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0aee67c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 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 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 | import pandas as pd
import plotly.express as px
import streamlit as st
import geopandas as gpd
# --- Configuration and Constants ---
MISSING_VALUE_PLACEHOLDER = -1
# --- Helper for Color Scale ---
def _generate_custom_colorscale(min_val, max_val, red_gradient=False):
if red_gradient:
reversed_rdylgn_colors = px.colors.sequential.Reds
return reversed_rdylgn_colors
else:
custom_colorscale = [[0.0, "lightgrey"]]
reversed_rdylgn_colors = px.colors.diverging.RdYlGn[::-1]
normalized_min_actual = (min_val - MISSING_VALUE_PLACEHOLDER) / (
max_val - MISSING_VALUE_PLACEHOLDER
)
normalized_max_actual = (max_val - MISSING_VALUE_PLACEHOLDER) / (
max_val - MISSING_VALUE_PLACEHOLDER
)
num_steps = len(reversed_rdylgn_colors)
for i, color in enumerate(reversed_rdylgn_colors):
normalized_point = normalized_min_actual + (
normalized_max_actual - normalized_min_actual
) * (i / (num_steps - 1))
if normalized_point > 0.0:
custom_colorscale.append([normalized_point, color])
return sorted(custom_colorscale, key=lambda x: x[0])
def display_choropleth_map_country(
df: pd.DataFrame,
geojson_data: dict,
metric_name: str = "prixm2moyen",
metric_description: str = "Average Price per Square Meter",
title: str = "Average Price per Square Meter in France (2015-2024)",
height: int = 1000,
width: int = 1400,
red_gradient: bool = False,
):
df_agg = (
df
.groupby(["code_departement", "annee"])[metric_name]
.mean()
.reset_index()
)
metric_min = df_agg[metric_name].min()
metric_max = df_agg[metric_name].max()
custom_colorscale = _generate_custom_colorscale(metric_min, metric_max, red_gradient)
fig = px.choropleth_map(
df_agg,
geojson=geojson_data,
locations="code_departement",
featureidkey="properties.code",
color=metric_name,
labels={metric_name: metric_description},
range_color=[metric_min, metric_max],
color_continuous_scale=custom_colorscale,
center={"lat": 46.603354, "lon": 1.888334},
zoom=5,
opacity=0.75,
hover_name="code_departement",
hover_data={metric_name: ":.1f", "annee": True},
title=title,
height=height,
width=width,
animation_frame="annee",
animation_group="code_departement",
)
fig.update_traces(marker_line_width=0)
if fig.layout.updatemenus:
try:
fig.layout.updatemenus[0].buttons[0].args[1]["frame"]["duration"] = 1500
fig.layout.updatemenus[0].buttons[0].args[1]["transition"]["duration"] = 500
except IndexError:
st.warning("Could not set animation speed.")
else:
st.warning("No animation updatemenus found.")
return fig
def display_choropleth_map_for_department(
df: pd.DataFrame,
department_code: str,
geojson_data: dict,
min_global_prixm2moyen: float,
max_global_prixm2moyen: float,
metric_name: str = "prixm2moyen",
metric_description: str = "Average Price per Square Meter",
title: str = "Average Price per Square Meter",
height_graph: int = 1000,
width_graph: int = 1400,
red_gradient: bool = False,
):
df_filtered = df[df["code_departement"] == department_code].copy()
filtered_features = [
feature
for feature in geojson_data["features"]
if feature["properties"]["code"].startswith(department_code)
]
filtered_geojson = {"type": "FeatureCollection", "features": filtered_features}
custom_colorscale = _generate_custom_colorscale(
min_global_prixm2moyen, max_global_prixm2moyen, red_gradient
)
center_lat, center_lon, zoom_level = 46.603354, 1.888334, 6
if filtered_features:
department_gdf = gpd.GeoDataFrame.from_features(filtered_geojson["features"])
if not department_gdf.empty:
dissolved_department = department_gdf.dissolve()
department_centroid = dissolved_department.geometry.centroid.iloc[0]
center_lat, center_lon = department_centroid.y, department_centroid.x
minx, miny, maxx, maxy = dissolved_department.total_bounds
width, height = maxx - minx, maxy - miny
if width < 0.2 and height < 0.2:
zoom_level = 10
elif width < 0.5 and height < 0.5:
zoom_level = 9
else:
zoom_level = 8
fig = px.choropleth_map(
df_filtered,
geojson=filtered_geojson,
locations="code_commune_insee",
featureidkey="properties.code",
color=metric_name,
labels={metric_name: metric_description},
range_color=[min_global_prixm2moyen, max_global_prixm2moyen],
# colorscale in red gradient
color_continuous_scale=custom_colorscale,
center={"lat": center_lat, "lon": center_lon},
zoom=zoom_level,
opacity=0.75,
hover_name="code_commune_insee",
hover_data={metric_name: ":.0f", "annee": True},
title=title,
height=height_graph,
width=width_graph,
animation_frame="annee",
animation_group="code_commune_insee",
)
fig.update_traces(marker_line_width=0)
if fig.layout.updatemenus:
try:
fig.layout.updatemenus[0].buttons[0].args[1]["frame"]["duration"] = 750
fig.layout.updatemenus[0].buttons[0].args[1]["transition"]["duration"] = 250
except IndexError:
st.warning("Could not set animation speed.")
else:
st.warning("No animation updatemenus found.")
return fig |