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Cartes avec cluster de points si > lignes #65

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Colin Maudry
2026-03-15 16:05:33 +01:00
parent 302d253e6e
commit adc8457abc
3 changed files with 55 additions and 24 deletions
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src/figures.py
+48 -22
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@@ -430,7 +430,7 @@ def get_geographic_maps(dff: pl.DataFrame) -> list | None:
"""
# Si les données sont trop importantes on utilise une carte chloropleth
if dff.height > 5000:
if dff.height > 50000:
return [
dbc.Col(
dcc.Graph(
@@ -443,21 +443,30 @@ def get_geographic_maps(dff: pl.DataFrame) -> list | None:
]
# Liste des codes départements Outre-Mer
dom_codes = ["971", "972", "973", "974", "976"]
region_codes: list = ["Métropole", "971", "972", "973", "974", "976"]
dom_codes = region_codes[1:]
# Couleurs accessibles (Okabe-Ito)
color_acheteur = "#E69F00" # Orange
color_titulaire = "#56B4E9" # Bleu ciel
regions = {
"Métropole": dff.filter(~pl.col("acheteur_departement_code").is_in(dom_codes))
}
regions = {}
# Ajout des DOM s'ils ont des données
for code in dom_codes:
dff_dom = dff.filter(pl.col("acheteur_departement_code") == code)
if dff_dom.height > 0:
name = f"Département {code}"
for code in region_codes:
if code == "Métropole":
dff_region = dff.filter(
(
~pl.col("acheteur_departement_code").is_in(dom_codes)
| (~pl.col("titulaire_departement_code").is_in(dom_codes))
)
)
else:
dff_region = dff.filter(
(pl.col("acheteur_departement_code") == code)
| (pl.col("titulaire_departement_code") == code)
)
if dff_region.height > 0:
if code == "971":
name = "Guadeloupe"
elif code == "972":
@@ -468,16 +477,21 @@ def get_geographic_maps(dff: pl.DataFrame) -> list | None:
name = "La Réunion"
elif code == "976":
name = "Mayotte"
regions[name] = dff_dom
elif code == "Métropole":
name = "Métropole"
else:
name = f"Département {code}"
regions[name] = dff_region
# Region centers for dash-leaflet
region_centers = {
"Métropole": ([46.6, 2.2], 6),
"Métropole": ([46.6, 2.2], 5),
"Guadeloupe": ([16.23, -61.55], 9),
"Martinique": ([14.64, -61.02], 10),
"Guyane": ([3.93, -53.12], 7),
"La Réunion": ([-21.11, 55.53], 10),
"Mayotte": ([-12.82, 45.16], 11),
"La Réunion": ([-21.11, 55.53], 9),
"Mayotte": ([-12.82, 45.16], 10),
}
# JavaScript functions for styling
@@ -487,9 +501,6 @@ def get_geographic_maps(dff: pl.DataFrame) -> list | None:
cols = []
for name, region_df in regions.items():
# Prepare data for GeoJSON
marker_dicts = []
# Trace Acheteurs
mask_acheteur = (
region_df.select(
@@ -503,9 +514,11 @@ def get_geographic_maps(dff: pl.DataFrame) -> list | None:
)
)
acheteurs_marker_dicts = []
if mask_acheteur.height > 0:
for row in mask_acheteur.to_dicts():
marker_dicts.append(
acheteurs_marker_dicts.append(
{
"lat": row["acheteur_latitude"],
"lon": row["acheteur_longitude"],
@@ -527,9 +540,11 @@ def get_geographic_maps(dff: pl.DataFrame) -> list | None:
)
)
titulaires_marker_dicts = []
if mask_titulaire.height > 0:
for row in mask_titulaire.to_dicts():
marker_dicts.append(
titulaires_marker_dicts.append(
{
"lat": row["titulaire_latitude"],
"lon": row["titulaire_longitude"],
@@ -538,10 +553,11 @@ def get_geographic_maps(dff: pl.DataFrame) -> list | None:
}
)
geojson_data = dlx.dicts_to_geojson(marker_dicts)
acheteurs_geojson_data = dlx.dicts_to_geojson(acheteurs_marker_dicts)
titulaires_geojson_data = dlx.dicts_to_geojson(titulaires_marker_dicts)
center, zoom = region_centers.get(name, ([46.6, 2.2], 6))
col_width = 12 if name == "Métropole" else 3
col_width = 6 if name == "Métropole" else 3
region_id = name.lower().replace(" ", "-")
cols.append(
dbc.Col(
@@ -551,12 +567,22 @@ def get_geographic_maps(dff: pl.DataFrame) -> list | None:
[
dl.TileLayer(),
dl.GeoJSON(
data=geojson_data,
data=titulaires_geojson_data,
cluster=True,
zoomToBoundsOnClick=True,
pointToLayer=point_to_layer,
clusterToLayer=cluster_to_layer,
id=f"geojson-{region_id}",
id=f"geojson-{region_id}-titulaires",
options={"fillColor": color_titulaire},
),
dl.GeoJSON(
data=acheteurs_geojson_data,
cluster=True,
zoomToBoundsOnClick=True,
pointToLayer=point_to_layer,
clusterToLayer=cluster_to_layer,
id=f"geojson-{region_id}-acheteurs",
options={"fillColor": color_acheteur},
),
],
center=center,