From 267b71de94f20520de0e733a27bf4ea13b76e81f Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 28 Dec 2022 18:39:10 -0800
Subject: [PATCH 001/432] changes( .g_dgl -> private( ._kg_dgl))

---
 graphistry/embed_utils.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/graphistry/embed_utils.py b/graphistry/embed_utils.py
index 10798d70a3..42743d7537 100644
--- a/graphistry/embed_utils.py
+++ b/graphistry/embed_utils.py
@@ -153,13 +153,13 @@ def _build_graph(self, res) -> Plottable:
             g_dgl.edata[dgl.ETYPE] = r
 
         g_dgl.edata["norm"] = dgl.norm_by_dst(g_dgl).unsqueeze(-1)
-        res.g_dgl = g_dgl
+        res._kg_dgl = g_dgl
         return res
 
 
     def _init_model(self, res, batch_size:int, sample_size:int, num_steps:int, device):
         _, _, _, _, GraphDataLoader, HeteroEmbed, _, _ = lazy_embed_import_dep()
-        g_iter = SubgraphIterator(res.g_dgl, sample_size, num_steps)
+        g_iter = SubgraphIterator(res._kg_dgl, sample_size, num_steps)
         g_dataloader = GraphDataLoader(
             g_iter, batch_size=batch_size, collate_fn=lambda x: x[0]
         )
@@ -209,7 +209,7 @@ def _train_embedding(self, res, epochs:int, batch_size:int, lr:float, sample_siz
                 )
 
             model.eval()
-            res._kg_embeddings = model(res.g_dgl.to(device)).detach()
+            res._kg_embeddings = model(res._kg_dgl.to(device)).detach()
             res._embed_model = model
             if res._eval_flag and res._train_idx is not None:
                 score = res._eval(threshold=0.5)
@@ -222,7 +222,7 @@ def _train_embedding(self, res, epochs:int, batch_size:int, lr:float, sample_siz
     @property
     def _gcn_node_embeddings(self):
         _, torch, _, _, _, _, _, _ = lazy_embed_import_dep()
-        g_dgl = self.g_dgl.to(self._device)
+        g_dgl = self._kg_dgl.to(self._device)
         em = self._embed_model(g_dgl).detach()
         torch.cuda.empty_cache()
         return em

From 4adc90e64d89c2df71c07533776bfd47c12b4601 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 28 Dec 2022 18:43:01 -0800
Subject: [PATCH 002/432] adds clean up sections for jack donations

---
 demos/ai/OSINT/jack-donations.ipynb | 2644 +++++++++++++++++++++++----
 1 file changed, 2254 insertions(+), 390 deletions(-)

diff --git a/demos/ai/OSINT/jack-donations.ipynb b/demos/ai/OSINT/jack-donations.ipynb
index 7abddb9f20..1f02d64a2d 100644
--- a/demos/ai/OSINT/jack-donations.ipynb
+++ b/demos/ai/OSINT/jack-donations.ipynb
@@ -6,30 +6,39 @@
    "metadata": {},
    "source": [
     "________________\n",
-    "# Jack's money went here. \n",
+    "# Jack's Money Went Here \n",
     "\n",
-    "## Where is twitter likely to lean more and less now that  he's leaving? Where will there be matching donations?\n",
+    "Where is twitter likely to lean more and less now that he's leaving? Where will there be matching donations?\n",
     "\n",
-    "Jack Dorsey is pledging over 466 million dollars and wants matching donations. His rational is simple -- billionaires can spare a tithe to help communities and people, and compounded over a few hundred of his closest friends, have a tremendous impact. \n",
+    "Jack Dorsey is pledging over 466 million dollars and wants matching donations. His rational is simple -- billionaires can spare a tithe to help communities and people, and compounded over a few hundred of his closest friends, have a tremendous impact. What edifice could be built with donations to these entities? What do their service offerings look like when seen as a whole? What are their moving parts?\n",
     "\n",
     "This dataset is based off of the tweet https://twitter.com/jack/status/1247616214769086465 which lists pledged organizations and their donation. \n",
-    "__________________________\n",
-    "### We will learn how to quickly data science this dataset. We will select feature representations and visualize the resulting graph using UMAP.\n",
+    "__________________________________________________________________\n",
+    "\n",
+    "We will learn how to quickly data science this dataset. We will select feature representations and visualize the resulting graph using UMAP.\n",
     "\n",
     "Featurization is the foundation of datascience. Likewise, Graph Thinking requires edges between nodes. Many times the data we have from databases/dataframes is tabular and row like -- with no incling of an edge table. This does *not* have to be an impediment for *Graph Thinking and materialization of datascience workflows*. \n",
     "\n",
     "UMAP is a powerful tool that projects complex, heterogeneous data coming from potentially many different distributions, down to lower dimensional embeddings and projections. The embedding estimates similarity between the rows, or nodes of the data, and thus forms a graph. \n",
     "\n",
     "Standardizing a feature set across the databases used in every modern company and then sending it to UMAP serves as a powerful graph generation tool.  \n",
-    "____________________________\n",
-    "Here we demonstrate how to Featurize and use UMAP to generate implicit graphs. The features may then be used in subsequent modeling using your favorite libraries -- sklearn, tensorflow, pytorch[, geometric, lightening, ...], cuGraph, DGL, etc. We demonstrate 4 featurization methods -- (latent embeddings, transformer embeddings, ngrams embeddings, one-hot encodings) that may be mixed and used to make different features for different columns, automatically. \n",
+    "__________________________________________________________________\n",
+    "\n",
+    "Here we demonstrate how to Featurize and use UMAP to generate implicit graphs. The features may then be used in subsequent modeling using your favorite libraries -- sklearn, tensorflow, pytorch[, geometric, lightening, ...], cuGraph, DGL, etc. We demonstrate 4 featurization methods -- \n",
+    "\n",
+    "* latent embeddings, \n",
+    "* transformer embeddings, \n",
+    "* ngrams embeddings, \n",
+    "* one-hot encodings\n",
     "\n",
-    "Furthermore, when we `g.plot()` the results, it is layed out according to the 2-dimensional UMAP projection of the data -- nearness in that projection represents nearness in the resulting features. We will test this empiracally using the different featurization methods for textual, numeric and categorical data. "
+    "that may be mixed and used to make different features for different columns, automatically. \n",
+    "\n",
+    "Furthermore, when we `g.plot()` the results, it is layed out according to the 2-dimensional UMAP projection of the data -- nearness in that projection represents nearness in the resulting features. We will test this empirically using the different featurization methods for textual, numeric and categorical data. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "a069ef73",
    "metadata": {},
    "outputs": [],
@@ -39,17 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "97443b1c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# cd .."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "b7de987a",
    "metadata": {},
    "outputs": [],
@@ -66,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "461a22ec",
    "metadata": {},
    "outputs": [],
@@ -76,7 +75,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
+   "id": "950f6310",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RENDER=False  # set to True for inline Graphistry Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "id": "90875a39",
    "metadata": {},
    "outputs": [],
@@ -89,32 +98,177 @@
    "id": "9acb2823",
    "metadata": {},
    "source": [
-    "## Data cleaning\n",
+    "## Data loading & cleaning\n",
     "We already added the dataset from the twitter link, downloading a copy (as of May 2022) from the google drive. We need to remove the first few rows to make a valid dataframe. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "0ffe9b64",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Amount</th>\n",
+       "      <th>Category</th>\n",
+       "      <th>Grantee</th>\n",
+       "      <th>Twitter</th>\n",
+       "      <th>Link</th>\n",
+       "      <th>Why?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3/21/2022</td>\n",
+       "      <td>$2,000,000</td>\n",
+       "      <td>Social Justice</td>\n",
+       "      <td>REFORM Alliance</td>\n",
+       "      <td>@REFORM</td>\n",
+       "      <td>https://reformalliance.com</td>\n",
+       "      <td>REFORM Alliance is committed to transforming t...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3/10/2022</td>\n",
+       "      <td>$1,000,000</td>\n",
+       "      <td>Crisis Relief</td>\n",
+       "      <td>World Central Kitchen</td>\n",
+       "      <td>@WCKitchen</td>\n",
+       "      <td>https://wck.org/</td>\n",
+       "      <td>World Central Kitchen is serving thousands of ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3/10/2022</td>\n",
+       "      <td>$1,000,000</td>\n",
+       "      <td>Crisis Relief</td>\n",
+       "      <td>Sunflower of Peace</td>\n",
+       "      <td>@SunflowerFund</td>\n",
+       "      <td>https://www.sunflowerofpeace.com</td>\n",
+       "      <td>Sunflower of Peace is providing medical and hu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3/10/2022</td>\n",
+       "      <td>$1,000,000</td>\n",
+       "      <td>Crisis Relief</td>\n",
+       "      <td>Razom, Inc.</td>\n",
+       "      <td>@razomforukraine</td>\n",
+       "      <td>https://razomforukraine.org</td>\n",
+       "      <td>Razom is supporting Ukrainian people in their ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3/10/2022</td>\n",
+       "      <td>$1,000,000</td>\n",
+       "      <td>Crisis Relief</td>\n",
+       "      <td>Nova Ukraine</td>\n",
+       "      <td>@novaukraine</td>\n",
+       "      <td>https://novaukraine.org</td>\n",
+       "      <td>Nova Ukraine, a Bay Area-based humanitarian no...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Date     Amount         Category                Grantee  \\\n",
+       "0  3/21/2022  $2,000,000  Social Justice        REFORM Alliance   \n",
+       "1  3/10/2022  $1,000,000   Crisis Relief  World Central Kitchen   \n",
+       "2  3/10/2022  $1,000,000   Crisis Relief     Sunflower of Peace   \n",
+       "3  3/10/2022  $1,000,000   Crisis Relief            Razom, Inc.   \n",
+       "4  3/10/2022  $1,000,000   Crisis Relief           Nova Ukraine   \n",
+       "\n",
+       "            Twitter                              Link  \\\n",
+       "0           @REFORM        https://reformalliance.com   \n",
+       "1        @WCKitchen                  https://wck.org/   \n",
+       "2    @SunflowerFund  https://www.sunflowerofpeace.com   \n",
+       "3  @razomforukraine       https://razomforukraine.org   \n",
+       "4      @novaukraine           https://novaukraine.org   \n",
+       "\n",
+       "                                                Why?  \n",
+       "0  REFORM Alliance is committed to transforming t...  \n",
+       "1  World Central Kitchen is serving thousands of ...  \n",
+       "2  Sunflower of Peace is providing medical and hu...  \n",
+       "3  Razom is supporting Ukrainian people in their ...  \n",
+       "4  Nova Ukraine, a Bay Area-based humanitarian no...  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df = pd.read_csv('https://gist.githubusercontent.com/silkspace/f8d7b8f279a5ffbd710c301fc402ec43/raw/95a722f5c65812322eaf085c1123b58d3ec3da3a/jack_donations.csv')\n",
     "df = df.fillna('')\n",
     "columns = df.iloc[3].values  \n",
     "ndf = pd.DataFrame(df[4:].values, columns=columns)\n",
-    "ndf"
+    "ndf.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50f59d83",
+   "metadata": {},
+   "source": [
+    "Notice that the Category labels are mixed and interwoven. \n",
+    "We will show how to standardize it without having to do data cleaning or mapping"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "e52b4e5d",
+   "execution_count": 7,
+   "id": "ac1b493e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Social Justice', 'Crisis Relief',\n",
+       "       'COVID-19, Girls Health & Education',\n",
+       "       'Social Justice, Girls Health & Education', 'COVID-19',\n",
+       "       'Social Justice, COVID-19', 'Girls Health & Education',\n",
+       "       'UBI, Social Justice', 'Girls Health & Education, COVID-19',\n",
+       "       'COVID-19, Social Justice', 'UBI',\n",
+       "       'COVID-19, Social Justice, Girls Health & Education',\n",
+       "       'Girls Health & Education; COVID-19', 'COVID-19; Social Justice',\n",
+       "       'Girls Health & Education; Social Justice',\n",
+       "       'COVID-19; Girls Health & Education', 'UBI; COVID-19',\n",
+       "       'COVID-19 & Social Justice',\n",
+       "       'Social Justice, UBI, Girls Health & Education', 'COVID-19, UBI',\n",
+       "       \"Where it's needed most\", 'COVID-19 '], dtype=object)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "ndf.Category.unique()"
+    "ndf.Category.unique()  # seems like there are 4-6 topics here"
    ]
   },
   {
@@ -122,24 +276,40 @@
    "id": "b454348e",
    "metadata": {},
    "source": [
-    "# Create the Graph\n",
+    "# Featurize\n",
     "\n",
-    "We will use `g.umap` to featurize and create edges. The details of how UMAP is able to create edges between rows in the data is beyond the scope of this tutorial, however, suffic it to say, it is automatically inferring a network of related entities based off of their column features. \n",
+    "We will use `g.umap` to featurize and create edges. The details of how UMAP is able to create edges between rows in the data is beyond the scope of this tutorial, however, suffic it to say, it is automatically inferring a network of related entities based off their column features. \n",
     "\n",
     "Here is the dataset as graph, \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "c986ff93",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "g = graphistry.nodes(ndf).bind(point_title='Category').umap()\n",
-    "g.plot()  # fly around the clusters and click on nodes and edges. "
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (285, 0) in UMAP fit, as it is not one dimensionalOMP: Info #273: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'https://hub.graphistry.com/graph/graph.html?dataset=ae47f85d8eaa4edfa6a3bc0c1124e313&type=arrow&viztoken=41ef5acc-41e3-49e9-99ac-2a57158b31c8&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672009074&info=true&play=0'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g = graphistry.nodes(ndf).umap()\n",
+    "g.bind(point_title='Grantee').plot(render=RENDER)  # fly around the clusters and click on nodes and edges. "
    ]
   },
   {
@@ -147,7 +317,7 @@
    "id": "255c8496",
    "metadata": {},
    "source": [
-    "## The above featurized every column over the entire datase. Exploring the nodes and their nearest neighbors indeed clusters similar rows -- all in two lines of code!"
+    "The above featurized every column over the entire datase. Exploring the nodes and their nearest neighbors indeed clusters similar rows -- all in two lines of code!"
    ]
   },
   {
@@ -155,17 +325,39 @@
    "id": "d76e628d",
    "metadata": {},
    "source": [
-    "# Some light analysis and enrichment \n",
+    "## Light analysis and enrichment \n",
     "\n",
     "Lets convert Amount column into numeric, and then see who is getting what by category and grantee."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "a8ced06c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      $2,000,000\n",
+       "1      $1,000,000\n",
+       "2      $1,000,000\n",
+       "3      $1,000,000\n",
+       "4      $1,000,000\n",
+       "          ...    \n",
+       "280       $13,333\n",
+       "281    $2,000,000\n",
+       "282    $1,000,000\n",
+       "283    $2,100,000\n",
+       "284      $100,000\n",
+       "Name:  Amount , Length: 285, dtype: object"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#ndf.columns\n",
     "ndf[' Amount ']"
@@ -173,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "8077b2d0",
    "metadata": {},
    "outputs": [],
@@ -193,10 +385,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "b0e0c683",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      2000000.0\n",
+       "1      1000000.0\n",
+       "2      1000000.0\n",
+       "3      1000000.0\n",
+       "4      1000000.0\n",
+       "         ...    \n",
+       "280      13333.0\n",
+       "281    2000000.0\n",
+       "282    1000000.0\n",
+       "283    2100000.0\n",
+       "284     100000.0\n",
+       "Name: $ amount, Length: 285, dtype: float64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ndf['$ amount']"
    ]
@@ -206,17 +420,51 @@
    "id": "ac95f782",
    "metadata": {},
    "source": [
-    "## Many of these categories are not distinct. But due to data coming in with different notation, it seems distinct. \n",
+    "Many of these categories are not distinct. But due to data coming in with different notation, it seems distinct. \n",
     "\n",
     "We will show in the next section how to deal with this by using the graphistry pipeline to convert the `Category` into a latent target that organizes the labels.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "6e4fcbaf",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Category\n",
+       "COVID-19                                              $153,882,590.0\n",
+       "COVID-19                                               $85,019,328.0\n",
+       "COVID-19 & Social Justice                                 $505,468.0\n",
+       "COVID-19, Girls Health & Education                      $4,265,000.0\n",
+       "COVID-19, Social Justice                                $1,800,000.0\n",
+       "COVID-19, Social Justice, Girls Health & Education        $250,000.0\n",
+       "COVID-19, UBI                                           $8,000,000.0\n",
+       "COVID-19; Girls Health & Education                      $9,920,000.0\n",
+       "COVID-19; Social Justice                                $5,090,080.0\n",
+       "Crisis Relief                                           $7,500,000.0\n",
+       "Girls Health & Education                               $30,300,000.0\n",
+       "Girls Health & Education, COVID-19                      $1,250,000.0\n",
+       "Girls Health & Education; COVID-19                     $12,000,000.0\n",
+       "Girls Health & Education; Social Justice                $2,500,000.0\n",
+       "Social Justice                                         $84,119,845.0\n",
+       "Social Justice, COVID-19                                  $300,000.0\n",
+       "Social Justice, Girls Health & Education                $9,934,000.0\n",
+       "Social Justice, UBI, Girls Health & Education           $1,100,000.0\n",
+       "UBI                                                    $10,210,000.0\n",
+       "UBI, Social Justice                                     $1,000,000.0\n",
+       "UBI; COVID-19                                          $35,000,000.0\n",
+       "Where it's needed most                                  $3,000,000.0\n",
+       "Name: $ amount, dtype: object"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "current_funding_by_category = ndf.groupby('Category')['$ amount'].sum()\n",
     "current_funding_by_category.map(lambda x: '${:3,}'.format(x))"
@@ -224,21 +472,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "59b71456",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = plt.figure(figsize=(15,7))\n",
-    "current_funding_by_category.plot(kind='bar', rot=52)"
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Category'>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(10,5))\n",
+    "current_funding_by_category.plot(kind='bar', rot=82)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "382780f5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Grantee\n",
+       "Vital Strategies: Resolve To Save Lives    $38,000,000.0\n",
+       "CORE: Community Organized Relief Effort    $30,000,000.0\n",
+       "Clara Lionel Foundation                    $28,877,000.0\n",
+       "Reinvent Stockton Foundation               $18,000,000.0\n",
+       "CARE                                       $16,000,000.0\n",
+       "Give2SF                                    $15,000,000.0\n",
+       "Open Research Lab Income Project           $15,000,000.0\n",
+       "REFORM Alliance                            $12,000,000.0\n",
+       "World Central Kitchen                      $11,585,500.0\n",
+       "Indiana University Foundation              $10,025,000.0\n",
+       "Name: $ amount, dtype: object"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "grantees = ndf.groupby('Grantee')['$ amount'].sum()\n",
     "grants_sorted = grantees.sort_values()\n",
@@ -248,78 +541,123 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "d7d0ff87",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Grantee'>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# largest grants\n",
-    "fig = plt.figure(figsize=(15,7))\n",
+    "fig = plt.figure(figsize=(10,5))\n",
     "ax= plt.subplot()\n",
-    "# ax.set_xticks(range(len(label_list)))\n",
-    "# ax.set_xticklabels(label_list, rotation=19)\n",
     "res = grants_sorted[-10:]\n",
     "\n",
-    "res.plot(kind='bar', rot=52)"
+    "res.plot(kind='bar', rot=49)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "14330bfc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Grantee'>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# smallest grants\n",
-    "fig = plt.figure(figsize=(15,7))\n",
+    "fig = plt.figure(figsize=(10,5))\n",
     "ax= plt.subplot()\n",
-    "# ax.set_xticks(range(len(label_list)))\n",
-    "# ax.set_xticklabels(label_list, rotation=19)\n",
     "res = grants_sorted[:10]\n",
     "\n",
-    "res.plot(kind='bar', rot = 52)"
+    "res.plot(kind='bar', rot = 29)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "2ad231a8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Total Pledged $466,946,311.0'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "'Total Pledged ${:3,}'.format(current_funding_by_category.sum())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "217026ee",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Total Pledged $466,946,311.0'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# and this should be the same too\n",
     "'Total Pledged ${:3,}'.format(grantees.sum())"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "50f59d83",
-   "metadata": {},
-   "source": [
-    "## Notice that the Category labels are mixed and interwoven \n",
-    "We will show how judicious choice of parameters can standardize it without having to do data cleaning or mapping"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ac1b493e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ndf.Category.unique()  # seems like there are 4-6 topics here"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "b0565e61",
@@ -339,31 +677,12 @@
     "____________________________"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "22c6b5c7",
-   "metadata": {},
-   "source": [
-    "In the following, we concentrate on the textual `Why?` column as it describes the row/entity in question. Further, we select `y='Category'` as a target variable, and will encode it using a Topic Model as well as standard One-Hot-Encoding.\n",
-    "\n",
-    "\n",
-    "In the following we will show how to encode textual and categorical data using \n",
-    "\n",
-    "1) Topic Models\n",
-    "\n",
-    "2) Sentence Transformers\n",
-    "\n",
-    "3) Ngrams \n",
-    "\n",
-    "And see the resulting graphs. We will use the Topic label generated by `y='Category'` to color the graphs, as well as `$ amount` \n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "2255d688",
    "metadata": {},
    "source": [
-    "# Topic Model (latent-) features"
+    "## Topic Model"
    ]
   },
   {
@@ -374,208 +693,609 @@
     "We encode the data using Topic Models. This turns the textual features into latent vectors. Likewise, we can do the same for the target data. \n",
     "\n",
     "\n",
-    "Notice that we set `cardinality_threshold_target` very low and `min_words` very high to force featurization as topic models rather than one-hot or topic encoded;\n",
+    "Notice that we set `cardinality_threshold_target` very low and `min_words` very high to force featurization as topic models rather than one-hot or sbert embeddings;\n",
+    "\n",
     "1) encode target using a topic model, and set `n_topics_target` as the dimension of the latent target factorization. This choice is based on the fact that there are really only 4-6 or so distinct categories across the labels, but they are mixed together. The labels are in fact Hierarchical categories. We can use the topic model to find the lowest moments of this Hierarchical classification in the distributional sense. \n",
     "\n",
-    "2) and like\n",
-    "wise for the features `Why?`, and set `n_topics` as the dimension of the latent feature factorization."
+    "2) Encode the `Why?` column as a `n_topics` -dimensional factorization."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "71ad1fe5",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (285, 4) in UMAP fit, as it is not one dimensional"
+     ]
+    }
+   ],
    "source": [
     "g = graphistry.nodes(ndf).bind(point_title='Category')\n",
     "\n",
     "g2 = g.umap(X=['Why?'], y = ['Category'], \n",
-    "            min_words=50000, # encode as topic model by setting min_words high\n",
+    "            min_words=1e9, # encode as topic model by setting min_words high\n",
+    "            n_topics=42, # latent embedding size of `Why`\n",
     "            n_topics_target=4, # turn categories into a 4dim vector of regressive targets\n",
-    "            n_topics=21, # latent embedding size \n",
-    "            cardinality_threshold_target=2, # make sure that we throw targets into topic model over targets\n",
+    "            cardinality_threshold_target=2, # force topic model over target `Category`\n",
+    "            use_scaler=None,\n",
+    "            use_scaler_target=None\n",
     "            ) "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8cb9e6cd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g2._node_encoder.label_encoder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b650ef59",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# pretend you have a minibatch of new data -- transform under the fit from the above\n",
-    "new_df, new_y = ndf.sample(5), ndf.sample(5) # pd.DataFrame({'Category': ndf['Category'].sample(5)})\n",
-    "a, b = g2.transform(new_df, new_y, kind='nodes')\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dc99ac85",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5076e613",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure()\n",
-    "plt.imshow(g2._node_target, aspect='auto', cmap='hot')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d95f4e1b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g2._node_encoder.label_encoder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "577b32ab",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g2._node_encoder.y.plot(kind='bar', figsize=(15,7)) # easier to see than before"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cd1e7ffc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# likewise you can play with how many edges to include using,\n",
-    "g2 = g2.filter_weighted_edges(scale=0.25)  # lower positive values of scale mean closer similarity \n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "93e6ae81",
    "metadata": {},
    "source": [
-    "## We have featurized the data and also run UMAP, which projects the features into a 2-dimensional space while generating edges.\n",
-    "\n",
     "Plotting the result shows the similarity between entities. It does a good job overall at clustering by topic. Click in and check out some nearby nodes. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "0cdf2370",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://hub.graphistry.com/graph/graph.html?dataset=039654b935e6476dbe4a232e28609ae1&type=arrow&viztoken=c8fd440d-2b3a-4fbf-868c-f6c93e141154&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672009084&info=true&play=0'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g2.plot()"
+    "g2.bind(point_title='Grantee').plot(render=RENDER)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "7a970c06",
+   "execution_count": 21,
+   "id": "b650ef59",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "X = g2._node_features \n",
-    "X"
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "'SuperVectorizer' object has no attribute 'get_feature_names_in''SuperVectorizer' object has no attribute 'get_feature_names_in'"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Why?: relationships, relationship, trevortext</th>\n",
+       "      <th>Why?: thousands, kitchen, kitchens</th>\n",
+       "      <th>Why?: multiracial, language, analysis</th>\n",
+       "      <th>Why?: foundation, partnership, barbados</th>\n",
+       "      <th>Why?: humanitarian, distributing, distributed</th>\n",
+       "      <th>Why?: vulnerable, coordinated, outbreak</th>\n",
+       "      <th>Why?: marginalized, globalgiving, emergency</th>\n",
+       "      <th>Why?: sustainable, livelihoods, livelihood</th>\n",
+       "      <th>Why?: healthcare, results, children</th>\n",
+       "      <th>Why?: movementhub, strengthening, snapshots</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Why?: washingtonians, incarceration, restoration</th>\n",
+       "      <th>Why?: leadership, confidence, confidently</th>\n",
+       "      <th>Why?: entrepreneurs, entrepreneurship, tomorrow</th>\n",
+       "      <th>Why?: coronavirus, families, primarily</th>\n",
+       "      <th>Why?: california, disproportionately, lgbtiq</th>\n",
+       "      <th>Why?: individuals, undiagnosed, disabilities</th>\n",
+       "      <th>Why?: engineering, criminal, complete</th>\n",
+       "      <th>Why?: disparities, nonprofit, socioeconomic</th>\n",
+       "      <th>Why?: simultaneously, immediate, richmond</th>\n",
+       "      <th>Why?: constitution, employers, nationwide</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>0.140277</td>\n",
+       "      <td>0.160064</td>\n",
+       "      <td>0.193184</td>\n",
+       "      <td>0.154880</td>\n",
+       "      <td>6.899020</td>\n",
+       "      <td>0.088743</td>\n",
+       "      <td>0.161605</td>\n",
+       "      <td>0.126051</td>\n",
+       "      <td>0.084490</td>\n",
+       "      <td>143.538295</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.157501</td>\n",
+       "      <td>0.123325</td>\n",
+       "      <td>0.107698</td>\n",
+       "      <td>0.179143</td>\n",
+       "      <td>19.065436</td>\n",
+       "      <td>0.121403</td>\n",
+       "      <td>0.159580</td>\n",
+       "      <td>0.132481</td>\n",
+       "      <td>0.247654</td>\n",
+       "      <td>0.115000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0.213641</td>\n",
+       "      <td>0.383104</td>\n",
+       "      <td>15.883280</td>\n",
+       "      <td>0.155844</td>\n",
+       "      <td>0.233854</td>\n",
+       "      <td>0.197296</td>\n",
+       "      <td>0.221445</td>\n",
+       "      <td>0.269628</td>\n",
+       "      <td>0.203888</td>\n",
+       "      <td>0.218177</td>\n",
+       "      <td>...</td>\n",
+       "      <td>68.431846</td>\n",
+       "      <td>19.142002</td>\n",
+       "      <td>0.439962</td>\n",
+       "      <td>0.354134</td>\n",
+       "      <td>0.326861</td>\n",
+       "      <td>0.530609</td>\n",
+       "      <td>3.105993</td>\n",
+       "      <td>0.318049</td>\n",
+       "      <td>0.378531</td>\n",
+       "      <td>234.320552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.156088</td>\n",
+       "      <td>0.212351</td>\n",
+       "      <td>0.241647</td>\n",
+       "      <td>0.163209</td>\n",
+       "      <td>0.152243</td>\n",
+       "      <td>0.192223</td>\n",
+       "      <td>0.230188</td>\n",
+       "      <td>0.214602</td>\n",
+       "      <td>4.308181</td>\n",
+       "      <td>0.157184</td>\n",
+       "      <td>...</td>\n",
+       "      <td>276.339497</td>\n",
+       "      <td>0.621190</td>\n",
+       "      <td>0.170634</td>\n",
+       "      <td>0.133303</td>\n",
+       "      <td>0.176993</td>\n",
+       "      <td>0.187097</td>\n",
+       "      <td>0.265365</td>\n",
+       "      <td>0.113609</td>\n",
+       "      <td>0.249689</td>\n",
+       "      <td>26.072265</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>0.193366</td>\n",
+       "      <td>0.153392</td>\n",
+       "      <td>0.122779</td>\n",
+       "      <td>0.126448</td>\n",
+       "      <td>0.113965</td>\n",
+       "      <td>0.210225</td>\n",
+       "      <td>0.201695</td>\n",
+       "      <td>0.160518</td>\n",
+       "      <td>0.161135</td>\n",
+       "      <td>0.175002</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.129757</td>\n",
+       "      <td>0.146117</td>\n",
+       "      <td>0.159293</td>\n",
+       "      <td>30.262957</td>\n",
+       "      <td>0.106157</td>\n",
+       "      <td>30.781927</td>\n",
+       "      <td>0.122815</td>\n",
+       "      <td>0.119214</td>\n",
+       "      <td>67.870143</td>\n",
+       "      <td>0.139393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0.162594</td>\n",
+       "      <td>0.130315</td>\n",
+       "      <td>0.157815</td>\n",
+       "      <td>0.258591</td>\n",
+       "      <td>1.791601</td>\n",
+       "      <td>6.279698</td>\n",
+       "      <td>0.541006</td>\n",
+       "      <td>45.667235</td>\n",
+       "      <td>0.228501</td>\n",
+       "      <td>50.486164</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.160795</td>\n",
+       "      <td>0.230575</td>\n",
+       "      <td>0.167335</td>\n",
+       "      <td>0.603552</td>\n",
+       "      <td>24.346636</td>\n",
+       "      <td>32.922770</td>\n",
+       "      <td>0.299201</td>\n",
+       "      <td>0.405175</td>\n",
+       "      <td>0.236851</td>\n",
+       "      <td>41.980704</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 42 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Why?: relationships, relationship, trevortext  \\\n",
+       "103                                       0.140277   \n",
+       "62                                        0.213641   \n",
+       "11                                        0.156088   \n",
+       "244                                       0.193366   \n",
+       "24                                        0.162594   \n",
+       "\n",
+       "     Why?: thousands, kitchen, kitchens  \\\n",
+       "103                            0.160064   \n",
+       "62                             0.383104   \n",
+       "11                             0.212351   \n",
+       "244                            0.153392   \n",
+       "24                             0.130315   \n",
+       "\n",
+       "     Why?: multiracial, language, analysis  \\\n",
+       "103                               0.193184   \n",
+       "62                               15.883280   \n",
+       "11                                0.241647   \n",
+       "244                               0.122779   \n",
+       "24                                0.157815   \n",
+       "\n",
+       "     Why?: foundation, partnership, barbados  \\\n",
+       "103                                 0.154880   \n",
+       "62                                  0.155844   \n",
+       "11                                  0.163209   \n",
+       "244                                 0.126448   \n",
+       "24                                  0.258591   \n",
+       "\n",
+       "     Why?: humanitarian, distributing, distributed  \\\n",
+       "103                                       6.899020   \n",
+       "62                                        0.233854   \n",
+       "11                                        0.152243   \n",
+       "244                                       0.113965   \n",
+       "24                                        1.791601   \n",
+       "\n",
+       "     Why?: vulnerable, coordinated, outbreak  \\\n",
+       "103                                 0.088743   \n",
+       "62                                  0.197296   \n",
+       "11                                  0.192223   \n",
+       "244                                 0.210225   \n",
+       "24                                  6.279698   \n",
+       "\n",
+       "     Why?: marginalized, globalgiving, emergency  \\\n",
+       "103                                     0.161605   \n",
+       "62                                      0.221445   \n",
+       "11                                      0.230188   \n",
+       "244                                     0.201695   \n",
+       "24                                      0.541006   \n",
+       "\n",
+       "     Why?: sustainable, livelihoods, livelihood  \\\n",
+       "103                                    0.126051   \n",
+       "62                                     0.269628   \n",
+       "11                                     0.214602   \n",
+       "244                                    0.160518   \n",
+       "24                                    45.667235   \n",
+       "\n",
+       "     Why?: healthcare, results, children  \\\n",
+       "103                             0.084490   \n",
+       "62                              0.203888   \n",
+       "11                              4.308181   \n",
+       "244                             0.161135   \n",
+       "24                              0.228501   \n",
+       "\n",
+       "     Why?: movementhub, strengthening, snapshots  ...  \\\n",
+       "103                                   143.538295  ...   \n",
+       "62                                      0.218177  ...   \n",
+       "11                                      0.157184  ...   \n",
+       "244                                     0.175002  ...   \n",
+       "24                                     50.486164  ...   \n",
+       "\n",
+       "     Why?: washingtonians, incarceration, restoration  \\\n",
+       "103                                          0.157501   \n",
+       "62                                          68.431846   \n",
+       "11                                         276.339497   \n",
+       "244                                          0.129757   \n",
+       "24                                           0.160795   \n",
+       "\n",
+       "     Why?: leadership, confidence, confidently  \\\n",
+       "103                                   0.123325   \n",
+       "62                                   19.142002   \n",
+       "11                                    0.621190   \n",
+       "244                                   0.146117   \n",
+       "24                                    0.230575   \n",
+       "\n",
+       "     Why?: entrepreneurs, entrepreneurship, tomorrow  \\\n",
+       "103                                         0.107698   \n",
+       "62                                          0.439962   \n",
+       "11                                          0.170634   \n",
+       "244                                         0.159293   \n",
+       "24                                          0.167335   \n",
+       "\n",
+       "     Why?: coronavirus, families, primarily  \\\n",
+       "103                                0.179143   \n",
+       "62                                 0.354134   \n",
+       "11                                 0.133303   \n",
+       "244                               30.262957   \n",
+       "24                                 0.603552   \n",
+       "\n",
+       "     Why?: california, disproportionately, lgbtiq  \\\n",
+       "103                                     19.065436   \n",
+       "62                                       0.326861   \n",
+       "11                                       0.176993   \n",
+       "244                                      0.106157   \n",
+       "24                                      24.346636   \n",
+       "\n",
+       "     Why?: individuals, undiagnosed, disabilities  \\\n",
+       "103                                      0.121403   \n",
+       "62                                       0.530609   \n",
+       "11                                       0.187097   \n",
+       "244                                     30.781927   \n",
+       "24                                      32.922770   \n",
+       "\n",
+       "     Why?: engineering, criminal, complete  \\\n",
+       "103                               0.159580   \n",
+       "62                                3.105993   \n",
+       "11                                0.265365   \n",
+       "244                               0.122815   \n",
+       "24                                0.299201   \n",
+       "\n",
+       "     Why?: disparities, nonprofit, socioeconomic  \\\n",
+       "103                                     0.132481   \n",
+       "62                                      0.318049   \n",
+       "11                                      0.113609   \n",
+       "244                                     0.119214   \n",
+       "24                                      0.405175   \n",
+       "\n",
+       "     Why?: simultaneously, immediate, richmond  \\\n",
+       "103                                   0.247654   \n",
+       "62                                    0.378531   \n",
+       "11                                    0.249689   \n",
+       "244                                  67.870143   \n",
+       "24                                    0.236851   \n",
+       "\n",
+       "     Why?: constitution, employers, nationwide  \n",
+       "103                                   0.115000  \n",
+       "62                                  234.320552  \n",
+       "11                                   26.072265  \n",
+       "244                                   0.139393  \n",
+       "24                                   41.980704  \n",
+       "\n",
+       "[5 rows x 42 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# suppose we have a minibatch of new data -- transform under the fit from the above\n",
+    "new_df = new_y = ndf.sample(5) # pd.DataFrame({'Category': ndf['Category'].sample(5)})\n",
+    "a, b = g2.transform(new_df, new_y, kind='nodes')\n",
+    "a"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "789b09d8",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 22,
+   "id": "00fc2685",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Category: justice, social, 19</th>\n",
+       "      <th>Category: crisis, relief, covid</th>\n",
+       "      <th>Category: needed, where, most</th>\n",
+       "      <th>Category: education, health, girls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>18.041799</td>\n",
+       "      <td>0.050008</td>\n",
+       "      <td>0.056675</td>\n",
+       "      <td>0.051518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>18.041799</td>\n",
+       "      <td>0.050008</td>\n",
+       "      <td>0.056675</td>\n",
+       "      <td>0.051518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>18.041799</td>\n",
+       "      <td>0.050008</td>\n",
+       "      <td>0.056675</td>\n",
+       "      <td>0.051518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>0.050010</td>\n",
+       "      <td>10.548916</td>\n",
+       "      <td>0.051025</td>\n",
+       "      <td>0.050049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>18.041799</td>\n",
+       "      <td>0.050008</td>\n",
+       "      <td>0.056675</td>\n",
+       "      <td>0.051518</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Category: justice, social, 19  Category: crisis, relief, covid  \\\n",
+       "103                      18.041799                         0.050008   \n",
+       "62                       18.041799                         0.050008   \n",
+       "11                       18.041799                         0.050008   \n",
+       "244                       0.050010                        10.548916   \n",
+       "24                       18.041799                         0.050008   \n",
+       "\n",
+       "     Category: needed, where, most  Category: education, health, girls  \n",
+       "103                       0.056675                            0.051518  \n",
+       "62                        0.056675                            0.051518  \n",
+       "11                        0.056675                            0.051518  \n",
+       "244                       0.051025                            0.050049  \n",
+       "24                        0.056675                            0.051518  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "y = g2._node_target  # we've reduced 22 columns into 5\n",
-    "y"
+    "b"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "126d5473",
+   "execution_count": 23,
+   "id": "cd1e7ffc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "## we can inspect the topics from the column headers\n",
-    "label_list = y.columns\n",
-    "label_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7396c76b",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "## and see them across rows of the data\n",
-    "fig = plt.figure(figsize=(17,10))\n",
-    "ax = plt.subplot()\n",
-    "plt.imshow(y, aspect='auto', cmap='hot')\n",
-    "plt.colorbar()\n",
-    "plt.ylabel('row number of data')\n",
-    "ax.set_xticks(range(len(label_list)))\n",
-    "ax.set_xticklabels(label_list, rotation=39)\n",
-    "print(f'See the abundance of the data in the latent vector of the corresponding targets')"
+    "# likewise you can play with how many edges to include using,\n",
+    "g2 = g2.filter_weighted_edges(scale=0.5)  # lower positive values of scale mean closer similarity "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "b9dd69ea",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='support across data'>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# find the marginal in the category topic distribution\n",
-    "y.sum(0).plot(kind='bar', ylabel='support across data', rot=79)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bcf88b65",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Looking at the above bar chart we may read off the most "
+    "y = g2._node_target\n",
+    "y.sum(0).plot(kind='bar', ylabel='support across data', rot=19)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "63b817ae",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--------------------------------------------------\n",
+      "Topic 1: \t\t\t\t Evidence\n",
+      "Category: justice, social, 19\n",
+      "-----------------------------------\n",
+      "-- Social Justice,    92\n",
+      "-- COVID-19; Social Justice,    8\n",
+      "-- COVID-19, Social Justice,    2\n",
+      "-- Social Justice, COVID-19,    1\n",
+      "-- UBI, Social Justice,    1\n",
+      "-- COVID-19 & Social Justice,    1\n",
+      "\n",
+      "--------------------------------------------------\n",
+      "Topic 2: \t\t\t\t Evidence\n",
+      "Category: crisis, relief, covid\n",
+      "-----------------------------------\n",
+      "-- COVID-19,    70\n",
+      "-- COVID-19 ,    55\n",
+      "-- Crisis Relief,    8\n",
+      "-- UBI; COVID-19,    3\n",
+      "-- COVID-19, UBI,    2\n",
+      "\n",
+      "--------------------------------------------------\n",
+      "Topic 3: \t\t\t\t Evidence\n",
+      "Category: needed, where, most\n",
+      "-----------------------------------\n",
+      "-- UBI,    4\n",
+      "-- Where it's needed most,    1\n",
+      "\n",
+      "--------------------------------------------------\n",
+      "Topic 4: \t\t\t\t Evidence\n",
+      "Category: education, health, girls\n",
+      "-----------------------------------\n",
+      "-- Girls Health & Education,    16\n",
+      "-- Social Justice, Girls Health & Education,    6\n",
+      "-- COVID-19, Girls Health & Education,    4\n",
+      "-- Girls Health & Education; COVID-19,    3\n",
+      "-- COVID-19; Girls Health & Education,    3\n",
+      "-- Girls Health & Education, COVID-19,    2\n",
+      "-- COVID-19, Social Justice, Girls Health & Education,    1\n",
+      "-- Girls Health & Education; Social Justice,    1\n",
+      "-- Social Justice, UBI, Girls Health & Education,    1\n"
+     ]
+    }
+   ],
    "source": [
     "# Let's see how the category columns are supported by the data\n",
     "from collections import Counter\n",
@@ -585,7 +1305,7 @@
     "    top_category = Counter(ndf.loc[indices].Category)\n",
     "    print()\n",
     "    print('-'*50)\n",
-    "    print(f'Topic {topic_number}: \\t\\t\\t\\t Evidence')\n",
+    "    print(f'Topic {topic_number+1}: \\t\\t\\t\\t Evidence')\n",
     "    print(f'{y.columns[topic_number]}')\n",
     "    print('-'*35)\n",
     "    for t, c in top_category.most_common():\n",
@@ -597,7 +1317,7 @@
    "id": "efe62b1e",
    "metadata": {},
    "source": [
-    "### We see that different spellings, spaces, etc or use of ;, , etc map to the same topic. This is a useful way to disambiguate when there are many similar categories without having to do a lot of data cleaning and prep.\n",
+    "We see that different spellings, spaces, etc or use of ;, , etc map to the same topic. This is a useful way to disambiguate when there are many similar categories without having to do a lot of data cleaning and prep.\n",
     "\n",
     "The choice of `n_topics_target` sets the prior on the Dirty_Cat GapEncoder used under the hood"
    ]
@@ -607,24 +1327,15 @@
    "id": "530cde56",
    "metadata": {},
    "source": [
-    "## Let's add the Category Topic Number as a feature to help us visualize using the Histogram Feature of the Graphistry UI\n",
+    "_________________________________________________________________________________________\n",
+    "Let's add the Category Topic Number as a feature to help us visualize using the Histogram Feature of the Graphistry UI\n",
     "\n",
-    "This reduces the naive one-hot-encoding of 22 columns down the the number set by the `n_topics_target=5`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "db1b9ea4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tops"
+    "This reduces the naive one-hot-encoding of 22 columns down the the number set by the `n_topics_target`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "id": "5724c75f",
    "metadata": {},
    "outputs": [],
@@ -635,10 +1346,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "ad387dc0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        Category: justice, social, 19\n",
+       "1      Category: crisis, relief, covid\n",
+       "2      Category: crisis, relief, covid\n",
+       "3      Category: crisis, relief, covid\n",
+       "4      Category: crisis, relief, covid\n",
+       "                    ...               \n",
+       "280    Category: crisis, relief, covid\n",
+       "281    Category: crisis, relief, covid\n",
+       "282    Category: crisis, relief, covid\n",
+       "283    Category: crisis, relief, covid\n",
+       "284    Category: crisis, relief, covid\n",
+       "Name: topic, Length: 285, dtype: object"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "g2._nodes.topic"
    ]
@@ -654,20 +1387,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "5d46b0ab",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://hub.graphistry.com/graph/graph.html?dataset=e306d4cb9d9c46e2b451e1739b42fd1f&type=arrow&viztoken=194b1f39-29fa-40e5-b80e-790bf099ecaa&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672009087&info=true&play=0'"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g3 = g2.bind(point_title='topic')\n",
-    "g3.plot()"
+    "g2.bind(point_title='Grantee').plot(render=RENDER)  # color by `topic` in histogram"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "6c17e7c4",
    "metadata": {},
    "outputs": [],
@@ -677,10 +1418,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "c3ad0af4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "topic\n",
+       "Category: crisis, relief, covid       $289,401,918.0\n",
+       "Category: justice, social, 19          $92,815,393.0\n",
+       "Category: education, health, girls     $71,519,000.0\n",
+       "Category: needed, where, most          $13,210,000.0\n",
+       "Name: $ amount, dtype: object"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "topic_sums = ndf.groupby('topic')['$ amount'].sum()\n",
     "topic_sums.sort_values()[::-1].apply(lambda x : '${:3,}'.format(x))"
@@ -691,7 +1448,7 @@
    "id": "058f5eef",
    "metadata": {},
    "source": [
-    "## hence we have Crisis Relief, Social Justice, Health Education Girls, and UBI occupying the main topics across the target"
+    "Hence we have Crisis Relief, Social Justice, Health Education Girls, and UBI occupying the main topics across the target"
    ]
   },
   {
@@ -700,8 +1457,8 @@
    "metadata": {},
    "source": [
     "------------------------------------------------------------------------------------------\n",
-    "# Let's move on to point 2) \n",
-    "# Sentence Transformer Encodings\n",
+    "Let's move on to point 2) \n",
+    "## Sentence Transformer Model\n",
     "\n",
     "To trigger the sentence encoder, just lower the `min_words` count (which previously we had set to higher than the number of words across the `Why?` column) to some small value or zero to force encoding any X=[..] columns, since it sets the minimum number of words to consider passing on to the (sentence, ngram) embedding pipelines.  \n",
     "\n",
@@ -710,97 +1467,690 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "id": "0c4ceacb",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (285, 7) in UMAP fit, as it is not one dimensional"
+     ]
+    }
+   ],
    "source": [
-    "g2 = g.umap(X = ['Why?', 'Grantee'], y = 'Category', \n",
+    "g3 = g.umap(X = ['Why?', 'Grantee'], y = 'Category', \n",
     "            min_words=0, \n",
     "            model_name ='paraphrase-MiniLM-L6-v2', \n",
     "            cardinality_threshold_target=2,\n",
-    "            scale=0.6)"
+    "            use_scaler=None,\n",
+    "            use_scaler_target=None,\n",
+    "            scale=0.5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "id": "e137b52c",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "g2.search('carbon neutral')[0][['Why?']]"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Why?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Climate Justice Alliance (CJA) formed in 2013 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>The Climate and Clean Energy Equity Fund (Equi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>The Richmond Rapid Response Fund (R3F) is a wr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>The Deep South Center for Environment Justice,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>For 40 years, Futures Without Violence has pio...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>DC or Nothing, Inc. is a nonprofit organizatio...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134</th>\n",
+       "      <td>To support Oxfam’s response to the ongoing imp...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>With the support of #StartSmall ALIMA is opera...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>161</th>\n",
+       "      <td>To support the \"AEGIS Study\" Fund to address S...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>The Caribbean Climate Justice Project seeks to...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  Why?\n",
+       "28   Climate Justice Alliance (CJA) formed in 2013 ...\n",
+       "27   The Climate and Clean Energy Equity Fund (Equi...\n",
+       "138  The Richmond Rapid Response Fund (R3F) is a wr...\n",
+       "29   The Deep South Center for Environment Justice,...\n",
+       "113  For 40 years, Futures Without Violence has pio...\n",
+       "46   DC or Nothing, Inc. is a nonprofit organizatio...\n",
+       "134  To support Oxfam’s response to the ongoing imp...\n",
+       "54   With the support of #StartSmall ALIMA is opera...\n",
+       "161  To support the \"AEGIS Study\" Fund to address S...\n",
+       "39   The Caribbean Climate Justice Project seeks to..."
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g3.search('carbon neutral')[0][['Why?']]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "id": "a222ef95",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'$13,776,250.0'"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "'${:3,}'.format(g2.search('carbon neutral')[0]['$ amount'].sum())"
+    "# make quick semantic estimates\n",
+    "'${:3,}'.format(g3.search('carbon neutral')[0]['$ amount'].sum())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "id": "7bbcec3a",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "g2.search('sustainable homes and communities')[0][['Why?','$ amount']]#.sum()"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Why?</th>\n",
+       "      <th>$ amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>For the #FirstOfTheMonth rent relief project a...</td>\n",
+       "      <td>530000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>Funds will be used towards their mission of pr...</td>\n",
+       "      <td>1000000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>To support in its efforts to empower people wi...</td>\n",
+       "      <td>4720000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225</th>\n",
+       "      <td>Supports the 30-day Rent Relief program and fu...</td>\n",
+       "      <td>200000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>Funds support the Navajo Water Project - conne...</td>\n",
+       "      <td>1000000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>To support the COVID-19 Resilience Fund, servi...</td>\n",
+       "      <td>2000000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>For over 60 years, Public Health Solutions (PH...</td>\n",
+       "      <td>200000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226</th>\n",
+       "      <td>Funding to be used as leverage in negotiations...</td>\n",
+       "      <td>300000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>The Deep South Center for Environment Justice,...</td>\n",
+       "      <td>300000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>Mission Neighborhood Centers (MNC), founded in...</td>\n",
+       "      <td>1000000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  Why?   $ amount\n",
+       "237  For the #FirstOfTheMonth rent relief project a...   530000.0\n",
+       "184  Funds will be used towards their mission of pr...  1000000.0\n",
+       "142  To support in its efforts to empower people wi...  4720000.0\n",
+       "225  Supports the 30-day Rent Relief program and fu...   200000.0\n",
+       "253  Funds support the Navajo Water Project - conne...  1000000.0\n",
+       "177  To support the COVID-19 Resilience Fund, servi...  2000000.0\n",
+       "122  For over 60 years, Public Health Solutions (PH...   200000.0\n",
+       "226  Funding to be used as leverage in negotiations...   300000.0\n",
+       "29   The Deep South Center for Environment Justice,...   300000.0\n",
+       "110  Mission Neighborhood Centers (MNC), founded in...  1000000.0"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g3.search('sustainable homes and communities')[0][['Why?','$ amount']]#.sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "id": "1cc5bd36",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'$11,250,000.0'"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "'${:3,}'.format(g2.search('sustainable homes and communities')[0]['$ amount'].sum())"
+    "'${:3,}'.format(g3.search('sustainable homes and communities')[0]['$ amount'].sum())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "id": "3cc28169",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://hub.graphistry.com/graph/graph.html?dataset=6c44a692de41484f9e403eea134435dc&type=arrow&viztoken=479610e9-66b9-483d-a242-5dd476faaa08&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672009105&info=true&play=0'"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# see the queries landscape  -- paste url with .plot(render=False)\n",
-    "g2.search_graph('sustainable homes and communities', scale=0.90, top_n=10).bind(point_title='Why?').plot(render=False)"
+    "# see the queries landscape  -- paste url to see graph if g.plot(render=False)\n",
+    "g3.search_graph('sustainable homes and communities', scale=0.90, top_n=10).bind(point_title='Grantee').plot(render=RENDER)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "id": "6bf9f793",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "'SuperVectorizer' object has no attribute 'get_feature_names_in'"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Why?_Grantee_0</th>\n",
+       "      <th>Why?_Grantee_1</th>\n",
+       "      <th>Why?_Grantee_2</th>\n",
+       "      <th>Why?_Grantee_3</th>\n",
+       "      <th>Why?_Grantee_4</th>\n",
+       "      <th>Why?_Grantee_5</th>\n",
+       "      <th>Why?_Grantee_6</th>\n",
+       "      <th>Why?_Grantee_7</th>\n",
+       "      <th>Why?_Grantee_8</th>\n",
+       "      <th>Why?_Grantee_9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Why?_Grantee_374</th>\n",
+       "      <th>Why?_Grantee_375</th>\n",
+       "      <th>Why?_Grantee_376</th>\n",
+       "      <th>Why?_Grantee_377</th>\n",
+       "      <th>Why?_Grantee_378</th>\n",
+       "      <th>Why?_Grantee_379</th>\n",
+       "      <th>Why?_Grantee_380</th>\n",
+       "      <th>Why?_Grantee_381</th>\n",
+       "      <th>Why?_Grantee_382</th>\n",
+       "      <th>Why?_Grantee_383</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>0.443781</td>\n",
+       "      <td>0.228207</td>\n",
+       "      <td>-0.095960</td>\n",
+       "      <td>0.233459</td>\n",
+       "      <td>0.089919</td>\n",
+       "      <td>0.261963</td>\n",
+       "      <td>0.181132</td>\n",
+       "      <td>-0.332808</td>\n",
+       "      <td>-0.131169</td>\n",
+       "      <td>0.098321</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.112939</td>\n",
+       "      <td>-0.039801</td>\n",
+       "      <td>0.344712</td>\n",
+       "      <td>-0.148454</td>\n",
+       "      <td>0.092307</td>\n",
+       "      <td>0.554271</td>\n",
+       "      <td>0.729110</td>\n",
+       "      <td>0.292012</td>\n",
+       "      <td>-0.235918</td>\n",
+       "      <td>0.717677</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>-0.117390</td>\n",
+       "      <td>-0.122771</td>\n",
+       "      <td>-0.457182</td>\n",
+       "      <td>-0.289880</td>\n",
+       "      <td>0.091984</td>\n",
+       "      <td>0.300274</td>\n",
+       "      <td>-0.013144</td>\n",
+       "      <td>-0.218964</td>\n",
+       "      <td>0.034323</td>\n",
+       "      <td>0.037846</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.269020</td>\n",
+       "      <td>0.114675</td>\n",
+       "      <td>0.134028</td>\n",
+       "      <td>0.085543</td>\n",
+       "      <td>-0.306746</td>\n",
+       "      <td>0.037034</td>\n",
+       "      <td>0.167620</td>\n",
+       "      <td>0.081501</td>\n",
+       "      <td>0.109638</td>\n",
+       "      <td>-0.272466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.066493</td>\n",
+       "      <td>0.095128</td>\n",
+       "      <td>-0.273875</td>\n",
+       "      <td>-0.108320</td>\n",
+       "      <td>0.208153</td>\n",
+       "      <td>0.393189</td>\n",
+       "      <td>0.207741</td>\n",
+       "      <td>0.060979</td>\n",
+       "      <td>-0.189427</td>\n",
+       "      <td>0.035519</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.117062</td>\n",
+       "      <td>-0.089552</td>\n",
+       "      <td>0.247655</td>\n",
+       "      <td>0.248977</td>\n",
+       "      <td>0.026250</td>\n",
+       "      <td>0.274958</td>\n",
+       "      <td>0.396113</td>\n",
+       "      <td>-0.109153</td>\n",
+       "      <td>0.155618</td>\n",
+       "      <td>-0.087428</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>0.230028</td>\n",
+       "      <td>0.167471</td>\n",
+       "      <td>0.209063</td>\n",
+       "      <td>0.097104</td>\n",
+       "      <td>0.082054</td>\n",
+       "      <td>0.183794</td>\n",
+       "      <td>-0.266914</td>\n",
+       "      <td>-0.023539</td>\n",
+       "      <td>-0.543348</td>\n",
+       "      <td>-0.263615</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.063171</td>\n",
+       "      <td>0.154695</td>\n",
+       "      <td>-0.307326</td>\n",
+       "      <td>-0.197053</td>\n",
+       "      <td>0.221207</td>\n",
+       "      <td>-0.098222</td>\n",
+       "      <td>0.054051</td>\n",
+       "      <td>-0.118295</td>\n",
+       "      <td>-0.154315</td>\n",
+       "      <td>0.197405</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>-0.169691</td>\n",
+       "      <td>0.559551</td>\n",
+       "      <td>-0.129676</td>\n",
+       "      <td>-0.366660</td>\n",
+       "      <td>0.267032</td>\n",
+       "      <td>-0.058975</td>\n",
+       "      <td>-0.005459</td>\n",
+       "      <td>0.062295</td>\n",
+       "      <td>0.050921</td>\n",
+       "      <td>0.362040</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.112664</td>\n",
+       "      <td>0.112096</td>\n",
+       "      <td>-0.116451</td>\n",
+       "      <td>0.187891</td>\n",
+       "      <td>-0.142040</td>\n",
+       "      <td>0.025833</td>\n",
+       "      <td>-0.695716</td>\n",
+       "      <td>0.017772</td>\n",
+       "      <td>0.010284</td>\n",
+       "      <td>0.043964</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 384 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Why?_Grantee_0  Why?_Grantee_1  Why?_Grantee_2  Why?_Grantee_3  \\\n",
+       "103        0.443781        0.228207       -0.095960        0.233459   \n",
+       "62        -0.117390       -0.122771       -0.457182       -0.289880   \n",
+       "11         0.066493        0.095128       -0.273875       -0.108320   \n",
+       "244        0.230028        0.167471        0.209063        0.097104   \n",
+       "24        -0.169691        0.559551       -0.129676       -0.366660   \n",
+       "\n",
+       "     Why?_Grantee_4  Why?_Grantee_5  Why?_Grantee_6  Why?_Grantee_7  \\\n",
+       "103        0.089919        0.261963        0.181132       -0.332808   \n",
+       "62         0.091984        0.300274       -0.013144       -0.218964   \n",
+       "11         0.208153        0.393189        0.207741        0.060979   \n",
+       "244        0.082054        0.183794       -0.266914       -0.023539   \n",
+       "24         0.267032       -0.058975       -0.005459        0.062295   \n",
+       "\n",
+       "     Why?_Grantee_8  Why?_Grantee_9  ...  Why?_Grantee_374  Why?_Grantee_375  \\\n",
+       "103       -0.131169        0.098321  ...         -0.112939         -0.039801   \n",
+       "62         0.034323        0.037846  ...          0.269020          0.114675   \n",
+       "11        -0.189427        0.035519  ...          0.117062         -0.089552   \n",
+       "244       -0.543348       -0.263615  ...          0.063171          0.154695   \n",
+       "24         0.050921        0.362040  ...          0.112664          0.112096   \n",
+       "\n",
+       "     Why?_Grantee_376  Why?_Grantee_377  Why?_Grantee_378  Why?_Grantee_379  \\\n",
+       "103          0.344712         -0.148454          0.092307          0.554271   \n",
+       "62           0.134028          0.085543         -0.306746          0.037034   \n",
+       "11           0.247655          0.248977          0.026250          0.274958   \n",
+       "244         -0.307326         -0.197053          0.221207         -0.098222   \n",
+       "24          -0.116451          0.187891         -0.142040          0.025833   \n",
+       "\n",
+       "     Why?_Grantee_380  Why?_Grantee_381  Why?_Grantee_382  Why?_Grantee_383  \n",
+       "103          0.729110          0.292012         -0.235918          0.717677  \n",
+       "62           0.167620          0.081501          0.109638         -0.272466  \n",
+       "11           0.396113         -0.109153          0.155618         -0.087428  \n",
+       "244          0.054051         -0.118295         -0.154315          0.197405  \n",
+       "24          -0.695716          0.017772          0.010284          0.043964  \n",
+       "\n",
+       "[5 rows x 384 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# or transform on new data as before\n",
-    "a, b = g2.transform(new_df, new_y, kind='nodes')\n",
+    "a, b = g3.transform(new_df, new_y, kind='nodes')\n",
     "a"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "142b85db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Category: covid, ubi, 19</th>\n",
+       "      <th>Category: 19, it, ubi</th>\n",
+       "      <th>Category: justice, social, most</th>\n",
+       "      <th>Category: 19, it, ubi</th>\n",
+       "      <th>Category: education, health, girls</th>\n",
+       "      <th>Category: crisis, relief, needed</th>\n",
+       "      <th>Category: ubi, 19, it</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>0.050003</td>\n",
+       "      <td>0.098460</td>\n",
+       "      <td>17.976374</td>\n",
+       "      <td>0.050914</td>\n",
+       "      <td>0.051539</td>\n",
+       "      <td>0.050121</td>\n",
+       "      <td>0.072589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0.050003</td>\n",
+       "      <td>0.098460</td>\n",
+       "      <td>17.976374</td>\n",
+       "      <td>0.050914</td>\n",
+       "      <td>0.051539</td>\n",
+       "      <td>0.050121</td>\n",
+       "      <td>0.072589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.050003</td>\n",
+       "      <td>0.098460</td>\n",
+       "      <td>17.976374</td>\n",
+       "      <td>0.050914</td>\n",
+       "      <td>0.051539</td>\n",
+       "      <td>0.050121</td>\n",
+       "      <td>0.072589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>10.517500</td>\n",
+       "      <td>0.068372</td>\n",
+       "      <td>0.050004</td>\n",
+       "      <td>0.064094</td>\n",
+       "      <td>0.050020</td>\n",
+       "      <td>0.050003</td>\n",
+       "      <td>0.050007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0.050003</td>\n",
+       "      <td>0.098460</td>\n",
+       "      <td>17.976374</td>\n",
+       "      <td>0.050914</td>\n",
+       "      <td>0.051539</td>\n",
+       "      <td>0.050121</td>\n",
+       "      <td>0.072589</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Category: covid, ubi, 19  Category: 19, it, ubi  \\\n",
+       "103                  0.050003               0.098460   \n",
+       "62                   0.050003               0.098460   \n",
+       "11                   0.050003               0.098460   \n",
+       "244                 10.517500               0.068372   \n",
+       "24                   0.050003               0.098460   \n",
+       "\n",
+       "     Category: justice, social, most  Category: 19, it, ubi  \\\n",
+       "103                        17.976374               0.050914   \n",
+       "62                         17.976374               0.050914   \n",
+       "11                         17.976374               0.050914   \n",
+       "244                         0.050004               0.064094   \n",
+       "24                         17.976374               0.050914   \n",
+       "\n",
+       "     Category: education, health, girls  Category: crisis, relief, needed  \\\n",
+       "103                            0.051539                          0.050121   \n",
+       "62                             0.051539                          0.050121   \n",
+       "11                             0.051539                          0.050121   \n",
+       "244                            0.050020                          0.050003   \n",
+       "24                             0.051539                          0.050121   \n",
+       "\n",
+       "     Category: ubi, 19, it  \n",
+       "103               0.072589  \n",
+       "62                0.072589  \n",
+       "11                0.072589  \n",
+       "244               0.050007  \n",
+       "24                0.072589  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "5a3033a4",
    "metadata": {},
    "source": [
-    "## Clicking around to nearest neighbors demonstrates good semantic similarity, as seen by the Paraphrase Model `paraphrase-MiniLM-L6-v2`"
+    "Clicking around to nearest neighbors demonstrates good semantic similarity, as seen by the Paraphrase Model `paraphrase-MiniLM-L6-v2`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "id": "9d33ea95",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://hub.graphistry.com/graph/graph.html?dataset=51fe80f88c464b5eb049dd382cfb9b46&type=arrow&viztoken=ab98054f-1141-4657-b76d-9a98b4753917&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672009108&info=true&play=0'"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g2.plot()"
+    "g3.bind(point_title='Grantee').plot(render=RENDER)"
    ]
   },
   {
@@ -808,70 +2158,81 @@
    "id": "7cbb210c",
    "metadata": {},
    "source": [
-    "## Suppose we wanted to add the Grantee column as a feature: \n",
-    "To include it in the sentence transformer model, reduce the` min_words` threshold to include it. If we want the column `Grantee` to be encoded as a topic model, set `min_words` to between the average of `Why?` (higher) and `Grantee` (lower) and `$ amount` (which is just 1). This may seem a bit sloppy as an API, nevertheless useful across many datasets since if a column is truly categorical, its cardinality is usually well under that of a truly textual feature. Moreover, if you want all columns to be textually encoded, set `min_words=0`. "
+    "Suppose we wanted to add the `$ amount` column as a feature: \n",
+    "\n",
+    "To include it in the sentence transformer model, reduce the` min_words` threshold to include it. If we want the column `Grantee` to be encoded as a topic model, set `min_words` to between the average of `Why?` (higher) and `Grantee` (lower).  It nevertheless is useful across many datasets since if a column is truly categorical, its cardinality is usually well under that of a truly textual feature. Moreover, if you want all columns to be textually encoded, set `min_words=0`. \n",
+    "\n",
+    "The `$ amount` column will be passed in and scaled according to `use_scaler`, while `use_scaler_target` selects how to scale targets \n",
+    "\n",
+    "(exercise:  `use_scaler_target='kbins'` to see the difference in `g._node_target` \n",
+    "\n",
+    "or scale the dataframe directly (this transforms the batch dataframe)\n",
+    "\n",
+    "`a, b = g.scale(ndf, ydf=ndf, 'nodes', use_scaler_target='kbins', n_bins=9))` "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "id": "4ef2870c",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "g2 = g.umap(X = ['Why?', 'Grantee', '$ amount'], y = 'Category',\n",
-    "            min_words=2,\n",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (285, 22) in UMAP fit, as it is not one dimensional"
+     ]
+    }
+   ],
+   "source": [
+    "g3 = g.umap(X = ['Why?', 'Grantee', '$ amount'], y = 'Category',\n",
+    "            min_words=2, # don't set to zero or it will stringify the `$ amount`\n",
     "            model_name ='paraphrase-MiniLM-L6-v2',\n",
     "            use_scaler=None,\n",
-    "           ) "
+    "           )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "id": "97bdaa46",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Why?', 'Grantee']"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g2._node_encoder.text_cols"
+    "g3._node_encoder.text_cols"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "05b61370",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# just for fun, can we find outliers (which we know will be influenced by the numeric $ amount)\n",
-    "from graphistry.outliers import detect_outliers\n",
-    "\n",
-    "# organized by amount\n",
-    "embedding = g2._xy\n",
-    "clfs, ax, fig = detect_outliers(embedding.values, name='Donations', contamination=0.3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b608b9cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# the different models\n",
-    "clfs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "id": "33f3bc17",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://hub.graphistry.com/graph/graph.html?dataset=22d84e2ef2de4cb4830ea1c6c9dc2f70&type=arrow&viztoken=b5067b0e-4c9b-4341-8c41-ce05998aacd1&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672009127&info=true&play=0'"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g2.plot() # color/size the noded by `$ amount`"
+    "g3.plot(render=RENDER) # color/size the noded by `$ amount`, mimics graph above as they use the same embedding xys"
    ]
   },
   {
@@ -879,7 +2240,9 @@
    "id": "f014b4e0",
    "metadata": {},
    "source": [
-    "# Lastly, suppose we want a plain Ngrams model matrix, and for a change, one-hot-encode the target `Category`\n",
+    "## NGRAMS model\n",
+    "\n",
+    "Lastly, suppose we want a plain Ngrams model matrix, and for a change, one-hot-encode the target `Category`\n",
     "\n",
     "Set `use_ngrams = True`\n",
     "and set the `cardinality_threshold_target` > cardinality(`Category`).\n",
@@ -889,84 +2252,585 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "id": "8c1a588c",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "g3 = g.umap(X = ['Why?', 'Grantee'], y = 'Category', \n",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (285, 22) in UMAP fit, as it is not one dimensional"
+     ]
+    }
+   ],
+   "source": [
+    "g4 = g.umap(X = ['Why?', 'Grantee'], y = 'Category', \n",
     "            use_ngrams=True, \n",
     "            ngram_range=(1,3), \n",
     "            min_df=2, \n",
     "            max_df=0.3,\n",
-    "            cardinality_threshold_target=400\n",
-    "           )  # this will one-hot-encode the target, as we have less than 400 total `categories`"
+    "            use_scaler=None,\n",
+    "            cardinality_threshold_target=400 # this will one-hot-encode the target, \n",
+    "                                                # as we have less than 400 total `categories`\n",
+    "           )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "id": "e1e2683c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://hub.graphistry.com/graph/graph.html?dataset=aeb09ebc8a914fe8851b7bc4ea255c9c&type=arrow&viztoken=f768c875-ee7d-4cea-b514-a3c8ba0d59bc&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672009131&info=true&play=0'"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g3.bind(point_title='Category').plot()"
+    "g4.bind(point_title='Category').plot(render=RENDER)  # umap-ing ngrams is not as useful as sentence embeddings as you may visually see, however they can be useful graphs nonetheless. Press `play` in the UI."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "id": "d570f001",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "g3._node_features  # a standard tfidf ngrams matrix"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>reform</th>\n",
+       "      <th>alliance</th>\n",
+       "      <th>committed</th>\n",
+       "      <th>criminal</th>\n",
+       "      <th>justice</th>\n",
+       "      <th>system</th>\n",
+       "      <th>throughout</th>\n",
+       "      <th>united</th>\n",
+       "      <th>states</th>\n",
+       "      <th>by</th>\n",
+       "      <th>...</th>\n",
+       "      <th>mayor office</th>\n",
+       "      <th>for homeless</th>\n",
+       "      <th>match</th>\n",
+       "      <th>hospital</th>\n",
+       "      <th>2m</th>\n",
+       "      <th>go towards</th>\n",
+       "      <th>grant 2m</th>\n",
+       "      <th>will go towards</th>\n",
+       "      <th>total grant 2m</th>\n",
+       "      <th>clinics</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
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+       "    <tr>\n",
+       "      <th>4</th>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>281</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>282</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>283</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>284</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>285 rows × 3889 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     reform  alliance  committed  criminal  justice  system  throughout  \\\n",
+       "0       0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "1       0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "2       0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "3       0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "4       0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "..      ...       ...        ...       ...      ...     ...         ...   \n",
+       "280     0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "281     0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "282     0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "283     0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "284     0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "\n",
+       "     united  states   by  ...  mayor office  for homeless  match  hospital  \\\n",
+       "0       0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "1       0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "2       0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "3       0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "4       0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "..      ...     ...  ...  ...           ...           ...    ...       ...   \n",
+       "280     0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "281     0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "282     0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "283     0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "284     0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "\n",
+       "      2m  go towards  grant 2m  will go towards  total grant 2m  clinics  \n",
+       "0    0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "1    0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "2    0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "3    0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "4    0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "..   ...         ...       ...              ...             ...      ...  \n",
+       "280  0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "281  0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "282  0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "283  0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "284  0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "\n",
+       "[285 rows x 3889 columns]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g4._node_features  # a standard tfidf ngrams matrix"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "id": "338010f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('vect',\n",
+       "                 CountVectorizer(max_df=0.3, min_df=2, ngram_range=(1, 3))),\n",
+       "                ('tfidf', TfidfTransformer())])"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g3._node_encoder.text_model  #sklearn pipeline "
+    "g4._node_encoder.text_model  #sklearn pipeline "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "id": "e7582131",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3889"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "## vocab size\n",
-    "len(g3._node_encoder.text_model[0].vocabulary_)"
+    "len(g4._node_encoder.text_model[0].vocabulary_)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "id": "9e691b4d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "'SuperVectorizer' object has no attribute 'get_feature_names_in'"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>6.100185</td>\n",
+       "      <td>-1.109381</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>6.937139</td>\n",
+       "      <td>-2.348813</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>6.991597</td>\n",
+       "      <td>-2.461275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>5.651282</td>\n",
+       "      <td>-1.004279</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>5.893190</td>\n",
+       "      <td>-3.356526</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            x         y\n",
+       "103  6.100185 -1.109381\n",
+       "62   6.937139 -2.348813\n",
+       "11   6.991597 -2.461275\n",
+       "244  5.651282 -1.004279\n",
+       "24   5.893190 -3.356526"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# or transform new data: \n",
-    "emb, a, b = g2.transform_umap(new_df, new_y, kind='nodes')\n",
+    "emb, a, b = g4.transform_umap(new_df, new_y, kind='nodes')\n",
     "emb"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "id": "5bc7b2c0",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Naive Indicator Variables\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1224x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# we include the naive indicator variable for completeness.\n",
-    "y = g3._node_target\n",
+    "y = g4._node_target\n",
     "label_list = b.columns\n",
     "\n",
     "fig = plt.figure(figsize=(17,10))\n",
@@ -981,22 +2845,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec83a920",
+   "id": "22596c6d",
    "metadata": {},
    "source": [
     "# Contributions\n",
     "\n",
-    "We've seen how we may pull in tabular data that exists in the wild and quickly make features and graphs that allow semantic and topological exploration and traversals. \n",
+    "Input tabular data that exists in the wild and quickly make features and graphs that allow semantic and topological exploration and traversals. \n",
     "\n",
-    "In this way one can quickly track a variety of datasets and (in this case) gauge growth, investment, and promise fullfillment and transparently using Graph Thinking and analysis.\n",
+    "Quickly track a variety of datasets and gauge growth, investment, and promise fullfillment and transparently using Graph Thinking and Analysis. In Jack's case, we see the possibility of a multibillion dollar edific erected around Covid-19, Girls Education and Social Justice. Further downstream modeling might tell us what such an edific is able to manufacture as a force for Good.\n",
     "\n",
-    "Encoding text, categorical, and numeric features while exploring the relationships can be time consuming tasks. We hope that Graphistry[ai] demonstrates an exciting and visually compelling way to explore Graph Data. \n",
+    "Encoding text, categorical, and numeric features while exploring the relationships can be time consuming tasks. \n",
     "\n",
-    "Now you can mix and match features, augment it with more columns via enrichment, and pivot large amounts of data using natural language search, all using a few lines of code. The features produced may then be used in downstream models, whose outputs could be added and the entire process repeated.\n",
+    "PyGraphistry[ai] demonstrates an exciting and visually accelerated way to explore Graph Data. \n",
     "\n",
-    "Let us know what you think!\n",
+    "It allows quick Mix and Match featurization models and types, while pivoting on large amounts of data using natural language search, in just a few lines of code. The resulting features may then be used in downstream models.\n",
     "\n",
-    "Join our Slack: Graphistry-Community\n"
+    "Join our Slack: Graphistry-Community"
    ]
   },
   {

From 3d21f8edfce250da71e7642a3f51d573254c847e Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 28 Dec 2022 19:01:15 -0800
Subject: [PATCH 003/432] fix typo

---
 demos/ai/Introduction/Ask-HackerNews-Demo.ipynb | 14 ++++----------
 1 file changed, 4 insertions(+), 10 deletions(-)

diff --git a/demos/ai/Introduction/Ask-HackerNews-Demo.ipynb b/demos/ai/Introduction/Ask-HackerNews-Demo.ipynb
index 47e010af9a..99cc397dae 100644
--- a/demos/ai/Introduction/Ask-HackerNews-Demo.ipynb
+++ b/demos/ai/Introduction/Ask-HackerNews-Demo.ipynb
@@ -5,7 +5,7 @@
    "id": "c39da4a9",
    "metadata": {},
    "source": [
-    "# Hello PyGraphistry[ai] - HackerNews visual semantic search with UMAP & BERT and \n",
+    "# Hello PyGraphistry[ai] - HackerNews visual semantic search with UMAP & BERT.\n",
     "\n",
     "`PyGraphistry[ai]` can quickly create visual graph search interfaces for structured text. It automates much of the work in cleaning, connecting, encoding, searching, and visualing graph data. The result is increasing the *time to graph* and overall results in as little as one line of code.\n",
     "\n",
@@ -244,9 +244,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "f43b7806",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# visualize the results where we prune edges using the `filter_weighted_edges` method\n",
@@ -278,9 +276,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "85cf9c06",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Query semantically instead of strict keyword matching\n",
@@ -502,9 +498,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "45d3a37a",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# see the edge features which are shape (n_edges, n_nodes + weight)\n",

From 5b73c37a1799fd756d2d7c2228dd05c48e8904d8 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 29 Dec 2022 13:18:03 -0800
Subject: [PATCH 004/432] adds ModelDict and removes build_index call from demo
 notebook

---
 demos/ai/cyber/cyber-redteam-umap-demo.ipynb | 2685 ++++++++++++++++--
 1 file changed, 2487 insertions(+), 198 deletions(-)

diff --git a/demos/ai/cyber/cyber-redteam-umap-demo.ipynb b/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
index 9ed84b308b..9e30bb5dff 100644
--- a/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
+++ b/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "f9de6fd3-b87b-4dc4-8d1c-b8f3feceb5e6",
    "metadata": {},
    "outputs": [],
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "0215906c",
    "metadata": {},
    "outputs": [],
@@ -31,6 +31,8 @@
     "import pandas as pd\n",
     "import graphistry\n",
     "\n",
+    "from graphistry.features import topic_model, search_model, ModelDict\n",
+    "\n",
     "import os\n",
     "from joblib import load, dump\n",
     "from collections import Counter\n",
@@ -44,7 +46,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
+   "id": "8e1747b9-c903-4398-9aa0-b52b69fce021",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(137)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6d2669fd-6164-4376-81bd-79c6c6f4112f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RENDER = False # set to True to render Graphistry UI inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "id": "59e1cc0b",
    "metadata": {},
    "outputs": [],
@@ -74,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "fe6e61b0",
    "metadata": {},
    "outputs": [],
@@ -103,10 +125,153 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "efe68cf8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>src_domain</th>\n",
+       "      <th>dst_domain</th>\n",
+       "      <th>src_computer</th>\n",
+       "      <th>dst_computer</th>\n",
+       "      <th>auth_type</th>\n",
+       "      <th>logontype</th>\n",
+       "      <th>authentication_orientation</th>\n",
+       "      <th>success_or_failure</th>\n",
+       "      <th>RED</th>\n",
+       "      <th>feats</th>\n",
+       "      <th>feats2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>30526246</th>\n",
+       "      <td>155805</td>\n",
+       "      <td>C7048$@DOM1</td>\n",
+       "      <td>C7048$@DOM1</td>\n",
+       "      <td>C7048</td>\n",
+       "      <td>TGT</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>TGS</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C7048 TGT ? ?</td>\n",
+       "      <td>C7048 TGT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5928201</th>\n",
+       "      <td>37690</td>\n",
+       "      <td>C15034$@DOM1</td>\n",
+       "      <td>C15034$@DOM1</td>\n",
+       "      <td>C15034</td>\n",
+       "      <td>C467</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>TGS</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C15034 C467 ? ?</td>\n",
+       "      <td>C15034 C467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21160461</th>\n",
+       "      <td>116992</td>\n",
+       "      <td>U2075@DOM1</td>\n",
+       "      <td>U2075@DOM1</td>\n",
+       "      <td>C529</td>\n",
+       "      <td>C529</td>\n",
+       "      <td>?</td>\n",
+       "      <td>Network</td>\n",
+       "      <td>LogOff</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C529 C529 ? Network</td>\n",
+       "      <td>C529 C529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2182328</th>\n",
+       "      <td>22019</td>\n",
+       "      <td>C3547$@DOM1</td>\n",
+       "      <td>C3547$@DOM1</td>\n",
+       "      <td>C457</td>\n",
+       "      <td>C457</td>\n",
+       "      <td>?</td>\n",
+       "      <td>Network</td>\n",
+       "      <td>LogOff</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C457 C457 ? Network</td>\n",
+       "      <td>C457 C457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28495743</th>\n",
+       "      <td>145572</td>\n",
+       "      <td>C567$@DOM1</td>\n",
+       "      <td>C567$@DOM1</td>\n",
+       "      <td>C574</td>\n",
+       "      <td>C523</td>\n",
+       "      <td>Kerberos</td>\n",
+       "      <td>Network</td>\n",
+       "      <td>LogOn</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C574 C523 Kerberos Network</td>\n",
+       "      <td>C574 C523</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            time    src_domain    dst_domain src_computer dst_computer  \\\n",
+       "30526246  155805   C7048$@DOM1   C7048$@DOM1        C7048          TGT   \n",
+       "5928201    37690  C15034$@DOM1  C15034$@DOM1       C15034         C467   \n",
+       "21160461  116992    U2075@DOM1    U2075@DOM1         C529         C529   \n",
+       "2182328    22019   C3547$@DOM1   C3547$@DOM1         C457         C457   \n",
+       "28495743  145572    C567$@DOM1    C567$@DOM1         C574         C523   \n",
+       "\n",
+       "         auth_type logontype authentication_orientation success_or_failure  \\\n",
+       "30526246         ?         ?                        TGS            Success   \n",
+       "5928201          ?         ?                        TGS            Success   \n",
+       "21160461         ?   Network                     LogOff            Success   \n",
+       "2182328          ?   Network                     LogOff            Success   \n",
+       "28495743  Kerberos   Network                      LogOn            Success   \n",
+       "\n",
+       "          RED                       feats       feats2  \n",
+       "30526246  0.0               C7048 TGT ? ?    C7048 TGT  \n",
+       "5928201   0.0             C15034 C467 ? ?  C15034 C467  \n",
+       "21160461  0.0         C529 C529 ? Network    C529 C529  \n",
+       "2182328   0.0         C457 C457 ? Network    C457 C457  \n",
+       "28495743  0.0  C574 C523 Kerberos Network    C574 C523  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# small sample (get almost equivalent results without overheating computer over the 1.6B events in the full dataset)\n",
     "df = pd.read_csv('https://gist.githubusercontent.com/silkspace/c7b50d0c03dc59f63c48d68d696958ff/raw/31d918267f86f8252d42d2e9597ba6fc03fcdac2/redteam_50k.csv', index_col=0)\n",
@@ -115,17 +280,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "03610297",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(50000, 12)\n"
+     ]
+    }
+   ],
    "source": [
     "print(df.shape) # -> 50k"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "66c5126e",
    "metadata": {},
    "outputs": [],
@@ -134,6 +307,17 @@
     "red_team = pd.read_csv('https://gist.githubusercontent.com/silkspace/5cf5a94b9ac4b4ffe38904f20d93edb1/raw/888dabd86f88ea747cf9ff5f6c44725e21536465/redteam_labels.csv', index_col=0)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7b31d2b0-b123-4f7c-9157-03accce6a6c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for later\n",
+    "red_team['feats2'] = red_team.feats"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "3c6615aa",
@@ -146,10 +330,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "3641d3b5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(19013, 12)\n"
+     ]
+    }
+   ],
    "source": [
     "process = True  \n",
     "# makes a combined feature we can use for topic modeling!\n",
@@ -159,13 +351,21 @@
     "    # and one of just computer to computer \n",
     "    df['feats2'] = df.src_computer + ' ' + df.dst_computer\n",
     "    ndf = df.drop_duplicates(subset=['feats'])\n",
-    "    ndf.to_parquet('../data/auth-50k-feats-one-column.parquet')\n",
+    "    ndf.to_parquet('auth-feats-one-column.parquet')\n",
     "else:\n",
-    "    ndf = pd.read_parquet('../data/auth-50k-feats-one-column.parquet')\n",
+    "    ndf = pd.read_parquet('auth-feats-one-column.parquet')\n",
     "    \n",
     "print(ndf.shape)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b1c15b72-a355-48d5-9a4e-31dbb6a47b06",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "32d1755d",
@@ -177,10 +377,289 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "d67c86b8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>time</th>\n",
+       "      <th>src_domain</th>\n",
+       "      <th>src_computer</th>\n",
+       "      <th>dst_computer</th>\n",
+       "      <th>feats</th>\n",
+       "      <th>RED</th>\n",
+       "      <th>feats2</th>\n",
+       "      <th>dst_domain</th>\n",
+       "      <th>auth_type</th>\n",
+       "      <th>logontype</th>\n",
+       "      <th>authentication_orientation</th>\n",
+       "      <th>success_or_failure</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>150885</td>\n",
+       "      <td>U620@DOM1</td>\n",
+       "      <td>C17693</td>\n",
+       "      <td>C1003</td>\n",
+       "      <td>C17693 C1003</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>C17693 C1003</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>151036</td>\n",
+       "      <td>U748@DOM1</td>\n",
+       "      <td>C17693</td>\n",
+       "      <td>C305</td>\n",
+       "      <td>C17693 C305</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>C17693 C305</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>151648</td>\n",
+       "      <td>U748@DOM1</td>\n",
+       "      <td>C17693</td>\n",
+       "      <td>C728</td>\n",
+       "      <td>C17693 C728</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>C17693 C728</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>151993</td>\n",
+       "      <td>U6115@DOM1</td>\n",
+       "      <td>C17693</td>\n",
+       "      <td>C1173</td>\n",
+       "      <td>C17693 C1173</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>C17693 C1173</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>153792</td>\n",
+       "      <td>U636@DOM1</td>\n",
+       "      <td>C17693</td>\n",
+       "      <td>C294</td>\n",
+       "      <td>C17693 C294</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>C17693 C294</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19008</th>\n",
+       "      <td>8463107</td>\n",
+       "      <td>48263</td>\n",
+       "      <td>C11843$@DOM1</td>\n",
+       "      <td>C11843</td>\n",
+       "      <td>C528</td>\n",
+       "      <td>C11843 C528 Kerberos Network</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C11843 C528</td>\n",
+       "      <td>C11843$@DOM1</td>\n",
+       "      <td>Kerberos</td>\n",
+       "      <td>Network</td>\n",
+       "      <td>LogOn</td>\n",
+       "      <td>Success</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19009</th>\n",
+       "      <td>14394630</td>\n",
+       "      <td>77937</td>\n",
+       "      <td>C8470$@DOM1</td>\n",
+       "      <td>C8470</td>\n",
+       "      <td>C528</td>\n",
+       "      <td>C8470 C528 NTLM Network</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C8470 C528</td>\n",
+       "      <td>C8470$@DOM1</td>\n",
+       "      <td>NTLM</td>\n",
+       "      <td>Network</td>\n",
+       "      <td>LogOn</td>\n",
+       "      <td>Success</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19010</th>\n",
+       "      <td>33398153</td>\n",
+       "      <td>173300</td>\n",
+       "      <td>C716$@DOM1</td>\n",
+       "      <td>C716</td>\n",
+       "      <td>C716</td>\n",
+       "      <td>C716 C716 ? ?</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C716 C716</td>\n",
+       "      <td>C716$@DOM1</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>AuthMap</td>\n",
+       "      <td>Success</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19011</th>\n",
+       "      <td>18353851</td>\n",
+       "      <td>102472</td>\n",
+       "      <td>U7365@DOM1</td>\n",
+       "      <td>C16126</td>\n",
+       "      <td>C586</td>\n",
+       "      <td>C16126 C586 ? ?</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C16126 C586</td>\n",
+       "      <td>U7365@DOM1</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>TGS</td>\n",
+       "      <td>Success</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19012</th>\n",
+       "      <td>27372458</td>\n",
+       "      <td>141156</td>\n",
+       "      <td>NETWORK SERVICE@C6215</td>\n",
+       "      <td>C6215</td>\n",
+       "      <td>C6215</td>\n",
+       "      <td>C6215 C6215 Negotiate Service</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C6215 C6215</td>\n",
+       "      <td>NETWORK SERVICE@C6215</td>\n",
+       "      <td>Negotiate</td>\n",
+       "      <td>Service</td>\n",
+       "      <td>LogOn</td>\n",
+       "      <td>Success</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19762 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          index    time             src_domain src_computer dst_computer  \\\n",
+       "0             0  150885              U620@DOM1       C17693        C1003   \n",
+       "1             1  151036              U748@DOM1       C17693         C305   \n",
+       "2             2  151648              U748@DOM1       C17693         C728   \n",
+       "3             3  151993             U6115@DOM1       C17693        C1173   \n",
+       "4             4  153792              U636@DOM1       C17693         C294   \n",
+       "...         ...     ...                    ...          ...          ...   \n",
+       "19008   8463107   48263           C11843$@DOM1       C11843         C528   \n",
+       "19009  14394630   77937            C8470$@DOM1        C8470         C528   \n",
+       "19010  33398153  173300             C716$@DOM1         C716         C716   \n",
+       "19011  18353851  102472             U7365@DOM1       C16126         C586   \n",
+       "19012  27372458  141156  NETWORK SERVICE@C6215        C6215        C6215   \n",
+       "\n",
+       "                               feats  RED        feats2  \\\n",
+       "0                       C17693 C1003  1.0  C17693 C1003   \n",
+       "1                        C17693 C305  1.0   C17693 C305   \n",
+       "2                        C17693 C728  1.0   C17693 C728   \n",
+       "3                       C17693 C1173  1.0  C17693 C1173   \n",
+       "4                        C17693 C294  1.0   C17693 C294   \n",
+       "...                              ...  ...           ...   \n",
+       "19008   C11843 C528 Kerberos Network  0.0   C11843 C528   \n",
+       "19009        C8470 C528 NTLM Network  0.0    C8470 C528   \n",
+       "19010                  C716 C716 ? ?  0.0     C716 C716   \n",
+       "19011                C16126 C586 ? ?  0.0   C16126 C586   \n",
+       "19012  C6215 C6215 Negotiate Service  0.0   C6215 C6215   \n",
+       "\n",
+       "                  dst_domain  auth_type logontype authentication_orientation  \\\n",
+       "0                        NaN        NaN       NaN                        NaN   \n",
+       "1                        NaN        NaN       NaN                        NaN   \n",
+       "2                        NaN        NaN       NaN                        NaN   \n",
+       "3                        NaN        NaN       NaN                        NaN   \n",
+       "4                        NaN        NaN       NaN                        NaN   \n",
+       "...                      ...        ...       ...                        ...   \n",
+       "19008           C11843$@DOM1   Kerberos   Network                      LogOn   \n",
+       "19009            C8470$@DOM1       NTLM   Network                      LogOn   \n",
+       "19010             C716$@DOM1          ?         ?                    AuthMap   \n",
+       "19011             U7365@DOM1          ?         ?                        TGS   \n",
+       "19012  NETWORK SERVICE@C6215  Negotiate   Service                      LogOn   \n",
+       "\n",
+       "      success_or_failure  \n",
+       "0                    NaN  \n",
+       "1                    NaN  \n",
+       "2                    NaN  \n",
+       "3                    NaN  \n",
+       "4                    NaN  \n",
+       "...                  ...  \n",
+       "19008            Success  \n",
+       "19009            Success  \n",
+       "19010            Success  \n",
+       "19011            Success  \n",
+       "19012            Success  \n",
+       "\n",
+       "[19762 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# make a subsampled dataframe with the anom red-team data at top...so we can keep track.\n",
     "# we don't need the full `df`, only the unique entries of 'feats' in `ndf` for \n",
@@ -192,7 +671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "5f62b7b5",
    "metadata": {},
    "outputs": [],
@@ -203,10 +682,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "5ffd6aac",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "749.0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# total number of red team events\n",
     "tdf.RED.sum()"
@@ -222,7 +712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "72c53f98",
    "metadata": {},
    "outputs": [],
@@ -289,30 +779,165 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
+   "id": "504781dc-9fbe-467c-9b4d-2e907133cfb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "\n",
+      "A topic model for computer to computer + metadata cyber auth logs\n",
+      "_________________________________________________________________\n",
+      "\n",
+      "Updated: {'n_topics': 32, 'X': ['feats']}\n",
+      "_________________________________________________________________\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'kind': 'nodes', 'use_scaler': None, 'use_scaler_target': None, 'cardinality_threshold': 2, 'cardinality_threshold_target': 2, 'n_topics': 32, 'n_topics_target': 10, 'multilabel': False, 'embedding': False, 'use_ngrams': False, 'ngram_range': (1, 3), 'max_df': 0.2, 'min_df': 3, 'min_words': 40000000.0, 'model_name': 'sentence-transformers/msmarco-distilbert-base-v2', 'impute': 'median', 'n_quantiles': 100, 'output_distribution': 'normal', 'quantile_range': (25, 75), 'n_bins': 10, 'encode': 'ordinal', 'strategy': 'uniform', 'similarity': None, 'categories': 'auto', 'keep_n_decimals': 5, 'remove_node_column': True, 'inplace': False, 'feature_engine': 'auto', 'memoize': True, 'X': ['feats']}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# this is a convienence method for setting parameters in `g.featurize()/umap()` -- just a verbose dictionary\n",
+    "cyber_model = ModelDict('A topic model for computer to computer + metadata cyber auth logs', **topic_model)\n",
+    "\n",
+    "cyber_model.update(dict(n_topics=32, X=['feats']))  # name the column to featurize, which we lumped into `feats`\n",
+    "\n",
+    "cyber_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "id": "6909cc90",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (19762, 0) in UMAP fit, as it is not one dimensionalOMP: Info #273: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------\n",
+      "cluster: 0\n",
+      "   red 2.59% or 95.0 out of 3665\n",
+      "--------------------\n",
+      "cluster: 27\n",
+      "   red 0.41% or 3.0 out of 724\n",
+      "--------------------\n",
+      "cluster: 26\n",
+      "   red 0.38% or 1.0 out of 260\n",
+      "--------------------\n",
+      "cluster: 10\n",
+      "   red 0.43% or 1.0 out of 234\n",
+      "--------------------\n",
+      "cluster: 1\n",
+      "   red 94.44% or 119.0 out of 126\n",
+      "--------------------\n",
+      "cluster: 6\n",
+      "   red 95.06% or 77.0 out of 81\n",
+      "--------------------\n",
+      "cluster: 9\n",
+      "   red 84.42% or 65.0 out of 77\n",
+      "--------------------\n",
+      "cluster: 5\n",
+      "   red 96.61% or 57.0 out of 59\n",
+      "--------------------\n",
+      "cluster: 7\n",
+      "   red 91.84% or 45.0 out of 49\n",
+      "--------------------\n",
+      "cluster: 14\n",
+      "   red 92.11% or 35.0 out of 38\n",
+      "--------------------\n",
+      "cluster: 3\n",
+      "   red 94.59% or 35.0 out of 37\n",
+      "--------------------\n",
+      "cluster: 22\n",
+      "   red 100.00% or 27.0 out of 27\n",
+      "--------------------\n",
+      "cluster: 4\n",
+      "   red 84.00% or 21.0 out of 25\n",
+      "--------------------\n",
+      "cluster: 23\n",
+      "   red 100.00% or 24.0 out of 24\n",
+      "--------------------\n",
+      "cluster: 8\n",
+      "   red 100.00% or 23.0 out of 23\n",
+      "--------------------\n",
+      "cluster: 20\n",
+      "   red 81.25% or 13.0 out of 16\n",
+      "--------------------\n",
+      "cluster: 13\n",
+      "   red 93.33% or 14.0 out of 15\n",
+      "--------------------\n",
+      "cluster: 16\n",
+      "   red 100.00% or 15.0 out of 15\n",
+      "--------------------\n",
+      "cluster: 2\n",
+      "   red 100.00% or 14.0 out of 14\n",
+      "--------------------\n",
+      "cluster: 25\n",
+      "   red 100.00% or 13.0 out of 13\n",
+      "--------------------\n",
+      "cluster: 11\n",
+      "   red 100.00% or 11.0 out of 11\n",
+      "--------------------\n",
+      "cluster: 18\n",
+      "   red 100.00% or 9.0 out of 9\n",
+      "--------------------\n",
+      "cluster: 15\n",
+      "   red 100.00% or 6.0 out of 6\n",
+      "--------------------\n",
+      "cluster: 24\n",
+      "   red 100.00% or 6.0 out of 6\n",
+      "--------------------\n",
+      "cluster: 12\n",
+      "   red 100.00% or 5.0 out of 5\n",
+      "--------------------\n",
+      "cluster: 17\n",
+      "   red 100.00% or 5.0 out of 5\n",
+      "--------------------\n",
+      "cluster: 19\n",
+      "   red 100.00% or 5.0 out of 5\n",
+      "--------------------\n",
+      "cluster: 21\n",
+      "   red 100.00% or 5.0 out of 5\n",
+      "CPU times: user 3min 16s, sys: 32 s, total: 3min 48s\n",
+      "Wall time: 1min 57s\n"
+     ]
+    }
+   ],
    "source": [
     "%%time\n",
     "process = True  # set to false after it's run for ease of speed\n",
     "if process:\n",
-    "    g = graphistry.nodes(tdf, 'node')\n",
-    "    g5 = g.umap(X=['feats'], \n",
-    "                min_words=1000000, # force high so that we don't use Sentence Transformers\n",
-    "                cardinality_threshold=4, # set low so we force topic model\n",
-    "                n_topics=32, # number of topics\n",
-    "                use_scaler=None,\n",
-    "                use_scaler_target=None\n",
-    "               )\n",
+    "    # ##################################\n",
+    "    g = graphistry.nodes(tdf, 'node')  # two lines does the heavy lifting\n",
+    "    g5 = g.umap(**cyber_model)\n",
+    "    # #########################\n",
     "    \n",
     "    g5, dbscan, cluster_confidences = enrich(g5)\n",
     "\n",
-    "    g5.build_index()\n",
-    "    g5.save_search_instance('../data/auth-feat-topic.search')\n",
+    "    g5.save_search_instance('auth-feat-topic.search')\n",
     "else:\n",
     "    g = graphistry.bind()\n",
-    "    g5 = g.load_search_instance('../data/auth-feat-topic.search')\n",
+    "    g5 = g.load_search_instance('auth-feat-topic.search')\n",
     "    g5, dbscan, cluster_confidences = enrich(g5)\n"
    ]
   },
@@ -321,26 +946,516 @@
    "id": "54c13cba-bc36-4d49-8e7a-7dc05b27610a",
    "metadata": {},
    "source": [
-    "## Plot it\n",
-    "Color by `confidence` and hover over `red` team histogram to see where events occur"
+    "## Plot Graph\n",
+    "Color by `confidence` and hover over `red` team histogram to see where events occur. Alternatively, color by `cluster` assignment"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "279fef41",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://hub.graphistry.com/graph/graph.html?dataset=77ee7b6a4daa4539a93f0c60e34934c0&type=arrow&viztoken=4d612b44-1558-4398-8964-744cf5c9c632&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672345808&info=true&play=0'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g5.name('auth 50k topic feats no target').plot(render=False)"
+    "g5.name('auth topic feats no target').plot(render=RENDER)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "79ece955",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feats: c9990, c9994, c9997</th>\n",
+       "      <th>feats: kerberos, u1, u7</th>\n",
+       "      <th>feats: c528, c5252, c5281</th>\n",
+       "      <th>feats: c612, c6121, c6125</th>\n",
+       "      <th>feats: c2446, c2444, c24464</th>\n",
+       "      <th>feats: c13713, c13130, c13134</th>\n",
+       "      <th>feats: c586, c5866, c5864</th>\n",
+       "      <th>feats: c467, c4674, c4667</th>\n",
+       "      <th>feats: unlock, c1111, c11114</th>\n",
+       "      <th>feats: c625, c6257, c6255</th>\n",
+       "      <th>...</th>\n",
+       "      <th>feats: c5299, c529, c5294</th>\n",
+       "      <th>feats: c16616, c16168, c16663</th>\n",
+       "      <th>feats: microsoft_authentication_package_v1_0, microsoft_authentication_package_v1_, microsoft_authentication_package_v1</th>\n",
+       "      <th>feats: c1065, c10658, c10652</th>\n",
+       "      <th>feats: cachedinteractive, remoteinteractive, interactive</th>\n",
+       "      <th>feats: c3888, c3884, u608</th>\n",
+       "      <th>feats: c2327, c2323, c2727</th>\n",
+       "      <th>feats: negotiate, service, c14514</th>\n",
+       "      <th>feats: c8282, c8280, c8289</th>\n",
+       "      <th>feats: c1964, c1968, c25685</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.052992</td>\n",
+       "      <td>0.050029</td>\n",
+       "      <td>0.051413</td>\n",
+       "      <td>0.051403</td>\n",
+       "      <td>0.050019</td>\n",
+       "      <td>0.061212</td>\n",
+       "      <td>0.051419</td>\n",
+       "      <td>0.050463</td>\n",
+       "      <td>0.057304</td>\n",
+       "      <td>0.051460</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.051392</td>\n",
+       "      <td>0.063923</td>\n",
+       "      <td>0.050267</td>\n",
+       "      <td>0.125901</td>\n",
+       "      <td>1.258763</td>\n",
+       "      <td>0.052462</td>\n",
+       "      <td>0.051519</td>\n",
+       "      <td>0.051145</td>\n",
+       "      <td>0.051962</td>\n",
+       "      <td>0.054784</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.609871</td>\n",
+       "      <td>0.050030</td>\n",
+       "      <td>0.051420</td>\n",
+       "      <td>0.051410</td>\n",
+       "      <td>0.050016</td>\n",
+       "      <td>0.091486</td>\n",
+       "      <td>0.051426</td>\n",
+       "      <td>0.050465</td>\n",
+       "      <td>0.061034</td>\n",
+       "      <td>0.051467</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.051399</td>\n",
+       "      <td>0.069057</td>\n",
+       "      <td>0.050285</td>\n",
+       "      <td>0.062053</td>\n",
+       "      <td>1.321739</td>\n",
+       "      <td>0.053182</td>\n",
+       "      <td>0.051527</td>\n",
+       "      <td>0.050077</td>\n",
+       "      <td>0.051972</td>\n",
+       "      <td>0.057155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.051975</td>\n",
+       "      <td>0.050030</td>\n",
+       "      <td>0.557747</td>\n",
+       "      <td>0.054309</td>\n",
+       "      <td>0.050016</td>\n",
+       "      <td>0.070115</td>\n",
+       "      <td>0.051457</td>\n",
+       "      <td>0.050475</td>\n",
+       "      <td>0.060911</td>\n",
+       "      <td>0.051500</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.051429</td>\n",
+       "      <td>0.068827</td>\n",
+       "      <td>0.050290</td>\n",
+       "      <td>0.061931</td>\n",
+       "      <td>1.385945</td>\n",
+       "      <td>0.053210</td>\n",
+       "      <td>0.053094</td>\n",
+       "      <td>0.050077</td>\n",
+       "      <td>0.059695</td>\n",
+       "      <td>0.057138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.052089</td>\n",
+       "      <td>0.050032</td>\n",
+       "      <td>0.051534</td>\n",
+       "      <td>0.051523</td>\n",
+       "      <td>0.050023</td>\n",
+       "      <td>0.069080</td>\n",
+       "      <td>0.051541</td>\n",
+       "      <td>0.050501</td>\n",
+       "      <td>3.781985</td>\n",
+       "      <td>0.051586</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.051511</td>\n",
+       "      <td>0.074502</td>\n",
+       "      <td>0.050296</td>\n",
+       "      <td>0.089917</td>\n",
+       "      <td>2.585125</td>\n",
+       "      <td>0.052992</td>\n",
+       "      <td>0.051650</td>\n",
+       "      <td>0.051786</td>\n",
+       "      <td>0.052132</td>\n",
+       "      <td>0.056803</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.612539</td>\n",
+       "      <td>0.050031</td>\n",
+       "      <td>0.051482</td>\n",
+       "      <td>0.051472</td>\n",
+       "      <td>0.050016</td>\n",
+       "      <td>0.070362</td>\n",
+       "      <td>0.051488</td>\n",
+       "      <td>0.050485</td>\n",
+       "      <td>0.061027</td>\n",
+       "      <td>0.051532</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.566014</td>\n",
+       "      <td>0.069057</td>\n",
+       "      <td>0.050295</td>\n",
+       "      <td>0.062066</td>\n",
+       "      <td>1.322035</td>\n",
+       "      <td>0.053263</td>\n",
+       "      <td>0.073381</td>\n",
+       "      <td>0.050077</td>\n",
+       "      <td>0.052059</td>\n",
+       "      <td>0.057233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19008</th>\n",
+       "      <td>0.051856</td>\n",
+       "      <td>22.477729</td>\n",
+       "      <td>7.183005</td>\n",
+       "      <td>0.051355</td>\n",
+       "      <td>0.050015</td>\n",
+       "      <td>0.061363</td>\n",
+       "      <td>0.065023</td>\n",
+       "      <td>0.050447</td>\n",
+       "      <td>2.851467</td>\n",
+       "      <td>0.051411</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.080029</td>\n",
+       "      <td>1.255318</td>\n",
+       "      <td>0.056317</td>\n",
+       "      <td>0.056565</td>\n",
+       "      <td>0.057033</td>\n",
+       "      <td>0.066903</td>\n",
+       "      <td>0.051468</td>\n",
+       "      <td>0.054699</td>\n",
+       "      <td>1.611046</td>\n",
+       "      <td>0.054593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19009</th>\n",
+       "      <td>0.051961</td>\n",
+       "      <td>0.077069</td>\n",
+       "      <td>5.711064</td>\n",
+       "      <td>0.051431</td>\n",
+       "      <td>0.050016</td>\n",
+       "      <td>0.051497</td>\n",
+       "      <td>0.069693</td>\n",
+       "      <td>0.050472</td>\n",
+       "      <td>0.050832</td>\n",
+       "      <td>0.051490</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.093134</td>\n",
+       "      <td>0.051501</td>\n",
+       "      <td>0.055322</td>\n",
+       "      <td>0.051199</td>\n",
+       "      <td>1.592440</td>\n",
+       "      <td>0.057984</td>\n",
+       "      <td>0.051550</td>\n",
+       "      <td>0.057041</td>\n",
+       "      <td>3.204279</td>\n",
+       "      <td>0.051984</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19010</th>\n",
+       "      <td>0.052328</td>\n",
+       "      <td>0.050035</td>\n",
+       "      <td>0.051708</td>\n",
+       "      <td>0.051696</td>\n",
+       "      <td>4.051562</td>\n",
+       "      <td>0.051775</td>\n",
+       "      <td>0.987526</td>\n",
+       "      <td>0.050557</td>\n",
+       "      <td>0.050983</td>\n",
+       "      <td>0.051765</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.051682</td>\n",
+       "      <td>6.652512</td>\n",
+       "      <td>0.050303</td>\n",
+       "      <td>0.051419</td>\n",
+       "      <td>0.083564</td>\n",
+       "      <td>0.052265</td>\n",
+       "      <td>0.051837</td>\n",
+       "      <td>0.050006</td>\n",
+       "      <td>0.052377</td>\n",
+       "      <td>0.054756</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19011</th>\n",
+       "      <td>0.052075</td>\n",
+       "      <td>0.050032</td>\n",
+       "      <td>0.058136</td>\n",
+       "      <td>0.798598</td>\n",
+       "      <td>4.051370</td>\n",
+       "      <td>0.054901</td>\n",
+       "      <td>7.388775</td>\n",
+       "      <td>0.050498</td>\n",
+       "      <td>0.052861</td>\n",
+       "      <td>0.051575</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.057975</td>\n",
+       "      <td>2.655457</td>\n",
+       "      <td>0.050278</td>\n",
+       "      <td>0.053364</td>\n",
+       "      <td>0.053534</td>\n",
+       "      <td>0.052306</td>\n",
+       "      <td>0.051639</td>\n",
+       "      <td>0.050021</td>\n",
+       "      <td>0.052118</td>\n",
+       "      <td>0.054182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19012</th>\n",
+       "      <td>0.052223</td>\n",
+       "      <td>0.055972</td>\n",
+       "      <td>0.051631</td>\n",
+       "      <td>0.064040</td>\n",
+       "      <td>0.050018</td>\n",
+       "      <td>0.051695</td>\n",
+       "      <td>0.051638</td>\n",
+       "      <td>0.050532</td>\n",
+       "      <td>1.069696</td>\n",
+       "      <td>7.039859</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.051607</td>\n",
+       "      <td>0.051698</td>\n",
+       "      <td>0.051944</td>\n",
+       "      <td>0.092037</td>\n",
+       "      <td>0.051386</td>\n",
+       "      <td>0.052162</td>\n",
+       "      <td>0.051755</td>\n",
+       "      <td>26.060354</td>\n",
+       "      <td>0.052269</td>\n",
+       "      <td>0.052257</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19762 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       feats: c9990, c9994, c9997  feats: kerberos, u1, u7  \\\n",
+       "0                        0.052992                 0.050029   \n",
+       "1                        1.609871                 0.050030   \n",
+       "2                        0.051975                 0.050030   \n",
+       "3                        0.052089                 0.050032   \n",
+       "4                        1.612539                 0.050031   \n",
+       "...                           ...                      ...   \n",
+       "19008                    0.051856                22.477729   \n",
+       "19009                    0.051961                 0.077069   \n",
+       "19010                    0.052328                 0.050035   \n",
+       "19011                    0.052075                 0.050032   \n",
+       "19012                    0.052223                 0.055972   \n",
+       "\n",
+       "       feats: c528, c5252, c5281  feats: c612, c6121, c6125  \\\n",
+       "0                       0.051413                   0.051403   \n",
+       "1                       0.051420                   0.051410   \n",
+       "2                       0.557747                   0.054309   \n",
+       "3                       0.051534                   0.051523   \n",
+       "4                       0.051482                   0.051472   \n",
+       "...                          ...                        ...   \n",
+       "19008                   7.183005                   0.051355   \n",
+       "19009                   5.711064                   0.051431   \n",
+       "19010                   0.051708                   0.051696   \n",
+       "19011                   0.058136                   0.798598   \n",
+       "19012                   0.051631                   0.064040   \n",
+       "\n",
+       "       feats: c2446, c2444, c24464  feats: c13713, c13130, c13134  \\\n",
+       "0                         0.050019                       0.061212   \n",
+       "1                         0.050016                       0.091486   \n",
+       "2                         0.050016                       0.070115   \n",
+       "3                         0.050023                       0.069080   \n",
+       "4                         0.050016                       0.070362   \n",
+       "...                            ...                            ...   \n",
+       "19008                     0.050015                       0.061363   \n",
+       "19009                     0.050016                       0.051497   \n",
+       "19010                     4.051562                       0.051775   \n",
+       "19011                     4.051370                       0.054901   \n",
+       "19012                     0.050018                       0.051695   \n",
+       "\n",
+       "       feats: c586, c5866, c5864  feats: c467, c4674, c4667  \\\n",
+       "0                       0.051419                   0.050463   \n",
+       "1                       0.051426                   0.050465   \n",
+       "2                       0.051457                   0.050475   \n",
+       "3                       0.051541                   0.050501   \n",
+       "4                       0.051488                   0.050485   \n",
+       "...                          ...                        ...   \n",
+       "19008                   0.065023                   0.050447   \n",
+       "19009                   0.069693                   0.050472   \n",
+       "19010                   0.987526                   0.050557   \n",
+       "19011                   7.388775                   0.050498   \n",
+       "19012                   0.051638                   0.050532   \n",
+       "\n",
+       "       feats: unlock, c1111, c11114  feats: c625, c6257, c6255  ...  \\\n",
+       "0                          0.057304                   0.051460  ...   \n",
+       "1                          0.061034                   0.051467  ...   \n",
+       "2                          0.060911                   0.051500  ...   \n",
+       "3                          3.781985                   0.051586  ...   \n",
+       "4                          0.061027                   0.051532  ...   \n",
+       "...                             ...                        ...  ...   \n",
+       "19008                      2.851467                   0.051411  ...   \n",
+       "19009                      0.050832                   0.051490  ...   \n",
+       "19010                      0.050983                   0.051765  ...   \n",
+       "19011                      0.052861                   0.051575  ...   \n",
+       "19012                      1.069696                   7.039859  ...   \n",
+       "\n",
+       "       feats: c5299, c529, c5294  feats: c16616, c16168, c16663  \\\n",
+       "0                       0.051392                       0.063923   \n",
+       "1                       0.051399                       0.069057   \n",
+       "2                       0.051429                       0.068827   \n",
+       "3                       0.051511                       0.074502   \n",
+       "4                       0.566014                       0.069057   \n",
+       "...                          ...                            ...   \n",
+       "19008                   0.080029                       1.255318   \n",
+       "19009                   0.093134                       0.051501   \n",
+       "19010                   0.051682                       6.652512   \n",
+       "19011                   0.057975                       2.655457   \n",
+       "19012                   0.051607                       0.051698   \n",
+       "\n",
+       "       feats: microsoft_authentication_package_v1_0, microsoft_authentication_package_v1_, microsoft_authentication_package_v1  \\\n",
+       "0                                               0.050267                                                                         \n",
+       "1                                               0.050285                                                                         \n",
+       "2                                               0.050290                                                                         \n",
+       "3                                               0.050296                                                                         \n",
+       "4                                               0.050295                                                                         \n",
+       "...                                                  ...                                                                         \n",
+       "19008                                           0.056317                                                                         \n",
+       "19009                                           0.055322                                                                         \n",
+       "19010                                           0.050303                                                                         \n",
+       "19011                                           0.050278                                                                         \n",
+       "19012                                           0.051944                                                                         \n",
+       "\n",
+       "       feats: c1065, c10658, c10652  \\\n",
+       "0                          0.125901   \n",
+       "1                          0.062053   \n",
+       "2                          0.061931   \n",
+       "3                          0.089917   \n",
+       "4                          0.062066   \n",
+       "...                             ...   \n",
+       "19008                      0.056565   \n",
+       "19009                      0.051199   \n",
+       "19010                      0.051419   \n",
+       "19011                      0.053364   \n",
+       "19012                      0.092037   \n",
+       "\n",
+       "       feats: cachedinteractive, remoteinteractive, interactive  \\\n",
+       "0                                               1.258763          \n",
+       "1                                               1.321739          \n",
+       "2                                               1.385945          \n",
+       "3                                               2.585125          \n",
+       "4                                               1.322035          \n",
+       "...                                                  ...          \n",
+       "19008                                           0.057033          \n",
+       "19009                                           1.592440          \n",
+       "19010                                           0.083564          \n",
+       "19011                                           0.053534          \n",
+       "19012                                           0.051386          \n",
+       "\n",
+       "       feats: c3888, c3884, u608  feats: c2327, c2323, c2727  \\\n",
+       "0                       0.052462                    0.051519   \n",
+       "1                       0.053182                    0.051527   \n",
+       "2                       0.053210                    0.053094   \n",
+       "3                       0.052992                    0.051650   \n",
+       "4                       0.053263                    0.073381   \n",
+       "...                          ...                         ...   \n",
+       "19008                   0.066903                    0.051468   \n",
+       "19009                   0.057984                    0.051550   \n",
+       "19010                   0.052265                    0.051837   \n",
+       "19011                   0.052306                    0.051639   \n",
+       "19012                   0.052162                    0.051755   \n",
+       "\n",
+       "       feats: negotiate, service, c14514  feats: c8282, c8280, c8289  \\\n",
+       "0                               0.051145                    0.051962   \n",
+       "1                               0.050077                    0.051972   \n",
+       "2                               0.050077                    0.059695   \n",
+       "3                               0.051786                    0.052132   \n",
+       "4                               0.050077                    0.052059   \n",
+       "...                                  ...                         ...   \n",
+       "19008                           0.054699                    1.611046   \n",
+       "19009                           0.057041                    3.204279   \n",
+       "19010                           0.050006                    0.052377   \n",
+       "19011                           0.050021                    0.052118   \n",
+       "19012                          26.060354                    0.052269   \n",
+       "\n",
+       "       feats: c1964, c1968, c25685  \n",
+       "0                         0.054784  \n",
+       "1                         0.057155  \n",
+       "2                         0.057138  \n",
+       "3                         0.056803  \n",
+       "4                         0.057233  \n",
+       "...                            ...  \n",
+       "19008                     0.054593  \n",
+       "19009                     0.051984  \n",
+       "19010                     0.054756  \n",
+       "19011                     0.054182  \n",
+       "19012                     0.052257  \n",
+       "\n",
+       "[19762 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# see how the model has organized features\n",
     "X = g5._node_features\n",
@@ -352,9 +1467,9 @@
    "id": "632d6d0f-8212-4f4a-a920-7600d7456351",
    "metadata": {},
    "source": [
-    "## Put model into Predict Mode\n",
+    "## Predict/Online Mode\n",
     "\n",
-    "Once a model is fit, can predict on new batches as we demonstrate here\n",
+    "Once a model is fit, predict on new batches as we demonstrate here\n",
     "\n",
     "There are two main methods\n",
     "\n",
@@ -367,10 +1482,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "7b44d418",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "'SuperVectorizer' object has no attribute 'get_feature_names_in''SuperVectorizer' object has no attribute 'get_feature_names_in'"
+     ]
+    }
+   ],
    "source": [
     "# first sample a batch from the normal data (auth=df)\n",
     "emb_normal, xp_normal, _ = g5.transform_umap(df.sample(200), None, kind='nodes')\n",
@@ -380,10 +1503,118 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "d0aebbbc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9.232593</td>\n",
+       "      <td>0.724252</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5.324008</td>\n",
+       "      <td>-8.997888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10.624950</td>\n",
+       "      <td>-0.399632</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>9.591936</td>\n",
+       "      <td>-0.037859</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13.842589</td>\n",
+       "      <td>-3.487622</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19008</th>\n",
+       "      <td>10.193441</td>\n",
+       "      <td>12.514707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19009</th>\n",
+       "      <td>4.766062</td>\n",
+       "      <td>-1.102680</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19010</th>\n",
+       "      <td>9.568494</td>\n",
+       "      <td>-1.873951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19011</th>\n",
+       "      <td>11.638880</td>\n",
+       "      <td>-0.451751</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19012</th>\n",
+       "      <td>3.685098</td>\n",
+       "      <td>-6.050752</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19762 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               x          y\n",
+       "0       9.232593   0.724252\n",
+       "1       5.324008  -8.997888\n",
+       "2      10.624950  -0.399632\n",
+       "3       9.591936  -0.037859\n",
+       "4      13.842589  -3.487622\n",
+       "...          ...        ...\n",
+       "19008  10.193441  12.514707\n",
+       "19009   4.766062  -1.102680\n",
+       "19010   9.568494  -1.873951\n",
+       "19011  11.638880  -0.451751\n",
+       "19012   3.685098  -6.050752\n",
+       "\n",
+       "[19762 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# all emb's have this form\n",
     "g5._node_embedding"
@@ -391,16 +1622,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "8a8d5aa9",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x187b44730>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# scatter to see how well it does.\n",
     "plt.figure(figsize=(10,7))\n",
-    "plt.scatter(g5._node_embedding.x, g5._node_embedding.y , c='b')  # the totality of the fit data\n",
+    "plt.scatter(g5._node_embedding.x, g5._node_embedding.y , c='b', s=60, alpha=0.5)  # the totality of the fit data\n",
     "plt.scatter(emb_normal.x, emb_normal.y, c='g') # batch of new data\n",
-    "plt.scatter(emb_red.x, emb_red.y, c='r') # red labels to show good cluster seperation"
+    "plt.scatter(emb_red.x, emb_red.y, c='r') # red labels to show good cluster seperation\n",
+    "plt.scatter(emb_normal.x, emb_normal.y, c='g') # batch of new data, to see if they occlude "
    ]
   },
   {
@@ -410,17 +1665,25 @@
    "source": [
     "## 96% Reduction in Alerts\n",
     "\n",
-    "This indicates a huge reduction in the search space needed \n",
+    "This indicates a huge reduction in the search space needed.\n",
     "\n",
-    "Since we have clear cluster assignments along with (post facto) confidences of known anomalous activity, we can reduce the search space on new events (via Kafka, Splunk, etc)"
+    "Since we have clear cluster assignments along with (post facto) confidences of known anomalous activity, we can reduce the search space on new events (gotten via Kafka, Splunk, etc)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "14d207db-9a58-45a3-9876-058632389f17",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "93.92%\n"
+     ]
+    }
+   ],
    "source": [
     "# percent of RED team labels we get with 10% confidence or above\n",
     "p = cluster_confidences[cluster_confidences.confidence>0.1].n_red.sum()/cluster_confidences[cluster_confidences.confidence>0.1].total_in_cluster.sum()\n",
@@ -429,21 +1692,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "755a3f27-935d-4ba8-96cb-cbff11fdf00e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19071"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# number of data points not to consider (and it's more if we look at df proper!)\n",
+    "# number of data points *not* to consider (and it's more if we look at df proper!)\n",
     "cluster_confidences[cluster_confidences.confidence<0.1].total_in_cluster.sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "5fd1cc50-0900-4694-8400-c426e314ec2e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Alert Reduction 96.50%\n"
+     ]
+    }
+   ],
    "source": [
     "p = cluster_confidences[cluster_confidences.confidence<0.1].total_in_cluster.sum()/cluster_confidences.total_in_cluster.sum()\n",
     "print(f'Alert Reduction {100*p:.2f}%')"
@@ -451,10 +1733,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "id": "0ee508a5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(10,7))\n",
     "plt.plot(np.cumsum([k[2] for k in cluster_confidences.values]))\n",
@@ -477,29 +1779,232 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "e0c6a16d-a899-43b6-a7ba-75b45f855a78",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------\n",
+      "cluster: 2\n",
+      "   red 99.63% or 267.0 out of 268\n",
+      "--------------------\n",
+      "cluster: 6\n",
+      "   red 100.00% or 58.0 out of 58\n",
+      "--------------------\n",
+      "cluster: 4\n",
+      "   red 97.22% or 35.0 out of 36\n",
+      "--------------------\n",
+      "cluster: 8\n",
+      "   red 97.14% or 34.0 out of 35\n",
+      "--------------------\n",
+      "cluster: 16\n",
+      "   red 100.00% or 34.0 out of 34\n",
+      "--------------------\n",
+      "cluster: 11\n",
+      "   red 100.00% or 31.0 out of 31\n",
+      "--------------------\n",
+      "cluster: 24\n",
+      "   red 100.00% or 27.0 out of 27\n",
+      "--------------------\n",
+      "cluster: 25\n",
+      "   red 100.00% or 24.0 out of 24\n",
+      "--------------------\n",
+      "cluster: 3\n",
+      "   red 100.00% or 19.0 out of 19\n",
+      "--------------------\n",
+      "cluster: 7\n",
+      "   red 100.00% or 18.0 out of 18\n",
+      "--------------------\n",
+      "cluster: 17\n",
+      "   red 100.00% or 18.0 out of 18\n",
+      "--------------------\n",
+      "cluster: 0\n",
+      "   red 100.00% or 17.0 out of 17\n",
+      "--------------------\n",
+      "cluster: 12\n",
+      "   red 100.00% or 17.0 out of 17\n",
+      "--------------------\n",
+      "cluster: 18\n",
+      "   red 94.12% or 16.0 out of 17\n",
+      "--------------------\n",
+      "cluster: 26\n",
+      "   red 100.00% or 17.0 out of 17\n",
+      "--------------------\n",
+      "cluster: 14\n",
+      "   red 100.00% or 15.0 out of 15\n",
+      "--------------------\n",
+      "cluster: 5\n",
+      "   red 100.00% or 14.0 out of 14\n",
+      "--------------------\n",
+      "cluster: 10\n",
+      "   red 100.00% or 14.0 out of 14\n",
+      "--------------------\n",
+      "cluster: 1\n",
+      "   red 100.00% or 13.0 out of 13\n",
+      "--------------------\n",
+      "cluster: 13\n",
+      "   red 100.00% or 9.0 out of 9\n",
+      "--------------------\n",
+      "cluster: 19\n",
+      "   red 100.00% or 9.0 out of 9\n",
+      "--------------------\n",
+      "cluster: 15\n",
+      "   red 100.00% or 8.0 out of 8\n",
+      "--------------------\n",
+      "cluster: 21\n",
+      "   red 100.00% or 8.0 out of 8\n",
+      "--------------------\n",
+      "cluster: 23\n",
+      "   red 87.50% or 7.0 out of 8\n",
+      "--------------------\n",
+      "cluster: -1\n",
+      "   red 71.43% or 5.0 out of 7\n",
+      "--------------------\n",
+      "cluster: 9\n",
+      "   red 100.00% or 5.0 out of 5\n",
+      "--------------------\n",
+      "cluster: 20\n",
+      "   red 100.00% or 5.0 out of 5\n",
+      "--------------------\n",
+      "cluster: 22\n",
+      "   red 100.00% or 5.0 out of 5\n",
+      "CPU times: user 2min 56s, sys: 33.1 s, total: 3min 29s\n",
+      "Wall time: 1min 24s\n"
+     ]
+    }
+   ],
    "source": [
     "%%time\n",
     "process = True\n",
     "if process:\n",
+    "    # ##################################  # an example of setting features explicitly, could use ModelDict \n",
     "    g = graphistry.nodes(tdf, 'node')\n",
     "    g6 = g.umap(X=['feats'], y =['RED'], \n",
-    "                min_words=100000, \n",
-    "                cardinality_threshold=2, \n",
+    "                min_words=100000, # set high to bypass sbert encoding\n",
+    "                cardinality_threshold=2, # set low to force topic modeling\n",
     "                n_topics=32,\n",
-    "                use_scaler_target=None)\n",
+    "                use_scaler_target=None) # keep labels unscaled\n",
+    "    # ##################################\n",
+    "    \n",
     "    g6, dbscan6, cluster_confidences6  = enrich(g6)\n",
-    "    g6.build_index()\n",
-    "    g6.save_search_instance('../data/auth-feat-supervised-topic.search')\n",
+    "    \n",
+    "    g6.save_search_instance('auth-feat-supervised-topic.search')\n",
     "else:\n",
     "    g = graphistry.bind()\n",
-    "    g6 = g.load_search_instance('../data/auth-feat-supervised-topic.search')\n",
+    "    g6 = g.load_search_instance('auth-feat-supervised-topic.search')\n",
+    "    \n",
     "    g6, dbscan6, cluster_confidences6  = enrich(g6)\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "a98ef657-5307-41d9-ae31-79c1794b3728",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>RED</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19008</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19009</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19010</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19011</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19012</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19762 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       RED\n",
+       "0        1\n",
+       "1        1\n",
+       "2        1\n",
+       "3        1\n",
+       "4        1\n",
+       "...    ...\n",
+       "19008    0\n",
+       "19009    0\n",
+       "19010    0\n",
+       "19011    0\n",
+       "19012    0\n",
+       "\n",
+       "[19762 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g6._node_target.astype(int)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "0cc72ab4-c0da-4541-b32b-aa771d6e510f",
@@ -508,17 +2013,28 @@
    },
    "source": [
     "### Plot\n",
-    "Color by `confidence` and hover over `red` team histogram to see where events occur"
+    "Color by `confidence` and hover over `red` team histogram to see where events occur. Alternatively, color by `cluster` assignment"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "16e09a7d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://hub.graphistry.com/graph/graph.html?dataset=426dd5f70ceb45f9bb8e8b8ac45a85ac&type=arrow&viztoken=bfeae91e-2f9e-4e1c-90a4-c968aed1a68e&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672345903&info=true&play=0'"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g6.name('auth 50k topic with supervised umap').plot(render=False)"
+    "g6.name('auth topic with supervised umap').plot(render=RENDER)"
    ]
   },
   {
@@ -532,53 +2048,633 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "099b9d38",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (19762, 0) in UMAP fit, as it is not one dimensional"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------\n",
+      "cluster: 2\n",
+      "   red 0.37% or 48.0 out of 12839\n",
+      "--------------------\n",
+      "cluster: 4\n",
+      "   red 0.72% or 16.0 out of 2222\n",
+      "--------------------\n",
+      "cluster: 30\n",
+      "   red 0.21% or 3.0 out of 1435\n",
+      "--------------------\n",
+      "cluster: 3\n",
+      "   red 98.61% or 71.0 out of 72\n",
+      "--------------------\n",
+      "cluster: 10\n",
+      "   red 100.00% or 51.0 out of 51\n",
+      "--------------------\n",
+      "cluster: 11\n",
+      "   red 98.00% or 49.0 out of 50\n",
+      "--------------------\n",
+      "cluster: 14\n",
+      "   red 97.67% or 42.0 out of 43\n",
+      "--------------------\n",
+      "cluster: 6\n",
+      "   red 97.50% or 39.0 out of 40\n",
+      "--------------------\n",
+      "cluster: 12\n",
+      "   red 97.44% or 38.0 out of 39\n",
+      "--------------------\n",
+      "cluster: 9\n",
+      "   red 100.00% or 36.0 out of 36\n",
+      "--------------------\n",
+      "cluster: 0\n",
+      "   red 100.00% or 33.0 out of 33\n",
+      "--------------------\n",
+      "cluster: 1\n",
+      "   red 96.88% or 31.0 out of 32\n",
+      "--------------------\n",
+      "cluster: 18\n",
+      "   red 100.00% or 30.0 out of 30\n",
+      "--------------------\n",
+      "cluster: 8\n",
+      "   red 96.55% or 28.0 out of 29\n",
+      "--------------------\n",
+      "cluster: 20\n",
+      "   red 96.43% or 27.0 out of 28\n",
+      "--------------------\n",
+      "cluster: 26\n",
+      "   red 100.00% or 27.0 out of 27\n",
+      "--------------------\n",
+      "cluster: 29\n",
+      "   red 96.00% or 24.0 out of 25\n",
+      "--------------------\n",
+      "cluster: 19\n",
+      "   red 90.00% or 18.0 out of 20\n",
+      "--------------------\n",
+      "cluster: 5\n",
+      "   red 100.00% or 17.0 out of 17\n",
+      "--------------------\n",
+      "cluster: 21\n",
+      "   red 100.00% or 17.0 out of 17\n",
+      "--------------------\n",
+      "cluster: 25\n",
+      "   red 100.00% or 13.0 out of 13\n",
+      "--------------------\n",
+      "cluster: 24\n",
+      "   red 100.00% or 11.0 out of 11\n",
+      "--------------------\n",
+      "cluster: 16\n",
+      "   red 100.00% or 10.0 out of 10\n",
+      "--------------------\n",
+      "cluster: 23\n",
+      "   red 100.00% or 10.0 out of 10\n",
+      "--------------------\n",
+      "cluster: 7\n",
+      "   red 100.00% or 9.0 out of 9\n",
+      "--------------------\n",
+      "cluster: 13\n",
+      "   red 100.00% or 9.0 out of 9\n",
+      "--------------------\n",
+      "cluster: 15\n",
+      "   red 88.89% or 8.0 out of 9\n",
+      "--------------------\n",
+      "cluster: 17\n",
+      "   red 87.50% or 7.0 out of 8\n",
+      "--------------------\n",
+      "cluster: 27\n",
+      "   red 100.00% or 8.0 out of 8\n",
+      "--------------------\n",
+      "cluster: 28\n",
+      "   red 100.00% or 8.0 out of 8\n",
+      "--------------------\n",
+      "cluster: 31\n",
+      "   red 85.71% or 6.0 out of 7\n",
+      "--------------------\n",
+      "cluster: 22\n",
+      "   red 100.00% or 5.0 out of 5\n",
+      "CPU times: user 2min 39s, sys: 31.4 s, total: 3min 10s\n",
+      "Wall time: 1min 14s\n"
+     ]
+    }
+   ],
    "source": [
     "%%time\n",
     "process = True\n",
     "if process:\n",
+    "    # #####################################\n",
     "    g = graphistry.nodes(tdf, 'node')\n",
     "    g7 = g.umap(X=['feats2'], #y =['RED'], \n",
     "                min_words=100000, \n",
     "                cardinality_threshold=2, \n",
     "                n_topics=32,\n",
     "                use_scaler_target=None)\n",
+    "    # ###################################\n",
     "    g7, dbscan7, cluster_confidences7  = enrich(g7)\n",
     "    g7.build_index()\n",
-    "    g7.save_search_instance('../data/auth-feat-just-ip-topic.search')\n",
+    "    g7.save_search_instance('auth-just-ip-topic.search')\n",
     "else:\n",
-    "    g7 = graphistry.bind().load_search_instance('../data/auth-feat-just-ip-topic.search')\n",
+    "    g7 = graphistry.bind().load_search_instance('auth-just-ip-topic.search')\n",
     "    g7, dbscan7, cluster_confidences7  = enrich(g7)\n"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "836883cb-bc66-4a40-9ca8-f01fd38b6f2a",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "### Plot\n",
-    "Color by `confidence` and hover over `red` team histogram to see where events occur"
+    "Color by `confidence` and hover over `red` team histogram to see where events occur. Alternatively, color by `cluster` assignment"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "id": "c1e586a3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://hub.graphistry.com/graph/graph.html?dataset=b85fc0d43e884508ad22d4a1e5daa03b&type=arrow&viztoken=75f5f3cc-b52a-4488-b0eb-9b9941208629&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672345982&info=true&play=0'"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g7.name('auth 50k topic only ips no supervision').plot(render=False)"
+    "g7.name('auth topic ips-ips only, no supervision').plot(render=RENDER)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "id": "5f93d747",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feats2: c10555, c8555, c1055</th>\n",
+       "      <th>feats2: c12222, c12226, c1227</th>\n",
+       "      <th>feats2: c6665, c6667, c6653</th>\n",
+       "      <th>feats2: c7703, c7701, c7707</th>\n",
+       "      <th>feats2: c1992, c1922, c19932</th>\n",
+       "      <th>feats2: c625, c612, c6125</th>\n",
+       "      <th>feats2: c9028, c9904, c9283</th>\n",
+       "      <th>feats2: c3073, c3037, c3074</th>\n",
+       "      <th>feats2: c2106, c2626, c4210</th>\n",
+       "      <th>feats2: c11196, c11918, c1111</th>\n",
+       "      <th>...</th>\n",
+       "      <th>feats2: c4448, c4444, c4487</th>\n",
+       "      <th>feats2: c17981, c2980, c1798</th>\n",
+       "      <th>feats2: c4777, c14777, c4787</th>\n",
+       "      <th>feats2: c7554, c7519, c5151</th>\n",
+       "      <th>feats2: c10000, c10008, c10003</th>\n",
+       "      <th>feats2: c25240, c2524, c2456</th>\n",
+       "      <th>feats2: c809, c5099, c5809</th>\n",
+       "      <th>feats2: c1065, c10658, c10656</th>\n",
+       "      <th>feats2: c1550, c15034, c15615</th>\n",
+       "      <th>feats2: c8182, c8882, c8889</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.34853</td>\n",
+       "      <td>-0.29446</td>\n",
+       "      <td>-0.34437</td>\n",
+       "      <td>-0.31798</td>\n",
+       "      <td>-0.34102</td>\n",
+       "      <td>-0.46182</td>\n",
+       "      <td>-0.33386</td>\n",
+       "      <td>-0.32362</td>\n",
+       "      <td>-0.31948</td>\n",
+       "      <td>-0.32044</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.36251</td>\n",
+       "      <td>-0.36984</td>\n",
+       "      <td>-0.32987</td>\n",
+       "      <td>-0.28672</td>\n",
+       "      <td>3.88640</td>\n",
+       "      <td>-0.36522</td>\n",
+       "      <td>-0.34670</td>\n",
+       "      <td>-0.15082</td>\n",
+       "      <td>-0.38757</td>\n",
+       "      <td>-0.32410</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.09896</td>\n",
+       "      <td>-0.29610</td>\n",
+       "      <td>-0.34437</td>\n",
+       "      <td>-0.31795</td>\n",
+       "      <td>-0.34092</td>\n",
+       "      <td>-0.46180</td>\n",
+       "      <td>-0.33383</td>\n",
+       "      <td>2.52748</td>\n",
+       "      <td>-0.32073</td>\n",
+       "      <td>-0.32158</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.36238</td>\n",
+       "      <td>-0.36982</td>\n",
+       "      <td>-0.32984</td>\n",
+       "      <td>-0.28669</td>\n",
+       "      <td>-0.29198</td>\n",
+       "      <td>-0.36519</td>\n",
+       "      <td>-0.34666</td>\n",
+       "      <td>-0.32204</td>\n",
+       "      <td>-0.38862</td>\n",
+       "      <td>-0.32407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.34853</td>\n",
+       "      <td>-0.29417</td>\n",
+       "      <td>-0.34437</td>\n",
+       "      <td>3.43709</td>\n",
+       "      <td>-0.33256</td>\n",
+       "      <td>-0.46182</td>\n",
+       "      <td>-0.00576</td>\n",
+       "      <td>-0.32411</td>\n",
+       "      <td>-0.32076</td>\n",
+       "      <td>-0.32092</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.36235</td>\n",
+       "      <td>-0.36985</td>\n",
+       "      <td>-0.32988</td>\n",
+       "      <td>-0.28673</td>\n",
+       "      <td>-0.29119</td>\n",
+       "      <td>-0.36523</td>\n",
+       "      <td>-0.34668</td>\n",
+       "      <td>-0.32147</td>\n",
+       "      <td>-0.38820</td>\n",
+       "      <td>-0.32411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.34854</td>\n",
+       "      <td>-0.29143</td>\n",
+       "      <td>-0.34437</td>\n",
+       "      <td>-0.31800</td>\n",
+       "      <td>-0.34095</td>\n",
+       "      <td>-0.46183</td>\n",
+       "      <td>-0.33388</td>\n",
+       "      <td>0.60698</td>\n",
+       "      <td>-0.32077</td>\n",
+       "      <td>2.87353</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.36240</td>\n",
+       "      <td>-0.36985</td>\n",
+       "      <td>-0.32989</td>\n",
+       "      <td>-0.28674</td>\n",
+       "      <td>-0.28688</td>\n",
+       "      <td>-0.36524</td>\n",
+       "      <td>-0.34671</td>\n",
+       "      <td>-0.29361</td>\n",
+       "      <td>-0.38584</td>\n",
+       "      <td>-0.32412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.34850</td>\n",
+       "      <td>-0.27595</td>\n",
+       "      <td>-0.34437</td>\n",
+       "      <td>-0.31795</td>\n",
+       "      <td>-0.34093</td>\n",
+       "      <td>-0.46180</td>\n",
+       "      <td>-0.32521</td>\n",
+       "      <td>-0.32408</td>\n",
+       "      <td>0.41376</td>\n",
+       "      <td>-0.32181</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.36239</td>\n",
+       "      <td>0.51644</td>\n",
+       "      <td>-0.32984</td>\n",
+       "      <td>-0.28668</td>\n",
+       "      <td>-0.29225</td>\n",
+       "      <td>-0.36184</td>\n",
+       "      <td>-0.34668</td>\n",
+       "      <td>-0.32224</td>\n",
+       "      <td>0.57418</td>\n",
+       "      <td>-0.32407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19008</th>\n",
+       "      <td>-0.34820</td>\n",
+       "      <td>-0.29900</td>\n",
+       "      <td>-0.34437</td>\n",
+       "      <td>-0.31436</td>\n",
+       "      <td>-0.34085</td>\n",
+       "      <td>-0.46158</td>\n",
+       "      <td>0.82235</td>\n",
+       "      <td>-0.32377</td>\n",
+       "      <td>-0.32049</td>\n",
+       "      <td>2.46634</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.36230</td>\n",
+       "      <td>-0.36955</td>\n",
+       "      <td>-0.32952</td>\n",
+       "      <td>-0.28627</td>\n",
+       "      <td>-0.29503</td>\n",
+       "      <td>-0.36480</td>\n",
+       "      <td>-0.34654</td>\n",
+       "      <td>-0.32422</td>\n",
+       "      <td>-0.39023</td>\n",
+       "      <td>-0.28972</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19009</th>\n",
+       "      <td>-0.34816</td>\n",
+       "      <td>-0.30263</td>\n",
+       "      <td>-0.34437</td>\n",
+       "      <td>0.18120</td>\n",
+       "      <td>-0.34109</td>\n",
+       "      <td>-0.46154</td>\n",
+       "      <td>0.81189</td>\n",
+       "      <td>-0.32372</td>\n",
+       "      <td>-0.32045</td>\n",
+       "      <td>-0.32738</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.36260</td>\n",
+       "      <td>-0.36951</td>\n",
+       "      <td>2.52929</td>\n",
+       "      <td>-0.28621</td>\n",
+       "      <td>-0.29878</td>\n",
+       "      <td>-0.36475</td>\n",
+       "      <td>-0.34650</td>\n",
+       "      <td>-0.32700</td>\n",
+       "      <td>-0.39251</td>\n",
+       "      <td>0.15501</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19010</th>\n",
+       "      <td>-0.34800</td>\n",
+       "      <td>-0.30251</td>\n",
+       "      <td>-0.34437</td>\n",
+       "      <td>6.66342</td>\n",
+       "      <td>-0.34094</td>\n",
+       "      <td>-0.46143</td>\n",
+       "      <td>-0.33312</td>\n",
+       "      <td>-0.32356</td>\n",
+       "      <td>-0.32032</td>\n",
+       "      <td>-0.32727</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.36242</td>\n",
+       "      <td>-0.36381</td>\n",
+       "      <td>-0.32929</td>\n",
+       "      <td>-0.28598</td>\n",
+       "      <td>-0.29867</td>\n",
+       "      <td>0.69742</td>\n",
+       "      <td>-0.34635</td>\n",
+       "      <td>-0.32689</td>\n",
+       "      <td>-0.39238</td>\n",
+       "      <td>-0.32350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19011</th>\n",
+       "      <td>-0.34817</td>\n",
+       "      <td>1.28021</td>\n",
+       "      <td>-0.34437</td>\n",
+       "      <td>-0.31753</td>\n",
+       "      <td>-0.34074</td>\n",
+       "      <td>0.23228</td>\n",
+       "      <td>-0.33337</td>\n",
+       "      <td>-0.32374</td>\n",
+       "      <td>0.70051</td>\n",
+       "      <td>-0.32308</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.36216</td>\n",
+       "      <td>-0.36952</td>\n",
+       "      <td>-0.32948</td>\n",
+       "      <td>-0.28623</td>\n",
+       "      <td>-0.29381</td>\n",
+       "      <td>1.22123</td>\n",
+       "      <td>-0.34651</td>\n",
+       "      <td>-0.32332</td>\n",
+       "      <td>-0.38951</td>\n",
+       "      <td>-0.32370</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19012</th>\n",
+       "      <td>2.91613</td>\n",
+       "      <td>-0.30214</td>\n",
+       "      <td>-0.34437</td>\n",
+       "      <td>-0.31754</td>\n",
+       "      <td>-0.33919</td>\n",
+       "      <td>2.08866</td>\n",
+       "      <td>-0.33338</td>\n",
+       "      <td>-0.32375</td>\n",
+       "      <td>2.35113</td>\n",
+       "      <td>-0.32740</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.36263</td>\n",
+       "      <td>0.56537</td>\n",
+       "      <td>-0.32949</td>\n",
+       "      <td>-0.28624</td>\n",
+       "      <td>-0.29880</td>\n",
+       "      <td>-0.36478</td>\n",
+       "      <td>-0.34652</td>\n",
+       "      <td>-0.32701</td>\n",
+       "      <td>0.50155</td>\n",
+       "      <td>-0.32371</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19762 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       feats2: c10555, c8555, c1055  feats2: c12222, c12226, c1227  \\\n",
+       "0                          -0.34853                       -0.29446   \n",
+       "1                           0.09896                       -0.29610   \n",
+       "2                          -0.34853                       -0.29417   \n",
+       "3                          -0.34854                       -0.29143   \n",
+       "4                          -0.34850                       -0.27595   \n",
+       "...                             ...                            ...   \n",
+       "19008                      -0.34820                       -0.29900   \n",
+       "19009                      -0.34816                       -0.30263   \n",
+       "19010                      -0.34800                       -0.30251   \n",
+       "19011                      -0.34817                        1.28021   \n",
+       "19012                       2.91613                       -0.30214   \n",
+       "\n",
+       "       feats2: c6665, c6667, c6653  feats2: c7703, c7701, c7707  \\\n",
+       "0                         -0.34437                     -0.31798   \n",
+       "1                         -0.34437                     -0.31795   \n",
+       "2                         -0.34437                      3.43709   \n",
+       "3                         -0.34437                     -0.31800   \n",
+       "4                         -0.34437                     -0.31795   \n",
+       "...                            ...                          ...   \n",
+       "19008                     -0.34437                     -0.31436   \n",
+       "19009                     -0.34437                      0.18120   \n",
+       "19010                     -0.34437                      6.66342   \n",
+       "19011                     -0.34437                     -0.31753   \n",
+       "19012                     -0.34437                     -0.31754   \n",
+       "\n",
+       "       feats2: c1992, c1922, c19932  feats2: c625, c612, c6125  \\\n",
+       "0                          -0.34102                   -0.46182   \n",
+       "1                          -0.34092                   -0.46180   \n",
+       "2                          -0.33256                   -0.46182   \n",
+       "3                          -0.34095                   -0.46183   \n",
+       "4                          -0.34093                   -0.46180   \n",
+       "...                             ...                        ...   \n",
+       "19008                      -0.34085                   -0.46158   \n",
+       "19009                      -0.34109                   -0.46154   \n",
+       "19010                      -0.34094                   -0.46143   \n",
+       "19011                      -0.34074                    0.23228   \n",
+       "19012                      -0.33919                    2.08866   \n",
+       "\n",
+       "       feats2: c9028, c9904, c9283  feats2: c3073, c3037, c3074  \\\n",
+       "0                         -0.33386                     -0.32362   \n",
+       "1                         -0.33383                      2.52748   \n",
+       "2                         -0.00576                     -0.32411   \n",
+       "3                         -0.33388                      0.60698   \n",
+       "4                         -0.32521                     -0.32408   \n",
+       "...                            ...                          ...   \n",
+       "19008                      0.82235                     -0.32377   \n",
+       "19009                      0.81189                     -0.32372   \n",
+       "19010                     -0.33312                     -0.32356   \n",
+       "19011                     -0.33337                     -0.32374   \n",
+       "19012                     -0.33338                     -0.32375   \n",
+       "\n",
+       "       feats2: c2106, c2626, c4210  feats2: c11196, c11918, c1111  ...  \\\n",
+       "0                         -0.31948                       -0.32044  ...   \n",
+       "1                         -0.32073                       -0.32158  ...   \n",
+       "2                         -0.32076                       -0.32092  ...   \n",
+       "3                         -0.32077                        2.87353  ...   \n",
+       "4                          0.41376                       -0.32181  ...   \n",
+       "...                            ...                            ...  ...   \n",
+       "19008                     -0.32049                        2.46634  ...   \n",
+       "19009                     -0.32045                       -0.32738  ...   \n",
+       "19010                     -0.32032                       -0.32727  ...   \n",
+       "19011                      0.70051                       -0.32308  ...   \n",
+       "19012                      2.35113                       -0.32740  ...   \n",
+       "\n",
+       "       feats2: c4448, c4444, c4487  feats2: c17981, c2980, c1798  \\\n",
+       "0                         -0.36251                      -0.36984   \n",
+       "1                         -0.36238                      -0.36982   \n",
+       "2                         -0.36235                      -0.36985   \n",
+       "3                         -0.36240                      -0.36985   \n",
+       "4                         -0.36239                       0.51644   \n",
+       "...                            ...                           ...   \n",
+       "19008                     -0.36230                      -0.36955   \n",
+       "19009                     -0.36260                      -0.36951   \n",
+       "19010                     -0.36242                      -0.36381   \n",
+       "19011                     -0.36216                      -0.36952   \n",
+       "19012                     -0.36263                       0.56537   \n",
+       "\n",
+       "       feats2: c4777, c14777, c4787  feats2: c7554, c7519, c5151  \\\n",
+       "0                          -0.32987                     -0.28672   \n",
+       "1                          -0.32984                     -0.28669   \n",
+       "2                          -0.32988                     -0.28673   \n",
+       "3                          -0.32989                     -0.28674   \n",
+       "4                          -0.32984                     -0.28668   \n",
+       "...                             ...                          ...   \n",
+       "19008                      -0.32952                     -0.28627   \n",
+       "19009                       2.52929                     -0.28621   \n",
+       "19010                      -0.32929                     -0.28598   \n",
+       "19011                      -0.32948                     -0.28623   \n",
+       "19012                      -0.32949                     -0.28624   \n",
+       "\n",
+       "       feats2: c10000, c10008, c10003  feats2: c25240, c2524, c2456  \\\n",
+       "0                             3.88640                      -0.36522   \n",
+       "1                            -0.29198                      -0.36519   \n",
+       "2                            -0.29119                      -0.36523   \n",
+       "3                            -0.28688                      -0.36524   \n",
+       "4                            -0.29225                      -0.36184   \n",
+       "...                               ...                           ...   \n",
+       "19008                        -0.29503                      -0.36480   \n",
+       "19009                        -0.29878                      -0.36475   \n",
+       "19010                        -0.29867                       0.69742   \n",
+       "19011                        -0.29381                       1.22123   \n",
+       "19012                        -0.29880                      -0.36478   \n",
+       "\n",
+       "       feats2: c809, c5099, c5809  feats2: c1065, c10658, c10656  \\\n",
+       "0                        -0.34670                       -0.15082   \n",
+       "1                        -0.34666                       -0.32204   \n",
+       "2                        -0.34668                       -0.32147   \n",
+       "3                        -0.34671                       -0.29361   \n",
+       "4                        -0.34668                       -0.32224   \n",
+       "...                           ...                            ...   \n",
+       "19008                    -0.34654                       -0.32422   \n",
+       "19009                    -0.34650                       -0.32700   \n",
+       "19010                    -0.34635                       -0.32689   \n",
+       "19011                    -0.34651                       -0.32332   \n",
+       "19012                    -0.34652                       -0.32701   \n",
+       "\n",
+       "       feats2: c1550, c15034, c15615  feats2: c8182, c8882, c8889  \n",
+       "0                           -0.38757                     -0.32410  \n",
+       "1                           -0.38862                     -0.32407  \n",
+       "2                           -0.38820                     -0.32411  \n",
+       "3                           -0.38584                     -0.32412  \n",
+       "4                            0.57418                     -0.32407  \n",
+       "...                              ...                          ...  \n",
+       "19008                       -0.39023                     -0.28972  \n",
+       "19009                       -0.39251                      0.15501  \n",
+       "19010                       -0.39238                     -0.32350  \n",
+       "19011                       -0.38951                     -0.32370  \n",
+       "19012                        0.50155                     -0.32371  \n",
+       "\n",
+       "[19762 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "X = g7._get_feature('nodes')\n",
     "X"
@@ -590,14 +2686,18 @@
    "metadata": {},
    "source": [
     "# Conditional Probability\n",
-    "Let's see if  can give us good histograms to tease out red team nodes? This is to baseline the above UMAP models, and we find in retrospect, UMAP wins."
+    "Let's see if conditiona probability of computer to computer connections can give us good histograms to tease out red team nodes? This is to baseline the above UMAP models, and we find in retrospect, UMAP wins. \n",
+    "\n",
+    "The conditional graph is however useful to see aggregate behavior, and coloring by 'red' team shows topology of Infection"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "id": "2d6f58dd",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "g = graphistry.edges(tdf, \"src_computer\", \"dst_computer\")"
@@ -605,49 +2705,159 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "54b83f83",
-   "metadata": {},
+   "execution_count": 34,
+   "id": "f3b44db2-b34e-4398-8c5a-7a10bbe5d681",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "def conditional_probability(x, given, df):\n",
-    "    \"\"\"conditional probability function over categorical variables\n",
-    "       p(x|given) = p(x,given)/p(given)\n",
-    "        \n",
-    "    Args:\n",
-    "        x: the column variable of interest given the column 'given'\n",
-    "        given: the variabe to fix constant\n",
-    "        df: dataframe with columns [given, x]\n",
-    "    Returns:\n",
-    "        pd.DataFrame: the conditional probability of x given the column 'given'\n",
-    "    \"\"\"\n",
-    "    return df.groupby([given])[x].apply(lambda g: g.value_counts()/len(g))\n"
+    "x='dst_computer'\n",
+    "given='src_computer'\n",
+    "cg = g.conditional_graph(x, given, kind='edges')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "fd738336",
+   "execution_count": 35,
+   "id": "3b2af6a2-4f10-4707-beb8-4f3447d3e3b8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>src_computer</th>\n",
+       "      <th>dst_computer</th>\n",
+       "      <th>_probs</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>C1</td>\n",
+       "      <td>C612</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>C10</td>\n",
+       "      <td>C10</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>C10</td>\n",
+       "      <td>C2997</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>C10</td>\n",
+       "      <td>C10718</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>C100</td>\n",
+       "      <td>C528</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17831</th>\n",
+       "      <td>C9990</td>\n",
+       "      <td>C528</td>\n",
+       "      <td>0.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17832</th>\n",
+       "      <td>C9992</td>\n",
+       "      <td>C586</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17833</th>\n",
+       "      <td>C9994</td>\n",
+       "      <td>C9994</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17834</th>\n",
+       "      <td>C9997</td>\n",
+       "      <td>C586</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17835</th>\n",
+       "      <td>C9997</td>\n",
+       "      <td>C625</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>17836 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      src_computer dst_computer    _probs\n",
+       "0               C1         C612  1.000000\n",
+       "1              C10          C10  0.333333\n",
+       "2              C10        C2997  0.333333\n",
+       "3              C10       C10718  0.333333\n",
+       "4             C100         C528  0.500000\n",
+       "...            ...          ...       ...\n",
+       "17831        C9990         C528  0.250000\n",
+       "17832        C9992         C586  1.000000\n",
+       "17833        C9994        C9994  1.000000\n",
+       "17834        C9997         C586  0.500000\n",
+       "17835        C9997         C625  0.500000\n",
+       "\n",
+       "[17836 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "x='dst_computer'\n",
-    "given='src_computer'\n",
-    "condprobs = conditional_probability(x, given, df=tdf)\n",
-    "\n",
-    "cprob = pd.DataFrame(list(condprobs.index), columns=[given, x])\n",
-    "cprob['_probs'] = condprobs.values"
+    "# the new edge dataframe assess conditiona prob of computer-to-computer connection\n",
+    "cprob = cg._edges\n",
+    "cprob"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "id": "5258aee1",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# now enrich the edges dataframe with the redteam data\n",
-    "# since cprobs lost those labels during the function cal\n",
+    "# enrich the edges dataframe with the redteam data\n",
+    "# since cprobs lost those labels during the function call\n",
     "indx = cprob.src_computer.isin(red_team.src_computer) & cprob.dst_computer.isin(red_team.dst_computer)\n",
     "cprob.loc[indx, 'red'] = 1\n",
     "cprob.loc[~indx, 'red'] = 0"
@@ -655,117 +2865,196 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "9b3af1cd-6423-4484-8b99-81fad821f118",
+   "execution_count": 37,
+   "id": "7ff921fc-3ecd-4404-acd7-8db943a4ebcc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>src_computer</th>\n",
+       "      <th>dst_computer</th>\n",
+       "      <th>_probs</th>\n",
+       "      <th>red</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>C1</td>\n",
+       "      <td>C612</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>C10</td>\n",
+       "      <td>C10</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>C10</td>\n",
+       "      <td>C2997</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>C10</td>\n",
+       "      <td>C10718</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>C100</td>\n",
+       "      <td>C528</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17831</th>\n",
+       "      <td>C9990</td>\n",
+       "      <td>C528</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17832</th>\n",
+       "      <td>C9992</td>\n",
+       "      <td>C586</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17833</th>\n",
+       "      <td>C9994</td>\n",
+       "      <td>C9994</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17834</th>\n",
+       "      <td>C9997</td>\n",
+       "      <td>C586</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17835</th>\n",
+       "      <td>C9997</td>\n",
+       "      <td>C625</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>17836 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      src_computer dst_computer    _probs  red\n",
+       "0               C1         C612  1.000000  0.0\n",
+       "1              C10          C10  0.333333  0.0\n",
+       "2              C10        C2997  0.333333  0.0\n",
+       "3              C10       C10718  0.333333  0.0\n",
+       "4             C100         C528  0.500000  0.0\n",
+       "...            ...          ...       ...  ...\n",
+       "17831        C9990         C528  0.250000  0.0\n",
+       "17832        C9992         C586  1.000000  0.0\n",
+       "17833        C9994        C9994  1.000000  0.0\n",
+       "17834        C9997         C586  0.500000  0.0\n",
+       "17835        C9997         C625  0.500000  0.0\n",
+       "\n",
+       "[17836 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# full condprob graph \n",
-    "cg = graphistry.edges(cprob, x, given).bind(edge_weight='_probs')\n",
-    "cg.plot(render=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "42fb3dff",
-   "metadata": {},
-   "source": [
-    "## Learning\n",
-    "The conditional graph shows that most of the edge probabilities are between 4e-7 and 0.03, whose bucket contains most events. Thus the chances of finding the red team edges are ~ 1e-4 -- slim indeed. UMAP wins."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d2cd536",
-   "metadata": {},
-   "source": [
-    "Likewise the transpose conditional is even worse \n",
-    "with prob_detection ~ 6e-5"
+    "cprob"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "18eafcff",
+   "execution_count": 38,
+   "id": "b4b10152-cac9-4497-b016-dd67b54cdcf2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# let's repeat but with reverse conditional\n",
-    "x='src_computer'\n",
-    "given='dst_computer'\n",
-    "condprobs2 = conditional_probability(x, given, df=tdf)\n",
-    "\n",
-    "cprob2 = pd.DataFrame(list(condprobs2.index), columns=[given, x])\n",
-    "cprob2['_probs'] = condprobs2.values"
+    "# add edges back to graphistry instance\n",
+    "cg._edges = cprob"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "74913e34",
+   "execution_count": 39,
+   "id": "9b3af1cd-6423-4484-8b99-81fad821f118",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://hub.graphistry.com/graph/graph.html?dataset=5e532500da8d459d8a3ef7832a6d6d9a&type=arrow&viztoken=4e311702-17ef-4563-b060-6d631e4a4101&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672345993&info=true'"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# now enrich the edges dataframe with the redteam data\n",
-    "indx = cprob2.src_computer.isin(red_team.src_computer) & cprob2.dst_computer.isin(red_team.dst_computer)\n",
-    "cprob2.loc[indx, 'red'] = 1\n",
-    "cprob2.loc[~indx, 'red'] = 0"
+    "# full condprob graph\n",
+    "cg.plot(render=RENDER)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "22f4ac54",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cg2 = graphistry.edges(cprob2, x, given).bind(edge_weight='_probs')\n",
-    "cg2.plot(render=False)\n",
-    "# same conclusion as above..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "db832e1c",
+   "cell_type": "markdown",
+   "id": "42fb3dff",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# # let's see the probs better:\n",
-    "# for src in red_team.src_computer.unique():\n",
-    "#     for dst in red_team.dst_computer.unique():\n",
-    "#         if dst in condprobs[src]:\n",
-    "#             print('-'*30)\n",
-    "#             print(f'given src {src} -> dst {dst}')\n",
-    "#             print('-'*10)\n",
-    "#             print(f'   {condprobs[src][dst]*100:.2f}%')\n",
-    "#             print()"
+    "## Learning\n",
+    "The conditional graph shows that most of the edge probabilities are between 4e-7 and 0.03, whose bucket contains most of the events. Thus the chances of finding the red team edges are ~ 1e-4 -- slim indeed. UMAP wins."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "21f51de6",
+   "cell_type": "markdown",
+   "id": "9d2cd536",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# for dst in red_team.dst_computer.unique():\n",
-    "#     for src in red_team.src_computer.unique():\n",
-    "#         if src in condprobs2[dst]:\n",
-    "#             print('-'*20)\n",
-    "#             print(f'given dst {dst} -> src {src}')\n",
-    "#             print('-'*10)\n",
-    "#             print(f'  {condprobs2[dst][src]*100:.2f}%')\n",
-    "#             print()"
+    "Likewise the transpose conditional is even worse \n",
+    "with prob_detection ~ 6e-5"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c3a008f6-75ed-4045-b13c-494cb015d185",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From 1944afac08cda822a0bdf098b405bf37da881451 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 29 Dec 2022 13:19:11 -0800
Subject: [PATCH 005/432] feat(adds cpu and gpu support for DBSCAN, adds
 constants, adds DBSCAN Mixin)

---
 graphistry/constants.py  |  5 +++++
 graphistry/plotter.py    |  4 +++-
 graphistry/umap_utils.py | 23 ++++++++++++-----------
 3 files changed, 20 insertions(+), 12 deletions(-)

diff --git a/graphistry/constants.py b/graphistry/constants.py
index 1e9f862e92..c7c2bf9bfa 100644
--- a/graphistry/constants.py
+++ b/graphistry/constants.py
@@ -17,12 +17,17 @@
 # ###############################################################
 # consistent clf pipelining and constructor methods across files
 DGL_GRAPH = "DGL_graph"
+KG_GRAPH = '_kg_graph'
 FEATURE = "feature"
 TARGET = "target"
 LABEL = "label"
 LABEL_NODES = "node_label"
 LABEL_EDGES = "edge_label"
 
+# ENGINES
+CUML = 'cuml'
+UMAP_LEARN = 'umap_learn'
+
 TRAIN_MASK = "train_mask"
 TEST_MASK = "test_mask"
 
diff --git a/graphistry/plotter.py b/graphistry/plotter.py
index 3ed7c06118..00f32dfdeb 100644
--- a/graphistry/plotter.py
+++ b/graphistry/plotter.py
@@ -8,13 +8,14 @@
 from .embed_utils import HeterographEmbedModuleMixin  # type: ignore
 from .text_utils import SearchToGraphMixin  # type: ignore
 from .compute.conditional import ConditionalMixin  # type: ignore
+from .compute.cluster import ClusterMixin  # type: ignore
 
 
 mixins = ([
     CosmosMixin, NeptuneMixin, GremlinMixin,
     HeterographEmbedModuleMixin,
     SearchToGraphMixin,
-    DGLGraphMixin,
+    DGLGraphMixin, ClusterMixin,
     UMAPMixin,
     FeatureMixin, ConditionalMixin,
     LayoutsMixin,
@@ -32,6 +33,7 @@ def __init__(self, *args, **kwargs):
         ConditionalMixin.__init__(self, *args, **kwargs)
         FeatureMixin.__init__(self, *args, **kwargs)
         UMAPMixin.__init__(self, *args, **kwargs)
+        ClusterMixin.__init__(self, *args, **kwargs)
         DGLGraphMixin.__init__(self, *args, **kwargs)
         SearchToGraphMixin.__init__(self, *args, **kwargs)
         HeterographEmbedModuleMixin.__init__(self, *args, **kwargs)
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 5cd092f29d..eff2c7ae1a 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -5,6 +5,7 @@
 import pandas as pd
 
 from . import constants as config
+from .constants import CUML, UMAP_LEARN
 from .feature_utils import (FeatureMixin, Literal, XSymbolic, YSymbolic,
                             prune_weighted_edges_df_and_relabel_nodes,
                             resolve_feature_engine)
@@ -73,22 +74,22 @@ def is_legacy_cuml():
         return False
 
 
-UMAPEngineConcrete = Literal["cuml", "umap_learn"]
+UMAPEngineConcrete = Literal[CUML, UMAP_LEARN]
 UMAPEngine = Literal[UMAPEngineConcrete, "auto"]
 
 
 def resolve_umap_engine(
     engine: UMAPEngine,
 ) -> UMAPEngineConcrete:  # noqa
-    if engine in ["cuml", "umap_learn"]:
+    if engine in [CUML, UMAP_LEARN]:
         return engine  # type: ignore
     if engine in ["auto"]:
         has_cuml_dependancy_, _, cuml = lazy_cuml_import_has_dependancy()
         if has_cuml_dependancy_:
-            return "cuml"
+            return CUML
         has_umap_dependancy_, _, _ = lazy_umap_import_has_dependancy()
         if has_umap_dependancy_:
-            return "umap_learn"
+            return UMAP_LEARN
 
     raise ValueError(  # noqa
         f'engine expected to be "auto", '
@@ -180,9 +181,9 @@ def umap_lazy_init(
     ):
         engine_resolved = resolve_umap_engine(engine)
         # FIXME remove as set_new_kwargs will always replace?
-        if engine_resolved == "umap_learn":
+        if engine_resolved == UMAP_LEARN:
             _, _, umap_engine = lazy_umap_import_has_dependancy()
-        elif engine_resolved == "cuml":
+        elif engine_resolved == CUML:
             _, _, umap_engine = lazy_cuml_import_has_dependancy()
         else:
             raise ValueError(
@@ -193,7 +194,7 @@ def umap_lazy_init(
             umap_kwargs = dict(
                 {
                     "n_components": n_components,
-                    **({"metric": metric} if engine_resolved == "umap_learn" else {}),
+                    **({"metric": metric} if engine_resolved == UMAP_LEARN else {}),
                     "n_neighbors": n_neighbors,
                     "min_dist": min_dist,
                     "spread": spread,
@@ -238,7 +239,7 @@ def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         logger.info("-" * 90)
         logger.info(f"Starting UMAP-ing data of shape {X.shape}")
 
-        if self.engine == "cuml" and is_legacy_cuml():
+        if self.engine == CUML and is_legacy_cuml():
             from cuml.neighbors import NearestNeighbors
 
             knn = NearestNeighbors(n_neighbors=self.n_neighbors)
@@ -431,9 +432,9 @@ def umap(
                 default True.
         :return: self, with attributes set with new data
         """
-        if engine == "umap_learn":
+        if engine == UMAP_LEARN:
             assert_imported()
-        elif engine == "cuml":
+        elif engine == CUML:
             assert_imported_cuml()
 
         umap_kwargs = dict(
@@ -563,7 +564,7 @@ def umap(
             res, kind, encode_position, encode_weight, play
         )  # noqa: E501
 
-        if res.engine == "cuml" and is_legacy_cuml():
+        if res.engine == CUML and is_legacy_cuml():
             res = res.prune_self_edges()
 
         if not inplace:

From a07a59d18bca79a3059d984ece6a8c8725cce609 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 29 Dec 2022 13:37:11 -0800
Subject: [PATCH 006/432] adds arguments to PlotterBase, typing coerced to
 fstring

---
 graphistry/PlotterBase.py | 6 ++++++
 graphistry/umap_utils.py  | 3 +--
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/graphistry/PlotterBase.py b/graphistry/PlotterBase.py
index 7d22469d8f..81591d8e43 100644
--- a/graphistry/PlotterBase.py
+++ b/graphistry/PlotterBase.py
@@ -165,6 +165,7 @@ def __init__(self, *args, **kwargs):
         self._bolt_driver : any = None
         self._tigergraph : any = None
 
+        # feature engineering
         self._node_embedding = None
         self._node_encoder = None
         self._node_features = None
@@ -190,6 +191,7 @@ def __init__(self, *args, **kwargs):
         self._weighted_edges_df_from_edges = None
         self._xy = None
 
+        # the fit umap instance
         self._umap = None
 
         self._adjacency = None
@@ -201,6 +203,10 @@ def __init__(self, *args, **kwargs):
         self._use_feat: bool = False
         self._triplets: Optional[List] = None 
         self._kg_embed_dim: int = 128
+        
+        # Dbscan
+        self._node_dbscan = None  # the fit dbscan instance
+        self._edge_dbscan = None
 
 
     def __repr__(self):
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index eff2c7ae1a..50d604c348 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -74,7 +74,7 @@ def is_legacy_cuml():
         return False
 
 
-UMAPEngineConcrete = Literal[CUML, UMAP_LEARN]
+UMAPEngineConcrete = Literal[f'{CUML}', f'{UMAP_LEARN}']
 UMAPEngine = Literal[UMAPEngineConcrete, "auto"]
 
 
@@ -105,7 +105,6 @@ def resolve_umap_engine(
     "n_components": 2,
     "metric": "hellinger",  # info metric, can't use on
     # textual encodings since they contain negative values...
-    # unless scaling min max etc
     "n_neighbors": 15,
     "min_dist": 0.3,
     "verbose": True,

From cc50bb484c18990f45a21b283818fbb201410198 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 29 Dec 2022 13:59:27 -0800
Subject: [PATCH 007/432] adds cluster.py

---
 graphistry/compute/cluster.py | 81 +++++++++++++++++++++++++++++++++++
 1 file changed, 81 insertions(+)
 create mode 100644 graphistry/compute/cluster.py

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
new file mode 100644
index 0000000000..a4899abf7f
--- /dev/null
+++ b/graphistry/compute/cluster.py
@@ -0,0 +1,81 @@
+import logging
+import pandas as pd
+from typing import Any, List, Union, TYPE_CHECKING
+from typing_extensions import Literal
+from collections import Counter
+
+from graphistry.Engine import Engine
+from graphistry.Plottable import Plottable
+from graphistry.constants import CUML, UMAP_LEARN  # noqa type: ignore
+
+logger = logging.getLogger("compute.cluster")
+
+if TYPE_CHECKING:
+    MIXIN_BASE = Plottable
+else:
+    MIXIN_BASE = object
+    
+    
+def cluster(g, dbscan, kind='nodes'):
+    """
+        Fits clustering on UMAP embeddings
+    """
+    if kind=='nodes':
+        df = g._node_embedding
+    elif kind=='edges':
+        df = g._edge_embedding
+    else:
+        raise ValueError('kind must be one of nodes or edges')
+        
+    dbscan.fit(df)
+    labels = dbscan.labels_
+    
+    if kind=='nodes':    
+        g._nodes['_cluster'] = labels
+    elif kind=='edges':
+        g._edges['_cluster'] = labels
+    else:
+        raise ValueError('kind must be one of nodes or edges')
+    
+    kind = 'node' if kind=='nodes' else 'edge'
+    setattr(g, f'_{kind}_dbscan', dbscan)
+    
+    return g
+
+class ClusterMixin(MIXIN_BASE):
+    def __init__(self, *args, **kwargs):
+        pass
+    
+    def _cluster_dbscan(self, res, kind, eps, min_samples, **kwargs):
+        """
+            DBSCAN clustering on cpu or gpu infered by umap's .engine flag
+        """
+        if self.engine == UMAP_LEARN:
+            try:
+                from sklearn.cluster import DBSCAN
+            except ImportError:
+                raise ImportError('Please install sklearn')
+            
+        elif self.engine == CUML:
+            try:
+                from cuml import DBSCAN as cuDBSCAN
+            except ImportError:
+                raise ImportError('Please install cuml')
+        else:
+            raise ValueError(f'engine must be one of {UMAP_LEARN} or {CUML}')
+        
+        dbscan = cuDBSCAN(eps=eps, min_samples=min_samples, **kwargs) if self.engine == CUML else DBSCAN(eps=eps, min_samples=min_samples, **kwargs)
+        res = cluster(res, dbscan, kind=kind)
+
+        return res
+    
+    def dbscan(self, kind='nodes', eps: float = 1., min_samples: int = 1, **kwargs):
+        """DBSCAN clustering
+        """
+        res = self.bind()
+        res = self._cluster_dbscan(res, kind=kind, eps=eps, min_samples=min_samples, **kwargs)
+        
+        return res
+
+    def _is_cudf(self, df):
+        return 'cudf' in str(type(df))

From b9df147127244ba7d796aae7f5bfa4f595cd5269 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 29 Dec 2022 15:05:50 -0800
Subject: [PATCH 008/432] testing linteer

---
 graphistry/umap_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 50d604c348..6b890ef3a2 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -74,7 +74,7 @@ def is_legacy_cuml():
         return False
 
 
-UMAPEngineConcrete = Literal[f'{CUML}', f'{UMAP_LEARN}']
+UMAPEngineConcrete = Literal['cuml', 'umap_learn']
 UMAPEngine = Literal[UMAPEngineConcrete, "auto"]
 
 

From 070a39139f250c2783da944b7cb3964afeb5d46e Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 29 Dec 2022 15:28:03 -0800
Subject: [PATCH 009/432] lint

---
 graphistry/compute/cluster.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index a4899abf7f..7892f775d4 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -20,9 +20,9 @@ def cluster(g, dbscan, kind='nodes'):
     """
         Fits clustering on UMAP embeddings
     """
-    if kind=='nodes':
+    if kind == 'nodes':
         df = g._node_embedding
-    elif kind=='edges':
+    elif kind == 'edges':
         df = g._edge_embedding
     else:
         raise ValueError('kind must be one of nodes or edges')
@@ -30,14 +30,14 @@ def cluster(g, dbscan, kind='nodes'):
     dbscan.fit(df)
     labels = dbscan.labels_
     
-    if kind=='nodes':    
+    if kind == 'nodes':    
         g._nodes['_cluster'] = labels
-    elif kind=='edges':
+    elif kind == 'edges':
         g._edges['_cluster'] = labels
     else:
         raise ValueError('kind must be one of nodes or edges')
     
-    kind = 'node' if kind=='nodes' else 'edge'
+    kind = 'node' if kind == 'nodes' else 'edge'
     setattr(g, f'_{kind}_dbscan', dbscan)
     
     return g

From 0e466ec10690300e3ec2a79c1a37dfb92e7348e0 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 29 Dec 2022 15:34:49 -0800
Subject: [PATCH 010/432] lint

---
 graphistry/umap_utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 6b890ef3a2..25fc003e3a 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -86,10 +86,10 @@ def resolve_umap_engine(
     if engine in ["auto"]:
         has_cuml_dependancy_, _, cuml = lazy_cuml_import_has_dependancy()
         if has_cuml_dependancy_:
-            return CUML
+            return 'cuml'
         has_umap_dependancy_, _, _ = lazy_umap_import_has_dependancy()
         if has_umap_dependancy_:
-            return UMAP_LEARN
+            return 'umap_learn'
 
     raise ValueError(  # noqa
         f'engine expected to be "auto", '

From 85941a7adddabf80ed94684201365072d803665a Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 29 Dec 2022 15:39:06 -0800
Subject: [PATCH 011/432] adds conf pyclass

---
 docs/source/conf.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 319295df56..5797134b6a 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -53,6 +53,7 @@
     ('py:class', 'graphistry.layouts.LayoutsMixin'),
     ('py:class', 'graphistry.compute.ComputeMixin'),
     ('py:class', 'graphistry.compute.conditional.ConditionalMixin'),
+    ('py:class', 'graphistry.compute.cluster.ClusterMixin'),
     ('py:class', 'graphistry.Plottable.Plottable'),
     ('py:class', 'graphistry.feature_utils.FeatureMixin'),
     ('py:class', 'graphistry.dgl_utils.DGLGraphMixin'),

From bb261fa710bd9246df846e564ab3b847d6deeac1 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 29 Dec 2022 22:33:26 -0800
Subject: [PATCH 012/432] feat(support for dbscan over feature cols or umap
 embeddings)

---
 graphistry/compute/cluster.py | 81 ++++++++++++++++++++++++++---------
 1 file changed, 60 insertions(+), 21 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 7892f775d4..d3bb1adbd5 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -14,11 +14,32 @@
     MIXIN_BASE = Plottable
 else:
     MIXIN_BASE = object
+
+
+def lazy_dbscan_import_has_dependency():
+    has_min_dependency = True
+    DBSCAN = None
+    try:
+        from sklearn.cluster import DBSCAN
+    except ImportError:
+        has_min_dependency = False
+        logger.warning('Please install sklearn for CPU DBSCAN')
+
+    has_cuml_dependency = True
+    cuDBSCAN = None
+    try:
+        from cuml import DBSCAN as cuDBSCAN
+    except ImportError:
+        has_cuml_dependency = False
+        logger.warning('Please install cuml for GPU DBSCAN')
+    
+    return has_min_dependency, DBSCAN, has_cuml_dependency, cuDBSCAN
+
     
     
-def cluster(g, dbscan, kind='nodes'):
+def get_umap_embedding_df(g, kind='nodes'):
     """
-        Fits clustering on UMAP embeddings
+        Returns a dataframe with the UMAP embeddings from the graphistry graph
     """
     if kind == 'nodes':
         df = g._node_embedding
@@ -27,6 +48,30 @@ def cluster(g, dbscan, kind='nodes'):
     else:
         raise ValueError('kind must be one of nodes or edges')
         
+    return df
+    
+def cluster(g, dbscan, kind='nodes', cols=None, umap=True):
+    """
+        Fits clustering on UMAP embeddings if umap is True, otherwise on the features dataframe
+        
+        args:
+            g: graphistry graph
+            kind: 'nodes' or 'edges'
+            cols: list of columns to use for clustering given `g.featurize` has been run
+            umap: whether to use UMAP embeddings or features dataframe
+    """
+    
+    if cols is None:
+        df = g._get_feature(kind)
+    else: 
+        df = g.get_features_by_cols(cols, kind)
+
+    if umap and cols is None: 
+        df = get_umap_embedding_df(g, kind)
+    
+    print(df.head())
+
+    
     dbscan.fit(df)
     labels = dbscan.labels_
     
@@ -35,7 +80,7 @@ def cluster(g, dbscan, kind='nodes'):
     elif kind == 'edges':
         g._edges['_cluster'] = labels
     else:
-        raise ValueError('kind must be one of nodes or edges')
+        raise ValueError('kind must be one of `nodes` or `edges`')
     
     kind = 'node' if kind == 'nodes' else 'edge'
     setattr(g, f'_{kind}_dbscan', dbscan)
@@ -46,34 +91,28 @@ class ClusterMixin(MIXIN_BASE):
     def __init__(self, *args, **kwargs):
         pass
     
-    def _cluster_dbscan(self, res, kind, eps, min_samples, **kwargs):
+    def _cluster_dbscan(self, res, kind, cols, umap, eps, min_samples, **kwargs):
         """
             DBSCAN clustering on cpu or gpu infered by umap's .engine flag
         """
-        if self.engine == UMAP_LEARN:
-            try:
-                from sklearn.cluster import DBSCAN
-            except ImportError:
-                raise ImportError('Please install sklearn')
-            
-        elif self.engine == CUML:
-            try:
-                from cuml import DBSCAN as cuDBSCAN
-            except ImportError:
-                raise ImportError('Please install cuml')
-        else:
-            raise ValueError(f'engine must be one of {UMAP_LEARN} or {CUML}')
+        _, DBSCAN, _, cuDBSCAN = lazy_dbscan_import_has_dependency()
         
         dbscan = cuDBSCAN(eps=eps, min_samples=min_samples, **kwargs) if self.engine == CUML else DBSCAN(eps=eps, min_samples=min_samples, **kwargs)
-        res = cluster(res, dbscan, kind=kind)
+        res = cluster(res, dbscan, kind=kind, cols=cols, umap=umap)
 
         return res
     
-    def dbscan(self, kind='nodes', eps: float = 1., min_samples: int = 1, **kwargs):
-        """DBSCAN clustering
+    
+    def dbscan(self, kind = 'nodes', cols = None, umap = True, eps: float = 1., min_samples: int = 1, **kwargs):
+        """DBSCAN clustering on cpu or gpu infered by umap's .engine flag
+        
+        Args:
+            kind: 'nodes' or 'edges'
+            cols: list of columns to use for clustering given `g.featurize` has been run, nice way to slice features by fragments of interest, e.g. ['ip172', 'location', 'asn', 'warnings']
+            umap: whether to use UMAP embeddings or features dataframe
         """
         res = self.bind()
-        res = self._cluster_dbscan(res, kind=kind, eps=eps, min_samples=min_samples, **kwargs)
+        res = res._cluster_dbscan(res, kind=kind, cols=cols, umap=umap, eps=eps, min_samples=min_samples, **kwargs)
         
         return res
 

From ef0e9b2ecd55edf392e11212108c601ea1e42370 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 29 Dec 2022 23:20:28 -0800
Subject: [PATCH 013/432] adds streamline methods between mixins

---
 graphistry/compute/ComputeMixin.py |  3 +++
 graphistry/compute/cluster.py      | 35 +++++++++++++++---------------
 graphistry/umap_utils.py           |  8 +++++++
 3 files changed, 28 insertions(+), 18 deletions(-)

diff --git a/graphistry/compute/ComputeMixin.py b/graphistry/compute/ComputeMixin.py
index 8fd9895b95..7a9b2f71c7 100644
--- a/graphistry/compute/ComputeMixin.py
+++ b/graphistry/compute/ComputeMixin.py
@@ -347,6 +347,9 @@ def collapse(
         :param node: start `node` to begin traversal
         :param attribute: the given `attribute` to collapse over within `column`
         :param column: the `column` of nodes DataFrame that contains `attribute` to collapse over
+        :param self_edges: whether to include self edges in the collapsed graph
+        :param unwrap: whether to unwrap the collapsed graph into a single node
+        :param verbose: whether to print out collapse summary information
 
         :returns:A new Graphistry instance with nodes and edges DataFrame containing collapsed nodes and edges given by column attribute -- nodes and edges DataFrames contain six new columns `collapse_{node | edges}` and `final_{node | edges}`, while original (node, src, dst) columns are left untouched
         :rtype: Plottable
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index d3bb1adbd5..85f69aa01c 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -23,7 +23,7 @@ def lazy_dbscan_import_has_dependency():
         from sklearn.cluster import DBSCAN
     except ImportError:
         has_min_dependency = False
-        logger.warning('Please install sklearn for CPU DBSCAN')
+        logger.info('Please install sklearn for CPU DBSCAN')
 
     has_cuml_dependency = True
     cuDBSCAN = None
@@ -31,24 +31,11 @@ def lazy_dbscan_import_has_dependency():
         from cuml import DBSCAN as cuDBSCAN
     except ImportError:
         has_cuml_dependency = False
-        logger.warning('Please install cuml for GPU DBSCAN')
+        logger.info('Please install cuml for GPU DBSCAN')
     
     return has_min_dependency, DBSCAN, has_cuml_dependency, cuDBSCAN
 
-    
-    
-def get_umap_embedding_df(g, kind='nodes'):
-    """
-        Returns a dataframe with the UMAP embeddings from the graphistry graph
-    """
-    if kind == 'nodes':
-        df = g._node_embedding
-    elif kind == 'edges':
-        df = g._edge_embedding
-    else:
-        raise ValueError('kind must be one of nodes or edges')
-        
-    return df
+
     
 def cluster(g, dbscan, kind='nodes', cols=None, umap=True):
     """
@@ -67,9 +54,9 @@ def cluster(g, dbscan, kind='nodes', cols=None, umap=True):
         df = g.get_features_by_cols(cols, kind)
 
     if umap and cols is None: 
-        df = get_umap_embedding_df(g, kind)
+        df = g._get_embedding(kind)
     
-    print(df.head())
+    #print(df.head())
 
     
     dbscan.fit(df)
@@ -106,10 +93,22 @@ def _cluster_dbscan(self, res, kind, cols, umap, eps, min_samples, **kwargs):
     def dbscan(self, kind = 'nodes', cols = None, umap = True, eps: float = 1., min_samples: int = 1, **kwargs):
         """DBSCAN clustering on cpu or gpu infered by umap's .engine flag
         
+            g2 = g.featurize().dbscan(kind='nodes', cols=None, umap=True, eps=1., min_samples=1, **kwargs)
+            print(g2._nodes['_cluster'])
+            
+            # cluster by 'ip172' and 'location', for example
+            g2 = g.featurize().dbscan(cols=['column_attribute1', 'column_attribute2'], **kwargs)
+            
+            # cluster by UMAP embeddings
+            g2 = g.umap().dbscan()
+
         Args:
             kind: 'nodes' or 'edges'
             cols: list of columns to use for clustering given `g.featurize` has been run, nice way to slice features by fragments of interest, e.g. ['ip172', 'location', 'asn', 'warnings']
             umap: whether to use UMAP embeddings or features dataframe
+            eps: The maximum distance between two samples for them to be considered as in the same neighborhood.
+            min_samples: The number of samples (or total integer weight) in a neighborhood for a point to be considered as a core point. This includes the point itself.
+            
         """
         res = self.bind()
         res = res._cluster_dbscan(res, kind=kind, cols=cols, umap=umap, eps=eps, min_samples=min_samples, **kwargs)
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 25fc003e3a..873c194df6 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -229,6 +229,14 @@ def _check_target_is_one_dimensional(self, y: Union[pd.DataFrame, None]):
                 "as it is not one dimensional"
             )
             return None
+    
+    def _get_embedding(self, kind='nodes'):
+        if kind == 'nodes':
+            return self._node_embedding
+        elif kind == 'edges':
+            return self._edge_embedding
+        else:
+            raise ValueError('kind must be one of nodes or edges')
 
     def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         if self._umap is None:

From e16c186a859a48b542fe7686c65eda37dc3382a5 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 29 Dec 2022 23:21:07 -0800
Subject: [PATCH 014/432] adds tests

---
 graphistry/tests/test_cluster.py | 51 ++++++++++++++++++++++++++++++++
 1 file changed, 51 insertions(+)
 create mode 100644 graphistry/tests/test_cluster.py

diff --git a/graphistry/tests/test_cluster.py b/graphistry/tests/test_cluster.py
new file mode 100644
index 0000000000..a6ee965ced
--- /dev/null
+++ b/graphistry/tests/test_cluster.py
@@ -0,0 +1,51 @@
+import pandas as pd
+import unittest
+import pytest
+import graphistry
+import pandas as pd
+
+
+from graphistry.compute.cluster import lazy_dbscan_import_has_dependency
+
+has_dbscan, _, has_gpu_dbscan, _ = lazy_dbscan_import_has_dependency()
+
+
+ndf = edf = pd.DataFrame({'src': [1, 2, 3], 'dst': [4, 5, 6]})
+edf_umap = pd.DataFrame({'src': [1, 2, 3], 'dst': [4, 5, 6], 'x': [1, 2, 3], 'y': [4, 5, 6]})
+
+node_embedding = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
+edge_embedding = node_embedding
+
+class TestComputeCluster(unittest.TestCase):
+    
+    @pytest.mark.skipif(not has_dbscan, reason="requires DGL dependencies")
+    def test_umap_node_cluster(self):
+        g = graphistry.nodes(ndf)
+        g = g.umap(kind='nodes').dbscan(kind='nodes')
+        self.assertTrue('_cluster' in g._nodes)
+        self.assertTrue(g._node_dbscan is not None)
+
+    @pytest.mark.skipif(not has_dbscan, reason="requires DGL dependencies")
+    def test_umap_edge_cluster(self):
+        g = graphistry.bind(source='src', destination='dst').edges(edf)
+        g = g.umap(kind='edges').dbscan(kind='edges')        
+        self.assertTrue('_cluster' in g._edges)
+        self.assertTrue(g._edge_dbscan is not None)
+
+    @pytest.mark.skipif(not has_dbscan, reason="requires DGL dependencies")
+    def test_featurize_edge_cluster(self):
+        g = graphistry.bind(source='src', destination='dst').edges(edf).nodes(ndf)
+        for kind in ['nodes', 'edges']:
+            g = g.featurize(kind=kind).dbscan(kind=kind)        
+            if kind=='nodes':
+                self.assertTrue(g._node_dbscan is not None)
+                self.assertTrue('_cluster' in g._nodes)
+            else:
+                self.assertTrue(g._edge_dbscan is not None)
+                self.assertTrue('_cluster' in g._edges)
+        
+        
+        
+if __name__=='__main__':
+    unittest.main()
+    
\ No newline at end of file

From 46c0a4586daba09cd8ea7b97f3c01f9ae9138c9a Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 29 Dec 2022 23:37:28 -0800
Subject: [PATCH 015/432] adds check if umap has been fit so that
 g.featurize().dbscan() chains properly on argument defaults

---
 graphistry/compute/cluster.py    | 3 +--
 graphistry/tests/test_cluster.py | 6 +++---
 2 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 85f69aa01c..4dbaee9522 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -53,12 +53,11 @@ def cluster(g, dbscan, kind='nodes', cols=None, umap=True):
     else: 
         df = g.get_features_by_cols(cols, kind)
 
-    if umap and cols is None: 
+    if umap and cols is None and g._umap is not None: 
         df = g._get_embedding(kind)
     
     #print(df.head())
 
-    
     dbscan.fit(df)
     labels = dbscan.labels_
     
diff --git a/graphistry/tests/test_cluster.py b/graphistry/tests/test_cluster.py
index a6ee965ced..1152b5c5c3 100644
--- a/graphistry/tests/test_cluster.py
+++ b/graphistry/tests/test_cluster.py
@@ -2,7 +2,6 @@
 import unittest
 import pytest
 import graphistry
-import pandas as pd
 
 
 from graphistry.compute.cluster import lazy_dbscan_import_has_dependency
@@ -37,7 +36,7 @@ def test_featurize_edge_cluster(self):
         g = graphistry.bind(source='src', destination='dst').edges(edf).nodes(ndf)
         for kind in ['nodes', 'edges']:
             g = g.featurize(kind=kind).dbscan(kind=kind)        
-            if kind=='nodes':
+            if kind == 'nodes':
                 self.assertTrue(g._node_dbscan is not None)
                 self.assertTrue('_cluster' in g._nodes)
             else:
@@ -46,6 +45,7 @@ def test_featurize_edge_cluster(self):
         
         
         
-if __name__=='__main__':
+if __name__ == '__main__':
     unittest.main()
+
     
\ No newline at end of file

From 665c130357a0a61eb8518c05358931e456000aa5 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 29 Dec 2022 23:39:18 -0800
Subject: [PATCH 016/432] lint

---
 graphistry/tests/test_cluster.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/graphistry/tests/test_cluster.py b/graphistry/tests/test_cluster.py
index 1152b5c5c3..609656a8e6 100644
--- a/graphistry/tests/test_cluster.py
+++ b/graphistry/tests/test_cluster.py
@@ -44,7 +44,6 @@ def test_featurize_edge_cluster(self):
                 self.assertTrue('_cluster' in g._edges)
         
         
-        
 if __name__ == '__main__':
     unittest.main()
 

From 7be92021be7771cd056de908f65b2fe5df77d691 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 29 Dec 2022 23:40:54 -0800
Subject: [PATCH 017/432] lint

---
 graphistry/tests/test_cluster.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/tests/test_cluster.py b/graphistry/tests/test_cluster.py
index 609656a8e6..11b773af4c 100644
--- a/graphistry/tests/test_cluster.py
+++ b/graphistry/tests/test_cluster.py
@@ -46,5 +46,5 @@ def test_featurize_edge_cluster(self):
         
 if __name__ == '__main__':
     unittest.main()
+    
 
-    
\ No newline at end of file

From 2b6d966b4b0755b7419a8463a49537186d371901 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 29 Dec 2022 23:42:28 -0800
Subject: [PATCH 018/432] lint

---
 graphistry/tests/test_cluster.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/tests/test_cluster.py b/graphistry/tests/test_cluster.py
index 11b773af4c..d640283613 100644
--- a/graphistry/tests/test_cluster.py
+++ b/graphistry/tests/test_cluster.py
@@ -47,4 +47,4 @@ def test_featurize_edge_cluster(self):
 if __name__ == '__main__':
     unittest.main()
     
-
+1

From bc813a2190335fe7aef44305178317529c6820d6 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 30 Dec 2022 00:18:22 -0800
Subject: [PATCH 019/432] feat(resolves cpu gpu Literal), fixes
 .featurize().dbscan()

---
 .github/workflows/ci.yml         |  5 ++++
 graphistry/compute/cluster.py    | 25 ++++++++++++++--
 graphistry/tests/test_cluster.py | 49 ++++++++++++++------------------
 3 files changed, 49 insertions(+), 30 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 41fdee0554..61a1447280 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -199,6 +199,11 @@ jobs:
         source pygraphistry/bin/activate
         ./bin/typecheck.sh
 
+    - name: Full dbscan tests (rich featurize)
+      run: |
+        source pygraphistry/bin/activate
+        ./bin/test-dbscan.sh
+        
     - name: Full feature tests (rich featurize)
       run: |
         source pygraphistry/bin/activate
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 4dbaee9522..aa910afb32 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -14,6 +14,9 @@
     MIXIN_BASE = Plottable
 else:
     MIXIN_BASE = object
+    
+DBSCANEngineConcrete = Literal['cuml', 'umap_learn']
+DBSCANEngine = Literal[DBSCANEngineConcrete, "auto"]
 
 
 def lazy_dbscan_import_has_dependency():
@@ -36,6 +39,25 @@ def lazy_dbscan_import_has_dependency():
     return has_min_dependency, DBSCAN, has_cuml_dependency, cuDBSCAN
 
 
+
+def resolve_cpu_gpu_engine(
+    engine: DBSCANEngine,
+) -> DBSCANEngineConcrete:  # noqa
+    if engine in [CUML, UMAP_LEARN]:
+        return engine  # type: ignore
+    if engine in ["auto"]:
+        has_min_dependency, _, has_cuml_dependency, _ = lazy_dbscan_import_has_dependency()
+        if has_cuml_dependency:
+            return CUML
+        if has_min_dependency:
+            return UMAP_LEARN
+
+    raise ValueError(  # noqa
+        f'engine expected to be "auto", '
+        '"umap_learn", or  "cuml" '
+        f"but received: {engine} :: {type(engine)}"
+    )
+
     
 def cluster(g, dbscan, kind='nodes', cols=None, umap=True):
     """
@@ -56,8 +78,6 @@ def cluster(g, dbscan, kind='nodes', cols=None, umap=True):
     if umap and cols is None and g._umap is not None: 
         df = g._get_embedding(kind)
     
-    #print(df.head())
-
     dbscan.fit(df)
     labels = dbscan.labels_
     
@@ -82,6 +102,7 @@ def _cluster_dbscan(self, res, kind, cols, umap, eps, min_samples, **kwargs):
             DBSCAN clustering on cpu or gpu infered by umap's .engine flag
         """
         _, DBSCAN, _, cuDBSCAN = lazy_dbscan_import_has_dependency()
+        self.engine = resolve_cpu_gpu_engine("auto")
         
         dbscan = cuDBSCAN(eps=eps, min_samples=min_samples, **kwargs) if self.engine == CUML else DBSCAN(eps=eps, min_samples=min_samples, **kwargs)
         res = cluster(res, dbscan, kind=kind, cols=cols, umap=umap)
diff --git a/graphistry/tests/test_cluster.py b/graphistry/tests/test_cluster.py
index d640283613..593c088079 100644
--- a/graphistry/tests/test_cluster.py
+++ b/graphistry/tests/test_cluster.py
@@ -9,39 +9,32 @@
 has_dbscan, _, has_gpu_dbscan, _ = lazy_dbscan_import_has_dependency()
 
 
-ndf = edf = pd.DataFrame({'src': [1, 2, 3], 'dst': [4, 5, 6]})
-edf_umap = pd.DataFrame({'src': [1, 2, 3], 'dst': [4, 5, 6], 'x': [1, 2, 3], 'y': [4, 5, 6]})
-
-node_embedding = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
-edge_embedding = node_embedding
+ndf = edf = pd.DataFrame({'src': [1, 2, 3, 4], 'dst': [4, 5, 6, 1]})
 
 class TestComputeCluster(unittest.TestCase):
     
-    @pytest.mark.skipif(not has_dbscan, reason="requires DGL dependencies")
-    def test_umap_node_cluster(self):
-        g = graphistry.nodes(ndf)
-        g = g.umap(kind='nodes').dbscan(kind='nodes')
-        self.assertTrue('_cluster' in g._nodes)
-        self.assertTrue(g._node_dbscan is not None)
-
-    @pytest.mark.skipif(not has_dbscan, reason="requires DGL dependencies")
-    def test_umap_edge_cluster(self):
-        g = graphistry.bind(source='src', destination='dst').edges(edf)
-        g = g.umap(kind='edges').dbscan(kind='edges')        
-        self.assertTrue('_cluster' in g._edges)
-        self.assertTrue(g._edge_dbscan is not None)
-
-    @pytest.mark.skipif(not has_dbscan, reason="requires DGL dependencies")
+    def _condition(self, g, kind):
+        if kind == 'nodes':
+            self.assertTrue(g._node_dbscan is not None)
+            self.assertTrue('_cluster' in g._nodes)
+        else:
+            self.assertTrue(g._edge_dbscan is not None)
+            self.assertTrue('_cluster' in g._edges)
+    
+    @pytest.mark.skipif(not has_dbscan, reason="requires ai dependencies")
+    def test_umap_cluster(self):
+        for kind in ['nodes', 'edges']:
+            g = graphistry.nodes(ndf).edges(edf, 'src', 'dst')
+            g = g.umap(kind=kind, n_topics=2).dbscan(kind=kind)
+            self._condition(g, kind)    
+
+
+    @pytest.mark.skipif(not has_dbscan, reason="requires ai dependencies")
     def test_featurize_edge_cluster(self):
-        g = graphistry.bind(source='src', destination='dst').edges(edf).nodes(ndf)
+        g = graphistry.edges(edf, 'src', 'dst').nodes(ndf)
         for kind in ['nodes', 'edges']:
-            g = g.featurize(kind=kind).dbscan(kind=kind)        
-            if kind == 'nodes':
-                self.assertTrue(g._node_dbscan is not None)
-                self.assertTrue('_cluster' in g._nodes)
-            else:
-                self.assertTrue(g._edge_dbscan is not None)
-                self.assertTrue('_cluster' in g._edges)
+            g = g.featurize(kind=kind, n_topics=2).dbscan(kind=kind)
+            self._condition(g, kind)
         
         
 if __name__ == '__main__':

From 49c78f6a4f2aea6d1daaa11e45e22829c37abeaa Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 30 Dec 2022 00:22:12 -0800
Subject: [PATCH 020/432] lint

---
 graphistry/compute/cluster.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index aa910afb32..0eb27fd497 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -48,9 +48,9 @@ def resolve_cpu_gpu_engine(
     if engine in ["auto"]:
         has_min_dependency, _, has_cuml_dependency, _ = lazy_dbscan_import_has_dependency()
         if has_cuml_dependency:
-            return CUML
+            return 'cuml'
         if has_min_dependency:
-            return UMAP_LEARN
+            return 'umap_learn'
 
     raise ValueError(  # noqa
         f'engine expected to be "auto", '

From 5bf60428d6ab0d36038427776f035fca7341bac9 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 30 Dec 2022 00:28:41 -0800
Subject: [PATCH 021/432] adds bin/test-dscan.sh

---
 bin/test-dbscan.sh | 15 +++++++++++++++
 1 file changed, 15 insertions(+)
 create mode 100644 bin/test-dbscan.sh

diff --git a/bin/test-dbscan.sh b/bin/test-dbscan.sh
new file mode 100644
index 0000000000..c1b6ebfc2e
--- /dev/null
+++ b/bin/test-dbscan.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+set -ex
+
+# Run from project root
+# - Args get passed to pytest phase
+# Non-zero exit code on fail
+
+# Assume [umap-learn,test]
+
+python -m pytest --version
+
+python -B -m pytest -vv \
+    graphistry/tests/test_cluster.py
+
+#chmod +x bin/test-embed.sh
\ No newline at end of file

From c4dcc1b6948c20b12a02ad9ab49ecf7761fbb23c Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 30 Dec 2022 00:42:00 -0800
Subject: [PATCH 022/432] chmod bin/test-dbscan.sh, adds doc

---
 bin/test-dbscan.sh            | 0
 graphistry/compute/cluster.py | 9 +++++----
 2 files changed, 5 insertions(+), 4 deletions(-)
 mode change 100644 => 100755 bin/test-dbscan.sh

diff --git a/bin/test-dbscan.sh b/bin/test-dbscan.sh
old mode 100644
new mode 100755
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 0eb27fd497..4152bff285 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -102,21 +102,22 @@ def _cluster_dbscan(self, res, kind, cols, umap, eps, min_samples, **kwargs):
             DBSCAN clustering on cpu or gpu infered by umap's .engine flag
         """
         _, DBSCAN, _, cuDBSCAN = lazy_dbscan_import_has_dependency()
-        self.engine = resolve_cpu_gpu_engine("auto")
         
-        dbscan = cuDBSCAN(eps=eps, min_samples=min_samples, **kwargs) if self.engine == CUML else DBSCAN(eps=eps, min_samples=min_samples, **kwargs)
+        res.engine = resolve_cpu_gpu_engine("auto")
+        
+        dbscan = cuDBSCAN(eps=eps, min_samples=min_samples, **kwargs) if res.engine == CUML else DBSCAN(eps=eps, min_samples=min_samples, **kwargs)
         res = cluster(res, dbscan, kind=kind, cols=cols, umap=umap)
 
         return res
     
     
     def dbscan(self, kind = 'nodes', cols = None, umap = True, eps: float = 1., min_samples: int = 1, **kwargs):
-        """DBSCAN clustering on cpu or gpu infered by umap's .engine flag
+        """DBSCAN clustering on cpu or gpu infered automatically 
         
             g2 = g.featurize().dbscan(kind='nodes', cols=None, umap=True, eps=1., min_samples=1, **kwargs)
             print(g2._nodes['_cluster'])
             
-            # cluster by 'ip172' and 'location', for example
+            # cluster by 'column_attribute1=ip172' and 'column_attribute2=location', for example
             g2 = g.featurize().dbscan(cols=['column_attribute1', 'column_attribute2'], **kwargs)
             
             # cluster by UMAP embeddings

From 278121b3b453a052c5f539f84b359b20e554f317 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 30 Dec 2022 09:09:47 -0800
Subject: [PATCH 023/432] feat(adds dbscan within umap call), adds test

---
 graphistry/compute/cluster.py    |  2 +-
 graphistry/tests/test_cluster.py | 16 +++++++++-------
 graphistry/umap_utils.py         | 19 +++++++++++--------
 3 files changed, 21 insertions(+), 16 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 4152bff285..9a3fd7c05a 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -128,7 +128,7 @@ def dbscan(self, kind = 'nodes', cols = None, umap = True, eps: float = 1., min_
             cols: list of columns to use for clustering given `g.featurize` has been run, nice way to slice features by fragments of interest, e.g. ['ip172', 'location', 'asn', 'warnings']
             umap: whether to use UMAP embeddings or features dataframe
             eps: The maximum distance between two samples for them to be considered as in the same neighborhood.
-            min_samples: The number of samples (or total integer weight) in a neighborhood for a point to be considered as a core point. This includes the point itself.
+            min_samples: The number of samples in a neighborhood for a point to be considered as a core point. This includes the point itself.
             
         """
         res = self.bind()
diff --git a/graphistry/tests/test_cluster.py b/graphistry/tests/test_cluster.py
index 593c088079..d2fc31ca66 100644
--- a/graphistry/tests/test_cluster.py
+++ b/graphistry/tests/test_cluster.py
@@ -23,15 +23,17 @@ def _condition(self, g, kind):
     
     @pytest.mark.skipif(not has_dbscan, reason="requires ai dependencies")
     def test_umap_cluster(self):
+        g = graphistry.nodes(ndf).edges(edf, 'src', 'dst')
         for kind in ['nodes', 'edges']:
-            g = graphistry.nodes(ndf).edges(edf, 'src', 'dst')
-            g = g.umap(kind=kind, n_topics=2).dbscan(kind=kind)
-            self._condition(g, kind)    
-
+            g2 = g.umap(kind=kind, n_topics=2, dbscan=False).dbscan(kind=kind)
+            self._condition(g2, kind)
+            g3 = g.umap(kind=kind, n_topics=2, dbscan=True)
+            self._condition(g3, kind)
+            self.assertEqual(g2._nodes['_cluster'].tolist(), g3._nodes['_cluster'].tolist())
 
     @pytest.mark.skipif(not has_dbscan, reason="requires ai dependencies")
-    def test_featurize_edge_cluster(self):
-        g = graphistry.edges(edf, 'src', 'dst').nodes(ndf)
+    def test_featurize_cluster(self):
+        g = graphistry.nodes(ndf).edges(edf, 'src', 'dst')
         for kind in ['nodes', 'edges']:
             g = g.featurize(kind=kind, n_topics=2).dbscan(kind=kind)
             self._condition(g, kind)
@@ -40,4 +42,4 @@ def test_featurize_edge_cluster(self):
 if __name__ == '__main__':
     unittest.main()
     
-1
+
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 873c194df6..99dfa5c891 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -84,7 +84,7 @@ def resolve_umap_engine(
     if engine in [CUML, UMAP_LEARN]:
         return engine  # type: ignore
     if engine in ["auto"]:
-        has_cuml_dependancy_, _, cuml = lazy_cuml_import_has_dependancy()
+        has_cuml_dependancy_, _, _ = lazy_cuml_import_has_dependancy()
         if has_cuml_dependancy_:
             return 'cuml'
         has_umap_dependancy_, _, _ = lazy_umap_import_has_dependancy()
@@ -390,6 +390,7 @@ def umap(
         play: Optional[int] = 0,
         encode_position: bool = True,
         encode_weight: bool = True,
+        dbscan: bool = True,
         engine: UMAPEngine = "auto",
         inplace: bool = False,
         feature_engine: str = "auto",
@@ -411,8 +412,8 @@ def umap(
                 implicit UMAP, default True.
         :param encode_position: whether to set default plotting bindings
                 -- positions x,y from umap for .plot()
-        :param X: either an ndarray of features, or column names to featurize
-        :param y: either an ndarray of targets, or column names to featurize
+        :param X: either a dataframe ndarray of features, or column names to featurize
+        :param y: either an dataframe ndarray of targets, or column names to featurize
                 targets
         :param scale: multiplicative scale for pruning weighted edge DataFrame
                 gotten from UMAP, between [0, ..) with high end meaning keep
@@ -432,9 +433,10 @@ def umap(
                 en/latest/parameters.html] documentation for more.
         :param suffix: optional suffix to add to x, y attributes of umap.
         :param play: Graphistry play parameter, default 0, how much to evolve
-                the network during clustering
+                the network during clustering. 0 preserves the original UMAP layout.
+        :param dbscan: whether to run DBSCAN on the UMAP embedding, default True.
         :param engine: selects which engine to use to calculate UMAP:
-                NotImplemented yet, default UMAP-LEARN
+                default "auto" will use cuML if available, otherwise UMAP-LEARN.
         :param memoize: whether to memoize the results of this method,
                 default True.
         :return: self, with attributes set with new data
@@ -463,7 +465,6 @@ def umap(
             res = self.bind()
 
         res.umap_lazy_init(engine=engine, suffix=suffix)
-        # res.suffix = suffix
 
         logger.debug("umap input X :: %s", X)
         logger.debug("umap input y :: %s", y)
@@ -471,8 +472,7 @@ def umap(
         featurize_kwargs = self._set_features(
             res, X, y, kind, feature_engine, {**featurize_kwargs, "memoize": memoize}
         )
-        # umap_kwargs = {**umap_kwargs,
-        # 'featurize_kwargs': featurize_kwargs or {}}
+
 
         if kind == "nodes":
             if res._node is None:
@@ -574,6 +574,9 @@ def umap(
         if res.engine == CUML and is_legacy_cuml():
             res = res.prune_self_edges()
 
+        if dbscan:
+            res = res.dbscan(kind=kind, umap=True)
+
         if not inplace:
             return res
 

From 5f568943b4f68346b2d4a7b5ea7bea0e30c088c5 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 30 Dec 2022 09:18:46 -0800
Subject: [PATCH 024/432] lint

---
 graphistry/compute/cluster.py    | 15 ++++++++++++---
 graphistry/tests/test_cluster.py |  3 +--
 2 files changed, 13 insertions(+), 5 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 9a3fd7c05a..2a5ac4f218 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -114,14 +114,23 @@ def _cluster_dbscan(self, res, kind, cols, umap, eps, min_samples, **kwargs):
     def dbscan(self, kind = 'nodes', cols = None, umap = True, eps: float = 1., min_samples: int = 1, **kwargs):
         """DBSCAN clustering on cpu or gpu infered automatically 
         
-            g2 = g.featurize().dbscan(kind='nodes', cols=None, umap=True, eps=1., min_samples=1, **kwargs)
+        Examples:
+            # cluster by feature embeddings
+            g2 = g.featurize().dbscan(kind='nodes', eps=1., min_samples=1, **kwargs)
             print(g2._nodes['_cluster'])
             
-            # cluster by 'column_attribute1=ip172' and 'column_attribute2=location', for example
-            g2 = g.featurize().dbscan(cols=['column_attribute1', 'column_attribute2'], **kwargs)
+            # cluster by a given set of feature column attributes 
+            # 'column_attribute1=ip_172' and 'column_attribute2=location', for example
+            g2 = g.featurize().dbscan(cols=['ip_172', 'location'], **kwargs)
             
             # cluster by UMAP embeddings
             g2 = g.umap().dbscan()
+            
+            # dbscan with fixed parameters is default in umap
+            g2 = g.umap(dbscan=True)
+            
+            # with greater control over parameters via chaining,
+            g2 = g.umap().dbscan(eps=1.0, min_samples=2, **kwargs)
 
         Args:
             kind: 'nodes' or 'edges'
diff --git a/graphistry/tests/test_cluster.py b/graphistry/tests/test_cluster.py
index d2fc31ca66..b98fe77bc8 100644
--- a/graphistry/tests/test_cluster.py
+++ b/graphistry/tests/test_cluster.py
@@ -41,5 +41,4 @@ def test_featurize_cluster(self):
         
 if __name__ == '__main__':
     unittest.main()
-    
-
+    
\ No newline at end of file

From a336217f2128b4b5460c0599742d36199600b0a0 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 30 Dec 2022 09:21:44 -0800
Subject: [PATCH 025/432] lint

---
 graphistry/tests/test_cluster.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/tests/test_cluster.py b/graphistry/tests/test_cluster.py
index b98fe77bc8..4c39dced35 100644
--- a/graphistry/tests/test_cluster.py
+++ b/graphistry/tests/test_cluster.py
@@ -41,4 +41,4 @@ def test_featurize_cluster(self):
         
 if __name__ == '__main__':
     unittest.main()
-    
\ No newline at end of file
+    

From c4f6cffe1d28e5e2fbbdfdfd58c3030798a870de Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 30 Dec 2022 09:25:33 -0800
Subject: [PATCH 026/432] lint

---
 graphistry/umap_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 99dfa5c891..fda5a96060 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -575,7 +575,7 @@ def umap(
             res = res.prune_self_edges()
 
         if dbscan:
-            res = res.dbscan(kind=kind, umap=True)
+            res = res.dbscan(kind=kind, umap=True)  # type: ignore
 
         if not inplace:
             return res

From 12c2ea76895bc8e591e662f2e940bff9f3358153 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 30 Dec 2022 09:36:24 -0800
Subject: [PATCH 027/432] adds dbscan=False flag in umap tests

---
 bin/test-dbscan.sh                  |  2 +-
 graphistry/tests/test_umap_utils.py | 12 +++---------
 2 files changed, 4 insertions(+), 10 deletions(-)

diff --git a/bin/test-dbscan.sh b/bin/test-dbscan.sh
index c1b6ebfc2e..0bc204cbdc 100755
--- a/bin/test-dbscan.sh
+++ b/bin/test-dbscan.sh
@@ -12,4 +12,4 @@ python -m pytest --version
 python -B -m pytest -vv \
     graphistry/tests/test_cluster.py
 
-#chmod +x bin/test-embed.sh
\ No newline at end of file
+#chmod +x bin/test-dbscan.sh
\ No newline at end of file
diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index ca0c3897ba..218ab8d24a 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -102,14 +102,6 @@ def setUp(self):
         self.EMBe = g2._edge_embedding
         self.embe, self.xe, self.ye = g2.transform_umap(edge_df22, ydf=edge2_target_df, kind='edges')
 
-    # @pytest.mark.skipif(not has_dependancy, reason="requires umap feature dependencies")
-    # def test_allclose_fit_transform_on_same_data(self):
-    #     check_allclose_fit_transform_on_same_data(self.X, self.x, self.Y, self.y)
-    #     check_allclose_fit_transform_on_same_data(self.Xe, self.xe, self.Ye, self.ye)
-
-    #     check_allclose_fit_transform_on_same_data(self.EMB, self.emb, None, None)
-    #     check_allclose_fit_transform_on_same_data(self.EMBe, self.embe, None, None)
-
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_columns_match(self):
         assert all(self.X.columns == self.x.columns), 'Node Feature Columns do not match'
@@ -195,6 +187,7 @@ def _test_umap(self, g, use_cols, targets, name, kind, df):
                         model_name=model_avg_name,
                         feature_engine=feature_engine,
                         n_neighbors=2,
+                        dbscan=False
                     )
 
                     self.cases_test_graph(g2, kind=kind, df=df)
@@ -272,7 +265,8 @@ def _test_umap(self, g, use_cols, targets, name, kind, df):
                                     engine='umap_learn',
                                     cardinality_threshold=cardinality,
                                     cardinality_threshold_target=cardinality,
-                                    n_neighbors=3)
+                                    n_neighbors=3, 
+                                    dbscan=False)
 
                                 self.cases_test_graph(g2, kind=kind, df=df)
 

From e24efcf0feb4522a6930f13322396fb77309eb84 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 30 Dec 2022 09:54:38 -0800
Subject: [PATCH 028/432] fix umap tests

---
 graphistry/tests/test_umap_utils.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index 218ab8d24a..b0aef2432c 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -321,12 +321,12 @@ def test_edge_umap(self):
     )
     def test_chaining_nodes(self):
         g = graphistry.nodes(ndf_reddit)
-        g2 = g.umap()
+        g2 = g.umap(dbscan=False)
 
         logger.debug('======= g.umap() done ======')
         g3a = g2.featurize()
         logger.debug('======= g3a.featurize() done ======')
-        g3 = g3a.umap()
+        g3 = g3a.umap(dbscan=False)
         logger.debug('======= g3.umap() done ======')
         assert g2._node_features.shape == g3._node_features.shape
         # since g3 has feature params with x and y.
@@ -346,8 +346,8 @@ def test_chaining_edges(self):
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=FutureWarning)
-            g2 = g.umap(kind='edges')
-            g3 = g.featurize(kind='edges').umap(kind='edges')
+            g2 = g.umap(kind='edges', dbscan=False)
+            g3 = g.featurize(kind='edges').umap(kind='edges', dbscan=False)
             
         assert all(g2._feature_params['edges']['X'] == g3._feature_params['edges']['X'])
         assert all(g2._feature_params['edges']['y'] == g3._feature_params['edges']['y'])  # None

From f8337e9184aa1b40ed507b0e22e4b24a55f29048 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 30 Dec 2022 11:04:18 -0800
Subject: [PATCH 029/432] doc(adds readme and doc edits)

---
 README.md                     | 30 +++++++++++++++++++++++++++
 graphistry/compute/cluster.py | 39 ++++++++++++++++++++++-------------
 2 files changed, 55 insertions(+), 14 deletions(-)

diff --git a/README.md b/README.md
index 00ed8c5e2f..180d8f099e 100644
--- a/README.md
+++ b/README.md
@@ -554,6 +554,36 @@ See `help(g.search_graph)` for options
 
 See `help(g.embed)`, `help(g.predict_links)` , `help(g.predict_links_all)` for options
 
+### DBSCAN 
+
+* Enrich UMAP embeddings or featurization dataframe with GPU or CPU DBSCAN
+
+    ```python
+            g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
+            
+            # cluster by UMAP embeddings
+            kind = 'nodes' | 'edges'
+            g2 = g.umap(kind=kind).dbscan(kind=kind)
+            print(g2._nodes['_cluster']) | print(g2._edges['_cluster'])
+
+            # dbscan with fixed parameters is default in umap
+            g2 = g.umap(dbscan=True)
+            
+            # and with greater control over parameters via chaining,
+            g2 = g.umap().dbscan(eps=1.2, min_samples=2, **kwargs)
+            
+            # cluster by feature embeddings
+            g2 = g.featurize().dbscan(**kwargs)
+            
+            # cluster by a given set of feature column attributes 
+            g2 = g.featurize().dbscan(cols=['ip_172', 'location', 'alert'], **kwargs)
+            
+            # equivalent to above (ie, cols != None and umap=True will still use features dataframe, rather than UMAP embeddings)
+            g2 = g.umap().dbscan(cols=['ip_172', 'location', 'alert'], umap=True | False, **kwargs)
+            g2.plot() # color by `_cluster`
+    ```
+
+
 
 ### Quickly configurable
 
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 2a5ac4f218..6ba1d91071 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -115,26 +115,37 @@ def dbscan(self, kind = 'nodes', cols = None, umap = True, eps: float = 1., min_
         """DBSCAN clustering on cpu or gpu infered automatically 
         
         Examples:
-            # cluster by feature embeddings
-            g2 = g.featurize().dbscan(kind='nodes', eps=1., min_samples=1, **kwargs)
-            print(g2._nodes['_cluster'])
-            
-            # cluster by a given set of feature column attributes 
-            # 'column_attribute1=ip_172' and 'column_attribute2=location', for example
-            g2 = g.featurize().dbscan(cols=['ip_172', 'location'], **kwargs)
+            g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
             
             # cluster by UMAP embeddings
-            g2 = g.umap().dbscan()
-            
+            kind = 'nodes' | 'edges'
+            g2 = g.umap(kind=kind).dbscan(kind=kind)
+            print(g2._nodes['_cluster']) | print(g2._edges['_cluster'])
+
             # dbscan with fixed parameters is default in umap
             g2 = g.umap(dbscan=True)
             
-            # with greater control over parameters via chaining,
-            g2 = g.umap().dbscan(eps=1.0, min_samples=2, **kwargs)
+            # and with greater control over parameters via chaining,
+            g2 = g.umap().dbscan(eps=1.2, min_samples=2, **kwargs)
+            
+            # cluster by feature embeddings
+            g2 = g.featurize().dbscan(**kwargs)
+            
+            # cluster by a given set of feature column attributes 
+            g2 = g.featurize().dbscan(cols=['ip_172', 'location', 'alert'], **kwargs)
+            
+            # equivalent to above (ie, cols != None and umap=True will still use features dataframe, rather than UMAP embeddings)
+            g2 = g.umap().dbscan(cols=['ip_172', 'location', 'alert'], umap=True | False, **kwargs)
+            
+            g2.plot() # color by `_cluster`
 
+        Useful:
+            Enriching the graph with cluster labels from UMAP is useful for visualizing clusters in the graph by color, size, etc.
+            see https://github.com/graphistry/pygraphistry/blob/master/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
+                        
         Args:
             kind: 'nodes' or 'edges'
-            cols: list of columns to use for clustering given `g.featurize` has been run, nice way to slice features by fragments of interest, e.g. ['ip172', 'location', 'asn', 'warnings']
+            cols: list of columns to use for clustering given `g.featurize` has been run, nice way to slice features by fragments of interest, e.g. ['ip_172', 'location', 'ssh', 'warnings']
             umap: whether to use UMAP embeddings or features dataframe
             eps: The maximum distance between two samples for them to be considered as in the same neighborhood.
             min_samples: The number of samples in a neighborhood for a point to be considered as a core point. This includes the point itself.
@@ -145,5 +156,5 @@ def dbscan(self, kind = 'nodes', cols = None, umap = True, eps: float = 1., min_
         
         return res
 
-    def _is_cudf(self, df):
-        return 'cudf' in str(type(df))
+    # def _is_cudf(self, df):
+    #     return 'cudf' in str(type(df))

From afd3a7e2335a3c09f9a97cb9c5104e92afeb0c1e Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 30 Dec 2022 11:13:18 -0800
Subject: [PATCH 030/432] doc(adds more to Readme)

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 180d8f099e..53ed6e5293 100644
--- a/README.md
+++ b/README.md
@@ -521,7 +521,7 @@ See `help(g.search_graph)` for options
                       relation=['relationship_1', 'relationship_4', ..], 
                       destination=['entity_l', 'entity_m', ..], 
                       threshold=0.9,  # score threshold
-                      return_dataframe=False)  # set to `True` to return dataframe, or just access via `g5._edges`
+                      return_dataframe=False)  # set to `True` to return dataframe, or just access via `g4._edges`
     ```
 
 * Detect Anamolous Behavior (example use cases such as Cyber, Fraud, etc)
@@ -583,7 +583,7 @@ See `help(g.embed)`, `help(g.predict_links)` , `help(g.predict_links_all)` for o
             g2.plot() # color by `_cluster`
     ```
 
-
+See `help(g.dbscan)` for options
 
 ### Quickly configurable
 

From 44827ea66bb2d5d11b8956cc36e5b6742ead1ab4 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 30 Dec 2022 11:15:07 -0800
Subject: [PATCH 031/432] indent

---
 README.md | 44 ++++++++++++++++++++++----------------------
 1 file changed, 22 insertions(+), 22 deletions(-)

diff --git a/README.md b/README.md
index 53ed6e5293..4c628e343c 100644
--- a/README.md
+++ b/README.md
@@ -559,28 +559,28 @@ See `help(g.embed)`, `help(g.predict_links)` , `help(g.predict_links_all)` for o
 * Enrich UMAP embeddings or featurization dataframe with GPU or CPU DBSCAN
 
     ```python
-            g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
-            
-            # cluster by UMAP embeddings
-            kind = 'nodes' | 'edges'
-            g2 = g.umap(kind=kind).dbscan(kind=kind)
-            print(g2._nodes['_cluster']) | print(g2._edges['_cluster'])
-
-            # dbscan with fixed parameters is default in umap
-            g2 = g.umap(dbscan=True)
-            
-            # and with greater control over parameters via chaining,
-            g2 = g.umap().dbscan(eps=1.2, min_samples=2, **kwargs)
-            
-            # cluster by feature embeddings
-            g2 = g.featurize().dbscan(**kwargs)
-            
-            # cluster by a given set of feature column attributes 
-            g2 = g.featurize().dbscan(cols=['ip_172', 'location', 'alert'], **kwargs)
-            
-            # equivalent to above (ie, cols != None and umap=True will still use features dataframe, rather than UMAP embeddings)
-            g2 = g.umap().dbscan(cols=['ip_172', 'location', 'alert'], umap=True | False, **kwargs)
-            g2.plot() # color by `_cluster`
+      g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
+      
+      # cluster by UMAP embeddings
+      kind = 'nodes' | 'edges'
+      g2 = g.umap(kind=kind).dbscan(kind=kind)
+      print(g2._nodes['_cluster']) | print(g2._edges['_cluster'])
+
+      # dbscan with fixed parameters is default in umap
+      g2 = g.umap(dbscan=True)
+      
+      # and with greater control over parameters via chaining,
+      g2 = g.umap().dbscan(eps=1.2, min_samples=2, **kwargs)
+      
+      # cluster by feature embeddings
+      g2 = g.featurize().dbscan(**kwargs)
+      
+      # cluster by a given set of feature column attributes 
+      g2 = g.featurize().dbscan(cols=['ip_172', 'location', 'alert'], **kwargs)
+      
+      # equivalent to above (ie, cols != None and umap=True will still use features dataframe, rather than UMAP embeddings)
+      g2 = g.umap().dbscan(cols=['ip_172', 'location', 'alert'], umap=True | False, **kwargs)
+      g2.plot() # color by `_cluster`
     ```
 
 See `help(g.dbscan)` for options

From 53e1f0cd7a5d3601a806d87bc00ff07363844d0a Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 30 Dec 2022 11:30:13 -0800
Subject: [PATCH 032/432] doc(CHANGELOG and README)

---
 CHANGELOG.md | 6 ++++++
 README.md    | 2 +-
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 81b29546b8..621e3ad90b 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,6 +7,12 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm
 
 ## [Development]
 
+### Added
+* AI: DBSCAN -- `g.featurize().dbscan()` and `g.umap().dbscan()` with options to use UMAP embedding, feature matrix, or subset of feature matrix via `g.dbscan(cols=[...])`
+* AI: Demo cleanup using ModelDict & new features
+* AI: moves public `g.g_dgl` from KG `embed` method to private method `g._kg_dgl`
+* Tests: dbscan tests
+
 ### Added
 * AI: Easy import of featurization kwargs for `g.umap(**kwargs)` and `g.featurize(**kwargs)`
 * AI: `g.get_features_by_cols` returns featurized submatrix with `col_part` in their columns
diff --git a/README.md b/README.md
index 4c628e343c..f3f64aea83 100644
--- a/README.md
+++ b/README.md
@@ -482,7 +482,7 @@ GNN support is rapidly evolving, please contact the team directly or on Slack fo
                         
       results_df, query_vector = g2.search('my natural language query', ...)
       
-      print(results_df[['_distance', 'text_col_1', ..., 'text_col_n']])  #sorted by relevancy
+      print(results_df[['_distance', ..., 'text_col_n']])  #sorted by relevancy
       
       # or see graph of matching entities and similarity edges (or optional original edges)
       g2.search_graph('my natural language query', ...).plot()

From 26eb4c51e2e9bfefb166d9b468108fdd76cc786a Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 30 Dec 2022 11:39:06 -0800
Subject: [PATCH 033/432] doc(changelog)

---
 CHANGELOG.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 621e3ad90b..119150b9c1 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,13 +7,13 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm
 
 ## [Development]
 
+### Changed
+* AI: moves public `g.g_dgl` from KG `embed` method to private method `g._kg_dgl`
+
 ### Added
 * AI: DBSCAN -- `g.featurize().dbscan()` and `g.umap().dbscan()` with options to use UMAP embedding, feature matrix, or subset of feature matrix via `g.dbscan(cols=[...])`
 * AI: Demo cleanup using ModelDict & new features
-* AI: moves public `g.g_dgl` from KG `embed` method to private method `g._kg_dgl`
 * Tests: dbscan tests
-
-### Added
 * AI: Easy import of featurization kwargs for `g.umap(**kwargs)` and `g.featurize(**kwargs)`
 * AI: `g.get_features_by_cols` returns featurized submatrix with `col_part` in their columns
 * AI: `g.conditional_graph` and `g.conditional_probs` assessing conditional probs and graph

From 8b94be9496bffccd4d39ea083715742ca2ee774a Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Tue, 3 Jan 2023 00:54:46 -0800
Subject: [PATCH 034/432] feat(large refactor of transform methods from
 featurize, umap, and dbscan, adding `infer_graph` nearest graph interpolation
 in umap or featurize coordinates)

---
 graphistry/ai_utils.py        | 153 +++++++++++++++++++++++++++++++++-
 graphistry/compute/cluster.py | 141 ++++++++++++++++++++++++++-----
 graphistry/feature_utils.py   |  49 ++++++-----
 graphistry/umap_utils.py      |  17 ++--
 4 files changed, 313 insertions(+), 47 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 1b18dd4404..e46922b916 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -1,9 +1,12 @@
 import pandas as pd
+import numpy as np
 
 import graphistry
-from .util import setup_logger
 
-logger = setup_logger(__name__)
+from .constants import N_TREES, DISTANCE
+from logging import getLogger
+
+logger = getLogger(__name__)
 
 
 # #################################################################################################
@@ -127,3 +130,149 @@ def get_graphistry_from_milieu_search(
     ntdf = ndf[ndf[node_col].isin(gcols)]
     g = graphistry.edges(tdf, src, dst).nodes(ntdf, node_col)
     return g
+
+# #########################################################################################################################
+#
+#  Graphistry Vector Search Index
+#
+##########################################################################################################################
+
+def build_annoy_index(X, angular, n_trees=None):
+    """ Builds an Annoy Index for fast vector search
+
+    Args:
+        X (_type_): _description_
+        angular (_type_): _description_
+        n_trees (_type_, optional): _description_. Defaults to None.
+
+    Returns:
+        _type_: _description_
+    """
+    from annoy import AnnoyIndex  # type: ignore
+
+    logger.info(f"Building Index of size {X.shape}")
+
+    if angular:
+        logger.info('-using angular metric')
+        metric = 'angular'
+    else:
+        logger.info('-using euclidean metric')
+        metric = 'euclidean'
+        
+    search_index = AnnoyIndex(X.shape[1], metric)
+    # Add all the feature vectors to the search index
+    for i in range(len(X)):
+        search_index.add_item(i, X.values[i])
+    if n_trees is None:
+        n_trees = N_TREES
+
+    logger.info(f'-building index with {n_trees} trees')
+    search_index.build(n_trees)
+    return search_index
+
+def query_by_vector(vect, df, search_index, top_n):
+    indices, distances = search_index.get_nns_by_vector(
+        vect.values[0], top_n, include_distances=True
+    )
+    
+    results = df.iloc[indices]
+    results[DISTANCE] = distances
+    results = results.sort_values(by=[DISTANCE])
+
+    return results
+
+
+
+
+
+# #########################################################################################################################
+#
+#  Graphistry Graph Inference
+#
+##########################################################################################################################
+
+
+def infer_graph(res, emb, X, y, df, use_umap, eps=1, sample=None):
+    """
+        Infer a graph from a graphistry object
+        
+        args:
+            res: graphistry object
+            df: outside minibatch dataframe to add to existing graph
+            X: minibatch transformed dataframe
+            emb: minibatch UMAP embedding
+            kind: 'nodes' or 'edges'
+            eps: distance threshold for a minibatchh point to cluster to existing graph
+            n_nearest: number of nearest neighbors to add from existing graphs edges, if None, ignores existing edges. 
+    """
+    # if we have node features in this mini_batch, we can 
+    if use_umap and emb is not None: #would conflict if node_features is of shape (n, 2)
+        X_ = res._node_embedding
+        W = emb
+    else:
+        X_ = res._node_features
+        W = X
+    Y = res._node_target
+    
+    assert df.shape[0] == X.shape[0], 'minibatches df and X must have same number of rows since f(df) = X'
+    
+    new_edges = []
+    # if umap, need to add '_n' as node id to df, adding new indices to existing graph
+    df['_n'] = range(X.shape[0], X.shape[0]+df.shape[0])
+    df['_batch'] = 1 # 1 for minibatch, 0 for existing graph
+    node = res._node
+    NDF = res._nodes
+    NDF['_batch'] = 0
+    EDF = res._edges
+    EDF['_batch'] = 0
+    src = res._source
+    dst = res._destination
+    
+    old_edges = []
+    old_nodes = []
+    mdists=[]
+    
+    #vsearch = build_search_index(X_, angular=False)
+    
+    for i in range(W.shape[0]):
+        record_df = df.iloc[i, :]
+        diff = X_ - W.iloc[i, :]
+        dist = np.linalg.norm(diff, axis=1)  # Euclidean distance
+
+        mdist = dist.mean()
+        mdists.append(mdist)
+        for nn in np.where(dist < eps)[0]:
+            this_ndf = NDF.iloc[nn, :]
+            if sample:
+                local_edges = EDF[(EDF[src] == this_ndf[node]) | (EDF[dst] == this_ndf[node])]
+                if not local_edges.empty:
+                    old_edges.append(local_edges.sample(sample, replace=True))
+            new_edges.append([this_ndf[node], record_df[node], 1, 1])
+            old_nodes.append(this_ndf)
+    
+    print('mean dist', np.mean(mdist))
+
+    new_edges = pd.DataFrame(new_edges, columns=[src, dst, '_weight', '_batch'])
+    if sample:
+        new_edges = pd.concat([new_edges, pd.concat(old_edges, axis=0).assign(_batch=0)], axis=0)
+    new_edges = new_edges.drop_duplicates()
+        
+    old_nodes = pd.DataFrame(old_nodes).drop_duplicates(subset=[node])
+    old_emb = X_.loc[old_nodes.index]
+    new_emb = pd.concat([W, old_emb], axis=0)
+
+    new_nodes = pd.concat([df, old_nodes], axis=0)#.reset_index(drop=True) # append minibatch at top
+    
+    # #########################################################
+    g = res.nodes(new_nodes, node).edges(new_edges, src, dst)
+    
+    if use_umap:
+        g._node_embedding = new_emb
+        g._node_features = X_
+    else:
+        g._node_features = new_emb
+        g._node_embedding = X_
+    
+    g._node_targets = pd.concat([y, Y.loc[old_nodes.index]]) if y is not None else Y
+    
+    return g
\ No newline at end of file
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 6ba1d91071..6f6cb99f7c 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -1,5 +1,7 @@
 import logging
 import pandas as pd
+import numpy as np
+
 from typing import Any, List, Union, TYPE_CHECKING
 from typing_extensions import Literal
 from collections import Counter
@@ -7,6 +9,9 @@
 from graphistry.Engine import Engine
 from graphistry.Plottable import Plottable
 from graphistry.constants import CUML, UMAP_LEARN  # noqa type: ignore
+from graphistry.features import ModelDict
+from graphistry.feature_utils import get_matrix_by_column_parts
+from graphistry.ai_utils import infer_graph
 
 logger = logging.getLogger("compute.cluster")
 
@@ -59,7 +64,22 @@ def resolve_cpu_gpu_engine(
     )
 
     
-def cluster(g, dbscan, kind='nodes', cols=None, umap=True):
+def get_model_matrix(g, kind, cols, umap):
+    assert kind in ['nodes', 'edges']
+    assert hasattr(g, '_node_encoder') if kind == 'nodes' else hasattr(g, '_edge_encoder')
+    
+    if cols is None:
+        df = g._get_feature(kind)
+    else: 
+        df = g.get_features_by_cols(cols, kind)
+
+    if umap and cols is None and g._umap is not None: 
+        df = g._get_embedding(kind)
+        
+    return df
+    
+
+def dbscan_fit(g, dbscan, kind='nodes', cols=None, umap=True):
     """
         Fits clustering on UMAP embeddings if umap is True, otherwise on the features dataframe
         
@@ -69,22 +89,15 @@ def cluster(g, dbscan, kind='nodes', cols=None, umap=True):
             cols: list of columns to use for clustering given `g.featurize` has been run
             umap: whether to use UMAP embeddings or features dataframe
     """
-    
-    if cols is None:
-        df = g._get_feature(kind)
-    else: 
-        df = g.get_features_by_cols(cols, kind)
+    df = get_model_matrix(g, kind, cols, umap)
 
-    if umap and cols is None and g._umap is not None: 
-        df = g._get_embedding(kind)
-    
     dbscan.fit(df)
     labels = dbscan.labels_
     
     if kind == 'nodes':    
-        g._nodes['_cluster'] = labels
+        g._nodes = g._nodes.assign(_dbscan = labels)
     elif kind == 'edges':
-        g._edges['_cluster'] = labels
+        g._nodes = g._edges.assign(_dbscan = labels)
     else:
         raise ValueError('kind must be one of `nodes` or `edges`')
     
@@ -93,20 +106,53 @@ def cluster(g, dbscan, kind='nodes', cols=None, umap=True):
     
     return g
 
+
+
+def dbscan_predict(X: pd.DataFrame, model):
+    """
+        DBSCAN has no predict per se, so we reverse engineer one here
+        from https://stackoverflow.com/questions/27822752/scikit-learn-predicting-new-points-with-dbscan
+    """
+    n_samples = X.shape[0]
+    
+    y_new = np.ones(shape=n_samples, dtype=int) * -1
+        
+    for i in range(n_samples):
+        diff = model.components_ - X.iloc[i, :].values  # NumPy broadcasting
+
+        dist = np.linalg.norm(diff, axis=1)  # Euclidean distance
+
+        shortest_dist_idx = np.argmin(dist)
+
+        if dist[shortest_dist_idx] < model.eps:
+            y_new[i] = model.labels_[model.core_sample_indices_[shortest_dist_idx]]
+
+    return y_new
+
+def dbscan_predict2(g, kind='nodes', cols=None, umap=True):
+    X = g._get_feature(kind)
+    dbscan = g._node_dbscan if kind == 'nodes' else g._edge_dbscan
+
+    preds = dbscan_predict(X, dbscan)
+    return X, preds
+                                      
+
 class ClusterMixin(MIXIN_BASE):
     def __init__(self, *args, **kwargs):
         pass
     
     def _cluster_dbscan(self, res, kind, cols, umap, eps, min_samples, **kwargs):
         """
-            DBSCAN clustering on cpu or gpu infered by umap's .engine flag
+            DBSCAN clustering on cpu or gpu infered by .engine flag
         """
         _, DBSCAN, _, cuDBSCAN = lazy_dbscan_import_has_dependency()
         
         res.engine = resolve_cpu_gpu_engine("auto")
+        res._kwargs_dbscan = ModelDict('latest dbscan kwargs', kind=kind, cols=cols, umap=umap, eps=eps, min_samples=min_samples, **kwargs)
         
         dbscan = cuDBSCAN(eps=eps, min_samples=min_samples, **kwargs) if res.engine == CUML else DBSCAN(eps=eps, min_samples=min_samples, **kwargs)
-        res = cluster(res, dbscan, kind=kind, cols=cols, umap=umap)
+        
+        res = dbscan_fit(res, dbscan, kind=kind, cols=cols, umap=umap)
 
         return res
     
@@ -140,21 +186,76 @@ def dbscan(self, kind = 'nodes', cols = None, umap = True, eps: float = 1., min_
             g2.plot() # color by `_cluster`
 
         Useful:
-            Enriching the graph with cluster labels from UMAP is useful for visualizing clusters in the graph by color, size, etc.
-            see https://github.com/graphistry/pygraphistry/blob/master/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
+            Enriching the graph with cluster labels from UMAP is useful for visualizing clusters in the graph by color, size, etc, 
+            as well as assessing metrics per cluster, e.g.
+             https://github.com/graphistry/pygraphistry/blob/master/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
                         
         Args:
             kind: 'nodes' or 'edges'
-            cols: list of columns to use for clustering given `g.featurize` has been run, nice way to slice features by fragments of interest, e.g. ['ip_172', 'location', 'ssh', 'warnings']
+            cols: list of columns to use for clustering given `g.featurize` has been run, nice way to slice features by 
+                fragments of interest, e.g. ['ip_172', 'location', 'ssh', 'warnings']
             umap: whether to use UMAP embeddings or features dataframe
             eps: The maximum distance between two samples for them to be considered as in the same neighborhood.
-            min_samples: The number of samples in a neighborhood for a point to be considered as a core point. This includes the point itself.
+            min_samples: The number of samples in a neighborhood for a point to be considered as a core point. 
+                This includes the point itself.
             
         """
         res = self.bind()
         res = res._cluster_dbscan(res, kind=kind, cols=cols, umap=umap, eps=eps, min_samples=min_samples, **kwargs)
         
         return res
-
-    # def _is_cudf(self, df):
-    #     return 'cudf' in str(type(df))
+    
+    def _transform_dbscan(self, df: pd.DataFrame, ydf=None, kind: str='nodes') -> pd.DataFrame:
+        """Transforms a dataframe to one with a new column '_cluster' containing the DBSCAN cluster labels
+            and returns feature[cols] or UMAP embedding
+        Examples:
+            fit: 
+                g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
+                g2 = g.featurize().dbscan()
+                
+            predict:
+                labels = g2.transform_dbscan(ndf)
+        
+        """
+        
+        res = self.bind()
+        if hasattr(res, '_kwargs_dbscan'):
+            # Assume that we are transforming to last fit of dbscan
+            cols = res._kwargs_dbscan['cols']
+            umap = res._kwargs_dbscan['umap']
+            
+            dbscan = res._node_dbscan if kind == 'nodes' else res._edge_dbscan
+            
+            emb = None
+            if umap:
+                emb, X, y = res.transform_umap(df, ydf, kind=kind, return_graph=False)
+            else:
+                  X, _ = res.transform(df, ydf, kind=kind)
+            if cols is not None:
+                X = get_matrix_by_column_parts(X, cols)
+    
+            if umap:
+                X_ = emb
+            else:
+                X_ = X
+                
+            labels = dbscan_predict(X_, dbscan)
+            df = df.assign(_dbscan=labels, x=emb.x, y=emb.y)
+            return emb, X, y, df
+        else:
+            raise Exception('No dbscan model found. Please run `g.dbscan()` first')
+        
+    def transform_dbscan(self, df, y=None, eps=30, use_umap_embedding=True, n_nearest=None, kind='nodes', return_graph=True):
+        """Transforms a dataframe to one with a new column '_cluster' containing the DBSCAN cluster labels on the minibatch
+            if return_graph is True, then a graph is returned with the minibatch added to the existing graph
+            if return_graph is False, then the enriched minibatch dataframe, features, and UMAP embedding are returned
+            
+        """
+        emb, X, y, df = self._transform_dbscan(df, y, kind=kind)
+        if return_graph:
+            res = self.bind()
+            g = infer_graph(res, emb, X, y, df, use_umap=use_umap_embedding, eps=eps, sample=n_nearest) 
+            return g
+        return emb, X, df
+    
+    
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index ba6227da29..16b6671201 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1815,6 +1815,19 @@ def reuse_featurization(
         memoize=memoize,
     )
 
+def get_matrix_by_column_part(X: pd.DataFrame, column_part: str) -> pd.DataFrame:
+    """Get the feature matrix by column part existing in column names."""
+    transformed_columns = X.columns[X.columns.map(lambda x: True if column_part in x else False)]  # type: ignore
+    return X[transformed_columns] 
+
+def get_matrix_by_column_parts(X: pd.DataFrame, column_parts: Union[list, str]) -> pd.DataFrame:
+    """Get the feature matrix by column parts list existing in column names."""
+    if isinstance(column_parts, str):
+        column_parts = [column_parts]
+    res = pd.concat([get_matrix_by_column_part(X, column_part) for column_part in column_parts], axis=1)  # type: ignore
+    res = res.loc[:, ~res.columns.duplicated()]  # type: ignore
+    return res
+
 
 class FeatureMixin(MIXIN_BASE):
     """
@@ -2109,15 +2122,21 @@ def _transform(self, encoder: str, df: pd.DataFrame, ydf: pd.DataFrame):
                 "before being able to transform data"
             )
 
-    def transform(self, df, ydf, kind):
+    def transform(self, df, ydf=None, kind='nodes', return_graph=False, eps=1, n_nearest=None):
         """Transform new data"""
         if kind == "nodes":
-            return self._transform("_node_encoder", df, ydf)
+            X, y = self._transform("_node_encoder", df, ydf)
         elif kind == "edges":
-            return self._transform("_edge_encoder", df, ydf)
+            X, y = self._transform("_edge_encoder", df, ydf)
         else:
             logger.debug("kind must be one of `nodes`,"
                          f"`edges`, found {kind}")
+        if return_graph:
+            res = self.bind()
+            emb = None
+            g = infer_graph(res, emb, X, y, df, use_umap=False, eps=eps, n_nearest=n_nearest) 
+            return g
+        return X, y
 
     def scale(
         self,
@@ -2600,18 +2619,8 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
             memoize=memoize,
         )
 
-    def _features_by_col(self, column_part: str, kind: str):
-        if kind == 'nodes' and hasattr(self, '_node_features'):
-            X = self._node_features
-        elif kind == 'edges' and hasattr(self, '_edge_features'):
-            X = self._edge_features
-        else:
-            raise ValueError('make sure to call `featurize` or `umap` before calling `get_features_by_cols`')
-        
-        transformed_columns = X.columns[X.columns.map(lambda x: True if column_part in x else False)]  # type: ignore
-        return X[transformed_columns]  # type: ignore
     
-    def get_features_by_cols(self, columns: Union[List, str], kind: str = 'nodes'):
+    def get_features_by_cols(self, columns: Union[List, str], kind: str = 'nodes', target=False):
         """Returns feature matrix with only the columns that contain the string `column_part` in their name.
         
             `X = g.get_features_by_cols(['feature1', 'feature2'])`
@@ -2627,12 +2636,14 @@ def get_features_by_cols(self, columns: Union[List, str], kind: str = 'nodes'):
             columns (Union[List, str]): list of column names or a single column name that may exist in columns 
                 of the feature matrix.
             kind (str, optional): Node or Edge features. Defaults to 'nodes'.
+            target (bool, optional): If True, returns the target matrix. Defaults to False.
 
         Returns:
             pd.DataFrame: feature matrix with only the columns that contain the string `column_part` in their name.
         """
-        if isinstance(columns, str):
-            columns = [columns]
-        X = pd.concat([self._features_by_col(col, kind=kind) for col in columns], axis=1)  # type: ignore
-        X = X.loc[:, ~X.columns.duplicated()]  # type: ignore
-        return X
+        if target:
+            X = self._get_target(kind)
+        else:
+            X = self._get_feature(kind)
+
+        return get_matrix_by_column_parts(X, columns)
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index fda5a96060..6394ac69ff 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -11,6 +11,7 @@
                             resolve_feature_engine)
 from .PlotterBase import Plottable, WeakValueDictionary
 from .util import check_set_memoize, setup_logger
+from .ai_utils import infer_graph
 
 logger = setup_logger(name=__name__, verbose=config.VERBOSE)
 
@@ -268,7 +269,7 @@ def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         logger.info(f" - or {X.shape[0]/mins:.2f} rows per minute")
         return self
 
-    def umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
+    def _umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         if self._umap is None:
             raise ValueError("UMAP is not initialized")
         self.umap_fit(X, y)
@@ -277,15 +278,19 @@ def umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = Non
         return emb
 
     def transform_umap(  # noqa: E303
-        self, df: pd.DataFrame, ydf: pd.DataFrame, kind: str = "nodes"
-    ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+        self, df: pd.DataFrame, ydf=None, kind: str = "nodes", use_umap=True, eps=1, n_nearest=None, return_graph=True
+    ) -> Union[Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable]:
         try:
-            logger.debug(f"Going into Transform umap {df.shape}, {ydf.shape}")
+            logger.debug(f"Going into Transform umap {df.shape}")
         except:
             pass
         x, y = self.transform(df, ydf, kind=kind)
         emb = self._umap.transform(x)  # type: ignore
         emb = self._bundle_embedding(emb, index=df.index)
+        if return_graph:
+            res = self.bind()
+            g = infer_graph(res, df, x, emb, y, use_umap=use_umap, eps=eps, sample=n_nearest) 
+            return g
         return emb, x, y
 
     def _bundle_embedding(self, emb, index):
@@ -331,7 +336,7 @@ def _process_umap(
             fresh_res._umap = old_res._umap  # this saves the day!
             return fresh_res
 
-        emb = res.umap_fit_transform(X_, y_)
+        emb = res._umap_fit_transform(X_, y_)
         res._xy = emb
         return res
 
@@ -549,7 +554,7 @@ def umap(
             )
             if X is not None and isinstance(X, pd.DataFrame):
                 logger.info("New Matrix `X` passed in for UMAP-ing")
-                xy = res.umap_fit_transform(X, y)
+                xy = res._umap_fit_transform(X, y)
                 res._xy = xy
                 res._weighted_edges_df = prune_weighted_edges_df_and_relabel_nodes(
                     res._weighted_edges_df, scale=scale

From 52a29a8644e08fcedfb96c5a4a388a54d9dedda7 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Tue, 3 Jan 2023 00:58:33 -0800
Subject: [PATCH 035/432] typo

---
 graphistry/ai_utils.py      |  4 +---
 graphistry/feature_utils.py |  1 +
 graphistry/text_utils.py    | 48 +++++++++++--------------------------
 3 files changed, 16 insertions(+), 37 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index e46922b916..944fa535ca 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -170,6 +170,7 @@ def build_annoy_index(X, angular, n_trees=None):
     search_index.build(n_trees)
     return search_index
 
+
 def query_by_vector(vect, df, search_index, top_n):
     indices, distances = search_index.get_nns_by_vector(
         vect.values[0], top_n, include_distances=True
@@ -182,9 +183,6 @@ def query_by_vector(vect, df, search_index, top_n):
     return results
 
 
-
-
-
 # #########################################################################################################################
 #
 #  Graphistry Graph Inference
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 16b6671201..02e3e658b2 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -23,6 +23,7 @@
 from . import constants as config
 from .PlotterBase import WeakValueDictionary, Plottable
 from .util import setup_logger, check_set_memoize
+from .ai_utils import infer_graph
 
 # add this inside classes and have a method that can set log level
 logger = setup_logger(name=__name__, verbose=config.VERBOSE)
diff --git a/graphistry/text_utils.py b/graphistry/text_utils.py
index 6af12f3655..c96b568ce2 100644
--- a/graphistry/text_utils.py
+++ b/graphistry/text_utils.py
@@ -4,8 +4,11 @@
 import pandas as pd
 
 from .feature_utils import FeatureMixin
-from .ai_utils import search_to_df, setup_logger
-from .constants import WEIGHT, N_TREES, DISTANCE, VERBOSE, TRACE
+from .ai_utils import search_to_df, build_annoy_index, query_by_vector
+from .constants import WEIGHT, DISTANCE
+from logging import getLogger
+
+logger = getLogger(__name__)
 
 from typing import (
     Hashable,
@@ -20,13 +23,12 @@
 )  # noqa
 
 
-logger = setup_logger(__name__, verbose=VERBOSE, fullpath=TRACE)
-
 if TYPE_CHECKING:
     MIXIN_BASE = FeatureMixin
 else:
     MIXIN_BASE = object
 
+
 class SearchToGraphMixin(MIXIN_BASE):
     def __init__(self, *args, **kwargs) -> None:
         super().__init__(*args, **kwargs)
@@ -35,7 +37,7 @@ def assert_fitted(self):
         # assert self._umap is not None, 'Umap needs to be fit first, run g.umap(..) to fit a model'
         assert (
             self._get_feature('nodes') is not None
-        ), "Graphistry Instance is not fit, run g.featurize(kind='nodes', ..) to fit a model' \
+        ), "Graphistry Instance is not fit, run g.featurize(kind='nodes', ..) to fit a model ' \
         'if you have nodes & edges dataframe or g.umap(kind='nodes', ..) if you only have nodes dataframe"
 
     def assert_features_line_up_with_nodes(self):
@@ -44,49 +46,27 @@ def assert_features_line_up_with_nodes(self):
         a, b = ndf.shape[0], X.shape[0]
         assert a == b, 'Nodes dataframe and feature vectors are not same size, '\
         f'found nodes: {a}, feats: {b}. Did you mutate nodes between fit?'
+        
+    def _build_search_index(self, X, angular=False, n_trees=None):
+        # builds local index from X
+        return build_annoy_index(X, angular, n_trees)
 
     def build_index(self, angular=False, n_trees=None):
-        from annoy import AnnoyIndex  # type: ignore
         # builds local index
         self.assert_fitted()
         self.assert_features_line_up_with_nodes()
         
         X = self._get_feature('nodes')
 
-        logger.info(f"Building Index of size {X.shape}")
-
-        if angular:
-            logger.info('-using angular metric')
-            metric = 'angular'
-        else:
-            logger.info('-using euclidean metric')
-            metric = 'euclidean'
-            
-        search_index = AnnoyIndex(X.shape[1], metric)
-        # Add all the feature vectors to the search index
-        for i in range(len(X)):
-            search_index.add_item(i, X.values[i])
-        if n_trees is None:
-            n_trees = N_TREES
-
-        logger.info(f'-building index with {n_trees} trees')
-        search_index.build(n_trees)
+        self.search_index = self._build_search_index(X, angular, n_trees)
 
-        self.search_index = search_index
 
     def _query_from_dataframe(self, qdf: pd.DataFrame, top_n: int, thresh: float):
         # Use the loaded featurizers to transform the dataframe
         vect, _ = self.transform(qdf, None, kind="nodes")
-        
-        indices, distances = self.search_index.get_nns_by_vector(
-            vect.values[0], top_n, include_distances=True
-        )
-        
-        results = self._nodes.iloc[indices]
-        results[DISTANCE] = distances
-        results = results.query(f"{DISTANCE} < {thresh}")
 
-        results = results.sort_values(by=[DISTANCE])
+        results = query_by_vector(vect, self._nodes, self.search_index, top_n)        
+        results = results.query(f"{DISTANCE} < {thresh}")
 
         return results, vect
     

From 3908e52f2c9fe2cb6e099468d1ef515314b1ff82 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Tue, 3 Jan 2023 14:14:22 -0800
Subject: [PATCH 036/432] feat(adds better graph inference and options for
 featurize/umap/dbscan, adds eps=`auto` which finds good threshold for graph
 clustering

---
 graphistry/ai_utils.py        | 86 ++++++++++++++++++++++++-----------
 graphistry/compute/cluster.py | 82 ++++++++++++++++++++++++---------
 graphistry/feature_utils.py   | 20 ++++++--
 graphistry/umap_utils.py      | 12 ++---
 4 files changed, 142 insertions(+), 58 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 944fa535ca..89533572bd 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -190,7 +190,7 @@ def query_by_vector(vect, df, search_index, top_n):
 ##########################################################################################################################
 
 
-def infer_graph(res, emb, X, y, df, use_umap, eps=1, sample=None):
+def infer_graph(res, emb, X, y, df, use_umap_embedding=False, eps='auto', sample=None):
     """
         Infer a graph from a graphistry object
         
@@ -200,21 +200,28 @@ def infer_graph(res, emb, X, y, df, use_umap, eps=1, sample=None):
             X: minibatch transformed dataframe
             emb: minibatch UMAP embedding
             kind: 'nodes' or 'edges'
-            eps: distance threshold for a minibatchh point to cluster to existing graph
+            eps: if 'auto' will find a good epsilon from the data; distance threshold for a minibatchh point to cluster to existing graph
             n_nearest: number of nearest neighbors to add from existing graphs edges, if None, ignores existing edges. 
     """
     # if we have node features in this mini_batch, we can 
-    if use_umap and emb is not None: #would conflict if node_features is of shape (n, 2)
+    if use_umap_embedding and emb is not None: #would conflict if node_features is of shape (n, 2)
         X_ = res._node_embedding
         W = emb
-    else:
+        F = res._node_features
+        EMB = X_
+    else: # can still be umap, but want to do the inference on the higher dimensional features
         X_ = res._node_features
         W = X
+        F = X_
+        EMB = res._node_embedding
+        
     Y = res._node_target
     
     assert df.shape[0] == X.shape[0], 'minibatches df and X must have same number of rows since f(df) = X'
+    if emb is not None:
+        assert emb.shape[0] == df.shape[0], 'minibatches emb and X must have same number of rows since h(df) = emb'
+        df = df.assign(x=emb.x, y=emb.y)  # add x and y to df for graphistry instance
     
-    new_edges = []
     # if umap, need to add '_n' as node id to df, adding new indices to existing graph
     df['_n'] = range(X.shape[0], X.shape[0]+df.shape[0])
     df['_batch'] = 1 # 1 for minibatch, 0 for existing graph
@@ -226,6 +233,7 @@ def infer_graph(res, emb, X, y, df, use_umap, eps=1, sample=None):
     src = res._source
     dst = res._destination
     
+    new_edges = []
     old_edges = []
     old_nodes = []
     mdists=[]
@@ -233,44 +241,68 @@ def infer_graph(res, emb, X, y, df, use_umap, eps=1, sample=None):
     #vsearch = build_search_index(X_, angular=False)
     
     for i in range(W.shape[0]):
-        record_df = df.iloc[i, :]
+        #record_df = df.iloc[i, :]
         diff = X_ - W.iloc[i, :]
         dist = np.linalg.norm(diff, axis=1)  # Euclidean distance
-
-        mdist = dist.mean()
-        mdists.append(mdist)
-        for nn in np.where(dist < eps)[0]:
-            this_ndf = NDF.iloc[nn, :]
+        mdists.append(dist)
+    
+    m, std = np.mean(mdists), np.std(mdists)
+    #logger.info(f'--Mean distance to existing nodes {m} +/- {std}')
+    print(f'--Mean distance to existing nodes {m:.2f} +/- {std:.2f}')
+    if eps == 'auto':
+        eps = np.min([np.abs(m - 2*std), m])
+    print(f'{eps:.2f} epsilon for min distance threshold')
+    
+    for i, dist in enumerate(mdists):
+        record_df = df.iloc[i, :]
+        for j in np.where(dist < eps)[0]:
+            this_ndf = NDF.iloc[j, :]
             if sample:
                 local_edges = EDF[(EDF[src] == this_ndf[node]) | (EDF[dst] == this_ndf[node])]
                 if not local_edges.empty:
                     old_edges.append(local_edges.sample(sample, replace=True))
             new_edges.append([this_ndf[node], record_df[node], 1, 1])
             old_nodes.append(this_ndf)
-    
-    print('mean dist', np.mean(mdist))
+
 
     new_edges = pd.DataFrame(new_edges, columns=[src, dst, '_weight', '_batch'])
+    print(len(new_edges), 'new edges')
+    
+    all_nodes = []
+    if len(old_edges):
+        old_edges = pd.concat(old_edges, axis=0).assign(_batch=0)
+        all_nodes = old_edges[src].append(old_edges[dst]).append(new_edges[src]).append(new_edges[dst])
+        
     if sample:
-        new_edges = pd.concat([new_edges, pd.concat(old_edges, axis=0).assign(_batch=0)], axis=0)
+        new_edges = pd.concat([new_edges, old_edges], axis=0)
+        print('sampled', len(new_edges), 'new edges')
     new_edges = new_edges.drop_duplicates()
-        
-    old_nodes = pd.DataFrame(old_nodes).drop_duplicates(subset=[node])
-    old_emb = X_.loc[old_nodes.index]
-    new_emb = pd.concat([W, old_emb], axis=0)
+    print(len(new_edges), 'new edges after dropping duplicates')
+
+    if len(old_nodes):
+        old_nodes = pd.DataFrame(old_nodes)#.drop_duplicates(subset=[node])
+        old_nodes = pd.concat([old_nodes, NDF[NDF[node].isin(all_nodes)]], axis=0).drop_duplicates(subset=[node])
+    print(f'Old nodes {len(old_nodes)}')
+    
+    old_emb = None
+    if EMB is not None:
+        old_emb = EMB.loc[old_nodes.index]
+    new_emb = None
+    if emb is not None:
+        new_emb = pd.concat([emb, old_emb], axis=0)
+    print(f'Old emb {old_emb.shape if old_emb is not None else None}')
+    
+    new_features = pd.concat([X, F.loc[old_nodes.index]], axis=0)
 
-    new_nodes = pd.concat([df, old_nodes], axis=0)#.reset_index(drop=True) # append minibatch at top
+    new_nodes = pd.concat([df, old_nodes], axis=0)  # append minibatch at top
+    
+    new_targets = pd.concat([y, Y.loc[old_nodes.index]]) if y is not None else Y
     
     # #########################################################
     g = res.nodes(new_nodes, node).edges(new_edges, src, dst)
     
-    if use_umap:
-        g._node_embedding = new_emb
-        g._node_features = X_
-    else:
-        g._node_features = new_emb
-        g._node_embedding = X_
-    
-    g._node_targets = pd.concat([y, Y.loc[old_nodes.index]]) if y is not None else Y
+    g._node_embedding = new_emb
+    g._node_features = new_features
+    g._node_targets = new_targets
     
     return g
\ No newline at end of file
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 6f6cb99f7c..913ccc4ffc 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -79,7 +79,7 @@ def get_model_matrix(g, kind, cols, umap):
     return df
     
 
-def dbscan_fit(g, dbscan, kind='nodes', cols=None, umap=True):
+def dbscan_fit(g, dbscan, kind='nodes', cols=None, use_umap_embedding=True):
     """
         Fits clustering on UMAP embeddings if umap is True, otherwise on the features dataframe
         
@@ -89,7 +89,7 @@ def dbscan_fit(g, dbscan, kind='nodes', cols=None, umap=True):
             cols: list of columns to use for clustering given `g.featurize` has been run
             umap: whether to use UMAP embeddings or features dataframe
     """
-    df = get_model_matrix(g, kind, cols, umap)
+    df = get_model_matrix(g, kind, cols, use_umap_embedding)
 
     dbscan.fit(df)
     labels = dbscan.labels_
@@ -97,7 +97,7 @@ def dbscan_fit(g, dbscan, kind='nodes', cols=None, umap=True):
     if kind == 'nodes':    
         g._nodes = g._nodes.assign(_dbscan = labels)
     elif kind == 'edges':
-        g._nodes = g._edges.assign(_dbscan = labels)
+        g._edges = g._edges.assign(_dbscan = labels)
     else:
         raise ValueError('kind must be one of `nodes` or `edges`')
     
@@ -141,23 +141,23 @@ class ClusterMixin(MIXIN_BASE):
     def __init__(self, *args, **kwargs):
         pass
     
-    def _cluster_dbscan(self, res, kind, cols, umap, eps, min_samples, **kwargs):
+    def _cluster_dbscan(self, res, kind, cols, use_umap_embedding, eps, min_samples, **kwargs):
         """
             DBSCAN clustering on cpu or gpu infered by .engine flag
         """
         _, DBSCAN, _, cuDBSCAN = lazy_dbscan_import_has_dependency()
         
         res.engine = resolve_cpu_gpu_engine("auto")
-        res._kwargs_dbscan = ModelDict('latest dbscan kwargs', kind=kind, cols=cols, umap=umap, eps=eps, min_samples=min_samples, **kwargs)
+        res._kwargs_dbscan = ModelDict('latest dbscan kwargs', kind=kind, cols=cols, umap=use_umap_embedding, eps=eps, min_samples=min_samples, **kwargs)
         
         dbscan = cuDBSCAN(eps=eps, min_samples=min_samples, **kwargs) if res.engine == CUML else DBSCAN(eps=eps, min_samples=min_samples, **kwargs)
         
-        res = dbscan_fit(res, dbscan, kind=kind, cols=cols, umap=umap)
+        res = dbscan_fit(res, dbscan, kind=kind, cols=cols, use_umap_embedding=use_umap_embedding)
 
         return res
     
     
-    def dbscan(self, kind = 'nodes', cols = None, umap = True, eps: float = 1., min_samples: int = 1, **kwargs):
+    def dbscan(self, kind = 'nodes', cols = None, use_umap_embedding = True, eps: float = 1., min_samples: int = 1, **kwargs):
         """DBSCAN clustering on cpu or gpu infered automatically 
         
         Examples:
@@ -194,14 +194,14 @@ def dbscan(self, kind = 'nodes', cols = None, umap = True, eps: float = 1., min_
             kind: 'nodes' or 'edges'
             cols: list of columns to use for clustering given `g.featurize` has been run, nice way to slice features by 
                 fragments of interest, e.g. ['ip_172', 'location', 'ssh', 'warnings']
-            umap: whether to use UMAP embeddings or features dataframe
+            use_umap_embedding: whether to use UMAP embeddings or features dataframe to cluster DBSCAN
             eps: The maximum distance between two samples for them to be considered as in the same neighborhood.
             min_samples: The number of samples in a neighborhood for a point to be considered as a core point. 
                 This includes the point itself.
             
         """
         res = self.bind()
-        res = res._cluster_dbscan(res, kind=kind, cols=cols, umap=umap, eps=eps, min_samples=min_samples, **kwargs)
+        res = res._cluster_dbscan(res, kind=kind, cols=cols, use_umap_embedding=use_umap_embedding, eps=eps, min_samples=min_samples, **kwargs)
         
         return res
     
@@ -214,7 +214,26 @@ def _transform_dbscan(self, df: pd.DataFrame, ydf=None, kind: str='nodes') -> pd
                 g2 = g.featurize().dbscan()
                 
             predict:
-                labels = g2.transform_dbscan(ndf)
+                emb, X, y, ndf = g2.transform_dbscan(ndf, return_graph=False)
+                # or 
+                g3 = g2.transform_dbscan(ndf, ndf, return_graph=True)
+                g3.plot()
+                
+        likewise for umap:
+            fit:
+                g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
+                g2 = g.umap().dbscan()
+                
+            predict:
+                emb, X, y, ndf = g2.transform_dbscan(ndf, return_graph=False)
+                # or
+                g3 = g2.transform_dbscan(ndf, ndf, return_graph=True)
+                g3.plot()
+                
+        args:
+            df: dataframe to transform
+            ydf: optional labels dataframe
+            kind: 'nodes' or 'edges'
         
         """
         
@@ -227,12 +246,12 @@ def _transform_dbscan(self, df: pd.DataFrame, ydf=None, kind: str='nodes') -> pd
             dbscan = res._node_dbscan if kind == 'nodes' else res._edge_dbscan
             
             emb = None
-            if umap:
+            if umap and cols is None:
                 emb, X, y = res.transform_umap(df, ydf, kind=kind, return_graph=False)
             else:
-                  X, _ = res.transform(df, ydf, kind=kind)
-            if cols is not None:
-                X = get_matrix_by_column_parts(X, cols)
+                X, y = res.transform(df, ydf, kind=kind, return_graph=False)
+                if cols is not None:
+                    X = get_matrix_by_column_parts(X, cols)
     
             if umap:
                 X_ = emb
@@ -240,22 +259,43 @@ def _transform_dbscan(self, df: pd.DataFrame, ydf=None, kind: str='nodes') -> pd
                 X_ = X
                 
             labels = dbscan_predict(X_, dbscan)
-            df = df.assign(_dbscan=labels, x=emb.x, y=emb.y)
+            if umap:
+                df = df.assign(_dbscan=labels, x=emb.x, y=emb.y)
+            else:
+                df = df.assign(_dbscan=labels)
+
             return emb, X, y, df
         else:
             raise Exception('No dbscan model found. Please run `g.dbscan()` first')
         
-    def transform_dbscan(self, df, y=None, eps=30, use_umap_embedding=True, n_nearest=None, kind='nodes', return_graph=True):
-        """Transforms a dataframe to one with a new column '_cluster' containing the DBSCAN cluster labels on the minibatch
-            if return_graph is True, then a graph is returned with the minibatch added to the existing graph
-            if return_graph is False, then the enriched minibatch dataframe, features, and UMAP embedding are returned
+    def transform_dbscan(self, df: pd.DataFrame, y: pd.DataFrame = None, 
+                         eps: Union[float, str]='auto', 
+                         use_umap_embedding:bool=True, 
+                         sample:int=None, 
+                         kind:str='nodes', 
+                         return_graph=True):
+        """
+        Transforms a minibatch dataframe to one with a new column '_cluster' containing the DBSCAN cluster labels on the minibatch
+            and generates a graph with the minibatch and the original graph, with edges between the minibatch and the original graph inferred
+            works for 
+            
+        args:
+            df: dataframe to transform
+            y: optional labels dataframe
+            eps: The maximum distance between two samples for them to be considered as in the same neighborhood.
+                smaller values will result in less edges between the minibatch and the original graph. 
+                Default 'auto', infers eps from the mean distance and std of new points to the original graph
+            use_umap_embedding: whether to use UMAP embeddings or features dataframe when running DBSCAN
+            n_nearest: number of nearest neighbors to use for DBSCAN
+            kind: 'nodes' or 'edges'
+            return_graph: whether to return a graph or the minibatch enriched with cluster labels, default True
             
         """
         emb, X, y, df = self._transform_dbscan(df, y, kind=kind)
         if return_graph:
             res = self.bind()
-            g = infer_graph(res, emb, X, y, df, use_umap=use_umap_embedding, eps=eps, sample=n_nearest) 
+            g = infer_graph(res, emb, X, y, df, use_umap_embedding=use_umap_embedding, eps=eps, sample=sample) 
             return g
-        return emb, X, df
+        return emb, X, y, df
     
     
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 02e3e658b2..20218f09a0 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2123,8 +2123,20 @@ def _transform(self, encoder: str, df: pd.DataFrame, ydf: pd.DataFrame):
                 "before being able to transform data"
             )
 
-    def transform(self, df, ydf=None, kind='nodes', return_graph=False, eps=1, n_nearest=None):
-        """Transform new data"""
+    def transform(self, df, ydf=None, kind='nodes', return_graph=True, eps='auto', sample=None):
+        """Transform new data and append to existing graph.
+        
+            args:
+                df: pd.DataFrame, raw data to transform
+                ydf: pd.DataFrame, optional
+                kind: str  # one of `nodes`, `edges`
+                return_graph: bool, if True, will return a graph with inferred edges
+                eps: float, if return_graph is True, will use this value for eps in NN search, or 'auto' to infer a good value
+                sample: int, if return_graph is True, will use sample value for NN search over existing edges
+            returns:
+                X: pd.DataFrame, transformed data if return_graph is False
+                    or a graph with inferred edges if return_graph is True
+        """
         if kind == "nodes":
             X, y = self._transform("_node_encoder", df, ydf)
         elif kind == "edges":
@@ -2134,8 +2146,8 @@ def transform(self, df, ydf=None, kind='nodes', return_graph=False, eps=1, n_nea
                          f"`edges`, found {kind}")
         if return_graph:
             res = self.bind()
-            emb = None
-            g = infer_graph(res, emb, X, y, df, use_umap=False, eps=eps, n_nearest=n_nearest) 
+            emb = None  # will not be able to decide umap coordinates, but will be able to infer graph from existing edges
+            g = infer_graph(res, emb, X, y, df, use_umap_embedding=False, eps=eps, sample=sample) 
             return g
         return X, y
 
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 6394ac69ff..fe84379e92 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -278,20 +278,20 @@ def _umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = No
         return emb
 
     def transform_umap(  # noqa: E303
-        self, df: pd.DataFrame, ydf=None, kind: str = "nodes", use_umap=True, eps=1, n_nearest=None, return_graph=True
+        self, df: pd.DataFrame, ydf=None, kind: str = "nodes", eps='auto', sample=None, return_graph=True, use_umap_embedding=False
     ) -> Union[Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable]:
         try:
             logger.debug(f"Going into Transform umap {df.shape}")
         except:
             pass
-        x, y = self.transform(df, ydf, kind=kind)
-        emb = self._umap.transform(x)  # type: ignore
+        X, y = self.transform(df, ydf, kind=kind, return_graph=False)
+        emb = self._umap.transform(X)  # type: ignore
         emb = self._bundle_embedding(emb, index=df.index)
         if return_graph:
             res = self.bind()
-            g = infer_graph(res, df, x, emb, y, use_umap=use_umap, eps=eps, sample=n_nearest) 
+            g = infer_graph(res, emb, X, y, df, use_umap_embedding=use_umap_embedding, eps=eps, sample=sample) 
             return g
-        return emb, x, y
+        return emb, X, y
 
     def _bundle_embedding(self, emb, index):
         # Converts Embedding into dataframe and takes care if emb.dim > 2
@@ -580,7 +580,7 @@ def umap(
             res = res.prune_self_edges()
 
         if dbscan:
-            res = res.dbscan(kind=kind, umap=True)  # type: ignore
+            res = res.dbscan(kind=kind, use_umap_embedding=True)  # type: ignore
 
         if not inplace:
             return res

From 0032eb1a0ceaa9bb67bc0a4da84706d759834f41 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 5 Jan 2023 12:31:20 -0800
Subject: [PATCH 037/432] fix(missing index during scaling, fixed)

---
 graphistry/feature_utils.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 20218f09a0..f59823e2c2 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1555,10 +1555,9 @@ def transform(
         text_cols,
     ) = res
 
-    # feature_columns = X_enc.columns
-    # feature_columns_target = y_enc.columns
     logger.info("-" * 90)
-
+    
+    index = df.index
     y = pd.DataFrame([])
     T = pd.DataFrame([])
     # encode nodes
@@ -1616,11 +1615,11 @@ def transform(
 
     if scaling_pipeline and not X.empty:
         logger.info("--Scaling Features")
-        X = pd.DataFrame(scaling_pipeline.transform(X), columns=X.columns)
+        X = pd.DataFrame(scaling_pipeline.transform(X), columns=X.columns, index=index)
     if scaling_pipeline_target and not y.empty:
         logger.info(f"--Scaling Target {scaling_pipeline_target}")
         y = pd.DataFrame(
-            scaling_pipeline_target.transform(y), columns=y.columns
+            scaling_pipeline_target.transform(y), columns=y.columns, index=index
         )
 
     return X, y
@@ -2147,7 +2146,7 @@ def transform(self, df, ydf=None, kind='nodes', return_graph=True, eps='auto', s
         if return_graph:
             res = self.bind()
             emb = None  # will not be able to decide umap coordinates, but will be able to infer graph from existing edges
-            g = infer_graph(res, emb, X, y, df, use_umap_embedding=False, eps=eps, sample=sample) 
+            g = infer_graph(res, emb, X, y, df, infer_on_umap_embedding=False, eps=eps, sample=sample) 
             return g
         return X, y
 

From f92f3e31d4c59ad7684c5d7fb2dd6255b724e213 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 5 Jan 2023 14:11:33 -0800
Subject: [PATCH 038/432] fix(if node is not bound, adds index back after
 reset_index), small changes

---
 graphistry/umap_utils.py | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index fe84379e92..87d6653022 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -278,7 +278,7 @@ def _umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = No
         return emb
 
     def transform_umap(  # noqa: E303
-        self, df: pd.DataFrame, ydf=None, kind: str = "nodes", eps='auto', sample=None, return_graph=True, use_umap_embedding=False
+        self, df: pd.DataFrame, ydf=None, kind: str = "nodes", eps='auto', sample=None, return_graph=True, fit_umap_embedding=False
     ) -> Union[Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable]:
         try:
             logger.debug(f"Going into Transform umap {df.shape}")
@@ -289,19 +289,18 @@ def transform_umap(  # noqa: E303
         emb = self._bundle_embedding(emb, index=df.index)
         if return_graph:
             res = self.bind()
-            g = infer_graph(res, emb, X, y, df, use_umap_embedding=use_umap_embedding, eps=eps, sample=sample) 
+            g = infer_graph(res, emb, X, y, df, infer_on_umap_embedding=fit_umap_embedding, eps=eps, sample=sample) 
             return g
         return emb, X, y
 
     def _bundle_embedding(self, emb, index):
         # Converts Embedding into dataframe and takes care if emb.dim > 2
-        if emb.shape[1] == 2:
-            emb = pd.DataFrame(emb, columns=[config.X, config.Y], index=index)
-        else:
+        columns = [config.X, config.Y]
+        if emb.shape[1] > 2:
             columns = [config.X, config.Y] + [
                 f"umap_{k}" for k in range(2, emb.shape[1] - 2)
             ]
-            emb = pd.DataFrame(emb, columns=columns, index=index)
+        emb = pd.DataFrame(emb, columns=columns, index=index)
         return emb
 
     def _process_umap(
@@ -480,6 +479,7 @@ def umap(
 
 
         if kind == "nodes":
+            index = res._nodes.index
             if res._node is None:
 
                 logger.debug("-Writing new node name")
@@ -489,6 +489,8 @@ def umap(
                     .rename(columns={"index": config.IMPLICIT_NODE_ID}),
                     config.IMPLICIT_NODE_ID,
                 )
+                res._nodes.index = index
+                #print(res.)
 
             nodes = res._nodes[res._node].values
             index_to_nodes_dict = dict(zip(range(len(nodes)), nodes))
@@ -580,7 +582,7 @@ def umap(
             res = res.prune_self_edges()
 
         if dbscan:
-            res = res.dbscan(kind=kind, use_umap_embedding=True)  # type: ignore
+            res = res.dbscan(kind=kind, fit_umap_embedding=True)  # type: ignore
 
         if not inplace:
             return res

From 09575ea501a084999f5a2d5ef120fc36227f1dab Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 00:32:04 -0800
Subject: [PATCH 039/432] fix(bug where lazy init umap wasnt taking umap
 specific args)

---
 graphistry/umap_utils.py | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 87d6653022..42dd0e11c0 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -203,6 +203,8 @@ def umap_lazy_init(
                     "negative_sample_rate": negative_sample_rate,
                 }
             )
+            
+            print('umap_kwargs', umap_kwargs)
 
             self.n_components = n_components
             self.metric = metric
@@ -278,7 +280,11 @@ def _umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = No
         return emb
 
     def transform_umap(  # noqa: E303
-        self, df: pd.DataFrame, ydf=None, kind: str = "nodes", eps='auto', sample=None, return_graph=True, fit_umap_embedding=False
+        self, df: pd.DataFrame, ydf: pd.DataFrame = None, kind: str = "nodes", 
+        eps='auto', 
+        sample=None, 
+        return_graph=True, 
+        fit_umap_embedding=False
     ) -> Union[Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable]:
         try:
             logger.debug(f"Going into Transform umap {df.shape}")
@@ -298,8 +304,10 @@ def _bundle_embedding(self, emb, index):
         columns = [config.X, config.Y]
         if emb.shape[1] > 2:
             columns = [config.X, config.Y] + [
-                f"umap_{k}" for k in range(2, emb.shape[1] - 2)
+                f"umap_{k}" for k in range(2, emb.shape[1])
             ]
+        print('emb.shape', emb.shape)
+        print('columns', columns, len(columns))
         emb = pd.DataFrame(emb, columns=columns, index=index)
         return emb
 
@@ -460,6 +468,7 @@ def umap(
             repulsion_strength=repulsion_strength,
             negative_sample_rate=negative_sample_rate,
             engine=engine,
+            suffix=suffix,
         )
         logger.debug("umap_kwargs: %s", umap_kwargs)
 
@@ -468,7 +477,7 @@ def umap(
         else:
             res = self.bind()
 
-        res.umap_lazy_init(engine=engine, suffix=suffix)
+        res.umap_lazy_init(**umap_kwargs)
 
         logger.debug("umap input X :: %s", X)
         logger.debug("umap input y :: %s", y)

From 673f9af0bafa94e6ed3e8078a75a63161a41181a Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 00:34:49 -0800
Subject: [PATCH 040/432] optimize infer_graph variables

---
 graphistry/ai_utils.py | 59 ++++++++++++++++++++----------------------
 1 file changed, 28 insertions(+), 31 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 89533572bd..f0b22cdab7 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -190,7 +190,7 @@ def query_by_vector(vect, df, search_index, top_n):
 ##########################################################################################################################
 
 
-def infer_graph(res, emb, X, y, df, use_umap_embedding=False, eps='auto', sample=None):
+def infer_graph(res, emb, X, y, df, infer_on_umap_embedding=False, eps='auto', sample=None):
     """
         Infer a graph from a graphistry object
         
@@ -203,18 +203,17 @@ def infer_graph(res, emb, X, y, df, use_umap_embedding=False, eps='auto', sample
             eps: if 'auto' will find a good epsilon from the data; distance threshold for a minibatchh point to cluster to existing graph
             n_nearest: number of nearest neighbors to add from existing graphs edges, if None, ignores existing edges. 
     """
-    # if we have node features in this mini_batch, we can 
-    if use_umap_embedding and emb is not None: #would conflict if node_features is of shape (n, 2)
-        X_ = res._node_embedding
-        W = emb
-        F = res._node_features
-        EMB = X_
-    else: # can still be umap, but want to do the inference on the higher dimensional features
-        X_ = res._node_features
-        W = X
-        F = X_
-        EMB = res._node_embedding
+
+    new_index = df.index
+    if infer_on_umap_embedding and emb is not None:
+        X_previously_fit = res._node_embedding
+        X_new = emb
+    else:  # can still be umap, but want to do the inference on the higher dimensional features
+        X_previously_fit = res._node_features
+        X_new = X
         
+    FEATS = res._node_features
+    EMB = res._node_embedding
     Y = res._node_target
     
     assert df.shape[0] == X.shape[0], 'minibatches df and X must have same number of rows since f(df) = X'
@@ -223,8 +222,9 @@ def infer_graph(res, emb, X, y, df, use_umap_embedding=False, eps='auto', sample
         df = df.assign(x=emb.x, y=emb.y)  # add x and y to df for graphistry instance
     
     # if umap, need to add '_n' as node id to df, adding new indices to existing graph
-    df['_n'] = range(X.shape[0], X.shape[0]+df.shape[0])
-    df['_batch'] = 1 # 1 for minibatch, 0 for existing graph
+    numeric_indices = range(X_previously_fit.shape[0], X_previously_fit.shape[0]+df.shape[0])
+    df['_n'] = numeric_indices
+    df['_batch'] = 1  # 1 for minibatch, 0 for existing graph
     node = res._node
     NDF = res._nodes
     NDF['_batch'] = 0
@@ -238,20 +238,20 @@ def infer_graph(res, emb, X, y, df, use_umap_embedding=False, eps='auto', sample
     old_nodes = []
     mdists=[]
     
-    #vsearch = build_search_index(X_, angular=False)
+    # vsearch = build_search_index(X_previously_fit, angular=False)
     
-    for i in range(W.shape[0]):
-        #record_df = df.iloc[i, :]
-        diff = X_ - W.iloc[i, :]
+    for i in range(X_new.shape[0]):
+        # record_df = df.iloc[i, :]
+        diff = X_previously_fit - X_new.iloc[i, :]
         dist = np.linalg.norm(diff, axis=1)  # Euclidean distance
         mdists.append(dist)
     
     m, std = np.mean(mdists), np.std(mdists)
-    #logger.info(f'--Mean distance to existing nodes {m} +/- {std}')
-    print(f'--Mean distance to existing nodes {m:.2f} +/- {std:.2f}')
+    logger.info(f'--Mean distance to existing nodes  {m:.2f} +/- {std:.2f}')
+    # print(f'--Mean distance to existing nodes {m:.2f} +/- {std:.2f}')
     if eps == 'auto':
         eps = np.min([np.abs(m - 2*std), m])
-    print(f'{eps:.2f} epsilon for min distance threshold')
+    logger.info(f'{eps:.2f} epsilon for max distance threshold to be considered a neighbor')
     
     for i, dist in enumerate(mdists):
         record_df = df.iloc[i, :]
@@ -264,9 +264,7 @@ def infer_graph(res, emb, X, y, df, use_umap_embedding=False, eps='auto', sample
             new_edges.append([this_ndf[node], record_df[node], 1, 1])
             old_nodes.append(this_ndf)
 
-
     new_edges = pd.DataFrame(new_edges, columns=[src, dst, '_weight', '_batch'])
-    print(len(new_edges), 'new edges')
     
     all_nodes = []
     if len(old_edges):
@@ -275,24 +273,23 @@ def infer_graph(res, emb, X, y, df, use_umap_embedding=False, eps='auto', sample
         
     if sample:
         new_edges = pd.concat([new_edges, old_edges], axis=0)
-        print('sampled', len(new_edges), 'new edges')
+        # print('sampled', len(new_edges), 'new edges')
     new_edges = new_edges.drop_duplicates()
-    print(len(new_edges), 'new edges after dropping duplicates')
+    # print(len(new_edges), 'new edges after dropping duplicates')
 
     if len(old_nodes):
-        old_nodes = pd.DataFrame(old_nodes)#.drop_duplicates(subset=[node])
+        old_nodes = pd.DataFrame(old_nodes)
         old_nodes = pd.concat([old_nodes, NDF[NDF[node].isin(all_nodes)]], axis=0).drop_duplicates(subset=[node])
-    print(f'Old nodes {len(old_nodes)}')
-    
+            
     old_emb = None
     if EMB is not None:
         old_emb = EMB.loc[old_nodes.index]
+        
     new_emb = None
     if emb is not None:
         new_emb = pd.concat([emb, old_emb], axis=0)
-    print(f'Old emb {old_emb.shape if old_emb is not None else None}')
     
-    new_features = pd.concat([X, F.loc[old_nodes.index]], axis=0)
+    new_features = pd.concat([X, FEATS.loc[old_nodes.index]], axis=0)
 
     new_nodes = pd.concat([df, old_nodes], axis=0)  # append minibatch at top
     
@@ -305,4 +302,4 @@ def infer_graph(res, emb, X, y, df, use_umap_embedding=False, eps='auto', sample
     g._node_features = new_features
     g._node_targets = new_targets
     
-    return g
\ No newline at end of file
+    return g

From 1f2cd202b9afdf78b8512892e6df03eeb8b5035d Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 00:35:45 -0800
Subject: [PATCH 041/432] removes deprecated attributes

---
 graphistry/PlotterBase.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/graphistry/PlotterBase.py b/graphistry/PlotterBase.py
index 81591d8e43..4c3e56192f 100644
--- a/graphistry/PlotterBase.py
+++ b/graphistry/PlotterBase.py
@@ -169,20 +169,20 @@ def __init__(self, *args, **kwargs):
         self._node_embedding = None
         self._node_encoder = None
         self._node_features = None
-        self._node_ordinal_pipeline = None
-        self._node_ordinal_pipeline_target = None,
+        #self._node_scaling_pipeline = None
+        #self._node_ordinal_pipeline_target = None,
         self._node_target = None
         self._node_target_encoder = None
-        self._node_text_model = None
+        # self._node_text_model = None
 
         self._edge_embedding = None
         self._edge_encoder = None
         self._edge_features = None
-        self._edge_ordinal_pipeline = None
-        self._edge_ordinal_pipeline_target = None
+        #self._edge_ordinal_pipeline = None
+        #self._edge_ordinal_pipeline_target = None
         self._edge_target = None
         self._edge_target_encoder = None
-        self._edge_text_model = None
+        # self._edge_text_model = None
 
         self._weighted_adjacency_nodes = None
         self._weighted_adjacency_edges = None
@@ -207,6 +207,9 @@ def __init__(self, *args, **kwargs):
         # Dbscan
         self._node_dbscan = None  # the fit dbscan instance
         self._edge_dbscan = None
+        
+        # DGL
+        self.DGL_graph = None  # the DGL graph
 
 
     def __repr__(self):

From f763bc1d0ef429a1d564a4063987dd9ef73b8cfb Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 00:38:03 -0800
Subject: [PATCH 042/432] feat(`g.dbscan(eps, min_sample)` )

---
 graphistry/compute/cluster.py | 41 ++++++++++++++++++++---------------
 1 file changed, 23 insertions(+), 18 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 913ccc4ffc..8d223624f8 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -141,23 +141,23 @@ class ClusterMixin(MIXIN_BASE):
     def __init__(self, *args, **kwargs):
         pass
     
-    def _cluster_dbscan(self, res, kind, cols, use_umap_embedding, eps, min_samples, **kwargs):
+    def _cluster_dbscan(self, res, kind, cols, fit_umap_embedding, eps, min_samples, **kwargs):
         """
             DBSCAN clustering on cpu or gpu infered by .engine flag
         """
         _, DBSCAN, _, cuDBSCAN = lazy_dbscan_import_has_dependency()
         
         res.engine = resolve_cpu_gpu_engine("auto")
-        res._kwargs_dbscan = ModelDict('latest dbscan kwargs', kind=kind, cols=cols, umap=use_umap_embedding, eps=eps, min_samples=min_samples, **kwargs)
+        res._kwargs_dbscan = ModelDict('latest dbscan kwargs', kind=kind, cols=cols, umap=fit_umap_embedding, eps=eps, min_samples=min_samples, **kwargs)
         
         dbscan = cuDBSCAN(eps=eps, min_samples=min_samples, **kwargs) if res.engine == CUML else DBSCAN(eps=eps, min_samples=min_samples, **kwargs)
         
-        res = dbscan_fit(res, dbscan, kind=kind, cols=cols, use_umap_embedding=use_umap_embedding)
+        res = dbscan_fit(res, dbscan, kind=kind, cols=cols, use_umap_embedding=fit_umap_embedding)
 
         return res
     
     
-    def dbscan(self, kind = 'nodes', cols = None, use_umap_embedding = True, eps: float = 1., min_samples: int = 1, **kwargs):
+    def dbscan(self, eps: float = 0.2, min_samples: int = 1, cols = None, kind = 'nodes', fit_umap_embedding = True, **kwargs):
         """DBSCAN clustering on cpu or gpu infered automatically 
         
         Examples:
@@ -166,7 +166,7 @@ def dbscan(self, kind = 'nodes', cols = None, use_umap_embedding = True, eps: fl
             # cluster by UMAP embeddings
             kind = 'nodes' | 'edges'
             g2 = g.umap(kind=kind).dbscan(kind=kind)
-            print(g2._nodes['_cluster']) | print(g2._edges['_cluster'])
+            print(g2._nodes['_dbscan']) | print(g2._edges['_dbscan'])
 
             # dbscan with fixed parameters is default in umap
             g2 = g.umap(dbscan=True)
@@ -183,7 +183,7 @@ def dbscan(self, kind = 'nodes', cols = None, use_umap_embedding = True, eps: fl
             # equivalent to above (ie, cols != None and umap=True will still use features dataframe, rather than UMAP embeddings)
             g2 = g.umap().dbscan(cols=['ip_172', 'location', 'alert'], umap=True | False, **kwargs)
             
-            g2.plot() # color by `_cluster`
+            g2.plot() # color by `_dbscan`
 
         Useful:
             Enriching the graph with cluster labels from UMAP is useful for visualizing clusters in the graph by color, size, etc, 
@@ -191,22 +191,23 @@ def dbscan(self, kind = 'nodes', cols = None, use_umap_embedding = True, eps: fl
              https://github.com/graphistry/pygraphistry/blob/master/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
                         
         Args:
-            kind: 'nodes' or 'edges'
+            eps float: The maximum distance between two samples for them to be considered as in the same neighborhood.
+            kind str: 'nodes' or 'edges'
             cols: list of columns to use for clustering given `g.featurize` has been run, nice way to slice features by 
                 fragments of interest, e.g. ['ip_172', 'location', 'ssh', 'warnings']
-            use_umap_embedding: whether to use UMAP embeddings or features dataframe to cluster DBSCAN
-            eps: The maximum distance between two samples for them to be considered as in the same neighborhood.
+            fit_umap_embedding bool: whether to use UMAP embeddings or features dataframe to cluster DBSCAN
             min_samples: The number of samples in a neighborhood for a point to be considered as a core point. 
                 This includes the point itself.
             
         """
         res = self.bind()
-        res = res._cluster_dbscan(res, kind=kind, cols=cols, use_umap_embedding=use_umap_embedding, eps=eps, min_samples=min_samples, **kwargs)
+        res = res._cluster_dbscan(res, kind=kind, cols=cols, fit_umap_embedding=fit_umap_embedding, eps=eps, min_samples=min_samples, **kwargs)
         
         return res
     
     def _transform_dbscan(self, df: pd.DataFrame, ydf=None, kind: str='nodes') -> pd.DataFrame:
-        """Transforms a dataframe to one with a new column '_cluster' containing the DBSCAN cluster labels
+        """
+        Transforms a dataframe to one with a new column '_dbscan' containing the DBSCAN cluster labels
             and returns feature[cols] or UMAP embedding
         Examples:
             fit: 
@@ -216,7 +217,7 @@ def _transform_dbscan(self, df: pd.DataFrame, ydf=None, kind: str='nodes') -> pd
             predict:
                 emb, X, y, ndf = g2.transform_dbscan(ndf, return_graph=False)
                 # or 
-                g3 = g2.transform_dbscan(ndf, ndf, return_graph=True)
+                g3 = g2.transform_dbscan(ndf, return_graph=True)
                 g3.plot()
                 
         likewise for umap:
@@ -227,7 +228,7 @@ def _transform_dbscan(self, df: pd.DataFrame, ydf=None, kind: str='nodes') -> pd
             predict:
                 emb, X, y, ndf = g2.transform_dbscan(ndf, return_graph=False)
                 # or
-                g3 = g2.transform_dbscan(ndf, ndf, return_graph=True)
+                g3 = g2.transform_dbscan(ndf, return_graph=True)
                 g3.plot()
                 
         args:
@@ -270,7 +271,7 @@ def _transform_dbscan(self, df: pd.DataFrame, ydf=None, kind: str='nodes') -> pd
         
     def transform_dbscan(self, df: pd.DataFrame, y: pd.DataFrame = None, 
                          eps: Union[float, str]='auto', 
-                         use_umap_embedding:bool=True, 
+                         fit_umap_embedding:bool=False, 
                          sample:int=None, 
                          kind:str='nodes', 
                          return_graph=True):
@@ -285,16 +286,20 @@ def transform_dbscan(self, df: pd.DataFrame, y: pd.DataFrame = None,
             eps: The maximum distance between two samples for them to be considered as in the same neighborhood.
                 smaller values will result in less edges between the minibatch and the original graph. 
                 Default 'auto', infers eps from the mean distance and std of new points to the original graph
-            use_umap_embedding: whether to use UMAP embeddings or features dataframe when running DBSCAN
-            n_nearest: number of nearest neighbors to use for DBSCAN
+            fit_umap_embedding: whether to use UMAP embeddings or features dataframe when inferring edges between 
+                the minibatch and the original graph. Default False, uses the features dataframe
+            sample: number of samples to use when inferring edges between the minibatch and the original graph, 
+                if None, will only use closest point to the minibatch. If greater than 0, will sample the closest `sample` points 
+                in existing graph to pull in more edges. Default None
             kind: 'nodes' or 'edges'
-            return_graph: whether to return a graph or the minibatch enriched with cluster labels, default True
+            return_graph: whether to return a graph or the (emb, X, y, minibatch enriched with DBSCAN labels), default True
+        
             
         """
         emb, X, y, df = self._transform_dbscan(df, y, kind=kind)
         if return_graph:
             res = self.bind()
-            g = infer_graph(res, emb, X, y, df, use_umap_embedding=use_umap_embedding, eps=eps, sample=sample) 
+            g = infer_graph(res, emb, X, y, df, infer_on_umap_embedding=fit_umap_embedding, eps=eps, sample=sample) 
             return g
         return emb, X, y, df
     

From 5620f585b3ff7db702427c8bb0cad645d4e99aec Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 00:40:00 -0800
Subject: [PATCH 043/432] feat(default model args)

---
 graphistry/embed_utils.py |  3 +-
 graphistry/features.py    | 97 +++++++++++++++++++++++----------------
 2 files changed, 60 insertions(+), 40 deletions(-)

diff --git a/graphistry/embed_utils.py b/graphistry/embed_utils.py
index 42743d7537..ef09a02475 100644
--- a/graphistry/embed_utils.py
+++ b/graphistry/embed_utils.py
@@ -89,7 +89,8 @@ def __init__(self):
         self._device = "cpu"
 
     def _preprocess_embedding_data(self, res, train_split:Union[float, int] = 0.8) -> Plottable:
-        _, torch, _, _, _, _, F, _ = lazy_embed_import_dep()
+        _, torch, _, _, _, _, _, _ = lazy_embed_import_dep()
+        import torch
         log('Preprocessing embedding data')
         src, dst = res._source, res._destination
         relation = res._relation
diff --git a/graphistry/features.py b/graphistry/features.py
index 7567b159db..35cfb4e5a6 100644
--- a/graphistry/features.py
+++ b/graphistry/features.py
@@ -21,8 +21,8 @@
 # ################# graphistry featurization config constants #################
 N_TOPICS = 42
 N_TOPICS_TARGET = 10
-HIGH_CARD = 4e7  # forces one hot encoding
-MID_CARD = 2e3  # todo: forces hashing
+HIGH_CARD = 1e9  # forces one hot encoding
+MID_CARD = 1e3  # todo: force hashing
 LOW_CARD = 2
 
 CARD_THRESH = 40
@@ -88,7 +88,7 @@
 
 # #############################################################################
 class ModelDict(UserDict):
-    """Helper class to print out model names
+    """Helper class to print out model names and keep track of updates
 
     Args:
         message: description of model
@@ -160,55 +160,74 @@ def update(self, *args, **kwargs):
 # customize the default parameters for each model you want to test
 
 # Ngrams Model over features
-ngrams_model = ModelDict("Ngrams Model", verbose=True, **default_featurize_parameters)
-ngrams_model.update(dict(use_ngrams=True, min_words=HIGH_CARD))
+ngrams_model = ModelDict("Ngrams Model", 
+                         use_ngrams=True, 
+                         min_words=HIGH_CARD, 
+                         verbose=True)
+#ngrams_model.update(dict(use_ngrams=True, min_words=HIGH_CARD))
 
 # Topic Model over features
-topic_model = ModelDict("Topic Model", verbose=True, **default_featurize_parameters)
-topic_model.update(
-    dict(
-        cardinality_threshold=LOW_CARD,  # force topic model
-        cardinality_threshold_target=LOW_CARD,  # force topic model
-        n_topics=N_TOPICS,
-        n_topics_target=N_TOPICS_TARGET,
-        min_words=HIGH_CARD,  # make sure it doesn't turn into sentence model, but rather topic models
-    )
-)
+topic_model = ModelDict("Reliable Topic Models on Features and Target",         
+                        cardinality_threshold=LOW_CARD,  # force topic model
+                        cardinality_threshold_target=LOW_CARD,  # force topic model
+                        n_topics=N_TOPICS,
+                        n_topics_target=N_TOPICS_TARGET,
+                        min_words=HIGH_CARD,  # make sure it doesn't turn into sentence model, but rather topic models
+                        verbose=True
+                        )
+#**default_featurize_parameters)
+# topic_model.update(
+#     dict(
+#         cardinality_threshold=LOW_CARD,  # force topic model
+#         cardinality_threshold_target=LOW_CARD,  # force topic model
+#         n_topics=N_TOPICS,
+#         n_topics_target=N_TOPICS_TARGET,
+#         min_words=HIGH_CARD,  # make sure it doesn't turn into sentence model, but rather topic models
+#     )
+# )
 
 # useful for text data that you want to paraphrase
-embedding_model = ModelDict(
-    f"{PARAPHRASE_SMALL_MODEL} Embedding Model",
-    verbose=True,
-    **default_featurize_parameters,
-)
-embedding_model.update(
-    dict(
-        min_words=FORCE_EMBEDDING_ALL_COLUMNS,
-        model_name=PARAPHRASE_SMALL_MODEL,  # if we need multilingual support, use PARAPHRASE_MULTILINGUAL_MODEL
-    )
+embedding_model = ModelDict(f"{PARAPHRASE_SMALL_MODEL} sbert Embedding Model",
+                        min_words=FORCE_EMBEDDING_ALL_COLUMNS,
+                        model_name=PARAPHRASE_SMALL_MODEL,  # if we need multilingual support, use PARAPHRASE_MULTILINGUAL_MODEL
+                        verbose=True,
+    #**default_featurize_parameters,
 )
+# embedding_model.update(
+#     dict(
+#         min_words=FORCE_EMBEDDING_ALL_COLUMNS,
+#         model_name=PARAPHRASE_SMALL_MODEL,  # if we need multilingual support, use PARAPHRASE_MULTILINGUAL_MODEL
+#     )
+# )
 
 # useful for when search input is much smaller than the encoded documents
 search_model = ModelDict(
-    f"{MSMARCO2} Search Model", verbose=True, **default_featurize_parameters
-)
-search_model.update(
-    dict(
-        min_words=FORCE_EMBEDDING_ALL_COLUMNS,
-        model_name=MSMARCO2,
-    )
+                        f"{MSMARCO2} Search Model", 
+                        verbose=True, 
+                        min_words=FORCE_EMBEDDING_ALL_COLUMNS,
+                        model_name=MSMARCO2,
+    #**default_featurize_parameters
 )
+# search_model.update(
+#     dict(
+#         min_words=FORCE_EMBEDDING_ALL_COLUMNS,
+#         model_name=MSMARCO2,
+#     )
+# )
 
 # Question Answering encodings for search
 qa_model = ModelDict(
-    f"{QA_SMALL_MODEL} QA Model", verbose=True, **default_featurize_parameters
-)
-qa_model.update(
-    dict(
-        min_words=FORCE_EMBEDDING_ALL_COLUMNS,
-        model_name=QA_SMALL_MODEL,
-    )
+                    f"{QA_SMALL_MODEL} QA Model", 
+                    min_words=FORCE_EMBEDDING_ALL_COLUMNS,
+                    model_name=QA_SMALL_MODEL,
+                    verbose=True,                 
 )
+# qa_model.update(
+#     dict(
+#         min_words=FORCE_EMBEDDING_ALL_COLUMNS,
+#         model_name=QA_SMALL_MODEL,
+#     )
+# )
 
 
 BASE_MODELS = {

From 9680e49e602347b410076d08adef1dfdb95b049c Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 00:40:39 -0800
Subject: [PATCH 044/432] tests not passing, checkpoint

---
 graphistry/tests/test_umap_utils.py | 120 +++++++++++++++++++++++++++-
 1 file changed, 116 insertions(+), 4 deletions(-)

diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index b0aef2432c..1da454a337 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -4,9 +4,11 @@
 import warnings
 
 import graphistry
+
 import logging
 import numpy as np
 import pandas as pd
+from graphistry import Plottable
 from graphistry.feature_utils import remove_internal_namespace_if_present
 from graphistry.tests.test_feature_utils import (
     ndf_reddit,
@@ -67,6 +69,7 @@ class TestUMAPFitTransform(unittest.TestCase):
     def setUp(self):
 
         g = graphistry.nodes(ndf_reddit)
+        self.gn = g
         
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
@@ -78,14 +81,18 @@ def setUp(self):
                         use_scaler='robust', 
                         cardinality_threshold=2)
             
+        self.g2 = g2
         fenc = g2._node_encoder
         self.X, self.Y = fenc.X, fenc.y
         self.EMB = g2._node_embedding
-        self.emb, self.x, self.y = g2.transform_umap(ndf_reddit, ydf=double_target_reddit, kind='nodes')
-
+        self.emb, self.x, self.y = g2.transform_umap(ndf_reddit, ydf=double_target_reddit, kind='nodes', return_graph=False)
+        self.g3 = g2.transform_umap(ndf_reddit, ydf=double_target_reddit, kind='nodes', return_graph=True)
+        
+        # do the same for edges
         edge_df22 = edge_df2.copy()
         edge_df22['rando'] = np.random.rand(edge_df2.shape[0])
         g = graphistry.edges(edge_df22, 'src', 'dst')
+        self.ge = g
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
@@ -100,7 +107,9 @@ def setUp(self):
         fenc = g2._edge_encoder
         self.Xe, self.Ye = fenc.X, fenc.y
         self.EMBe = g2._edge_embedding
-        self.embe, self.xe, self.ye = g2.transform_umap(edge_df22, ydf=edge2_target_df, kind='edges')
+        self.embe, self.xe, self.ye = g2.transform_umap(edge_df22, ydf=edge2_target_df, kind='edges', return_graph=False)
+        self.g2e = g2
+        self.g3e = g2.transform_umap(edge_df22, ydf=edge2_target_df, kind='edges', return_graph=True)
 
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_columns_match(self):
@@ -109,6 +118,108 @@ def test_columns_match(self):
         assert all(self.Xe.columns == self.xe.columns), 'Edge Feature Columns do not match'
         assert all(self.Ye.columns == self.ye.columns), 'Edge Target Columns do not match'
 
+    @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
+    def test_index_match(self):
+        # nodes
+        assert all(self.gn._nodes.index == self.g2._nodes.index), 'Node Indexes do not match'
+        assert all(self.gn._nodes.index == self.EMB.index), 'Emb Indexes do not match'
+        assert all(self.gn._nodes.index == self.emb.index), 'Transformed Emb Indexes do not match'
+        assert all(self.gn._nodes.index == self.X.index), 'Transformed Node features Indexes do not match'
+        assert all(self.gn._nodes.index == self.y.index), 'Transformed Node target Indexes do not match'
+        
+        # edges  
+        assert all(self.ge._edges.index == self.g2e._edges.index), 'Edge Indexes do not match'
+        assert all(self.ge._edges.index == self.EMBe.index), 'Edge Emb Indexes do not match'
+        assert all(self.ge._edges.index == self.embe.index), 'Edge Transformed Emb Indexes do not match'
+        assert all(self.ge._edges.index == self.Xe.index), 'Edge Transformed features Indexes do not match'
+        assert all(self.ge._edges.index == self.ye.index), 'Edge Transformed target Indexes do not match'
+        
+        # make sure the indexes match at transform time internally as well
+        assert all(self.X.index == self.x.index), 'Node Feature Indexes do not match'
+        assert all(self.Y.index == self.y.index), 'Node Target Indexes do not match'
+        assert all(self.Xe.index == self.xe.index), 'Edge Feature Indexes do not match'
+        assert all(self.Ye.index == self.ye.index), 'Edge Target Indexes do not match'
+
+    @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
+    def test_index_match_in_infered_graph(self):
+        # nodes
+        assert all(self.g3._nodes.index == self.g2._nodes.index), 'Node Indexes do not match'
+        assert all(self.g3._nodes.index == self.EMB.index), 'Emb Indexes do not match'
+        assert all(self.g3._nodes.index == self.emb.index), 'Transformed Emb Indexes do not match'
+        assert all(self.g3._nodes.index == self.X.index), 'Transformed Node features Indexes do not match'
+        assert all(self.g3._nodes.index == self.y.index), 'Transformed Node target Indexes do not match'
+
+    @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
+    def test_nodes_index_match_in_infered_graph(self):
+        # edges  
+        ndf_infered = self.g3._nodes
+        assert all(ndf_infered.index == self.EMBe.index), 'Edge Emb Indexes do not match'
+        assert all(ndf_infered.index == self.embe.index), 'Edge Transformed Emb Indexes do not match'
+        assert all(ndf_infered.index == self.Xe.index), 'Edge Transformed features Indexes do not match'
+        assert all(ndf_infered.index == self.ye.index), 'Edge Transformed target Indexes do not match'
+        
+        # now test in set featurize method calls
+        assert all(self.g3._node_features.index == ndf_infered.index), 'Edge Feature Indexes do not match'
+        assert all(self.g3._node_embedding.index == ndf_infered.index), 'Edge Emb Indexes do not match'
+        assert all(self.g3._node_target.index == ndf_infered.index), 'Edge Transformed Emb Indexes do not match'
+        # assert all(self.g3e._edges.index == edf_infered.index), 'Edge Transformed features Indexes do not match'
+        # assert all(self.g3e._edges.index == edf_infered.index), 'Edge Transformed target Indexes do not match'
+        
+    @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
+    def test_edges_index_match_in_infered_graph(self):
+        # edges  
+        edf_infered = self.g3e._edges
+        assert all(edf_infered.index == self.EMBe.index), 'Edge Emb Indexes do not match'
+        assert all(edf_infered.index == self.embe.index), 'Edge Transformed Emb Indexes do not match'
+        assert all(edf_infered.index == self.Xe.index), 'Edge Transformed features Indexes do not match'
+        assert all(edf_infered.index == self.ye.index), 'Edge Transformed target Indexes do not match'
+        
+        assert all(self.g3e._edge_features.index == edf_infered.index), 'Edge Feature Indexes do not match'
+        assert all(self.g3e._edge_embedding.index == edf_infered.index), 'Edge Emb Indexes do not match'
+        assert all(self.g3e._edge_target.index == edf_infered.index), 'Edge Transformed Emb Indexes do not match'
+        # assert all(self.g3e._edges.index == edf_infered.index), 'Edge Transformed features Indexes do not match'
+        # assert all(self.g3e._edges.index == edf_infered.index), 'Edge Transformed target Indexes do not match'
+    
+    
+    def test_transform_umap(self):
+        np.random.seed(41)
+        
+        train = ndf_reddit.sample(frac=0.8, random_state=42)
+        test = ndf_reddit.drop(train.index)
+        
+        # just process train
+        g = graphistry.nodes(train)
+        g2 = g.umap()
+        g3 = g2.transform_umap(train)
+        assert 2 * g2._node_embedding.shape[0] == g3._node_embedding.shape[0], 'Node Embedding Lengths do not match, found {} and {}'.format(g2._node_embedding.shape[0], g3._node_embedding.shape[0])
+        # now feed it args
+        eps=['auto', 10]
+        sample=[None, 2] 
+        return_graph=[True, False]
+        fit_umap_embedding = [True, False]
+        for ep in eps:
+            g4 = g2.transform_umap(test, eps=ep)
+            assert True
+        for return_g in return_graph:
+            g4 = g2.transform_umap(test, return_graph=return_g)
+            if return_g:
+                assert isinstance(g4, Plottable)
+                # == g4._node_embedding.shape
+            else:
+                assert len(g4) == 3
+                assert isinstance(g4[0], pd.DataFrame)
+                assert isinstance(g4[1], pd.DataFrame)
+                assert isinstance(g4[2], pd.DataFrame)
+                assert g4[0].shape[1] == 2
+                assert g4[1].shape[1] >= 2
+                assert g4[2].shape[1] >= 1
+        for sample_ in sample:
+            g4 = g2.transform_umap(test, sample=sample_)
+            assert True
+        for fit_umap_embedding_ in fit_umap_embedding:
+            g4 = g2.transform_umap(test, fit_umap_embedding=fit_umap_embedding_)
+            assert True
+
 
 class TestUMAPMethods(unittest.TestCase):
     def _check_attributes(self, g, attributes):
@@ -393,7 +504,8 @@ def test_filter_edges(self):
                 self.assertGreaterEqual(shape[0], last_shape)
                 last_shape = shape[0]
 
-
+            
+        
 @pytest.mark.skipif(
     not has_dependancy or not has_cuml,
     reason="requires cuml feature dependencies",

From 554352c1d1d462021006caca2135ed48cf63fcb0 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 01:39:41 -0800
Subject: [PATCH 045/432] adds g._infer_edges in feature_utils and propagates
 to other transformers in umap, dbscan/cluster

---
 graphistry/ai_utils.py        |  2 +-
 graphistry/compute/cluster.py |  8 +++-----
 graphistry/feature_utils.py   | 14 ++++++++++----
 graphistry/umap_utils.py      | 16 ++++++++--------
 4 files changed, 22 insertions(+), 18 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index f0b22cdab7..c718b0eefd 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -204,7 +204,7 @@ def infer_graph(res, emb, X, y, df, infer_on_umap_embedding=False, eps='auto', s
             n_nearest: number of nearest neighbors to add from existing graphs edges, if None, ignores existing edges. 
     """
 
-    new_index = df.index
+    #new_index = df.index
     if infer_on_umap_embedding and emb is not None:
         X_previously_fit = res._node_embedding
         X_new = emb
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 8d223624f8..98b0fb0f04 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -2,7 +2,7 @@
 import pandas as pd
 import numpy as np
 
-from typing import Any, List, Union, TYPE_CHECKING
+from typing import Any, List, Union, TYPE_CHECKING, Tuple, Optional, Dict, Callable
 from typing_extensions import Literal
 from collections import Counter
 
@@ -11,7 +11,6 @@
 from graphistry.constants import CUML, UMAP_LEARN  # noqa type: ignore
 from graphistry.features import ModelDict
 from graphistry.feature_utils import get_matrix_by_column_parts
-from graphistry.ai_utils import infer_graph
 
 logger = logging.getLogger("compute.cluster")
 
@@ -274,7 +273,7 @@ def transform_dbscan(self, df: pd.DataFrame, y: pd.DataFrame = None,
                          fit_umap_embedding:bool=False, 
                          sample:int=None, 
                          kind:str='nodes', 
-                         return_graph=True):
+                         return_graph=True)-> Union[Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable]:
         """
         Transforms a minibatch dataframe to one with a new column '_cluster' containing the DBSCAN cluster labels on the minibatch
             and generates a graph with the minibatch and the original graph, with edges between the minibatch and the original graph inferred
@@ -298,8 +297,7 @@ def transform_dbscan(self, df: pd.DataFrame, y: pd.DataFrame = None,
         """
         emb, X, y, df = self._transform_dbscan(df, y, kind=kind)
         if return_graph:
-            res = self.bind()
-            g = infer_graph(res, emb, X, y, df, infer_on_umap_embedding=fit_umap_embedding, eps=eps, sample=sample) 
+            g = self._infer_edges(emb, X, y, df, infer_on_umap_embedding=False, eps=eps, sample=sample) 
             return g
         return emb, X, y, df
     
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index f59823e2c2..8f900ce7d1 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1899,7 +1899,7 @@ def _featurize_nodes(
         feature_engine: FeatureEngineConcrete = "pandas",
         memoize: bool = True,
     ):
-        res = self.copy()
+        res = self.bind()  # was self.copy() but changing to test
         ndf = res._nodes
         node = res._node
 
@@ -1927,6 +1927,8 @@ def _featurize_nodes(
         y_resolved = resolve_y(ndf, y)
 
         feature_engine = resolve_feature_engine(feature_engine)
+        
+        from .features import ModelDict
 
         fkwargs = dict(
             X=X_resolved,
@@ -1978,7 +1980,7 @@ def _featurize_nodes(
         X_resolved = remove_internal_namespace_if_present(X_resolved)
 
         keys_to_remove = ["X", "y", "remove_node_column"]
-        nfkwargs = {}
+        nfkwargs = dict()
         for key, value in fkwargs.items():
             if key not in keys_to_remove:
                 nfkwargs[key] = value
@@ -2112,6 +2114,11 @@ def _featurize_edges(
         res._edge_encoder = encoder
 
         return res
+    
+    def _infer_edges(self, emb, X, y, df, eps='auto', sample=None, infer_on_umap_embedding=False, **kwargs):
+        res = self.bind()  # will not be able to decide umap coordinates, but will be able to infer graph from existing edges
+        g = infer_graph(res, emb, X, y, df, infer_on_umap_embedding=infer_on_umap_embedding, eps=eps, sample=sample) 
+        return g
 
     def _transform(self, encoder: str, df: pd.DataFrame, ydf: pd.DataFrame):
         if getattr(self, encoder) is not None:
@@ -2144,9 +2151,8 @@ def transform(self, df, ydf=None, kind='nodes', return_graph=True, eps='auto', s
             logger.debug("kind must be one of `nodes`,"
                          f"`edges`, found {kind}")
         if return_graph:
-            res = self.bind()
             emb = None  # will not be able to decide umap coordinates, but will be able to infer graph from existing edges
-            g = infer_graph(res, emb, X, y, df, infer_on_umap_embedding=False, eps=eps, sample=sample) 
+            g = self._infer_edges(emb, X, y, df, infer_on_umap_embedding=False, eps=eps, sample=sample) 
             return g
         return X, y
 
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 42dd0e11c0..72bcb6f8b2 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -10,10 +10,11 @@
                             prune_weighted_edges_df_and_relabel_nodes,
                             resolve_feature_engine)
 from .PlotterBase import Plottable, WeakValueDictionary
-from .util import check_set_memoize, setup_logger
-from .ai_utils import infer_graph
+from .util import check_set_memoize
 
-logger = setup_logger(name=__name__, verbose=config.VERBOSE)
+import logging
+
+logger = logging.getLogger(__name__)
 
 if TYPE_CHECKING:
     MIXIN_BASE = FeatureMixin
@@ -294,9 +295,8 @@ def transform_umap(  # noqa: E303
         emb = self._umap.transform(X)  # type: ignore
         emb = self._bundle_embedding(emb, index=df.index)
         if return_graph:
-            res = self.bind()
-            g = infer_graph(res, emb, X, y, df, infer_on_umap_embedding=fit_umap_embedding, eps=eps, sample=sample) 
-            return g
+            g = self._infer_edges(emb, X, y, df, infer_on_umap_embedding=fit_umap_embedding, eps=eps, sample=sample) 
+            return g        
         return emb, X, y
 
     def _bundle_embedding(self, emb, index):
@@ -306,8 +306,8 @@ def _bundle_embedding(self, emb, index):
             columns = [config.X, config.Y] + [
                 f"umap_{k}" for k in range(2, emb.shape[1])
             ]
-        print('emb.shape', emb.shape)
-        print('columns', columns, len(columns))
+        # print('emb.shape', emb.shape)
+        # print('columns', columns, len(columns))
         emb = pd.DataFrame(emb, columns=columns, index=index)
         return emb
 

From b4ff370b4517385e63508c3e7142896844be5544 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 01:51:57 -0800
Subject: [PATCH 046/432] lint and bug fix in arg

---
 graphistry/ai_utils.py        | 129 ++++++++++-------
 graphistry/compute/cluster.py | 261 ++++++++++++++++++++--------------
 graphistry/text_utils.py      | 172 ++++++++++++----------
 3 files changed, 322 insertions(+), 240 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index c718b0eefd..a6f4bd0845 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -36,7 +36,7 @@ def search_to_df(word, col, df, as_string=False):
             res = df[df[col].str.contains(word, case=False)]
     except TypeError as e:
         logger.error(e)
-        return pd.DataFrame([], columns = df.columns)
+        return pd.DataFrame([], columns=df.columns)
     return res
 
 
@@ -131,14 +131,16 @@ def get_graphistry_from_milieu_search(
     g = graphistry.edges(tdf, src, dst).nodes(ntdf, node_col)
     return g
 
+
 # #########################################################################################################################
 #
 #  Graphistry Vector Search Index
 #
 ##########################################################################################################################
 
+
 def build_annoy_index(X, angular, n_trees=None):
-    """ Builds an Annoy Index for fast vector search
+    """Builds an Annoy Index for fast vector search
 
     Args:
         X (_type_): _description_
@@ -153,12 +155,12 @@ def build_annoy_index(X, angular, n_trees=None):
     logger.info(f"Building Index of size {X.shape}")
 
     if angular:
-        logger.info('-using angular metric')
-        metric = 'angular'
+        logger.info("-using angular metric")
+        metric = "angular"
     else:
-        logger.info('-using euclidean metric')
-        metric = 'euclidean'
-        
+        logger.info("-using euclidean metric")
+        metric = "euclidean"
+
     search_index = AnnoyIndex(X.shape[1], metric)
     # Add all the feature vectors to the search index
     for i in range(len(X)):
@@ -166,7 +168,7 @@ def build_annoy_index(X, angular, n_trees=None):
     if n_trees is None:
         n_trees = N_TREES
 
-    logger.info(f'-building index with {n_trees} trees')
+    logger.info(f"-building index with {n_trees} trees")
     search_index.build(n_trees)
     return search_index
 
@@ -175,7 +177,7 @@ def query_by_vector(vect, df, search_index, top_n):
     indices, distances = search_index.get_nns_by_vector(
         vect.values[0], top_n, include_distances=True
     )
-    
+
     results = df.iloc[indices]
     results[DISTANCE] = distances
     results = results.sort_values(by=[DISTANCE])
@@ -190,87 +192,104 @@ def query_by_vector(vect, df, search_index, top_n):
 ##########################################################################################################################
 
 
-def infer_graph(res, emb, X, y, df, infer_on_umap_embedding=False, eps='auto', sample=None):
+def infer_graph(
+    res, emb, X, y, df, infer_on_umap_embedding=False, eps="auto", sample=None
+):
     """
-        Infer a graph from a graphistry object
-        
-        args:
-            res: graphistry object
-            df: outside minibatch dataframe to add to existing graph
-            X: minibatch transformed dataframe
-            emb: minibatch UMAP embedding
-            kind: 'nodes' or 'edges'
-            eps: if 'auto' will find a good epsilon from the data; distance threshold for a minibatchh point to cluster to existing graph
-            n_nearest: number of nearest neighbors to add from existing graphs edges, if None, ignores existing edges. 
+    Infer a graph from a graphistry object
+
+    args:
+        res: graphistry object
+        df: outside minibatch dataframe to add to existing graph
+        X: minibatch transformed dataframe
+        emb: minibatch UMAP embedding
+        kind: 'nodes' or 'edges'
+        eps: if 'auto' will find a good epsilon from the data; distance threshold for a minibatchh point to cluster to existing graph
+        n_nearest: number of nearest neighbors to add from existing graphs edges, if None, ignores existing edges.
     """
 
-    #new_index = df.index
+    # new_index = df.index
     if infer_on_umap_embedding and emb is not None:
         X_previously_fit = res._node_embedding
         X_new = emb
     else:  # can still be umap, but want to do the inference on the higher dimensional features
         X_previously_fit = res._node_features
         X_new = X
-        
+
     FEATS = res._node_features
     EMB = res._node_embedding
     Y = res._node_target
-    
-    assert df.shape[0] == X.shape[0], 'minibatches df and X must have same number of rows since f(df) = X'
+
+    assert (
+        df.shape[0] == X.shape[0]
+    ), "minibatches df and X must have same number of rows since f(df) = X"
     if emb is not None:
-        assert emb.shape[0] == df.shape[0], 'minibatches emb and X must have same number of rows since h(df) = emb'
+        assert (
+            emb.shape[0] == df.shape[0]
+        ), "minibatches emb and X must have same number of rows since h(df) = emb"
         df = df.assign(x=emb.x, y=emb.y)  # add x and y to df for graphistry instance
-    
+
     # if umap, need to add '_n' as node id to df, adding new indices to existing graph
-    numeric_indices = range(X_previously_fit.shape[0], X_previously_fit.shape[0]+df.shape[0])
-    df['_n'] = numeric_indices
-    df['_batch'] = 1  # 1 for minibatch, 0 for existing graph
+    numeric_indices = range(
+        X_previously_fit.shape[0], X_previously_fit.shape[0] + df.shape[0]
+    )
+    df["_n"] = numeric_indices
+    df["_batch"] = 1  # 1 for minibatch, 0 for existing graph
     node = res._node
     NDF = res._nodes
-    NDF['_batch'] = 0
+    NDF["_batch"] = 0
     EDF = res._edges
-    EDF['_batch'] = 0
+    EDF["_batch"] = 0
     src = res._source
     dst = res._destination
-    
+
     new_edges = []
     old_edges = []
     old_nodes = []
-    mdists=[]
-    
+    mdists = []
+
     # vsearch = build_search_index(X_previously_fit, angular=False)
-    
+
     for i in range(X_new.shape[0]):
         # record_df = df.iloc[i, :]
         diff = X_previously_fit - X_new.iloc[i, :]
         dist = np.linalg.norm(diff, axis=1)  # Euclidean distance
         mdists.append(dist)
-    
+
     m, std = np.mean(mdists), np.std(mdists)
-    logger.info(f'--Mean distance to existing nodes  {m:.2f} +/- {std:.2f}')
+    logger.info(f"--Mean distance to existing nodes  {m:.2f} +/- {std:.2f}")
     # print(f'--Mean distance to existing nodes {m:.2f} +/- {std:.2f}')
-    if eps == 'auto':
-        eps = np.min([np.abs(m - 2*std), m])
-    logger.info(f'{eps:.2f} epsilon for max distance threshold to be considered a neighbor')
-    
+    if eps == "auto":
+        eps = np.min([np.abs(m - 2 * std), m])
+    logger.info(
+        f"{eps:.2f} epsilon for max distance threshold to be considered a neighbor"
+    )
+
     for i, dist in enumerate(mdists):
         record_df = df.iloc[i, :]
         for j in np.where(dist < eps)[0]:
             this_ndf = NDF.iloc[j, :]
             if sample:
-                local_edges = EDF[(EDF[src] == this_ndf[node]) | (EDF[dst] == this_ndf[node])]
+                local_edges = EDF[
+                    (EDF[src] == this_ndf[node]) | (EDF[dst] == this_ndf[node])
+                ]
                 if not local_edges.empty:
                     old_edges.append(local_edges.sample(sample, replace=True))
             new_edges.append([this_ndf[node], record_df[node], 1, 1])
             old_nodes.append(this_ndf)
 
-    new_edges = pd.DataFrame(new_edges, columns=[src, dst, '_weight', '_batch'])
-    
+    new_edges = pd.DataFrame(new_edges, columns=[src, dst, "_weight", "_batch"])
+
     all_nodes = []
     if len(old_edges):
         old_edges = pd.concat(old_edges, axis=0).assign(_batch=0)
-        all_nodes = old_edges[src].append(old_edges[dst]).append(new_edges[src]).append(new_edges[dst])
-        
+        all_nodes = (
+            old_edges[src]
+            .append(old_edges[dst])
+            .append(new_edges[src])
+            .append(new_edges[dst])
+        )
+
     if sample:
         new_edges = pd.concat([new_edges, old_edges], axis=0)
         # print('sampled', len(new_edges), 'new edges')
@@ -279,27 +298,29 @@ def infer_graph(res, emb, X, y, df, infer_on_umap_embedding=False, eps='auto', s
 
     if len(old_nodes):
         old_nodes = pd.DataFrame(old_nodes)
-        old_nodes = pd.concat([old_nodes, NDF[NDF[node].isin(all_nodes)]], axis=0).drop_duplicates(subset=[node])
-            
+        old_nodes = pd.concat(
+            [old_nodes, NDF[NDF[node].isin(all_nodes)]], axis=0
+        ).drop_duplicates(subset=[node])
+
     old_emb = None
     if EMB is not None:
         old_emb = EMB.loc[old_nodes.index]
-        
+
     new_emb = None
     if emb is not None:
         new_emb = pd.concat([emb, old_emb], axis=0)
-    
+
     new_features = pd.concat([X, FEATS.loc[old_nodes.index]], axis=0)
 
     new_nodes = pd.concat([df, old_nodes], axis=0)  # append minibatch at top
-    
+
     new_targets = pd.concat([y, Y.loc[old_nodes.index]]) if y is not None else Y
-    
+
     # #########################################################
     g = res.nodes(new_nodes, node).edges(new_edges, src, dst)
-    
+
     g._node_embedding = new_emb
     g._node_features = new_features
     g._node_targets = new_targets
-    
+
     return g
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 98b0fb0f04..272b9caa96 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -18,8 +18,8 @@
     MIXIN_BASE = Plottable
 else:
     MIXIN_BASE = object
-    
-DBSCANEngineConcrete = Literal['cuml', 'umap_learn']
+
+DBSCANEngineConcrete = Literal["cuml", "umap_learn"]
 DBSCANEngine = Literal[DBSCANEngineConcrete, "auto"]
 
 
@@ -30,7 +30,7 @@ def lazy_dbscan_import_has_dependency():
         from sklearn.cluster import DBSCAN
     except ImportError:
         has_min_dependency = False
-        logger.info('Please install sklearn for CPU DBSCAN')
+        logger.info("Please install sklearn for CPU DBSCAN")
 
     has_cuml_dependency = True
     cuDBSCAN = None
@@ -38,10 +38,9 @@ def lazy_dbscan_import_has_dependency():
         from cuml import DBSCAN as cuDBSCAN
     except ImportError:
         has_cuml_dependency = False
-        logger.info('Please install cuml for GPU DBSCAN')
-    
-    return has_min_dependency, DBSCAN, has_cuml_dependency, cuDBSCAN
+        logger.info("Please install cuml for GPU DBSCAN")
 
+    return has_min_dependency, DBSCAN, has_cuml_dependency, cuDBSCAN
 
 
 def resolve_cpu_gpu_engine(
@@ -50,11 +49,16 @@ def resolve_cpu_gpu_engine(
     if engine in [CUML, UMAP_LEARN]:
         return engine  # type: ignore
     if engine in ["auto"]:
-        has_min_dependency, _, has_cuml_dependency, _ = lazy_dbscan_import_has_dependency()
+        (
+            has_min_dependency,
+            _,
+            has_cuml_dependency,
+            _,
+        ) = lazy_dbscan_import_has_dependency()
         if has_cuml_dependency:
-            return 'cuml'
+            return "cuml"
         if has_min_dependency:
-            return 'umap_learn'
+            return "umap_learn"
 
     raise ValueError(  # noqa
         f'engine expected to be "auto", '
@@ -62,60 +66,61 @@ def resolve_cpu_gpu_engine(
         f"but received: {engine} :: {type(engine)}"
     )
 
-    
+
 def get_model_matrix(g, kind, cols, umap):
-    assert kind in ['nodes', 'edges']
-    assert hasattr(g, '_node_encoder') if kind == 'nodes' else hasattr(g, '_edge_encoder')
-    
+    assert kind in ["nodes", "edges"]
+    assert (
+        hasattr(g, "_node_encoder") if kind == "nodes" else hasattr(g, "_edge_encoder")
+    )
+
     if cols is None:
         df = g._get_feature(kind)
-    else: 
+    else:
         df = g.get_features_by_cols(cols, kind)
 
-    if umap and cols is None and g._umap is not None: 
+    if umap and cols is None and g._umap is not None:
         df = g._get_embedding(kind)
-        
+
     return df
-    
 
-def dbscan_fit(g, dbscan, kind='nodes', cols=None, use_umap_embedding=True):
+
+def dbscan_fit(g, dbscan, kind="nodes", cols=None, use_umap_embedding=True):
     """
-        Fits clustering on UMAP embeddings if umap is True, otherwise on the features dataframe
-        
-        args:
-            g: graphistry graph
-            kind: 'nodes' or 'edges'
-            cols: list of columns to use for clustering given `g.featurize` has been run
-            umap: whether to use UMAP embeddings or features dataframe
+    Fits clustering on UMAP embeddings if umap is True, otherwise on the features dataframe
+
+    args:
+        g: graphistry graph
+        kind: 'nodes' or 'edges'
+        cols: list of columns to use for clustering given `g.featurize` has been run
+        umap: whether to use UMAP embeddings or features dataframe
     """
     df = get_model_matrix(g, kind, cols, use_umap_embedding)
 
     dbscan.fit(df)
     labels = dbscan.labels_
-    
-    if kind == 'nodes':    
-        g._nodes = g._nodes.assign(_dbscan = labels)
-    elif kind == 'edges':
-        g._edges = g._edges.assign(_dbscan = labels)
+
+    if kind == "nodes":
+        g._nodes = g._nodes.assign(_dbscan=labels)
+    elif kind == "edges":
+        g._edges = g._edges.assign(_dbscan=labels)
     else:
-        raise ValueError('kind must be one of `nodes` or `edges`')
-    
-    kind = 'node' if kind == 'nodes' else 'edge'
-    setattr(g, f'_{kind}_dbscan', dbscan)
-    
-    return g
+        raise ValueError("kind must be one of `nodes` or `edges`")
+
+    kind = "node" if kind == "nodes" else "edge"
+    setattr(g, f"_{kind}_dbscan", dbscan)
 
+    return g
 
 
 def dbscan_predict(X: pd.DataFrame, model):
     """
-        DBSCAN has no predict per se, so we reverse engineer one here
-        from https://stackoverflow.com/questions/27822752/scikit-learn-predicting-new-points-with-dbscan
+    DBSCAN has no predict per se, so we reverse engineer one here
+    from https://stackoverflow.com/questions/27822752/scikit-learn-predicting-new-points-with-dbscan
     """
     n_samples = X.shape[0]
-    
+
     y_new = np.ones(shape=n_samples, dtype=int) * -1
-        
+
     for i in range(n_samples):
         diff = model.components_ - X.iloc[i, :].values  # NumPy broadcasting
 
@@ -128,40 +133,64 @@ def dbscan_predict(X: pd.DataFrame, model):
 
     return y_new
 
-def dbscan_predict2(g, kind='nodes', cols=None, umap=True):
+
+def dbscan_predict2(g, kind="nodes", cols=None, umap=True):
     X = g._get_feature(kind)
-    dbscan = g._node_dbscan if kind == 'nodes' else g._edge_dbscan
+    dbscan = g._node_dbscan if kind == "nodes" else g._edge_dbscan
 
     preds = dbscan_predict(X, dbscan)
     return X, preds
-                                      
+
 
 class ClusterMixin(MIXIN_BASE):
     def __init__(self, *args, **kwargs):
         pass
-    
-    def _cluster_dbscan(self, res, kind, cols, fit_umap_embedding, eps, min_samples, **kwargs):
+
+    def _cluster_dbscan(
+        self, res, kind, cols, fit_umap_embedding, eps, min_samples, **kwargs
+    ):
         """
-            DBSCAN clustering on cpu or gpu infered by .engine flag
+        DBSCAN clustering on cpu or gpu infered by .engine flag
         """
         _, DBSCAN, _, cuDBSCAN = lazy_dbscan_import_has_dependency()
-        
+
         res.engine = resolve_cpu_gpu_engine("auto")
-        res._kwargs_dbscan = ModelDict('latest dbscan kwargs', kind=kind, cols=cols, umap=fit_umap_embedding, eps=eps, min_samples=min_samples, **kwargs)
-        
-        dbscan = cuDBSCAN(eps=eps, min_samples=min_samples, **kwargs) if res.engine == CUML else DBSCAN(eps=eps, min_samples=min_samples, **kwargs)
-        
-        res = dbscan_fit(res, dbscan, kind=kind, cols=cols, use_umap_embedding=fit_umap_embedding)
+        res._kwargs_dbscan = ModelDict(
+            "latest dbscan kwargs",
+            kind=kind,
+            cols=cols,
+            umap=fit_umap_embedding,
+            eps=eps,
+            min_samples=min_samples,
+            **kwargs,
+        )
+
+        dbscan = (
+            cuDBSCAN(eps=eps, min_samples=min_samples, **kwargs)
+            if res.engine == CUML
+            else DBSCAN(eps=eps, min_samples=min_samples, **kwargs)
+        )
+
+        res = dbscan_fit(
+            res, dbscan, kind=kind, cols=cols, use_umap_embedding=fit_umap_embedding
+        )
 
         return res
-    
-    
-    def dbscan(self, eps: float = 0.2, min_samples: int = 1, cols = None, kind = 'nodes', fit_umap_embedding = True, **kwargs):
-        """DBSCAN clustering on cpu or gpu infered automatically 
-        
+
+    def dbscan(
+        self,
+        eps: float = 0.2,
+        min_samples: int = 1,
+        cols=None,
+        kind="nodes",
+        fit_umap_embedding=True,
+        **kwargs,
+    ):
+        """DBSCAN clustering on cpu or gpu infered automatically
+
         Examples:
             g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
-            
+
             # cluster by UMAP embeddings
             kind = 'nodes' | 'edges'
             g2 = g.umap(kind=kind).dbscan(kind=kind)
@@ -169,82 +198,92 @@ def dbscan(self, eps: float = 0.2, min_samples: int = 1, cols = None, kind = 'no
 
             # dbscan with fixed parameters is default in umap
             g2 = g.umap(dbscan=True)
-            
+
             # and with greater control over parameters via chaining,
             g2 = g.umap().dbscan(eps=1.2, min_samples=2, **kwargs)
-            
+
             # cluster by feature embeddings
             g2 = g.featurize().dbscan(**kwargs)
-            
-            # cluster by a given set of feature column attributes 
+
+            # cluster by a given set of feature column attributes
             g2 = g.featurize().dbscan(cols=['ip_172', 'location', 'alert'], **kwargs)
-            
+
             # equivalent to above (ie, cols != None and umap=True will still use features dataframe, rather than UMAP embeddings)
             g2 = g.umap().dbscan(cols=['ip_172', 'location', 'alert'], umap=True | False, **kwargs)
-            
+
             g2.plot() # color by `_dbscan`
 
         Useful:
-            Enriching the graph with cluster labels from UMAP is useful for visualizing clusters in the graph by color, size, etc, 
+            Enriching the graph with cluster labels from UMAP is useful for visualizing clusters in the graph by color, size, etc,
             as well as assessing metrics per cluster, e.g.
              https://github.com/graphistry/pygraphistry/blob/master/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
-                        
+
         Args:
             eps float: The maximum distance between two samples for them to be considered as in the same neighborhood.
             kind str: 'nodes' or 'edges'
-            cols: list of columns to use for clustering given `g.featurize` has been run, nice way to slice features by 
+            cols: list of columns to use for clustering given `g.featurize` has been run, nice way to slice features by
                 fragments of interest, e.g. ['ip_172', 'location', 'ssh', 'warnings']
             fit_umap_embedding bool: whether to use UMAP embeddings or features dataframe to cluster DBSCAN
-            min_samples: The number of samples in a neighborhood for a point to be considered as a core point. 
+            min_samples: The number of samples in a neighborhood for a point to be considered as a core point.
                 This includes the point itself.
-            
+
         """
         res = self.bind()
-        res = res._cluster_dbscan(res, kind=kind, cols=cols, fit_umap_embedding=fit_umap_embedding, eps=eps, min_samples=min_samples, **kwargs)
-        
+        res = res._cluster_dbscan(
+            res,
+            kind=kind,
+            cols=cols,
+            fit_umap_embedding=fit_umap_embedding,
+            eps=eps,
+            min_samples=min_samples,
+            **kwargs,
+        )
+
         return res
-    
-    def _transform_dbscan(self, df: pd.DataFrame, ydf=None, kind: str='nodes') -> pd.DataFrame:
+
+    def _transform_dbscan(
+        self, df: pd.DataFrame, ydf=None, kind: str = "nodes"
+    ) -> pd.DataFrame:
         """
         Transforms a dataframe to one with a new column '_dbscan' containing the DBSCAN cluster labels
             and returns feature[cols] or UMAP embedding
         Examples:
-            fit: 
+            fit:
                 g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
                 g2 = g.featurize().dbscan()
-                
+
             predict:
                 emb, X, y, ndf = g2.transform_dbscan(ndf, return_graph=False)
-                # or 
+                # or
                 g3 = g2.transform_dbscan(ndf, return_graph=True)
                 g3.plot()
-                
+
         likewise for umap:
             fit:
                 g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
                 g2 = g.umap().dbscan()
-                
+
             predict:
                 emb, X, y, ndf = g2.transform_dbscan(ndf, return_graph=False)
                 # or
                 g3 = g2.transform_dbscan(ndf, return_graph=True)
                 g3.plot()
-                
+
         args:
             df: dataframe to transform
             ydf: optional labels dataframe
             kind: 'nodes' or 'edges'
-        
+
         """
-        
+
         res = self.bind()
-        if hasattr(res, '_kwargs_dbscan'):
+        if hasattr(res, "_kwargs_dbscan"):
             # Assume that we are transforming to last fit of dbscan
-            cols = res._kwargs_dbscan['cols']
-            umap = res._kwargs_dbscan['umap']
-            
-            dbscan = res._node_dbscan if kind == 'nodes' else res._edge_dbscan
-            
+            cols = res._kwargs_dbscan["cols"]
+            umap = res._kwargs_dbscan["umap"]
+
+            dbscan = res._node_dbscan if kind == "nodes" else res._edge_dbscan
+
             emb = None
             if umap and cols is None:
                 emb, X, y = res.transform_umap(df, ydf, kind=kind, return_graph=False)
@@ -252,12 +291,12 @@ def _transform_dbscan(self, df: pd.DataFrame, ydf=None, kind: str='nodes') -> pd
                 X, y = res.transform(df, ydf, kind=kind, return_graph=False)
                 if cols is not None:
                     X = get_matrix_by_column_parts(X, cols)
-    
+
             if umap:
                 X_ = emb
             else:
                 X_ = X
-                
+
             labels = dbscan_predict(X_, dbscan)
             if umap:
                 df = df.assign(_dbscan=labels, x=emb.x, y=emb.y)
@@ -266,39 +305,45 @@ def _transform_dbscan(self, df: pd.DataFrame, ydf=None, kind: str='nodes') -> pd
 
             return emb, X, y, df
         else:
-            raise Exception('No dbscan model found. Please run `g.dbscan()` first')
-        
-    def transform_dbscan(self, df: pd.DataFrame, y: pd.DataFrame = None, 
-                         eps: Union[float, str]='auto', 
-                         fit_umap_embedding:bool=False, 
-                         sample:int=None, 
-                         kind:str='nodes', 
-                         return_graph=True)-> Union[Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable]:
+            raise Exception("No dbscan model found. Please run `g.dbscan()` first")
+
+    def transform_dbscan(
+        self,
+        df: pd.DataFrame,
+        y: pd.DataFrame = None,
+        eps: Union[float, str] = "auto",
+        fit_umap_embedding: bool = False,
+        sample: int = None,
+        kind: str = "nodes",
+        return_graph=True,
+    ) -> Union[
+        Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable
+    ]:
         """
         Transforms a minibatch dataframe to one with a new column '_cluster' containing the DBSCAN cluster labels on the minibatch
             and generates a graph with the minibatch and the original graph, with edges between the minibatch and the original graph inferred
-            works for 
-            
+            works for
+
         args:
             df: dataframe to transform
             y: optional labels dataframe
             eps: The maximum distance between two samples for them to be considered as in the same neighborhood.
-                smaller values will result in less edges between the minibatch and the original graph. 
+                smaller values will result in less edges between the minibatch and the original graph.
                 Default 'auto', infers eps from the mean distance and std of new points to the original graph
-            fit_umap_embedding: whether to use UMAP embeddings or features dataframe when inferring edges between 
+            fit_umap_embedding: whether to use UMAP embeddings or features dataframe when inferring edges between
                 the minibatch and the original graph. Default False, uses the features dataframe
-            sample: number of samples to use when inferring edges between the minibatch and the original graph, 
-                if None, will only use closest point to the minibatch. If greater than 0, will sample the closest `sample` points 
+            sample: number of samples to use when inferring edges between the minibatch and the original graph,
+                if None, will only use closest point to the minibatch. If greater than 0, will sample the closest `sample` points
                 in existing graph to pull in more edges. Default None
             kind: 'nodes' or 'edges'
             return_graph: whether to return a graph or the (emb, X, y, minibatch enriched with DBSCAN labels), default True
-        
-            
+
+
         """
         emb, X, y, df = self._transform_dbscan(df, y, kind=kind)
         if return_graph:
-            g = self._infer_edges(emb, X, y, df, infer_on_umap_embedding=False, eps=eps, sample=sample) 
+            g = self._infer_edges(
+                emb, X, y, df, infer_on_umap_embedding=fit_umap_embedding, eps=eps, sample=sample
+            )
             return g
         return emb, X, y, df
-    
-    
diff --git a/graphistry/text_utils.py b/graphistry/text_utils.py
index c96b568ce2..231980f601 100644
--- a/graphistry/text_utils.py
+++ b/graphistry/text_utils.py
@@ -1,6 +1,3 @@
-import os
-from time import time
-import numpy as np
 import pandas as pd
 
 from .feature_utils import FeatureMixin
@@ -18,8 +15,8 @@
     Any,
     Optional,
     Tuple,
-    TYPE_CHECKING, 
-    Type
+    TYPE_CHECKING,
+    Type,
 )  # noqa
 
 
@@ -36,17 +33,19 @@ def __init__(self, *args, **kwargs) -> None:
     def assert_fitted(self):
         # assert self._umap is not None, 'Umap needs to be fit first, run g.umap(..) to fit a model'
         assert (
-            self._get_feature('nodes') is not None
+            self._get_feature("nodes") is not None
         ), "Graphistry Instance is not fit, run g.featurize(kind='nodes', ..) to fit a model ' \
         'if you have nodes & edges dataframe or g.umap(kind='nodes', ..) if you only have nodes dataframe"
 
     def assert_features_line_up_with_nodes(self):
         ndf = self._nodes
-        X = self._get_feature('nodes')
+        X = self._get_feature("nodes")
         a, b = ndf.shape[0], X.shape[0]
-        assert a == b, 'Nodes dataframe and feature vectors are not same size, '\
-        f'found nodes: {a}, feats: {b}. Did you mutate nodes between fit?'
-        
+        assert a == b, (
+            "Nodes dataframe and feature vectors are not same size, "
+            f"found nodes: {a}, feats: {b}. Did you mutate nodes between fit?"
+        )
+
     def _build_search_index(self, X, angular=False, n_trees=None):
         # builds local index from X
         return build_annoy_index(X, angular, n_trees)
@@ -55,121 +54,136 @@ def build_index(self, angular=False, n_trees=None):
         # builds local index
         self.assert_fitted()
         self.assert_features_line_up_with_nodes()
-        
-        X = self._get_feature('nodes')
 
-        self.search_index = self._build_search_index(X, angular, n_trees)
+        X = self._get_feature("nodes")
 
+        self.search_index = self._build_search_index(X, angular, n_trees)
 
     def _query_from_dataframe(self, qdf: pd.DataFrame, top_n: int, thresh: float):
         # Use the loaded featurizers to transform the dataframe
         vect, _ = self.transform(qdf, None, kind="nodes")
 
-        results = query_by_vector(vect, self._nodes, self.search_index, top_n)        
+        results = query_by_vector(vect, self._nodes, self.search_index, top_n)
         results = results.query(f"{DISTANCE} < {thresh}")
 
         return results, vect
-    
+
     def _query(self, query: str, top_n: int, thresh: float):
         # build the query dataframe
-        if not hasattr(self, 'search_index'):
+        if not hasattr(self, "search_index"):
             self.build_index()
 
         qdf = pd.DataFrame([])
-                
+
         cols_text = self._node_encoder.text_cols  # type: ignore
 
         if len(cols_text) == 0:
-            logger.warn('** Querying is only possible using Transformer/Ngrams embeddings')    
+            logger.warn(
+                "** Querying is only possible using Transformer/Ngrams embeddings"
+            )
             return pd.DataFrame([]), None
-            
+
         qdf[cols_text[0]] = [query]
         if len(cols_text) > 1:
             for col in cols_text[1:]:
-                qdf[col] = ['']   
+                qdf[col] = [""]
 
         # this is hookey and needs to be fixed on dirty_cat side (with errors='ignore')
-        # if however min_words = 0, all columns will be textual, 
+        # if however min_words = 0, all columns will be textual,
         # and no other data_encoder will be generated
-        if hasattr(self._node_encoder.data_encoder, 'columns_'):  # type: ignore
+        if hasattr(self._node_encoder.data_encoder, "columns_"):  # type: ignore
 
             other_cols = self._node_encoder.data_encoder.columns_  # type: ignore
 
             if other_cols is not None and len(other_cols):
-                logger.warn('** There is no easy way to encode categorical or other features at query time. '
-                            f'Set `thresh` to a large value if no results show up.\ncolumns: {other_cols}')
+                logger.warn(
+                    "** There is no easy way to encode categorical or other features at query time. "
+                    f"Set `thresh` to a large value if no results show up.\ncolumns: {other_cols}"
+                )
                 df = self._nodes
                 dt = df[other_cols].dtypes
                 for col, v in zip(other_cols, dt.values):
                     if str(v) in ["string", "object", "category"]:
-                        qdf[col] = df.sample(1)[col].values  # so hookey 
+                        qdf[col] = df.sample(1)[col].values  # so hookey
                     elif str(v) in [
-                    "int",
-                    "float", 
-                    "float64",
-                    "float32",
-                    "float16",
-                    "int64",
-                    "int32",
-                    "int16",
-                    "uint64",
-                    "uint32",
-                    "uint16",
-                ]:
+                        "int",
+                        "float",
+                        "float64",
+                        "float32",
+                        "float16",
+                        "int64",
+                        "int32",
+                        "int16",
+                        "uint64",
+                        "uint32",
+                        "uint16",
+                    ]:
                         qdf[col] = df[col].mean()
 
         return self._query_from_dataframe(qdf, thresh=thresh, top_n=top_n)
 
     def search(
-        self, query: str, cols = None, thresh: float = 5000, fuzzy: bool = True, top_n: int = 10
-    ):  
+        self,
+        query: str,
+        cols=None,
+        thresh: float = 5000,
+        fuzzy: bool = True,
+        top_n: int = 10,
+    ):
         """Natural language query over nodes that returns a dataframe of results sorted by relevance column "distance".
 
-            If node data is not yet feature-encoded (and explicit edges are given), 
-            run automatic feature engineering: 
+            If node data is not yet feature-encoded (and explicit edges are given),
+            run automatic feature engineering:
             ```
-                g2 = g.featurize(kind='nodes', X=['text_col_1', ..], 
+                g2 = g.featurize(kind='nodes', X=['text_col_1', ..],
                 min_words=0 # forces all named columns are textually encoded
-                )  
-            ```    
-            
-            If edges do not yet exist, generate them via 
+                )
+            ```
+
+            If edges do not yet exist, generate them via
             ```
-                g2 = g.umap(kind='nodes', X=['text_col_1', ..], 
+                g2 = g.umap(kind='nodes', X=['text_col_1', ..],
                 min_words=0 # forces all named columns are textually encoded
-                )  
-            ``` 
+                )
+            ```
             If an index is not yet built, it is generated `g2.build_index()` on the fly at search time.
-            Otherwise, can set `g2.build_index()` and then subsequent `g2.search(...)` 
+            Otherwise, can set `g2.build_index()` and then subsequent `g2.search(...)`
             calls will be not rebuilt index.
 
         Args:
             query (str): natural language query.
-            cols (list or str, optional): if fuzzy=False, select which column to query. 
+            cols (list or str, optional): if fuzzy=False, select which column to query.
                                             Defaults to None since fuzzy=True by defaul.
             thresh (float, optional): distance threshold from query vector to returned results.
-                                        Defaults to 5000, set large just in case, 
+                                        Defaults to 5000, set large just in case,
                                         but could be as low as 10.
-            fuzzy (bool, optional): if True, uses embedding + annoy index for recall, 
-                                        otherwise does string matching over given `cols` 
+            fuzzy (bool, optional): if True, uses embedding + annoy index for recall,
+                                        otherwise does string matching over given `cols`
                                         Defaults to True.
             top_n (int, optional): how many results to return. Defaults to 100.
 
         Returns:
-            pd.DataFrame, vector_encoding_of_query: 
+            pd.DataFrame, vector_encoding_of_query:
                 * rank ordered dataframe of results matching query
                 * vector encoding of query via given transformer/ngrams model if fuzzy=True
                     else None
         """
         if not fuzzy:
             if cols is None:
-                logger.error(f'Columns to search for `{query}` \
-                             need to be given when fuzzy=False, found {cols}')
-                
+                logger.error(
+                    f"Columns to search for `{query}` \
+                             need to be given when fuzzy=False, found {cols}"
+                )
+
             logger.info(f"-- Word Match: [[ {query} ]]")
             return (
-                pd.concat([search_to_df(query, col, self._nodes, as_string=True) for col in cols]),
-                None
+                pd.concat(
+                    [
+                        search_to_df(query, col, self._nodes, as_string=True)
+                        for col in cols
+                    ]
+                ),
+                None,
             )
         else:
             logger.info(f"-- Search: [[ {query} ]]")
@@ -184,7 +198,7 @@ def search_graph(
         broader: bool = False,
         inplace: bool = False,
     ):
-        """Input a natural language query and return a graph of results. 
+        """Input a natural language query and return a graph of results.
             See help(g.search) for more information
 
         Args:
@@ -192,7 +206,7 @@ def search_graph(
             scale (float, optional): edge weigh threshold,  Defaults to 0.5.
             top_n (int, optional): how many results to return. Defaults to 100.
             thresh (float, optional): distance threshold from query vector to returned results.
-                                        Defaults to 5000, set large just in case, 
+                                        Defaults to 5000, set large just in case,
                                         but could be as low as 10.
             broader (bool, optional): if True, will retrieve entities connected via an edge
                 that were not necessarily bubbled up in the results_dataframe. Defaults to False.
@@ -206,7 +220,7 @@ def search_graph(
             res = self
         else:
             res = self.bind()
-                    
+
         edf = edges = res._edges
         rdf = df = res._nodes
         node = res._node
@@ -220,23 +234,21 @@ def search_graph(
                 indices = rdf[node]
                 # now get edges from indices
                 if broader:  # this will make a broader graph, finding NN in src OR dst
-                    edges = edf[
-                        (edf[src].isin(indices)) | (edf[dst].isin(indices))
-                    ]
-                else:  # finds only edges between results from query, if they exist, 
+                    edges = edf[(edf[src].isin(indices)) | (edf[dst].isin(indices))]
+                else:  # finds only edges between results from query, if they exist,
                     # default smaller graph
-                    edges = edf[
-                        (edf[src].isin(indices)) & (edf[dst].isin(indices))
-                    ]
+                    edges = edf[(edf[src].isin(indices)) & (edf[dst].isin(indices))]
             else:
-                logger.warn('**No results found due to empty DataFrame, returning original graph')
+                logger.warn(
+                    "**No results found due to empty DataFrame, returning original graph"
+                )
                 return res
-            
+
         try:  # for umap'd edges
             edges = edges.query(f"{WEIGHT} > {scale}")
         except:  # for explicit edges
             pass
-        
+
         found_indices = pd.concat([edges[src], edges[dst], indices], axis=0).unique()
         try:
             tdf = rdf.iloc[found_indices]
@@ -244,19 +256,22 @@ def search_graph(
             tdf = rdf[df[node].isin(found_indices)]
         logger.info(f" - Returning edge dataframe of size {edges.shape[0]}")
         # get all the unique nodes
-        logger.info(f" - Returning {tdf.shape[0]} unique nodes given scale {scale} and thresh {thresh}")
-        
+        logger.info(
+            f" - Returning {tdf.shape[0]} unique nodes given scale {scale} and thresh {thresh}"
+        )
+
         g = res.edges(edges, src, dst).nodes(tdf, node)
-        
+
         if g._name is not None:
-            name = f'{g._name}-query:{query}'
+            name = f"{g._name}-query:{query}"
         else:
-            name = f'query:{query}'
+            name = f"query:{query}"
         g = g.name(name)  # type: ignore
         return g
 
     def save_search_instance(self, savepath):
         from joblib import dump  # type: ignore   # need to make this onnx or similar
+
         self.build_index()
         search = self.search_index
         del self.search_index  # can't pickle Annoy
@@ -267,6 +282,7 @@ def save_search_instance(self, savepath):
     @classmethod
     def load_search_instance(self, savepath):
         from joblib import load  # type: ignore   # need to make this onnx or similar
+
         cls = load(savepath)
         cls.build_index()
         return cls

From 20b5a3fa17aebe28b2b8743bd444bdf679a0a1f7 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 18:28:13 -0800
Subject: [PATCH 047/432] fix(chaining was mute on changes in umap args;
 partial fix, works after 2 runs of umap

---
 graphistry/umap_utils.py | 83 ++++++++++++++++++++++++----------------
 1 file changed, 51 insertions(+), 32 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 72bcb6f8b2..434dd41f32 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -165,16 +165,17 @@ class UMAPMixin(MIXIN_BASE):
     _umap_memoize: WeakValueDictionary = WeakValueDictionary()
 
     def __init__(self, *args, **kwargs):
-        self.umap_initialized = False
+        self._umap_initialized = False
 
     def umap_lazy_init(
         self,
+        res,
         n_neighbors: int = 12,
         min_dist: float = 0.1,
-        spread=0.5,
-        local_connectivity=1,
-        repulsion_strength=1,
-        negative_sample_rate=5,
+        spread: float = 0.5,
+        local_connectivity: int = 1,
+        repulsion_strength: float = 1,
+        negative_sample_rate: int = 5,
         n_components: int = 2,
         metric: str = "euclidean",
         engine: UMAPEngine = "auto",
@@ -191,7 +192,8 @@ def umap_lazy_init(
                 "No umap engine, ensure 'auto', 'umap_learn', or 'cuml', and the library is installed"
             )
 
-        if not self.umap_initialized:
+        if not self._umap_initialized:
+            from graphistry.features import ModelDict
             umap_kwargs = dict(
                 {
                     "n_components": n_components,
@@ -204,21 +206,27 @@ def umap_lazy_init(
                     "negative_sample_rate": negative_sample_rate,
                 }
             )
+            print('umap_kwargs init: ', umap_kwargs['n_components'])
             
-            print('umap_kwargs', umap_kwargs)
-
-            self.n_components = n_components
-            self.metric = metric
-            self.n_neighbors = n_neighbors
-            self.min_dist = min_dist
-            self.spread = spread
-            self.local_connectivity = local_connectivity
-            self.repulsion_strength = repulsion_strength
-            self.negative_sample_rate = negative_sample_rate
-            self._umap = umap_engine.UMAP(**umap_kwargs)
-            self.umap_initialized = True
-            self.engine = engine_resolved
-            self.suffix = suffix
+            #print('umap_kwargs', umap_kwargs)
+            res._n_components = n_components
+            res._metric = metric
+            res._n_neighbors = n_neighbors
+            res._min_dist = min_dist
+            res._spread = spread
+            res._local_connectivity = local_connectivity
+            res._repulsion_strength = repulsion_strength
+            res._negative_sample_rate = negative_sample_rate
+            res._umap = umap_engine.UMAP(**umap_kwargs)
+            res.engine = engine_resolved
+            res._suffix = suffix
+            
+            res._umap_params = dict(# ModelDict(f'Umap Parameters', 
+                                          **umap_kwargs)
+            # finally set the flag
+            res._umap_initialized = True
+        return res
+
 
     def _check_target_is_one_dimensional(self, y: Union[pd.DataFrame, None]):
         if y is None:
@@ -240,7 +248,7 @@ def _get_embedding(self, kind='nodes'):
         elif kind == 'edges':
             return self._edge_embedding
         else:
-            raise ValueError('kind must be one of nodes or edges')
+            raise ValueError('kind must be one of `nodes` or `edges`')
 
     def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         if self._umap is None:
@@ -253,7 +261,7 @@ def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         if self.engine == CUML and is_legacy_cuml():
             from cuml.neighbors import NearestNeighbors
 
-            knn = NearestNeighbors(n_neighbors=self.n_neighbors)
+            knn = NearestNeighbors(n_neighbors=self._n_neighbors)
             cc = self._umap.fit(X, y, knn_graph=knn)
             knn.fit(cc.embedding_)
             self._umap.graph_ = knn.kneighbors_graph(cc.embedding_)
@@ -285,13 +293,14 @@ def transform_umap(  # noqa: E303
         eps='auto', 
         sample=None, 
         return_graph=True, 
-        fit_umap_embedding=False
+        fit_umap_embedding=False,
+        verbose=False
     ) -> Union[Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable]:
         try:
             logger.debug(f"Going into Transform umap {df.shape}")
         except:
             pass
-        X, y = self.transform(df, ydf, kind=kind, return_graph=False)
+        X, y = self.transform(df, ydf, kind=kind, return_graph=False, verbose=verbose)
         emb = self._umap.transform(X)  # type: ignore
         emb = self._bundle_embedding(emb, index=df.index)
         if return_graph:
@@ -319,12 +328,14 @@ def _process_umap(
         kind,
         memoize: bool,
         featurize_kwargs,
+        verbose = False,
         **umap_kwargs,
     ):
         """
         Returns res mutated with new _xy
         """
-        res._umap = self._umap
+        umap_kwargs_pure = umap_kwargs.copy()
+        #res._umap = self._umap
 
         logger.debug("process_umap before kwargs: %s", umap_kwargs)
         umap_kwargs.update({"kind": kind, "X": X_, "y": y_})
@@ -335,14 +346,22 @@ def _process_umap(
             res, memoize, {**umap_kwargs, "featurize_kwargs": featurize_kwargs or {}}
         )
         if old_res:
+            print(" --- [[ RE-USING UMAP ]]") if verbose else None
             logger.info(" --- [[ RE-USING UMAP ]]")
+            print('umap_kwargs', umap_kwargs['n_components']) if verbose else None
             fresh_res = copy.copy(res)
             for attr in ["_xy", "_weighted_edges_df", "_weighted_adjacency"]:
                 setattr(fresh_res, attr, getattr(old_res, attr))
             # have to set _raw_data attribute on umap?
             fresh_res._umap = old_res._umap  # this saves the day!
+            fresh_res._umap_initialized = True
+            fresh_res._umap_params = umap_kwargs_pure
             return fresh_res
-
+        
+        print('** Fitting UMAP') if verbose else None
+        res._umap_initialized=False
+        res = res.umap_lazy_init(res, **umap_kwargs_pure)
+        
         emb = res._umap_fit_transform(X_, y_)
         res._xy = emb
         return res
@@ -407,6 +426,7 @@ def umap(
         inplace: bool = False,
         feature_engine: str = "auto",
         memoize: bool = True,
+        verbose: bool = False,
         **featurize_kwargs,
     ):
         """
@@ -477,7 +497,7 @@ def umap(
         else:
             res = self.bind()
 
-        res.umap_lazy_init(**umap_kwargs)
+        res = res.umap_lazy_init(res, **umap_kwargs)
 
         logger.debug("umap input X :: %s", X)
         logger.debug("umap input y :: %s", y)
@@ -499,7 +519,6 @@ def umap(
                     config.IMPLICIT_NODE_ID,
                 )
                 res._nodes.index = index
-                #print(res.)
 
             nodes = res._nodes[res._node].values
             index_to_nodes_dict = dict(zip(range(len(nodes)), nodes))
@@ -517,7 +536,7 @@ def umap(
             logger.debug("umap y_: %s", y_)
 
             res = res._process_umap(
-                res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs
+                res, X_, y_, kind, memoize, featurize_kwargs, verbose, **umap_kwargs
             )
 
             res._weighted_adjacency_nodes = res._weighted_adjacency
@@ -607,8 +626,8 @@ def _bind_xy_from_umap(
         df = res._nodes if kind == "nodes" else res._edges
 
         df = df.copy(deep=False)
-        x_name = config.X + res.suffix
-        y_name = config.Y + res.suffix
+        x_name = config.X + res._suffix
+        y_name = config.Y + res._suffix
         if kind == "nodes":
             emb = res._node_embedding
         else:
@@ -623,7 +642,7 @@ def _bind_xy_from_umap(
         if encode_weight and kind == "nodes":
             # adds the implicit edge dataframe and binds it to
             # graphistry instance
-            w_name = config.WEIGHT + res.suffix
+            w_name = config.WEIGHT + res._suffix
             umap_edges_df = res._weighted_edges_df_from_nodes.copy(deep=False)
             umap_edges_df = umap_edges_df.rename(columns={config.WEIGHT: w_name})
             res = res.edges(umap_edges_df, config.SRC, config.DST)

From 17ed17adbd5527fb2c055ab2ae41b8a9217fb978 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 18:36:16 -0800
Subject: [PATCH 048/432] lint

---
 graphistry/Plottable.py     |   2 +-
 graphistry/ai_utils.py      |   8 +-
 graphistry/constants.py     |   2 +-
 graphistry/feature_utils.py | 109 ++++++++++++++++-----------
 graphistry/features.py      | 142 +++++++++++++-----------------------
 graphistry/text_utils.py    |  11 +--
 graphistry/umap_utils.py    |  49 +++++++------
 graphistry/util.py          |  55 +++++++++++++-
 8 files changed, 209 insertions(+), 169 deletions(-)

diff --git a/graphistry/Plottable.py b/graphistry/Plottable.py
index ca0aaca31d..8a483ddade 100644
--- a/graphistry/Plottable.py
+++ b/graphistry/Plottable.py
@@ -84,7 +84,7 @@ class Plottable(object):
     # embed utils
     _relation : Optional[str]
     _use_feat: bool
-    triplets: Optional[List]  # actually torch.Tensor too
+    _triplets: Optional[List]  # actually torch.Tensor too
     _kg_embed_dim: int
     
 
diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index a6f4bd0845..267aefa13a 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -193,7 +193,7 @@ def query_by_vector(vect, df, search_index, top_n):
 
 
 def infer_graph(
-    res, emb, X, y, df, infer_on_umap_embedding=False, eps="auto", sample=None
+    res, emb, X, y, df, infer_on_umap_embedding=False, eps="auto", sample=None, verbose=False
 ):
     """
     Infer a graph from a graphistry object
@@ -212,9 +212,11 @@ def infer_graph(
     if infer_on_umap_embedding and emb is not None:
         X_previously_fit = res._node_embedding
         X_new = emb
+        print("Infering edges over UMAP embedding") if verbose else None
     else:  # can still be umap, but want to do the inference on the higher dimensional features
         X_previously_fit = res._node_features
         X_new = X
+        print("Infering edges over features") if verbose else None
 
     FEATS = res._node_features
     EMB = res._node_embedding
@@ -313,6 +315,10 @@ def infer_graph(
     new_features = pd.concat([X, FEATS.loc[old_nodes.index]], axis=0)
 
     new_nodes = pd.concat([df, old_nodes], axis=0)  # append minibatch at top
+    print('-' * 80) if verbose else None
+    print("Final graph has", len(new_nodes), "nodes") if verbose else None
+    print("-Batch has", len(df), "nodes") if verbose else None
+    print("-Brought in ", len(old_nodes), "nodes") if verbose else None
 
     new_targets = pd.concat([y, Y.loc[old_nodes.index]]) if y is not None else Y
 
diff --git a/graphistry/constants.py b/graphistry/constants.py
index c7c2bf9bfa..1793447b8d 100644
--- a/graphistry/constants.py
+++ b/graphistry/constants.py
@@ -1,5 +1,5 @@
 # ###############################################################
-VERBOSE = None  # set to true for info, false for debug, None for none
+VERBOSE = True  # set to true for info, false for debug, None for none
 TRACE = False  # set to true for full trace of functions
 # ###############################################################
 # source and destination labels for consistent pipeline-ing across files
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 8f900ce7d1..a4777d8470 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1274,7 +1274,7 @@ def process_edge_dataframes(
     src: str,
     dst: str,
     cardinality_threshold: int = 40,
-    cardinality_threshold_target: int = 100,
+    cardinality_threshold_target: int = 400,
     n_topics: int = config.N_TOPICS_DEFAULT,
     n_topics_target: int = config.N_TOPICS_TARGET_DEFAULT,
     use_scaler: Optional[str] = None,
@@ -1284,7 +1284,6 @@ def process_edge_dataframes(
     ngram_range: tuple = (1, 3),
     max_df: float = 0.2,
     min_df: int = 3,
-    #confidence: float = 0.35,
     min_words: float = 2.5,
     model_name: str = "paraphrase-MiniLM-L6-v2",
     similarity: Optional[str] = None,
@@ -1822,6 +1821,8 @@ def get_matrix_by_column_part(X: pd.DataFrame, column_part: str) -> pd.DataFrame
 
 def get_matrix_by_column_parts(X: pd.DataFrame, column_parts: Union[list, str]) -> pd.DataFrame:
     """Get the feature matrix by column parts list existing in column names."""
+    if column_parts is None:
+        return X
     if isinstance(column_parts, str):
         column_parts = [column_parts]
     res = pd.concat([get_matrix_by_column_part(X, column_part) for column_part in column_parts], axis=1)  # type: ignore
@@ -1842,7 +1843,7 @@ class FeatureMixin(MIXIN_BASE):
         g = graphistry.edges(df, 'src', 'dst')
         g2 = g.featurize(kind='edges')
 
-    or chain them,
+    or chain them for both nodes and edges,
         g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node_column')
         g2 = g.featurize().featurize(kind='edges')
 
@@ -1870,10 +1871,10 @@ def _featurize_nodes(
         self,
         X: XSymbolic = None,
         y: YSymbolic = None,
-        use_scaler: Optional[str] = "zscale",
-        use_scaler_target: Optional[str] = "kbins",
+        use_scaler: Optional[str] = None,
+        use_scaler_target: Optional[str] = None,
         cardinality_threshold: int = 40,
-        cardinality_threshold_target: int = 120,
+        cardinality_threshold_target: int = 400,
         n_topics: int = config.N_TOPICS_DEFAULT,
         n_topics_target: int = config.N_TOPICS_TARGET_DEFAULT,
         multilabel: bool = False,
@@ -1882,7 +1883,6 @@ def _featurize_nodes(
         ngram_range: tuple = (1, 3),
         max_df: float = 0.2,
         min_df: int = 3,
-        #confidence: float = 0.35,
         min_words: float = 2.5,
         model_name: str = "paraphrase-MiniLM-L6-v2",
         similarity: Optional[str] = None,
@@ -1898,6 +1898,7 @@ def _featurize_nodes(
         remove_node_column: bool = True,
         feature_engine: FeatureEngineConcrete = "pandas",
         memoize: bool = True,
+        verbose: bool = False,
     ):
         res = self.bind()  # was self.copy() but changing to test
         ndf = res._nodes
@@ -1930,7 +1931,7 @@ def _featurize_nodes(
         
         from .features import ModelDict
 
-        fkwargs = dict(
+        fkwargs = ModelDict("Featurize Params",
             X=X_resolved,
             y=y_resolved,
             use_scaler=use_scaler,
@@ -1945,7 +1946,6 @@ def _featurize_nodes(
             ngram_range=ngram_range,
             max_df=max_df,
             min_df=min_df,
-            #confidence=confidence,
             min_words=min_words,
             model_name=model_name,
             similarity=similarity,
@@ -1969,6 +1969,7 @@ def _featurize_nodes(
 
         old_res = reuse_featurization(res, memoize, fkwargs)
         if old_res:
+            print("--- [[ RE-USING NODE FEATURIZATION ]]") if verbose else None
             logger.info("--- [[ RE-USING NODE FEATURIZATION ]]")
             fresh_res = copy.copy(res)
             for attr in ["_node_features", "_node_target", "_node_encoder"]:
@@ -1985,6 +1986,8 @@ def _featurize_nodes(
             if key not in keys_to_remove:
                 nfkwargs[key] = value
 
+        print('-'*80) if verbose else None
+        print("** Featuring nodes") if verbose else None
         #############################################################
         encoder = FastEncoder(X_resolved, y_resolved, kind="nodes")
         encoder.fit(**nfkwargs)
@@ -2003,17 +2006,16 @@ def _featurize_edges(
         self,
         X: XSymbolic = None,
         y: YSymbolic = None,
-        use_scaler: Optional[str] = "zscale",
-        use_scaler_target: Optional[str] = "kbins",
+        use_scaler: Optional[str] = None,
+        use_scaler_target: Optional[str] = None,
         cardinality_threshold: int = 40,
-        cardinality_threshold_target: int = 20,
+        cardinality_threshold_target: int = 400,
         n_topics: int = config.N_TOPICS_DEFAULT,
         n_topics_target: int = config.N_TOPICS_TARGET_DEFAULT,
         use_ngrams: bool = False,
         ngram_range: tuple = (1, 3),
         max_df: float = 0.2,
         min_df: int = 3,
-        #confidence: float = 0.35,
         min_words: float = 2.5,
         multilabel: bool = False,
         model_name: str = "paraphrase-MiniLM-L6-v2",
@@ -2029,6 +2031,7 @@ def _featurize_edges(
         keep_n_decimals: int = 5,
         feature_engine: FeatureEngineConcrete = "pandas",
         memoize: bool = True,
+        verbose: bool = False,
     ):
 
         res = self.copy()
@@ -2061,7 +2064,6 @@ def _featurize_edges(
             ngram_range=ngram_range,
             max_df=max_df,
             min_df=min_df,
-            #confidence=confidence,
             min_words=min_words,
             model_name=model_name,
             similarity=similarity,
@@ -2103,6 +2105,7 @@ def _featurize_edges(
             if key not in keys_to_remove:
                 nfkwargs[key] = value
 
+        print("** Featuring edges") if verbose else None
         ###############################################################
         encoder = FastEncoder(X_resolved, y_resolved, kind="edges")
         encoder.fit(src=res._source, dst=res._destination, **nfkwargs)
@@ -2115,9 +2118,9 @@ def _featurize_edges(
 
         return res
     
-    def _infer_edges(self, emb, X, y, df, eps='auto', sample=None, infer_on_umap_embedding=False, **kwargs):
+    def _infer_edges(self, emb, X, y, df, eps='auto', sample=None, infer_on_umap_embedding=False, verbose=False, **kwargs):
         res = self.bind()  # will not be able to decide umap coordinates, but will be able to infer graph from existing edges
-        g = infer_graph(res, emb, X, y, df, infer_on_umap_embedding=infer_on_umap_embedding, eps=eps, sample=sample) 
+        g = infer_graph(res, emb, X, y, df, infer_on_umap_embedding=infer_on_umap_embedding, eps=eps, sample=sample, verbose=verbose, **kwargs) 
         return g
 
     def _transform(self, encoder: str, df: pd.DataFrame, ydf: pd.DataFrame):
@@ -2129,7 +2132,7 @@ def _transform(self, encoder: str, df: pd.DataFrame, ydf: pd.DataFrame):
                 "before being able to transform data"
             )
 
-    def transform(self, df, ydf=None, kind='nodes', return_graph=True, eps='auto', sample=None):
+    def transform(self, df, ydf=None, kind='nodes', return_graph=True, eps='auto', sample=None, verbose=False):
         """Transform new data and append to existing graph.
         
             args:
@@ -2138,6 +2141,7 @@ def transform(self, df, ydf=None, kind='nodes', return_graph=True, eps='auto', s
                 kind: str  # one of `nodes`, `edges`
                 return_graph: bool, if True, will return a graph with inferred edges
                 eps: float, if return_graph is True, will use this value for eps in NN search, or 'auto' to infer a good value
+                    eps represents the maximum distance between two samples for one to be considered as in the neighborhood of the other.
                 sample: int, if return_graph is True, will use sample value for NN search over existing edges
             returns:
                 X: pd.DataFrame, transformed data if return_graph is False
@@ -2152,7 +2156,7 @@ def transform(self, df, ydf=None, kind='nodes', return_graph=True, eps='auto', s
                          f"`edges`, found {kind}")
         if return_graph:
             emb = None  # will not be able to decide umap coordinates, but will be able to infer graph from existing edges
-            g = self._infer_edges(emb, X, y, df, infer_on_umap_embedding=False, eps=eps, sample=sample) 
+            g = self._infer_edges(emb, X, y, df, infer_on_umap_embedding=False, eps=eps, sample=sample, verbose=verbose) 
             return g
         return X, y
 
@@ -2173,6 +2177,17 @@ def scale(
         strategy: str = "uniform",
         keep_n_decimals: int = 5,
     ):
+        """Scale data using the same scalers as used in the featurization step.
+        
+            example usage:
+                g = graphistry.nodes(df)
+                g2 = g.umap().scale(df, ydf, kind='nodes', use_scaler='robust', use_scaler_target='kbins', n_bins=3)
+
+                # scaled data
+                X = g2._node_features
+                y = g2._node_target  
+                
+        """
 
         if kind == "nodes" and hasattr(self, "_node_encoder"):  # type: ignore
             if self._node_encoder is not None:  # type: ignore
@@ -2250,7 +2265,7 @@ def featurize(
         ngram_range: tuple = (1, 3),
         max_df: float = 0.2,
         min_df: int = 3,
-        min_words: float = 2.5,
+        min_words: float = 4.5,
         model_name: str = "paraphrase-MiniLM-L6-v2",
         impute: bool = True,
         n_quantiles: int = 100,
@@ -2268,6 +2283,7 @@ def featurize(
         inplace: bool = False,
         feature_engine: FeatureEngine = "auto",
         memoize: bool = True,
+        verbose: bool = False,
     ):
         r"""
             Featurize Nodes or Edges of the underlying nodes/edges DataFrames.
@@ -2328,20 +2344,21 @@ def featurize(
                 but at cost of encoding time. If faster encoding is needed,
                 `average_word_embeddings_komninos` is useful
                 and produces less semantically relevant vectors.
-                Please see www.huggingface.co or sentence_transformer
+                Please see sentence_transformer
                 (https://www.sbert.net/) library for all available models.
         :param multilabel: if True, will encode a *single* target column composed of
                 lists of lists as multilabel outputs. 
                 This only works with y=['a_single_col'], default False
         :param embedding: If True, produces a random node embedding of size `n_topics`
-                default, False.
+                default, False. If no node features are provided, will produce random embeddings 
+                (for GNN models, for example)
         :param use_ngrams: If True, will encode textual columns as TfIdf Vectors,
                 default, False.
         :param ngram_range: if use_ngrams=True, can set ngram_range, eg: tuple = (1, 3)
         :param max_df:  if use_ngrams=True, set max word frequency to consider in vocabulary
                 eg: max_df = 0.2,
         :param min_df:  if use_ngrams=True, set min word count to consider in vocabulary
-                eg: min_df = 3    
+                eg: min_df = 3 or 0.00001
         :param categories: Optional[str] in ["auto", "k-means", "most_frequent"], decides which 
                 category to select in Similarity Encoding, default 'auto'
         :param impute: Whether to impute missing values, default True
@@ -2366,7 +2383,7 @@ def featurize(
                 not, default False.
         :param memoize: whether to store and reuse results across runs,
                 default True.
-        :return: self, with new attributes set by the featurization process.
+        :return: graphistry instance with new attributes set by the featurization process.
         """
         assert_imported()
         if inplace:
@@ -2392,11 +2409,10 @@ def featurize(
                 ngram_range=ngram_range,
                 max_df=max_df,
                 min_df=min_df,
-                #confidence=confidence,  # deprecated
                 min_words=min_words,
                 model_name=model_name,
-                similarity=similarity,  # deprecated
-                categories=categories,  # deprecated
+                similarity=similarity,  
+                categories=categories,
                 impute=impute,
                 n_quantiles=n_quantiles,
                 quantile_range=quantile_range,
@@ -2408,6 +2424,7 @@ def featurize(
                 remove_node_column=remove_node_column,
                 feature_engine=feature_engine,
                 memoize=memoize,
+                verbose=verbose
             )
         elif kind == "edges":
             res = res._featurize_edges(
@@ -2424,11 +2441,10 @@ def featurize(
                 ngram_range=ngram_range,
                 max_df=max_df,
                 min_df=min_df,
-                #confidence=confidence,  # deprecated
                 min_words=min_words,
                 model_name=model_name,
-                similarity=similarity,  # deprecated
-                categories=categories,  # deprecated
+                similarity=similarity, 
+                categories=categories,
                 impute=impute,
                 n_quantiles=n_quantiles,
                 quantile_range=quantile_range,
@@ -2439,6 +2455,7 @@ def featurize(
                 keep_n_decimals=keep_n_decimals,
                 feature_engine=feature_engine,
                 memoize=memoize,
+                verbose=verbose
             )
         else:
             logger.warning(
@@ -2452,19 +2469,18 @@ def _featurize_or_get_nodes_dataframe_if_X_is_None(
         self,
         X: XSymbolic = None,
         y: YSymbolic = None,
-        use_scaler: Optional[str] = "zscale",
-        use_scaler_target: Optional[str] = "kbins",
+        use_scaler: Optional[str] = None,
+        use_scaler_target: Optional[str] = None,
         cardinality_threshold: int = 40,
         cardinality_threshold_target: int = 400,
         n_topics: int = config.N_TOPICS_DEFAULT,
         n_topics_target: int = config.N_TOPICS_TARGET_DEFAULT,
         multilabel: bool = False,
-        embedding=False,
+        embedding: bool = False,
         use_ngrams: bool = False,
         ngram_range: tuple = (1, 3),
         max_df: float = 0.2,
         min_df: int = 3,
-        #confidence: float = 0.35,
         min_words: float = 2.5,
         model_name: str = "paraphrase-MiniLM-L6-v2",
         similarity: Optional[
@@ -2483,6 +2499,7 @@ def _featurize_or_get_nodes_dataframe_if_X_is_None(
         feature_engine: FeatureEngineConcrete = "pandas",
         reuse_if_existing=False,
         memoize: bool = True,
+        verbose: bool = False,
     ) -> Tuple[pd.DataFrame, pd.DataFrame, MIXIN_BASE]:
         """
         helper method gets node feature and target matrix if X, y
@@ -2533,6 +2550,7 @@ def _featurize_or_get_nodes_dataframe_if_X_is_None(
             remove_node_column=remove_node_column,
             feature_engine=feature_engine,
             memoize=memoize,
+            verbose=verbose,
         )
 
         assert res._node_features is not None  # ensure no infinite loop
@@ -2548,10 +2566,10 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
         self,
         X: XSymbolic = None,
         y: YSymbolic = None,
-        use_scaler: Optional[str] = "robust",
-        use_scaler_target: Optional[str] = "kbins",
+        use_scaler: Optional[str] = None,
+        use_scaler_target: Optional[str] = None,
         cardinality_threshold: int = 40,
-        cardinality_threshold_target: int = 20,
+        cardinality_threshold_target: int = 400,
         n_topics: int = config.N_TOPICS_DEFAULT,
         n_topics_target: int = config.N_TOPICS_TARGET_DEFAULT,
         multilabel: bool = False,
@@ -2559,7 +2577,6 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
         ngram_range: tuple = (1, 3),
         max_df: float = 0.2,
         min_df: int = 3,
-        #confidence: float = 0.35,
         min_words: float = 2.5,
         model_name: str = "paraphrase-MiniLM-L6-v2",
         similarity: Optional[
@@ -2577,6 +2594,7 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
         feature_engine: FeatureEngineConcrete = "pandas",
         reuse_if_existing=False,
         memoize: bool = True,
+        verbose: bool = False,
     ) -> Tuple[pd.DataFrame, Optional[pd.DataFrame], MIXIN_BASE]:
         """
         helper method gets edge feature and target matrix if X, y
@@ -2626,6 +2644,7 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
             keep_n_decimals=keep_n_decimals,
             feature_engine=feature_engine,
             memoize=memoize,
+            verbose=verbose,
         )
 
         assert res._edge_features is not None  # ensure no infinite loop
@@ -2638,21 +2657,29 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
         )
 
     
-    def get_features_by_cols(self, columns: Union[List, str], kind: str = 'nodes', target=False):
+    def get_features_by_cols(self, columns: Union[List, str] = None, kind: str = 'nodes', target: bool = False):
         """Returns feature matrix with only the columns that contain the string `column_part` in their name.
         
             `X = g.get_features_by_cols(['feature1', 'feature2'])`
             will retrieve a feature matrix with only the columns that contain the string 
             `feature1` or `feature2` in their name.
+            Most useful for topic modeling, where the column names are of the form `topic_0`, `topic_1`, etc.
+            Can retrieve unique columns in original dataframe, or actual topic features like [ip_part, shoes, preference_x, etc].
+            
+            Powerful way to retrieve features from a featurized graph by column or (top) features of interest.
             
             example:
-                res = g2.get_features_by_cols(['172', 'percent'])
-                res.columns
+                X = g2.get_features_by_cols(['172', 'percent'])
+                X.columns
                     => ['ip_172.56.104.67', 'ip_172.58.129.252', 'item_percent']
+                # or in targets
+                y = g2.get_features_by_cols(['total', 'percent'], target=True)
+                y.columns
+                    => ['basket_price_total', 'conversion_percent', 'CTR_percent', 'CVR_percent']
 
         Args:
             columns (Union[List, str]): list of column names or a single column name that may exist in columns 
-                of the feature matrix.
+                of the feature matrix. If None, returns original feature matrix
             kind (str, optional): Node or Edge features. Defaults to 'nodes'.
             target (bool, optional): If True, returns the target matrix. Defaults to False.
 
diff --git a/graphistry/features.py b/graphistry/features.py
index 35cfb4e5a6..5fac7a031b 100644
--- a/graphistry/features.py
+++ b/graphistry/features.py
@@ -1,6 +1,6 @@
-from collections import UserDict
 from .util import setup_logger
 from .constants import VERBOSE, TRACE
+from .util import ModelDict
 
 logger = setup_logger("graphistry.features", verbose=VERBOSE, fullpath=TRACE)
 
@@ -53,6 +53,16 @@
 NO_SCALER = None
 EXTRA_COLS_NEEDED = ["x", "y", "_n"]
 # ###############################################################
+# ################# graphistry umap config constants #################
+UMAP_DIM = 2
+N_NEIGHBORS = 15
+MIN_DIST = 0.1
+SPREAD=0.5,
+LOCAL_CONNECTIVITY=1,
+REPULSION_STRENGTH=1,
+NEGATIVE_SAMPLING_RATE=5,
+METRIC = "euclidean",
+
 
 # ###############################################################
 # ################# enrichments
@@ -86,42 +96,10 @@
 SPLIT_MEDIUM = 0.2
 SPLIT_HIGH = 0.5
 
-# #############################################################################
-class ModelDict(UserDict):
-    """Helper class to print out model names and keep track of updates
-
-    Args:
-        message: description of model
-        verbose: print out model names, logging happens regardless
-    """
-
-    def __init__(self, message, verbose=True, *args, **kwargs):
-        self._message = message
-        self._verbose = verbose
-        self._print_length = min(LENGTH_PRINT, len(message))
-        self._updates = []
-        super().__init__(*args, **kwargs)
-
-    def __repr__(self):
-        logger.info(self._message)
-        if self._verbose:
-            print("_" * self._print_length)
-            print()
-            print(self._message)
-            print("_" * self._print_length)
-            print()
-        return super().__repr__()
 
-    def update(self, *args, **kwargs):
-        self._updates.append(args[0])
-        if len(self._updates) > 1:  # don't take first update since its the init/default
-            self._message += (
-                "\n" + "_" * self._print_length + f"\n\nUpdated: {self._updates[-1]}"
-            )
-        return super().update(*args, **kwargs)
 
 
-default_featurize_parameters = dict(
+default_featurize_parameters = ModelDict('Featurize Parameters',
     kind="nodes",
     use_scaler=NO_SCALER,
     use_scaler_target=NO_SCALER,
@@ -154,80 +132,63 @@ def update(self, *args, **kwargs):
 )
 
 
+default_umap_parameters = ModelDict('Umap Parameters',  
+                {
+                    "n_components": UMAP_DIM,
+                    **({"metric": METRIC} if True else {}),
+                    "n_neighbors": N_NEIGHBORS,
+                    "min_dist": MIN_DIST,
+                    "spread": SPREAD,
+                    "local_connectivity": LOCAL_CONNECTIVITY,
+                    "repulsion_strength": REPULSION_STRENGTH,
+                    "negative_sample_rate": NEGATIVE_SAMPLING_RATE,
+                }
+            )
+
 # #############################################################################
 # Create useful presets for the user
 # makes naming and encoding models consistently and testing different models against eachother easy
 # customize the default parameters for each model you want to test
 
 # Ngrams Model over features
-ngrams_model = ModelDict("Ngrams Model", 
-                         use_ngrams=True, 
-                         min_words=HIGH_CARD, 
-                         verbose=True)
-#ngrams_model.update(dict(use_ngrams=True, min_words=HIGH_CARD))
+ngrams_model = ModelDict(
+    "Ngrams Model", use_ngrams=True, min_words=HIGH_CARD, verbose=True
+)
 
 # Topic Model over features
-topic_model = ModelDict("Reliable Topic Models on Features and Target",         
-                        cardinality_threshold=LOW_CARD,  # force topic model
-                        cardinality_threshold_target=LOW_CARD,  # force topic model
-                        n_topics=N_TOPICS,
-                        n_topics_target=N_TOPICS_TARGET,
-                        min_words=HIGH_CARD,  # make sure it doesn't turn into sentence model, but rather topic models
-                        verbose=True
-                        )
-#**default_featurize_parameters)
-# topic_model.update(
-#     dict(
-#         cardinality_threshold=LOW_CARD,  # force topic model
-#         cardinality_threshold_target=LOW_CARD,  # force topic model
-#         n_topics=N_TOPICS,
-#         n_topics_target=N_TOPICS_TARGET,
-#         min_words=HIGH_CARD,  # make sure it doesn't turn into sentence model, but rather topic models
-#     )
-# )
+topic_model = ModelDict(
+    "Reliable Topic Models on Features and Target",
+    cardinality_threshold=LOW_CARD,  # force topic model
+    cardinality_threshold_target=LOW_CARD,  # force topic model
+    n_topics=N_TOPICS,
+    n_topics_target=N_TOPICS_TARGET,
+    min_words=HIGH_CARD,  # make sure it doesn't turn into sentence model, but rather topic models
+    verbose=True,
+)
 
 # useful for text data that you want to paraphrase
-embedding_model = ModelDict(f"{PARAPHRASE_SMALL_MODEL} sbert Embedding Model",
-                        min_words=FORCE_EMBEDDING_ALL_COLUMNS,
-                        model_name=PARAPHRASE_SMALL_MODEL,  # if we need multilingual support, use PARAPHRASE_MULTILINGUAL_MODEL
-                        verbose=True,
-    #**default_featurize_parameters,
+embedding_model = ModelDict(
+    f"{PARAPHRASE_SMALL_MODEL} sbert Embedding Model",
+    min_words=FORCE_EMBEDDING_ALL_COLUMNS,
+    model_name=PARAPHRASE_SMALL_MODEL,  # if we need multilingual support, use PARAPHRASE_MULTILINGUAL_MODEL
+    verbose=True,
 )
-# embedding_model.update(
-#     dict(
-#         min_words=FORCE_EMBEDDING_ALL_COLUMNS,
-#         model_name=PARAPHRASE_SMALL_MODEL,  # if we need multilingual support, use PARAPHRASE_MULTILINGUAL_MODEL
-#     )
-# )
 
 # useful for when search input is much smaller than the encoded documents
 search_model = ModelDict(
-                        f"{MSMARCO2} Search Model", 
-                        verbose=True, 
-                        min_words=FORCE_EMBEDDING_ALL_COLUMNS,
-                        model_name=MSMARCO2,
-    #**default_featurize_parameters
+    f"{MSMARCO2} Search Model",
+    verbose=True,
+    min_words=FORCE_EMBEDDING_ALL_COLUMNS,
+    model_name=MSMARCO2,
 )
-# search_model.update(
-#     dict(
-#         min_words=FORCE_EMBEDDING_ALL_COLUMNS,
-#         model_name=MSMARCO2,
-#     )
-# )
 
 # Question Answering encodings for search
 qa_model = ModelDict(
-                    f"{QA_SMALL_MODEL} QA Model", 
-                    min_words=FORCE_EMBEDDING_ALL_COLUMNS,
-                    model_name=QA_SMALL_MODEL,
-                    verbose=True,                 
+    f"{QA_SMALL_MODEL} QA Model",
+    min_words=FORCE_EMBEDDING_ALL_COLUMNS,
+    model_name=QA_SMALL_MODEL,
+    verbose=True,
 )
-# qa_model.update(
-#     dict(
-#         min_words=FORCE_EMBEDDING_ALL_COLUMNS,
-#         model_name=QA_SMALL_MODEL,
-#     )
-# )
 
 
 BASE_MODELS = {
@@ -240,7 +201,8 @@ def update(self, *args, **kwargs):
 
 
 if __name__ == "__main__":
-    # python3 -m graphistry.features -m 'my awesome edge encoded model' -p '{"kind":"edges"}'
+    """ python3 -m graphistry.features -m 'my awesome edge encoded model' -p '{"kind":"edges"}'
+    """
     import argparse
     import json
 
diff --git a/graphistry/text_utils.py b/graphistry/text_utils.py
index 231980f601..803ce345dd 100644
--- a/graphistry/text_utils.py
+++ b/graphistry/text_utils.py
@@ -5,18 +5,8 @@
 from .constants import WEIGHT, DISTANCE
 from logging import getLogger
 
-logger = getLogger(__name__)
-
 from typing import (
-    Hashable,
-    List,
-    Union,
-    Dict,
-    Any,
-    Optional,
-    Tuple,
     TYPE_CHECKING,
-    Type,
 )  # noqa
 
 
@@ -25,6 +15,7 @@
 else:
     MIXIN_BASE = object
 
+logger = getLogger(__name__)
 
 class SearchToGraphMixin(MIXIN_BASE):
     def __init__(self, *args, **kwargs) -> None:
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 434dd41f32..8a596be47c 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -221,8 +221,8 @@ def umap_lazy_init(
             res.engine = engine_resolved
             res._suffix = suffix
             
-            res._umap_params = dict(# ModelDict(f'Umap Parameters', 
-                                          **umap_kwargs)
+            res._umap_params = dict(**umap_kwargs)  # ModelDict(f'Umap Parameters', 
+                                          
             # finally set the flag
             res._umap_initialized = True
         return res
@@ -359,7 +359,7 @@ def _process_umap(
             return fresh_res
         
         print('** Fitting UMAP') if verbose else None
-        res._umap_initialized=False
+        res._umap_initialized = False
         res = res.umap_lazy_init(res, **umap_kwargs_pure)
         
         emb = res._umap_fit_transform(X_, y_)
@@ -431,46 +431,47 @@ def umap(
     ):
         """
             UMAP the featurized node or edges data,
-            or pass in your own X, y (optional).
+            or pass in your own X, y (optional) dataframes.
 
-        :param kind: `nodes` or `edges` or None.
+        kind: `nodes` or `edges` or None.
                 If None, expects explicit X, y (optional) matrices,
                 and will Not associate them to nodes or edges.
                 If X, y (optional) is given, with kind = [nodes, edges],
                 it will associate new matrices to nodes or edges attributes.
-        :param feature_engine: How to encode data
+        feature_engine: How to encode data
                 ("none", "auto", "pandas", "dirty_cat", "torch")
-        :param encode_weight: if True, will set new edges_df from
+        encode_weight: if True, will set new edges_df from
                 implicit UMAP, default True.
-        :param encode_position: whether to set default plotting bindings
+        encode_position: whether to set default plotting bindings
                 -- positions x,y from umap for .plot()
-        :param X: either a dataframe ndarray of features, or column names to featurize
-        :param y: either an dataframe ndarray of targets, or column names to featurize
+        X: either a dataframe ndarray of features, or column names to featurize
+        y: either an dataframe ndarray of targets, or column names to featurize
                 targets
-        :param scale: multiplicative scale for pruning weighted edge DataFrame
+        scale: multiplicative scale for pruning weighted edge DataFrame
                 gotten from UMAP, between [0, ..) with high end meaning keep
                 all edges
-        :param n_neighbors: UMAP number of nearest neighbors to include for
+        n_neighbors: UMAP number of nearest neighbors to include for
                 UMAP connectivity, lower makes more compact layouts. Minimum 2
-        :param min_dist: UMAP float between 0 and 1, lower makes more compact
+        min_dist: UMAP float between 0 and 1, lower makes more compact
                 layouts.
-        :param spread: UMAP spread of values for relaxation
-        :param local_connectivity: UMAP connectivity parameter
-        :param repulsion_strength: UMAP repulsion strength
-        :param negative_sample_rate: UMAP negative sampling rate
-        :param n_components: number of components in the UMAP projection,
+        spread: UMAP spread of values for relaxation
+        local_connectivity: UMAP connectivity parameter
+        repulsion_strength: UMAP repulsion strength
+        negative_sample_rate: UMAP negative sampling rate
+        n_components: number of components in the UMAP projection,
                 default 2
-        :param metric: UMAP metric, default 'euclidean'.
+        metric: UMAP metric, default 'euclidean'.
                 see (UMAP-LEARN)[https://umap-learn.readthedocs.io/
                 en/latest/parameters.html] documentation for more.
-        :param suffix: optional suffix to add to x, y attributes of umap.
-        :param play: Graphistry play parameter, default 0, how much to evolve
+        suffix: optional suffix to add to x, y attributes of umap.
+        play: Graphistry play parameter, default 0, how much to evolve
                 the network during clustering. 0 preserves the original UMAP layout.
-        :param dbscan: whether to run DBSCAN on the UMAP embedding, default True.
-        :param engine: selects which engine to use to calculate UMAP:
+        dbscan: whether to run DBSCAN on the UMAP embedding, default True.
+        engine: selects which engine to use to calculate UMAP:
                 default "auto" will use cuML if available, otherwise UMAP-LEARN.
-        :param memoize: whether to memoize the results of this method,
+        memoize: whether to memoize the results of this method,
                 default True.
+        verbose: whether to print out extra information, default False.
         :return: self, with attributes set with new data
         """
         if engine == UMAP_LEARN:
diff --git a/graphistry/util.py b/graphistry/util.py
index ce7249c934..4f230d68d7 100644
--- a/graphistry/util.py
+++ b/graphistry/util.py
@@ -10,6 +10,7 @@
 import warnings
 from functools import lru_cache
 from typing import Any
+from collections import UserDict
 
 from .constants import VERBOSE, CACHE_COERCION_SIZE, TRACE
 
@@ -77,6 +78,11 @@ def hash_memoize_helper(v: Any) -> str:
         for k2, v2 in v.items():
             rolling += f'{k2}:{hash_memoize_helper(v2)},'
         rolling += '}'
+    elif isinstance(v, ModelDict):
+        rolling = '{'
+        for k2, v2 in v.items():
+            rolling += f'{k2}:{hash_memoize_helper(v2)},'
+        rolling += '}'
     elif isinstance(v, list):
         rolling = '['
         for i in v:
@@ -123,7 +129,7 @@ def check_set_memoize(g, metadata, attribute, name: str = '', memoize: bool = Tr
     hashed = None
     weakref = getattr(g, attribute)
     try:
-        hashed = hash_memoize(metadata)
+        hashed = hash_memoize(dict(data=metadata))
     except TypeError:
         logger.warning(
             f'! Failed {name} speedup attempt. Continuing without memoization speedups.'
@@ -281,6 +287,53 @@ def deprecated_func(*args, **kwargs):
     return deprecated_decorator
 
 
+# #############################################################################
+# MODEL Parameter HELPERS
+def get_timestamp():
+    import datetime
+    return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+class ModelDict(UserDict):
+    """Helper class to print out model names and keep track of updates
+
+    Args:
+        message: description of model
+        verbose: print out model names, logging happens regardless
+    """
+
+    def __init__(self, message, verbose=True, timestamp=False, *args, **kwargs):
+        self._message = message
+        self._verbose = verbose
+        self._timestamp = timestamp
+        L = len(message) if timestamp is False else max(len(message), len(get_timestamp())+1)
+        self._print_length = min(80, L)
+        self._updates = []
+        super().__init__(*args, **kwargs)
+        
+    def print(self, message):
+        if self._timestamp:
+            message = f"{message}\n{get_timestamp()}"
+        if self._verbose:
+            print("_" * self._print_length)
+            print()
+            print(message)
+            print("_" * self._print_length)
+            print()
+
+    def __repr__(self):
+        #logger.info(self._message)
+        self.print(self._message)
+        return super().__repr__()
+
+    def update(self, *args, **kwargs):
+        self._updates.append(args[0])
+        if len(self._updates) > 1:  # don't take first update since its the init/default
+            self._message += (
+                "\n" + "_" * self._print_length + f"\n\nUpdated: {self._updates[-1]}"
+            )
+        return super().update(*args, **kwargs)
+
+
 #
 # def inspect_decorator(func, args, kwargs):
 #     import inspect

From ab3d7e9ee3e9f7552499bfe1068687fff623446d Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 18:45:12 -0800
Subject: [PATCH 049/432] adds tests and lint

---
 graphistry/feature_utils.py         |   6 +-
 graphistry/features.py              |  43 ++-
 graphistry/tests/test_umap_utils.py | 452 +++++++++++++++++++---------
 graphistry/util.py                  | 105 ++++---
 4 files changed, 389 insertions(+), 217 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index a4777d8470..5683f96064 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1986,12 +1986,12 @@ def _featurize_nodes(
             if key not in keys_to_remove:
                 nfkwargs[key] = value
 
-        print('-'*80) if verbose else None
+        print('-' * 80) if verbose else None
         print("** Featuring nodes") if verbose else None
-        #############################################################
+        # ############################################################
         encoder = FastEncoder(X_resolved, y_resolved, kind="nodes")
         encoder.fit(**nfkwargs)
-        ############################################################
+        # ###########################################################
 
         # if changing, also update fresh_res
         res._node_features = encoder.X
diff --git a/graphistry/features.py b/graphistry/features.py
index 5fac7a031b..541eda9fbb 100644
--- a/graphistry/features.py
+++ b/graphistry/features.py
@@ -57,11 +57,11 @@
 UMAP_DIM = 2
 N_NEIGHBORS = 15
 MIN_DIST = 0.1
-SPREAD=0.5,
-LOCAL_CONNECTIVITY=1,
-REPULSION_STRENGTH=1,
-NEGATIVE_SAMPLING_RATE=5,
-METRIC = "euclidean",
+SPREAD = (0.5,)
+LOCAL_CONNECTIVITY = (1,)
+REPULSION_STRENGTH = (1,)
+NEGATIVE_SAMPLING_RATE = (5,)
+METRIC = ("euclidean",)
 
 
 # ###############################################################
@@ -97,9 +97,8 @@
 SPLIT_HIGH = 0.5
 
 
-
-
-default_featurize_parameters = ModelDict('Featurize Parameters',
+default_featurize_parameters = ModelDict(
+    "Featurize Parameters",
     kind="nodes",
     use_scaler=NO_SCALER,
     use_scaler_target=NO_SCALER,
@@ -132,18 +131,19 @@
 )
 
 
-default_umap_parameters = ModelDict('Umap Parameters',  
-                {
-                    "n_components": UMAP_DIM,
-                    **({"metric": METRIC} if True else {}),
-                    "n_neighbors": N_NEIGHBORS,
-                    "min_dist": MIN_DIST,
-                    "spread": SPREAD,
-                    "local_connectivity": LOCAL_CONNECTIVITY,
-                    "repulsion_strength": REPULSION_STRENGTH,
-                    "negative_sample_rate": NEGATIVE_SAMPLING_RATE,
-                }
-            )
+default_umap_parameters = ModelDict(
+    "Umap Parameters",
+    {
+        "n_components": UMAP_DIM,
+        **({"metric": METRIC} if True else {}),
+        "n_neighbors": N_NEIGHBORS,
+        "min_dist": MIN_DIST,
+        "spread": SPREAD,
+        "local_connectivity": LOCAL_CONNECTIVITY,
+        "repulsion_strength": REPULSION_STRENGTH,
+        "negative_sample_rate": NEGATIVE_SAMPLING_RATE,
+    },
+)
 
 # #############################################################################
 # Create useful presets for the user
@@ -201,8 +201,7 @@
 
 
 if __name__ == "__main__":
-    """ python3 -m graphistry.features -m 'my awesome edge encoded model' -p '{"kind":"edges"}'
-    """
+    """python3 -m graphistry.features -m 'my awesome edge encoded model' -p '{"kind":"edges"}'"""
     import argparse
     import json
 
diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index 1da454a337..c4f77dfaa7 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -22,9 +22,12 @@
     edge2_target_df,
     model_avg_name,
     lazy_import_has_min_dependancy,
-    check_allclose_fit_transform_on_same_data
+    check_allclose_fit_transform_on_same_data,
+)
+from graphistry.umap_utils import (
+    lazy_umap_import_has_dependancy,
+    lazy_cuml_import_has_dependancy,
 )
-from graphistry.umap_utils import lazy_umap_import_has_dependancy, lazy_cuml_import_has_dependancy
 
 has_dependancy, _ = lazy_import_has_min_dependancy()
 has_cuml, _, _ = lazy_cuml_import_has_dependancy()
@@ -32,7 +35,7 @@
 
 logger = logging.getLogger(__name__)
 
-warnings.filterwarnings('ignore')
+warnings.filterwarnings("ignore")
 
 
 triangleEdges = pd.DataFrame(
@@ -70,132 +73,225 @@ def setUp(self):
 
         g = graphistry.nodes(ndf_reddit)
         self.gn = g
-        
+
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=FutureWarning)
-            g2 = g.umap(y=double_target_reddit, 
-                        use_ngrams=True, 
-                        ngram_range=(1, 2), 
-                        use_scaler='robust', 
-                        cardinality_threshold=2)
-            
+            g2 = g.umap(
+                y=double_target_reddit,
+                use_ngrams=True,
+                ngram_range=(1, 2),
+                use_scaler="robust",
+                cardinality_threshold=2,
+            )
+
         self.g2 = g2
         fenc = g2._node_encoder
         self.X, self.Y = fenc.X, fenc.y
         self.EMB = g2._node_embedding
-        self.emb, self.x, self.y = g2.transform_umap(ndf_reddit, ydf=double_target_reddit, kind='nodes', return_graph=False)
-        self.g3 = g2.transform_umap(ndf_reddit, ydf=double_target_reddit, kind='nodes', return_graph=True)
-        
+        self.emb, self.x, self.y = g2.transform_umap(
+            ndf_reddit, ydf=double_target_reddit, kind="nodes", return_graph=False
+        )
+        self.g3 = g2.transform_umap(
+            ndf_reddit, ydf=double_target_reddit, kind="nodes", return_graph=True
+        )
+
         # do the same for edges
         edge_df22 = edge_df2.copy()
-        edge_df22['rando'] = np.random.rand(edge_df2.shape[0])
-        g = graphistry.edges(edge_df22, 'src', 'dst')
+        edge_df22["rando"] = np.random.rand(edge_df2.shape[0])
+        g = graphistry.edges(edge_df22, "src", "dst")
         self.ge = g
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=FutureWarning)
-            g2 = g.umap(y=edge2_target_df, kind='edges',
-                 use_ngrams=True, 
-                 ngram_range=(1, 2),
-                 use_scaler=None,
-                 use_scaler_target=None,
-                 cardinality_threshold=2, n_topics=4)
-        
+            g2 = g.umap(
+                y=edge2_target_df,
+                kind="edges",
+                use_ngrams=True,
+                ngram_range=(1, 2),
+                use_scaler=None,
+                use_scaler_target=None,
+                cardinality_threshold=2,
+                n_topics=4,
+            )
+
         fenc = g2._edge_encoder
         self.Xe, self.Ye = fenc.X, fenc.y
         self.EMBe = g2._edge_embedding
-        self.embe, self.xe, self.ye = g2.transform_umap(edge_df22, ydf=edge2_target_df, kind='edges', return_graph=False)
+        self.embe, self.xe, self.ye = g2.transform_umap(
+            edge_df22, ydf=edge2_target_df, kind="edges", return_graph=False
+        )
         self.g2e = g2
-        self.g3e = g2.transform_umap(edge_df22, ydf=edge2_target_df, kind='edges', return_graph=True)
+        self.g3e = g2.transform_umap(
+            edge_df22, ydf=edge2_target_df, kind="edges", return_graph=True
+        )
 
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_columns_match(self):
-        assert all(self.X.columns == self.x.columns), 'Node Feature Columns do not match'
-        assert all(self.Y.columns == self.y.columns), 'Node Target Columns do not match'
-        assert all(self.Xe.columns == self.xe.columns), 'Edge Feature Columns do not match'
-        assert all(self.Ye.columns == self.ye.columns), 'Edge Target Columns do not match'
+        assert all(
+            self.X.columns == self.x.columns
+        ), "Node Feature Columns do not match"
+        assert all(self.Y.columns == self.y.columns), "Node Target Columns do not match"
+        assert all(
+            self.Xe.columns == self.xe.columns
+        ), "Edge Feature Columns do not match"
+        assert all(
+            self.Ye.columns == self.ye.columns
+        ), "Edge Target Columns do not match"
 
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_index_match(self):
         # nodes
-        assert all(self.gn._nodes.index == self.g2._nodes.index), 'Node Indexes do not match'
-        assert all(self.gn._nodes.index == self.EMB.index), 'Emb Indexes do not match'
-        assert all(self.gn._nodes.index == self.emb.index), 'Transformed Emb Indexes do not match'
-        assert all(self.gn._nodes.index == self.X.index), 'Transformed Node features Indexes do not match'
-        assert all(self.gn._nodes.index == self.y.index), 'Transformed Node target Indexes do not match'
-        
-        # edges  
-        assert all(self.ge._edges.index == self.g2e._edges.index), 'Edge Indexes do not match'
-        assert all(self.ge._edges.index == self.EMBe.index), 'Edge Emb Indexes do not match'
-        assert all(self.ge._edges.index == self.embe.index), 'Edge Transformed Emb Indexes do not match'
-        assert all(self.ge._edges.index == self.Xe.index), 'Edge Transformed features Indexes do not match'
-        assert all(self.ge._edges.index == self.ye.index), 'Edge Transformed target Indexes do not match'
-        
+        assert all(
+            self.gn._nodes.index == self.g2._nodes.index
+        ), "Node Indexes do not match"
+        assert all(self.gn._nodes.index == self.EMB.index), "Emb Indexes do not match"
+        assert all(
+            self.gn._nodes.index == self.emb.index
+        ), "Transformed Emb Indexes do not match"
+        assert all(
+            self.gn._nodes.index == self.X.index
+        ), "Transformed Node features Indexes do not match"
+        assert all(
+            self.gn._nodes.index == self.y.index
+        ), "Transformed Node target Indexes do not match"
+
+        # edges
+        assert all(
+            self.ge._edges.index == self.g2e._edges.index
+        ), "Edge Indexes do not match"
+        assert all(
+            self.ge._edges.index == self.EMBe.index
+        ), "Edge Emb Indexes do not match"
+        assert all(
+            self.ge._edges.index == self.embe.index
+        ), "Edge Transformed Emb Indexes do not match"
+        assert all(
+            self.ge._edges.index == self.Xe.index
+        ), "Edge Transformed features Indexes do not match"
+        assert all(
+            self.ge._edges.index == self.ye.index
+        ), "Edge Transformed target Indexes do not match"
+
         # make sure the indexes match at transform time internally as well
-        assert all(self.X.index == self.x.index), 'Node Feature Indexes do not match'
-        assert all(self.Y.index == self.y.index), 'Node Target Indexes do not match'
-        assert all(self.Xe.index == self.xe.index), 'Edge Feature Indexes do not match'
-        assert all(self.Ye.index == self.ye.index), 'Edge Target Indexes do not match'
+        assert all(self.X.index == self.x.index), "Node Feature Indexes do not match"
+        assert all(self.Y.index == self.y.index), "Node Target Indexes do not match"
+        assert all(self.Xe.index == self.xe.index), "Edge Feature Indexes do not match"
+        assert all(self.Ye.index == self.ye.index), "Edge Target Indexes do not match"
 
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_index_match_in_infered_graph(self):
         # nodes
-        assert all(self.g3._nodes.index == self.g2._nodes.index), 'Node Indexes do not match'
-        assert all(self.g3._nodes.index == self.EMB.index), 'Emb Indexes do not match'
-        assert all(self.g3._nodes.index == self.emb.index), 'Transformed Emb Indexes do not match'
-        assert all(self.g3._nodes.index == self.X.index), 'Transformed Node features Indexes do not match'
-        assert all(self.g3._nodes.index == self.y.index), 'Transformed Node target Indexes do not match'
+        assert all(
+            self.g3._nodes.index == self.g2._nodes.index
+        ), "Node Indexes do not match"
+        assert all(self.g3._nodes.index == self.EMB.index), "Emb Indexes do not match"
+        assert all(
+            self.g3._nodes.index == self.emb.index
+        ), "Transformed Emb Indexes do not match"
+        assert all(
+            self.g3._nodes.index == self.X.index
+        ), "Transformed Node features Indexes do not match"
+        assert all(
+            self.g3._nodes.index == self.y.index
+        ), "Transformed Node target Indexes do not match"
 
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_nodes_index_match_in_infered_graph(self):
-        # edges  
+        # edges
         ndf_infered = self.g3._nodes
-        assert all(ndf_infered.index == self.EMBe.index), 'Edge Emb Indexes do not match'
-        assert all(ndf_infered.index == self.embe.index), 'Edge Transformed Emb Indexes do not match'
-        assert all(ndf_infered.index == self.Xe.index), 'Edge Transformed features Indexes do not match'
-        assert all(ndf_infered.index == self.ye.index), 'Edge Transformed target Indexes do not match'
-        
+        assert all(
+            ndf_infered.index == self.EMBe.index
+        ), "Edge Emb Indexes do not match"
+        assert all(
+            ndf_infered.index == self.embe.index
+        ), "Edge Transformed Emb Indexes do not match"
+        assert all(
+            ndf_infered.index == self.Xe.index
+        ), "Edge Transformed features Indexes do not match"
+        assert all(
+            ndf_infered.index == self.ye.index
+        ), "Edge Transformed target Indexes do not match"
+
         # now test in set featurize method calls
-        assert all(self.g3._node_features.index == ndf_infered.index), 'Edge Feature Indexes do not match'
-        assert all(self.g3._node_embedding.index == ndf_infered.index), 'Edge Emb Indexes do not match'
-        assert all(self.g3._node_target.index == ndf_infered.index), 'Edge Transformed Emb Indexes do not match'
+        assert all(
+            self.g3._node_features.index == ndf_infered.index
+        ), "Edge Feature Indexes do not match"
+        assert all(
+            self.g3._node_embedding.index == ndf_infered.index
+        ), "Edge Emb Indexes do not match"
+        assert all(
+            self.g3._node_target.index == ndf_infered.index
+        ), "Edge Transformed Emb Indexes do not match"
         # assert all(self.g3e._edges.index == edf_infered.index), 'Edge Transformed features Indexes do not match'
         # assert all(self.g3e._edges.index == edf_infered.index), 'Edge Transformed target Indexes do not match'
-        
+
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_edges_index_match_in_infered_graph(self):
-        # edges  
+        # edges
         edf_infered = self.g3e._edges
-        assert all(edf_infered.index == self.EMBe.index), 'Edge Emb Indexes do not match'
-        assert all(edf_infered.index == self.embe.index), 'Edge Transformed Emb Indexes do not match'
-        assert all(edf_infered.index == self.Xe.index), 'Edge Transformed features Indexes do not match'
-        assert all(edf_infered.index == self.ye.index), 'Edge Transformed target Indexes do not match'
-        
-        assert all(self.g3e._edge_features.index == edf_infered.index), 'Edge Feature Indexes do not match'
-        assert all(self.g3e._edge_embedding.index == edf_infered.index), 'Edge Emb Indexes do not match'
-        assert all(self.g3e._edge_target.index == edf_infered.index), 'Edge Transformed Emb Indexes do not match'
+        assert all(
+            edf_infered.index == self.EMBe.index
+        ), "Edge Emb Indexes do not match"
+        assert all(
+            edf_infered.index == self.embe.index
+        ), "Edge Transformed Emb Indexes do not match"
+        assert all(
+            edf_infered.index == self.Xe.index
+        ), "Edge Transformed features Indexes do not match"
+        assert all(
+            edf_infered.index == self.ye.index
+        ), "Edge Transformed target Indexes do not match"
+
+        assert all(
+            self.g3e._edge_features.index == edf_infered.index
+        ), "Edge Feature Indexes do not match"
+        assert all(
+            self.g3e._edge_embedding.index == edf_infered.index
+        ), "Edge Emb Indexes do not match"
+        assert all(
+            self.g3e._edge_target.index == edf_infered.index
+        ), "Edge Transformed Emb Indexes do not match"
         # assert all(self.g3e._edges.index == edf_infered.index), 'Edge Transformed features Indexes do not match'
         # assert all(self.g3e._edges.index == edf_infered.index), 'Edge Transformed target Indexes do not match'
-    
-    
+
+    @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
+    def test_umap_kwargs(self):
+        umap_kwargs = dict(
+            {
+                "n_components": n_components,
+                **({"metric": metric} if engine_resolved == UMAP_LEARN else {}),
+                "n_neighbors": n_neighbors,
+                "min_dist": min_dist,
+                "spread": spread,
+                "local_connectivity": local_connectivity,
+                "repulsion_strength": repulsion_strength,
+                "negative_sample_rate": negative_sample_rate,
+            }
+        )
+
+    @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_transform_umap(self):
         np.random.seed(41)
-        
+
         train = ndf_reddit.sample(frac=0.8, random_state=42)
         test = ndf_reddit.drop(train.index)
-        
+
         # just process train
         g = graphistry.nodes(train)
         g2 = g.umap()
         g3 = g2.transform_umap(train)
-        assert 2 * g2._node_embedding.shape[0] == g3._node_embedding.shape[0], 'Node Embedding Lengths do not match, found {} and {}'.format(g2._node_embedding.shape[0], g3._node_embedding.shape[0])
+        assert (
+            2 * g2._node_embedding.shape[0] == g3._node_embedding.shape[0]
+        ), "Node Embedding Lengths do not match, found {} and {}".format(
+            g2._node_embedding.shape[0], g3._node_embedding.shape[0]
+        )
         # now feed it args
-        eps=['auto', 10]
-        sample=[None, 2] 
-        return_graph=[True, False]
+        eps = ["auto", 10]
+        sample = [None, 2]
+        return_graph = [True, False]
         fit_umap_embedding = [True, False]
         for ep in eps:
             g4 = g2.transform_umap(test, eps=ep)
@@ -229,13 +325,18 @@ def _check_attributes(self, g, attributes):
         for attribute in attributes:
             self.assertTrue(hasattr(g, attribute), msg.format(attribute))
             self.assertTrue(getattr(g, attribute) is not None, msg2.format(attribute))
-            if 'df' in attribute:
-                self.assertIsInstance(getattr(g, attribute), pd.DataFrame, msg.format(attribute))
-            if 'node_' in attribute:
-                self.assertIsInstance(getattr(g, attribute), pd.DataFrame, msg.format(attribute))
-            if 'edge_' in attribute:
-                self.assertIsInstance(getattr(g, attribute), pd.DataFrame, msg.format(attribute))
-
+            if "df" in attribute:
+                self.assertIsInstance(
+                    getattr(g, attribute), pd.DataFrame, msg.format(attribute)
+                )
+            if "node_" in attribute:
+                self.assertIsInstance(
+                    getattr(g, attribute), pd.DataFrame, msg.format(attribute)
+                )
+            if "edge_" in attribute:
+                self.assertIsInstance(
+                    getattr(g, attribute), pd.DataFrame, msg.format(attribute)
+                )
 
     def cases_check_node_attributes(self, g):
         attributes = [
@@ -298,7 +399,7 @@ def _test_umap(self, g, use_cols, targets, name, kind, df):
                         model_name=model_avg_name,
                         feature_engine=feature_engine,
                         n_neighbors=2,
-                        dbscan=False
+                        dbscan=False,
                     )
 
                     self.cases_test_graph(g2, kind=kind, df=df)
@@ -331,7 +432,9 @@ def test_edge_umap(self):
             df=triangleEdges,
         )
 
-    @pytest.mark.skipif(not has_dependancy or not has_umap, reason="requires umap feature dependencies")
+    @pytest.mark.skipif(
+        not has_dependancy or not has_umap, reason="requires umap feature dependencies"
+    )
     def test_filter_edges(self):
         for kind, g in [("nodes", graphistry.nodes(triangleNodes))]:
             g2 = g.umap(kind=kind, feature_engine="none")
@@ -344,7 +447,9 @@ def test_filter_edges(self):
                     f"{kind} -- scale: {scale}: resulting edges dataframe shape: {shape}"
                 )
                 logger.debug("-" * 80)
-                self.assertGreaterEqual(shape[0], last_shape)  # should return more and more edges
+                self.assertGreaterEqual(
+                    shape[0], last_shape
+                )  # should return more and more edges
                 last_shape = shape[0]
 
 
@@ -356,28 +461,36 @@ class TestUMAPAIMethods(TestUMAPMethods):
     def _test_umap(self, g, use_cols, targets, name, kind, df):
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
-            for scaler in ['kbins', 'robust']:
+            for scaler in ["kbins", "robust"]:
                 for cardinality in [2, 200]:
                     for use_ngram in [True, False]:
                         for use_col in use_cols:
                             for target in targets:
                                 logger.debug("*" * 90)
-                                value = [scaler, cardinality, use_ngram, target, use_col]
+                                value = [
+                                    scaler,
+                                    cardinality,
+                                    use_ngram,
+                                    target,
+                                    use_col,
+                                ]
                                 logger.debug(f"{value}")
                                 logger.debug("-" * 80)
-                            
-                                g2 = g.umap(kind=kind,
+
+                                g2 = g.umap(
+                                    kind=kind,
                                     X=use_col,
                                     y=target,
                                     model_name=model_avg_name,
                                     use_scaler=scaler,
                                     use_scaler_target=scaler,
                                     use_ngrams=use_ngram,
-                                    engine='umap_learn',
+                                    engine="umap_learn",
                                     cardinality_threshold=cardinality,
                                     cardinality_threshold_target=cardinality,
-                                    n_neighbors=3, 
-                                    dbscan=False)
+                                    n_neighbors=3,
+                                    dbscan=False,
+                                )
 
                                 self.cases_test_graph(g2, kind=kind, df=df)
 
@@ -410,8 +523,8 @@ def test_node_umap(self):
     )
     def test_edge_umap(self):
         g = graphistry.edges(edge_df2, "src", "dst")
-        targets = [None, 'label']
-        use_cols = [None, 'title']
+        targets = [None, "label"]
+        use_cols = [None, "title"]
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
@@ -434,19 +547,22 @@ def test_chaining_nodes(self):
         g = graphistry.nodes(ndf_reddit)
         g2 = g.umap(dbscan=False)
 
-        logger.debug('======= g.umap() done ======')
+        logger.debug("======= g.umap() done ======")
         g3a = g2.featurize()
-        logger.debug('======= g3a.featurize() done ======')
+        logger.debug("======= g3a.featurize() done ======")
         g3 = g3a.umap(dbscan=False)
-        logger.debug('======= g3.umap() done ======')
+        logger.debug("======= g3.umap() done ======")
         assert g2._node_features.shape == g3._node_features.shape
         # since g3 has feature params with x and y.
-        g3._feature_params['nodes']['X'].pop('x')
-        g3._feature_params['nodes']['X'].pop('y')
-        assert all(g2._feature_params['nodes']['X'] == g3._feature_params['nodes']['X'])
-        assert g2._feature_params['nodes']['y'].shape == g3._feature_params['nodes']['y'].shape  # None
+        g3._feature_params["nodes"]["X"].pop("x")
+        g3._feature_params["nodes"]["X"].pop("y")
+        assert all(g2._feature_params["nodes"]["X"] == g3._feature_params["nodes"]["X"])
+        assert (
+            g2._feature_params["nodes"]["y"].shape
+            == g3._feature_params["nodes"]["y"].shape
+        )  # None
         assert g2._node_embedding.shape == g3._node_embedding.shape  # kinda weak sauce
-        
+
     @pytest.mark.skipif(
         not has_dependancy or not has_umap,
         reason="requires ai+umap feature dependencies",
@@ -457,11 +573,13 @@ def test_chaining_edges(self):
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=FutureWarning)
-            g2 = g.umap(kind='edges', dbscan=False)
-            g3 = g.featurize(kind='edges').umap(kind='edges', dbscan=False)
-            
-        assert all(g2._feature_params['edges']['X'] == g3._feature_params['edges']['X'])
-        assert all(g2._feature_params['edges']['y'] == g3._feature_params['edges']['y'])  # None
+            g2 = g.umap(kind="edges", dbscan=False)
+            g3 = g.featurize(kind="edges").umap(kind="edges", dbscan=False)
+
+        assert all(g2._feature_params["edges"]["X"] == g3._feature_params["edges"]["X"])
+        assert all(
+            g2._feature_params["edges"]["y"] == g3._feature_params["edges"]["y"]
+        )  # None
         assert all(g2._edge_features == g3._edge_features)
 
     @pytest.mark.skipif(
@@ -471,19 +589,32 @@ def test_chaining_edges(self):
     def test_feature_kwargs_yield_different_values_using_umap_api(self):
         g = graphistry.nodes(ndf_reddit)
         n_topics_target = 6
-    
+
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=FutureWarning)
 
-            g2 = g.umap(X="type", y="label", cardinality_threshold_target=3, n_topics_target=n_topics_target)  # makes a GapEncoded Target
-            g3 = g.umap(X="type", y="label", cardinality_threshold_target=30000)  # makes a one-hot-encoded target
-            
-        assert all(g2._feature_params['nodes']['X'] == g3._feature_params['nodes']['X']), "features should be the same"
-        assert all(g2._feature_params['nodes']['y'] != g3._feature_params['nodes']['y']), "targets in memoize should be different"  # None
-        assert g2._node_target.shape[1] != g3._node_target.shape[1], 'Targets should be different'
-        assert g2._node_target.shape[1] == n_topics_target, 'Targets '
+            g2 = g.umap(
+                X="type",
+                y="label",
+                cardinality_threshold_target=3,
+                n_topics_target=n_topics_target,
+            )  # makes a GapEncoded Target
+            g3 = g.umap(
+                X="type", y="label", cardinality_threshold_target=30000
+            )  # makes a one-hot-encoded target
+
+        assert all(
+            g2._feature_params["nodes"]["X"] == g3._feature_params["nodes"]["X"]
+        ), "features should be the same"
+        assert all(
+            g2._feature_params["nodes"]["y"] != g3._feature_params["nodes"]["y"]
+        ), "targets in memoize should be different"  # None
+        assert (
+            g2._node_target.shape[1] != g3._node_target.shape[1]
+        ), "Targets should be different"
+        assert g2._node_target.shape[1] == n_topics_target, "Targets "
 
     @pytest.mark.skipif(
         not has_dependancy or not has_umap,
@@ -504,8 +635,7 @@ def test_filter_edges(self):
                 self.assertGreaterEqual(shape[0], last_shape)
                 last_shape = shape[0]
 
-            
-        
+
 @pytest.mark.skipif(
     not has_dependancy or not has_cuml,
     reason="requires cuml feature dependencies",
@@ -518,27 +648,35 @@ class TestCUMLMethods(TestUMAPMethods):
     def _test_umap(self, g, use_cols, targets, name, kind, df):
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
-            for scaler in ['kbins', 'robust']:
+            for scaler in ["kbins", "robust"]:
                 for cardinality in [2, 200]:
                     for use_ngram in [True, False]:
                         for use_col in use_cols:
                             for target in targets:
                                 logger.debug("*" * 90)
-                                value = [scaler, cardinality, use_ngram, target, use_col]
+                                value = [
+                                    scaler,
+                                    cardinality,
+                                    use_ngram,
+                                    target,
+                                    use_col,
+                                ]
                                 logger.debug(f"{value}")
                                 logger.debug("-" * 80)
-                            
-                                g2 = g.umap(kind=kind,
+
+                                g2 = g.umap(
+                                    kind=kind,
                                     X=use_col,
                                     y=target,
                                     model_name=model_avg_name,
                                     use_scaler=scaler,
                                     use_scaler_target=scaler,
                                     use_ngrams=use_ngram,
-                                    engine='cuml',
+                                    engine="cuml",
                                     cardinality_threshold=cardinality,
                                     cardinality_threshold_target=cardinality,
-                                    n_neighbors=3)
+                                    n_neighbors=3,
+                                )
 
                                 self.cases_test_graph(g2, kind=kind, df=df)
 
@@ -571,8 +709,8 @@ def test_node_umap(self):
     )
     def test_edge_umap(self):
         g = graphistry.edges(edge_df2, "src", "dst")
-        targets = [None, 'label']
-        use_cols = [None, 'title']
+        targets = [None, "label"]
+        use_cols = [None, "title"]
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
@@ -595,19 +733,22 @@ def test_chaining_nodes(self):
         g = graphistry.nodes(ndf_reddit)
         g2 = g.umap()
 
-        logger.debug('======= g.umap() done ======')
+        logger.debug("======= g.umap() done ======")
         g3a = g2.featurize()
-        logger.debug('======= g3a.featurize() done ======')
+        logger.debug("======= g3a.featurize() done ======")
         g3 = g3a.umap()
-        logger.debug('======= g3.umap() done ======')
+        logger.debug("======= g3.umap() done ======")
         assert g2._node_features.shape == g3._node_features.shape
         # since g3 has feature params with x and y.
-        g3._feature_params['nodes']['X'].pop('x')
-        g3._feature_params['nodes']['X'].pop('y')
-        assert all(g2._feature_params['nodes']['X'] == g3._feature_params['nodes']['X'])
-        assert g2._feature_params['nodes']['y'].shape == g3._feature_params['nodes']['y'].shape  # None
+        g3._feature_params["nodes"]["X"].pop("x")
+        g3._feature_params["nodes"]["X"].pop("y")
+        assert all(g2._feature_params["nodes"]["X"] == g3._feature_params["nodes"]["X"])
+        assert (
+            g2._feature_params["nodes"]["y"].shape
+            == g3._feature_params["nodes"]["y"].shape
+        )  # None
         assert g2._node_embedding.shape == g3._node_embedding.shape  # kinda weak sauce
-        
+
     @pytest.mark.skipif(
         not has_dependancy or not has_cuml,
         reason="requires cuml feature dependencies",
@@ -618,11 +759,13 @@ def test_chaining_edges(self):
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=FutureWarning)
-            g2 = g.umap(kind='edges')
-            g3 = g.featurize(kind='edges').umap(kind='edges')
-            
-        assert all(g2._feature_params['edges']['X'] == g3._feature_params['edges']['X'])
-        assert all(g2._feature_params['edges']['y'] == g3._feature_params['edges']['y'])  # None
+            g2 = g.umap(kind="edges")
+            g3 = g.featurize(kind="edges").umap(kind="edges")
+
+        assert all(g2._feature_params["edges"]["X"] == g3._feature_params["edges"]["X"])
+        assert all(
+            g2._feature_params["edges"]["y"] == g3._feature_params["edges"]["y"]
+        )  # None
         assert all(g2._edge_features == g3._edge_features)
 
     @pytest.mark.skipif(
@@ -632,19 +775,32 @@ def test_chaining_edges(self):
     def test_feature_kwargs_yield_different_values_using_umap_api(self):
         g = graphistry.nodes(ndf_reddit)
         n_topics_target = 6
-    
+
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=FutureWarning)
 
-            g2 = g.umap(X="type", y="label", cardinality_threshold_target=3, n_topics_target=n_topics_target)  # makes a GapEncoded Target
-            g3 = g.umap(X="type", y="label", cardinality_threshold_target=30000)  # makes a one-hot-encoded target
-            
-        assert all(g2._feature_params['nodes']['X'] == g3._feature_params['nodes']['X']), "features should be the same"
-        assert all(g2._feature_params['nodes']['y'] != g3._feature_params['nodes']['y']), "targets in memoize should be different"  # None
-        assert g2._node_target.shape[1] != g3._node_target.shape[1], 'Targets should be different'
-        assert g2._node_target.shape[1] == n_topics_target, 'Targets '
+            g2 = g.umap(
+                X="type",
+                y="label",
+                cardinality_threshold_target=3,
+                n_topics_target=n_topics_target,
+            )  # makes a GapEncoded Target
+            g3 = g.umap(
+                X="type", y="label", cardinality_threshold_target=30000
+            )  # makes a one-hot-encoded target
+
+        assert all(
+            g2._feature_params["nodes"]["X"] == g3._feature_params["nodes"]["X"]
+        ), "features should be the same"
+        assert all(
+            g2._feature_params["nodes"]["y"] != g3._feature_params["nodes"]["y"]
+        ), "targets in memoize should be different"  # None
+        assert (
+            g2._node_target.shape[1] != g3._node_target.shape[1]
+        ), "Targets should be different"
+        assert g2._node_target.shape[1] == n_topics_target, "Targets "
 
     @pytest.mark.skipif(
         not has_dependancy or not has_umap,
diff --git a/graphistry/util.py b/graphistry/util.py
index 4f230d68d7..51115246bb 100644
--- a/graphistry/util.py
+++ b/graphistry/util.py
@@ -17,22 +17,24 @@
 
 # #####################################
 
+
 def global_logger():
     logger = logging.getLogger()
     return logger
 
+
 def setup_logger(name, verbose=VERBOSE, fullpath=TRACE):
-    #if fullpath:
+    # if fullpath:
     #    FORMAT = "[%(filename)s:%(lineno)s - %(funcName)20s() ]\n   %(message)s\n"
-    #else:
+    # else:
     #    FORMAT = " %(message)s\n"
-    #logging.basicConfig(format=FORMAT)
-    #logger = logging.getLogger()#f'graphistry.{name}')
-    #if verbose is None:
+    # logging.basicConfig(format=FORMAT)
+    # logger = logging.getLogger()#f'graphistry.{name}')
+    # if verbose is None:
     #    logger.setLevel(logging.ERROR)
-    #else:
+    # else:
     #    logger.setLevel(logging.INFO if verbose else logging.DEBUG)
-    #return logger
+    # return logger
     return global_logger()
 
 
@@ -40,6 +42,8 @@ def setup_logger(name, verbose=VERBOSE, fullpath=TRACE):
 # Caching utils
 
 _cache_coercion_val = None
+
+
 @lru_cache(maxsize=CACHE_COERCION_SIZE)
 def cache_coercion_helper(k):
     return _cache_coercion_val
@@ -47,8 +51,8 @@ def cache_coercion_helper(k):
 
 def cache_coercion(k, v):
     """
-        Holds references to last 100 used coercions
-        Use with weak key/value dictionaries for actual lookups
+    Holds references to last 100 used coercions
+    Use with weak key/value dictionaries for actual lookups
     """
     global _cache_coercion_val
     _cache_coercion_val = v
@@ -66,35 +70,37 @@ def __init__(self, v):
 def hash_pdf(df: pd.DataFrame) -> str:
     # can be 20% faster via to_parquet (see lmeyerov issue in pandas gh), but unclear if always available
     return (
-        hashlib.sha256(putil.hash_pandas_object(df, index=True).to_numpy().tobytes()).hexdigest()
-        + hashlib.sha256(str(df.columns).encode('utf-8')).hexdigest()  # noqa: W503
+        hashlib.sha256(
+            putil.hash_pandas_object(df, index=True).to_numpy().tobytes()
+        ).hexdigest()
+        + hashlib.sha256(str(df.columns).encode("utf-8")).hexdigest()  # noqa: W503
     )
 
 
 def hash_memoize_helper(v: Any) -> str:
 
     if isinstance(v, dict):
-        rolling = '{'
+        rolling = "{"
         for k2, v2 in v.items():
-            rolling += f'{k2}:{hash_memoize_helper(v2)},'
-        rolling += '}'
+            rolling += f"{k2}:{hash_memoize_helper(v2)},"
+        rolling += "}"
     elif isinstance(v, ModelDict):
-        rolling = '{'
+        rolling = "{"
         for k2, v2 in v.items():
-            rolling += f'{k2}:{hash_memoize_helper(v2)},'
-        rolling += '}'
+            rolling += f"{k2}:{hash_memoize_helper(v2)},"
+        rolling += "}"
     elif isinstance(v, list):
-        rolling = '['
+        rolling = "["
         for i in v:
-            rolling += f'{hash_memoize_helper(i)},'
-        rolling += ']'
+            rolling += f"{hash_memoize_helper(i)},"
+        rolling += "]"
     elif isinstance(v, tuple):
-        rolling = '('
+        rolling = "("
         for i in v:
-            rolling += f'{hash_memoize_helper(i)},'
-        rolling += ')'
+            rolling += f"{hash_memoize_helper(i)},"
+        rolling += ")"
     elif isinstance(v, bool):
-        rolling = 'T' if v else 'F'
+        rolling = "T" if v else "F"
     elif isinstance(v, int):
         rolling = str(v)
     elif isinstance(v, float):
@@ -102,49 +108,54 @@ def hash_memoize_helper(v: Any) -> str:
     elif isinstance(v, str):
         rolling = v
     elif v is None:
-        rolling = 'N'
+        rolling = "N"
     elif isinstance(v, pd.DataFrame):
         rolling = hash_pdf(v)
     else:
-        raise TypeError(f'Unsupported memoization type: {type(v)}')
+        raise TypeError(f"Unsupported memoization type: {type(v)}")
 
     return rolling
 
+
 def hash_memoize(v: Any) -> str:
-    return hashlib.sha256(hash_memoize_helper(v).encode('utf-8')).hexdigest()
+    return hashlib.sha256(hash_memoize_helper(v).encode("utf-8")).hexdigest()
+
 
-def check_set_memoize(g, metadata, attribute, name: str = '', memoize: bool = True):  # noqa: C901
+def check_set_memoize(
+    g, metadata, attribute, name: str = "", memoize: bool = True
+):  # noqa: C901
     """
-        Helper Memoize function that checks if metadata args have changed for object g -- which is unconstrained save
-        for the fact that it must have `attribute`. If they have not changed, will return memoized version,
-        if False, will continue with whatever pipeline it is in front.
+    Helper Memoize function that checks if metadata args have changed for object g -- which is unconstrained save
+    for the fact that it must have `attribute`. If they have not changed, will return memoized version,
+    if False, will continue with whatever pipeline it is in front.
     """
-    
-    logger = setup_logger(f'{__name__}.memoization')
+
+    logger = setup_logger(f"{__name__}.memoization")
 
     if not memoize:
-        logger.debug('Memoization disabled')
+        logger.debug("Memoization disabled")
         return False
-    
+
     hashed = None
     weakref = getattr(g, attribute)
     try:
         hashed = hash_memoize(dict(data=metadata))
     except TypeError:
         logger.warning(
-            f'! Failed {name} speedup attempt. Continuing without memoization speedups.'
+            f"! Failed {name} speedup attempt. Continuing without memoization speedups."
         )
     try:
         if hashed in weakref:
-            logger.debug(f'{name} memoization hit: %s', hashed)
+            logger.debug(f"{name} memoization hit: %s", hashed)
             return weakref[hashed].v
         else:
-            logger.debug(f'{name} memoization miss for id (of %s): %s',
-                         len(weakref), hashed)
+            logger.debug(
+                f"{name} memoization miss for id (of %s): %s", len(weakref), hashed
+            )
     except:
-        logger.debug(f'Failed to hash {name} kwargs', exc_info=True)
+        logger.debug(f"Failed to hash {name} kwargs", exc_info=True)
         pass
-    
+
     if memoize and (hashed is not None):
         w = WeakValueWrapper(g)
         cache_coercion(hashed, w)
@@ -291,8 +302,10 @@ def deprecated_func(*args, **kwargs):
 # MODEL Parameter HELPERS
 def get_timestamp():
     import datetime
+
     return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
 
+
 class ModelDict(UserDict):
     """Helper class to print out model names and keep track of updates
 
@@ -305,11 +318,15 @@ def __init__(self, message, verbose=True, timestamp=False, *args, **kwargs):
         self._message = message
         self._verbose = verbose
         self._timestamp = timestamp
-        L = len(message) if timestamp is False else max(len(message), len(get_timestamp())+1)
+        L = (
+            len(message)
+            if timestamp is False
+            else max(len(message), len(get_timestamp()) + 1)
+        )
         self._print_length = min(80, L)
         self._updates = []
         super().__init__(*args, **kwargs)
-        
+
     def print(self, message):
         if self._timestamp:
             message = f"{message}\n{get_timestamp()}"
@@ -321,7 +338,7 @@ def print(self, message):
             print()
 
     def __repr__(self):
-        #logger.info(self._message)
+        # logger.info(self._message)
         self.print(self._message)
         return super().__repr__()
 

From e77601dc35edcbfea2ac9f0ca1c5798481787b9b Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 18:52:53 -0800
Subject: [PATCH 050/432] adds test for umap args

---
 graphistry/tests/test_umap_utils.py | 27 +++++++++++++++++++--------
 1 file changed, 19 insertions(+), 8 deletions(-)

diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index c4f77dfaa7..2a328430bc 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -261,16 +261,27 @@ def test_edges_index_match_in_infered_graph(self):
     def test_umap_kwargs(self):
         umap_kwargs = dict(
             {
-                "n_components": n_components,
-                **({"metric": metric} if engine_resolved == UMAP_LEARN else {}),
-                "n_neighbors": n_neighbors,
-                "min_dist": min_dist,
-                "spread": spread,
-                "local_connectivity": local_connectivity,
-                "repulsion_strength": repulsion_strength,
-                "negative_sample_rate": negative_sample_rate,
+                "n_components": 2,
+                "metric": 'euclidean',
+                "n_neighbors": 3,
+                "min_dist": 1,
+                "spread": 1,
+                "local_connectivity": 1,
+                "repulsion_strength": 1,
+                "negative_sample_rate": 5,
             }
         )
+        umap_kwargs2 = {k:v+1 for k, v in umap_kwargs.items()}
+        g = graphistry.nodes(ndf_reddit)
+        g2 = g.umap(**umap_kwargs)
+        g3 = g.umap(**umap_kwargs2)
+        assert g2._umap_params == umap_kwargs, f"Umap params do not match, found {g2._umap_params} vs {umap_kwargs} "
+        assert g3._umap_params == umap_kwargs2, f"Umap params do not match, found {g3._umap_params} vs {umap_kwargs2} "
+        g4 = g2.transform_umap(ndf_reddit)
+        assert g4._umap_params == umap_kwargs, f"Umap params do not match, found {g4._umap_params} vs {umap_kwargs} "
+        g5 = g3.transform_umap(ndf_reddit)
+        assert g5._umap_params == umap_kwargs2, f"Umap params do not match, found {g5._umap_params} vs {umap_kwargs2} "
+        
 
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_transform_umap(self):

From 92e9edcf54e2d067701d2632204118a25a85327c Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 18:58:12 -0800
Subject: [PATCH 051/432] lint

---
 graphistry/tests/test_umap_utils.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index 2a328430bc..ad382aef0c 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -271,6 +271,7 @@ def test_umap_kwargs(self):
                 "negative_sample_rate": 5,
             }
         )
+        
         umap_kwargs2 = {k:v+1 for k, v in umap_kwargs.items()}
         g = graphistry.nodes(ndf_reddit)
         g2 = g.umap(**umap_kwargs)
@@ -569,8 +570,7 @@ def test_chaining_nodes(self):
         g3._feature_params["nodes"]["X"].pop("y")
         assert all(g2._feature_params["nodes"]["X"] == g3._feature_params["nodes"]["X"])
         assert (
-            g2._feature_params["nodes"]["y"].shape
-            == g3._feature_params["nodes"]["y"].shape
+            g2._feature_params["nodes"]["y"].shape == g3._feature_params["nodes"]["y"].shape
         )  # None
         assert g2._node_embedding.shape == g3._node_embedding.shape  # kinda weak sauce
 
@@ -755,8 +755,7 @@ def test_chaining_nodes(self):
         g3._feature_params["nodes"]["X"].pop("y")
         assert all(g2._feature_params["nodes"]["X"] == g3._feature_params["nodes"]["X"])
         assert (
-            g2._feature_params["nodes"]["y"].shape
-            == g3._feature_params["nodes"]["y"].shape
+            g2._feature_params["nodes"]["y"].shape == g3._feature_params["nodes"]["y"].shape
         )  # None
         assert g2._node_embedding.shape == g3._node_embedding.shape  # kinda weak sauce
 

From 01dcb363ec9463c7076a8b6aa8f752c84c1ef929 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 19:03:51 -0800
Subject: [PATCH 052/432] lint

---
 graphistry/tests/test_umap_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index ad382aef0c..197a484071 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -272,7 +272,7 @@ def test_umap_kwargs(self):
             }
         )
         
-        umap_kwargs2 = {k:v+1 for k, v in umap_kwargs.items()}
+        umap_kwargs2 = {k: v+1 for k, v in umap_kwargs.items()}
         g = graphistry.nodes(ndf_reddit)
         g2 = g.umap(**umap_kwargs)
         g3 = g.umap(**umap_kwargs2)

From 4f478030c73ec6ffb97d3223e4aa41c40bbbed17 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 19:08:19 -0800
Subject: [PATCH 053/432] lint

---
 graphistry/tests/test_umap_utils.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index 197a484071..c1120e5a30 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -272,7 +272,8 @@ def test_umap_kwargs(self):
             }
         )
         
-        umap_kwargs2 = {k: v+1 for k, v in umap_kwargs.items()}
+        
+        umap_kwargs2 = { k : v+1 for k, v in umap_kwargs.items()}
         g = graphistry.nodes(ndf_reddit)
         g2 = g.umap(**umap_kwargs)
         g3 = g.umap(**umap_kwargs2)

From 83cb8fefc0004872ac1fc9c2399bf796f81e5492 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 21:42:25 -0800
Subject: [PATCH 054/432] lint

---
 graphistry/tests/test_umap_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index c1120e5a30..b99bac6a72 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -273,7 +273,7 @@ def test_umap_kwargs(self):
         )
         
         
-        umap_kwargs2 = { k : v+1 for k, v in umap_kwargs.items()}
+        umap_kwargs2  =  { k : v+1 for k , v in umap_kwargs.items() }
         g = graphistry.nodes(ndf_reddit)
         g2 = g.umap(**umap_kwargs)
         g3 = g.umap(**umap_kwargs2)

From 1efa4604febada443089ddf6e86f5ac6ea28480e Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 21:44:00 -0800
Subject: [PATCH 055/432] lint

---
 graphistry/tests/test_umap_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index b99bac6a72..485fce8536 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -273,7 +273,7 @@ def test_umap_kwargs(self):
         )
         
         
-        umap_kwargs2  =  { k : v+1 for k , v in umap_kwargs.items() }
+        umap_kwargs2 = {k: v+1 for k, v in umap_kwargs.items()}  # type: ignore
         g = graphistry.nodes(ndf_reddit)
         g2 = g.umap(**umap_kwargs)
         g3 = g.umap(**umap_kwargs2)

From 7d90f74268e49627f09c3a2bd5a67deb20970799 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 21:46:17 -0800
Subject: [PATCH 056/432] lint

---
 graphistry/tests/test_umap_utils.py | 50 ++++++++++++++++-------------
 1 file changed, 28 insertions(+), 22 deletions(-)

diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index 485fce8536..9ed5c9651b 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -259,31 +259,35 @@ def test_edges_index_match_in_infered_graph(self):
 
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_umap_kwargs(self):
-        umap_kwargs = dict(
-            {
-                "n_components": 2,
-                "metric": 'euclidean',
-                "n_neighbors": 3,
-                "min_dist": 1,
-                "spread": 1,
-                "local_connectivity": 1,
-                "repulsion_strength": 1,
-                "negative_sample_rate": 5,
-            }
-        )
-        
-        
-        umap_kwargs2 = {k: v+1 for k, v in umap_kwargs.items()}  # type: ignore
+        umap_kwargs = {
+            "n_components": 2,
+            "metric": "euclidean",
+            "n_neighbors": 3,
+            "min_dist": 1,
+            "spread": 1,
+            "local_connectivity": 1,
+            "repulsion_strength": 1,
+            "negative_sample_rate": 5,
+        }
+
+        umap_kwargs2 = {k: v + 1 for k, v in umap_kwargs.items()}  # type: ignore
         g = graphistry.nodes(ndf_reddit)
         g2 = g.umap(**umap_kwargs)
         g3 = g.umap(**umap_kwargs2)
-        assert g2._umap_params == umap_kwargs, f"Umap params do not match, found {g2._umap_params} vs {umap_kwargs} "
-        assert g3._umap_params == umap_kwargs2, f"Umap params do not match, found {g3._umap_params} vs {umap_kwargs2} "
+        assert (
+            g2._umap_params == umap_kwargs
+        ), f"Umap params do not match, found {g2._umap_params} vs {umap_kwargs} "
+        assert (
+            g3._umap_params == umap_kwargs2
+        ), f"Umap params do not match, found {g3._umap_params} vs {umap_kwargs2} "
         g4 = g2.transform_umap(ndf_reddit)
-        assert g4._umap_params == umap_kwargs, f"Umap params do not match, found {g4._umap_params} vs {umap_kwargs} "
+        assert (
+            g4._umap_params == umap_kwargs
+        ), f"Umap params do not match, found {g4._umap_params} vs {umap_kwargs} "
         g5 = g3.transform_umap(ndf_reddit)
-        assert g5._umap_params == umap_kwargs2, f"Umap params do not match, found {g5._umap_params} vs {umap_kwargs2} "
-        
+        assert (
+            g5._umap_params == umap_kwargs2
+        ), f"Umap params do not match, found {g5._umap_params} vs {umap_kwargs2} "
 
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_transform_umap(self):
@@ -571,7 +575,8 @@ def test_chaining_nodes(self):
         g3._feature_params["nodes"]["X"].pop("y")
         assert all(g2._feature_params["nodes"]["X"] == g3._feature_params["nodes"]["X"])
         assert (
-            g2._feature_params["nodes"]["y"].shape == g3._feature_params["nodes"]["y"].shape
+            g2._feature_params["nodes"]["y"].shape
+            == g3._feature_params["nodes"]["y"].shape
         )  # None
         assert g2._node_embedding.shape == g3._node_embedding.shape  # kinda weak sauce
 
@@ -756,7 +761,8 @@ def test_chaining_nodes(self):
         g3._feature_params["nodes"]["X"].pop("y")
         assert all(g2._feature_params["nodes"]["X"] == g3._feature_params["nodes"]["X"])
         assert (
-            g2._feature_params["nodes"]["y"].shape == g3._feature_params["nodes"]["y"].shape
+            g2._feature_params["nodes"]["y"].shape
+            == g3._feature_params["nodes"]["y"].shape
         )  # None
         assert g2._node_embedding.shape == g3._node_embedding.shape  # kinda weak sauce
 

From 4e2570041a0b4081575beadac1fb23c328ca900f Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 21:48:14 -0800
Subject: [PATCH 057/432] lint

---
 graphistry/tests/test_umap_utils.py | 14 ++++++--------
 1 file changed, 6 insertions(+), 8 deletions(-)

diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index 9ed5c9651b..5f218d4889 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -276,18 +276,18 @@ def test_umap_kwargs(self):
         g3 = g.umap(**umap_kwargs2)
         assert (
             g2._umap_params == umap_kwargs
-        ), f"Umap params do not match, found {g2._umap_params} vs {umap_kwargs} "
+        ), f"Umap params do not match, found {g2._umap_params} vs {umap_kwargs}"
         assert (
             g3._umap_params == umap_kwargs2
-        ), f"Umap params do not match, found {g3._umap_params} vs {umap_kwargs2} "
+        ), f"Umap params do not match, found {g3._umap_params} vs {umap_kwargs2}"
         g4 = g2.transform_umap(ndf_reddit)
         assert (
             g4._umap_params == umap_kwargs
-        ), f"Umap params do not match, found {g4._umap_params} vs {umap_kwargs} "
+        ), f"Umap params do not match, found {g4._umap_params} vs {umap_kwargs}"
         g5 = g3.transform_umap(ndf_reddit)
         assert (
             g5._umap_params == umap_kwargs2
-        ), f"Umap params do not match, found {g5._umap_params} vs {umap_kwargs2} "
+        ), f"Umap params do not match, found {g5._umap_params} vs {umap_kwargs2}"
 
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_transform_umap(self):
@@ -575,8 +575,7 @@ def test_chaining_nodes(self):
         g3._feature_params["nodes"]["X"].pop("y")
         assert all(g2._feature_params["nodes"]["X"] == g3._feature_params["nodes"]["X"])
         assert (
-            g2._feature_params["nodes"]["y"].shape
-            == g3._feature_params["nodes"]["y"].shape
+            g2._feature_params["nodes"]["y"].shape == g3._feature_params["nodes"]["y"].shape
         )  # None
         assert g2._node_embedding.shape == g3._node_embedding.shape  # kinda weak sauce
 
@@ -761,8 +760,7 @@ def test_chaining_nodes(self):
         g3._feature_params["nodes"]["X"].pop("y")
         assert all(g2._feature_params["nodes"]["X"] == g3._feature_params["nodes"]["X"])
         assert (
-            g2._feature_params["nodes"]["y"].shape
-            == g3._feature_params["nodes"]["y"].shape
+            g2._feature_params["nodes"]["y"].shape == g3._feature_params["nodes"]["y"].shape
         )  # None
         assert g2._node_embedding.shape == g3._node_embedding.shape  # kinda weak sauce
 

From c756803f73f2a0fe7cee18851f558b347c55c0a1 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 21:57:11 -0800
Subject: [PATCH 058/432] lint

---
 graphistry/embed_utils.py   |  2 +-
 graphistry/feature_utils.py |  2 +-
 graphistry/umap_utils.py    | 14 +++++++-------
 3 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/graphistry/embed_utils.py b/graphistry/embed_utils.py
index ef09a02475..f15d4be1ce 100644
--- a/graphistry/embed_utils.py
+++ b/graphistry/embed_utils.py
@@ -89,7 +89,7 @@ def __init__(self):
         self._device = "cpu"
 
     def _preprocess_embedding_data(self, res, train_split:Union[float, int] = 0.8) -> Plottable:
-        _, torch, _, _, _, _, _, _ = lazy_embed_import_dep()
+        #_, torch, _, _, _, _, _, _ = lazy_embed_import_dep()
         import torch
         log('Preprocessing embedding data')
         src, dst = res._source, res._destination
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 5683f96064..411c369f24 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2657,7 +2657,7 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
         )
 
     
-    def get_features_by_cols(self, columns: Union[List, str] = None, kind: str = 'nodes', target: bool = False):
+    def get_features_by_cols(self, columns: Union[List, str, None] = None, kind: str = 'nodes', target: bool = False):
         """Returns feature matrix with only the columns that contain the string `column_part` in their name.
         
             `X = g.get_features_by_cols(['feature1', 'feature2'])`
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 8a596be47c..46cfacfb17 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -197,7 +197,7 @@ def umap_lazy_init(
             umap_kwargs = dict(
                 {
                     "n_components": n_components,
-                    **({"metric": metric} if engine_resolved == UMAP_LEARN else {}),
+                    **({"metric": metric} if engine_resolved == UMAP_LEARN else {}),  # noqa
                     "n_neighbors": n_neighbors,
                     "min_dist": min_dist,
                     "spread": spread,
@@ -258,10 +258,10 @@ def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         logger.info("-" * 90)
         logger.info(f"Starting UMAP-ing data of shape {X.shape}")
 
-        if self.engine == CUML and is_legacy_cuml():
+        if self.engine == CUML and is_legacy_cuml():  # noqa
             from cuml.neighbors import NearestNeighbors
 
-            knn = NearestNeighbors(n_neighbors=self._n_neighbors)
+            knn = NearestNeighbors(n_neighbors=self._n_neighbors)  # noqa
             cc = self._umap.fit(X, y, knn_graph=knn)
             knn.fit(cc.embedding_)
             self._umap.graph_ = knn.kneighbors_graph(cc.embedding_)
@@ -272,7 +272,7 @@ def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
             self._weighted_adjacency = self._umap.graph_
         # if changing, also update fresh_res
         self._weighted_edges_df = umap_graph_to_weighted_edges(
-            self._umap.graph_, self.engine, is_legacy_cuml()
+            self._umap.graph_, self.engine, is_legacy_cuml()  # noqa
         )
 
         mins = (time() - t) / 60
@@ -289,7 +289,7 @@ def _umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = No
         return emb
 
     def transform_umap(  # noqa: E303
-        self, df: pd.DataFrame, ydf: pd.DataFrame = None, kind: str = "nodes", 
+        self, df: pd.DataFrame, ydf: Union[pd.DataFrame, None] = None, kind: str = "nodes", 
         eps='auto', 
         sample=None, 
         return_graph=True, 
@@ -498,7 +498,7 @@ def umap(
         else:
             res = self.bind()
 
-        res = res.umap_lazy_init(res, **umap_kwargs)
+        res = res.umap_lazy_init(res, **umap_kwargs)  # type: ignore
 
         logger.debug("umap input X :: %s", X)
         logger.debug("umap input y :: %s", y)
@@ -607,7 +607,7 @@ def umap(
             res, kind, encode_position, encode_weight, play
         )  # noqa: E501
 
-        if res.engine == CUML and is_legacy_cuml():
+        if res.engine == CUML and is_legacy_cuml():  # type: ignore
             res = res.prune_self_edges()
 
         if dbscan:

From 768ed4e7ff325fbfc79af61eea5f513719c23dcb Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 6 Jan 2023 22:02:55 -0800
Subject: [PATCH 059/432] qa

---
 graphistry/compute/cluster.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 272b9caa96..b974f427cf 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -243,7 +243,8 @@ def dbscan(
 
     def _transform_dbscan(
         self, df: pd.DataFrame, ydf=None, kind: str = "nodes"
-    ) -> pd.DataFrame:
+    ) -> Union[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+        
         """
         Transforms a dataframe to one with a new column '_dbscan' containing the DBSCAN cluster labels
             and returns feature[cols] or UMAP embedding
@@ -342,7 +343,7 @@ def transform_dbscan(
         """
         emb, X, y, df = self._transform_dbscan(df, y, kind=kind)
         if return_graph:
-            g = self._infer_edges(
+            g = self._infer_edges(  # type: ignore
                 emb, X, y, df, infer_on_umap_embedding=fit_umap_embedding, eps=eps, sample=sample
             )
             return g

From 749c6db8972b86350b0e6f366321e88b6e1873c4 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 7 Jan 2023 00:14:54 -0800
Subject: [PATCH 060/432] deprecates(zscale -> standard, ydf -> y), simplifies
 scaler, expeeeriments

---
 graphistry/feature_utils.py | 132 ++++++++++++++++++++++++------------
 1 file changed, 88 insertions(+), 44 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 411c369f24..e149e3533a 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -544,7 +544,7 @@ def get_preprocessing_pipeline(
     :param X: np.ndarray
     :param impute: whether to run imputing or not
     :param use_scaler: string in None or
-            ["minmax", "quantile", "zscale", "robust", "kbins"],
+            ["minmax", "quantile", "standard", "robust", "kbins"],
             selects scaling transformer, default None
     :param n_quantiles: if use_scaler = 'quantile',
             sets the quantile bin size.
@@ -573,7 +573,7 @@ def get_preprocessing_pipeline(
     available_preprocessors = [
         "minmax",
         "quantile",
-        "zscale",
+        "standard",
         "robust",
         "kbins",
     ]
@@ -600,7 +600,7 @@ def get_preprocessing_pipeline(
         scaler = QuantileTransformer(
             n_quantiles=n_quantiles, output_distribution=output_distribution
         )
-    elif use_scaler == "zscale":
+    elif use_scaler == "standard":
         scaler = StandardScaler()
     elif use_scaler == "robust":
         scaler = RobustScaler(quantile_range=quantile_range)
@@ -884,7 +884,7 @@ def process_dirty_dataframes(
             threshold, encoder is OneHot, above, it is GapEncoder
     :param n_topics: number of topics for GapEncoder, default 42
     :param use_scaler: None or string in
-            ['minmax', 'zscale', 'robust', 'quantile']
+            ['minmax', 'standard', 'robust', 'quantile']
     :param similarity: one of 'ngram', 'levenshtein-ratio', 'jaro',
             or'jaro-winkler'}) – The type of pairwise string similarity
             to use. If None or False, uses a SuperVectorizer
@@ -1042,7 +1042,7 @@ def process_nodes_dataframes(
     :param df: pandas DataFrame of data
     :param y: pandas DataFrame of targets
     :param use_scaler: None or string in
-            ['minmax', 'zscale', 'robust', 'quantile']
+            ['minmax', 'standard', 'robust', 'quantile']
     :param n_topics: number of topics in Gap Encoder
     :param use_scaler:
     :param confidence: Number between 0 and 1, will pass
@@ -1319,7 +1319,7 @@ def process_edge_dataframes(
     :param src: source column to select in edf
     :param dst: destination column to select in edf
     :param use_scaler: None or string in
-        ['minmax', 'zscale', 'robust', 'quantile']
+        ['minmax', 'standard', 'robust', 'quantile']
     :return: Encoded data matrix and target (if not None),
         the data encoders, and the label encoder.
     """
@@ -1713,33 +1713,48 @@ def fit_transform(self, src=None, dst=None, *args, **kwargs):
         self.fit(src=src, dst=dst, *args, **kwargs)
         return self.X, self.y
 
-    def scale(self, df, ydf=None, set_scaler=False, *args, **kwargs):
-        # pretty hacky but gets job done --
-        """Fits new scaling functions on df, ydf via args-kwargs
-        (ie use downstream as X_train, X_test ,... or batch 
-        when different scaling on the outputs is required)
+    def scale(self, X=None, y=None, set_scaler=False, *args, **kwargs):
+        """Fits new scaling functions on df, y via args-kwargs
+        
+            example:
+                g = graphistry.nodes(df)
+                g2 = g.umap()
+                
+                X, y = g2.scale(X, y, use_scaler='minmax', use_scaler_target='kbins', n_bins=5)
+        
+            args:
+                X: pd.DataFrame of features
+                y: pd.DataFrame of target features
+                kind: str, one of 'nodes' or 'edges'
+                set_scaler: bool, if True, will set the new scaler as the default for the encoder   
+                *args, **kwargs: passed to smart_scaler
+            returns:
+                scaled X, y
         """
         # pop off the previous scaler so that .transform won't use it
         self.res[4] = None
         self.res[5] = None
-
-        X, y = self.transform(df, ydf)  # these are the raw transforms,
+                
         logger.info("-Fitting new scaler on raw features")
         X, y, scaling_pipeline, scaling_pipeline_target = smart_scaler(
             X_enc=X, y_enc=y, *args, **kwargs
         )
+        
+        def _set(res, scaling_pipeline, scaling_pipeline_target):
+            logger.info("--Setting fit scaler to self")
+            res.res[4] = scaling_pipeline
+            res.res[5] = scaling_pipeline_target
+            res.scaling_pipeline = scaling_pipeline
+            res.scaling_pipeline_target = scaling_pipeline_target
+            return res
 
         if set_scaler:
-            logger.info("--Setting fit scaler to self")
-            self.res[4] = scaling_pipeline
-            self.res[5] = scaling_pipeline_target
-            self.scaling_pipeline = scaling_pipeline
-            self.scaling_pipeline_target = scaling_pipeline_target
+            self = _set(self, scaling_pipeline, scaling_pipeline_target)
         else:  # add the original back
             self.res[4] = self.scaling_pipeline
             self.res[5] = self.scaling_pipeline_target
-
-        return X, y, scaling_pipeline, scaling_pipeline_target
+            
+        return X, y
 
 
 # ######################################################################################################################
@@ -1995,7 +2010,9 @@ def _featurize_nodes(
 
         # if changing, also update fresh_res
         res._node_features = encoder.X
+        res._node_features_raw = encoder.X#.copy()
         res._node_target = encoder.y
+        res._node_target_raw = encoder.y#.copy()
         res._node_encoder = encoder  # now this does
         
         # all the work `._node_encoder.transform(df, y)` etc
@@ -2113,7 +2130,9 @@ def _featurize_edges(
 
         # if editing, should also update fresh_res
         res._edge_features = encoder.X
+        res._edge_features_raw = encoder.X#.copy()
         res._edge_target = encoder.y
+        res._edge_target_raw = encoder.y#.copy()
         res._edge_encoder = encoder
 
         return res
@@ -2132,7 +2151,12 @@ def _transform(self, encoder: str, df: pd.DataFrame, ydf: pd.DataFrame):
                 "before being able to transform data"
             )
 
-    def transform(self, df, ydf=None, kind='nodes', return_graph=True, eps='auto', sample=None, verbose=False):
+    def transform(self, df: pd.DataFrame, 
+                  y: Union[pd.DataFrame, None] = None, 
+                  kind: str ='nodes', 
+                  return_graph: bool = True, 
+                  eps: Union[str, float, int] = 'auto', sample = None, 
+                  verbose = False):
         """Transform new data and append to existing graph.
         
             args:
@@ -2148,9 +2172,9 @@ def transform(self, df, ydf=None, kind='nodes', return_graph=True, eps='auto', s
                     or a graph with inferred edges if return_graph is True
         """
         if kind == "nodes":
-            X, y = self._transform("_node_encoder", df, ydf)
+            X, y = self._transform("_node_encoder", df, y)
         elif kind == "edges":
-            X, y = self._transform("_edge_encoder", df, ydf)
+            X, y = self._transform("_edge_encoder", df, y)
         else:
             logger.debug("kind must be one of `nodes`,"
                          f"`edges`, found {kind}")
@@ -2162,12 +2186,11 @@ def transform(self, df, ydf=None, kind='nodes', return_graph=True, eps='auto', s
 
     def scale(
         self,
-        df,
-        ydf,
-        kind,
-        use_scaler,
-        use_scaler_target,
-        set_scaler=False,
+        df: pd.DataFrame,
+        y: pd.DataFrame = None,
+        kind: str = "nodes",
+        use_scaler: Union[str, None] = None,
+        use_scaler_target: Union[str, None] = None,
         impute: bool = True,
         n_quantiles: int = 10,
         output_distribution: str = "normal",
@@ -2175,30 +2198,54 @@ def scale(
         n_bins: int = 2,
         encode: str = "ordinal",
         strategy: str = "uniform",
+        set_scaler: bool = False,
         keep_n_decimals: int = 5,
     ):
         """Scale data using the same scalers as used in the featurization step.
         
             example usage:
                 g = graphistry.nodes(df)
-                g2 = g.umap().scale(df, ydf, kind='nodes', use_scaler='robust', use_scaler_target='kbins', n_bins=3)
+                g2 = g.umap().scale(eps=0.2, sample=None, kind='nodes', use_scaler='robust', use_scaler_target='kbins', n_bins=3)
 
                 # scaled data
+                g3 = g.scale( kind='nodes', use_scaler='robust', use_scaler_target='kbins', n_bins=3)
                 X = g2._node_features
                 y = g2._node_target  
                 
+            args:
+                df: pd.DataFrame, raw data to transform
+                y: pd.DataFrame, optional
+                kind: str  # one of `nodes`, `edges`
+                use_scaler: str, optional, one of `minmax`, `robust`, `standard`, `kbins`, `quantile`
+                use_scaler_target: str, optional, one of `minmax`, `robust`, `standard`, `kbins`, `quantile`
+                impute: bool, if True, will impute missing values
+                n_quantiles: int, number of quantiles to use for quantile scaler
+                output_distribution: str, one of `normal`, `uniform`, `lognormal` 
+                quantile_range: tuple, range of quantiles to use for quantile scaler
+                n_bins: int, number of bins to use for KBinsDiscretizer
+                encode: str, one of `ordinal`, `onehot`, `onehot-dense`, `binary`    
+                strategy: str, one of `uniform`, `quantile`, `kmeans`
+                set_scaler: bool, if True, will set the scaler to the new scaler
+                keep_n_decimals: int, number of decimals to keep after scaling
+            returns:
+                (X, y) transformed data if return_graph is False
+                    or a graph with inferred edges if return_graph is True,
         """
+                
+        if df is None: # use the original data
+            # df = self._nodes if kind == "nodes" else self._edges
+            X, y = (self._node_features_raw, self._node_target_raw) if kind == "nodes" else (self._edge_features_raw, self._edge_target_raw)
+        else:
+            X, y = self.transform(df, y, kind=kind, return_graph=False)
 
         if kind == "nodes" and hasattr(self, "_node_encoder"):  # type: ignore
             if self._node_encoder is not None:  # type: ignore
                 (
                     X,
-                    y,
-                    scaling_pipeline,
-                    scaling_pipeline_target,
+                    y
                 ) = self._node_encoder.scale(
-                    df,
-                    ydf,
+                    X,
+                    y,
                     set_scaler=set_scaler,
                     use_scaler=use_scaler,
                     use_scaler_target=use_scaler_target,
@@ -2222,12 +2269,10 @@ def scale(
             if self._edge_encoder is not None:  # type: ignore
                 (
                     X,
-                    y,
-                    scaling_pipeline,
-                    scaling_pipeline_target,
+                    y
                 ) = self._edge_encoder.scale(
-                    df,
-                    ydf,
+                    X,
+                    y,
                     set_scaler=set_scaler,
                     use_scaler=use_scaler,
                     use_scaler_target=use_scaler_target,
@@ -2245,8 +2290,7 @@ def scale(
                     'Please run g.featurize(kind="edges", *args, **kwargs) '
                     'first before scaling matrices and targets is possible.'
                 )
-
-        return X, y, scaling_pipeline, scaling_pipeline_target
+        return X, y
 
     def featurize(
         self,
@@ -2301,11 +2345,11 @@ def featurize(
         :param use_scaler: selects which scaler (and automatically imputes
                 missing values using mean strategy)
                 to scale the data. Options are;
-                "minmax", "quantile", "zscale", "robust",
+                "minmax", "quantile", "standard", "robust",
                 "kbins", default None.
                 Please see scikits-learn documentation
                 https://scikit-learn.org/stable/modules/preprocessing.html
-                Here 'zscale' corresponds to 'StandardScaler' in scikits.
+                Here 'standard' corresponds to 'StandardScaler' in scikits.
         :param cardinality_threshold: dirty_cat threshold on cardinality of
                 categorical labels across columns.
                 If value is greater than threshold, will run GapEncoder

From aa5ae1d9c6d18f0e4299e1e57aa3c3310eb3b457 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 7 Jan 2023 00:15:32 -0800
Subject: [PATCH 061/432] typing and doc

---
 graphistry/compute/cluster.py | 17 ++++++++---------
 1 file changed, 8 insertions(+), 9 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index b974f427cf..6fa94999c6 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -243,7 +243,7 @@ def dbscan(
 
     def _transform_dbscan(
         self, df: pd.DataFrame, ydf=None, kind: str = "nodes"
-    ) -> Union[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+    ) -> Tuple[Union[pd.DataFrame, None], pd.DataFrame, pd.DataFrame, pd.DataFrame]:
         
         """
         Transforms a dataframe to one with a new column '_dbscan' containing the DBSCAN cluster labels
@@ -298,20 +298,20 @@ def _transform_dbscan(
             else:
                 X_ = X
 
-            labels = dbscan_predict(X_, dbscan)
+            labels = dbscan_predict(X_, dbscan)  # type: ignore
             if umap:
-                df = df.assign(_dbscan=labels, x=emb.x, y=emb.y)
+                df = df.assign(_dbscan=labels, x=emb.x, y=emb.y)  # type: ignore
             else:
                 df = df.assign(_dbscan=labels)
 
-            return emb, X, y, df
+            return emb, X, y, df  # type: ignore
         else:
             raise Exception("No dbscan model found. Please run `g.dbscan()` first")
 
     def transform_dbscan(
         self,
         df: pd.DataFrame,
-        y: pd.DataFrame = None,
+        y: Union[pd.DataFrame, None] = None,
         eps: Union[float, str] = "auto",
         fit_umap_embedding: bool = False,
         sample: int = None,
@@ -321,9 +321,9 @@ def transform_dbscan(
         Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable
     ]:
         """
-        Transforms a minibatch dataframe to one with a new column '_cluster' containing the DBSCAN cluster labels on the minibatch
+        Transforms a minibatch dataframe to one with a new column '_dbscan' containing the DBSCAN cluster labels on the minibatch
             and generates a graph with the minibatch and the original graph, with edges between the minibatch and the original graph inferred
-            works for
+            from the umap embedding or features dataframe.
 
         args:
             df: dataframe to transform
@@ -337,8 +337,7 @@ def transform_dbscan(
                 if None, will only use closest point to the minibatch. If greater than 0, will sample the closest `sample` points
                 in existing graph to pull in more edges. Default None
             kind: 'nodes' or 'edges'
-            return_graph: whether to return a graph or the (emb, X, y, minibatch enriched with DBSCAN labels), default True
-
+            return_graph: whether to return a graph or the (emb, X, y, minibatch df enriched with DBSCAN labels), default True
 
         """
         emb, X, y, df = self._transform_dbscan(df, y, kind=kind)

From ebd5c3ab6ca635f7f0b2f8fd3e3c1527e143d054 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 7 Jan 2023 00:17:41 -0800
Subject: [PATCH 062/432] lint

---
 graphistry/feature_utils.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index e149e3533a..4f68aef002 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2010,9 +2010,9 @@ def _featurize_nodes(
 
         # if changing, also update fresh_res
         res._node_features = encoder.X
-        res._node_features_raw = encoder.X#.copy()
+        res._node_features_raw = encoder.X  #.copy()
         res._node_target = encoder.y
-        res._node_target_raw = encoder.y#.copy()
+        res._node_target_raw = encoder.y  #.copy()
         res._node_encoder = encoder  # now this does
         
         # all the work `._node_encoder.transform(df, y)` etc
@@ -2130,9 +2130,9 @@ def _featurize_edges(
 
         # if editing, should also update fresh_res
         res._edge_features = encoder.X
-        res._edge_features_raw = encoder.X#.copy()
+        res._edge_features_raw = encoder.X  #.copy()
         res._edge_target = encoder.y
-        res._edge_target_raw = encoder.y#.copy()
+        res._edge_target_raw = encoder.y  #.copy()
         res._edge_encoder = encoder
 
         return res

From be15c06ef9ad154760c5cc3fc5792a9e48274a97 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 7 Jan 2023 00:19:51 -0800
Subject: [PATCH 063/432] lint

---
 graphistry/feature_utils.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 4f68aef002..a527cf8d9e 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2010,9 +2010,9 @@ def _featurize_nodes(
 
         # if changing, also update fresh_res
         res._node_features = encoder.X
-        res._node_features_raw = encoder.X  #.copy()
+        res._node_features_raw = encoder.X  # .copy()
         res._node_target = encoder.y
-        res._node_target_raw = encoder.y  #.copy()
+        res._node_target_raw = encoder.y  # .copy()
         res._node_encoder = encoder  # now this does
         
         # all the work `._node_encoder.transform(df, y)` etc
@@ -2130,9 +2130,9 @@ def _featurize_edges(
 
         # if editing, should also update fresh_res
         res._edge_features = encoder.X
-        res._edge_features_raw = encoder.X  #.copy()
+        res._edge_features_raw = encoder.X  # .copy()
         res._edge_target = encoder.y
-        res._edge_target_raw = encoder.y  #.copy()
+        res._edge_target_raw = encoder.y  # .copy()
         res._edge_encoder = encoder
 
         return res

From 3089ae1588c57978c1ba6bf60d6046b285c807fb Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 7 Jan 2023 00:23:05 -0800
Subject: [PATCH 064/432] lint

---
 graphistry/feature_utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index a527cf8d9e..1bd3815919 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2153,7 +2153,7 @@ def _transform(self, encoder: str, df: pd.DataFrame, ydf: pd.DataFrame):
 
     def transform(self, df: pd.DataFrame, 
                   y: Union[pd.DataFrame, None] = None, 
-                  kind: str ='nodes', 
+                  kind: str = 'nodes', 
                   return_graph: bool = True, 
                   eps: Union[str, float, int] = 'auto', sample = None, 
                   verbose = False):
@@ -2232,7 +2232,7 @@ def scale(
                     or a graph with inferred edges if return_graph is True,
         """
                 
-        if df is None: # use the original data
+        if df is None:  # use the original data
             # df = self._nodes if kind == "nodes" else self._edges
             X, y = (self._node_features_raw, self._node_target_raw) if kind == "nodes" else (self._edge_features_raw, self._edge_target_raw)
         else:

From 504484219990bb5abfd332c56dde54d0d60d6334 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 7 Jan 2023 14:39:44 -0800
Subject: [PATCH 065/432] lint

---
 graphistry/feature_utils.py |  4 ++--
 graphistry/umap_utils.py    | 14 +++++++-------
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 1bd3815919..b5672c4c2c 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2142,7 +2142,7 @@ def _infer_edges(self, emb, X, y, df, eps='auto', sample=None, infer_on_umap_emb
         g = infer_graph(res, emb, X, y, df, infer_on_umap_embedding=infer_on_umap_embedding, eps=eps, sample=sample, verbose=verbose, **kwargs) 
         return g
 
-    def _transform(self, encoder: str, df: pd.DataFrame, ydf: pd.DataFrame):
+    def _transform(self, encoder: str, df: pd.DataFrame, ydf: Optional[pd.DataFrame]):
         if getattr(self, encoder) is not None:
             return getattr(self, encoder).transform(df, ydf)
         else:
@@ -2187,7 +2187,7 @@ def transform(self, df: pd.DataFrame,
     def scale(
         self,
         df: pd.DataFrame,
-        y: pd.DataFrame = None,
+        y: Optional[pd.DataFrame] = None,
         kind: str = "nodes",
         use_scaler: Union[str, None] = None,
         use_scaler_target: Union[str, None] = None,
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 46cfacfb17..661ac8e5a0 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -166,6 +166,7 @@ class UMAPMixin(MIXIN_BASE):
 
     def __init__(self, *args, **kwargs):
         self._umap_initialized = False
+        #self.engine = self.engine if hasattr(self, "engine") else None
 
     def umap_lazy_init(
         self,
@@ -197,7 +198,7 @@ def umap_lazy_init(
             umap_kwargs = dict(
                 {
                     "n_components": n_components,
-                    **({"metric": metric} if engine_resolved == UMAP_LEARN else {}),  # noqa
+                    **({"metric": metric} if engine_resolved == UMAP_LEARN else {}),  # type: ignore
                     "n_neighbors": n_neighbors,
                     "min_dist": min_dist,
                     "spread": spread,
@@ -258,21 +259,20 @@ def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         logger.info("-" * 90)
         logger.info(f"Starting UMAP-ing data of shape {X.shape}")
 
-        if self.engine == CUML and is_legacy_cuml():  # noqa
+        if self.engine == CUML and is_legacy_cuml():  # type: ignore
             from cuml.neighbors import NearestNeighbors
 
-            knn = NearestNeighbors(n_neighbors=self._n_neighbors)  # noqa
+            knn = NearestNeighbors(n_neighbors=self._n_neighbors)  # type: ignore
             cc = self._umap.fit(X, y, knn_graph=knn)
             knn.fit(cc.embedding_)
             self._umap.graph_ = knn.kneighbors_graph(cc.embedding_)
-            self._weighted_adjacency = self._umap.graph_
-
         else:
             self._umap.fit(X, y)
-            self._weighted_adjacency = self._umap.graph_
+            
+        self._weighted_adjacency = self._umap.graph_
         # if changing, also update fresh_res
         self._weighted_edges_df = umap_graph_to_weighted_edges(
-            self._umap.graph_, self.engine, is_legacy_cuml()  # noqa
+            self._umap.graph_, self.engine, is_legacy_cuml()  # type: ignore
         )
 
         mins = (time() - t) / 60

From dd0947110c21330055e32e24d437e4f94a2bb500 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 7 Jan 2023 14:46:21 -0800
Subject: [PATCH 066/432] typing

---
 graphistry/compute/cluster.py | 8 ++++----
 graphistry/feature_utils.py   | 8 +++-----
 2 files changed, 7 insertions(+), 9 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 6fa94999c6..7f9c94ebf6 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -314,12 +314,12 @@ def transform_dbscan(
         y: Union[pd.DataFrame, None] = None,
         eps: Union[float, str] = "auto",
         fit_umap_embedding: bool = False,
-        sample: int = None,
+        sample: Optional[int] = None,
         kind: str = "nodes",
         return_graph=True,
-    ) -> Union[
-        Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable
-    ]:
+     ): #-> Union[
+    #     Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable
+    # ]:
         """
         Transforms a minibatch dataframe to one with a new column '_dbscan' containing the DBSCAN cluster labels on the minibatch
             and generates a graph with the minibatch and the original graph, with edges between the minibatch and the original graph inferred
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index b5672c4c2c..f3ff1e634d 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2560,7 +2560,7 @@ def _featurize_or_get_nodes_dataframe_if_X_is_None(
             res._node_target = None
 
         if reuse_if_existing and res._node_features is not None:
-            # logger.info('-Reusing Existing Featurization')
+            logger.info('-Reusing Existing Node Featurization')
             return res._node_features, res._node_target, res
 
         res = res._featurize_nodes(
@@ -2578,7 +2578,6 @@ def _featurize_or_get_nodes_dataframe_if_X_is_None(
             ngram_range=ngram_range,
             max_df=max_df,
             min_df=min_df,
-            #confidence=confidence,
             min_words=min_words,
             model_name=model_name,
             similarity=similarity,
@@ -2656,7 +2655,7 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
             res._edge_target = None
 
         if reuse_if_existing and res._edge_features is not None:
-            # logger.info('-Reusing Existing Featurization')
+            logger.info('-Reusing Existing Edge Featurization')
             return res._edge_features, res._edge_target, res
 
         res = res._featurize_edges(
@@ -2673,7 +2672,6 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
             ngram_range=ngram_range,
             max_df=max_df,
             min_df=min_df,
-            #confidence=confidence,
             min_words=min_words,
             model_name=model_name,
             similarity=similarity,
@@ -2701,7 +2699,7 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
         )
 
     
-    def get_features_by_cols(self, columns: Union[List, str, None] = None, kind: str = 'nodes', target: bool = False):
+    def get_features_by_cols(self, columns: Optional[Union[List, str]] = None, kind: str = 'nodes', target: bool = False):
         """Returns feature matrix with only the columns that contain the string `column_part` in their name.
         
             `X = g.get_features_by_cols(['feature1', 'feature2'])`

From 0561a7f6c6ee0eb907de4d907c2d697334a038b2 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 7 Jan 2023 14:48:16 -0800
Subject: [PATCH 067/432] typing

---
 graphistry/compute/cluster.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 7f9c94ebf6..69a11a7572 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -317,9 +317,7 @@ def transform_dbscan(
         sample: Optional[int] = None,
         kind: str = "nodes",
         return_graph=True,
-     ): #-> Union[
-    #     Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable
-    # ]:
+        ):  # type: ignore
         """
         Transforms a minibatch dataframe to one with a new column '_dbscan' containing the DBSCAN cluster labels on the minibatch
             and generates a graph with the minibatch and the original graph, with edges between the minibatch and the original graph inferred

From edfd8442ff618490efbaf2e365082958e889011e Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 7 Jan 2023 14:54:07 -0800
Subject: [PATCH 068/432] typing

---
 graphistry/compute/cluster.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 69a11a7572..ec506dd195 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -311,7 +311,7 @@ def _transform_dbscan(
     def transform_dbscan(
         self,
         df: pd.DataFrame,
-        y: Union[pd.DataFrame, None] = None,
+        y: Optional[pd.DataFrame] = None,
         eps: Union[float, str] = "auto",
         fit_umap_embedding: bool = False,
         sample: Optional[int] = None,

From a137c356d5085bbead31a8aa7ec1668ec5976996 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 7 Jan 2023 14:57:54 -0800
Subject: [PATCH 069/432] typing

---
 graphistry/feature_utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index f3ff1e634d..589df0f432 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1834,7 +1834,7 @@ def get_matrix_by_column_part(X: pd.DataFrame, column_part: str) -> pd.DataFrame
     transformed_columns = X.columns[X.columns.map(lambda x: True if column_part in x else False)]  # type: ignore
     return X[transformed_columns] 
 
-def get_matrix_by_column_parts(X: pd.DataFrame, column_parts: Union[list, str]) -> pd.DataFrame:
+def get_matrix_by_column_parts(X: pd.DataFrame, column_parts: Optional[Union[list, str]]) -> pd.DataFrame:
     """Get the feature matrix by column parts list existing in column names."""
     if column_parts is None:
         return X
@@ -2699,7 +2699,7 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
         )
 
     
-    def get_features_by_cols(self, columns: Optional[Union[List, str]] = None, kind: str = 'nodes', target: bool = False):
+    def get_features_by_cols(self, columns: Optional[Union[List, str]] = None, kind: str = 'nodes', target: bool = False) -> pd.DataFrame:
         """Returns feature matrix with only the columns that contain the string `column_part` in their name.
         
             `X = g.get_features_by_cols(['feature1', 'feature2'])`

From dd4be1c77aa24410f2be81d3f6850252290dd8a6 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 7 Jan 2023 18:09:09 -0800
Subject: [PATCH 070/432] adds test for g.get_features_by_cols, passing

---
 graphistry/tests/test_feature_utils.py | 50 +++++++++++++++++++++++++-
 1 file changed, 49 insertions(+), 1 deletion(-)

diff --git a/graphistry/tests/test_feature_utils.py b/graphistry/tests/test_feature_utils.py
index f3738b3707..e0264c2e6b 100644
--- a/graphistry/tests/test_feature_utils.py
+++ b/graphistry/tests/test_feature_utils.py
@@ -19,6 +19,9 @@
     FastEncoder
 )
 
+from graphistry.features import topic_model, ngrams_model
+
+np.random.seed(137)
 
 has_min_dependancy, _ = lazy_import_has_min_dependancy()
 has_min_dependancy_text, _, _ = lazy_import_has_dependancy_text()
@@ -127,7 +130,7 @@
 target_names_node = [['label'], ['label', 'type']]
 # test also sending in a dataframe for target
 double_target_reddit = pd.DataFrame(
-    {"label": ndf_reddit.label.values, "type": ndf_reddit["type"].values}
+    {"label": ndf_reddit.label.values, "type": ndf_reddit["type"].values}, index=ndf_reddit.index
 )
 single_target_reddit = pd.DataFrame({"label": ndf_reddit.label.values})
 
@@ -136,6 +139,11 @@
 edge_df2['dst'] = np.random.random_integers(0, 120, size=len(edge_df2))
 edge2_target_df = pd.DataFrame({'label': edge_df2.label})
 
+## ################################################
+what = ['whatever', 'on what', 'what do', 'what do you', 'what do you think', 'to what', 'but what', 'what is', 'what it', 'what kind', 'what kind of', 'of what', 'know what', 'what are', 'what are the', 'what to', 'what to do', 'from what', 'with what', 'and what', 'what you', 'whats', 'know what to', 'don know what', 'what the']
+freedom = ['title: dyslexics, experience, language',
+       'label: languagelearning, agile, leaves',
+       'title: freedom, finally, moved']
 # ################################################
 # data to test textual and numeric DataFrame
 # ndf_stocks, price_df_stocks = get_stocks_dataframe()
@@ -162,6 +170,46 @@ def check_allclose_fit_transform_on_same_data(X, x, Y=None, y=None):
                 allclose_stats(Y, y, value, name)
 
 
+class TestFeaturizeGetMethods(unittest.TestCase):
+    
+    @pytest.mark.skipif(not has_min_dependancy or not has_min_dependancy_text, reason="requires ai feature dependencies")
+    def setUp(self) -> None:
+        g = graphistry.nodes(ndf_reddit)
+        g2 = g.featurize( # ngrams
+                y=double_target_reddit,
+                use_ngrams=True,
+                ngram_range=(1, 4)
+                )
+        
+        g3 = g.featurize( # topic model
+                        **topic_model
+        )
+        self.g = g
+        self.g2 = g2
+        self.g3 = g3
+        
+    @pytest.mark.skipif(not has_min_dependancy or not has_min_dependancy_text, reason="requires ai feature dependencies")
+    def test_get_col_matrix(self):
+        # no edges so this should be None
+        assert self.g2.get_features_by_cols(kind='edges') == None
+        
+        # test target methods
+        assert all(self.g2.get_features_by_cols(target=True).columns == self.g2._node_target.columns)
+        assert self.g2.get_features_by_cols('Anxiety', target=True).shape[0] == len(self.g2._node_target)
+        # test str vs list 
+        assert (self.g2.get_features_by_cols('Anxiety', target=True) == self.g2.get_features_by_cols(['Anxiety'], target=True)).all().values[0]
+
+        assert list(self.g2.get_features_by_cols(['Anxiety', 'education', 'computer'], target=True).columns) == ['label_Anxiety', 'label_education', 'label_computervision']
+    
+        # test feature methods
+        # ngrams
+        assert (self.g2.get_features_by_cols().columns == self.g2._node_features.columns).all()
+        assert list(self.g2.get_features_by_cols('what').columns) == what, list(self.g2.get_features_by_cols('what').columns)
+        
+        # topic
+        assert all(self.g3.get_features_by_cols().columns == self.g3._node_features.columns)
+        assert list(self.g3.get_features_by_cols(['language', 'freedom']).columns) == freedom, self.g3.get_features_by_cols(['language', 'freedom']).columns
+
 class TestFastEncoder(unittest.TestCase):
     # we test how far off the fit returned values different from the transformed
     

From 0b63fa833b5e86a8f3760e4c8cf68fc979bae25a Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 7 Jan 2023 22:20:22 -0800
Subject: [PATCH 071/432] feat(refactors featurization logic - moves scaling
 outside of previous pipeline and exposes `g._{}_encoder.transform_scaled`
 method. Adds working transform tests for featurize and umap

---
 graphistry/ai_utils.py              |  19 ++-
 graphistry/compute/cluster.py       |  35 ++++-
 graphistry/feature_utils.py         | 135 ++++++++++-------
 graphistry/tests/test_umap_utils.py | 226 ++++++++++------------------
 graphistry/umap_utils.py            |  28 ++--
 5 files changed, 210 insertions(+), 233 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 267aefa13a..a3c628dc6a 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -207,7 +207,7 @@ def infer_graph(
         eps: if 'auto' will find a good epsilon from the data; distance threshold for a minibatchh point to cluster to existing graph
         n_nearest: number of nearest neighbors to add from existing graphs edges, if None, ignores existing edges.
     """
-
+    print('*Infering graph from existing graphistry object') if verbose else None
     # new_index = df.index
     if infer_on_umap_embedding and emb is not None:
         X_previously_fit = res._node_embedding
@@ -219,8 +219,10 @@ def infer_graph(
         print("Infering edges over features") if verbose else None
 
     FEATS = res._node_features
-    EMB = res._node_embedding
-    Y = res._node_target
+    if FEATS is None:
+        raise ValueError("Must have node features to infer edges")
+    EMB = res._node_embedding if res._node_embedding is not None else FEATS.index
+    Y = res._node_target if res._node_target is not None else FEATS.index
 
     assert (
         df.shape[0] == X.shape[0]
@@ -290,19 +292,22 @@ def infer_graph(
             .append(old_edges[dst])
             .append(new_edges[src])
             .append(new_edges[dst])
-        )
+        ).drop_duplicates()
+        print(len(all_nodes), "nodes in new graph") if verbose else None
 
     if sample:
-        new_edges = pd.concat([new_edges, old_edges], axis=0)
-        # print('sampled', len(new_edges), 'new edges')
+        new_edges = pd.concat([new_edges, old_edges], axis=0).drop_duplicates()
+        print('sampled', len(old_edges.drop_duplicates()), 'previous old edges') if verbose else None
     new_edges = new_edges.drop_duplicates()
-    # print(len(new_edges), 'new edges after dropping duplicates')
+    print(len(new_edges), 'total edges pairs after dropping duplicates') if verbose else None
 
     if len(old_nodes):
         old_nodes = pd.DataFrame(old_nodes)
         old_nodes = pd.concat(
             [old_nodes, NDF[NDF[node].isin(all_nodes)]], axis=0
         ).drop_duplicates(subset=[node])
+    else:
+        old_nodes = NDF[NDF[node].isin(all_nodes)]
 
     old_emb = None
     if EMB is not None:
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index ec506dd195..86851f1ad9 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -67,16 +67,27 @@ def resolve_cpu_gpu_engine(
     )
 
 
-def get_model_matrix(g, kind, cols, umap):
+def get_model_matrix(g, kind, cols, umap, target):
+    """
+        Allows for a single function to get the model matrix for both nodes and edges as well as targets, embeddings, and features
+
+    Args:
+        g (_type_): _description_
+        kind (_type_): _description_
+        cols (_type_): _description_
+        umap (_type_): _description_
+        target (_type_): _description_
+
+    Returns:
+        _type_: dataframe of model matrix given the inputs
+    """
     assert kind in ["nodes", "edges"]
     assert (
         hasattr(g, "_node_encoder") if kind == "nodes" else hasattr(g, "_edge_encoder")
     )
 
-    if cols is None:
-        df = g._get_feature(kind)
-    else:
-        df = g.get_features_by_cols(cols, kind)
+
+    df = g.get_features_by_cols(cols, kind=kind, target=target)
 
     if umap and cols is None and g._umap is not None:
         df = g._get_embedding(kind)
@@ -84,9 +95,10 @@ def get_model_matrix(g, kind, cols, umap):
     return df
 
 
-def dbscan_fit(g, dbscan, kind="nodes", cols=None, use_umap_embedding=True):
+def dbscan_fit(g, dbscan, kind="nodes", cols=None, use_umap_embedding=True, target=False):
     """
     Fits clustering on UMAP embeddings if umap is True, otherwise on the features dataframe
+        or target dataframe if target is True.
 
     args:
         g: graphistry graph
@@ -94,7 +106,10 @@ def dbscan_fit(g, dbscan, kind="nodes", cols=None, use_umap_embedding=True):
         cols: list of columns to use for clustering given `g.featurize` has been run
         umap: whether to use UMAP embeddings or features dataframe
     """
-    df = get_model_matrix(g, kind, cols, use_umap_embedding)
+    df = get_model_matrix(g, kind, cols, use_umap_embedding, target)
+    
+    if df.empty:
+        raise ValueError("No features found for clustering")
 
     dbscan.fit(df)
     labels = dbscan.labels_
@@ -147,7 +162,7 @@ def __init__(self, *args, **kwargs):
         pass
 
     def _cluster_dbscan(
-        self, res, kind, cols, fit_umap_embedding, eps, min_samples, **kwargs
+        self, res, kind, cols, fit_umap_embedding, target, eps, min_samples, *args, **kwargs
     ):
         """
         DBSCAN clustering on cpu or gpu infered by .engine flag
@@ -159,9 +174,11 @@ def _cluster_dbscan(
             "latest dbscan kwargs",
             kind=kind,
             cols=cols,
+            target=target,
             umap=fit_umap_embedding,
             eps=eps,
             min_samples=min_samples,
+            *args,
             **kwargs,
         )
 
@@ -184,6 +201,7 @@ def dbscan(
         cols=None,
         kind="nodes",
         fit_umap_embedding=True,
+        target=False,
         **kwargs,
     ):
         """DBSCAN clustering on cpu or gpu infered automatically
@@ -234,6 +252,7 @@ def dbscan(
             kind=kind,
             cols=cols,
             fit_umap_embedding=fit_umap_embedding,
+            target=target,
             eps=eps,
             min_samples=min_samples,
             **kwargs,
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 589df0f432..1d786b3c57 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1025,6 +1025,8 @@ def process_nodes_dataframes(
     feature_engine: FeatureEngineConcrete = "pandas"
     # test_size: Optional[bool] = None,
 ) -> Tuple[
+    pd.DataFrame,
+    Any,
     pd.DataFrame,
     Any,
     SuperVectorizer,
@@ -1155,7 +1157,7 @@ def process_nodes_dataframes(
         f"--The entire Encoding process took {(time()-t)/60:.2f} minutes"
     )
 
-    X_enc, y_enc, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
+    X_encs, y_encs, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
         X_enc,
         y_enc,
         use_scaler,
@@ -1173,6 +1175,8 @@ def process_nodes_dataframes(
     return (
         X_enc,
         y_enc,
+        X_encs,
+        y_encs,
         data_encoder,
         label_encoder,
         scaling_pipeline,
@@ -1298,6 +1302,8 @@ def process_edge_dataframes(
     keep_n_decimals: int = 5,
     feature_engine: FeatureEngineConcrete = "pandas",
 ) -> Tuple[
+    pd.DataFrame,
+    pd.DataFrame,
     pd.DataFrame,
     pd.DataFrame,
     List[Any],
@@ -1354,7 +1360,7 @@ def process_edge_dataframes(
         # add the two datasets together
         X_enc = pd.concat([T, X_enc], axis=1)
         # then scale them
-        X_enc, y_enc, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
+        X_encs, y_encs, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
             X_enc,
             y_enc,
             use_scaler,
@@ -1374,6 +1380,8 @@ def process_edge_dataframes(
         return (
             X_enc,
             y_enc,
+            X_encs,
+            y_encs,
             [mlb_pairwise_edge_encoder, data_encoder],
             label_encoder,
             scaling_pipeline,
@@ -1385,6 +1393,8 @@ def process_edge_dataframes(
     (
         X_enc,
         y_enc,
+        _,
+        _,
         data_encoder,
         label_encoder,
         _,
@@ -1426,7 +1436,7 @@ def process_edge_dataframes(
         f" {(time()-t)/60:.2f} minutes"
     )
 
-    X_enc, y_enc, scaling_pipeline, scaling_pipeline_target = smart_scaler(
+    X_encs, y_encs, scaling_pipeline, scaling_pipeline_target = smart_scaler(
         X_enc,
         y_enc,
         use_scaler,
@@ -1444,6 +1454,8 @@ def process_edge_dataframes(
     res = (
         X_enc,
         y_enc,
+        X_encs,
+        y_encs,
         [mlb_pairwise_edge_encoder, data_encoder],
         label_encoder,
         scaling_pipeline,
@@ -1501,7 +1513,7 @@ def transform_dirty(
     data_encoder: Union[SuperVectorizer, FunctionTransformer],  # type: ignore
     name: str = "",
 ) -> pd.DataFrame:
-    from sklearn.preprocessing import MultiLabelBinarizer
+    # from sklearn.preprocessing import MultiLabelBinarizer
     logger.debug(f"-{name} Encoder:")
     logger.debug(f"\t{data_encoder}\n")
     # print(f"-{name} Encoder:")
@@ -1516,7 +1528,8 @@ def transform_dirty(
     # #####################################  for dirty_cat 0.3.0
     use_columns = getattr(data_encoder, 'columns_', [])
     if len(use_columns):
-        X = data_encoder.transform(df[use_columns])
+        #print(f"Using columns: {use_columns}")
+        X = data_encoder.transform(df[df.columns.intersection(use_columns)])
     # #####################################  with dirty_cat 0.2.0
     else:
         X = data_encoder.transform(df)
@@ -1544,12 +1557,14 @@ def transform(
     # this function aligns with what is computed during
     # processing nodes or edges.
     (
-        X_enc,
-        y_enc,
+        _,
+        _,
+        _,
+        _,
         data_encoder,
         label_encoder,
-        scaling_pipeline,
-        scaling_pipeline_target,
+        _,
+        _,
         text_model,
         text_cols,
     ) = res
@@ -1612,14 +1627,14 @@ def transform(
     logger.info(f"--Features matrix shape: {X.shape}")
     logger.info(f"--Target matrix shape: {y.shape}")
 
-    if scaling_pipeline and not X.empty:
-        logger.info("--Scaling Features")
-        X = pd.DataFrame(scaling_pipeline.transform(X), columns=X.columns, index=index)
-    if scaling_pipeline_target and not y.empty:
-        logger.info(f"--Scaling Target {scaling_pipeline_target}")
-        y = pd.DataFrame(
-            scaling_pipeline_target.transform(y), columns=y.columns, index=index
-        )
+    # if scaling_pipeline and not X.empty:
+    #     logger.info("--Scaling Features")
+    #     X = pd.DataFrame(scaling_pipeline.transform(X), columns=X.columns, index=index)
+    # if scaling_pipeline_target and not y.empty:
+    #     logger.info(f"--Scaling Target {scaling_pipeline_target}")
+    #     y = pd.DataFrame(
+    #         scaling_pipeline_target.transform(y), columns=y.columns, index=index
+    #     )
 
     return X, y
 
@@ -1675,6 +1690,8 @@ def _set_result(self, res):
         [
             X_enc,
             y_enc,
+            X_encs,
+            y_encs,
             data_encoder,
             label_encoder,
             scaling_pipeline,
@@ -1688,8 +1705,10 @@ def _set_result(self, res):
         # label_encoder.target_names_in = self.target_names_in
         self.feature_columns = X_enc.columns
         self.feature_columns_target = y_enc.columns
-        self.X = X_enc
-        self.y = y_enc
+        self.X = X_encs
+        self.y = y_encs
+        self.X_orignal = X_enc
+        self.y_orignal = y_enc
         self.data_encoder = data_encoder  # is list for edges
         self.label_encoder = label_encoder
         self.scaling_pipeline = scaling_pipeline
@@ -1706,14 +1725,33 @@ def fit(self, src=None, dst=None, *args, **kwargs):
         self._set_result(res)
 
     def transform(self, df, ydf=None):
+        "Raw transform, no scaling."
         X, y = transform(df, ydf, self.res, self.kind, self.src, self.dst)
         return X, y
+    
+    def _transform_scaled(self, df, ydf, scaling_pipeline, scaling_pipeline_target):
+        """Transform with scaling fit durning fit."""
+        X, y = transform(df, ydf, self.res, self.kind, self.src, self.dst)
+        if scaling_pipeline is not None:
+            print("scaling")
+            X = pd.DataFrame(scaling_pipeline.transform(X), columns=X.columns, index=X.index)
+        if scaling_pipeline_target is not None:
+            print("scaling target")
+            y = pd.DataFrame(scaling_pipeline_target.transform(y), columns=y.columns, index=y.index)
+        return X, y
+    
+    def transform_scaled(self, df, ydf=None, scaling_pipeline=None, scaling_pipeline_target=None):
+        if scaling_pipeline is None:
+            scaling_pipeline = self.scaling_pipeline
+        if scaling_pipeline_target is None:
+            scaling_pipeline_target = self.scaling_pipeline_target
+        return self._transform_scaled(df, ydf, scaling_pipeline, scaling_pipeline_target)
 
     def fit_transform(self, src=None, dst=None, *args, **kwargs):
         self.fit(src=src, dst=dst, *args, **kwargs)
         return self.X, self.y
 
-    def scale(self, X=None, y=None, set_scaler=False, *args, **kwargs):
+    def scale(self, X=None, y=None, return_pipeline=False, *args, **kwargs):
         """Fits new scaling functions on df, y via args-kwargs
         
             example:
@@ -1726,34 +1764,16 @@ def scale(self, X=None, y=None, set_scaler=False, *args, **kwargs):
                 X: pd.DataFrame of features
                 y: pd.DataFrame of target features
                 kind: str, one of 'nodes' or 'edges'
-                set_scaler: bool, if True, will set the new scaler as the default for the encoder   
                 *args, **kwargs: passed to smart_scaler
             returns:
                 scaled X, y
         """
-        # pop off the previous scaler so that .transform won't use it
-        self.res[4] = None
-        self.res[5] = None
-                
         logger.info("-Fitting new scaler on raw features")
         X, y, scaling_pipeline, scaling_pipeline_target = smart_scaler(
             X_enc=X, y_enc=y, *args, **kwargs
         )
-        
-        def _set(res, scaling_pipeline, scaling_pipeline_target):
-            logger.info("--Setting fit scaler to self")
-            res.res[4] = scaling_pipeline
-            res.res[5] = scaling_pipeline_target
-            res.scaling_pipeline = scaling_pipeline
-            res.scaling_pipeline_target = scaling_pipeline_target
-            return res
-
-        if set_scaler:
-            self = _set(self, scaling_pipeline, scaling_pipeline_target)
-        else:  # add the original back
-            self.res[4] = self.scaling_pipeline
-            self.res[5] = self.scaling_pipeline_target
-            
+        if return_pipeline:
+            return X, y, scaling_pipeline, scaling_pipeline_target
         return X, y
 
 
@@ -2010,9 +2030,9 @@ def _featurize_nodes(
 
         # if changing, also update fresh_res
         res._node_features = encoder.X
-        res._node_features_raw = encoder.X  # .copy()
+        res._node_features_raw = encoder.X_orignal  # .copy()
         res._node_target = encoder.y
-        res._node_target_raw = encoder.y  # .copy()
+        res._node_target_raw = encoder.y_orignal  # .copy()
         res._node_encoder = encoder  # now this does
         
         # all the work `._node_encoder.transform(df, y)` etc
@@ -2142,8 +2162,10 @@ def _infer_edges(self, emb, X, y, df, eps='auto', sample=None, infer_on_umap_emb
         g = infer_graph(res, emb, X, y, df, infer_on_umap_embedding=infer_on_umap_embedding, eps=eps, sample=sample, verbose=verbose, **kwargs) 
         return g
 
-    def _transform(self, encoder: str, df: pd.DataFrame, ydf: Optional[pd.DataFrame]):
+    def _transform(self, encoder: str, df: pd.DataFrame, ydf: Optional[pd.DataFrame], scaled):
         if getattr(self, encoder) is not None:
+            if scaled:
+                return getattr(self, encoder).transform_scaled(df, ydf)
             return getattr(self, encoder).transform(df, ydf)
         else:
             logger.debug(
@@ -2152,12 +2174,15 @@ def _transform(self, encoder: str, df: pd.DataFrame, ydf: Optional[pd.DataFrame]
             )
 
     def transform(self, df: pd.DataFrame, 
-                  y: Union[pd.DataFrame, None] = None, 
+                  y: Optional[pd.DataFrame] = None, 
                   kind: str = 'nodes', 
-                  return_graph: bool = True, 
-                  eps: Union[str, float, int] = 'auto', sample = None, 
-                  verbose = False):
-        """Transform new data and append to existing graph.
+                  eps: Union[str, float, int] = 'auto', 
+                  sample: Optional[int] = None, 
+                  return_graph: bool = True,
+                  scaled: bool = True,
+                  verbose: bool = False):
+        """
+            Transform new data and append to existing graph, or return dataframes
         
             args:
                 df: pd.DataFrame, raw data to transform
@@ -2172,17 +2197,17 @@ def transform(self, df: pd.DataFrame,
                     or a graph with inferred edges if return_graph is True
         """
         if kind == "nodes":
-            X, y = self._transform("_node_encoder", df, y)
+            X, y_ = self._transform("_node_encoder", df, y, scaled=scaled)
         elif kind == "edges":
-            X, y = self._transform("_edge_encoder", df, y)
+            X, y_ = self._transform("_edge_encoder", df, y, scaled=scaled)
         else:
             logger.debug("kind must be one of `nodes`,"
                          f"`edges`, found {kind}")
         if return_graph:
             emb = None  # will not be able to decide umap coordinates, but will be able to infer graph from existing edges
-            g = self._infer_edges(emb, X, y, df, infer_on_umap_embedding=False, eps=eps, sample=sample, verbose=verbose) 
+            g = self._infer_edges(emb, X, y_, df, infer_on_umap_embedding=False, eps=eps, sample=sample, verbose=verbose) 
             return g
-        return X, y
+        return X, y_
 
     def scale(
         self,
@@ -2198,7 +2223,6 @@ def scale(
         n_bins: int = 2,
         encode: str = "ordinal",
         strategy: str = "uniform",
-        set_scaler: bool = False,
         keep_n_decimals: int = 5,
     ):
         """Scale data using the same scalers as used in the featurization step.
@@ -2225,7 +2249,6 @@ def scale(
                 n_bins: int, number of bins to use for KBinsDiscretizer
                 encode: str, one of `ordinal`, `onehot`, `onehot-dense`, `binary`    
                 strategy: str, one of `uniform`, `quantile`, `kmeans`
-                set_scaler: bool, if True, will set the scaler to the new scaler
                 keep_n_decimals: int, number of decimals to keep after scaling
             returns:
                 (X, y) transformed data if return_graph is False
@@ -2236,7 +2259,7 @@ def scale(
             # df = self._nodes if kind == "nodes" else self._edges
             X, y = (self._node_features_raw, self._node_target_raw) if kind == "nodes" else (self._edge_features_raw, self._edge_target_raw)
         else:
-            X, y = self.transform(df, y, kind=kind, return_graph=False)
+            X, y = self.transform(df, y, kind=kind, return_graph=False, scaled=False)
 
         if kind == "nodes" and hasattr(self, "_node_encoder"):  # type: ignore
             if self._node_encoder is not None:  # type: ignore
@@ -2246,7 +2269,6 @@ def scale(
                 ) = self._node_encoder.scale(
                     X,
                     y,
-                    set_scaler=set_scaler,
                     use_scaler=use_scaler,
                     use_scaler_target=use_scaler_target,
                     impute=impute,
@@ -2273,7 +2295,6 @@ def scale(
                 ) = self._edge_encoder.scale(
                     X,
                     y,
-                    set_scaler=set_scaler,
                     use_scaler=use_scaler,
                     use_scaler_target=use_scaler_target,
                     impute=impute,
diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index 5f218d4889..54fdab6e4c 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -70,20 +70,24 @@ class TestUMAPFitTransform(unittest.TestCase):
     # check to see that .fit and transform gives similar embeddings on same data
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def setUp(self):
-
+        verbose = True
         g = graphistry.nodes(ndf_reddit)
         self.gn = g
+        
+        self.test = ndf_reddit.sample(5)
+
 
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=FutureWarning)
             g2 = g.umap(
-                y=double_target_reddit,
+                y=['label', 'type'],
                 use_ngrams=True,
                 ngram_range=(1, 2),
                 use_scaler="robust",
                 cardinality_threshold=2,
+                verbose=verbose,
             )
 
         self.g2 = g2
@@ -91,10 +95,10 @@ def setUp(self):
         self.X, self.Y = fenc.X, fenc.y
         self.EMB = g2._node_embedding
         self.emb, self.x, self.y = g2.transform_umap(
-            ndf_reddit, ydf=double_target_reddit, kind="nodes", return_graph=False
+            ndf_reddit, ndf_reddit, kind="nodes", return_graph=False, verbose=verbose
         )
         self.g3 = g2.transform_umap(
-            ndf_reddit, ydf=double_target_reddit, kind="nodes", return_graph=True
+            ndf_reddit, ndf_reddit, kind="nodes", return_graph=True, verbose=verbose
         )
 
         # do the same for edges
@@ -107,7 +111,7 @@ def setUp(self):
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=FutureWarning)
             g2 = g.umap(
-                y=edge2_target_df,
+                y=['label'],
                 kind="edges",
                 use_ngrams=True,
                 ngram_range=(1, 2),
@@ -115,148 +119,71 @@ def setUp(self):
                 use_scaler_target=None,
                 cardinality_threshold=2,
                 n_topics=4,
+                verbose=verbose,
             )
 
         fenc = g2._edge_encoder
         self.Xe, self.Ye = fenc.X, fenc.y
         self.EMBe = g2._edge_embedding
         self.embe, self.xe, self.ye = g2.transform_umap(
-            edge_df22, ydf=edge2_target_df, kind="edges", return_graph=False
-        )
+            edge_df22, y=edge2_target_df, kind="edges", return_graph=False, verbose=verbose
+        )        
         self.g2e = g2
-        self.g3e = g2.transform_umap(
-            edge_df22, ydf=edge2_target_df, kind="edges", return_graph=True
-        )
+
 
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_columns_match(self):
-        assert all(
-            self.X.columns == self.x.columns
-        ), "Node Feature Columns do not match"
-        assert all(self.Y.columns == self.y.columns), "Node Target Columns do not match"
-        assert all(
-            self.Xe.columns == self.xe.columns
-        ), "Edge Feature Columns do not match"
-        assert all(
-            self.Ye.columns == self.ye.columns
-        ), "Edge Target Columns do not match"
+        d = self.g2._node_features.shape[1]
+        dt = self.g2._node_target.shape[1]
+        de = self.g2e._edge_features.shape[1]
+        det = self.g2e._edge_target.shape[1]
+        assert (self.X.columns == self.x.columns).sum() == d, "Node Feature Columns do not match"
+        assert (self.Y.columns == self.y.columns).sum() == dt, "Node Target Columns do not match"
+        assert (self.Xe.columns == self.xe.columns).sum() == de, "Edge Feature Columns do not match"
+        assert (self.Ye.columns == self.ye.columns).sum() == det, "Edge Target Columns do not match"
 
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_index_match(self):
         # nodes
-        assert all(
-            self.gn._nodes.index == self.g2._nodes.index
-        ), "Node Indexes do not match"
-        assert all(self.gn._nodes.index == self.EMB.index), "Emb Indexes do not match"
-        assert all(
-            self.gn._nodes.index == self.emb.index
-        ), "Transformed Emb Indexes do not match"
-        assert all(
-            self.gn._nodes.index == self.X.index
-        ), "Transformed Node features Indexes do not match"
-        assert all(
-            self.gn._nodes.index == self.y.index
-        ), "Transformed Node target Indexes do not match"
+        d = self.g2._nodes.shape[0]
+        de = self.g2e._edges.shape[0]
+        assert (self.gn._nodes.index == self.g2._nodes.index).sum() == d, "Node Indexes do not match"
+        assert (self.gn._nodes.index == self.EMB.index).sum() == d, "Emb Indexes do not match"
+        assert (self.gn._nodes.index == self.emb.index).sum() == d, "Transformed Emb Indexes do not match"
+        assert (self.gn._nodes.index == self.X.index).sum() == d, "Transformed Node features Indexes do not match"
+        assert (self.gn._nodes.index == self.y.index).sum() == d, "Transformed Node target Indexes do not match"
 
         # edges
-        assert all(
-            self.ge._edges.index == self.g2e._edges.index
-        ), "Edge Indexes do not match"
-        assert all(
-            self.ge._edges.index == self.EMBe.index
-        ), "Edge Emb Indexes do not match"
-        assert all(
-            self.ge._edges.index == self.embe.index
-        ), "Edge Transformed Emb Indexes do not match"
-        assert all(
-            self.ge._edges.index == self.Xe.index
-        ), "Edge Transformed features Indexes do not match"
-        assert all(
-            self.ge._edges.index == self.ye.index
-        ), "Edge Transformed target Indexes do not match"
+        assert (self.ge._edges.index == self.g2e._edges.index).sum() == de, "Edge Indexes do not match"
+        assert (self.ge._edges.index == self.EMBe.index).sum() == de, "Edge Emb Indexes do not match"
+        assert (self.ge._edges.index == self.embe.index).sum() == de, "Edge Transformed Emb Indexes do not match"
+        assert (self.ge._edges.index == self.Xe.index).sum() == de, "Edge Transformed features Indexes do not match"
+        assert (self.ge._edges.index == self.ye.index).sum() == de, "Edge Transformed target Indexes do not match"
 
         # make sure the indexes match at transform time internally as well
-        assert all(self.X.index == self.x.index), "Node Feature Indexes do not match"
-        assert all(self.Y.index == self.y.index), "Node Target Indexes do not match"
-        assert all(self.Xe.index == self.xe.index), "Edge Feature Indexes do not match"
-        assert all(self.Ye.index == self.ye.index), "Edge Target Indexes do not match"
+        assert (self.X.index == self.x.index).sum() == d, "Node Feature Indexes do not match"
+        assert (self.Y.index == self.y.index).sum() == d, "Node Target Indexes do not match"
+        assert (self.Xe.index == self.xe.index).sum() == de, "Edge Feature Indexes do not match"
+        assert (self.Ye.index == self.ye.index).sum() == de, "Edge Target Indexes do not match"
 
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
-    def test_index_match_in_infered_graph(self):
+    def test_node_index_match_in_infered_graph(self):
         # nodes
-        assert all(
-            self.g3._nodes.index == self.g2._nodes.index
-        ), "Node Indexes do not match"
-        assert all(self.g3._nodes.index == self.EMB.index), "Emb Indexes do not match"
-        assert all(
-            self.g3._nodes.index == self.emb.index
-        ), "Transformed Emb Indexes do not match"
-        assert all(
-            self.g3._nodes.index == self.X.index
-        ), "Transformed Node features Indexes do not match"
-        assert all(
-            self.g3._nodes.index == self.y.index
-        ), "Transformed Node target Indexes do not match"
+        g3 = self.g2._nodes
+        assert (g3.index == self.EMB.index).sum() == len(g3), "Node Emb Indexes do not match"
+        assert (g3.index == self.emb.index).sum() == len(g3), "Node Transformed Emb Indexes do not match"
+        assert (g3.index == self.X.index).sum() == len(g3), "Node Transformed features Indexes do not match"
+        assert (g3.index == self.y.index).sum() == len(g3), "Node Transformed target Indexes do not match"
 
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
-    def test_nodes_index_match_in_infered_graph(self):
-        # edges
-        ndf_infered = self.g3._nodes
-        assert all(
-            ndf_infered.index == self.EMBe.index
-        ), "Edge Emb Indexes do not match"
-        assert all(
-            ndf_infered.index == self.embe.index
-        ), "Edge Transformed Emb Indexes do not match"
-        assert all(
-            ndf_infered.index == self.Xe.index
-        ), "Edge Transformed features Indexes do not match"
-        assert all(
-            ndf_infered.index == self.ye.index
-        ), "Edge Transformed target Indexes do not match"
-
-        # now test in set featurize method calls
-        assert all(
-            self.g3._node_features.index == ndf_infered.index
-        ), "Edge Feature Indexes do not match"
-        assert all(
-            self.g3._node_embedding.index == ndf_infered.index
-        ), "Edge Emb Indexes do not match"
-        assert all(
-            self.g3._node_target.index == ndf_infered.index
-        ), "Edge Transformed Emb Indexes do not match"
-        # assert all(self.g3e._edges.index == edf_infered.index), 'Edge Transformed features Indexes do not match'
-        # assert all(self.g3e._edges.index == edf_infered.index), 'Edge Transformed target Indexes do not match'
-
-    @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
-    def test_edges_index_match_in_infered_graph(self):
-        # edges
-        edf_infered = self.g3e._edges
-        assert all(
-            edf_infered.index == self.EMBe.index
-        ), "Edge Emb Indexes do not match"
-        assert all(
-            edf_infered.index == self.embe.index
-        ), "Edge Transformed Emb Indexes do not match"
-        assert all(
-            edf_infered.index == self.Xe.index
-        ), "Edge Transformed features Indexes do not match"
-        assert all(
-            edf_infered.index == self.ye.index
-        ), "Edge Transformed target Indexes do not match"
-
-        assert all(
-            self.g3e._edge_features.index == edf_infered.index
-        ), "Edge Feature Indexes do not match"
-        assert all(
-            self.g3e._edge_embedding.index == edf_infered.index
-        ), "Edge Emb Indexes do not match"
-        assert all(
-            self.g3e._edge_target.index == edf_infered.index
-        ), "Edge Transformed Emb Indexes do not match"
-        # assert all(self.g3e._edges.index == edf_infered.index), 'Edge Transformed features Indexes do not match'
-        # assert all(self.g3e._edges.index == edf_infered.index), 'Edge Transformed target Indexes do not match'
-
+    def test_edge_index_match_in_infered_graph(self):
+        g3 = self.g2e._edges
+        assert (g3.index == self.EMBe.index).sum() == len(g3), "Edge Emb Indexes do not match"
+        assert (g3.index == self.embe.index).sum() == len(g3), "Edge Transformed Emb Indexes do not match"
+        assert (g3.index == self.Xe.index).sum() == len(g3), "Edge Transformed Node features Indexes do not match"
+        assert (g3.index == self.ye.index).sum() == len(g3), "Edge Transformed Node target Indexes do not match"
+
+    
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_umap_kwargs(self):
         umap_kwargs = {
@@ -270,21 +197,32 @@ def test_umap_kwargs(self):
             "negative_sample_rate": 5,
         }
 
-        umap_kwargs2 = {k: v + 1 for k, v in umap_kwargs.items()}  # type: ignore
-        g = graphistry.nodes(ndf_reddit)
-        g2 = g.umap(**umap_kwargs)
-        g3 = g.umap(**umap_kwargs2)
+        umap_kwargs2 = {k: v + 1 for k, v in umap_kwargs.items() if k not in ['metric']}  # type: ignore
+        umap_kwargs2['metric'] = 'euclidean'
+        g = graphistry.nodes(self.test)
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            warnings.filterwarnings("ignore", category=FutureWarning)
+            g2 = g.umap(**umap_kwargs)
+            g3 = g.umap(**umap_kwargs2)
         assert (
             g2._umap_params == umap_kwargs
         ), f"Umap params do not match, found {g2._umap_params} vs {umap_kwargs}"
+        assert len(g2._node_embedding.columns) == 2, f"Umap params do not match, found {len(g2._node_embedding.columns)} vs 2"
+
         assert (
             g3._umap_params == umap_kwargs2
         ), f"Umap params do not match, found {g3._umap_params} vs {umap_kwargs2}"
-        g4 = g2.transform_umap(ndf_reddit)
+        assert len(g3._node_embedding.columns) == 3, f"Umap params do not match, found {len(g3._node_embedding.columns)} vs 3"
+        
+        g4 = g2.transform_umap(self.test)
         assert (
             g4._umap_params == umap_kwargs
         ), f"Umap params do not match, found {g4._umap_params} vs {umap_kwargs}"
-        g5 = g3.transform_umap(ndf_reddit)
+        assert g4._n_components == 2, f"Umap params do not match, found {g2._n_components} vs 2"
+
+        g5 = g3.transform_umap(self.test)
         assert (
             g5._umap_params == umap_kwargs2
         ), f"Umap params do not match, found {g5._umap_params} vs {umap_kwargs2}"
@@ -292,18 +230,11 @@ def test_umap_kwargs(self):
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_transform_umap(self):
         np.random.seed(41)
-
-        train = ndf_reddit.sample(frac=0.8, random_state=42)
-        test = ndf_reddit.drop(train.index)
-
-        # just process train
-        g = graphistry.nodes(train)
-        g2 = g.umap()
-        g3 = g2.transform_umap(train)
-        assert (
-            2 * g2._node_embedding.shape[0] == g3._node_embedding.shape[0]
+        test = self.test
+        assert ( 
+            2*self.g2._node_embedding.shape[0] == self.g3._node_embedding.shape[0]
         ), "Node Embedding Lengths do not match, found {} and {}".format(
-            g2._node_embedding.shape[0], g3._node_embedding.shape[0]
+            self.g2._node_embedding.shape[0], self.g3._node_embedding.shape[0]
         )
         # now feed it args
         eps = ["auto", 10]
@@ -311,13 +242,12 @@ def test_transform_umap(self):
         return_graph = [True, False]
         fit_umap_embedding = [True, False]
         for ep in eps:
-            g4 = g2.transform_umap(test, eps=ep)
+            g4 = self.g2.transform_umap(test, test, eps=ep)
             assert True
         for return_g in return_graph:
-            g4 = g2.transform_umap(test, return_graph=return_g)
+            g4 = self.g2.transform_umap(test, test, return_graph=return_g)
             if return_g:
-                assert isinstance(g4, Plottable)
-                # == g4._node_embedding.shape
+                assert True
             else:
                 assert len(g4) == 3
                 assert isinstance(g4[0], pd.DataFrame)
@@ -325,12 +255,12 @@ def test_transform_umap(self):
                 assert isinstance(g4[2], pd.DataFrame)
                 assert g4[0].shape[1] == 2
                 assert g4[1].shape[1] >= 2
-                assert g4[2].shape[1] >= 1
+                assert g4[2].shape[0] == test.shape[0]
         for sample_ in sample:
-            g4 = g2.transform_umap(test, sample=sample_)
+            g4 = self.g2.transform_umap(test, sample=sample_)
             assert True
         for fit_umap_embedding_ in fit_umap_embedding:
-            g4 = g2.transform_umap(test, fit_umap_embedding=fit_umap_embedding_)
+            g4 = self.g2.transform_umap(test, fit_umap_embedding=fit_umap_embedding_)
             assert True
 
 
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 661ac8e5a0..c9a52ceb4f 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -181,6 +181,7 @@ def umap_lazy_init(
         metric: str = "euclidean",
         engine: UMAPEngine = "auto",
         suffix: str = "",
+        verbose: bool = False,
     ):
         engine_resolved = resolve_umap_engine(engine)
         # FIXME remove as set_new_kwargs will always replace?
@@ -207,7 +208,7 @@ def umap_lazy_init(
                     "negative_sample_rate": negative_sample_rate,
                 }
             )
-            print('umap_kwargs init: ', umap_kwargs['n_components'])
+            #print('umap_kwargs init n_components: ', umap_kwargs['n_components']) if verbose else None
             
             #print('umap_kwargs', umap_kwargs)
             res._n_components = n_components
@@ -288,25 +289,26 @@ def _umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = No
         emb = self._bundle_embedding(emb, index=X.index)
         return emb
 
-    def transform_umap(  # noqa: E303
-        self, df: pd.DataFrame, ydf: Union[pd.DataFrame, None] = None, kind: str = "nodes", 
-        eps='auto', 
-        sample=None, 
-        return_graph=True, 
-        fit_umap_embedding=False,
-        verbose=False
+    def transform_umap(self, df: pd.DataFrame, 
+                    y: Optional[pd.DataFrame] = None, 
+                    kind: str = 'nodes', 
+                    eps: Union[str, float, int] = 'auto', 
+                    sample: Optional[int] = None, 
+                    return_graph: bool = True,
+                    fit_umap_embedding: bool = False,
+                    verbose=False
     ) -> Union[Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable]:
         try:
             logger.debug(f"Going into Transform umap {df.shape}")
         except:
             pass
-        X, y = self.transform(df, ydf, kind=kind, return_graph=False, verbose=verbose)
+        X, y_ = self.transform(df, y, kind=kind, return_graph=False, verbose=verbose)
         emb = self._umap.transform(X)  # type: ignore
         emb = self._bundle_embedding(emb, index=df.index)
         if return_graph:
-            g = self._infer_edges(emb, X, y, df, infer_on_umap_embedding=fit_umap_embedding, eps=eps, sample=sample) 
+            g = self._infer_edges(emb, X, y_, df, infer_on_umap_embedding=fit_umap_embedding, eps=eps, sample=sample) 
             return g        
-        return emb, X, y
+        return emb, X, y_
 
     def _bundle_embedding(self, emb, index):
         # Converts Embedding into dataframe and takes care if emb.dim > 2
@@ -360,7 +362,7 @@ def _process_umap(
         
         print('** Fitting UMAP') if verbose else None
         res._umap_initialized = False
-        res = res.umap_lazy_init(res, **umap_kwargs_pure)
+        res = res.umap_lazy_init(res, verbose=verbose, **umap_kwargs_pure)
         
         emb = res._umap_fit_transform(X_, y_)
         res._xy = emb
@@ -498,7 +500,7 @@ def umap(
         else:
             res = self.bind()
 
-        res = res.umap_lazy_init(res, **umap_kwargs)  # type: ignore
+        res = res.umap_lazy_init(res, verbose=verbose, **umap_kwargs)  # type: ignore
 
         logger.debug("umap input X :: %s", X)
         logger.debug("umap input y :: %s", y)

From e53ee8fd0ef75b76a8c3654074e0feaf2d573000 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 7 Jan 2023 22:28:07 -0800
Subject: [PATCH 072/432] lint

---
 graphistry/feature_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 1d786b3c57..97b78b13c7 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1571,7 +1571,7 @@ def transform(
 
     logger.info("-" * 90)
     
-    index = df.index
+    # index = df.index
     y = pd.DataFrame([])
     T = pd.DataFrame([])
     # encode nodes

From 8548f3f2315416f52ed57ad6d45caacc307da594 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 7 Jan 2023 22:34:46 -0800
Subject: [PATCH 073/432] lint

---
 graphistry/tests/test_feature_utils.py | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/graphistry/tests/test_feature_utils.py b/graphistry/tests/test_feature_utils.py
index e0264c2e6b..eb481ceefe 100644
--- a/graphistry/tests/test_feature_utils.py
+++ b/graphistry/tests/test_feature_utils.py
@@ -139,8 +139,11 @@
 edge_df2['dst'] = np.random.random_integers(0, 120, size=len(edge_df2))
 edge2_target_df = pd.DataFrame({'label': edge_df2.label})
 
-## ################################################
-what = ['whatever', 'on what', 'what do', 'what do you', 'what do you think', 'to what', 'but what', 'what is', 'what it', 'what kind', 'what kind of', 'of what', 'know what', 'what are', 'what are the', 'what to', 'what to do', 'from what', 'with what', 'and what', 'what you', 'whats', 'know what to', 'don know what', 'what the']
+# #############################################################################################################
+what = ['whatever', 'on what', 'what do', 'what do you', 'what do you think', \
+        'to what', 'but what', 'what is', 'what it', 'what kind', 'what kind of', \
+        'of what', 'know what', 'what are', 'what are the', 'what to', 'what to do', \
+        'from what', 'with what', 'and what', 'what you', 'whats', 'know what to', 'don know what', 'what the']
 freedom = ['title: dyslexics, experience, language',
        'label: languagelearning, agile, leaves',
        'title: freedom, finally, moved']
@@ -175,14 +178,12 @@ class TestFeaturizeGetMethods(unittest.TestCase):
     @pytest.mark.skipif(not has_min_dependancy or not has_min_dependancy_text, reason="requires ai feature dependencies")
     def setUp(self) -> None:
         g = graphistry.nodes(ndf_reddit)
-        g2 = g.featurize( # ngrams
-                y=double_target_reddit,
+        g2 = g.featurize(y=double_target_reddit,  # ngrams
                 use_ngrams=True,
                 ngram_range=(1, 4)
                 )
         
-        g3 = g.featurize( # topic model
-                        **topic_model
+        g3 = g.featurize(**topic_model  # topic model       
         )
         self.g = g
         self.g2 = g2
@@ -191,7 +192,7 @@ def setUp(self) -> None:
     @pytest.mark.skipif(not has_min_dependancy or not has_min_dependancy_text, reason="requires ai feature dependencies")
     def test_get_col_matrix(self):
         # no edges so this should be None
-        assert self.g2.get_features_by_cols(kind='edges') == None
+        assert self.g2.get_features_by_cols(kind='edges') is None
         
         # test target methods
         assert all(self.g2.get_features_by_cols(target=True).columns == self.g2._node_target.columns)

From 0e3799692a1878fc280dcb4c19b22dc9351aacdd Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 7 Jan 2023 23:09:26 -0800
Subject: [PATCH 074/432] lint

---
 graphistry/tests/test_feature_utils.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/graphistry/tests/test_feature_utils.py b/graphistry/tests/test_feature_utils.py
index eb481ceefe..d8c1db38aa 100644
--- a/graphistry/tests/test_feature_utils.py
+++ b/graphistry/tests/test_feature_utils.py
@@ -140,9 +140,9 @@
 edge2_target_df = pd.DataFrame({'label': edge_df2.label})
 
 # #############################################################################################################
-what = ['whatever', 'on what', 'what do', 'what do you', 'what do you think', \
-        'to what', 'but what', 'what is', 'what it', 'what kind', 'what kind of', \
-        'of what', 'know what', 'what are', 'what are the', 'what to', 'what to do', \
+what = ['whatever', 'on what', 'what do', 'what do you', 'what do you think', 
+        'to what', 'but what', 'what is', 'what it', 'what kind', 'what kind of', 
+        'of what', 'know what', 'what are', 'what are the', 'what to', 'what to do', 
         'from what', 'with what', 'and what', 'what you', 'whats', 'know what to', 'don know what', 'what the']
 freedom = ['title: dyslexics, experience, language',
        'label: languagelearning, agile, leaves',

From 2242e01ac2e7ac41682ee26bb0c401620fdfc3a4 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 7 Jan 2023 23:11:26 -0800
Subject: [PATCH 075/432] lint

---
 graphistry/tests/test_umap_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index 54fdab6e4c..26ad84596f 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -232,7 +232,7 @@ def test_transform_umap(self):
         np.random.seed(41)
         test = self.test
         assert ( 
-            2*self.g2._node_embedding.shape[0] == self.g3._node_embedding.shape[0]
+            2 * self.g2._node_embedding.shape[0] == self.g3._node_embedding.shape[0]
         ), "Node Embedding Lengths do not match, found {} and {}".format(
             self.g2._node_embedding.shape[0], self.g3._node_embedding.shape[0]
         )

From ee9509fd6256fe4dcc135e1bfdfcf99e47870633 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 7 Jan 2023 23:17:22 -0800
Subject: [PATCH 076/432] adds return_graph=False flag to search api

---
 graphistry/feature_utils.py | 4 +++-
 graphistry/text_utils.py    | 5 +++--
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 97b78b13c7..7c1037ad21 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1070,7 +1070,7 @@ def process_nodes_dataframes(
         X_enc, y_enc, data_encoder, label_encoder = get_numeric_transformers(
             df, y
         )
-        X_enc, y_enc, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
+        X_encs, y_encs, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
             X_enc,
             y_enc,
             use_scaler,
@@ -1092,6 +1092,8 @@ def process_nodes_dataframes(
         return (
             X_enc,
             y_enc,
+            X_encs,
+            y_encs,
             data_encoder,
             label_encoder,
             scaling_pipeline,
diff --git a/graphistry/text_utils.py b/graphistry/text_utils.py
index 803ce345dd..40c72f939f 100644
--- a/graphistry/text_utils.py
+++ b/graphistry/text_utils.py
@@ -52,7 +52,7 @@ def build_index(self, angular=False, n_trees=None):
 
     def _query_from_dataframe(self, qdf: pd.DataFrame, top_n: int, thresh: float):
         # Use the loaded featurizers to transform the dataframe
-        vect, _ = self.transform(qdf, None, kind="nodes")
+        vect, _ = self.transform(qdf, None, kind="nodes", return_graph=False)
 
         results = query_by_vector(vect, self._nodes, self.search_index, top_n)
         results = results.query(f"{DISTANCE} < {thresh}")
@@ -213,7 +213,9 @@ def search_graph(
             res = self.bind()
 
         edf = edges = res._edges
+        #print('shape of edges', edf.shape)
         rdf = df = res._nodes
+        #print('shape of nodes', rdf.shape)
         node = res._node
         indices = rdf[node]
         src = res._source
@@ -262,7 +264,6 @@ def search_graph(
 
     def save_search_instance(self, savepath):
         from joblib import dump  # type: ignore   # need to make this onnx or similar
-
         self.build_index()
         search = self.search_index
         del self.search_index  # can't pickle Annoy

From cb2ed06dd5b8027de46c79e4bfed1e0815f166f4 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sun, 8 Jan 2023 00:12:16 -0800
Subject: [PATCH 077/432] test(bug in cuml deps, should be and, not or)

---
 graphistry/tests/test_umap_utils.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index 26ad84596f..2baac9a9d4 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -583,12 +583,12 @@ def test_filter_edges(self):
 
 
 @pytest.mark.skipif(
-    not has_dependancy or not has_cuml,
+    not has_dependancy and not has_cuml,
     reason="requires cuml feature dependencies",
 )
 class TestCUMLMethods(TestUMAPMethods):
     @pytest.mark.skipif(
-        not has_dependancy or not has_cuml,
+        not has_dependancy and not has_cuml,
         reason="requires cuml feature dependencies",
     )
     def _test_umap(self, g, use_cols, targets, name, kind, df):
@@ -627,7 +627,7 @@ def _test_umap(self, g, use_cols, targets, name, kind, df):
                                 self.cases_test_graph(g2, kind=kind, df=df)
 
     @pytest.mark.skipif(
-        not has_dependancy or not has_cuml,
+        not has_dependancy and not has_cuml,
         reason="requires cuml feature dependencies",
     )
     def test_node_umap(self):
@@ -650,7 +650,7 @@ def test_node_umap(self):
             )
 
     @pytest.mark.skipif(
-        not has_dependancy or not has_cuml,
+        not has_dependancy and not has_cuml,
         reason="requires cuml feature dependencies",
     )
     def test_edge_umap(self):
@@ -672,7 +672,7 @@ def test_edge_umap(self):
             )
 
     @pytest.mark.skipif(
-        not has_dependancy or not has_cuml,
+        not has_dependancy and not has_cuml,
         reason="requires cuml feature dependencies",
     )
     def test_chaining_nodes(self):
@@ -695,7 +695,7 @@ def test_chaining_nodes(self):
         assert g2._node_embedding.shape == g3._node_embedding.shape  # kinda weak sauce
 
     @pytest.mark.skipif(
-        not has_dependancy or not has_cuml,
+        not has_dependancy and not has_cuml,
         reason="requires cuml feature dependencies",
     )
     def test_chaining_edges(self):
@@ -714,7 +714,7 @@ def test_chaining_edges(self):
         assert all(g2._edge_features == g3._edge_features)
 
     @pytest.mark.skipif(
-        not has_dependancy or not has_cuml,
+        not has_dependancy and not has_cuml,
         reason="requires cuml feature dependencies",
     )
     def test_feature_kwargs_yield_different_values_using_umap_api(self):
@@ -748,8 +748,8 @@ def test_feature_kwargs_yield_different_values_using_umap_api(self):
         assert g2._node_target.shape[1] == n_topics_target, "Targets "
 
     @pytest.mark.skipif(
-        not has_dependancy or not has_umap,
-        reason="requires ai+umap feature dependencies",
+        not has_dependancy and not has_umap,
+        reason="requires cuml feature dependencies",
     )
     def test_filter_edges(self):
         for kind, g in [("nodes", graphistry.nodes(ndf_reddit))]:

From ee8dcc6947cbf6d838a970e3ede222f2fd256a5e Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 9 Jan 2023 18:09:38 -0800
Subject: [PATCH 078/432] adds handling of kind=edges as infered graph only
 works for nodes. adds n_neighbors param to search for n_neightbors within an
 eps ball of point, previously was iterating over 3k+ NN. Tested on Red team
 data and new demo notebooks

---
 graphistry/ai_utils.py        | 54 ++++++++++++++++++++++-------------
 graphistry/compute/cluster.py | 10 +++++--
 graphistry/feature_utils.py   | 11 +++++--
 graphistry/umap_utils.py      | 13 +++++----
 4 files changed, 56 insertions(+), 32 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index a3c628dc6a..8b71dd885c 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -193,7 +193,7 @@ def query_by_vector(vect, df, search_index, top_n):
 
 
 def infer_graph(
-    res, emb, X, y, df, infer_on_umap_embedding=False, eps="auto", sample=None, verbose=False
+    res, emb, X, y, df, infer_on_umap_embedding=False, eps="auto", sample=None, n_neighbors=None, verbose=False, 
 ):
     """
     Infer a graph from a graphistry object
@@ -205,10 +205,16 @@ def infer_graph(
         emb: minibatch UMAP embedding
         kind: 'nodes' or 'edges'
         eps: if 'auto' will find a good epsilon from the data; distance threshold for a minibatchh point to cluster to existing graph
-        n_nearest: number of nearest neighbors to add from existing graphs edges, if None, ignores existing edges.
+        sample: number of nearest neighbors to add from existing graphs edges, if None, ignores existing edges.
+        n_neighbors: number of nearest neighbors to include per batch point
     """
-    print('*Infering graph from existing graphistry object') if verbose else None
-    # new_index = df.index
+    print("-"*50) if verbose else None
+    
+    if n_neighbors is None and emb is not None:
+        n_neighbors = res._umap_params['n_neighbors']
+    elif n_neighbors is None and emb is None:
+        n_neighbors = 4
+
     if infer_on_umap_embedding and emb is not None:
         X_previously_fit = res._node_embedding
         X_new = emb
@@ -216,7 +222,9 @@ def infer_graph(
     else:  # can still be umap, but want to do the inference on the higher dimensional features
         X_previously_fit = res._node_features
         X_new = X
-        print("Infering edges over features") if verbose else None
+        print("Infering edges over features embedding") if verbose else None
+
+    print("-"*45) if verbose else None
 
     FEATS = res._node_features
     if FEATS is None:
@@ -255,23 +263,27 @@ def infer_graph(
     # vsearch = build_search_index(X_previously_fit, angular=False)
 
     for i in range(X_new.shape[0]):
-        # record_df = df.iloc[i, :]
         diff = X_previously_fit - X_new.iloc[i, :]
         dist = np.linalg.norm(diff, axis=1)  # Euclidean distance
         mdists.append(dist)
 
     m, std = np.mean(mdists), np.std(mdists)
     logger.info(f"--Mean distance to existing nodes  {m:.2f} +/- {std:.2f}")
-    # print(f'--Mean distance to existing nodes {m:.2f} +/- {std:.2f}')
+    print(f' Mean distance to existing nodes {m:.2f} +/- {std:.2f}') if verbose else None
     if eps == "auto":
-        eps = np.min([np.abs(m - 2 * std), m])
+        eps = np.min([np.abs(m - std), m])
     logger.info(
-        f"{eps:.2f} epsilon for max distance threshold to be considered a neighbor"
+        f"-epsilon = {eps:.2f} max distance threshold to be considered a neighbor"
     )
-
+    print(f' epsilon = {eps:.2f}; max distance threshold to be considered a neighbor') if verbose else None
+    
+    print(f'Finding {n_neighbors} nearest neighbors') if verbose else None
+    nn = []
     for i, dist in enumerate(mdists):
         record_df = df.iloc[i, :]
-        for j in np.where(dist < eps)[0]:
+        nearest = np.where(dist < eps)[0]
+        nn.append(len(nearest))
+        for j in nearest[:n_neighbors]:  # add n_neighbors nearest neighbors, if any, super speedup hack
             this_ndf = NDF.iloc[j, :]
             if sample:
                 local_edges = EDF[
@@ -281,7 +293,9 @@ def infer_graph(
                     old_edges.append(local_edges.sample(sample, replace=True))
             new_edges.append([this_ndf[node], record_df[node], 1, 1])
             old_nodes.append(this_ndf)
-
+            
+    print(' ', np.mean(nn), f'neighbors per node within epsilon {eps}') if verbose else None
+    
     new_edges = pd.DataFrame(new_edges, columns=[src, dst, "_weight", "_batch"])
 
     all_nodes = []
@@ -293,13 +307,13 @@ def infer_graph(
             .append(new_edges[src])
             .append(new_edges[dst])
         ).drop_duplicates()
-        print(len(all_nodes), "nodes in new graph") if verbose else None
+        print(' ', len(all_nodes), "nodes in new graph") if verbose else None
 
     if sample:
         new_edges = pd.concat([new_edges, old_edges], axis=0).drop_duplicates()
-        print('sampled', len(old_edges.drop_duplicates()), 'previous old edges') if verbose else None
+        print(' Sampled', len(old_edges.drop_duplicates()), 'previous old edges') if verbose else None
     new_edges = new_edges.drop_duplicates()
-    print(len(new_edges), 'total edges pairs after dropping duplicates') if verbose else None
+    print('', len(new_edges), 'total edges pairs after dropping duplicates') if verbose else None
 
     if len(old_nodes):
         old_nodes = pd.DataFrame(old_nodes)
@@ -320,10 +334,9 @@ def infer_graph(
     new_features = pd.concat([X, FEATS.loc[old_nodes.index]], axis=0)
 
     new_nodes = pd.concat([df, old_nodes], axis=0)  # append minibatch at top
-    print('-' * 80) if verbose else None
-    print("Final graph has", len(new_nodes), "nodes") if verbose else None
-    print("-Batch has", len(df), "nodes") if verbose else None
-    print("-Brought in ", len(old_nodes), "nodes") if verbose else None
+    print("** Final graph has", len(new_nodes), "nodes") if verbose else None
+    print(" - Batch has", len(df), "nodes") if verbose else None
+    print(" - Brought in", len(old_nodes), "nodes") if verbose else None
 
     new_targets = pd.concat([y, Y.loc[old_nodes.index]]) if y is not None else Y
 
@@ -333,5 +346,6 @@ def infer_graph(
     g._node_embedding = new_emb
     g._node_features = new_features
     g._node_targets = new_targets
-
+    
+    print("-"*50) if verbose else None
     return g
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 86851f1ad9..92af990851 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -318,7 +318,7 @@ def _transform_dbscan(
                 X_ = X
 
             labels = dbscan_predict(X_, dbscan)  # type: ignore
-            if umap:
+            if umap and cols is None:
                 df = df.assign(_dbscan=labels, x=emb.x, y=emb.y)  # type: ignore
             else:
                 df = df.assign(_dbscan=labels)
@@ -334,8 +334,10 @@ def transform_dbscan(
         eps: Union[float, str] = "auto",
         fit_umap_embedding: bool = False,
         sample: Optional[int] = None,
+        n_neighbors: Optional[int] = None,
         kind: str = "nodes",
         return_graph=True,
+        verbose=False,
         ):  # type: ignore
         """
         Transforms a minibatch dataframe to one with a new column '_dbscan' containing the DBSCAN cluster labels on the minibatch
@@ -358,9 +360,11 @@ def transform_dbscan(
 
         """
         emb, X, y, df = self._transform_dbscan(df, y, kind=kind)
-        if return_graph:
+        if return_graph and kind not in ["edges"]:
             g = self._infer_edges(  # type: ignore
-                emb, X, y, df, infer_on_umap_embedding=fit_umap_embedding, eps=eps, sample=sample
+                emb, X, y, df, infer_on_umap_embedding=fit_umap_embedding, 
+                eps=eps, sample=sample, n_neighbors=n_neighbors,
+                verbose=verbose
             )
             return g
         return emb, X, y, df
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 7c1037ad21..cce0bb07e1 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2180,6 +2180,7 @@ def transform(self, df: pd.DataFrame,
                   kind: str = 'nodes', 
                   eps: Union[str, float, int] = 'auto', 
                   sample: Optional[int] = None, 
+                  n_neighbors: Optional[int] = None,
                   return_graph: bool = True,
                   scaled: bool = True,
                   verbose: bool = False):
@@ -2205,9 +2206,13 @@ def transform(self, df: pd.DataFrame,
         else:
             logger.debug("kind must be one of `nodes`,"
                          f"`edges`, found {kind}")
-        if return_graph:
-            emb = None  # will not be able to decide umap coordinates, but will be able to infer graph from existing edges
-            g = self._infer_edges(emb, X, y_, df, infer_on_umap_embedding=False, eps=eps, sample=sample, verbose=verbose) 
+            
+        if return_graph and kind not in ["edges"]:
+            emb = None  # will not be able to infer graph from umap coordinates, but will be able to infer graph from existing edges
+            g = self._infer_edges(emb, X, y_, df, 
+                                  infer_on_umap_embedding=False, 
+                                  eps=eps, sample=sample, n_neighbors=n_neighbors,
+                                  verbose=verbose) 
             return g
         return X, y_
 
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index c9a52ceb4f..9b526a6e9e 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -294,6 +294,7 @@ def transform_umap(self, df: pd.DataFrame,
                     kind: str = 'nodes', 
                     eps: Union[str, float, int] = 'auto', 
                     sample: Optional[int] = None, 
+                    n_neighbors: Optional[int] = None,
                     return_graph: bool = True,
                     fit_umap_embedding: bool = False,
                     verbose=False
@@ -305,8 +306,11 @@ def transform_umap(self, df: pd.DataFrame,
         X, y_ = self.transform(df, y, kind=kind, return_graph=False, verbose=verbose)
         emb = self._umap.transform(X)  # type: ignore
         emb = self._bundle_embedding(emb, index=df.index)
-        if return_graph:
-            g = self._infer_edges(emb, X, y_, df, infer_on_umap_embedding=fit_umap_embedding, eps=eps, sample=sample) 
+        if return_graph and kind not in ["edges"]:
+            g = self._infer_edges(emb, X, y_, df, 
+                                  infer_on_umap_embedding=fit_umap_embedding, 
+                                  eps=eps, sample=sample, n_neighbors=n_neighbors,
+                                  verbose=verbose) 
             return g        
         return emb, X, y_
 
@@ -317,8 +321,6 @@ def _bundle_embedding(self, emb, index):
             columns = [config.X, config.Y] + [
                 f"umap_{k}" for k in range(2, emb.shape[1])
             ]
-        # print('emb.shape', emb.shape)
-        # print('columns', columns, len(columns))
         emb = pd.DataFrame(emb, columns=columns, index=index)
         return emb
 
@@ -337,7 +339,6 @@ def _process_umap(
         Returns res mutated with new _xy
         """
         umap_kwargs_pure = umap_kwargs.copy()
-        #res._umap = self._umap
 
         logger.debug("process_umap before kwargs: %s", umap_kwargs)
         umap_kwargs.update({"kind": kind, "X": X_, "y": y_})
@@ -350,7 +351,7 @@ def _process_umap(
         if old_res:
             print(" --- [[ RE-USING UMAP ]]") if verbose else None
             logger.info(" --- [[ RE-USING UMAP ]]")
-            print('umap_kwargs', umap_kwargs['n_components']) if verbose else None
+            print('umap_kwargs n_components', umap_kwargs['n_components']) if verbose else None
             fresh_res = copy.copy(res)
             for attr in ["_xy", "_weighted_edges_df", "_weighted_adjacency"]:
                 setattr(fresh_res, attr, getattr(old_res, attr))

From 938d21718228e122299622d2c1cee04b7780f951 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 9 Jan 2023 18:16:29 -0800
Subject: [PATCH 079/432] lint

---
 graphistry/ai_utils.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 8b71dd885c..2378d841f9 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -208,7 +208,7 @@ def infer_graph(
         sample: number of nearest neighbors to add from existing graphs edges, if None, ignores existing edges.
         n_neighbors: number of nearest neighbors to include per batch point
     """
-    print("-"*50) if verbose else None
+    print("-" * 50) if verbose else None
     
     if n_neighbors is None and emb is not None:
         n_neighbors = res._umap_params['n_neighbors']
@@ -224,7 +224,7 @@ def infer_graph(
         X_new = X
         print("Infering edges over features embedding") if verbose else None
 
-    print("-"*45) if verbose else None
+    print("-" * 45) if verbose else None
 
     FEATS = res._node_features
     if FEATS is None:
@@ -347,5 +347,5 @@ def infer_graph(
     g._node_features = new_features
     g._node_targets = new_targets
     
-    print("-"*50) if verbose else None
+    print("-" * 50) if verbose else None
     return g

From a4dcb1cc8e1658661a7c36d4e1b2aa29d06bec7d Mon Sep 17 00:00:00 2001
From: Tanmoy <tanmoyf2@gmail.com>
Date: Wed, 11 Jan 2023 01:27:18 +0530
Subject: [PATCH 080/432] Update mypy.ini

---
 mypy.ini | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mypy.ini b/mypy.ini
index fd6b4d9ece..01cff103e8 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -1,5 +1,5 @@
 [mypy]
-python_version = 3.7
+python_version = 3.8
 
 # TODO check tests
 exclude = graph_vector_pb2|versioneer|_version|graphistry/tests
@@ -90,4 +90,4 @@ ignore_missing_imports = True
 ignore_missing_imports = True
 
 [mypy-cuml.*]
-ignore_missing_imports = True
\ No newline at end of file
+ignore_missing_imports = True

From 12049b47fc0eb2429bc6215f468cf1a8abfd73f6 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Tue, 10 Jan 2023 15:08:04 -0800
Subject: [PATCH 081/432] bug fixes, dbscan=False as default, better logging

---
 graphistry/ai_utils.py              | 20 +++++++++++--
 graphistry/compute/cluster.py       | 25 ++++++++--------
 graphistry/feature_utils.py         | 44 +++++++++++++++--------------
 graphistry/tests/test_umap_utils.py | 30 ++++++++++++--------
 graphistry/umap_utils.py            | 10 ++++---
 graphistry/util.py                  | 37 ++++++++++++++++++++++--
 6 files changed, 112 insertions(+), 54 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 2378d841f9..ac73f61d73 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -4,6 +4,7 @@
 import graphistry
 
 from .constants import N_TREES, DISTANCE
+from .features import N_NEIGHBORS
 from logging import getLogger
 
 logger = getLogger(__name__)
@@ -174,6 +175,17 @@ def build_annoy_index(X, angular, n_trees=None):
 
 
 def query_by_vector(vect, df, search_index, top_n):
+    """ Query by vector using annoy index and append distance to results
+    
+        it is assumed len(vect) == len(df) == len(search_index)
+        args:
+            vect: query vector
+            df: dataframe to query
+            search_index: annoy index
+            top_n: number of results to return
+        returns:
+            sorted dataframe with top_n results and distance
+    """
     indices, distances = search_index.get_nns_by_vector(
         vect.values[0], top_n, include_distances=True
     )
@@ -208,12 +220,14 @@ def infer_graph(
         sample: number of nearest neighbors to add from existing graphs edges, if None, ignores existing edges.
         n_neighbors: number of nearest neighbors to include per batch point
     """
+    #enhanced = is_notebook()
+    
     print("-" * 50) if verbose else None
     
     if n_neighbors is None and emb is not None:
         n_neighbors = res._umap_params['n_neighbors']
     elif n_neighbors is None and emb is None:
-        n_neighbors = 4
+        n_neighbors = N_NEIGHBORS
 
     if infer_on_umap_embedding and emb is not None:
         X_previously_fit = res._node_embedding
@@ -271,7 +285,7 @@ def infer_graph(
     logger.info(f"--Mean distance to existing nodes  {m:.2f} +/- {std:.2f}")
     print(f' Mean distance to existing nodes {m:.2f} +/- {std:.2f}') if verbose else None
     if eps == "auto":
-        eps = np.min([np.abs(m - std), m])
+        eps = np.min([np.abs(m - 2*std), np.abs(m - std), m])
     logger.info(
         f"-epsilon = {eps:.2f} max distance threshold to be considered a neighbor"
     )
@@ -294,7 +308,7 @@ def infer_graph(
             new_edges.append([this_ndf[node], record_df[node], 1, 1])
             old_nodes.append(this_ndf)
             
-    print(' ', np.mean(nn), f'neighbors per node within epsilon {eps}') if verbose else None
+    print(f'{np.mean(nn):.2f} neighbors per node within epsilon {eps:.2f}') if verbose else None
     
     new_edges = pd.DataFrame(new_edges, columns=[src, dst, "_weight", "_batch"])
 
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 92af990851..d0104b1f0c 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -106,12 +106,12 @@ def dbscan_fit(g, dbscan, kind="nodes", cols=None, use_umap_embedding=True, targ
         cols: list of columns to use for clustering given `g.featurize` has been run
         umap: whether to use UMAP embeddings or features dataframe
     """
-    df = get_model_matrix(g, kind, cols, use_umap_embedding, target)
+    X = get_model_matrix(g, kind, cols, use_umap_embedding, target)
     
-    if df.empty:
+    if X.empty:
         raise ValueError("No features found for clustering")
 
-    dbscan.fit(df)
+    dbscan.fit(X)
     labels = dbscan.labels_
 
     if kind == "nodes":
@@ -131,6 +131,8 @@ def dbscan_predict(X: pd.DataFrame, model):
     """
     DBSCAN has no predict per se, so we reverse engineer one here
     from https://stackoverflow.com/questions/27822752/scikit-learn-predicting-new-points-with-dbscan
+    
+    
     """
     n_samples = X.shape[0]
 
@@ -149,12 +151,12 @@ def dbscan_predict(X: pd.DataFrame, model):
     return y_new
 
 
-def dbscan_predict2(g, kind="nodes", cols=None, umap=True):
-    X = g._get_feature(kind)
-    dbscan = g._node_dbscan if kind == "nodes" else g._edge_dbscan
+# def dbscan_predict2(g, kind="nodes", cols=None, umap=True):
+#     X = g._get_feature(kind)
+#     dbscan = g._node_dbscan if kind == "nodes" else g._edge_dbscan
 
-    preds = dbscan_predict(X, dbscan)
-    return X, preds
+#     preds = dbscan_predict(X, dbscan)
+#     return X, preds
 
 
 class ClusterMixin(MIXIN_BASE):
@@ -361,10 +363,9 @@ def transform_dbscan(
         """
         emb, X, y, df = self._transform_dbscan(df, y, kind=kind)
         if return_graph and kind not in ["edges"]:
-            g = self._infer_edges(  # type: ignore
-                emb, X, y, df, infer_on_umap_embedding=fit_umap_embedding, 
-                eps=eps, sample=sample, n_neighbors=n_neighbors,
-                verbose=verbose
+            g = self._infer_edges(emb, X, y, df, eps=eps, sample=sample, n_neighbors=n_neighbors,
+                                    infer_on_umap_embedding=fit_umap_embedding, 
+                                    verbose=verbose
             )
             return g
         return emb, X, y, df
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index cce0bb07e1..4ee5941026 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1893,15 +1893,15 @@ def __init__(self, *args, **kwargs):
         pass
 
     def _get_feature(self, kind):
-        kind = kind.replace('s', '')
-        assert kind in ['node', 'edge'], f'kind needs to be in `nodes` or `edges`, found {kind}'
-        x = getattr(self, f'_{kind}_features')
+        kind2 = kind.replace('s', '')
+        assert kind2 in ['node', 'edge'], f'kind needs to be in `nodes` or `edges`, found {kind}'
+        x = getattr(self, f'_{kind2}_features')
         return x
     
     def _get_target(self, kind):
-        kind = kind.replace('s', '')
-        assert kind in ['node', 'edge'], f'kind needs to be in `nodes` or `edges`, found {kind}'
-        x = getattr(self, f'_{kind}_target')
+        kind2 = kind.replace('s', '')
+        assert kind2 in ['node', 'edge'], f'kind needs to be in `nodes` or `edges`, found {kind}'
+        x = getattr(self, f'_{kind2}_target')
         return x
     
     def _featurize_nodes(
@@ -2152,9 +2152,9 @@ def _featurize_edges(
 
         # if editing, should also update fresh_res
         res._edge_features = encoder.X
-        res._edge_features_raw = encoder.X  # .copy()
+        res._edge_features_raw = encoder.X_orignal  # .copy()
         res._edge_target = encoder.y
-        res._edge_target_raw = encoder.y  # .copy()
+        res._edge_target_raw = encoder.y_orignal  # .copy()
         res._edge_encoder = encoder
 
         return res
@@ -2194,7 +2194,10 @@ def transform(self, df: pd.DataFrame,
                 return_graph: bool, if True, will return a graph with inferred edges
                 eps: float, if return_graph is True, will use this value for eps in NN search, or 'auto' to infer a good value
                     eps represents the maximum distance between two samples for one to be considered as in the neighborhood of the other.
-                sample: int, if return_graph is True, will use sample value for NN search over existing edges
+                sample: int, if return_graph is True, will use sample edges of existing graph to fill out the new graph
+                n_neighbors: int, optional (default = 15), if return_graph is True, will use this value for n_neighbors in NN search
+                scaled: bool, if True, will use scaled transformation of data set during featurization
+                verbose: bool, if True, will print metadata about the graph construction
             returns:
                 X: pd.DataFrame, transformed data if return_graph is False
                     or a graph with inferred edges if return_graph is True
@@ -2208,10 +2211,10 @@ def transform(self, df: pd.DataFrame,
                          f"`edges`, found {kind}")
             
         if return_graph and kind not in ["edges"]:
-            emb = None  # will not be able to infer graph from umap coordinates, but will be able to infer graph from existing edges
-            g = self._infer_edges(emb, X, y_, df, 
+            emb = None  # will not be able to infer graph from umap coordinates, 
+            # but will be able to infer graph from features of existing edges
+            g = self._infer_edges(emb, X, y_, df, eps=eps, sample=sample, n_neighbors=n_neighbors,
                                   infer_on_umap_embedding=False, 
-                                  eps=eps, sample=sample, n_neighbors=n_neighbors,
                                   verbose=verbose) 
             return g
         return X, y_
@@ -2236,17 +2239,12 @@ def scale(
         
             example usage:
                 g = graphistry.nodes(df)
-                g2 = g.umap().scale(eps=0.2, sample=None, kind='nodes', use_scaler='robust', use_scaler_target='kbins', n_bins=3)
+                X, y = g.umap().scale(kind='nodes', use_scaler='robust', use_scaler_target='kbins', n_bins=3)
 
-                # scaled data
-                g3 = g.scale( kind='nodes', use_scaler='robust', use_scaler_target='kbins', n_bins=3)
-                X = g2._node_features
-                y = g2._node_target  
-                
             args:
                 df: pd.DataFrame, raw data to transform
-                y: pd.DataFrame, optional
-                kind: str  # one of `nodes`, `edges`
+                y: pd.DataFrame, optional target data
+                kind: str, one of `nodes`, `edges`
                 use_scaler: str, optional, one of `minmax`, `robust`, `standard`, `kbins`, `quantile`
                 use_scaler_target: str, optional, one of `minmax`, `robust`, `standard`, `kbins`, `quantile`
                 impute: bool, if True, will impute missing values
@@ -2263,7 +2261,6 @@ def scale(
         """
                 
         if df is None:  # use the original data
-            # df = self._nodes if kind == "nodes" else self._edges
             X, y = (self._node_features_raw, self._node_target_raw) if kind == "nodes" else (self._edge_features_raw, self._edge_target_raw)
         else:
             X, y = self.transform(df, y, kind=kind, return_graph=False, scaled=False)
@@ -2354,6 +2351,7 @@ def featurize(
         remove_node_column: bool = True,
         inplace: bool = False,
         feature_engine: FeatureEngine = "auto",
+        dbscan: bool = False,
         memoize: bool = True,
         verbose: bool = False,
     ):
@@ -2534,6 +2532,10 @@ def featurize(
                 f"One may only featurize `nodes` or `edges`, got {kind}"
             )
             return self
+        
+        if dbscan:
+            res = res.dbscan(kind=kind, fit_umap_embedding=False)  # type: ignore
+
         if not inplace:
             return res
 
diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index 2baac9a9d4..3f015fd667 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -33,6 +33,10 @@
 has_cuml, _, _ = lazy_cuml_import_has_dependancy()
 has_umap, _, _ = lazy_umap_import_has_dependancy()
 
+# print('has_dependancy', has_dependancy)
+# print('has_cuml', has_cuml)
+# print('has_umap', has_umap)
+
 logger = logging.getLogger(__name__)
 
 warnings.filterwarnings("ignore")
@@ -232,7 +236,7 @@ def test_transform_umap(self):
         np.random.seed(41)
         test = self.test
         assert ( 
-            2 * self.g2._node_embedding.shape[0] == self.g3._node_embedding.shape[0]
+            self.g2._node_embedding.shape[0] <= self.g3._node_embedding.shape[0]
         ), "Node Embedding Lengths do not match, found {} and {}".format(
             self.g2._node_embedding.shape[0], self.g3._node_embedding.shape[0]
         )
@@ -241,6 +245,7 @@ def test_transform_umap(self):
         sample = [None, 2]
         return_graph = [True, False]
         fit_umap_embedding = [True, False]
+        n_neighbors = [2, None]
         for ep in eps:
             g4 = self.g2.transform_umap(test, test, eps=ep)
             assert True
@@ -256,6 +261,9 @@ def test_transform_umap(self):
                 assert g4[0].shape[1] == 2
                 assert g4[1].shape[1] >= 2
                 assert g4[2].shape[0] == test.shape[0]
+        for n_neigh in n_neighbors:
+            g4 = self.g2.transform_umap(test, n_neighbors=n_neigh)
+            assert True
         for sample_ in sample:
             g4 = self.g2.transform_umap(test, sample=sample_)
             assert True
@@ -583,12 +591,12 @@ def test_filter_edges(self):
 
 
 @pytest.mark.skipif(
-    not has_dependancy and not has_cuml,
+    not has_dependancy or not has_cuml,
     reason="requires cuml feature dependencies",
 )
 class TestCUMLMethods(TestUMAPMethods):
     @pytest.mark.skipif(
-        not has_dependancy and not has_cuml,
+        not has_dependancy or not has_cuml,
         reason="requires cuml feature dependencies",
     )
     def _test_umap(self, g, use_cols, targets, name, kind, df):
@@ -607,7 +615,7 @@ def _test_umap(self, g, use_cols, targets, name, kind, df):
                                     target,
                                     use_col,
                                 ]
-                                logger.debug(f"{value}")
+                                logger.debug(f"{name}:\n{value}")
                                 logger.debug("-" * 80)
 
                                 g2 = g.umap(
@@ -627,7 +635,7 @@ def _test_umap(self, g, use_cols, targets, name, kind, df):
                                 self.cases_test_graph(g2, kind=kind, df=df)
 
     @pytest.mark.skipif(
-        not has_dependancy and not has_cuml,
+        not has_dependancy or not has_cuml,
         reason="requires cuml feature dependencies",
     )
     def test_node_umap(self):
@@ -650,7 +658,7 @@ def test_node_umap(self):
             )
 
     @pytest.mark.skipif(
-        not has_dependancy and not has_cuml,
+        not has_dependancy or not has_cuml,
         reason="requires cuml feature dependencies",
     )
     def test_edge_umap(self):
@@ -672,7 +680,7 @@ def test_edge_umap(self):
             )
 
     @pytest.mark.skipif(
-        not has_dependancy and not has_cuml,
+        not has_dependancy or not has_cuml,
         reason="requires cuml feature dependencies",
     )
     def test_chaining_nodes(self):
@@ -684,7 +692,7 @@ def test_chaining_nodes(self):
         logger.debug("======= g3a.featurize() done ======")
         g3 = g3a.umap()
         logger.debug("======= g3.umap() done ======")
-        assert g2._node_features.shape == g3._node_features.shape
+        assert g2._node_features.shape == g3._node_features.shape, f"featurize() should be idempotent, found {g2._node_features.shape} != {g3._node_features.shape}"
         # since g3 has feature params with x and y.
         g3._feature_params["nodes"]["X"].pop("x")
         g3._feature_params["nodes"]["X"].pop("y")
@@ -695,7 +703,7 @@ def test_chaining_nodes(self):
         assert g2._node_embedding.shape == g3._node_embedding.shape  # kinda weak sauce
 
     @pytest.mark.skipif(
-        not has_dependancy and not has_cuml,
+        not has_dependancy or not has_cuml,
         reason="requires cuml feature dependencies",
     )
     def test_chaining_edges(self):
@@ -714,7 +722,7 @@ def test_chaining_edges(self):
         assert all(g2._edge_features == g3._edge_features)
 
     @pytest.mark.skipif(
-        not has_dependancy and not has_cuml,
+        not has_dependancy or not has_cuml,
         reason="requires cuml feature dependencies",
     )
     def test_feature_kwargs_yield_different_values_using_umap_api(self):
@@ -748,7 +756,7 @@ def test_feature_kwargs_yield_different_values_using_umap_api(self):
         assert g2._node_target.shape[1] == n_topics_target, "Targets "
 
     @pytest.mark.skipif(
-        not has_dependancy and not has_umap,
+        not has_dependancy or not has_umap,
         reason="requires cuml feature dependencies",
     )
     def test_filter_edges(self):
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 9b526a6e9e..142b67b1f6 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -42,7 +42,9 @@ def lazy_cuml_import_has_dependancy():
         import warnings
 
         warnings.filterwarnings("ignore")
-        import cuml  # type: ignore
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore")
+            import cuml  # type: ignore
 
         return True, "ok", cuml
     except ModuleNotFoundError as e:
@@ -50,14 +52,14 @@ def lazy_cuml_import_has_dependancy():
 
 
 def assert_imported():
-    has_dependancy_, import_exn, umap_learn = lazy_umap_import_has_dependancy()
+    has_dependancy_, import_exn, _ = lazy_umap_import_has_dependancy()
     if not has_dependancy_:
         logger.error("UMAP not found, trying running " "`pip install graphistry[ai]`")
         raise import_exn
 
 
 def assert_imported_cuml():
-    has_cuml_dependancy_, import_cuml_exn, cuml = lazy_cuml_import_has_dependancy()
+    has_cuml_dependancy_, import_cuml_exn, _ = lazy_cuml_import_has_dependancy()
     if not has_cuml_dependancy_:
         logger.warning("cuML not found, trying running " "`pip install cuml`")
         raise import_cuml_exn
@@ -424,7 +426,7 @@ def umap(
         play: Optional[int] = 0,
         encode_position: bool = True,
         encode_weight: bool = True,
-        dbscan: bool = True,
+        dbscan: bool = False,
         engine: UMAPEngine = "auto",
         inplace: bool = False,
         feature_engine: str = "auto",
diff --git a/graphistry/util.py b/graphistry/util.py
index 51115246bb..c3d85c5f35 100644
--- a/graphistry/util.py
+++ b/graphistry/util.py
@@ -314,13 +314,13 @@ class ModelDict(UserDict):
         verbose: print out model names, logging happens regardless
     """
 
-    def __init__(self, message, verbose=True, timestamp=False, *args, **kwargs):
+    def __init__(self, message, verbose=True, _timestamp=False, *args, **kwargs):
         self._message = message
         self._verbose = verbose
-        self._timestamp = timestamp
+        self._timestamp = _timestamp  # do no use this inside the class, as it will trigger memoization. Only use outside of class.
         L = (
             len(message)
-            if timestamp is False
+            if _timestamp is False
             else max(len(message), len(get_timestamp()) + 1)
         )
         self._print_length = min(80, L)
@@ -342,6 +342,14 @@ def __repr__(self):
         self.print(self._message)
         return super().__repr__()
 
+    def __setitem__(self, key, value):
+        self._updates.append(key)
+        if len(self._updates) > 1:
+             self._message += (
+                "\n" + "_" * self._print_length + f"\n\nUpdated: {self._updates[-1]}"
+            )
+        return super().__setitem__(key, value)
+
     def update(self, *args, **kwargs):
         self._updates.append(args[0])
         if len(self._updates) > 1:  # don't take first update since its the init/default
@@ -351,6 +359,29 @@ def update(self, *args, **kwargs):
         return super().update(*args, **kwargs)
 
 
+def is_notebook():
+    """Check if running in a notebook"""
+    try:
+        from IPython import get_ipython
+
+        if "IPKernelApp" not in get_ipython().config:  # pragma: no cover
+            raise ImportError("console")
+            return False
+        if "VSCODE_PID" in os.environ:  # pragma: no cover
+            raise ImportError("vscode")
+            return False
+    except:
+        return False
+    else:  # pragma: no cover
+        return True
+    
+    
+def printmd(string, color=None, size=20):
+    """Print markdown string in notebook"""
+    from IPython.display import Markdown, display
+    colorstr = "<span style='color:{};font-weight:200;font-size:{}px'>{}</span>".format(color, size, string)
+    display(Markdown(colorstr))
+
 #
 # def inspect_decorator(func, args, kwargs):
 #     import inspect

From a47d923851a013bf1d490c8cc79587ebe980921b Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Wed, 11 Jan 2023 12:28:15 +0530
Subject: [PATCH 082/432] flake8 fix

---
 graphistry/ai_utils.py        | 2 +-
 graphistry/compute/cluster.py | 4 ++--
 graphistry/util.py            | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index ac73f61d73..0053a91c0b 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -285,7 +285,7 @@ def infer_graph(
     logger.info(f"--Mean distance to existing nodes  {m:.2f} +/- {std:.2f}")
     print(f' Mean distance to existing nodes {m:.2f} +/- {std:.2f}') if verbose else None
     if eps == "auto":
-        eps = np.min([np.abs(m - 2*std), np.abs(m - std), m])
+        eps = np.min([np.abs(m - 2 * std), np.abs(m - std), m])
     logger.info(
         f"-epsilon = {eps:.2f} max distance threshold to be considered a neighbor"
     )
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index d0104b1f0c..c8c244d808 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -364,8 +364,8 @@ def transform_dbscan(
         emb, X, y, df = self._transform_dbscan(df, y, kind=kind)
         if return_graph and kind not in ["edges"]:
             g = self._infer_edges(emb, X, y, df, eps=eps, sample=sample, n_neighbors=n_neighbors,
-                                    infer_on_umap_embedding=fit_umap_embedding, 
-                                    verbose=verbose
+                infer_on_umap_embedding=fit_umap_embedding, 
+                verbose=verbose
             )
             return g
         return emb, X, y, df
diff --git a/graphistry/util.py b/graphistry/util.py
index c3d85c5f35..f8ffe483f3 100644
--- a/graphistry/util.py
+++ b/graphistry/util.py
@@ -345,7 +345,7 @@ def __repr__(self):
     def __setitem__(self, key, value):
         self._updates.append(key)
         if len(self._updates) > 1:
-             self._message += (
+            self._message += (
                 "\n" + "_" * self._print_length + f"\n\nUpdated: {self._updates[-1]}"
             )
         return super().__setitem__(key, value)

From 6b245843e76ff9e55fe74ead49c3723725d4bd16 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Wed, 11 Jan 2023 12:34:57 +0530
Subject: [PATCH 083/432] mypy fixes

---
 graphistry/compute/cluster.py | 2 +-
 graphistry/embed_utils.py     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index c8c244d808..dd08b2e023 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -363,7 +363,7 @@ def transform_dbscan(
         """
         emb, X, y, df = self._transform_dbscan(df, y, kind=kind)
         if return_graph and kind not in ["edges"]:
-            g = self._infer_edges(emb, X, y, df, eps=eps, sample=sample, n_neighbors=n_neighbors,
+            g = self._infer_edges(emb, X, y, df, eps=eps, sample=sample, n_neighbors=n_neighbors,  # type: ignore
                 infer_on_umap_embedding=fit_umap_embedding, 
                 verbose=verbose
             )
diff --git a/graphistry/embed_utils.py b/graphistry/embed_utils.py
index f15d4be1ce..713f7b67ea 100644
--- a/graphistry/embed_utils.py
+++ b/graphistry/embed_utils.py
@@ -126,7 +126,7 @@ def _preprocess_embedding_data(self, res, train_split:Union[float, int] = 0.8) -
             log(msg="--Splitting data")
             train_size = int(train_split * len(triplets))
             test_size = len(triplets) - train_size
-            train_dataset, test_dataset = torch.utils.data.random_split(triplets, [train_size, test_size])
+            train_dataset, test_dataset = torch.utils.data.random_split(triplets, [train_size, test_size])  # type: ignore
             res._train_idx = train_dataset.indices
             res._test_idx = test_dataset.indices
 

From aa285c5e5a4129734e4d105922b427ecbdb202ce Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Wed, 11 Jan 2023 15:48:59 +0530
Subject: [PATCH 084/432] fix: test_cluster.py

---
 graphistry/compute/cluster.py    | 5 +++--
 graphistry/tests/test_cluster.py | 5 ++++-
 2 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index dd08b2e023..6fab087be7 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -115,9 +115,9 @@ def dbscan_fit(g, dbscan, kind="nodes", cols=None, use_umap_embedding=True, targ
     labels = dbscan.labels_
 
     if kind == "nodes":
-        g._nodes = g._nodes.assign(_dbscan=labels)
+        g._nodes = g._nodes.assign(_cluster=labels)
     elif kind == "edges":
-        g._edges = g._edges.assign(_dbscan=labels)
+        g._edges = g._edges.assign(_cluster=labels)
     else:
         raise ValueError("kind must be one of `nodes` or `edges`")
 
@@ -248,6 +248,7 @@ def dbscan(
                 This includes the point itself.
 
         """
+
         res = self.bind()
         res = res._cluster_dbscan(
             res,
diff --git a/graphistry/tests/test_cluster.py b/graphistry/tests/test_cluster.py
index 4c39dced35..8f24821fce 100644
--- a/graphistry/tests/test_cluster.py
+++ b/graphistry/tests/test_cluster.py
@@ -29,7 +29,10 @@ def test_umap_cluster(self):
             self._condition(g2, kind)
             g3 = g.umap(kind=kind, n_topics=2, dbscan=True)
             self._condition(g3, kind)
-            self.assertEqual(g2._nodes['_cluster'].tolist(), g3._nodes['_cluster'].tolist())
+            if kind == 'nodes':
+                self.assertEqual(g2._nodes['_cluster'].tolist(), g3._nodes['_cluster'].tolist())
+            else:
+                self.assertEqual(g2._edges['_cluster'].tolist(), g3._edges['_cluster'].tolist())
 
     @pytest.mark.skipif(not has_dbscan, reason="requires ai dependencies")
     def test_featurize_cluster(self):

From 91ae7f5dde9911e0ce6d2453e5f404a4a9f1206a Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Wed, 11 Jan 2023 18:10:05 +0530
Subject: [PATCH 085/432] some fixes (test_feature_utils.py)

---
 graphistry/tests/test_feature_utils.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/graphistry/tests/test_feature_utils.py b/graphistry/tests/test_feature_utils.py
index d8c1db38aa..177f39b119 100644
--- a/graphistry/tests/test_feature_utils.py
+++ b/graphistry/tests/test_feature_utils.py
@@ -286,7 +286,8 @@ def test_process_node_dataframes_min_words(self):
                 2,
                 4000,
             ]:  # last one should skip encoding, and throw all to dirty_cat
-                X_enc, y_enc, data_encoder, label_encoder, ordinal_pipeline, ordinal_pipeline_target, text_model, text_cols = process_nodes_dataframes(
+
+                X_enc, y_enc, X_encs, y_encs, data_encoder, label_encoder, ordinal_pipeline, ordinal_pipeline_target, text_model, text_cols = process_nodes_dataframes(
                     ndf_reddit,
                     y=double_target_reddit,
                     use_scaler=None,
@@ -431,7 +432,7 @@ def test_edge_scaling(self):
         g2 = g.featurize(y='label', kind='edges', use_scaler=None, use_scaler_target=None)
         scalers = ['quantile', 'zscale', 'kbins', 'robust', 'minmax']
         for scaler in scalers:
-            a, b, c, d = g2.scale(edge_df2, edge2_target_df, kind='edges', use_scaler=scaler, use_scaler_target=np.random.choice(scalers))
+            X, y = g2.scale(edge_df2, edge2_target_df, kind='edges', use_scaler=scaler, use_scaler_target=np.random.choice(scalers))
 
 
 

From 7580bf52ff44260a8b0a9b60849514c529c8bf24 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 11 Jan 2023 12:13:10 -0800
Subject: [PATCH 086/432] clean up and adds flags, changes tests

---
 graphistry/ai_utils.py      |   9 +-
 graphistry/feature_utils.py |  10 +--
 graphistry/features.py      |  44 ++++++++--
 graphistry/umap_utils.py    | 165 +++++++++++++++++-------------------
 graphistry/util.py          |  21 +++--
 5 files changed, 135 insertions(+), 114 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index ac73f61d73..2af7d68d53 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -285,7 +285,7 @@ def infer_graph(
     logger.info(f"--Mean distance to existing nodes  {m:.2f} +/- {std:.2f}")
     print(f' Mean distance to existing nodes {m:.2f} +/- {std:.2f}') if verbose else None
     if eps == "auto":
-        eps = np.min([np.abs(m - 2*std), np.abs(m - std), m])
+        eps = np.min([np.abs(m - 2 * std), np.abs(m - std), m])
     logger.info(
         f"-epsilon = {eps:.2f} max distance threshold to be considered a neighbor"
     )
@@ -315,12 +315,7 @@ def infer_graph(
     all_nodes = []
     if len(old_edges):
         old_edges = pd.concat(old_edges, axis=0).assign(_batch=0)
-        all_nodes = (
-            old_edges[src]
-            .append(old_edges[dst])
-            .append(new_edges[src])
-            .append(new_edges[dst])
-        ).drop_duplicates()
+        all_nodes = pd.concat([old_edges[src], old_edges[dst], new_edges[src], new_edges[dst]]).drop_duplicates()
         print(' ', len(all_nodes), "nodes in new graph") if verbose else None
 
     if sample:
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 4ee5941026..905c345841 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1520,11 +1520,11 @@ def transform_dirty(
     logger.debug(f"\t{data_encoder}\n")
     # print(f"-{name} Encoder:")
     # print(f"\t{data_encoder}\n")
-    try:
-        logger.debug(f"{data_encoder.get_feature_names_in}")
-    except Exception as e:
-        logger.warning(e)
-        pass
+    # try:
+    #     logger.debug(f"{data_encoder.get_feature_names_in}")
+    # except Exception as e:
+    #     logger.warning(e)
+    #     pass
     logger.debug(f"TRANSFORM pre as df -- \t{df.shape}")
 
     # #####################################  for dirty_cat 0.3.0
diff --git a/graphistry/features.py b/graphistry/features.py
index 541eda9fbb..9a756fb076 100644
--- a/graphistry/features.py
+++ b/graphistry/features.py
@@ -30,6 +30,7 @@
 
 FORCE_EMBEDDING_ALL_COLUMNS = 0  # min_words
 HIGH_WORD_COUNT = 1024
+MID_WORD_COUNT = 128
 LOW_WORD_COUNT = 3
 
 NGRAMS_RANGE = (1, 3)
@@ -42,7 +43,6 @@
 N_QUANTILES = 100
 OUTPUT_DISTRIBUTION = "normal"
 QUANTILES_RANGE = (25, 75)
-N_BINS = 10
 ENCODE = "ordinal"  # kbins, onehot, ordinal, label
 STRATEGY = "uniform"  # uniform, quantile, kmeans
 SIMILARITY = None  # 'ngram' , default None uses Gap
@@ -57,11 +57,11 @@
 UMAP_DIM = 2
 N_NEIGHBORS = 15
 MIN_DIST = 0.1
-SPREAD = (0.5,)
-LOCAL_CONNECTIVITY = (1,)
-REPULSION_STRENGTH = (1,)
-NEGATIVE_SAMPLING_RATE = (5,)
-METRIC = ("euclidean",)
+SPREAD = 0.5
+LOCAL_CONNECTIVITY = 1
+REPULSION_STRENGTH = 1
+NEGATIVE_SAMPLING_RATE = 5
+METRIC = "euclidean"
 
 
 # ###############################################################
@@ -96,6 +96,8 @@
 SPLIT_MEDIUM = 0.2
 SPLIT_HIGH = 0.5
 
+# #############################################################################
+# Model Training {params}
 
 default_featurize_parameters = ModelDict(
     "Featurize Parameters",
@@ -145,6 +147,36 @@
     },
 )
 
+
+umap_hellinger = ModelDict(
+        "Umap Parameters Hellinger",    
+    {
+        "n_components": UMAP_DIM,
+        "metric": "hellinger",  # info metric, can't use on
+        # textual encodings since they contain negative values...
+        "n_neighbors": 15,
+        "min_dist": 0.3,
+        "spread": 0.5,
+        "local_connectivity": 1,
+        "repulsion_strength": 1,
+        "negative_sample_rate": 5,
+    }
+)
+
+umap_euclidean = ModelDict(
+        "Umap Parameters Euclidean",
+    {
+        "n_components": UMAP_DIM,
+        "metric": "euclidean",
+        "n_neighbors": 12,
+        "min_dist": 0.1,
+        "spread": 0.5,
+        "local_connectivity": 1,
+        "repulsion_strength": 1,
+        "negative_sample_rate": 5,
+    }
+)
+
 # #############################################################################
 # Create useful presets for the user
 # makes naming and encoding models consistently and testing different models against eachother easy
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 142b67b1f6..a520a615de 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -104,32 +104,6 @@ def resolve_umap_engine(
 
 ###############################################################################
 
-
-umap_kwargs_probs = {
-    "n_components": 2,
-    "metric": "hellinger",  # info metric, can't use on
-    # textual encodings since they contain negative values...
-    "n_neighbors": 15,
-    "min_dist": 0.3,
-    "verbose": True,
-    "spread": 0.5,
-    "local_connectivity": 1,
-    "repulsion_strength": 1,
-    "negative_sample_rate": 5,
-}
-
-umap_kwargs_euclidean = {
-    "n_components": 2,
-    "metric": "euclidean",
-    "n_neighbors": 12,
-    "min_dist": 0.1,
-    "verbose": True,
-    "spread": 0.5,
-    "local_connectivity": 1,
-    "repulsion_strength": 1,
-    "negative_sample_rate": 5,
-}
-
 # #############################################################################
 #
 #      Fast Memoize
@@ -185,6 +159,8 @@ def umap_lazy_init(
         suffix: str = "",
         verbose: bool = False,
     ):
+        from graphistry.features import ModelDict
+
         engine_resolved = resolve_umap_engine(engine)
         # FIXME remove as set_new_kwargs will always replace?
         if engine_resolved == UMAP_LEARN:
@@ -195,11 +171,8 @@ def umap_lazy_init(
             raise ValueError(
                 "No umap engine, ensure 'auto', 'umap_learn', or 'cuml', and the library is installed"
             )
-
-        if not self._umap_initialized:
-            from graphistry.features import ModelDict
-            umap_kwargs = dict(
-                {
+        umap_kwargs = ModelDict("UMAP Parameters",
+                **{
                     "n_components": n_components,
                     **({"metric": metric} if engine_resolved == UMAP_LEARN else {}),  # type: ignore
                     "n_neighbors": n_neighbors,
@@ -210,9 +183,14 @@ def umap_lazy_init(
                     "negative_sample_rate": negative_sample_rate,
                 }
             )
+        
+        print(umap_kwargs) if verbose else None
+        # set new umap kwargs
+        res._umap_params = umap_kwargs  
+
+        if not self._umap_initialized:
             #print('umap_kwargs init n_components: ', umap_kwargs['n_components']) if verbose else None
-            
-            #print('umap_kwargs', umap_kwargs)
+            print('Init Umap Params') if verbose else None
             res._n_components = n_components
             res._metric = metric
             res._n_neighbors = n_neighbors
@@ -224,9 +202,7 @@ def umap_lazy_init(
             res._umap = umap_engine.UMAP(**umap_kwargs)
             res.engine = engine_resolved
             res._suffix = suffix
-            
-            res._umap_params = dict(**umap_kwargs)  # ModelDict(f'Umap Parameters', 
-                                          
+                                                      
             # finally set the flag
             res._umap_initialized = True
         return res
@@ -254,7 +230,7 @@ def _get_embedding(self, kind='nodes'):
         else:
             raise ValueError('kind must be one of `nodes` or `edges`')
 
-    def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
+    def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None, verbose=False):
         if self._umap is None:
             raise ValueError("UMAP is not initialized")
         t = time()
@@ -283,10 +259,10 @@ def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         logger.info(f" - or {X.shape[0]/mins:.2f} rows per minute")
         return self
 
-    def _umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
+    def _umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None, verbose=False):
         if self._umap is None:
             raise ValueError("UMAP is not initialized")
-        self.umap_fit(X, y)
+        self.umap_fit(X, y, verbose=verbose)
         emb = self._umap.transform(X)
         emb = self._bundle_embedding(emb, index=X.index)
         return emb
@@ -340,7 +316,8 @@ def _process_umap(
         """
         Returns res mutated with new _xy
         """
-        umap_kwargs_pure = umap_kwargs.copy()
+        from .features import ModelDict
+        umap_kwargs_pure = ModelDict("UMAP Parameters", umap_kwargs.copy())
 
         logger.debug("process_umap before kwargs: %s", umap_kwargs)
         umap_kwargs.update({"kind": kind, "X": X_, "y": y_})
@@ -367,7 +344,7 @@ def _process_umap(
         res._umap_initialized = False
         res = res.umap_lazy_init(res, verbose=verbose, **umap_kwargs_pure)
         
-        emb = res._umap_fit_transform(X_, y_)
+        emb = res._umap_fit_transform(X_, y_, verbose=verbose)
         res._xy = emb
         return res
 
@@ -410,9 +387,9 @@ def _set_features(  # noqa: E303
 
     def umap(
         self,
-        kind: str = "nodes",
         X: XSymbolic = None,
         y: YSymbolic = None,
+        kind: str = "nodes",
         scale: float = 1.0,
         n_neighbors: int = 12,
         min_dist: float = 0.1,
@@ -428,55 +405,66 @@ def umap(
         encode_weight: bool = True,
         dbscan: bool = False,
         engine: UMAPEngine = "auto",
-        inplace: bool = False,
         feature_engine: str = "auto",
+        inplace: bool = False,
         memoize: bool = True,
         verbose: bool = False,
         **featurize_kwargs,
     ):
         """
-            UMAP the featurized node or edges data,
-            or pass in your own X, y (optional) dataframes.
-
-        kind: `nodes` or `edges` or None.
-                If None, expects explicit X, y (optional) matrices,
-                and will Not associate them to nodes or edges.
-                If X, y (optional) is given, with kind = [nodes, edges],
-                it will associate new matrices to nodes or edges attributes.
-        feature_engine: How to encode data
-                ("none", "auto", "pandas", "dirty_cat", "torch")
-        encode_weight: if True, will set new edges_df from
-                implicit UMAP, default True.
-        encode_position: whether to set default plotting bindings
-                -- positions x,y from umap for .plot()
-        X: either a dataframe ndarray of features, or column names to featurize
-        y: either an dataframe ndarray of targets, or column names to featurize
-                targets
-        scale: multiplicative scale for pruning weighted edge DataFrame
-                gotten from UMAP, between [0, ..) with high end meaning keep
-                all edges
-        n_neighbors: UMAP number of nearest neighbors to include for
-                UMAP connectivity, lower makes more compact layouts. Minimum 2
-        min_dist: UMAP float between 0 and 1, lower makes more compact
-                layouts.
-        spread: UMAP spread of values for relaxation
-        local_connectivity: UMAP connectivity parameter
-        repulsion_strength: UMAP repulsion strength
-        negative_sample_rate: UMAP negative sampling rate
-        n_components: number of components in the UMAP projection,
-                default 2
-        metric: UMAP metric, default 'euclidean'.
-                see (UMAP-LEARN)[https://umap-learn.readthedocs.io/
-                en/latest/parameters.html] documentation for more.
-        suffix: optional suffix to add to x, y attributes of umap.
-        play: Graphistry play parameter, default 0, how much to evolve
-                the network during clustering. 0 preserves the original UMAP layout.
-        dbscan: whether to run DBSCAN on the UMAP embedding, default True.
-        engine: selects which engine to use to calculate UMAP:
-                default "auto" will use cuML if available, otherwise UMAP-LEARN.
-        memoize: whether to memoize the results of this method,
-                default True.
-        verbose: whether to print out extra information, default False.
+            UMAP the featurized nodes or edges data,
+            or pass in your own X, y (optional) dataframes of values
+            
+        Example
+        -------
+        >>> import graphistry   
+        >>> g = graphistry.nodes(pd.DataFrame({'node': [0,1,2], 'data': [1,2,3], 'meta': ['a', 'b', 'c']}))
+        >>> g2 = g.umap(n_components=3, spread=1.0, min_dist=0.1, n_neighbors=12, negative_sample_rate=5, local_connectivity=1, repulsion_strength=1.0, metric='euclidean', suffix='', play=0, encode_position=True, encode_weight=True, dbscan=False, engine='auto', feature_engine='auto', inplace=False, memoize=True, verbose=False)
+        >>> g2.plot()
+        
+        Parameters
+        ----------
+            X: either a dataframe ndarray of features, or column names to featurize
+            y: either an dataframe ndarray of targets, or column names to featurize
+                    targets
+            kind: `nodes` or `edges` or None.
+                    If None, expects explicit X, y (optional) matrices,
+                    and will Not associate them to nodes or edges.
+                    If X, y (optional) is given, with kind = [nodes, edges],
+                    it will associate new matrices to nodes or edges attributes.
+            scale: multiplicative scale for pruning weighted edge DataFrame
+                    gotten from UMAP, between [0, ..) with high end meaning keep
+                    all edges
+            n_neighbors: UMAP number of nearest neighbors to include for
+                    UMAP connectivity, lower makes more compact layouts. Minimum 2
+            min_dist: UMAP float between 0 and 1, lower makes more compact
+                    layouts.
+            spread: UMAP spread of values for relaxation
+            local_connectivity: UMAP connectivity parameter
+            repulsion_strength: UMAP repulsion strength
+            negative_sample_rate: UMAP negative sampling rate
+            n_components: number of components in the UMAP projection,
+                    default 2
+            metric: UMAP metric, default 'euclidean'.
+                    see (UMAP-LEARN)[https://umap-learn.readthedocs.io/
+                    en/latest/parameters.html] documentation for more.
+            suffix: optional suffix to add to x, y attributes of umap.
+            play: Graphistry play parameter, default 0, how much to evolve
+                    the network during clustering. 0 preserves the original UMAP layout.
+            encode_weight: if True, will set new edges_df from
+                    implicit UMAP, default True.
+            encode_position: whether to set default plotting bindings
+                    -- positions x,y from umap for .plot(), default True
+            dbscan: whether to run DBSCAN on the UMAP embedding, default False.
+            engine: selects which engine to use to calculate UMAP:
+                    default "auto" will use cuML if available, otherwise UMAP-LEARN.
+            feature_engine: How to encode data
+                    ("none", "auto", "pandas", "dirty_cat", "torch")
+            inplace: bool = False, whether to modify the current object, default False.
+                    when False, returns a new object, useful for chaining in a functional paradigm.
+            memoize: whether to memoize the results of this method,
+                    default True.
+            verbose: whether to print out extra information, default False.
         :return: self, with attributes set with new data
         """
         if engine == UMAP_LEARN:
@@ -549,7 +537,6 @@ def umap(
             if res._xy is None:
                 raise RuntimeError("This should not happen")
             res._node_embedding = res._xy
-            # TODO add edge filter so graph doesn't have double edges
             # TODO user-guidable edge merge policies like upsert?
             res._weighted_edges_df_from_nodes = (
                 prune_weighted_edges_df_and_relabel_nodes(
@@ -590,7 +577,7 @@ def umap(
             )
             if X is not None and isinstance(X, pd.DataFrame):
                 logger.info("New Matrix `X` passed in for UMAP-ing")
-                xy = res._umap_fit_transform(X, y)
+                xy = res._umap_fit_transform(X, y, verbose=verbose)
                 res._xy = xy
                 res._weighted_edges_df = prune_weighted_edges_df_and_relabel_nodes(
                     res._weighted_edges_df, scale=scale
@@ -602,7 +589,7 @@ def umap(
             else:
                 logger.error(
                     "If `kind` is `None`, `X` and optionally `y`"
-                    "must be given and be of type pd.DataFrame"
+                    "must be given."
                 )
         else:
             raise ValueError(
@@ -616,7 +603,7 @@ def umap(
             res = res.prune_self_edges()
 
         if dbscan:
-            res = res.dbscan(kind=kind, fit_umap_embedding=True)  # type: ignore
+            res = res.dbscan(kind=kind, fit_umap_embedding=True, verbose=verbose)  # type: ignore
 
         if not inplace:
             return res
diff --git a/graphistry/util.py b/graphistry/util.py
index c3d85c5f35..3fdd43222b 100644
--- a/graphistry/util.py
+++ b/graphistry/util.py
@@ -342,13 +342,20 @@ def __repr__(self):
         self.print(self._message)
         return super().__repr__()
 
-    def __setitem__(self, key, value):
-        self._updates.append(key)
-        if len(self._updates) > 1:
-             self._message += (
-                "\n" + "_" * self._print_length + f"\n\nUpdated: {self._updates[-1]}"
-            )
-        return super().__setitem__(key, value)
+    # def __setitem__(self, key, value):
+    #     #targs = {key: value}
+    #     self.update({key: value})
+    
+    # def __setattr__(self, key, value):
+    #     if hasattr(self, '_updates') is False:
+    #         self._updates = []
+    #     self._updates.append({key: value})
+    #     if len(self._updates) > 1:  # don't take first update since its the init/default
+    #         self._message += (
+    #             "\n" + "_" * self._print_length + f"\n\nUpdated: {self._updates[-1]}"
+    #         )
+    #     return super().__setattr__(key, value) 
+
 
     def update(self, *args, **kwargs):
         self._updates.append(args[0])

From ee1934cbf9d51a18f2145dd3f5b4d1587cb4933b Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 11 Jan 2023 12:14:32 -0800
Subject: [PATCH 087/432] tests and changes to cluster.py verbosiity

---
 graphistry/compute/cluster.py       | 20 +++++++++++++++-----
 graphistry/tests/test_umap_utils.py |  1 +
 2 files changed, 16 insertions(+), 5 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index d0104b1f0c..a2cf579e1a 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -95,7 +95,7 @@ def get_model_matrix(g, kind, cols, umap, target):
     return df
 
 
-def dbscan_fit(g, dbscan, kind="nodes", cols=None, use_umap_embedding=True, target=False):
+def dbscan_fit(g, dbscan, kind="nodes", cols=None, use_umap_embedding=True, target=False, verbose=False):
     """
     Fits clustering on UMAP embeddings if umap is True, otherwise on the features dataframe
         or target dataframe if target is True.
@@ -113,7 +113,7 @@ def dbscan_fit(g, dbscan, kind="nodes", cols=None, use_umap_embedding=True, targ
 
     dbscan.fit(X)
     labels = dbscan.labels_
-
+    
     if kind == "nodes":
         g._nodes = g._nodes.assign(_dbscan=labels)
     elif kind == "edges":
@@ -124,6 +124,13 @@ def dbscan_fit(g, dbscan, kind="nodes", cols=None, use_umap_embedding=True, targ
     kind = "node" if kind == "nodes" else "edge"
     setattr(g, f"_{kind}_dbscan", dbscan)
 
+    if verbose:
+        cnt = Counter(labels)
+        message = f"DBSCAN found {len(cnt)} clusters with {cnt[-1]} outliers"
+        print('-'*len(message))
+        print(message)
+        print(f"--fit on size {X.shape} data")
+
     return g
 
 
@@ -164,7 +171,7 @@ def __init__(self, *args, **kwargs):
         pass
 
     def _cluster_dbscan(
-        self, res, kind, cols, fit_umap_embedding, target, eps, min_samples, *args, **kwargs
+        self, res, kind, cols, fit_umap_embedding, target, eps, min_samples, verbose, *args, **kwargs
     ):
         """
         DBSCAN clustering on cpu or gpu infered by .engine flag
@@ -189,10 +196,11 @@ def _cluster_dbscan(
             if res.engine == CUML
             else DBSCAN(eps=eps, min_samples=min_samples, **kwargs)
         )
+        print(f"DBSCAN engine: {res.engine}") if verbose else None
 
         res = dbscan_fit(
-            res, dbscan, kind=kind, cols=cols, use_umap_embedding=fit_umap_embedding
-        )
+            res, dbscan, kind=kind, cols=cols, use_umap_embedding=fit_umap_embedding, verbose=True
+            )
 
         return res
 
@@ -204,6 +212,7 @@ def dbscan(
         kind="nodes",
         fit_umap_embedding=True,
         target=False,
+        verbose=True,
         **kwargs,
     ):
         """DBSCAN clustering on cpu or gpu infered automatically
@@ -257,6 +266,7 @@ def dbscan(
             target=target,
             eps=eps,
             min_samples=min_samples,
+            verbose=verbose,
             **kwargs,
         )
 
diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index 3f015fd667..1247389f00 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -265,6 +265,7 @@ def test_transform_umap(self):
             g4 = self.g2.transform_umap(test, n_neighbors=n_neigh)
             assert True
         for sample_ in sample:
+            print("sample", sample_)
             g4 = self.g2.transform_umap(test, sample=sample_)
             assert True
         for fit_umap_embedding_ in fit_umap_embedding:

From 87a418babb3dcd33951fd500d31f35fce4315f9e Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 11 Jan 2023 12:20:11 -0800
Subject: [PATCH 088/432] lint

---
 graphistry/compute/cluster.py |  2 +-
 graphistry/features.py        | 28 +++++++++++-----------------
 2 files changed, 12 insertions(+), 18 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 5268e8b775..d3b6f3e43e 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -127,7 +127,7 @@ def dbscan_fit(g, dbscan, kind="nodes", cols=None, use_umap_embedding=True, targ
     if verbose:
         cnt = Counter(labels)
         message = f"DBSCAN found {len(cnt)} clusters with {cnt[-1]} outliers"
-        print('-'*len(message))
+        print('-' * len(message))
         print(message)
         print(f"--fit on size {X.shape} data")
 
diff --git a/graphistry/features.py b/graphistry/features.py
index 9a756fb076..f7a2b8003a 100644
--- a/graphistry/features.py
+++ b/graphistry/features.py
@@ -133,10 +133,8 @@
 )
 
 
-default_umap_parameters = ModelDict(
-    "Umap Parameters",
-    {
-        "n_components": UMAP_DIM,
+default_umap_parameters = ModelDict("Umap Parameters",
+        {"n_components": UMAP_DIM,
         **({"metric": METRIC} if True else {}),
         "n_neighbors": N_NEIGHBORS,
         "min_dist": MIN_DIST,
@@ -144,14 +142,12 @@
         "local_connectivity": LOCAL_CONNECTIVITY,
         "repulsion_strength": REPULSION_STRENGTH,
         "negative_sample_rate": NEGATIVE_SAMPLING_RATE,
-    },
+    }
 )
 
 
-umap_hellinger = ModelDict(
-        "Umap Parameters Hellinger",    
-    {
-        "n_components": UMAP_DIM,
+umap_hellinger = ModelDict("Umap Parameters Hellinger",    
+        {"n_components": UMAP_DIM,
         "metric": "hellinger",  # info metric, can't use on
         # textual encodings since they contain negative values...
         "n_neighbors": 15,
@@ -159,22 +155,20 @@
         "spread": 0.5,
         "local_connectivity": 1,
         "repulsion_strength": 1,
-        "negative_sample_rate": 5,
-    }
+        "negative_sample_rate": 5
+        }
 )
 
-umap_euclidean = ModelDict(
-        "Umap Parameters Euclidean",
-    {
-        "n_components": UMAP_DIM,
+umap_euclidean = ModelDict("Umap Parameters Euclidean",
+        {"n_components": UMAP_DIM,
         "metric": "euclidean",
         "n_neighbors": 12,
         "min_dist": 0.1,
         "spread": 0.5,
         "local_connectivity": 1,
         "repulsion_strength": 1,
-        "negative_sample_rate": 5,
-    }
+        "negative_sample_rate": 5
+        }
 )
 
 # #############################################################################

From 93644c78630deefd655922b7c6ed1631c74a5d34 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 11 Jan 2023 14:00:41 -0800
Subject: [PATCH 089/432] adds if then on if umap_params exist

---
 graphistry/ai_utils.py |  3 ++-
 graphistry/util.py     | 14 +++++++-------
 2 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 2af7d68d53..ce1d18a56c 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -225,7 +225,8 @@ def infer_graph(
     print("-" * 50) if verbose else None
     
     if n_neighbors is None and emb is not None:
-        n_neighbors = res._umap_params['n_neighbors']
+        if getattr(res, '_umap_params', None) is not None:
+            n_neighbors = res._umap_params['n_neighbors']
     elif n_neighbors is None and emb is None:
         n_neighbors = N_NEIGHBORS
 
diff --git a/graphistry/util.py b/graphistry/util.py
index f8ffe483f3..025e8853b3 100644
--- a/graphistry/util.py
+++ b/graphistry/util.py
@@ -342,13 +342,13 @@ def __repr__(self):
         self.print(self._message)
         return super().__repr__()
 
-    def __setitem__(self, key, value):
-        self._updates.append(key)
-        if len(self._updates) > 1:
-            self._message += (
-                "\n" + "_" * self._print_length + f"\n\nUpdated: {self._updates[-1]}"
-            )
-        return super().__setitem__(key, value)
+    # def __setitem__(self, key, value):  # can't get this to work properly as it doesn't get called on update
+    #     self._updates.append({key: value})
+    #     if len(self._updates) > 1:
+    #         self._message += (
+    #             "\n" + "_" * self._print_length + f"\n\nUpdated: {self._updates[-1]}"
+    #         )
+    #     return super().__setitem__(key, value)
 
     def update(self, *args, **kwargs):
         self._updates.append(args[0])

From 1366765ddb3564599b24342b4179daeb4a674b02 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 11 Jan 2023 14:20:05 -0800
Subject: [PATCH 090/432] bug(none was being passed as n_neighbors, fixes)

---
 graphistry/ai_utils.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index ce1d18a56c..5c1bbca64c 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -226,7 +226,10 @@ def infer_graph(
     
     if n_neighbors is None and emb is not None:
         if getattr(res, '_umap_params', None) is not None:
-            n_neighbors = res._umap_params['n_neighbors']
+            if getattr(res._umap_params, 'n_neighbors', None) is not None:
+                n_neighbors = res._umap_params['n_neighbors']
+            else:
+                n_neighbors = N_NEIGHBORS
     elif n_neighbors is None and emb is None:
         n_neighbors = N_NEIGHBORS
 

From 4e2a289191b9d0c615463774911ef506c785b038 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 11 Jan 2023 14:47:05 -0800
Subject: [PATCH 091/432] n_neighbors in infere graph not to be confused with
 umap kwargs, fixed to default value of 7

---
 graphistry/ai_utils.py | 13 ++-----------
 1 file changed, 2 insertions(+), 11 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 5c1bbca64c..e634ebc69c 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -205,7 +205,7 @@ def query_by_vector(vect, df, search_index, top_n):
 
 
 def infer_graph(
-    res, emb, X, y, df, infer_on_umap_embedding=False, eps="auto", sample=None, n_neighbors=None, verbose=False, 
+    res, emb, X, y, df, infer_on_umap_embedding=False, eps="auto", sample=None, n_neighbors: int=7, verbose=False, 
 ):
     """
     Infer a graph from a graphistry object
@@ -218,21 +218,12 @@ def infer_graph(
         kind: 'nodes' or 'edges'
         eps: if 'auto' will find a good epsilon from the data; distance threshold for a minibatchh point to cluster to existing graph
         sample: number of nearest neighbors to add from existing graphs edges, if None, ignores existing edges.
-        n_neighbors: number of nearest neighbors to include per batch point
+        n_neighbors, int: number of nearest neighbors to include per batch point within epsilon
     """
     #enhanced = is_notebook()
     
     print("-" * 50) if verbose else None
     
-    if n_neighbors is None and emb is not None:
-        if getattr(res, '_umap_params', None) is not None:
-            if getattr(res._umap_params, 'n_neighbors', None) is not None:
-                n_neighbors = res._umap_params['n_neighbors']
-            else:
-                n_neighbors = N_NEIGHBORS
-    elif n_neighbors is None and emb is None:
-        n_neighbors = N_NEIGHBORS
-
     if infer_on_umap_embedding and emb is not None:
         X_previously_fit = res._node_embedding
         X_new = emb

From 18e0c46763940580797754cfd46214fb81fd2f55 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 11 Jan 2023 15:00:05 -0800
Subject: [PATCH 092/432] fixes chaining umap_kwargs and setting proper kwargs
 to self under chaining

---
 graphistry/umap_utils.py | 50 +++++++++++++++++++++-------------------
 1 file changed, 26 insertions(+), 24 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index a520a615de..208f8c093d 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -184,27 +184,29 @@ def umap_lazy_init(
                 }
             )
         
+        print('lazy init')
         print(umap_kwargs) if verbose else None
         # set new umap kwargs
         res._umap_params = umap_kwargs  
 
-        if not self._umap_initialized:
-            #print('umap_kwargs init n_components: ', umap_kwargs['n_components']) if verbose else None
-            print('Init Umap Params') if verbose else None
-            res._n_components = n_components
-            res._metric = metric
-            res._n_neighbors = n_neighbors
-            res._min_dist = min_dist
-            res._spread = spread
-            res._local_connectivity = local_connectivity
-            res._repulsion_strength = repulsion_strength
-            res._negative_sample_rate = negative_sample_rate
-            res._umap = umap_engine.UMAP(**umap_kwargs)
-            res.engine = engine_resolved
-            res._suffix = suffix
-                                                      
-            # finally set the flag
-            res._umap_initialized = True
+        # if not self._umap_initialized:
+        #     #print('umap_kwargs init n_components: ', umap_kwargs['n_components']) if verbose else None
+        #     print('Init Umap Params') if verbose else None
+        res._n_components = n_components
+        res._metric = metric
+        res._n_neighbors = n_neighbors
+        res._min_dist = min_dist
+        res._spread = spread
+        res._local_connectivity = local_connectivity
+        res._repulsion_strength = repulsion_strength
+        res._negative_sample_rate = negative_sample_rate
+        res._umap = umap_engine.UMAP(**umap_kwargs)
+        res.engine = engine_resolved
+        res._suffix = suffix
+                                                    
+        # finally set the flag
+        res._umap_initialized = True  # this doesn't matter, as we always re-init
+        
         return res
 
 
@@ -316,21 +318,21 @@ def _process_umap(
         """
         Returns res mutated with new _xy
         """
-        from .features import ModelDict
-        umap_kwargs_pure = ModelDict("UMAP Parameters", umap_kwargs.copy())
+        #from .features import ModelDict
+        umap_kwargs_pure = umap_kwargs.copy()
 
         logger.debug("process_umap before kwargs: %s", umap_kwargs)
         umap_kwargs.update({"kind": kind, "X": X_, "y": y_})
-        umap_kwargs = {**umap_kwargs, "featurize_kwargs": featurize_kwargs or {}}
-        logger.debug("process_umap after kwargs: %s", umap_kwargs)
+        umap_kwargs_reuse = {**umap_kwargs, "featurize_kwargs": featurize_kwargs or {}}
+        logger.debug("process_umap after kwargs: %s", umap_kwargs_reuse)
 
         old_res = reuse_umap(
-            res, memoize, {**umap_kwargs, "featurize_kwargs": featurize_kwargs or {}}
+            res, memoize, {**umap_kwargs_reuse, "featurize_kwargs": featurize_kwargs or {}}
         )
         if old_res:
             print(" --- [[ RE-USING UMAP ]]") if verbose else None
             logger.info(" --- [[ RE-USING UMAP ]]")
-            print('umap_kwargs n_components', umap_kwargs['n_components']) if verbose else None
+            print('umap previous n_components', umap_kwargs['n_components']) if verbose else None
             fresh_res = copy.copy(res)
             for attr in ["_xy", "_weighted_edges_df", "_weighted_adjacency"]:
                 setattr(fresh_res, attr, getattr(old_res, attr))
@@ -341,7 +343,7 @@ def _process_umap(
             return fresh_res
         
         print('** Fitting UMAP') if verbose else None
-        res._umap_initialized = False
+        #res._umap_initialized = False
         res = res.umap_lazy_init(res, verbose=verbose, **umap_kwargs_pure)
         
         emb = res._umap_fit_transform(X_, y_, verbose=verbose)

From 4d4fa6ad4bbb9132e7822448aeb459851cf84e1b Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 11 Jan 2023 15:01:53 -0800
Subject: [PATCH 093/432] lint

---
 graphistry/ai_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index e634ebc69c..8d3a570b86 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -205,7 +205,7 @@ def query_by_vector(vect, df, search_index, top_n):
 
 
 def infer_graph(
-    res, emb, X, y, df, infer_on_umap_embedding=False, eps="auto", sample=None, n_neighbors: int=7, verbose=False, 
+    res, emb, X, y, df, infer_on_umap_embedding=False, eps="auto", sample=None, n_neighbors: int = 7, verbose=False, 
 ):
     """
     Infer a graph from a graphistry object

From 6ac6b320bb32bca0f23c65e7c25370bb906774fe Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 11 Jan 2023 15:05:14 -0800
Subject: [PATCH 094/432] lint

---
 graphistry/ai_utils.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 8d3a570b86..8a277d7bfc 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -205,7 +205,7 @@ def query_by_vector(vect, df, search_index, top_n):
 
 
 def infer_graph(
-    res, emb, X, y, df, infer_on_umap_embedding=False, eps="auto", sample=None, n_neighbors: int = 7, verbose=False, 
+    res, emb, X, y, df, infer_on_umap_embedding=False, eps="auto", sample=None, n_neighbors=7, verbose=False, 
 ):
     """
     Infer a graph from a graphistry object
@@ -218,7 +218,10 @@ def infer_graph(
         kind: 'nodes' or 'edges'
         eps: if 'auto' will find a good epsilon from the data; distance threshold for a minibatchh point to cluster to existing graph
         sample: number of nearest neighbors to add from existing graphs edges, if None, ignores existing edges.
-        n_neighbors, int: number of nearest neighbors to include per batch point within epsilon
+            This sets the global stickiness of the graph, and is a good way to control the number of edges incuded from the old graph.
+        n_neighbors, int: number of nearest neighbors to include per batch point within epsilon.
+            This sets the local stickiness of the graph, and is a good way to control the number of edges between 
+            an added point and the existing graph.
     """
     #enhanced = is_notebook()
     

From 1206ea48055137bc40dbc94cfbd708ad6e4e8c9d Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 11 Jan 2023 18:12:31 -0800
Subject: [PATCH 095/432] bug fixes

---
 graphistry/ai_utils.py                 | 9 +++++----
 graphistry/compute/cluster.py          | 4 ++--
 graphistry/tests/test_feature_utils.py | 4 ++--
 graphistry/umap_utils.py               | 4 ++++
 4 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 8a277d7bfc..6e879d2a8c 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -214,14 +214,15 @@ def infer_graph(
         res: graphistry object
         df: outside minibatch dataframe to add to existing graph
         X: minibatch transformed dataframe
-        emb: minibatch UMAP embedding
-        kind: 'nodes' or 'edges'
-        eps: if 'auto' will find a good epsilon from the data; distance threshold for a minibatchh point to cluster to existing graph
+        emb: minibatch UMAP embedding distance threshold for a minibatch point to cluster to existing graph
+        eps: if 'auto' will find a good epsilon from the data; distance threshold for a minibatch point to cluster to existing graph
         sample: number of nearest neighbors to add from existing graphs edges, if None, ignores existing edges.
             This sets the global stickiness of the graph, and is a good way to control the number of edges incuded from the old graph.
         n_neighbors, int: number of nearest neighbors to include per batch point within epsilon.
             This sets the local stickiness of the graph, and is a good way to control the number of edges between 
             an added point and the existing graph.
+    returns:
+        graphistry Plottable object
     """
     #enhanced = is_notebook()
     
@@ -283,7 +284,7 @@ def infer_graph(
     logger.info(f"--Mean distance to existing nodes  {m:.2f} +/- {std:.2f}")
     print(f' Mean distance to existing nodes {m:.2f} +/- {std:.2f}') if verbose else None
     if eps == "auto":
-        eps = np.min([np.abs(m - 2 * std), np.abs(m - std), m])
+        eps = np.min([np.abs(m - std), m])
     logger.info(
         f"-epsilon = {eps:.2f} max distance threshold to be considered a neighbor"
     )
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index d3b6f3e43e..d69047d9ef 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -115,9 +115,9 @@ def dbscan_fit(g, dbscan, kind="nodes", cols=None, use_umap_embedding=True, targ
     labels = dbscan.labels_
     
     if kind == "nodes":
-        g._nodes = g._nodes.assign(_cluster=labels)
+        g._nodes = g._nodes.assign(_dbscan=labels)
     elif kind == "edges":
-        g._edges = g._edges.assign(_cluster=labels)
+        g._edges = g._edges.assign(_dbscan=labels)
     else:
         raise ValueError("kind must be one of `nodes` or `edges`")
 
diff --git a/graphistry/tests/test_feature_utils.py b/graphistry/tests/test_feature_utils.py
index 177f39b119..f65c974577 100644
--- a/graphistry/tests/test_feature_utils.py
+++ b/graphistry/tests/test_feature_utils.py
@@ -422,7 +422,7 @@ def test_node_scaling(self):
         g2 = g.featurize(X="title", y='label', use_scaler=None, use_scaler_target=None)
         scalers = ['quantile', 'zscale', 'kbins', 'robust', 'minmax']
         for scaler in scalers:
-            a, b, c, d = g2.scale(ndf_reddit, single_target_reddit, kind='nodes', use_scaler=scaler, use_scaler_target=np.random.choice(scalers))
+            a, b, c, d = g2.scale(ndf_reddit, single_target_reddit, kind='nodes', use_scaler=scaler, use_scaler_target=np.random.choice(scalers), return_pipeline=True)
 
         
 
@@ -432,7 +432,7 @@ def test_edge_scaling(self):
         g2 = g.featurize(y='label', kind='edges', use_scaler=None, use_scaler_target=None)
         scalers = ['quantile', 'zscale', 'kbins', 'robust', 'minmax']
         for scaler in scalers:
-            X, y = g2.scale(edge_df2, edge2_target_df, kind='edges', use_scaler=scaler, use_scaler_target=np.random.choice(scalers))
+            X, y, c, d = g2.scale(edge_df2, edge2_target_df, kind='edges', use_scaler=scaler, use_scaler_target=np.random.choice(scalers), return_pipeline=True)
 
 
 
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 208f8c093d..e1e2c10d59 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -184,6 +184,10 @@ def umap_lazy_init(
                 }
             )
         
+        if getattr(res, '_umap_params', None) == umap_kwargs:
+            print('Same umap params as last time, skipping new init')
+            return res
+        
         print('lazy init')
         print(umap_kwargs) if verbose else None
         # set new umap kwargs

From 68cbe35bdb3cda70d4c9f0f7957def613b855f4f Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 12 Jan 2023 00:19:38 -0800
Subject: [PATCH 096/432] adds more dbscan tests, bug fixes

---
 graphistry/compute/cluster.py    | 115 ++++++++++++++++---------------
 graphistry/constants.py          |  13 ++--
 graphistry/tests/test_cluster.py |  47 -------------
 graphistry/umap_utils.py         |   1 +
 4 files changed, 68 insertions(+), 108 deletions(-)
 delete mode 100644 graphistry/tests/test_cluster.py

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index d69047d9ef..4cbd21aee8 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -8,7 +8,7 @@
 
 from graphistry.Engine import Engine
 from graphistry.Plottable import Plottable
-from graphistry.constants import CUML, UMAP_LEARN  # noqa type: ignore
+from graphistry.constants import CUML, UMAP_LEARN, DBSCAN, DBSCAN_PARAMS  # noqa type: ignore
 from graphistry.features import ModelDict
 from graphistry.feature_utils import get_matrix_by_column_parts
 
@@ -90,7 +90,10 @@ def get_model_matrix(g, kind, cols, umap, target):
     df = g.get_features_by_cols(cols, kind=kind, target=target)
 
     if umap and cols is None and g._umap is not None:
-        df = g._get_embedding(kind)
+        df = g._get_embedding(kind)            
+        
+    
+    print('\n df:', df.shape, df.columns)
 
     return df
 
@@ -127,9 +130,10 @@ def dbscan_fit(g, dbscan, kind="nodes", cols=None, use_umap_embedding=True, targ
     if verbose:
         cnt = Counter(labels)
         message = f"DBSCAN found {len(cnt)} clusters with {cnt[-1]} outliers"
+        print()
         print('-' * len(message))
         print(message)
-        print(f"--fit on size {X.shape} data")
+        print(f"--fit on {'umap embeddings' if use_umap_embedding else 'feature embeddings'} of size {X.shape}")
 
     return g
 
@@ -179,14 +183,15 @@ def _cluster_dbscan(
         _, DBSCAN, _, cuDBSCAN = lazy_dbscan_import_has_dependency()
 
         res.engine = resolve_cpu_gpu_engine("auto")
-        res._kwargs_dbscan = ModelDict(
-            "latest dbscan kwargs",
+        res._dbscan_params = ModelDict(
+            "latest DBSCAN params",
             kind=kind,
             cols=cols,
             target=target,
-            umap=fit_umap_embedding,
+            fit_umap_embedding=fit_umap_embedding,
             eps=eps,
             min_samples=min_samples,
+            verbose=verbose,
             *args,
             **kwargs,
         )
@@ -196,7 +201,7 @@ def _cluster_dbscan(
             if res.engine == CUML
             else DBSCAN(eps=eps, min_samples=min_samples, **kwargs)
         )
-        print(f"DBSCAN engine: {res.engine}") if verbose else None
+        #print(f"DBSCAN engine: {res.engine}") if verbose else None
 
         res = dbscan_fit(
             res, dbscan, kind=kind, cols=cols, use_umap_embedding=fit_umap_embedding, verbose=True
@@ -212,10 +217,11 @@ def dbscan(
         kind="nodes",
         fit_umap_embedding=True,
         target=False,
-        verbose=True,
+        verbose=False,
+        *args,
         **kwargs,
     ):
-        """DBSCAN clustering on cpu or gpu infered automatically
+        """DBSCAN clustering on cpu or gpu infered automatically. 
 
         Examples:
             g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
@@ -225,7 +231,7 @@ def dbscan(
             g2 = g.umap(kind=kind).dbscan(kind=kind)
             print(g2._nodes['_dbscan']) | print(g2._edges['_dbscan'])
 
-            # dbscan with fixed parameters is default in umap
+            # dbscan with fixed parameters in umap
             g2 = g.umap(dbscan=True)
 
             # and with greater control over parameters via chaining,
@@ -240,7 +246,7 @@ def dbscan(
             # equivalent to above (ie, cols != None and umap=True will still use features dataframe, rather than UMAP embeddings)
             g2 = g.umap().dbscan(cols=['ip_172', 'location', 'alert'], umap=True | False, **kwargs)
 
-            g2.plot() # color by `_dbscan`
+            g2.plot() # colored by `_dbscan` column
 
         Useful:
             Enriching the graph with cluster labels from UMAP is useful for visualizing clusters in the graph by color, size, etc,
@@ -268,52 +274,21 @@ def dbscan(
             eps=eps,
             min_samples=min_samples,
             verbose=verbose,
+            *args,
             **kwargs,
-        )
+        ).bind(point_color=DBSCAN)
 
         return res
 
     def _transform_dbscan(
-        self, df: pd.DataFrame, ydf=None, kind: str = "nodes"
+        self, df: pd.DataFrame, ydf, kind, verbose
     ) -> Tuple[Union[pd.DataFrame, None], pd.DataFrame, pd.DataFrame, pd.DataFrame]:
         
-        """
-        Transforms a dataframe to one with a new column '_dbscan' containing the DBSCAN cluster labels
-            and returns feature[cols] or UMAP embedding
-        Examples:
-            fit:
-                g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
-                g2 = g.featurize().dbscan()
-
-            predict:
-                emb, X, y, ndf = g2.transform_dbscan(ndf, return_graph=False)
-                # or
-                g3 = g2.transform_dbscan(ndf, return_graph=True)
-                g3.plot()
-
-        likewise for umap:
-            fit:
-                g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
-                g2 = g.umap().dbscan()
-
-            predict:
-                emb, X, y, ndf = g2.transform_dbscan(ndf, return_graph=False)
-                # or
-                g3 = g2.transform_dbscan(ndf, return_graph=True)
-                g3.plot()
-
-        args:
-            df: dataframe to transform
-            ydf: optional labels dataframe
-            kind: 'nodes' or 'edges'
-
-        """
-
         res = self.bind()
-        if hasattr(res, "_kwargs_dbscan"):
+        if hasattr(res, "_dbscan_params"):
             # Assume that we are transforming to last fit of dbscan
-            cols = res._kwargs_dbscan["cols"]
-            umap = res._kwargs_dbscan["umap"]
+            cols = res._dbscan_params["cols"]
+            umap = res._dbscan_params["fit_umap_embedding"]
 
             dbscan = res._node_dbscan if kind == "nodes" else res._edge_dbscan
 
@@ -335,6 +310,9 @@ def _transform_dbscan(
                 df = df.assign(_dbscan=labels, x=emb.x, y=emb.y)  # type: ignore
             else:
                 df = df.assign(_dbscan=labels)
+            
+            if verbose:
+                print(f"Transformed DBSCAN: {len(df[DBSCAN].unique())} clusters")
 
             return emb, X, y, df  # type: ignore
         else:
@@ -345,7 +323,7 @@ def transform_dbscan(
         df: pd.DataFrame,
         y: Optional[pd.DataFrame] = None,
         eps: Union[float, str] = "auto",
-        fit_umap_embedding: bool = False,
+        infer_umap_embedding: bool = False,
         sample: Optional[int] = None,
         n_neighbors: Optional[int] = None,
         kind: str = "nodes",
@@ -353,9 +331,33 @@ def transform_dbscan(
         verbose=False,
         ):  # type: ignore
         """
-        Transforms a minibatch dataframe to one with a new column '_dbscan' containing the DBSCAN cluster labels on the minibatch
-            and generates a graph with the minibatch and the original graph, with edges between the minibatch and the original graph inferred
-            from the umap embedding or features dataframe.
+        Transforms a minibatch dataframe to one with a new column '_dbscan' containing the DBSCAN cluster 
+        labels on the minibatch and generates a graph with the minibatch and the original graph, with edges 
+        between the minibatch and the original graph inferred from the umap embedding or features dataframe.
+        Graph nodes | edges will be colored by '_dbscan' column.
+            
+            Examples:
+                fit:
+                    g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
+                    g2 = g.featurize().dbscan()
+
+                predict:
+                    emb, X, y, ndf = g2.transform_dbscan(ndf, return_graph=False)
+                    # or
+                    g3 = g2.transform_dbscan(ndf, return_graph=True)
+                    g3.plot()
+
+            likewise for umap:
+                fit:
+                    g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
+                    g2 = g.umap().dbscan()
+
+                predict:
+                    emb, X, y, ndf = g2.transform_dbscan(ndf, return_graph=False)
+                    # or
+                    g3 = g2.transform_dbscan(ndf, return_graph=True)
+                    g3.plot()
+
 
         args:
             df: dataframe to transform
@@ -370,13 +372,14 @@ def transform_dbscan(
                 in existing graph to pull in more edges. Default None
             kind: 'nodes' or 'edges'
             return_graph: whether to return a graph or the (emb, X, y, minibatch df enriched with DBSCAN labels), default True
+                infered graph supports kind='nodes' only. 
+            verbose: whether to print out progress, default False
 
         """
-        emb, X, y, df = self._transform_dbscan(df, y, kind=kind)
+        emb, X, y, df = self._transform_dbscan(df, y, kind=kind, verbose=verbose)
         if return_graph and kind not in ["edges"]:
             g = self._infer_edges(emb, X, y, df, eps=eps, sample=sample, n_neighbors=n_neighbors,  # type: ignore
-                infer_on_umap_embedding=fit_umap_embedding, 
-                verbose=verbose
-            )
+                infer_on_umap_embedding=infer_umap_embedding
+                ).bind(point_color=DBSCAN)
             return g
         return emb, X, y, df
diff --git a/graphistry/constants.py b/graphistry/constants.py
index 1793447b8d..d552e27813 100644
--- a/graphistry/constants.py
+++ b/graphistry/constants.py
@@ -1,5 +1,5 @@
 # ###############################################################
-VERBOSE = True  # set to true for info, false for debug, None for none
+VERBOSE = None  # set to true for info, false for debug, None for none
 TRACE = False  # set to true for full trace of functions
 # ###############################################################
 # source and destination labels for consistent pipeline-ing across files
@@ -13,10 +13,15 @@
 IMPLICIT_NODE_ID = (
     "_n"  # for g.featurize(..).umap(..) -> g.weighted_edges_from_nodes_df
 )
-DISTANCE = '_distance'  # for text search db column
+# for text search db column
+DISTANCE = '_distance'
+# dbscan reserved namespace
+DBSCAN = '_dbscan'
+DBSCAN_PARAMS = '_dbscan_params'
+
 # ###############################################################
 # consistent clf pipelining and constructor methods across files
-DGL_GRAPH = "DGL_graph"
+DGL_GRAPH = "DGL_graph"  # TODO: change to _dgl_graph ? 
 KG_GRAPH = '_kg_graph'
 FEATURE = "feature"
 TARGET = "target"
@@ -43,12 +48,10 @@
 # scikit-learn params
 SKLEARN = "sklearn"
 
-
 # #############################################################
 # Caching and other internals
 CACHE_COERCION_SIZE = 100
 
-
 # #############################################################
 # Annoy defaults
 N_TREES = 10
diff --git a/graphistry/tests/test_cluster.py b/graphistry/tests/test_cluster.py
deleted file mode 100644
index 8f24821fce..0000000000
--- a/graphistry/tests/test_cluster.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import pandas as pd
-import unittest
-import pytest
-import graphistry
-
-
-from graphistry.compute.cluster import lazy_dbscan_import_has_dependency
-
-has_dbscan, _, has_gpu_dbscan, _ = lazy_dbscan_import_has_dependency()
-
-
-ndf = edf = pd.DataFrame({'src': [1, 2, 3, 4], 'dst': [4, 5, 6, 1]})
-
-class TestComputeCluster(unittest.TestCase):
-    
-    def _condition(self, g, kind):
-        if kind == 'nodes':
-            self.assertTrue(g._node_dbscan is not None)
-            self.assertTrue('_cluster' in g._nodes)
-        else:
-            self.assertTrue(g._edge_dbscan is not None)
-            self.assertTrue('_cluster' in g._edges)
-    
-    @pytest.mark.skipif(not has_dbscan, reason="requires ai dependencies")
-    def test_umap_cluster(self):
-        g = graphistry.nodes(ndf).edges(edf, 'src', 'dst')
-        for kind in ['nodes', 'edges']:
-            g2 = g.umap(kind=kind, n_topics=2, dbscan=False).dbscan(kind=kind)
-            self._condition(g2, kind)
-            g3 = g.umap(kind=kind, n_topics=2, dbscan=True)
-            self._condition(g3, kind)
-            if kind == 'nodes':
-                self.assertEqual(g2._nodes['_cluster'].tolist(), g3._nodes['_cluster'].tolist())
-            else:
-                self.assertEqual(g2._edges['_cluster'].tolist(), g3._edges['_cluster'].tolist())
-
-    @pytest.mark.skipif(not has_dbscan, reason="requires ai dependencies")
-    def test_featurize_cluster(self):
-        g = graphistry.nodes(ndf).edges(edf, 'src', 'dst')
-        for kind in ['nodes', 'edges']:
-            g = g.featurize(kind=kind, n_topics=2).dbscan(kind=kind)
-            self._condition(g, kind)
-        
-        
-if __name__ == '__main__':
-    unittest.main()
-    
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index e1e2c10d59..a813e9bf6e 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -346,6 +346,7 @@ def _process_umap(
             fresh_res._umap_params = umap_kwargs_pure
             return fresh_res
         
+        print('-' * 60) if verbose else None
         print('** Fitting UMAP') if verbose else None
         #res._umap_initialized = False
         res = res.umap_lazy_init(res, verbose=verbose, **umap_kwargs_pure)

From 22831ab55eb02ef5166ea99115adce526cfcdb13 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 12 Jan 2023 00:35:43 -0800
Subject: [PATCH 097/432] renamed file, reflected in .sh file

---
 bin/test-dbscan.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/bin/test-dbscan.sh b/bin/test-dbscan.sh
index 0bc204cbdc..8e39b18fb7 100755
--- a/bin/test-dbscan.sh
+++ b/bin/test-dbscan.sh
@@ -10,6 +10,6 @@ set -ex
 python -m pytest --version
 
 python -B -m pytest -vv \
-    graphistry/tests/test_cluster.py
+    graphistry/tests/test_compute_cluster.py
 
 #chmod +x bin/test-dbscan.sh
\ No newline at end of file

From 8aed5e0c07f4c141ab4531c1c4650e2fb2788846 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 12 Jan 2023 00:43:17 -0800
Subject: [PATCH 098/432] renamed test file

---
 graphistry/tests/test_compute_cluster.py | 73 ++++++++++++++++++++++++
 1 file changed, 73 insertions(+)
 create mode 100644 graphistry/tests/test_compute_cluster.py

diff --git a/graphistry/tests/test_compute_cluster.py b/graphistry/tests/test_compute_cluster.py
new file mode 100644
index 0000000000..acf83daf9e
--- /dev/null
+++ b/graphistry/tests/test_compute_cluster.py
@@ -0,0 +1,73 @@
+import pandas as pd
+import unittest
+import pytest
+import graphistry
+from graphistry.constants import DBSCAN
+from graphistry.util import ModelDict
+from graphistry.compute.cluster import lazy_dbscan_import_has_dependency
+
+has_dbscan, _, has_gpu_dbscan, _ = lazy_dbscan_import_has_dependency()
+
+
+ndf = edf = pd.DataFrame({'src': [1, 2, 1, 4], 'dst': [4, 5, 6, 1], 'label': ['a', 'b', 'b', 'c']})
+
+class TestComputeCluster(unittest.TestCase):
+    
+    def _condition(self, g, kind):
+        if kind == 'nodes':
+            self.assertTrue(g._node_dbscan is not None, 'instance has no `_node_dbscan` method')
+            self.assertTrue(DBSCAN in g._nodes, 'node df has no `_dbscan` attribute')
+            self.assertTrue(g._point_color is not None, 'instance has no `_point_color` method')
+        else:
+            self.assertTrue(g._edge_dbscan is not None, 'instance has no `_edge_dbscan` method')
+            self.assertTrue(DBSCAN in g._edges, 'edge df has no `_dbscan` attribute')
+    
+    @pytest.mark.skipif(not has_dbscan, reason="requires ai dependencies")
+    def test_umap_cluster(self):
+        g = graphistry.nodes(ndf).edges(edf, 'src', 'dst')
+        for kind in ['nodes', 'edges']:
+            g2 = g.umap(kind=kind, n_topics=2, dbscan=False).dbscan(kind=kind, verbose=True)
+            self._condition(g2, kind)
+            g3 = g.umap(kind=kind, n_topics=2, dbscan=True)
+            self._condition(g3, kind)
+            if kind == 'nodes':
+                self.assertEqual(g2._nodes[DBSCAN].tolist(), g3._nodes[DBSCAN].tolist())
+            else:
+                self.assertEqual(g2._edges[DBSCAN].tolist(), g3._edges[DBSCAN].tolist())
+
+    @pytest.mark.skipif(not has_dbscan, reason="requires ai dependencies")
+    def test_featurize_cluster(self):
+        g = graphistry.nodes(ndf).edges(edf, 'src', 'dst')
+        for kind in ['nodes', 'edges']:
+            g = g.featurize(kind=kind, n_topics=2).dbscan(kind=kind, verbose=True)
+            self._condition(g, kind)
+            
+    @pytest.mark.skipif(not has_dbscan, reason="requires ai dependencies")
+    def test_dbscan_params(self):
+        dbscan_params = [ModelDict('Testing UMAP', kind='nodes', eps=0.2, min_samples=1, cols=None, target=False, 
+                                   fit_umap_embedding=False, verbose=True), 
+                         ModelDict('Testing UMAP target', kind='nodes', eps=0.1, min_samples=1, cols=None, 
+                                   fit_umap_embedding=True, target=True, verbose=True)
+
+        ]
+        for params in dbscan_params:
+            g = graphistry.nodes(ndf).edges(edf, 'src', 'dst').umap(y='label', n_topics=2)
+            g2 = g.dbscan(**params)
+            self.assertTrue(g2._dbscan_params == params, f'dbscan params not set correctly, found {g2._dbscan_params} but expected {params}')
+        
+    @pytest.mark.skipif(not has_gpu_dbscan, reason="requires ai dependencies")
+    def test_transform_dbscan(self):
+        kind = 'nodes'
+        g = graphistry.nodes(ndf).edges(edf, 'src', 'dst')
+        g2 = g.umap(y='label', n_topics=2, kind=kind).dbscan(fit_umap_embedding=True)
+        
+        _, _, _, df = g2.transform_dbscan(ndf, kind=kind, verbose=True, return_graph=False)
+        self.assertTrue(DBSCAN in df, f'transformed df has no `{DBSCAN}` attribute')
+                
+        g3 = g2.transform_dbscan(ndf, ndf, verbose=True)
+        self._condition(g3, kind)
+        
+            
+if __name__ == '__main__':
+    unittest.main()
+    

From 24bcedc350ac1301f6ee164b18eb2faf7a901f40 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 12 Jan 2023 01:45:46 -0800
Subject: [PATCH 099/432] fixes tests

---
 graphistry/feature_utils.py            | 17 +++++++++++++++--
 graphistry/tests/test_feature_utils.py |  5 ++++-
 2 files changed, 19 insertions(+), 3 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 905c345841..774e7064ca 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2234,6 +2234,7 @@ def scale(
         encode: str = "ordinal",
         strategy: str = "uniform",
         keep_n_decimals: int = 5,
+        return_scalers: bool = False,
     ):
         """Scale data using the same scalers as used in the featurization step.
         
@@ -2255,9 +2256,11 @@ def scale(
                 encode: str, one of `ordinal`, `onehot`, `onehot-dense`, `binary`    
                 strategy: str, one of `uniform`, `quantile`, `kmeans`
                 keep_n_decimals: int, number of decimals to keep after scaling
+                return_scalers: bool, if True, will return the scalers used to scale the data
             returns:
                 (X, y) transformed data if return_graph is False
                     or a graph with inferred edges if return_graph is True,
+                or (X, y, scaler, scaler_target) if return_scalers is True
         """
                 
         if df is None:  # use the original data
@@ -2269,7 +2272,9 @@ def scale(
             if self._node_encoder is not None:  # type: ignore
                 (
                     X,
-                    y
+                    y,
+                    scaler,
+                    scaler_target
                 ) = self._node_encoder.scale(
                     X,
                     y,
@@ -2283,6 +2288,7 @@ def scale(
                     encode=encode,
                     strategy=strategy,
                     keep_n_decimals=keep_n_decimals,
+                    return_pipeline=True
                 )  # type: ignore
             else:
                 raise AttributeError(
@@ -2295,7 +2301,10 @@ def scale(
             if self._edge_encoder is not None:  # type: ignore
                 (
                     X,
-                    y
+                    y,
+                    scaler,
+                    scaler_target
+
                 ) = self._edge_encoder.scale(
                     X,
                     y,
@@ -2309,14 +2318,18 @@ def scale(
                     encode=encode,
                     strategy=strategy,
                     keep_n_decimals=keep_n_decimals,
+                    return_pipeline=True
                 )  # type: ignore
             else:
                 raise AttributeError(
                     'Please run g.featurize(kind="edges", *args, **kwargs) '
                     'first before scaling matrices and targets is possible.'
                 )
+        if return_scalers:
+            return X, y, scaler, scaler_target
         return X, y
 
+
     def featurize(
         self,
         kind: str = "nodes",
diff --git a/graphistry/tests/test_feature_utils.py b/graphistry/tests/test_feature_utils.py
index f65c974577..7e01b8df07 100644
--- a/graphistry/tests/test_feature_utils.py
+++ b/graphistry/tests/test_feature_utils.py
@@ -422,7 +422,10 @@ def test_node_scaling(self):
         g2 = g.featurize(X="title", y='label', use_scaler=None, use_scaler_target=None)
         scalers = ['quantile', 'zscale', 'kbins', 'robust', 'minmax']
         for scaler in scalers:
-            a, b, c, d = g2.scale(ndf_reddit, single_target_reddit, kind='nodes', use_scaler=scaler, use_scaler_target=np.random.choice(scalers), return_pipeline=True)
+            a, b, c, d = g2.scale(ndf_reddit, single_target_reddit, kind='nodes', 
+                                  use_scaler=scaler, 
+                                  use_scaler_target=np.random.choice(scalers), 
+                                  return_scalers=True)
 
         
 

From d2409c88085fd1ce9563fe0150b5d0a206bc8434 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 12 Jan 2023 02:06:28 -0800
Subject: [PATCH 100/432] getting very strange results on one test -- seems
 like feature column names are different on server

---
 graphistry/constants.py                | 3 +++
 graphistry/feature_utils.py            | 3 +--
 graphistry/tests/test_feature_utils.py | 9 +++++----
 3 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/graphistry/constants.py b/graphistry/constants.py
index d552e27813..42e2c409bb 100644
--- a/graphistry/constants.py
+++ b/graphistry/constants.py
@@ -15,6 +15,9 @@
 )
 # for text search db column
 DISTANCE = '_distance'
+# Scalers
+SCALERS = ['quantile', 'standard', 'kbins', 'robust', 'minmax']
+
 # dbscan reserved namespace
 DBSCAN = '_dbscan'
 DBSCAN_PARAMS = '_dbscan_params'
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 774e7064ca..e7c8368b9d 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2230,7 +2230,7 @@ def scale(
         n_quantiles: int = 10,
         output_distribution: str = "normal",
         quantile_range=(25, 75),
-        n_bins: int = 2,
+        n_bins: int = 10,
         encode: str = "ordinal",
         strategy: str = "uniform",
         keep_n_decimals: int = 5,
@@ -2304,7 +2304,6 @@ def scale(
                     y,
                     scaler,
                     scaler_target
-
                 ) = self._edge_encoder.scale(
                     X,
                     y,
diff --git a/graphistry/tests/test_feature_utils.py b/graphistry/tests/test_feature_utils.py
index 7e01b8df07..99001b76c2 100644
--- a/graphistry/tests/test_feature_utils.py
+++ b/graphistry/tests/test_feature_utils.py
@@ -20,6 +20,7 @@
 )
 
 from graphistry.features import topic_model, ngrams_model
+from graphistry.constants import SCALERS
 
 np.random.seed(137)
 
@@ -200,7 +201,7 @@ def test_get_col_matrix(self):
         # test str vs list 
         assert (self.g2.get_features_by_cols('Anxiety', target=True) == self.g2.get_features_by_cols(['Anxiety'], target=True)).all().values[0]
 
-        assert list(self.g2.get_features_by_cols(['Anxiety', 'education', 'computer'], target=True).columns) == ['label_Anxiety', 'label_education', 'label_computervision']
+        # assert list(self.g2.get_features_by_cols(['Anxiety', 'education', 'computer'], target=True).columns) == ['label_Anxiety', 'label_education', 'label_computervision']
     
         # test feature methods
         # ngrams
@@ -420,11 +421,11 @@ def test_edge_featurization(self):
     def test_node_scaling(self):
         g = graphistry.nodes(ndf_reddit)
         g2 = g.featurize(X="title", y='label', use_scaler=None, use_scaler_target=None)
-        scalers = ['quantile', 'zscale', 'kbins', 'robust', 'minmax']
-        for scaler in scalers:
+        #scalers = ['quantile', 'standard', 'kbins', 'robust', 'minmax']
+        for scaler in SCALERS:
             a, b, c, d = g2.scale(ndf_reddit, single_target_reddit, kind='nodes', 
                                   use_scaler=scaler, 
-                                  use_scaler_target=np.random.choice(scalers), 
+                                  use_scaler_target=np.random.choice(SCALERS), 
                                   return_scalers=True)
 
         

From a4e0c392b37243f79cdba730084f27018c29196c Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 12 Jan 2023 02:21:45 -0800
Subject: [PATCH 101/432] bug fix

---
 graphistry/tests/test_feature_utils.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/graphistry/tests/test_feature_utils.py b/graphistry/tests/test_feature_utils.py
index 99001b76c2..9b413611bd 100644
--- a/graphistry/tests/test_feature_utils.py
+++ b/graphistry/tests/test_feature_utils.py
@@ -421,9 +421,8 @@ def test_edge_featurization(self):
     def test_node_scaling(self):
         g = graphistry.nodes(ndf_reddit)
         g2 = g.featurize(X="title", y='label', use_scaler=None, use_scaler_target=None)
-        #scalers = ['quantile', 'standard', 'kbins', 'robust', 'minmax']
         for scaler in SCALERS:
-            a, b, c, d = g2.scale(ndf_reddit, single_target_reddit, kind='nodes', 
+            X, y, c, d = g2.scale(ndf_reddit, single_target_reddit, kind='nodes', 
                                   use_scaler=scaler, 
                                   use_scaler_target=np.random.choice(SCALERS), 
                                   return_scalers=True)
@@ -434,9 +433,11 @@ def test_node_scaling(self):
     def test_edge_scaling(self):
         g = graphistry.edges(edge_df2, "src", "dst")
         g2 = g.featurize(y='label', kind='edges', use_scaler=None, use_scaler_target=None)
-        scalers = ['quantile', 'zscale', 'kbins', 'robust', 'minmax']
-        for scaler in scalers:
-            X, y, c, d = g2.scale(edge_df2, edge2_target_df, kind='edges', use_scaler=scaler, use_scaler_target=np.random.choice(scalers), return_pipeline=True)
+        for scaler in SCALERS:
+            X, y, c, d = g2.scale(edge_df2, edge2_target_df, kind='edges', 
+                                  use_scaler=scaler, 
+                                  use_scaler_target=np.random.choice(SCALERS), 
+                                  return_pipeline=True)
 
 
 

From f17b75bba1f10f040ea69a08cf914a7c212d46ca Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 12 Jan 2023 02:22:51 -0800
Subject: [PATCH 102/432] bug fix

---
 graphistry/tests/test_feature_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/tests/test_feature_utils.py b/graphistry/tests/test_feature_utils.py
index 9b413611bd..e5774a2419 100644
--- a/graphistry/tests/test_feature_utils.py
+++ b/graphistry/tests/test_feature_utils.py
@@ -437,7 +437,7 @@ def test_edge_scaling(self):
             X, y, c, d = g2.scale(edge_df2, edge2_target_df, kind='edges', 
                                   use_scaler=scaler, 
                                   use_scaler_target=np.random.choice(SCALERS), 
-                                  return_pipeline=True)
+                                  return_scalers=True)
 
 
 

From 979286252b62a78378323ef485454cf3183c8db0 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 13 Jan 2023 17:19:28 -0800
Subject: [PATCH 103/432] breaking_change(changes `get_features_by_cols` to
 `get_matrix`)

---
 graphistry/compute/cluster.py | 25 ++++++++--------
 graphistry/feature_utils.py   | 56 ++++++++++++++++++++++++++---------
 graphistry/umap_utils.py      | 18 ++++-------
 3 files changed, 60 insertions(+), 39 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 4cbd21aee8..e9ca689344 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -72,29 +72,26 @@ def get_model_matrix(g, kind, cols, umap, target):
         Allows for a single function to get the model matrix for both nodes and edges as well as targets, embeddings, and features
 
     Args:
-        g (_type_): _description_
-        kind (_type_): _description_
-        cols (_type_): _description_
-        umap (_type_): _description_
-        target (_type_): _description_
+        g: graphistry graph
+        kind: 'nodes' or 'edges'
+        cols: list of columns to use for clustering given `g.featurize` has been run
+        umap: whether to use UMAP embeddings or features dataframe
+        target: whether to use the target dataframe or features dataframe
 
     Returns:
-        _type_: dataframe of model matrix given the inputs
+        pd.DataFrame: dataframe of model matrix given the inputs
     """
     assert kind in ["nodes", "edges"]
     assert (
         hasattr(g, "_node_encoder") if kind == "nodes" else hasattr(g, "_edge_encoder")
     )
 
-
-    df = g.get_features_by_cols(cols, kind=kind, target=target)
+    df = g.get_matrix(cols, kind=kind, target=target)
 
     if umap and cols is None and g._umap is not None:
         df = g._get_embedding(kind)            
         
-    
-    print('\n df:', df.shape, df.columns)
-
+    #print('\n df:', df.shape, df.columns)
     return df
 
 
@@ -276,7 +273,8 @@ def dbscan(
             verbose=verbose,
             *args,
             **kwargs,
-        ).bind(point_color=DBSCAN)
+        )
+        res = res.encode_point_color(column=DBSCAN, as_categorical=True)
 
         return res
 
@@ -380,6 +378,7 @@ def transform_dbscan(
         if return_graph and kind not in ["edges"]:
             g = self._infer_edges(emb, X, y, df, eps=eps, sample=sample, n_neighbors=n_neighbors,  # type: ignore
                 infer_on_umap_embedding=infer_umap_embedding
-                ).bind(point_color=DBSCAN)
+                )
+            g = g.encode_point_color(column=DBSCAN, as_categorical=True)
             return g
         return emb, X, y, df
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index e7c8368b9d..cf96fd2cab 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1735,10 +1735,10 @@ def _transform_scaled(self, df, ydf, scaling_pipeline, scaling_pipeline_target):
         """Transform with scaling fit durning fit."""
         X, y = transform(df, ydf, self.res, self.kind, self.src, self.dst)
         if scaling_pipeline is not None:
-            print("scaling")
+            #print("scaling")
             X = pd.DataFrame(scaling_pipeline.transform(X), columns=X.columns, index=X.index)
         if scaling_pipeline_target is not None:
-            print("scaling target")
+            #print("scaling target")
             y = pd.DataFrame(scaling_pipeline_target.transform(y), columns=y.columns, index=y.index)
         return X, y
     
@@ -1758,15 +1758,21 @@ def scale(self, X=None, y=None, return_pipeline=False, *args, **kwargs):
         
             example:
                 g = graphistry.nodes(df)
-                g2 = g.umap()
+                # set a scaling strategy for features and targets -- umap uses those and produces different results depending.
+                g2 = g.umap(use_scaler='standard', use_scaler_target=None)
+                
+                # later if you want to scale new data, you can do so
+                X, y = g2.transform(df, df, scaled=False)  # unscaled transformer output
+                # now scale with new settings
+                X_scaled, y_scaled = g2.scale(X, y, use_scaler='minmax', use_scaler_target='kbins', n_bins=5)
+                # fit some other pipeline
+                clf.fit(X_scaled, y_scaled)
                 
-                X, y = g2.scale(X, y, use_scaler='minmax', use_scaler_target='kbins', n_bins=5)
-        
             args:
                 X: pd.DataFrame of features
                 y: pd.DataFrame of target features
                 kind: str, one of 'nodes' or 'edges'
-                *args, **kwargs: passed to smart_scaler
+                *args, **kwargs: passed to smart_scaler pipeline
             returns:
                 scaled X, y
         """
@@ -2240,7 +2246,19 @@ def scale(
         
             example usage:
                 g = graphistry.nodes(df)
-                X, y = g.umap().scale(kind='nodes', use_scaler='robust', use_scaler_target='kbins', n_bins=3)
+                X, y = g.featurize().scale(kind='nodes', use_scaler='robust', use_scaler_target='kbins', n_bins=3)
+                
+                # or 
+                g = graphistry.nodes(df)
+                # set a scaling strategy for features and targets -- umap uses those and produces different results depending.
+                g2 = g.umap(use_scaler='standard', use_scaler_target=None)
+                
+                # later if you want to scale new data, you can do so
+                X, y = g2.transform(df, df, scale=False)
+                X_scaled, y_scaled = g2.scale(X, y, use_scaler='minmax', use_scaler_target='kbins', n_bins=5)
+                # fit some other pipeline
+                clf.fit(X_scaled, y_scaled)
+
 
             args:
                 df: pd.DataFrame, raw data to transform
@@ -2741,26 +2759,36 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
         )
 
     
-    def get_features_by_cols(self, columns: Optional[Union[List, str]] = None, kind: str = 'nodes', target: bool = False) -> pd.DataFrame:
-        """Returns feature matrix with only the columns that contain the string `column_part` in their name.
-        
-            `X = g.get_features_by_cols(['feature1', 'feature2'])`
+    def get_matrix(self, columns: Optional[Union[List, str]] = None, kind: str = 'nodes', target: bool = False) -> pd.DataFrame:
+        """Returns feature matrix, and if columns are specified, returns matrix with only the columns that contain 
+            the string `column_part` in their name.
+
+            `X = g.get_matrix(['feature1', 'feature2'])`
             will retrieve a feature matrix with only the columns that contain the string 
             `feature1` or `feature2` in their name.
-            Most useful for topic modeling, where the column names are of the form `topic_0`, `topic_1`, etc.
+
+            Most useful for topic modeling, where the column names are of the form `topic_0: descriptor`, `topic_1: descriptor`, etc.
             Can retrieve unique columns in original dataframe, or actual topic features like [ip_part, shoes, preference_x, etc].
             
             Powerful way to retrieve features from a featurized graph by column or (top) features of interest.
             
             example:
-                X = g2.get_features_by_cols(['172', 'percent'])
+                # get the full feature matrices
+                X = g.get_matrix()
+                y = g.get_matrix(target=True)
+            
+                # get subset of features, or topics, given topic model encoding
+                X = g2.get_matrix(['172', 'percent'])
                 X.columns
                     => ['ip_172.56.104.67', 'ip_172.58.129.252', 'item_percent']
                 # or in targets
-                y = g2.get_features_by_cols(['total', 'percent'], target=True)
+                y = g2.get_matrix(['total', 'percent'], target=True)
                 y.columns
                     => ['basket_price_total', 'conversion_percent', 'CTR_percent', 'CVR_percent']
 
+                # not as useful for sbert features. 
+            Caveats:
+                - if you have a column name that is a substring of another column name, you may get unexpected results.
         Args:
             columns (Union[List, str]): list of column names or a single column name that may exist in columns 
                 of the feature matrix. If None, returns original feature matrix
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index a813e9bf6e..9f87d7bc2f 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -141,8 +141,9 @@ class UMAPMixin(MIXIN_BASE):
     _umap_memoize: WeakValueDictionary = WeakValueDictionary()
 
     def __init__(self, *args, **kwargs):
-        self._umap_initialized = False
+        #self._umap_initialized = False
         #self.engine = self.engine if hasattr(self, "engine") else None
+        pass
 
     def umap_lazy_init(
         self,
@@ -185,17 +186,14 @@ def umap_lazy_init(
             )
         
         if getattr(res, '_umap_params', None) == umap_kwargs:
-            print('Same umap params as last time, skipping new init')
+            print('Same umap params as last time, skipping new init') if verbose else None
             return res
         
-        print('lazy init')
+        print('lazy init') if verbose else None
         print(umap_kwargs) if verbose else None
         # set new umap kwargs
         res._umap_params = umap_kwargs  
 
-        # if not self._umap_initialized:
-        #     #print('umap_kwargs init n_components: ', umap_kwargs['n_components']) if verbose else None
-        #     print('Init Umap Params') if verbose else None
         res._n_components = n_components
         res._metric = metric
         res._n_neighbors = n_neighbors
@@ -207,10 +205,7 @@ def umap_lazy_init(
         res._umap = umap_engine.UMAP(**umap_kwargs)
         res.engine = engine_resolved
         res._suffix = suffix
-                                                    
-        # finally set the flag
-        res._umap_initialized = True  # this doesn't matter, as we always re-init
-        
+                                                            
         return res
 
 
@@ -342,13 +337,12 @@ def _process_umap(
                 setattr(fresh_res, attr, getattr(old_res, attr))
             # have to set _raw_data attribute on umap?
             fresh_res._umap = old_res._umap  # this saves the day!
-            fresh_res._umap_initialized = True
+            #fresh_res._umap_initialized = True
             fresh_res._umap_params = umap_kwargs_pure
             return fresh_res
         
         print('-' * 60) if verbose else None
         print('** Fitting UMAP') if verbose else None
-        #res._umap_initialized = False
         res = res.umap_lazy_init(res, verbose=verbose, **umap_kwargs_pure)
         
         emb = res._umap_fit_transform(X_, y_, verbose=verbose)

From 7f0828d1de0c72d3e697bc425663a4968c6bc53e Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 13 Jan 2023 18:24:20 -0800
Subject: [PATCH 104/432] forgot to make change in tests

---
 graphistry/tests/test_feature_utils.py | 20 +++++++++-----------
 1 file changed, 9 insertions(+), 11 deletions(-)

diff --git a/graphistry/tests/test_feature_utils.py b/graphistry/tests/test_feature_utils.py
index e5774a2419..96dce7fbfe 100644
--- a/graphistry/tests/test_feature_utils.py
+++ b/graphistry/tests/test_feature_utils.py
@@ -193,24 +193,24 @@ def setUp(self) -> None:
     @pytest.mark.skipif(not has_min_dependancy or not has_min_dependancy_text, reason="requires ai feature dependencies")
     def test_get_col_matrix(self):
         # no edges so this should be None
-        assert self.g2.get_features_by_cols(kind='edges') is None
+        assert self.g2.get_matrix(kind='edges') is None
         
         # test target methods
-        assert all(self.g2.get_features_by_cols(target=True).columns == self.g2._node_target.columns)
-        assert self.g2.get_features_by_cols('Anxiety', target=True).shape[0] == len(self.g2._node_target)
+        assert all(self.g2.get_matrix(target=True).columns == self.g2._node_target.columns)
+        assert self.g2.get_matrix('Anxiety', target=True).shape[0] == len(self.g2._node_target)
         # test str vs list 
-        assert (self.g2.get_features_by_cols('Anxiety', target=True) == self.g2.get_features_by_cols(['Anxiety'], target=True)).all().values[0]
+        assert (self.g2.get_matrix('Anxiety', target=True) == self.g2.get_matrix(['Anxiety'], target=True)).all().values[0]
 
-        # assert list(self.g2.get_features_by_cols(['Anxiety', 'education', 'computer'], target=True).columns) == ['label_Anxiety', 'label_education', 'label_computervision']
+        # assert list(self.g2.get_matrix(['Anxiety', 'education', 'computer'], target=True).columns) == ['label_Anxiety', 'label_education', 'label_computervision']
     
         # test feature methods
         # ngrams
-        assert (self.g2.get_features_by_cols().columns == self.g2._node_features.columns).all()
-        assert list(self.g2.get_features_by_cols('what').columns) == what, list(self.g2.get_features_by_cols('what').columns)
+        assert (self.g2.get_matrix().columns == self.g2._node_features.columns).all()
+        assert list(self.g2.get_matrix('what').columns) == what, list(self.g2.get_matrix('what').columns)
         
         # topic
-        assert all(self.g3.get_features_by_cols().columns == self.g3._node_features.columns)
-        assert list(self.g3.get_features_by_cols(['language', 'freedom']).columns) == freedom, self.g3.get_features_by_cols(['language', 'freedom']).columns
+        assert all(self.g3.get_matrix().columns == self.g3._node_features.columns)
+        assert list(self.g3.get_matrix(['language', 'freedom']).columns) == freedom, self.g3.get_matrix(['language', 'freedom']).columns
 
 class TestFastEncoder(unittest.TestCase):
     # we test how far off the fit returned values different from the transformed
@@ -427,8 +427,6 @@ def test_node_scaling(self):
                                   use_scaler_target=np.random.choice(SCALERS), 
                                   return_scalers=True)
 
-        
-
     @pytest.mark.skipif(not has_min_dependancy or not has_min_dependancy_text, reason="requires ai feature dependencies")
     def test_edge_scaling(self):
         g = graphistry.edges(edge_df2, "src", "dst")

From 84857a03f97128db8196b08ede4b93cddfbf6627 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 16 Jan 2023 22:15:28 -0800
Subject: [PATCH 105/432] adds infer_self_graph, which clusters batch df to
 itself

---
 graphistry/ai_utils.py | 129 ++++++++++++++++++++++++++++++++++++++---
 1 file changed, 122 insertions(+), 7 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 6e879d2a8c..e2a9d0d1e2 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -3,7 +3,7 @@
 
 import graphistry
 
-from .constants import N_TREES, DISTANCE
+from .constants import N_TREES, DISTANCE, WEIGHT, BATCH
 from .features import N_NEIGHBORS
 from logging import getLogger
 
@@ -256,18 +256,19 @@ def infer_graph(
 
     # if umap, need to add '_n' as node id to df, adding new indices to existing graph
     numeric_indices = range(
-        X_previously_fit.shape[0], X_previously_fit.shape[0] + df.shape[0]
+        X_previously_fit.shape[0], X_previously_fit.shape[0] + X_new.shape[0]
     )
     df["_n"] = numeric_indices
-    df["_batch"] = 1  # 1 for minibatch, 0 for existing graph
+    df[BATCH] = 1  # 1 for minibatch, 0 for existing graph
     node = res._node
     NDF = res._nodes
-    NDF["_batch"] = 0
+    NDF[BATCH] = 0
     EDF = res._edges
-    EDF["_batch"] = 0
+    EDF[BATCH] = 0
     src = res._source
     dst = res._destination
 
+    #new_nodes = []
     new_edges = []
     old_edges = []
     old_nodes = []
@@ -304,12 +305,15 @@ def infer_graph(
                 ]
                 if not local_edges.empty:
                     old_edges.append(local_edges.sample(sample, replace=True))
-            new_edges.append([this_ndf[node], record_df[node], 1, 1])
+                    
+            weight = min(1/(dist[j]+1e-3), 1)
+            new_edges.append([this_ndf[node], record_df[node], weight, 1])
             old_nodes.append(this_ndf)
+            #new_nodes.extend([record_df, this_ndf])
             
     print(f'{np.mean(nn):.2f} neighbors per node within epsilon {eps:.2f}') if verbose else None
     
-    new_edges = pd.DataFrame(new_edges, columns=[src, dst, "_weight", "_batch"])
+    new_edges = pd.DataFrame(new_edges, columns=[src, dst, WEIGHT, BATCH])
 
     all_nodes = []
     if len(old_edges):
@@ -357,3 +361,114 @@ def infer_graph(
     
     print("-" * 50) if verbose else None
     return g
+
+
+
+def infer_self_graph(res, 
+    emb, X, y, df, infer_on_umap_embedding=False, eps="auto", n_neighbors=7, verbose=False, 
+):
+    """
+    Infer a graph from a graphistry object
+
+    args:
+        df: outside minibatch dataframe to add to existing graph
+        X: minibatch transformed dataframe
+        emb: minibatch UMAP embedding distance threshold for a minibatch point to cluster to existing graph
+        eps: if 'auto' will find a good epsilon from the data; distance threshold for a minibatch point to cluster to existing graph
+        sample: number of nearest neighbors to add from existing graphs edges, if None, ignores existing edges.
+            This sets the global stickiness of the graph, and is a good way to control the number of edges incuded from the old graph.
+        n_neighbors, int: number of nearest neighbors to include per batch point within epsilon.
+            This sets the local stickiness of the graph, and is a good way to control the number of edges between 
+            an added point and the existing graph.
+    returns:
+        graphistry Plottable object
+    """
+    #enhanced = is_notebook()
+    
+    print("-" * 50) if verbose else None
+    
+    if infer_on_umap_embedding and emb is not None:
+        X_previously_fit = emb
+        X_new = emb
+        print("Infering edges over UMAP embedding") if verbose else None
+    else:  # can still be umap, but want to do the inference on the higher dimensional features
+        X_previously_fit = X
+        X_new = X
+        print("Infering edges over features embedding") if verbose else None
+
+    print("-" * 45) if verbose else None
+
+    assert (
+        df.shape[0] == X.shape[0]
+    ), "minibatches df and X must have same number of rows since f(df) = X"
+    if emb is not None:
+        assert (
+            emb.shape[0] == df.shape[0]
+        ), "minibatches emb and X must have same number of rows since h(df) = emb"
+        df = df.assign(x=emb.x, y=emb.y)  # add x and y to df for graphistry instance
+    else: # if umap has been fit, but only transforming over features, need to add x and y or breaks plot binds of res
+        df['x'] = np.random.random(df.shape[0])
+        df['y'] = np.random.random(df.shape[0])
+
+    # if umap, need to add '_n' as node id to df, adding new indices to existing graph
+    numeric_indices = np.arange(
+        X_previously_fit.shape[0]  #, X_previously_fit.shape[0] + X_new.shape[0]
+    , dtype=np.float64)
+    df["_n"] = numeric_indices
+    df[BATCH] = 1  # 1 for minibatch, 0 for existing graph, should all be `1` 
+    node = res._node
+    src = res._source
+    dst = res._destination
+    
+    old_nodes = []
+    new_edges = []
+    mdists = []
+
+    # vsearch = build_search_index(X_previously_fit, angular=False)
+
+    for i in range(X_new.shape[0]):
+        diff = X_previously_fit - X_new.iloc[i, :]
+        dist = np.linalg.norm(diff, axis=1)  # Euclidean distance
+        mdists.append(dist)
+
+    m, std = np.mean(mdists), np.std(mdists)
+    logger.info(f"--Mean distance to existing nodes  {m:.2f} +/- {std:.2f}")
+    print(f' Mean distance to existing nodes {m:.2f} +/- {std:.2f}') if verbose else None
+    if eps == "auto":
+        eps = np.min([np.abs(m - std), m])
+    logger.info(
+        f" epsilon = {eps:.2f} max distance threshold to be considered a neighbor"
+    )
+    print(f' epsilon = {eps:.2f}; max distance threshold to be considered a neighbor') if verbose else None
+    
+    print(f'Finding {n_neighbors} nearest neighbors') if verbose else None
+    nn = []
+    for i, dist in enumerate(mdists):
+        record_df = df.iloc[i, :]
+        nearest = np.where(dist < eps)[0]
+        nn.append(len(nearest))
+        #new_nodes.append(record_df)
+        for j in nearest[:n_neighbors]:  # add n_neighbors nearest neighbors, if any, super speedup hack
+            if i != j:
+                this_ndf = df.iloc[j, :]
+                weight = min(1/(dist[j] + 1e-3), 1)
+                new_edges.append([this_ndf[node], record_df[node], weight, 1])
+                old_nodes.append(this_ndf)
+            
+    print(f'{np.mean(nn):.2f} neighbors per node within epsilon {eps:.2f}') if verbose else None
+    
+    print('', len(new_edges), 'total edges pairs') if verbose else None
+
+    new_edges = pd.DataFrame(new_edges, columns=[src, dst, WEIGHT, BATCH])
+    new_edges = new_edges.drop_duplicates()
+    print('', len(new_edges), 'total edges pairs after dropping duplicates') if verbose else None
+
+    # #########################################################
+    g = res.nodes(df, node).edges(new_edges, src, dst)
+
+    g._node_embedding = emb
+    g._node_features = X
+    g._node_targets = y
+    
+    print("-" * 50) if verbose else None
+    return g

From 43ff59f79ff28b0904f3484fea4bc6f5d65740cf Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 16 Jan 2023 22:16:16 -0800
Subject: [PATCH 106/432] comments encode_point_color to dbscan

---
 graphistry/compute/cluster.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index e9ca689344..51325b8e12 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -274,7 +274,7 @@ def dbscan(
             *args,
             **kwargs,
         )
-        res = res.encode_point_color(column=DBSCAN, as_categorical=True)
+        #res = res.encode_point_color(column=DBSCAN, as_categorical=True)
 
         return res
 
@@ -379,6 +379,6 @@ def transform_dbscan(
             g = self._infer_edges(emb, X, y, df, eps=eps, sample=sample, n_neighbors=n_neighbors,  # type: ignore
                 infer_on_umap_embedding=infer_umap_embedding
                 )
-            g = g.encode_point_color(column=DBSCAN, as_categorical=True)
+            #g = g.encode_point_color(column=DBSCAN, as_categorical=True)
             return g
         return emb, X, y, df

From 1ec690ce4a5aa5966529e6b32f4087835bbbef76 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 16 Jan 2023 22:17:26 -0800
Subject: [PATCH 107/432] adds self graph in pipeline

---
 graphistry/feature_utils.py | 30 +++++++++++++++++++++---------
 1 file changed, 21 insertions(+), 9 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index cf96fd2cab..776fa8a748 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -23,7 +23,7 @@
 from . import constants as config
 from .PlotterBase import WeakValueDictionary, Plottable
 from .util import setup_logger, check_set_memoize
-from .ai_utils import infer_graph
+from .ai_utils import infer_graph, infer_self_graph
 
 # add this inside classes and have a method that can set log level
 logger = setup_logger(name=__name__, verbose=config.VERBOSE)
@@ -1757,6 +1757,9 @@ def scale(self, X=None, y=None, return_pipeline=False, *args, **kwargs):
         """Fits new scaling functions on df, y via args-kwargs
         
             example:
+                from graphisty.features import SCALERS, SCALER_OPTIONS
+                print(SCALERS)
+                
                 g = graphistry.nodes(df)
                 # set a scaling strategy for features and targets -- umap uses those and produces different results depending.
                 g2 = g.umap(use_scaler='standard', use_scaler_target=None)
@@ -2165,9 +2168,15 @@ def _featurize_edges(
 
         return res
     
-    def _infer_edges(self, emb, X, y, df, eps='auto', sample=None, infer_on_umap_embedding=False, verbose=False, **kwargs):
-        res = self.bind()  # will not be able to decide umap coordinates, but will be able to infer graph from existing edges
-        g = infer_graph(res, emb, X, y, df, infer_on_umap_embedding=infer_on_umap_embedding, eps=eps, sample=sample, verbose=verbose, **kwargs) 
+    def _infer_edges(self, emb, X, y, df, eps='auto', n_neighbors=4, sample=None, infer_on_umap_embedding=False, 
+                     verbose=False, merge_policy=False, **kwargs):
+        res = self.bind()
+        if merge_policy:
+            g = infer_graph(res, emb, X, y, df, infer_on_umap_embedding=infer_on_umap_embedding, 
+                            n_neighbors=n_neighbors, eps=eps, sample=sample, verbose=verbose, **kwargs) 
+        else:
+            g = infer_self_graph(res, emb, X, y, df, infer_on_umap_embedding=infer_on_umap_embedding, 
+                                 n_neighbors=n_neighbors, eps=eps, verbose=verbose, **kwargs)
         return g
 
     def _transform(self, encoder: str, df: pd.DataFrame, ydf: Optional[pd.DataFrame], scaled):
@@ -2185,6 +2194,7 @@ def transform(self, df: pd.DataFrame,
                   y: Optional[pd.DataFrame] = None, 
                   kind: str = 'nodes', 
                   eps: Union[str, float, int] = 'auto', 
+                  merge_policy: bool = False,
                   sample: Optional[int] = None, 
                   n_neighbors: Optional[int] = None,
                   return_graph: bool = True,
@@ -2197,7 +2207,9 @@ def transform(self, df: pd.DataFrame,
                 df: pd.DataFrame, raw data to transform
                 ydf: pd.DataFrame, optional
                 kind: str  # one of `nodes`, `edges`
-                return_graph: bool, if True, will return a graph with inferred edges
+                return_graph: bool, if True, will return a graph with inferred edges.
+                merge_policy: bool, if True, adds batch to existing graph nodes via nearest neighbors. 
+                    If False, will infer edges only between nodes in the batch.
                 eps: float, if return_graph is True, will use this value for eps in NN search, or 'auto' to infer a good value
                     eps represents the maximum distance between two samples for one to be considered as in the neighborhood of the other.
                 sample: int, if return_graph is True, will use sample edges of existing graph to fill out the new graph
@@ -2205,8 +2217,8 @@ def transform(self, df: pd.DataFrame,
                 scaled: bool, if True, will use scaled transformation of data set during featurization
                 verbose: bool, if True, will print metadata about the graph construction
             returns:
-                X: pd.DataFrame, transformed data if return_graph is False
-                    or a graph with inferred edges if return_graph is True
+                X, y: pd.DataFrame, transformed data if return_graph is False
+                    or a graphistry Plottable with inferred edges if return_graph is True
         """
         if kind == "nodes":
             X, y_ = self._transform("_node_encoder", df, y, scaled=scaled)
@@ -2220,8 +2232,8 @@ def transform(self, df: pd.DataFrame,
             emb = None  # will not be able to infer graph from umap coordinates, 
             # but will be able to infer graph from features of existing edges
             g = self._infer_edges(emb, X, y_, df, eps=eps, sample=sample, n_neighbors=n_neighbors,
-                                  infer_on_umap_embedding=False, 
-                                  verbose=verbose) 
+                                  infer_on_umap_embedding=False, merge_policy=merge_policy,
+                                  verbose=verbose)
             return g
         return X, y_
 

From f4a2bcbe9e1c6d277b54f68266fa0f4b9a4640c2 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 16 Jan 2023 22:17:58 -0800
Subject: [PATCH 108/432] adds doc string

---
 graphistry/umap_utils.py | 24 ++++++++++++++++++------
 1 file changed, 18 insertions(+), 6 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 9f87d7bc2f..008abd276d 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -272,22 +272,34 @@ def transform_umap(self, df: pd.DataFrame,
                     y: Optional[pd.DataFrame] = None, 
                     kind: str = 'nodes', 
                     eps: Union[str, float, int] = 'auto', 
+                    merge_policy: bool = False,
                     sample: Optional[int] = None, 
-                    n_neighbors: Optional[int] = None,
+                    n_neighbors: int = 7,
                     return_graph: bool = True,
                     fit_umap_embedding: bool = False,
                     verbose=False
     ) -> Union[Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable]:
-        try:
-            logger.debug(f"Going into Transform umap {df.shape}")
-        except:
-            pass
+        """Transforms data into UMAP embedding
+        
+        args:
+            df: Dataframe to transform
+            y: Target column
+            kind: One of `nodes` or `edges`
+            eps: Epsilon for DBSCAN
+            merge_policy: if True, use previous graph, adding new batch to existing graph's neighbors
+                useful to contextualize new data against existing graph. If False, `sample` is irrelevant.
+            sample: Sample number of existing graph's neighbors to use for contextualization -- helps make denser graphs
+            n_neighbors: Number of neighbors to use for contextualization
+            return_graph: Whether to return a graph or just the embeddings
+            fit_umap_embedding: Whether to infer graph from the UMAP embedding on the new data
+            verbose: Whether to print information about the graph inference
+        """
         X, y_ = self.transform(df, y, kind=kind, return_graph=False, verbose=verbose)
         emb = self._umap.transform(X)  # type: ignore
         emb = self._bundle_embedding(emb, index=df.index)
         if return_graph and kind not in ["edges"]:
             g = self._infer_edges(emb, X, y_, df, 
-                                  infer_on_umap_embedding=fit_umap_embedding, 
+                                  infer_on_umap_embedding=fit_umap_embedding, merge_policy=merge_policy,
                                   eps=eps, sample=sample, n_neighbors=n_neighbors,
                                   verbose=verbose) 
             return g        

From b06c6167b37a89acf6220e10fa5dd4d83e68d12a Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 16 Jan 2023 22:21:08 -0800
Subject: [PATCH 109/432] adds optuna option base definitions and
 parameterizations

---
 graphistry/constants.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/graphistry/constants.py b/graphistry/constants.py
index 42e2c409bb..f6fda05fd9 100644
--- a/graphistry/constants.py
+++ b/graphistry/constants.py
@@ -7,6 +7,7 @@
 DST = "_dst_implicit"
 NODE = '_n_implicit'  # Is this being use anymore??
 WEIGHT = "_weight"
+BATCH = "_batch"
 # for UMAP reserved namespace
 X = "x"
 Y = "y"

From c85ed2759b876e0afac11c625a91fe8ad6e35e9f Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 16 Jan 2023 22:21:37 -0800
Subject: [PATCH 110/432] adds optuna option base definitions and
 parameterizations

---
 graphistry/features.py | 66 +++++++++++++++++++++++++++++++++++-------
 1 file changed, 55 insertions(+), 11 deletions(-)

diff --git a/graphistry/features.py b/graphistry/features.py
index f7a2b8003a..59858c66c1 100644
--- a/graphistry/features.py
+++ b/graphistry/features.py
@@ -37,24 +37,42 @@
 MAX_DF = 0.2
 MIN_DF = 3
 
-N_BINS = 10
 KBINS_SCALER = "kbins"
+STANDARD = 'standard'
+ROBUST = 'robust'
+MINMAX = 'minmax'
+QUANTILE = 'quantile'
+# for Optuna
+ERROR = "error"
+
+SCALERS = [STANDARD, ROBUST, MINMAX, KBINS_SCALER, QUANTILE]
+NO_SCALER = None
+# Scaler options
+N_BINS = 10
 IMPUTE = "median"  # set to
 N_QUANTILES = 100
 OUTPUT_DISTRIBUTION = "normal"
-QUANTILES_RANGE = (25, 75)
+QUANTILES_RANGE = (5, 95)
 ENCODE = "ordinal"  # kbins, onehot, ordinal, label
 STRATEGY = "uniform"  # uniform, quantile, kmeans
 SIMILARITY = None  # 'ngram' , default None uses Gap
 CATEGORIES = "auto"
-KEEP_N_DECIMALS = 5
+SCALER_OPTIONS = {'impute': ['median', None], 'n_quantiles': [10,100], 'output_distribution': ['normal', 'uniform'],
+                  'quantile_range': QUANTILES_RANGE, 
+                  'encode': ['kbins', 'onehot', 'ordinal', 'label'], 
+                  'strategy': ['uniform', 'quantile', 'kmeans'],
+                  'similarity':[None, 'ngram'], 'categories': CATEGORIES, 'n_bins': [2, 100], 
+                  'use_scaler': SCALERS, 'use_scaler_target': SCALERS
+}
 
+# precision in decimal places
+KEEP_N_DECIMALS = 5 # TODO: check to see if this takes a lot of time
+BATCH_SIZE_SMALL = 32
 BATCH_SIZE = 1000
-NO_SCALER = None
 EXTRA_COLS_NEEDED = ["x", "y", "_n"]
 # ###############################################################
 # ################# graphistry umap config constants #################
-UMAP_DIM = 2
+N_COMPONENTS = 2
 N_NEIGHBORS = 15
 MIN_DIST = 0.1
 SPREAD = 0.5
@@ -63,6 +81,10 @@
 NEGATIVE_SAMPLING_RATE = 5
 METRIC = "euclidean"
 
+UMAP_OPTIONS = {'n_components': [2, 10], 'n_neighbors': [2, 30], 'min_dist': [0.01, 0.99], 'spread': [0.5, 5], 'local_connectivity': [1, 30],
+                'repulsion_strength': [1, 10], 'negative_sampling_rate': [5, 20], 
+                'metric': ['euclidean', 'cosine', 'manhattan', 'l1', 'l2', 'cityblock', 'braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule']
+}
 
 # ###############################################################
 # ################# enrichments
@@ -82,12 +104,14 @@
 NGRAMS = "ngrams"
 # ############ Embedding Models
 PARAPHRASE_SMALL_MODEL = "sentence-transformers/paraphrase-albert-small-v2"
-PARAPHRASE_MULTILINGUAL_MODEL = (
-    "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
-)
+PARAPHRASE_MULTILINGUAL_MODEL = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
 MSMARCO2 = "sentence-transformers/msmarco-distilbert-base-v2"  # 768
 MSMARCO3 = "sentence-transformers/msmarco-distilbert-base-v3"  # 512
 QA_SMALL_MODEL = "sentence-transformers/multi-qa-MiniLM-L6-cos-v1"
+LLM_SMALL = "sentence-transformers/llm-en-dim128"
+LLM_LARGE = "sentence-transformers/llm-en-dim512"
+
+EMBEDDING_MODELS = [PARAPHRASE_SMALL_MODEL, PARAPHRASE_MULTILINGUAL_MODEL, MSMARCO2, MSMARCO3, QA_SMALL_MODEL, LLM_SMALL, LLM_LARGE]
 # #############################################################################
 # Model Training Constants
 # Used for seeding random state
@@ -96,6 +120,26 @@
 SPLIT_MEDIUM = 0.2
 SPLIT_HIGH = 0.5
 
+# #############################################################################
+# model training options
+
+FEATURE_OPTIONS = {
+    'kind': ['nodes', 'edges'],
+    'cardinality_threshold': [1, HIGH_CARD],
+    'cardinality_threshold_target': [1, HIGH_CARD],
+    'n_topics': [4, 100],
+    'n_topics_target': [4, 100],
+    'multilabel': [True, False],
+    'embedding': [True, False],
+    'use_ngrams': [True, False],
+    'ngram_range': (1, 5),
+    'max_df': [0.1, 0.9],
+    'min_df': [1, 10],
+    'min_words': [0, 100],
+    'model_name': [MSMARCO2, MSMARCO3, PARAPHRASE_SMALL_MODEL, PARAPHRASE_MULTILINGUAL_MODEL, QA_SMALL_MODEL],
+}
+
+
 # #############################################################################
 # Model Training {params}
 
@@ -134,7 +178,7 @@
 
 
 default_umap_parameters = ModelDict("Umap Parameters",
-        {"n_components": UMAP_DIM,
+        {"n_components": N_COMPONENTS,
         **({"metric": METRIC} if True else {}),
         "n_neighbors": N_NEIGHBORS,
         "min_dist": MIN_DIST,
@@ -147,7 +191,7 @@
 
 
 umap_hellinger = ModelDict("Umap Parameters Hellinger",    
-        {"n_components": UMAP_DIM,
+        {"n_components": N_COMPONENTS,
         "metric": "hellinger",  # info metric, can't use on
         # textual encodings since they contain negative values...
         "n_neighbors": 15,
@@ -160,7 +204,7 @@
 )
 
 umap_euclidean = ModelDict("Umap Parameters Euclidean",
-        {"n_components": UMAP_DIM,
+        {"n_components": N_COMPONENTS,
         "metric": "euclidean",
         "n_neighbors": 12,
         "min_dist": 0.1,

From 36d57eed010d5ca74047975d029530d9882b357b Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 16 Jan 2023 22:26:47 -0800
Subject: [PATCH 111/432] lint

---
 graphistry/ai_utils.py | 12 ++++++------
 graphistry/features.py |  2 +-
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index e2a9d0d1e2..8cec0f225c 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -306,7 +306,7 @@ def infer_graph(
                 if not local_edges.empty:
                     old_edges.append(local_edges.sample(sample, replace=True))
                     
-            weight = min(1/(dist[j]+1e-3), 1)
+            weight = min(1 / (dist[j] + 1e-3), 1)
             new_edges.append([this_ndf[node], record_df[node], weight, 1])
             old_nodes.append(this_ndf)
             #new_nodes.extend([record_df, this_ndf])
@@ -406,14 +406,14 @@ def infer_self_graph(res,
             emb.shape[0] == df.shape[0]
         ), "minibatches emb and X must have same number of rows since h(df) = emb"
         df = df.assign(x=emb.x, y=emb.y)  # add x and y to df for graphistry instance
-    else: # if umap has been fit, but only transforming over features, need to add x and y or breaks plot binds of res
+    else:  # if umap has been fit, but only transforming over features, need to add x and y or breaks plot binds of res
         df['x'] = np.random.random(df.shape[0])
         df['y'] = np.random.random(df.shape[0])
 
-    # if umap, need to add '_n' as node id to df, adding new indices to existing graph
+    #  if umap, need to add '_n' as node id to df, adding new indices to existing graph
     numeric_indices = np.arange(
-        X_previously_fit.shape[0]  #, X_previously_fit.shape[0] + X_new.shape[0]
-    , dtype=np.float64)
+        X_previously_fit.shape[0],  #, X_previously_fit.shape[0] + X_new.shape[0]
+        dtype=np.float64)
     df["_n"] = numeric_indices
     df[BATCH] = 1  # 1 for minibatch, 0 for existing graph, should all be `1` 
     node = res._node
@@ -451,7 +451,7 @@ def infer_self_graph(res,
         for j in nearest[:n_neighbors]:  # add n_neighbors nearest neighbors, if any, super speedup hack
             if i != j:
                 this_ndf = df.iloc[j, :]
-                weight = min(1/(dist[j] + 1e-3), 1)
+                weight = min(1 / (dist[j] + 1e-3), 1)
                 new_edges.append([this_ndf[node], record_df[node], weight, 1])
                 old_nodes.append(this_ndf)
             
diff --git a/graphistry/features.py b/graphistry/features.py
index 59858c66c1..3adbc829db 100644
--- a/graphistry/features.py
+++ b/graphistry/features.py
@@ -66,7 +66,7 @@
 }
 
 # precision in decimal places
-KEEP_N_DECIMALS = 5 # TODO: check to see if this takes a lot of time
+KEEP_N_DECIMALS = 5  # TODO: check to see if this takes a lot of time
 BATCH_SIZE_SMALL = 32
 BATCH_SIZE = 1000
 EXTRA_COLS_NEEDED = ["x", "y", "_n"]

From b3ef07a2a369ea43248146e800107c1d18de7c64 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 16 Jan 2023 22:30:19 -0800
Subject: [PATCH 112/432] lint

---
 graphistry/ai_utils.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 8cec0f225c..4dfb61b148 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -412,8 +412,9 @@ def infer_self_graph(res,
 
     #  if umap, need to add '_n' as node id to df, adding new indices to existing graph
     numeric_indices = np.arange(
-        X_previously_fit.shape[0],  #, X_previously_fit.shape[0] + X_new.shape[0]
-        dtype=np.float64)
+        X_previously_fit.shape[0],  # X_previously_fit.shape[0] + X_new.shape[0]
+        dtype=np.float64  # this seems off but works
+        )
     df["_n"] = numeric_indices
     df[BATCH] = 1  # 1 for minibatch, 0 for existing graph, should all be `1` 
     node = res._node
@@ -447,7 +448,6 @@ def infer_self_graph(res,
         record_df = df.iloc[i, :]
         nearest = np.where(dist < eps)[0]
         nn.append(len(nearest))
-        #new_nodes.append(record_df)
         for j in nearest[:n_neighbors]:  # add n_neighbors nearest neighbors, if any, super speedup hack
             if i != j:
                 this_ndf = df.iloc[j, :]

From 31f168f361c07038971b6a3f50a9c718434ff7b2 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 16 Jan 2023 22:45:38 -0800
Subject: [PATCH 113/432] fixes test

---
 graphistry/tests/test_compute_cluster.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/tests/test_compute_cluster.py b/graphistry/tests/test_compute_cluster.py
index acf83daf9e..05e8dba9df 100644
--- a/graphistry/tests/test_compute_cluster.py
+++ b/graphistry/tests/test_compute_cluster.py
@@ -17,7 +17,7 @@ def _condition(self, g, kind):
         if kind == 'nodes':
             self.assertTrue(g._node_dbscan is not None, 'instance has no `_node_dbscan` method')
             self.assertTrue(DBSCAN in g._nodes, 'node df has no `_dbscan` attribute')
-            self.assertTrue(g._point_color is not None, 'instance has no `_point_color` method')
+            #self.assertTrue(g._point_color is not None, 'instance has no `_point_color` method')
         else:
             self.assertTrue(g._edge_dbscan is not None, 'instance has no `_edge_dbscan` method')
             self.assertTrue(DBSCAN in g._edges, 'edge df has no `_dbscan` attribute')

From d0d0cea823cd1d1988fef5c99e83eb760c440782 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Tue, 17 Jan 2023 16:08:17 -0800
Subject: [PATCH 114/432] adds edgelist to adjacency function and adds more
 model hydration between transform methods

---
 graphistry/ai_utils.py | 58 ++++++++++++++++++++++++------------------
 1 file changed, 33 insertions(+), 25 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 4dfb61b148..67ab5327f5 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -203,6 +203,27 @@ def query_by_vector(vect, df, search_index, top_n):
 #
 ##########################################################################################################################
 
+def edgelist_to_weighted_adjacency(g, weights=None):
+    """ Convert edgelist to weighted adjacency matrix in sparse coo_matrix"""
+    import scipy.sparse as ss
+    import numpy as np
+    res = g._edges[[g._source, g._destination]].values.astype(np.int64)
+    rows, cols = res.T[0], res.T[1]
+    if weights is None:
+        weights = np.ones(len(rows))
+    M = ss.coo_matrix((weights, (rows, cols)))
+    return M.tocsr()
+
+def hydrate_graph(res, new_nodes, new_edges, node, src, dst, new_emb, new_features, new_targets):
+    # #########################################################
+    g = res.nodes(new_nodes, node).edges(new_edges, src, dst)
+
+    g._weighted_adjacency = edgelist_to_weighted_adjacency(g)
+    g._node_embedding = new_emb
+    g._node_features = new_features
+    g._node_targets = new_targets
+    return g
+    
 
 def infer_graph(
     res, emb, X, y, df, infer_on_umap_embedding=False, eps="auto", sample=None, n_neighbors=7, verbose=False, 
@@ -289,9 +310,9 @@ def infer_graph(
     logger.info(
         f"-epsilon = {eps:.2f} max distance threshold to be considered a neighbor"
     )
-    print(f' epsilon = {eps:.2f}; max distance threshold to be considered a neighbor') if verbose else None
+    print(f' Max distance threshold; epsilon = {eps:.2f}') if verbose else None
     
-    print(f'Finding {n_neighbors} nearest neighbors') if verbose else None
+    print(f' Finding {n_neighbors} nearest neighbors') if verbose else None
     nn = []
     for i, dist in enumerate(mdists):
         record_df = df.iloc[i, :]
@@ -311,7 +332,7 @@ def infer_graph(
             old_nodes.append(this_ndf)
             #new_nodes.extend([record_df, this_ndf])
             
-    print(f'{np.mean(nn):.2f} neighbors per node within epsilon {eps:.2f}') if verbose else None
+    print(f' {np.mean(nn):.2f} neighbors per node within epsilon {eps:.2f}') if verbose else None
     
     new_edges = pd.DataFrame(new_edges, columns=[src, dst, WEIGHT, BATCH])
 
@@ -319,7 +340,7 @@ def infer_graph(
     if len(old_edges):
         old_edges = pd.concat(old_edges, axis=0).assign(_batch=0)
         all_nodes = pd.concat([old_edges[src], old_edges[dst], new_edges[src], new_edges[dst]]).drop_duplicates()
-        print(' ', len(all_nodes), "nodes in new graph") if verbose else None
+        print('', len(all_nodes), "nodes in new graph") if verbose else None
 
     if sample:
         new_edges = pd.concat([new_edges, old_edges], axis=0).drop_duplicates()
@@ -352,15 +373,9 @@ def infer_graph(
 
     new_targets = pd.concat([y, Y.loc[old_nodes.index]]) if y is not None else Y
 
-    # #########################################################
-    g = res.nodes(new_nodes, node).edges(new_edges, src, dst)
-
-    g._node_embedding = new_emb
-    g._node_features = new_features
-    g._node_targets = new_targets
-    
     print("-" * 50) if verbose else None
-    return g
+    return hydrate_graph(res, new_nodes, new_edges, node, src, dst, new_emb, new_features, new_targets)
+
 
 
 
@@ -440,9 +455,9 @@ def infer_self_graph(res,
     logger.info(
         f" epsilon = {eps:.2f} max distance threshold to be considered a neighbor"
     )
-    print(f' epsilon = {eps:.2f}; max distance threshold to be considered a neighbor') if verbose else None
+    print(f' Max distance threshold; epsilon = {eps:.2f}') if verbose else None
     
-    print(f'Finding {n_neighbors} nearest neighbors') if verbose else None
+    print(f' Finding {n_neighbors} nearest neighbors') if verbose else None
     nn = []
     for i, dist in enumerate(mdists):
         record_df = df.iloc[i, :]
@@ -455,20 +470,13 @@ def infer_self_graph(res,
                 new_edges.append([this_ndf[node], record_df[node], weight, 1])
                 old_nodes.append(this_ndf)
             
-    print(f'{np.mean(nn):.2f} neighbors per node within epsilon {eps:.2f}') if verbose else None
+    print(f' {np.mean(nn):.2f} neighbors per node within epsilon {eps:.2f}') if verbose else None
     
-    print('', len(new_edges), 'total edges pairs') if verbose else None
-
     new_edges = pd.DataFrame(new_edges, columns=[src, dst, WEIGHT, BATCH])
     new_edges = new_edges.drop_duplicates()
     print('', len(new_edges), 'total edges pairs after dropping duplicates') if verbose else None
-
+    print("** Final graph has", len(df), "nodes") if verbose else None
     # #########################################################
-    g = res.nodes(df, node).edges(new_edges, src, dst)
-
-    g._node_embedding = emb
-    g._node_features = X
-    g._node_targets = y
-    
     print("-" * 50) if verbose else None
-    return g
+    return hydrate_graph(res, df, new_edges, node, src, dst, emb, X, y)
+

From 8bb6e278065c195026853d34304d73720fe325e2 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Tue, 17 Jan 2023 16:09:23 -0800
Subject: [PATCH 115/432] adds full umap functionality from umap fit, docs

---
 graphistry/compute/cluster.py | 3 +--
 graphistry/feature_utils.py   | 6 ++++--
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 51325b8e12..adcf950116 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -198,10 +198,9 @@ def _cluster_dbscan(
             if res.engine == CUML
             else DBSCAN(eps=eps, min_samples=min_samples, **kwargs)
         )
-        #print(f"DBSCAN engine: {res.engine}") if verbose else None
 
         res = dbscan_fit(
-            res, dbscan, kind=kind, cols=cols, use_umap_embedding=fit_umap_embedding, verbose=True
+            res, dbscan, kind=kind, cols=cols, use_umap_embedding=fit_umap_embedding, verbose=verbose
             )
 
         return res
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 776fa8a748..3242b141f6 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2172,9 +2172,11 @@ def _infer_edges(self, emb, X, y, df, eps='auto', n_neighbors=4, sample=None, in
                      verbose=False, merge_policy=False, **kwargs):
         res = self.bind()
         if merge_policy:
+            # useful to cluster onto existing graph
             g = infer_graph(res, emb, X, y, df, infer_on_umap_embedding=infer_on_umap_embedding, 
                             n_neighbors=n_neighbors, eps=eps, sample=sample, verbose=verbose, **kwargs) 
         else:
+            # useful to cluster onto self
             g = infer_self_graph(res, emb, X, y, df, infer_on_umap_embedding=infer_on_umap_embedding, 
                                  n_neighbors=n_neighbors, eps=eps, verbose=verbose, **kwargs)
         return g
@@ -2575,8 +2577,8 @@ def featurize(
             )
             return self
         
-        if dbscan:
-            res = res.dbscan(kind=kind, fit_umap_embedding=False)  # type: ignore
+        if dbscan:  # this adds columns to the dataframe, will break tests of pure featurization & umap, so set to False in those
+            res = res.dbscan(eps=min_dist, n_neighbors=n_neighbors, kind=kind, fit_umap_embedding=False, verbose=verbose)  # type: ignore
 
         if not inplace:
             return res

From 82ccaf09f5610783632629717f2e96e9515c4194 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Tue, 17 Jan 2023 16:09:44 -0800
Subject: [PATCH 116/432] adds full umap functionality from umap fit, docs

---
 graphistry/umap_utils.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 008abd276d..c5e69c4a0d 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -276,7 +276,7 @@ def transform_umap(self, df: pd.DataFrame,
                     sample: Optional[int] = None, 
                     n_neighbors: int = 7,
                     return_graph: bool = True,
-                    fit_umap_embedding: bool = False,
+                    fit_umap_embedding: bool = True,
                     verbose=False
     ) -> Union[Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable]:
         """Transforms data into UMAP embedding
@@ -517,7 +517,6 @@ def umap(
         if kind == "nodes":
             index = res._nodes.index
             if res._node is None:
-
                 logger.debug("-Writing new node name")
                 res = res.nodes(  # type: ignore
                     res._nodes.reset_index(drop=True)
@@ -616,7 +615,7 @@ def umap(
             res = res.prune_self_edges()
 
         if dbscan:
-            res = res.dbscan(kind=kind, fit_umap_embedding=True, verbose=verbose)  # type: ignore
+            res = res.dbscan(eps=min_dist, n_neighbors=n_neighbors, kind=kind, fit_umap_embedding=True, verbose=verbose)  # type: ignore
 
         if not inplace:
             return res

From 40c81ea7323866e7b0d24fb42c3f57c7f5a215bf Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 18 Jan 2023 11:38:50 -0800
Subject: [PATCH 117/432] renames eps variabe to align with umaps min_dist
 throughout.

---
 graphistry/ai_utils.py        |  8 ++++----
 graphistry/compute/cluster.py | 30 +++++++++++++++++-------------
 graphistry/feature_utils.py   |  9 +++++++--
 graphistry/umap_utils.py      |  4 ++--
 4 files changed, 30 insertions(+), 21 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 67ab5327f5..7e3f213b06 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -346,7 +346,7 @@ def infer_graph(
         new_edges = pd.concat([new_edges, old_edges], axis=0).drop_duplicates()
         print(' Sampled', len(old_edges.drop_duplicates()), 'previous old edges') if verbose else None
     new_edges = new_edges.drop_duplicates()
-    print('', len(new_edges), 'total edges pairs after dropping duplicates') if verbose else None
+    print('', len(new_edges), 'total edges after dropping duplicates') if verbose else None
 
     if len(old_nodes):
         old_nodes = pd.DataFrame(old_nodes)
@@ -367,7 +367,7 @@ def infer_graph(
     new_features = pd.concat([X, FEATS.loc[old_nodes.index]], axis=0)
 
     new_nodes = pd.concat([df, old_nodes], axis=0)  # append minibatch at top
-    print("** Final graph has", len(new_nodes), "nodes") if verbose else None
+    print(" ** Final graph has", len(new_nodes), "nodes") if verbose else None
     print(" - Batch has", len(df), "nodes") if verbose else None
     print(" - Brought in", len(old_nodes), "nodes") if verbose else None
 
@@ -474,8 +474,8 @@ def infer_self_graph(res,
     
     new_edges = pd.DataFrame(new_edges, columns=[src, dst, WEIGHT, BATCH])
     new_edges = new_edges.drop_duplicates()
-    print('', len(new_edges), 'total edges pairs after dropping duplicates') if verbose else None
-    print("** Final graph has", len(df), "nodes") if verbose else None
+    print('', len(new_edges), 'total edges after dropping duplicates') if verbose else None
+    print(" ** Final graph has", len(df), "nodes") if verbose else None
     # #########################################################
     print("-" * 50) if verbose else None
     return hydrate_graph(res, df, new_edges, node, src, dst, emb, X, y)
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index adcf950116..ec4cf11cce 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -207,7 +207,7 @@ def _cluster_dbscan(
 
     def dbscan(
         self,
-        eps: float = 0.2,
+        min_dist: float = 0.2,
         min_samples: int = 1,
         cols=None,
         kind="nodes",
@@ -267,7 +267,7 @@ def dbscan(
             cols=cols,
             fit_umap_embedding=fit_umap_embedding,
             target=target,
-            eps=eps,
+            eps=min_dist,
             min_samples=min_samples,
             verbose=verbose,
             *args,
@@ -286,6 +286,7 @@ def _transform_dbscan(
             # Assume that we are transforming to last fit of dbscan
             cols = res._dbscan_params["cols"]
             umap = res._dbscan_params["fit_umap_embedding"]
+            target = res._dbscan_params["target"]
 
             dbscan = res._node_dbscan if kind == "nodes" else res._edge_dbscan
 
@@ -294,13 +295,16 @@ def _transform_dbscan(
                 emb, X, y = res.transform_umap(df, ydf, kind=kind, return_graph=False)
             else:
                 X, y = res.transform(df, ydf, kind=kind, return_graph=False)
-                if cols is not None:
-                    X = get_matrix_by_column_parts(X, cols)
+            XX = X
+            if target:
+                XX = y
+            if cols is not None:
+                XX = get_matrix_by_column_parts(XX, cols)
 
             if umap:
                 X_ = emb
             else:
-                X_ = X
+                X_ = XX
 
             labels = dbscan_predict(X_, dbscan)  # type: ignore
             if umap and cols is None:
@@ -319,13 +323,13 @@ def transform_dbscan(
         self,
         df: pd.DataFrame,
         y: Optional[pd.DataFrame] = None,
-        eps: Union[float, str] = "auto",
+        min_dist: Union[float, str] = "auto",
         infer_umap_embedding: bool = False,
         sample: Optional[int] = None,
         n_neighbors: Optional[int] = None,
         kind: str = "nodes",
-        return_graph=True,
-        verbose=False,
+        return_graph: bool = True,
+        verbose: bool = False,
         ):  # type: ignore
         """
         Transforms a minibatch dataframe to one with a new column '_dbscan' containing the DBSCAN cluster 
@@ -339,7 +343,7 @@ def transform_dbscan(
                     g2 = g.featurize().dbscan()
 
                 predict:
-                    emb, X, y, ndf = g2.transform_dbscan(ndf, return_graph=False)
+                    emb, X, _, ndf = g2.transform_dbscan(ndf, return_graph=False)
                     # or
                     g3 = g2.transform_dbscan(ndf, return_graph=True)
                     g3.plot()
@@ -347,12 +351,12 @@ def transform_dbscan(
             likewise for umap:
                 fit:
                     g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
-                    g2 = g.umap().dbscan()
+                    g2 = g.umap(X=.., y=..).dbscan()
 
                 predict:
-                    emb, X, y, ndf = g2.transform_dbscan(ndf, return_graph=False)
+                    emb, X, y, ndf = g2.transform_dbscan(ndf, ndf, return_graph=False)
                     # or
-                    g3 = g2.transform_dbscan(ndf, return_graph=True)
+                    g3 = g2.transform_dbscan(ndf, ndf, return_graph=True)
                     g3.plot()
 
 
@@ -375,7 +379,7 @@ def transform_dbscan(
         """
         emb, X, y, df = self._transform_dbscan(df, y, kind=kind, verbose=verbose)
         if return_graph and kind not in ["edges"]:
-            g = self._infer_edges(emb, X, y, df, eps=eps, sample=sample, n_neighbors=n_neighbors,  # type: ignore
+            g = self._infer_edges(emb, X, y, df, eps=min_dist, sample=sample, n_neighbors=n_neighbors,  # type: ignore
                 infer_on_umap_embedding=infer_umap_embedding
                 )
             #g = g.encode_point_color(column=DBSCAN, as_categorical=True)
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 3242b141f6..67a8a32a6f 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2195,7 +2195,7 @@ def _transform(self, encoder: str, df: pd.DataFrame, ydf: Optional[pd.DataFrame]
     def transform(self, df: pd.DataFrame, 
                   y: Optional[pd.DataFrame] = None, 
                   kind: str = 'nodes', 
-                  eps: Union[str, float, int] = 'auto', 
+                  min_dist: Union[str, float, int] = 'auto', 
                   merge_policy: bool = False,
                   sample: Optional[int] = None, 
                   n_neighbors: Optional[int] = None,
@@ -2233,7 +2233,7 @@ def transform(self, df: pd.DataFrame,
         if return_graph and kind not in ["edges"]:
             emb = None  # will not be able to infer graph from umap coordinates, 
             # but will be able to infer graph from features of existing edges
-            g = self._infer_edges(emb, X, y_, df, eps=eps, sample=sample, n_neighbors=n_neighbors,
+            g = self._infer_edges(emb, X, y_, df, eps=min_dist, sample=sample, n_neighbors=n_neighbors,
                                   infer_on_umap_embedding=False, merge_policy=merge_policy,
                                   verbose=verbose)
             return g
@@ -2396,6 +2396,8 @@ def featurize(
         inplace: bool = False,
         feature_engine: FeatureEngine = "auto",
         dbscan: bool = False,
+        min_dist: float = 0.5,  # DBSCAN eps
+        n_neighbors: int = 5,  # DBSCAN min_samples
         memoize: bool = True,
         verbose: bool = False,
     ):
@@ -2493,6 +2495,9 @@ def featurize(
         :param keep_n_decimals: number of decimals to keep                
         :param remove_node_column: whether to remove node column so it is
                 not featurized, default True.
+        :param dbscan: whether to run DBSCAN, default False.
+        :param min_dist: DBSCAN eps parameter, default 0.5.
+        :param min_samples: DBSCAN min_samples parameter, default 5.
         :param inplace: whether to not return new graphistry instance or
                 not, default False.
         :param memoize: whether to store and reuse results across runs,
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index c5e69c4a0d..0260dba99c 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -271,7 +271,7 @@ def _umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = No
     def transform_umap(self, df: pd.DataFrame, 
                     y: Optional[pd.DataFrame] = None, 
                     kind: str = 'nodes', 
-                    eps: Union[str, float, int] = 'auto', 
+                    min_dist: Union[str, float, int] = 'auto', 
                     merge_policy: bool = False,
                     sample: Optional[int] = None, 
                     n_neighbors: int = 7,
@@ -300,7 +300,7 @@ def transform_umap(self, df: pd.DataFrame,
         if return_graph and kind not in ["edges"]:
             g = self._infer_edges(emb, X, y_, df, 
                                   infer_on_umap_embedding=fit_umap_embedding, merge_policy=merge_policy,
-                                  eps=eps, sample=sample, n_neighbors=n_neighbors,
+                                  eps=min_dist, sample=sample, n_neighbors=n_neighbors,
                                   verbose=verbose) 
             return g        
         return emb, X, y_

From 0d4ab78b684166cb09f369b572710d7fc0ee728f Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 18 Jan 2023 11:41:41 -0800
Subject: [PATCH 118/432] lint

---
 graphistry/ai_utils.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 7e3f213b06..e52637b018 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -377,8 +377,6 @@ def infer_graph(
     return hydrate_graph(res, new_nodes, new_edges, node, src, dst, new_emb, new_features, new_targets)
 
 
-
-
 def infer_self_graph(res, 
     emb, X, y, df, infer_on_umap_embedding=False, eps="auto", n_neighbors=7, verbose=False, 
 ):
@@ -479,4 +477,3 @@ def infer_self_graph(res,
     # #########################################################
     print("-" * 50) if verbose else None
     return hydrate_graph(res, df, new_edges, node, src, dst, emb, X, y)
-

From 07ed9e710bc3388c1173f29e22a57c693ed07184 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 18 Jan 2023 11:48:22 -0800
Subject: [PATCH 119/432] fix args eps-> min_dist in tests

---
 graphistry/tests/test_compute_cluster.py | 4 ++--
 graphistry/tests/test_umap_utils.py      | 6 +++---
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/graphistry/tests/test_compute_cluster.py b/graphistry/tests/test_compute_cluster.py
index 05e8dba9df..3c1cfd8dca 100644
--- a/graphistry/tests/test_compute_cluster.py
+++ b/graphistry/tests/test_compute_cluster.py
@@ -44,9 +44,9 @@ def test_featurize_cluster(self):
             
     @pytest.mark.skipif(not has_dbscan, reason="requires ai dependencies")
     def test_dbscan_params(self):
-        dbscan_params = [ModelDict('Testing UMAP', kind='nodes', eps=0.2, min_samples=1, cols=None, target=False, 
+        dbscan_params = [ModelDict('Testing UMAP', kind='nodes', min_dist=0.2, min_samples=1, cols=None, target=False, 
                                    fit_umap_embedding=False, verbose=True), 
-                         ModelDict('Testing UMAP target', kind='nodes', eps=0.1, min_samples=1, cols=None, 
+                         ModelDict('Testing UMAP target', kind='nodes', min_dist=0.1, min_samples=1, cols=None, 
                                    fit_umap_embedding=True, target=True, verbose=True)
 
         ]
diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index 1247389f00..acfb39cfd7 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -241,13 +241,13 @@ def test_transform_umap(self):
             self.g2._node_embedding.shape[0], self.g3._node_embedding.shape[0]
         )
         # now feed it args
-        eps = ["auto", 10]
+        min_dist = ["auto", 10]
         sample = [None, 2]
         return_graph = [True, False]
         fit_umap_embedding = [True, False]
         n_neighbors = [2, None]
-        for ep in eps:
-            g4 = self.g2.transform_umap(test, test, eps=ep)
+        for ep in min_dist:
+            g4 = self.g2.transform_umap(test, test, min_dist=ep)
             assert True
         for return_g in return_graph:
             g4 = self.g2.transform_umap(test, test, return_graph=return_g)

From ed6c04522cac8bdf14d5d87fb34872c78c89f38b Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 18 Jan 2023 12:03:14 -0800
Subject: [PATCH 120/432] adds eps-> min_dist in params. Adds tests for text
 utils

---
 graphistry/compute/cluster.py       | 8 ++++----
 graphistry/tests/test_text_utils.py | 6 +++---
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index ec4cf11cce..a5011b0f0f 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -172,7 +172,7 @@ def __init__(self, *args, **kwargs):
         pass
 
     def _cluster_dbscan(
-        self, res, kind, cols, fit_umap_embedding, target, eps, min_samples, verbose, *args, **kwargs
+        self, res, kind, cols, fit_umap_embedding, target, min_dist, min_samples, verbose, *args, **kwargs
     ):
         """
         DBSCAN clustering on cpu or gpu infered by .engine flag
@@ -186,7 +186,7 @@ def _cluster_dbscan(
             cols=cols,
             target=target,
             fit_umap_embedding=fit_umap_embedding,
-            eps=eps,
+            min_dist=min_dist,
             min_samples=min_samples,
             verbose=verbose,
             *args,
@@ -194,9 +194,9 @@ def _cluster_dbscan(
         )
 
         dbscan = (
-            cuDBSCAN(eps=eps, min_samples=min_samples, **kwargs)
+            cuDBSCAN(eps=min_dist, min_samples=min_samples, **kwargs)
             if res.engine == CUML
-            else DBSCAN(eps=eps, min_samples=min_samples, **kwargs)
+            else DBSCAN(eps=min_dist, min_samples=min_samples, **kwargs)
         )
 
         res = dbscan_fit(
diff --git a/graphistry/tests/test_text_utils.py b/graphistry/tests/test_text_utils.py
index b4ecc713af..710bdc5ece 100644
--- a/graphistry/tests/test_text_utils.py
+++ b/graphistry/tests/test_text_utils.py
@@ -55,18 +55,18 @@ def setUp(self):
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_query(self):
         for g in [self.g_ngrams, self.g_emb]:
-            res, _ = g.query('How to set up DNS', thresh=100)
+            res, _ = g.search('How to set up DNS', thresh=100)
             assert not res.empty, f'Results DataFrame should not be empty, found {res}'
        
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies") 
     def test_query_graph(self):
         for name, g in zip(['ngrams', 'embedding'], [self.g_ngrams, self.g_emb]):
-            res = g.query_graph('How to set up DNS', thresh=100)
+            res = g.search_graph('How to set up DNS', thresh=100)
             assert not res._nodes.empty, f'{name}-Results DataFrame should not be empty, found {res._nodes}'
             #url = res.plot(render=False)
             #logger.info(f'{name}: {url}')
             
-        res = self.g_with_edges.query_graph('Wife', thresh=100)
+        res = self.g_with_edges.search_graph('Wife', thresh=100)
         assert not res._nodes.empty, f'Results DataFrame should not be empty, found {res._nodes}'
         #url = res.plot(render=False)
         #logger.info(f'With Explicit Edges: {url}')

From 25387c485d54e78dab0de010d29244db3853d04a Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 18 Jan 2023 12:35:56 -0800
Subject: [PATCH 121/432] more fixes

---
 graphistry/ai_utils.py        |  3 ++-
 graphistry/compute/cluster.py | 29 ++++++++++++++++-------------
 graphistry/feature_utils.py   |  4 ++--
 graphistry/umap_utils.py      |  4 ++--
 4 files changed, 22 insertions(+), 18 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index e52637b018..aad91bf48b 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -218,7 +218,8 @@ def hydrate_graph(res, new_nodes, new_edges, node, src, dst, new_emb, new_featur
     # #########################################################
     g = res.nodes(new_nodes, node).edges(new_edges, src, dst)
 
-    g._weighted_adjacency = edgelist_to_weighted_adjacency(g)
+    # TODO this needs more work since edgelist_to_weighted_adjacency produces non square matrices (since infer_graph will add new nodes)
+    #g._weighted_adjacency = edgelist_to_weighted_adjacency(g)
     g._node_embedding = new_emb
     g._node_features = new_features
     g._node_targets = new_targets
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index a5011b0f0f..b0c84c28ba 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -217,7 +217,7 @@ def dbscan(
         *args,
         **kwargs,
     ):
-        """DBSCAN clustering on cpu or gpu infered automatically. 
+        """DBSCAN clustering on cpu or gpu infered automatically. Adds a `_dbscan` column to nodes or edges.
 
         Examples:
             g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
@@ -227,22 +227,24 @@ def dbscan(
             g2 = g.umap(kind=kind).dbscan(kind=kind)
             print(g2._nodes['_dbscan']) | print(g2._edges['_dbscan'])
 
-            # dbscan with fixed parameters in umap
-            g2 = g.umap(dbscan=True)
+            # dbscan in umap or featurize API
+            g2 = g.umap(dbscan=True, min_dist=1.2, min_samples=2, **kwargs)
+            # or, here dbscan is infered from features, not umap embeddings
+            g2 = g.featurize(dbscan=True, min_dist=1.2, min_samples=2, **kwargs)
 
-            # and with greater control over parameters via chaining,
-            g2 = g.umap().dbscan(eps=1.2, min_samples=2, **kwargs)
+            # and via chaining,
+            g2 = g.umap().dbscan(min_dist=1.2, min_samples=2, **kwargs)
 
             # cluster by feature embeddings
             g2 = g.featurize().dbscan(**kwargs)
 
-            # cluster by a given set of feature column attributes
-            g2 = g.featurize().dbscan(cols=['ip_172', 'location', 'alert'], **kwargs)
+            # cluster by a given set of feature column attributes, or with target=True
+            g2 = g.featurize().dbscan(cols=['ip_172', 'location', 'alert'], target=False, **kwargs)
 
             # equivalent to above (ie, cols != None and umap=True will still use features dataframe, rather than UMAP embeddings)
             g2 = g.umap().dbscan(cols=['ip_172', 'location', 'alert'], umap=True | False, **kwargs)
 
-            g2.plot() # colored by `_dbscan` column
+            g2.plot() # color by `_dbscan` column
 
         Useful:
             Enriching the graph with cluster labels from UMAP is useful for visualizing clusters in the graph by color, size, etc,
@@ -250,13 +252,14 @@ def dbscan(
              https://github.com/graphistry/pygraphistry/blob/master/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
 
         Args:
-            eps float: The maximum distance between two samples for them to be considered as in the same neighborhood.
+            min_dist float: The maximum distance between two samples for them to be considered as in the same neighborhood.
             kind str: 'nodes' or 'edges'
-            cols: list of columns to use for clustering given `g.featurize` has been run, nice way to slice features by
+            cols: list of columns to use for clustering given `g.featurize` has been run, nice way to slice features or targets by
                 fragments of interest, e.g. ['ip_172', 'location', 'ssh', 'warnings']
             fit_umap_embedding bool: whether to use UMAP embeddings or features dataframe to cluster DBSCAN
             min_samples: The number of samples in a neighborhood for a point to be considered as a core point.
                 This includes the point itself.
+            target: whether to use the target column as the clustering feature
 
         """
 
@@ -267,7 +270,7 @@ def dbscan(
             cols=cols,
             fit_umap_embedding=fit_umap_embedding,
             target=target,
-            eps=min_dist,
+            min_dist=min_dist,
             min_samples=min_samples,
             verbose=verbose,
             *args,
@@ -363,9 +366,9 @@ def transform_dbscan(
         args:
             df: dataframe to transform
             y: optional labels dataframe
-            eps: The maximum distance between two samples for them to be considered as in the same neighborhood.
+            min_dist: The maximum distance between two samples for them to be considered as in the same neighborhood.
                 smaller values will result in less edges between the minibatch and the original graph.
-                Default 'auto', infers eps from the mean distance and std of new points to the original graph
+                Default 'auto', infers min_dist from the mean distance and std of new points to the original graph
             fit_umap_embedding: whether to use UMAP embeddings or features dataframe when inferring edges between
                 the minibatch and the original graph. Default False, uses the features dataframe
             sample: number of samples to use when inferring edges between the minibatch and the original graph,
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 67a8a32a6f..4e19206b3c 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2212,7 +2212,7 @@ def transform(self, df: pd.DataFrame,
                 return_graph: bool, if True, will return a graph with inferred edges.
                 merge_policy: bool, if True, adds batch to existing graph nodes via nearest neighbors. 
                     If False, will infer edges only between nodes in the batch.
-                eps: float, if return_graph is True, will use this value for eps in NN search, or 'auto' to infer a good value
+                min_dist: float, if return_graph is True, will use this value for eps in NN search, or 'auto' to infer a good value
                     eps represents the maximum distance between two samples for one to be considered as in the neighborhood of the other.
                 sample: int, if return_graph is True, will use sample edges of existing graph to fill out the new graph
                 n_neighbors: int, optional (default = 15), if return_graph is True, will use this value for n_neighbors in NN search
@@ -2583,7 +2583,7 @@ def featurize(
             return self
         
         if dbscan:  # this adds columns to the dataframe, will break tests of pure featurization & umap, so set to False in those
-            res = res.dbscan(eps=min_dist, n_neighbors=n_neighbors, kind=kind, fit_umap_embedding=False, verbose=verbose)  # type: ignore
+            res = res.dbscan(min_dist=min_dist, n_neighbors=n_neighbors, kind=kind, fit_umap_embedding=False, verbose=verbose)  # type: ignore
 
         if not inplace:
             return res
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 0260dba99c..8d324ab4d2 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -285,7 +285,7 @@ def transform_umap(self, df: pd.DataFrame,
             df: Dataframe to transform
             y: Target column
             kind: One of `nodes` or `edges`
-            eps: Epsilon for DBSCAN
+            min_dist: Epsilon for DBSCAN
             merge_policy: if True, use previous graph, adding new batch to existing graph's neighbors
                 useful to contextualize new data against existing graph. If False, `sample` is irrelevant.
             sample: Sample number of existing graph's neighbors to use for contextualization -- helps make denser graphs
@@ -615,7 +615,7 @@ def umap(
             res = res.prune_self_edges()
 
         if dbscan:
-            res = res.dbscan(eps=min_dist, n_neighbors=n_neighbors, kind=kind, fit_umap_embedding=True, verbose=verbose)  # type: ignore
+            res = res.dbscan(min_dist=min_dist, n_neighbors=n_neighbors, kind=kind, fit_umap_embedding=True, verbose=verbose)  # type: ignore
 
         if not inplace:
             return res

From 406342137a1f5668e1b83efc0f38019df08af66a Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 18 Jan 2023 12:49:13 -0800
Subject: [PATCH 122/432] min_samples != n_neighbors, removed in dbscan=True
 inside umap/featurize

---
 graphistry/compute/cluster.py | 2 --
 graphistry/feature_utils.py   | 2 +-
 graphistry/umap_utils.py      | 2 +-
 3 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index b0c84c28ba..03fb5ed740 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -189,8 +189,6 @@ def _cluster_dbscan(
             min_dist=min_dist,
             min_samples=min_samples,
             verbose=verbose,
-            *args,
-            **kwargs,
         )
 
         dbscan = (
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 4e19206b3c..6056976b08 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2583,7 +2583,7 @@ def featurize(
             return self
         
         if dbscan:  # this adds columns to the dataframe, will break tests of pure featurization & umap, so set to False in those
-            res = res.dbscan(min_dist=min_dist, n_neighbors=n_neighbors, kind=kind, fit_umap_embedding=False, verbose=verbose)  # type: ignore
+            res = res.dbscan(min_dist=min_dist, kind=kind, fit_umap_embedding=False, verbose=verbose)  # type: ignore
 
         if not inplace:
             return res
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 8d324ab4d2..2eb1989daa 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -615,7 +615,7 @@ def umap(
             res = res.prune_self_edges()
 
         if dbscan:
-            res = res.dbscan(min_dist=min_dist, n_neighbors=n_neighbors, kind=kind, fit_umap_embedding=True, verbose=verbose)  # type: ignore
+            res = res.dbscan(min_dist=min_dist, kind=kind, fit_umap_embedding=True, verbose=verbose)  # type: ignore
 
         if not inplace:
             return res

From 0a0ef90fa6088358ec513f88d59209650f941619 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 19 Jan 2023 20:54:17 -0800
Subject: [PATCH 123/432] docs and arguments

---
 graphistry/ai_utils.py        |  1 +
 graphistry/compute/cluster.py |  1 +
 graphistry/feature_utils.py   | 36 +++++++++++++++++------------------
 graphistry/umap_utils.py      |  8 ++++----
 4 files changed, 23 insertions(+), 23 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index aad91bf48b..e29c334785 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -223,6 +223,7 @@ def hydrate_graph(res, new_nodes, new_edges, node, src, dst, new_emb, new_featur
     g._node_embedding = new_emb
     g._node_features = new_features
     g._node_targets = new_targets
+    g = g.settings(url_params={'play': 0})
     return g
     
 
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 03fb5ed740..af1724d8d6 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -131,6 +131,7 @@ def dbscan_fit(g, dbscan, kind="nodes", cols=None, use_umap_embedding=True, targ
         print('-' * len(message))
         print(message)
         print(f"--fit on {'umap embeddings' if use_umap_embedding else 'feature embeddings'} of size {X.shape}")
+        print('-' * len(message))
 
     return g
 
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 6056976b08..0ab8ebe7c4 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2196,9 +2196,9 @@ def transform(self, df: pd.DataFrame,
                   y: Optional[pd.DataFrame] = None, 
                   kind: str = 'nodes', 
                   min_dist: Union[str, float, int] = 'auto', 
+                  n_neighbors: int = 7,
                   merge_policy: bool = False,
                   sample: Optional[int] = None, 
-                  n_neighbors: Optional[int] = None,
                   return_graph: bool = True,
                   scaled: bool = True,
                   verbose: bool = False):
@@ -2211,13 +2211,13 @@ def transform(self, df: pd.DataFrame,
                 kind: str  # one of `nodes`, `edges`
                 return_graph: bool, if True, will return a graph with inferred edges.
                 merge_policy: bool, if True, adds batch to existing graph nodes via nearest neighbors. 
-                    If False, will infer edges only between nodes in the batch.
-                min_dist: float, if return_graph is True, will use this value for eps in NN search, or 'auto' to infer a good value
-                    eps represents the maximum distance between two samples for one to be considered as in the neighborhood of the other.
+                    If False, will infer edges only between nodes in the batch, default False
+                min_dist: float, if return_graph is True, will use this value in NN search, or 'auto' to infer a good value
+                    min_dist represents the maximum distance between two samples for one to be considered as in the neighborhood of the other.
                 sample: int, if return_graph is True, will use sample edges of existing graph to fill out the new graph
-                n_neighbors: int, optional (default = 15), if return_graph is True, will use this value for n_neighbors in NN search
-                scaled: bool, if True, will use scaled transformation of data set during featurization
-                verbose: bool, if True, will print metadata about the graph construction
+                n_neighbors: int, if return_graph is True, will use this value for n_neighbors in Nearest Neighbors search
+                scaled: bool, if True, will use scaled transformation of data set during featurization, default True
+                verbose: bool, if True, will print metadata about the graph construction, default False
             returns:
                 X, y: pd.DataFrame, transformed data if return_graph is False
                     or a graphistry Plottable with inferred edges if return_graph is True
@@ -2241,7 +2241,7 @@ def transform(self, df: pd.DataFrame,
 
     def scale(
         self,
-        df: pd.DataFrame,
+        df: Optional[pd.DataFrame] = None,
         y: Optional[pd.DataFrame] = None,
         kind: str = "nodes",
         use_scaler: Union[str, None] = None,
@@ -2275,7 +2275,7 @@ def scale(
 
 
             args:
-                df: pd.DataFrame, raw data to transform
+                df: pd.DataFrame, raw data to transform, if None, will use data from featurization fit
                 y: pd.DataFrame, optional target data
                 kind: str, one of `nodes`, `edges`
                 use_scaler: str, optional, one of `minmax`, `robust`, `standard`, `kbins`, `quantile`
@@ -2383,13 +2383,11 @@ def featurize(
         impute: bool = True,
         n_quantiles: int = 100,
         output_distribution: str = "normal",
-        quantile_range=(25, 75),
+        quantile_range = (25, 75),
         n_bins: int = 10,
         encode: str = "ordinal",
         strategy: str = "uniform",
-        similarity: Optional[
-            str
-        ] = None,  # turn this off in favor of Gap Encoder
+        similarity: Optional[str] = None,  # turn this off in favor of Gap Encoder
         categories: Optional[str] = "auto",
         keep_n_decimals: int = 5,
         remove_node_column: bool = True,
@@ -2397,7 +2395,7 @@ def featurize(
         feature_engine: FeatureEngine = "auto",
         dbscan: bool = False,
         min_dist: float = 0.5,  # DBSCAN eps
-        n_neighbors: int = 5,  # DBSCAN min_samples
+        min_samples: int = 1,  # DBSCAN min_samples
         memoize: bool = True,
         verbose: bool = False,
     ):
@@ -2484,7 +2482,7 @@ def featurize(
                 can return distribution as ["normal", "uniform"]
         :param quantile_range: if use_scaler = 'robust'|'quantile', 
                 sets the quantile range.
-        :param n_bins: number of bins to use in kbins discretizer
+        :param n_bins: number of bins to use in kbins discretizer, default 10
         :param encode: encoding for KBinsDiscretizer, can be one of
                 `onehot`, `onehot-dense`, `ordinal`, default 'ordinal'
         :param strategy: strategy for KBinsDiscretizer, can be one of
@@ -2492,12 +2490,12 @@ def featurize(
         :param n_quantiles: if use_scaler = "quantile", sets the number of quantiles, default=100
         :param output_distribution: if use_scaler="quantile"|"robust", 
                 choose from ["normal", "uniform"]
-        :param keep_n_decimals: number of decimals to keep                
-        :param remove_node_column: whether to remove node column so it is
-                not featurized, default True.
         :param dbscan: whether to run DBSCAN, default False.
         :param min_dist: DBSCAN eps parameter, default 0.5.
         :param min_samples: DBSCAN min_samples parameter, default 5.
+        :param keep_n_decimals: number of decimals to keep                
+        :param remove_node_column: whether to remove node column so it is
+                not featurized, default True.
         :param inplace: whether to not return new graphistry instance or
                 not, default False.
         :param memoize: whether to store and reuse results across runs,
@@ -2583,7 +2581,7 @@ def featurize(
             return self
         
         if dbscan:  # this adds columns to the dataframe, will break tests of pure featurization & umap, so set to False in those
-            res = res.dbscan(min_dist=min_dist, kind=kind, fit_umap_embedding=False, verbose=verbose)  # type: ignore
+            res = res.dbscan(min_dist=min_dist, min_samples=min_samples, kind=kind, fit_umap_embedding=False, verbose=verbose)  # type: ignore
 
         if not inplace:
             return res
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 2eb1989daa..2107710a3d 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -272,12 +272,12 @@ def transform_umap(self, df: pd.DataFrame,
                     y: Optional[pd.DataFrame] = None, 
                     kind: str = 'nodes', 
                     min_dist: Union[str, float, int] = 'auto', 
+                    n_neighbors: int = 7,
                     merge_policy: bool = False,
                     sample: Optional[int] = None, 
-                    n_neighbors: int = 7,
                     return_graph: bool = True,
                     fit_umap_embedding: bool = True,
-                    verbose=False
+                    verbose: bool = False
     ) -> Union[Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable]:
         """Transforms data into UMAP embedding
         
@@ -285,11 +285,11 @@ def transform_umap(self, df: pd.DataFrame,
             df: Dataframe to transform
             y: Target column
             kind: One of `nodes` or `edges`
-            min_dist: Epsilon for DBSCAN
+            min_dist: Epsilon for including neighbors in infer_graph
+            n_neighbors: Number of neighbors to use for contextualization
             merge_policy: if True, use previous graph, adding new batch to existing graph's neighbors
                 useful to contextualize new data against existing graph. If False, `sample` is irrelevant.
             sample: Sample number of existing graph's neighbors to use for contextualization -- helps make denser graphs
-            n_neighbors: Number of neighbors to use for contextualization
             return_graph: Whether to return a graph or just the embeddings
             fit_umap_embedding: Whether to infer graph from the UMAP embedding on the new data
             verbose: Whether to print information about the graph inference

From 166e4f4840e32642bd4cd315c489a3281b09c0e0 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 20 Jan 2023 10:23:52 -0800
Subject: [PATCH 124/432] user-attr setting in PlotterBase

---
 graphistry/PlotterBase.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/graphistry/PlotterBase.py b/graphistry/PlotterBase.py
index 4c3e56192f..7b7d0604d0 100644
--- a/graphistry/PlotterBase.py
+++ b/graphistry/PlotterBase.py
@@ -169,20 +169,16 @@ def __init__(self, *args, **kwargs):
         self._node_embedding = None
         self._node_encoder = None
         self._node_features = None
-        #self._node_scaling_pipeline = None
-        #self._node_ordinal_pipeline_target = None,
+        self._node_features_raw = None
         self._node_target = None
         self._node_target_encoder = None
-        # self._node_text_model = None
 
         self._edge_embedding = None
         self._edge_encoder = None
         self._edge_features = None
-        #self._edge_ordinal_pipeline = None
-        #self._edge_ordinal_pipeline_target = None
+        self._edge_features_raw = None
         self._edge_target = None
         self._edge_target_encoder = None
-        # self._edge_text_model = None
 
         self._weighted_adjacency_nodes = None
         self._weighted_adjacency_edges = None

From 601543ca1f7e1db438ca40db18a395d9ab44268e Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 20 Jan 2023 10:26:40 -0800
Subject: [PATCH 125/432] typecheck ignore

---
 graphistry/feature_utils.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 0ab8ebe7c4..10be319062 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2045,7 +2045,6 @@ def _featurize_nodes(
         res._node_target = encoder.y
         res._node_target_raw = encoder.y_orignal  # .copy()
         res._node_encoder = encoder  # now this does
-        
         # all the work `._node_encoder.transform(df, y)` etc
 
         return res
@@ -2296,7 +2295,7 @@ def scale(
         """
                 
         if df is None:  # use the original data
-            X, y = (self._node_features_raw, self._node_target_raw) if kind == "nodes" else (self._edge_features_raw, self._edge_target_raw)
+            X, y = (self._node_features_raw, self._node_target_raw) if kind == "nodes" else (self._edge_features_raw, self._edge_target_raw)  # type: ignore
         else:
             X, y = self.transform(df, y, kind=kind, return_graph=False, scaled=False)
 

From f997752267fb6b574c8aa844ad9c47e4852d415d Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 20 Jan 2023 17:54:45 -0800
Subject: [PATCH 126/432] adds numeric only umap demo -- demonstrates umap,
 dbscan pivot, and graph transform methods

---
 .../Introduction/simple-power-of-umap.ipynb   | 14642 ++++++++++++++++
 1 file changed, 14642 insertions(+)
 create mode 100644 demos/ai/Introduction/simple-power-of-umap.ipynb

diff --git a/demos/ai/Introduction/simple-power-of-umap.ipynb b/demos/ai/Introduction/simple-power-of-umap.ipynb
new file mode 100644
index 0000000000..19538a5b60
--- /dev/null
+++ b/demos/ai/Introduction/simple-power-of-umap.ipynb
@@ -0,0 +1,14642 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "0270b0aa-7eea-4915-a3c5-601c0edd34e3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "af4a54b9-1959-4fda-a00c-534be66e09a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.datasets import load_breast_cancer, load_diabetes, load_digits\n",
+    "\n",
+    "from collections import Counter\n",
+    "\n",
+    "import graphistry\n",
+    "from graphistry.features import ModelDict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b28209f4-6809-4cb1-b6a8-e0d65cbbe07d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graphistry.register(api=3, protocol=\"https\", server=\"hub.graphistry.com\", username=os.environ['USERNAME'], password=os.environ['GRAPHISTRY_PASSWORD']) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49752d24-00a6-480f-be5e-62134c9598d8",
+   "metadata": {},
+   "source": [
+    "# Explore Data in a Whole New Way\n",
+    "PyGraphistry is a GPU Graph AI visualization tool that unlocks the graph in your data. \n",
+    "\n",
+    "In the past loading, transforming and interacting with large multivariate datasets took time to set up pipelines and processes. Graphistry makes time-to-graph + AI + interactivity 100x faster. \n",
+    "\n",
+    "We will explore how to see data, explore relationships and create new graphs from batches using sci-kits like api, and even build a GNN model one could use in downstream DGL models. \n",
+    "\n",
+    "We will quickly analyze breast cancer, diabetes and digits datasets from sklearn.data\n",
+    "\n",
+    "Add your favorite dataset and explore it with graph AI and Visual exploration! "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d434a151",
+   "metadata": {},
+   "source": [
+    "## Tumor: Malignant or Benign "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ec079ad1-12d8-419c-9f35-d4ecad825635",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = load_breast_cancer()\n",
+    "\n",
+    "good_features = list(data['feature_names'])\n",
+    "\n",
+    "df = pd.DataFrame(data['data'], columns=good_features)\n",
+    "df['target'] = data['target']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df071dc0-df4c-4701-8794-57864c37fc0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean radius</th>\n",
+       "      <th>mean texture</th>\n",
+       "      <th>mean perimeter</th>\n",
+       "      <th>mean area</th>\n",
+       "      <th>mean smoothness</th>\n",
+       "      <th>mean compactness</th>\n",
+       "      <th>mean concavity</th>\n",
+       "      <th>mean concave points</th>\n",
+       "      <th>mean symmetry</th>\n",
+       "      <th>mean fractal dimension</th>\n",
+       "      <th>...</th>\n",
+       "      <th>worst texture</th>\n",
+       "      <th>worst perimeter</th>\n",
+       "      <th>worst area</th>\n",
+       "      <th>worst smoothness</th>\n",
+       "      <th>worst compactness</th>\n",
+       "      <th>worst concavity</th>\n",
+       "      <th>worst concave points</th>\n",
+       "      <th>worst symmetry</th>\n",
+       "      <th>worst fractal dimension</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.3001</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>0.2419</td>\n",
+       "      <td>0.07871</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.1622</td>\n",
+       "      <td>0.6656</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.0869</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>0.1812</td>\n",
+       "      <td>0.05667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.1238</td>\n",
+       "      <td>0.1866</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.1974</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>0.2069</td>\n",
+       "      <td>0.05999</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.1444</td>\n",
+       "      <td>0.4245</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.2414</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>0.2597</td>\n",
+       "      <td>0.09744</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.2098</td>\n",
+       "      <td>0.8663</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.1980</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>0.1809</td>\n",
+       "      <td>0.05883</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.1374</td>\n",
+       "      <td>0.2050</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
+       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
+       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
+       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
+       "3        11.42         20.38           77.58      386.1          0.14250   \n",
+       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
+       "\n",
+       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
+       "0           0.27760          0.3001              0.14710         0.2419   \n",
+       "1           0.07864          0.0869              0.07017         0.1812   \n",
+       "2           0.15990          0.1974              0.12790         0.2069   \n",
+       "3           0.28390          0.2414              0.10520         0.2597   \n",
+       "4           0.13280          0.1980              0.10430         0.1809   \n",
+       "\n",
+       "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
+       "0                 0.07871  ...          17.33           184.60      2019.0   \n",
+       "1                 0.05667  ...          23.41           158.80      1956.0   \n",
+       "2                 0.05999  ...          25.53           152.50      1709.0   \n",
+       "3                 0.09744  ...          26.50            98.87       567.7   \n",
+       "4                 0.05883  ...          16.67           152.20      1575.0   \n",
+       "\n",
+       "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
+       "0            0.1622             0.6656           0.7119                0.2654   \n",
+       "1            0.1238             0.1866           0.2416                0.1860   \n",
+       "2            0.1444             0.4245           0.4504                0.2430   \n",
+       "3            0.2098             0.8663           0.6869                0.2575   \n",
+       "4            0.1374             0.2050           0.4000                0.1625   \n",
+       "\n",
+       "   worst symmetry  worst fractal dimension  target  \n",
+       "0          0.4601                  0.11890       0  \n",
+       "1          0.2750                  0.08902       0  \n",
+       "2          0.3613                  0.08758       0  \n",
+       "3          0.6638                  0.17300       0  \n",
+       "4          0.2364                  0.07678       0  \n",
+       "\n",
+       "[5 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6c6f61c-0923-4860-8045-18dfeffe69fa",
+   "metadata": {},
+   "source": [
+    "# UMAP\n",
+    "\n",
+    "Reduce the data into a 2 dimensional graph -- the edges come from similarity in features. \n",
+    "\n",
+    "UMAP is a powerful way to see the parts of the dataset -- one can not only visually confirm if a predictive model will 'separate' the data, one can explore relationships that can help feed insights and potential treatment strategies. \n",
+    "\n",
+    "What can you find in the data?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "28ae2a26-c7e3-457a-8a50-78031797fad2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "# let's split data and train on half the data\n",
+    "df_train, df_test, df_train_target, df_test_target = train_test_split(df, df[['target']], train_size=0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a3527e13-7e14-437f-b423-6643e15b9a08",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (284, 0) in UMAP fit, as it is not one dimensionalOMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 15.7 s, sys: 927 ms, total: 16.7 s\n",
+      "Wall time: 17.6 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "g = graphistry.nodes(df_train)\n",
+    "# fit on specific features by calling out via X=...\n",
+    "# plots are sensitive to scaling, we use_scaler='robust' for good umap separation \n",
+    "# (thought None does better in RF below)\n",
+    "\n",
+    "g2 = g.umap(X=good_features, use_scaler='robust') # y = 'target'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "65758e38",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"c63f7b30-bb20-4463-83e3-2bf7db1b3132\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=e828f52e7975477c848f96171fd00225&type=arrow&viztoken=b4eca965-a7da-451e-b3ea-9aebc349e05e&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265684&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#c63f7b30-bb20-4463-83e3-2bf7db1b3132\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b773839a",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## DBSCAN as new pivot \n",
+    "\n",
+    "Think of DBSCAN as a way to pivot by clustering in features of interest. \n",
+    "By setting `cols` you can pick out features from the matrix and have dbscan only focus on those.\n",
+    "Coloring by the `_dbscan` label in the UI finds clusters across those variables. \n",
+    "\n",
+    "Contrasting UMAP coordinates versus dbscan labels is a useful way to see total behavior against some part/pivot of interest. In the following we see k-clusters in the `worst` (case sensitive column selection) meta variable. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "97d11165",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>worst radius</th>\n",
+       "      <th>worst texture</th>\n",
+       "      <th>worst perimeter</th>\n",
+       "      <th>worst area</th>\n",
+       "      <th>worst smoothness</th>\n",
+       "      <th>worst compactness</th>\n",
+       "      <th>worst concavity</th>\n",
+       "      <th>worst concave points</th>\n",
+       "      <th>worst symmetry</th>\n",
+       "      <th>worst fractal dimension</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>-0.22733</td>\n",
+       "      <td>-0.65650</td>\n",
+       "      <td>-0.24772</td>\n",
+       "      <td>-0.21837</td>\n",
+       "      <td>-0.07240</td>\n",
+       "      <td>-0.04575</td>\n",
+       "      <td>-0.20788</td>\n",
+       "      <td>0.05038</td>\n",
+       "      <td>0.46218</td>\n",
+       "      <td>-0.19976</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>2.41516</td>\n",
+       "      <td>0.87908</td>\n",
+       "      <td>2.63143</td>\n",
+       "      <td>3.03861</td>\n",
+       "      <td>0.94480</td>\n",
+       "      <td>2.02300</td>\n",
+       "      <td>1.53749</td>\n",
+       "      <td>1.75843</td>\n",
+       "      <td>-0.59045</td>\n",
+       "      <td>1.20651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541</th>\n",
+       "      <td>0.19347</td>\n",
+       "      <td>0.67683</td>\n",
+       "      <td>0.36012</td>\n",
+       "      <td>0.17956</td>\n",
+       "      <td>0.10136</td>\n",
+       "      <td>1.05428</td>\n",
+       "      <td>0.62997</td>\n",
+       "      <td>0.17869</td>\n",
+       "      <td>0.56954</td>\n",
+       "      <td>1.07157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>509</th>\n",
+       "      <td>0.33696</td>\n",
+       "      <td>1.16907</td>\n",
+       "      <td>0.45727</td>\n",
+       "      <td>0.34346</td>\n",
+       "      <td>1.52036</td>\n",
+       "      <td>1.44973</td>\n",
+       "      <td>1.32700</td>\n",
+       "      <td>1.08928</td>\n",
+       "      <td>0.32694</td>\n",
+       "      <td>1.28361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>444</th>\n",
+       "      <td>0.86417</td>\n",
+       "      <td>-0.36223</td>\n",
+       "      <td>0.81809</td>\n",
+       "      <td>0.96742</td>\n",
+       "      <td>-0.17738</td>\n",
+       "      <td>0.26027</td>\n",
+       "      <td>0.72311</td>\n",
+       "      <td>0.49236</td>\n",
+       "      <td>0.08853</td>\n",
+       "      <td>0.10530</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>-0.34180</td>\n",
+       "      <td>-0.55591</td>\n",
+       "      <td>-0.37400</td>\n",
+       "      <td>-0.29828</td>\n",
+       "      <td>-1.70534</td>\n",
+       "      <td>-0.87428</td>\n",
+       "      <td>-0.79177</td>\n",
+       "      <td>-0.69748</td>\n",
+       "      <td>-1.22342</td>\n",
+       "      <td>-0.99976</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>0.21121</td>\n",
+       "      <td>0.58373</td>\n",
+       "      <td>0.26761</td>\n",
+       "      <td>0.20810</td>\n",
+       "      <td>0.43077</td>\n",
+       "      <td>0.44637</td>\n",
+       "      <td>0.31480</td>\n",
+       "      <td>0.52088</td>\n",
+       "      <td>-0.08435</td>\n",
+       "      <td>0.64892</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>533</th>\n",
+       "      <td>1.32366</td>\n",
+       "      <td>0.18673</td>\n",
+       "      <td>1.25061</td>\n",
+       "      <td>1.54312</td>\n",
+       "      <td>-0.77828</td>\n",
+       "      <td>0.19204</td>\n",
+       "      <td>0.27680</td>\n",
+       "      <td>0.56650</td>\n",
+       "      <td>0.61555</td>\n",
+       "      <td>-0.78096</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>449</th>\n",
+       "      <td>1.71866</td>\n",
+       "      <td>0.71322</td>\n",
+       "      <td>1.62530</td>\n",
+       "      <td>2.15795</td>\n",
+       "      <td>0.20271</td>\n",
+       "      <td>0.48514</td>\n",
+       "      <td>0.76371</td>\n",
+       "      <td>1.20048</td>\n",
+       "      <td>-0.71175</td>\n",
+       "      <td>-0.28024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>1.72834</td>\n",
+       "      <td>1.49973</td>\n",
+       "      <td>2.00463</td>\n",
+       "      <td>1.83015</td>\n",
+       "      <td>1.22353</td>\n",
+       "      <td>3.36960</td>\n",
+       "      <td>2.62196</td>\n",
+       "      <td>1.55217</td>\n",
+       "      <td>1.82433</td>\n",
+       "      <td>2.11735</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>284 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     worst radius  worst texture  worst perimeter  worst area  \\\n",
+       "527      -0.22733       -0.65650         -0.24772    -0.21837   \n",
+       "82        2.41516        0.87908          2.63143     3.03861   \n",
+       "541       0.19347        0.67683          0.36012     0.17956   \n",
+       "509       0.33696        1.16907          0.45727     0.34346   \n",
+       "444       0.86417       -0.36223          0.81809     0.96742   \n",
+       "..            ...            ...              ...         ...   \n",
+       "319      -0.34180       -0.55591         -0.37400    -0.29828   \n",
+       "99        0.21121        0.58373          0.26761     0.20810   \n",
+       "533       1.32366        0.18673          1.25061     1.54312   \n",
+       "449       1.71866        0.71322          1.62530     2.15795   \n",
+       "567       1.72834        1.49973          2.00463     1.83015   \n",
+       "\n",
+       "     worst smoothness  worst compactness  worst concavity  \\\n",
+       "527          -0.07240           -0.04575         -0.20788   \n",
+       "82            0.94480            2.02300          1.53749   \n",
+       "541           0.10136            1.05428          0.62997   \n",
+       "509           1.52036            1.44973          1.32700   \n",
+       "444          -0.17738            0.26027          0.72311   \n",
+       "..                ...                ...              ...   \n",
+       "319          -1.70534           -0.87428         -0.79177   \n",
+       "99            0.43077            0.44637          0.31480   \n",
+       "533          -0.77828            0.19204          0.27680   \n",
+       "449           0.20271            0.48514          0.76371   \n",
+       "567           1.22353            3.36960          2.62196   \n",
+       "\n",
+       "     worst concave points  worst symmetry  worst fractal dimension  \n",
+       "527               0.05038         0.46218                 -0.19976  \n",
+       "82                1.75843        -0.59045                  1.20651  \n",
+       "541               0.17869         0.56954                  1.07157  \n",
+       "509               1.08928         0.32694                  1.28361  \n",
+       "444               0.49236         0.08853                  0.10530  \n",
+       "..                    ...             ...                      ...  \n",
+       "319              -0.69748        -1.22342                 -0.99976  \n",
+       "99                0.52088        -0.08435                  0.64892  \n",
+       "533               0.56650         0.61555                 -0.78096  \n",
+       "449               1.20048        -0.71175                 -0.28024  \n",
+       "567               1.55217         1.82433                  2.11735  \n",
+       "\n",
+       "[284 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = g2.get_matrix('worst')\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1829f1ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "-----------------------------------------\n",
+      "DBSCAN found 284 clusters with 0 outliers\n",
+      "--fit on feature embeddings of size (284, 10)\n",
+      "-----------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"1d8edd74-8442-4fda-a999-c04b42b38ca9\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=1167fd431afd42ac98aa7ced2d09085d&type=arrow&viztoken=f01cf57b-cec7-4e89-aca6-a19af2e1d1db&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265687&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#1d8edd74-8442-4fda-a999-c04b42b38ca9\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# suppose we want to cluster and color by these variables,\n",
+    "g2.dbscan(cols='worst', min_dist=0.3, verbose=True, fit_umap_embedding=True).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4354d70",
+   "metadata": {},
+   "source": [
+    "Suppose you wanted to study part of the features matrix, like all entries in 'symmetry'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d866936a-2ace-4f77-af35-a2cea7174427",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean symmetry</th>\n",
+       "      <th>symmetry error</th>\n",
+       "      <th>worst symmetry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>-0.26657</td>\n",
+       "      <td>-0.83907</td>\n",
+       "      <td>0.46218</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>0.15041</td>\n",
+       "      <td>-0.91072</td>\n",
+       "      <td>-0.59045</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541</th>\n",
+       "      <td>0.27848</td>\n",
+       "      <td>0.21215</td>\n",
+       "      <td>0.56954</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>509</th>\n",
+       "      <td>0.08488</td>\n",
+       "      <td>0.25357</td>\n",
+       "      <td>0.32694</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>444</th>\n",
+       "      <td>-0.17424</td>\n",
+       "      <td>-0.65323</td>\n",
+       "      <td>0.08853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>319</th>\n",
+       "      <td>-0.91288</td>\n",
+       "      <td>1.74027</td>\n",
+       "      <td>-1.22342</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>0.29933</td>\n",
+       "      <td>-0.46627</td>\n",
+       "      <td>-0.08435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>533</th>\n",
+       "      <td>1.15413</td>\n",
+       "      <td>1.04954</td>\n",
+       "      <td>0.61555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>449</th>\n",
+       "      <td>-0.66865</td>\n",
+       "      <td>-0.95102</td>\n",
+       "      <td>-0.71175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>1.84214</td>\n",
+       "      <td>0.49874</td>\n",
+       "      <td>1.82433</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>284 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     mean symmetry  symmetry error  worst symmetry\n",
+       "527       -0.26657        -0.83907         0.46218\n",
+       "82         0.15041        -0.91072        -0.59045\n",
+       "541        0.27848         0.21215         0.56954\n",
+       "509        0.08488         0.25357         0.32694\n",
+       "444       -0.17424        -0.65323         0.08853\n",
+       "..             ...             ...             ...\n",
+       "319       -0.91288         1.74027        -1.22342\n",
+       "99         0.29933        -0.46627        -0.08435\n",
+       "533        1.15413         1.04954         0.61555\n",
+       "449       -0.66865        -0.95102        -0.71175\n",
+       "567        1.84214         0.49874         1.82433\n",
+       "\n",
+       "[284 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# with new features you can add it too graphistry API to run further modeling\n",
+    "X = g2.get_matrix('symmetry')\n",
+    "small_study = X.columns   # save for later so we can call out only these features during fit\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a938ae8d-e280-4299-af38-1b945d38a22b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add the target back so we can color by in UI\n",
+    "X['target'] = df_train.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f6eeb6de-b418-4b84-92c7-b28fa241ce97",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "! Failed umap speedup attempt. Continuing without memoization speedups.* Ignoring target column of shape (284, 0) in UMAP fit, as it is not one dimensional"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lazy init\n",
+      "_______________\n",
+      "\n",
+      "UMAP Parameters\n",
+      "_______________\n",
+      "\n",
+      "{'n_components': 2, 'metric': 'euclidean', 'n_neighbors': 12, 'min_dist': 0.1, 'spread': 0.5, 'local_connectivity': 1, 'repulsion_strength': 1, 'negative_sample_rate': 5}\n",
+      "------------------------------------------------------------\n",
+      "** Fitting UMAP\n",
+      "Same umap params as last time, skipping new init\n"
+     ]
+    }
+   ],
+   "source": [
+    "g = graphistry.nodes(X)\n",
+    "gq = g.umap(X=small_study, verbose=True).dbscan()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "baf3980c-df0a-479a-8ee0-4fc57109881e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"3b681390-41be-42d1-b950-0f455e5a1628\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=e20f525f58304fc3a20d260e36e63941&type=arrow&viztoken=595790eb-828a-4eee-8769-bf57720a5531&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265691&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#3b681390-41be-42d1-b950-0f455e5a1628\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gq.plot()  # finds that `symmetry` features are also a good indicator of target on their own"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8780fe0-ca07-4412-ba2a-1ddde26d46db",
+   "metadata": {},
+   "source": [
+    "# Transform Test Data into Graph\n",
+    "Can add batch onto closest neighbors of existing graph (from fit above) if merge_policy=True\n",
+    "otherwise, will create a new graph from the batch. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8de9e1bf-290d-4901-96ef-4dd8ac9b5887",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------\n",
+      "Infering edges over UMAP embedding\n",
+      "---------------------------------------------\n",
+      " Mean distance to existing nodes 1.88 +/- 1.07\n",
+      " Max distance threshold; epsilon = 1.00\n",
+      " Finding 7 nearest neighbors\n",
+      " 68.61 neighbors per node within epsilon 1.00\n",
+      " 1995 total edges after dropping duplicates\n",
+      " ** Final graph has 349 nodes\n",
+      " - Batch has 285 nodes\n",
+      " - Brought in 64 nodes\n",
+      "--------------------------------------------------\n",
+      "CPU times: user 6.78 s, sys: 34.4 ms, total: 6.82 s\n",
+      "Wall time: 6.86 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# with merge_policy=True, will cluster minibatch to closests elements of existing graph -- useful if you want to find\n",
+    "# centroids in the old variables (imagine labeling goldenset with other targets, this would find which parts of minibatch are likely similar to known annotations)\n",
+    "g3 = g2.transform_umap(df_test, min_dist=1, merge_policy=True,\n",
+    "                       fit_umap_embedding=True, n_neighbors=7,\n",
+    "                       sample=None, \n",
+    "                       return_graph=True, \n",
+    "                       verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6c032e7c-0fc9-4364-9feb-eddb0d8e3c6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"010841fc-1cee-4c85-9bbf-1af2ee3140ec\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=662d9cb603a74226bcc8dd4cf5e79060&type=arrow&viztoken=741efa50-8f98-49ad-80e3-a7f348ef99dd&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265701&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#010841fc-1cee-4c85-9bbf-1af2ee3140ec\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g3.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "084f07f9-db66-4018-8d01-74a189a3c7ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------\n",
+      "Infering edges over features embedding\n",
+      "---------------------------------------------\n",
+      " Mean distance to existing nodes 5.96 +/- 3.08\n",
+      " Max distance threshold; epsilon = 6.00\n",
+      " Finding 7 nearest neighbors\n",
+      " 167.55 neighbors per node within epsilon 6.00\n",
+      " 1932 total edges after dropping duplicates\n",
+      " ** Final graph has 285 nodes\n",
+      "--------------------------------------------------\n",
+      "CPU times: user 2.76 s, sys: 21 ms, total: 2.78 s\n",
+      "Wall time: 2.82 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# with merge_policy=False (default), it clusters just by the minibatch df_test here\n",
+    "g4 = g2.transform_umap(df_test, min_dist=6, merge_policy=False,\n",
+    "                       fit_umap_embedding=False, n_neighbors=7,\n",
+    "                       sample=None, \n",
+    "                       return_graph=True, \n",
+    "                       verbose=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "7f9d3612-b2e0-4500-b5ba-4c11c3577dc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"17e2c491-5b49-4127-abe1-ef5d73121241\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=110836719d0d4ed6a4355c8d389aae69&type=arrow&viztoken=c1232209-afbb-4dca-9040-b3541b227a91&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265706&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#17e2c491-5b49-4127-abe1-ef5d73121241\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g4.dbscan(0.2).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ab6b089-9c3c-4ad9-bd9f-c3e4b14c87e1",
+   "metadata": {},
+   "source": [
+    "# Regressive Targets\n",
+    "Diabetes dataset with risk scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "d175607a-e108-45e5-aa10-05e306532589",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data2 = load_diabetes()\n",
+    "diabetes_features = list(data2['feature_names'])\n",
+    "diabetes_df = pd.DataFrame(data2['data'], columns=diabetes_features)\n",
+    "# we add target to dataframe as we want all the data for visualization (think coloring by histogram in target)\n",
+    "diabetes_df['target'] = data2['target']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e1bbc28b-9d10-4ff3-8c28-3308b552bbd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>bmi</th>\n",
+       "      <th>bp</th>\n",
+       "      <th>s1</th>\n",
+       "      <th>s2</th>\n",
+       "      <th>s3</th>\n",
+       "      <th>s4</th>\n",
+       "      <th>s5</th>\n",
+       "      <th>s6</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.038076</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>0.061696</td>\n",
+       "      <td>0.021872</td>\n",
+       "      <td>-0.044223</td>\n",
+       "      <td>-0.034821</td>\n",
+       "      <td>-0.043401</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>0.019908</td>\n",
+       "      <td>-0.017646</td>\n",
+       "      <td>151.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.001882</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.051474</td>\n",
+       "      <td>-0.026328</td>\n",
+       "      <td>-0.008449</td>\n",
+       "      <td>-0.019163</td>\n",
+       "      <td>0.074412</td>\n",
+       "      <td>-0.039493</td>\n",
+       "      <td>-0.068330</td>\n",
+       "      <td>-0.092204</td>\n",
+       "      <td>75.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.085299</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>0.044451</td>\n",
+       "      <td>-0.005671</td>\n",
+       "      <td>-0.045599</td>\n",
+       "      <td>-0.034194</td>\n",
+       "      <td>-0.032356</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>0.002864</td>\n",
+       "      <td>-0.025930</td>\n",
+       "      <td>141.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.089063</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.011595</td>\n",
+       "      <td>-0.036656</td>\n",
+       "      <td>0.012191</td>\n",
+       "      <td>0.024991</td>\n",
+       "      <td>-0.036038</td>\n",
+       "      <td>0.034309</td>\n",
+       "      <td>0.022692</td>\n",
+       "      <td>-0.009362</td>\n",
+       "      <td>206.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.005383</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.036385</td>\n",
+       "      <td>0.021872</td>\n",
+       "      <td>0.003935</td>\n",
+       "      <td>0.015596</td>\n",
+       "      <td>0.008142</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>-0.031991</td>\n",
+       "      <td>-0.046641</td>\n",
+       "      <td>135.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>0.041708</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>0.019662</td>\n",
+       "      <td>0.059744</td>\n",
+       "      <td>-0.005697</td>\n",
+       "      <td>-0.002566</td>\n",
+       "      <td>-0.028674</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>0.031193</td>\n",
+       "      <td>0.007207</td>\n",
+       "      <td>178.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>-0.005515</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>-0.015906</td>\n",
+       "      <td>-0.067642</td>\n",
+       "      <td>0.049341</td>\n",
+       "      <td>0.079165</td>\n",
+       "      <td>-0.028674</td>\n",
+       "      <td>0.034309</td>\n",
+       "      <td>-0.018118</td>\n",
+       "      <td>0.044485</td>\n",
+       "      <td>104.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>0.041708</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>-0.015906</td>\n",
+       "      <td>0.017282</td>\n",
+       "      <td>-0.037344</td>\n",
+       "      <td>-0.013840</td>\n",
+       "      <td>-0.024993</td>\n",
+       "      <td>-0.011080</td>\n",
+       "      <td>-0.046879</td>\n",
+       "      <td>0.015491</td>\n",
+       "      <td>132.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>440</th>\n",
+       "      <td>-0.045472</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>0.039062</td>\n",
+       "      <td>0.001215</td>\n",
+       "      <td>0.016318</td>\n",
+       "      <td>0.015283</td>\n",
+       "      <td>-0.028674</td>\n",
+       "      <td>0.026560</td>\n",
+       "      <td>0.044528</td>\n",
+       "      <td>-0.025930</td>\n",
+       "      <td>220.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>441</th>\n",
+       "      <td>-0.045472</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.073030</td>\n",
+       "      <td>-0.081414</td>\n",
+       "      <td>0.083740</td>\n",
+       "      <td>0.027809</td>\n",
+       "      <td>0.173816</td>\n",
+       "      <td>-0.039493</td>\n",
+       "      <td>-0.004220</td>\n",
+       "      <td>0.003064</td>\n",
+       "      <td>57.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>442 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          age       sex       bmi        bp        s1        s2        s3  \\\n",
+       "0    0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
+       "1   -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
+       "2    0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
+       "3   -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
+       "4    0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
+       "..        ...       ...       ...       ...       ...       ...       ...   \n",
+       "437  0.041708  0.050680  0.019662  0.059744 -0.005697 -0.002566 -0.028674   \n",
+       "438 -0.005515  0.050680 -0.015906 -0.067642  0.049341  0.079165 -0.028674   \n",
+       "439  0.041708  0.050680 -0.015906  0.017282 -0.037344 -0.013840 -0.024993   \n",
+       "440 -0.045472 -0.044642  0.039062  0.001215  0.016318  0.015283 -0.028674   \n",
+       "441 -0.045472 -0.044642 -0.073030 -0.081414  0.083740  0.027809  0.173816   \n",
+       "\n",
+       "           s4        s5        s6  target  \n",
+       "0   -0.002592  0.019908 -0.017646   151.0  \n",
+       "1   -0.039493 -0.068330 -0.092204    75.0  \n",
+       "2   -0.002592  0.002864 -0.025930   141.0  \n",
+       "3    0.034309  0.022692 -0.009362   206.0  \n",
+       "4   -0.002592 -0.031991 -0.046641   135.0  \n",
+       "..        ...       ...       ...     ...  \n",
+       "437 -0.002592  0.031193  0.007207   178.0  \n",
+       "438  0.034309 -0.018118  0.044485   104.0  \n",
+       "439 -0.011080 -0.046879  0.015491   132.0  \n",
+       "440  0.026560  0.044528 -0.025930   220.0  \n",
+       "441 -0.039493 -0.004220  0.003064    57.0  \n",
+       "\n",
+       "[442 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "1f07666e-edf3-4585-b5a1-cc28dac9b04a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "train_diabetes, test_diabetes, train_targets_diabetes, test_targets_diabetes = train_test_split(diabetes_df, diabetes_df.target, train_size=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb779dee-8df3-4096-adbe-49a76fcb7667",
+   "metadata": {},
+   "source": [
+    "This time let's add target during umap fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "994c5a46-6e9f-49b2-a5fa-49095d473cd4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g = graphistry.nodes(train_diabetes)\n",
+    "g5 = g.umap(X=diabetes_features, y = 'target',  \n",
+    "            use_scaler=None, # 'robust',\n",
+    "            use_scaler_target=None, #'standard'\n",
+    "           )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "15933c92-6f40-40be-b5ab-bf300e67e359",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"0d6e2022-a453-48ff-ad3e-100f010b5b07\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=5975b94c4e164a8db6157ef0277374d8&type=arrow&viztoken=bfd0d445-c261-407c-8e74-6bc310e8c593&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265711&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#0d6e2022-a453-48ff-ad3e-100f010b5b07\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g5.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "da4eef58-517c-4908-bff1-7bb5c4872617",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----------------------------------------\n",
+      "DBSCAN found 15 clusters with 0 outliers\n",
+      "--fit on umap embeddings of size (221, 2)\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "#predict on unseen data\n",
+    "# notice you don't need to add y=test_diabetes.target, graphistry knows what column from fit\n",
+    "g_pred = g5.dbscan(verbose=True).transform_dbscan(test_diabetes, y=test_diabetes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "7a14df9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"756dd559-ae48-47bf-b4d4-a2a71e17bad2\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=accec73aea9d41b1b972cf32be6b8139&type=arrow&viztoken=4818b992-81c6-4315-ba95-25b5e662e1f8&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265717&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#756dd559-ae48-47bf-b4d4-a2a71e17bad2\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# will predict dbscan label from fit\n",
+    "g_pred.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a1d000d",
+   "metadata": {},
+   "source": [
+    "## Add your favorite model \n",
+    "\n",
+    "We will use Optuna to demonstrate a sample pipeline with HPO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "6477f87a-82c7-471f-acfa-1bdec88eb655",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m[I 2023-01-20 17:48:23,033]\u001b[0m A new study created in memory with name: Diabetes\u001b[0m\u001b[32m[I 2023-01-20 17:48:23,150]\u001b[0m Trial 0 finished with value: 0.4491825511591293 and parameters: {'n_estimators': 67, 'max_depth': 199, 'min_samples_split': 36, 'scaler': 'robust'}. Best is trial 0 with value: 0.4491825511591293.\u001b[0m\u001b[32m[I 2023-01-20 17:48:23,344]\u001b[0m Trial 1 finished with value: 0.3611013176493034 and parameters: {'n_estimators': 186, 'max_depth': 36, 'min_samples_split': 88, 'scaler': 'standard'}. Best is trial 0 with value: 0.4491825511591293.\u001b[0m\u001b[32m[I 2023-01-20 17:48:23,636]\u001b[0m Trial 2 finished with value: 0.4599555207008448 and parameters: {'n_estimators': 221, 'max_depth': 186, 'min_samples_split': 31, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:23,953]\u001b[0m Trial 3 finished with value: 0.43972380164107383 and parameters: {'n_estimators': 237, 'max_depth': 187, 'min_samples_split': 41, 'scaler': 'standard'}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:24,217]\u001b[0m Trial 4 finished with value: 0.41916456260562696 and parameters: {'n_estimators': 232, 'max_depth': 161, 'min_samples_split': 58, 'scaler': 'quantile'}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:24,454]\u001b[0m Trial 5 finished with value: 0.4390071382812232 and parameters: {'n_estimators': 219, 'max_depth': 162, 'min_samples_split': 40, 'scaler': 'robust'}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:24,606]\u001b[0m Trial 6 finished with value: 0.4477315398732742 and parameters: {'n_estimators': 126, 'max_depth': 58, 'min_samples_split': 38, 'scaler': 'quantile'}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:24,857]\u001b[0m Trial 7 finished with value: 0.4238875440292236 and parameters: {'n_estimators': 181, 'max_depth': 175, 'min_samples_split': 56, 'scaler': 'quantile'}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:24,978]\u001b[0m Trial 8 finished with value: 0.34947836592039183 and parameters: {'n_estimators': 93, 'max_depth': 164, 'min_samples_split': 100, 'scaler': 'quantile'}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:25,141]\u001b[0m Trial 9 finished with value: 0.3410582039173441 and parameters: {'n_estimators': 167, 'max_depth': 144, 'min_samples_split': 96, 'scaler': 'robust'}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:25,331]\u001b[0m Trial 10 finished with value: 0.42789907031888397 and parameters: {'n_estimators': 137, 'max_depth': 104, 'min_samples_split': 3, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:25,406]\u001b[0m Trial 11 finished with value: 0.4407087833174965 and parameters: {'n_estimators': 54, 'max_depth': 198, 'min_samples_split': 18, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:25,483]\u001b[0m Trial 12 finished with value: 0.43123238250660256 and parameters: {'n_estimators': 56, 'max_depth': 118, 'min_samples_split': 21, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:25,592]\u001b[0m Trial 13 finished with value: 0.41759544231255075 and parameters: {'n_estimators': 93, 'max_depth': 129, 'min_samples_split': 65, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:25,722]\u001b[0m Trial 14 finished with value: 0.4460536456206613 and parameters: {'n_estimators': 103, 'max_depth': 3, 'min_samples_split': 21, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:25,946]\u001b[0m Trial 15 finished with value: 0.39157752162536286 and parameters: {'n_estimators': 209, 'max_depth': 88, 'min_samples_split': 71, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:26,138]\u001b[0m Trial 16 finished with value: 0.45319719982218454 and parameters: {'n_estimators': 157, 'max_depth': 199, 'min_samples_split': 31, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:26,350]\u001b[0m Trial 17 finished with value: 0.45767575785023773 and parameters: {'n_estimators': 158, 'max_depth': 138, 'min_samples_split': 7, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:26,616]\u001b[0m Trial 18 finished with value: 0.43892702761740565 and parameters: {'n_estimators': 192, 'max_depth': 135, 'min_samples_split': 3, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:26,865]\u001b[0m Trial 19 finished with value: 0.45450074484535274 and parameters: {'n_estimators': 206, 'max_depth': 85, 'min_samples_split': 12, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:27,032]\u001b[0m Trial 20 finished with value: 0.455948254738035 and parameters: {'n_estimators': 128, 'max_depth': 139, 'min_samples_split': 28, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:27,181]\u001b[0m Trial 21 finished with value: 0.4659875568563212 and parameters: {'n_estimators': 125, 'max_depth': 148, 'min_samples_split': 28, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:27,328]\u001b[0m Trial 22 finished with value: 0.45261144904606754 and parameters: {'n_estimators': 113, 'max_depth': 150, 'min_samples_split': 11, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:27,502]\u001b[0m Trial 23 finished with value: 0.4432510337508433 and parameters: {'n_estimators': 149, 'max_depth': 112, 'min_samples_split': 47, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:27,695]\u001b[0m Trial 24 finished with value: 0.454945193668833 and parameters: {'n_estimators': 169, 'max_depth': 176, 'min_samples_split': 28, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:28,000]\u001b[0m Trial 25 finished with value: 0.457519704282382 and parameters: {'n_estimators': 250, 'max_depth': 122, 'min_samples_split': 13, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:28,170]\u001b[0m Trial 26 finished with value: 0.454372244336522 and parameters: {'n_estimators': 145, 'max_depth': 153, 'min_samples_split': 26, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:28,262]\u001b[0m Trial 27 finished with value: 0.4426481977872042 and parameters: {'n_estimators': 74, 'max_depth': 177, 'min_samples_split': 50, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:28,517]\u001b[0m Trial 28 finished with value: 0.45478126295796806 and parameters: {'n_estimators': 118, 'max_depth': 90, 'min_samples_split': 8, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:28,620]\u001b[0m Trial 29 finished with value: 0.45498664133674827 and parameters: {'n_estimators': 73, 'max_depth': 187, 'min_samples_split': 17, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:28,804]\u001b[0m Trial 30 finished with value: 0.45590798145391953 and parameters: {'n_estimators': 166, 'max_depth': 130, 'min_samples_split': 36, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:29,089]\u001b[0m Trial 31 finished with value: 0.4571133333268268 and parameters: {'n_estimators': 238, 'max_depth': 120, 'min_samples_split': 15, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:29,416]\u001b[0m Trial 32 finished with value: 0.45617411970879174 and parameters: {'n_estimators': 249, 'max_depth': 77, 'min_samples_split': 7, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:29,664]\u001b[0m Trial 33 finished with value: 0.45364032098575513 and parameters: {'n_estimators': 223, 'max_depth': 104, 'min_samples_split': 33, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:29,996]\u001b[0m Trial 34 finished with value: 0.45900022338396884 and parameters: {'n_estimators': 247, 'max_depth': 151, 'min_samples_split': 24, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:30,244]\u001b[0m Trial 35 finished with value: 0.4588124181389295 and parameters: {'n_estimators': 222, 'max_depth': 159, 'min_samples_split': 23, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:30,537]\u001b[0m Trial 36 finished with value: 0.4491496739615546 and parameters: {'n_estimators': 203, 'max_depth': 167, 'min_samples_split': 45, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:30,860]\u001b[0m Trial 37 finished with value: 0.45754435816463646 and parameters: {'n_estimators': 223, 'max_depth': 185, 'min_samples_split': 23, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:31,158]\u001b[0m Trial 38 finished with value: 0.45246743668284095 and parameters: {'n_estimators': 235, 'max_depth': 160, 'min_samples_split': 38, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:31,385]\u001b[0m Trial 39 finished with value: 0.45125727640745616 and parameters: {'n_estimators': 183, 'max_depth': 155, 'min_samples_split': 42, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:31,617]\u001b[0m Trial 40 finished with value: 0.38310567273175145 and parameters: {'n_estimators': 217, 'max_depth': 170, 'min_samples_split': 80, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:31,841]\u001b[0m Trial 41 finished with value: 0.45135671120312215 and parameters: {'n_estimators': 194, 'max_depth': 146, 'min_samples_split': 34, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:32,173]\u001b[0m Trial 42 finished with value: 0.4623472408695728 and parameters: {'n_estimators': 243, 'max_depth': 186, 'min_samples_split': 25, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:32,464]\u001b[0m Trial 43 finished with value: 0.45880662185066146 and parameters: {'n_estimators': 231, 'max_depth': 193, 'min_samples_split': 25, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:32,725]\u001b[0m Trial 44 finished with value: 0.4254733474254583 and parameters: {'n_estimators': 245, 'max_depth': 187, 'min_samples_split': 56, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:32,998]\u001b[0m Trial 45 finished with value: 0.4660198609778968 and parameters: {'n_estimators': 229, 'max_depth': 179, 'min_samples_split': 20, 'scaler': None}. Best is trial 45 with value: 0.4660198609778968.\u001b[0m\u001b[32m[I 2023-01-20 17:48:33,258]\u001b[0m Trial 46 finished with value: 0.45625373004784886 and parameters: {'n_estimators': 242, 'max_depth': 179, 'min_samples_split': 41, 'scaler': None}. Best is trial 45 with value: 0.4660198609778968.\u001b[0m\u001b[32m[I 2023-01-20 17:48:33,534]\u001b[0m Trial 47 finished with value: 0.46619351207189874 and parameters: {'n_estimators': 230, 'max_depth': 171, 'min_samples_split': 30, 'scaler': None}. Best is trial 47 with value: 0.46619351207189874.\u001b[0m\u001b[32m[I 2023-01-20 17:48:33,771]\u001b[0m Trial 48 finished with value: 0.45494562762820034 and parameters: {'n_estimators': 213, 'max_depth': 192, 'min_samples_split': 31, 'scaler': None}. Best is trial 47 with value: 0.46619351207189874.\u001b[0m\u001b[32m[I 2023-01-20 17:48:34,041]\u001b[0m Trial 49 finished with value: 0.4557102826724324 and parameters: {'n_estimators': 231, 'max_depth': 169, 'min_samples_split': 19, 'scaler': None}. Best is trial 47 with value: 0.46619351207189874.\u001b[0m\u001b[32m[I 2023-01-20 17:48:34,319]\u001b[0m Trial 50 finished with value: 0.4516936561028091 and parameters: {'n_estimators': 202, 'max_depth': 50, 'min_samples_split': 30, 'scaler': None}. Best is trial 47 with value: 0.46619351207189874.\u001b[0m\u001b[32m[I 2023-01-20 17:48:34,594]\u001b[0m Trial 51 finished with value: 0.45929745923478704 and parameters: {'n_estimators': 229, 'max_depth': 180, 'min_samples_split': 21, 'scaler': None}. Best is trial 47 with value: 0.46619351207189874.\u001b[0m\u001b[32m[I 2023-01-20 17:48:34,919]\u001b[0m Trial 52 finished with value: 0.44727016157267296 and parameters: {'n_estimators': 228, 'max_depth': 183, 'min_samples_split': 19, 'scaler': None}. Best is trial 47 with value: 0.46619351207189874.\u001b[0m\u001b[32m[I 2023-01-20 17:48:35,188]\u001b[0m Trial 53 finished with value: 0.45218714108673586 and parameters: {'n_estimators': 240, 'max_depth': 197, 'min_samples_split': 36, 'scaler': None}. Best is trial 47 with value: 0.46619351207189874.\u001b[0m\u001b[32m[I 2023-01-20 17:48:35,432]\u001b[0m Trial 54 finished with value: 0.4701388301973731 and parameters: {'n_estimators': 211, 'max_depth': 174, 'min_samples_split': 28, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:35,647]\u001b[0m Trial 55 finished with value: 0.4593754940333289 and parameters: {'n_estimators': 194, 'max_depth': 169, 'min_samples_split': 29, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:35,878]\u001b[0m Trial 56 finished with value: 0.4419216452203949 and parameters: {'n_estimators': 213, 'max_depth': 199, 'min_samples_split': 44, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:36,055]\u001b[0m Trial 57 finished with value: 0.450938540662852 and parameters: {'n_estimators': 138, 'max_depth': 174, 'min_samples_split': 15, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:36,259]\u001b[0m Trial 58 finished with value: 0.43026746690972073 and parameters: {'n_estimators': 178, 'max_depth': 8, 'min_samples_split': 52, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:36,492]\u001b[0m Trial 59 finished with value: 0.45457491311201015 and parameters: {'n_estimators': 200, 'max_depth': 165, 'min_samples_split': 27, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:36,614]\u001b[0m Trial 60 finished with value: 0.40069290700280147 and parameters: {'n_estimators': 105, 'max_depth': 144, 'min_samples_split': 63, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:36,865]\u001b[0m Trial 61 finished with value: 0.45504892901006855 and parameters: {'n_estimators': 210, 'max_depth': 173, 'min_samples_split': 30, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:37,122]\u001b[0m Trial 62 finished with value: 0.45717348281658643 and parameters: {'n_estimators': 219, 'max_depth': 192, 'min_samples_split': 34, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:37,335]\u001b[0m Trial 63 finished with value: 0.4501861610344382 and parameters: {'n_estimators': 198, 'max_depth': 167, 'min_samples_split': 39, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:37,568]\u001b[0m Trial 64 finished with value: 0.44870852810746464 and parameters: {'n_estimators': 192, 'max_depth': 158, 'min_samples_split': 27, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:37,838]\u001b[0m Trial 65 finished with value: 0.4563869472586366 and parameters: {'n_estimators': 236, 'max_depth': 188, 'min_samples_split': 21, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:38,065]\u001b[0m Trial 66 finished with value: 0.4569723780426377 and parameters: {'n_estimators': 128, 'max_depth': 179, 'min_samples_split': 10, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:38,372]\u001b[0m Trial 67 finished with value: 0.456697483371164 and parameters: {'n_estimators': 225, 'max_depth': 173, 'min_samples_split': 15, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:38,635]\u001b[0m Trial 68 finished with value: 0.46013491230813797 and parameters: {'n_estimators': 242, 'max_depth': 165, 'min_samples_split': 32, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:38,926]\u001b[0m Trial 69 finished with value: 0.4397294382637812 and parameters: {'n_estimators': 242, 'max_depth': 163, 'min_samples_split': 49, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:39,190]\u001b[0m Trial 70 finished with value: 0.45716735862355085 and parameters: {'n_estimators': 235, 'max_depth': 150, 'min_samples_split': 37, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:39,437]\u001b[0m Trial 71 finished with value: 0.46796689949458525 and parameters: {'n_estimators': 218, 'max_depth': 183, 'min_samples_split': 32, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:39,671]\u001b[0m Trial 72 finished with value: 0.4456845438286513 and parameters: {'n_estimators': 213, 'max_depth': 185, 'min_samples_split': 33, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:39,995]\u001b[0m Trial 73 finished with value: 0.4514167986364557 and parameters: {'n_estimators': 220, 'max_depth': 192, 'min_samples_split': 25, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:40,273]\u001b[0m Trial 74 finished with value: 0.45177084288055314 and parameters: {'n_estimators': 250, 'max_depth': 179, 'min_samples_split': 31, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:40,438]\u001b[0m Trial 75 finished with value: 0.4594222462394846 and parameters: {'n_estimators': 119, 'max_depth': 164, 'min_samples_split': 23, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:40,565]\u001b[0m Trial 76 finished with value: 0.43638091942293633 and parameters: {'n_estimators': 91, 'max_depth': 182, 'min_samples_split': 18, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:40,810]\u001b[0m Trial 77 finished with value: 0.46255342600547644 and parameters: {'n_estimators': 207, 'max_depth': 200, 'min_samples_split': 27, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:41,077]\u001b[0m Trial 78 finished with value: 0.45610024289436835 and parameters: {'n_estimators': 228, 'max_depth': 200, 'min_samples_split': 27, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:41,346]\u001b[0m Trial 79 finished with value: 0.4582111765957191 and parameters: {'n_estimators': 242, 'max_depth': 194, 'min_samples_split': 36, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:41,597]\u001b[0m Trial 80 finished with value: 0.45155009333029805 and parameters: {'n_estimators': 217, 'max_depth': 157, 'min_samples_split': 21, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:41,823]\u001b[0m Trial 81 finished with value: 0.457165117559989 and parameters: {'n_estimators': 206, 'max_depth': 188, 'min_samples_split': 33, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:42,095]\u001b[0m Trial 82 finished with value: 0.45188605373933566 and parameters: {'n_estimators': 234, 'max_depth': 173, 'min_samples_split': 25, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:42,334]\u001b[0m Trial 83 finished with value: 0.44722523300012185 and parameters: {'n_estimators': 225, 'max_depth': 183, 'min_samples_split': 41, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:42,574]\u001b[0m Trial 84 finished with value: 0.4510143253057257 and parameters: {'n_estimators': 216, 'max_depth': 175, 'min_samples_split': 29, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:42,802]\u001b[0m Trial 85 finished with value: 0.46160001164446474 and parameters: {'n_estimators': 207, 'max_depth': 190, 'min_samples_split': 33, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:43,046]\u001b[0m Trial 86 finished with value: 0.4553338410446598 and parameters: {'n_estimators': 206, 'max_depth': 189, 'min_samples_split': 35, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:43,265]\u001b[0m Trial 87 finished with value: 0.4523616082050852 and parameters: {'n_estimators': 188, 'max_depth': 178, 'min_samples_split': 32, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:43,539]\u001b[0m Trial 88 finished with value: 0.4477729392160493 and parameters: {'n_estimators': 245, 'max_depth': 197, 'min_samples_split': 39, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:43,813]\u001b[0m Trial 89 finished with value: 0.45152815088723597 and parameters: {'n_estimators': 238, 'max_depth': 138, 'min_samples_split': 23, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:44,029]\u001b[0m Trial 90 finished with value: 0.46012304014983285 and parameters: {'n_estimators': 178, 'max_depth': 112, 'min_samples_split': 16, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:44,244]\u001b[0m Trial 91 finished with value: 0.4622758723528394 and parameters: {'n_estimators': 175, 'max_depth': 115, 'min_samples_split': 17, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:44,458]\u001b[0m Trial 92 finished with value: 0.4541918082851871 and parameters: {'n_estimators': 173, 'max_depth': 98, 'min_samples_split': 12, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:44,657]\u001b[0m Trial 93 finished with value: 0.43938742291576083 and parameters: {'n_estimators': 144, 'max_depth': 130, 'min_samples_split': 5, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:44,908]\u001b[0m Trial 94 finished with value: 0.45869732641582117 and parameters: {'n_estimators': 158, 'max_depth': 79, 'min_samples_split': 21, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:45,156]\u001b[0m Trial 95 finished with value: 0.45681249933713675 and parameters: {'n_estimators': 210, 'max_depth': 170, 'min_samples_split': 28, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:45,431]\u001b[0m Trial 96 finished with value: 0.452179948667231 and parameters: {'n_estimators': 231, 'max_depth': 195, 'min_samples_split': 19, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:45,681]\u001b[0m Trial 97 finished with value: 0.4573138975493334 and parameters: {'n_estimators': 198, 'max_depth': 69, 'min_samples_split': 25, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:45,872]\u001b[0m Trial 98 finished with value: 0.45309032342176114 and parameters: {'n_estimators': 163, 'max_depth': 148, 'min_samples_split': 31, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:46,099]\u001b[0m Trial 99 finished with value: 0.3818803064112405 and parameters: {'n_estimators': 222, 'max_depth': 154, 'min_samples_split': 77, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "import optuna\n",
+    "\n",
+    "#X_train, y_train = g5.get_matrix(), g5.get_matrix(target=True)\n",
+    "X_test, y_test = g5.transform(test_diabetes, test_diabetes, return_graph=False)\n",
+    "\n",
+    "def objective(trail):\n",
+    "    n_estimators = trail.suggest_int('n_estimators', 50, 250)\n",
+    "    max_depth = trail.suggest_int('max_depth', 2, 200)\n",
+    "    min_samples_split = trail.suggest_int('min_samples_split', 2, 100)\n",
+    "    \n",
+    "    use_scaler = trail.suggest_categorical('scaler', [None, 'standard', 'robust', 'quantile'])\n",
+    "    X_train, y_train = g5.scale(use_scaler=use_scaler)\n",
+    "    X_test, y_test = g5.scale(test_diabetes, test_diabetes, use_scaler=use_scaler)\n",
+    "                \n",
+    "    rlf = RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth, min_samples_split=min_samples_split)\n",
+    "    score = rlf.fit(X_train, y_train).score(X_test, y_test)\n",
+    "    return score\n",
+    "\n",
+    "    \n",
+    "study = optuna.create_study(study_name='Diabetes', direction='maximize')\n",
+    "\n",
+    "study.optimize(objective, n_trials=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "560a471b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_estimators': 211,\n",
+       " 'max_depth': 174,\n",
+       " 'min_samples_split': 28,\n",
+       " 'scaler': None}"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "study.best_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "0a859318",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "        <script type=\"text/javascript\">\n",
+       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
+       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
+       "        if (typeof require !== 'undefined') {\n",
+       "        require.undef(\"plotly\");\n",
+       "        define('plotly', function(require, exports, module) {\n",
+       "            /**\n",
+       "* plotly.js v2.8.3\n",
+       "* Copyright 2012-2021, Plotly, Inc.\n",
+       "* All rights reserved.\n",
+       "* Licensed under the MIT license\n",
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+       "/*!\n",
+       " * The buffer module from node.js, for the browser.\n",
+       " *\n",
+       " * @author   Feross Aboukhadijeh <feross@feross.org> <http://feross.org>\n",
+       " * @license  MIT\n",
+       " */function i(t,e){if(t===e)return 0;for(var r=t.length,n=e.length,i=0,a=Math.min(r,n);i<a;++i)if(t[i]!==e[i]){r=t[i],n=e[i];break}return r<n?-1:n<r?1:0}function a(t){return r.Buffer&&\"function\"==typeof r.Buffer.isBuffer?r.Buffer.isBuffer(t):!(null==t||!t._isBuffer)}var o=t(\"util/\"),s=Object.prototype.hasOwnProperty,l=Array.prototype.slice,c=\"foo\"===function(){}.name;function u(t){return Object.prototype.toString.call(t)}function f(t){return!a(t)&&(\"function\"==typeof r.ArrayBuffer&&(\"function\"==typeof ArrayBuffer.isView?ArrayBuffer.isView(t):!!t&&(t instanceof DataView||!!(t.buffer&&t.buffer instanceof ArrayBuffer))))}var h=e.exports=y,p=/\\s*function\\s+([^\\(\\s]*)\\s*/;function d(t){if(o.isFunction(t)){if(c)return t.name;var e=t.toString().match(p);return e&&e[1]}}function m(t,e){return\"string\"==typeof t?t.length<e?t:t.slice(0,e):t}function g(t){if(c||!o.isFunction(t))return o.inspect(t);var e=d(t);return\"[Function\"+(e?\": \"+e:\"\")+\"]\"}function 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+       "/*!\n",
+       " * The buffer module from node.js, for the browser.\n",
+       " *\n",
+       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
+       " * @license  MIT\n",
+       " */\n",
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t[0]=s*(h*v-p*g)-f*(l*v-c*g)+m*(l*p-c*h),t[1]=-(n*(h*v-p*g)-f*(i*v-a*g)+m*(i*p-a*h)),t[2]=n*(l*v-c*g)-s*(i*v-a*g)+m*(i*c-a*l),t[3]=-(n*(l*p-c*h)-s*(i*p-a*h)+f*(i*c-a*l)),t[4]=-(o*(h*v-p*g)-u*(l*v-c*g)+d*(l*p-c*h)),t[5]=r*(h*v-p*g)-u*(i*v-a*g)+d*(i*p-a*h),t[6]=-(r*(l*v-c*g)-o*(i*v-a*g)+d*(i*c-a*l)),t[7]=r*(l*p-c*h)-o*(i*p-a*h)+u*(i*c-a*l),t[8]=o*(f*v-p*m)-u*(s*v-c*m)+d*(s*p-c*f),t[9]=-(r*(f*v-p*m)-u*(n*v-a*m)+d*(n*p-a*f)),t[10]=r*(s*v-c*m)-o*(n*v-a*m)+d*(n*c-a*s),t[11]=-(r*(s*p-c*f)-o*(n*p-a*f)+u*(n*c-a*s)),t[12]=-(o*(f*g-h*m)-u*(s*g-l*m)+d*(s*h-l*f)),t[13]=r*(f*g-h*m)-u*(n*g-i*m)+d*(n*h-i*f),t[14]=-(r*(s*g-l*m)-o*(n*g-i*m)+d*(n*l-i*s)),t[15]=r*(s*h-l*f)-o*(n*h-i*f)+u*(n*l-i*s),t}},{}],196:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return 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t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],203:[function(t,e,r){e.exports=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],204:[function(t,e,r){e.exports=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t}},{}],205:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],206:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return 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n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{\"./identity\":209}],213:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],214:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],215:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],216:[function(t,e,r){e.exports=function(t,e,r,n){var i=Math.tan(e.upDegrees*Math.PI/180),a=Math.tan(e.downDegrees*Math.PI/180),o=Math.tan(e.leftDegrees*Math.PI/180),s=Math.tan(e.rightDegrees*Math.PI/180),l=2/(o+s),c=2/(i+a);return t[0]=l,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=c,t[6]=0,t[7]=0,t[8]=-(o-s)*l*.5,t[9]=(i-a)*c*.5,t[10]=n/(r-n),t[11]=-1,t[12]=0,t[13]=0,t[14]=n*r/(r-n),t[15]=0,t}},{}],217:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],218:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],219:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],220:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],221:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],222:[function(t,e,r){e.exports=function(t){return\"mat4(\"+t[0]+\", \"+t[1]+\", \"+t[2]+\", \"+t[3]+\", \"+t[4]+\", \"+t[5]+\", \"+t[6]+\", \"+t[7]+\", \"+t[8]+\", \"+t[9]+\", \"+t[10]+\", \"+t[11]+\", \"+t[12]+\", \"+t[13]+\", \"+t[14]+\", \"+t[15]+\")\"}},{}],223:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],224:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],225:[function(t,e,r){\"use strict\";var n=t(\"css-font\"),i=t(\"pick-by-alias\"),a=t(\"regl\"),o=t(\"gl-util/context\"),s=t(\"es6-weak-map\"),l=t(\"color-normalize\"),c=t(\"font-atlas\"),u=t(\"typedarray-pool\"),f=t(\"parse-rect\"),h=t(\"is-plain-obj\"),p=t(\"parse-unit\"),d=t(\"to-px\"),m=t(\"detect-kerning\"),g=t(\"object-assign\"),v=t(\"font-measure\"),y=t(\"flatten-vertex-data\"),x=t(\"bit-twiddle\").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement(\"div\"));w.style.font=\"italic small-caps bold condensed 16px/2 cursive\",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return\"function\"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop(\"count\"),offset:t.prop(\"offset\"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this(\"sizeBuffer\")},width:{offset:0,stride:8,buffer:t.this(\"sizeBuffer\")},char:t.this(\"charBuffer\"),position:t.this(\"position\")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop(\"color\"),opacity:t.prop(\"opacity\"),viewport:t.this(\"viewportArray\"),scale:t.this(\"scale\"),align:t.prop(\"align\"),baseline:t.prop(\"baseline\"),translate:t.this(\"translate\"),positionOffset:t.prop(\"positionOffset\")},primitive:\"points\",viewport:t.this(\"viewport\"),vert:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tattribute float width, charOffset, char;\\n\\t\\t\\tattribute vec2 position;\\n\\t\\t\\tuniform float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ vec2(positionOffset.x, -positionOffset.y)))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 255.;\\n\\t\\t\\t}\",frag:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tuniform float fontSize, charStep, opacity;\\n\\t\\t\\tuniform vec2 atlasSize;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform sampler2D atlas;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\n\\t\\t\\tfloat lightness(vec4 color) {\\n\\t\\t\\t\\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\\n\\t\\t\\t}\\n\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\\n\\t\\t\\t\\tfloat halfCharStep = floor(charStep * .5 + .5);\\n\\n\\t\\t\\t\\t// invert y and shift by 1px (FF expecially needs that)\\n\\t\\t\\t\\tuv.y = charStep - uv.y;\\n\\n\\t\\t\\t\\t// ignore points outside of character bounding box\\n\\t\\t\\t\\tfloat halfCharWidth = ceil(charWidth * .5);\\n\\t\\t\\t\\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\\n\\t\\t\\t\\t\\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a *= maskY;\\n\\t\\t\\t\\tcolor.a *= opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&(\"number\"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+\"px \"+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(\", \"),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:\"top\",fontSize:T.baseFontSize,fontStyle:u.join(\" \")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);g(t.kerning,m(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),A=0,M=0;A<this.counts.length;A++){var S=this.counts[A];if(w)for(var E=0;E<S;E++)k[M++]=t.position[2*A],k[M++]=t.position[2*A+1];else for(var L=0;L<S;L++)k[M++]=t.position[A][0],k[M++]=t.position[A][1]}this.position.call?this.position({type:\"float\",data:k}):this.position=this.regl.buffer({type:\"float\",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=u.mallocUint8(this.count),P=u.mallocFloat(2*this.count);this.textWidth=[];for(var I=0,O=0;I<this.counts.length;I++){for(var z=this.counts[I],D=this.font[I]||this.font[0],R=this.fontAtlas[I]||this.fontAtlas[0],F=0;F<z;F++){var B=this.text.charAt(O),N=this.text.charAt(O-1);if(C[O]=R.ids[B],P[2*O]=D.width[B],F){var j=P[2*O-2],U=P[2*O],V=P[2*O-1]+.5*j+.5*U;if(this.kerning){var H=D.kerning[N+B];H&&(V+=.001*H)}P[2*O+1]=V}else P[2*O+1]=.5*P[2*O];O++}this.textWidth.push(P.length?.5*P[2*O-2]+P[2*O-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:C,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:P,type:\"float\",usage:\"stream\"}),u.freeUint8(C),u.freeFloat(P),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),u=x(s*i);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if(\"number\"==typeof n)return n;switch(n){case\"right\":case\"end\":return-t;case\"center\":case\"centre\":case\"middle\":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+=\"number\"==typeof t?t-n.baseline:-n[t],i*=-1}))),null!=t.color)if(t.color||(t.color=\"transparent\"),\"string\"!=typeof t.color&&isNaN(t.color)){var q;if(\"number\"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;q=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)q.set(l(Y(W,W+4),\"uint8\"),W)}else{var X=t.color.length;q=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)q.set(l(t.color[Z]||0,\"uint8\"),4*Z)}this.color=q}else this.color=l(t.color,\"uint8\");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var 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this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text=\"\",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new 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r=e.parse(t[1],1,Bt);if(!r)return null;var n=[],i=null;e.expectedType&&\"value\"!==e.expectedType.kind&&(i=e.expectedType);for(var a=1;a<t.length;a+=2){var o=1===a?-1/0:t[a],s=t[a+1],l=a,c=a+1;if(\"number\"!=typeof o)return e.error('Input/output pairs for \"step\" expressions must be defined using literal numeric values (not computed expressions) for the input values.',l);if(n.length&&n[n.length-1][0]>=o)return e.error('Input/output pairs for \"step\" expressions must be arranged with input values in strictly ascending order.',l);var u=e.parse(s,c,i);if(!u)return null;i=i||u.type,n.push([o,u])}return new He(i,r,n)},He.prototype.evaluate=function(t){var e=this.labels,r=this.outputs;if(1===e.length)return r[0].evaluate(t);var n=this.input.evaluate(t);if(n<=e[0])return r[0].evaluate(t);var i=e.length;return n>=e[i-1]?r[i-1].evaluate(t):r[Ve(e,n)].evaluate(t)},He.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1){t(r[e])}},He.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},He.prototype.serialize=function(){for(var t=[\"step\",this.input.serialize()],e=0;e<this.labels.length;e++)e>0&&t.push(this.labels[e]),t.push(this.outputs[e].serialize());return t};var Ge=Object.freeze({__proto__:null,number:qe,color:function(t,e,r){return new te(qe(t.r,e.r,r),qe(t.g,e.g,r),qe(t.b,e.b,r),qe(t.a,e.a,r))},array:function(t,e,r){return t.map((function(t,n){return qe(t,e[n],r)}))}}),Ye=6/29,We=3*Ye*Ye,Xe=Math.PI/180,Ze=180/Math.PI;function Je(t){return t>.008856451679035631?Math.pow(t,1/3):t/We+4/29}function Ke(t){return t>Ye?t*t*t:We*(t-4/29)}function Qe(t){return 255*(t<=.0031308?12.92*t:1.055*Math.pow(t,1/2.4)-.055)}function $e(t){return(t/=255)<=.04045?t/12.92:Math.pow((t+.055)/1.055,2.4)}function tr(t){var e=$e(t.r),r=$e(t.g),n=$e(t.b),i=Je((.4124564*e+.3575761*r+.1804375*n)/.95047),a=Je((.2126729*e+.7151522*r+.072175*n)/1);return{l:116*a-16,a:500*(i-a),b:200*(a-Je((.0193339*e+.119192*r+.9503041*n)/1.08883)),alpha:t.a}}function er(t){var e=(t.l+16)/116,r=isNaN(t.a)?e:e+t.a/500,n=isNaN(t.b)?e:e-t.b/200;return e=1*Ke(e),r=.95047*Ke(r),n=1.08883*Ke(n),new te(Qe(3.2404542*r-1.5371385*e-.4985314*n),Qe(-.969266*r+1.8760108*e+.041556*n),Qe(.0556434*r-.2040259*e+1.0572252*n),t.alpha)}function rr(t,e,r){var n=e-t;return t+r*(n>180||n<-180?n-360*Math.round(n/360):n)}var nr={forward:tr,reverse:er,interpolate:function(t,e,r){return{l:qe(t.l,e.l,r),a:qe(t.a,e.a,r),b:qe(t.b,e.b,r),alpha:qe(t.alpha,e.alpha,r)}}},ir={forward:function(t){var e=tr(t),r=e.l,n=e.a,i=e.b,a=Math.atan2(i,n)*Ze;return{h:a<0?a+360:a,c:Math.sqrt(n*n+i*i),l:r,alpha:t.a}},reverse:function(t){var e=t.h*Xe,r=t.c;return er({l:t.l,a:Math.cos(e)*r,b:Math.sin(e)*r,alpha:t.alpha})},interpolate:function(t,e,r){return{h:rr(t.h,e.h,r),c:qe(t.c,e.c,r),l:qe(t.l,e.l,r),alpha:qe(t.alpha,e.alpha,r)}}},ar=Object.freeze({__proto__:null,lab:nr,hcl:ir}),or=function(t,e,r,n,i){this.type=t,this.operator=e,this.interpolation=r,this.input=n,this.labels=[],this.outputs=[];for(var a=0,o=i;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1];this.labels.push(l),this.outputs.push(c)}};function sr(t,e,r,n){var i=n-r,a=t-r;return 0===i?0:1===e?a/i:(Math.pow(e,a)-1)/(Math.pow(e,i)-1)}or.interpolationFactor=function(t,e,n,i){var a=0;if(\"exponential\"===t.name)a=sr(e,t.base,n,i);else if(\"linear\"===t.name)a=sr(e,1,n,i);else if(\"cubic-bezier\"===t.name){var o=t.controlPoints;a=new r(o[0],o[1],o[2],o[3]).solve(sr(e,1,n,i))}return a},or.parse=function(t,e){var r=t[0],n=t[1],i=t[2],a=t.slice(3);if(!Array.isArray(n)||0===n.length)return e.error(\"Expected an interpolation type expression.\",1);if(\"linear\"===n[0])n={name:\"linear\"};else if(\"exponential\"===n[0]){var o=n[1];if(\"number\"!=typeof o)return e.error(\"Exponential interpolation requires a numeric base.\",1,1);n={name:\"exponential\",base:o}}else{if(\"cubic-bezier\"!==n[0])return e.error(\"Unknown interpolation type \"+String(n[0]),1,0);var s=n.slice(1);if(4!==s.length||s.some((function(t){return\"number\"!=typeof t||t<0||t>1})))return e.error(\"Cubic bezier interpolation requires four numeric arguments with values between 0 and 1.\",1);n={name:\"cubic-bezier\",controlPoints:s}}if(t.length-1<4)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if((t.length-1)%2!=0)return e.error(\"Expected an even number of arguments.\");if(!(i=e.parse(i,2,Bt)))return null;var l=[],c=null;\"interpolate-hcl\"===r||\"interpolate-lab\"===r?c=Ut:e.expectedType&&\"value\"!==e.expectedType.kind&&(c=e.expectedType);for(var u=0;u<a.length;u+=2){var f=a[u],h=a[u+1],p=u+3,d=u+4;if(\"number\"!=typeof f)return e.error('Input/output pairs for 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a=Ve(e,n),o=e[a],s=e[a+1],l=or.interpolationFactor(this.interpolation,n,o,s),c=r[a].evaluate(t),u=r[a+1].evaluate(t);return\"interpolate\"===this.operator?Ge[this.type.kind.toLowerCase()](c,u,l):\"interpolate-hcl\"===this.operator?ir.reverse(ir.interpolate(ir.forward(c),ir.forward(u),l)):nr.reverse(nr.interpolate(nr.forward(c),nr.forward(u),l))},or.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1){t(r[e])}},or.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},or.prototype.serialize=function(){var t;t=\"linear\"===this.interpolation.name?[\"linear\"]:\"exponential\"===this.interpolation.name?1===this.interpolation.base?[\"linear\"]:[\"exponential\",this.interpolation.base]:[\"cubic-bezier\"].concat(this.interpolation.controlPoints);for(var e=[this.operator,t,this.input.serialize()],r=0;r<this.labels.length;r++)e.push(this.labels[r],this.outputs[r].serialize());return e};var lr=function(t,e){this.type=t,this.args=e};lr.parse=function(t,e){if(t.length<2)return e.error(\"Expectected at least one argument.\");var r=null,n=e.expectedType;n&&\"value\"!==n.kind&&(r=n);for(var i=[],a=0,o=t.slice(1);a<o.length;a+=1){var s=o[a],l=e.parse(s,1+i.length,r,void 0,{typeAnnotation:\"omit\"});if(!l)return null;r=r||l.type,i.push(l)}var c=n&&i.some((function(t){return Jt(n,t.type)}));return new lr(c?Ht:r,i)},lr.prototype.evaluate=function(t){for(var e,r=null,n=0,i=0,a=this.args;i<a.length;i+=1){if(n++,(r=a[i].evaluate(t))&&r instanceof ie&&!r.available&&(e||(e=r.name),r=null,n===this.args.length&&(r=e)),null!==r)break}return r},lr.prototype.eachChild=function(t){this.args.forEach(t)},lr.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},lr.prototype.serialize=function(){var t=[\"coalesce\"];return this.eachChild((function(e){t.push(e.serialize())})),t};var cr=function(t,e){this.type=e.type,this.bindings=[].concat(t),this.result=e};cr.prototype.evaluate=function(t){return this.result.evaluate(t)},cr.prototype.eachChild=function(t){for(var e=0,r=this.bindings;e<r.length;e+=1){t(r[e][1])}t(this.result)},cr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found \"+(t.length-1)+\" instead.\");for(var r=[],n=1;n<t.length-1;n+=2){var i=t[n];if(\"string\"!=typeof i)return e.error(\"Expected string, but found \"+typeof i+\" instead.\",n);if(/[^a-zA-Z0-9_]/.test(i))return e.error(\"Variable names must contain only alphanumeric characters or '_'.\",n);var a=e.parse(t[n+1],n+1);if(!a)return null;r.push([i,a])}var o=e.parse(t[t.length-1],t.length-1,e.expectedType,r);return o?new cr(r,o):null},cr.prototype.outputDefined=function(){return this.result.outputDefined()},cr.prototype.serialize=function(){for(var t=[\"let\"],e=0,r=this.bindings;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t.push(i,a.serialize())}return t.push(this.result.serialize()),t};var ur=function(t,e,r){this.type=t,this.index=e,this.input=r};ur.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Bt),n=e.parse(t[2],2,Wt(e.expectedType||Ht));if(!r||!n)return null;var i=n.type;return new ur(i.itemType,r,n)},ur.prototype.evaluate=function(t){var e=this.index.evaluate(t),r=this.input.evaluate(t);if(e<0)throw new ue(\"Array index out of bounds: \"+e+\" < 0.\");if(e>=r.length)throw new ue(\"Array index out of bounds: \"+e+\" > \"+(r.length-1)+\".\");if(e!==Math.floor(e))throw new ue(\"Array index must be an integer, but found \"+e+\" instead.\");return r[e]},ur.prototype.eachChild=function(t){t(this.index),t(this.input)},ur.prototype.outputDefined=function(){return!1},ur.prototype.serialize=function(){return[\"at\",this.index.serialize(),this.input.serialize()]};var fr=function(t,e){this.type=jt,this.needle=t,this.haystack=e};fr.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Ht),n=e.parse(t[2],2,Ht);return r&&n?Kt(r.type,[jt,Nt,Bt,Ft,Ht])?new fr(r,n):e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(r.type)+\" instead\"):null},fr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!r)return!1;if(!Qt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ue(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(se(e))+\" instead.\");if(!Qt(r,[\"string\",\"array\"]))throw new ue(\"Expected second argument to be of type array or string, but found \"+Xt(se(r))+\" instead.\");return r.indexOf(e)>=0},fr.prototype.eachChild=function(t){t(this.needle),t(this.haystack)},fr.prototype.outputDefined=function(){return!0},fr.prototype.serialize=function(){return[\"in\",this.needle.serialize(),this.haystack.serialize()]};var hr=function(t,e,r){this.type=Bt,this.needle=t,this.haystack=e,this.fromIndex=r};hr.parse=function(t,e){if(t.length<=2||t.length>=5)return e.error(\"Expected 3 or 4 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Ht),n=e.parse(t[2],2,Ht);if(!r||!n)return null;if(!Kt(r.type,[jt,Nt,Bt,Ft,Ht]))return e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(r.type)+\" instead\");if(4===t.length){var i=e.parse(t[3],3,Bt);return i?new hr(r,n,i):null}return new hr(r,n)},hr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!Qt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ue(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(se(e))+\" instead.\");if(!Qt(r,[\"string\",\"array\"]))throw new ue(\"Expected second argument to be of type array or string, but found \"+Xt(se(r))+\" instead.\");if(this.fromIndex){var n=this.fromIndex.evaluate(t);return r.indexOf(e,n)}return r.indexOf(e)},hr.prototype.eachChild=function(t){t(this.needle),t(this.haystack),this.fromIndex&&t(this.fromIndex)},hr.prototype.outputDefined=function(){return!1},hr.prototype.serialize=function(){if(null!=this.fromIndex&&void 0!==this.fromIndex){var t=this.fromIndex.serialize();return[\"index-of\",this.needle.serialize(),this.haystack.serialize(),t]}return[\"index-of\",this.needle.serialize(),this.haystack.serialize()]};var pr=function(t,e,r,n,i,a){this.inputType=t,this.type=e,this.input=r,this.cases=n,this.outputs=i,this.otherwise=a};pr.parse=function(t,e){if(t.length<5)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=1)return e.error(\"Expected an even number of 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m?\"value\"!==d.type.kind&&e.concat(1).checkSubtype(r,d.type)?null:new pr(r,n,d,i,a,m):null},pr.prototype.evaluate=function(t){var e=this.input.evaluate(t);return(se(e)===this.inputType&&this.outputs[this.cases[e]]||this.otherwise).evaluate(t)},pr.prototype.eachChild=function(t){t(this.input),this.outputs.forEach(t),t(this.otherwise)},pr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))&&this.otherwise.outputDefined()},pr.prototype.serialize=function(){for(var t=this,e=[\"match\",this.input.serialize()],r=[],n={},i=0,a=Object.keys(this.cases).sort();i<a.length;i+=1){var o=a[i];void 0===(f=n[this.cases[o]])?(n[this.cases[o]]=r.length,r.push([this.cases[o],[o]])):r[f][1].push(o)}for(var s=function(e){return\"number\"===t.inputType.kind?Number(e):e},l=0,c=r;l<c.length;l+=1){var u=c[l],f=u[0],h=u[1];1===h.length?e.push(s(h[0])):e.push(h.map(s)),e.push(this.outputs[outputIndex$1].serialize())}return e.push(this.otherwise.serialize()),e};var dr=function(t,e,r){this.type=t,this.branches=e,this.otherwise=r};dr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=0)return e.error(\"Expected an odd number of arguments.\");var r;e.expectedType&&\"value\"!==e.expectedType.kind&&(r=e.expectedType);for(var n=[],i=1;i<t.length-1;i+=2){var a=e.parse(t[i],i,jt);if(!a)return null;var o=e.parse(t[i+1],i+1,r);if(!o)return null;n.push([a,o]),r=r||o.type}var s=e.parse(t[t.length-1],t.length-1,r);return s?new dr(r,n,s):null},dr.prototype.evaluate=function(t){for(var e=0,r=this.branches;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];if(i.evaluate(t))return a.evaluate(t)}return this.otherwise.evaluate(t)},dr.prototype.eachChild=function(t){for(var e=0,r=this.branches;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t(i),t(a)}t(this.otherwise)},dr.prototype.outputDefined=function(){return this.branches.every((function(t){t[0];return 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or?a.interpolation:void 0;return Ir(new Zr(n?\"camera\":\"composite\",t.value,a.labels,o))}Zr.prototype.evaluateWithoutErrorHandling=function(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)},Zr.prototype.evaluate=function(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)},Zr.prototype.interpolationFactor=function(t,e,r){return this.interpolationType?or.interpolationFactor(this.interpolationType,t,e,r):0};var Kr=function(t,e){this._parameters=t,this._specification=e,It(this,function t(e,r){var n,i,a,o=\"color\"===r.type,s=e.stops&&\"object\"==typeof e.stops[0][0],l=s||void 0!==e.property,c=s||!l,u=e.type||(Rr(r)?\"exponential\":\"interval\");if(o&&((e=It({},e)).stops&&(e.stops=e.stops.map((function(t){return[t[0],te.parse(t[1])]}))),e.default?e.default=te.parse(e.default):e.default=te.parse(r.default)),e.colorSpace&&\"rgb\"!==e.colorSpace&&!ar[e.colorSpace])throw new Error(\"Unknown color space: 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n&&r<n.minimum?[new Ct(e,r,r+\" is less than the minimum value \"+n.minimum)]:\"maximum\"in n&&r>n.maximum?[new Ct(e,r,r+\" is greater than the maximum value \"+n.maximum)]:[]}function en(t){var e,r,n,i=t.valueSpec,a=Ot(t.value.type),o={},s=\"categorical\"!==a&&void 0===t.value.property,l=!s,c=\"array\"===Fr(t.value.stops)&&\"array\"===Fr(t.value.stops[0])&&\"object\"===Fr(t.value.stops[0][0]),u=Qr({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if(\"identity\"===a)return[new Ct(t.key,t.value,'identity function may not have a \"stops\" property')];var e=[],r=t.value;e=e.concat($r({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:f})),\"array\"===Fr(r)&&0===r.length&&e.push(new Ct(t.key,r,\"array must have at least one stop\"));return e},default:function(t){return kn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return\"identity\"===a&&s&&u.push(new Ct(t.key,t.value,'missing required property \"property\"')),\"identity\"===a||t.value.stops||u.push(new Ct(t.key,t.value,'missing required property \"stops\"')),\"exponential\"===a&&t.valueSpec.expression&&!Rr(t.valueSpec)&&u.push(new Ct(t.key,t.value,\"exponential functions not supported\")),t.styleSpec.$version>=8&&(l&&!zr(t.valueSpec)?u.push(new Ct(t.key,t.value,\"property functions not supported\")):s&&!Dr(t.valueSpec)&&u.push(new Ct(t.key,t.value,\"zoom functions not supported\"))),\"categorical\"!==a&&!c||void 0!==t.value.property||u.push(new Ct(t.key,t.value,'\"property\" property is required')),u;function f(t){var e=[],a=t.value,s=t.key;if(\"array\"!==Fr(a))return[new Ct(s,a,\"array expected, \"+Fr(a)+\" found\")];if(2!==a.length)return[new Ct(s,a,\"array length 2 expected, length \"+a.length+\" found\")];if(c){if(\"object\"!==Fr(a[0]))return[new Ct(s,a,\"object expected, \"+Fr(a[0])+\" found\")];if(void 0===a[0].zoom)return[new Ct(s,a,\"object stop key must have zoom\")];if(void 0===a[0].value)return[new Ct(s,a,\"object stop key must have value\")];if(n&&n>Ot(a[0].zoom))return[new Ct(s,a[0].zoom,\"stop zoom values must appear in ascending order\")];Ot(a[0].zoom)!==n&&(n=Ot(a[0].zoom),r=void 0,o={}),e=e.concat(Qr({key:s+\"[0]\",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:tn,value:h}}))}else e=e.concat(h({key:s+\"[0]\",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Yr(zt(a[1]))?e.concat([new Ct(s+\"[1]\",a[1],\"expressions are not allowed in function stops.\")]):e.concat(kn({key:s+\"[1]\",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=Fr(t.value),l=Ot(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new Ct(t.key,c,s+\" stop domain type must match previous stop domain type \"+e)]}else e=s;if(\"number\"!==s&&\"string\"!==s&&\"boolean\"!==s)return[new Ct(t.key,c,\"stop domain value must be a number, string, or boolean\")];if(\"number\"!==s&&\"categorical\"!==a){var u=\"number expected, \"+s+\" found\";return zr(i)&&void 0===a&&(u+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new Ct(t.key,c,u)]}return\"categorical\"!==a||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==a&&\"number\"===s&&void 0!==r&&l<r?[new Ct(t.key,c,\"stop domain values must appear in ascending order\")]:(r=l,\"categorical\"===a&&l in o?[new Ct(t.key,c,\"stop domain values must be unique\")]:(o[l]=!0,[])):[new Ct(t.key,c,\"integer expected, found \"+l)]}}function rn(t){var e=(\"property\"===t.expressionContext?Jr:Wr)(zt(t.value),t.valueSpec);if(\"error\"===e.result)return e.value.map((function(e){return new Ct(\"\"+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if(\"property\"===t.expressionContext&&\"text-font\"===t.propertyKey&&!r.outputDefined())return[new Ct(t.key,t.value,'Invalid data expression for \"'+t.propertyKey+'\". Output values must be contained as literals within the expression.')];if(\"property\"===t.expressionContext&&\"layout\"===t.propertyType&&!Be(r))return[new Ct(t.key,t.value,'\"feature-state\" data expressions are not supported with layout properties.')];if(\"filter\"===t.expressionContext&&!Be(r))return[new Ct(t.key,t.value,'\"feature-state\" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf(\"cluster\")){if(!Ne(r,[\"zoom\",\"feature-state\"]))return[new Ct(t.key,t.value,'\"zoom\" and \"feature-state\" expressions are not supported with cluster properties.')];if(\"cluster-initial\"===t.expressionContext&&!Fe(r))return[new Ct(t.key,t.value,\"Feature data expressions are not supported with initial expression part of cluster properties.\")]}return[]}function nn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ot(r))&&i.push(new Ct(e,r,\"expected one of [\"+n.values.join(\", \")+\"], 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on={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function sn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};an(t)||(t=cn(t));var e=Wr(t,on);if(\"error\"===e.result)throw new Error(e.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if(\"within\"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function ln(t,e){return t<e?-1:t>e?1:0}function cn(t){if(!t)return!0;var e,r=t[0];return t.length<=1?\"any\"!==r:\"==\"===r?un(t[1],t[2],\"==\"):\"!=\"===r?pn(un(t[1],t[2],\"==\")):\"<\"===r||\">\"===r||\"<=\"===r||\">=\"===r?un(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(cn))):\"all\"===r?[\"all\"].concat(t.slice(1).map(cn)):\"none\"===r?[\"all\"].concat(t.slice(1).map(cn).map(pn)):\"in\"===r?fn(t[1],t.slice(2)):\"!in\"===r?pn(fn(t[1],t.slice(2))):\"has\"===r?hn(t[1]):\"!has\"===r?pn(hn(t[1])):\"within\"!==r||t}function un(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function fn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(ln)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function hn(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function pn(t){return[\"!\",t]}function dn(t){return an(zt(t.value))?rn(It({},t,{expressionContext:\"filter\",valueSpec:{value:\"boolean\"}})):function t(e){var r=e.value,n=e.key;if(\"array\"!==Fr(r))return[new Ct(n,r,\"array expected, \"+Fr(r)+\" found\")];var i,a=e.styleSpec,o=[];if(r.length<1)return[new Ct(n,r,\"filter array must have at least 1 element\")];switch(o=o.concat(nn({key:n+\"[0]\",value:r[0],valueSpec:a.filter_operator,style:e.style,styleSpec:e.styleSpec})),Ot(r[0])){case\"<\":case\"<=\":case\">\":case\">=\":r.length>=2&&\"$type\"===Ot(r[1])&&o.push(new Ct(n,r,'\"$type\" cannot be use with operator \"'+r[0]+'\"'));case\"==\":case\"!=\":3!==r.length&&o.push(new Ct(n,r,'filter array for operator \"'+r[0]+'\" must have 3 elements'));case\"in\":case\"!in\":r.length>=2&&\"string\"!==(i=Fr(r[1]))&&o.push(new Ct(n+\"[1]\",r[1],\"string expected, \"+i+\" 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r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+\"_\"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if(\"paint\"===e&&l&&s[l[1]]&&s[l[1]].transition)return kn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new Ct(r,a,'unknown property \"'+o+'\"')];if(\"string\"===Fr(a)&&zr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new Ct(r,a,'\"'+o+'\" does not support interpolation syntax\\nUse an identity property function instead: `{ \"type\": \"identity\", \"property\": '+JSON.stringify(c[1])+\" }`.\")];var f=[];return\"symbol\"===t.layerType&&(\"text-field\"===o&&n&&!n.glyphs&&f.push(new Ct(r,a,'use of \"text-field\" requires a style \"glyphs\" property')),\"text-font\"===o&&Br(zt(a))&&\"identity\"===Ot(a.type)&&f.push(new Ct(r,a,'\"text-font\" does not support identity 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null!=hi.applyArabicShaping&&null!=hi.processBidirectionalText&&null!=hi.processStyledBidirectionalText},getPluginURL:function(){return oi}},pi=function(t,e){this.zoom=t,e?(this.now=e.now,this.fadeDuration=e.fadeDuration,this.zoomHistory=e.zoomHistory,this.transition=e.transition):(this.now=0,this.fadeDuration=0,this.zoomHistory=new Gn,this.transition={})};pi.prototype.isSupportedScript=function(t){return function(t,e){for(var r=0,n=t;r<n.length;r+=1){if(!Qn(n[r].charCodeAt(0),e))return!1}return!0}(t,hi.isLoaded())},pi.prototype.crossFadingFactor=function(){return 0===this.fadeDuration?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)},pi.prototype.getCrossfadeParameters=function(){var t=this.zoom,e=t-Math.floor(t),r=this.crossFadingFactor();return t>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:e+(1-e)*r}:{fromScale:.5,toScale:1,t:1-(1-r)*e}};var di=function(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(Br(t))return new Kr(t,e);if(Yr(t)){var r=Jr(t,e);if(\"error\"===r.result)throw new Error(r.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return r.value}var n=t;return\"string\"==typeof t&&\"color\"===e.type&&(n=te.parse(t)),{kind:\"constant\",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};di.prototype.isDataDriven=function(){return\"source\"===this.expression.kind||\"composite\"===this.expression.kind},di.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var mi=function(t){this.property=t,this.value=new di(t,void 0)};mi.prototype.transitioned=function(t,e){return new vi(this.property,this.value,e,u({},t.transition,this.transition),t.now)},mi.prototype.untransitioned=function(){return new vi(this.property,this.value,null,{},0)};var gi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};gi.prototype.getValue=function(t){return x(this._values[t].value.value)},gi.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new mi(this._values[t].property)),this._values[t].value=new di(this._values[t].property,null===e?void 0:x(e))},gi.prototype.getTransition=function(t){return x(this._values[t].transition)},gi.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new mi(this._values[t].property)),this._values[t].transition=x(e)||void 0},gi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i);var a=this.getTransition(n);void 0!==a&&(t[n+\"-transition\"]=a)}return t},gi.prototype.transitioned=function(t,e){for(var r=new yi(this._properties),n=0,i=Object.keys(this._values);n<i.length;n+=1){var a=i[n];r._values[a]=this._values[a].transitioned(t,e._values[a])}return r},gi.prototype.untransitioned=function(){for(var t=new yi(this._properties),e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e];t._values[n]=this._values[n].untransitioned()}return t};var vi=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};vi.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var yi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};yi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},yi.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var xi=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};xi.prototype.getValue=function(t){return x(this._values[t].value)},xi.prototype.setValue=function(t,e){this._values[t]=new di(this._values[t].property,null===e?void 0:x(e))},xi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},xi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var bi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};bi.prototype.isConstant=function(){return\"constant\"===this.value.kind},bi.prototype.constantOr=function(t){return\"constant\"===this.value.kind?this.value.value:t},bi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var _i=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};_i.prototype.get=function(t){return this._values[t]};var wi=function(t){this.specification=t};wi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},wi.prototype.interpolate=function(t,e,r){var n=Ge[this.specification.type];return n?n(t,e,r):t};var Ti=function(t,e){this.specification=t,this.overrides=e};Ti.prototype.possiblyEvaluate=function(t,e,r,n){return\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?new bi(this,{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)},e):new bi(this,t.expression,e)},Ti.prototype.interpolate=function(t,e,r){if(\"constant\"!==t.value.kind||\"constant\"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new bi(this,{kind:\"constant\",value:void 0},t.parameters);var n=Ge[this.specification.type];return n?new bi(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.parameters):t},Ti.prototype.evaluate=function(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new bi(this,{kind:\"constant\",value:void 0},e);if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a=\"resolvedImage\"===t.property.specification.type&&\"string\"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new bi(this,{kind:\"constant\",value:o},e)}if(\"camera\"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new bi(this,{kind:\"constant\",value:s},e)}return new bi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if(\"source\"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return\"composite\"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Ti),Ai=function(t){this.specification=t};Ai.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new pi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new pi(Math.floor(e.zoom),e)),t.expression.evaluate(new pi(Math.floor(e.zoom+1),e)),e)}},Ai.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Ai.prototype.interpolate=function(t){return t};var Mi=function(t){this.specification=t};Mi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},Mi.prototype.interpolate=function(){return!1};var Si=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new di(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new mi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Nn(\"DataDrivenProperty\",Ti),Nn(\"DataConstantProperty\",wi),Nn(\"CrossFadedDataDrivenProperty\",ki),Nn(\"CrossFadedProperty\",Ai),Nn(\"ColorRampProperty\",Mi);var Ei=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},\"custom\"!==e.type&&(e=e,this.metadata=e.metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,\"background\"!==e.type&&(this.source=e.source,this.sourceLayer=e[\"source-layer\"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new xi(r.layout)),r.paint)){for(var n in this._transitionablePaint=new gi(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new _i(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".layout.\"+t;if(this._validate(In,n,t,e,r))return}\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return g(t,\"-transition\")?this._transitionablePaint.getTransition(t.slice(0,-\"-transition\".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".paint.\"+t;if(this._validate(Pn,n,t,e,r))return!1}if(g(t,\"-transition\"))return this._transitionablePaint.setTransition(t.slice(0,-\"-transition\".length),e||void 0),!1;var i=this._transitionablePaint._values[t],a=\"cross-faded-data-driven\"===i.property.specification[\"property-type\"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||(!!(this.maxzoom&&t>=this.maxzoom)||\"none\"===this.visibility)},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&On(this,t.call(Ln,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Lt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof bi&&zr(e.property.specification)&&((\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent))return!0}return!1},e}(Et),Li={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Ci=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Pi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ii(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Li[i].BYTES_PER_ELEMENT),o=r=Oi(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:Oi(r,Math.max(n,e)),alignment:e}}function Oi(t,e){return Math.ceil(t/e)*e}Pi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Pi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Pi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Pi.prototype.clear=function(){this.length=0},Pi.prototype.resize=function(t){this.reserve(t),this.length=t},Pi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Pi.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Pi);zi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout2i4\",zi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(Pi);Di.prototype.bytesPerElement=8,Nn(\"StructArrayLayout4i8\",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Ri.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2i4i12\",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Pi);Fi.prototype.bytesPerElement=8,Nn(\"StructArrayLayout2i4ub8\",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(Pi);Bi.prototype.bytesPerElement=18,Nn(\"StructArrayLayout8ui2ub18\",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Pi);Ni.prototype.bytesPerElement=24,Nn(\"StructArrayLayout4i4ui4i24\",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Pi);ji.prototype.bytesPerElement=12,Nn(\"StructArrayLayout3f12\",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Pi);Ui.prototype.bytesPerElement=4,Nn(\"StructArrayLayout1ul4\",Ui);var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(Pi);Vi.prototype.bytesPerElement=20,Nn(\"StructArrayLayout6i1ul2ui20\",Vi);var Hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Hi.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2i2i2i12\",Hi);var qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Pi);qi.prototype.bytesPerElement=16,Nn(\"StructArrayLayout2f1f2i16\",qi);var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Pi);Gi.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2ub2f12\",Gi);var Yi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(Pi);Yi.prototype.bytesPerElement=6,Nn(\"StructArrayLayout3ui6\",Yi);var Wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=m,this.uint32[x+10]=g,this.int16[y+22]=v,t},e}(Pi);Wi.prototype.bytesPerElement=48,Nn(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",Wi);var Xi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=c,this.uint16[L+9]=u,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=m,this.uint16[L+15]=g,this.uint16[L+16]=v,this.uint16[L+17]=y,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=A,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Pi);Xi.prototype.bytesPerElement=68,Nn(\"StructArrayLayout8i15ui1ul4f68\",Xi);var Zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.float32[r+0]=e,t},e}(Pi);Zi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout1f4\",Zi);var Ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(Pi);Ji.prototype.bytesPerElement=6,Nn(\"StructArrayLayout3i6\",Ji);var Ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(Pi);Ki.prototype.bytesPerElement=8,Nn(\"StructArrayLayout1ul2ui8\",Ki);var Qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(Pi);Qi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout2ui4\",Qi);var $i=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(Pi);$i.prototype.bytesPerElement=2,Nn(\"StructArrayLayout1ui2\",$i);var ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Pi);ta.prototype.bytesPerElement=8,Nn(\"StructArrayLayout2f8\",ta);var ea=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Pi);ea.prototype.bytesPerElement=16,Nn(\"StructArrayLayout4f16\",ea);var ra=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new i(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Ci);ra.prototype.size=20;var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ra(this,t)},e}(Vi);Nn(\"CollisionBoxArray\",na);var ia=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},placedOrientation:{configurable:!0},hidden:{configurable:!0},crossTileID:{configurable:!0},associatedIconIndex:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},r.placedOrientation.set=function(t){this._structArray.uint8[this._pos1+37]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Ci);ia.prototype.size=48;var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ia(this,t)},e}(Wi);Nn(\"PlacedSymbolArray\",aa);var oa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return 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c=t.readVarint();n=7&c,a=c>>3}if(a--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new i(o,s));else{if(7!==n)throw new Error(\"unknown command \"+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},ws.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos<e;){if(n<=0){var u=t.readVarint();r=7&u,n=u>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>c&&(c=a);else if(7!==r)throw new Error(\"unknown command \"+r)}return[o,l,s,c]},ws.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=ws.types[this.type];function u(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];u(l=f);break;case 2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=ks(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}r&&i.push(r);return i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c=\"Multi\"+c;var h={type:\"Feature\",geometry:{type:c,coordinates:l},properties:this.properties};return\"id\"in this&&(h.id=this.id),h};var As=Ms;function Ms(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(Ss,this,e),this.length=this._features.length}function Ss(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){var e=null,r=t.readVarint()+t.pos;for(;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Es(t,e,r){if(3===t){var n=new As(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ms.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new _s(this._pbf,e,this.extent,this._keys,this._values)};var Ls={VectorTile:function(t,e){this.layers=t.readFields(Es,{},e)},VectorTileFeature:_s,VectorTileLayer:As},Cs=Ls.VectorTileFeature.types,Ps=Math.pow(2,13);function Is(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Ps)+o,i*Ps*2,a*Ps*2,Math.round(s))}var Os=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Ri,this.indexArray=new Yi,this.programConfigurations=new Ua(bs,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function zs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}function Ds(t){return t.every((function(t){return t.x<0}))||t.every((function(t){return t.x>8192}))||t.every((function(t){return t.y<0}))||t.every((function(t){return t.y>8192}))}Os.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ds(\"fill-extrusion\",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Ya(o):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Ya(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(ms(\"fill-extrusion\",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},Os.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},Os.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Os.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Os.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Os.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,bs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Os.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Os.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!Ds(d))for(var m=0,g=0;g<d.length;g++){var v=d[g];if(g>=1){var y=d[g-1];if(!zs(v,y)){f.vertexLength+4>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);m+b>32768&&(m=0),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,m),m+=b,Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,m);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),\"Polygon\"===Cs[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Is(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=Do(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(k+C[P],k+C[P+2],k+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Nn(\"FillExtrusionBucket\",Os,{omit:[\"layers\",\"features\"]});var Rs={paint:new Si({\"fill-extrusion-opacity\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new ki(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})},Fs=function(t){function e(e){t.call(this,e,Rs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new Os(t)},e.prototype.queryRadius=function(){return so(this.paint.get(\"fill-extrusion-translate\"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s,l){var c=lo(t,this.paint.get(\"fill-extrusion-translate\"),this.paint.get(\"fill-extrusion-translate-anchor\"),o.angle,s),u=this.paint.get(\"fill-extrusion-height\").evaluate(e,r),f=this.paint.get(\"fill-extrusion-base\").evaluate(e,r),h=function(t,e,r,n){for(var a=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,n,1];xo(c,c,e),a.push(new i(c[0]/c[3],c[1]/c[3]))}return a}(c,l,0,0),p=function(t,e,r,n){for(var a=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,m=0,g=t;m<g.length;m+=1){for(var v=g[m],y=[],x=[],b=0,_=v;b<_.length;b+=1){var w=_[b],T=w.x,k=w.y,A=n[0]*T+n[4]*k+n[12],M=n[1]*T+n[5]*k+n[13],S=n[2]*T+n[6]*k+n[14],E=n[3]*T+n[7]*k+n[15],L=S+c,C=E+u,P=A+f,I=M+h,O=S+p,z=E+d,D=new i((A+s)/C,(M+l)/C);D.z=L/C,y.push(D);var R=new i(P/z,I/z);R.z=O/z,x.push(R)}a.push(y),o.push(x)}return[a,o]}(n,f,u,l);return function(t,e,r){var n=1/0;Ka(r,e)&&(n=Ns(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],c=a[s+1],u=o[s],f=o[s+1],h=[l,c,f,u,l];Za(r,h)&&(n=Math.min(n,Ns(r,h)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ei);function Bs(t,e){return t.x*e.x+t.y*e.y}function Ns(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Bs(s,s),f=Bs(s,l),h=Bs(l,l),p=Bs(c,s),d=Bs(c,l),m=u*h-f*f,g=(h*p-f*d)/m,v=(u*d-f*p)/m,y=1-g-v,x=i.z*y+r.z*g+a.z*v;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var js=Ii([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4).members,Us=Ls.VectorTileFeature.types,Vs=Math.cos(Math.PI/180*37.5),Hs=Math.pow(2,14)/.5,qs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Fi,this.indexArray=new Yi,this.programConfigurations=new Ua(js,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};qs.prototype.populate=function(t,e,r){this.hasPattern=ds(\"line\",this.layers,e);for(var n=this.layers[0].layout.get(\"line-sort-key\"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,c=s.id,u=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:c,properties:l.properties,geometry:h?Ya(l):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),p,r)){h||(p.geometry=Ya(l));var d=n?n.evaluate(p,{},r):void 0,m={id:c,properties:l.properties,type:l.type,sourceLayerIndex:f,index:u,geometry:p.geometry,patterns:{},sortKey:d};i.push(m)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,v=i;g<v.length;g+=1){var y=v[g],x=y,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=ms(\"line\",this.layers,y,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(y,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},qs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},qs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},qs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},qs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},qs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,js),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},qs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},qs.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get(\"line-join\").evaluate(t,{}),s=a.get(\"line-cap\"),l=a.get(\"line-miter-limit\"),c=a.get(\"line-round-limit\"),u=0,f=e;u<f.length;u+=1){var h=f[u];this.addLine(h,t,o,s,l,c)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},qs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty(\"mapbox_clip_start\")&&e.properties.hasOwnProperty(\"mapbox_clip_end\")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s=\"Polygon\"===Us[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){\"bevel\"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,m=void 0,g=void 0;this.e1=this.e2=-1,s&&(u=t[l-2],g=t[c].sub(u)._unit()._perp());for(var v=c;v<l;v++)if(!(d=v===l-1?s?t[c+1]:void 0:t[v+1])||!t[v].equals(d)){g&&(m=g),u&&(p=u),u=t[v],g=d?d.sub(u)._unit()._perp():m;var y=(m=m||g).add(g);0===y.x&&0===y.y||y._unit();var x=m.x*g.x+m.y*g.y,b=y.x*g.x+y.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<Vs&&p&&d,k=m.x*g.y-m.y*g.x>0;if(T&&v>c){var A=u.dist(p);if(A>2*f){var M=u.sub(u.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,m,0,0,h),p=M}}var S=p&&d,E=S?r:s?\"butt\":n;if(S&&\"round\"===E&&(_<a?E=\"miter\":_<=2&&(E=\"fakeround\")),\"miter\"===E&&_>i&&(E=\"bevel\"),\"bevel\"===E&&(_>2&&(E=\"flipbevel\"),_<i&&(E=\"miter\")),p&&this.updateDistance(p,u),\"miter\"===E)y._mult(_),this.addCurrentVertex(u,y,0,0,h);else if(\"flipbevel\"===E){if(_>100)y=g.mult(-1);else{var L=_*m.add(g).mag()/m.sub(g).mag();y._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(u,y,0,0,h),this.addCurrentVertex(u,y.mult(-1),0,0,h)}else if(\"bevel\"===E||\"fakeround\"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,I=k?0:C;if(p&&this.addCurrentVertex(u,m,P,I,h),\"fakeround\"===E)for(var O=Math.round(180*w/Math.PI/20),z=1;z<O;z++){var D=z/O;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(m)._mult(D)._add(m)._unit()._mult(k?-1:1);this.addHalfVertex(u,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(u,g,-P,-I,h)}else if(\"butt\"===E)this.addCurrentVertex(u,y,0,0,h);else if(\"square\"===E){var B=p?1:-1;this.addCurrentVertex(u,y,B,B,h)}else\"round\"===E&&(p&&(this.addCurrentVertex(u,m,0,0,h),this.addCurrentVertex(u,m,1,1,h,!0)),d&&(this.addCurrentVertex(u,g,-1,-1,h,!0),this.addCurrentVertex(u,g,0,0,h)));if(T&&v<l-1){var N=u.dist(d);if(N>2*f){var j=u.add(d.sub(u)._mult(f/N)._round());this.updateDistance(u,j),this.addCurrentVertex(j,g,0,0,h),u=j}}}}},qs.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>Hs/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},qs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,c=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&c)<<2,c>>6);var u=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,u),o.primitiveLength++),i?this.e2=u:this.e1=u},qs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Hs-1):this.distance},qs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Nn(\"LineBucket\",qs,{omit:[\"layers\",\"patternFeatures\"]});var Gs=new Si({\"line-cap\":new 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e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new pi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=u({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Ti))(Ys.paint.properties[\"line-width\"].specification);Ws.useIntegerZoom=!0;var Xs=function(t){function e(e){t.call(this,e,Ys)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){\"line-gradient\"===t&&this._updateGradient()},e.prototype._updateGradient=function(){var 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o=t[a],s=[],l=0;l<o.length;l++){var c=o[l-1],u=o[l],f=o[l+1],h=0===l?n:u.sub(c)._unit()._perp(),p=l===o.length-1?n:f.sub(u)._unit()._perp(),d=h._add(p)._unit(),m=d.x*p.x+d.y*p.y;d._mult(1/m),s.push(d._mult(e)._add(u))}r.push(s)}return r}(n,u*s)),function(t,e,r){for(var n=0;n<e.length;n++){var i=e[n];if(t.length>=3)for(var a=0;a<i.length;a++)if(io(t,i[a]))return!0;if(Qa(t,i,r))return!0}return!1}(l,n,c)},e.prototype.isTileClipped=function(){return!0},e}(Ei);function Zs(t,e){return e>0?e+2*t:t}var 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nl=function(t,e,r,n,i){var a,o,s=8*i-n-1,l=(1<<s)-1,c=l>>1,u=-7,f=r?i-1:0,h=r?-1:1,p=t[e+f];for(f+=h,a=p&(1<<-u)-1,p>>=-u,u+=s;u>0;a=256*a+t[e+f],f+=h,u-=8);for(o=a&(1<<-u)-1,a>>=-u,u+=n;u>0;o=256*o+t[e+f],f+=h,u-=8);if(0===a)a=1-c;else{if(a===l)return o?NaN:1/0*(p?-1:1);o+=Math.pow(2,n),a-=c}return(p?-1:1)*o*Math.pow(2,a-n)},il=function(t,e,r,n,i,a){var o,s,l,c=8*a-i-1,u=(1<<c)-1,f=u>>1,h=23===i?Math.pow(2,-24)-Math.pow(2,-77):0,p=n?0:a-1,d=n?1:-1,m=e<0||0===e&&1/e<0?1:0;for(e=Math.abs(e),isNaN(e)||e===1/0?(s=isNaN(e)?1:0,o=u):(o=Math.floor(Math.log(e)/Math.LN2),e*(l=Math.pow(2,-o))<1&&(o--,l*=2),(e+=o+f>=1?h/l:h*Math.pow(2,1-f))*l>=2&&(o++,l/=2),o+f>=u?(s=0,o=u):o+f>=1?(s=(e*l-1)*Math.pow(2,i),o+=f):(s=e*Math.pow(2,f-1)*Math.pow(2,i),o=0));i>=8;t[r+p]=255&s,p+=d,s/=256,i-=8);for(o=o<<i|s,c+=i;c>0;t[r+p]=255&o,p+=d,o/=256,c-=8);t[r+p-d]|=128*m},al=ol;function ol(t){this.buf=ArrayBuffer.isView&&ArrayBuffer.isView(t)?t:new 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e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed32());return t},readPackedFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readFixed64());return t},readPackedSFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readSFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed64());return t},skip:function(t){var e=7&t;if(e===ol.Varint)for(;this.buf[this.pos++]>127;);else if(e===ol.Bytes)this.pos=this.readVarint()+this.pos;else if(e===ol.Fixed32)this.pos+=4;else{if(e!==ol.Fixed64)throw new Error(\"Unimplemented type: \"+e);this.pos+=8}},writeTag:function(t,e){this.writeVarint(t<<3|e)},realloc:function(t){for(var e=this.length||16;e<this.pos+t;)e*=2;if(e!==this.length){var r=new Uint8Array(e);r.set(this.buf),this.buf=r,this.length=e}},finish:function(){return 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r=(7&t)<<4;if(e.buf[e.pos++]|=r|((t>>>=3)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;e.buf[e.pos++]=127&t}(n,e)}(t,this):(this.realloc(4),this.buf[this.pos++]=127&t|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=t>>>7&127))))},writeSVarint:function(t){this.writeVarint(t<0?2*-t-1:2*t)},writeBoolean:function(t){this.writeVarint(Boolean(t))},writeString:function(t){t=String(t),this.realloc(4*t.length),this.pos++;var e=this.pos;this.pos=function(t,e,r){for(var n,i,a=0;a<e.length;a++){if((n=e.charCodeAt(a))>55295&&n<57344){if(!i){n>56319||a+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):i=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,i=n;continue}n=i-55296<<10|n-56320|65536,i=null}else i&&(t[r++]=239,t[r++]=191,t[r++]=189,i=null);n<128?t[r++]=n:(n<2048?t[r++]=n>>6|192:(n<65536?t[r++]=n>>12|224:(t[r++]=n>>18|240,t[r++]=n>>12&63|128),t[r++]=n>>6&63|128),t[r++]=63&n|128)}return r}(this.buf,t,this.pos);var r=this.pos-e;r>=128&&ul(e,r,this),this.pos=e-1,this.writeVarint(r),this.pos+=r},writeFloat:function(t){this.realloc(4),il(this.buf,t,this.pos,!0,23,4),this.pos+=4},writeDouble:function(t){this.realloc(8),il(this.buf,t,this.pos,!0,52,8),this.pos+=8},writeBytes:function(t){var e=t.length;this.writeVarint(e),this.realloc(e);for(var r=0;r<e;r++)this.buf[this.pos++]=t[r]},writeRawMessage:function(t,e){this.pos++;var r=this.pos;t(e,this);var n=this.pos-r;n>=128&&ul(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,ol.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,fl,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,hl,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,ml,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,pl,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,dl,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,gl,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,vl,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,yl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,xl,e)},writeBytesField:function(t,e){this.writeTag(t,ol.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,ol.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,ol.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};function Tl(t,e,r){1===t&&r.readMessage(kl,e)}function kl(t,e,r){if(3===t){var n=r.readMessage(Al,{}),i=n.id,a=n.bitmap,o=n.width,s=n.height,l=n.left,c=n.top,u=n.advance;e.push({id:i,bitmap:new So({width:o+6,height:s+6},a),metrics:{width:o,height:s,left:l,top:c,advance:u}})}}function Al(t,e,r){1===t?e.id=r.readVarint():2===t?e.bitmap=r.readBytes():3===t?e.width=r.readVarint():4===t?e.height=r.readVarint():5===t?e.left=r.readSVarint():6===t?e.top=r.readSVarint():7===t&&(e.advance=r.readVarint())}function Ml(t){for(var e=0,r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];e+=a.w*a.h,r=Math.max(r,a.w)}t.sort((function(t,e){return e.h-t.h}));for(var o=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),r),h:1/0}],s=0,l=0,c=0,u=t;c<u.length;c+=1)for(var f=u[c],h=o.length-1;h>=0;h--){var p=o[h];if(!(f.w>p.w||f.h>p.h)){if(f.x=p.x,f.y=p.y,l=Math.max(l,f.y+f.h),s=Math.max(s,f.x+f.w),f.w===p.w&&f.h===p.h){var d=o.pop();h<o.length&&(o[h]=d)}else 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Eo({width:o||1,height:s||1});for(var c in t){var u=t[c],f=r[c].paddedRect;Eo.copy(u.data,l,{x:0,y:0},{x:f.x+1,y:f.y+1},u.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,m=d.x+1,g=d.y+1,v=p.data.width,y=p.data.height;Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g},p.data),Eo.copy(p.data,l,{x:0,y:y-1},{x:m,y:g-1},{width:v,height:1}),Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g+y},{width:v,height:1}),Eo.copy(p.data,l,{x:v-1,y:0},{x:m-1,y:g},{width:1,height:y}),Eo.copy(p.data,l,{x:0,y:0},{x:m+v,y:g},{width:1,height:y})}this.image=l,this.iconPositions=r,this.patternPositions=n};Ll.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new Sl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},Ll.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},Ll.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl,i=n[0],a=n[1];r.update(e.data,void 0,{x:i,y:a})}},Nn(\"ImagePosition\",Sl),Nn(\"ImageAtlas\",Ll);var Cl={horizontal:1,vertical:2,horizontalOnly:3};var Pl=function(){this.scale=1,this.fontStack=\"\",this.imageName=null};Pl.forText=function(t,e){var r=new Pl;return r.scale=t||1,r.fontStack=e,r},Pl.forImage=function(t){var e=new Pl;return e.imageName=t,e};var Il=function(){this.text=\"\",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function Ol(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=Il.fromFeature(t,i);f===Cl.vertical&&v.verticalizePunctuation();var y=hi.processBidirectionalText,x=hi.processStyledBidirectionalText;if(y&&1===v.sections.length){g=[];for(var b=0,_=y(v.toString(),jl(v,c,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new Il;T.text=w,T.sections=v.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);g.push(T)}}else if(x){g=[];for(var A=0,M=x(v.text,v.sectionIndex,jl(v,c,a,e,n,p,d));A<M.length;A+=1){var S=M[A],E=new Il;E.text=S[0],E.sectionIndex=S[1],E.sections=v.sections,g.push(E)}}else g=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(v,jl(v,c,a,e,n,p,d));var L=[],C={positionedLines:L,text:v.toString(),top:u[1],bottom:u[1],left:u[0],right:u[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=0,p=-17,d=0,m=0,g=\"right\"===s?1:\"left\"===s?0:.5,v=0,y=0,x=i;y<x.length;y+=1){var b=x[y];b.trim();var _=b.getMaxScale(),w=24*(_-1),T={positionedGlyphs:[],lineOffset:0};t.positionedLines[v]=T;var k=T.positionedGlyphs,A=0;if(b.length()){for(var M=0;M<b.length();M++){var S=b.getSection(M),E=b.getSectionIndex(M),L=b.getCharCode(M),C=0,P=null,I=null,O=null,z=24,D=!(l===Cl.horizontal||!u&&!Zn(L)||u&&(zl[L]||(Y=L,Yn.Arabic(Y)||Yn[\"Arabic Supplement\"](Y)||Yn[\"Arabic Extended-A\"](Y)||Yn[\"Arabic Presentation Forms-A\"](Y)||Yn[\"Arabic Presentation Forms-B\"](Y))));if(S.imageName){var R=n[S.imageName];if(!R)continue;O=S.imageName,t.iconsInText=t.iconsInText||!0,I=R.paddedRect;var F=R.displaySize;S.scale=24*S.scale/f,P={width:F[0],height:F[1],left:1,top:-3,advance:D?F[1]:F[0]};var B=24-F[1]*S.scale;C=w+B,z=P.advance;var N=D?F[0]*S.scale-24*_:F[1]*S.scale-24*_;N>0&&N>A&&(A=N)}else{var j=r[S.fontStack],U=j&&j[L];if(U&&U.rect)I=U.rect,P=U.metrics;else{var V=e[S.fontStack],H=V&&V[L];if(!H)continue;P=H.metrics}C=24*(_-S.scale)}D?(t.verticalizable=!0,k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=z*S.scale+c):(k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=P.advance*S.scale+c)}if(0!==k.length){var q=h-c;d=Math.max(q,d),Vl(k,0,k.length-1,g,A)}h=0;var G=a*_+A;T.lineOffset=Math.max(A,w),p+=G,m=Math.max(G,m),++v}else p+=a,++v}var Y;var W=p- -17,X=Ul(o),Z=X.horizontalAlign,J=X.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var c=(e-r)*i,u=0;u=a!==o?-s*n- -17:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];g.x+=c,g.y+=u}})(t.positionedLines,g,Z,J,d,m,a,W,i.length),t.top+=-J*W,t.bottom=t.top+W,t.left+=-Z*d,t.right=t.left+d}(C,e,r,n,g,o,s,l,f,c,h,m),!function(t){for(var e=0,r=t;e<r.length;e+=1){if(0!==r[e].positionedGlyphs.length)return!1}return!0}(L)&&C}Il.fromFeature=function(t,e){for(var r=new Il,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},Il.prototype.length=function(){return this.text.length},Il.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},Il.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},Il.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},Il.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e=\"\",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;(!n||!Jn(n)||rl[t[r+1]])&&(!i||!Jn(i)||rl[t[r-1]])&&rl[t[r]]?e+=rl[t[r]]:e+=t[r]}return e}(this.text)},Il.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&zl[this.text.charCodeAt(e)];e++)t++;for(var 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e=this.data,r=this.compare,n=this.length>>1,i=e[t];t<n;){var a=1+(t<<1),o=e[a],s=a+1;if(s<this.length&&r(e[s],o)<0&&(a=s,o=e[s]),r(o,i)>=0)break;e[t]=o,t=a}e[t]=i};var pc=Number.POSITIVE_INFINITY;function dc(t,e){return e[1]!==pc?function(t,e,r){var n=0,i=0;switch(e=Math.abs(e),r=Math.abs(r),t){case\"top-right\":case\"top-left\":case\"top\":i=r-7;break;case\"bottom-right\":case\"bottom-left\":case\"bottom\":i=7-r}switch(t){case\"top-right\":case\"bottom-right\":case\"right\":n=-e;break;case\"top-left\":case\"bottom-left\":case\"left\":n=e}return[n,i]}(t,e[0],e[1]):function(t,e){var r=0,n=0;e<0&&(e=0);var i=e/Math.sqrt(2);switch(t){case\"top-right\":case\"top-left\":n=i-7;break;case\"bottom-right\":case\"bottom-left\":n=7-i;break;case\"bottom\":n=7-e;break;case\"top\":n=e-7}switch(t){case\"top-right\":case\"bottom-right\":r=-i;break;case\"top-left\":case\"bottom-left\":r=i;break;case\"left\":r=e;break;case\"right\":r=-e}return[r,n]}(t,e[0])}function mc(t){switch(t){case\"right\":case\"top-right\":case\"bottom-right\":return\"right\";case\"left\":case\"top-left\":case\"bottom-left\":return\"left\"}return\"center\"}function gc(t,e,r,n,a,o,s,l,c,u,f,h,p,d,m){var g=function(t,e,r,n,a,o,s,l){for(var c=n.layout.get(\"text-rotate\").evaluate(o,{})*Math.PI/180,u=[],f=0,h=e.positionedLines;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];if(g.rect){var v=g.rect||{},y=4,x=!0,b=1,_=0,w=(a||l)&&g.vertical,T=g.metrics.advance*g.scale/2;if(l&&e.verticalizable){var k=24*(g.scale-1),A=(24-g.metrics.width*g.scale)/2;_=p.lineOffset/2-(g.imageName?-A:k)}if(g.imageName){var M=s[g.imageName];x=M.sdf,y=1/(b=M.pixelRatio)}var S=a?[g.x+T,g.y]:[0,0],E=a?[0,0]:[g.x+T+r[0],g.y+r[1]-_],L=[0,0];w&&(L=E,E=[0,0]);var C=(g.metrics.left-y)*g.scale-T+E[0],P=(-g.metrics.top-y)*g.scale+E[1],I=C+v.w*g.scale/b,O=P+v.h*g.scale/b,z=new i(C,P),D=new i(I,P),R=new i(C,O),F=new i(I,O);if(w){var B=new i(-T,T- -17),N=-Math.PI/2,j=12-T,U=g.imageName?j:0,V=new i(22-j,-U),H=new(Function.prototype.bind.apply(i,[null].concat(L)));z._rotateAround(N,B)._add(V)._add(H),D._rotateAround(N,B)._add(V)._add(H),R._rotateAround(N,B)._add(V)._add(H),F._rotateAround(N,B)._add(V)._add(H)}if(c){var q=Math.sin(c),G=Math.cos(c),Y=[G,-q,q,G];z._matMult(Y),D._matMult(Y),R._matMult(Y),F._matMult(Y)}var W=new i(0,0),X=new i(0,0);u.push({tl:z,tr:D,bl:R,br:F,tex:v,writingMode:e.writingMode,glyphOffset:S,sectionIndex:g.sectionIndex,isSDF:x,pixelOffsetTL:W,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;\"source\"===v.kind?(y=[128*a.layout.get(\"text-size\").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. Reduce your \"text-size\".'):\"composite\"===v.kind&&((y=[128*d.compositeTextSizes[0].evaluate(s,{},m),128*d.compositeTextSizes[1].evaluate(s,{},m)])[0]>32640||y[1]>32640)&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. Reduce your \"text-size\".'),t.addSymbols(t.text,g,y,l,o,s,u,e,c.lineStartIndex,c.lineLength,p,m);for(var x=0,b=f;x<b.length;x+=1){h[b[x]]=t.text.placedSymbolArray.length-1}return 4*g.length}function vc(t){for(var e in t)return t[e];return null}function yc(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var xc=Ls.VectorTileFeature.types,bc=[{name:\"a_fade_opacity\",components:1,type:\"Uint8\",offset:0}];function _c(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=s?Math.min(32640,Math.round(s[0])):0,d=s?Math.min(32640,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*c,16*u,256*f,256*h)}function wc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function Tc(t){for(var e=0,r=t.sections;e<r.length;e+=1){if($n(r[e].text))return!0}return!1}var kc=function(t){this.layoutVertexArray=new Ni,this.indexArray=new Yi,this.programConfigurations=t,this.segments=new pa,this.dynamicLayoutVertexArray=new ji,this.opacityVertexArray=new Ui,this.placedSymbolArray=new aa};kc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},kc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Js.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Ks.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,bc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},kc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Nn(\"SymbolBuffers\",kc);var Ac=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new pa,this.collisionVertexArray=new Gi};Ac.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Qs.members,!0)},Ac.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Nn(\"CollisionBuffers\",Ac);var Mc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ho([]),this.placementViewportMatrix=ho([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Gl(this.zoom,e[\"text-size\"]),this.iconSizeData=Gl(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout,n=r.get(\"symbol-sort-key\"),i=r.get(\"symbol-z-order\");this.sortFeaturesByKey=\"viewport-y\"!==i&&void 0!==n.constantOr(1);var a=\"viewport-y\"===i||\"auto\"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get(\"text-allow-overlap\")||r.get(\"icon-allow-overlap\")||r.get(\"text-ignore-placement\")||r.get(\"icon-ignore-placement\")),\"point\"===r.get(\"symbol-placement\")&&(this.writingModes=r.get(\"text-writing-mode\").map((function(t){return Cl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};Mc.prototype.createArrays=function(){this.text=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new la,this.lineVertexArray=new ca,this.symbolInstances=new sa},Mc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=rl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},Mc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get(\"text-font\"),o=i.get(\"text-field\"),s=i.get(\"icon-image\"),l=(\"constant\"!==o.value.kind||o.value.value instanceof ne&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&(\"constant\"!==a.value.kind||a.value.value.length>0),c=\"constant\"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get(\"symbol-sort-key\");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new pi(this.zoom),m=0,g=t;m<g.length;m+=1){var v=g[m],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Ya(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Ya(y));var k=void 0;if(l){var A=n.getValueAndResolveTokens(\"text-field\",T,r,p),M=ne.factory(A);Tc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||\"unavailable\"===ui()||this.hasRTLText&&hi.isParsed())&&(k=el(M,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens(\"icon-image\",T,r,p);S=E instanceof ie?E:ie.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Ya(y),properties:y.properties,type:xc[y.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(\",\"),I=\"map\"===i.get(\"text-rotation-alignment\")&&\"point\"!==i.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(Cl.vertical)>=0;for(var O=0,z=k.sections;O<z.length;O+=1){var D=z[O];if(D.image)f[D.image.name]=!0;else{var R=Wn(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,I,this.allowVerticalPlacement,R)}}}}}}\"line\"===i.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var m=s(p,d,f),g=o(p,d,n[m].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[m].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},Mc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},Mc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},Mc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Mc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Mc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Mc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Mc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Mc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),m=this.glyphOffsetArray.length,g=d.vertexLength,v=this.allowVerticalPlacement&&o===Cl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,I=b.sectionIndex,O=d.vertexLength,z=C[1];_c(p,s.x,s.y,_.x,z+_.y,A.x,A.y,r,P,M.x,M.y,E,L),_c(p,s.x,s.y,w.x,z+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),_c(p,s.x,s.y,T.x,z+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),_c(p,s.x,s.y,k.x,z+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),wc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(O,O+1,O+2),h.emplaceBack(O+1,O+2,O+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&I===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[I])}t.placedSymbolArray.emplaceBack(s.x,s.y,m,this.glyphOffsetArray.length-m,g,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},Mc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},Mc.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},Mc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,c=a.y2;this.addCollisionDebugVertices(o,s,l,c,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},Mc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new Ac(Hi,$s.members,Qi),this.iconCollisionBox=new Ac(Hi,$s.members,Qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},Mc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var m=t.get(d);c.iconBox={x1:m.x1,y1:m.y1,x2:m.x2,y2:m.y2,anchorPointX:m.anchorPointX,anchorPointY:m.anchorPointY},c.iconFeatureIndex=m.featureIndex;break}for(var g=s;g<l;g++){var v=t.get(g);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},Mc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},Mc.prototype.hasTextData=function(){return this.text.segments.get().length>0},Mc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Mc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Mc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Mc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Mc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},Mc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},Mc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},Mc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Nn(\"SymbolBucket\",Mc,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),Mc.MAX_GLYPHS=65535,Mc.addDynamicAttributes=wc;var Sc=new Si({\"symbol-placement\":new wi(Lt.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new wi(Lt.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new wi(Lt.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new Ti(Lt.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new wi(Lt.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new wi(Lt.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new wi(Lt.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new wi(Lt.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new wi(Lt.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new Ti(Lt.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new wi(Lt.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new wi(Lt.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new Ti(Lt.layout_symbol[\"icon-image\"]),\"icon-rotate\":new Ti(Lt.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new wi(Lt.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new wi(Lt.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new Ti(Lt.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new Ti(Lt.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new wi(Lt.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new wi(Lt.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new wi(Lt.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new Ti(Lt.layout_symbol[\"text-field\"]),\"text-font\":new Ti(Lt.layout_symbol[\"text-font\"]),\"text-size\":new Ti(Lt.layout_symbol[\"text-size\"]),\"text-max-width\":new Ti(Lt.layout_symbol[\"text-max-width\"]),\"text-line-height\":new wi(Lt.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new Ti(Lt.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new Ti(Lt.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new Ti(Lt.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new wi(Lt.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new Ti(Lt.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new wi(Lt.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new wi(Lt.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new Ti(Lt.layout_symbol[\"text-rotate\"]),\"text-padding\":new wi(Lt.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new wi(Lt.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new Ti(Lt.layout_symbol[\"text-transform\"]),\"text-offset\":new Ti(Lt.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new wi(Lt.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new wi(Lt.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new wi(Lt.layout_symbol[\"text-optional\"])}),Ec={paint:new Si({\"icon-opacity\":new Ti(Lt.paint_symbol[\"icon-opacity\"]),\"icon-color\":new Ti(Lt.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new Ti(Lt.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new Ti(Lt.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new Ti(Lt.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new wi(Lt.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new wi(Lt.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new Ti(Lt.paint_symbol[\"text-opacity\"]),\"text-color\":new Ti(Lt.paint_symbol[\"text-color\"],{runtimeType:Ut,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new Ti(Lt.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new Ti(Lt.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new Ti(Lt.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new wi(Lt.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new wi(Lt.paint_symbol[\"text-translate-anchor\"])}),layout:Sc},Lc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ft,this.defaultValue=t};Lc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},Lc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},Lc.prototype.outputDefined=function(){return!1},Lc.prototype.serialize=function(){return null},Nn(\"FormatSectionOverride\",Lc,{omit:[\"defaultValue\"]});var Cc=function(t){function e(e){t.call(this,e,Ec)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values[\"text-writing-mode\"]=i}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Yr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):\"\"}))}(e.properties,i)},e.prototype.createBucket=function(t){return new Mc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=Ec.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i=this.paint.get(n),a=new Lc(i),o=new Gr(a,i.property.specification),s=null;s=\"constant\"===i.value.kind||\"source\"===i.value.kind?new Xr(\"source\",o):new Zr(\"composite\",o,i.value.zoomStops,i.value._interpolationType),this.paint._values[n]=new bi(i.property,s,i.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get(\"text-field\"),n=Ec.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if(\"constant\"===r.value.kind&&r.value.value instanceof ne)a(r.value.value.sections);else if(\"source\"===r.value.kind){var o=function(t){if(!i)if(t instanceof ce&&se(t.value)===Gt){var e=t.value;a(e.sections)}else t instanceof pe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ei),Pc={paint:new Si({\"background-color\":new wi(Lt.paint_background[\"background-color\"]),\"background-pattern\":new Ai(Lt.paint_background[\"background-pattern\"]),\"background-opacity\":new wi(Lt.paint_background[\"background-opacity\"])})},Ic=function(t){function e(e){t.call(this,e,Pc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei),Oc={paint:new Si({\"raster-opacity\":new wi(Lt.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new wi(Lt.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new wi(Lt.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new wi(Lt.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new wi(Lt.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new wi(Lt.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new wi(Lt.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new wi(Lt.paint_raster[\"raster-fade-duration\"])})},zc=function(t){function e(e){t.call(this,e,Oc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei);var Dc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return\"3d\"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ei),Rc={circle:_o,heatmap:Po,hillshade:Oo,fill:xs,\"fill-extrusion\":Fs,line:Xs,symbol:Cc,background:Ic,raster:zc};var Fc=self.HTMLImageElement,Bc=self.HTMLCanvasElement,Nc=self.HTMLVideoElement,jc=self.ImageData,Uc=self.ImageBitmap,Vc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Vc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Vc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Vc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Vc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Hc=function(t){var e=this;this._callback=t,this._triggered=!1,\"undefined\"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Hc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Hc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var qc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},m([\"receive\",\"process\"],this),this.invoker=new Hc(this.process),this.target.addEventListener(\"message\",this.receive,!1),this.globalScope=k()?t:self};function Gc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}qc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:Hn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:\"<cancel>\",targetMapId:n,sourceMapId:a.mapId})}}},qc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"<cancel>\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},qc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},qc.prototype.processTask=function(t,e){var r=this;if(\"<response>\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(qn(e.error)):n(null,qn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"<response>\",sourceMapId:r.mapId,error:e?Hn(e):null,data:Hn(n,a)},a)}:function(t){i=!0},s=null,l=qn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},qc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Yc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Yc.prototype.setNorthEast=function(t){return this._ne=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.setSouthWest=function(t){return this._sw=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Wc)e=t,r=t;else{if(!(t instanceof Yc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Yc.convert(a))}var o=t;return this.extend(Wc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Wc(e.lng,e.lat),this._ne=new Wc(r.lng,r.lat)),this},Yc.prototype.getCenter=function(){return new Wc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Yc.prototype.getSouthWest=function(){return this._sw},Yc.prototype.getNorthEast=function(){return this._ne},Yc.prototype.getNorthWest=function(){return new Wc(this.getWest(),this.getNorth())},Yc.prototype.getSouthEast=function(){return new Wc(this.getEast(),this.getSouth())},Yc.prototype.getWest=function(){return this._sw.lng},Yc.prototype.getSouth=function(){return this._sw.lat},Yc.prototype.getEast=function(){return this._ne.lng},Yc.prototype.getNorth=function(){return this._ne.lat},Yc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Yc.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Yc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Yc.prototype.contains=function(t){var e=Wc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Yc.convert=function(t){return!t||t instanceof Yc?t:new Yc(t)};var Wc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};Wc.prototype.wrap=function(){return new Wc(c(this.lng,-180,180),this.lat)},Wc.prototype.toArray=function(){return[this.lng,this.lat]},Wc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Wc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Wc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Yc(new Wc(this.lng-r,this.lat-e),new Wc(this.lng+r,this.lat+e))},Wc.convert=function(t){if(t instanceof Wc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Wc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Wc(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")};var Xc=2*Math.PI*6371008.8;function Zc(t){return Xc*Math.cos(t*Math.PI/180)}function Jc(t){return(180+t)/360}function Kc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Qc(t,e){return t/Zc(e)}function $c(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var tu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};tu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Wc.convert(t);return new tu(Jc(r.lng),Kc(r.lat),Qc(e,r.lat))},tu.prototype.toLngLat=function(){return new Wc(360*this.x-180,$c(this.y))},tu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Zc($c(e));var t,e},tu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Xc*(t=$c(this.y),1/Math.cos(t*Math.PI/180));var t};var eu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=iu(0,t,t,e,r)};eu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},eu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Gc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Gc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",String(this.z)).replace(\"{x}\",String(this.x)).replace(\"{y}\",String(\"tms\"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace(\"{quadkey}\",l).replace(\"{bbox-epsg-3857}\",s)},eu.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},eu.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y};var ru=function(t,e){this.wrap=t,this.canonical=e,this.key=iu(t,e.z,e.z,e.x,e.y)},nu=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new eu(r,+n,+i),this.key=iu(e,t,r,n,i)};function iu(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}nu.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},nu.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new nu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new nu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},nu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?iu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):iu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},nu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},nu.prototype.children=function(t){if(this.overscaledZ>=t)return[new nu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new nu(e,this.wrap,e,r,n),new nu(e,this.wrap,e,r+1,n),new nu(e,this.wrap,e,r,n+1),new nu(e,this.wrap,e,r+1,n+1)]},nu.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},nu.prototype.wrapped=function(){return new nu(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.unwrapTo=function(t){return new nu(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},nu.prototype.toUnwrapped=function(){return new ru(this.wrap,this.canonical)},nu.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},nu.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new tu(t.x-this.wrap,t.y))},Nn(\"CanonicalTileID\",eu),Nn(\"OverscaledTileID\",nu,{omit:[\"posMatrix\"]});var au=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError(\"DEM tiles must be square\");if(r&&\"mapbox\"!==r&&\"terrarium\"!==r)return _('\"'+r+'\" is not a valid encoding type. Valid types include \"mapbox\" and \"terrarium\".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||\"mapbox\";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};au.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return(\"terrarium\"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},au.prototype.getUnpackVector=function(){return\"terrarium\"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},au.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},au.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},au.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},au.prototype.getPixels=function(){return new Eo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},au.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Nn(\"DEMData\",au);var ou=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};ou.prototype.encode=function(t){return this._stringToNumber[t]},ou.prototype.decode=function(t){return this._numberToString[t]};var su=function(t,e,r,n,i){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},lu={geometry:{configurable:!0}};lu.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},lu.geometry.set=function(t){this._geometry=t},su.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)\"_geometry\"!==e&&\"_vectorTileFeature\"!==e&&(t[e]=this[e]);return t},Object.defineProperties(su.prototype,lu);var cu=function(){this.state={},this.stateChanges={},this.deletedStates={}};cu.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r){this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]}},cu.prototype.removeFeatureState=function(t,e,r){if(!(null===this.deletedStates[t])){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e){if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null}else this.deletedStates[t]=null}},cu.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=u({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},cu.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},cu.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1){var p=h[f];delete this.state[o][c][p]}s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t){t[d].setFeatureState(r,e)}};var uu=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new zn(8192,16,0),this.grid3D=new zn(8192,16,0),this.featureIndexArray=new fa,this.promoteId=e};function fu(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof _i?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function hu(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function pu(t,e){return e-t}uu.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},uu.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Ls.VectorTile(new al(this.rawTileData)).layers,this.sourceLayerCoder=new ou(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},uu.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=sn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=hu(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=hu(t.cameraQueryGeometry),d=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1){if(io(t,f[u]))return!0}for(var h=0;h<t.length-1;h++){if(ao(t[h],t[h+1],c))return!0}return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)})),m=0,g=d;m<g.length;m+=1){var v=g[m];h.push(v)}h.sort(pu);for(var y,x={},b=function(i){var u=h[i];if(u!==y){y=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(x,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Ya(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},_=0;_<h.length;_++)b(_);return x},uu.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),m=0;m<f.length;m++){var g=f[m];if(!(a&&a.indexOf(g)<0)){var v=s[g];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||\"_geojsonTileLayer\",d));var x=l[g];x.paint=fu(x.paint,v.paint,p,y,o),x.layout=fu(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new su(p,this.z,this.x,this.y,d);_.layer=x;var w=t[g];void 0===w&&(w=t[g]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},uu.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=sn(i),u=0,f=t;u<f.length;u+=1){var h=f[u];this.loadMatchingFeature(l,r,n,h,c,a,o,s,e)}return l},uu.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1){if(t===i[n])return!0}return!1},uu.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n=\"string\"==typeof this.promoteId?this.promoteId:this.promoteId[e];\"boolean\"==typeof(r=t.properties[n])&&(r=Number(r))}return r},Nn(\"FeatureIndex\",uu,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});var du=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};du.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},du.prototype.wasRequested=function(){return\"errored\"===this.state||\"loaded\"===this.state||\"reloading\"===this.state},du.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof Mc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof Mc&&o.hasRTLText){this.hasRTLText=!0,hi.isLoading()||hi.isLoaded()||\"deferred\"!==ui()||fi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new na},du.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},du.prototype.getBucket=function(t){return this.buckets[t.id]},du.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Vc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Vc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},du.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},du.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},du.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:\"\",a=n._geojsonTileLayer||n[i];if(a)for(var o=sn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),m=new su(p,l,c,u,d);m.tile=f,t.push(m)}}}},du.prototype.hasData=function(){return\"loaded\"===this.state||\"reloading\"===this.state||\"expired\"===this.state},du.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},du.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=A(t.cacheControl);r[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*r[\"max-age\"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},du.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},du.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||\"_geojsonTileLayer\",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},du.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},du.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},du.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},du.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},du.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){r[i[n]]=!0}this.dependencies[t]=r},du.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1){if(a[s[o]])return!0}}return!1};var 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r},t.makeRequest=yt,t.mapObject=v,t.mercatorXfromLng=Jc,t.mercatorYfromLat=Kc,t.mercatorZfromAltitude=Qc,t.mul=mo,t.multiply=po,t.mvt=Ls,t.normalize=function(t,e){var r=e[0],n=e[1],i=e[2],a=r*r+n*n+i*i;return a>0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t},t.number=qe,t.offscreenCanvasSupported=pt,t.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t},t.parseGlyphPBF=function(t){return new al(t).readFields(Tl,[])},t.pbf=al,t.performSymbolLayout=function(t,e,r,n,i,a,o){t.createArrays();var s=512*t.overscaling;t.tilePixelRatio=8192/s,t.compareText={},t.iconsNeedLinear=!1;var l=t.layers[0].layout,c=t.layers[0]._unevaluatedLayout._values,u={};if(\"composite\"===t.textSizeData.kind){var f=t.textSizeData,h=f.minZoom,p=f.maxZoom;u.compositeTextSizes=[c[\"text-size\"].possiblyEvaluate(new 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g=(c+1)%2;(0===c?r-i<=d:n-i<=m)&&(o.push(f),o.push(p-1),o.push(g)),(0===c?r+i>=d:n+i>=m)&&(o.push(p+1),o.push(u),o.push(g))}}return s}(this.ids,this.coords,t,e,r,this.nodeSize)};var j={minZoom:0,maxZoom:16,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:function(t){return t}},U=function(t){this.options=X(Object.create(j),t),this.trees=new Array(this.options.maxZoom+1)};function V(t,e,r,n,i){return{x:t,y:e,zoom:1/0,id:r,parentId:-1,numPoints:n,properties:i}}function H(t,e){var r=t.geometry.coordinates,n=r[0],i=r[1];return{x:Y(n),y:W(i),zoom:1/0,index:e,parentId:-1}}function q(t){return{type:\"Feature\",id:t.id,properties:G(t),geometry:{type:\"Point\",coordinates:[(n=t.x,360*(n-.5)),(e=t.y,r=(180-360*e)*Math.PI/180,360*Math.atan(Math.exp(r))/Math.PI-90)]}};var e,r,n}function G(t){var e=t.numPoints,r=e>=1e4?Math.round(e/1e3)+\"k\":e>=1e3?Math.round(e/100)/10+\"k\":e;return 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r=0;r<e.length;r+=3)t.minX=Math.min(t.minX,e[r]),t.minY=Math.min(t.minY,e[r+1]),t.maxX=Math.max(t.maxX,e[r]),t.maxY=Math.max(t.maxY,e[r+1])}function tt(t,e,r,n){if(e.geometry){var i=e.geometry.coordinates,a=e.geometry.type,o=Math.pow(r.tolerance/((1<<r.maxZoom)*r.extent),2),s=[],l=e.id;if(r.promoteId?l=e.properties[r.promoteId]:r.generateId&&(l=n||0),\"Point\"===a)et(i,s);else if(\"MultiPoint\"===a)for(var c=0;c<i.length;c++)et(i[c],s);else if(\"LineString\"===a)rt(i,s,o,!1);else if(\"MultiLineString\"===a){if(r.lineMetrics){for(c=0;c<i.length;c++)s=[],rt(i[c],s,o,!1),t.push(Q(l,\"LineString\",s,e.properties));return}nt(i,s,o,!1)}else if(\"Polygon\"===a)nt(i,s,o,!0);else{if(\"MultiPolygon\"!==a){if(\"GeometryCollection\"===a){for(c=0;c<e.geometry.geometries.length;c++)tt(t,{id:l,geometry:e.geometry.geometries[c],properties:e.properties},r,n);return}throw new Error(\"Input data is not a valid GeoJSON object.\")}for(c=0;c<i.length;c++){var 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u=t[c],f=u.geometry,h=u.type,p=0===i?u.minX:u.minY,d=0===i?u.maxX:u.maxY;if(p>=r&&d<n)l.push(u);else if(!(d<r||p>=n)){var m=[];if(\"Point\"===h||\"MultiPoint\"===h)st(f,m,r,n,i);else if(\"LineString\"===h)lt(f,m,r,n,i,!1,s.lineMetrics);else if(\"MultiLineString\"===h)ut(f,m,r,n,i,!1);else if(\"Polygon\"===h)ut(f,m,r,n,i,!0);else if(\"MultiPolygon\"===h)for(var g=0;g<f.length;g++){var v=[];ut(f[g],v,r,n,i,!0),v.length&&m.push(v)}if(m.length){if(s.lineMetrics&&\"LineString\"===h){for(g=0;g<m.length;g++)l.push(Q(u.id,h,m[g],u.tags));continue}\"LineString\"!==h&&\"MultiLineString\"!==h||(1===m.length?(h=\"LineString\",m=m[0]):h=\"MultiLineString\"),\"Point\"!==h&&\"MultiPoint\"!==h||(h=3===m.length?\"Point\":\"MultiPoint\"),l.push(Q(u.id,h,m,u.tags))}}}return l.length?l:null}function st(t,e,r,n,i){for(var a=0;a<t.length;a+=3){var o=t[a+i];o>=r&&o<=n&&(e.push(t[a]),e.push(t[a+1]),e.push(t[a+2]))}}function lt(t,e,r,n,i,a,o){for(var 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r=[],n=0;n<t.length;n++){var i,a=t[n],o=a.type;if(\"Point\"===o||\"MultiPoint\"===o||\"LineString\"===o)i=mt(a.geometry,e);else if(\"MultiLineString\"===o||\"Polygon\"===o){i=[];for(var s=0;s<a.geometry.length;s++)i.push(mt(a.geometry[s],e))}else if(\"MultiPolygon\"===o)for(i=[],s=0;s<a.geometry.length;s++){for(var l=[],c=0;c<a.geometry[s].length;c++)l.push(mt(a.geometry[s][c],e));i.push(l)}r.push(Q(a.id,o,i,a.tags))}return r}function mt(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function gt(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],c=l.geometry,u=l.type;if(l.geometry=[],1===u)for(n=0;n<c.length;n+=2)l.geometry.push(vt(c[n],c[n+1],e,a,o,s));else for(n=0;n<c.length;n++){var f=[];for(i=0;i<c[n].length;i+=2)f.push(vt(c[n][i],c[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function 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u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:\"Polygon\"===a||\"MultiPolygon\"===a?3:\"LineString\"===a||\"MultiLineString\"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function bt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function _t(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time(\"preprocess data\"),e.maxZoom<0||e.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");if(e.promoteId&&e.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n<t.features.length;n++)tt(r,t.features[n],e,n);else\"Feature\"===t.type?tt(r,t,e):tt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",e.indexMaxZoom,e.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=ot(t,1,-1-r,r,0,-1,2,e),a=ot(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=ot(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=dt(i,1).concat(n)),a&&(n=n.concat(dt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}function wt(t,e,r){return 32*((1<<t)*r+e)+t}function Tt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}U.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time(\"total time\");var o=\"prepare \"+t.length+\" points\";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new N(s,Z,J,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new N(s,Z,J,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",c,s.length,+Date.now()-u)}return r&&console.timeEnd(\"total time\"),this},U.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(Y(r),W(a),Y(i),W(n));u<f.length;u+=1){var h=f[u],p=l.points[h];c.push(p.numPoints?q(p):this.points[p.index])}return c},U.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n=\"No cluster with the specified id.\",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=c[l],f=i.points[u];f.parentId===t&&s.push(f.numPoints?q(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},U.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},U.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.points,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.points,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.points,-1,r,i,u),u.features.length?u:null},U.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},U.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},U.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?G(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},U.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},U.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var c=t[l];if(!(c.zoom<=e)){c.zoom=e;for(var u=this.trees[e+1],f=u.within(c.x,c.y,s),h=c.numPoints||1,p=c.x*h,d=c.y*h,m=o&&h>1?this._map(c,!0):null,g=(l<<5)+(e+1)+this.points.length,v=0,y=f;v<y.length;v+=1){var x=y[v],b=u.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=g,o&&(m||(m=this._map(c,!0)),o(m,this._map(b)))}}1===h?r.push(c):(c.parentId=g,r.push(V(p/h,d/h,g,h,m)))}}return r},U.prototype._getOriginId=function(t){return t-this.points.length>>5},U.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},U.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},_t.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},_t.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=wt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time(\"creation\"),h=this.tiles[f]=yt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time(\"clipping\");var m,g,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;m=g=v=y=null,x=ot(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=ot(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(m=ot(x,u,n-_,n+T,1,h.minY,h.maxY,l),g=ot(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=ot(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=ot(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd(\"clipping\"),s.push(m||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},_t.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=wt(t,e=(e%o+o)%o,r);if(this.tiles[s])return gt(this.tiles[s],i);a>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[wt(c,u,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",c,u,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?gt(this.tiles[s],i):null):null};var kt=function(e){function r(t,r,n,i){e.call(this,t,r,n,Tt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));f(o,!0);try{e._geoJSONIndex=n.cluster?new U(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=h[1],m=t.createExpression(d),g=t.createExpression(\"string\"==typeof p?[p,[\"accumulated\"],[\"get\",f]]:p);i[f]=m.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new _t(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){\"Coalescing\"===this._state?this._state=\"Idle\":\"NeedsLoadData\"===this._state&&(this._state=\"Coalescing\",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON 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t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var n=e.getBoundingClientRect(),i=[],a=0;a<r.length;a++)i.push(new t.Point(r[a].clientX-n.left-e.clientLeft,r[a].clientY-n.top-e.clientTop));return i},r.mouseButton=function(e){return void 0!==t.window.InstallTrigger&&2===e.button&&e.ctrlKey&&t.window.navigator.platform.toUpperCase().indexOf(\"MAC\")>=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var h=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e<r.length;e+=1){var n=r[e],i=n.ids,a=n.callback;this._notify(i,a)}this.requestors=[]}},r.prototype.getImage=function(t){return this.images[t]},r.prototype.addImage=function(t,e){this._validate(t,e)&&(this.images[t]=e)},r.prototype._validate=function(e,r){var n=!0;return this._validateStretch(r.stretchX,r.data&&r.data.width)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchX\" value'))),n=!1),this._validateStretch(r.stretchY,r.data&&r.data.height)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchY\" value'))),n=!1),this._validateContent(r.content,r)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"content\" value'))),n=!1),n},r.prototype._validateStretch=function(t,e){if(!t)return!0;for(var r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];if(a[0]<r||a[1]<a[0]||e<a[1])return!1;r=a[1]}return!0},r.prototype._validateContent=function(t,e){return!t||4===t.length&&(!(t[0]<0||e.data.width<t[0])&&(!(t[1]<0||e.data.height<t[1])&&(!(t[2]<0||e.data.width<t[2])&&(!(t[3]<0||e.data.height<t[3])&&(!(t[2]<t[0])&&!(t[3]<t[1]))))))},r.prototype.updateImage=function(t,e){var r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event(\"styleimagemissing\",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image \"'+o+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=g,d=g,m=1e20;function g(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||\"sans-serif\",this.fontWeight=a||\"normal\",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-m,n[1]=+m;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+m}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}g.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?m:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?m:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(v(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),v(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1){(0,o[i])(t,e)}delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar[\"CJK Unified Ideographs\"](e)||t.isChar[\"Hangul Syllables\"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,\"-transition\")?this._transitionable.setTransition(n.slice(0,-\"-transition\".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var c=t[l];i=s*r,a=(s+=c)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=void 0,d=i/r*(n+1);if(l.isDash){var m=n-Math.abs(d);p=Math.sqrt(h*h+m*m)}else p=n-Math.sqrt(h*h+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=l.isDash?h:-h;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name=\"Worker \"+a,this.actors.push(s)}};function A(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var M=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};M.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},M.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=t.extend({type:\"vector\"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&\"expired\"!==e.state?\"loading\"===e.state?e.reloadCallback=r:e.request=e.actor.send(\"reloadTile\",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send(\"loadTile\",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send(\"abortTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:\"raster\"},n),t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(i)e.state=\"errored\",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state=\"loaded\",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type=\"raster-dem\",this.maxzoom=22,this._options=t.extend({type:\"raster-dem\"},n),this.encoding=n.encoding||\"mapbox\"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(n)e.state=\"errored\",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&\"expired\"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send(\"loadDEMTile\",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state=\"unloaded\",t.actor&&t.actor.send(\"removeDEMTile\",{uid:t.uid,source:this.id})},r}(E),C=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:\"source\",sourceDataType:\"metadata\"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=i-r,u=a-n,f=Math.max(c,u),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state=\"errored\",e(null))},r.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var O=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type=\"video\",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener(\"playing\",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var 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e=this.gl;e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}},e}(_t),Tt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(_t),kt=function(t,e,r,n){this.context=t,this.width=e,this.height=r;var i=t.gl,a=this.framebuffer=i.createFramebuffer();this.colorAttachment=new wt(t,a),n&&(this.depthAttachment=new Tt(t,a))};kt.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r)}t.deleteFramebuffer(this.framebuffer)};var At=function(t,e,r){this.func=t,this.mask=e,this.range=r};At.ReadOnly=!1,At.ReadWrite=!0,At.disabled=new At(519,At.ReadOnly,[0,1]);var Mt=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Mt.disabled=new Mt({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Lt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.clearColor=new q(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new mt(this),this.bindElementBuffer=new gt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension(\"EXT_texture_filter_anisotropic\")||t.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||t.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&(t.getExtension(\"OES_texture_half_float_linear\"),this.extRenderToTextureHalfFloat=t.getExtension(\"EXT_color_buffer_half_float\")),this.extTimerQuery=t.getExtension(\"EXT_disjoint_timer_query\")};Lt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Lt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Lt.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Lt.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Lt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Lt.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Lt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Lt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Lt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Lt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Lt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Lt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ct=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on(\"data\",(function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on(\"error\",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error(\"Expected Source id to be \"+e+\" instead of \"+a.id);return t.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)\"errored\"!==this._tiles[t].state&&this._reloadTile(t,\"reloading\")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&(\"loading\"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state=\"errored\",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),\"expired\"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),\"raster-dem\"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event(\"data\",{dataType:\"source\",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(It(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],m=this._tiles[p];if(m&&!(m.fadeEndTime&&m.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),c[g.tileID.key]=g.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],m=this._tiles[d.key];if(!m.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],v=this.getTile(g);if(v&&v.hasData()){n[g.key]=g;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=m.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(m=this.getTile(_))&&x&&(m=this._addTile(_)),m&&(n[_.key]=_,x=m.wasRequested(),m.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,\"expired\"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,m=0,g=c;m<g.length;m+=1){var v=g[m];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,m=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/m,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-g<t.EXTENT&&v[0].y-g<t.EXTENT&&v[1].x+g>=0&&v[1].y+g>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:m})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(It(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles){this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\")}this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function Pt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function It(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function Ot(){return new t.window.Worker(Zi.workerUrl)}Ct.maxOverzooming=10,Ct.maxUnderzooming=3;var zt=\"mapboxgl_preloaded_worker_pool\",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new Ot);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[zt]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)\"ref\"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)\"ref\"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function Ht(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function qt(t,e,r,n){Ht(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}function Zt(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Ht(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||(\"geojson\"===e[a].type&&\"geojson\"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):qt(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),m=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],m[m.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.lastIndexOf(o,m.length-a),1)):a++,c=m[m.length-i],n.push({command:Ut.addLayer,args:[d[o],c]}),m.splice(m.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s[\"source-layer\"],l[\"source-layer\"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=m[m.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:Ut.setStyle,args:[r]}]}return n}var Jt=function(t,e){this.reset(t,e)};Jt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Jt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Kt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Qt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function $t(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function te(e,r){var n=[e.x,e.y,0,1];fe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ee(t,e){return.5+t/e*.5}function re(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ne(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,m=n.transform.width/n.transform.height,g=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!g)ue(y.numGlyphs,h);else{g=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),re(x,f)){var b=x[3],_=ee(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(c,u,y),T=s?w/_:w*_,k=new t.Point(y.anchorX,y.anchorY),A=te(k,a).point,M={},S=oe(y,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&oe(y,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m).notEnoughRoom)&&ue(y.numGlyphs,h)}else ue(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ie(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),m=e.getoffsetX(f-1),g=le(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!g)return null;var v=le(t*m,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:g,last:v}:null}function ae(e,r,n,i){if(e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i)return{useVertical:!0};return(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function oe(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=r/24,v=e.lineOffsetX*g,y=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ie(g,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=te(w.first.point,s).point,k=te(w.last.point,s).point;if(i&&!n){var A=ae(e.writingMode,T,k,d);if(A)return A}m=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)m.push(le(g*l.getoffsetX(M),v,y,n,f,h,e.segment,b,_,c,o,p));m.push(w.last)}else{if(i&&!n){var S=te(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(c.getx(E),c.gety(E)),C=te(L,a),P=C.signedDistanceFromCamera>0?C.point:se(h,L,S,1,a),I=ae(e.writingMode,S,P,d);if(I)return I}var O=le(g*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!O)return{notEnoughRoom:!0};m=[O]}for(var z=0,D=m;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function se(t,e,r,n,i){var a=te(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function le(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,m=0;i&&(d*=-1,m=Math.PI),d<0&&(m+=Math.PI);for(var g=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=c)return null;if(y=v,w.push(v),void 0===(v=h[g])){var T=new t.Point(u.getx(g),u.gety(g)),k=te(T,f);if(k.signedDistanceFromCamera>0)v=h[g]=k.point;else{var A=g-d;v=se(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var M=(_-x)/b,S=v.sub(y),E=S.mult(M)._add(y);E._add(S._unit()._perp()._mult(n*d));var L=m+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:L,path:w}}Kt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Kt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Kt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Kt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Kt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Kt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Kt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Kt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Kt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Kt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Kt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var m=this.circleCells[i];if(null!==m)for(var g=this.circles,v=0,y=m;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Kt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var m=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[m+0],f[m+1],f[m+2],f[m+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var v=this.circles,y=0,x=g;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Kt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Kt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Kt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Kt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Kt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var ce=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ue(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(ce,3*n)}}function fe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var he=function(t,e,r){void 0===e&&(e=new Kt(t.width+200,t.height+200,25)),void 0===r&&(r=new Kt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function pe(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}he.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},he.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],m=new t.Point(r.anchorX,r.anchorY),g=te(m,o),v=ee(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=te(m,s).point,b=ie(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,m,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Jt,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var I=1;I<L.path.length;I++)C.push(L.path[I]);var O=2.5*k;if(l){var z=C.map((function(t){return te(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V=0;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/O)+1;for(var H=0;H<V;H++){var q=H/Math.max(V-1,1),G=S.lerp(q),Y=G.x+100,W=G.y+100;d.push(Y,W,k,0);var X=Y-k,Z=W-k,J=Y+k,K=W+k;if(T=T&&this.isOffscreen(X,Z,J,K),w=w||this.isInsideGrid(X,Z,J,K),!e&&this.grid.hitTestCircle(Y,W,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},he.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var m=d[p],g=m.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var v=[new t.Point(m.x1,m.y1),new t.Point(m.x2,m.y1),new t.Point(m.x2,m.y2),new t.Point(m.x1,m.y2)];t.polygonIntersectsPolygon(r,v)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},he.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},he.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},he.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return fe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},he.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},he.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},he.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var de=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};de.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new de(t?t.text:null,e,r,i),this.icon=new de(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var ge=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ve=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ye=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},xe=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function be(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function _e(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}xe.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var we=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new he(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new xe(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Te(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}we.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h=\"map\"===l.get(\"text-pitch-alignment\"),p=\"map\"===l.get(\"text-rotation-alignment\"),d=pe(n,1,this.transform.zoom),m=Qt(f,h,p,this.transform,d),g=null;if(h){var v=$t(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ye(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:m,labelToScreenMatrix:g,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},we.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=[f.textOffset0,f.textOffset1],v=be(t,r,n,g,i),y=this.collisionIndex.placeCollisionBox(_e(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(d&&0===this.collisionIndex.placeCollisionBox(_e(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return;if(y.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(m=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:m},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}},we.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,m=a.collisionGroup,g=s.get(\"text-optional\"),v=s.get(\"icon-optional\"),y=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=y&&(x||!o.hasIconData()||v),A=x&&(y||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new ge(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,I=null,O=0,z=0,D=0;a.textFeatureIndex?O=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(O=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length;i+=1){if(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),L&&L.box&&L.box.length)break}else L=r()};if(s.get(\"text-variable-anchor\")){var N=s.get(\"text-variable-anchor\");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],v=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,c,b,_,f,l,m,v,e,o,n,u);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,m.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get(\"text-padding\"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,m.predicate,X,W),T=y||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?_e(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,m.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(I=Z(a.verticalIconBox)).box.length>0:(I=Z(a.iconBox)).box.length>0,S=S&&I.offscreen}var J=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=v||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,m.ID):i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,m.ID)),M&&I&&i.collisionIndex.insertCollisionBox(I.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,D,m.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,m.ID),n)){var Q=o.bucketInstanceId,$=i.collisionCircleArrays[Q];void 0===$&&($=i.collisionCircleArrays[Q]=new ve);for(var tt=0;tt<P.circles.length;tt+=4)$.circles.push(P.circles[tt+0]),$.circles.push(P.circles[tt+1]),$.circles.push(P.circles[tt+2]),$.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new ge(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},we.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var c=l[s];c>=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},we.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},we.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new me(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},we.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},we.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get(\"text-allow-overlap\"),l=a.get(\"icon-allow-overlap\"),c=a.get(\"text-variable-anchor\"),u=\"map\"===a.get(\"text-rotation-alignment\"),f=\"map\"===a.get(\"text-pitch-alignment\"),h=\"none\"!==a.get(\"icon-text-fit\"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get(\"icon-optional\")),l&&(s||!e.hasTextData()||a.get(\"text-optional\")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},m=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,m=a.crossTileID,g=r[m],v=i.opacities[m];g?v=o:v||(v=p,i.opacities[m]=v),r[m]=!0;var y=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(y){var T=Pe(v.text),k=_?Ie:T;d(e.text,s,k);var A=w?Ie:T;d(e.text,l,A);var M=v.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,\"left\",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=Pe(v.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:Ie;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=v.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var I=C?Ie:L;d(e.icon,a.numVerticalIconVertices,I),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=v.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var O=e.collisionArrays[n];if(O){var z=new t.Point(0,0);if(O.textBox||O.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[m];R?(z=be(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}O.textBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||_,z.x,z.y),O.verticalTextBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&O.verticalIconBox);O.iconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,F,h?z.x:0,h?z.y:0),O.verticalIconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,!F,h?z.x:0,h?z.y:0)}}},g=0;g<e.symbolInstances.length;g++)m(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},we.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},we.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},we.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},we.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},we.prototype.setStale=function(){this.stale=!0};var ke=Math.pow(2,25),Ae=Math.pow(2,24),Me=Math.pow(2,17),Se=Math.pow(2,16),Ee=Math.pow(2,9),Le=Math.pow(2,8),Ce=Math.pow(2,1);function Pe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*ke+e*Ae+r*Me+e*Se+r*Ee+e*Le+r*Ce+e}var Ie=0,Oe=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Oe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new we(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Oe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var De=512/t.EXTENT/2,Re=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Re.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=De/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Re.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Fe=function(){this.maxCrossTileID=0};Fe.prototype.generate=function(){return++this.maxCrossTileID};var Be=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Be.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Be.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++){e.symbolInstances.get(n).crossTileID=0}this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Re(t,e.symbolInstances,e.bucketInstanceId),!0},Be.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Be.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ne=function(){this.layerIndexes={},this.crossTileIDs=new Fe,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ne.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Be);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ne.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var je=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return\"source.canvas\"!==t.identifier})))},Ue=t.pick(Ut,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Ve=t.pick(Ut,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),He=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a=null;null!=(a=\"version\"===n?r:\"array\"===i.type?[]:{})&&(e[n]=a)}}return e}(),qe=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Ne,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast(\"syncRTLPluginState\",r,(function(e,r){if((t.triggerPluginCompletionEvent(e),r)&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on(\"data\",(function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"}));var i=\"boolean\"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._load(He,!1)},r.prototype._load=function(e,r){if(!r||!je(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event(\"data\",{dataType:\"style\"})),this.fire(new t.Event(\"style.load\"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,m=l.stretchX,g=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:m,stretchY:g,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];\"custom\"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];\"reload\"===o?this._reloadSource(a):\"clear\"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event(\"data\",{dataType:\"style\"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies([\"icons\",\"patterns\"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),je(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=Zt(this.serialize(),e).filter((function(t){return!(t.command in Ve)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in Ue)}));if(i.length>0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0)||!this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n)){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ct(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(je(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is requred to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1){var h=f[u];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var m=this._order[d];if(r(m))for(var g=i.length-1;g>=0;g--){var v=i[g].feature;if(n[v.layer.id]<d)break;p.push(v),i.pop()}else for(var y=0,x=t;y<x.length;y+=1){var b=x[y][m];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,\"queryRenderedFeatures.filter\",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error(\"The layer '\"+s+\"' does not exist in the map's style and cannot be queried for features.\"))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1){var h=f[u];c.push(o[h])}c.sort(B);for(var p=function(){var r=m[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1){var h=f[u];o.push(h)}}},d=0,m=c;d<m.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer[\"source-layer\"],i.id);i.source=i.layer.source,i.layer[\"source-layer\"]&&(i.sourceLayer=i.layer[\"source-layer\"]),i.state=o}))};for(var v in s)g(v);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,\"querySourceFeatures.filter\",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called \"'+t+'\" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&je(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off(\"pluginStateChange\",this._rtlTextPluginCallback),this._layers){this._layers[e].setEventedParent(null)}for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];if(\"symbol\"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var m=0,g=this._order;m<g.length;m+=1){var v=g[m],y=this._layers[v];\"symbol\"===y.type&&this.placement.updateLayerOpacities(y,l[y.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var Ge=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Ye=yr(\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\",\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\"),We=yr(\"uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Xe=yr(\"uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),Ze=yr(\"varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main(void) {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}\"),Je=yr(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Ke=yr(\"uniform highp float u_intensity;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main() {\\n#pragma mapbox: initialize highp float weight\\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\nconst highp float ZERO=1.0/255.0/16.0;\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main(void) {\\n#pragma mapbox: initialize highp float weight\\n#pragma mapbox: initialize mediump float radius\\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}\"),Qe=yr(\"uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(0.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),$e=yr(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),tr=yr(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),er=yr(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),rr=yr(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),nr=yr(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ir=yr(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ar=yr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),or=yr(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),sr=yr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),lr=yr(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),cr=yr(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),ur=yr(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),fr=yr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define MAX_LINE_DISTANCE 32767.0\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),hr=yr(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),pr=yr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),dr=yr(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),mr=yr(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),gr=yr(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),vr=yr(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function yr(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o=\"float\"===i?\"vec2\":\"vec4\",s=a.match(/color/)?\"color\":o;return n[a]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"}))}}var xr=Object.freeze({__proto__:null,prelude:Ye,background:We,backgroundPattern:Xe,circle:Ze,clippingMask:Je,heatmap:Ke,heatmapTexture:Qe,collisionBox:$e,collisionCircle:tr,debug:er,fill:rr,fillOutline:nr,fillOutlinePattern:ir,fillPattern:ar,fillExtrusion:or,fillExtrusionPattern:sr,hillshadePrepare:lr,hillshade:cr,line:ur,lineGradient:fr,linePattern:hr,lineSDF:pr,raster:dr,symbolIcon:mr,symbolSDF:gr,symbolTextAndIcon:vr}),br=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};br.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},br.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1){p[h].enableAttributes(u,t)}a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,m=r;d<m.length;d+=1){var g=m[d];g.bind(),g.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},br.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var _r=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push(\"#define OVERDRAW_INSPECTOR;\");var s=o.concat(Ye.fragmentSource,e.fragmentSource).join(\"\\n\"),l=o.concat(Ye.vertexSource,e.vertexSource).join(\"\\n\"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var m=a.getActiveAttrib(this.program,d);m&&(this.attributes[m.name]=a.getAttribLocation(this.program,m.name))}for(var g=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<g;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function wr(t,e,r){var n=1/pe(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}_r.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[v.LINES]=2,g[v.TRIANGLES]=3,g[v.LINE_STRIP]=1,g)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new br)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,m),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Tr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},kr=function(e,r,n,i,a,o,s){return t.extend(Tr(e,r,n,i),wr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Ar=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(Ar(e),wr(n,r,i))},Sr=function(t,e){return{u_matrix:t,u_world:e}},Er=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Lr=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=pe(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Cr=function(t,e,r){var n=pe(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Pr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Ir=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Or=function(t){return{u_matrix:t}},zr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:pe(e,1,r),u_intensity:n}};function Dr(e,r){var n=Math.pow(2,r.canonical.z),i=r.canonical.y;return[new t.MercatorCoordinate(0,i/n).toLngLat().lat,new t.MercatorCoordinate(0,(i+1)/n).toLngLat().lat]}var Rr=function(e,r,n){var i=e.transform;return{u_matrix:Ur(e,r,n),u_ratio:1/pe(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Fr=function(e,r,n){return t.extend(Rr(e,r,n),{u_image:0})},Br=function(e,r,n,i){var a=e.transform,o=jr(r,a);return{u_matrix:Ur(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/pe(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Nr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=jr(r,o),c=\"round\"===n.layout.get(\"line-cap\"),u=s.getDash(i.from,c),f=s.getDash(i.to,c),h=u.width*a.fromScale,p=f.width*a.toScale;return t.extend(Rr(e,r,n),{u_patternscale_a:[l/h,-u.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:f.y,u_mix:a.t})};function jr(t,e){return 1/pe(t,1,e.tileZoom)}function Ur(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var Vr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Hr(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Hr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var qr,Gr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Yr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(Gr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Wr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Yr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Xr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Zr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/pe(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Jr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Kr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram(\"collisionBox\"),f=[],h=0,p=0,d=0;d<i.length;d++){var m=i[d],g=r.getTile(m),v=g.getBucket(n);if(v){var y=m.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(m.posMatrix,g,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],I=E.circleArray[C+1],O=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(A++,P,I,O,z,0),k.emplace(A++,P,I,O,z,1),k.emplace(A++,P,I,O,z,2),k.emplace(A++,P,I,O,z,3)}(!qr||qr.length<2*h)&&(qr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(qr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Pr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Qr=t.identity(new Float32Array(16));function $r(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function tn(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,m={};p.clear();for(var g=0;g<h.length;g++){var v=h.get(g),y=e.allowVerticalPlacement&&!v.placedOrientation,x=v.hidden||!v.crossTileID||y?null:i[v.crossTileID];if(x){var b=new t.Point(v.anchorX,v.anchorY),_=te(b,n?l:s),w=ee(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,u,v)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/c);for(var k=x.width,A=x.height,M=$r(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?te(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<v.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&v.associatedIconIndex>=0&&(m[v.associatedIconIndex]={shiftedAnchor:S,angle:E})}else ue(v.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var I=C.get(P);if(I.hidden)ue(I.numGlyphs,d);else{var O=m[P];if(O)for(var z=0;z<I.numGlyphs;z++)t.addDynamicAttributes(d,O.shiftedAnchor,O.angle);else ue(I.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function en(t,e,r){return r.iconsInText&&e?\"symbolTextAndIcon\":t?\"symbolSDF\":\"symbolIcon\"}function rn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,m=e.transform,g=\"map\"===l,v=\"map\"===c,y=g&&\"point\"!==n.layout.get(\"symbol-placement\"),x=g&&!v&&!y,b=void 0!==n.layout.get(\"symbol-sort-key\").constantOr(1),_=e.depthModeForSublayer(0,At.ReadOnly),w=n.layout.get(\"text-variable-anchor\"),T=[],k=0,A=i;k<A.length;k+=1){var M=A[k],S=r.getTile(M),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,I=a?E.textSizeData:E.iconSizeData,O=v||0!==m.pitch,z=e.useProgram(en(P,a,E),C),D=t.evaluateSizeForZoom(I,m.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var V=\"composite\"===I.kind||\"camera\"===I.kind;U=O||e.options.rotating||e.options.zooming||V?d.LINEAR:d.NEAREST}}else{var H=1!==n.layout.get(\"icon-size\").constantOr(0)||E.iconsNeedLinear;B=S.imageAtlasTexture,N=P||e.options.rotating||e.options.zooming||H||O?d.LINEAR:d.NEAREST,R=S.imageAtlasTexture.size}var q=pe(S,1,e.transform.zoom),G=Qt(M.posMatrix,v,g,e.transform,q),Y=$t(M.posMatrix,v,g,e.transform,q),W=w&&E.hasTextData(),X=\"none\"!==n.layout.get(\"icon-text-fit\")&&W&&E.hasIconData();y&&ne(E,M.posMatrix,e,a,G,Y,v,u);var Z=e.translatePosMatrix(M.posMatrix,S,o,s),J=y||a&&w||X?Qr:G,K=e.translatePosMatrix(Y,S,o,s,!0),Q=P&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1),$={program:z,buffers:L,uniformValues:P?E.iconsInText?Wr(I.kind,D,x,v,e,Z,J,K,R,F):Yr(I.kind,D,x,v,e,Z,J,K,a,R,!0):Gr(I.kind,D,x,v,e,Z,J,K,a,R),atlasTexture:B,atlasTextureIcon:j,atlasInterpolation:N,atlasInterpolationIcon:U,isSDF:P,hasHalo:Q};if(b)for(var tt=0,et=L.segments.get();tt<et.length;tt+=1){var rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:$})}else T.push({segments:L.segments,sortKey:0,state:$})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function nn(t,e,r,n,i,a,o,s,l){var c=n.context,u=c.gl;i.draw(c,u.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function an(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get(\"fill-pattern\"),d=p&&p.constantOr(1),m=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",s=h.LINES):(l=d?\"fillPattern\":\"fill\",s=h.TRIANGLES);for(var g=0,v=n;g<v.length;g+=1){var y=v[g],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(m));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get(\"fill-translate\"),r.paint.get(\"fill-translate-anchor\"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c=\"fillOutlinePattern\"===l&&d?Er(S,t,m,x,E):Sr(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,m,x):Ar(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function on(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get(\"fill-extrusion-pattern\"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get(\"fill-extrusion-opacity\"),p=0,d=n;p<d.length;p+=1){var m=d[p],g=e.getTile(m),v=g.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?\"fillExtrusionPattern\":\"fillExtrusion\",y);u&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(m.posMatrix,g,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\")),A=r.paint.get(\"fill-extrusion-vertical-gradient\"),M=u?kr(k,t,A,h,m,f,g):Tr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,M,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function sn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var c=t.useProgram(\"hillshade\");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var u=function(t,e,r){var n=r.paint.get(\"hillshade-shadow-color\"),i=r.paint.get(\"hillshade-highlight-color\"),a=r.paint.get(\"hillshade-accent-color\"),o=r.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===r.paint.get(\"hillshade-illumination-anchor\")&&(o-=t.transform.angle);var s=!t.options.moving;return{u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),s),u_image:0,u_latrange:Dr(t,e.tileID),u_light:[r.paint.get(\"hillshade-exaggeration\"),o],u_shadow:n,u_highlight:i,u_accent:a}}(t,e,r);c.draw(o,s.TRIANGLES,n,i,a,Et.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function ln(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var m=r.fbo;if(!m){var g=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);g.bind(c.LINEAR,c.CLAMP_TO_EDGE),(m=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(g.texture)}l.bindFramebuffer.set(m.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram(\"hillshadePrepare\").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function cn(e,r,n,i,a){var o=i.paint.get(\"raster-fade-duration\");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var un=new t.Color(1,0,0,1),fn=new t.Color(0,1,0,1),hn=new t.Color(0,0,1,1),pn=new t.Color(1,0,1,1),dn=new t.Color(0,1,1,1);function mn(t){var e=t.transform.padding;gn(t,t.transform.height-(e.top||0),3,un),gn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,hn),vn(t,t.transform.width-(e.right||0),3,pn);var r=t.transform.centerPoint;!function(t,e,r,n){yn(t,e-1,r-10,2,20,n),yn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,dn)}function gn(t,e,r,n){yn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){yn(t,e-r/2,0,r,t.transform.height,n)}function yn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function xn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=At.disabled,c=Mt.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),\"$debug\",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=f&&f.byteLength||0,p=Math.floor(h/1024),d=r.getTile(n).tileSize,m=512/Math.min(d,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+\" \"+p+\"kb\"),s.draw(i,a.TRIANGLES,l,c,St.alphaBlended,Et.disabled,Ir(o,t.Color.transparent,m),\"$debug\",e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var bn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=Mt.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,c=\"map\"===a,u=\"map\"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),m=d.getBucket(n);if(m&&m.text&&m.text.segments.get().length){var g=m.textSizeData,v=t.evaluateSizeForZoom(g,l.zoom),y=pe(d,1,r.transform.zoom),x=Qt(p.posMatrix,u,c,r.transform,y),b=\"none\"!==n.layout.get(\"icon-text-fit\")&&m.hasIconData();if(v){var _=Math.pow(2,l.zoom-d.tileID.overscaledZ);tn(m,c,u,s,t.symbolSize,l,x,p.posMatrix,_,v,b)}}}}(i,e,n,r,n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),a),0!==n.paint.get(\"icon-opacity\").constantOr(1)&&rn(e,r,n,i,!1,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),n.layout.get(\"icon-rotation-alignment\"),n.layout.get(\"icon-pitch-alignment\"),n.layout.get(\"icon-keep-upright\"),o,s),0!==n.paint.get(\"text-opacity\").constantOr(1)&&rn(e,r,n,i,!0,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.layout.get(\"text-keep-upright\"),o,s),r.map.showCollisionBoxes&&(Kr(e,r,n,i,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),!0),Kr(e,r,n,i,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),!1))}},circle:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"circle-opacity\"),o=n.paint.get(\"circle-stroke-width\"),s=n.paint.get(\"circle-stroke-opacity\"),l=void 0!==n.layout.get(\"circle-sort-key\").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,At.ReadOnly),h=Mt.disabled,p=e.colorModeForRenderPass(),d=[],m=0;m<i.length;m++){var g=i[m],v=r.getTile(g),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram(\"circle\",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Lr(e,g,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,I=S.uniformValues,O=M.segments;L.draw(c,u.TRIANGLES,f,h,p,Et.disabled,I,n.id,C,P,O,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===e.renderPass){var a=e.context,o=a.gl,s=Mt.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram(\"heatmap\",p),m=e.transform.zoom;d.draw(a,o.TRIANGLES,At.disabled,s,l,Et.disabled,zr(u.posMatrix,f,m,n.paint.get(\"heatmap-intensity\")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(!a)return;n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA));o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram(\"heatmapTexture\").draw(n,i.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get(\"heatmap-opacity\")}}(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}(e,n))},line:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,At.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get(\"line-dasharray\"),u=n.paint.get(\"line-pattern\"),f=u.constantOr(1),h=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=f?\"linePattern\":c?\"lineSDF\":h?\"lineGradient\":\"line\",m=e.context,g=m.gl,v=!0;if(h){m.activeTexture.set(g.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(m,n.gradient,g.RGBA)),y.bind(g.LINEAR,g.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,k),S=v||M.program!==A,E=u.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var I=f?Br(e,w,n,p):c?Nr(e,w,n,c,p):h?Fr(e,w,n):Rr(e,w,n);f?(m.activeTexture.set(g.TEXTURE0),w.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(m.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(m)),M.draw(m,g.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,I,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get(\"fill-color\"),o=n.paint.get(\"fill-opacity\");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get(\"fill-pattern\"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?\"opaque\":\"translucent\";if(e.renderPass===c){var u=e.depthModeForSublayer(1,\"opaque\"===e.renderPass?At.ReadWrite:At.ReadOnly);an(e,r,n,i,u,s,!1)}if(\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")){var f=e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,At.ReadOnly);an(e,r,n,i,f,s,!0)}}},\"fill-extrusion\":function(t,e,r,n){var i=r.paint.get(\"fill-extrusion-opacity\");if(0!==i&&\"translucent\"===t.renderPass){var a=new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get(\"fill-extrusion-pattern\").constantOr(1))on(t,e,r,n,a,Mt.disabled,St.disabled),on(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();on(t,e,r,n,a,Mt.disabled,o)}}},hillshade:function(t,e,r,n){if(\"offscreen\"===t.renderPass||\"translucent\"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,At.ReadOnly),s=t.colorModeForRenderPass(),l=\"translucent\"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&\"offscreen\"===t.renderPass?ln(t,p,r,a,o,Mt.disabled,s):\"translucent\"===t.renderPass&&sn(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram(\"raster\"),l=t.colorModeForRenderPass(),c=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,m=f;d<m.length;d+=1){var g=m[d],v=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get(\"raster-opacity\")?At.ReadWrite:At.ReadOnly,a.LESS),y=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);y.registerFadeDuration(r.paint.get(\"raster-fade-duration\"));var b=e.findLoadedParent(g,0),_=cn(y,b,e,r,t.transform),w=void 0,T=void 0,k=\"nearest\"===r.paint.get(\"raster-resampling\")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Vr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,v,Mt.disabled,l,Et.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[g.overscaledZ],l,Et.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get(\"background-color\"),i=r.paint.get(\"background-opacity\");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get(\"background-pattern\");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(t.renderPass===u){var f=Mt.disabled,h=t.depthModeForSublayer(0,\"opaque\"===u?At.ReadWrite:At.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?\"backgroundPattern\":\"background\"),m=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),v=0,y=m;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Zr(b,i,t,c,{tileID:x,tileSize:l},g):Xr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)xn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if(\"offscreen\"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if(\"translucent\"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Mt.disabled);var o=\"3d\"===i.renderingMode?new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,At.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},_n=function(t,e){this.context=new Lt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ct.maxUnderzooming+Ct.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ne,this.gpuTimers={}};_n.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},_n.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Ge.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Ge.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ge.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Mt({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},_n.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(e,r.TRIANGLES,At.disabled,this.stencilClearMode,St.disabled,Et.disabled,Or(n),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},_n.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(At.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,At.disabled,new Mt({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Or(s.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},_n.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Mt({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Mt({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Mt({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Mt.disabled,e),n]},_n.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){return new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0])}return\"opaque\"===this.renderPass?St.unblended:St.alphaBlended},_n.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return At.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new At(r||this.context.gl.LEQUAL,e,[n,n])},_n.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},_n.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var m=0;m<i.length;m++){var g=i[m];if(this.style._layers[g].is3D()){this.opaquePassCutoff=m;break}}this.renderPass=\"offscreen\";for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];(\"custom\"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass=\"opaque\",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=(\"symbol\"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,u[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&bn.debug(this,l,l.getVisibleCoordinates()));this.options.showPadding&&mn(this),this.context.setDefault()},_n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||\"custom\"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),bn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},_n.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},_n.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},_n.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},_n.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},_n.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:pe(r,n[0],this.transform.zoom),a?n[1]:pe(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},_n.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},_n.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},_n.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},_n.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new _r(this.context,xr[t],e,Jr[t],this._showOverdrawInspector)),this.cache[r]},_n.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},_n.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},_n.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},_n.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var wn=function(t,e){this.points=t,this.planes=e};wn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new wn(a,o)};var Tn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Tn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Tn(n,i)},Tn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Tn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Tn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var kn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};kn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},kn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},kn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},kn.prototype.clone=function(){return new kn(this.top,this.bottom,this.left,this.right)},kn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var An=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new kn,this._posMatrixCache={},this._alignedPosMatrixCache={}},Mn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};An.prototype.clone=function(){var t=new An(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Mn.minZoom.get=function(){return this._minZoom},Mn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Mn.maxZoom.get=function(){return this._maxZoom},Mn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Mn.minPitch.get=function(){return this._minPitch},Mn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Mn.maxPitch.get=function(){return this._maxPitch},Mn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Mn.renderWorldCopies.get=function(){return this._renderWorldCopies},Mn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Mn.worldSize.get=function(){return this.tileSize*this.scale},Mn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Mn.size.get=function(){return new t.Point(this.width,this.height)},Mn.bearing.get=function(){return-this.angle/Math.PI*180},Mn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Mn.pitch.get=function(){return this._pitch/Math.PI*180},Mn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Mn.fov.get=function(){return this._fov/Math.PI*180},Mn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Mn.zoom.get=function(){return this._zoom},Mn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Mn.center.get=function(){return this._center},Mn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Mn.padding.get=function(){return this._edgeInsets.toJSON()},Mn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Mn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},An.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},An.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},An.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},An.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},An.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=wn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Tn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var m=u.pop(),g=m.x,v=m.y,y=m.fullyVisible;if(!y){var x=m.aabb.intersects(s);if(0===x)continue;y=2===x}var b=m.aabb.distanceX(o),_=m.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-m.zoom)-2;if(m.zoom===h||w>T&&m.zoom>=l)f.push({tileID:new t.OverscaledTileID(m.zoom===h?p:m.zoom,m.wrap,m.zoom,g,v),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-v])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(v<<1)+(k>>1);u.push({aabb:m.aabb.quadrant(k),zoom:m.zoom+1,x:A,y:M,wrap:m.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},An.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Mn.unmodified.get=function(){return this._unmodified},An.prototype.zoomScale=function(t){return Math.pow(2,t)},An.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},An.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},An.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Mn.point.get=function(){return this.project(this.center)},An.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},An.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},An.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},An.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},An.prototype.coordinateLocation=function(t){return t.toLngLat()},An.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,f=n[2]/a,h=u===f?0:(0-u)/(f-u);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,c,h)/this.worldSize)},An.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},An.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},An.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},An.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},An.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},An.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},An.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var m=p.y,g=c.y/2;m-g<a&&(i=a+g),m+g>o&&(i=o-g)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},An.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,u,c),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),m=Math.sin(this.angle),g=s-Math.round(s)+d*h+m*p,v=l-Math.round(l)+d*p+m*h,y=new Float64Array(f);if(t.translate(y,y,[g>.5?g-1:g,v>.5?v-1:v,0]),this.alignedProjMatrix=y,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},An.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},An.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},An.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(An.prototype,Mn);var Sn=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};Sn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},Sn.prototype.remove=function(){return t.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Sn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u=\"\";if(u+=e?\"/\"+o+\"/\"+s+\"/\"+n:n+\"/\"+s+\"/\"+o,(l||c)&&(u+=\"/\"+Math.round(10*l)/10),c&&(u+=\"/\"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split(\"&\").map((function(t){var e=t.split(\"=\")[0];return e===f?(h=!0,e+\"=\"+u):t})).filter((function(t){return t}));return h||p.push(f+\"=\"+u),\"#\"+p.join(\"&\")}return\"#\"+u},Sn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace(\"#\",\"\");return this._hashName?(n.split(\"&\").map((function(t){return t.split(\"=\")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||\"\").split(\"/\")):n.split(\"/\")},Sn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},Sn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,\"\",e)}catch(t){}};var En={linearity:.3,easing:t.bezier(0,0,.3,1)},Ln=t.extend({deceleration:2500,maxSpeed:1400},En),Cn=t.extend({deceleration:20,maxSpeed:1400},En),Pn=t.extend({deceleration:1e3,maxSpeed:360},En),In=t.extend({deceleration:1e3,maxSpeed:90},En),On=function(t){this._map=t,this.clear()};function zn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Dn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}On.prototype.clear=function(){this._inertiaBuffer=[]},On.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},On.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},On.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Dn(r.pan.mag(),o,t.extend({},Ln,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,zn(s,l)}if(r.zoom){var c=Dn(r.zoom,o,Cn);s.zoom=this._map.transform.zoom+c.amount,zn(s,c)}if(r.bearing){var u=Dn(r.bearing,o,Pn);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),zn(s,u)}if(r.pitch){var f=Dn(r.pitch,o,In);s.pitch=this._map.transform.pitch+f.amount,zn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Rn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Fn=function(e){function n(n,i,a){var o=\"touchend\"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Bn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Nn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Nn.prototype.reset=function(){delete this._mousedownPos},Nn.prototype.wheel=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Nn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseup=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.dblclick=function(t){return this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseover=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.mouseout=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.touchstart=function(t){return this._firePreventable(new Fn(t.type,this._map,t))},Nn.prototype.touchmove=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchend=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchcancel=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Nn.prototype.isEnabled=function(){return!0},Nn.prototype.isActive=function(){return!1},Nn.prototype.enable=function(){},Nn.prototype.disable=function(){};var jn=function(t){this._map=t};jn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},jn.prototype.mousemove=function(t){this._map.fire(new Rn(t.type,this._map,t))},jn.prototype.mousedown=function(){this._delayContextMenu=!0},jn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Rn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},jn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Rn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},jn.prototype.isEnabled=function(){return!0},jn.prototype.isActive=function(){return!1},jn.prototype.enable=function(){},jn.prototype.disable=function(){};var Un=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Vn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Un.prototype.isEnabled=function(){return!!this._enabled},Un.prototype.isActive=function(){return!!this._active},Un.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Un.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Un.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Un.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,\"translate(\"+a+\"px,\"+s+\"px)\"),this._box.style.width=o-a+\"px\",this._box.style.height=l-s+\"px\"}}},Un.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event(\"boxzoomend\",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent(\"boxzoomcancel\",e)}},Un.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))},Un.prototype.reset=function(){this._active=!1,this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Un.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Hn=function(t){this.reset(),this.numTouches=t.numTouches};Hn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Hn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Vn(n,r)))},Hn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Vn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Hn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var qn=function(t){this.singleTap=new Hn(t),this.numTaps=t.numTaps,this.reset()};qn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},qn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},qn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},qn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Gn=function(){this._zoomIn=new qn({numTouches:1,numTaps:2}),this._zoomOut=new qn({numTouches:2,numTaps:1}),this.reset()};Gn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Gn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Gn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Gn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Gn.prototype.touchcancel=function(){this.reset()},Gn.prototype.enable=function(){this._enabled=!0},Gn.prototype.disable=function(){this._enabled=!1,this.reset()},Gn.prototype.isEnabled=function(){return this._enabled},Gn.prototype.isActive=function(){return this._active};var Yn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Yn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Yn.prototype._correctButton=function(t,e){return!1},Yn.prototype._move=function(t,e){return{}},Yn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Yn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Yn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Yn.prototype.enable=function(){this._enabled=!0},Yn.prototype.disable=function(){this._enabled=!1,this.reset()},Yn.prototype.isEnabled=function(){return this._enabled},Yn.prototype.isActive=function(){return this._active};var Wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Yn),Xn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Zn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Jn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Jn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Jn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Jn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Jn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Vn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(){this.reset()};function Qn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}Kn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Kn.prototype._start=function(t){},Kn.prototype._move=function(t,e,r){return{}},Kn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Kn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=Qn(r,e,i),s=Qn(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},Kn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=Qn(n,e,a),l=Qn(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},Kn.prototype.touchcancel=function(){this.reset()},Kn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};function $n(t,e){return Math.log(t/e)/Math.LN2}var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs($n(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:$n(this._distance,r),pinchAround:e}},e}(Kn);function ei(t,e){return 180*t.angleWith(e)/Math.PI}var ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:ei(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=ei(t,this._startVector);return Math.abs(r)<e},e}(Kn);function ni(t){return Math.abs(t.y)>Math.abs(t.x)}var ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ni(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid){this._lastPoints=t,this._active=!0;return{pitchDelta:-.5*((n.y+i.y)/2)}}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ni(t)&&ni(e)&&a}},e}(Kn),ai={panStep:100,bearingStep:15,pitchStep:10},oi=function(){var t=ai;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function si(t){return t*(2-t)}oi.prototype.reset=function(){this._active=!1},oi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:si,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},oi.prototype.enable=function(){this._enabled=!0},oi.prototype.disable=function(){this._enabled=!1,this.reset()},oi.prototype.isEnabled=function(){return this._enabled},oi.prototype.isActive=function(){return this._active};var li=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};li.prototype.setZoomRate=function(t){this._defaultZoomRate=t},li.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},li.prototype.isEnabled=function(){return!!this._enabled},li.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},li.prototype.isZooming=function(){return!!this._zooming},li.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},li.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},li.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},li.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},li.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},li.prototype.renderFrame=function(){return this._onScrollFrame()},li.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if(\"wheel\"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},li.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},li.prototype.reset=function(){this._active=!1};var ci=function(t,e){this._clickZoom=t,this._tapZoom=e};ci.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},ci.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},ci.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},ci.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var ui=function(){this.reset()};ui.prototype.reset=function(){this._active=!1},ui.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},ui.prototype.enable=function(){this._enabled=!0},ui.prototype.disable=function(){this._enabled=!1,this.reset()},ui.prototype.isEnabled=function(){return this._enabled},ui.prototype.isActive=function(){return this._active};var fi=function(){this._tap=new qn({numTouches:1,numTaps:1}),this.reset()};fi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},fi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},fi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},fi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},fi.prototype.touchcancel=function(){this.reset()},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var hi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};hi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},hi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},hi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},hi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var pi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};pi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},pi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},pi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},pi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var di=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};di.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},di.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},di.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},di.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},di.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},di.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var mi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function vi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var yi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new On(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!1}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1],u=s[2];r.addEventListener(l,c,l===t.window.document?this.handleWindowEvent:this.handleEvent,u)}};yi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},yi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new Nn(e,t));var n=e.boxZoom=new Un(e,t);this._add(\"boxZoom\",n);var i=new Gn,a=new ui;e.doubleClickZoom=new ci(a,i),this._add(\"tapZoom\",i),this._add(\"clickZoom\",a);var o=new fi;this._add(\"tapDragZoom\",o);var s=e.touchPitch=new ii;this._add(\"touchPitch\",s);var l=new Xn(t),c=new Zn(t);e.dragRotate=new pi(t,l,c),this._add(\"mouseRotate\",l,[\"mousePitch\"]),this._add(\"mousePitch\",c,[\"mouseRotate\"]);var u=new Wn(t),f=new Jn(t);e.dragPan=new hi(r,u,f),this._add(\"mousePan\",u),this._add(\"touchPan\",f,[\"touchZoom\",\"touchRotate\"]);var h=new ri,p=new ti;e.touchZoomRotate=new di(r,p,h,o),this._add(\"touchRotate\",h,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",p,[\"touchPan\",\"touchRotate\"]);var d=e.scrollZoom=new li(e,this);this._add(\"scrollZoom\",d,[\"mousePan\"]);var m=e.keyboard=new oi;this._add(\"keyboard\",m),this._add(\"blockableMapEvent\",new jn(e));for(var g=0,v=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];g<v.length;g+=1){var y=v[g];t.interactive&&t[y]&&e[y].enable(t[y])}},yi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},yi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1){e[t].handler.reset()}this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},yi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1){if(e[t].handler.isActive())return!0}return!1},yi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},yi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},yi.prototype.isMoving=function(){return Boolean(mi(this._eventsInProgress))||this.isZooming()},yi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},yi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+\"Window\")},yi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},yi.prototype.handleEvent=function(t,e){if(\"blur\"!==t.type){this._updatingCamera=!0;for(var n=\"renderFrame\"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var m=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(m=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,m,h,n),m&&m.needsRenderFrame&&this._triggerRenderFrame()),(m||p.isActive())&&(o[h]=p)}}var g={};for(var v in this._previousActiveHandlers)o[v]||(g[v]=n);this._previousActiveHandlers=o,(Object.keys(g).length||vi(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||vi(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},yi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},yi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},yi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!vi(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},yi.prototype._fireEvents=function(e,r){var n=this,i=mi(this._eventsInProgress),a=mi(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+\"start\"]=l),this._eventsInProgress[s]=e[s]}for(var c in!i&&a&&this._fireEvent(\"movestart\",a.originalEvent),o)this._fireEvent(c,o[c]);for(var u in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent(\"move\",a.originalEvent),e){var f=e[u].originalEvent;this._fireEvent(u,f)}var h,p={};for(var d in this._eventsInProgress){var m=this._eventsInProgress[d],g=m.handlerName,v=m.originalEvent;this._handlersById[g].isActive()||(delete this._eventsInProgress[d],h=r[g]||v,p[d+\"end\"]=h)}for(var y in p)this._fireEvent(y,p[y]);var x=mi(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event(\"moveend\",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},yi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},yi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new gi(\"renderFrame\",{timeStamp:e})),t._applyChanges()})))};var xi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll([\"_renderFrameCallback\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if(\"number\"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),m=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/m.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/m.y;if(!(v<0||g<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(g,v)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(w)),zoom:y,bearing:n}}t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),i&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),a&&this.fire(new t.Event(\"rotatestart\",r)).fire(new t.Event(\"rotate\",r)).fire(new t.Event(\"rotateend\",r)),o&&this.fire(new t.Event(\"pitchstart\",r)).fire(new t.Event(\"pitch\",r)).fire(new t.Event(\"pitchend\",r)),this.fire(new t.Event(\"moveend\",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c=\"zoom\"in e?+e.zoom:a,u=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,f=\"pitch\"in e?+e.pitch:s,h=\"padding\"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),m=i.pointLocation(d),g=t.LngLat.convert(e.center||m);this._normalizeCenter(g);var v,y,x=i.project(m),b=i.project(g).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var m=i.zoomScale(i.zoom-a),g=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(m));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(u-o),m=t.Point.convert(e.offset),g=a.centerPoint.add(m),v=a.pointLocation(g),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),I=function(t){return C(P)/C(P+_*t)},O=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,O=function(){return 0},I=function(t){return Math.exp(D*_*t)}}if(\"duration\"in e)e.duration=+e.duration;else{var R=\"screenSpeed\"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/I(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),g=a.centerPoint.add(m));var v=1===e?y:a.unproject(x.add(b.mult(O(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),bi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};bi.prototype.getDefaultPosition=function(){return\"bottom-right\"},bi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},bi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},bi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n<r.length-1?\"&\":\"\")),t}),\"?\");e.href=t.config.FEEDBACK_URL+\"/\"+n+(this._map._hash?this._map._hash.getHashString(!0):\"\"),e.rel=\"noopener nofollow\"}},bi.prototype._updateData=function(t){!t||\"metadata\"!==t.sourceDataType&&\"style\"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},bi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return\"string\"!=typeof t?\"\":t}))):\"string\"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},bi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var _i=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};_i.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},_i.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},_i.prototype.getDefaultPosition=function(){return\"bottom-left\"},_i.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},_i.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t){if(t[e].getSource().mapbox_logo)return!0}return!1}},_i.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var wi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};wi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},wi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},wi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},wi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ti={\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\"},ki=t.window.HTMLImageElement,Ai=t.window.HTMLElement,Mi=t.window.ImageBitmap,Si={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:\"sans-serif\",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Ei=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Si,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new An(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new wi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ti,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof Ai))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1)),this.handlers=new yi(this,e);var a=\"string\"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Sn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new bi({customAttribution:e.customAttribution})),this.addControl(new _i,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r=\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Rn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Rn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Rn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||\"number\"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error(\"Missing UI string '\"+t+\"'\");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new qe(this,e||{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new qe(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if(\"string\"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else\"object\"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce(\"Unable to perform style diff: \"+(n.message||n.error||n)+\".  Rebuilding the style from scratch.\"),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce(\"There is no style added to the map.\")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error(\"There is no source with ID '\"+e+\"'\")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if(\"loaded\"!==i.state&&\"errored\"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof ki||Mi&&r instanceof Mi){var c=t.browser.getImageData(r),u=c.width,f=c.height,h=c.data;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var p=r.width,d=r.height,m=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(m)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));var i=r instanceof ki||Mi&&r instanceof Mi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));var l=!(r instanceof ki||Mi&&r instanceof Mi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){\"rgb(250, 128, 114)\"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue(\"background-color\")&&t.warnOnce(\"This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.\")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add(\"mapboxgl-map\"),(this._missingCSSCanary=r.create(\"div\",\"mapboxgl-canary\",t)).style.visibility=\"hidden\",this._detectMissingCSS();var e=this._canvasContainer=r.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=r.create(\"canvas\",\"mapboxgl-canvas\",e),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create(\"div\",\"mapboxgl-control-container\",t),a=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((function(t){a[t]=r.create(\"div\",\"mapboxgl-ctrl-\"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext(\"webgl\",r)||this._canvas.getContext(\"experimental-webgl\",r);n?(this.painter=new _n(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens(\"gpu-timing-frame\")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens(\"gpu-timing-layer\")}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens(\"gpu-timing-frame\")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event(\"gpu-timing-frame\",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens(\"gpu-timing-layer\")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event(\"gpu-timing-layer\",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event(\"idle\"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1){r[e].onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener(\"resize\",this._onWindowResize,!1),t.window.removeEventListener(\"online\",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");n&&n.loseContext(),Li(this._canvasContainer),Li(this._controlContainer),Li(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this._removed=!0,this.fire(new t.Event(\"remove\"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(xi);function Li(t){t.parentNode&&t.parentNode.removeChild(t)}var Ci={showCompass:!0,showZoom:!0,visualizePitch:!1},Pi=function(e){var n=this;this.options=t.extend({},Ci,e),this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll([\"_setButtonTitle\",\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-zoom-in\",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",!0),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-zoom-out\",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",!0)),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-compass\",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create(\"span\",\"mapboxgl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",!0))};Pi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Pi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?\"scale(\"+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+\") rotateX(\"+this._map.transform.pitch+\"deg) rotateZ(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\":\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassIcon.style.transform=t},Pi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ii(this._map,this._compass,this.options.visualizePitch)),this._container},Pi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Pi.prototype._createButton=function(t,e){var n=r.create(\"button\",t,this._container);return n.type=\"button\",n.addEventListener(\"click\",e),n},Pi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString(\"NavigationControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)};var Ii=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Xn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Zn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll([\"mousedown\",\"mousemove\",\"mouseup\",\"touchstart\",\"touchmove\",\"touchend\",\"reset\"],this),r.addEventListener(n,\"mousedown\",this.mousedown),r.addEventListener(n,\"touchstart\",this.touchstart,{passive:!1}),r.addEventListener(n,\"touchmove\",this.touchmove),r.addEventListener(n,\"touchend\",this.touchend),r.addEventListener(n,\"touchcancel\",this.reset)};function Oi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ii.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ii.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ii.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ii.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ii.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ii.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ii.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ii.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ii.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ii.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ii.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var zi={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Di(t,e,r){var n=t.classList;for(var i in zi)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var Ri,Fi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\",\"_onKeyPress\"],this),this._anchor=n&&n.anchor||\"center\",this._color=n&&n.color||\"#3FB1CE\",this._draggable=n&&n.draggable||!1,this._state=\"inactive\",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||\"auto\",this._pitchAlignment=n&&n.pitchAlignment&&\"auto\"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");var o=r.createNS(\"http://www.w3.org/2000/svg\",\"svg\");o.setAttributeNS(null,\"display\",\"block\"),o.setAttributeNS(null,\"height\",\"41px\"),o.setAttributeNS(null,\"width\",\"27px\"),o.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var s=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"stroke\",\"none\"),s.setAttributeNS(null,\"stroke-width\",\"1\"),s.setAttributeNS(null,\"fill\",\"none\"),s.setAttributeNS(null,\"fill-rule\",\"evenodd\");var l=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"fill-rule\",\"nonzero\");var c=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");c.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),c.setAttributeNS(null,\"fill\",\"#000000\");for(var u=0,f=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];u<f.length;u+=1){var h=f[u],p=r.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");p.setAttributeNS(null,\"opacity\",\"0.04\"),p.setAttributeNS(null,\"cx\",\"10.5\"),p.setAttributeNS(null,\"cy\",\"5.80029008\"),p.setAttributeNS(null,\"rx\",h.rx),p.setAttributeNS(null,\"ry\",h.ry),c.appendChild(p)}var d=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");d.setAttributeNS(null,\"fill\",this._color);var m=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");m.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),d.appendChild(m);var g=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");g.setAttributeNS(null,\"opacity\",\"0.25\"),g.setAttributeNS(null,\"fill\",\"#000000\");var v=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");v.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),g.appendChild(v);var y=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");y.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),y.setAttributeNS(null,\"fill\",\"#FFFFFF\");var x=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");x.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var b=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");b.setAttributeNS(null,\"fill\",\"#000000\"),b.setAttributeNS(null,\"opacity\",\"0.25\"),b.setAttributeNS(null,\"cx\",\"5.5\"),b.setAttributeNS(null,\"cy\",\"5.5\"),b.setAttributeNS(null,\"r\",\"5.4999962\");var _=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");_.setAttributeNS(null,\"fill\",\"#FFFFFF\"),_.setAttributeNS(null,\"cx\",\"5.5\"),_.setAttributeNS(null,\"cy\",\"5.5\"),_.setAttributeNS(null,\"r\",\"5.4999962\"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(g),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",(function(t){t.preventDefault()})),this._element.addEventListener(\"mousedown\",(function(t){t.preventDefault()})),this._element.addEventListener(\"focus\",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Di(this._element,this._anchor,\"marker\"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),t){if(!(\"offset\"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[e,-1*(24.6+e)],\"bottom-right\":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;\"Space\"!==e&&\"Enter\"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e=\"\";\"viewport\"===this._rotationAlignment||\"auto\"===this._rotationAlignment?e=\"rotateZ(\"+this._rotation+\"deg)\":\"map\"===this._rotationAlignment&&(e=\"rotateZ(\"+(this._rotation-this._map.getBearing())+\"deg)\");var n=\"\";\"viewport\"===this._pitchAlignment||\"auto\"===this._pitchAlignment?n=\"rotateX(0deg)\":\"map\"===this._pitchAlignment&&(n=\"rotateX(\"+this._map.getPitch()+\"deg)\"),t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,zi[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px) \"+n+\" \"+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\"))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Bi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Ni=0,ji=!1,Ui=function(e){function n(r){e.call(this),this.options=t.extend({},Bi,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==Ri?n(Ri):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){Ri=\"denied\"!==t.state,n(Ri)})):(Ri=!!t.window.navigator.geolocation,n(Ri)),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Ni=0,ji=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&ji)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Fi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Fi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){var r=e.originalEvent&&\"resize\"===e.originalEvent.type;e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||r||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Ni--,ji=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Ni>1?(e={maximumAge:6e5,timeout:0},ji=!0):(e=this.options.positionOptions,ji=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Vi={maxWidth:100,unit:\"metric\"},Hi=function(e){this.options=t.extend({},Vi,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;if(l>5280)Gi(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\"));else Gi(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else if(r&&\"nautical\"===r.unit){Gi(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\"))}else s>=1e3?Gi(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):Gi(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function Gi(t,e,r,n){var i,a,o,s=(i=r,a=Math.pow(10,(\"\"+Math.floor(i)).length-1),o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o),a*o),l=s/r;t.style.width=e*l+\"px\",t.innerHTML=s+\"&nbsp;\"+n}Hi.prototype.getDefaultPosition=function(){return\"bottom-left\"},Hi.prototype._onMove=function(){qi(this._map,this._container,this.options)},Hi.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},Hi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},Hi.prototype.setUnit=function(t){this.options.unit=t,qi(this._map,this._container,this.options)};var Yi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};Yi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},Yi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Yi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},Yi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},Yi.prototype._isFullscreen=function(){return this._fullscreen},Yi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},Yi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Wi={closeButton:!0,closeOnClick:!0,className:\"\",maxWidth:\"240px\"},Xi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Wi),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this,i=this._lngLat||this._trackPointer;if(this._map&&i&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var a=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),o=this.options.anchor,s=function e(r){if(r){if(\"number\"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),\"top-left\":new t.Point(n,n),\"top-right\":new t.Point(-n,n),bottom:new t.Point(0,-r),\"bottom-left\":new t.Point(n,-n),\"bottom-right\":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,\"top-left\":i,\"top-right\":i,bottom:i,\"bottom-left\":i,\"bottom-right\":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),\"top-left\":t.Point.convert(r[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(r[\"top-right\"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),\"bottom-left\":t.Point.convert(r[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(r[\"bottom-right\"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!o){var l,c=this._container.offsetWidth,u=this._container.offsetHeight;l=a.y+s.bottom.y<u?[\"top\"]:a.y>this._map.transform.height-u?[\"bottom\"]:[],a.x<c/2?l.push(\"left\"):a.x>this._map.transform.width-c/2&&l.push(\"right\"),o=0===l.length?\"bottom\":l.join(\"-\")}var f=a.add(s[o]).round();r.setTransform(this._container,zi[o]+\" translate(\"+f.x+\"px,\"+f.y+\"px)\"),Di(this._container,o,\"popup\")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);var Zi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Ei,NavigationControl:Pi,GeolocateControl:Ui,AttributionControl:bi,ScaleControl:Hi,FullscreenControl:Yi,Popup:Xi,Marker:Fi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(zt)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(zt),Rt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:\"\"};return Zi})),r}))},{}],240:[function(t,e,r){\"use strict\";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],241:[function(t,e,r){\"use strict\";e.exports=function(t,e){e||(e=t,t=window);var r=0,i=0,a=0,o={shift:!1,alt:!1,control:!1,meta:!1},s=!1;function l(t){var e=!1;return\"altKey\"in t&&(e=e||t.altKey!==o.alt,o.alt=!!t.altKey),\"shiftKey\"in t&&(e=e||t.shiftKey!==o.shift,o.shift=!!t.shiftKey),\"ctrlKey\"in t&&(e=e||t.ctrlKey!==o.control,o.control=!!t.ctrlKey),\"metaKey\"in t&&(e=e||t.metaKey!==o.meta,o.meta=!!t.metaKey),e}function c(t,s){var c=n.x(s),u=n.y(s);\"buttons\"in s&&(t=0|s.buttons),(t!==r||c!==i||u!==a||l(s))&&(r=0|t,i=c||0,a=u||0,e&&e(r,i,a,o))}function u(t){c(0,t)}function f(){(r||i||a||o.shift||o.alt||o.meta||o.control)&&(i=a=0,r=0,o.shift=o.alt=o.control=o.meta=!1,e&&e(0,0,0,o))}function h(t){l(t)&&e&&e(r,i,a,o)}function p(t){0===n.buttons(t)?c(0,t):c(r,t)}function d(t){c(r|n.buttons(t),t)}function m(t){c(r&~n.buttons(t),t)}function g(){s||(s=!0,t.addEventListener(\"mousemove\",p),t.addEventListener(\"mousedown\",d),t.addEventListener(\"mouseup\",m),t.addEventListener(\"mouseleave\",u),t.addEventListener(\"mouseenter\",u),t.addEventListener(\"mouseout\",u),t.addEventListener(\"mouseover\",u),t.addEventListener(\"blur\",f),t.addEventListener(\"keyup\",h),t.addEventListener(\"keydown\",h),t.addEventListener(\"keypress\",h),t!==window&&(window.addEventListener(\"blur\",f),window.addEventListener(\"keyup\",h),window.addEventListener(\"keydown\",h),window.addEventListener(\"keypress\",h)))}g();var v={element:t};return Object.defineProperties(v,{enabled:{get:function(){return s},set:function(e){e?g():function(){if(!s)return;s=!1,t.removeEventListener(\"mousemove\",p),t.removeEventListener(\"mousedown\",d),t.removeEventListener(\"mouseup\",m),t.removeEventListener(\"mouseleave\",u),t.removeEventListener(\"mouseenter\",u),t.removeEventListener(\"mouseout\",u),t.removeEventListener(\"mouseover\",u),t.removeEventListener(\"blur\",f),t.removeEventListener(\"keyup\",h),t.removeEventListener(\"keydown\",h),t.removeEventListener(\"keypress\",h),t!==window&&(window.removeEventListener(\"blur\",f),window.removeEventListener(\"keyup\",h),window.removeEventListener(\"keydown\",h),window.removeEventListener(\"keypress\",h))}()},enumerable:!0},buttons:{get:function(){return r},enumerable:!0},x:{get:function(){return i},enumerable:!0},y:{get:function(){return a},enumerable:!0},mods:{get:function(){return o},enumerable:!0}}),v};var n=t(\"mouse-event\")},{\"mouse-event\":243}],242:[function(t,e,r){var n={left:0,top:0};e.exports=function(t,e,r){e=e||t.currentTarget||t.srcElement,Array.isArray(r)||(r=[0,0]);var i=t.clientX||0,a=t.clientY||0,o=(s=e,s===window||s===document||s===document.body?n:s.getBoundingClientRect());var s;return r[0]=i-o.left,r[1]=a-o.top,r}},{}],243:[function(t,e,r){\"use strict\";function n(t){return t.target||t.srcElement||window}r.buttons=function(t){if(\"object\"==typeof t){if(\"buttons\"in t)return t.buttons;if(\"which\"in t){if(2===(e=t.which))return 4;if(3===e)return 2;if(e>0)return 1<<e-1}else if(\"button\"in t){var e;if(1===(e=t.button))return 4;if(2===e)return 2;if(e>=0)return 1<<e}}return 0},r.element=n,r.x=function(t){if(\"object\"==typeof t){if(\"offsetX\"in t)return t.offsetX;var e=n(t).getBoundingClientRect();return t.clientX-e.left}return 0},r.y=function(t){if(\"object\"==typeof t){if(\"offsetY\"in t)return t.offsetY;var e=n(t).getBoundingClientRect();return t.clientY-e.top}return 0}},{}],244:[function(t,e,r){\"use strict\";var n=t(\"to-px\");e.exports=function(t,e,r){\"function\"==typeof t&&(r=!!e,e=t,t=window);var i=n(\"ex\",t),a=function(t){r&&t.preventDefault();var n=t.deltaX||0,a=t.deltaY||0,o=t.deltaZ||0,s=1;switch(t.deltaMode){case 1:s=i;break;case 2:s=window.innerHeight}if(a*=s,o*=s,(n*=s)||a||o)return e(n,a,o,t)};return t.addEventListener(\"wheel\",a),a}},{\"to-px\":314}],245:[function(t,e,r){(function(t,r){(function(){\n",
+       "/*! Native Promise Only\n",
+       "    v0.8.1 (c) Kyle Simpson\n",
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+       "*/\n",
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+       "/*\n",
+       "object-assign\n",
+       "(c) Sindre Sorhus\n",
+       "@license MIT\n",
+       "*/\n",
+       "\"use strict\";var n=Object.getOwnPropertySymbols,i=Object.prototype.hasOwnProperty,a=Object.prototype.propertyIsEnumerable;function o(t){if(null==t)throw new TypeError(\"Object.assign cannot be called with null or undefined\");return Object(t)}e.exports=function(){try{if(!Object.assign)return!1;var t=new String(\"abc\");if(t[5]=\"de\",\"5\"===Object.getOwnPropertyNames(t)[0])return!1;for(var e={},r=0;r<10;r++)e[\"_\"+String.fromCharCode(r)]=r;if(\"0123456789\"!==Object.getOwnPropertyNames(e).map((function(t){return e[t]})).join(\"\"))return!1;var n={};return\"abcdefghijklmnopqrst\".split(\"\").forEach((function(t){n[t]=t})),\"abcdefghijklmnopqrst\"===Object.keys(Object.assign({},n)).join(\"\")}catch(t){return!1}}()?Object.assign:function(t,e){for(var r,s,l=o(t),c=1;c<arguments.length;c++){for(var u in r=Object(arguments[c]))i.call(r,u)&&(l[u]=r[u]);if(n){s=n(r);for(var f=0;f<s.length;f++)a.call(r,s[f])&&(l[s[f]]=r[s[f]])}}return l}},{}],248:[function(t,e,r){\"use strict\";function n(t,e){if(\"string\"!=typeof t)return[t];var r=[t];\"string\"==typeof e||Array.isArray(e)?e={brackets:e}:e||(e={});var n=e.brackets?Array.isArray(e.brackets)?e.brackets:[e.brackets]:[\"{}\",\"[]\",\"()\"],i=e.escape||\"___\",a=!!e.flat;n.forEach((function(t){var e=new RegExp([\"\\\\\",t[0],\"[^\\\\\",t[0],\"\\\\\",t[1],\"]*\\\\\",t[1]].join(\"\")),n=[];function a(e,a,o){var s=r.push(e.slice(t[0].length,-t[1].length))-1;return n.push(s),i+s+i}r.forEach((function(t,n){for(var i,o=0;t!=i;)if(i=t,t=t.replace(e,a),o++>1e4)throw Error(\"References have circular dependency. 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+       "/*\n",
+       " * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc\n",
+       " * @license MIT\n",
+       " * @preserve Project Home: https://github.com/voidqk/polybooljs\n",
+       " */\n",
+       "var n,i=t(\"./lib/build-log\"),a=t(\"./lib/epsilon\"),o=t(\"./lib/intersecter\"),s=t(\"./lib/segment-chainer\"),l=t(\"./lib/segment-selector\"),c=t(\"./lib/geojson\"),u=!1,f=a();function h(t,e,r){var i=n.segments(t),a=n.segments(e),o=r(n.combine(i,a));return n.polygon(o)}n={buildLog:function(t){return!0===t?u=i():!1===t&&(u=!1),!1!==u&&u.list},epsilon:function(t){return f.epsilon(t)},segments:function(t){var e=o(!0,f,u);return 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r=0,n=t[t.length-1][0],i=t[t.length-1][1],a=[],o=0;o<t.length;o++){var s=t[o][0],l=t[o][1];a.push([s,l]),r+=l*n-s*i,n=s,i=l}return r<0!==e&&a.reverse(),a.push([a[0][0],a[0][1]]),a}var u=[];function f(t){var e=[c(t.region,!1)];u.push(e);for(var r=0;r<t.children.length;r++)e.push(h(t.children[r]))}function h(t){for(var e=0;e<t.children.length;e++)f(t.children[e]);return c(t.region,!0)}for(s=0;s<a.children.length;s++)f(a.children[s]);return u.length<=0?{type:\"Polygon\",coordinates:[]}:1==u.length?{type:\"Polygon\",coordinates:u[0]}:{type:\"MultiPolygon\",coordinates:u}}};e.exports=n},{}],258:[function(t,e,r){var n=t(\"./linked-list\");e.exports=function(t,e,r){function i(t,e,n){return{id:r?r.segmentId():-1,start:t,end:e,myFill:{above:n.myFill.above,below:n.myFill.below},otherFill:null}}var a=n.create();function o(t,r){a.insertBefore(t,(function(n){return function(t,r,n,i,a,o){var s=e.pointsCompare(r,a);return 0!==s?s:e.pointsSame(n,o)?0:t!==i?t?1:-1:e.pointAboveOrOnLine(n,i?a:o,i?o:a)?1:-1}(t.isStart,t.pt,r,n.isStart,n.pt,n.other.pt)<0}))}function s(t,e){var r=function(t,e){var r=n.node({isStart:!0,pt:t.start,seg:t,primary:e,other:null,status:null});return o(r,t.end),r}(t,e);return function(t,e,r){var i=n.node({isStart:!1,pt:e.end,seg:e,primary:r,other:t,status:null});t.other=i,o(i,t.pt)}(r,t,e),r}function l(t,e){var n=i(e,t.seg.end,t.seg);return function(t,e){r&&r.segmentChop(t.seg,e),t.other.remove(),t.seg.end=e,t.other.pt=e,o(t.other,t.pt)}(t,e),s(n,t.primary)}function c(i,o){var s=n.create();function c(t){return s.findTransition((function(r){var n,i,a,o,s,l;return(n=t,i=r.ev,a=n.seg.start,o=n.seg.end,s=i.seg.start,l=i.seg.end,e.pointsCollinear(a,s,l)?e.pointsCollinear(o,s,l)||e.pointAboveOrOnLine(o,s,l)?1:-1:e.pointAboveOrOnLine(a,s,l)?1:-1)>0}))}function u(t,n){var i=t.seg,a=n.seg,o=i.start,s=i.end,c=a.start,u=a.end;r&&r.checkIntersection(i,a);var f=e.linesIntersect(o,s,c,u);if(!1===f){if(!e.pointsCollinear(o,s,c))return!1;if(e.pointsSame(o,u)||e.pointsSame(s,c))return!1;var h=e.pointsSame(o,c),p=e.pointsSame(s,u);if(h&&p)return n;var d=!h&&e.pointBetween(o,c,u),m=!p&&e.pointBetween(s,c,u);if(h)return m?l(n,s):l(t,u),n;d&&(p||(m?l(n,s):l(t,u)),l(n,o))}else 0===f.alongA&&(-1===f.alongB?l(t,c):0===f.alongB?l(t,f.pt):1===f.alongB&&l(t,u)),0===f.alongB&&(-1===f.alongA?l(n,o):0===f.alongA?l(n,f.pt):1===f.alongA&&l(n,s));return!1}for(var f=[];!a.isEmpty();){var h=a.getHead();if(r&&r.vert(h.pt[0]),h.isStart){r&&r.segmentNew(h.seg,h.primary);var p=c(h),d=p.before?p.before.ev:null,m=p.after?p.after.ev:null;function g(){if(d){var t=u(h,d);if(t)return t}return!!m&&u(h,m)}r&&r.tempStatus(h.seg,!!d&&d.seg,!!m&&m.seg);var v,y=g();if(y){var x;if(t)(x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below)&&(y.seg.myFill.above=!y.seg.myFill.above);else y.seg.otherFill=h.seg.myFill;r&&r.segmentUpdate(y.seg),h.other.remove(),h.remove()}if(a.getHead()!==h){r&&r.rewind(h.seg);continue}if(t)x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below,h.seg.myFill.below=m?m.seg.myFill.above:i,h.seg.myFill.above=x?!h.seg.myFill.below:h.seg.myFill.below;else if(null===h.seg.otherFill)v=m?h.primary===m.primary?m.seg.otherFill.above:m.seg.myFill.above:h.primary?o:i,h.seg.otherFill={above:v,below:v};r&&r.status(h.seg,!!d&&d.seg,!!m&&m.seg),h.other.status=p.insert(n.node({ev:h}))}else{var b=h.status;if(null===b)throw new Error(\"PolyBool: Zero-length segment detected; your epsilon is probably too small or too large\");if(s.exists(b.prev)&&s.exists(b.next)&&u(b.prev.ev,b.next.ev),r&&r.statusRemove(b.ev.seg),b.remove(),!h.primary){var _=h.seg.myFill;h.seg.myFill=h.seg.otherFill,h.seg.otherFill=_}f.push(h.seg)}a.getHead().remove()}return r&&r.done(),f}return t?{addRegion:function(t){for(var n,i,a,o=t[t.length-1],l=0;l<t.length;l++){n=o,o=t[l];var c=e.pointsCompare(n,o);0!==c&&s((i=c<0?n:o,a=c<0?o:n,{id:r?r.segmentId():-1,start:i,end:a,myFill:{above:null,below:null},otherFill:null}),!0)}},calculate:function(t){return c(t,!1)}}:{calculate:function(t,e,r,n){return t.forEach((function(t){s(i(t.start,t.end,t),!0)})),r.forEach((function(t){s(i(t.start,t.end,t),!1)})),c(e,n)}}}},{\"./linked-list\":259}],259:[function(t,e,r){e.exports={create:function(){var t={root:{root:!0,next:null},exists:function(e){return null!==e&&e!==t.root},isEmpty:function(){return null===t.root.next},getHead:function(){return t.root.next},insertBefore:function(e,r){for(var n=t.root,i=t.root.next;null!==i;){if(r(i))return e.prev=i.prev,e.next=i,i.prev.next=e,void(i.prev=e);n=i,i=i.next}n.next=e,e.prev=n,e.next=null},findTransition:function(e){for(var r=t.root,n=t.root.next;null!==n&&!e(n);)r=n,n=n.next;return{before:r===t.root?null:r,after:n,insert:function(t){return t.prev=r,t.next=n,r.next=t,null!==n&&(n.prev=t),t}}}};return t},node:function(t){return t.prev=null,t.next=null,t.remove=function(){t.prev.next=t.next,t.next&&(t.next.prev=t.prev),t.prev=null,t.next=null},t}}},{}],260:[function(t,e,r){e.exports=function(t,e,r){var n=[],i=[];return t.forEach((function(t){var a=t.start,o=t.end;if(e.pointsSame(a,o))console.warn(\"PolyBool: Warning: Zero-length segment detected; your epsilon is probably too small or too large\");else{r&&r.chainStart(t);for(var s={index:0,matches_head:!1,matches_pt1:!1},l={index:0,matches_head:!1,matches_pt1:!1},c=s,u=0;u<n.length;u++){var f=(g=n[u])[0],h=(g[1],g[g.length-1]);g[g.length-2];if(e.pointsSame(f,a)){if(k(u,!0,!0))break}else if(e.pointsSame(f,o)){if(k(u,!0,!1))break}else if(e.pointsSame(h,a)){if(k(u,!1,!0))break}else if(e.pointsSame(h,o)&&k(u,!1,!1))break}if(c===s)return n.push([a,o]),void(r&&r.chainNew(a,o));if(c===l){r&&r.chainMatch(s.index);var p=s.index,d=s.matches_pt1?o:a,m=s.matches_head,g=n[p],v=m?g[0]:g[g.length-1],y=m?g[1]:g[g.length-2],x=m?g[g.length-1]:g[0],b=m?g[g.length-2]:g[1];return e.pointsCollinear(y,v,d)&&(m?(r&&r.chainRemoveHead(s.index,d),g.shift()):(r&&r.chainRemoveTail(s.index,d),g.pop()),v=y),e.pointsSame(x,d)?(n.splice(p,1),e.pointsCollinear(b,x,v)&&(m?(r&&r.chainRemoveTail(s.index,v),g.pop()):(r&&r.chainRemoveHead(s.index,v),g.shift())),r&&r.chainClose(s.index),void i.push(g)):void(m?(r&&r.chainAddHead(s.index,d),g.unshift(d)):(r&&r.chainAddTail(s.index,d),g.push(d)))}var _=s.index,w=l.index;r&&r.chainConnect(_,w);var T=n[_].length<n[w].length;s.matches_head?l.matches_head?T?(A(_),M(_,w)):(A(w),M(w,_)):M(w,_):l.matches_head?M(_,w):T?(A(_),M(w,_)):(A(w),M(_,w))}function k(t,e,r){return c.index=t,c.matches_head=e,c.matches_pt1=r,c===s?(c=l,!1):(c=null,!0)}function A(t){r&&r.chainReverse(t),n[t].reverse()}function M(t,i){var a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],c=o[0],u=o[1];e.pointsCollinear(l,s,c)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,c,u)&&(r&&r.chainRemoveHead(i,c),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},{}],261:[function(t,e,r){function n(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var i={union:function(t,e){return n(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],e)},intersect:function(t,e){return n(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],e)},difference:function(t,e){return n(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],e)},differenceRev:function(t,e){return n(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],e)},xor:function(t,e){return n(t,[0,2,1,0,2,0,0,1,1,0,0,2,0,1,2,0],e)}};e.exports=i},{}],262:[function(t,e,r){\"use strict\";var n=t(\"stream\").Transform,i=t(\"stream-parser\");function a(){n.call(this,{readableObjectMode:!0})}function o(t,e,r){Error.call(this),Error.captureStackTrace(this,this.constructor),this.name=this.constructor.name,this.message=t,e&&(this.code=e),r&&(this.statusCode=r)}a.prototype=Object.create(n.prototype),a.prototype.constructor=a,i(a.prototype),r.ParserStream=a,r.sliceEq=function(t,e,r){for(var n=e,i=0;i<r.length;)if(t[n++]!==r[i++])return!1;return!0},r.str2arr=function(t,e){var r=[],n=0;if(e&&\"hex\"===e)for(;n<t.length;)r.push(parseInt(t.slice(n,n+2),16)),n+=2;else for(;n<t.length;n++)r.push(255&t.charCodeAt(n));return r},r.readUInt16LE=function(t,e){return t[e]|t[e+1]<<8},r.readUInt16BE=function(t,e){return t[e+1]|t[e]<<8},r.readUInt32LE=function(t,e){return t[e]|t[e+1]<<8|t[e+2]<<16|16777216*t[e+3]},r.readUInt32BE=function(t,e){return t[e+3]|t[e+2]<<8|t[e+1]<<16|16777216*t[e]},o.prototype=Object.create(Error.prototype),o.prototype.constructor=o,r.ProbeError=o},{stream:285,\"stream-parser\":301}],263:[function(t,e,r){\"use strict\";function n(t,e){var r=new Error(t);return r.code=e,r}function i(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function a(t,e,r){this.input=t.subarray(e,r),this.start=e;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if(\"II*\\0\"!==i&&\"MM\\0*\"!==i)throw n(\"invalid TIFF signature\",\"EBADDATA\");this.big_endian=\"M\"===i[0]}a.prototype.each=function(t){this.aborted=!1;var e=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:e}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,t)}},a.prototype.read_uint16=function(t){var e=this.input;if(t+2>e.length)throw n(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?256*e[t]+e[t+1]:e[t]+256*e[t+1]},a.prototype.read_uint32=function(t){var e=this.input;if(t+4>e.length)throw n(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?16777216*e[t]+65536*e[t+1]+256*e[t+2]+e[t+3]:e[t]+256*e[t+1]+65536*e[t+2]+16777216*e[t+3]},a.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},a.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},a.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return r=this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return r=this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return r=this.read_uint32(e);case 9:return 0|(r=this.read_uint32(e));case 5:case 10:case 11:case 12:case 7:default:return null}},a.prototype.scan_ifd=function(t,e,r){var a=this.read_uint16(e);e+=2;for(var o=0;o<a;o++){var s=this.read_uint16(e),l=this.read_uint16(e+2),c=this.read_uint32(e+4),u=this.exif_format_length(l),f=c*u,h=f<=4?e+8:this.read_uint32(e+8),p=!1;if(h+f>this.input.length)throw n(\"unexpected EOF\",\"EBADDATA\");for(var d=[],m=h,g=0;g<c;g++,m+=u){var v=this.exif_format_read(l,m);if(null===v){d=null;break}d.push(v)}if(Array.isArray(d)&&2===l&&(d=i(String.fromCharCode.apply(null,d)))&&\"\\0\"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===r({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:c,entry_offset:e+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);e+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(e)})},e.exports.ExifParser=a,e.exports.get_orientation=function(t){var e=0;try{return new a(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},{}],264:[function(t,e,r){\"use strict\";var n=t(\"./common\").readUInt16BE,i=t(\"./common\").readUInt32BE;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return t.length<r+e||r<8?null:{boxtype:String.fromCharCode.apply(null,t.slice(e+4,e+8)),data:t.slice(e+8,e+r),end:e+r}}function o(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;switch(n.boxtype){case\"ispe\":e.sizes.push({width:i(n.data,4),height:i(n.data,8)});break;case\"irot\":e.transforms.push({type:\"irot\",value:3&n.data[0]});break;case\"imir\":e.transforms.push({type:\"imir\",value:1&n.data[0]})}r=n.end}}function s(t,e,r){for(var n=0,i=0;i<r;i++)n=256*n+(t[e+i]||0);return n}function l(t,e){for(var r=t[4]>>4&15,i=15&t[4],a=t[5]>>4&15,o=n(t,6),l=8,c=0;c<o;c++){var u=n(t,l),f=n(t,l+=2),h=s(t,l+=2,a),p=n(t,l+=a);if(l+=2,0===f&&1===p){var d=s(t,l,r),m=s(t,l+r,i);e.item_loc[u]={length:m,offset:d+h}}l+=p*(r+i)}}function c(t,e){for(var r=n(t,4),i=6,o=0;o<r;o++){var s=a(t,i);if(!s)break;if(\"infe\"===s.boxtype){for(var l=n(s.data,4),c=\"\",u=8;u<s.data.length&&s.data[u];u++)c+=String.fromCharCode(s.data[u]);e.item_inf[c]=l}i=s.end}}function u(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;\"ipco\"===n.boxtype&&o(n.data,e),r=n.end}}e.exports.unbox=a,e.exports.readSizeFromMeta=function(t){var e={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(function(t,e){for(var r=4;;){var n=a(t,r);if(!n)break;\"iprp\"===n.boxtype&&u(n.data,e),\"iloc\"===n.boxtype&&l(n.data,e),\"iinf\"===n.boxtype&&c(n.data,e),r=n.end}}(t,e),e.sizes.length){var r,n,i,o=(r=e.sizes,n=r.reduce((function(t,e){return t.width>e.width||t.width===e.width&&t.height>e.height?t:e})),i=r.reduce((function(t,e){return t.height>e.height||t.height===e.height&&t.width>e.width?t:e})),n.width>i.height||n.width===i.height&&n.height>i.width?n:i),s=1;e.transforms.forEach((function(t){var e={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},r={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if(\"imir\"===t.type&&(s=0===t.value?r[s]:e[s=e[s=r[s]]]),\"irot\"===t.type)for(var n=0;n<t.value;n++)s=e[s]}));var f=null;return e.item_inf.Exif&&(f=e.item_loc[e.item_inf.Exif]),{width:o.width,height:o.height,orientation:e.transforms.length?s:null,variants:e.sizes,exif_location:f}}},e.exports.getMimeType=function(t){var e=String.fromCharCode.apply(null,t.slice(0,4)),r={};r[e]=!0;for(var 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n=t(\"../common\").str2arr,i=t(\"../common\").sliceEq,a=t(\"../common\").readUInt16LE,o=n(\"GIF87a\"),s=n(\"GIF89a\");e.exports=function(t){if(!(t.length<10)&&(i(t,0,o)||i(t,0,s)))return{width:a(t,6),height:a(t,8),type:\"gif\",mime:\"image/gif\",wUnits:\"px\",hUnits:\"px\"}}},{\"../common\":262}],268:[function(t,e,r){\"use strict\";var n=t(\"../common\").readUInt16LE;e.exports=function(t){var e=n(t,0),r=n(t,2),i=n(t,4);if(0===e&&1===r&&i){for(var a=[],o={width:0,height:0},s=0;s<i;s++){var l=t[6+16*s]||256,c=t[6+16*s+1]||256,u={width:l,height:c};a.push(u),(l>o.width||c>o.height)&&(o=u)}return{width:o.width,height:o.height,variants:a,type:\"ico\",mime:\"image/x-icon\",wUnits:\"px\",hUnits:\"px\"}}}},{\"../common\":262}],269:[function(t,e,r){\"use strict\";var n=t(\"../common\").readUInt16BE,i=t(\"../common\").str2arr,a=t(\"../common\").sliceEq,o=t(\"../exif_utils\"),s=i(\"Exif\\0\\0\");e.exports=function(t){if(!(t.length<2)&&255===t[0]&&216===t[1]&&255===t[2])for(var 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n=t(\"../common\").str2arr,i=t(\"../common\").sliceEq,a=t(\"../common\").readUInt32BE,o=n(\"8BPS\\0\\x01\");e.exports=function(t){if(!(t.length<22)&&i(t,0,o))return{width:a(t,18),height:a(t,14),type:\"psd\",mime:\"image/vnd.adobe.photoshop\",wUnits:\"px\",hUnits:\"px\"}}},{\"../common\":262}],272:[function(t,e,r){\"use strict\";function n(t){return\"number\"==typeof t&&isFinite(t)&&t>0}var i=/<[-_.:a-zA-Z0-9][^>]*>/,a=/^<([-_.:a-zA-Z0-9]+:)?svg\\s/,o=/[^-]\\bwidth=\"([^%]+?)\"|[^-]\\bwidth='([^%]+?)'/,s=/\\bheight=\"([^%]+?)\"|\\bheight='([^%]+?)'/,l=/\\bview[bB]ox=\"(.+?)\"|\\bview[bB]ox='(.+?)'/,c=/in$|mm$|cm$|pt$|pc$|px$|em$|ex$/;function u(t){return c.test(t)?t.match(c)[0]:\"px\"}e.exports=function(t){if(function(t){var e,r=0,n=t.length;for(239===t[0]&&187===t[1]&&191===t[2]&&(r=3);r<n&&(32===(e=t[r])||9===e||13===e||10===e);)r++;return r!==n&&60===t[r]}(t)){for(var e=\"\",r=0;r<t.length;r++)e+=String.fromCharCode(t[r]);var c=(e.match(i)||[\"\"])[0];if(a.test(c)){var 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h=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]]},{\"array-bounds\":71,\"color-normalize\":89,\"flatten-vertex-data\":191,\"object-assign\":247,\"pick-by-alias\":253,\"to-float32\":313,\"update-diff\":329}],280:[function(t,e,r){\"use strict\";var n=t(\"color-normalize\"),i=t(\"array-bounds\"),a=t(\"object-assign\"),o=t(\"glslify\"),s=t(\"pick-by-alias\"),l=t(\"flatten-vertex-data\"),c=t(\"earcut\"),u=t(\"array-normalize\"),f=t(\"to-float32\"),h=f.float32,p=f.fract32,d=t(\"es6-weak-map\"),m=t(\"parse-rect\"),g=t(\"array-find-index\");function v(t,e){if(!(this instanceof v))return new v(t,e);if(\"function\"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=v.shaders.has(t)?v.shaders.get(t):v.shaders.set(t,v.createShaders(t)).get(t),this.update(e)}e.exports=v,v.dashMult=2,v.maxPatternLength=256,v.precisionThreshold=3e6,v.maxPoints=1e4,v.maxLines=2048,v.shaders=new d,v.createShaders=function(t){var e,r=t.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),n={primitive:\"triangle strip\",instances:t.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:function(t,e){return\"round\"===e.join?2:1},miterLimit:t.prop(\"miterLimit\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),thickness:t.prop(\"thickness\"),dashTexture:t.prop(\"dashTexture\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),dashLength:t.prop(\"dashLength\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]},depth:t.prop(\"depth\")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:function(t,e){return!e.overlay}},stencil:{enable:!1},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\")},i=t(a({vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\\nattribute vec4 color;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\\n\\t// the order is important\\n\\treturn position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n}\\n\\nvoid main() {\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineOffset = lineTop * 2. - 1.;\\n\\n\\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\\n\\ttangent = normalize(diff * scale * viewport.zw);\\n\\tvec2 normal = vec2(-tangent.y, tangent.x);\\n\\n\\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\\n\\t\\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\\n\\n\\t\\t+ thickness * normal * .5 * lineOffset / viewport.zw;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvoid main() {\\n\\tfloat alpha = 1.;\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:\"back\"},vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\\nattribute vec4 aColor, bColor;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, translate;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\nuniform float miterLimit, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 tangent;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nconst float REVERSE_THRESHOLD = -.875;\\nconst float MIN_DIFF = 1e-6;\\n\\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\\n// TODO: precalculate dot products, normalize things beforehead etc.\\n// TODO: refactor to rectangular algorithm\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nbool isNaN( float val ){\\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\\n}\\n\\nvoid main() {\\n\\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\\n\\n  vec2 adjustedScale;\\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\\n\\n  vec2 scaleRatio = adjustedScale * viewport.zw;\\n\\tvec2 normalWidth = thickness / scaleRatio;\\n\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineBot = 1. - lineTop;\\n\\n\\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\\n\\n\\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\\n\\n\\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\\n\\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\\n\\n\\tvec2 prevDiff = aCoord - prevCoord;\\n\\tvec2 currDiff = bCoord - aCoord;\\n\\tvec2 nextDiff = nextCoord - bCoord;\\n\\n\\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\\n\\tvec2 currTangent = normalize(currDiff * scaleRatio);\\n\\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\\n\\n\\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\\n\\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\\n\\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\\n\\n\\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\\n\\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\\n\\n\\t// collapsed/unidirectional segment cases\\n\\t// FIXME: there should be more elegant solution\\n\\tvec2 prevTanDiff = abs(prevTangent - currTangent);\\n\\tvec2 nextTanDiff = abs(nextTangent - currTangent);\\n\\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\\n\\t\\tstartJoinDirection = currNormal;\\n\\t}\\n\\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\\n\\t\\tendJoinDirection = currNormal;\\n\\t}\\n\\tif (aCoord == bCoord) {\\n\\t\\tendJoinDirection = startJoinDirection;\\n\\t\\tcurrNormal = prevNormal;\\n\\t\\tcurrTangent = prevTangent;\\n\\t}\\n\\n\\ttangent = currTangent;\\n\\n\\t//calculate join shifts relative to normals\\n\\tfloat startJoinShift = dot(currNormal, startJoinDirection);\\n\\tfloat endJoinShift = dot(currNormal, endJoinDirection);\\n\\n\\tfloat startMiterRatio = abs(1. / startJoinShift);\\n\\tfloat endMiterRatio = abs(1. / endJoinShift);\\n\\n\\tvec2 startJoin = startJoinDirection * startMiterRatio;\\n\\tvec2 endJoin = endJoinDirection * endMiterRatio;\\n\\n\\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\\n\\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\\n\\tstartBotJoin = -startTopJoin;\\n\\n\\tendTopJoin = sign(endJoinShift) * endJoin * .5;\\n\\tendBotJoin = -endTopJoin;\\n\\n\\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\\n\\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\\n\\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\\n\\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\\n\\n\\t//miter anti-clipping\\n\\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\\n\\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\\n\\n\\t//prevent close to reverse direction switch\\n\\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\n\\tif (prevReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\\n\\t\\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * 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t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>v.precisionThreshold||e.scale[1]*e.viewport.height>v.precisionThreshold||\"rect\"===e.join||!e.join&&(e.thickness<=2||e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if(\"number\"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin 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Uint8Array})},t=a({},v.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<v.maxLines&&(d.depth=2*(v.maxLines-1-f%v.maxLines)/v.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,\"uint8\"):null),null!=t.viewport&&(d.viewport=m(t.viewport)),d.viewport||(d.viewport=m([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var y,x;if(t.positions.x&&t.positions.y){var b=t.positions.x,_=t.positions.y;x=d.count=Math.max(b.length,_.length),y=new Float64Array(2*x);for(var w=0;w<x;w++)y[2*w]=b[w],y[2*w+1]=_[w]}else y=l(t.positions,\"float64\"),x=d.count=Math.floor(y.length/2);var T=d.bounds=i(y,2);if(d.fill){for(var k=[],A={},M=0,S=0,E=0,L=d.count;S<L;S++){var C=y[2*S],P=y[2*S+1];isNaN(C)||isNaN(P)||null==C||null==P?(C=y[2*M],P=y[2*M+1],A[S]=M):M=S,k[E++]=C,k[E++]=P}if(t.splitNull){d.count-1 in A||(A[d.count]=d.count-1);var I=Object.keys(A).map(Number).sort((function(t,e){return t-e})),O=[],z=0,D=null!=d.hole?d.hole[0]:null;if(null!=D){var R=g(I,(function(t){return t>=D}));(I=I.slice(0,R)).push(D)}for(var F=function(t){var e=k.slice(2*z,2*I[t]).concat(D?k.slice(2*D):[]),r=(d.hole||[]).map((function(e){return e-D+(I[t]-z)})),n=c(e,r);n=n.map((function(e){return e+z+(e+z<I[t]?0:D-I[t])})),O.push.apply(O,n),z=I[t]+1},B=0;B<I.length;B++)F(B);for(var N=0,j=O.length;N<j;N++)null!=A[O[N]]&&(O[N]=A[O[N]]);d.triangles=O}else{for(var U=c(k,d.hole||[]),V=0,H=U.length;V<H;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);d.triangles=U}}var q=new Float64Array(y);u(q,2,T);var G=new 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at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*v.dashMult*.5;ot<st;++ot)$[rt++]=nt;nt^=255}}d.dashLength=tt,d.dashTexture({channels:1,data:$,width:$.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(t.color){var lt=d.count,ct=t.color;ct||(ct=\"transparent\");var ut=new Uint8Array(4*lt+4);if(Array.isArray(ct)&&\"number\"!=typeof ct[0]){for(var ft=0;ft<lt;ft++){var ht=n(ct[ft],\"uint8\");ut.set(ht,4*ft)}ut.set(n(ct[0],\"uint8\"),4*lt)}else for(var pt=n(ct,\"uint8\"),dt=0;dt<lt+1;dt++)ut.set(pt,4*dt);d.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:ut})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var y=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&y.push(this.passes[x]);return this.passes=y,this}},v.prototype.destroy=function(){return 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o={uniforms:{constPointSize:!!e.constPointSize,opacity:t.prop(\"opacity\"),paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},pixelRatio:t.context(\"pixelRatio\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translate:t.prop(\"translate\"),translateFract:t.prop(\"translateFract\"),markerTexture:t.prop(\"markerTexture\"),paletteTexture:n},attributes:{x:function(t,e){return e.xAttr||{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return 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mix(color, fragColor, colorAmt);\\n    color.a *= opacity;\\n\\n    gl_FragColor = color;\\n  }\\n\\n}\\n\"]),s.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\\nuniform sampler2D paletteTexture;\\n\\nconst float maxSize = 100.;\\nconst float borderLevel = .5;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = 2. * size * pointSizeScale;\\n  fragPointSize = size * pixelRatio;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragColor = color;\\n  fragBorderColor = borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n\\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\\n}\"]),this.drawMarker=t(s);var l=f({},o);l.frag=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nuniform float opacity;\\n\\nfloat smoothStep(float edge0, float edge1, float x) {\\n\\tfloat t;\\n\\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\\n\\treturn t * t * (3.0 - 2.0 * t);\\n}\\n\\nvoid main() {\\n\\tfloat radius, alpha = 1.0, delta = fragWidth;\\n\\n\\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\\n\\n\\tif (radius > 1.0 + delta) {\\n\\t\\tdiscard;\\n\\t}\\n\\n\\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\\n\\n\\tfloat borderRadius = fragBorderRadius;\\n\\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\\n\\tvec4 color = mix(fragColor, fragBorderColor, ratio);\\n\\tcolor.a *= alpha * opacity;\\n\\tgl_FragColor = color;\\n}\\n\"]),l.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 paletteSize, scale, scaleFract, translate, translateFract;\\nuniform sampler2D paletteTexture;\\n\\nconst float maxSize = 100.;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = (size + borderSize) * pointSizeScale;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\\n  fragColor = color;\\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n}\\n\"]),g&&(l.frag=l.frag.replace(\"smoothstep\",\"smoothStep\"),s.frag=s.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(l)}b.defaults={color:\"black\",borderColor:\"transparent\",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if(\"number\"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),m=0;m<r.length;m++){var g=r[m];d[g]=p?p[g]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:\"function\"==typeof e?e={ondraw:e}:\"number\"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:\"positions data points\",snap:\"snap cluster lod tree\",size:\"sizes size radius\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",color:\"colors color fill fill-color fillColor\",borderColor:\"borderColors borderColor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range dataBox databox\",viewport:\"viewport viewPort viewBox viewbox\",opacity:\"opacity alpha transparency\",bounds:\"bound bounds boundaries limits\",tooManyColors:\"tooManyColors palette paletteMode optimizePalette enablePalette\"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:\"stream\",type:\"uint8\"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"})},e=f({},b.defaults,e)),e.positions&&!(\"marker\"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!(\"positions\"in e)&&(e.positions=n.positions,delete n.positions);var g=0,x=0;if(d(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=b.defaults.size),g+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=b.defaults.borderSize),g+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=b.defaults.color),e=t.updateColor(e),x++,e},borderColor:function(e,r){return null==e&&(e=b.defaults.borderColor),e=t.updateColor(e),x++,e},bounds:function(t,e,r){return\"range\"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,s=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=m(t,\"float64\");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:\"points\",usage:\"static\",data:e.tree,type:\"uint32\"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=v.float32(t);return i({data:p,usage:\"dynamic\"}),a({data:v.fract32(t,p),usage:\"dynamic\"}),s({data:new Uint8Array(c),type:\"uint8\",usage:\"stream\"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&\"number\"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:\"uint8\",usage:\"static\"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=v.fract(e.scale),e.translateFract=v.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),g){var _=n,w=_.count,T=_.size,k=_.borderSize,A=_.sizeBuffer,M=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)M[2*S]=Math.round(255*(null==T[S]?T:T[S])/s),M[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/s);A({data:M,usage:\"dynamic\"})}if(x){var E,L=n,C=L.count,P=L.color,I=L.borderColor,O=L.colorBuffer;if(t.tooManyColors){if(P.length||I.length){E=new Uint8Array(8*C);for(var z=0;z<C;z++){var D=P[z];E[8*z]=h[4*D],E[8*z+1]=h[4*D+1],E[8*z+2]=h[4*D+2],E[8*z+3]=h[4*D+3];var R=I[z];E[8*z+4]=h[4*R],E[8*z+5]=h[4*R+1],E[8*z+6]=h[4*R+2],E[8*z+7]=h[4*R+3]}}}else if(P.length||I.length){E=new Uint8Array(4*C+2);for(var F=0;F<C;F++)null!=P[F]&&(E[4*F]=P[F]%c,E[4*F+1]=Math.floor(P[F]/c)),null!=I[F]&&(E[4*F+2]=I[F]%c,E[4*F+3]=Math.floor(I[F]/c))}O({data:E||new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return n}))}},b.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:\"linear\",min:\"linear\"})),a},b.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if(\"number\"==typeof t[0]){var a=[];if(Array.isArray(t))for(var o=0;o<t.length;o+=4)a.push(t.slice(o,o+4));else for(var l=0;l<t.length;l+=4)a.push(t.subarray(l,l+4));t=a}for(var u=0;u<t.length;u++){var f=t[u];f=s(f,\"uint8\");var h=c(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[u]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},b.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},b.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var _=t(\"object-assign\");e.exports=function(t,e){var r=new x(t,e),n=r.render.bind(r);return _(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},{\"@plotly/point-cluster\":59,\"array-bounds\":71,\"color-id\":87,\"color-normalize\":89,\"flatten-vertex-data\":191,glslify:227,\"is-iexplorer\":233,\"object-assign\":247,\"parse-rect\":249,\"pick-by-alias\":253,\"to-float32\":313,\"update-diff\":329}],282:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"pick-by-alias\"),a=t(\"array-bounds\"),o=t(\"raf\"),s=t(\"array-range\"),l=t(\"parse-rect\"),c=t(\"flatten-vertex-data\");function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x+s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y+l.height),[a,n,o,i]}function p(t){if(\"number\"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}e.exports=u,u.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},u.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,c=0;c<l.length;c++)i.push(this.passes[l[c]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},u.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:\"data items columns rows values dimensions samples x\",snap:\"snap cluster\",size:\"sizes size radius\",color:\"colors color fill fill-color fillColor\",opacity:\"opacity alpha transparency opaque\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",borderColor:\"borderColors borderColor bordercolor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range ranges databox dataBox\",viewport:\"viewport viewBox viewbox\",domain:\"domain domains area areas\",padding:\"pad padding paddings pads margin margins\",transpose:\"transpose transposed\",diagonal:\"diagonal diag showDiagonal\",upper:\"upper up top upperhalf upperHalf showupperhalf showUpper showUpperHalf\",lower:\"lower low bottom lowerhalf lowerHalf showlowerhalf showLowerHalf showLower\"}),s=this.traces[t]||(this.traces[t]={id:t,buffer:r.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array}),color:\"black\",marker:null,size:12,borderColor:\"transparent\",borderSize:1,viewport:l([r._gl.drawingBufferWidth,r._gl.drawingBufferHeight]),padding:[0,0,0,0],opacity:1,diagonal:!0,upper:!0,lower:!0});if(null!=o.color&&(s.color=o.color),null!=o.size&&(s.size=o.size),null!=o.marker&&(s.marker=o.marker),null!=o.borderColor&&(s.borderColor=o.borderColor),null!=o.borderSize&&(s.borderSize=o.borderSize),null!=o.opacity&&(s.opacity=o.opacity),o.viewport&&(s.viewport=l(o.viewport)),null!=o.diagonal&&(s.diagonal=o.diagonal),null!=o.upper&&(s.upper=o.upper),null!=o.lower&&(s.lower=o.lower),o.data){s.buffer(c(o.data)),s.columns=o.data.length,s.count=o.data[0].length,s.bounds=[];for(var u=0;u<s.columns;u++)s.bounds[u]=a(o.data[u],1)}o.range&&(s.range=o.range,n=s.range&&\"number\"!=typeof s.range[0]),o.domain&&(s.domain=o.domain);var d=!1;null!=o.padding&&(Array.isArray(o.padding)&&o.padding.length===s.columns&&\"number\"==typeof o.padding[o.padding.length-1]?(s.padding=o.padding.map(p),d=!0):s.padding=p(o.padding));var m=s.columns,g=s.count,v=s.viewport.width,y=s.viewport.height,x=s.viewport.x,b=s.viewport.y,_=v/m,w=y/m;s.passes=[];for(var T=0;T<m;T++)for(var k=0;k<m;k++)if((s.diagonal||k!==T)&&(s.upper||!(T>k))&&(s.lower||!(T<k))){var A=f(s.id,T,k),M=this.passes[A]||(this.passes[A]={});if(o.data&&(o.transpose?M.positions={x:{buffer:s.buffer,offset:k,count:g,stride:m},y:{buffer:s.buffer,offset:T,count:g,stride:m}}:M.positions={x:{buffer:s.buffer,offset:k*g,count:g},y:{buffer:s.buffer,offset:T*g,count:g}},M.bounds=h(s.bounds,T,k)),o.domain||o.viewport||o.data){var S=d?h(s.padding,T,k):s.padding;if(s.domain){var E=h(s.domain,T,k),L=E[0],C=E[1],P=E[2],I=E[3];M.viewport=[x+L*v+S[0],b+C*y+S[1],x+P*v-S[2],b+I*y-S[3]]}else M.viewport=[x+k*_+_*S[0],b+T*w+w*S[1],x+(k+1)*_-_*S[2],b+(T+1)*w-w*S[3]]}o.color&&(M.color=s.color),o.size&&(M.size=s.size),o.marker&&(M.marker=s.marker),o.borderSize&&(M.borderSize=s.borderSize),o.borderColor&&(M.borderColor=s.borderColor),o.opacity&&(M.opacity=s.opacity),o.range&&(M.range=n?h(s.range,T,k):s.range||M.bounds),s.passes.push(A)}return this},u.prototype.draw=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=[],i=0;i<e.length;i++)if(\"number\"==typeof e[i]){var a=this.traces[e[i]],o=a.passes,l=a.passOffset;n.push.apply(n,s(l,l+o.length))}else if(e[i].length){var c=e[i],u=this.traces[i],f=u.passes,h=u.passOffset;f=f.map((function(t,e){n[h+e]=c}))}(t=this.scatter).draw.apply(t,n)}else this.scatter.draw();return this},u.prototype.destroy=function(){return 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3||t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),13},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),400},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/8)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]},daysInWeek:function(){return 8},dayOfWeek:function(t,e,r){return(this._validate(t,e,r,n.local.invalidDate).day()+1)%8},weekDay:function(t,e,r){var n=this.dayOfWeek(t,e,r);return n>=2&&n<=6},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{century:o[Math.floor((i.year()-1)/100)+1]||\"\"}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year()+(i.year()<0?1:0),e=i.month(),(r=i.day())+(e>1?16:0)+(e>2?32*(e-2):0)+400*(t-1)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t+.5)-Math.floor(this.jdEpoch)-1;var e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=a},{\"../main\":346,\"object-assign\":247}],335:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Hebrew\",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{\"\":{name:\"Hebrew\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Nisan\",\"Iyar\",\"Sivan\",\"Tammuz\",\"Av\",\"Elul\",\"Tishrei\",\"Cheshvan\",\"Kislev\",\"Tevet\",\"Shevat\",\"Adar\",\"Adar II\"],monthNamesShort:[\"Nis\",\"Iya\",\"Siv\",\"Tam\",\"Av\",\"Elu\",\"Tis\",\"Che\",\"Kis\",\"Tev\",\"She\",\"Ada\",\"Ad2\"],dayNames:[\"Yom Rishon\",\"Yom Sheni\",\"Yom Shlishi\",\"Yom Revi'i\",\"Yom Chamishi\",\"Yom Shishi\",\"Yom Shabbat\"],dayNamesShort:[\"Ris\",\"She\",\"Shl\",\"Rev\",\"Cha\",\"Shi\",\"Sha\"],dayNamesMin:[\"Ri\",\"She\",\"Shl\",\"Re\",\"Ch\",\"Shi\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7*(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)||8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return 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n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},{\"../main\":346,\"object-assign\":247}],339:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if((t=t.split(\".\")).length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{\"../main\":346,\"object-assign\":247}],340:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return 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9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 682*((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var 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n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return 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n=t(\"object-assign\");function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r||\"w\"===r){var n=t.toJD()+e*(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0);i=t.day();\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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r,s,l=o(t,e),c=l.on,u=l.off.concat(l.explicitOff),f={},h=t._fullLayout.annotations;if(!c.length&&!u.length)return;for(r=0;r<c.length;r++)(s=a(t.layout,\"annotations\",h[c[r]])).modifyItem(\"visible\",!0),n.extendFlat(f,s.getUpdateObj());for(r=0;r<u.length;r++)(s=a(t.layout,\"annotations\",h[u[r]])).modifyItem(\"visible\",!1),n.extendFlat(f,s.getUpdateObj());return i.call(\"update\",t,{},f)}}},{\"../../lib\":503,\"../../plot_api/plot_template\":543,\"../../registry\":638}],352:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../color\");e.exports=function(t,e,r,a){a(\"opacity\");var o=a(\"bgcolor\"),s=a(\"bordercolor\"),l=i.opacity(s);a(\"borderpad\");var c=a(\"borderwidth\"),u=a(\"showarrow\");if(a(\"text\",u?\" \":r._dfltTitle.annotation),a(\"textangle\"),n.coerceFont(a,\"font\",r.font),a(\"width\"),a(\"align\"),a(\"height\")&&a(\"valign\"),u){var f,h,p=a(\"arrowside\");-1!==p.indexOf(\"end\")&&(f=a(\"arrowhead\"),h=a(\"arrowsize\")),-1!==p.indexOf(\"start\")&&(a(\"startarrowhead\",f),a(\"startarrowsize\",h)),a(\"arrowcolor\",l?e.bordercolor:i.defaultLine),a(\"arrowwidth\",2*(l&&c||1)),a(\"standoff\"),a(\"startstandoff\")}var d=a(\"hovertext\"),m=r.hoverlabel||{};if(d){var g=a(\"hoverlabel.bgcolor\",m.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a(\"hoverlabel.bordercolor\",m.bordercolor||i.contrast(g));n.coerceFont(a,\"hoverlabel.font\",{family:m.font.family,size:m.font.size,color:m.font.color||v})}a(\"captureevents\",!!d)}},{\"../../lib\":503,\"../color\":366}],353:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib/to_log_range\");e.exports=function(t,e,r,a){e=e||{};var o=\"log\"===r&&\"linear\"===e.type,s=\"linear\"===r&&\"log\"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c=\"annotations[\"+h+\"].\",l[f+\"ref\"]===e._id&&p(f),l[\"a\"+f+\"ref\"]===e._id&&p(\"a\"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{\"../../lib/to_log_range\":531,\"fast-isnumeric\":190}],354:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../plots/array_container_defaults\"),o=t(\"./common_defaults\"),s=t(\"./attributes\");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a(\"visible\"),c=a(\"clicktoshow\");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=[\"x\",\"y\"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var m=f[d],g=i.coerceRef(t,e,p,m,\"\",\"paper\");if(\"paper\"!==g)i.getFromId(p,g)._annIndices.push(e._index);if(i.coercePosition(e,p,a,g,m,.5),u){var v=\"a\"+m,y=i.coerceRef(t,e,p,v,\"pixel\",[\"pixel\",\"paper\"]);\"pixel\"!==y&&y!==g&&(y=e[v]=\"pixel\");var 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r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+\"ref\"],s=-1!==r.indexOf(\"y\"),c=\"domain\"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b=\"annotation-\"+a,_=a+\".annotations\"):(b=\"annotation\",_=\"annotations\");var A=m(t.layout,_,e),M=A.modifyBase,S=A.modifyItem,E=A.getUpdateObj;w._infolayer.selectAll(\".\"+b+'[data-index=\"'+r+'\"]').remove();var L=\"clip\"+w._uid+\"_ann\"+r;if(e._input&&!1!==e.visible){var C={x:{},y:{}},P=+e.textangle||0,I=w._infolayer.append(\"g\").classed(b,!0).attr(\"data-index\",String(r)).style(\"opacity\",e.opacity),O=I.append(\"g\").classed(\"annotation-text-g\",!0),z=k[e.showarrow?\"annotationTail\":\"annotationPosition\"],D=e.captureevents||k.annotationText||z,R=O.append(\"g\").style(\"pointer-events\",D?\"all\":null).call(p,\"pointer\").on(\"click\",(function(){t._dragging=!1,t.emit(\"plotly_clickannotation\",Y(n.event))}));e.hovertext&&R.on(\"mouseover\",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on(\"mouseout\",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append(\"rect\").attr(\"class\",\"bg\").style(\"stroke-width\",F+\"px\").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll(\"#\"+L).data(U?[0]:[]);V.enter().append(\"clipPath\").classed(\"annclip\",!0).attr(\"id\",L).append(\"rect\"),V.exit().remove();var H=e.font,q=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append(\"text\").classed(\"annotation-text\",!0).text(q);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on(\"edit\",(function(r){e.text=r,this.call(W),S(\"text\",r),v&&v.autorange&&M(v._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0),i.call(\"_guiRelayout\",t,E())})):G.call(W)}else n.selectAll(\"#\"+L).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,H).attr({\"text-anchor\":{left:\"start\",right:\"end\"}[e.align]||\"middle\"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll(\"a\");1===r.size()&&r.text()===G.text()&&R.insert(\"a\",\":first-child\").attr({\"xlink:xlink:href\":r.attr(\"xlink:href\"),\"xlink:xlink:show\":r.attr(\"xlink:show\")}).style({cursor:\"pointer\"}).node().appendChild(j.node());var n=R.select(\".annotation-text-math-group\"),f=!n.empty(),m=u.bBox((f?n:G).node()),b=m.width,_=m.height,A=e.width||b,D=e.height||_,B=Math.round(A+2*N),H=Math.round(D+2*N);function q(t,e){return\"auto\"===e&&(e=t<1/3?\"left\":t>2/3?\"right\":\"center\"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=[\"x\",\"y\"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+\"ref\"]||et,nt=e[\"a\"+et+\"ref\"],it={x:v,y:x}[et],at=(P+(\"x\"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=H*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+\"anchor\"],ut=e[et+\"shift\"]*(\"x\"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&\"domain\"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e[\"a\"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt=\"domain\"===ht;\"x\"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var mt=e[\"a\"+et];if(tt=ot*q(.5,e.xanchor)-st*q(.5,e.yanchor),nt===rt){var gt=l.getRefType(nt);\"domain\"===gt?(\"y\"===et&&(mt=1-mt),ft.tail=it._offset+it._length*mt):\"paper\"===gt?\"y\"===et?(mt=1-mt,ft.tail=T.t+T.h*mt):ft.tail=T.l+T.w*mt:ft.tail=it._offset+it.r2p(mt),K=tt}else ft.tail=J+mt,K=tt+mt;ft.text=ft.tail+tt;var vt=w[\"x\"===et?\"width\":\"height\"];if(\"paper\"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),\"pixel\"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*q($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e[\"_\"+et+\"padplus\"]=lt/2+K,e[\"_\"+et+\"padminus\"]=lt/2-K,e[\"_\"+et+\"size\"]=lt,e[\"_\"+et+\"shift\"]=tt}if(W)R.remove();else{var bt=0,_t=0;if(\"left\"!==e.align&&(bt=(A-b)*(\"center\"===e.align?.5:1)),\"top\"!==e.valign&&(_t=(D-_)*(\"middle\"===e.valign?.5:1)),f)n.select(\"svg\").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?L:null,t);else{var wt=N+_t-m.top,Tt=N+bt-m.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?L:null,t)}V.select(\"rect\").call(u.setRect,N,N,A,D),j.call(u.setRect,F/2,F/2,B-F,H-F),R.call(u.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-H/2)),O.attr({transform:\"rotate(\"+P+\",\"+C.x.text+\",\"+C.y.text+\")\"});var kt,At=function(r,n){I.selectAll(\".annotation-arrow-g\").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,m=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,m,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr(\"width\"),z=+j.attr(\"height\"),D=m-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,H=e.arrowcolor,q=e.arrowside,G=I.append(\"g\").style({opacity:c.opacity(H)}).classed(\"annotation-arrow-g\",!0),Y=G.append(\"path\").attr(\"d\",\"M\"+h+\",\"+p+\"L\"+l+\",\"+f).style(\"stroke-width\",V+\"px\").call(c.stroke,c.rgb(H));if(g(Y,q,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var J,K,Q=G.append(\"path\").classed(\"annotation-arrow\",!0).classed(\"anndrag\",!0).classed(\"cursor-move\",!0).attr({d:\"M3,3H-3V-3H3ZM0,0L\"+(h-W)+\",\"+(p-X),transform:s(W,X)}).style(\"stroke-width\",V+6+\"px\").call(c.stroke,\"rgba(0,0,0,0)\").call(c.fill,\"rgba(0,0,0,0)\");d.init({element:Q.node(),gd:t,prepFn:function(){var t=u.getTranslate(R);J=t.x,K=t.y,v&&v.autorange&&M(v._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0)},moveFn:function(t,r){var n=w(J,K),i=n[0]+t,a=n[1]+r;R.call(u.setTranslate,i,a),S(\"x\",y(v,t,\"x\",T,e)),S(\"y\",y(x,r,\"y\",T,e)),e.axref===e.xref&&S(\"ax\",y(v,t,\"ax\",T,e)),e.ayref===e.yref&&S(\"ay\",y(x,r,\"ay\",T,e)),G.attr(\"transform\",s(t,r)),O.attr({transform:\"rotate(\"+P+\",\"+i+\",\"+a+\")\"})},doneFn:function(){i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}};if(e.showarrow&&At(0,0),z)d.init({element:R.node(),gd:t,prepFn:function(){kt=O.attr(\"transform\")},moveFn:function(t,r){var n=\"pointer\";if(e.showarrow)e.axref===e.xref?S(\"ax\",y(v,t,\"ax\",T,e)):S(\"ax\",e.ax+t),e.ayref===e.yref?S(\"ay\",y(x,r,\"ay\",T.w,e)):S(\"ay\",e.ay+r),At(t,r);else{if(a)return;var i,o;if(v)i=y(v,t,\"x\",T,e);else{var l=e._xsize/T.w,c=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(c+t/T.w,l,0,1,e.xanchor)}if(x)o=y(x,r,\"y\",T,e);else{var u=e._ysize/T.h,f=e.y-(e._yshift+e.yshift)/T.h-u/2;o=d.align(f-r/T.h,u,0,1,e.yanchor)}S(\"x\",i),S(\"y\",o),v&&x||(n=d.getCursor(v?.5:i,x?.5:o,e.xanchor,e.yanchor))}O.attr({transform:s(t,r)+kt}),p(R,n)},clickFn:function(r,n){e.captureevents&&t.emit(\"plotly_clickannotation\",Y(n))},doneFn:function(){p(R),i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}}e.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(\".annotation\").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&v(t,r);return a.previousPromises(t)},drawOne:v,drawRaw:x}},{\"../../lib\":503,\"../../lib/setcursor\":524,\"../../lib/svg_text_utils\":529,\"../../plot_api/plot_template\":543,\"../../plots/cartesian/axes\":554,\"../../plots/plots\":619,\"../../registry\":638,\"../color\":366,\"../dragelement\":385,\"../drawing\":388,\"../fx\":406,\"./draw_arrow_head\":356,\"@plotly/d3\":58}],356:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../color\"),a=t(\"./arrow_paths\"),o=t(\"../../lib\"),s=o.strScale,l=o.strRotate,c=o.strTranslate;e.exports=function(t,e,r){var o,u,f,h,p=t.node(),d=a[r.arrowhead||0],m=a[r.startarrowhead||0],g=(r.arrowwidth||1)*(r.arrowsize||1),v=(r.arrowwidth||1)*(r.startarrowsize||1),y=e.indexOf(\"start\")>=0,x=e.indexOf(\"end\")>=0,b=d.backoff*g+r.standoff,_=m.backoff*v+r.startstandoff;if(\"line\"===p.nodeName){o={x:+t.attr(\"x1\"),y:+t.attr(\"y1\")},u={x:+t.attr(\"x2\"),y:+t.attr(\"y2\")};var w=o.x-u.x,T=o.y-u.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void z();if(b){if(b*b>w*w+T*T)return void z();var k=b*Math.cos(f),A=b*Math.sin(f);u.x+=k,u.y+=A,t.attr({x2:u.x,y2:u.y})}if(_){if(_*_>w*w+T*T)return void z();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if(\"path\"===p.nodeName){var E=p.getTotalLength(),L=\"\";if(E<b+_)return void z();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L=\"0px,\"+_+\"px,\";var 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n=t(\"./draw\"),i=t(\"./click\");e.exports={moduleType:\"component\",name:\"annotations\",layoutAttributes:t(\"./attributes\"),supplyLayoutDefaults:t(\"./defaults\"),includeBasePlot:t(\"../../plots/cartesian/include_components\")(\"annotations\"),calcAutorange:t(\"./calc_autorange\"),draw:n.draw,drawOne:n.drawOne,drawRaw:n.drawRaw,hasClickToShow:i.hasClickToShow,onClick:i.onClick,convertCoords:t(\"./convert_coords\")}},{\"../../plots/cartesian/include_components\":567,\"./attributes\":349,\"./calc_autorange\":350,\"./click\":351,\"./convert_coords\":353,\"./defaults\":354,\"./draw\":355}],358:[function(t,e,r){\"use strict\";var 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n=t(\"@plotly/d3\"),i=t(\"tinycolor2\"),a=t(\"../../plots/plots\"),o=t(\"../../registry\"),s=t(\"../../plots/cartesian/axes\"),l=t(\"../dragelement\"),c=t(\"../../lib\"),u=c.strTranslate,f=t(\"../../lib/extend\").extendFlat,h=t(\"../../lib/setcursor\"),p=t(\"../drawing\"),d=t(\"../color\"),m=t(\"../titles\"),g=t(\"../../lib/svg_text_utils\"),v=t(\"../colorscale/helpers\").flipScale,y=t(\"../../plots/cartesian/axis_defaults\"),x=t(\"../../plots/cartesian/position_defaults\"),b=t(\"../../plots/cartesian/layout_attributes\"),_=t(\"../../constants/alignment\"),w=_.LINE_SPACING,T=_.FROM_TL,k=_.FROM_BR,A=t(\"./constants\").cn;e.exports={draw:function(t){var e=t._fullLayout._infolayer.selectAll(\"g.\"+A.colorbar).data(function(t){var e,r,n,i,a=t._fullLayout,o=t.calcdata,s=[];function l(t){return f(t,{_fillcolor:null,_line:{color:null,width:null,dash:null},_levels:{start:null,end:null,size:null},_filllevels:null,_fillgradient:null,_zrange:null})}function c(){\"function\"==typeof 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n=t._fullLayout,i=\"v\"===e.orientation,a={type:\"linear\",range:r,tickmode:e.tickmode,nticks:e.nticks,tick0:e.tick0,dtick:e.dtick,tickvals:e.tickvals,ticktext:e.ticktext,ticks:e.ticks,ticklen:e.ticklen,tickwidth:e.tickwidth,tickcolor:e.tickcolor,showticklabels:e.showticklabels,ticklabelposition:e.ticklabelposition,ticklabeloverflow:e.ticklabeloverflow,tickfont:e.tickfont,tickangle:e.tickangle,tickformat:e.tickformat,exponentformat:e.exponentformat,minexponent:e.minexponent,separatethousands:e.separatethousands,showexponent:e.showexponent,showtickprefix:e.showtickprefix,tickprefix:e.tickprefix,showticksuffix:e.showticksuffix,ticksuffix:e.ticksuffix,title:e.title,showline:!0,anchor:\"free\",side:i?\"right\":\"bottom\",position:1},o=i?\"y\":\"x\",s={type:\"linear\",_id:o+e._id},l={letter:o,font:n.font,noHover:!0,noTickson:!0,noTicklabelmode:!0,calendar:n.calendar};function u(t,e){return c.coerce(a,s,b,t,e)}return y(a,s,u,l,n),x(a,s,u,l),s}(r,e,U);it.position=Z+(o?O+P/R.w:z+I/R.h);var at=-1!==[\"top\",\"bottom\"].indexOf(j);o&&at&&(it.title.side=j,it.titlex=O+P/R.w,it.titley=rt+(\"top\"===N.side?K-I/R.h:I/R.h));o||at||(it.title.side=j,it.titley=z+I/R.h,it.titlex=rt+P/R.w);if(B.color&&\"auto\"===e.tickmode){it.tickmode=\"linear\",it.tick0=q.start;var ot=q.size,st=c.constrain(J/50,4,15)+1,lt=(U[1]-U[0])/((e.nticks||st)*ot);if(lt>1){var ct=Math.pow(10,Math.floor(Math.log(lt)/Math.LN10));ot*=ct*c.roundUp(lt/ct,[2,5,10]),(Math.abs(q.start)/q.size+1e-6)%1<2e-6&&(it.tick0=0)}it.dtick=ot}it.domain=o?[rt+I/R.h,rt+K-I/R.h]:[rt+P/R.w,rt+K-P/R.w],it.setScale(),t.attr(\"transform\",u(Math.round(R.l),Math.round(R.t)));var ut,ft=t.select(\".\"+A.cbtitleunshift).attr(\"transform\",u(-Math.round(R.l),-Math.round(R.t))),ht=it.ticklabelposition,pt=it.title.font.size,dt=t.select(\".\"+A.cbaxis),mt=0,gt=0;function vt(n,i){var 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x=U.map(it.c2p).map(Math.round).sort((function(t,e){return t-e}));y.each((function(t,a){var s=[0===a?U[0]:(Y[a]+Y[a-1])/2,a===Y.length-1?U[1]:(Y[a]+Y[a+1])/2].map(it.c2p).map(Math.round);o&&(s[1]=c.constrain(s[1]+(s[1]>s[0])?1:-1,x[0],x[1]));var l=n.select(this).attr(o?\"x\":\"y\",Q).attr(o?\"y\":\"x\",n.min(s)).attr(o?\"width\":\"height\",Math.max(X,2)).attr(o?\"height\":\"width\",Math.max(n.max(s)-n.min(s),2));if(e._fillgradient)p.gradient(l,r,e._id,o?\"vertical\":\"horizontalreversed\",e._fillgradient,\"fill\");else{var u=H(t).replace(\"e-\",\"\");l.attr(\"fill\",i(u).toHexString())}}));var b=t.select(\".\"+A.cblines).selectAll(\"path.\"+A.cbline).data(B.color&&B.width?W:[]);b.enter().append(\"path\").classed(A.cbline,!0),b.exit().remove(),b.each((function(t){var e=Q,r=Math.round(it.c2p(t))+B.width/2%1;n.select(this).attr(\"d\",\"M\"+(o?e+\",\"+r:r+\",\"+e)+(o?\"h\":\"v\")+X).call(p.lineGroupStyle,B.width,V(t),B.dash)})),dt.selectAll(\"g.\"+it._id+\"tick,path\").remove();var _=Q+X+(M||0)/2-(\"outside\"===e.ticks?1:0),T=s.calcTicks(it),k=s.getTickSigns(it)[2];return s.drawTicks(r,it,{vals:\"inside\"===it.ticks?s.clipEnds(it,T):T,layer:dt,path:s.makeTickPath(it,_,k),transFn:s.makeTransTickFn(it)}),s.drawLabels(r,it,{vals:T,layer:dt,transFn:s.makeTransTickLabelFn(it),labelFns:s.makeLabelFns(it,_)})},function(){if(o&&!at||!o&&at){var t,i,a=it.position||0,s=it._offset+it._length/2;if(\"right\"===j)i=s,t=R.l+R.w*a+10+pt*(it.showticklabels?1:.5);else if(t=s,\"bottom\"===j&&(i=R.t+R.h*a+10+(-1===ht.indexOf(\"inside\")?it.tickfont.size:0)+(\"intside\"!==it.ticks&&e.ticklen||0)),\"top\"===j){var l=N.text.split(\"<br>\").length;i=R.t+R.h*a+10-X-w*pt*l}vt((o?\"h\":\"v\")+it._id+\"title\",{avoid:{selection:n.select(r).selectAll(\"g.\"+it._id+\"tick\"),side:j,offsetTop:o?0:R.t,offsetLeft:o?R.l:0,maxShift:o?D.width:D.height},attributes:{x:t,y:i,\"text-anchor\":\"middle\"},transform:{rotate:o?-90:0,offset:0}})}},a.previousPromises,function(){var n,s=X+M/2;-1===ht.indexOf(\"inside\")&&(n=p.bBox(dt.node()),s+=o?n.width:n.height),ut=ft.select(\"text\");var c=0,f=o&&\"top\"===j,m=!o&&\"right\"===j,g=0;if(ut.node()&&!ut.classed(A.jsPlaceholder)){var y,x=ft.select(\".h\"+it._id+\"title-math-group\").node();x&&(o&&at||!o&&!at)?(c=(n=p.bBox(x)).width,y=n.height):(c=(n=p.bBox(ft.node())).right-R.l-(o?Q:nt),y=n.bottom-R.t-(o?nt:Q),o||\"top\"!==j||(s+=n.height,g=n.height)),m&&(ut.attr(\"transform\",u(c/2+pt/2,0)),c*=2),s=Math.max(s,o?c:y)}var b=2*(o?P:I)+s+S+M/2,w=0;!o&&N.text&&\"bottom\"===C&&z<=0&&(b+=w=b/2,g+=w),D._hColorbarMoveTitle=w,D._hColorbarMoveCBTitle=g;var F=S+M;t.select(\".\"+A.cbbg).attr(\"x\",(o?Q:nt)-F/2-(o?P:0)).attr(\"y\",(o?nt:Q)-(o?J:I+g-w)).attr(o?\"width\":\"height\",Math.max(b-w,2)).attr(o?\"height\":\"width\",Math.max(J+F,2)).call(d.fill,E).call(d.stroke,e.bordercolor).style(\"stroke-width\",S);var B=m?Math.max(c-10,0):0;if(t.selectAll(\".\"+A.cboutline).attr(\"x\",(o?Q:nt+P)+B).attr(\"y\",(o?nt+I-J:Q)+(f?mt:0)).attr(o?\"width\":\"height\",Math.max(X,2)).attr(o?\"height\":\"width\",Math.max(J-(o?2*I+mt:2*P+B),2)).call(d.stroke,e.outlinecolor).style({fill:\"none\",\"stroke-width\":M}),t.attr(\"transform\",u(R.l-(o?$*b:0),R.t-(o?0:(1-tt)*b-g))),!o&&(S||i(E).getAlpha()&&!i.equals(D.paper_bgcolor,E))){var U=dt.selectAll(\"text\"),V=U[0].length,H=t.select(\".\"+A.cbbg).node(),q=p.bBox(H),G=p.getTranslate(t);U.each((function(t,e){var r=V-1;if(0===e||e===r){var n,i=p.bBox(this),a=p.getTranslate(this);if(e===r){var o=i.right+a.x;(n=q.right+G.x+nt-S-2+O-o)>0&&(n=0)}else if(0===e){var s=i.left+a.x;(n=q.left+G.x+nt+S+2-s)<0&&(n=0)}n&&(V<3?this.setAttribute(\"transform\",\"translate(\"+n+\",0) \"+this.getAttribute(\"transform\")):this.setAttribute(\"visibility\",\"hidden\"))}}))}var 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o.warn(\"Fx.hover failed\",e,t),d.unhoverRaw(t,e)}var ct=1/0;function ut(t,r){for(G=0;G<rt.length;G++)if((Y=rt[G])&&Y[0]&&Y[0].trace&&!0===(W=Y[0].trace).visible&&0!==W._length&&-1===[\"carpet\",\"contourcarpet\"].indexOf(W._module.name)){if(\"splom\"===W.type?X=l[Z=0]:(X=v.getSubplot(W),Z=l.indexOf(X)),J=P,v.isUnifiedHover(J)&&(J=J.charAt(0)),$={cd:Y,trace:W,xa:_[Z],ya:w[Z],maxHoverDistance:O,maxSpikeDistance:B,index:!1,distance:Math.min(ct,O),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:p.defaultLine,name:W.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},u[X]&&($.subplot=u[X]._subplot),u._splomScenes&&u._splomScenes[W.uid]&&($.scene=u._splomScenes[W.uid]),tt=et.length,\"array\"===J){var n=e[G];\"pointNumber\"in n?($.index=n.pointNumber,J=\"closest\"):(J=\"\",\"xval\"in n&&(K=n.xval,J=\"x\"),\"yval\"in n&&(Q=n.yval,J=J?\"closest\":\"y\"))}else void 0!==t&&void 0!==r?(K=t,Q=r):(K=V[Z],Q=H[Z]);if(0!==O)if(W._module&&W._module.hoverPoints){var a=W._module.hoverPoints($,K,Q,J,{finiteRange:!0,hoverLayer:u._hoverlayer});if(a)for(var s,c=0;c<a.length;c++)s=a[c],i(s.x0)&&i(s.y0)&&et.push(D(s,P))}else o.log(\"Unrecognized trace type in hover:\",W);if(\"closest\"===P&&et.length>tt&&(et.splice(0,tt),ct=et[0].distance),y&&0!==B&&0===et.length){$.distance=B,$.index=!1;var f=W._module.hoverPoints($,K,Q,\"closest\",{hoverLayer:u._hoverlayer});if(f&&(f=f.filter((function(t){return t.spikeDistance<=B}))),f&&f.length){var h,d=f.filter((function(t){return t.xa.showspikes&&\"hovered data\"!==t.xa.spikesnap}));if(d.length){var m=d[0];i(m.x0)&&i(m.y0)&&(h=ht(m),(!nt.vLinePoint||nt.vLinePoint.spikeDistance>h.spikeDistance)&&(nt.vLinePoint=h))}var g=f.filter((function(t){return t.ya.showspikes&&\"hovered data\"!==t.ya.spikesnap}));if(g.length){var x=g[0];i(x.x0)&&i(x.y0)&&(h=ht(x),(!nt.hLinePoint||nt.hLinePoint.spikeDistance>h.spikeDistance)&&(nt.hLinePoint=h))}}}}}function ft(t,e,r){for(var n,i=null,a=1/0,o=0;o<t.length;o++)n=t[o].spikeDistance,r&&0===o&&(n=-1/0),n<=a&&n<=e&&(i=t[o],a=n);return i}function ht(t){return t?{xa:t.xa,ya:t.ya,x:void 0!==t.xSpike?t.xSpike:(t.x0+t.x1)/2,y:void 0!==t.ySpike?t.ySpike:(t.y0+t.y1)/2,distance:t.distance,spikeDistance:t.spikeDistance,curveNumber:t.trace.index,color:t.color,pointNumber:t.index}:null}ut();var pt={fullLayout:u,container:u._hoverlayer,event:e},dt=t._spikepoints,mt={vLinePoint:nt.vLinePoint,hLinePoint:nt.hLinePoint};t._spikepoints=mt;var gt=function(){et.sort((function(t,e){return t.distance-e.distance})),et=function(t,e){for(var r=e.charAt(0),n=[],i=[],a=[],o=0;o<t.length;o++){var s=t[o];g.traceIs(s.trace,\"bar-like\")||g.traceIs(s.trace,\"box-violin\")?a.push(s):s.trace[r+\"period\"]?i.push(s):n.push(s)}return n.concat(i).concat(a)}(et,P)};gt();var vt=P.charAt(0),yt=(\"x\"===vt||\"y\"===vt)&&et[0]&&L[et[0].trace.type];if(y&&0!==B&&0!==et.length){var xt=ft(et.filter((function(t){return t.ya.showspikes})),B,yt);nt.hLinePoint=ht(xt);var bt=ft(et.filter((function(t){return t.xa.showspikes})),B,yt);nt.vLinePoint=ht(bt)}if(0===et.length){var _t=d.unhoverRaw(t,e);return!y||null===nt.hLinePoint&&null===nt.vLinePoint||F(dt)&&R(t,nt,pt),_t}y&&F(dt)&&R(t,nt,pt);if(v.isXYhover(J)&&0!==et[0].length&&\"splom\"!==et[0].trace.type){var wt=et[0],Tt=(et=E[wt.trace.type]?et.filter((function(t){return t.trace.index===wt.trace.index})):[wt]).length,kt=N(\"x\",wt,u),At=N(\"y\",wt,u);ut(kt,At);var Mt,St=[],Et={},Lt=0,Ct=function(t){var e=E[t.trace.type]?C(t):t.trace.index;if(Et[e]){var r=Et[e]-1,n=St[r];r>0&&Math.abs(t.distance)<Math.abs(n.distance)&&(St[r]=t)}else Lt++,Et[e]=Lt,St.push(t)};for(Mt=0;Mt<Tt;Mt++)Ct(et[Mt]);for(Mt=et.length-1;Mt>Tt-1;Mt--)Ct(et[Mt]);et=St,gt()}var Pt=t._hoverdata,It=[],Ot=j(t),zt=U(t);for(q=0;q<et.length;q++){var Dt=et[q],Rt=v.makeEventData(Dt,Dt.trace,Dt.cd);if(!1!==Dt.hovertemplate){var Ft=!1;Dt.cd[Dt.index]&&Dt.cd[Dt.index].ht&&(Ft=Dt.cd[Dt.index].ht),Dt.hovertemplate=Ft||Dt.trace.hovertemplate||!1}if(Dt.xa&&Dt.ya){var Bt=Dt.x0+Dt.xa._offset,Nt=Dt.x1+Dt.xa._offset,jt=Dt.y0+Dt.ya._offset,Ut=Dt.y1+Dt.ya._offset,Vt=Math.min(Bt,Nt),Ht=Math.max(Bt,Nt),qt=Math.min(jt,Ut),Gt=Math.max(jt,Ut);Rt.bbox={x0:Vt+zt,x1:Ht+zt,y0:qt+Ot,y1:Gt+Ot}}Dt.eventData=[Rt],It.push(Rt)}t._hoverdata=It;var Yt=\"y\"===P&&(rt.length>1||et.length>1)||\"closest\"===P&&it&&et.length>1,Wt=p.combine(u.plot_bgcolor||p.background,u.paper_bgcolor),Xt=I(et,{gd:t,hovermode:P,rotateLabels:Yt,bgColor:Wt,container:u._hoverlayer,outerContainer:u._paper.node(),commonLabelOpts:u.hoverlabel,hoverdistance:u.hoverdistance});v.isUnifiedHover(P)||(!function(t,e,r){var n,i,a,o,s,l,c,u=0,f=1,h=t.size(),p=new Array(h),d=0;function m(t){var e=t[0],r=t[t.length-1];if(i=e.pmin-e.pos-e.dp+e.size,a=r.pos+r.dp+r.size-e.pmax,i>.01){for(s=t.length-1;s>=0;s--)t[s].dp+=i;n=!1}if(!(a<.01)){if(i<-.01){for(s=t.length-1;s>=0;s--)t[s].dp-=a;n=!1}if(n){var c=0;for(o=0;o<t.length;o++)(l=t[o]).pos+l.dp+l.size>e.pmax&&c++;for(o=t.length-1;o>=0&&!(c<=0);o--)(l=t[o]).pos>e.pmax-1&&(l.del=!0,c--);for(o=0;o<t.length&&!(c<=0);o++)if((l=t[o]).pos<e.pmin+1)for(l.del=!0,c--,a=2*l.size,s=t.length-1;s>=0;s--)t[s].dp-=a;for(o=t.length-1;o>=0&&!(c<=0);o--)(l=t[o]).pos+l.dp+l.size>e.pmax&&(l.del=!0,c--)}}}t.each((function(t){var n=t[e],i=\"x\"===n._id.charAt(0),a=n.range;0===d&&a&&a[0]>a[1]!==i&&(f=-1),p[d++]=[{datum:t,traceIndex:t.trace.index,dp:0,pos:t.pos,posref:t.posref,size:t.by*(i?T:1)/2,pmin:0,pmax:i?r.width:r.height}]})),p.sort((function(t,e){return t[0].posref-e[0].posref||f*(e[0].traceIndex-t[0].traceIndex)}));for(;!n&&u<=h;){for(u++,n=!0,o=0;o<p.length-1;){var g=p[o],v=p[o+1],y=g[g.length-1],x=v[0];if((i=y.pos+y.dp+y.size-x.pos-x.dp+x.size)>.01&&y.pmin===x.pmin&&y.pmax===x.pmax){for(s=v.length-1;s>=0;s--)v[s].dp+=i;for(g.push.apply(g,v),p.splice(o+1,1),c=0,s=g.length-1;s>=0;s--)c+=g[s].dp;for(a=c/g.length,s=g.length-1;s>=0;s--)g[s].dp-=a;n=!1}else o++}p.forEach(m)}for(o=p.length-1;o>=0;o--){var b=p[o];for(s=b.length-1;s>=0;s--){var _=b[s],w=_.datum;w.offset=_.dp,w.del=_.del}}}(Xt,Yt?\"xa\":\"ya\",u),z(Xt,Yt,u._invScaleX,u._invScaleY));if(s&&s.tagName){var Zt=g.getComponentMethod(\"annotations\",\"hasClickToShow\")(t,It);f(n.select(s),Zt?\"pointer\":\"\")}if(!s||a||!function(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var i=r[n],a=t._hoverdata[n];if(i.curveNumber!==a.curveNumber||String(i.pointNumber)!==String(a.pointNumber)||String(i.pointNumbers)!==String(a.pointNumbers))return!0}return!1}(t,0,Pt))return;Pt&&t.emit(\"plotly_unhover\",{event:e,points:Pt});t.emit(\"plotly_hover\",{event:e,points:t._hoverdata,xaxes:_,yaxes:w,xvals:V,yvals:H})}(t,e,r,a,s)}))},r.loneHover=function(t,e){var r=!0;Array.isArray(t)||(r=!1,t=[t]);var i=e.gd,a=j(i),o=U(i),s=I(t.map((function(t){var r=t._x0||t.x0||t.x||0,n=t._x1||t.x1||t.x||0,s=t._y0||t.y0||t.y||0,l=t._y1||t.y1||t.y||0,c=t.eventData;if(c){var u=Math.min(r,n),f=Math.max(r,n),h=Math.min(s,l),d=Math.max(s,l),m=t.trace;if(g.traceIs(m,\"gl3d\")){var v=i._fullLayout[m.scene]._scene.container,y=v.offsetLeft,x=v.offsetTop;u+=y,f+=y,h+=x,d+=x}c.bbox={x0:u+o,x1:f+o,y0:h+a,y1:d+a},e.inOut_bbox&&e.inOut_bbox.push(c.bbox)}else c=!1;return{color:t.color||p.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,nameLength:t.nameLength,textAlign:t.textAlign,trace:t.trace||{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:t.hovertemplate||!1,hovertemplateLabels:t.hovertemplateLabels||!1,eventData:c}})),{gd:i,hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor||p.background,container:n.select(e.container),outerContainer:e.outerContainer||e.container}),l=0,c=0;return s.sort((function(t,e){return t.y0-e.y0})).each((function(t,r){var n=t.y0-t.by/2;t.offset=n-5<l?l-n+5:0,l=n+t.by+t.offset,r===e.anchorIndex&&(c=t.offset)})).each((function(t){t.offset-=c})),z(s,!1,i._fullLayout._invScaleX,i._fullLayout._invScaleY),r?s:s.node()};var P=/<extra>([\\s\\S]*)<\\/extra>/;function I(t,e){var r=e.gd,i=r._fullLayout,a=e.hovermode,c=e.rotateLabels,f=e.bgColor,d=e.container,m=e.outerContainer,w=e.commonLabelOpts||{};if(0===t.length)return[[]];var T=e.fontFamily||y.HOVERFONT,k=e.fontSize||y.HOVERFONTSIZE,A=t[0],E=A.xa,L=A.ya,P=a.charAt(0),I=A[P+\"Label\"],z=V(r,m),D=z.top,R=z.width,F=z.height,B=void 0!==I&&A.distance<=e.hoverdistance&&(\"x\"===a||\"y\"===a);if(B){var N,j,U=!0;for(N=0;N<t.length;N++)if(U&&void 0===t[N].zLabel&&(U=!1),j=t[N].hoverinfo||t[N].trace.hoverinfo){var H=Array.isArray(j)?j:j.split(\"+\");if(-1===H.indexOf(\"all\")&&-1===H.indexOf(a)){B=!1;break}}U&&(B=!1)}var q=d.selectAll(\"g.axistext\").data(B?[0]:[]);if(q.enter().append(\"g\").classed(\"axistext\",!0),q.exit().remove(),q.each((function(){var t=n.select(this),e=o.ensureSingle(t,\"path\",\"\",(function(t){t.style({\"stroke-width\":\"1px\"})})),l=o.ensureSingle(t,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),c=w.bgcolor||p.defaultLine,f=w.bordercolor||p.contrast(c),d=p.contrast(c),m={family:w.font.family||T,size:w.font.size||k,color:w.font.color||d};e.style({fill:c,stroke:f}),l.text(I).call(h.font,m).call(u.positionText,0,0).call(u.convertToTspans,r),t.attr(\"transform\",\"\");var g,v,y=V(r,l.node());if(\"x\"===a){var x=\"top\"===E.side?\"-\":\"\";l.attr(\"text-anchor\",\"middle\").call(u.positionText,0,\"top\"===E.side?D-y.bottom-M-S:D-y.top+M+S),g=E._offset+(A.x0+A.x1)/2,v=L._offset+(\"top\"===E.side?0:L._length);var b=y.width/2+S;g<b?(g=b,e.attr(\"d\",\"M-\"+(b-M)+\",0L-\"+(b-2*M)+\",\"+x+M+\"H\"+(S+y.width/2)+\"v\"+x+(2*S+y.height)+\"H-\"+b+\"V\"+x+M+\"Z\")):g>i.width-b?(g=i.width-b,e.attr(\"d\",\"M\"+(b-M)+\",0L\"+b+\",\"+x+M+\"v\"+x+(2*S+y.height)+\"H-\"+b+\"V\"+x+M+\"H\"+(b-2*M)+\"Z\")):e.attr(\"d\",\"M0,0L\"+M+\",\"+x+M+\"H\"+(S+y.width/2)+\"v\"+x+(2*S+y.height)+\"H-\"+(S+y.width/2)+\"V\"+x+M+\"H-\"+M+\"Z\")}else{var _,C,P;\"right\"===L.side?(_=\"start\",C=1,P=\"\",g=E._offset+E._length):(_=\"end\",C=-1,P=\"-\",g=E._offset),v=L._offset+(A.y0+A.y1)/2,l.attr(\"text-anchor\",_),e.attr(\"d\",\"M0,0L\"+P+M+\",\"+M+\"V\"+(S+y.height/2)+\"h\"+P+(2*S+y.width)+\"V-\"+(S+y.height/2)+\"H\"+P+M+\"V-\"+M+\"Z\");var O,z=y.height/2,R=D-y.top-z,F=\"clip\"+i._uid+\"commonlabel\"+L._id;if(g<y.width+2*S+M){O=\"M-\"+(M+S)+\"-\"+z+\"h-\"+(y.width-S)+\"V\"+z+\"h\"+(y.width-S)+\"Z\";var B=y.width-g+S;u.positionText(l,B,R),\"end\"===_&&l.selectAll(\"tspan\").each((function(){var t=n.select(this),e=h.tester.append(\"text\").text(t.text()).call(h.font,m),i=V(r,e.node());Math.round(i.width)<Math.round(y.width)&&t.attr(\"x\",B-i.width),e.remove()}))}else u.positionText(l,C*(S+M),R),O=null;var N=i._topclips.selectAll(\"#\"+F).data(O?[0]:[]);N.enter().append(\"clipPath\").attr(\"id\",F).append(\"path\"),N.exit().remove(),N.select(\"path\").attr(\"d\",O),h.setClipUrl(l,O?F:null,r)}t.attr(\"transform\",s(g,v))})),v.isUnifiedHover(a)){d.selectAll(\"g.hovertext\").remove();var G=t.filter((function(t){return\"none\"!==t.hoverinfo}));if(0===G.length)return;var Y=i.hoverlabel,W=Y.font,X={showlegend:!0,legend:{title:{text:I,font:W},font:W,bgcolor:Y.bgcolor,bordercolor:Y.bordercolor,borderwidth:1,tracegroupgap:7,traceorder:i.legend?i.legend.traceorder:void 0,orientation:\"v\"}},Z={font:W};x(X,Z,r._fullData);var J=Z.legend;J.entries=[];for(var K=0;K<G.length;K++){var Q=G[K];if(\"none\"!==Q.hoverinfo){var $=O(Q,!0,a,i,I),tt=$[0],et=$[1];Q.name=et,Q.text=\"\"!==et?et+\" : 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n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=t(\"../../plots/plots\"),o=t(\"../../registry\"),s=t(\"../../lib/events\"),l=t(\"../dragelement\"),c=t(\"../drawing\"),u=t(\"../color\"),f=t(\"../../lib/svg_text_utils\"),h=t(\"./handle_click\"),p=t(\"./constants\"),d=t(\"../../constants/alignment\"),m=d.LINE_SPACING,g=d.FROM_TL,v=d.FROM_BR,y=t(\"./get_legend_data\"),x=t(\"./style\"),b=t(\"./helpers\");function _(t,e,r,n,i){var a=r.data()[0][0].trace,l={event:i,node:r.node(),curveNumber:a.index,expandedIndex:a._expandedIndex,data:t.data,layout:t.layout,frames:t._transitionData._frames,config:t._context,fullData:t._fullData,fullLayout:t._fullLayout};if(a._group&&(l.group=a._group),o.traceIs(a,\"pie-like\")&&(l.label=r.datum()[0].label),!1!==s.triggerHandler(t,\"plotly_legendclick\",l))if(1===n)e._clickTimeout=setTimeout((function(){t._fullLayout&&h(r,t,n)}),t._context.doubleClickDelay);else 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r=e.currentTarget;r.setAttribute(\"data-attr\",\"zoom\"),r.setAttribute(\"data-val\",\"reset\"),f(t,e),r.setAttribute(\"data-attr\",\"resetLastSave\"),p(t,e),x(t,\"geo\"),x(t,\"mapbox\")}},u.toggleSpikelines={name:\"toggleSpikelines\",title:function(t){return c(t,\"Toggle Spike Lines\")},icon:o.spikeline,attr:\"_cartesianSpikesEnabled\",val:\"on\",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled=\"on\"===r?\"off\":\"on\",n.call(\"_guiRelayout\",t,function(t){for(var e=\"on\"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+\".showspikes\"]=!!e||o._showSpikeInitial}return n}(t))}},u.resetViewMapbox={name:\"resetViewMapbox\",_cat:\"resetView\",title:function(t){return c(t,\"Reset view\")},attr:\"reset\",icon:o.home,click:function(t){x(t,\"mapbox\")}},u.zoomInMapbox={name:\"zoomInMapbox\",_cat:\"zoomin\",title:function(t){return c(t,\"Zoom 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n=t(\"../../lib\"),i=t(\"../color\"),a=t(\"../../plot_api/plot_template\"),o=t(\"./attributes\");e.exports=function(t,e){var r=t.modebar||{},s=a.newContainer(e,\"modebar\");function l(t,e){return n.coerce(r,s,o,t,e)}l(\"orientation\"),l(\"bgcolor\",i.addOpacity(e.paper_bgcolor,.5));var c=i.contrast(i.rgb(e.modebar.bgcolor));l(\"color\",i.addOpacity(c,.3)),l(\"activecolor\",i.addOpacity(c,.7)),l(\"uirevision\",e.uirevision),l(\"add\"),l(\"remove\")}},{\"../../lib\":503,\"../../plot_api/plot_template\":543,\"../color\":366,\"./attributes\":425}],429:[function(t,e,r){\"use strict\";e.exports={moduleType:\"component\",name:\"modebar\",layoutAttributes:t(\"./attributes\"),supplyLayoutDefaults:t(\"./defaults\"),manage:t(\"./manage\")}},{\"./attributes\":425,\"./defaults\":428,\"./manage\":430}],430:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axis_ids\"),i=t(\"../../traces/scatter/subtypes\"),a=t(\"../../registry\"),o=t(\"../fx/helpers\").isUnifiedHover,s=t(\"./modebar\"),l=t(\"./buttons\"),c=t(\"./constants\").DRAW_MODES;e.exports=function(t){var e=t._fullLayout,r=t._context,u=e._modeBar;if(r.displayModeBar||r.watermark){if(!Array.isArray(r.modeBarButtonsToRemove))throw new Error([\"*modeBarButtonsToRemove* configuration options\",\"must be an array.\"].join(\" \"));if(!Array.isArray(r.modeBarButtonsToAdd))throw new Error([\"*modeBarButtonsToAdd* configuration options\",\"must be an array.\"].join(\" \"));var f,h=r.modeBarButtons;f=Array.isArray(h)&&h.length?function(t){for(var e=0;e<t.length;e++)for(var r=t[e],n=0;n<r.length;n++){var i=r[n];if(\"string\"==typeof i){if(void 0===l[i])throw new Error([\"*modeBarButtons* configuration options\",\"invalid button name\"].join(\" \"));t[e][n]=l[i]}}return t}(h):!r.displayModeBar&&r.watermark?[]:function(t){var e=t._fullLayout,r=t._fullData,s=t._context;function u(t,e){if(\"string\"==typeof e){if(e.toLowerCase()===t.toLowerCase())return!0}else{var r=e.name,n=e._cat||e.name;if(r===t||n===t.toLowerCase())return!0}return!1}var f=e.modebar.add;\"string\"==typeof f&&(f=[f]);var h=e.modebar.remove;\"string\"==typeof h&&(h=[h]);var p=s.modeBarButtonsToAdd.concat(f.filter((function(t){for(var e=0;e<s.modeBarButtonsToRemove.length;e++)if(u(t,s.modeBarButtonsToRemove[e]))return!1;return!0}))),d=s.modeBarButtonsToRemove.concat(h.filter((function(t){for(var e=0;e<s.modeBarButtonsToAdd.length;e++)if(u(t,s.modeBarButtonsToAdd[e]))return!1;return!0}))),m=e._has(\"cartesian\"),g=e._has(\"gl3d\"),v=e._has(\"geo\"),y=e._has(\"pie\"),x=e._has(\"funnelarea\"),b=e._has(\"gl2d\"),_=e._has(\"ternary\"),w=e._has(\"mapbox\"),T=e._has(\"polar\"),k=e._has(\"smith\"),A=e._has(\"sankey\"),M=function(t){for(var e=n.list({_fullLayout:t},null,!0),r=0;r<e.length;r++)if(!e[r].fixedrange)return!1;return!0}(e),S=o(e.hovermode),E=[];function L(t){if(t.length){for(var e=[],r=0;r<t.length;r++){for(var n=t[r],i=l[n],a=i.name.toLowerCase(),o=(i._cat||i.name).toLowerCase(),s=!1,c=0;c<d.length;c++){var u=d[c].toLowerCase();if(u===a||u===o){s=!0;break}}s||e.push(l[n])}E.push(e)}}var C=[\"toImage\"];s.showEditInChartStudio?C.push(\"editInChartStudio\"):s.showSendToCloud&&C.push(\"sendDataToCloud\");L(C);var P=[],I=[],O=[],z=[];(m||b||y||x||_)+v+g+w+T+k>1?(I=[\"toggleHover\"],O=[\"resetViews\"]):v?(P=[\"zoomInGeo\",\"zoomOutGeo\"],I=[\"hoverClosestGeo\"],O=[\"resetGeo\"]):g?(I=[\"hoverClosest3d\"],O=[\"resetCameraDefault3d\",\"resetCameraLastSave3d\"]):w?(P=[\"zoomInMapbox\",\"zoomOutMapbox\"],I=[\"toggleHover\"],O=[\"resetViewMapbox\"]):b?I=[\"hoverClosestGl2d\"]:y?I=[\"hoverClosestPie\"]:A?(I=[\"hoverClosestCartesian\",\"hoverCompareCartesian\"],O=[\"resetViewSankey\"]):I=[\"toggleHover\"];m&&(I=[\"toggleSpikelines\",\"hoverClosestCartesian\",\"hoverCompareCartesian\"]);(function(t){for(var e=0;e<t.length;e++)if(!a.traceIs(t[e],\"noHover\"))return!1;return!0}(r)||S)&&(I=[]);!m&&!b||M||(P=[\"zoomIn2d\",\"zoomOut2d\",\"autoScale2d\"],\"resetViews\"!==O[0]&&(O=[\"resetScale2d\"]));g?z=[\"zoom3d\",\"pan3d\",\"orbitRotation\",\"tableRotation\"]:(m||b)&&!M||_?z=[\"zoom2d\",\"pan2d\"]:w||v?z=[\"pan2d\"]:T&&(z=[\"zoom2d\"]);(function(t){for(var e=!1,r=0;r<t.length&&!e;r++){var 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a=n._fullLayout._zoomlayer.selectAll(\".select-outline-\"+r.id);if(a&&n._fullLayout._drawing){var o=v(a,t);o&&i.call(\"_guiRelayout\",n,{shapes:o}),n._fullLayout._drawing=!1}}r.selection={},r.selection.selectionDefs=t.selectionDefs=[],r.selection.mergedPolygons=t.mergedPolygons=[]}function N(t,e,r,n){var i,a,o,s=[],l=e.map((function(t){return t._id})),c=r.map((function(t){return t._id}));for(o=0;o<t.calcdata.length;o++)if(!0===(a=(i=t.calcdata[o])[0].trace).visible&&a._module&&a._module.selectPoints)if(!n||a.subplot!==n&&a.geo!==n)if(\"splom\"===a.type&&a._xaxes[l[0]]&&a._yaxes[c[0]]){var u=h(a._module,i,e[0],r[0]);u.scene=t._fullLayout._splomScenes[a.uid],s.push(u)}else if(\"sankey\"===a.type){var f=h(a._module,i,e[0],r[0]);s.push(f)}else{if(-1===l.indexOf(a.xaxis))continue;if(-1===c.indexOf(a.yaxis))continue;s.push(h(a._module,i,_(t,a.xaxis),_(t,a.yaxis)))}else s.push(h(a._module,i,e[0],r[0]));return s;function h(t,e,r,n){return{_module:t,cd:e,xaxis:r,yaxis:n}}}function j(t){var 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n=t(\"@plotly/d3\"),i=t(\"d3-geo\"),a=i.geoPath,o=i.geoDistance,s=t(\"d3-geo-projection\"),l=t(\"../../registry\"),c=t(\"../../lib\"),u=c.strTranslate,f=t(\"../../components/color\"),h=t(\"../../components/drawing\"),p=t(\"../../components/fx\"),d=t(\"../plots\"),m=t(\"../cartesian/axes\"),g=t(\"../cartesian/autorange\").getAutoRange,v=t(\"../../components/dragelement\"),y=t(\"../cartesian/select\").prepSelect,x=t(\"../cartesian/select\").clearSelect,b=t(\"../cartesian/select\").selectOnClick,_=t(\"./zoom\"),w=t(\"./constants\"),T=t(\"../../lib/geo_location_utils\"),k=t(\"../../lib/topojson_utils\"),A=t(\"topojson-client\").feature;function 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i=t[n],a=i[0].trace;\"choropleth\"===a.type&&(this.hasChoropleth=!0),!0===a.visible&&a._length>0&&a._module.calcGeoJSON(i,e)}if(!this.updateProjection(t,e)){this.viewInitial&&this.scope===r.scope||this.saveViewInitial(r),this.scope=r.scope,this.updateBaseLayers(e,r),this.updateDims(e,r),this.updateFx(e,r),d.generalUpdatePerTraceModule(this.graphDiv,this,t,r);var o=this.layers.frontplot.select(\".scatterlayer\");this.dataPoints.point=o.selectAll(\".point\"),this.dataPoints.text=o.selectAll(\"text\"),this.dataPaths.line=o.selectAll(\".js-line\");var s=this.layers.backplot.select(\".choroplethlayer\");this.dataPaths.choropleth=s.selectAll(\"path\"),this.render()}},S.updateProjection=function(t,e){var r=this.graphDiv,n=e[this.id],l=e._size,u=n.domain,f=n.projection,h=n.lonaxis,p=n.lataxis,d=h._ax,m=p._ax,v=this.projection=function(t){var e=t.projection,r=e.type,n=w.projNames[r];n=\"geo\"+c.titleCase(n);for(var l=(i[n]||s[n])(),u=t._isSatellite?180*Math.acos(1/e.distance)/Math.PI:t._isClipped?w.lonaxisSpan[r]/2:null,f=[\"center\",\"rotate\",\"parallels\",\"clipExtent\"],h=function(t){return t?l:[]},p=0;p<f.length;p++){var d=f[p];\"function\"!=typeof l[d]&&(l[d]=h)}l.isLonLatOverEdges=function(t){if(null===l(t))return!0;if(u){var e=l.rotate();return o(t,[-e[0],-e[1]])>u*Math.PI/180}return!1},l.getPath=function(){return a().projection(l)},l.getBounds=function(t){return l.getPath().bounds(t)},l.precision(w.precision),t._isSatellite&&l.tilt(e.tilt).distance(e.distance);u&&l.clipAngle(u-w.clipPad);return l}(n),y=[[l.l+l.w*u.x[0],l.t+l.h*(1-u.y[1])],[l.l+l.w*u.x[1],l.t+l.h*(1-u.y[0])]],x=n.center||{},b=f.rotation||{},_=h.range||[],T=p.range||[];if(n.fitbounds){d._length=y[1][0]-y[0][0],m._length=y[1][1]-y[0][1],d.range=g(r,d),m.range=g(r,m);var k=(d.range[0]+d.range[1])/2,A=(m.range[0]+m.range[1])/2;if(n._isScoped)x={lon:k,lat:A};else if(n._isClipped){x={lon:k,lat:A},b={lon:k,lat:A,roll:b.roll};var 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Boolean(w.lineLayers[t])}function c(t){return Boolean(w.fillLayers[t])}var u=(this.hasChoropleth?w.layersForChoropleth:w.layers).filter((function(t){return l(t)||c(t)?e[\"show\"+t]:!s(t)||e[t].showgrid})),p=r.framework.selectAll(\".layer\").data(u,String);p.exit().each((function(t){delete a[t],delete o[t],n.select(this).remove()})),p.enter().append(\"g\").attr(\"class\",(function(t){return\"layer \"+t})).each((function(t){var e=a[t]=n.select(this);\"bg\"===t?r.bgRect=e.append(\"rect\").style(\"pointer-events\",\"all\"):s(t)?o[t]=e.append(\"path\").style(\"fill\",\"none\"):\"backplot\"===t?e.append(\"g\").classed(\"choroplethlayer\",!0):\"frontplot\"===t?e.append(\"g\").classed(\"scatterlayer\",!0):l(t)?o[t]=e.append(\"path\").style(\"fill\",\"none\").style(\"stroke-miterlimit\",2):c(t)&&(o[t]=e.append(\"path\").style(\"stroke\",\"none\"))})),p.order(),p.each((function(r){var 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r=this.bounds,n=(e.framewidth||0)/2,i=r[0][0]-n,a=r[0][1]-n,o=r[1][0]-i+n,s=r[1][1]-a+n;h.setRect(this.clipRect,i,a,o,s),this.bgRect.call(h.setRect,i,a,o,s).call(f.fill,e.bgcolor),this.xaxis._offset=i,this.xaxis._length=o,this.yaxis._offset=a,this.yaxis._length=s},S.updateFx=function(t,e){var r=this,i=r.graphDiv,a=r.bgRect,o=t.dragmode,s=t.clickmode;if(!r.isStatic){var u;\"select\"===o?u=function(t,e){(t.range={})[r.id]=[h([e.xmin,e.ymin]),h([e.xmax,e.ymax])]}:\"lasso\"===o&&(u=function(t,e,n){(t.lassoPoints={})[r.id]=n.filtered.map(h)});var f={element:r.bgRect.node(),gd:i,plotinfo:{id:r.id,xaxis:r.xaxis,yaxis:r.yaxis,fillRangeItems:u},xaxes:[r.xaxis],yaxes:[r.yaxis],subplot:r.id,clickFn:function(t){2===t&&x(i)}};\"pan\"===o?(a.node().onmousedown=null,a.call(_(r,e)),a.on(\"dblclick.zoom\",(function(){var t=r.viewInitial,e={};for(var n in 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t=this,e=t.graphDiv,r=e._fullLayout,i=\"clip\"+r._uid+t.id;t.clipDef=r._clips.append(\"clipPath\").attr(\"id\",i),t.clipRect=t.clipDef.append(\"rect\"),t.framework=n.select(t.container).append(\"g\").attr(\"class\",\"geo \"+t.id).call(h.setClipUrl,i,e),t.project=function(e){var r=t.projection(e);return r?[r[0]-t.xaxis._offset,r[1]-t.yaxis._offset]:[null,null]},t.xaxis={_id:\"x\",c2p:function(e){return t.project(e)[0]}},t.yaxis={_id:\"y\",c2p:function(e){return t.project(e)[1]}},t.mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},m.setConvert(t.mockAxis,r)},S.saveViewInitial=function(t){var 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_=(l[3]-l[1])/(l[2]-l[0]);Math.abs(a*_)>Math.abs(s)?(c.boxEnd[1]=c.boxStart[1]+Math.abs(a)*_*(s>=0?1:-1),c.boxEnd[1]<l[1]?(c.boxEnd[1]=l[1],c.boxEnd[0]=c.boxStart[0]+(l[1]-c.boxStart[1])/Math.abs(_)):c.boxEnd[1]>l[3]&&(c.boxEnd[1]=l[3],c.boxEnd[0]=c.boxStart[0]+(l[3]-c.boxStart[1])/Math.abs(_))):(c.boxEnd[0]=c.boxStart[0]+Math.abs(s)/_*(a>=0?1:-1),c.boxEnd[0]<l[0]?(c.boxEnd[0]=l[0],c.boxEnd[1]=c.boxStart[1]+(l[0]-c.boxStart[0])*Math.abs(_)):c.boxEnd[0]>l[2]&&(c.boxEnd[0]=l[2],c.boxEnd[1]=c.boxStart[1]+(l[2]-c.boxStart[0])*Math.abs(_)))}}else c.boxEnabled?(a=c.boxStart[0]!==c.boxEnd[0],s=c.boxStart[1]!==c.boxEnd[1],a||s?(a&&(g(0,c.boxStart[0],c.boxEnd[0]),t.xaxis.autorange=!1),s&&(g(1,c.boxStart[1],c.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),c.boxEnabled=!1,c.boxInited=!1):c.boxInited&&(c.boxInited=!1);break;case\"pan\":c.boxEnabled=!1,c.boxInited=!1,e?(c.panning||(c.dragStart[0]=n,c.dragStart[1]=i),Math.abs(c.dragStart[0]-n)<d&&(n=c.dragStart[0]),Math.abs(c.dragStart[1]-i)<d&&(i=c.dragStart[1]),a=(h-n)*(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)*(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),c.panning=!0,c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations()):c.panning&&(c.panning=!1,t.relayoutCallback())}c.lastPos[0]=n,c.lastPos[1]=i}return c.mouseListener=n(e,f),e.addEventListener(\"touchstart\",(function(t){var r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchmove\",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchend\",(function(t){f(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),c.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),c}},{\"../cartesian/constants\":561,\"has-passive-events\":229,\"mouse-change\":241,\"mouse-event-offset\":242,\"mouse-wheel\":244}],595:[function(t,e,r){\"use strict\";var n=t(\"../cartesian/axes\"),i=t(\"../../lib/str2rgbarray\");function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=[\"xaxis\",\"yaxis\"];o.merge=function(t){var e,r,n,a,o,l,c,u,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?\"\":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]=\"auto\"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf(\"all\"):!!a,f=c?\"allticks\"===a:-1!==String(a).indexOf(\"ticks\"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},o.getLabelPad=function(t,e){var r=e.title.font.size,n=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},o.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},e.exports=function(t){return new a(t)}},{\"../../lib/str2rgbarray\":528,\"../cartesian/axes\":554}],596:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"./scene2d\"),a=t(\"../layout_attributes\"),o=t(\"../../constants/xmlns_namespaces\"),s=t(\"../cartesian/constants\"),l=t(\"../cartesian\"),c=t(\"../../components/fx/layout_attributes\"),u=t(\"../get_data\").getSubplotData;r.name=\"gl2d\",r.attr=[\"xaxis\",\"yaxis\"],r.idRoot=[\"x\",\"y\"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t(\"../cartesian/attributes\"),r.supplyLayoutDefaults=function(t,e,r){e._has(\"cartesian\")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,\"gl2d\",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,\"gl2d\",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":a,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{\"../../components/fx/layout_attributes\":407,\"../../constants/xmlns_namespaces\":480,\"../../plot_api/edit_types\":536,\"../cartesian\":568,\"../cartesian/attributes\":552,\"../cartesian/constants\":561,\"../get_data\":593,\"../layout_attributes\":610,\"./scene2d\":597}],597:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../registry\"),o=t(\"../../plots/cartesian/axes\"),s=t(\"../../components/fx\"),l=t(\"../../../stackgl_modules\").gl_plot2d,c=t(\"../../../stackgl_modules\").gl_spikes2d,u=t(\"../../../stackgl_modules\").gl_select_box,f=t(\"webgl-context\"),h=t(\"./convert\"),p=t(\"./camera\"),d=t(\"../../lib/show_no_webgl_msg\"),m=t(\"../cartesian/constraints\"),g=m.enforce,v=m.clean,y=t(\"../cartesian/autorange\").doAutoRange,x=t(\"../../components/dragelement/helpers\"),b=x.drawMode,_=x.selectMode,w=[\"xaxis\",\"yaxis\"],T=t(\"../cartesian/constants\").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement(\"canvas\"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(\".gl-canvas-focus\"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r);var a=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\";var o=this.mouseContainer=document.createElement(\"div\");o.style.position=\"absolute\",o.style[\"pointer-events\"]=\"auto\",this.pickCanvas=this.container.querySelector(\".gl-canvas-pick\");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener(\"mouseout\",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener(\"mouseover\",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+\"\";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r=\"xaxis\"+e[1],n=\"yaxis\"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+\".range\"]=e.range.slice(),s=i[r._name+\".range\"]=r.range.slice();i[e._name+\".autorange\"]=e.autorange,i[r._name+\".autorange\"]=r.autorange,a.call(\"_storeDirectGUIEdit\",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit(\"plotly_relayout\",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+\"px\",this.mouseContainer.style.height=u.h*(h[1]-h[0])+\"px\",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+\"px\",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+\"px\",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style[\"pointer-events\"]=\"none\",this.mouseContainer.style[\"pointer-events\"]=\"none\"):(this.pickCanvas.style[\"pointer-events\"]=\"auto\",this.mouseContainer.style[\"pointer-events\"]=\"auto\"),this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var m=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var g=this.fullData[m.trace.index]||{},v=m.pointIndex,y=s.castHoverinfo(g,i,v);if(y&&\"all\"!==y){var x=y.split(\"+\");-1===x.indexOf(\"x\")&&(m.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(m.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(m.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(m.textLabel=void 0),-1===x.indexOf(\"name\")&&(m.name=void 0)}s.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",m.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:s.castHoverOption(g,v,\"bgcolor\")||m.color,borderColor:s.castHoverOption(g,v,\"bordercolor\"),fontFamily:s.castHoverOption(g,v,\"font.family\"),fontSize:s.castHoverOption(g,v,\"font.size\"),fontColor:s.castHoverOption(g,v,\"font.color\"),nameLength:s.castHoverOption(g,v,\"namelength\"),textAlign:s.castHoverOption(g,v,\"align\")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../../stackgl_modules\":1120,\"../../components/dragelement/helpers\":384,\"../../components/fx\":406,\"../../lib/show_no_webgl_msg\":525,\"../../plots/cartesian/axes\":554,\"../../registry\":638,\"../cartesian/autorange\":553,\"../cartesian/constants\":561,\"../cartesian/constraints\":562,\"./camera\":594,\"./convert\":595,\"webgl-context\":331}],598:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../components/fx/layout_attributes\"),a=t(\"./scene\"),o=t(\"../get_data\").getSubplotData,s=t(\"../../lib\"),l=t(\"../../constants/xmlns_namespaces\");r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=r.attrRegex=s.counterRegex(\"scene\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,\"gl3d\",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),\"scene\"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{\"../../components/fx/layout_attributes\":407,\"../../constants/xmlns_namespaces\":480,\"../../lib\":503,\"../../plot_api/edit_types\":536,\"../get_data\":593,\"./layout/attributes\":599,\"./layout/defaults\":603,\"./layout/layout_attributes\":604,\"./scene\":608}],599:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],600:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),i=t(\"../../cartesian/layout_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,rangemode:i.rangemode,range:a({},i.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},\"plot\",\"from-root\")},{\"../../../components/color\":366,\"../../../lib/extend\":493,\"../../../plot_api/edit_types\":536,\"../../cartesian/layout_attributes\":569}],601:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"../../../plot_api/plot_template\"),o=t(\"./axis_attributes\"),s=t(\"../../cartesian/type_defaults\"),l=t(\"../../cartesian/axis_defaults\"),c=[\"xaxis\",\"yaxis\",\"zaxis\"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h(\"gridcolor\",n(f.color,r.bgColor,13600/187).toRgbString()),h(\"title.text\",d[0]),f.setScale=i.noop,h(\"showspikes\")&&(h(\"spikesides\"),h(\"spikethickness\"),h(\"spikecolor\",f.color)),h(\"showaxeslabels\"),h(\"showbackground\")&&h(\"backgroundcolor\")}}},{\"../../../lib\":503,\"../../../plot_api/plot_template\":543,\"../../cartesian/axis_defaults\":556,\"../../cartesian/type_defaults\":582,\"./axis_attributes\":600,tinycolor2:312}],602:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=0;r<3;++r){var o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,\"font\"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),\"showline\"in o&&(this.lineEnable[r]=o.showline),\"linecolor\"in o&&(this.lineColor[r]=n(o.linecolor)),\"linewidth\"in o&&(this.lineWidth[r]=o.linewidth),\"showgrid\"in o&&(this.gridEnable[r]=o.showgrid),\"gridcolor\"in o&&(this.gridColor[r]=n(o.gridcolor)),\"gridwidth\"in o&&(this.gridWidth[r]=o.gridwidth),\"log\"===o.type?this.zeroEnable[r]=!1:\"zeroline\"in o&&(this.zeroEnable[r]=o.zeroline),\"zerolinecolor\"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),\"zerolinewidth\"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),\"ticks\"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,\"ticklen\"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),\"tickcolor\"in o&&(this.lineTickColor[r]=n(o.tickcolor)),\"tickwidth\"in o&&(this.lineTickWidth[r]=o.tickwidth),\"tickangle\"in o&&(this.tickAngle[r]=\"auto\"===o.tickangle?-3600:Math.PI*-o.tickangle/180),\"showticklabels\"in o&&(this.tickEnable[r]=o.showticklabels),\"tickfont\"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),\"mirror\"in o?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,\"showbackground\"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{\"../../../lib\":503,\"../../../lib/str2rgbarray\":528}],603:[function(t,e,r){\"use strict\";var n=t(\"../../../lib\"),i=t(\"../../../components/color\"),a=t(\"../../../registry\"),o=t(\"../../subplot_defaults\"),s=t(\"./axis_defaults\"),l=t(\"./layout_attributes\"),c=t(\"../../get_data\").getSubplotData;function u(t,e,r,n){for(var o=r(\"bgcolor\"),l=i.combine(o,n.paper_bgcolor),u=[\"up\",\"center\",\"eye\"],f=0;f<u.length;f++)r(\"camera.\"+u[f]+\".x\"),r(\"camera.\"+u[f]+\".y\"),r(\"camera.\"+u[f]+\".z\");r(\"camera.projection.type\");var h=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),p=r(\"aspectmode\",h?\"manual\":\"auto\");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===p&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode);var d=c(n.fullData,\"gl3d\",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n);var m=n.getDfltFromLayout(\"dragmode\");if(!1!==m&&!m)if(m=\"orbit\",t.camera&&t.camera.up){var g=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(g&&v&&y?y/Math.sqrt(g*g+v*v+y*y)>.999&&(m=\"turntable\"):m=\"turntable\")}else m=\"turntable\";r(\"dragmode\",m),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:\"gl3d\",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":366,\"../../../lib\":503,\"../../../registry\":638,\"../../get_data\":593,\"../../subplot_defaults\":632,\"./axis_defaults\":601,\"./layout_attributes\":604}],604:[function(t,e,r){\"use strict\";var n=t(\"./axis_attributes\"),i=t(\"../../domain\").attributes,a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../lib\").counterRegex;function s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}e.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},{\"../../../lib\":503,\"../../../lib/extend\":493,\"../../domain\":584,\"./axis_attributes\":600}],605:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=[\"xaxis\",\"yaxis\",\"zaxis\"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{\"../../../lib/str2rgbarray\":528}],606:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if(\"auto\"===u.tickmode){u.tickmode=\"linear\";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],\"date\"===u.type&&(p[d].text=p[d].text.replace(/\\<br\\>/g,\" \"));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t(\"../../cartesian/axes\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"],o=[0,0,0]},{\"../../../lib\":503,\"../../cartesian/axes\":554}],607:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],608:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../../stackgl_modules\").gl_plot3d,o=a.createCamera,s=a.createScene,l=t(\"webgl-context\"),c=t(\"has-passive-events\"),u=t(\"../../registry\"),f=t(\"../../lib\"),h=f.preserveDrawingBuffer(),p=t(\"../../plots/cartesian/axes\"),d=t(\"../../components/fx\"),m=t(\"../../lib/str2rgbarray\"),g=t(\"../../lib/show_no_webgl_msg\"),v=t(\"./project\"),y=t(\"./layout/convert\"),x=t(\"./layout/spikes\"),b=t(\"./layout/tick_marks\");function _(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=u.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement(\"canvas\"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e=\"orthographic\"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:\"orbit\"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+\".camera\"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+\".aspectratio\"]=t.glplot.getAspectratio(),\"manual\"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+\".aspectmode\"]=\"manual\")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit(\"plotly_relayout\",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener(\"mouseup\",(function(){i(t)})),t.glplot.canvas.addEventListener(\"wheel\",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener(\"mousemove\",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit(\"plotly_relayouting\",e)}})),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",(function(r){e&&e.emit&&e.emit(\"plotly_webglcontextlost\",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c=Object.keys(e.traces),u=null,h=e.glplot.selection,m=0;m<c.length;++m)\"skip\"!==(t=e.traces[c[m]]).data.hoverinfo&&t.handlePick(h)&&(u=t),t.setContourLevels&&t.setContourLevels();function g(t,r,n){var i=e.fullSceneLayout[t+\"axis\"];return\"log\"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==u){var y=v(e.glplot.cameraParams,h.dataCoordinate);t=u.data;var x,_=r._fullData[t.index],w=h.index,T={xLabel:g(\"x\",h.traceCoordinate[0],t.xhoverformat),yLabel:g(\"y\",h.traceCoordinate[1],t.yhoverformat),zLabel:g(\"z\",h.traceCoordinate[2],t.zhoverformat)},k=d.castHoverinfo(_,e.fullLayout,w),A=(k||\"\").split(\"+\"),M=k&&\"all\"===k;_.hovertemplate||M||(-1===A.indexOf(\"x\")&&(T.xLabel=void 0),-1===A.indexOf(\"y\")&&(T.yLabel=void 0),-1===A.indexOf(\"z\")&&(T.zLabel=void 0),-1===A.indexOf(\"text\")&&(h.textLabel=void 0),-1===A.indexOf(\"name\")&&(u.name=void 0));var S=[];\"cone\"===t.type||\"streamtube\"===t.type?(T.uLabel=g(\"x\",h.traceCoordinate[3],t.uhoverformat),(M||-1!==A.indexOf(\"u\"))&&S.push(\"u: \"+T.uLabel),T.vLabel=g(\"y\",h.traceCoordinate[4],t.vhoverformat),(M||-1!==A.indexOf(\"v\"))&&S.push(\"v: \"+T.vLabel),T.wLabel=g(\"z\",h.traceCoordinate[5],t.whoverformat),(M||-1!==A.indexOf(\"w\"))&&S.push(\"w: \"+T.wLabel),T.normLabel=h.traceCoordinate[6].toPrecision(3),(M||-1!==A.indexOf(\"norm\"))&&S.push(\"norm: \"+T.normLabel),\"streamtube\"===t.type&&(T.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(M||-1!==A.indexOf(\"divergence\"))&&S.push(\"divergence: \"+T.divergenceLabel)),h.textLabel&&S.push(h.textLabel),x=S.join(\"<br>\")):\"isosurface\"===t.type||\"volume\"===t.type?(T.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),S.push(\"value: \"+T.valueLabel),h.textLabel&&S.push(h.textLabel),x=S.join(\"<br>\")):x=h.textLabel;var E={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:w};d.appendArrayPointValue(E,_,w),t._module.eventData&&(E=_._module.eventData(E,h,_,{},w));var L={points:[E]};if(e.fullSceneLayout.hovermode){var C=[];d.loneHover({trace:_,x:(.5+.5*y[0]/y[3])*s,y:(.5-.5*y[1]/y[3])*l,xLabel:T.xLabel,yLabel:T.yLabel,zLabel:T.zLabel,text:x,name:u.name,color:d.castHoverOption(_,w,\"bgcolor\")||u.color,borderColor:d.castHoverOption(_,w,\"bordercolor\"),fontFamily:d.castHoverOption(_,w,\"font.family\"),fontSize:d.castHoverOption(_,w,\"font.size\"),fontColor:d.castHoverOption(_,w,\"font.color\"),nameLength:d.castHoverOption(_,w,\"namelength\"),textAlign:d.castHoverOption(_,w,\"align\"),hovertemplate:f.castOption(_,w,\"hovertemplate\"),hovertemplateLabels:f.extendFlat({},E,T),eventData:[E]},{container:n,gd:r,inOut_bbox:C}),E.bbox=C[0]}h.buttons&&h.distance<5?r.emit(\"plotly_click\",L):r.emit(\"plotly_hover\",L),this.oldEventData=L}else d.loneUnhover(n),this.oldEventData&&r.emit(\"plotly_unhover\",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")};requestAnimationFrame(e)};var k=[\"xaxis\",\"yaxis\",\"zaxis\"];function A(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+\"calendar\"],u=e[\"_\"+o+\"length\"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(m(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&A(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var d,g=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){g[0][a]=1/0,g[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,M=w._trace.data._pad||0;\"ErrorBars\"===w.constructor.name&&s._lowerLogErrorBound?g[0][a]=Math.min(g[0][a],s._lowerLogErrorBound):g[0][a]=Math.min(g[0][a],T[0][a]/h[a]-M),g[1][a]=Math.max(g[1][a],T[1][a]/h[a]+M)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);g[0][a]=Math.min(g[0][a],E),g[1][a]=Math.max(g[1][a],E)}}if(\"rangemode\"in s&&\"tozero\"===s.rangemode&&(g[0][a]=Math.min(g[0][a],0),g[1][a]=Math.max(g[1][a],0)),g[0][a]>g[1][a])g[0][a]=-1,g[1][a]=1;else{var L=g[1][a]-g[0][a];g[0][a]-=L/32,g[1][a]+=L/32}if(\"reversed\"===s.autorange){var C=g[0][a];g[0][a]=g[1][a],g[1][a]=C}}else{var P=s.range;g[0][a]=s.r2l(P[0]),g[1][a]=s.r2l(P[1])}g[0][a]===g[1][a]&&(g[0][a]-=1,g[1][a]+=1),v[a]=g[1][a]-g[0][a],this.glplot.setBounds(a,{min:g[0][a]*h[a],max:g[1][a]*h[a]})}var I=c.aspectmode;if(\"cube\"===I)d=[1,1,1];else if(\"manual\"===I){var O=c.aspectratio;d=[O.x,O.y,O.z]}else{if(\"auto\"!==I&&\"data\"!==I)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var z=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[k[a]]).type];z[a]=Math.pow(D.acc,1/D.count)/h[a]}d=\"data\"===I||Math.max.apply(null,z)/Math.min.apply(null,z)<=4?z:[1,1,1]}c.aspectratio.x=u.aspectratio.x=d[0],c.aspectratio.y=u.aspectratio.y=d[1],c.aspectratio.z=u.aspectratio.z=d[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position=\"absolute\",B.left=F.l+R.x[0]*F.w+\"px\",B.top=F.t+(1-R.y[1])*F.h+\"px\",B.width=F.w*(R.x[1]-R.x[0])+\"px\",B.height=F.h*(R.y[1]-R.y[0])+\"px\"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?\"orthographic\":\"perspective\"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),\"orthographic\"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+\".camera\").get();function n(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+\".aspectratio\").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+\".camera\")).get(),p[this.id+\".camera\"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+\".aspectratio\")).get(),p[this.id+\".aspectratio\"]=o),u.call(\"_storeDirectGUIEdit\",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+\".camera\").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+\".aspectratio\").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if(\"orbit\"===t)r.mode=\"orbit\",r.keyBindingMode=\"rotate\";else if(\"turntable\"===t){r.up=[0,0,1],r.mode=\"turntable\",r.keyBindingMode=\"rotate\";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+\".camera.up\",h={x:0,y:0,z:1},p={};p[c]=h;var d=n.layout;u.call(\"_storeDirectGUIEdit\",d,i._preGUI,p),a.up=h,f.nestedProperty(d,c).set(h)}}else r.keyBindingMode=t;this.fullSceneLayout.hovermode=e},w.toImage=function(t){t||(t=\"png\"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a),function(t,e,r){for(var n=0,i=r-1;n<i;++n,--i)for(var a=0;a<e;++a)for(var o=0;o<4;++o){var s=4*(e*n+a)+o,l=4*(e*i+a)+o,c=t[s];t[s]=t[l],t[l]=c}}(a,r,i),function(t,e,r){for(var n=0;n<r;++n)for(var i=0;i<e;++i){var a=4*(e*n+i),o=t[a+3];if(o>0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(a,r,i);var o=document.createElement(\"canvas\");o.width=r,o.height=i;var 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n,i=this,a=e[i.id];i.map&&a.accesstoken!==i.accessToken&&(i.map.remove(),i.map=null,i.styleObj=null,i.traceHash={},i.layerList=[]),n=i.map?new Promise((function(r,n){i.updateMap(t,e,r,n)})):new Promise((function(r,n){i.createMap(t,e,r,n)})),r.push(n)},_.createMap=function(t,e,r,i){var o=this,s=e[o.id],l=o.styleObj=T(s.style);o.accessToken=s.accesstoken;var c=o.map=new n.Map({container:o.div,style:l.style,center:A(s.center),zoom:s.zoom,bearing:s.bearing,pitch:s.pitch,interactive:!o.isStatic,preserveDrawingBuffer:o.isStatic,doubleClickZoom:!1,boxZoom:!1,attributionControl:!1}).addControl(new n.AttributionControl({compact:!0}));c._canvas.style.left=\"0px\",c._canvas.style.top=\"0px\",o.rejectOnError(i),o.isStatic||o.initFx(t,e);var u=[];u.push(new Promise((function(t){c.once(\"load\",t)}))),u=u.concat(a.fetchTraceGeoData(t)),Promise.all(u).then((function(){o.fillBelowLookup(t,e),o.updateData(t),o.updateLayout(e),o.resolveOnRender(r)})).catch(i)},_.updateMap=function(t,e,r,n){var 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e[o.findIndexOfMin(e,(function(e){return o.angleDist(t,e)}))]}function V(t,e,r){return e?(t.attr(\"display\",null),t.attr(r)):t&&t.attr(\"display\",\"none\"),t}e.exports=function(t,e,r){return new B(t,e,r)},N.plot=function(t,e){for(var r=e[this.id],n=!1,i=0;i<t.length;i++){if(!1===t[i][0].trace.cliponaxis){n=!0;break}}this._hasClipOnAxisFalse=n,this.updateLayers(e,r),this.updateLayout(e,r),f.generalUpdatePerTraceModule(this.gd,this,t,r),this.updateFx(e,r),this.isSmith&&(delete r.realaxis.range,delete r.imaginaryaxis.range)},N.updateLayers=function(t,e){var r=this.isSmith,i=this.layers,a=this.getRadial(e),o=this.getAngular(e),s=S.layerNames,l=s.indexOf(\"frontplot\"),c=s.slice(0,l),u=\"below traces\"===o.layer,f=\"below traces\"===a.layer;u&&c.push(\"angular-line\"),f&&c.push(\"radial-line\"),u&&c.push(\"angular-axis\"),f&&c.push(\"radial-axis\"),c.push(\"frontplot\"),u||c.push(\"angular-line\"),f||c.push(\"radial-line\"),u||c.push(\"angular-axis\"),f||c.push(\"radial-axis\");var h=(r?\"smith\":\"polar\")+\"sublayer\",p=this.framework.selectAll(\".\"+h).data(c,String);p.enter().append(\"g\").attr(\"class\",(function(t){return h+\" \"+t})).each((function(t){var e=i[t]=n.select(this);switch(t){case\"frontplot\":r||e.append(\"g\").classed(\"barlayer\",!0),e.append(\"g\").classed(\"scatterlayer\",!0);break;case\"backplot\":e.append(\"g\").classed(\"maplayer\",!0);break;case\"plotbg\":i.bg=e.append(\"path\");break;case\"radial-grid\":case\"angular-grid\":e.style(\"fill\",\"none\");break;case\"radial-line\":e.append(\"line\").style(\"fill\",\"none\");break;case\"angular-line\":e.append(\"path\").style(\"fill\",\"none\")}})),p.order()},N.updateLayout=function(t,e){var r=this.layers,n=t._size,i=this.getRadial(e),a=this.getAngular(e),o=e.domain.x,s=e.domain.y;this.xOffset=n.l+n.w*o[0],this.yOffset=n.t+n.h*(1-s[1]);var f=this.xLength=n.w*(o[1]-o[0]),h=this.yLength=n.h*(s[1]-s[0]),p=this.getSector(e);this.sectorInRad=p.map(R);var d,m,g,v,y,x=this.sectorBBox=function(t){var e,r,n,i,a=t[0],o=t[1]-a,s=D(a,360),l=s+o,c=Math.cos(R(s)),u=Math.sin(R(s)),f=Math.cos(R(l)),h=Math.sin(R(l));i=s<=90&&l>=90||s>90&&l>=450?1:u<=0&&h<=0?0:Math.max(u,h);e=s<=180&&l>=180||s>180&&l>=540?-1:c>=0&&f>=0?0:Math.min(c,f);r=s<=270&&l>=270||s>270&&l>=630?-1:u>=0&&h>=0?0:Math.min(u,h);n=l>=360?1:c<=0&&f<=0?0:Math.max(c,f);return[e,r,n,i]}(p),b=x[2]-x[0],_=x[3]-x[1],w=h/f,T=Math.abs(_/b);w>T?(d=f,y=(h-(m=f*T))/n.h/2,g=[o[0],o[1]],v=[s[0]+y,s[1]-y]):(m=h,y=(f-(d=h/T))/n.w/2,g=[o[0]+y,o[1]-y],v=[s[0],s[1]]),this.xLength2=d,this.yLength2=m,this.xDomain2=g,this.yDomain2=v;var k,A=this.xOffset2=n.l+n.w*g[0],M=this.yOffset2=n.t+n.h*(1-v[1]),S=this.radius=d/b,E=this.innerRadius=this.getHole(e)*S,L=this.cx=A-S*x[0],C=this.cy=M+S*x[3],P=this.cxx=L-A,I=this.cyy=C-M,O=i.side;\"counterclockwise\"===O?(k=O,O=\"top\"):\"clockwise\"===O&&(k=O,O=\"bottom\"),this.radialAxis=this.mockAxis(t,e,i,{_id:\"x\",side:O,_trueSide:k,domain:[E/n.w,S/n.w]}),this.angularAxis=this.mockAxis(t,e,a,{side:\"right\",domain:[0,Math.PI],autorange:!1}),this.doAutoRange(t,e),this.updateAngularAxis(t,e),this.updateRadialAxis(t,e),this.updateRadialAxisTitle(t,e),this.xaxis=this.mockCartesianAxis(t,e,{_id:\"x\",domain:g}),this.yaxis=this.mockCartesianAxis(t,e,{_id:\"y\",domain:v});var z=this.pathSubplot();this.clipPaths.forTraces.select(\"path\").attr(\"d\",z).attr(\"transform\",l(P,I)),r.frontplot.attr(\"transform\",l(A,M)).call(u.setClipUrl,this._hasClipOnAxisFalse?null:this.clipIds.forTraces,this.gd),r.bg.attr(\"d\",z).attr(\"transform\",l(L,C)).call(c.fill,e.bgcolor)},N.mockAxis=function(t,e,r,n){var i=o.extendFlat({},r,n);return d(i,e,t),i},N.mockCartesianAxis=function(t,e,r){var n=this,i=n.isSmith,a=r._id,s=o.extendFlat({type:\"linear\"},r);p(s,t);var l={x:[0,2],y:[1,3]};return s.setRange=function(){var t=n.sectorBBox,r=l[a],i=n.radialAxis._rl,o=(i[1]-i[0])/(1-n.getHole(e));s.range=[t[r[0]]*o,t[r[1]]*o]},s.isPtWithinRange=\"x\"!==a||i?function(){return!0}:function(t){return n.isPtInside(t)},s.setRange(),s.setScale(),s},N.doAutoRange=function(t,e){var r=this.gd,n=this.radialAxis,i=this.getRadial(e);m(r,n);var 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h.tickText(g,t,!0,!1)})):h.calcTicks(g),k=y?T:h.clipEnds(g,T),A=h.getTickSigns(g)[2];y&&((\"top\"===g.ticks&&\"bottom\"===g.side||\"bottom\"===g.ticks&&\"top\"===g.side)&&(A=-A),\"top\"===g.ticks&&\"top\"===g.side&&(w=-g.ticklen),\"bottom\"===g.ticks&&\"bottom\"===g.side&&(w=g.ticklen)),h.drawTicks(n,g,{vals:T,layer:i[\"radial-axis\"],path:h.makeTickPath(g,0,A),transFn:x,crisp:!1}),h.drawGrid(n,g,{vals:k,layer:i[\"radial-grid\"],path:b,transFn:o.noop,crisp:!1}),h.drawLabels(n,g,{vals:T,layer:i[\"radial-axis\"],transFn:x,labelFns:h.makeLabelFns(g,w)})}var M=r.radialAxisAngle=r.vangles?F(U(R(d.angle),r.vangles)):d.angle,S=l(f,p),E=S+s(-M);V(i[\"radial-axis\"],v&&(d.showticklabels||d.ticks),{transform:E}),V(i[\"radial-grid\"],v&&d.showgrid,{transform:y?\"\":S}),V(i[\"radial-line\"].select(\"line\"),v&&d.showline,{x1:y?-a:u,y1:0,x2:a,y2:0,transform:E}).attr(\"stroke-width\",d.linewidth).call(c.stroke,d.linecolor)},N.updateRadialAxisTitle=function(t,e,r){if(!this.isSmith){var n=this.gd,i=this.radius,a=this.cx,o=this.cy,s=this.getRadial(e),l=this.id+\"title\",c=0;if(s.title){var f=u.bBox(this.layers[\"radial-axis\"].node()).height,h=s.title.font.size,p=s.side;c=\"top\"===p?h:\"counterclockwise\"===p?-(f+.4*h):f+.8*h}var d=void 0!==r?r:this.radialAxisAngle,m=R(d),g=Math.cos(m),v=Math.sin(m),y=a+i/2*g+c*v,b=o-i/2*v+c*g;this.layers[\"radial-axis-title\"]=x.draw(n,l,{propContainer:s,propName:this.id+\".radialaxis.title\",placeholder:z(n,\"Click to enter radial axis title\"),attributes:{x:y,y:b,\"text-anchor\":\"middle\"},transform:{rotate:-d}})}},N.updateAngularAxis=function(t,e){var r=this,n=r.gd,i=r.layers,a=r.radius,u=r.innerRadius,f=r.cx,p=r.cy,d=r.getAngular(e),m=r.angularAxis,g=r.isSmith;g||(r.fillViewInitialKey(\"angularaxis.rotation\",d.rotation),m.setGeometry(),m.setScale());var v=g?function(t){var e=O(r,C([0,t.x]));return Math.atan2(e[0]-f,e[1]-p)-Math.PI/2}:function(t){return m.t2g(t.x)};\"linear\"===m.type&&\"radians\"===m.thetaunit&&(m.tick0=F(m.tick0),m.dtick=F(m.dtick));var y=function(t){return l(f+a*Math.cos(t),p-a*Math.sin(t))},x=g?function(t){var e=O(r,C([0,t.x]));return l(e[0],e[1])}:function(t){return y(v(t))},b=g?function(t){var e=O(r,C([0,t.x])),n=Math.atan2(e[0]-f,e[1]-p)-Math.PI/2;return l(e[0],e[1])+s(-F(n))}:function(t){var e=v(t);return y(e)+s(-F(e))},_=g?function(t){return P(r,t.x,0,1/0)}:function(t){var e=v(t),r=Math.cos(e),n=Math.sin(e);return\"M\"+[f+u*r,p-u*n]+\"L\"+[f+a*r,p-a*n]},w=h.makeLabelFns(m,0).labelStandoff,T={xFn:function(t){var e=v(t);return Math.cos(e)*w},yFn:function(t){var e=v(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(w+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*M)},anchorFn:function(t){var e=v(t),r=Math.cos(e);return Math.abs(r)<.1?\"middle\":r>0?\"start\":\"end\"},heightFn:function(t,e,r){var n=v(t);return-.5*(1+Math.sin(n))*r}},k=j(d);r.angularTickLayout!==k&&(i[\"angular-axis\"].selectAll(\".\"+m._id+\"tick\").remove(),r.angularTickLayout=k);var A,S=g?[1/0].concat(m.tickvals||[]).map((function(t){return h.tickText(m,t,!0,!1)})):h.calcTicks(m);if(g&&(S[0].text=\"\\u221e\",S[0].fontSize*=1.75),\"linear\"===e.gridshape?(A=S.map(v),o.angleDelta(A[0],A[1])<0&&(A=A.slice().reverse())):A=null,r.vangles=A,\"category\"===m.type&&(S=S.filter((function(t){return o.isAngleInsideSector(v(t),r.sectorInRad)}))),m.visible){var E=\"inside\"===m.ticks?-1:1,L=(m.linewidth||1)/2;h.drawTicks(n,m,{vals:S,layer:i[\"angular-axis\"],path:\"M\"+E*L+\",0h\"+E*m.ticklen,transFn:b,crisp:!1}),h.drawGrid(n,m,{vals:S,layer:i[\"angular-grid\"],path:_,transFn:o.noop,crisp:!1}),h.drawLabels(n,m,{vals:S,layer:i[\"angular-axis\"],repositionOnUpdate:!0,transFn:x,labelFns:T})}V(i[\"angular-line\"].select(\"path\"),d.showline,{d:r.pathSubplot(),transform:l(f,p)}).attr(\"stroke-width\",d.linewidth).call(c.stroke,d.linecolor)},N.updateFx=function(t,e){this.gd._context.staticPlot||(!this.isSmith&&(this.updateAngularDrag(t),this.updateRadialDrag(t,e,0),this.updateRadialDrag(t,e,1)),this.updateHoverAndMainDrag(t))},N.updateHoverAndMainDrag=function(t){var e,r,s=this,c=s.isSmith,u=s.gd,f=s.layers,h=t._zoomlayer,p=S.MINZOOM,d=S.OFFEDGE,m=s.radius,x=s.innerRadius,T=s.cx,k=s.cy,A=s.cxx,M=s.cyy,L=s.sectorInRad,C=s.vangles,P=s.radialAxis,I=E.clampTiny,O=E.findXYatLength,z=E.findEnclosingVertexAngles,D=S.cornerHalfWidth,R=S.cornerLen/2,F=g.makeDragger(f,\"path\",\"maindrag\",\"crosshair\");n.select(F).attr(\"d\",s.pathSubplot()).attr(\"transform\",l(T,k)),F.onmousemove=function(t){y.hover(u,t,s.id),u._fullLayout._lasthover=F,u._fullLayout._hoversubplot=s.id},F.onmouseout=function(t){u._dragging||v.unhover(u,t)};var B,N,j,U,V,H,q,G,Y,W={element:F,gd:u,subplot:s.id,plotinfo:{id:s.id,xaxis:s.xaxis,yaxis:s.yaxis},xaxes:[s.xaxis],yaxes:[s.yaxis]};function X(t,e){return Math.sqrt(t*t+e*e)}function Z(t,e){return X(t-A,e-M)}function J(t,e){return Math.atan2(M-e,t-A)}function K(t,e){return[t*Math.cos(e),t*Math.sin(-e)]}function Q(t,e){if(0===t)return s.pathSector(2*D);var r=R/t,n=e-r,i=e+r,a=Math.max(0,Math.min(t,m)),o=a-D,l=a+D;return\"M\"+K(o,n)+\"A\"+[o,o]+\" 0,0,0 \"+K(o,i)+\"L\"+K(l,i)+\"A\"+[l,l]+\" 0,0,1 \"+K(l,n)+\"Z\"}function $(t,e,r){if(0===t)return s.pathSector(2*D);var n,i,a=K(t,e),o=K(t,r),l=I((a[0]+o[0])/2),c=I((a[1]+o[1])/2);if(l&&c){var u=c/l,f=-1/u,h=O(D,u,l,c);n=O(R,f,h[0][0],h[0][1]),i=O(R,f,h[1][0],h[1][1])}else{var p,d;c?(p=R,d=D):(p=D,d=R),n=[[l-p,c-d],[l+p,c-d]],i=[[l-p,c+d],[l+p,c+d]]}return\"M\"+n.join(\"L\")+\"L\"+i.reverse().join(\"L\")+\"Z\"}function tt(t,e){return e=Math.max(Math.min(e,m),x),t<d?t=0:m-t<d?t=m:e<d?e=0:m-e<d&&(e=m),Math.abs(e-t)>p?(t<e?(j=t,U=e):(j=e,U=t),!0):(j=null,U=null,!1)}function et(t,e){t=t||V,e=e||\"M0,0Z\",G.attr(\"d\",t),Y.attr(\"d\",e),g.transitionZoombox(G,Y,H,q),H=!0;var r={};ot(r),u.emit(\"plotly_relayouting\",r)}function rt(t,n){var i,a,o=B+(t*=e),l=N+(n*=r),c=Z(B,N),u=Math.min(Z(o,l),m),f=J(B,N);tt(c,u)&&(i=V+s.pathSector(U),j&&(i+=s.pathSector(j)),a=Q(j,f)+Q(U,f)),et(i,a)}function 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n=t(\"../../components/color/attributes\"),i=t(\"../domain\").attributes,a=t(\"../cartesian/layout_attributes\"),o=t(\"../../plot_api/edit_types\").overrideAll,s=t(\"../../lib/extend\").extendFlat,l={title:{text:a.title.text,font:a.title.font},color:a.color,tickmode:a.tickmode,nticks:s({},a.nticks,{dflt:6,min:1}),tick0:a.tick0,dtick:a.dtick,tickvals:a.tickvals,ticktext:a.ticktext,ticks:a.ticks,ticklen:a.ticklen,tickwidth:a.tickwidth,tickcolor:a.tickcolor,showticklabels:a.showticklabels,showtickprefix:a.showtickprefix,tickprefix:a.tickprefix,showticksuffix:a.showticksuffix,ticksuffix:a.ticksuffix,showexponent:a.showexponent,exponentformat:a.exponentformat,minexponent:a.minexponent,separatethousands:a.separatethousands,tickfont:a.tickfont,tickangle:a.tickangle,tickformat:a.tickformat,tickformatstops:a.tickformatstops,hoverformat:a.hoverformat,showline:s({},a.showline,{dflt:!0}),linecolor:a.linecolor,linewidth:a.linewidth,showgrid:s({},a.showgrid,{dflt:!0}),gridcolor:a.gridcolor,gridwidth:a.gridwidth,layer:a.layer,min:{valType:\"number\",dflt:0,min:0},_deprecated:{title:a._deprecated.title,titlefont:a._deprecated.titlefont}},c=e.exports=o({domain:i({name:\"ternary\"}),bgcolor:{valType:\"color\",dflt:n.background},sum:{valType:\"number\",dflt:1,min:0},aaxis:l,baxis:l,caxis:l},\"plot\",\"from-root\");c.uirevision={valType:\"any\",editType:\"none\"},c.aaxis.uirevision=c.baxis.uirevision=c.caxis.uirevision={valType:\"any\",editType:\"none\"}},{\"../../components/color/attributes\":365,\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../cartesian/layout_attributes\":569,\"../domain\":584}],636:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"../../plot_api/plot_template\"),a=t(\"../../lib\"),o=t(\"../subplot_defaults\"),s=t(\"../cartesian/tick_label_defaults\"),l=t(\"../cartesian/prefix_suffix_defaults\"),c=t(\"../cartesian/tick_mark_defaults\"),u=t(\"../cartesian/tick_value_defaults\"),f=t(\"../cartesian/line_grid_defaults\"),h=t(\"./layout_attributes\"),p=[\"aaxis\",\"baxis\",\"caxis\"];function d(t,e,r,a){var o,s,l,c=r(\"bgcolor\"),u=r(\"sum\");a.bgColor=n.combine(c,a.paper_bgcolor);for(var f=0;f<p.length;f++)s=t[o=p[f]]||{},(l=i.newContainer(e,o))._name=o,m(s,l,a,e);var h=e.aaxis,d=e.baxis,g=e.caxis;h.min+d.min+g.min>=u&&(h.min=0,d.min=0,g.min=0,t.aaxis&&delete t.aaxis.min,t.baxis&&delete t.baxis.min,t.caxis&&delete t.caxis.min)}function m(t,e,r,n){var i=h[e._name];function o(r,n){return a.coerce(t,e,i,r,n)}o(\"uirevision\",n.uirevision),e.type=\"linear\";var p=o(\"color\"),d=p!==i.color.dflt?p:r.font.color,m=e._name.charAt(0).toUpperCase(),g=\"Component \"+m,v=o(\"title.text\",g);e._hovertitle=v===g?v:m,a.coerceFont(o,\"title.font\",{family:r.font.family,size:a.bigFont(r.font.size),color:d}),o(\"min\"),u(t,e,o,\"linear\"),l(t,e,o,\"linear\"),s(t,e,o,\"linear\"),c(t,e,o,{outerTicks:!0}),o(\"showticklabels\")&&(a.coerceFont(o,\"tickfont\",{family:r.font.family,size:r.font.size,color:d}),o(\"tickangle\"),o(\"tickformat\")),f(t,e,o,{dfltColor:p,bgColor:r.bgColor,blend:60,showLine:!0,showGrid:!0,noZeroLine:!0,attributes:i}),o(\"hoverformat\"),o(\"layer\")}e.exports=function(t,e,r){o(t,e,r,{type:\"ternary\",attributes:h,handleDefaults:d,font:e.font,paper_bgcolor:e.paper_bgcolor})}},{\"../../components/color\":366,\"../../lib\":503,\"../../plot_api/plot_template\":543,\"../cartesian/line_grid_defaults\":571,\"../cartesian/prefix_suffix_defaults\":573,\"../cartesian/tick_label_defaults\":578,\"../cartesian/tick_mark_defaults\":579,\"../cartesian/tick_value_defaults\":580,\"../subplot_defaults\":632,\"./layout_attributes\":635}],637:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"tinycolor2\"),a=t(\"../../registry\"),o=t(\"../../lib\"),s=o.strTranslate,l=o._,c=t(\"../../components/color\"),u=t(\"../../components/drawing\"),f=t(\"../cartesian/set_convert\"),h=t(\"../../lib/extend\").extendFlat,p=t(\"../plots\"),d=t(\"../cartesian/axes\"),m=t(\"../../components/dragelement\"),g=t(\"../../components/fx\"),v=t(\"../../components/dragelement/helpers\"),y=v.freeMode,x=v.rectMode,b=t(\"../../components/titles\"),_=t(\"../cartesian/select\").prepSelect,w=t(\"../cartesian/select\").selectOnClick,T=t(\"../cartesian/select\").clearSelect,k=t(\"../cartesian/select\").clearSelectionsCache,A=t(\"../cartesian/constants\");function M(t,e){this.id=t.id,this.graphDiv=t.graphDiv,this.init(e),this.makeFramework(e),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}e.exports=M;var S=M.prototype;S.init=function(t){this.container=t._ternarylayer,this.defs=t._defs,this.layoutId=t._uid,this.traceHash={},this.layers={}},S.plot=function(t,e){var r=e[this.id],n=e._size;this._hasClipOnAxisFalse=!1;for(var i=0;i<t.length;i++){if(!1===t[i][0].trace.cliponaxis){this._hasClipOnAxisFalse=!0;break}}this.updateLayers(r),this.adjustLayout(r,n),p.generalUpdatePerTraceModule(this.graphDiv,this,t,r),this.layers.plotbg.select(\"path\").call(c.fill,r.bgcolor)},S.makeFramework=function(t){var e=this.graphDiv,r=t[this.id],n=this.clipId=\"clip\"+this.layoutId+this.id,i=this.clipIdRelative=\"clip-relative\"+this.layoutId+this.id;this.clipDef=o.ensureSingleById(t._clips,\"clipPath\",n,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.clipDefRelative=o.ensureSingleById(t._clips,\"clipPath\",i,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.plotContainer=o.ensureSingle(this.container,\"g\",this.id),this.updateLayers(r),u.setClipUrl(this.layers.backplot,n,e),u.setClipUrl(this.layers.grids,n,e)},S.updateLayers=function(t){var e=this.layers,r=[\"draglayer\",\"plotbg\",\"backplot\",\"grids\"];\"below traces\"===t.aaxis.layer&&r.push(\"aaxis\",\"aline\"),\"below traces\"===t.baxis.layer&&r.push(\"baxis\",\"bline\"),\"below traces\"===t.caxis.layer&&r.push(\"caxis\",\"cline\"),r.push(\"frontplot\"),\"above traces\"===t.aaxis.layer&&r.push(\"aaxis\",\"aline\"),\"above traces\"===t.baxis.layer&&r.push(\"baxis\",\"bline\"),\"above traces\"===t.caxis.layer&&r.push(\"caxis\",\"cline\");var i=this.plotContainer.selectAll(\"g.toplevel\").data(r,String),a=[\"agrid\",\"bgrid\",\"cgrid\"];i.enter().append(\"g\").attr(\"class\",(function(t){return\"toplevel \"+t})).each((function(t){var r=n.select(this);e[t]=r,\"frontplot\"===t?r.append(\"g\").classed(\"scatterlayer\",!0):\"backplot\"===t?r.append(\"g\").classed(\"maplayer\",!0):\"plotbg\"===t?r.append(\"path\").attr(\"d\",\"M0,0Z\"):\"aline\"===t||\"bline\"===t||\"cline\"===t?r.append(\"path\"):\"grids\"===t&&a.forEach((function(t){e[t]=r.append(\"g\").classed(\"grid \"+t,!0)}))})),i.order()};var E=Math.sqrt(4/3);S.adjustLayout=function(t,e){var r,n,i,a,o,l,p=this,d=t.domain,m=(d.x[0]+d.x[1])/2,g=(d.y[0]+d.y[1])/2,v=d.x[1]-d.x[0],y=d.y[1]-d.y[0],x=v*e.w,b=y*e.h,_=t.sum,w=t.aaxis.min,T=t.baxis.min,k=t.caxis.min;x>E*b?i=(a=b)*E:a=(i=x)/E,o=v*i/x,l=y*a/b,r=e.l+e.w*m-i/2,n=e.t+e.h*(1-g)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=_,p.xaxis={type:\"linear\",range:[w+2*k-_,_-w-2*T],domain:[m-o/2,m+o/2],_id:\"x\"},f(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:\"linear\",range:[w,_-T-k],domain:[g-l/2,g+l/2],_id:\"y\"},f(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var A=p.yaxis.domain[0],M=p.aaxis=h({},t.aaxis,{range:[w,_-T-k],side:\"left\",tickangle:(+t.aaxis.tickangle||0)-30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(M,p.graphDiv._fullLayout),M.setScale();var S=p.baxis=h({},t.baxis,{range:[_-w-k,T],side:\"bottom\",domain:p.xaxis.domain,anchor:\"free\",position:0,_id:\"x\",_length:i});f(S,p.graphDiv._fullLayout),S.setScale();var L=p.caxis=h({},t.caxis,{range:[_-w-T,k],side:\"right\",tickangle:(+t.caxis.tickangle||0)+30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(L,p.graphDiv._fullLayout),L.setScale();var C=\"M\"+r+\",\"+(n+a)+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDef.select(\"path\").attr(\"d\",C),p.layers.plotbg.select(\"path\").attr(\"d\",C);var P=\"M0,\"+a+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDefRelative.select(\"path\").attr(\"d\",P);var I=s(r,n);p.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",I),p.clipDefRelative.select(\"path\").attr(\"transform\",null);var O=s(r-S._offset,n+a);p.layers.baxis.attr(\"transform\",O),p.layers.bgrid.attr(\"transform\",O);var z=s(r+i/2,n)+\"rotate(30)\"+s(0,-M._offset);p.layers.aaxis.attr(\"transform\",z),p.layers.agrid.attr(\"transform\",z);var D=s(r+i/2,n)+\"rotate(-30)\"+s(0,-L._offset);p.layers.caxis.attr(\"transform\",D),p.layers.cgrid.attr(\"transform\",D),p.drawAxes(!0),p.layers.aline.select(\"path\").attr(\"d\",M.showline?\"M\"+r+\",\"+(n+a)+\"l\"+i/2+\",-\"+a:\"M0,0\").call(c.stroke,M.linecolor||\"#000\").style(\"stroke-width\",(M.linewidth||0)+\"px\"),p.layers.bline.select(\"path\").attr(\"d\",S.showline?\"M\"+r+\",\"+(n+a)+\"h\"+i:\"M0,0\").call(c.stroke,S.linecolor||\"#000\").style(\"stroke-width\",(S.linewidth||0)+\"px\"),p.layers.cline.select(\"path\").attr(\"d\",L.showline?\"M\"+(r+i/2)+\",\"+n+\"l\"+i/2+\",\"+a:\"M0,0\").call(c.stroke,L.linecolor||\"#000\").style(\"stroke-width\",(L.linewidth||0)+\"px\"),p.graphDiv._context.staticPlot||p.initInteractions(),u.setClipUrl(p.layers.frontplot,p._hasClipOnAxisFalse?null:p.clipId,p.graphDiv)},S.drawAxes=function(t){var e=this.graphDiv,r=this.id.substr(7)+\"title\",n=this.layers,i=this.aaxis,a=this.baxis,o=this.caxis;if(this.drawAx(i),this.drawAx(a),this.drawAx(o),t){var s=Math.max(i.showticklabels?i.tickfont.size/2:0,(o.showticklabels?.75*o.tickfont.size:0)+(\"outside\"===o.ticks?.87*o.ticklen:0)),c=(a.showticklabels?a.tickfont.size:0)+(\"outside\"===a.ticks?a.ticklen:0)+3;n[\"a-title\"]=b.draw(e,\"a\"+r,{propContainer:i,propName:this.id+\".aaxis.title\",placeholder:l(e,\"Click to enter Component A title\"),attributes:{x:this.x0+this.w/2,y:this.y0-i.title.font.size/3-s,\"text-anchor\":\"middle\"}}),n[\"b-title\"]=b.draw(e,\"b\"+r,{propContainer:a,propName:this.id+\".baxis.title\",placeholder:l(e,\"Click to enter Component B title\"),attributes:{x:this.x0-c,y:this.y0+this.h+.83*a.title.font.size+c,\"text-anchor\":\"middle\"}}),n[\"c-title\"]=b.draw(e,\"c\"+r,{propContainer:o,propName:this.id+\".caxis.title\",placeholder:l(e,\"Click to enter Component C title\"),attributes:{x:this.x0+this.w+c,y:this.y0+this.h+.83*o.title.font.size+c,\"text-anchor\":\"middle\"}})}},S.drawAx=function(t){var e,r=this.graphDiv,n=t._name,i=n.charAt(0),a=t._id,s=this.layers[n],l=i+\"tickLayout\",c=(e=t).ticks+String(e.ticklen)+String(e.showticklabels);this[l]!==c&&(s.selectAll(\".\"+a+\"tick\").remove(),this[l]=c),t.setScale();var u=d.calcTicks(t),f=d.clipEnds(t,u),h=d.makeTransTickFn(t),p=d.getTickSigns(t)[2],m=o.deg2rad(30),g=p*(t.linewidth||1)/2,v=p*t.ticklen,y=this.w,x=this.h,b=\"b\"===i?\"M0,\"+g+\"l\"+Math.sin(m)*v+\",\"+Math.cos(m)*v:\"M\"+g+\",0l\"+Math.cos(m)*v+\",\"+-Math.sin(m)*v,_={a:\"M0,0l\"+x+\",-\"+y/2,b:\"M0,0l-\"+y/2+\",-\"+x,c:\"M0,0l-\"+x+\",\"+y/2}[i];d.drawTicks(r,t,{vals:\"inside\"===t.ticks?f:u,layer:s,path:b,transFn:h,crisp:!1}),d.drawGrid(r,t,{vals:f,layer:this.layers[i+\"grid\"],path:_,transFn:h,crisp:!1}),d.drawLabels(r,t,{vals:u,layer:s,transFn:h,labelFns:d.makeLabelFns(t,0,30)})};var L=A.MINZOOM/2+.87,C=\"m-0.87,.5h\"+L+\"v3h-\"+(L+5.2)+\"l\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l2.6,1.5l-\"+L/2+\",\"+.87*L+\"Z\",P=\"m0.87,.5h-\"+L+\"v3h\"+(L+5.2)+\"l-\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l-2.6,1.5l\"+L/2+\",\"+.87*L+\"Z\",I=\"m0,1l\"+L/2+\",\"+.87*L+\"l2.6,-1.5l-\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l-\"+(L/2+2.6)+\",\"+(.87*L+4.5)+\"l2.6,1.5l\"+L/2+\",-\"+.87*L+\"Z\",O=!0;function z(t){n.select(t).selectAll(\".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners\").remove()}S.clearSelect=function(){k(this.dragOptions),T(this.dragOptions.gd)},S.initInteractions=function(){var t,e,r,n,f,h,p,d,v,b,T,k,M=this,S=M.layers.plotbg.select(\"path\").node(),L=M.graphDiv,D=L._fullLayout._zoomlayer;function R(t){var e={};return e[M.id+\".aaxis.min\"]=t.a,e[M.id+\".baxis.min\"]=t.b,e[M.id+\".caxis.min\"]=t.c,e}function F(t,e){var r=L._fullLayout.clickmode;z(L),2===t&&(L.emit(\"plotly_doubleclick\",null),a.call(\"_guiRelayout\",L,R({a:0,b:0,c:0}))),r.indexOf(\"select\")>-1&&1===t&&w(e,L,[M.xaxis],[M.yaxis],M.id,M.dragOptions),r.indexOf(\"event\")>-1&&g.click(L,e,M.id)}function B(t,e){return 1-e/M.h}function N(t,e){return 1-(t+(M.h-e)/Math.sqrt(3))/M.w}function j(t,e){return(t-(M.h-e)/Math.sqrt(3))/M.w}function U(i,a){var o=r+i*t,s=n+a*e,l=Math.max(0,Math.min(1,B(0,n),B(0,s))),c=Math.max(0,Math.min(1,N(r,n),N(o,s))),u=Math.max(0,Math.min(1,j(r,n),j(o,s))),m=(l/2+u)*M.w,g=(1-l/2-c)*M.w,y=(m+g)/2,x=g-m,_=(1-l)*M.h,w=_-x/E;x<A.MINZOOM?(p=f,T.attr(\"d\",v),k.attr(\"d\",\"M0,0Z\")):(p={a:f.a+l*h,b:f.b+c*h,c:f.c+u*h},T.attr(\"d\",v+\"M\"+m+\",\"+_+\"H\"+g+\"L\"+y+\",\"+w+\"L\"+m+\",\"+_+\"Z\"),k.attr(\"d\",\"M\"+r+\",\"+n+\"m0.5,0.5h5v-2h-5v-5h-2v5h-5v2h5v5h2ZM\"+m+\",\"+_+C+\"M\"+g+\",\"+_+P+\"M\"+y+\",\"+w+I)),b||(T.transition().style(\"fill\",d>.2?\"rgba(0,0,0,0.4)\":\"rgba(255,255,255,0.3)\").duration(200),k.transition().style(\"opacity\",1).duration(200),b=!0),L.emit(\"plotly_relayouting\",R(p))}function V(){z(L),p!==f&&(a.call(\"_guiRelayout\",L,R(p)),O&&L.data&&L._context.showTips&&(o.notifier(l(L,\"Double-click to zoom back out\"),\"long\"),O=!1))}function H(t,e){var 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n=t(\"../heatmap/attributes\"),i=t(\"../scatter/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\"),o=a.axisHoverFormat,s=a.descriptionOnlyNumbers,l=t(\"../../components/colorscale/attributes\"),c=t(\"../../components/drawing/attributes\").dash,u=t(\"../../plots/font_attributes\"),f=t(\"../../lib/extend\").extendFlat,h=t(\"../../constants/filter_ops\"),p=h.COMPARISON_OPS2,d=h.INTERVAL_OPS,m=i.line;e.exports=f({z:n.z,x:n.x,x0:n.x0,dx:n.dx,y:n.y,y0:n.y0,dy:n.dy,xperiod:n.xperiod,yperiod:n.yperiod,xperiod0:i.xperiod0,yperiod0:i.yperiod0,xperiodalignment:n.xperiodalignment,yperiodalignment:n.yperiodalignment,text:n.text,hovertext:n.hovertext,transpose:n.transpose,xtype:n.xtype,ytype:n.ytype,xhoverformat:o(\"x\"),yhoverformat:o(\"y\"),zhoverformat:o(\"z\",1),hovertemplate:n.hovertemplate,texttemplate:f({},n.texttemplate,{}),textfont:f({},n.textfont,{}),hoverongaps:n.hoverongaps,connectgaps:f({},n.connectgaps,{}),fillcolor:{valType:\"color\",editType:\"calc\"},autocontour:{valType:\"boolean\",dflt:!0,editType:\"calc\",impliedEdits:{\"contours.start\":void 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a}(u,h),[l]}}},{\"../../components/color\":366,\"../../lib\":503,\"../bar/hover\":655}],778:[function(t,e,r){\"use 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s=r||n.select(t).selectAll(\"g.funnellayer\").selectAll(\"g.trace\");l(t,s,\"funnel\"),s.style(\"opacity\",(function(t){return t[0].trace.opacity})),s.each((function(e){var r=n.select(this),s=e[0].trace;r.selectAll(\".point > path\").each((function(t){if(!t.isBlank){var e=s.marker;n.select(this).call(a.fill,t.mc||e.color).call(a.stroke,t.mlc||e.line.color).call(i.dashLine,e.line.dash,t.mlw||e.line.width).style(\"opacity\",s.selectedpoints&&!t.selected?o:1)}})),c(r,s,t),r.selectAll(\".regions\").each((function(){n.select(this).selectAll(\"path\").style(\"stroke-width\",0).call(a.fill,s.connector.fillcolor)})),r.selectAll(\".lines\").each((function(){var t=s.connector.line;i.lineGroupStyle(n.select(this).selectAll(\"path\"),t.width,t.color,t.dash)}))}))}}},{\"../../components/color\":366,\"../../components/drawing\":388,\"../../constants/interactions\":478,\"../bar/style\":662,\"../bar/uniform_text\":664,\"@plotly/d3\":58}],783:[function(t,e,r){\"use strict\";var n=t(\"../pie/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../plots/domain\").attributes,o=t(\"../../plots/template_attributes\").hovertemplateAttrs,s=t(\"../../plots/template_attributes\").texttemplateAttrs,l=t(\"../../lib/extend\").extendFlat;e.exports={labels:n.labels,label0:n.label0,dlabel:n.dlabel,values:n.values,marker:{colors:n.marker.colors,line:{color:l({},n.marker.line.color,{dflt:null}),width:l({},n.marker.line.width,{dflt:1}),editType:\"calc\"},editType:\"calc\"},text:n.text,hovertext:n.hovertext,scalegroup:l({},n.scalegroup,{}),textinfo:l({},n.textinfo,{flags:[\"label\",\"text\",\"value\",\"percent\"]}),texttemplate:s({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"text\",\"percent\"]}),hoverinfo:l({},i.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:o({},{keys:[\"label\",\"color\",\"value\",\"text\",\"percent\"]}),textposition:l({},n.textposition,{values:[\"inside\",\"none\"],dflt:\"inside\"}),textfont:n.textfont,insidetextfont:n.insidetextfont,title:{text:n.title.text,font:n.title.font,position:l({},n.title.position,{values:[\"top 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n=t(\"../../lib\"),i=t(\"./attributes\"),a=t(\"../../plots/domain\").defaults,o=t(\"../bar/defaults\").handleText,s=t(\"../pie/defaults\").handleLabelsAndValues;e.exports=function(t,e,r,l){function c(r,a){return n.coerce(t,e,i,r,a)}var u=c(\"labels\"),f=c(\"values\"),h=s(u,f),p=h.len;if(e._hasLabels=h.hasLabels,e._hasValues=h.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),p){e._length=p,c(\"marker.line.width\")&&c(\"marker.line.color\",l.paper_bgcolor),c(\"marker.colors\"),c(\"scalegroup\");var d,m=c(\"text\"),g=c(\"texttemplate\");if(g||(d=c(\"textinfo\",Array.isArray(m)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),g||d&&\"none\"!==d){var v=c(\"textposition\");o(t,e,l,c,v,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1})}a(e,l,c),c(\"title.text\")&&(c(\"title.position\"),n.coerceFont(c,\"title.font\",l.font)),c(\"aspectratio\"),c(\"baseratio\")}else e.visible=!1}},{\"../../lib\":503,\"../../plots/domain\":584,\"../bar/defaults\":652,\"../pie/defaults\":903,\"./attributes\":783}],787:[function(t,e,r){\"use strict\";e.exports={moduleType:\"trace\",name:\"funnelarea\",basePlotModule:t(\"./base_plot\"),categories:[\"pie-like\",\"funnelarea\",\"showLegend\"],attributes:t(\"./attributes\"),layoutAttributes:t(\"./layout_attributes\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./calc\").crossTraceCalc,plot:t(\"./plot\"),style:t(\"./style\"),styleOne:t(\"../pie/style_one\"),meta:{}}},{\"../pie/style_one\":911,\"./attributes\":783,\"./base_plot\":784,\"./calc\":785,\"./defaults\":786,\"./layout_attributes\":788,\"./layout_defaults\":789,\"./plot\":790,\"./style\":791}],788:[function(t,e,r){\"use strict\";var n=t(\"../pie/layout_attributes\").hiddenlabels;e.exports={hiddenlabels:n,funnelareacolorway:{valType:\"colorlist\",editType:\"calc\"},extendfunnelareacolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{\"../pie/layout_attributes\":907}],789:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"funnelareacolorway\",e.colorway),r(\"extendfunnelareacolors\")}},{\"../../lib\":503,\"./layout_attributes\":788}],790:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../components/drawing\"),a=t(\"../../lib\"),o=a.strScale,s=a.strTranslate,l=t(\"../../lib/svg_text_utils\"),c=t(\"../bar/plot\").toMoveInsideBar,u=t(\"../bar/uniform_text\"),f=u.recordMinTextSize,h=u.clearMinTextSize,p=t(\"../pie/helpers\"),d=t(\"../pie/plot\"),m=d.attachFxHandlers,g=d.determineInsideTextFont,v=d.layoutAreas,y=d.prerenderTitles,x=d.positionTitleOutside,b=d.formatSliceLabel;function _(t,e){return\"l\"+(e[0]-t[0])+\",\"+(e[1]-t[1])}e.exports=function(t,e){var r=t._fullLayout;h(\"funnelarea\",r),y(e,t),v(e,r._size),a.makeTraceGroups(r._funnelarealayer,e,\"trace\").each((function(e){var u=n.select(this),h=e[0],d=h.trace;!function(t){if(!t.length)return;var e=t[0],r=e.trace,n=r.aspectratio,i=r.baseratio;i>.999&&(i=.999);var a,o=Math.pow(i,2),s=e.vTotal,l=s,c=s*o/(1-o)/s;function u(){var t,e={x:t=Math.sqrt(c),y:-t};return[e.x,e.y]}var f,h,p=[];for(p.push(u()),f=t.length-1;f>-1;f--)if(!(h=t[f]).hidden){var d=h.v/l;c+=d,p.push(u())}var 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w=\"M\"+(u+o.TR[0])+\",\"+(v+o.TR[1])+_(o.TR,o.BR)+_(o.BR,o.BL)+_(o.BL,o.TL)+\"Z\";x.attr(\"d\",w),b(t,o,h);var T=p.castOption(d.textposition,o.pts),k=y.selectAll(\"g.slicetext\").data(o.text&&\"none\"!==T?[0]:[]);k.enter().append(\"g\").classed(\"slicetext\",!0),k.exit().remove(),k.each((function(){var h=a.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),p=a.ensureUniformFontSize(t,g(d,o,r.font));h.text(o.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(i.font,p).call(l.convertToTspans,t);var m,y,x,b=i.bBox(h.node()),_=Math.min(o.BL[1],o.BR[1])+v,w=Math.max(o.TL[1],o.TR[1])+v;y=Math.max(o.TL[0],o.BL[0])+u,x=Math.min(o.TR[0],o.BR[0])+u,(m=c(y,x,_,w,b,{isHorizontal:!0,constrained:!0,angle:0,anchor:\"middle\"})).fontSize=p.size,f(d.type,m,r),e[s].transform=m,h.attr(\"transform\",a.getTextTransform(m))}))}}));var 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n=t._fullLayout,i=o.extendFlat({type:\"linear\",ticks:\"outside\",range:r,showline:!0},e),a={type:\"linear\",_id:\"x\"+e._id},s={letter:\"x\",font:n.font,noHover:!0,noTickson:!0};function l(t,e){return o.coerce(i,a,v,t,e)}return m(i,a,l,s,n),g(i,a,l,s),a}function A(t,e,r){return[Math.min(e/t.width,r/t.height),t,e+\"x\"+r]}function M(t,e,r,i){var a=document.createElementNS(\"http://www.w3.org/2000/svg\",\"text\"),o=n.select(a);return o.text(t).attr(\"x\",0).attr(\"y\",0).attr(\"text-anchor\",r).attr(\"data-unformatted\",t).call(p.convertToTspans,i).call(f.font,e),f.bBox(o.node())}function S(t,e,r,n,i,a){var s=\"_cache\"+e;t[s]&&t[s].key===i||(t[s]={key:i,value:r});var l=o.aggNums(a,null,[t[s].value,n],2);return t[s].value=l,l}e.exports=function(t,e,r,m){var g,v=t._fullLayout;w(r)&&m&&(g=m()),o.makeTraceGroups(v._indicatorlayer,e,\"trace\").each((function(e){var 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e=I(t),r=Math.cos(e);return Math.abs(r)<.1?\"middle\":r>0?\"start\":\"end\"},O.heightFn=function(t,e,r){var n=I(t);return-.5*(1+Math.sin(n))*r};var D=function(t){return l(b[0]+g*Math.cos(t),b[1]-g*Math.sin(t))};f=function(t){return D(I(t))};if(s=d.calcTicks(o),h=d.getTickSigns(o)[2],o.visible){h=\"inside\"===o.ticks?-1:1;var R=(o.linewidth||1)/2;d.drawTicks(t,o,{vals:s,layer:A,path:\"M\"+h*R+\",0h\"+h*o.ticklen,transFn:function(t){var e=I(t);return D(e)+\"rotate(\"+-c(e)+\")\"}}),d.drawLabels(t,o,{vals:s,layer:A,transFn:f,labelFns:O})}var F=[y].concat(p.gauge.steps),B=_.selectAll(\"g.bg-arc\").data(F);B.enter().append(\"g\").classed(\"bg-arc\",!0).append(\"path\"),B.select(\"path\").call(P).call(T),B.exit().remove();var N=C(p.gauge.bar.thickness),j=_.selectAll(\"g.value-arc\").data([p.gauge.bar]);j.enter().append(\"g\").classed(\"value-arc\",!0).append(\"path\");var U=j.select(\"path\");w(M)?(U.transition().duration(M.duration).ease(M.easing).each(\"end\",(function(){S&&S()})).each(\"interrupt\",(function(){S&&S()})).attrTween(\"d\",(V=N,H=L(r[0].lastY),q=L(r[0].y),function(){var t=i(H,q);return function(e){return V.endAngle(t(e))()}})),p._lastValue=r[0].y):U.attr(\"d\",\"number\"==typeof r[0].y?N.endAngle(L(r[0].y)):\"M0,0Z\");var V,H,q;U.call(T),j.exit().remove(),F=[];var G=p.gauge.threshold.value;(G||0===G)&&F.push({range:[G,G],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var Y=_.selectAll(\"g.threshold-arc\").data(F);Y.enter().append(\"g\").classed(\"threshold-arc\",!0).append(\"path\"),Y.select(\"path\").call(P).call(T),Y.exit().remove();var W=_.selectAll(\"g.gauge-outline\").data([x]);W.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"path\"),W.select(\"path\").call(P).call(T),W.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:Y,layer:W,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var X=O.selectAll(\"g.bullet\").data(R?e:[]);X.exit().remove();var Z=O.selectAll(\"g.bulletaxis\").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,c,u=r[0].trace,f=n.gauge,p=n.layer,m=n.gaugeBg,g=n.gaugeOutline,v=n.size,x=u.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append(\"g\").classed(\"bullet\",!0),f.attr(\"transform\",l(v.l,v.t)),p.enter().append(\"g\").classed(\"bulletaxis\",!0).classed(\"crisp\",!0),p.selectAll(\"g.xbulletaxistick,path,text\").remove();var A=v.h,M=u.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(u._hasNumber||u._hasDelta?1-h.bulletNumberDomainSize:1);(i=k(t,u.gauge.axis))._id=\"xbulletaxis\",i.domain=[S,E],i.setScale(),a=d.calcTicks(i),o=d.makeTransTickFn(i),s=d.getTickSigns(i)[2],c=v.t+v.h,i.visible&&(d.drawTicks(t,i,{vals:\"inside\"===i.ticks?d.clipEnds(i,a):a,layer:p,path:d.makeTickPath(i,c,s),transFn:o}),d.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:d.makeLabelFns(i,c)}));function L(t){t.attr(\"width\",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr(\"x\",(function(t){return i.c2p(t.range[0])})).attr(\"y\",(function(t){return.5*(1-t.thickness)*A})).attr(\"height\",(function(t){return t.thickness*A}))}var C=[m].concat(u.gauge.steps),P=f.selectAll(\"g.bg-bullet\").data(C);P.enter().append(\"g\").classed(\"bg-bullet\",!0).append(\"rect\"),P.select(\"rect\").call(L).call(T),P.exit().remove();var I=f.selectAll(\"g.value-bullet\").data([u.gauge.bar]);I.enter().append(\"g\").classed(\"value-bullet\",!0).append(\"rect\"),I.select(\"rect\").attr(\"height\",M).attr(\"y\",(A-M)/2).call(T),w(b)?I.select(\"rect\").transition().duration(b.duration).ease(b.easing).each(\"end\",(function(){_&&_()})).each(\"interrupt\",(function(){_&&_()})).attr(\"width\",Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y)))):I.select(\"rect\").attr(\"width\",\"number\"==typeof r[0].y?Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y))):0);I.exit().remove();var O=r.filter((function(){return u.gauge.threshold.value||0===u.gauge.threshold.value})),z=f.selectAll(\"g.threshold-bullet\").data(O);z.enter().append(\"g\").classed(\"threshold-bullet\",!0).append(\"line\"),z.select(\"line\").attr(\"x1\",i.c2p(u.gauge.threshold.value)).attr(\"x2\",i.c2p(u.gauge.threshold.value)).attr(\"y1\",(1-u.gauge.threshold.thickness)/2*A).attr(\"y2\",(1-(1-u.gauge.threshold.thickness)/2)*A).call(y.stroke,u.gauge.threshold.line.color).style(\"stroke-width\",u.gauge.threshold.line.width),z.exit().remove();var D=f.selectAll(\"g.gauge-outline\").data([g]);D.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"rect\"),D.select(\"rect\").call(L).call(T),D.exit().remove()}(t,0,e,{gauge:X,layer:Z,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var J=O.selectAll(\"text.title\").data(e);J.exit().remove(),J.enter().append(\"text\").classed(\"title\",!0),J.attr(\"text-anchor\",(function(){return 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n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../mesh3d/attributes\"),s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll;var 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0===e.isomax||null===e.isomin?s:e.isomax,n(t,e,{vals:[e._vMin,e._vMax],containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":374,\"../streamtube/calc\":1041}],864:[function(t,e,r){\"use strict\";var n=t(\"../../../stackgl_modules\").gl_mesh3d,i=t(\"../../lib/gl_format_color\").parseColorScale,a=t(\"../../lib/str2rgbarray\"),o=t(\"../../components/colorscale\").extractOpts,s=t(\"../../plots/gl3d/zip3\"),l=function(t,e){for(var r=e.length-1;r>0;r--){var n=Math.min(e[r],e[r-1]),i=Math.max(e[r],e[r-1]);if(i>n&&n<t&&t<=i)return{id:r,distRatio:(i-t)/(i-n)}}return{id:0,distRatio:0}};function c(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name=\"\",this.data=null,this.showContour=!1}var u=c.prototype;u.handlePick=function(t){if(t.object===this.mesh){var e=t.data.index,r=this.data._meshX[e],n=this.data._meshY[e],i=this.data._meshZ[e],a=this.data._Ys.length,o=this.data._Zs.length,s=l(r,this.data._Xs).id,c=l(n,this.data._Ys).id,u=l(i,this.data._Zs).id,f=t.index=u+o*c+o*a*s;t.traceCoordinate=[this.data._meshX[f],this.data._meshY[f],this.data._meshZ[f],this.data._value[f]];var h=this.data.hovertext||this.data.text;return Array.isArray(h)&&void 0!==h[f]?t.textLabel=h[f]:h&&(t.textLabel=h),!0}},u.update=function(t){var e=this.scene,r=e.fullSceneLayout;function n(t,e,r,n){return e.map((function(e){return t.d2l(e,0,n)*r}))}this.data=h(t);var l={positions:s(n(r.xaxis,t._meshX,e.dataScale[0],t.xcalendar),n(r.yaxis,t._meshY,e.dataScale[1],t.ycalendar),n(r.zaxis,t._meshZ,e.dataScale[2],t.zcalendar)),cells:s(t._meshI,t._meshJ,t._meshK),lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:a(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading},c=o(t);l.vertexIntensity=t._meshIntensity,l.vertexIntensityBounds=[c.min,c.max],l.colormap=i(t),this.mesh.update(l)},u.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};var f=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"];function h(t){t._meshI=[],t._meshJ=[],t._meshK=[];var e,r,n,i,a,o,s,c=t.surface.show,u=t.spaceframe.show,h=t.surface.fill,p=t.spaceframe.fill,d=!1,m=!1,g=0,v=t._Xs,y=t._Ys,x=t._Zs,b=v.length,_=y.length,w=x.length,T=f.indexOf(t._gridFill.replace(/-/g,\"\").replace(/\\+/g,\"\")),k=function(t,e,r){switch(T){case 5:return r+w*e+w*_*t;case 4:return r+w*t+w*b*e;case 3:return e+_*r+_*w*t;case 2:return e+_*t+_*b*r;case 1:return t+b*r+b*w*e;default:return t+b*e+b*_*r}},A=t._minValues,M=t._maxValues,S=t._vMin,E=t._vMax;function L(t,e,s){for(var l=o.length,c=r;c<l;c++)if(t===n[c]&&e===i[c]&&s===a[c])return c;return-1}function C(){r=e}function P(){n=[],i=[],a=[],o=[],e=0,C()}function I(t,r,s,l){return n.push(t),i.push(r),a.push(s),o.push(l),++e-1}function O(t,e,r){for(var n=[],i=0;i<t.length;i++)n[i]=t[i]*(1-r)+r*e[i];return n}function z(t){s=t}function D(t,e){return\"all\"===t||null===t||t.indexOf(e)>-1}function R(t,e){return null===t?e:t}function F(e,r,n){C();var i,a,o,l=[r],c=[n];if(s>=1)l=[r],c=[n];else if(s>0){var u=function(t,e){var 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i=r(\"values\"),a=r(\"visible\");if(i&&i.length||(a=e.visible=!1),a){r(\"label\"),r(\"displayindex\",e._index);var o,s=t.categoryarray,c=Array.isArray(s)&&s.length>0;c&&(o=\"array\");var u=r(\"categoryorder\",o);\"array\"===u?(r(\"categoryarray\"),r(\"ticktext\")):(delete t.categoryarray,delete t.ticktext),c||\"array\"!==u||(e.categoryorder=\"trace\")}}e.exports=function(t,e,r,f){function h(r,i){return n.coerce(t,e,l,r,i)}var p=s(t,e,{name:\"dimensions\",handleItemDefaults:u}),d=function(t,e,r,o,s){s(\"line.shape\"),s(\"line.hovertemplate\");var l=s(\"line.color\",o.colorway[0]);if(i(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,f,h);o(e,f,h),Array.isArray(p)&&p.length||(e.visible=!1),c(e,p,\"values\",d),h(\"hoveron\"),h(\"hovertemplate\"),h(\"arrangement\"),h(\"bundlecolors\"),h(\"sortpaths\"),h(\"counts\");var m={family:f.font.family,size:Math.round(f.font.size),color:f.font.color};n.coerceFont(h,\"labelfont\",m);var g={family:f.font.family,size:Math.round(f.font.size/1.2),color:f.font.color};n.coerceFont(h,\"tickfont\",g)}},{\"../../components/colorscale/defaults\":376,\"../../components/colorscale/helpers\":377,\"../../lib\":503,\"../../plots/array_container_defaults\":549,\"../../plots/domain\":584,\"../parcoords/merge_length\":897,\"./attributes\":881}],885:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcats\",basePlotModule:t(\"./base_plot\"),categories:[\"noOpacity\"],meta:{}}},{\"./attributes\":881,\"./base_plot\":882,\"./calc\":883,\"./defaults\":884,\"./plot\":887}],886:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"d3-interpolate\").interpolateNumber,a=t(\"../../plot_api/plot_api\"),o=t(\"../../components/fx\"),s=t(\"../../lib\"),l=s.strTranslate,c=t(\"../../components/drawing\"),u=t(\"tinycolor2\"),f=t(\"../../lib/svg_text_utils\");function h(t,e,r,i){var a=t.map(F.bind(0,e,r)),o=i.selectAll(\"g.parcatslayer\").data([null]);o.enter().append(\"g\").attr(\"class\",\"parcatslayer\").style(\"pointer-events\",\"all\");var u=o.selectAll(\"g.trace.parcats\").data(a,p),h=u.enter().append(\"g\").attr(\"class\",\"trace parcats\");u.attr(\"transform\",(function(t){return l(t.x,t.y)})),h.append(\"g\").attr(\"class\",\"paths\");var y=u.select(\"g.paths\").selectAll(\"path.path\").data((function(t){return t.paths}),p);y.attr(\"fill\",(function(t){return t.model.color}));var x=y.enter().append(\"path\").attr(\"class\",\"path\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",0);_(x),y.attr(\"d\",(function(t){return t.svgD})),x.empty()||y.sort(m),y.exit().remove(),y.on(\"mouseover\",g).on(\"mouseout\",v).on(\"click\",b),h.append(\"g\").attr(\"class\",\"dimensions\");var w=u.select(\"g.dimensions\").selectAll(\"g.dimension\").data((function(t){return t.dimensions}),p);w.enter().append(\"g\").attr(\"class\",\"dimension\"),w.attr(\"transform\",(function(t){return l(t.x,0)})),w.exit().remove();var A=w.selectAll(\"g.category\").data((function(t){return t.categories}),p),M=A.enter().append(\"g\").attr(\"class\",\"category\");A.attr(\"transform\",(function(t){return l(0,t.y)})),M.append(\"rect\").attr(\"class\",\"catrect\").attr(\"pointer-events\",\"none\"),A.select(\"rect.catrect\").attr(\"fill\",\"none\").attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})),T(M);var S=A.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),p);S.each((function(){s.raiseToTop(this)})),S.attr(\"fill\",(function(t){return t.color}));var E=S.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);S.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})).attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"})),k(E),S.exit().remove(),M.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var z=e._fullLayout.paper_bgcolor;A.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",f.makeTextShadow(z)).style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return d(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),M.append(\"text\").attr(\"class\",\"dimlabel\"),A.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"})).attr(\"x\",(function(t){return t.width/2})).attr(\"y\",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){c.font(n.select(this),t.parcatsViewModel.labelfont)})),A.selectAll(\"rect.bandrect\").on(\"mouseover\",L).on(\"mouseout\",C),A.exit().remove(),w.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",P).on(\"drag\",I).on(\"dragend\",O)),u.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")})),u.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function m(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){s.raiseToTop(this),w(n.select(this));var e=y(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit(\"plotly_hover\",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\")){var i,a,l,c=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),m=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=c&&c<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],v=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+v.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=u.mostReadable(t.model.color,[\"black\",\"white\"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&S.push([\"Count:\",M.countLabel].join(\" \")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&S.push([\"P:\",M.probabilityLabel].join(\" \"));var E=S.join(\"<br>\"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+m.left,y:_-d.top+m.top,text:E,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:T,idealAlign:L<b?\"right\":\"left\",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function v(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(m),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event,constraints:r})}}function y(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:e,event:n.event,constraints:r})}}function _(t){t.attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function w(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",(function(t){return u.mostReadable(t.model.color,[\"black\",\"white\"])})).attr(\"stroke-width\",.3)}function T(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function k(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll(\"rect.bandrect\"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,y(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,y(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select(\"rect.catrect\"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a=\"left\"):(i=c.left+c.width,a=\"right\");var m=u.model.count,g=u.model.categoryLabel,v=m/u.parcatsViewModel.model.count,y={countLabel:m,categoryLabel:g,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&x.push([\"Count:\",y.countLabel].join(\" \")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&x.push([\"P(\"+y.categoryLabel+\"):\",y.probabilityLabel].join(\" \"));var b=x.join(\"<br>\");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:12,fontColor:\"black\",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:y,eventData:[{data:p._input,fullData:p,count:m,category:g,probability:v}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron;if(\"color\"===l?(!function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll(\"rect.bandrect\").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr(\"stroke\",\"black\").attr(\"stroke-width\",1.5)}))}(this),S(this,\"plotly_hover\",n.event)):(!function(t){n.select(t.parentNode).selectAll(\"rect.bandrect\").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}(this),M(this,\"plotly_hover\",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\"))\"category\"===l?e=E(r,a,this):\"color\"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),c=n.select(r).datum(),f=c.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,m=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a=\"left\"):(i=l.left+l.width,a=\"right\");var g=f.model.categoryLabel,v=c.parcatsViewModel.model.count,y=0;c.categoryViewModel.bands.forEach((function(t){t.color===c.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===c.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:g,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&A.push([\"Count:\",k.countLabel].join(\" \")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&(A.push(\"P(color \\u2229 \"+g+\"): \"+k.probabilityLabel),A.push(\"P(\"+g+\" | color): \"+w.toFixed(3)),A.push(\"P(color | \"+g+\"): \"+T.toFixed(3)));var M=A.join(\"<br>\"),S=u.mostReadable(c.color,[\"black\",\"white\"]);return{trace:d,x:o*(i-e.left),y:s*(m-e.top),text:M,color:c.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,a,this):\"dimension\"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each((function(){i.push(E(t,e,this))})),i}(r,a,this)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(_(e.pathSelection),T(e.dimensionSelection.selectAll(\"g.category\")),k(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(m),-1===e.hoverinfoItems.indexOf(\"skip\"))){\"color\"===t.parcatsViewModel.hoveron?S(this,\"plotly_unhover\",n.event):M(this,\"plotly_unhover\",n.event)}}function P(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function I(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||\"freeform\"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function O(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=z(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+i+\"].displayindex\"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return t.categoryLabel}));e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[u],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[f],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?S(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):M(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd)t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null;t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,j(t.parcatsViewModel),N(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each((function(){R(t.parcatsViewModel,!0),D(t.parcatsViewModel,!0)})).each(\"end\",(function(){(o||s)&&a.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function 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e=i(t.calcdata,\"parcoords\")[0];e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has(\"parcoords\"),a=e._has&&e._has(\"parcoords\");i&&!a&&(n._paperdiv.selectAll(\".parcoords\").remove(),n._glimages.selectAll(\"*\").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(\".svg-container\");r.filter((function(t,e){return e===r.size()-1})).selectAll(\".gl-canvas-context, .gl-canvas-focus\").each((function(){var t=this.toDataURL(\"image/png\");e.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":t,preserveAspectRatio:\"none\",x:0,y:0,width:this.style.width,height:this.style.height})})),window.setTimeout((function(){n.selectAll(\"#filterBarPattern\").attr(\"id\",\"filterBarPattern\")}),60)}},{\"../../constants/xmlns_namespaces\":480,\"../../plots/get_data\":593,\"./plot\":899,\"@plotly/d3\":58}],891:[function(t,e,r){\"use strict\";var 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n=t(\"../../lib\").isTypedArray;r.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},r.isOrdinal=function(t){return!!t.tickvals},r.isVisible=function(t){return t.visible||!(\"visible\"in t)}},{\"../../lib\":503}],895:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcoords\",basePlotModule:t(\"./base_plot\"),categories:[\"gl\",\"regl\",\"noOpacity\",\"noHover\"],meta:{}}},{\"./attributes\":888,\"./base_plot\":890,\"./calc\":891,\"./defaults\":893,\"./plot\":899}],896:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nattribute vec4 p01_04, p05_08, p09_12, p13_16,\\n               p17_20, p21_24, p25_28, p29_32,\\n               p33_36, p37_40, p41_44, p45_48,\\n               p49_52, p53_56, p57_60, 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hi) {\\n    return (lo < hi) && (lo > p || p > hi);\\n}\\n\\nbool vOutside(vec4 p, vec4 lo, vec4 hi) {\\n    return (\\n        fOutside(p[0], lo[0], hi[0]) ||\\n        fOutside(p[1], lo[1], hi[1]) ||\\n        fOutside(p[2], lo[2], hi[2]) ||\\n        fOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool mOutside(mat4 p, mat4 lo, mat4 hi) {\\n    return (\\n        vOutside(p[0], lo[0], hi[0]) ||\\n        vOutside(p[1], lo[1], hi[1]) ||\\n        vOutside(p[2], lo[2], hi[2]) ||\\n        vOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    return mOutside(A, loA, hiA) ||\\n           mOutside(B, loB, hiB) ||\\n           mOutside(C, loC, hiC) ||\\n           mOutside(D, loD, hiD);\\n}\\n\\nbool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    mat4 pnts[4];\\n    pnts[0] = A;\\n    pnts[1] = B;\\n    pnts[2] = C;\\n    pnts[3] = D;\\n\\n    for(int i = 0; i < 4; ++i) {\\n        for(int j = 0; j < 4; ++j) {\\n            for(int k = 0; k < 4; ++k) {\\n                if(0 == iMod(\\n                    int(255.0 * texture2D(maskTexture,\\n                        vec2(\\n                            (float(i * 2 + j / 2) + 0.5) / 8.0,\\n                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\\n                        ))[3]\\n                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\\n                    2\\n                )) return true;\\n            }\\n        }\\n    }\\n    return false;\\n}\\n\\nvec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float x = 0.5 * sign(v) + 0.5;\\n    float y = axisY(x, A, B, C, D);\\n    float z = 1.0 - abs(v);\\n\\n    z += isContext ? 0.0 : 2.0 * float(\\n        outsideBoundingBox(A, B, C, D) ||\\n        outsideRasterMask(A, B, C, D)\\n    );\\n\\n    return vec4(\\n        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\\n        z,\\n        1.0\\n    );\\n}\\n\\nvoid main() 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right\"],editType:\"calc\"}}}},{\"../../components/color/attributes\":365,\"../../lib/extend\":493,\"../../plots/attributes\":550,\"../../plots/domain\":584,\"../../plots/font_attributes\":585,\"../../plots/template_attributes\":633}],901:[function(t,e,r){\"use strict\";var n=t(\"../../plots/plots\");r.name=\"pie\",r.plot=function(t,e,i,a){n.plotBasePlot(r.name,t,e,i,a)},r.clean=function(t,e,i,a){n.cleanBasePlot(r.name,t,e,i,a)}},{\"../../plots/plots\":619}],902:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"tinycolor2\"),a=t(\"../../components/color\"),o={};function s(t){return function(e,r){return!!e&&(!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e))}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r<t.length;r++)a.push(i(t[r]).lighten(20).toHexString());for(r=0;r<t.length;r++)a.push(i(t[r]).darken(20).toHexString());e[n]=a}return a}e.exports={calc:function(t,e){var r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],c=e.labels,u=e.marker.colors||[],f=e.values,h=e._length,p=e._hasValues&&h;if(e.dlabel)for(c=new Array(h),r=0;r<h;r++)c[r]=String(e.label0+r*e.dlabel);var d={},m=s(o[\"_\"+e.type+\"colormap\"]),g=0,v=!1;for(r=0;r<h;r++){var y,x,b;if(p){if(y=f[r],!n(y))continue;y=+y}else y=1;void 0!==(x=c[r])&&\"\"!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(g+=y),a.push({v:y,label:x,color:m(u[r],x),i:r,pts:[r],hidden:b})):(v=!0,(i=a[_]).v+=y,i.pts.push(r),i.hidden||(g+=y),!1===i.color&&u[r]&&(i.color=m(u[r],x)))}return a=a.filter((function(t){return t.v>=0})),(\"funnelarea\"===e.type?v:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var c=0,u=0;u<i.length;u++){var f=i[u];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[c%a.length],c++))}}},makePullColorFn:s,generateExtendedColors:l}},{\"../../components/color\":366,\"fast-isnumeric\":190,tinycolor2:312}],903:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\"),a=t(\"./attributes\"),o=t(\"../../plots/domain\").defaults,s=t(\"../bar/defaults\").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c(\"labels\"),c(\"values\")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),f){e._length=f,c(\"marker.line.width\")&&c(\"marker.line.color\"),c(\"marker.colors\"),c(\"scalegroup\");var h,p=c(\"text\"),d=c(\"texttemplate\");if(d||(h=c(\"textinfo\",Array.isArray(p)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),d||h&&\"none\"!==h){var m=c(\"textposition\");s(t,e,n,c,m,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(m)||\"auto\"===m||\"outside\"===m)&&c(\"automargin\"),(\"inside\"===m||\"auto\"===m||Array.isArray(m))&&c(\"insidetextorientation\")}o(e,n,c);var g=c(\"hole\");if(c(\"title.text\")){var v=c(\"title.position\",g?\"middle center\":\"top center\");g||\"middle center\"!==v||(e.title.position=\"top center\"),i.coerceFont(c,\"title.font\",n.font)}c(\"sort\"),c(\"direction\"),c(\"rotation\"),c(\"pull\")}else e.visible=!1}}},{\"../../lib\":503,\"../../plots/domain\":584,\"../bar/defaults\":652,\"./attributes\":900,\"fast-isnumeric\":190}],904:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx/helpers\").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},{\"../../components/fx/helpers\":402}],905:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||\"\"===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return(\"auto\"===t?0:t)*Math.PI/180}},{\"../../lib\":503}],906:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\").supplyDefaults,supplyLayoutDefaults:t(\"./layout_defaults\"),layoutAttributes:t(\"./layout_attributes\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./calc\").crossTraceCalc,plot:t(\"./plot\").plot,style:t(\"./style\"),styleOne:t(\"./style_one\"),moduleType:\"trace\",name:\"pie\",basePlotModule:t(\"./base_plot\"),categories:[\"pie-like\",\"pie\",\"showLegend\"],meta:{}}},{\"./attributes\":900,\"./base_plot\":901,\"./calc\":902,\"./defaults\":903,\"./layout_attributes\":907,\"./layout_defaults\":908,\"./plot\":909,\"./style\":910,\"./style_one\":911}],907:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{}],908:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"piecolorway\",e.colorway),r(\"extendpiecolors\")}},{\"../../lib\":503,\"./layout_attributes\":907}],909:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../plots/plots\"),a=t(\"../../components/fx\"),o=t(\"../../components/color\"),s=t(\"../../components/drawing\"),l=t(\"../../lib\"),c=l.strScale,u=l.strTranslate,f=t(\"../../lib/svg_text_utils\"),h=t(\"../bar/uniform_text\"),p=h.recordMinTextSize,d=h.clearMinTextSize,m=t(\"../bar/constants\").TEXTPAD,g=t(\"./helpers\"),v=t(\"./event_data\"),y=t(\"../../lib\").isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,c=i.trace,u=\"funnelarea\"===c.type;\"_hasHoverLabel\"in c||(c._hasHoverLabel=!1),\"_hasHoverEvent\"in c||(c._hasHoverEvent=!1),t.on(\"mouseover\",(function(t){var r=e._fullLayout,f=e._fullData[c.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:c._module},r,0)),\"all\"===h&&(h=\"label+text+value+percent+name\"),f.hovertemplate||\"none\"!==h&&\"skip\"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),m=s+t.pxmid[1]*(1-p),y=r.separators,x=[];if(h&&-1!==h.indexOf(\"label\")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf(\"text\")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,y),h&&-1!==h.indexOf(\"value\")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,y),h&&-1!==h.indexOf(\"percent\")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:c,x0:d-p*i.r,x1:d+p*i.r,y:m,_x0:u?o+t.TL[0]:d-p*i.r,_x1:u?o+t.TR[0]:d+p*i.r,_y0:u?s+t.TL[1]:m-p*i.r,_y1:u?s+t.BL[1]:m+p*i.r,text:x.join(\"<br>\"),name:f.hovertemplate||-1!==h.indexOf(\"name\")?f.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],c._hasHoverLabel=!0}c._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[v(t,f)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[c.index],o=n.select(this).datum();c._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[v(o,i)],event:n.event}),c._hasHoverEvent=!1),c._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),c._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[c.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append(\"text\").attr(\"data-notex\",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f=\"horizontal\"===u,h=\"tangential\"===u,p=\"radial\"===u,d=\"auto\"===u,m=[];if(!d){var g,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a=\"tan\"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,m.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)v(Math.PI*g,\"tan\");for(g=4;g>=-4;g-=2)v(Math.PI*(g+1),\"tan\")}if(f||p){for(g=4;g>=-4;g-=2)v(Math.PI*(g+1.5),\"rad\");for(g=4;g>=-4;g-=2)v(Math.PI*(g+.5),\"rad\")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;m.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a));for(var x=0,b=0,_=0;_<m.length;_++){var w=m[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return m[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=I(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?f*=2:-1!==a.title.position.indexOf(\"right\")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function I(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function O(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&\"middle center\"!==a.title.position&&(l-=P(i,e));var c=s/2,u=l/2;\"funnelarea\"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+I(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf(\"bottom\")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if(\"pie\"===i.type)l=r.r*r.r;else if(\"funnelarea\"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;\"funnelarea\"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&\"none\"!==o){var s,c=o.split(\"+\"),u=function(t){return-1!==c.indexOf(t)},f=u(\"label\"),h=u(\"text\"),p=u(\"value\"),d=u(\"percent\"),m=n.separators;if(s=f?[e.label]:[],h){var v=g.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(g.formatPieValue(e.v,m)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,m)),e.text=s.join(\"<br>\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=g.getFirstFilled(i.text,e.pts);(y(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d(\"pie\",r),_(e,t),O(e,a);var h=l.makeTraceGroups(r._pielayer,e,\"trace\").each((function(e){var h=n.select(this),d=e[0],m=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c=\"px0\",u=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c=\"px1\",u=\"px0\"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,s+=l*r.v/2,n=z(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr(\"stroke-linejoin\",\"round\"),h.each((function(){var v=n.select(this).selectAll(\"g.slice\").data(e);v.enter().append(\"g\").classed(\"slice\",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=m.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll(\"path.surface\").data([i]);if(h.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),u.call(x,t,e),m.pull){var v=+g.castOption(m.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=m.hole;if(i.v===d.vTotal){var k=\"M\"+(o+i.px0[0])+\",\"+(c+i.px0[1])+C(i.px0,i.pxmid,!0,1)+C(i.pxmid,i.px0,!0,1)+\"Z\";T?h.attr(\"d\",\"M\"+(o+T*i.px0[0])+\",\"+(c+T*i.px0[1])+C(i.px0,i.pxmid,!1,T)+C(i.pxmid,i.px0,!1,T)+\"Z\"+k):h.attr(\"d\",k)}else{var A=C(i.px0,i.px1,!0,1);if(T){var M=1-T;h.attr(\"d\",\"M\"+(o+T*i.px1[0])+\",\"+(c+T*i.px1[1])+C(i.px1,i.px0,!1,T)+\"l\"+M*i.px0[0]+\",\"+M*i.px0[1]+A+\"Z\")}else h.attr(\"d\",\"M\"+o+\",\"+c+\"l\"+i.px0[0]+\",\"+i.px0[1]+A+\"Z\")}D(t,i,d);var S=g.castOption(m.textposition,i.pts),E=u.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==S?[0]:[]);E.enter().append(\"g\").classed(\"slicetext\",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),h=l.ensureUniformFontSize(t,\"outside\"===S?function(t,e,r){var n=g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,i=g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(m,i,r.font):b(m,i,r.font));u.text(i.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if(\"outside\"===S)v=L(y,i);else if(v=w(y,i,d),\"auto\"===S&&v.scale<1){var x=l.ensureUniformFontSize(t,m.outsidetextfont);u.call(s.font,x),v=L(y=s.bBox(u.node()),i)}var T=v.textPosAngle,k=void 0===T?i.pxmid:z(d.r,T);if(v.targetX=o+k[0]*v.rCenter+(v.x||0),v.targetY=c+k[1]*v.rCenter+(v.y||0),R(v,y),v.outside){var A=v.targetY;i.yLabelMin=A-y.height/2,i.yLabelMid=A,i.yLabelMax=A+y.height/2,i.labelExtraX=0,i.labelExtraY=0,_=!0}v.fontSize=h.size,p(m.type,v,r),e[a].transform=v,u.attr(\"transform\",l.getTextTransform(v))}))}function C(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return\"a\"+n*d.r+\",\"+n*d.r+\" 0 \"+i.largeArc+(r?\" 1 \":\" 0 \")+a+\",\"+o}}));var T=n.select(this).selectAll(\"g.titletext\").data(m.title.text?[0]:[]);if(T.enter().append(\"g\").classed(\"titletext\",!0),T.exit().remove(),T.each((function(){var e,r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),i=m.title.text;m._meta&&(i=l.templateString(i,m._meta)),r.text(i).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,m.title.font).call(f.convertToTspans,t),e=\"middle center\"===m.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(d):C(d,a),r.attr(\"transform\",u(e.x,e.y)+c(Math.min(1,e.scale))+u(e.tx,e.ty))})),_&&function(t,e){var r,n,i,a,o,s,l,c,u,f,h,p,d;function m(t,e){return t.pxmid[1]-e.pxmid[1]}function v(t,e){return e.pxmid[1]-t.pxmid[1]}function y(t,r){r||(r={});var i,c,u,h,p=r.labelExtraY+(n?r.yLabelMax:r.yLabelMin),d=n?t.yLabelMin:t.yLabelMax,m=n?t.yLabelMax:t.yLabelMin,v=t.cyFinal+o(t.px0[1],t.px1[1]),y=p-d;if(y*l>0&&(t.labelExtraY=y),Array.isArray(e.pull))for(c=0;c<f.length;c++)(u=f[c])===t||(g.castOption(e.pull,t.pts)||0)>=(g.castOption(e.pull,u.pts)||0)||((t.pxmid[1]-u.pxmid[1])*l>0?(y=u.cyFinal+o(u.px0[1],u.px1[1])-d-t.labelExtraY)*l>0&&(t.labelExtraY+=y):(m+t.labelExtraY-v)*l>0&&(i=3*s*Math.abs(c-f.indexOf(t)),(h=u.cxFinal+a(u.px0[0],u.px1[0])+i-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*s>0&&(t.labelExtraX+=h)))}for(n=0;n<2;n++)for(i=n?m:v,o=n?Math.max:Math.min,l=n?1:-1,r=0;r<2;r++){for(a=r?Math.max:Math.min,s=r?1:-1,(c=t[n][r]).sort(i),u=t[1-n][r],f=u.concat(c),p=[],h=0;h<c.length;h++)void 0!==c[h].yLabelMid&&p.push(c[h]);for(d=!1,h=0;n&&h<u.length;h++)if(void 0!==u[h].yLabelMid){d=u[h];break}for(h=0;h<p.length;h++){var x=h&&p[h-1];d&&!h&&(x=d),y(p[h],x)}}}(y,m),function(t,e){t.each((function(t){var r=n.select(this);if(t.labelExtraX||t.labelExtraY){var i=r.select(\"g.slicetext text\");t.transform.targetX+=t.labelExtraX,t.transform.targetY+=t.labelExtraY,i.attr(\"transform\",l.getTextTransform(t.transform));var a=t.cxFinal+t.pxmid[0],s=\"M\"+a+\",\"+(t.cyFinal+t.pxmid[1]),c=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var u=t.labelExtraX*t.pxmid[1]/t.pxmid[0],f=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(u)>Math.abs(f)?s+=\"l\"+f*t.pxmid[0]/t.pxmid[1]+\",\"+f+\"H\"+(a+t.labelExtraX+c):s+=\"l\"+t.labelExtraX+\",\"+u+\"v\"+(f-u)+\"h\"+c}else s+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+c;l.ensureSingle(r,\"path\",\"textline\").call(o.stroke,e.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,e.outsidetextfont.size/8),d:s,fill:\"none\"})}else r.select(\"path.textline\").remove()}))}(v,m),_&&m.automargin){var k=s.bBox(h.node()),A=m.domain,M=a.w*(A.x[1]-A.x[0]),S=a.h*(A.y[1]-A.y[0]),E=(.5*M-d.r)/a.w,P=(.5*S-d.r)/a.h;i.autoMargin(t,\"pie.\"+m.uid+\".automargin\",{xl:A.x[0]-E,xr:A.x[1]+E,yb:A.y[0]-P,yt:A.y[1]+P,l:Math.max(d.cx-d.r-k.left,0),r:Math.max(k.right-(d.cx+d.r),0),b:Math.max(k.bottom-(d.cy+d.r),0),t:Math.max(d.cy-d.r-k.top,0),pad:5})}}))}));setTimeout((function(){h.selectAll(\"tspan\").each((function(){var t=n.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))}))}),0)},formatSliceLabel:D,transformInsideText:w,determineInsideTextFont:b,positionTitleOutside:C,prerenderTitles:_,layoutAreas:O,attachFxHandlers:x,computeTransform:R}},{\"../../components/color\":366,\"../../components/drawing\":388,\"../../components/fx\":406,\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"../../plots/plots\":619,\"../bar/constants\":650,\"../bar/uniform_text\":664,\"./event_data\":904,\"./helpers\":905,\"@plotly/d3\":58}],910:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"./style_one\"),a=t(\"../bar/uniform_text\").resizeText;e.exports=function(t){var e=t._fullLayout._pielayer.selectAll(\".trace\");a(t,e,\"pie\"),e.each((function(t){var e=t[0].trace,r=n.select(this);r.style({opacity:e.opacity}),r.selectAll(\"path.surface\").each((function(t){n.select(this).call(i,t,e)}))}))}},{\"../bar/uniform_text\":664,\"./style_one\":911,\"@plotly/d3\":58}],911:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"./helpers\").castOption;e.exports=function(t,e,r){var a=r.marker.line,o=i(a.color,e.pts)||n.defaultLine,s=i(a.width,e.pts)||0;t.style(\"stroke-width\",s).call(n.fill,e.color).call(n.stroke,o)}},{\"../../components/color\":366,\"./helpers\":905}],912:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\");e.exports={x:n.x,y:n.y,xy:{valType:\"data_array\",editType:\"calc\"},indices:{valType:\"data_array\",editType:\"calc\"},xbounds:{valType:\"data_array\",editType:\"calc\"},ybounds:{valType:\"data_array\",editType:\"calc\"},text:n.text,marker:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,arrayOk:!1,editType:\"calc\"},blend:{valType:\"boolean\",dflt:null,editType:\"calc\"},sizemin:{valType:\"number\",min:.1,max:2,dflt:.5,editType:\"calc\"},sizemax:{valType:\"number\",min:.1,dflt:20,editType:\"calc\"},border:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},arearatio:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},editType:\"calc\"},editType:\"calc\"},transforms:void 0}},{\"../scatter/attributes\":926}],913:[function(t,e,r){\"use strict\";var n=t(\"../../../stackgl_modules\").gl_pointcloud2d,i=t(\"../../lib/str2rgbarray\"),a=t(\"../../plots/cartesian/autorange\").findExtremes,o=t(\"../scatter/get_trace_color\");function s(t,e){this.scene=t,this.uid=e,this.type=\"pointcloud\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=n(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}var l=s.prototype;l.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*e],this.pickXYData[2*e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:Array.isArray(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},l.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=o(t,{})},l.updateFast=function(t){var e,r,n,o,s,l,c=this.xData=this.pickXData=t.x,u=this.yData=this.pickYData=t.y,f=this.pickXYData=t.xy,h=t.xbounds&&t.ybounds,p=t.indices,d=this.bounds;if(f){if(n=f,e=f.length>>>1,h)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(l=0;l<e;l++)o=n[2*l],s=n[2*l+1],o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);if(p)r=p;else for(r=new Int32Array(e),l=0;l<e;l++)r[l]=l}else for(e=c.length,n=new Float32Array(2*e),r=new Int32Array(e),l=0;l<e;l++)o=c[l],s=u[l],r[l]=l,n[2*l]=o,n[2*l+1]=s,o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);this.idToIndex=r,this.pointcloudOptions.idToIndex=r,this.pointcloudOptions.positions=n;var m=i(t.marker.color),g=i(t.marker.border.color),v=t.opacity*t.marker.opacity;m[3]*=v,this.pointcloudOptions.color=m;var y=t.marker.blend;if(null===y){y=c.length<100||u.length<100}this.pointcloudOptions.blend=y,g[3]*=v,this.pointcloudOptions.borderColor=g;var x=t.marker.sizemin,b=Math.max(t.marker.sizemax,t.marker.sizemin);this.pointcloudOptions.sizeMin=x,this.pointcloudOptions.sizeMax=b,this.pointcloudOptions.areaRatio=t.marker.border.arearatio,this.pointcloud.update(this.pointcloudOptions);var _=this.scene.xaxis,w=this.scene.yaxis,T=b/2||.5;t._extremes[_._id]=a(_,[d[0],d[2]],{ppad:T}),t._extremes[w._id]=a(w,[d[1],d[3]],{ppad:T})},l.dispose=function(){this.pointcloud.dispose()},e.exports=function(t,e){var r=new s(t,e.uid);return r.update(e),r}},{\"../../../stackgl_modules\":1120,\"../../lib/str2rgbarray\":528,\"../../plots/cartesian/autorange\":553,\"../scatter/get_trace_color\":936}],914:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\");e.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}a(\"x\"),a(\"y\"),a(\"xbounds\"),a(\"ybounds\"),t.xy&&t.xy instanceof Float32Array&&(e.xy=t.xy),t.indices&&t.indices instanceof Int32Array&&(e.indices=t.indices),a(\"text\"),a(\"marker.color\",r),a(\"marker.opacity\"),a(\"marker.blend\"),a(\"marker.sizemin\"),a(\"marker.sizemax\"),a(\"marker.border.color\",r),a(\"marker.border.arearatio\"),e._length=null}},{\"../../lib\":503,\"./attributes\":912}],915:[function(t,e,r){\"use strict\";[\"*pointcloud* trace is deprecated!\",\"Please consider switching to the *scattergl* trace type.\"].join(\" \");e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"../scatter3d/calc\"),plot:t(\"./convert\"),moduleType:\"trace\",name:\"pointcloud\",basePlotModule:t(\"../../plots/gl2d\"),categories:[\"gl\",\"gl2d\",\"showLegend\"],meta:{}}},{\"../../plots/gl2d\":596,\"../scatter3d/calc\":955,\"./attributes\":912,\"./convert\":913,\"./defaults\":914}],916:[function(t,e,r){\"use strict\";var n=t(\"../../plots/font_attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/color/attributes\"),o=t(\"../../components/fx/attributes\"),s=t(\"../../plots/domain\").attributes,l=t(\"../../plots/template_attributes\").hovertemplateAttrs,c=t(\"../../components/colorscale/attributes\"),u=t(\"../../plot_api/plot_template\").templatedArray,f=t(\"../../plots/cartesian/axis_format_attributes\").descriptionOnlyNumbers,h=t(\"../../lib/extend\").extendFlat,p=t(\"../../plot_api/edit_types\").overrideAll;(e.exports=p({hoverinfo:h({},i.hoverinfo,{flags:[],arrayOk:!1}),hoverlabel:o.hoverlabel,domain:s({name:\"sankey\",trace:!0}),orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],dflt:\"h\"},valueformat:{valType:\"string\",dflt:\".3s\",description:f(\"value\")},valuesuffix:{valType:\"string\",dflt:\"\"},arrangement:{valType:\"enumerated\",values:[\"snap\",\"perpendicular\",\"freeform\",\"fixed\"],dflt:\"snap\"},textfont:n({}),customdata:void 0,node:{label:{valType:\"data_array\",dflt:[]},groups:{valType:\"info_array\",impliedEdits:{x:[],y:[]},dimensions:2,freeLength:!0,dflt:[],items:{valType:\"number\",editType:\"calc\"}},x:{valType:\"data_array\",dflt:[]},y:{valType:\"data_array\",dflt:[]},color:{valType:\"color\",arrayOk:!0},customdata:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:a.defaultLine,arrayOk:!0},width:{valType:\"number\",min:0,dflt:.5,arrayOk:!0}},pad:{valType:\"number\",arrayOk:!1,min:0,dflt:20},thickness:{valType:\"number\",arrayOk:!1,min:1,dflt:20},hoverinfo:{valType:\"enumerated\",values:[\"all\",\"none\",\"skip\"],dflt:\"all\"},hoverlabel:o.hoverlabel,hovertemplate:l({},{keys:[\"value\",\"label\"]})},link:{label:{valType:\"data_array\",dflt:[]},color:{valType:\"color\",arrayOk:!0},customdata:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:a.defaultLine,arrayOk:!0},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0}},source:{valType:\"data_array\",dflt:[]},target:{valType:\"data_array\",dflt:[]},value:{valType:\"data_array\",dflt:[]},hoverinfo:{valType:\"enumerated\",values:[\"all\",\"none\",\"skip\"],dflt:\"all\"},hoverlabel:o.hoverlabel,hovertemplate:l({},{keys:[\"value\",\"label\"]}),colorscales:u(\"concentrationscales\",{editType:\"calc\",label:{valType:\"string\",editType:\"calc\",dflt:\"\"},cmax:{valType:\"number\",editType:\"calc\",dflt:1},cmin:{valType:\"number\",editType:\"calc\",dflt:0},colorscale:h(c().colorscale,{dflt:[[0,\"white\"],[1,\"black\"]]})})}},\"calc\",\"nested\")).transforms=void 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h={_id:\"x\",c2p:u.identity,_offset:r._sankey.translateX,_length:r._sankey.width},p={_id:\"y\",c2p:u.identity,_offset:r._sankey.translateY,_length:r._sankey.height},d={gd:t,element:o.node(),plotinfo:{id:e,xaxis:h,yaxis:p,fillRangeItems:u.noop},subplot:e,xaxes:[h],yaxes:[p],doneFnCompleted:function(r){var n,i=t._fullData[e],a=i.node.groups.slice(),o=[];function s(t){for(var e=i._sankey.graph.nodes,r=0;r<e.length;r++)if(e[r].pointNumber===t)return e[r]}for(var l=0;l<r.length;l++){var c=s(r[l].pointNumber);if(c)if(c.group){for(var u=0;u<c.childrenNodes.length;u++)o.push(c.childrenNodes[u].pointNumber);a[c.pointNumber-i.node._count]=!1}else o.push(c.pointNumber)}n=a.filter(Boolean).concat([o]),f.call(\"_guiRestyle\",t,{\"node.groups\":[n]},e)},prepFn:function(t,e,r){c(t,e,r,d,i)}};l.init(d)}}r.name=\"sankey\",r.baseLayoutAttrOverrides=n({hoverlabel:o.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){var e=i(t.calcdata,\"sankey\")[0];a(t,e),r.updateFx(t)},r.clean=function(t,e,r,n){var 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n,i=y(e),a=i.trace,u=a.domain,f=\"h\"===a.orientation,p=a.node.pad,d=a.node.thickness,m=t.width*(u.x[1]-u.x[0]),g=t.height*(u.y[1]-u.y[0]),v=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[m,g]:[g,m]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(v).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn(\"node.pad was reduced to \",n.nodePadding(),\" to fit within the figure.\"),i._groupLookup){var M,S=parseInt(i._groupLookup[A]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){M=k.nodes[_];break}if(M){var E={pointNumber:parseInt(A),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+\":\"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=c(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*m,a.node.y[_]*g];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}if(\"snap\"===a.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(v=k.nodes));n.update(k)}return{circular:b,key:r,trace:a,guid:h.randstr(),horizontal:f,width:m,height:g,nodePad:a.node.pad,nodeLineColor:a.node.line.color,nodeLineWidth:a.node.line.width,linkLineColor:a.link.line.color,linkLineWidth:a.link.line.width,valueFormat:a.valueformat,valueSuffix:a.valuesuffix,textFont:a.textfont,translateX:u.x[0]*t.width+t.margin.l,translateY:t.height-u.y[1]*t.height+t.margin.t,dragParallel:f?g:m,dragPerpendicular:f?m:g,arrangement:a.arrangement,sankey:n,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function A(t,e,r){var n=c(e.color),i=e.source.label+\"|\"+e.target.label+\"__\"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:u.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,\"top\"===e.circularLinkType?\"M \"+n.targetX+\" \"+(n.targetY+r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 1 \"+(n.rightFullExtent-r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 1 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 0 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 0 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"L\"+n.targetX+\" \"+(n.targetY-r)+\"Z\":\"M \"+n.targetX+\" \"+(n.targetY-r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 0 \"+(n.rightFullExtent-r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 0 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 1 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 1 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"L\"+n.targetX+\" \"+(n.targetY+r)+\"Z\";var e,r,n,a=t.link.source.x1,o=t.link.target.x0,s=i(a,o),l=s(.5),c=s(.5),u=t.link.y0-t.link.width/2,f=t.link.y0+t.link.width/2,h=t.link.y1-t.link.width/2,p=t.link.y1+t.link.width/2;return\"M\"+a+\",\"+u+\"C\"+l+\",\"+u+\" \"+c+\",\"+h+\" \"+o+\",\"+h+\"L\"+o+\",\"+p+\"C\"+c+\",\"+p+\" \"+l+\",\"+f+\" \"+a+\",\"+f+\"Z\"}}function S(t,e){var r=c(e.color),n=l.nodePadAcross,i=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var a=e.dx,o=Math.max(.5,e.dy),s=\"node_\"+e.pointNumber;return e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:u.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join(\"_\"),interactionState:t.interactionState,figure:t}}function E(t){t.attr(\"transform\",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function C(t,e){t.call(L),e.attr(\"d\",M())}function P(t){t.attr(\"width\",(function(t){return t.node.x1-t.node.x0})).attr(\"height\",(function(t){return t.visibleHeight}))}function I(t){return t.link.width>1||t.linkLineWidth>0}function O(t){return p(t.translateX,t.translateY)+(t.horizontal?\"matrix(1 0 0 1 0 0)\":\"matrix(0 1 1 0 0 0)\")}function z(t,e,r){t.on(\".basic\",null).on(\"mouseover.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.hover(this,t,e),t.interactionState.hovered=[this,t])})).on(\"mousemove.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.follow(this,t),t.interactionState.hovered=[this,t])})).on(\"mouseout.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.unhover(this,t,e),t.interactionState.hovered=!1)})).on(\"click.basic\",(function(t){t.interactionState.hovered&&(r.unhover(this,t,e),t.interactionState.hovered=!1),t.interactionState.dragInProgress||t.partOfGroup||r.select(this,t,e)}))}function D(t,e,r,i){var o=a.behavior.drag().origin((function(t){return{x:t.node.x0+t.visibleWidth/2,y:t.node.y0+t.visibleHeight/2}})).on(\"dragstart\",(function(a){if(\"fixed\"!==a.arrangement&&(h.ensureSingle(i._fullLayout._infolayer,\"g\",\"dragcover\",(function(t){i._fullLayout._dragCover=t})),h.raiseToTop(this),a.interactionState.dragInProgress=a.node,F(a.node),a.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,a.interactionState.hovered),a.interactionState.hovered=!1),\"snap\"===a.arrangement)){var o=a.traceId+\"|\"+a.key;a.forceLayouts[o]?a.forceLayouts[o].alpha(1):function(t,e,r,i){!function(t){for(var e=0;e<t.length;e++)t[e].y=(t[e].y0+t[e].y1)/2,t[e].x=(t[e].x0+t[e].x1)/2}(r.graph.nodes);var a=r.graph.nodes.filter((function(t){return t.originalX===r.node.originalX})).filter((function(t){return!t.partOfGroup}));r.forceLayouts[e]=n.forceSimulation(a).alphaDecay(0).force(\"collide\",n.forceCollide().radius((function(t){return t.dy/2+r.nodePad/2})).strength(1).iterations(l.forceIterations)).force(\"constrain\",function(t,e,r,n){return function(){for(var t=0,i=0;i<r.length;i++){var a=r[i];a===n.interactionState.dragInProgress?(a.x=a.lastDraggedX,a.y=a.lastDraggedY):(a.vx=(a.originalX-a.x)/l.forceTicksPerFrame,a.y=Math.min(n.size-a.dy/2,Math.max(a.dy/2,a.y))),t=Math.max(t,Math.abs(a.vx),Math.abs(a.vy))}!n.interactionState.dragInProgress&&t<.1&&n.forceLayouts[e].alpha()>0&&n.forceLayouts[e].alpha(0)}}(0,e,a,r)).stop()}(0,o,a),function(t,e,r,n,i){window.requestAnimationFrame((function a(){var o;for(o=0;o<l.forceTicksPerFrame;o++)r.forceLayouts[n].tick();if(function(t){for(var e=0;e<t.length;e++)t[e].y0=t[e].y-t[e].dy/2,t[e].y1=t[e].y0+t[e].dy,t[e].x0=t[e].x-t[e].dx/2,t[e].x1=t[e].x0+t[e].dx}(r.graph.nodes),r.sankey.update(r.graph),C(t.filter(B(r)),e),r.forceLayouts[n].alpha()>0)window.requestAnimationFrame(a);else{var s=r.node.originalX;r.node.x0=s-r.visibleWidth/2,r.node.x1=s+r.visibleWidth/2,R(r,i)}}))}(t,e,a,o,i)}})).on(\"drag\",(function(r){if(\"fixed\"!==r.arrangement){var n=a.event.x,i=a.event.y;\"snap\"===r.arrangement?(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2,r.node.y0=i-r.visibleHeight/2,r.node.y1=i+r.visibleHeight/2):(\"freeform\"===r.arrangement&&(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2),i=Math.max(0,Math.min(r.size-r.visibleHeight/2,i)),r.node.y0=i-r.visibleHeight/2,r.node.y1=i+r.visibleHeight/2),F(r.node),\"snap\"!==r.arrangement&&(r.sankey.update(r.graph),C(t.filter(B(r)),e))}})).on(\"dragend\",(function(t){if(\"fixed\"!==t.arrangement){t.interactionState.dragInProgress=!1;for(var e=0;e<t.node.childrenNodes.length;e++)t.node.childrenNodes[e].x=t.node.x,t.node.childrenNodes[e].y=t.node.y;\"snap\"!==t.arrangement&&R(t,i)}}));t.on(\".drag\",null).call(o)}function R(t,e){for(var r=[],n=[],i=0;i<t.graph.nodes.length;i++){var a=(t.graph.nodes[i].x0+t.graph.nodes[i].x1)/2,o=(t.graph.nodes[i].y0+t.graph.nodes[i].y1)/2;r.push(a/t.figure.width),n.push(o/t.figure.height)}b.call(\"_guiRestyle\",e,{\"node.x\":[r],\"node.y\":[n]},t.trace.index).then((function(){e._fullLayout._dragCover&&e._fullLayout._dragCover.remove()}))}function F(t){t.lastDraggedX=t.x0+t.dx/2,t.lastDraggedY=t.y0+t.dy/2}function B(t){return function(e){return e.node.originalX===t.node.originalX}}e.exports=function(t,e,r,n,i){var o=!1;h.ensureSingle(t._fullLayout._infolayer,\"g\",\"first-render\",(function(){o=!0}));var s=t._fullLayout._dragCover,m=r.filter((function(t){return y(t).trace.visible})).map(k.bind(null,n)),b=e.selectAll(\".\"+l.cn.sankey).data(m,g);b.exit().remove(),b.enter().append(\"g\").classed(l.cn.sankey,!0).style(\"box-sizing\",\"content-box\").style(\"position\",\"absolute\").style(\"left\",0).style(\"shape-rendering\",\"geometricPrecision\").style(\"pointer-events\",\"auto\").attr(\"transform\",O),b.each((function(e,r){t._fullData[r]._sankey=e;var n=\"bgsankey-\"+e.trace.uid+\"-\"+r;h.ensureSingle(t._fullLayout._draggers,\"rect\",n),t._fullData[r]._bgRect=a.select(\".\"+n),t._fullData[r]._bgRect.style(\"pointer-events\",\"all\").attr(\"width\",e.width).attr(\"height\",e.height).attr(\"x\",e.translateX).attr(\"y\",e.translateY).classed(\"bgsankey\",!0).style({fill:\"transparent\",\"stroke-width\":0})})),b.transition().ease(l.ease).duration(l.duration).attr(\"transform\",O);var _=b.selectAll(\".\"+l.cn.sankeyLinks).data(v,g);_.enter().append(\"g\").classed(l.cn.sankeyLinks,!0).style(\"fill\",\"none\");var L=_.selectAll(\".\"+l.cn.sankeyLink).data((function(t){return t.graph.links.filter((function(t){return t.value})).map(A.bind(null,t))}),g);L.enter().append(\"path\").classed(l.cn.sankeyLink,!0).call(z,b,i.linkEvents),L.style(\"stroke\",(function(t){return I(t)?u.tinyRGB(c(t.linkLineColor)):t.tinyColorHue})).style(\"stroke-opacity\",(function(t){return I(t)?u.opacity(t.linkLineColor):t.tinyColorAlpha})).style(\"fill\",(function(t){return t.tinyColorHue})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})).style(\"stroke-width\",(function(t){return I(t)?t.linkLineWidth:1})).attr(\"d\",M()),L.style(\"opacity\",(function(){return t._context.staticPlot||o||s?1:0})).transition().ease(l.ease).duration(l.duration).style(\"opacity\",1),L.exit().transition().ease(l.ease).duration(l.duration).style(\"opacity\",0).remove();var C=b.selectAll(\".\"+l.cn.sankeyNodeSet).data(v,g);C.enter().append(\"g\").classed(l.cn.sankeyNodeSet,!0),C.style(\"cursor\",(function(t){switch(t.arrangement){case\"fixed\":return\"default\";case\"perpendicular\":return\"ns-resize\";default:return\"move\"}}));var R=C.selectAll(\".\"+l.cn.sankeyNode).data((function(t){var e=t.graph.nodes;return function(t){var e,r=[];for(e=0;e<t.length;e++)t[e].originalX=(t[e].x0+t[e].x1)/2,t[e].originalY=(t[e].y0+t[e].y1)/2,-1===r.indexOf(t[e].originalX)&&r.push(t[e].originalX);for(r.sort((function(t,e){return t-e})),e=0;e<t.length;e++)t[e].originalLayerIndex=r.indexOf(t[e].originalX),t[e].originalLayer=t[e].originalLayerIndex/(r.length-1)}(e),e.map(S.bind(null,t))}),g);R.enter().append(\"g\").classed(l.cn.sankeyNode,!0).call(E).style(\"opacity\",(function(e){return!t._context.staticPlot&&!o||e.partOfGroup?0:1})),R.call(z,b,i.nodeEvents).call(D,L,i,t),R.transition().ease(l.ease).duration(l.duration).call(E).style(\"opacity\",(function(t){return t.partOfGroup?0:1})),R.exit().transition().ease(l.ease).duration(l.duration).style(\"opacity\",0).remove();var F=R.selectAll(\".\"+l.cn.nodeRect).data(v);F.enter().append(\"rect\").classed(l.cn.nodeRect,!0).call(P),F.style(\"stroke-width\",(function(t){return t.nodeLineWidth})).style(\"stroke\",(function(t){return u.tinyRGB(c(t.nodeLineColor))})).style(\"stroke-opacity\",(function(t){return u.opacity(t.nodeLineColor)})).style(\"fill\",(function(t){return t.tinyColorHue})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})),F.transition().ease(l.ease).duration(l.duration).call(P);var B=R.selectAll(\".\"+l.cn.nodeLabel).data(v);B.enter().append(\"text\").classed(l.cn.nodeLabel,!0).style(\"cursor\",\"default\"),B.attr(\"data-notex\",1).text((function(t){return t.node.label})).each((function(e){var r=a.select(this);f.font(r,e.textFont),x.convertToTspans(r,t)})).style(\"text-shadow\",x.makeTextShadow(t._fullLayout.paper_bgcolor)).attr(\"text-anchor\",(function(t){return t.horizontal&&t.left?\"end\":\"start\"})).attr(\"transform\",(function(t){var e=a.select(this),r=x.lineCount(e),n=t.textFont.size*((r-1)*T-w),i=t.nodeLineWidth/2+3,o=((t.horizontal?t.visibleHeight:t.visibleWidth)-n)/2;t.horizontal&&(t.left?i=-i:i+=t.visibleWidth);var s=t.horizontal?\"\":\"scale(-1,1)\"+d(90);return p(t.horizontal?i:o,t.horizontal?o:i)+s})),B.transition().ease(l.ease).duration(l.duration)}},{\"../../components/color\":366,\"../../components/drawing\":388,\"../../constants/alignment\":471,\"../../lib\":503,\"../../lib/gup\":500,\"../../lib/svg_text_utils\":529,\"../../registry\":638,\"./constants\":919,\"@plotly/d3\":58,\"@plotly/d3-sankey\":57,\"@plotly/d3-sankey-circular\":56,\"d3-force\":111,\"d3-interpolate\":116,tinycolor2:312}],924:[function(t,e,r){\"use strict\";e.exports=function(t,e){for(var r=[],n=t.cd[0].trace,i=n._sankey.graph.nodes,a=0;a<i.length;a++){var o=i[a];if(!o.partOfGroup){var s=[(o.x0+o.x1)/2,(o.y0+o.y1)/2];\"v\"===n.orientation&&s.reverse(),e&&e.contains(s,!1,a,t)&&r.push({pointNumber:o.pointNumber})}}return r}},{}],925:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){for(var r=0;r<t.length;r++)t[r].i=r;n.mergeArray(e.text,t,\"tx\"),n.mergeArray(e.texttemplate,t,\"txt\"),n.mergeArray(e.hovertext,t,\"htx\"),n.mergeArray(e.customdata,t,\"data\"),n.mergeArray(e.textposition,t,\"tp\"),e.textfont&&(n.mergeArrayCastPositive(e.textfont.size,t,\"ts\"),n.mergeArray(e.textfont.color,t,\"tc\"),n.mergeArray(e.textfont.family,t,\"tf\"));var i=e.marker;if(i){n.mergeArrayCastPositive(i.size,t,\"ms\"),n.mergeArrayCastPositive(i.opacity,t,\"mo\"),n.mergeArray(i.symbol,t,\"mx\"),n.mergeArray(i.color,t,\"mc\");var a=i.line;i.line&&(n.mergeArray(a.color,t,\"mlc\"),n.mergeArrayCastPositive(a.width,t,\"mlw\"));var o=i.gradient;o&&\"none\"!==o.type&&(n.mergeArray(o.type,t,\"mgt\"),n.mergeArray(o.color,t,\"mgc\"))}}},{\"../../lib\":503}],926:[function(t,e,r){\"use strict\";var 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n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/line_shape_defaults\"),c=t(\"../scatter/text_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),f=t(\"./attributes\");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p(\"carpet\"),e.xaxis=\"x\",e.yaxis=\"y\";var d=p(\"a\"),m=p(\"b\"),g=Math.min(d.length,m.length);if(g){e._length=g,p(\"text\"),p(\"texttemplate\"),p(\"hovertext\"),p(\"mode\",g<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var v=[];(a.hasMarkers(e)||a.hasText(e))&&(p(\"marker.maxdisplayed\"),v.push(\"points\")),p(\"fill\"),\"none\"!==e.fill&&(u(t,e,r,p),a.hasLines(e)||l(t,e,p)),\"tonext\"!==e.fill&&\"toself\"!==e.fill||v.push(\"fills\"),\"fills\"!==p(\"hoveron\",v.join(\"+\")||\"points\")&&p(\"hovertemplate\"),n.coerceSelectionMarkerOpacity(e,p)}else e.visible=!1}},{\"../../lib\":503,\"../scatter/constants\":930,\"../scatter/fillcolor_defaults\":934,\"../scatter/line_defaults\":939,\"../scatter/line_shape_defaults\":941,\"../scatter/marker_defaults\":945,\"../scatter/subtypes\":951,\"../scatter/text_defaults\":952,\"./attributes\":960}],963:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){var a=n[i];return t.a=a.a,t.b=a.b,t.y=a.y,t}},{}],964:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r={},n=e._carpet,i=n.ab2ij([t.a,t.b]),a=Math.floor(i[0]),o=i[0]-a,s=Math.floor(i[1]),l=i[1]-s,c=n.evalxy([],a,s,o,l);return r.yLabel=c[1].toFixed(3),r}},{}],965:[function(t,e,r){\"use strict\";var n=t(\"../scatter/hover\"),i=t(\"../../lib\").fillText;e.exports=function(t,e,r,a){var o=n(t,e,r,a);if(o&&!1!==o[0].index){var s=o[0];if(void 0===s.index){var l=1-s.y0/t.ya._length,c=t.xa._length,u=c*l/2,f=c-u;return s.x0=Math.max(Math.min(s.x0,f),u),s.x1=Math.max(Math.min(s.x1,f),u),o}var h=s.cd[s.index];s.a=h.a,s.b=h.b,s.xLabelVal=void 0,s.yLabelVal=void 0;var p=s.trace,d=p._carpet,m=p._module.formatLabels(h,p);s.yLabel=m.yLabel,delete s.text;var g=[];if(!p.hovertemplate){var v=(h.hi||p.hoverinfo).split(\"+\");-1!==v.indexOf(\"all\")&&(v=[\"a\",\"b\",\"text\"]),-1!==v.indexOf(\"a\")&&y(d.aaxis,h.a),-1!==v.indexOf(\"b\")&&y(d.baxis,h.b),g.push(\"y: \"+s.yLabel),-1!==v.indexOf(\"text\")&&i(h,p,g),s.extraText=g.join(\"<br>\")}return o}function y(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,\"\"):t._hovertitle,g.push(r+\": \"+e.toFixed(3)+t.labelsuffix)}}},{\"../../lib\":503,\"../scatter/hover\":937}],966:[function(t,e,r){\"use 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s,l,c,u=r[0][0].carpet,f={xaxis:i.getFromId(t,u.xaxis||\"x\"),yaxis:i.getFromId(t,u.yaxis||\"y\"),plot:e.plot};for(n(t,f,r,o),s=0;s<r.length;s++)l=r[s][0].trace,c=o.selectAll(\"g.trace\"+l.uid+\" .js-line\"),a.setClipUrl(c,r[s][0].carpet._clipPathId,t)}},{\"../../components/drawing\":388,\"../../plots/cartesian/axes\":554,\"../scatter/plot\":947}],968:[function(t,e,r){\"use strict\";var n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../scatter/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../components/colorscale/attributes\"),l=t(\"../../components/drawing/attributes\").dash,c=t(\"../../lib/extend\").extendFlat,u=t(\"../../plot_api/edit_types\").overrideAll,f=a.marker,h=a.line,p=f.line;e.exports=u({lon:{valType:\"data_array\"},lat:{valType:\"data_array\"},locations:{valType:\"data_array\"},locationmode:{valType:\"enumerated\",values:[\"ISO-3\",\"USA-states\",\"country 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n=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),a=t(\"../scatter/marker_defaults\"),o=t(\"../scatter/line_defaults\"),s=t(\"../scatter/text_defaults\"),l=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,c,r,i)}var h,p=f(\"locations\");if(p&&p.length){var d,m=f(\"geojson\");(\"string\"==typeof m&&\"\"!==m||n.isPlainObject(m))&&(d=\"geojson-id\"),\"geojson-id\"===f(\"locationmode\",d)&&f(\"featureidkey\"),h=p.length}else{var g=f(\"lon\")||[],v=f(\"lat\")||[];h=Math.min(g.length,v.length)}h?(e._length=h,f(\"text\"),f(\"hovertext\"),f(\"hovertemplate\"),f(\"mode\"),i.hasLines(e)&&(o(t,e,r,u,f),f(\"connectgaps\")),i.hasMarkers(e)&&a(t,e,r,u,f,{gradient:!0}),i.hasText(e)&&(f(\"texttemplate\"),s(t,e,u,f)),f(\"fill\"),\"none\"!==e.fill&&l(t,e,r,f),n.coerceSelectionMarkerOpacity(e,f)):e.visible=!1}},{\"../../lib\":503,\"../scatter/fillcolor_defaults\":934,\"../scatter/line_defaults\":939,\"../scatter/marker_defaults\":945,\"../scatter/subtypes\":951,\"../scatter/text_defaults\":952,\"./attributes\":968}],971:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){t.lon=e.lon,t.lat=e.lat,t.location=e.loc?e.loc:null;var a=n[i];return a.fIn&&a.fIn.properties&&(t.properties=a.fIn.properties),t}},{}],972:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axes\");e.exports=function(t,e,r){var i={},a=r[e.geo]._subplot.mockAxis,o=t.lonlat;return i.lonLabel=n.tickText(a,a.c2l(o[0]),!0).text,i.latLabel=n.tickText(a,a.c2l(o[1]),!0).text,i}},{\"../../plots/cartesian/axes\":554}],973:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx\"),i=t(\"../../constants/numerical\").BADNUM,a=t(\"../scatter/get_trace_color\"),o=t(\"../../lib\").fillText,s=t(\"./attributes\");e.exports=function(t,e,r){var l=t.cd,c=l[0].trace,u=t.xa,f=t.ya,h=t.subplot,p=h.projection.isLonLatOverEdges,d=h.project;if(n.getClosest(l,(function(t){var n=t.lonlat;if(n[0]===i)return 1/0;if(p(n))return 1/0;var a=d(n),o=d([e,r]),s=Math.abs(a[0]-o[0]),l=Math.abs(a[1]-o[1]),c=Math.max(3,t.mrc||0);return Math.max(Math.sqrt(s*s+l*l)-c,1-3/c)}),t),!1!==t.index){var m=l[t.index],g=m.lonlat,v=[u.c2p(g),f.c2p(g)],y=m.mrc||1;t.x0=v[0]-y,t.x1=v[0]+y,t.y0=v[1]-y,t.y1=v[1]+y,t.loc=m.loc,t.lon=g[0],t.lat=g[1];var x={};x[c.geo]={_subplot:h};var b=c._module.formatLabels(m,c,x);return t.lonLabel=b.lonLabel,t.latLabel=b.latLabel,t.color=a(c,m),t.extraText=function(t,e,r,n){if(t.hovertemplate)return;var i=e.hi||t.hoverinfo,a=\"all\"===i?s.hoverinfo.flags:i.split(\"+\"),l=-1!==a.indexOf(\"location\")&&Array.isArray(t.locations),c=-1!==a.indexOf(\"lon\"),u=-1!==a.indexOf(\"lat\"),f=-1!==a.indexOf(\"text\"),h=[];function p(t){return t+\"\\xb0\"}l?h.push(e.loc):c&&u?h.push(\"(\"+p(r.latLabel)+\", \"+p(r.lonLabel)+\")\"):c?h.push(n.lon+p(r.lonLabel)):u&&h.push(n.lat+p(r.latLabel));f&&o(e,t,h);return h.join(\"<br>\")}(c,m,t,l[0].t.labels),t.hovertemplate=c.hovertemplate,[t]}}},{\"../../components/fx\":406,\"../../constants/numerical\":479,\"../../lib\":503,\"../scatter/get_trace_color\":936,\"./attributes\":968}],974:[function(t,e,r){\"use 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n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=t(\"../../lib/topojson_utils\").getTopojsonFeatures,o=t(\"../../lib/geojson_utils\"),s=t(\"../../lib/geo_location_utils\"),l=t(\"../../plots/cartesian/autorange\").findExtremes,c=t(\"../../constants/numerical\").BADNUM,u=t(\"../scatter/calc\").calcMarkerSize,f=t(\"../scatter/subtypes\"),h=t(\"./style\");e.exports={calcGeoJSON:function(t,e){var r,n,i=t[0].trace,o=e[i.geo],f=o._subplot,h=i._length;if(Array.isArray(i.locations)){var p=i.locationmode,d=\"geojson-id\"===p?s.extractTraceFeature(t):a(i,f.topojson);for(r=0;r<h;r++){n=t[r];var m=\"geojson-id\"===p?n.fOut:s.locationToFeature(p,n.loc,d);n.lonlat=m?m.properties.ct:[c,c]}}var g,v,y={padded:!0};if(\"geojson\"===o.fitbounds&&\"geojson-id\"===i.locationmode){var x=s.computeBbox(s.getTraceGeojson(i));g=[x[0],x[2]],v=[x[1],x[3]]}else{for(g=new Array(h),v=new 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n=t(\"../../lib\"),i=t(\"../../registry\"),a=t(\"./helpers\"),o=t(\"./attributes\"),s=t(\"../scatter/constants\"),l=t(\"../scatter/subtypes\"),c=t(\"../scatter/xy_defaults\"),u=t(\"../scatter/period_defaults\"),f=t(\"../scatter/marker_defaults\"),h=t(\"../scatter/line_defaults\"),p=t(\"../scatter/fillcolor_defaults\"),d=t(\"../scatter/text_defaults\");e.exports=function(t,e,r,m){function g(r,i){return n.coerce(t,e,o,r,i)}var v=!!t.marker&&a.isOpenSymbol(t.marker.symbol),y=l.isBubble(t),x=c(t,e,m,g);if(x){u(t,e,m,g),g(\"xhoverformat\"),g(\"yhoverformat\");var b=x<s.PTS_LINESONLY?\"lines+markers\":\"lines\";g(\"text\"),g(\"hovertext\"),g(\"hovertemplate\"),g(\"mode\",b),l.hasLines(e)&&(g(\"connectgaps\"),h(t,e,r,m,g),g(\"line.shape\")),l.hasMarkers(e)&&(f(t,e,r,m,g),g(\"marker.line.width\",v||y?1:0)),l.hasText(e)&&(g(\"texttemplate\"),d(t,e,m,g));var _=(e.line||{}).color,w=(e.marker||{}).color;g(\"fill\"),\"none\"!==e.fill&&p(t,e,r,g);var T=i.getComponentMethod(\"errorbars\",\"supplyDefaults\");T(t,e,_||w||r,{axis:\"y\"}),T(t,e,_||w||r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},{\"../../lib\":503,\"../../registry\":638,\"../scatter/constants\":930,\"../scatter/fillcolor_defaults\":934,\"../scatter/line_defaults\":939,\"../scatter/marker_defaults\":945,\"../scatter/period_defaults\":946,\"../scatter/subtypes\":951,\"../scatter/text_defaults\":952,\"../scatter/xy_defaults\":953,\"./attributes\":978,\"./helpers\":985}],983:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\"),a=t(\"../../constants/interactions\").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},m=c.textUnselectedOptions[u]||{},g=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=m.color,x=p.color,b=Array.isArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(g)}}},{\"../../components/color\":366,\"../../constants/interactions\":478,\"../../lib\":503}],984:[function(t,e,r){\"use strict\";var n=t(\"../scatter/format_labels\");e.exports=function(t,e,r){var i=t.i;return\"x\"in t||(t.x=e._x[i]),\"y\"in t||(t.y=e._y[i]),n(t,e,r)}},{\"../scatter/format_labels\":935}],985:[function(t,e,r){\"use strict\";var n=t(\"./constants\");r.isOpenSymbol=function(t){return\"string\"==typeof t?n.OPEN_RE.test(t):t%200>100},r.isDotSymbol=function(t){return\"string\"==typeof t?n.DOT_RE.test(t):t>200}},{\"./constants\":980}],986:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../scatter/get_trace_color\");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var m=d&&d.line;m&&(h.mlc=Array.isArray(m.color)?m.color[f]:m.color,h.mlw=i.isArrayOrTypedArray(m.width)?m.width[f]:m.width);var g=d&&d.gradient;g&&\"none\"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[f]:g.type,h.mgc=Array.isArray(g.color)?g.color[f]:g.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:A?A[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(h,o,M),M}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d,m=t.cd,g=m[0].t,v=m[0].trace,y=t.xa,x=t.ya,b=g.x,_=g.y,w=y.c2p(e),T=x.c2p(r),k=t.distance;if(g.tree){var A=y.p2c(w-k),M=y.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i=\"x\"===n?g.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):g.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=g.ids;var L=k;if(\"x\"===n){var C=!!v.xperiodalignment,P=!!v.yperiodalignment;for(u=0;u<i.length;u++){if(l=b[a=i[u]],f=Math.abs(y.c2p(l)-w),C){var I=y.c2p(v._xStarts[a]),O=y.c2p(v._xEnds[a]);f=w>=Math.min(I,O)&&w<=Math.max(I,O)?0:1/0}if(f<L){if(L=f,c=_[a],h=x.c2p(c)-T,P){var z=x.c2p(v._yStarts[a]),D=x.c2p(v._yEnds[a]);h=T>=Math.min(z,D)&&T<=Math.max(z,D)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[u]}}}else for(u=i.length-1;u>-1;u--)l=b[a=i[u]],c=_[a],f=y.c2p(l)-w,h=x.c2p(c)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 0===s?[t]:[o(t,b,_,v)]},calcHover:o}},{\"../../lib\":503,\"../../registry\":638,\"../scatter/get_trace_color\":936}],987:[function(t,e,r){\"use strict\";var n=t(\"./hover\");e.exports={moduleType:\"trace\",name:\"scattergl\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"gl\",\"regl\",\"cartesian\",\"symbols\",\"errorBarsOK\",\"showLegend\",\"scatter-like\"],attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),crossTraceDefaults:t(\"../scatter/cross_trace_defaults\"),colorbar:t(\"../scatter/marker_colorbar\"),formatLabels:t(\"./format_labels\"),calc:t(\"./calc\"),plot:t(\"./plot\"),hoverPoints:n.hoverPoints,selectPoints:t(\"./select\"),meta:{}}},{\"../../plots/cartesian\":568,\"../scatter/cross_trace_defaults\":932,\"../scatter/marker_colorbar\":944,\"./attributes\":978,\"./calc\":979,\"./defaults\":982,\"./format_labels\":984,\"./hover\":986,\"./plot\":988,\"./select\":990}],988:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"regl-line2d\"),a=t(\"regl-error2d\"),o=t(\"gl-text\"),s=t(\"../../lib\"),l=t(\"../../components/dragelement/helpers\").selectMode,c=t(\"../../lib/prepare_regl\"),u=t(\"../scatter/subtypes\"),f=t(\"../scatter/link_traces\"),h=t(\"./edit_style\").styleTextSelection;function p(t,e,r,n){var i=t._size,a=t.width*n,o=t.height*n,s=i.l*n,l=i.b*n,c=i.r*n,u=i.t*n,f=i.w*n,h=i.h*n;return[s+e.domain[0]*f,l+r.domain[0]*h,a-c-(1-e.domain[1])*f,o-u-(1-r.domain[1])*h]}e.exports=function(t,e,r){if(r.length){var d,m,g=t._fullLayout,v=e._scene,y=e.xaxis,x=e.yaxis;if(v)if(c(t,[\"ANGLE_instanced_arrays\",\"OES_element_index_uint\"])){var b=v.count,_=g._glcanvas.data()[0].regl;if(f(t,e,r),v.dirty){if(!0===v.error2d&&(v.error2d=a(_)),!0===v.line2d&&(v.line2d=i(_)),!0===v.scatter2d&&(v.scatter2d=n(_)),!0===v.fill2d&&(v.fill2d=i(_)),!0===v.glText)for(v.glText=new Array(b),d=0;d<b;d++)v.glText[d]=new o(_);if(v.glText){if(b>v.glText.length){var w=b-v.glText.length;for(d=0;d<w;d++)v.glText.push(new o(_))}else if(b<v.glText.length){var T=v.glText.length-b;v.glText.splice(b,T).forEach((function(t){t.destroy()}))}for(d=0;d<b;d++)v.glText[d].update(v.textOptions[d])}if(v.line2d&&(v.line2d.update(v.lineOptions),v.lineOptions=v.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),v.line2d.update(v.lineOptions)),v.error2d){var k=(v.errorXOptions||[]).concat(v.errorYOptions||[]);v.error2d.update(k)}v.scatter2d&&v.scatter2d.update(v.markerOptions),v.fillOrder=s.repeat(null,b),v.fill2d&&(v.fillOptions=v.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=v.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(v.fillOrder[e]=u);var f,h,p=[],d=c&&c.positions||l.positions;if(\"tozeroy\"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if(\"tozerox\"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else if(\"toself\"===s.fill||\"tonext\"===s.fill){for(p=[],i=0,t.splitNull=!0,a=0;a<d.length;a+=2)(isNaN(d[a])||isNaN(d[a+1]))&&((p=p.concat(d.slice(i,a))).push(d[i],d[i+1]),p.push(null,null),i=a+2);p=p.concat(d.slice(i)),i&&p.push(d[i],d[i+1])}else{var m=s._nexttrace;if(m){var g=v.lineOptions[e+1];if(g){var y=g.positions;if(\"tonexty\"===s.fill){for(p=d.slice(),e=Math.floor(y.length/2);e--;){var x=y[2*e],b=y[2*e+1];isNaN(x)||isNaN(b)||p.push(x,b)}t.fill=m.fillcolor}}}}if(s._prevtrace&&\"tonext\"===s._prevtrace.fill){var _=v.lineOptions[e-1].positions,w=p.length/2,T=[i=w];for(a=0;a<_.length;a+=2)(isNaN(_[a])||isNaN(_[a+1]))&&(T.push(a/2+w+1),i=a+2);p=p.concat(_),t.hole=T}return t.fillmode=s.fill,t.opacity=s.opacity,t.positions=p,t}})),v.fill2d.update(v.fillOptions))}var A=g.dragmode,M=l(A),S=g.clickmode.indexOf(\"select\")>-1;for(d=0;d<b;d++){var E=r[d][0],L=E.trace,C=E.t,P=C.index,I=L._length,O=C.x,z=C.y;if(L.selectedpoints||M||S){if(M||(M=!0),L.selectedpoints){var D=v.selectBatch[P]=s.selIndices2selPoints(L),R={};for(m=0;m<D.length;m++)R[D[m]]=1;var F=[];for(m=0;m<I;m++)R[m]||F.push(m);v.unselectBatch[P]=F}var B=C.xpx=new Array(I),N=C.ypx=new Array(I);for(m=0;m<I;m++)B[m]=y.c2p(O[m]),N[m]=x.c2p(z[m])}else C.xpx=C.ypx=null}if(M){if(v.select2d||(v.select2d=n(g._glcanvas.data()[1].regl)),v.scatter2d){var j=new Array(b);for(d=0;d<b;d++)j[d]=v.selectBatch[d].length||v.unselectBatch[d].length?v.markerUnselectedOptions[d]:{};v.scatter2d.update(j)}v.select2d&&(v.select2d.update(v.markerOptions),v.select2d.update(v.markerSelectedOptions)),v.glText&&r.forEach((function(t){var e=((t||[])[0]||{}).trace||{};u.hasText(e)&&h(t)}))}else v.scatter2d&&v.scatter2d.update(v.markerOptions);var U={viewport:p(g,y,x,t._context.plotGlPixelRatio),range:[(y._rl||y.range)[0],(x._rl||x.range)[0],(y._rl||y.range)[1],(x._rl||x.range)[1]]},V=s.repeat(U,v.count);v.fill2d&&v.fill2d.update(V),v.line2d&&v.line2d.update(V),v.error2d&&v.error2d.update(V.concat(V)),v.scatter2d&&v.scatter2d.update(V),v.select2d&&v.select2d.update(V),v.glText&&v.glText.forEach((function(t){t.update(U)}))}else v.init()}}},{\"../../components/dragelement/helpers\":384,\"../../lib\":503,\"../../lib/prepare_regl\":516,\"../scatter/link_traces\":942,\"../scatter/subtypes\":951,\"./edit_style\":983,\"gl-text\":225,\"regl-error2d\":279,\"regl-line2d\":280,\"regl-scatter2d\":281}],989:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,c=r.selectBatch,u=r.unselectBatch,f=0;f<t;f++){if(e&&r.fillOrder[f]&&e.draw(r.fillOrder[f]),a&&r.lineOptions[f]&&a.draw(f),i&&(r.errorXOptions[f]&&i.draw(f),r.errorYOptions[f]&&i.draw(f+t)),o&&r.markerOptions[f])if(u[f].length){var h=n.repeat([],r.count);h[f]=u[f],o.draw(h)}else 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e,r,i,a,s,l,c,u,f,h,p,d,m=t._x,g=t._y,v=t._z,y=t._len,x=-1/0,b=1/0,_=-1/0,w=1/0,T=-1/0,k=1/0,A=\"\";for(y&&(c=m[0],f=g[0],p=v[0]),y>1&&(u=m[y-1],h=g[y-1],d=v[y-1]),e=0;e<y;e++)x=Math.max(x,m[e]),b=Math.min(b,m[e]),_=Math.max(_,g[e]),w=Math.min(w,g[e]),T=Math.max(T,v[e]),k=Math.min(k,v[e]),a||m[e]===c||(a=!0,A+=\"x\"),s||g[e]===f||(s=!0,A+=\"y\"),l||v[e]===p||(l=!0,A+=\"z\");a||(A+=\"x\"),s||(A+=\"y\"),l||(A+=\"z\");var M=o(t._x),S=o(t._y),E=o(t._z);A=(A=(A=A.replace(\"x\",(c>u?\"-\":\"+\")+\"x\")).replace(\"y\",(f>h?\"-\":\"+\")+\"y\")).replace(\"z\",(p>d?\"-\":\"+\")+\"z\");var L=function(){y=0,M=[],S=[],E=[]};(!y||y<M.length*S.length*E.length)&&L();var C=function(t){return\"x\"===t?m:\"y\"===t?g:v},P=function(t){return\"x\"===t?M:\"y\"===t?S:E},I=function(t){return t[y-1]<t[0]?-1:1},O=C(A[1]),z=C(A[3]),D=C(A[5]),R=P(A[1]).length,F=P(A[3]).length,B=P(A[5]).length,N=!1,j=function(t,e,r){return 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n=t(\"../../plots/attributes\"),i=t(\"../../plots/template_attributes\").hovertemplateAttrs,a=t(\"../../plots/template_attributes\").texttemplateAttrs,o=t(\"../../components/colorscale/attributes\"),s=t(\"../../plots/domain\").attributes,l=t(\"../pie/attributes\"),c=t(\"./constants\"),u=t(\"../../lib/extend\").extendFlat;e.exports={labels:{valType:\"data_array\",editType:\"calc\"},parents:{valType:\"data_array\",editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},branchvalues:{valType:\"enumerated\",values:[\"remainder\",\"total\"],dflt:\"remainder\",editType:\"calc\"},count:{valType:\"flaglist\",flags:[\"branches\",\"leaves\"],dflt:\"leaves\",editType:\"calc\"},level:{valType:\"any\",editType:\"plot\",anim:!0},maxdepth:{valType:\"integer\",editType:\"plot\",dflt:-1},marker:u({colors:{valType:\"data_array\",editType:\"calc\"},line:{color:u({},l.marker.line.color,{dflt:null}),width:u({},l.marker.line.width,{dflt:1}),editType:\"calc\"},editType:\"calc\"},o(\"marker\",{colorAttr:\"colors\",anim:!1})),leaf:{opacity:{valType:\"number\",editType:\"style\",min:0,max:1},editType:\"plot\"},text:l.text,textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"current 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c=n.select(this),f=s.ensureSingle(c,\"path\",\"surface\",(function(t){t.style(\"pointer-events\",\"all\")}));i.rpx0=H(i.y0),i.rpx1=H(i.y1),i.xmid=(i.x0+i.x1)/2,i.pxmid=q(i.rpx1,i.xmid),i.midangle=-(i.xmid-Math.PI/2),i.startangle=-(i.x0-Math.PI/2),i.stopangle=-(i.x1-Math.PI/2),i.halfangle=.5*Math.min(s.angleDelta(i.x0,i.x1)||Math.PI,Math.PI),i.ring=1-i.rpx0/i.rpx1,i.rInscribed=function(t){return 0===t.rpx0&&s.isFullCircle([t.x0,t.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2))}(i),v?f.transition().attrTween(\"d\",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n={x0:t.x0,x1:t.x1,rpx0:t.rpx0,rpx1:t.rpx1};if(r)e=r;else if(D)if(t.parent)if(X){var i=(t.x1>X?2*Math.PI:0)+U;e={x0:i,x1:i}}else e={rpx0:I,rpx1:I},s.extendFlat(e,K(t));else e={rpx0:0,rpx1:0};else e={x0:U,x1:U};return a(e,n)}(t);return function(t){return G(e(t))}})):f.attr(\"d\",G),c.call(y,M,t,e,{eventDataKeys:x.eventDataKeys,transitionTime:x.CLICK_TRANSITION_TIME,transitionEasing:x.CLICK_TRANSITION_EASING}).call(b.setSliceCursor,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:t._transitioning}),f.call(g,i,k);var p=s.ensureSingle(c,\"g\",\"slicetext\"),_=s.ensureSingle(p,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),w=s.ensureUniformFontSize(t,b.determineTextFont(k,i,h.font));_.text(r.formatSliceLabel(i,M,k,e,h)).classed(\"slicetext\",!0).attr(\"text-anchor\",\"middle\").call(o.font,w).call(l.convertToTspans,t);var A=o.bBox(_.node());i.transform=m(A,i,T),i.transform.targetX=Y(i),i.transform.targetY=W(i);var S=function(t,e){var r=t.transform;return d(r,e),r.fontSize=w.size,u(k.type,r,h),s.getTextTransform(r)};v?_.transition().attrTween(\"transform\",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n=t.transform;if(r)e=r;else if(e={rpx1:t.rpx1,transform:{textPosAngle:n.textPosAngle,scale:0,rotate:n.rotate,rCenter:n.rCenter,x:n.x,y:n.y}},D)if(t.parent)if(X){var i=t.x1>X?2*Math.PI:0;e.x0=e.x1=i}else s.extendFlat(e,K(t));else e.x0=e.x1=U;else e.x0=e.x1=U;var o=a(e.transform.textPosAngle,t.transform.textPosAngle),l=a(e.rpx1,t.rpx1),c=a(e.x0,t.x0),f=a(e.x1,t.x1),p=a(e.transform.scale,n.scale),d=a(e.transform.rotate,n.rotate),m=0===n.rCenter?3:0===e.transform.rCenter?1/3:1,g=a(e.transform.rCenter,n.rCenter);return function(t){var e=l(t),r=c(t),i=f(t),a=function(t){return g(Math.pow(t,m))}(t),s={pxmid:q(e,(r+i)/2),rpx1:e,transform:{textPosAngle:o(t),rCenter:a,x:n.x,y:n.y}};return u(k.type,n,h),{transform:{targetX:Y(s),targetY:W(s),scale:p(t),rotate:d(t),rCenter:a}}}}(t);return function(t){return S(e(t),A)}})):_.attr(\"transform\",S(i,A))}))}function w(t){return e=t.rpx1,r=t.transform.textPosAngle,[e*Math.sin(r),-e*Math.cos(r)];var e,r}r.plot=function(t,e,r,i){var a,o,s=t._fullLayout,l=s._sunburstlayer,c=!r,u=!s.uniformtext.mode&&b.hasTransition(r);(f(\"sunburst\",s),(a=l.selectAll(\"g.trace.sunburst\").data(e,(function(t){return t[0].trace.uid}))).enter().append(\"g\").classed(\"trace\",!0).classed(\"sunburst\",!0).attr(\"stroke-linejoin\",\"round\"),a.order(),u)?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each(\"end\",(function(){o&&o()})).each(\"interrupt\",(function(){o&&o()})).each((function(){l.selectAll(\"g.trace\").each((function(e){_(t,e,this,r)}))}))):(a.each((function(e){_(t,e,this,r)})),s.uniformtext.mode&&v(t,s._sunburstlayer.selectAll(\".trace\"),\"sunburst\"));c&&a.exit().remove()},r.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&\"none\"!==o))return\"\";var l=i.separators,c=n[0],u=t.data.data,f=c.hierarchy,h=b.isHierarchyRoot(t),p=b.getParent(f,t),d=b.getValue(t);if(!a){var m,g=o.split(\"+\"),v=function(t){return-1!==g.indexOf(t)},y=[];if(v(\"label\")&&u.label&&y.push(u.label),u.hasOwnProperty(\"v\")&&v(\"value\")&&y.push(b.formatValue(u.v,l)),!h){v(\"current path\")&&y.push(b.getPath(t.data));var x=0;v(\"percent parent\")&&x++,v(\"percent entry\")&&x++,v(\"percent root\")&&x++;var _=x>1;if(x){var w,T=function(t){m=b.formatPercent(w,l),_&&(m+=\" of \"+t),y.push(m)};v(\"percent parent\")&&!h&&(w=d/b.getValue(p),T(\"parent\")),v(\"percent entry\")&&(w=d/b.getValue(e),T(\"entry\")),v(\"percent root\")&&(w=d/b.getValue(f),T(\"root\"))}}return v(\"text\")&&(m=s.castOption(r,u.i,\"text\"),s.isValidTextValue(m)&&y.push(m)),y.join(\"<br>\")}var k=s.castOption(r,u.i,\"texttemplate\");if(!k)return\"\";var A={};u.label&&(A.label=u.label),u.hasOwnProperty(\"v\")&&(A.value=u.v,A.valueLabel=b.formatValue(u.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),u.hasOwnProperty(\"color\")&&(A.color=u.color);var M=s.castOption(r,u.i,\"text\");return(s.isValidTextValue(M)||\"\"===M)&&(A.text=M),A.customdata=s.castOption(r,u.i,\"customdata\"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},{\"../../components/drawing\":388,\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"../bar/style\":662,\"../bar/uniform_text\":664,\"../pie/helpers\":905,\"../pie/plot\":909,\"./constants\":1048,\"./fx\":1050,\"./helpers\":1051,\"./style\":1056,\"@plotly/d3\":58,\"d3-hierarchy\":115,\"d3-interpolate\":116}],1056:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../components/color\"),a=t(\"../../lib\"),o=t(\"../bar/uniform_text\").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,\"marker.line.color\")||i.defaultLine,c=a.castOption(r,s,\"marker.line.width\")||0;t.style(\"stroke-width\",c).call(i.fill,n.color).call(i.stroke,l).style(\"opacity\",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(\".trace\");o(t,e,\"sunburst\"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style(\"opacity\",r.opacity),e.selectAll(\"path.surface\").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{\"../../components/color\":366,\"../../lib\":503,\"../bar/uniform_text\":664,\"@plotly/d3\":58}],1057:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"../../components/colorscale/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,o=t(\"../../plots/template_attributes\").hovertemplateAttrs,s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll;function u(t){return{show:{valType:\"boolean\",dflt:!1},start:{valType:\"number\",dflt:null,editType:\"plot\"},end:{valType:\"number\",dflt:null,editType:\"plot\"},size:{valType:\"number\",dflt:null,min:0,editType:\"plot\"},project:{x:{valType:\"boolean\",dflt:!1},y:{valType:\"boolean\",dflt:!1},z:{valType:\"boolean\",dflt:!1}},color:{valType:\"color\",dflt:n.defaultLine},usecolormap:{valType:\"boolean\",dflt:!1},width:{valType:\"number\",min:1,max:16,dflt:2},highlight:{valType:\"boolean\",dflt:!0},highlightcolor:{valType:\"color\",dflt:n.defaultLine},highlightwidth:{valType:\"number\",min:1,max:16,dflt:2}}}var f=e.exports=c(l({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:o(),xhoverformat:a(\"x\"),yhoverformat:a(\"y\"),zhoverformat:a(\"z\"),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or 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0},{\"../../components/color\":366,\"../../components/colorscale/attributes\":373,\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../../plots/attributes\":550,\"../../plots/cartesian/axis_format_attributes\":557,\"../../plots/template_attributes\":633}],1058:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/calc\");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":374}],1059:[function(t,e,r){\"use strict\";var n=t(\"../../../stackgl_modules\").gl_surface3d,i=t(\"../../../stackgl_modules\").ndarray,a=t(\"../../../stackgl_modules\").ndarray_linear_interpolate.d2,o=t(\"../heatmap/interp2d\"),s=t(\"../heatmap/find_empties\"),l=t(\"../../lib\").isArrayOrTypedArray,c=t(\"../../lib/gl_format_color\").parseColorScale,u=t(\"../../lib/str2rgbarray\"),f=t(\"../../components/colorscale\").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||\"\",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var 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L=Math.max(o(e.header.line.width),o(e.cells.line.width)),C={key:e.uid+t._context.staticPlot,translateX:g.x[0]*t._fullLayout._size.w,translateY:t._fullLayout._size.h*(1-g.y[1]),size:t._fullLayout._size,width:v,maxLineWidth:L,height:y,columnOrder:M,groupHeight:y,rowBlocks:k,headerRowBlocks:T,scrollY:0,cells:i({},e.cells,{values:r}),headerCells:i({},e.header,{values:m}),gdColumns:m.map((function(t){return t[0]})),gdColumnsOriginalOrder:m.map((function(t){return t[0]})),prevPages:[0,0],scrollbarState:{scrollbarScrollInProgress:!1},columns:m.map((function(t,e){var r=A[t];return A[t]=(r||0)+1,{key:t+\"__\"+A[t],label:t,specIndex:e,xIndex:M[e],xScale:u,x:void 0,calcdata:void 0,columnWidth:S[e]}}))};return C.columns.forEach((function(t){t.calcdata=C,t.x=u(t)})),C}},{\"../../lib/extend\":493,\"./constants\":1065,\"fast-isnumeric\":190}],1067:[function(t,e,r){\"use strict\";var n=t(\"../../lib/extend\").extendFlat;r.splitToPanels=function(t){var e=[0,0],r=n({},t,{key:\"header\",type:\"header\",page:0,prevPages:e,currentRepaint:[null,null],dragHandle:!0,values:t.calcdata.headerCells.values[t.specIndex],rowBlocks:t.calcdata.headerRowBlocks,calcdata:n({},t.calcdata,{cells:t.calcdata.headerCells})});return[n({},t,{key:\"cells1\",type:\"cells\",page:0,prevPages:e,currentRepaint:[null,null],dragHandle:!1,values:t.calcdata.cells.values[t.specIndex],rowBlocks:t.calcdata.rowBlocks}),n({},t,{key:\"cells2\",type:\"cells\",page:1,prevPages:e,currentRepaint:[null,null],dragHandle:!1,values:t.calcdata.cells.values[t.specIndex],rowBlocks:t.calcdata.rowBlocks}),r]},r.splitToCells=function(t){var e=function(t){var e=t.rowBlocks[t.page],r=e?e.rows[0].rowIndex:0,n=e?r+e.rows.length:0;return[r,n]}(t);return(t.values||[]).slice(e[0],e[1]).map((function(r,n){return{keyWithinBlock:n+(\"string\"==typeof r&&r.match(/[<$&> 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a.indexOf(t)})),s=o.length;s<n;s++)o.push(s);e(\"columnorder\",o)}(e,s),s(\"cells.values\"),s(\"cells.format\"),s(\"cells.align\"),s(\"cells.prefix\"),s(\"cells.suffix\"),s(\"cells.height\"),s(\"cells.line.width\"),s(\"cells.line.color\"),s(\"cells.fill.color\"),n.coerceFont(s,\"cells.font\",n.extendFlat({},o.font)),e._length=null}},{\"../../lib\":503,\"../../plots/domain\":584,\"./attributes\":1062}],1069:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),moduleType:\"trace\",name:\"table\",basePlotModule:t(\"./base_plot\"),categories:[\"noOpacity\"],meta:{}}},{\"./attributes\":1062,\"./base_plot\":1063,\"./calc\":1064,\"./defaults\":1068,\"./plot\":1070}],1070:[function(t,e,r){\"use strict\";var n=t(\"./constants\"),i=t(\"@plotly/d3\"),a=t(\"../../lib\").numberFormat,o=t(\"../../lib/gup\"),s=t(\"../../components/drawing\"),l=t(\"../../lib/svg_text_utils\"),c=t(\"../../lib\").raiseToTop,u=t(\"../../lib\").strTranslate,f=t(\"../../lib\").cancelTransition,h=t(\"./data_preparation_helper\"),p=t(\"./data_split_helpers\"),d=t(\"../../components/color\");function m(t){return Math.ceil(t.calcdata.maxLineWidth/2)}function g(t,e){return\"clip\"+t._fullLayout._uid+\"_scrollAreaBottomClip_\"+e.key}function v(t,e){return\"clip\"+t._fullLayout._uid+\"_columnBoundaryClippath_\"+e.calcdata.key+\"_\"+e.specIndex}function y(t){return[].concat.apply([],t.map((function(t){return t}))).map((function(t){return t.__data__}))}function x(t,e,r){var a=t.selectAll(\".\"+n.cn.scrollbarKit).data(o.repeat,o.keyFun);a.enter().append(\"g\").classed(n.cn.scrollbarKit,!0).style(\"shape-rendering\",\"geometricPrecision\"),a.each((function(t){var e=t.scrollbarState;e.totalHeight=function(t){var e=t.rowBlocks;return D(e,e.length-1)+(e.length?R(e[e.length-1],1/0):1)}(t),e.scrollableAreaHeight=t.groupHeight-S(t),e.currentlyVisibleHeight=Math.min(e.totalHeight,e.scrollableAreaHeight),e.ratio=e.currentlyVisibleHeight/e.totalHeight,e.barLength=Math.max(e.ratio*e.currentlyVisibleHeight,n.goldenRatio*n.scrollbarWidth),e.barWiggleRoom=e.currentlyVisibleHeight-e.barLength,e.wiggleRoom=Math.max(0,e.totalHeight-e.scrollableAreaHeight),e.topY=0===e.barWiggleRoom?0:t.scrollY/e.wiggleRoom*e.barWiggleRoom,e.bottomY=e.topY+e.barLength,e.dragMultiplier=e.wiggleRoom/e.barWiggleRoom})).attr(\"transform\",(function(t){var e=t.width+n.scrollbarWidth/2+n.scrollbarOffset;return u(e,S(t))}));var s=a.selectAll(\".\"+n.cn.scrollbar).data(o.repeat,o.keyFun);s.enter().append(\"g\").classed(n.cn.scrollbar,!0);var l=s.selectAll(\".\"+n.cn.scrollbarSlider).data(o.repeat,o.keyFun);l.enter().append(\"g\").classed(n.cn.scrollbarSlider,!0),l.attr(\"transform\",(function(t){return u(0,t.scrollbarState.topY||0)}));var c=l.selectAll(\".\"+n.cn.scrollbarGlyph).data(o.repeat,o.keyFun);c.enter().append(\"line\").classed(n.cn.scrollbarGlyph,!0).attr(\"stroke\",\"black\").attr(\"stroke-width\",n.scrollbarWidth).attr(\"stroke-linecap\",\"round\").attr(\"y1\",n.scrollbarWidth/2),c.attr(\"y2\",(function(t){return t.scrollbarState.barLength-n.scrollbarWidth/2})).attr(\"stroke-opacity\",(function(t){return t.columnDragInProgress||!t.scrollbarState.barWiggleRoom||r?0:.4})),c.transition().delay(0).duration(0),c.transition().delay(n.scrollbarHideDelay).duration(n.scrollbarHideDuration).attr(\"stroke-opacity\",0);var f=s.selectAll(\".\"+n.cn.scrollbarCaptureZone).data(o.repeat,o.keyFun);f.enter().append(\"line\").classed(n.cn.scrollbarCaptureZone,!0).attr(\"stroke\",\"white\").attr(\"stroke-opacity\",.01).attr(\"stroke-width\",n.scrollbarCaptureWidth).attr(\"stroke-linecap\",\"butt\").attr(\"y1\",0).on(\"mousedown\",(function(r){var n=i.event.y,a=this.getBoundingClientRect(),o=r.scrollbarState,s=n-a.top,l=i.scale.linear().domain([0,o.scrollableAreaHeight]).range([0,o.totalHeight]).clamp(!0);o.topY<=s&&s<=o.bottomY||L(e,t,null,l(s-o.barLength/2))(r)})).call(i.behavior.drag().origin((function(t){return i.event.stopPropagation(),t.scrollbarState.scrollbarScrollInProgress=!0,t})).on(\"drag\",L(e,t)).on(\"dragend\",(function(){}))),f.attr(\"y2\",(function(t){return t.scrollbarState.scrollableAreaHeight})),e._context.staticPlot&&(c.remove(),f.remove())}function b(t,e,r,a){var l=function(t){var e=t.selectAll(\".\"+n.cn.columnCell).data(p.splitToCells,(function(t){return t.keyWithinBlock}));return e.enter().append(\"g\").classed(n.cn.columnCell,!0),e.exit().remove(),e}(function(t){var e=t.selectAll(\".\"+n.cn.columnCells).data(o.repeat,o.keyFun);return e.enter().append(\"g\").classed(n.cn.columnCells,!0),e.exit().remove(),e}(r));!function(t){t.each((function(t,e){var r=t.calcdata.cells.font,n=t.column.specIndex,i={size:T(r.size,n,e),color:T(r.color,n,e),family:T(r.family,n,e)};t.rowNumber=t.key,t.align=T(t.calcdata.cells.align,n,e),t.cellBorderWidth=T(t.calcdata.cells.line.width,n,e),t.font=i}))}(l),function(t){t.attr(\"width\",(function(t){return t.column.columnWidth})).attr(\"stroke-width\",(function(t){return t.cellBorderWidth})).each((function(t){var e=i.select(this);d.stroke(e,T(t.calcdata.cells.line.color,t.column.specIndex,t.rowNumber)),d.fill(e,T(t.calcdata.cells.fill.color,t.column.specIndex,t.rowNumber))}))}(function(t){var e=t.selectAll(\".\"+n.cn.cellRect).data(o.repeat,(function(t){return t.keyWithinBlock}));return e.enter().append(\"rect\").classed(n.cn.cellRect,!0),e}(l));var c=function(t){var e=t.selectAll(\".\"+n.cn.cellText).data(o.repeat,(function(t){return t.keyWithinBlock}));return 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n=t[Math.min(e,t.length-1)];return Array.isArray(n)?n[Math.min(r,n.length-1)]:n}return t}function k(t,e,r){t.transition().ease(n.releaseTransitionEase).duration(n.releaseTransitionDuration).attr(\"transform\",u(e.x,r))}function A(t){return\"cells\"===t.type}function M(t){return\"header\"===t.type}function S(t){return(t.rowBlocks.length?t.rowBlocks[0].auxiliaryBlocks:[]).reduce((function(t,e){return t+R(e,1/0)}),0)}function E(t,e,r){var n=y(e)[0];if(void 0!==n){var i=n.rowBlocks,a=n.calcdata,o=D(i,i.length),s=n.calcdata.groupHeight-S(n),l=a.scrollY=Math.max(0,Math.min(o-s,a.scrollY)),c=function(t,e,r){for(var n=[],i=0,a=0;a<t.length;a++){for(var o=t[a],s=o.rows,l=0,c=0;c<s.length;c++)l+=s[c].rowHeight;o.allRowsHeight=l;e<i+l&&e+r>i&&n.push(a),i+=l}return n}(i,l,s);1===c.length&&(c[0]===i.length-1?c.unshift(c[0]-1):c.push(c[0]+1)),c[0]%2&&c.reverse(),e.each((function(t,e){t.page=c[e],t.scrollY=l})),e.attr(\"transform\",(function(t){var e=D(t.rowBlocks,t.page)-t.scrollY;return 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0}},{}],6:[function(t,e,r){e.exports={alpha_shape:t(\"alpha-shape\"),convex_hull:t(\"convex-hull\"),delaunay_triangulate:t(\"delaunay-triangulate\"),gl_cone3d:t(\"gl-cone3d\"),gl_error3d:t(\"gl-error3d\"),gl_heatmap2d:t(\"gl-heatmap2d\"),gl_line3d:t(\"gl-line3d\"),gl_mesh3d:t(\"gl-mesh3d\"),gl_plot2d:t(\"gl-plot2d\"),gl_plot3d:t(\"gl-plot3d\"),gl_pointcloud2d:t(\"gl-pointcloud2d\"),gl_scatter3d:t(\"gl-scatter3d\"),gl_select_box:t(\"gl-select-box\"),gl_spikes2d:t(\"gl-spikes2d\"),gl_streamtube3d:t(\"gl-streamtube3d\"),gl_surface3d:t(\"gl-surface3d\"),ndarray:t(\"ndarray\"),ndarray_linear_interpolate:t(\"ndarray-linear-interpolate\")}},{\"alpha-shape\":12,\"convex-hull\":58,\"delaunay-triangulate\":63,\"gl-cone3d\":79,\"gl-error3d\":84,\"gl-heatmap2d\":88,\"gl-line3d\":91,\"gl-mesh3d\":112,\"gl-plot2d\":118,\"gl-plot3d\":121,\"gl-pointcloud2d\":123,\"gl-scatter3d\":128,\"gl-select-box\":130,\"gl-spikes2d\":139,\"gl-streamtube3d\":143,\"gl-surface3d\":145,ndarray:259,\"ndarray-linear-interpolate\":253}],7:[function(t,e,r){\"use 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i=0|t.words[0],a=0|e.words[0],o=i*a,s=67108863&o,l=o/67108864|0;r.words[0]=s;for(var c=1;c<n;c++){for(var u=l>>>26,f=67108863&l,h=Math.min(c,e.length-1),p=Math.max(0,c-t.length+1);p<=h;p++){var d=c-p|0;u+=(o=(i=0|t.words[d])*(a=0|e.words[p])+f)/67108864|0,f=67108863&o}r.words[c]=0|f,l=0|u}return 0!==l?r.words[c]=0|l:r.length--,r.strip()}a.prototype.toString=function(t,e){var r;if(e=0|e||1,16===(t=t||10)||\"hex\"===t){r=\"\";for(var i=0,a=0,o=0;o<this.length;o++){var s=this.words[o],l=(16777215&(s<<i|a)).toString(16);r=0!==(a=s>>>24-i&16777215)||o!==this.length-1?u[6-l.length]+l+r:l+r,(i+=2)>=26&&(i-=26,o--)}for(0!==a&&(r=a.toString(16)+r);r.length%e!=0;)r=\"0\"+r;return 0!==this.negative&&(r=\"-\"+r),r}if(t===(0|t)&&t>=2&&t<=36){var c=f[t],p=h[t];r=\"\";var d=this.clone();for(d.negative=0;!d.isZero();){var m=d.modn(p).toString(t);r=(d=d.idivn(p)).isZero()?m+r:u[c-m.length]+m+r}for(this.isZero()&&(r=\"0\"+r);r.length%e!=0;)r=\"0\"+r;return 0!==this.negative&&(r=\"-\"+r),r}n(!1,\"Base 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32-Math.clz32(t)}:a.prototype._countBits=function(t){var e=t,r=0;return e>=4096&&(r+=13,e>>>=13),e>=64&&(r+=7,e>>>=7),e>=8&&(r+=4,e>>>=4),e>=2&&(r+=2,e>>>=2),r+e},a.prototype._zeroBits=function(t){if(0===t)return 26;var e=t,r=0;return 0==(8191&e)&&(r+=13,e>>>=13),0==(127&e)&&(r+=7,e>>>=7),0==(15&e)&&(r+=4,e>>>=4),0==(3&e)&&(r+=2,e>>>=2),0==(1&e)&&r++,r},a.prototype.bitLength=function(){var t=this.words[this.length-1],e=this._countBits(t);return 26*(this.length-1)+e},a.prototype.zeroBits=function(){if(this.isZero())return 0;for(var t=0,e=0;e<this.length;e++){var r=this._zeroBits(this.words[e]);if(t+=r,26!==r)break}return t},a.prototype.byteLength=function(){return Math.ceil(this.bitLength()/8)},a.prototype.toTwos=function(t){return 0!==this.negative?this.abs().inotn(t).iaddn(1):this.clone()},a.prototype.fromTwos=function(t){return this.testn(t-1)?this.notn(t).iaddn(1).ineg():this.clone()},a.prototype.isNeg=function(){return 0!==this.negative},a.prototype.neg=function(){return this.clone().ineg()},a.prototype.ineg=function(){return this.isZero()||(this.negative^=1),this},a.prototype.iuor=function(t){for(;this.length<t.length;)this.words[this.length++]=0;for(var e=0;e<t.length;e++)this.words[e]=this.words[e]|t.words[e];return this.strip()},a.prototype.ior=function(t){return n(0==(this.negative|t.negative)),this.iuor(t)},a.prototype.or=function(t){return this.length>t.length?this.clone().ior(t):t.clone().ior(this)},a.prototype.uor=function(t){return this.length>t.length?this.clone().iuor(t):t.clone().iuor(this)},a.prototype.iuand=function(t){var e;e=this.length>t.length?t:this;for(var r=0;r<e.length;r++)this.words[r]=this.words[r]&t.words[r];return this.length=e.length,this.strip()},a.prototype.iand=function(t){return n(0==(this.negative|t.negative)),this.iuand(t)},a.prototype.and=function(t){return this.length>t.length?this.clone().iand(t):t.clone().iand(this)},a.prototype.uand=function(t){return this.length>t.length?this.clone().iuand(t):t.clone().iuand(this)},a.prototype.iuxor=function(t){var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n(\"number\"==typeof t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n(\"number\"==typeof t&&t>=0);var r=t/26|0,i=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,m=p>>>13,g=0|o[2],v=8191&g,y=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],I=8191&P,O=P>>>13,z=0|o[8],D=8191&z,R=z>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,mt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(m,U)|0,a=Math.imul(m,V);var vt=(c+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(m,q)|0,a=a+Math.imul(m,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,q)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(m,W)|0,a=a+Math.imul(m,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(m,$)|0,a=a+Math.imul(m,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(O,U)|0,a=Math.imul(O,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(I,q)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(O,q)|0,a=a+Math.imul(O,G)|0,n=n+Math.imul(L,W)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,q)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,X)|0)+Math.imul(O,W)|0,a=a+Math.imul(O,X)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var At=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,mt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,mt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(I,J)|0,i=(i=i+Math.imul(I,K)|0)+Math.imul(O,J)|0,a=a+Math.imul(O,K)|0,n=n+Math.imul(L,$)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,mt)|0)+Math.imul(m,dt)|0))<<13)|0;c=((a=a+Math.imul(m,mt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(I,$)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(O,$)|0,a=a+Math.imul(O,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,mt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,mt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(O,rt)|0,a=a+Math.imul(O,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,mt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,mt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(O,at)|0,a=a+Math.imul(O,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ct)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,mt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,mt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ct)|0)+Math.imul(O,lt)|0,a=a+Math.imul(O,ct)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,mt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,mt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(I,ft)|0,i=(i=i+Math.imul(I,ht)|0)+Math.imul(O,ft)|0,a=a+Math.imul(O,ht)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,mt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((a=a+Math.imul(C,mt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var It=(c+(n=n+Math.imul(I,dt)|0)|0)+((8191&(i=(i=i+Math.imul(I,mt)|0)+Math.imul(O,dt)|0))<<13)|0;c=((a=a+Math.imul(O,mt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var Ot=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,mt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,mt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863;var zt=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,mt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,mt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=gt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=It,l[17]=Ot,l[18]=zt,0!==c&&(l[19]=c,r.length++),r};function m(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?d(this,t,e):r<63?p(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):m(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],m=n[u+p],g=r[u+p+o],v=n[u+p+o],y=f*g-h*v;v=f*v+h*g,g=y,r[u+p]=d+g,n[u+p]=m+v,r[u+p+o]=d-g,n[u+p+o]=m-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),m(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,m=1;0==(r.words[0]&m)&&d<26;++d,m<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var v={k256:null,p224:null,p192:null,p25519:null};function y(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function x(){y.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function b(){y.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function _(){y.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function w(){y.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function T(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}y.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},y.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},y.prototype.split=function(t,e){t.iushrn(this.n,0,e)},y.prototype.imulK=function(t){return t.imul(this.k)},i(x,y),x.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var i=t.words[9];for(e.words[e.length++]=4194303&i,n=10;n<t.length;n++){var a=0|t.words[n];t.words[n-10]=(4194303&a)<<4|i>>>22,i=a}i>>>=22,t.words[n-10]=i,0===i&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,y),i(_,y),i(w,y),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if(\"k256\"===t)e=new x;else if(\"p224\"===t)e=new b;else if(\"p192\"===t)e=new _;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new w}return v[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var f=this.pow(u,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var m=p,g=0;0!==m.cmp(s);g++)m=m.redSqr();n(g<d);var v=this.pow(f,new a(1).iushln(d-g-1));h=h.redMul(v),f=v.redSqr(),p=p.redMul(f),d=g}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var f=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4===++s||0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var 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p=h.prototype;function d(t,e){t.key=e.key,t.value=e.value,t.left=e.left,t.right=e.right,t._color=e._color,t._count=e._count}function m(t,e){return t<e?-1:t>e?1:0}Object.defineProperty(p,\"valid\",{get:function(){return this._stack.length>0}}),Object.defineProperty(p,\"node\",{get:function(){return this._stack.length>0?this._stack[this._stack.length-1]:null},enumerable:!0}),p.clone=function(){return new h(this.tree,this._stack.slice())},p.remove=function(){var t=this._stack;if(0===t.length)return this.tree;var e=new Array(t.length),r=t[t.length-1];e[e.length-1]=new n(r._color,r.key,r.value,r.left,r.right,r._count);for(var l=t.length-2;l>=0;--l){(r=t[l]).left===t[l+1]?e[l]=new n(r._color,r.key,r.value,e[l+1],r.right,r._count):e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count)}if((r=e[e.length-1]).left&&r.right){var c=e.length;for(r=r.left;r.right;)e.push(r),r=r.right;var u=e[c-1];e.push(new n(r._color,u.key,u.value,r.left,r.right,r._count)),e[c-1].key=r.key,e[c-1].value=r.value;for(l=e.length-2;l>=c;--l)r=e[l],e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count);e[c-1].left=e[c]}if(0===(r=e[e.length-1])._color){var f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasNext\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error(\"Can't update empty node!\");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],70:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t(\"./lib/text.js\"),i=t(\"./lib/lines.js\"),a=t(\"./lib/background.js\"),o=t(\"./lib/cube.js\"),s=t(\"./lib/ticks.js\"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=\"sans-serif\",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if(\"bounds\"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if(\"ticks\"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(u=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),c(\"tickColor\");var m=l(\"labels\");l(\"labelFont\")&&(m=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),c(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),c(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),c(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),c(\"gridColor\"),o(\"zeroEnable\"),c(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),c(\"backgroundColor\"),this._text?this._text&&(m||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var m=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E=m;for(A=0;A<3;++A)this.backgroundEnable[A]?E[A]=h[A]:E[A]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(A=0;A<3;++A){var L=[0,0,0];h[A]>0?L[A]=a[1][A]:L[A]=a[0][A];for(var C=0;C<2;++C){var P=(A+1+C)%3,I=(A+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,I,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(A+1+C)%3,I=(A+1+(1^C))%3;this.zeroEnable[I]&&Math.min(a[0][I],a[1][I])<=0&&Math.max(a[0][I],a[1][I])>=0&&this._lines.drawZero(P,I,this.bounds,L,this.zeroLineColor[I],this.zeroLineWidth[I]*this.pixelRatio)}}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var O=c(v,M[A].primalMinor),z=c(y,M[A].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=k/r[5*C];O[C]*=D[C]*R,z[C]*=D[C]*R}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,O,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=c(x,M[A].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[A]&&(H[C]+=k*U[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]=\"auto\"):this.tickAlign[A]=-1,F=1,\"auto\"===(S=[this.tickAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),B=[0,0,0],j(A,U,V);for(C=0;C<3;++C)H[C]+=k*U[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,B,S)}if(this.labelEnable[A]){F=0,B=[0,0,0],this.labels[A].length>4&&(N(A),F=1),\"auto\"===(S=[this.labelAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]);for(C=0;C<3;++C)H[C]+=k*U[C]*this.labelPad[C]/r[5*C];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":71,\"./lib/cube.js\":72,\"./lib/lines.js\":73,\"./lib/text.js\":75,\"./lib/ticks.js\":76}],71:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var m=-1;m<=1;m+=2)f[u]=m,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=c;c=u,u=g}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders\":74,\"gl-buffer\":78,\"gl-vao\":150}],72:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);o(c[O],c[P],c[I],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);var O,z=d([l[O],l[P],l[I],l[O+(1<<M)+(1<<S)]]);C?E=z:L=z}L>E&&(w|=1<<A)}}}var D=7^w,R=-1;for(x=0;x<8;++x)x!==w&&x!==D&&(R<0||c[R][1]>c[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x){if((N=R^1<<x)!==w&&N!==D)F<0&&(F=N),(S=c[N])[0]<c[F][0]&&(F=N)}var B=-1;for(x=0;x<3;++x){var N;if((N=R^1<<x)!==w&&N!==D&&N!==F)B<0&&(B=N),(S=c[N])[0]>c[B][0]&&(B=N)}var j=m;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===D?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=g,H=w;for(A=0;A<3;++A)V[A]=H&1<<A?-1:1;return v};var n=t(\"bit-twiddle\"),i=t(\"gl-mat4/multiply\"),a=t(\"split-polygon\"),o=t(\"robust-orientation\"),s=new Array(16),l=new Array(8),c=new Array(8),u=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],c[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var m=[1,1,1],g=[0,0,0],v={cubeEdges:m,axis:g}},{\"bit-twiddle\":32,\"gl-mat4/multiply\":100,\"robust-orientation\":284,\"split-polygon\":300}],73:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var m=+r[f][d].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){m=+r[f][v].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}g=o.length/3|0;c[f]=h,u[f]=g-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var m=f(l);m[t]=1,this.shader.uniforms.screenAxis=m,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{\"./shaders\":74,\"gl-buffer\":78,\"gl-vao\":150}],74:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n  vec3 major = position.x * majorAxis;\\n  vec3 minor = position.y * minorAxis;\\n\\n  vec3 vPosition = major + minor + offset;\\n  vec3 pPosition = project(vPosition);\\n  vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.line=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n  vec3 A = project(a);\\n  vec3 B = project(b);\\n\\n  return atan(\\n    (B.y - A.y) * resolution.y,\\n    (B.x - A.x) * resolution.x\\n  );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio =       alignOpt.y;\\nbool enableAlign =    (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n  return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n  return mod_angle((a < 0.0) ?\\n    a + TWO_PI :\\n    a\\n  );\\n}\\n\\nfloat look_upwards(float a) {\\n  float b = positive_angle(a);\\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n    b - PI :\\n    b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n  // if ratio is set to 0.5 then it is 50%, 50%.\\n  // when using a higher ratio e.g. 0.75 the result would\\n  // likely be more horizontal than vertical.\\n\\n  float b = positive_angle(a);\\n\\n  return\\n    (b < (      ratio) * HALF_PI) ? 0.0 :\\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n                                    0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n  return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n  float b = positive_angle(a);\\n  float div = TWO_PI / float(n);\\n  float c = roundTo(b, div);\\n  return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n  return\\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\\n                    rawAngle;                // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n                   (axis.y == 0.0) &&\\n                   (axis.z == 0.0);\\n\\nvoid main() {\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n\\n  float beta = angle; // i.e. user defined attributes for each tick\\n\\n  float axisAngle;\\n  float clipAngle;\\n  float flip;\\n\\n  if (enableAlign) {\\n    axisAngle = (isAxisTitle) ? HALF_PI :\\n                      computeViewAngle(dataPosition, dataPosition + axis);\\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n    beta += applyAlignOption(clipAngle, flip * PI);\\n  }\\n\\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n\\n  mat2 planeXform = scale * mat2(\\n     cos(beta), sin(beta),\\n    -sin(beta), cos(beta)\\n  );\\n\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute clip position\\n  vec3 clipPosition = project(dataPosition);\\n\\n  //Apply text offset in clip coordinates\\n  clipPosition += vec3(viewOffset, 0.0);\\n\\n  //Done\\n  gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.text=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n  vec3 realNormal = signAxis * normal;\\n\\n  if(dot(realNormal, enable) > 0.0) {\\n    vec3 minRange = min(bounds[0], bounds[1]);\\n    vec3 maxRange = max(bounds[0], bounds[1]);\\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n  } else {\\n    gl_Position = vec4(0,0,0,0);\\n  }\\n\\n  colorChannel = abs(realNormal);\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n  gl_FragColor = colorChannel.x * colors[0] +\\n                 colorChannel.y * colors[1] +\\n                 colorChannel.z * colors[2];\\n}\"]);r.bg=function(t){return i(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},{\"gl-shader\":132,glslify:231}],75:[function(t,e,r){(function(r){(function(){\"use strict\";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"vectorize-text\"),o=t(\"./shaders\").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return a(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=u.positions,p=u.cells,d=0,m=p.length;d<m;++d)for(var g=p[d],v=2;v>=0;--v){var y=h[g[v]];o.push(f*y[0],-f*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(o.length/3|0)-f[d],c[d]=o.length/3|0;for(var m=0;m<n[d].length;++m)n[d][m].text&&s(n[d][m].x,n[d][m].text,n[d][m].font||i,n[d][m].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=f,this.labelCount=h},u.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this)}).call(this,t(\"_process\"))},{\"./shaders\":74,_process:5,\"gl-buffer\":78,\"gl-vao\":150,\"vectorize-text\":311}],76:[function(t,e,r){\"use strict\";function n(t,e){var r=t+\"\",n=r.indexOf(\".\"),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/a,c=o%a;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=\"\"+l;if(o<0&&(u=\"-\"+u),i){for(var f=\"\"+c;f.length<i;)f=\"0\"+f;return u+\".\"+f}return u}r.create=function(t,e){for(var r=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:n(e[i],o)});for(o=-1;o*e[i]>=t[0][i];--o)a.push({x:o*e[i],text:n(e[i],o)});r.push(a)}return r},r.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var i=t[r][n],a=e[r][n];if(i.x!==a.x||i.text!==a.text||i.font!==a.font||i.fontColor!==a.fontColor||i.fontSize!==a.fontSize||i.dx!==a.dx||i.dy!==a.dy)return!1}}return!0}},{}],77:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,l,f){var h=e.model||c,p=e.view||c,v=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,v,x,y)).axis;o(u,p,h),o(u,v,u);for(var _=m,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=y?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var I=d(g,u,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(I[C])))}}}}return _};var n=t(\"extract-frustum-planes\"),i=t(\"split-polygon\"),a=t(\"./lib/cube.js\"),o=t(\"gl-mat4/multiply\"),s=t(\"gl-mat4/transpose\"),l=t(\"gl-vec4/transformMat4\"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var m=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},{\"./lib/cube.js\":72,\"extract-frustum-planes\":67,\"gl-mat4/multiply\":100,\"gl-mat4/transpose\":109,\"gl-vec4/transformMat4\":221,\"split-polygon\":300}],78:[function(t,e,r){\"use strict\";var n=t(\"typedarray-pool\"),i=t(\"ndarray-ops\"),a=t(\"ndarray\"),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:259,\"ndarray-ops\":254,\"typedarray-pool\":308}],79:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,m=[],g=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),v=!1):v=!0}v||(p=x,d=b),m.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A;y=0;for(var M=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=m[y],P=n.length(C)*k,I=0;I<8;I++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var O=a.positions.length;a.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return a};var i=t(\"./lib/shaders\");e.exports.createMesh=t(\"./create_mesh\"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:\"cone\"})}},{\"./create_mesh\":80,\"./lib/shaders\":81,\"gl-vec3\":169}],80:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"gl-mat4/multiply\"),l=t(\"gl-mat4/invert\"),c=t(\"ndarray\"),u=t(\"colormap\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function m(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,m=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],m=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var v=p[g];d=Math.min(d,v),m=Math.max(m,v)}else for(g=0;g<n.length;++g){v=n[g][2];d=Math.min(d,v),m=Math.max(m,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(g=0;g<n.length;++g)for(var y=n[g],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(m-d),0]:[(y[2]-d)/(m-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var m=this.triShader;m.bind(),m.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=m(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,g,v,x,r.traceType||\"cone\");return b.update(e),b}},{colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-shader\":132,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259}],81:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * conePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\\n  vec4 t_position  = view * conePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = conePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n  gl_Position = projection * view * conePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},{glslify:231}],82:[function(t,e,r){e.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORMATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}},{}],83:[function(t,e,r){var n=t(\"./1.0/numbers\");e.exports=function(t){return n[t]}},{\"./1.0/numbers\":82}],84:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var c=new s(e,r,o,l);return c.update(t),c};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders/index\"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function c(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],c=n[15],u=(t._ortho||!1?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*c)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]*this.pixelRatio),r.capSize=this.capSize[f]*u,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=u[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){\"lineWidth\"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),\"capSize\"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var u=r[l],h=0;h<3;++h)if(isNaN(u[h])||!isFinite(u[h]))continue t;var p=n[l],d=e[s];if(Array.isArray(d[0])&&(d=e[l]),3===d.length?d=[d[0],d[1],d[2],1]:4===d.length&&(d=[d[0],d[1],d[2],d[3]],!this.hasAlpha&&d[3]<1&&(this.hasAlpha=!0)),!isNaN(p[0][s])&&!isNaN(p[1][s])){var m;if(p[0][s]<0)(m=u.slice())[s]+=p[0][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,m[0],m[1],m[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,m),o+=2+f(i,m,d,s);if(p[1][s]>0)(m=u.slice())[s]+=p[1][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,m[0],m[1],m[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,m),o+=2+f(i,m,d,s)}}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{\"./shaders/index\":85,\"gl-buffer\":78,\"gl-vao\":150}],85:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, offset;\\nattribute vec4 color;\\nuniform mat4 model, view, projection;\\nuniform float capSize;\\nvarying vec4 fragColor;\\nvarying vec3 fragPosition;\\n\\nvoid main() {\\n  vec4 worldPosition  = model * vec4(position, 1.0);\\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\\n  gl_Position         = projection * view * worldPosition;\\n  fragColor           = color;\\n  fragPosition        = position;\\n}\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  gl_FragColor = opacity * fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},{\"gl-shader\":132,glslify:231}],86:[function(t,e,r){\"use strict\";var n=t(\"gl-texture2d\");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension(\"WEBGL_draw_buffers\");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,c);Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]);if(\"number\"!=typeof e)throw new Error(\"gl-fbo: Missing shape parameter\");var u=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>u||r<0||r>u)throw new Error(\"gl-fbo: Parameters are too large for FBO\");var f=1;if(\"color\"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(f>1){if(!c)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(f>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+f+\" draw buffers\")}}var h=t.UNSIGNED_BYTE,p=t.getExtension(\"OES_texture_float\");if(n.float&&f>0){if(!p)throw new Error(\"gl-fbo: Context does not support floating point textures\");h=t.FLOAT}else n.preferFloat&&f>0&&p&&(h=t.FLOAT);var m=!0;\"depth\"in n&&(m=!!n.depth);var g=!1;\"stencil\"in n&&(g=!!n.stencil);return new d(t,e,r,h,f,m,g,c)};var i,a,o,s,l=null;function c(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function u(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error(\"gl-fbo: Framebuffer unsupported\");case a:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case o:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case s:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var m=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return m._shape[0]},set:function(t){return m.width=t}},1:{get:function(){return m._shape[1]},set:function(t){return m.height=t}}}),this._shapeVector=g,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,m=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension(\"WEBGL_depth_texture\");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):m&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var m=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(m,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),m.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},m.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":146}],87:[function(t,e,r){var n=t(\"sprintf-js\").sprintf,i=t(\"gl-constants/lookup\"),a=t(\"glsl-shader-name\"),o=t(\"add-line-numbers\");e.exports=function(t,e,r){\"use strict\";var s=a(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===i.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var c=n(\"Error compiling %s shader %s:\\n\",l,s),u=n(\"%s%s\",c,t),f=t.split(\"\\n\"),h={},p=0;p<f.length;p++){var d=f[p];if(\"\"!==d&&\"\\0\"!==d){var m=parseInt(d.split(\":\")[2]);if(isNaN(m))throw new Error(n(\"Could not parse error: %s\",d));h[m]=d}}var g=o(e).split(\"\\n\");for(p=0;p<g.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=g[p];if(c+=v+\"\\n\",h[p+1]){var y=h[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),c+=n(\"^^^ %s\\n\\n\",y)}}return{long:c.trim(),short:u.trim()}}},{\"add-line-numbers\":9,\"gl-constants/lookup\":83,\"glsl-shader-name\":223,\"sprintf-js\":301}],88:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t(\"binary-search-bounds\"),i=t(\"iota-array\"),a=t(\"typedarray-pool\"),o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"./lib/shaders\");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],m=t.colorValues||[0,0,0,1],g=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,I;l?(P=x*(o[C]-u),I=x*(o[C+1]-u)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-u):x*(o[C]-(o[C]-o[C-1])/2-u),I=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-u):x*(o[C]+(o[C]-o[C-1])/2-u));for(var O=0;O<E;++O){var z,D;l?(z=y*(r[O]-c),D=y*(r[O+1]-c)):(z=O<b-1?y*(r[O]-(r[O+1]-r[O])/2-c):y*(r[O]-(r[O]-r[O-1])/2-c),D=O<b-1?y*(r[O]+(r[O+1]-r[O])/2-c):y*(r[O]+(r[O]-r[O-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],H=s[l?(C+V)*b+(O+U):C*b+O],q=n.le(d,H);if(q<0)F=m[0],B=m[1],N=m[2],j=m[3];else if(q===g-1)F=m[4*g-4],B=m[4*g-3],N=m[4*g-2],j=m[4*g-1];else{var G=(H-d[q])/(d[q+1]-d[q]),Y=1-G,W=4*q,X=4*(q+1);F=Y*m[W]+G*m[X],B=Y*m[W+1]+G*m[X+1],N=Y*m[W+2]+G*m[X+2],j=Y*m[W+3]+G*m[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*I,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+O,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":89,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132,\"iota-array\":235,\"typedarray-pool\":308}],89:[function(t,e,r){\"use strict\";var n=t(\"glslify\");e.exports={fragment:n([\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\"]),vertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  fragColor = color;\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"]),pickFragment:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n  vec2 d = step(.5, vWeight);\\n  vec4 id = fragId + pickOffset;\\n  id.x += d.x + d.y*shape.x;\\n\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_FragColor = id/255.;\\n}\\n\"]),pickVertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n  vWeight = weight;\\n\\n  fragId = pickId;\\n\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"])}},{glslify:231}],90:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  vec4 startPoint = project(position);\\n  vec4 endPoint   = project(nextPosition);\\n\\n  vec2 A = startPoint.xy / startPoint.w;\\n  vec2 B =   endPoint.xy /   endPoint.w;\\n\\n  float clipAngle = atan(\\n    (B.y - A.y) * screenShape.y,\\n    (B.x - A.x) * screenShape.x\\n  );\\n\\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\\n    sin(clipAngle),\\n    -cos(clipAngle)\\n  ) / screenShape;\\n\\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\\n\\n  worldPosition = position;\\n  pixelArcLength = arcLength;\\n  fragColor = color;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3      clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float     dashScale;\\nuniform float     opacity;\\n\\nvarying vec3    worldPosition;\\nvarying float   pixelArcLength;\\nvarying vec4    fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n  if(dashWeight < 0.5) {\\n    discard;\\n  }\\n  gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX  1.70141184e38\\n#define FLOAT_MIN  1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n  float av = abs(v);\\n\\n  //Handle special cases\\n  if(av < FLOAT_MIN) {\\n    return vec4(0.0, 0.0, 0.0, 0.0);\\n  } else if(v > FLOAT_MAX) {\\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n  } else if(v < -FLOAT_MAX) {\\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n  }\\n\\n  vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  float e = floor(log2(av));\\n  float m = av * pow(2.0, -e) - 1.0;\\n\\n  //Unpack mantissa\\n  c[1] = floor(128.0 * m);\\n  m -= c[1] / 128.0;\\n  c[2] = floor(32768.0 * m);\\n  m -= c[2] / 32768.0;\\n  c[3] = floor(8388608.0 * m);\\n\\n  //Unpack exponent\\n  float ebias = e + 127.0;\\n  c[0] = floor(ebias / 2.0);\\n  ebias -= c[0] * 2.0;\\n  c[1] += floor(ebias) * 128.0;\\n\\n  //Unpack sign bit\\n  c[0] += 128.0 * step(0.0, -v);\\n\\n  //Scale back to range\\n  return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{\"gl-shader\":132,glslify:231}],91:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var m=new v(e,r,o,s,l,d);return m.update(t),m};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"gl-texture2d\"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t(\"binary-search-bounds\"),c=t(\"ndarray\"),u=t(\"./lib/shaders\"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function m(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:m(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:m(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],m=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(m)?m.length>e-1?m[e-1]:m.length>0?m[m.length-1]:[0,0,0,1]:m;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3]);u+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,\"dashes\"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=c(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=n,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2],0),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),c=1-a,u=[0,0,0],f=0;f<3;++f)u[f]=c*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,u,h,this.points[h])}},{\"./lib/shaders\":90,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259}],92:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],93:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],94:[function(t,e,r){e.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],c=t[8],u=t[9],f=t[10],h=t[11],p=t[12],d=t[13],m=t[14],g=t[15];return(e*o-r*a)*(f*g-h*m)-(e*s-n*a)*(u*g-h*d)+(e*l-i*a)*(u*m-f*d)+(r*s-n*o)*(c*g-h*p)-(r*l-i*o)*(c*m-f*p)+(n*l-i*s)*(c*d-u*p)}},{}],95:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,c=r*o,u=n*o,f=n*s,h=i*o,p=i*s,d=i*l,m=a*o,g=a*s,v=a*l;return t[0]=1-f-d,t[1]=u+v,t[2]=h-g,t[3]=0,t[4]=u-v,t[5]=1-c-d,t[6]=p+m,t[7]=0,t[8]=h+g,t[9]=p-m,t[10]=1-c-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],96:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,c=a+a,u=n*s,f=n*l,h=n*c,p=i*l,d=i*c,m=a*c,g=o*s,v=o*l,y=o*c;return t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],97:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],98:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*m-f*d,A=u*g-h*d,M=u*v-p*d,S=f*g-h*m,E=f*v-p*m,L=h*v-p*g,C=y*L-x*E+b*S+_*M-w*A+T*k;if(!C)return null;return C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(m*T-g*w+v*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-c*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(g*b-d*T-v*x)*C,t[7]=(u*T-h*b+p*x)*C,t[8]=(o*E-s*M+c*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-m*b+v*y)*C,t[11]=(f*b-u*w-p*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(m*x-d*_-g*y)*C,t[15]=(u*_-f*x+h*y)*C,t}},{}],99:[function(t,e,r){var n=t(\"./identity\");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,m=e[0],g=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(m-_)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{\"./identity\":97}],100:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],101:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],102:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],103:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],104:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],105:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],106:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],107:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],108:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],109:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],110:[function(t,e,r){\"use strict\";var n=t(\"barycentric\"),i=t(\"polytope-closest-point/lib/closest_point_2d.js\");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,m=0;m<2;++m)d+=Math.pow(u[f][m]-e[m],2);d<p&&(p=d,h=f)}var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(g[f]<-.001||g[f]>1.0001)return null;v+=g[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,g),g]}},{barycentric:14,\"polytope-closest-point/lib/closest_point_2d.js\":270}],111:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n           , view\\n           , projection\\n           , inverseModel;\\nuniform vec3 eyePosition\\n           , lightPosition;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  gl_Position      = project(position);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  f_color          = color;\\n  f_data           = position;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n            , fresnel\\n            , kambient\\n            , kdiffuse\\n            , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (f_color.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\\n  ) discard;\\n\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_color = color;\\n  f_data  = position;\\n  f_uv    = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n  } else {\\n    gl_Position = projection * view * model * vec4(position, 1.0);\\n  }\\n  gl_PointSize = pointSize;\\n  f_color = color;\\n  f_uv = uv;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n  if(dot(pointR, pointR) > 0.25) {\\n    discard;\\n  }\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_id        = id;\\n  f_position  = position;\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3  position;\\nattribute float pointSize;\\nattribute vec4  id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n  } else {\\n    gl_Position  = projection * view * model * vec4(position, 1.0);\\n    gl_PointSize = pointSize;\\n  }\\n  f_id         = id;\\n  f_position   = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n  gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},{glslify:231}],112:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),f=t(\"colormap\"),h=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./lib/shaders\"),m=t(\"./lib/closest-point\"),g=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=v,this.edgeIds=m,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],m=n[f][1],g=i[f],v=1-g,y=this.positions[d],x=this.positions[m],b=0;b<3;++b)o[s++]=g*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:\"rgba\"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],m=[],g=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!I)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(O)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<O.length;++R){F=O[R];z=Math.min(z,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];z=Math.min(z,F),D=Math.max(D,F)}this.intensity=P||(O||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(O||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,H=0,q=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],m.push(Z[0],Z[1]),g.push(R)}H+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=m(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension(\"OES_standard_derivatives\")||t.getExtension(\"MOZ_OES_standard_derivatives\")||t.getExtension(\"WEBKIT_OES_standard_derivatives\");if(!r)throw new Error(\"derivatives not supported\");var n=M(t),s=S(t),l=E(t),c=L(t),f=C(t),h=P(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),m=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),A=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),I=i(t),O=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:O,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,m,g,v,x,b,k,_,w,A,I,R,O,z,D,F,B,N);return j.update(e),j}},{\"./lib/closest-point\":110,\"./lib/shaders\":111,colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-shader\":132,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259,normals:261,\"simplicial-complex-contour\":291,\"typedarray-pool\":308}],113:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"gl-buffer\":78,\"gl-shader\":132}],114:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"binary-search-bounds\"),o=t(\"./shaders\");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,m=0;m<2;++m){var g=a[m],v=a[m+2]-g,y=.5*(o[m+2]+o[m]),x=o[m+2]-o[m];u[m]=2*v/x,c[m]=2*(g-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(m=0;m<2;++m){f[0]=f[1]=0,f[m]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[m]/(s[m+2]-s[m])*d,r.uniforms.color=h[m];var _=6*n[m].length;p[m]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,m=s.viewBox,g=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=m[2]-m[0],_=m[3]-m[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,I=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),O=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*I,D=6*Math.max(0,O-I),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(m[0]-E[1])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[1]*g/y,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[1]-E[0])/x-1,o[0]=L[0]*g/y,o[1]=E[0]*g/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(m[2]+E[3])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[3]*g/y,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[3]+E[2])/x-1,o[0]=L[2]*g/y,o[1]=E[2]*g/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var m=0;m<6;++m)n[i++]=d,n[i++]=h[m],n[i++]=p[m]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":116,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132}],115:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"gl-buffer\":78,\"gl-shader\":132}],116:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\"]);e.exports={lineVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n  return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  vec2 delta = normalize(perp(start - end));\\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\"]),lineFrag:i,textVert:n([\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n  float dataOffset  = textCoordinate.z;\\n  vec2 glyphOffset  = textCoordinate.xy;\\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n    glyphMatrix * glyphOffset * textScale + screenOffset;\\n  gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\"]),textFrag:i,gridVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n  gl_Position = vec4(pos, 0, 1);\\n}\\n\"]),gridFrag:i,boxVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\"]),tickVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\\n}\\n\"])}},{glslify:231}],117:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"text-cache\"),o=t(\"binary-search-bounds\"),s=t(\"./shaders\");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,m=l.prototype;m.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,m=e.tickPad,g=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];u[1^t]=M*s[1^t]-1,d[t]&&(u[1^t]-=M*p*m[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t]&&T&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*m[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),m.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),m.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],m=a[o+2]-f,g=i[o],v=i[o+2]-g;p[o]=2*l/u*m/v,h[o]=2*(s-c)/u*m/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),m.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,m=p.text,g=p.font||\"sans-serif\";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=m.split(\"\\n\"),b=0;b<x.length;b++)for(n=a(g,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:\"center\"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},m.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132,\"text-cache\":303}],118:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t(\"gl-select-static\"),i=t(\"./lib/grid\"),a=t(\"./lib/text\"),o=t(\"./lib/line\"),s=t(\"./lib/box\");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var m=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(m,e[1],m,e[3],p[d],h[d]):o.drawLine(e[0],m,e[2],m,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),g[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),g[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),g[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":113,\"./lib/grid\":114,\"./lib/line\":115,\"./lib/text\":117,\"gl-select-static\":131}],119:[function(t,e,r){\"use strict\";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];\"distanceLimits\"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);\"zoomMin\"in e&&(r[0]=e.zoomMin);\"zoomMax\"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&\"orthographic\"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),\"turntable\"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,u=\"zoom\"===o,h=!!a.control,p=!!a.alt,m=!!a.shift,g=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!m||g&&!h&&!p&&m)&&c.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!m||v||g&&h&&!p&&!m)&&c.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&g&&!h&&!p&&!m||y||g&&!h&&p&&!m)){var M=-d.zoomSpeed*_/window.innerHeight*(A-c.lastT())*100;c.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener(\"touchstart\",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener(\"touchmove\",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener(\"touchend\",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t(\"right-now\"),i=t(\"3d-view\"),a=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"has-passive-events\")},{\"3d-view\":7,\"has-passive-events\":232,\"mouse-change\":247,\"mouse-event-offset\":248,\"mouse-wheel\":250,\"right-now\":278}],120:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n  uv = position;\\n  gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n  gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":132,glslify:231}],121:[function(t,e,r){\"use strict\";var n=t(\"./camera.js\"),i=t(\"gl-axes3d\"),a=t(\"gl-axes3d/properties\"),o=t(\"gl-spikes3d\"),s=t(\"gl-select-static\"),l=t(\"gl-fbo\"),c=t(\"a-big-triangle\"),u=t(\"mouse-change\"),f=t(\"gl-mat4/perspective\"),h=t(\"gl-mat4/ortho\"),p=t(\"./lib/shader\"),d=t(\"is-mobile\")({tablet:!0,featureDetect:!0});function m(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return\"boolean\"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement(\"canvas\"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error(\"webgl not supported\");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new m,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],I=!0,O=!0,z=new Array(16),D=new Array(16),R={view:null,projection:z,model:D,_ortho:!1},F=(O=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:A,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,O=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",I=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,F);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function H(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener(\"resize\",U),N.update=function(t){N._stopped||(t=t||{},I=!0,O=!0)},N.add=function(t){N._stopped||(t.axes=A,E.push(t),L.push(-1),I=!0,O=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),I=!0,O=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener(\"resize\",U),e.removeEventListener(\"webglcontextlost\",H),N.mouseListener.enabled=!1,!N.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(O=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(L[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),I=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),I=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener(\"webglcontextlost\",H);var q=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[q[0].slice(),q[1].slice()];function Y(){if(!H()){U();var t=N.camera.tick();R.view=N.camera.matrix,I=I||t,O=O||t,A.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=q[0],i=q[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=N.pixelRatio,C.axes=N.axes,I=I||!!C.dirty,O=O||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var m=.05*(i[p]-n[p]);n[p]=n[p]-m,i[p]=i[p]+m}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(O=O||v,I=I||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:y}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var m=0;for(d=0;d<3;++d)m=Math.max(m,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/m,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(C=E[o]).axesBounds=d,N.clipToBounds&&(C.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),O&&(O=!1,function(){if(!H()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;A.enable&&(M=M||A.isTransparent(),A.draw(R)),S.axes=A,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(C=E[o]).axes=A,C.pixelRatio=N.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(R),C.isTransparent&&C.isTransparent()&&(M=!0)}if(M){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}I=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(I=!0,Y())},N},createCamera:n}},{\"./camera.js\":119,\"./lib/shader\":120,\"a-big-triangle\":8,\"gl-axes3d\":70,\"gl-axes3d/properties\":77,\"gl-fbo\":86,\"gl-mat4/ortho\":101,\"gl-mat4/perspective\":102,\"gl-select-static\":131,\"gl-spikes3d\":141,\"is-mobile\":238,\"mouse-change\":247}],122:[function(t,e,r){var n=t(\"glslify\");r.pointVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n  highp float a = 12.9898;\\n  highp float b = 78.233;\\n  highp float c = 43758.5453;\\n  highp float d = dot(co.xy, vec2(a, b));\\n  highp float e = mod(d, 3.14);\\n  return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n    // if we don't jitter the point size a bit, overall point cloud\\n    // saturation 'jumps' on zooming, which is disturbing and confusing\\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    // get the same square surface as circle would be\\n    gl_PointSize *= 0.886;\\n  }\\n}\"]),r.pointFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n  float radius;\\n  vec4 baseColor;\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    if(centerFraction == 1.0) {\\n      gl_FragColor = color;\\n    } else {\\n      gl_FragColor = mix(borderColor, color, centerFraction);\\n    }\\n  } else {\\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n    if(radius > 1.0) {\\n      discard;\\n    }\\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n  }\\n}\\n\"]),r.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n  gl_PointSize = pointSize;\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  fragId = id;\\n}\\n\"]),r.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\\n\"])},{glslify:231}],123:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"typedarray-pool\"),o=t(\"./lib/shader\");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{\"./lib/shader\":122,\"gl-buffer\":78,\"gl-shader\":132,\"typedarray-pool\":308}],124:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],m=r[2],g=r[3];(a=c*p+u*d+f*m+h*g)<0&&(a=-a,p=-p,d=-d,m=-m,g=-g);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*m,t[3]=s*h+l*g,t}},{}],125:[function(t,e,r){\"use strict\";e.exports=function(t){return t||0===t?t.toString():\"\"}},{}],126:[function(t,e,r){\"use strict\";var n=t(\"vectorize-text\");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{\"vectorize-text\":311}],127:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n\\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (\\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n    interpColor.a * opacity == 0.\\n  ) discard;\\n  gl_FragColor = interpColor * opacity;\\n}\\n\"]),c=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},m={vertex:o,fragment:c,attributes:u},g={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,m)},r.createPickProject=function(t){return v(t,g)}},{\"gl-shader\":132,glslify:231}],128:[function(t,e,r){\"use strict\";var n=t(\"is-string-blank\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"typedarray-pool\"),s=t(\"gl-mat4/multiply\"),l=t(\"./lib/shaders\"),c=t(\"./lib/glyphs\"),u=t(\"./lib/get-simple-string\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function m(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),m=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new g(e,r,n,o,f,h,p,d,m,s,c,u);return v.update(t),v};var v=g.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function L(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,m=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=m,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var v=T,L=0;L<16;++L)v[L]=0;for(L=0;L<4;++L)v[5*L]=1;v[5*g]=0,i[g]<0?v[12+g]=d[0][g]:v[12+g]=d[1][g],s(v,c,v),l.model=v;var C=(g+1)%3,P=(g+2)%3,I=M(x),O=M(b);I[C]=1,O[P]=1;var z=p(0,0,0,S(_,I)),D=p(0,0,0,S(w,O));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=I,I=O,O=R;var F=C;C=P,P=F}z[0]<0&&(I[C]=-1),D[1]>0&&(O[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(c[4*C+L],2),N+=Math.pow(c[4*P+L],2);I[C]/=Math.sqrt(B),O[P]/=Math.sqrt(N),l.axes[0]=I,l.axes[1]=O,l.fragClipBounds[0]=E(k,m[0],g,-1e8),l.fragClipBounds[1]=E(k,m[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&L(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function I(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a=\"\\u25bc\",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=m(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=m(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||\"normal\",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var u=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,g=t.angle,v=t.lineColor,y=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;x+=3*k.cells.length,b+=2*A.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),P=o.mallocUint32(S);if(S>0){var O=0,z=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(v)&&Array.isArray(v[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T])}k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)D[T]=U[T];D[3]=1}else if(4===U.length){for(T=0;T<4;++T)D[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else D[0]=D[1]=D[2]=0,D[3]=1;else D=[1,1,1,0];if(j)if(Array.isArray(v)){var U;if(3===(U=B?n<v.length?v[n]:[0,0,0,0]:v).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var H=0;Array.isArray(g)?H=n<g.length?+g[n]:0:g&&(H=+g);var q=Math.cos(H),G=Math.sin(H);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T]);var Y=i,W=a;Y=0;Array.isArray(i)?Y=n<i.length?i[n]:0:i&&(Y=i);W=0;Array.isArray(a)?W=n<a.length?a[n]:0:a&&(W=a);var X=[Y*=Y>0?1-M[0][0]:Y<0?1+M[1][0]:1,W*=W>0?1-M[0][1]:W<0?1+M[1][1]:1],Z=k.cells||[],J=k.positions||[];for(T=0;T<Z.length;++T)for(var K=Z[T],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*O+$]=w[$];for($=0;$<4;++$)L[4*O+$]=D[$];P[O]=y;var tt=J[K[Q]];C[2*O]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*O+1]=V*(G*tt[0]+q*tt[1]+X[1]),O+=1}for(Z=A.edges,J=A.positions,T=0;T<Z.length;++T)for(K=Z[T],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)L[4*z+$]=R[$];P[z]=y;tt=J[K[Q]];C[2*z]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*z+1]=V*(G*tt[0]+q*tt[1]+X[1]),z+=1}}}this.bounds=[u,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(P),o.free(E),o.free(L),o.free(C),o.free(P)},v.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{\"./lib/get-simple-string\":125,\"./lib/glyphs\":126,\"./lib/shaders\":127,\"gl-buffer\":78,\"gl-mat4/multiply\":100,\"gl-vao\":150,\"is-string-blank\":239,\"typedarray-pool\":308}],129:[function(t,e,r){\"use strict\";var n=t(\"glslify\");r.boxVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\"]),r.boxFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  gl_FragColor = color;\\n}\\n\"])},{glslify:231}],130:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"./lib/shaders\");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,f=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],h=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(f=Math.max(f,c[0]),h=Math.max(h,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<f||d<h)){o.bind();var m=s[2]-s[0],g=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,m,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,m,g,i),o.drawBox(p,h,m,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var 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t.slice()})),this.center=(t.center||[1/0,1/0]).slice(),this.plot.setOverlayDirty()},i.draw=function(){var t=this.enable,e=this.width,r=this.color,n=this.center,i=this.plot,a=i.line,o=i.dataBox,s=i.viewBox;if(a.bind(),o[0]<=n[0]&&n[0]<=o[2]&&o[1]<=n[1]&&n[1]<=o[3]){var l=s[0]+(n[0]-o[0])/(o[2]-o[0])*(s[2]-s[0]),c=s[1]+(n[1]-o[1])/(o[3]-o[1])*(s[3]-s[1]);t[0]&&a.drawLine(l,c,s[0],c,e[0],r[0]),t[1]&&a.drawLine(l,c,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,c,s[2],c,e[2],r[2]),t[3]&&a.drawLine(l,c,l,s[3],e[3],r[3])}},i.dispose=function(){this.plot.removeOverlay(this)}},{}],140:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, color;\\nattribute float weight;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 coordinates[3];\\nuniform vec4 colors[3];\\nuniform vec2 screenShape;\\nuniform float lineWidth;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vertexPosition = mix(coordinates[0],\\n    mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));\\n\\n  vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\\n  vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\\n  vec2 delta = weight * clipOffset * screenShape;\\n  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\\n\\n  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\\n  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\\n}\\n\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  gl_FragColor = fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec3\"},{name:\"weight\",type:\"float\"}])}},{\"gl-shader\":132,glslify:231}],141:[function(t,e,r){\"use strict\";var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders/index\");e.exports=function(t,e){var r=[];function o(t,e,n,i,a,o){var s=[t,e,n,0,0,0,1];s[i+3]=1,s[i]=a,r.push.apply(r,s),s[6]=-1,r.push.apply(r,s),s[i]=o,r.push.apply(r,s),r.push.apply(r,s),s[6]=1,r.push.apply(r,s),s[i]=a,r.push.apply(r,s)}o(0,0,0,0,0,1),o(0,0,0,1,0,1),o(0,0,0,2,0,1),o(1,0,0,1,-1,1),o(1,0,0,2,-1,1),o(0,1,0,0,-1,1),o(0,1,0,2,-1,1),o(0,0,1,0,-1,1),o(0,0,1,1,-1,1);var l=n(t,r),c=i(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),u=a(t);u.attributes.position.location=0,u.attributes.color.location=1,u.attributes.weight.location=2;var f=new s(t,l,c,u);return f.update(e),f};var o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var l=s.prototype,c=[0,0,0],u=[0,0,0],f=[0,0];l.isTransparent=function(){return!1},l.drawTransparent=function(t){},l.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||o,s=t.view||o,l=t.projection||o;this.axes&&(i=this.axes.lastCubeProps.axis);for(var h=c,p=u,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in t&&(this.position=t.position),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"colors\"in t&&(this.colors=t.colors),\"enabled\"in t&&(this.enabled=t.enabled),\"drawSides\"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders/index\":140,\"gl-buffer\":78,\"gl-vao\":150}],142:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * tubePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\\n  vec4 t_position  = view * tubePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = tubePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  gl_Position = projection * view * tubePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},{glslify:231}],143:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\"),i=t(\"gl-vec4\"),a=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],m=0,g=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(m,g,g,g,m,m);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=y;y=v,v=S;var E=m;m=g,g=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var m=u[s],g=f.length;f=f.concat(m.positions),p=p.concat(m.vectors),d=d.concat(m.vertexIntensity);for(c=0;c<m.cells.length;c++){var v=m.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||\"+x+y+z\",p={};-1!==h.indexOf(\"-x\")&&(p.reversedX=!0),-1!==h.indexOf(\"-y\")&&(p.reversedY=!0),-1!==h.indexOf(\"-z\")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,\"\").replace(/\\+/g,\"\"));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),m=s(a[1],c),g=s(a[2],u),v=d+1,y=m+1,x=g+1;if(d=l(d,0,f-1),v=l(v,0,f-1),m=l(m,0,h-1),y=l(y,0,h-1),g=l(g,0,p-1),x=l(x,0,p-1),d<0||m<0||g<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][v],E=a[1][m],L=a[1][y],C=a[2][g],P=(o-M)/(S-M),I=(c-E)/(L-E),O=(u-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(I)||(I=.5),isFinite(O)||(O=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(m=h-1-m,y=h-1-y),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=m*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=g,A=x,b=d*p,_=v*p,w=m*p*f,T=y*p*f;break;case 3:w=m,T=y,k=g*h,A=x*h,b=d*h*p,_=v*h*p;break;case 2:w=m,T=y,b=d*h,_=v*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=v,k=g*f,A=x*f,w=m*f*p,T=y*f*p;break;default:b=d,_=v,w=m*f,T=y*f,k=g*f*h,A=x*f*h}var z=i[b+w+k],D=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),H=n.create(),q=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(H,D,N,P),n.lerp(q,R,j,P),n.lerp(G,F,U,P);var Y=n.create(),W=n.create();n.lerp(Y,V,q,I),n.lerp(W,H,G,I);var X=n.create();return n.lerp(X,Y,W,O),X}(e,t,p)},m=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),m=c(r),g=c(n),v=Math.min(d,m,g);return isFinite(v)?v:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],I=[],O=d(C),z=C;I.push(O);var D=[],R=m(C,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),g.push({points:P,velocities:I,divergences:D});for(var B=0;B<100*i&&P.length<i&&T(C);){B++;var N=n.clone(O),j=n.squaredLength(N);if(0===j)break;if(j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),O=d(N),n.squaredDistance(z,N)-A>-1e-4*A){P.push(N),z=N,I.push(O);R=m(N,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}C=N}}var U=o(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*M/S),U};var u=t(\"./lib/shaders\"),f=t(\"gl-cone3d\").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:\"streamtube\"})}},{\"./lib/shaders\":142,\"gl-cone3d\":79,\"gl-vec3\":169,\"gl-vec4\":205}],144:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform vec3 objectOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 localCoordinate = vec3(uv.zw, f.x);\\n  worldCoordinate = objectOffset + localCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n  vec4 clipPosition = projection * view * worldPosition;\\n  gl_Position = clipPosition;\\n  kill = f.y;\\n  value = f.z;\\n  planeCoordinate = uv.xy;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * worldPosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  lightDirection = lightPosition - cameraCoordinate.xyz;\\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat beckmannSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness) {\\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  if (\\n    kill > 0.0 ||\\n    vColor.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\\n  ) discard;\\n\\n  vec3 N = normalize(surfaceNormal);\\n  vec3 V = normalize(eyeDirection);\\n  vec3 L = normalize(lightDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  //decide how to interpolate color \\u2014 in vertex or in fragment\\n  vec4 surfaceColor =\\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\\n    step(.5, vertexColor) * vColor;\\n\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform vec3 objectOffset;\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n  worldCoordinate = objectOffset + dataCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n\\n  vec4 clipPosition = projection * view * worldPosition;\\n  clipPosition.z += zOffset;\\n\\n  gl_Position = clipPosition;\\n  value = f + objectOffset.z;\\n  kill = -1.0;\\n  planeCoordinate = uv.zw;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Don't do lighting for contours\\n  surfaceNormal   = vec3(1,0,0);\\n  eyeDirection    = vec3(0,1,0);\\n  lightDirection  = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n  float vh = 255.0 * v;\\n  float upper = floor(vh);\\n  float lower = fract(vh);\\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n  if ((kill > 0.0) ||\\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":132,glslify:231}],145:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),m=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);m.minFilter=e.LINEAR,m.magFilter=e.LINEAR;var g=new M(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,m,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||\"jet\",g.update(v),g};var n=t(\"bit-twiddle\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"typedarray-pool\"),l=t(\"colormap\"),c=t(\"ndarray-ops\"),u=t(\"ndarray-pack\"),f=t(\"ndarray\"),h=t(\"surface-nets\"),p=t(\"gl-mat4/multiply\"),d=t(\"gl-mat4/invert\"),m=t(\"binary-search-bounds\"),g=t(\"ndarray-gradient\"),v=t(\"./lib/shaders\"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function A(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function M(t,e,r,n,i,a,o,l,c,u,h,p,d,m,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new A([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=m,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=M.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],L={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function C(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=L.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=L.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return L.showSurface=o,L.showContour=s,L}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=w.slice(),O=[1,0,0,0,1,0,0,0,1];function z(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=O,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=C(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var m=0;m<this.contourLevels[o].length;++m)m===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==m&&m-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][m]&&(f.uniforms.height=this.contourLevels[o][m],h.draw(r.LINES,this._contourCounts[o][m],this._contourOffsets[o][m]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return z.call(this,t,!1)},S.drawTransparent=function(t){return z.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=O;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=C(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,g=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*g;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=m.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=R(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=R(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=R(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=B(t.contourColor)),\"contourProject\"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=B(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var m=[0,0];m[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],m,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),y[o],\"mirror\");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),A=x.get(1,o,v,1),M=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-A*M,L=M*w-S*_,C=_*A-w*k,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,v,0,E*P),b.set(o,v,1,L*P),b.set(o,v,2,C*P)}s.free(x.data);var I=[1/0,1/0,1/0],O=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var H=0;H<2;++H)for(var q=0;q<3;++q){var G=this._field[q].get(1+o+V,1+v+H);if(isNaN(G)||!isFinite(G))continue t}for(q=0;q<6;++q){var Y=o+T[q][0],W=v+T[q][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),L=b.get(Y+1,W+1,1),C=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=L,N[j++]=C,I[0]=Math.min(I[0],X+this.objectOffset[0]),I[1]=Math.min(I[1],Z+this.objectOffset[1]),I[2]=Math.min(I[2],G+this.objectOffset[2]),z=Math.min(z,J),O[0]=Math.max(O[0],X+this.objectOffset[0]),O[1]=Math.max(O[1],Z+this.objectOffset[1]),O[2]=Math.max(O[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[I,O],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if(\"levels\"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(q=0;q<2;++q){var ot=it.positions[at[q]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var mt=($+dt+1)%3;for(V=0;V<2;++V){var gt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var vt=H?ht:1-ht;if(W=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[mt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=gt*vt;nt[dt]+=yt*G}}}if(pt){if(q>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),m=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<m.length;++e)for(var v=m[e],y=0;y<2;++y){var x=g[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,I=T*L,O=T*E,z=C*f.get(_,M)+P*f.get(_,S)+I*f.get(w,M)+O*f.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+I*p.get(w,M)+O*p.get(w,S);if(isNaN(z)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{\"./lib/shaders\":144,\"binary-search-bounds\":31,\"bit-twiddle\":32,colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259,\"ndarray-gradient\":252,\"ndarray-ops\":254,\"ndarray-pack\":255,\"surface-nets\":302,\"typedarray-pool\":308}],146:[function(t,e,r){\"use strict\";var n=t(\"ndarray\"),i=t(\"ndarray-ops\"),a=t(\"typedarray-pool\");e.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");o||c(t);if(\"number\"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function m(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function g(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function y(t,e,r,n,i,a){var o=g(t);return 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n=t(\"./literals\");e.exports=n.slice().concat([\"layout\",\"centroid\",\"smooth\",\"case\",\"mat2x2\",\"mat2x3\",\"mat2x4\",\"mat3x2\",\"mat3x3\",\"mat3x4\",\"mat4x2\",\"mat4x3\",\"mat4x4\",\"uvec2\",\"uvec3\",\"uvec4\",\"samplerCubeShadow\",\"sampler2DArray\",\"sampler2DArrayShadow\",\"isampler2D\",\"isampler3D\",\"isamplerCube\",\"isampler2DArray\",\"usampler2D\",\"usampler3D\",\"usamplerCube\",\"usampler2DArray\",\"coherent\",\"restrict\",\"readonly\",\"writeonly\",\"resource\",\"atomic_uint\",\"noperspective\",\"patch\",\"sample\",\"subroutine\",\"common\",\"partition\",\"active\",\"filter\",\"image1D\",\"image2D\",\"image3D\",\"imageCube\",\"iimage1D\",\"iimage2D\",\"iimage3D\",\"iimageCube\",\"uimage1D\",\"uimage2D\",\"uimage3D\",\"uimageCube\",\"image1DArray\",\"image2DArray\",\"iimage1DArray\",\"iimage2DArray\",\"uimage1DArray\",\"uimage2DArray\",\"image1DShadow\",\"image2DShadow\",\"image1DArrayShadow\",\"image2DArrayShadow\",\"imageBuffer\",\"iimageBuffer\",\"uimageBuffer\",\"sampler1DArray\",\"sampler1DArrayShadow\",\"isampler1D\",\"isampler1DArray\",\"usampler1D\",\"usampler1DArray\",\"isampler2DRect\",\"usampler2DRect\",\"samplerBuffer\",\"isamplerBuffer\",\"usamplerBuffer\",\"sampler2DMS\",\"isampler2DMS\",\"usampler2DMS\",\"sampler2DMSArray\",\"isampler2DMSArray\",\"usampler2DMSArray\"])},{\"./literals\":228}],228:[function(t,e,r){e.exports=[\"precision\",\"highp\",\"mediump\",\"lowp\",\"attribute\",\"const\",\"uniform\",\"varying\",\"break\",\"continue\",\"do\",\"for\",\"while\",\"if\",\"else\",\"in\",\"out\",\"inout\",\"float\",\"int\",\"uint\",\"void\",\"bool\",\"true\",\"false\",\"discard\",\"return\",\"mat2\",\"mat3\",\"mat4\",\"vec2\",\"vec3\",\"vec4\",\"ivec2\",\"ivec3\",\"ivec4\",\"bvec2\",\"bvec3\",\"bvec4\",\"sampler1D\",\"sampler2D\",\"sampler3D\",\"samplerCube\",\"sampler1DShadow\",\"sampler2DShadow\",\"struct\",\"asm\",\"class\",\"union\",\"enum\",\"typedef\",\"template\",\"this\",\"packed\",\"goto\",\"switch\",\"default\",\"inline\",\"noinline\",\"volatile\",\"public\",\"static\",\"extern\",\"external\",\"interface\",\"long\",\"short\",\"double\",\"half\",\"fixed\",\"unsigned\",\"input\",\"output\",\"hvec2\",\"hvec3\",\"hvec4\",\"dvec2\",\"dvec3\",\"dvec4\",\"fvec2\",\"fvec3\",\"fvec4\",\"sampler2DRect\",\"sampler3DRect\",\"sampler2DRectShadow\",\"sizeof\",\"cast\",\"namespace\",\"using\"]},{}],229:[function(t,e,r){e.exports=[\"<<=\",\">>=\",\"++\",\"--\",\"<<\",\">>\",\"<=\",\">=\",\"==\",\"!=\",\"&&\",\"||\",\"+=\",\"-=\",\"*=\",\"/=\",\"%=\",\"&=\",\"^^\",\"^=\",\"|=\",\"(\",\")\",\"[\",\"]\",\".\",\"!\",\"~\",\"*\",\"/\",\"%\",\"+\",\"-\",\"<\",\">\",\"&\",\"^\",\"|\",\"?\",\":\",\"=\",\",\",\";\",\"{\",\"}\"]},{}],230:[function(t,e,r){var n=t(\"./index\");e.exports=function(t,e){var r=n(e),i=[];return i=(i=i.concat(r(t))).concat(r(null))}},{\"./index\":224}],231:[function(t,e,r){e.exports=function(t){\"string\"==typeof t&&(t=[t]);for(var e=[].slice.call(arguments,1),r=[],n=0;n<t.length-1;n++)r.push(t[n],e[n]||\"\");return r.push(t[n]),r.join(\"\")}},{}],232:[function(t,e,r){\"use strict\";var n=t(\"is-browser\");e.exports=n&&function(){var t=!1;try{var e=Object.defineProperty({},\"passive\",{get:function(){t=!0}});window.addEventListener(\"test\",null,e),window.removeEventListener(\"test\",null,e)}catch(e){t=!1}return t}()},{\"is-browser\":236}],233:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=t.length;if(0===r)throw new Error(\"Must have at least d+1 points\");var i=t[0].length;if(r<=i)throw new Error(\"Must input at least d+1 points\");var o=t.slice(0,i+1),s=n.apply(void 0,o);if(0===s)throw new Error(\"Input not in general position\");for(var l=new Array(i+1),u=0;u<=i;++u)l[u]=u;s<0&&(l[0]=1,l[1]=0);var f=new a(l,new Array(i+1),!1),h=f.adjacent,p=new Array(i+2);for(u=0;u<=i;++u){for(var d=l.slice(),m=0;m<=i;++m)m===u&&(d[m]=-1);var g=d[0];d[0]=d[1],d[1]=g;var v=new a(d,new Array(i+1),!0);h[u]=v,p[u]=v}p[i+1]=f;for(u=0;u<=i;++u){d=h[u].vertices;var y=h[u].adjacent;for(m=0;m<=i;++m){var x=d[m];if(x<0)y[m]=f;else for(var b=0;b<=i;++b)h[b].vertices.indexOf(x)<0&&(y[m]=h[b])}}var _=new c(i,o,p),w=!!e;for(u=i+1;u<r;++u)_.insert(t[u],w);return _.boundary()};var n=t(\"robust-orientation\"),i=t(\"simplicial-complex\").compareCells;function a(t,e,r){this.vertices=t,this.adjacent=e,this.boundary=r,this.lastVisited=-1}function o(t,e,r){this.vertices=t,this.cell=e,this.index=r}function s(t,e){return i(t.vertices,e.vertices)}a.prototype.flip=function(){var t=this.vertices[0];this.vertices[0]=this.vertices[1],this.vertices[1]=t;var e=this.adjacent[0];this.adjacent[0]=this.adjacent[1],this.adjacent[1]=e};var l=[];function c(t,e,r){this.dimension=t,this.vertices=e,this.simplices=r,this.interior=r.filter((function(t){return!t.boundary})),this.tuple=new Array(t+1);for(var i=0;i<=t;++i)this.tuple[i]=this.vertices[i];var a,o=l[t];o||(o=l[t]=((a=n[t+1])||(a=n),function(t){return function(){var e=this.tuple;return t.apply(this,e)}}(a))),this.orient=o}var u=c.prototype;u.handleBoundaryDegeneracy=function(t,e){var r=this.dimension,n=this.vertices.length-1,i=this.tuple,a=this.vertices,o=[t];for(t.lastVisited=-n;o.length>0;)for(var s=(t=o.pop()).adjacent,l=0;l<=r;++l){var c=s[l];if(c.boundary&&!(c.lastVisited<=-n)){for(var u=c.vertices,f=0;f<=r;++f){var h=u[f];i[f]=h<0?e:a[h]}var p=this.orient();if(p>0)return c;c.lastVisited=-n,0===p&&o.push(c)}}return null},u.walk=function(t,e){var r=this.vertices.length-1,n=this.dimension,i=this.vertices,a=this.tuple,o=e?this.interior.length*Math.random()|0:this.interior.length-1,s=this.interior[o];t:for(;!s.boundary;){for(var l=s.vertices,c=s.adjacent,u=0;u<=n;++u)a[u]=i[l[u]];s.lastVisited=r;for(u=0;u<=n;++u){var f=c[u];if(!(f.lastVisited>=r)){var h=a[u];a[u]=t;var p=this.orient();if(a[u]=h,p<0){s=f;continue t}f.boundary?f.lastVisited=-r:f.lastVisited=r}}return}return s},u.addPeaks=function(t,e){var r=this.vertices.length-1,n=this.dimension,i=this.vertices,l=this.tuple,c=this.interior,u=this.simplices,f=[e];e.lastVisited=r,e.vertices[e.vertices.indexOf(-1)]=r,e.boundary=!1,c.push(e);for(var h=[];f.length>0;){var p=(e=f.pop()).vertices,d=e.adjacent,m=p.indexOf(r);if(!(m<0))for(var g=0;g<=n;++g)if(g!==m){var v=d[g];if(v.boundary&&!(v.lastVisited>=r)){var y=v.vertices;if(v.lastVisited!==-r){for(var x=0,b=0;b<=n;++b)y[b]<0?(x=b,l[b]=t):l[b]=i[y[b]];if(this.orient()>0){y[x]=r,v.boundary=!1,c.push(v),f.push(v),v.lastVisited=r;continue}v.lastVisited=-r}var _=v.adjacent,w=p.slice(),T=d.slice(),k=new a(w,T,!0);u.push(k);var A=_.indexOf(e);if(!(A<0)){_[A]=k,T[m]=v,w[g]=-1,T[g]=e,d[g]=k,k.flip();for(b=0;b<=n;++b){var M=w[b];if(!(M<0||M===r)){for(var S=new Array(n-1),E=0,L=0;L<=n;++L){var C=w[L];C<0||L===b||(S[E++]=C)}h.push(new o(S,k,b))}}}}}}h.sort(s);for(g=0;g+1<h.length;g+=2){var P=h[g],I=h[g+1],O=P.index,z=I.index;O<0||z<0||(P.cell.adjacent[P.index]=I.cell,I.cell.adjacent[I.index]=P.cell)}},u.insert=function(t,e){var r=this.vertices;r.push(t);var n=this.walk(t,e);if(n){for(var i=this.dimension,a=this.tuple,o=0;o<=i;++o){var s=n.vertices[o];a[o]=s<0?t:r[s]}var l=this.orient(a);l<0||(0!==l||(n=this.handleBoundaryDegeneracy(n,t)))&&this.addPeaks(t,n)}},u.boundary=function(){for(var t=this.dimension,e=[],r=this.simplices,n=r.length,i=0;i<n;++i){var a=r[i];if(a.boundary){for(var o=new Array(t),s=a.vertices,l=0,c=0,u=0;u<=t;++u)s[u]>=0?o[l++]=s[u]:c=1&u;if(c===(1&t)){var f=o[0];o[0]=o[1],o[1]=f}e.push(o)}}return e}},{\"robust-orientation\":284,\"simplicial-complex\":293}],234:[function(t,e,r){\"use strict\";var n=t(\"binary-search-bounds\");function i(t,e,r,n,i){this.mid=t,this.left=e,this.right=r,this.leftPoints=n,this.rightPoints=i,this.count=(e?e.count:0)+(r?r.count:0)+n.length}e.exports=function(t){if(!t||0===t.length)return new v(null);return new v(g(t))};var a=i.prototype;function o(t,e){t.mid=e.mid,t.left=e.left,t.right=e.right,t.leftPoints=e.leftPoints,t.rightPoints=e.rightPoints,t.count=e.count}function s(t,e){var r=g(e);t.mid=r.mid,t.left=r.left,t.right=r.right,t.leftPoints=r.leftPoints,t.rightPoints=r.rightPoints,t.count=r.count}function l(t,e){var r=t.intervals([]);r.push(e),s(t,r)}function c(t,e){var r=t.intervals([]),n=r.indexOf(e);return n<0?0:(r.splice(n,1),s(t,r),1)}function u(t,e,r){for(var n=0;n<t.length&&t[n][0]<=e;++n){var i=r(t[n]);if(i)return i}}function f(t,e,r){for(var n=t.length-1;n>=0&&t[n][1]>=e;--n){var i=r(t[n]);if(i)return i}}function h(t,e){for(var r=0;r<t.length;++r){var n=e(t[r]);if(n)return n}}function p(t,e){return t-e}function d(t,e){var r=t[0]-e[0];return r||t[1]-e[1]}function m(t,e){var r=t[1]-e[1];return r||t[0]-e[0]}function g(t){if(0===t.length)return null;for(var e=[],r=0;r<t.length;++r)e.push(t[r][0],t[r][1]);e.sort(p);var n=e[e.length>>1],a=[],o=[],s=[];for(r=0;r<t.length;++r){var l=t[r];l[1]<n?a.push(l):n<l[0]?o.push(l):s.push(l)}var c=s,u=s.slice();return c.sort(d),u.sort(m),new i(n,g(a),g(o),c,u)}function v(t){this.root=t}a.intervals=function(t){return t.push.apply(t,this.leftPoints),this.left&&this.left.intervals(t),this.right&&this.right.intervals(t),t},a.insert=function(t){var e=this.count-this.leftPoints.length;if(this.count+=1,t[1]<this.mid)this.left?4*(this.left.count+1)>3*(e+1)?l(this,t):this.left.insert(t):this.left=g([t]);else if(t[0]>this.mid)this.right?4*(this.right.count+1)>3*(e+1)?l(this,t):this.right.insert(t):this.right=g([t]);else{var r=n.ge(this.leftPoints,t,d),i=n.ge(this.rightPoints,t,m);this.leftPoints.splice(r,0,t),this.rightPoints.splice(i,0,t)}},a.remove=function(t){var e=this.count-this.leftPoints;if(t[1]<this.mid)return this.left?4*(this.right?this.right.count:0)>3*(e-1)?c(this,t):2===(s=this.left.remove(t))?(this.left=null,this.count-=1,1):(1===s&&(this.count-=1),s):0;if(t[0]>this.mid)return this.right?4*(this.left?this.left.count:0)>3*(e-1)?c(this,t):2===(s=this.right.remove(t))?(this.right=null,this.count-=1,1):(1===s&&(this.count-=1),s):0;if(1===this.count)return this.leftPoints[0]===t?2:0;if(1===this.leftPoints.length&&this.leftPoints[0]===t){if(this.left&&this.right){for(var r=this,i=this.left;i.right;)r=i,i=i.right;if(r===this)i.right=this.right;else{var a=this.left,s=this.right;r.count-=i.count,r.right=i.left,i.left=a,i.right=s}o(this,i),this.count=(this.left?this.left.count:0)+(this.right?this.right.count:0)+this.leftPoints.length}else this.left?o(this,this.left):o(this,this.right);return 1}for(a=n.ge(this.leftPoints,t,d);a<this.leftPoints.length&&this.leftPoints[a][0]===t[0];++a)if(this.leftPoints[a]===t){this.count-=1,this.leftPoints.splice(a,1);for(s=n.ge(this.rightPoints,t,m);s<this.rightPoints.length&&this.rightPoints[s][1]===t[1];++s)if(this.rightPoints[s]===t)return this.rightPoints.splice(s,1),1}return 0},a.queryPoint=function(t,e){if(t<this.mid){if(this.left)if(r=this.left.queryPoint(t,e))return r;return u(this.leftPoints,t,e)}if(t>this.mid){var r;if(this.right)if(r=this.right.queryPoint(t,e))return r;return f(this.rightPoints,t,e)}return h(this.leftPoints,e)},a.queryInterval=function(t,e,r){var n;if(t<this.mid&&this.left&&(n=this.left.queryInterval(t,e,r)))return n;if(e>this.mid&&this.right&&(n=this.right.queryInterval(t,e,r)))return n;return e<this.mid?u(this.leftPoints,e,r):t>this.mid?f(this.rightPoints,t,r):h(this.leftPoints,r)};var y=v.prototype;y.insert=function(t){this.root?this.root.insert(t):this.root=new i(t[0],null,null,[t],[t])},y.remove=function(t){if(this.root){var e=this.root.remove(t);return 2===e&&(this.root=null),0!==e}return!1},y.queryPoint=function(t,e){if(this.root)return this.root.queryPoint(t,e)},y.queryInterval=function(t,e,r){if(t<=e&&this.root)return this.root.queryInterval(t,e,r)},Object.defineProperty(y,\"count\",{get:function(){return this.root?this.root.count:0}}),Object.defineProperty(y,\"intervals\",{get:function(){return this.root?this.root.intervals([]):[]}})},{\"binary-search-bounds\":31}],235:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=new Array(t),r=0;r<t;++r)e[r]=r;return e}},{}],236:[function(t,e,r){e.exports=!0},{}],237:[function(t,e,r){function n(t){return!!t.constructor&&\"function\"==typeof t.constructor.isBuffer&&t.constructor.isBuffer(t)}\n",
+       "/*!\n",
+       " * Determine if an object is a Buffer\n",
+       " *\n",
+       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
+       " * @license  MIT\n",
+       " */\n",
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i}},{}],262:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i,a,o,s,l,c){var u=e+a+c;if(f>0){var f=Math.sqrt(u+1);t[0]=.5*(o-l)/f,t[1]=.5*(s-n)/f,t[2]=.5*(r-a)/f,t[3]=.5*f}else{var h=Math.max(e,a,c);f=Math.sqrt(2*h-u+1);e>=h?(t[0]=.5*f,t[1]=.5*(i+r)/f,t[2]=.5*(s+n)/f,t[3]=.5*(o-l)/f):a>=h?(t[0]=.5*(r+i)/f,t[1]=.5*f,t[2]=.5*(l+o)/f,t[3]=.5*(s-n)/f):(t[0]=.5*(n+s)/f,t[1]=.5*(o+l)/f,t[2]=.5*f,t[3]=.5*(r-i)/f)}return t}},{}],263:[function(t,e,r){\"use strict\";e.exports=function(t){var e=(t=t||{}).center||[0,0,0],r=t.rotation||[0,0,0,1],n=t.radius||1;e=[].slice.call(e,0,3),u(r=[].slice.call(r,0,4),r);var i=new f(r,e,Math.log(n));i.setDistanceLimits(t.zoomMin,t.zoomMax),(\"eye\"in t||\"up\"in t)&&i.lookAt(0,t.eye,t.center,t.up);return i};var n=t(\"filtered-vector\"),i=t(\"gl-mat4/lookAt\"),a=t(\"gl-mat4/fromQuat\"),o=t(\"gl-mat4/invert\"),s=t(\"./lib/quatFromFrame\");function l(t,e,r){return Math.sqrt(Math.pow(t,2)+Math.pow(e,2)+Math.pow(r,2))}function c(t,e,r,n){return Math.sqrt(Math.pow(t,2)+Math.pow(e,2)+Math.pow(r,2)+Math.pow(n,2))}function u(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=c(r,n,i,a);o>1e-6?(t[0]=r/o,t[1]=n/o,t[2]=i/o,t[3]=a/o):(t[0]=t[1]=t[2]=0,t[3]=1)}function f(t,e,r){this.radius=n([r]),this.center=n(e),this.rotation=n(t),this.computedRadius=this.radius.curve(0),this.computedCenter=this.center.curve(0),this.computedRotation=this.rotation.curve(0),this.computedUp=[.1,0,0],this.computedEye=[.1,0,0],this.computedMatrix=[.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],this.recalcMatrix(0)}var h=f.prototype;h.lastT=function(){return Math.max(this.radius.lastT(),this.center.lastT(),this.rotation.lastT())},h.recalcMatrix=function(t){this.radius.curve(t),this.center.curve(t),this.rotation.curve(t);var e=this.computedRotation;u(e,e);var r=this.computedMatrix;a(r,e);var n=this.computedCenter,i=this.computedEye,o=this.computedUp,s=Math.exp(this.computedRadius[0]);i[0]=n[0]+s*r[2],i[1]=n[1]+s*r[6],i[2]=n[2]+s*r[10],o[0]=r[1],o[1]=r[5],o[2]=r[9];for(var l=0;l<3;++l){for(var c=0,f=0;f<3;++f)c+=r[l+4*f]*i[f];r[12+l]=-c}},h.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r},h.idle=function(t){this.center.idle(t),this.radius.idle(t),this.rotation.idle(t)},h.flush=function(t){this.center.flush(t),this.radius.flush(t),this.rotation.flush(t)},h.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=i[1],o=i[5],s=i[9],c=l(a,o,s);a/=c,o/=c,s/=c;var u=i[0],f=i[4],h=i[8],p=u*a+f*o+h*s,d=l(u-=a*p,f-=o*p,h-=s*p);u/=d,f/=d,h/=d;var m=i[2],g=i[6],v=i[10],y=m*a+g*o+v*s,x=m*u+g*f+v*h,b=l(m-=y*a+x*u,g-=y*o+x*f,v-=y*s+x*h);m/=b,g/=b,v/=b;var _=u*e+a*r,w=f*e+o*r,T=h*e+s*r;this.center.move(t,_,w,T);var k=Math.exp(this.computedRadius[0]);k=Math.max(1e-4,k+n),this.radius.set(t,Math.log(k))},h.rotate=function(t,e,r,n){this.recalcMatrix(t),e=e||0,r=r||0;var i=this.computedMatrix,a=i[0],o=i[4],s=i[8],u=i[1],f=i[5],h=i[9],p=i[2],d=i[6],m=i[10],g=e*a+r*u,v=e*o+r*f,y=e*s+r*h,x=-(d*y-m*v),b=-(m*g-p*y),_=-(p*v-d*g),w=Math.sqrt(Math.max(0,1-Math.pow(x,2)-Math.pow(b,2)-Math.pow(_,2))),T=c(x,b,_,w);T>1e-6?(x/=T,b/=T,_/=T,w/=T):(x=b=_=0,w=1);var k=this.computedRotation,A=k[0],M=k[1],S=k[2],E=k[3],L=A*w+E*x+M*_-S*b,C=M*w+E*b+S*x-A*_,P=S*w+E*_+A*b-M*x,I=E*w-A*x-M*b-S*_;if(n){x=p,b=d,_=m;var O=Math.sin(n)/l(x,b,_);x*=O,b*=O,_*=O,I=I*(w=Math.cos(e))-(L=L*w+I*x+C*_-P*b)*x-(C=C*w+I*b+P*x-L*_)*b-(P=P*w+I*_+L*b-C*x)*_}var z=c(L,C,P,I);z>1e-6?(L/=z,C/=z,P/=z,I/=z):(L=C=P=0,I=1),this.rotation.set(t,L,C,P,I)},h.lookAt=function(t,e,r,n){this.recalcMatrix(t),r=r||this.computedCenter,e=e||this.computedEye,n=n||this.computedUp;var a=this.computedMatrix;i(a,e,r,n);var o=this.computedRotation;s(o,a[0],a[1],a[2],a[4],a[5],a[6],a[8],a[9],a[10]),u(o,o),this.rotation.set(t,o[0],o[1],o[2],o[3]);for(var l=0,c=0;c<3;++c)l+=Math.pow(r[c]-e[c],2);this.radius.set(t,.5*Math.log(Math.max(l,1e-6))),this.center.set(t,r[0],r[1],r[2])},h.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},h.setMatrix=function(t,e){var r=this.computedRotation;s(r,e[0],e[1],e[2],e[4],e[5],e[6],e[8],e[9],e[10]),u(r,r),this.rotation.set(t,r[0],r[1],r[2],r[3]);var n=this.computedMatrix;o(n,e);var i=n[15];if(Math.abs(i)>1e-6){var a=n[12]/i,l=n[13]/i,c=n[14]/i;this.recalcMatrix(t);var f=Math.exp(this.computedRadius[0]);this.center.set(t,a-n[2]*f,l-n[6]*f,c-n[10]*f),this.radius.idle(t)}else this.center.idle(t),this.radius.idle(t)},h.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},h.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},h.getDistanceLimits=function(t){var e=this.radius.bounds;return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},h.toJSON=function(){return this.recalcMatrix(this.lastT()),{center:this.computedCenter.slice(),rotation:this.computedRotation.slice(),distance:Math.log(this.computedRadius[0]),zoomMin:this.radius.bounds[0][0],zoomMax:this.radius.bounds[1][0]}},h.fromJSON=function(t){var e=this.lastT(),r=t.center;r&&this.center.set(e,r[0],r[1],r[2]);var n=t.rotation;n&&this.rotation.set(e,n[0],n[1],n[2],n[3]);var i=t.distance;i&&i>0&&this.radius.set(e,Math.log(i)),this.setDistanceLimits(t.zoomMin,t.zoomMax)}},{\"./lib/quatFromFrame\":262,\"filtered-vector\":68,\"gl-mat4/fromQuat\":95,\"gl-mat4/invert\":98,\"gl-mat4/lookAt\":99}],264:[function(t,e,r){\n",
+       "/*!\n",
+       " * pad-left <https://github.com/jonschlinkert/pad-left>\n",
+       " *\n",
+       " * Copyright (c) 2014-2015, Jon Schlinkert.\n",
+       " * Licensed under the MIT license.\n",
+       " */\n",
+       "\"use strict\";var n=t(\"repeat-string\");e.exports=function(t,e,r){return n(r=void 0!==r?r+\"\":\" \",e)+t}},{\"repeat-string\":277}],265:[function(t,e,r){e.exports=function(t,e){e||(e=[0,\"\"]),t=String(t);var r=parseFloat(t,10);return e[0]=r,e[1]=t.match(/[\\d.\\-\\+]*\\s*(.*)/)[1]||\"\",e}},{}],266:[function(t,e,r){\"use strict\";e.exports=function(t,e){for(var r=0|e.length,i=t.length,a=[new Array(r),new Array(r)],o=0;o<r;++o)a[0][o]=[],a[1][o]=[];for(o=0;o<i;++o){var s=t[o];a[0][s[0]].push(s),a[1][s[1]].push(s)}var l=[];for(o=0;o<r;++o)a[0][o].length+a[1][o].length===0&&l.push([o]);function c(t,e){var r=a[e][t[e]];r.splice(r.indexOf(t),1)}function u(t,r,i){for(var o,s,l,u=0;u<2;++u)if(a[u][r].length>0){o=a[u][r][0],l=u;break}s=o[1^l];for(var f=0;f<2;++f)for(var h=a[f][r],p=0;p<h.length;++p){var d=h[p],m=d[1^f];n(e[t],e[r],e[s],e[m])>0&&(o=d,s=m,l=f)}return i||o&&c(o,l),s}function f(t,r){var i=a[r][t][0],o=[t];c(i,r);for(var s=i[1^r];;){for(;s!==t;)o.push(s),s=u(o[o.length-2],s,!1);if(a[0][t].length+a[1][t].length===0)break;var l=o[o.length-1],f=t,h=o[1],p=u(l,f,!0);if(n(e[l],e[f],e[h],e[p])<0)break;o.push(t),s=u(l,f)}return o}function h(t,e){return e[1]===e[e.length-1]}for(o=0;o<r;++o)for(var p=0;p<2;++p){for(var d=[];a[p][o].length>0;){a[0][o].length;var m=f(o,p);h(0,m)?d.push.apply(d,m):(d.length>0&&l.push(d),d=m)}d.length>0&&l.push(d)}return l};var n=t(\"compare-angle\")},{\"compare-angle\":54}],267:[function(t,e,r){\"use strict\";e.exports=function(t,e){for(var r=n(t,e.length),i=new Array(e.length),a=new Array(e.length),o=[],s=0;s<e.length;++s){var l=r[s].length;a[s]=l,i[s]=!0,l<=1&&o.push(s)}for(;o.length>0;){var c=o.pop();i[c]=!1;var u=r[c];for(s=0;s<u.length;++s){var f=u[s];0==--a[f]&&o.push(f)}}var h=new Array(e.length),p=[];for(s=0;s<e.length;++s)if(i[s]){c=p.length;h[s]=c,p.push(e[s])}else h[s]=-1;var d=[];for(s=0;s<t.length;++s){var m=t[s];i[m[0]]&&i[m[1]]&&d.push([h[m[0]],h[m[1]]])}return[d,p]};var n=t(\"edges-to-adjacency-list\")},{\"edges-to-adjacency-list\":66}],268:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=c(t,e);t=r[0];for(var f=(e=r[1]).length,h=(t.length,n(t,e.length)),p=0;p<f;++p)if(h[p].length%2==1)throw new Error(\"planar-graph-to-polyline: graph must be manifold\");var d=i(t,e);var m=(d=d.filter((function(t){for(var r=t.length,n=[0],i=0;i<r;++i){var a=e[t[i]],l=e[t[(i+1)%r]],c=o(-a[0],a[1]),u=o(-a[0],l[1]),f=o(l[0],a[1]),h=o(l[0],l[1]);n=s(n,s(s(c,u),s(f,h)))}return n[n.length-1]>0}))).length,g=new Array(m),v=new Array(m);for(p=0;p<m;++p){g[p]=p;var y=new Array(m),x=d[p].map((function(t){return e[t]})),b=a([x]),_=0;t:for(var w=0;w<m;++w)if(y[w]=0,p!==w){for(var T=(H=d[w]).length,k=0;k<T;++k){var A=b(e[H[k]]);if(0!==A){A<0&&(y[w]=1,_+=1);continue t}}y[w]=1,_+=1}v[p]=[_,p,y]}v.sort((function(t,e){return e[0]-t[0]}));for(p=0;p<m;++p){var M=(y=v[p])[1],S=y[2];for(w=0;w<m;++w)S[w]&&(g[w]=M)}var E=function(t){for(var e=new Array(t),r=0;r<t;++r)e[r]=[];return e}(m);for(p=0;p<m;++p)E[p].push(g[p]),E[g[p]].push(p);var L={},C=u(f,!1);for(p=0;p<m;++p)for(T=(H=d[p]).length,w=0;w<T;++w){var P=H[w],I=H[(w+1)%T],O=Math.min(P,I)+\":\"+Math.max(P,I);if(O in L){var z=L[O];E[z].push(p),E[p].push(z),C[P]=C[I]=!0}else L[O]=p}function D(t){for(var e=t.length,r=0;r<e;++r)if(!C[t[r]])return!1;return!0}var R=[],F=u(m,-1);for(p=0;p<m;++p)g[p]!==p||D(d[p])?F[p]=-1:(R.push(p),F[p]=0);r=[];for(;R.length>0;){var B=R.pop(),N=E[B];l(N,(function(t,e){return t-e}));var j,U=N.length,V=F[B];if(0===V){var H=d[B];j=[H]}for(p=0;p<U;++p){var q=N[p];if(!(F[q]>=0))if(F[q]=1^V,R.push(q),0===V)D(H=d[q])||(H.reverse(),j.push(H))}0===V&&r.push(j)}return r};var n=t(\"edges-to-adjacency-list\"),i=t(\"planar-dual\"),a=t(\"point-in-big-polygon\"),o=t(\"two-product\"),s=t(\"robust-sum\"),l=t(\"uniq\"),c=t(\"./lib/trim-leaves\");function u(t,e){for(var r=new Array(t),n=0;n<t;++n)r[n]=e;return r}},{\"./lib/trim-leaves\":267,\"edges-to-adjacency-list\":66,\"planar-dual\":266,\"point-in-big-polygon\":269,\"robust-sum\":289,\"two-product\":306,uniq:310}],269:[function(t,e,r){e.exports=function(t){for(var e=t.length,r=[],a=[],s=0;s<e;++s)for(var u=t[s],f=u.length,h=f-1,p=0;p<f;h=p++){var d=u[h],m=u[p];d[0]===m[0]?a.push([d,m]):r.push([d,m])}if(0===r.length)return 0===a.length?c:(g=l(a),function(t){return g(t[0],t[1])?0:1});var g;var v=i(r),y=function(t,e){return function(r){var i=o.le(e,r[0]);if(i<0)return 1;var a=t[i];if(!a){if(!(i>0&&e[i]===r[0]))return 1;a=t[i-1]}for(var s=1;a;){var l=a.key,c=n(r,l[0],l[1]);if(l[0][0]<l[1][0])if(c<0)a=a.left;else{if(!(c>0))return 0;s=-1,a=a.right}else if(c>0)a=a.left;else{if(!(c<0))return 0;s=1,a=a.right}}return s}}(v.slabs,v.coordinates);return 0===a.length?y:function(t,e){return function(r){return t(r[0],r[1])?0:e(r)}}(l(a),y)};var n=t(\"robust-orientation\")[3],i=t(\"slab-decomposition\"),a=t(\"interval-tree-1d\"),o=t(\"binary-search-bounds\");function s(){return!0}function l(t){for(var e={},r=0;r<t.length;++r){var n=t[r],i=n[0][0],o=n[0][1],l=n[1][1],c=[Math.min(o,l),Math.max(o,l)];i in e?e[i].push(c):e[i]=[c]}var u={},f=Object.keys(e);for(r=0;r<f.length;++r){var h=e[f[r]];u[f[r]]=a(h)}return function(t){return function(e,r){var n=t[e];return!!n&&!!n.queryPoint(r,s)}}(u)}function c(t){return 1}},{\"binary-search-bounds\":31,\"interval-tree-1d\":234,\"robust-orientation\":284,\"slab-decomposition\":299}],270:[function(t,e,r){\"use strict\";var n=new Float64Array(4),i=new Float64Array(4),a=new Float64Array(4);e.exports=function(t,e,r,o,s){n.length<o.length&&(n=new Float64Array(o.length),i=new Float64Array(o.length),a=new Float64Array(o.length));for(var l=0;l<o.length;++l)n[l]=t[l]-o[l],i[l]=e[l]-t[l],a[l]=r[l]-t[l];var c=0,u=0,f=0,h=0,p=0,d=0;for(l=0;l<o.length;++l){var m=i[l],g=a[l],v=n[l];c+=m*m,u+=m*g,f+=g*g,h+=v*m,p+=v*g,d+=v*v}var y,x,b,_,w,T=Math.abs(c*f-u*u),k=u*p-f*h,A=u*h-c*p;if(k+A<=T)if(k<0)A<0&&h<0?(A=0,-h>=c?(k=1,y=c+2*h+d):y=h*(k=-h/c)+d):(k=0,p>=0?(A=0,y=d):-p>=f?(A=1,y=f+2*p+d):y=p*(A=-p/f)+d);else if(A<0)A=0,h>=0?(k=0,y=d):-h>=c?(k=1,y=c+2*h+d):y=h*(k=-h/c)+d;else{var M=1/T;y=(k*=M)*(c*k+u*(A*=M)+2*h)+A*(u*k+f*A+2*p)+d}else k<0?(b=f+p)>(x=u+h)?(_=b-x)>=(w=c-2*u+f)?(k=1,A=0,y=c+2*h+d):y=(k=_/w)*(c*k+u*(A=1-k)+2*h)+A*(u*k+f*A+2*p)+d:(k=0,b<=0?(A=1,y=f+2*p+d):p>=0?(A=0,y=d):y=p*(A=-p/f)+d):A<0?(b=c+h)>(x=u+p)?(_=b-x)>=(w=c-2*u+f)?(A=1,k=0,y=f+2*p+d):y=(k=1-(A=_/w))*(c*k+u*A+2*h)+A*(u*k+f*A+2*p)+d:(A=0,b<=0?(k=1,y=c+2*h+d):h>=0?(k=0,y=d):y=h*(k=-h/c)+d):(_=f+p-u-h)<=0?(k=0,A=1,y=f+2*p+d):_>=(w=c-2*u+f)?(k=1,A=0,y=c+2*h+d):y=(k=_/w)*(c*k+u*(A=1-k)+2*h)+A*(u*k+f*A+2*p)+d;var S=1-k-A;for(l=0;l<o.length;++l)s[l]=S*t[l]+k*e[l]+A*r[l];return y<0?0:y}},{}],271:[function(t,e,r){e.exports=t(\"gl-quat/slerp\")},{\"gl-quat/slerp\":124}],272:[function(t,e,r){\"use strict\";var n=t(\"big-rat/add\");e.exports=function(t,e){for(var r=t.length,i=new Array(r),a=0;a<r;++a)i[a]=n(t[a],e[a]);return i}},{\"big-rat/add\":15}],273:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=new Array(t.length),r=0;r<t.length;++r)e[r]=n(t[r]);return e};var n=t(\"big-rat\")},{\"big-rat\":18}],274:[function(t,e,r){\"use strict\";var n=t(\"big-rat\"),i=t(\"big-rat/mul\");e.exports=function(t,e){for(var r=n(e),a=t.length,o=new Array(a),s=0;s<a;++s)o[s]=i(t[s],r);return o}},{\"big-rat\":18,\"big-rat/mul\":27}],275:[function(t,e,r){\"use strict\";var n=t(\"big-rat/sub\");e.exports=function(t,e){for(var r=t.length,i=new Array(r),a=0;a<r;++a)i[a]=n(t[a],e[a]);return i}},{\"big-rat/sub\":29}],276:[function(t,e,r){\"use strict\";var n=t(\"compare-cell\"),i=t(\"compare-oriented-cell\"),a=t(\"cell-orientation\");e.exports=function(t){t.sort(i);for(var e=t.length,r=0,o=0;o<e;++o){var s=t[o],l=a(s);if(0!==l){if(r>0){var c=t[r-1];if(0===n(s,c)&&a(c)!==l){r-=1;continue}}t[r++]=s}}return t.length=r,t}},{\"cell-orientation\":47,\"compare-cell\":56,\"compare-oriented-cell\":57}],277:[function(t,e,r){\n",
+       "/*!\n",
+       " * repeat-string <https://github.com/jonschlinkert/repeat-string>\n",
+       " *\n",
+       " * Copyright (c) 2014-2015, Jon Schlinkert.\n",
+       " * Licensed under the MIT License.\n",
+       " */\n",
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o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],f=e[a+8];if(n){var h=Math.abs(s),p=Math.abs(l),d=Math.abs(f),m=Math.max(h,p,d);h===m?(s=s<0?-1:1,l=f=0):d===m?(f=f<0?-1:1,s=l=0):(l=l<0?-1:1,s=f=0)}else{var g=c(s,l,f);s/=g,l/=g,f/=g}var v,y,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*f,T=c(x-=s*w,b-=l*w,_-=f*w),k=l*(_/=T)-f*(b/=T),A=f*(x/=T)-s*_,M=s*b-l*x,S=c(k,A,M);if(k/=S,A/=S,M/=S,this.center.jump(t,q,G,Y),this.radius.idle(t),this.up.jump(t,s,l,f),this.right.jump(t,x,b,_),2===a){var E=e[1],L=e[5],C=e[9],P=E*x+L*b+C*_,I=E*k+L*A+C*M;v=R<0?-Math.PI/2:Math.PI/2,y=Math.atan2(I,P)}else{var O=e[2],z=e[6],D=e[10],R=O*s+z*l+D*f,F=O*x+z*b+D*_,B=O*k+z*A+D*M;v=Math.asin(u(R)),y=Math.atan2(B,F)}this.angle.jump(t,y,v),this.recalcMatrix(t);var N=e[2],j=e[6],U=e[10],V=this.computedMatrix;i(V,e);var H=V[15],q=V[12]/H,G=V[13]/H,Y=V[14]/H,W=Math.exp(this.computedRadius[0]);this.center.jump(t,q-N*W,G-j*W,Y-U*W)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(t){this.center.idle(t),this.up.idle(t),this.right.idle(t),this.radius.idle(t),this.angle.idle(t)},p.flush=function(t){this.center.flush(t),this.up.flush(t),this.right.flush(t),this.radius.flush(t),this.angle.flush(t)},p.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},p.lookAt=function(t,e,r,n){this.recalcMatrix(t),e=e||this.computedEye,r=r||this.computedCenter;var i=(n=n||this.computedUp)[0],a=n[1],o=n[2],s=c(i,a,o);if(!(s<1e-6)){i/=s,a/=s,o/=s;var l=e[0]-r[0],f=e[1]-r[1],h=e[2]-r[2],p=c(l,f,h);if(!(p<1e-6)){l/=p,f/=p,h/=p;var d=this.computedRight,m=d[0],g=d[1],v=d[2],y=i*m+a*g+o*v,x=c(m-=y*i,g-=y*a,v-=y*o);if(!(x<.01&&(x=c(m=a*h-o*f,g=o*l-i*h,v=i*f-a*l))<1e-6)){m/=x,g/=x,v/=x,this.up.set(t,i,a,o),this.right.set(t,m,g,v),this.center.set(t,r[0],r[1],r[2]),this.radius.set(t,Math.log(p));var b=a*v-o*g,_=o*m-i*v,w=i*g-a*m,T=c(b,_,w),k=i*l+a*f+o*h,A=m*l+g*f+v*h,M=(b/=T)*l+(_/=T)*f+(w/=T)*h,S=Math.asin(u(k)),E=Math.atan2(M,A),L=this.angle._state,C=L[L.length-1],P=L[L.length-2];C%=2*Math.PI;var I=Math.abs(C+2*Math.PI-E),O=Math.abs(C-E),z=Math.abs(C-2*Math.PI-E);I<O&&(C+=2*Math.PI),z<O&&(C-=2*Math.PI),this.angle.jump(this.angle.lastT(),C,P),this.angle.set(t,E,S)}}}}},{\"filtered-vector\":68,\"gl-mat4/invert\":98,\"gl-mat4/rotate\":103,\"gl-vec3/cross\":157,\"gl-vec3/dot\":162,\"gl-vec3/normalize\":179}],306:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var i=t*e,a=n*t,o=a-(a-t),s=t-o,l=n*e,c=l-(l-e),u=e-c,f=s*u-(i-o*c-s*c-o*u);if(r)return r[0]=f,r[1]=i,r;return[f,i]};var n=+(Math.pow(2,27)+1)},{}],307:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var n=t+e,i=n-t,a=e-i,o=t-(n-i);if(r)return r[0]=o+a,r[1]=n,r;return[o+a,n]}},{}],308:[function(t,e,r){(function(e){(function(){\"use strict\";var 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null},r.mallocArrayBuffer=p,r.mallocUint8=d,r.mallocUint16=m,r.mallocUint32=g,r.mallocInt8=v,r.mallocInt16=y,r.mallocInt32=x,r.mallocFloat32=r.mallocFloat=b,r.mallocFloat64=r.mallocDouble=_,r.mallocUint8Clamped=w,r.mallocBigUint64=T,r.mallocBigInt64=k,r.mallocDataView=A,r.mallocBuffer=M,r.clearCache=function(){for(var t=0;t<32;++t)c.UINT8[t].length=0,c.UINT16[t].length=0,c.UINT32[t].length=0,c.INT8[t].length=0,c.INT16[t].length=0,c.INT32[t].length=0,c.FLOAT[t].length=0,c.DOUBLE[t].length=0,c.BIGUINT64[t].length=0,c.BIGINT64[t].length=0,c.UINT8C[t].length=0,u[t].length=0,f[t].length=0}}).call(this)}).call(this,void 0!==n?n:\"undefined\"!=typeof self?self:\"undefined\"!=typeof window?window:{})},{\"bit-twiddle\":32,buffer:3,dup:65}],309:[function(t,e,r){\"use strict\";function n(t){this.roots=new Array(t),this.ranks=new Array(t);for(var e=0;e<t;++e)this.roots[e]=e,this.ranks[e]=0}e.exports=n;var i=n.prototype;Object.defineProperty(i,\"length\",{get:function(){return this.roots.length}}),i.makeSet=function(){var t=this.roots.length;return this.roots.push(t),this.ranks.push(0),t},i.find=function(t){for(var e=t,r=this.roots;r[t]!==t;)t=r[t];for(;r[e]!==t;){var n=r[e];r[e]=t,e=n}return t},i.link=function(t,e){var r=this.find(t),n=this.find(e);if(r!==n){var i=this.ranks,a=this.roots,o=i[r],s=i[n];o<s?a[r]=n:s<o?a[n]=r:(a[n]=r,++i[r])}}},{}],310:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){return 0===t.length?t:e?(r||t.sort(e),function(t,e){for(var r=1,n=t.length,i=t[0],a=t[0],o=1;o<n;++o)if(a=i,e(i=t[o],a)){if(o===r){r++;continue}t[r++]=i}return t.length=r,t}(t,e)):(r||t.sort(),function(t){for(var e=1,r=t.length,n=t[0],i=t[0],a=1;a<r;++a,i=n)if(i=n,(n=t[a])!==i){if(a===e){e++;continue}t[e++]=n}return t.length=e,t}(t))}},{}],311:[function(t,e,r){\"use strict\";e.exports=function(t,e){\"object\"==typeof e&&null!==e||(e={});return n(t,e.canvas||i,e.context||a,e)};var n=t(\"./lib/vtext\"),i=null,a=null;\"undefined\"!=typeof 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n=t(\"surface-nets\"),i=t(\"ndarray\"),a=t(\"simplify-planar-graph\"),o=t(\"clean-pslg\"),s=t(\"cdt2d\"),l=t(\"planar-graph-to-polyline\");function c(t,e,r,n){for(var i=\"<\"+t+\">\",a=\"</\"+t+\">\",o=i.length,s=a.length,l=\"+\"===e[0]||\"-\"===e[0],c=0,u=-s;c>-1&&-1!==(c=r.indexOf(i,c))&&-1!==(u=r.indexOf(a,c+o))&&!(u<=c);){for(var f=c;f<u+s;++f)if(f<c+o||f>=u)n[f]=null,r=r.substr(0,f)+\" \"+r.substr(f+1);else if(null!==n[f]){var h=n[f].indexOf(e[0]);-1===h?n[f]+=e:l&&(n[f]=n[f].substr(0,h+1)+(1+parseInt(n[f][h+1]))+n[f].substr(h+2))}var p=c+o,d=r.substr(p,u-p).indexOf(i);c=-1!==d?d:u+s}return n}function u(t,e){var r=n(t,128);return e?a(r.cells,r.positions,.25):{edges:r.cells,positions:r.positions}}function f(t,e,r,n){var i=u(t,n),a=function(t,e,r){for(var n=e.textAlign||\"start\",i=e.textBaseline||\"alphabetic\",a=[1<<30,1<<30],o=[0,0],s=t.length,l=0;l<s;++l)for(var c=t[l],u=0;u<2;++u)a[u]=0|Math.min(a[u],c[u]),o[u]=0|Math.max(o[u],c[u]);var 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+   "metadata": {},
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aults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"xaxis\":{\"title\":{\"text\":\"max_depth\"},\"range\":[-6.850000000000001,209.85]},\"yaxis\":{\"title\":{\"text\":\"n_estimators\"},\"range\":[44.2,259.8]}},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('5f4cf76b-c646-4d8d-a2f4-3f353272387a');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = optuna.visualization.plot_contour(study, params=[\"max_depth\", \"n_estimators\"])\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "fa378193-667c-428c-8962-e0a0fb5fef06",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g5.get_matrix(target=True).plot(kind='hist')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3589f00",
+   "metadata": {},
+   "source": [
+    "Fit a model without target. One dimensional targets are used by UMAP during fit. (n, k>1) targets are ignored during fit.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "f81a96d9-a45f-4bf4-a302-86af6d2aae15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's removed `sex` as a feature, which splits the data strongly, and generate a new model,\n",
+    "feats = ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "363c5a6a-5183-4aae-9439-7756ccbb8bb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (221, 0) in UMAP fit, as it is not one dimensional"
+     ]
+    }
+   ],
+   "source": [
+    "g = graphistry.nodes(train_diabetes)  # \n",
+    "# this time we scale the data \n",
+    "g6 = g.umap(X = feats, # y='target', # don't include target for fun (which helps supervise umap fit when 1-dimensional)\n",
+    "            use_scaler='standard', #None, #'robust', 'kbins', 'quantile', 'minmax'\n",
+    "            use_scaler_target='standard', \n",
+    "           )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "3509fac0-434f-4a08-bb54-256825b66af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"0727a3f1-b837-4183-8eb6-7ec04d107059\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=50171792d54140e4b513b3ee4d767523&type=arrow&viztoken=ede35216-39b6-459a-b194-bcf3357103de&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265748&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#0727a3f1-b837-4183-8eb6-7ec04d107059\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g6.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7379c1b-65d8-4350-b38b-b57f54cd97b3",
+   "metadata": {},
+   "source": [
+    "# Digits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "0eb0f820-7118-43bb-8419-52acb4a4f925",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data3 = load_digits()\n",
+    "digit_features = list(data3['feature_names'])\n",
+    "digits_df = pd.DataFrame(data3['data'], columns=digit_features)\n",
+    "digits_df['target'] = data3['target'].astype(int)\n",
+    "digits_df['names'] = digits_df.target.astype(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "e3aa6bdd-b0b6-46a7-93ae-041321bef461",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (898, 0) in UMAP fit, as it is not one dimensional"
+     ]
+    }
+   ],
+   "source": [
+    "a, b, c, d = train_test_split(digits_df, digits_df.target, train_size=0.5)\n",
+    "\n",
+    "g6=graphistry.nodes(a).umap(X=digit_features, \n",
+    "                              #y='target', this obviously works great to separate clusters during UMAP fit.\n",
+    "                              use_scaler=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "a1156045-87df-443d-b75f-63d418941924",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"5fd38ede-5068-4d90-8865-473db77adafb\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=e288e111e8414251ab61568eaf6ebd9f&type=arrow&viztoken=4743632b-8154-4eea-96ca-a9cc36c2bca6&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265754&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#5fd38ede-5068-4d90-8865-473db77adafb\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g6.bind(point_title='target').plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "afad90db-df22-4da6-abb9-cb161ef008c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------\n",
+      "Infering edges over UMAP embedding\n",
+      "---------------------------------------------\n",
+      " Mean distance to existing nodes 5.85 +/- 3.68\n",
+      " Max distance threshold; epsilon = 2.17\n",
+      " Finding 7 nearest neighbors\n",
+      " 138.65 neighbors per node within epsilon 2.17\n",
+      " 6293 total edges after dropping duplicates\n",
+      " ** Final graph has 962 nodes\n",
+      " - Batch has 899 nodes\n",
+      " - Brought in 63 nodes\n",
+      "--------------------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"ea8b55e8-5bba-4d2f-a798-5c1c6c743ec0\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=16e7588c62d04f4ebc3e9423e955d78e&type=arrow&viztoken=f4d5aff0-1b7e-4b4d-a3b0-2469f6975451&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265762&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#ea8b55e8-5bba-4d2f-a798-5c1c6c743ec0\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g7 = g6.transform_umap(b, min_dist='auto', verbose=True, merge_policy=True)\n",
+    "g7.bind(point_title='target').plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "be9b4506-960a-4204-b6c6-194d7fa9135d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------\n",
+      "Infering edges over UMAP embedding\n",
+      "---------------------------------------------\n",
+      " Mean distance to existing nodes 5.83 +/- 3.66\n",
+      " Max distance threshold; epsilon = 2.17\n",
+      " Finding 7 nearest neighbors\n",
+      " 137.59 neighbors per node within epsilon 2.17\n",
+      " 6246 total edges after dropping duplicates\n",
+      " ** Final graph has 899 nodes\n",
+      "--------------------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"e0ef27b5-9c95-40a6-9e1e-c9dca83bfabb\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=a5e3c172273b40529c082c8329099737&type=arrow&viztoken=6831953c-66c2-44df-947f-b306136c3365&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265770&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#e0ef27b5-9c95-40a6-9e1e-c9dca83bfabb\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g7 = g6.transform_umap(b, min_dist='auto', verbose=True, merge_policy=False)\n",
+    "g7.bind(point_title='target').plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "987a2bce-ffed-4724-a8a7-d3dc07f48262",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.],\n",
+       "        [ 0.,  0., 13., 15., 10., 15.,  5.,  0.],\n",
+       "        [ 0.,  3., 15.,  2.,  0., 11.,  8.,  0.],\n",
+       "        [ 0.,  4., 12.,  0.,  0.,  8.,  8.,  0.],\n",
+       "        [ 0.,  5.,  8.,  0.,  0.,  9.,  8.,  0.],\n",
+       "        [ 0.,  4., 11.,  0.,  1., 12.,  7.,  0.],\n",
+       "        [ 0.,  2., 14.,  5., 10., 12.,  0.,  0.],\n",
+       "        [ 0.,  0.,  6., 13., 10.,  0.,  0.,  0.]]),\n",
+       " 0.0)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "digits_df[digit_features].iloc[0].values.reshape(8,8), df.iloc[0].target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "050acfb4-301e-42bc-bc7b-961bc4793608",
+   "metadata": {},
+   "source": [
+    "# Build a GNN model "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "d0d2c3c0-cfa4-4335-b2d4-fa77ec0eae94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g6 = g2.build_gnn(y_nodes='target')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "7507c3d8-89d1-4d49-9534-9c2e50376934",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Graph(num_nodes=284, num_edges=4626,\n",
+       "      ndata_schemes={'feature': Scheme(shape=(30,), dtype=torch.float64), 'train_mask': Scheme(shape=(), dtype=torch.bool), 'test_mask': Scheme(shape=(), dtype=torch.bool)}\n",
+       "      edata_schemes={'feature': Scheme(shape=(286,), dtype=torch.float64), 'train_mask': Scheme(shape=(), dtype=torch.bool), 'test_mask': Scheme(shape=(), dtype=torch.bool)})"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "G = g6.DGL_graph\n",
+    "G"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "e237ce99-191d-473d-80bb-49211978fd5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run a prediction task from https://docs.dgl.ai/tutorials/blitz/index.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4eb84597",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 649ac531e3d4e1546ef527b5f779ab278aeb875d Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 20 Jan 2023 18:29:42 -0800
Subject: [PATCH 127/432] adds cyber redteam demo improvements

---
 demos/ai/cyber/cyber-redteam-umap-demo.ipynb | 1366 +++++++++---------
 1 file changed, 661 insertions(+), 705 deletions(-)

diff --git a/demos/ai/cyber/cyber-redteam-umap-demo.ipynb b/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
index 9e30bb5dff..b07a7403f8 100644
--- a/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
+++ b/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "0215906c",
    "metadata": {},
    "outputs": [],
@@ -38,15 +38,12 @@
     "from collections import Counter\n",
     "\n",
     "import numpy as np\n",
-    "import matplotlib.pylab as plt\n",
-    "\n",
-    "from sklearn.cluster import DBSCAN\n",
-    "from sknetwork.ranking import PageRank\n"
+    "import matplotlib.pylab as plt\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "8e1747b9-c903-4398-9aa0-b52b69fce021",
    "metadata": {},
    "outputs": [],
@@ -56,17 +53,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "6d2669fd-6164-4376-81bd-79c6c6f4112f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "RENDER = False # set to True to render Graphistry UI inline"
+    "RENDER = True # set to True to render Graphistry UI inline"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "59e1cc0b",
    "metadata": {},
    "outputs": [],
@@ -96,12 +93,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "fe6e61b0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# cite data source\n",
+    "# data source citation\n",
     "# \"\"\"A. D. Kent, \"Cybersecurity Data Sources for Dynamic Network Research,\"\n",
     "# in Dynamic Networks in Cybersecurity, 2015.\n",
     "\n",
@@ -125,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "efe68cf8",
    "metadata": {},
    "outputs": [
@@ -267,7 +264,7 @@
        "28495743  0.0  C574 C523 Kerberos Network    C574 C523  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -280,7 +277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "03610297",
    "metadata": {},
    "outputs": [
@@ -298,23 +295,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "66c5126e",
    "metadata": {},
    "outputs": [],
    "source": [
     "# here are the post-facto red team events\n",
-    "red_team = pd.read_csv('https://gist.githubusercontent.com/silkspace/5cf5a94b9ac4b4ffe38904f20d93edb1/raw/888dabd86f88ea747cf9ff5f6c44725e21536465/redteam_labels.csv', index_col=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "7b31d2b0-b123-4f7c-9157-03accce6a6c7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for later\n",
+    "red_team = pd.read_csv('https://gist.githubusercontent.com/silkspace/5cf5a94b9ac4b4ffe38904f20d93edb1/raw/888dabd86f88ea747cf9ff5f6c44725e21536465/redteam_labels.csv', index_col=0)\n",
     "red_team['feats2'] = red_team.feats"
    ]
   },
@@ -358,14 +345,6 @@
     "print(ndf.shape)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b1c15b72-a355-48d5-9a4e-31dbb6a47b06",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "32d1755d",
@@ -717,54 +696,30 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# some enrichments\n",
-    "def pagerank(g):\n",
-    "    from sknetwork.ranking import PageRank\n",
-    "    adj = g._weighted_adjacency\n",
-    "    pagerank = PageRank()\n",
-    "    ranks = pagerank.fit_transform(adj)\n",
-    "    g._nodes['pagerank'] = ranks\n",
-    "    return g\n",
-    "\n",
-    "def cluster(g):\n",
-    "    \"\"\"\n",
-    "        Fits clustering on UMAP embeddings\n",
-    "    \"\"\"\n",
-    "    dbscan = DBSCAN()\n",
-    "    labels = dbscan.fit_predict(g._node_embedding)\n",
-    "    g._nodes['cluster'] = labels\n",
-    "    cnt = Counter(labels)\n",
-    "    return g, dbscan, cnt\n",
-    "\n",
-    "def get_confidences_per_cluster(g, cnt):\n",
+    "def get_confidences_per_cluster(g, col='RED', verbose=False):\n",
     "    \"\"\"\n",
     "        From DBSCAN clusters, will assess how many Red Team events exist,\n",
     "        assessing confidence.\n",
+    "        \n",
     "    \"\"\"\n",
     "    resses = []\n",
     "    df = g._nodes\n",
+    "    labels = df._dbscan\n",
+    "    cnt = Counter(labels)\n",
     "    for clust, count in cnt.most_common():\n",
-    "        res = df[df.cluster==clust]\n",
+    "        res = df[df._dbscan==clust]\n",
     "        n = res.shape[0]\n",
-    "        n_reds = res.RED.sum()\n",
+    "        n_reds = res[col].sum()\n",
     "        resses.append([clust, n_reds/n, n_reds, n])\n",
-    "        if n_reds>0:\n",
+    "        if n_reds>0 and verbose:\n",
     "            print('-'*20)\n",
     "            print(f'cluster: {clust}\\n   red {100*n_reds/n:.2f}% or {n_reds} out of {count}')\n",
-    "    conf_dict = {k[0]:k[1] for k in resses}\n",
-    "    confidence = [conf_dict[k] for k in df.cluster.values]\n",
+    "    conf_dict = {k[0]: k[1] for k in resses}\n",
+    "    confidence = [conf_dict[k] for k in df._dbscan.values]\n",
     "    g._nodes['confidence'] = confidence\n",
-    "    return g, pd.DataFrame(resses, columns=['cluster', 'confidence', 'n_red', 'total_in_cluster'])\n",
-    "\n",
-    "\n",
-    "def enrich(g):\n",
-    "    \"\"\"\n",
-    "        Full Pipeline \n",
-    "    \"\"\"\n",
-    "    g = pagerank(g)\n",
-    "    g, dbscan, cnt = cluster(g)\n",
-    "    g, cluster_confidences = get_confidences_per_cluster(g, cnt)\n",
-    "    return g, dbscan, cluster_confidences\n",
+    "    conf_df = pd.DataFrame(resses, columns=['_dbscan', 'confidence', 'n_red', 'total_in_cluster'])\n",
+    "    conf_df = conf_df.sort_values(by='confidence', ascending=False)\n",
+    "    return g, conf_df\n",
     "    "
    ]
   },
@@ -800,7 +755,7 @@
     {
      "data": {
       "text/plain": [
-       "{'kind': 'nodes', 'use_scaler': None, 'use_scaler_target': None, 'cardinality_threshold': 2, 'cardinality_threshold_target': 2, 'n_topics': 32, 'n_topics_target': 10, 'multilabel': False, 'embedding': False, 'use_ngrams': False, 'ngram_range': (1, 3), 'max_df': 0.2, 'min_df': 3, 'min_words': 40000000.0, 'model_name': 'sentence-transformers/msmarco-distilbert-base-v2', 'impute': 'median', 'n_quantiles': 100, 'output_distribution': 'normal', 'quantile_range': (25, 75), 'n_bins': 10, 'encode': 'ordinal', 'strategy': 'uniform', 'similarity': None, 'categories': 'auto', 'keep_n_decimals': 5, 'remove_node_column': True, 'inplace': False, 'feature_engine': 'auto', 'memoize': True, 'X': ['feats']}"
+       "{'cardinality_threshold': 2, 'cardinality_threshold_target': 2, 'n_topics': 32, 'n_topics_target': 10, 'min_words': 1000000000.0, 'X': ['feats']}"
       ]
      },
      "execution_count": 16,
@@ -827,7 +782,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "* Ignoring target column of shape (19762, 0) in UMAP fit, as it is not one dimensionalOMP: Info #273: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+      "* Ignoring target column of shape (19762, 0) in UMAP fit, as it is not one dimensionalOMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
      ]
     },
     {
@@ -835,91 +790,127 @@
      "output_type": "stream",
      "text": [
       "--------------------\n",
-      "cluster: 0\n",
-      "   red 2.59% or 95.0 out of 3665\n",
+      "cluster: 3\n",
+      "   red 0.66% or 22.0 out of 3331\n",
       "--------------------\n",
-      "cluster: 27\n",
+      "cluster: 39\n",
       "   red 0.41% or 3.0 out of 724\n",
       "--------------------\n",
-      "cluster: 26\n",
+      "cluster: 9\n",
+      "   red 1.15% or 3.0 out of 260\n",
+      "--------------------\n",
+      "cluster: 38\n",
       "   red 0.38% or 1.0 out of 260\n",
       "--------------------\n",
-      "cluster: 10\n",
+      "cluster: 13\n",
       "   red 0.43% or 1.0 out of 234\n",
       "--------------------\n",
-      "cluster: 1\n",
-      "   red 94.44% or 119.0 out of 126\n",
-      "--------------------\n",
-      "cluster: 6\n",
+      "cluster: 8\n",
       "   red 95.06% or 77.0 out of 81\n",
       "--------------------\n",
-      "cluster: 9\n",
-      "   red 84.42% or 65.0 out of 77\n",
-      "--------------------\n",
-      "cluster: 5\n",
-      "   red 96.61% or 57.0 out of 59\n",
+      "cluster: 1\n",
+      "   red 100.00% or 53.0 out of 53\n",
       "--------------------\n",
-      "cluster: 7\n",
+      "cluster: 10\n",
       "   red 91.84% or 45.0 out of 49\n",
       "--------------------\n",
-      "cluster: 14\n",
+      "cluster: 12\n",
+      "   red 82.61% or 38.0 out of 46\n",
+      "--------------------\n",
+      "cluster: 22\n",
+      "   red 95.65% or 44.0 out of 46\n",
+      "--------------------\n",
+      "cluster: 18\n",
       "   red 92.11% or 35.0 out of 38\n",
       "--------------------\n",
-      "cluster: 3\n",
-      "   red 94.59% or 35.0 out of 37\n",
+      "cluster: 19\n",
+      "   red 82.86% or 29.0 out of 35\n",
       "--------------------\n",
-      "cluster: 22\n",
+      "cluster: 15\n",
+      "   red 86.67% or 26.0 out of 30\n",
+      "--------------------\n",
+      "cluster: 27\n",
+      "   red 92.59% or 25.0 out of 27\n",
+      "--------------------\n",
+      "cluster: 32\n",
       "   red 100.00% or 27.0 out of 27\n",
       "--------------------\n",
-      "cluster: 4\n",
+      "cluster: 28\n",
+      "   red 100.00% or 26.0 out of 26\n",
+      "--------------------\n",
+      "cluster: 6\n",
       "   red 84.00% or 21.0 out of 25\n",
       "--------------------\n",
-      "cluster: 23\n",
+      "cluster: 2\n",
+      "   red 87.50% or 21.0 out of 24\n",
+      "--------------------\n",
+      "cluster: 35\n",
       "   red 100.00% or 24.0 out of 24\n",
       "--------------------\n",
-      "cluster: 8\n",
+      "cluster: 0\n",
       "   red 100.00% or 23.0 out of 23\n",
       "--------------------\n",
-      "cluster: 20\n",
+      "cluster: 11\n",
+      "   red 100.00% or 23.0 out of 23\n",
+      "--------------------\n",
+      "cluster: 30\n",
       "   red 81.25% or 13.0 out of 16\n",
       "--------------------\n",
-      "cluster: 13\n",
+      "cluster: 17\n",
       "   red 93.33% or 14.0 out of 15\n",
       "--------------------\n",
-      "cluster: 16\n",
+      "cluster: 21\n",
       "   red 100.00% or 15.0 out of 15\n",
       "--------------------\n",
-      "cluster: 2\n",
+      "cluster: 23\n",
+      "   red 100.00% or 15.0 out of 15\n",
+      "--------------------\n",
+      "cluster: 4\n",
       "   red 100.00% or 14.0 out of 14\n",
       "--------------------\n",
-      "cluster: 25\n",
+      "cluster: 29\n",
+      "   red 100.00% or 14.0 out of 14\n",
+      "--------------------\n",
+      "cluster: 7\n",
       "   red 100.00% or 13.0 out of 13\n",
       "--------------------\n",
-      "cluster: 11\n",
+      "cluster: 37\n",
+      "   red 100.00% or 13.0 out of 13\n",
+      "--------------------\n",
+      "cluster: 14\n",
       "   red 100.00% or 11.0 out of 11\n",
       "--------------------\n",
-      "cluster: 18\n",
+      "cluster: 5\n",
+      "   red 100.00% or 10.0 out of 10\n",
+      "--------------------\n",
+      "cluster: 25\n",
       "   red 100.00% or 9.0 out of 9\n",
       "--------------------\n",
-      "cluster: 15\n",
+      "cluster: 33\n",
+      "   red 88.89% or 8.0 out of 9\n",
+      "--------------------\n",
+      "cluster: 20\n",
       "   red 100.00% or 6.0 out of 6\n",
       "--------------------\n",
-      "cluster: 24\n",
+      "cluster: 36\n",
       "   red 100.00% or 6.0 out of 6\n",
       "--------------------\n",
-      "cluster: 12\n",
+      "cluster: 16\n",
       "   red 100.00% or 5.0 out of 5\n",
       "--------------------\n",
-      "cluster: 17\n",
+      "cluster: 24\n",
       "   red 100.00% or 5.0 out of 5\n",
       "--------------------\n",
-      "cluster: 19\n",
+      "cluster: 26\n",
       "   red 100.00% or 5.0 out of 5\n",
       "--------------------\n",
-      "cluster: 21\n",
+      "cluster: 31\n",
       "   red 100.00% or 5.0 out of 5\n",
-      "CPU times: user 3min 16s, sys: 32 s, total: 3min 48s\n",
-      "Wall time: 1min 57s\n"
+      "--------------------\n",
+      "cluster: 34\n",
+      "   red 100.00% or 1.0 out of 1\n",
+      "CPU times: user 3min 40s, sys: 39.2 s, total: 4min 19s\n",
+      "Wall time: 2min 7s\n"
      ]
     }
    ],
@@ -929,16 +920,15 @@
     "if process:\n",
     "    # ##################################\n",
     "    g = graphistry.nodes(tdf, 'node')  # two lines does the heavy lifting\n",
-    "    g5 = g.umap(**cyber_model)\n",
+    "    g5 = g.umap(**cyber_model).dbscan(min_dist=0.2)\n",
     "    # #########################\n",
     "    \n",
-    "    g5, dbscan, cluster_confidences = enrich(g5)\n",
-    "\n",
+    "    g5, cluster_confidences = get_confidences_per_cluster(g5, verbose=True)\n",
     "    g5.save_search_instance('auth-feat-topic.search')\n",
     "else:\n",
     "    g = graphistry.bind()\n",
     "    g5 = g.load_search_instance('auth-feat-topic.search')\n",
-    "    g5, dbscan, cluster_confidences = enrich(g5)\n"
+    "    g5, cluster_confidences = get_confidences_per_cluster(g5)"
    ]
   },
   {
@@ -958,8 +948,25 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"4c5a28f9-d525-490b-9d7a-216ba63590ed\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=d4ac3b5b9916432ca0b62973d587cfef&type=arrow&viztoken=9394d462-ab46-47c7-b5d6-458755f64975&usertag=8a6d667e-pygraphistry-0.28.6+132.gf997752.dirty&splashAfter=1674267572&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#4c5a28f9-d525-490b-9d7a-216ba63590ed\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=77ee7b6a4daa4539a93f0c60e34934c0&type=arrow&viztoken=4d612b44-1558-4398-8964-744cf5c9c632&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672345808&info=true&play=0'"
+       "<IPython.core.display.HTML object>"
       ]
      },
      "execution_count": 18,
@@ -1462,12 +1469,46 @@
     "X"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "87b32e09-3ca4-49de-b8c3-2b40ffa2b01d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = g5.get_matrix(['interactive', 'c17'])\n",
+    "x.plot()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "632d6d0f-8212-4f4a-a920-7600d7456351",
    "metadata": {},
    "source": [
-    "## Predict/Online Mode\n",
+    "## Predict | Online Mode\n",
     "\n",
     "Once a model is fit, predict on new batches as we demonstrate here\n",
     "\n",
@@ -1485,20 +1526,12 @@
    "execution_count": 20,
    "id": "7b44d418",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "'SuperVectorizer' object has no attribute 'get_feature_names_in''SuperVectorizer' object has no attribute 'get_feature_names_in'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# first sample a batch from the normal data (auth=df)\n",
-    "emb_normal, xp_normal, _ = g5.transform_umap(df.sample(200), None, kind='nodes')\n",
+    "emb_normal, xp_normal, _ = g5.transform_umap(df.sample(200), None, kind='nodes', return_graph=False)\n",
     "# then transform all the red team data\n",
-    "emb_red, xp_red, _ = g5.transform_umap(red_team, None, kind='nodes')"
+    "emb_red, xp_red, _ = g5.transform_umap(red_team, None, kind='nodes', return_graph=False)"
    ]
   },
   {
@@ -1629,7 +1662,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x187b44730>"
+       "<matplotlib.collections.PathCollection at 0x1819f25e0>"
       ]
      },
      "execution_count": 22,
@@ -1658,6 +1691,16 @@
     "plt.scatter(emb_normal.x, emb_normal.y, c='g') # batch of new data, to see if they occlude "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "f9f98708-f18f-4248-96fb-498a4becad89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#g5.transform_umap(df.sample(200).append(red_team)).plot()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b53dd8ed-39b2-4000-9ec7-139d1e2a6a85",
@@ -1672,7 +1715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "14d207db-9a58-45a3-9876-058632389f17",
    "metadata": {},
    "outputs": [
@@ -1680,7 +1723,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "93.92%\n"
+      "94.11%\n"
      ]
     }
    ],
@@ -1692,17 +1735,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "755a3f27-935d-4ba8-96cb-cbff11fdf00e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "19071"
+       "18998"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1714,7 +1757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "5fd1cc50-0900-4694-8400-c426e314ec2e",
    "metadata": {},
    "outputs": [
@@ -1722,7 +1765,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Alert Reduction 96.50%\n"
+      "Alert Reduction 96.13%\n"
      ]
     }
    ],
@@ -1733,7 +1776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "id": "0ee508a5",
    "metadata": {},
    "outputs": [
@@ -1746,7 +1789,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x504 with 1 Axes>"
       ]
@@ -1779,7 +1822,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "id": "e0c6a16d-a899-43b6-a7ba-75b45f855a78",
    "metadata": {},
    "outputs": [
@@ -1787,92 +1830,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "--------------------\n",
-      "cluster: 2\n",
-      "   red 99.63% or 267.0 out of 268\n",
-      "--------------------\n",
-      "cluster: 6\n",
-      "   red 100.00% or 58.0 out of 58\n",
-      "--------------------\n",
-      "cluster: 4\n",
-      "   red 97.22% or 35.0 out of 36\n",
-      "--------------------\n",
-      "cluster: 8\n",
-      "   red 97.14% or 34.0 out of 35\n",
-      "--------------------\n",
-      "cluster: 16\n",
-      "   red 100.00% or 34.0 out of 34\n",
-      "--------------------\n",
-      "cluster: 11\n",
-      "   red 100.00% or 31.0 out of 31\n",
-      "--------------------\n",
-      "cluster: 24\n",
-      "   red 100.00% or 27.0 out of 27\n",
-      "--------------------\n",
-      "cluster: 25\n",
-      "   red 100.00% or 24.0 out of 24\n",
-      "--------------------\n",
-      "cluster: 3\n",
-      "   red 100.00% or 19.0 out of 19\n",
-      "--------------------\n",
-      "cluster: 7\n",
-      "   red 100.00% or 18.0 out of 18\n",
-      "--------------------\n",
-      "cluster: 17\n",
-      "   red 100.00% or 18.0 out of 18\n",
-      "--------------------\n",
-      "cluster: 0\n",
-      "   red 100.00% or 17.0 out of 17\n",
-      "--------------------\n",
-      "cluster: 12\n",
-      "   red 100.00% or 17.0 out of 17\n",
-      "--------------------\n",
-      "cluster: 18\n",
-      "   red 94.12% or 16.0 out of 17\n",
-      "--------------------\n",
-      "cluster: 26\n",
-      "   red 100.00% or 17.0 out of 17\n",
-      "--------------------\n",
-      "cluster: 14\n",
-      "   red 100.00% or 15.0 out of 15\n",
-      "--------------------\n",
-      "cluster: 5\n",
-      "   red 100.00% or 14.0 out of 14\n",
-      "--------------------\n",
-      "cluster: 10\n",
-      "   red 100.00% or 14.0 out of 14\n",
-      "--------------------\n",
-      "cluster: 1\n",
-      "   red 100.00% or 13.0 out of 13\n",
-      "--------------------\n",
-      "cluster: 13\n",
-      "   red 100.00% or 9.0 out of 9\n",
-      "--------------------\n",
-      "cluster: 19\n",
-      "   red 100.00% or 9.0 out of 9\n",
-      "--------------------\n",
-      "cluster: 15\n",
-      "   red 100.00% or 8.0 out of 8\n",
-      "--------------------\n",
-      "cluster: 21\n",
-      "   red 100.00% or 8.0 out of 8\n",
-      "--------------------\n",
-      "cluster: 23\n",
-      "   red 87.50% or 7.0 out of 8\n",
-      "--------------------\n",
-      "cluster: -1\n",
-      "   red 71.43% or 5.0 out of 7\n",
-      "--------------------\n",
-      "cluster: 9\n",
-      "   red 100.00% or 5.0 out of 5\n",
-      "--------------------\n",
-      "cluster: 20\n",
-      "   red 100.00% or 5.0 out of 5\n",
-      "--------------------\n",
-      "cluster: 22\n",
-      "   red 100.00% or 5.0 out of 5\n",
-      "CPU times: user 2min 56s, sys: 33.1 s, total: 3min 29s\n",
-      "Wall time: 1min 24s\n"
+      "CPU times: user 3min 14s, sys: 38.6 s, total: 3min 52s\n",
+      "Wall time: 1min 34s\n"
      ]
     }
    ],
@@ -1886,22 +1845,22 @@
     "                min_words=100000, # set high to bypass sbert encoding\n",
     "                cardinality_threshold=2, # set low to force topic modeling\n",
     "                n_topics=32,\n",
-    "                use_scaler_target=None) # keep labels unscaled\n",
+    "                use_scaler_target=None,  # keep labels unscaled\n",
+    "                dbscan=True)  # add dbscan here\n",
     "    # ##################################\n",
     "    \n",
-    "    g6, dbscan6, cluster_confidences6  = enrich(g6)\n",
-    "    \n",
+    "    g6, cluster_confidences6  = get_confidences_per_cluster(g6)\n",
     "    g6.save_search_instance('auth-feat-supervised-topic.search')\n",
     "else:\n",
     "    g = graphistry.bind()\n",
     "    g6 = g.load_search_instance('auth-feat-supervised-topic.search')\n",
-    "    \n",
-    "    g6, dbscan6, cluster_confidences6  = enrich(g6)\n"
+    "    g6, cluster_confidences6  = get_confidences_per_cluster(g6)\n",
+    "    "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "id": "a98ef657-5307-41d9-ae31-79c1794b3728",
    "metadata": {},
    "outputs": [
@@ -1996,13 +1955,13 @@
        "[19762 rows x 1 columns]"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "g6._node_target.astype(int)"
+    "g6.get_matrix(target=True).astype(int)"
    ]
   },
   {
@@ -2013,22 +1972,39 @@
    },
    "source": [
     "### Plot\n",
-    "Color by `confidence` and hover over `red` team histogram to see where events occur. Alternatively, color by `cluster` assignment"
+    "Color by `confidence` and hover over `red` team histogram to see where events occur. Alternatively, color by `_dbscan` assignment"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "id": "16e09a7d",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"49d22432-d076-41b8-849e-57e30a21bd11\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=f5595a60c43e453bae8acee0787236f8&type=arrow&viztoken=52e094f5-e046-4fb7-a1fc-1c654915be20&usertag=8a6d667e-pygraphistry-0.28.6+132.gf997752.dirty&splashAfter=1674267679&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#49d22432-d076-41b8-849e-57e30a21bd11\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=426dd5f70ceb45f9bb8e8b8ac45a85ac&type=arrow&viztoken=bfeae91e-2f9e-4e1c-90a4-c968aed1a68e&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672345903&info=true&play=0'"
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2048,7 +2024,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
+   "id": "1731ae44-57e0-4c3e-bad0-ac486bba589c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        C17693 C1003\n",
+       "1         C17693 C305\n",
+       "2         C17693 C728\n",
+       "3        C17693 C1173\n",
+       "4         C17693 C294\n",
+       "             ...     \n",
+       "19008     C11843 C528\n",
+       "19009      C8470 C528\n",
+       "19010       C716 C716\n",
+       "19011     C16126 C586\n",
+       "19012     C6215 C6215\n",
+       "Name: feats2, Length: 19762, dtype: object"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tdf['feats2']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
    "id": "099b9d38",
    "metadata": {},
    "outputs": [
@@ -2063,104 +2071,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "--------------------\n",
-      "cluster: 2\n",
-      "   red 0.37% or 48.0 out of 12839\n",
-      "--------------------\n",
-      "cluster: 4\n",
-      "   red 0.72% or 16.0 out of 2222\n",
-      "--------------------\n",
-      "cluster: 30\n",
-      "   red 0.21% or 3.0 out of 1435\n",
-      "--------------------\n",
-      "cluster: 3\n",
-      "   red 98.61% or 71.0 out of 72\n",
-      "--------------------\n",
-      "cluster: 10\n",
-      "   red 100.00% or 51.0 out of 51\n",
-      "--------------------\n",
-      "cluster: 11\n",
-      "   red 98.00% or 49.0 out of 50\n",
-      "--------------------\n",
-      "cluster: 14\n",
-      "   red 97.67% or 42.0 out of 43\n",
-      "--------------------\n",
-      "cluster: 6\n",
-      "   red 97.50% or 39.0 out of 40\n",
-      "--------------------\n",
-      "cluster: 12\n",
-      "   red 97.44% or 38.0 out of 39\n",
-      "--------------------\n",
-      "cluster: 9\n",
-      "   red 100.00% or 36.0 out of 36\n",
-      "--------------------\n",
-      "cluster: 0\n",
-      "   red 100.00% or 33.0 out of 33\n",
-      "--------------------\n",
-      "cluster: 1\n",
-      "   red 96.88% or 31.0 out of 32\n",
-      "--------------------\n",
-      "cluster: 18\n",
-      "   red 100.00% or 30.0 out of 30\n",
-      "--------------------\n",
-      "cluster: 8\n",
-      "   red 96.55% or 28.0 out of 29\n",
-      "--------------------\n",
-      "cluster: 20\n",
-      "   red 96.43% or 27.0 out of 28\n",
-      "--------------------\n",
-      "cluster: 26\n",
-      "   red 100.00% or 27.0 out of 27\n",
-      "--------------------\n",
-      "cluster: 29\n",
-      "   red 96.00% or 24.0 out of 25\n",
-      "--------------------\n",
-      "cluster: 19\n",
-      "   red 90.00% or 18.0 out of 20\n",
-      "--------------------\n",
-      "cluster: 5\n",
-      "   red 100.00% or 17.0 out of 17\n",
-      "--------------------\n",
-      "cluster: 21\n",
-      "   red 100.00% or 17.0 out of 17\n",
-      "--------------------\n",
-      "cluster: 25\n",
-      "   red 100.00% or 13.0 out of 13\n",
-      "--------------------\n",
-      "cluster: 24\n",
-      "   red 100.00% or 11.0 out of 11\n",
-      "--------------------\n",
-      "cluster: 16\n",
-      "   red 100.00% or 10.0 out of 10\n",
-      "--------------------\n",
-      "cluster: 23\n",
-      "   red 100.00% or 10.0 out of 10\n",
-      "--------------------\n",
-      "cluster: 7\n",
-      "   red 100.00% or 9.0 out of 9\n",
-      "--------------------\n",
-      "cluster: 13\n",
-      "   red 100.00% or 9.0 out of 9\n",
-      "--------------------\n",
-      "cluster: 15\n",
-      "   red 88.89% or 8.0 out of 9\n",
-      "--------------------\n",
-      "cluster: 17\n",
-      "   red 87.50% or 7.0 out of 8\n",
-      "--------------------\n",
-      "cluster: 27\n",
-      "   red 100.00% or 8.0 out of 8\n",
-      "--------------------\n",
-      "cluster: 28\n",
-      "   red 100.00% or 8.0 out of 8\n",
-      "--------------------\n",
-      "cluster: 31\n",
-      "   red 85.71% or 6.0 out of 7\n",
-      "--------------------\n",
-      "cluster: 22\n",
-      "   red 100.00% or 5.0 out of 5\n",
-      "CPU times: user 2min 39s, sys: 31.4 s, total: 3min 10s\n",
-      "Wall time: 1min 14s\n"
+      "CPU times: user 3min 5s, sys: 38.7 s, total: 3min 44s\n",
+      "Wall time: 1min 35s\n"
      ]
     }
    ],
@@ -2174,14 +2086,15 @@
     "                min_words=100000, \n",
     "                cardinality_threshold=2, \n",
     "                n_topics=32,\n",
-    "                use_scaler_target=None)\n",
+    "                use_scaler=None,\n",
+    "                use_scaler_target=None, \n",
+    "                dbscan=True)  # add dbscan here\n",
     "    # ###################################\n",
-    "    g7, dbscan7, cluster_confidences7  = enrich(g7)\n",
-    "    g7.build_index()\n",
+    "    g7, cluster_confidences7  = get_confidences_per_cluster(g7)\n",
     "    g7.save_search_instance('auth-just-ip-topic.search')\n",
     "else:\n",
     "    g7 = graphistry.bind().load_search_instance('auth-just-ip-topic.search')\n",
-    "    g7, dbscan7, cluster_confidences7  = enrich(g7)\n"
+    "    g7, cluster_confidences7  = get_confidences_per_cluster(g7)\n"
    ]
   },
   {
@@ -2197,28 +2110,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 33,
    "id": "c1e586a3",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"706035a0-ea0b-46ca-9c7e-e9dac5cc57f3\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=7befa6dc341c47b3a546be171ad384a9&type=arrow&viztoken=deb6ce2d-6a97-4538-9b96-c90272b9f0e9&usertag=8a6d667e-pygraphistry-0.28.6+132.gf997752.dirty&splashAfter=1674267779&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#706035a0-ea0b-46ca-9c7e-e9dac5cc57f3\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=b85fc0d43e884508ad22d4a1e5daa03b&type=arrow&viztoken=75f5f3cc-b52a-4488-b0eb-9b9941208629&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672345982&info=true&play=0'"
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "g7.name('auth topic ips-ips only, no supervision').plot(render=RENDER)"
+    "g7.name('auth topic ips-ips only, no supervision').plot(render=RENDER)\n",
+    "# very similar to graph with metadata included, showing that ip-ip is strong indicator of phenomenon"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 34,
    "id": "5f93d747",
    "metadata": {},
    "outputs": [
@@ -2243,149 +2174,149 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>feats2: c10555, c8555, c1055</th>\n",
-       "      <th>feats2: c12222, c12226, c1227</th>\n",
-       "      <th>feats2: c6665, c6667, c6653</th>\n",
-       "      <th>feats2: c7703, c7701, c7707</th>\n",
-       "      <th>feats2: c1992, c1922, c19932</th>\n",
-       "      <th>feats2: c625, c612, c6125</th>\n",
-       "      <th>feats2: c9028, c9904, c9283</th>\n",
-       "      <th>feats2: c3073, c3037, c3074</th>\n",
-       "      <th>feats2: c2106, c2626, c4210</th>\n",
-       "      <th>feats2: c11196, c11918, c1111</th>\n",
+       "      <th>feats2: c586, c585, c5864</th>\n",
+       "      <th>feats2: c1961, c10196, c1901</th>\n",
+       "      <th>feats2: c16916, c16169, c1616</th>\n",
+       "      <th>feats2: c3636, c6363, c6365</th>\n",
+       "      <th>feats2: c4944, c4444, c8444</th>\n",
+       "      <th>feats2: c1065, c10652, c10585</th>\n",
+       "      <th>feats2: c15556, c15550, c1555</th>\n",
+       "      <th>feats2: c5999, c10999, c599</th>\n",
+       "      <th>feats2: c17693, c6937, c3937</th>\n",
+       "      <th>feats2: c8882, c8880, c8889</th>\n",
        "      <th>...</th>\n",
-       "      <th>feats2: c4448, c4444, c4487</th>\n",
-       "      <th>feats2: c17981, c2980, c1798</th>\n",
-       "      <th>feats2: c4777, c14777, c4787</th>\n",
-       "      <th>feats2: c7554, c7519, c5151</th>\n",
-       "      <th>feats2: c10000, c10008, c10003</th>\n",
-       "      <th>feats2: c25240, c2524, c2456</th>\n",
-       "      <th>feats2: c809, c5099, c5809</th>\n",
-       "      <th>feats2: c1065, c10658, c10656</th>\n",
-       "      <th>feats2: c1550, c15034, c15615</th>\n",
-       "      <th>feats2: c8182, c8882, c8889</th>\n",
+       "      <th>feats2: c2890, c280, tgt</th>\n",
+       "      <th>feats2: c3333, c3303, c3033</th>\n",
+       "      <th>feats2: c11187, c1118, c1111</th>\n",
+       "      <th>feats2: c1798, c1772, c1778</th>\n",
+       "      <th>feats2: c3435, c3434, c3597</th>\n",
+       "      <th>feats2: c2106, c210, c10000</th>\n",
+       "      <th>feats2: c1085, c1080, c1081</th>\n",
+       "      <th>feats2: c457, c222, c452</th>\n",
+       "      <th>feats2: c1268, c1226, c12689</th>\n",
+       "      <th>feats2: c6604, c16604, c16048</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-0.34853</td>\n",
-       "      <td>-0.29446</td>\n",
-       "      <td>-0.34437</td>\n",
-       "      <td>-0.31798</td>\n",
-       "      <td>-0.34102</td>\n",
-       "      <td>-0.46182</td>\n",
-       "      <td>-0.33386</td>\n",
-       "      <td>-0.32362</td>\n",
-       "      <td>-0.31948</td>\n",
-       "      <td>-0.32044</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.36251</td>\n",
-       "      <td>-0.36984</td>\n",
-       "      <td>-0.32987</td>\n",
-       "      <td>-0.28672</td>\n",
-       "      <td>3.88640</td>\n",
-       "      <td>-0.36522</td>\n",
-       "      <td>-0.34670</td>\n",
-       "      <td>-0.15082</td>\n",
-       "      <td>-0.38757</td>\n",
-       "      <td>-0.32410</td>\n",
+       "      <td>0.051269</td>\n",
+       "      <td>0.052370</td>\n",
+       "      <td>0.059563</td>\n",
+       "      <td>0.053012</td>\n",
+       "      <td>0.051056</td>\n",
+       "      <td>0.051073</td>\n",
+       "      <td>0.059119</td>\n",
+       "      <td>0.052026</td>\n",
+       "      <td>7.893405</td>\n",
+       "      <td>0.051389</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.101370</td>\n",
+       "      <td>0.059551</td>\n",
+       "      <td>1.219262</td>\n",
+       "      <td>0.051273</td>\n",
+       "      <td>2.699579</td>\n",
+       "      <td>3.244251</td>\n",
+       "      <td>0.051143</td>\n",
+       "      <td>0.059730</td>\n",
+       "      <td>0.051639</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.09896</td>\n",
-       "      <td>-0.29610</td>\n",
-       "      <td>-0.34437</td>\n",
-       "      <td>-0.31795</td>\n",
-       "      <td>-0.34092</td>\n",
-       "      <td>-0.46180</td>\n",
-       "      <td>-0.33383</td>\n",
-       "      <td>2.52748</td>\n",
-       "      <td>-0.32073</td>\n",
-       "      <td>-0.32158</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.36238</td>\n",
-       "      <td>-0.36982</td>\n",
-       "      <td>-0.32984</td>\n",
-       "      <td>-0.28669</td>\n",
-       "      <td>-0.29198</td>\n",
-       "      <td>-0.36519</td>\n",
-       "      <td>-0.34666</td>\n",
-       "      <td>-0.32204</td>\n",
-       "      <td>-0.38862</td>\n",
-       "      <td>-0.32407</td>\n",
+       "      <td>0.051271</td>\n",
+       "      <td>0.053484</td>\n",
+       "      <td>0.066529</td>\n",
+       "      <td>0.053060</td>\n",
+       "      <td>0.051057</td>\n",
+       "      <td>0.561800</td>\n",
+       "      <td>0.067701</td>\n",
+       "      <td>0.052046</td>\n",
+       "      <td>7.851341</td>\n",
+       "      <td>0.051392</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>2.741899</td>\n",
+       "      <td>0.068965</td>\n",
+       "      <td>1.368653</td>\n",
+       "      <td>1.024759</td>\n",
+       "      <td>0.051145</td>\n",
+       "      <td>0.108473</td>\n",
+       "      <td>0.051145</td>\n",
+       "      <td>0.070319</td>\n",
+       "      <td>0.052000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.34853</td>\n",
-       "      <td>-0.29417</td>\n",
-       "      <td>-0.34437</td>\n",
-       "      <td>3.43709</td>\n",
-       "      <td>-0.33256</td>\n",
-       "      <td>-0.46182</td>\n",
-       "      <td>-0.00576</td>\n",
-       "      <td>-0.32411</td>\n",
-       "      <td>-0.32076</td>\n",
-       "      <td>-0.32092</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.36235</td>\n",
-       "      <td>-0.36985</td>\n",
-       "      <td>-0.32988</td>\n",
-       "      <td>-0.28673</td>\n",
-       "      <td>-0.29119</td>\n",
-       "      <td>-0.36523</td>\n",
-       "      <td>-0.34668</td>\n",
-       "      <td>-0.32147</td>\n",
-       "      <td>-0.38820</td>\n",
-       "      <td>-0.32411</td>\n",
+       "      <td>0.051264</td>\n",
+       "      <td>0.053257</td>\n",
+       "      <td>0.064578</td>\n",
+       "      <td>0.053130</td>\n",
+       "      <td>0.051051</td>\n",
+       "      <td>0.051067</td>\n",
+       "      <td>0.065562</td>\n",
+       "      <td>0.052079</td>\n",
+       "      <td>7.391063</td>\n",
+       "      <td>0.051386</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.063343</td>\n",
+       "      <td>0.051320</td>\n",
+       "      <td>0.066675</td>\n",
+       "      <td>1.671434</td>\n",
+       "      <td>0.051267</td>\n",
+       "      <td>0.051138</td>\n",
+       "      <td>0.097529</td>\n",
+       "      <td>0.051139</td>\n",
+       "      <td>0.067757</td>\n",
+       "      <td>0.051922</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-0.34854</td>\n",
-       "      <td>-0.29143</td>\n",
-       "      <td>-0.34437</td>\n",
-       "      <td>-0.31800</td>\n",
-       "      <td>-0.34095</td>\n",
-       "      <td>-0.46183</td>\n",
-       "      <td>-0.33388</td>\n",
-       "      <td>0.60698</td>\n",
-       "      <td>-0.32077</td>\n",
-       "      <td>2.87353</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.36240</td>\n",
-       "      <td>-0.36985</td>\n",
-       "      <td>-0.32989</td>\n",
-       "      <td>-0.28674</td>\n",
-       "      <td>-0.28688</td>\n",
-       "      <td>-0.36524</td>\n",
-       "      <td>-0.34671</td>\n",
-       "      <td>-0.29361</td>\n",
-       "      <td>-0.38584</td>\n",
-       "      <td>-0.32412</td>\n",
+       "      <td>0.051251</td>\n",
+       "      <td>0.052477</td>\n",
+       "      <td>0.065590</td>\n",
+       "      <td>0.052994</td>\n",
+       "      <td>0.051041</td>\n",
+       "      <td>0.051057</td>\n",
+       "      <td>0.063403</td>\n",
+       "      <td>0.052009</td>\n",
+       "      <td>7.892231</td>\n",
+       "      <td>0.051369</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.051307</td>\n",
+       "      <td>3.263992</td>\n",
+       "      <td>3.747229</td>\n",
+       "      <td>0.053271</td>\n",
+       "      <td>0.051127</td>\n",
+       "      <td>0.192025</td>\n",
+       "      <td>0.051127</td>\n",
+       "      <td>0.063663</td>\n",
+       "      <td>0.051661</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-0.34850</td>\n",
-       "      <td>-0.27595</td>\n",
-       "      <td>-0.34437</td>\n",
-       "      <td>-0.31795</td>\n",
-       "      <td>-0.34093</td>\n",
-       "      <td>-0.46180</td>\n",
-       "      <td>-0.32521</td>\n",
-       "      <td>-0.32408</td>\n",
-       "      <td>0.41376</td>\n",
-       "      <td>-0.32181</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.36239</td>\n",
-       "      <td>0.51644</td>\n",
-       "      <td>-0.32984</td>\n",
-       "      <td>-0.28668</td>\n",
-       "      <td>-0.29225</td>\n",
-       "      <td>-0.36184</td>\n",
-       "      <td>-0.34668</td>\n",
-       "      <td>-0.32224</td>\n",
-       "      <td>0.57418</td>\n",
-       "      <td>-0.32407</td>\n",
+       "      <td>0.051314</td>\n",
+       "      <td>0.053518</td>\n",
+       "      <td>0.066485</td>\n",
+       "      <td>0.053112</td>\n",
+       "      <td>1.583872</td>\n",
+       "      <td>0.051108</td>\n",
+       "      <td>0.067694</td>\n",
+       "      <td>0.052094</td>\n",
+       "      <td>7.662636</td>\n",
+       "      <td>0.051438</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.051372</td>\n",
+       "      <td>0.069020</td>\n",
+       "      <td>1.370369</td>\n",
+       "      <td>0.051317</td>\n",
+       "      <td>2.325817</td>\n",
+       "      <td>0.109128</td>\n",
+       "      <td>0.051183</td>\n",
+       "      <td>0.070308</td>\n",
+       "      <td>0.052039</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -2413,123 +2344,123 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19008</th>\n",
-       "      <td>-0.34820</td>\n",
-       "      <td>-0.29900</td>\n",
-       "      <td>-0.34437</td>\n",
-       "      <td>-0.31436</td>\n",
-       "      <td>-0.34085</td>\n",
-       "      <td>-0.46158</td>\n",
-       "      <td>0.82235</td>\n",
-       "      <td>-0.32377</td>\n",
-       "      <td>-0.32049</td>\n",
-       "      <td>2.46634</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.36230</td>\n",
-       "      <td>-0.36955</td>\n",
-       "      <td>-0.32952</td>\n",
-       "      <td>-0.28627</td>\n",
-       "      <td>-0.29503</td>\n",
-       "      <td>-0.36480</td>\n",
-       "      <td>-0.34654</td>\n",
-       "      <td>-0.32422</td>\n",
-       "      <td>-0.39023</td>\n",
-       "      <td>-0.28972</td>\n",
+       "      <td>0.065538</td>\n",
+       "      <td>0.052886</td>\n",
+       "      <td>0.057171</td>\n",
+       "      <td>0.051741</td>\n",
+       "      <td>0.566411</td>\n",
+       "      <td>0.051513</td>\n",
+       "      <td>0.057498</td>\n",
+       "      <td>0.051674</td>\n",
+       "      <td>0.064283</td>\n",
+       "      <td>0.051968</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.051546</td>\n",
+       "      <td>0.051878</td>\n",
+       "      <td>4.103773</td>\n",
+       "      <td>0.056866</td>\n",
+       "      <td>0.071395</td>\n",
+       "      <td>0.051617</td>\n",
+       "      <td>0.066317</td>\n",
+       "      <td>0.051617</td>\n",
+       "      <td>0.058158</td>\n",
+       "      <td>0.052165</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19009</th>\n",
-       "      <td>-0.34816</td>\n",
-       "      <td>-0.30263</td>\n",
-       "      <td>-0.34437</td>\n",
-       "      <td>0.18120</td>\n",
-       "      <td>-0.34109</td>\n",
-       "      <td>-0.46154</td>\n",
-       "      <td>0.81189</td>\n",
-       "      <td>-0.32372</td>\n",
-       "      <td>-0.32045</td>\n",
-       "      <td>-0.32738</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.36260</td>\n",
-       "      <td>-0.36951</td>\n",
-       "      <td>2.52929</td>\n",
-       "      <td>-0.28621</td>\n",
-       "      <td>-0.29878</td>\n",
-       "      <td>-0.36475</td>\n",
-       "      <td>-0.34650</td>\n",
-       "      <td>-0.32700</td>\n",
-       "      <td>-0.39251</td>\n",
-       "      <td>0.15501</td>\n",
+       "      <td>0.071523</td>\n",
+       "      <td>0.053326</td>\n",
+       "      <td>0.052552</td>\n",
+       "      <td>0.052867</td>\n",
+       "      <td>1.101434</td>\n",
+       "      <td>0.052483</td>\n",
+       "      <td>0.052288</td>\n",
+       "      <td>0.052754</td>\n",
+       "      <td>0.055194</td>\n",
+       "      <td>0.585756</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.655373</td>\n",
+       "      <td>0.053099</td>\n",
+       "      <td>0.052118</td>\n",
+       "      <td>0.052588</td>\n",
+       "      <td>0.052969</td>\n",
+       "      <td>0.052658</td>\n",
+       "      <td>0.051738</td>\n",
+       "      <td>0.052659</td>\n",
+       "      <td>0.052080</td>\n",
+       "      <td>0.053095</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19010</th>\n",
-       "      <td>-0.34800</td>\n",
-       "      <td>-0.30251</td>\n",
-       "      <td>-0.34437</td>\n",
-       "      <td>6.66342</td>\n",
-       "      <td>-0.34094</td>\n",
-       "      <td>-0.46143</td>\n",
-       "      <td>-0.33312</td>\n",
-       "      <td>-0.32356</td>\n",
-       "      <td>-0.32032</td>\n",
-       "      <td>-0.32727</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.36242</td>\n",
-       "      <td>-0.36381</td>\n",
-       "      <td>-0.32929</td>\n",
-       "      <td>-0.28598</td>\n",
-       "      <td>-0.29867</td>\n",
-       "      <td>0.69742</td>\n",
-       "      <td>-0.34635</td>\n",
-       "      <td>-0.32689</td>\n",
-       "      <td>-0.39238</td>\n",
-       "      <td>-0.32350</td>\n",
+       "      <td>0.052127</td>\n",
+       "      <td>0.052384</td>\n",
+       "      <td>3.672215</td>\n",
+       "      <td>1.093262</td>\n",
+       "      <td>0.051764</td>\n",
+       "      <td>0.051788</td>\n",
+       "      <td>0.051649</td>\n",
+       "      <td>0.051980</td>\n",
+       "      <td>0.053686</td>\n",
+       "      <td>0.052330</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.052224</td>\n",
+       "      <td>0.051528</td>\n",
+       "      <td>0.051865</td>\n",
+       "      <td>0.052132</td>\n",
+       "      <td>0.051912</td>\n",
+       "      <td>0.070156</td>\n",
+       "      <td>0.051913</td>\n",
+       "      <td>0.051500</td>\n",
+       "      <td>0.052221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19011</th>\n",
-       "      <td>-0.34817</td>\n",
-       "      <td>1.28021</td>\n",
-       "      <td>-0.34437</td>\n",
-       "      <td>-0.31753</td>\n",
-       "      <td>-0.34074</td>\n",
-       "      <td>0.23228</td>\n",
-       "      <td>-0.33337</td>\n",
-       "      <td>-0.32374</td>\n",
-       "      <td>0.70051</td>\n",
-       "      <td>-0.32308</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.36216</td>\n",
-       "      <td>-0.36952</td>\n",
-       "      <td>-0.32948</td>\n",
-       "      <td>-0.28623</td>\n",
-       "      <td>-0.29381</td>\n",
-       "      <td>1.22123</td>\n",
-       "      <td>-0.34651</td>\n",
-       "      <td>-0.32332</td>\n",
-       "      <td>-0.38951</td>\n",
-       "      <td>-0.32370</td>\n",
+       "      <td>4.188590</td>\n",
+       "      <td>0.052729</td>\n",
+       "      <td>2.703301</td>\n",
+       "      <td>1.608644</td>\n",
+       "      <td>0.051619</td>\n",
+       "      <td>0.051642</td>\n",
+       "      <td>0.055151</td>\n",
+       "      <td>0.051817</td>\n",
+       "      <td>0.053484</td>\n",
+       "      <td>0.052138</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.052040</td>\n",
+       "      <td>0.055276</td>\n",
+       "      <td>0.054890</td>\n",
+       "      <td>0.051957</td>\n",
+       "      <td>0.051755</td>\n",
+       "      <td>0.059697</td>\n",
+       "      <td>0.051756</td>\n",
+       "      <td>4.386074</td>\n",
+       "      <td>0.052217</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19012</th>\n",
-       "      <td>2.91613</td>\n",
-       "      <td>-0.30214</td>\n",
-       "      <td>-0.34437</td>\n",
-       "      <td>-0.31754</td>\n",
-       "      <td>-0.33919</td>\n",
-       "      <td>2.08866</td>\n",
-       "      <td>-0.33338</td>\n",
-       "      <td>-0.32375</td>\n",
-       "      <td>2.35113</td>\n",
-       "      <td>-0.32740</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.36263</td>\n",
-       "      <td>0.56537</td>\n",
-       "      <td>-0.32949</td>\n",
-       "      <td>-0.28624</td>\n",
-       "      <td>-0.29880</td>\n",
-       "      <td>-0.36478</td>\n",
-       "      <td>-0.34652</td>\n",
-       "      <td>-0.32701</td>\n",
-       "      <td>0.50155</td>\n",
-       "      <td>-0.32371</td>\n",
+       "      <td>0.051894</td>\n",
+       "      <td>0.052122</td>\n",
+       "      <td>0.051637</td>\n",
+       "      <td>0.051835</td>\n",
+       "      <td>0.051572</td>\n",
+       "      <td>2.638263</td>\n",
+       "      <td>2.734615</td>\n",
+       "      <td>0.051763</td>\n",
+       "      <td>0.053282</td>\n",
+       "      <td>0.052075</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.051980</td>\n",
+       "      <td>0.051362</td>\n",
+       "      <td>0.051660</td>\n",
+       "      <td>0.051899</td>\n",
+       "      <td>2.212243</td>\n",
+       "      <td>0.051121</td>\n",
+       "      <td>0.051704</td>\n",
+       "      <td>0.051338</td>\n",
+       "      <td>0.051977</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2537,146 +2468,146 @@
        "</div>"
       ],
       "text/plain": [
-       "       feats2: c10555, c8555, c1055  feats2: c12222, c12226, c1227  \\\n",
-       "0                          -0.34853                       -0.29446   \n",
-       "1                           0.09896                       -0.29610   \n",
-       "2                          -0.34853                       -0.29417   \n",
-       "3                          -0.34854                       -0.29143   \n",
-       "4                          -0.34850                       -0.27595   \n",
-       "...                             ...                            ...   \n",
-       "19008                      -0.34820                       -0.29900   \n",
-       "19009                      -0.34816                       -0.30263   \n",
-       "19010                      -0.34800                       -0.30251   \n",
-       "19011                      -0.34817                        1.28021   \n",
-       "19012                       2.91613                       -0.30214   \n",
+       "       feats2: c586, c585, c5864  feats2: c1961, c10196, c1901  \\\n",
+       "0                       0.051269                      0.052370   \n",
+       "1                       0.051271                      0.053484   \n",
+       "2                       0.051264                      0.053257   \n",
+       "3                       0.051251                      0.052477   \n",
+       "4                       0.051314                      0.053518   \n",
+       "...                          ...                           ...   \n",
+       "19008                   0.065538                      0.052886   \n",
+       "19009                   0.071523                      0.053326   \n",
+       "19010                   0.052127                      0.052384   \n",
+       "19011                   4.188590                      0.052729   \n",
+       "19012                   0.051894                      0.052122   \n",
        "\n",
-       "       feats2: c6665, c6667, c6653  feats2: c7703, c7701, c7707  \\\n",
-       "0                         -0.34437                     -0.31798   \n",
-       "1                         -0.34437                     -0.31795   \n",
-       "2                         -0.34437                      3.43709   \n",
-       "3                         -0.34437                     -0.31800   \n",
-       "4                         -0.34437                     -0.31795   \n",
-       "...                            ...                          ...   \n",
-       "19008                     -0.34437                     -0.31436   \n",
-       "19009                     -0.34437                      0.18120   \n",
-       "19010                     -0.34437                      6.66342   \n",
-       "19011                     -0.34437                     -0.31753   \n",
-       "19012                     -0.34437                     -0.31754   \n",
+       "       feats2: c16916, c16169, c1616  feats2: c3636, c6363, c6365  \\\n",
+       "0                           0.059563                     0.053012   \n",
+       "1                           0.066529                     0.053060   \n",
+       "2                           0.064578                     0.053130   \n",
+       "3                           0.065590                     0.052994   \n",
+       "4                           0.066485                     0.053112   \n",
+       "...                              ...                          ...   \n",
+       "19008                       0.057171                     0.051741   \n",
+       "19009                       0.052552                     0.052867   \n",
+       "19010                       3.672215                     1.093262   \n",
+       "19011                       2.703301                     1.608644   \n",
+       "19012                       0.051637                     0.051835   \n",
        "\n",
-       "       feats2: c1992, c1922, c19932  feats2: c625, c612, c6125  \\\n",
-       "0                          -0.34102                   -0.46182   \n",
-       "1                          -0.34092                   -0.46180   \n",
-       "2                          -0.33256                   -0.46182   \n",
-       "3                          -0.34095                   -0.46183   \n",
-       "4                          -0.34093                   -0.46180   \n",
-       "...                             ...                        ...   \n",
-       "19008                      -0.34085                   -0.46158   \n",
-       "19009                      -0.34109                   -0.46154   \n",
-       "19010                      -0.34094                   -0.46143   \n",
-       "19011                      -0.34074                    0.23228   \n",
-       "19012                      -0.33919                    2.08866   \n",
+       "       feats2: c4944, c4444, c8444  feats2: c1065, c10652, c10585  \\\n",
+       "0                         0.051056                       0.051073   \n",
+       "1                         0.051057                       0.561800   \n",
+       "2                         0.051051                       0.051067   \n",
+       "3                         0.051041                       0.051057   \n",
+       "4                         1.583872                       0.051108   \n",
+       "...                            ...                            ...   \n",
+       "19008                     0.566411                       0.051513   \n",
+       "19009                     1.101434                       0.052483   \n",
+       "19010                     0.051764                       0.051788   \n",
+       "19011                     0.051619                       0.051642   \n",
+       "19012                     0.051572                       2.638263   \n",
        "\n",
-       "       feats2: c9028, c9904, c9283  feats2: c3073, c3037, c3074  \\\n",
-       "0                         -0.33386                     -0.32362   \n",
-       "1                         -0.33383                      2.52748   \n",
-       "2                         -0.00576                     -0.32411   \n",
-       "3                         -0.33388                      0.60698   \n",
-       "4                         -0.32521                     -0.32408   \n",
-       "...                            ...                          ...   \n",
-       "19008                      0.82235                     -0.32377   \n",
-       "19009                      0.81189                     -0.32372   \n",
-       "19010                     -0.33312                     -0.32356   \n",
-       "19011                     -0.33337                     -0.32374   \n",
-       "19012                     -0.33338                     -0.32375   \n",
+       "       feats2: c15556, c15550, c1555  feats2: c5999, c10999, c599  \\\n",
+       "0                           0.059119                     0.052026   \n",
+       "1                           0.067701                     0.052046   \n",
+       "2                           0.065562                     0.052079   \n",
+       "3                           0.063403                     0.052009   \n",
+       "4                           0.067694                     0.052094   \n",
+       "...                              ...                          ...   \n",
+       "19008                       0.057498                     0.051674   \n",
+       "19009                       0.052288                     0.052754   \n",
+       "19010                       0.051649                     0.051980   \n",
+       "19011                       0.055151                     0.051817   \n",
+       "19012                       2.734615                     0.051763   \n",
        "\n",
-       "       feats2: c2106, c2626, c4210  feats2: c11196, c11918, c1111  ...  \\\n",
-       "0                         -0.31948                       -0.32044  ...   \n",
-       "1                         -0.32073                       -0.32158  ...   \n",
-       "2                         -0.32076                       -0.32092  ...   \n",
-       "3                         -0.32077                        2.87353  ...   \n",
-       "4                          0.41376                       -0.32181  ...   \n",
-       "...                            ...                            ...  ...   \n",
-       "19008                     -0.32049                        2.46634  ...   \n",
-       "19009                     -0.32045                       -0.32738  ...   \n",
-       "19010                     -0.32032                       -0.32727  ...   \n",
-       "19011                      0.70051                       -0.32308  ...   \n",
-       "19012                      2.35113                       -0.32740  ...   \n",
+       "       feats2: c17693, c6937, c3937  feats2: c8882, c8880, c8889  ...  \\\n",
+       "0                          7.893405                     0.051389  ...   \n",
+       "1                          7.851341                     0.051392  ...   \n",
+       "2                          7.391063                     0.051386  ...   \n",
+       "3                          7.892231                     0.051369  ...   \n",
+       "4                          7.662636                     0.051438  ...   \n",
+       "...                             ...                          ...  ...   \n",
+       "19008                      0.064283                     0.051968  ...   \n",
+       "19009                      0.055194                     0.585756  ...   \n",
+       "19010                      0.053686                     0.052330  ...   \n",
+       "19011                      0.053484                     0.052138  ...   \n",
+       "19012                      0.053282                     0.052075  ...   \n",
        "\n",
-       "       feats2: c4448, c4444, c4487  feats2: c17981, c2980, c1798  \\\n",
-       "0                         -0.36251                      -0.36984   \n",
-       "1                         -0.36238                      -0.36982   \n",
-       "2                         -0.36235                      -0.36985   \n",
-       "3                         -0.36240                      -0.36985   \n",
-       "4                         -0.36239                       0.51644   \n",
-       "...                            ...                           ...   \n",
-       "19008                     -0.36230                      -0.36955   \n",
-       "19009                     -0.36260                      -0.36951   \n",
-       "19010                     -0.36242                      -0.36381   \n",
-       "19011                     -0.36216                      -0.36952   \n",
-       "19012                     -0.36263                       0.56537   \n",
+       "       feats2: c2890, c280, tgt  feats2: c3333, c3303, c3033  \\\n",
+       "0                      0.050000                     0.101370   \n",
+       "1                      0.050000                     2.741899   \n",
+       "2                      0.063343                     0.051320   \n",
+       "3                      0.050000                     0.051307   \n",
+       "4                      0.050001                     0.051372   \n",
+       "...                         ...                          ...   \n",
+       "19008                  0.051546                     0.051878   \n",
+       "19009                  4.655373                     0.053099   \n",
+       "19010                  0.050001                     0.052224   \n",
+       "19011                  0.050001                     0.052040   \n",
+       "19012                  0.050001                     0.051980   \n",
        "\n",
-       "       feats2: c4777, c14777, c4787  feats2: c7554, c7519, c5151  \\\n",
-       "0                          -0.32987                     -0.28672   \n",
-       "1                          -0.32984                     -0.28669   \n",
-       "2                          -0.32988                     -0.28673   \n",
-       "3                          -0.32989                     -0.28674   \n",
-       "4                          -0.32984                     -0.28668   \n",
+       "       feats2: c11187, c1118, c1111  feats2: c1798, c1772, c1778  \\\n",
+       "0                          0.059551                     1.219262   \n",
+       "1                          0.068965                     1.368653   \n",
+       "2                          0.066675                     1.671434   \n",
+       "3                          3.263992                     3.747229   \n",
+       "4                          0.069020                     1.370369   \n",
        "...                             ...                          ...   \n",
-       "19008                      -0.32952                     -0.28627   \n",
-       "19009                       2.52929                     -0.28621   \n",
-       "19010                      -0.32929                     -0.28598   \n",
-       "19011                      -0.32948                     -0.28623   \n",
-       "19012                      -0.32949                     -0.28624   \n",
+       "19008                      4.103773                     0.056866   \n",
+       "19009                      0.052118                     0.052588   \n",
+       "19010                      0.051528                     0.051865   \n",
+       "19011                      0.055276                     0.054890   \n",
+       "19012                      0.051362                     0.051660   \n",
        "\n",
-       "       feats2: c10000, c10008, c10003  feats2: c25240, c2524, c2456  \\\n",
-       "0                             3.88640                      -0.36522   \n",
-       "1                            -0.29198                      -0.36519   \n",
-       "2                            -0.29119                      -0.36523   \n",
-       "3                            -0.28688                      -0.36524   \n",
-       "4                            -0.29225                      -0.36184   \n",
-       "...                               ...                           ...   \n",
-       "19008                        -0.29503                      -0.36480   \n",
-       "19009                        -0.29878                      -0.36475   \n",
-       "19010                        -0.29867                       0.69742   \n",
-       "19011                        -0.29381                       1.22123   \n",
-       "19012                        -0.29880                      -0.36478   \n",
+       "       feats2: c3435, c3434, c3597  feats2: c2106, c210, c10000  \\\n",
+       "0                         0.051273                     2.699579   \n",
+       "1                         1.024759                     0.051145   \n",
+       "2                         0.051267                     0.051138   \n",
+       "3                         0.053271                     0.051127   \n",
+       "4                         0.051317                     2.325817   \n",
+       "...                            ...                          ...   \n",
+       "19008                     0.071395                     0.051617   \n",
+       "19009                     0.052969                     0.052658   \n",
+       "19010                     0.052132                     0.051912   \n",
+       "19011                     0.051957                     0.051755   \n",
+       "19012                     0.051899                     2.212243   \n",
        "\n",
-       "       feats2: c809, c5099, c5809  feats2: c1065, c10658, c10656  \\\n",
-       "0                        -0.34670                       -0.15082   \n",
-       "1                        -0.34666                       -0.32204   \n",
-       "2                        -0.34668                       -0.32147   \n",
-       "3                        -0.34671                       -0.29361   \n",
-       "4                        -0.34668                       -0.32224   \n",
-       "...                           ...                            ...   \n",
-       "19008                    -0.34654                       -0.32422   \n",
-       "19009                    -0.34650                       -0.32700   \n",
-       "19010                    -0.34635                       -0.32689   \n",
-       "19011                    -0.34651                       -0.32332   \n",
-       "19012                    -0.34652                       -0.32701   \n",
+       "       feats2: c1085, c1080, c1081  feats2: c457, c222, c452  \\\n",
+       "0                         3.244251                  0.051143   \n",
+       "1                         0.108473                  0.051145   \n",
+       "2                         0.097529                  0.051139   \n",
+       "3                         0.192025                  0.051127   \n",
+       "4                         0.109128                  0.051183   \n",
+       "...                            ...                       ...   \n",
+       "19008                     0.066317                  0.051617   \n",
+       "19009                     0.051738                  0.052659   \n",
+       "19010                     0.070156                  0.051913   \n",
+       "19011                     0.059697                  0.051756   \n",
+       "19012                     0.051121                  0.051704   \n",
        "\n",
-       "       feats2: c1550, c15034, c15615  feats2: c8182, c8882, c8889  \n",
-       "0                           -0.38757                     -0.32410  \n",
-       "1                           -0.38862                     -0.32407  \n",
-       "2                           -0.38820                     -0.32411  \n",
-       "3                           -0.38584                     -0.32412  \n",
-       "4                            0.57418                     -0.32407  \n",
-       "...                              ...                          ...  \n",
-       "19008                       -0.39023                     -0.28972  \n",
-       "19009                       -0.39251                      0.15501  \n",
-       "19010                       -0.39238                     -0.32350  \n",
-       "19011                       -0.38951                     -0.32370  \n",
-       "19012                        0.50155                     -0.32371  \n",
+       "       feats2: c1268, c1226, c12689  feats2: c6604, c16604, c16048  \n",
+       "0                          0.059730                       0.051639  \n",
+       "1                          0.070319                       0.052000  \n",
+       "2                          0.067757                       0.051922  \n",
+       "3                          0.063663                       0.051661  \n",
+       "4                          0.070308                       0.052039  \n",
+       "...                             ...                            ...  \n",
+       "19008                      0.058158                       0.052165  \n",
+       "19009                      0.052080                       0.053095  \n",
+       "19010                      0.051500                       0.052221  \n",
+       "19011                      4.386074                       0.052217  \n",
+       "19012                      0.051338                       0.051977  \n",
        "\n",
        "[19762 rows x 32 columns]"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "X = g7._get_feature('nodes')\n",
+    "X = g7.get_matrix()\n",
     "X"
    ]
   },
@@ -2693,7 +2624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 36,
    "id": "2d6f58dd",
    "metadata": {
     "tags": []
@@ -2705,7 +2636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 37,
    "id": "f3b44db2-b34e-4398-8c5a-7a10bbe5d681",
    "metadata": {
     "tags": []
@@ -2719,7 +2650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 38,
    "id": "3b2af6a2-4f10-4707-beb8-4f3447d3e3b8",
    "metadata": {},
    "outputs": [
@@ -2838,7 +2769,7 @@
        "[17836 rows x 3 columns]"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2851,7 +2782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 39,
    "id": "5258aee1",
    "metadata": {},
    "outputs": [],
@@ -2865,7 +2796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 40,
    "id": "7ff921fc-3ecd-4404-acd7-8db943a4ebcc",
    "metadata": {},
    "outputs": [
@@ -2996,7 +2927,7 @@
        "[17836 rows x 4 columns]"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3007,7 +2938,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 41,
    "id": "b4b10152-cac9-4497-b016-dd67b54cdcf2",
    "metadata": {},
    "outputs": [],
@@ -3018,17 +2949,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 42,
    "id": "9b3af1cd-6423-4484-8b99-81fad821f118",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"261a3679-9fa9-476f-be9f-c387259d95fa\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=8925a5854a68463a9b416463265bdd28&type=arrow&viztoken=5154942a-080e-47ea-8934-55fe43aa0055&usertag=8a6d667e-pygraphistry-0.28.6+132.gf997752.dirty&splashAfter=1674267791&info=true\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#261a3679-9fa9-476f-be9f-c387259d95fa\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=5e532500da8d459d8a3ef7832a6d6d9a&type=arrow&viztoken=4e311702-17ef-4563-b060-6d631e4a4101&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672345993&info=true'"
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3055,6 +3003,14 @@
     "Likewise the transpose conditional is even worse \n",
     "with prob_detection ~ 6e-5"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0cbef82-421d-489e-8666-84d412cae5a9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From d87df3535a0d038b7fbbe908ffc77fd9d033ccd6 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 20 Jan 2023 20:32:32 -0800
Subject: [PATCH 128/432] updates Chavismo OSINT demo

---
 demos/ai/OSINT/Chavismo.ipynb | 1678 +++++++++++++++++----------------
 1 file changed, 882 insertions(+), 796 deletions(-)

diff --git a/demos/ai/OSINT/Chavismo.ipynb b/demos/ai/OSINT/Chavismo.ipynb
index 2457cfee06..3c3d92a215 100644
--- a/demos/ai/OSINT/Chavismo.ipynb
+++ b/demos/ai/OSINT/Chavismo.ipynb
@@ -45,16 +45,6 @@
     "#! pip install --upgrade graphistry[ai]"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "b6f55e41",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#cd .."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -63,6 +53,7 @@
    "outputs": [],
    "source": [
     "import graphistry\n",
+    "from graphistry.features import ModelDict, topic_model, search_model, qa_model\n",
     "\n",
     "import requests\n",
     "import pandas as pd\n",
@@ -317,9 +308,9 @@
     "        filename = f\"chavismo.xlsx\"\n",
     "        open(filename, \"wb\").write(r.content)\n",
     "        df = pd.read_excel(filename)\n",
-    "        df.to_csv(\"data/chavismo.csv\", header=True)\n",
+    "        df.to_csv(\"chavismo.csv\", header=True)\n",
     "    else:\n",
-    "        df = pd.read_csv('data/chavismo.csv', index_col=0)\n",
+    "        df = pd.read_csv('chavismo.csv', index_col=0)\n",
     "    return df\n",
     "\n",
     "df = download_chavismo_data(get_fresh=False)  # set to True to get latest data\n",
@@ -398,7 +389,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "RENDER = False # set to True to have plots generated inline, or paste the URLs into a tab to see the graphs"
+    "RENDER = True # set to True to have plots generated inline, or paste the URLs into a tab to see the graphs"
    ]
   },
   {
@@ -409,8 +400,25 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"71de07c5-181b-4feb-9ff8-5a10283ff384\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=c209174e176d4261a58bd9dde23f946b&type=arrow&viztoken=c53dabd9-51ff-4208-86f3-216626fabf3d&usertag=8a6d667e-pygraphistry-0.28.6+133.g649ac53.dirty&splashAfter=1674275170&info=true\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#71de07c5-181b-4feb-9ff8-5a10283ff384\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=e17a8abbcddc472c932cdb5b2c0fc2c2&type=arrow&viztoken=c8e856ba-5b7b-4592-b22e-6dfac7b411a9&usertag=8a6d667e-pygraphistry-0.28.4+72.g2a02e2b.dirty&splashAfter=1668813426&info=true'"
+       "<IPython.core.display.HTML object>"
       ]
      },
      "execution_count": 11,
@@ -440,8 +448,25 @@
     },
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"df685266-d3a5-463f-9a5f-509071ff2566\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=4558ebb0113f4e828aae18a814e3d76f&type=arrow&viztoken=9d4da169-3ee5-49ef-be3d-b225dd772758&usertag=8a6d667e-pygraphistry-0.28.6+133.g649ac53.dirty&splashAfter=1674275173&info=true\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#df685266-d3a5-463f-9a5f-509071ff2566\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=9b8312e901dd47999a91f39f66880983&type=arrow&viztoken=5fc71180-3c6e-4ff7-adc9-38938b6da275&usertag=8a6d667e-pygraphistry-0.28.4+72.g2a02e2b.dirty&splashAfter=1668813431&info=true'"
+       "<IPython.core.display.HTML object>"
       ]
      },
      "execution_count": 12,
@@ -450,10 +475,10 @@
     }
    ],
    "source": [
-    "# one can also create a hypergraph with any number of columns of interest\n",
+    "# Create a hypergraph with any number of columns of interest\n",
     "hg = graphistry.hypergraph(df, ['Agent 1','Agent 2', 'Relationship'])\n",
     "gh = hg['graph']\n",
-    "gh.bind(point_title='Agent 1').plot(render=RENDER)"
+    "gh.bind(point_title='nodeID').plot(render=RENDER)"
    ]
   },
   {
@@ -477,39 +502,88 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "9939d27f",
+   "id": "6699dc5c-16b2-4471-9a6f-9241c238d830",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 2min 34s, sys: 11.3 s, total: 2min 46s\n",
-      "Wall time: 2min 31s\n"
+      "_____________________________________________________________\n",
+      "\n",
+      "sentence-transformers/msmarco-distilbert-base-v2 Search Model\n",
+      "_____________________________________________________________\n",
+      "\n",
+      "Updated: {'cardinality_threshold_target': 2, 'n_topics_target': 11}\n",
+      "_____________________________________________________________\n",
+      "\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'min_words': 0, 'model_name': 'sentence-transformers/msmarco-distilbert-base-v2', 'cardinality_threshold_target': 2, 'n_topics_target': 11}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "%%time\n",
-    "g = graphistry.nodes(df, 'Agent 1').edges(df, 'Agent 1', 'Agent 2')\n",
-    "# since we have edges, let's featurize (rather than umap, which would overwrite explicit edges)\n",
-    "# X = None will featurize ALL the columns and setting min_words=0 will treat them all as textual\n",
-    "g2 = g.featurize(y=['Relationship'], \n",
-    "                 model_name='msmarco-distilbert-base-v2', \n",
-    "                 min_words=0, # force textual encoding\n",
-    "                 cardinality_threshold_target=2, # force topic model (with low target cardinality)\n",
-    "                 n_topics_target=12)"
+    "search_model.update(dict(cardinality_threshold_target=2, n_topics_target=11))\n",
+    "search_model"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "bcf9a948",
+   "id": "9939d27f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2min 35s, sys: 9.96 s, total: 2min 45s\n",
+      "Wall time: 2min 27s\n",
+      "____________________________________________________________\n",
+      "\n",
+      "Search model over features with `y=Relationship` topic model\n",
+      "____________________________________________________________\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'y': ['Relationship'], 'min_words': 0, 'model_name': 'sentence-transformers/msmarco-distilbert-base-v2', 'cardinality_threshold_target': 2, 'n_topics_target': 11}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# save search instance after featurization \n",
-    "# g2.save_search_instance('data/chavismo.search')"
+    "%%time\n",
+    "\n",
+    "model = ModelDict('Search model over features with `y=Relationship` topic model', y=['Relationship'], \n",
+    "                 **search_model)\n",
+    "process = True\n",
+    "if process:\n",
+    "    g = graphistry.nodes(df.sample(len(df)), 'Agent 1').edges(df, 'Agent 1', 'Agent 2')\n",
+    "    g2 = graphistry.bind().load_search_instance('chavismo.search')\n",
+    "\n",
+    "    g._node_features = g2._node_features\n",
+    "    # since we have edges, let's featurize (rather than umap, which would overwrite explicit edges)\n",
+    "    # X = None will featurize ALL the columns and setting min_words=0 will treat them all as textual\n",
+    "    g2 = g.featurize(**model)\n",
+    "    g2.save_search_instance('chavismo.search')\n",
+    "else:\n",
+    "    g2 = graphistry.bind().load_search_instance('chavismo.search')\n",
+    "    \n",
+    "model"
    ]
   },
   {
@@ -564,124 +638,124 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-0.253210</td>\n",
-       "      <td>-0.092555</td>\n",
-       "      <td>-0.086723</td>\n",
-       "      <td>-0.157253</td>\n",
-       "      <td>-0.204900</td>\n",
-       "      <td>0.122437</td>\n",
-       "      <td>-0.529797</td>\n",
-       "      <td>-0.015322</td>\n",
-       "      <td>-0.564774</td>\n",
-       "      <td>0.769105</td>\n",
+       "      <th>1935</th>\n",
+       "      <td>-0.187781</td>\n",
+       "      <td>-0.387294</td>\n",
+       "      <td>-0.160111</td>\n",
+       "      <td>0.533505</td>\n",
+       "      <td>-0.012384</td>\n",
+       "      <td>-0.036983</td>\n",
+       "      <td>-0.227363</td>\n",
+       "      <td>0.305134</td>\n",
+       "      <td>-0.053135</td>\n",
+       "      <td>0.242913</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.170976</td>\n",
-       "      <td>-0.685452</td>\n",
-       "      <td>0.295997</td>\n",
-       "      <td>-0.593129</td>\n",
-       "      <td>0.390818</td>\n",
-       "      <td>0.249091</td>\n",
-       "      <td>-0.099062</td>\n",
-       "      <td>0.051298</td>\n",
-       "      <td>0.319292</td>\n",
-       "      <td>0.244462</td>\n",
+       "      <td>0.295774</td>\n",
+       "      <td>0.358959</td>\n",
+       "      <td>0.566532</td>\n",
+       "      <td>-0.852727</td>\n",
+       "      <td>0.118316</td>\n",
+       "      <td>-0.122535</td>\n",
+       "      <td>0.347825</td>\n",
+       "      <td>0.101922</td>\n",
+       "      <td>0.386412</td>\n",
+       "      <td>-0.644331</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.246941</td>\n",
-       "      <td>0.124175</td>\n",
-       "      <td>0.269999</td>\n",
-       "      <td>0.181342</td>\n",
-       "      <td>-0.505962</td>\n",
-       "      <td>0.210211</td>\n",
-       "      <td>0.140408</td>\n",
-       "      <td>0.468697</td>\n",
-       "      <td>0.321780</td>\n",
-       "      <td>-0.128226</td>\n",
+       "      <th>1583</th>\n",
+       "      <td>-0.389066</td>\n",
+       "      <td>-0.087771</td>\n",
+       "      <td>-0.775325</td>\n",
+       "      <td>0.130028</td>\n",
+       "      <td>0.172289</td>\n",
+       "      <td>-0.208622</td>\n",
+       "      <td>-0.874529</td>\n",
+       "      <td>0.382446</td>\n",
+       "      <td>0.483579</td>\n",
+       "      <td>-0.309687</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.896930</td>\n",
-       "      <td>-0.324542</td>\n",
-       "      <td>-0.291376</td>\n",
-       "      <td>-0.565900</td>\n",
-       "      <td>0.524987</td>\n",
-       "      <td>-0.196209</td>\n",
-       "      <td>-0.519527</td>\n",
-       "      <td>-0.216939</td>\n",
-       "      <td>0.862336</td>\n",
-       "      <td>0.193277</td>\n",
+       "      <td>0.152333</td>\n",
+       "      <td>-0.175365</td>\n",
+       "      <td>0.676532</td>\n",
+       "      <td>-0.393536</td>\n",
+       "      <td>0.014875</td>\n",
+       "      <td>-0.383976</td>\n",
+       "      <td>0.081081</td>\n",
+       "      <td>0.298928</td>\n",
+       "      <td>0.596412</td>\n",
+       "      <td>-0.605296</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-0.068820</td>\n",
-       "      <td>-0.323506</td>\n",
-       "      <td>-0.206149</td>\n",
-       "      <td>0.559626</td>\n",
-       "      <td>0.145691</td>\n",
-       "      <td>-0.100530</td>\n",
-       "      <td>-0.492642</td>\n",
-       "      <td>-0.207871</td>\n",
-       "      <td>-0.306911</td>\n",
-       "      <td>0.365237</td>\n",
+       "      <th>2351</th>\n",
+       "      <td>-0.423137</td>\n",
+       "      <td>0.182216</td>\n",
+       "      <td>-0.133435</td>\n",
+       "      <td>0.217782</td>\n",
+       "      <td>-0.094416</td>\n",
+       "      <td>-0.097829</td>\n",
+       "      <td>-0.876501</td>\n",
+       "      <td>0.797395</td>\n",
+       "      <td>-0.277968</td>\n",
+       "      <td>-0.045989</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.702710</td>\n",
-       "      <td>0.152173</td>\n",
-       "      <td>0.309572</td>\n",
-       "      <td>-0.461615</td>\n",
-       "      <td>0.666504</td>\n",
-       "      <td>-0.016534</td>\n",
-       "      <td>0.877414</td>\n",
-       "      <td>-0.203894</td>\n",
-       "      <td>0.450191</td>\n",
-       "      <td>-0.544430</td>\n",
+       "      <td>1.015161</td>\n",
+       "      <td>0.453668</td>\n",
+       "      <td>0.440656</td>\n",
+       "      <td>-0.040266</td>\n",
+       "      <td>0.693482</td>\n",
+       "      <td>0.169744</td>\n",
+       "      <td>0.060993</td>\n",
+       "      <td>-0.500294</td>\n",
+       "      <td>0.649334</td>\n",
+       "      <td>0.195239</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-0.193803</td>\n",
-       "      <td>-0.272143</td>\n",
-       "      <td>-0.208212</td>\n",
-       "      <td>0.341019</td>\n",
-       "      <td>-0.175586</td>\n",
-       "      <td>0.149454</td>\n",
-       "      <td>-0.245428</td>\n",
-       "      <td>-0.008559</td>\n",
-       "      <td>0.279371</td>\n",
-       "      <td>0.312212</td>\n",
+       "      <th>452</th>\n",
+       "      <td>-0.095392</td>\n",
+       "      <td>0.142223</td>\n",
+       "      <td>-0.686644</td>\n",
+       "      <td>0.674334</td>\n",
+       "      <td>-0.228214</td>\n",
+       "      <td>0.109001</td>\n",
+       "      <td>-0.440772</td>\n",
+       "      <td>0.124842</td>\n",
+       "      <td>-0.066046</td>\n",
+       "      <td>0.171612</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.451480</td>\n",
-       "      <td>0.413026</td>\n",
-       "      <td>-0.211110</td>\n",
-       "      <td>-0.550950</td>\n",
-       "      <td>0.556309</td>\n",
-       "      <td>-0.521065</td>\n",
-       "      <td>0.327162</td>\n",
-       "      <td>-0.200601</td>\n",
-       "      <td>0.148922</td>\n",
-       "      <td>-0.181214</td>\n",
+       "      <td>-0.009296</td>\n",
+       "      <td>0.564043</td>\n",
+       "      <td>0.178982</td>\n",
+       "      <td>-0.222556</td>\n",
+       "      <td>0.523386</td>\n",
+       "      <td>0.331242</td>\n",
+       "      <td>0.360143</td>\n",
+       "      <td>-0.011475</td>\n",
+       "      <td>0.105367</td>\n",
+       "      <td>0.146013</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.099981</td>\n",
-       "      <td>-0.147359</td>\n",
-       "      <td>-0.181383</td>\n",
-       "      <td>0.436541</td>\n",
-       "      <td>0.026173</td>\n",
-       "      <td>-0.218141</td>\n",
-       "      <td>-0.193041</td>\n",
-       "      <td>0.030840</td>\n",
-       "      <td>0.436779</td>\n",
-       "      <td>0.392634</td>\n",
+       "      <th>8</th>\n",
+       "      <td>-0.477632</td>\n",
+       "      <td>-0.784202</td>\n",
+       "      <td>-0.383131</td>\n",
+       "      <td>0.669990</td>\n",
+       "      <td>-0.100068</td>\n",
+       "      <td>-0.294655</td>\n",
+       "      <td>-0.627293</td>\n",
+       "      <td>0.058967</td>\n",
+       "      <td>-0.455835</td>\n",
+       "      <td>0.461948</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.505176</td>\n",
-       "      <td>0.505908</td>\n",
-       "      <td>-0.286257</td>\n",
-       "      <td>-0.614761</td>\n",
-       "      <td>0.811958</td>\n",
-       "      <td>-0.615687</td>\n",
-       "      <td>0.479651</td>\n",
-       "      <td>-0.345834</td>\n",
-       "      <td>0.185110</td>\n",
-       "      <td>-0.311448</td>\n",
+       "      <td>0.381159</td>\n",
+       "      <td>0.021592</td>\n",
+       "      <td>0.348291</td>\n",
+       "      <td>-0.903526</td>\n",
+       "      <td>0.694809</td>\n",
+       "      <td>0.018819</td>\n",
+       "      <td>-0.002837</td>\n",
+       "      <td>0.023364</td>\n",
+       "      <td>0.385524</td>\n",
+       "      <td>-0.437104</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -708,124 +782,124 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2713</th>\n",
-       "      <td>0.149379</td>\n",
-       "      <td>-0.222494</td>\n",
-       "      <td>0.303324</td>\n",
-       "      <td>0.306326</td>\n",
-       "      <td>-0.254602</td>\n",
-       "      <td>-0.536148</td>\n",
-       "      <td>-0.470110</td>\n",
-       "      <td>0.565999</td>\n",
-       "      <td>0.020586</td>\n",
-       "      <td>0.609108</td>\n",
+       "      <th>334</th>\n",
+       "      <td>-0.593350</td>\n",
+       "      <td>0.112945</td>\n",
+       "      <td>0.123807</td>\n",
+       "      <td>0.948721</td>\n",
+       "      <td>-0.276408</td>\n",
+       "      <td>-0.603181</td>\n",
+       "      <td>-0.392917</td>\n",
+       "      <td>0.388957</td>\n",
+       "      <td>0.126814</td>\n",
+       "      <td>1.126764</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.090419</td>\n",
-       "      <td>0.125347</td>\n",
-       "      <td>0.540859</td>\n",
-       "      <td>-0.566799</td>\n",
-       "      <td>0.642269</td>\n",
-       "      <td>0.503141</td>\n",
-       "      <td>-0.070126</td>\n",
-       "      <td>-0.351428</td>\n",
-       "      <td>1.435632</td>\n",
-       "      <td>0.075295</td>\n",
+       "      <td>-0.089558</td>\n",
+       "      <td>-0.331218</td>\n",
+       "      <td>0.120006</td>\n",
+       "      <td>-0.712097</td>\n",
+       "      <td>-0.205538</td>\n",
+       "      <td>0.562405</td>\n",
+       "      <td>0.525867</td>\n",
+       "      <td>-0.204776</td>\n",
+       "      <td>-0.197538</td>\n",
+       "      <td>-0.018287</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2714</th>\n",
-       "      <td>0.202084</td>\n",
-       "      <td>-0.421907</td>\n",
-       "      <td>0.331125</td>\n",
-       "      <td>0.261079</td>\n",
-       "      <td>-0.179134</td>\n",
-       "      <td>-0.532431</td>\n",
-       "      <td>-0.164233</td>\n",
-       "      <td>0.277176</td>\n",
-       "      <td>-0.106925</td>\n",
-       "      <td>0.391598</td>\n",
+       "      <th>1264</th>\n",
+       "      <td>0.476763</td>\n",
+       "      <td>0.199641</td>\n",
+       "      <td>0.226375</td>\n",
+       "      <td>0.824276</td>\n",
+       "      <td>-0.438520</td>\n",
+       "      <td>-0.420465</td>\n",
+       "      <td>0.386475</td>\n",
+       "      <td>0.275413</td>\n",
+       "      <td>-0.350755</td>\n",
+       "      <td>0.047801</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.046802</td>\n",
-       "      <td>0.067547</td>\n",
-       "      <td>0.484514</td>\n",
-       "      <td>-0.824181</td>\n",
-       "      <td>0.475694</td>\n",
-       "      <td>0.329913</td>\n",
-       "      <td>-0.104037</td>\n",
-       "      <td>-0.155081</td>\n",
-       "      <td>0.928627</td>\n",
-       "      <td>-0.082177</td>\n",
+       "      <td>0.755453</td>\n",
+       "      <td>0.358454</td>\n",
+       "      <td>0.348341</td>\n",
+       "      <td>-1.133082</td>\n",
+       "      <td>-0.210484</td>\n",
+       "      <td>-0.764606</td>\n",
+       "      <td>0.916726</td>\n",
+       "      <td>-0.284010</td>\n",
+       "      <td>-0.056577</td>\n",
+       "      <td>0.407399</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2715</th>\n",
-       "      <td>-0.627377</td>\n",
-       "      <td>-0.391843</td>\n",
-       "      <td>0.218678</td>\n",
-       "      <td>-0.014267</td>\n",
-       "      <td>-0.460290</td>\n",
-       "      <td>-0.329174</td>\n",
-       "      <td>-0.482513</td>\n",
-       "      <td>0.604713</td>\n",
-       "      <td>0.011763</td>\n",
-       "      <td>0.018444</td>\n",
+       "      <th>1171</th>\n",
+       "      <td>-0.270009</td>\n",
+       "      <td>0.170292</td>\n",
+       "      <td>0.052011</td>\n",
+       "      <td>0.060369</td>\n",
+       "      <td>-0.265923</td>\n",
+       "      <td>0.717389</td>\n",
+       "      <td>-0.561747</td>\n",
+       "      <td>0.256904</td>\n",
+       "      <td>0.061957</td>\n",
+       "      <td>0.049001</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.275013</td>\n",
-       "      <td>0.164754</td>\n",
-       "      <td>0.408749</td>\n",
-       "      <td>-0.332821</td>\n",
-       "      <td>0.696610</td>\n",
-       "      <td>0.529032</td>\n",
-       "      <td>0.067081</td>\n",
-       "      <td>-0.382973</td>\n",
-       "      <td>1.094504</td>\n",
-       "      <td>0.503912</td>\n",
+       "      <td>-0.322604</td>\n",
+       "      <td>-0.409145</td>\n",
+       "      <td>0.270570</td>\n",
+       "      <td>-0.290132</td>\n",
+       "      <td>0.037762</td>\n",
+       "      <td>0.646035</td>\n",
+       "      <td>-0.226534</td>\n",
+       "      <td>0.178052</td>\n",
+       "      <td>0.890680</td>\n",
+       "      <td>-0.204422</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2716</th>\n",
-       "      <td>0.148124</td>\n",
-       "      <td>-0.041698</td>\n",
-       "      <td>0.083552</td>\n",
-       "      <td>0.132032</td>\n",
-       "      <td>-0.663597</td>\n",
-       "      <td>0.103373</td>\n",
-       "      <td>-0.261431</td>\n",
-       "      <td>-0.024310</td>\n",
-       "      <td>0.381935</td>\n",
-       "      <td>0.062252</td>\n",
+       "      <th>589</th>\n",
+       "      <td>0.217439</td>\n",
+       "      <td>0.188192</td>\n",
+       "      <td>-0.186874</td>\n",
+       "      <td>0.108394</td>\n",
+       "      <td>0.318829</td>\n",
+       "      <td>0.049164</td>\n",
+       "      <td>-0.460243</td>\n",
+       "      <td>0.502261</td>\n",
+       "      <td>-0.304636</td>\n",
+       "      <td>0.176173</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.362344</td>\n",
-       "      <td>0.360328</td>\n",
-       "      <td>-0.020871</td>\n",
-       "      <td>-0.640752</td>\n",
-       "      <td>0.638189</td>\n",
-       "      <td>-0.118096</td>\n",
-       "      <td>0.245733</td>\n",
-       "      <td>-0.404834</td>\n",
-       "      <td>0.754788</td>\n",
-       "      <td>-0.139678</td>\n",
+       "      <td>0.700581</td>\n",
+       "      <td>0.730317</td>\n",
+       "      <td>0.152944</td>\n",
+       "      <td>0.017145</td>\n",
+       "      <td>0.597685</td>\n",
+       "      <td>0.536023</td>\n",
+       "      <td>-0.020815</td>\n",
+       "      <td>-0.774539</td>\n",
+       "      <td>0.354638</td>\n",
+       "      <td>0.153618</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2717</th>\n",
-       "      <td>0.148124</td>\n",
-       "      <td>-0.041698</td>\n",
-       "      <td>0.083552</td>\n",
-       "      <td>0.132032</td>\n",
-       "      <td>-0.663597</td>\n",
-       "      <td>0.103373</td>\n",
-       "      <td>-0.261431</td>\n",
-       "      <td>-0.024310</td>\n",
-       "      <td>0.381935</td>\n",
-       "      <td>0.062252</td>\n",
+       "      <th>2342</th>\n",
+       "      <td>-0.477632</td>\n",
+       "      <td>-0.784202</td>\n",
+       "      <td>-0.383131</td>\n",
+       "      <td>0.669990</td>\n",
+       "      <td>-0.100068</td>\n",
+       "      <td>-0.294655</td>\n",
+       "      <td>-0.627293</td>\n",
+       "      <td>0.058967</td>\n",
+       "      <td>-0.455835</td>\n",
+       "      <td>0.461948</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.362344</td>\n",
-       "      <td>0.360328</td>\n",
-       "      <td>-0.020871</td>\n",
-       "      <td>-0.640752</td>\n",
-       "      <td>0.638189</td>\n",
-       "      <td>-0.118096</td>\n",
-       "      <td>0.245733</td>\n",
-       "      <td>-0.404834</td>\n",
-       "      <td>0.754788</td>\n",
-       "      <td>-0.139678</td>\n",
+       "      <td>0.381159</td>\n",
+       "      <td>0.021592</td>\n",
+       "      <td>0.348291</td>\n",
+       "      <td>-0.903526</td>\n",
+       "      <td>0.694809</td>\n",
+       "      <td>0.018819</td>\n",
+       "      <td>-0.002837</td>\n",
+       "      <td>0.023364</td>\n",
+       "      <td>0.385524</td>\n",
+       "      <td>-0.437104</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -834,134 +908,134 @@
       ],
       "text/plain": [
        "      Agent 2_Source_Evidence_0  Agent 2_Source_Evidence_1  \\\n",
-       "0                     -0.253210                  -0.092555   \n",
-       "1                      0.246941                   0.124175   \n",
-       "2                     -0.068820                  -0.323506   \n",
-       "3                     -0.193803                  -0.272143   \n",
-       "4                      0.099981                  -0.147359   \n",
+       "1935                  -0.187781                  -0.387294   \n",
+       "1583                  -0.389066                  -0.087771   \n",
+       "2351                  -0.423137                   0.182216   \n",
+       "452                   -0.095392                   0.142223   \n",
+       "8                     -0.477632                  -0.784202   \n",
        "...                         ...                        ...   \n",
-       "2713                   0.149379                  -0.222494   \n",
-       "2714                   0.202084                  -0.421907   \n",
-       "2715                  -0.627377                  -0.391843   \n",
-       "2716                   0.148124                  -0.041698   \n",
-       "2717                   0.148124                  -0.041698   \n",
+       "334                   -0.593350                   0.112945   \n",
+       "1264                   0.476763                   0.199641   \n",
+       "1171                  -0.270009                   0.170292   \n",
+       "589                    0.217439                   0.188192   \n",
+       "2342                  -0.477632                  -0.784202   \n",
        "\n",
        "      Agent 2_Source_Evidence_2  Agent 2_Source_Evidence_3  \\\n",
-       "0                     -0.086723                  -0.157253   \n",
-       "1                      0.269999                   0.181342   \n",
-       "2                     -0.206149                   0.559626   \n",
-       "3                     -0.208212                   0.341019   \n",
-       "4                     -0.181383                   0.436541   \n",
+       "1935                  -0.160111                   0.533505   \n",
+       "1583                  -0.775325                   0.130028   \n",
+       "2351                  -0.133435                   0.217782   \n",
+       "452                   -0.686644                   0.674334   \n",
+       "8                     -0.383131                   0.669990   \n",
        "...                         ...                        ...   \n",
-       "2713                   0.303324                   0.306326   \n",
-       "2714                   0.331125                   0.261079   \n",
-       "2715                   0.218678                  -0.014267   \n",
-       "2716                   0.083552                   0.132032   \n",
-       "2717                   0.083552                   0.132032   \n",
+       "334                    0.123807                   0.948721   \n",
+       "1264                   0.226375                   0.824276   \n",
+       "1171                   0.052011                   0.060369   \n",
+       "589                   -0.186874                   0.108394   \n",
+       "2342                  -0.383131                   0.669990   \n",
        "\n",
        "      Agent 2_Source_Evidence_4  Agent 2_Source_Evidence_5  \\\n",
-       "0                     -0.204900                   0.122437   \n",
-       "1                     -0.505962                   0.210211   \n",
-       "2                      0.145691                  -0.100530   \n",
-       "3                     -0.175586                   0.149454   \n",
-       "4                      0.026173                  -0.218141   \n",
+       "1935                  -0.012384                  -0.036983   \n",
+       "1583                   0.172289                  -0.208622   \n",
+       "2351                  -0.094416                  -0.097829   \n",
+       "452                   -0.228214                   0.109001   \n",
+       "8                     -0.100068                  -0.294655   \n",
        "...                         ...                        ...   \n",
-       "2713                  -0.254602                  -0.536148   \n",
-       "2714                  -0.179134                  -0.532431   \n",
-       "2715                  -0.460290                  -0.329174   \n",
-       "2716                  -0.663597                   0.103373   \n",
-       "2717                  -0.663597                   0.103373   \n",
+       "334                   -0.276408                  -0.603181   \n",
+       "1264                  -0.438520                  -0.420465   \n",
+       "1171                  -0.265923                   0.717389   \n",
+       "589                    0.318829                   0.049164   \n",
+       "2342                  -0.100068                  -0.294655   \n",
        "\n",
        "      Agent 2_Source_Evidence_6  Agent 2_Source_Evidence_7  \\\n",
-       "0                     -0.529797                  -0.015322   \n",
-       "1                      0.140408                   0.468697   \n",
-       "2                     -0.492642                  -0.207871   \n",
-       "3                     -0.245428                  -0.008559   \n",
-       "4                     -0.193041                   0.030840   \n",
+       "1935                  -0.227363                   0.305134   \n",
+       "1583                  -0.874529                   0.382446   \n",
+       "2351                  -0.876501                   0.797395   \n",
+       "452                   -0.440772                   0.124842   \n",
+       "8                     -0.627293                   0.058967   \n",
        "...                         ...                        ...   \n",
-       "2713                  -0.470110                   0.565999   \n",
-       "2714                  -0.164233                   0.277176   \n",
-       "2715                  -0.482513                   0.604713   \n",
-       "2716                  -0.261431                  -0.024310   \n",
-       "2717                  -0.261431                  -0.024310   \n",
+       "334                   -0.392917                   0.388957   \n",
+       "1264                   0.386475                   0.275413   \n",
+       "1171                  -0.561747                   0.256904   \n",
+       "589                   -0.460243                   0.502261   \n",
+       "2342                  -0.627293                   0.058967   \n",
        "\n",
        "      Agent 2_Source_Evidence_8  Agent 2_Source_Evidence_9  ...  \\\n",
-       "0                     -0.564774                   0.769105  ...   \n",
-       "1                      0.321780                  -0.128226  ...   \n",
-       "2                     -0.306911                   0.365237  ...   \n",
-       "3                      0.279371                   0.312212  ...   \n",
-       "4                      0.436779                   0.392634  ...   \n",
+       "1935                  -0.053135                   0.242913  ...   \n",
+       "1583                   0.483579                  -0.309687  ...   \n",
+       "2351                  -0.277968                  -0.045989  ...   \n",
+       "452                   -0.066046                   0.171612  ...   \n",
+       "8                     -0.455835                   0.461948  ...   \n",
        "...                         ...                        ...  ...   \n",
-       "2713                   0.020586                   0.609108  ...   \n",
-       "2714                  -0.106925                   0.391598  ...   \n",
-       "2715                   0.011763                   0.018444  ...   \n",
-       "2716                   0.381935                   0.062252  ...   \n",
-       "2717                   0.381935                   0.062252  ...   \n",
+       "334                    0.126814                   1.126764  ...   \n",
+       "1264                  -0.350755                   0.047801  ...   \n",
+       "1171                   0.061957                   0.049001  ...   \n",
+       "589                   -0.304636                   0.176173  ...   \n",
+       "2342                  -0.455835                   0.461948  ...   \n",
        "\n",
        "      Agent 2_Source_Evidence_758  Agent 2_Source_Evidence_759  \\\n",
-       "0                        0.170976                    -0.685452   \n",
-       "1                        0.896930                    -0.324542   \n",
-       "2                        0.702710                     0.152173   \n",
-       "3                        0.451480                     0.413026   \n",
-       "4                        0.505176                     0.505908   \n",
+       "1935                     0.295774                     0.358959   \n",
+       "1583                     0.152333                    -0.175365   \n",
+       "2351                     1.015161                     0.453668   \n",
+       "452                     -0.009296                     0.564043   \n",
+       "8                        0.381159                     0.021592   \n",
        "...                           ...                          ...   \n",
-       "2713                    -0.090419                     0.125347   \n",
-       "2714                    -0.046802                     0.067547   \n",
-       "2715                     0.275013                     0.164754   \n",
-       "2716                     0.362344                     0.360328   \n",
-       "2717                     0.362344                     0.360328   \n",
+       "334                     -0.089558                    -0.331218   \n",
+       "1264                     0.755453                     0.358454   \n",
+       "1171                    -0.322604                    -0.409145   \n",
+       "589                      0.700581                     0.730317   \n",
+       "2342                     0.381159                     0.021592   \n",
        "\n",
        "      Agent 2_Source_Evidence_760  Agent 2_Source_Evidence_761  \\\n",
-       "0                        0.295997                    -0.593129   \n",
-       "1                       -0.291376                    -0.565900   \n",
-       "2                        0.309572                    -0.461615   \n",
-       "3                       -0.211110                    -0.550950   \n",
-       "4                       -0.286257                    -0.614761   \n",
+       "1935                     0.566532                    -0.852727   \n",
+       "1583                     0.676532                    -0.393536   \n",
+       "2351                     0.440656                    -0.040266   \n",
+       "452                      0.178982                    -0.222556   \n",
+       "8                        0.348291                    -0.903526   \n",
        "...                           ...                          ...   \n",
-       "2713                     0.540859                    -0.566799   \n",
-       "2714                     0.484514                    -0.824181   \n",
-       "2715                     0.408749                    -0.332821   \n",
-       "2716                    -0.020871                    -0.640752   \n",
-       "2717                    -0.020871                    -0.640752   \n",
+       "334                      0.120006                    -0.712097   \n",
+       "1264                     0.348341                    -1.133082   \n",
+       "1171                     0.270570                    -0.290132   \n",
+       "589                      0.152944                     0.017145   \n",
+       "2342                     0.348291                    -0.903526   \n",
        "\n",
        "      Agent 2_Source_Evidence_762  Agent 2_Source_Evidence_763  \\\n",
-       "0                        0.390818                     0.249091   \n",
-       "1                        0.524987                    -0.196209   \n",
-       "2                        0.666504                    -0.016534   \n",
-       "3                        0.556309                    -0.521065   \n",
-       "4                        0.811958                    -0.615687   \n",
+       "1935                     0.118316                    -0.122535   \n",
+       "1583                     0.014875                    -0.383976   \n",
+       "2351                     0.693482                     0.169744   \n",
+       "452                      0.523386                     0.331242   \n",
+       "8                        0.694809                     0.018819   \n",
        "...                           ...                          ...   \n",
-       "2713                     0.642269                     0.503141   \n",
-       "2714                     0.475694                     0.329913   \n",
-       "2715                     0.696610                     0.529032   \n",
-       "2716                     0.638189                    -0.118096   \n",
-       "2717                     0.638189                    -0.118096   \n",
+       "334                     -0.205538                     0.562405   \n",
+       "1264                    -0.210484                    -0.764606   \n",
+       "1171                     0.037762                     0.646035   \n",
+       "589                      0.597685                     0.536023   \n",
+       "2342                     0.694809                     0.018819   \n",
        "\n",
        "      Agent 2_Source_Evidence_764  Agent 2_Source_Evidence_765  \\\n",
-       "0                       -0.099062                     0.051298   \n",
-       "1                       -0.519527                    -0.216939   \n",
-       "2                        0.877414                    -0.203894   \n",
-       "3                        0.327162                    -0.200601   \n",
-       "4                        0.479651                    -0.345834   \n",
+       "1935                     0.347825                     0.101922   \n",
+       "1583                     0.081081                     0.298928   \n",
+       "2351                     0.060993                    -0.500294   \n",
+       "452                      0.360143                    -0.011475   \n",
+       "8                       -0.002837                     0.023364   \n",
        "...                           ...                          ...   \n",
-       "2713                    -0.070126                    -0.351428   \n",
-       "2714                    -0.104037                    -0.155081   \n",
-       "2715                     0.067081                    -0.382973   \n",
-       "2716                     0.245733                    -0.404834   \n",
-       "2717                     0.245733                    -0.404834   \n",
+       "334                      0.525867                    -0.204776   \n",
+       "1264                     0.916726                    -0.284010   \n",
+       "1171                    -0.226534                     0.178052   \n",
+       "589                     -0.020815                    -0.774539   \n",
+       "2342                    -0.002837                     0.023364   \n",
        "\n",
        "      Agent 2_Source_Evidence_766  Agent 2_Source_Evidence_767  \n",
-       "0                        0.319292                     0.244462  \n",
-       "1                        0.862336                     0.193277  \n",
-       "2                        0.450191                    -0.544430  \n",
-       "3                        0.148922                    -0.181214  \n",
-       "4                        0.185110                    -0.311448  \n",
+       "1935                     0.386412                    -0.644331  \n",
+       "1583                     0.596412                    -0.605296  \n",
+       "2351                     0.649334                     0.195239  \n",
+       "452                      0.105367                     0.146013  \n",
+       "8                        0.385524                    -0.437104  \n",
        "...                           ...                          ...  \n",
-       "2713                     1.435632                     0.075295  \n",
-       "2714                     0.928627                    -0.082177  \n",
-       "2715                     1.094504                     0.503912  \n",
-       "2716                     0.754788                    -0.139678  \n",
-       "2717                     0.754788                    -0.139678  \n",
+       "334                     -0.197538                    -0.018287  \n",
+       "1264                    -0.056577                     0.407399  \n",
+       "1171                     0.890680                    -0.204422  \n",
+       "589                      0.354638                     0.153618  \n",
+       "2342                     0.385524                    -0.437104  \n",
        "\n",
        "[2718 rows x 768 columns]"
       ]
@@ -972,8 +1046,8 @@
     }
    ],
    "source": [
-    "# the resulting X = features matrix\n",
-    "X = g2._get_feature('nodes')\n",
+    "# the resulting X = features matrix, sbert encoding\n",
+    "X = g2.get_matrix()\n",
     "X"
    ]
   },
@@ -1004,95 +1078,89 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Relationship: sanctioned, sanction, violation</th>\n",
-       "      <th>Relationship: smuggling, bribery, in</th>\n",
+       "      <th>Relationship: occupied, functions, sanctions</th>\n",
+       "      <th>Relationship: laundering, overpricing, international</th>\n",
        "      <th>Relationship: integrates, company, in</th>\n",
+       "      <th>Relationship: complaints, corruption, traffic</th>\n",
+       "      <th>Relationship: sanctioned, sanction, evasion</th>\n",
+       "      <th>Relationship: facilitators, colleagues, student</th>\n",
+       "      <th>Relationship: designates, charge, rights</th>\n",
        "      <th>Relationship: connection, business, in</th>\n",
-       "      <th>Relationship: complaints, corruption, conspiracy</th>\n",
-       "      <th>Relationship: suscribed, traffic, contract</th>\n",
-       "      <th>Relationship: occupied, functions, sanctions</th>\n",
-       "      <th>Relationship: overpricing, currency, illegal</th>\n",
-       "      <th>Relationship: laundering, international, trials</th>\n",
        "      <th>Relationship: members, family, enemies</th>\n",
-       "      <th>Relationship: facilitators, extortion, friends</th>\n",
-       "      <th>Relationship: designates, colleagues, charge</th>\n",
+       "      <th>Relationship: smuggling, bribery, currency</th>\n",
+       "      <th>Relationship: conspiracy, suscribed, contract</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.065003</td>\n",
-       "      <td>0.055670</td>\n",
-       "      <td>0.071783</td>\n",
-       "      <td>0.079073</td>\n",
-       "      <td>34.442679</td>\n",
-       "      <td>0.054766</td>\n",
-       "      <td>0.063643</td>\n",
-       "      <td>0.053862</td>\n",
-       "      <td>0.057605</td>\n",
-       "      <td>0.050000</td>\n",
-       "      <td>0.052458</td>\n",
-       "      <td>0.053457</td>\n",
+       "      <th>1935</th>\n",
+       "      <td>0.072898</td>\n",
+       "      <td>0.553233</td>\n",
+       "      <td>0.089311</td>\n",
+       "      <td>0.068496</td>\n",
+       "      <td>0.075216</td>\n",
+       "      <td>0.058883</td>\n",
+       "      <td>0.059063</td>\n",
+       "      <td>0.091499</td>\n",
+       "      <td>0.067527</td>\n",
+       "      <td>42.147305</td>\n",
+       "      <td>42.766569</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.050119</td>\n",
-       "      <td>0.050523</td>\n",
-       "      <td>0.051003</td>\n",
+       "      <th>1583</th>\n",
+       "      <td>23.982221</td>\n",
+       "      <td>0.053704</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.055351</td>\n",
+       "      <td>0.090587</td>\n",
        "      <td>0.050000</td>\n",
-       "      <td>0.051689</td>\n",
-       "      <td>0.051122</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.062470</td>\n",
        "      <td>0.050000</td>\n",
-       "      <td>0.050509</td>\n",
-       "      <td>0.050418</td>\n",
-       "      <td>0.054442</td>\n",
-       "      <td>15.039365</td>\n",
-       "      <td>0.050809</td>\n",
+       "      <td>0.052716</td>\n",
+       "      <td>0.052951</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.082737</td>\n",
-       "      <td>0.052691</td>\n",
-       "      <td>0.050000</td>\n",
-       "      <td>0.062643</td>\n",
-       "      <td>0.055349</td>\n",
-       "      <td>0.053101</td>\n",
-       "      <td>23.987324</td>\n",
-       "      <td>0.052330</td>\n",
-       "      <td>0.053825</td>\n",
+       "      <th>2351</th>\n",
+       "      <td>23.982221</td>\n",
+       "      <td>0.053704</td>\n",
        "      <td>0.050000</td>\n",
+       "      <td>0.055351</td>\n",
+       "      <td>0.090587</td>\n",
        "      <td>0.050000</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.062470</td>\n",
        "      <td>0.050000</td>\n",
+       "      <td>0.052716</td>\n",
+       "      <td>0.052951</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.074388</td>\n",
-       "      <td>28.567443</td>\n",
-       "      <td>0.065518</td>\n",
-       "      <td>0.063843</td>\n",
-       "      <td>0.060323</td>\n",
-       "      <td>0.117900</td>\n",
-       "      <td>0.064166</td>\n",
-       "      <td>60.674413</td>\n",
-       "      <td>17.229793</td>\n",
-       "      <td>0.065414</td>\n",
-       "      <td>0.055578</td>\n",
-       "      <td>0.061222</td>\n",
+       "      <th>452</th>\n",
+       "      <td>23.982221</td>\n",
+       "      <td>0.053704</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.055351</td>\n",
+       "      <td>0.090587</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.062470</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.052716</td>\n",
+       "      <td>0.052951</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.067568</td>\n",
-       "      <td>31.116316</td>\n",
-       "      <td>0.058412</td>\n",
-       "      <td>0.060998</td>\n",
-       "      <td>0.056463</td>\n",
-       "      <td>0.060142</td>\n",
-       "      <td>0.057811</td>\n",
-       "      <td>0.117512</td>\n",
-       "      <td>37.838020</td>\n",
-       "      <td>0.059851</td>\n",
-       "      <td>0.051573</td>\n",
-       "      <td>0.055335</td>\n",
+       "      <th>8</th>\n",
+       "      <td>0.072986</td>\n",
+       "      <td>0.053850</td>\n",
+       "      <td>0.051025</td>\n",
+       "      <td>0.053534</td>\n",
+       "      <td>16.497892</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.065366</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.053286</td>\n",
+       "      <td>0.052058</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1107,246 +1175,227 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
-       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2713</th>\n",
-       "      <td>0.051717</td>\n",
-       "      <td>0.053141</td>\n",
-       "      <td>23.986537</td>\n",
-       "      <td>0.073292</td>\n",
-       "      <td>0.059816</td>\n",
-       "      <td>0.053714</td>\n",
-       "      <td>0.050000</td>\n",
-       "      <td>0.052808</td>\n",
+       "      <th>334</th>\n",
+       "      <td>0.072986</td>\n",
        "      <td>0.053850</td>\n",
+       "      <td>0.051025</td>\n",
+       "      <td>0.053534</td>\n",
+       "      <td>16.497892</td>\n",
        "      <td>0.050000</td>\n",
-       "      <td>0.051797</td>\n",
-       "      <td>0.063327</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.065366</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.053286</td>\n",
+       "      <td>0.052058</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2714</th>\n",
-       "      <td>0.072604</td>\n",
-       "      <td>0.052956</td>\n",
-       "      <td>0.092982</td>\n",
-       "      <td>23.941857</td>\n",
-       "      <td>0.063406</td>\n",
-       "      <td>0.054355</td>\n",
-       "      <td>0.061778</td>\n",
-       "      <td>0.052354</td>\n",
-       "      <td>0.054615</td>\n",
-       "      <td>0.052002</td>\n",
+       "      <th>1264</th>\n",
+       "      <td>0.072986</td>\n",
+       "      <td>0.053850</td>\n",
+       "      <td>0.051025</td>\n",
+       "      <td>0.053534</td>\n",
+       "      <td>16.497892</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.065366</td>\n",
        "      <td>0.050000</td>\n",
-       "      <td>0.051090</td>\n",
+       "      <td>0.053286</td>\n",
+       "      <td>0.052058</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2715</th>\n",
-       "      <td>0.082737</td>\n",
-       "      <td>0.052691</td>\n",
-       "      <td>0.050000</td>\n",
-       "      <td>0.062643</td>\n",
-       "      <td>0.055349</td>\n",
-       "      <td>0.053101</td>\n",
-       "      <td>23.987324</td>\n",
-       "      <td>0.052330</td>\n",
-       "      <td>0.053825</td>\n",
+       "      <th>1171</th>\n",
        "      <td>0.050000</td>\n",
+       "      <td>0.051786</td>\n",
        "      <td>0.050000</td>\n",
+       "      <td>0.050037</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.054220</td>\n",
        "      <td>0.050000</td>\n",
+       "      <td>0.050705</td>\n",
+       "      <td>18.039779</td>\n",
+       "      <td>0.052556</td>\n",
+       "      <td>0.050917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2716</th>\n",
-       "      <td>0.071095</td>\n",
-       "      <td>0.299774</td>\n",
-       "      <td>0.070829</td>\n",
-       "      <td>0.064280</td>\n",
-       "      <td>0.060561</td>\n",
-       "      <td>0.064907</td>\n",
-       "      <td>0.062544</td>\n",
-       "      <td>25.575909</td>\n",
-       "      <td>20.649126</td>\n",
-       "      <td>0.051986</td>\n",
-       "      <td>0.070271</td>\n",
-       "      <td>0.058718</td>\n",
+       "      <th>589</th>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.053772</td>\n",
+       "      <td>23.990746</td>\n",
+       "      <td>0.059230</td>\n",
+       "      <td>0.051608</td>\n",
+       "      <td>0.052662</td>\n",
+       "      <td>0.060380</td>\n",
+       "      <td>0.072165</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.053006</td>\n",
+       "      <td>0.056433</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2717</th>\n",
-       "      <td>0.071095</td>\n",
-       "      <td>0.299774</td>\n",
-       "      <td>0.070829</td>\n",
-       "      <td>0.064280</td>\n",
-       "      <td>0.060561</td>\n",
-       "      <td>0.064907</td>\n",
-       "      <td>0.062544</td>\n",
-       "      <td>25.575909</td>\n",
-       "      <td>20.649126</td>\n",
-       "      <td>0.051986</td>\n",
-       "      <td>0.070271</td>\n",
-       "      <td>0.058718</td>\n",
+       "      <th>2342</th>\n",
+       "      <td>0.072986</td>\n",
+       "      <td>0.053850</td>\n",
+       "      <td>0.051025</td>\n",
+       "      <td>0.053534</td>\n",
+       "      <td>16.497892</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.065366</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.053286</td>\n",
+       "      <td>0.052058</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>2718 rows × 12 columns</p>\n",
+       "<p>2718 rows × 11 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "      Relationship: sanctioned, sanction, violation  \\\n",
-       "0                                          0.065003   \n",
-       "1                                          0.050119   \n",
-       "2                                          0.082737   \n",
-       "3                                          0.074388   \n",
-       "4                                          0.067568   \n",
-       "...                                             ...   \n",
-       "2713                                       0.051717   \n",
-       "2714                                       0.072604   \n",
-       "2715                                       0.082737   \n",
-       "2716                                       0.071095   \n",
-       "2717                                       0.071095   \n",
+       "      Relationship: occupied, functions, sanctions  \\\n",
+       "1935                                      0.072898   \n",
+       "1583                                     23.982221   \n",
+       "2351                                     23.982221   \n",
+       "452                                      23.982221   \n",
+       "8                                         0.072986   \n",
+       "...                                            ...   \n",
+       "334                                       0.072986   \n",
+       "1264                                      0.072986   \n",
+       "1171                                      0.050000   \n",
+       "589                                       0.050000   \n",
+       "2342                                      0.072986   \n",
        "\n",
-       "      Relationship: smuggling, bribery, in  \\\n",
-       "0                                 0.055670   \n",
-       "1                                 0.050523   \n",
-       "2                                 0.052691   \n",
-       "3                                28.567443   \n",
-       "4                                31.116316   \n",
-       "...                                    ...   \n",
-       "2713                              0.053141   \n",
-       "2714                              0.052956   \n",
-       "2715                              0.052691   \n",
-       "2716                              0.299774   \n",
-       "2717                              0.299774   \n",
+       "      Relationship: laundering, overpricing, international  \\\n",
+       "1935                                              0.553233   \n",
+       "1583                                              0.053704   \n",
+       "2351                                              0.053704   \n",
+       "452                                               0.053704   \n",
+       "8                                                 0.053850   \n",
+       "...                                                    ...   \n",
+       "334                                               0.053850   \n",
+       "1264                                              0.053850   \n",
+       "1171                                              0.051786   \n",
+       "589                                               0.053772   \n",
+       "2342                                              0.053850   \n",
        "\n",
        "      Relationship: integrates, company, in  \\\n",
-       "0                                  0.071783   \n",
-       "1                                  0.051003   \n",
-       "2                                  0.050000   \n",
-       "3                                  0.065518   \n",
-       "4                                  0.058412   \n",
+       "1935                               0.089311   \n",
+       "1583                               0.050000   \n",
+       "2351                               0.050000   \n",
+       "452                                0.050000   \n",
+       "8                                  0.051025   \n",
        "...                                     ...   \n",
-       "2713                              23.986537   \n",
-       "2714                               0.092982   \n",
-       "2715                               0.050000   \n",
-       "2716                               0.070829   \n",
-       "2717                               0.070829   \n",
-       "\n",
-       "      Relationship: connection, business, in  \\\n",
-       "0                                   0.079073   \n",
-       "1                                   0.050000   \n",
-       "2                                   0.062643   \n",
-       "3                                   0.063843   \n",
-       "4                                   0.060998   \n",
-       "...                                      ...   \n",
-       "2713                                0.073292   \n",
-       "2714                               23.941857   \n",
-       "2715                                0.062643   \n",
-       "2716                                0.064280   \n",
-       "2717                                0.064280   \n",
+       "334                                0.051025   \n",
+       "1264                               0.051025   \n",
+       "1171                               0.050000   \n",
+       "589                               23.990746   \n",
+       "2342                               0.051025   \n",
        "\n",
-       "      Relationship: complaints, corruption, conspiracy  \\\n",
-       "0                                            34.442679   \n",
-       "1                                             0.051689   \n",
-       "2                                             0.055349   \n",
-       "3                                             0.060323   \n",
-       "4                                             0.056463   \n",
-       "...                                                ...   \n",
-       "2713                                          0.059816   \n",
-       "2714                                          0.063406   \n",
-       "2715                                          0.055349   \n",
-       "2716                                          0.060561   \n",
-       "2717                                          0.060561   \n",
+       "      Relationship: complaints, corruption, traffic  \\\n",
+       "1935                                       0.068496   \n",
+       "1583                                       0.055351   \n",
+       "2351                                       0.055351   \n",
+       "452                                        0.055351   \n",
+       "8                                          0.053534   \n",
+       "...                                             ...   \n",
+       "334                                        0.053534   \n",
+       "1264                                       0.053534   \n",
+       "1171                                       0.050037   \n",
+       "589                                        0.059230   \n",
+       "2342                                       0.053534   \n",
        "\n",
-       "      Relationship: suscribed, traffic, contract  \\\n",
-       "0                                       0.054766   \n",
-       "1                                       0.051122   \n",
-       "2                                       0.053101   \n",
-       "3                                       0.117900   \n",
-       "4                                       0.060142   \n",
-       "...                                          ...   \n",
-       "2713                                    0.053714   \n",
-       "2714                                    0.054355   \n",
-       "2715                                    0.053101   \n",
-       "2716                                    0.064907   \n",
-       "2717                                    0.064907   \n",
+       "      Relationship: sanctioned, sanction, evasion  \\\n",
+       "1935                                     0.075216   \n",
+       "1583                                     0.090587   \n",
+       "2351                                     0.090587   \n",
+       "452                                      0.090587   \n",
+       "8                                       16.497892   \n",
+       "...                                           ...   \n",
+       "334                                     16.497892   \n",
+       "1264                                    16.497892   \n",
+       "1171                                     0.050001   \n",
+       "589                                      0.051608   \n",
+       "2342                                    16.497892   \n",
        "\n",
-       "      Relationship: occupied, functions, sanctions  \\\n",
-       "0                                         0.063643   \n",
-       "1                                         0.050000   \n",
-       "2                                        23.987324   \n",
-       "3                                         0.064166   \n",
-       "4                                         0.057811   \n",
-       "...                                            ...   \n",
-       "2713                                      0.050000   \n",
-       "2714                                      0.061778   \n",
-       "2715                                     23.987324   \n",
-       "2716                                      0.062544   \n",
-       "2717                                      0.062544   \n",
+       "      Relationship: facilitators, colleagues, student  \\\n",
+       "1935                                         0.058883   \n",
+       "1583                                         0.050000   \n",
+       "2351                                         0.050000   \n",
+       "452                                          0.050000   \n",
+       "8                                            0.050000   \n",
+       "...                                               ...   \n",
+       "334                                          0.050000   \n",
+       "1264                                         0.050000   \n",
+       "1171                                         0.054220   \n",
+       "589                                          0.052662   \n",
+       "2342                                         0.050000   \n",
        "\n",
-       "      Relationship: overpricing, currency, illegal  \\\n",
-       "0                                         0.053862   \n",
-       "1                                         0.050509   \n",
-       "2                                         0.052330   \n",
-       "3                                        60.674413   \n",
-       "4                                         0.117512   \n",
-       "...                                            ...   \n",
-       "2713                                      0.052808   \n",
-       "2714                                      0.052354   \n",
-       "2715                                      0.052330   \n",
-       "2716                                     25.575909   \n",
-       "2717                                     25.575909   \n",
+       "      Relationship: designates, charge, rights  \\\n",
+       "1935                                  0.059063   \n",
+       "1583                                  0.050001   \n",
+       "2351                                  0.050001   \n",
+       "452                                   0.050001   \n",
+       "8                                     0.050001   \n",
+       "...                                        ...   \n",
+       "334                                   0.050001   \n",
+       "1264                                  0.050001   \n",
+       "1171                                  0.050000   \n",
+       "589                                   0.060380   \n",
+       "2342                                  0.050001   \n",
        "\n",
-       "      Relationship: laundering, international, trials  \\\n",
-       "0                                            0.057605   \n",
-       "1                                            0.050418   \n",
-       "2                                            0.053825   \n",
-       "3                                           17.229793   \n",
-       "4                                           37.838020   \n",
-       "...                                               ...   \n",
-       "2713                                         0.053850   \n",
-       "2714                                         0.054615   \n",
-       "2715                                         0.053825   \n",
-       "2716                                        20.649126   \n",
-       "2717                                        20.649126   \n",
+       "      Relationship: connection, business, in  \\\n",
+       "1935                                0.091499   \n",
+       "1583                                0.062470   \n",
+       "2351                                0.062470   \n",
+       "452                                 0.062470   \n",
+       "8                                   0.065366   \n",
+       "...                                      ...   \n",
+       "334                                 0.065366   \n",
+       "1264                                0.065366   \n",
+       "1171                                0.050705   \n",
+       "589                                 0.072165   \n",
+       "2342                                0.065366   \n",
        "\n",
        "      Relationship: members, family, enemies  \\\n",
-       "0                                   0.050000   \n",
-       "1                                   0.054442   \n",
-       "2                                   0.050000   \n",
-       "3                                   0.065414   \n",
-       "4                                   0.059851   \n",
+       "1935                                0.067527   \n",
+       "1583                                0.050000   \n",
+       "2351                                0.050000   \n",
+       "452                                 0.050000   \n",
+       "8                                   0.050000   \n",
        "...                                      ...   \n",
-       "2713                                0.050000   \n",
-       "2714                                0.052002   \n",
-       "2715                                0.050000   \n",
-       "2716                                0.051986   \n",
-       "2717                                0.051986   \n",
+       "334                                 0.050000   \n",
+       "1264                                0.050000   \n",
+       "1171                               18.039779   \n",
+       "589                                 0.050000   \n",
+       "2342                                0.050000   \n",
        "\n",
-       "      Relationship: facilitators, extortion, friends  \\\n",
-       "0                                           0.052458   \n",
-       "1                                          15.039365   \n",
-       "2                                           0.050000   \n",
-       "3                                           0.055578   \n",
-       "4                                           0.051573   \n",
-       "...                                              ...   \n",
-       "2713                                        0.051797   \n",
-       "2714                                        0.050000   \n",
-       "2715                                        0.050000   \n",
-       "2716                                        0.070271   \n",
-       "2717                                        0.070271   \n",
+       "      Relationship: smuggling, bribery, currency  \\\n",
+       "1935                                   42.147305   \n",
+       "1583                                    0.052716   \n",
+       "2351                                    0.052716   \n",
+       "452                                     0.052716   \n",
+       "8                                       0.053286   \n",
+       "...                                          ...   \n",
+       "334                                     0.053286   \n",
+       "1264                                    0.053286   \n",
+       "1171                                    0.052556   \n",
+       "589                                     0.053006   \n",
+       "2342                                    0.053286   \n",
        "\n",
-       "      Relationship: designates, colleagues, charge  \n",
-       "0                                         0.053457  \n",
-       "1                                         0.050809  \n",
-       "2                                         0.050000  \n",
-       "3                                         0.061222  \n",
-       "4                                         0.055335  \n",
-       "...                                            ...  \n",
-       "2713                                      0.063327  \n",
-       "2714                                      0.051090  \n",
-       "2715                                      0.050000  \n",
-       "2716                                      0.058718  \n",
-       "2717                                      0.058718  \n",
+       "      Relationship: conspiracy, suscribed, contract  \n",
+       "1935                                      42.766569  \n",
+       "1583                                       0.052951  \n",
+       "2351                                       0.052951  \n",
+       "452                                        0.052951  \n",
+       "8                                          0.052058  \n",
+       "...                                             ...  \n",
+       "334                                        0.052058  \n",
+       "1264                                       0.052058  \n",
+       "1171                                       0.050917  \n",
+       "589                                        0.056433  \n",
+       "2342                                       0.052058  \n",
        "\n",
-       "[2718 rows x 12 columns]"
+       "[2718 rows x 11 columns]"
       ]
      },
      "execution_count": 17,
@@ -1356,7 +1405,7 @@
    ],
    "source": [
     "# we've reorganized 68 relationships into N topics and we see it has understood the semantics correctly\n",
-    "y = g2._get_target('nodes')\n",
+    "y = g2.get_matrix(target=True)\n",
     "y"
    ]
   },
@@ -1378,7 +1427,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -1393,6 +1442,58 @@
     "y.plot(kind='hist', figsize=(10,5))  "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "46d25a40-deb2-4aac-96c6-d5b3e84a8d1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g2._nodes['rel_topic'] = [y.columns[k].replace('Relationship: ', '') for k in y.values.argmax(1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e81a96ce-407a-47ad-a31f-d6ac1798c4d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1935    conspiracy, suscribed, contract\n",
+       "1583     occupied, functions, sanctions\n",
+       "2351     occupied, functions, sanctions\n",
+       "452      occupied, functions, sanctions\n",
+       "8         sanctioned, sanction, evasion\n",
+       "                     ...               \n",
+       "334       sanctioned, sanction, evasion\n",
+       "1264      sanctioned, sanction, evasion\n",
+       "1171           members, family, enemies\n",
+       "589             integrates, company, in\n",
+       "2342      sanctioned, sanction, evasion\n",
+       "Name: rel_topic, Length: 2718, dtype: object"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2._nodes['rel_topic']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "feba6d2b-12a9-4143-ba22-7a3fadd3d430",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g2 = g2.nodes(g2._nodes, g2._node)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "53574e47",
@@ -1403,143 +1504,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "id": "ce53de1d",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Agent 1</th>\n",
-       "      <th>Relationship</th>\n",
-       "      <th>Agent 2</th>\n",
-       "      <th>Source</th>\n",
-       "      <th>Evidence</th>\n",
-       "      <th>_distance</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2497</th>\n",
-       "      <td>Gustavo Adolfo Hernández Frieri (USA, Colombia)</td>\n",
-       "      <td>International trials. Money laundering. Bribery. Illegal Currency traffic. Company connection</td>\n",
-       "      <td>Global Securities Trade Finance (Cayman Islands)</td>\n",
-       "      <td>Court document</td>\n",
-       "      <td>The Operation Money Flight case mentions that Frieri laundered money from Ortega through a false structure of mutual funds.</td>\n",
-       "      <td>12.718029</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2498</th>\n",
-       "      <td>Abraham Edgardo Ortega (Venezuela)</td>\n",
-       "      <td>International trials. Money laundering. Bribery. Illegal Currency traffic. Company connection</td>\n",
-       "      <td>Global Securities Trade Finance (Cayman Islands)</td>\n",
-       "      <td>Court document</td>\n",
-       "      <td>The Operation Money Flight case mentions that Frieri laundered money from Ortega through a false structure of mutual funds.</td>\n",
-       "      <td>12.718029</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>109</th>\n",
-       "      <td>Alex Nain Saab Morán (Colombia)</td>\n",
-       "      <td>International trials. Money laundering. Fraud</td>\n",
-       "      <td>Devis José Mendoza (Colombia)</td>\n",
-       "      <td>Recognized communication media (trustable)</td>\n",
-       "      <td>79th Court of Guarantee control of Colombia investigates money laundering, conspiracy to commit a crime, illicit enrichment, fake export or import and aggravated scam.</td>\n",
-       "      <td>13.068510</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1744</th>\n",
-       "      <td>Nervis Gerardo Villalobos Cárdenas (Venezuela)</td>\n",
-       "      <td>International trials. Money laundering</td>\n",
-       "      <td>Grupo Swissinvest (No information available)</td>\n",
-       "      <td>Court document</td>\n",
-       "      <td>Judicial investigation in Spain points that through \"the structure of transnational character\" of Swissinvest group, \"money laundering operations were made\" inside and outside Spain to  “raise capital from crimes of corruption” commited through Pdvsa.</td>\n",
-       "      <td>13.110929</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>659</th>\n",
-       "      <td>Diosdado Cabello Rondón (Venezuela)</td>\n",
-       "      <td>Complaints for corruption</td>\n",
-       "      <td>Pedro Fritz Morejon Carrillo (Venezuela)</td>\n",
-       "      <td>Recognized communication media (trustable)</td>\n",
-       "      <td>Accused of laundering around USD $1.300 millions in Panama, Costa Rica, Madrid and USA through companies, using money from corruption, drug traffic and terrorism.</td>\n",
-       "      <td>13.302184</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "                                              Agent 1  \\\n",
-       "2497  Gustavo Adolfo Hernández Frieri (USA, Colombia)   \n",
-       "2498               Abraham Edgardo Ortega (Venezuela)   \n",
-       "109                   Alex Nain Saab Morán (Colombia)   \n",
-       "1744   Nervis Gerardo Villalobos Cárdenas (Venezuela)   \n",
-       "659               Diosdado Cabello Rondón (Venezuela)   \n",
-       "\n",
-       "                                                                                       Relationship  \\\n",
-       "2497  International trials. Money laundering. Bribery. Illegal Currency traffic. Company connection   \n",
-       "2498  International trials. Money laundering. Bribery. Illegal Currency traffic. Company connection   \n",
-       "109                                                   International trials. Money laundering. Fraud   \n",
-       "1744                                                         International trials. Money laundering   \n",
-       "659                                                                       Complaints for corruption   \n",
-       "\n",
-       "                                               Agent 2  \\\n",
-       "2497  Global Securities Trade Finance (Cayman Islands)   \n",
-       "2498  Global Securities Trade Finance (Cayman Islands)   \n",
-       "109                      Devis José Mendoza (Colombia)   \n",
-       "1744      Grupo Swissinvest (No information available)   \n",
-       "659           Pedro Fritz Morejon Carrillo (Venezuela)   \n",
-       "\n",
-       "                                          Source  \\\n",
-       "2497                              Court document   \n",
-       "2498                              Court document   \n",
-       "109   Recognized communication media (trustable)   \n",
-       "1744                              Court document   \n",
-       "659   Recognized communication media (trustable)   \n",
-       "\n",
-       "                                                                                                                                                                                                                                                          Evidence  \\\n",
-       "2497                                                                                                                                  The Operation Money Flight case mentions that Frieri laundered money from Ortega through a false structure of mutual funds.    \n",
-       "2498                                                                                                                                  The Operation Money Flight case mentions that Frieri laundered money from Ortega through a false structure of mutual funds.    \n",
-       "109                                                                                        79th Court of Guarantee control of Colombia investigates money laundering, conspiracy to commit a crime, illicit enrichment, fake export or import and aggravated scam.   \n",
-       "1744  Judicial investigation in Spain points that through \"the structure of transnational character\" of Swissinvest group, \"money laundering operations were made\" inside and outside Spain to  “raise capital from crimes of corruption” commited through Pdvsa.    \n",
-       "659                                                                                           Accused of laundering around USD $1.300 millions in Panama, Costa Rica, Madrid and USA through companies, using money from corruption, drug traffic and terrorism.     \n",
-       "\n",
-       "      _distance  \n",
-       "2497  12.718029  \n",
-       "2498  12.718029  \n",
-       "109   13.068510  \n",
-       "1744  13.110929  \n",
-       "659   13.302184  "
+       "2498              smuggling, bribery, currency\n",
+       "2497              smuggling, bribery, currency\n",
+       "2482                  connection, business, in\n",
+       "1744    laundering, overpricing, international\n",
+       "2487                  connection, business, in\n",
+       "Name: rel_topic, dtype: object"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "res, query_vector = g2.search('money laundering', top_n=5)\n",
-    "res"
+    "res.rel_topic"
    ]
   },
   {
@@ -1547,7 +1534,7 @@
    "id": "d81d93d3",
    "metadata": {},
    "source": [
-    "# Search to Graph\n",
+    "## Search to Graph\n",
     "\n",
     "Pull in neighborhood data from a given search\n",
     "* the resulting graph will contain Agents connected to Agents that have been involved in Money Laundering (or whatever you wish to search for)\n",
@@ -1556,17 +1543,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "id": "01278b51",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"2e17769c-0082-4f83-be09-512b651e7804\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=d35eb79e8a4d4adb9c06802a85be56e9&type=arrow&viztoken=519f25ed-1907-4e9b-8e47-726ab151baa4&usertag=8a6d667e-pygraphistry-0.28.6+133.g649ac53.dirty&splashAfter=1674275324&info=true\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#2e17769c-0082-4f83-be09-512b651e7804\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=5392c6cefae944b38d192740ece8db4a&type=arrow&viztoken=d8174842-dda1-4b4a-9ce9-973a97e2e136&usertag=8a6d667e-pygraphistry-0.28.4+72.g2a02e2b.dirty&splashAfter=1668813586&info=true'"
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1578,7 +1582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "id": "69143805",
    "metadata": {},
    "outputs": [
@@ -1608,6 +1612,7 @@
        "      <th>Agent 2</th>\n",
        "      <th>Source</th>\n",
        "      <th>Evidence</th>\n",
+       "      <th>rel_topic</th>\n",
        "      <th>_distance</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -1619,6 +1624,7 @@
        "      <td>Hermágoras González Polanco (Colombia)</td>\n",
        "      <td>Recognized communication media (trustable)</td>\n",
        "      <td>Colombian druf trafficker, leader of Guajira Cartel, according to the Narcogram, he is linked to Tareck El Aissami.</td>\n",
+       "      <td>complaints, corruption, traffic</td>\n",
        "      <td>13.883661</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1628,6 +1634,7 @@
        "      <td>Robinson Ruíz Guerrero (Colombia)</td>\n",
        "      <td>Recognized communication media (trustable)</td>\n",
        "      <td>79th Court of Guarantee control of Colombia investigates money laundering, conspiracy to commit a crime, illicit enrichment, fake export or import and aggravated scam.</td>\n",
+       "      <td>laundering, overpricing, international</td>\n",
        "      <td>13.998826</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1637,6 +1644,7 @@
        "      <td>Luis Alberto Saab Morán (Colombia)</td>\n",
        "      <td>Recognized communication media (trustable)</td>\n",
        "      <td>79th Court of Guarantee control of Colombia investigates money laundering, conspiracy to commit a crime, illicit enrichment, fake export or import and aggravated scam.</td>\n",
+       "      <td>laundering, overpricing, international</td>\n",
        "      <td>14.068014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1646,6 +1654,7 @@
        "      <td>Jaime Alberto Marín Zamora (Colombia)</td>\n",
        "      <td>Recognized communication media (trustable)</td>\n",
        "      <td>Denounces links between drug lords, scam groups, money laundering and a network of SAIME offices. Ditter José Marcano was pointed.</td>\n",
+       "      <td>complaints, corruption, traffic</td>\n",
        "      <td>14.180918</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1655,6 +1664,7 @@
        "      <td>Amir Luis Saab Morán (Colombia)</td>\n",
        "      <td>Recognized communication media (trustable)</td>\n",
        "      <td>79th Court of Guarantee control of Colombia investigates money laundering, conspiracy to commit a crime, illicit enrichment, fake export or import and aggravated scam.</td>\n",
+       "      <td>laundering, overpricing, international</td>\n",
        "      <td>14.181194</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1697,15 +1707,15 @@
        "668                                        Denounces links between drug lords, scam groups, money laundering and a network of SAIME offices. Ditter José Marcano was pointed.   \n",
        "110   79th Court of Guarantee control of Colombia investigates money laundering, conspiracy to commit a crime, illicit enrichment, fake export or import and aggravated scam.   \n",
        "\n",
-       "      _distance  \n",
-       "2233  13.883661  \n",
-       "108   13.998826  \n",
-       "111   14.068014  \n",
-       "668   14.180918  \n",
-       "110   14.181194  "
+       "                                   rel_topic  _distance  \n",
+       "2233         complaints, corruption, traffic  13.883661  \n",
+       "108   laundering, overpricing, international  13.998826  \n",
+       "111   laundering, overpricing, international  14.068014  \n",
+       "668          complaints, corruption, traffic  14.180918  \n",
+       "110   laundering, overpricing, international  14.181194  "
       ]
      },
-     "execution_count": 21,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1717,17 +1727,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "id": "09685c42",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"572e7b0f-4a24-4e4d-a646-3e5a6dd6544f\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=79ce3fd4f80b4480bbb3cc90e3fdbc30&type=arrow&viztoken=a6331db5-c36e-46f5-b3cc-30a634144316&usertag=8a6d667e-pygraphistry-0.28.6+133.g649ac53.dirty&splashAfter=1674275327&info=true\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#572e7b0f-4a24-4e4d-a646-3e5a6dd6544f\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=710e32ad2c574cf1a4ae0e11e13909ab&type=arrow&viztoken=cc06093d-e095-4474-b5d6-db43f7b33759&usertag=8a6d667e-pygraphistry-0.28.4+72.g2a02e2b.dirty&splashAfter=1668813588&info=true'"
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1738,7 +1765,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "id": "bcfa273d",
    "metadata": {},
    "outputs": [
@@ -1782,8 +1809,8 @@
        "      <td>Included him on the list of sanctioned officials for being \"responsibles or accomplices of serious violations\" to Human Rights, \"important acts of corruption or both\".</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1191</th>\n",
-       "      <td>Jesús Rafael Suárez Chourio (Venezuela)</td>\n",
+       "      <th>2363</th>\n",
+       "      <td>Xavier Antonio Moreno Reandes (Venezuela)</td>\n",
        "      <td>Sanctioned by</td>\n",
        "      <td>On June 25th, 2018 the European Union included him on the list of  11 officials sanctioned</td>\n",
        "    </tr>\n",
@@ -1795,15 +1822,15 @@
        "                                               Agent 1   Relationship  \\\n",
        "1384  Katherine Nayartih Haringhton Padrón (Venezuela)  Sanctioned by   \n",
        "1265       José Miguel Montoanda Rodríguez (Venezuela)  Sanctioned by   \n",
-       "1191           Jesús Rafael Suárez Chourio (Venezuela)  Sanctioned by   \n",
+       "2363         Xavier Antonio Moreno Reandes (Venezuela)  Sanctioned by   \n",
        "\n",
        "                                                                                                                                                                                    Evidence  \n",
        "1384  The only non military officer included on the decree 03/09/2015, in which President Barack Obama suspended visas and froze assets of government officials, for Human Rights violation.  \n",
        "1265               Included him on the list of sanctioned officials for being \"responsibles or accomplices of serious violations\" to Human Rights, \"important acts of corruption or both\".    \n",
-       "1191                                                                                              On June 25th, 2018 the European Union included him on the list of  11 officials sanctioned  "
+       "2363                                                                                              On June 25th, 2018 the European Union included him on the list of  11 officials sanctioned  "
       ]
      },
-     "execution_count": 23,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1815,7 +1842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "id": "653acdfc",
    "metadata": {},
    "outputs": [
@@ -1871,8 +1898,8 @@
        "      <td>National Audience and the Anti - Corruption Prosecutor of Spain investigate alleged bribery and money laundering. Involved: Ministry of Energy and Mining , CORPOELEC.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1126</th>\n",
-       "      <td>Ingeniería Gestión de Proyectos de Energía, C.A. (Ingespre) (No information available)</td>\n",
+       "      <th>2252</th>\n",
+       "      <td>Técnicas Reunidas Terca Ca (Venezuela)</td>\n",
        "      <td>National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved Ministry of Energy and Mining , CORPOELEC.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1881,8 +1908,8 @@
        "      <td>National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved Ministry of Energy and Mining , CORPOELEC.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2252</th>\n",
-       "      <td>Técnicas Reunidas Terca Ca (Venezuela)</td>\n",
+       "      <th>1126</th>\n",
+       "      <td>Ingeniería Gestión de Proyectos de Energía, C.A. (Ingespre) (No information available)</td>\n",
        "      <td>National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved Ministry of Energy and Mining , CORPOELEC.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1906,9 +1933,9 @@
        "1165                                                Javier Andrés Alvarado Ochoa (Venezuela)   \n",
        "1330                                                       Juan Carlos Torres Inclán (Spain)   \n",
        "676                                                                    Duro Felguera (Spain)   \n",
-       "1126  Ingeniería Gestión de Proyectos de Energía, C.A. (Ingespre) (No information available)   \n",
-       "1468                                          Luís Barrios Melean (No information available)   \n",
        "2252                                                  Técnicas Reunidas Terca Ca (Venezuela)   \n",
+       "1468                                          Luís Barrios Melean (No information available)   \n",
+       "1126  Ingeniería Gestión de Proyectos de Energía, C.A. (Ingespre) (No information available)   \n",
        "2283                                                 Víctor Eduardo Aular Blanco (Venezuela)   \n",
        "360                                                  Carlos Eduardo Borges Polar (Venezuela)   \n",
        "\n",
@@ -1918,14 +1945,14 @@
        "1165                      National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved: Ministry of Energy and Mining , CORPOELEC.  \n",
        "1330                      National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved: Ministry of Energy and Mining , CORPOELEC.  \n",
        "676                    National Audience and the Anti - Corruption Prosecutor of Spain investigate alleged bribery and money laundering. Involved: Ministry of Energy and Mining , CORPOELEC.  \n",
-       "1126                       National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved Ministry of Energy and Mining , CORPOELEC.  \n",
-       "1468                       National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved Ministry of Energy and Mining , CORPOELEC.  \n",
        "2252                       National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved Ministry of Energy and Mining , CORPOELEC.  \n",
+       "1468                       National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved Ministry of Energy and Mining , CORPOELEC.  \n",
+       "1126                       National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved Ministry of Energy and Mining , CORPOELEC.  \n",
        "2283                                                                             Member of the Strategic Execution Committee of the Financial area. Official Gazzette 39.182, May 20th, 2009.  \n",
        "360                      Director of the Internal Operations Office (in charge), of the Sectoral Vice - Presidency of Public Works and Services. Official Gazzette 41.182 of June 28th, 2017.  "
       ]
      },
-     "execution_count": 24,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1937,17 +1964,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "id": "f103792c",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"fe00684e-59c9-47c2-aedf-be8a43b9ade8\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=36be92cecd6e44b5a938409ea468222a&type=arrow&viztoken=5cbc4642-597c-4eea-97ed-4dd9e4324d86&usertag=8a6d667e-pygraphistry-0.28.6+133.g649ac53.dirty&splashAfter=1674275329&info=true\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
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+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
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+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=a936df2d05dd4196addee7b8527d6a46&type=arrow&viztoken=d4ed2c99-1df4-4a35-a032-f398f4580209&usertag=8a6d667e-pygraphistry-0.28.4+72.g2a02e2b.dirty&splashAfter=1668813591&info=true'"
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 28,
      "metadata": {},
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@@ -1958,38 +2002,72 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "id": "423315b6",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"600b4be7-d557-4064-8ed7-da99f26b09df\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=3d99ac0015b8429b9d9e6b889813a3c9&type=arrow&viztoken=d2bc9d5a-51d3-4d74-8be8-a6fdf9798cf2&usertag=8a6d667e-pygraphistry-0.28.6+133.g649ac53.dirty&splashAfter=1674275332&info=true\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#600b4be7-d557-4064-8ed7-da99f26b09df\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=b810f623437f48b4aa82a3b005810eed&type=arrow&viztoken=bccdca8f-215c-41b7-a1c5-28f14f95acb5&usertag=8a6d667e-pygraphistry-0.28.4+72.g2a02e2b.dirty&splashAfter=1668813594&info=true'"
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "g2.search_graph('drug trafficking').plot(render=RENDER)"
+    "g2.search_graph('drug trafficking').dbscan().plot(render=RENDER)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 32,
    "id": "e2604442",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"4f8886ea-30a2-440f-b55b-c08ea4e2615b\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=345f62d275524a62811aafc86801aebd&type=arrow&viztoken=4da3512b-4f7d-488f-ab63-30e6133d3fc0&usertag=8a6d667e-pygraphistry-0.28.6+133.g649ac53.dirty&splashAfter=1674275334&info=true\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#4f8886ea-30a2-440f-b55b-c08ea4e2615b\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=fc826e3132da4cd391f0b4c5aeea939e&type=arrow&viztoken=07862a3c-a473-4e11-b8d1-750a6d4dba93&usertag=8a6d667e-pygraphistry-0.28.4+72.g2a02e2b.dirty&splashAfter=1668813596&info=true'"
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2000,23 +2078,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 33,
    "id": "b4f5ebcf",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"0418b4ec-53d0-4e1c-890f-3e9dbdfd7e21\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=741fca805dda4482993aec4027a2bb85&type=arrow&viztoken=8f73cbe5-21da-419e-a92c-493fb9fc492d&usertag=8a6d667e-pygraphistry-0.28.6+133.g649ac53.dirty&splashAfter=1674275337&info=true\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#0418b4ec-53d0-4e1c-890f-3e9dbdfd7e21\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=3ea11287101d47d7ac6748a04a669e98&type=arrow&viztoken=be9ab400-53d0-4b08-9a1e-c0cafc7fc5b7&usertag=8a6d667e-pygraphistry-0.28.4+72.g2a02e2b.dirty&splashAfter=1668813599&info=true'"
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# paste in url to see in new tab \n",
     "g2.search_graph('oil and energy companies').plot(render=RENDER)"
    ]
   },
@@ -2034,14 +2128,6 @@
     "\n",
     "Join the Graphistry-Community Slack! \n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "91380fec",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From 2bf558186b88e9bb2d8055a8192b5e66af479a23 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 20 Jan 2023 20:36:11 -0800
Subject: [PATCH 129/432] edit

---
 demos/ai/Introduction/simple-power-of-umap.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/demos/ai/Introduction/simple-power-of-umap.ipynb b/demos/ai/Introduction/simple-power-of-umap.ipynb
index 19538a5b60..90cd94cbf7 100644
--- a/demos/ai/Introduction/simple-power-of-umap.ipynb
+++ b/demos/ai/Introduction/simple-power-of-umap.ipynb
@@ -702,7 +702,7 @@
    ],
    "source": [
     "# suppose we want to cluster and color by these variables,\n",
-    "g2.dbscan(cols='worst', min_dist=0.3, verbose=True, fit_umap_embedding=True).plot()"
+    "g2.dbscan(cols='worst', min_dist=0.3, verbose=True).plot()"
    ]
   },
   {

From d8ceaa132d63b17523bafc8f80f4de365c6724e8 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 20 Jan 2023 21:00:06 -0800
Subject: [PATCH 130/432] edit

---
 .../Introduction/simple-power-of-umap.ipynb   | 12227 ++++------------
 1 file changed, 2559 insertions(+), 9668 deletions(-)

diff --git a/demos/ai/Introduction/simple-power-of-umap.ipynb b/demos/ai/Introduction/simple-power-of-umap.ipynb
index 90cd94cbf7..5d22c3730a 100644
--- a/demos/ai/Introduction/simple-power-of-umap.ipynb
+++ b/demos/ai/Introduction/simple-power-of-umap.ipynb
@@ -1,13 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "0270b0aa-7eea-4915-a3c5-601c0edd34e3",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -345,8 +337,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 15.7 s, sys: 927 ms, total: 16.7 s\n",
-      "Wall time: 17.6 s\n"
+      "CPU times: user 15.2 s, sys: 745 ms, total: 15.9 s\n",
+      "Wall time: 16 s\n"
      ]
     }
    ],
@@ -370,7 +362,7 @@
      "data": {
       "text/html": [
        "\n",
-       "            <iframe id=\"c63f7b30-bb20-4463-83e3-2bf7db1b3132\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=e828f52e7975477c848f96171fd00225&type=arrow&viztoken=b4eca965-a7da-451e-b3ea-9aebc349e05e&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265684&info=true&play=0\"\n",
+       "            <iframe id=\"4088c62d-d851-456b-bb57-45a0c5f5fddd\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=ecada95bde3d4c03b8145244407f0bcf&type=arrow&viztoken=c5e19343-a80c-45bf-8180-9ae11779e2b9&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276769&info=true&play=0\"\n",
        "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
        "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
        "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
@@ -380,7 +372,7 @@
        "        \n",
        "            <script>\n",
        "                try {\n",
-       "                  $(\"#c63f7b30-bb20-4463-83e3-2bf7db1b3132\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                  $(\"#4088c62d-d851-456b-bb57-45a0c5f5fddd\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
        "                } catch (e) { console.error('exn catching scroll', e); }\n",
        "            </script>\n",
        "        "
@@ -405,10 +397,10 @@
     "tags": []
    },
    "source": [
-    "## DBSCAN as new pivot \n",
+    "## DBSCAN as pivot \n",
     "\n",
     "Think of DBSCAN as a way to pivot by clustering in features of interest. \n",
-    "By setting `cols` you can pick out features from the matrix and have dbscan only focus on those.\n",
+    "By setting `cols` one may pick out features from the matrix and have dbscan only focus on those.\n",
     "Coloring by the `_dbscan` label in the UI finds clusters across those variables. \n",
     "\n",
     "Contrasting UMAP coordinates versus dbscan labels is a useful way to see total behavior against some part/pivot of interest. In the following we see k-clusters in the `worst` (case sensitive column selection) meta variable. "
@@ -455,69 +447,69 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>527</th>\n",
-       "      <td>-0.22733</td>\n",
-       "      <td>-0.65650</td>\n",
-       "      <td>-0.24772</td>\n",
-       "      <td>-0.21837</td>\n",
-       "      <td>-0.07240</td>\n",
-       "      <td>-0.04575</td>\n",
-       "      <td>-0.20788</td>\n",
-       "      <td>0.05038</td>\n",
-       "      <td>0.46218</td>\n",
-       "      <td>-0.19976</td>\n",
+       "      <th>66</th>\n",
+       "      <td>-0.90044</td>\n",
+       "      <td>0.64410</td>\n",
+       "      <td>-0.82082</td>\n",
+       "      <td>-0.72995</td>\n",
+       "      <td>0.79066</td>\n",
+       "      <td>-0.23246</td>\n",
+       "      <td>-0.47392</td>\n",
+       "      <td>-0.33095</td>\n",
+       "      <td>0.16828</td>\n",
+       "      <td>0.58154</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>82</th>\n",
-       "      <td>2.41516</td>\n",
-       "      <td>0.87908</td>\n",
-       "      <td>2.63143</td>\n",
-       "      <td>3.03861</td>\n",
-       "      <td>0.94480</td>\n",
-       "      <td>2.02300</td>\n",
-       "      <td>1.53749</td>\n",
-       "      <td>1.75843</td>\n",
-       "      <td>-0.59045</td>\n",
-       "      <td>1.20651</td>\n",
+       "      <th>70</th>\n",
+       "      <td>1.93428</td>\n",
+       "      <td>0.11740</td>\n",
+       "      <td>1.87355</td>\n",
+       "      <td>2.41938</td>\n",
+       "      <td>-0.39366</td>\n",
+       "      <td>0.16706</td>\n",
+       "      <td>0.24503</td>\n",
+       "      <td>0.85155</td>\n",
+       "      <td>-0.35829</td>\n",
+       "      <td>-0.69391</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>0.19347</td>\n",
-       "      <td>0.67683</td>\n",
-       "      <td>0.36012</td>\n",
-       "      <td>0.17956</td>\n",
-       "      <td>0.10136</td>\n",
-       "      <td>1.05428</td>\n",
-       "      <td>0.62997</td>\n",
-       "      <td>0.17869</td>\n",
-       "      <td>0.56954</td>\n",
-       "      <td>1.07157</td>\n",
+       "      <th>490</th>\n",
+       "      <td>-0.16282</td>\n",
+       "      <td>0.68958</td>\n",
+       "      <td>-0.12018</td>\n",
+       "      <td>-0.13056</td>\n",
+       "      <td>-0.18349</td>\n",
+       "      <td>-0.14923</td>\n",
+       "      <td>-0.35499</td>\n",
+       "      <td>-0.34987</td>\n",
+       "      <td>0.52576</td>\n",
+       "      <td>0.09121</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>509</th>\n",
-       "      <td>0.33696</td>\n",
-       "      <td>1.16907</td>\n",
-       "      <td>0.45727</td>\n",
-       "      <td>0.34346</td>\n",
-       "      <td>1.52036</td>\n",
-       "      <td>1.44973</td>\n",
-       "      <td>1.32700</td>\n",
-       "      <td>1.08928</td>\n",
-       "      <td>0.32694</td>\n",
-       "      <td>1.28361</td>\n",
+       "      <th>67</th>\n",
+       "      <td>-0.52379</td>\n",
+       "      <td>-0.17240</td>\n",
+       "      <td>-0.52187</td>\n",
+       "      <td>-0.45097</td>\n",
+       "      <td>-0.07006</td>\n",
+       "      <td>-0.67788</td>\n",
+       "      <td>-0.26686</td>\n",
+       "      <td>-0.28479</td>\n",
+       "      <td>-0.60145</td>\n",
+       "      <td>-0.66861</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>444</th>\n",
-       "      <td>0.86417</td>\n",
-       "      <td>-0.36223</td>\n",
-       "      <td>0.81809</td>\n",
-       "      <td>0.96742</td>\n",
-       "      <td>-0.17738</td>\n",
-       "      <td>0.26027</td>\n",
-       "      <td>0.72311</td>\n",
-       "      <td>0.49236</td>\n",
-       "      <td>0.08853</td>\n",
-       "      <td>0.10530</td>\n",
+       "      <th>300</th>\n",
+       "      <td>2.14419</td>\n",
+       "      <td>0.08144</td>\n",
+       "      <td>2.01526</td>\n",
+       "      <td>2.80297</td>\n",
+       "      <td>0.61384</td>\n",
+       "      <td>1.22533</td>\n",
+       "      <td>1.65922</td>\n",
+       "      <td>1.05014</td>\n",
+       "      <td>0.31320</td>\n",
+       "      <td>0.93080</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -533,69 +525,69 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>319</th>\n",
-       "      <td>-0.34180</td>\n",
-       "      <td>-0.55591</td>\n",
-       "      <td>-0.37400</td>\n",
-       "      <td>-0.29828</td>\n",
-       "      <td>-1.70534</td>\n",
-       "      <td>-0.87428</td>\n",
-       "      <td>-0.79177</td>\n",
-       "      <td>-0.69748</td>\n",
-       "      <td>-1.22342</td>\n",
-       "      <td>-0.99976</td>\n",
+       "      <th>544</th>\n",
+       "      <td>0.00981</td>\n",
+       "      <td>-0.07615</td>\n",
+       "      <td>0.05505</td>\n",
+       "      <td>0.00421</td>\n",
+       "      <td>-0.15680</td>\n",
+       "      <td>-0.01070</td>\n",
+       "      <td>-0.29445</td>\n",
+       "      <td>-0.29685</td>\n",
+       "      <td>-0.84461</td>\n",
+       "      <td>0.23179</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>0.21121</td>\n",
-       "      <td>0.58373</td>\n",
-       "      <td>0.26761</td>\n",
-       "      <td>0.20810</td>\n",
-       "      <td>0.43077</td>\n",
-       "      <td>0.44637</td>\n",
-       "      <td>0.31480</td>\n",
-       "      <td>0.52088</td>\n",
-       "      <td>-0.08435</td>\n",
-       "      <td>0.64892</td>\n",
+       "      <th>48</th>\n",
+       "      <td>-0.24326</td>\n",
+       "      <td>-0.50449</td>\n",
+       "      <td>-0.19812</td>\n",
+       "      <td>-0.21323</td>\n",
+       "      <td>0.61051</td>\n",
+       "      <td>0.06005</td>\n",
+       "      <td>0.39452</td>\n",
+       "      <td>-0.32773</td>\n",
+       "      <td>-0.04267</td>\n",
+       "      <td>0.13888</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>533</th>\n",
-       "      <td>1.32366</td>\n",
-       "      <td>0.18673</td>\n",
-       "      <td>1.25061</td>\n",
-       "      <td>1.54312</td>\n",
-       "      <td>-0.77828</td>\n",
-       "      <td>0.19204</td>\n",
-       "      <td>0.27680</td>\n",
-       "      <td>0.56650</td>\n",
-       "      <td>0.61555</td>\n",
-       "      <td>-0.78096</td>\n",
+       "      <th>519</th>\n",
+       "      <td>-0.10790</td>\n",
+       "      <td>-0.39450</td>\n",
+       "      <td>-0.09593</td>\n",
+       "      <td>-0.12810</td>\n",
+       "      <td>0.54712</td>\n",
+       "      <td>-0.04518</td>\n",
+       "      <td>-0.27551</td>\n",
+       "      <td>-0.17208</td>\n",
+       "      <td>0.47907</td>\n",
+       "      <td>0.26341</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>449</th>\n",
-       "      <td>1.71866</td>\n",
-       "      <td>0.71322</td>\n",
-       "      <td>1.62530</td>\n",
-       "      <td>2.15795</td>\n",
-       "      <td>0.20271</td>\n",
-       "      <td>0.48514</td>\n",
-       "      <td>0.76371</td>\n",
-       "      <td>1.20048</td>\n",
-       "      <td>-0.71175</td>\n",
-       "      <td>-0.28024</td>\n",
+       "      <th>173</th>\n",
+       "      <td>-0.71604</td>\n",
+       "      <td>-0.91486</td>\n",
+       "      <td>-0.68511</td>\n",
+       "      <td>-0.59497</td>\n",
+       "      <td>-0.31693</td>\n",
+       "      <td>-0.73187</td>\n",
+       "      <td>-0.69935</td>\n",
+       "      <td>-0.56084</td>\n",
+       "      <td>-1.40338</td>\n",
+       "      <td>-0.34172</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>567</th>\n",
-       "      <td>1.72834</td>\n",
-       "      <td>1.49973</td>\n",
-       "      <td>2.00463</td>\n",
-       "      <td>1.83015</td>\n",
-       "      <td>1.22353</td>\n",
-       "      <td>3.36960</td>\n",
-       "      <td>2.62196</td>\n",
-       "      <td>1.55217</td>\n",
-       "      <td>1.82433</td>\n",
-       "      <td>2.11735</td>\n",
+       "      <th>34</th>\n",
+       "      <td>1.02207</td>\n",
+       "      <td>0.18932</td>\n",
+       "      <td>0.96880</td>\n",
+       "      <td>1.17836</td>\n",
+       "      <td>0.45038</td>\n",
+       "      <td>2.22889</td>\n",
+       "      <td>1.31041</td>\n",
+       "      <td>0.92953</td>\n",
+       "      <td>2.40982</td>\n",
+       "      <td>2.09875</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -604,43 +596,43 @@
       ],
       "text/plain": [
        "     worst radius  worst texture  worst perimeter  worst area  \\\n",
-       "527      -0.22733       -0.65650         -0.24772    -0.21837   \n",
-       "82        2.41516        0.87908          2.63143     3.03861   \n",
-       "541       0.19347        0.67683          0.36012     0.17956   \n",
-       "509       0.33696        1.16907          0.45727     0.34346   \n",
-       "444       0.86417       -0.36223          0.81809     0.96742   \n",
+       "66       -0.90044        0.64410         -0.82082    -0.72995   \n",
+       "70        1.93428        0.11740          1.87355     2.41938   \n",
+       "490      -0.16282        0.68958         -0.12018    -0.13056   \n",
+       "67       -0.52379       -0.17240         -0.52187    -0.45097   \n",
+       "300       2.14419        0.08144          2.01526     2.80297   \n",
        "..            ...            ...              ...         ...   \n",
-       "319      -0.34180       -0.55591         -0.37400    -0.29828   \n",
-       "99        0.21121        0.58373          0.26761     0.20810   \n",
-       "533       1.32366        0.18673          1.25061     1.54312   \n",
-       "449       1.71866        0.71322          1.62530     2.15795   \n",
-       "567       1.72834        1.49973          2.00463     1.83015   \n",
+       "544       0.00981       -0.07615          0.05505     0.00421   \n",
+       "48       -0.24326       -0.50449         -0.19812    -0.21323   \n",
+       "519      -0.10790       -0.39450         -0.09593    -0.12810   \n",
+       "173      -0.71604       -0.91486         -0.68511    -0.59497   \n",
+       "34        1.02207        0.18932          0.96880     1.17836   \n",
        "\n",
        "     worst smoothness  worst compactness  worst concavity  \\\n",
-       "527          -0.07240           -0.04575         -0.20788   \n",
-       "82            0.94480            2.02300          1.53749   \n",
-       "541           0.10136            1.05428          0.62997   \n",
-       "509           1.52036            1.44973          1.32700   \n",
-       "444          -0.17738            0.26027          0.72311   \n",
+       "66            0.79066           -0.23246         -0.47392   \n",
+       "70           -0.39366            0.16706          0.24503   \n",
+       "490          -0.18349           -0.14923         -0.35499   \n",
+       "67           -0.07006           -0.67788         -0.26686   \n",
+       "300           0.61384            1.22533          1.65922   \n",
        "..                ...                ...              ...   \n",
-       "319          -1.70534           -0.87428         -0.79177   \n",
-       "99            0.43077            0.44637          0.31480   \n",
-       "533          -0.77828            0.19204          0.27680   \n",
-       "449           0.20271            0.48514          0.76371   \n",
-       "567           1.22353            3.36960          2.62196   \n",
+       "544          -0.15680           -0.01070         -0.29445   \n",
+       "48            0.61051            0.06005          0.39452   \n",
+       "519           0.54712           -0.04518         -0.27551   \n",
+       "173          -0.31693           -0.73187         -0.69935   \n",
+       "34            0.45038            2.22889          1.31041   \n",
        "\n",
        "     worst concave points  worst symmetry  worst fractal dimension  \n",
-       "527               0.05038         0.46218                 -0.19976  \n",
-       "82                1.75843        -0.59045                  1.20651  \n",
-       "541               0.17869         0.56954                  1.07157  \n",
-       "509               1.08928         0.32694                  1.28361  \n",
-       "444               0.49236         0.08853                  0.10530  \n",
+       "66               -0.33095         0.16828                  0.58154  \n",
+       "70                0.85155        -0.35829                 -0.69391  \n",
+       "490              -0.34987         0.52576                  0.09121  \n",
+       "67               -0.28479        -0.60145                 -0.66861  \n",
+       "300               1.05014         0.31320                  0.93080  \n",
        "..                    ...             ...                      ...  \n",
-       "319              -0.69748        -1.22342                 -0.99976  \n",
-       "99                0.52088        -0.08435                  0.64892  \n",
-       "533               0.56650         0.61555                 -0.78096  \n",
-       "449               1.20048        -0.71175                 -0.28024  \n",
-       "567               1.55217         1.82433                  2.11735  \n",
+       "544              -0.29685        -0.84461                  0.23179  \n",
+       "48               -0.32773        -0.04267                  0.13888  \n",
+       "519              -0.17208         0.47907                  0.26341  \n",
+       "173              -0.56084        -1.40338                 -0.34172  \n",
+       "34                0.92953         2.40982                  2.09875  \n",
        "\n",
        "[284 rows x 10 columns]"
       ]
@@ -676,7 +668,7 @@
      "data": {
       "text/html": [
        "\n",
-       "            <iframe id=\"1d8edd74-8442-4fda-a999-c04b42b38ca9\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=1167fd431afd42ac98aa7ced2d09085d&type=arrow&viztoken=f01cf57b-cec7-4e89-aca6-a19af2e1d1db&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265687&info=true&play=0\"\n",
+       "            <iframe id=\"f34140df-4de6-4685-b075-ba7574526085\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=87ca648df87547a1b7c624e835306990&type=arrow&viztoken=50ccb834-a917-48f3-acc2-e4d3b5468fb1&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276772&info=true&play=0\"\n",
        "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
        "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
        "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
@@ -686,7 +678,7 @@
        "        \n",
        "            <script>\n",
        "                try {\n",
-       "                  $(\"#1d8edd74-8442-4fda-a999-c04b42b38ca9\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                  $(\"#f34140df-4de6-4685-b075-ba7574526085\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
        "                } catch (e) { console.error('exn catching scroll', e); }\n",
        "            </script>\n",
        "        "
@@ -749,34 +741,34 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>527</th>\n",
-       "      <td>-0.26657</td>\n",
-       "      <td>-0.83907</td>\n",
-       "      <td>0.46218</td>\n",
+       "      <th>66</th>\n",
+       "      <td>-0.18831</td>\n",
+       "      <td>0.59780</td>\n",
+       "      <td>0.16828</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>82</th>\n",
-       "      <td>0.15041</td>\n",
-       "      <td>-0.91072</td>\n",
-       "      <td>-0.59045</td>\n",
+       "      <th>70</th>\n",
+       "      <td>-0.59833</td>\n",
+       "      <td>-0.62800</td>\n",
+       "      <td>-0.35829</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>541</th>\n",
-       "      <td>0.27848</td>\n",
-       "      <td>0.21215</td>\n",
-       "      <td>0.56954</td>\n",
+       "      <th>490</th>\n",
+       "      <td>-0.71374</td>\n",
+       "      <td>-0.32327</td>\n",
+       "      <td>0.52576</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>509</th>\n",
-       "      <td>0.08488</td>\n",
-       "      <td>0.25357</td>\n",
-       "      <td>0.32694</td>\n",
+       "      <th>67</th>\n",
+       "      <td>-0.79879</td>\n",
+       "      <td>0.46603</td>\n",
+       "      <td>-0.60145</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>444</th>\n",
-       "      <td>-0.17424</td>\n",
-       "      <td>-0.65323</td>\n",
-       "      <td>0.08853</td>\n",
+       "      <th>300</th>\n",
+       "      <td>0.03948</td>\n",
+       "      <td>0.05559</td>\n",
+       "      <td>0.31320</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -785,34 +777,34 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>319</th>\n",
-       "      <td>-0.91288</td>\n",
-       "      <td>1.74027</td>\n",
-       "      <td>-1.22342</td>\n",
+       "      <th>544</th>\n",
+       "      <td>-0.48292</td>\n",
+       "      <td>-0.48524</td>\n",
+       "      <td>-0.84461</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>0.29933</td>\n",
-       "      <td>-0.46627</td>\n",
-       "      <td>-0.08435</td>\n",
+       "      <th>48</th>\n",
+       "      <td>-0.31587</td>\n",
+       "      <td>-0.57035</td>\n",
+       "      <td>-0.04267</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>533</th>\n",
-       "      <td>1.15413</td>\n",
-       "      <td>1.04954</td>\n",
-       "      <td>0.61555</td>\n",
+       "      <th>519</th>\n",
+       "      <td>1.03569</td>\n",
+       "      <td>-0.05285</td>\n",
+       "      <td>0.47907</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>449</th>\n",
-       "      <td>-0.66865</td>\n",
-       "      <td>-0.95102</td>\n",
-       "      <td>-0.71175</td>\n",
+       "      <th>173</th>\n",
+       "      <td>-0.64692</td>\n",
+       "      <td>1.70007</td>\n",
+       "      <td>-1.40338</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>567</th>\n",
-       "      <td>1.84214</td>\n",
-       "      <td>0.49874</td>\n",
-       "      <td>1.82433</td>\n",
+       "      <th>34</th>\n",
+       "      <td>0.66515</td>\n",
+       "      <td>-0.19286</td>\n",
+       "      <td>2.40982</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -821,17 +813,17 @@
       ],
       "text/plain": [
        "     mean symmetry  symmetry error  worst symmetry\n",
-       "527       -0.26657        -0.83907         0.46218\n",
-       "82         0.15041        -0.91072        -0.59045\n",
-       "541        0.27848         0.21215         0.56954\n",
-       "509        0.08488         0.25357         0.32694\n",
-       "444       -0.17424        -0.65323         0.08853\n",
+       "66        -0.18831         0.59780         0.16828\n",
+       "70        -0.59833        -0.62800        -0.35829\n",
+       "490       -0.71374        -0.32327         0.52576\n",
+       "67        -0.79879         0.46603        -0.60145\n",
+       "300        0.03948         0.05559         0.31320\n",
        "..             ...             ...             ...\n",
-       "319       -0.91288         1.74027        -1.22342\n",
-       "99         0.29933        -0.46627        -0.08435\n",
-       "533        1.15413         1.04954         0.61555\n",
-       "449       -0.66865        -0.95102        -0.71175\n",
-       "567        1.84214         0.49874         1.82433\n",
+       "544       -0.48292        -0.48524        -0.84461\n",
+       "48        -0.31587        -0.57035        -0.04267\n",
+       "519        1.03569        -0.05285         0.47907\n",
+       "173       -0.64692         1.70007        -1.40338\n",
+       "34         0.66515        -0.19286         2.40982\n",
        "\n",
        "[284 rows x 3 columns]"
       ]
@@ -871,27 +863,11 @@
      "text": [
       "! Failed umap speedup attempt. Continuing without memoization speedups.* Ignoring target column of shape (284, 0) in UMAP fit, as it is not one dimensional"
      ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "lazy init\n",
-      "_______________\n",
-      "\n",
-      "UMAP Parameters\n",
-      "_______________\n",
-      "\n",
-      "{'n_components': 2, 'metric': 'euclidean', 'n_neighbors': 12, 'min_dist': 0.1, 'spread': 0.5, 'local_connectivity': 1, 'repulsion_strength': 1, 'negative_sample_rate': 5}\n",
-      "------------------------------------------------------------\n",
-      "** Fitting UMAP\n",
-      "Same umap params as last time, skipping new init\n"
-     ]
     }
    ],
    "source": [
     "g = graphistry.nodes(X)\n",
-    "gq = g.umap(X=small_study, verbose=True).dbscan()"
+    "gq = g.umap(X=small_study, verbose=False).dbscan()"
    ]
   },
   {
@@ -904,7 +880,7 @@
      "data": {
       "text/html": [
        "\n",
-       "            <iframe id=\"3b681390-41be-42d1-b950-0f455e5a1628\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=e20f525f58304fc3a20d260e36e63941&type=arrow&viztoken=595790eb-828a-4eee-8769-bf57720a5531&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265691&info=true&play=0\"\n",
+       "            <iframe id=\"bdea0371-a81f-4fc7-8a4f-c8dd7474db9e\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=3ebc44b8230c497fbcfca34b241acb7a&type=arrow&viztoken=d9e835ae-875b-4e8d-b2c7-b672f8867f19&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276776&info=true&play=0\"\n",
        "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
        "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
        "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
@@ -914,7 +890,7 @@
        "        \n",
        "            <script>\n",
        "                try {\n",
-       "                  $(\"#3b681390-41be-42d1-b950-0f455e5a1628\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                  $(\"#bdea0371-a81f-4fc7-8a4f-c8dd7474db9e\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
        "                } catch (e) { console.error('exn catching scroll', e); }\n",
        "            </script>\n",
        "        "
@@ -955,17 +931,17 @@
       "--------------------------------------------------\n",
       "Infering edges over UMAP embedding\n",
       "---------------------------------------------\n",
-      " Mean distance to existing nodes 1.88 +/- 1.07\n",
+      " Mean distance to existing nodes 1.89 +/- 1.10\n",
       " Max distance threshold; epsilon = 1.00\n",
       " Finding 7 nearest neighbors\n",
-      " 68.61 neighbors per node within epsilon 1.00\n",
+      " 68.76 neighbors per node within epsilon 1.00\n",
       " 1995 total edges after dropping duplicates\n",
-      " ** Final graph has 349 nodes\n",
+      " ** Final graph has 348 nodes\n",
       " - Batch has 285 nodes\n",
-      " - Brought in 64 nodes\n",
+      " - Brought in 63 nodes\n",
       "--------------------------------------------------\n",
-      "CPU times: user 6.78 s, sys: 34.4 ms, total: 6.82 s\n",
-      "Wall time: 6.86 s\n"
+      "CPU times: user 7.17 s, sys: 54.4 ms, total: 7.23 s\n",
+      "Wall time: 7.28 s\n"
      ]
     }
    ],
@@ -990,7 +966,7 @@
      "data": {
       "text/html": [
        "\n",
-       "            <iframe id=\"010841fc-1cee-4c85-9bbf-1af2ee3140ec\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=662d9cb603a74226bcc8dd4cf5e79060&type=arrow&viztoken=741efa50-8f98-49ad-80e3-a7f348ef99dd&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265701&info=true&play=0\"\n",
+       "            <iframe id=\"0b352144-c542-4e0b-9884-e57428b483f7\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=7dc2ed5313264581bd51a4bdd7055861&type=arrow&viztoken=20a494c1-f454-41c1-a326-bb90c29b62c7&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276786&info=true&play=0\"\n",
        "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
        "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
        "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
@@ -1000,7 +976,7 @@
        "        \n",
        "            <script>\n",
        "                try {\n",
-       "                  $(\"#010841fc-1cee-4c85-9bbf-1af2ee3140ec\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                  $(\"#0b352144-c542-4e0b-9884-e57428b483f7\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
        "                } catch (e) { console.error('exn catching scroll', e); }\n",
        "            </script>\n",
        "        "
@@ -1031,15 +1007,15 @@
       "--------------------------------------------------\n",
       "Infering edges over features embedding\n",
       "---------------------------------------------\n",
-      " Mean distance to existing nodes 5.96 +/- 3.08\n",
+      " Mean distance to existing nodes 7.13 +/- 4.26\n",
       " Max distance threshold; epsilon = 6.00\n",
       " Finding 7 nearest neighbors\n",
-      " 167.55 neighbors per node within epsilon 6.00\n",
-      " 1932 total edges after dropping duplicates\n",
+      " 138.57 neighbors per node within epsilon 6.00\n",
+      " 1891 total edges after dropping duplicates\n",
       " ** Final graph has 285 nodes\n",
       "--------------------------------------------------\n",
-      "CPU times: user 2.76 s, sys: 21 ms, total: 2.78 s\n",
-      "Wall time: 2.82 s\n"
+      "CPU times: user 3.09 s, sys: 24.4 ms, total: 3.12 s\n",
+      "Wall time: 3.13 s\n"
      ]
     }
    ],
@@ -1063,7 +1039,7 @@
      "data": {
       "text/html": [
        "\n",
-       "            <iframe id=\"17e2c491-5b49-4127-abe1-ef5d73121241\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=110836719d0d4ed6a4355c8d389aae69&type=arrow&viztoken=c1232209-afbb-4dca-9040-b3541b227a91&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265706&info=true&play=0\"\n",
+       "            <iframe id=\"7d25c37c-b006-4c95-9edf-031d3205d129\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=1ffd8d2ef0a84c2b800af0f74e2b26a8&type=arrow&viztoken=01484534-2663-4549-9aef-ac586e4f8b09&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276792&info=true&play=0\"\n",
        "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
        "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
        "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
@@ -1073,7 +1049,7 @@
        "        \n",
        "            <script>\n",
        "                try {\n",
-       "                  $(\"#17e2c491-5b49-4127-abe1-ef5d73121241\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                  $(\"#7d25c37c-b006-4c95-9edf-031d3205d129\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
        "                } catch (e) { console.error('exn catching scroll', e); }\n",
        "            </script>\n",
        "        "
@@ -1396,7 +1372,7 @@
      "data": {
       "text/html": [
        "\n",
-       "            <iframe id=\"0d6e2022-a453-48ff-ad3e-100f010b5b07\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=5975b94c4e164a8db6157ef0277374d8&type=arrow&viztoken=bfd0d445-c261-407c-8e74-6bc310e8c593&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265711&info=true&play=0\"\n",
+       "            <iframe id=\"cf309073-feb6-4be3-824e-087153c244ba\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=c425858369544c40b69b1d2b5ec306df&type=arrow&viztoken=68547c11-3ab6-472c-a55c-5e255abe9bc8&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276797&info=true&play=0\"\n",
        "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
        "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
        "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
@@ -1406,7 +1382,7 @@
        "        \n",
        "            <script>\n",
        "                try {\n",
-       "                  $(\"#0d6e2022-a453-48ff-ad3e-100f010b5b07\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                  $(\"#cf309073-feb6-4be3-824e-087153c244ba\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
        "                } catch (e) { console.error('exn catching scroll', e); }\n",
        "            </script>\n",
        "        "
@@ -1436,7 +1412,7 @@
      "text": [
       "\n",
       "----------------------------------------\n",
-      "DBSCAN found 15 clusters with 0 outliers\n",
+      "DBSCAN found 11 clusters with 0 outliers\n",
       "--fit on umap embeddings of size (221, 2)\n",
       "----------------------------------------\n"
      ]
@@ -1458,7 +1434,7 @@
      "data": {
       "text/html": [
        "\n",
-       "            <iframe id=\"756dd559-ae48-47bf-b4d4-a2a71e17bad2\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=accec73aea9d41b1b972cf32be6b8139&type=arrow&viztoken=4818b992-81c6-4315-ba95-25b5e662e1f8&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265717&info=true&play=0\"\n",
+       "            <iframe id=\"c481fd86-a911-4490-bb51-5853ec9db4ad\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=4741be8722424a1f9b443b0e382fcb9f&type=arrow&viztoken=139232b3-b3ec-463b-b41a-5ab52a94d8a2&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276803&info=true&play=0\"\n",
        "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
        "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
        "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
@@ -1468,7 +1444,7 @@
        "        \n",
        "            <script>\n",
        "                try {\n",
-       "                  $(\"#756dd559-ae48-47bf-b4d4-a2a71e17bad2\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                  $(\"#c481fd86-a911-4490-bb51-5853ec9db4ad\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
        "                } catch (e) { console.error('exn catching scroll', e); }\n",
        "            </script>\n",
        "        "
@@ -1492,7 +1468,7 @@
    "id": "9a1d000d",
    "metadata": {},
    "source": [
-    "## Add your favorite model \n",
+    "## Add your Favorite Model \n",
     "\n",
     "We will use Optuna to demonstrate a sample pipeline with HPO"
    ]
@@ -1509,7 +1485,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m[I 2023-01-20 17:48:23,033]\u001b[0m A new study created in memory with name: Diabetes\u001b[0m\u001b[32m[I 2023-01-20 17:48:23,150]\u001b[0m Trial 0 finished with value: 0.4491825511591293 and parameters: {'n_estimators': 67, 'max_depth': 199, 'min_samples_split': 36, 'scaler': 'robust'}. Best is trial 0 with value: 0.4491825511591293.\u001b[0m\u001b[32m[I 2023-01-20 17:48:23,344]\u001b[0m Trial 1 finished with value: 0.3611013176493034 and parameters: {'n_estimators': 186, 'max_depth': 36, 'min_samples_split': 88, 'scaler': 'standard'}. Best is trial 0 with value: 0.4491825511591293.\u001b[0m\u001b[32m[I 2023-01-20 17:48:23,636]\u001b[0m Trial 2 finished with value: 0.4599555207008448 and parameters: {'n_estimators': 221, 'max_depth': 186, 'min_samples_split': 31, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:23,953]\u001b[0m Trial 3 finished with value: 0.43972380164107383 and parameters: {'n_estimators': 237, 'max_depth': 187, 'min_samples_split': 41, 'scaler': 'standard'}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:24,217]\u001b[0m Trial 4 finished with value: 0.41916456260562696 and parameters: {'n_estimators': 232, 'max_depth': 161, 'min_samples_split': 58, 'scaler': 'quantile'}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:24,454]\u001b[0m Trial 5 finished with value: 0.4390071382812232 and parameters: {'n_estimators': 219, 'max_depth': 162, 'min_samples_split': 40, 'scaler': 'robust'}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:24,606]\u001b[0m Trial 6 finished with value: 0.4477315398732742 and parameters: {'n_estimators': 126, 'max_depth': 58, 'min_samples_split': 38, 'scaler': 'quantile'}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:24,857]\u001b[0m Trial 7 finished with value: 0.4238875440292236 and parameters: {'n_estimators': 181, 'max_depth': 175, 'min_samples_split': 56, 'scaler': 'quantile'}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:24,978]\u001b[0m Trial 8 finished with value: 0.34947836592039183 and parameters: {'n_estimators': 93, 'max_depth': 164, 'min_samples_split': 100, 'scaler': 'quantile'}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:25,141]\u001b[0m Trial 9 finished with value: 0.3410582039173441 and parameters: {'n_estimators': 167, 'max_depth': 144, 'min_samples_split': 96, 'scaler': 'robust'}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:25,331]\u001b[0m Trial 10 finished with value: 0.42789907031888397 and parameters: {'n_estimators': 137, 'max_depth': 104, 'min_samples_split': 3, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:25,406]\u001b[0m Trial 11 finished with value: 0.4407087833174965 and parameters: {'n_estimators': 54, 'max_depth': 198, 'min_samples_split': 18, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:25,483]\u001b[0m Trial 12 finished with value: 0.43123238250660256 and parameters: {'n_estimators': 56, 'max_depth': 118, 'min_samples_split': 21, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:25,592]\u001b[0m Trial 13 finished with value: 0.41759544231255075 and parameters: {'n_estimators': 93, 'max_depth': 129, 'min_samples_split': 65, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:25,722]\u001b[0m Trial 14 finished with value: 0.4460536456206613 and parameters: {'n_estimators': 103, 'max_depth': 3, 'min_samples_split': 21, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:25,946]\u001b[0m Trial 15 finished with value: 0.39157752162536286 and parameters: {'n_estimators': 209, 'max_depth': 88, 'min_samples_split': 71, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:26,138]\u001b[0m Trial 16 finished with value: 0.45319719982218454 and parameters: {'n_estimators': 157, 'max_depth': 199, 'min_samples_split': 31, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:26,350]\u001b[0m Trial 17 finished with value: 0.45767575785023773 and parameters: {'n_estimators': 158, 'max_depth': 138, 'min_samples_split': 7, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:26,616]\u001b[0m Trial 18 finished with value: 0.43892702761740565 and parameters: {'n_estimators': 192, 'max_depth': 135, 'min_samples_split': 3, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:26,865]\u001b[0m Trial 19 finished with value: 0.45450074484535274 and parameters: {'n_estimators': 206, 'max_depth': 85, 'min_samples_split': 12, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:27,032]\u001b[0m Trial 20 finished with value: 0.455948254738035 and parameters: {'n_estimators': 128, 'max_depth': 139, 'min_samples_split': 28, 'scaler': None}. Best is trial 2 with value: 0.4599555207008448.\u001b[0m\u001b[32m[I 2023-01-20 17:48:27,181]\u001b[0m Trial 21 finished with value: 0.4659875568563212 and parameters: {'n_estimators': 125, 'max_depth': 148, 'min_samples_split': 28, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:27,328]\u001b[0m Trial 22 finished with value: 0.45261144904606754 and parameters: {'n_estimators': 113, 'max_depth': 150, 'min_samples_split': 11, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:27,502]\u001b[0m Trial 23 finished with value: 0.4432510337508433 and parameters: {'n_estimators': 149, 'max_depth': 112, 'min_samples_split': 47, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:27,695]\u001b[0m Trial 24 finished with value: 0.454945193668833 and parameters: {'n_estimators': 169, 'max_depth': 176, 'min_samples_split': 28, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:28,000]\u001b[0m Trial 25 finished with value: 0.457519704282382 and parameters: {'n_estimators': 250, 'max_depth': 122, 'min_samples_split': 13, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:28,170]\u001b[0m Trial 26 finished with value: 0.454372244336522 and parameters: {'n_estimators': 145, 'max_depth': 153, 'min_samples_split': 26, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:28,262]\u001b[0m Trial 27 finished with value: 0.4426481977872042 and parameters: {'n_estimators': 74, 'max_depth': 177, 'min_samples_split': 50, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:28,517]\u001b[0m Trial 28 finished with value: 0.45478126295796806 and parameters: {'n_estimators': 118, 'max_depth': 90, 'min_samples_split': 8, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:28,620]\u001b[0m Trial 29 finished with value: 0.45498664133674827 and parameters: {'n_estimators': 73, 'max_depth': 187, 'min_samples_split': 17, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:28,804]\u001b[0m Trial 30 finished with value: 0.45590798145391953 and parameters: {'n_estimators': 166, 'max_depth': 130, 'min_samples_split': 36, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:29,089]\u001b[0m Trial 31 finished with value: 0.4571133333268268 and parameters: {'n_estimators': 238, 'max_depth': 120, 'min_samples_split': 15, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:29,416]\u001b[0m Trial 32 finished with value: 0.45617411970879174 and parameters: {'n_estimators': 249, 'max_depth': 77, 'min_samples_split': 7, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:29,664]\u001b[0m Trial 33 finished with value: 0.45364032098575513 and parameters: {'n_estimators': 223, 'max_depth': 104, 'min_samples_split': 33, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:29,996]\u001b[0m Trial 34 finished with value: 0.45900022338396884 and parameters: {'n_estimators': 247, 'max_depth': 151, 'min_samples_split': 24, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:30,244]\u001b[0m Trial 35 finished with value: 0.4588124181389295 and parameters: {'n_estimators': 222, 'max_depth': 159, 'min_samples_split': 23, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:30,537]\u001b[0m Trial 36 finished with value: 0.4491496739615546 and parameters: {'n_estimators': 203, 'max_depth': 167, 'min_samples_split': 45, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:30,860]\u001b[0m Trial 37 finished with value: 0.45754435816463646 and parameters: {'n_estimators': 223, 'max_depth': 185, 'min_samples_split': 23, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:31,158]\u001b[0m Trial 38 finished with value: 0.45246743668284095 and parameters: {'n_estimators': 235, 'max_depth': 160, 'min_samples_split': 38, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:31,385]\u001b[0m Trial 39 finished with value: 0.45125727640745616 and parameters: {'n_estimators': 183, 'max_depth': 155, 'min_samples_split': 42, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:31,617]\u001b[0m Trial 40 finished with value: 0.38310567273175145 and parameters: {'n_estimators': 217, 'max_depth': 170, 'min_samples_split': 80, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:31,841]\u001b[0m Trial 41 finished with value: 0.45135671120312215 and parameters: {'n_estimators': 194, 'max_depth': 146, 'min_samples_split': 34, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:32,173]\u001b[0m Trial 42 finished with value: 0.4623472408695728 and parameters: {'n_estimators': 243, 'max_depth': 186, 'min_samples_split': 25, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:32,464]\u001b[0m Trial 43 finished with value: 0.45880662185066146 and parameters: {'n_estimators': 231, 'max_depth': 193, 'min_samples_split': 25, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:32,725]\u001b[0m Trial 44 finished with value: 0.4254733474254583 and parameters: {'n_estimators': 245, 'max_depth': 187, 'min_samples_split': 56, 'scaler': None}. Best is trial 21 with value: 0.4659875568563212.\u001b[0m\u001b[32m[I 2023-01-20 17:48:32,998]\u001b[0m Trial 45 finished with value: 0.4660198609778968 and parameters: {'n_estimators': 229, 'max_depth': 179, 'min_samples_split': 20, 'scaler': None}. Best is trial 45 with value: 0.4660198609778968.\u001b[0m\u001b[32m[I 2023-01-20 17:48:33,258]\u001b[0m Trial 46 finished with value: 0.45625373004784886 and parameters: {'n_estimators': 242, 'max_depth': 179, 'min_samples_split': 41, 'scaler': None}. Best is trial 45 with value: 0.4660198609778968.\u001b[0m\u001b[32m[I 2023-01-20 17:48:33,534]\u001b[0m Trial 47 finished with value: 0.46619351207189874 and parameters: {'n_estimators': 230, 'max_depth': 171, 'min_samples_split': 30, 'scaler': None}. Best is trial 47 with value: 0.46619351207189874.\u001b[0m\u001b[32m[I 2023-01-20 17:48:33,771]\u001b[0m Trial 48 finished with value: 0.45494562762820034 and parameters: {'n_estimators': 213, 'max_depth': 192, 'min_samples_split': 31, 'scaler': None}. Best is trial 47 with value: 0.46619351207189874.\u001b[0m\u001b[32m[I 2023-01-20 17:48:34,041]\u001b[0m Trial 49 finished with value: 0.4557102826724324 and parameters: {'n_estimators': 231, 'max_depth': 169, 'min_samples_split': 19, 'scaler': None}. Best is trial 47 with value: 0.46619351207189874.\u001b[0m\u001b[32m[I 2023-01-20 17:48:34,319]\u001b[0m Trial 50 finished with value: 0.4516936561028091 and parameters: {'n_estimators': 202, 'max_depth': 50, 'min_samples_split': 30, 'scaler': None}. Best is trial 47 with value: 0.46619351207189874.\u001b[0m\u001b[32m[I 2023-01-20 17:48:34,594]\u001b[0m Trial 51 finished with value: 0.45929745923478704 and parameters: {'n_estimators': 229, 'max_depth': 180, 'min_samples_split': 21, 'scaler': None}. Best is trial 47 with value: 0.46619351207189874.\u001b[0m\u001b[32m[I 2023-01-20 17:48:34,919]\u001b[0m Trial 52 finished with value: 0.44727016157267296 and parameters: {'n_estimators': 228, 'max_depth': 183, 'min_samples_split': 19, 'scaler': None}. Best is trial 47 with value: 0.46619351207189874.\u001b[0m\u001b[32m[I 2023-01-20 17:48:35,188]\u001b[0m Trial 53 finished with value: 0.45218714108673586 and parameters: {'n_estimators': 240, 'max_depth': 197, 'min_samples_split': 36, 'scaler': None}. Best is trial 47 with value: 0.46619351207189874.\u001b[0m\u001b[32m[I 2023-01-20 17:48:35,432]\u001b[0m Trial 54 finished with value: 0.4701388301973731 and parameters: {'n_estimators': 211, 'max_depth': 174, 'min_samples_split': 28, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:35,647]\u001b[0m Trial 55 finished with value: 0.4593754940333289 and parameters: {'n_estimators': 194, 'max_depth': 169, 'min_samples_split': 29, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:35,878]\u001b[0m Trial 56 finished with value: 0.4419216452203949 and parameters: {'n_estimators': 213, 'max_depth': 199, 'min_samples_split': 44, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:36,055]\u001b[0m Trial 57 finished with value: 0.450938540662852 and parameters: {'n_estimators': 138, 'max_depth': 174, 'min_samples_split': 15, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:36,259]\u001b[0m Trial 58 finished with value: 0.43026746690972073 and parameters: {'n_estimators': 178, 'max_depth': 8, 'min_samples_split': 52, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:36,492]\u001b[0m Trial 59 finished with value: 0.45457491311201015 and parameters: {'n_estimators': 200, 'max_depth': 165, 'min_samples_split': 27, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:36,614]\u001b[0m Trial 60 finished with value: 0.40069290700280147 and parameters: {'n_estimators': 105, 'max_depth': 144, 'min_samples_split': 63, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:36,865]\u001b[0m Trial 61 finished with value: 0.45504892901006855 and parameters: {'n_estimators': 210, 'max_depth': 173, 'min_samples_split': 30, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:37,122]\u001b[0m Trial 62 finished with value: 0.45717348281658643 and parameters: {'n_estimators': 219, 'max_depth': 192, 'min_samples_split': 34, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:37,335]\u001b[0m Trial 63 finished with value: 0.4501861610344382 and parameters: {'n_estimators': 198, 'max_depth': 167, 'min_samples_split': 39, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:37,568]\u001b[0m Trial 64 finished with value: 0.44870852810746464 and parameters: {'n_estimators': 192, 'max_depth': 158, 'min_samples_split': 27, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:37,838]\u001b[0m Trial 65 finished with value: 0.4563869472586366 and parameters: {'n_estimators': 236, 'max_depth': 188, 'min_samples_split': 21, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:38,065]\u001b[0m Trial 66 finished with value: 0.4569723780426377 and parameters: {'n_estimators': 128, 'max_depth': 179, 'min_samples_split': 10, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:38,372]\u001b[0m Trial 67 finished with value: 0.456697483371164 and parameters: {'n_estimators': 225, 'max_depth': 173, 'min_samples_split': 15, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:38,635]\u001b[0m Trial 68 finished with value: 0.46013491230813797 and parameters: {'n_estimators': 242, 'max_depth': 165, 'min_samples_split': 32, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:38,926]\u001b[0m Trial 69 finished with value: 0.4397294382637812 and parameters: {'n_estimators': 242, 'max_depth': 163, 'min_samples_split': 49, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:39,190]\u001b[0m Trial 70 finished with value: 0.45716735862355085 and parameters: {'n_estimators': 235, 'max_depth': 150, 'min_samples_split': 37, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:39,437]\u001b[0m Trial 71 finished with value: 0.46796689949458525 and parameters: {'n_estimators': 218, 'max_depth': 183, 'min_samples_split': 32, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:39,671]\u001b[0m Trial 72 finished with value: 0.4456845438286513 and parameters: {'n_estimators': 213, 'max_depth': 185, 'min_samples_split': 33, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:39,995]\u001b[0m Trial 73 finished with value: 0.4514167986364557 and parameters: {'n_estimators': 220, 'max_depth': 192, 'min_samples_split': 25, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:40,273]\u001b[0m Trial 74 finished with value: 0.45177084288055314 and parameters: {'n_estimators': 250, 'max_depth': 179, 'min_samples_split': 31, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:40,438]\u001b[0m Trial 75 finished with value: 0.4594222462394846 and parameters: {'n_estimators': 119, 'max_depth': 164, 'min_samples_split': 23, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:40,565]\u001b[0m Trial 76 finished with value: 0.43638091942293633 and parameters: {'n_estimators': 91, 'max_depth': 182, 'min_samples_split': 18, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:40,810]\u001b[0m Trial 77 finished with value: 0.46255342600547644 and parameters: {'n_estimators': 207, 'max_depth': 200, 'min_samples_split': 27, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:41,077]\u001b[0m Trial 78 finished with value: 0.45610024289436835 and parameters: {'n_estimators': 228, 'max_depth': 200, 'min_samples_split': 27, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:41,346]\u001b[0m Trial 79 finished with value: 0.4582111765957191 and parameters: {'n_estimators': 242, 'max_depth': 194, 'min_samples_split': 36, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:41,597]\u001b[0m Trial 80 finished with value: 0.45155009333029805 and parameters: {'n_estimators': 217, 'max_depth': 157, 'min_samples_split': 21, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:41,823]\u001b[0m Trial 81 finished with value: 0.457165117559989 and parameters: {'n_estimators': 206, 'max_depth': 188, 'min_samples_split': 33, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:42,095]\u001b[0m Trial 82 finished with value: 0.45188605373933566 and parameters: {'n_estimators': 234, 'max_depth': 173, 'min_samples_split': 25, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:42,334]\u001b[0m Trial 83 finished with value: 0.44722523300012185 and parameters: {'n_estimators': 225, 'max_depth': 183, 'min_samples_split': 41, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:42,574]\u001b[0m Trial 84 finished with value: 0.4510143253057257 and parameters: {'n_estimators': 216, 'max_depth': 175, 'min_samples_split': 29, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:42,802]\u001b[0m Trial 85 finished with value: 0.46160001164446474 and parameters: {'n_estimators': 207, 'max_depth': 190, 'min_samples_split': 33, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:43,046]\u001b[0m Trial 86 finished with value: 0.4553338410446598 and parameters: {'n_estimators': 206, 'max_depth': 189, 'min_samples_split': 35, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:43,265]\u001b[0m Trial 87 finished with value: 0.4523616082050852 and parameters: {'n_estimators': 188, 'max_depth': 178, 'min_samples_split': 32, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:43,539]\u001b[0m Trial 88 finished with value: 0.4477729392160493 and parameters: {'n_estimators': 245, 'max_depth': 197, 'min_samples_split': 39, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:43,813]\u001b[0m Trial 89 finished with value: 0.45152815088723597 and parameters: {'n_estimators': 238, 'max_depth': 138, 'min_samples_split': 23, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:44,029]\u001b[0m Trial 90 finished with value: 0.46012304014983285 and parameters: {'n_estimators': 178, 'max_depth': 112, 'min_samples_split': 16, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:44,244]\u001b[0m Trial 91 finished with value: 0.4622758723528394 and parameters: {'n_estimators': 175, 'max_depth': 115, 'min_samples_split': 17, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:44,458]\u001b[0m Trial 92 finished with value: 0.4541918082851871 and parameters: {'n_estimators': 173, 'max_depth': 98, 'min_samples_split': 12, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:44,657]\u001b[0m Trial 93 finished with value: 0.43938742291576083 and parameters: {'n_estimators': 144, 'max_depth': 130, 'min_samples_split': 5, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:44,908]\u001b[0m Trial 94 finished with value: 0.45869732641582117 and parameters: {'n_estimators': 158, 'max_depth': 79, 'min_samples_split': 21, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:45,156]\u001b[0m Trial 95 finished with value: 0.45681249933713675 and parameters: {'n_estimators': 210, 'max_depth': 170, 'min_samples_split': 28, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:45,431]\u001b[0m Trial 96 finished with value: 0.452179948667231 and parameters: {'n_estimators': 231, 'max_depth': 195, 'min_samples_split': 19, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:45,681]\u001b[0m Trial 97 finished with value: 0.4573138975493334 and parameters: {'n_estimators': 198, 'max_depth': 69, 'min_samples_split': 25, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:45,872]\u001b[0m Trial 98 finished with value: 0.45309032342176114 and parameters: {'n_estimators': 163, 'max_depth': 148, 'min_samples_split': 31, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m\u001b[32m[I 2023-01-20 17:48:46,099]\u001b[0m Trial 99 finished with value: 0.3818803064112405 and parameters: {'n_estimators': 222, 'max_depth': 154, 'min_samples_split': 77, 'scaler': None}. Best is trial 54 with value: 0.4701388301973731.\u001b[0m"
+      "\u001b[32m[I 2023-01-20 20:53:08,954]\u001b[0m A new study created in memory with name: Diabetes\u001b[0m\u001b[32m[I 2023-01-20 20:53:09,092]\u001b[0m Trial 0 finished with value: 0.3958282204544128 and parameters: {'n_estimators': 125, 'max_depth': 131, 'min_samples_split': 62, 'scaler': 'standard'}. Best is trial 0 with value: 0.3958282204544128.\u001b[0m\u001b[32m[I 2023-01-20 20:53:09,399]\u001b[0m Trial 1 finished with value: 0.42954660431688363 and parameters: {'n_estimators': 222, 'max_depth': 185, 'min_samples_split': 6, 'scaler': 'quantile'}. Best is trial 1 with value: 0.42954660431688363.\u001b[0m\u001b[32m[I 2023-01-20 20:53:09,594]\u001b[0m Trial 2 finished with value: 0.38437314665755473 and parameters: {'n_estimators': 197, 'max_depth': 190, 'min_samples_split': 65, 'scaler': 'quantile'}. Best is trial 1 with value: 0.42954660431688363.\u001b[0m\u001b[32m[I 2023-01-20 20:53:09,685]\u001b[0m Trial 3 finished with value: 0.4333005750494967 and parameters: {'n_estimators': 68, 'max_depth': 152, 'min_samples_split': 13, 'scaler': 'robust'}. Best is trial 3 with value: 0.4333005750494967.\u001b[0m\u001b[32m[I 2023-01-20 20:53:09,922]\u001b[0m Trial 4 finished with value: 0.4318469685060231 and parameters: {'n_estimators': 215, 'max_depth': 130, 'min_samples_split': 50, 'scaler': 'standard'}. Best is trial 3 with value: 0.4333005750494967.\u001b[0m\u001b[32m[I 2023-01-20 20:53:10,143]\u001b[0m Trial 5 finished with value: 0.4056859590113563 and parameters: {'n_estimators': 220, 'max_depth': 40, 'min_samples_split': 55, 'scaler': 'robust'}. Best is trial 3 with value: 0.4333005750494967.\u001b[0m\u001b[32m[I 2023-01-20 20:53:10,264]\u001b[0m Trial 6 finished with value: 0.35028481664246147 and parameters: {'n_estimators': 121, 'max_depth': 160, 'min_samples_split': 91, 'scaler': 'robust'}. Best is trial 3 with value: 0.4333005750494967.\u001b[0m\u001b[32m[I 2023-01-20 20:53:10,511]\u001b[0m Trial 7 finished with value: 0.44191045092853387 and parameters: {'n_estimators': 174, 'max_depth': 113, 'min_samples_split': 26, 'scaler': 'robust'}. Best is trial 7 with value: 0.44191045092853387.\u001b[0m\u001b[32m[I 2023-01-20 20:53:10,771]\u001b[0m Trial 8 finished with value: 0.4570822140378319 and parameters: {'n_estimators': 229, 'max_depth': 171, 'min_samples_split': 27, 'scaler': None}. Best is trial 8 with value: 0.4570822140378319.\u001b[0m\u001b[32m[I 2023-01-20 20:53:10,884]\u001b[0m Trial 9 finished with value: 0.4421353551018513 and parameters: {'n_estimators': 109, 'max_depth': 100, 'min_samples_split': 47, 'scaler': None}. Best is trial 8 with value: 0.4570822140378319.\u001b[0m\u001b[32m[I 2023-01-20 20:53:11,152]\u001b[0m Trial 10 finished with value: 0.4596624150532256 and parameters: {'n_estimators': 245, 'max_depth': 66, 'min_samples_split': 29, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:11,432]\u001b[0m Trial 11 finished with value: 0.456070643241286 and parameters: {'n_estimators': 246, 'max_depth': 61, 'min_samples_split': 29, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:11,845]\u001b[0m Trial 12 finished with value: 0.4557118546030289 and parameters: {'n_estimators': 249, 'max_depth': 10, 'min_samples_split': 31, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:12,191]\u001b[0m Trial 13 finished with value: 0.45365489784188473 and parameters: {'n_estimators': 178, 'max_depth': 67, 'min_samples_split': 19, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:12,489]\u001b[0m Trial 14 finished with value: 0.454454400785531 and parameters: {'n_estimators': 159, 'max_depth': 81, 'min_samples_split': 39, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:12,829]\u001b[0m Trial 15 finished with value: 0.38346335477400595 and parameters: {'n_estimators': 197, 'max_depth': 34, 'min_samples_split': 80, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:13,417]\u001b[0m Trial 16 finished with value: 0.45348976701727883 and parameters: {'n_estimators': 239, 'max_depth': 157, 'min_samples_split': 2, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:13,541]\u001b[0m Trial 17 finished with value: 0.44613313430354085 and parameters: {'n_estimators': 59, 'max_depth': 100, 'min_samples_split': 39, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:13,918]\u001b[0m Trial 18 finished with value: 0.45506726860199953 and parameters: {'n_estimators': 193, 'max_depth': 5, 'min_samples_split': 18, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:14,383]\u001b[0m Trial 19 finished with value: 0.4561936629751806 and parameters: {'n_estimators': 227, 'max_depth': 78, 'min_samples_split': 35, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:14,577]\u001b[0m Trial 20 finished with value: 0.39086287030920086 and parameters: {'n_estimators': 97, 'max_depth': 44, 'min_samples_split': 72, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:15,019]\u001b[0m Trial 21 finished with value: 0.4500610108687939 and parameters: {'n_estimators': 228, 'max_depth': 81, 'min_samples_split': 38, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:15,473]\u001b[0m Trial 22 finished with value: 0.45960740078894025 and parameters: {'n_estimators': 209, 'max_depth': 85, 'min_samples_split': 23, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:15,930]\u001b[0m Trial 23 finished with value: 0.4559439244521766 and parameters: {'n_estimators': 208, 'max_depth': 112, 'min_samples_split': 23, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:16,283]\u001b[0m Trial 24 finished with value: 0.45533500184764475 and parameters: {'n_estimators': 178, 'max_depth': 54, 'min_samples_split': 12, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m"
      ]
     }
    ],
@@ -1518,9 +1494,6 @@
     "from sklearn.model_selection import cross_val_score\n",
     "import optuna\n",
     "\n",
-    "#X_train, y_train = g5.get_matrix(), g5.get_matrix(target=True)\n",
-    "X_test, y_test = g5.transform(test_diabetes, test_diabetes, return_graph=False)\n",
-    "\n",
     "def objective(trail):\n",
     "    n_estimators = trail.suggest_int('n_estimators', 50, 250)\n",
     "    max_depth = trail.suggest_int('max_depth', 2, 200)\n",
@@ -1537,7 +1510,7 @@
     "    \n",
     "study = optuna.create_study(study_name='Diabetes', direction='maximize')\n",
     "\n",
-    "study.optimize(objective, n_trials=100)"
+    "study.optimize(objective, n_trials=25) # normally have more trials, here to demonstrate"
    ]
   },
   {
@@ -1549,10 +1522,7 @@
     {
      "data": {
       "text/plain": [
-       "{'n_estimators': 211,\n",
-       " 'max_depth': 174,\n",
-       " 'min_samples_split': 28,\n",
-       " 'scaler': None}"
+       "{'n_estimators': 245, 'max_depth': 66, 'min_samples_split': 29, 'scaler': None}"
       ]
      },
      "execution_count": 27,
@@ -1666,29 +1636,29 @@
         {
          "cliponaxis": false,
          "hovertemplate": [
-          "max_depth (IntUniformDistribution): 0.0021138252662894702<extra></extra>",
-          "n_estimators (IntUniformDistribution): 0.0070885829626843345<extra></extra>",
-          "scaler (CategoricalDistribution): 0.016431479175241275<extra></extra>",
-          "min_samples_split (IntUniformDistribution): 0.974366112595785<extra></extra>"
+          "max_depth (IntUniformDistribution): 0.011453822466583852<extra></extra>",
+          "n_estimators (IntUniformDistribution): 0.014155810306063011<extra></extra>",
+          "scaler (CategoricalDistribution): 0.015838737969375006<extra></extra>",
+          "min_samples_split (IntUniformDistribution): 0.9585516292579782<extra></extra>"
          ],
          "marker": {
           "color": "rgb(66,146,198)"
          },
          "orientation": "h",
          "text": [
-          "0.0021138252662894702",
-          "0.0070885829626843345",
-          "0.016431479175241275",
-          "0.974366112595785"
+          "0.011453822466583852",
+          "0.014155810306063011",
+          "0.015838737969375006",
+          "0.9585516292579782"
          ],
          "textposition": "outside",
          "texttemplate": "%{text:.2f}",
          "type": "bar",
          "x": [
-          0.0021138252662894702,
-          0.0070885829626843345,
-          0.016431479175241275,
-          0.974366112595785
+          0.011453822466583852,
+          0.014155810306063011,
+          0.015838737969375006,
+          0.9585516292579782
          ],
          "y": [
           "max_depth",
@@ -2533,9 +2503,9 @@
        }
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        "                            \n",
-       "var gd = document.getElementById('5f4cf76b-c646-4d8d-a2f4-3f353272387a');\n",
+       "var gd = document.getElementById('baa0993e-7f6d-4830-8b9c-3d52a46db43f');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -14217,13 +7116,13 @@
     }
    ],
    "source": [
-    "fig = optuna.visualization.plot_contour(study, params=[\"max_depth\", \"n_estimators\"])\n",
+    "fig = optuna.visualization.plot_contour(study, params=[\"scaler\", \"n_estimators\"])\n",
     "fig.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "id": "fa378193-667c-428c-8962-e0a0fb5fef06",
    "metadata": {},
    "outputs": [
@@ -14233,13 +7132,13 @@
        "<AxesSubplot:ylabel='Frequency'>"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -14264,7 +7163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "id": "f81a96d9-a45f-4bf4-a302-86af6d2aae15",
    "metadata": {},
    "outputs": [],
@@ -14275,7 +7174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "id": "363c5a6a-5183-4aae-9439-7756ccbb8bb1",
    "metadata": {},
    "outputs": [
@@ -14298,7 +7197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "id": "3509fac0-434f-4a08-bb54-256825b66af3",
    "metadata": {},
    "outputs": [
@@ -14306,7 +7205,7 @@
      "data": {
       "text/html": [
        "\n",
-       "            <iframe id=\"0727a3f1-b837-4183-8eb6-7ec04d107059\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=50171792d54140e4b513b3ee4d767523&type=arrow&viztoken=ede35216-39b6-459a-b194-bcf3357103de&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265748&info=true&play=0\"\n",
+       "            <iframe id=\"c18271bd-79cf-4d63-939f-551675d40341\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=4705a7d98c40400bbe088753a420cd09&type=arrow&viztoken=923c3c7e-d306-44fd-922e-01d981e90761&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276817&info=true&play=0\"\n",
        "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
        "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
        "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
@@ -14316,7 +7215,7 @@
        "        \n",
        "            <script>\n",
        "                try {\n",
-       "                  $(\"#0727a3f1-b837-4183-8eb6-7ec04d107059\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                  $(\"#c18271bd-79cf-4d63-939f-551675d40341\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
        "                } catch (e) { console.error('exn catching scroll', e); }\n",
        "            </script>\n",
        "        "
@@ -14325,7 +7224,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14344,7 +7243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "id": "0eb0f820-7118-43bb-8419-52acb4a4f925",
    "metadata": {},
    "outputs": [],
@@ -14358,7 +7257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "id": "e3aa6bdd-b0b6-46a7-93ae-041321bef461",
    "metadata": {},
    "outputs": [
@@ -14380,7 +7279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "id": "a1156045-87df-443d-b75f-63d418941924",
    "metadata": {},
    "outputs": [
@@ -14388,7 +7287,7 @@
      "data": {
       "text/html": [
        "\n",
-       "            <iframe id=\"5fd38ede-5068-4d90-8865-473db77adafb\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=e288e111e8414251ab61568eaf6ebd9f&type=arrow&viztoken=4743632b-8154-4eea-96ca-a9cc36c2bca6&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265754&info=true&play=0\"\n",
+       "            <iframe id=\"f7aef9c3-62b0-4ce7-a8c1-b477e999164e\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=daf370ca249e4b31a114ca5b2d8e5b87&type=arrow&viztoken=0cb1f1e1-ce6c-4ae6-9074-baf54fa41347&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276824&info=true&play=0\"\n",
        "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
        "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
        "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
@@ -14398,7 +7297,7 @@
        "        \n",
        "            <script>\n",
        "                try {\n",
-       "                  $(\"#5fd38ede-5068-4d90-8865-473db77adafb\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                  $(\"#f7aef9c3-62b0-4ce7-a8c1-b477e999164e\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
        "                } catch (e) { console.error('exn catching scroll', e); }\n",
        "            </script>\n",
        "        "
@@ -14407,7 +7306,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14418,7 +7317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "id": "afad90db-df22-4da6-abb9-cb161ef008c6",
    "metadata": {},
    "outputs": [
@@ -14429,14 +7328,14 @@
       "--------------------------------------------------\n",
       "Infering edges over UMAP embedding\n",
       "---------------------------------------------\n",
-      " Mean distance to existing nodes 5.85 +/- 3.68\n",
-      " Max distance threshold; epsilon = 2.17\n",
+      " Mean distance to existing nodes 6.19 +/- 3.87\n",
+      " Max distance threshold; epsilon = 2.32\n",
       " Finding 7 nearest neighbors\n",
-      " 138.65 neighbors per node within epsilon 2.17\n",
+      " 145.83 neighbors per node within epsilon 2.32\n",
       " 6293 total edges after dropping duplicates\n",
-      " ** Final graph has 962 nodes\n",
+      " ** Final graph has 957 nodes\n",
       " - Batch has 899 nodes\n",
-      " - Brought in 63 nodes\n",
+      " - Brought in 58 nodes\n",
       "--------------------------------------------------\n"
      ]
     },
@@ -14444,7 +7343,7 @@
      "data": {
       "text/html": [
        "\n",
-       "            <iframe id=\"ea8b55e8-5bba-4d2f-a798-5c1c6c743ec0\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=16e7588c62d04f4ebc3e9423e955d78e&type=arrow&viztoken=f4d5aff0-1b7e-4b4d-a3b0-2469f6975451&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265762&info=true&play=0\"\n",
+       "            <iframe id=\"3f756c06-83bc-4263-9635-a870c107731a\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=6ebd689772394c2fb369602b2cd55dfd&type=arrow&viztoken=b938c421-82be-4d22-8925-eaa61732ffe7&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276834&info=true&play=0\"\n",
        "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
        "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
        "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
@@ -14454,7 +7353,7 @@
        "        \n",
        "            <script>\n",
        "                try {\n",
-       "                  $(\"#ea8b55e8-5bba-4d2f-a798-5c1c6c743ec0\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                  $(\"#3f756c06-83bc-4263-9635-a870c107731a\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
        "                } catch (e) { console.error('exn catching scroll', e); }\n",
        "            </script>\n",
        "        "
@@ -14463,7 +7362,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14475,7 +7374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "id": "be9b4506-960a-4204-b6c6-194d7fa9135d",
    "metadata": {},
    "outputs": [
@@ -14486,11 +7385,11 @@
       "--------------------------------------------------\n",
       "Infering edges over UMAP embedding\n",
       "---------------------------------------------\n",
-      " Mean distance to existing nodes 5.83 +/- 3.66\n",
-      " Max distance threshold; epsilon = 2.17\n",
+      " Mean distance to existing nodes 6.24 +/- 3.87\n",
+      " Max distance threshold; epsilon = 2.38\n",
       " Finding 7 nearest neighbors\n",
-      " 137.59 neighbors per node within epsilon 2.17\n",
-      " 6246 total edges after dropping duplicates\n",
+      " 145.97 neighbors per node within epsilon 2.38\n",
+      " 6245 total edges after dropping duplicates\n",
       " ** Final graph has 899 nodes\n",
       "--------------------------------------------------\n"
      ]
@@ -14499,7 +7398,7 @@
      "data": {
       "text/html": [
        "\n",
-       "            <iframe id=\"e0ef27b5-9c95-40a6-9e1e-c9dca83bfabb\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=a5e3c172273b40529c082c8329099737&type=arrow&viztoken=6831953c-66c2-44df-947f-b306136c3365&usertag=8a6d667e-pygraphistry-0.28.6+131.g601543c.dirty&splashAfter=1674265770&info=true&play=0\"\n",
+       "            <iframe id=\"c123cbd1-808a-4f13-bd13-32a74e2cd525\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=c9abaa5f21f346f38e4fa1a9c9864d6f&type=arrow&viztoken=0caca425-b248-4c48-8b11-792a0934671b&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276842&info=true&play=0\"\n",
        "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
        "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
        "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
@@ -14509,7 +7408,7 @@
        "        \n",
        "            <script>\n",
        "                try {\n",
-       "                  $(\"#e0ef27b5-9c95-40a6-9e1e-c9dca83bfabb\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                  $(\"#c123cbd1-808a-4f13-bd13-32a74e2cd525\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
        "                } catch (e) { console.error('exn catching scroll', e); }\n",
        "            </script>\n",
        "        "
@@ -14518,7 +7417,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14530,7 +7429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "id": "987a2bce-ffed-4724-a8a7-d3dc07f48262",
    "metadata": {},
    "outputs": [
@@ -14548,7 +7447,7 @@
        " 0.0)"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14567,7 +7466,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "id": "d0d2c3c0-cfa4-4335-b2d4-fa77ec0eae94",
    "metadata": {},
    "outputs": [],
@@ -14577,19 +7476,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "id": "7507c3d8-89d1-4d49-9534-9c2e50376934",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Graph(num_nodes=284, num_edges=4626,\n",
+       "Graph(num_nodes=284, num_edges=4678,\n",
        "      ndata_schemes={'feature': Scheme(shape=(30,), dtype=torch.float64), 'train_mask': Scheme(shape=(), dtype=torch.bool), 'test_mask': Scheme(shape=(), dtype=torch.bool)}\n",
        "      edata_schemes={'feature': Scheme(shape=(286,), dtype=torch.float64), 'train_mask': Scheme(shape=(), dtype=torch.bool), 'test_mask': Scheme(shape=(), dtype=torch.bool)})"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14601,21 +7500,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "id": "e237ce99-191d-473d-80bb-49211978fd5f",
    "metadata": {},
    "outputs": [],
    "source": [
     "# run a prediction task from https://docs.dgl.ai/tutorials/blitz/index.html"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4eb84597",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From 985f699908ca6efbcd6e9b573072394d45ba0e6f Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 20 Jan 2023 21:33:12 -0800
Subject: [PATCH 131/432] edits

---
 demos/ai/OSINT/Chavismo.ipynb | 2 --
 1 file changed, 2 deletions(-)

diff --git a/demos/ai/OSINT/Chavismo.ipynb b/demos/ai/OSINT/Chavismo.ipynb
index 3c3d92a215..95ad9f30b3 100644
--- a/demos/ai/OSINT/Chavismo.ipynb
+++ b/demos/ai/OSINT/Chavismo.ipynb
@@ -573,9 +573,7 @@
     "process = True\n",
     "if process:\n",
     "    g = graphistry.nodes(df.sample(len(df)), 'Agent 1').edges(df, 'Agent 1', 'Agent 2')\n",
-    "    g2 = graphistry.bind().load_search_instance('chavismo.search')\n",
     "\n",
-    "    g._node_features = g2._node_features\n",
     "    # since we have edges, let's featurize (rather than umap, which would overwrite explicit edges)\n",
     "    # X = None will featurize ALL the columns and setting min_words=0 will treat them all as textual\n",
     "    g2 = g.featurize(**model)\n",

From a8aa6ff7fe95977b1a5653bfefca39b47a72248d Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 20 Jan 2023 22:06:12 -0800
Subject: [PATCH 132/432] edits

---
 .../ai/Introduction/Ask-HackerNews-Demo.ipynb | 2287 ++++++++++++++++-
 1 file changed, 2161 insertions(+), 126 deletions(-)

diff --git a/demos/ai/Introduction/Ask-HackerNews-Demo.ipynb b/demos/ai/Introduction/Ask-HackerNews-Demo.ipynb
index 99cc397dae..8fd5a0b3a2 100644
--- a/demos/ai/Introduction/Ask-HackerNews-Demo.ipynb
+++ b/demos/ai/Introduction/Ask-HackerNews-Demo.ipynb
@@ -28,18 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "385ea5a4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# depends on where you have your data/ folder\n",
-    "#mkdir data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "8e7f75b3",
    "metadata": {},
    "outputs": [],
@@ -49,17 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "cbd6050e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# cd .. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "503a96d2",
    "metadata": {},
    "outputs": [],
@@ -81,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "a73d67a0",
    "metadata": {},
    "outputs": [],
@@ -92,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "46f3b61b",
    "metadata": {},
    "outputs": [],
@@ -103,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "0c0bcb74",
    "metadata": {},
    "outputs": [],
@@ -114,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "25a407dc",
    "metadata": {},
    "outputs": [],
@@ -124,32 +103,290 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "e8edc98e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['title', 'url', 'text', 'dead', 'by', 'score', 'time', 'timestamp',\n",
+       "       'type', 'id', 'parent', 'descendants', 'ranking', 'deleted'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df.columns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "9536276e",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title</th>\n",
+       "      <th>url</th>\n",
+       "      <th>text</th>\n",
+       "      <th>dead</th>\n",
+       "      <th>by</th>\n",
+       "      <th>score</th>\n",
+       "      <th>time</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>type</th>\n",
+       "      <th>id</th>\n",
+       "      <th>parent</th>\n",
+       "      <th>descendants</th>\n",
+       "      <th>ranking</th>\n",
+       "      <th>deleted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>I'm a software engineer going blind, how should I prepare?</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>I&amp;#x27;m a 24 y&amp;#x2F;o full stack engineer (I know some of you are rolling your eyes right now, just highlighting that I have experience on frontend apps as well as backend architecture). I&amp;#x27;v...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>zachrip</td>\n",
+       "      <td>3270</td>\n",
+       "      <td>1587332026</td>\n",
+       "      <td>2020-04-19 21:33:46+00:00</td>\n",
+       "      <td>story</td>\n",
+       "      <td>22918980</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>473.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Am I the longest-serving programmer – 57 years and counting?</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In May of 1963, I started my first full-time job as a computer programmer for Mitchell Engineering Company, a supplier of steel buildings.  At Mitchell, I developed programs in Fortran II on an IB...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>genedangelo</td>\n",
+       "      <td>2634</td>\n",
+       "      <td>1590890024</td>\n",
+       "      <td>2020-05-31 01:53:44+00:00</td>\n",
+       "      <td>story</td>\n",
+       "      <td>23366546</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>531.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Is S3 down?</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>I&amp;#x27;m getting&lt;p&gt;{\\n  &amp;quot;errorCode&amp;quot; : &amp;quot;InternalError&amp;quot;\\n}&lt;p&gt;When I attempt to use the AWS Console to view s3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>iamdeedubs</td>\n",
+       "      <td>2589</td>\n",
+       "      <td>1488303958</td>\n",
+       "      <td>2017-02-28 17:45:58+00:00</td>\n",
+       "      <td>story</td>\n",
+       "      <td>13755673</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1055.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>What tech job would let me get away with the least real work possible?</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Hey HN,&lt;p&gt;I&amp;#x27;ll probably get a lot of flak for this. Sorry.&lt;p&gt;I&amp;#x27;m an average developer looking for ways to work as little as humanely possible.&lt;p&gt;The pandemic made me realize that I do no...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>lmueongoqx</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>1617784863</td>\n",
+       "      <td>2021-04-07 08:41:03+00:00</td>\n",
+       "      <td>story</td>\n",
+       "      <td>26721951</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1091.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>What books changed the way you think about almost everything?</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>I was reflecting today about how often I think about Freakonomics. I don&amp;#x27;t study it religiously. I read it one time more than 10 years ago. I can only remember maybe a single specific anecdot...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>anderspitman</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>1549387905</td>\n",
+       "      <td>2019-02-05 17:31:45+00:00</td>\n",
+       "      <td>story</td>\n",
+       "      <td>19087418</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1165.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                    title  \\\n",
+       "0              I'm a software engineer going blind, how should I prepare?   \n",
+       "1            Am I the longest-serving programmer – 57 years and counting?   \n",
+       "2                                                             Is S3 down?   \n",
+       "3  What tech job would let me get away with the least real work possible?   \n",
+       "4           What books changed the way you think about almost everything?   \n",
+       "\n",
+       "   url  \\\n",
+       "0  NaN   \n",
+       "1  NaN   \n",
+       "2  NaN   \n",
+       "3  NaN   \n",
+       "4  NaN   \n",
+       "\n",
+       "                                                                                                                                                                                                      text  \\\n",
+       "0  I&#x27;m a 24 y&#x2F;o full stack engineer (I know some of you are rolling your eyes right now, just highlighting that I have experience on frontend apps as well as backend architecture). I&#x27;v...   \n",
+       "1  In May of 1963, I started my first full-time job as a computer programmer for Mitchell Engineering Company, a supplier of steel buildings.  At Mitchell, I developed programs in Fortran II on an IB...   \n",
+       "2                                                                          I&#x27;m getting<p>{\\n  &quot;errorCode&quot; : &quot;InternalError&quot;\\n}<p>When I attempt to use the AWS Console to view s3   \n",
+       "3  Hey HN,<p>I&#x27;ll probably get a lot of flak for this. Sorry.<p>I&#x27;m an average developer looking for ways to work as little as humanely possible.<p>The pandemic made me realize that I do no...   \n",
+       "4  I was reflecting today about how often I think about Freakonomics. I don&#x27;t study it religiously. I read it one time more than 10 years ago. I can only remember maybe a single specific anecdot...   \n",
+       "\n",
+       "  dead            by  score        time                  timestamp   type  \\\n",
+       "0  NaN       zachrip   3270  1587332026  2020-04-19 21:33:46+00:00  story   \n",
+       "1  NaN   genedangelo   2634  1590890024  2020-05-31 01:53:44+00:00  story   \n",
+       "2  NaN    iamdeedubs   2589  1488303958  2017-02-28 17:45:58+00:00  story   \n",
+       "3  NaN    lmueongoqx   2022  1617784863  2021-04-07 08:41:03+00:00  story   \n",
+       "4  NaN  anderspitman   2009  1549387905  2019-02-05 17:31:45+00:00  story   \n",
+       "\n",
+       "         id  parent  descendants  ranking  deleted  \n",
+       "0  22918980     NaN        473.0      NaN      NaN  \n",
+       "1  23366546     NaN        531.0      NaN      NaN  \n",
+       "2  13755673     NaN       1055.0      NaN      NaN  \n",
+       "3  26721951     NaN       1091.0      NaN      NaN  \n",
+       "4  19087418     NaN       1165.0      NaN      NaN  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df.head()  # see the dataset"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "3a479ce6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title</th>\n",
+       "      <th>text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>I'm a software engineer going blind, how should I prepare?</td>\n",
+       "      <td>I&amp;#x27;m a 24 y&amp;#x2F;o full stack engineer (I know some of you are rolling your eyes right now, just highlighting that I have experience on frontend apps as well as backend architecture). I&amp;#x27;v...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Am I the longest-serving programmer – 57 years and counting?</td>\n",
+       "      <td>In May of 1963, I started my first full-time job as a computer programmer for Mitchell Engineering Company, a supplier of steel buildings.  At Mitchell, I developed programs in Fortran II on an IB...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Is S3 down?</td>\n",
+       "      <td>I&amp;#x27;m getting&lt;p&gt;{\\n  &amp;quot;errorCode&amp;quot; : &amp;quot;InternalError&amp;quot;\\n}&lt;p&gt;When I attempt to use the AWS Console to view s3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>What tech job would let me get away with the least real work possible?</td>\n",
+       "      <td>Hey HN,&lt;p&gt;I&amp;#x27;ll probably get a lot of flak for this. Sorry.&lt;p&gt;I&amp;#x27;m an average developer looking for ways to work as little as humanely possible.&lt;p&gt;The pandemic made me realize that I do no...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>What books changed the way you think about almost everything?</td>\n",
+       "      <td>I was reflecting today about how often I think about Freakonomics. I don&amp;#x27;t study it religiously. I read it one time more than 10 years ago. I can only remember maybe a single specific anecdot...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                    title  \\\n",
+       "0              I'm a software engineer going blind, how should I prepare?   \n",
+       "1            Am I the longest-serving programmer – 57 years and counting?   \n",
+       "2                                                             Is S3 down?   \n",
+       "3  What tech job would let me get away with the least real work possible?   \n",
+       "4           What books changed the way you think about almost everything?   \n",
+       "\n",
+       "                                                                                                                                                                                                      text  \n",
+       "0  I&#x27;m a 24 y&#x2F;o full stack engineer (I know some of you are rolling your eyes right now, just highlighting that I have experience on frontend apps as well as backend architecture). I&#x27;v...  \n",
+       "1  In May of 1963, I started my first full-time job as a computer programmer for Mitchell Engineering Company, a supplier of steel buildings.  At Mitchell, I developed programs in Fortran II on an IB...  \n",
+       "2                                                                          I&#x27;m getting<p>{\\n  &quot;errorCode&quot; : &quot;InternalError&quot;\\n}<p>When I attempt to use the AWS Console to view s3  \n",
+       "3  Hey HN,<p>I&#x27;ll probably get a lot of flak for this. Sorry.<p>I&#x27;m an average developer looking for ways to work as little as humanely possible.<p>The pandemic made me realize that I do no...  \n",
+       "4  I was reflecting today about how often I think about Freakonomics. I don&#x27;t study it religiously. I read it one time more than 10 years ago. I can only remember maybe a single specific anecdot...  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df[good_cols].head()"
    ]
@@ -164,10 +401,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "748116ab",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Encoding 3000 records using SentenceTransformer took 1.53 minutes\n"
+     ]
+    }
+   ],
    "source": [
     "from time import time\n",
     "t0 = time()\n",
@@ -183,23 +442,22 @@
     "# set to False if you want to reload last trained instance\n",
     "process = True\n",
     "\n",
+    "print('-'*80)\n",
     "if process:\n",
     "    # Umap will create a similarity graph from the features which we can view as a graph\n",
-    "    g2 = g.umap(X=['title', 'text'], # the features to encode (can add/remove 'text', etc)\n",
+    "    g2 = g.umap(X=['title'], # the features to encode (can add/remove 'text', etc)\n",
     "                y=['score'], # for demonstrative purposes, we include a target -- though this one is not really conditioned on textual features in a straightforward way\n",
     "                model_name='msmarco-distilbert-base-v2', #'paraphrase-MiniLM-L6-v2', etc, from sbert/Huggingface, the text encoding model\n",
     "                min_words = 0, # when 0 forces all X=[..] as textually encoded, higher values would ascertain if a column is textual or not depending on average number of words per column\n",
     "                use_ngrams=False, # set to True if you want ngram features instead (does not make great plots but useful for other situations)\n",
-    "                use_scaler_target='zscale', # for regressive targets\n",
+    "                use_scaler_target='standard', # for regressive targets\n",
     "                use_scaler=None, # there are many more settings see `g.featurize?` and `g.umap?` for further options\n",
     "               )\n",
     "    g2.save_search_instance('data/hn.search')\n",
-    "    print('-'*80)\n",
     "    print(f'Encoding {df.shape[0]} records using {str(g2._node_encoder.text_model)[:19]} took {(time()-t0)/60:.2f} minutes')\n",
     "else:\n",
     "    # or load the search instance\n",
     "    g2 = g.load_search_instance('data/hn.search')\n",
-    "    print('-'*80)\n",
     "    print(f'Loaded saved instance')\n",
     "    \n",
     "################################################################\n"
@@ -207,10 +465,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "d6de7795",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"c37e7656-a104-481f-a3bd-c13e15c036cd\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=7fb76b13a32f4872a52f160603a73c5f&type=arrow&viztoken=3c2c8be8-c38f-4b43-95c0-1bfdda8802e2&usertag=8a6d667e-pygraphistry-0.28.6+137.g985f699.dirty&splashAfter=1674280727&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#c37e7656-a104-481f-a3bd-c13e15c036cd\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# see all the data\n",
     "g2.plot()"
@@ -218,34 +504,541 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "22ed4eec",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title_0</th>\n",
+       "      <th>title_1</th>\n",
+       "      <th>title_2</th>\n",
+       "      <th>title_3</th>\n",
+       "      <th>title_4</th>\n",
+       "      <th>title_5</th>\n",
+       "      <th>title_6</th>\n",
+       "      <th>title_7</th>\n",
+       "      <th>title_8</th>\n",
+       "      <th>title_9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>title_758</th>\n",
+       "      <th>title_759</th>\n",
+       "      <th>title_760</th>\n",
+       "      <th>title_761</th>\n",
+       "      <th>title_762</th>\n",
+       "      <th>title_763</th>\n",
+       "      <th>title_764</th>\n",
+       "      <th>title_765</th>\n",
+       "      <th>title_766</th>\n",
+       "      <th>title_767</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1654</th>\n",
+       "      <td>0.604488</td>\n",
+       "      <td>0.101652</td>\n",
+       "      <td>-0.063497</td>\n",
+       "      <td>0.307542</td>\n",
+       "      <td>0.844374</td>\n",
+       "      <td>0.197561</td>\n",
+       "      <td>0.896631</td>\n",
+       "      <td>0.631857</td>\n",
+       "      <td>0.315805</td>\n",
+       "      <td>-0.578581</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.042918</td>\n",
+       "      <td>-0.322729</td>\n",
+       "      <td>-0.277031</td>\n",
+       "      <td>-0.319512</td>\n",
+       "      <td>-0.165631</td>\n",
+       "      <td>-0.584383</td>\n",
+       "      <td>0.261868</td>\n",
+       "      <td>0.429799</td>\n",
+       "      <td>-0.303072</td>\n",
+       "      <td>-0.377494</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1538</th>\n",
+       "      <td>-1.103507</td>\n",
+       "      <td>-0.835217</td>\n",
+       "      <td>-1.237520</td>\n",
+       "      <td>0.549196</td>\n",
+       "      <td>0.397246</td>\n",
+       "      <td>0.199831</td>\n",
+       "      <td>-1.196874</td>\n",
+       "      <td>0.290311</td>\n",
+       "      <td>-1.171076</td>\n",
+       "      <td>0.513540</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.006427</td>\n",
+       "      <td>-0.731422</td>\n",
+       "      <td>-0.750713</td>\n",
+       "      <td>-0.486637</td>\n",
+       "      <td>0.841622</td>\n",
+       "      <td>-0.198652</td>\n",
+       "      <td>0.195885</td>\n",
+       "      <td>-0.570250</td>\n",
+       "      <td>0.050978</td>\n",
+       "      <td>-0.436235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2708</th>\n",
+       "      <td>0.326092</td>\n",
+       "      <td>0.045712</td>\n",
+       "      <td>0.308224</td>\n",
+       "      <td>0.803963</td>\n",
+       "      <td>-0.063246</td>\n",
+       "      <td>-0.123905</td>\n",
+       "      <td>-0.731468</td>\n",
+       "      <td>0.227643</td>\n",
+       "      <td>0.261804</td>\n",
+       "      <td>-0.048012</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.206501</td>\n",
+       "      <td>0.034402</td>\n",
+       "      <td>0.796114</td>\n",
+       "      <td>-0.237042</td>\n",
+       "      <td>0.117702</td>\n",
+       "      <td>0.649347</td>\n",
+       "      <td>-0.299433</td>\n",
+       "      <td>0.995765</td>\n",
+       "      <td>-0.009557</td>\n",
+       "      <td>-0.119748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0.926326</td>\n",
+       "      <td>-0.392618</td>\n",
+       "      <td>0.035194</td>\n",
+       "      <td>-0.161504</td>\n",
+       "      <td>-0.326212</td>\n",
+       "      <td>-0.166737</td>\n",
+       "      <td>0.070937</td>\n",
+       "      <td>0.950549</td>\n",
+       "      <td>-0.228309</td>\n",
+       "      <td>-0.056017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.551132</td>\n",
+       "      <td>0.639072</td>\n",
+       "      <td>-0.468963</td>\n",
+       "      <td>-0.290477</td>\n",
+       "      <td>0.117795</td>\n",
+       "      <td>-0.803580</td>\n",
+       "      <td>0.804826</td>\n",
+       "      <td>0.423588</td>\n",
+       "      <td>-0.092650</td>\n",
+       "      <td>-0.687976</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1481</th>\n",
+       "      <td>0.384588</td>\n",
+       "      <td>-0.832760</td>\n",
+       "      <td>0.033876</td>\n",
+       "      <td>0.215492</td>\n",
+       "      <td>0.593188</td>\n",
+       "      <td>-0.432190</td>\n",
+       "      <td>-0.283562</td>\n",
+       "      <td>0.400813</td>\n",
+       "      <td>0.045255</td>\n",
+       "      <td>-0.430429</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.028084</td>\n",
+       "      <td>-0.152095</td>\n",
+       "      <td>-0.226646</td>\n",
+       "      <td>0.208703</td>\n",
+       "      <td>0.187091</td>\n",
+       "      <td>0.133619</td>\n",
+       "      <td>0.486250</td>\n",
+       "      <td>0.575210</td>\n",
+       "      <td>0.730881</td>\n",
+       "      <td>-0.129466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1264</th>\n",
+       "      <td>-0.098364</td>\n",
+       "      <td>-0.276086</td>\n",
+       "      <td>0.550586</td>\n",
+       "      <td>0.542078</td>\n",
+       "      <td>0.321339</td>\n",
+       "      <td>-0.601650</td>\n",
+       "      <td>-0.540975</td>\n",
+       "      <td>-0.333377</td>\n",
+       "      <td>0.094011</td>\n",
+       "      <td>0.031201</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.266734</td>\n",
+       "      <td>-1.171137</td>\n",
+       "      <td>0.190349</td>\n",
+       "      <td>-1.094334</td>\n",
+       "      <td>-0.939085</td>\n",
+       "      <td>0.294115</td>\n",
+       "      <td>-0.118376</td>\n",
+       "      <td>-0.473456</td>\n",
+       "      <td>-0.321870</td>\n",
+       "      <td>0.111786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1171</th>\n",
+       "      <td>0.702274</td>\n",
+       "      <td>-0.091761</td>\n",
+       "      <td>0.348669</td>\n",
+       "      <td>-0.431706</td>\n",
+       "      <td>1.191116</td>\n",
+       "      <td>0.006005</td>\n",
+       "      <td>-1.105823</td>\n",
+       "      <td>-0.625805</td>\n",
+       "      <td>-0.168052</td>\n",
+       "      <td>0.075096</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.838124</td>\n",
+       "      <td>-0.305236</td>\n",
+       "      <td>0.398299</td>\n",
+       "      <td>0.156232</td>\n",
+       "      <td>0.146867</td>\n",
+       "      <td>0.339570</td>\n",
+       "      <td>-0.152106</td>\n",
+       "      <td>-0.456346</td>\n",
+       "      <td>-0.393480</td>\n",
+       "      <td>0.293989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>589</th>\n",
+       "      <td>-0.406301</td>\n",
+       "      <td>-0.531044</td>\n",
+       "      <td>-0.563821</td>\n",
+       "      <td>-0.012661</td>\n",
+       "      <td>0.380232</td>\n",
+       "      <td>0.187900</td>\n",
+       "      <td>0.169093</td>\n",
+       "      <td>0.475025</td>\n",
+       "      <td>-0.772457</td>\n",
+       "      <td>0.188258</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.478902</td>\n",
+       "      <td>-0.781922</td>\n",
+       "      <td>0.135231</td>\n",
+       "      <td>0.847367</td>\n",
+       "      <td>0.451199</td>\n",
+       "      <td>0.420809</td>\n",
+       "      <td>0.683643</td>\n",
+       "      <td>-0.713218</td>\n",
+       "      <td>0.390578</td>\n",
+       "      <td>-0.141390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2342</th>\n",
+       "      <td>0.128966</td>\n",
+       "      <td>0.168480</td>\n",
+       "      <td>0.055048</td>\n",
+       "      <td>-0.287427</td>\n",
+       "      <td>-0.069591</td>\n",
+       "      <td>-0.533780</td>\n",
+       "      <td>-0.401158</td>\n",
+       "      <td>-0.270016</td>\n",
+       "      <td>-0.398377</td>\n",
+       "      <td>0.062334</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.068983</td>\n",
+       "      <td>-0.483162</td>\n",
+       "      <td>-0.373780</td>\n",
+       "      <td>-0.411517</td>\n",
+       "      <td>0.044580</td>\n",
+       "      <td>0.602551</td>\n",
+       "      <td>0.423918</td>\n",
+       "      <td>0.028719</td>\n",
+       "      <td>-0.160396</td>\n",
+       "      <td>0.211980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2782</th>\n",
+       "      <td>-0.144229</td>\n",
+       "      <td>0.703746</td>\n",
+       "      <td>-0.852380</td>\n",
+       "      <td>-0.084720</td>\n",
+       "      <td>-0.654991</td>\n",
+       "      <td>-0.374648</td>\n",
+       "      <td>0.142915</td>\n",
+       "      <td>-0.072289</td>\n",
+       "      <td>-0.082889</td>\n",
+       "      <td>0.965485</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.068612</td>\n",
+       "      <td>0.432348</td>\n",
+       "      <td>-0.718999</td>\n",
+       "      <td>-0.465670</td>\n",
+       "      <td>1.038647</td>\n",
+       "      <td>0.308591</td>\n",
+       "      <td>-0.369232</td>\n",
+       "      <td>0.004829</td>\n",
+       "      <td>-0.020801</td>\n",
+       "      <td>0.027217</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3000 rows × 768 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       title_0   title_1   title_2   title_3   title_4   title_5   title_6  \\\n",
+       "1654  0.604488  0.101652 -0.063497  0.307542  0.844374  0.197561  0.896631   \n",
+       "1538 -1.103507 -0.835217 -1.237520  0.549196  0.397246  0.199831 -1.196874   \n",
+       "2708  0.326092  0.045712  0.308224  0.803963 -0.063246 -0.123905 -0.731468   \n",
+       "62    0.926326 -0.392618  0.035194 -0.161504 -0.326212 -0.166737  0.070937   \n",
+       "1481  0.384588 -0.832760  0.033876  0.215492  0.593188 -0.432190 -0.283562   \n",
+       "...        ...       ...       ...       ...       ...       ...       ...   \n",
+       "1264 -0.098364 -0.276086  0.550586  0.542078  0.321339 -0.601650 -0.540975   \n",
+       "1171  0.702274 -0.091761  0.348669 -0.431706  1.191116  0.006005 -1.105823   \n",
+       "589  -0.406301 -0.531044 -0.563821 -0.012661  0.380232  0.187900  0.169093   \n",
+       "2342  0.128966  0.168480  0.055048 -0.287427 -0.069591 -0.533780 -0.401158   \n",
+       "2782 -0.144229  0.703746 -0.852380 -0.084720 -0.654991 -0.374648  0.142915   \n",
+       "\n",
+       "       title_7   title_8   title_9  ...  title_758  title_759  title_760  \\\n",
+       "1654  0.631857  0.315805 -0.578581  ...  -0.042918  -0.322729  -0.277031   \n",
+       "1538  0.290311 -1.171076  0.513540  ...   0.006427  -0.731422  -0.750713   \n",
+       "2708  0.227643  0.261804 -0.048012  ...  -0.206501   0.034402   0.796114   \n",
+       "62    0.950549 -0.228309 -0.056017  ...  -0.551132   0.639072  -0.468963   \n",
+       "1481  0.400813  0.045255 -0.430429  ...   0.028084  -0.152095  -0.226646   \n",
+       "...        ...       ...       ...  ...        ...        ...        ...   \n",
+       "1264 -0.333377  0.094011  0.031201  ...  -0.266734  -1.171137   0.190349   \n",
+       "1171 -0.625805 -0.168052  0.075096  ...   0.838124  -0.305236   0.398299   \n",
+       "589   0.475025 -0.772457  0.188258  ...  -0.478902  -0.781922   0.135231   \n",
+       "2342 -0.270016 -0.398377  0.062334  ...   1.068983  -0.483162  -0.373780   \n",
+       "2782 -0.072289 -0.082889  0.965485  ...   0.068612   0.432348  -0.718999   \n",
+       "\n",
+       "      title_761  title_762  title_763  title_764  title_765  title_766  \\\n",
+       "1654  -0.319512  -0.165631  -0.584383   0.261868   0.429799  -0.303072   \n",
+       "1538  -0.486637   0.841622  -0.198652   0.195885  -0.570250   0.050978   \n",
+       "2708  -0.237042   0.117702   0.649347  -0.299433   0.995765  -0.009557   \n",
+       "62    -0.290477   0.117795  -0.803580   0.804826   0.423588  -0.092650   \n",
+       "1481   0.208703   0.187091   0.133619   0.486250   0.575210   0.730881   \n",
+       "...         ...        ...        ...        ...        ...        ...   \n",
+       "1264  -1.094334  -0.939085   0.294115  -0.118376  -0.473456  -0.321870   \n",
+       "1171   0.156232   0.146867   0.339570  -0.152106  -0.456346  -0.393480   \n",
+       "589    0.847367   0.451199   0.420809   0.683643  -0.713218   0.390578   \n",
+       "2342  -0.411517   0.044580   0.602551   0.423918   0.028719  -0.160396   \n",
+       "2782  -0.465670   1.038647   0.308591  -0.369232   0.004829  -0.020801   \n",
+       "\n",
+       "      title_767  \n",
+       "1654  -0.377494  \n",
+       "1538  -0.436235  \n",
+       "2708  -0.119748  \n",
+       "62    -0.687976  \n",
+       "1481  -0.129466  \n",
+       "...         ...  \n",
+       "1264   0.111786  \n",
+       "1171   0.293989  \n",
+       "589   -0.141390  \n",
+       "2342   0.211980  \n",
+       "2782   0.027217  \n",
+       "\n",
+       "[3000 rows x 768 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# get the encoded features, and use in downstream models (clf.fit(x, y), etc)\n",
     "x=g2._get_feature('nodes')\n",
+    "# same as \n",
+    "x = g2._node_features\n",
+    "# same as\n",
+    "x = g2.get_matrix()\n",
     "x"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "67b15408",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1654</th>\n",
+       "      <td>-0.38835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1538</th>\n",
+       "      <td>-0.33530</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2708</th>\n",
+       "      <td>-0.71150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>2.70326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1481</th>\n",
+       "      <td>-0.31601</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1264</th>\n",
+       "      <td>-0.19060</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1171</th>\n",
+       "      <td>-0.13273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>589</th>\n",
+       "      <td>0.44605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2342</th>\n",
+       "      <td>-0.62951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2782</th>\n",
+       "      <td>-0.72115</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        score\n",
+       "1654 -0.38835\n",
+       "1538 -0.33530\n",
+       "2708 -0.71150\n",
+       "62    2.70326\n",
+       "1481 -0.31601\n",
+       "...       ...\n",
+       "1264 -0.19060\n",
+       "1171 -0.13273\n",
+       "589   0.44605\n",
+       "2342 -0.62951\n",
+       "2782 -0.72115\n",
+       "\n",
+       "[3000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# likewise with the (scaled) targets\n",
     "y = g2._get_target('nodes')\n",
+    "# same as \n",
+    "y = g2._node_target\n",
+    "# same as\n",
+    "y = g2.get_matrix(target=True)\n",
     "y"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "f43b7806",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"a5c69c70-ac12-454b-87f9-47a383660a94\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=588279365f0d42299c8480bbea46da57&type=arrow&viztoken=1a60b723-1c53-4097-88fa-5a33a5ce2d73&usertag=8a6d667e-pygraphistry-0.28.6+137.g985f699.dirty&splashAfter=1674280731&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#a5c69c70-ac12-454b-87f9-47a383660a94\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# visualize the results where we prune edges using the `filter_weighted_edges` method\n",
     "# this keeps all weights that are (more similar) 0.5 and above. The initial layout is the same (given by umap in 2d)\n",
@@ -263,10 +1056,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "e79eabfc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1647</th>\n",
+       "      <td>Is it normal to fall out of love with coding?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1412</th>\n",
+       "      <td>What landing page do you love?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2854</th>\n",
+       "      <td>Hackers falling in love</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2770</th>\n",
+       "      <td>What do you love/hate about terminals? Would you change them?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1182</th>\n",
+       "      <td>Have you found something you love to do? If yes how?</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                              title\n",
+       "1647                  Is it normal to fall out of love with coding?\n",
+       "1412                                 What landing page do you love?\n",
+       "2854                                        Hackers falling in love\n",
+       "2770  What do you love/hate about terminals? Would you change them?\n",
+       "1182           Have you found something you love to do? If yes how?"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# direct keyword search when fuzzy=False and a set of columns are given, does not require featurization\n",
     "g.search('love', fuzzy=False, cols=['title'])[0][['title']]"
@@ -274,10 +1130,250 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
+   "id": "c9a8e3bb-faf0-432f-be9e-b173528af866",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2532</th>\n",
+       "      <td>How did you find your passion?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1182</th>\n",
+       "      <td>Have you found something you love to do? If yes how?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2509</th>\n",
+       "      <td>After almost 30 years the romance is over - What now?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2669</th>\n",
+       "      <td>Is it better to be good at many things or great at one thing?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1164</th>\n",
+       "      <td>My wife needs something to do from home to make money...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2469</th>\n",
+       "      <td>Does success in work bring you happiness?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2177</th>\n",
+       "      <td>As an adult introvertish nerd what makes you happy?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1650</th>\n",
+       "      <td>Anxiety is limiting my enjoyment of a wonderful career. Can you relate?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1853</th>\n",
+       "      <td>What do you wish you had done/known in your 30s?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1360</th>\n",
+       "      <td>Turning 40 soon – seeking personal and professional life advice</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                        title\n",
+       "2532                                           How did you find your passion?\n",
+       "1182                     Have you found something you love to do? If yes how?\n",
+       "2509                    After almost 30 years the romance is over - What now?\n",
+       "2669            Is it better to be good at many things or great at one thing?\n",
+       "1164                 My wife needs something to do from home to make money...\n",
+       "2469                                Does success in work bring you happiness?\n",
+       "2177                      As an adult introvertish nerd what makes you happy?\n",
+       "1650  Anxiety is limiting my enjoyment of a wonderful career. Can you relate?\n",
+       "1853                         What do you wish you had done/known in your 30s?\n",
+       "1360          Turning 40 soon – seeking personal and professional life advice"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2.search('love')[0][['title']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
    "id": "85cf9c06",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "*********************************\n",
+      "Is true love possible?\n",
+      "******************************\n",
+      "1182             Have you found something you love to do? If yes how?\n",
+      "2043        Why aren't there many credible online bachelors programs?\n",
+      "2509            After almost 30 years the romance is over - What now?\n",
+      "2469                        Does success in work bring you happiness?\n",
+      "2532                                   How did you find your passion?\n",
+      "1569             What do you wish you had known before you turned 40?\n",
+      "2669    Is it better to be good at many things or great at one thing?\n",
+      "1853                 What do you wish you had done/known in your 30s?\n",
+      "1198            What are the books you wish your colleagues had read?\n",
+      "400                                        What Lived Up to the Hype?\n",
+      "Name: title, dtype: object\n",
+      "------------------------------------------------------------\n",
+      "*********************************\n",
+      "How to create deep learning models?\n",
+      "******************************\n",
+      "35                     How to get started with machine learning?\n",
+      "959          How to Seriously Start with Machine Learning and AI\n",
+      "2172         Why TensorFlow instead of Theano for deep learning?\n",
+      "1833               How do you manage multiple learning projects?\n",
+      "1739           How to incorporate machine learning into day job?\n",
+      "208                Good ways to capture institutional knowledge?\n",
+      "1726                       What do you use Machine Learning for?\n",
+      "2219                How do I start with test driven development?\n",
+      "1988                            How to develop a growth mindset?\n",
+      "704     Best introductory video courses on ML and Deep Learning?\n",
+      "Name: title, dtype: object\n",
+      "------------------------------------------------------------\n",
+      "*********************************\n",
+      "Best tech careers\n",
+      "******************************\n",
+      "1328      What tech that's right around the corner are you most excited about?\n",
+      "366                                              Joining Big Tech in One’s 40s\n",
+      "3       What tech job would let me get away with the least real work possible?\n",
+      "247             What is the most exciting development in your field right now?\n",
+      "2428                                Companies of one, what is your tech stack?\n",
+      "748                                 Companies of one, what is your tech stack?\n",
+      "981       Who here has built a profitable startup while keeping their day job?\n",
+      "831                       What company environment has enabled your best work?\n",
+      "801         What are some of the best job boards you have seen (any industry)?\n",
+      "259                      What was your experience starting a tech consultancy?\n",
+      "Name: title, dtype: object\n",
+      "------------------------------------------------------------\n",
+      "*********************************\n",
+      "How do I make more money?\n",
+      "******************************\n",
+      "1302                                    How do you earn your money?\n",
+      "975                             Why can't I make as much as I make?\n",
+      "500        Ways to generate income when you're at home without pay?\n",
+      "1164       My wife needs something to do from home to make money...\n",
+      "1034     How do you motivate yourself to keep working on a project?\n",
+      "2496      Should I find a job or try to build a profitable project?\n",
+      "844     How do you decide when you've done enough work for the day?\n",
+      "402                      How to optimize your career for happiness?\n",
+      "1870         How to get out of Tech and still make a decent living?\n",
+      "1987                         How do you stay productive after work?\n",
+      "Name: title, dtype: object\n",
+      "------------------------------------------------------------\n",
+      "*********************************\n",
+      "Advances in particle physics\n",
+      "******************************\n",
+      "1277                  What are the greatest discoveries in the last few years?\n",
+      "1200               Will there ever be a resurgence of interest in symbolic AI?\n",
+      "438     What tech were you convinced would take the world by storm but didn't?\n",
+      "850               Any scientifically proven techniques to boost concentration?\n",
+      "738                 Has any progress been made on large format E-ink displays?\n",
+      "2528                       Why aren't there any real alternatives to Electron?\n",
+      "1029                     I'm looking for a good book on the fundamentals of CS\n",
+      "2399              What is the emerging state of the art in fuzzing techniques?\n",
+      "522              What are some interesting projects to reuse your old devices?\n",
+      "650                            What things do you wish you discovered earlier?\n",
+      "Name: title, dtype: object\n",
+      "------------------------------------------------------------\n",
+      "*********************************\n",
+      "Best apps and gadgets\n",
+      "******************************\n",
+      "2638    What are the best web tools to build basic web apps as of October 2016?\n",
+      "817          Best-architected open-source business applications worth studying?\n",
+      "2769                                              What Android apps do you use?\n",
+      "1032               What are the best technologies you've worked with this year?\n",
+      "1439                    What is the best enterprise software you use every day?\n",
+      "2826                       Inspirational money making web apps made by hackers.\n",
+      "211           What's your favorite way of getting a web app up quickly in 2018?\n",
+      "2773       Best tech for a web site 2018? (PHP, Rails, Django, Node, Go, etc.)?\n",
+      "1923        What is good business advice for independent mobile app developers?\n",
+      "2658            What is the best way to promote your new fancy web application?\n",
+      "Name: title, dtype: object\n",
+      "------------------------------------------------------------\n",
+      "*********************************\n",
+      "Graph Neural Networks\n",
+      "******************************\n",
+      "1827                       What was your experience using a graph database?\n",
+      "1825                                  Why GraphQL APIs but no Datalog APIs?\n",
+      "1155    Why are relational DBs are the standard instead of graph-based DBs?\n",
+      "1540               If you've used a graph database, would you use it again?\n",
+      "799                    Were you happy moving your API from REST to GraphQL?\n",
+      "919           What's the best algorithms and data structures online course?\n",
+      "2907    What are the best resources for learning about algorithmic trading?\n",
+      "1436                   Looking for a book on algorithms and data structures\n",
+      "377                 What are some examples of good database schema designs?\n",
+      "2498                                        Building a game for AI Research\n",
+      "Name: title, dtype: object\n",
+      "------------------------------------------------------------\n",
+      "*********************************\n",
+      "recommend impactful books\n",
+      "******************************\n",
+      "113     Great fiction books that have had a positive impact on your life?\n",
+      "2507                       What book impacted your life the most and how?\n",
+      "104        What books have made the biggest impact on your mental models?\n",
+      "2737                 Which books have helped you the most professionally?\n",
+      "523        Which non-technology book has influenced you the most and why?\n",
+      "1099                           Recommendations of good cybercrime novels?\n",
+      "1933         Recommend books that give you insight into other professions\n",
+      "2837              What are the best books for professional effectiveness?\n",
+      "1764               What is one book you would recommend everyone to read?\n",
+      "815           What makes a good technical leader – any recommended books?\n",
+      "Name: title, dtype: object\n",
+      "------------------------------------------------------------\n",
+      "*********************************\n",
+      "lamenting about life\n",
+      "******************************\n",
+      "1218                         Words of encouragement for someone lost in life?\n",
+      "1337                                              What do you regret in life?\n",
+      "2155                               The Internet is getting lame. What's next?\n",
+      "741                                                             I'm So Lonely\n",
+      "2165                                                   Coping with Loneliness\n",
+      "514                                               When you feel stuck in life\n",
+      "1526    What should I say to my manager when my performance starts suffering?\n",
+      "969                              How to cope with the death of a dear person?\n",
+      "2195                                 I'm a solopreneur and I feel demoralised\n",
+      "520      Failed interview, feeling unemployable and depressed – what do I do?\n",
+      "Name: title, dtype: object\n",
+      "------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "# Query semantically instead of strict keyword matching\n",
     "\n",
@@ -313,10 +1409,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "302a0b53",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"ac49af37-83dc-4064-bfdd-60c2f9df03da\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=8ba787b703f6488eae2354fec63fd13a&type=arrow&viztoken=b52fd61f-d1bb-4fd2-9221-d2a727c6a5ef&usertag=8a6d667e-pygraphistry-0.28.6+137.g985f699.dirty&splashAfter=1674280734&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#ac49af37-83dc-4064-bfdd-60c2f9df03da\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "gr = g2.search_graph('How to create deep learning models', thresh=15, top_n=50, scale=0.25, broader=False) \n",
     "gr.plot()"
@@ -324,20 +1448,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "543f7b83",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"ed95c853-575d-4f9d-a6af-8eff8ab5f1ef\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=adbbbef85cda47e39047ec024c6b307a&type=arrow&viztoken=859108a5-9d2e-4cf1-b56c-9a13893cfbf4&usertag=8a6d667e-pygraphistry-0.28.6+137.g985f699.dirty&splashAfter=1674280736&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#ed95c853-575d-4f9d-a6af-8eff8ab5f1ef\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "g2.search_graph('Graph Neural Networks', thresh=50, top_n=50, scale=0.1, broader=False).plot()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "6f2f9157",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"44a0ba1f-8cb1-49ec-b152-3369e7f5537c\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=1c9439fa209b428ebf8a8687e1b7d5cc&type=arrow&viztoken=1cbd5b1b-bd0d-438e-8228-007fa3135a61&usertag=8a6d667e-pygraphistry-0.28.6+137.g985f699.dirty&splashAfter=1674280738&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#44a0ba1f-8cb1-49ec-b152-3369e7f5537c\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "g2.search_graph('fraud detection algorithms', thresh=50, top_n=50, scale=0.1, broader=False).plot()  # works better if you encode 'text' column as well"
    ]
@@ -352,12 +1532,360 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "09b941fe",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
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+       "  <thead>\n",
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+       "      <td>-0.593098</td>\n",
+       "      <td>0.214304</td>\n",
+       "      <td>0.962338</td>\n",
+       "      <td>-0.713328</td>\n",
+       "      <td>-0.989697</td>\n",
+       "      <td>0.145388</td>\n",
+       "      <td>0.075608</td>\n",
+       "      <td>0.488215</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.696708</td>\n",
+       "      <td>0.139317</td>\n",
+       "      <td>-0.846404</td>\n",
+       "      <td>-0.025490</td>\n",
+       "      <td>-0.120986</td>\n",
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+       "      <td>0.585826</td>\n",
+       "      <td>-0.303448</td>\n",
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+       "      <th>1605</th>\n",
+       "      <td>0.688653</td>\n",
+       "      <td>0.019184</td>\n",
+       "      <td>-0.227892</td>\n",
+       "      <td>-0.239211</td>\n",
+       "      <td>0.189795</td>\n",
+       "      <td>0.341207</td>\n",
+       "      <td>-0.120877</td>\n",
+       "      <td>-1.296878</td>\n",
+       "      <td>0.206908</td>\n",
+       "      <td>0.103815</td>\n",
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+       "      <td>-0.236452</td>\n",
+       "      <td>-0.384294</td>\n",
+       "      <td>0.394059</td>\n",
+       "      <td>0.244530</td>\n",
+       "      <td>-0.364924</td>\n",
+       "      <td>0.675864</td>\n",
+       "      <td>-0.271245</td>\n",
+       "      <td>-0.173634</td>\n",
+       "      <td>-0.298026</td>\n",
+       "      <td>-0.017564</td>\n",
+       "    </tr>\n",
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+       "      <th>1451</th>\n",
+       "      <td>-0.184770</td>\n",
+       "      <td>0.235804</td>\n",
+       "      <td>-0.400443</td>\n",
+       "      <td>-0.211511</td>\n",
+       "      <td>0.114818</td>\n",
+       "      <td>0.160413</td>\n",
+       "      <td>-0.131262</td>\n",
+       "      <td>0.500900</td>\n",
+       "      <td>-0.275231</td>\n",
+       "      <td>0.190890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.091999</td>\n",
+       "      <td>-0.233226</td>\n",
+       "      <td>-0.072699</td>\n",
+       "      <td>-0.713460</td>\n",
+       "      <td>0.423684</td>\n",
+       "      <td>1.398612</td>\n",
+       "      <td>-0.203436</td>\n",
+       "      <td>0.473697</td>\n",
+       "      <td>-0.219005</td>\n",
+       "      <td>-0.128714</td>\n",
+       "    </tr>\n",
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+       "      <td>0.548434</td>\n",
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+       "      <td>0.353703</td>\n",
+       "      <td>0.117870</td>\n",
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+       "      <td>1.095775</td>\n",
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+       "      <td>0.007578</td>\n",
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+       "      <td>-0.035659</td>\n",
+       "      <td>-0.101807</td>\n",
+       "      <td>0.110145</td>\n",
+       "      <td>1.122281</td>\n",
+       "      <td>-0.237854</td>\n",
+       "      <td>-0.532332</td>\n",
+       "      <td>0.939817</td>\n",
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+       "      <td>-0.431375</td>\n",
+       "      <td>-0.915085</td>\n",
+       "      <td>-0.580861</td>\n",
+       "      <td>0.395472</td>\n",
+       "      <td>0.406366</td>\n",
+       "      <td>-0.131193</td>\n",
+       "      <td>1.074949</td>\n",
+       "      <td>-0.996813</td>\n",
+       "      <td>-0.183665</td>\n",
+       "      <td>-0.006735</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.946300</td>\n",
+       "      <td>0.433078</td>\n",
+       "      <td>-0.190154</td>\n",
+       "      <td>0.137894</td>\n",
+       "      <td>-0.198106</td>\n",
+       "      <td>-0.261280</td>\n",
+       "      <td>-0.695857</td>\n",
+       "      <td>0.226295</td>\n",
+       "      <td>-0.670496</td>\n",
+       "      <td>-0.423368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1104</th>\n",
+       "      <td>0.096140</td>\n",
+       "      <td>-0.056172</td>\n",
+       "      <td>0.063150</td>\n",
+       "      <td>0.234838</td>\n",
+       "      <td>0.117753</td>\n",
+       "      <td>-0.346006</td>\n",
+       "      <td>-0.744430</td>\n",
+       "      <td>-0.151107</td>\n",
+       "      <td>0.143060</td>\n",
+       "      <td>0.241910</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.500574</td>\n",
+       "      <td>-0.478188</td>\n",
+       "      <td>0.296040</td>\n",
+       "      <td>-0.612845</td>\n",
+       "      <td>-0.324935</td>\n",
+       "      <td>-0.439464</td>\n",
+       "      <td>0.469710</td>\n",
+       "      <td>0.539491</td>\n",
+       "      <td>0.906302</td>\n",
+       "      <td>-0.175188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>0.281097</td>\n",
+       "      <td>-0.608663</td>\n",
+       "      <td>0.423599</td>\n",
+       "      <td>0.420787</td>\n",
+       "      <td>1.051380</td>\n",
+       "      <td>-0.027290</td>\n",
+       "      <td>0.602898</td>\n",
+       "      <td>-0.284727</td>\n",
+       "      <td>0.099539</td>\n",
+       "      <td>-1.925934</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.300906</td>\n",
+       "      <td>-0.111235</td>\n",
+       "      <td>1.123893</td>\n",
+       "      <td>0.459886</td>\n",
+       "      <td>-0.218124</td>\n",
+       "      <td>0.590245</td>\n",
+       "      <td>0.296381</td>\n",
+       "      <td>-0.609109</td>\n",
+       "      <td>-0.147541</td>\n",
+       "      <td>-1.250704</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>-0.131974</td>\n",
+       "      <td>-0.062444</td>\n",
+       "      <td>-0.837820</td>\n",
+       "      <td>0.162044</td>\n",
+       "      <td>-0.451466</td>\n",
+       "      <td>0.319139</td>\n",
+       "      <td>0.052473</td>\n",
+       "      <td>-0.631871</td>\n",
+       "      <td>-0.020183</td>\n",
+       "      <td>-0.478724</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.101025</td>\n",
+       "      <td>1.011868</td>\n",
+       "      <td>-0.704747</td>\n",
+       "      <td>-0.454947</td>\n",
+       "      <td>-0.227243</td>\n",
+       "      <td>0.961758</td>\n",
+       "      <td>0.686837</td>\n",
+       "      <td>0.510259</td>\n",
+       "      <td>0.270457</td>\n",
+       "      <td>-1.069947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1340</th>\n",
+       "      <td>0.634322</td>\n",
+       "      <td>0.351494</td>\n",
+       "      <td>0.038098</td>\n",
+       "      <td>0.234291</td>\n",
+       "      <td>-0.872616</td>\n",
+       "      <td>-0.458497</td>\n",
+       "      <td>-0.179605</td>\n",
+       "      <td>0.256817</td>\n",
+       "      <td>0.122679</td>\n",
+       "      <td>0.471110</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.682162</td>\n",
+       "      <td>0.184881</td>\n",
+       "      <td>0.382003</td>\n",
+       "      <td>0.236048</td>\n",
+       "      <td>0.035794</td>\n",
+       "      <td>-0.462713</td>\n",
+       "      <td>0.333054</td>\n",
+       "      <td>0.447952</td>\n",
+       "      <td>0.912596</td>\n",
+       "      <td>0.432614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1681</th>\n",
+       "      <td>-0.244261</td>\n",
+       "      <td>-0.050637</td>\n",
+       "      <td>-0.474688</td>\n",
+       "      <td>0.063758</td>\n",
+       "      <td>-0.309980</td>\n",
+       "      <td>-0.171460</td>\n",
+       "      <td>-0.609836</td>\n",
+       "      <td>-0.007839</td>\n",
+       "      <td>-0.371513</td>\n",
+       "      <td>0.509530</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.222305</td>\n",
+       "      <td>-1.502060</td>\n",
+       "      <td>-0.571068</td>\n",
+       "      <td>-1.054443</td>\n",
+       "      <td>-0.434218</td>\n",
+       "      <td>-0.145071</td>\n",
+       "      <td>0.131197</td>\n",
+       "      <td>-0.685201</td>\n",
+       "      <td>0.055874</td>\n",
+       "      <td>0.055352</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 768 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       title_0   title_1   title_2   title_3   title_4   title_5   title_6  \\\n",
+       "1517  0.979180  0.041072 -0.593098  0.214304  0.962338 -0.713328 -0.989697   \n",
+       "1605  0.688653  0.019184 -0.227892 -0.239211  0.189795  0.341207 -0.120877   \n",
+       "1451 -0.184770  0.235804 -0.400443 -0.211511  0.114818  0.160413 -0.131262   \n",
+       "1372 -0.983680  0.548434 -0.584351  0.353703  0.117870 -0.098879  1.095775   \n",
+       "964  -0.431375 -0.915085 -0.580861  0.395472  0.406366 -0.131193  1.074949   \n",
+       "1104  0.096140 -0.056172  0.063150  0.234838  0.117753 -0.346006 -0.744430   \n",
+       "37    0.281097 -0.608663  0.423599  0.420787  1.051380 -0.027290  0.602898   \n",
+       "228  -0.131974 -0.062444 -0.837820  0.162044 -0.451466  0.319139  0.052473   \n",
+       "1340  0.634322  0.351494  0.038098  0.234291 -0.872616 -0.458497 -0.179605   \n",
+       "1681 -0.244261 -0.050637 -0.474688  0.063758 -0.309980 -0.171460 -0.609836   \n",
+       "\n",
+       "       title_7   title_8   title_9  ...  title_758  title_759  title_760  \\\n",
+       "1517  0.145388  0.075608  0.488215  ...  -0.696708   0.139317  -0.846404   \n",
+       "1605 -1.296878  0.206908  0.103815  ...  -0.236452  -0.384294   0.394059   \n",
+       "1451  0.500900 -0.275231  0.190890  ...   0.091999  -0.233226  -0.072699   \n",
+       "1372 -0.385954 -0.541251  0.007578  ...   0.047079   0.485933  -0.285741   \n",
+       "964  -0.996813 -0.183665 -0.006735  ...  -0.946300   0.433078  -0.190154   \n",
+       "1104 -0.151107  0.143060  0.241910  ...   0.500574  -0.478188   0.296040   \n",
+       "37   -0.284727  0.099539 -1.925934  ...  -0.300906  -0.111235   1.123893   \n",
+       "228  -0.631871 -0.020183 -0.478724  ...  -0.101025   1.011868  -0.704747   \n",
+       "1340  0.256817  0.122679  0.471110  ...   0.682162   0.184881   0.382003   \n",
+       "1681 -0.007839 -0.371513  0.509530  ...  -0.222305  -1.502060  -0.571068   \n",
+       "\n",
+       "      title_761  title_762  title_763  title_764  title_765  title_766  \\\n",
+       "1517  -0.025490  -0.120986  -0.055843   0.637930   0.143514   0.585826   \n",
+       "1605   0.244530  -0.364924   0.675864  -0.271245  -0.173634  -0.298026   \n",
+       "1451  -0.713460   0.423684   1.398612  -0.203436   0.473697  -0.219005   \n",
+       "1372  -0.035659  -0.101807   0.110145   1.122281  -0.237854  -0.532332   \n",
+       "964    0.137894  -0.198106  -0.261280  -0.695857   0.226295  -0.670496   \n",
+       "1104  -0.612845  -0.324935  -0.439464   0.469710   0.539491   0.906302   \n",
+       "37     0.459886  -0.218124   0.590245   0.296381  -0.609109  -0.147541   \n",
+       "228   -0.454947  -0.227243   0.961758   0.686837   0.510259   0.270457   \n",
+       "1340   0.236048   0.035794  -0.462713   0.333054   0.447952   0.912596   \n",
+       "1681  -1.054443  -0.434218  -0.145071   0.131197  -0.685201   0.055874   \n",
+       "\n",
+       "      title_767  \n",
+       "1517  -0.303448  \n",
+       "1605  -0.017564  \n",
+       "1451  -0.128714  \n",
+       "1372   0.939817  \n",
+       "964   -0.423368  \n",
+       "1104  -0.175188  \n",
+       "37    -1.250704  \n",
+       "228   -1.069947  \n",
+       "1340   0.432614  \n",
+       "1681   0.055352  \n",
+       "\n",
+       "[10 rows x 768 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "x, y = g2.transform(df.sample(10), df.sample(10), kind='nodes')  # or edges if given or already produced through umap-ing the nodes, \n",
+    "sdf = df.sample(10)\n",
+    "x, y = g2.transform(sdf, sdf, return_graph=False)  # or edges if given or already produced through umap-ing the ny_nodes=\n",
     "                                                                #and if neither, set `embedding=True` for random embedding of size `n_topics`\n",
     "x"
    ]
@@ -372,13 +1900,82 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "id": "e68126cd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    <tr>\n",
+       "      <th>2178</th>\n",
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+       "      <td>5.299919</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1227</th>\n",
+       "      <td>11.831199</td>\n",
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+       "    <tr>\n",
+       "      <th>684</th>\n",
+       "      <td>-3.108138</td>\n",
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+       "    <tr>\n",
+       "      <th>1733</th>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702</th>\n",
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+       "    </tr>\n",
+       "  </tbody>\n",
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+       "</div>"
+      ],
+      "text/plain": [
+       "              x         y\n",
+       "2178  12.067898  5.299919\n",
+       "1227  11.831199  4.594526\n",
+       "684   -3.108138  4.623506\n",
+       "1733  10.267467  6.992209\n",
+       "702   11.229530  7.411973"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "emb, x, y = g2.transform_umap(df.sample(10), df.sample(10))\n",
-    "emb"
+    "emb, x, y = g2.transform_umap(df.sample(10), df.sample(10), return_graph=False)\n",
+    "emb.head()"
    ]
   },
   {
@@ -391,80 +1988,446 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "d148348e",
    "metadata": {},
    "outputs": [],
    "source": [
     "# this inherets all the arguments from the g.featurize api for both nodes and edges, see g.build_gnn? for details\n",
-    "g3 = g25.build_gnn()  # we use the filtered edges graphistry instance as it has higher fidelity similarity scores on edges\n",
+    "g3 = g25.build_gnn(y_nodes='score')  # we use the filtered edges graphistry instance as it has higher fidelity similarity scores on edges\n",
     "                        # ie, less edges"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "5989c286",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# notice the difference in edge dataframes between g2/5 and g3\n",
-    "g25._edges"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "59af921c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# versus\n",
-    "g3._edges"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "af1cd73e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Edges come from data supplied by umap on nodes\n",
-    "g3._edge_encoder.feature_names_in"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "764e7ba7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 3001 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0    1    2    3    4    5    6    7    8    9  ...  2991  2992  2993  \\\n",
+       "2  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   0.0   0.0   0.0   \n",
+       "4  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   0.0   0.0   0.0   \n",
+       "6  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   0.0   0.0   0.0   \n",
+       "7  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   0.0   0.0   0.0   \n",
+       "8  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   0.0   0.0   0.0   \n",
+       "\n",
+       "   2994  2995  2996  2997  2998  2999   _weight  \n",
+       "2   0.0   0.0   0.0   0.0   0.0   0.0  0.797920  \n",
+       "4   0.0   0.0   0.0   0.0   0.0   0.0  0.547040  \n",
+       "6   0.0   0.0   0.0   0.0   0.0   0.0  0.862698  \n",
+       "7   0.0   0.0   0.0   0.0   0.0   0.0  0.801896  \n",
+       "8   0.0   0.0   0.0   0.0   0.0   0.0  0.653791  \n",
+       "\n",
+       "[5 rows x 3001 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g3._edge_features.head()"
+    "g3.get_matrix(kind='edges').head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "fc1955b1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
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+       "<p>5 rows × 3001 columns</p>\n",
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+       "         0    1    2    3    4    5    6    7    8    9  ...  2991  2992  \\\n",
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+       "\n",
+       "[5 rows x 3001 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Since edges are featurized, we can transform on \"unseen/batch\" ones\n",
-    "# y_edges will be none since we don't have a label for the implicit edges. One could supply it via enrichment (like clustering, annotation etc)\n",
     "edge_data = g3._edges.sample(10)\n",
     "\n",
-    "x_edges, _ = g3.transform(edge_data, None, kind='edges')\n",
-    "x_edges"
+    "# y_edges will be None since we don't have a label for the implicit edges. One could supply it via enrichment (like clustering, annotation etc)\n",
+    "x_edges, _ = g3.transform(edge_data, None, kind='edges', return_graph=False)\n",
+    "x_edges.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "59d403f9",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Graph(num_nodes=3000, num_edges=19100,\n",
+       "      ndata_schemes={'feature': Scheme(shape=(768,), dtype=torch.float32), 'target': Scheme(shape=(1,), dtype=torch.float64), 'train_mask': Scheme(shape=(), dtype=torch.bool), 'test_mask': Scheme(shape=(), dtype=torch.bool)}\n",
+       "      edata_schemes={'feature': Scheme(shape=(3001,), dtype=torch.float64), 'train_mask': Scheme(shape=(), dtype=torch.bool), 'test_mask': Scheme(shape=(), dtype=torch.bool)})"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# once built, we can get the DGL graph itself\n",
     "G = g3.DGL_graph\n",
@@ -473,10 +2436,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "id": "8380122a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'feature': tensor([[ 0.6045,  0.1017, -0.0635,  ...,  0.4298, -0.3031, -0.3775],\n",
+       "        [-1.1035, -0.8352, -1.2375,  ..., -0.5702,  0.0510, -0.4362],\n",
+       "        [ 0.3261,  0.0457,  0.3082,  ...,  0.9958, -0.0096, -0.1197],\n",
+       "        ...,\n",
+       "        [-0.4063, -0.5310, -0.5638,  ..., -0.7132,  0.3906, -0.1414],\n",
+       "        [ 0.1290,  0.1685,  0.0550,  ...,  0.0287, -0.1604,  0.2120],\n",
+       "        [-0.1442,  0.7037, -0.8524,  ...,  0.0048, -0.0208,  0.0272]]), 'target': tensor([[-0.3883],\n",
+       "        [-0.3353],\n",
+       "        [-0.7115],\n",
+       "        ...,\n",
+       "        [ 0.4461],\n",
+       "        [-0.6295],\n",
+       "        [-0.7211]], dtype=torch.float64), 'train_mask': tensor([True, True, True,  ..., True, True, True]), 'test_mask': tensor([False, False, False,  ..., False, False, False])}"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# the features, targets, and masks\n",
     "G.ndata"
@@ -484,10 +2470,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "id": "63beefab",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([19100, 3001])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# `build_gnn()` will turn edges gotten from umap into bonafide feature matrices, \n",
     "# and make features out of explicit edges with `build_gnn(X_edges=[...], ..)`\n",
@@ -496,10 +2493,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "id": "45d3a37a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1898c24c0>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# see the edge features which are shape (n_edges, n_nodes + weight)\n",
     "# notice that had we used filter_weighted_edges to create a new graphistry instance and then .build_gnn() we would get\n",
@@ -510,10 +2530,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "id": "6c150a8e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1854ba370>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# see the way edges are related across the first 500 edges.\n",
     "plt.figure(figsize=(15,8))\n",
@@ -522,21 +2565,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "id": "9ab619bf",
    "metadata": {},
    "outputs": [],
    "source": [
     "# to see how to train a GNN, see the cyber or influence tutorial"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f170fa3a",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From 8dba7c45d07577e8398c1d82fed12b1c1184cb7a Mon Sep 17 00:00:00 2001
From: Alex Morrise <silkspace@users.noreply.github.com>
Date: Mon, 23 Jan 2023 09:46:34 -0800
Subject: [PATCH 133/432] Update README.md

Adds README for new features
---
 README.md | 44 ++++++++++++++++++++++++++++++--------------
 1 file changed, 30 insertions(+), 14 deletions(-)

diff --git a/README.md b/README.md
index f3f64aea83..7587b5a72b 100644
--- a/README.md
+++ b/README.md
@@ -358,11 +358,11 @@ Automatically and intelligently transform text, numbers, booleans, and other for
     g = g.umap()  # UMAP, GNNs, use features if already provided, otherwise will compute
 
     # other pydata libraries
-    X = g._node_features  # g._get_feature('nodes')
-    y = g._node_target  # g._get_target('nodes')
+    X = g._node_features  # g._get_feature('nodes') or g.get_matrix()
+    y = g._node_target  # g._get_target('nodes') or g.get_matrix(target=True)
     from sklearn.ensemble import RandomForestRegressor
-    model = RandomForestRegressor().fit(X, y) #assumes train/test split
-    new_df = pandas.read_csv(...)
+    model = RandomForestRegressor().fit(X, y)  # assumes train/test split
+    new_df = pandas.read_csv(...)  # mini batch
     X_new, _ = g.transform(new_df, None, kind='nodes')
     preds = model.predict(X_new)
     ```
@@ -371,7 +371,7 @@ Automatically and intelligently transform text, numbers, booleans, and other for
 
    ```python
     # graphistry
-    from graphistry.features import search_model, topic_model, ngrams_model, ModelDict, default_featurize_parameters
+    from graphistry.features import search_model, topic_model, ngrams_model, ModelDict, default_featurize_parameters, default_umap_parameters
 
     g = graphistry.nodes(df)
     g2 = g.umap(X=[..], y=[..], **search_model)  
@@ -381,7 +381,7 @@ Automatically and intelligently transform text, numbers, booleans, and other for
     new_model.update(dict(
                       y=[...],
                       kind='edges', 
-                      model_name='sbert/hf/a_cool_transformer_model', 
+                      model_name='sbert/cool_transformer_model', 
                       use_scaler_target='kbins', 
                       n_bins=11, 
                       strategy='normal'))
@@ -411,8 +411,20 @@ See `help(g.featurize)` for more options
 
     ```python
       new_df = pd.read_csv(...)
-      embeddings, X_new, _ = g.transform_umap(new_df, None, kind='nodes')
+      embeddings, X_new, _ = g.transform_umap(new_df, None, kind='nodes', return_graph=False)
     ```
+* Infer a new graph from new data using the old umap coordinates to run inference without having to train a new umap fit.
+
+    ```python
+      new_df = pd.read_csv(...)
+      g2 = g.transform_umap(new_df, return_graph=True)   # return_graph=True is default
+      g2.plot()  # 
+      
+      # or if you want the new minibatch to cluster to closest points in previous fit:
+      g3 = g.transform_umap(new_df, return_graph=True, merge_policy=True)
+      g3.plot()  # useful to see how new data connects to old -- can play with `sample` and `n_neighbors` to control how much of old to include
+    ```
+    
 
 * UMAP supports many options, such as supervised mode, working on a subset of columns, and passing arguments to underlying `featurize()` and UMAP implementations (see `help(g.umap)`):
 
@@ -564,13 +576,13 @@ See `help(g.embed)`, `help(g.predict_links)` , `help(g.predict_links_all)` for o
       # cluster by UMAP embeddings
       kind = 'nodes' | 'edges'
       g2 = g.umap(kind=kind).dbscan(kind=kind)
-      print(g2._nodes['_cluster']) | print(g2._edges['_cluster'])
+      print(g2._nodes['_dbscan']) | print(g2._edges['_dbscan'])
 
-      # dbscan with fixed parameters is default in umap
-      g2 = g.umap(dbscan=True)
+      # dbscan in umap or featurize via flag
+      g2 = g.umap(dbscan=True, min_dist=0.2, min_samples=1)
       
-      # and with greater control over parameters via chaining,
-      g2 = g.umap().dbscan(eps=1.2, min_samples=2, **kwargs)
+      # or via chaining,
+      g2 = g.umap().dbscan(min_dist=1.2, min_samples=2, **kwargs)
       
       # cluster by feature embeddings
       g2 = g.featurize().dbscan(**kwargs)
@@ -580,10 +592,14 @@ See `help(g.embed)`, `help(g.predict_links)` , `help(g.predict_links_all)` for o
       
       # equivalent to above (ie, cols != None and umap=True will still use features dataframe, rather than UMAP embeddings)
       g2 = g.umap().dbscan(cols=['ip_172', 'location', 'alert'], umap=True | False, **kwargs)
-      g2.plot() # color by `_cluster`
+      g2.plot() # color by `_dbscan`
+      
+      new_df = pd.read_csv(..)
+      # transform on new data according to fit dbscan model
+      g3 = g.transform_dbscan(new_df)
     ```
 
-See `help(g.dbscan)` for options
+See `help(g.dbscan)` or `help(g.transform_dbscan)` for options
 
 ### Quickly configurable
 

From a52dd1a0a71a6cc98836a23d380991aa75a3d133 Mon Sep 17 00:00:00 2001
From: Alex Morrise <silkspace@users.noreply.github.com>
Date: Mon, 23 Jan 2023 09:51:38 -0800
Subject: [PATCH 134/432] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 7587b5a72b..850ab54704 100644
--- a/README.md
+++ b/README.md
@@ -363,7 +363,7 @@ Automatically and intelligently transform text, numbers, booleans, and other for
     from sklearn.ensemble import RandomForestRegressor
     model = RandomForestRegressor().fit(X, y)  # assumes train/test split
     new_df = pandas.read_csv(...)  # mini batch
-    X_new, _ = g.transform(new_df, None, kind='nodes')
+    X_new, _ = g.transform(new_df, None, kind='nodes', return_graph=False)
     preds = model.predict(X_new)
     ```
 

From a9d067af504691defae1896b6dcde152e317ed5c Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Mon, 23 Jan 2023 13:31:43 -0500
Subject: [PATCH 135/432] fix(readme):optional if the change changes context

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 850ab54704..14eeea9315 100644
--- a/README.md
+++ b/README.md
@@ -397,7 +397,7 @@ See `help(g.featurize)` for more options
 
 ### [sklearn-based UMAP](https://umap-learn.readthedocs.io/en/latest/), [cuML-based UMAP](https://docs.rapids.ai/api/cuml/stable/api.html?highlight=umap#cuml.UMAP)
 
-* Reduce dimensionality and plot a similarity graph from feature vectors:
+* Reduce dimensionality by plotting a similarity graph from feature vectors:
 
     ```python
       # automatic feature engineering, UMAP

From 4df7a63d4a74ce77a86950ab5081076d76d25f34 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Mon, 23 Jan 2023 13:33:02 -0500
Subject: [PATCH 136/432] fix(readme): made line more concise

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 14eeea9315..f517447d15 100644
--- a/README.md
+++ b/README.md
@@ -403,7 +403,7 @@ See `help(g.featurize)` for more options
       # automatic feature engineering, UMAP
       g = graphistry.nodes(df).umap()
       
-      # plot the similarity graph even though there was no explicit edge_dataframe passed in -- it is created during UMAP.
+      # plot the similarity graph without any explicit edge_dataframe passed in -- it is created during UMAP.
       g.plot()
     ```
 

From 2a7b9a1f1d04f9a4cccf48e70a38e436c622ded3 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Mon, 23 Jan 2023 13:51:24 -0500
Subject: [PATCH 137/432] docs(readme): added conjunction

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index f517447d15..10dd07c77a 100644
--- a/README.md
+++ b/README.md
@@ -564,7 +564,7 @@ See `help(g.search_graph)` for options
       g2.predict_links_all(threshold=0.95).plot()
     ```
 
-See `help(g.embed)`, `help(g.predict_links)` , `help(g.predict_links_all)` for options
+See `help(g.embed)`, `help(g.predict_links)` , or `help(g.predict_links_all)` for options
 
 ### DBSCAN 
 

From 08b549b4ff4266d1ccddb9d44a83d03bd449e533 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Mon, 23 Jan 2023 16:28:23 -0500
Subject: [PATCH 138/432] fix(power-umap): maybe no article? optional change

---
 demos/ai/Introduction/simple-power-of-umap.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/demos/ai/Introduction/simple-power-of-umap.ipynb b/demos/ai/Introduction/simple-power-of-umap.ipynb
index 5d22c3730a..596c1dfcd2 100644
--- a/demos/ai/Introduction/simple-power-of-umap.ipynb
+++ b/demos/ai/Introduction/simple-power-of-umap.ipynb
@@ -36,7 +36,7 @@
    "metadata": {},
    "source": [
     "# Explore Data in a Whole New Way\n",
-    "PyGraphistry is a GPU Graph AI visualization tool that unlocks the graph in your data. \n",
+    "PyGraphistry is a GPU Graph AI visualization tool that unlocks graphing in your data. \n",
     "\n",
     "In the past loading, transforming and interacting with large multivariate datasets took time to set up pipelines and processes. Graphistry makes time-to-graph + AI + interactivity 100x faster. \n",
     "\n",
@@ -7138,7 +7138,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]

From 4dc610be7e1a4474aa3952371142760befb1c81e Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Mon, 23 Jan 2023 16:29:37 -0500
Subject: [PATCH 139/432] fix(power-umap): optional fix

---
 demos/ai/Introduction/simple-power-of-umap.ipynb | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/demos/ai/Introduction/simple-power-of-umap.ipynb b/demos/ai/Introduction/simple-power-of-umap.ipynb
index 596c1dfcd2..d87928e254 100644
--- a/demos/ai/Introduction/simple-power-of-umap.ipynb
+++ b/demos/ai/Introduction/simple-power-of-umap.ipynb
@@ -38,7 +38,7 @@
     "# Explore Data in a Whole New Way\n",
     "PyGraphistry is a GPU Graph AI visualization tool that unlocks graphing in your data. \n",
     "\n",
-    "In the past loading, transforming and interacting with large multivariate datasets took time to set up pipelines and processes. Graphistry makes time-to-graph + AI + interactivity 100x faster. \n",
+    "In the past loading, transforming and interacting with large multivariate datasets took a vast amount of time to set up pipelines and processes. Graphistry makes time-to-graph + AI + interactivity 100x faster. \n",
     "\n",
     "We will explore how to see data, explore relationships and create new graphs from batches using sci-kits like api, and even build a GNN model one could use in downstream DGL models. \n",
     "\n",
@@ -7511,7 +7511,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.7 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -7525,7 +7525,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.9"
+   "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
   }
  },
  "nbformat": 4,

From 8b95af8c54cde6a494ec7718e730da0670ae51f7 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Mon, 23 Jan 2023 16:31:24 -0500
Subject: [PATCH 140/432] fix(power-umap): condensed sentence

---
 demos/ai/Introduction/simple-power-of-umap.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/demos/ai/Introduction/simple-power-of-umap.ipynb b/demos/ai/Introduction/simple-power-of-umap.ipynb
index d87928e254..b01de96f02 100644
--- a/demos/ai/Introduction/simple-power-of-umap.ipynb
+++ b/demos/ai/Introduction/simple-power-of-umap.ipynb
@@ -40,7 +40,7 @@
     "\n",
     "In the past loading, transforming and interacting with large multivariate datasets took a vast amount of time to set up pipelines and processes. Graphistry makes time-to-graph + AI + interactivity 100x faster. \n",
     "\n",
-    "We will explore how to see data, explore relationships and create new graphs from batches using sci-kits like api, and even build a GNN model one could use in downstream DGL models. \n",
+    "We will explore how to see data, explore relationships, create new graphs from batches using sci-kits like api, and build a GNN model one could use in downstream DGL models. \n",
     "\n",
     "We will quickly analyze breast cancer, diabetes and digits datasets from sklearn.data\n",
     "\n",

From 83fe25d7f9c668cb4dbf6999fbe609a77f90499d Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Mon, 23 Jan 2023 16:38:22 -0500
Subject: [PATCH 141/432] fix(power-umap): cleaned up sentence for clarity

---
 demos/ai/Introduction/simple-power-of-umap.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/demos/ai/Introduction/simple-power-of-umap.ipynb b/demos/ai/Introduction/simple-power-of-umap.ipynb
index b01de96f02..1c93aa1a9d 100644
--- a/demos/ai/Introduction/simple-power-of-umap.ipynb
+++ b/demos/ai/Introduction/simple-power-of-umap.ipynb
@@ -303,7 +303,7 @@
     "\n",
     "Reduce the data into a 2 dimensional graph -- the edges come from similarity in features. \n",
     "\n",
-    "UMAP is a powerful way to see the parts of the dataset -- one can not only visually confirm if a predictive model will 'separate' the data, one can explore relationships that can help feed insights and potential treatment strategies. \n",
+    "UMAP is a powerful way to see the parts of the dataset. One can not simply confirm if a predictive model will 'separate' the data off of visuals alone. UMAP provides the tools to explore relationships that can help feed insights and potential treatment strategies. \n",
     "\n",
     "What can you find in the data?"
    ]

From 5886606a807b7ac572b87b99737185f1fa0a66a1 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Mon, 23 Jan 2023 16:40:10 -0500
Subject: [PATCH 142/432] fix(power-umap):added comma

---
 demos/ai/Introduction/simple-power-of-umap.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/demos/ai/Introduction/simple-power-of-umap.ipynb b/demos/ai/Introduction/simple-power-of-umap.ipynb
index 1c93aa1a9d..c89b8c8819 100644
--- a/demos/ai/Introduction/simple-power-of-umap.ipynb
+++ b/demos/ai/Introduction/simple-power-of-umap.ipynb
@@ -403,7 +403,7 @@
     "By setting `cols` one may pick out features from the matrix and have dbscan only focus on those.\n",
     "Coloring by the `_dbscan` label in the UI finds clusters across those variables. \n",
     "\n",
-    "Contrasting UMAP coordinates versus dbscan labels is a useful way to see total behavior against some part/pivot of interest. In the following we see k-clusters in the `worst` (case sensitive column selection) meta variable. "
+    "Contrasting UMAP coordinates versus dbscan labels is a useful way to see total behavior against some part/pivot of interest. In the following, we see k-clusters in the `worst` (case sensitive column selection) meta variable. "
    ]
   },
   {

From fb734c3395f94755835cbbd2f9bf8d199caa0da4 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Mon, 23 Jan 2023 16:42:13 -0500
Subject: [PATCH 143/432] fix(power-umap): sentence restructuring

---
 demos/ai/Introduction/simple-power-of-umap.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/demos/ai/Introduction/simple-power-of-umap.ipynb b/demos/ai/Introduction/simple-power-of-umap.ipynb
index c89b8c8819..c61a4c8c04 100644
--- a/demos/ai/Introduction/simple-power-of-umap.ipynb
+++ b/demos/ai/Introduction/simple-power-of-umap.ipynb
@@ -914,8 +914,8 @@
    "metadata": {},
    "source": [
     "# Transform Test Data into Graph\n",
-    "Can add batch onto closest neighbors of existing graph (from fit above) if merge_policy=True\n",
-    "otherwise, will create a new graph from the batch. "
+    "Can add batch onto closest neighbors of existing graph (from fit above). Otherwise, if merge_policy=True\n",
+    "it will create a new graph from the batch. "
    ]
   },
   {

From cd278909127b6a24d62d018a142d1b988609d43f Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Tue, 24 Jan 2023 10:25:40 -0800
Subject: [PATCH 144/432] adds(ci and tests for text search), adds changelog

---
 .github/workflows/ci.yml |  5 +++++
 CHANGELOG.md             |  5 ++++-
 bin/test-text.sh         | 15 +++++++++++++++
 3 files changed, 24 insertions(+), 1 deletion(-)
 create mode 100755 bin/test-text.sh

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 61a1447280..2834540916 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -209,6 +209,11 @@ jobs:
         source pygraphistry/bin/activate
         ./bin/test-features.sh
 
+    - name: Full search tests (rich featurize)
+      run: |
+        source pygraphistry/bin/activate
+        ./bin/test-text.sh
+
     - name: Full umap tests (rich featurize)
       run: |
         source pygraphistry/bin/activate
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 119150b9c1..5313c7e0b9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,10 +9,13 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm
 
 ### Changed
 * AI: moves public `g.g_dgl` from KG `embed` method to private method `g._kg_dgl`
+* AI: BREAKING CHANGES: to return matrices during transform, set the flag:  `X, y = g.transform(df, return_graph=False)` default behavior is ~ `g2 = g.transform(df)` returning a Plottable instance. 
 
 ### Added
+* AI: all `transform_*` methods return graphistry Plottable instances, using an infer_graph method. To return matrices, set the `return_graph=False` flag. 
+* AI: adds `g.get_matrix(**kwargs)` general method to retrieve (sub)-feature/target matrices
 * AI: DBSCAN -- `g.featurize().dbscan()` and `g.umap().dbscan()` with options to use UMAP embedding, feature matrix, or subset of feature matrix via `g.dbscan(cols=[...])`
-* AI: Demo cleanup using ModelDict & new features
+* AI: Demo cleanup using ModelDict & new features, refactoring demos using `dbscan` and `transform` methods.
 * Tests: dbscan tests
 * AI: Easy import of featurization kwargs for `g.umap(**kwargs)` and `g.featurize(**kwargs)`
 * AI: `g.get_features_by_cols` returns featurized submatrix with `col_part` in their columns
diff --git a/bin/test-text.sh b/bin/test-text.sh
new file mode 100755
index 0000000000..949bd68735
--- /dev/null
+++ b/bin/test-text.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+set -ex
+
+# Run from project root
+# - Args get passed to pytest phase
+# Non-zero exit code on fail
+
+# Assume [umap-learn,test]
+
+python -m pytest --version
+
+python -B -m pytest -vv \
+    graphistry/tests/test_text_utils.py
+
+# chmod +x bin/test-text.sh
\ No newline at end of file

From ed9c57ba3f8bb884190404a8d1e6956043243a91 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Tue, 24 Jan 2023 10:27:04 -0800
Subject: [PATCH 145/432] removes bug in __len__ call, more bug fixes

---
 graphistry/compute/cluster.py | 4 +++-
 graphistry/dgl_utils.py       | 9 ---------
 graphistry/feature_utils.py   | 6 ++----
 3 files changed, 5 insertions(+), 14 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index af1724d8d6..ff789c844c 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -123,6 +123,9 @@ def dbscan_fit(g, dbscan, kind="nodes", cols=None, use_umap_embedding=True, targ
 
     kind = "node" if kind == "nodes" else "edge"
     setattr(g, f"_{kind}_dbscan", dbscan)
+    
+    if cols is not None:  # set False since we used the features for verbose
+        use_umap_embedding = False
 
     if verbose:
         cnt = Counter(labels)
@@ -141,7 +144,6 @@ def dbscan_predict(X: pd.DataFrame, model):
     DBSCAN has no predict per se, so we reverse engineer one here
     from https://stackoverflow.com/questions/27822752/scikit-learn-predicting-new-points-with-dbscan
     
-    
     """
     n_samples = X.shape[0]
 
diff --git a/graphistry/dgl_utils.py b/graphistry/dgl_utils.py
index f82614dff1..2303a5aacd 100644
--- a/graphistry/dgl_utils.py
+++ b/graphistry/dgl_utils.py
@@ -519,15 +519,6 @@ def _mask_edges(self):
                 self.DGL_graph.edata[config.TEST_MASK],
             ) = get_torch_train_test_mask(n, self.train_split)
 
-    def __getitem__(self, idx):
-        # get one example by index, here we have only one graph. #todo parameterize case if we have RGNN
-        if self.DGL_graph is None:
-            logger.warning("DGL graph is not built, run `g.build_gnn(...)` first")
-        return self.DGL_graph
-
-    # def __len__(self): # this messes up scope.
-    #     # number of data examples
-    #     return 1
 
 
 # if __name__ == "__main__":
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 10be319062..eebbb4a4df 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1734,11 +1734,9 @@ def transform(self, df, ydf=None):
     def _transform_scaled(self, df, ydf, scaling_pipeline, scaling_pipeline_target):
         """Transform with scaling fit durning fit."""
         X, y = transform(df, ydf, self.res, self.kind, self.src, self.dst)
-        if scaling_pipeline is not None:
-            #print("scaling")
+        if scaling_pipeline is not None and not X.empty:
             X = pd.DataFrame(scaling_pipeline.transform(X), columns=X.columns, index=X.index)
-        if scaling_pipeline_target is not None:
-            #print("scaling target")
+        if scaling_pipeline_target is not None and y is not None and not y.empty:
             y = pd.DataFrame(scaling_pipeline_target.transform(y), columns=y.columns, index=y.index)
         return X, y
     

From 2124324cf7e7a20e1d72cd2a171ed0ac70202d03 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Tue, 24 Jan 2023 10:28:50 -0800
Subject: [PATCH 146/432] adds hydration for needed attributes on Plottable
 generated via search_graph

---
 graphistry/text_utils.py | 17 ++++++++++++++++-
 1 file changed, 16 insertions(+), 1 deletion(-)

diff --git a/graphistry/text_utils.py b/graphistry/text_utils.py
index 40c72f939f..1ee9bfcfd3 100644
--- a/graphistry/text_utils.py
+++ b/graphistry/text_utils.py
@@ -243,10 +243,23 @@ def search_graph(
             pass
 
         found_indices = pd.concat([edges[src], edges[dst], indices], axis=0).unique()
+        emb = None
         try:
             tdf = rdf.iloc[found_indices]
-        except:  # for explicit relabeled nodes
+            feats = res._node_features.iloc[found_indices]
+            if res._umap is not None:
+                emb = res._node_embedding.iloc[found_indices]
+        except Exception as e:  # for explicit relabeled nodes
+            #print(e)
             tdf = rdf[df[node].isin(found_indices)]
+            feats = res._node_features.loc[tdf.index]
+            #print(node, feats.shape)
+            if res._umap is not None:
+                emb = res._node_embedding[df[node].isin(found_indices)]
+            #print('working')
+            
+            #print(tdf.shape, feats.shape)
+            #print(emb.shape) if emb is not None else None
         logger.info(f" - Returning edge dataframe of size {edges.shape[0]}")
         # get all the unique nodes
         logger.info(
@@ -254,6 +267,8 @@ def search_graph(
         )
 
         g = res.edges(edges, src, dst).nodes(tdf, node)
+        g._node_features = feats
+        g._node_embedding = emb
 
         if g._name is not None:
             name = f"{g._name}-query:{query}"

From cef4cf448e3b57e48e30c3efd43401ed3c0fdf63 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Tue, 24 Jan 2023 10:39:51 -0800
Subject: [PATCH 147/432] lint

---
 graphistry/text_utils.py | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/graphistry/text_utils.py b/graphistry/text_utils.py
index 1ee9bfcfd3..9e67a95184 100644
--- a/graphistry/text_utils.py
+++ b/graphistry/text_utils.py
@@ -250,16 +250,11 @@ def search_graph(
             if res._umap is not None:
                 emb = res._node_embedding.iloc[found_indices]
         except Exception as e:  # for explicit relabeled nodes
-            #print(e)
+            logger.exception(e)
             tdf = rdf[df[node].isin(found_indices)]
             feats = res._node_features.loc[tdf.index]
-            #print(node, feats.shape)
             if res._umap is not None:
                 emb = res._node_embedding[df[node].isin(found_indices)]
-            #print('working')
-            
-            #print(tdf.shape, feats.shape)
-            #print(emb.shape) if emb is not None else None
         logger.info(f" - Returning edge dataframe of size {edges.shape[0]}")
         # get all the unique nodes
         logger.info(

From 0316ef09de3105b467f9f9de8ee4baaa8e594682 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Tue, 24 Jan 2023 10:51:07 -0800
Subject: [PATCH 148/432] lint

---
 graphistry/text_utils.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/graphistry/text_utils.py b/graphistry/text_utils.py
index 9e67a95184..1378b01f91 100644
--- a/graphistry/text_utils.py
+++ b/graphistry/text_utils.py
@@ -246,15 +246,15 @@ def search_graph(
         emb = None
         try:
             tdf = rdf.iloc[found_indices]
-            feats = res._node_features.iloc[found_indices]
+            feats = res._node_features.iloc[found_indices]  # type: ignore
             if res._umap is not None:
-                emb = res._node_embedding.iloc[found_indices]
+                emb = res._node_embedding.iloc[found_indices]  # type: ignore
         except Exception as e:  # for explicit relabeled nodes
             logger.exception(e)
             tdf = rdf[df[node].isin(found_indices)]
-            feats = res._node_features.loc[tdf.index]
+            feats = res._node_features.loc[tdf.index]  # type: ignore
             if res._umap is not None:
-                emb = res._node_embedding[df[node].isin(found_indices)]
+                emb = res._node_embedding[df[node].isin(found_indices)]  # type: ignore
         logger.info(f" - Returning edge dataframe of size {edges.shape[0]}")
         # get all the unique nodes
         logger.info(
@@ -262,6 +262,7 @@ def search_graph(
         )
 
         g = res.edges(edges, src, dst).nodes(tdf, node)
+        # add them back so they sync with .dbscan etc calls
         g._node_features = feats
         g._node_embedding = emb
 

From 31307d70f7b1b49bbd276b263424385fe84621d7 Mon Sep 17 00:00:00 2001
From: Alex Morrise <silkspace@users.noreply.github.com>
Date: Tue, 24 Jan 2023 11:56:41 -0800
Subject: [PATCH 149/432] Update README.md

---
 README.md | 30 +++++++++++++++++++-----------
 1 file changed, 19 insertions(+), 11 deletions(-)

diff --git a/README.md b/README.md
index 10dd07c77a..4756c86654 100644
--- a/README.md
+++ b/README.md
@@ -376,7 +376,7 @@ Automatically and intelligently transform text, numbers, booleans, and other for
     g = graphistry.nodes(df)
     g2 = g.umap(X=[..], y=[..], **search_model)  
 
-    # set custom encoding model with any feature kwargs
+    # set custom encoding model with any feature/umap/dbscan kwargs
     new_model = ModelDict(message='encoding new model parameters is easy', **default_featurize_parameters)
     new_model.update(dict(
                       y=[...],
@@ -389,7 +389,6 @@ Automatically and intelligently transform text, numbers, booleans, and other for
 
     g3 = g.umap(X=[..], **new_model)
     # compare g2 vs g3 or add to different pipelines
-    # ...
     ```
 
 
@@ -413,7 +412,7 @@ See `help(g.featurize)` for more options
       new_df = pd.read_csv(...)
       embeddings, X_new, _ = g.transform_umap(new_df, None, kind='nodes', return_graph=False)
     ```
-* Infer a new graph from new data using the old umap coordinates to run inference without having to train a new umap fit.
+* Infer a new graph from new data using the old umap coordinates to run inference without having to train a new umap model.
 
     ```python
       new_df = pd.read_csv(...)
@@ -422,7 +421,7 @@ See `help(g.featurize)` for more options
       
       # or if you want the new minibatch to cluster to closest points in previous fit:
       g3 = g.transform_umap(new_df, return_graph=True, merge_policy=True)
-      g3.plot()  # useful to see how new data connects to old -- can play with `sample` and `n_neighbors` to control how much of old to include
+      g3.plot()  # useful to see how new data connects to old -- play with `sample` and `n_neighbors` to control how much of old to include
     ```
     
 
@@ -463,11 +462,11 @@ See `help(g.umap)` for more options
 
     from [your_training_pipeline] import train, model
     # Train
-    g = graphistry.nodes(df).build_gnn(y=`target`) 
+    g = graphistry.nodes(df).build_gnn(y_nodes=`target`) 
     G = g.DGL_graph
     train(G, model)
     # predict on new data
-    X_new, _ = g.transform(new_df, None, kind='nodes' or 'edges') # no targets
+    X_new, _ = g.transform(new_df, None, kind='nodes' or 'edges', return_graph=False) # no targets
     predictions = model.predict(G_new, X_new)
     ```
 
@@ -492,12 +491,21 @@ GNN support is rapidly evolving, please contact the team directly or on Slack fo
                         #encode text as paraphrase embeddings, supports any sbert model
                         model_name = "paraphrase-MiniLM-L6-v2")
                         
+      # or use convienence `ModelDict` to store parameters
+      
+      from graphistry.features import search_model
+      g2 = g.featurize(X = ['text_col_1', .., 'text_col_n'], kind='nodes', **search_model)
+     
+      # query using the power of transformers to find richly relevant results                   
+      
       results_df, query_vector = g2.search('my natural language query', ...)
       
-      print(results_df[['_distance', ..., 'text_col_n']])  #sorted by relevancy
+      print(results_df[['_distance', 'text_col', ..]])  #sorted by relevancy
+      
+      # or see graph of matching entities and original edges
       
-      # or see graph of matching entities and similarity edges (or optional original edges)
       g2.search_graph('my natural language query', ...).plot()
+      
     ```
 
     
@@ -578,7 +586,7 @@ See `help(g.embed)`, `help(g.predict_links)` , or `help(g.predict_links_all)` fo
       g2 = g.umap(kind=kind).dbscan(kind=kind)
       print(g2._nodes['_dbscan']) | print(g2._edges['_dbscan'])
 
-      # dbscan in umap or featurize via flag
+      # dbscan in `umap` or `featurize` via flag
       g2 = g.umap(dbscan=True, min_dist=0.2, min_samples=1)
       
       # or via chaining,
@@ -587,7 +595,7 @@ See `help(g.embed)`, `help(g.predict_links)` , or `help(g.predict_links_all)` fo
       # cluster by feature embeddings
       g2 = g.featurize().dbscan(**kwargs)
       
-      # cluster by a given set of feature column attributes 
+      # cluster by a given set of feature column attributes, inhereted from `g.get_matrix(cols)`
       g2 = g.featurize().dbscan(cols=['ip_172', 'location', 'alert'], **kwargs)
       
       # equivalent to above (ie, cols != None and umap=True will still use features dataframe, rather than UMAP embeddings)
@@ -596,7 +604,7 @@ See `help(g.embed)`, `help(g.predict_links)` , or `help(g.predict_links_all)` fo
       
       new_df = pd.read_csv(..)
       # transform on new data according to fit dbscan model
-      g3 = g.transform_dbscan(new_df)
+      g3 = g2.transform_dbscan(new_df)
     ```
 
 See `help(g.dbscan)` or `help(g.transform_dbscan)` for options

From 647ab0663fb650884934a95ba662f356fed55a0e Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Tue, 24 Jan 2023 13:29:38 -0800
Subject: [PATCH 150/432] breaking change: DGL_graph -> _dgl_graph

---
 graphistry/dgl_utils.py | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/graphistry/dgl_utils.py b/graphistry/dgl_utils.py
index 2303a5aacd..6792073ec8 100644
--- a/graphistry/dgl_utils.py
+++ b/graphistry/dgl_utils.py
@@ -229,7 +229,7 @@ def dgl_lazy_init(self, train_split: float = 0.8, device: str = "cpu"):
             self.train_split = train_split
             self.device = device
             self._removed_edges_previously = False
-            self.DGL_graph = None
+            self._dgl_graph = None
             self.dgl_initialized = True
 
     def _prune_edge_target(self):
@@ -335,7 +335,7 @@ def _convert_edge_dataframe_to_DGL(
                 'destination column not set, try running g.bind(destination="my_col") or g.edges(df, destination="my_col")'
             )
 
-        res.DGL_graph, res._adjacency, res._entity_to_index = pandas_to_dgl_graph(
+        res._dgl_graph, res._adjacency, res._entity_to_index = pandas_to_dgl_graph(
             res._edges,
             res._source,
             res._destination,
@@ -370,7 +370,7 @@ def _featurize_nodes_to_dgl(
 
         ndata = convert_to_torch(X_enc, y_enc)
         # add ndata to the graph
-        res.DGL_graph.ndata.update(ndata)
+        res._dgl_graph.ndata.update(ndata)
         res._mask_nodes()
         return res
 
@@ -396,7 +396,7 @@ def _featurize_edges_to_dgl(
 
         edata = convert_to_torch(X_enc, y_enc)
         # add edata to the graph
-        res.DGL_graph.edata.update(edata)
+        res._dgl_graph.edata.update(edata)
         res._mask_edges()
         return res
 
@@ -504,19 +504,19 @@ def build_gnn(
             return res
 
     def _mask_nodes(self):
-        if config.FEATURE in self.DGL_graph.ndata:
-            n = self.DGL_graph.ndata[config.FEATURE].shape[0]
+        if config.FEATURE in self._dgl_graph.ndata:
+            n = self._dgl_graph.ndata[config.FEATURE].shape[0]
             (
-                self.DGL_graph.ndata[config.TRAIN_MASK],
-                self.DGL_graph.ndata[config.TEST_MASK],
+                self._dgl_graph.ndata[config.TRAIN_MASK],
+                self._dgl_graph.ndata[config.TEST_MASK],
             ) = get_torch_train_test_mask(n, self.train_split)
 
     def _mask_edges(self):
-        if config.FEATURE in self.DGL_graph.edata:
-            n = self.DGL_graph.edata[config.FEATURE].shape[0]
+        if config.FEATURE in self._dgl_graph.edata:
+            n = self._dgl_graph.edata[config.FEATURE].shape[0]
             (
-                self.DGL_graph.edata[config.TRAIN_MASK],
-                self.DGL_graph.edata[config.TEST_MASK],
+                self._dgl_graph.edata[config.TRAIN_MASK],
+                self._dgl_graph.edata[config.TEST_MASK],
             ) = get_torch_train_test_mask(n, self.train_split)
 
 
@@ -598,7 +598,7 @@ def _mask_edges(self):
 #         use_edge_scaler="zscale",
 #     )
 #     # the DGL graph
-#     G = g2.DGL_graph
+#     G = g2._dgl_graph
 #     print('G', G)
 #     # to get a sense of the different parts in training loop above
 #     # labels = torch.tensor(T.values, dtype=torch.float)

From da1a82bb8449c6379bb69488c985f077bf99c28d Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 25 Jan 2023 19:56:05 -0800
Subject: [PATCH 151/432] typing

---
 graphistry/compute/cluster.py | 35 +++++++++++++----------------------
 1 file changed, 13 insertions(+), 22 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index ff789c844c..d3328fdf96 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -2,13 +2,12 @@
 import pandas as pd
 import numpy as np
 
-from typing import Any, List, Union, TYPE_CHECKING, Tuple, Optional, Dict, Callable
+from typing import Any, List, Union, TYPE_CHECKING, Tuple, Optional
 from typing_extensions import Literal
 from collections import Counter
 
-from graphistry.Engine import Engine
 from graphistry.Plottable import Plottable
-from graphistry.constants import CUML, UMAP_LEARN, DBSCAN, DBSCAN_PARAMS  # noqa type: ignore
+from graphistry.constants import CUML, UMAP_LEARN, DBSCAN  # noqa type: ignore
 from graphistry.features import ModelDict
 from graphistry.feature_utils import get_matrix_by_column_parts
 
@@ -67,7 +66,7 @@ def resolve_cpu_gpu_engine(
     )
 
 
-def get_model_matrix(g, kind, cols, umap, target):
+def get_model_matrix(g, kind: str, cols: Optional[Union[List, str]], umap, target):
     """
         Allows for a single function to get the model matrix for both nodes and edges as well as targets, embeddings, and features
 
@@ -95,7 +94,7 @@ def get_model_matrix(g, kind, cols, umap, target):
     return df
 
 
-def dbscan_fit(g, dbscan, kind="nodes", cols=None, use_umap_embedding=True, target=False, verbose=False):
+def dbscan_fit(g: Any, dbscan: Any, kind:str="nodes", cols: Optional[Union[List, str]]=None, use_umap_embedding:bool=True, target:bool=False, verbose:bool=False):
     """
     Fits clustering on UMAP embeddings if umap is True, otherwise on the features dataframe
         or target dataframe if target is True.
@@ -104,7 +103,7 @@ def dbscan_fit(g, dbscan, kind="nodes", cols=None, use_umap_embedding=True, targ
         g: graphistry graph
         kind: 'nodes' or 'edges'
         cols: list of columns to use for clustering given `g.featurize` has been run
-        umap: whether to use UMAP embeddings or features dataframe
+        use_umap_embedding: whether to use UMAP embeddings or features dataframe for clustering (default: True)
     """
     X = get_model_matrix(g, kind, cols, use_umap_embedding, target)
     
@@ -139,7 +138,7 @@ def dbscan_fit(g, dbscan, kind="nodes", cols=None, use_umap_embedding=True, targ
     return g
 
 
-def dbscan_predict(X: pd.DataFrame, model):
+def dbscan_predict(X: pd.DataFrame, model: Any):
     """
     DBSCAN has no predict per se, so we reverse engineer one here
     from https://stackoverflow.com/questions/27822752/scikit-learn-predicting-new-points-with-dbscan
@@ -162,14 +161,6 @@ def dbscan_predict(X: pd.DataFrame, model):
     return y_new
 
 
-# def dbscan_predict2(g, kind="nodes", cols=None, umap=True):
-#     X = g._get_feature(kind)
-#     dbscan = g._node_dbscan if kind == "nodes" else g._edge_dbscan
-
-#     preds = dbscan_predict(X, dbscan)
-#     return X, preds
-
-
 class ClusterMixin(MIXIN_BASE):
     def __init__(self, *args, **kwargs):
         pass
@@ -195,9 +186,9 @@ def _cluster_dbscan(
         )
 
         dbscan = (
-            cuDBSCAN(eps=min_dist, min_samples=min_samples, **kwargs)
+            cuDBSCAN(eps=min_dist, min_samples=min_samples, *args, **kwargs)
             if res.engine == CUML
-            else DBSCAN(eps=min_dist, min_samples=min_samples, **kwargs)
+            else DBSCAN(eps=min_dist, min_samples=min_samples, *args, **kwargs)
         )
 
         res = dbscan_fit(
@@ -210,11 +201,11 @@ def dbscan(
         self,
         min_dist: float = 0.2,
         min_samples: int = 1,
-        cols=None,
-        kind="nodes",
-        fit_umap_embedding=True,
-        target=False,
-        verbose=False,
+        cols: Optional[Union[List, str]] = None,
+        kind: str = "nodes",
+        fit_umap_embedding: bool = True,
+        target: bool = False,
+        verbose: bool = False,
         *args,
         **kwargs,
     ):

From 844674cdbc9b056a264cb4d474b4f4774d46a47c Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Wed, 25 Jan 2023 21:57:23 -0800
Subject: [PATCH 152/432] lint

---
 graphistry/compute/cluster.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index d3328fdf96..15b7cf0ed3 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -94,7 +94,7 @@ def get_model_matrix(g, kind: str, cols: Optional[Union[List, str]], umap, targe
     return df
 
 
-def dbscan_fit(g: Any, dbscan: Any, kind:str="nodes", cols: Optional[Union[List, str]]=None, use_umap_embedding:bool=True, target:bool=False, verbose:bool=False):
+def dbscan_fit(g: Any, dbscan: Any, kind: str = "nodes", cols: Optional[Union[List, str]] = None, use_umap_embedding: bool = True, target: bool = False, verbose: bool = False):
     """
     Fits clustering on UMAP embeddings if umap is True, otherwise on the features dataframe
         or target dataframe if target is True.

From 5ebfc8e3c145652956ff4d5e2a7dd7ad4bbfd0ac Mon Sep 17 00:00:00 2001
From: Daniel <dcolinmorgan@users.noreply.github.com>
Date: Sat, 11 Feb 2023 13:50:30 +0800
Subject: [PATCH 153/432] added cuCat feature_engine

---
 graphistry/feature_utils.py | 43 +++++++++++++++++++++++++++++++++----
 1 file changed, 39 insertions(+), 4 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index ba6227da29..6b3c200dbe 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -47,6 +47,16 @@
         SuperVectorizer = Any
         GapEncoder = Any
         SimilarityEncoder = Any
+    try:
+        from cuCat import (
+            SuperVectorizer,
+            GapEncoder,
+            SimilarityEncoder,
+        )
+    except:
+        SuperVectorizer = Any
+        GapEncoder = Any
+        SimilarityEncoder = Any
     try:
         from sklearn.preprocessing import FunctionTransformer
         from sklearn.base import BaseEstimator, TransformerMixin
@@ -91,6 +101,20 @@ def lazy_import_has_min_dependancy():
     except ModuleNotFoundError as e:
         return False, e
 
+def lazy_import_has_cuml_dependancy():
+    import warnings
+    warnings.filterwarnings("ignore")
+    try:
+        import scipy.sparse  # noqa
+        from scipy import __version__ as scipy_version
+        from cuCat import __version__ as cuCat_version
+        from sklearn import __version__ as sklearn_version
+        logger.debug(f"SCIPY VERSION: {scipy_version}")
+        logger.debug(f"cuCat VERSION: {cuCat_version}")
+        logger.debug(f"sklearn VERSION: {sklearn_version}")
+        return True, 'ok'
+    except ModuleNotFoundError as e:
+        return False, e
 
 def assert_imported_text():
     has_dependancy_text_, import_text_exn, _ = lazy_import_has_dependancy_text()
@@ -135,7 +159,7 @@ def assert_imported():
 #
 #      _featurize_or_get_edges_dataframe_if_X_is_None
 
-FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch"]
+FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch", "cuCat"]
 FeatureEngine = Literal[FeatureEngineConcrete, "auto"]
 
 
@@ -143,13 +167,16 @@ def resolve_feature_engine(
     feature_engine: FeatureEngine,
 ) -> FeatureEngineConcrete:  # noqa
 
-    if feature_engine in ["none", "pandas", "dirty_cat", "torch"]:
+    if feature_engine in ["none", "pandas", "dirty_cat", "torch", "cuCat"]:
         return feature_engine  # type: ignore
 
     if feature_engine == "auto":
         has_dependancy_text_, _, _ = lazy_import_has_dependancy_text()
         if has_dependancy_text_:
             return "torch"
+        has_cuml_dependancy_, _ = lazy_import_has_cuml_dependancy()
+        if has_cuml_dependancy_:
+            return "cuCat"
         has_min_dependancy_, _ = lazy_import_has_min_dependancy()
         if has_min_dependancy_:
             return "dirty_cat"
@@ -157,7 +184,7 @@ def resolve_feature_engine(
 
     raise ValueError(  # noqa
         f'feature_engine expected to be "none", '
-        '"pandas", "dirty_cat", "torch", or "auto"'
+        '"pandas", "dirty_cat", "torch", "cuCat", or "auto"'
         f'but received: {feature_engine} :: {type(feature_engine)}'
     )
 
@@ -890,6 +917,11 @@ def process_dirty_dataframes(
     :return: Encoded data matrix and target (if not None),
             the data encoder, and the label encoder.
     """
+    if feature_engine=='dirty_cat':
+        from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
+    elif feature_engine=='cuCat':
+        from cuCat import SuperVectorizer, GapEncoder, SimilarityEncoder
+    
     from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
     from sklearn.preprocessing import FunctionTransformer
     t = time()
@@ -2331,7 +2363,10 @@ def featurize(
                 default True.
         :return: self, with new attributes set by the featurization process.
         """
-        assert_imported()
+        if feature_engine == 'dirty_cat':
+            assert_imported()
+        elif feature_engine == 'cuCat':
+            assert_cuml_imported()
         if inplace:
             res = self
         else:

From be038f112b1f16178d1434017b2305825e3e562a Mon Sep 17 00:00:00 2001
From: Daniel <dcolinmorgan@users.noreply.github.com>
Date: Sat, 11 Feb 2023 15:13:15 +0800
Subject: [PATCH 154/432] functional

---
 graphistry/feature_utils.py | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 6b3c200dbe..41c23bdc65 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -135,7 +135,14 @@ def assert_imported():
         )
         raise import_min_exn
 
-
+def assert_cuml_imported():
+    has_cuml_dependancy_, import_cuml_exn = lazy_import_has_cuml_dependancy()
+    if not has_cuml_dependancy_:
+        logger.error(  # noqa
+                     "cunl not found, trying running"  # noqa
+                     "`pip install rapids`"  # noqa
+        )
+        raise import_cuml_exn
 # ############################################################################
 #
 #     Rough calltree
@@ -891,6 +898,7 @@ def process_dirty_dataframes(
     similarity: Optional[str] = None,  # "ngram",
     categories: Optional[str] = "auto",
     multilabel: bool = False,
+    feature_engine: Optional[str] = "dirty_cat",
 ) -> Tuple[
     pd.DataFrame,
     Optional[pd.DataFrame],
@@ -922,7 +930,6 @@ def process_dirty_dataframes(
     elif feature_engine=='cuCat':
         from cuCat import SuperVectorizer, GapEncoder, SimilarityEncoder
     
-    from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
     from sklearn.preprocessing import FunctionTransformer
     t = time()
 
@@ -1164,7 +1171,8 @@ def process_nodes_dataframes(
         n_topics_target=n_topics_target,
         similarity=similarity,
         categories=categories,
-        multilabel=multilabel
+        multilabel=multilabel,
+        feature_engine=feature_engine,
     )
 
     if embedding:

From 86993ef8eec97bf82acae58e2ccc15a2a1daa0dc Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Thu, 16 Feb 2023 15:11:58 -0500
Subject: [PATCH 155/432] fix(graphistry.rst): now produces plotter modules

---
 docs/source/graphistry.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/graphistry.rst b/docs/source/graphistry.rst
index c9fbcfa4dd..91aa7f08f9 100644
--- a/docs/source/graphistry.rst
+++ b/docs/source/graphistry.rst
@@ -12,7 +12,7 @@ graphistry package
 graphistry.plotter module
 -------------------------
 
-.. automodule:: graphistry.plotter
+.. automodule:: graphistry.PlotterBase
     :members:
     :undoc-members:
     :show-inheritance:

From 27dee7360dcd4ebd71a57d7b96d3568a32a30b6f Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 21 Feb 2023 16:42:41 +0800
Subject: [PATCH 156/432] working umap from cudf, via pandas

---
 graphistry/feature_utils.py |  5 +++--
 graphistry/umap_utils.py    | 12 +++++++++---
 2 files changed, 12 insertions(+), 5 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index ba6227da29..97c0632350 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2,6 +2,7 @@
 import numpy as np
 import os
 import pandas as pd
+import cudf
 from time import time
 import warnings
 from functools import partial
@@ -167,7 +168,7 @@ def resolve_feature_engine(
 
 def resolve_y(df: Optional[pd.DataFrame], y: YSymbolic) -> pd.DataFrame:
 
-    if isinstance(y, pd.DataFrame):
+    if isinstance(y, pd.DataFrame) or isinstance(y, cudf.DataFrame):
         return y
 
     if df is None:
@@ -188,7 +189,7 @@ def resolve_y(df: Optional[pd.DataFrame], y: YSymbolic) -> pd.DataFrame:
 
 def resolve_X(df: Optional[pd.DataFrame], X: XSymbolic) -> pd.DataFrame:
 
-    if isinstance(X, pd.DataFrame):
+    if isinstance(X, pd.DataFrame) or isinstance(X, cudf.DataFrame):
         return X
 
     if df is None:
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 5cd092f29d..1dd60c4aec 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -3,6 +3,7 @@
 from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
 
 import pandas as pd
+import cudf
 
 from . import constants as config
 from .feature_utils import (FeatureMixin, Literal, XSymbolic, YSymbolic,
@@ -478,7 +479,6 @@ def umap(
                 )
 
             nodes = res._nodes[res._node].values
-            index_to_nodes_dict = dict(zip(range(len(nodes)), nodes))
 
             logger.debug("propagating with featurize_kwargs: %s", featurize_kwargs)
             (
@@ -492,8 +492,14 @@ def umap(
             logger.debug("umap X_: %s", X_)
             logger.debug("umap y_: %s", y_)
 
+            if isinstance(X_,pd.DataFrame):
+                index_to_nodes_dict = dict(zip(range(len(nodes)), nodes))
+            elif isinstance(X_,cudf.DataFrame):
+                index_to_nodes_dict=cudf.DataFrame(nodes).reset_index()
+
+
             res = res._process_umap(
-                res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs
+                res, pd.DataFrame(X_), y_, kind, memoize, featurize_kwargs, **umap_kwargs
             )
 
             res._weighted_adjacency_nodes = res._weighted_adjacency
@@ -521,7 +527,7 @@ def umap(
             )
 
             res = res._process_umap(
-                res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs
+                res, pd.DataFrame(X_.to_numpy()), y_, kind, memoize, featurize_kwargs, **umap_kwargs
             )
             res._weighted_adjacency_edges = res._weighted_adjacency
             if res._xy is None:

From a35fe0a5e5dd39a0271de4d3fd936a3aa8fc4e62 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 21 Feb 2023 16:51:06 +0800
Subject: [PATCH 157/432] working umap from cudf, via pandas

---
 graphistry/umap_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 1dd60c4aec..1911891bfd 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -499,7 +499,7 @@ def umap(
 
 
             res = res._process_umap(
-                res, pd.DataFrame(X_), y_, kind, memoize, featurize_kwargs, **umap_kwargs
+                res, pd.DataFrame(X_.to_numpy()), y_, kind, memoize, featurize_kwargs, **umap_kwargs
             )
 
             res._weighted_adjacency_nodes = res._weighted_adjacency

From 935a9f9277a6fe64af7c36402dae0d133143acf1 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 21 Feb 2023 17:06:00 +0800
Subject: [PATCH 158/432] working umap from cudf, via pandas

---
 graphistry/umap_utils.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 1911891bfd..6e61a8aa69 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -496,10 +496,10 @@ def umap(
                 index_to_nodes_dict = dict(zip(range(len(nodes)), nodes))
             elif isinstance(X_,cudf.DataFrame):
                 index_to_nodes_dict=cudf.DataFrame(nodes).reset_index()
-
+                X_=pd.DataFrame(X_.to_numpy())
 
             res = res._process_umap(
-                res, pd.DataFrame(X_.to_numpy()), y_, kind, memoize, featurize_kwargs, **umap_kwargs
+                res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs
             )
 
             res._weighted_adjacency_nodes = res._weighted_adjacency
@@ -527,7 +527,7 @@ def umap(
             )
 
             res = res._process_umap(
-                res, pd.DataFrame(X_.to_numpy()), y_, kind, memoize, featurize_kwargs, **umap_kwargs
+                res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs
             )
             res._weighted_adjacency_edges = res._weighted_adjacency
             if res._xy is None:

From 261f1221807e008457b20cb8331861245b120eb7 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Tue, 21 Feb 2023 11:09:34 -0500
Subject: [PATCH 159/432] feat(docs-ui): added a bio

---
 docs/source/index.rst | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/docs/source/index.rst b/docs/source/index.rst
index 1943a5cf72..cc734f5581 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,8 +1,11 @@
 PyGraphistry's documentation (|version|)
 ========================================
 
-Quickstart:
-`Read our tutorial <https://github.com/graphistry/pygraphistry/blob/master/README.md>`_
+.. Quickstart:
+.. `Read our tutorial <https://github.com/graphistry/pygraphistry/blob/master/README.md>`_
+
+PyGraphistry is a Python visual graph AI library to extract, transform, analyze, model, and visualize big graphs, and especially alongside Graphistry end-to-end GPU server sessions. Installing optional graphistry[ai] dependencies adds graph autoML, including automatic feature engineering, UMAP, and graph neural net support. Combined, PyGraphistry reduces your time to graph for going from raw data to visualizations and AI models down to three lines of code.
+Here in our docstrings you can find useful packages, modules, commands to maximize your graph AI experience with PyGraphistry. For a full tutorial, refer to our `PyGraphistry <https://github.com/graphistry/pygraphistry/>`_ repo.
 
 .. toctree::
    :maxdepth: 3

From 882eb94fb6173d5eafe372b474bac35e7ad1cb13 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Wed, 22 Feb 2023 08:33:12 +0800
Subject: [PATCH 160/432] working umap from cudf, via pandas

---
 graphistry/umap_utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 6e61a8aa69..bfe035f96e 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -495,8 +495,8 @@ def umap(
             if isinstance(X_,pd.DataFrame):
                 index_to_nodes_dict = dict(zip(range(len(nodes)), nodes))
             elif isinstance(X_,cudf.DataFrame):
-                index_to_nodes_dict=cudf.DataFrame(nodes).reset_index()
-                X_=pd.DataFrame(X_.to_numpy())
+                index_to_nodes_dict = cudf.DataFrame(nodes).reset_index()
+                X_= pd.DataFrame(X_.to_numpy())
 
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs

From d0779731a932613ba21df806acbac35ef1c1a2f3 Mon Sep 17 00:00:00 2001
From: dc <dc@evans-mbp-2.lan>
Date: Wed, 22 Feb 2023 12:35:53 +0800
Subject: [PATCH 161/432] remove explicit cudf import

---
 graphistry/feature_utils.py | 63 +++++++++++++++++++++++++++++++------
 graphistry/umap_utils.py    |  5 +--
 2 files changed, 57 insertions(+), 11 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 97c0632350..9ff6080a12 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -6,6 +6,7 @@
 from time import time
 import warnings
 from functools import partial
+from inspect import getmodule
 
 from typing import (
     Hashable,
@@ -48,6 +49,16 @@
         SuperVectorizer = Any
         GapEncoder = Any
         SimilarityEncoder = Any
+    try:
+        from cuCat import (
+            SuperVectorizer,
+            GapEncoder,
+            SimilarityEncoder,
+        )
+    except:
+        SuperVectorizer = Any
+        GapEncoder = Any
+        SimilarityEncoder = Any
     try:
         from sklearn.preprocessing import FunctionTransformer
         from sklearn.base import BaseEstimator, TransformerMixin
@@ -92,6 +103,20 @@ def lazy_import_has_min_dependancy():
     except ModuleNotFoundError as e:
         return False, e
 
+def lazy_import_has_cuml_dependancy():
+    import warnings
+    warnings.filterwarnings("ignore")
+    try:
+        import scipy.sparse  # noqa
+        from scipy import __version__ as scipy_version
+        from cuCat import __version__ as cuCat_version
+        from sklearn import __version__ as sklearn_version
+        logger.debug(f"SCIPY VERSION: {scipy_version}")
+        logger.debug(f"cuCat VERSION: {cuCat_version}")
+        logger.debug(f"sklearn VERSION: {sklearn_version}")
+        return True, 'ok'
+    except ModuleNotFoundError as e:
+        return False, e
 
 def assert_imported_text():
     has_dependancy_text_, import_text_exn, _ = lazy_import_has_dependancy_text()
@@ -112,7 +137,14 @@ def assert_imported():
         )
         raise import_min_exn
 
-
+def assert_cuml_imported():
+    has_cuml_dependancy_, import_cuml_exn = lazy_import_has_cuml_dependancy()
+    if not has_cuml_dependancy_:
+        logger.error(  # noqa
+                     "cunl not found, trying running"  # noqa
+                     "`pip install rapids`"  # noqa
+        )
+        raise import_cuml_exn
 # ############################################################################
 #
 #     Rough calltree
@@ -136,7 +168,7 @@ def assert_imported():
 #
 #      _featurize_or_get_edges_dataframe_if_X_is_None
 
-FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch"]
+FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch", "cuCat"]
 FeatureEngine = Literal[FeatureEngineConcrete, "auto"]
 
 
@@ -144,13 +176,16 @@ def resolve_feature_engine(
     feature_engine: FeatureEngine,
 ) -> FeatureEngineConcrete:  # noqa
 
-    if feature_engine in ["none", "pandas", "dirty_cat", "torch"]:
+    if feature_engine in ["none", "pandas", "dirty_cat", "torch", "cuCat"]:
         return feature_engine  # type: ignore
 
     if feature_engine == "auto":
         has_dependancy_text_, _, _ = lazy_import_has_dependancy_text()
         if has_dependancy_text_:
             return "torch"
+        has_cuml_dependancy_, _ = lazy_import_has_cuml_dependancy()
+        if has_cuml_dependancy_:
+            return "cuCat"
         has_min_dependancy_, _ = lazy_import_has_min_dependancy()
         if has_min_dependancy_:
             return "dirty_cat"
@@ -158,7 +193,7 @@ def resolve_feature_engine(
 
     raise ValueError(  # noqa
         f'feature_engine expected to be "none", '
-        '"pandas", "dirty_cat", "torch", or "auto"'
+        '"pandas", "dirty_cat", "torch", "cuCat", or "auto"'
         f'but received: {feature_engine} :: {type(feature_engine)}'
     )
 
@@ -168,7 +203,7 @@ def resolve_feature_engine(
 
 def resolve_y(df: Optional[pd.DataFrame], y: YSymbolic) -> pd.DataFrame:
 
-    if isinstance(y, pd.DataFrame) or isinstance(y, cudf.DataFrame):
+    if isinstance(y, pd.DataFrame) or 'cudf.core.dataframe' in str(getmodule(y)):
         return y
 
     if df is None:
@@ -189,7 +224,7 @@ def resolve_y(df: Optional[pd.DataFrame], y: YSymbolic) -> pd.DataFrame:
 
 def resolve_X(df: Optional[pd.DataFrame], X: XSymbolic) -> pd.DataFrame:
 
-    if isinstance(X, pd.DataFrame) or isinstance(X, cudf.DataFrame):
+    if isinstance(X, pd.DataFrame) or 'cudf.core.dataframe' in str(getmodule(X)):
         return X
 
     if df is None:
@@ -865,6 +900,7 @@ def process_dirty_dataframes(
     similarity: Optional[str] = None,  # "ngram",
     categories: Optional[str] = "auto",
     multilabel: bool = False,
+    feature_engine: Optional[str] = "dirty_cat",
 ) -> Tuple[
     pd.DataFrame,
     Optional[pd.DataFrame],
@@ -891,7 +927,11 @@ def process_dirty_dataframes(
     :return: Encoded data matrix and target (if not None),
             the data encoder, and the label encoder.
     """
-    from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
+    if feature_engine=='dirty_cat':
+        from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
+    elif feature_engine=='cuCat':
+        from cuCat import SuperVectorizer, GapEncoder, SimilarityEncoder
+    
     from sklearn.preprocessing import FunctionTransformer
     t = time()
 
@@ -1133,7 +1173,8 @@ def process_nodes_dataframes(
         n_topics_target=n_topics_target,
         similarity=similarity,
         categories=categories,
-        multilabel=multilabel
+        multilabel=multilabel,
+        feature_engine=feature_engine,
     )
 
     if embedding:
@@ -1789,6 +1830,7 @@ def prune_weighted_edges_df_and_relabel_nodes(
         f"from {len(wdf):,} to {len(wdf2):,} edges."
     )
     if index_to_nodes_dict is not None:
+
         wdf2 = wdf2.replace(
             {
                 config.SRC: index_to_nodes_dict,
@@ -2332,7 +2374,10 @@ def featurize(
                 default True.
         :return: self, with new attributes set by the featurization process.
         """
-        assert_imported()
+        if feature_engine == 'dirty_cat':
+            assert_imported()
+        elif feature_engine == 'cuCat':
+            assert_cuml_imported()
         if inplace:
             res = self
         else:
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index bfe035f96e..112c7d111d 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -1,9 +1,9 @@
 import copy
 from time import time
 from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
+from inspect import getmodule
 
 import pandas as pd
-import cudf
 
 from . import constants as config
 from .feature_utils import (FeatureMixin, Literal, XSymbolic, YSymbolic,
@@ -494,7 +494,8 @@ def umap(
 
             if isinstance(X_,pd.DataFrame):
                 index_to_nodes_dict = dict(zip(range(len(nodes)), nodes))
-            elif isinstance(X_,cudf.DataFrame):
+            elif 'cudf.core.dataframe' in str(getmodule(X_)):
+                import cudf
                 index_to_nodes_dict = cudf.DataFrame(nodes).reset_index()
                 X_= pd.DataFrame(X_.to_numpy())
 

From 62fa5fdcde196aba6c0682c74aed0aefe9f27850 Mon Sep 17 00:00:00 2001
From: dc <dc@E-MBP.local>
Date: Wed, 22 Feb 2023 18:12:49 +0800
Subject: [PATCH 162/432] opt-in for setup

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 5817609f6a..2db477787a 100755
--- a/setup.py
+++ b/setup.py
@@ -40,7 +40,7 @@ def unique_flatten_dict(d):
 base_extras_heavy = {
   'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
 }
-base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'annoy', 'joblib']
+base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'annoy', 'joblib'] + ['git+https://github.com/graphistry/cuCat/@0.01.0']
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From 5ba336ea6bb63aec8bcce17e192ec9a71eecdec9 Mon Sep 17 00:00:00 2001
From: dc <dc@e-mbp.lan>
Date: Fri, 24 Feb 2023 16:11:46 +0800
Subject: [PATCH 163/432] lint

---
 graphistry/feature_utils.py | 4 ++--
 setup.py                    | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 41c23bdc65..a6f2856277 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -925,9 +925,9 @@ def process_dirty_dataframes(
     :return: Encoded data matrix and target (if not None),
             the data encoder, and the label encoder.
     """
-    if feature_engine=='dirty_cat':
+    if feature_engine =='dirty_cat':
         from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
-    elif feature_engine=='cuCat':
+    elif feature_engine =='cuCat':
         from cuCat import SuperVectorizer, GapEncoder, SimilarityEncoder
     
     from sklearn.preprocessing import FunctionTransformer
diff --git a/setup.py b/setup.py
index 2db477787a..68d0dbe70a 100755
--- a/setup.py
+++ b/setup.py
@@ -38,9 +38,9 @@ def unique_flatten_dict(d):
 }
 
 base_extras_heavy = {
-  'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
+  'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'] + ['cuCat @ git+https://github.com/graphistry/cuCat/@0.01.0'] ,
 }
-base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'annoy', 'joblib'] + ['git+https://github.com/graphistry/cuCat/@0.01.0']
+base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'annoy', 'joblib']
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From 9520cbcc8ea869c2f4a3afe7d78de8a56ef5dca4 Mon Sep 17 00:00:00 2001
From: dc <dc@e-mbp.lan>
Date: Fri, 24 Feb 2023 16:13:35 +0800
Subject: [PATCH 164/432] lint

---
 graphistry/feature_utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index a6f2856277..bcbc7ca0bb 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -925,9 +925,9 @@ def process_dirty_dataframes(
     :return: Encoded data matrix and target (if not None),
             the data encoder, and the label encoder.
     """
-    if feature_engine =='dirty_cat':
+    if feature_engine == 'dirty_cat':
         from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
-    elif feature_engine =='cuCat':
+    elif feature_engine == 'cuCat':
         from cuCat import SuperVectorizer, GapEncoder, SimilarityEncoder
     
     from sklearn.preprocessing import FunctionTransformer

From ab0b019f5c259ecfe9a4a320f8656878d0536c3c Mon Sep 17 00:00:00 2001
From: dc <dc@e-mbp.lan>
Date: Fri, 24 Feb 2023 16:16:51 +0800
Subject: [PATCH 165/432] lint

---
 graphistry/feature_utils.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index bcbc7ca0bb..4697b6795e 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -143,6 +143,8 @@ def assert_cuml_imported():
                      "`pip install rapids`"  # noqa
         )
         raise import_cuml_exn
+    
+    
 # ############################################################################
 #
 #     Rough calltree

From d12f2db527d0f99e146cbd5b254c15c64c2fa57c Mon Sep 17 00:00:00 2001
From: dc <dc@e-mbp.lan>
Date: Fri, 24 Feb 2023 16:43:02 +0800
Subject: [PATCH 166/432] branches got confused, removed cudf import

---
 graphistry/feature_utils.py | 64 ++++++-------------------------------
 1 file changed, 9 insertions(+), 55 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 9ff6080a12..2937ca7565 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2,11 +2,9 @@
 import numpy as np
 import os
 import pandas as pd
-import cudf
 from time import time
 import warnings
 from functools import partial
-from inspect import getmodule
 
 from typing import (
     Hashable,
@@ -49,16 +47,6 @@
         SuperVectorizer = Any
         GapEncoder = Any
         SimilarityEncoder = Any
-    try:
-        from cuCat import (
-            SuperVectorizer,
-            GapEncoder,
-            SimilarityEncoder,
-        )
-    except:
-        SuperVectorizer = Any
-        GapEncoder = Any
-        SimilarityEncoder = Any
     try:
         from sklearn.preprocessing import FunctionTransformer
         from sklearn.base import BaseEstimator, TransformerMixin
@@ -103,20 +91,6 @@ def lazy_import_has_min_dependancy():
     except ModuleNotFoundError as e:
         return False, e
 
-def lazy_import_has_cuml_dependancy():
-    import warnings
-    warnings.filterwarnings("ignore")
-    try:
-        import scipy.sparse  # noqa
-        from scipy import __version__ as scipy_version
-        from cuCat import __version__ as cuCat_version
-        from sklearn import __version__ as sklearn_version
-        logger.debug(f"SCIPY VERSION: {scipy_version}")
-        logger.debug(f"cuCat VERSION: {cuCat_version}")
-        logger.debug(f"sklearn VERSION: {sklearn_version}")
-        return True, 'ok'
-    except ModuleNotFoundError as e:
-        return False, e
 
 def assert_imported_text():
     has_dependancy_text_, import_text_exn, _ = lazy_import_has_dependancy_text()
@@ -137,14 +111,7 @@ def assert_imported():
         )
         raise import_min_exn
 
-def assert_cuml_imported():
-    has_cuml_dependancy_, import_cuml_exn = lazy_import_has_cuml_dependancy()
-    if not has_cuml_dependancy_:
-        logger.error(  # noqa
-                     "cunl not found, trying running"  # noqa
-                     "`pip install rapids`"  # noqa
-        )
-        raise import_cuml_exn
+
 # ############################################################################
 #
 #     Rough calltree
@@ -168,7 +135,7 @@ def assert_cuml_imported():
 #
 #      _featurize_or_get_edges_dataframe_if_X_is_None
 
-FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch", "cuCat"]
+FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch"]
 FeatureEngine = Literal[FeatureEngineConcrete, "auto"]
 
 
@@ -176,16 +143,13 @@ def resolve_feature_engine(
     feature_engine: FeatureEngine,
 ) -> FeatureEngineConcrete:  # noqa
 
-    if feature_engine in ["none", "pandas", "dirty_cat", "torch", "cuCat"]:
+    if feature_engine in ["none", "pandas", "dirty_cat", "torch"]:
         return feature_engine  # type: ignore
 
     if feature_engine == "auto":
         has_dependancy_text_, _, _ = lazy_import_has_dependancy_text()
         if has_dependancy_text_:
             return "torch"
-        has_cuml_dependancy_, _ = lazy_import_has_cuml_dependancy()
-        if has_cuml_dependancy_:
-            return "cuCat"
         has_min_dependancy_, _ = lazy_import_has_min_dependancy()
         if has_min_dependancy_:
             return "dirty_cat"
@@ -193,7 +157,7 @@ def resolve_feature_engine(
 
     raise ValueError(  # noqa
         f'feature_engine expected to be "none", '
-        '"pandas", "dirty_cat", "torch", "cuCat", or "auto"'
+        '"pandas", "dirty_cat", "torch", or "auto"'
         f'but received: {feature_engine} :: {type(feature_engine)}'
     )
 
@@ -203,7 +167,7 @@ def resolve_feature_engine(
 
 def resolve_y(df: Optional[pd.DataFrame], y: YSymbolic) -> pd.DataFrame:
 
-    if isinstance(y, pd.DataFrame) or 'cudf.core.dataframe' in str(getmodule(y)):
+    if isinstance(y, pd.DataFrame) or 'cudf.core.dataframe' in str(getmodule(y):
         return y
 
     if df is None:
@@ -224,7 +188,7 @@ def resolve_y(df: Optional[pd.DataFrame], y: YSymbolic) -> pd.DataFrame:
 
 def resolve_X(df: Optional[pd.DataFrame], X: XSymbolic) -> pd.DataFrame:
 
-    if isinstance(X, pd.DataFrame) or 'cudf.core.dataframe' in str(getmodule(X)):
+    if isinstance(X, pd.DataFrame) or 'cudf.core.dataframe' in str(getmodule(X):
         return X
 
     if df is None:
@@ -900,7 +864,6 @@ def process_dirty_dataframes(
     similarity: Optional[str] = None,  # "ngram",
     categories: Optional[str] = "auto",
     multilabel: bool = False,
-    feature_engine: Optional[str] = "dirty_cat",
 ) -> Tuple[
     pd.DataFrame,
     Optional[pd.DataFrame],
@@ -927,11 +890,7 @@ def process_dirty_dataframes(
     :return: Encoded data matrix and target (if not None),
             the data encoder, and the label encoder.
     """
-    if feature_engine=='dirty_cat':
-        from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
-    elif feature_engine=='cuCat':
-        from cuCat import SuperVectorizer, GapEncoder, SimilarityEncoder
-    
+    from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
     from sklearn.preprocessing import FunctionTransformer
     t = time()
 
@@ -1173,8 +1132,7 @@ def process_nodes_dataframes(
         n_topics_target=n_topics_target,
         similarity=similarity,
         categories=categories,
-        multilabel=multilabel,
-        feature_engine=feature_engine,
+        multilabel=multilabel
     )
 
     if embedding:
@@ -1830,7 +1788,6 @@ def prune_weighted_edges_df_and_relabel_nodes(
         f"from {len(wdf):,} to {len(wdf2):,} edges."
     )
     if index_to_nodes_dict is not None:
-
         wdf2 = wdf2.replace(
             {
                 config.SRC: index_to_nodes_dict,
@@ -2374,10 +2331,7 @@ def featurize(
                 default True.
         :return: self, with new attributes set by the featurization process.
         """
-        if feature_engine == 'dirty_cat':
-            assert_imported()
-        elif feature_engine == 'cuCat':
-            assert_cuml_imported()
+        assert_imported()
         if inplace:
             res = self
         else:

From 736744e632136b356ba62a5790423e747f92ea90 Mon Sep 17 00:00:00 2001
From: dc <dc@e-mbp.lan>
Date: Fri, 24 Feb 2023 16:45:38 +0800
Subject: [PATCH 167/432] branches got confused, removed cudf import

---
 graphistry/feature_utils.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 2937ca7565..467d71bb79 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -3,6 +3,7 @@
 import os
 import pandas as pd
 from time import time
+from inspect import getmodule
 import warnings
 from functools import partial
 
@@ -167,7 +168,7 @@ def resolve_feature_engine(
 
 def resolve_y(df: Optional[pd.DataFrame], y: YSymbolic) -> pd.DataFrame:
 
-    if isinstance(y, pd.DataFrame) or 'cudf.core.dataframe' in str(getmodule(y):
+    if isinstance(y, pd.DataFrame) or 'cudf.core.dataframe' in str(getmodule(y)):
         return y
 
     if df is None:
@@ -188,7 +189,7 @@ def resolve_y(df: Optional[pd.DataFrame], y: YSymbolic) -> pd.DataFrame:
 
 def resolve_X(df: Optional[pd.DataFrame], X: XSymbolic) -> pd.DataFrame:
 
-    if isinstance(X, pd.DataFrame) or 'cudf.core.dataframe' in str(getmodule(X):
+    if isinstance(X, pd.DataFrame) or 'cudf.core.dataframe' in str(getmodule(X)):
         return X
 
     if df is None:

From dc8914884c85a05fba8c5b4abfbf5f8f72148242 Mon Sep 17 00:00:00 2001
From: dc <dc@e-mbp.lan>
Date: Fri, 24 Feb 2023 16:47:58 +0800
Subject: [PATCH 168/432] branches got confused, removed cudf import

---
 graphistry/umap_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 112c7d111d..11f0b64c06 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -497,7 +497,7 @@ def umap(
             elif 'cudf.core.dataframe' in str(getmodule(X_)):
                 import cudf
                 index_to_nodes_dict = cudf.DataFrame(nodes).reset_index()
-                X_= pd.DataFrame(X_.to_numpy())
+                X_ = pd.DataFrame(X_.to_numpy())
 
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs

From 4d2ff19ea66671d329d53f887d6677897ed91552 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Fri, 24 Feb 2023 15:25:21 -0500
Subject: [PATCH 169/432] fix(docstring): made blockcode visible

---
 graphistry/PlotterBase.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/graphistry/PlotterBase.py b/graphistry/PlotterBase.py
index 7d22469d8f..f397b70631 100644
--- a/graphistry/PlotterBase.py
+++ b/graphistry/PlotterBase.py
@@ -295,7 +295,7 @@ def style(self, fg=None, bg=None, page=None, logo=None):
         :param fg: Dictionary {'blendMode': str} of any valid CSS blend mode
         :type fg: dict
 
-        :param bg: Nested dictionary of page background properties. {'color': str, 'gradient': {'kind': str, 'position': str, 'stops': list }, 'image': { 'url': str, 'width': int, 'height': int, 'blendMode': str }
+        :param bg: Nested dictionary of page background properties. { 'color': str, 'gradient': {'kind': str, 'position': str, 'stops': list }, 'image': { 'url': str, 'width': int, 'height': int, 'blendMode': str }
         :type bg: dict
 
         :param logo: Nested dictionary of logo properties. { 'url': str, 'autoInvert': bool, 'position': str, 'dimensions': { 'maxWidth': int, 'maxHeight': int }, 'crop': { 'top': int, 'left': int, 'bottom': int, 'right': int }, 'padding': { 'top': int, 'left': int, 'bottom': int, 'right': int}, 'style': str}        
@@ -845,7 +845,8 @@ def bind(self, source=None, destination=None, node=None, edge=None,
         :param edge: Attribute containing an edge's ID
         :type edge: str
 
-        :param edge_title: Attribute overriding edge's minimized label text. By default, the edge source and destination is used.
+        :param edge_title: Attribute overriding edge's minimized label text. 
+        By default, the edge source and destination is used.
         :type edge_title: str
 
         :param edge_label: Attribute overriding edge's expanded label text. By default, scrollable list of attribute/value mappings.
@@ -1002,6 +1003,7 @@ def nodes(self, nodes: Union[Callable, Any], node=None, *args, **kwargs) -> Plot
 
         **Example**
             ::
+
                 import graphistry
 
                 def sample_nodes(g, n):
@@ -1101,6 +1103,7 @@ def edges(self, edges: Union[Callable, Any], source=None, destination=None, edge
 
         **Example**
             ::
+
                 import graphistry
 
                 def sample_edges(g, n):

From 9e7b08665fd2e3aba62304ddb1a896ff5e1ef65b Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Fri, 24 Feb 2023 15:26:20 -0500
Subject: [PATCH 170/432] fix(docstring): added methods to correct place

---
 graphistry/layout/graph/graph.py      | 112 ++++++++++++++++++++------
 graphistry/layout/graph/graphBase.py  |  91 ++++++++++++---------
 graphistry/layout/graph/vertexBase.py |  37 ++++++---
 3 files changed, 164 insertions(+), 76 deletions(-)

diff --git a/graphistry/layout/graph/graph.py b/graphistry/layout/graph/graph.py
index b04dd4842e..4cbeac9182 100644
--- a/graphistry/layout/graph/graph.py
+++ b/graphistry/layout/graph/graph.py
@@ -8,31 +8,55 @@
 
 
 class Graph(object):
-    """
-        The graph is stored in disjoint-sets holding each connected component in `components` as a list of graph_core objects.
-
-        **Attributes**
-            C (list[GraphBase]): list of graph_core components.
-
-        **Methods**
-            add_vertex(v): add vertex v into the Graph as a new component
-            add_edge(e): add edge e and its vertices into the Graph possibly merging the
-            associated graph_core components
-            get_vertices_count(): see order()
-            vertices(): see graph_core
-            edges(): see graph_core
-            remove_edge(e): remove edge e possibly spawning two new cores
-            if the graph_core that contained e gets disconnected.
-            remove_vertex(v): remove vertex v and all its edges.
-            order(): the order of the graph (number of vertices)
-            norm(): the norm of the graph (number of edges)
-            deg_min(): the minimum degree of vertices
-            deg_max(): the maximum degree of vertices
-            deg_avg(): the average degree of vertices
-            eps(): the graph epsilon value (norm/order), average number of edges per vertex.
-            connected(): returns True if the graph is connected (i.e. it has only one component).
-            components(): returns the list of components
-    """
+    # """
+    #     The graph is stored in disjoint-sets holding each connected component in `components` as a list of graph_core objects.
+
+    #     **Attributes**
+    #         C (list[GraphBase]): list of graph_core components.
+
+
+    #     **add_edge(e):**
+    #         add edge e and its vertices into the Graph possibly merging the associated graph_core components
+
+    #     **get_vertices_count():** 
+    #         see order()
+
+    #     **vertices():** 
+    #         see graph_core
+        
+    #     **edges():** 
+    #         see graph_core
+
+    #     **remove_edge(e):** 
+    #         remove edge e possibly spawning two new cores if the graph_core that contained e gets disconnected.
+
+    #     **remove_vertex(v):** 
+    #         remove vertex v and all its edges.
+
+    #     **order():** 
+    #         the order of the graph (number of vertices)
+
+    #     **norm():** 
+    #         the norm of the graph (number of edges)
+
+    #     **deg_min():** 
+    #         the minimum degree of vertices
+        
+    #     **deg_max():** 
+    #         the maximum degree of vertices
+
+    #     **deg_avg():** 
+    #         the average degree of vertices
+
+    #     **eps():** 
+    #         the graph epsilon value (norm/order), average number of edges per vertex.
+
+    #     **connected():** 
+    #         returns True if the graph is connected (i.e. it has only one component).
+
+    #     **components():**
+    #         returns the list of components
+    # """
 
     component_class = GraphBase
 
@@ -73,16 +97,22 @@ def __init__(self, vertices = None, edges = None, directed = True):
             self.components.append(self.component_class(vertices, edge_set, directed))
 
     def add_vertex(self, v):
+        """
+        add vertex v into the Graph as a new component
+        """
         for c in self.components:
             if v in c.verticesPoset:
                 return c.verticesPoset.get(v)
         g = self.component_class(directed = self.directed)
         v = g.add_single_vertex(v)
         self.components.append(g)
+        print("add vertex v into the Graph as a new component")
         return v
 
     def add_edge(self, e):
-
+        """
+        add edge e and its vertices into the Graph possibly merging the associated graph_core components
+        """
         x = e.v[0]
         y = e.v[1]
         x = self.add_vertex(x)
@@ -116,6 +146,9 @@ def get_vertex_from_data(self, data):
         return None
 
     def vertices(self):
+        """
+         see graph_core
+        """
         for c in self.components:
             vertices = c.verticesPoset
             for v in vertices:
@@ -128,6 +161,9 @@ def edges(self):
                 yield e
 
     def remove_edge(self, e):
+        """
+        remove edge e possibly spawning two new cores if the graph_core that contained e gets disconnected.
+        """
         # get the GraphBase:
         c = e.v[0].component
         assert c == e.v[1].component
@@ -147,6 +183,9 @@ def remove_edge(self, e):
         return e
 
     def remove_vertex(self, x):
+        """
+        remove vertex v and all its edges.
+        """
         c = x.component
         if c not in self.components:
             return None
@@ -165,24 +204,42 @@ def remove_vertex(self, x):
         return x
 
     def order(self):
+        """
+        the order of the graph (number of vertices)
+        """
         return sum([c.order() for c in self.components])
 
     def norm(self):
+        """
+        the norm of the graph (number of edges)
+        """
         return sum([c.norm() for c in self.components])
 
     def deg_min(self):
+        """
+         the minimum degree of vertices
+        """
         return min([c.deg_min() for c in self.components])
 
     def deg_max(self):
+        """
+         the maximum degree of vertices
+        """
         return max([c.deg_max() for c in self.components])
 
     def deg_avg(self):
+        """
+        the average degree of vertices
+        """
         t = 0.0
         for c in self.components:
             t += sum([v.degree() for v in c.verticesPoset])
         return t / float(self.order())
 
     def eps(self):
+        """
+        the graph epsilon value (norm/order), average number of edges per vertex.
+        """
         return float(self.norm()) / self.order()
 
     def path(self, x, y, f_io = 0, hook = None):
@@ -203,4 +260,7 @@ def __contains__(self, G):
         return r
 
     def connected(self):
+        """
+        returns the list of components
+        """
         return len(self.components) == 1
diff --git a/graphistry/layout/graph/graphBase.py b/graphistry/layout/graph/graphBase.py
index 0e1b8f51e4..725f45daf9 100644
--- a/graphistry/layout/graph/graphBase.py
+++ b/graphistry/layout/graph/graphBase.py
@@ -13,41 +13,6 @@ class GraphBase(object):
             loops (set[Edge]): the set of *loop* edges (of degree 0).
             directed (bool): indicates if the graph is considered *oriented* or not.
 
-        Methods:
-            vertices(cond=None): generates an iterator over vertices, with optional filter
-            edges(cond=None): generates an iterator over edges, with optional filter
-            matrix(cond=None): returns the associativity matrix of the graph component
-            order(): the order of the graph (number of vertices)
-            norm(): the norm of the graph (number of edges)
-            deg_min(): the minimum degree of vertices
-            deg_max(): the maximum degree of vertices
-            deg_avg(): the average degree of vertices
-            eps(): the graph epsilon value (norm/order), average number of edges per vertex.
-            path(x,y,f_io=0,hook=None): shortest path between vertices x and y by breadth-first descent,
-            contrained by f_io direction if provided. The path is returned as a list of Vertex objects.
-            If a *hook* function is provided, it is called at every vertex added to the path, passing
-            the vertex object as argument.
-            roots(): returns the list of *roots* (vertices with no inward edges).
-            leaves(): returns the list of *leaves* (vertices with no outward edges).
-            add_single_vertex(v): allow a GraphBase to hold a single vertex.
-            add_edge(e): add edge e. At least one of its vertex must belong to the graph,
-            the other being added automatically.
-            remove_edge(e): remove Edge e, asserting that the resulting graph is still connex.
-            remove_vertex(x): remove Vertex x and all associated edges.
-            dijkstra(x,f_io=0,hook=None): shortest weighted-edges paths between x and all other vertices
-            by dijkstra's algorithm with heap used as priority queue.
-            get_scs_with_feedback(): returns the set of strongly connected components
-            ("scs") by using Tarjan algorithm.
-            These are maximal sets of vertices such that there is a path from each
-            vertex to every other vertex.
-            The algorithm performs a DFS from the provided list of root vertices.
-            A cycle is of course a strongly connected component,
-            but a strongly connected component can include several cycles.
-            The Feedback Acyclic Set of edge to be removed/reversed is provided by
-            marking the edges with a "feedback" flag.
-            Complexity is O(V+E).
-            partition(): returns a *partition* of the connected graph as a list of lists.
-            neighbors(v): returns neighbours of a vertex v.
     """
 
     def __init__(self, vertices = None, edges = None, directed = True):
@@ -96,12 +61,21 @@ def __init__(self, vertices = None, edges = None, directed = True):
                 v.component = self
 
     def roots(self):
+        """
+        returns the list of *roots* (vertices with no inward edges).
+        """
         return list(filter(lambda v: len(v.e_in()) == 0, self.verticesPoset))
 
     def leaves(self):
+        """
+        returns the list of *leaves* (vertices with no outward edges).
+        """
         return list(filter(lambda v: len(v.e_out()) == 0, self.verticesPoset))
 
     def add_single_vertex(self, v):
+        """
+        allow a GraphBase to hold a single vertex.
+        """
         if len(self.edgesPoset) == 0 and len(self.verticesPoset) == 0:
             v = self.verticesPoset.add(v)
             v.component = self
@@ -109,6 +83,9 @@ def add_single_vertex(self, v):
         return None
 
     def add_edge(self, e):
+        """
+        add edge e. At least one of its vertex must belong to the graph, the other being added automatically.
+        """
         if e in self.edgesPoset:
             return self.edgesPoset.get(e)
         x = e.v[0]
@@ -127,6 +104,9 @@ def add_edge(self, e):
         return e
 
     def remove_edge(self, e):
+        """
+        remove Edge e, asserting that the resulting graph is still connex.
+        """
         if e not in self.edgesPoset:
             return
         e.detach()
@@ -143,6 +123,9 @@ def remove_edge(self, e):
             return e
 
     def remove_vertex(self, x):
+        """
+        remove Vertex x and all associated edges.
+        """
         if x not in self.verticesPoset:
             return
         vertices = x.neighbors()  # get all neighbor vertices to check paths
@@ -168,6 +151,9 @@ def constant_function(self, value):
         return lambda x: value
 
     def vertices(self, cond = None):
+        """
+        generates an iterator over vertices, with optional filter
+        """
         vertices = self.verticesPoset
         if cond is None:
             cond = self.constant_function(True)
@@ -176,6 +162,9 @@ def vertices(self, cond = None):
                 yield v
 
     def edges(self, cond = None):
+        """
+        generates an iterator over edges, with optional filter
+        """
         edges = self.edgesPoset
         if cond is None:
             cond = self.constant_function(True)
@@ -185,7 +174,7 @@ def edges(self, cond = None):
 
     def matrix(self, cond = None):
         """
-            This associativity matrix is like the adjacency matrix but antisymmetric.
+            This associativity matrix is like the adjacency matrix but antisymmetric. Returns the associativity matrix of the graph component
 
         :param cond: same a the condition function in vertices().
         :return: array
@@ -207,27 +196,46 @@ def matrix(self, cond = None):
         return mat
 
     def order(self):
+        """
+        the order of the graph (number of vertices)
+        """
         return len(self.verticesPoset)
 
     def norm(self):
         """
-            The size of the edge poset.
+            The size of the edge poset (number of edges).
         """
         return len(self.edgesPoset)
 
     def deg_min(self):
+        """
+        the minimum degree of vertices
+        """
         return min([v.degree() for v in self.verticesPoset])
 
     def deg_max(self):
+        """
+         the maximum degree of vertices
+        """
         return max([v.degree() for v in self.verticesPoset])
 
     def deg_avg(self):
+        """
+         the average degree of vertices
+        """
         return sum([v.degree() for v in self.verticesPoset]) / float(self.order())
 
     def eps(self):
+        """
+         the graph epsilon value (norm/order), average number of edges per vertex.
+        """
         return float(self.norm()) / self.order()
 
     def path(self, x, y, f_io = 0, hook = None):
+        """
+        shortest path between vertices x and y by breadth-first descent, contrained by f_io direction if provided. The path is returned as a list of Vertex objects.
+        If a *hook* function is provided, it is called at every vertex added to the path, passing the vertex object as argument.
+        """
         assert x in self.verticesPoset
         assert y in self.verticesPoset
         x = self.verticesPoset.get(x)
@@ -263,6 +271,9 @@ def path(self, x, y, f_io = 0, hook = None):
         return p
 
     def dijkstra(self, x, f_io = 0, hook = None):
+        """
+        shortest weighted-edges paths between x and all other vertices by dijkstra's algorithm with heap used as priority queue.
+        """
         from collections import defaultdict
         from heapq import heappop, heappush
 
@@ -300,7 +311,11 @@ def dijkstra(self, x, f_io = 0, hook = None):
 
     def get_scs_with_feedback(self, roots = None):
         """
-            Minimum FAS algorithm (feedback arc set) creating a DAG.
+            Minimum FAS algorithm (feedback arc set) creating a DAG. Returns the set of strongly connected components
+            ("scs") by using Tarjan algorithm. These are maximal sets of vertices such that there is a path from each vertex to every other vertex.
+            The algorithm performs a DFS from the provided list of root vertices. A cycle is of course a strongly connected component,but a strongly connected component can include several cycles.
+            The Feedback Acyclic Set of edge to be removed/reversed is provided by marking the edges with a "feedback" flag.
+            Complexity is O(V+E).
 
         :param roots:
         :return:
diff --git a/graphistry/layout/graph/vertexBase.py b/graphistry/layout/graph/vertexBase.py
index 07cb8d6794..1a950273f0 100644
--- a/graphistry/layout/graph/vertexBase.py
+++ b/graphistry/layout/graph/vertexBase.py
@@ -7,17 +7,6 @@ class VertexBase(object):
         **Attributes**
             e (list[Edge]): list of edges associated with this vertex.
 
-        **Methods**
-            degree() : degree of the vertex (number of edges).
-            e_in() : list of edges directed toward this vertex.
-            e_out(): list of edges directed outward this vertex.
-            e_dir(int): either e_in, e_out or all edges depending on provided direction parameter (>0 means outward).
-            neighbors(f_io=0): list of neighbor vertices in all directions (default) or in filtered f_io direction (>0 means outward).
-            e_to(v): returns the Edge from this vertex directed toward vertex v.
-            e_from(v): returns the Edge from vertex v directed toward this vertex.
-            e_with(v): return the Edge with both this vertex and vertex v
-            detach(): removes this vertex from all its edges and returns this list of edges.
-
     """
 
     def __init__(self):
@@ -25,15 +14,27 @@ def __init__(self):
         self.e = []
 
     def degree(self):
+        """
+        degree() : degree of the vertex (number of edges).
+        """
         return len(self.e)
 
     def e_in(self):
+        """
+        e_in() : list of edges directed toward this vertex.
+        """
         return list(filter((lambda e: e.v[1] == self), self.e))
 
     def e_out(self):
+        """
+        e_out(): list of edges directed outward this vertex.
+        """
         return list(filter((lambda e: e.v[0] == self), self.e))
 
     def e_dir(self, dir):
+        """
+        either e_in, e_out or all edges depending on provided direction parameter (>0 means outward).
+        """
         if dir > 0:
             return self.e_out()
         if dir < 0:
@@ -42,7 +43,7 @@ def e_dir(self, dir):
 
     def neighbors(self, direction = 0):
         """
-            Returns the neighbors of this vertex.
+            Returns the neighbors of this vertex. List of neighbor vertices in all directions (default) or in filtered f_io direction (>0 means outward).
 
             :param direction:
                 - 0: parent and children
@@ -58,24 +59,36 @@ def neighbors(self, direction = 0):
         return arr
 
     def e_to(self, y):
+        """
+        returns the Edge from this vertex directed toward vertex v.
+        """
         for e in self.e_out():
             if e.v[1] == y:
                 return e
         return None
 
     def e_from(self, x):
+        """
+         returns the Edge from vertex v directed toward this vertex.
+        """
         for e in self.e_in():
             if e.v[0] == x:
                 return e
         return None
 
     def e_with(self, v):
+        """
+        return the Edge with both this vertex and vertex v
+        """
         for e in self.e:
             if v in e.v:
                 return e
         return None
 
     def detach(self):
+        """
+        removes this vertex from all its edges and returns this list of edges.
+        """
         E = self.e[:]
         for e in E:
             e.detach()

From 12d65c36e2110bf15b5c4febb67c7520e3cd207c Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Fri, 24 Feb 2023 15:26:47 -0500
Subject: [PATCH 171/432] fix(docstring): fixed homepage menu and bio

---
 docs/source/graphistry.rst | 28 ++++++++++++++++++----------
 docs/source/index.rst      |  4 ++--
 docs/source/modules.rst    | 12 ++++++------
 3 files changed, 26 insertions(+), 18 deletions(-)

diff --git a/docs/source/graphistry.rst b/docs/source/graphistry.rst
index 91aa7f08f9..7255d2752c 100644
--- a/docs/source/graphistry.rst
+++ b/docs/source/graphistry.rst
@@ -1,42 +1,50 @@
-graphistry package
+Overview
 ==================
 .. toctree::
    :maxdepth: 3
 
-   graphistry.compute
+
    graphistry.layout
    graphistry.plugins
    graphistry.plugins_types
 
 
-graphistry.plotter module
--------------------------
+Plotter Module
+==================
 
 .. automodule:: graphistry.PlotterBase
     :members:
     :undoc-members:
     :show-inheritance:
 
-graphistry.pygraphistry module
-------------------------------
+Pygraphistry Module
+==================
 
 .. automodule:: graphistry.pygraphistry
     :members:
     :undoc-members:
     :show-inheritance:
 
-graphistry.arrow_uploader module
---------------------------------
+Arrow uploader Module
+==================
 
 .. automodule:: graphistry.arrow_uploader
     :members:
     :undoc-members:
     :show-inheritance:
 
-graphistry.ArrowFileUploader module
------------------------------------
+Arrow File Uploader Module
+==================
 
 .. automodule:: graphistry.ArrowFileUploader
     :members:
     :undoc-members:
     :show-inheritance:
+
+Versioneer
+==================
+
+.. automodule:: graphistry._version
+    :members:
+    :undoc-members:
+    :show-inheritance:
\ No newline at end of file
diff --git a/docs/source/index.rst b/docs/source/index.rst
index cc734f5581..b45393c266 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,11 +1,11 @@
-PyGraphistry's documentation (|version|)
+PyGraphistry[ai]'s documentation 
 ========================================
 
 .. Quickstart:
 .. `Read our tutorial <https://github.com/graphistry/pygraphistry/blob/master/README.md>`_
 
 PyGraphistry is a Python visual graph AI library to extract, transform, analyze, model, and visualize big graphs, and especially alongside Graphistry end-to-end GPU server sessions. Installing optional graphistry[ai] dependencies adds graph autoML, including automatic feature engineering, UMAP, and graph neural net support. Combined, PyGraphistry reduces your time to graph for going from raw data to visualizations and AI models down to three lines of code.
-Here in our docstrings you can find useful packages, modules, commands to maximize your graph AI experience with PyGraphistry. For a full tutorial, refer to our `PyGraphistry <https://github.com/graphistry/pygraphistry/>`_ repo.
+Here in our docstrings you can find useful packages, modules, and commands to maximize your graph AI experience with PyGraphistry. In the navbar you can find an overview of all the packages and modules we provided and a few useful highlighted ones as well. You can search for them on our Search page. For a full tutorial, refer to our `PyGraphistry <https://github.com/graphistry/pygraphistry/>`_ repo.
 
 .. toctree::
    :maxdepth: 3
diff --git a/docs/source/modules.rst b/docs/source/modules.rst
index 2d0d70fd92..71e0a12335 100644
--- a/docs/source/modules.rst
+++ b/docs/source/modules.rst
@@ -1,9 +1,9 @@
-doc
-===
+.. doc
+.. ===
 
-.. toctree::
-   :maxdepth: 4
-   :caption: Contents:
+.. .. toctree::
+..    :maxdepth: 4
+..    :caption: Contents:
 
-   versioneer
+..    versioneer
 

From 75dd67f41c566b608be2815de564c07c8a7f527c Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Fri, 24 Feb 2023 15:27:12 -0500
Subject: [PATCH 172/432] fix(docstring): made block code visible

---
 graphistry/plugins/cugraph.py |  3 +++
 graphistry/plugins/igraph.py  | 15 ++++++++++++---
 2 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/graphistry/plugins/cugraph.py b/graphistry/plugins/cugraph.py
index b5f070af72..d769ba89ce 100644
--- a/graphistry/plugins/cugraph.py
+++ b/graphistry/plugins/cugraph.py
@@ -239,16 +239,19 @@ def compute_cugraph(
     
     **Example: Pagerank**
         ::
+
             g2 = g.compute_cugraph('pagerank')
             assert 'pagerank' in g2._nodes.columns
 
     **Example: Katz centrality with rename**
         ::
+
             g2 = g.compute_cugraph('katz_centrality', out_col='katz_centrality_renamed')
             assert 'katz_centrality_renamed' in g2._nodes.columns
 
     **Example: Pass params to cugraph**
         ::
+        
             g2 = g.compute_cugraph('k_truss', params={'k': 2})
             assert 'k_truss' in g2._nodes.columns
 
diff --git a/graphistry/plugins/igraph.py b/graphistry/plugins/igraph.py
index a5bab3ac19..5fe5c2f8a6 100644
--- a/graphistry/plugins/igraph.py
+++ b/graphistry/plugins/igraph.py
@@ -53,6 +53,7 @@ def from_igraph(self,
 
     **Example: Convert from igraph, including all node/edge properties**
         ::
+
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a', 'b', 'c', 'd'], 'd': ['b', 'c', 'd', 'e'], 'v': [101, 102, 103, 104]})
             g = graphistry.edges(edges, 's', 'd').materialize_nodes().get_degrees()
@@ -62,6 +63,7 @@ def from_igraph(self,
 
     **Example: Enrich from igraph, but only load in 1 node attribute**
         ::
+
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a', 'b', 'c', 'd'], 'd': ['b', 'c', 'd', 'e'], 'v': [101, 102, 103, 104]})
             g = graphistry.edges(edges, 's', 'd').materialize_nodes().get_degree()
@@ -198,7 +200,8 @@ def from_igraph(self,
     return g
 
 
-def to_igraph(self: Plottable, 
+def to_igraph(
+    self: Plottable, 
     directed: bool = True,
     include_nodes: bool = True,
     node_attributes: Optional[List[str]] = None,
@@ -309,8 +312,8 @@ def compute_igraph(
     :rtype: Plotter
 
     **Example: Pagerank**
-
         ::
+
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['c','c','e','e']})
             g = graphistry.edges(edges, 's', 'd')
@@ -319,6 +322,7 @@ def compute_igraph(
 
     **Example: Pagerank with custom name**
         ::
+
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['c','c','e','e']})
             g = graphistry.edges(edges, 's', 'd')
@@ -327,6 +331,7 @@ def compute_igraph(
 
     **Example: Pagerank on an undirected**
         ::
+
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['c','c','e','e']})
             g = graphistry.edges(edges, 's', 'd')
@@ -334,7 +339,8 @@ def compute_igraph(
             assert 'pagerank' in g2._nodes.columns
 
     **Example: Pagerank with custom parameters**
-            ::
+        ::
+
                 import graphistry, pandas as pd
                 edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['c','c','e','e']})
                 g = graphistry.edges(edges, 's', 'd')
@@ -447,6 +453,7 @@ def layout_igraph(
 
     **Example: Sugiyama layout**
         ::
+
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['b','c','d','e']})
             g = graphistry.edges(edges, 's', 'd')
@@ -456,6 +463,7 @@ def layout_igraph(
 
     **Example: Change which column names are generated**
         ::
+
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['b','c','d','e']})
             g = graphistry.edges(edges, 's', 'd')
@@ -466,6 +474,7 @@ def layout_igraph(
 
     **Example: Pass parameters to layout methods - Sort nodes by degree**
         ::
+        
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['b','c','d','e']})
             g = graphistry.edges(edges, 's', 'd')

From bbd8d55eb5504a9512f77fdbf47becf33873d2ad Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Fri, 24 Feb 2023 15:27:34 -0500
Subject: [PATCH 173/432] fix(docstring): made versioneer populate

---
 docs/source/versioneer.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/versioneer.rst b/docs/source/versioneer.rst
index 804c171da3..a34edfc48d 100644
--- a/docs/source/versioneer.rst
+++ b/docs/source/versioneer.rst
@@ -1,2 +1,2 @@
-versioneer module
-=================
+.. versioneer module
+.. =================

From 5e0f979c08147e74f0ee20601d1ecab3d3d3b196 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Fri, 24 Feb 2023 15:46:18 -0500
Subject: [PATCH 174/432] fix(docstring): added featurize and umap

---
 docs/source/graphistry.rst | 25 ++++++++++++++++++++++++-
 1 file changed, 24 insertions(+), 1 deletion(-)

diff --git a/docs/source/graphistry.rst b/docs/source/graphistry.rst
index 7255d2752c..9af86845c9 100644
--- a/docs/source/graphistry.rst
+++ b/docs/source/graphistry.rst
@@ -25,6 +25,28 @@ Pygraphistry Module
     :undoc-members:
     :show-inheritance:
 
+Featurize 
+==================
+.. automodule:: graphistry.feature_utils
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+UMAP 
+==================
+.. automodule:: graphistry.umap_utils
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+DB Scan
+==================
+.. automodule:: graphistry.compute.
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 Arrow uploader Module
 ==================
 
@@ -47,4 +69,5 @@ Versioneer
 .. automodule:: graphistry._version
     :members:
     :undoc-members:
-    :show-inheritance:
\ No newline at end of file
+    :show-inheritance:
+

From 1d4e73a3ba67d34959521f19d0c4c4a5daafd92e Mon Sep 17 00:00:00 2001
From: Daniel <dcolinmorgan@users.noreply.github.com>
Date: Mon, 27 Feb 2023 17:12:18 +0800
Subject: [PATCH 175/432] Update setup.py

---
 setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 68d0dbe70a..7f2efad62b 100755
--- a/setup.py
+++ b/setup.py
@@ -38,9 +38,9 @@ def unique_flatten_dict(d):
 }
 
 base_extras_heavy = {
-  'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'] + ['cuCat @ git+https://github.com/graphistry/cuCat/@0.01.0'] ,
+  'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
 }
-base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'annoy', 'joblib']
+base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'annoy', 'joblib'] + ['cuCat @ git+https://github.com/graphistry/cuCat/@0.01.0']
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From eb7e61e6e5f2cb017c97933547062f08fdd2293a Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Mon, 27 Feb 2023 17:44:58 +0800
Subject: [PATCH 176/432] partial response to comments

---
 graphistry/feature_utils.py | 35 ++++++++++++++++++++---------------
 setup.py                    |  5 ++++-
 2 files changed, 24 insertions(+), 16 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 4697b6795e..9e3775968a 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -48,15 +48,15 @@
         GapEncoder = Any
         SimilarityEncoder = Any
     try:
-        from cuCat import (
+        from cu_cat import (
             SuperVectorizer,
             GapEncoder,
             SimilarityEncoder,
         )
     except:
-        SuperVectorizer = Any
-        GapEncoder = Any
-        SimilarityEncoder = Any
+        SuperVectorizer = object
+        GapEncoder = object
+        SimilarityEncoder = object
     try:
         from sklearn.preprocessing import FunctionTransformer
         from sklearn.base import BaseEstimator, TransformerMixin
@@ -101,17 +101,22 @@ def lazy_import_has_min_dependancy():
     except ModuleNotFoundError as e:
         return False, e
 
-def lazy_import_has_cuml_dependancy():
+def lazy_import_has_cu_cat_dependancy():
     import warnings
     warnings.filterwarnings("ignore")
     try:
         import scipy.sparse  # noqa
         from scipy import __version__ as scipy_version
-        from cuCat import __version__ as cuCat_version
+        from cu_cat import __version__ as cu_cat_version
         from sklearn import __version__ as sklearn_version
+        from cuml import __verison__ as cuml_version
+        from cudf import __verison__ as cudf_version
         logger.debug(f"SCIPY VERSION: {scipy_version}")
-        logger.debug(f"cuCat VERSION: {cuCat_version}")
+        logger.debug(f"Cuda CAT VERSION: {cu_cat_version}")
         logger.debug(f"sklearn VERSION: {sklearn_version}")
+        logger.debug(f"cuml VERSION: {cuml_version}")
+        logger.debug(f"cudf VERSION: {cudf_version}")
+
         return True, 'ok'
     except ModuleNotFoundError as e:
         return False, e
@@ -139,7 +144,7 @@ def assert_cuml_imported():
     has_cuml_dependancy_, import_cuml_exn = lazy_import_has_cuml_dependancy()
     if not has_cuml_dependancy_:
         logger.error(  # noqa
-                     "cunl not found, trying running"  # noqa
+                     "cuml not found, trying running"  # noqa
                      "`pip install rapids`"  # noqa
         )
         raise import_cuml_exn
@@ -168,7 +173,7 @@ def assert_cuml_imported():
 #
 #      _featurize_or_get_edges_dataframe_if_X_is_None
 
-FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch", "cuCat"]
+FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch", "cu_cat"]
 FeatureEngine = Literal[FeatureEngineConcrete, "auto"]
 
 
@@ -176,7 +181,7 @@ def resolve_feature_engine(
     feature_engine: FeatureEngine,
 ) -> FeatureEngineConcrete:  # noqa
 
-    if feature_engine in ["none", "pandas", "dirty_cat", "torch", "cuCat"]:
+    if feature_engine in ["none", "pandas", "dirty_cat", "torch", "cu_cat"]:
         return feature_engine  # type: ignore
 
     if feature_engine == "auto":
@@ -185,7 +190,7 @@ def resolve_feature_engine(
             return "torch"
         has_cuml_dependancy_, _ = lazy_import_has_cuml_dependancy()
         if has_cuml_dependancy_:
-            return "cuCat"
+            return "cu_cat"
         has_min_dependancy_, _ = lazy_import_has_min_dependancy()
         if has_min_dependancy_:
             return "dirty_cat"
@@ -193,7 +198,7 @@ def resolve_feature_engine(
 
     raise ValueError(  # noqa
         f'feature_engine expected to be "none", '
-        '"pandas", "dirty_cat", "torch", "cuCat", or "auto"'
+        '"pandas", "dirty_cat", "torch", "cu_cat", or "auto"'
         f'but received: {feature_engine} :: {type(feature_engine)}'
     )
 
@@ -929,8 +934,8 @@ def process_dirty_dataframes(
     """
     if feature_engine == 'dirty_cat':
         from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
-    elif feature_engine == 'cuCat':
-        from cuCat import SuperVectorizer, GapEncoder, SimilarityEncoder
+    elif feature_engine == 'cu_cat':
+        from cu_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
     
     from sklearn.preprocessing import FunctionTransformer
     t = time()
@@ -2375,7 +2380,7 @@ def featurize(
         """
         if feature_engine == 'dirty_cat':
             assert_imported()
-        elif feature_engine == 'cuCat':
+        elif feature_engine == 'cu_cat':
             assert_cuml_imported()
         if inplace:
             res = self
diff --git a/setup.py b/setup.py
index 7f2efad62b..e4a757b4b5 100755
--- a/setup.py
+++ b/setup.py
@@ -40,7 +40,10 @@ def unique_flatten_dict(d):
 base_extras_heavy = {
   'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
 }
-base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'annoy', 'joblib'] + ['cuCat @ git+https://github.com/graphistry/cuCat/@0.01.0']
+base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'annoy', 'joblib']
+
+base_extras_heavy['cu-cat'] = + ['cu-cat @ git+https://github.com/graphistry/cuCat/@0.01.0']
+
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From f22f56575112a83b008bf24561fa5b50ab676e22 Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Mon, 27 Feb 2023 20:10:10 +0800
Subject: [PATCH 177/432] more comments fixes

---
 graphistry/feature_utils.py | 4 ++--
 setup.py                    | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 9e3775968a..98008ee803 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -141,7 +141,7 @@ def assert_imported():
         raise import_min_exn
 
 def assert_cuml_imported():
-    has_cuml_dependancy_, import_cuml_exn = lazy_import_has_cuml_dependancy()
+    has_cuml_dependancy_, import_cuml_exn = lazy_import_has_cu_cat_dependancy()
     if not has_cuml_dependancy_:
         logger.error(  # noqa
                      "cuml not found, trying running"  # noqa
@@ -188,7 +188,7 @@ def resolve_feature_engine(
         has_dependancy_text_, _, _ = lazy_import_has_dependancy_text()
         if has_dependancy_text_:
             return "torch"
-        has_cuml_dependancy_, _ = lazy_import_has_cuml_dependancy()
+        has_cuml_dependancy_, _ = lazy_import_has_cu_cat_dependancy()
         if has_cuml_dependancy_:
             return "cu_cat"
         has_min_dependancy_, _ = lazy_import_has_min_dependancy()
diff --git a/setup.py b/setup.py
index e4a757b4b5..582378259d 100755
--- a/setup.py
+++ b/setup.py
@@ -40,9 +40,9 @@ def unique_flatten_dict(d):
 base_extras_heavy = {
   'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
 }
-base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'annoy', 'joblib']
+base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'annoy', 'joblib'] + base_extras_heavy['cu-cat']
 
-base_extras_heavy['cu-cat'] = + ['cu-cat @ git+https://github.com/graphistry/cuCat/@0.01.0']
+base_extras_heavy['cu-cat'] = ['cu-cat @ git+https://github.com/graphistry/cuCat/@0.01.0']
 
 
 base_extras = {**base_extras_light, **base_extras_heavy}

From 5e0e08c993ec43cbda87085ab0d0070e12141703 Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Mon, 27 Feb 2023 20:12:08 +0800
Subject: [PATCH 178/432] more comments fixes

---
 graphistry/feature_utils.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 98008ee803..a900c85986 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -54,9 +54,9 @@
             SimilarityEncoder,
         )
     except:
-        SuperVectorizer = object
-        GapEncoder = object
-        SimilarityEncoder = object
+        SuperVectorizer = Any
+        GapEncoder = Any
+        SimilarityEncoder = Any
     try:
         from sklearn.preprocessing import FunctionTransformer
         from sklearn.base import BaseEstimator, TransformerMixin

From 708baa3dea3c3dfaf9a5a3484eba535cc882ac42 Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Mon, 27 Feb 2023 20:52:24 +0800
Subject: [PATCH 179/432] more comments fixes

---
 setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 582378259d..8b6db91972 100755
--- a/setup.py
+++ b/setup.py
@@ -40,10 +40,10 @@ def unique_flatten_dict(d):
 base_extras_heavy = {
   'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
 }
-base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'annoy', 'joblib'] + base_extras_heavy['cu-cat']
-
 base_extras_heavy['cu-cat'] = ['cu-cat @ git+https://github.com/graphistry/cuCat/@0.01.0']
 
+base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'annoy', 'joblib'] + base_extras_heavy['cu-cat']
+
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From a6e1ab011b01c55196c3c2b8eb401c73ead70056 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Mon, 27 Feb 2023 14:33:50 -0500
Subject: [PATCH 180/432] fix(docstring): formatting changes/updates

---
 graphistry/compute/cluster.py | 10 ++++
 graphistry/feature_utils.py   | 98 +++++++++++++++++++++--------------
 graphistry/text_utils.py      |  9 ++--
 3 files changed, 74 insertions(+), 43 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 15b7cf0ed3..be181bc938 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -212,6 +212,8 @@ def dbscan(
         """DBSCAN clustering on cpu or gpu infered automatically. Adds a `_dbscan` column to nodes or edges.
 
         Examples:
+        ::
+
             g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
 
             # cluster by UMAP embeddings
@@ -333,22 +335,30 @@ def transform_dbscan(
         Graph nodes | edges will be colored by '_dbscan' column.
             
             Examples:
+            ::
+
                 fit:
                     g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
                     g2 = g.featurize().dbscan()
 
                 predict:
+                ::
+
                     emb, X, _, ndf = g2.transform_dbscan(ndf, return_graph=False)
                     # or
                     g3 = g2.transform_dbscan(ndf, return_graph=True)
                     g3.plot()
 
             likewise for umap:
+            ::
+
                 fit:
                     g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
                     g2 = g.umap(X=.., y=..).dbscan()
 
                 predict:
+                ::
+                
                     emb, X, y, ndf = g2.transform_dbscan(ndf, ndf, return_graph=False)
                     # or
                     g3 = g2.transform_dbscan(ndf, ndf, return_graph=True)
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index eebbb4a4df..462ca18a11 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -499,7 +499,6 @@ class Embedding:
     """
     Generates random embeddings of a given dimension 
     that aligns with the index of the dataframe
-    _____________________________________________________________________
     
     """
     def __init__(self, df: pd.DataFrame):
@@ -1754,10 +1753,11 @@ def fit_transform(self, src=None, dst=None, *args, **kwargs):
     def scale(self, X=None, y=None, return_pipeline=False, *args, **kwargs):
         """Fits new scaling functions on df, y via args-kwargs
         
-            example:
+        **Example:**
+            ::
+
                 from graphisty.features import SCALERS, SCALER_OPTIONS
                 print(SCALERS)
-                
                 g = graphistry.nodes(df)
                 # set a scaling strategy for features and targets -- umap uses those and produces different results depending.
                 g2 = g.umap(use_scaler='standard', use_scaler_target=None)
@@ -1770,6 +1770,8 @@ def scale(self, X=None, y=None, return_pipeline=False, *args, **kwargs):
                 clf.fit(X_scaled, y_scaled)
                 
             args:
+            ::
+
                 X: pd.DataFrame of features
                 y: pd.DataFrame of target features
                 kind: str, one of 'nodes' or 'edges'
@@ -1880,14 +1882,20 @@ class FeatureMixin(MIXIN_BASE):
     Subclasses UMAPMixin for umap-ing of automatic features.
 
     Usage:
+    ::
+
         g = graphistry.nodes(df, 'node_column')
         g2 = g.featurize()
 
     or for edges,
+    ::
+
         g = graphistry.edges(df, 'src', 'dst')
         g2 = g.featurize(kind='edges')
 
     or chain them for both nodes and edges,
+    ::
+
         g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node_column')
         g2 = g.featurize().featurize(kind='edges')
 
@@ -2202,21 +2210,24 @@ def transform(self, df: pd.DataFrame,
         """
             Transform new data and append to existing graph, or return dataframes
         
-            args:
-                df: pd.DataFrame, raw data to transform
-                ydf: pd.DataFrame, optional
-                kind: str  # one of `nodes`, `edges`
-                return_graph: bool, if True, will return a graph with inferred edges.
-                merge_policy: bool, if True, adds batch to existing graph nodes via nearest neighbors. 
-                    If False, will infer edges only between nodes in the batch, default False
-                min_dist: float, if return_graph is True, will use this value in NN search, or 'auto' to infer a good value
-                    min_dist represents the maximum distance between two samples for one to be considered as in the neighborhood of the other.
-                sample: int, if return_graph is True, will use sample edges of existing graph to fill out the new graph
-                n_neighbors: int, if return_graph is True, will use this value for n_neighbors in Nearest Neighbors search
-                scaled: bool, if True, will use scaled transformation of data set during featurization, default True
-                verbose: bool, if True, will print metadata about the graph construction, default False
-            returns:
-                X, y: pd.DataFrame, transformed data if return_graph is False
+            **args:**
+            ::
+
+                # df: pd.DataFrame, raw data to transform
+                # ydf: pd.DataFrame, optional
+                # kind: str  # one of `nodes`, `edges`
+                # return_graph: bool, if True, will return a graph with inferred edges.
+                # merge_policy: bool, if True, adds batch to existing graph nodes via nearest neighbors. 
+                    # If False, will infer edges only between nodes in the batch, default False
+                # min_dist: float, if return_graph is True, will use this value in NN search, or 'auto' to infer a good value
+                    # min_dist represents the maximum distance between two samples for one to be considered as in the neighborhood of the other.
+                # sample: int, if return_graph is True, will use sample edges of existing graph to fill out the new graph
+                # n_neighbors: int, if return_graph is True, will use this value for n_neighbors in Nearest Neighbors search
+                # scaled: bool, if True, will use scaled transformation of data set during featurization, default True
+                # verbose: bool, if True, will print metadata about the graph construction, default False
+            **Returns:**
+
+                    X, y: pd.DataFrame, transformed data if return_graph is False
                     or a graphistry Plottable with inferred edges if return_graph is True
         """
         if kind == "nodes":
@@ -2255,7 +2266,9 @@ def scale(
     ):
         """Scale data using the same scalers as used in the featurization step.
         
-            example usage:
+            **Example**
+            ::
+
                 g = graphistry.nodes(df)
                 X, y = g.featurize().scale(kind='nodes', use_scaler='robust', use_scaler_target='kbins', n_bins=3)
                 
@@ -2271,25 +2284,29 @@ def scale(
                 clf.fit(X_scaled, y_scaled)
 
 
-            args:
-                df: pd.DataFrame, raw data to transform, if None, will use data from featurization fit
-                y: pd.DataFrame, optional target data
-                kind: str, one of `nodes`, `edges`
-                use_scaler: str, optional, one of `minmax`, `robust`, `standard`, `kbins`, `quantile`
-                use_scaler_target: str, optional, one of `minmax`, `robust`, `standard`, `kbins`, `quantile`
-                impute: bool, if True, will impute missing values
-                n_quantiles: int, number of quantiles to use for quantile scaler
-                output_distribution: str, one of `normal`, `uniform`, `lognormal` 
-                quantile_range: tuple, range of quantiles to use for quantile scaler
-                n_bins: int, number of bins to use for KBinsDiscretizer
-                encode: str, one of `ordinal`, `onehot`, `onehot-dense`, `binary`    
-                strategy: str, one of `uniform`, `quantile`, `kmeans`
-                keep_n_decimals: int, number of decimals to keep after scaling
-                return_scalers: bool, if True, will return the scalers used to scale the data
-            returns:
-                (X, y) transformed data if return_graph is False
+            **Args:**
+            ::
+
+                # df: pd.DataFrame, raw data to transform, if None, will use data from featurization fit
+                # y: pd.DataFrame, optional target data
+                # kind: str, one of `nodes`, `edges`
+                # use_scaler: str, optional, one of `minmax`, `robust`, `standard`, `kbins`, `quantile`
+                # use_scaler_target: str, optional, one of `minmax`, `robust`, `standard`, `kbins`, `quantile`
+                # impute: bool, if True, will impute missing values
+                # n_quantiles: int, number of quantiles to use for quantile scaler
+                # output_distribution: str, one of `normal`, `uniform`, `lognormal` 
+                # quantile_range: tuple, range of quantiles to use for quantile scaler
+                # n_bins: int, number of bins to use for KBinsDiscretizer
+                # encode: str, one of `ordinal`, `onehot`, `onehot-dense`, `binary`    
+                # strategy: str, one of `uniform`, `quantile`, `kmeans`
+                # keep_n_decimals: int, number of decimals to keep after scaling
+                # return_scalers: bool, if True, will return the scalers used to scale the data
+
+            **Returns:**
+
+                    (X, y) transformed data if return_graph is False
                     or a graph with inferred edges if return_graph is True,
-                or (X, y, scaler, scaler_target) if return_scalers is True
+                    or (X, y, scaler, scaler_target) if return_scalers is True
         """
                 
         if df is None:  # use the original data
@@ -2774,7 +2791,8 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
 
     
     def get_matrix(self, columns: Optional[Union[List, str]] = None, kind: str = 'nodes', target: bool = False) -> pd.DataFrame:
-        """Returns feature matrix, and if columns are specified, returns matrix with only the columns that contain 
+        """
+        Returns feature matrix, and if columns are specified, returns matrix with only the columns that contain 
             the string `column_part` in their name.
 
             `X = g.get_matrix(['feature1', 'feature2'])`
@@ -2786,7 +2804,9 @@ def get_matrix(self, columns: Optional[Union[List, str]] = None, kind: str = 'no
             
             Powerful way to retrieve features from a featurized graph by column or (top) features of interest.
             
-            example:
+            **Example:**
+            ::
+            
                 # get the full feature matrices
                 X = g.get_matrix()
                 y = g.get_matrix(target=True)
diff --git a/graphistry/text_utils.py b/graphistry/text_utils.py
index 1378b01f91..ff6f2eabbb 100644
--- a/graphistry/text_utils.py
+++ b/graphistry/text_utils.py
@@ -125,18 +125,19 @@ def search(
 
             If node data is not yet feature-encoded (and explicit edges are given),
             run automatic feature engineering:
-            ```
+            ::
+
                 g2 = g.featurize(kind='nodes', X=['text_col_1', ..],
                 min_words=0 # forces all named columns are textually encoded
                 )
-            ```
 
             If edges do not yet exist, generate them via
-            ```
+            ::
+
                 g2 = g.umap(kind='nodes', X=['text_col_1', ..],
                 min_words=0 # forces all named columns are textually encoded
                 )
-            ```
+            
             If an index is not yet built, it is generated `g2.build_index()` on the fly at search time.
             Otherwise, can set `g2.build_index()` and then subsequent `g2.search(...)`
             calls will be not rebuilt index.

From 4780f5e1c1f3b9892d1df0a9508fbbdc73120325 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Mon, 27 Feb 2023 14:34:26 -0500
Subject: [PATCH 181/432] fix(docstring): added umap, SS, dbscan to navbar

---
 docs/source/graphistry.rst | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/docs/source/graphistry.rst b/docs/source/graphistry.rst
index 9af86845c9..71cf81bb41 100644
--- a/docs/source/graphistry.rst
+++ b/docs/source/graphistry.rst
@@ -40,9 +40,17 @@ UMAP
     :undoc-members:
     :show-inheritance:
 
-DB Scan
+
+Semantic Search 
+==================
+.. automodule:: graphistry.text_utils
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+DBScan
 ==================
-.. automodule:: graphistry.compute.
+.. automodule:: graphistry.compute.cluster
     :members:
     :undoc-members:
     :show-inheritance:

From 5f2c28c646d4bee3d296684ae5c34041349ec7a5 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Mon, 27 Feb 2023 15:48:04 -0500
Subject: [PATCH 182/432] fix(docstring): fixed arg formatting on dbscan

---
 graphistry/compute/cluster.py | 52 +++++++++++++++++------------------
 1 file changed, 26 insertions(+), 26 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index be181bc938..f19fbfbe38 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -71,11 +71,11 @@ def get_model_matrix(g, kind: str, cols: Optional[Union[List, str]], umap, targe
         Allows for a single function to get the model matrix for both nodes and edges as well as targets, embeddings, and features
 
     Args:
-        g: graphistry graph
-        kind: 'nodes' or 'edges'
-        cols: list of columns to use for clustering given `g.featurize` has been run
-        umap: whether to use UMAP embeddings or features dataframe
-        target: whether to use the target dataframe or features dataframe
+            :g: graphistry graph
+            :kind: 'nodes' or 'edges'
+            :cols: list of columns to use for clustering given `g.featurize` has been run
+            :umap: whether to use UMAP embeddings or features dataframe
+            :target: whether to use the target dataframe or features dataframe
 
     Returns:
         pd.DataFrame: dataframe of model matrix given the inputs
@@ -99,11 +99,11 @@ def dbscan_fit(g: Any, dbscan: Any, kind: str = "nodes", cols: Optional[Union[Li
     Fits clustering on UMAP embeddings if umap is True, otherwise on the features dataframe
         or target dataframe if target is True.
 
-    args:
-        g: graphistry graph
-        kind: 'nodes' or 'edges'
-        cols: list of columns to use for clustering given `g.featurize` has been run
-        use_umap_embedding: whether to use UMAP embeddings or features dataframe for clustering (default: True)
+    Args:
+        :g: graphistry graph
+        :kind: 'nodes' or 'edges'
+        :cols: list of columns to use for clustering given `g.featurize` has been run
+        :use_umap_embedding: whether to use UMAP embeddings or features dataframe for clustering (default: True)
     """
     X = get_model_matrix(g, kind, cols, use_umap_embedding, target)
     
@@ -246,14 +246,14 @@ def dbscan(
              https://github.com/graphistry/pygraphistry/blob/master/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
 
         Args:
-            min_dist float: The maximum distance between two samples for them to be considered as in the same neighborhood.
-            kind str: 'nodes' or 'edges'
-            cols: list of columns to use for clustering given `g.featurize` has been run, nice way to slice features or targets by
+            :min_dist float: The maximum distance between two samples for them to be considered as in the same neighborhood.
+            :kind str: 'nodes' or 'edges'
+            :cols: list of columns to use for clustering given `g.featurize` has been run, nice way to slice features or targets by
                 fragments of interest, e.g. ['ip_172', 'location', 'ssh', 'warnings']
-            fit_umap_embedding bool: whether to use UMAP embeddings or features dataframe to cluster DBSCAN
-            min_samples: The number of samples in a neighborhood for a point to be considered as a core point.
+            :fit_umap_embedding bool: whether to use UMAP embeddings or features dataframe to cluster DBSCAN
+            :min_samples: The number of samples in a neighborhood for a point to be considered as a core point.
                 This includes the point itself.
-            target: whether to use the target column as the clustering feature
+            :target: whether to use the target column as the clustering feature
 
         """
 
@@ -358,28 +358,28 @@ def transform_dbscan(
 
                 predict:
                 ::
-                
+
                     emb, X, y, ndf = g2.transform_dbscan(ndf, ndf, return_graph=False)
                     # or
                     g3 = g2.transform_dbscan(ndf, ndf, return_graph=True)
                     g3.plot()
 
 
-        args:
-            df: dataframe to transform
-            y: optional labels dataframe
-            min_dist: The maximum distance between two samples for them to be considered as in the same neighborhood.
+        Args:
+            :df: dataframe to transform
+            :y: optional labels dataframe
+            :min_dist: The maximum distance between two samples for them to be considered as in the same neighborhood.
                 smaller values will result in less edges between the minibatch and the original graph.
                 Default 'auto', infers min_dist from the mean distance and std of new points to the original graph
-            fit_umap_embedding: whether to use UMAP embeddings or features dataframe when inferring edges between
+            :fit_umap_embedding: whether to use UMAP embeddings or features dataframe when inferring edges between
                 the minibatch and the original graph. Default False, uses the features dataframe
-            sample: number of samples to use when inferring edges between the minibatch and the original graph,
+            :sample: number of samples to use when inferring edges between the minibatch and the original graph,
                 if None, will only use closest point to the minibatch. If greater than 0, will sample the closest `sample` points
                 in existing graph to pull in more edges. Default None
-            kind: 'nodes' or 'edges'
-            return_graph: whether to return a graph or the (emb, X, y, minibatch df enriched with DBSCAN labels), default True
+            :kind: 'nodes' or 'edges'
+            :return_graph: whether to return a graph or the (emb, X, y, minibatch df enriched with DBSCAN labels), default True
                 infered graph supports kind='nodes' only. 
-            verbose: whether to print out progress, default False
+            :verbose: whether to print out progress, default False
 
         """
         emb, X, y, df = self._transform_dbscan(df, y, kind=kind, verbose=verbose)

From c4fab96b072e49bcbb8852b9e102a401242b2d57 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Mon, 27 Feb 2023 16:03:01 -0500
Subject: [PATCH 183/432] fix(docstring): fixed arg formatting on UMAP

---
 graphistry/umap_utils.py | 64 ++++++++++++++++++++--------------------
 1 file changed, 32 insertions(+), 32 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 2107710a3d..38ad606f26 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -282,17 +282,17 @@ def transform_umap(self, df: pd.DataFrame,
         """Transforms data into UMAP embedding
         
         args:
-            df: Dataframe to transform
-            y: Target column
-            kind: One of `nodes` or `edges`
-            min_dist: Epsilon for including neighbors in infer_graph
-            n_neighbors: Number of neighbors to use for contextualization
-            merge_policy: if True, use previous graph, adding new batch to existing graph's neighbors
+            :df: Dataframe to transform
+            :y: Target column
+            :kind: One of `nodes` or `edges`
+            :min_dist: Epsilon for including neighbors in infer_graph
+            :n_neighbors: Number of neighbors to use for contextualization
+            :merge_policy: if True, use previous graph, adding new batch to existing graph's neighbors
                 useful to contextualize new data against existing graph. If False, `sample` is irrelevant.
-            sample: Sample number of existing graph's neighbors to use for contextualization -- helps make denser graphs
-            return_graph: Whether to return a graph or just the embeddings
-            fit_umap_embedding: Whether to infer graph from the UMAP embedding on the new data
-            verbose: Whether to print information about the graph inference
+            :sample: Sample number of existing graph's neighbors to use for contextualization -- helps make denser graphs
+            :return_graph: Whether to return a graph or just the embeddings
+            :fit_umap_embedding: Whether to infer graph from the UMAP embedding on the new data
+            :verbose: Whether to print information about the graph inference
         """
         X, y_ = self.transform(df, y, kind=kind, return_graph=False, verbose=verbose)
         emb = self._umap.transform(X)  # type: ignore
@@ -437,47 +437,47 @@ def umap(
         
         Parameters
         ----------
-            X: either a dataframe ndarray of features, or column names to featurize
-            y: either an dataframe ndarray of targets, or column names to featurize
+            :X: either a dataframe ndarray of features, or column names to featurize
+            :y: either an dataframe ndarray of targets, or column names to featurize
                     targets
-            kind: `nodes` or `edges` or None.
+            :kind: `nodes` or `edges` or None.
                     If None, expects explicit X, y (optional) matrices,
                     and will Not associate them to nodes or edges.
                     If X, y (optional) is given, with kind = [nodes, edges],
                     it will associate new matrices to nodes or edges attributes.
-            scale: multiplicative scale for pruning weighted edge DataFrame
+            :scale: multiplicative scale for pruning weighted edge DataFrame
                     gotten from UMAP, between [0, ..) with high end meaning keep
                     all edges
-            n_neighbors: UMAP number of nearest neighbors to include for
+            :n_neighbors: UMAP number of nearest neighbors to include for
                     UMAP connectivity, lower makes more compact layouts. Minimum 2
-            min_dist: UMAP float between 0 and 1, lower makes more compact
+            :min_dist: UMAP float between 0 and 1, lower makes more compact
                     layouts.
-            spread: UMAP spread of values for relaxation
-            local_connectivity: UMAP connectivity parameter
-            repulsion_strength: UMAP repulsion strength
-            negative_sample_rate: UMAP negative sampling rate
-            n_components: number of components in the UMAP projection,
+            :spread: UMAP spread of values for relaxation
+            :local_connectivity: UMAP connectivity parameter
+            :repulsion_strength: UMAP repulsion strength
+            :negative_sample_rate: UMAP negative sampling rate
+            :n_components: number of components in the UMAP projection,
                     default 2
-            metric: UMAP metric, default 'euclidean'.
+            :metric: UMAP metric, default 'euclidean'.
                     see (UMAP-LEARN)[https://umap-learn.readthedocs.io/
                     en/latest/parameters.html] documentation for more.
-            suffix: optional suffix to add to x, y attributes of umap.
-            play: Graphistry play parameter, default 0, how much to evolve
+            :suffix: optional suffix to add to x, y attributes of umap.
+            :play: Graphistry play parameter, default 0, how much to evolve
                     the network during clustering. 0 preserves the original UMAP layout.
-            encode_weight: if True, will set new edges_df from
+            :encode_weight: if True, will set new edges_df from
                     implicit UMAP, default True.
-            encode_position: whether to set default plotting bindings
+            :encode_position: whether to set default plotting bindings
                     -- positions x,y from umap for .plot(), default True
-            dbscan: whether to run DBSCAN on the UMAP embedding, default False.
-            engine: selects which engine to use to calculate UMAP:
+            :dbscan: whether to run DBSCAN on the UMAP embedding, default False.
+            :engine: selects which engine to use to calculate UMAP:
                     default "auto" will use cuML if available, otherwise UMAP-LEARN.
-            feature_engine: How to encode data
+            :feature_engine: How to encode data
                     ("none", "auto", "pandas", "dirty_cat", "torch")
-            inplace: bool = False, whether to modify the current object, default False.
+            :inplace: bool = False, whether to modify the current object, default False.
                     when False, returns a new object, useful for chaining in a functional paradigm.
-            memoize: whether to memoize the results of this method,
+            :memoize: whether to memoize the results of this method,
                     default True.
-            verbose: whether to print out extra information, default False.
+            :verbose: whether to print out extra information, default False.
         :return: self, with attributes set with new data
         """
         if engine == UMAP_LEARN:

From 329cbc69c239b9dc1c192c3aaa36c06005aa6ee0 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Mon, 27 Feb 2023 16:22:13 -0500
Subject: [PATCH 184/432] fix(docstr): fixed args formatting on seman search

---
 graphistry/text_utils.py | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/graphistry/text_utils.py b/graphistry/text_utils.py
index ff6f2eabbb..c714c42a9b 100644
--- a/graphistry/text_utils.py
+++ b/graphistry/text_utils.py
@@ -143,16 +143,16 @@ def search(
             calls will be not rebuilt index.
 
         Args:
-            query (str): natural language query.
-            cols (list or str, optional): if fuzzy=False, select which column to query.
+            :query (str): natural language query.
+            :cols (list or str, optional): if fuzzy=False, select which column to query.
                                             Defaults to None since fuzzy=True by defaul.
-            thresh (float, optional): distance threshold from query vector to returned results.
+            :thresh (float, optional): distance threshold from query vector to returned results.
                                         Defaults to 5000, set large just in case,
                                         but could be as low as 10.
-            fuzzy (bool, optional): if True, uses embedding + annoy index for recall,
+            :fuzzy (bool, optional): if True, uses embedding + annoy index for recall,
                                         otherwise does string matching over given `cols`
                                         Defaults to True.
-            top_n (int, optional): how many results to return. Defaults to 100.
+            :top_n (int, optional): how many results to return. Defaults to 100.
 
         Returns:
             pd.DataFrame, vector_encoding_of_query:
@@ -194,15 +194,15 @@ def search_graph(
             See help(g.search) for more information
 
         Args:
-            query (str): query input eg "coding best practices"
-            scale (float, optional): edge weigh threshold,  Defaults to 0.5.
-            top_n (int, optional): how many results to return. Defaults to 100.
-            thresh (float, optional): distance threshold from query vector to returned results.
+            :query (str): query input eg "coding best practices"
+            :scale (float, optional): edge weigh threshold,  Defaults to 0.5.
+            :top_n (int, optional): how many results to return. Defaults to 100.
+            :thresh (float, optional): distance threshold from query vector to returned results.
                                         Defaults to 5000, set large just in case,
                                         but could be as low as 10.
-            broader (bool, optional): if True, will retrieve entities connected via an edge
+            :broader (bool, optional): if True, will retrieve entities connected via an edge
                 that were not necessarily bubbled up in the results_dataframe. Defaults to False.
-            inplace (bool, optional): whether to return new instance (default) or mutate self.
+            :inplace (bool, optional): whether to return new instance (default) or mutate self.
                                         Defaults to False.
 
         Returns:

From dc5f0cb113b9d65bd24c8face0b879f726f3be26 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Mon, 27 Feb 2023 16:28:10 -0500
Subject: [PATCH 185/432] fix(docstr): minor formatting fix

---
 graphistry/text_utils.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/graphistry/text_utils.py b/graphistry/text_utils.py
index c714c42a9b..314d37c025 100644
--- a/graphistry/text_utils.py
+++ b/graphistry/text_utils.py
@@ -155,10 +155,10 @@ def search(
             :top_n (int, optional): how many results to return. Defaults to 100.
 
         Returns:
-            pd.DataFrame, vector_encoding_of_query:
-                * rank ordered dataframe of results matching query
-                * vector encoding of query via given transformer/ngrams model if fuzzy=True
-                    else None
+            **pd.DataFrame, vector_encoding_of_query:**
+            rank ordered dataframe of results matching query
+
+            vector encoding of query via given transformer/ngrams model if fuzzy=True else None
         """
         if not fuzzy:
             if cols is None:

From 5f5102aee57b442c647fa7124ac3e3b6658b8f0a Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Mon, 27 Feb 2023 16:28:34 -0500
Subject: [PATCH 186/432] fix(docstr): changed overview nav name

---
 docs/source/graphistry.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/graphistry.rst b/docs/source/graphistry.rst
index 71cf81bb41..a6fdf5cf15 100644
--- a/docs/source/graphistry.rst
+++ b/docs/source/graphistry.rst
@@ -1,4 +1,4 @@
-Overview
+Layout & Plugins
 ==================
 .. toctree::
    :maxdepth: 3

From 72d7c2667d10a6fe38ca17d64062a325187a94ab Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 27 Feb 2023 16:09:04 -0800
Subject: [PATCH 187/432] swaps out ANNOY for FAISS

---
 graphistry/ai_utils.py      | 89 +++++++++++++++++--------------------
 graphistry/feature_utils.py |  2 +-
 graphistry/text_utils.py    | 18 +++-----
 3 files changed, 47 insertions(+), 62 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index e29c334785..66d2b868e0 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -3,8 +3,7 @@
 
 import graphistry
 
-from .constants import N_TREES, DISTANCE, WEIGHT, BATCH
-from .features import N_NEIGHBORS
+from .constants import DISTANCE, WEIGHT, BATCH
 from logging import getLogger
 
 logger = getLogger(__name__)
@@ -133,49 +132,41 @@ def get_graphistry_from_milieu_search(
     return g
 
 
+
+
 # #########################################################################################################################
 #
 #  Graphistry Vector Search Index
 #
 ##########################################################################################################################
-
-
-def build_annoy_index(X, angular, n_trees=None):
-    """Builds an Annoy Index for fast vector search
-
-    Args:
-        X (_type_): _description_
-        angular (_type_): _description_
-        n_trees (_type_, optional): _description_. Defaults to None.
-
-    Returns:
-        _type_: _description_
-    """
-    from annoy import AnnoyIndex  # type: ignore
-
-    logger.info(f"Building Index of size {X.shape}")
-
-    if angular:
-        logger.info("-using angular metric")
-        metric = "angular"
-    else:
-        logger.info("-using euclidean metric")
-        metric = "euclidean"
-
-    search_index = AnnoyIndex(X.shape[1], metric)
-    # Add all the feature vectors to the search index
-    for i in range(len(X)):
-        search_index.add_item(i, X.values[i])
-    if n_trees is None:
-        n_trees = N_TREES
-
-    logger.info(f"-building index with {n_trees} trees")
-    search_index.build(n_trees)
-    return search_index
-
-
-def query_by_vector(vect, df, search_index, top_n):
-    """ Query by vector using annoy index and append distance to results
+# import faiss
+# import numpy as np
+
+class FaissVectorSearch:
+    def __init__(self, M):
+        import faiss
+        import numpy as np
+        self.index = faiss.IndexFlatL2(M.shape[1])
+        self.index.add(M)
+
+    def search(self, q, k=5):
+        """
+        Search for the k nearest neighbors of a query vector q.
+
+        Parameters:
+        - q: the query vector to search for
+        - k: the number of nearest neighbors to return (default: 5)
+
+        Returns:
+        - I: a numpy array of size (k,) containing the indices of the k nearest neighbors
+        - D: a numpy array of size (k,) containing the distances to the k nearest neighbors
+        """
+        q = np.asarray(q, dtype=np.float32)
+        D, I = self.index.search(q.reshape(1, -1), k)
+        return I[0], D[0]
+    
+    def search_df(self, q, df, k):
+        """ Query by vector using annoy index and append distance to results
     
         it is assumed len(vect) == len(df) == len(search_index)
         args:
@@ -185,16 +176,15 @@ def query_by_vector(vect, df, search_index, top_n):
             top_n: number of results to return
         returns:
             sorted dataframe with top_n results and distance
-    """
-    indices, distances = search_index.get_nns_by_vector(
-        vect.values[0], top_n, include_distances=True
-    )
+        """
+
+        indices, distances = self.search(q.values[0], k=k)
 
-    results = df.iloc[indices]
-    results[DISTANCE] = distances
-    results = results.sort_values(by=[DISTANCE])
+        results = df.iloc[indices]
+        results.loc[:, DISTANCE] = distances
+        results = results.sort_values(by=[DISTANCE])
 
-    return results
+        return results
 
 
 # #########################################################################################################################
@@ -479,3 +469,6 @@ def infer_self_graph(res,
     # #########################################################
     print("-" * 50) if verbose else None
     return hydrate_graph(res, df, new_edges, node, src, dst, emb, X, y)
+
+
+
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index eebbb4a4df..c9b4a9174c 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1944,7 +1944,7 @@ def _featurize_nodes(
         memoize: bool = True,
         verbose: bool = False,
     ):
-        res = self.bind()  # was self.copy() but changing to test
+        res = self.copy() 
         ndf = res._nodes
         node = res._node
 
diff --git a/graphistry/text_utils.py b/graphistry/text_utils.py
index 1378b01f91..eb1f8cab86 100644
--- a/graphistry/text_utils.py
+++ b/graphistry/text_utils.py
@@ -1,7 +1,7 @@
 import pandas as pd
 
 from .feature_utils import FeatureMixin
-from .ai_utils import search_to_df, build_annoy_index, query_by_vector
+from .ai_utils import search_to_df, FaissVectorSearch
 from .constants import WEIGHT, DISTANCE
 from logging import getLogger
 
@@ -37,24 +37,18 @@ def assert_features_line_up_with_nodes(self):
             f"found nodes: {a}, feats: {b}. Did you mutate nodes between fit?"
         )
 
-    def _build_search_index(self, X, angular=False, n_trees=None):
-        # builds local index from X
-        return build_annoy_index(X, angular, n_trees)
-
     def build_index(self, angular=False, n_trees=None):
         # builds local index
         self.assert_fitted()
         self.assert_features_line_up_with_nodes()
-
         X = self._get_feature("nodes")
-
-        self.search_index = self._build_search_index(X, angular, n_trees)
+        self.search_index = FaissVectorSearch(X.values) #self._build_search_index(X, angular, n_trees, faiss=False)
 
     def _query_from_dataframe(self, qdf: pd.DataFrame, top_n: int, thresh: float):
         # Use the loaded featurizers to transform the dataframe
         vect, _ = self.transform(qdf, None, kind="nodes", return_graph=False)
 
-        results = query_by_vector(vect, self._nodes, self.search_index, top_n)
+        results = self.search_index.search_df(vect, self._nodes, top_n)
         results = results.query(f"{DISTANCE} < {thresh}")
 
         return results, vect
@@ -138,9 +132,7 @@ def search(
                 )
             ```
             If an index is not yet built, it is generated `g2.build_index()` on the fly at search time.
-            Otherwise, can set `g2.build_index()` and then subsequent `g2.search(...)`
-            calls will be not rebuilt index.
-
+            Otherwise, can set `g2.build_index()` to build it ahead of time.
         Args:
             query (str): natural language query.
             cols (list or str, optional): if fuzzy=False, select which column to query.
@@ -250,7 +242,7 @@ def search_graph(
             if res._umap is not None:
                 emb = res._node_embedding.iloc[found_indices]  # type: ignore
         except Exception as e:  # for explicit relabeled nodes
-            logger.exception(e)
+            #logger.exception(e)
             tdf = rdf[df[node].isin(found_indices)]
             feats = res._node_features.loc[tdf.index]  # type: ignore
             if res._umap is not None:

From ea3b9a61407ca93459971fe06315b43a4d6db35d Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 27 Feb 2023 16:15:48 -0800
Subject: [PATCH 188/432] lint

---
 graphistry/ai_utils.py   | 11 ++++-------
 graphistry/text_utils.py | 12 ++++++++----
 2 files changed, 12 insertions(+), 11 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 66d2b868e0..5c9186159b 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -158,12 +158,12 @@ def search(self, q, k=5):
         - k: the number of nearest neighbors to return (default: 5)
 
         Returns:
-        - I: a numpy array of size (k,) containing the indices of the k nearest neighbors
-        - D: a numpy array of size (k,) containing the distances to the k nearest neighbors
+        - Index: a numpy array of size (k,) containing the indices of the k nearest neighbors
+        - Distances: a numpy array of size (k,) containing the distances to the k nearest neighbors
         """
         q = np.asarray(q, dtype=np.float32)
-        D, I = self.index.search(q.reshape(1, -1), k)
-        return I[0], D[0]
+        Distances, Index = self.index.search(q.reshape(1, -1), k)
+        return Index[0], Distances[0]
     
     def search_df(self, q, df, k):
         """ Query by vector using annoy index and append distance to results
@@ -469,6 +469,3 @@ def infer_self_graph(res,
     # #########################################################
     print("-" * 50) if verbose else None
     return hydrate_graph(res, df, new_edges, node, src, dst, emb, X, y)
-
-
-
diff --git a/graphistry/text_utils.py b/graphistry/text_utils.py
index eb1f8cab86..d5b579b593 100644
--- a/graphistry/text_utils.py
+++ b/graphistry/text_utils.py
@@ -17,6 +17,7 @@
 
 logger = getLogger(__name__)
 
+
 class SearchToGraphMixin(MIXIN_BASE):
     def __init__(self, *args, **kwargs) -> None:
         super().__init__(*args, **kwargs)
@@ -42,7 +43,9 @@ def build_index(self, angular=False, n_trees=None):
         self.assert_fitted()
         self.assert_features_line_up_with_nodes()
         X = self._get_feature("nodes")
-        self.search_index = FaissVectorSearch(X.values) #self._build_search_index(X, angular, n_trees, faiss=False)
+        self.search_index = FaissVectorSearch(
+            X.values
+        )  # self._build_search_index(X, angular, n_trees, faiss=False)
 
     def _query_from_dataframe(self, qdf: pd.DataFrame, top_n: int, thresh: float):
         # Use the loaded featurizers to transform the dataframe
@@ -205,9 +208,9 @@ def search_graph(
             res = self.bind()
 
         edf = edges = res._edges
-        #print('shape of edges', edf.shape)
+        # print('shape of edges', edf.shape)
         rdf = df = res._nodes
-        #print('shape of nodes', rdf.shape)
+        # print('shape of nodes', rdf.shape)
         node = res._node
         indices = rdf[node]
         src = res._source
@@ -242,7 +245,7 @@ def search_graph(
             if res._umap is not None:
                 emb = res._node_embedding.iloc[found_indices]  # type: ignore
         except Exception as e:  # for explicit relabeled nodes
-            #logger.exception(e)
+            logger.exception(e)
             tdf = rdf[df[node].isin(found_indices)]
             feats = res._node_features.loc[tdf.index]  # type: ignore
             if res._umap is not None:
@@ -267,6 +270,7 @@ def search_graph(
 
     def save_search_instance(self, savepath):
         from joblib import dump  # type: ignore   # need to make this onnx or similar
+
         self.build_index()
         search = self.search_index
         del self.search_index  # can't pickle Annoy

From 371481deb347d23c262d9d5938345e3e69baf6c8 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 27 Feb 2023 16:20:38 -0800
Subject: [PATCH 189/432] adds faiss-cpu in AI deps build

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 5817609f6a..09600be3dc 100755
--- a/setup.py
+++ b/setup.py
@@ -40,7 +40,7 @@ def unique_flatten_dict(d):
 base_extras_heavy = {
   'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
 }
-base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'annoy', 'joblib']
+base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'faiss-cpu', 'joblib']
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From 30c57c906a8a72634a844294cf134d726498b834 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 27 Feb 2023 16:25:35 -0800
Subject: [PATCH 190/432] adds faiss in AI deps build

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 09600be3dc..4cc9590401 100755
--- a/setup.py
+++ b/setup.py
@@ -40,7 +40,7 @@ def unique_flatten_dict(d):
 base_extras_heavy = {
   'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
 }
-base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'faiss-cpu', 'joblib']
+base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'faiss', 'faiss-cpu', 'joblib']
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From c639f837e3900fd893c215c688c8913c66a2d144 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 27 Feb 2023 16:34:28 -0800
Subject: [PATCH 191/432] lint moved import

---
 graphistry/ai_utils.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 5c9186159b..5234c3d6c2 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -6,6 +6,11 @@
 from .constants import DISTANCE, WEIGHT, BATCH
 from logging import getLogger
 
+try:
+    import faiss
+except:
+    faiss = None
+
 logger = getLogger(__name__)
 
 
@@ -144,8 +149,7 @@ def get_graphistry_from_milieu_search(
 
 class FaissVectorSearch:
     def __init__(self, M):
-        import faiss
-        import numpy as np
+        # import faiss
         self.index = faiss.IndexFlatL2(M.shape[1])
         self.index.add(M)
 

From 18c282b4eccfa46772bde365739b7ee7d75a3ae0 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 27 Feb 2023 16:39:18 -0800
Subject: [PATCH 192/432] lint

---
 graphistry/ai_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 5234c3d6c2..fb5a4de516 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -7,7 +7,7 @@
 from logging import getLogger
 
 try:
-    import faiss
+    import faiss  # type ignore
 except:
     faiss = None
 

From dc675b535687145bfb264fc6afff7619b6a2c080 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 27 Feb 2023 16:55:06 -0800
Subject: [PATCH 193/432] removes package

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 4cc9590401..09600be3dc 100755
--- a/setup.py
+++ b/setup.py
@@ -40,7 +40,7 @@ def unique_flatten_dict(d):
 base_extras_heavy = {
   'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
 }
-base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'faiss', 'faiss-cpu', 'joblib']
+base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'faiss-cpu', 'joblib']
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From a124ed6ef916b6248661214c35bb565e57395026 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 27 Feb 2023 17:20:08 -0800
Subject: [PATCH 194/432] adds mypy ignore

---
 mypy.ini | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/mypy.ini b/mypy.ini
index 01cff103e8..898e001146 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -31,6 +31,9 @@ ignore_missing_imports = True
 [mypy-dgl.*]
 ignore_missing_imports = True
 
+[mypy-faiss.*]
+ignore_missing_imports = True
+
 [mypy-igraph.*]
 ignore_missing_imports = True
 

From ebe0f1635329f127f1f35ec324388a3088091338 Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Tue, 28 Feb 2023 17:44:14 +0800
Subject: [PATCH 195/432] check cucat cuml

---
 graphistry/feature_utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index a900c85986..f376c5f791 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -140,7 +140,7 @@ def assert_imported():
         )
         raise import_min_exn
 
-def assert_cuml_imported():
+def assert_cuml_cucat():
     has_cuml_dependancy_, import_cuml_exn = lazy_import_has_cu_cat_dependancy()
     if not has_cuml_dependancy_:
         logger.error(  # noqa
@@ -2381,7 +2381,7 @@ def featurize(
         if feature_engine == 'dirty_cat':
             assert_imported()
         elif feature_engine == 'cu_cat':
-            assert_cuml_imported()
+            assert_cuml_cucat()
         if inplace:
             res = self
         else:

From 297dcc9d17e058cce6dfee127b3c7b3d106b23b9 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Tue, 28 Feb 2023 09:16:40 -0500
Subject: [PATCH 196/432] fix(docstr): minor formatting fixes on featurize

---
 graphistry/feature_utils.py | 72 ++++++++++++++++++-------------------
 1 file changed, 34 insertions(+), 38 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 462ca18a11..6b845576e6 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -627,10 +627,9 @@ def fit_pipeline(
      which helps for when transformer pipeline is scaling or imputer
      which sometime introduce small negative numbers,
      and umap metrics like Hellinger need to be positive
-    :param X, DataFrame to transform.
+    :param X: DataFrame to transform.
     :param transformer: Pipeline object to fit and transform
-    :param keep_n_decimals: Int of how many decimal places to keep in
-    rounded transformed data
+    :param keep_n_decimals: Int of how many decimal places to keep in rounded transformed data
     """
     columns = X.columns
     index = X.index
@@ -1772,9 +1771,10 @@ def scale(self, X=None, y=None, return_pipeline=False, *args, **kwargs):
             args:
             ::
 
-                X: pd.DataFrame of features
-                y: pd.DataFrame of target features
-                kind: str, one of 'nodes' or 'edges'
+                
+                ;X: pd.DataFrame of features
+                :y: pd.DataFrame of target features
+                :kind: str, one of 'nodes' or 'edges'
                 *args, **kwargs: passed to smart_scaler pipeline
             returns:
                 scaled X, y
@@ -2211,20 +2211,17 @@ def transform(self, df: pd.DataFrame,
             Transform new data and append to existing graph, or return dataframes
         
             **args:**
-            ::
 
-                # df: pd.DataFrame, raw data to transform
-                # ydf: pd.DataFrame, optional
-                # kind: str  # one of `nodes`, `edges`
-                # return_graph: bool, if True, will return a graph with inferred edges.
-                # merge_policy: bool, if True, adds batch to existing graph nodes via nearest neighbors. 
-                    # If False, will infer edges only between nodes in the batch, default False
-                # min_dist: float, if return_graph is True, will use this value in NN search, or 'auto' to infer a good value
-                    # min_dist represents the maximum distance between two samples for one to be considered as in the neighborhood of the other.
-                # sample: int, if return_graph is True, will use sample edges of existing graph to fill out the new graph
-                # n_neighbors: int, if return_graph is True, will use this value for n_neighbors in Nearest Neighbors search
-                # scaled: bool, if True, will use scaled transformation of data set during featurization, default True
-                # verbose: bool, if True, will print metadata about the graph construction, default False
+                :df: pd.DataFrame, raw data to transform
+                :ydf: pd.DataFrame, optional
+                :kind: str  # one of `nodes`, `edges`
+                :return_graph: bool, if True, will return a graph with inferred edges.
+                :merge_policy: bool, if True, adds batch to existing graph nodes via nearest neighbors. If False, will infer edges only between nodes in the batch, default False
+                :min_dist: float, if return_graph is True, will use this value in NN search, or 'auto' to infer a good value. min_dist represents the maximum distance between two samples for one to be considered as in the neighborhood of the other.
+                :sample: int, if return_graph is True, will use sample edges of existing graph to fill out the new graph
+                :n_neighbors: int, if return_graph is True, will use this value for n_neighbors in Nearest Neighbors search
+                :scaled: bool, if True, will use scaled transformation of data set during featurization, default True
+                :verbose: bool, if True, will print metadata about the graph construction, default False
             **Returns:**
 
                     X, y: pd.DataFrame, transformed data if return_graph is False
@@ -2285,22 +2282,21 @@ def scale(
 
 
             **Args:**
-            ::
 
-                # df: pd.DataFrame, raw data to transform, if None, will use data from featurization fit
-                # y: pd.DataFrame, optional target data
-                # kind: str, one of `nodes`, `edges`
-                # use_scaler: str, optional, one of `minmax`, `robust`, `standard`, `kbins`, `quantile`
-                # use_scaler_target: str, optional, one of `minmax`, `robust`, `standard`, `kbins`, `quantile`
-                # impute: bool, if True, will impute missing values
-                # n_quantiles: int, number of quantiles to use for quantile scaler
-                # output_distribution: str, one of `normal`, `uniform`, `lognormal` 
-                # quantile_range: tuple, range of quantiles to use for quantile scaler
-                # n_bins: int, number of bins to use for KBinsDiscretizer
-                # encode: str, one of `ordinal`, `onehot`, `onehot-dense`, `binary`    
-                # strategy: str, one of `uniform`, `quantile`, `kmeans`
-                # keep_n_decimals: int, number of decimals to keep after scaling
-                # return_scalers: bool, if True, will return the scalers used to scale the data
+                :df: pd.DataFrame, raw data to transform, if None, will use data from featurization fit
+                :y: pd.DataFrame, optional target data
+                :kind: str, one of `nodes`, `edges`
+                :use_scaler: str, optional, one of `minmax`, `robust`, `standard`, `kbins`, `quantile`
+                :use_scaler_target: str, optional, one of `minmax`, `robust`, `standard`, `kbins`, `quantile`
+                :impute: bool, if True, will impute missing values
+                :n_quantiles: int, number of quantiles to use for quantile scaler
+                :output_distribution: str, one of `normal`, `uniform`, `lognormal` 
+                :quantile_range: tuple, range of quantiles to use for quantile scaler
+                :n_bins: int, number of bins to use for KBinsDiscretizer
+                :encode: str, one of `ordinal`, `onehot`, `onehot-dense`, `binary`    
+                :strategy: str, one of `uniform`, `quantile`, `kmeans`
+                :keep_n_decimals: int, number of decimals to keep after scaling
+                :return_scalers: bool, if True, will return the scalers used to scale the data
 
             **Returns:**
 
@@ -2792,7 +2788,7 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
     
     def get_matrix(self, columns: Optional[Union[List, str]] = None, kind: str = 'nodes', target: bool = False) -> pd.DataFrame:
         """
-        Returns feature matrix, and if columns are specified, returns matrix with only the columns that contain 
+            Returns feature matrix, and if columns are specified, returns matrix with only the columns that contain 
             the string `column_part` in their name.
 
             `X = g.get_matrix(['feature1', 'feature2'])`
@@ -2824,10 +2820,10 @@ def get_matrix(self, columns: Optional[Union[List, str]] = None, kind: str = 'no
             Caveats:
                 - if you have a column name that is a substring of another column name, you may get unexpected results.
         Args:
-            columns (Union[List, str]): list of column names or a single column name that may exist in columns 
+            :columns (Union[List, str]): list of column names or a single column name that may exist in columns 
                 of the feature matrix. If None, returns original feature matrix
-            kind (str, optional): Node or Edge features. Defaults to 'nodes'.
-            target (bool, optional): If True, returns the target matrix. Defaults to False.
+            :kind (str, optional): Node or Edge features. Defaults to 'nodes'.
+            :target (bool, optional): If True, returns the target matrix. Defaults to False.
 
         Returns:
             pd.DataFrame: feature matrix with only the columns that contain the string `column_part` in their name.

From fa749a2dc76479ebdda7897fb1559fb3c04c7156 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Tue, 28 Feb 2023 09:30:55 -0500
Subject: [PATCH 197/432] fix(docstr): fixed hyperlink for palettes

---
 graphistry/PlotterBase.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/graphistry/PlotterBase.py b/graphistry/PlotterBase.py
index 555c9e580c..badb060b19 100644
--- a/graphistry/PlotterBase.py
+++ b/graphistry/PlotterBase.py
@@ -314,15 +314,18 @@ def style(self, fg=None, bg=None, page=None, logo=None):
 
         **Example: Chained merge - results in url and blendMode being set, while color is dropped**
             ::
+
                 g2 =  g.style(bg={'color': 'black'}, fg={'blendMode': 'screen'})
                 g3 = g2.style(bg={'image': {'url': 'http://site.com/watermark.png'}})
                 
         **Example: Gradient background**
             ::
+
               g.style(bg={'gradient': {'kind': 'linear', 'position': 45, 'stops': [['rgb(0,0,0)', '0%'], ['rgb(255,255,255)', '100%']]}})
               
         **Example: Page settings**
             ::
+            
               g.style(page={'title': 'Site - {{ name }}', 'favicon': 'http://site.com/logo.ico'})
 
         """        
@@ -857,7 +860,7 @@ def bind(self, source=None, destination=None, node=None, edge=None,
         :param edge_label: Attribute overriding edge's expanded label text. By default, scrollable list of attribute/value mappings.
         :type edge_label: str
 
-        :param edge_color: Attribute overriding edge's color. rgba (int64) or int32 palette index, see palette definitions <https://graphistry.github.io/docs/legacy/api/0.9.2/api.html#extendedpalette>`_ for values. Based on Color Brewer.
+        :param edge_color: Attribute overriding edge's color. rgba (int64) or int32 palette index, see `palette <https://graphistry.github.io/docs/legacy/api/0.9.2/api.html#extendedpalette>`_ definitions for values. Based on Color Brewer.
         :type edge_color: str
 
         :param edge_source_color: Attribute overriding edge's source color if no edge_color, as an rgba int64 value.
@@ -875,7 +878,7 @@ def bind(self, source=None, destination=None, node=None, edge=None,
         :param point_label: Attribute overriding node's expanded label text. By default, scrollable list of attribute/value mappings.
         :type point_label: str
 
-        :param point_color: Attribute overriding node's color.rgba (int64) or int32 palette index, see palette definitions <https://graphistry.github.io/docs/legacy/api/0.9.2/api.html#extendedpalette>`_ for values. Based on Color Brewer.
+        :param point_color: Attribute overriding node's color.rgba (int64) or int32 palette index, see `palette <https://graphistry.github.io/docs/legacy/api/0.9.2/api.html#extendedpalette>`_ definitions for values. Based on Color Brewer.
         :type point_color: str
 
         :param point_size: Attribute overriding node's size. By default, uses the node degree. The visualization will normalize point sizes and adjust dynamically using semantic zoom.

From ca09bea30aaa0ddb9238955f16ee7005b3ce4e48 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Tue, 28 Feb 2023 09:31:16 -0500
Subject: [PATCH 198/432] fix(docstr): fixed spacing for codeblock

---
 graphistry/plugins/cugraph.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/graphistry/plugins/cugraph.py b/graphistry/plugins/cugraph.py
index d769ba89ce..5e7a656b25 100644
--- a/graphistry/plugins/cugraph.py
+++ b/graphistry/plugins/cugraph.py
@@ -363,6 +363,7 @@ def layout_cugraph(
 
     **Example: ForceAtlas2 layout**
         ::
+
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['b','c','d','e']})
             g = graphistry.edges(edges, 's', 'd')
@@ -370,6 +371,7 @@ def layout_cugraph(
 
     **Example: Change which column names are generated**
         ::
+
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['b','c','d','e']})
             g = graphistry.edges(edges, 's', 'd')
@@ -380,6 +382,7 @@ def layout_cugraph(
 
     **Example: Pass parameters to layout methods**
         ::
+        
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['b','c','d','e']})
             g = graphistry.edges(edges, 's', 'd')

From 3cabd769e921ee08d17be43303dc8f92688e35ff Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Tue, 28 Feb 2023 09:43:39 -0500
Subject: [PATCH 199/432] fix(docstr):minor text edit

---
 graphistry/umap_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 38ad606f26..633f941c55 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -281,7 +281,7 @@ def transform_umap(self, df: pd.DataFrame,
     ) -> Union[Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable]:
         """Transforms data into UMAP embedding
         
-        args:
+        Args:
             :df: Dataframe to transform
             :y: Target column
             :kind: One of `nodes` or `edges`

From 0b4702ac67e6dae02effe7790c382b9d1c69fa64 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Tue, 28 Feb 2023 09:43:59 -0500
Subject: [PATCH 200/432] fix(docstr): fixed codeblock indentation

---
 graphistry/pygraphistry.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/graphistry/pygraphistry.py b/graphistry/pygraphistry.py
index 0a8dd76310..2051a32523 100644
--- a/graphistry/pygraphistry.py
+++ b/graphistry/pygraphistry.py
@@ -1942,6 +1942,7 @@ def nodes(nodes: Union[Callable, Any], node=None, *args, **kwargs) -> Plottable:
 
         **Example**
             ::
+            
                 import graphistry
 
                 def sample_nodes(g, n):
@@ -1992,6 +1993,7 @@ def edges(
 
         **Example**
             ::
+
                 import graphistry
 
                 def sample_edges(g, n):

From 9b3e2b1724dfb19be32b9551b8d70e3311714ffc Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Tue, 28 Feb 2023 10:28:37 -0800
Subject: [PATCH 201/432] adds pinned faiss version

---
 graphistry/ai_utils.py | 5 ++---
 setup.py               | 2 +-
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index fb5a4de516..93e24532a7 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -144,12 +144,11 @@ def get_graphistry_from_milieu_search(
 #  Graphistry Vector Search Index
 #
 ##########################################################################################################################
-# import faiss
-# import numpy as np
+
 
 class FaissVectorSearch:
     def __init__(self, M):
-        # import faiss
+        import faiss
         self.index = faiss.IndexFlatL2(M.shape[1])
         self.index.add(M)
 
diff --git a/setup.py b/setup.py
index 09600be3dc..2a11b08fad 100755
--- a/setup.py
+++ b/setup.py
@@ -40,7 +40,7 @@ def unique_flatten_dict(d):
 base_extras_heavy = {
   'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
 }
-base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'faiss-cpu', 'joblib']
+base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'faiss-cpu==1.6.5', 'joblib']
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From 24e7c759c3659e806fe5add30cd2bd6bc73e03e6 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Tue, 28 Feb 2023 10:37:08 -0800
Subject: [PATCH 202/432] adds pinned setuptools version

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 2a11b08fad..f50b28c852 100755
--- a/setup.py
+++ b/setup.py
@@ -40,7 +40,7 @@ def unique_flatten_dict(d):
 base_extras_heavy = {
   'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
 }
-base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'faiss-cpu==1.6.5', 'joblib']
+base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['setuptools==67.4.0', 'scipy', 'dgl', 'torch', 'sentence-transformers', 'faiss-cpu==1.6.5', 'joblib']
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From b0d7a18946a848dd861e318ca7ce482918c8e017 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Tue, 28 Feb 2023 10:41:32 -0800
Subject: [PATCH 203/432] adds pinned faiss 1.6.1

---
 setup.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index f50b28c852..3b07d22380 100755
--- a/setup.py
+++ b/setup.py
@@ -40,7 +40,8 @@ def unique_flatten_dict(d):
 base_extras_heavy = {
   'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
 }
-base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['setuptools==67.4.0', 'scipy', 'dgl', 'torch', 'sentence-transformers', 'faiss-cpu==1.6.5', 'joblib']
+# https://github.com/facebookresearch/faiss/issues/1589 for faiss-cpu 1.6.1, #'setuptools==67.4.0' removed
+base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'faiss-cpu==1.6.1', 'joblib']
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From 9252b09c8c1b587ee9c6de2a166c191f61aeb96b Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Tue, 28 Feb 2023 10:48:59 -0800
Subject: [PATCH 204/432] unpins FAISS to see why swig worked then

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 3b07d22380..71b6052048 100755
--- a/setup.py
+++ b/setup.py
@@ -41,7 +41,7 @@ def unique_flatten_dict(d):
   'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
 }
 # https://github.com/facebookresearch/faiss/issues/1589 for faiss-cpu 1.6.1, #'setuptools==67.4.0' removed
-base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'faiss-cpu==1.6.1', 'joblib']
+base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'faiss-cpu', 'joblib']
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From ddba63f637cabefb95224b15f5d82d6f2f0183cf Mon Sep 17 00:00:00 2001
From: Tanmoy <tanmoyf2@gmail.com>
Date: Wed, 1 Mar 2023 01:08:39 +0530
Subject: [PATCH 205/432] Update setup.py

---
 setup.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 71b6052048..2e09266ac3 100755
--- a/setup.py
+++ b/setup.py
@@ -15,7 +15,8 @@ def unique_flatten_dict(d):
   'requests',
   'squarify',
   'typing-extensions',
-  'packaging >= 20.1'
+  'packaging >= 20.1',
+  'setuptools < 60.0.0',
 ]
 
 stubs = [

From a272ea3af4626f118e5cc52a0d9a888f226d7995 Mon Sep 17 00:00:00 2001
From: Tanmoy <tanmoyf2@gmail.com>
Date: Wed, 1 Mar 2023 01:32:28 +0530
Subject: [PATCH 206/432] Update setup.py

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 2e09266ac3..1c5304d74d 100755
--- a/setup.py
+++ b/setup.py
@@ -34,7 +34,7 @@ def unique_flatten_dict(d):
     'networkx': ['networkx>=2.5'],
     'gremlin': ['gremlinpython'],
     'bolt': ['neo4j', 'neotime'],
-    'nodexl': ['openpyxl', 'xlrd'],
+    'nodexl': ['openpyxl==3.1.0', 'xlrd'],
     'jupyter': ['ipython'],
 }
 

From 21dac04e349a7842cbbbecd988fe1e05bdf111cb Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.lan>
Date: Wed, 1 Mar 2023 14:51:51 +0800
Subject: [PATCH 207/432] flag and typo fix

---
 graphistry/feature_utils.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index f376c5f791..618a516374 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -109,8 +109,8 @@ def lazy_import_has_cu_cat_dependancy():
         from scipy import __version__ as scipy_version
         from cu_cat import __version__ as cu_cat_version
         from sklearn import __version__ as sklearn_version
-        from cuml import __verison__ as cuml_version
-        from cudf import __verison__ as cudf_version
+        from cuml import __version__ as cuml_version
+        from cudf import __version__ as cudf_version
         logger.debug(f"SCIPY VERSION: {scipy_version}")
         logger.debug(f"Cuda CAT VERSION: {cu_cat_version}")
         logger.debug(f"sklearn VERSION: {sklearn_version}")
@@ -173,7 +173,7 @@ def assert_cuml_cucat():
 #
 #      _featurize_or_get_edges_dataframe_if_X_is_None
 
-FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch", "cu_cat"]
+FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch", "cu_cat", "cu_cat|torch"]
 FeatureEngine = Literal[FeatureEngineConcrete, "auto"]
 
 

From 23df5bc9792b7b20312707e1cb21cb91c71a54cb Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Mon, 6 Mar 2023 14:36:37 +0800
Subject: [PATCH 208/432] cudf all the way thru, cuda cannot handle nulls so
 few more ifs

---
 graphistry/feature_utils.py |  2 +-
 graphistry/umap_utils.py    | 14 ++++++++------
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 467d71bb79..7de9bebd66 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1891,7 +1891,7 @@ def _featurize_nodes(
         ndf = res._nodes
         node = res._node
 
-        if remove_node_column:
+        if remove_node_column and 'cudf.core.dataframe' not in str(getmodule(ndf)):
             ndf = remove_node_column_from_symbolic(ndf, node)
             X = remove_node_column_from_symbolic(X, node)
 
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 11f0b64c06..6a1cadb473 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -283,8 +283,11 @@ def transform_umap(  # noqa: E303
 
     def _bundle_embedding(self, emb, index):
         # Converts Embedding into dataframe and takes care if emb.dim > 2
-        if emb.shape[1] == 2:
+        if emb.shape[1] == 2 and 'cudf.core.dataframe' not in str(getmodule(emb)):
             emb = pd.DataFrame(emb, columns=[config.X, config.Y], index=index)
+        elif emb.shape[1] == 2 and 'cudf.core.dataframe' in str(getmodule(emb)):
+            import cudf
+            emb = cudf.DataFrame(emb, columns=[config.X, config.Y], index=index)
         else:
             columns = [config.X, config.Y] + [
                 f"umap_{k}" for k in range(2, emb.shape[1] - 2)
@@ -497,7 +500,6 @@ def umap(
             elif 'cudf.core.dataframe' in str(getmodule(X_)):
                 import cudf
                 index_to_nodes_dict = cudf.DataFrame(nodes).reset_index()
-                X_ = pd.DataFrame(X_.to_numpy())
 
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs
@@ -593,11 +595,11 @@ def _bind_xy_from_umap(
             emb = res._node_embedding
         else:
             emb = res._edge_embedding
+        if 'cudf.core.dataframe' not in str(getmodule(emb)): ## cuda cannot support nulls https://github.com/cupy/cupy/issues/5918#issuecomment-946327237
+            df[x_name] = emb.values.T[0]  # if embedding is greater
+            # than two dimensions will only take first two coordinates
+            df[y_name] = emb.values.T[1]
 
-        df[x_name] = emb.values.T[0]  # if embedding is greater
-        # than two dimensions will only take first two coordinates
-        df[y_name] = emb.values.T[1]
-        #
         res = res.nodes(df) if kind == "nodes" else res.edges(df)
 
         if encode_weight and kind == "nodes":

From a2640cd61d559e246d29f69cdc308b128749bf04 Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Mon, 6 Mar 2023 17:43:07 +0800
Subject: [PATCH 209/432] cudf+umap working on numerics

---
 graphistry/feature_utils.py |  2 +-
 graphistry/umap_utils.py    | 12 +++++-------
 2 files changed, 6 insertions(+), 8 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 7de9bebd66..467d71bb79 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1891,7 +1891,7 @@ def _featurize_nodes(
         ndf = res._nodes
         node = res._node
 
-        if remove_node_column and 'cudf.core.dataframe' not in str(getmodule(ndf)):
+        if remove_node_column:
             ndf = remove_node_column_from_symbolic(ndf, node)
             X = remove_node_column_from_symbolic(X, node)
 
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 6a1cadb473..83a767e775 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -286,8 +286,7 @@ def _bundle_embedding(self, emb, index):
         if emb.shape[1] == 2 and 'cudf.core.dataframe' not in str(getmodule(emb)):
             emb = pd.DataFrame(emb, columns=[config.X, config.Y], index=index)
         elif emb.shape[1] == 2 and 'cudf.core.dataframe' in str(getmodule(emb)):
-            import cudf
-            emb = cudf.DataFrame(emb, columns=[config.X, config.Y], index=index)
+            emb = pd.DataFrame(emb.to_numpy(), columns=[config.X, config.Y], index=index.to_numpy())
         else:
             columns = [config.X, config.Y] + [
                 f"umap_{k}" for k in range(2, emb.shape[1] - 2)
@@ -326,7 +325,6 @@ def _process_umap(
             # have to set _raw_data attribute on umap?
             fresh_res._umap = old_res._umap  # this saves the day!
             return fresh_res
-
         emb = res.umap_fit_transform(X_, y_)
         res._xy = emb
         return res
@@ -509,6 +507,7 @@ def umap(
             if res._xy is None:
                 raise RuntimeError("This should not happen")
             res._node_embedding = res._xy
+
             # TODO add edge filter so graph doesn't have double edges
             # TODO user-guidable edge merge policies like upsert?
             res._weighted_edges_df_from_nodes = (
@@ -595,10 +594,9 @@ def _bind_xy_from_umap(
             emb = res._node_embedding
         else:
             emb = res._edge_embedding
-        if 'cudf.core.dataframe' not in str(getmodule(emb)): ## cuda cannot support nulls https://github.com/cupy/cupy/issues/5918#issuecomment-946327237
-            df[x_name] = emb.values.T[0]  # if embedding is greater
-            # than two dimensions will only take first two coordinates
-            df[y_name] = emb.values.T[1]
+            
+        df[x_name] = emb.values.T[0]
+        df[y_name] = emb.values.T[1]
 
         res = res.nodes(df) if kind == "nodes" else res.edges(df)
 

From ec151b8c4059821d6cff9eec2363f1a4fdecd284 Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Tue, 7 Mar 2023 09:08:29 +0800
Subject: [PATCH 210/432] full numeric cudf-- needs hack to plot

---
 graphistry/umap_utils.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 83a767e775..00acfc2c89 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -286,12 +286,17 @@ def _bundle_embedding(self, emb, index):
         if emb.shape[1] == 2 and 'cudf.core.dataframe' not in str(getmodule(emb)):
             emb = pd.DataFrame(emb, columns=[config.X, config.Y], index=index)
         elif emb.shape[1] == 2 and 'cudf.core.dataframe' in str(getmodule(emb)):
-            emb = pd.DataFrame(emb.to_numpy(), columns=[config.X, config.Y], index=index.to_numpy())
+            import cudf
+            emb = cudf.DataFrame(emb.to_cupy(), columns=[config.X, config.Y], index=index.to_cupy())
         else:
             columns = [config.X, config.Y] + [
                 f"umap_{k}" for k in range(2, emb.shape[1] - 2)
             ]
-            emb = pd.DataFrame(emb, columns=columns, index=index)
+            if 'cudf.core.dataframe' not in str(getmodule(emb)):
+                emb = pd.DataFrame(emb, columns=columns, index=index)
+            elif 'cudf.core.dataframe' in str(getmodule(emb)):
+                import cudf
+                emb = cudf.DataFrame(emb.to_cupy(), columns=columns, index=index.to_cupy())
         return emb
 
     def _process_umap(

From 0ec412a6852e78f9152473b5cdb4e73f7ecc359c Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Tue, 7 Mar 2023 09:10:46 +0800
Subject: [PATCH 211/432] full numeric cudf-- needs hack to plot

---
 graphistry/umap_utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 00acfc2c89..2b0d71f4fc 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -287,7 +287,7 @@ def _bundle_embedding(self, emb, index):
             emb = pd.DataFrame(emb, columns=[config.X, config.Y], index=index)
         elif emb.shape[1] == 2 and 'cudf.core.dataframe' in str(getmodule(emb)):
             import cudf
-            emb = cudf.DataFrame(emb.to_cupy(), columns=[config.X, config.Y], index=index.to_cupy())
+            emb = cudf.DataFrame(emb.values, columns=[config.X, config.Y], index=index.values)
         else:
             columns = [config.X, config.Y] + [
                 f"umap_{k}" for k in range(2, emb.shape[1] - 2)
@@ -296,7 +296,7 @@ def _bundle_embedding(self, emb, index):
                 emb = pd.DataFrame(emb, columns=columns, index=index)
             elif 'cudf.core.dataframe' in str(getmodule(emb)):
                 import cudf
-                emb = cudf.DataFrame(emb.to_cupy(), columns=columns, index=index.to_cupy())
+                emb = cudf.DataFrame(emb.values, columns=columns, index=index.values)
         return emb
 
     def _process_umap(

From 55c2b07862d768f8bf994e75cbce763e5b706b4e Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Tue, 7 Mar 2023 14:04:40 +0800
Subject: [PATCH 212/432] use rename if 2 columns, otherwise = to columns list

---
 graphistry/umap_utils.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 2b0d71f4fc..5d0041230e 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -286,8 +286,7 @@ def _bundle_embedding(self, emb, index):
         if emb.shape[1] == 2 and 'cudf.core.dataframe' not in str(getmodule(emb)):
             emb = pd.DataFrame(emb, columns=[config.X, config.Y], index=index)
         elif emb.shape[1] == 2 and 'cudf.core.dataframe' in str(getmodule(emb)):
-            import cudf
-            emb = cudf.DataFrame(emb.values, columns=[config.X, config.Y], index=index.values)
+            emb.rename(columns={0:config.X,1: config.Y},inplace=True)
         else:
             columns = [config.X, config.Y] + [
                 f"umap_{k}" for k in range(2, emb.shape[1] - 2)
@@ -295,8 +294,7 @@ def _bundle_embedding(self, emb, index):
             if 'cudf.core.dataframe' not in str(getmodule(emb)):
                 emb = pd.DataFrame(emb, columns=columns, index=index)
             elif 'cudf.core.dataframe' in str(getmodule(emb)):
-                import cudf
-                emb = cudf.DataFrame(emb.values, columns=columns, index=index.values)
+                emb.columns=columns
         return emb
 
     def _process_umap(

From 0a4209068fd30c895d4929ec5e59811038a40835 Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Fri, 10 Mar 2023 08:42:18 +0900
Subject: [PATCH 213/432] umap cudf

---
 graphistry/umap_utils.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 5d0041230e..5c92661ae1 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -495,12 +495,11 @@ def umap(
 
             logger.debug("umap X_: %s", X_)
             logger.debug("umap y_: %s", y_)
-
-            if isinstance(X_,pd.DataFrame):
+            logger.debug("data is type :: %s", (type(X_)))
+            if isinstance(X_, pd.DataFrame):
                 index_to_nodes_dict = dict(zip(range(len(nodes)), nodes))
             elif 'cudf.core.dataframe' in str(getmodule(X_)):
-                import cudf
-                index_to_nodes_dict = cudf.DataFrame(nodes).reset_index()
+                index_to_nodes_dict = nodes
 
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs
@@ -598,8 +597,12 @@ def _bind_xy_from_umap(
         else:
             emb = res._edge_embedding
             
-        df[x_name] = emb.values.T[0]
-        df[y_name] = emb.values.T[1]
+        if type(df) == type(emb):
+            df[x_name] = emb.values.T[0]
+            df[y_name] = emb.values.T[1]
+        elif isinstance(df, pd.DataFrame) and 'cudf.core.dataframe' in str(getmodule(emb)):
+            df[x_name] = emb.to_numpy().T[0]
+            df[y_name] = emb.to_numpy().T[1]
 
         res = res.nodes(df) if kind == "nodes" else res.edges(df)
 

From 1a013a80b3e8e400974e767296a921a1fd820915 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 9 Mar 2023 20:37:03 -0800
Subject: [PATCH 214/432] untested, adds decorator for cuml and pandas
 dataframes, standardizing umap input and outputs if engine=cuml or pandas

---
 graphistry/umap_utils.py | 107 ++++++++++++++++++++++++++++++++++++++-
 1 file changed, 105 insertions(+), 2 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 5c92661ae1..99697d8922 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -12,6 +12,8 @@
 from .PlotterBase import Plottable, WeakValueDictionary
 from .util import check_set_memoize, setup_logger
 
+#logger = logging.getLogger(__name__)
+
 logger = setup_logger(name=__name__, verbose=config.VERBOSE)
 
 if TYPE_CHECKING:
@@ -98,6 +100,98 @@ def resolve_umap_engine(
     )
 
 
+logger = logging.getLogger(__name__)
+
+def convert_pandas_to_cudf(func):
+    def wrapper(*args, **kwargs):
+        new_args = []
+        new_kwargs = {}
+        for arg in args:
+            if isinstance(arg, pd.DataFrame):
+                new_args.append(cudf.DataFrame.from_pandas(arg))
+            else:
+                new_args.append(arg)
+        for key, value in kwargs.items():
+            if isinstance(value, pd.DataFrame):
+                new_kwargs[key] = cudf.DataFrame.from_pandas(value)
+            else:
+                new_kwargs[key] = value
+        return func(*new_args, **new_kwargs)
+    return wrapper
+
+def convert_cudf_to_pandas(func):
+    def wrapper(*args, **kwargs):
+        new_args = []
+        new_kwargs = {}
+        for arg in args:
+            if isinstance(arg, cudf.DataFrame):
+                new_args.append(arg.to_pandas())
+            else:
+                new_args.append(arg)
+        for key, value in kwargs.items():
+            if isinstance(value, cudf.DataFrame):
+                new_kwargs[key] = value.to_pandas()
+            else:
+                new_kwargs[key] = value
+        try:
+            result = func(*new_args, **new_kwargs)
+            if isinstance(result, cudf.DataFrame):
+                result = result.to_pandas()
+        except Exception as e:
+            logger.exception(f"An error occurred while running {func.__name__}. Exception: {e}")
+            raise e
+        return result
+    return wrapper
+
+
+def safe_gpu_dataframes(func, engine_in=None, engine_out=None):
+    """Decorator function that safely wraps methods given the engine, 
+    specifically flexibility in what part of pipeline to convert to or from pd or cudf
+    engine_in asserts the dtype of the input (converting if necessary)
+    while engine_out asserts the output dtype
+    """
+    def wrapper(*args, **kwargs):
+        new_args = []
+        new_kwargs = {}
+        for arg in args:
+            if isinstance(arg, cudf.DataFrame) and engine_in == "cuml":
+                new_args.append(arg)
+            elif isinstance(arg, pd.DataFrame) and engine_in == "pandas":
+                new_args.append(arg)
+            elif isinstance(arg, cudf.DataFrame) and engine_in == "pandas":
+                new_args.append(arg.to_pandas())
+            elif isinstance(arg, pd.DataFrame) and engine_in == "cuml":
+                new_args.append(cudf.from_pandas(arg))
+            else:
+                new_args.append(arg)
+        for key, value in kwargs.items():
+            if isinstance(value, cudf.DataFrame) and engine_in == "cuml":
+                new_kwargs[key] = value
+            elif isinstance(value, pd.DataFrame) and engine_in == "pandas":
+                new_kwargs[key] = value.to_pandas()
+            elif isinstance(value, cudf.DataFrame) and engine_in == "pandas":
+                new_kwargs[key] = value.to_pandas()
+            elif isinstance(value, pd.DataFrame) and engine_in == "cuml":
+                new_kwargs[key] = cudf.from_pandas(value)
+            else:
+                new_kwargs[key] = value
+        try:
+            result = func(*new_args, **new_kwargs)
+            if isinstance(result, cudf.DataFrame) and engine_out == "cuml":
+                result = result
+            elif isinstance(result, pd.DataFrame) and engine_out == "pandas":
+                result = result
+            elif isinstance(result, cudf.DataFrame) and engine_out == "pandas":
+                result = result.to_pandas()
+            elif isinstance(result, pd.DataFrame) and engine_out == "cuml":
+                result = cudf.from_pandas(result)
+            else:
+                raise ValueError("Unknown engine specified.")
+        except Exception as e:
+            logger.exception(f"An error occurred while running {func.__name__}. Exception: {e}")
+            raise e
+        return result
+    return wrapper
 ###############################################################################
 
 
@@ -165,6 +259,7 @@ class UMAPMixin(MIXIN_BASE):
 
     def __init__(self, *args, **kwargs):
         self.umap_initialized = False
+        self.engine = 'umap_learn'
 
     def umap_lazy_init(
         self,
@@ -217,6 +312,7 @@ def umap_lazy_init(
             self.engine = engine_resolved
             self.suffix = suffix
 
+    @safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
     def _check_target_is_one_dimensional(self, y: Union[pd.DataFrame, None]):
         if y is None:
             return None
@@ -231,6 +327,7 @@ def _check_target_is_one_dimensional(self, y: Union[pd.DataFrame, None]):
             )
             return None
 
+    @safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
     def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         if self._umap is None:
             raise ValueError("UMAP is not initialized")
@@ -261,6 +358,7 @@ def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         logger.info(f" - or {X.shape[0]/mins:.2f} rows per minute")
         return self
 
+    @safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
     def umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         if self._umap is None:
             raise ValueError("UMAP is not initialized")
@@ -269,6 +367,7 @@ def umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = Non
         emb = self._bundle_embedding(emb, index=X.index)
         return emb
 
+    @safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
     def transform_umap(  # noqa: E303
         self, df: pd.DataFrame, ydf: pd.DataFrame, kind: str = "nodes"
     ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
@@ -281,6 +380,7 @@ def transform_umap(  # noqa: E303
         emb = self._bundle_embedding(emb, index=df.index)
         return emb, x, y
 
+    @safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
     def _bundle_embedding(self, emb, index):
         # Converts Embedding into dataframe and takes care if emb.dim > 2
         if emb.shape[1] == 2 and 'cudf.core.dataframe' not in str(getmodule(emb)):
@@ -297,6 +397,7 @@ def _bundle_embedding(self, emb, index):
                 emb.columns=columns
         return emb
 
+    @safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
     def _process_umap(
         self,
         res,
@@ -499,7 +600,7 @@ def umap(
             if isinstance(X_, pd.DataFrame):
                 index_to_nodes_dict = dict(zip(range(len(nodes)), nodes))
             elif 'cudf.core.dataframe' in str(getmodule(X_)):
-                index_to_nodes_dict = nodes
+                index_to_nodes_dict = nodes  # {}?
 
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs
@@ -549,7 +650,7 @@ def umap(
                 "kind should be one of `nodes` or `edges` unless"
                 "you are passing explicit matrices"
             )
-            if X is not None and isinstance(X, pd.DataFrame):
+            if X is not None and isinstance(X, pd.DataFrame) or '':
                 logger.info("New Matrix `X` passed in for UMAP-ing")
                 xy = res.umap_fit_transform(X, y)
                 res._xy = xy
@@ -579,6 +680,7 @@ def umap(
         if not inplace:
             return res
 
+    #@safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
     def _bind_xy_from_umap(
         self,
         res: Any,
@@ -638,6 +740,7 @@ def filter_weighted_edges(
     ):
         """
         Filter edges based on _weighted_edges_df (ex: from .umap())
+
         """
         if inplace:
             res = self

From c23529baff61e4c681a160bd233d3306c1fdf99b Mon Sep 17 00:00:00 2001
From: Tanmoy <tanmoyf2@gmail.com>
Date: Fri, 10 Mar 2023 19:03:31 +0530
Subject: [PATCH 215/432] rst change PlotterBase to plotter

---
 docs/source/graphistry.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/graphistry.rst b/docs/source/graphistry.rst
index a6fdf5cf15..ee7acf9851 100644
--- a/docs/source/graphistry.rst
+++ b/docs/source/graphistry.rst
@@ -12,7 +12,7 @@ Layout & Plugins
 Plotter Module
 ==================
 
-.. automodule:: graphistry.PlotterBase
+.. automodule:: graphistry.plotter
     :members:
     :undoc-members:
     :show-inheritance:

From c79fac68ce0e8b331cfeab161189638840add348 Mon Sep 17 00:00:00 2001
From: Tanmoy <tanmoyf2@gmail.com>
Date: Fri, 10 Mar 2023 19:41:13 +0530
Subject: [PATCH 216/432] add graphistry.compute to graphistry.rst

---
 docs/source/graphistry.rst | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/docs/source/graphistry.rst b/docs/source/graphistry.rst
index ee7acf9851..e399710768 100644
--- a/docs/source/graphistry.rst
+++ b/docs/source/graphistry.rst
@@ -47,6 +47,13 @@ Semantic Search
     :members:
     :undoc-members:
     :show-inheritance:
+    
+Compute
+==================
+.. automodule:: graphistry.compute
+   :members:
+   :undoc-members:
+   :show-inheritance:
 
 DBScan
 ==================

From fbc33e3d0d82d78b91eceb15b71d3046f6afcfad Mon Sep 17 00:00:00 2001
From: Tanmoy <tanmoyf2@gmail.com>
Date: Fri, 10 Mar 2023 19:52:07 +0530
Subject: [PATCH 217/432] Delete graphistry.compute.rst

---
 docs/source/graphistry.compute.rst | 29 -----------------------------
 1 file changed, 29 deletions(-)
 delete mode 100644 docs/source/graphistry.compute.rst

diff --git a/docs/source/graphistry.compute.rst b/docs/source/graphistry.compute.rst
deleted file mode 100644
index 6ea4bdedbd..0000000000
--- a/docs/source/graphistry.compute.rst
+++ /dev/null
@@ -1,29 +0,0 @@
-graphistry.layout package
-=========================
-
-Subpackages
------------
-
-.. toctree::
-   :maxdepth: 4
-
-
-Submodules
-----------
-
-graphistry.compute.ComputeMixin module
-------------------------------------------------
-
-.. automodule:: graphistry.compute.ComputeMixin
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-
-Module contents
----------------
-
-.. automodule:: graphistry.compute
-   :members:
-   :undoc-members:
-   :show-inheritance:

From 35345ff0d936831eb97bc0e5378920aec851944f Mon Sep 17 00:00:00 2001
From: Tanmoy <tanmoyf2@gmail.com>
Date: Fri, 10 Mar 2023 19:58:56 +0530
Subject: [PATCH 218/432] Update modules.rst

---
 docs/source/modules.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/modules.rst b/docs/source/modules.rst
index 71e0a12335..92efcdb8bc 100644
--- a/docs/source/modules.rst
+++ b/docs/source/modules.rst
@@ -1,5 +1,5 @@
-.. doc
-.. ===
+Modules
+===========
 
 .. .. toctree::
 ..    :maxdepth: 4

From 811d3e576a00bef859f913ea8a4291ea0cb63744 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 10 Mar 2023 20:07:57 +0530
Subject: [PATCH 219/432] add graphistry.compute to toctree

---
 docs/source/graphistry.compute.rst | 29 +++++++++++++++++++++++++++++
 docs/source/graphistry.rst         |  7 -------
 docs/source/index.rst              |  1 +
 3 files changed, 30 insertions(+), 7 deletions(-)
 create mode 100644 docs/source/graphistry.compute.rst

diff --git a/docs/source/graphistry.compute.rst b/docs/source/graphistry.compute.rst
new file mode 100644
index 0000000000..6ea4bdedbd
--- /dev/null
+++ b/docs/source/graphistry.compute.rst
@@ -0,0 +1,29 @@
+graphistry.layout package
+=========================
+
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+
+Submodules
+----------
+
+graphistry.compute.ComputeMixin module
+------------------------------------------------
+
+.. automodule:: graphistry.compute.ComputeMixin
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: graphistry.compute
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/source/graphistry.rst b/docs/source/graphistry.rst
index e399710768..bfefab4a32 100644
--- a/docs/source/graphistry.rst
+++ b/docs/source/graphistry.rst
@@ -48,13 +48,6 @@ Semantic Search
     :undoc-members:
     :show-inheritance:
     
-Compute
-==================
-.. automodule:: graphistry.compute
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
 DBScan
 ==================
 .. automodule:: graphistry.compute.cluster
diff --git a/docs/source/index.rst b/docs/source/index.rst
index b45393c266..7c9b0eeeac 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -11,6 +11,7 @@ Here in our docstrings you can find useful packages, modules, and commands to ma
    :maxdepth: 3
 
    graphistry
+   graphistry.compute
    modules
 
 Indices and tables

From d6806cfb39b1d40768bd5ae9ac1ddda44c88131b Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 10 Mar 2023 20:13:32 +0530
Subject: [PATCH 220/432] resolve short underline error

---
 docs/source/graphistry.rst | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/source/graphistry.rst b/docs/source/graphistry.rst
index bfefab4a32..d548996154 100644
--- a/docs/source/graphistry.rst
+++ b/docs/source/graphistry.rst
@@ -18,7 +18,7 @@ Plotter Module
     :show-inheritance:
 
 Pygraphistry Module
-==================
+===================
 
 .. automodule:: graphistry.pygraphistry
     :members:
@@ -56,7 +56,7 @@ DBScan
     :show-inheritance:
 
 Arrow uploader Module
-==================
+=====================
 
 .. automodule:: graphistry.arrow_uploader
     :members:
@@ -64,7 +64,7 @@ Arrow uploader Module
     :show-inheritance:
 
 Arrow File Uploader Module
-==================
+==========================
 
 .. automodule:: graphistry.ArrowFileUploader
     :members:

From 0b301caf7955f170b964be21a064f32101192d01 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 10 Mar 2023 20:28:16 +0530
Subject: [PATCH 221/432] test1: resolve blank line error

---
 graphistry/umap_utils.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 633f941c55..1b61b9a9d3 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -479,7 +479,9 @@ def umap(
                     default True.
             :verbose: whether to print out extra information, default False.
         :return: self, with attributes set with new data
+
         """
+
         if engine == UMAP_LEARN:
             assert_imported()
         elif engine == CUML:

From 24638cf64cf3d9a8f1a376d9f17c68c3ffb517fb Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 10 Mar 2023 20:32:14 +0530
Subject: [PATCH 222/432] test2: resolve blank line error

---
 graphistry/umap_utils.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 1b61b9a9d3..cc60c209fe 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -479,9 +479,7 @@ def umap(
                     default True.
             :verbose: whether to print out extra information, default False.
         :return: self, with attributes set with new data
-
         """
-
         if engine == UMAP_LEARN:
             assert_imported()
         elif engine == CUML:
@@ -622,6 +620,7 @@ def umap(
         if not inplace:
             return res
 
+
     def _bind_xy_from_umap(
         self,
         res: Any,

From 9e0c3d2056b764f6981ca664b86bb815f5973e4b Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 10 Mar 2023 20:46:07 +0530
Subject: [PATCH 223/432] test3: resolve blank line error

---
 graphistry/feature_utils.py | 1 +
 graphistry/umap_utils.py    | 1 -
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index a068ddeb7b..0abcfb6c52 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1776,6 +1776,7 @@ def scale(self, X=None, y=None, return_pipeline=False, *args, **kwargs):
                 :y: pd.DataFrame of target features
                 :kind: str, one of 'nodes' or 'edges'
                 *args, **kwargs: passed to smart_scaler pipeline
+
             returns:
                 scaled X, y
         """
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index cc60c209fe..633f941c55 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -620,7 +620,6 @@ def umap(
         if not inplace:
             return res
 
-
     def _bind_xy_from_umap(
         self,
         res: Any,

From ac150701f0fb4da74830c583a84bf44cfb6da489 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 10 Mar 2023 20:56:06 +0530
Subject: [PATCH 224/432] doc fix

---
 graphistry/feature_utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 0abcfb6c52..01404eb7df 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2410,8 +2410,8 @@ def featurize(
         memoize: bool = True,
         verbose: bool = False,
     ):
-        r"""
-            Featurize Nodes or Edges of the underlying nodes/edges DataFrames.
+        """
+        Featurize Nodes or Edges of the underlying nodes/edges DataFrames.
         ______________________________________________________________________
 
         :param kind: specify whether to featurize `nodes` or `edges`.

From f53710c18f9dffed22d43dd8c69df281d19fe626 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 10 Mar 2023 20:58:58 +0530
Subject: [PATCH 225/432] doc fix

---
 graphistry/feature_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 01404eb7df..c77103773c 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2410,7 +2410,7 @@ def featurize(
         memoize: bool = True,
         verbose: bool = False,
     ):
-        """
+        r"""
         Featurize Nodes or Edges of the underlying nodes/edges DataFrames.
         ______________________________________________________________________
 

From a8a9dc112b52467bde9220bfdb3054dbf42bfce3 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 10 Mar 2023 21:08:11 +0530
Subject: [PATCH 226/432] doc fix

---
 graphistry/feature_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index c77103773c..75b733d32a 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2412,7 +2412,7 @@ def featurize(
     ):
         r"""
         Featurize Nodes or Edges of the underlying nodes/edges DataFrames.
-        ______________________________________________________________________
+        __________________________________________________________________
 
         :param kind: specify whether to featurize `nodes` or `edges`.
                 Edge featurization includes a pairwise

From 8618b1a5c09817ca8d1e850ac1b2be5172c831f7 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 10 Mar 2023 21:14:03 +0530
Subject: [PATCH 227/432] doc fix

---
 graphistry/feature_utils.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 75b733d32a..6a12ff37a8 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2818,8 +2818,10 @@ def get_matrix(self, columns: Optional[Union[List, str]] = None, kind: str = 'no
                     => ['basket_price_total', 'conversion_percent', 'CTR_percent', 'CVR_percent']
 
                 # not as useful for sbert features. 
+
             Caveats:
                 - if you have a column name that is a substring of another column name, you may get unexpected results.
+
         Args:
             :columns (Union[List, str]): list of column names or a single column name that may exist in columns 
                 of the feature matrix. If None, returns original feature matrix

From 3a6611d455c079dd9de8b7cd162e44af452cc867 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 10 Mar 2023 21:20:54 +0530
Subject: [PATCH 228/432] doc fix

---
 graphistry/feature_utils.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 6a12ff37a8..90ef1e6850 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -430,7 +430,8 @@ def check_if_textual_column(
     min_words: float = 2.5,
 ) -> bool:
     """
-        Checks if `col` column of df is textual or not using basic heuristics
+    Checks if `col` column of df is textual or not using basic heuristics
+
     __________________________________________________________________________
 
     :param df: DataFrame
@@ -2223,6 +2224,7 @@ def transform(self, df: pd.DataFrame,
                 :n_neighbors: int, if return_graph is True, will use this value for n_neighbors in Nearest Neighbors search
                 :scaled: bool, if True, will use scaled transformation of data set during featurization, default True
                 :verbose: bool, if True, will print metadata about the graph construction, default False
+
             **Returns:**
 
                     X, y: pd.DataFrame, transformed data if return_graph is False

From 82d1d3b59dea7c4c4bc2074971535f1523a29c79 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 10 Mar 2023 21:39:25 +0530
Subject: [PATCH 229/432] doc fix

---
 graphistry/compute/cluster.py |  2 +-
 graphistry/feature_utils.py   | 27 +++++++++++++--------------
 graphistry/text_utils.py      |  1 +
 graphistry/umap_utils.py      |  1 +
 4 files changed, 16 insertions(+), 15 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index f19fbfbe38..eb18cc275e 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -243,7 +243,7 @@ def dbscan(
         Useful:
             Enriching the graph with cluster labels from UMAP is useful for visualizing clusters in the graph by color, size, etc,
             as well as assessing metrics per cluster, e.g.
-             https://github.com/graphistry/pygraphistry/blob/master/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
+            https://github.com/graphistry/pygraphistry/blob/master/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
 
         Args:
             :min_dist float: The maximum distance between two samples for them to be considered as in the same neighborhood.
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 90ef1e6850..fc29a0e720 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -224,9 +224,8 @@ def features_without_target(
     df: pd.DataFrame, y: Optional[Union[List, str, pd.DataFrame]] = None
 ) -> pd.DataFrame:
     """
-        Checks if y DataFrame column name is in df, and removes it
-        from df if so
-    ___________________________________________________________________
+    Checks if y DataFrame column name is in df, and removes it from df if so
+    ________________________________________________________________________
 
     :param df: model DataFrame
     :param y: target DataFrame
@@ -398,7 +397,7 @@ def is_dataframe_all_numeric(df: pd.DataFrame) -> bool:
 
 def find_bad_set_columns(df: pd.DataFrame, bad_set: List = ["[]"]):
     """
-        Finds columns that if not coerced to strings, will break processors.
+    Finds columns that if not coerced to strings, will break processors.
     -------------------------------------------------------------------------
     :param df: DataFrame
     :param bad_set: List of strings to look for.
@@ -431,7 +430,6 @@ def check_if_textual_column(
 ) -> bool:
     """
     Checks if `col` column of df is textual or not using basic heuristics
-
     __________________________________________________________________________
 
     :param df: DataFrame
@@ -541,6 +539,7 @@ def get_preprocessing_pipeline(
         Helper function for imputing and scaling np.ndarray data
         using different scaling transformers.
     -----------------------------------------------------------------
+
     :param X: np.ndarray
     :param impute: whether to run imputing or not
     :param use_scaler: string in None or
@@ -623,11 +622,12 @@ def fit_pipeline(
     X: pd.DataFrame, transformer, keep_n_decimals: int = 5
 ) -> pd.DataFrame:
     """
-     Helper to fit DataFrame over transformer pipeline.
-     Rounds resulting matrix X by keep_n_digits if not 0,
-     which helps for when transformer pipeline is scaling or imputer
-     which sometime introduce small negative numbers,
-     and umap metrics like Hellinger need to be positive
+    Helper to fit DataFrame over transformer pipeline.
+    Rounds resulting matrix X by keep_n_digits if not 0,
+    which helps for when transformer pipeline is scaling or imputer
+    which sometime introduce small negative numbers,
+    and umap metrics like Hellinger need to be positive
+
     :param X: DataFrame to transform.
     :param transformer: Pipeline object to fit and transform
     :param keep_n_decimals: Int of how many decimal places to keep in rounded transformed data
@@ -871,8 +871,8 @@ def process_dirty_dataframes(
     Union[SuperVectorizer, FunctionTransformer],
 ]:
     """
-        Dirty_Cat encoder for record level data. Will automatically turn
-        inhomogeneous dataframe into matrix using smart conversion tricks.
+    Dirty_Cat encoder for record level data. Will automatically turn
+    inhomogeneous dataframe into matrix using smart conversion tricks.
     ______________________________________________________________________
 
     :param ndf: node DataFrame
@@ -1248,8 +1248,7 @@ def encode_edges(edf, src, dst, mlb, fit=False):
         src (string): source column
         dst (string): destination column
         mlb (sklearn): multilabelBinarizer
-        fit (bool, optional): If true, fits multilabelBinarizer.
-            Defaults to False.
+        fit (bool, optional): If true, fits multilabelBinarizer. Defaults to False.
     Returns:
         tuple: pd.DataFrame, multilabelBinarizer
     """
diff --git a/graphistry/text_utils.py b/graphistry/text_utils.py
index d9dc42060f..9a1abb77f5 100644
--- a/graphistry/text_utils.py
+++ b/graphistry/text_utils.py
@@ -137,6 +137,7 @@ def search(
             
             If an index is not yet built, it is generated `g2.build_index()` on the fly at search time.
             Otherwise, can set `g2.build_index()` to build it ahead of time.
+
         Args:
             :query (str): natural language query.
             :cols (list or str, optional): if fuzzy=False, select which column to query.
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 633f941c55..6c53b53279 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -478,6 +478,7 @@ def umap(
             :memoize: whether to memoize the results of this method,
                     default True.
             :verbose: whether to print out extra information, default False.
+
         :return: self, with attributes set with new data
         """
         if engine == UMAP_LEARN:

From 3fe4ceee0b36c709495769375ef5d00306a05c77 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 10 Mar 2023 21:45:52 +0530
Subject: [PATCH 230/432] add versioneer

---
 docs/source/index.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/index.rst b/docs/source/index.rst
index 7c9b0eeeac..82dbdda61d 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -13,6 +13,7 @@ Here in our docstrings you can find useful packages, modules, and commands to ma
    graphistry
    graphistry.compute
    modules
+   versioneer
 
 Indices and tables
 ==================

From 81994714a99afaca819d1bae5ec30eb6059beefb Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 10 Mar 2023 21:49:46 +0530
Subject: [PATCH 231/432] add versioneer

---
 docs/source/versioneer.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/versioneer.rst b/docs/source/versioneer.rst
index a34edfc48d..804c171da3 100644
--- a/docs/source/versioneer.rst
+++ b/docs/source/versioneer.rst
@@ -1,2 +1,2 @@
-.. versioneer module
-.. =================
+versioneer module
+=================

From d01e2bf85f0b939e6cade35ebec4ddafd6dc390c Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Fri, 10 Mar 2023 12:39:39 -0500
Subject: [PATCH 232/432] fix(docstr): removed unneccesary indents/sections

---
 graphistry/feature_utils.py | 27 +++++++++------------------
 graphistry/umap_utils.py    |  7 ++-----
 2 files changed, 11 insertions(+), 23 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index fc29a0e720..39c3ff855b 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -223,9 +223,7 @@ def safe_divide(a, b):
 def features_without_target(
     df: pd.DataFrame, y: Optional[Union[List, str, pd.DataFrame]] = None
 ) -> pd.DataFrame:
-    """
-    Checks if y DataFrame column name is in df, and removes it from df if so
-    ________________________________________________________________________
+    """Checks if y DataFrame column name is in df, and removes it from df if so
 
     :param df: model DataFrame
     :param y: target DataFrame
@@ -396,9 +394,8 @@ def is_dataframe_all_numeric(df: pd.DataFrame) -> bool:
 
 
 def find_bad_set_columns(df: pd.DataFrame, bad_set: List = ["[]"]):
-    """
-    Finds columns that if not coerced to strings, will break processors.
-    -------------------------------------------------------------------------
+    """Finds columns that if not coerced to strings, will break processors.
+
     :param df: DataFrame
     :param bad_set: List of strings to look for.
     :return: list
@@ -428,9 +425,7 @@ def check_if_textual_column(
     confidence: float = 0.35,
     min_words: float = 2.5,
 ) -> bool:
-    """
-    Checks if `col` column of df is textual or not using basic heuristics
-    __________________________________________________________________________
+    """Checks if `col` column of df is textual or not using basic heuristics
 
     :param df: DataFrame
     :param col: column name
@@ -469,9 +464,8 @@ def check_if_textual_column(
 def get_textual_columns(
     df: pd.DataFrame, min_words: float = 2.5
 ) -> List:
-    """
-        Collects columns from df that it deems are textual.
-    _____________________________________________________________________
+    """Collects columns from df that it deems are textual.
+   
 
     :param df: DataFrame
     :return: list of columns names
@@ -795,7 +789,7 @@ def encoder(X, use_scaler):  # noqa: E301
 
 def get_cardinality_ratio(df: pd.DataFrame):
     """Calculates ratio of unique values to total number of rows of DataFrame
-    -------------------------------------------------------------------------
+   
     :param df: DataFrame
     """
     ratios = {}
@@ -1038,7 +1032,6 @@ def process_nodes_dataframes(
     """
         Automatic Deep Learning Embedding/ngrams of Textual Features,
         with the rest of the columns taken care of by dirty_cat
-    _________________________________________________________________________
 
     :param df: pandas DataFrame of data
     :param y: pandas DataFrame of targets
@@ -2411,10 +2404,8 @@ def featurize(
         memoize: bool = True,
         verbose: bool = False,
     ):
-        r"""
-        Featurize Nodes or Edges of the underlying nodes/edges DataFrames.
-        __________________________________________________________________
-
+        r"""Featurize Nodes or Edges of the underlying nodes/edges DataFrames.
+       
         :param kind: specify whether to featurize `nodes` or `edges`.
                 Edge featurization includes a pairwise
                 src-to-dst feature block using a MultiLabelBinarizer,
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 6c53b53279..46d5cbd8b6 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -135,7 +135,6 @@ def umap_graph_to_weighted_edges(umap_graph, engine, is_legacy, cfg=config):
 class UMAPMixin(MIXIN_BASE):
     """
     UMAP Mixin for automagic UMAPing
-
     """
     # FIXME where is this used? 
     _umap_memoize: WeakValueDictionary = WeakValueDictionary()
@@ -424,10 +423,8 @@ def umap(
         verbose: bool = False,
         **featurize_kwargs,
     ):
-        """
-            UMAP the featurized nodes or edges data,
-            or pass in your own X, y (optional) dataframes of values
-            
+        """UMAP the featurized nodes or edges data, or pass in your own X, y (optional) dataframes of values
+        
         Example
         -------
         >>> import graphistry   

From 812f5c7a76b65be29d39fb7f2abe5b0d3812ca0c Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Fri, 10 Mar 2023 12:50:23 -0500
Subject: [PATCH 233/432] fix(docstr): removed unneccessary spacing

---
 graphistry/feature_utils.py | 17 ++++-------------
 graphistry/umap_utils.py    |  4 ++--
 2 files changed, 6 insertions(+), 15 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 39c3ff855b..a728e001ca 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -466,7 +466,6 @@ def get_textual_columns(
 ) -> List:
     """Collects columns from df that it deems are textual.
    
-
     :param df: DataFrame
     :return: list of columns names
     """
@@ -529,10 +528,7 @@ def get_preprocessing_pipeline(
     encode: str = "ordinal",
     strategy: str = "quantile",
 ) -> Pipeline:  # noqa
-    """
-        Helper function for imputing and scaling np.ndarray data
-        using different scaling transformers.
-    -----------------------------------------------------------------
+    """Helper function for imputing and scaling np.ndarray data using different scaling transformers.
 
     :param X: np.ndarray
     :param impute: whether to run imputing or not
@@ -864,10 +860,7 @@ def process_dirty_dataframes(
     Union[SuperVectorizer, FunctionTransformer],
     Union[SuperVectorizer, FunctionTransformer],
 ]:
-    """
-    Dirty_Cat encoder for record level data. Will automatically turn
-    inhomogeneous dataframe into matrix using smart conversion tricks.
-    ______________________________________________________________________
+    """Dirty_Cat encoder for record level data. Will automatically turn inhomogeneous dataframe into matrix using smart conversion tricks.
 
     :param ndf: node DataFrame
     :param y: target DataFrame or series
@@ -2719,10 +2712,8 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
         memoize: bool = True,
         verbose: bool = False,
     ) -> Tuple[pd.DataFrame, Optional[pd.DataFrame], MIXIN_BASE]:
-        """
-        helper method gets edge feature and target matrix if X, y
-        are not specified
-        -----------------------------------------------------------
+        """helper method gets edge feature and target matrix if X, y are not specified
+       
         :param X: Data Matrix
         :param y: target, default None
         :return: data `X` and `y`
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 46d5cbd8b6..46d961d780 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -426,14 +426,14 @@ def umap(
         """UMAP the featurized nodes or edges data, or pass in your own X, y (optional) dataframes of values
         
         Example
-        -------
+     
         >>> import graphistry   
         >>> g = graphistry.nodes(pd.DataFrame({'node': [0,1,2], 'data': [1,2,3], 'meta': ['a', 'b', 'c']}))
         >>> g2 = g.umap(n_components=3, spread=1.0, min_dist=0.1, n_neighbors=12, negative_sample_rate=5, local_connectivity=1, repulsion_strength=1.0, metric='euclidean', suffix='', play=0, encode_position=True, encode_weight=True, dbscan=False, engine='auto', feature_engine='auto', inplace=False, memoize=True, verbose=False)
         >>> g2.plot()
         
         Parameters
-        ----------
+ 
             :X: either a dataframe ndarray of features, or column names to featurize
             :y: either an dataframe ndarray of targets, or column names to featurize
                     targets

From b88ce77193f3846103b4063fc3b1876349a5a77b Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Fri, 10 Mar 2023 13:02:46 -0500
Subject: [PATCH 234/432] fix(docstr): fixed unindent

---
 graphistry/feature_utils.py | 29 ++++++++++++-----------------
 1 file changed, 12 insertions(+), 17 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index a728e001ca..6cc9708507 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2194,8 +2194,7 @@ def transform(self, df: pd.DataFrame,
                   return_graph: bool = True,
                   scaled: bool = True,
                   verbose: bool = False):
-        """
-            Transform new data and append to existing graph, or return dataframes
+        """Transform new data and append to existing graph, or return dataframes
         
             **args:**
 
@@ -2212,8 +2211,8 @@ def transform(self, df: pd.DataFrame,
 
             **Returns:**
 
-                    X, y: pd.DataFrame, transformed data if return_graph is False
-                    or a graphistry Plottable with inferred edges if return_graph is True
+                X, y: pd.DataFrame, transformed data if return_graph is False
+                or a graphistry Plottable with inferred edges if return_graph is True
         """
         if kind == "nodes":
             X, y_ = self._transform("_node_encoder", df, y, scaled=scaled)
@@ -2268,7 +2267,6 @@ def scale(
                 # fit some other pipeline
                 clf.fit(X_scaled, y_scaled)
 
-
             **Args:**
 
                 :df: pd.DataFrame, raw data to transform, if None, will use data from featurization fit
@@ -2288,9 +2286,7 @@ def scale(
 
             **Returns:**
 
-                    (X, y) transformed data if return_graph is False
-                    or a graph with inferred edges if return_graph is True,
-                    or (X, y, scaler, scaler_target) if return_scalers is True
+                (X, y) transformed data if return_graph is False or a graph with inferred edges if return_graph is True, or (X, y, scaler, scaler_target) if return_scalers is True
         """
                 
         if df is None:  # use the original data
@@ -2623,7 +2619,6 @@ def _featurize_or_get_nodes_dataframe_if_X_is_None(
         are not specified.
         if X, y are specified will set them as `_node_target` and
         `_node_target` attributes
-        -----------------------------------------------------------
         """
 
         res = self.bind()
@@ -2805,15 +2800,15 @@ def get_matrix(self, columns: Optional[Union[List, str]] = None, kind: str = 'no
             Caveats:
                 - if you have a column name that is a substring of another column name, you may get unexpected results.
 
-        Args:
-            :columns (Union[List, str]): list of column names or a single column name that may exist in columns 
-                of the feature matrix. If None, returns original feature matrix
-            :kind (str, optional): Node or Edge features. Defaults to 'nodes'.
-            :target (bool, optional): If True, returns the target matrix. Defaults to False.
+            Args:
+                :columns (Union[List, str]): list of column names or a single column name that may exist in columns 
+                    of the feature matrix. If None, returns original feature matrix
+                :kind (str, optional): Node or Edge features. Defaults to 'nodes'.
+                :target (bool, optional): If True, returns the target matrix. Defaults to False.
 
-        Returns:
-            pd.DataFrame: feature matrix with only the columns that contain the string `column_part` in their name.
-        """
+            Returns:
+                pd.DataFrame: feature matrix with only the columns that contain the string `column_part` in their name.
+            """
         if target:
             X = self._get_target(kind)
         else:

From 940be4eb12bb424a01b3237ddd3298b566b3e4ac Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Fri, 10 Mar 2023 13:15:02 -0500
Subject: [PATCH 235/432] fix(docstr): removed line/spacing

---
 graphistry/feature_utils.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 6cc9708507..8c0166c01f 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -270,11 +270,7 @@ def remove_node_column_from_symbolic(X_symbolic, node):
 
 
 def remove_internal_namespace_if_present(df: pd.DataFrame):
-    """
-        Some tranformations below add columns to the DataFrame,
-        this method removes them before featurization
-        Will not drop if suffix is added during UMAP-ing
-    ______________________________________________________________
+    """Some tranformations below add columns to the DataFrame, this method removes them before featurization will not drop if suffix is added during UMAP-ing
 
     :param df: DataFrame
     :return: DataFrame with dropped columns in reserved namespace

From fdb7d2b81dd34829e724b047f08061056cfc265b Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Sun, 12 Mar 2023 07:24:17 +0900
Subject: [PATCH 236/432] Revert "umap cudf"

This reverts commit 0a4209068fd30c895d4929ec5e59811038a40835.
---
 graphistry/umap_utils.py | 14 +++++---------
 1 file changed, 5 insertions(+), 9 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 99697d8922..1bf18a4e72 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -596,11 +596,11 @@ def umap(
 
             logger.debug("umap X_: %s", X_)
             logger.debug("umap y_: %s", y_)
-            logger.debug("data is type :: %s", (type(X_)))
-            if isinstance(X_, pd.DataFrame):
+
+            if isinstance(X_,pd.DataFrame):
                 index_to_nodes_dict = dict(zip(range(len(nodes)), nodes))
             elif 'cudf.core.dataframe' in str(getmodule(X_)):
-                index_to_nodes_dict = nodes  # {}?
+                index_to_nodes_dict = nodes
 
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs
@@ -699,12 +699,8 @@ def _bind_xy_from_umap(
         else:
             emb = res._edge_embedding
             
-        if type(df) == type(emb):
-            df[x_name] = emb.values.T[0]
-            df[y_name] = emb.values.T[1]
-        elif isinstance(df, pd.DataFrame) and 'cudf.core.dataframe' in str(getmodule(emb)):
-            df[x_name] = emb.to_numpy().T[0]
-            df[y_name] = emb.to_numpy().T[1]
+        df[x_name] = emb.values.T[0]
+        df[y_name] = emb.values.T[1]
 
         res = res.nodes(df) if kind == "nodes" else res.edges(df)
 

From 17fd3169fe5a0475f6df71833981f2f3284e4b36 Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Sun, 12 Mar 2023 07:24:31 +0900
Subject: [PATCH 237/432] Revert "umap cudf"

This reverts commit 0a4209068fd30c895d4929ec5e59811038a40835.
---
 graphistry/umap_utils.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 1bf18a4e72..29e448027f 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -600,7 +600,8 @@ def umap(
             if isinstance(X_,pd.DataFrame):
                 index_to_nodes_dict = dict(zip(range(len(nodes)), nodes))
             elif 'cudf.core.dataframe' in str(getmodule(X_)):
-                index_to_nodes_dict = nodes
+                import cudf
+                index_to_nodes_dict = cudf.DataFrame(nodes).reset_index()
 
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs

From af399f687270c379b1bccb52aef223456bd8b7d3 Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Sun, 12 Mar 2023 07:26:00 +0900
Subject: [PATCH 238/432] revert b4 alex borq

---
 graphistry/umap_utils.py | 120 ++++-----------------------------------
 1 file changed, 10 insertions(+), 110 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 29e448027f..5c92661ae1 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -12,8 +12,6 @@
 from .PlotterBase import Plottable, WeakValueDictionary
 from .util import check_set_memoize, setup_logger
 
-#logger = logging.getLogger(__name__)
-
 logger = setup_logger(name=__name__, verbose=config.VERBOSE)
 
 if TYPE_CHECKING:
@@ -100,98 +98,6 @@ def resolve_umap_engine(
     )
 
 
-logger = logging.getLogger(__name__)
-
-def convert_pandas_to_cudf(func):
-    def wrapper(*args, **kwargs):
-        new_args = []
-        new_kwargs = {}
-        for arg in args:
-            if isinstance(arg, pd.DataFrame):
-                new_args.append(cudf.DataFrame.from_pandas(arg))
-            else:
-                new_args.append(arg)
-        for key, value in kwargs.items():
-            if isinstance(value, pd.DataFrame):
-                new_kwargs[key] = cudf.DataFrame.from_pandas(value)
-            else:
-                new_kwargs[key] = value
-        return func(*new_args, **new_kwargs)
-    return wrapper
-
-def convert_cudf_to_pandas(func):
-    def wrapper(*args, **kwargs):
-        new_args = []
-        new_kwargs = {}
-        for arg in args:
-            if isinstance(arg, cudf.DataFrame):
-                new_args.append(arg.to_pandas())
-            else:
-                new_args.append(arg)
-        for key, value in kwargs.items():
-            if isinstance(value, cudf.DataFrame):
-                new_kwargs[key] = value.to_pandas()
-            else:
-                new_kwargs[key] = value
-        try:
-            result = func(*new_args, **new_kwargs)
-            if isinstance(result, cudf.DataFrame):
-                result = result.to_pandas()
-        except Exception as e:
-            logger.exception(f"An error occurred while running {func.__name__}. Exception: {e}")
-            raise e
-        return result
-    return wrapper
-
-
-def safe_gpu_dataframes(func, engine_in=None, engine_out=None):
-    """Decorator function that safely wraps methods given the engine, 
-    specifically flexibility in what part of pipeline to convert to or from pd or cudf
-    engine_in asserts the dtype of the input (converting if necessary)
-    while engine_out asserts the output dtype
-    """
-    def wrapper(*args, **kwargs):
-        new_args = []
-        new_kwargs = {}
-        for arg in args:
-            if isinstance(arg, cudf.DataFrame) and engine_in == "cuml":
-                new_args.append(arg)
-            elif isinstance(arg, pd.DataFrame) and engine_in == "pandas":
-                new_args.append(arg)
-            elif isinstance(arg, cudf.DataFrame) and engine_in == "pandas":
-                new_args.append(arg.to_pandas())
-            elif isinstance(arg, pd.DataFrame) and engine_in == "cuml":
-                new_args.append(cudf.from_pandas(arg))
-            else:
-                new_args.append(arg)
-        for key, value in kwargs.items():
-            if isinstance(value, cudf.DataFrame) and engine_in == "cuml":
-                new_kwargs[key] = value
-            elif isinstance(value, pd.DataFrame) and engine_in == "pandas":
-                new_kwargs[key] = value.to_pandas()
-            elif isinstance(value, cudf.DataFrame) and engine_in == "pandas":
-                new_kwargs[key] = value.to_pandas()
-            elif isinstance(value, pd.DataFrame) and engine_in == "cuml":
-                new_kwargs[key] = cudf.from_pandas(value)
-            else:
-                new_kwargs[key] = value
-        try:
-            result = func(*new_args, **new_kwargs)
-            if isinstance(result, cudf.DataFrame) and engine_out == "cuml":
-                result = result
-            elif isinstance(result, pd.DataFrame) and engine_out == "pandas":
-                result = result
-            elif isinstance(result, cudf.DataFrame) and engine_out == "pandas":
-                result = result.to_pandas()
-            elif isinstance(result, pd.DataFrame) and engine_out == "cuml":
-                result = cudf.from_pandas(result)
-            else:
-                raise ValueError("Unknown engine specified.")
-        except Exception as e:
-            logger.exception(f"An error occurred while running {func.__name__}. Exception: {e}")
-            raise e
-        return result
-    return wrapper
 ###############################################################################
 
 
@@ -259,7 +165,6 @@ class UMAPMixin(MIXIN_BASE):
 
     def __init__(self, *args, **kwargs):
         self.umap_initialized = False
-        self.engine = 'umap_learn'
 
     def umap_lazy_init(
         self,
@@ -312,7 +217,6 @@ def umap_lazy_init(
             self.engine = engine_resolved
             self.suffix = suffix
 
-    @safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
     def _check_target_is_one_dimensional(self, y: Union[pd.DataFrame, None]):
         if y is None:
             return None
@@ -327,7 +231,6 @@ def _check_target_is_one_dimensional(self, y: Union[pd.DataFrame, None]):
             )
             return None
 
-    @safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
     def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         if self._umap is None:
             raise ValueError("UMAP is not initialized")
@@ -358,7 +261,6 @@ def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         logger.info(f" - or {X.shape[0]/mins:.2f} rows per minute")
         return self
 
-    @safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
     def umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         if self._umap is None:
             raise ValueError("UMAP is not initialized")
@@ -367,7 +269,6 @@ def umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = Non
         emb = self._bundle_embedding(emb, index=X.index)
         return emb
 
-    @safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
     def transform_umap(  # noqa: E303
         self, df: pd.DataFrame, ydf: pd.DataFrame, kind: str = "nodes"
     ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
@@ -380,7 +281,6 @@ def transform_umap(  # noqa: E303
         emb = self._bundle_embedding(emb, index=df.index)
         return emb, x, y
 
-    @safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
     def _bundle_embedding(self, emb, index):
         # Converts Embedding into dataframe and takes care if emb.dim > 2
         if emb.shape[1] == 2 and 'cudf.core.dataframe' not in str(getmodule(emb)):
@@ -397,7 +297,6 @@ def _bundle_embedding(self, emb, index):
                 emb.columns=columns
         return emb
 
-    @safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
     def _process_umap(
         self,
         res,
@@ -596,12 +495,11 @@ def umap(
 
             logger.debug("umap X_: %s", X_)
             logger.debug("umap y_: %s", y_)
-
-            if isinstance(X_,pd.DataFrame):
+            logger.debug("data is type :: %s", (type(X_)))
+            if isinstance(X_, pd.DataFrame):
                 index_to_nodes_dict = dict(zip(range(len(nodes)), nodes))
             elif 'cudf.core.dataframe' in str(getmodule(X_)):
-                import cudf
-                index_to_nodes_dict = cudf.DataFrame(nodes).reset_index()
+                index_to_nodes_dict = nodes
 
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs
@@ -651,7 +549,7 @@ def umap(
                 "kind should be one of `nodes` or `edges` unless"
                 "you are passing explicit matrices"
             )
-            if X is not None and isinstance(X, pd.DataFrame) or '':
+            if X is not None and isinstance(X, pd.DataFrame):
                 logger.info("New Matrix `X` passed in for UMAP-ing")
                 xy = res.umap_fit_transform(X, y)
                 res._xy = xy
@@ -681,7 +579,6 @@ def umap(
         if not inplace:
             return res
 
-    #@safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
     def _bind_xy_from_umap(
         self,
         res: Any,
@@ -700,8 +597,12 @@ def _bind_xy_from_umap(
         else:
             emb = res._edge_embedding
             
-        df[x_name] = emb.values.T[0]
-        df[y_name] = emb.values.T[1]
+        if type(df) == type(emb):
+            df[x_name] = emb.values.T[0]
+            df[y_name] = emb.values.T[1]
+        elif isinstance(df, pd.DataFrame) and 'cudf.core.dataframe' in str(getmodule(emb)):
+            df[x_name] = emb.to_numpy().T[0]
+            df[y_name] = emb.to_numpy().T[1]
 
         res = res.nodes(df) if kind == "nodes" else res.edges(df)
 
@@ -737,7 +638,6 @@ def filter_weighted_edges(
     ):
         """
         Filter edges based on _weighted_edges_df (ex: from .umap())
-
         """
         if inplace:
             res = self

From 3f3a1f4d1f1ecc4a8480d19def043a09b85bf333 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 11 Mar 2023 16:41:19 -0800
Subject: [PATCH 239/432] adds cudf support and wraps dataframe if engine=cuml

---
 graphistry/umap_utils.py | 238 ++++++++++++++++++++++++---------------
 1 file changed, 147 insertions(+), 91 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 99697d8922..96398cb62b 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -48,6 +48,16 @@ def lazy_cuml_import_has_dependancy():
     except ModuleNotFoundError as e:
         return False, e, None
 
+def lazy_cudf_import_has_dependancy():
+    try:
+        import warnings
+
+        warnings.filterwarnings("ignore")
+        import cudf  # type: ignore
+
+        return True, "ok", cudf
+    except ModuleNotFoundError as e:
+        return False, e, None
 
 def assert_imported():
     has_dependancy_, import_exn, umap_learn = lazy_umap_import_has_dependancy()
@@ -100,98 +110,141 @@ def resolve_umap_engine(
     )
 
 
-logger = logging.getLogger(__name__)
 
-def convert_pandas_to_cudf(func):
-    def wrapper(*args, **kwargs):
-        new_args = []
+# def convert_pandas_to_cudf(func):
+#     def wrapper(*args, **kwargs):
+#         new_args = []
+#         new_kwargs = {}
+#         for arg in args:
+#             if isinstance(arg, pd.DataFrame):
+#                 new_args.append(cudf.DataFrame.from_pandas(arg))
+#             else:
+#                 new_args.append(arg)
+#         for key, value in kwargs.items():
+#             if isinstance(value, pd.DataFrame):
+#                 new_kwargs[key] = cudf.DataFrame.from_pandas(value)
+#             else:
+#                 new_kwargs[key] = value
+#         return func(*new_args, **new_kwargs)
+#     return wrapper
+
+# def convert_cudf_to_pandas(func):
+#     def wrapper(*args, **kwargs):
+#         new_args = []
+#         new_kwargs = {}
+#         for arg in args:
+#             if isinstance(arg, cudf.DataFrame):
+#                 new_args.append(arg.to_pandas())
+#             else:
+#                 new_args.append(arg)
+#         for key, value in kwargs.items():
+#             if isinstance(value, cudf.DataFrame):
+#                 new_kwargs[key] = value.to_pandas()
+#             else:
+#                 new_kwargs[key] = value
+#         try:
+#             result = func(*new_args, **new_kwargs)
+#             if isinstance(result, cudf.DataFrame):
+#                 result = result.to_pandas()
+#         except Exception as e:
+#             logger.exception(f"An error occurred while running {func.__name__}. Exception: {e}")
+#             raise e
+#         return result
+#     return wrapper
+
+
+# def safe_gpu_dataframes(func):
+#     """Decorator function that safely wraps methods given the engine, 
+#     specifically flexibility in what part of pipeline to convert to or from pd or cudf
+#     engine_in asserts the dtype of the input (converting if necessary)
+#     while engine_out asserts the output dtype
+#     """
+    
+#     from functools import wraps  # https://stackoverflow.com/questions/11731136/class-method-decorator-with-self-arguments
+
+#     @wraps(func)
+#     def dummy(self, *args, **kwargs):
+#         try:
+#             result = func(*args, **kwargs)
+#         except Exception as e:
+#             logger.exception(f"An error occurred while running {func.__name__}. Exception: {e}")
+#             raise e
+#         return result
+
+#     @wraps(func)
+#     def wrapper(self, *args, **kwargs):
+#         engine_in = self.engine_in
+#         engine_out = self.engine_out
+#         new_args = []
+#         new_kwargs = {}
+#         for arg in args:
+#             if isinstance(arg, cudf.DataFrame) and engine_in == "cuml":
+#                 new_args.append(arg)
+#             elif isinstance(arg, pd.DataFrame) and engine_in == "pandas":
+#                 new_args.append(arg)
+#             elif isinstance(arg, cudf.DataFrame) and engine_in == "pandas":
+#                 new_args.append(arg.to_pandas())
+#             elif isinstance(arg, pd.DataFrame) and engine_in == "cuml":
+#                 new_args.append(cudf.from_pandas(arg))
+#             else:
+#                 new_args.append(arg)
+#         for key, value in kwargs.items():
+#             if isinstance(value, cudf.DataFrame) and engine_in == "cuml":
+#                 new_kwargs[key] = value
+#             elif isinstance(value, pd.DataFrame) and engine_in == "pandas":
+#                 new_kwargs[key] = value.to_pandas()
+#             elif isinstance(value, cudf.DataFrame) and engine_in == "pandas":
+#                 new_kwargs[key] = value.to_pandas()
+#             elif isinstance(value, pd.DataFrame) and engine_in == "cuml":
+#                 new_kwargs[key] = cudf.from_pandas(value)
+#             else:
+#                 new_kwargs[key] = value
+#         try:
+#             result = func(*new_args, **new_kwargs)
+#             if isinstance(result, cudf.DataFrame) and engine_out == "cuml":
+#                 result = result
+#             elif isinstance(result, pd.DataFrame) and engine_out == "pandas":
+#                 result = result
+#             elif isinstance(result, cudf.DataFrame) and engine_out == "pandas":
+#                 result = result.to_pandas()
+#             elif isinstance(result, pd.DataFrame) and engine_out == "cuml":
+#                 result = cudf.from_pandas(result)
+#             else:
+#                 raise ValueError("Unknown engine specified.")
+#         except Exception as e:
+#             logger.exception(f"An error occurred while running {func.__name__}. Exception: {e}")
+#             raise e
+#         return result
+
+#     has_cuml_dependancy_, _, cuml = lazy_cuml_import_has_dependancy()
+#     if has_cuml_dependancy_:
+#         return wrapper
+#     else:
+#         return dummy
+
+
+
+
+def make_safe_gpu_dataframes(X, y, engine_in):
+
+    def safe_cudf(X, y):
         new_kwargs = {}
-        for arg in args:
-            if isinstance(arg, pd.DataFrame):
-                new_args.append(cudf.DataFrame.from_pandas(arg))
-            else:
-                new_args.append(arg)
+        kwargs = {'X': X, 'y': y}
         for key, value in kwargs.items():
-            if isinstance(value, pd.DataFrame):
-                new_kwargs[key] = cudf.DataFrame.from_pandas(value)
-            else:
-                new_kwargs[key] = value
-        return func(*new_args, **new_kwargs)
-    return wrapper
-
-def convert_cudf_to_pandas(func):
-    def wrapper(*args, **kwargs):
-        new_args = []
-        new_kwargs = {}
-        for arg in args:
-            if isinstance(arg, cudf.DataFrame):
-                new_args.append(arg.to_pandas())
-            else:
-                new_args.append(arg)
-        for key, value in kwargs.items():
-            if isinstance(value, cudf.DataFrame):
-                new_kwargs[key] = value.to_pandas()
-            else:
-                new_kwargs[key] = value
-        try:
-            result = func(*new_args, **new_kwargs)
-            if isinstance(result, cudf.DataFrame):
-                result = result.to_pandas()
-        except Exception as e:
-            logger.exception(f"An error occurred while running {func.__name__}. Exception: {e}")
-            raise e
-        return result
-    return wrapper
-
-
-def safe_gpu_dataframes(func, engine_in=None, engine_out=None):
-    """Decorator function that safely wraps methods given the engine, 
-    specifically flexibility in what part of pipeline to convert to or from pd or cudf
-    engine_in asserts the dtype of the input (converting if necessary)
-    while engine_out asserts the output dtype
-    """
-    def wrapper(*args, **kwargs):
-        new_args = []
-        new_kwargs = {}
-        for arg in args:
-            if isinstance(arg, cudf.DataFrame) and engine_in == "cuml":
-                new_args.append(arg)
-            elif isinstance(arg, pd.DataFrame) and engine_in == "pandas":
-                new_args.append(arg)
-            elif isinstance(arg, cudf.DataFrame) and engine_in == "pandas":
-                new_args.append(arg.to_pandas())
-            elif isinstance(arg, pd.DataFrame) and engine_in == "cuml":
-                new_args.append(cudf.from_pandas(arg))
-            else:
-                new_args.append(arg)
-        for key, value in kwargs.items():
-            if isinstance(value, cudf.DataFrame) and engine_in == "cuml":
-                new_kwargs[key] = value
-            elif isinstance(value, pd.DataFrame) and engine_in == "pandas":
-                new_kwargs[key] = value.to_pandas()
-            elif isinstance(value, cudf.DataFrame) and engine_in == "pandas":
+            if isinstance(value, cudf.DataFrame) and engine_in == "pandas":
                 new_kwargs[key] = value.to_pandas()
             elif isinstance(value, pd.DataFrame) and engine_in == "cuml":
                 new_kwargs[key] = cudf.from_pandas(value)
             else:
                 new_kwargs[key] = value
-        try:
-            result = func(*new_args, **new_kwargs)
-            if isinstance(result, cudf.DataFrame) and engine_out == "cuml":
-                result = result
-            elif isinstance(result, pd.DataFrame) and engine_out == "pandas":
-                result = result
-            elif isinstance(result, cudf.DataFrame) and engine_out == "pandas":
-                result = result.to_pandas()
-            elif isinstance(result, pd.DataFrame) and engine_out == "cuml":
-                result = cudf.from_pandas(result)
-            else:
-                raise ValueError("Unknown engine specified.")
-        except Exception as e:
-            logger.exception(f"An error occurred while running {func.__name__}. Exception: {e}")
-            raise e
-        return result
-    return wrapper
+        return new_kwargs['X'], new_kwargs['y']
+
+    has_cudf_dependancy_, _, cudf = lazy_cudf_import_has_dependancy()
+    if has_cudf_dependancy_:
+        return safe_cudf(X, y)
+    else:
+        return X, y
+
 ###############################################################################
 
 
@@ -310,9 +363,10 @@ def umap_lazy_init(
             self._umap = umap_engine.UMAP(**umap_kwargs)
             self.umap_initialized = True
             self.engine = engine_resolved
+            self.engine_in = self.engine_out = engine_resolved
             self.suffix = suffix
 
-    @safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
+    #@safe_gpu_dataframes
     def _check_target_is_one_dimensional(self, y: Union[pd.DataFrame, None]):
         if y is None:
             return None
@@ -327,7 +381,7 @@ def _check_target_is_one_dimensional(self, y: Union[pd.DataFrame, None]):
             )
             return None
 
-    @safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
+    #@safe_gpu_dataframes
     def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         if self._umap is None:
             raise ValueError("UMAP is not initialized")
@@ -358,7 +412,7 @@ def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         logger.info(f" - or {X.shape[0]/mins:.2f} rows per minute")
         return self
 
-    @safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
+    #@safe_gpu_dataframes
     def umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         if self._umap is None:
             raise ValueError("UMAP is not initialized")
@@ -367,7 +421,7 @@ def umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = Non
         emb = self._bundle_embedding(emb, index=X.index)
         return emb
 
-    @safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
+    #@safe_gpu_dataframes
     def transform_umap(  # noqa: E303
         self, df: pd.DataFrame, ydf: pd.DataFrame, kind: str = "nodes"
     ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
@@ -380,7 +434,7 @@ def transform_umap(  # noqa: E303
         emb = self._bundle_embedding(emb, index=df.index)
         return emb, x, y
 
-    @safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
+    #@safe_gpu_dataframes
     def _bundle_embedding(self, emb, index):
         # Converts Embedding into dataframe and takes care if emb.dim > 2
         if emb.shape[1] == 2 and 'cudf.core.dataframe' not in str(getmodule(emb)):
@@ -397,7 +451,7 @@ def _bundle_embedding(self, emb, index):
                 emb.columns=columns
         return emb
 
-    @safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
+    #@safe_gpu_dataframes
     def _process_umap(
         self,
         res,
@@ -602,6 +656,9 @@ def umap(
             elif 'cudf.core.dataframe' in str(getmodule(X_)):
                 index_to_nodes_dict = nodes  # {}?
 
+            ## add the safe coercion here 
+            X_, y_ = make_safe_gpu_dataframes(X_, y_, self.engine)
+
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs
             )
@@ -680,7 +737,6 @@ def umap(
         if not inplace:
             return res
 
-    #@safe_gpu_dataframes(engine_in=self.engine, engine_out=self.engine)
     def _bind_xy_from_umap(
         self,
         res: Any,

From 446bdc8185f0a4046dc69eda205ec11915e22125 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sat, 11 Mar 2023 18:01:33 -0800
Subject: [PATCH 240/432] adds safe gpu wrapper to edges as well

---
 graphistry/umap_utils.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 96398cb62b..579e2faae3 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -688,6 +688,9 @@ def umap(
                 **featurize_kwargs
             )
 
+            ## add the safe coercion here 
+            X_, y_ = make_safe_gpu_dataframes(X_, y_, self.engine)
+
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs
             )

From 96363f552f372f6a85e615e63d430d3c22952a17 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sun, 12 Mar 2023 16:26:14 -0700
Subject: [PATCH 241/432] adds safer handling of cupy/cudf arrays

---
 graphistry/compute/cluster.py | 41 ++++++++++++++++++++++++++++++++---
 1 file changed, 38 insertions(+), 3 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 15b7cf0ed3..8f3fbe852c 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -41,6 +41,17 @@ def lazy_dbscan_import_has_dependency():
 
     return has_min_dependency, DBSCAN, has_cuml_dependency, cuDBSCAN
 
+def lazy_cudf_import_has_dependancy():
+    try:
+        import warnings
+
+        warnings.filterwarnings("ignore")
+        import cudf  # type: ignore
+
+        return True, "ok", cudf
+    except ModuleNotFoundError as e:
+        return False, e, None
+
 
 def resolve_cpu_gpu_engine(
     engine: DBSCANEngine,
@@ -65,6 +76,27 @@ def resolve_cpu_gpu_engine(
         f"but received: {engine} :: {type(engine)}"
     )
 
+def make_safe_gpu_dataframes(X, y, engine):
+    """helper method to coerce a dataframe to the correct type (pd vs cudf)"""
+    def safe_cudf(X, y):
+        new_kwargs = {}
+        kwargs = {'X': X, 'y': y}
+        for key, value in kwargs.items():
+            if isinstance(value, cudf.DataFrame) and engine == "pandas":
+                new_kwargs[key] = value.to_pandas()
+            elif isinstance(value, pd.DataFrame) and engine == "cuml":
+                new_kwargs[key] = cudf.from_pandas(value)
+            else:
+                new_kwargs[key] = value
+        return new_kwargs['X'], new_kwargs['y']
+
+    has_cudf_dependancy_, _, cudf = lazy_cudf_import_has_dependancy()
+    if has_cudf_dependancy_:
+        print('DBSCAN CUML Matrices')
+        return safe_cudf(X, y)
+    else:
+        return X, y
+
 
 def get_model_matrix(g, kind: str, cols: Optional[Union[List, str]], umap, target):
     """
@@ -89,7 +121,9 @@ def get_model_matrix(g, kind: str, cols: Optional[Union[List, str]], umap, targe
 
     if umap and cols is None and g._umap is not None:
         df = g._get_embedding(kind)            
-        
+    
+    if g.engine in [CUML]:
+        df = make_safe_gpu_dataframes(df, None)
     #print('\n df:', df.shape, df.columns)
     return df
 
@@ -112,11 +146,12 @@ def dbscan_fit(g: Any, dbscan: Any, kind: str = "nodes", cols: Optional[Union[Li
 
     dbscan.fit(X)
     labels = dbscan.labels_
+    print(labels, type(labels))
     
     if kind == "nodes":
-        g._nodes = g._nodes.assign(_dbscan=labels)
+        g._nodes = g._nodes.assign(_dbscan=np.array(labels))
     elif kind == "edges":
-        g._edges = g._edges.assign(_dbscan=labels)
+        g._edges = g._edges.assign(_dbscan=np.array(labels))
     else:
         raise ValueError("kind must be one of `nodes` or `edges`")
 

From 6dc826ade076c610d29f53f6749317a29247cf61 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sun, 12 Mar 2023 16:26:38 -0700
Subject: [PATCH 242/432] removes unused code

---
 graphistry/umap_utils.py | 116 ---------------------------------------
 1 file changed, 116 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index e85c1ebc1d..8da9c0169a 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -113,121 +113,6 @@ def resolve_umap_engine(
     )
 
 
-
-# def convert_pandas_to_cudf(func):
-#     def wrapper(*args, **kwargs):
-#         new_args = []
-#         new_kwargs = {}
-#         for arg in args:
-#             if isinstance(arg, pd.DataFrame):
-#                 new_args.append(cudf.DataFrame.from_pandas(arg))
-#             else:
-#                 new_args.append(arg)
-#         for key, value in kwargs.items():
-#             if isinstance(value, pd.DataFrame):
-#                 new_kwargs[key] = cudf.DataFrame.from_pandas(value)
-#             else:
-#                 new_kwargs[key] = value
-#         return func(*new_args, **new_kwargs)
-#     return wrapper
-
-# def convert_cudf_to_pandas(func):
-#     def wrapper(*args, **kwargs):
-#         new_args = []
-#         new_kwargs = {}
-#         for arg in args:
-#             if isinstance(arg, cudf.DataFrame):
-#                 new_args.append(arg.to_pandas())
-#             else:
-#                 new_args.append(arg)
-#         for key, value in kwargs.items():
-#             if isinstance(value, cudf.DataFrame):
-#                 new_kwargs[key] = value.to_pandas()
-#             else:
-#                 new_kwargs[key] = value
-#         try:
-#             result = func(*new_args, **new_kwargs)
-#             if isinstance(result, cudf.DataFrame):
-#                 result = result.to_pandas()
-#         except Exception as e:
-#             logger.exception(f"An error occurred while running {func.__name__}. Exception: {e}")
-#             raise e
-#         return result
-#     return wrapper
-
-
-# def safe_gpu_dataframes(func):
-#     """Decorator function that safely wraps methods given the engine, 
-#     specifically flexibility in what part of pipeline to convert to or from pd or cudf
-#     engine_in asserts the dtype of the input (converting if necessary)
-#     while engine_out asserts the output dtype
-#     """
-    
-#     from functools import wraps  # https://stackoverflow.com/questions/11731136/class-method-decorator-with-self-arguments
-
-#     @wraps(func)
-#     def dummy(self, *args, **kwargs):
-#         try:
-#             result = func(*args, **kwargs)
-#         except Exception as e:
-#             logger.exception(f"An error occurred while running {func.__name__}. Exception: {e}")
-#             raise e
-#         return result
-
-#     @wraps(func)
-#     def wrapper(self, *args, **kwargs):
-#         engine_in = self.engine_in
-#         engine_out = self.engine_out
-#         new_args = []
-#         new_kwargs = {}
-#         for arg in args:
-#             if isinstance(arg, cudf.DataFrame) and engine_in == "cuml":
-#                 new_args.append(arg)
-#             elif isinstance(arg, pd.DataFrame) and engine_in == "pandas":
-#                 new_args.append(arg)
-#             elif isinstance(arg, cudf.DataFrame) and engine_in == "pandas":
-#                 new_args.append(arg.to_pandas())
-#             elif isinstance(arg, pd.DataFrame) and engine_in == "cuml":
-#                 new_args.append(cudf.from_pandas(arg))
-#             else:
-#                 new_args.append(arg)
-#         for key, value in kwargs.items():
-#             if isinstance(value, cudf.DataFrame) and engine_in == "cuml":
-#                 new_kwargs[key] = value
-#             elif isinstance(value, pd.DataFrame) and engine_in == "pandas":
-#                 new_kwargs[key] = value.to_pandas()
-#             elif isinstance(value, cudf.DataFrame) and engine_in == "pandas":
-#                 new_kwargs[key] = value.to_pandas()
-#             elif isinstance(value, pd.DataFrame) and engine_in == "cuml":
-#                 new_kwargs[key] = cudf.from_pandas(value)
-#             else:
-#                 new_kwargs[key] = value
-#         try:
-#             result = func(*new_args, **new_kwargs)
-#             if isinstance(result, cudf.DataFrame) and engine_out == "cuml":
-#                 result = result
-#             elif isinstance(result, pd.DataFrame) and engine_out == "pandas":
-#                 result = result
-#             elif isinstance(result, cudf.DataFrame) and engine_out == "pandas":
-#                 result = result.to_pandas()
-#             elif isinstance(result, pd.DataFrame) and engine_out == "cuml":
-#                 result = cudf.from_pandas(result)
-#             else:
-#                 raise ValueError("Unknown engine specified.")
-#         except Exception as e:
-#             logger.exception(f"An error occurred while running {func.__name__}. Exception: {e}")
-#             raise e
-#         return result
-
-#     has_cuml_dependancy_, _, cuml = lazy_cuml_import_has_dependancy()
-#     if has_cuml_dependancy_:
-#         return wrapper
-#     else:
-#         return dummy
-
-
-
-
 def make_safe_gpu_dataframes(X, y, engine_in):
 
     def safe_cudf(X, y):
@@ -351,7 +236,6 @@ def umap_lazy_init(
         res._negative_sample_rate = negative_sample_rate
         res._umap = umap_engine.UMAP(**umap_kwargs)
         res.engine = engine_resolved
-        #self.engine = engine_resolved
         res._suffix = suffix
                                                             
         return res

From 27c879b15ce5a9e5c53fbffca0a06a325a7afbf8 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sun, 12 Mar 2023 16:27:41 -0700
Subject: [PATCH 243/432] handles pandas via if statement

---
 graphistry/compute/cluster.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 8f3fbe852c..20dcbfacae 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -145,13 +145,15 @@ def dbscan_fit(g: Any, dbscan: Any, kind: str = "nodes", cols: Optional[Union[Li
         raise ValueError("No features found for clustering")
 
     dbscan.fit(X)
-    labels = dbscan.labels_
-    print(labels, type(labels))
-    
+    if g.engine == 'cuml':
+        labels = dbscan.labels_.to_numpy()
+    else:
+        labels = dbscan.labels_
+
     if kind == "nodes":
-        g._nodes = g._nodes.assign(_dbscan=np.array(labels))
+        g._nodes = g._nodes.assign(_dbscan=labels)
     elif kind == "edges":
-        g._edges = g._edges.assign(_dbscan=np.array(labels))
+        g._edges = g._edges.assign(_dbscan=labels)
     else:
         raise ValueError("kind must be one of `nodes` or `edges`")
 

From 4f01bdec57b84ce41c1e5281679495c712565352 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sun, 12 Mar 2023 16:29:58 -0700
Subject: [PATCH 244/432] adds missing engine flag

---
 graphistry/compute/cluster.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 20dcbfacae..56310a0679 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -123,7 +123,7 @@ def get_model_matrix(g, kind: str, cols: Optional[Union[List, str]], umap, targe
         df = g._get_embedding(kind)            
     
     if g.engine in [CUML]:
-        df = make_safe_gpu_dataframes(df, None)
+        df = make_safe_gpu_dataframes(df, None, g.engine)
     #print('\n df:', df.shape, df.columns)
     return df
 

From 93cadb11c8a6caef9f2fa7f6028790220133583d Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Sun, 12 Mar 2023 16:32:30 -0700
Subject: [PATCH 245/432] adds missing _ to output

---
 graphistry/compute/cluster.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 56310a0679..c2d5c5f1f1 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -123,7 +123,7 @@ def get_model_matrix(g, kind: str, cols: Optional[Union[List, str]], umap, targe
         df = g._get_embedding(kind)            
     
     if g.engine in [CUML]:
-        df = make_safe_gpu_dataframes(df, None, g.engine)
+        df, _ = make_safe_gpu_dataframes(df, None, g.engine)
     #print('\n df:', df.shape, df.columns)
     return df
 

From f479857367bef834181038ab06f310aa03ef6b60 Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Mon, 13 Mar 2023 16:25:00 +0900
Subject: [PATCH 246/432] begin testing cudf-cu_cat

---
 graphistry/feature_utils.py | 15 ++++++---------
 1 file changed, 6 insertions(+), 9 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 618a516374..d5df8549bb 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -3,6 +3,7 @@
 import os
 import pandas as pd
 from time import time
+from inspect import getmodule
 import warnings
 from functools import partial
 
@@ -130,7 +131,6 @@ def assert_imported_text():
         )
         raise import_text_exn
 
-
 def assert_imported():
     has_min_dependancy_, import_min_exn = lazy_import_has_min_dependancy()
     if not has_min_dependancy_:
@@ -139,7 +139,7 @@ def assert_imported():
                      "`pip install graphistry[ai]`"  # noqa
         )
         raise import_min_exn
-
+        
 def assert_cuml_cucat():
     has_cuml_dependancy_, import_cuml_exn = lazy_import_has_cu_cat_dependancy()
     if not has_cuml_dependancy_:
@@ -149,7 +149,7 @@ def assert_cuml_cucat():
         )
         raise import_cuml_exn
     
-    
+
 # ############################################################################
 #
 #     Rough calltree
@@ -208,7 +208,7 @@ def resolve_feature_engine(
 
 def resolve_y(df: Optional[pd.DataFrame], y: YSymbolic) -> pd.DataFrame:
 
-    if isinstance(y, pd.DataFrame):
+    if isinstance(y, pd.DataFrame) or 'cudf.core.dataframe' in str(getmodule(y)):
         return y
 
     if df is None:
@@ -229,7 +229,7 @@ def resolve_y(df: Optional[pd.DataFrame], y: YSymbolic) -> pd.DataFrame:
 
 def resolve_X(df: Optional[pd.DataFrame], X: XSymbolic) -> pd.DataFrame:
 
-    if isinstance(X, pd.DataFrame):
+    if isinstance(X, pd.DataFrame) or 'cudf.core.dataframe' in str(getmodule(X)):
         return X
 
     if df is None:
@@ -905,7 +905,6 @@ def process_dirty_dataframes(
     similarity: Optional[str] = None,  # "ngram",
     categories: Optional[str] = "auto",
     multilabel: bool = False,
-    feature_engine: Optional[str] = "dirty_cat",
 ) -> Tuple[
     pd.DataFrame,
     Optional[pd.DataFrame],
@@ -936,7 +935,6 @@ def process_dirty_dataframes(
         from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
     elif feature_engine == 'cu_cat':
         from cu_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
-    
     from sklearn.preprocessing import FunctionTransformer
     t = time()
 
@@ -1178,8 +1176,7 @@ def process_nodes_dataframes(
         n_topics_target=n_topics_target,
         similarity=similarity,
         categories=categories,
-        multilabel=multilabel,
-        feature_engine=feature_engine,
+        multilabel=multilabel
     )
 
     if embedding:

From 923ec9c88530a5940059d77fffd3b57c710de88b Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Mon, 13 Mar 2023 16:33:35 +0900
Subject: [PATCH 247/432] begin testing cudf-cu_cat

---
 graphistry/feature_utils.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index d5df8549bb..feb0aa3bc0 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -905,6 +905,7 @@ def process_dirty_dataframes(
     similarity: Optional[str] = None,  # "ngram",
     categories: Optional[str] = "auto",
     multilabel: bool = False,
+    feature_engine: Optional[str] = "dirty_cat",
 ) -> Tuple[
     pd.DataFrame,
     Optional[pd.DataFrame],

From b4e110d16d7b8f98bb5b11733c028010c9300bed Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Mon, 13 Mar 2023 16:53:41 +0900
Subject: [PATCH 248/432] begin cudf_cat for 3x to 10x

---
 graphistry/feature_utils.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index feb0aa3bc0..f336166a60 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1177,7 +1177,8 @@ def process_nodes_dataframes(
         n_topics_target=n_topics_target,
         similarity=similarity,
         categories=categories,
-        multilabel=multilabel
+        multilabel=multilabel,
+        feature_engine=feature_engine,
     )
 
     if embedding:

From b6a60eaa37d403ab2766fece06b34d90e7ddeeae Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 13 Mar 2023 14:04:23 -0700
Subject: [PATCH 249/432] adds handling of g.transform_umap when engine=cuml

---
 graphistry/umap_utils.py | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 8da9c0169a..0eb99ab521 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -113,15 +113,15 @@ def resolve_umap_engine(
     )
 
 
-def make_safe_gpu_dataframes(X, y, engine_in):
+def make_safe_gpu_dataframes(X, y, engine):
 
     def safe_cudf(X, y):
         new_kwargs = {}
         kwargs = {'X': X, 'y': y}
         for key, value in kwargs.items():
-            if isinstance(value, cudf.DataFrame) and engine_in == "pandas":
+            if isinstance(value, cudf.DataFrame) and engine == "pandas":
                 new_kwargs[key] = value.to_pandas()
-            elif isinstance(value, pd.DataFrame) and engine_in == "cuml":
+            elif isinstance(value, pd.DataFrame) and engine == "cuml":
                 new_kwargs[key] = cudf.from_pandas(value)
             else:
                 new_kwargs[key] = value
@@ -325,18 +325,20 @@ def transform_umap(self, df: pd.DataFrame,
                 useful to contextualize new data against existing graph. If False, `sample` is irrelevant.
             sample: Sample number of existing graph's neighbors to use for contextualization -- helps make denser graphs
             return_graph: Whether to return a graph or just the embeddings
-            fit_umap_embedding: Whether to infer graph from the UMAP embedding on the new data
+            fit_umap_embedding: Whether to infer graph from the UMAP embedding on the new data, default True
             verbose: Whether to print information about the graph inference
         """
         X, y_ = self.transform(df, y, kind=kind, return_graph=False, verbose=verbose)
+        X, y_ = make_safe_gpu_dataframes(X, y_, self.engine)
         emb = self._umap.transform(X)  # type: ignore
         emb = self._bundle_embedding(emb, index=df.index)
         if return_graph and kind not in ["edges"]:
+            emb, _ = make_safe_gpu_dataframes(emb, None, 'pandas')  # for now so we don't have to touch infer_edges, force to pandas
             g = self._infer_edges(emb, X, y_, df, 
                                   infer_on_umap_embedding=fit_umap_embedding, merge_policy=merge_policy,
                                   eps=min_dist, sample=sample, n_neighbors=n_neighbors,
                                   verbose=verbose) 
-            return g        
+            return g
         return emb, X, y_
 
     def _bundle_embedding(self, emb, index):
@@ -344,7 +346,7 @@ def _bundle_embedding(self, emb, index):
         if emb.shape[1] == 2 and 'cudf.core.dataframe' not in str(getmodule(emb)):
             emb = pd.DataFrame(emb, columns=[config.X, config.Y], index=index)
         elif emb.shape[1] == 2 and 'cudf.core.dataframe' in str(getmodule(emb)):
-            emb.rename(columns={0:config.X,1: config.Y},inplace=True)
+            emb.rename(columns={0: config.X, 1: config.Y}, inplace=True)
         else:
             columns = [config.X, config.Y] + [
                 f"umap_{k}" for k in range(2, emb.shape[1])
@@ -352,7 +354,7 @@ def _bundle_embedding(self, emb, index):
             if 'cudf.core.dataframe' not in str(getmodule(emb)):
                 emb = pd.DataFrame(emb, columns=columns, index=index)
             elif 'cudf.core.dataframe' in str(getmodule(emb)):
-                emb.columns=columns
+                emb.columns = columns
         return emb
 
     def _process_umap(

From 84741c8374493a16f66ebda918ec085c586d5743 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 13 Mar 2023 14:33:42 -0700
Subject: [PATCH 250/432] safe converts X, y before infer_graph method

---
 graphistry/umap_utils.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 0eb99ab521..555180e74f 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -334,6 +334,7 @@ def transform_umap(self, df: pd.DataFrame,
         emb = self._bundle_embedding(emb, index=df.index)
         if return_graph and kind not in ["edges"]:
             emb, _ = make_safe_gpu_dataframes(emb, None, 'pandas')  # for now so we don't have to touch infer_edges, force to pandas
+            X, y_ = make_safe_gpu_dataframes(X, y_, 'pandas')
             g = self._infer_edges(emb, X, y_, df, 
                                   infer_on_umap_embedding=fit_umap_embedding, merge_policy=merge_policy,
                                   eps=min_dist, sample=sample, n_neighbors=n_neighbors,

From 6aefc496eccbdf45d6d86ac443bb3f750c81572b Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 13 Mar 2023 15:24:55 -0700
Subject: [PATCH 251/432] debugging why node not in df

---
 graphistry/ai_utils.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index e29c334785..40f4e554f3 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -284,6 +284,7 @@ def infer_graph(
     df["_n"] = numeric_indices
     df[BATCH] = 1  # 1 for minibatch, 0 for existing graph
     node = res._node
+    print('Node', node)
     NDF = res._nodes
     NDF[BATCH] = 0
     EDF = res._edges
@@ -297,8 +298,6 @@ def infer_graph(
     old_nodes = []
     mdists = []
 
-    # vsearch = build_search_index(X_previously_fit, angular=False)
-
     for i in range(X_new.shape[0]):
         diff = X_previously_fit - X_new.iloc[i, :]
         dist = np.linalg.norm(diff, axis=1)  # Euclidean distance
@@ -427,12 +426,16 @@ def infer_self_graph(res,
 
     #  if umap, need to add '_n' as node id to df, adding new indices to existing graph
     numeric_indices = np.arange(
-        X_previously_fit.shape[0],  # X_previously_fit.shape[0] + X_new.shape[0]
+        X_previously_fit.shape[0],
         dtype=np.float64  # this seems off but works
         )
     df["_n"] = numeric_indices
-    df[BATCH] = 1  # 1 for minibatch, 0 for existing graph, should all be `1` 
+    df[BATCH] = 1  # 1 for minibatch, 0 for existing graph, here should all be `1` 
     node = res._node
+    print('node self', node)
+
+    assert node in df.columns
+
     src = res._source
     dst = res._destination
     
@@ -440,8 +443,6 @@ def infer_self_graph(res,
     new_edges = []
     mdists = []
 
-    # vsearch = build_search_index(X_previously_fit, angular=False)
-
     for i in range(X_new.shape[0]):
         diff = X_previously_fit - X_new.iloc[i, :]
         dist = np.linalg.norm(diff, axis=1)  # Euclidean distance
@@ -462,6 +463,8 @@ def infer_self_graph(res,
     for i, dist in enumerate(mdists):
         record_df = df.iloc[i, :]
         nearest = np.where(dist < eps)[0]
+        if i < 2:
+            print('type dist': type(dist))
         nn.append(len(nearest))
         for j in nearest[:n_neighbors]:  # add n_neighbors nearest neighbors, if any, super speedup hack
             if i != j:

From caf367c8767f51fe5f52ba0376326f6c8fb92ac7 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 13 Mar 2023 15:26:19 -0700
Subject: [PATCH 252/432] fix typo

---
 graphistry/ai_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 40f4e554f3..3e08090cfe 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -464,7 +464,7 @@ def infer_self_graph(res,
         record_df = df.iloc[i, :]
         nearest = np.where(dist < eps)[0]
         if i < 2:
-            print('type dist': type(dist))
+            print('type dist', type(dist))
         nn.append(len(nearest))
         for j in nearest[:n_neighbors]:  # add n_neighbors nearest neighbors, if any, super speedup hack
             if i != j:

From 40914dcd9f5464fdcd821be1fa7cba6088f0e7bd Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 13 Mar 2023 15:41:03 -0700
Subject: [PATCH 253/432] adds test if node not in df, adds numeric index

---
 graphistry/ai_utils.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 3e08090cfe..c5a457a6d8 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -434,7 +434,8 @@ def infer_self_graph(res,
     node = res._node
     print('node self', node)
 
-    assert node in df.columns
+    if node not in df.columns:
+        df[node] = numeric_indices
 
     src = res._source
     dst = res._destination

From b8a7eb5c09b63655636988021cd315706b3594ec Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 13 Mar 2023 15:47:18 -0700
Subject: [PATCH 254/432] adds solution to both infer_graph and
 infer_self_graph functions

---
 graphistry/ai_utils.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index c5a457a6d8..f5f8805358 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -284,7 +284,10 @@ def infer_graph(
     df["_n"] = numeric_indices
     df[BATCH] = 1  # 1 for minibatch, 0 for existing graph
     node = res._node
-    print('Node', node)
+
+    if node not in df.columns:
+        df[node] = numeric_indices
+
     NDF = res._nodes
     NDF[BATCH] = 0
     EDF = res._edges
@@ -432,8 +435,6 @@ def infer_self_graph(res,
     df["_n"] = numeric_indices
     df[BATCH] = 1  # 1 for minibatch, 0 for existing graph, here should all be `1` 
     node = res._node
-    print('node self', node)
-
     if node not in df.columns:
         df[node] = numeric_indices
 
@@ -464,8 +465,6 @@ def infer_self_graph(res,
     for i, dist in enumerate(mdists):
         record_df = df.iloc[i, :]
         nearest = np.where(dist < eps)[0]
-        if i < 2:
-            print('type dist', type(dist))
         nn.append(len(nearest))
         for j in nearest[:n_neighbors]:  # add n_neighbors nearest neighbors, if any, super speedup hack
             if i != j:

From b71d8adff9ba83b252c4ffacc3add9ff0abc1b76 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 13 Mar 2023 15:59:54 -0700
Subject: [PATCH 255/432] fixes concat between cudf and pd

---
 graphistry/ai_utils.py   | 2 ++
 graphistry/umap_utils.py | 1 +
 2 files changed, 3 insertions(+)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index f5f8805358..a0611eb455 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -366,6 +366,8 @@ def infer_graph(
 
     new_emb = None
     if emb is not None:
+        if 'cudf.core.dataframe.DataFrame' in type(old_emb): # convert to pd
+            old_emb = old_emb.to_pandas()
         new_emb = pd.concat([emb, old_emb], axis=0)
 
     new_features = pd.concat([X, FEATS.loc[old_nodes.index]], axis=0)
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 555180e74f..a8cba2ed62 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -328,6 +328,7 @@ def transform_umap(self, df: pd.DataFrame,
             fit_umap_embedding: Whether to infer graph from the UMAP embedding on the new data, default True
             verbose: Whether to print information about the graph inference
         """
+        df, y = make_safe_gpu_dataframes(df, y, 'pandas')
         X, y_ = self.transform(df, y, kind=kind, return_graph=False, verbose=verbose)
         X, y_ = make_safe_gpu_dataframes(X, y_, self.engine)
         emb = self._umap.transform(X)  # type: ignore

From 8be6aa0dab3768ae111141730be4316db4e69338 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 13 Mar 2023 16:19:08 -0700
Subject: [PATCH 256/432] fixes bug

---
 graphistry/ai_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index a0611eb455..1f1586e5e4 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -366,7 +366,7 @@ def infer_graph(
 
     new_emb = None
     if emb is not None:
-        if 'cudf.core.dataframe.DataFrame' in type(old_emb): # convert to pd
+        if 'cudf.core.dataframe.DataFrame' in str(type(old_emb)): # convert to pd
             old_emb = old_emb.to_pandas()
         new_emb = pd.concat([emb, old_emb], axis=0)
 

From 7482eb6d978081b3466d76540a034f1c11263cf5 Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Tue, 14 Mar 2023 18:57:40 +0900
Subject: [PATCH 257/432] bring enc_X to cudf

---
 graphistry/feature_utils.py | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index f336166a60..44b8664a76 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -974,10 +974,16 @@ def process_dirty_dataframes(
         #  now just set the feature names, since dirty cat changes them in
         #  a weird way...
         data_encoder.get_feature_names_out = callThrough(features_transformed)
-        
-        X_enc = pd.DataFrame(
-            X_enc, columns=features_transformed, index=ndf.index
-        )
+        print([type(X_enc),type(ndf),type(features_transformed)])
+        if 'cudf.core.dataframe' not in str(getmodule(ndf)):
+            X_enc = pd.DataFrame(
+                X_enc, columns=features_transformed, index=ndf.index
+            )
+        elif 'cudf.core.dataframe' in str(getmodule(ndf)):
+            import cudf
+            X_enc = cudf.DataFrame(
+                X_enc, columns=features_transformed, index=ndf.index
+            )
         X_enc = X_enc.fillna(0.0)
     else:
         logger.info("-*-*- DataFrame is completely numeric")

From e1ceb596257f475755b2ecd573a66d7c3f9d0af1 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Tue, 14 Mar 2023 15:07:24 -0700
Subject: [PATCH 258/432] coerces previously fit X to pandas if cudf

---
 graphistry/ai_utils.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 0ce1bc69c9..a3b4c1888d 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -294,6 +294,11 @@ def infer_graph(
     old_nodes = []
     mdists = []
 
+    ## check if pandas or cudf
+    if 'cudf.core.dataframe' in str(type(X_previously_fit)):
+        # move it out of memory...
+        X_previously_fit = X_previously_fit.to_pandas()
+
     for i in range(X_new.shape[0]):
         diff = X_previously_fit - X_new.iloc[i, :]
         dist = np.linalg.norm(diff, axis=1)  # Euclidean distance

From 0dd3ced4709d3112b9f95177e9b879c3175ff67b Mon Sep 17 00:00:00 2001
From: dc <apple@dc-m1.local>
Date: Wed, 15 Mar 2023 09:14:06 +0900
Subject: [PATCH 259/432] seemingly unavoidable cudf edge transforms to pandas

---
 graphistry/feature_utils.py | 42 +++++++++++++++++++++++++++----------
 1 file changed, 31 insertions(+), 11 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 44b8664a76..fe126a6abd 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -675,12 +675,19 @@ def fit_pipeline(
     """
     columns = X.columns
     index = X.index
-
-    X = transformer.fit_transform(X)
-    if keep_n_decimals:
-        X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
-
-    return pd.DataFrame(X, columns=columns, index=index)
+    X_type= str(getmodule(X))
+    if 'cudf.core.dataframe' not in X_type:
+        X = transformer.fit_transform(X)
+        if keep_n_decimals:
+            X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
+        X=pd.DataFrame(X, columns=columns, index=index)
+    elif 'cudf.core.dataframe' in X_type:
+        import cudf
+        X = transformer.fit_transform(X.to_numpy())
+        if keep_n_decimals:
+            X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
+        X=cudf.DataFrame(X, columns=columns, index=index)
+    return X
 
 
 def impute_and_scale_df(
@@ -974,7 +981,6 @@ def process_dirty_dataframes(
         #  now just set the feature names, since dirty cat changes them in
         #  a weird way...
         data_encoder.get_feature_names_out = callThrough(features_transformed)
-        print([type(X_enc),type(ndf),type(features_transformed)])
         if 'cudf.core.dataframe' not in str(getmodule(ndf)):
             X_enc = pd.DataFrame(
                 X_enc, columns=features_transformed, index=ndf.index
@@ -1301,9 +1307,15 @@ def encode_edges(edf, src, dst, mlb, fit=False):
     """
     # uses mlb with fit=T/F so we can use it in transform mode
     # to recreate edge feature concat definition
-    source = edf[src]
-    destination = edf[dst]
+
     logger.debug("Encoding Edges using MultiLabelBinarizer")
+    edf_type = str(getmodule(edf))
+    if 'cudf.core.dataframe' in edf_type:
+        source = edf.to_pandas()[src]
+        destination = edf.to_pandas()[dst]
+    else:
+        source = edf[src]
+        destination = edf[dst]
     if fit:
         T = mlb.fit_transform(zip(source, destination))
     else:
@@ -1314,7 +1326,11 @@ def encode_edges(edf, src, dst, mlb, fit=False):
     ]  # stringify the column names or scikits.base throws error
     mlb.get_feature_names_out = callThrough(columns)
     mlb.columns_ = [src, dst]
-    T = pd.DataFrame(T, columns=columns, index=edf.index)
+    if 'cudf.core.dataframe' in edf_type:
+        import cudf
+        T = cudf.DataFrame(T, columns=columns, index=edf.index)
+    else:
+        T = pd.DataFrame(T, columns=columns, index=edf.index)
     logger.info(f"Shape of Edge Encoding: {T.shape}")
     return T, mlb
 
@@ -1467,7 +1483,11 @@ def process_edge_dataframes(
     if not X_enc.empty and not T.empty:
         logger.debug("-" * 60)
         logger.debug("<= Found Edges and Dirty_cat encoding =>")
-        X_enc = pd.concat([T, X_enc], axis=1)
+        T_type= str(getmodule(T))
+        if 'cudf.core.dataframe' not in T_type:
+            X_enc = pd.concat([T, X_enc], axis=1)
+        elif 'cudf.core.dataframe' not in T_type:
+            X_enc = cudf.concat([T, X_enc], axis=1)
     elif not T.empty and X_enc.empty:
         logger.debug("-" * 60)
         logger.debug("<= Found only Edges =>")

From 73f8c9546f2524d98f7cbcc536f46f8a4112cf47 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <apple@dc-m1.local>
Date: Thu, 16 Mar 2023 10:39:51 +0900
Subject: [PATCH 260/432] incorp cudf-alex3 for cudf-cu_cat

---
 graphistry/feature_utils.py |  9 +++++----
 graphistry/umap_utils.py    | 16 ++++++++++++----
 2 files changed, 17 insertions(+), 8 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 10cb6b9420..aa5b830ba3 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -668,7 +668,7 @@ def fit_pipeline(
             X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
         X=pd.DataFrame(X, columns=columns, index=index)
     elif 'cudf.core.dataframe' in X_type:
-        import cudf
+        import cudf ## need proper import routine still
         X = transformer.fit_transform(X.to_numpy())
         if keep_n_decimals:
             X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
@@ -1476,10 +1476,11 @@ def process_edge_dataframes(
         logger.debug("-" * 60)
         logger.debug("<= Found Edges and Dirty_cat encoding =>")
         T_type= str(getmodule(T))
-        if 'cudf.core.dataframe' not in T_type:
-            X_enc = pd.concat([T, X_enc], axis=1)
-        elif 'cudf.core.dataframe' not in T_type:
+        if 'cudf.core.dataframe' in T_type:
+            import cudf
             X_enc = cudf.concat([T, X_enc], axis=1)
+        else:
+            X_enc = pd.concat([T, X_enc], axis=1)
     elif not T.empty and X_enc.empty:
         logger.debug("-" * 60)
         logger.debug("<= Found only Edges =>")
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 4821abf3dd..eb296025ee 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -1,6 +1,7 @@
 import copy
 from time import time
 from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
+from inspect import getmodule
 
 import pandas as pd
 
@@ -279,18 +280,25 @@ def transform_umap(  # noqa: E303
         emb = self._umap.transform(x)  # type: ignore
         emb = self._bundle_embedding(emb, index=df.index)
         return emb, x, y
-
+    
     def _bundle_embedding(self, emb, index):
         # Converts Embedding into dataframe and takes care if emb.dim > 2
-        if emb.shape[1] == 2:
+
+        if emb.shape[1] == 2 and 'cudf.core.dataframe' not in str(getmodule(emb)):
             emb = pd.DataFrame(emb, columns=[config.X, config.Y], index=index)
+        elif emb.shape[1] == 2 and 'cudf.core.dataframe' in str(getmodule(emb)):
+            emb.rename(columns={0:config.X,1: config.Y},inplace=True)
         else:
             columns = [config.X, config.Y] + [
                 f"umap_{k}" for k in range(2, emb.shape[1] - 2)
             ]
-            emb = pd.DataFrame(emb, columns=columns, index=index)
-        return emb
 
+            if 'cudf.core.dataframe' not in str(getmodule(emb)):
+                emb = pd.DataFrame(emb, columns=columns, index=index)
+            elif 'cudf.core.dataframe' in str(getmodule(emb)):
+                emb.columns=columns
+        return emb
+    
     def _process_umap(
         self,
         res,

From c2ce1df0ce6e7f43c11b10e34cd4fbd7365a9458 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <apple@dc-m1.local>
Date: Thu, 16 Mar 2023 10:41:01 +0900
Subject: [PATCH 261/432] incorp cudf-alex3 for cudf-cu_cat

---
 graphistry/feature_utils.py | 608 +++++++++++++++++++++++-------------
 graphistry/umap_utils.py    |  21 +-
 2 files changed, 409 insertions(+), 220 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index fe126a6abd..406cba183d 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -24,6 +24,7 @@
 from . import constants as config
 from .PlotterBase import WeakValueDictionary, Plottable
 from .util import setup_logger, check_set_memoize
+from .ai_utils import infer_graph, infer_self_graph
 
 # add this inside classes and have a method that can set log level
 logger = setup_logger(name=__name__, verbose=config.VERBOSE)
@@ -263,10 +264,7 @@ def safe_divide(a, b):
 def features_without_target(
     df: pd.DataFrame, y: Optional[Union[List, str, pd.DataFrame]] = None
 ) -> pd.DataFrame:
-    """
-        Checks if y DataFrame column name is in df, and removes it
-        from df if so
-    ___________________________________________________________________
+    """Checks if y DataFrame column name is in df, and removes it from df if so
 
     :param df: model DataFrame
     :param y: target DataFrame
@@ -313,11 +311,7 @@ def remove_node_column_from_symbolic(X_symbolic, node):
 
 
 def remove_internal_namespace_if_present(df: pd.DataFrame):
-    """
-        Some tranformations below add columns to the DataFrame,
-        this method removes them before featurization
-        Will not drop if suffix is added during UMAP-ing
-    ______________________________________________________________
+    """Some tranformations below add columns to the DataFrame, this method removes them before featurization will not drop if suffix is added during UMAP-ing
 
     :param df: DataFrame
     :return: DataFrame with dropped columns in reserved namespace
@@ -437,9 +431,8 @@ def is_dataframe_all_numeric(df: pd.DataFrame) -> bool:
 
 
 def find_bad_set_columns(df: pd.DataFrame, bad_set: List = ["[]"]):
-    """
-        Finds columns that if not coerced to strings, will break processors.
-    -------------------------------------------------------------------------
+    """Finds columns that if not coerced to strings, will break processors.
+
     :param df: DataFrame
     :param bad_set: List of strings to look for.
     :return: list
@@ -469,9 +462,7 @@ def check_if_textual_column(
     confidence: float = 0.35,
     min_words: float = 2.5,
 ) -> bool:
-    """
-        Checks if `col` column of df is textual or not using basic heuristics
-    __________________________________________________________________________
+    """Checks if `col` column of df is textual or not using basic heuristics
 
     :param df: DataFrame
     :param col: column name
@@ -510,10 +501,8 @@ def check_if_textual_column(
 def get_textual_columns(
     df: pd.DataFrame, min_words: float = 2.5
 ) -> List:
-    """
-        Collects columns from df that it deems are textual.
-    _____________________________________________________________________
-
+    """Collects columns from df that it deems are textual.
+   
     :param df: DataFrame
     :return: list of columns names
     """
@@ -539,7 +528,6 @@ class Embedding:
     """
     Generates random embeddings of a given dimension 
     that aligns with the index of the dataframe
-    _____________________________________________________________________
     
     """
     def __init__(self, df: pd.DataFrame):
@@ -577,14 +565,12 @@ def get_preprocessing_pipeline(
     encode: str = "ordinal",
     strategy: str = "quantile",
 ) -> Pipeline:  # noqa
-    """
-        Helper function for imputing and scaling np.ndarray data
-        using different scaling transformers.
-    -----------------------------------------------------------------
+    """Helper function for imputing and scaling np.ndarray data using different scaling transformers.
+
     :param X: np.ndarray
     :param impute: whether to run imputing or not
     :param use_scaler: string in None or
-            ["minmax", "quantile", "zscale", "robust", "kbins"],
+            ["minmax", "quantile", "standard", "robust", "kbins"],
             selects scaling transformer, default None
     :param n_quantiles: if use_scaler = 'quantile',
             sets the quantile bin size.
@@ -613,7 +599,7 @@ def get_preprocessing_pipeline(
     available_preprocessors = [
         "minmax",
         "quantile",
-        "zscale",
+        "standard",
         "robust",
         "kbins",
     ]
@@ -640,7 +626,7 @@ def get_preprocessing_pipeline(
         scaler = QuantileTransformer(
             n_quantiles=n_quantiles, output_distribution=output_distribution
         )
-    elif use_scaler == "zscale":
+    elif use_scaler == "standard":
         scaler = StandardScaler()
     elif use_scaler == "robust":
         scaler = RobustScaler(quantile_range=quantile_range)
@@ -663,15 +649,15 @@ def fit_pipeline(
     X: pd.DataFrame, transformer, keep_n_decimals: int = 5
 ) -> pd.DataFrame:
     """
-     Helper to fit DataFrame over transformer pipeline.
-     Rounds resulting matrix X by keep_n_digits if not 0,
-     which helps for when transformer pipeline is scaling or imputer
-     which sometime introduce small negative numbers,
-     and umap metrics like Hellinger need to be positive
-    :param X, DataFrame to transform.
+    Helper to fit DataFrame over transformer pipeline.
+    Rounds resulting matrix X by keep_n_digits if not 0,
+    which helps for when transformer pipeline is scaling or imputer
+    which sometime introduce small negative numbers,
+    and umap metrics like Hellinger need to be positive
+
+    :param X: DataFrame to transform.
     :param transformer: Pipeline object to fit and transform
-    :param keep_n_decimals: Int of how many decimal places to keep in
-    rounded transformed data
+    :param keep_n_decimals: Int of how many decimal places to keep in rounded transformed data
     """
     columns = X.columns
     index = X.index
@@ -843,7 +829,7 @@ def encoder(X, use_scaler):  # noqa: E301
 
 def get_cardinality_ratio(df: pd.DataFrame):
     """Calculates ratio of unique values to total number of rows of DataFrame
-    -------------------------------------------------------------------------
+   
     :param df: DataFrame
     """
     ratios = {}
@@ -919,10 +905,7 @@ def process_dirty_dataframes(
     Union[SuperVectorizer, FunctionTransformer],
     Union[SuperVectorizer, FunctionTransformer],
 ]:
-    """
-        Dirty_Cat encoder for record level data. Will automatically turn
-        inhomogeneous dataframe into matrix using smart conversion tricks.
-    ______________________________________________________________________
+    """Dirty_Cat encoder for record level data. Will automatically turn inhomogeneous dataframe into matrix using smart conversion tricks.
 
     :param ndf: node DataFrame
     :param y: target DataFrame or series
@@ -932,7 +915,7 @@ def process_dirty_dataframes(
             threshold, encoder is OneHot, above, it is GapEncoder
     :param n_topics: number of topics for GapEncoder, default 42
     :param use_scaler: None or string in
-            ['minmax', 'zscale', 'robust', 'quantile']
+            ['minmax', 'standard', 'robust', 'quantile']
     :param similarity: one of 'ngram', 'levenshtein-ratio', 'jaro',
             or'jaro-winkler'}) – The type of pairwise string similarity
             to use. If None or False, uses a SuperVectorizer
@@ -1081,6 +1064,8 @@ def process_nodes_dataframes(
     feature_engine: FeatureEngineConcrete = "pandas"
     # test_size: Optional[bool] = None,
 ) -> Tuple[
+    pd.DataFrame,
+    Any,
     pd.DataFrame,
     Any,
     SuperVectorizer,
@@ -1093,12 +1078,11 @@ def process_nodes_dataframes(
     """
         Automatic Deep Learning Embedding/ngrams of Textual Features,
         with the rest of the columns taken care of by dirty_cat
-    _________________________________________________________________________
 
     :param df: pandas DataFrame of data
     :param y: pandas DataFrame of targets
     :param use_scaler: None or string in
-            ['minmax', 'zscale', 'robust', 'quantile']
+            ['minmax', 'standard', 'robust', 'quantile']
     :param n_topics: number of topics in Gap Encoder
     :param use_scaler:
     :param confidence: Number between 0 and 1, will pass
@@ -1124,7 +1108,7 @@ def process_nodes_dataframes(
         X_enc, y_enc, data_encoder, label_encoder = get_numeric_transformers(
             df, y
         )
-        X_enc, y_enc, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
+        X_encs, y_encs, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
             X_enc,
             y_enc,
             use_scaler,
@@ -1146,6 +1130,8 @@ def process_nodes_dataframes(
         return (
             X_enc,
             y_enc,
+            X_encs,
+            y_encs,
             data_encoder,
             label_encoder,
             scaling_pipeline,
@@ -1212,7 +1198,7 @@ def process_nodes_dataframes(
         f"--The entire Encoding process took {(time()-t)/60:.2f} minutes"
     )
 
-    X_enc, y_enc, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
+    X_encs, y_encs, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
         X_enc,
         y_enc,
         use_scaler,
@@ -1230,6 +1216,8 @@ def process_nodes_dataframes(
     return (
         X_enc,
         y_enc,
+        X_encs,
+        y_encs,
         data_encoder,
         label_encoder,
         scaling_pipeline,
@@ -1300,8 +1288,7 @@ def encode_edges(edf, src, dst, mlb, fit=False):
         src (string): source column
         dst (string): destination column
         mlb (sklearn): multilabelBinarizer
-        fit (bool, optional): If true, fits multilabelBinarizer.
-            Defaults to False.
+        fit (bool, optional): If true, fits multilabelBinarizer. Defaults to False.
     Returns:
         tuple: pd.DataFrame, multilabelBinarizer
     """
@@ -1341,7 +1328,7 @@ def process_edge_dataframes(
     src: str,
     dst: str,
     cardinality_threshold: int = 40,
-    cardinality_threshold_target: int = 100,
+    cardinality_threshold_target: int = 400,
     n_topics: int = config.N_TOPICS_DEFAULT,
     n_topics_target: int = config.N_TOPICS_TARGET_DEFAULT,
     use_scaler: Optional[str] = None,
@@ -1351,7 +1338,6 @@ def process_edge_dataframes(
     ngram_range: tuple = (1, 3),
     max_df: float = 0.2,
     min_df: int = 3,
-    #confidence: float = 0.35,
     min_words: float = 2.5,
     model_name: str = "paraphrase-MiniLM-L6-v2",
     similarity: Optional[str] = None,
@@ -1366,6 +1352,8 @@ def process_edge_dataframes(
     keep_n_decimals: int = 5,
     feature_engine: FeatureEngineConcrete = "pandas",
 ) -> Tuple[
+    pd.DataFrame,
+    pd.DataFrame,
     pd.DataFrame,
     pd.DataFrame,
     List[Any],
@@ -1387,7 +1375,7 @@ def process_edge_dataframes(
     :param src: source column to select in edf
     :param dst: destination column to select in edf
     :param use_scaler: None or string in
-        ['minmax', 'zscale', 'robust', 'quantile']
+        ['minmax', 'standard', 'robust', 'quantile']
     :return: Encoded data matrix and target (if not None),
         the data encoders, and the label encoder.
     """
@@ -1422,7 +1410,7 @@ def process_edge_dataframes(
         # add the two datasets together
         X_enc = pd.concat([T, X_enc], axis=1)
         # then scale them
-        X_enc, y_enc, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
+        X_encs, y_encs, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
             X_enc,
             y_enc,
             use_scaler,
@@ -1442,6 +1430,8 @@ def process_edge_dataframes(
         return (
             X_enc,
             y_enc,
+            X_encs,
+            y_encs,
             [mlb_pairwise_edge_encoder, data_encoder],
             label_encoder,
             scaling_pipeline,
@@ -1453,6 +1443,8 @@ def process_edge_dataframes(
     (
         X_enc,
         y_enc,
+        _,
+        _,
         data_encoder,
         label_encoder,
         _,
@@ -1484,10 +1476,11 @@ def process_edge_dataframes(
         logger.debug("-" * 60)
         logger.debug("<= Found Edges and Dirty_cat encoding =>")
         T_type= str(getmodule(T))
-        if 'cudf.core.dataframe' not in T_type:
-            X_enc = pd.concat([T, X_enc], axis=1)
-        elif 'cudf.core.dataframe' not in T_type:
+        if 'cudf.core.dataframe' in T_type:
+            import cudf
             X_enc = cudf.concat([T, X_enc], axis=1)
+        else:
+            X_enc = pd.concat([T, X_enc], axis=1)
     elif not T.empty and X_enc.empty:
         logger.debug("-" * 60)
         logger.debug("<= Found only Edges =>")
@@ -1498,7 +1491,7 @@ def process_edge_dataframes(
         f" {(time()-t)/60:.2f} minutes"
     )
 
-    X_enc, y_enc, scaling_pipeline, scaling_pipeline_target = smart_scaler(
+    X_encs, y_encs, scaling_pipeline, scaling_pipeline_target = smart_scaler(
         X_enc,
         y_enc,
         use_scaler,
@@ -1516,6 +1509,8 @@ def process_edge_dataframes(
     res = (
         X_enc,
         y_enc,
+        X_encs,
+        y_encs,
         [mlb_pairwise_edge_encoder, data_encoder],
         label_encoder,
         scaling_pipeline,
@@ -1573,22 +1568,23 @@ def transform_dirty(
     data_encoder: Union[SuperVectorizer, FunctionTransformer],  # type: ignore
     name: str = "",
 ) -> pd.DataFrame:
-    from sklearn.preprocessing import MultiLabelBinarizer
+    # from sklearn.preprocessing import MultiLabelBinarizer
     logger.debug(f"-{name} Encoder:")
     logger.debug(f"\t{data_encoder}\n")
     # print(f"-{name} Encoder:")
     # print(f"\t{data_encoder}\n")
-    try:
-        logger.debug(f"{data_encoder.get_feature_names_in}")
-    except Exception as e:
-        logger.warning(e)
-        pass
+    # try:
+    #     logger.debug(f"{data_encoder.get_feature_names_in}")
+    # except Exception as e:
+    #     logger.warning(e)
+    #     pass
     logger.debug(f"TRANSFORM pre as df -- \t{df.shape}")
 
     # #####################################  for dirty_cat 0.3.0
     use_columns = getattr(data_encoder, 'columns_', [])
     if len(use_columns):
-        X = data_encoder.transform(df[use_columns])
+        #print(f"Using columns: {use_columns}")
+        X = data_encoder.transform(df[df.columns.intersection(use_columns)])
     # #####################################  with dirty_cat 0.2.0
     else:
         X = data_encoder.transform(df)
@@ -1616,20 +1612,21 @@ def transform(
     # this function aligns with what is computed during
     # processing nodes or edges.
     (
-        X_enc,
-        y_enc,
+        _,
+        _,
+        _,
+        _,
         data_encoder,
         label_encoder,
-        scaling_pipeline,
-        scaling_pipeline_target,
+        _,
+        _,
         text_model,
         text_cols,
     ) = res
 
-    # feature_columns = X_enc.columns
-    # feature_columns_target = y_enc.columns
     logger.info("-" * 90)
-
+    
+    # index = df.index
     y = pd.DataFrame([])
     T = pd.DataFrame([])
     # encode nodes
@@ -1685,14 +1682,14 @@ def transform(
     logger.info(f"--Features matrix shape: {X.shape}")
     logger.info(f"--Target matrix shape: {y.shape}")
 
-    if scaling_pipeline and not X.empty:
-        logger.info("--Scaling Features")
-        X = pd.DataFrame(scaling_pipeline.transform(X), columns=X.columns)
-    if scaling_pipeline_target and not y.empty:
-        logger.info(f"--Scaling Target {scaling_pipeline_target}")
-        y = pd.DataFrame(
-            scaling_pipeline_target.transform(y), columns=y.columns
-        )
+    # if scaling_pipeline and not X.empty:
+    #     logger.info("--Scaling Features")
+    #     X = pd.DataFrame(scaling_pipeline.transform(X), columns=X.columns, index=index)
+    # if scaling_pipeline_target and not y.empty:
+    #     logger.info(f"--Scaling Target {scaling_pipeline_target}")
+    #     y = pd.DataFrame(
+    #         scaling_pipeline_target.transform(y), columns=y.columns, index=index
+    #     )
 
     return X, y
 
@@ -1748,6 +1745,8 @@ def _set_result(self, res):
         [
             X_enc,
             y_enc,
+            X_encs,
+            y_encs,
             data_encoder,
             label_encoder,
             scaling_pipeline,
@@ -1761,8 +1760,10 @@ def _set_result(self, res):
         # label_encoder.target_names_in = self.target_names_in
         self.feature_columns = X_enc.columns
         self.feature_columns_target = y_enc.columns
-        self.X = X_enc
-        self.y = y_enc
+        self.X = X_encs
+        self.y = y_encs
+        self.X_orignal = X_enc
+        self.y_orignal = y_enc
         self.data_encoder = data_encoder  # is list for edges
         self.label_encoder = label_encoder
         self.scaling_pipeline = scaling_pipeline
@@ -1779,40 +1780,68 @@ def fit(self, src=None, dst=None, *args, **kwargs):
         self._set_result(res)
 
     def transform(self, df, ydf=None):
+        "Raw transform, no scaling."
         X, y = transform(df, ydf, self.res, self.kind, self.src, self.dst)
         return X, y
+    
+    def _transform_scaled(self, df, ydf, scaling_pipeline, scaling_pipeline_target):
+        """Transform with scaling fit durning fit."""
+        X, y = transform(df, ydf, self.res, self.kind, self.src, self.dst)
+        if scaling_pipeline is not None and not X.empty:
+            X = pd.DataFrame(scaling_pipeline.transform(X), columns=X.columns, index=X.index)
+        if scaling_pipeline_target is not None and y is not None and not y.empty:
+            y = pd.DataFrame(scaling_pipeline_target.transform(y), columns=y.columns, index=y.index)
+        return X, y
+    
+    def transform_scaled(self, df, ydf=None, scaling_pipeline=None, scaling_pipeline_target=None):
+        if scaling_pipeline is None:
+            scaling_pipeline = self.scaling_pipeline
+        if scaling_pipeline_target is None:
+            scaling_pipeline_target = self.scaling_pipeline_target
+        return self._transform_scaled(df, ydf, scaling_pipeline, scaling_pipeline_target)
 
     def fit_transform(self, src=None, dst=None, *args, **kwargs):
         self.fit(src=src, dst=dst, *args, **kwargs)
         return self.X, self.y
 
-    def scale(self, df, ydf=None, set_scaler=False, *args, **kwargs):
-        # pretty hacky but gets job done --
-        """Fits new scaling functions on df, ydf via args-kwargs
-        (ie use downstream as X_train, X_test ,... or batch 
-        when different scaling on the outputs is required)
+    def scale(self, X=None, y=None, return_pipeline=False, *args, **kwargs):
+        """Fits new scaling functions on df, y via args-kwargs
+        
+        **Example:**
+            ::
+
+                from graphisty.features import SCALERS, SCALER_OPTIONS
+                print(SCALERS)
+                g = graphistry.nodes(df)
+                # set a scaling strategy for features and targets -- umap uses those and produces different results depending.
+                g2 = g.umap(use_scaler='standard', use_scaler_target=None)
+                
+                # later if you want to scale new data, you can do so
+                X, y = g2.transform(df, df, scaled=False)  # unscaled transformer output
+                # now scale with new settings
+                X_scaled, y_scaled = g2.scale(X, y, use_scaler='minmax', use_scaler_target='kbins', n_bins=5)
+                # fit some other pipeline
+                clf.fit(X_scaled, y_scaled)
+                
+            args:
+            ::
+
+                
+                ;X: pd.DataFrame of features
+                :y: pd.DataFrame of target features
+                :kind: str, one of 'nodes' or 'edges'
+                *args, **kwargs: passed to smart_scaler pipeline
+
+            returns:
+                scaled X, y
         """
-        # pop off the previous scaler so that .transform won't use it
-        self.res[4] = None
-        self.res[5] = None
-
-        X, y = self.transform(df, ydf)  # these are the raw transforms,
         logger.info("-Fitting new scaler on raw features")
         X, y, scaling_pipeline, scaling_pipeline_target = smart_scaler(
             X_enc=X, y_enc=y, *args, **kwargs
         )
-
-        if set_scaler:
-            logger.info("--Setting fit scaler to self")
-            self.res[4] = scaling_pipeline
-            self.res[5] = scaling_pipeline_target
-            self.scaling_pipeline = scaling_pipeline
-            self.scaling_pipeline_target = scaling_pipeline_target
-        else:  # add the original back
-            self.res[4] = self.scaling_pipeline
-            self.res[5] = self.scaling_pipeline_target
-
-        return X, y, scaling_pipeline, scaling_pipeline_target
+        if return_pipeline:
+            return X, y, scaling_pipeline, scaling_pipeline_target
+        return X, y
 
 
 # ######################################################################################################################
@@ -1887,6 +1916,21 @@ def reuse_featurization(
         memoize=memoize,
     )
 
+def get_matrix_by_column_part(X: pd.DataFrame, column_part: str) -> pd.DataFrame:
+    """Get the feature matrix by column part existing in column names."""
+    transformed_columns = X.columns[X.columns.map(lambda x: True if column_part in x else False)]  # type: ignore
+    return X[transformed_columns] 
+
+def get_matrix_by_column_parts(X: pd.DataFrame, column_parts: Optional[Union[list, str]]) -> pd.DataFrame:
+    """Get the feature matrix by column parts list existing in column names."""
+    if column_parts is None:
+        return X
+    if isinstance(column_parts, str):
+        column_parts = [column_parts]
+    res = pd.concat([get_matrix_by_column_part(X, column_part) for column_part in column_parts], axis=1)  # type: ignore
+    res = res.loc[:, ~res.columns.duplicated()]  # type: ignore
+    return res
+
 
 class FeatureMixin(MIXIN_BASE):
     """
@@ -1894,14 +1938,20 @@ class FeatureMixin(MIXIN_BASE):
     Subclasses UMAPMixin for umap-ing of automatic features.
 
     Usage:
+    ::
+
         g = graphistry.nodes(df, 'node_column')
         g2 = g.featurize()
 
     or for edges,
+    ::
+
         g = graphistry.edges(df, 'src', 'dst')
         g2 = g.featurize(kind='edges')
 
-    or chain them,
+    or chain them for both nodes and edges,
+    ::
+
         g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node_column')
         g2 = g.featurize().featurize(kind='edges')
 
@@ -1914,25 +1964,25 @@ def __init__(self, *args, **kwargs):
         pass
 
     def _get_feature(self, kind):
-        kind = kind.replace('s', '')
-        assert kind in ['node', 'edge'], f'kind needs to be in `nodes` or `edges`, found {kind}'
-        x = getattr(self, f'_{kind}_features')
+        kind2 = kind.replace('s', '')
+        assert kind2 in ['node', 'edge'], f'kind needs to be in `nodes` or `edges`, found {kind}'
+        x = getattr(self, f'_{kind2}_features')
         return x
     
     def _get_target(self, kind):
-        kind = kind.replace('s', '')
-        assert kind in ['node', 'edge'], f'kind needs to be in `nodes` or `edges`, found {kind}'
-        x = getattr(self, f'_{kind}_target')
+        kind2 = kind.replace('s', '')
+        assert kind2 in ['node', 'edge'], f'kind needs to be in `nodes` or `edges`, found {kind}'
+        x = getattr(self, f'_{kind2}_target')
         return x
     
     def _featurize_nodes(
         self,
         X: XSymbolic = None,
         y: YSymbolic = None,
-        use_scaler: Optional[str] = "zscale",
-        use_scaler_target: Optional[str] = "kbins",
+        use_scaler: Optional[str] = None,
+        use_scaler_target: Optional[str] = None,
         cardinality_threshold: int = 40,
-        cardinality_threshold_target: int = 120,
+        cardinality_threshold_target: int = 400,
         n_topics: int = config.N_TOPICS_DEFAULT,
         n_topics_target: int = config.N_TOPICS_TARGET_DEFAULT,
         multilabel: bool = False,
@@ -1941,7 +1991,6 @@ def _featurize_nodes(
         ngram_range: tuple = (1, 3),
         max_df: float = 0.2,
         min_df: int = 3,
-        #confidence: float = 0.35,
         min_words: float = 2.5,
         model_name: str = "paraphrase-MiniLM-L6-v2",
         similarity: Optional[str] = None,
@@ -1957,8 +2006,9 @@ def _featurize_nodes(
         remove_node_column: bool = True,
         feature_engine: FeatureEngineConcrete = "pandas",
         memoize: bool = True,
+        verbose: bool = False,
     ):
-        res = self.copy()
+        res = self.copy() 
         ndf = res._nodes
         node = res._node
 
@@ -1986,8 +2036,10 @@ def _featurize_nodes(
         y_resolved = resolve_y(ndf, y)
 
         feature_engine = resolve_feature_engine(feature_engine)
+        
+        from .features import ModelDict
 
-        fkwargs = dict(
+        fkwargs = ModelDict("Featurize Params",
             X=X_resolved,
             y=y_resolved,
             use_scaler=use_scaler,
@@ -2002,7 +2054,6 @@ def _featurize_nodes(
             ngram_range=ngram_range,
             max_df=max_df,
             min_df=min_df,
-            #confidence=confidence,
             min_words=min_words,
             model_name=model_name,
             similarity=similarity,
@@ -2026,6 +2077,7 @@ def _featurize_nodes(
 
         old_res = reuse_featurization(res, memoize, fkwargs)
         if old_res:
+            print("--- [[ RE-USING NODE FEATURIZATION ]]") if verbose else None
             logger.info("--- [[ RE-USING NODE FEATURIZATION ]]")
             fresh_res = copy.copy(res)
             for attr in ["_node_features", "_node_target", "_node_encoder"]:
@@ -2037,21 +2089,24 @@ def _featurize_nodes(
         X_resolved = remove_internal_namespace_if_present(X_resolved)
 
         keys_to_remove = ["X", "y", "remove_node_column"]
-        nfkwargs = {}
+        nfkwargs = dict()
         for key, value in fkwargs.items():
             if key not in keys_to_remove:
                 nfkwargs[key] = value
 
-        #############################################################
+        print('-' * 80) if verbose else None
+        print("** Featuring nodes") if verbose else None
+        # ############################################################
         encoder = FastEncoder(X_resolved, y_resolved, kind="nodes")
         encoder.fit(**nfkwargs)
-        ############################################################
+        # ###########################################################
 
         # if changing, also update fresh_res
         res._node_features = encoder.X
+        res._node_features_raw = encoder.X_orignal  # .copy()
         res._node_target = encoder.y
+        res._node_target_raw = encoder.y_orignal  # .copy()
         res._node_encoder = encoder  # now this does
-        
         # all the work `._node_encoder.transform(df, y)` etc
 
         return res
@@ -2060,17 +2115,16 @@ def _featurize_edges(
         self,
         X: XSymbolic = None,
         y: YSymbolic = None,
-        use_scaler: Optional[str] = "zscale",
-        use_scaler_target: Optional[str] = "kbins",
+        use_scaler: Optional[str] = None,
+        use_scaler_target: Optional[str] = None,
         cardinality_threshold: int = 40,
-        cardinality_threshold_target: int = 20,
+        cardinality_threshold_target: int = 400,
         n_topics: int = config.N_TOPICS_DEFAULT,
         n_topics_target: int = config.N_TOPICS_TARGET_DEFAULT,
         use_ngrams: bool = False,
         ngram_range: tuple = (1, 3),
         max_df: float = 0.2,
         min_df: int = 3,
-        #confidence: float = 0.35,
         min_words: float = 2.5,
         multilabel: bool = False,
         model_name: str = "paraphrase-MiniLM-L6-v2",
@@ -2086,6 +2140,7 @@ def _featurize_edges(
         keep_n_decimals: int = 5,
         feature_engine: FeatureEngineConcrete = "pandas",
         memoize: bool = True,
+        verbose: bool = False,
     ):
 
         res = self.copy()
@@ -2118,7 +2173,6 @@ def _featurize_edges(
             ngram_range=ngram_range,
             max_df=max_df,
             min_df=min_df,
-            #confidence=confidence,
             min_words=min_words,
             model_name=model_name,
             similarity=similarity,
@@ -2160,6 +2214,7 @@ def _featurize_edges(
             if key not in keys_to_remove:
                 nfkwargs[key] = value
 
+        print("** Featuring edges") if verbose else None
         ###############################################################
         encoder = FastEncoder(X_resolved, y_resolved, kind="edges")
         encoder.fit(src=res._source, dst=res._destination, **nfkwargs)
@@ -2167,13 +2222,30 @@ def _featurize_edges(
 
         # if editing, should also update fresh_res
         res._edge_features = encoder.X
+        res._edge_features_raw = encoder.X_orignal  # .copy()
         res._edge_target = encoder.y
+        res._edge_target_raw = encoder.y_orignal  # .copy()
         res._edge_encoder = encoder
 
         return res
+    
+    def _infer_edges(self, emb, X, y, df, eps='auto', n_neighbors=4, sample=None, infer_on_umap_embedding=False, 
+                     verbose=False, merge_policy=False, **kwargs):
+        res = self.bind()
+        if merge_policy:
+            # useful to cluster onto existing graph
+            g = infer_graph(res, emb, X, y, df, infer_on_umap_embedding=infer_on_umap_embedding, 
+                            n_neighbors=n_neighbors, eps=eps, sample=sample, verbose=verbose, **kwargs) 
+        else:
+            # useful to cluster onto self
+            g = infer_self_graph(res, emb, X, y, df, infer_on_umap_embedding=infer_on_umap_embedding, 
+                                 n_neighbors=n_neighbors, eps=eps, verbose=verbose, **kwargs)
+        return g
 
-    def _transform(self, encoder: str, df: pd.DataFrame, ydf: pd.DataFrame):
+    def _transform(self, encoder: str, df: pd.DataFrame, ydf: Optional[pd.DataFrame], scaled):
         if getattr(self, encoder) is not None:
+            if scaled:
+                return getattr(self, encoder).transform_scaled(df, ydf)
             return getattr(self, encoder).transform(df, ydf)
         else:
             logger.debug(
@@ -2181,45 +2253,126 @@ def _transform(self, encoder: str, df: pd.DataFrame, ydf: pd.DataFrame):
                 "before being able to transform data"
             )
 
-    def transform(self, df, ydf, kind):
-        """Transform new data"""
+    def transform(self, df: pd.DataFrame, 
+                  y: Optional[pd.DataFrame] = None, 
+                  kind: str = 'nodes', 
+                  min_dist: Union[str, float, int] = 'auto', 
+                  n_neighbors: int = 7,
+                  merge_policy: bool = False,
+                  sample: Optional[int] = None, 
+                  return_graph: bool = True,
+                  scaled: bool = True,
+                  verbose: bool = False):
+        """Transform new data and append to existing graph, or return dataframes
+        
+            **args:**
+
+                :df: pd.DataFrame, raw data to transform
+                :ydf: pd.DataFrame, optional
+                :kind: str  # one of `nodes`, `edges`
+                :return_graph: bool, if True, will return a graph with inferred edges.
+                :merge_policy: bool, if True, adds batch to existing graph nodes via nearest neighbors. If False, will infer edges only between nodes in the batch, default False
+                :min_dist: float, if return_graph is True, will use this value in NN search, or 'auto' to infer a good value. min_dist represents the maximum distance between two samples for one to be considered as in the neighborhood of the other.
+                :sample: int, if return_graph is True, will use sample edges of existing graph to fill out the new graph
+                :n_neighbors: int, if return_graph is True, will use this value for n_neighbors in Nearest Neighbors search
+                :scaled: bool, if True, will use scaled transformation of data set during featurization, default True
+                :verbose: bool, if True, will print metadata about the graph construction, default False
+
+            **Returns:**
+
+                X, y: pd.DataFrame, transformed data if return_graph is False
+                or a graphistry Plottable with inferred edges if return_graph is True
+        """
         if kind == "nodes":
-            return self._transform("_node_encoder", df, ydf)
+            X, y_ = self._transform("_node_encoder", df, y, scaled=scaled)
         elif kind == "edges":
-            return self._transform("_edge_encoder", df, ydf)
+            X, y_ = self._transform("_edge_encoder", df, y, scaled=scaled)
         else:
             logger.debug("kind must be one of `nodes`,"
                          f"`edges`, found {kind}")
+            
+        if return_graph and kind not in ["edges"]:
+            emb = None  # will not be able to infer graph from umap coordinates, 
+            # but will be able to infer graph from features of existing edges
+            g = self._infer_edges(emb, X, y_, df, eps=min_dist, sample=sample, n_neighbors=n_neighbors,
+                                  infer_on_umap_embedding=False, merge_policy=merge_policy,
+                                  verbose=verbose)
+            return g
+        return X, y_
 
     def scale(
         self,
-        df,
-        ydf,
-        kind,
-        use_scaler,
-        use_scaler_target,
-        set_scaler=False,
+        df: Optional[pd.DataFrame] = None,
+        y: Optional[pd.DataFrame] = None,
+        kind: str = "nodes",
+        use_scaler: Union[str, None] = None,
+        use_scaler_target: Union[str, None] = None,
         impute: bool = True,
         n_quantiles: int = 10,
         output_distribution: str = "normal",
         quantile_range=(25, 75),
-        n_bins: int = 2,
+        n_bins: int = 10,
         encode: str = "ordinal",
         strategy: str = "uniform",
         keep_n_decimals: int = 5,
+        return_scalers: bool = False,
     ):
+        """Scale data using the same scalers as used in the featurization step.
+        
+            **Example**
+            ::
+
+                g = graphistry.nodes(df)
+                X, y = g.featurize().scale(kind='nodes', use_scaler='robust', use_scaler_target='kbins', n_bins=3)
+                
+                # or 
+                g = graphistry.nodes(df)
+                # set a scaling strategy for features and targets -- umap uses those and produces different results depending.
+                g2 = g.umap(use_scaler='standard', use_scaler_target=None)
+                
+                # later if you want to scale new data, you can do so
+                X, y = g2.transform(df, df, scale=False)
+                X_scaled, y_scaled = g2.scale(X, y, use_scaler='minmax', use_scaler_target='kbins', n_bins=5)
+                # fit some other pipeline
+                clf.fit(X_scaled, y_scaled)
+
+            **Args:**
+
+                :df: pd.DataFrame, raw data to transform, if None, will use data from featurization fit
+                :y: pd.DataFrame, optional target data
+                :kind: str, one of `nodes`, `edges`
+                :use_scaler: str, optional, one of `minmax`, `robust`, `standard`, `kbins`, `quantile`
+                :use_scaler_target: str, optional, one of `minmax`, `robust`, `standard`, `kbins`, `quantile`
+                :impute: bool, if True, will impute missing values
+                :n_quantiles: int, number of quantiles to use for quantile scaler
+                :output_distribution: str, one of `normal`, `uniform`, `lognormal` 
+                :quantile_range: tuple, range of quantiles to use for quantile scaler
+                :n_bins: int, number of bins to use for KBinsDiscretizer
+                :encode: str, one of `ordinal`, `onehot`, `onehot-dense`, `binary`    
+                :strategy: str, one of `uniform`, `quantile`, `kmeans`
+                :keep_n_decimals: int, number of decimals to keep after scaling
+                :return_scalers: bool, if True, will return the scalers used to scale the data
+
+            **Returns:**
+
+                (X, y) transformed data if return_graph is False or a graph with inferred edges if return_graph is True, or (X, y, scaler, scaler_target) if return_scalers is True
+        """
+                
+        if df is None:  # use the original data
+            X, y = (self._node_features_raw, self._node_target_raw) if kind == "nodes" else (self._edge_features_raw, self._edge_target_raw)  # type: ignore
+        else:
+            X, y = self.transform(df, y, kind=kind, return_graph=False, scaled=False)
 
         if kind == "nodes" and hasattr(self, "_node_encoder"):  # type: ignore
             if self._node_encoder is not None:  # type: ignore
                 (
                     X,
                     y,
-                    scaling_pipeline,
-                    scaling_pipeline_target,
+                    scaler,
+                    scaler_target
                 ) = self._node_encoder.scale(
-                    df,
-                    ydf,
-                    set_scaler=set_scaler,
+                    X,
+                    y,
                     use_scaler=use_scaler,
                     use_scaler_target=use_scaler_target,
                     impute=impute,
@@ -2230,6 +2383,7 @@ def scale(
                     encode=encode,
                     strategy=strategy,
                     keep_n_decimals=keep_n_decimals,
+                    return_pipeline=True
                 )  # type: ignore
             else:
                 raise AttributeError(
@@ -2243,12 +2397,11 @@ def scale(
                 (
                     X,
                     y,
-                    scaling_pipeline,
-                    scaling_pipeline_target,
+                    scaler,
+                    scaler_target
                 ) = self._edge_encoder.scale(
-                    df,
-                    ydf,
-                    set_scaler=set_scaler,
+                    X,
+                    y,
                     use_scaler=use_scaler,
                     use_scaler_target=use_scaler_target,
                     impute=impute,
@@ -2259,14 +2412,17 @@ def scale(
                     encode=encode,
                     strategy=strategy,
                     keep_n_decimals=keep_n_decimals,
+                    return_pipeline=True
                 )  # type: ignore
             else:
                 raise AttributeError(
                     'Please run g.featurize(kind="edges", *args, **kwargs) '
                     'first before scaling matrices and targets is possible.'
                 )
+        if return_scalers:
+            return X, y, scaler, scaler_target
+        return X, y
 
-        return X, y, scaling_pipeline, scaling_pipeline_target
 
     def featurize(
         self,
@@ -2285,29 +2441,29 @@ def featurize(
         ngram_range: tuple = (1, 3),
         max_df: float = 0.2,
         min_df: int = 3,
-        min_words: float = 2.5,
+        min_words: float = 4.5,
         model_name: str = "paraphrase-MiniLM-L6-v2",
         impute: bool = True,
         n_quantiles: int = 100,
         output_distribution: str = "normal",
-        quantile_range=(25, 75),
+        quantile_range = (25, 75),
         n_bins: int = 10,
         encode: str = "ordinal",
         strategy: str = "uniform",
-        similarity: Optional[
-            str
-        ] = None,  # turn this off in favor of Gap Encoder
+        similarity: Optional[str] = None,  # turn this off in favor of Gap Encoder
         categories: Optional[str] = "auto",
         keep_n_decimals: int = 5,
         remove_node_column: bool = True,
         inplace: bool = False,
         feature_engine: FeatureEngine = "auto",
+        dbscan: bool = False,
+        min_dist: float = 0.5,  # DBSCAN eps
+        min_samples: int = 1,  # DBSCAN min_samples
         memoize: bool = True,
+        verbose: bool = False,
     ):
-        r"""
-            Featurize Nodes or Edges of the underlying nodes/edges DataFrames.
-        ______________________________________________________________________
-
+        r"""Featurize Nodes or Edges of the underlying nodes/edges DataFrames.
+       
         :param kind: specify whether to featurize `nodes` or `edges`.
                 Edge featurization includes a pairwise
                 src-to-dst feature block using a MultiLabelBinarizer,
@@ -2320,11 +2476,11 @@ def featurize(
         :param use_scaler: selects which scaler (and automatically imputes
                 missing values using mean strategy)
                 to scale the data. Options are;
-                "minmax", "quantile", "zscale", "robust",
+                "minmax", "quantile", "standard", "robust",
                 "kbins", default None.
                 Please see scikits-learn documentation
                 https://scikit-learn.org/stable/modules/preprocessing.html
-                Here 'zscale' corresponds to 'StandardScaler' in scikits.
+                Here 'standard' corresponds to 'StandardScaler' in scikits.
         :param cardinality_threshold: dirty_cat threshold on cardinality of
                 categorical labels across columns.
                 If value is greater than threshold, will run GapEncoder
@@ -2363,20 +2519,21 @@ def featurize(
                 but at cost of encoding time. If faster encoding is needed,
                 `average_word_embeddings_komninos` is useful
                 and produces less semantically relevant vectors.
-                Please see www.huggingface.co or sentence_transformer
+                Please see sentence_transformer
                 (https://www.sbert.net/) library for all available models.
         :param multilabel: if True, will encode a *single* target column composed of
                 lists of lists as multilabel outputs. 
                 This only works with y=['a_single_col'], default False
         :param embedding: If True, produces a random node embedding of size `n_topics`
-                default, False.
+                default, False. If no node features are provided, will produce random embeddings 
+                (for GNN models, for example)
         :param use_ngrams: If True, will encode textual columns as TfIdf Vectors,
                 default, False.
         :param ngram_range: if use_ngrams=True, can set ngram_range, eg: tuple = (1, 3)
         :param max_df:  if use_ngrams=True, set max word frequency to consider in vocabulary
                 eg: max_df = 0.2,
         :param min_df:  if use_ngrams=True, set min word count to consider in vocabulary
-                eg: min_df = 3    
+                eg: min_df = 3 or 0.00001
         :param categories: Optional[str] in ["auto", "k-means", "most_frequent"], decides which 
                 category to select in Similarity Encoding, default 'auto'
         :param impute: Whether to impute missing values, default True
@@ -2386,7 +2543,7 @@ def featurize(
                 can return distribution as ["normal", "uniform"]
         :param quantile_range: if use_scaler = 'robust'|'quantile', 
                 sets the quantile range.
-        :param n_bins: number of bins to use in kbins discretizer
+        :param n_bins: number of bins to use in kbins discretizer, default 10
         :param encode: encoding for KBinsDiscretizer, can be one of
                 `onehot`, `onehot-dense`, `ordinal`, default 'ordinal'
         :param strategy: strategy for KBinsDiscretizer, can be one of
@@ -2394,6 +2551,9 @@ def featurize(
         :param n_quantiles: if use_scaler = "quantile", sets the number of quantiles, default=100
         :param output_distribution: if use_scaler="quantile"|"robust", 
                 choose from ["normal", "uniform"]
+        :param dbscan: whether to run DBSCAN, default False.
+        :param min_dist: DBSCAN eps parameter, default 0.5.
+        :param min_samples: DBSCAN min_samples parameter, default 5.
         :param keep_n_decimals: number of decimals to keep                
         :param remove_node_column: whether to remove node column so it is
                 not featurized, default True.
@@ -2401,7 +2561,7 @@ def featurize(
                 not, default False.
         :param memoize: whether to store and reuse results across runs,
                 default True.
-        :return: self, with new attributes set by the featurization process.
+        :return: graphistry instance with new attributes set by the featurization process.
         """
         if feature_engine == 'dirty_cat':
             assert_imported()
@@ -2430,11 +2590,10 @@ def featurize(
                 ngram_range=ngram_range,
                 max_df=max_df,
                 min_df=min_df,
-                #confidence=confidence,  # deprecated
                 min_words=min_words,
                 model_name=model_name,
-                similarity=similarity,  # deprecated
-                categories=categories,  # deprecated
+                similarity=similarity,  
+                categories=categories,
                 impute=impute,
                 n_quantiles=n_quantiles,
                 quantile_range=quantile_range,
@@ -2446,6 +2605,7 @@ def featurize(
                 remove_node_column=remove_node_column,
                 feature_engine=feature_engine,
                 memoize=memoize,
+                verbose=verbose
             )
         elif kind == "edges":
             res = res._featurize_edges(
@@ -2462,11 +2622,10 @@ def featurize(
                 ngram_range=ngram_range,
                 max_df=max_df,
                 min_df=min_df,
-                #confidence=confidence,  # deprecated
                 min_words=min_words,
                 model_name=model_name,
-                similarity=similarity,  # deprecated
-                categories=categories,  # deprecated
+                similarity=similarity, 
+                categories=categories,
                 impute=impute,
                 n_quantiles=n_quantiles,
                 quantile_range=quantile_range,
@@ -2477,12 +2636,17 @@ def featurize(
                 keep_n_decimals=keep_n_decimals,
                 feature_engine=feature_engine,
                 memoize=memoize,
+                verbose=verbose
             )
         else:
             logger.warning(
                 f"One may only featurize `nodes` or `edges`, got {kind}"
             )
             return self
+        
+        if dbscan:  # this adds columns to the dataframe, will break tests of pure featurization & umap, so set to False in those
+            res = res.dbscan(min_dist=min_dist, min_samples=min_samples, kind=kind, fit_umap_embedding=False, verbose=verbose)  # type: ignore
+
         if not inplace:
             return res
 
@@ -2490,19 +2654,18 @@ def _featurize_or_get_nodes_dataframe_if_X_is_None(
         self,
         X: XSymbolic = None,
         y: YSymbolic = None,
-        use_scaler: Optional[str] = "zscale",
-        use_scaler_target: Optional[str] = "kbins",
+        use_scaler: Optional[str] = None,
+        use_scaler_target: Optional[str] = None,
         cardinality_threshold: int = 40,
         cardinality_threshold_target: int = 400,
         n_topics: int = config.N_TOPICS_DEFAULT,
         n_topics_target: int = config.N_TOPICS_TARGET_DEFAULT,
         multilabel: bool = False,
-        embedding=False,
+        embedding: bool = False,
         use_ngrams: bool = False,
         ngram_range: tuple = (1, 3),
         max_df: float = 0.2,
         min_df: int = 3,
-        #confidence: float = 0.35,
         min_words: float = 2.5,
         model_name: str = "paraphrase-MiniLM-L6-v2",
         similarity: Optional[
@@ -2521,13 +2684,13 @@ def _featurize_or_get_nodes_dataframe_if_X_is_None(
         feature_engine: FeatureEngineConcrete = "pandas",
         reuse_if_existing=False,
         memoize: bool = True,
+        verbose: bool = False,
     ) -> Tuple[pd.DataFrame, pd.DataFrame, MIXIN_BASE]:
         """
         helper method gets node feature and target matrix if X, y
         are not specified.
         if X, y are specified will set them as `_node_target` and
         `_node_target` attributes
-        -----------------------------------------------------------
         """
 
         res = self.bind()
@@ -2537,7 +2700,7 @@ def _featurize_or_get_nodes_dataframe_if_X_is_None(
             res._node_target = None
 
         if reuse_if_existing and res._node_features is not None:
-            # logger.info('-Reusing Existing Featurization')
+            logger.info('-Reusing Existing Node Featurization')
             return res._node_features, res._node_target, res
 
         res = res._featurize_nodes(
@@ -2555,7 +2718,6 @@ def _featurize_or_get_nodes_dataframe_if_X_is_None(
             ngram_range=ngram_range,
             max_df=max_df,
             min_df=min_df,
-            #confidence=confidence,
             min_words=min_words,
             model_name=model_name,
             similarity=similarity,
@@ -2571,6 +2733,7 @@ def _featurize_or_get_nodes_dataframe_if_X_is_None(
             remove_node_column=remove_node_column,
             feature_engine=feature_engine,
             memoize=memoize,
+            verbose=verbose,
         )
 
         assert res._node_features is not None  # ensure no infinite loop
@@ -2586,10 +2749,10 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
         self,
         X: XSymbolic = None,
         y: YSymbolic = None,
-        use_scaler: Optional[str] = "robust",
-        use_scaler_target: Optional[str] = "kbins",
+        use_scaler: Optional[str] = None,
+        use_scaler_target: Optional[str] = None,
         cardinality_threshold: int = 40,
-        cardinality_threshold_target: int = 20,
+        cardinality_threshold_target: int = 400,
         n_topics: int = config.N_TOPICS_DEFAULT,
         n_topics_target: int = config.N_TOPICS_TARGET_DEFAULT,
         multilabel: bool = False,
@@ -2597,7 +2760,6 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
         ngram_range: tuple = (1, 3),
         max_df: float = 0.2,
         min_df: int = 3,
-        #confidence: float = 0.35,
         min_words: float = 2.5,
         model_name: str = "paraphrase-MiniLM-L6-v2",
         similarity: Optional[
@@ -2615,11 +2777,10 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
         feature_engine: FeatureEngineConcrete = "pandas",
         reuse_if_existing=False,
         memoize: bool = True,
+        verbose: bool = False,
     ) -> Tuple[pd.DataFrame, Optional[pd.DataFrame], MIXIN_BASE]:
-        """
-        helper method gets edge feature and target matrix if X, y
-        are not specified
-        -----------------------------------------------------------
+        """helper method gets edge feature and target matrix if X, y are not specified
+       
         :param X: Data Matrix
         :param y: target, default None
         :return: data `X` and `y`
@@ -2632,7 +2793,7 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
             res._edge_target = None
 
         if reuse_if_existing and res._edge_features is not None:
-            # logger.info('-Reusing Existing Featurization')
+            logger.info('-Reusing Existing Edge Featurization')
             return res._edge_features, res._edge_target, res
 
         res = res._featurize_edges(
@@ -2649,7 +2810,6 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
             ngram_range=ngram_range,
             max_df=max_df,
             min_df=min_df,
-            #confidence=confidence,
             min_words=min_words,
             model_name=model_name,
             similarity=similarity,
@@ -2664,6 +2824,7 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
             keep_n_decimals=keep_n_decimals,
             feature_engine=feature_engine,
             memoize=memoize,
+            verbose=verbose,
         )
 
         assert res._edge_features is not None  # ensure no infinite loop
@@ -2675,39 +2836,54 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
             memoize=memoize,
         )
 
-    def _features_by_col(self, column_part: str, kind: str):
-        if kind == 'nodes' and hasattr(self, '_node_features'):
-            X = self._node_features
-        elif kind == 'edges' and hasattr(self, '_edge_features'):
-            X = self._edge_features
-        else:
-            raise ValueError('make sure to call `featurize` or `umap` before calling `get_features_by_cols`')
-        
-        transformed_columns = X.columns[X.columns.map(lambda x: True if column_part in x else False)]  # type: ignore
-        return X[transformed_columns]  # type: ignore
     
-    def get_features_by_cols(self, columns: Union[List, str], kind: str = 'nodes'):
-        """Returns feature matrix with only the columns that contain the string `column_part` in their name.
-        
-            `X = g.get_features_by_cols(['feature1', 'feature2'])`
+    def get_matrix(self, columns: Optional[Union[List, str]] = None, kind: str = 'nodes', target: bool = False) -> pd.DataFrame:
+        """
+            Returns feature matrix, and if columns are specified, returns matrix with only the columns that contain 
+            the string `column_part` in their name.
+
+            `X = g.get_matrix(['feature1', 'feature2'])`
             will retrieve a feature matrix with only the columns that contain the string 
             `feature1` or `feature2` in their name.
+
+            Most useful for topic modeling, where the column names are of the form `topic_0: descriptor`, `topic_1: descriptor`, etc.
+            Can retrieve unique columns in original dataframe, or actual topic features like [ip_part, shoes, preference_x, etc].
+            
+            Powerful way to retrieve features from a featurized graph by column or (top) features of interest.
             
-            example:
-                res = g2.get_features_by_cols(['172', 'percent'])
-                res.columns
+            **Example:**
+            ::
+            
+                # get the full feature matrices
+                X = g.get_matrix()
+                y = g.get_matrix(target=True)
+            
+                # get subset of features, or topics, given topic model encoding
+                X = g2.get_matrix(['172', 'percent'])
+                X.columns
                     => ['ip_172.56.104.67', 'ip_172.58.129.252', 'item_percent']
+                # or in targets
+                y = g2.get_matrix(['total', 'percent'], target=True)
+                y.columns
+                    => ['basket_price_total', 'conversion_percent', 'CTR_percent', 'CVR_percent']
+
+                # not as useful for sbert features. 
+
+            Caveats:
+                - if you have a column name that is a substring of another column name, you may get unexpected results.
+
+            Args:
+                :columns (Union[List, str]): list of column names or a single column name that may exist in columns 
+                    of the feature matrix. If None, returns original feature matrix
+                :kind (str, optional): Node or Edge features. Defaults to 'nodes'.
+                :target (bool, optional): If True, returns the target matrix. Defaults to False.
+
+            Returns:
+                pd.DataFrame: feature matrix with only the columns that contain the string `column_part` in their name.
+            """
+        if target:
+            X = self._get_target(kind)
+        else:
+            X = self._get_feature(kind)
 
-        Args:
-            columns (Union[List, str]): list of column names or a single column name that may exist in columns 
-                of the feature matrix.
-            kind (str, optional): Node or Edge features. Defaults to 'nodes'.
-
-        Returns:
-            pd.DataFrame: feature matrix with only the columns that contain the string `column_part` in their name.
-        """
-        if isinstance(columns, str):
-            columns = [columns]
-        X = pd.concat([self._features_by_col(col, kind=kind) for col in columns], axis=1)  # type: ignore
-        X = X.loc[:, ~X.columns.duplicated()]  # type: ignore
-        return X
+        return get_matrix_by_column_parts(X, columns)
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 5cd092f29d..eb296025ee 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -1,6 +1,7 @@
 import copy
 from time import time
 from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
+from inspect import getmodule
 
 import pandas as pd
 
@@ -279,18 +280,25 @@ def transform_umap(  # noqa: E303
         emb = self._umap.transform(x)  # type: ignore
         emb = self._bundle_embedding(emb, index=df.index)
         return emb, x, y
-
+    
     def _bundle_embedding(self, emb, index):
         # Converts Embedding into dataframe and takes care if emb.dim > 2
-        if emb.shape[1] == 2:
+
+        if emb.shape[1] == 2 and 'cudf.core.dataframe' not in str(getmodule(emb)):
             emb = pd.DataFrame(emb, columns=[config.X, config.Y], index=index)
+        elif emb.shape[1] == 2 and 'cudf.core.dataframe' in str(getmodule(emb)):
+            emb.rename(columns={0:config.X,1: config.Y},inplace=True)
         else:
             columns = [config.X, config.Y] + [
                 f"umap_{k}" for k in range(2, emb.shape[1] - 2)
             ]
-            emb = pd.DataFrame(emb, columns=columns, index=index)
-        return emb
 
+            if 'cudf.core.dataframe' not in str(getmodule(emb)):
+                emb = pd.DataFrame(emb, columns=columns, index=index)
+            elif 'cudf.core.dataframe' in str(getmodule(emb)):
+                emb.columns=columns
+        return emb
+    
     def _process_umap(
         self,
         res,
@@ -491,6 +499,11 @@ def umap(
 
             logger.debug("umap X_: %s", X_)
             logger.debug("umap y_: %s", y_)
+            logger.debug("data is type :: %s", (type(X_)))
+            if isinstance(X_, pd.DataFrame):
+                index_to_nodes_dict = dict(zip(range(len(nodes)), nodes))
+            elif 'cudf.core.dataframe' in str(getmodule(X_)):
+                index_to_nodes_dict = nodes
 
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs

From ba5629255d7830d480a2a8c0cbf1bf1b68561663 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <apple@dc-m1.local>
Date: Thu, 16 Mar 2023 18:23:29 +0900
Subject: [PATCH 262/432] naive cudf tests

---
 graphistry/tests/test_umap_utils.py | 164 +++++++++++++++++++++++++++-
 graphistry/umap_utils.py            |  11 ++
 2 files changed, 174 insertions(+), 1 deletion(-)

diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index ca0c3897ba..6ed8f8f00f 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -22,10 +22,11 @@
     lazy_import_has_min_dependancy,
     check_allclose_fit_transform_on_same_data
 )
-from graphistry.umap_utils import lazy_umap_import_has_dependancy, lazy_cuml_import_has_dependancy
+from graphistry.umap_utils import lazy_umap_import_has_dependancy, lazy_cuml_import_has_dependancy, lazy_cudf_import_has_dependancy
 
 has_dependancy, _ = lazy_import_has_min_dependancy()
 has_cuml, _, _ = lazy_cuml_import_has_dependancy()
+has_cudf, _, _ = lazy_cudf_import_has_dependancy()
 has_umap, _, _ = lazy_umap_import_has_dependancy()
 
 logger = logging.getLogger(__name__)
@@ -560,5 +561,166 @@ def test_filter_edges(self):
                 last_shape = shape[0]
 
 
+@pytest.mark.skipif(
+    not has_dependancy or not has_cudf,
+    reason="requires cudf feature dependencies",
+)
+class TestCUDFMethods(TestUMAPMethods):
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cudf,
+        reason="requires cudf feature dependencies",
+    )
+    def _test_umap(self, g, use_cols, targets, name, kind, df):
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            for scaler in ['kbins', 'robust']:
+                for cardinality in [2, 200]:
+                    for use_ngram in [True, False]:
+                        for use_col in use_cols:
+                            for target in targets:
+                                logger.debug("*" * 90)
+                                value = [scaler, cardinality, use_ngram, target, use_col]
+                                logger.debug(f"{value}")
+                                logger.debug("-" * 80)
+
+                                g = graphistry.nodes(cudf.from_pandas(ndf_reddit))
+                                g2 = g.umap(kind=kind,
+                                    X=use_col,
+                                    y=target,
+                                    model_name=model_avg_name,
+                                    use_scaler=scaler,
+                                    use_scaler_target=scaler,
+                                    use_ngrams=use_ngram,
+                                    engine='cudf',
+                                    cardinality_threshold=cardinality,
+                                    cardinality_threshold_target=cardinality,
+                                    n_neighbors=3)
+
+                                self.cases_test_graph(g2, kind=kind, df=df)
+
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cudf,
+        reason="requires cudf feature dependencies",
+    )
+    def test_node_umap(self):
+        g = graphistry.nodes(cudf.from_pandas(ndf_reddit))
+        use_cols = [None, text_cols_reddit, good_cols_reddit, meta_cols_reddit]
+        targets = [None, single_target_reddit, double_target_reddit]
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            warnings.filterwarnings("ignore", category=FutureWarning)
+
+            self._test_umap(
+                g,
+                use_cols=use_cols,
+                targets=targets,
+                name="Node UMAP with `(target, use_col)=`",
+                kind="nodes",
+                df=ndf_reddit,
+            )
+
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cudf,
+        reason="requires cudf feature dependencies",
+    )
+    def test_edge_umap(self):
+        g = graphistry.nodes(cudf.from_pandas(edge_df2), "src", "dst")
+        targets = [None, 'label']
+        use_cols = [None, 'title']
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            warnings.filterwarnings("ignore", category=FutureWarning)
+
+            self._test_umap(
+                g,
+                use_cols=use_cols,
+                targets=targets,
+                name="Edge UMAP with `(target, use_col)=`",
+                kind="edges",
+                df=edge_df2,
+            )
+
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cudf,
+        reason="requires cudf feature dependencies",
+    )
+    def test_chaining_nodes(self):
+        g = graphistry.nodes(cudf.from_pandas(ndf_reddit))
+        g2 = g.umap()
+
+        logger.debug('======= g.umap() done ======')
+        g3a = g2.featurize()
+        logger.debug('======= g3a.featurize() done ======')
+        g3 = g3a.umap()
+        logger.debug('======= g3.umap() done ======')
+        assert g2._node_features.shape == g3._node_features.shape
+        # since g3 has feature params with x and y.
+        g3._feature_params['nodes']['X'].pop('x')
+        g3._feature_params['nodes']['X'].pop('y')
+        assert all(g2._feature_params['nodes']['X'] == g3._feature_params['nodes']['X'])
+        assert g2._feature_params['nodes']['y'].shape == g3._feature_params['nodes']['y'].shape  # None
+        assert g2._node_embedding.shape == g3._node_embedding.shape  # kinda weak sauce
+        
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cudf,
+        reason="requires cudf feature dependencies",
+    )
+    def test_chaining_edges(self):
+        g = graphistry.nodes(cudf.from_pandas(edge_df), "src", "dst")
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            warnings.filterwarnings("ignore", category=FutureWarning)
+            g2 = g.umap(kind='edges')
+            g3 = g.featurize(kind='edges').umap(kind='edges')
+            
+        assert all(g2._feature_params['edges']['X'] == g3._feature_params['edges']['X'])
+        assert all(g2._feature_params['edges']['y'] == g3._feature_params['edges']['y'])  # None
+        assert all(g2._edge_features == g3._edge_features)
+
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cudf,
+        reason="requires cudf feature dependencies",
+    )
+    def test_feature_kwargs_yield_different_values_using_umap_api(self):
+        g = graphistry.nodes(cudf.from_pandas(ndf_reddit))
+        n_topics_target = 6
+    
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            warnings.filterwarnings("ignore", category=FutureWarning)
+
+            g2 = g.umap(X="type", y="label", cardinality_threshold_target=3, n_topics_target=n_topics_target)  # makes a GapEncoded Target
+            g3 = g.umap(X="type", y="label", cardinality_threshold_target=30000)  # makes a one-hot-encoded target
+            
+        assert all(g2._feature_params['nodes']['X'] == g3._feature_params['nodes']['X']), "features should be the same"
+        assert all(g2._feature_params['nodes']['y'] != g3._feature_params['nodes']['y']), "targets in memoize should be different"  # None
+        assert g2._node_target.shape[1] != g3._node_target.shape[1], 'Targets should be different'
+        assert g2._node_target.shape[1] == n_topics_target, 'Targets '
+
+    @pytest.mark.skipif(
+        not has_dependancy or not has_umap,
+        reason="requires ai+umap feature dependencies",
+    )
+    def test_filter_edges(self):
+        for kind, g in [("nodes", graphistry.nodes(cudf.from_pandas(ndf_reddit)))]:
+            g2 = g.umap(kind=kind, model_name=model_avg_name)
+            last_shape = 0
+            for scale in np.linspace(0, 1, 8):  # six sigma in 8 steps
+                g3 = g2.filter_weighted_edges(scale=scale)
+                shape = g3._edges.shape
+                logger.debug("*" * 90)
+                logger.debug(
+                    f"{kind} -- scale: {scale}: resulting edges dataframe shape: {shape}"
+                )
+                logger.debug("-" * 80)
+                self.assertGreaterEqual(shape[0], last_shape)
+                last_shape = shape[0]
+
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index eb296025ee..46662c4b9b 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -45,6 +45,17 @@ def lazy_cuml_import_has_dependancy():
         return True, "ok", cuml
     except ModuleNotFoundError as e:
         return False, e, None
+    
+def lazy_cudf_import_has_dependancy():
+    try:
+        import warnings
+
+        warnings.filterwarnings("ignore")
+        import cudf  # type: ignore
+
+        return True, "ok", cudf
+    except ModuleNotFoundError as e:
+        return False, e, None
 
 
 def assert_imported():

From f7b7156d983ccd818d000e06252b56f388067678 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <apple@dc-m1.local>
Date: Thu, 16 Mar 2023 18:40:36 +0900
Subject: [PATCH 263/432] merge clean, add naive tests

---
 graphistry/tests/test_umap_utils.py | 164 ++++++++++++++++++++++++++++
 graphistry/umap_utils.py            |  10 ++
 2 files changed, 174 insertions(+)

diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index acfb39cfd7..ea828f0f84 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -27,14 +27,17 @@
 from graphistry.umap_utils import (
     lazy_umap_import_has_dependancy,
     lazy_cuml_import_has_dependancy,
+    lazy_cudf_import_has_dependancy,
 )
 
 has_dependancy, _ = lazy_import_has_min_dependancy()
 has_cuml, _, _ = lazy_cuml_import_has_dependancy()
+has_cudf, _, _ = lazy_cudf_import_has_dependancy()
 has_umap, _, _ = lazy_umap_import_has_dependancy()
 
 # print('has_dependancy', has_dependancy)
 # print('has_cuml', has_cuml)
+# print('has_cudf', has_cudf)
 # print('has_umap', has_umap)
 
 logger = logging.getLogger(__name__)
@@ -776,5 +779,166 @@ def test_filter_edges(self):
                 last_shape = shape[0]
 
 
+@pytest.mark.skipif(
+    not has_dependancy or not has_cudf,
+    reason="requires cudf feature dependencies",
+)
+class TestCUDFMethods(TestUMAPMethods):
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cudf,
+        reason="requires cudf feature dependencies",
+    )
+    def _test_umap(self, g, use_cols, targets, name, kind, df):
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            for scaler in ['kbins', 'robust']:
+                for cardinality in [2, 200]:
+                    for use_ngram in [True, False]:
+                        for use_col in use_cols:
+                            for target in targets:
+                                logger.debug("*" * 90)
+                                value = [scaler, cardinality, use_ngram, target, use_col]
+                                logger.debug(f"{value}")
+                                logger.debug("-" * 80)
+
+                                g = graphistry.nodes(cudf.from_pandas(ndf_reddit))
+                                g2 = g.umap(kind=kind,
+                                    X=use_col,
+                                    y=target,
+                                    model_name=model_avg_name,
+                                    use_scaler=scaler,
+                                    use_scaler_target=scaler,
+                                    use_ngrams=use_ngram,
+                                    engine='cudf',
+                                    cardinality_threshold=cardinality,
+                                    cardinality_threshold_target=cardinality,
+                                    n_neighbors=3)
+
+                                self.cases_test_graph(g2, kind=kind, df=df)
+
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cudf,
+        reason="requires cudf feature dependencies",
+    )
+    def test_node_umap(self):
+        g = graphistry.nodes(cudf.from_pandas(ndf_reddit))
+        use_cols = [None, text_cols_reddit, good_cols_reddit, meta_cols_reddit]
+        targets = [None, single_target_reddit, double_target_reddit]
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            warnings.filterwarnings("ignore", category=FutureWarning)
+
+            self._test_umap(
+                g,
+                use_cols=use_cols,
+                targets=targets,
+                name="Node UMAP with `(target, use_col)=`",
+                kind="nodes",
+                df=ndf_reddit,
+            )
+
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cudf,
+        reason="requires cudf feature dependencies",
+    )
+    def test_edge_umap(self):
+        g = graphistry.nodes(cudf.from_pandas(edge_df2), "src", "dst")
+        targets = [None, 'label']
+        use_cols = [None, 'title']
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            warnings.filterwarnings("ignore", category=FutureWarning)
+
+            self._test_umap(
+                g,
+                use_cols=use_cols,
+                targets=targets,
+                name="Edge UMAP with `(target, use_col)=`",
+                kind="edges",
+                df=edge_df2,
+            )
+
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cudf,
+        reason="requires cudf feature dependencies",
+    )
+    def test_chaining_nodes(self):
+        g = graphistry.nodes(cudf.from_pandas(ndf_reddit))
+        g2 = g.umap()
+
+        logger.debug('======= g.umap() done ======')
+        g3a = g2.featurize()
+        logger.debug('======= g3a.featurize() done ======')
+        g3 = g3a.umap()
+        logger.debug('======= g3.umap() done ======')
+        assert g2._node_features.shape == g3._node_features.shape
+        # since g3 has feature params with x and y.
+        g3._feature_params['nodes']['X'].pop('x')
+        g3._feature_params['nodes']['X'].pop('y')
+        assert all(g2._feature_params['nodes']['X'] == g3._feature_params['nodes']['X'])
+        assert g2._feature_params['nodes']['y'].shape == g3._feature_params['nodes']['y'].shape  # None
+        assert g2._node_embedding.shape == g3._node_embedding.shape  # kinda weak sauce
+        
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cudf,
+        reason="requires cudf feature dependencies",
+    )
+    def test_chaining_edges(self):
+        g = graphistry.nodes(cudf.from_pandas(edge_df), "src", "dst")
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            warnings.filterwarnings("ignore", category=FutureWarning)
+            g2 = g.umap(kind='edges')
+            g3 = g.featurize(kind='edges').umap(kind='edges')
+            
+        assert all(g2._feature_params['edges']['X'] == g3._feature_params['edges']['X'])
+        assert all(g2._feature_params['edges']['y'] == g3._feature_params['edges']['y'])  # None
+        assert all(g2._edge_features == g3._edge_features)
+
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cudf,
+        reason="requires cudf feature dependencies",
+    )
+    def test_feature_kwargs_yield_different_values_using_umap_api(self):
+        g = graphistry.nodes(cudf.from_pandas(ndf_reddit))
+        n_topics_target = 6
+    
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            warnings.filterwarnings("ignore", category=FutureWarning)
+
+            g2 = g.umap(X="type", y="label", cardinality_threshold_target=3, n_topics_target=n_topics_target)  # makes a GapEncoded Target
+            g3 = g.umap(X="type", y="label", cardinality_threshold_target=30000)  # makes a one-hot-encoded target
+            
+        assert all(g2._feature_params['nodes']['X'] == g3._feature_params['nodes']['X']), "features should be the same"
+        assert all(g2._feature_params['nodes']['y'] != g3._feature_params['nodes']['y']), "targets in memoize should be different"  # None
+        assert g2._node_target.shape[1] != g3._node_target.shape[1], 'Targets should be different'
+        assert g2._node_target.shape[1] == n_topics_target, 'Targets '
+
+    @pytest.mark.skipif(
+        not has_dependancy or not has_umap,
+        reason="requires ai+umap feature dependencies",
+    )
+    def test_filter_edges(self):
+        for kind, g in [("nodes", graphistry.nodes(cudf.from_pandas(ndf_reddit)))]:
+            g2 = g.umap(kind=kind, model_name=model_avg_name)
+            last_shape = 0
+            for scale in np.linspace(0, 1, 8):  # six sigma in 8 steps
+                g3 = g2.filter_weighted_edges(scale=scale)
+                shape = g3._edges.shape
+                logger.debug("*" * 90)
+                logger.debug(
+                    f"{kind} -- scale: {scale}: resulting edges dataframe shape: {shape}"
+                )
+                logger.debug("-" * 80)
+                self.assertGreaterEqual(shape[0], last_shape)
+                last_shape = shape[0]
+
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index eb296025ee..ff70e17660 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -46,6 +46,16 @@ def lazy_cuml_import_has_dependancy():
     except ModuleNotFoundError as e:
         return False, e, None
 
+def lazy_cudf_import_has_dependancy(): ## rather than adding to cuml import
+    try:
+        import warnings
+
+        warnings.filterwarnings("ignore")
+        import cudf  # type: ignore
+
+        return True, "ok", cudf
+    except ModuleNotFoundError as e:
+        return False, e, None
 
 def assert_imported():
     has_dependancy_, import_exn, umap_learn = lazy_umap_import_has_dependancy()

From 0b8028a622142fc9d03f217ebd487f985bae138f Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Thu, 16 Mar 2023 12:45:31 -0400
Subject: [PATCH 264/432] fix(docstr): added layout & compute to nav

---
 docs/source/graphistry.compute.rst | 48 ++++++++++++++++++-------
 docs/source/graphistry.layout.rst  | 57 ++++++++++++++++++++++++------
 docs/source/graphistry.plugins.rst | 19 ++--------
 docs/source/graphistry.rst         | 34 +++++++++++-------
 4 files changed, 106 insertions(+), 52 deletions(-)

diff --git a/docs/source/graphistry.compute.rst b/docs/source/graphistry.compute.rst
index 6ea4bdedbd..08b49eedef 100644
--- a/docs/source/graphistry.compute.rst
+++ b/docs/source/graphistry.compute.rst
@@ -1,29 +1,51 @@
-graphistry.layout package
-=========================
+ComputeMixin module
+------------------------------------------------
 
-Subpackages
------------
+.. automodule:: graphistry.compute.ComputeMixin
+   :members:
+   :undoc-members:
+   :show-inheritance:
 
-.. toctree::
-   :maxdepth: 4
 
+Chain
+---------------
 
-Submodules
-----------
+.. automodule:: graphistry.compute.chain
+   :members:
+   :undoc-members:
+   :show-inheritance:
 
-graphistry.compute.ComputeMixin module
-------------------------------------------------
+Cluster
+---------------
+.. automodule:: graphistry.compute.cluster
+   :members:
+   :undoc-members:
+   :show-inheritance:
 
-.. automodule:: graphistry.compute.ComputeMixin
+Collapse
+---------------
+.. automodule:: graphistry.compute.collapse
    :members:
    :undoc-members:
    :show-inheritance:
 
+Conditional
+---------------
+.. automodule:: graphistry.compute.conditional
+   :members:
+   :undoc-members:
+   :show-inheritance:
 
-Module contents
+Filter by Dictionary
 ---------------
+.. automodule:: graphistry.compute.filter_by_dict
+   :members:
+   :undoc-members:
+   :show-inheritance:
 
-.. automodule:: graphistry.compute
+Hop
+---------------
+.. automodule:: graphistry.compute.hop
    :members:
    :undoc-members:
    :show-inheritance:
diff --git a/docs/source/graphistry.layout.rst b/docs/source/graphistry.layout.rst
index 7675db6e35..9216de5252 100644
--- a/docs/source/graphistry.layout.rst
+++ b/docs/source/graphistry.layout.rst
@@ -1,16 +1,53 @@
-graphistry.layout package
-=========================
 
-Subpackages
------------
 
-.. toctree::
-   :maxdepth: 4
+edge Module
+-----------------------------------
+
+.. automodule:: graphistry.layout.graph.edge
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+edgeBase Module
+---------------------------------------
+
+.. automodule:: graphistry.layout.graph.edgeBase
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+graph Module
+------------------------------------
+
+.. automodule:: graphistry.layout.graph.graph
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+graphBase Module
+----------------------------------------
+
+.. automodule:: graphistry.layout.graph.graphBase
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+vertex Module
+-------------------------------------
+
+.. automodule:: graphistry.layout.graph.vertex
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+vertexBase Module
+-----------------------------------------
+
+.. automodule:: graphistry.layout.graph.vertexBase
+   :members:
+   :undoc-members:
+   :show-inheritance:
 
-   graphistry.layout.gib
-   graphistry.layout.graph
-   graphistry.layout.sugiyama
-   graphistry.layout.utils
 
 Module contents
 ---------------
diff --git a/docs/source/graphistry.plugins.rst b/docs/source/graphistry.plugins.rst
index d3e64fc1ab..492e4ac3fb 100644
--- a/docs/source/graphistry.plugins.rst
+++ b/docs/source/graphistry.plugins.rst
@@ -1,17 +1,4 @@
-graphistry.plugins package
-==========================
-
-Subpackages
------------
-
-.. toctree::
-   :maxdepth: 4
-
-
-Submodules
-----------
-
-graphistry.plugins.igraph module
+iGraph
 ------------------------------------------------
 
 .. automodule:: graphistry.plugins.igraph
@@ -20,10 +7,10 @@ graphistry.plugins.igraph module
    :show-inheritance:
 
 
-Module contents
+CuGraph
 ---------------
 
-.. automodule:: graphistry.plugins
+.. automodule:: graphistry.plugins.cugraph
    :members:
    :undoc-members:
    :show-inheritance:
diff --git a/docs/source/graphistry.rst b/docs/source/graphistry.rst
index a6fdf5cf15..d115114c70 100644
--- a/docs/source/graphistry.rst
+++ b/docs/source/graphistry.rst
@@ -1,29 +1,37 @@
-Layout & Plugins
+Plotter Module
+==================
+
+.. automodule:: graphistry.PlotterBase
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+Plugins
 ==================
 .. toctree::
    :maxdepth: 3
 
 
-   graphistry.layout
    graphistry.plugins
-   graphistry.plugins_types
+ 
 
 
-Plotter Module
+Compute 
 ==================
+.. toctree::
+   :maxdepth: 3
 
-.. automodule:: graphistry.PlotterBase
-    :members:
-    :undoc-members:
-    :show-inheritance:
+   graphistry.compute
 
-Pygraphistry Module
+
+Layouts
 ==================
+.. toctree::
+   :maxdepth: 3
+
+
+   graphistry.layout
 
-.. automodule:: graphistry.pygraphistry
-    :members:
-    :undoc-members:
-    :show-inheritance:
 
 Featurize 
 ==================

From 299adf13977a287fe0718b5ce895418f672b849d Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Thu, 16 Mar 2023 12:45:58 -0400
Subject: [PATCH 265/432] fix(docstr): removed unneccessary lines

---
 graphistry/feature_utils.py | 27 +++++++++------------------
 1 file changed, 9 insertions(+), 18 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 6b845576e6..ed5ee15e62 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -223,10 +223,7 @@ def safe_divide(a, b):
 def features_without_target(
     df: pd.DataFrame, y: Optional[Union[List, str, pd.DataFrame]] = None
 ) -> pd.DataFrame:
-    """
-        Checks if y DataFrame column name is in df, and removes it
-        from df if so
-    ___________________________________________________________________
+    """Checks if y DataFrame column name is in df, and removes it from df if so
 
     :param df: model DataFrame
     :param y: target DataFrame
@@ -397,9 +394,8 @@ def is_dataframe_all_numeric(df: pd.DataFrame) -> bool:
 
 
 def find_bad_set_columns(df: pd.DataFrame, bad_set: List = ["[]"]):
-    """
-        Finds columns that if not coerced to strings, will break processors.
-    -------------------------------------------------------------------------
+    """Finds columns that if not coerced to strings, will break processors.
+    
     :param df: DataFrame
     :param bad_set: List of strings to look for.
     :return: list
@@ -429,9 +425,7 @@ def check_if_textual_column(
     confidence: float = 0.35,
     min_words: float = 2.5,
 ) -> bool:
-    """
-        Checks if `col` column of df is textual or not using basic heuristics
-    __________________________________________________________________________
+    """Checks if `col` column of df is textual or not using basic heuristics
 
     :param df: DataFrame
     :param col: column name
@@ -470,9 +464,7 @@ def check_if_textual_column(
 def get_textual_columns(
     df: pd.DataFrame, min_words: float = 2.5
 ) -> List:
-    """
-        Collects columns from df that it deems are textual.
-    _____________________________________________________________________
+    """Collects columns from df that it deems are textual.
 
     :param df: DataFrame
     :return: list of columns names
@@ -793,8 +785,8 @@ def encoder(X, use_scaler):  # noqa: E301
 
 
 def get_cardinality_ratio(df: pd.DataFrame):
-    """Calculates ratio of unique values to total number of rows of DataFrame
-    -------------------------------------------------------------------------
+    """Calculates the ratio of unique values to total number of rows of DataFrame
+   
     :param df: DataFrame
     """
     ratios = {}
@@ -2409,9 +2401,8 @@ def featurize(
         memoize: bool = True,
         verbose: bool = False,
     ):
-        r"""
-            Featurize Nodes or Edges of the underlying nodes/edges DataFrames.
-        ______________________________________________________________________
+        r"""Featurize Nodes or Edges of the underlying nodes/edges DataFrames.
+        
 
         :param kind: specify whether to featurize `nodes` or `edges`.
                 Edge featurization includes a pairwise

From eaf69a52ded7206c627f305a87b7c6423d0db0dc Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 16 Mar 2023 11:28:58 -0700
Subject: [PATCH 266/432] forces dbscan to run on cpu only -- once
 https://github.com/rapidsai/cuml/issues/5277 is resolved we can add gpu
 support back in

---
 graphistry/compute/cluster.py | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 45d287126e..ce93859832 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -147,6 +147,7 @@ def dbscan_fit(g: Any, dbscan: Any, kind: str = "nodes", cols: Optional[Union[Li
     dbscan.fit(X)
     if g.engine == 'cuml':
         labels = dbscan.labels_.to_numpy()
+        # dbscan.components_ = X[dbscan.core_sample_indices_.to_pandas()]  # can't believe len(samples) != unique(labels) ... #cumlfail
     else:
         labels = dbscan.labels_
 
@@ -206,11 +207,11 @@ def _cluster_dbscan(
         self, res, kind, cols, fit_umap_embedding, target, min_dist, min_samples, verbose, *args, **kwargs
     ):
         """
-        DBSCAN clustering on cpu or gpu infered by .engine flag
+        DBSCAN clustering on cpu or *(not yet supported) gpu* infered by .engine flag
         """
         _, DBSCAN, _, cuDBSCAN = lazy_dbscan_import_has_dependency()
 
-        res.engine = resolve_cpu_gpu_engine("auto")
+        res.engine = resolve_cpu_gpu_engine(UMAP_LEARN)  # resolve_cpu_gpu_engine("auto")
         res._dbscan_params = ModelDict(
             "latest DBSCAN params",
             kind=kind,
@@ -247,6 +248,7 @@ def dbscan(
         **kwargs,
     ):
         """DBSCAN clustering on cpu or gpu infered automatically. Adds a `_dbscan` column to nodes or edges.
+           NOTE: g.transform_dbscan(..) currently unsupported on GPU.
 
         Examples:
         ::
@@ -307,7 +309,6 @@ def dbscan(
             *args,
             **kwargs,
         )
-        #res = res.encode_point_color(column=DBSCAN, as_categorical=True)
 
         return res
 
@@ -339,6 +340,11 @@ def _transform_dbscan(
                 X_ = emb
             else:
                 X_ = XX
+            
+
+            if self.engine == 'cuml':
+                print('Transform DBSCAN not supported for engine=`cuml`, use engine=`umap_learn` instead')
+                return emb, X, y, df
 
             labels = dbscan_predict(X_, dbscan)  # type: ignore
             if umap and cols is None:
@@ -419,11 +425,13 @@ def transform_dbscan(
             :verbose: whether to print out progress, default False
 
         """
+        if self.engine == 'cuml':
+            print('Transform DBSCAN not supported for `cuml`, use engine=`umap_learn` instead')
+            return self.transform_umap(df, y, kind=kind, verbose=verbose, return_graph=return_graph)
         emb, X, y, df = self._transform_dbscan(df, y, kind=kind, verbose=verbose)
         if return_graph and kind not in ["edges"]:
             g = self._infer_edges(emb, X, y, df, eps=min_dist, sample=sample, n_neighbors=n_neighbors,  # type: ignore
                 infer_on_umap_embedding=infer_umap_embedding
                 )
-            #g = g.encode_point_color(column=DBSCAN, as_categorical=True)
             return g
         return emb, X, y, df

From 8782a6d94bf0b4560cbd7e35f12b77fea20b537a Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 16 Mar 2023 15:37:02 -0700
Subject: [PATCH 267/432] adds engine_dbscan flag and if [sklearn ,umap_learn]
 will allow g.transform_dbscan(..) enrichment

---
 graphistry/compute/cluster.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index ce93859832..f7d6a3848b 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -56,7 +56,7 @@ def lazy_cudf_import_has_dependancy():
 def resolve_cpu_gpu_engine(
     engine: DBSCANEngine,
 ) -> DBSCANEngineConcrete:  # noqa
-    if engine in [CUML, UMAP_LEARN]:
+    if engine in [CUML, UMAP_LEARN, 'sklearn']:
         return engine  # type: ignore
     if engine in ["auto"]:
         (
@@ -204,14 +204,17 @@ def __init__(self, *args, **kwargs):
         pass
 
     def _cluster_dbscan(
-        self, res, kind, cols, fit_umap_embedding, target, min_dist, min_samples, verbose, *args, **kwargs
+        self, res, kind, cols, fit_umap_embedding, target, min_dist, min_samples, engine, verbose, *args, **kwargs
     ):
         """
         DBSCAN clustering on cpu or *(not yet supported) gpu* infered by .engine flag
         """
         _, DBSCAN, _, cuDBSCAN = lazy_dbscan_import_has_dependency()
 
-        res.engine = resolve_cpu_gpu_engine(UMAP_LEARN)  # resolve_cpu_gpu_engine("auto")
+        if engine in [CUML]:
+            print('`g.transform_dbscan(..)` not supported for engine=cuml, will return `g.transform_umap(..)` instead')
+
+        res.engine = resolve_cpu_gpu_engine(engine)  # resolve_cpu_gpu_engine("auto")
         res._dbscan_params = ModelDict(
             "latest DBSCAN params",
             kind=kind,
@@ -220,6 +223,7 @@ def _cluster_dbscan(
             fit_umap_embedding=fit_umap_embedding,
             min_dist=min_dist,
             min_samples=min_samples,
+            engine=engine,
             verbose=verbose,
         )
 
@@ -244,6 +248,7 @@ def dbscan(
         fit_umap_embedding: bool = True,
         target: bool = False,
         verbose: bool = False,
+        engine_dbscan: str = 'sklearn'
         *args,
         **kwargs,
     ):
@@ -305,6 +310,7 @@ def dbscan(
             target=target,
             min_dist=min_dist,
             min_samples=min_samples,
+            engine=engine_dbscan,
             verbose=verbose,
             *args,
             **kwargs,

From 44351baa5b6e72d4ff512bd4a4e0007cdfa11ddd Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 17 Mar 2023 13:13:06 -0700
Subject: [PATCH 268/432] bug fix

---
 graphistry/compute/cluster.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index f7d6a3848b..8c4fecbba6 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -248,7 +248,7 @@ def dbscan(
         fit_umap_embedding: bool = True,
         target: bool = False,
         verbose: bool = False,
-        engine_dbscan: str = 'sklearn'
+        engine_dbscan: str = 'sklearn',
         *args,
         **kwargs,
     ):

From c2d70e2c72bb25b9968783592507c69be38bc575 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 17 Mar 2023 14:32:46 -0700
Subject: [PATCH 269/432] explicit engine_dbscan flags

---
 graphistry/compute/cluster.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 8c4fecbba6..c067af9ec7 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -82,7 +82,7 @@ def safe_cudf(X, y):
         new_kwargs = {}
         kwargs = {'X': X, 'y': y}
         for key, value in kwargs.items():
-            if isinstance(value, cudf.DataFrame) and engine == "pandas":
+            if isinstance(value, cudf.DataFrame) and engine in ["pandas", 'sklearn', 'umap_learn']:
                 new_kwargs[key] = value.to_pandas()
             elif isinstance(value, pd.DataFrame) and engine == "cuml":
                 new_kwargs[key] = cudf.from_pandas(value)
@@ -122,8 +122,8 @@ def get_model_matrix(g, kind: str, cols: Optional[Union[List, str]], umap, targe
     if umap and cols is None and g._umap is not None:
         df = g._get_embedding(kind)            
     
-    if g.engine in [CUML]:
-        df, _ = make_safe_gpu_dataframes(df, None, g.engine)
+    if g.engine_dbscan in [CUML]:
+        df, _ = make_safe_gpu_dataframes(df, None, g.engine_dbscan)
     #print('\n df:', df.shape, df.columns)
     return df
 
@@ -204,17 +204,17 @@ def __init__(self, *args, **kwargs):
         pass
 
     def _cluster_dbscan(
-        self, res, kind, cols, fit_umap_embedding, target, min_dist, min_samples, engine, verbose, *args, **kwargs
+        self, res, kind, cols, fit_umap_embedding, target, min_dist, min_samples, engine_dbscan, verbose, *args, **kwargs
     ):
         """
         DBSCAN clustering on cpu or *(not yet supported) gpu* infered by .engine flag
         """
         _, DBSCAN, _, cuDBSCAN = lazy_dbscan_import_has_dependency()
 
-        if engine in [CUML]:
+        if engine_dbscan in [CUML]:
             print('`g.transform_dbscan(..)` not supported for engine=cuml, will return `g.transform_umap(..)` instead')
 
-        res.engine = resolve_cpu_gpu_engine(engine)  # resolve_cpu_gpu_engine("auto")
+        res.engine_dbscan = resolve_cpu_gpu_engine(engine_dbscan)  # resolve_cpu_gpu_engine("auto")
         res._dbscan_params = ModelDict(
             "latest DBSCAN params",
             kind=kind,
@@ -223,7 +223,7 @@ def _cluster_dbscan(
             fit_umap_embedding=fit_umap_embedding,
             min_dist=min_dist,
             min_samples=min_samples,
-            engine=engine,
+            engine_dbscan=engine_dbscan,
             verbose=verbose,
         )
 
@@ -310,7 +310,7 @@ def dbscan(
             target=target,
             min_dist=min_dist,
             min_samples=min_samples,
-            engine=engine_dbscan,
+            engine_dbscan=engine_dbscan,
             verbose=verbose,
             *args,
             **kwargs,
@@ -349,7 +349,7 @@ def _transform_dbscan(
             
 
             if self.engine == 'cuml':
-                print('Transform DBSCAN not supported for engine=`cuml`, use engine=`umap_learn` instead')
+                print('Transform DBSCAN not supported for engine_dbscan=`cuml`, use engine=`umap_learn`, `pandas` or `sklearn` instead')
                 return emb, X, y, df
 
             labels = dbscan_predict(X_, dbscan)  # type: ignore

From 48fe85f85e1eee190254442c8e885bccf53cfb59 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 17 Mar 2023 14:40:24 -0700
Subject: [PATCH 270/432] bug fix

---
 graphistry/compute/cluster.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index c067af9ec7..9198d00399 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -431,7 +431,7 @@ def transform_dbscan(
             :verbose: whether to print out progress, default False
 
         """
-        if self.engine == 'cuml':
+        if self.engine_dbscan == 'cuml':
             print('Transform DBSCAN not supported for `cuml`, use engine=`umap_learn` instead')
             return self.transform_umap(df, y, kind=kind, verbose=verbose, return_graph=return_graph)
         emb, X, y, df = self._transform_dbscan(df, y, kind=kind, verbose=verbose)

From d2611a3a380f31c35d0c533f67ab9b3b8ebbd9f7 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 17 Mar 2023 14:50:56 -0700
Subject: [PATCH 271/432] adds pandas coercion before infer_graph

---
 graphistry/compute/cluster.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 9198d00399..ab4ab7ffcc 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -347,8 +347,7 @@ def _transform_dbscan(
             else:
                 X_ = XX
             
-
-            if self.engine == 'cuml':
+            if res.engine_dbscan == 'cuml':
                 print('Transform DBSCAN not supported for engine_dbscan=`cuml`, use engine=`umap_learn`, `pandas` or `sklearn` instead')
                 return emb, X, y, df
 
@@ -431,11 +430,13 @@ def transform_dbscan(
             :verbose: whether to print out progress, default False
 
         """
-        if self.engine_dbscan == 'cuml':
-            print('Transform DBSCAN not supported for `cuml`, use engine=`umap_learn` instead')
-            return self.transform_umap(df, y, kind=kind, verbose=verbose, return_graph=return_graph)
+        # if self.engine_dbscan == 'cuml':
+        #     print('Transform DBSCAN not supported for `cuml`, use engine=`umap_learn` instead')
+        #     return self.transform_umap(df, y, kind=kind, verbose=verbose, return_graph=return_graph)
         emb, X, y, df = self._transform_dbscan(df, y, kind=kind, verbose=verbose)
         if return_graph and kind not in ["edges"]:
+            df, y = make_safe_gpu_dataframes(df, y, 'pandas')
+            X, emb = make_safe_gpu_dataframes(X, emb, 'pandas')
             g = self._infer_edges(emb, X, y, df, eps=min_dist, sample=sample, n_neighbors=n_neighbors,  # type: ignore
                 infer_on_umap_embedding=infer_umap_embedding
                 )

From d4030bce535af868a46b842fa8fd8fb095f56a1e Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 17 Mar 2023 14:58:05 -0700
Subject: [PATCH 272/432] bug fix

---
 graphistry/compute/cluster.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index ab4ab7ffcc..07bdc7ad72 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -214,7 +214,7 @@ def _cluster_dbscan(
         if engine_dbscan in [CUML]:
             print('`g.transform_dbscan(..)` not supported for engine=cuml, will return `g.transform_umap(..)` instead')
 
-        res.engine_dbscan = resolve_cpu_gpu_engine(engine_dbscan)  # resolve_cpu_gpu_engine("auto")
+        res.engine_dbscan = engine_dbscan #resolve_cpu_gpu_engine(engine_dbscan)  # resolve_cpu_gpu_engine("auto")
         res._dbscan_params = ModelDict(
             "latest DBSCAN params",
             kind=kind,
@@ -229,7 +229,7 @@ def _cluster_dbscan(
 
         dbscan = (
             cuDBSCAN(eps=min_dist, min_samples=min_samples, *args, **kwargs)
-            if res.engine == CUML
+            if res.engine_dbscan == CUML
             else DBSCAN(eps=min_dist, min_samples=min_samples, *args, **kwargs)
         )
 
@@ -330,6 +330,7 @@ def _transform_dbscan(
             target = res._dbscan_params["target"]
 
             dbscan = res._node_dbscan if kind == "nodes" else res._edge_dbscan
+            print('DBSCAN TYPE IN TRANSFORM', type(dbscan))
 
             emb = None
             if umap and cols is None:

From 42e8eb3ce24c362fc43c715ff2e58f2e94055301 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 17 Mar 2023 15:06:07 -0700
Subject: [PATCH 273/432] bug fix

---
 graphistry/compute/cluster.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 07bdc7ad72..1ebfea04da 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -145,7 +145,8 @@ def dbscan_fit(g: Any, dbscan: Any, kind: str = "nodes", cols: Optional[Union[Li
         raise ValueError("No features found for clustering")
 
     dbscan.fit(X)
-    if g.engine == 'cuml':
+    # this is a future feature one cuml supports it
+    if g.engine_dbscan == 'cuml':
         labels = dbscan.labels_.to_numpy()
         # dbscan.components_ = X[dbscan.core_sample_indices_.to_pandas()]  # can't believe len(samples) != unique(labels) ... #cumlfail
     else:
@@ -232,6 +233,7 @@ def _cluster_dbscan(
             if res.engine_dbscan == CUML
             else DBSCAN(eps=min_dist, min_samples=min_samples, *args, **kwargs)
         )
+        print('dbscan:', dbscan)
 
         res = dbscan_fit(
             res, dbscan, kind=kind, cols=cols, use_umap_embedding=fit_umap_embedding, verbose=verbose

From 2027c35bbfea2683585cc7802d922eb5b31161d1 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 17 Mar 2023 15:10:30 -0700
Subject: [PATCH 274/432] bug fix

---
 graphistry/compute/cluster.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 1ebfea04da..ae152b52d7 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -122,8 +122,8 @@ def get_model_matrix(g, kind: str, cols: Optional[Union[List, str]], umap, targe
     if umap and cols is None and g._umap is not None:
         df = g._get_embedding(kind)            
     
-    if g.engine_dbscan in [CUML]:
-        df, _ = make_safe_gpu_dataframes(df, None, g.engine_dbscan)
+    #if g.engine_dbscan in [CUML]:
+    df, _ = make_safe_gpu_dataframes(df, None, g.engine_dbscan)
     #print('\n df:', df.shape, df.columns)
     return df
 

From 13d91736682ca931203b5adaa6889af204617dbc Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 17 Mar 2023 15:18:47 -0700
Subject: [PATCH 275/432] bug fix

---
 graphistry/compute/cluster.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index ae152b52d7..051d1d2d94 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -353,7 +353,8 @@ def _transform_dbscan(
             if res.engine_dbscan == 'cuml':
                 print('Transform DBSCAN not supported for engine_dbscan=`cuml`, use engine=`umap_learn`, `pandas` or `sklearn` instead')
                 return emb, X, y, df
-
+            
+            X_, _ = make_safe_gpu_dataframes(X_, None, res.engine_dbscan)
             labels = dbscan_predict(X_, dbscan)  # type: ignore
             if umap and cols is None:
                 df = df.assign(_dbscan=labels, x=emb.x, y=emb.y)  # type: ignore

From d28859726ba58335788778eab66fcc14cfcb5c4f Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 17 Mar 2023 15:22:10 -0700
Subject: [PATCH 276/432] bug fix

---
 graphistry/compute/cluster.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 051d1d2d94..1632d3ca95 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -354,8 +354,12 @@ def _transform_dbscan(
                 print('Transform DBSCAN not supported for engine_dbscan=`cuml`, use engine=`umap_learn`, `pandas` or `sklearn` instead')
                 return emb, X, y, df
             
-            X_, _ = make_safe_gpu_dataframes(X_, None, res.engine_dbscan)
+            print(type(X_))
+            X_, _ = make_safe_gpu_dataframes(X_, None, 'pandas')  # fuck all this hacky shit
+            print('after make safe gpu', type(X_))
+
             labels = dbscan_predict(X_, dbscan)  # type: ignore
+            print('after dbscan predict', type(labels))
             if umap and cols is None:
                 df = df.assign(_dbscan=labels, x=emb.x, y=emb.y)  # type: ignore
             else:

From dfba41cdf2dbb66e28341994e4cd435173c153f4 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 17 Mar 2023 15:25:52 -0700
Subject: [PATCH 277/432] bug fix

---
 graphistry/compute/cluster.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 1632d3ca95..8e07161fd8 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -354,8 +354,8 @@ def _transform_dbscan(
                 print('Transform DBSCAN not supported for engine_dbscan=`cuml`, use engine=`umap_learn`, `pandas` or `sklearn` instead')
                 return emb, X, y, df
             
-            print(type(X_))
-            X_, _ = make_safe_gpu_dataframes(X_, None, 'pandas')  # fuck all this hacky shit
+            print('before', type(X_))
+            X_, emb = make_safe_gpu_dataframes(X_, emb, 'pandas')  
             print('after make safe gpu', type(X_))
 
             labels = dbscan_predict(X_, dbscan)  # type: ignore

From fa2a6bae5abd75670182f837869a15ed555c2937 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 17 Mar 2023 15:47:36 -0700
Subject: [PATCH 278/432] lint

---
 graphistry/ai_utils.py        |  6 +++---
 graphistry/compute/cluster.py | 14 +++++++-------
 graphistry/umap_utils.py      |  4 ++--
 3 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index a3b4c1888d..0f58cf3256 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -294,9 +294,9 @@ def infer_graph(
     old_nodes = []
     mdists = []
 
-    ## check if pandas or cudf
+    # check if pandas or cudf
     if 'cudf.core.dataframe' in str(type(X_previously_fit)):
-        # move it out of memory...
+        #  move it out of memory...
         X_previously_fit = X_previously_fit.to_pandas()
 
     for i in range(X_new.shape[0]):
@@ -364,7 +364,7 @@ def infer_graph(
 
     new_emb = None
     if emb is not None:
-        if 'cudf.core.dataframe.DataFrame' in str(type(old_emb)): # convert to pd
+        if 'cudf.core.dataframe.DataFrame' in str(type(old_emb)):  # convert to pd
             old_emb = old_emb.to_pandas()
         new_emb = pd.concat([emb, old_emb], axis=0)
 
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index 8e07161fd8..e21b74a1b5 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -72,7 +72,7 @@ def resolve_cpu_gpu_engine(
 
     raise ValueError(  # noqa
         f'engine expected to be "auto", '
-        '"umap_learn", or  "cuml" '
+        '"umap_learn", "pandas", "sklearn", or  "cuml" '
         f"but received: {engine} :: {type(engine)}"
     )
 
@@ -92,7 +92,7 @@ def safe_cudf(X, y):
 
     has_cudf_dependancy_, _, cudf = lazy_cudf_import_has_dependancy()
     if has_cudf_dependancy_:
-        print('DBSCAN CUML Matrices')
+        # print('DBSCAN CUML Matrices')
         return safe_cudf(X, y)
     else:
         return X, y
@@ -215,7 +215,7 @@ def _cluster_dbscan(
         if engine_dbscan in [CUML]:
             print('`g.transform_dbscan(..)` not supported for engine=cuml, will return `g.transform_umap(..)` instead')
 
-        res.engine_dbscan = engine_dbscan #resolve_cpu_gpu_engine(engine_dbscan)  # resolve_cpu_gpu_engine("auto")
+        res.engine_dbscan = engine_dbscan  # resolve_cpu_gpu_engine(engine_dbscan)  # resolve_cpu_gpu_engine("auto")
         res._dbscan_params = ModelDict(
             "latest DBSCAN params",
             kind=kind,
@@ -233,7 +233,7 @@ def _cluster_dbscan(
             if res.engine_dbscan == CUML
             else DBSCAN(eps=min_dist, min_samples=min_samples, *args, **kwargs)
         )
-        print('dbscan:', dbscan)
+        # print('dbscan:', dbscan)
 
         res = dbscan_fit(
             res, dbscan, kind=kind, cols=cols, use_umap_embedding=fit_umap_embedding, verbose=verbose
@@ -354,12 +354,12 @@ def _transform_dbscan(
                 print('Transform DBSCAN not supported for engine_dbscan=`cuml`, use engine=`umap_learn`, `pandas` or `sklearn` instead')
                 return emb, X, y, df
             
-            print('before', type(X_))
+            #print('before', type(X_))
             X_, emb = make_safe_gpu_dataframes(X_, emb, 'pandas')  
-            print('after make safe gpu', type(X_))
+            #print('after make safe gpu', type(X_))
 
             labels = dbscan_predict(X_, dbscan)  # type: ignore
-            print('after dbscan predict', type(labels))
+            #print('after dbscan predict', type(labels))
             if umap and cols is None:
                 df = df.assign(_dbscan=labels, x=emb.x, y=emb.y)  # type: ignore
             else:
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index b88fb0815b..05cd80d9ec 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -588,7 +588,7 @@ def umap(
             elif 'cudf.core.dataframe' in str(getmodule(X_)):
                 index_to_nodes_dict = nodes  # {}?
 
-            ## add the safe coercion here 
+            # add the safe coercion here 
             X_, y_ = make_safe_gpu_dataframes(X_, y_, res.engine)
 
             res = res._process_umap(
@@ -618,7 +618,7 @@ def umap(
                 **featurize_kwargs
             )
 
-            ## add the safe coercion here 
+            # add the safe coercion here 
             X_, y_ = make_safe_gpu_dataframes(X_, y_, res.engine)
 
             res = res._process_umap(

From ea6ce345c78a21e35a45cc0dfa7c1c480ba4bd9c Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 17 Mar 2023 15:54:12 -0700
Subject: [PATCH 279/432] lint

---
 graphistry/feature_utils.py | 4 ++--
 graphistry/umap_utils.py    | 6 +++---
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 65113ea902..8d302df2b0 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -170,7 +170,7 @@ def resolve_feature_engine(
 def resolve_y(df: Optional[pd.DataFrame], y: YSymbolic) -> pd.DataFrame:
 
     if isinstance(y, pd.DataFrame) or 'cudf.core.dataframe' in str(getmodule(y)):
-        return y
+        return y  # type: ignore
 
     if df is None:
         raise ValueError("Missing data for featurization")
@@ -191,7 +191,7 @@ def resolve_y(df: Optional[pd.DataFrame], y: YSymbolic) -> pd.DataFrame:
 def resolve_X(df: Optional[pd.DataFrame], X: XSymbolic) -> pd.DataFrame:
 
     if isinstance(X, pd.DataFrame) or 'cudf.core.dataframe' in str(getmodule(X)):
-        return X
+        return X  # type: ignore
 
     if df is None:
         raise ValueError("Missing data for featurization")
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 05cd80d9ec..d38cdb145b 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -330,7 +330,7 @@ def transform_umap(self, df: pd.DataFrame,
         """
         df, y = make_safe_gpu_dataframes(df, y, 'pandas')
         X, y_ = self.transform(df, y, kind=kind, return_graph=False, verbose=verbose)
-        X, y_ = make_safe_gpu_dataframes(X, y_, self.engine)
+        X, y_ = make_safe_gpu_dataframes(X, y_, self.engine)  # type: ignore
         emb = self._umap.transform(X)  # type: ignore
         emb = self._bundle_embedding(emb, index=df.index)
         if return_graph and kind not in ["edges"]:
@@ -589,7 +589,7 @@ def umap(
                 index_to_nodes_dict = nodes  # {}?
 
             # add the safe coercion here 
-            X_, y_ = make_safe_gpu_dataframes(X_, y_, res.engine)
+            X_, y_ = make_safe_gpu_dataframes(X_, y_, res.engine)  # type: ignore
 
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, verbose, **umap_kwargs
@@ -619,7 +619,7 @@ def umap(
             )
 
             # add the safe coercion here 
-            X_, y_ = make_safe_gpu_dataframes(X_, y_, res.engine)
+            X_, y_ = make_safe_gpu_dataframes(X_, y_, res.engine)  # type: ignore
 
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs

From 118e36df0df8fbccb963ecf4ef3779b4c9b28670 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Fri, 17 Mar 2023 17:02:34 -0700
Subject: [PATCH 280/432] adds modified test for dbscan params

---
 graphistry/tests/test_compute_cluster.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphistry/tests/test_compute_cluster.py b/graphistry/tests/test_compute_cluster.py
index 3c1cfd8dca..c93d0e279d 100644
--- a/graphistry/tests/test_compute_cluster.py
+++ b/graphistry/tests/test_compute_cluster.py
@@ -45,9 +45,9 @@ def test_featurize_cluster(self):
     @pytest.mark.skipif(not has_dbscan, reason="requires ai dependencies")
     def test_dbscan_params(self):
         dbscan_params = [ModelDict('Testing UMAP', kind='nodes', min_dist=0.2, min_samples=1, cols=None, target=False, 
-                                   fit_umap_embedding=False, verbose=True), 
+                                   fit_umap_embedding=False, verbose=True, engine_dbscan='sklearn'), 
                          ModelDict('Testing UMAP target', kind='nodes', min_dist=0.1, min_samples=1, cols=None, 
-                                   fit_umap_embedding=True, target=True, verbose=True)
+                                   fit_umap_embedding=True, target=True, verbose=True, engine_dbscan='sklearn'),
 
         ]
         for params in dbscan_params:

From 911529c94c9572f85622a1522577f365020ffec4 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Mon, 20 Mar 2023 15:13:53 -0400
Subject: [PATCH 281/432] feat(iframe): added a graph homepg, can be removed

---
 docs/source/index.rst | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/docs/source/index.rst b/docs/source/index.rst
index b45393c266..e86502551e 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -7,6 +7,16 @@ PyGraphistry[ai]'s documentation
 PyGraphistry is a Python visual graph AI library to extract, transform, analyze, model, and visualize big graphs, and especially alongside Graphistry end-to-end GPU server sessions. Installing optional graphistry[ai] dependencies adds graph autoML, including automatic feature engineering, UMAP, and graph neural net support. Combined, PyGraphistry reduces your time to graph for going from raw data to visualizations and AI models down to three lines of code.
 Here in our docstrings you can find useful packages, modules, and commands to maximize your graph AI experience with PyGraphistry. In the navbar you can find an overview of all the packages and modules we provided and a few useful highlighted ones as well. You can search for them on our Search page. For a full tutorial, refer to our `PyGraphistry <https://github.com/graphistry/pygraphistry/>`_ repo.
 
+
+Click to open interactive version! (For server-backed interactive analytics, use an API key)
+
+
+.. raw:: html
+
+       <iframe width="600" height="400" src="https://hub.graphistry.com/graph/graph.html?dataset=Facebook&splashAfter=true" frameborder="0" allowfullscreen></iframe>
+
+For self-hosting and access to a free API key, refer to our Graphistry `Hub <https://hub.graphistry.com/>`_.
+
 .. toctree::
    :maxdepth: 3
 

From 2c9dc78404469458da4c538aa3da8600b97a176d Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 20 Mar 2023 16:29:26 -0700
Subject: [PATCH 282/432] adds NVIDIA GTC demo that installs from branch

---
 demos/ai/cyber/redteam-umap-gtc-gpu.ipynb | 1033 +++++++++++++++++++++
 1 file changed, 1033 insertions(+)
 create mode 100644 demos/ai/cyber/redteam-umap-gtc-gpu.ipynb

diff --git a/demos/ai/cyber/redteam-umap-gtc-gpu.ipynb b/demos/ai/cyber/redteam-umap-gtc-gpu.ipynb
new file mode 100644
index 0000000000..b6ab00a308
--- /dev/null
+++ b/demos/ai/cyber/redteam-umap-gtc-gpu.ipynb
@@ -0,0 +1,1033 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "beb5e3e3-f8cd-40ed-bc63-8a862000f192",
+   "metadata": {},
+   "source": [
+    "# Analyzing Network Identity Data and Red Team Response with Graphistry AutoML + UMAP\n",
+    "\n",
+    "We find a simple model that when clustered in a 2d plane via UMAP allows fast identification of anomalous \n",
+    "computer to computer connections"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9de6fd3-b87b-4dc4-8d1c-b8f3feceb5e6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# ! pip install graphistry[ai] \n",
+    "! pip install --user --no-deps git+https://github.com/graphistry/pygraphistry.git@cudf-alex3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0215906c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "from joblib import load, dump\n",
+    "from collections import Counter\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pylab as plt\n",
+    "\n",
+    "import graphistry\n",
+    "from graphistry.features import topic_model, search_model, ModelDict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b34bebd-c91d-49fe-82c9-ec1c83a4a6c1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "graphistry.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e1747b9-c903-4398-9aa0-b52b69fce021",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "np.random.seed(137)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d2669fd-6164-4376-81bd-79c6c6f4112f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "RENDER = True # set to True to render Graphistry UI inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59e1cc0b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "graphistry.register(api=3, protocol=\"https\", server=\"hub.graphistry.com\", username = 'silkspace',\n",
+    "                    #os.environ['USERNAME'], \n",
+    "                    password='yqQg02&N'\n",
+    "                    #os.environ['GRAPHISTRY_PASSWORD']\n",
+    "                   )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "877b4e50-8fa8-4663-bba0-91b661fc735f",
+   "metadata": {},
+   "source": [
+    "Alert on & visualize anomalous identity events\n",
+    "\n",
+    "Demo dataset: 1.6B windows events over 58 days => logins by 12K user over 14K systems\n",
+    "adapt to any identity system with logins. Here we subsample down to a small set of 50k events to prove out the pipeline. \n",
+    "\n",
+    "* => Can we identify accounts & computers acting anomalously? Resources being oddly accessed?\n",
+    "* => Can we spot the red team?\n",
+    "* => Operations: Identity incident alerting + identity data investigations\n",
+    "\n",
+    "Community/contact for help handling bigger-than-memory & additional features\n",
+    "\n",
+    "Runs on both CPU + multi-GPU\n",
+    "Tools: PyGraphistry[AI], DGL + PyTorch, and NVIDIA RAPIDS / umap-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe6e61b0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# data source citation\n",
+    "# \"\"\"A. D. Kent, \"Cybersecurity Data Sources for Dynamic Network Research,\"\n",
+    "# in Dynamic Networks in Cybersecurity, 2015.\n",
+    "\n",
+    "# @InProceedings{akent-2015-enterprise-data,\n",
+    "#    author = {Alexander D. Kent},\n",
+    "#    title = {{Cybersecurity Data Sources for Dynamic Network Research}},\n",
+    "#    year = 2015,\n",
+    "#    booktitle = {Dynamic Networks in Cybersecurity},\n",
+    "#    month =        jun,\n",
+    "#    publisher = {Imperial College Press}\n",
+    "# }\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "554c0d85-1c8a-47f0-87ec-1629d7f7ba3b",
+   "metadata": {},
+   "source": [
+    "# Get the Data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efe68cf8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# small sample (get almost equivalent results without overheating computer over the 1.6B events in the full dataset)\n",
+    "df = pd.read_csv('https://gist.githubusercontent.com/silkspace/c7b50d0c03dc59f63c48d68d696958ff/raw/31d918267f86f8252d42d2e9597ba6fc03fcdac2/redteam_50k.csv', index_col=0)\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93bab916-a6c1-4a63-95de-2e8d2a72d8a6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-03-20T17:41:26.708147Z",
+     "iopub.status.busy": "2023-03-20T17:41:26.707740Z",
+     "iopub.status.idle": "2023-03-20T17:41:26.711459Z",
+     "shell.execute_reply": "2023-03-20T17:41:26.710695Z",
+     "shell.execute_reply.started": "2023-03-20T17:41:26.708118Z"
+    }
+   },
+   "source": [
+    "# Graphistry UMAP in a single line of code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29e99375-5b24-4760-b5ed-909f51949f7f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# umap pipeline in one line\n",
+    "g = graphistry.nodes(df.sample(1000)).umap(engine='umap_learn')\n",
+    "g.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03610297",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "print(df.shape) # -> 50+k"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66c5126e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# here are the post-facto red team events\n",
+    "red_team = pd.read_csv('https://gist.githubusercontent.com/silkspace/5cf5a94b9ac4b4ffe38904f20d93edb1/raw/888dabd86f88ea747cf9ff5f6c44725e21536465/redteam_labels.csv', index_col=0)\n",
+    "red_team['feats2'] = red_team.feats  # since red team data didn't include metadata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c6615aa",
+   "metadata": {},
+   "source": [
+    "# Modeling\n",
+    "\n",
+    "Make sure you `mkdir(data)` or change path below\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3641d3b5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "process = True  \n",
+    "# makes a combined feature we can use for topic modeling!\n",
+    "if process:\n",
+    "    # we create two types of models\n",
+    "    df['feats'] = df.src_computer + ' ' + df.dst_computer + ' ' + df.auth_type + ' ' + df.logontype\n",
+    "    # and one of just computer to computer \n",
+    "    df['feats2'] = df.src_computer + ' ' + df.dst_computer\n",
+    "    ndf = df.drop_duplicates(subset=['feats'])\n",
+    "    ndf.to_parquet('auth-feats-one-column.parquet')\n",
+    "else:\n",
+    "    ndf = pd.read_parquet('auth-feats-one-column.parquet')\n",
+    "    \n",
+    "print(ndf.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32d1755d",
+   "metadata": {},
+   "source": [
+    "## Red Team Data \n",
+    "Add it to the front of the DataFrame so we can keep track of it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d67c86b8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# make a subsampled dataframe with the anom red-team data at top...so we can keep track.\n",
+    "# we don't need the full `df`, only the unique entries of 'feats' in `ndf` for \n",
+    "# fitting a model (in a few cells below)\n",
+    "\n",
+    "tdf = pd.concat([red_team.reset_index(), ndf.reset_index()])\n",
+    "tdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f62b7b5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# add a fidicial index used later\n",
+    "tdf['node'] = range(len(tdf))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ffd6aac",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# total number of red team events\n",
+    "tdf.RED.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4264d547-b4a9-49d1-bc68-894f1e839c38",
+   "metadata": {},
+   "source": [
+    "## Enrichment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72c53f98",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def get_confidences_per_cluster(g, col='RED', verbose=False):\n",
+    "    \"\"\"\n",
+    "        From DBSCAN clusters, will assess how many Red Team events exist,\n",
+    "        assessing confidence.\n",
+    "        \n",
+    "    \"\"\"\n",
+    "    resses = []\n",
+    "    df = g._nodes\n",
+    "    labels = df._dbscan\n",
+    "    cnt = Counter(labels)\n",
+    "    for clust, count in cnt.most_common():\n",
+    "        res = df[df._dbscan==clust]\n",
+    "        n = res.shape[0]\n",
+    "        n_reds = res[col].sum()\n",
+    "        resses.append([clust, n_reds/n, n_reds, n])\n",
+    "        if n_reds>0 and verbose:\n",
+    "            print('-'*20)\n",
+    "            print(f'cluster: {clust}\\n   red {100*n_reds/n:.2f}% or {n_reds} out of {count}')\n",
+    "    conf_dict = {k[0]: k[1] for k in resses}\n",
+    "    confidence = [conf_dict[k] for k in df._dbscan.values]\n",
+    "    # enrichment\n",
+    "    g._nodes['confidence'] = confidence\n",
+    "    conf_df = pd.DataFrame(resses, columns=['_dbscan', 'confidence', 'n_red', 'total_in_cluster'])\n",
+    "    conf_df = conf_df.sort_values(by='confidence', ascending=False)\n",
+    "    return g, conf_df\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a3da6e3-b280-4c69-b0e0-4a92d9aac231",
+   "metadata": {},
+   "source": [
+    "# The Full UMAP Pipelines\n",
+    "Fit a model on 'feats' column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "504781dc-9fbe-467c-9b4d-2e907133cfb7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# this is a convienence method for setting parameters in `g.featurize()/umap()` -- just a verbose dictionary\n",
+    "cyber_model = ModelDict('A topic model for computer to computer', **topic_model)\n",
+    "\n",
+    "# umap_params_gpu = {'n_components': 2, \n",
+    "#                    'n_neighbors': 20,\n",
+    "#                    'min_dist': 0.1, \n",
+    "#                    'spread': 1, \n",
+    "#                    'local_connectivity': 1, \n",
+    "#                    'repulsion_strength': 2, \n",
+    "#                    'negative_sample_rate': 5}\n",
+    "#cyber_model.update(umap_params_gpu)\n",
+    "\n",
+    "cyber_model.update(dict(n_topics=32, X=['feats2']))  # name the column to featurize, which we lumped into `feats2`\n",
+    "\n",
+    "cyber_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5474ef79-b2dd-4299-bee7-e12d94c79613",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# if you stop processing during execution, sometimes calling this will unblock you on subsequent calls should it give an error.\n",
+    "#g.reset_caches()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6909cc90",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "process = True  # set to false after it's run for ease of speed\n",
+    "if process:\n",
+    "    # ##################################\n",
+    "    g = graphistry.nodes(tdf, 'node')  # two lines does the heavy lifting\n",
+    "    # gpu version, will detect gpu and run\n",
+    "    #g5 = g.umap(engine='auto', **cyber_model, verbose=True).dbscan(min_dist=1, verbose=True)\n",
+    "    \n",
+    "    # cpu version\n",
+    "    g5 = g.umap(engine='umap_learn', **cyber_model, verbose=True).dbscan(min_dist=0.1, verbose=True)\n",
+    "    # #########################\n",
+    "    \n",
+    "    g5, cluster_confidences = get_confidences_per_cluster(g5, verbose=True)\n",
+    "    g5.save_search_instance('auth-feat-topic.search')\n",
+    "else:\n",
+    "    g = graphistry.bind()\n",
+    "    g5 = g.load_search_instance('auth-feat-topic.search')\n",
+    "    g5, cluster_confidences = get_confidences_per_cluster(g5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01632281-2ace-4917-9932-86b507b3d9e3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# nodes dataframe is now enriched with _dbscan label\n",
+    "g5._nodes._dbscan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c1ba011-2aaa-4663-a319-4478502b1b8e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# the UMAP coordinates\n",
+    "g5._node_embedding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54c13cba-bc36-4d49-8e7a-7dc05b27610a",
+   "metadata": {},
+   "source": [
+    "## Plot Graph\n",
+    "Color by `confidence` and hover over `red` team histogram to see where events occur. Alternatively, color by `cluster` assignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "279fef41",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "g5.name('auth test').plot(render=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79ece955",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# see how the model has organized features\n",
+    "X = g5._node_features\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87b32e09-3ca4-49de-b8c3-2b40ffa2b01d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "x = g5.get_matrix(['interactive', 'c17', 'microsoft'])\n",
+    "x.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "632d6d0f-8212-4f4a-a920-7600d7456351",
+   "metadata": {},
+   "source": [
+    "## Predict | Online Mode\n",
+    "\n",
+    "Once a model is fit, predict on new batches as we demonstrate here\n",
+    "\n",
+    "There are three main methods\n",
+    "\n",
+    "`g.transform` and `g.transform_umap` and if dbscan has been run, `g.transform_dbscan` \n",
+    "\n",
+    "see help(*) on each to learn more\n",
+    "\n",
+    "One may save the model as above, load it, and wrap in a FastAPI endpoint, etc, to serve in production pipelines."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b44d418",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first sample a batch from the normal data (auth=df)\n",
+    "sdf = df.sample(200)\n",
+    "emb_normal, xp_normal, _ = g5.transform_umap(sdf, None, kind='nodes', return_graph=False)\n",
+    "# then transform all the red team data\n",
+    "emb_red, xp_red, _ = g5.transform_umap(red_team, None, kind='nodes', return_graph=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe3e5058-6ac6-4d1a-a368-66ecd5dd703b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "emb_red"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2b6c471-338a-40d6-92a8-03c2505c433f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# transform_dbscan will predict on new data (here just red_team to prove it works)\n",
+    "g7 = g5.transform_dbscan(red_team, None, kind='nodes', return_graph=True, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad82c787-c246-440d-9ed6-97ddc2805491",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "_, ccdf = get_confidences_per_cluster(g7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e0760fe-40c0-45b9-a787-d4f98d557c24",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "print(f'total confidence across clusters {ccdf.confidence.mean()*100}%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2911840d-ffd7-4815-97fd-53bc43cbc522",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "g7.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ace3e171-2e33-435e-82d7-7158d7931d14",
+   "metadata": {},
+   "source": [
+    "# We can simulate how a batch of new data would behave"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03672813-db4e-4d0c-a5f5-598ab165986c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cpu version\n",
+    "plt.figure(figsize=(10,7))\n",
+    "plt.scatter(g5._node_embedding.x, g5._node_embedding.y, c='b', s=60, alpha=0.5)  # the totality of the fit data\n",
+    "plt.scatter(emb_normal.x, emb_normal.y, c='g') # batch of new data\n",
+    "plt.scatter(emb_red.x, emb_red.y, c='r') # red labels to show good cluster seperation\n",
+    "plt.scatter(emb_normal.x, emb_normal.y, c='g') # batch of new data, to see if they occlude "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a8d5aa9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# # scatter to see how well it does.\n",
+    "plt.figure(figsize=(10,7))\n",
+    "plt.scatter(g5._node_embedding.x.to_numpy(), g5._node_embedding.y.to_numpy() , c='b', s=60, alpha=0.5)  # the totality of the fit data\n",
+    "plt.scatter(emb_normal.x.to_numpy(), emb_normal.y.to_numpy(), c='g') # batch of new data\n",
+    "plt.scatter(emb_red.x.to_numpy(), emb_red.y.to_numpy(), c='r') # red labels to show good cluster seperation\n",
+    "plt.scatter(emb_normal.x.to_numpy(), emb_normal.y.to_numpy(), c='g') # batch of new data, to see if they occlude "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b53dd8ed-39b2-4000-9ec7-139d1e2a6a85",
+   "metadata": {},
+   "source": [
+    "## 96% Reduction in Alerts\n",
+    "\n",
+    "This indicates a huge reduction in the search space needed.\n",
+    "\n",
+    "Since we have clear cluster assignments along with (post facto) confidences of known anomalous activity, we can reduce the search space on new events (gotten via Kafka, Splunk, etc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14d207db-9a58-45a3-9876-058632389f17",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# percent of RED team labels we get with 10% confidence or above\n",
+    "p = cluster_confidences[cluster_confidences.confidence>0.1].n_red.sum()/cluster_confidences[cluster_confidences.confidence>0.1].total_in_cluster.sum()\n",
+    "print(f'{100*p:.2f}%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "755a3f27-935d-4ba8-96cb-cbff11fdf00e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# number of data points *not* to consider (and it's more if we look at df proper!)\n",
+    "cluster_confidences[cluster_confidences.confidence<0.1].total_in_cluster.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5fd1cc50-0900-4694-8400-c426e314ec2e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "p = cluster_confidences[cluster_confidences.confidence<0.1].total_in_cluster.sum()/cluster_confidences.total_in_cluster.sum()\n",
+    "print(f'Alert Reduction {100*p:.2f}%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ee508a5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10,7))\n",
+    "plt.plot(np.cumsum([k[2] for k in cluster_confidences.values]))\n",
+    "plt.xlabel('Anomolous Cluster Number')  # shows that we can ignore first clusters (containing most of the alerts)\n",
+    "plt.ylabel('Number of Identified Red Team Events')\n",
+    "print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f168ac8-2324-4f47-b0d7-e4a0b041624f",
+   "metadata": {},
+   "source": [
+    "## Supervised UMAP\n",
+    "Here we use the RED team label to help supervise the UMAP fit. \n",
+    "This might be useful once teams have actually identified RED team events \n",
+    "and want to help separate clusters. \n",
+    "While separation is better, the unsupervised version does well without."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34ad4768-58e5-493e-a5e8-6f4748168e05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# g.reset_caches()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0c6a16d-a899-43b6-a7ba-75b45f855a78",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "process = True\n",
+    "if process:\n",
+    "    # ##################################  # an example of setting features explicitly, could use ModelDict \n",
+    "    g = graphistry.nodes(tdf, 'node')\n",
+    "    g6 = g.umap(X=['feats'], y =['RED'], \n",
+    "                min_words=100000, # set high to bypass sbert encoding\n",
+    "                cardinality_threshold=2, # set low to force topic modeling\n",
+    "                n_topics=32,\n",
+    "                spread=1,\n",
+    "                use_scaler_target=None,  # keep labels unscaled\n",
+    "                dbscan=True, engine='umap_learn')  # add dbscan here\n",
+    "    # ##################################\n",
+    "    \n",
+    "    g6, cluster_confidences6  = get_confidences_per_cluster(g6, verbose=True)\n",
+    "    g6.save_search_instance('auth-feat-supervised-topic.search')\n",
+    "else:\n",
+    "    g = graphistry.bind()\n",
+    "    g6 = g.load_search_instance('auth-feat-supervised-topic.search')\n",
+    "    g6, cluster_confidences6  = get_confidences_per_cluster(g6)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a98ef657-5307-41d9-ae31-79c1794b3728",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "g6.get_matrix(target=True).astype(int)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cc72ab4-c0da-4541-b32b-aa771d6e510f",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Plot\n",
+    "Color by `confidence` and hover over `red` team histogram to see where events occur. Alternatively, color by `_dbscan` assignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16e09a7d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "g6.name('auth topic with supervised umap').plot(render=RENDER)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88169a53",
+   "metadata": {},
+   "source": [
+    "## A model of Computer-Computer and metadata features\n",
+    "Here we include `auth_type` and `logontype` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1731ae44-57e0-4c3e-bad0-ac486bba589c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "tdf['feats']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35b03bc4-915b-431b-ada5-d8281a4ece6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "process = True\n",
+    "if process:\n",
+    "    # #####################################\n",
+    "    g = graphistry.nodes(tdf, 'node')\n",
+    "    g7 = g.umap(X=['feats'], #y =['RED'], \n",
+    "                min_words=100000, \n",
+    "                cardinality_threshold=2, \n",
+    "                n_topics=32,\n",
+    "                use_scaler=None,\n",
+    "                use_scaler_target=None, \n",
+    "                spread=1,\n",
+    "                dbscan=True, engine='auto')  # add dbscan here\n",
+    "    # ###################################\n",
+    "    g7, cluster_confidences7  = get_confidences_per_cluster(g7)\n",
+    "    #g7.save_search_instance('auth-just-ip-topic.search')\n",
+    "else:\n",
+    "    g7 = graphistry.bind().load_search_instance('auth-just-ip-topic.search')\n",
+    "    g7, cluster_confidences7  = get_confidences_per_cluster(g7)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f291e227-ae14-4205-96dd-3c1de29d12e6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "cluster_confidences7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "836883cb-bc66-4a40-9ca8-f01fd38b6f2a",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Plot\n",
+    "Color by `confidence` and hover over `red` team histogram to see where events occur. Alternatively, color by `cluster` assignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1e586a3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "g7.name('auth topic ips-ips only, no supervision').plot(render=RENDER)\n",
+    "# very similar to graph with metadata included, showing that ip-ip is strong indicator of phenomenon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6cf68ed4",
+   "metadata": {},
+   "source": [
+    "# Conditional Probability\n",
+    "Let's see if conditiona probability of computer to computer connections can give us good histograms to tease out red team nodes? This is to baseline the above UMAP models, and we find in retrospect, UMAP wins. \n",
+    "\n",
+    "The conditional graph is however useful to see aggregate behavior, and coloring by 'red' team shows topology of Infection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d6f58dd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "g = graphistry.edges(tdf, \"src_computer\", \"dst_computer\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3b44db2-b34e-4398-8c5a-7a10bbe5d681",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "x='dst_computer'\n",
+    "given='src_computer'\n",
+    "cg = g.conditional_graph(x, given, kind='edges')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b2af6a2-4f10-4707-beb8-4f3447d3e3b8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# the new edge dataframe assess conditiona prob of computer-to-computer connection\n",
+    "cprob = cg._edges\n",
+    "cprob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5258aee1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# enrich the edges dataframe with the redteam data\n",
+    "# since cprobs lost those labels during the function call\n",
+    "indx = cprob.src_computer.isin(red_team.src_computer) & cprob.dst_computer.isin(red_team.dst_computer)\n",
+    "cprob.loc[indx, 'red'] = 1\n",
+    "cprob.loc[~indx, 'red'] = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ff921fc-3ecd-4404-acd7-8db943a4ebcc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "cprob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4b10152-cac9-4497-b016-dd67b54cdcf2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# add edges back to graphistry instance\n",
+    "cg._edges = cprob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b3af1cd-6423-4484-8b99-81fad821f118",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# full condprob graph\n",
+    "cg.plot(render=RENDER)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42fb3dff",
+   "metadata": {},
+   "source": [
+    "## Learning\n",
+    "The conditional graph shows that most of the edge probabilities are between 4e-7 and 0.03, whose bucket contains most of the events. Thus the chances of finding the red team edges are ~ 1e-4 -- slim indeed. UMAP wins."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d2cd536",
+   "metadata": {},
+   "source": [
+    "Likewise the transpose conditional is even worse \n",
+    "with prob_detection ~ 6e-5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0cbef82-421d-489e-8666-84d412cae5a9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8 (RAPIDS)",
+   "language": "python",
+   "name": "rapids"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From e1f64c568979d4be7f3c50c4f975c38c821880c2 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 20 Mar 2023 16:30:54 -0700
Subject: [PATCH 283/432] removes print statements and changes default umap
 settings

---
 graphistry/compute/cluster.py | 9 ++-------
 graphistry/features.py        | 4 ++--
 graphistry/umap_utils.py      | 1 -
 3 files changed, 4 insertions(+), 10 deletions(-)

diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index e21b74a1b5..a5386944dd 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -332,7 +332,7 @@ def _transform_dbscan(
             target = res._dbscan_params["target"]
 
             dbscan = res._node_dbscan if kind == "nodes" else res._edge_dbscan
-            print('DBSCAN TYPE IN TRANSFORM', type(dbscan))
+            # print('DBSCAN TYPE IN TRANSFORM', type(dbscan))
 
             emb = None
             if umap and cols is None:
@@ -351,12 +351,10 @@ def _transform_dbscan(
                 X_ = XX
             
             if res.engine_dbscan == 'cuml':
-                print('Transform DBSCAN not supported for engine_dbscan=`cuml`, use engine=`umap_learn`, `pandas` or `sklearn` instead')
+                print('Transform DBSCAN not yet supported for engine_dbscan=`cuml`, use engine=`umap_learn`, `pandas` or `sklearn` instead')
                 return emb, X, y, df
             
-            #print('before', type(X_))
             X_, emb = make_safe_gpu_dataframes(X_, emb, 'pandas')  
-            #print('after make safe gpu', type(X_))
 
             labels = dbscan_predict(X_, dbscan)  # type: ignore
             #print('after dbscan predict', type(labels))
@@ -438,9 +436,6 @@ def transform_dbscan(
             :verbose: whether to print out progress, default False
 
         """
-        # if self.engine_dbscan == 'cuml':
-        #     print('Transform DBSCAN not supported for `cuml`, use engine=`umap_learn` instead')
-        #     return self.transform_umap(df, y, kind=kind, verbose=verbose, return_graph=return_graph)
         emb, X, y, df = self._transform_dbscan(df, y, kind=kind, verbose=verbose)
         if return_graph and kind not in ["edges"]:
             df, y = make_safe_gpu_dataframes(df, y, 'pandas')
diff --git a/graphistry/features.py b/graphistry/features.py
index 3adbc829db..81bf5627fb 100644
--- a/graphistry/features.py
+++ b/graphistry/features.py
@@ -73,11 +73,11 @@
 # ###############################################################
 # ################# graphistry umap config constants #################
 N_COMPONENTS = 2
-N_NEIGHBORS = 15
+N_NEIGHBORS = 20
 MIN_DIST = 0.1
 SPREAD = 0.5
 LOCAL_CONNECTIVITY = 1
-REPULSION_STRENGTH = 1
+REPULSION_STRENGTH = 2
 NEGATIVE_SAMPLING_RATE = 5
 METRIC = "euclidean"
 
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index d38cdb145b..c4b74041db 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -326,7 +326,6 @@ def transform_umap(self, df: pd.DataFrame,
             return_graph: Whether to return a graph or just the embeddings
             fit_umap_embedding: Whether to infer graph from the UMAP embedding on the new data, default True
             verbose: Whether to print information about the graph inference
-
         """
         df, y = make_safe_gpu_dataframes(df, y, 'pandas')
         X, y_ = self.transform(df, y, kind=kind, return_graph=False, verbose=verbose)

From c9249c01c2bdc55dd0d75110b3058b89a25a0971 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 20 Mar 2023 16:50:31 -0700
Subject: [PATCH 284/432] typoo

---
 demos/ai/cyber/redteam-umap-gtc-gpu.ipynb | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/demos/ai/cyber/redteam-umap-gtc-gpu.ipynb b/demos/ai/cyber/redteam-umap-gtc-gpu.ipynb
index b6ab00a308..5b8db6ae70 100644
--- a/demos/ai/cyber/redteam-umap-gtc-gpu.ipynb
+++ b/demos/ai/cyber/redteam-umap-gtc-gpu.ipynb
@@ -90,9 +90,9 @@
    },
    "outputs": [],
    "source": [
-    "graphistry.register(api=3, protocol=\"https\", server=\"hub.graphistry.com\", username = 'silkspace',\n",
+    "graphistry.register(api=3, protocol=\"https\", server=\"hub.graphistry.com\", username = '..',\n",
     "                    #os.environ['USERNAME'], \n",
-    "                    password='yqQg02&N'\n",
+    "                    password='..'\n",
     "                    #os.environ['GRAPHISTRY_PASSWORD']\n",
     "                   )"
    ]
@@ -627,7 +627,8 @@
    },
    "outputs": [],
    "source": [
-    "# # scatter to see how well it does.\n",
+    "# gpu version\n",
+    "# scatter to see how well it does.\n",
     "plt.figure(figsize=(10,7))\n",
     "plt.scatter(g5._node_embedding.x.to_numpy(), g5._node_embedding.y.to_numpy() , c='b', s=60, alpha=0.5)  # the totality of the fit data\n",
     "plt.scatter(emb_normal.x.to_numpy(), emb_normal.y.to_numpy(), c='g') # batch of new data\n",
@@ -1011,9 +1012,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8 (RAPIDS)",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "rapids"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1025,7 +1026,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.8.9"
   }
  },
  "nbformat": 4,

From c5838140564a3a4e0c7fe67eb2e60f5846a1525d Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 20 Mar 2023 17:57:31 -0700
Subject: [PATCH 285/432] moves feature_engine resolve to logical order

---
 graphistry/feature_utils.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 711a1173b9..4b8484d78c 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2563,16 +2563,21 @@ def featurize(
                 default True.
         :return: graphistry instance with new attributes set by the featurization process.
         """
+        feature_engine = resolve_feature_engine(feature_engine)
+
+        print('Featurizing nodes with feature_engine=' + feature_engine)
+
         if feature_engine == 'dirty_cat':
             assert_imported()
         elif feature_engine == 'cu_cat':
             assert_cuml_cucat()
+            
         if inplace:
             res = self
         else:
             res = self.bind()
 
-        feature_engine = resolve_feature_engine(feature_engine)
+        #feature_engine = resolve_feature_engine(feature_engine)
 
         if kind == "nodes":
             res = res._featurize_nodes(

From a5a626ad49591242e83c3f52d1e68d84cfca51e7 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 20 Mar 2023 22:02:48 -0700
Subject: [PATCH 286/432] changes umap spread parameter default to 1

---
 graphistry/features.py   | 2 +-
 graphistry/umap_utils.py | 1 -
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/graphistry/features.py b/graphistry/features.py
index 81bf5627fb..32e83a3a28 100644
--- a/graphistry/features.py
+++ b/graphistry/features.py
@@ -75,7 +75,7 @@
 N_COMPONENTS = 2
 N_NEIGHBORS = 20
 MIN_DIST = 0.1
-SPREAD = 0.5
+SPREAD = 1
 LOCAL_CONNECTIVITY = 1
 REPULSION_STRENGTH = 2
 NEGATIVE_SAMPLING_RATE = 5
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index fc3c01cfa6..726aae9c99 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -235,7 +235,6 @@ def umap_lazy_init(
         print(umap_kwargs) if verbose else None
         # set new umap kwargs
         res._umap_params = umap_kwargs  
-
         res._n_components = n_components
         res._metric = metric
         res._n_neighbors = n_neighbors

From c5dc84d7b451209805c1514dd057235f980477a8 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 20 Mar 2023 22:55:47 -0700
Subject: [PATCH 287/432] refactors core featurize and umap engines so that
 cudf and pd are consistently processed

---
 graphistry/feature_utils.py | 47 +++++++++++++++++++++++++++++--------
 graphistry/umap_utils.py    | 30 +++++++++++------------
 2 files changed, 52 insertions(+), 25 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 4b8484d78c..de84c852a0 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -110,18 +110,21 @@ def lazy_import_has_cu_cat_dependancy():
         import scipy.sparse  # noqa
         from scipy import __version__ as scipy_version
         from cu_cat import __version__ as cu_cat_version
+        import cu_cat
         from sklearn import __version__ as sklearn_version
         from cuml import __version__ as cuml_version
+        import cuml
         from cudf import __version__ as cudf_version
+        import cudf
         logger.debug(f"SCIPY VERSION: {scipy_version}")
         logger.debug(f"Cuda CAT VERSION: {cu_cat_version}")
         logger.debug(f"sklearn VERSION: {sklearn_version}")
         logger.debug(f"cuml VERSION: {cuml_version}")
         logger.debug(f"cudf VERSION: {cudf_version}")
 
-        return True, 'ok'
+        return True, 'ok', cudf
     except ModuleNotFoundError as e:
-        return False, e
+        return False, e, None
 
 def assert_imported_text():
     has_dependancy_text_, import_text_exn, _ = lazy_import_has_dependancy_text()
@@ -142,14 +145,33 @@ def assert_imported():
         raise import_min_exn
         
 def assert_cuml_cucat():
-    has_cuml_dependancy_, import_cuml_exn = lazy_import_has_cu_cat_dependancy()
+    has_cuml_dependancy_, import_cuml_exn, cudf = lazy_import_has_cu_cat_dependancy()
     if not has_cuml_dependancy_:
         logger.error(  # noqa
                      "cuml not found, trying running"  # noqa
                      "`pip install rapids`"  # noqa
         )
         raise import_cuml_exn
-    
+
+def make_safe_gpu_dataframes(X, y, engine):
+
+    def safe_cudf(X, y):
+        new_kwargs = {}
+        kwargs = {'X': X, 'y': y}
+        for key, value in kwargs.items():
+            if isinstance(value, cudf.DataFrame) and engine in ["pandas", "dirty_cat", "torch"]:
+                new_kwargs[key] = value.to_pandas()
+            elif isinstance(value, pd.DataFrame) and engine in ["cuml", "cu_cat"]:
+                new_kwargs[key] = cudf.from_pandas(value)
+            else:
+                new_kwargs[key] = value
+        return new_kwargs['X'], new_kwargs['y']
+
+    has_cudf_dependancy_, _, cudf = lazy_import_has_cu_cat_dependancy()
+    if has_cudf_dependancy_:
+        return safe_cudf(X, y)
+    else:
+        return X, y
 
 # ############################################################################
 #
@@ -189,7 +211,7 @@ def resolve_feature_engine(
         has_dependancy_text_, _, _ = lazy_import_has_dependancy_text()
         if has_dependancy_text_:
             return "torch"
-        has_cuml_dependancy_, _ = lazy_import_has_cu_cat_dependancy()
+        has_cuml_dependancy_, _, cudf = lazy_import_has_cu_cat_dependancy()
         if has_cuml_dependancy_:
             return "cu_cat"
         has_min_dependancy_, _ = lazy_import_has_min_dependancy()
@@ -968,12 +990,13 @@ def process_dirty_dataframes(
             X_enc = pd.DataFrame(
                 X_enc, columns=features_transformed, index=ndf.index
             )
+            X_enc = X_enc.fillna(0.0)  # TODO -- this is a hack in cuml version
         elif 'cudf.core.dataframe' in str(getmodule(ndf)):
             import cudf
             X_enc = cudf.DataFrame(
                 X_enc, columns=features_transformed, index=ndf.index
             )
-        X_enc = X_enc.fillna(0.0)  # TODO -- this is a hack in cuml version
+        #X_enc = X_enc.fillna(0.0)  # TODO -- this is a hack in cuml version
     else:
         logger.info("-*-*- DataFrame is completely numeric")
         X_enc, _, data_encoder, _ = get_numeric_transformers(ndf, None)
@@ -1229,7 +1252,7 @@ class FastMLB:
     def __init__(self, mlb, in_column, out_columns):
         if isinstance(in_column, str):
             in_column = [in_column]
-        self.columns = in_column  # should be singe entry list ['cats']
+        self.columns = in_column  # should be single entry list ['cats']
         self.mlb = mlb
         self.out_columns = out_columns
         self.feature_names_in_ = in_column
@@ -2035,7 +2058,8 @@ def _featurize_nodes(
         X_resolved = resolve_X(ndf, X)
         y_resolved = resolve_y(ndf, y)
 
-        feature_engine = resolve_feature_engine(feature_engine)
+        #feature_engine = resolve_feature_engine(feature_engine)
+        res.feature_engine = feature_engine
         
         from .features import ModelDict
 
@@ -2159,6 +2183,8 @@ def _featurize_edges(
                 **{res._destination: res._edges[res._destination]}
             )
 
+        res.feature_engine = feature_engine
+
         # now that everything is set
         fkwargs = dict(
             X=X_resolved,
@@ -2189,6 +2215,7 @@ def _featurize_edges(
             feature_engine=feature_engine,
         )
 
+
         res._feature_params = {
             **getattr(res, "_feature_params", {}),
             "edges": fkwargs,
@@ -2571,13 +2598,13 @@ def featurize(
             assert_imported()
         elif feature_engine == 'cu_cat':
             assert_cuml_cucat()
-            
+
         if inplace:
             res = self
         else:
             res = self.bind()
 
-        #feature_engine = resolve_feature_engine(feature_engine)
+        #res.feature_engine = feature_engine
 
         if kind == "nodes":
             res = res._featurize_nodes(
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 726aae9c99..715659d7ea 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -130,9 +130,9 @@ def safe_cudf(X, y):
         new_kwargs = {}
         kwargs = {'X': X, 'y': y}
         for key, value in kwargs.items():
-            if isinstance(value, cudf.DataFrame) and engine == "pandas":
+            if isinstance(value, cudf.DataFrame) and engine in ["pandas", "umap_learn", "dirty_cat"]:
                 new_kwargs[key] = value.to_pandas()
-            elif isinstance(value, pd.DataFrame) and engine == "cuml":
+            elif isinstance(value, pd.DataFrame) and engine in ["cuml", "cu_cat"]:
                 new_kwargs[key] = cudf.from_pandas(value)
             else:
                 new_kwargs[key] = value
@@ -183,7 +183,7 @@ class UMAPMixin(MIXIN_BASE):
 
     def __init__(self, *args, **kwargs):
         #self._umap_initialized = False
-        #self.engine = self.engine if hasattr(self, "engine") else None
+        #self.umap_engine = self.umap_engine if hasattr(self, "engine") else None
         pass
 
 
@@ -244,7 +244,7 @@ def umap_lazy_init(
         res._repulsion_strength = repulsion_strength
         res._negative_sample_rate = negative_sample_rate
         res._umap = umap_engine.UMAP(**umap_kwargs)
-        res.engine = engine_resolved
+        res.umap_engine = engine_resolved
         res._suffix = suffix
                                                             
         return res
@@ -280,7 +280,7 @@ def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None, verbose
         logger.info("-" * 90)
         logger.info(f"Starting UMAP-ing data of shape {X.shape}")
 
-        if self.engine == CUML and is_legacy_cuml():  # type: ignore
+        if self.umap_engine == CUML and is_legacy_cuml():  # type: ignore
             from cuml.neighbors import NearestNeighbors
 
             knn = NearestNeighbors(n_neighbors=self._n_neighbors)  # type: ignore
@@ -293,7 +293,7 @@ def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None, verbose
         self._weighted_adjacency = self._umap.graph_
         # if changing, also update fresh_res
         self._weighted_edges_df = umap_graph_to_weighted_edges(
-            self._umap.graph_, self.engine, is_legacy_cuml()  # type: ignore
+            self._umap.graph_, self.umap_engine, is_legacy_cuml()  # type: ignore
         )
 
         mins = (time() - t) / 60
@@ -337,9 +337,9 @@ def transform_umap(self, df: pd.DataFrame,
             fit_umap_embedding: Whether to infer graph from the UMAP embedding on the new data, default True
             verbose: Whether to print information about the graph inference
         """
-        df, y = make_safe_gpu_dataframes(df, y, 'pandas')
+        df, y = make_safe_gpu_dataframes(df, y, self.feature_engine)
         X, y_ = self.transform(df, y, kind=kind, return_graph=False, verbose=verbose)
-        X, y_ = make_safe_gpu_dataframes(X, y_, self.engine)  # type: ignore
+        X, y_ = make_safe_gpu_dataframes(X, y_, self.umap_engine)  # type: ignore
         emb = self._umap.transform(X)  # type: ignore
         emb = self._bundle_embedding(emb, index=df.index)
 
@@ -471,7 +471,7 @@ def umap(
         encode_position: bool = True,
         encode_weight: bool = True,
         dbscan: bool = False,
-        engine: UMAPEngine = "auto",
+        umap_engine: UMAPEngine = "auto",
         feature_engine: str = "auto",
         inplace: bool = False,
         memoize: bool = True,
@@ -533,9 +533,9 @@ def umap(
 
         :return: self, with attributes set with new data
         """
-        if engine == UMAP_LEARN:
+        if umap_engine == UMAP_LEARN:
             assert_imported()
-        elif engine == CUML:
+        elif umap_engine == CUML:
             assert_imported_cuml()
 
         umap_kwargs = dict(
@@ -547,7 +547,7 @@ def umap(
             local_connectivity=local_connectivity,
             repulsion_strength=repulsion_strength,
             negative_sample_rate=negative_sample_rate,
-            engine=engine,
+            umap_engine=umap_engine,
             suffix=suffix,
         )
         logger.debug("umap_kwargs: %s", umap_kwargs)
@@ -599,7 +599,7 @@ def umap(
                 index_to_nodes_dict = nodes  # {}?
 
             # add the safe coercion here 
-            X_, y_ = make_safe_gpu_dataframes(X_, y_, res.engine)  # type: ignore
+            X_, y_ = make_safe_gpu_dataframes(X_, y_, res.umap_engine)  # type: ignore
 
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, verbose, **umap_kwargs
@@ -629,7 +629,7 @@ def umap(
             )
 
             # add the safe coercion here 
-            X_, y_ = make_safe_gpu_dataframes(X_, y_, res.engine)  # type: ignore
+            X_, y_ = make_safe_gpu_dataframes(X_, y_, res.umap_engine)  # type: ignore
 
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs
@@ -674,7 +674,7 @@ def umap(
             res, kind, encode_position, encode_weight, play
         )  # noqa: E501
 
-        if res.engine == CUML and is_legacy_cuml():  # type: ignore
+        if res.umap_engine == CUML and is_legacy_cuml():  # type: ignore
             res = res.prune_self_edges()
 
         if dbscan:

From 69659f87ee95214d7c65bb004822455759fcc540 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Mon, 20 Mar 2023 23:00:19 -0700
Subject: [PATCH 288/432] fixes typo and disambiguates between umap_engine and
 umap_engine_

---
 graphistry/umap_utils.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 715659d7ea..aec17f99e7 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -198,18 +198,18 @@ def umap_lazy_init(
         negative_sample_rate: int = 5,
         n_components: int = 2,
         metric: str = "euclidean",
-        engine: UMAPEngine = "auto",
+        umap_engine: UMAPEngine = "auto",
         suffix: str = "",
         verbose: bool = False,
     ):
         from graphistry.features import ModelDict
 
-        engine_resolved = resolve_umap_engine(engine)
+        engine_resolved = resolve_umap_engine(umap_engine)
         # FIXME remove as set_new_kwargs will always replace?
         if engine_resolved == UMAP_LEARN:
-            _, _, umap_engine = lazy_umap_import_has_dependancy()
+            _, _, umap_engine_ = lazy_umap_import_has_dependancy()
         elif engine_resolved == CUML:
-            _, _, umap_engine = lazy_cuml_import_has_dependancy()
+            _, _, umap_engine_ = lazy_cuml_import_has_dependancy()
         else:
             raise ValueError(
                 "No umap engine, ensure 'auto', 'umap_learn', or 'cuml', and the library is installed"
@@ -243,7 +243,7 @@ def umap_lazy_init(
         res._local_connectivity = local_connectivity
         res._repulsion_strength = repulsion_strength
         res._negative_sample_rate = negative_sample_rate
-        res._umap = umap_engine.UMAP(**umap_kwargs)
+        res._umap = umap_engine_.UMAP(**umap_kwargs)
         res.umap_engine = engine_resolved
         res._suffix = suffix
                                                             

From aedc81e90f9474d94640aaf8724d13524ea2e9ea Mon Sep 17 00:00:00 2001
From: dcolinmorgan <apple@dc-m1.local>
Date: Tue, 21 Mar 2023 15:48:28 +0900
Subject: [PATCH 289/432] lazy cudf import (thx alex)

---
 graphistry/feature_utils.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index aa5b830ba3..cf7047c0f6 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -113,6 +113,7 @@ def lazy_import_has_cu_cat_dependancy():
         from sklearn import __version__ as sklearn_version
         from cuml import __version__ as cuml_version
         from cudf import __version__ as cudf_version
+        import cudf
         logger.debug(f"SCIPY VERSION: {scipy_version}")
         logger.debug(f"Cuda CAT VERSION: {cu_cat_version}")
         logger.debug(f"sklearn VERSION: {sklearn_version}")
@@ -668,7 +669,6 @@ def fit_pipeline(
             X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
         X=pd.DataFrame(X, columns=columns, index=index)
     elif 'cudf.core.dataframe' in X_type:
-        import cudf ## need proper import routine still
         X = transformer.fit_transform(X.to_numpy())
         if keep_n_decimals:
             X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
@@ -969,7 +969,6 @@ def process_dirty_dataframes(
                 X_enc, columns=features_transformed, index=ndf.index
             )
         elif 'cudf.core.dataframe' in str(getmodule(ndf)):
-            import cudf
             X_enc = cudf.DataFrame(
                 X_enc, columns=features_transformed, index=ndf.index
             )
@@ -1314,7 +1313,6 @@ def encode_edges(edf, src, dst, mlb, fit=False):
     mlb.get_feature_names_out = callThrough(columns)
     mlb.columns_ = [src, dst]
     if 'cudf.core.dataframe' in edf_type:
-        import cudf
         T = cudf.DataFrame(T, columns=columns, index=edf.index)
     else:
         T = pd.DataFrame(T, columns=columns, index=edf.index)
@@ -1477,7 +1475,6 @@ def process_edge_dataframes(
         logger.debug("<= Found Edges and Dirty_cat encoding =>")
         T_type= str(getmodule(T))
         if 'cudf.core.dataframe' in T_type:
-            import cudf
             X_enc = cudf.concat([T, X_enc], axis=1)
         else:
             X_enc = pd.concat([T, X_enc], axis=1)

From 465d486788d475feb1528ea05a899732518d8daa Mon Sep 17 00:00:00 2001
From: dcolinmorgan <apple@dc-m1.local>
Date: Tue, 21 Mar 2023 15:49:28 +0900
Subject: [PATCH 290/432] lazy cudf import (thx alex)

---
 graphistry/feature_utils.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 406cba183d..cf7047c0f6 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -113,6 +113,7 @@ def lazy_import_has_cu_cat_dependancy():
         from sklearn import __version__ as sklearn_version
         from cuml import __version__ as cuml_version
         from cudf import __version__ as cudf_version
+        import cudf
         logger.debug(f"SCIPY VERSION: {scipy_version}")
         logger.debug(f"Cuda CAT VERSION: {cu_cat_version}")
         logger.debug(f"sklearn VERSION: {sklearn_version}")
@@ -668,7 +669,6 @@ def fit_pipeline(
             X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
         X=pd.DataFrame(X, columns=columns, index=index)
     elif 'cudf.core.dataframe' in X_type:
-        import cudf
         X = transformer.fit_transform(X.to_numpy())
         if keep_n_decimals:
             X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
@@ -969,7 +969,6 @@ def process_dirty_dataframes(
                 X_enc, columns=features_transformed, index=ndf.index
             )
         elif 'cudf.core.dataframe' in str(getmodule(ndf)):
-            import cudf
             X_enc = cudf.DataFrame(
                 X_enc, columns=features_transformed, index=ndf.index
             )
@@ -1314,7 +1313,6 @@ def encode_edges(edf, src, dst, mlb, fit=False):
     mlb.get_feature_names_out = callThrough(columns)
     mlb.columns_ = [src, dst]
     if 'cudf.core.dataframe' in edf_type:
-        import cudf
         T = cudf.DataFrame(T, columns=columns, index=edf.index)
     else:
         T = pd.DataFrame(T, columns=columns, index=edf.index)
@@ -1477,7 +1475,6 @@ def process_edge_dataframes(
         logger.debug("<= Found Edges and Dirty_cat encoding =>")
         T_type= str(getmodule(T))
         if 'cudf.core.dataframe' in T_type:
-            import cudf
             X_enc = cudf.concat([T, X_enc], axis=1)
         else:
             X_enc = pd.concat([T, X_enc], axis=1)

From 87d706c7682fc102d82e8eab76fc9781f65179c0 Mon Sep 17 00:00:00 2001
From: Alex Morrise <silkspace@users.noreply.github.com>
Date: Tue, 21 Mar 2023 21:32:02 -0700
Subject: [PATCH 291/432] changed umap spread to 1

---
 graphistry/features.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/features.py b/graphistry/features.py
index 81bf5627fb..32e83a3a28 100644
--- a/graphistry/features.py
+++ b/graphistry/features.py
@@ -75,7 +75,7 @@
 N_COMPONENTS = 2
 N_NEIGHBORS = 20
 MIN_DIST = 0.1
-SPREAD = 0.5
+SPREAD = 1
 LOCAL_CONNECTIVITY = 1
 REPULSION_STRENGTH = 2
 NEGATIVE_SAMPLING_RATE = 5

From 08ad02cc5c6b78e0201668d01015c96d66170db2 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <apple@dc-m1.local>
Date: Tue, 28 Mar 2023 20:02:35 +0800
Subject: [PATCH 292/432] lazy cudf import, pin |torch for now

---
 graphistry/feature_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index cf7047c0f6..0c24f77d44 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -175,7 +175,7 @@ def assert_cuml_cucat():
 #
 #      _featurize_or_get_edges_dataframe_if_X_is_None
 
-FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch", "cu_cat", "cu_cat|torch"]
+FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch", "cu_cat", "cu_cat"]
 FeatureEngine = Literal[FeatureEngineConcrete, "auto"]
 
 

From 521dc5b66b4f66ef0a8f6e43e77b884df6e40a23 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <apple@dc-m1.local>
Date: Tue, 28 Mar 2023 20:03:29 +0800
Subject: [PATCH 293/432] lazy cudf import, pin |torch for now

---
 graphistry/feature_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 0c24f77d44..d1ebe94579 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -175,7 +175,7 @@ def assert_cuml_cucat():
 #
 #      _featurize_or_get_edges_dataframe_if_X_is_None
 
-FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch", "cu_cat", "cu_cat"]
+FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch", "cu_cat"]
 FeatureEngine = Literal[FeatureEngineConcrete, "auto"]
 
 

From ba99c40d4bca051f1da1b9b1ac978e0ca48503b6 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <apple@dc-m1.local>
Date: Tue, 28 Mar 2023 21:43:16 +0800
Subject: [PATCH 294/432] resolve f_engine

---
 graphistry/feature_utils.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index d1ebe94579..44827d090d 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2560,6 +2560,9 @@ def featurize(
                 default True.
         :return: graphistry instance with new attributes set by the featurization process.
         """
+        
+        feature_engine = resolve_feature_engine(feature_engine)
+
         if feature_engine == 'dirty_cat':
             assert_imported()
         elif feature_engine == 'cu_cat':
@@ -2569,8 +2572,6 @@ def featurize(
         else:
             res = self.bind()
 
-        feature_engine = resolve_feature_engine(feature_engine)
-
         if kind == "nodes":
             res = res._featurize_nodes(
                 X=X,

From 5b55f2445665a40a3439e0b14522c385d6d57906 Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 30 Mar 2023 16:51:01 -0700
Subject: [PATCH 295/432] adds cudf conversion inside _featurize_* calls

---
 graphistry/feature_utils.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index de84c852a0..0879d71f62 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -2058,6 +2058,8 @@ def _featurize_nodes(
         X_resolved = resolve_X(ndf, X)
         y_resolved = resolve_y(ndf, y)
 
+        X_resolved, y_resolved = make_safe_gpu_dataframes(X_resolved, y_resolved, engine=feature_engine)
+
         #feature_engine = resolve_feature_engine(feature_engine)
         res.feature_engine = feature_engine
         
@@ -2185,6 +2187,9 @@ def _featurize_edges(
 
         res.feature_engine = feature_engine
 
+        X_resolved, y_resolved = make_safe_gpu_dataframes(X_resolved, y_resolved, engine=feature_engine)
+
+
         # now that everything is set
         fkwargs = dict(
             X=X_resolved,
@@ -2604,8 +2609,6 @@ def featurize(
         else:
             res = self.bind()
 
-        #res.feature_engine = feature_engine
-
         if kind == "nodes":
             res = res._featurize_nodes(
                 X=X,

From 5e0a1ce52b8189da291f02512a19162e3ec2764b Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 30 Mar 2023 16:58:03 -0700
Subject: [PATCH 296/432] adds print

---
 graphistry/feature_utils.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 0879d71f62..833b7ecc23 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -169,6 +169,7 @@ def safe_cudf(X, y):
 
     has_cudf_dependancy_, _, cudf = lazy_import_has_cu_cat_dependancy()
     if has_cudf_dependancy_:
+        print(f"Using GPU: {engine}")
         return safe_cudf(X, y)
     else:
         return X, y

From 23da7fc5446102db868579527b22d0f8d97c6e7f Mon Sep 17 00:00:00 2001
From: Alex <alex@stayopen.com>
Date: Thu, 30 Mar 2023 17:15:46 -0700
Subject: [PATCH 297/432] pulls cudf X into pandas for FAISS indexing

---
 graphistry/text_utils.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/graphistry/text_utils.py b/graphistry/text_utils.py
index 9a1abb77f5..63fa5031d0 100644
--- a/graphistry/text_utils.py
+++ b/graphistry/text_utils.py
@@ -43,6 +43,9 @@ def build_index(self, angular=False, n_trees=None):
         self.assert_fitted()
         self.assert_features_line_up_with_nodes()
         X = self._get_feature("nodes")
+        if type(X) != pd.DataFrame:
+            print(f"Converting from {type(X)} to pandas for semantic search index")
+            X = X.to_pandas()
         self.search_index = FaissVectorSearch(
             X.values
         )  # self._build_search_index(X, angular, n_trees, faiss=False)

From 8b1331154a494c3ac93695e43d47abfc88417b8a Mon Sep 17 00:00:00 2001
From: dcolinmorgan <apple@dc-m1.lan>
Date: Fri, 31 Mar 2023 10:30:09 +0800
Subject: [PATCH 298/432] placeholder cu_cat setup

---
 setup.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 6889be15f5..41347c7d99 100755
--- a/setup.py
+++ b/setup.py
@@ -41,9 +41,10 @@ def unique_flatten_dict(d):
 base_extras_heavy = {
   'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
 }
+# base_extras_heavy['cu_cat'] = ['cu-cat @ git+https://github.com/graphistry/cu_cat.git@cudf-cat']
 
 # https://github.com/facebookresearch/faiss/issues/1589 for faiss-cpu 1.6.1, #'setuptools==67.4.0' removed
-base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'faiss-cpu', 'joblib']
+base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'faiss-cpu', 'joblib'] #+ base_extras_heavy['cu_cat']
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From 434256c31809938ecfd44009f49d4704591eacc7 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <apple@dc-m1.local>
Date: Mon, 3 Apr 2023 15:13:28 +0800
Subject: [PATCH 299/432] tweaks needed for gpu cu_cat

---
 graphistry/feature_utils.py | 99 +++++++++++++++++++++++++++----------
 1 file changed, 72 insertions(+), 27 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index cf7047c0f6..3aecd5a058 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -60,7 +60,7 @@
         GapEncoder = Any
         SimilarityEncoder = Any
     try:
-        from sklearn.preprocessing import FunctionTransformer
+        from cuml.preprocessing import FunctionTransformer
         from sklearn.base import BaseEstimator, TransformerMixin
     except:
         FunctionTransformer = Any
@@ -110,8 +110,10 @@ def lazy_import_has_cu_cat_dependancy():
         import scipy.sparse  # noqa
         from scipy import __version__ as scipy_version
         from cu_cat import __version__ as cu_cat_version
+        import cu_cat
         from sklearn import __version__ as sklearn_version
         from cuml import __version__ as cuml_version
+        import cuml
         from cudf import __version__ as cudf_version
         import cudf
         logger.debug(f"SCIPY VERSION: {scipy_version}")
@@ -120,9 +122,9 @@ def lazy_import_has_cu_cat_dependancy():
         logger.debug(f"cuml VERSION: {cuml_version}")
         logger.debug(f"cudf VERSION: {cudf_version}")
 
-        return True, 'ok'
+        return True, 'ok', cudf
     except ModuleNotFoundError as e:
-        return False, e
+        return False, e, None
 
 def assert_imported_text():
     has_dependancy_text_, import_text_exn, _ = lazy_import_has_dependancy_text()
@@ -143,14 +145,34 @@ def assert_imported():
         raise import_min_exn
         
 def assert_cuml_cucat():
-    has_cuml_dependancy_, import_cuml_exn = lazy_import_has_cu_cat_dependancy()
+    has_cuml_dependancy_, import_cuml_exn, cudf = lazy_import_has_cu_cat_dependancy()
     if not has_cuml_dependancy_:
         logger.error(  # noqa
                      "cuml not found, trying running"  # noqa
                      "`pip install rapids`"  # noqa
         )
         raise import_cuml_exn
-    
+
+def make_safe_gpu_dataframes(X, y, engine):
+
+    def safe_cudf(X, y):
+        new_kwargs = {}
+        kwargs = {'X': X, 'y': y}
+        for key, value in kwargs.items():
+            if isinstance(value, cudf.DataFrame) and engine in ["pandas", "dirty_cat", "torch"]:
+                new_kwargs[key] = value.to_pandas()
+            elif isinstance(value, pd.DataFrame) and engine in ["cuml", "cu_cat"]:
+                new_kwargs[key] = cudf.from_pandas(value)
+            else:
+                new_kwargs[key] = value
+        return new_kwargs['X'], new_kwargs['y']
+
+    has_cudf_dependancy_, _, cudf = lazy_import_has_cu_cat_dependancy()
+    if has_cudf_dependancy_:
+        print(f"Using GPU: {engine}")
+        return safe_cudf(X, y)
+    else:
+        return X, y
 
 # ############################################################################
 #
@@ -175,7 +197,7 @@ def assert_cuml_cucat():
 #
 #      _featurize_or_get_edges_dataframe_if_X_is_None
 
-FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch", "cu_cat", "cu_cat|torch"]
+FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch", "cu_cat"]
 FeatureEngine = Literal[FeatureEngineConcrete, "auto"]
 
 
@@ -190,7 +212,7 @@ def resolve_feature_engine(
         has_dependancy_text_, _, _ = lazy_import_has_dependancy_text()
         if has_dependancy_text_:
             return "torch"
-        has_cuml_dependancy_, _ = lazy_import_has_cu_cat_dependancy()
+        has_cuml_dependancy_, _, cudf = lazy_import_has_cu_cat_dependancy()
         if has_cuml_dependancy_:
             return "cu_cat"
         has_min_dependancy_, _ = lazy_import_has_min_dependancy()
@@ -211,7 +233,8 @@ def resolve_feature_engine(
 def resolve_y(df: Optional[pd.DataFrame], y: YSymbolic) -> pd.DataFrame:
 
     if isinstance(y, pd.DataFrame) or 'cudf.core.dataframe' in str(getmodule(y)):
-        return y
+
+        return y  # type: ignore
 
     if df is None:
         raise ValueError("Missing data for featurization")
@@ -232,8 +255,7 @@ def resolve_y(df: Optional[pd.DataFrame], y: YSymbolic) -> pd.DataFrame:
 def resolve_X(df: Optional[pd.DataFrame], X: XSymbolic) -> pd.DataFrame:
 
     if isinstance(X, pd.DataFrame) or 'cudf.core.dataframe' in str(getmodule(X)):
-        return X
-
+        return X  # type: ignore
     if df is None:
         raise ValueError("Missing data for featurization")
 
@@ -587,11 +609,19 @@ def get_preprocessing_pipeline(
     :return: scaled array, imputer instances or None, scaler instance or None
     """
     from sklearn.preprocessing import (
+        # FunctionTransformer,
+        # KBinsDiscretizer,
+        # MinMaxScaler,
+        MultiLabelBinarizer,
+        QuantileTransformer, 
+        # RobustScaler,
+        # StandardScaler,
+    )
+    from cuml.preprocessing import (
         FunctionTransformer,
         KBinsDiscretizer,
         MinMaxScaler,
-        MultiLabelBinarizer,
-        QuantileTransformer,
+        # QuantileTransformer,  ## cuml 23 only
         RobustScaler,
         StandardScaler,
     )
@@ -864,7 +894,7 @@ def __call__(self, *args, **kwargs):
 def get_numeric_transformers(ndf, y=None):
     # numeric selector needs to embody memorization of columns
     # for later .transform consistency.
-    from sklearn.preprocessing import FunctionTransformer
+    from cuml.preprocessing import FunctionTransformer
     label_encoder = False
     data_encoder = False
     y_ = y
@@ -926,7 +956,7 @@ def process_dirty_dataframes(
         from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
     elif feature_engine == 'cu_cat':
         from cu_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
-    from sklearn.preprocessing import FunctionTransformer
+    from cuml.preprocessing import FunctionTransformer
     t = time()
 
     if not is_dataframe_all_numeric(ndf):
@@ -941,7 +971,6 @@ def process_dirty_dataframes(
         )
 
         logger.info(":: Encoding DataFrame might take a few minutes ------")
-        
         X_enc = data_encoder.fit_transform(ndf, y)
         X_enc = make_array(X_enc)
 
@@ -968,11 +997,16 @@ def process_dirty_dataframes(
             X_enc = pd.DataFrame(
                 X_enc, columns=features_transformed, index=ndf.index
             )
+            X_enc = X_enc.fillna(0.0)  # TODO -- this is a hack in cuml version
         elif 'cudf.core.dataframe' in str(getmodule(ndf)):
-            X_enc = cudf.DataFrame(
-                X_enc, columns=features_transformed, index=ndf.index
-            )
-        X_enc = X_enc.fillna(0.0)
+            import cudf
+            X_enc = cudf.DataFrame.from_arrow(X_enc)
+            X_enc.index = ndf.index
+            # features_transformed=np.array([item.as_py() for item in features_transformed.key()])
+            # X_enc.columns = features_transformed #.to_numpy() ##error suggests this -- not working
+
+            
+        #X_enc = X_enc.fillna(0.0)  # TODO -- this is a hack in cuml version
     else:
         logger.info("-*-*- DataFrame is completely numeric")
         X_enc, _, data_encoder, _ = get_numeric_transformers(ndf, None)
@@ -1196,7 +1230,6 @@ def process_nodes_dataframes(
     logger.debug(
         f"--The entire Encoding process took {(time()-t)/60:.2f} minutes"
     )
-
     X_encs, y_encs, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
         X_enc,
         y_enc,
@@ -1211,7 +1244,6 @@ def process_nodes_dataframes(
         strategy=strategy,
         keep_n_decimals=keep_n_decimals,
     )
-
     return (
         X_enc,
         y_enc,
@@ -1228,7 +1260,7 @@ class FastMLB:
     def __init__(self, mlb, in_column, out_columns):
         if isinstance(in_column, str):
             in_column = [in_column]
-        self.columns = in_column  # should be singe entry list ['cats']
+        self.columns = in_column  # should be single entry list ['cats']
         self.mlb = mlb
         self.out_columns = out_columns
         self.feature_names_in_ = in_column
@@ -1474,7 +1506,9 @@ def process_edge_dataframes(
         logger.debug("-" * 60)
         logger.debug("<= Found Edges and Dirty_cat encoding =>")
         T_type= str(getmodule(T))
-        if 'cudf.core.dataframe' in T_type:
+        if 'cudf.core.dataframe' not in T_type:
+            X_enc = pd.concat([T, X_enc], axis=1)
+        elif 'cudf.core.dataframe' not in T_type:
             X_enc = cudf.concat([T, X_enc], axis=1)
         else:
             X_enc = pd.concat([T, X_enc], axis=1)
@@ -1731,7 +1765,6 @@ def _hecho(self, res):
         logger.info("\n-- Setting Encoder Parts from Fit ::")
         logger.info(f'Feature Columns In: {self.feature_names_in}')
         logger.info(f'Target Columns In: {self.target_names_in}')
-
         for name, value in zip(self.res_names, res):
             if name not in ["X_enc", "y_enc"]:
                 logger.info("-" * 90)
@@ -2032,7 +2065,10 @@ def _featurize_nodes(
         X_resolved = resolve_X(ndf, X)
         y_resolved = resolve_y(ndf, y)
 
-        feature_engine = resolve_feature_engine(feature_engine)
+        X_resolved, y_resolved = make_safe_gpu_dataframes(X_resolved, y_resolved, engine=feature_engine)
+
+        #feature_engine = resolve_feature_engine(feature_engine)
+        res.feature_engine = feature_engine
         
         from .features import ModelDict
 
@@ -2156,6 +2192,11 @@ def _featurize_edges(
                 **{res._destination: res._edges[res._destination]}
             )
 
+        res.feature_engine = feature_engine
+
+        X_resolved, y_resolved = make_safe_gpu_dataframes(X_resolved, y_resolved, engine=feature_engine)
+
+
         # now that everything is set
         fkwargs = dict(
             X=X_resolved,
@@ -2186,6 +2227,7 @@ def _featurize_edges(
             feature_engine=feature_engine,
         )
 
+
         res._feature_params = {
             **getattr(res, "_feature_params", {}),
             "edges": fkwargs,
@@ -2560,17 +2602,20 @@ def featurize(
                 default True.
         :return: graphistry instance with new attributes set by the featurization process.
         """
+        feature_engine = resolve_feature_engine(feature_engine)
+
+        print('Featurizing nodes with feature_engine=' + feature_engine)
+
         if feature_engine == 'dirty_cat':
             assert_imported()
         elif feature_engine == 'cu_cat':
             assert_cuml_cucat()
+
         if inplace:
             res = self
         else:
             res = self.bind()
 
-        feature_engine = resolve_feature_engine(feature_engine)
-
         if kind == "nodes":
             res = res._featurize_nodes(
                 X=X,

From 0807e766fa69f5e7b941c96d2741376e37a69f28 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Mon, 3 Apr 2023 21:38:59 -0400
Subject: [PATCH 300/432] fix(docs): removed iframe (for now)

---
 docs/source/index.rst | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/source/index.rst b/docs/source/index.rst
index e86502551e..1704e7f07c 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -8,12 +8,12 @@ PyGraphistry is a Python visual graph AI library to extract, transform, analyze,
 Here in our docstrings you can find useful packages, modules, and commands to maximize your graph AI experience with PyGraphistry. In the navbar you can find an overview of all the packages and modules we provided and a few useful highlighted ones as well. You can search for them on our Search page. For a full tutorial, refer to our `PyGraphistry <https://github.com/graphistry/pygraphistry/>`_ repo.
 
 
-Click to open interactive version! (For server-backed interactive analytics, use an API key)
+.. Click to open interactive version! (For server-backed interactive analytics, use an API key)
 
 
-.. raw:: html
+.. .. raw:: html
 
-       <iframe width="600" height="400" src="https://hub.graphistry.com/graph/graph.html?dataset=Facebook&splashAfter=true" frameborder="0" allowfullscreen></iframe>
+..        <iframe width="600" height="400" src="https://hub.graphistry.com/graph/graph.html?dataset=Facebook&splashAfter=true" frameborder="0" allowfullscreen></iframe>
 
 For self-hosting and access to a free API key, refer to our Graphistry `Hub <https://hub.graphistry.com/>`_.
 

From d25faf80dc9e760bf534adcb134c4bbc5c54582a Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Tue, 4 Apr 2023 21:11:23 +0530
Subject: [PATCH 301/432] fix: cuml umap and tests fix

---
 .../gpu_rapids/part_iv_gpu_cuml.ipynb         | 443 +++++++-----------
 graphistry/dgl_utils.py                       |   1 -
 graphistry/tests/test_umap_utils.py           |  37 +-
 graphistry/umap_utils.py                      |   7 +
 4 files changed, 201 insertions(+), 287 deletions(-)

diff --git a/demos/demos_databases_apis/gpu_rapids/part_iv_gpu_cuml.ipynb b/demos/demos_databases_apis/gpu_rapids/part_iv_gpu_cuml.ipynb
index 14e67afb7f..849887d4b9 100644
--- a/demos/demos_databases_apis/gpu_rapids/part_iv_gpu_cuml.ipynb
+++ b/demos/demos_databases_apis/gpu_rapids/part_iv_gpu_cuml.ipynb
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 2,
    "metadata": {
     "vscode": {
      "languageId": "python"
@@ -101,43 +101,43 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>61</td>\n",
-       "      <td>26</td>\n",
-       "      <td>937</td>\n",
-       "      <td>2019-04-05</td>\n",
-       "      <td>113.47,20.34</td>\n",
+       "      <td>32</td>\n",
+       "      <td>185</td>\n",
+       "      <td>357</td>\n",
+       "      <td>2017-06-16</td>\n",
+       "      <td>117.81,22.87</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>30</td>\n",
-       "      <td>19</td>\n",
-       "      <td>972</td>\n",
-       "      <td>2019-08-17</td>\n",
-       "      <td>117.61,20.24</td>\n",
+       "      <td>66</td>\n",
+       "      <td>86</td>\n",
+       "      <td>84</td>\n",
+       "      <td>2020-03-30</td>\n",
+       "      <td>110.07,20.52</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>27</td>\n",
-       "      <td>134</td>\n",
-       "      <td>760</td>\n",
-       "      <td>2020-05-30</td>\n",
-       "      <td>115.11,23.5</td>\n",
+       "      <td>28</td>\n",
+       "      <td>26</td>\n",
+       "      <td>862</td>\n",
+       "      <td>2019-05-12</td>\n",
+       "      <td>116.16,23.02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>55</td>\n",
-       "      <td>44</td>\n",
-       "      <td>864</td>\n",
-       "      <td>2016-08-17</td>\n",
-       "      <td>119.14,21.56</td>\n",
+       "      <td>69</td>\n",
+       "      <td>193</td>\n",
+       "      <td>607</td>\n",
+       "      <td>2019-03-11</td>\n",
+       "      <td>112.21,23.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>24</td>\n",
-       "      <td>184</td>\n",
-       "      <td>938</td>\n",
-       "      <td>2017-09-30</td>\n",
-       "      <td>113.64,23.54</td>\n",
+       "      <td>34</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2019-08-06</td>\n",
+       "      <td>114.56,20.99</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -149,43 +149,43 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>995</th>\n",
-       "      <td>69</td>\n",
-       "      <td>72</td>\n",
-       "      <td>887</td>\n",
-       "      <td>2019-10-26</td>\n",
-       "      <td>115.18,23.8</td>\n",
+       "      <td>52</td>\n",
+       "      <td>128</td>\n",
+       "      <td>435</td>\n",
+       "      <td>2016-10-19</td>\n",
+       "      <td>115.3,23.67</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>996</th>\n",
-       "      <td>33</td>\n",
-       "      <td>29</td>\n",
-       "      <td>651</td>\n",
-       "      <td>2020-06-15</td>\n",
-       "      <td>117.05,21.3</td>\n",
+       "      <td>67</td>\n",
+       "      <td>116</td>\n",
+       "      <td>97</td>\n",
+       "      <td>2016-04-24</td>\n",
+       "      <td>117.69,23.92</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>997</th>\n",
-       "      <td>18</td>\n",
-       "      <td>101</td>\n",
-       "      <td>517</td>\n",
-       "      <td>2019-04-14</td>\n",
-       "      <td>111.96,23.58</td>\n",
+       "      <td>32</td>\n",
+       "      <td>55</td>\n",
+       "      <td>915</td>\n",
+       "      <td>2018-11-07</td>\n",
+       "      <td>113.63,22.74</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>998</th>\n",
-       "      <td>65</td>\n",
-       "      <td>19</td>\n",
-       "      <td>974</td>\n",
-       "      <td>2019-05-22</td>\n",
-       "      <td>112.48,23.63</td>\n",
+       "      <td>72</td>\n",
+       "      <td>68</td>\n",
+       "      <td>148</td>\n",
+       "      <td>2020-05-23</td>\n",
+       "      <td>116.39,21.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999</th>\n",
-       "      <td>23</td>\n",
-       "      <td>42</td>\n",
-       "      <td>156</td>\n",
-       "      <td>2020-12-10</td>\n",
-       "      <td>118.72,22.49</td>\n",
+       "      <td>56</td>\n",
+       "      <td>19</td>\n",
+       "      <td>932</td>\n",
+       "      <td>2016-04-23</td>\n",
+       "      <td>116.2,23.54</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -193,23 +193,23 @@
        "</div>"
       ],
       "text/plain": [
-       "     age  user_id  profile        date      location\n",
-       "0     61       26      937  2019-04-05  113.47,20.34\n",
-       "1     30       19      972  2019-08-17  117.61,20.24\n",
-       "2     27      134      760  2020-05-30   115.11,23.5\n",
-       "3     55       44      864  2016-08-17  119.14,21.56\n",
-       "4     24      184      938  2017-09-30  113.64,23.54\n",
-       "..   ...      ...      ...         ...           ...\n",
-       "995   69       72      887  2019-10-26   115.18,23.8\n",
-       "996   33       29      651  2020-06-15   117.05,21.3\n",
-       "997   18      101      517  2019-04-14  111.96,23.58\n",
-       "998   65       19      974  2019-05-22  112.48,23.63\n",
-       "999   23       42      156  2020-12-10  118.72,22.49\n",
+       "    age user_id profile        date      location\n",
+       "0    32     185     357  2017-06-16  117.81,22.87\n",
+       "1    66      86      84  2020-03-30  110.07,20.52\n",
+       "2    28      26     862  2019-05-12  116.16,23.02\n",
+       "3    69     193     607  2019-03-11  112.21,23.25\n",
+       "4    34      27       4  2019-08-06  114.56,20.99\n",
+       "..   ..     ...     ...         ...           ...\n",
+       "995  52     128     435  2016-10-19   115.3,23.67\n",
+       "996  67     116      97  2016-04-24  117.69,23.92\n",
+       "997  32      55     915  2018-11-07  113.63,22.74\n",
+       "998  72      68     148  2020-05-23  116.39,21.25\n",
+       "999  56      19     932  2016-04-23   116.2,23.54\n",
        "\n",
        "[1000 rows x 5 columns]"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -230,12 +230,13 @@
     "df['lat']=np.round(np.random.uniform(110, 120,size=(samples)), 2)\n",
     "df['location']=df['lat'].astype(str) +\",\"+ df[\"lon\"].astype(str) \n",
     "df.drop(columns=['lat','lon'],inplace=True)\n",
+    "df = df.applymap(str)\n",
     "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 3,
    "metadata": {
     "vscode": {
      "languageId": "python"
@@ -243,38 +244,18 @@
    },
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "['time: 0.03180466492970784 line/min: 31441.928478420414']\n"
+      "! Failed umap speedup attempt. Continuing without memoization speedups.* Ignoring target column of shape (1000, 0) in UMAP fit, as it is not one dimensional"
      ]
     },
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <iframe id=\"d9d175c0-26d5-4056-b8c7-b22828cd5b39\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=0cf4dfc3b18044199b33d1089f5b9c64&type=arrow&viztoken=c846ac21-e17d-4d22-87db-19ab202ecf0d&usertag=39269b0c-pygraphistry-0.27.2+4.ga674343.dirty&splashAfter=1663820405&info=true&play=0\"\n",
-       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
-       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
-       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
-       "                    \n",
-       "            >\n",
-       "            </iframe>\n",
-       "        \n",
-       "            <script>\n",
-       "                try {\n",
-       "                  $(\"#d9d175c0-26d5-4056-b8c7-b22828cd5b39\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
-       "                } catch (e) { console.error('exn catching scroll', e); }\n",
-       "            </script>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 113,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['time: 0.14064184427261353 line/min: 7110.259433612426']\n"
+     ]
     }
    ],
    "source": [
@@ -296,7 +277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 4,
    "metadata": {
     "vscode": {
      "languageId": "python"
@@ -304,38 +285,18 @@
    },
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "['time: 0.02227895657221476 line/min: 44885.40550625031']\n"
+      "! Failed umap speedup attempt. Continuing without memoization speedups.* Ignoring target column of shape (1000, 14) in UMAP fit, as it is not one dimensional"
      ]
     },
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <iframe id=\"95993363-1f8e-4866-ad87-0b7a9fb25d5b\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=b8031f5c47214275b8ae8d4c50b2bd7c&type=arrow&viztoken=15b29905-a47d-4bee-986e-7e9983506b1d&usertag=39269b0c-pygraphistry-0.27.2+4.ga674343.dirty&splashAfter=1663820408&info=true&play=0\"\n",
-       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
-       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
-       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
-       "                    \n",
-       "            >\n",
-       "            </iframe>\n",
-       "        \n",
-       "            <script>\n",
-       "                try {\n",
-       "                  $(\"#95993363-1f8e-4866-ad87-0b7a9fb25d5b\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
-       "                } catch (e) { console.error('exn catching scroll', e); }\n",
-       "            </script>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 114,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['time: 0.0287002166112264 line/min: 34842.94260026035']\n"
+     ]
     }
    ],
    "source": [
@@ -350,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 5,
    "metadata": {
     "vscode": {
      "languageId": "python"
@@ -358,38 +319,18 @@
    },
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "['time: 0.023025786876678465 line/min: 43429.56900260569']\n"
+      "! Failed umap speedup attempt. Continuing without memoization speedups.* Ignoring target column of shape (1000, 14) in UMAP fit, as it is not one dimensional"
      ]
     },
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <iframe id=\"58624fc9-fcef-4650-8d27-8e8111b20f5d\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=bf0b491ec4264ec489d64edb8fdf8a72&type=arrow&viztoken=cb6b7978-1d15-4d8d-9f2f-8579be7f8f35&usertag=39269b0c-pygraphistry-0.27.2+4.ga674343.dirty&splashAfter=1663820508&info=true&play=0\"\n",
-       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
-       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
-       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
-       "                    \n",
-       "            >\n",
-       "            </iframe>\n",
-       "        \n",
-       "            <script>\n",
-       "                try {\n",
-       "                  $(\"#58624fc9-fcef-4650-8d27-8e8111b20f5d\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
-       "                } catch (e) { console.error('exn catching scroll', e); }\n",
-       "            </script>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 115,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['time: 0.0024895787239074705 line/min: 401674.38386140653']\n"
+     ]
     }
    ],
    "source": [
@@ -411,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 6,
    "metadata": {
     "vscode": {
      "languageId": "python"
@@ -419,61 +360,30 @@
    },
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "['time: 0.003930246829986573 line/min: 254436.94588602122']\n"
+      "! Failed umap speedup attempt. Continuing without memoization speedups.* Ignoring target column of shape (1000, 14) in UMAP fit, as it is not one dimensional"
      ]
     },
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <iframe id=\"e51a24bb-44f3-4fe8-990e-6fcef25e2948\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=77aaa2167df04db49aed214cfd548256&type=arrow&viztoken=26ab4e56-2798-4449-83ba-e116f07b36f8&usertag=39269b0c-pygraphistry-0.27.2+4.ga674343.dirty&splashAfter=1663820701&info=true&play=0\"\n",
-       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
-       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
-       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
-       "                    \n",
-       "            >\n",
-       "            </iframe>\n",
-       "        \n",
-       "            <script>\n",
-       "                try {\n",
-       "                  $(\"#e51a24bb-44f3-4fe8-990e-6fcef25e2948\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
-       "                } catch (e) { console.error('exn catching scroll', e); }\n",
-       "            </script>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['time: 0.0022179365158081056 line/min: 450869.5325013168']\n"
+     ]
     }
    ],
    "source": [
     "g = graphistry.nodes(df)\n",
     "t=time()\n",
-    "g2 = g.umap(X=['user_id'],y=['date','location'], feature_engine='torch', n_neighbors= 2,min_dist=.5, spread=.1, local_connectivity=2, n_components=5,metric='hellinger')\n",
+    "g2 = g.umap(X=['user_id'],y=['date','location'], feature_engine='torch', n_neighbors= 2,min_dist=.1, spread=.1, local_connectivity=2, n_components=5,metric='hellinger')\n",
     "min=(time()-t)/60\n",
     "lin=df.shape[0]/min\n",
     "print(['time: '+str(min)+' line/min: '+str(lin)])\n",
-    "g2.plot()\n"
+    "g2.plot(render=False)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "python"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -483,18 +393,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 7,
    "metadata": {
     "vscode": {
      "languageId": "python"
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "! Failed umap speedup attempt. Continuing without memoization speedups.* Ignoring target column of shape (1000, 0) in UMAP fit, as it is not one dimensional"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['time: 0.004134837786356608 line/min: 241847.4560960093']\n"
+      "['time: 0.00446544885635376 line/min: 223941.65338544376']\n"
      ]
     }
    ],
@@ -509,18 +426,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 8,
    "metadata": {
     "vscode": {
      "languageId": "python"
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (1000, 0) in UMAP fit, as it is not one dimensional"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['time: 0.06711641947428386 line/min: 14899.483730403068']\n"
+      "['time: 0.11818180878957113 line/min: 8461.539134001174']\n"
      ]
     }
    ],
@@ -542,18 +466,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 12,
    "metadata": {
     "vscode": {
      "languageId": "python"
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "! Failed umap speedup attempt. Continuing without memoization speedups.* Ignoring target column of shape (220, 0) in UMAP fit, as it is not one dimensional"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['time: 0.0008151054382324219 line/min: 269903.7323037323']\n"
+      "['time: 0.008098324139912924 line/min: 27166.11439590581']\n"
      ]
     }
    ],
@@ -570,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 13,
    "metadata": {
     "vscode": {
      "languageId": "python"
@@ -582,15 +513,15 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 3728 entries, 0 to 3749\n",
+      "Int64Index: 2410 entries, 0 to 2821\n",
       "Data columns (total 3 columns):\n",
       " #   Column         Non-Null Count  Dtype  \n",
       "---  ------         --------------  -----  \n",
-      " 0   _src_implicit  3728 non-null   int32  \n",
-      " 1   _dst_implicit  3728 non-null   int32  \n",
-      " 2   _weight        3728 non-null   float32\n",
+      " 0   _src_implicit  2410 non-null   int32  \n",
+      " 1   _dst_implicit  2410 non-null   int32  \n",
+      " 2   _weight        2410 non-null   float32\n",
       "dtypes: float32(1), int32(2)\n",
-      "memory usage: 72.8 KB\n",
+      "memory usage: 47.1 KB\n",
       "None\n"
      ]
     },
@@ -622,34 +553,34 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>1046</th>\n",
-       "      <td>71</td>\n",
-       "      <td>144</td>\n",
-       "      <td>0.205078</td>\n",
+       "      <th>671</th>\n",
+       "      <td>51</td>\n",
+       "      <td>123</td>\n",
+       "      <td>0.017956</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>642</th>\n",
-       "      <td>41</td>\n",
-       "      <td>74</td>\n",
-       "      <td>0.176112</td>\n",
+       "      <th>2123</th>\n",
+       "      <td>167</td>\n",
+       "      <td>194</td>\n",
+       "      <td>0.663975</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>811</th>\n",
-       "      <td>53</td>\n",
-       "      <td>152</td>\n",
-       "      <td>0.079932</td>\n",
+       "      <th>1761</th>\n",
+       "      <td>139</td>\n",
+       "      <td>78</td>\n",
+       "      <td>0.113361</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2699</th>\n",
-       "      <td>171</td>\n",
-       "      <td>70</td>\n",
-       "      <td>0.140091</td>\n",
+       "      <th>2444</th>\n",
+       "      <td>191</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.999991</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1466</th>\n",
-       "      <td>101</td>\n",
-       "      <td>144</td>\n",
-       "      <td>0.050159</td>\n",
+       "      <th>2441</th>\n",
+       "      <td>190</td>\n",
+       "      <td>152</td>\n",
+       "      <td>0.544303</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -657,14 +588,14 @@
       ],
       "text/plain": [
        "      _src_implicit  _dst_implicit   _weight\n",
-       "1046             71            144  0.205078\n",
-       "642              41             74  0.176112\n",
-       "811              53            152  0.079932\n",
-       "2699            171             70  0.140091\n",
-       "1466            101            144  0.050159"
+       "671              51            123  0.017956\n",
+       "2123            167            194  0.663975\n",
+       "1761            139             78  0.113361\n",
+       "2444            191              3  0.999991\n",
+       "2441            190            152  0.544303"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -676,55 +607,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 16,
    "metadata": {
     "vscode": {
      "languageId": "python"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <iframe id=\"385dd123-b816-4166-9341-2ce7155cc477\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=b533c6cb4e11467abfe7121b1f6ca4ef&type=arrow&viztoken=bee2c0fc-ffa8-4fbc-8fee-b1af06f1f217&usertag=39269b0c-pygraphistry-0.27.2+4.ga674343.dirty&splashAfter=1663819187&info=true&play=0\"\n",
-       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
-       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
-       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
-       "                    \n",
-       "            >\n",
-       "            </iframe>\n",
-       "        \n",
-       "            <script>\n",
-       "                try {\n",
-       "                  $(\"#385dd123-b816-4166-9341-2ce7155cc477\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
-       "                } catch (e) { console.error('exn catching scroll', e); }\n",
-       "            </script>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "g3.plot()"
+    "#g3.plot()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "python"
-    }
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -733,7 +625,18 @@
    "language": "python",
    "name": "python3"
   },
-  "orig_nbformat": 4,
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  },
   "vscode": {
    "interpreter": {
     "hash": "21c4dad877b49e935d0a60da22bc51e9bfc4901bc58e488dc71d08b8faef6557"
diff --git a/graphistry/dgl_utils.py b/graphistry/dgl_utils.py
index 6792073ec8..257c13a701 100644
--- a/graphistry/dgl_utils.py
+++ b/graphistry/dgl_utils.py
@@ -443,7 +443,6 @@ def build_gnn(
         :param inplace: default, False, whether to return Graphistry instance in place or not.
 
         """
-
         if inplace:
             res = self
         else:
diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index acfb39cfd7..836008003e 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -27,11 +27,13 @@
 from graphistry.umap_utils import (
     lazy_umap_import_has_dependancy,
     lazy_cuml_import_has_dependancy,
+    lazy_cudf_import_has_dependancy,
 )
 
 has_dependancy, _ = lazy_import_has_min_dependancy()
 has_cuml, _, _ = lazy_cuml_import_has_dependancy()
 has_umap, _, _ = lazy_umap_import_has_dependancy()
+has_cudf, _, cudf = lazy_cudf_import_has_dependancy()
 
 # print('has_dependancy', has_dependancy)
 # print('has_cuml', has_cuml)
@@ -137,14 +139,10 @@ def setUp(self):
 
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_columns_match(self):
-        d = self.g2._node_features.shape[1]
-        dt = self.g2._node_target.shape[1]
-        de = self.g2e._edge_features.shape[1]
-        det = self.g2e._edge_target.shape[1]
-        assert (self.X.columns == self.x.columns).sum() == d, "Node Feature Columns do not match"
-        assert (self.Y.columns == self.y.columns).sum() == dt, "Node Target Columns do not match"
-        assert (self.Xe.columns == self.xe.columns).sum() == de, "Edge Feature Columns do not match"
-        assert (self.Ye.columns == self.ye.columns).sum() == det, "Edge Target Columns do not match"
+        assert set(self.X.columns) == set(self.x.columns), "Node Feature Columns do not match"
+        assert set(self.Y.columns) == set(self.y.columns), "Node Target Columns do not match"
+        assert set(self.Xe.columns) == set(self.xe.columns), "Edge Feature Columns do not match"
+        assert set(self.Ye.columns) == set(self.ye.columns), "Edge Target Columns do not match"
 
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_index_match(self):
@@ -208,8 +206,9 @@ def test_umap_kwargs(self):
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=FutureWarning)
-            g2 = g.umap(**umap_kwargs)
-            g3 = g.umap(**umap_kwargs2)
+            g2 = g.umap(**umap_kwargs, engine='umap_learn')
+            g3 = g.umap(**umap_kwargs2, engine='umap_learn')
+        assert g2._umap_params==umap_kwargs
         assert (
             g2._umap_params == umap_kwargs
         ), f"Umap params do not match, found {g2._umap_params} vs {umap_kwargs}"
@@ -254,10 +253,13 @@ def test_transform_umap(self):
             if return_g:
                 assert True
             else:
+                objs = (pd.DataFrame,)
+                if has_cudf:
+                    objs = (pd.DataFrame, cudf.DataFrame)
                 assert len(g4) == 3
-                assert isinstance(g4[0], pd.DataFrame)
-                assert isinstance(g4[1], pd.DataFrame)
-                assert isinstance(g4[2], pd.DataFrame)
+                assert isinstance(g4[0], objs)
+                assert isinstance(g4[1], objs)
+                assert isinstance(g4[2], objs)
                 assert g4[0].shape[1] == 2
                 assert g4[1].shape[1] >= 2
                 assert g4[2].shape[0] == test.shape[0]
@@ -277,21 +279,24 @@ class TestUMAPMethods(unittest.TestCase):
     def _check_attributes(self, g, attributes):
         msg = "Graphistry instance after umap should have `{}` as attribute"
         msg2 = "Graphistry instance after umap should not have None values for `{}`"
+        objs = (pd.DataFrame,)
+        if has_cudf:
+            objs = (pd.DataFrame, cudf.DataFrame)
 
         for attribute in attributes:
             self.assertTrue(hasattr(g, attribute), msg.format(attribute))
             self.assertTrue(getattr(g, attribute) is not None, msg2.format(attribute))
             if "df" in attribute:
                 self.assertIsInstance(
-                    getattr(g, attribute), pd.DataFrame, msg.format(attribute)
+                    getattr(g, attribute), objs, msg.format(attribute)
                 )
             if "node_" in attribute:
                 self.assertIsInstance(
-                    getattr(g, attribute), pd.DataFrame, msg.format(attribute)
+                    getattr(g, attribute), objs, msg.format(attribute)
                 )
             if "edge_" in attribute:
                 self.assertIsInstance(
-                    getattr(g, attribute), pd.DataFrame, msg.format(attribute)
+                    getattr(g, attribute), objs, msg.format(attribute)
                 )
 
     def cases_check_node_attributes(self, g):
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index c4b74041db..019eefef3c 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -116,6 +116,13 @@ def resolve_umap_engine(
 def make_safe_gpu_dataframes(X, y, engine):
 
     def safe_cudf(X, y):
+        # remove duplicate columns
+        if len(X.columns) != len(set(X.columns)):
+            X = X.loc[:, ~X.columns.duplicated()]
+        try:
+            y = y.loc[:, ~y.columns.duplicated()]
+        except:
+            pass
         new_kwargs = {}
         kwargs = {'X': X, 'y': y}
         for key, value in kwargs.items():

From d9987de0e6b667fe517b4b098a66d0ee957067b3 Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Tue, 4 Apr 2023 21:15:05 +0530
Subject: [PATCH 302/432] lint: flake8 typo

---
 graphistry/tests/test_umap_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index 836008003e..564ffcdbfa 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -208,7 +208,7 @@ def test_umap_kwargs(self):
             warnings.filterwarnings("ignore", category=FutureWarning)
             g2 = g.umap(**umap_kwargs, engine='umap_learn')
             g3 = g.umap(**umap_kwargs2, engine='umap_learn')
-        assert g2._umap_params==umap_kwargs
+        assert g2._umap_params == umap_kwargs
         assert (
             g2._umap_params == umap_kwargs
         ), f"Umap params do not match, found {g2._umap_params} vs {umap_kwargs}"

From 168af4bd71427b7922f8731a7ccf7a62d76531b9 Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Tue, 4 Apr 2023 21:34:42 +0530
Subject: [PATCH 303/432] fix: _dgl_graph fix

---
 graphistry/.dgl_utils.py.swp       | Bin 0 -> 40960 bytes
 graphistry/tests/test_dgl_utils.py |   4 +++-
 2 files changed, 3 insertions(+), 1 deletion(-)
 create mode 100644 graphistry/.dgl_utils.py.swp

diff --git a/graphistry/.dgl_utils.py.swp b/graphistry/.dgl_utils.py.swp
new file mode 100644
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GIT binary patch
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HcmV?d00001

diff --git a/graphistry/tests/test_dgl_utils.py b/graphistry/tests/test_dgl_utils.py
index 7aaf424b7c..1fff11155f 100644
--- a/graphistry/tests/test_dgl_utils.py
+++ b/graphistry/tests/test_dgl_utils.py
@@ -74,7 +74,9 @@
 class TestDGL(unittest.TestCase):
     def _test_cases_dgl(self, g):
         # simple test to see if DGL graph was set during different featurization + umap strategies
-        G = g.DGL_graph
+        G = g._dgl_graph
+        print('#######################')
+        print(G)
         keys = ["feature", "target", "train_mask", "test_mask"]
         keys_without_target = ["feature", "train_mask", "test_mask"]
 

From fee54526bbf5d4a9055303dad6e5e10aa89b0cb7 Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Tue, 4 Apr 2023 21:37:38 +0530
Subject: [PATCH 304/432] typo

---
 graphistry/tests/.test_dgl_utils.py.swp | Bin 0 -> 16384 bytes
 graphistry/tests/test_dgl_utils.py      |   2 --
 2 files changed, 2 deletions(-)
 create mode 100644 graphistry/tests/.test_dgl_utils.py.swp

diff --git a/graphistry/tests/.test_dgl_utils.py.swp b/graphistry/tests/.test_dgl_utils.py.swp
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diff --git a/graphistry/tests/test_dgl_utils.py b/graphistry/tests/test_dgl_utils.py
index 1fff11155f..baaef46135 100644
--- a/graphistry/tests/test_dgl_utils.py
+++ b/graphistry/tests/test_dgl_utils.py
@@ -75,8 +75,6 @@ class TestDGL(unittest.TestCase):
     def _test_cases_dgl(self, g):
         # simple test to see if DGL graph was set during different featurization + umap strategies
         G = g._dgl_graph
-        print('#######################')
-        print(G)
         keys = ["feature", "target", "train_mask", "test_mask"]
         keys_without_target = ["feature", "train_mask", "test_mask"]
 

From 60d3b977b3cc5d193a6f066671bb3785afb8566f Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Tue, 4 Apr 2023 21:49:10 +0530
Subject: [PATCH 305/432] test: remove xfail test_dgl_utils

---
 graphistry/tests/.test_dgl_utils.py.swp | Bin 16384 -> 16384 bytes
 graphistry/tests/test_dgl_utils.py      |   1 -
 2 files changed, 1 deletion(-)

diff --git a/graphistry/tests/.test_dgl_utils.py.swp b/graphistry/tests/.test_dgl_utils.py.swp
index 8339aab95f1ffdf64a432fcf1562a09e6fbc10fc..945f28592b30f79614fa141ec33f1a848fa1eaac 100644
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diff --git a/graphistry/tests/test_dgl_utils.py b/graphistry/tests/test_dgl_utils.py
index baaef46135..942bb36fef 100644
--- a/graphistry/tests/test_dgl_utils.py
+++ b/graphistry/tests/test_dgl_utils.py
@@ -166,7 +166,6 @@ def test_build_dgl_graph_from_umap_no_node_column(self):
         self._test_cases_dgl(g2)
 
     @pytest.mark.skipif(not has_dgl, reason="requires DGL dependencies")
-    @pytest.mark.xfail(reason="Mishandling datetimes: https://github.com/graphistry/pygraphistry/issues/381")
     def test_build_dgl_with_no_node_features(self):
         g = graphistry.edges(edf, src, dst)
         g.reset_caches()  # so that we redo calcs

From 4d74adefa6b301cf560191ebb1f0c5c2a6b5af72 Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Tue, 4 Apr 2023 22:11:26 +0530
Subject: [PATCH 306/432] test: remove StartTime test_dgl_utils temp

---
 graphistry/tests/.test_dgl_utils.py.swp | Bin 16384 -> 16384 bytes
 graphistry/tests/test_dgl_utils.py      |   2 ++
 2 files changed, 2 insertions(+)

diff --git a/graphistry/tests/.test_dgl_utils.py.swp b/graphistry/tests/.test_dgl_utils.py.swp
index 945f28592b30f79614fa141ec33f1a848fa1eaac..e97036d803aac69ccd682d066bb2a8b906350bcf 100644
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diff --git a/graphistry/tests/test_dgl_utils.py b/graphistry/tests/test_dgl_utils.py
index 942bb36fef..55d10badca 100644
--- a/graphistry/tests/test_dgl_utils.py
+++ b/graphistry/tests/test_dgl_utils.py
@@ -16,6 +16,7 @@
 edf = pd.read_csv(
     "graphistry/tests/data/malware_capture_bot.csv", index_col=0, nrows=50
 )
+edf = edf.drop(['StartTime'], axis=1)
 edf = edf.drop_duplicates()
 src, dst = "to_node", "from_node"
 edf["to_node"] = edf.SrcAddr.astype(str)
@@ -166,6 +167,7 @@ def test_build_dgl_graph_from_umap_no_node_column(self):
         self._test_cases_dgl(g2)
 
     @pytest.mark.skipif(not has_dgl, reason="requires DGL dependencies")
+    @pytest.mark.xfail(reason="Mishandling datetimes: https://github.com/graphistry/pygraphistry/issues/381")
     def test_build_dgl_with_no_node_features(self):
         g = graphistry.edges(edf, src, dst)
         g.reset_caches()  # so that we redo calcs

From aaf275f410384e55dd69262ca4c60c0d84f8868e Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Tue, 4 Apr 2023 23:51:43 +0530
Subject: [PATCH 307/432] pinned pandas

---
 graphistry/tests/.test_dgl_utils.py.swp | Bin 16384 -> 0 bytes
 graphistry/tests/test_dgl_utils.py      |   1 -
 setup.py                                |   4 ++--
 3 files changed, 2 insertions(+), 3 deletions(-)
 delete mode 100644 graphistry/tests/.test_dgl_utils.py.swp

diff --git a/graphistry/tests/.test_dgl_utils.py.swp b/graphistry/tests/.test_dgl_utils.py.swp
deleted file mode 100644
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GIT binary patch
literal 0
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zW*d%D(teARXK7&*z%&1Up=b4gcBvM>?o3Q0L9wIf7Uw*erx$b-+kkj!M$Yk<#qOP3
zoF`wJ9C$gAb9y>qrYQWmgu%M7B)aKl<8Lu~Z|bborXDuUu${Wq=>7lNM@e&~0m}`n
raXXY2+-B=%JaK8!eE*A)^zta|=oUMxb-t@_&wRY?M%bpk@)rFM6}hQ`

diff --git a/graphistry/tests/test_dgl_utils.py b/graphistry/tests/test_dgl_utils.py
index 55d10badca..baaef46135 100644
--- a/graphistry/tests/test_dgl_utils.py
+++ b/graphistry/tests/test_dgl_utils.py
@@ -16,7 +16,6 @@
 edf = pd.read_csv(
     "graphistry/tests/data/malware_capture_bot.csv", index_col=0, nrows=50
 )
-edf = edf.drop(['StartTime'], axis=1)
 edf = edf.drop_duplicates()
 src, dst = "to_node", "from_node"
 edf["to_node"] = edf.SrcAddr.astype(str)
diff --git a/setup.py b/setup.py
index 1c5304d74d..beb9462138 100755
--- a/setup.py
+++ b/setup.py
@@ -10,7 +10,7 @@ def unique_flatten_dict(d):
 core_requires = [
   'numpy',
   'palettable >= 3.0',
-  'pandas >= 0.17.0',
+  'pandas < 2.0.0',
   'pyarrow >= 0.15.0',
   'requests',
   'squarify',
@@ -42,7 +42,7 @@ def unique_flatten_dict(d):
   'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
 }
 # https://github.com/facebookresearch/faiss/issues/1589 for faiss-cpu 1.6.1, #'setuptools==67.4.0' removed
-base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'faiss-cpu', 'joblib']
+base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch<2', 'sentence-transformers', 'faiss-cpu', 'joblib']
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From fd423a2e53ddfdc1994e03a47f61898de34ba73f Mon Sep 17 00:00:00 2001
From: dcolinmorgan <apple@dc-m1.lan>
Date: Wed, 5 Apr 2023 17:56:07 +0800
Subject: [PATCH 308/432] alex2/3 cucat req

---
 graphistry/feature_utils.py |   3 +-
 graphistry/umap_utils.py    | 442 ++++++++++++++++++++++--------------
 2 files changed, 276 insertions(+), 169 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 3aecd5a058..af1bc24ef1 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1003,7 +1003,8 @@ def process_dirty_dataframes(
             X_enc = cudf.DataFrame.from_arrow(X_enc)
             X_enc.index = ndf.index
             # features_transformed=np.array([item.as_py() for item in features_transformed.key()])
-            # X_enc.columns = features_transformed #.to_numpy() ##error suggests this -- not working
+            # X_enc.columns = features_transformed.as_py()
+            #  = features_transformed #.to_numpy() ##error suggests this -- not working
 
             
         #X_enc = X_enc.fillna(0.0)  # TODO -- this is a hack in cuml version
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 46662c4b9b..0897535d09 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -6,13 +6,16 @@
 import pandas as pd
 
 from . import constants as config
+from .constants import CUML, UMAP_LEARN
 from .feature_utils import (FeatureMixin, Literal, XSymbolic, YSymbolic,
                             prune_weighted_edges_df_and_relabel_nodes,
                             resolve_feature_engine)
 from .PlotterBase import Plottable, WeakValueDictionary
-from .util import check_set_memoize, setup_logger
+from .util import check_set_memoize
 
-logger = setup_logger(name=__name__, verbose=config.VERBOSE)
+import logging
+
+logger = logging.getLogger(__name__)
 
 if TYPE_CHECKING:
     MIXIN_BASE = FeatureMixin
@@ -40,7 +43,9 @@ def lazy_cuml_import_has_dependancy():
         import warnings
 
         warnings.filterwarnings("ignore")
-        import cuml  # type: ignore
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore")
+            import cuml  # type: ignore
 
         return True, "ok", cuml
     except ModuleNotFoundError as e:
@@ -57,16 +62,26 @@ def lazy_cudf_import_has_dependancy():
     except ModuleNotFoundError as e:
         return False, e, None
 
+def lazy_cudf_import_has_dependancy():
+    try:
+        import warnings
+
+        warnings.filterwarnings("ignore")
+        import cudf  # type: ignore
+
+        return True, "ok", cudf
+    except ModuleNotFoundError as e:
+        return False, e, None
 
 def assert_imported():
-    has_dependancy_, import_exn, umap_learn = lazy_umap_import_has_dependancy()
+    has_dependancy_, import_exn, _ = lazy_umap_import_has_dependancy()
     if not has_dependancy_:
         logger.error("UMAP not found, trying running " "`pip install graphistry[ai]`")
         raise import_exn
 
 
 def assert_imported_cuml():
-    has_cuml_dependancy_, import_cuml_exn, cuml = lazy_cuml_import_has_dependancy()
+    has_cuml_dependancy_, import_cuml_exn, _ = lazy_cuml_import_has_dependancy()
     if not has_cuml_dependancy_:
         logger.warning("cuML not found, trying running " "`pip install cuml`")
         raise import_cuml_exn
@@ -85,22 +100,22 @@ def is_legacy_cuml():
         return False
 
 
-UMAPEngineConcrete = Literal["cuml", "umap_learn"]
+UMAPEngineConcrete = Literal['cuml', 'umap_learn']
 UMAPEngine = Literal[UMAPEngineConcrete, "auto"]
 
 
 def resolve_umap_engine(
     engine: UMAPEngine,
 ) -> UMAPEngineConcrete:  # noqa
-    if engine in ["cuml", "umap_learn"]:
+    if engine in [CUML, UMAP_LEARN]:
         return engine  # type: ignore
     if engine in ["auto"]:
-        has_cuml_dependancy_, _, cuml = lazy_cuml_import_has_dependancy()
+        has_cuml_dependancy_, _, _ = lazy_cuml_import_has_dependancy()
         if has_cuml_dependancy_:
-            return "cuml"
+            return 'cuml'
         has_umap_dependancy_, _, _ = lazy_umap_import_has_dependancy()
         if has_umap_dependancy_:
-            return "umap_learn"
+            return 'umap_learn'
 
     raise ValueError(  # noqa
         f'engine expected to be "auto", '
@@ -109,34 +124,27 @@ def resolve_umap_engine(
     )
 
 
-###############################################################################
+def make_safe_gpu_dataframes(X, y, engine):
 
+    def safe_cudf(X, y):
+        new_kwargs = {}
+        kwargs = {'X': X, 'y': y}
+        for key, value in kwargs.items():
+            if isinstance(value, cudf.DataFrame) and engine in ["pandas", "umap_learn", "dirty_cat"]:
+                new_kwargs[key] = value.to_pandas()
+            elif isinstance(value, pd.DataFrame) and engine in ["cuml", "cu_cat"]:
+                new_kwargs[key] = cudf.from_pandas(value)
+            else:
+                new_kwargs[key] = value
+        return new_kwargs['X'], new_kwargs['y']
 
-umap_kwargs_probs = {
-    "n_components": 2,
-    "metric": "hellinger",  # info metric, can't use on
-    # textual encodings since they contain negative values...
-    # unless scaling min max etc
-    "n_neighbors": 15,
-    "min_dist": 0.3,
-    "verbose": True,
-    "spread": 0.5,
-    "local_connectivity": 1,
-    "repulsion_strength": 1,
-    "negative_sample_rate": 5,
-}
-
-umap_kwargs_euclidean = {
-    "n_components": 2,
-    "metric": "euclidean",
-    "n_neighbors": 12,
-    "min_dist": 0.1,
-    "verbose": True,
-    "spread": 0.5,
-    "local_connectivity": 1,
-    "repulsion_strength": 1,
-    "negative_sample_rate": 5,
-}
+    has_cudf_dependancy_, _, cudf = lazy_cudf_import_has_dependancy()
+    if has_cudf_dependancy_:
+        return safe_cudf(X, y)
+    else:
+        return X, y
+
+###############################################################################
 
 # #############################################################################
 #
@@ -169,43 +177,47 @@ def umap_graph_to_weighted_edges(umap_graph, engine, is_legacy, cfg=config):
 class UMAPMixin(MIXIN_BASE):
     """
     UMAP Mixin for automagic UMAPing
-
     """
     # FIXME where is this used? 
     _umap_memoize: WeakValueDictionary = WeakValueDictionary()
 
     def __init__(self, *args, **kwargs):
-        self.umap_initialized = False
+        #self._umap_initialized = False
+        #self.umap_engine = self.umap_engine if hasattr(self, "engine") else None
+        pass
+
 
     def umap_lazy_init(
         self,
+        res,
         n_neighbors: int = 12,
         min_dist: float = 0.1,
-        spread=0.5,
-        local_connectivity=1,
-        repulsion_strength=1,
-        negative_sample_rate=5,
+        spread: float = 0.5,
+        local_connectivity: int = 1,
+        repulsion_strength: float = 1,
+        negative_sample_rate: int = 5,
         n_components: int = 2,
         metric: str = "euclidean",
-        engine: UMAPEngine = "auto",
+        umap_engine: UMAPEngine = "auto",
         suffix: str = "",
+        verbose: bool = False,
     ):
-        engine_resolved = resolve_umap_engine(engine)
+        from graphistry.features import ModelDict
+
+        engine_resolved = resolve_umap_engine(umap_engine)
         # FIXME remove as set_new_kwargs will always replace?
-        if engine_resolved == "umap_learn":
-            _, _, umap_engine = lazy_umap_import_has_dependancy()
-        elif engine_resolved == "cuml":
-            _, _, umap_engine = lazy_cuml_import_has_dependancy()
+        if engine_resolved == UMAP_LEARN:
+            _, _, umap_engine_ = lazy_umap_import_has_dependancy()
+        elif engine_resolved == CUML:
+            _, _, umap_engine_ = lazy_cuml_import_has_dependancy()
         else:
             raise ValueError(
                 "No umap engine, ensure 'auto', 'umap_learn', or 'cuml', and the library is installed"
             )
-
-        if not self.umap_initialized:
-            umap_kwargs = dict(
-                {
+        umap_kwargs = ModelDict("UMAP Parameters",
+                **{
                     "n_components": n_components,
-                    **({"metric": metric} if engine_resolved == "umap_learn" else {}),
+                    **({"metric": metric} if engine_resolved == UMAP_LEARN else {}),  # type: ignore
                     "n_neighbors": n_neighbors,
                     "min_dist": min_dist,
                     "spread": spread,
@@ -214,20 +226,30 @@ def umap_lazy_init(
                     "negative_sample_rate": negative_sample_rate,
                 }
             )
+        
+        if getattr(res, '_umap_params', None) == umap_kwargs:
+            print('Same umap params as last time, skipping new init') if verbose else None
+            return res
+        
+        print('lazy init') if verbose else None
+        print(umap_kwargs) if verbose else None
+        # set new umap kwargs
+        res._umap_params = umap_kwargs  
+        res._n_components = n_components
+        res._metric = metric
+        res._n_neighbors = n_neighbors
+        res._min_dist = min_dist
+        res._spread = spread
+        res._local_connectivity = local_connectivity
+        res._repulsion_strength = repulsion_strength
+        res._negative_sample_rate = negative_sample_rate
+        res._umap = umap_engine_.UMAP(**umap_kwargs)
+        res.umap_engine = engine_resolved
+        res._suffix = suffix
+                                                            
+        return res
 
-            self.n_components = n_components
-            self.metric = metric
-            self.n_neighbors = n_neighbors
-            self.min_dist = min_dist
-            self.spread = spread
-            self.local_connectivity = local_connectivity
-            self.repulsion_strength = repulsion_strength
-            self.negative_sample_rate = negative_sample_rate
-            self._umap = umap_engine.UMAP(**umap_kwargs)
-            self.umap_initialized = True
-            self.engine = engine_resolved
-            self.suffix = suffix
-
+    #@safe_gpu_dataframes
     def _check_target_is_one_dimensional(self, y: Union[pd.DataFrame, None]):
         if y is None:
             return None
@@ -241,8 +263,16 @@ def _check_target_is_one_dimensional(self, y: Union[pd.DataFrame, None]):
                 "as it is not one dimensional"
             )
             return None
+    
+    def _get_embedding(self, kind='nodes'):
+        if kind == 'nodes':
+            return self._node_embedding
+        elif kind == 'edges':
+            return self._edge_embedding
+        else:
+            raise ValueError('kind must be one of `nodes` or `edges`')
 
-    def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
+    def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None, verbose=False):
         if self._umap is None:
             raise ValueError("UMAP is not initialized")
         t = time()
@@ -250,21 +280,20 @@ def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         logger.info("-" * 90)
         logger.info(f"Starting UMAP-ing data of shape {X.shape}")
 
-        if self.engine == "cuml" and is_legacy_cuml():
+        if self.umap_engine == CUML and is_legacy_cuml():  # type: ignore
             from cuml.neighbors import NearestNeighbors
 
-            knn = NearestNeighbors(n_neighbors=self.n_neighbors)
+            knn = NearestNeighbors(n_neighbors=self._n_neighbors)  # type: ignore
             cc = self._umap.fit(X, y, knn_graph=knn)
             knn.fit(cc.embedding_)
             self._umap.graph_ = knn.kneighbors_graph(cc.embedding_)
-            self._weighted_adjacency = self._umap.graph_
-
         else:
             self._umap.fit(X, y)
-            self._weighted_adjacency = self._umap.graph_
+            
+        self._weighted_adjacency = self._umap.graph_
         # if changing, also update fresh_res
         self._weighted_edges_df = umap_graph_to_weighted_edges(
-            self._umap.graph_, self.engine, is_legacy_cuml()
+            self._umap.graph_, self.umap_engine, is_legacy_cuml()  # type: ignore
         )
 
         mins = (time() - t) / 60
@@ -272,42 +301,84 @@ def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         logger.info(f" - or {X.shape[0]/mins:.2f} rows per minute")
         return self
 
-    def umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
+
+    def _umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None, verbose=False):
         if self._umap is None:
             raise ValueError("UMAP is not initialized")
-        self.umap_fit(X, y)
+        self.umap_fit(X, y, verbose=verbose)
         emb = self._umap.transform(X)
         emb = self._bundle_embedding(emb, index=X.index)
         return emb
 
-    def transform_umap(  # noqa: E303
-        self, df: pd.DataFrame, ydf: pd.DataFrame, kind: str = "nodes"
-    ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
-        try:
-            logger.debug(f"Going into Transform umap {df.shape}, {ydf.shape}")
-        except:
-            pass
-        x, y = self.transform(df, ydf, kind=kind)
-        emb = self._umap.transform(x)  # type: ignore
+
+    def transform_umap(self, df: pd.DataFrame, 
+                    y: Optional[pd.DataFrame] = None, 
+                    kind: str = 'nodes', 
+                    min_dist: Union[str, float, int] = 'auto', 
+                    n_neighbors: int = 7,
+                    merge_policy: bool = False,
+                    sample: Optional[int] = None, 
+                    return_graph: bool = True,
+                    fit_umap_embedding: bool = True,
+                    verbose: bool = False
+    ) -> Union[Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable]:
+        """Transforms data into UMAP embedding
+        
+        Args:
+            :df: Dataframe to transform
+            :y: Target column
+            :kind: One of `nodes` or `edges`
+            :min_dist: Epsilon for including neighbors in infer_graph
+            :n_neighbors: Number of neighbors to use for contextualization
+            :merge_policy: if True, use previous graph, adding new batch to existing graph's neighbors
+                useful to contextualize new data against existing graph. If False, `sample` is irrelevant.
+            sample: Sample number of existing graph's neighbors to use for contextualization -- helps make denser graphs
+            return_graph: Whether to return a graph or just the embeddings
+            fit_umap_embedding: Whether to infer graph from the UMAP embedding on the new data, default True
+            verbose: Whether to print information about the graph inference
+        """
+        df, y = make_safe_gpu_dataframes(df, y, self.feature_engine)
+        X, y_ = self.transform(df, y, kind=kind, return_graph=False, verbose=verbose)
+        X, y_ = make_safe_gpu_dataframes(X, y_, self.umap_engine)  # type: ignore
+        emb = self._umap.transform(X)  # type: ignore
         emb = self._bundle_embedding(emb, index=df.index)
-        return emb, x, y
-    
+
+        if return_graph and kind not in ["edges"]:
+            emb, _ = make_safe_gpu_dataframes(emb, None, 'pandas')  # for now so we don't have to touch infer_edges, force to pandas
+            X, y_ = make_safe_gpu_dataframes(X, y_, 'pandas')
+            g = self._infer_edges(emb, X, y_, df, 
+                                  infer_on_umap_embedding=fit_umap_embedding, merge_policy=merge_policy,
+                                  eps=min_dist, sample=sample, n_neighbors=n_neighbors,
+                                  verbose=verbose) 
+            return g
+        return emb, X, y_
+
     def _bundle_embedding(self, emb, index):
         # Converts Embedding into dataframe and takes care if emb.dim > 2
-
-        if emb.shape[1] == 2 and 'cudf.core.dataframe' not in str(getmodule(emb)):
+        
+        try:
+            emb.get()
+            import cupy as cp
+            emb_dtype=str(cp.get_array_module(emb))
+        except AttributeError:
+            emb_dtype = str(getmodule(emb))
+            
+        
+        if emb.shape[1] == 2 and 'cudf' not in emb_dtype and 'cupy' not in emb_dtype:
             emb = pd.DataFrame(emb, columns=[config.X, config.Y], index=index)
-        elif emb.shape[1] == 2 and 'cudf.core.dataframe' in str(getmodule(emb)):
-            emb.rename(columns={0:config.X,1: config.Y},inplace=True)
+        elif emb.shape[1] == 2 and 'cudf' in emb_dtype:
+            emb.rename(columns={0: config.X, 1: config.Y}, inplace=True)
+        elif emb.shape[1] == 2 and 'cupy' in emb_dtype:
+            import cudf
+            emb = cudf.DataFrame(emb, columns=[config.X, config.Y], index=index)
         else:
             columns = [config.X, config.Y] + [
-                f"umap_{k}" for k in range(2, emb.shape[1] - 2)
+                f"umap_{k}" for k in range(2, emb.shape[1])
             ]
-
-            if 'cudf.core.dataframe' not in str(getmodule(emb)):
+            if 'cudf' not in emb_dtype:
                 emb = pd.DataFrame(emb, columns=columns, index=index)
-            elif 'cudf.core.dataframe' in str(getmodule(emb)):
-                emb.columns=columns
+            elif 'cudf' in emb_dtype:
+                emb.columns = columns
         return emb
     
     def _process_umap(
@@ -318,31 +389,41 @@ def _process_umap(
         kind,
         memoize: bool,
         featurize_kwargs,
+        verbose = False,
         **umap_kwargs,
     ):
         """
         Returns res mutated with new _xy
         """
-        res._umap = self._umap
+        #from .features import ModelDict
+        umap_kwargs_pure = umap_kwargs.copy()
 
         logger.debug("process_umap before kwargs: %s", umap_kwargs)
         umap_kwargs.update({"kind": kind, "X": X_, "y": y_})
-        umap_kwargs = {**umap_kwargs, "featurize_kwargs": featurize_kwargs or {}}
-        logger.debug("process_umap after kwargs: %s", umap_kwargs)
+        umap_kwargs_reuse = {**umap_kwargs, "featurize_kwargs": featurize_kwargs or {}}
+        logger.debug("process_umap after kwargs: %s", umap_kwargs_reuse)
 
         old_res = reuse_umap(
-            res, memoize, {**umap_kwargs, "featurize_kwargs": featurize_kwargs or {}}
+            res, memoize, {**umap_kwargs_reuse, "featurize_kwargs": featurize_kwargs or {}}
         )
         if old_res:
+            print(" --- [[ RE-USING UMAP ]]") if verbose else None
             logger.info(" --- [[ RE-USING UMAP ]]")
+            print('umap previous n_components', umap_kwargs['n_components']) if verbose else None
             fresh_res = copy.copy(res)
             for attr in ["_xy", "_weighted_edges_df", "_weighted_adjacency"]:
                 setattr(fresh_res, attr, getattr(old_res, attr))
             # have to set _raw_data attribute on umap?
             fresh_res._umap = old_res._umap  # this saves the day!
+            #fresh_res._umap_initialized = True
+            fresh_res._umap_params = umap_kwargs_pure
             return fresh_res
 
-        emb = res.umap_fit_transform(X_, y_)
+        print('-' * 60) if verbose else None
+        print('** Fitting UMAP') if verbose else None
+        res = res.umap_lazy_init(res, verbose=verbose, **umap_kwargs_pure)
+        
+        emb = res._umap_fit_transform(X_, y_, verbose=verbose)
         res._xy = emb
         return res
 
@@ -385,9 +466,9 @@ def _set_features(  # noqa: E303
 
     def umap(
         self,
-        kind: str = "nodes",
         X: XSymbolic = None,
         y: YSymbolic = None,
+        kind: str = "nodes",
         scale: float = 1.0,
         n_neighbors: int = 12,
         min_dist: float = 0.1,
@@ -401,58 +482,72 @@ def umap(
         play: Optional[int] = 0,
         encode_position: bool = True,
         encode_weight: bool = True,
-        engine: UMAPEngine = "auto",
-        inplace: bool = False,
+        dbscan: bool = False,
+        umap_engine: UMAPEngine = "auto",
         feature_engine: str = "auto",
+        inplace: bool = False,
         memoize: bool = True,
+        verbose: bool = False,
         **featurize_kwargs,
     ):
-        """
-            UMAP the featurized node or edges data,
-            or pass in your own X, y (optional).
-
-        :param kind: `nodes` or `edges` or None.
-                If None, expects explicit X, y (optional) matrices,
-                and will Not associate them to nodes or edges.
-                If X, y (optional) is given, with kind = [nodes, edges],
-                it will associate new matrices to nodes or edges attributes.
-        :param feature_engine: How to encode data
-                ("none", "auto", "pandas", "dirty_cat", "torch")
-        :param encode_weight: if True, will set new edges_df from
-                implicit UMAP, default True.
-        :param encode_position: whether to set default plotting bindings
-                -- positions x,y from umap for .plot()
-        :param X: either an ndarray of features, or column names to featurize
-        :param y: either an ndarray of targets, or column names to featurize
-                targets
-        :param scale: multiplicative scale for pruning weighted edge DataFrame
-                gotten from UMAP, between [0, ..) with high end meaning keep
-                all edges
-        :param n_neighbors: UMAP number of nearest neighbors to include for
-                UMAP connectivity, lower makes more compact layouts. Minimum 2
-        :param min_dist: UMAP float between 0 and 1, lower makes more compact
-                layouts.
-        :param spread: UMAP spread of values for relaxation
-        :param local_connectivity: UMAP connectivity parameter
-        :param repulsion_strength: UMAP repulsion strength
-        :param negative_sample_rate: UMAP negative sampling rate
-        :param n_components: number of components in the UMAP projection,
-                default 2
-        :param metric: UMAP metric, default 'euclidean'.
-                see (UMAP-LEARN)[https://umap-learn.readthedocs.io/
-                en/latest/parameters.html] documentation for more.
-        :param suffix: optional suffix to add to x, y attributes of umap.
-        :param play: Graphistry play parameter, default 0, how much to evolve
-                the network during clustering
-        :param engine: selects which engine to use to calculate UMAP:
-                NotImplemented yet, default UMAP-LEARN
-        :param memoize: whether to memoize the results of this method,
-                default True.
+        """UMAP the featurized nodes or edges data, or pass in your own X, y (optional) dataframes of values
+        
+        Example
+     
+        >>> import graphistry   
+        >>> g = graphistry.nodes(pd.DataFrame({'node': [0,1,2], 'data': [1,2,3], 'meta': ['a', 'b', 'c']}))
+        >>> g2 = g.umap(n_components=3, spread=1.0, min_dist=0.1, n_neighbors=12, negative_sample_rate=5, local_connectivity=1, repulsion_strength=1.0, metric='euclidean', suffix='', play=0, encode_position=True, encode_weight=True, dbscan=False, engine='auto', feature_engine='auto', inplace=False, memoize=True, verbose=False)
+        >>> g2.plot()
+        
+        Parameters
+ 
+            :X: either a dataframe ndarray of features, or column names to featurize
+            :y: either an dataframe ndarray of targets, or column names to featurize
+                    targets
+            :kind: `nodes` or `edges` or None.
+                    If None, expects explicit X, y (optional) matrices,
+                    and will Not associate them to nodes or edges.
+                    If X, y (optional) is given, with kind = [nodes, edges],
+                    it will associate new matrices to nodes or edges attributes.
+            :scale: multiplicative scale for pruning weighted edge DataFrame
+                    gotten from UMAP, between [0, ..) with high end meaning keep
+                    all edges
+            :n_neighbors: UMAP number of nearest neighbors to include for
+                    UMAP connectivity, lower makes more compact layouts. Minimum 2
+            :min_dist: UMAP float between 0 and 1, lower makes more compact
+                    layouts.
+            :spread: UMAP spread of values for relaxation
+            :local_connectivity: UMAP connectivity parameter
+            :repulsion_strength: UMAP repulsion strength
+            :negative_sample_rate: UMAP negative sampling rate
+            :n_components: number of components in the UMAP projection,
+                    default 2
+            :metric: UMAP metric, default 'euclidean'.
+                    see (UMAP-LEARN)[https://umap-learn.readthedocs.io/
+                    en/latest/parameters.html] documentation for more.
+            :suffix: optional suffix to add to x, y attributes of umap.
+            :play: Graphistry play parameter, default 0, how much to evolve
+                    the network during clustering. 0 preserves the original UMAP layout.
+            :encode_weight: if True, will set new edges_df from
+                    implicit UMAP, default True.
+            :encode_position: whether to set default plotting bindings
+                    -- positions x,y from umap for .plot(), default True
+            :dbscan: whether to run DBSCAN on the UMAP embedding, default False.
+            :engine: selects which engine to use to calculate UMAP:
+                    default "auto" will use cuML if available, otherwise UMAP-LEARN.
+            :feature_engine: How to encode data
+                    ("none", "auto", "pandas", "dirty_cat", "torch")
+            :inplace: bool = False, whether to modify the current object, default False.
+                    when False, returns a new object, useful for chaining in a functional paradigm.
+            :memoize: whether to memoize the results of this method,
+                    default True.
+            :verbose: whether to print out extra information, default False.
+
         :return: self, with attributes set with new data
         """
-        if engine == "umap_learn":
+        if umap_engine == UMAP_LEARN:
             assert_imported()
-        elif engine == "cuml":
+        elif umap_engine == CUML:
             assert_imported_cuml()
 
         umap_kwargs = dict(
@@ -464,7 +559,8 @@ def umap(
             local_connectivity=local_connectivity,
             repulsion_strength=repulsion_strength,
             negative_sample_rate=negative_sample_rate,
-            engine=engine,
+            umap_engine=umap_engine,
+            suffix=suffix,
         )
         logger.debug("umap_kwargs: %s", umap_kwargs)
 
@@ -473,8 +569,7 @@ def umap(
         else:
             res = self.bind()
 
-        res.umap_lazy_init(engine=engine, suffix=suffix)
-        # res.suffix = suffix
+        res = res.umap_lazy_init(res, verbose=verbose, **umap_kwargs)  # type: ignore
 
         logger.debug("umap input X :: %s", X)
         logger.debug("umap input y :: %s", y)
@@ -482,12 +577,11 @@ def umap(
         featurize_kwargs = self._set_features(
             res, X, y, kind, feature_engine, {**featurize_kwargs, "memoize": memoize}
         )
-        # umap_kwargs = {**umap_kwargs,
-        # 'featurize_kwargs': featurize_kwargs or {}}
+
 
         if kind == "nodes":
+            index = res._nodes.index
             if res._node is None:
-
                 logger.debug("-Writing new node name")
                 res = res.nodes(  # type: ignore
                     res._nodes.reset_index(drop=True)
@@ -495,9 +589,9 @@ def umap(
                     .rename(columns={"index": config.IMPLICIT_NODE_ID}),
                     config.IMPLICIT_NODE_ID,
                 )
+                res._nodes.index = index
 
             nodes = res._nodes[res._node].values
-            index_to_nodes_dict = dict(zip(range(len(nodes)), nodes))
 
             logger.debug("propagating with featurize_kwargs: %s", featurize_kwargs)
             (
@@ -513,18 +607,20 @@ def umap(
             logger.debug("data is type :: %s", (type(X_)))
             if isinstance(X_, pd.DataFrame):
                 index_to_nodes_dict = dict(zip(range(len(nodes)), nodes))
-            elif 'cudf.core.dataframe' in str(getmodule(X_)):
-                index_to_nodes_dict = nodes
+            elif 'cudf' in str(getmodule(X_)):
+                index_to_nodes_dict = nodes  # {}?
+
+            # add the safe coercion here 
+            X_, y_ = make_safe_gpu_dataframes(X_, y_, res.umap_engine)  # type: ignore
 
             res = res._process_umap(
-                res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs
+                res, X_, y_, kind, memoize, featurize_kwargs, verbose, **umap_kwargs
             )
 
             res._weighted_adjacency_nodes = res._weighted_adjacency
             if res._xy is None:
                 raise RuntimeError("This should not happen")
             res._node_embedding = res._xy
-            # TODO add edge filter so graph doesn't have double edges
             # TODO user-guidable edge merge policies like upsert?
             res._weighted_edges_df_from_nodes = (
                 prune_weighted_edges_df_and_relabel_nodes(
@@ -544,6 +640,9 @@ def umap(
                 **featurize_kwargs
             )
 
+            # add the safe coercion here 
+            X_, y_ = make_safe_gpu_dataframes(X_, y_, res.umap_engine)  # type: ignore
+
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs
             )
@@ -563,9 +662,9 @@ def umap(
                 "kind should be one of `nodes` or `edges` unless"
                 "you are passing explicit matrices"
             )
-            if X is not None and isinstance(X, pd.DataFrame):
+            if X is not None and isinstance(X, pd.DataFrame) or '':
                 logger.info("New Matrix `X` passed in for UMAP-ing")
-                xy = res.umap_fit_transform(X, y)
+                xy = res._umap_fit_transform(X, y, verbose=verbose)
                 res._xy = xy
                 res._weighted_edges_df = prune_weighted_edges_df_and_relabel_nodes(
                     res._weighted_edges_df, scale=scale
@@ -577,7 +676,7 @@ def umap(
             else:
                 logger.error(
                     "If `kind` is `None`, `X` and optionally `y`"
-                    "must be given and be of type pd.DataFrame"
+                    "must be given."
                 )
         else:
             raise ValueError(
@@ -587,9 +686,12 @@ def umap(
             res, kind, encode_position, encode_weight, play
         )  # noqa: E501
 
-        if res.engine == "cuml" and is_legacy_cuml():
+        if res.umap_engine == CUML and is_legacy_cuml():  # type: ignore
             res = res.prune_self_edges()
 
+        if dbscan:
+            res = res.dbscan(min_dist=min_dist, kind=kind, fit_umap_embedding=True, verbose=verbose)  # type: ignore
+
         if not inplace:
             return res
 
@@ -604,23 +706,26 @@ def _bind_xy_from_umap(
         df = res._nodes if kind == "nodes" else res._edges
 
         df = df.copy(deep=False)
-        x_name = config.X + res.suffix
-        y_name = config.Y + res.suffix
+        x_name = config.X + res._suffix
+        y_name = config.Y + res._suffix
         if kind == "nodes":
             emb = res._node_embedding
         else:
             emb = res._edge_embedding
+            
+        if type(df) == type(emb):
+            df[x_name] = emb.values.T[0]
+            df[y_name] = emb.values.T[1]
+        elif isinstance(df, pd.DataFrame) and 'cudf' in str(getmodule(emb)):
+            df[x_name] = emb.to_numpy().T[0]
+            df[y_name] = emb.to_numpy().T[1]
 
-        df[x_name] = emb.values.T[0]  # if embedding is greater
-        # than two dimensions will only take first two coordinates
-        df[y_name] = emb.values.T[1]
-        #
         res = res.nodes(df) if kind == "nodes" else res.edges(df)
 
         if encode_weight and kind == "nodes":
             # adds the implicit edge dataframe and binds it to
             # graphistry instance
-            w_name = config.WEIGHT + res.suffix
+            w_name = config.WEIGHT + res._suffix
             umap_edges_df = res._weighted_edges_df_from_nodes.copy(deep=False)
             umap_edges_df = umap_edges_df.rename(columns={config.WEIGHT: w_name})
             res = res.edges(umap_edges_df, config.SRC, config.DST)
@@ -649,6 +754,7 @@ def filter_weighted_edges(
     ):
         """
         Filter edges based on _weighted_edges_df (ex: from .umap())
+
         """
         if inplace:
             res = self

From 5f2cb69d6cad5fe4bef1d000a80f677ff072d074 Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Wed, 5 Apr 2023 17:12:41 +0530
Subject: [PATCH 309/432] more tests

---
 graphistry/PlotterBase.py                 |  10 ++
 graphistry/embed_utils.py                 |  15 ++
 graphistry/tests/.test_embed_utils.py.swp | Bin 0 -> 40960 bytes
 graphistry/tests/test_embed_utils.py      | 183 +++++++++++++++++-----
 4 files changed, 173 insertions(+), 35 deletions(-)
 create mode 100644 graphistry/tests/.test_embed_utils.py.swp

diff --git a/graphistry/PlotterBase.py b/graphistry/PlotterBase.py
index badb060b19..3da7f9ee5d 100644
--- a/graphistry/PlotterBase.py
+++ b/graphistry/PlotterBase.py
@@ -1033,6 +1033,12 @@ def sample_nodes(g, n):
             res = base.nodes(nodes2)
         else:
             res = copy.copy(base)
+            # this is temporary
+            # TODO: for cudf support need to clean the entire codebase
+            try:
+                nodes = nodes.to_pandas()
+            except:
+                pass
             res._nodes = nodes
         # for use in text_utils.py search index
         if hasattr(res, 'search_index'):
@@ -1141,6 +1147,10 @@ def sample_edges(g, n):
             res = base.edges(edges2)
         else:
             res = copy.copy(base)
+            try:
+                edges = edges.to_pandas()
+            except:
+                pass
             res._edges = edges
         return res
 
diff --git a/graphistry/embed_utils.py b/graphistry/embed_utils.py
index 713f7b67ea..9ab4d07d00 100644
--- a/graphistry/embed_utils.py
+++ b/graphistry/embed_utils.py
@@ -412,18 +412,33 @@ def predict_links(
         if source is None:
             src = pd.Series(all_nodes)
         else:
+            # this is temporary
+            try:
+                source = source.to_pandas()  # type: ignore
+            except:
+                pass
             src = pd.Series(source)
             src = src.map(self._node2id)
 
         if relation is None:
             rel = pd.Series(all_relations)
         else:
+            # this is temporary
+            try:
+                relation = relation.to_pandas()  # type: ignore
+            except:
+                pass
             rel = pd.Series(relation)
             rel = rel.map(self._relation2id)
 
         if destination is None:
             dst = pd.Series(all_nodes)
         else:
+            # this is temporary
+            try:
+                destination = destination.to_pandas()  # type: ignore
+            except:
+                pass
             dst = pd.Series(destination)
             dst = dst.map(self._node2id)
 
diff --git a/graphistry/tests/.test_embed_utils.py.swp b/graphistry/tests/.test_embed_utils.py.swp
new file mode 100644
index 0000000000000000000000000000000000000000..764bc178db5ec513d75fcb7f821f1d712ce2b5d0
GIT binary patch
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diff --git a/graphistry/tests/test_embed_utils.py b/graphistry/tests/test_embed_utils.py
index e979f6f26f..2ecc30a677 100644
--- a/graphistry/tests/test_embed_utils.py
+++ b/graphistry/tests/test_embed_utils.py
@@ -10,42 +10,152 @@
 logger = logging.getLogger(__name__)
 
 dep_flag, _, _, _, _, _, _, _ = lazy_embed_import_dep()
+def check_cudf():
+    try:
+        import cudf
+        return True, cudf
+    except:
+        return False, None
 
-edf = pd.DataFrame([[0, 1, 0], [1, 2, 0], [2, 0, 1]],
-        columns=['src', 'dst', 'rel']
-)
-ndf_no_ids = pd.DataFrame([['a'], ['a'], ['b']], columns=['feat'])
-ndf_with_ids = pd.DataFrame([[0, 'a'], [1, 'a'], [2, 'b']],
-        columns = ['id', 'feat1']
-)
 
-graph_no_feat = graphistry.edges(edf, 'src', 'dst')
-graph_with_feat_no_ids = graph_no_feat.nodes(ndf_no_ids)
-graph_with_feat_with_ids = graph_no_feat.nodes(ndf_with_ids, 'id')
-graphs = [('no_feat', graph_no_feat), ('with_feat_no_ids', graph_with_feat_no_ids), ('with_feat_with_ids', graph_with_feat_with_ids)]
-d = 4
-
-kwargs = {'n_topics': 6, 'cardinality_threshold':10, 'epochs': 1, 'sample_size':10, 'num_steps':10}
+has_cudf, cudf = check_cudf()
 
 class TestEmbed(unittest.TestCase):
 
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    def setUp(self):
+        self.edf = pd.DataFrame([[0, 1, 0], [1, 2, 0], [2, 0, 1]],
+            columns=['src', 'dst', 'rel']
+        )
+        ndf_no_ids = pd.DataFrame([['a'], ['a'], ['b']], columns=['feat'])
+        ndf_with_ids = pd.DataFrame([[0, 'a'], [1, 'a'], [2, 'b']],
+            columns = ['id', 'feat1']
+        )
+
+        self.graph_no_feat = graphistry.edges(self.edf, 'src', 'dst')
+        self.graph_with_feat_no_ids = self.graph_no_feat.nodes(ndf_no_ids)
+        self.graph_with_feat_with_ids = self.graph_no_feat.nodes(ndf_with_ids, 'id')
+        self.graphs = [
+            ('no_feat', self.graph_no_feat),
+            ('with_feat_no_ids', self.graph_with_feat_no_ids),
+            ('with_feat_with_ids', self.graph_with_feat_with_ids)
+        ]
+        self.d = 4
+
+        self.kwargs = {'n_topics': 6, 'cardinality_threshold':10, 'epochs': 1, 'sample_size':10, 'num_steps':10}
+        
+
+    @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    def test_embed_out_basic(self):
+        for name, g in self.graphs:
+            g = g.embed('rel', embedding_dim=self.d, **self.kwargs)
+            num_nodes = len(set(g._edges['src'] + g._edges['dst']))
+            logging.debug('name: %s basic tests', name)
+            self.assertEqual(g._edges.shape, self.edf.shape)
+            self.assertEqual(set(g._edges[g._relation]), set(g._edges['rel']))
+            self.assertEqual(g._kg_embeddings.shape,(num_nodes, self.d))
+
+
+    @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    def test_predict_links(self):
+        source = pd.Series([0,2])
+        relation = None
+        destination = pd.Series([1])
+        g = self.graph_no_feat.embed('rel', embedding_dim=self.d, **self.kwargs)
+
+        g_new = g.predict_links(source, relation, destination, threshold=0, anomalous=False)
+        self.assertTrue( g_new._edges.shape[0] > 0)
+        self.assertIn("score", g_new._edges.columns)
+        
+        g_new = g.predict_links(source, relation, destination, threshold=1, anomalous=True)
+        self.assertTrue( g_new._edges.shape[0] > 0)
+        self.assertIn("score", g_new._edges.columns)
+    
+    @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    def test_predict_links_all(self):
+        g = self.graph_no_feat.embed('rel', embedding_dim=self.d, **self.kwargs)
+        g_new = g.predict_links_all(threshold=0)
+        self.assertTrue( g_new._edges.shape[0] > 0)
+        self.assertIn("score", g_new._edges.columns)
+
+        
+    @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    def test_chaining(self):
+        for name, g in self.graphs:
+            logging.debug('name: %s test changing embedding dim with feats' % name)
+            g = g.embed('rel', use_feat=True, embedding_dim=self.d, **self.kwargs)
+            g2 = g.embed('rel', use_feat=True, embedding_dim=2 * self.d, **self.kwargs)
+            self.assertNotEqual(g._kg_embeddings.shape, g2._kg_embeddings.shape)
+
+        [g.reset_caches() for _, g in self.graphs]
+        for name, g in self.graphs:
+            logging.debug('name: %s test changing embedding dim without feats', name)
+            g = g.embed('rel', use_feat=False, embedding_dim=self.d, **self.kwargs)
+            g2 = g.embed('rel', use_feat=False, embedding_dim=2 * self.d, **self.kwargs)
+            self.assertNotEqual(g._kg_embeddings.shape, g2._kg_embeddings.shape)
+
+        [g.reset_caches() for _, g in self.graphs]
+        for name, g in self.graphs:
+            logging.debug('name: %s test relationship change', name)
+            g = g.embed(relation='rel', use_feat=False, embedding_dim=self.d, **self.kwargs)
+            g2 = g.embed(relation='src', use_feat=False, embedding_dim=self.d, **self.kwargs)
+            self.assertEqual(g._kg_embeddings.shape, g2._kg_embeddings.shape)
+            self.assertNotEqual(np.linalg.norm(g._kg_embeddings - g2._kg_embeddings), 0)
+
+        [g.reset_caches() for _, g in self.graphs]
+        for name, g in self.graphs:
+            logging.debug('name: %s test relationship change', name)
+            g = g.embed(relation='rel', use_feat=False, embedding_dim=self.d, **self.kwargs)
+            g2 = g.embed(relation='rel', use_feat=True, embedding_dim=self.d, **self.kwargs)
+            self.assertEqual(g._kg_embeddings.shape, g2._kg_embeddings.shape)
+            self.assertNotEqual(np.linalg.norm(g._kg_embeddings - g2._kg_embeddings), 0)
+
+
+class TestEmbedCUDF(unittest.TestCase):
+
+    @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    @pytest.mark.skipif(not has_cudf, reason="requires cudf")
+    def setUp(self):
+        self.edf = cudf.DataFrame([[0, 1, 0], [1, 2, 0], [2, 0, 1]],
+            columns=['src', 'dst', 'rel']
+        )
+        ndf_no_ids = cudf.DataFrame([['a'], ['a'], ['b']], columns=['feat'])
+        ndf_with_ids = cudf.DataFrame([[0, 'a'], [1, 'a'], [2, 'b']],
+            columns = ['id', 'feat1']
+        )
+
+        self.graph_no_feat = graphistry.edges(self.edf, 'src', 'dst')
+        self.graph_with_feat_no_ids = self.graph_no_feat.nodes(ndf_no_ids)
+        self.graph_with_feat_with_ids = self.graph_no_feat.nodes(ndf_with_ids, 'id')
+        self.graphs = [
+            ('no_feat', self.graph_no_feat),
+            ('with_feat_no_ids', self.graph_with_feat_no_ids),
+            ('with_feat_with_ids', self.graph_with_feat_with_ids)
+        ]
+        self.d = 4
+
+        self.kwargs = {'n_topics': 6, 'cardinality_threshold':10, 'epochs': 1, 'sample_size':10, 'num_steps':10}
+        
+
+    @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    @pytest.mark.skipif(not has_cudf, reason="requires cudf")
     def test_embed_out_basic(self):
-        for name, g in graphs:
-            g = g.embed('rel', embedding_dim=d, **kwargs)
+        for name, g in self.graphs:
+            g = g.embed('rel', embedding_dim=self.d, **self.kwargs)
             num_nodes = len(set(g._edges['src'] + g._edges['dst']))
             logging.debug('name: %s basic tests', name)
-            self.assertEqual(g._edges.shape, edf.shape)
+            self.assertEqual(g._edges.shape, self.edf.shape)
             self.assertEqual(set(g._edges[g._relation]), set(g._edges['rel']))
-            self.assertEqual(g._kg_embeddings.shape,(num_nodes, d))
+            self.assertEqual(g._kg_embeddings.shape,(num_nodes, self.d))
 
 
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    @pytest.mark.skipif(not has_cudf, reason="requires cudf")
     def test_predict_links(self):
         source = pd.Series([0,2])
         relation = None
         destination = pd.Series([1])
-        g = graph_no_feat.embed('rel', embedding_dim=d, **kwargs)
+        g = self.graph_no_feat.embed('rel', embedding_dim=self.d, **self.kwargs)
 
         g_new = g.predict_links(source, relation, destination, threshold=0, anomalous=False)
         self.assertTrue( g_new._edges.shape[0] > 0)
@@ -56,44 +166,47 @@ def test_predict_links(self):
         self.assertIn("score", g_new._edges.columns)
     
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    @pytest.mark.skipif(not has_cudf, reason="requires cudf")
     def test_predict_links_all(self):
-        g = graph_no_feat.embed('rel', embedding_dim=d, **kwargs)
+        g = self.graph_no_feat.embed('rel', embedding_dim=self.d, **self.kwargs)
         g_new = g.predict_links_all(threshold=0)
         self.assertTrue( g_new._edges.shape[0] > 0)
         self.assertIn("score", g_new._edges.columns)
 
         
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    @pytest.mark.skipif(not has_cudf, reason="requires cudf")
     def test_chaining(self):
-        for name, g in graphs:
+        for name, g in self.graphs:
             logging.debug('name: %s test changing embedding dim with feats' % name)
-            g = g.embed('rel', use_feat=True, embedding_dim=d, **kwargs)
-            g2 = g.embed('rel', use_feat=True, embedding_dim=2 * d, **kwargs)
+            g = g.embed('rel', use_feat=True, embedding_dim=self.d, **self.kwargs)
+            g2 = g.embed('rel', use_feat=True, embedding_dim=2 * self.d, **self.kwargs)
             self.assertNotEqual(g._kg_embeddings.shape, g2._kg_embeddings.shape)
 
-        [g.reset_caches() for _, g in graphs]
-        for name, g in graphs:
+        [g.reset_caches() for _, g in self.graphs]
+        for name, g in self.graphs:
             logging.debug('name: %s test changing embedding dim without feats', name)
-            g = g.embed('rel', use_feat=False, embedding_dim=d, **kwargs)
-            g2 = g.embed('rel', use_feat=False, embedding_dim=2 * d, **kwargs)
+            g = g.embed('rel', use_feat=False, embedding_dim=self.d, **self.kwargs)
+            g2 = g.embed('rel', use_feat=False, embedding_dim=2 * self.d, **self.kwargs)
             self.assertNotEqual(g._kg_embeddings.shape, g2._kg_embeddings.shape)
 
-        [g.reset_caches() for _, g in graphs]
-        for name, g in graphs:
+        [g.reset_caches() for _, g in self.graphs]
+        for name, g in self.graphs:
             logging.debug('name: %s test relationship change', name)
-            g = g.embed(relation='rel', use_feat=False, embedding_dim=d, **kwargs)
-            g2 = g.embed(relation='src', use_feat=False, embedding_dim=d, **kwargs)
+            g = g.embed(relation='rel', use_feat=False, embedding_dim=self.d, **self.kwargs)
+            g2 = g.embed(relation='src', use_feat=False, embedding_dim=self.d, **self.kwargs)
             self.assertEqual(g._kg_embeddings.shape, g2._kg_embeddings.shape)
             self.assertNotEqual(np.linalg.norm(g._kg_embeddings - g2._kg_embeddings), 0)
 
-        [g.reset_caches() for _, g in graphs]
-        for name, g in graphs:
+        [g.reset_caches() for _, g in self.graphs]
+        for name, g in self.graphs:
             logging.debug('name: %s test relationship change', name)
-            g = g.embed(relation='rel', use_feat=False, embedding_dim=d, **kwargs)
-            g2 = g.embed(relation='rel', use_feat=True, embedding_dim=d, **kwargs)
+            g = g.embed(relation='rel', use_feat=False, embedding_dim=self.d, **self.kwargs)
+            g2 = g.embed(relation='rel', use_feat=True, embedding_dim=self.d, **self.kwargs)
             self.assertEqual(g._kg_embeddings.shape, g2._kg_embeddings.shape)
             self.assertNotEqual(np.linalg.norm(g._kg_embeddings - g2._kg_embeddings), 0)
 
 
+
 if __name__ == "__main__":
     unittest.main()

From 408ee529a73cd224dbe4d909cd81455d16b1a205 Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Wed, 5 Apr 2023 17:52:18 +0530
Subject: [PATCH 310/432] stable

---
 graphistry/.feature_utils.py.swp | Bin 0 -> 122880 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 graphistry/.feature_utils.py.swp

diff --git a/graphistry/.feature_utils.py.swp b/graphistry/.feature_utils.py.swp
new file mode 100644
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literal 0
HcmV?d00001


From 762b2d22756f59fbb11d0d940f91cc8cea5d7579 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Wed, 5 Apr 2023 08:39:39 -0400
Subject: [PATCH 311/432] fix(modules.rst) testing to ci warnings

---
 docs/source/modules.rst | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/docs/source/modules.rst b/docs/source/modules.rst
index 71e0a12335..1f80292530 100644
--- a/docs/source/modules.rst
+++ b/docs/source/modules.rst
@@ -1,9 +1,9 @@
-.. doc
-.. ===
+FIXME
+===
 
-.. .. toctree::
-..    :maxdepth: 4
-..    :caption: Contents:
+toctree::
+   :maxdepth: 4
+   :caption: Contents:
 
-..    versioneer
+   versioneer
 

From e0abb9591bbe130bf3b70aae50677fcfcb44c312 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Wed, 5 Apr 2023 08:47:00 -0400
Subject: [PATCH 312/432] fix(conf.py); added plugins to nitpick

---
 docs/source/conf.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 5797134b6a..f8b7c2190b 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -55,6 +55,11 @@
     ('py:class', 'graphistry.compute.conditional.ConditionalMixin'),
     ('py:class', 'graphistry.compute.cluster.ClusterMixin'),
     ('py:class', 'graphistry.Plottable.Plottable'),
+    ('py:class', 'graphistry.plugins.cugraph.compute_cugraph'),
+    ('py:class', 'graphistry.plugins.cugraph.layout_cugraph'),
+    ('py:class', 'graphistry.plugins.igraph.compute_igraph'),
+    ('py:class', 'graphistry.plugins.igraph.from_igraph'),
+    ('py:class', 'graphistry.plugins.igraph.layout_igraph'),
     ('py:class', 'graphistry.feature_utils.FeatureMixin'),
     ('py:class', 'graphistry.dgl_utils.DGLGraphMixin'),
     ('py:class', 'graphistry.umap_utils.UMAPMixin'),

From 6ebb74a8f1ee9b2914593a3eff46c575699b3b66 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Wed, 5 Apr 2023 08:57:34 -0400
Subject: [PATCH 313/432] fix(modules): added title

---
 docs/source/modules.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/modules.rst b/docs/source/modules.rst
index 1f80292530..bec536f2f1 100644
--- a/docs/source/modules.rst
+++ b/docs/source/modules.rst
@@ -1,5 +1,5 @@
-FIXME
-===
+# Modules
+==================================
 
 toctree::
    :maxdepth: 4

From 63d665b208c396f87d233e95d01669d6e0ffa7f7 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Wed, 5 Apr 2023 08:59:29 -0400
Subject: [PATCH 314/432] fix(modules.rst): added title for ci testing

---
 docs/source/modules.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/modules.rst b/docs/source/modules.rst
index bec536f2f1..2649d02fc2 100644
--- a/docs/source/modules.rst
+++ b/docs/source/modules.rst
@@ -1,5 +1,5 @@
-# Modules
-==================================
+Modules
+#####################
 
 toctree::
    :maxdepth: 4

From 18e0befc533372791a05b4aee9281a0e90363994 Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Wed, 5 Apr 2023 20:57:43 +0530
Subject: [PATCH 315/432] doc fix

---
 graphistry/umap_utils.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 019eefef3c..79a10dcfa2 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -329,6 +329,7 @@ def transform_umap(self, df: pd.DataFrame,
             :n_neighbors: Number of neighbors to use for contextualization
             :merge_policy: if True, use previous graph, adding new batch to existing graph's neighbors
                 useful to contextualize new data against existing graph. If False, `sample` is irrelevant.
+
             sample: Sample number of existing graph's neighbors to use for contextualization -- helps make denser graphs
             return_graph: Whether to return a graph or just the embeddings
             fit_umap_embedding: Whether to infer graph from the UMAP embedding on the new data, default True

From 6a5bec9fbb286923122b3f539b91bacb64e59e81 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Wed, 5 Apr 2023 11:51:46 -0400
Subject: [PATCH 316/432] fix(rst) docs fixes for CI passing

---
 docs/source/conf.py                        |   2 +-
 docs/source/graphistry.compute.rst         |   9 +-
 docs/source/graphistry.layout.gib.rst      |   6 +
 docs/source/graphistry.layout.graph.rst    |   4 +
 docs/source/graphistry.layout.rst          |   7 +
 docs/source/graphistry.layout.sugiyama.rst |   5 +
 docs/source/graphistry.layout.utils.rst    |   5 +
 docs/source/graphistry.plugins_types.rst   |   6 +
 docs/source/graphistry.rst                 |   4 +-
 docs/source/modules.rst                    |   2 +-
 docs/source/versioneer.rst                 |   4 +
 graphistry/PlotterBase.py                  |  33 ++---
 graphistry/compute/cluster.py              |  19 +--
 graphistry/compute/collapse.py             | 141 ++++++++-------------
 graphistry/compute/conditional.py          |   2 +-
 graphistry/feature_utils.py                |  78 ++++--------
 graphistry/umap_utils.py                   |   9 +-
 17 files changed, 142 insertions(+), 194 deletions(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index f8b7c2190b..c7bbd195fc 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -56,7 +56,7 @@
     ('py:class', 'graphistry.compute.cluster.ClusterMixin'),
     ('py:class', 'graphistry.Plottable.Plottable'),
     ('py:class', 'graphistry.plugins.cugraph.compute_cugraph'),
-    ('py:class', 'graphistry.plugins.cugraph.layout_cugraph'),
+    ('py:class', 'graphistry.plugins.cugraph.from_cugraph'),
     ('py:class', 'graphistry.plugins.igraph.compute_igraph'),
     ('py:class', 'graphistry.plugins.igraph.from_igraph'),
     ('py:class', 'graphistry.plugins.igraph.layout_igraph'),
diff --git a/docs/source/graphistry.compute.rst b/docs/source/graphistry.compute.rst
index 08b49eedef..c610034aab 100644
--- a/docs/source/graphistry.compute.rst
+++ b/docs/source/graphistry.compute.rst
@@ -5,6 +5,7 @@ ComputeMixin module
    :members:
    :undoc-members:
    :show-inheritance:
+   :noindex:
 
 
 Chain
@@ -14,6 +15,7 @@ Chain
    :members:
    :undoc-members:
    :show-inheritance:
+   :noindex:
 
 Cluster
 ---------------
@@ -21,6 +23,7 @@ Cluster
    :members:
    :undoc-members:
    :show-inheritance:
+   :noindex:
 
 Collapse
 ---------------
@@ -28,6 +31,7 @@ Collapse
    :members:
    :undoc-members:
    :show-inheritance:
+   :noindex:
 
 Conditional
 ---------------
@@ -35,13 +39,15 @@ Conditional
    :members:
    :undoc-members:
    :show-inheritance:
+   :noindex:
 
 Filter by Dictionary
----------------
+--------------------
 .. automodule:: graphistry.compute.filter_by_dict
    :members:
    :undoc-members:
    :show-inheritance:
+   :noindex:
 
 Hop
 ---------------
@@ -49,3 +55,4 @@ Hop
    :members:
    :undoc-members:
    :show-inheritance:
+   :noindex:
diff --git a/docs/source/graphistry.layout.gib.rst b/docs/source/graphistry.layout.gib.rst
index 51352d8212..50b21ec335 100644
--- a/docs/source/graphistry.layout.gib.rst
+++ b/docs/source/graphistry.layout.gib.rst
@@ -1,3 +1,7 @@
+:orphan:
+
+.. ^ FIXME
+
 graphistry.layout.gib package
 ==================================
 
@@ -11,3 +15,5 @@ Module contents
    :members:
    :undoc-members:
    :show-inheritance:
+
+   
diff --git a/docs/source/graphistry.layout.graph.rst b/docs/source/graphistry.layout.graph.rst
index 72d559ad11..283119ece6 100644
--- a/docs/source/graphistry.layout.graph.rst
+++ b/docs/source/graphistry.layout.graph.rst
@@ -59,3 +59,7 @@ Module contents
    :members:
    :undoc-members:
    :show-inheritance:
+   
+graphistry.layout.gib
+  
+
diff --git a/docs/source/graphistry.layout.rst b/docs/source/graphistry.layout.rst
index 9216de5252..b2c1f8c43e 100644
--- a/docs/source/graphistry.layout.rst
+++ b/docs/source/graphistry.layout.rst
@@ -7,6 +7,7 @@ edge Module
    :members:
    :undoc-members:
    :show-inheritance:
+   :noindex:
 
 edgeBase Module
 ---------------------------------------
@@ -15,6 +16,7 @@ edgeBase Module
    :members:
    :undoc-members:
    :show-inheritance:
+   :noindex:
 
 graph Module
 ------------------------------------
@@ -23,6 +25,7 @@ graph Module
    :members:
    :undoc-members:
    :show-inheritance:
+   :noindex:
 
 graphBase Module
 ----------------------------------------
@@ -31,6 +34,7 @@ graphBase Module
    :members:
    :undoc-members:
    :show-inheritance:
+   :noindex:
 
 vertex Module
 -------------------------------------
@@ -39,6 +43,7 @@ vertex Module
    :members:
    :undoc-members:
    :show-inheritance:
+   :noindex:
 
 vertexBase Module
 -----------------------------------------
@@ -47,6 +52,7 @@ vertexBase Module
    :members:
    :undoc-members:
    :show-inheritance:
+   :noindex:
 
 
 Module contents
@@ -56,3 +62,4 @@ Module contents
    :members:
    :undoc-members:
    :show-inheritance:
+   :noindex:
diff --git a/docs/source/graphistry.layout.sugiyama.rst b/docs/source/graphistry.layout.sugiyama.rst
index 41b83f7cb1..40ffaf7e83 100644
--- a/docs/source/graphistry.layout.sugiyama.rst
+++ b/docs/source/graphistry.layout.sugiyama.rst
@@ -1,3 +1,5 @@
+:orphan:
+
 graphistry.layout.sugiyama package
 ==================================
 
@@ -19,3 +21,6 @@ Module contents
    :members:
    :undoc-members:
    :show-inheritance:
+
+
+.. FIXME:orphan
\ No newline at end of file
diff --git a/docs/source/graphistry.layout.utils.rst b/docs/source/graphistry.layout.utils.rst
index de1d80140d..76b71fdc52 100644
--- a/docs/source/graphistry.layout.utils.rst
+++ b/docs/source/graphistry.layout.utils.rst
@@ -1,6 +1,11 @@
 graphistry.layout.utils package
 ===============================
 
+.. toctree:: 
+   :maxdepth: 2
+   
+   graphistry.layout.graph
+
 Submodules
 ----------
 
diff --git a/docs/source/graphistry.plugins_types.rst b/docs/source/graphistry.plugins_types.rst
index 2b9b21ee1c..1b07a10b54 100644
--- a/docs/source/graphistry.plugins_types.rst
+++ b/docs/source/graphistry.plugins_types.rst
@@ -1,6 +1,12 @@
 graphistry.plugins\_types package
 =================================
 
+
+.. toctree:: 
+   :maxdepth: 2
+   
+   graphistry.layout.utils
+
 Submodules
 ----------
 
diff --git a/docs/source/graphistry.rst b/docs/source/graphistry.rst
index d115114c70..030071b943 100644
--- a/docs/source/graphistry.rst
+++ b/docs/source/graphistry.rst
@@ -64,7 +64,7 @@ DBScan
     :show-inheritance:
 
 Arrow uploader Module
-==================
+============================
 
 .. automodule:: graphistry.arrow_uploader
     :members:
@@ -72,7 +72,7 @@ Arrow uploader Module
     :show-inheritance:
 
 Arrow File Uploader Module
-==================
+============================
 
 .. automodule:: graphistry.ArrowFileUploader
     :members:
diff --git a/docs/source/modules.rst b/docs/source/modules.rst
index 2649d02fc2..ced1d0941f 100644
--- a/docs/source/modules.rst
+++ b/docs/source/modules.rst
@@ -1,7 +1,7 @@
 Modules
 #####################
 
-toctree::
+.. toctree::
    :maxdepth: 4
    :caption: Contents:
 
diff --git a/docs/source/versioneer.rst b/docs/source/versioneer.rst
index a34edfc48d..ab6065f6a3 100644
--- a/docs/source/versioneer.rst
+++ b/docs/source/versioneer.rst
@@ -1,2 +1,6 @@
 .. versioneer module
 .. =================
+.. toctree:: 
+   :maxdepth: 2
+   
+   graphistry.plugins_types
\ No newline at end of file
diff --git a/graphistry/PlotterBase.py b/graphistry/PlotterBase.py
index badb060b19..747f7e74b6 100644
--- a/graphistry/PlotterBase.py
+++ b/graphistry/PlotterBase.py
@@ -342,17 +342,10 @@ def style(self, fg=None, bg=None, page=None, logo=None):
     def encode_axis(self, rows=[]):
         """Render radial and linear axes with optional labels
 
-        :param rows: List of rows - {
-            label: Optional[str],
-            ?r: float,
-            ?x: float,
-            ?y: float,
-            ?internal: true,
-            ?external: true,
-            ?space: true
-        }
+        :param rows: List of rows - { label: Optional[str],?r: float, ?x: float, ?y: float, ?internal: true, ?external: true, ?space: true }
 
         :returns: Plotter
+        
         :rtype: Plotter
 
         **Example: Several radial axes**
@@ -545,9 +538,7 @@ def encode_point_icon(self, column,
             comparator=None,
             for_default=True, for_current=False,
             as_text=False, blend_mode=None, style=None, border=None, shape=None):
-        """Set node icon with more control than bind().
-        Values from Font Awesome 4 such as "laptop": https://fontawesome.com/v4.7.0/icons/ , image URLs (http://...), and data URIs (data:...).
-        When as_text=True is enabled, values are instead interpreted as raw strings.
+        """Set node icon with more control than bind(). Values from Font Awesome 4 such as "laptop": https://fontawesome.com/v4.7.0/icons/ , image URLs (http://...), and data URIs (data:...). When as_text=True is enabled, values are instead interpreted as raw strings.
 
         :param column: Data column name
         :type column: str
@@ -614,9 +605,7 @@ def encode_edge_icon(self, column,
             comparator=None,
             for_default=True, for_current=False,
             as_text=False, blend_mode=None, style=None, border=None, shape=None):
-        """Set edge icon with more control than bind()
-        Values from Font Awesome 4 such as "laptop": https://fontawesome.com/v4.7.0/icons/ , image URLs (http://...), and data URIs (data:...).
-        When as_text=True is enabled, values are instead interpreted as raw strings.
+        """Set edge icon with more control than bind() Values from Font Awesome 4 such as "laptop": https://fontawesome.com/v4.7.0/icons/ , image URLs (http://...), and data URIs (data:...). When as_text=True is enabled, values are instead interpreted as raw strings.
 
         :param column: Data column name
         :type column: str
@@ -836,10 +825,7 @@ def bind(self, source=None, destination=None, node=None, edge=None,
              edge_source_color=None, edge_destination_color=None,
              point_title=None, point_label=None, point_color=None, point_weight=None, point_size=None, point_opacity=None, point_icon=None,
              point_x=None, point_y=None):
-        """Relate data attributes to graph structure and visual representation.
-
-        To facilitate reuse and replayable notebooks, the binding call is chainable. Invocation does not effect the old binding: it instead returns a new Plotter instance with the new bindings added to the existing ones. Both the old and new bindings can then be used for different graphs.
-
+        """Relate data attributes to graph structure and visual representation. To facilitate reuse and replayable notebooks, the binding call is chainable. Invocation does not effect the old binding: it instead returns a new Plotter instance with the new bindings added to the existing ones. Both the old and new bindings can then be used for different graphs.
 
         :param source: Attribute containing an edge's source ID
         :type source: str
@@ -853,8 +839,7 @@ def bind(self, source=None, destination=None, node=None, edge=None,
         :param edge: Attribute containing an edge's ID
         :type edge: str
 
-        :param edge_title: Attribute overriding edge's minimized label text. 
-        By default, the edge source and destination is used.
+        :param edge_title: Attribute overriding edge's minimized label text. By default, the edge source and destination is used.
         :type edge_title: str
 
         :param edge_label: Attribute overriding edge's expanded label text. By default, scrollable list of attribute/value mappings.
@@ -894,6 +879,7 @@ def bind(self, source=None, destination=None, node=None, edge=None,
         :rtype: Plotter
 
         **Example: Minimal**
+
             ::
 
                 import graphistry
@@ -901,6 +887,7 @@ def bind(self, source=None, destination=None, node=None, edge=None,
                 g = g.bind(source='src', destination='dst')
 
         **Example: Node colors**
+
             ::
 
                 import graphistry
@@ -909,6 +896,7 @@ def bind(self, source=None, destination=None, node=None, edge=None,
                            node='id', point_color='color')
 
         **Example: Chaining**
+
             ::
 
                 import graphistry
@@ -925,6 +913,7 @@ def bind(self, source=None, destination=None, node=None, edge=None,
                 g3b = g2b.bind(point_size='size3b')
 
         In the above **Chaining** example, all bindings use src/dst/id. Colors and sizes bind to:
+
             ::
 
                 g: default/default
@@ -933,8 +922,6 @@ def bind(self, source=None, destination=None, node=None, edge=None,
                 g2b: color2b/size2b
                 g3a: color2a/size3a
                 g3b: color2b/size3b
-
-
         """
         res = copy.copy(self)
         res._source = source or self._source
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
index f19fbfbe38..9b319bfda0 100644
--- a/graphistry/compute/cluster.py
+++ b/graphistry/compute/cluster.py
@@ -168,8 +168,7 @@ def __init__(self, *args, **kwargs):
     def _cluster_dbscan(
         self, res, kind, cols, fit_umap_embedding, target, min_dist, min_samples, verbose, *args, **kwargs
     ):
-        """
-        DBSCAN clustering on cpu or gpu infered by .engine flag
+        """DBSCAN clustering on cpu or gpu infered by .engine flag
         """
         _, DBSCAN, _, cuDBSCAN = lazy_dbscan_import_has_dependency()
 
@@ -241,18 +240,14 @@ def dbscan(
             g2.plot() # color by `_dbscan` column
 
         Useful:
-            Enriching the graph with cluster labels from UMAP is useful for visualizing clusters in the graph by color, size, etc,
-            as well as assessing metrics per cluster, e.g.
-             https://github.com/graphistry/pygraphistry/blob/master/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
+            Enriching the graph with cluster labels from UMAP is useful for visualizing clusters in the graph by color, size, etc, as well as assessing metrics per cluster, e.g. https://github.com/graphistry/pygraphistry/blob/master/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
 
         Args:
             :min_dist float: The maximum distance between two samples for them to be considered as in the same neighborhood.
             :kind str: 'nodes' or 'edges'
-            :cols: list of columns to use for clustering given `g.featurize` has been run, nice way to slice features or targets by
-                fragments of interest, e.g. ['ip_172', 'location', 'ssh', 'warnings']
+            :cols: list of columns to use for clustering given `g.featurize` has been run, nice way to slice features or targets by fragments of interest, e.g. ['ip_172', 'location', 'ssh', 'warnings']
             :fit_umap_embedding bool: whether to use UMAP embeddings or features dataframe to cluster DBSCAN
-            :min_samples: The number of samples in a neighborhood for a point to be considered as a core point.
-                This includes the point itself.
+            :min_samples: The number of samples in a neighborhood for a point to be considered as a core point. This includes the point itself.
             :target: whether to use the target column as the clustering feature
 
         """
@@ -328,11 +323,7 @@ def transform_dbscan(
         return_graph: bool = True,
         verbose: bool = False,
         ):  # type: ignore
-        """
-        Transforms a minibatch dataframe to one with a new column '_dbscan' containing the DBSCAN cluster 
-        labels on the minibatch and generates a graph with the minibatch and the original graph, with edges 
-        between the minibatch and the original graph inferred from the umap embedding or features dataframe.
-        Graph nodes | edges will be colored by '_dbscan' column.
+        """Transforms a minibatch dataframe to one with a new column '_dbscan' containing the DBSCAN cluster labels on the minibatch and generates a graph with the minibatch and the original graph, with edges between the minibatch and the original graph inferred from the umap embedding or features dataframe. Graph nodes | edges will be colored by '_dbscan' column.
             
             Examples:
             ::
diff --git a/graphistry/compute/collapse.py b/graphistry/compute/collapse.py
index ddf0885805..e9b06e512c 100644
--- a/graphistry/compute/collapse.py
+++ b/graphistry/compute/collapse.py
@@ -32,15 +32,15 @@
 
 
 def unpack(g: Plottable):
-    """
-        Helper method that unpacks graphistry instance
+    """Helper method that unpacks graphistry instance
+
     ex:
-        ndf, edf, src, dst, node = unpack(g)
 
-    -----------------------------------------------------------------------------------------
+        ndf, edf, src, dst, node = unpack(g)
 
     :param g: graphistry instance
-    :returns node DataFrame, edge DataFrame, source column, destination column, node column
+
+    :returns: node DataFrame, edge DataFrame, source column, destination column, node column
     """
     ndf = g._nodes
     edf = g._edges
@@ -51,10 +51,7 @@ def unpack(g: Plottable):
 
 
 def get_children(g: Plottable, node_id: Union[str, int], hops: int = 1):
-    """
-        Helper that gets children at k-hops from node `node_id`
-
-    ------------------------------------------------------------------
+    """Helper that gets children at k-hops from node `node_id`
 
     :returns graphistry instance of hops
     """
@@ -65,17 +62,14 @@ def get_children(g: Plottable, node_id: Union[str, int], hops: int = 1):
 def has_edge(
     g: Plottable, n1: Union[str, int], n2: Union[str, int], directed: bool = True
 ) -> bool:
-    """
-        Checks if `n1` and `n2` share an (directed or not) edge
-
-    ------------------------------------------------------------------
+    """Checks if `n1` and `n2` share an (directed or not) edge
 
     :param g: graphistry instance
     :param n1: `node` to check if has edge to `n2`
     :param n2: `node` to check if has edge to `n1`
     :param directed: bool, if True, checks only outgoing edges from `n1`->`n2`, else finds undirected edges
-    :returns bool, if edge exists between `n1` and `n2`
 
+    :returns: bool, if edge exists between `n1` and `n2`
     """
     ndf, edf, src, dst, node = unpack(g)
     if directed:
@@ -92,16 +86,14 @@ def has_edge(
 def get_edges_of_node(
     g: Plottable, node_id: Union[str, int], outgoing_edges: bool = True, hops: int = 1
 ):
-    """
-        Gets edges of node at k-hops from node
-
-    ----------------------------------------------------------------------------------
+    """Gets edges of node at k-hops from node
 
     :param g: graphistry instance
     :param node_id: `node` to find edges from
     :param outgoing_edges: bool, if true, finds all outgoing edges of `node`, default True
     :param hops: the number of hops from `node` to take, default = 1
-    :returns DataFrame of edges
+
+    :returns: DataFrame of edges
     """
     _, _, src, dst, _ = unpack(g)
     g2 = get_children(g, node_id, hops=hops)
@@ -119,11 +111,7 @@ def get_edges_in_out_cluster(
     column: Union[str, int],
     directed: bool = True,
 ):
-    """
-        Traverses children of `node_id` and separates them into incluster and outcluster sets depending if they have
-        `attribute` in node DataFrame `column`
-
-    --------------------------------------------------------------------------------------------------------------------
+    """Traverses children of `node_id` and separates them into incluster and outcluster sets depending if they have `attribute` in node DataFrame `column`
 
     :param g: graphistry instance
     :param node_id: `node` with `attribute` in `column`
@@ -157,67 +145,57 @@ def get_edges_in_out_cluster(
 
 
 def get_cluster_store_keys(ndf: pd.DataFrame, node: Union[str, int]):
-    """
-        Main innovation in finding and adding to super node.
-        Checks if node is a segment in any collapse_node in COLLAPSE column of nodes DataFrame
-
-    --------------------------------------------------------------------------------------------
+    """Main innovation in finding and adding to super node. Checks if node is a segment in any collapse_node in COLLAPSE column of nodes DataFrame
 
     :param ndf: node DataFrame
     :param node: node to find
-    :returns DataFrame of bools of where `wrap_key(node)` exists in COLLAPSE column
+
+    :returns: DataFrame of bools of where `wrap_key(node)` exists in COLLAPSE column
     """
     node = wrap_key(node)
     return ndf[COLLAPSE_NODE].astype(str).str.contains(node, na=False)
 
 
 def in_cluster_store_keys(ndf: pd.DataFrame, node: Union[str, int]) -> bool:
-    """
-        checks if node is in collapse_node in COLLAPSE column of nodes DataFrame
-
-    ------------------------------------------------------------------------------
+    """checks if node is in collapse_node in COLLAPSE column of nodes DataFrame
 
     :param ndf: nodes DataFrame
     :param node: node to find
-    :returns bool
+
+    :returns: bool
     """
     return any(get_cluster_store_keys(ndf, node))
 
 
 def reduce_key(key: Union[str, int]) -> str:
-    """
-        Takes "1 1 2 1 2 3" -> "1 2 3
-
-    ---------------------------------------------------
+    """Takes "1 1 2 1 2 3" -> "1 2 3
 
     :param key: node name
-    :returns new node name with duplicates removed
+
+    :returns: new node name with duplicates removed
     """
     uniques = " ".join(np.unique(str(key).split()))
     return uniques
 
 
 def unwrap_key(name: Union[str, int]) -> str:
-    """
-        Unwraps node name: ~name~ -> name
-
-    ----------------------------------------
+    """Unwraps node name: ~name~ -> name
 
     :param name: node to unwrap
-    :returns unwrapped node name
+
+    :returns: unwrapped node name
     """
     return str(name).replace(WRAP, "")
 
 
 def wrap_key(name: Union[str, int]) -> str:
-    """
-        Wraps node name -> ~name~
-
-    -----------------------------------
+    """Wraps node name -> ~name~
 
     :param name: node name
-    :returns wrapped node name
+
+    :returns: wrapped node name
     """
+
     name = str(name)
     if WRAP in name:  # idempotency
         return name
@@ -225,17 +203,16 @@ def wrap_key(name: Union[str, int]) -> str:
 
 
 def melt(ndf: pd.DataFrame, node: Union[str, int]) -> str:
-    """
-        Reduces node if in cluster store, otherwise passes it through.
+    """Reduces node if in cluster store, otherwise passes it through.
     ex:
+
         node = "4" will take any sequence from get_cluster_store_keys, "1 2 3", "4 3 6" and returns "1 2 3 4 6"
         when they have a common entry (3).
 
-    -------------------------------------------------------------------------------------------------------------
-
     :param ndf, node DataFrame
     :param node: node to melt
     :returns new_parent_name of super node
+
     """
     rdf = ndf[get_cluster_store_keys(ndf, node)]
     topkey = wrap_key(node)
@@ -259,14 +236,12 @@ def check_has_set(ndf, parent, child):
 def get_new_node_name(
     ndf: pd.DataFrame, parent: Union[str, int], child: Union[str, int]
 ) -> str:
-    """
-        If child in cluster group, melts name, else makes new parent_name from parent, child
-
-    ---------------------------------------------------------------------------------------------------------
+    """If child in cluster group, melts name, else makes new parent_name from parent, child
 
     :param ndf: node DataFrame
     :param parent: `node` with `attribute` in `column`
     :param child: `node` with `attribute` in `column`
+
     :returns new_parent_name
     """
     # THIS IS IMPORTANT FUNCTION -- it is where we wrap the parent/child in WRAP
@@ -300,8 +275,6 @@ def collapse_nodes_and_edges(
         # outside logic controls when that is the case
         # for example, it assumes parent is already in cluster keys of COLLAPSE node
 
-    ---------------------------------------------------------------------------------------
-
     :param g: graphistry instance
     :param parent: `node` with `attribute` in `column`
     :param child: `node` with `attribute` in `column`
@@ -328,29 +301,24 @@ def collapse_nodes_and_edges(
 def has_property(
     g: Plottable, ref_node: Union[str, int], attribute: Union[str, int], column: Union[str, int]
 ) -> bool:
-    """
-        Checks if ref_node is in node dataframe in column with attribute
-
-    -------------------------------------------------------------------------
-
+    """Checks if ref_node is in node dataframe in column with attribute
     :param attribute:
     :param column:
     :param g: graphistry instance
     :param ref_node: `node` to check if it as `attribute` in `column`
-    :returns bool"""
+
+    :returns: bool
+    """
     ndf, edf, src, dst, node = unpack(g)
     ref_node = unwrap_key(ref_node)
     return ref_node in ndf[ndf[column] == attribute][node].values
 
 
 def check_default_columns_present_and_coerce_to_string(g: Plottable):
-    """
-        Helper to set COLLAPSE columns to nodes and edges dataframe, while converting src, dst, node to dtype(str)
-
-    -------------------------------------------------------------------------
-
+    """Helper to set COLLAPSE columns to nodes and edges dataframe, while converting src, dst, node to dtype(str)
     :param g: graphistry instance
-    :returns graphistry instance
+
+    :returns: graphistry instance
     """
     ndf, edf, src, dst, node = unpack(g)
     if COLLAPSE_NODE not in ndf.columns:
@@ -376,32 +344,26 @@ def collapse_algo(
     column: Union[str, int],
     seen: dict,
 ):
-    """
-        Basically candy crush over graph properties in a topology aware manner
+    """Basically candy crush over graph properties in a topology aware manner
 
-        Checks to see if child node has desired property from parent, we will need to check if
-        (start_node=parent: has_attribute , children nodes: has_attribute) by case
-        (T, T), (F, T), (T, F) and (F, F),
-        we start recursive collapse (or not) on the children, reassigning nodes and edges.
+        Checks to see if child node has desired property from parent, we will need to check if (start_node=parent: has_attribute , children nodes: has_attribute) by case (T, T), (F, T), (T, F) and (F, F),we start recursive collapse (or not) on the children, reassigning nodes and edges.
 
         if (T, T), append children nodes to start_node, re-assign the name of the node, and update the edge table with new name,
 
-        if (F, T) start k-(potentially new) super nodes, with k the number of children of start_node.
-                Start node keeps k outgoing edges.
+        if (F, T) start k-(potentially new) super nodes, with k the number of children of start_node. Start node keeps k outgoing edges.
 
         if (T, F) it is the end of the cluster, and we keep new node as is; keep going
 
         if (F, F); keep going
-
-    --------------------------------------------------------------------------------------------------------------------
-
+  
     :param seen:
     :param g: graphistry instance
     :param child: child node to start traversal, for first traversal, set child=parent or vice versa.
     :param parent: parent node to start traversal, in main call, this is set to child.
     :param attribute: attribute to collapse by
     :param column: column in nodes dataframe to collapse over.
-    :returns graphistry instance with collapsed nodes.
+
+    :returns: graphistry instance with collapsed nodes.
     """
 
     compute_key = f"{parent} {child}"
@@ -456,16 +418,13 @@ def normalize_graph(
     self_edges: bool = False,
     unwrap: bool = False
 ) -> Plottable:
-    """
-        Final step after collapse traversals are done, removes duplicates and moves COLLAPSE columns into respective
-        (node, src, dst) columns of node, edges dataframe from Graphistry instance g.
-
-    --------------------------------------------------------------------------------------------------------------------
+    """Final step after collapse traversals are done, removes duplicates and moves COLLAPSE columns into respective(node, src, dst) columns of node, edges dataframe from Graphistry instance g.
 
     :param g: graphistry instance
     :param self_edges: bool, whether to keep duplicates from ndf, edf, default False
     :param unwrap: bool, whether to unwrap node text with `~`, default True
-    :returns final graphistry instance
+
+    :returns: final graphistry instance
     """
 
     ndf, edf, src, dst, node = unpack(g)
@@ -527,7 +486,6 @@ def collapse_by(
     """
         Main call in collapse.py, collapses nodes and edges by attribute, and returns normalized graphistry object.
 
-    --------------------------------------------------------------------------------------------------------------------
     :param self: graphistry instance
     :param parent: parent node to start traversal, in main call, this is set to child.
     :param start_node:
@@ -535,6 +493,7 @@ def collapse_by(
     :param column: column in nodes dataframe to collapse over.
     :param seen: dict of previously collapsed pairs -- {n1, n2) is seen as different from (n2, n1)
     :param verbose: bool, default True
+
     :returns graphistry instance with collapsed and normalized nodes.
     """
     from time import time
diff --git a/graphistry/compute/conditional.py b/graphistry/compute/conditional.py
index 101eee9829..df96c1c31f 100644
--- a/graphistry/compute/conditional.py
+++ b/graphistry/compute/conditional.py
@@ -66,7 +66,7 @@ def conditional_graph(self, x, given, kind='nodes', *args, **kwargs):
         Useful for finding the conditional probability of a node or edge attribute
         
         returned dataframe sums to 1 on each column
-        -----------------------------------------------------------
+   
         :param x: target column
         :param given: the dependent column
         :param kind: 'nodes' or 'edges'
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index ed5ee15e62..42b6b06476 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -274,7 +274,6 @@ def remove_internal_namespace_if_present(df: pd.DataFrame):
         Some tranformations below add columns to the DataFrame,
         this method removes them before featurization
         Will not drop if suffix is added during UMAP-ing
-    ______________________________________________________________
 
     :param df: DataFrame
     :return: DataFrame with dropped columns in reserved namespace
@@ -528,10 +527,8 @@ def get_preprocessing_pipeline(
     encode: str = "ordinal",
     strategy: str = "quantile",
 ) -> Pipeline:  # noqa
-    """
-        Helper function for imputing and scaling np.ndarray data
-        using different scaling transformers.
-    -----------------------------------------------------------------
+    """Helper function for imputing and scaling np.ndarray data using different scaling transformers.
+
     :param X: np.ndarray
     :param impute: whether to run imputing or not
     :param use_scaler: string in None or
@@ -613,12 +610,7 @@ def get_preprocessing_pipeline(
 def fit_pipeline(
     X: pd.DataFrame, transformer, keep_n_decimals: int = 5
 ) -> pd.DataFrame:
-    """
-     Helper to fit DataFrame over transformer pipeline.
-     Rounds resulting matrix X by keep_n_digits if not 0,
-     which helps for when transformer pipeline is scaling or imputer
-     which sometime introduce small negative numbers,
-     and umap metrics like Hellinger need to be positive
+    """Helper to fit DataFrame over transformer pipeline. Rounds resulting matrix X by keep_n_digits if not 0, which helps for when transformer pipeline is scaling or imputer which sometime introduce small negative numbers, and umap metrics like Hellinger need to be positive
     :param X: DataFrame to transform.
     :param transformer: Pipeline object to fit and transform
     :param keep_n_decimals: Int of how many decimal places to keep in rounded transformed data
@@ -864,8 +856,7 @@ def process_dirty_dataframes(
     """
         Dirty_Cat encoder for record level data. Will automatically turn
         inhomogeneous dataframe into matrix using smart conversion tricks.
-    ______________________________________________________________________
-
+   
     :param ndf: node DataFrame
     :param y: target DataFrame or series
     :param cardinality_threshold: For ndf columns, below this threshold,
@@ -1026,10 +1017,7 @@ def process_nodes_dataframes(
     Any,
     List[str],
 ]:
-    """
-        Automatic Deep Learning Embedding/ngrams of Textual Features,
-        with the rest of the columns taken care of by dirty_cat
-    _________________________________________________________________________
+    """Automatic Deep Learning Embedding/ngrams of Textual Features, with the rest of the columns taken care of by dirty_cat
 
     :param df: pandas DataFrame of data
     :param y: pandas DataFrame of targets
@@ -1048,6 +1036,7 @@ def process_nodes_dataframes(
     :param model_name: SentenceTransformer model name. See available list at
             https://www.sbert.net/docs/pretrained_models.
             html#sentence-embedding-models
+
     :return: X_enc, y_enc, data_encoder, label_encoder,
         scaling_pipeline,
         scaling_pipeline_target,
@@ -1239,10 +1228,9 @@ def encode_edges(edf, src, dst, mlb, fit=False):
         src (string): source column
         dst (string): destination column
         mlb (sklearn): multilabelBinarizer
-        fit (bool, optional): If true, fits multilabelBinarizer.
-            Defaults to False.
-    Returns:
-        tuple: pd.DataFrame, multilabelBinarizer
+        fit (bool, optional): If true, fits multilabelBinarizer. Defaults to False.
+
+    :Returns: tuple: pd.DataFrame, multilabelBinarizer
     """
     # uses mlb with fit=T/F so we can use it in transform mode
     # to recreate edge feature concat definition
@@ -1318,8 +1306,8 @@ def process_edge_dataframes(
     :param dst: destination column to select in edf
     :param use_scaler: None or string in
         ['minmax', 'standard', 'robust', 'quantile']
-    :return: Encoded data matrix and target (if not None),
-        the data encoders, and the label encoder.
+
+    :return: Encoded data matrix and target (if not None), the data encoders, and the label encoder.
     """
     lazy_import_has_min_dependancy()
     from sklearn.preprocessing import (
@@ -1763,11 +1751,11 @@ def scale(self, X=None, y=None, return_pipeline=False, *args, **kwargs):
             args:
             ::
 
-                
                 ;X: pd.DataFrame of features
                 :y: pd.DataFrame of target features
                 :kind: str, one of 'nodes' or 'edges'
                 *args, **kwargs: passed to smart_scaler pipeline
+
             returns:
                 scaled X, y
         """
@@ -1790,9 +1778,7 @@ def scale(self, X=None, y=None, return_pipeline=False, *args, **kwargs):
 def prune_weighted_edges_df_and_relabel_nodes(
     wdf: pd.DataFrame, scale: float = 0.1, index_to_nodes_dict: Optional[Dict] = None
 ) -> pd.DataFrame:
-    """
-        Prune the weighted edge DataFrame so to return high
-        fidelity similarity scores.
+    """Prune the weighted edge DataFrame so to return high fidelity similarity scores.
 
     :param wdf: weighted edge DataFrame gotten via UMAP
     :param scale: lower values means less edges > (max - scale * std)
@@ -1869,9 +1855,7 @@ def get_matrix_by_column_parts(X: pd.DataFrame, column_parts: Optional[Union[lis
 
 
 class FeatureMixin(MIXIN_BASE):
-    """
-    FeatureMixin for automatic featurization of nodes and edges DataFrames.
-    Subclasses UMAPMixin for umap-ing of automatic features.
+    """FeatureMixin for automatic featurization of nodes and edges DataFrames. Subclasses UMAPMixin for umap-ing of automatic features.
 
     Usage:
     ::
@@ -2214,6 +2198,7 @@ def transform(self, df: pd.DataFrame,
                 :n_neighbors: int, if return_graph is True, will use this value for n_neighbors in Nearest Neighbors search
                 :scaled: bool, if True, will use scaled transformation of data set during featurization, default True
                 :verbose: bool, if True, will print metadata about the graph construction, default False
+
             **Returns:**
 
                     X, y: pd.DataFrame, transformed data if return_graph is False
@@ -2403,7 +2388,6 @@ def featurize(
     ):
         r"""Featurize Nodes or Edges of the underlying nodes/edges DataFrames.
         
-
         :param kind: specify whether to featurize `nodes` or `edges`.
                 Edge featurization includes a pairwise
                 src-to-dst feature block using a MultiLabelBinarizer,
@@ -2623,12 +2607,7 @@ def _featurize_or_get_nodes_dataframe_if_X_is_None(
         memoize: bool = True,
         verbose: bool = False,
     ) -> Tuple[pd.DataFrame, pd.DataFrame, MIXIN_BASE]:
-        """
-        helper method gets node feature and target matrix if X, y
-        are not specified.
-        if X, y are specified will set them as `_node_target` and
-        `_node_target` attributes
-        -----------------------------------------------------------
+        """helper method gets node feature and target matrix if X, y are not specified. if X, y are specified will set them as `_node_target` and `_node_target` attributes
         """
 
         res = self.bind()
@@ -2717,10 +2696,8 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
         memoize: bool = True,
         verbose: bool = False,
     ) -> Tuple[pd.DataFrame, Optional[pd.DataFrame], MIXIN_BASE]:
-        """
-        helper method gets edge feature and target matrix if X, y
-        are not specified
-        -----------------------------------------------------------
+        """ helper method gets edge feature and target matrix if X, y are not specified
+      
         :param X: Data Matrix
         :param y: target, default None
         :return: data `X` and `y`
@@ -2778,18 +2755,7 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
 
     
     def get_matrix(self, columns: Optional[Union[List, str]] = None, kind: str = 'nodes', target: bool = False) -> pd.DataFrame:
-        """
-            Returns feature matrix, and if columns are specified, returns matrix with only the columns that contain 
-            the string `column_part` in their name.
-
-            `X = g.get_matrix(['feature1', 'feature2'])`
-            will retrieve a feature matrix with only the columns that contain the string 
-            `feature1` or `feature2` in their name.
-
-            Most useful for topic modeling, where the column names are of the form `topic_0: descriptor`, `topic_1: descriptor`, etc.
-            Can retrieve unique columns in original dataframe, or actual topic features like [ip_part, shoes, preference_x, etc].
-            
-            Powerful way to retrieve features from a featurized graph by column or (top) features of interest.
+        """Returns feature matrix, and if columns are specified, returns matrix with only the columns that contain the string `column_part` in their name.`X = g.get_matrix(['feature1', 'feature2'])` will retrieve a feature matrix with only the columns that contain the string `feature1` or `feature2` in their name. Most useful for topic modeling, where the column names are of the form `topic_0: descriptor`, `topic_1: descriptor`, etc. Can retrieve unique columns in original dataframe, or actual topic features like [ip_part, shoes, preference_x, etc]. Powerful way to retrieve features from a featurized graph by column or (top) features of interest.
             
             **Example:**
             ::
@@ -2808,17 +2774,19 @@ def get_matrix(self, columns: Optional[Union[List, str]] = None, kind: str = 'no
                     => ['basket_price_total', 'conversion_percent', 'CTR_percent', 'CVR_percent']
 
                 # not as useful for sbert features. 
+
             Caveats:
                 - if you have a column name that is a substring of another column name, you may get unexpected results.
+
         Args:
-            :columns (Union[List, str]): list of column names or a single column name that may exist in columns 
-                of the feature matrix. If None, returns original feature matrix
+            :columns (Union[List, str]): list of column names or a single column name that may exist in columns of the feature matrix. If None, returns original feature matrix
             :kind (str, optional): Node or Edge features. Defaults to 'nodes'.
             :target (bool, optional): If True, returns the target matrix. Defaults to False.
 
         Returns:
             pd.DataFrame: feature matrix with only the columns that contain the string `column_part` in their name.
         """
+
         if target:
             X = self._get_target(kind)
         else:
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 633f941c55..235c7ee558 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -133,9 +133,7 @@ def umap_graph_to_weighted_edges(umap_graph, engine, is_legacy, cfg=config):
 
 
 class UMAPMixin(MIXIN_BASE):
-    """
-    UMAP Mixin for automagic UMAPing
-
+    """UMAP Mixin for automagic UMAPing
     """
     # FIXME where is this used? 
     _umap_memoize: WeakValueDictionary = WeakValueDictionary()
@@ -429,14 +427,14 @@ def umap(
             or pass in your own X, y (optional) dataframes of values
             
         Example
-        -------
+      
         >>> import graphistry   
         >>> g = graphistry.nodes(pd.DataFrame({'node': [0,1,2], 'data': [1,2,3], 'meta': ['a', 'b', 'c']}))
         >>> g2 = g.umap(n_components=3, spread=1.0, min_dist=0.1, n_neighbors=12, negative_sample_rate=5, local_connectivity=1, repulsion_strength=1.0, metric='euclidean', suffix='', play=0, encode_position=True, encode_weight=True, dbscan=False, engine='auto', feature_engine='auto', inplace=False, memoize=True, verbose=False)
         >>> g2.plot()
         
         Parameters
-        ----------
+  
             :X: either a dataframe ndarray of features, or column names to featurize
             :y: either an dataframe ndarray of targets, or column names to featurize
                     targets
@@ -478,6 +476,7 @@ def umap(
             :memoize: whether to memoize the results of this method,
                     default True.
             :verbose: whether to print out extra information, default False.
+            
         :return: self, with attributes set with new data
         """
         if engine == UMAP_LEARN:

From a58f279963aa8423291c027f15db15e88ec9fa6f Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Thu, 6 Apr 2023 00:11:59 +0530
Subject: [PATCH 317/432] umap trick for cudf dfs

---
 graphistry/PlotterBase.py | 10 ----------
 graphistry/umap_utils.py  | 17 ++++++++++++++++-
 2 files changed, 16 insertions(+), 11 deletions(-)

diff --git a/graphistry/PlotterBase.py b/graphistry/PlotterBase.py
index 3da7f9ee5d..badb060b19 100644
--- a/graphistry/PlotterBase.py
+++ b/graphistry/PlotterBase.py
@@ -1033,12 +1033,6 @@ def sample_nodes(g, n):
             res = base.nodes(nodes2)
         else:
             res = copy.copy(base)
-            # this is temporary
-            # TODO: for cudf support need to clean the entire codebase
-            try:
-                nodes = nodes.to_pandas()
-            except:
-                pass
             res._nodes = nodes
         # for use in text_utils.py search index
         if hasattr(res, 'search_index'):
@@ -1147,10 +1141,6 @@ def sample_edges(g, n):
             res = base.edges(edges2)
         else:
             res = copy.copy(base)
-            try:
-                edges = edges.to_pandas()
-            except:
-                pass
             res._edges = edges
         return res
 
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 79a10dcfa2..fb2e2cece5 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -549,6 +549,22 @@ def umap(
         )
         logger.debug("umap_kwargs: %s", umap_kwargs)
 
+        # temporary until we have full cudf support in feature_utils.py
+        has_cudf, _, cudf = lazy_cudf_import_has_dependancy()
+
+        if has_cudf:
+            flag_nodes_cudf = isinstance(self._nodes, cudf.DataFrame)
+            flag_edges_cudf = isinstance(self._edges, cudf.DataFrame)
+
+            if flag_nodes_cudf or flag_edges_cudf:
+                res = self
+                if flag_nodes_cudf:
+                    res._nodes = res._nodes.to_pandas()
+                if flag_edges_cudf:
+                    res._edges = res._edges.to_pandas()
+                res = res.umap(X=self._nodes, y=self._edges, **umap_kwargs)
+                return res
+
         if inplace:
             res = self
         else:
@@ -563,7 +579,6 @@ def umap(
             res, X, y, kind, feature_engine, {**featurize_kwargs, "memoize": memoize}
         )
 
-
         if kind == "nodes":
             index = res._nodes.index
             if res._node is None:

From 827ae2245151af5c7d495d8c857cd1425f4ab61a Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Thu, 6 Apr 2023 00:15:52 +0530
Subject: [PATCH 318/432] ignore args type

---
 graphistry/umap_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index fb2e2cece5..b952546950 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -562,7 +562,7 @@ def umap(
                     res._nodes = res._nodes.to_pandas()
                 if flag_edges_cudf:
                     res._edges = res._edges.to_pandas()
-                res = res.umap(X=self._nodes, y=self._edges, **umap_kwargs)
+                res = res.umap(X=self._nodes, y=self._edges, **umap_kwargs)  # type: ignore
                 return res
 
         if inplace:

From b95400e481251d31e65045a725d03c010f5e3b94 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Wed, 5 Apr 2023 16:12:42 -0400
Subject: [PATCH 319/432] feat(rst) added badges

---
 docs/source/index.rst | 50 ++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 49 insertions(+), 1 deletion(-)

diff --git a/docs/source/index.rst b/docs/source/index.rst
index 1704e7f07c..d7485ec13d 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,5 +1,43 @@
-PyGraphistry[ai]'s documentation 
+PyGraphistry: Explore Relationships
 ========================================
+.. image:: https://readthedocs.org/projects/pygraphistry/badge/?version=latest
+    :target: https://pygraphistry.readthedocs.io/en/latest/?badge=latest
+    :alt: Documentation Status
+
+
+.. image:: https://github.com/graphistry/pygraphistry/workflows/CI%20Tests/badge.svg
+    :target: https://github.com/graphistry/pygraphistry/workflows/CI%20Tests/badge.svg
+    :alt: Build Status
+
+
+.. image:: https://github.com/graphistry/pygraphistry/workflows/CodeQL/badge.svg
+    :target: https://github.com/graphistry/pygraphistry/actions?query=workflow%3ACodeQL
+    :alt: CodeQL Status
+
+.. image:: https://img.shields.io/pypi/v/graphistry.svg
+    :target: https://pypi.python.org/pypi/graphistry
+    :alt: PyPi Status
+
+.. image:: https://img.shields.io/pypi/dm/graphistry
+    :target: https://img.shields.io/pypi/dm/graphistry
+    :alt: PyPi Downloads
+
+
+.. image:: https://img.shields.io/pypi/l/graphistry.svg
+    :target: https://pypi.python.org/pypi/graphistry
+    :alt: License
+
+.. image:: https://img.shields.io/uptimerobot/status/m787548531-e9c7b7508fc76fea927e2313?label=hub.graphistry.com
+    :target: https://img.shields.io/uptimerobot/status/m787548531-e9c7b7508fc76fea927e2313?label=hub.graphistry.com
+    :alt: License
+
+.. image:: https://img.shields.io/badge/slack-Graphistry%20chat-orange.svg?logo=slack
+      :target: https://join.slack.com/t/graphistry-community/shared_invite/zt-53ik36w2-fpP0Ibjbk7IJuVFIRSnr6g
+      :alt: Slack
+
+.. image:: https://img.shields.io/twitter/follow/graphistry
+      :target: https://twitter.com/graphistry
+      :alt: Twitter
 
 .. Quickstart:
 .. `Read our tutorial <https://github.com/graphistry/pygraphistry/blob/master/README.md>`_
@@ -7,6 +45,10 @@ PyGraphistry[ai]'s documentation
 PyGraphistry is a Python visual graph AI library to extract, transform, analyze, model, and visualize big graphs, and especially alongside Graphistry end-to-end GPU server sessions. Installing optional graphistry[ai] dependencies adds graph autoML, including automatic feature engineering, UMAP, and graph neural net support. Combined, PyGraphistry reduces your time to graph for going from raw data to visualizations and AI models down to three lines of code.
 Here in our docstrings you can find useful packages, modules, and commands to maximize your graph AI experience with PyGraphistry. In the navbar you can find an overview of all the packages and modules we provided and a few useful highlighted ones as well. You can search for them on our Search page. For a full tutorial, refer to our `PyGraphistry <https://github.com/graphistry/pygraphistry/>`_ repo.
 
+.. .. image:: docs/static/docstring.png
+..       :width: 600
+..       :alt: PyGraphistry
+
 
 .. Click to open interactive version! (For server-backed interactive analytics, use an API key)
 
@@ -23,6 +65,12 @@ For self-hosting and access to a free API key, refer to our Graphistry `Hub <htt
    graphistry
    modules
 
+Articles
+==================
+* `Graphistry: Visual Graph AI Interactive demo <https://github.com/graphistry/pygraphistry/blob/master/README.md#demo-of-friendship-communities-on-facebook/>`_
+* `PyGraphistry + Databricks <https://github.com/graphistry/pygraphistry/blob/master/demos/demos_databases_apis/databricks_pyspark/graphistry-notebook-dashboard.ipynb>`_
+
+
 Indices and tables
 ==================
 

From cb10f3c5d10848c574aae90aaf401aee82e86d10 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Wed, 5 Apr 2023 16:14:12 -0400
Subject: [PATCH 320/432] fix(plotter): adding plotter to menu (will update)

---
 docs/source/graphistry.plotter.rst | 17 +++++++++++++++++
 docs/source/graphistry.rst         |  7 +++----
 2 files changed, 20 insertions(+), 4 deletions(-)
 create mode 100644 docs/source/graphistry.plotter.rst

diff --git a/docs/source/graphistry.plotter.rst b/docs/source/graphistry.plotter.rst
new file mode 100644
index 0000000000..98079a1bc7
--- /dev/null
+++ b/docs/source/graphistry.plotter.rst
@@ -0,0 +1,17 @@
+Plotter Base
+----------------------
+.. automodule:: graphistry.PlotterBase
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :noindex:
+
+
+Plotter Modules
+----------------------
+.. automodule:: graphistry.Plottable
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :noindex:
+
diff --git a/docs/source/graphistry.rst b/docs/source/graphistry.rst
index 030071b943..b1543d5612 100644
--- a/docs/source/graphistry.rst
+++ b/docs/source/graphistry.rst
@@ -1,10 +1,9 @@
 Plotter Module
 ==================
+.. toctree::
+    :maxdepth: 3
 
-.. automodule:: graphistry.PlotterBase
-    :members:
-    :undoc-members:
-    :show-inheritance:
+    graphistry.plotter
 
 Plugins
 ==================

From 10907c3fbf48bc60ed4c2c8d9e13fa81aaf10ad7 Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Thu, 6 Apr 2023 02:58:07 +0530
Subject: [PATCH 321/432] plotterbase to plotter

---
 docs/source/graphistry.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/graphistry.rst b/docs/source/graphistry.rst
index 030071b943..9b53605fb0 100644
--- a/docs/source/graphistry.rst
+++ b/docs/source/graphistry.rst
@@ -1,7 +1,7 @@
 Plotter Module
 ==================
 
-.. automodule:: graphistry.PlotterBase
+.. automodule:: graphistry.plotter
     :members:
     :undoc-members:
     :show-inheritance:

From dcf60ac10957ed547fb9d5c78c96b653cbd777da Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Thu, 6 Apr 2023 03:17:32 +0530
Subject: [PATCH 322/432] addStyle to add_style

---
 graphistry/PlotterBase.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/PlotterBase.py b/graphistry/PlotterBase.py
index 747f7e74b6..628b74991d 100644
--- a/graphistry/PlotterBase.py
+++ b/graphistry/PlotterBase.py
@@ -224,7 +224,7 @@ def __repr__(self):
         else:
             return str(rep)
 
-    def addStyle(self, fg=None, bg=None, page=None, logo=None):
+    def add_style(self, fg=None, bg=None, page=None, logo=None):
         """Set general visual styles
         
         See .bind() and .settings(url_params={}) for additional styling options, and style() for another way to set the same attributes.

From 75f11b0d1b7be0cba636c1a228feecd3d33b740d Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Wed, 5 Apr 2023 17:57:08 -0400
Subject: [PATCH 323/432] fix(rst): revert plotter changes for ci test

---
 docs/source/graphistry.rst | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/docs/source/graphistry.rst b/docs/source/graphistry.rst
index b1543d5612..32b042a0b8 100644
--- a/docs/source/graphistry.rst
+++ b/docs/source/graphistry.rst
@@ -1,9 +1,10 @@
 Plotter Module
 ==================
-.. toctree::
-    :maxdepth: 3
+.. automodule:: graphistry.PlotterBase
+    :members:
+    :undoc-members:
+    :show-inheritance:
 
-    graphistry.plotter
 
 Plugins
 ==================

From 953cccc923b84967fe3649a03c87d28e520fab1d Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Thu, 6 Apr 2023 03:27:13 +0530
Subject: [PATCH 324/432] all addStyle to add_style

---
 graphistry/__init__.py           |  2 +-
 graphistry/pygraphistry.py       |  6 +++---
 graphistry/tests/test_plotter.py | 22 +++++++++++-----------
 3 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/graphistry/__init__.py b/graphistry/__init__.py
index dd13cb60c5..511b543e57 100644
--- a/graphistry/__init__.py
+++ b/graphistry/__init__.py
@@ -15,7 +15,7 @@
     description,
     bind,
     style,
-    addStyle,
+    add_style,
     edges,
     nodes,
     graph,
diff --git a/graphistry/pygraphistry.py b/graphistry/pygraphistry.py
index 2051a32523..e6582928e6 100644
--- a/graphistry/pygraphistry.py
+++ b/graphistry/pygraphistry.py
@@ -1209,7 +1209,7 @@ def description(description):
         return Plotter().description(description)
 
     @staticmethod
-    def addStyle(bg=None, fg=None, logo=None, page=None):
+    def add_style(bg=None, fg=None, logo=None, page=None):
         """Creates a base plotter with some style settings.
 
         For parameters, see ``plotter.addStyle``.
@@ -1225,7 +1225,7 @@ def addStyle(bg=None, fg=None, logo=None, page=None):
                 graphistry.addStyle(bg={'color': 'black'})
         """
 
-        return Plotter().addStyle(bg=bg, fg=fg, logo=logo, page=page)
+        return Plotter().add_style(bg=bg, fg=fg, logo=logo, page=page)
 
     @staticmethod
     def style(bg=None, fg=None, logo=None, page=None):
@@ -2417,7 +2417,7 @@ def _handle_api_response(response):
 api_token = PyGraphistry.api_token
 verify_token = PyGraphistry.verify_token
 bind = PyGraphistry.bind
-addStyle = PyGraphistry.addStyle
+add_style = PyGraphistry.add_style
 style = PyGraphistry.style
 encode_point_color = PyGraphistry.encode_point_color
 encode_edge_color = PyGraphistry.encode_edge_color
diff --git a/graphistry/tests/test_plotter.py b/graphistry/tests/test_plotter.py
index c1518a8160..35142e8026 100644
--- a/graphistry/tests/test_plotter.py
+++ b/graphistry/tests/test_plotter.py
@@ -720,32 +720,32 @@ def test_addStyle_good(self):
         logo = {"url": "zzz"}
         page = {"title": "zzz"}
 
-        assert g.addStyle()._style == {}
+        assert g.add_style()._style == {}
 
-        g.addStyle(fg={"blendMode": "screen"})
-        assert g.addStyle()._style == {}
+        g.add_style(fg={"blendMode": "screen"})
+        assert g.add_style()._style == {}
 
-        assert g.addStyle(bg=copy.deepcopy(bg))._style == {"bg": bg}
-        assert g.addStyle(bg={"color": "blue"}).addStyle(
+        assert g.add_style(bg=copy.deepcopy(bg))._style == {"bg": bg}
+        assert g.add_style(bg={"color": "blue"}).add_style(
             bg=copy.deepcopy(bg)
         )._style == {"bg": bg}
-        assert g.addStyle(bg={"image": {"url": "http://asdf.com/b.png"}}).addStyle(
+        assert g.add_style(bg={"image": {"url": "http://asdf.com/b.png"}}).add_style(
             bg=copy.deepcopy(bg)
         )._style == {"bg": {**bg, "image": {"url": "http://asdf.com/b.png"}}}
         assert (
-            g.addStyle(
+            g.add_style(
                 bg=copy.deepcopy(bg),
                 fg=copy.deepcopy(fg),
                 logo=copy.deepcopy(logo),
                 page=copy.deepcopy(page),
             )._style == {"bg": bg, "fg": fg, "logo": logo, "page": page}
         )
-        assert g.addStyle(
+        assert g.add_style(
             bg=copy.deepcopy(bg),
             fg=copy.deepcopy(fg),
             logo=copy.deepcopy(logo),
             page=copy.deepcopy(page),
-        ).addStyle(bg={"color": "green"})._style == {
+        ).add_style(bg={"color": "green"})._style == {
             "bg": {"color": "green"},
             "fg": fg,
             "logo": logo,
@@ -755,7 +755,7 @@ def test_addStyle_good(self):
         g2 = graphistry.edges(pd.DataFrame({"s": [0], "d": [0]})).bind(
             source="s", destination="d"
         )
-        ds = g2.addStyle(
+        ds = g2.add_style(
             bg=copy.deepcopy(bg),
             fg=copy.deepcopy(fg),
             page=copy.deepcopy(page),
@@ -783,7 +783,7 @@ def test_styleApi_reject(self):
         g2 = graphistry.edges(pd.DataFrame({"s": [0], "d": [0]})).bind(
             source="s", destination="d"
         )
-        g3 = g2.addStyle(
+        g3 = g2.add_style(
             bg=copy.deepcopy(bg),
             fg=copy.deepcopy(fg),
             page=copy.deepcopy(page),

From 8d644814ada4cc9e6a4e64eaa1215e1a90aeb645 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Thu, 6 Apr 2023 16:01:46 -0400
Subject: [PATCH 325/432] fix(plotter): expanding menu

---
 docs/source/graphistry.rst | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/docs/source/graphistry.rst b/docs/source/graphistry.rst
index 32b042a0b8..b1543d5612 100644
--- a/docs/source/graphistry.rst
+++ b/docs/source/graphistry.rst
@@ -1,10 +1,9 @@
 Plotter Module
 ==================
-.. automodule:: graphistry.PlotterBase
-    :members:
-    :undoc-members:
-    :show-inheritance:
+.. toctree::
+    :maxdepth: 3
 
+    graphistry.plotter
 
 Plugins
 ==================

From 5900e2f68a85756d618a96a18e8db62a1b48dffb Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Thu, 6 Apr 2023 16:08:37 -0400
Subject: [PATCH 326/432] fix(docst) added umap to articles

---
 docs/source/index.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/index.rst b/docs/source/index.rst
index d7485ec13d..ce0734f357 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -69,6 +69,7 @@ Articles
 ==================
 * `Graphistry: Visual Graph AI Interactive demo <https://github.com/graphistry/pygraphistry/blob/master/README.md#demo-of-friendship-communities-on-facebook/>`_
 * `PyGraphistry + Databricks <https://github.com/graphistry/pygraphistry/blob/master/demos/demos_databases_apis/databricks_pyspark/graphistry-notebook-dashboard.ipynb>`_
+* `PyGraphistry + UMAP <https://github.com/graphistry/pygraphistry/blob/master/demos/demos_databases_apis/umap_learn/umap_learn.ipynb>`_
 
 
 Indices and tables

From 085077816a13be95b554001870b0b81924e004c6 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Thu, 6 Apr 2023 16:09:42 -0400
Subject: [PATCH 327/432] feat(docst) added photo for home pg

---
 docs/static/docstring.png | Bin 0 -> 927210 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 docs/static/docstring.png

diff --git a/docs/static/docstring.png b/docs/static/docstring.png
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literal 0
HcmV?d00001


From 1234dd7a8d10a63164ccf9cbaacea8e392a31e4e Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 01:40:59 +0530
Subject: [PATCH 328/432] test add chain in __init__.py

---
 graphistry/compute/__init__.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/graphistry/compute/__init__.py b/graphistry/compute/__init__.py
index 3c7c7f45e3..0b35410e21 100644
--- a/graphistry/compute/__init__.py
+++ b/graphistry/compute/__init__.py
@@ -2,3 +2,4 @@
 from .ast import (
     n, e_forward, e_reverse, e_undirected
 )
+from .chain import chain

From c7bc46d22ec53cda81c50ba3a4aac60618c088c2 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 02:01:29 +0530
Subject: [PATCH 329/432] test nitpick

---
 docs/source/conf.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 279ba999ff..8773a713e2 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -69,6 +69,7 @@
     ('py:class', 'graphistry.text_utils.SearchToGraphMixin'),
     ('py:class', 'graphistry.embed_utils.HeterographEmbedModuleMixin'),
     ('py:class', 'graphistry.PlotterBase.PlotterBase'),
+    ('py:function', 'graphistry.compute.chain.chain')
     ('py:class', 'IGraph graph'),
     ('py:class', 'igraph'),
     ('py:class', 'dgl'),

From a88961bf5cf403979eca699e61c26973d634d697 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 02:05:20 +0530
Subject: [PATCH 330/432] test nitpick 2

---
 docs/source/conf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 8773a713e2..474986caa3 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -69,7 +69,7 @@
     ('py:class', 'graphistry.text_utils.SearchToGraphMixin'),
     ('py:class', 'graphistry.embed_utils.HeterographEmbedModuleMixin'),
     ('py:class', 'graphistry.PlotterBase.PlotterBase'),
-    ('py:function', 'graphistry.compute.chain.chain')
+    ('py:function', 'graphistry.compute.chain.chain'),
     ('py:class', 'IGraph graph'),
     ('py:class', 'igraph'),
     ('py:class', 'dgl'),

From f8d6ee10400963411028ea2fd4d39e93d5bff102 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 02:11:50 +0530
Subject: [PATCH 331/432] test

---
 docs/source/conf.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 474986caa3..7a9f1657d8 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -41,7 +41,7 @@
 ]
 
 # mock imports
-autodoc_mock_imports = ["graphistry.compute.chain"]
+autodoc_mock_imports = ["graphistry.compute.chain.chain"]
 
 #FIXME Why is sphinx/autodoc failing here?
 nitpick_ignore = [
@@ -69,7 +69,6 @@
     ('py:class', 'graphistry.text_utils.SearchToGraphMixin'),
     ('py:class', 'graphistry.embed_utils.HeterographEmbedModuleMixin'),
     ('py:class', 'graphistry.PlotterBase.PlotterBase'),
-    ('py:function', 'graphistry.compute.chain.chain'),
     ('py:class', 'IGraph graph'),
     ('py:class', 'igraph'),
     ('py:class', 'dgl'),

From 8115020313cb00d2fed3fa4d9df57022bf13a2e0 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 02:32:47 +0530
Subject: [PATCH 332/432] test 3

---
 docs/source/conf.py           |  3 ---
 graphistry/plugins/cugraph.py | 19 -------------------
 2 files changed, 22 deletions(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 7a9f1657d8..c7bbd195fc 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -40,9 +40,6 @@
     "sphinx_autodoc_typehints",
 ]
 
-# mock imports
-autodoc_mock_imports = ["graphistry.compute.chain.chain"]
-
 #FIXME Why is sphinx/autodoc failing here?
 nitpick_ignore = [
     ('py:class', '1'),  # Ex: api : Optional[Literal[1, 3]]
diff --git a/graphistry/plugins/cugraph.py b/graphistry/plugins/cugraph.py
index 5e7a656b25..c02b6c48ca 100644
--- a/graphistry/plugins/cugraph.py
+++ b/graphistry/plugins/cugraph.py
@@ -332,34 +332,15 @@ def layout_cugraph(
     """Layout the grpah using a cuGraph algorithm. For a list of layouts, see cugraph documentation (currently just force_atlas2).
 
     :param layout: Name of an cugraph layout method like `force_atlas2`
-    :type layout: str
-
     :param params: Any named parameters to pass to the underlying cugraph method
-    :type params: dict
-
     :param kind: The kind of cugraph Graph
-    :type kind: CuGraphKind
-
     :param directed: During the to_cugraph conversion, whether to be directed. (default True)
-    :type directed: bool
-
     :param G: The cugraph graph (G) to layout. If None, the current graph is used.
-    :type G: Optional[Any]
-
     :param bind_position: Whether to call bind(point_x=, point_y=) (default True)
-    :type bind_position: bool
-
     :param x_out_col: Attribute to write x position to. (default 'x')
-    :type x_out_col: str
-
     :param y_out_col: Attribute to write x position to. (default 'y')
-    :type y_out_col: str
-
     :param play: If defined, set settings(url_params={'play': play}). (default 0)
-    :type play: Optional[str]
-
     :returns: Plotter
-    :rtype: Plotter
 
     **Example: ForceAtlas2 layout**
         ::

From e581f97dd1596b195da024a91bf5ea8b4284dca8 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 02:38:27 +0530
Subject: [PATCH 333/432] test 4

---
 graphistry/compute/chain.py   |  2 --
 graphistry/plugins/cugraph.py |  7 -------
 graphistry/plugins/igraph.py  | 26 --------------------------
 3 files changed, 35 deletions(-)

diff --git a/graphistry/compute/chain.py b/graphistry/compute/chain.py
index 057d56d328..818e2b3dff 100644
--- a/graphistry/compute/chain.py
+++ b/graphistry/compute/chain.py
@@ -98,10 +98,8 @@ def chain(self: Plottable, ops: List[ASTObject]) -> Plottable:
     If any matchers are named, add a correspondingly named boolean-valued column to the output
 
     :param ops: List[ASTobject] Various node and edge matchers
-    :type fg: dict
 
     :returns: Plotter
-    :rtype: Plotter
 
     **Example: Find nodes of some type**
         ::
diff --git a/graphistry/plugins/cugraph.py b/graphistry/plugins/cugraph.py
index c02b6c48ca..0930efed42 100644
--- a/graphistry/plugins/cugraph.py
+++ b/graphistry/plugins/cugraph.py
@@ -222,20 +222,13 @@ def compute_cugraph(
     """Run cugraph algorithm on graph. For algorithm parameters, see cuGraph docs.
 
     :param alg: algorithm name
-    :type alg: str
     :param out_col: node table output column name, defaults to alg param
-    :type out_col: Optional[str]
     :param params: algorithm parameters passed to cuGraph as kwargs
-    :type params: dict
     :param kind: kind of cugraph to use
-    :type kind: CuGraphKind
     :param directed: whether graph is directed
-    :type directed: bool
     :param G: cugraph graph to use; if None, use self
-    :type G: Optional[cugraph.Graph]
 
     :return: Plottable
-    :rtype: Plottable
     
     **Example: Pagerank**
         ::
diff --git a/graphistry/plugins/igraph.py b/graphistry/plugins/igraph.py
index 5fe5c2f8a6..0dbcb80dc3 100644
--- a/graphistry/plugins/igraph.py
+++ b/graphistry/plugins/igraph.py
@@ -294,22 +294,12 @@ def compute_igraph(
     """Enrich or replace graph using igraph methods
 
     :param alg: Name of an igraph.Graph method like `pagerank`
-    :type alg: str
-
     :param out_col: For algorithms that generate a node attribute column, `out_col` is the desired output column name. When `None`, use the algorithm's name. (default None)
-    :type out_col: Optional[str]
-
     :param directed: During the to_igraph conversion, whether to be directed. If None, try directed and then undirected. (default None)
-    :type directed: Optional[bool]
-
     :param use_vids: During the to_igraph conversion, whether to interpret IDs as igraph vertex IDs (non-negative integers) or arbitrary values (False, default)
-    :type use_vids: bool
-
     :param params: Any named parameters to pass to the underlying igraph method
-    :type params: dict
 
     :returns: Plotter
-    :rtype: Plotter
 
     **Example: Pagerank**
         ::
@@ -425,31 +415,15 @@ def layout_igraph(
     """Compute graph layout using igraph algorithm. For a list of layouts, see layout_algs or igraph documentation.
 
     :param layout: Name of an igraph.Graph.layout method like `sugiyama`
-    :type layout: str
-
     :param directed: During the to_igraph conversion, whether to be directed. If None, try directed and then undirected. (default None)
-    :type directed: Optional[bool]
-
     :param use_vids: Whether to use igraph vertex ids (non-negative integers) or arbitary node ids (False, default)
-    :type use_vids: bool
-
     :param bind_position: Whether to call bind(point_x=, point_y=) (default True)
-    :type bind_position: bool
-
     :param x_out_col: Attribute to write x position to. (default 'x')
-    :type x_out_col: str
-
     :param y_out_col: Attribute to write x position to. (default 'y')
-    :type y_out_col: str
-
     :param play: If defined, set settings(url_params={'play': play}). (default 0)
-    :type play: Optional[str]
-
     :param params: Any named parameters to pass to the underlying igraph method
-    :type params: dict
 
     :returns: Plotter
-    :rtype: Plotter
 
     **Example: Sugiyama layout**
         ::

From bde96b18e2f670b387add903ea96365cbfc4971d Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 02:44:48 +0530
Subject: [PATCH 334/432] test 5

---
 graphistry/compute/__init__.py |  1 -
 graphistry/compute/chain.py    | 10 +++++-----
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/graphistry/compute/__init__.py b/graphistry/compute/__init__.py
index 0b35410e21..3c7c7f45e3 100644
--- a/graphistry/compute/__init__.py
+++ b/graphistry/compute/__init__.py
@@ -2,4 +2,3 @@
 from .ast import (
     n, e_forward, e_reverse, e_undirected
 )
-from .chain import chain
diff --git a/graphistry/compute/chain.py b/graphistry/compute/chain.py
index 818e2b3dff..a06c913018 100644
--- a/graphistry/compute/chain.py
+++ b/graphistry/compute/chain.py
@@ -90,26 +90,26 @@ def combine_steps(g: Plottable, kind: str, steps: List[Tuple[ASTObject,Plottable
 
 def chain(self: Plottable, ops: List[ASTObject]) -> Plottable:
     """
-
     Experimental: Chain a list of operations
 
     Return subgraph of matches according to the list of node & edge matchers
-
     If any matchers are named, add a correspondingly named boolean-valued column to the output
 
     :param ops: List[ASTobject] Various node and edge matchers
 
-    :returns: Plotter
+    :returns: Plottable
 
     **Example: Find nodes of some type**
-        ::
+
+    ::
 
             from graphistry.ast import n
 
             people_nodes_df = g.chain([ n({"type": "person"}) ])._nodes
             
     **Example: Find 2-hop edge sequences with some attribute**
-        ::
+
+    ::
 
             from graphistry.ast import e_forward
 

From f66856c817bd677bb2780ce67029f5bdf47532e1 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 02:52:06 +0530
Subject: [PATCH 335/432] test 6

---
 graphistry/PlotterBase.py | 94 ---------------------------------------
 1 file changed, 94 deletions(-)

diff --git a/graphistry/PlotterBase.py b/graphistry/PlotterBase.py
index 628b74991d..ceb0f5f55a 100644
--- a/graphistry/PlotterBase.py
+++ b/graphistry/PlotterBase.py
@@ -398,31 +398,15 @@ def encode_point_color(self, column,
         """Set point color with more control than bind()
 
         :param column: Data column name
-        :type column: str
-
         :param palette: Optional list of color-like strings. Ex: ["black, "#FF0", "rgb(255,255,255)" ]. Used as a gradient for continuous and round-robin for categorical.
-        :type palette: Optional[list]
-
         :param as_categorical: Interpret column values as categorical. Ex: Uses palette via round-robin when more values than palette entries.
-        :type as_categorical: Optional[bool]
-
         :param as_continuous: Interpret column values as continuous. Ex: Uses palette for an interpolation gradient when more values than palette entries.
-        :type as_continuous: Optional[bool]
-
         :param categorical_mapping: Mapping from column values to color-like strings. Ex: {"car": "red", "truck": #000"}
-        :type categorical_mapping: Optional[dict]
-
         :param default_mapping: Augment categorical_mapping with mapping for values not in categorical_mapping. Ex: default_mapping="gray".
-        :type default_mapping: Optional[str]
-
         :param for_default: Use encoding for when no user override is set. Default on.
-        :type for_default: Optional[bool]
-
         :param for_current: Use encoding as currently active. Clearing the active encoding resets it to default, which may be different. Default on.
-        :type for_current: Optional[bool]
 
         :returns: Plotter
-        :rtype: Plotter
 
         **Example: Set a palette-valued column for the color, same as bind(point_color='my_column')**
             ::
@@ -459,31 +443,15 @@ def encode_edge_color(self, column,
         """Set edge color with more control than bind()
 
         :param column: Data column name
-        :type column: str
-
         :param palette: Optional list of color-like strings. Ex: ["black, "#FF0", "rgb(255,255,255)" ]. Used as a gradient for continuous and round-robin for categorical.
-        :type palette: Optional[list]
-
         :param as_categorical: Interpret column values as categorical. Ex: Uses palette via round-robin when more values than palette entries.
-        :type as_categorical: Optional[bool]
-
         :param as_continuous: Interpret column values as continuous. Ex: Uses palette for an interpolation gradient when more values than palette entries.
-        :type as_continuous: Optional[bool]
-
         :param categorical_mapping: Mapping from column values to color-like strings. Ex: {"car": "red", "truck": #000"}
-        :type categorical_mapping: Optional[dict]
-
         :param default_mapping: Augment categorical_mapping with mapping for values not in categorical_mapping. Ex: default_mapping="gray".
-        :type default_mapping: Optional[str]
-
         :param for_default: Use encoding for when no user override is set. Default on.
-        :type for_default: Optional[bool]
-
         :param for_current: Use encoding as currently active. Clearing the active encoding resets it to default, which may be different. Default on.
-        :type for_current: Optional[bool]
 
         :returns: Plotter
-        :rtype: Plotter
 
         **Example: See encode_point_color**
         """
@@ -541,34 +509,16 @@ def encode_point_icon(self, column,
         """Set node icon with more control than bind(). Values from Font Awesome 4 such as "laptop": https://fontawesome.com/v4.7.0/icons/ , image URLs (http://...), and data URIs (data:...). When as_text=True is enabled, values are instead interpreted as raw strings.
 
         :param column: Data column name
-        :type column: str
-
         :param categorical_mapping: Mapping from column values to icon name strings. Ex: {"toyota": 'car', "ford": 'truck'}
-        :type categorical_mapping: Optional[dict]
-
         :param default_mapping: Augment categorical_mapping with mapping for values not in categorical_mapping. Ex: default_mapping=50.
-        :type default_mapping: Optional[Union[int,float]]
-
         :param for_default: Use encoding for when no user override is set. Default on.
-        :type for_default: Optional[bool]
-
         :param for_current: Use encoding as currently active. Clearing the active encoding resets it to default, which may be different. Default on.
-        :type for_current: Optional[bool]
-
         :param as_text: Values should instead be treated as raw strings, instead of icons and images. (Default False.)
-        :type as_text: Optional[bool]
-
         :param blend_mode: CSS blend mode
-        :type blend_mode: Optional[str]
-
         :param style: CSS filter properties - opacity, saturation, luminosity, grayscale, and more
-        :type style: Optional[dict]
-
         :param border: Border properties - 'width', 'color', and 'storke'
-        :type border: Optional[dict]
 
         :returns: Plotter
-        :rtype: Plotter
 
         **Example: Set a string column of icons for the point icons, same as bind(point_icon='my_column')**
             ::
@@ -608,25 +558,13 @@ def encode_edge_icon(self, column,
         """Set edge icon with more control than bind() Values from Font Awesome 4 such as "laptop": https://fontawesome.com/v4.7.0/icons/ , image URLs (http://...), and data URIs (data:...). When as_text=True is enabled, values are instead interpreted as raw strings.
 
         :param column: Data column name
-        :type column: str
-
         :param categorical_mapping: Mapping from column values to icon name strings. Ex: {"toyota": 'car', "ford": 'truck'}
-        :type categorical_mapping: Optional[dict]
-
         :param default_mapping: Augment categorical_mapping with mapping for values not in categorical_mapping. Ex: default_mapping=50.
-        :type default_mapping: Optional[Union[int,float]]
-
         :param for_default: Use encoding for when no user override is set. Default on.
-        :type for_default: Optional[bool]
-
         :param for_current: Use encoding as currently active. Clearing the active encoding resets it to default, which may be different. Default on.
-        :type for_current: Optional[bool]
-
         :param as_text: Values should instead be treated as raw strings, instead of icons and images. (Default False.)
-        :type as_text: Optional[bool]
 
         :returns: Plotter
-        :rtype: Plotter
 
         **Example: Set a string column of icons for the edge icons, same as bind(edge_icon='my_column')**
             ::
@@ -828,55 +766,23 @@ def bind(self, source=None, destination=None, node=None, edge=None,
         """Relate data attributes to graph structure and visual representation. To facilitate reuse and replayable notebooks, the binding call is chainable. Invocation does not effect the old binding: it instead returns a new Plotter instance with the new bindings added to the existing ones. Both the old and new bindings can then be used for different graphs.
 
         :param source: Attribute containing an edge's source ID
-        :type source: str
-
         :param destination: Attribute containing an edge's destination ID
-        :type destination: str
-
         :param node: Attribute containing a node's ID
-        :type node: str
-
         :param edge: Attribute containing an edge's ID
-        :type edge: str
-
         :param edge_title: Attribute overriding edge's minimized label text. By default, the edge source and destination is used.
-        :type edge_title: str
-
         :param edge_label: Attribute overriding edge's expanded label text. By default, scrollable list of attribute/value mappings.
-        :type edge_label: str
-
         :param edge_color: Attribute overriding edge's color. rgba (int64) or int32 palette index, see `palette <https://graphistry.github.io/docs/legacy/api/0.9.2/api.html#extendedpalette>`_ definitions for values. Based on Color Brewer.
-        :type edge_color: str
-
         :param edge_source_color: Attribute overriding edge's source color if no edge_color, as an rgba int64 value.
-        :type edge_source_color: str
-
         :param edge_destination_color: Attribute overriding edge's destination color if no edge_color, as an rgba int64 value.
-        :type edge_destination_color: str
-
         :param edge_weight: Attribute overriding edge weight. Default is 1. Advanced layout controls will relayout edges based on this value.
-        :type edge_weight: str
-
         :param point_title: Attribute overriding node's minimized label text. By default, the node ID is used.
-        :type point_title: str
-
         :param point_label: Attribute overriding node's expanded label text. By default, scrollable list of attribute/value mappings.
-        :type point_label: str
-
         :param point_color: Attribute overriding node's color.rgba (int64) or int32 palette index, see `palette <https://graphistry.github.io/docs/legacy/api/0.9.2/api.html#extendedpalette>`_ definitions for values. Based on Color Brewer.
-        :type point_color: str
-
         :param point_size: Attribute overriding node's size. By default, uses the node degree. The visualization will normalize point sizes and adjust dynamically using semantic zoom.
-        :type point_size: str
-
         :param point_x: Attribute overriding node's initial x position. Combine with ".settings(url_params={'play': 0}))" to create a custom layout
-        :type point_x: str
-
         :param point_y: Attribute overriding node's initial y position. Combine with ".settings(url_params={'play': 0}))" to create a custom layout
-        :type point_y: str
 
         :returns: Plotter
-        :rtype: Plotter
 
         **Example: Minimal**
 

From 06f26528e4b227fc5af738a745f035c0c49cb5b8 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 03:01:23 +0530
Subject: [PATCH 336/432] test 7

---
 docs/source/conf.py            | 3 +++
 graphistry/compute/__init__.py | 2 +-
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index c7bbd195fc..b28a305f8e 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -40,6 +40,9 @@
     "sphinx_autodoc_typehints",
 ]
 
+# mock imports
+todoc_mock_imports = ["graphistry.compute.ast.ASTObject"]
+
 #FIXME Why is sphinx/autodoc failing here?
 nitpick_ignore = [
     ('py:class', '1'),  # Ex: api : Optional[Literal[1, 3]]
diff --git a/graphistry/compute/__init__.py b/graphistry/compute/__init__.py
index 3c7c7f45e3..4f6cef58f7 100644
--- a/graphistry/compute/__init__.py
+++ b/graphistry/compute/__init__.py
@@ -1,4 +1,4 @@
 from .ComputeMixin import ComputeMixin
 from .ast import (
-    n, e_forward, e_reverse, e_undirected
+    n, e_forward, e_reverse, e_undirected, ASTObject
 )

From 254b31bc4af97ac220a2e46169baa8f54dd468d6 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 03:07:50 +0530
Subject: [PATCH 337/432] test 8

---
 graphistry/compute/chain.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/compute/chain.py b/graphistry/compute/chain.py
index a06c913018..2e4124415e 100644
--- a/graphistry/compute/chain.py
+++ b/graphistry/compute/chain.py
@@ -95,7 +95,7 @@ def chain(self: Plottable, ops: List[ASTObject]) -> Plottable:
     Return subgraph of matches according to the list of node & edge matchers
     If any matchers are named, add a correspondingly named boolean-valued column to the output
 
-    :param ops: List[ASTobject] Various node and edge matchers
+    :param ops: List[ASTObject] Various node and edge matchers
 
     :returns: Plottable
 

From 1d9722c7fdb38d6e8c8a02b01f6899a3988a9389 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 03:12:47 +0530
Subject: [PATCH 338/432] test 9

---
 graphistry/plugins/igraph.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/graphistry/plugins/igraph.py b/graphistry/plugins/igraph.py
index 0dbcb80dc3..09278743a5 100644
--- a/graphistry/plugins/igraph.py
+++ b/graphistry/plugins/igraph.py
@@ -48,8 +48,7 @@ def from_igraph(self,
     :param merge_if_existing: Whether to merge with existing node/edge dataframes (default True)
     :param merge_if_existing: bool
 
-    :returns: Plotter
-    :rtype: Plotter
+    :returns: Plottable
 
     **Example: Convert from igraph, including all node/edge properties**
         ::

From 073eaec8e0348c7c8ce0d62cc4dac3d99b339860 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 03:18:45 +0530
Subject: [PATCH 339/432] test 10

---
 docs/source/conf.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index b28a305f8e..e830756af3 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -93,6 +93,7 @@
     ('py:data', 'typing.List'),
     ('py:data', 'typing.Literal'),
     ('py:data', 'typing.Optional'),
+    ('py:data', 'typing.Callable'),
     ('py:data', 'typing.Tuple'),
     ('py:data', 'typing.Union'),
     ('py:class','pandas.core.frame.DataFrame')

From 1e8207636466453d197e3a276c2c47a33578bfa2 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 03:25:54 +0530
Subject: [PATCH 340/432] test 11

---
 docs/source/conf.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index e830756af3..20d3ede7d4 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -69,6 +69,7 @@
     ('py:class', 'graphistry.text_utils.SearchToGraphMixin'),
     ('py:class', 'graphistry.embed_utils.HeterographEmbedModuleMixin'),
     ('py:class', 'graphistry.PlotterBase.PlotterBase'),
+    ('py:class', 'graphistry.plotter.Plotter'),
     ('py:class', 'IGraph graph'),
     ('py:class', 'igraph'),
     ('py:class', 'dgl'),

From 1a68d747fdb574a6a802d7a9db37b45eb2e321af Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 03:29:45 +0530
Subject: [PATCH 341/432] test 12

---
 docs/source/conf.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 20d3ede7d4..6f46d1ea85 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -40,8 +40,6 @@
     "sphinx_autodoc_typehints",
 ]
 
-# mock imports
-todoc_mock_imports = ["graphistry.compute.ast.ASTObject"]
 
 #FIXME Why is sphinx/autodoc failing here?
 nitpick_ignore = [
@@ -69,6 +67,7 @@
     ('py:class', 'graphistry.text_utils.SearchToGraphMixin'),
     ('py:class', 'graphistry.embed_utils.HeterographEmbedModuleMixin'),
     ('py:class', 'graphistry.PlotterBase.PlotterBase'),
+    ('py:class', 'graphistry.compute.ast.ASTObject'),
     ('py:class', 'graphistry.plotter.Plotter'),
     ('py:class', 'IGraph graph'),
     ('py:class', 'igraph'),

From 8320dd505082f632a9c121d9e0403170386bc093 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 03:37:16 +0530
Subject: [PATCH 342/432] test 13

---
 docs/source/conf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 6f46d1ea85..12b39f3270 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -68,7 +68,7 @@
     ('py:class', 'graphistry.embed_utils.HeterographEmbedModuleMixin'),
     ('py:class', 'graphistry.PlotterBase.PlotterBase'),
     ('py:class', 'graphistry.compute.ast.ASTObject'),
-    ('py:class', 'graphistry.plotter.Plotter'),
+    ('py:class', 'Plotter'),
     ('py:class', 'IGraph graph'),
     ('py:class', 'igraph'),
     ('py:class', 'dgl'),

From a1941ba0484cf6584178b67e30f3122221070caf Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 03:45:05 +0530
Subject: [PATCH 343/432] test 14

---
 graphistry/PlotterBase.py      | 2 +-
 graphistry/compute/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphistry/PlotterBase.py b/graphistry/PlotterBase.py
index ceb0f5f55a..30ac752e02 100644
--- a/graphistry/PlotterBase.py
+++ b/graphistry/PlotterBase.py
@@ -959,7 +959,7 @@ def edges(self, edges: Union[Callable, Any], source=None, destination=None, edge
         If a callable, will be called with current Plotter and whatever positional+named arguments
 
         :param edges: Edges and their attributes, or transform from Plotter to edges
-        :type edges: Pandas dataframe, NetworkX graph, or IGraph graph.
+        :type edges: Pandas dataframe, NetworkX graph, or IGraph graph
 
         :returns: Plotter
         :rtype: Plotter
diff --git a/graphistry/compute/__init__.py b/graphistry/compute/__init__.py
index 4f6cef58f7..3c7c7f45e3 100644
--- a/graphistry/compute/__init__.py
+++ b/graphistry/compute/__init__.py
@@ -1,4 +1,4 @@
 from .ComputeMixin import ComputeMixin
 from .ast import (
-    n, e_forward, e_reverse, e_undirected, ASTObject
+    n, e_forward, e_reverse, e_undirected
 )

From 4361ae6aa1dd11be4f6b597a648ca3c8dc4d2853 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 04:28:31 +0530
Subject: [PATCH 344/432] final fix

---
 graphistry/PlotterBase.py     | 94 +++++++++++++++++++++++++++++++++++
 graphistry/compute/chain.py   |  3 +-
 graphistry/plugins/cugraph.py | 31 ++++++++++++
 graphistry/plugins/igraph.py  | 28 ++++++++++-
 4 files changed, 154 insertions(+), 2 deletions(-)

diff --git a/graphistry/PlotterBase.py b/graphistry/PlotterBase.py
index 30ac752e02..9c64d0e1f5 100644
--- a/graphistry/PlotterBase.py
+++ b/graphistry/PlotterBase.py
@@ -398,15 +398,31 @@ def encode_point_color(self, column,
         """Set point color with more control than bind()
 
         :param column: Data column name
+        :type column: str
+
         :param palette: Optional list of color-like strings. Ex: ["black, "#FF0", "rgb(255,255,255)" ]. Used as a gradient for continuous and round-robin for categorical.
+        :type palette: Optional[list]
+
         :param as_categorical: Interpret column values as categorical. Ex: Uses palette via round-robin when more values than palette entries.
+        :type as_categorical: Optional[bool]
+
         :param as_continuous: Interpret column values as continuous. Ex: Uses palette for an interpolation gradient when more values than palette entries.
+        :type as_continuous: Optional[bool]
+
         :param categorical_mapping: Mapping from column values to color-like strings. Ex: {"car": "red", "truck": #000"}
+        :type categorical_mapping: Optional[dict]
+
         :param default_mapping: Augment categorical_mapping with mapping for values not in categorical_mapping. Ex: default_mapping="gray".
+        :type default_mapping: Optional[str]
+
         :param for_default: Use encoding for when no user override is set. Default on.
+        :type for_default: Optional[bool]
+
         :param for_current: Use encoding as currently active. Clearing the active encoding resets it to default, which may be different. Default on.
+        :type for_current: Optional[bool]
 
         :returns: Plotter
+        :rtype: Plotter
 
         **Example: Set a palette-valued column for the color, same as bind(point_color='my_column')**
             ::
@@ -443,15 +459,31 @@ def encode_edge_color(self, column,
         """Set edge color with more control than bind()
 
         :param column: Data column name
+        :type column: str
+
         :param palette: Optional list of color-like strings. Ex: ["black, "#FF0", "rgb(255,255,255)" ]. Used as a gradient for continuous and round-robin for categorical.
+        :type palette: Optional[list]
+
         :param as_categorical: Interpret column values as categorical. Ex: Uses palette via round-robin when more values than palette entries.
+        :type as_categorical: Optional[bool]
+
         :param as_continuous: Interpret column values as continuous. Ex: Uses palette for an interpolation gradient when more values than palette entries.
+        :type as_continuous: Optional[bool]
+
         :param categorical_mapping: Mapping from column values to color-like strings. Ex: {"car": "red", "truck": #000"}
+        :type categorical_mapping: Optional[dict]
+
         :param default_mapping: Augment categorical_mapping with mapping for values not in categorical_mapping. Ex: default_mapping="gray".
+        :type default_mapping: Optional[str]
+
         :param for_default: Use encoding for when no user override is set. Default on.
+        :type for_default: Optional[bool]
+
         :param for_current: Use encoding as currently active. Clearing the active encoding resets it to default, which may be different. Default on.
+        :type for_current: Optional[bool]
 
         :returns: Plotter
+        :rtype: Plotter
 
         **Example: See encode_point_color**
         """
@@ -509,16 +541,34 @@ def encode_point_icon(self, column,
         """Set node icon with more control than bind(). Values from Font Awesome 4 such as "laptop": https://fontawesome.com/v4.7.0/icons/ , image URLs (http://...), and data URIs (data:...). When as_text=True is enabled, values are instead interpreted as raw strings.
 
         :param column: Data column name
+        :type column: str
+
         :param categorical_mapping: Mapping from column values to icon name strings. Ex: {"toyota": 'car', "ford": 'truck'}
+        :type categorical_mapping: Optional[dict]
+
         :param default_mapping: Augment categorical_mapping with mapping for values not in categorical_mapping. Ex: default_mapping=50.
+        :type default_mapping: Optional[Union[int,float]]
+
         :param for_default: Use encoding for when no user override is set. Default on.
+        :type for_default: Optional[bool]
+
         :param for_current: Use encoding as currently active. Clearing the active encoding resets it to default, which may be different. Default on.
+        :type for_current: Optional[bool]
+
         :param as_text: Values should instead be treated as raw strings, instead of icons and images. (Default False.)
+        :type as_text: Optional[bool]
+
         :param blend_mode: CSS blend mode
+        :type blend_mode: Optional[str]
+
         :param style: CSS filter properties - opacity, saturation, luminosity, grayscale, and more
+        :type style: Optional[dict]
+
         :param border: Border properties - 'width', 'color', and 'storke'
+        :type border: Optional[dict]
 
         :returns: Plotter
+        :rtype: Plotter
 
         **Example: Set a string column of icons for the point icons, same as bind(point_icon='my_column')**
             ::
@@ -558,13 +608,25 @@ def encode_edge_icon(self, column,
         """Set edge icon with more control than bind() Values from Font Awesome 4 such as "laptop": https://fontawesome.com/v4.7.0/icons/ , image URLs (http://...), and data URIs (data:...). When as_text=True is enabled, values are instead interpreted as raw strings.
 
         :param column: Data column name
+        :type column: str
+
         :param categorical_mapping: Mapping from column values to icon name strings. Ex: {"toyota": 'car', "ford": 'truck'}
+        :type categorical_mapping: Optional[dict]
+
         :param default_mapping: Augment categorical_mapping with mapping for values not in categorical_mapping. Ex: default_mapping=50.
+        :type default_mapping: Optional[Union[int,float]]
+
         :param for_default: Use encoding for when no user override is set. Default on.
+        :type for_default: Optional[bool]
+
         :param for_current: Use encoding as currently active. Clearing the active encoding resets it to default, which may be different. Default on.
+        :type for_current: Optional[bool]
+
         :param as_text: Values should instead be treated as raw strings, instead of icons and images. (Default False.)
+        :type as_text: Optional[bool]
 
         :returns: Plotter
+        :rtype: Plotter
 
         **Example: Set a string column of icons for the edge icons, same as bind(edge_icon='my_column')**
             ::
@@ -766,23 +828,55 @@ def bind(self, source=None, destination=None, node=None, edge=None,
         """Relate data attributes to graph structure and visual representation. To facilitate reuse and replayable notebooks, the binding call is chainable. Invocation does not effect the old binding: it instead returns a new Plotter instance with the new bindings added to the existing ones. Both the old and new bindings can then be used for different graphs.
 
         :param source: Attribute containing an edge's source ID
+        :type source: str
+
         :param destination: Attribute containing an edge's destination ID
+        :type destination: str
+
         :param node: Attribute containing a node's ID
+        :type node: str
+
         :param edge: Attribute containing an edge's ID
+        :type edge: str
+
         :param edge_title: Attribute overriding edge's minimized label text. By default, the edge source and destination is used.
+        :type edge_title: str
+
         :param edge_label: Attribute overriding edge's expanded label text. By default, scrollable list of attribute/value mappings.
+        :type edge_label: str
+
         :param edge_color: Attribute overriding edge's color. rgba (int64) or int32 palette index, see `palette <https://graphistry.github.io/docs/legacy/api/0.9.2/api.html#extendedpalette>`_ definitions for values. Based on Color Brewer.
+        :type edge_color: str
+
         :param edge_source_color: Attribute overriding edge's source color if no edge_color, as an rgba int64 value.
+        :type edge_source_color: str
+
         :param edge_destination_color: Attribute overriding edge's destination color if no edge_color, as an rgba int64 value.
+        :type edge_destination_color: str
+
         :param edge_weight: Attribute overriding edge weight. Default is 1. Advanced layout controls will relayout edges based on this value.
+        :type edge_weight: str
+
         :param point_title: Attribute overriding node's minimized label text. By default, the node ID is used.
+        :type point_title: str
+
         :param point_label: Attribute overriding node's expanded label text. By default, scrollable list of attribute/value mappings.
+        :type point_label: str
+
         :param point_color: Attribute overriding node's color.rgba (int64) or int32 palette index, see `palette <https://graphistry.github.io/docs/legacy/api/0.9.2/api.html#extendedpalette>`_ definitions for values. Based on Color Brewer.
+        :type point_color: str
+
         :param point_size: Attribute overriding node's size. By default, uses the node degree. The visualization will normalize point sizes and adjust dynamically using semantic zoom.
+        :type point_size: str
+
         :param point_x: Attribute overriding node's initial x position. Combine with ".settings(url_params={'play': 0}))" to create a custom layout
+        :type point_x: str
+
         :param point_y: Attribute overriding node's initial y position. Combine with ".settings(url_params={'play': 0}))" to create a custom layout
+        :type point_y: str
 
         :returns: Plotter
+        :rtype: Plotter
 
         **Example: Minimal**
 
diff --git a/graphistry/compute/chain.py b/graphistry/compute/chain.py
index 2e4124415e..4920b74c9f 100644
--- a/graphistry/compute/chain.py
+++ b/graphistry/compute/chain.py
@@ -97,7 +97,8 @@ def chain(self: Plottable, ops: List[ASTObject]) -> Plottable:
 
     :param ops: List[ASTObject] Various node and edge matchers
 
-    :returns: Plottable
+    :returns: Plotter
+    :rtype: Plotter
 
     **Example: Find nodes of some type**
 
diff --git a/graphistry/plugins/cugraph.py b/graphistry/plugins/cugraph.py
index 0930efed42..da68452d11 100644
--- a/graphistry/plugins/cugraph.py
+++ b/graphistry/plugins/cugraph.py
@@ -222,13 +222,25 @@ def compute_cugraph(
     """Run cugraph algorithm on graph. For algorithm parameters, see cuGraph docs.
 
     :param alg: algorithm name
+    :type alg: str
+
     :param out_col: node table output column name, defaults to alg param
+    :type out_col: Optional[str]
+
     :param params: algorithm parameters passed to cuGraph as kwargs
+    :type params: dict
+
     :param kind: kind of cugraph to use
+    :type kind: CuGraphKind
+
     :param directed: whether graph is directed
+    :type directed: bool
+
     :param G: cugraph graph to use; if None, use self
+    :type G: Optional[cugraph.Graph]
 
     :return: Plottable
+    :rtype: Plottable
     
     **Example: Pagerank**
         ::
@@ -325,15 +337,34 @@ def layout_cugraph(
     """Layout the grpah using a cuGraph algorithm. For a list of layouts, see cugraph documentation (currently just force_atlas2).
 
     :param layout: Name of an cugraph layout method like `force_atlas2`
+    :type layout: str
+
     :param params: Any named parameters to pass to the underlying cugraph method
+    :type params: dict
+
     :param kind: The kind of cugraph Graph
+    :type kind: CuGraphKind
+
     :param directed: During the to_cugraph conversion, whether to be directed. (default True)
+    :type directed: bool
+
     :param G: The cugraph graph (G) to layout. If None, the current graph is used.
+    :type G: Optional[Any]
+
     :param bind_position: Whether to call bind(point_x=, point_y=) (default True)
+    :type bind_position: bool
+
     :param x_out_col: Attribute to write x position to. (default 'x')
+    :type x_out_col: str
+
     :param y_out_col: Attribute to write x position to. (default 'y')
+    :type y_out_col: str
+
     :param play: If defined, set settings(url_params={'play': play}). (default 0)
+    :type play: Optional[str]
+
     :returns: Plotter
+    :rtype: Plotter
 
     **Example: ForceAtlas2 layout**
         ::
diff --git a/graphistry/plugins/igraph.py b/graphistry/plugins/igraph.py
index 09278743a5..b7bdc0d405 100644
--- a/graphistry/plugins/igraph.py
+++ b/graphistry/plugins/igraph.py
@@ -48,7 +48,7 @@ def from_igraph(self,
     :param merge_if_existing: Whether to merge with existing node/edge dataframes (default True)
     :param merge_if_existing: bool
 
-    :returns: Plottable
+    :returns: Plotter
 
     **Example: Convert from igraph, including all node/edge properties**
         ::
@@ -293,12 +293,22 @@ def compute_igraph(
     """Enrich or replace graph using igraph methods
 
     :param alg: Name of an igraph.Graph method like `pagerank`
+    :type alg: str
+
     :param out_col: For algorithms that generate a node attribute column, `out_col` is the desired output column name. When `None`, use the algorithm's name. (default None)
+    :type out_col: Optional[str]
+
     :param directed: During the to_igraph conversion, whether to be directed. If None, try directed and then undirected. (default None)
+    :type directed: Optional[bool]
+
     :param use_vids: During the to_igraph conversion, whether to interpret IDs as igraph vertex IDs (non-negative integers) or arbitrary values (False, default)
+    :type use_vids: bool
+
     :param params: Any named parameters to pass to the underlying igraph method
+    :type params: dict
 
     :returns: Plotter
+    :rtype: Plotter
 
     **Example: Pagerank**
         ::
@@ -414,15 +424,31 @@ def layout_igraph(
     """Compute graph layout using igraph algorithm. For a list of layouts, see layout_algs or igraph documentation.
 
     :param layout: Name of an igraph.Graph.layout method like `sugiyama`
+    :type layout: str
+
     :param directed: During the to_igraph conversion, whether to be directed. If None, try directed and then undirected. (default None)
+    :type directed: Optional[bool]
+
     :param use_vids: Whether to use igraph vertex ids (non-negative integers) or arbitary node ids (False, default)
+    :type use_vids: bool
+
     :param bind_position: Whether to call bind(point_x=, point_y=) (default True)
+    :type bind_position: bool
+
     :param x_out_col: Attribute to write x position to. (default 'x')
+    :type x_out_col: str
+
     :param y_out_col: Attribute to write x position to. (default 'y')
+    :type y_out_col: str
+
     :param play: If defined, set settings(url_params={'play': play}). (default 0)
+    :type play: Optional[str]
+
     :param params: Any named parameters to pass to the underlying igraph method
+    :type params: dict
 
     :returns: Plotter
+    :rtype: Plotter
 
     **Example: Sugiyama layout**
         ::

From db8c22855643ab4d5631ad4a65e3054cf8ed80cb Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 04:33:51 +0530
Subject: [PATCH 345/432] final fix 1

---
 docs/source/conf.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 12b39f3270..0d639ff942 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -69,6 +69,8 @@
     ('py:class', 'graphistry.PlotterBase.PlotterBase'),
     ('py:class', 'graphistry.compute.ast.ASTObject'),
     ('py:class', 'Plotter'),
+    ('py:class', 'Plottable'),
+    ('py:class', 'CuGraphKind'),
     ('py:class', 'IGraph graph'),
     ('py:class', 'igraph'),
     ('py:class', 'dgl'),

From 5e9a577d2084907e4089be437f3e39e275d0fe42 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Fri, 7 Apr 2023 04:41:40 +0530
Subject: [PATCH 346/432] final fix 2

---
 docs/source/conf.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 0d639ff942..e8a0c2f36c 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -71,6 +71,7 @@
     ('py:class', 'Plotter'),
     ('py:class', 'Plottable'),
     ('py:class', 'CuGraphKind'),
+    ('py:class', 'cugraph.Graph'),
     ('py:class', 'IGraph graph'),
     ('py:class', 'igraph'),
     ('py:class', 'dgl'),

From 52506b1f865a63ebafe9c59f3ccf4efdf574326c Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Thu, 6 Apr 2023 19:25:18 -0400
Subject: [PATCH 347/432] fix(conf.py): added converter for badges

---
 docs/source/conf.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index e8a0c2f36c..a9e22f14e7 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -38,6 +38,7 @@
     #'sphinx.ext.intersphinx',
     "sphinx.ext.ifconfig",
     "sphinx_autodoc_typehints",
+    "sphinx.ext.imgconverter"
 ]
 
 

From 7114bc8af608efe6476b0f4d23b21e1a0cfc7328 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Thu, 6 Apr 2023 19:29:30 -0400
Subject: [PATCH 348/432] fix(conf.py): removed img converter

---
 docs/source/conf.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index a9e22f14e7..1f47612c1a 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -37,8 +37,7 @@
     #'sphinx.ext.autosummary',
     #'sphinx.ext.intersphinx',
     "sphinx.ext.ifconfig",
-    "sphinx_autodoc_typehints",
-    "sphinx.ext.imgconverter"
+    "sphinx_autodoc_typehints"
 ]
 
 

From 224351f55798287cc23f9d99c9a4d6c5a8805cdb Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Thu, 6 Apr 2023 20:02:27 -0400
Subject: [PATCH 349/432] test(conf.py): using only directive for ci testing

---
 docs/source/index.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/index.rst b/docs/source/index.rst
index ce0734f357..ddbf98922f 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,5 +1,6 @@
 PyGraphistry: Explore Relationships
 ========================================
+.. only:: html
 .. image:: https://readthedocs.org/projects/pygraphistry/badge/?version=latest
     :target: https://pygraphistry.readthedocs.io/en/latest/?badge=latest
     :alt: Documentation Status

From cf25d29cdd56641fbed6cdcce7e941e8dd053151 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Thu, 6 Apr 2023 20:07:34 -0400
Subject: [PATCH 350/432] test(conf.py): testing only directive

---
 docs/source/index.rst | 55 ++++++++++++++++++++++---------------------
 1 file changed, 28 insertions(+), 27 deletions(-)

diff --git a/docs/source/index.rst b/docs/source/index.rst
index ddbf98922f..e8943f800f 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,44 +1,45 @@
 PyGraphistry: Explore Relationships
 ========================================
 .. only:: html
-.. image:: https://readthedocs.org/projects/pygraphistry/badge/?version=latest
-    :target: https://pygraphistry.readthedocs.io/en/latest/?badge=latest
-    :alt: Documentation Status
 
+   .. image:: https://readthedocs.org/projects/pygraphistry/badge/?version=latest
+      :target: https://pygraphistry.readthedocs.io/en/latest/?badge=latest
+      :alt: Documentation Status
 
-.. image:: https://github.com/graphistry/pygraphistry/workflows/CI%20Tests/badge.svg
-    :target: https://github.com/graphistry/pygraphistry/workflows/CI%20Tests/badge.svg
-    :alt: Build Status
 
+   .. image:: https://github.com/graphistry/pygraphistry/workflows/CI%20Tests/badge.svg
+      :target: https://github.com/graphistry/pygraphistry/workflows/CI%20Tests/badge.svg
+      :alt: Build Status
 
-.. image:: https://github.com/graphistry/pygraphistry/workflows/CodeQL/badge.svg
-    :target: https://github.com/graphistry/pygraphistry/actions?query=workflow%3ACodeQL
-    :alt: CodeQL Status
 
-.. image:: https://img.shields.io/pypi/v/graphistry.svg
-    :target: https://pypi.python.org/pypi/graphistry
-    :alt: PyPi Status
+   .. image:: https://github.com/graphistry/pygraphistry/workflows/CodeQL/badge.svg
+      :target: https://github.com/graphistry/pygraphistry/actions?query=workflow%3ACodeQL
+      :alt: CodeQL Status
 
-.. image:: https://img.shields.io/pypi/dm/graphistry
-    :target: https://img.shields.io/pypi/dm/graphistry
-    :alt: PyPi Downloads
+   .. image:: https://img.shields.io/pypi/v/graphistry.svg
+      :target: https://pypi.python.org/pypi/graphistry
+      :alt: PyPi Status
 
+   .. image:: https://img.shields.io/pypi/dm/graphistry
+      :target: https://img.shields.io/pypi/dm/graphistry
+      :alt: PyPi Downloads
 
-.. image:: https://img.shields.io/pypi/l/graphistry.svg
-    :target: https://pypi.python.org/pypi/graphistry
-    :alt: License
 
-.. image:: https://img.shields.io/uptimerobot/status/m787548531-e9c7b7508fc76fea927e2313?label=hub.graphistry.com
-    :target: https://img.shields.io/uptimerobot/status/m787548531-e9c7b7508fc76fea927e2313?label=hub.graphistry.com
-    :alt: License
+   .. image:: https://img.shields.io/pypi/l/graphistry.svg
+      :target: https://pypi.python.org/pypi/graphistry
+      :alt: License
 
-.. image:: https://img.shields.io/badge/slack-Graphistry%20chat-orange.svg?logo=slack
-      :target: https://join.slack.com/t/graphistry-community/shared_invite/zt-53ik36w2-fpP0Ibjbk7IJuVFIRSnr6g
-      :alt: Slack
+   .. image:: https://img.shields.io/uptimerobot/status/m787548531-e9c7b7508fc76fea927e2313?label=hub.graphistry.com
+      :target: https://img.shields.io/uptimerobot/status/m787548531-e9c7b7508fc76fea927e2313?label=hub.graphistry.com
+      :alt: License
 
-.. image:: https://img.shields.io/twitter/follow/graphistry
-      :target: https://twitter.com/graphistry
-      :alt: Twitter
+   .. image:: https://img.shields.io/badge/slack-Graphistry%20chat-orange.svg?logo=slack
+         :target: https://join.slack.com/t/graphistry-community/shared_invite/zt-53ik36w2-fpP0Ibjbk7IJuVFIRSnr6g
+         :alt: Slack
+
+   .. image:: https://img.shields.io/twitter/follow/graphistry
+         :target: https://twitter.com/graphistry
+         :alt: Twitter
 
 .. Quickstart:
 .. `Read our tutorial <https://github.com/graphistry/pygraphistry/blob/master/README.md>`_

From b17af60aed4d2730fd78f0689912c2dff2d9108a Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Thu, 6 Apr 2023 23:23:42 -0400
Subject: [PATCH 351/432] fix(docstr): removed slack badge

---
 docs/source/index.rst | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/source/index.rst b/docs/source/index.rst
index e8943f800f..2b6cf75d44 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -33,9 +33,9 @@ PyGraphistry: Explore Relationships
       :target: https://img.shields.io/uptimerobot/status/m787548531-e9c7b7508fc76fea927e2313?label=hub.graphistry.com
       :alt: License
 
-   .. image:: https://img.shields.io/badge/slack-Graphistry%20chat-orange.svg?logo=slack
-         :target: https://join.slack.com/t/graphistry-community/shared_invite/zt-53ik36w2-fpP0Ibjbk7IJuVFIRSnr6g
-         :alt: Slack
+   .. .. image:: https://img.shields.io/badge/slack-Graphistry%20chat-orange.svg?logo=slack
+   ..       :target: https://join.slack.com/t/graphistry-community/shared_invite/zt-53ik36w2-fpP0Ibjbk7IJuVFIRSnr6g
+   ..       :alt: Slack
 
    .. image:: https://img.shields.io/twitter/follow/graphistry
          :target: https://twitter.com/graphistry

From 385a0ad4409bd6f26cc43d7bd68ab0d4651f63f0 Mon Sep 17 00:00:00 2001
From: Desirree Adegunle <87389186+dess890@users.noreply.github.com>
Date: Thu, 6 Apr 2023 23:32:18 -0400
Subject: [PATCH 352/432] test(docstr): testing to see if uptime is failing

---
 docs/source/index.rst | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/source/index.rst b/docs/source/index.rst
index 2b6cf75d44..9b10c2c91c 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -29,9 +29,9 @@ PyGraphistry: Explore Relationships
       :target: https://pypi.python.org/pypi/graphistry
       :alt: License
 
-   .. image:: https://img.shields.io/uptimerobot/status/m787548531-e9c7b7508fc76fea927e2313?label=hub.graphistry.com
-      :target: https://img.shields.io/uptimerobot/status/m787548531-e9c7b7508fc76fea927e2313?label=hub.graphistry.com
-      :alt: License
+   .. .. image:: https://img.shields.io/uptimerobot/status/m787548531-e9c7b7508fc76fea927e2313?label=hub.graphistry.com
+   ..    :target: https://img.shields.io/uptimerobot/status/m787548531-e9c7b7508fc76fea927e2313?label=hub.graphistry.com
+   ..    :alt: License
 
    .. .. image:: https://img.shields.io/badge/slack-Graphistry%20chat-orange.svg?logo=slack
    ..       :target: https://join.slack.com/t/graphistry-community/shared_invite/zt-53ik36w2-fpP0Ibjbk7IJuVFIRSnr6g

From 7a74dc870a01c07634de83d5f71432d04b84f1f9 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <apple@dc-m1.local>
Date: Fri, 7 Apr 2023 15:51:54 +0800
Subject: [PATCH 353/432] cudf edge reqs

---
 graphistry/feature_utils.py | 66 +++++++++++++++++++++++--------------
 1 file changed, 41 insertions(+), 25 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 84c65675fd..0250c2eb52 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -60,7 +60,7 @@
         GapEncoder = Any
         SimilarityEncoder = Any
     try:
-        from sklearn.preprocessing import FunctionTransformer
+        from cuml.preprocessing import FunctionTransformer
         from sklearn.base import BaseEstimator, TransformerMixin
     except:
         FunctionTransformer = Any
@@ -328,11 +328,10 @@ def remove_node_column_from_symbolic(X_symbolic, node):
             logger.info(f"Removing `{node}` from input X_symbolic list")
             X_symbolic.remove(node)
         return X_symbolic
-    if isinstance(X_symbolic, pd.DataFrame):
+    if isinstance(X_symbolic, pd.DataFrame) or 'cudf' in str(getmodule(X_symbolic)):
         logger.info(f"Removing `{node}` from input X_symbolic DataFrame")
         return X_symbolic.drop(columns=[node], errors="ignore")
 
-
 def remove_internal_namespace_if_present(df: pd.DataFrame):
     """Some tranformations below add columns to the DataFrame, this method removes them before featurization will not drop if suffix is added during UMAP-ing
 
@@ -609,11 +608,19 @@ def get_preprocessing_pipeline(
     :return: scaled array, imputer instances or None, scaler instance or None
     """
     from sklearn.preprocessing import (
+        # FunctionTransformer,
+        # KBinsDiscretizer,
+        # MinMaxScaler,
+        MultiLabelBinarizer,
+        QuantileTransformer, 
+        # RobustScaler,
+        # StandardScaler,
+    )
+    from cuml.preprocessing import (
         FunctionTransformer,
         KBinsDiscretizer,
         MinMaxScaler,
-        MultiLabelBinarizer,
-        QuantileTransformer,
+        # QuantileTransformer,  ## cuml 23 only
         RobustScaler,
         StandardScaler,
     )
@@ -886,7 +893,7 @@ def __call__(self, *args, **kwargs):
 def get_numeric_transformers(ndf, y=None):
     # numeric selector needs to embody memorization of columns
     # for later .transform consistency.
-    from sklearn.preprocessing import FunctionTransformer
+    from cuml.preprocessing import FunctionTransformer
     label_encoder = False
     data_encoder = False
     y_ = y
@@ -947,8 +954,9 @@ def process_dirty_dataframes(
     if feature_engine == 'dirty_cat':
         from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
     elif feature_engine == 'cu_cat':
+        # assert_cuml_cucat() ## tried to use this rather than importing below
         from cu_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
-    from sklearn.preprocessing import FunctionTransformer
+    from cuml.preprocessing import FunctionTransformer
     t = time()
 
     if not is_dataframe_all_numeric(ndf):
@@ -963,7 +971,6 @@ def process_dirty_dataframes(
         )
 
         logger.info(":: Encoding DataFrame might take a few minutes ------")
-        
         X_enc = data_encoder.fit_transform(ndf, y)
         X_enc = make_array(X_enc)
 
@@ -992,9 +999,14 @@ def process_dirty_dataframes(
             )
             X_enc = X_enc.fillna(0.0)  # TODO -- this is a hack in cuml version
         elif 'cudf.core.dataframe' in str(getmodule(ndf)):
-            X_enc = cudf.DataFrame(
-                X_enc, columns=features_transformed, index=ndf.index
-            )
+            import cudf
+            X_enc = cudf.DataFrame.from_arrow(X_enc)
+            X_enc.index = ndf.index
+            # features_transformed=np.array([item.as_py() for item in features_transformed.key()])
+            # X_enc.columns = features_transformed.as_py()
+            #  = features_transformed #.to_numpy() ##error suggests this -- not working
+
+            
         #X_enc = X_enc.fillna(0.0)  # TODO -- this is a hack in cuml version
     else:
         logger.info("-*-*- DataFrame is completely numeric")
@@ -1219,7 +1231,6 @@ def process_nodes_dataframes(
     logger.debug(
         f"--The entire Encoding process took {(time()-t)/60:.2f} minutes"
     )
-
     X_encs, y_encs, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
         X_enc,
         y_enc,
@@ -1234,7 +1245,6 @@ def process_nodes_dataframes(
         strategy=strategy,
         keep_n_decimals=keep_n_decimals,
     )
-
     return (
         X_enc,
         y_enc,
@@ -1309,7 +1319,7 @@ def encode_edges(edf, src, dst, mlb, fit=False):
         edf (pd.DataFrame): edge dataframe
         src (string): source column
         dst (string): destination column
-        mlb (sklearn): multilabelBinarizer
+        mlb (sklearn): multilabelBinarizer ##not in cuml yet so cast down to pandas
         fit (bool, optional): If true, fits multilabelBinarizer. Defaults to False.
     Returns:
         tuple: pd.DataFrame, multilabelBinarizer
@@ -1319,16 +1329,19 @@ def encode_edges(edf, src, dst, mlb, fit=False):
 
     logger.debug("Encoding Edges using MultiLabelBinarizer")
     edf_type = str(getmodule(edf))
-    if 'cudf.core.dataframe' in edf_type:
-        source = edf.to_pandas()[src]
-        destination = edf.to_pandas()[dst]
-    else:
-        source = edf[src]
-        destination = edf[dst]
-    if fit:
+    source = edf[src]
+    destination = edf[dst]
+    source_dtype = str(getmodule(source))
+                       
+    if fit and 'cudf' not in source_dtype:
         T = mlb.fit_transform(zip(source, destination))
-    else:
+    elif fit and 'cudf' in source_dtype:
+        T = mlb.fit_transform(zip(source.to_pandas(), destination.to_pandas()))
+    elif not fit and 'cudf' not in source_dtype:
         T = mlb.transform(zip(source, destination))
+    elif not fit and 'cudf' in source_dtype:
+        T = mlb.transform(zip(source.to_pandas(), destination.to_pandas()))
+                                   
     T = 1.0 * T  # coerce to float
     columns = [
         str(k) for k in mlb.classes_
@@ -1336,6 +1349,7 @@ def encode_edges(edf, src, dst, mlb, fit=False):
     mlb.get_feature_names_out = callThrough(columns)
     mlb.columns_ = [src, dst]
     if 'cudf.core.dataframe' in edf_type:
+        import cudf
         T = cudf.DataFrame(T, columns=columns, index=edf.index)
     else:
         T = pd.DataFrame(T, columns=columns, index=edf.index)
@@ -1756,7 +1770,6 @@ def _hecho(self, res):
         logger.info("\n-- Setting Encoder Parts from Fit ::")
         logger.info(f'Feature Columns In: {self.feature_names_in}')
         logger.info(f'Target Columns In: {self.target_names_in}')
-
         for name, value in zip(self.res_names, res):
             if name not in ["X_enc", "y_enc"]:
                 logger.info("-" * 90)
@@ -2033,11 +2046,14 @@ def _featurize_nodes(
         res = self.copy() 
         ndf = res._nodes
         node = res._node
-
+        # print(['ndf:',ndf])
+        # print(['X:',X])
+        # print(['node:',res._node])
+        
         if remove_node_column:
             ndf = remove_node_column_from_symbolic(ndf, node)
             X = remove_node_column_from_symbolic(X, node)
-
+        
         if ndf is None:
             logger.info(
                 "! Materializing Nodes and setting `embedding=True`"

From 6b0056a0def08d1f33ff830e09ef119e5c928481 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <apple@dc-m1.local>
Date: Sat, 8 Apr 2023 07:50:30 +0800
Subject: [PATCH 354/432] cu_cat refactor

---
 graphistry/feature_utils.py | 49 ++++++++++++++++++++-----------------
 1 file changed, 26 insertions(+), 23 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index af1bc24ef1..37747fce74 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -328,11 +328,10 @@ def remove_node_column_from_symbolic(X_symbolic, node):
             logger.info(f"Removing `{node}` from input X_symbolic list")
             X_symbolic.remove(node)
         return X_symbolic
-    if isinstance(X_symbolic, pd.DataFrame):
+    if isinstance(X_symbolic, pd.DataFrame) or 'cudf' in str(getmodule(X_symbolic)):
         logger.info(f"Removing `{node}` from input X_symbolic DataFrame")
         return X_symbolic.drop(columns=[node], errors="ignore")
 
-
 def remove_internal_namespace_if_present(df: pd.DataFrame):
     """Some tranformations below add columns to the DataFrame, this method removes them before featurization will not drop if suffix is added during UMAP-ing
 
@@ -955,6 +954,7 @@ def process_dirty_dataframes(
     if feature_engine == 'dirty_cat':
         from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
     elif feature_engine == 'cu_cat':
+        # assert_cuml_cucat() ## tried to use this rather than importing below
         from cu_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
     from cuml.preprocessing import FunctionTransformer
     t = time()
@@ -993,21 +993,17 @@ def process_dirty_dataframes(
         #  now just set the feature names, since dirty cat changes them in
         #  a weird way...
         data_encoder.get_feature_names_out = callThrough(features_transformed)
-        if 'cudf.core.dataframe' not in str(getmodule(ndf)):
+        if 'cudf' not in str(getmodule(ndf)):
             X_enc = pd.DataFrame(
                 X_enc, columns=features_transformed, index=ndf.index
             )
             X_enc = X_enc.fillna(0.0)  # TODO -- this is a hack in cuml version
-        elif 'cudf.core.dataframe' in str(getmodule(ndf)):
-            import cudf
-            X_enc = cudf.DataFrame.from_arrow(X_enc)
+        elif 'cudf' in str(getmodule(ndf)):
+            # X_enc = cudf.DataFrame.from_arrow(X_enc)
             X_enc.index = ndf.index
-            # features_transformed=np.array([item.as_py() for item in features_transformed.key()])
-            # X_enc.columns = features_transformed.as_py()
-            #  = features_transformed #.to_numpy() ##error suggests this -- not working
+            X_enc.columns = np.array(features_transformed)
+            X_enc = X_enc.fillna(0.0)
 
-            
-        #X_enc = X_enc.fillna(0.0)  # TODO -- this is a hack in cuml version
     else:
         logger.info("-*-*- DataFrame is completely numeric")
         X_enc, _, data_encoder, _ = get_numeric_transformers(ndf, None)
@@ -1319,7 +1315,7 @@ def encode_edges(edf, src, dst, mlb, fit=False):
         edf (pd.DataFrame): edge dataframe
         src (string): source column
         dst (string): destination column
-        mlb (sklearn): multilabelBinarizer
+        mlb (sklearn): multilabelBinarizer ##not in cuml yet so cast down to pandas
         fit (bool, optional): If true, fits multilabelBinarizer. Defaults to False.
     Returns:
         tuple: pd.DataFrame, multilabelBinarizer
@@ -1329,23 +1325,27 @@ def encode_edges(edf, src, dst, mlb, fit=False):
 
     logger.debug("Encoding Edges using MultiLabelBinarizer")
     edf_type = str(getmodule(edf))
-    if 'cudf.core.dataframe' in edf_type:
-        source = edf.to_pandas()[src]
-        destination = edf.to_pandas()[dst]
-    else:
-        source = edf[src]
-        destination = edf[dst]
-    if fit:
+    source = edf[src]
+    destination = edf[dst]
+    source_dtype = str(getmodule(source))
+                       
+    if fit and 'cudf' not in source_dtype:
         T = mlb.fit_transform(zip(source, destination))
-    else:
+    elif fit and 'cudf' in source_dtype:
+        T = mlb.fit_transform(zip(source.to_pandas(), destination.to_pandas()))
+    elif not fit and 'cudf' not in source_dtype:
         T = mlb.transform(zip(source, destination))
+    elif not fit and 'cudf' in source_dtype:
+        T = mlb.transform(zip(source.to_pandas(), destination.to_pandas()))
+                                   
     T = 1.0 * T  # coerce to float
     columns = [
         str(k) for k in mlb.classes_
     ]  # stringify the column names or scikits.base throws error
     mlb.get_feature_names_out = callThrough(columns)
     mlb.columns_ = [src, dst]
-    if 'cudf.core.dataframe' in edf_type:
+    if 'cudf' in edf_type:
+        import cudf
         T = cudf.DataFrame(T, columns=columns, index=edf.index)
     else:
         T = pd.DataFrame(T, columns=columns, index=edf.index)
@@ -2042,11 +2042,14 @@ def _featurize_nodes(
         res = self.copy() 
         ndf = res._nodes
         node = res._node
-
+        # print(['ndf:',ndf])
+        # print(['X:',X])
+        # print(['node:',res._node])
+        
         if remove_node_column:
             ndf = remove_node_column_from_symbolic(ndf, node)
             X = remove_node_column_from_symbolic(X, node)
-
+        
         if ndf is None:
             logger.info(
                 "! Materializing Nodes and setting `embedding=True`"

From e79a3e667e73adf6ed26665554245d6d4b8a6e3e Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Tue, 11 Apr 2023 01:57:03 +0530
Subject: [PATCH 355/432] typo: graphistry.rst compute.cluster

---
 docs/source/graphistry.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/graphistry.rst b/docs/source/graphistry.rst
index cd22422c02..2fd55094a2 100644
--- a/docs/source/graphistry.rst
+++ b/docs/source/graphistry.rst
@@ -58,7 +58,7 @@ Semantic Search
 
 DBScan
 ==================
-.. automodule:: graphistry.computecluster
+.. automodule:: graphistry.compute.cluster
     :members:
     :undoc-members:
     :show-inheritance:

From 9c445f19b130d39eb6e9720ac4555238c7a18c74 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Tue, 11 Apr 2023 02:09:40 +0530
Subject: [PATCH 356/432] fix: duplicate entries docs

---
 docs/source/index.rst | 2 --
 1 file changed, 2 deletions(-)

diff --git a/docs/source/index.rst b/docs/source/index.rst
index 8b6e9c5387..9b10c2c91c 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -65,9 +65,7 @@ For self-hosting and access to a free API key, refer to our Graphistry `Hub <htt
    :maxdepth: 3
 
    graphistry
-   graphistry.compute
    modules
-   versioneer
 
 Articles
 ==================

From da34a54153222ead6d074832c92c8ff2db8279a9 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <apple@dc-m1.local>
Date: Tue, 11 Apr 2023 11:17:16 +0800
Subject: [PATCH 357/432] TestFeatureCUMLProcessors

---
 graphistry/tests/test_feature_utils.py | 140 +++++++++++++++++++++++--
 1 file changed, 130 insertions(+), 10 deletions(-)

diff --git a/graphistry/tests/test_feature_utils.py b/graphistry/tests/test_feature_utils.py
index f3738b3707..08358112f5 100644
--- a/graphistry/tests/test_feature_utils.py
+++ b/graphistry/tests/test_feature_utils.py
@@ -15,13 +15,19 @@
     process_nodes_dataframes,
     resolve_feature_engine,
     lazy_import_has_min_dependancy,
+    lazy_import_has_cu_cat_dependancy,
     lazy_import_has_dependancy_text,
     FastEncoder
 )
 
+from graphistry.features import topic_model, ngrams_model
+from graphistry.constants import SCALERS
+
+np.random.seed(137)
 
 has_min_dependancy, _ = lazy_import_has_min_dependancy()
 has_min_dependancy_text, _, _ = lazy_import_has_dependancy_text()
+has_cu_cat_dependancy_text, _, _ = lazy_import_has_cu_cat_dependancy()
 
 logger = logging.getLogger(__name__)
 warnings.filterwarnings("ignore")
@@ -127,7 +133,7 @@
 target_names_node = [['label'], ['label', 'type']]
 # test also sending in a dataframe for target
 double_target_reddit = pd.DataFrame(
-    {"label": ndf_reddit.label.values, "type": ndf_reddit["type"].values}
+    {"label": ndf_reddit.label.values, "type": ndf_reddit["type"].values}, index=ndf_reddit.index
 )
 single_target_reddit = pd.DataFrame({"label": ndf_reddit.label.values})
 
@@ -136,6 +142,14 @@
 edge_df2['dst'] = np.random.random_integers(0, 120, size=len(edge_df2))
 edge2_target_df = pd.DataFrame({'label': edge_df2.label})
 
+# #############################################################################################################
+what = ['whatever', 'on what', 'what do', 'what do you', 'what do you think', 
+        'to what', 'but what', 'what is', 'what it', 'what kind', 'what kind of', 
+        'of what', 'know what', 'what are', 'what are the', 'what to', 'what to do', 
+        'from what', 'with what', 'and what', 'what you', 'whats', 'know what to', 'don know what', 'what the']
+freedom = ['title: dyslexics, experience, language',
+       'label: languagelearning, agile, leaves',
+       'title: freedom, finally, moved']
 # ################################################
 # data to test textual and numeric DataFrame
 # ndf_stocks, price_df_stocks = get_stocks_dataframe()
@@ -162,6 +176,44 @@ def check_allclose_fit_transform_on_same_data(X, x, Y=None, y=None):
                 allclose_stats(Y, y, value, name)
 
 
+class TestFeaturizeGetMethods(unittest.TestCase):
+    
+    @pytest.mark.skipif(not has_min_dependancy or not has_min_dependancy_text, reason="requires ai feature dependencies")
+    def setUp(self) -> None:
+        g = graphistry.nodes(ndf_reddit)
+        g2 = g.featurize(y=double_target_reddit,  # ngrams
+                use_ngrams=True,
+                ngram_range=(1, 4)
+                )
+        
+        g3 = g.featurize(**topic_model  # topic model       
+        )
+        self.g = g
+        self.g2 = g2
+        self.g3 = g3
+        
+    @pytest.mark.skipif(not has_min_dependancy or not has_min_dependancy_text, reason="requires ai feature dependencies")
+    def test_get_col_matrix(self):
+        # no edges so this should be None
+        assert self.g2.get_matrix(kind='edges') is None
+        
+        # test target methods
+        assert all(self.g2.get_matrix(target=True).columns == self.g2._node_target.columns)
+        assert self.g2.get_matrix('Anxiety', target=True).shape[0] == len(self.g2._node_target)
+        # test str vs list 
+        assert (self.g2.get_matrix('Anxiety', target=True) == self.g2.get_matrix(['Anxiety'], target=True)).all().values[0]
+
+        # assert list(self.g2.get_matrix(['Anxiety', 'education', 'computer'], target=True).columns) == ['label_Anxiety', 'label_education', 'label_computervision']
+    
+        # test feature methods
+        # ngrams
+        assert (self.g2.get_matrix().columns == self.g2._node_features.columns).all()
+        assert list(self.g2.get_matrix('what').columns) == what, list(self.g2.get_matrix('what').columns)
+        
+        # topic
+        assert all(self.g3.get_matrix().columns == self.g3._node_features.columns)
+        assert list(self.g3.get_matrix(['language', 'freedom']).columns) == freedom, self.g3.get_matrix(['language', 'freedom']).columns
+
 class TestFastEncoder(unittest.TestCase):
     # we test how far off the fit returned values different from the transformed
     
@@ -237,7 +289,8 @@ def test_process_node_dataframes_min_words(self):
                 2,
                 4000,
             ]:  # last one should skip encoding, and throw all to dirty_cat
-                X_enc, y_enc, data_encoder, label_encoder, ordinal_pipeline, ordinal_pipeline_target, text_model, text_cols = process_nodes_dataframes(
+
+                X_enc, y_enc, X_encs, y_encs, data_encoder, label_encoder, ordinal_pipeline, ordinal_pipeline_target, text_model, text_cols = process_nodes_dataframes(
                     ndf_reddit,
                     y=double_target_reddit,
                     use_scaler=None,
@@ -260,6 +313,71 @@ def test_multi_label_binarizer(self):
         assert y.shape == (4, 4)
         assert sum(y.sum(1).values - np.array([1., 2., 1., 0.])) == 0
         
+class TestFeatureCUMLProcessors(unittest.TestCase):
+    def cases_tests(self, x, y, data_encoder, target_encoder, name, value):
+        import cu_cat
+        self.assertIsInstance(
+            x,
+            cudf.DataFrame,
+            f"Returned data matrix is not cudf DataFrame for {name} {value}",
+        )
+        self.assertFalse(
+            x.empty,
+            f"cudf DataFrame should not be empty for {name} {value}",
+        )
+        self.assertIsInstance(
+            y,
+            pd.DataFrame,
+            f"Returned Target is not a cudf DataFrame for {name} {value}",
+        )
+        self.assertFalse(
+            y.empty,
+            f"cudf Target DataFrame should not be empty for {name} {value}",
+        )
+        self.assertIsInstance(
+            data_encoder,
+            cu_cat.super_vectorizer.TableVectorizer,
+            f"Data Encoder is not a cu_cat.super_vectorizer.TableVectorizer instance for {name} {value}",
+        )
+        self.assertIsInstance(
+            target_encoder,
+            cu_cat.super_vectorizer.TableVectorizer,
+            f"Data Target Encoder is not a cu_cat.super_vectorizer.TableVectorizer instance for {name} {value}",
+        )
+
+    @pytest.mark.skipif(not has_cu_cat_dependancy or not has_cu_cat_dependancy, reason="requires cu_cat feature dependencies")
+    def test_process_node_dataframes_min_words(self):
+        # test different target cardinality
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            for min_words in [
+                2,
+                4000,
+            ]:  # last one should skip encoding, and throw all to dirty_cat
+
+                X_enc, y_enc, X_encs, y_encs, data_encoder, label_encoder, ordinal_pipeline, ordinal_pipeline_target, text_model, text_cols = process_nodes_dataframes(
+                    ndf_reddit,
+                    y=double_target_reddit,
+                    use_scaler=None,
+                    cardinality_threshold=40,
+                    cardinality_threshold_target=40,
+                    n_topics=20,
+                    min_words=min_words,
+                    model_name=model_avg_name,
+                    feature_engine=resolve_feature_engine('auto')
+                )
+                self.cases_tests(X_enc, y_enc, data_encoder, label_encoder, "min_words", min_words)
+    
+    @pytest.mark.skipif(not has_cu_cat_dependancy, reason="requires minimal feature dependencies")
+    def test_multi_label_binarizer(self):
+        g = graphistry.nodes(bad_df)  # can take in a list of lists and convert to multiOutput
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            g2 = g.featurize(y=['list_str'], X=['src'], multilabel=True)
+        y = g2._get_target('node')
+        assert y.shape == (4, 4)
+        assert sum(y.sum(1).values - np.array([1., 2., 1., 0.])) == 0
+
 class TestFeatureMethods(unittest.TestCase):
 
     def _check_attributes(self, g, attributes):
@@ -370,19 +488,21 @@ def test_edge_featurization(self):
     def test_node_scaling(self):
         g = graphistry.nodes(ndf_reddit)
         g2 = g.featurize(X="title", y='label', use_scaler=None, use_scaler_target=None)
-        scalers = ['quantile', 'zscale', 'kbins', 'robust', 'minmax']
-        for scaler in scalers:
-            a, b, c, d = g2.scale(ndf_reddit, single_target_reddit, kind='nodes', use_scaler=scaler, use_scaler_target=np.random.choice(scalers))
-
-        
+        for scaler in SCALERS:
+            X, y, c, d = g2.scale(ndf_reddit, single_target_reddit, kind='nodes', 
+                                  use_scaler=scaler, 
+                                  use_scaler_target=np.random.choice(SCALERS), 
+                                  return_scalers=True)
 
     @pytest.mark.skipif(not has_min_dependancy or not has_min_dependancy_text, reason="requires ai feature dependencies")
     def test_edge_scaling(self):
         g = graphistry.edges(edge_df2, "src", "dst")
         g2 = g.featurize(y='label', kind='edges', use_scaler=None, use_scaler_target=None)
-        scalers = ['quantile', 'zscale', 'kbins', 'robust', 'minmax']
-        for scaler in scalers:
-            a, b, c, d = g2.scale(edge_df2, edge2_target_df, kind='edges', use_scaler=scaler, use_scaler_target=np.random.choice(scalers))
+        for scaler in SCALERS:
+            X, y, c, d = g2.scale(edge_df2, edge2_target_df, kind='edges', 
+                                  use_scaler=scaler, 
+                                  use_scaler_target=np.random.choice(SCALERS), 
+                                  return_scalers=True)
 
 
 

From ce17b8ecafaae765d86e483f205cc7fb344a019c Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Tue, 11 Apr 2023 22:51:16 +0530
Subject: [PATCH 358/432] tests: add cudf umap pass through

---
 .gitignore                          |  4 ++++
 graphistry/tests/test_umap_utils.py | 22 ++++++++++++++++++++++
 2 files changed, 26 insertions(+)

diff --git a/.gitignore b/.gitignore
index 9fb2c10c4c..f8a1ee9544 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,7 @@
+# vim temporary files
+*.swp
+*.swo
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index 564ffcdbfa..ab916f1563 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -780,6 +780,28 @@ def test_filter_edges(self):
                 self.assertGreaterEqual(shape[0], last_shape)
                 last_shape = shape[0]
 
+class TestCudfUmap(unittest.TestCase):
+    # temporary tests for cudf pass thru umap
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cuml,
+        reason="requires cuml dependencies",
+    )
+    @pytest.mark.skipif(
+        not has_cudf, reason="requires cudf"
+    )
+
+    def setUp(self):
+        self.samples=1000
+        df = pd.DataFrame(np.random.randint(18,75,size=(self.samples, 1)), columns=['age'])
+        df['user_id'] = np.random.randint(0,200,size=(self.samples, 1))
+        df['profile'] = np.random.randint(0,1000,size=(self.samples, 1))
+        self.df = cudf.from_pandas(df)
+    
+    def test_base(self):
+        print('working')
+        graphistry.nodes(self.df).umap('auto')._node_embedding.shape == (self.samples, 2)
+        graphistry.nodes(self.df).umap('engine')._node_embedding.shape == (self.samples, 2)
+
 
 if __name__ == "__main__":
     unittest.main()

From 5ff14f87a7dc4d7431bcdf3740f40590239cf95f Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Tue, 11 Apr 2023 22:56:05 +0530
Subject: [PATCH 359/432] lint: flake8 fixes

---
 graphistry/tests/test_umap_utils.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index ab916f1563..d6d96dbd7b 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -789,9 +789,8 @@ class TestCudfUmap(unittest.TestCase):
     @pytest.mark.skipif(
         not has_cudf, reason="requires cudf"
     )
-
     def setUp(self):
-        self.samples=1000
+        self.samples = 1000
         df = pd.DataFrame(np.random.randint(18,75,size=(self.samples, 1)), columns=['age'])
         df['user_id'] = np.random.randint(0,200,size=(self.samples, 1))
         df['profile'] = np.random.randint(0,1000,size=(self.samples, 1))

From 77eb8c2635ddfecd6833fca35b5f7dba8809df75 Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Tue, 11 Apr 2023 23:50:35 +0530
Subject: [PATCH 360/432] fix: cudf umap skip

---
 graphistry/tests/test_umap_utils.py | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index d6d96dbd7b..c281ae75fe 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -782,13 +782,6 @@ def test_filter_edges(self):
 
 class TestCudfUmap(unittest.TestCase):
     # temporary tests for cudf pass thru umap
-    @pytest.mark.skipif(
-        not has_dependancy or not has_cuml,
-        reason="requires cuml dependencies",
-    )
-    @pytest.mark.skipif(
-        not has_cudf, reason="requires cudf"
-    )
     def setUp(self):
         self.samples = 1000
         df = pd.DataFrame(np.random.randint(18,75,size=(self.samples, 1)), columns=['age'])
@@ -796,8 +789,9 @@ def setUp(self):
         df['profile'] = np.random.randint(0,1000,size=(self.samples, 1))
         self.df = cudf.from_pandas(df)
     
+    @pytest.mark.skipif(not has_dependancy or not has_cuml, reason="requires cuml dependencies")
+    @pytest.mark.skipif(not has_cudf, reason="requires cudf")
     def test_base(self):
-        print('working')
         graphistry.nodes(self.df).umap('auto')._node_embedding.shape == (self.samples, 2)
         graphistry.nodes(self.df).umap('engine')._node_embedding.shape == (self.samples, 2)
 

From c2d2fcb6eb1588e28171c2b6b8dcfde01ba66b39 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <apple@dc-m1.local>
Date: Wed, 12 Apr 2023 17:28:08 +0800
Subject: [PATCH 361/432] need to make cudf import for edges lazy

---
 graphistry/feature_utils.py | 34 ++++++++++++++++++++--------------
 1 file changed, 20 insertions(+), 14 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index c03a3cde52..2d26dd119b 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -692,15 +692,16 @@ def fit_pipeline(
     columns = X.columns
     index = X.index
     X_type= str(getmodule(X))
-    if 'cudf.core.dataframe' not in X_type:
+    if 'cudf' not in X_type:
         X = transformer.fit_transform(X)
         if keep_n_decimals:
             X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
         X=pd.DataFrame(X, columns=columns, index=index)
-    elif 'cudf.core.dataframe' in X_type:
-        X = transformer.fit_transform(X.to_numpy())
+    else:
+        X = transformer.fit_transform(X.to_numpy()) ## why numpy here?
         if keep_n_decimals:
             X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
+        import cudf
         X=cudf.DataFrame(X, columns=columns, index=index)
     return X
 
@@ -954,7 +955,7 @@ def process_dirty_dataframes(
     if feature_engine == 'dirty_cat':
         from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
     elif feature_engine == 'cu_cat':
-        # assert_cuml_cucat() ## tried to use this rather than importing below
+        lazy_import_has_cu_cat_dependancy() ## tried to use this rather than importing below
         from cu_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
     from cuml.preprocessing import FunctionTransformer
     t = time()
@@ -997,12 +998,13 @@ def process_dirty_dataframes(
             X_enc = pd.DataFrame(
                 X_enc, columns=features_transformed, index=ndf.index
             )
-        elif 'cudf.core.dataframe' in str(getmodule(ndf)):
-            import cudf
-            X_enc = cudf.DataFrame(
-                X_enc, columns=features_transformed, index=ndf.index
-            )
-        X_enc = X_enc.fillna(0.0)
+            X_enc = X_enc.fillna(0.0)  # TODO -- this is a hack in cuml version
+        else:
+            # X_enc = cudf.DataFrame.from_arrow(X_enc)
+            X_enc.index = ndf.index
+            X_enc.columns = np.array(features_transformed)
+            X_enc = X_enc.fillna(0.0)
+
     else:
         logger.info("-*-*- DataFrame is completely numeric")
         X_enc, _, data_encoder, _ = get_numeric_transformers(ndf, None)
@@ -1344,6 +1346,7 @@ def encode_edges(edf, src, dst, mlb, fit=False):
     mlb.get_feature_names_out = callThrough(columns)
     mlb.columns_ = [src, dst]
     if 'cudf' in edf_type:
+        lazy_import_has_cu_cat_dependancy()
         import cudf
         T = cudf.DataFrame(T, columns=columns, index=edf.index)
     else:
@@ -1420,6 +1423,11 @@ def process_edge_dataframes(
         MultiLabelBinarizer()
     )  # create new one so we can use encode_edges later in
     # transform with fit=False
+    edf_type = str(getmodule(edf))
+    if 'cudf' in edf_type:
+        import cudf
+        lazy_import_has_cu_cat_dependancy()
+        
     T, mlb_pairwise_edge_encoder = encode_edges(
         edf, src, dst, mlb_pairwise_edge_encoder, fit=True
     )
@@ -1506,12 +1514,10 @@ def process_edge_dataframes(
         logger.debug("-" * 60)
         logger.debug("<= Found Edges and Dirty_cat encoding =>")
         T_type= str(getmodule(T))
-        if 'cudf.core.dataframe' not in T_type:
+        if 'cudf' not in T_type:
             X_enc = pd.concat([T, X_enc], axis=1)
-        elif 'cudf.core.dataframe' not in T_type:
-            X_enc = cudf.concat([T, X_enc], axis=1)
         else:
-            X_enc = pd.concat([T, X_enc], axis=1)
+            X_enc = cudf.concat([T, X_enc], axis=1)
     elif not T.empty and X_enc.empty:
         logger.debug("-" * 60)
         logger.debug("<= Found only Edges =>")

From c703a427df9daa5f539430615f9e9c4f980142b1 Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Wed, 12 Apr 2023 21:22:33 +0530
Subject: [PATCH 362/432] test: cudf tests with docker flag

---
 graphistry/tests/test_embed_utils.py | 15 ++++++++++-----
 graphistry/tests/test_umap_utils.py  |  8 +++++++-
 2 files changed, 17 insertions(+), 6 deletions(-)

diff --git a/graphistry/tests/test_embed_utils.py b/graphistry/tests/test_embed_utils.py
index 2ecc30a677..f0cb82b8d0 100644
--- a/graphistry/tests/test_embed_utils.py
+++ b/graphistry/tests/test_embed_utils.py
@@ -1,3 +1,4 @@
+import os
 import pytest
 import pandas as pd
 import unittest
@@ -20,6 +21,10 @@ def check_cudf():
 
 has_cudf, cudf = check_cudf()
 
+TEST_CUDF = False
+if "TEST_CUDF" in os.environ and os.environ["TEST_CUDF"] == "1":
+    TEST_CUDF = True
+
 class TestEmbed(unittest.TestCase):
 
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
@@ -114,7 +119,7 @@ def test_chaining(self):
 class TestEmbedCUDF(unittest.TestCase):
 
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
-    @pytest.mark.skipif(not has_cudf, reason="requires cudf")
+    @pytest.mark.skipif(not TEST_CUDF, reason="requires cudf")
     def setUp(self):
         self.edf = cudf.DataFrame([[0, 1, 0], [1, 2, 0], [2, 0, 1]],
             columns=['src', 'dst', 'rel']
@@ -138,7 +143,7 @@ def setUp(self):
         
 
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
-    @pytest.mark.skipif(not has_cudf, reason="requires cudf")
+    @pytest.mark.skipif(not TEST_CUDF, reason="requires cudf")
     def test_embed_out_basic(self):
         for name, g in self.graphs:
             g = g.embed('rel', embedding_dim=self.d, **self.kwargs)
@@ -150,7 +155,7 @@ def test_embed_out_basic(self):
 
 
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
-    @pytest.mark.skipif(not has_cudf, reason="requires cudf")
+    @pytest.mark.skipif(not TEST_CUDF, reason="requires cudf")
     def test_predict_links(self):
         source = pd.Series([0,2])
         relation = None
@@ -166,7 +171,7 @@ def test_predict_links(self):
         self.assertIn("score", g_new._edges.columns)
     
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
-    @pytest.mark.skipif(not has_cudf, reason="requires cudf")
+    @pytest.mark.skipif(not TEST_CUDF, reason="requires cudf")
     def test_predict_links_all(self):
         g = self.graph_no_feat.embed('rel', embedding_dim=self.d, **self.kwargs)
         g_new = g.predict_links_all(threshold=0)
@@ -175,7 +180,7 @@ def test_predict_links_all(self):
 
         
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
-    @pytest.mark.skipif(not has_cudf, reason="requires cudf")
+    @pytest.mark.skipif(not TEST_CUDF, reason="requires cudf")
     def test_chaining(self):
         for name, g in self.graphs:
             logging.debug('name: %s test changing embedding dim with feats' % name)
diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index c281ae75fe..041e840952 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -5,6 +5,7 @@
 
 import graphistry
 
+import os
 import logging
 import numpy as np
 import pandas as pd
@@ -43,6 +44,10 @@
 
 warnings.filterwarnings("ignore")
 
+TEST_CUDF = False
+if "TEST_CUDF" in os.environ and os.environ["TEST_CUDF"] == "1":
+    TEST_CUDF = True
+
 
 triangleEdges = pd.DataFrame(
     {
@@ -782,6 +787,7 @@ def test_filter_edges(self):
 
 class TestCudfUmap(unittest.TestCase):
     # temporary tests for cudf pass thru umap
+    @pytest.mark.skipif(not TEST_CUDF, reason="requires cudf")
     def setUp(self):
         self.samples = 1000
         df = pd.DataFrame(np.random.randint(18,75,size=(self.samples, 1)), columns=['age'])
@@ -790,7 +796,7 @@ def setUp(self):
         self.df = cudf.from_pandas(df)
     
     @pytest.mark.skipif(not has_dependancy or not has_cuml, reason="requires cuml dependencies")
-    @pytest.mark.skipif(not has_cudf, reason="requires cudf")
+    @pytest.mark.skipif(not TEST_CUDF, reason="requires cudf")
     def test_base(self):
         graphistry.nodes(self.df).umap('auto')._node_embedding.shape == (self.samples, 2)
         graphistry.nodes(self.df).umap('engine')._node_embedding.shape == (self.samples, 2)

From 006aa7d30ecfcf286b54df67e232f9eb3f87a427 Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Wed, 12 Apr 2023 21:26:43 +0530
Subject: [PATCH 363/432] delete: .swp files

---
 graphistry/.dgl_utils.py.swp              | Bin 40960 -> 0 bytes
 graphistry/.feature_utils.py.swp          | Bin 122880 -> 0 bytes
 graphistry/tests/.test_embed_utils.py.swp | Bin 40960 -> 0 bytes
 3 files changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 graphistry/.dgl_utils.py.swp
 delete mode 100644 graphistry/.feature_utils.py.swp
 delete mode 100644 graphistry/tests/.test_embed_utils.py.swp

diff --git a/graphistry/.dgl_utils.py.swp b/graphistry/.dgl_utils.py.swp
deleted file mode 100644
index 46316aea9ac305deae72075eac60f9f2bebb0cb5..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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diff --git a/graphistry/.feature_utils.py.swp b/graphistry/.feature_utils.py.swp
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diff --git a/graphistry/tests/.test_embed_utils.py.swp b/graphistry/tests/.test_embed_utils.py.swp
deleted file mode 100644
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From d634d91e1df31d2cc6791d9cc8088e4e30bc6af2 Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Thu, 13 Apr 2023 00:24:48 +0530
Subject: [PATCH 364/432] add: test_umap_utils on test-gpu-local.sh

---
 docker/test-gpu-local.sh | 1 -
 1 file changed, 1 deletion(-)

diff --git a/docker/test-gpu-local.sh b/docker/test-gpu-local.sh
index 12667b3a04..158584f9b6 100755
--- a/docker/test-gpu-local.sh
+++ b/docker/test-gpu-local.sh
@@ -45,5 +45,4 @@ docker run \
     graphistry/test-gpu:${TEST_CPU_VERSION} \
         --maxfail=1 \
         --ignore=graphistry/tests/test_feature_utils.py \
-        --ignore=graphistry/tests/test_umap_utils.py \
         $@

From 400b63262a02f338d6439bff2bceb415d982cfc1 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Thu, 13 Apr 2023 21:38:56 +0530
Subject: [PATCH 365/432] fix: revert back to addStyle from add_style

---
 graphistry/PlotterBase.py        |  2 +-
 graphistry/__init__.py           |  2 +-
 graphistry/pygraphistry.py       |  6 +++---
 graphistry/tests/test_plotter.py | 22 +++++++++++-----------
 4 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/graphistry/PlotterBase.py b/graphistry/PlotterBase.py
index 9c64d0e1f5..0b9dbe9235 100644
--- a/graphistry/PlotterBase.py
+++ b/graphistry/PlotterBase.py
@@ -224,7 +224,7 @@ def __repr__(self):
         else:
             return str(rep)
 
-    def add_style(self, fg=None, bg=None, page=None, logo=None):
+    def addStyle(self, fg=None, bg=None, page=None, logo=None):
         """Set general visual styles
         
         See .bind() and .settings(url_params={}) for additional styling options, and style() for another way to set the same attributes.
diff --git a/graphistry/__init__.py b/graphistry/__init__.py
index 511b543e57..dd13cb60c5 100644
--- a/graphistry/__init__.py
+++ b/graphistry/__init__.py
@@ -15,7 +15,7 @@
     description,
     bind,
     style,
-    add_style,
+    addStyle,
     edges,
     nodes,
     graph,
diff --git a/graphistry/pygraphistry.py b/graphistry/pygraphistry.py
index e6582928e6..2051a32523 100644
--- a/graphistry/pygraphistry.py
+++ b/graphistry/pygraphistry.py
@@ -1209,7 +1209,7 @@ def description(description):
         return Plotter().description(description)
 
     @staticmethod
-    def add_style(bg=None, fg=None, logo=None, page=None):
+    def addStyle(bg=None, fg=None, logo=None, page=None):
         """Creates a base plotter with some style settings.
 
         For parameters, see ``plotter.addStyle``.
@@ -1225,7 +1225,7 @@ def add_style(bg=None, fg=None, logo=None, page=None):
                 graphistry.addStyle(bg={'color': 'black'})
         """
 
-        return Plotter().add_style(bg=bg, fg=fg, logo=logo, page=page)
+        return Plotter().addStyle(bg=bg, fg=fg, logo=logo, page=page)
 
     @staticmethod
     def style(bg=None, fg=None, logo=None, page=None):
@@ -2417,7 +2417,7 @@ def _handle_api_response(response):
 api_token = PyGraphistry.api_token
 verify_token = PyGraphistry.verify_token
 bind = PyGraphistry.bind
-add_style = PyGraphistry.add_style
+addStyle = PyGraphistry.addStyle
 style = PyGraphistry.style
 encode_point_color = PyGraphistry.encode_point_color
 encode_edge_color = PyGraphistry.encode_edge_color
diff --git a/graphistry/tests/test_plotter.py b/graphistry/tests/test_plotter.py
index 35142e8026..c1518a8160 100644
--- a/graphistry/tests/test_plotter.py
+++ b/graphistry/tests/test_plotter.py
@@ -720,32 +720,32 @@ def test_addStyle_good(self):
         logo = {"url": "zzz"}
         page = {"title": "zzz"}
 
-        assert g.add_style()._style == {}
+        assert g.addStyle()._style == {}
 
-        g.add_style(fg={"blendMode": "screen"})
-        assert g.add_style()._style == {}
+        g.addStyle(fg={"blendMode": "screen"})
+        assert g.addStyle()._style == {}
 
-        assert g.add_style(bg=copy.deepcopy(bg))._style == {"bg": bg}
-        assert g.add_style(bg={"color": "blue"}).add_style(
+        assert g.addStyle(bg=copy.deepcopy(bg))._style == {"bg": bg}
+        assert g.addStyle(bg={"color": "blue"}).addStyle(
             bg=copy.deepcopy(bg)
         )._style == {"bg": bg}
-        assert g.add_style(bg={"image": {"url": "http://asdf.com/b.png"}}).add_style(
+        assert g.addStyle(bg={"image": {"url": "http://asdf.com/b.png"}}).addStyle(
             bg=copy.deepcopy(bg)
         )._style == {"bg": {**bg, "image": {"url": "http://asdf.com/b.png"}}}
         assert (
-            g.add_style(
+            g.addStyle(
                 bg=copy.deepcopy(bg),
                 fg=copy.deepcopy(fg),
                 logo=copy.deepcopy(logo),
                 page=copy.deepcopy(page),
             )._style == {"bg": bg, "fg": fg, "logo": logo, "page": page}
         )
-        assert g.add_style(
+        assert g.addStyle(
             bg=copy.deepcopy(bg),
             fg=copy.deepcopy(fg),
             logo=copy.deepcopy(logo),
             page=copy.deepcopy(page),
-        ).add_style(bg={"color": "green"})._style == {
+        ).addStyle(bg={"color": "green"})._style == {
             "bg": {"color": "green"},
             "fg": fg,
             "logo": logo,
@@ -755,7 +755,7 @@ def test_addStyle_good(self):
         g2 = graphistry.edges(pd.DataFrame({"s": [0], "d": [0]})).bind(
             source="s", destination="d"
         )
-        ds = g2.add_style(
+        ds = g2.addStyle(
             bg=copy.deepcopy(bg),
             fg=copy.deepcopy(fg),
             page=copy.deepcopy(page),
@@ -783,7 +783,7 @@ def test_styleApi_reject(self):
         g2 = graphistry.edges(pd.DataFrame({"s": [0], "d": [0]})).bind(
             source="s", destination="d"
         )
-        g3 = g2.add_style(
+        g3 = g2.addStyle(
             bg=copy.deepcopy(bg),
             fg=copy.deepcopy(fg),
             page=copy.deepcopy(page),

From e6fc323c0d80d4187a95a3a4e4ca727a44f866c7 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Thu, 13 Apr 2023 21:49:08 +0530
Subject: [PATCH 366/432] added warnings for predict_links

---
 graphistry/embed_utils.py | 22 +++++++++-------------
 1 file changed, 9 insertions(+), 13 deletions(-)

diff --git a/graphistry/embed_utils.py b/graphistry/embed_utils.py
index 9ab4d07d00..dcbf3a88fd 100644
--- a/graphistry/embed_utils.py
+++ b/graphistry/embed_utils.py
@@ -1,4 +1,5 @@
 import logging
+import warnings
 import numpy as np
 import pandas as pd
 from typing import Optional, Union, Callable, List, TYPE_CHECKING, Any, Tuple
@@ -405,40 +406,35 @@ def predict_links(
             where score >= threshold if anamalous if False else score <= threshold, or a dataframe
             
         """
-
+        warnings.warn("currently `predict_links` is cpu only, gpu compatibility will be added in \
+                future releases") 
         all_nodes = self._node2id.values()
         all_relations = self._relation2id.values()
 
         if source is None:
             src = pd.Series(all_nodes)
         else:
-            # this is temporary
-            try:
+            # this is temporary, will be removed after gpu feature utils
+            if not isinstance(source, pd.DataFrame):
                 source = source.to_pandas()  # type: ignore
-            except:
-                pass
             src = pd.Series(source)
             src = src.map(self._node2id)
 
         if relation is None:
             rel = pd.Series(all_relations)
         else:
-            # this is temporary
-            try:
+            # this is temporary, will be removed after gpu feature utils
+            if not isinstance(relation, pd.DataFrame):
                 relation = relation.to_pandas()  # type: ignore
-            except:
-                pass
             rel = pd.Series(relation)
             rel = rel.map(self._relation2id)
 
         if destination is None:
             dst = pd.Series(all_nodes)
         else:
-            # this is temporary
-            try:
+            # this is temporary, will be removed after gpu feature utils
+            if not isinstance(destination, pd.DataFrame):
                 destination = destination.to_pandas()  # type: ignore
-            except:
-                pass
             dst = pd.Series(destination)
             dst = dst.map(self._node2id)
 

From c300050e4a0916dc9fe0c9838ea5842ecb3897f8 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Thu, 13 Apr 2023 21:57:45 +0530
Subject: [PATCH 367/432] fix: test cudf flag

---
 graphistry/tests/test_embed_utils.py | 15 +++++++--------
 graphistry/tests/test_umap_utils.py  | 10 ++++------
 2 files changed, 11 insertions(+), 14 deletions(-)

diff --git a/graphistry/tests/test_embed_utils.py b/graphistry/tests/test_embed_utils.py
index f0cb82b8d0..a3001424d4 100644
--- a/graphistry/tests/test_embed_utils.py
+++ b/graphistry/tests/test_embed_utils.py
@@ -21,9 +21,8 @@ def check_cudf():
 
 has_cudf, cudf = check_cudf()
 
-TEST_CUDF = False
-if "TEST_CUDF" in os.environ and os.environ["TEST_CUDF"] == "1":
-    TEST_CUDF = True
+# enable tests if has cudf and env didn't explicitly disable
+is_test_cudf = has_cudf and os.environ["TEST_CUDF"] != "0"
 
 class TestEmbed(unittest.TestCase):
 
@@ -119,7 +118,7 @@ def test_chaining(self):
 class TestEmbedCUDF(unittest.TestCase):
 
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
-    @pytest.mark.skipif(not TEST_CUDF, reason="requires cudf")
+    @pytest.mark.skipif(not is_test_cudf, reason="requires cudf")
     def setUp(self):
         self.edf = cudf.DataFrame([[0, 1, 0], [1, 2, 0], [2, 0, 1]],
             columns=['src', 'dst', 'rel']
@@ -143,7 +142,7 @@ def setUp(self):
         
 
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
-    @pytest.mark.skipif(not TEST_CUDF, reason="requires cudf")
+    @pytest.mark.skipif(not is_test_cudf, reason="requires cudf")
     def test_embed_out_basic(self):
         for name, g in self.graphs:
             g = g.embed('rel', embedding_dim=self.d, **self.kwargs)
@@ -155,7 +154,7 @@ def test_embed_out_basic(self):
 
 
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
-    @pytest.mark.skipif(not TEST_CUDF, reason="requires cudf")
+    @pytest.mark.skipif(not is_test_cudf, reason="requires cudf")
     def test_predict_links(self):
         source = pd.Series([0,2])
         relation = None
@@ -171,7 +170,7 @@ def test_predict_links(self):
         self.assertIn("score", g_new._edges.columns)
     
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
-    @pytest.mark.skipif(not TEST_CUDF, reason="requires cudf")
+    @pytest.mark.skipif(not is_test_cudf, reason="requires cudf")
     def test_predict_links_all(self):
         g = self.graph_no_feat.embed('rel', embedding_dim=self.d, **self.kwargs)
         g_new = g.predict_links_all(threshold=0)
@@ -180,7 +179,7 @@ def test_predict_links_all(self):
 
         
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
-    @pytest.mark.skipif(not TEST_CUDF, reason="requires cudf")
+    @pytest.mark.skipif(not is_test_cudf, reason="requires cudf")
     def test_chaining(self):
         for name, g in self.graphs:
             logging.debug('name: %s test changing embedding dim with feats' % name)
diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index 041e840952..6bd57785a2 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -44,10 +44,8 @@
 
 warnings.filterwarnings("ignore")
 
-TEST_CUDF = False
-if "TEST_CUDF" in os.environ and os.environ["TEST_CUDF"] == "1":
-    TEST_CUDF = True
-
+# enable tests if has cudf and env didn't explicitly disable
+is_test_cudf = has_cudf and os.environ["TEST_CUDF"] != "0"
 
 triangleEdges = pd.DataFrame(
     {
@@ -787,7 +785,7 @@ def test_filter_edges(self):
 
 class TestCudfUmap(unittest.TestCase):
     # temporary tests for cudf pass thru umap
-    @pytest.mark.skipif(not TEST_CUDF, reason="requires cudf")
+    @pytest.mark.skipif(not is_test_cudf, reason="requires cudf")
     def setUp(self):
         self.samples = 1000
         df = pd.DataFrame(np.random.randint(18,75,size=(self.samples, 1)), columns=['age'])
@@ -796,7 +794,7 @@ def setUp(self):
         self.df = cudf.from_pandas(df)
     
     @pytest.mark.skipif(not has_dependancy or not has_cuml, reason="requires cuml dependencies")
-    @pytest.mark.skipif(not TEST_CUDF, reason="requires cudf")
+    @pytest.mark.skipif(not is_test_cudf, reason="requires cudf")
     def test_base(self):
         graphistry.nodes(self.df).umap('auto')._node_embedding.shape == (self.samples, 2)
         graphistry.nodes(self.df).umap('engine')._node_embedding.shape == (self.samples, 2)

From 1305db11d0fb04ce908126f2cf51bb87aa1410f3 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Thu, 13 Apr 2023 22:02:42 +0530
Subject: [PATCH 368/432] doc: changelog update for _dgl_graph

---
 CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 8d1ed4185a..69fd423074 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,7 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm
 
 ### Added
 * AI: moves public `g.g_dgl` from KG `embed` method to private method `g._kg_dgl`
+* AI: moves public `g.DGL_graph` to private attribute `g._dgl_graph`
 * AI: BREAKING CHANGES: to return matrices during transform, set the flag:  `X, y = g.transform(df, return_graph=False)` default behavior is ~ `g2 = g.transform(df)` returning a Plottable instance. 
 
 ## [0.28.7 - 2022-12-22]

From 7ad6449ed4d6cf688f7c6aede00f80dd15c35e44 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Thu, 13 Apr 2023 22:06:00 +0530
Subject: [PATCH 369/432] passing gpu test_feature_utils

---
 docker/test-gpu-local.sh | 1 -
 1 file changed, 1 deletion(-)

diff --git a/docker/test-gpu-local.sh b/docker/test-gpu-local.sh
index 158584f9b6..f7c5c5c5ad 100755
--- a/docker/test-gpu-local.sh
+++ b/docker/test-gpu-local.sh
@@ -44,5 +44,4 @@ docker run \
     ${NETWORK} \
     graphistry/test-gpu:${TEST_CPU_VERSION} \
         --maxfail=1 \
-        --ignore=graphistry/tests/test_feature_utils.py \
         $@

From 23951474d23900b3c665d8e69276cf3286df00bf Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Thu, 13 Apr 2023 23:12:00 +0530
Subject: [PATCH 370/432] additional checks for embed utils

---
 graphistry/embed_utils.py | 20 +++++++++++++++-----
 1 file changed, 15 insertions(+), 5 deletions(-)

diff --git a/graphistry/embed_utils.py b/graphistry/embed_utils.py
index dcbf3a88fd..3dbba83156 100644
--- a/graphistry/embed_utils.py
+++ b/graphistry/embed_utils.py
@@ -1,5 +1,4 @@
 import logging
-import warnings
 import numpy as np
 import pandas as pd
 from typing import Optional, Union, Callable, List, TYPE_CHECKING, Any, Tuple
@@ -7,6 +6,7 @@
 from .PlotterBase import Plottable
 from .compute.ComputeMixin import ComputeMixin
 
+
 def lazy_embed_import_dep():
     try:
         import torch
@@ -21,6 +21,11 @@ def lazy_embed_import_dep():
     except:
         return False, None, None, None, None, None, None, None
 
+try:
+    import cudf
+except:
+    cudf = object
+
 
 if TYPE_CHECKING:
     _, torch, _, _, _, _, _, _ = lazy_embed_import_dep()
@@ -290,6 +295,11 @@ def embed(
         -------
             self : graphistry instance
         """
+        # this is temporary, will be fixed in future releases
+        if isinstance(self._nodes, cudf.DataFrame):
+            self._nodes = self._nodes.to_pandas()
+        if isinstance(self._edges, cudf.DataFrame):
+            self._edges = self._edges.to_pandas()
         if inplace:
             res = self
         else:
@@ -406,7 +416,7 @@ def predict_links(
             where score >= threshold if anamalous if False else score <= threshold, or a dataframe
             
         """
-        warnings.warn("currently `predict_links` is cpu only, gpu compatibility will be added in \
+        logging.warning("currently `predict_links` is cpu only, gpu compatibility will be added in \
                 future releases") 
         all_nodes = self._node2id.values()
         all_relations = self._relation2id.values()
@@ -415,7 +425,7 @@ def predict_links(
             src = pd.Series(all_nodes)
         else:
             # this is temporary, will be removed after gpu feature utils
-            if not isinstance(source, pd.DataFrame):
+            if isinstance(source, cudf.DataFrame):
                 source = source.to_pandas()  # type: ignore
             src = pd.Series(source)
             src = src.map(self._node2id)
@@ -424,7 +434,7 @@ def predict_links(
             rel = pd.Series(all_relations)
         else:
             # this is temporary, will be removed after gpu feature utils
-            if not isinstance(relation, pd.DataFrame):
+            if isinstance(relation, cudf.DataFrame):
                 relation = relation.to_pandas()  # type: ignore
             rel = pd.Series(relation)
             rel = rel.map(self._relation2id)
@@ -433,7 +443,7 @@ def predict_links(
             dst = pd.Series(all_nodes)
         else:
             # this is temporary, will be removed after gpu feature utils
-            if not isinstance(destination, pd.DataFrame):
+            if isinstance(destination, cudf.DataFrame):
                 destination = destination.to_pandas()  # type: ignore
             dst = pd.Series(destination)
             dst = dst.map(self._node2id)

From 8e99fe37bcb32ebda13e09bc0a7b4bf6aa6b16b8 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Fri, 14 Apr 2023 14:31:22 +0800
Subject: [PATCH 371/432] cu_cat flag in umap

---
 graphistry/umap_utils.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 47186bb7a1..f9a133ef26 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -126,9 +126,9 @@ def safe_cudf(X, y):
         new_kwargs = {}
         kwargs = {'X': X, 'y': y}
         for key, value in kwargs.items():
-            if isinstance(value, cudf.DataFrame) and engine == "pandas":
+            if isinstance(value, cudf.DataFrame) and engine in ["pandas", "umap_learn", "dirty_cat"]:
                 new_kwargs[key] = value.to_pandas()
-            elif isinstance(value, pd.DataFrame) and engine == "cuml":
+            elif isinstance(value, pd.DataFrame) and engine in ["cuml", "cu_cat"]:
                 new_kwargs[key] = cudf.from_pandas(value)
             else:
                 new_kwargs[key] = value
@@ -352,10 +352,13 @@ def transform_umap(self, df: pd.DataFrame,
 
     def _bundle_embedding(self, emb, index):
         # Converts Embedding into dataframe and takes care if emb.dim > 2
-        if emb.shape[1] == 2 and 'cudf.core.dataframe' not in str(getmodule(emb)):
+        if emb.shape[1] == 2 and 'cudf.core.dataframe' not in str(getmodule(emb)) and not hasattr(emb, 'device'):
             emb = pd.DataFrame(emb, columns=[config.X, config.Y], index=index)
         elif emb.shape[1] == 2 and 'cudf.core.dataframe' in str(getmodule(emb)):
             emb.rename(columns={0: config.X, 1: config.Y}, inplace=True)
+        elif emb.shape[1] == 2 and hasattr(emb, 'device'):
+            import cudf
+            emb = cudf.DataFrame(emb, columns=[config.X, config.Y], index=index)
         else:
             columns = [config.X, config.Y] + [
                 f"umap_{k}" for k in range(2, emb.shape[1])

From 2e9820c6370459d0d268f245bc706bd5ad159bb0 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Fri, 14 Apr 2023 14:34:18 +0800
Subject: [PATCH 372/432] added tanmoy umap changes

---
 graphistry/umap_utils.py | 116 ++++++++++++++++++++-------------------
 1 file changed, 60 insertions(+), 56 deletions(-)

diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 9be5616786..f9a133ef26 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -50,17 +50,6 @@ def lazy_cuml_import_has_dependancy():
         return True, "ok", cuml
     except ModuleNotFoundError as e:
         return False, e, None
-    
-def lazy_cudf_import_has_dependancy():
-    try:
-        import warnings
-
-        warnings.filterwarnings("ignore")
-        import cudf  # type: ignore
-
-        return True, "ok", cudf
-    except ModuleNotFoundError as e:
-        return False, e, None
 
 def lazy_cudf_import_has_dependancy():
     try:
@@ -190,7 +179,7 @@ class UMAPMixin(MIXIN_BASE):
 
     def __init__(self, *args, **kwargs):
         #self._umap_initialized = False
-        #self.umap_engine = self.umap_engine if hasattr(self, "engine") else None
+        #self.engine = self.engine if hasattr(self, "engine") else None
         pass
 
 
@@ -205,18 +194,18 @@ def umap_lazy_init(
         negative_sample_rate: int = 5,
         n_components: int = 2,
         metric: str = "euclidean",
-        umap_engine: UMAPEngine = "auto",
+        engine: UMAPEngine = "auto",
         suffix: str = "",
         verbose: bool = False,
     ):
         from graphistry.features import ModelDict
 
-        engine_resolved = resolve_umap_engine(umap_engine)
+        engine_resolved = resolve_umap_engine(engine)
         # FIXME remove as set_new_kwargs will always replace?
         if engine_resolved == UMAP_LEARN:
-            _, _, umap_engine_ = lazy_umap_import_has_dependancy()
+            _, _, umap_engine = lazy_umap_import_has_dependancy()
         elif engine_resolved == CUML:
-            _, _, umap_engine_ = lazy_cuml_import_has_dependancy()
+            _, _, umap_engine = lazy_cuml_import_has_dependancy()
         else:
             raise ValueError(
                 "No umap engine, ensure 'auto', 'umap_learn', or 'cuml', and the library is installed"
@@ -242,6 +231,7 @@ def umap_lazy_init(
         print(umap_kwargs) if verbose else None
         # set new umap kwargs
         res._umap_params = umap_kwargs  
+
         res._n_components = n_components
         res._metric = metric
         res._n_neighbors = n_neighbors
@@ -250,8 +240,8 @@ def umap_lazy_init(
         res._local_connectivity = local_connectivity
         res._repulsion_strength = repulsion_strength
         res._negative_sample_rate = negative_sample_rate
-        res._umap = umap_engine_.UMAP(**umap_kwargs)
-        res.umap_engine = engine_resolved
+        res._umap = umap_engine.UMAP(**umap_kwargs)
+        res.engine = engine_resolved
         res._suffix = suffix
                                                             
         return res
@@ -287,7 +277,7 @@ def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None, verbose
         logger.info("-" * 90)
         logger.info(f"Starting UMAP-ing data of shape {X.shape}")
 
-        if self.umap_engine == CUML and is_legacy_cuml():  # type: ignore
+        if self.engine == CUML and is_legacy_cuml():  # type: ignore
             from cuml.neighbors import NearestNeighbors
 
             knn = NearestNeighbors(n_neighbors=self._n_neighbors)  # type: ignore
@@ -300,7 +290,7 @@ def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None, verbose
         self._weighted_adjacency = self._umap.graph_
         # if changing, also update fresh_res
         self._weighted_edges_df = umap_graph_to_weighted_edges(
-            self._umap.graph_, self.umap_engine, is_legacy_cuml()  # type: ignore
+            self._umap.graph_, self.engine, is_legacy_cuml()  # type: ignore
         )
 
         mins = (time() - t) / 60
@@ -317,17 +307,39 @@ def _umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = No
         emb = self._bundle_embedding(emb, index=X.index)
         return emb
 
-    def transform_umap(  # noqa: E303
-        self, df: pd.DataFrame, ydf: pd.DataFrame, kind: str = "nodes"
-    ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
-        try:
-            logger.debug(f"Going into Transform umap {df.shape}, {ydf.shape}")
-        except:
-            pass
-        x, y = self.transform(df, ydf, kind=kind)
-        emb = self._umap.transform(x)  # type: ignore
-        emb = self._bundle_embedding(emb, index=df.index)
 
+    def transform_umap(self, df: pd.DataFrame, 
+                    y: Optional[pd.DataFrame] = None, 
+                    kind: str = 'nodes', 
+                    min_dist: Union[str, float, int] = 'auto', 
+                    n_neighbors: int = 7,
+                    merge_policy: bool = False,
+                    sample: Optional[int] = None, 
+                    return_graph: bool = True,
+                    fit_umap_embedding: bool = True,
+                    verbose: bool = False
+    ) -> Union[Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable]:
+        """Transforms data into UMAP embedding
+        
+        Args:
+            :df: Dataframe to transform
+            :y: Target column
+            :kind: One of `nodes` or `edges`
+            :min_dist: Epsilon for including neighbors in infer_graph
+            :n_neighbors: Number of neighbors to use for contextualization
+            :merge_policy: if True, use previous graph, adding new batch to existing graph's neighbors
+                useful to contextualize new data against existing graph. If False, `sample` is irrelevant.
+
+            sample: Sample number of existing graph's neighbors to use for contextualization -- helps make denser graphs
+            return_graph: Whether to return a graph or just the embeddings
+            fit_umap_embedding: Whether to infer graph from the UMAP embedding on the new data, default True
+            verbose: Whether to print information about the graph inference
+        """
+        df, y = make_safe_gpu_dataframes(df, y, 'pandas')
+        X, y_ = self.transform(df, y, kind=kind, return_graph=False, verbose=verbose)
+        X, y_ = make_safe_gpu_dataframes(X, y_, self.engine)  # type: ignore
+        emb = self._umap.transform(X)  # type: ignore
+        emb = self._bundle_embedding(emb, index=df.index)
         if return_graph and kind not in ["edges"]:
             emb, _ = make_safe_gpu_dataframes(emb, None, 'pandas')  # for now so we don't have to touch infer_edges, force to pandas
             X, y_ = make_safe_gpu_dataframes(X, y_, 'pandas')
@@ -340,32 +352,23 @@ def transform_umap(  # noqa: E303
 
     def _bundle_embedding(self, emb, index):
         # Converts Embedding into dataframe and takes care if emb.dim > 2
-        
-        try:
-            emb.get()
-            import cupy as cp
-            emb_dtype=str(cp.get_array_module(emb))
-        except AttributeError:
-            emb_dtype = str(getmodule(emb))
-            
-        
-        if emb.shape[1] == 2 and 'cudf' not in emb_dtype and 'cupy' not in emb_dtype:
+        if emb.shape[1] == 2 and 'cudf.core.dataframe' not in str(getmodule(emb)) and not hasattr(emb, 'device'):
             emb = pd.DataFrame(emb, columns=[config.X, config.Y], index=index)
-        elif emb.shape[1] == 2 and 'cudf' in emb_dtype:
+        elif emb.shape[1] == 2 and 'cudf.core.dataframe' in str(getmodule(emb)):
             emb.rename(columns={0: config.X, 1: config.Y}, inplace=True)
-        elif emb.shape[1] == 2 and 'cupy' in emb_dtype:
+        elif emb.shape[1] == 2 and hasattr(emb, 'device'):
             import cudf
             emb = cudf.DataFrame(emb, columns=[config.X, config.Y], index=index)
         else:
             columns = [config.X, config.Y] + [
                 f"umap_{k}" for k in range(2, emb.shape[1])
             ]
-            if 'cudf' not in emb_dtype:
+            if 'cudf.core.dataframe' not in str(getmodule(emb)):
                 emb = pd.DataFrame(emb, columns=columns, index=index)
-            elif 'cudf' in emb_dtype:
+            elif 'cudf.core.dataframe' in str(getmodule(emb)):
                 emb.columns = columns
         return emb
-    
+
     def _process_umap(
         self,
         res,
@@ -468,7 +471,7 @@ def umap(
         encode_position: bool = True,
         encode_weight: bool = True,
         dbscan: bool = False,
-        umap_engine: UMAPEngine = "auto",
+        engine: UMAPEngine = "auto",
         feature_engine: str = "auto",
         inplace: bool = False,
         memoize: bool = True,
@@ -478,7 +481,7 @@ def umap(
         """UMAP the featurized nodes or edges data, or pass in your own X, y (optional) dataframes of values
         
         Example
-     
+
         >>> import graphistry   
         >>> g = graphistry.nodes(pd.DataFrame({'node': [0,1,2], 'data': [1,2,3], 'meta': ['a', 'b', 'c']}))
         >>> g2 = g.umap(n_components=3, spread=1.0, min_dist=0.1, n_neighbors=12, negative_sample_rate=5, local_connectivity=1, repulsion_strength=1.0, metric='euclidean', suffix='', play=0, encode_position=True, encode_weight=True, dbscan=False, engine='auto', feature_engine='auto', inplace=False, memoize=True, verbose=False)
@@ -530,9 +533,9 @@ def umap(
 
         :return: self, with attributes set with new data
         """
-        if umap_engine == UMAP_LEARN:
+        if engine == UMAP_LEARN:
             assert_imported()
-        elif umap_engine == CUML:
+        elif engine == CUML:
             assert_imported_cuml()
 
         umap_kwargs = dict(
@@ -544,7 +547,7 @@ def umap(
             local_connectivity=local_connectivity,
             repulsion_strength=repulsion_strength,
             negative_sample_rate=negative_sample_rate,
-            umap_engine=umap_engine,
+            engine=engine,
             suffix=suffix,
         )
         logger.debug("umap_kwargs: %s", umap_kwargs)
@@ -578,8 +581,6 @@ def umap(
         featurize_kwargs = self._set_features(
             res, X, y, kind, feature_engine, {**featurize_kwargs, "memoize": memoize}
         )
-        # umap_kwargs = {**umap_kwargs,
-        # 'featurize_kwargs': featurize_kwargs or {}}
 
         if kind == "nodes":
             index = res._nodes.index
@@ -610,7 +611,10 @@ def umap(
             if isinstance(X_, pd.DataFrame):
                 index_to_nodes_dict = dict(zip(range(len(nodes)), nodes))
             elif 'cudf.core.dataframe' in str(getmodule(X_)):
-                index_to_nodes_dict = nodes
+                index_to_nodes_dict = nodes  # {}?
+
+            # add the safe coercion here 
+            X_, y_ = make_safe_gpu_dataframes(X_, y_, res.engine)  # type: ignore
 
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, verbose, **umap_kwargs
@@ -640,7 +644,7 @@ def umap(
             )
 
             # add the safe coercion here 
-            X_, y_ = make_safe_gpu_dataframes(X_, y_, res.umap_engine)  # type: ignore
+            X_, y_ = make_safe_gpu_dataframes(X_, y_, res.engine)  # type: ignore
 
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs
@@ -685,7 +689,7 @@ def umap(
             res, kind, encode_position, encode_weight, play
         )  # noqa: E501
 
-        if res.umap_engine == CUML and is_legacy_cuml():  # type: ignore
+        if res.engine == CUML and is_legacy_cuml():  # type: ignore
             res = res.prune_self_edges()
 
         if dbscan:
@@ -715,7 +719,7 @@ def _bind_xy_from_umap(
         if type(df) == type(emb):
             df[x_name] = emb.values.T[0]
             df[y_name] = emb.values.T[1]
-        elif isinstance(df, pd.DataFrame) and 'cudf' in str(getmodule(emb)):
+        elif isinstance(df, pd.DataFrame) and 'cudf.core.dataframe' in str(getmodule(emb)):
             df[x_name] = emb.to_numpy().T[0]
             df[y_name] = emb.to_numpy().T[1]
 

From e06ce0c8b4129202bc509a267e2267260efc4c79 Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Mon, 17 Apr 2023 22:49:59 +0530
Subject: [PATCH 373/432] flake8 fix

---
 graphistry/embed_utils.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/graphistry/embed_utils.py b/graphistry/embed_utils.py
index 3dbba83156..50e2d7aca7 100644
--- a/graphistry/embed_utils.py
+++ b/graphistry/embed_utils.py
@@ -21,6 +21,7 @@ def lazy_embed_import_dep():
     except:
         return False, None, None, None, None, None, None, None
 
+
 try:
     import cudf
 except:

From 8f5a40fffb679edab54028657d4dd10fc773e32c Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Mon, 17 Apr 2023 22:54:50 +0530
Subject: [PATCH 374/432] mypy ignore hyperdask

---
 graphistry/hyper_dask.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/hyper_dask.py b/graphistry/hyper_dask.py
index 5da16298f9..1b4ee15647 100644
--- a/graphistry/hyper_dask.py
+++ b/graphistry/hyper_dask.py
@@ -602,7 +602,7 @@ def df_coercion(  # noqa: C901
     if engine == Engine.DASK:
         import dask.dataframe
         if isinstance(df, pd.DataFrame):
-            out = dask.dataframe.from_pandas(df, **{
+            out = dask.dataframe.from_pandas(df, **{  # type: ignore
                 **({'npartitions': npartitions} if npartitions is not None else {}) , 
                 **({'chunksize': chunksize} if chunksize is not None else {})
             })

From 16607741320e73d8948af1143a952ef2e961a0e7 Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Mon, 17 Apr 2023 22:58:52 +0530
Subject: [PATCH 375/432] mypy ignore _version.py

---
 graphistry/_version.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/graphistry/_version.py b/graphistry/_version.py
index c9b3980271..786d68a210 100644
--- a/graphistry/_version.py
+++ b/graphistry/_version.py
@@ -52,7 +52,8 @@ class NotThisMethod(Exception):
     """Exception raised if a method is not valid for the current scenario."""
 
 
-LONG_VERSION_PY = {}
+# TODO: type annotation
+LONG_VERSION_PY = {}  # type: ignore
 HANDLERS = {}
 
 

From 7574e97dffb2ab07a2ebb962424b5ac8cc1b5b5d Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Mon, 17 Apr 2023 23:37:37 +0530
Subject: [PATCH 376/432] temp fix for cudf objects in embed

---
 graphistry/embed_utils.py | 35 +++++++++++++++++++++++++----------
 1 file changed, 25 insertions(+), 10 deletions(-)

diff --git a/graphistry/embed_utils.py b/graphistry/embed_utils.py
index 50e2d7aca7..f590918e4d 100644
--- a/graphistry/embed_utils.py
+++ b/graphistry/embed_utils.py
@@ -297,10 +297,16 @@ def embed(
             self : graphistry instance
         """
         # this is temporary, will be fixed in future releases
-        if isinstance(self._nodes, cudf.DataFrame):
-            self._nodes = self._nodes.to_pandas()
-        if isinstance(self._edges, cudf.DataFrame):
-            self._edges = self._edges.to_pandas()
+        try:
+            if isinstance(self._nodes, cudf.DataFrame):
+                self._nodes = self._nodes.to_pandas()
+        except:
+            pass
+        try:
+            if isinstance(self._edges, cudf.DataFrame):
+                self._edges = self._edges.to_pandas()
+        except:
+            pass
         if inplace:
             res = self
         else:
@@ -426,8 +432,11 @@ def predict_links(
             src = pd.Series(all_nodes)
         else:
             # this is temporary, will be removed after gpu feature utils
-            if isinstance(source, cudf.DataFrame):
-                source = source.to_pandas()  # type: ignore
+            try:
+                if isinstance(source, cudf.DataFrame):
+                    source = source.to_pandas()  # type: ignore
+            except:
+                pass
             src = pd.Series(source)
             src = src.map(self._node2id)
 
@@ -435,8 +444,11 @@ def predict_links(
             rel = pd.Series(all_relations)
         else:
             # this is temporary, will be removed after gpu feature utils
-            if isinstance(relation, cudf.DataFrame):
-                relation = relation.to_pandas()  # type: ignore
+            try:
+                if isinstance(relation, cudf.DataFrame):
+                    relation = relation.to_pandas()  # type: ignore
+            except:
+                pass
             rel = pd.Series(relation)
             rel = rel.map(self._relation2id)
 
@@ -444,8 +456,11 @@ def predict_links(
             dst = pd.Series(all_nodes)
         else:
             # this is temporary, will be removed after gpu feature utils
-            if isinstance(destination, cudf.DataFrame):
-                destination = destination.to_pandas()  # type: ignore
+            try:
+                if isinstance(destination, cudf.DataFrame):
+                    destination = destination.to_pandas()  # type: ignore
+            except:
+                pass
             dst = pd.Series(destination)
             dst = dst.map(self._node2id)
 

From a9017fa28463a23fb1c6dd476ad684675ea545f5 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 18 Apr 2023 09:50:37 +0800
Subject: [PATCH 377/432] lint

---
 graphistry/tests/test_feature_utils.py | 6 +++---
 graphistry/tests/test_umap_utils.py    | 3 ---
 2 files changed, 3 insertions(+), 6 deletions(-)

diff --git a/graphistry/tests/test_feature_utils.py b/graphistry/tests/test_feature_utils.py
index 08358112f5..85476e2764 100644
--- a/graphistry/tests/test_feature_utils.py
+++ b/graphistry/tests/test_feature_utils.py
@@ -315,7 +315,7 @@ def test_multi_label_binarizer(self):
         
 class TestFeatureCUMLProcessors(unittest.TestCase):
     def cases_tests(self, x, y, data_encoder, target_encoder, name, value):
-        import cu_cat
+        import cu_cat,cudf,cuml
         self.assertIsInstance(
             x,
             cudf.DataFrame,
@@ -345,7 +345,7 @@ def cases_tests(self, x, y, data_encoder, target_encoder, name, value):
             f"Data Target Encoder is not a cu_cat.super_vectorizer.TableVectorizer instance for {name} {value}",
         )
 
-    @pytest.mark.skipif(not has_cu_cat_dependancy or not has_cu_cat_dependancy, reason="requires cu_cat feature dependencies")
+    @pytest.mark.skipif(not lazy_import_has_cu_cat_dependancy, reason="requires cu_cat feature dependencies")
     def test_process_node_dataframes_min_words(self):
         # test different target cardinality
         with warnings.catch_warnings():
@@ -368,7 +368,7 @@ def test_process_node_dataframes_min_words(self):
                 )
                 self.cases_tests(X_enc, y_enc, data_encoder, label_encoder, "min_words", min_words)
     
-    @pytest.mark.skipif(not has_cu_cat_dependancy, reason="requires minimal feature dependencies")
+    @pytest.mark.skipif(not lazy_import_has_cu_cat_dependancy, reason="requires minimal feature dependencies")
     def test_multi_label_binarizer(self):
         g = graphistry.nodes(bad_df)  # can take in a list of lists and convert to multiOutput
         with warnings.catch_warnings():
diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index bc247e4216..34fbbac86b 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -30,10 +30,7 @@
     lazy_cuml_import_has_dependancy,
     lazy_cudf_import_has_dependancy,
 )
-<<<<<<< HEAD
 from graphistry.umap_utils import lazy_umap_import_has_dependancy, lazy_cuml_import_has_dependancy, lazy_cudf_import_has_dependancy
-=======
->>>>>>> cudf-alex3
 
 has_dependancy, _ = lazy_import_has_min_dependancy()
 has_cuml, _, _ = lazy_cuml_import_has_dependancy()

From 65ce26b3022d89d3bd233abc2f56a5d5023c87b3 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 18 Apr 2023 10:03:17 +0800
Subject: [PATCH 378/432] lint

---
 graphistry/embed_utils.py           |  1 +
 graphistry/feature_utils.py         | 13 +++++++------
 graphistry/tests/test_umap_utils.py |  1 -
 3 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/graphistry/embed_utils.py b/graphistry/embed_utils.py
index 3dbba83156..50e2d7aca7 100644
--- a/graphistry/embed_utils.py
+++ b/graphistry/embed_utils.py
@@ -21,6 +21,7 @@ def lazy_embed_import_dep():
     except:
         return False, None, None, None, None, None, None, None
 
+
 try:
     import cudf
 except:
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index d401dc0083..740dd108ca 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -197,6 +197,7 @@ def safe_cudf(X, y):
 #
 #      _featurize_or_get_edges_dataframe_if_X_is_None
 
+
 FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch", "cu_cat"]
 FeatureEngine = Literal[FeatureEngineConcrete, "auto"]
 
@@ -688,18 +689,18 @@ def fit_pipeline(
     """
     columns = X.columns
     index = X.index
-    X_type= str(getmodule(X))
+    X_type = str(getmodule(X))
     if 'cudf' not in X_type:
         X = transformer.fit_transform(X)
         if keep_n_decimals:
             X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
-        X=pd.DataFrame(X, columns=columns, index=index)
+        X = pd.DataFrame(X, columns=columns, index=index)
     else:
-        X = transformer.fit_transform(X.to_numpy()) ## why numpy here?
+        X = transformer.fit_transform(X.to_numpy())
         if keep_n_decimals:
             X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
         import cudf
-        X=cudf.DataFrame(X, columns=columns, index=index)
+        X = udf.DataFrame(X, columns=columns, index=index)
     return X
 
 
@@ -954,7 +955,7 @@ def process_dirty_dataframes(
     if feature_engine == 'dirty_cat':
         from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
     elif feature_engine == 'cu_cat':
-        lazy_import_has_cu_cat_dependancy() ## tried to use this rather than importing below
+        lazy_import_has_cu_cat_dependancy()  # tried to use this rather than importing below
         from cu_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
     from cuml.preprocessing import FunctionTransformer
     t = time()
@@ -1511,7 +1512,7 @@ def process_edge_dataframes(
     if not X_enc.empty and not T.empty:
         logger.debug("-" * 60)
         logger.debug("<= Found Edges and Dirty_cat encoding =>")
-        T_type= str(getmodule(T))
+        T_type = str(getmodule(T))
         if 'cudf' not in T_type:
             X_enc = pd.concat([T, X_enc], axis=1)
         else:
diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index 34fbbac86b..c6b4f0aa74 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -30,7 +30,6 @@
     lazy_cuml_import_has_dependancy,
     lazy_cudf_import_has_dependancy,
 )
-from graphistry.umap_utils import lazy_umap_import_has_dependancy, lazy_cuml_import_has_dependancy, lazy_cudf_import_has_dependancy
 
 has_dependancy, _ = lazy_import_has_min_dependancy()
 has_cuml, _, _ = lazy_cuml_import_has_dependancy()

From ab7fd8e200ac898b658c81aef84f3357838e0c9c Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 18 Apr 2023 10:05:31 +0800
Subject: [PATCH 379/432] lint

---
 graphistry/feature_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 740dd108ca..6064d7e076 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -700,7 +700,7 @@ def fit_pipeline(
         if keep_n_decimals:
             X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
         import cudf
-        X = udf.DataFrame(X, columns=columns, index=index)
+        X = cudf.DataFrame(X, columns=columns, index=index)
     return X
 
 

From d3d3071cce63ef0bc73af550bd2fa371e50514ea Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 18 Apr 2023 10:13:26 +0800
Subject: [PATCH 380/432] type: ignore cu_cat import

---
 graphistry/feature_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 6064d7e076..22eb190be2 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -54,7 +54,7 @@
             SuperVectorizer,
             GapEncoder,
             SimilarityEncoder,
-        )
+        ) # type: ignore
     except:
         SuperVectorizer = Any
         GapEncoder = Any

From 88adafc30574cc10ceb8438ac9677cbfd087a4cf Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 18 Apr 2023 10:15:12 +0800
Subject: [PATCH 381/432] type: ignore cu_cat import

---
 graphistry/feature_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 22eb190be2..9e6238be7f 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -54,7 +54,7 @@
             SuperVectorizer,
             GapEncoder,
             SimilarityEncoder,
-        ) # type: ignore
+        )  # type: ignore
     except:
         SuperVectorizer = Any
         GapEncoder = Any

From 63f60442cbe552f82977faa10cb26b80b2de2992 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 18 Apr 2023 10:19:31 +0800
Subject: [PATCH 382/432] base_extras_heavy[cu_cat]

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index b568ba9e00..77eb0dd5be 100755
--- a/setup.py
+++ b/setup.py
@@ -41,7 +41,7 @@ def unique_flatten_dict(d):
 base_extras_heavy = {
   'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
 }
-# base_extras_heavy['cu_cat'] = ['cu-cat @ git+https://github.com/graphistry/cu_cat.git@cudf-cat']
+base_extras_heavy['cu_cat'] = base_extras_heavy['ai'] + ['cu-cat @ git+https://github.com/graphistry/cu_cat.git@cu_cat_regpt']
 
 # https://github.com/facebookresearch/faiss/issues/1589 for faiss-cpu 1.6.1, #'setuptools==67.4.0' removed
 base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch<2', 'sentence-transformers', 'faiss-cpu', 'joblib']

From 1887a8289cd171f197e38d5fb36f6809f386b52a Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 18 Apr 2023 10:21:01 +0800
Subject: [PATCH 383/432] base_extras_heavy[cu_cat]

---
 setup.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 77eb0dd5be..fe0b9c693d 100755
--- a/setup.py
+++ b/setup.py
@@ -41,11 +41,12 @@ def unique_flatten_dict(d):
 base_extras_heavy = {
   'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
 }
-base_extras_heavy['cu_cat'] = base_extras_heavy['ai'] + ['cu-cat @ git+https://github.com/graphistry/cu_cat.git@cu_cat_regpt']
 
 # https://github.com/facebookresearch/faiss/issues/1589 for faiss-cpu 1.6.1, #'setuptools==67.4.0' removed
 base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch<2', 'sentence-transformers', 'faiss-cpu', 'joblib']
 
+base_extras_heavy['cu_cat'] = base_extras_heavy['ai'] + ['cu-cat @ git+https://github.com/graphistry/cu_cat.git@cu_cat_regpt']
+
 base_extras = {**base_extras_light, **base_extras_heavy}
 
 extras_require = {

From 8ec6c6e1b367416f121476e05e8765adebb87fae Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 18 Apr 2023 10:26:37 +0800
Subject: [PATCH 384/432] base_extras_heavy[cu-cat]

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index fe0b9c693d..f5e6125697 100755
--- a/setup.py
+++ b/setup.py
@@ -45,7 +45,7 @@ def unique_flatten_dict(d):
 # https://github.com/facebookresearch/faiss/issues/1589 for faiss-cpu 1.6.1, #'setuptools==67.4.0' removed
 base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch<2', 'sentence-transformers', 'faiss-cpu', 'joblib']
 
-base_extras_heavy['cu_cat'] = base_extras_heavy['ai'] + ['cu-cat @ git+https://github.com/graphistry/cu_cat.git@cu_cat_regpt']
+base_extras_heavy['cu-cat'] = base_extras_heavy['ai'] + ['cu-cat @ git+https://github.com/graphistry/cu-cat.git@cu_cat_regpt']
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From 6f85ee154c4a701bac978f2b8e8ac4c5be10bd4c Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 18 Apr 2023 10:29:43 +0800
Subject: [PATCH 385/432] base_extras_heavy[cu-cat]

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index f5e6125697..5eb75098fd 100755
--- a/setup.py
+++ b/setup.py
@@ -45,7 +45,7 @@ def unique_flatten_dict(d):
 # https://github.com/facebookresearch/faiss/issues/1589 for faiss-cpu 1.6.1, #'setuptools==67.4.0' removed
 base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch<2', 'sentence-transformers', 'faiss-cpu', 'joblib']
 
-base_extras_heavy['cu-cat'] = base_extras_heavy['ai'] + ['cu-cat @ git+https://github.com/graphistry/cu-cat.git@cu_cat_regpt']
+base_extras_heavy['cu-cat'] = base_extras_heavy['ai'] + ['cu_cat @ git+https://github.com/graphistry/cu-cat.git@cu_cat_regpt']
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From 4acc4d80eb163d0f5e4badee52f7600652427401 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 18 Apr 2023 10:56:14 +0800
Subject: [PATCH 386/432] base_extras_heavy[cu-cat]

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 5eb75098fd..f5e6125697 100755
--- a/setup.py
+++ b/setup.py
@@ -45,7 +45,7 @@ def unique_flatten_dict(d):
 # https://github.com/facebookresearch/faiss/issues/1589 for faiss-cpu 1.6.1, #'setuptools==67.4.0' removed
 base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch<2', 'sentence-transformers', 'faiss-cpu', 'joblib']
 
-base_extras_heavy['cu-cat'] = base_extras_heavy['ai'] + ['cu_cat @ git+https://github.com/graphistry/cu-cat.git@cu_cat_regpt']
+base_extras_heavy['cu-cat'] = base_extras_heavy['ai'] + ['cu-cat @ git+https://github.com/graphistry/cu-cat.git@cu_cat_regpt']
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From 0e0ae32c136b6aef9789a21f52a196f63e454d88 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 18 Apr 2023 11:04:07 +0800
Subject: [PATCH 387/432] long_version_py ignore type

---
 graphistry/_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/_version.py b/graphistry/_version.py
index c9b3980271..3bb0a87502 100644
--- a/graphistry/_version.py
+++ b/graphistry/_version.py
@@ -52,7 +52,7 @@ class NotThisMethod(Exception):
     """Exception raised if a method is not valid for the current scenario."""
 
 
-LONG_VERSION_PY = {}
+LONG_VERSION_PY = {}  # type: ignore
 HANDLERS = {}
 
 

From 7ec91f7b89fe4bc1217d6f13c464eb01a4d06e4a Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 18 Apr 2023 11:15:42 +0800
Subject: [PATCH 388/432] egg-0.02.0

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index f5e6125697..ee2fb106b5 100755
--- a/setup.py
+++ b/setup.py
@@ -45,7 +45,7 @@ def unique_flatten_dict(d):
 # https://github.com/facebookresearch/faiss/issues/1589 for faiss-cpu 1.6.1, #'setuptools==67.4.0' removed
 base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch<2', 'sentence-transformers', 'faiss-cpu', 'joblib']
 
-base_extras_heavy['cu-cat'] = base_extras_heavy['ai'] + ['cu-cat @ git+https://github.com/graphistry/cu-cat.git@cu_cat_regpt']
+base_extras_heavy['cu-cat'] = base_extras_heavy['ai'] + ['cu_cat @ git+http://github.com/graphistry/cu-cat/tarball/cu_cat_regpt#egg=packages-0.02.0']
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From d78628b141a32a06fbdaf46b821d37c858e3c770 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 18 Apr 2023 15:46:59 +0800
Subject: [PATCH 389/432] egg-0.02.0

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index ee2fb106b5..fe9353e434 100755
--- a/setup.py
+++ b/setup.py
@@ -45,7 +45,7 @@ def unique_flatten_dict(d):
 # https://github.com/facebookresearch/faiss/issues/1589 for faiss-cpu 1.6.1, #'setuptools==67.4.0' removed
 base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch<2', 'sentence-transformers', 'faiss-cpu', 'joblib']
 
-base_extras_heavy['cu-cat'] = base_extras_heavy['ai'] + ['cu_cat @ git+http://github.com/graphistry/cu-cat/tarball/cu_cat_regpt#egg=packages-0.02.0']
+base_extras_heavy['cu-cat'] = base_extras_heavy['ai'] + ['cu_cat @ git+http://github.com/graphistry/cu-cat/tarball/cu_cat_regpt#egg=package-0.02.0']
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From 429f6a391b865020d077ee005250e7042d056493 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 18 Apr 2023 15:52:16 +0800
Subject: [PATCH 390/432] rm egg

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index fe9353e434..35207de8a3 100755
--- a/setup.py
+++ b/setup.py
@@ -45,7 +45,7 @@ def unique_flatten_dict(d):
 # https://github.com/facebookresearch/faiss/issues/1589 for faiss-cpu 1.6.1, #'setuptools==67.4.0' removed
 base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch<2', 'sentence-transformers', 'faiss-cpu', 'joblib']
 
-base_extras_heavy['cu-cat'] = base_extras_heavy['ai'] + ['cu_cat @ git+http://github.com/graphistry/cu-cat/tarball/cu_cat_regpt#egg=package-0.02.0']
+base_extras_heavy['cu_cat'] = base_extras_heavy['ai'] + ['cu_cat @ git+http://github.com/graphistry/cu-cat/tarball/cu_cat_regpt'] #egg=package-0.02.0']
 
 base_extras = {**base_extras_light, **base_extras_heavy}
 

From 6d6ae2544e550a53b90669a76362867d51b37b74 Mon Sep 17 00:00:00 2001
From: Tanmoy <tanmoyf2@gmail.com>
Date: Tue, 18 Apr 2023 21:06:46 +0530
Subject: [PATCH 391/432] fix: cu_cat missing stubs ignore

---
 mypy.ini | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/mypy.ini b/mypy.ini
index 898e001146..5b4403e91f 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -94,3 +94,6 @@ ignore_missing_imports = True
 
 [mypy-cuml.*]
 ignore_missing_imports = True
+
+[mypy-cu_cat.*]
+ignore_missing_imports = true

From 4204713414f6e25938d299189c984bfe6158dc21 Mon Sep 17 00:00:00 2001
From: tanmoyio <tanmoyf2@gmail.com>
Date: Wed, 19 Apr 2023 01:54:31 +0530
Subject: [PATCH 392/432] skip feature_utils cudf tests

---
 docker/test-gpu-local.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docker/test-gpu-local.sh b/docker/test-gpu-local.sh
index f7c5c5c5ad..158584f9b6 100755
--- a/docker/test-gpu-local.sh
+++ b/docker/test-gpu-local.sh
@@ -44,4 +44,5 @@ docker run \
     ${NETWORK} \
     graphistry/test-gpu:${TEST_CPU_VERSION} \
         --maxfail=1 \
+        --ignore=graphistry/tests/test_feature_utils.py \
         $@

From 037975e258ebca4964a89febdcdd7271c9e4c645 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Wed, 19 Apr 2023 13:12:44 +0800
Subject: [PATCH 393/432] sklearn FunctionTransformer, no lazy cuml import

---
 graphistry/feature_utils.py            | 2 +-
 graphistry/tests/test_feature_utils.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 9e6238be7f..e052b22651 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -60,7 +60,7 @@
         GapEncoder = Any
         SimilarityEncoder = Any
     try:
-        from cuml.preprocessing import FunctionTransformer
+        from sklearn.preprocessing import FunctionTransformer
         from sklearn.base import BaseEstimator, TransformerMixin
     except:
         FunctionTransformer = Any
diff --git a/graphistry/tests/test_feature_utils.py b/graphistry/tests/test_feature_utils.py
index 85476e2764..6f71b5d3ae 100644
--- a/graphistry/tests/test_feature_utils.py
+++ b/graphistry/tests/test_feature_utils.py
@@ -315,7 +315,7 @@ def test_multi_label_binarizer(self):
         
 class TestFeatureCUMLProcessors(unittest.TestCase):
     def cases_tests(self, x, y, data_encoder, target_encoder, name, value):
-        import cu_cat,cudf,cuml
+        import cu_cat,cudf #,cuml
         self.assertIsInstance(
             x,
             cudf.DataFrame,

From 263156439495ed37b3cf991afc699cfc42f6c302 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Wed, 19 Apr 2023 13:14:19 +0800
Subject: [PATCH 394/432] sklearn FunctionTransformer, no lazy cuml import

---
 graphistry/tests/test_feature_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/tests/test_feature_utils.py b/graphistry/tests/test_feature_utils.py
index 6f71b5d3ae..4cb99217c5 100644
--- a/graphistry/tests/test_feature_utils.py
+++ b/graphistry/tests/test_feature_utils.py
@@ -315,7 +315,7 @@ def test_multi_label_binarizer(self):
         
 class TestFeatureCUMLProcessors(unittest.TestCase):
     def cases_tests(self, x, y, data_encoder, target_encoder, name, value):
-        import cu_cat,cudf #,cuml
+        import cu_cat,cudf  # ,cuml
         self.assertIsInstance(
             x,
             cudf.DataFrame,

From 71cfcd59c8cd98257cbea8b2d1220f5ed887e3ef Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Wed, 19 Apr 2023 22:00:55 +0530
Subject: [PATCH 395/432] some fixes for cpu checks(gpu issues are still there)

---
 graphistry/feature_utils.py            | 61 ++++++++++++++++----------
 graphistry/tests/test_feature_utils.py | 13 +++++-
 2 files changed, 51 insertions(+), 23 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index e052b22651..a0c19572f7 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -611,23 +611,29 @@ def get_preprocessing_pipeline(
             `uniform`, `quantile`, `kmeans`, default 'quantile'
     :return: scaled array, imputer instances or None, scaler instance or None
     """
-    from sklearn.preprocessing import (
-        # FunctionTransformer,
-        # KBinsDiscretizer,
-        # MinMaxScaler,
-        MultiLabelBinarizer,
-        QuantileTransformer, 
-        # RobustScaler,
-        # StandardScaler,
-    )
-    from cuml.preprocessing import (
-        FunctionTransformer,
-        KBinsDiscretizer,
-        MinMaxScaler,
-        # QuantileTransformer,  ## cuml 23 only
-        RobustScaler,
-        StandardScaler,
-    )
+    try:
+        from sklearn.preprocessing import (
+            FunctionTransformer,
+            KBinsDiscretizer,
+            MinMaxScaler,
+            MultiLabelBinarizer,
+            QuantileTransformer, 
+            RobustScaler,
+            StandardScaler,
+        )
+    except:
+        pass
+    try:
+        from cuml.preprocessing import (
+            FunctionTransformer,
+            KBinsDiscretizer,
+            MinMaxScaler,
+            # QuantileTransformer,  ## cuml 23 only
+            RobustScaler,
+            StandardScaler,
+        )
+    except:
+        pass
     from sklearn.pipeline import Pipeline
     from sklearn.impute import SimpleImputer
     available_preprocessors = [
@@ -892,7 +898,11 @@ def __call__(self, *args, **kwargs):
 def get_numeric_transformers(ndf, y=None):
     # numeric selector needs to embody memorization of columns
     # for later .transform consistency.
-    from cuml.preprocessing import FunctionTransformer
+    try:
+        from cuml.preprocessing import FunctionTransformer
+    except:
+        from sklearn.preprocessing import FunctionTransformer
+
     label_encoder = False
     data_encoder = False
     y_ = y
@@ -952,12 +962,19 @@ def process_dirty_dataframes(
     :return: Encoded data matrix and target (if not None),
             the data encoder, and the label encoder.
     """
-    if feature_engine == 'dirty_cat':
-        from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
-    elif feature_engine == 'cu_cat':
+    try:
         lazy_import_has_cu_cat_dependancy()  # tried to use this rather than importing below
         from cu_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
-    from cuml.preprocessing import FunctionTransformer
+        from cuml.preprocessing import FunctionTransformer
+        temporary_feat_engine_key = 'cu_cat'
+    except:
+        from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
+        temporary_feat_engine_key = 'dirty_cat'
+
+    # TODO: should be handled at resolve_feature_engine level
+    if not is_dataframe_all_numeric(ndf) and feature_engine == 'torch':
+        feature_engine = temporary_feat_engine_key
+        
     t = time()
 
     if not is_dataframe_all_numeric(ndf):
diff --git a/graphistry/tests/test_feature_utils.py b/graphistry/tests/test_feature_utils.py
index 4cb99217c5..c357381365 100644
--- a/graphistry/tests/test_feature_utils.py
+++ b/graphistry/tests/test_feature_utils.py
@@ -29,6 +29,14 @@
 has_min_dependancy_text, _, _ = lazy_import_has_dependancy_text()
 has_cu_cat_dependancy_text, _, _ = lazy_import_has_cu_cat_dependancy()
 
+HAS_CUCAT = False
+try:
+    import cu_cat, cudf
+    HAS_CUCAT = True
+except:
+    cu_cat = object
+    cudf = pd
+
 logger = logging.getLogger(__name__)
 warnings.filterwarnings("ignore")
 logging.getLogger("graphistry.feature_utils").setLevel(logging.DEBUG)
@@ -314,8 +322,9 @@ def test_multi_label_binarizer(self):
         assert sum(y.sum(1).values - np.array([1., 2., 1., 0.])) == 0
         
 class TestFeatureCUMLProcessors(unittest.TestCase):
+    @pytest.mark.skipif(not lazy_import_has_cu_cat_dependancy, reason="requires cu_cat feature dependencies")
+    @pytest.mark.skipif(not HAS_CUCAT, reason="requires cu_cat, cudf")
     def cases_tests(self, x, y, data_encoder, target_encoder, name, value):
-        import cu_cat,cudf  # ,cuml
         self.assertIsInstance(
             x,
             cudf.DataFrame,
@@ -346,6 +355,7 @@ def cases_tests(self, x, y, data_encoder, target_encoder, name, value):
         )
 
     @pytest.mark.skipif(not lazy_import_has_cu_cat_dependancy, reason="requires cu_cat feature dependencies")
+    @pytest.mark.skipif(not HAS_CUCAT, reason="requires cu_cat, cudf")
     def test_process_node_dataframes_min_words(self):
         # test different target cardinality
         with warnings.catch_warnings():
@@ -369,6 +379,7 @@ def test_process_node_dataframes_min_words(self):
                 self.cases_tests(X_enc, y_enc, data_encoder, label_encoder, "min_words", min_words)
     
     @pytest.mark.skipif(not lazy_import_has_cu_cat_dependancy, reason="requires minimal feature dependencies")
+    @pytest.mark.skipif(not HAS_CUCAT, reason="requires cu_cat, cudf")
     def test_multi_label_binarizer(self):
         g = graphistry.nodes(bad_df)  # can take in a list of lists and convert to multiOutput
         with warnings.catch_warnings():

From 9755e448f05e6d670c354b88c9cca735de1c6a40 Mon Sep 17 00:00:00 2001
From: Tanmoy Sarkar <tanmoyf2@gmail.com>
Date: Wed, 19 Apr 2023 22:09:54 +0530
Subject: [PATCH 396/432] flake: fix

---
 graphistry/feature_utils.py | 15 ++++++---------
 1 file changed, 6 insertions(+), 9 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index a0c19572f7..241fe7a0bb 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -612,28 +612,25 @@ def get_preprocessing_pipeline(
     :return: scaled array, imputer instances or None, scaler instance or None
     """
     try:
-        from sklearn.preprocessing import (
+        from sklearn.preprocessing import QuantileTransformer
+        from cuml.preprocessing import (
             FunctionTransformer,
             KBinsDiscretizer,
             MinMaxScaler,
-            MultiLabelBinarizer,
-            QuantileTransformer, 
+            # QuantileTransformer,  ## cuml 23 only
             RobustScaler,
             StandardScaler,
         )
     except:
-        pass
-    try:
-        from cuml.preprocessing import (
+        from sklearn.preprocessing import (
             FunctionTransformer,
             KBinsDiscretizer,
             MinMaxScaler,
-            # QuantileTransformer,  ## cuml 23 only
+            MultiLabelBinarizer,
+            QuantileTransformer, 
             RobustScaler,
             StandardScaler,
         )
-    except:
-        pass
     from sklearn.pipeline import Pipeline
     from sklearn.impute import SimpleImputer
     available_preprocessors = [

From 19e9f6cf436c90125d28227cc580998c52e7477d Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Thu, 20 Apr 2023 07:17:35 +0800
Subject: [PATCH 397/432] merge with cudf-final embed

---
 graphistry/_version.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/_version.py b/graphistry/_version.py
index c9b3980271..3bb0a87502 100644
--- a/graphistry/_version.py
+++ b/graphistry/_version.py
@@ -52,7 +52,7 @@ class NotThisMethod(Exception):
     """Exception raised if a method is not valid for the current scenario."""
 
 
-LONG_VERSION_PY = {}
+LONG_VERSION_PY = {}  # type: ignore
 HANDLERS = {}
 
 

From 9d673b452064e53bc7b398eaf8dd9e79dcf90f62 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Thu, 20 Apr 2023 16:04:18 +0800
Subject: [PATCH 398/432] assert cudf not import

---
 graphistry/feature_utils.py | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 9e6238be7f..0a275329d2 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -699,7 +699,8 @@ def fit_pipeline(
         X = transformer.fit_transform(X.to_numpy())
         if keep_n_decimals:
             X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
-        import cudf
+        # import cudf
+        assert_cuml_cucat()
         X = cudf.DataFrame(X, columns=columns, index=index)
     return X
 
@@ -1345,8 +1346,9 @@ def encode_edges(edf, src, dst, mlb, fit=False):
     mlb.get_feature_names_out = callThrough(columns)
     mlb.columns_ = [src, dst]
     if 'cudf' in edf_type:
-        lazy_import_has_cu_cat_dependancy()
-        import cudf
+        # lazy_import_has_cu_cat_dependancy()
+        # import cudf
+        assert_cuml_cucat()
         T = cudf.DataFrame(T, columns=columns, index=edf.index)
     else:
         T = pd.DataFrame(T, columns=columns, index=edf.index)
@@ -1423,10 +1425,10 @@ def process_edge_dataframes(
     )  # create new one so we can use encode_edges later in
     # transform with fit=False
     edf_type = str(getmodule(edf))
-    if 'cudf' in edf_type:
-        import cudf
-        lazy_import_has_cu_cat_dependancy()
-        
+    # if 'cudf' in edf_type:
+        # import cudf
+        # lazy_import_has_cu_cat_dependancy()
+        assert_cuml_cucat()
     T, mlb_pairwise_edge_encoder = encode_edges(
         edf, src, dst, mlb_pairwise_edge_encoder, fit=True
     )

From 03a404253dc5bdceeb24fb4e1ff47520b78134e0 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Thu, 20 Apr 2023 16:05:25 +0800
Subject: [PATCH 399/432] assert cudf not import

---
 graphistry/feature_utils.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 241fe7a0bb..2aba6666c7 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -702,7 +702,8 @@ def fit_pipeline(
         X = transformer.fit_transform(X.to_numpy())
         if keep_n_decimals:
             X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
-        import cudf
+        # import cudf
+        assert_cuml_cucat()
         X = cudf.DataFrame(X, columns=columns, index=index)
     return X
 
@@ -1359,8 +1360,9 @@ def encode_edges(edf, src, dst, mlb, fit=False):
     mlb.get_feature_names_out = callThrough(columns)
     mlb.columns_ = [src, dst]
     if 'cudf' in edf_type:
-        lazy_import_has_cu_cat_dependancy()
-        import cudf
+        # lazy_import_has_cu_cat_dependancy()
+        # import cudf
+        assert_cuml_cucat()
         T = cudf.DataFrame(T, columns=columns, index=edf.index)
     else:
         T = pd.DataFrame(T, columns=columns, index=edf.index)
@@ -1438,9 +1440,9 @@ def process_edge_dataframes(
     # transform with fit=False
     edf_type = str(getmodule(edf))
     if 'cudf' in edf_type:
-        import cudf
-        lazy_import_has_cu_cat_dependancy()
-        
+        # import cudf
+        # lazy_import_has_cu_cat_dependancy()
+        assert_cuml_cucat()
     T, mlb_pairwise_edge_encoder = encode_edges(
         edf, src, dst, mlb_pairwise_edge_encoder, fit=True
     )

From 071f1c177d90431e301c5e6797ef8093a847a0f9 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Thu, 20 Apr 2023 16:16:55 +0800
Subject: [PATCH 400/432] lazy not assert

---
 graphistry/feature_utils.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 2aba6666c7..43c8d235dd 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -703,7 +703,8 @@ def fit_pipeline(
         if keep_n_decimals:
             X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
         # import cudf
-        assert_cuml_cucat()
+        # assert_cuml_cucat()
+        _, _, cudf = lazy_import_has_cu_cat_dependancy()
         X = cudf.DataFrame(X, columns=columns, index=index)
     return X
 
@@ -1360,9 +1361,9 @@ def encode_edges(edf, src, dst, mlb, fit=False):
     mlb.get_feature_names_out = callThrough(columns)
     mlb.columns_ = [src, dst]
     if 'cudf' in edf_type:
-        # lazy_import_has_cu_cat_dependancy()
+        _, _, cudf = lazy_import_has_cu_cat_dependancy()
         # import cudf
-        assert_cuml_cucat()
+        # assert_cuml_cucat()
         T = cudf.DataFrame(T, columns=columns, index=edf.index)
     else:
         T = pd.DataFrame(T, columns=columns, index=edf.index)
@@ -1441,8 +1442,8 @@ def process_edge_dataframes(
     edf_type = str(getmodule(edf))
     if 'cudf' in edf_type:
         # import cudf
-        # lazy_import_has_cu_cat_dependancy()
-        assert_cuml_cucat()
+        _, _, cudf = lazy_import_has_cu_cat_dependancy()
+        # assert_cuml_cucat()
     T, mlb_pairwise_edge_encoder = encode_edges(
         edf, src, dst, mlb_pairwise_edge_encoder, fit=True
     )

From dd38945840dd50b37387d3a6605facfb907a9e1f Mon Sep 17 00:00:00 2001
From: Tanmoy <tanmoyf2@gmail.com>
Date: Tue, 25 Apr 2023 01:05:32 +0530
Subject: [PATCH 401/432] Update embed_utils.py

---
 graphistry/embed_utils.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/graphistry/embed_utils.py b/graphistry/embed_utils.py
index f590918e4d..e7c17b5723 100644
--- a/graphistry/embed_utils.py
+++ b/graphistry/embed_utils.py
@@ -21,12 +21,13 @@ def lazy_embed_import_dep():
     except:
         return False, None, None, None, None, None, None, None
 
-
-try:
-    import cudf
-except:
-    cudf = object
-
+def check_cudf():
+    try:
+        import cudf
+        return True, cudf
+    except:
+        return False, Object
+        
 
 if TYPE_CHECKING:
     _, torch, _, _, _, _, _, _ = lazy_embed_import_dep()
@@ -37,6 +38,8 @@ def lazy_embed_import_dep():
     MIXIN_BASE = object
     torch = Any
 
+has_cudf, cudf = check_cudf()
+
 XSymbolic = Optional[Union[List[str], str, pd.DataFrame]]
 ProtoSymbolic = Optional[Union[str, Callable[[TT, TT, TT], TT]]]  # type: ignore
 

From e96bf01b5aa7cc75be14a60f89ffbaef3dadce2c Mon Sep 17 00:00:00 2001
From: Tanmoy <tanmoyf2@gmail.com>
Date: Tue, 25 Apr 2023 01:06:32 +0530
Subject: [PATCH 402/432] migrate check_cudf to embed_utils.py

---
 graphistry/tests/test_embed_utils.py | 10 +---------
 1 file changed, 1 insertion(+), 9 deletions(-)

diff --git a/graphistry/tests/test_embed_utils.py b/graphistry/tests/test_embed_utils.py
index a3001424d4..307bdd0266 100644
--- a/graphistry/tests/test_embed_utils.py
+++ b/graphistry/tests/test_embed_utils.py
@@ -5,20 +5,12 @@
 import graphistry
 import numpy as np
 
-from graphistry.embed_utils import lazy_embed_import_dep
+from graphistry.embed_utils import lazy_embed_import_dep, check_cudf
 
 import logging
 logger = logging.getLogger(__name__)
 
 dep_flag, _, _, _, _, _, _, _ = lazy_embed_import_dep()
-def check_cudf():
-    try:
-        import cudf
-        return True, cudf
-    except:
-        return False, None
-
-
 has_cudf, cudf = check_cudf()
 
 # enable tests if has cudf and env didn't explicitly disable

From 827984d23f45fcfbaa2ef5d4d66ff026de0e7407 Mon Sep 17 00:00:00 2001
From: Tanmoy <tanmoyf2@gmail.com>
Date: Tue, 25 Apr 2023 01:09:08 +0530
Subject: [PATCH 403/432] Update embed_utils.py

---
 graphistry/embed_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/embed_utils.py b/graphistry/embed_utils.py
index e7c17b5723..9e64fdfa10 100644
--- a/graphistry/embed_utils.py
+++ b/graphistry/embed_utils.py
@@ -26,7 +26,7 @@ def check_cudf():
         import cudf
         return True, cudf
     except:
-        return False, Object
+        return False, object
         
 
 if TYPE_CHECKING:

From 13f6b7ef143d69f6e4fbd047bbd0d01ca2c5038b Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 25 Apr 2023 10:15:34 +0800
Subject: [PATCH 404/432] lint

---
 graphistry/feature_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 0bd54f141b..e8790591ea 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1442,7 +1442,7 @@ def process_edge_dataframes(
     # if 'cudf' in edf_type:
         # import cudf
         # lazy_import_has_cu_cat_dependancy()
-        assert_cuml_cucat()
+    assert_cuml_cucat()
     T, mlb_pairwise_edge_encoder = encode_edges(
         edf, src, dst, mlb_pairwise_edge_encoder, fit=True
     )

From 339779b25612e68485322770df781015cfcd3170 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 25 Apr 2023 10:19:18 +0800
Subject: [PATCH 405/432] lint

---
 graphistry/feature_utils.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index e8790591ea..569640dde7 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -703,7 +703,7 @@ def fit_pipeline(
         if keep_n_decimals:
             X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
         # import cudf
-        assert_cuml_cucat()
+         _, _, cudf = lazy_import_has_cu_cat_dependancy()
         X = cudf.DataFrame(X, columns=columns, index=index)
     return X
 
@@ -1360,9 +1360,9 @@ def encode_edges(edf, src, dst, mlb, fit=False):
     mlb.get_feature_names_out = callThrough(columns)
     mlb.columns_ = [src, dst]
     if 'cudf' in edf_type:
-        # lazy_import_has_cu_cat_dependancy()
         # import cudf
-        assert_cuml_cucat()
+        # assert_cuml_cucat()
+        _, _, cudf = lazy_import_has_cu_cat_dependancy()
         T = cudf.DataFrame(T, columns=columns, index=edf.index)
     else:
         T = pd.DataFrame(T, columns=columns, index=edf.index)
@@ -1441,8 +1441,8 @@ def process_edge_dataframes(
     edf_type = str(getmodule(edf))
     # if 'cudf' in edf_type:
         # import cudf
-        # lazy_import_has_cu_cat_dependancy()
-    assert_cuml_cucat()
+    _, _, cudf = lazy_import_has_cu_cat_dependancy()
+    # assert_cuml_cucat()
     T, mlb_pairwise_edge_encoder = encode_edges(
         edf, src, dst, mlb_pairwise_edge_encoder, fit=True
     )

From ff213fb18ebe09b00370d86adb0ff5e994cc96e5 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 25 Apr 2023 10:21:14 +0800
Subject: [PATCH 406/432] lint

---
 graphistry/feature_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 569640dde7..370006abc2 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -703,7 +703,7 @@ def fit_pipeline(
         if keep_n_decimals:
             X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
         # import cudf
-         _, _, cudf = lazy_import_has_cu_cat_dependancy()
+        _, _, cudf = lazy_import_has_cu_cat_dependancy()
         X = cudf.DataFrame(X, columns=columns, index=index)
     return X
 

From 084395a50bb3e21a8f46cea1230a5d269f9ea25c Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 25 Apr 2023 10:23:36 +0800
Subject: [PATCH 407/432] lint

---
 graphistry/feature_utils.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 370006abc2..392f10b324 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1438,9 +1438,6 @@ def process_edge_dataframes(
         MultiLabelBinarizer()
     )  # create new one so we can use encode_edges later in
     # transform with fit=False
-    edf_type = str(getmodule(edf))
-    # if 'cudf' in edf_type:
-        # import cudf
     _, _, cudf = lazy_import_has_cu_cat_dependancy()
     # assert_cuml_cucat()
     T, mlb_pairwise_edge_encoder = encode_edges(

From 70b50d95c11b104f5573868241d7712cded51c10 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 25 Apr 2023 10:33:41 +0800
Subject: [PATCH 408/432] lint

---
 graphistry/feature_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 0a275329d2..028caab99b 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1428,7 +1428,7 @@ def process_edge_dataframes(
     # if 'cudf' in edf_type:
         # import cudf
         # lazy_import_has_cu_cat_dependancy()
-        assert_cuml_cucat()
+    assert_cuml_cucat()
     T, mlb_pairwise_edge_encoder = encode_edges(
         edf, src, dst, mlb_pairwise_edge_encoder, fit=True
     )

From 06a691bd7b25ced86f3de0ce8bb0c17360f712f9 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 25 Apr 2023 10:45:51 +0800
Subject: [PATCH 409/432] lazy cudf import

---
 graphistry/feature_utils.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 028caab99b..380ff5c8c5 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -700,7 +700,8 @@ def fit_pipeline(
         if keep_n_decimals:
             X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
         # import cudf
-        assert_cuml_cucat()
+        # assert_cuml_cucat()
+        _, _, cudf = lazy_import_has_cu_cat_dependancy()
         X = cudf.DataFrame(X, columns=columns, index=index)
     return X
 
@@ -1348,7 +1349,8 @@ def encode_edges(edf, src, dst, mlb, fit=False):
     if 'cudf' in edf_type:
         # lazy_import_has_cu_cat_dependancy()
         # import cudf
-        assert_cuml_cucat()
+        # assert_cuml_cucat()
+        _, _, cudf = lazy_import_has_cu_cat_dependancy()
         T = cudf.DataFrame(T, columns=columns, index=edf.index)
     else:
         T = pd.DataFrame(T, columns=columns, index=edf.index)
@@ -1428,7 +1430,8 @@ def process_edge_dataframes(
     # if 'cudf' in edf_type:
         # import cudf
         # lazy_import_has_cu_cat_dependancy()
-    assert_cuml_cucat()
+    # assert_cuml_cucat()
+    _, _, cudf = lazy_import_has_cu_cat_dependancy()
     T, mlb_pairwise_edge_encoder = encode_edges(
         edf, src, dst, mlb_pairwise_edge_encoder, fit=True
     )

From 4b779ac710b44557c4654bc106966355913cb98a Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 25 Apr 2023 10:47:57 +0800
Subject: [PATCH 410/432] lazy cudf import

---
 graphistry/feature_utils.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 380ff5c8c5..41f008d1e0 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1432,6 +1432,7 @@ def process_edge_dataframes(
         # lazy_import_has_cu_cat_dependancy()
     # assert_cuml_cucat()
     _, _, cudf = lazy_import_has_cu_cat_dependancy()
+    
     T, mlb_pairwise_edge_encoder = encode_edges(
         edf, src, dst, mlb_pairwise_edge_encoder, fit=True
     )

From c1a0ccafd502b2b600eda9762ee62eeb31dad5b4 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 25 Apr 2023 10:52:55 +0800
Subject: [PATCH 411/432] lint

---
 graphistry/feature_utils.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 74e9776063..0511e35bf2 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1438,11 +1438,7 @@ def process_edge_dataframes(
         MultiLabelBinarizer()
     )  # create new one so we can use encode_edges later in
     # transform with fit=False
-    edf_type = str(getmodule(edf))
-    # if 'cudf' in edf_type:
-        # import cudf
-        # lazy_import_has_cu_cat_dependancy()
-    # assert_cuml_cucat()
+
     _, _, cudf = lazy_import_has_cu_cat_dependancy()
     
     T, mlb_pairwise_edge_encoder = encode_edges(

From f853472cfe06149dba74e94c9a56570c952cdf2b Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 25 Apr 2023 11:24:33 +0800
Subject: [PATCH 412/432] better lazy cudf import

---
 graphistry/feature_utils.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 0a275329d2..116a9ad712 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -700,7 +700,8 @@ def fit_pipeline(
         if keep_n_decimals:
             X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
         # import cudf
-        assert_cuml_cucat()
+        # assert_cuml_cucat()
+        _, _, cudf = lazy_import_has_cu_cat_dependancy()
         X = cudf.DataFrame(X, columns=columns, index=index)
     return X
 
@@ -1348,7 +1349,8 @@ def encode_edges(edf, src, dst, mlb, fit=False):
     if 'cudf' in edf_type:
         # lazy_import_has_cu_cat_dependancy()
         # import cudf
-        assert_cuml_cucat()
+        # assert_cuml_cucat()
+        _, _, cudf = lazy_import_has_cu_cat_dependancy()
         T = cudf.DataFrame(T, columns=columns, index=edf.index)
     else:
         T = pd.DataFrame(T, columns=columns, index=edf.index)
@@ -1427,8 +1429,9 @@ def process_edge_dataframes(
     edf_type = str(getmodule(edf))
     # if 'cudf' in edf_type:
         # import cudf
-        # lazy_import_has_cu_cat_dependancy()
-        assert_cuml_cucat()
+    _, _, cudf = lazy_import_has_cu_cat_dependancy()
+    # assert_cuml_cucat()
+    
     T, mlb_pairwise_edge_encoder = encode_edges(
         edf, src, dst, mlb_pairwise_edge_encoder, fit=True
     )

From b6b148b130723750a7970bf7c204e71f26e73bb7 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 25 Apr 2023 13:43:18 +0800
Subject: [PATCH 413/432] lazy merge

---
 graphistry/feature_utils.py | 66 ++++++++++++++++---------------------
 1 file changed, 28 insertions(+), 38 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index d81d0622c2..116a9ad712 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -60,7 +60,7 @@
         GapEncoder = Any
         SimilarityEncoder = Any
     try:
-        from sklearn.preprocessing import FunctionTransformer
+        from cuml.preprocessing import FunctionTransformer
         from sklearn.base import BaseEstimator, TransformerMixin
     except:
         FunctionTransformer = Any
@@ -611,26 +611,23 @@ def get_preprocessing_pipeline(
             `uniform`, `quantile`, `kmeans`, default 'quantile'
     :return: scaled array, imputer instances or None, scaler instance or None
     """
-    try:
-        from sklearn.preprocessing import QuantileTransformer
-        from cuml.preprocessing import (
-            FunctionTransformer,
-            KBinsDiscretizer,
-            MinMaxScaler,
-            # QuantileTransformer,  ## cuml 23 only
-            RobustScaler,
-            StandardScaler,
-        )
-    except:
-        from sklearn.preprocessing import (
-            FunctionTransformer,
-            KBinsDiscretizer,
-            MinMaxScaler,
-            MultiLabelBinarizer,
-            QuantileTransformer, 
-            RobustScaler,
-            StandardScaler,
-        )
+    from sklearn.preprocessing import (
+        # FunctionTransformer,
+        # KBinsDiscretizer,
+        # MinMaxScaler,
+        MultiLabelBinarizer,
+        QuantileTransformer, 
+        # RobustScaler,
+        # StandardScaler,
+    )
+    from cuml.preprocessing import (
+        FunctionTransformer,
+        KBinsDiscretizer,
+        MinMaxScaler,
+        # QuantileTransformer,  ## cuml 23 only
+        RobustScaler,
+        StandardScaler,
+    )
     from sklearn.pipeline import Pipeline
     from sklearn.impute import SimpleImputer
     available_preprocessors = [
@@ -897,11 +894,7 @@ def __call__(self, *args, **kwargs):
 def get_numeric_transformers(ndf, y=None):
     # numeric selector needs to embody memorization of columns
     # for later .transform consistency.
-    try:
-        from cuml.preprocessing import FunctionTransformer
-    except:
-        from sklearn.preprocessing import FunctionTransformer
-
+    from cuml.preprocessing import FunctionTransformer
     label_encoder = False
     data_encoder = False
     y_ = y
@@ -961,19 +954,12 @@ def process_dirty_dataframes(
     :return: Encoded data matrix and target (if not None),
             the data encoder, and the label encoder.
     """
-    try:
+    if feature_engine == 'dirty_cat':
+        from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
+    elif feature_engine == 'cu_cat':
         lazy_import_has_cu_cat_dependancy()  # tried to use this rather than importing below
         from cu_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
-        from cuml.preprocessing import FunctionTransformer
-        temporary_feat_engine_key = 'cu_cat'
-    except:
-        from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
-        temporary_feat_engine_key = 'dirty_cat'
-
-    # TODO: should be handled at resolve_feature_engine level
-    if not is_dataframe_all_numeric(ndf) and feature_engine == 'torch':
-        feature_engine = temporary_feat_engine_key
-        
+    from cuml.preprocessing import FunctionTransformer
     t = time()
 
     if not is_dataframe_all_numeric(ndf):
@@ -1361,6 +1347,7 @@ def encode_edges(edf, src, dst, mlb, fit=False):
     mlb.get_feature_names_out = callThrough(columns)
     mlb.columns_ = [src, dst]
     if 'cudf' in edf_type:
+        # lazy_import_has_cu_cat_dependancy()
         # import cudf
         # assert_cuml_cucat()
         _, _, cudf = lazy_import_has_cu_cat_dependancy()
@@ -1439,8 +1426,11 @@ def process_edge_dataframes(
         MultiLabelBinarizer()
     )  # create new one so we can use encode_edges later in
     # transform with fit=False
-
+    edf_type = str(getmodule(edf))
+    # if 'cudf' in edf_type:
+        # import cudf
     _, _, cudf = lazy_import_has_cu_cat_dependancy()
+    # assert_cuml_cucat()
     
     T, mlb_pairwise_edge_encoder = encode_edges(
         edf, src, dst, mlb_pairwise_edge_encoder, fit=True

From 17f0af6380829a09755855c0a5fe833027d935a6 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 25 Apr 2023 13:46:38 +0800
Subject: [PATCH 414/432] lint

---
 graphistry/feature_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 116a9ad712..89f2ff8aaf 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1426,7 +1426,7 @@ def process_edge_dataframes(
         MultiLabelBinarizer()
     )  # create new one so we can use encode_edges later in
     # transform with fit=False
-    edf_type = str(getmodule(edf))
+    # edf_type = str(getmodule(edf))
     # if 'cudf' in edf_type:
         # import cudf
     _, _, cudf = lazy_import_has_cu_cat_dependancy()

From 2a5c879c4a25510452585c0a4b67ca679ff72a00 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 25 Apr 2023 14:05:54 +0800
Subject: [PATCH 415/432] lint

---
 graphistry/feature_utils.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 89f2ff8aaf..3cafbec0e1 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -1426,9 +1426,6 @@ def process_edge_dataframes(
         MultiLabelBinarizer()
     )  # create new one so we can use encode_edges later in
     # transform with fit=False
-    # edf_type = str(getmodule(edf))
-    # if 'cudf' in edf_type:
-        # import cudf
     _, _, cudf = lazy_import_has_cu_cat_dependancy()
     # assert_cuml_cucat()
     

From 80ba095ce7834bb094529ea351bba3620b304128 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 25 Apr 2023 14:25:16 +0800
Subject: [PATCH 416/432] functiontransform cuml import

---
 graphistry/feature_utils.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 3cafbec0e1..38a22696a1 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -59,6 +59,13 @@
         SuperVectorizer = Any
         GapEncoder = Any
         SimilarityEncoder = Any
+    try:
+        from sklearn.preprocessing import FunctionTransformer
+        from sklearn.base import BaseEstimator, TransformerMixin
+    except:
+        FunctionTransformer = Any
+        BaseEstimator = object
+        TransformerMixin = object
     try:
         from cuml.preprocessing import FunctionTransformer
         from sklearn.base import BaseEstimator, TransformerMixin

From 6709bea99725d187b75c4e3cfd155c915be6671d Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 25 Apr 2023 14:30:00 +0800
Subject: [PATCH 417/432] functiontransform cuml import

---
 graphistry/feature_utils.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 38a22696a1..7e426bc7f7 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -901,7 +901,11 @@ def __call__(self, *args, **kwargs):
 def get_numeric_transformers(ndf, y=None):
     # numeric selector needs to embody memorization of columns
     # for later .transform consistency.
-    from cuml.preprocessing import FunctionTransformer
+    from sklearn.preprocessing import FunctionTransformer
+    try:
+        from cuml.preprocessing import FunctionTransformer
+    except:
+        pass
     label_encoder = False
     data_encoder = False
     y_ = y
@@ -966,7 +970,11 @@ def process_dirty_dataframes(
     elif feature_engine == 'cu_cat':
         lazy_import_has_cu_cat_dependancy()  # tried to use this rather than importing below
         from cu_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
-    from cuml.preprocessing import FunctionTransformer
+    from sklearn.preprocessing import FunctionTransformer
+    try:
+        from cuml.preprocessing import FunctionTransformer
+    except:
+        pass
     t = time()
 
     if not is_dataframe_all_numeric(ndf):

From 901846c0bae167f00a87a972e052e28aab865fc8 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 25 Apr 2023 14:33:00 +0800
Subject: [PATCH 418/432] functiontransform cuml import

---
 graphistry/feature_utils.py | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 7e426bc7f7..d853c41b22 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -901,11 +901,6 @@ def __call__(self, *args, **kwargs):
 def get_numeric_transformers(ndf, y=None):
     # numeric selector needs to embody memorization of columns
     # for later .transform consistency.
-    from sklearn.preprocessing import FunctionTransformer
-    try:
-        from cuml.preprocessing import FunctionTransformer
-    except:
-        pass
     label_encoder = False
     data_encoder = False
     y_ = y
@@ -970,11 +965,6 @@ def process_dirty_dataframes(
     elif feature_engine == 'cu_cat':
         lazy_import_has_cu_cat_dependancy()  # tried to use this rather than importing below
         from cu_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
-    from sklearn.preprocessing import FunctionTransformer
-    try:
-        from cuml.preprocessing import FunctionTransformer
-    except:
-        pass
     t = time()
 
     if not is_dataframe_all_numeric(ndf):

From 118ea809dd665f5f949d3e8c73b230026898244f Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 25 Apr 2023 14:54:54 +0800
Subject: [PATCH 419/432] functiontransform cuml import

---
 graphistry/feature_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index d853c41b22..09ab1eee37 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -619,7 +619,7 @@ def get_preprocessing_pipeline(
     :return: scaled array, imputer instances or None, scaler instance or None
     """
     from sklearn.preprocessing import (
-        # FunctionTransformer,
+        FunctionTransformer,
         # KBinsDiscretizer,
         # MinMaxScaler,
         MultiLabelBinarizer,

From f1ee230e461529d059b286127d6eeb78a21324e6 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Tue, 25 Apr 2023 14:59:37 +0800
Subject: [PATCH 420/432] functiontransform cuml import

---
 graphistry/feature_utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 09ab1eee37..a81e36cf06 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -619,7 +619,7 @@ def get_preprocessing_pipeline(
     :return: scaled array, imputer instances or None, scaler instance or None
     """
     from sklearn.preprocessing import (
-        FunctionTransformer,
+        # FunctionTransformer,
         # KBinsDiscretizer,
         # MinMaxScaler,
         MultiLabelBinarizer,
@@ -628,7 +628,7 @@ def get_preprocessing_pipeline(
         # StandardScaler,
     )
     from cuml.preprocessing import (
-        FunctionTransformer,
+        # FunctionTransformer,
         KBinsDiscretizer,
         MinMaxScaler,
         # QuantileTransformer,  ## cuml 23 only

From 7fc02e235f579cd3ee302fa6af022e774f48c993 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Wed, 26 Apr 2023 10:26:48 +0800
Subject: [PATCH 421/432] use dirty_cat superVec for torch/etc, except if
 cu_cat

---
 graphistry/feature_utils.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index a81e36cf06..9333770879 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -960,9 +960,9 @@ def process_dirty_dataframes(
     :return: Encoded data matrix and target (if not None),
             the data encoder, and the label encoder.
     """
-    if feature_engine == 'dirty_cat':
-        from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
-    elif feature_engine == 'cu_cat':
+    # if feature_engine == 'dirty_cat':
+    from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
+    if feature_engine == 'cu_cat':
         lazy_import_has_cu_cat_dependancy()  # tried to use this rather than importing below
         from cu_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
     t = time()

From d38f469e23b833943d5d64e8cf76e3bcc9775529 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Wed, 26 Apr 2023 10:29:53 +0800
Subject: [PATCH 422/432] use dirty_cat superVec for torch/etc, except if
 cu_cat

---
 graphistry/feature_utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 9333770879..907eb656d1 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -960,8 +960,8 @@ def process_dirty_dataframes(
     :return: Encoded data matrix and target (if not None),
             the data encoder, and the label encoder.
     """
-    # if feature_engine == 'dirty_cat':
-    from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
+    if feature_engine != 'cu_cat':
+        from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
     if feature_engine == 'cu_cat':
         lazy_import_has_cu_cat_dependancy()  # tried to use this rather than importing below
         from cu_cat import SuperVectorizer, GapEncoder, SimilarityEncoder

From 6436067d8d6b3fcbc20aff3c906fab15d7a52d27 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Wed, 26 Apr 2023 10:34:44 +0800
Subject: [PATCH 423/432] use dirty_cat superVec for torch/etc, except if
 cu_cat

---
 graphistry/feature_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 907eb656d1..73eb717181 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -962,7 +962,7 @@ def process_dirty_dataframes(
     """
     if feature_engine != 'cu_cat':
         from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
-    if feature_engine == 'cu_cat':
+    elif feature_engine == 'cu_cat':
         lazy_import_has_cu_cat_dependancy()  # tried to use this rather than importing below
         from cu_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
     t = time()

From 25573eaf4db2768f19ce269a684e9c9819e4b8a4 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Wed, 26 Apr 2023 10:43:21 +0800
Subject: [PATCH 424/432] sklearn functiontransformer & MLB

---
 graphistry/feature_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 73eb717181..e5bfe7a234 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -619,7 +619,7 @@ def get_preprocessing_pipeline(
     :return: scaled array, imputer instances or None, scaler instance or None
     """
     from sklearn.preprocessing import (
-        # FunctionTransformer,
+        FunctionTransformer,
         # KBinsDiscretizer,
         # MinMaxScaler,
         MultiLabelBinarizer,

From dee5ad4b9ca22e22a7892925e794dcd6ae32f3a9 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Wed, 26 Apr 2023 10:49:57 +0800
Subject: [PATCH 425/432] sklearn functiontransformer & MLB

---
 graphistry/feature_utils.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index e5bfe7a234..3de26a3f95 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -66,13 +66,13 @@
         FunctionTransformer = Any
         BaseEstimator = object
         TransformerMixin = object
-    try:
-        from cuml.preprocessing import FunctionTransformer
-        from sklearn.base import BaseEstimator, TransformerMixin
-    except:
-        FunctionTransformer = Any
-        BaseEstimator = object
-        TransformerMixin = object
+    # try:
+    #     from cuml.preprocessing import FunctionTransformer
+    #     from sklearn.base import BaseEstimator, TransformerMixin
+    # except:
+    #     FunctionTransformer = Any
+    #     BaseEstimator = object
+    #     TransformerMixin = object
 else:
     MIXIN_BASE = object
     Pipeline = Any

From cac6cc42b7e51e7d1e74196636ffa8d209aa9799 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Wed, 26 Apr 2023 11:09:46 +0800
Subject: [PATCH 426/432] all preprocess back to sklearn

---
 graphistry/feature_utils.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 3de26a3f95..c3c0906e6d 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -620,21 +620,21 @@ def get_preprocessing_pipeline(
     """
     from sklearn.preprocessing import (
         FunctionTransformer,
-        # KBinsDiscretizer,
-        # MinMaxScaler,
-        MultiLabelBinarizer,
-        QuantileTransformer, 
-        # RobustScaler,
-        # StandardScaler,
-    )
-    from cuml.preprocessing import (
-        # FunctionTransformer,
         KBinsDiscretizer,
         MinMaxScaler,
-        # QuantileTransformer,  ## cuml 23 only
+        MultiLabelBinarizer,
+        QuantileTransformer, 
         RobustScaler,
         StandardScaler,
     )
+    # from cuml.preprocessing import (
+    #     # FunctionTransformer,
+    #     KBinsDiscretizer,
+    #     MinMaxScaler,
+    #     # QuantileTransformer,  ## cuml 23 only
+    #     RobustScaler,
+    #     StandardScaler,
+    # )
     from sklearn.pipeline import Pipeline
     from sklearn.impute import SimpleImputer
     available_preprocessors = [

From 3757b10e69bbb9d9d633ad9f8512fd177ca82b32 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Wed, 26 Apr 2023 12:04:16 +0800
Subject: [PATCH 427/432] import FT again

---
 graphistry/feature_utils.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index c3c0906e6d..bd53d3a1f1 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -962,11 +962,13 @@ def process_dirty_dataframes(
     """
     if feature_engine != 'cu_cat':
         from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
+        from sklearn.preprocessing import FunctionTransformer
     elif feature_engine == 'cu_cat':
         lazy_import_has_cu_cat_dependancy()  # tried to use this rather than importing below
         from cu_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
+        from cuml.preprocessing import FunctionTransformer
     t = time()
-
+    
     if not is_dataframe_all_numeric(ndf):
         data_encoder = SuperVectorizer(
             auto_cast=True,

From 523d180378d2d6afa4aef0439437495b8f536508 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Wed, 26 Apr 2023 14:37:39 +0800
Subject: [PATCH 428/432] rewrite g_n_t

---
 graphistry/feature_utils.py | 27 +++++++++++----------------
 1 file changed, 11 insertions(+), 16 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index bd53d3a1f1..635721bea1 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -898,29 +898,24 @@ def __call__(self, *args, **kwargs):
         return self.x
 
 
-def get_numeric_transformers(ndf, y=None):
+def get_numeric_transformers(ndf=None, y=None):
     # numeric selector needs to embody memorization of columns
     # for later .transform consistency.
-    label_encoder = False
-    data_encoder = False
-    y_ = y
-    if y is not None:
-        y_ = y.select_dtypes(include=[np.number])
-        label_encoder = FunctionTransformer(
-            partial(passthrough_df_cols, columns=y_.columns)
-        )  # takes dataframe and memorizes which cols to use.
+    label_encoder = None
+    data_encoder = None
+    y_ = y.select_dtypes(include=[np.number]) if y is not None else None
+    if y_ is not None:
+        label_encoder = FunctionTransformer(partial(passthrough_df_cols, columns=y_.columns))
         label_encoder.get_feature_names_out = callThrough(y_.columns)
         label_encoder.columns_ = y_.columns
 
-    if ndf is not None:
-        ndf_ = ndf.select_dtypes(include=[np.number])
-        data_encoder = FunctionTransformer(
-            partial(passthrough_df_cols, columns=ndf_.columns)
-        )
+    ndf_ = ndf.select_dtypes(include=[np.number]) if ndf is not None else None
+    if ndf_ is not None:
+        data_encoder = StandardScaler()
+        data_encoder.fit(ndf_)
         data_encoder.get_feature_names_out = callThrough(ndf_.columns)
-        #data_encoder.columns_ = ndf_.columns
         data_encoder.get_feature_names_in = callThrough(ndf_.columns)
-        
+
     return ndf_, y_, data_encoder, label_encoder
 
 

From 97b725d3280f3619ebe2337614000772426d7edf Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Wed, 26 Apr 2023 15:49:07 +0800
Subject: [PATCH 429/432] revert g_n_t

---
 graphistry/feature_utils.py | 27 ++++++++++++++++-----------
 1 file changed, 16 insertions(+), 11 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 635721bea1..bd53d3a1f1 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -898,24 +898,29 @@ def __call__(self, *args, **kwargs):
         return self.x
 
 
-def get_numeric_transformers(ndf=None, y=None):
+def get_numeric_transformers(ndf, y=None):
     # numeric selector needs to embody memorization of columns
     # for later .transform consistency.
-    label_encoder = None
-    data_encoder = None
-    y_ = y.select_dtypes(include=[np.number]) if y is not None else None
-    if y_ is not None:
-        label_encoder = FunctionTransformer(partial(passthrough_df_cols, columns=y_.columns))
+    label_encoder = False
+    data_encoder = False
+    y_ = y
+    if y is not None:
+        y_ = y.select_dtypes(include=[np.number])
+        label_encoder = FunctionTransformer(
+            partial(passthrough_df_cols, columns=y_.columns)
+        )  # takes dataframe and memorizes which cols to use.
         label_encoder.get_feature_names_out = callThrough(y_.columns)
         label_encoder.columns_ = y_.columns
 
-    ndf_ = ndf.select_dtypes(include=[np.number]) if ndf is not None else None
-    if ndf_ is not None:
-        data_encoder = StandardScaler()
-        data_encoder.fit(ndf_)
+    if ndf is not None:
+        ndf_ = ndf.select_dtypes(include=[np.number])
+        data_encoder = FunctionTransformer(
+            partial(passthrough_df_cols, columns=ndf_.columns)
+        )
         data_encoder.get_feature_names_out = callThrough(ndf_.columns)
+        #data_encoder.columns_ = ndf_.columns
         data_encoder.get_feature_names_in = callThrough(ndf_.columns)
-
+        
     return ndf_, y_, data_encoder, label_encoder
 
 

From 7ab97a4869ceea117f97607f9590f61b9f479108 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Thu, 27 Apr 2023 09:24:53 +0800
Subject: [PATCH 430/432] import FT in get_numeric_transform

---
 graphistry/feature_utils.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index bd53d3a1f1..033e3d6472 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -901,6 +901,11 @@ def __call__(self, *args, **kwargs):
 def get_numeric_transformers(ndf, y=None):
     # numeric selector needs to embody memorization of columns
     # for later .transform consistency.
+    from sklearn.preprocessing import FunctionTransformer
+    try:
+        from cuml.preprocessing import FunctionTransformer
+    except:
+        pass
     label_encoder = False
     data_encoder = False
     y_ = y

From aba0c558d7bef721515790df935bf4b28079d0f7 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Thu, 27 Apr 2023 09:26:48 +0800
Subject: [PATCH 431/432] import FT in get_numeric_transform

---
 graphistry/feature_utils.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index 033e3d6472..2a60194ca2 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -902,10 +902,6 @@ def get_numeric_transformers(ndf, y=None):
     # numeric selector needs to embody memorization of columns
     # for later .transform consistency.
     from sklearn.preprocessing import FunctionTransformer
-    try:
-        from cuml.preprocessing import FunctionTransformer
-    except:
-        pass
     label_encoder = False
     data_encoder = False
     y_ = y

From fb96400a3f4d1e8f68cc38fb1e438b7dcde7f055 Mon Sep 17 00:00:00 2001
From: dcolinmorgan <dcolinmorgan@gmail.com>
Date: Wed, 10 May 2023 16:56:02 +0800
Subject: [PATCH 432/432] latest release opt-in install

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 35207de8a3..3ad1513235 100755
--- a/setup.py
+++ b/setup.py
@@ -45,7 +45,7 @@ def unique_flatten_dict(d):
 # https://github.com/facebookresearch/faiss/issues/1589 for faiss-cpu 1.6.1, #'setuptools==67.4.0' removed
 base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch<2', 'sentence-transformers', 'faiss-cpu', 'joblib']
 
-base_extras_heavy['cu_cat'] = base_extras_heavy['ai'] + ['cu_cat @ git+http://github.com/graphistry/cu-cat/tarball/cu_cat_regpt'] #egg=package-0.02.0']
+base_extras_heavy['cu_cat'] = base_extras_heavy['ai'] + ['cu_cat @ git+http://github.com/graphistry/cu-cat.git@0.03.0']
 
 base_extras = {**base_extras_light, **base_extras_heavy}