Update views/maps.py

views/maps.py

@@ -1,187 +1,187 @@
-import folium
-import geopandas as gpd
-import plotly.express as px
-from branca.colormap import LinearColormap
-from folium.plugins import HeatMap
-from streamlit_folium import folium_static
-
-import streamlit as st
-from streamlit_extras.stylable_container import stylable_container
-from streamlit_extras.add_vertical_space import add_vertical_space
-import streamlit_shadcn_ui as ui
-
-with stylable_container(
-    key="banner",
-    css_styles="""
-    img {
-        width: 1800px;
-        height: 400px;
-        overflow: hidden;
-        position: relative;
-        object-fit: cover;
-        border-radius: 20px; /* Adiciona bordas arredondadas */
-        mask-image: linear-gradient(to bottom, rgba(0, 0, 0, 1), rgba(0, 0, 0, 0));
-        -webkit-mask-image: linear-gradient(to bottom, rgba(0, 0, 0, 1), rgba(0, 0, 0, 0)); /* For Safari */
-    }
-    """,
-):
-    st.image("src/images/mp.jpg")
-
-st.title("Mapas da área")
-add_vertical_space(5)
-st.markdown(""" ### :world_map: **UBS Flamengo: (IBGE 2022)** """)
-add_vertical_space(5)
-
-
-@st.cache_data
-def load_data():
-    """
-    A function that loads and reads geojson data for UBS Flamengo and converts it to the specified coordinate reference system.
-    """
-    return gpd.read_file("views\\flamengo_ibge2022.geojson").to_crs(epsg=4326)
-
-gdf = load_data()
-LATITUDE = -19.971591804
-LONGITUDE = -44.057912815
-lat = -19.96214
-long = -44.05603
-
-
-
-total_pop = gdf["POP"].sum()
-col1, col2, col3 = st.columns([1, 1, 5])
-
-with col1:
-    # st.write(f"###### População Total: {total_pop:,}")
-    map_type = st.selectbox("Tipo de mapa", ["População", "Densidade", "Heatmap"])
-
-with col2:
-    # st.write(f"###### Número de Setores Censitários: {len(gdf)}")
-    base_map = st.selectbox("Mapa base", ["Cartodb Positron", "OpenStreetMap"])
-
-with col3:
-    total_pop = gdf["POP"].sum()
-    st.write(
-        f"### 👪 População Total: {total_pop:,} habitantes, dados do Censo 2022 IBGE"
-    )
-    st.write(f"### 🗺️ Número de Setores Censitários: {len(gdf)}")
-
-add_vertical_space(5)
-add_vertical_space(5)
-
-col1, col2 = st.columns(2)
-with col1:
-    m = folium.Map(
-        location=[LATITUDE, LONGITUDE], tiles=base_map, zoom_start=15
-    )
-    dns_p = "Densidade pop. (hab/km²) UBS Flamengo - IBGE 2022"
-    if map_type in ["População", "Densidade"]:
-        if map_type == "População":
-            column = "POP"
-            caption = "Pop. residente UBS Flamengo IBGE 2022"
-        else:
-            gdf["DENSIDADE"] = gdf["POP"] / gdf["AREA_KM2"]
-            column = "DENSIDADE"
-            caption = dns_p
-        colorscale = px.colors.sequential.Viridis
-        colormap = LinearColormap(
-            colors=colorscale,
-            vmin=gdf[column].min(),
-            vmax=gdf[column].max(),
-            caption=caption,
-        )
-        folium.GeoJson(
-            gdf,
-            style_function=lambda feature: {
-                "fillColor": colormap(feature["properties"][column]),
-                "color": "black",
-                "weight": 1,
-                "fillOpacity": 0.7,
-            },
-            highlight_function=lambda feature: {
-                "fillColor": "#ffaf00",
-                "color": "green",
-                "weight": 3,
-                "fillOpacity": 0.9,
-            },
-            tooltip=folium.features.GeoJsonTooltip(
-                fields=["CD_SETOR", column, "AREA_KM2"],
-                aliases=[
-                    "Setor Censitário:",
-                    f"{caption}:",
-                    "Área (km²):",
-                ],
-                style=(
-                    "background-color: white; color: #333333;"
-                    "font-family: calibri; font-size: 12px;"
-                    "padding: 10px;"
-                ),
-            ),
-        ).add_to(m)
-        colormap.add_to(m)
-
-    elif map_type == "Heatmap":
-        heat_data = [
-            [
-                row["geometry"].centroid.y,
-                row["geometry"].centroid.x,
-                row["POP"],
-            ]
-            for idx, row in gdf.iterrows()
-        ]
-        HeatMap(heat_data).add_to(m)
-    folium.Marker(
-        [lat, long],
-        popup="UBS Flamengo",
-        tooltip="UBS Flamengo",
-        icon=folium.Icon(color="red", icon="info-sign"),
-    ).add_to(m)
-
-    STYLE_STATEMENT = (
-        "<style>.leaflet-control-layers"
-        "{ position: fixed; top: 10px; left: 50px; } </style>"
-    )
-    m.get_root().html.add_child(folium.Element(STYLE_STATEMENT))
-    folium_static(m)
-
-with col2:
-    fig = px.bar(
-        gdf,
-        x="CD_SETOR",
-        y="POP",
-        title="Distribuição da População por Setor Censitário",
-        color="POP",
-        color_continuous_scale=px.colors.sequential.Viridis,
-    )
-    st.plotly_chart(fig)
-
-    age_columns = [
-        "POP_0A4",
-        "POP_5A9",
-        "POP_10A14",
-        "POP_15A19",
-        "POP_20A24",
-        "POP_25A29",
-        "POP_30A34",
-        "POP_35A39",
-        "POP_40A44",
-        "POP_45A49",
-        "POP_50A54",
-        "POP_55A59",
-        "POP_60A64",
-        "POP_65A69",
-        "POP_70A74",
-        "POP_75A79",
-        "POP_80A84",
-        "POP_85A89",
-        "POP_90A94",
-        "POP_95A99",
-        "POP_100OUMAIS",
-    ]
-
-
-
-add_vertical_space(10)
-
-
+import folium
+import geopandas as gpd
+import plotly.express as px
+from branca.colormap import LinearColormap
+from folium.plugins import HeatMap
+from streamlit_folium import folium_static
+
+import streamlit as st
+from streamlit_extras.stylable_container import stylable_container
+from streamlit_extras.add_vertical_space import add_vertical_space
+import streamlit_shadcn_ui as ui
+
+with stylable_container(
+    key="banner",
+    css_styles="""
+    img {
+        width: 1800px;
+        height: 400px;
+        overflow: hidden;
+        position: relative;
+        object-fit: cover;
+        border-radius: 20px; /* Adiciona bordas arredondadas */
+        mask-image: linear-gradient(to bottom, rgba(0, 0, 0, 1), rgba(0, 0, 0, 0));
+        -webkit-mask-image: linear-gradient(to bottom, rgba(0, 0, 0, 1), rgba(0, 0, 0, 0)); /* For Safari */
+    }
+    """,
+):
+    st.image("mp.jpg")
+
+st.title("Mapas da área")
+add_vertical_space(5)
+st.markdown(""" ### :world_map: **UBS Flamengo: (IBGE 2022)** """)
+add_vertical_space(5)
+
+
+@st.cache_data
+def load_data():
+    """
+    A function that loads and reads geojson data for UBS Flamengo and converts it to the specified coordinate reference system.
+    """
+    return gpd.read_file("views\\flamengo_ibge2022.geojson").to_crs(epsg=4326)
+
+gdf = load_data()
+LATITUDE = -19.971591804
+LONGITUDE = -44.057912815
+lat = -19.96214
+long = -44.05603
+
+
+
+total_pop = gdf["POP"].sum()
+col1, col2, col3 = st.columns([1, 1, 5])
+
+with col1:
+    # st.write(f"###### População Total: {total_pop:,}")
+    map_type = st.selectbox("Tipo de mapa", ["População", "Densidade", "Heatmap"])
+
+with col2:
+    # st.write(f"###### Número de Setores Censitários: {len(gdf)}")
+    base_map = st.selectbox("Mapa base", ["Cartodb Positron", "OpenStreetMap"])
+
+with col3:
+    total_pop = gdf["POP"].sum()
+    st.write(
+        f"### 👪 População Total: {total_pop:,} habitantes, dados do Censo 2022 IBGE"
+    )
+    st.write(f"### 🗺️ Número de Setores Censitários: {len(gdf)}")
+
+add_vertical_space(5)
+add_vertical_space(5)
+
+col1, col2 = st.columns(2)
+with col1:
+    m = folium.Map(
+        location=[LATITUDE, LONGITUDE], tiles=base_map, zoom_start=15
+    )
+    dns_p = "Densidade pop. (hab/km²) UBS Flamengo - IBGE 2022"
+    if map_type in ["População", "Densidade"]:
+        if map_type == "População":
+            column = "POP"
+            caption = "Pop. residente UBS Flamengo IBGE 2022"
+        else:
+            gdf["DENSIDADE"] = gdf["POP"] / gdf["AREA_KM2"]
+            column = "DENSIDADE"
+            caption = dns_p
+        colorscale = px.colors.sequential.Viridis
+        colormap = LinearColormap(
+            colors=colorscale,
+            vmin=gdf[column].min(),
+            vmax=gdf[column].max(),
+            caption=caption,
+        )
+        folium.GeoJson(
+            gdf,
+            style_function=lambda feature: {
+                "fillColor": colormap(feature["properties"][column]),
+                "color": "black",
+                "weight": 1,
+                "fillOpacity": 0.7,
+            },
+            highlight_function=lambda feature: {
+                "fillColor": "#ffaf00",
+                "color": "green",
+                "weight": 3,
+                "fillOpacity": 0.9,
+            },
+            tooltip=folium.features.GeoJsonTooltip(
+                fields=["CD_SETOR", column, "AREA_KM2"],
+                aliases=[
+                    "Setor Censitário:",
+                    f"{caption}:",
+                    "Área (km²):",
+                ],
+                style=(
+                    "background-color: white; color: #333333;"
+                    "font-family: calibri; font-size: 12px;"
+                    "padding: 10px;"
+                ),
+            ),
+        ).add_to(m)
+        colormap.add_to(m)
+
+    elif map_type == "Heatmap":
+        heat_data = [
+            [
+                row["geometry"].centroid.y,
+                row["geometry"].centroid.x,
+                row["POP"],
+            ]
+            for idx, row in gdf.iterrows()
+        ]
+        HeatMap(heat_data).add_to(m)
+    folium.Marker(
+        [lat, long],
+        popup="UBS Flamengo",
+        tooltip="UBS Flamengo",
+        icon=folium.Icon(color="red", icon="info-sign"),
+    ).add_to(m)
+
+    STYLE_STATEMENT = (
+        "<style>.leaflet-control-layers"
+        "{ position: fixed; top: 10px; left: 50px; } </style>"
+    )
+    m.get_root().html.add_child(folium.Element(STYLE_STATEMENT))
+    folium_static(m)
+
+with col2:
+    fig = px.bar(
+        gdf,
+        x="CD_SETOR",
+        y="POP",
+        title="Distribuição da População por Setor Censitário",
+        color="POP",
+        color_continuous_scale=px.colors.sequential.Viridis,
+    )
+    st.plotly_chart(fig)
+
+    age_columns = [
+        "POP_0A4",
+        "POP_5A9",
+        "POP_10A14",
+        "POP_15A19",
+        "POP_20A24",
+        "POP_25A29",
+        "POP_30A34",
+        "POP_35A39",
+        "POP_40A44",
+        "POP_45A49",
+        "POP_50A54",
+        "POP_55A59",
+        "POP_60A64",
+        "POP_65A69",
+        "POP_70A74",
+        "POP_75A79",
+        "POP_80A84",
+        "POP_85A89",
+        "POP_90A94",
+        "POP_95A99",
+        "POP_100OUMAIS",
+    ]
+
+
+
+add_vertical_space(10)
+
+
 st.write('----')