From 58dc5227bac29ffa49632e78984495f47a261af5 Mon Sep 17 00:00:00 2001 From: Micah Johnson Date: Wed, 3 Jul 2024 13:40:40 -0600 Subject: [PATCH] CSV Files (#108) * Added in base class for downloading flat files * Added in snowex and csas flat files * Added a test for checking valid links * Added in test datasets * Rearranged the base validation to avoid complexity flake issues. Converted from assertions to exceptions! * Broke out validation checks. Added associated tests --- .gitignore | 1 + README.rst | 5 +- docs/gallery/csas_example.ipynb | 140 +++++ docs/usage.rst | 51 ++ metloom/pointdata/__init__.py | 6 +- metloom/pointdata/base.py | 58 +- metloom/pointdata/csas.py | 80 +++ metloom/pointdata/files.py | 256 +++++++++ metloom/pointdata/snowex.py | 45 ++ metloom/variables.py | 99 +++- tests/data/csas_mocks/PTSP_1hr.csv | 506 ++++++++++++++++++ tests/data/csas_mocks/SASP_1hr_2003-2009.csv | 506 ++++++++++++++++++ tests/data/csas_mocks/SASP_1hr_2010-2023.csv | 506 ++++++++++++++++++ tests/data/csas_mocks/SBSG_1hr.csv | 502 +++++++++++++++++ tests/data/csas_mocks/SBSP_1hr_2003-2009.csv | 506 ++++++++++++++++++ tests/data/csas_mocks/SBSP_1hr_2010-2023.csv | 505 +++++++++++++++++ .../SNEX_Met_GMSP2_final_output.csv | 361 +++++++++++++ .../SNEX_Met_LSOS_final_output.csv | 361 +++++++++++++ .../snowex_mocks/SNEX_Met_ME_final_output.csv | 361 +++++++++++++ .../snowex_mocks/SNEX_Met_MM_final_output.csv | 361 +++++++++++++ .../snowex_mocks/SNEX_Met_MW_final_output.csv | 361 +++++++++++++ tests/data/snowex_mocks/gm_polygon.dbf | Bin 0 -> 767 bytes tests/data/snowex_mocks/gm_polygon.prj | 1 + tests/data/snowex_mocks/gm_polygon.shp | Bin 0 -> 388 bytes tests/data/snowex_mocks/gm_polygon.shx | Bin 0 -> 108 bytes tests/test_csas.py | 182 +++++++ tests/test_point_data.py | 30 +- tests/test_snowex.py | 109 ++++ 28 files changed, 5877 insertions(+), 22 deletions(-) create mode 100644 docs/gallery/csas_example.ipynb create mode 100644 metloom/pointdata/csas.py create mode 100644 metloom/pointdata/files.py create mode 100644 metloom/pointdata/snowex.py create mode 100644 tests/data/csas_mocks/PTSP_1hr.csv create mode 100644 tests/data/csas_mocks/SASP_1hr_2003-2009.csv create mode 100644 tests/data/csas_mocks/SASP_1hr_2010-2023.csv create mode 100644 tests/data/csas_mocks/SBSG_1hr.csv create mode 100644 tests/data/csas_mocks/SBSP_1hr_2003-2009.csv create mode 100644 tests/data/csas_mocks/SBSP_1hr_2010-2023.csv create mode 100644 tests/data/snowex_mocks/SNEX_Met_GMSP2_final_output.csv create mode 100644 tests/data/snowex_mocks/SNEX_Met_LSOS_final_output.csv create mode 100644 tests/data/snowex_mocks/SNEX_Met_ME_final_output.csv create mode 100644 tests/data/snowex_mocks/SNEX_Met_MM_final_output.csv create mode 100644 tests/data/snowex_mocks/SNEX_Met_MW_final_output.csv create mode 100644 tests/data/snowex_mocks/gm_polygon.dbf create mode 100644 tests/data/snowex_mocks/gm_polygon.prj create mode 100644 tests/data/snowex_mocks/gm_polygon.shp create mode 100644 tests/data/snowex_mocks/gm_polygon.shx create mode 100644 tests/test_csas.py create mode 100644 tests/test_snowex.py diff --git a/.gitignore b/.gitignore index b6ecd47..57ba7a2 100644 --- a/.gitignore +++ b/.gitignore @@ -111,3 +111,4 @@ ENV/ # scratch dir scratch/ **/.ipynb_checkpoints/* +**/cache/** diff --git a/README.rst b/README.rst index 46f00b9..12e0ff3 100644 --- a/README.rst +++ b/README.rst @@ -19,8 +19,7 @@ metloom Location Oriented Observed Meteorology metloom is a python library created with the goal of consistent, simple sampling of -meteorology and snow related point measurments from a variety of datasources across the -Western US. metloom is developed by `M3 Works `_ as a tool for validating +meteorology and snow related point measurments from a variety of datasources is developed by `M3 Works `_ as a tool for validating computational hydrology model results. Contributions welcome! Warning - This software is provided as is (see the license), so use at your own risk. @@ -45,6 +44,8 @@ Features * `GEOSPHERE AUSTRIA `_ * `UCSB CUES `_ * `MET NORWAY `_ + * `SNOWEX MET STATIONS `_ + * `CENTER FOR SNOW AND AVALANCHE STUDIES (CSAS) `_ Requirements ------------ diff --git a/docs/gallery/csas_example.ipynb b/docs/gallery/csas_example.ipynb new file mode 100644 index 0000000..c496d60 --- /dev/null +++ b/docs/gallery/csas_example.ipynb @@ -0,0 +1,140 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "44d008c9-e968-4fc8-9673-4e47acecea3a", + "metadata": {}, + "source": [ + "# Studying Senator Beck Basin\n", + "Senator Beck and Swamp Angel studies plots are excellent sites to look at energy balance for studying snow. Now we can read these in using metloom! Below is a quick example of how to pull in various data from CSAS. \n", + "\n", + "Start by importing the Center for Snow and Avalanche Studies (CSAS) reader!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3bab34f2-ec02-4830-a4be-22252d7e7bcb", + "metadata": {}, + "outputs": [], + "source": [ + "from metloom.pointdata import CSASMet\n", + "from metloom.variables import CSASVariables\n", + "from datetime import datetime\n", + "import matplotlib.pyplot as plt " + ] + }, + { + "cell_type": "markdown", + "id": "d551f2b8-b81c-4925-92be-ceadbf73a55e", + "metadata": {}, + "source": [ + "Setup some dates and select the variables. The CSAS data are stored as csv's on their website so the call to CSAS will download the necessary files into a cache folder locally. Then it will be reused when the call is made. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8d28b4d0-d7bb-49de-88c8-93085e915b97", + "metadata": {}, + "outputs": [], + "source": [ + "start = datetime(2023, 1, 1)\n", + "end = datetime(2023, 6, 1)\n", + "\n", + "variable = CSASVariables.SNOWDEPTH\n", + "\n", + "# Senator Beck\n", + "sbsp = CSASMet('SBSP')\n", + "df_sbsp = sbsp.get_daily_data(start, end, [variable])\n", + "\n", + "# Swamp Angel Study plot\n", + "sasp = CSASMet('SASP')\n", + "df_sasp = sasp.get_daily_data(start, end, [variable])\n", + "\n", + "# Swamp Angel Study plot\n", + "sbsg = CSASMet('SBSG')\n", + "stream_var = CSASVariables.STREAMFLOW_CFS\n", + "df_sbsg = sbsg.get_daily_data(start, end, [stream_var])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0a300471-a039-41f2-a9c8-53e66c14a098", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1)\n", + "ax.plot(df_sbsp.index.get_level_values('datetime'), df_sbsp[variable.name], label=sbsp.id)\n", + "ax.plot(df_sasp.index.get_level_values('datetime'), df_sasp[variable.name], label=sasp.id)\n", + "\n", + "ax1 = ax.twinx()\n", + "ax1.plot(df_sbsg.index.get_level_values('datetime'), \n", + " df_sbsg[stream_var.name], label='streamflow', color='black', alpha=0.5)\n", + "ax.set_title('Snow Depth and Streamflow at Senator Beck Basin')\n", + "ax.set_ylabel('Snow Depth [m[')\n", + "ax1.set_ylabel('Streamflow [CFS]')\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b09a37f2-d050-4819-8992-0222ad9c8a75", + "metadata": {}, + "source": [ + "Thats all it takes! Putney Study plot is also available. Note: There are limitation in dates metloom is designed to pull all available data online which is lagging from realtime a bit. Happy metloom-ing!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "679ef3a4-77ad-4e86-86e6-0ad6fe48d0bf", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6a3b86f-1a94-4072-9a59-d50f77705c47", + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/usage.rst b/docs/usage.rst index b3bbd48..1fc4494 100644 --- a/docs/usage.rst +++ b/docs/usage.rst @@ -80,7 +80,58 @@ To pull stations using Mesowest:: ) print(df) +Center for Snow and Avalanche Studies (CSAS) +-------------------------------------------- +There are 4 stations of interest maintained by the CSAS. Senator Beck Study plot, +Swamp Angel Study Plot, Senator Beck Stream Gauge and Putney Study plot. These four stations +contain a wealth of data useful for studying and validating snow processes. The files exist as a +flat csv file so requests using this will simply download the file, interpret the datetime +index and crop according to your request. Since it is a CSV the file will be stored in a local cache +in the same directory you ran your code. This way the download times are reduced. + +Additionally, the CSAS data is not available in realtime (at least as of June 2024). +Data is updated annually and stored on the website. Metloom will try to stay as up to date as +possible when the files are updated. Please feel free to submit a PR if you know the data has been +updated. Checkout the `facilities page `_ on CSAS to see more about the stations. + +To pull stations using CSAS:: + + from metloom.pointdata import CSASMet + from metloom.variables import CSASVariables + from datetime import datetime + + start = datetime(2023, 1, 1) + end = datetime(2023, 6, 1) + sbsp = CSASMet('SBSP') + df_sbsp = sbsp.get_daily_data(start, end, [CSASVariables.SNOWDEPTH]) + +If you use these data, please use the `appropriate citations `_ and give credit to the +institution. + +SnowEx +------ +During the `NASA SnowEx campaign `_ +there were a handful of met stations deployed which are now published on the +`NSIDC `_. These stations have been +mapped into metloom to increase the utility/convenience of these data. The SnowEx +data is in a csv file format and thus any queries will download the appropriate +files to a local cache to reduce download times. For this to work you need to have +a `.netrc` and an account with the NSIDC. See the +`access guide `_ +for more help. + +To pull stations using SnowEx:: + + from metloom.pointdata import SnowExMet + from metloom.variables import SnowExVariables + from datetime import datetime + + start = datetime(2020, 1, 1) + end = datetime(2020, 6, 1) + # Grand Mesa Study Plot + gmsp = SnowExMet('GMSP') + df_gmsp = gmsp.get_daily_data(start, end, [SnowExVariables.SNOWDEPTH]) My variables aren't here ------------------------ diff --git a/metloom/pointdata/__init__.py b/metloom/pointdata/__init__.py index ef9397c..6c22af9 100644 --- a/metloom/pointdata/__init__.py +++ b/metloom/pointdata/__init__.py @@ -6,9 +6,13 @@ from .geosphere_austria import GeoSphereHistPointData, GeoSphereCurrentPointData from .norway import MetNorwayPointData from .cues import CuesLevel1 +from .files import CSVPointData, StationInfo +from .snowex import SnowExMet +from .csas import CSASMet __all__ = [ "PointData", "PointDataCollection", "CDECPointData", "SnotelPointData", "MesowestPointData", "USGSPointData", "GeoSphereHistPointData", - "GeoSphereCurrentPointData", "CuesLevel1", "MetNorwayPointData" + "GeoSphereCurrentPointData", "CuesLevel1", "MetNorwayPointData", + "CSVPointData", "StationInfo", "SnowExMet", "CSASMet" ] diff --git a/metloom/pointdata/base.py b/metloom/pointdata/base.py index 883adb6..eea3c2d 100644 --- a/metloom/pointdata/base.py +++ b/metloom/pointdata/base.py @@ -242,30 +242,29 @@ def points_from_geometry( raise NotImplementedError("points_from_geometry not implemented") @classmethod - def validate_sensor_df(cls, gdf: gpd.GeoDataFrame): - """ - Validate that the GeoDataFrame returned is formatted correctly. - The goal of this method is to ensure base classes are returning a - consistent format of dataframe - """ - if gdf is None: - return - assert isinstance(gdf, gpd.GeoDataFrame) - columns = gdf.columns - index_names = gdf.index.names - # check for required indexes + def _validate_geodataframe(cls, gdf: gpd.GeoDataFrame): + if not isinstance(gdf, gpd.GeoDataFrame): + raise DataValidationError('Returned DataFrame must be a GeoDataframe') + + @classmethod + def _validate_df_indicies(cls, gdf: gpd.GeoDataFrame): + """ Confirm the df is indexed properly""" for ei in cls.EXPECTED_INDICES: - if ei not in index_names: + if ei not in gdf.index.names: raise DataValidationError( f"{ei} was expected, but not found as an" f" index of the final dataframe" ) + + @classmethod + def _validate_df_columns(cls, gdf: gpd.GeoDataFrame, expected_columns: List[str]): # check for expected columns - avoid modifying at class level - expected_columns = copy.deepcopy(cls.EXPECTED_COLUMNS) possible_extras = ["measurementDate", "quality_code"] + columns = gdf.columns for pe in possible_extras: if pe in columns: expected_columns += [pe] + for column in expected_columns: if column not in columns: raise DataValidationError( @@ -273,11 +272,38 @@ def validate_sensor_df(cls, gdf: gpd.GeoDataFrame): f" column of the final dataframe" ) - remaining_columns = [c for c in columns if c not in expected_columns] + @classmethod + def _validate_df_units(cls, gdf: gpd.GeoDataFrame, expected_columns: List[str]): + """ + Check the variables requested have units associated + """ + remaining_columns = [c for c in gdf.columns if c not in expected_columns] # make sure all variables have a units column as well for rc in remaining_columns: if "_units" not in rc: - assert f"{rc}_units" in remaining_columns + if f"{rc}_units" not in remaining_columns: + raise DataValidationError(f'Missing units column for {rc}') + + @classmethod + def validate_sensor_df(cls, gdf: gpd.GeoDataFrame): + """ + Validate that the GeoDataFrame returned is formatted correctly. + The goal of this method is to ensure base classes are returning a + consistent format of dataframe + """ + if gdf is None: + return + + expected_columns = copy.deepcopy(cls.EXPECTED_COLUMNS) + + # Confirm the dataframe is a geodataframe + cls._validate_geodataframe(gdf) + # Confirm the df is indexed properly + cls._validate_df_indicies(gdf) + # Confirm the columns are correct + cls._validate_df_columns(gdf, expected_columns) + # Confirm that any columns from variables have associated units + cls._validate_df_units(gdf, expected_columns) def __repr__(self): return f"{self.__class__.__name__}({self.id!r}, {self.name!r})" diff --git a/metloom/pointdata/csas.py b/metloom/pointdata/csas.py new file mode 100644 index 0000000..501de1b --- /dev/null +++ b/metloom/pointdata/csas.py @@ -0,0 +1,80 @@ +""" +Data reader for the Center for Snow and Avalanche Studies +""" +from metloom.pointdata import CSVPointData, StationInfo +from metloom.variables import CSASVariables +import os +from datetime import datetime, timedelta + + +class InvalidDateRange(Exception): + """ + Exception to indicate there is no know data for the available date range + """ + + +class CSASStationInfo(StationInfo): + # Name, id, lat, long, elevation, http path + SENATOR_BECK = ("Senator Beck Study Plot", "SBSP", 37.90688, -107.72627, 12186, + "2023/11/SBSP_1hr_2003-2009.csv") + SWAMP_ANGEL = ("Swamp Angel Study Plot", "SASP", 37.90691, -107.71132, 11060, + "2023/11/SASP_1hr_2003-2009.csv") + PUTNEY = ("Putney Study Plot", "PTSP", 37.89233, -107.69577, 12323, + "2023/11/PTSP_1hr.csv") + SENATOR_BECK_STREAM_GAUGE = ("Senator Beck Stream Gauge", "SBSG", 37.90678, + -107.70943, 11030, "2023/11/SBSG_1hr.csv") + + +class CSASMet(CSVPointData): + """ + """ + ALLOWED_VARIABLES = CSASVariables + ALLOWED_STATIONS = CSASStationInfo + + # Data is in Mountain time + UTC_OFFSET_HOURS = -7 + + URL = "https://snowstudies.org/wp-content/uploads/" + DATASOURCE = "CSAS" + DOI = "" + + def _file_urls(self, station_id, start, end): + """ + Navigate the system using dates. Data for SASP and SBSP is stored in + two csvs. 2003-2009 and 2010-2023. Not sure what happens when the + next year is made available. This function will grab the necessary urls + depending on the requested data + """ + urls = [] + + if station_id in ['SASP', 'SBSP']: + current_available_year = datetime.today().year - 1 + + if start.year <= 2009: + urls.append(os.path.join(self.URL, self._station_info.path)) + + # Account for later file use or even straddling thge data + if start.year > 2009 or end.year > 2009: # TODO: add to the info enum? + partial = str(self._station_info.path).replace("2003", "2010") + + filename = partial.replace('2009', str(current_available_year)) + urls.append(os.path.join(self.URL, filename)) + + if start.year < 2003 or end.year > current_available_year: + raise InvalidDateRange(f"CSAS data is only available from 2003-" + f"{current_available_year}") + else: + urls.append(os.path.join(self.URL, self._station_info.path)) + + return urls + + @staticmethod + def _parse_datetime(row): + # Julian day is not zero based Jan 1 == DOY 1 + dt = timedelta(days=int(row['DOY']) - 1, hours=int(row['Hour'] / 100)) + return datetime(int(row['Year']), 1, 1) + dt + + def _assign_datetime(self, resp_df): + resp_df['datetime'] = resp_df.apply(lambda row: self._parse_datetime(row), + axis=1) + return resp_df.set_index('datetime') diff --git a/metloom/pointdata/files.py b/metloom/pointdata/files.py new file mode 100644 index 0000000..d277973 --- /dev/null +++ b/metloom/pointdata/files.py @@ -0,0 +1,256 @@ +""" +Location to keep readers that pull in a flat file like a csv. +""" +import os + +import pandas as pd + +from metloom.pointdata import PointData +from metloom.variables import SensorDescription +from enum import Enum +from pathlib import Path +from datetime import datetime, timezone, timedelta +import logging +import requests +from typing import List +import geopandas as gpd +import numpy as np + + +LOG = logging.getLogger(__name__) + + +class StationInfo(Enum): + """ + Since there is not enough info via an API, csv readers rely on a list of stations + and info associated with them to build the calls. This should allow for a common + dataset with multiple stations to be verified and isolated + """ + + # Defined: Name, id, lat, long, http sub path to file + + @property + def station_name(self): + return self.value[0] + + @property + def station_id(self): + return self.value[1] + + @property + def latitude(self): + return self.value[2] + + @property + def longitude(self): + return self.value[3] + + @property + def elevation(self): + return self.value[4] + + @property + def path(self): + return Path(self.value[5]) + + @classmethod + def all_station_ids(cls): + return [e.station_id for e in cls] + + @classmethod + def all_points(cls): + return [e.point for e in cls] + + @classmethod + def from_station_id(cls, station_id): + result = None + for e in cls: + if station_id == e.station_id: + result = e + break + return result + + @property + def point(self): + return gpd.points_from_xy([self.longitude], + [self.latitude], + [self.elevation])[0] + + +class CSVPointData(PointData): + """ + Some met station data is stored off in flat csv files. This class enables the + management of downloading those files while still allowing a similar interface + """ + ALLOWED_STATIONS = StationInfo + UTC_OFFSET_HOURS = 0 # Allows users to specificy the timezone of the datasets + + def __init__(self, station_id, name=None, metadata=None, cache='./cache'): + """ + See docstring for PointData.__init__ + """ + super(CSVPointData, self).__init__( + station_id, name, + metadata=metadata + ) + self._raw_metadata = None + self._tzinfo = timezone(timedelta(hours=self.UTC_OFFSET_HOURS)) + self._cache = Path(cache) + self._station_info = None + self.valid = False + + def _verify_station(self): + """ Verifies the station is valid using the associated enum""" + self._station_info = self.ALLOWED_STATIONS.from_station_id(self.id) + + if self._station_info is not None: + # Auto assign name + if self.name is None: + self.name = self._station_info.station_name + + return True + else: + LOG.error(f"Station ID {self.id} is not valid, allowed id's are " + f"{', '.join(self.ALLOWED_STATIONS.all_station_ids())}") + return False + + def _verify_sensor(self, resp_df, variable: SensorDescription): + """ Verifies the station is valid using the associated enum""" + if variable.code in resp_df.columns: + return True + else: + LOG.debug(f"{variable.name} not found in {self.id} data") + return False + + def _file_urls(self, *args): + """Returns the url to the file containing the station data""" + raise NotImplementedError('CSVPointData._file_urls() must be implemented to ' + 'download csv station data.') + + def _assign_datetime(self, resp_df): + raise NotImplementedError('CSVPointData._assign_datetime() must be implemented ' + 'to download csv station data.') + + def _download(self, urls): + """Download the file(s)""" + filenames = [] + for url in urls: + filename = self._cache.joinpath(Path(url).name) + filenames.append(filename) + if not filename.exists(): + with requests.get(url, stream=True) as r: + LOG.info(f'Downloading {Path(url).name}...') + lines = r.iter_lines() + with open(filename, mode='w+') as fp: + for line in lines: + fp.write(line.decode('utf-8') + '\n') + return filenames + + def _get_one_variable(self, resp_df, period, variable: SensorDescription): + """ + Retrieve a single variable and process it accordingly + """ + method = "sum" if variable.accumulated else "average" + if self._verify_sensor(resp_df, variable): + isolated = resp_df[variable.code].loc[:] + + # TODO: This may only be true for SNOWEX + isolated.loc[isolated == -9999] = np.nan + if method == 'average': + data = isolated.resample(period).mean() + elif method == 'sum': + data = isolated.resample(period).sum() + else: + raise Exception('Invalid aggregation method') + else: + data = None + LOG.debug(f"No data returned for {variable.name}") + + return data + + def _get_data(self, start_date, end_date, variables: List[SensorDescription], + period): + """ + Utilizes cached data or downloads the data + """ + self.valid = self._verify_station() + + if not self.valid: + return None + + urls = self._file_urls(self._station_info.station_id, + start_date, end_date) + + # Make the cache dir + if not self._cache.is_dir(): + os.mkdir(self._cache) + + # Download data if it doesn't exist locally. + files = self._download(urls) + dfs = [pd.read_csv(f, index_col=False, low_memory=False) for f in files] + resp_df = pd.concat(dfs) + resp_df = self._assign_datetime(resp_df) + + # use a predifined index to show nans in the event of patch data + df = pd.DataFrame(index=pd.date_range(start_date, end_date, freq=period, + name='datetime'), + columns=[v.name for v in variables]) + + # Use this instead .loc to avoid index on patchy data + ind = (resp_df.index >= start_date) & (resp_df.index < end_date) + isolated = resp_df.loc[ind, resp_df.columns] + for i, variable in enumerate(variables): + df_var = self._get_one_variable(isolated, period, variable) + if df_var is not None: + if not np.all(df_var.isnull()): + df[variable.name].loc[df_var.index] = df_var + df[f"{variable.name}_units"] = variable.units + else: + df = df.drop(columns=[variable.name]) + + # All nan data suggests no matching data + if np.all(df.isnull()): + return None + + # Make this a geodataframe + df["site"] = [self.id] * len(df) + df["datasource"] = [self.DATASOURCE] * len(df) + # Make this a geodataframe + df = gpd.GeoDataFrame(df, geometry=[self.metadata] * len(df)) + df = df.reset_index().set_index(["datetime", "site"]) + + self.validate_sensor_df(df) + return df + + def get_daily_data(self, start_date: datetime, end_date: datetime, + variables: List[SensorDescription]): + return self._get_data( + start_date, end_date, variables, "D" + ) + + def get_hourly_data(self, start_date: datetime, end_date: datetime, + variables: List[SensorDescription]): + return self._get_data( + start_date, end_date, variables, "h" + ) + + def _get_metadata(self): + return self._station_info.point + + @classmethod + def points_from_geometry(cls, geometry: gpd.GeoDataFrame, + variables: List[SensorDescription], within_geometry=True, + buffer=0.0, **kwargs): + # Avoid multiple polys and use a buffer if requested. + projected_geom = geometry.dissolve().buffer(buffer).to_crs(4326) + + gdf = gpd.GeoDataFrame(geometry=cls.ALLOWED_STATIONS.all_points(), data=[], + crs=4326) + # Use the exact geometry to filter, otherwise use the bounds of the poly + if within_geometry: + search_area = projected_geom.iloc[0] + else: + search_area = projected_geom.envelope.iloc[0] + + filtered_gdf = gdf[gdf.within(search_area)] + return filtered_gdf diff --git a/metloom/pointdata/snowex.py b/metloom/pointdata/snowex.py new file mode 100644 index 0000000..ef73681 --- /dev/null +++ b/metloom/pointdata/snowex.py @@ -0,0 +1,45 @@ +import logging +import pandas as pd +from metloom.pointdata import CSVPointData, StationInfo +from metloom.variables import SnowExVariables +import os + + +LOG = logging.getLogger(__name__) + + +class SnowExMetInfo(StationInfo): + # Name, id, lat, long, elevation, http filename + GM_STUDY_PLOT = ("Grand Mesa Study Plot", "GMSP", 39.05084, -108.06144, 10626, + "2017.06.21/SNEX_Met_GMSP2_final_output.csv") + LS_OBS_SITE = ("Local Scale Observation Site", "LSOS", 39.05225, -108.09792, 9791, + "2016.10.09/SNEX_Met_LSOS_final_output.csv") + MESA_EAST = ("Mesa East", "ME", 39.10358, -107.88383, 10105, + "2016.10.10/SNEX_Met_ME_final_output.csv") + MESA_MIDDLE = ("Mesa Middle", "MM", 39.03954, -107.94174, 10286, + "2016.10.10/SNEX_Met_MM_final_output.csv") + MESA_WEST = ("Mesa West", "MW", 39.03388, -108.21399, 9950, + "2016.10.09/SNEX_Met_MW_final_output.csv") + + +class SnowExMet(CSVPointData): + """ + These data are stored in csv data formats + """ + ALLOWED_VARIABLES = SnowExVariables + ALLOWED_STATIONS = SnowExMetInfo + + # Data is in UTC + UTC_OFFSET_HOURS = 0 + + URL = "https://n5eil01u.ecs.nsidc.org/SNOWEX/SNEX_Met.001/" + DATASOURCE = "NSIDC" + DOI = "https://doi.org/10.5067/497NQVJ0CBEX" + + def _file_urls(self, *args): + return [os.path.join(self.URL, self._station_info.path)] + + def _assign_datetime(self, resp_df): + resp_df['datetime'] = pd.to_datetime(resp_df['TIMESTAMP']) + resp_df = resp_df.drop(columns=['TIMESTAMP']).set_index('datetime') + return resp_df diff --git a/metloom/variables.py b/metloom/variables.py index 5e9cdd4..47cf14a 100644 --- a/metloom/variables.py +++ b/metloom/variables.py @@ -10,7 +10,8 @@ class SensorDescription: code: str = "-1" # code used within the applicable API name: str = "basename" # desired name for the sensor description: str = None # description of the sensor - accumulated: bool = False # whether or not the data is accumulated + accumulated: bool = False # whether the data is accumulated + units: str = None # Optional units kwarg @dataclass(eq=True, frozen=True) @@ -337,7 +338,7 @@ class MetNorwayVariables(VariableBase): "accumulated(precipitation_amount)", "ACCUMULATED PRECIPITATION", "Total precipitation amount in gauge" " (accumulated since last emptying). Timing for emptying and" - " algoritm for calculating the precipitation" + " algorithm for calculating the precipitation" " amount depends on sensortype" ) PRECIPITATION = SensorDescription( @@ -346,3 +347,97 @@ class MetNorwayVariables(VariableBase): " Each tip is registered along with the time stamp for the tip." " This is the basis for calcutation of precipitation sum per minute" ) + + +class SnowExVariables(VariableBase): + """ + Variables for SnowEx met stations data, refer to user guide for adding more + variables + + Metadata: + https://nsidc.org/sites/default/files/documents/user-guide/snex_met-v001-userguide.pdf + """ + + TEMP_20FT = SensorDescription( + 'AirTC_20ft_Avg', "AIR TEMP @20ft", units='deg C', + description="Air temperature measured at 20 ft") + + TEMP_10FT = SensorDescription( + 'AirTC_10ft_Avg', "AIR TEMP @10ft", units='deg C', + description="Air temperature measured at 10 ft") + + UPSHORTWAVE = SensorDescription( + "SUp_Avg", "UPWARD SHORTWAVE RADIATION", units='w/m^2', + description="Shortwave radiation measured with upward-facing sensor", + ) + DOWNSHORTWAVE = SensorDescription( + "SDn_Avg", "DOWNWARD SHORTWAVE RADIATION", + description="Shortwave radiation measured with downward-facing sensor", + ) + SNOWDEPTH = SensorDescription( + "SnowDepthFilter(m)", "SNOWDEPTH", + description="Snow surface height in meters w/ filtering") + + TEMPGROUND5CM = SensorDescription("TC_5cm_Avg", "SOIL TEMP @ 5cm") + TEMPGROUND20CM = SensorDescription("TC_20cm_Avg", "SOIL TEMP @ 20cm") + TEMPGROUND50CM = SensorDescription("TC_50cm_Avg", "SOIL TEMP @ 50cm") + + +class CSASVariables(VariableBase): + """ + Variable meta for the stations: + SASP - https://snowstudies.org/wp-content/uploads/2023/11/SASP_Variable_Table.xlsx + SBSP - https://snowstudies.org/wp-content/uploads/2023/11/SBSP_Variable_Table.xlsx + PTSP - https://snowstudies.org/wp-content/uploads/2023/11/PTSP_Variable_Table.xlsx + SGSB - https://snowstudies.org/wp-content/uploads/2023/11/SBSG_Variable_Table.xlsx + """ + SNOWDEPTH = SensorDescription("Sno_Height_M", "SNOWDEPTH", units='meters') + RH = SensorDescription("RH", "RELATIVE HUMIDITY", units='%') + STREAMFLOW_CFS = SensorDescription("Discharge_CFS", "STREAMFLOW", units='CFS') + SURF_TEMP = SensorDescription('Sno_IR_C', "SURFACE TEMP", units="deg C", + description="Snow surface temperature") + UPPER_WINDSPEED = SensorDescription("UpWind_Uavg_MS", "UPPER WIND SPEED", + units="m/s", + description="Wind speed at the upper location") + UPPER_WINDDIR = SensorDescription("UpWind_Dir_Uavg", "UPPER WIND DIRECTION", + units="degrees", + description="Wind direction at the upper " + "location") + LOWER_WINDSPEED = SensorDescription("LoWind_Uavg_MS", "LOWER WIND SPEED", + units="m/s", + description="Wind speed at the lower location") + LOWER_WINDDIR = SensorDescription("LoWind_Dir_Uavg", "LOWER WIND DIRECTION", + units='degrees', + description="Wind direction at the lower " + "location") + DOWN_BROADBAND = SensorDescription( + 'PyDwn_Unfilt_W', "DOWNWARD BROADBAND RADIATION", + units='w/m^2', description="Reflected Broadband radiation") + DOWN_NIR_SWIR = SensorDescription( + 'PyDwn_filt_W', "DOWNWARD NIR/SWIR RADIATION", + units='w/m^2', description="Reflected NIR/SWIR radiation") + UP_BROADBAND = SensorDescription( + 'PyUp_Unfilt_W', "UPWARD BROADBAND RADIATION", + units='w/m^2', description="Incoming Broadband radiation") + + UP_NIR_SWIR = SensorDescription( + 'PyUp_filt_W', "UPWARD NIR/SWIR RADIATION", + units='w/m^2', description="Incoming NIR/SWIR radiation") + + PRECIPITATION = SensorDescription( + 'Day_H2O_mm', "DAILY PRECIP", accumulated=True, + units='mm', + description="Daily accumulated precipitation in mm") + + TEMPGROUND = SensorDescription( + "Soil_Surf_C", "GROUND TEMPERATURE", units='deg C', + description='Temperature at soil interface') + TEMPGROUND10CM = SensorDescription( + "Soil_10cm_C", "GROUND TEMPERATURE -10CM", units='deg C', + description='Soil temperature at a depth of 10cm') + TEMPGROUND20CM = SensorDescription( + "Soil_20cm_C", "GROUND TEMPERATURE -20CM", units='deg C', + description='Soil temperature at a depth of 20cm') + TEMPGROUND40CM = SensorDescription( + "Soil_40cm_C", "GROUND TEMPERATURE -40CM", units='deg C', + description='Soil temperature at a depth of 40cm') diff --git a/tests/data/csas_mocks/PTSP_1hr.csv b/tests/data/csas_mocks/PTSP_1hr.csv new file mode 100644 index 0000000..ce62ffc --- /dev/null +++ b/tests/data/csas_mocks/PTSP_1hr.csv @@ -0,0 +1,506 @@ +ArrayID,Year,DOY,Hour,Air_Min_C,Air_Min_Time,Air_Max_C,Air_Max_Time,Air_Min_F,Air_Max_F,RH,Wind_Pgust_MPH,Wind_PGust_Time,Wind_PGust_MS,Wind_SAvg_MPH,Wind_Uavg_MPH,Wind_Dir_Uavg,Wind_Dir_StDev,Wind_SAvg_MS,Wind_Uavg_MS,Wind_Dir_Uavg_Copy,Wind_Dir_StDev_Copy,Sys_Volts +101,2023,60,100,-15.1,38,-14.43,51,4.826,6.035,89.1,20.03,33,8.96,12.76,12.29,178.5,15.58,5.705,5.494,178.5,15.58,13.37 +101,2023,60,200,-14.96,106,-14.09,157,5.068,6.639,89.7,30.43,156,13.6,18.87,18.42,183.6,12.56,8.44,8.23,183.6,12.56,13.29 +101,2023,60,300,-14.49,201,-13.89,257,5.914,7,90.2,42.26,259,18.89,25.03,24.54,187.5,11.29,11.19,10.97,187.5,11.29,13.21 +101,2023,60,400,-14.02,301,-13.59,348,6.76,7.54,90.3,59.89,357,26.77,41.49,41.06,193.5,8.23,18.55,18.36,193.5,8.23,13.14 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b/tests/data/snowex_mocks/SNEX_Met_ME_final_output.csv new file mode 100644 index 0000000..f16257f --- /dev/null +++ b/tests/data/snowex_mocks/SNEX_Met_ME_final_output.csv @@ -0,0 +1,361 @@ +TIMESTAMP,RH_10ft,RH_20ft,BP_kPa_Avg,AirTC_20ft_Avg,AirTC_10ft_Avg,WSms_20ft_Avg,WSms_10ft_Avg,WindDir_10ft_D1_WVT,WindDir_20ft_D1_WVT,SUp_Avg,SDn_Avg,LUpCo_Avg,LDnCo_Avg,SM_5cm_Avg,SM_20cm_Avg,SM_50cm_Avg,TC_5cm_Avg,TC_20cm_Avg,TC_50cm_Avg,DistanceSensToGnd(m),SnowDepthFilter(m) +2017-01-01 00:00:00,85.322,85.927,68.26,-5.633,-5.879,2.325,2.247,-7.869,-7.133,-1.417,3.656,184.117,250.2,0.322,0.174,0.322,0.6,1.208,2.068,0.898,0.898 +2017-01-01 01:00:00,91.46,91.4,68.214,-6.661,-6.944,2.145,2.083,3.058,1.641,-3.368,1.018,181.82,250.68,0.323,0.174,0.322,0.6,1.2,2.013,0.901,0.901 +2017-01-01 02:00:00,87.375,85.3,68.338,-7.237,-7.799,0.834,0.712,18.214,6.724,-2.88,1.239,180.1,236.075,0.323,0.174,0.322,0.6,1.2,2.002,0.903,0.903 +2017-01-01 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22:00:00,96.62,97.26,-9999.0,-0.71,-0.914,0.264,0.167,142.097,149.896,90.052,73.146,297.74,311.38,0.289,0.242,0.152,0.797,1.1,1.9,1.767,1.767 +2017-01-15 23:00:00,94.28,95.48,-9999.0,-2.242,-2.48,0.33,0.309,-70.777,-81.914,30.244,25.316,293.76,304.78,0.288,0.242,0.152,0.796,1.1,1.9,1.768,1.768 diff --git a/tests/data/snowex_mocks/SNEX_Met_MW_final_output.csv b/tests/data/snowex_mocks/SNEX_Met_MW_final_output.csv new file mode 100644 index 0000000..5ab3f5f --- /dev/null +++ b/tests/data/snowex_mocks/SNEX_Met_MW_final_output.csv @@ -0,0 +1,361 @@ +TIMESTAMP,RH_10ft,RH_20ft,BP_kPa_Avg,AirTC_20ft_Avg,AirTC_10ft_Avg,WSms_20ft_Avg,WSms_10ft_Avg,WindDir_10ft_D1_WVT,WindDir_20ft_D1_WVT,SUp_Avg,SDn_Avg,LUpCo_Avg,LDnCo_Avg,SM_5cm_Avg,SM_20cm_Avg,SM_50cm_Avg,TC_5cm_Avg,TC_20cm_Avg,TC_50cm_Avg,DistanceSensToGnd(m),SnowDepthFilter(m) +2017-01-01 00:00:00,80.086,76.452,-9999.0,-6.871,-7.799,-9999.0,-9999.0,-47.111,-39.079,0.812,2.275,183.88,230.86,0.094,0.2,0.298,0.6,1.031,1.884,0.932,0.932 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Jan 1 + +Mocked data has been cropped to: + +* SASP/SBSP 2003-2009 -> 2009 DOY 60-80 +* SASP/SBSP 2010-2023 -> 2023 DOY 60-80 +* SBSG - 2023 DOY 100-120 +* PTSP - 2023 DOY 60-80 + +""" +import pytest + +from metloom.pointdata import CSASMet +from metloom.pointdata.csas import InvalidDateRange +from metloom.variables import CSASVariables +from datetime import datetime, timedelta +from pathlib import Path +import shutil +from unittest.mock import patch, MagicMock +import requests + + +DATA_DIR = str(Path(__file__).parent.joinpath("data/csas_mocks")) + +# Convenient Dates for testing +DT_20090301 = datetime(2009, 3, 1) +DT_20090315 = datetime(2009, 3, 15) +DT_20230301 = datetime(2023, 3, 1) +DT_20230315 = datetime(2023, 3, 15) +DT_20230601 = datetime(2023, 6, 1) +DT_20230615 = datetime(2023, 6, 15) + + +class TestCSASMet: + + @classmethod + def get_side_effect(cls, *args, **kwargs): + url = Path(args[0]) + local_path = Path(__file__).parent.joinpath(DATA_DIR).joinpath(url.name) + + obj = MagicMock() + with open(local_path, 'rb') as f: + lines = f.readlines() + + obj.iter_lines.return_value = lines + obj.__enter__.return_value = obj # Black magic? + obj.__exit__.return_value = None + return obj + + @pytest.fixture(scope="class") + def mocked_requests(self): + with patch("metloom.pointdata.files.requests.get") as mock_get: + mock_get.side_effect = self.get_side_effect + yield mock_get + + def copy_files(self, urls): + files = [] + for url in urls: + file = Path(DATA_DIR).joinpath(Path(url).name) + cache = Path(__file__).parent.joinpath('cache') + shutil.copy(file, cache.joinpath(file.name)) + files.append(file) + return files + + @pytest.fixture(scope='function') + def cache_dir(self): + """Cachae dir where data is being downloaded to""" + cache = Path(__file__).parent.joinpath('cache') + yield cache + if cache.is_dir(): + shutil.rmtree(cache) + + @pytest.fixture(scope='function') + def station(self, mocked_requests, cache_dir, station_id): + pnt = CSASMet(station_id, cache=cache_dir) + yield pnt + + @pytest.mark.parametrize('year, doy, hour, expected', [ + (2024, 92, 1400, datetime(2024, 4, 1, 14)), + # Check doy 1 is jan 1 + (2024, 1, 0, datetime(2024, 1, 1)) + + ]) + def test_parse_datetime(self, year, doy, hour, expected): + dt = CSASMet._parse_datetime({'Year': year, 'DOY': doy, 'Hour': hour}) + assert dt == expected + + @pytest.mark.parametrize("station_id, start, end, expected", [ + # Two stations have different files depending on the years + ('SBSP', datetime(2003, 1, 1), + datetime(2003, 1, 1), ['SBSP_1hr_2003-2009.csv']), + ('SBSP', datetime(2010, 1, 1), + datetime(2010, 1, 1), ['SBSP_1hr_2010-2023.csv']), + # Test straddling the files + ('SBSP', datetime(2008, 1, 1), + datetime(2010, 1, 1), ['SBSP_1hr_2003-2009.csv', 'SBSP_1hr_2010-2023.csv']), + # test stations that don't have year specific timeframes + ('PTSP', datetime(2010, 1, 1), datetime(2010, 1, 2), ["PTSP_1hr.csv"]), + ('SBSG', datetime(2010, 1, 1), datetime(2010, 1, 2), ["SBSG_1hr.csv"]) + ]) + def test_file_urls(self, station_id, start, end, expected): + pnt = CSASMet(station_id) + pnt._verify_station() + urls = pnt._file_urls(station_id, start, end) + names = sorted([Path(url).name for url in urls]) + assert names == expected + + @pytest.mark.parametrize("station_id, start, end", [ + # Test pre 2003 + ('SBSP', datetime(2002, 1, 1), + datetime(2003, 1, 1)), + # Test post 2023 exception + ('SBSP', datetime(2010, 1, 1), + datetime(2024, 1, 1)) + ]) + def test_file_urls_exception(self, station_id, start, end): + """ + With files there are hard timeframes, test ane exception is raised when + this is the case + """ + pnt = CSASMet(station_id) + pnt._verify_station() + with pytest.raises(InvalidDateRange): + pnt._file_urls(station_id, start, end) + + @pytest.mark.parametrize("station_id, year", [ + # Test the two SBSP urls + ('SBSP', 2009), + ('SBSP', 2010), + # Test the two SASP urls + ('SASP', 2009), + ('SASP', 2010), + # Test the Stream gauges url + ('SBSG', 2005), + # Test the PTSP url + ('PTSP', 2003), + ]) + def test_links_are_valid(self, station_id, year): + """ + Seeking answers from CSAS on how and when these files are updated. Until then + this will serve as a nice way to check the files are workin still + """ + start = datetime(year, 1, 1) + end = start + timedelta(days=1) + + pnt = CSASMet(station_id) + pnt._verify_station() + urls = pnt._file_urls(station_id, start, end) + + resp = requests.head(urls[0]) + assert resp.ok + + @pytest.mark.parametrize('station_id, variable, start, end, expected_mean', [ + ('SASP', CSASVariables.SURF_TEMP, DT_20230301, DT_20230315, -11.59220), + # Span two files, Test data wont be complete but requires two files + ('SASP', CSASVariables.SURF_TEMP, DT_20090301, DT_20230315, -10.35284), + ]) + def test_get_daily_data(self, station, station_id, variable, start, end, + expected_mean): + """ Check pulling two weeks of data """ + + df = station.get_daily_data(start, end, [variable]) + # Assert it's a daily timeseries + assert df.index.get_level_values('datetime').inferred_freq == 'D' + assert df[variable.name].mean() == pytest.approx(expected_mean, abs=1e-5) + + @pytest.mark.parametrize('station_id, variable, start, end, expected_mean', [ + ('SASP', CSASVariables.SURF_TEMP, DT_20230301, DT_20230315, -11.49969), + ]) + def test_get_hourly_data(self, station, station_id, variable, start, end, + expected_mean): + """ Check pulling two weeks of data """ + + df = station.get_hourly_data(start, end, [variable]) + + # Assert it's a daily timeseries + assert df.index.get_level_values('datetime').inferred_freq.lower() == 'h' + assert df[variable.name].mean() == pytest.approx(expected_mean, abs=1e-5) diff --git a/tests/test_point_data.py b/tests/test_point_data.py index 7724be2..199b213 100644 --- a/tests/test_point_data.py +++ b/tests/test_point_data.py @@ -3,7 +3,7 @@ import pandas as pd from os import path -from metloom.pointdata.base import PointData +from metloom.pointdata.base import PointData, DataValidationError def side_effect_error(*args): @@ -60,3 +60,31 @@ def expected_response(dates, variables_map, station, points, ) df.set_index(keys=["datetime", "site"], inplace=True) return df.sort_index(axis=1) + + +class TestPointDataValidations: + def test_validate_gdf(self): + with pytest.raises(DataValidationError): + PointData._validate_geodataframe(pd.DataFrame()) + + def test_validate_indicies(self): + with pytest.raises(DataValidationError): + PointData._validate_df_indicies(gpd.GeoDataFrame(index=["datetime"])) + + @pytest.mark.parametrize("incoming_columns", [ + # Missing datasource + ["geometry", "measurementDate", "quality_code"], + ["datasource"], + ]) + def test_validate_columns(self, incoming_columns): + gdf = gpd.GeoDataFrame(columns=incoming_columns) + with pytest.raises(DataValidationError): + columns = PointData.EXPECTED_COLUMNS.copy() + PointData._validate_df_columns(gdf, columns) + + def test_validate_units(self): + # Missing units for this variables + incoming = ["Depth", 'SWE'] + gdf = gpd.GeoDataFrame(columns=incoming) + with pytest.raises(DataValidationError): + PointData._validate_df_units(gdf, PointData.EXPECTED_COLUMNS) diff --git a/tests/test_snowex.py b/tests/test_snowex.py new file mode 100644 index 0000000..c75aee0 --- /dev/null +++ b/tests/test_snowex.py @@ -0,0 +1,109 @@ +""" +Tests our snowex reader which is pulling down csv data from the nsidc +All mock data is cut to the first 2 weeks of january in 2017. 2018 for GMSP + +""" +import shutil +import pytest +from datetime import datetime, timedelta +from pathlib import Path +import geopandas as gpd +from unittest.mock import patch + +from metloom.variables import SnowExVariables +from metloom.pointdata import SnowExMet + + +DATA_DIR = str(Path(__file__).parent.joinpath("data/snowex_mocks")) + + +class TestSnowEx: + def copy_file(self, urls): + files = [] + for url in urls: + file = Path(DATA_DIR).joinpath(Path(url).name) + cache = Path(__file__).parent.joinpath('cache') + shutil.copy(file, cache.joinpath(file.name)) + files.append(file) + return files + + @pytest.fixture(scope='function') + def cache_dir(self): + """Cachae dir where data is being downloaded to""" + cache = Path(__file__).parent.joinpath('cache') + yield cache + if cache.is_dir(): + shutil.rmtree(cache) + + @pytest.fixture(scope='function') + def station(self, cache_dir, station_id): + with patch.object(SnowExMet, '_download', new=self.copy_file): + pnt = SnowExMet(station_id, cache=cache_dir) + yield pnt + + @pytest.mark.parametrize('station_id, expected', [ + ('GMSP', 'Grand Mesa Study Plot') + ]) + def test_station_name(self, station, station_id, expected): + """ Check auto assignment of the name""" + station._verify_station() + assert station.name == expected + + @pytest.mark.parametrize('station_id, variable, expected_mean', [ + ('LSOS', SnowExVariables.TEMP_10FT, -7.07817), + ('MW', SnowExVariables.SNOWDEPTH, 1.03503), + ]) + def test_get_daily_data(self, station, station_id, variable, expected_mean): + """ Check data pulling """ + df = station.get_daily_data(datetime(2017, 1, 1), + datetime(2017, 1, 15), [variable]) + + # Assert it's a daily timeseries + assert df.index.get_level_values('datetime').inferred_freq == 'D' + assert df[variable.name].mean() == pytest.approx(expected_mean, abs=1e-5) + + @pytest.mark.parametrize('station_id, variable, expected_mean', [ + # Test a couple stations with different variables + ('LSOS', SnowExVariables.TEMP_10FT, -7.86599), + ('MW', SnowExVariables.SNOWDEPTH, 0.98625), + + ]) + def test_get_hourly_data(self, station, station_id, variable, expected_mean): + """ Check auto assignment of the name""" + df = station.get_hourly_data(datetime(2017, 1, 1, 11), + datetime(2017, 1, 10, 23), + [variable]) + + # Assert it's hourly timeseries + assert df.index.get_level_values('datetime').inferred_freq.lower() == 'h' + assert df[variable.name].mean() == pytest.approx(expected_mean, abs=1e-5) + + @pytest.mark.parametrize("station_id, variable, start", [ + # GMSP doesnt have radiation + ('GMSP', SnowExVariables.UPSHORTWAVE, datetime(2018, 1, 1, 11)), + # GMSP has no data in Jan - 2017 + ('GMSP', SnowExVariables.SNOWDEPTH, datetime(2017, 1, 1, 11)) + + ]) + def test_get_daily_none(self, station, station_id, variable, start): + """ Test when a station doesn't have something, its handled""" + end = start + timedelta(days=1) + df = station.get_daily_data(start, end, [variable]) + assert df is None + + @pytest.mark.parametrize('within_geom, buffer, expected_count', [ + # Inside the geom is only one station + (True, 0.0, 1), + # inside the bounds of the geom is 3 stations + (False, 0.0, 3), + # Test Buffer use + (True, 0.1, 4) + ]) + def test_within_geometry(self, within_geom, buffer, expected_count): + """ Use the within geometry on downloaded stations """ + search_poly = gpd.read_file(Path(DATA_DIR).joinpath('gm_polygon.shp')) + df = SnowExMet.points_from_geometry(search_poly, + [SnowExVariables.TEMP_20FT], + within_geometry=within_geom, + buffer=buffer) + assert len(df.index) == expected_count