fixed chart filters

app.py

@@ -83,30 +83,76 @@ us_lower_48_area_m2 = 7.8e+12
 ## Helper functions
 
 #@st.cache_resource
-def ibis_connection(parquet):
-    return ibis.read_parquet(parquet)
-pad_data = ibis_connection(parquet)
+# def ibis_connection(parquet):
+#     return ibis.read_parquet(parquet)
+# pad_data = ibis_connection(parquet)
+
+con = ibis.duckdb.connect(extensions=["spatial"])
+pad_data = con.read_parquet(parquet)
+         
 
 #@st.cache_data()
-def summary_table(column, colors):
-    df = (pad_data
-        .rename(area = "area_square_meters")
-        .group_by(_[column])
-        .aggregate(hectares_protected =  (_.area.sum() / 10000).round(),
-                   percent_protected =  100 * _.area.sum() / us_lower_48_area_m2,
-                   mean_richness = (_.richness * _.area).sum() / _.area.sum(),
-                   mean_rsr = (_.rsr * _.area).sum() / _.area.sum(),
-                   carbon_lost = (_.deforest_carbon * _.area).sum() / _.area.sum(),
-                   crop_expansion = (_.crop_expansion * _.area).sum() / _.area.sum(),
-                   human_impact =  (_.human_impact * _.area).sum() / _.area.sum(),
-                   )
-        .mutate(percent_protected = _.percent_protected.round(1))
-        .inner_join(colors, column)
-        )
+# def summary_table(column, colors):
+#     df = (pad_data
+#         .rename(area = "area_square_meters")
+#         .group_by(_[column])
+#         .aggregate(
+#                    )
+#         .mutate(percent_protected = _.percent_protected.round(1))
+#         .inner_join(colors, column)
+#         )
+#     df = df.to_pandas()
+#     df[column] = df[column].astype(str)
+#     return df
+
+
+from functools import reduce
+
+def get_summary(pad_data, combined_filter, column, colors=None): #summary stats, based on filtered data
+    # ca = ca.filter(_.gap_code.isin([1,2])) #only gap 1 and 2
+    df = pad_data.filter(combined_filter)
+    df = (df
+            .rename(area = "area_square_meters")
+            .group_by(*column)  # unpack the list for grouping
+            .aggregate(hectares_protected =  (_.area.sum() / 10000).round(),
+                       percent_protected =  100 * _.area.sum() / us_lower_48_area_m2,
+                       mean_richness = (_.richness * _.area).sum() / _.area.sum(),
+                       mean_rsr = (_.rsr * _.area).sum() / _.area.sum(),
+                       mean_irrecoverable_carbon = (_.irrecoverable_carbon * _.area).sum() / _.area.sum(),
+                       mean_manageable_carbon = (_.manageable_carbon * _.area).sum() / _.area.sum(),
+                       mean_carbon_lost = (_.deforest_carbon * _.area).sum() / _.area.sum(),
+                       mean_crop_expansion = (_.crop_expansion * _.area).sum() / _.area.sum(),
+                       mean_human_impact =  (_.human_impact * _.area).sum() / _.area.sum(),
+                       mean_forest_integrity_loss = (_.forest_integrity_loss*_.area).sum() / _.area.sum(),
+                       mean_bio_intact_loss = (_.biodiversity_intactness_loss * _.area).sum() / _.area.sum(),
+                      )
+            .mutate(percent_protected=_.percent_protected.round(1))
+         )
+    if colors is not None and not colors.empty: #only the df will have colors, df_tab doesn't since we are printing it.
+        df = df.inner_join(colors, column) 
+    df = df.cast({col: "string" for col in column})
     df = df.to_pandas()
-    df[column] = df[column].astype(str)
     return df
 
+
+def summary_table(column, colors, filter_cols, filter_vals,colorby_vals): # get df for charts + df_tab for printed table + df_percent for percentage (only gap 1 and 2)
+    filters = [] 
+    if filter_cols and filter_vals: #if a filter is selected, add to list of filters 
+        for filter_col, filter_val in zip(filter_cols, filter_vals):
+            if len(filter_val) > 1:
+                filters.append(getattr(_, filter_col).isin(filter_val))
+            else:
+                filters.append(getattr(_, filter_col) == filter_val[0])
+    if column not in filter_cols: #show color_by column in table by adding it as a filter (if it's not already a filter)
+        filter_cols.append(column)
+        filters.append(getattr(_, column).isin(colorby_vals[column]))  
+    combined_filter = reduce(lambda x, y: x & y, filters) #combining all the filters into ibis filter expression 
+    df = get_summary(pad_data, combined_filter, [column], colors) # df used for charts 
+    df_tab = get_summary(pad_data, combined_filter, filter_cols, colors = None) #df used for printed table
+    df_percent = get_summary(pad_data.filter(_.gap_code.isin([1,2])), combined_filter, [column], colors) # only gap 1 and 2 count towards percentage
+    return df, df_tab, df_percent 
+
+
 def bar_chart(df, x, y):
     chart = alt.Chart(df).mark_bar().encode(
         x=x,
@@ -345,7 +391,9 @@ bil_fill = {
     #"fill-extrusion-height": ["*", .01, ["get", "FundingAmo"]],
     "fill-extrusion-height": ["*", 50, ["sqrt", ["get", "FundingAmo"]]],
     "fill-extrusion-opacity": 0.9,
-}
+} 
+
+
 
 ###########################################################################################################
 
@@ -383,66 +431,72 @@ with st.sidebar:
     # if st.toggle("Protected Areas", True):
     
     color_choice = st.radio("Color by:", style_options)
+    colorby_vals = getColorVals(style_options, color_choice) #get options for selected color_by column 
     alpha = st.slider("transparency", 0.0, 1.0, 0.5)
 
     "Data layers:"
     with st.expander("🦜 Biodiversity"):
+        a_bio = st.slider("transparency", 0.0, 1.0, 0.4, key = "biodiversity")
         show_richness = st.toggle("Species Richness", False)
+
         if show_richness:
             m.add_tile_layer(url="https://data.source.coop/cboettig/mobi/tiles/red/species-richness-all/{z}/{x}/{y}.png",
                             name="MOBI Species Richness",
                             attribution="NatureServe",
-                            opacity=0.9
+                            opacity=a_bio
                             )
         show_rsr = st.toggle("Range-Size Rarity")
         if show_rsr:
             m.add_tile_layer(url="https://data.source.coop/cboettig/mobi/tiles/green/range-size-rarity-all/{z}/{x}/{y}.png",
                             name="MOBI Range-Size Rarity",
                             attribution="NatureServe",
-                            opacity=0.9
+                            opacity=a_bio
                             )
             #m.add_cog_layer("https://data.source.coop/cboettig/mobi/range-size-rarity-all/RSR_All.tif",
             #                palette="greens", name="Range-Size Rarity", transparent_bg=True, opacity = 0.9, fit_bounds=False)
     with st.expander("⛅ Carbon & Climate"):
+        a_climate = st.slider("transparency", 0.0, 1.0, 0.3, key = "climate")
         show_carbon_lost = st.toggle("Carbon Lost (2002-2022)")
+
         if show_carbon_lost:
             m.add_cog_layer("https://data.source.coop/vizzuality/lg-land-carbon-data/deforest_carbon_100m_cog.tif",
-                            palette="reds", name="Carbon Lost (2002-2022)", transparent_bg=True, opacity = 0.8, fit_bounds=False)
-        
-        if st.toggle("Irrecoverable Carbon"):
+                            palette="reds", name="Carbon Lost (2002-2022)", transparent_bg=True, opacity = a_climate, fit_bounds=False)
+        show_irr_carbon =  st.toggle("Irrecoverable Carbon")
+        if show_irr_carbon:
             m.add_cog_layer("https://data.source.coop/cboettig/carbon/cogs/irrecoverable_c_total_2018.tif",
-                            palette="purples", name="Irrecoverable Carbon", transparent_bg=True, opacity = 0.8, fit_bounds=False)
-
-        if st.toggle("Manageable Carbon"):
+                            palette="purples", name="Irrecoverable Carbon", transparent_bg=True, opacity = a_climate, fit_bounds=False)
+        show_man_carbon = st.toggle("Manageable Carbon")
+        if show_man_carbon:
             m.add_cog_layer("https://data.source.coop/cboettig/carbon/cogs/manageable_c_total_2018.tif",
-                            palette="greens", name="Manageable Carbon", transparent_bg=True, opacity = 0.8, fit_bounds=False)
+                            palette="greens", name="Manageable Carbon", transparent_bg=True, opacity = a_climate, fit_bounds=False)
 
     with st.expander("🚜 Human Impacts"):
+        a_hi = st.slider("transparency", 0.0, 1.0, 0.5, key = "hi")
         show_human_impact = st.toggle("Human Impact")
         if show_human_impact:
             hi="https://data.source.coop/vizzuality/hfp-100/hfp_2021_100m_v1-2_cog.tif"
-            m.add_cog_layer(hi, palette="purples", name="Human Impact", transparent_bg=True, opacity = 0.8, fit_bounds=False)
+            m.add_cog_layer(hi, palette="purples", name="Human Impact", transparent_bg=True, opacity = a_hi, fit_bounds=False)
 
-        show_crop_expansion = st.toggle("cropland expansion")
+        show_crop_expansion = st.toggle("Cropland Expansion")
         if show_crop_expansion:
-            m.add_cog_layer("https://data.source.coop/vizzuality/lg-land-carbon-data/natcrop_expansion_100m_cog.tif",)
+            m.add_cog_layer("https://data.source.coop/vizzuality/lg-land-carbon-data/natcrop_expansion_100m_cog.tif",opacity = a_hi, name = "Cropland Expansion")
                          #   palette="greens", name="cropland expansion", transparent_bg=True, opacity = 0.8, fit_bounds=False)
-
-        if st.toggle("Biodiversity Intactness Loss"):
+        show_bio_loss = st.toggle("Biodiversity Intactness Loss")
+        if show_bio_loss:
             m.add_cog_layer("https://data.source.coop/vizzuality/lg-land-carbon-data/natcrop_bii_100m_cog.tif",
-                            palette="reds", name="biodiversity intactness loss", transparent_bg=True, opacity = 0.8, fit_bounds=False)
-    
-        if st.toggle("Forest Integrity Loss"):
+                            palette="reds", name="Biodiversity Intactness Loss", transparent_bg=True, opacity = a_hi, fit_bounds=False)
+        show_forest_loss = st.toggle("Forest Integrity Loss")
+        if show_forest_loss:
             m.add_cog_layer("https://data.source.coop/vizzuality/lg-land-carbon-data/natcrop_fii_100m_cog.tif",
-                            palette="reds", name="forest integrity loss", transparent_bg=True, opacity = 0.8, fit_bounds=False)
+                            palette="reds", name="Forest Integrity Loss", transparent_bg=True, opacity = a_hi, fit_bounds=False)
 
     
     with st.expander("💰 Conservation Investment"):
         if st.toggle("Bipartisan Infrastructure Law"):
             m.add_geojson(bil_url, layer_type="fill-extrusion", paint=bil_fill, name="BIL", fit_bounds=False)
-    
-    with st.expander("💻 Custom code"):
-        if st.toggle("Custom map layers"):
+
+    with st.expander("💻 Custom Code"):
+        if st.toggle("Custom Map Layers"):
             
             code = st.text_area(label = "leafmap code:",
                                 value = code_ex, 
@@ -519,6 +573,28 @@ colors = (ibis
 # +
 
 
+
+# get summary tables used for charts + printed table + percentage 
+# df - charts; df_tab - printed table (omits colors) + df_percent - only gap codes 1 & 2 count towards percentage 
+df,df_tab,df_percent = summary_table(column, colors, filter_cols, filter_vals, colorby_vals)
+
+# compute area covered (only gap 1 and 2)
+# df_onlygap = df[df.gap_code.isin([1,2])]
+total_percent = df_percent.percent_protected.sum().round(1) 
+
+
+# charts displayed based on color_by variable
+richness_chart = bar_chart(df, column, 'mean_richness')
+rsr_chart = bar_chart(df, column, 'mean_rsr')
+irr_carbon_chart = bar_chart(df, column, 'mean_irrecoverable_carbon')
+man_carbon_chart = bar_chart(df, column, 'mean_manageable_carbon')
+carbon_loss_chart = bar_chart(df, column, 'mean_carbon_lost')
+hi_chart = bar_chart(df, column, 'mean_human_impact')
+crop_expansion_chart = bar_chart(df, column, 'mean_crop_expansion')
+bio_intact_loss_chart = bar_chart(df, column, 'mean_bio_intact_loss')
+forest_integrity_loss_chart = bar_chart(df, column, 'mean_forest_integrity_loss')
+
+
 main = st.container()
 
 with main:
@@ -527,13 +603,13 @@ with main:
     with map_col:
         to_streamlit(m, height=700)
 
-    df = summary_table(column, colors)
-    total_percent = df.percent_protected.sum().round(1)
-    richness_chart = bar_chart(df, column, 'mean_richness')
-    rsr_chart = bar_chart(df, column, 'mean_rsr')
-    carbon_lost = bar_chart(df, column, 'carbon_lost')
-    crop_expansion = bar_chart(df, column, 'crop_expansion')
-    human_impact = bar_chart(df, column, 'human_impact')
+    # df = summary_table(column, colors)
+    # total_percent = df.percent_protected.sum().round(1)
+    # richness_chart = bar_chart(df, column, 'mean_richness')
+    # rsr_chart = bar_chart(df, column, 'mean_rsr')
+    # carbon_lost = bar_chart(df, column, 'carbon_lost')
+    # crop_expansion = bar_chart(df, column, 'crop_expansion')
+    # human_impact = bar_chart(df, column, 'human_impact')
 
     with stats_col:
         with st.container():
@@ -551,15 +627,33 @@ with main:
 
             if show_carbon_lost:
                 "Carbon Lost ('02-'22)"
-                st.altair_chart(carbon_lost, use_container_width=True)
+                st.altair_chart(carbon_loss_chart, use_container_width=True)
 
             if show_crop_expansion:
-                "Crop expansion"
-                st.altair_chart(crop_expansion, use_container_width=True)
+                "Crop Expansion"
+                st.altair_chart(crop_expansion_chart, use_container_width=True)
 
             if show_human_impact:
                 "Human Impact"
-                st.altair_chart(human_impact, use_container_width=True)
+                st.altair_chart(hi_chart, use_container_width=True)
+
+            if show_irr_carbon:
+                "Irrecoverable Carbon"
+                st.altair_chart(irr_carbon_chart, use_container_width=True)            
+            
+            if show_man_carbon:
+                "Manageable Carbon"
+                st.altair_chart(man_carbon_chart, use_container_width=True)
+
+            if show_bio_loss:
+                "Biodiversity Intactness Loss"
+                st.altair_chart(bio_intact_loss_chart, use_container_width=True)
+
+            if show_forest_loss:
+                "Forest Integrity Loss"
+                st.altair_chart(forest_integrity_loss_chart, use_container_width=True)
+# charts displayed based on color_by variable
+
 
 
 # +