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6 onglet hotspots #20

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merged 2 commits into from
Apr 25, 2024
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6 onglet hotspots #20

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0246942
Debug probleme zoom
linh-dinh-1012 Apr 25, 2024
93abb97
Merge branch '6-onglet-hotspots' of github.com:dataforgoodfr/12_zero_…
linh-dinh-1012 Apr 25, 2024
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135 changes: 70 additions & 65 deletions dashboards/app/pages/hotspots.py
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Original file line number Diff line number Diff line change
Expand Up @@ -438,72 +438,73 @@ def update_lieu_options(selected_milieu):
return ["Sélectionnez un lieu..."] + list(filtered_data['TYPE_LIEU2'].dropna().unique())
return ["Sélectionnez un lieu..."]

# Function to plot a density map
def plot_density_map(
data_zds: pd.DataFrame,
filtered_data: pd.DataFrame
) -> folium.Map:
@st.cache_data
def process_data(data_zds):
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Faudra peut-être transférer les traitements sur la partie ETL pour alléger l'app.

# Filtering data to ensure surface area is not zero
data_zds = data_zds[data_zds['SURFACE'] > 0]
# Calculating density and filtering out anomalous values
data_zds['DENSITE'] = data_zds['VOLUME_TOTAL'] / data_zds['SURFACE']
data_zds = data_zds[data_zds['DENSITE'] < 20]
# Rounding values for better display
data_zds['DENSITE'] = data_zds['DENSITE'].round(4)
data_zds['SURFACE_ROND'] = data_zds['SURFACE'].round(2)
return data_zds

#Zoom from admin level
if NIVEAU_ADMIN == "Commune":
zoom_admin = 12
elif NIVEAU_ADMIN == "EPCI":
zoom_admin = 13
elif NIVEAU_ADMIN == "Département":
zoom_admin = 10
else:
zoom_admin = 8

# Function to plot a density map
def plot_density_map(data_zds: pd.DataFrame, filtered_data: pd.DataFrame) -> folium.Map:
# Check if the primary dataset is empty
if data_zds.empty:
st.write("Aucune donnée disponible pour la région sélectionnée.")
# Initialize a basic map without any data-specific layers
m = folium.Map(location=[46.6358, 2.5614], zoom_start=5)
return folium.Map(location=[46.6358, 2.5614], zoom_start=5)

else:
# Use filtered data if available; otherwise, use the full dataset
if filtered_data.empty:
map_data = data_zds
else:
map_data = filtered_data
# Use processed data
processed_data = process_data(filtered_data if not filtered_data.empty else data_zds)

m = folium.Map(
location=[
processed_data['LIEU_COORD_GPS_Y'].mean(),
processed_data['LIEU_COORD_GPS_X'].mean()
],
zoom_start=zoom_admin
)

# Ensure the surface area is not zero to avoid division by zero
map_data = map_data[map_data['SURFACE'] > 0]
# Loop over each row in the DataFrame to place markers
for index, row in processed_data.iterrows():
popup_html = f"""
<div style="width: 300px; height: 170px;">
<h4>Densité: {row['DENSITE']} L/m²</h4>
<h4>Volume total : {row['VOLUME_TOTAL']} litres</h4>
<h4>Surface total : {row['SURFACE_ROND']} m²</h4>
<h4>Type de milieu : {row['TYPE_MILIEU']}</h4>
<h4>Type de lieu : {row['TYPE_LIEU']}</h4>
</div>
"""
lgd_txt = '<span style="color: {col};">{txt}</span>'
color = couleur_milieu(row['TYPE_MILIEU'])
folium.CircleMarker(
fg = folium.FeatureGroup(name= lgd_txt.format( txt= ['TYPE_MILIEU'], col= color)),
location=[row['LIEU_COORD_GPS_Y'], row['LIEU_COORD_GPS_X']],
radius=np.log(row['DENSITE'] + 1)*15,
popup=folium.Popup(popup_html, max_width=300),
color=color,
fill=True,

# Calculate density
map_data["DENSITE"] = map_data["VOLUME_TOTAL"] / map_data["SURFACE"]
map_data = map_data[map_data["DENSITE"] < 20] # Remove rows with anomalously high density values

# Round density values for display
map_data["DENSITE"] = map_data["DENSITE"].round(4)
# Round surface values for display
map_data["SURFACE_ROND"] = map_data["SURFACE"].round(2)

# Initialize a map centered at the mean coordinates of locations
if not map_data[['LIEU_COORD_GPS_Y', 'LIEU_COORD_GPS_X']].dropna().empty:
m = folium.Map(
location=[
map_data["LIEU_COORD_GPS_Y"].mean(),
map_data["LIEU_COORD_GPS_X"].mean(),
],
zoom_start=12
)
).add_to(m)

# Loop over each row in the DataFrame to place markers
for _, row in map_data.iterrows():
if pd.notna(row['LIEU_COORD_GPS_Y']) and pd.notna(row['LIEU_COORD_GPS_X']):
popup_html = f"""
<div style="width: 300px; height: 170px;">
<h4>Densité: {row['DENSITE']} L/m²</h4>
<h4>Volume total : {row['VOLUME_TOTAL']} litres</h4>
<h4>Surface total : {row['SURFACE_ROND']} m²</h4>
<h4>Type de milieu : {row['TYPE_MILIEU']}</h4>
<h4>Type de lieu : {row['TYPE_LIEU']}</h4>
</div>
"""
color = couleur_milieu(row["TYPE_MILIEU"])
folium.CircleMarker(
location=[row["LIEU_COORD_GPS_Y"], row["LIEU_COORD_GPS_X"]],
radius=np.log(row["DENSITE"] + 1) * 15,
popup=folium.Popup(popup_html, max_width=300),
color=color,
fill=True,
).add_to(m)

# Display the map in Streamlit
st_folium(m, width='100%', height=600) # Adjust width and height as needed
else:
st.write("Aucune donnée de localisation valide à afficher sur la carte.")
folium_static(m)

return m

# Function for 'milieu' density table
def density_table_milieu(
Expand Down Expand Up @@ -772,14 +773,18 @@ def create_contributors_table(data_zds: pd.DataFrame, multi_filter_dict: dict) -
lieu_options = update_lieu_options(selected_milieu)
selected_lieu = st.selectbox("Sélectionnez un lieu:", lieu_options)

st.markdown("### Carte des Densités")
# Automatically update the map based on the current selection
if selected_milieu != "Sélectionnez un milieu..." and selected_lieu != "Sélectionnez un lieu...":
filtered_data = data_zds_correct[(data_zds_correct['TYPE_MILIEU'] == selected_milieu) & (data_zds_correct['TYPE_LIEU2'] == selected_lieu)]
plot_density_map(data_zds_correct, filtered_data)
else:
# Optionally show the map with all data or display a message
plot_density_map(data_zds_correct, data_zds_correct) # Show all data by default

# Place the map centrally by using a wider column for the map and narrower ones on the sides
col1, map_col, col3 = st.columns([4, 10, 1]) # Adjust column ratios as needed

with map_col:
st.markdown("### Carte des Densités")
if selected_milieu != "Sélectionnez un milieu..." and selected_lieu != "Sélectionnez un lieu...":
filtered_data = data_zds_correct[(data_zds_correct['TYPE_MILIEU'] == selected_milieu) & (data_zds_correct['TYPE_LIEU2'] == selected_lieu)]
plot_density_map(data_zds_correct, filtered_data)
else:
plot_density_map(data_zds_correct, data_zds_correct) # Show all data by default


col1, col2, col3 = st.columns([3, 3, 2])

Expand Down
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