Change % to pct

app.py

@@ -170,7 +170,7 @@ def server(input, output, session):
         if simulation_mode == "Usar control deslizante":
             title = f"Estimación de cobertura arbórea necesaria para una disminución de {value_temp}°C"
         try:
-            fig = show_two_plots(gdf_temp, '%total_trees', '%CoberturaVeg', 
+            fig = show_two_plots(gdf_temp, '%total_trees', 'Pct_CoberturaVeg', 
              min_cobertura_veg, max_cobertura, 'YlGn', 
              title,"Cobertura arbórea antes",
              "Cobertura arbórea (%)", split=split)

data_utils.py

@@ -9,7 +9,7 @@ headers = {"Content-Type": "application/json"}
 
 cell_area = 100*100 #in square meters
 
-def make_XYT(df, treatment_col = '%CoberturaVeg', target_col = 'LST'):
+def make_XYT(df, treatment_col = 'Pct_CoberturaVeg', target_col = 'LST'):
     
     Y = df[target_col].values
 
@@ -27,7 +27,7 @@ def prepare_data(og_file):
     original_df = original_df.round(2)
     
     df = original_df[['Elevacion', 'Neighbor_NDBI', 'Proximidad_agua',
-       'Neighbor_%CoberturaVeg', '%CoberturaVeg', 'LST']].copy()
+       'Neighbor_%CoberturaVeg', 'Pct_CoberturaVeg', 'LST']].copy()
     df = df.astype(np.float32)
 
     X, Y, T = make_XYT(df)
@@ -36,8 +36,8 @@ def prepare_data(og_file):
 
 
 def prepare_treatment_df(original_df, df):
-    treatment_df = original_df[['x','y','%CoberturaVeg']].copy()
-    treatment_df.loc[:, '%Construccion'] = original_df['%Construccion'].values
+    treatment_df = original_df[['x','y','Pct_CoberturaVeg']].copy()
+    treatment_df.loc[:, 'Pct_Construccion'] = original_df['Pct_Construccion'].values
     treatment_df.loc[:, 'LST'] = df['LST'].values
     
     treatment_df = treatment_df.reset_index()
@@ -64,7 +64,7 @@ def run_treatment_increase(X, T, original_df, df, value, name, simulation_mode):
     
     treatment_df[name] = result["effect"]
     treatment_df['%total_trees'] = T_sim
-    treatment_df['pp_trees_increase'] = treatment_df['%total_trees'] - treatment_df['%CoberturaVeg']
+    treatment_df['pp_trees_increase'] = treatment_df['%total_trees'] - treatment_df['Pct_CoberturaVeg']
 
     return treatment_df
 
@@ -78,7 +78,7 @@ def process_data_trees_to_temp(value, simulation_mode, original_df, df, X, Y, T)
     treatment_df['sqm_trees_increase'] = (treatment_df['pp_trees_increase']/100)*cell_area
     treatment_df['sqm_total_trees'] = (treatment_df['%total_trees']/100)*cell_area
 
-    col_order = ['index', 'x', 'y', '%CoberturaVeg', '%Construccion', 'LST','temp_decrease', 
+    col_order = ['index', 'x', 'y', 'Pct_CoberturaVeg', 'Pct_Construccion', 'LST','temp_decrease', 
     'new_temp', 'pp_trees_increase','%total_trees', 'sqm_trees_increase','sqm_total_trees',]  # to match gdf trees
 
     treatment_df = treatment_df[col_order]
@@ -115,13 +115,13 @@ def process_data_temp_to_trees(goal, simulation_mode, original_df, df, X, Y, T):
     name = 'pp_trees_increase'
     treatment_df = run_temp_decrease(X, original_df, df, goal, name,simulation_mode)
 
-    treatment_df['%total_trees'] = treatment_df['%CoberturaVeg'] + treatment_df[name]
+    treatment_df['%total_trees'] = treatment_df['Pct_CoberturaVeg'] + treatment_df[name]
 
     #add columns translated to square meters
     treatment_df['sqm_total_trees'] = (treatment_df['%total_trees']/100)*cell_area
     treatment_df['sqm_trees_increase'] = (treatment_df['pp_trees_increase']/100)*cell_area
 
-    col_order = ['index', 'x', 'y', '%CoberturaVeg', '%Construccion', 'LST','temp_decrease', 
+    col_order = ['index', 'x', 'y', 'Pct_CoberturaVeg', 'Pct_Construccion', 'LST','temp_decrease', 
     'new_temp', 'pp_trees_increase','%total_trees', 'sqm_trees_increase','sqm_total_trees',]  # to match gdf trees
 
     treatment_df = treatment_df[col_order]

example.csv

@@ -1,4 +1,4 @@
-,x,y,Elevacion,Neighbor_NDBI,Proximidad_agua,Neighbor_%CoberturaVeg,%CoberturaVeg,LST,%Construccion,simulacion_temp,simulacion_arboles
+,x,y,Elevacion,Neighbor_NDBI,Proximidad_agua,Neighbor_%CoberturaVeg,Pct_CoberturaVeg,LST,Pct_Construccion,simulacion_temp,simulacion_arboles
 0,769975.0,1617705.0,1462.4218,-0.18309525,234.21106,14.933889,72.46039,26.467785,6.4757442,26.267784,77.46039
 1,770075.0,1617705.0,1460.5194,-0.17259958,132.40779,54.877888,87.98455,25.810371,2.7551615,25.61037,92.98455
 2,769875.0,1617605.0,1458.5167,-0.21486245,288.92615,44.12824,97.19963,25.18595,3.9044127,24.985949,102.19963

plot_utils.py

@@ -38,7 +38,7 @@ def show_two_plots(gdf, name1, name2, vmin, vmax, colorscheme, title1, title2, l
     else:
         # Split data into three GeoDataFrames
         # below_100 = gdf[gdf[split] <= 100]
-        above_capacity = gdf[gdf[split] > (100-gdf['%Construccion'])]
+        above_capacity = gdf[gdf[split] > (100-gdf['Pct_Construccion'])]
         above_100 = gdf[gdf[split] > 100]
         above_colors = {"capacity":"chocolate", "100":"firebrick"}