diff --git a/README.md b/README.md index 4420a7b..7493b4a 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,8 @@ due time. At this stage, the following APIs are covered by cl-hubeau: * [piezometry/piézométrie](https://hubeau.eaufrance.fr/page/api-piezometrie) * [hydrometry/hydrométrie](https://hubeau.eaufrance.fr/page/api-hydrometrie) -* [drinking water quality/qualité de l'eau potable](https://hubeau.eaufrance.fr/page/api-qualite-eau-potable#/qualite_eau_potable/communes) +* [drinking water quality/qualité de l'eau potable](https://hubeau.eaufrance.fr/page/api-qualite-eau-potable) +* [superficial waterbodies quality/qualité physico-chimique des cours d'eau'](https://hubeau.eaufrance.fr/page/api-qualite-cours-deau) For any help on available kwargs for each endpoint, please refer directly to the documentation on hubeau (this will not be covered @@ -159,11 +160,6 @@ Note that this query is heavy, even if this was already restricted to nitrates. In theory, you could also query the API without specifying the substance you're tracking, but you may hit the 20k threshold and trigger an exception. -As it is, the `get_control_results` function already implements a double loop: - -* on networks' codes (20 codes maximum) ; -* on periods, requesting only yearly datasets (which should be scalable over time **and** should work nicely with the cache algorithm). - You can also call the same function, using official city codes directly: ```python df = drinking_water_quality.get_control_results( @@ -185,4 +181,75 @@ with drinking_water_quality.DrinkingWaterQualitySession() as session: df = session.get_cities_networks(nom_commune="LILLE") df = session.get_control_results(code_departement='02', code_parametre="1340") -``` \ No newline at end of file +``` + +### Superficial waterbodies quality + +4 high level functions are available (and one class for low level operations). + + +Get all stations (uses a 30 days caching): + +```python +from cl_hubeau import superficial_waterbodies_quality +df = superficial_waterbodies_quality.get_all_stations() +``` + +Get all operations (uses a 30 days caching): + +```python +from cl_hubeau import superficial_waterbodies_quality +df = superficial_waterbodies_quality.get_all_operations() +``` + +Note that this query is heavy, users should restrict it to a given territory. +For instance, you could use : +```python +df = superficial_waterbodies_quality.get_all_operations(code_region="11") +``` + +Get all environmental conditions: + +```python +from cl_hubeau import superficial_waterbodies_quality +df = superficial_waterbodies_quality.get_all_environmental_conditions() +``` + +Note that this query is heavy, users should restrict it to a given territory. +For instance, you could use : +```python +df = superficial_waterbodies_quality.get_all_environmental_conditions(code_region="11") +``` + +Get all physicochemical analysis: +```python +from cl_hubeau import superficial_waterbodies_quality +df = superficial_waterbodies_quality.get_all_analysis() +``` + +Note that this query is heavy, users should restrict it to a given territory +and given parameters. For instance, you could use : +```python +df = superficial_waterbodies_quality.get_all_analysis( + code_departement="59", + code_parametre="1313" + ) +``` + + +Low level class to perform the same tasks: + + +Note that : + +* the API is forbidding results > 20k rows and you may need inner loops +* the cache handling will be your responsibility + +```python +with superficial_waterbodies_quality.SuperficialWaterbodiesQualitySession() as session: + df = session.get_stations(code_commune="59183") + df = session.get_operations(code_commune="59183") + df = session.get_environmental_conditions(code_commune="59183") + df = session.get_analysis(code_commune='59183', code_parametre="1340") + +``` diff --git a/cl_hubeau/drinking_water_quality/utils.py b/cl_hubeau/drinking_water_quality/utils.py index 9714c00..3f52dba 100644 --- a/cl_hubeau/drinking_water_quality/utils.py +++ b/cl_hubeau/drinking_water_quality/utils.py @@ -4,7 +4,7 @@ Convenience functions for hydrometry consumption """ -from datetime import date, datetime +from datetime import date from itertools import product import pandas as pd @@ -13,7 +13,7 @@ from cl_hubeau.drinking_water_quality import DrinkingWaterQualitySession from cl_hubeau import _config -from cl_hubeau.utils import get_cities +from cl_hubeau.utils import get_cities, prepare_kwargs_loops def get_all_water_networks(**kwargs) -> pd.DataFrame: @@ -66,8 +66,8 @@ def get_control_results( Retrieve sanitary controls' results. Uses a loop to avoid reaching 20k results threshold. - As queries may induce big datasets, loops are based on networks and years, - even if date_min_prelevement/date_max_prelevement are not set. + As queries may induce big datasets, loops are based on networks and 6 month + timeranges, even if date_min_prelevement/date_max_prelevement are not set. Note that `codes_reseaux` and `codes_communes` are mutually exclusive! @@ -112,41 +112,26 @@ def get_control_results( if "date_max_prelevement" not in kwargs: kwargs["date_max_prelevement"] = date.today().strftime("%Y-%m-%d") - ranges = pd.date_range( - start=datetime.strptime( - kwargs.pop("date_min_prelevement"), "%Y-%m-%d" - ).date(), - end=datetime.strptime( - kwargs.pop("date_max_prelevement"), "%Y-%m-%d" - ).date(), + kwargs_loop = prepare_kwargs_loops( + "date_min_prelevement", + "date_max_prelevement", + kwargs, + start_auto_determination, ) - dates = pd.Series(ranges).to_frame("date") - dates["year"] = dates["date"].dt.year - dates = dates.groupby("year")["date"].agg(["min", "max"]) - for d in "min", "max": - dates[d] = dates[d].dt.strftime("%Y-%m-%d") - if start_auto_determination: - dates = pd.concat( - [ - dates, - pd.DataFrame([{"min": "1900-01-01", "max": "2015-12-31"}]), - ], - ignore_index=False, - ).sort_index() - - args = list(product(codes, dates.values.tolist())) + + kwargs_loop = list(product(codes, kwargs_loop)) + [kwargs.update({codes_names: chunk}) for chunk, kwargs in kwargs_loop] + kwargs_loop = [x[1] for x in kwargs_loop] with DrinkingWaterQualitySession() as session: results = [ session.get_control_results( - date_min_prelevement=date_min, - date_max_prelevement=date_max, - **{codes_names: chunk}, - **kwargs + **kwargs, + **kw_loop, ) - for chunk, (date_min, date_max) in tqdm( - args, + for kw_loop in tqdm( + kwargs_loop, desc="querying network/network and year/year", leave=_config["TQDM_LEAVE"], position=tqdm._get_free_pos(), @@ -155,13 +140,3 @@ def get_control_results( results = [x.dropna(axis=1, how="all") for x in results if not x.empty] results = pd.concat(results, ignore_index=True) return results - - -# if __name__ == "__main__": -# df = get_control_results( -# codes_communes="59350", -# code_parametre="1340", -# date_min_prelevement="2023-01-01", -# date_max_prelevement="2023-12-31", -# ) -# print(df) diff --git a/cl_hubeau/hydrometry/hydrometry_scraper.py b/cl_hubeau/hydrometry/hydrometry_scraper.py index 748fd8a..1431ab2 100644 --- a/cl_hubeau/hydrometry/hydrometry_scraper.py +++ b/cl_hubeau/hydrometry/hydrometry_scraper.py @@ -264,6 +264,7 @@ def get_observations(self, **kwargs): ) except KeyError: pass + return df def get_realtime_observations(self, **kwargs): @@ -382,6 +383,7 @@ def get_realtime_observations(self, **kwargs): df["date_obs"] = pd.to_datetime(df["date_obs"]) except KeyError: pass + return df diff --git a/cl_hubeau/piezometry/piezometry_scraper.py b/cl_hubeau/piezometry/piezometry_scraper.py index 79eb28a..693e099 100644 --- a/cl_hubeau/piezometry/piezometry_scraper.py +++ b/cl_hubeau/piezometry/piezometry_scraper.py @@ -92,6 +92,13 @@ def get_stations(self, **kwargs): except KeyError: pass + if kwargs: + raise ValueError( + f"found unexpected arguments {kwargs}, " + "please have a look at the documentation on " + "https://hubeau.eaufrance.fr/page/api-piezometrie" + ) + method = "GET" url = self.BASE_URL + "/v1/niveaux_nappes/stations" @@ -122,12 +129,7 @@ def get_chronicles(self, **kwargs): kwargs.pop("code_bss"), 200 ) except KeyError: - # reset to default hubeau value, which is set even when code_bss is - # missing - code_bss = "07548X0009/F" - msg = f"code_bss is missing, will be set to {code_bss=} by hubeau" - logging.warning(msg) - params["code_bss"] = code_bss + pass for arg in "date_debut_mesure", "date_fin_mesure": try: @@ -153,6 +155,13 @@ def get_chronicles(self, **kwargs): except KeyError: pass + if kwargs: + raise ValueError( + f"found unexpected arguments {kwargs}, " + "please have a look at the documentation on " + "https://hubeau.eaufrance.fr/page/api-piezometrie" + ) + method = "GET" url = self.BASE_URL + "/v1/niveaux_nappes/chroniques" @@ -165,13 +174,6 @@ def get_chronicles(self, **kwargs): except KeyError: pass - if kwargs: - raise ValueError( - f"found unexpected arguments {kwargs}, " - "please have a look at the documentation on " - "https://hubeau.eaufrance.fr/page/api-piezometrie" - ) - return df def get_realtime_chronicles(self, **kwargs): @@ -250,6 +252,13 @@ def get_realtime_chronicles(self, **kwargs): except KeyError: pass + if kwargs: + raise ValueError( + f"found unexpected arguments {kwargs}, " + "please have a look at the documentation on " + "https://hubeau.eaufrance.fr/page/api-piezometrie" + ) + method = "GET" url = self.BASE_URL + "/v1/niveaux_nappes/chroniques_tr" @@ -266,13 +275,6 @@ def get_realtime_chronicles(self, **kwargs): except KeyError: pass - if kwargs: - raise ValueError( - f"found unexpected arguments {kwargs}, " - "please have a look at the documentation on " - "https://hubeau.eaufrance.fr/page/api-piezometrie" - ) - return df diff --git a/cl_hubeau/session/session.py b/cl_hubeau/session/session.py index da07091..d7ed88c 100644 --- a/cl_hubeau/session/session.py +++ b/cl_hubeau/session/session.py @@ -256,7 +256,7 @@ def request( error = str(r.content) raise ValueError( f"Connection error on {method=} {url=} with {kwargs=}, " - f"got {error}" + f"got {error} : got result {r.status_code}" ) return r @@ -314,6 +314,11 @@ def get_result( page = "page" if "page" in js["first"] else "cursor" count_rows = js["count"] + if count_rows > 20_000: + raise ValueError( + "this request won't be handled by hubeau " + f"( {count_rows} > 20k results)" + ) msg = f"{count_rows} expected results" logging.info(msg) count_pages = count_rows // self.size + ( diff --git a/cl_hubeau/superficial_waterbodies_quality/__init__.py b/cl_hubeau/superficial_waterbodies_quality/__init__.py new file mode 100644 index 0000000..b070e14 --- /dev/null +++ b/cl_hubeau/superficial_waterbodies_quality/__init__.py @@ -0,0 +1,21 @@ +# -*- coding: utf-8 -*- + +from .superficial_waterbodies_quality_scraper import ( + SuperficialWaterbodiesQualitySession, +) + +from .utils import ( + get_all_stations, + get_all_operations, + get_all_environmental_conditions, + get_all_analysis, +) + + +__all__ = [ + "get_all_stations", + "get_all_operations", + "get_all_environmental_conditions", + "get_all_analysis", + "SuperficialWaterbodiesQualitySession", +] diff --git a/cl_hubeau/superficial_waterbodies_quality/superficial_waterbodies_quality_scraper.py b/cl_hubeau/superficial_waterbodies_quality/superficial_waterbodies_quality_scraper.py new file mode 100644 index 0000000..080afaa --- /dev/null +++ b/cl_hubeau/superficial_waterbodies_quality/superficial_waterbodies_quality_scraper.py @@ -0,0 +1,527 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Jul 28 14:03:41 2024 + +low level class to collect data from the drinking water quality API from +hub'eau +""" +import pandas as pd +from cl_hubeau.session import BaseHubeauSession + + +class SuperficialWaterbodiesQualitySession(BaseHubeauSession): + """ + Base session class to handle the superifical waterbodies' quality API + """ + + def __init__(self, *args, **kwargs): + + super().__init__(version="2.0.0", *args, **kwargs) + + # Set default size for API queries, based on hub'eau piezo's doc + self.size = 5000 + + def get_stations(self, **kwargs): + """ + Lister les stations de mesures physicochimique + Endpoint /v2/qualite_rivieres/station_pc + + Ce service permet de rechercher des stations de mesures physicochimique + sur des cours d'eau et plan d'eau en France et les DROM. + + Doc: https://hubeau.eaufrance.fr/page/api-qualite-cours-deau + """ + + params = {} + + try: + variable = kwargs.pop("sort") + if variable not in ("asc", "desc"): + raise ValueError( + "sort must be among ('asc', 'sort'), " + f"found sort='{variable}' instead" + ) + params["sort"] = variable + except KeyError: + params["sort"] = "asc" + + try: + variable = kwargs.pop("format") + if variable not in ("json", "geojson"): + raise ValueError( + "format must be among ('json', 'geojson'), " + f"found {format=} instead" + ) + params["format"] = variable + except KeyError: + params["format"] = "json" + + try: + params["bbox"] = self.list_to_str_param( + kwargs.pop("bbox"), None, 4 + ) + except KeyError: + pass + + for arg in ( + "date_debut_maj", + "date_debut_prelevement", + "date_fin_maj", + "date_fin_prelevement", + ): + try: + variable = kwargs.pop(arg) + self.ensure_date_format_is_ok(variable) + params[arg] = variable + except KeyError: + continue + + for arg in ( + "code_banque_reference", + "code_bassin_dce", + "code_commune", + "code_cours_eau", + "code_departement", + "code_eu_masse_eau", + "code_fraction", + "code_groupe_parametres", + "code_masse_eau", + "code_parametre", + "code_qualification", + "code_region", + "code_reseau", + "code_sous_bassin", + "code_station", + "code_statut", + "code_support", + "libelle_commune", + "libelle_departement", + "libelle_fraction", + "libelle_masse_eau", + "libelle_parametre", + "libelle_qualification", + "libelle_region", + "libelle_reseau", + "libelle_station", + "libelle_support", + "mnemo_statut", + "nom_bassin_dce", + "nom_cours_eau", + "nom_groupe_parametres", + "nom_sous_bassin", + "type_entite_hydro", + ): + try: + variable = kwargs.pop(arg) + params[arg] = self.list_to_str_param(variable, 200) + except KeyError: + continue + + for arg in ("distance", "latitude", "longitude"): + try: + params[arg] = kwargs.pop(arg) + except KeyError: + continue + + try: + params["exact_count"] = kwargs.pop("exact_count") in ("true", True) + except KeyError: + params["exact_count"] = "true" + + try: + params["fields"] = self.list_to_str_param(kwargs.pop("fields")) + except KeyError: + pass + + if kwargs: + raise ValueError( + f"found unexpected arguments {kwargs}, " + "please have a look at the documentation on " + "https://hubeau.eaufrance.fr/page/api-qualite-cours-deau" + ) + + method = "GET" + url = self.BASE_URL + "/v2/qualite_rivieres/station_pc" + df = self.get_result(method, url, params=params) + + return df + + def get_operations(self, **kwargs): + """ + Lister les opérations physicochimique + Endpoint /v2/qualite_rivieres/operation_pc + + Ce service permet de rechercher des opérations physicochimiques sur des + cours d'eau et plan d'eau en France et les DROM. + + Doc: https://hubeau.eaufrance.fr/page/api-qualite-cours-deau + """ + + params = {} + + try: + variable = kwargs.pop("sort") + if variable not in ("asc", "desc"): + raise ValueError( + "sort must be among ('asc', 'sort'), " + f"found sort='{variable}' instead" + ) + params["sort"] = variable + except KeyError: + params["sort"] = "asc" + + try: + variable = kwargs.pop("format") + if variable not in ("json", "geojson"): + raise ValueError( + "format must be among ('json', 'geojson'), " + f"found {format=} instead" + ) + params["format"] = variable + except KeyError: + params["format"] = "json" + + try: + params["bbox"] = self.list_to_str_param( + kwargs.pop("bbox"), None, 4 + ) + except KeyError: + pass + + for arg in ( + "date_debut_maj", + "date_debut_prelevement", + "date_fin_maj", + "date_fin_prelevement", + ): + try: + variable = kwargs.pop(arg) + self.ensure_date_format_is_ok(variable) + params[arg] = variable + except KeyError: + continue + + for arg in ( + "code_banque_reference", + "code_bassin_dce", + "code_commune", + "code_cours_eau", + "code_departement", + "code_eu_masse_eau", + "code_fraction", + "code_groupe_parametres", + "code_masse_eau", + "code_parametre", + "code_prelevement", + "code_qualification", + "code_region", + "code_reseau", + "code_sous_bassin", + "code_station", + "code_statut", + "code_support", + "libelle_commune", + "libelle_departement", + "libelle_fraction", + "libelle_masse_eau", + "libelle_parametre", + "libelle_qualification", + "libelle_region", + "libelle_reseau", + "libelle_station", + "libelle_support", + "mnemo_statut", + "nom_bassin_dce", + "nom_cours_eau", + "nom_groupe_parametres", + "nom_sous_bassin", + "type_entite_hydro", + ): + try: + variable = kwargs.pop(arg) + params[arg] = self.list_to_str_param(variable, 200) + except KeyError: + continue + + for arg in ("distance", "latitude", "longitude"): + try: + params[arg] = kwargs.pop(arg) + except KeyError: + continue + + try: + params["exact_count"] = kwargs.pop("exact_count") in ("true", True) + except KeyError: + params["exact_count"] = "true" + + try: + params["fields"] = self.list_to_str_param(kwargs.pop("fields")) + except KeyError: + pass + + if kwargs: + raise ValueError( + f"found unexpected arguments {kwargs}, " + "please have a look at the documentation on " + "https://hubeau.eaufrance.fr/page/api-qualite-cours-deau" + ) + + method = "GET" + url = self.BASE_URL + "/v2/qualite_rivieres/operation_pc" + df = self.get_result(method, url, params=params) + + try: + df["date"] = pd.to_datetime(df["date"], format="%Y-%m-%d") + except KeyError: + pass + + return df + + def get_environmental_conditions(self, **kwargs): + """ + Lister les conditions environnementales physicochimique + Endpoint /v2/qualite_rivieres/condition_environnementale_pc + + Ce service permet de rechercher des conditions environnementales + physicochimique d'analyses (attention la pagination est basée sur le + nombre d'analyse) sur des cours d'eau et plan d'eau en France et les + DROM. + + Doc: https://hubeau.eaufrance.fr/page/api-qualite-cours-deau + """ + + params = {} + + try: + variable = kwargs.pop("sort") + if variable not in ("asc", "desc"): + raise ValueError( + "sort must be among ('asc', 'sort'), " + f"found sort='{variable}' instead" + ) + params["sort"] = variable + except KeyError: + params["sort"] = "asc" + + try: + variable = kwargs.pop("format") + if variable not in ("json", "geojson"): + raise ValueError( + "format must be among ('json', 'geojson'), " + f"found {format=} instead" + ) + params["format"] = variable + except KeyError: + params["format"] = "json" + + try: + params["bbox"] = self.list_to_str_param( + kwargs.pop("bbox"), None, 4 + ) + except KeyError: + pass + + for arg in ( + "date_debut_maj", + "date_debut_prelevement", + "date_fin_maj", + "date_fin_prelevement", + ): + try: + variable = kwargs.pop(arg) + self.ensure_date_format_is_ok(variable) + params[arg] = variable + except KeyError: + continue + + for arg in ( + "code_banque_reference", + "code_commune", + "code_cours_eau", + "code_departement", + "code_eu_masse_eau", + "code_groupe_parametres", + "code_masse_eau", + "code_parametre", + "code_prelevement", + "code_qualification", + "code_region", + "code_station", + "code_statut", + "libelle_commune", + "libelle_departement", + "libelle_masse_eau", + "libelle_parametre", + "libelle_qualification", + "libelle_region", + "libelle_station", + "mnemo_statut", + "nom_cours_eau", + "nom_groupe_parametres", + ): + try: + variable = kwargs.pop(arg) + params[arg] = self.list_to_str_param(variable, 200) + except KeyError: + continue + + for arg in ("distance", "latitude", "longitude"): + try: + params[arg] = kwargs.pop(arg) + except KeyError: + continue + + try: + params["fields"] = self.list_to_str_param(kwargs.pop("fields")) + except KeyError: + pass + + if kwargs: + raise ValueError( + f"found unexpected arguments {kwargs}, " + "please have a look at the documentation on " + "https://hubeau.eaufrance.fr/page/api-qualite-cours-deau" + ) + method = "GET" + url = ( + self.BASE_URL + + "/v2/qualite_rivieres/condition_environnementale_pc" + ) + df = self.get_result(method, url, params=params) + + try: + df["date_prelevement"] = pd.to_datetime( + df["date_prelevement"], format="%Y-%m-%d" + ) + except KeyError: + pass + + return df + + def get_analysis(self, **kwargs): + """ + Lister les analyses physicochimique + Endpoint /v2/qualite_rivieres/analyse_pc + + Ce service permet de rechercher des analyses physicochimique sur des + cours d'eau et plan d'eau en France et les DROM. + + Doc: https://hubeau.eaufrance.fr/page/api-qualite-cours-deau + """ + + params = {} + + try: + variable = kwargs.pop("sort") + if variable not in ("asc", "desc"): + raise ValueError( + "sort must be among ('asc', 'sort'), " + f"found sort='{variable}' instead" + ) + params["sort"] = variable + except KeyError: + params["sort"] = "asc" + + try: + variable = kwargs.pop("format") + if variable not in ("json", "geojson"): + raise ValueError( + "format must be among ('json', 'geojson'), " + f"found {format=} instead" + ) + params["format"] = variable + except KeyError: + params["format"] = "json" + + try: + params["bbox"] = self.list_to_str_param( + kwargs.pop("bbox"), None, 4 + ) + except KeyError: + pass + + for arg in ( + "date_debut_maj", + "date_debut_prelevement", + "date_fin_maj", + "date_fin_prelevement", + ): + try: + variable = kwargs.pop(arg) + self.ensure_date_format_is_ok(variable) + params[arg] = variable + except KeyError: + continue + + for arg in ( + "code_banque_reference", + "code_bassin_dce", + "code_commune", + "code_cours_eau", + "code_departement", + "code_eu_masse_eau", + "code_fraction", + "code_groupe_parametres", + "code_masse_eau", + "code_parametre", + "code_prelevement", + "code_qualification", + "code_region", + "code_reseau", + "code_sous_bassin", + "code_station", + "code_statut", + "code_support", + "libelle_commune", + "libelle_departement", + "libelle_fraction", + "libelle_masse_eau", + "libelle_parametre", + "libelle_qualification", + "libelle_region", + "libelle_reseau", + "libelle_station", + "libelle_support", + "mnemo_statut", + "nom_bassin_dce", + "nom_cours_eau", + "nom_groupe_parametres", + "nom_sous_bassin", + "type_entite_hydro", + ): + try: + variable = kwargs.pop(arg) + params[arg] = self.list_to_str_param(variable, 200) + except KeyError: + continue + + for arg in ("distance", "latitude", "longitude"): + try: + params[arg] = kwargs.pop(arg) + except KeyError: + continue + + try: + params["fields"] = self.list_to_str_param(kwargs.pop("fields")) + except KeyError: + pass + + if kwargs: + raise ValueError( + f"found unexpected arguments {kwargs}, " + "please have a look at the documentation on " + "https://hubeau.eaufrance.fr/page/api-qualite-cours-deau" + ) + + method = "GET" + url = self.BASE_URL + "/v2/qualite_rivieres/analyse_pc" + df = self.get_result(method, url, params=params) + + try: + df["date_prelevement"] = pd.to_datetime( + df["date_prelevement"], format="%Y-%m-%d" + ) + except KeyError: + pass + + return df diff --git a/cl_hubeau/superficial_waterbodies_quality/utils.py b/cl_hubeau/superficial_waterbodies_quality/utils.py new file mode 100644 index 0000000..27b8a91 --- /dev/null +++ b/cl_hubeau/superficial_waterbodies_quality/utils.py @@ -0,0 +1,302 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Convenience functions for superficial waterbodies quality inspections +""" + +from datetime import date +import warnings + +import geopandas as gpd +import pandas as pd +from tqdm import tqdm + + +from cl_hubeau.superficial_waterbodies_quality import ( + SuperficialWaterbodiesQualitySession, +) +from cl_hubeau import _config +from cl_hubeau.utils import ( + get_departements, + get_departements_from_regions, + prepare_kwargs_loops, +) + + +def get_all_stations(**kwargs) -> gpd.GeoDataFrame: + """ + Retrieve all stations for physical/chemical analysis on superficial + waterbodies + + Use a loop to avoid reaching 20k results threshold. Do not use + `code_departement` or `format` as they are set by the current function. + + Parameters + ---------- + **kwargs : + kwargs passed to SuperficialWaterbodiesQualitySession.get_stations + (hence mostly intended for hub'eau API's arguments). + + Returns + ------- + results : gpd.GeoDataFrame + DataFrame of networks (UDI) /cities coverage + + """ + + deps = get_departements() + + # Split by 20-something chunks + deps = [deps[i : i + 20] for i in range(0, len(deps), 20)] + + with SuperficialWaterbodiesQualitySession() as session: + results = [ + session.get_stations( + code_departement=dep, format="geojson", **kwargs + ) + for dep in tqdm( + deps, + desc="querying dep/dep", + leave=_config["TQDM_LEAVE"], + position=tqdm._get_free_pos(), + ) + ] + results = [x.dropna(axis=1, how="all") for x in results if not x.empty] + + results = gpd.pd.concat(results, ignore_index=True) + return results + + +def get_all_operations(**kwargs) -> gpd.GeoDataFrame: + """ + Retrieve operations for measures. + + Should only be used with additional arguments to avoid reaching the 20k + threshold, in conjonction with the built-in loop (which will operate + on yearly subsets, even if date_min_prelevement/date_max_prelevement are + not set.) + + Parameters + ---------- + **kwargs : + kwargs passed to SuperficialWaterbodiesQualitySession.get_operations + (hence mostly intended for hub'eau API's arguments). + Do not use `format` which is used by this function. + + Returns + ------- + results : gpd.GeoDataFrame + GeoDataFrame of operations + + """ + + if not kwargs: + warnings.warn( + "get_all_operations should only be used with " + "kwargs, for instance `get_operations(code_departement='02')`" + ) + + # Set a loop for yearly querying as dataset are big + + start_auto_determination = False + if "date_debut_prelevement" not in kwargs: + start_auto_determination = True + kwargs["date_debut_prelevement"] = "1960-01-01" + if "date_fin_prelevement" not in kwargs: + kwargs["date_fin_prelevement"] = date.today().strftime("%Y-%m-%d") + + if "code_region" in kwargs: + # let's downcast to departemental loops + reg = kwargs.pop("code_region") + if isinstance(reg, (list, tuple, set)): + deps = [ + dep for r in get_departements_from_regions(reg) for dep in r + ] + else: + deps = get_departements_from_regions(reg) + kwargs["code_departement"] = deps + + desc = "querying year/year" + ( + " & dep/dep" if "code_departement" in kwargs else "" + ) + + kwargs_loop = prepare_kwargs_loops( + "date_debut_prelevement", + "date_fin_prelevement", + kwargs, + start_auto_determination, + ) + + with SuperficialWaterbodiesQualitySession() as session: + + results = [ + session.get_operations( + format="geojson", + **kwargs, + **kw_loop, + ) + for kw_loop in tqdm( + kwargs_loop, + desc=desc, + leave=_config["TQDM_LEAVE"], + position=tqdm._get_free_pos(), + ) + ] + results = [x.dropna(axis=1, how="all") for x in results if not x.empty] + results = pd.concat(results, ignore_index=True) + return results + + +def get_all_environmental_conditions(**kwargs) -> gpd.GeoDataFrame: + """ + Retrieve environmental conditions for measures. + + Should only be used with additional arguments to avoid reaching the 20k + threshold, in conjonction with the built-in loop (which will operate + on yearly subsets, even if date_min_prelevement/date_max_prelevement are + not set.) + + Parameters + ---------- + **kwargs : + kwargs passed to SuperficialWaterbodiesQualitySession.get_environmental_conditions + (hence mostly intended for hub'eau API's arguments). + Do not use `format` which is used by this function. + + Returns + ------- + results : gpd.GeoDataFrame + GeoDataFrame of environmental conditions + + """ + + if not kwargs: + warnings.warn( + "get_all_environmental_conditions should only be used with " + "kwargs, for instance " + "`get_all_environmental_conditions(code_department='02')`" + ) + + # Set a loop for yearly querying as dataset are big + + start_auto_determination = False + if "date_debut_prelevement" not in kwargs: + start_auto_determination = True + kwargs["date_debut_prelevement"] = "1960-01-01" + if "date_fin_prelevement" not in kwargs: + kwargs["date_fin_prelevement"] = date.today().strftime("%Y-%m-%d") + + if "code_region" in kwargs: + # let's downcast to departemental loops + reg = kwargs.pop("code_region") + if isinstance(reg, (list, tuple, set)): + deps = [ + dep for r in get_departements_from_regions(reg) for dep in r + ] + else: + deps = get_departements_from_regions(reg) + kwargs["code_departement"] = deps + + desc = "querying year/year" + ( + " & dep/dep" if "code_departement" in kwargs else "" + ) + + kwargs_loop = prepare_kwargs_loops( + "date_debut_prelevement", + "date_fin_prelevement", + kwargs, + start_auto_determination, + ) + + with SuperficialWaterbodiesQualitySession() as session: + + results = [ + session.get_environmental_conditions( + format="geojson", **kwargs, **kw_loop + ) + for kw_loop in tqdm( + kwargs_loop, + desc=desc, + leave=_config["TQDM_LEAVE"], + position=tqdm._get_free_pos(), + ) + ] + results = [x.dropna(axis=1, how="all") for x in results if not x.empty] + results = pd.concat(results, ignore_index=True) + return results + + +def get_all_analysis(**kwargs) -> gpd.GeoDataFrame: + """ + Retrieve analysis results from measures. + + Should only be used with additional arguments to avoid reaching the 20k + threshold, in conjonction with the built-in loop (which will operate + on yearly subsets, even if date_min_prelevement/date_max_prelevement are + not set.) + + Parameters + ---------- + **kwargs : + kwargs passed to SuperficialWaterbodiesQualitySession.get_analysis + (hence mostly intended for hub'eau API's arguments). + + Returns + ------- + results : gpd.GeoDataFrame + GeoDataFrame of analysis results + + """ + + if not kwargs: + warnings.warn( + "get_all_analysis should only be used with " + "kwargs, for instance " + "`get_all_analysis(code_department='02')`" + ) + + # Set a loop for yearly querying as dataset are big + + start_auto_determination = False + if "date_debut_prelevement" not in kwargs: + start_auto_determination = True + kwargs["date_debut_prelevement"] = "1960-01-01" + if "date_fin_prelevement" not in kwargs: + kwargs["date_fin_prelevement"] = date.today().strftime("%Y-%m-%d") + + if "code_region" in kwargs: + # let's downcast to departemental loops + reg = kwargs.pop("code_region") + if isinstance(reg, (list, tuple, set)): + deps = [ + dep for r in get_departements_from_regions(reg) for dep in r + ] + else: + deps = get_departements_from_regions(reg) + kwargs["code_departement"] = deps + + desc = "querying year/year" + ( + " & dep/dep" if "code_departement" in kwargs else "" + ) + + kwargs_loop = prepare_kwargs_loops( + "date_debut_prelevement", + "date_fin_prelevement", + kwargs, + start_auto_determination, + ) + + with SuperficialWaterbodiesQualitySession() as session: + + results = [ + session.get_analysis(format="geojson", **kwargs, **kw_loop) + for kw_loop in tqdm( + kwargs_loop, + desc=desc, + leave=_config["TQDM_LEAVE"], + position=tqdm._get_free_pos(), + ) + ] + results = [x.dropna(axis=1, how="all") for x in results if not x.empty] + results = pd.concat(results, ignore_index=True) + return results diff --git a/cl_hubeau/utils/__init__.py b/cl_hubeau/utils/__init__.py index 93669ba..217587b 100644 --- a/cl_hubeau/utils/__init__.py +++ b/cl_hubeau/utils/__init__.py @@ -1,6 +1,12 @@ # -*- coding: utf-8 -*- from .clean_cache import clean_all_cache -from .cities_and_departments import get_cities, get_departements +from .cities_deps_regions import ( + get_cities, + get_departements, + get_regions, + get_departements_from_regions, +) +from .prepare_loops import prepare_kwargs_loops __all__ = ["clean_all_cache"] diff --git a/cl_hubeau/utils/cities_and_departments.py b/cl_hubeau/utils/cities_and_departments.py deleted file mode 100644 index 2e4f2f7..0000000 --- a/cl_hubeau/utils/cities_and_departments.py +++ /dev/null @@ -1,138 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Get list of cities' and departements' codes -""" - -import os - -from pynsee import get_area_list -from pynsee.utils.init_conn import init_conn - - -def init_pynsee_connection(): - """ - Initiate an INSEE API connection with tokens and proxies. - """ - keys = ["insee_key", "insee_secret", "http_proxy", "https_proxy"] - kwargs = {x: os.environ[x] for x in keys if x in os.environ} - init_conn(**kwargs) - - -def get_cities(): - init_pynsee_connection() - cities = get_area_list("communes", "*", silent=True) - return cities["CODE"].unique().tolist() - - -def get_departements(): - try: - init_pynsee_connection() - deps = get_area_list("departements", "*", silent=True) - return deps["CODE"].unique().tolist() - except Exception: - # pynsee not working, return a simple constant - return [ - "01", - "02", - "03", - "04", - "05", - "06", - "07", - "08", - "09", - "10", - "11", - "12", - "13", - "14", - "15", - "16", - "17", - "18", - "19", - "20", - "21", - "22", - "23", - "24", - "25", - "26", - "27", - "28", - "29", - "2A", - "2B", - "30", - "31", - "32", - "33", - "34", - "35", - "36", - "37", - "38", - "39", - "40", - "41", - "42", - "43", - "44", - "45", - "46", - "47", - "48", - "49", - "50", - "51", - "52", - "53", - "54", - "55", - "56", - "57", - "58", - "59", - "60", - "61", - "62", - "63", - "64", - "65", - "66", - "67", - "68", - "69", - "70", - "71", - "72", - "73", - "74", - "75", - "76", - "77", - "78", - "79", - "80", - "81", - "82", - "83", - "84", - "85", - "86", - "87", - "88", - "89", - "90", - "91", - "92", - "93", - "94", - "95", - "971", - "972", - "973", - "974", - "976", - ] diff --git a/cl_hubeau/utils/cities_deps_regions.py b/cl_hubeau/utils/cities_deps_regions.py new file mode 100644 index 0000000..a166095 --- /dev/null +++ b/cl_hubeau/utils/cities_deps_regions.py @@ -0,0 +1,256 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Get list of cities' and departements' codes +""" + +import os + +from pynsee import get_area_list, get_geo_list +from pynsee.utils.init_conn import init_conn + + +def init_pynsee_connection(): + """ + Initiate an INSEE API connection with tokens and proxies. + """ + keys = ["insee_key", "insee_secret", "http_proxy", "https_proxy"] + kwargs = {x: os.environ[x] for x in keys if x in os.environ} + kwargs.update( + {x: os.environ[x.upper()] for x in keys if x.upper() in os.environ} + ) + init_conn(**kwargs) + + +def get_cities(): + init_pynsee_connection() + cities = get_area_list("communes", "*", silent=True) + return cities["CODE"].unique().tolist() + + +def get_regions() -> list: + try: + init_pynsee_connection() + regs = get_area_list("regions", "*", silent=True) + return regs["CODE"].unique().tolist() + except Exception: + return [ + "01", + "02", + "03", + "04", + "06", + "11", + "21", + "22", + "23", + "24", + "25", + "26", + "27", + "28", + "31", + "32", + "41", + "42", + "43", + "44", + "52", + "53", + "54", + "72", + "73", + "74", + "75", + "76", + "82", + "83", + "84", + "91", + "93", + "94", + ] + + +def get_departements_from_regions(reg: str) -> list: + try: + init_pynsee_connection() + deps = get_geo_list("departements", silent=True) + deps = deps.groupby("CODE_REG")["CODE"].agg(list).to_dict() + except Exception: + # pynsee not working, return a simple constant + deps = { + "01": ["971"], + "02": ["972"], + "03": ["973"], + "04": ["974"], + "06": ["976"], + "11": ["75", "77", "78", "91", "92", "93", "94", "95"], + "24": ["18", "28", "36", "37", "41", "45"], + "27": ["21", "25", "39", "58", "70", "71", "89", "90"], + "28": ["14", "27", "50", "61", "76"], + "32": ["02", "59", "60", "62", "80"], + "44": ["08", "10", "51", "52", "54", "55", "57", "67", "68", "88"], + "52": ["44", "49", "53", "72", "85"], + "53": ["22", "29", "35", "56"], + "75": [ + "16", + "17", + "19", + "23", + "24", + "33", + "40", + "47", + "64", + "79", + "86", + "87", + ], + "76": [ + "09", + "11", + "12", + "30", + "31", + "32", + "34", + "46", + "48", + "65", + "66", + "81", + "82", + ], + "84": [ + "01", + "03", + "07", + "15", + "26", + "38", + "42", + "43", + "63", + "69", + "73", + "74", + ], + "93": ["04", "05", "06", "13", "83", "84"], + "94": ["2A", "2B"], + } + + return deps[reg] + + +def get_departements() -> list: + try: + init_pynsee_connection() + deps = get_area_list("departements", "*", silent=True) + return deps["CODE"].unique().tolist() + except Exception: + # pynsee not working, return a simple constant + return [ + "01", + "02", + "03", + "04", + "05", + "06", + "07", + "08", + "09", + "10", + "11", + "12", + "13", + "14", + "15", + "16", + "17", + "18", + "19", + "20", + "21", + "22", + "23", + "24", + "25", + "26", + "27", + "28", + "29", + "2A", + "2B", + "30", + "31", + "32", + "33", + "34", + "35", + "36", + "37", + "38", + "39", + "40", + "41", + "42", + "43", + "44", + "45", + "46", + "47", + "48", + "49", + "50", + "51", + "52", + "53", + "54", + "55", + "56", + "57", + "58", + "59", + "60", + "61", + "62", + "63", + "64", + "65", + "66", + "67", + "68", + "69", + "70", + "71", + "72", + "73", + "74", + "75", + "76", + "77", + "78", + "79", + "80", + "81", + "82", + "83", + "84", + "85", + "86", + "87", + "88", + "89", + "90", + "91", + "92", + "93", + "94", + "95", + "971", + "972", + "973", + "974", + "976", + ] diff --git a/cl_hubeau/utils/prepare_loops.py b/cl_hubeau/utils/prepare_loops.py new file mode 100644 index 0000000..1fac0fc --- /dev/null +++ b/cl_hubeau/utils/prepare_loops.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Utilitary functions to prepare temporal and territorial loops +""" +import pandas as pd +from datetime import datetime, timedelta + + +def prepare_kwargs_loops( + key_start: str, key_end: str, kwargs: dict, start_auto_determination: bool +) -> dict: + """ + Prepare a list of kwargs of arguments to prepare a temporal loop. + + WARNING : kwargs is changed by side-effect! + + Parameters + ---------- + key_start : str + Field representing the start of a timestep in the API (for instance, + "date_debut_prelevement") + key_end : str + Field representing the end of a timestep in the API (for instance, + "date_fin_prelevement") + kwargs : dict + kwargs passed to a higher level function. + start_auto_determination : bool + Whether the dates were automatically set by the algorithm. + + Returns + ------- + args : dict + List of dict (kwargs) for inner loops. It should have 2 or 3 keys: + * key_start + * key_end + * code_departement (optional) + + """ + start = datetime.strptime(kwargs.pop(key_start), "%Y-%m-%d").date() + end = datetime.strptime(kwargs.pop(key_end), "%Y-%m-%d").date() + ranges = pd.date_range(start, end=end, freq="6ME") + dates = pd.Series(ranges).to_frame("max") + dates["min"] = dates["max"].shift(1) + timedelta(days=1) + dates = dates.dropna().loc[:, ["min", "max"]] + for d in "min", "max": + dates[d] = dates[d].dt.strftime("%Y-%m-%d") + dates = dates.reset_index(drop=True) + + if start_auto_determination: + dates = pd.concat( + [ + dates, + pd.DataFrame( + [ + { + "min": "1900-01-01", + "max": ( + datetime.strptime( + dates["min"].min(), "%Y-%m-%d" + ) + - timedelta(days=1) + ).strftime("%Y-%m-%d"), + } + ] + ), + ], + ignore_index=True, + ).sort_values("min") + else: + dates.at[0, "min"] = start.strftime("%Y-%m-%d") + dates.at[dates.index.max(), "max"] = end.strftime("%Y-%m-%d") + args = dates.rename( + {"min": key_start, "max": key_end}, + axis=1, + ) + + if "code_departement" in kwargs: + deps = kwargs.pop("code_departement") + if isinstance(deps, str): + deps = [deps] + args = args.merge( + pd.Series(deps, name="code_departement"), how="cross" + ) + + # Force restitution of new results at first hand, to trigger + # ValueError >20k results faster + args = args.sort_values(key_end, ascending=False) + + args = args.to_dict(orient="records") + + return args diff --git a/docs/drinking_water_quality.md b/docs/drinking_water_quality.md index 12ea91a..b390fb2 100644 --- a/docs/drinking_water_quality.md +++ b/docs/drinking_water_quality.md @@ -68,8 +68,8 @@ la probabilité d'atteindre le seuil maximal de 20 000 résultats est élevé. En l'état, cette fonction implémente déjà une double boucle : * sur les codes UDI -* sur les périodes, en requêtant par années calendaires (ce qui permet une scalabilité de l'algorithme dans le temps -et optimise l'usage du cache). +* sur les périodes, en requêtant par plage de 6 mois calendaires (ce qui permet +une scalabilité de l'algorithme dans le temps et optimise l'usage du cache). Il est également possible de spécifier des arguments à la fonction, parmi ceux supportés par le point de sortie "chroniques" de l'API, à l'exception de `code_reseau`. diff --git a/docs/superficial_waterbodies_quality.md b/docs/superficial_waterbodies_quality.md new file mode 100644 index 0000000..98ff190 --- /dev/null +++ b/docs/superficial_waterbodies_quality.md @@ -0,0 +1,194 @@ +--- +layout: default +title: API Qualité des cours d'eau +language: fr +handle: /drinking-water-quality +nav_order: 7 + +--- +# API Qualité des cours d'eau + +[https://hubeau.eaufrance.fr/page/api-qualite-cours-deau](https://hubeau.eaufrance.fr/page/api-qualite-cours-deau) + +`cl-hubeau` définit : + +* des fonctions de haut niveau implémentant des boucles basiques ; +* des fonctions de bas niveau qui implémentent directement les différents points d'entrée de l'API. + +{: .warning } +Lors de l'utilisation des fonctions de bas niveau, l'utilisateur est responsable +de la consommation de l'API. En particulier, il s'agit d'être vigilant quant au seuil +de 20 000 résultats récupérables d'une seule requête. +Ce seuil sera également facilement atteint avec les fonctions de haut niveau, auquel cas +l'utilisateur devra envisager de restreindre ses requêtes. +Par ailleurs, la gestion du cache par les fonctions de bas niveau est de la responsabilité +de l'utilisateur. + +Dans les deux cas, les fonctions implémentées sont conçues pour boucler sur les résultats de la +requête : les arguments optionnels `size` et `page` ou `cursor` ne doivent pas être fournis +au client python. + +## Fonctions de haut niveau + +### Récupération de la totalité des stations + +Cette fonction permet de récupérer les stations de mesures physicochimique en cours d'eau de la France entière. + +```python +from cl_hubeau import superficial_waterbodies_quality +df = superficial_waterbodies_quality.get_all_stations() +``` + +Il est également possible de spécifier des arguments à la fonction, parmi ceux supportés +par le point de sortie "station_pc" de l'API, à l'exception de : +* `code_departement` (utilisé pour boucler sur les codes départements) + +Par exemple : +```python +from cl_hubeau import superficial_waterbodies_quality +gdf = superficial_waterbodies_quality.get_all_stations(code_region="32") +``` + +### Récupération des opérations + +Cette fonction permet de récupérer les opérations physicochimiques sur des cours d'eau et plan d'eau +en France métropolitaine et DROM. + +```python +from cl_hubeau import superficial_waterbodies_quality +df = superficial_waterbodies_quality.get_all_operations() +``` + +{: .warning } +Ce type de requêtage induit rapidement des résultats volumineux. +S'il est en théorie possible de requêter l'API sans paramétrage via cette fonction, il est fortement +conseillé d'utiliser des arguments suplémentaires pour restreindre les résultats. + +Il est ainsi possible de spécifier des arguments à la fonction, parmi ceux supportés +par le point de sortie "operation_pc" de l'API. + +En l'état, cette fonction implémente déjà des boucles : +* sur les codes départementaux (y compris en cas de recherche par code région) ; +* sur les périodes, en requêtant par tranche de 6 mois (ce qui permet une scalabilité de l'algorithme dans le temps +et optimise l'usage du cache). + +Par exemple : + +```python +from cl_hubeau import superficial_waterbodies_quality +gdf = superficial_waterbodies_quality.get_all_stations(code_region=['32']) +``` + +### Récupération des conditions environnementales + +Cette fonction permet de récupérer les conditions environnementales +physicochimiques d'analyses sur des cours d'eau et plan d'eau +en France métropolitaine et DROM. + +```python +from cl_hubeau import superficial_waterbodies_quality +df = superficial_waterbodies_quality.get_all_environmental_conditions() +``` + +{: .warning } +Ce type de requêtage induit rapidement des résultats volumineux. +S'il est en théorie possible de requêter l'API sans paramétrage via cette fonction, il est fortement +conseillé d'utiliser des arguments suplémentaires pour restreindre les résultats. + +Il est ainsi possible de spécifier des arguments à la fonction, parmi ceux supportés +par le point de sortie "condition_environnementale_pc" de l'API. + +En l'état, cette fonction implémente déjà des boucles : +* sur les codes départementaux (y compris en cas de recherche par code région) ; +* sur les périodes, en requêtant par tranche de 6 mois (ce qui permet une scalabilité de l'algorithme dans le temps +et optimise l'usage du cache). + +Par exemple : + +```python +from cl_hubeau import superficial_waterbodies_quality +gdf = superficial_waterbodies_quality.get_all_environmental_conditions(code_departement=['59']) +``` + +### Récupération des analyses physico-chimiques + +Cette fonction permet de récupérer les analyses physico-chimiques +physicochimiques d'analyses sur des cours d'eau et plan d'eau +en France métropolitaine et DROM. + +```python +from cl_hubeau import superficial_waterbodies_quality +df = superficial_waterbodies_quality.get_all_analysis() +``` + +{: .warning } +Ce type de requêtage induit des résultats volumineux. Cette API ne **peut pas** être +requêtée sans paramétrage supplémentaire (dépassement du nombre de 20 000 résultats). +Il est de la responsabilité de l'utilisateur d'optimiser le requêtage par +zone géographique (code département, etc.) et/ou en ciblant certaines substances. + +Il est ainsi possible de spécifier des arguments à la fonction, parmi ceux supportés +par le point de sortie "analyse_pc" de l'API. + +En l'état, cette fonction implémente déjà une boucle sur les périodes, en requêtant par +années calendaires (ce qui permet une scalabilité de l'algorithme dans le temps et optimise l'usage du cache). + +Par exemple : + +```python +from cl_hubeau import superficial_waterbodies_quality +gdf = superficial_waterbodies_quality.get_all_analysis(code_departement="59", code_parametre="1313") +``` + +## Fonctions de bas niveau + +Un objet session est défini pour consommer l'API à l'aide de méthodes de bas niveau. +Ces méthodes correspondent strictement aux fonctions disponibles via l'API : l'utilisateur +est invité à se reporter à la documentation de l'API concernant le détail des arguments +disponibles. + +### Lister les stations + +```python +from cl_hubeau import superficial_waterbodies_quality +with superficial_waterbodies_quality.SuperficialWaterbodiesQualitySession() as session: + df = session.get_stations(nom_commune="LILLE") +``` + +### Lister les opérations + +```python +from cl_hubeau import superficial_waterbodies_quality +with cl_hubeau.superficial_waterbodies_quality.SuperficialWaterbodiesQualitySession() as session: + df = session.get_operations( + code_departement='02', + code_parametre="1340", + date_debut_prelevement="2023-01-01", + ) +``` + +### Lister les conditions environnementales + +```python +from cl_hubeau import superficial_waterbodies_quality +with superficial_waterbodies_quality.SuperficialWaterbodiesQualitySession() as session: + df = session.get_environmental_conditions( + code_departement='59', + date_debut_prelevement="2023-01-01", + format="geojson", + ) +``` + +### Lister les analyses physico-chimiques + +```python +from cl_hubeau import superficial_waterbodies_quality +with superficial_waterbodies_quality.SuperficialWaterbodiesQualitySession() as session: + df = session.get_analysis( + code_departement='02', + code_parametre="1313", + date_debut_prelevement="2024-01-01", + format="geojson", + ) +``` + diff --git a/poetry.lock b/poetry.lock index d0df74a..50157cf 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,141 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "aiohappyeyeballs" +version = "2.4.2" +description = "Happy Eyeballs for asyncio" +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiohappyeyeballs-2.4.2-py3-none-any.whl", hash = "sha256:8522691d9a154ba1145b157d6d5c15e5c692527ce6a53c5e5f9876977f6dab2f"}, + {file = 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= ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", [metadata] lock-version = "2.0" python-versions = "^3.9" -content-hash = "2903cf28c03d93a3a78533e80a45a71d9229c86a250d24068560576840ac6c66" +content-hash = "42fa6646263d700ad761ee16c6174575f74c842a34cc6d10c306064e799a48f4" diff --git a/pyproject.toml b/pyproject.toml index fb208ab..92f0f60 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,13 +1,13 @@ [tool.poetry] name = "cl-hubeau" -version = "0.3.0" +version = "0.4.0" description = "Hubeau client to collect data from the different APIs" authors = ["Thomas Grandjean "] license = "GPL-3.0-or-later" readme = "README.md" homepage = "https://github.com/tgrandje/cl-hubeau/" repository = "https://github.com/tgrandje/cl-hubeau/" -documentation = "https://github.com/tgrandje/cl-hubeau/" +documentation = "https://tgrandje.github.io/cl-hubeau" keywords = ["france", "water", "hydrology"] classifiers = [ "Development Status :: 3 - Alpha", @@ -33,15 +33,17 @@ python-dotenv = "^1.0.1" [tool.poetry.group.dev.dependencies] -spyder = "^5.5.5" +spyder = "^6.0.1" pytest = "^8.2.2" black = "^24.4.2" pyqt5-qt5 = "5.15.2" pyqtwebengine-qt5 = "5.15.2" -matplotlib = "^3.9.1" +matplotlib = "^3.9.2" licensecheck = "^2024.2" jupyter = "^1.0.0" pyproj = "^3.6.1" +debugpy = "1.8.2" +seaborn = "^0.13.2" [build-system] requires = ["poetry-core"] diff --git a/tests/test_superficial_waterbodies_quality.py b/tests/test_superficial_waterbodies_quality.py new file mode 100644 index 0000000..4548bf2 --- /dev/null +++ b/tests/test_superficial_waterbodies_quality.py @@ -0,0 +1,130 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" + +Test high level functions +""" + +import pandas as pd +import pytest +import re +from requests_cache import CacheMixin + +from cl_hubeau import superficial_waterbodies_quality + + +class MockResponse: + def __init__(self, json_data): + self.json_data = json_data + self.ok = True + + def json(self): + return self.json_data + + +@pytest.fixture +def mock_get_data(monkeypatch): + + def mock_request(*args, **kwargs): + self, method, url, *args = args + + if re.search("station_pc$", url): + deps = kwargs["params"]["code_departement"].split(",") + data = { + "count": len(deps), + "first": "blah_page", + "features": [ + { + "type": "Feature", + "properties": { + "code_station": f"dummy_code_{dep}", + "libelle_station": "dummy_label", + "code_departement": dep, + }, + "geometry": { + "type": "Point", + "crs": { + "type": "name", + "properties": { + "name": "urn:ogc:def:crs:OGC:1.3:CRS84" + }, + }, + "coordinates": [0, 0], + }, + } + for dep in deps + ], + } + + elif ( + re.search("operation_pc$", url) + or re.search("condition_environnementale_pc$", url) + or re.search("analyse_pc$", url) + ): + code = kwargs["params"]["code_station"] + data = { + "count": 1, + "first": "blah_page", + "features": [ + { + "type": "Feature", + "properties": { + "date": "2020-06-01", + "code_station": code, + }, + "geometry": { + "type": "Point", + "crs": { + "type": "name", + "properties": { + "name": "urn:ogc:def:crs:OGC:1.3:CRS84" + }, + }, + "coordinates": [0, 0], + }, + } + ], + } + + return MockResponse(data) + + monkeypatch.setattr(CacheMixin, "request", mock_request) + + +def test_get_stations(mock_get_data): + data = superficial_waterbodies_quality.get_all_stations() + assert isinstance(data, pd.DataFrame) + assert len(data) == 102 + + +def test_get_operations(mock_get_data): + data = superficial_waterbodies_quality.get_all_operations( + code_station="dummy_code", + date_debut_prelevement="2020-01-01", + date_fin_prelevement="2020-12-31", + ) + data = data.drop_duplicates() + assert isinstance(data, pd.DataFrame) + assert len(data) == 1 + + +def test_get_environmental_conditions(mock_get_data): + data = superficial_waterbodies_quality.get_all_environmental_conditions( + code_station="dummy_code", + date_debut_prelevement="2020-01-01", + date_fin_prelevement="2020-12-31", + ) + data = data.drop_duplicates() + assert isinstance(data, pd.DataFrame) + assert len(data) == 1 + + +def test_get_analysis(mock_get_data): + data = superficial_waterbodies_quality.get_all_analysis( + code_station="dummy_code", + date_debut_prelevement="2020-01-01", + date_fin_prelevement="2020-12-31", + ) + data = data.drop_duplicates() + assert isinstance(data, pd.DataFrame) + assert len(data) == 1