cl-hubeau 0.9.0

Hubeau client to collect data from the different APIs

cl-hubeau

Simple hub'eau client for python

This package is currently under active development. Every API on Hub'eau will be covered by this package in due time.

At this stage, the following APIs are covered by cl-hubeau:

• phytopharmaceuticals transactions/vente et achat de produits phytopharmaceutiques
• watercourses flow/écoulement des cours d'eau
• drinking water quality/qualité de l'eau potable
• hydrometry/hydrométrie
• superficial waterbodies quality/qualité des cours d'eau
• ground waterbodies quality/qualité des nappes
• piezometry/piézométrie

For any help on available kwargs for each endpoint, please refer directly to the documentation on hubeau (this will not be covered by the current documentation).

Assume that each function from cl-hubeau will be consistent with it's hub'eau counterpart, with the exception of the size and page or cursor arguments (those will be set automatically by cl-hubeau to crawl allong the results).

Parallelization

cl-hubeau already uses simple multithreading pools to perform requests. In order not to endanger the webservers and share ressources among users, a rate limiter is set to 10 queries per second. This limiter should work fine on any given machine, whatever the context (even with a new parallelization overlay).

However cl-hubeau should NOT be used in containers (or pods) with parallelization. There is currently no way of tracking the queries' rate among multiple machines: greedy queries may end up blacklisted by the team managing Hub'eau.

Configuration

Starting with pynsee 0.2.0, no API keys are needed anymore.

Support

In case of bugs, please open an issue on the repo.

Contribution

Any help is welcome. Please refer to the CONTRIBUTING file.

Licence

GPL-3.0-or-later

Project Status

This package is currently under active development.

Basic examples

Clean cache

from cl_hubeau.utils import clean_all_cache
clean_all_cache()

Phyopharmaceuticals transactions

4 high level functions are available (and one class for low level operations).

Note that high level functions introduce new arguments (filter_regions and filter_departements to better target territorial data.

Get all active substances bought (uses a 30 days caching):

from cl_hubeau import phytopharmaceuticals_transactions as pt
df = pt.get_all_active_substances_bought()

# or to get regional data:
df = pt.get_all_active_substances_bought(
        type_territoire="Région", code_territoire="32"
    )

# or to get departemantal data:
df = pt.get_all_active_substances_bought(
        type_territoire="Département", filter_regions="32"
    )

# or to get postcode-zoned data:
df = pt.get_all_active_substances_bought(
        type_territoire="Zone postale", filter_departements=["59", "62"]
    )

Get all phytopharmaceutical products bought (uses a 30 days caching):

from cl_hubeau import phytopharmaceuticals_transactions as pt
df = pt.get_all_phytopharmaceutical_products_bought()

# or to get regional data:
df = pt.get_all_phytopharmaceutical_products_bought(
        type_territoire="Région", code_territoire="32"
    )

# or to get departemantal data:
df = pt.get_all_phytopharmaceutical_products_bought(
        type_territoire="Département", filter_regions="32"
    )

# or to get postcode-zoned data:
df = pt.get_all_phytopharmaceutical_products_bought(
        type_territoire="Zone postale", filter_departements=["59", "62"]
    )

Get all active substances sold (uses a 30 days caching):

from cl_hubeau import phytopharmaceuticals_transactions as pt
df = pt.get_all_active_substances_sold()

# or to get regional data:
df = pt.get_all_active_substances_sold(
        type_territoire="Région", code_territoire="32"
    )

# or to get departemantal data:
df = pt.get_all_active_substances_sold(
        type_territoire="Département", filter_regions="32"
    )

Get all phytopharmaceutical products sold (uses a 30 days caching):

from cl_hubeau import phytopharmaceuticals_transactions as pt
df = pt.get_all_phytopharmaceutical_products_sold()

# or to get regional data:
df = pt.get_all_phytopharmaceutical_products_sold(
        type_territoire="Région", code_territoire="32"
    )

# or to get departemantal data:
df = pt.get_all_phytopharmaceutical_products_sold(
        type_territoire="Département", filter_regions="32"
    )

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

with pt.PhytopharmaceuticalsSession() as session:
    df = session.active_substances_sold(
        annee_min=2010,
        annee_max=2015,
        code_territoire=["32"],
        type_territoire="Région",
        )
    df = session.phytopharmaceutical_products_sold(
        annee_min=2010,
        annee_max=2015,
        code_territoire=["32"],
        type_territoire="Région",
        eaj="Oui",
        unite="l",
    )
    df = session.active_substances_bought(
        annee_min=2010,
        annee_max=2015,
        code_territoire=["32"],
        type_territoire="Région",
    )
    df = session.phytopharmaceutical_products_bought(
        code_territoire=["32"],
        type_territoire="Région",
        eaj="Oui",
        unite="l",
    )

Watercourses flow

3 high level functions are available (and one class for low level operations).

Get all stations (uses a 30 days caching):

from cl_hubeau import watercourses_flow
df = watercourses_flow.get_all_stations()

Get all observations (uses a 30 days caching):

from cl_hubeau import watercourses_flow
df = watercourses_flow.get_all_observations()

Note that this query is heavy, users should restrict it to a given territory when possible. For instance, you could use :

df = watercourses_flow.get_all_observations(code_region="11")

Get all campaigns:

from cl_hubeau import watercourses_flow
df = watercourses_flow.get_all_campaigns()

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

with watercourses_flow.WatercoursesFlowSession() as session:
    df = session.get_stations(code_departement="59")
    df = session.get_campaigns(code_campagne=[12])
    df = session.get_observations(code_station="F6640008")

Drinking water quality

2 high level functions are available (and one class for low level operations).

Get all water networks (UDI) (uses a 30 days caching):

from cl_hubeau import drinking_water_quality
df = drinking_water_quality.get_all_water_networks()

Get the sanitary controls's results for nitrates on all networks of Paris, Lyon & Marseille (uses a 30 days caching) for nitrates

networks = drinking_water_quality.get_all_water_networks(code_region=["11", "84", "93"])
networks = networks[
    networks.nom_commune.isin(["PARIS", "MARSEILLE", "LYON"])
    ]["code_reseau"].unique().tolist()

df = drinking_water_quality.get_control_results(
    code_reseau=networks, code_parametre="1340"
)
df = df[df.nom_commune.isin(["PARIS", "MARSEILLE", "LYON"])]

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.

You can also call the same function, using official city codes directly:

df = drinking_water_quality.get_control_results(
    code_commune=['59350'],
    code_parametre="1340"
)

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

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")

Hydrometry

4 high level functions are available (and one class for low level operations).

Get all stations (uses a 30 days caching):

from cl_hubeau import hydrometry
gdf = hydrometry.get_all_stations()

Get all sites (uses a 30 days caching):

gdf = hydrometry.get_all_sites()

Get observations for the first 5 sites (uses a 30 days caching): Note that this will also work with stations (instead of sites).

df = hydrometry.get_observations(gdf["code_site"].head(5).tolist())

Get realtime data for the first 5 sites (no cache stored):

A small cache is stored to allow for realtime consumption (cache expires after only 15 minutes). Please, adopt a responsible usage with this functionnality !

df = hydrometry.get_realtime_observations(gdf["code_site"].head(5).tolist())

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, noticely for realtime data

with hydrometry.HydrometrySession() as session:
    df = session.get_stations(code_station="K437311001")
    df = session.get_sites(code_departement=['02', '59', '60', '62', '80'], format="geojson")
    df = session.get_realtime_observations(code_entite="K437311001")
    df = session.get_observations(code_entite="K437311001")

Superficial waterbodies quality

4 high level functions are available (and one class for low level operations).

Get all stations (uses a 30 days caching):

from cl_hubeau import superficial_waterbodies_quality
df = superficial_waterbodies_quality.get_all_stations()

Get all operations (uses a 30 days caching):

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 :

df = superficial_waterbodies_quality.get_all_operations(code_region="11")

Get all environmental conditions:

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 :

df = superficial_waterbodies_quality.get_all_environmental_conditions(code_region="11")

Get all physicochemical analyses:

from cl_hubeau import superficial_waterbodies_quality
df = superficial_waterbodies_quality.get_all_analyses()

Note that this query is heavy, users should restrict it to a given territory and given parameters. For instance, you could use :

df = superficial_waterbodies_quality.get_all_analyses(
    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

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_analyses(code_commune='59183', code_parametre="1340")

Ground waterbodies quality

2 high level functions are available (and one class for low level operations).

Get all stations (uses a 30 days caching):

from cl_hubeau import ground_water_quality
df = ground_water_quality.get_all_stations()

Get the tests results for nitrates :

df = ground_water_quality.df = get_all_analyses(code_param="1340")

Note that this query is heavy, even if this was already restricted to nitrates, and that it may fail. In theory, you could even query the API without specifying the substance you're tracking, but you will hit the 20k threshold and trigger an exception.

In practice, you should call the same function with a territorial restriction or with specific bss_ids. For instance, you could use official city codes directly:

df = ground_water_quality.get_all_analyses(
    num_departement=["59"]
    code_param="1340"
)

Note: a bit of caution is needed here, as the arguments are NOT the same in the two endpoints. Please have a look at the documentation on hubeau. For instance, the city's number is called "code_insee_actuel" on analyses' endpoint and "code_commune" on station's.

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

with ground_water_quality.GroundWaterQualitySession() as session:
    df = session.get_stations(bss_id="01832B0600")
    df = session.get_analyses(
        bss_id=["BSS000BMMA"],
        code_param="1461",
        )

Piezometry

3 high level functions are available (and one class for low level operations).

Get all piezometers (uses a 30 days caching):

from cl_hubeau import piezometry
gdf = piezometry.get_all_stations()

Get chronicles for the first 100 piezometers (uses a 30 days caching):

df = piezometry.get_chronicles(gdf["code_bss"].head(100).tolist())

Get realtime data for the first 100 piezometers:

A small cache is stored to allow for realtime consumption (cache expires after only 15 minutes). Please, adopt a responsible usage with this functionnality !

df = get_realtime_chronicles(gdf["code_bss"].head(100).tolist())

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, noticely for realtime data

with piezometry.PiezometrySession() as session:
    df = session.get_chronicles(code_bss="07548X0009/F")
    df = session.get_stations(code_departement=['02', '59', '60', '62', '80'], format="geojson")
    df = session.get_chronicles_real_time(code_bss="07548X0009/F")

Convenience functions

In order to ease queries on hydrographic territories, some convenience functions have been added to this module.

In these process, we are harvesting official geodatasets which are not available on hub'eau; afterwards, simple geospatial joins are performed with the latest geodataset of french cities.

These are convenience tools and there will be approximations (geographical precision of both datasets might not match).

SAGE (Schéma d'Aménagement et de Gestion des Eaux)

You can retrieve a SAGE's communal components using the following snippet:

from cl_hubeau.utils import cities_for_sage

d = cities_for_sage()

The official geodataset is eaufrance's SAGE.