Choix de carte dynamique, même pour les TOM #65
This commit is contained in:
Colin Maudry
+182
-170
@@ -1,4 +1,3 @@
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import json
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from typing import Literal
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from urllib.error import HTTPError, URLError
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@@ -11,57 +10,7 @@ import polars as pl
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from dash import dash_table, dcc, html
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from dash_extensions.javascript import Namespace
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from src.utils import data_schema, df, format_number
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def get_map_count_marches(dff: pl.DataFrame) -> go.Figure:
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lff: pl.LazyFrame = dff.lazy()
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lff = lff.rename({"acheteur_departement_code": "Département"})
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lff = (
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lff.select(["uid", "Département"])
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.drop_nulls()
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.group_by("uid")
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.agg(pl.col("Département").first())
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.group_by("Département")
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.len("uid")
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)
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# Suppression des infos pour les DOM/TOM pour l'instant
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lff = lff.filter(~pl.col("Département").str.head(2).is_in(["97", "98"]))
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with open("./data/departements-1000m.geojson") as f:
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departements = json.load(f)
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# Ajout de feature.id
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for f in departements["features"]:
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f["id"] = f["properties"]["code"]
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df_map = lff.collect(engine="streaming")
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fig = px.choropleth(
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df_map,
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geojson=departements,
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locations="Département",
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color="uid",
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color_continuous_scale="Reds",
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title="Nombres de marchés attribués par département (lieu d'exécution)",
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range_color=(df_map["uid"].min(), df_map["uid"].max()),
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labels={"uid": "Marchés attribués"},
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scope="europe",
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width=900,
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height=700,
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)
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fig.update_geos(fitbounds="locations", visible=False)
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fig.update_layout(
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mapbox={
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"style": "carto-positron",
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"center": {"lon": 10, "lat": 10},
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"zoom": 1,
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"domain": {"x": [0, 1], "y": [0, 1]},
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}
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)
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return fig
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from src.utils import data_schema, departements_geojson, df, format_number
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def get_yearly_statistics(statistics, today_str) -> html.Div:
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@@ -82,11 +31,11 @@ def get_yearly_statistics(statistics, today_str) -> html.Div:
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}
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)
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df = pl.DataFrame(data)
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dff = pl.DataFrame(data)
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# Create Dash DataTable
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table = dash_table.DataTable(
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data=df.to_dicts(),
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data=dff.to_dicts(),
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columns=[
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{"name": "Année", "id": "Année"},
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{"name": "Marchés et accord-cadres", "id": "Marchés et accord-cadres"},
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@@ -429,141 +378,211 @@ def get_geographic_maps(dff: pl.DataFrame) -> list | None:
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Génère les cartes géographiques pour la métropole et les DOM-TOM.
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"""
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# Si les données sont trop importantes on utilise une carte chloropleth
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if dff.height > 50000:
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return [
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dbc.Col(
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dcc.Graph(
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figure=get_map_count_marches(dff), config={"displayModeBar": False}
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),
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width=12,
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md=12,
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className="mb-4",
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)
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]
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regions: dict = {
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"Métropole": {
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"coordinates": [46.6, 2.2],
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"zoom_leaflet": 5,
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"zoom_chloropleth": 1,
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"name": "Métropole",
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},
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"971": {
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"coordinates": [16.23, -61.55],
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"zoom_leaflet": 9,
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"zoom_chloropleth": 1,
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"name": "Guadeloupe",
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},
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"972": {
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"coordinates": [14.64, -61.02],
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"zoom_leaflet": 10,
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"zoom_chloropleth": 1,
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"name": "Martinique",
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},
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"973": {
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"coordinates": [3.93, -53.12],
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"zoom_leaflet": 7,
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"zoom_chloropleth": 1,
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"name": "Guyane",
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},
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"974": {
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"coordinates": [-21.11, 55.53],
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"zoom_leaflet": 9,
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"zoom_chloropleth": 1,
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"name": "La Réunion",
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},
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"976": {
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"coordinates": [-12.82, 45.16],
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"zoom_leaflet": 10,
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"zoom_chloropleth": 1,
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"name": "Mayotte",
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},
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}
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# Liste des codes départements Outre-Mer
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region_codes: list = ["Métropole", "971", "972", "973", "974", "976"]
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dom_codes = region_codes[1:]
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dom_codes = [code for code in regions.keys() if code != "Métropole"]
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print("dom codes", dom_codes)
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# Couleurs accessibles (Okabe-Ito)
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color_acheteur = "#E69F00" # Orange
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color_titulaire = "#56B4E9" # Bleu ciel
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regions = {}
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# Ajout des DOM s'ils ont des données
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for code in region_codes:
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if code == "Métropole":
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dff_region = dff.filter(
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def make_map_data(region_code: str) -> tuple[list, str or None]:
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lff: pl.LazyFrame = dff.lazy()
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if region_code == "Métropole":
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lff = lff.filter(
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(
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~pl.col("acheteur_departement_code").is_in(dom_codes)
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| (~pl.col("titulaire_departement_code").is_in(dom_codes))
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)
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)
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else:
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dff_region = dff.filter(
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lff = lff.filter(
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(pl.col("acheteur_departement_code") == code)
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| (pl.col("titulaire_departement_code") == code)
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)
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if dff_region.height > 0:
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if code == "971":
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name = "Guadeloupe"
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elif code == "972":
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name = "Martinique"
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elif code == "973":
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name = "Guyane"
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elif code == "974":
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name = "La Réunion"
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elif code == "976":
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name = "Mayotte"
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elif code == "Métropole":
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name = "Métropole"
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nb_marches = lff.select("uid").group_by("uid").first().collect().height
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if nb_marches == 0:
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return [], None
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dfs = []
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if (code == "Métropole" and nb_marches > 20000) or (
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code != "Métropole" and nb_marches > 10000
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):
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_map_type: str = "chloropleth"
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lff = lff.rename({"acheteur_departement_code": "Département"})
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lff = (
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lff.select(["uid", "Département"])
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.drop_nulls()
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.group_by("uid")
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.agg(pl.col("Département").first())
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.group_by("Département")
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.len("uid")
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)
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dfs.append(lff.collect())
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else:
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name = f"Département {code}"
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_map_type: str = "clusters"
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for org_type in ["acheteur", "titulaire"]:
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lff_org = (
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lff.select(
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"uid",
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f"{org_type}_longitude",
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f"{org_type}_latitude",
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f"{org_type}_nom",
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)
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.group_by(
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f"{org_type}_longitude",
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f"{org_type}_latitude",
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f"{org_type}_nom",
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)
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.len("nb_marches")
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.filter(
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pl.col(f"{org_type}_latitude").is_not_null()
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& pl.col(f"{org_type}_longitude").is_not_null()
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)
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)
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regions[name] = dff_region
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markers = []
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# Region centers for dash-leaflet
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region_centers = {
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"Métropole": ([46.6, 2.2], 5),
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"Guadeloupe": ([16.23, -61.55], 9),
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"Martinique": ([14.64, -61.02], 10),
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"Guyane": ([3.93, -53.12], 7),
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"La Réunion": ([-21.11, 55.53], 9),
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"Mayotte": ([-12.82, 45.16], 10),
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# Couleurs accessibles (Okabe-Ito)
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colors = {
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"acheteur": "#E69F00", # orange
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"titulaire": "#56B4E9", # bleu ciel
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}
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for row in lff_org.collect().to_dicts():
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markers.append(
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{
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"lat": row[f"{org_type}_latitude"],
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"lon": row[f"{org_type}_longitude"],
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"tooltip": f"{row[f'{org_type}_nom']} ({row['nb_marches']} marchés)",
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"marker_color": colors[org_type],
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}
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)
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dfs.append(markers)
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return dfs, _map_type
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cols = []
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for code in regions.keys():
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regions[code]["data"], map_type = make_map_data(code)
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print("region", regions[code]["name"], len(regions[code]["data"][0]), map_type)
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if map_type == "chloropleth":
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map_graph = make_chloropleth_map(regions[code]) # call chloropleth function
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elif map_type == "clusters":
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map_graph = make_clusters_map(regions[code])
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elif map_type is None:
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continue
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else:
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raise ValueError(f"Map type '{map_type}' not recognised")
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col = dbc.Col(
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children=[
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html.H5(regions[code]["name"]),
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map_graph,
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],
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md=6 if code == "Métropole" else 3,
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width=12,
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className="mb-4",
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)
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cols.append(col)
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return cols
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def make_chloropleth_map(region: dict) -> dcc.Graph:
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df_map = region["data"][0]
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fig = px.choropleth(
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df_map,
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geojson=departements_geojson,
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locations="Département",
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color="uid",
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color_continuous_scale="Reds",
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title="Nombres de marchés attribués",
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range_color=(df_map["uid"].min(), df_map["uid"].max()),
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labels={"uid": "Marchés attribués"},
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scope="europe",
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width=400,
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height=400,
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)
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fig.update_geos(fitbounds="locations", visible=False)
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fig.update_layout(
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mapbox={
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"style": "carto-positron",
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"center": {"lon": 10, "lat": 10},
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"zoom": 8,
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"domain": {"x": [0, 1], "y": [0, 1]},
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}
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)
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graph = dcc.Graph(figure=fig, config={"displayModeBar": False})
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return graph
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def make_clusters_map(region: dict):
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# JavaScript functions for styling
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ns = Namespace("dash_clientside", "leaflet")
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point_to_layer = ns("pointToLayer")
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cluster_to_layer = ns("clusterToLayer")
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cols = []
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for name, region_df in regions.items():
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# Trace Acheteurs
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mask_acheteur = (
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region_df.select(
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"uid", "acheteur_longitude", "acheteur_latitude", "acheteur_nom"
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)
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.group_by("acheteur_longitude", "acheteur_latitude", "acheteur_nom")
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.len("nb_marches")
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.filter(
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pl.col("acheteur_latitude").is_not_null()
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& pl.col("acheteur_longitude").is_not_null()
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)
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)
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name = region["name"]
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acheteurs_marker_dicts = []
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# Données de la région
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region_acheteurs = region["data"][0]
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region_titulaires = region["data"][1]
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if mask_acheteur.height > 0:
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for row in mask_acheteur.to_dicts():
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acheteurs_marker_dicts.append(
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{
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"lat": row["acheteur_latitude"],
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"lon": row["acheteur_longitude"],
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"tooltip": f"{row['acheteur_nom']} ({row['nb_marches']} marchés)",
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"marker_color": color_acheteur,
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}
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)
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# Couleurs
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color_acheteur = region_acheteurs[0]["marker_color"]
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color_titulaire = region_titulaires[0]["marker_color"]
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# Trace Titulaires
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mask_titulaire = (
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region_df.select(
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"uid", "titulaire_longitude", "titulaire_latitude", "titulaire_nom"
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)
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.group_by("titulaire_longitude", "titulaire_latitude", "titulaire_nom")
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.len("nb_marches")
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.filter(
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pl.col("titulaire_latitude").is_not_null()
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& pl.col("titulaire_longitude").is_not_null()
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)
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)
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acheteurs_geojson_data = dlx.dicts_to_geojson(region_acheteurs)
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titulaires_geojson_data = dlx.dicts_to_geojson(region_titulaires)
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titulaires_marker_dicts = []
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if mask_titulaire.height > 0:
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for row in mask_titulaire.to_dicts():
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titulaires_marker_dicts.append(
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{
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"lat": row["titulaire_latitude"],
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"lon": row["titulaire_longitude"],
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"tooltip": f"{row['titulaire_nom']} ({row['nb_marches']} marchés)",
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"marker_color": color_titulaire,
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}
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)
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acheteurs_geojson_data = dlx.dicts_to_geojson(acheteurs_marker_dicts)
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titulaires_geojson_data = dlx.dicts_to_geojson(titulaires_marker_dicts)
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center, zoom = region_centers.get(name, ([46.6, 2.2], 6))
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col_width = 6 if name == "Métropole" else 3
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center, zoom = region["coordinates"], region["zoom_leaflet"]
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region_id = name.lower().replace(" ", "-")
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cols.append(
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dbc.Col(
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[
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html.H5(name),
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dl.Map(
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leaflet_map = dl.Map(
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[
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dl.TileLayer(),
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dl.GeoJSON(
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@@ -592,15 +611,8 @@ def get_geographic_maps(dff: pl.DataFrame) -> list | None:
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"height": "400px" if name == "Métropole" else "300px",
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},
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id=f"map-{region_id}",
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),
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],
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width=12,
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md=col_width,
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className="mb-4",
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)
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)
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return cols
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return leaflet_map
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def make_column_picker(page: str):
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@@ -286,6 +286,17 @@ def get_departements() -> dict:
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return data
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def get_departements_geojson() -> dict:
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with open("./data/departements-1000m.geojson") as f:
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geojson = json.load(f)
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# Ajout de feature.id
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for f in geojson["features"]:
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f["id"] = f["properties"]["code"]
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return geojson
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def get_departement_region(code_postal):
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if code_postal > "97000":
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code_departement = code_postal[:3]
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@@ -774,6 +785,7 @@ df_titulaires_marches: pl.DataFrame = (
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)
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departements = get_departements()
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departements_geojson = get_departements_geojson()
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domain_name = (
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"test.decp.info" if os.getenv("DEVELOPMENT").lower() == "true" else "decp.info"
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)
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