modified marker size to be dynamic

map-spike.py

@@ -1,162 +0,0 @@
-# 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

@@ -5,6 +5,13 @@ import matplotlib.gridspec as gridspec
 
 
 def show_two_plots(gdf, name1, name2, vmin, vmax, colorscheme, title1, title2, label, split=False):
+    #dynamically change marker size
+    n_points = len(gdf)
+    marker_size = 8500 / n_points  
+    marker_size = max(2, min(marker_size, 200))
+    filter_marker_size = marker_size*0.6
+    print(marker_size)
+
     print('split', split)
     if gdf.empty:
         raise ValueError("GeoDataFrame is empty")
@@ -26,7 +33,7 @@ def show_two_plots(gdf, name1, name2, vmin, vmax, colorscheme, title1, title2, l
 
     if split == False:
         # First plot 
-        gdf.plot(column=name1, cmap=cmap, norm=norm, ax=axes[0], marker='s', markersize=10)
+        gdf.plot(column=name1, cmap=cmap, norm=norm, ax=axes[0], marker='s', markersize=marker_size)
     
     else:
         # Split data into three GeoDataFrames
@@ -36,13 +43,13 @@ def show_two_plots(gdf, name1, name2, vmin, vmax, colorscheme, title1, title2, l
         above_colors = {"capacity":"chocolate", "100":"firebrick"}
         
         # Plot values ≤ 100 using colormap
-        gdf.plot(column=name1, cmap=colorscheme, ax=axes[0], vmin=vmin, vmax=vmax, marker='s', markersize=10)
+        gdf.plot(column=name1, cmap=colorscheme, ax=axes[0], vmin=vmin, vmax=vmax, marker='s', markersize=marker_size)
         
         # Plot values > capacity in orange
-        above_capacity.plot(color=above_colors['capacity'], ax=axes[0], label='> capacidad debido a la construcción', marker='x', markersize=6)
+        above_capacity.plot(color=above_colors['capacity'], ax=axes[0], label='> capacidad debido a la construcción', marker='x', markersize=filter_marker_size)
         
         # Plot values > 100 in red
-        above_100.plot(color=above_colors['100'], ax=axes[0], label='> 100% cobertura arbórea', marker='x', markersize=6)
+        above_100.plot(color=above_colors['100'], ax=axes[0], label='> 100% cobertura arbórea', marker='x', markersize=filter_marker_size)
         
         # Add legend manually for orange points
         orange_patch = plt.Line2D([0], [0], marker='o', color='w', label='> capacidad debido a la construcción', 
@@ -60,7 +67,7 @@ def show_two_plots(gdf, name1, name2, vmin, vmax, colorscheme, title1, title2, l
 
 
     # Second plot (original)
-    gdf.plot(column=name2, cmap=cmap, norm=norm, ax=axes[1],marker='s', markersize=10)
+    gdf.plot(column=name2, cmap=cmap, norm=norm, ax=axes[1],marker='s', markersize=marker_size)
     axes[1].set_title(title2, pad=-30)
     axes[1].set_axis_off()
     
@@ -74,6 +81,11 @@ def show_two_plots(gdf, name1, name2, vmin, vmax, colorscheme, title1, title2, l
     return fig
 
 def show_one_plot(gdf, name, vmin, vmax, colorscheme, title, label):
+    #dynamically change marker size
+    n_points = len(gdf)
+    marker_size = 8500 / n_points  
+    marker_size = max(2, min(marker_size, 200))
+
     # Second set of plots
     cmap = plt.colormaps[colorscheme]
     norm = colors.Normalize(vmin, vmax)
@@ -85,7 +97,7 @@ def show_one_plot(gdf, name, vmin, vmax, colorscheme, title, label):
     axes = [fig.add_subplot(gs[0]), fig.add_subplot(gs[1])]
     
 
-    gdf.plot(column=name, cmap=cmap, norm=norm, ax=axes[0], marker='s', markersize=10)
+    gdf.plot(column=name, cmap=cmap, norm=norm, ax=axes[0], marker='s', markersize=marker_size)
     axes[0].set_title(title, pad=-30)
     axes[0].set_axis_off()