Switch to matplotlib

.gitignore

@@ -2,3 +2,4 @@
 .venv/
 __pycache__
 .DS_Store
+venv/

app.py

@@ -1,53 +1,99 @@
 # Example taken from here - https://shinylive.io/py/examples/#map
 
 import os
-from shiny import reactive
-from shiny.express import input, render, ui
+from shiny import reactive, render, ui, App
+# from shiny.express import input, render, ui
 from shinywidgets import render_widget
-import ipyleaflet as ipyl
 
+from plot_utils import show_one_plot, show_two_plots
+from data_utils import prepare_data
 
-city_centers = {
-    "London": (51.5074, 0.1278),
-    "Paris": (48.8566, 2.3522),
-    "New York": (40.7128, -74.0060),
+import pandas as pd
+import geopandas as gpd
+
+# import matplotlib.pyplot as plt
+# import matplotlib.colors as colors
+# import matplotlib.cm as cm
+# import matplotlib.gridspec as gridspec
+import numpy as np
+
+import requests
+
+url = "https://Projects-by-IF-model-temp-api.hf.space/model-effect"
+headers = {"Content-Type": "application/json"}
+data = {
+    "X": [[1462, -0.18, 234, 8]], "T0":[10], "T1":[15]
 }
 
-ui.input_text("Text", "Some vector", "London")
+response = requests.post(url, headers=headers, json=data)
+
+if response.ok:
+    result = response.json()
+    print("Model response:", result["effect"])
+else:
+    print("Request failed:", response.status_code, response.text)
+
+treatment_df = pd.read_csv("example.csv", index_col=0)
+og_file = "cleaned_dataframe.csv"
+training_file = "training_dataframe.csv"
+original_df, df, X, Y, T = prepare_data(og_file, training_file)
 
+print(X)
 
-@render_widget
-def map():
-    return ipyl.Map(zoom=4)
 
+ui.input_slider("trees", "Percentage increase in tree coverage", 0, 100, 20)
 
-def use_in_model(x):
-    # TODO: Call some model loaded in huggingface_hub. 
-    # Define the client once then just call the
-    # client from here.
 
-    # from huggingface_hub import InferenceClient 
-    # client = InferenceClient(
-    #    api_key=os.environ["HF_TOKEN"],
-    #    provider="auto",
-    # )
-    #
-    # completion = client.chat.completions.create(
-    #    model="deepseek-ai/DeepSeek-V3-0324",
-    #    messages=[{"role": "user", "content": "A story about hiking in the mountains"}]
-    # )
+app_ui = ui.page_fluid(
+    ui.input_radio_buttons("tab_choice", "Choose page:", ["Effect of trees on temperature", "Effect of temperature on trees"]),
+    ui.output_ui("page_ui")
+)
 
-    return x
+def server(input, output, session):
+# def show_effect_trees_on_temp():  
+    value = 40
+    name = f'treatment_effect_{value}%'
+    gdf = gpd.GeoDataFrame(treatment_df, geometry=gpd.points_from_xy(treatment_df.x, treatment_df.y))
+    
+    @output
+    @render.ui
+    def page_ui():
+        if input.tab_choice() == "Effect of trees on temperature":
+            return ui.div(
+                ui.h2("Temperature Simulation Viewer", class_="text-center"),
+                ui.div(ui.output_plot("temp_after_treatment"), style="display: flex; justify-content: center;"),
+                ui.div(ui.output_plot("treatment_trees_effect"), style="display: flex; justify-content: center;"),
+            )
+        else: #Effect of temperature on trees
+            return ui.div(
+                ui.h2("Tree Simulation Viewer", class_="text-center"),
+                ui.div(ui.output_plot("temp_after_treatment"), style="display: flex; justify-content: center;"),
+            )
 
+    @output
+    @render.plot()
+    def temp_after_treatment():
+        try:
+            fig = show_two_plots(gdf, f'simulated_temp_{value}%', 'LST', 
+             gdf['LST'].min() + treatment_df[name].min(), 
+             gdf['LST'].max(), 'coolwarm',
+                   f"Temperature after treatment (+{value}% Trees)",
+            "Original temperature",
+                  'Temperature (°C)')
+            return fig
+        except Exception as e:
+            print("Plotting error:", e)
+            raise e
 
-@render.text
-def text():
-    return ("You entered: " + use_in_model(input.Text()))
 
+    @output
+    @render.plot()
+    def treatment_trees_effect():
+        fig = show_one_plot(gdf, name, gdf[name].min(), 
+                  gdf[name].max(), 'PuBu_r', 
+                  f"Decrease of temperature from treatment (+{value}% Trees)", 
+                  "Temperature decrease (°C)")
+        return fig
+    
 
-@reactive.effect
-def _():
-    try:
-        map.widget.center = city_centers[use_in_model(input.Text())]
-    except:
-        pass
+app = App(app_ui, server)

data_utils.py

@@ -0,0 +1,27 @@
+import pandas as pd
+import numpy as np
+
+def make_XYT(df, treatment_col = '%CoberturaVeg', target_col = 'LST'):
+    
+    Y = df[target_col].values
+
+    T = df[treatment_col].values
+
+    X = df.drop(columns=[treatment_col, target_col]).values
+    
+    
+    return X, Y, T
+    
+def prepare_data(og_file, training_file):
+    original_df = pd.read_csv(og_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 = df.astype(np.float32)
+
+    X, Y, T = make_XYT(df)
+    
+    return original_df, df, X, Y, T
+

example.csv

@@ -0,0 +1,905 @@
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map-spike.py

@@ -0,0 +1,162 @@
+# Example taken from here - https://shinylive.io/py/examples/#map
+
+import os
+from shiny import reactive
+from shiny.express import input, render, ui
+from shinywidgets import render_widget
+import ipyleaflet as ipyl
+
+import pandas as pd
+import geopandas as gpd
+from ipyleaflet import Map, Rectangle, basemaps, basemap_to_tiles, LayersControl, ColorMap, FeatureGroup
+from branca.colormap import linear
+
+from shapely.geometry import box
+
+def point_to_square(row, size=0.0009):
+    x, y = row.geometry.x, row.geometry.y
+    half = size / 2
+    return box(x - half, y - half, x + half, y + half)
+
+
+
+df = pd.read_csv("example.csv")
+
+# Step 2: Create GeoDataFrame with UTM Zone 15N
+gdf = gpd.GeoDataFrame(
+    df,
+    geometry=gpd.points_from_xy(df['x'], df['y']),
+    crs="EPSG:32615"  # <- Your current system
+)
+
+
+
+# Step 3: Reproject to WGS84 (lat/lon)
+gdf = gdf.to_crs("EPSG:4326")
+
+# Convert points to small square polygons
+gdf["geometry_box"] = gdf.apply(point_to_square, axis=1)
+
+print(gdf.head())
+
+# Step 4: Extract lat/lon columns for ipyleaflet
+gdf["lat"] = gdf.geometry.y
+gdf["lon"] = gdf.geometry.x
+
+temp_colormap = linear.YlOrRd_09.scale(df['LST'].min(), df['LST'].max())
+
+# def generate_temp_layer():
+#     rectangles = []
+#     for _, row in gdf.iterrows():
+#         lat = row['lat']
+#         lon = row['lon']
+#         size_deg = 0.0009  # Approx ~100m (latitude degrees)
+
+#         bounds = [
+#             (lat - size_deg / 2, lon - size_deg / 2),
+#             (lat + size_deg / 2, lon + size_deg / 2)
+#         ]
+
+#         color = temp_colormap(row['LST'])
+
+#         rect = Rectangle(
+#             bounds=bounds,
+#             color=color,
+#             fill_color=color,
+#             fill_opacity=0.5,
+#             weight=0
+#         )
+#         rectangles.append(rect)
+#     return rectangles
+
+city_centers = {
+    "London": (51.5074, 0.1278),
+    "Paris": (48.8566, 2.3522),
+    "New York": (40.7128, -74.0060),
+    "Guatemala city": (14.6349149, -90.5068824),
+    "Zone 15": (14.61925, -90.49386)
+}
+
+ui.input_text("Text", "Some vector", "Zone 15")
+
+
+
+
+@render_widget
+def map():
+    city = use_in_model(input.Text())
+    center = city_centers.get(city, (0, 0))
+    m = Map(center=center, zoom=13, basemap=basemaps.OpenStreetMap.Mapnik)
+        # Add rectangle overlay
+
+    geo_data = ipyl.GeoData(
+        geo_dataframe=gdf,
+            style={
+                "color": "black",
+                "fillColor": "#3366cc",
+                "opacity": 0.05,
+                "weight": 1.9,
+                "dashArray": "2",
+                "fillOpacity": 0.6,
+            },
+            hover_style={"fillColor": "red", "fillOpacity": 0.2},
+            name="Temperature",
+        )
+    m.add(geo_data)
+    m.add(ipyl.LayersControl())
+
+    return m
+
+# @render_widget
+# def map():
+#     city = use_in_model(input.Text())
+#     center = city_centers.get(city, (0, 0))
+
+#     # m = Map(center=center, zoom=13, basemap=basemaps.OpenStreetMap.Mapnik)
+#     m = Map(center=city_centers["Guatemala city"], zoom=13, basemap=basemaps.OpenStreetMap.Mapnik)
+
+#     print(f"Centering on city: {city}")
+
+#     # Add rectangle overlay
+#     for rect in generate_temp_layer():
+#         m.add_layer(rect)
+
+#     # Optional: Add color legend
+#     # m.add_control(temp_colormap.legend(title= "Temp"))
+
+#     # Add controls
+#     m.add_control(LayersControl(position="topright"))
+
+#     return m
+
+
+def use_in_model(x):
+    # TODO: Call some model loaded in huggingface_hub. 
+    # Define the client once then just call the
+    # client from here.
+
+    # from huggingface_hub import InferenceClient 
+    # client = InferenceClient(
+    #    api_key=os.environ["HF_TOKEN"],
+    #    provider="auto",
+    # )
+    #
+    # completion = client.chat.completions.create(
+    #    model="deepseek-ai/DeepSeek-V3-0324",
+    #    messages=[{"role": "user", "content": "A story about hiking in the mountains"}]
+    # )
+
+    return x
+
+
+@render.text
+def text():
+    return ("You entered: " + use_in_model(input.Text()))
+
+
+@reactive.effect
+def _():
+    try:
+        map.widget.center = city_centers[use_in_model(input.Text())]
+    except:
+        pass

plot_utils.py

@@ -0,0 +1,100 @@
+import matplotlib.pyplot as plt
+import matplotlib.colors as colors
+import matplotlib.cm as cm
+import matplotlib.gridspec as gridspec
+
+
+def show_two_plots(gdf, name1, name2, vmin, vmax, colorscheme, title1, title2, label, split=False):
+    if gdf.empty:
+        raise ValueError("GeoDataFrame is empty")
+
+    required_cols = [name1, name2]
+    for col in required_cols:
+        if col not in gdf.columns:
+            raise ValueError(f"Column '{col}' not found in gdf")
+
+    #Create a shared colormap and normalization
+    cmap = plt.colormaps[colorscheme]
+    norm = colors.Normalize(vmin=vmin, vmax=vmax)
+    
+    # Create subplots
+    fig = plt.figure(figsize=(12, 6))
+    gs = gridspec.GridSpec(1, 3, width_ratios=[1, 1, 0.05], wspace=0.3)
+
+    axes = [fig.add_subplot(gs[0]), fig.add_subplot(gs[1]), fig.add_subplot(gs[2]) ]
+
+    if split:
+        # Split data into three GeoDataFrames
+        below_100 = gdf[gdf[name1] <= 100]
+        above_capacity = gdf[gdf[name1] > (100-gdf['%Construccion'])]
+        above_100 = gdf[gdf[name1] > 100]
+    
+        
+        # Plot values ≤ 100 using colormap
+        below_100.plot(column=name1, cmap='YlGn', legend=True, ax=axes[0], vmin=vmin, vmax=vmax, marker='s', markersize=10)
+        
+        # Plot values > capacity in orange
+        above_capacity.plot(color='orange', ax=axes[0], label='> capacity due to construction', marker='x', markersize=10)
+        
+        # Plot values > 100 in red
+        above_100.plot(color='firebrick', ax=axes[0], label='> 100% trees', marker='s', markersize=10)
+        
+        # Add legend manually for orange points
+        orange_patch = plt.Line2D([0], [0], marker='o', color='w', label='> capacity due to construction', 
+                               markerfacecolor='orange', markersize=8)
+    
+        # Add legend manually for red points
+        red_patch = plt.Line2D([0], [0], marker='o', color='w', label='>100% trees', 
+                               markerfacecolor='firebrick', markersize=8)
+        
+        axes[0].legend(handles=[orange_patch, red_patch])
+
+
+    else:
+        # First plot 
+        gdf.plot(column=name1, cmap=cmap, norm=norm, ax=axes[0], marker='s', markersize=10)
+    axes[0].set_title(title1)
+    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_axis_off()
+    
+
+    # Shared colorbar
+    sm = cm.ScalarMappable(cmap=cmap, norm=norm)
+    sm._A = []  # Dummy array for the colormap
+    cbar = fig.colorbar(sm, cax=axes[2], orientation='vertical', fraction=0.03, pad=0.02)
+    cbar.set_label(label)
+    
+    return fig
+
+def show_one_plot(gdf, name, vmin, vmax, colorscheme, title, label):
+    # Second set of plots
+    cmap = plt.colormaps[colorscheme]
+    norm = colors.Normalize(vmin, vmax)
+
+    # Create subplots
+    # fig, axes = plt.subplots(1, 1, figsize=(6, 6))
+    fig = plt.figure(figsize=(12, 6))
+    gs = gridspec.GridSpec(1, 2, width_ratios=[1, 0.05], wspace=0.3)
+
+    axes = [fig.add_subplot(gs[0]), fig.add_subplot(gs[1])]
+    
+    # First plot (simulated)
+    gdf.plot(column=name, cmap=cmap, ax=axes[0], marker='s', markersize=10)
+    axes[0].set_title(title)
+    axes[0].set_axis_off()
+
+        # Add a shared colorbar
+    sm = cm.ScalarMappable(cmap=cmap, norm=norm)
+    sm._A = []  # Dummy data for the colorbar
+    cbar = fig.colorbar(sm, cax=axes[1], orientation='vertical', fraction=0.03, pad=0.02)
+    cbar.set_label(label)
+
+
+    return fig
+
+

requirements.txt

@@ -3,5 +3,7 @@ shiny
 shinywidgets
 plotly
 pandas
+geopandas
 ridgeplot
-ipyleaflet
+ipyleaflet
+requests

training_dataframe.csv

@@ -0,0 +1,905 @@
+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
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+004-007,-4,7,769575.0,1617405.0,1445.24,-0.24,349.77,27.22,81.96,24.35
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+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
+005-008,-5,8,769675.0,1617305.0,1486.73,-0.24,213.92,15.76,27.22,28.07
+005-009,-5,9,769775.0,1617305.0,1482.6,-0.26,129.16,17.47,35.18,28.21
+005-010,-5,10,769875.0,1617305.0,1453.86,-0.27,56.11,25.98,75.81,26.26
+006-004,-6,4,769275.0,1617205.0,1443.6,-0.2,565.09,59.8,70.96,24.72
+006-005,-6,5,769375.0,1617205.0,1474.39,-0.2,467.49,16.27,64.46,25.44
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