""" Weather Locations World Map Generator ==================================== This script creates a world map visualization showing weather stations with: - Different climate zones (color-coded) - Different data types: Real weather data vs TMY (Typical Meteorological Year) - Geographic distribution of weather monitoring stations Data Sources: - Real weather data: tables/weather_real.csv - Base TMY data: tables/weather_base.csv - Expanded TMY data: tables/weather.csv Author: Generated for weather data visualization Version: 2.0 (CSV file input version) """ import pandas as pd import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Rectangle import warnings warnings.filterwarnings('ignore') # Try to use cartopy for better world map, fall back to basic plotting if not available try: import cartopy.crs as ccrs import cartopy.feature as cfeature CARTOPY_AVAILABLE = True except ImportError: CARTOPY_AVAILABLE = False print("Cartopy not available. Using basic matplotlib plotting.") def create_location_database(): """Create a database of approximate coordinates for weather locations""" locations = { # UAE 'Dubai.Intl.AP': (25.2532, 55.3657), 'Abu.Dhabi.Intl.AP': (24.4539, 54.6511), 'Dubai.Minhad.Ab': (25.0269, 55.3625), # USA 'Atlanta-Hartsfield-Jackson Intl AP': (33.6407, -84.4277), 'Aurora-Buckley AFB': (39.7016, -104.7513), 'Fairbanks Intl AP': (64.8378, -147.8562), 'Tampa-MacDill AFB': (27.8492, -82.5203), 'Albuquerque Intl Sunport': (35.0402, -106.6093), 'El Paso Intl AP': (31.8072, -106.3781), 'Seattle-Tacoma Intl AP': (47.4502, -122.3088), 'Miami Intl AP': (25.7959, -80.2870), 'Rochester Intl AP': (43.9056, -92.4924), 'New York-Kennedy Intl AP': (40.6413, -73.7781), 'Great Falls Intl AP': (47.4820, -111.3706), 'International Falls-Falls Intl AP': (48.5663, -93.4030), 'Port Angeles-Fairchild Intl AP': (48.1202, -123.5004), 'Buffalo Niagara Intl AP': (42.9405, -78.7322), 'Tucscon-Davis-Monthan AFB': (32.1665, -110.8837), 'Chula Vista-Brown Muni Field AP': (32.5725, -117.0261), 'Chula Vista-Brown Field Muni AP': (32.639954, -117.1067), 'Salt Lake City Intl AP': (40.7899, -111.9791), 'Boise AP-Gowen Field ANGB': (43.5644, -116.2228), 'Twin Falls-Magic Valley Rgnl AP-Joslin Field': (42.4818, -114.4877), 'Mitchell Muni AP': (43.7058, -98.0428), 'Rapid City Rgnl AP': (44.0453, -103.0645), 'Sioux Falls Rgnl AP-Foss Field': (43.5820, -96.7419), # Vietnam 'Ho.Chi.Minh-Tan.Son.Nhat.Intl.AP': (10.8188, 106.6519), # India 'Jaipur': (26.8247, 75.8130), 'New.Delhi-Gandhi.Intl.AP': (28.5562, 77.1000), 'New.Delhi-Safdarjung.Intl.AP': (28.5844, 77.2066), 'Sikar': (27.6094, 75.1399), # Other countries 'Cotonou-Cadjehoun.AP': (6.3573, 2.3844), # Benin 'Foz.do.Iguacu-Cataratas.Intl.AP': (-25.6008, -54.4858), # Brazil 'Formosa.Intl.AP': (-26.2128, -58.2281), # Argentina 'Albany': (-35.0275, 117.8840), # Australia 'Bridgetown': (-33.9567, 116.1428), # Australia 'Busselton': (-33.6775, 115.4006), # Australia 'Alberni.Valley.Rgnl.AP': (49.3189, -124.9356), # Canada 'Campbell.River.AP': (50.0881, -125.2714), # Canada 'Nanaimo.AP': (49.0522, -123.8700), # Canada 'Ottawa-Macdonald-Cartier.Intl.AP': (45.3192, -75.6692), # Canada 'Montreal-Trudeau.Intl.AP': (45.4706, -73.7408), # Canada 'Trois.Rivieres': (46.3528, -72.5478), # Canada 'Nanjing': (32.0603, 118.7969), # China 'Shanghai-Hongqiao.Intl.AP': (31.1979, 121.3365), # China 'Hangzhou': (30.2741, 120.1551), # China 'Praha-Ruzyne.AP': (50.1008, 14.2632), # Czech Republic 'Dresden.AP': (51.1328, 13.7671), # Germany 'Tanta': (30.7865, 31.0004), # Egypt 'Alexandria-Nozha.Intl.AP': (31.1834, 29.9489), # Egypt 'Cairo.Intl.AP': (30.1219, 31.4056), # Egypt 'Leon.AP': (42.5886, -5.6556), # Spain 'Salamanca.AP': (40.9321, -5.5017), # Spain 'Rovaniemi.AP': (66.5648, 25.8307), # Finland 'Oulu.AP': (65.0324, 25.3540), # Finland 'Suva.Kings.Wharf': (-18.1416, 178.4419), # Fiji 'Labasa.ap': (-16.4667, 179.3397), # Fiji 'Lautoka.Queens.Wharf': (-17.6125, 177.4203), # Fiji 'Aktau': (43.6506, 51.2089), # Kazakhstan 'Kuryk-Eralievo': (43.2056, 51.3711), # Kazakhstan 'Wonju.WS': (37.3422, 127.9502), # South Korea 'Seoul.WS': (37.5665, 126.9780), # South Korea 'Daejeon.WS': (36.3504, 127.3845), # South Korea 'Ulaanbaatar-Chinggis.Khaan.Intl.AP': (47.8430, 106.7665), # Mongolia 'Lagos-Muhammed.Intl.AP': (6.5774, 3.3212), # Nigeria 'Muscat-Sultan.Qaboos.Port': (23.5859, 58.4059), # Oman 'Legnica': (51.2070, 16.1610), # Poland 'Braganca.AP': (41.8581, -6.7075), # Portugal 'Asuncion-Pettirossi.Intl.AP': (-25.2397, -57.5197), # Paraguay 'Ulan-Ude.AP': (51.8081, 107.4375), # Russia 'Chita-Kadala.AP': (52.0263, 113.3056), # Russia 'Lulea.AP': (65.5439, 22.1218), # Sweden 'Lome-Tokoin-Eyadema.Intl.AP': (6.1656, 1.2547), # Togo 'Turkmenbashi': (40.0775, 53.0072), # Turkmenistan 'Douglas': (-29.0414, 23.7519), # South Africa 'Kimberley.AP': (-28.8025, 24.7650), # South Africa 'Postmasburg': (-28.3289, 23.3678), # South Africa } return locations def standardize_dataframe_columns(df): """Standardize column names across different datasets""" # Create a mapping of possible column name variations column_mapping = { # Climate zone code variations 'climate zone code': 'climate_zone_code', 'climate_zone_code': 'climate_zone_code', 'Climate Zone Code': 'climate_zone_code', 'climatezonecode': 'climate_zone_code', # Climate zone description variations 'climate zone description': 'climate_zone_description', 'climate_zone_description': 'climate_zone_description', 'Climate Zone Description': 'climate_zone_description', 'climatzonedescription': 'climate_zone_description', # Place variations 'place': 'place', 'Place': 'place', 'location': 'place', 'Location': 'place', # Country variations 'country': 'country', 'Country': 'country', # ID variations 'id': 'id', 'ID': 'id', 'Id': 'id' } # Rename columns based on mapping df_renamed = df.rename(columns=column_mapping) return df_renamed def parse_weather_data(): """Read weather data from CSV files""" try: # Read Real weather data print("Reading real weather data from tables/weather_real.csv...") real_df = pd.read_csv('tables/weather_real.csv') real_df = standardize_dataframe_columns(real_df) # Read Base TMY data print("Reading base weather data from tables/weather_base.csv...") base_df = pd.read_csv('tables/weather_base.csv') base_df = standardize_dataframe_columns(base_df) # Read Expanded TMY data print("Reading expanded weather data from tables/weather.csv...") expanded_df = pd.read_csv('tables/weather.csv') expanded_df = standardize_dataframe_columns(expanded_df) return real_df, base_df, expanded_df except FileNotFoundError as e: print(f"Error: Could not find CSV file - {e}") print("Please ensure the following files exist:") print("- tables/weather_real.csv") print("- tables/weather_base.csv") print("- tables/weather.csv") raise except Exception as e: print(f"Error reading CSV files: {e}") raise def get_climate_zone_color(zone_code): """Return color for each climate zone""" colors = { '0A': '#8B0000', # Extremely Hot Humid - Dark Red '0B': '#FF4500', # Extremely Hot Dry - Orange Red '1A': '#DC143C', # Very Hot Humid - Crimson '1B': '#FF6347', # Very Hot Dry - Tomato '2A': '#FF69B4', # Hot Humid - Hot Pink '2B': '#FFB6C1', # Hot Dry - Light Pink '3A': '#32CD32', # Warm Humid - Lime Green '3B': '#90EE90', # Warm Dry - Light Green '3C': '#00CED1', # Warm Marine - Dark Turquoise '4A': '#4169E1', # Mixed Humid - Royal Blue '4B': '#87CEEB', # Mixed Dry - Sky Blue '4C': '#1E90FF', # Mixed Marine - Dodger Blue '5A': '#9370DB', # Cool Humid - Medium Purple '5B': '#DDA0DD', # Cool Dry - Plum '5C': '#6495ED', # Cool Marine - Cornflower Blue '6A': '#4B0082', # Cold Humid - Indigo '6B': '#9932CC', # Cold Dry - Dark Orchid '7': '#000080', # Very Cold - Navy '8': '#483D8B', # Subarctic/Arctic - Dark Slate Blue } return colors.get(zone_code, '#808080') # Default to gray def create_world_map_with_cartopy(real_df, base_df, expanded_df, location_coords): """Create world map using Cartopy""" fig = plt.figure(figsize=(20, 12)) ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree()) # Add map features ax.add_feature(cfeature.COASTLINE, linewidth=0.5) ax.add_feature(cfeature.BORDERS, linewidth=0.3) ax.add_feature(cfeature.LAND, color='lightgray', alpha=0.3) ax.add_feature(cfeature.OCEAN, color='lightblue', alpha=0.3) ax.set_global() # Plot locations plotted_zones = set() plotted_types = set() # Helper function to safely plot data def plot_dataset(df, marker, label, dataset_name): plotted_count = 0 if df is None or len(df) == 0: print(f"Warning: {dataset_name} dataset is empty or None") return plotted_count if 'place' not in df.columns or 'climate_zone_code' not in df.columns: print(f"Warning: {dataset_name} dataset missing required columns") print(f"Available columns: {list(df.columns)}") return plotted_count for _, row in df.iterrows(): place = row['place'] if pd.isna(place) or place not in location_coords: continue lat, lon = location_coords[place] climate_zone = row['climate_zone_code'] if pd.isna(climate_zone): continue color = get_climate_zone_color(climate_zone) ax.plot(lon, lat, marker, color=color, markersize=10 if marker == 'o' else 8, markeredgecolor='black', markeredgewidth=1, transform=ccrs.PlateCarree(), label=label if label not in plotted_types else '') plotted_types.add(label) plotted_zones.add(climate_zone) plotted_count += 1 return plotted_count # Plot each dataset real_plotted = plot_dataset(real_df, 'o', 'Real Data', 'Real') base_plotted = plot_dataset(base_df, 's', 'Base TMY', 'Base') expanded_plotted = plot_dataset(expanded_df, '^', 'Expanded TMY', 'Expanded') print(f"\nPlotted locations:") print(f"- Real data: {real_plotted} locations") print(f"- Base TMY: {base_plotted} locations") print(f"- Expanded TMY: {expanded_plotted} locations") print(f"- Total plotted: {real_plotted + base_plotted + expanded_plotted}") return fig, ax, plotted_zones, real_plotted, base_plotted, expanded_plotted def create_world_map_basic(real_df, base_df, expanded_df, location_coords): """Create world map using basic matplotlib""" fig, ax = plt.subplots(figsize=(20, 12)) # Simple world map outline (very basic) ax.set_xlim(-180, 180) ax.set_ylim(-90, 90) ax.set_xlabel('Longitude') ax.set_ylabel('Latitude') ax.grid(True, alpha=0.3) # Plot locations plotted_zones = set() plotted_types = set() # Helper function to safely plot data def plot_dataset(df, marker, label, dataset_name): plotted_count = 0 if df is None or len(df) == 0: print(f"Warning: {dataset_name} dataset is empty or None") return plotted_count if 'place' not in df.columns or 'climate_zone_code' not in df.columns: print(f"Warning: {dataset_name} dataset missing required columns") print(f"Available columns: {list(df.columns)}") return plotted_count for _, row in df.iterrows(): place = row['place'] if pd.isna(place) or place not in location_coords: continue lat, lon = location_coords[place] climate_zone = row['climate_zone_code'] if pd.isna(climate_zone): continue color = get_climate_zone_color(climate_zone) ax.plot(lon, lat, marker, color=color, markersize=10 if marker == 'o' else 8, markeredgecolor='black', markeredgewidth=1, label=label if label not in plotted_types else '') plotted_types.add(label) plotted_zones.add(climate_zone) plotted_count += 1 return plotted_count # Plot each dataset real_plotted = plot_dataset(real_df, 'o', 'Real Data', 'Real') base_plotted = plot_dataset(base_df, 's', 'Base TMY', 'Base') expanded_plotted = plot_dataset(expanded_df, '^', 'Expanded TMY', 'Expanded') print(f"\nPlotted locations:") print(f"- Real data: {real_plotted} locations") print(f"- Base TMY: {base_plotted} locations") print(f"- Expanded TMY: {expanded_plotted} locations") print(f"- Total plotted: {real_plotted + base_plotted + expanded_plotted}") return fig, ax, plotted_zones, real_plotted, base_plotted, expanded_plotted def add_legends(fig, ax, plotted_zones): """Add legends for climate zones and data types""" # Create legend for data types (markers) marker_legend_elements = [ plt.Line2D([0], [0], marker='o', color='gray', linestyle='None', markersize=10, markeredgecolor='black', label='Real Data'), plt.Line2D([0], [0], marker='s', color='gray', linestyle='None', markersize=8, markeredgecolor='black', label='Base TMY'), plt.Line2D([0], [0], marker='^', color='gray', linestyle='None', markersize=8, markeredgecolor='black', label='Expanded TMY') ] # Create legend for climate zones (colors) zone_descriptions = { '0A': 'Extremely Hot Humid', '0B': 'Extremely Hot Dry', '1A': 'Very Hot Humid', '1B': 'Very Hot Dry', '2A': 'Hot Humid', '2B': 'Hot Dry', '3A': 'Warm Humid', '3B': 'Warm Dry', '3C': 'Warm Marine', '4A': 'Mixed Humid', '4B': 'Mixed Dry', '4C': 'Mixed Marine', '5A': 'Cool Humid', '5B': 'Cool Dry', '5C': 'Cool Marine', '6A': 'Cold Humid', '6B': 'Cold Dry', '7': 'Very Cold', '8': 'Subarctic/Arctic', } color_legend_elements = [] for zone in sorted(plotted_zones): color = get_climate_zone_color(zone) description = zone_descriptions.get(zone, zone) color_legend_elements.append( plt.Line2D([0], [0], marker='o', color=color, linestyle='None', markersize=8, label=f'{zone}: {description}') ) # Position legends legend1 = ax.legend(handles=marker_legend_elements, loc='upper left', bbox_to_anchor=(0.02, 0.98), title='Data Types', fontsize=10) legend2 = ax.legend(handles=color_legend_elements, loc='upper right', bbox_to_anchor=(0.98, 0.98), title='Climate Zones', fontsize=9, ncol=2) ax.add_artist(legend1) # Add the first legend back plt.setp(legend1.get_title(), fontsize=12, fontweight='bold') plt.setp(legend2.get_title(), fontsize=12, fontweight='bold') def main(): """Main function to create the weather locations world map""" print("Creating Weather Locations World Map...") print("=" * 50) # Get location coordinates location_coords = create_location_database() # Parse weather data from CSV files try: real_df, base_df, expanded_df = parse_weather_data() print(f"\nSuccessfully loaded weather data:") print(f"- Real weather locations: {len(real_df)}") print(f"- Base TMY locations: {len(base_df)}") print(f"- Expanded TMY locations: {len(expanded_df)}") # Display column information for debugging print(f"\nDataset overview:") print(f"- Real data: {len(real_df)} locations") if len(real_df) > 0: print(f" Columns: {list(real_df.columns)}") print(f" Sample: {real_df['place'].iloc[0] if 'place' in real_df.columns else 'No place column'}") print(f"- Base data: {len(base_df)} locations") if len(base_df) > 0: print(f" Columns: {list(base_df.columns)}") print(f" Sample: {base_df['place'].iloc[0] if 'place' in base_df.columns else 'No place column'}") print(f"- Expanded data: {len(expanded_df)} locations") if len(expanded_df) > 0: print(f" Columns: {list(expanded_df.columns)}") print(f" Sample: {expanded_df['place'].iloc[0] if 'place' in expanded_df.columns else 'No place column'}") # Check for locations that can be mapped all_places = set() if 'place' in real_df.columns: all_places.update(real_df['place'].unique()) if 'place' in base_df.columns: all_places.update(base_df['place'].unique()) if 'place' in expanded_df.columns: all_places.update(expanded_df['place'].unique()) mappable_places = [place for place in all_places if place in location_coords] unmappable_places = [place for place in all_places if place not in location_coords] print(f"\nLocation mapping status:") print(f"- Total unique places in data: {len(all_places)}") print(f"- Places that can be mapped: {len(mappable_places)}") print(f"- Places missing coordinates: {len(unmappable_places)}") if unmappable_places: print(f"- Unmappable places: {unmappable_places[:10]}{'...' if len(unmappable_places) > 10 else ''}") except Exception as e: print(f"Error loading data: {e}") return # Create the map real_plotted = 0 base_plotted = 0 expanded_plotted = 0 if CARTOPY_AVAILABLE: print("Using Cartopy for world map...") fig, ax, plotted_zones, real_plotted, base_plotted, expanded_plotted = create_world_map_with_cartopy(real_df, base_df, expanded_df, location_coords) else: print("Using basic matplotlib for world map...") fig, ax, plotted_zones, real_plotted, base_plotted, expanded_plotted = create_world_map_basic(real_df, base_df, expanded_df, location_coords) # Add legends add_legends(fig, ax, plotted_zones) # Add grid and improve layout ax.grid(True, alpha=0.3, linestyle='--') # Add text box with information info_text = """Weather Data Types: • Circles (○): Real weather data (actual recorded years) • Squares (□): Base TMY (Typical Meteorological Year) • Triangles (△): Expanded TMY locations Climate zones based on international standards. Colors represent different thermal characteristics.""" ax.text(0.02, 0.02, info_text, transform=ax.transAxes, fontsize=10, verticalalignment='bottom', horizontalalignment='left', bbox=dict(boxstyle='round', facecolor='white', alpha=0.8), fontfamily='monospace') plt.tight_layout() # Show statistics total_plotted = real_plotted + base_plotted + expanded_plotted print(f"\n" + "="*50) print(f"MAP GENERATION COMPLETE") print(f"="*50) print(f"Final Statistics:") print(f"- Total locations plotted: {total_plotted}") print(f"- Unique climate zones: {len(plotted_zones)}") print(f"- Climate zones represented: {', '.join(sorted(plotted_zones))}") print(f"- Dataset breakdown:") print(f" • Real weather data: {real_plotted} locations") print(f" • Base TMY data: {base_plotted} locations") print(f" • Expanded TMY data: {expanded_plotted} locations") if total_plotted == 0: print("\nWARNING: No locations were plotted. This might be due to:") print("- Missing or incorrect column names in CSV files") print("- Location names not matching coordinate database") print("- Empty or corrupted CSV files") return # Save the plot try: plt.savefig('weather_locations_world_map.png', dpi=300, bbox_inches='tight') print(f"\n✅ Map saved as 'weather_locations_world_map.png'") except Exception as e: print(f"\n❌ Error saving map: {e}") try: plt.show() print("✅ Map displayed successfully") except Exception as e: print(f"❌ Error displaying map: {e}") print("Map file has been saved even if display failed.") if __name__ == "__main__": main()