import folium
import os
import time
import random
import pandas as pd
from pathlib import Path
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager
# Delhi center coordinates
DELHI_LAT = 28.7041
DELHI_LON = 77.1025
# Base hotspots data with AQI values and weight ratios
# Weight = how much this location deviates from average (>1 = higher, <1 = lower)
BASE_HOTSPOTS = [
{"name": "Chandni Chowk", "lat": 28.656, "lon": 77.231, "base_aqi": 384, "weight": 1.2092},
{"name": "Nehru Nagar", "lat": 28.5697, "lon": 77.253, "base_aqi": 384, "weight": 1.2092},
{"name": "New Moti Bagh", "lat": 28.582, "lon": 77.1717, "base_aqi": 377, "weight": 1.1872},
{"name": "Shadipur", "lat": 28.6517, "lon": 77.1582, "base_aqi": 375, "weight": 1.1809},
{"name": "Wazirpur", "lat": 28.6967, "lon": 77.1658, "base_aqi": 354, "weight": 1.1148},
{"name": "Mundka", "lat": 28.6794, "lon": 77.0284, "base_aqi": 353, "weight": 1.1116},
{"name": "Punjabi Bagh", "lat": 28.673, "lon": 77.1374, "base_aqi": 349, "weight": 1.0990},
{"name": "RK Puram", "lat": 28.5505, "lon": 77.1849, "base_aqi": 342, "weight": 1.0770},
{"name": "Jahangirpuri", "lat": 28.716, "lon": 77.1829, "base_aqi": 338, "weight": 1.0644},
{"name": "Okhla Phase-2", "lat": 28.5365, "lon": 77.2803, "base_aqi": 337, "weight": 1.0612},
{"name": "Dwarka Sector-8", "lat": 28.5656, "lon": 77.067, "base_aqi": 336, "weight": 1.0581},
{"name": "Dhyanchand Stadium", "lat": 28.6125, "lon": 77.2372, "base_aqi": 335, "weight": 1.0549},
{"name": "Patparganj", "lat": 28.612, "lon": 77.292, "base_aqi": 334, "weight": 1.0518},
{"name": "Bawana", "lat": 28.8, "lon": 77.03, "base_aqi": 328, "weight": 1.0329},
{"name": "Sirifort", "lat": 28.5521, "lon": 77.2193, "base_aqi": 326, "weight": 1.0266},
{"name": "ITO", "lat": 28.6294, "lon": 77.241, "base_aqi": 324, "weight": 1.0203},
{"name": "Karni Singh Shooting Range", "lat": 28.4998, "lon": 77.2668, "base_aqi": 323, "weight": 1.0171},
{"name": "Sonia Vihar", "lat": 28.7074, "lon": 77.2599, "base_aqi": 317, "weight": 0.9982},
{"name": "JLN Stadium", "lat": 28.5828, "lon": 77.2344, "base_aqi": 314, "weight": 0.9888},
{"name": "Rohini", "lat": 28.7019, "lon": 77.0984, "base_aqi": 312, "weight": 0.9825},
{"name": "Narela", "lat": 28.85, "lon": 77.1, "base_aqi": 311, "weight": 0.9793},
{"name": "Mandir Marg", "lat": 28.6325, "lon": 77.1994, "base_aqi": 311, "weight": 0.9793},
{"name": "IGI Airport T3", "lat": 28.5562, "lon": 77.1, "base_aqi": 305, "weight": 0.9605},
{"name": "Vivek Vihar", "lat": 28.6635, "lon": 77.3152, "base_aqi": 303, "weight": 0.9542},
{"name": "Pusa IMD", "lat": 28.6335, "lon": 77.1651, "base_aqi": 298, "weight": 0.9384},
{"name": "Burari Crossing", "lat": 28.7592, "lon": 77.1938, "base_aqi": 294, "weight": 0.9258},
{"name": "Aurobindo Marg", "lat": 28.545, "lon": 77.205, "base_aqi": 287, "weight": 0.9038},
{"name": "Dilshad Garden (IHBAS)", "lat": 28.681, "lon": 77.305, "base_aqi": 283, "weight": 0.8912},
{"name": "Lodhi Road", "lat": 28.5921, "lon": 77.2284, "base_aqi": 282, "weight": 0.8880},
{"name": "NSIT-Dwarka", "lat": 28.6081, "lon": 77.0193, "base_aqi": 275, "weight": 0.8660},
{"name": "Alipur", "lat": 28.8, "lon": 77.15, "base_aqi": 273, "weight": 0.8597},
{"name": "Najafgarh", "lat": 28.6125, "lon": 76.983, "base_aqi": 262, "weight": 0.8250},
{"name": "CRRI-Mathura Road", "lat": 28.5518, "lon": 77.2752, "base_aqi": 252, "weight": 0.7936},
{"name": "DTU", "lat": 28.7501, "lon": 77.1177, "base_aqi": 219, "weight": 0.6896},
]
# Baseline average AQI (average of all base_aqi values)
BASELINE_AVG_AQI = sum(h['base_aqi'] for h in BASE_HOTSPOTS) / len(BASE_HOTSPOTS)
def get_emission_scaling_factor(year: int) -> float:
"""
Calculate the scaling factor for a forecast year relative to 2025 baseline.
Returns the ratio of year's avg emissions to 2025's avg emissions.
"""
try:
# Load forecast data
forecast_path = Path(__file__).parent / "emission_forecast_3years_full.csv"
if not forecast_path.exists():
print(f"Forecast file not found: {forecast_path}")
return 1.0
df = pd.read_csv(forecast_path)
df['Date'] = pd.to_datetime(df['Date'])
df['Year'] = df['Date'].dt.year
# Get baseline 2025 average
baseline_2025 = df[df['Year'] == 2025]['Total_Emission'].mean()
# Get selected year average
year_avg = df[df['Year'] == year]['Total_Emission'].mean()
if pd.isna(baseline_2025) or pd.isna(year_avg) or baseline_2025 == 0:
print(f"Could not calculate scaling factor for year {year}")
return 1.0
scaling_factor = year_avg / baseline_2025
print(f"Year {year}: avg emission = {year_avg:.2f}, baseline = {baseline_2025:.2f}, scaling = {scaling_factor:.4f}")
return scaling_factor
except Exception as e:
print(f"Error calculating emission scaling factor: {e}")
return 1.0
def add_delhi_boundary_to_map(m):
"""Helper to fetch and add Delhi boundary to a map."""
import requests
try:
geojson_url = "https://raw.githubusercontent.com/datameet/Municipal_Spatial_Data/master/Delhi/Delhi_Boundary.geojson"
# Add Boundary Outline
folium.GeoJson(
geojson_url,
name="Delhi Boundary",
style_function=lambda x: {
'fillColor': 'none',
'color': 'black', # Black for visibility on light tiles
'weight': 3,
'dashArray': '5, 5',
'opacity': 0.6
}
).add_to(m)
return True
except Exception as e:
print(f"Error adding boundary: {e}")
return False
def generate_heatmap_html():
"""Generates a Folium map with Grid Heatmap (Clipped to Delhi)."""
# Create base map
m = folium.Map(
location=[DELHI_LAT, DELHI_LON],
zoom_start=10,
control_scale=True
)
# Heatmap Grid Generation
import requests
from shapely.geometry import shape, Point
from shapely.ops import unary_union
# Fetch Delhi Boundary for layout clipping & display
# We fetch manually here for the shapely polygon, AND add the visual layer
has_boundary = False
delhi_polygon = None
# Add visual boundary
add_delhi_boundary_to_map(m)
try:
geojson_url = "https://raw.githubusercontent.com/datameet/Municipal_Spatial_Data/master/Delhi/Delhi_Boundary.geojson"
resp = requests.get(geojson_url)
data = resp.json()
# Create a unified polygon for checking
features = data.get('features', [])
shapes = [shape(f['geometry']) for f in features]
delhi_polygon = unary_union(shapes)
has_boundary = True
except Exception as e:
print(f"Error processing boundary for clipping: {e}")
# Grid config
lat_min, lat_max = 28.40, 28.88
lon_min, lon_max = 76.85, 77.35
step = 0.015 # Approx 1.5km grid size
def get_color(value):
"""Red-scale heatmap colors like the reference image."""
if value < 100: return "#fee5d9" # Very Light Pink
if value < 200: return "#fcae91" # Light Pink/Orange
if value < 300: return "#fb6a4a" # Salmon
if value < 400: return "#de2d26" # Red
return "#a50f15" # Dark Red/Brown
lat = lat_min
while lat < lat_max:
lon = lon_min
while lon < lon_max:
# Check if center of grid is inside Delhi
center_point = Point(lon + step/2, lat + step/2)
if has_boundary and not delhi_polygon.contains(center_point):
# Skip if outside
lon += step
continue
# Generate simulated AQI/CO2 data with spatial coherence
# Higher near center (Delhi)
dist_center = ((lat - DELHI_LAT)**2 + (lon - DELHI_LON)**2)**0.5
# Base value + random noise - distance decay
base_value = 450 - (dist_center * 800)
val = base_value + random.randint(-50, 50)
val = max(50, min(500, val)) # Clamp 50-500
color = get_color(val)
# Draw grid cell
folium.Rectangle(
bounds=[[lat, lon], [lat + step, lon + step]],
color=None, # No border
fill=True,
fill_color=color,
fill_opacity=0.6, # Slightly transparent
tooltip=f"Zone AQI: {int(val)}"
).add_to(m)
lon += step
lat += step
return m.get_root().render()
def generate_hotspots_html():
"""Generates the original Hotspot Map with markers."""
m = folium.Map(location=[DELHI_LAT, DELHI_LON], zoom_start=11)
# Add Visual Boundary
add_delhi_boundary_to_map(m)
# Original Hotspots Data
hotspots = [
{"name": "Chandni Chowk", "lat": 28.656, "lon": 77.231, "aqi": 384},
{"name": "Nehru Nagar", "lat": 28.5697, "lon": 77.253, "aqi": 384},
{"name": "New Moti Bagh", "lat": 28.582, "lon": 77.1717, "aqi": 377},
{"name": "Shadipur", "lat": 28.6517, "lon": 77.1582, "aqi": 375},
{"name": "Wazirpur", "lat": 28.6967, "lon": 77.1658, "aqi": 354},
{"name": "Ashok Vihar", "lat": 28.6856, "lon": 77.178, "aqi": 231},
{"name": "Mundka", "lat": 28.6794, "lon": 77.0284, "aqi": 353},
{"name": "Punjabi Bagh", "lat": 28.673, "lon": 77.1374, "aqi": 349},
{"name": "RK Puram", "lat": 28.5505, "lon": 77.1849, "aqi": 342},
{"name": "Jahangirpuri", "lat": 28.716, "lon": 77.1829, "aqi": 338},
{"name": "Okhla Phase-2", "lat": 28.5365, "lon": 77.2803, "aqi": 337},
{"name": "Dwarka Sector-8", "lat": 28.5656, "lon": 77.067, "aqi": 336},
{"name": "Patparganj", "lat": 28.612, "lon": 77.292, "aqi": 334},
{"name": "Bawana", "lat": 28.8, "lon": 77.03, "aqi": 328},
{"name": "Sonia Vihar", "lat": 28.7074, "lon": 77.2599, "aqi": 317},
{"name": "Rohini", "lat": 28.7019, "lon": 77.0984, "aqi": 312},
{"name": "Narela", "lat": 28.85, "lon": 77.1, "aqi": 311},
{"name": "Mandir Marg", "lat": 28.6325, "lon": 77.1994, "aqi": 311},
{"name": "Vivek Vihar", "lat": 28.6635, "lon": 77.3152, "aqi": 303},
{"name": "Anand Vihar", "lat": 28.6508, "lon": 77.3152, "aqi": 335},
{"name": "Dhyanchand Stadium", "lat": 28.6125, "lon": 77.2372, "aqi": 335},
{"name": "Sirifort", "lat": 28.5521, "lon": 77.2193, "aqi": 326},
{"name": "Karni Singh Shooting Range", "lat": 28.4998, "lon": 77.2668, "aqi": 323},
{"name": "ITO", "lat": 28.6294, "lon": 77.241, "aqi": 324},
{"name": "JLN Stadium", "lat": 28.5828, "lon": 77.2344, "aqi": 314},
{"name": "IGI Airport T3", "lat": 28.5562, "lon": 77.1, "aqi": 305},
{"name": "Pusa IMD", "lat": 28.6335, "lon": 77.1651, "aqi": 298},
{"name": "Burari Crossing", "lat": 28.7592, "lon": 77.1938, "aqi": 294},
{"name": "Aurobindo Marg", "lat": 28.545, "lon": 77.205, "aqi": 287},
{"name": "Dilshad Garden (IHBAS)", "lat": 28.681, "lon": 77.305, "aqi": 283},
{"name": "Lodhi Road", "lat": 28.5921, "lon": 77.2284, "aqi": 282},
{"name": "NSIT-Dwarka", "lat": 28.6081, "lon": 77.0193, "aqi": 275},
{"name": "Alipur", "lat": 28.8, "lon": 77.15, "aqi": 273},
{"name": "Najafgarh", "lat": 28.6125, "lon": 76.983, "aqi": 262},
{"name": "CRRI-Mathura Road", "lat": 28.5518, "lon": 77.2752, "aqi": 252},
{"name": "DTU", "lat": 28.7501, "lon": 77.1177, "aqi": 219},
{"name": "DU North Campus", "lat": 28.69, "lon": 77.21, "aqi": 298},
{"name": "Ayanagar", "lat": 28.4809, "lon": 77.1255, "aqi": 342}
]
for spot in hotspots:
color = "gray"
aqi = spot['aqi']
if isinstance(aqi, (int, float)):
color = "green"
if aqi > 100: color = "yellow"
if aqi > 200: color = "orange"
if aqi > 300: color = "red"
if aqi > 400: color = "purple"
folium.CircleMarker(
location=[spot['lat'], spot['lon']],
radius=15,
popup=f"{spot['name']}: AQI {spot['aqi']}",
color=color,
fill=True,
fill_color=color,
fill_opacity=0.7
).add_to(m)
folium.Marker(
location=[spot['lat'], spot['lon']],
icon=folium.DivIcon(html=f'{spot["aqi"]}
')
).add_to(m)
return m.get_root().render()
def generate_forecast_hotspots_html(year: int):
"""
Generates a Hotspot Map with AQI values adjusted for the forecast year.
Uses emission scaling factor to project future AQI values.
"""
m = folium.Map(location=[DELHI_LAT, DELHI_LON], zoom_start=11)
# Add Visual Boundary
add_delhi_boundary_to_map(m)
# Get scaling factor for the year
scaling_factor = get_emission_scaling_factor(year)
# Generate adjusted hotspots
for spot in BASE_HOTSPOTS:
# Apply scaling: new_aqi = base_aqi * scaling_factor
# The weight is already embedded in base_aqi, so we just scale by emission change
adjusted_aqi = int(spot['base_aqi'] * scaling_factor)
# Determine color based on AQI
color = "gray"
if isinstance(adjusted_aqi, (int, float)):
color = "green"
if adjusted_aqi > 100: color = "yellow"
if adjusted_aqi > 200: color = "orange"
if adjusted_aqi > 300: color = "red"
if adjusted_aqi > 400: color = "purple"
folium.CircleMarker(
location=[spot['lat'], spot['lon']],
radius=15,
popup=f"{spot['name']}: AQI {adjusted_aqi} ({year} Forecast)",
color=color,
fill=True,
fill_color=color,
fill_opacity=0.7
).add_to(m)
folium.Marker(
location=[spot['lat'], spot['lon']],
icon=folium.DivIcon(html=f'{adjusted_aqi}
')
).add_to(m)
return m.get_root().render()
def render_map_to_png(html_content, output_path):
"""
Renders the HTML content to a PNG image using Selenium headless Chrome.
"""
import uuid
unique_id = uuid.uuid4()
tmp_html_path = os.path.abspath(f"temp_map_{unique_id}.html")
# Write HTML to temp file
with open(tmp_html_path, "w", encoding="utf-8") as f:
f.write(html_content)
chrome_options = Options()
chrome_options.add_argument("--headless")
chrome_options.add_argument("--window-size=800,600")
chrome_options.add_argument("--disable-gpu")
chrome_options.add_argument("--no-sandbox")
driver = None
try:
# Auto-install chromedriver
service = Service(ChromeDriverManager().install())
driver = webdriver.Chrome(service=service, options=chrome_options)
# Load local HTML file
driver.get(f"file:///{tmp_html_path}")
time.sleep(2) # Wait for map to render tiles
# Save screenshot
driver.save_screenshot(output_path)
print(f"Map image saved to {output_path}")
except Exception as e:
print(f"Error rendering map to PNG: {e}")
import traceback
traceback.print_exc()
finally:
if driver:
driver.quit()
# Cleanup temp file
if os.path.exists(tmp_html_path):
os.remove(tmp_html_path)
if __name__ == "__main__":
# Test run
html = generate_aqi_map_html()
render_map_to_png(html, "aqi_map_test.png")