Set up v2

app.py

@@ -12,27 +12,48 @@ import geopandas as gpd
 
 import numpy as np
 
-og_file = "cleaned_dataframe.csv"
-training_file = "training_dataframe.csv"
-original_df, df, X, Y, T = prepare_data(og_file, training_file)
+og_file = "example.csv"
+# original_df, df, X, Y, T = prepare_data(og_file)
 
 
 app_ui = ui.page_fluid(
-    ui.input_radio_buttons("tab_choice", "Seleccionar página:", ["Efecto de los árboles en la temperatura", "Efecto de la temperatura en los árboles"]),
-    ui.output_ui("tab_slider"),
-    ui.tooltip(
-        ui.input_radio_buttons(
-            "split_filter",
-            "¿Aplicar filtro?",
-            choices=["Sin filtro", "Filtro"],
-            selected="Sin filtro"
+    ui.row(
+        ui.column(8,
+            ui.input_radio_buttons("tab_choice", "Seleccionar página:", ["Efecto de los árboles en la temperatura", "Efecto de la temperatura en los árboles"]),
+    #     ui.input_file("csv_upload", "Subir archivo CSV", accept=[".csv"]),
+    #     ui.input_radio_buttons(
+    #     "simulation_mode", "Modo de simulación:",
+    #     choices=["Usar control deslizante", "Usar simulación personalizada del CSV"],
+    #     selected="Usar control deslizante"
+    # ),
+    #     ui.output_ui("tab_slider"),
+            ui.tooltip(
+                ui.output_ui("tab_slider"),
+                ui.input_radio_buttons(
+                    "split_filter",
+                    "¿Aplicar filtro?",
+                    choices=["Sin filtro", "Filtro"],
+                    selected="Sin filtro"
+                ),
+                "Indica exceso de cobertura: marrón = límite por construcción, rojo = más del 100%.",
+                placement="right"
+            )
+        # ui.output_ui("page_ui")
+        # ui.download_button("download_csv", "Descargar simulación (CSV)")
+),
+        ui.column(4,
+            ui.input_file("csv_upload", "Subir archivo CSV", accept=[".csv"]),
+            ui.input_radio_buttons(
+            "simulation_mode", "Modo de simulación:",
+            choices=["Usar control deslizante", "Usar simulación personalizada del CSV"],
+            selected="Usar control deslizante"
         ),
-        "Indica exceso de cobertura: marrón = límite por construcción, rojo = más del 100%.",
-        placement="right"
-    ),
-    ui.output_ui("page_ui"),
-    ui.download_button("download_csv", "Descargar simulación (CSV)")
+            ui.download_button("download_csv", "Descargar simulación (CSV)")
 )
+        ),
+        ui.row(ui.column(12,
+        ui.output_ui("page_ui")))
+        )
 
 def server(input, output, session):
     @output
@@ -43,18 +64,37 @@ def server(input, output, session):
         else:  # "Effect of temperature on trees"
             return ui.input_slider("temp_goal", "Disminución de la temperatura (°C)", min=0, max=2, value=0.5, step=0.1)
             
- 
+    @reactive.Calc
+    def user_data():
+        file_info = input.csv_upload()
+        if file_info is None:
+            return prepare_data(og_file)
+        else:
+            with open(file_info[0]["datapath"], "r", encoding="utf-8") as f:
+                return prepare_data(f)
+    
+    @reactive.Calc
+    def uploaded_filename():
+        file_info = input.csv_upload()
+        if file_info is None:
+            return "example_provided.csv"
+        return file_info[0]["name"]
+
     @reactive.Calc
     def get_gdf_trees():
         value_trees = input.tree_pct()
+        simulation_mode = input.simulation_mode()
         print(f"value inputed is {value_trees}")
-        return process_data_trees_to_temp(value_trees, original_df, df, X, Y, T)
+        original_df, df, X, Y, T = user_data()
+        return process_data_trees_to_temp(value_trees, simulation_mode, original_df, df, X, Y, T)
 
     @reactive.Calc
     def get_gdf_temp():
         value_temp = input.temp_goal()
+        simulation_mode = input.simulation_mode()
         print(f"value inputed is {value_temp}")
-        return process_data_temp_to_trees(value_temp, original_df, df, X, Y, T)
+        original_df, df, X, Y, T = user_data()
+        return process_data_temp_to_trees(value_temp, simulation_mode, original_df, df, X, Y, T)
     
     @output
     @render.ui
@@ -80,16 +120,19 @@ def server(input, output, session):
         apply_filter = input.split_filter()
 
         if apply_filter == "Filtro":
-            split = f'%total_trees_treatment_{value_trees}%'
+            split = '%total_trees'
         else:
             split = False
 
         min_temp = 15
         max_temp = 35
-
+        simulation_mode = input.simulation_mode()
+        title = "Temperatura estimada después del aumento de cobertura arbórea\n(simulación personalizada del CSV)"
+        if simulation_mode == "Usar control deslizante":
+            title = f"Temperatura estimada después del aumento de cobertura arbórea (+{value_trees}%)"
         try:
-            fig = show_two_plots(gdf_trees, f'simulated_temp_{value_trees}%', 'LST', 
-             min_temp, max_temp, 'coolwarm',f"Temperatura estimada después del aumento de cobertura arbórea (+{value_trees}% )",
+            fig = show_two_plots(gdf_trees, 'new_temp', 'LST', 
+             min_temp, max_temp, 'coolwarm',title,
              "Temperatura antes",'Temperatura (°C)',split=split)
             return fig
         except Exception as e:
@@ -105,9 +148,14 @@ def server(input, output, session):
 
         most_temp_change = -2
         no_temp_change = 0
+
+        simulation_mode = input.simulation_mode()
+        title = "Disminución de la temperatura después del aumento de cobertura arbórea\n(simulación personalizada del CSV)"
+        if simulation_mode == "Usar control deslizante":
+            title = f"Disminución de la temperatura después del aumento de cobertura arbórea (+{value_trees}%) "
         
-        fig = show_one_plot(gdf_trees, f'treatment_effect_{value_trees}%', most_temp_change, no_temp_change, 
-        'PuBu_r', f"Disminución de la temperatura por el tratamiento (+{value_trees}% cobertura arbórea)", 
+        fig = show_one_plot(gdf_trees, 'temp_decrease', most_temp_change, no_temp_change, 
+        'PuBu_r', title, 
         "Cambio de temperatura (°C)")
         return fig
     
@@ -119,16 +167,21 @@ def server(input, output, session):
         apply_filter = input.split_filter()
 
         if apply_filter == "Filtro":
-            split = f'%total_trees_needed_for_{value_temp}C'
+            split = '%total_trees'
         else:
             split = False
 
         min_cobertura_veg = 5
         max_cobertura = 100
+
+        simulation_mode = input.simulation_mode()
+        title = "Estimación de cobertura arbórea necesaria para la disminución de temperatura\n(simulación personalizada del CSV)"
+        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, f'%total_trees_needed_for_{value_temp}C', '%CoberturaVeg', 
+            fig = show_two_plots(gdf_temp, '%total_trees', '%CoberturaVeg', 
              min_cobertura_veg, max_cobertura, 'YlGn', 
-             f"Estimación de cobertura arbórea necesaria para una disminución de {value_temp}°C","Cobertura arbórea antes",
+             title,"Cobertura arbórea antes",
              "Cobertura arbórea (%)", split=split)
             return fig
         except Exception as e:
@@ -143,17 +196,36 @@ def server(input, output, session):
         value_temp = input.temp_goal()
         min_tree_increase = 0
         max_tree_increase = 90
-        fig = show_one_plot(gdf_temp, f'pp_trees_increase_for_{value_temp}C', min_tree_increase, 
+
+        simulation_mode = input.simulation_mode()
+        title = "Aumento de cobertura arbórea necesario para la disminución de temperatura\n(simulación personalizada del CSV)"
+        if simulation_mode == "Usar control deslizante":
+            title = f"Aumento de cobertura arbórea necesario para disminución de {value_temp}°C"
+        fig = show_one_plot(gdf_temp, 'pp_trees_increase', min_tree_increase, 
                   max_tree_increase, 'Greens', 
-                  f"Aumento de cobertura arbórea por disminución de {value_temp}°C",
+                  title,
                   "Cambio en cobertura arbórea (%pt)")
         return fig
 
+    def get_filename():
+        simulation_mode = input.simulation_mode() 
+        uploaded_name = uploaded_filename()    
+        print(uploaded_name)   
+        if simulation_mode == "Usar control deslizante":
+            if input.tab_choice() == "Efecto de los árboles en la temperatura":
+                filename = f"simulacion_arboles_{input.tree_pct()}pct.csv"
+            else:
+                filename = f"simulacion_temp_{input.temp_goal()}C.csv"
+        else:
+            if input.tab_choice() == "Efecto de los árboles en la temperatura":
+                filename = f"simulacion_arboles_desde_{uploaded_name}"
+            else:
+                filename = f"simulacion_temp_desde_{uploaded_name}"
+        
+        return filename
+
     @output
-    @render.download(filename=lambda: (
-        f"gdf_trees_{input.tree_pct()}pct.csv" if input.tab_choice() == "Efecto de los árboles en la temperatura"
-        else f"gdf_temp_{input.temp_goal()}C.csv"
-    ))
+    @render.download(filename=lambda: get_filename())
     def download_csv():
         if input.tab_choice() == "Efecto de los árboles en la temperatura":
             df = get_gdf_trees()

data_utils.py

@@ -20,13 +20,14 @@ def make_XYT(df, treatment_col = '%CoberturaVeg', target_col = 'LST'):
     
     return X, Y, T
     
-def prepare_data(og_file, training_file):
+def prepare_data(og_file):
     original_df = pd.read_csv(og_file, index_col=0)
-    df = pd.read_csv(training_file, index_col=0)
+    # df = pd.read_csv(training_file, index_col=0)
 
     original_df = original_df.round(2)
     
-    df = df.drop(columns=['row','column','x','y'])
+    df = original_df[['Elevacion', 'Neighbor_NDBI', 'Proximidad_agua',
+       'Neighbor_%CoberturaVeg', '%CoberturaVeg', 'LST']].copy()
     df = df.astype(np.float32)
 
     X, Y, T = make_XYT(df)
@@ -43,12 +44,16 @@ def prepare_treatment_df(original_df, df):
     return treatment_df
     
 
-def run_treatment_increase(X, T, original_df, df, value, name):
+def run_treatment_increase(X, T, original_df, df, value, name, simulation_mode):
     
     treatment_df = prepare_treatment_df(original_df, df)
-    T_sim = T.copy()  
-    value_num = 1+ (value/100)
-    T_sim = T_sim*value_num
+
+    if simulation_mode == "Usar control deslizante":
+        T_sim = T.copy()  
+        value_num = 1+ (value/100)
+        T_sim = T_sim*value_num
+    else:
+        T_sim = original_df['simulacion_arboles']
     data = {"X": X.tolist(), "T0":T.tolist(), "T1":(T_sim).tolist()}
     response = requests.post(url, headers=headers, json=data)
     if response.ok:
@@ -58,28 +63,32 @@ 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'pp_trees_increase_treatment_{value}%'] = treatment_df[f'%total_trees_treatment_{value}%'] - treatment_df['%CoberturaVeg']
+    treatment_df['%total_trees'] = T_sim
+    treatment_df['pp_trees_increase'] = treatment_df['%total_trees'] - treatment_df['%CoberturaVeg']
 
     return treatment_df
 
-def process_data_trees_to_temp(value, original_df, df, X, Y, T):
-
-    name = f'treatment_effect_{value}%'
-    treatment_df = run_treatment_increase(X, T, original_df, df, value, name)
+def process_data_trees_to_temp(value, simulation_mode, original_df, df, X, Y, T):
+    name = 'temp_decrease'
+    treatment_df = run_treatment_increase(X, T, original_df, df, value, name, simulation_mode)
 
-    treatment_df[f'simulated_temp_{value}%'] = treatment_df['LST'] + treatment_df[name]
+    treatment_df['new_temp'] = 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
+    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', 
+    'new_temp', 'pp_trees_increase','%total_trees', 'sqm_trees_increase','sqm_total_trees',]  # to match gdf trees
+
+    treatment_df = treatment_df[col_order]
 
     gdf = gpd.GeoDataFrame(treatment_df, geometry=gpd.points_from_xy(treatment_df.x, treatment_df.y))
 
     return gdf
 
 
-def run_temp_decrease(X, original_df, df, goal, name):
+def run_temp_decrease(X, original_df, df, goal, name,simulation_mode):
     
     treatment_df = prepare_treatment_df(original_df, df)
     data = {"X": X.tolist()}
@@ -90,24 +99,32 @@ def run_temp_decrease(X, original_df, df, goal, name):
     else:
         print("Request failed:", response.status_code, response.text)
     
-    treatment_effects = np.array(result["effect"])
-    
-    trees_needed = -goal / treatment_effects
+    if simulation_mode == "Usar control deslizante":
+        treatment_df['temp_decrease'] = -goal
+        treatment_df['new_temp'] = treatment_df['LST'] + treatment_df['temp_decrease']
+    else:
+        treatment_df['new_temp'] = original_df['simulacion_temp']
+        treatment_df['temp_decrease'] = treatment_df['new_temp'] - treatment_df['LST']
     
-    treatment_df[name] = trees_needed
-    treatment_df['goal_temp'] = treatment_df['LST'] - goal
+    treatment_effects = np.array(result["effect"])
+    treatment_df[name] = treatment_df['temp_decrease'] / treatment_effects
 
     return treatment_df
-def process_data_temp_to_trees(goal, original_df, df, X, Y, T):
+def process_data_temp_to_trees(goal, simulation_mode, 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)
+    name = 'pp_trees_increase'
+    treatment_df = run_temp_decrease(X, original_df, df, goal, name,simulation_mode)
 
-    treatment_df[f'%total_trees_needed_for_{goal}C'] = treatment_df['%CoberturaVeg'] + treatment_df[name]
+    treatment_df['%total_trees'] = treatment_df['%CoberturaVeg'] + treatment_df[name]
 
     #add columns translated to square meters
-    treatment_df[f'sqm_total_trees_needed_for_{goal}C'] = (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
+    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', 
+    'new_temp', 'pp_trees_increase','%total_trees', 'sqm_trees_increase','sqm_total_trees',]  # to match gdf trees
+
+    treatment_df = treatment_df[col_order]
 
     gdf = gpd.GeoDataFrame(treatment_df, geometry=gpd.points_from_xy(treatment_df.x, treatment_df.y))
 

example.csv

@@ -0,0 +1,864 @@
+,x,y,Elevacion,Neighbor_NDBI,Proximidad_agua,Neighbor_%CoberturaVeg,%CoberturaVeg,LST,%Construccion,simulacion_temp,simulacion_arboles
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plot_utils.py

@@ -53,13 +53,15 @@ def show_two_plots(gdf, name1, name2, vmin, vmax, colorscheme, title1, title2, l
                                markerfacecolor=above_colors['100'], markersize=8)
         
         axes[0].legend(handles=[orange_patch, red_patch])
-    axes[0].set_title(title1)
+    # axes[0].set_title(title1)
+    axes[0].set_title(title1, pad=-30)
     axes[0].set_axis_off()
-        
+    
+
 
     # Second plot (original)
     gdf.plot(column=name2, cmap=cmap, norm=norm, ax=axes[1],marker='s', markersize=10)
-    axes[1].set_title(title2)
+    axes[1].set_title(title2, pad=-30)
     axes[1].set_axis_off()
     
 
@@ -84,7 +86,7 @@ def show_one_plot(gdf, name, vmin, vmax, colorscheme, title, label):
     
 
     gdf.plot(column=name, cmap=cmap, norm=norm, ax=axes[0], marker='s', markersize=10)
-    axes[0].set_title(title)
+    axes[0].set_title(title, pad=-30)
     axes[0].set_axis_off()
 
     # Add a colorbar

training_dataframe.csv

@@ -1,905 +0,0 @@
-index,row,column,x,y,Elevacion,Neighbor_NDBI,Proximidad_agua,Neighbor_%CoberturaVeg,%CoberturaVeg,LST
-001-011,-1,11,769975.0,1617705.0,1462.42,-0.18,234.21,8.07,39.79,26.47
-001-012,-1,12,770075.0,1617705.0,1460.52,-0.17,132.41,8.07,66.87,25.81
-002-009,-2,9,769775.0,1617605.0,1432.93,-0.21,337.65,47.63,83.07,24.59
-002-010,-2,10,769875.0,1617605.0,1458.52,-0.21,288.93,38.65,91.83,25.19
-002-011,-2,11,769975.0,1617605.0,1477.37,-0.21,219.62,38.65,59.28,25.44
-002-012,-2,12,770075.0,1617605.0,1455.41,-0.21,129.07,38.65,40.68,25.12
-003-008,-3,8,769675.0,1617505.0,1438.17,-0.24,333.41,25.98,90.66,24.28
-003-009,-3,9,769775.0,1617505.0,1467.39,-0.21,269.63,25.98,74.5,25.49
-003-010,-3,10,769875.0,1617505.0,1481.73,-0.21,208.99,25.98,47.63,26.69
-003-011,-3,11,769975.0,1617505.0,1472.59,-0.26,148.56,27.46,38.65,25.86
-003-012,-3,12,770075.0,1617505.0,1432.38,-0.26,86.73,38.65,72.46,24.45
-004-006,-4,6,769475.0,1617405.0,1432.73,-0.24,419.78,39.36,92.47,24.31
-004-007,-4,7,769575.0,1617405.0,1445.24,-0.24,349.77,27.22,81.96,24.35
-004-008,-4,8,769675.0,1617405.0,1473.53,-0.24,270.35,25.98,67.13,25.7
-004-009,-4,9,769775.0,1617405.0,1484.71,-0.21,195.18,27.22,25.98,27.88
-004-010,-4,10,769875.0,1617405.0,1481.58,-0.27,130.69,25.98,27.46,27.82
-004-011,-4,11,769975.0,1617405.0,1449.39,-0.27,70.2,27.46,64.78,25.44
-005-005,-5,5,769375.0,1617305.0,1445.18,-0.23,483.28,16.27,90.41,24.74
-005-006,-5,6,769475.0,1617305.0,1468.29,-0.24,393.26,15.76,84.07,25.29
-005-007,-5,7,769575.0,1617305.0,1478.15,-0.24,302.02,15.76,61.16,26.4
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