Add sq meters translation

app.py

@@ -80,7 +80,7 @@ def server(input, output, session):
         apply_filter = input.split_filter()
 
         if apply_filter == "Filtro":
-            split = f'total_trees_treatment_{value_trees}%'
+            split = f'%total_trees_treatment_{value_trees}%'
         else:
             split = False
 
@@ -119,14 +119,14 @@ def server(input, output, session):
         apply_filter = input.split_filter()
 
         if apply_filter == "Filtro":
-            split = f'total_trees_needed_for_{value_temp}C'
+            split = f'%total_trees_needed_for_{value_temp}C'
         else:
             split = False
 
         min_cobertura_veg = 5
         max_cobertura = 100
         try:
-            fig = show_two_plots(gdf_temp, f'total_trees_needed_for_{value_temp}C', '%CoberturaVeg', 
+            fig = show_two_plots(gdf_temp, f'%total_trees_needed_for_{value_temp}C', '%CoberturaVeg', 
              min_cobertura_veg, max_cobertura, 'YlGn', 
              f"Estimación de cobertura arbórea necesaria para una disminución de {value_temp}°C","Cobertura arbórea antes",
              "Cobertura arbórea (%)", split=split)

data_utils.py

@@ -7,6 +7,8 @@ import requests
 url = "https://Projects-by-IF-model-temp-api.hf.space/model-effect"
 headers = {"Content-Type": "application/json"}
 
+cell_area = 100*100 #in square meters
+
 def make_XYT(df, treatment_col = '%CoberturaVeg', target_col = 'LST'):
     
     Y = df[target_col].values
@@ -56,7 +58,8 @@ def run_treatment_increase(X, T, original_df, df, value, name):
         print("Request failed:", response.status_code, response.text)
     
     treatment_df[name] = result["effect"]
-    treatment_df[f'total_trees_treatment_{value}%'] = T_sim
+    treatment_df[f'%total_trees_treatment_{value}%'] = T_sim
+    treatment_df[f'pp_trees_increase_treatment_{value}%'] = treatment_df[f'%total_trees_treatment_{value}%'] - treatment_df['%CoberturaVeg']
 
     return treatment_df
 
@@ -67,6 +70,10 @@ def process_data_trees_to_temp(value, original_df, df, X, Y, T):
 
     treatment_df[f'simulated_temp_{value}%'] = treatment_df['LST'] + treatment_df[name]
 
+    #add columns translated to square meters
+    treatment_df[f'sqm_total_trees_treatment_{value}%'] = (treatment_df[f'%total_trees_treatment_{value}%']/100)*cell_area
+    treatment_df[f'sqm_trees_increase_treatment_{value}%'] = (treatment_df[f'pp_trees_increase_treatment_{value}%']/100)*cell_area
+
     gdf = gpd.GeoDataFrame(treatment_df, geometry=gpd.points_from_xy(treatment_df.x, treatment_df.y))
 
     return gdf
@@ -88,6 +95,7 @@ def run_temp_decrease(X, original_df, df, goal, name):
     trees_needed = -goal / treatment_effects
     
     treatment_df[name] = trees_needed
+    treatment_df['goal_temp'] = treatment_df['LST'] - goal
 
     return treatment_df
 def process_data_temp_to_trees(goal, original_df, df, X, Y, T):
@@ -95,7 +103,11 @@ def process_data_temp_to_trees(goal, original_df, df, X, Y, T):
     name = f'pp_trees_increase_for_{goal}C'
     treatment_df = run_temp_decrease(X,original_df, df, goal, name)
 
-    treatment_df[f'total_trees_needed_for_{goal}C'] = treatment_df['%CoberturaVeg'] + treatment_df[name]
+    treatment_df[f'%total_trees_needed_for_{goal}C'] = treatment_df['%CoberturaVeg'] + treatment_df[name]
+
+    #add columns translated to square meters
+    treatment_df[f'sqm_total_trees_needed_for_{goal}'] = (treatment_df[f'%total_trees_needed_for_{goal}C']/100)*cell_area
+    treatment_df[f'sqm_trees_increase_for_{goal}C'] = (treatment_df[f'pp_trees_increase_for_{goal}C']/100)*cell_area
 
     gdf = gpd.GeoDataFrame(treatment_df, geometry=gpd.points_from_xy(treatment_df.x, treatment_df.y))