Enhance map generation

Author: frankfeng

.github/workflows/update_map.yml

@@ -30,7 +30,7 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install matplotlib pandas geopandas
+          pip install numpy matplotlib pandas geopandas requests
 
       # Step 4: Run your Python script to generate the map
       - name: Generate map

generate_map.py

@@ -3,53 +3,112 @@
 import geopandas
 import matplotlib.pyplot as plt
 import datetime
+import requests
+import numpy as np
+from pathlib import Path
 
-def create_map():
+def fetch_grid_coordinates():
     """
-    Generate a world map with the current time and several fixed points.
-    In practical applications, you should fetch your real-time data here.
+    Fetch grid coordinates from the registry file.
+    Returns a DataFrame with latitude and longitude columns.
     """
-    # --- 1. Fetch your data ---
-    # This is an example. You should replace it with your own data fetching logic.
-    # For example: Fetching the latest earthquake locations from an API, your movement trajectory, etc.
-    data = {
-        'City': ['London', 'New York', 'Tokyo', 'Sydney'],
-        'Latitude': [51.5074, 40.7128, 35.6895, -33.8688],
-        'Longitude': [-0.1278, -74.0060, 139.6917, 151.2093]
-    }
-    df = pd.DataFrame(data)
+    # Fetch the registry file from the URL
+    registry_url = "https://dl-1.tessera.wiki/v1/global_0.1_degree_representation/registry_2024.txt"
+    
+    print("Fetching registry file...")
+    response = requests.get(registry_url)
+    response.raise_for_status()
+    
+    # Parse the registry content
+    coordinates = set()  # Use set to avoid duplicates
+    
+    for line in response.text.strip().split('\n'):
+        if line:
+            # Extract the file path (first part before the hash)
+            file_path = line.split()[0]
+            
+            # Only process main grid files (not _scales files)
+            if file_path.endswith('.npy') and not '_scales.npy' in file_path:
+                # Extract grid name from path like "2024/grid_-0.05_10.05/grid_-0.05_10.05.npy"
+                parts = file_path.split('/')
+                if len(parts) >= 2:
+                    grid_name = parts[1]  # e.g., "grid_-0.05_10.05"
+                    
+                    if grid_name.startswith('grid_'):
+                        # Remove "grid_" prefix and split by underscore
+                        coords = grid_name[5:].split('_')
+                        if len(coords) == 2:
+                            try:
+                                lon = float(coords[0])
+                                lat = float(coords[1])
+                                coordinates.add((lat, lon))
+                            except ValueError:
+                                continue
+    
+    print(f"Found {len(coordinates)} unique grid points")
+    
+    # Convert to DataFrame
+    coords_list = list(coordinates)
+    df = pd.DataFrame(coords_list, columns=['Latitude', 'Longitude'])
+    
+    return df
 
+def create_map():
+    """
+    Generate a world map with all available grid points from the registry.
+    """
+    # --- 1. Fetch grid coordinates ---
+    df = fetch_grid_coordinates()
+    
     # Convert pandas DataFrame to GeoDataFrame
+    print("Creating GeoDataFrame...")
     gdf = geopandas.GeoDataFrame(
         df, geometry=geopandas.points_from_xy(df.Longitude, df.Latitude)
     )
-    gdf.set_crs("EPSG:4326", inplace=True) # Set the coordinate system to WGS84
+    gdf.set_crs("EPSG:4326", inplace=True)  # Set the coordinate system to WGS84
 
     # --- 2. Plot the map ---
-    # Directly load a local shapefile
-    world = geopandas.read_file('world_map/ne_110m_admin_0_countries.shp')
+    # Check if world map shapefile exists
+    world_map_path = Path('world_map/ne_110m_admin_0_countries.shp')
+    if not world_map_path.exists():
+        print("Warning: World map shapefile not found. Using built-in world map.")
+        world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
+    else:
+        world = geopandas.read_file(world_map_path)
 
     # Create the plotting area
-    fig, ax = plt.subplots(1, 1, figsize=(15, 10))
+    print("Creating map...")
+    fig, ax = plt.subplots(1, 1, figsize=(20, 12))
 
     # Plot the world map base layer
-    world.plot(ax=ax, color='lightgray', edgecolor='black')
+    world.plot(ax=ax, color='lightgray', edgecolor='black', linewidth=0.5)
 
-    # Plot your data points on the map
-    gdf.plot(ax=ax, marker='o', color='red', markersize=50, label='My Points')
+    # Plot grid points on the map with smaller markers
+    gdf.plot(ax=ax, marker='o', color='red', markersize=2, alpha=0.8, label='Available')
 
     # --- 3. Customize and save the map ---
-    # Add a title with the current time
+    # Add a title with the current time and grid count
     current_time_utc = datetime.datetime.now(datetime.timezone.utc).strftime('%Y-%m-%d %H:%M:%S UTC')
-    ax.set_title(f'Real-time Map\nLast Updated: {current_time_utc}', fontdict={'fontsize': '16', 'fontweight': '3'})
+    ax.set_title(f'GeoTessera Grid Coverage Map\nTotal Grid Points: {len(df)}\nLast Updated: {current_time_utc}', 
+                 fontdict={'fontsize': '18', 'fontweight': 'bold'})
+
+    # Add legend
+    ax.legend(loc='lower left', frameon=True, fancybox=True, shadow=True, fontsize=12)
+
+    # Set axis labels
+    ax.set_xlabel('Longitude', fontsize=14)
+    ax.set_ylabel('Latitude', fontsize=14)
 
-    # Hide the axes
-    ax.set_axis_off()
+    # Add grid for better readability
+    ax.grid(True, alpha=0.3, linestyle='--')
 
-    # Save the map as an image file, bbox_inches='tight' trims excess whitespace
-    # The filename 'map.png' is important and will be used later
-    plt.savefig('map.png', dpi=150, bbox_inches='tight')
-    print("Map generated successfully!")
+    # Save the map as an image file
+    plt.tight_layout()
+    plt.savefig('map.png', dpi=300, bbox_inches='tight')
+    print(f"Map generated successfully with {len(df)} grid points!")
+    
+    # Close the plot to free memory
+    plt.close()
 
 if __name__ == '__main__':
     create_map()