doc/api/data-api-tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CARD:Live Dashboard Data and API tutorial\n",
    "\n",
    "Welcome to the CARD:Live Dashboard API tutorial. This will show you how to download the results JSON files used by [CARD:Live](https://card.mcmaster.ca/live) and convert to a Python/Pandas DataFrame for custom data analysis.\n",
    "\n",
    "## Dependencies\n",
    "\n",
    "You will have to have installed the `card-live-dashboard` Python package as well as `matplotlib` like below:\n",
    "\n",
    "```bash\n",
    "python -m pip install card-live-dashboard matplotlib\n",
    "```\n",
    "\n",
    "I use matplotlib here for plotting (rather than plotly) since it's easier to integrate into Juptyer.\n",
    "\n",
    "Let's check the version of the package we are using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5.0\n"
     ]
    }
   ],
   "source": [
    "import card_live_dashboard\n",
    "print(card_live_dashboard.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download data\n",
    "\n",
    "The first step is to download a copy of the data. This can be directly downloaded from the main CARD:Live dashboard (<https://bergen.mcmaster.ca/cardlive>), however it's easier to run the `card-live-dash-init` command to setup a new home directory for the CARD:Live dashboard, which will download the current data as well as setup an NCBI taxonomic database for later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initalizing CARD:Live home directory as [card-live-home]\n",
      "Downloading latest CARD:Live data from [https://bergen.mcmaster.ca/cardlive/data/all]\n",
      "Loaded 3061 samples into [card-live-home/data/card_live]\n",
      "Writing example configurations to [card-live-home/config]\n",
      "Initializing NCBI Taxonomy database in [card-live-home/db]\n",
      "Downloading taxdump.tar.gz from NCBI FTP site (via HTTP)...\n",
      "Done. Parsing...\n",
      "Loading node names...\n",
      "2293283 names loaded.\n",
      "232616 synonyms loaded.\n",
      "Loading nodes...\n",
      "2293283 nodes loaded.\n",
      "Linking nodes...\n",
      "Tree is loaded.\n",
      "Updating database: card-live-home/db/taxa.sqlite ...\n",
      " 2293000 generating entries... \n",
      "Uploading to card-live-home/db/taxa.sqlite\n",
      "\n",
      "Inserting synonyms:      230000 \n",
      "Inserting taxid merges:  60000 \n",
      "Inserting taxids:       2290000 \n",
      "Finished initializing CARD:Live home directory as [card-live-home]\n",
      "Please add CARD:Live JSON data to [card-live-home/data/card_live] and start the CARD:Live dashboard by running \"card-live-dash-prod card-live-home\"\n"
     ]
    }
   ],
   "source": [
    "!card-live-dash-init card-live-home"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data will be in the directory `card-live-home/data/card_live`, which is a directory of JSON-formmated files containing the results of RGI/MLST/LMAT run on the different genomes. Let's take a quick look at one of these files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"rgi_main\": [\n",
      "        {\n",
      "            \"ORF_ID\": 1,\n",
      "            \"Cut_Off\": \"Strict\",\n",
      "            \"Pass_Bitscore\": \"450\",\n",
      "            \"Best_Hit_Bitscore\": \"452.2\",\n",
      "            \"Best_Hit_ARO\": \"OXA-405\",\n",
      "            \"Best_Identities\": \"80.37\",\n",
      "            \"ARO\": \"3001596\",\n"
     ]
    }
   ],
   "source": [
    "!python -m json.tool card-live-home/data/card_live/ffa57d033d9e5f4d3b191c4eae8c75ca | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Construct Dataframes\n",
    "\n",
    "To construct pandas Dataframes from these JSON files we can use part of the `card-live-dashboard` API. Specifically the [CardLiveDataLoader](https://github.com/arpcard/card-live-dashboard/blob/main/card_live_dashboard/service/CardLiveDataLoader.py) class, which will generate a [CardLiveData](https://github.com/arpcard/card-live-dashboard/blob/main/card_live_dashboard/model/CardLiveData.py) object containing JSON results as separate dataframes.\n",
    "\n",
    "Let's convert the data now (note this may take a few minutes to load all the JSON files)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>geo_area_code</th>\n",
       "      <th>geo_area_name</th>\n",
       "      <th>mlst_version</th>\n",
       "      <th>lmat_version</th>\n",
       "      <th>rgi_version</th>\n",
       "      <th>card_data_version</th>\n",
       "      <th>variants_data_version</th>\n",
       "      <th>analysis_valid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filename</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000e7e4842e3880db69532c0578e1960</th>\n",
       "      <td>2020-08-31 09:56:48.521536</td>\n",
       "      <td>35</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.1</td>\n",
       "      <td>3.1.0</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>003b60af692d11a14f10223d303a352f</th>\n",
       "      <td>2020-09-08 07:37:23.142951</td>\n",
       "      <td>-1</td>\n",
       "      <td>N/A</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.1</td>\n",
       "      <td>3.1.0</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>mlst and lmat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>003f7bbe769852d8a171865081ba1bc3</th>\n",
       "      <td>2020-08-10 16:56:26.607290</td>\n",
       "      <td>34</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.0</td>\n",
       "      <td>3.0.7</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0044d0a1783629ef652c8e8232d76b58</th>\n",
       "      <td>2020-08-10 14:02:57.663503</td>\n",
       "      <td>34</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.0</td>\n",
       "      <td>3.0.7</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0070cced7fef29dd8c43e68c417458d4</th>\n",
       "      <td>2020-09-15 16:39:58.648231</td>\n",
       "      <td>21</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.1</td>\n",
       "      <td>3.1.0</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  timestamp  geo_area_code  \\\n",
       "filename                                                                     \n",
       "000e7e4842e3880db69532c0578e1960 2020-08-31 09:56:48.521536             35   \n",
       "003b60af692d11a14f10223d303a352f 2020-09-08 07:37:23.142951             -1   \n",
       "003f7bbe769852d8a171865081ba1bc3 2020-08-10 16:56:26.607290             34   \n",
       "0044d0a1783629ef652c8e8232d76b58 2020-08-10 14:02:57.663503             34   \n",
       "0070cced7fef29dd8c43e68c417458d4 2020-09-15 16:39:58.648231             21   \n",
       "\n",
       "                                       geo_area_name mlst_version  \\\n",
       "filename                                                            \n",
       "000e7e4842e3880db69532c0578e1960  South-Eastern Asia       2.19.0   \n",
       "003b60af692d11a14f10223d303a352f                 N/A       2.19.0   \n",
       "003f7bbe769852d8a171865081ba1bc3       Southern Asia       2.19.0   \n",
       "0044d0a1783629ef652c8e8232d76b58       Southern Asia       2.19.0   \n",
       "0070cced7fef29dd8c43e68c417458d4    Northern America       2.19.0   \n",
       "\n",
       "                                 lmat_version rgi_version card_data_version  \\\n",
       "filename                                                                      \n",
       "000e7e4842e3880db69532c0578e1960        1.2.6       5.1.1             3.1.0   \n",
       "003b60af692d11a14f10223d303a352f        1.2.6       5.1.1             3.1.0   \n",
       "003f7bbe769852d8a171865081ba1bc3        1.2.6       5.1.0             3.0.7   \n",
       "0044d0a1783629ef652c8e8232d76b58        1.2.6       5.1.0             3.0.7   \n",
       "0070cced7fef29dd8c43e68c417458d4        1.2.6       5.1.1             3.1.0   \n",
       "\n",
       "                                 variants_data_version analysis_valid  \n",
       "filename                                                               \n",
       "000e7e4842e3880db69532c0578e1960                 3.0.6            all  \n",
       "003b60af692d11a14f10223d303a352f                 3.0.6  mlst and lmat  \n",
       "003f7bbe769852d8a171865081ba1bc3                 3.0.6            all  \n",
       "0044d0a1783629ef652c8e8232d76b58                 3.0.6            all  \n",
       "0070cced7fef29dd8c43e68c417458d4                 3.0.6            all  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "from card_live_dashboard.service.CardLiveDataLoader import CardLiveDataLoader\n",
    "\n",
    "data_dir = Path('card-live-home/data/card_live')\n",
    "loader = CardLiveDataLoader(data_dir)\n",
    "data = loader.read_data()\n",
    "data.main_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Awesome, we've loaded the data up into separate dataframes. The dataframe displayed above is `main_df` and contains the geographic information, timestamps, and other main information related to each file.\n",
    "\n",
    "There are four other possible dataframes: `rgi_df`, `rgi_kmer_df`, `lmat_df`, and `mlst_df`. Let's take a look at some of these (note the first few columns are the same among each dataframe)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geo_area_name</th>\n",
       "      <th>mlst_version</th>\n",
       "      <th>lmat_version</th>\n",
       "      <th>rgi_version</th>\n",
       "      <th>card_data_version</th>\n",
       "      <th>variants_data_version</th>\n",
       "      <th>analysis_valid</th>\n",
       "      <th>rgi_main.ORF_ID</th>\n",
       "      <th>rgi_main.Cut_Off</th>\n",
       "      <th>rgi_main.Pass_Bitscore</th>\n",
       "      <th>...</th>\n",
       "      <th>rgi_main.Other_SNPs</th>\n",
       "      <th>rgi_main.Drug Class</th>\n",
       "      <th>rgi_main.Resistance Mechanism</th>\n",
       "      <th>rgi_main.AMR Gene Family</th>\n",
       "      <th>rgi_main.CARD_Protein_Sequence</th>\n",
       "      <th>rgi_main.Percentage Length of Reference Sequence</th>\n",
       "      <th>rgi_main.ID</th>\n",
       "      <th>rgi_main.Model_ID</th>\n",
       "      <th>rgi_main.Nudged</th>\n",
       "      <th>rgi_main.Note</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filename</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000e7e4842e3880db69532c0578e1960</th>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.1</td>\n",
       "      <td>3.1.0</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Strict</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>cephalosporin; cephamycin; penam</td>\n",
       "      <td>antibiotic target alteration</td>\n",
       "      <td>Penicillin-binding protein mutations conferrin...</td>\n",
       "      <td>MVKFNSSRKSGKSKKTIRKLTAPETVKQNKPQKVFEKCFMRGRYML...</td>\n",
       "      <td>98.52</td>\n",
       "      <td>gnl|BL_ORD_ID|2560|hsp_num:0</td>\n",
       "      <td>2886</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000e7e4842e3880db69532c0578e1960</th>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.1</td>\n",
       "      <td>3.1.0</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Strict</td>\n",
       "      <td>700</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>elfamycin antibiotic</td>\n",
       "      <td>antibiotic target alteration</td>\n",
       "      <td>elfamycin resistant EF-Tu</td>\n",
       "      <td>MLSPEGESTIVRNIAVSKEKFERTKPHVNVGTIGHVDHGKTTLTAA...</td>\n",
       "      <td>96.09</td>\n",
       "      <td>gnl|BL_ORD_ID|2022|hsp_num:0</td>\n",
       "      <td>2158</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>003b60af692d11a14f10223d303a352f</th>\n",
       "      <td>N/A</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.1</td>\n",
       "      <td>3.1.0</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>mlst and lmat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>003f7bbe769852d8a171865081ba1bc3</th>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.0</td>\n",
       "      <td>3.0.7</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Strict</td>\n",
       "      <td>850</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>fluoroquinolone antibiotic; acridine dye; tric...</td>\n",
       "      <td>antibiotic efflux</td>\n",
       "      <td>multidrug and toxic compound extrusion (MATE) ...</td>\n",
       "      <td>MSNVTSFRSELKQLFHLMLPILITQFAQAGFGLIDTIMAGHLSAAD...</td>\n",
       "      <td>100.00</td>\n",
       "      <td>gnl|BL_ORD_ID|1284|hsp_num:0</td>\n",
       "      <td>1368</td>\n",
       "      <td>True</td>\n",
       "      <td>loose hit with at least 95 percent identity pu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>003f7bbe769852d8a171865081ba1bc3</th>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.0</td>\n",
       "      <td>3.0.7</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Perfect</td>\n",
       "      <td>700</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>fosfomycin</td>\n",
       "      <td>antibiotic efflux</td>\n",
       "      <td>major facilitator superfamily (MFS) antibiotic...</td>\n",
       "      <td>MTTTLKKVVAASMVGSVAEWYEFFLYGTASALVFGELFFQQTGNAI...</td>\n",
       "      <td>100.00</td>\n",
       "      <td>gnl|BL_ORD_ID|2769|hsp_num:0</td>\n",
       "      <td>3277</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       geo_area_name mlst_version  \\\n",
       "filename                                                            \n",
       "000e7e4842e3880db69532c0578e1960  South-Eastern Asia       2.19.0   \n",
       "000e7e4842e3880db69532c0578e1960  South-Eastern Asia       2.19.0   \n",
       "003b60af692d11a14f10223d303a352f                 N/A       2.19.0   \n",
       "003f7bbe769852d8a171865081ba1bc3       Southern Asia       2.19.0   \n",
       "003f7bbe769852d8a171865081ba1bc3       Southern Asia       2.19.0   \n",
       "\n",
       "                                 lmat_version rgi_version card_data_version  \\\n",
       "filename                                                                      \n",
       "000e7e4842e3880db69532c0578e1960        1.2.6       5.1.1             3.1.0   \n",
       "000e7e4842e3880db69532c0578e1960        1.2.6       5.1.1             3.1.0   \n",
       "003b60af692d11a14f10223d303a352f        1.2.6       5.1.1             3.1.0   \n",
       "003f7bbe769852d8a171865081ba1bc3        1.2.6       5.1.0             3.0.7   \n",
       "003f7bbe769852d8a171865081ba1bc3        1.2.6       5.1.0             3.0.7   \n",
       "\n",
       "                                 variants_data_version analysis_valid  \\\n",
       "filename                                                                \n",
       "000e7e4842e3880db69532c0578e1960                 3.0.6            all   \n",
       "000e7e4842e3880db69532c0578e1960                 3.0.6            all   \n",
       "003b60af692d11a14f10223d303a352f                 3.0.6  mlst and lmat   \n",
       "003f7bbe769852d8a171865081ba1bc3                 3.0.6            all   \n",
       "003f7bbe769852d8a171865081ba1bc3                 3.0.6            all   \n",
       "\n",
       "                                  rgi_main.ORF_ID rgi_main.Cut_Off  \\\n",
       "filename                                                             \n",
       "000e7e4842e3880db69532c0578e1960              1.0           Strict   \n",
       "000e7e4842e3880db69532c0578e1960              2.0           Strict   \n",
       "003b60af692d11a14f10223d303a352f              NaN              NaN   \n",
       "003f7bbe769852d8a171865081ba1bc3              1.0           Strict   \n",
       "003f7bbe769852d8a171865081ba1bc3              2.0          Perfect   \n",
       "\n",
       "                                 rgi_main.Pass_Bitscore  ...  \\\n",
       "filename                                                 ...   \n",
       "000e7e4842e3880db69532c0578e1960                    500  ...   \n",
       "000e7e4842e3880db69532c0578e1960                    700  ...   \n",
       "003b60af692d11a14f10223d303a352f                    NaN  ...   \n",
       "003f7bbe769852d8a171865081ba1bc3                    850  ...   \n",
       "003f7bbe769852d8a171865081ba1bc3                    700  ...   \n",
       "\n",
       "                                 rgi_main.Other_SNPs  \\\n",
       "filename                                               \n",
       "000e7e4842e3880db69532c0578e1960                None   \n",
       "000e7e4842e3880db69532c0578e1960                None   \n",
       "003b60af692d11a14f10223d303a352f                 NaN   \n",
       "003f7bbe769852d8a171865081ba1bc3                None   \n",
       "003f7bbe769852d8a171865081ba1bc3                None   \n",
       "\n",
       "                                                                rgi_main.Drug Class  \\\n",
       "filename                                                                              \n",
       "000e7e4842e3880db69532c0578e1960                   cephalosporin; cephamycin; penam   \n",
       "000e7e4842e3880db69532c0578e1960                               elfamycin antibiotic   \n",
       "003b60af692d11a14f10223d303a352f                                                NaN   \n",
       "003f7bbe769852d8a171865081ba1bc3  fluoroquinolone antibiotic; acridine dye; tric...   \n",
       "003f7bbe769852d8a171865081ba1bc3                                         fosfomycin   \n",
       "\n",
       "                                 rgi_main.Resistance Mechanism  \\\n",
       "filename                                                         \n",
       "000e7e4842e3880db69532c0578e1960  antibiotic target alteration   \n",
       "000e7e4842e3880db69532c0578e1960  antibiotic target alteration   \n",
       "003b60af692d11a14f10223d303a352f                           NaN   \n",
       "003f7bbe769852d8a171865081ba1bc3             antibiotic efflux   \n",
       "003f7bbe769852d8a171865081ba1bc3             antibiotic efflux   \n",
       "\n",
       "                                                           rgi_main.AMR Gene Family  \\\n",
       "filename                                                                              \n",
       "000e7e4842e3880db69532c0578e1960  Penicillin-binding protein mutations conferrin...   \n",
       "000e7e4842e3880db69532c0578e1960                          elfamycin resistant EF-Tu   \n",
       "003b60af692d11a14f10223d303a352f                                                NaN   \n",
       "003f7bbe769852d8a171865081ba1bc3  multidrug and toxic compound extrusion (MATE) ...   \n",
       "003f7bbe769852d8a171865081ba1bc3  major facilitator superfamily (MFS) antibiotic...   \n",
       "\n",
       "                                                     rgi_main.CARD_Protein_Sequence  \\\n",
       "filename                                                                              \n",
       "000e7e4842e3880db69532c0578e1960  MVKFNSSRKSGKSKKTIRKLTAPETVKQNKPQKVFEKCFMRGRYML...   \n",
       "000e7e4842e3880db69532c0578e1960  MLSPEGESTIVRNIAVSKEKFERTKPHVNVGTIGHVDHGKTTLTAA...   \n",
       "003b60af692d11a14f10223d303a352f                                                NaN   \n",
       "003f7bbe769852d8a171865081ba1bc3  MSNVTSFRSELKQLFHLMLPILITQFAQAGFGLIDTIMAGHLSAAD...   \n",
       "003f7bbe769852d8a171865081ba1bc3  MTTTLKKVVAASMVGSVAEWYEFFLYGTASALVFGELFFQQTGNAI...   \n",
       "\n",
       "                                 rgi_main.Percentage Length of Reference Sequence  \\\n",
       "filename                                                                            \n",
       "000e7e4842e3880db69532c0578e1960                                            98.52   \n",
       "000e7e4842e3880db69532c0578e1960                                            96.09   \n",
       "003b60af692d11a14f10223d303a352f                                              NaN   \n",
       "003f7bbe769852d8a171865081ba1bc3                                           100.00   \n",
       "003f7bbe769852d8a171865081ba1bc3                                           100.00   \n",
       "\n",
       "                                                   rgi_main.ID  \\\n",
       "filename                                                         \n",
       "000e7e4842e3880db69532c0578e1960  gnl|BL_ORD_ID|2560|hsp_num:0   \n",
       "000e7e4842e3880db69532c0578e1960  gnl|BL_ORD_ID|2022|hsp_num:0   \n",
       "003b60af692d11a14f10223d303a352f                           NaN   \n",
       "003f7bbe769852d8a171865081ba1bc3  gnl|BL_ORD_ID|1284|hsp_num:0   \n",
       "003f7bbe769852d8a171865081ba1bc3  gnl|BL_ORD_ID|2769|hsp_num:0   \n",
       "\n",
       "                                 rgi_main.Model_ID rgi_main.Nudged  \\\n",
       "filename                                                             \n",
       "000e7e4842e3880db69532c0578e1960              2886                   \n",
       "000e7e4842e3880db69532c0578e1960              2158                   \n",
       "003b60af692d11a14f10223d303a352f               NaN             NaN   \n",
       "003f7bbe769852d8a171865081ba1bc3              1368            True   \n",
       "003f7bbe769852d8a171865081ba1bc3              3277                   \n",
       "\n",
       "                                                                      rgi_main.Note  \n",
       "filename                                                                             \n",
       "000e7e4842e3880db69532c0578e1960                                                     \n",
       "000e7e4842e3880db69532c0578e1960                                                     \n",
       "003b60af692d11a14f10223d303a352f                                                NaN  \n",
       "003f7bbe769852d8a171865081ba1bc3  loose hit with at least 95 percent identity pu...  \n",
       "003f7bbe769852d8a171865081ba1bc3                                                     \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rgi_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geo_area_name</th>\n",
       "      <th>mlst_version</th>\n",
       "      <th>lmat_version</th>\n",
       "      <th>rgi_version</th>\n",
       "      <th>card_data_version</th>\n",
       "      <th>variants_data_version</th>\n",
       "      <th>analysis_valid</th>\n",
       "      <th>rgi_kmer.CARD*kmer Prediction</th>\n",
       "      <th>rgi_kmer.Taxonomic kmers</th>\n",
       "      <th>rgi_kmer.Genomic kmers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filename</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000e7e4842e3880db69532c0578e1960</th>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.1</td>\n",
       "      <td>3.1.0</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "      <td>Haemophilus influenzae (chromosome)</td>\n",
       "      <td>Haemophilus influenzae: 18; Haemophilus: 73;</td>\n",
       "      <td>chr + plasmid: 0; plasmid: 0; chr: 81;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>003b60af692d11a14f10223d303a352f</th>\n",
       "      <td>N/A</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.1</td>\n",
       "      <td>3.1.0</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>mlst and lmat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>003f7bbe769852d8a171865081ba1bc3</th>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.0</td>\n",
       "      <td>3.0.7</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "      <td>Acinetobacter baumannii (chromosome)</td>\n",
       "      <td>Acinetobacter baumannii: 785; Acinetobacter: 64;</td>\n",
       "      <td>chr + plasmid: 0; plasmid: 0; chr: 1287;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>003f7bbe769852d8a171865081ba1bc3</th>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.0</td>\n",
       "      <td>3.0.7</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "      <td>Acinetobacter baumannii (chromosome)</td>\n",
       "      <td>Acinetobacter baumannii: 458; Acinetobacter: 430;</td>\n",
       "      <td>chr + plasmid: 0; plasmid: 0; chr: 1230;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>003f7bbe769852d8a171865081ba1bc3</th>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.0</td>\n",
       "      <td>3.0.7</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "      <td>Acinetobacter baumannii (chromosome)</td>\n",
       "      <td>Acinetobacter baumannii: 70; Acinetobacter: 200;</td>\n",
       "      <td>chr + plasmid: 0; plasmid: 0; chr: 270;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       geo_area_name mlst_version  \\\n",
       "filename                                                            \n",
       "000e7e4842e3880db69532c0578e1960  South-Eastern Asia       2.19.0   \n",
       "003b60af692d11a14f10223d303a352f                 N/A       2.19.0   \n",
       "003f7bbe769852d8a171865081ba1bc3       Southern Asia       2.19.0   \n",
       "003f7bbe769852d8a171865081ba1bc3       Southern Asia       2.19.0   \n",
       "003f7bbe769852d8a171865081ba1bc3       Southern Asia       2.19.0   \n",
       "\n",
       "                                 lmat_version rgi_version card_data_version  \\\n",
       "filename                                                                      \n",
       "000e7e4842e3880db69532c0578e1960        1.2.6       5.1.1             3.1.0   \n",
       "003b60af692d11a14f10223d303a352f        1.2.6       5.1.1             3.1.0   \n",
       "003f7bbe769852d8a171865081ba1bc3        1.2.6       5.1.0             3.0.7   \n",
       "003f7bbe769852d8a171865081ba1bc3        1.2.6       5.1.0             3.0.7   \n",
       "003f7bbe769852d8a171865081ba1bc3        1.2.6       5.1.0             3.0.7   \n",
       "\n",
       "                                 variants_data_version analysis_valid  \\\n",
       "filename                                                                \n",
       "000e7e4842e3880db69532c0578e1960                 3.0.6            all   \n",
       "003b60af692d11a14f10223d303a352f                 3.0.6  mlst and lmat   \n",
       "003f7bbe769852d8a171865081ba1bc3                 3.0.6            all   \n",
       "003f7bbe769852d8a171865081ba1bc3                 3.0.6            all   \n",
       "003f7bbe769852d8a171865081ba1bc3                 3.0.6            all   \n",
       "\n",
       "                                         rgi_kmer.CARD*kmer Prediction  \\\n",
       "filename                                                                 \n",
       "000e7e4842e3880db69532c0578e1960   Haemophilus influenzae (chromosome)   \n",
       "003b60af692d11a14f10223d303a352f                                   NaN   \n",
       "003f7bbe769852d8a171865081ba1bc3  Acinetobacter baumannii (chromosome)   \n",
       "003f7bbe769852d8a171865081ba1bc3  Acinetobacter baumannii (chromosome)   \n",
       "003f7bbe769852d8a171865081ba1bc3  Acinetobacter baumannii (chromosome)   \n",
       "\n",
       "                                                           rgi_kmer.Taxonomic kmers  \\\n",
       "filename                                                                              \n",
       "000e7e4842e3880db69532c0578e1960       Haemophilus influenzae: 18; Haemophilus: 73;   \n",
       "003b60af692d11a14f10223d303a352f                                                NaN   \n",
       "003f7bbe769852d8a171865081ba1bc3   Acinetobacter baumannii: 785; Acinetobacter: 64;   \n",
       "003f7bbe769852d8a171865081ba1bc3  Acinetobacter baumannii: 458; Acinetobacter: 430;   \n",
       "003f7bbe769852d8a171865081ba1bc3   Acinetobacter baumannii: 70; Acinetobacter: 200;   \n",
       "\n",
       "                                                    rgi_kmer.Genomic kmers  \n",
       "filename                                                                    \n",
       "000e7e4842e3880db69532c0578e1960    chr + plasmid: 0; plasmid: 0; chr: 81;  \n",
       "003b60af692d11a14f10223d303a352f                                       NaN  \n",
       "003f7bbe769852d8a171865081ba1bc3  chr + plasmid: 0; plasmid: 0; chr: 1287;  \n",
       "003f7bbe769852d8a171865081ba1bc3  chr + plasmid: 0; plasmid: 0; chr: 1230;  \n",
       "003f7bbe769852d8a171865081ba1bc3   chr + plasmid: 0; plasmid: 0; chr: 270;  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rgi_kmer_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>lmat_version</th>\n",
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       "      <th>lmat.count</th>\n",
       "      <th>lmat.ncbi_taxon_id</th>\n",
       "      <th>lmat.rank</th>\n",
       "      <th>lmat.taxonomy_label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filename</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000e7e4842e3880db69532c0578e1960</th>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.1</td>\n",
       "      <td>3.1.0</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "      <td>10239.6</td>\n",
       "      <td>6686</td>\n",
       "      <td>747</td>\n",
       "      <td>species</td>\n",
       "      <td>Pasteurella multocida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000e7e4842e3880db69532c0578e1960</th>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.1</td>\n",
       "      <td>3.1.0</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "      <td>656.063</td>\n",
       "      <td>642</td>\n",
       "      <td>1032869</td>\n",
       "      <td>no rank</td>\n",
       "      <td>Pasteurella multocida subsp. multocida str. An...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000e7e4842e3880db69532c0578e1960</th>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.1</td>\n",
       "      <td>3.1.0</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "      <td>164.587</td>\n",
       "      <td>115</td>\n",
       "      <td>1169410</td>\n",
       "      <td>no rank</td>\n",
       "      <td>Pasteurella multocida subsp. gallicida X73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000e7e4842e3880db69532c0578e1960</th>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.1</td>\n",
       "      <td>3.1.0</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "      <td>54.48</td>\n",
       "      <td>52</td>\n",
       "      <td>44283</td>\n",
       "      <td>subspecies</td>\n",
       "      <td>Pasteurella multocida subsp. multocida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000e7e4842e3880db69532c0578e1960</th>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.1</td>\n",
       "      <td>3.1.0</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "      <td>47.046</td>\n",
       "      <td>22</td>\n",
       "      <td>32630</td>\n",
       "      <td>species</td>\n",
       "      <td>synthetic construct</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       geo_area_name mlst_version  \\\n",
       "filename                                                            \n",
       "000e7e4842e3880db69532c0578e1960  South-Eastern Asia       2.19.0   \n",
       "000e7e4842e3880db69532c0578e1960  South-Eastern Asia       2.19.0   \n",
       "000e7e4842e3880db69532c0578e1960  South-Eastern Asia       2.19.0   \n",
       "000e7e4842e3880db69532c0578e1960  South-Eastern Asia       2.19.0   \n",
       "000e7e4842e3880db69532c0578e1960  South-Eastern Asia       2.19.0   \n",
       "\n",
       "                                 lmat_version rgi_version card_data_version  \\\n",
       "filename                                                                      \n",
       "000e7e4842e3880db69532c0578e1960        1.2.6       5.1.1             3.1.0   \n",
       "000e7e4842e3880db69532c0578e1960        1.2.6       5.1.1             3.1.0   \n",
       "000e7e4842e3880db69532c0578e1960        1.2.6       5.1.1             3.1.0   \n",
       "000e7e4842e3880db69532c0578e1960        1.2.6       5.1.1             3.1.0   \n",
       "000e7e4842e3880db69532c0578e1960        1.2.6       5.1.1             3.1.0   \n",
       "\n",
       "                                 variants_data_version analysis_valid  \\\n",
       "filename                                                                \n",
       "000e7e4842e3880db69532c0578e1960                 3.0.6            all   \n",
       "000e7e4842e3880db69532c0578e1960                 3.0.6            all   \n",
       "000e7e4842e3880db69532c0578e1960                 3.0.6            all   \n",
       "000e7e4842e3880db69532c0578e1960                 3.0.6            all   \n",
       "000e7e4842e3880db69532c0578e1960                 3.0.6            all   \n",
       "\n",
       "                                 lmat.score lmat.count lmat.ncbi_taxon_id  \\\n",
       "filename                                                                    \n",
       "000e7e4842e3880db69532c0578e1960    10239.6       6686                747   \n",
       "000e7e4842e3880db69532c0578e1960    656.063        642            1032869   \n",
       "000e7e4842e3880db69532c0578e1960    164.587        115            1169410   \n",
       "000e7e4842e3880db69532c0578e1960      54.48         52              44283   \n",
       "000e7e4842e3880db69532c0578e1960     47.046         22              32630   \n",
       "\n",
       "                                   lmat.rank  \\\n",
       "filename                                       \n",
       "000e7e4842e3880db69532c0578e1960     species   \n",
       "000e7e4842e3880db69532c0578e1960     no rank   \n",
       "000e7e4842e3880db69532c0578e1960     no rank   \n",
       "000e7e4842e3880db69532c0578e1960  subspecies   \n",
       "000e7e4842e3880db69532c0578e1960     species   \n",
       "\n",
       "                                                                lmat.taxonomy_label  \n",
       "filename                                                                             \n",
       "000e7e4842e3880db69532c0578e1960                              Pasteurella multocida  \n",
       "000e7e4842e3880db69532c0578e1960  Pasteurella multocida subsp. multocida str. An...  \n",
       "000e7e4842e3880db69532c0578e1960         Pasteurella multocida subsp. gallicida X73  \n",
       "000e7e4842e3880db69532c0578e1960             Pasteurella multocida subsp. multocida  \n",
       "000e7e4842e3880db69532c0578e1960                                synthetic construct  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.lmat_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <td>RIRDC_gdh(20)</td>\n",
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       "      <td>mlst and lmat</td>\n",
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       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>003f7bbe769852d8a171865081ba1bc3</th>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.0</td>\n",
       "      <td>3.0.7</td>\n",
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       "      <td>abaumannii_2</td>\n",
       "      <td>2</td>\n",
       "      <td>Pas_cpn60(2)</td>\n",
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       "      <td>Pas_rplB(2)</td>\n",
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       "      <td>Southern Asia</td>\n",
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       "      <td>Pas_rplB(9)</td>\n",
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       "      <td>icd(13)</td>\n",
       "      <td>mdh(17)</td>\n",
       "      <td>purA(11)</td>\n",
       "      <td>recA(25)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "                                       geo_area_name mlst_version  \\\n",
       "filename                                                            \n",
       "000e7e4842e3880db69532c0578e1960  South-Eastern Asia       2.19.0   \n",
       "003b60af692d11a14f10223d303a352f                 N/A       2.19.0   \n",
       "003f7bbe769852d8a171865081ba1bc3       Southern Asia       2.19.0   \n",
       "0044d0a1783629ef652c8e8232d76b58       Southern Asia       2.19.0   \n",
       "0070cced7fef29dd8c43e68c417458d4    Northern America       2.19.0   \n",
       "\n",
       "                                 lmat_version rgi_version card_data_version  \\\n",
       "filename                                                                      \n",
       "000e7e4842e3880db69532c0578e1960        1.2.6       5.1.1             3.1.0   \n",
       "003b60af692d11a14f10223d303a352f        1.2.6       5.1.1             3.1.0   \n",
       "003f7bbe769852d8a171865081ba1bc3        1.2.6       5.1.0             3.0.7   \n",
       "0044d0a1783629ef652c8e8232d76b58        1.2.6       5.1.0             3.0.7   \n",
       "0070cced7fef29dd8c43e68c417458d4        1.2.6       5.1.1             3.1.0   \n",
       "\n",
       "                                 variants_data_version analysis_valid  \\\n",
       "filename                                                                \n",
       "000e7e4842e3880db69532c0578e1960                 3.0.6            all   \n",
       "003b60af692d11a14f10223d303a352f                 3.0.6  mlst and lmat   \n",
       "003f7bbe769852d8a171865081ba1bc3                 3.0.6            all   \n",
       "0044d0a1783629ef652c8e8232d76b58                 3.0.6            all   \n",
       "0070cced7fef29dd8c43e68c417458d4                 3.0.6            all   \n",
       "\n",
       "                                       mlst.scheme mlst.sequence_type  \\\n",
       "filename                                                                \n",
       "000e7e4842e3880db69532c0578e1960  pmultocida_rirdc                201   \n",
       "003b60af692d11a14f10223d303a352f              None               None   \n",
       "003f7bbe769852d8a171865081ba1bc3      abaumannii_2                  2   \n",
       "0044d0a1783629ef652c8e8232d76b58      abaumannii_2                149   \n",
       "0070cced7fef29dd8c43e68c417458d4             ecoli                 73   \n",
       "\n",
       "                                 mlst.alleles_1 mlst.alleles_2 mlst.alleles_3  \\\n",
       "filename                                                                        \n",
       "000e7e4842e3880db69532c0578e1960  RIRDC_adk(21)  RIRDC_est(33)  RIRDC_pmi(26)   \n",
       "003b60af692d11a14f10223d303a352f                                                \n",
       "003f7bbe769852d8a171865081ba1bc3   Pas_cpn60(2)    Pas_fusA(2)    Pas_gltA(2)   \n",
       "0044d0a1783629ef652c8e8232d76b58   Pas_cpn60(3)   Pas_fusA(12)   Pas_gltA(11)   \n",
       "0070cced7fef29dd8c43e68c417458d4        adk(36)       fumC(24)        gyrB(9)   \n",
       "\n",
       "                                 mlst.alleles_4 mlst.alleles_5 mlst.alleles_6  \\\n",
       "filename                                                                        \n",
       "000e7e4842e3880db69532c0578e1960   RIRDC_zwf(2)  RIRDC_mdh(17)  RIRDC_gdh(20)   \n",
       "003b60af692d11a14f10223d303a352f                                                \n",
       "003f7bbe769852d8a171865081ba1bc3    Pas_pyrG(2)    Pas_recA(2)    Pas_rplB(2)   \n",
       "0044d0a1783629ef652c8e8232d76b58    Pas_pyrG(2)   Pas_recA(14)    Pas_rplB(9)   \n",
       "0070cced7fef29dd8c43e68c417458d4        icd(13)        mdh(17)       purA(11)   \n",
       "\n",
       "                                 mlst.alleles_7 mlst.alleles_8 mlst.alleles_9  \\\n",
       "filename                                                                        \n",
       "000e7e4842e3880db69532c0578e1960  RIRDC_pgi(35)            NaN            NaN   \n",
       "003b60af692d11a14f10223d303a352f                           NaN            NaN   \n",
       "003f7bbe769852d8a171865081ba1bc3    Pas_rpoB(2)            NaN            NaN   \n",
       "0044d0a1783629ef652c8e8232d76b58   Pas_rpoB(14)            NaN            NaN   \n",
       "0070cced7fef29dd8c43e68c417458d4       recA(25)            NaN            NaN   \n",
       "\n",
       "                                 mlst.alleles_10  \n",
       "filename                                          \n",
       "000e7e4842e3880db69532c0578e1960             NaN  \n",
       "003b60af692d11a14f10223d303a352f             NaN  \n",
       "003f7bbe769852d8a171865081ba1bc3             NaN  \n",
       "0044d0a1783629ef652c8e8232d76b58             NaN  \n",
       "0070cced7fef29dd8c43e68c417458d4             NaN  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mlst_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframes with postprocessed data\n",
    "\n",
    "Some of the data displayed in CARD:Live is post-processed internally by the application to set standardized geographic region names or taxonomic ranks. This must be all setup before loading the data (see the [code](https://github.com/arpcard/card-live-dashboard/blob/05d8c41e16544f1dbe8572783c7ff5015349d04f/card_live_dashboard/service/CardLiveDataManager.py#L29-L34) for details). If you wish to deal with the post-procssed (instead of raw JSON) data you can load as follows: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>timestamp</th>\n",
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       "      <th>filename</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000e7e4842e3880db69532c0578e1960</th>\n",
       "      <td>2020-08-31 09:56:48.521536</td>\n",
       "      <td>35</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>2.19.0</td>\n",
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       "      <td>35</td>\n",
       "      <td>South-eastern Asia</td>\n",
       "      <td>Haemophilus influenzae</td>\n",
       "      <td>Pasteurella multocida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>003b60af692d11a14f10223d303a352f</th>\n",
       "      <td>2020-09-08 07:37:23.142951</td>\n",
       "      <td>-1</td>\n",
       "      <td>N/A</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.1</td>\n",
       "      <td>3.1.0</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>mlst and lmat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Lactiplantibacillus plantarum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>003f7bbe769852d8a171865081ba1bc3</th>\n",
       "      <td>2020-08-10 16:56:26.607290</td>\n",
       "      <td>34</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
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       "      <td>all</td>\n",
       "      <td>34</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Acinetobacter baumannii</td>\n",
       "      <td>Acinetobacter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0044d0a1783629ef652c8e8232d76b58</th>\n",
       "      <td>2020-08-10 14:02:57.663503</td>\n",
       "      <td>34</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.0</td>\n",
       "      <td>3.0.7</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "      <td>34</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Acinetobacter baumannii</td>\n",
       "      <td>Acinetobacter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0070cced7fef29dd8c43e68c417458d4</th>\n",
       "      <td>2020-09-15 16:39:58.648231</td>\n",
       "      <td>21</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>1.2.6</td>\n",
       "      <td>5.1.1</td>\n",
       "      <td>3.1.0</td>\n",
       "      <td>3.0.6</td>\n",
       "      <td>all</td>\n",
       "      <td>21</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>Escherichia coli</td>\n",
       "      <td>Enterobacteriaceae</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  timestamp  geo_area_code  \\\n",
       "filename                                                                     \n",
       "000e7e4842e3880db69532c0578e1960 2020-08-31 09:56:48.521536             35   \n",
       "003b60af692d11a14f10223d303a352f 2020-09-08 07:37:23.142951             -1   \n",
       "003f7bbe769852d8a171865081ba1bc3 2020-08-10 16:56:26.607290             34   \n",
       "0044d0a1783629ef652c8e8232d76b58 2020-08-10 14:02:57.663503             34   \n",
       "0070cced7fef29dd8c43e68c417458d4 2020-09-15 16:39:58.648231             21   \n",
       "\n",
       "                                       geo_area_name mlst_version  \\\n",
       "filename                                                            \n",
       "000e7e4842e3880db69532c0578e1960  South-Eastern Asia       2.19.0   \n",
       "003b60af692d11a14f10223d303a352f                 N/A       2.19.0   \n",
       "003f7bbe769852d8a171865081ba1bc3       Southern Asia       2.19.0   \n",
       "0044d0a1783629ef652c8e8232d76b58       Southern Asia       2.19.0   \n",
       "0070cced7fef29dd8c43e68c417458d4    Northern America       2.19.0   \n",
       "\n",
       "                                 lmat_version rgi_version card_data_version  \\\n",
       "filename                                                                      \n",
       "000e7e4842e3880db69532c0578e1960        1.2.6       5.1.1             3.1.0   \n",
       "003b60af692d11a14f10223d303a352f        1.2.6       5.1.1             3.1.0   \n",
       "003f7bbe769852d8a171865081ba1bc3        1.2.6       5.1.0             3.0.7   \n",
       "0044d0a1783629ef652c8e8232d76b58        1.2.6       5.1.0             3.0.7   \n",
       "0070cced7fef29dd8c43e68c417458d4        1.2.6       5.1.1             3.1.0   \n",
       "\n",
       "                                 variants_data_version analysis_valid  \\\n",
       "filename                                                                \n",
       "000e7e4842e3880db69532c0578e1960                 3.0.6            all   \n",
       "003b60af692d11a14f10223d303a352f                 3.0.6  mlst and lmat   \n",
       "003f7bbe769852d8a171865081ba1bc3                 3.0.6            all   \n",
       "0044d0a1783629ef652c8e8232d76b58                 3.0.6            all   \n",
       "0070cced7fef29dd8c43e68c417458d4                 3.0.6            all   \n",
       "\n",
       "                                 geo_area_toplevel_m49code  \\\n",
       "filename                                                     \n",
       "000e7e4842e3880db69532c0578e1960                        35   \n",
       "003b60af692d11a14f10223d303a352f                       NaN   \n",
       "003f7bbe769852d8a171865081ba1bc3                        34   \n",
       "0044d0a1783629ef652c8e8232d76b58                        34   \n",
       "0070cced7fef29dd8c43e68c417458d4                        21   \n",
       "\n",
       "                                 geo_area_name_standard  \\\n",
       "filename                                                  \n",
       "000e7e4842e3880db69532c0578e1960     South-eastern Asia   \n",
       "003b60af692d11a14f10223d303a352f                    N/A   \n",
       "003f7bbe769852d8a171865081ba1bc3          Southern Asia   \n",
       "0044d0a1783629ef652c8e8232d76b58          Southern Asia   \n",
       "0070cced7fef29dd8c43e68c417458d4       Northern America   \n",
       "\n",
       "                                        rgi_kmer_taxonomy  \\\n",
       "filename                                                    \n",
       "000e7e4842e3880db69532c0578e1960   Haemophilus influenzae   \n",
       "003b60af692d11a14f10223d303a352f                      N/A   \n",
       "003f7bbe769852d8a171865081ba1bc3  Acinetobacter baumannii   \n",
       "0044d0a1783629ef652c8e8232d76b58  Acinetobacter baumannii   \n",
       "0070cced7fef29dd8c43e68c417458d4         Escherichia coli   \n",
       "\n",
       "                                                  lmat_taxonomy  \n",
       "filename                                                         \n",
       "000e7e4842e3880db69532c0578e1960          Pasteurella multocida  \n",
       "003b60af692d11a14f10223d303a352f  Lactiplantibacillus plantarum  \n",
       "003f7bbe769852d8a171865081ba1bc3                  Acinetobacter  \n",
       "0044d0a1783629ef652c8e8232d76b58                  Acinetobacter  \n",
       "0070cced7fef29dd8c43e68c417458d4             Enterobacteriaceae  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import numpy as np\n",
    "\n",
    "from card_live_dashboard.service.CardLiveDataLoader import CardLiveDataLoader\n",
    "\n",
    "from card_live_dashboard.model.data_modifiers.AddGeographicNamesModifier import AddGeographicNamesModifier\n",
    "from card_live_dashboard.model.data_modifiers.AddTaxonomyModifier import AddTaxonomyModifier\n",
    "from card_live_dashboard.model.data_modifiers.AntarcticaNAModifier import AntarcticaNAModifier\n",
    "from card_live_dashboard.service import region_codes\n",
    "\n",
    "data_dir = Path('card_live')\n",
    "loader = CardLiveDataLoader(data_dir)\n",
    "loader.add_data_modifiers([\n",
    "    AntarcticaNAModifier(np.datetime64('2020-07-20')),\n",
    "    AddGeographicNamesModifier(region_codes),    \n",
    "    AddTaxonomyModifier(Path('card-live-home/db/taxa.sqlite')),\n",
    "])\n",
    "\n",
    "data = loader.read_data()\n",
    "data.main_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the modifications you can see here is the addition of the `geo_area_name_standard` column for the standardized geographic region name as well as the `rgi_kmer_taxonomy` and `lmat_taxonomy` columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figures\n",
    "\n",
    "Once you have the data loaded into dataframes, you can move on to producing figures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Count of samples by geographic area')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams.update({'font.size': 16})\n",
    "\n",
    "data.main_df['geo_area_name_standard'].value_counts().sort_values(ascending=True).plot(kind='barh', figsize=(8,8))\n",
    "plt.xlabel('Count')\n",
    "plt.title('Count of samples by geographic area')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Awesome :). You can now generate your own figures from the CARD:Live Dashboard data.\n",
    "\n",
    "In the CARD:Live Dashboard I make use [plotly](https://plotly.com/python/) instead of matplotlib for generating figures. The code for generating the dashboard figures is found in the [figures.py](https://github.com/arpcard/card-live-dashboard/blob/main/card_live_dashboard/layouts/figures.py) file in the code."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}