spatial_diffusion.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from shapely.geometry import Point, Polygon
import random
import datetime
import gradio as gr
import tempfile
import os
import requests
import json
from typing import List, Tuple, Optional, Dict, Any, Union

def fetch_osm_exclusion_zones(bounds: Tuple[float, float, float, float], exclusion_types: List[str]) -> Optional[Any]:
    """

    Fetch exclusion zones from OpenStreetMap using Overpass API.

    

    Args:

        bounds: (min_lat, min_lon, max_lat, max_lon) bounding box

        exclusion_types: List of exclusion types to fetch

        

    Returns:

        GeoDataFrame with exclusion polygons or None if failed

    """
    try:
        import geopandas as gpd
        from shapely.geometry import Polygon, MultiPolygon, LineString
        
        # Overpass API endpoint
        overpass_url = "http://overpass-api.de/api/interpreter"
        
        # Build Overpass query based on selected exclusion types
        queries = []
        
        if "Water bodies" in exclusion_types:
            # Get both water polygons AND linear waterways
            queries.extend([
                # Water area polygons
                f'way["natural"="water"]({bounds[0]},{bounds[1]},{bounds[2]},{bounds[3]});',
                f'relation["natural"="water"]({bounds[0]},{bounds[1]},{bounds[2]},{bounds[3]});',
                f'way["landuse"="reservoir"]({bounds[0]},{bounds[1]},{bounds[2]},{bounds[3]});',
                f'way["water"="lake"]({bounds[0]},{bounds[1]},{bounds[2]},{bounds[3]});',
                f'way["water"="pond"]({bounds[0]},{bounds[1]},{bounds[2]},{bounds[3]});',
                # Linear waterways (rivers, streams, canals)
                f'way["waterway"="river"]({bounds[0]},{bounds[1]},{bounds[2]},{bounds[3]});',
                f'way["waterway"="stream"]({bounds[0]},{bounds[1]},{bounds[2]},{bounds[3]});',
                f'way["waterway"="canal"]({bounds[0]},{bounds[1]},{bounds[2]},{bounds[3]});'
            ])
        
        if "Parks & green spaces" in exclusion_types:
            queries.extend([
                f'way["leisure"="park"]({bounds[0]},{bounds[1]},{bounds[2]},{bounds[3]});',
                f'way["landuse"="forest"]({bounds[0]},{bounds[1]},{bounds[2]},{bounds[3]});',
                f'way["landuse"="grass"]({bounds[0]},{bounds[1]},{bounds[2]},{bounds[3]});',
                f'way["natural"="wood"]({bounds[0]},{bounds[1]},{bounds[2]},{bounds[3]});'
            ])
        
        if "Industrial areas" in exclusion_types:
            queries.extend([
                f'way["landuse"="industrial"]({bounds[0]},{bounds[1]},{bounds[2]},{bounds[3]});',
                f'way["landuse"="commercial"]({bounds[0]},{bounds[1]},{bounds[2]},{bounds[3]});'
            ])
        
        if "Major roads" in exclusion_types:
            queries.extend([
                f'way["highway"~"motorway|trunk|primary"]({bounds[0]},{bounds[1]},{bounds[2]},{bounds[3]});'
            ])
        
        if not queries:
            return None
        
        # Build complete Overpass query
        overpass_query = f"""

        [out:json][timeout:25];

        (

        {chr(10).join(queries)}

        );

        out geom;

        """
        
        print(f"Fetching OSM data for exclusion zones: {exclusion_types}")
        
        # Make request to Overpass API
        response = requests.get(overpass_url, params={'data': overpass_query})
        response.raise_for_status()
        
        data = response.json()
        
        if 'elements' not in data or not data['elements']:
            print("No exclusion zones found in the specified area")
            return None
        
        # Convert OSM data to polygons
        polygons = []
        zone_types = []
        
        for element in data['elements']:
            try:
                if element['type'] == 'way' and 'geometry' in element:
                    tags = element.get('tags', {})
                    
                    # Determine what type of feature this is
                    zone_type = None
                    if 'natural' in tags and tags['natural'] == 'water':
                        zone_type = 'Water'
                    elif 'landuse' in tags and tags['landuse'] == 'reservoir':
                        zone_type = 'Water'
                    elif 'water' in tags:
                        zone_type = 'Water'
                    elif 'waterway' in tags and tags['waterway'] in ['river', 'stream', 'canal']:
                        zone_type = 'Water'
                    elif 'leisure' in tags and tags['leisure'] == 'park':
                        zone_type = 'Park'
                    elif 'landuse' in tags and tags['landuse'] in ['forest', 'grass']:
                        zone_type = 'Green space'
                    elif 'natural' in tags and tags['natural'] == 'wood':
                        zone_type = 'Forest'
                    elif 'landuse' in tags and tags['landuse'] in ['industrial', 'commercial']:
                        zone_type = 'Industrial/Commercial'
                    elif 'highway' in tags:
                        zone_type = 'Major road'
                    
                    if zone_type is None:
                        continue
                    
                    # Create polygon from way geometry
                    coords = [(node['lon'], node['lat']) for node in element['geometry']]
                    
                    # Handle different geometry types
                    if 'waterway' in tags or 'highway' in tags:
                        # For linear features (rivers, roads), create a buffered polygon from the line
                        if len(coords) >= 2:
                            try:
                                line = LineString(coords)
                                # Buffer size depends on feature type
                                if 'waterway' in tags:
                                    if tags['waterway'] == 'river':
                                        buffer_size = 50 / 111320  # Rivers: ~50 meters
                                    elif tags['waterway'] == 'canal':
                                        buffer_size = 30 / 111320  # Canals: ~30 meters  
                                    else:  # streams
                                        buffer_size = 20 / 111320  # Streams: ~20 meters
                                else:  # highways
                                    buffer_size = 25 / 111320  # Roads: ~25 meters
                                
                                polygon = line.buffer(buffer_size)
                                if polygon.is_valid and polygon.area > 0:
                                    polygons.append(polygon)
                                    zone_types.append(zone_type)
                            except Exception as e:
                                print(f"Error buffering linear feature: {str(e)}")
                                continue
                    else:
                        # For areas, create closed polygons
                        if len(coords) > 2:
                            # Close polygon if not already closed
                            if coords[0] != coords[-1]:
                                coords.append(coords[0])
                            
                            if len(coords) >= 4:  # Valid polygon needs at least 4 points
                                try:
                                    polygon = Polygon(coords)
                                    if polygon.is_valid and polygon.area > 0:
                                        polygons.append(polygon)
                                        zone_types.append(zone_type)
                                except Exception as e:
                                    print(f"Error creating polygon: {str(e)}")
                                    continue
                                    
            except Exception as e:
                print(f"Error processing OSM element: {str(e)}")
                continue
        
        if not polygons:
            print("No valid polygons found in OSM data")
            return None
        
        # Create GeoDataFrame
        gdf = gpd.GeoDataFrame(
            {'zone_type': zone_types}, 
            geometry=polygons, 
            crs='EPSG:4326'
        )
        
        print(f"Successfully fetched {len(gdf)} exclusion zones from OpenStreetMap")
        print(f"Zone types found: {gdf['zone_type'].value_counts().to_dict()}")
        return gdf
        
    except ImportError:
        print("GeoPandas not available for OSM processing")
        return None
    except requests.exceptions.RequestException as e:
        print(f"Error fetching data from OpenStreetMap: {str(e)}")
        return None
    except Exception as e:
        print(f"Error processing OpenStreetMap data: {str(e)}")
        return None

def calculate_bounds_from_points(input_df: pd.DataFrame, buffer_km: float = 2.0) -> Tuple[float, float, float, float]:
    """Calculate bounding box around input points with buffer"""
    # Get min/max coordinates
    min_lat = input_df['lat'].min()
    max_lat = input_df['lat'].max()
    min_lon = input_df['lon'].min()
    max_lon = input_df['lon'].max()
    
    # Add buffer (approximate conversion from km to degrees)
    buffer_deg = buffer_km / 111.0  # Rough conversion: 1 degree ≈ 111 km
    
    return (
        min_lat - buffer_deg,  # min_lat
        min_lon - buffer_deg,  # min_lon  
        max_lat + buffer_deg,  # max_lat
        max_lon + buffer_deg   # max_lon
    )

class SpatialDiffuser:
    """

    Class for performing spatial diffusion - takes points with counts and diffuses them

    according to specified distributions within given radii, with optional exclusion zones.

    """
    
    def __init__(self):
        self.distribution_methods = {
            "uniform": self._uniform_distribution,
            "normal": self._normal_distribution,
            "exponential_decay": self._exponential_decay,
            "distance_weighted": self._distance_weighted
        }
        
    def diffuse_points(self, 

                       input_data: pd.DataFrame, 

                       distribution_type: str = "uniform",

                       global_radius: Optional[float] = None,

                       time_start: Optional[datetime.datetime] = None, 

                       time_end: Optional[datetime.datetime] = None,

                       seed: Optional[int] = None,

                       exclusion_zones_gdf: Optional[Any] = None) -> pd.DataFrame:
        """

        Generate diffused points based on input coordinates and counts.

        

        Args:

            input_data: DataFrame with columns: lat, lon, count, radius (optional)

            distribution_type: Type of spatial distribution to use

            global_radius: Radius to use for all points if not specified individually (in meters)

            time_start: Start time for temporal distribution

            time_end: End time for temporal distribution

            seed: Random seed for reproducible results

            exclusion_zones_gdf: GeoDataFrame with polygons to exclude points from

            

        Returns:

            DataFrame with columns: lat, lon, source_id, timestamp (if temporal)

        """
        # Set random seed if provided
        if seed is not None:
            np.random.seed(seed)
            random.seed(seed)
            
        if distribution_type not in self.distribution_methods:
            raise ValueError(f"Distribution type '{distribution_type}' not supported. Choose from: {list(self.distribution_methods.keys())}")
        
        # Initialize list to hold all generated points
        all_points = []
        
        # Generate points for each input location
        for idx, row in input_data.iterrows():
            # Get radius (either from row or global)
            radius = row.get('radius', global_radius)
            if radius is None:
                raise ValueError("Radius must be specified either globally or per point")
            
            # Get count
            count = int(row['count'])
            if count <= 0:
                continue
                
            # Generate points with exclusion zone filtering
            new_points = self._generate_points_with_exclusions(
                lat=row['lat'],
                lon=row['lon'],
                count=count,
                radius=radius,
                distribution_type=distribution_type,
                exclusion_zones_gdf=exclusion_zones_gdf
            )
            
            # Add source identifier
            source_ids = [idx] * len(new_points)
            
            # Add timestamps if temporal distribution is requested
            if time_start is not None and time_end is not None:
                timestamps = self._generate_timestamps(len(new_points), time_start, time_end)
                
                # Combine points with metadata
                for i, point in enumerate(new_points):
                    all_points.append({
                        'lat': point[0],
                        'lon': point[1],
                        'source_id': source_ids[i],
                        'timestamp': timestamps[i]
                    })
            else:
                # Combine points with metadata without timestamps
                for i, point in enumerate(new_points):
                    all_points.append({
                        'lat': point[0],
                        'lon': point[1],
                        'source_id': source_ids[i]
                    })
        
        # Convert to DataFrame
        result = pd.DataFrame(all_points)
        return result
    
    def _generate_points_with_exclusions(self, lat: float, lon: float, count: int, radius: float, 

                                       distribution_type: str, exclusion_zones_gdf: Optional[Any] = None) -> List[Tuple[float, float]]:
        """Generate points while avoiding exclusion zones"""
        
        if exclusion_zones_gdf is None or len(exclusion_zones_gdf) == 0:
            # No exclusion zones, use normal generation
            return self.distribution_methods[distribution_type](lat, lon, count, radius)
        
        try:
            import geopandas as gpd
            from shapely.geometry import Point
            
            valid_points = []
            max_attempts = count * 10  # Generate up to 10x more points to account for exclusions
            attempts = 0
            
            # Ensure exclusion zones are in the same CRS as our points (WGS84)
            if exclusion_zones_gdf.crs is None:
                exclusion_zones_gdf = exclusion_zones_gdf.set_crs('EPSG:4326')
            elif exclusion_zones_gdf.crs != 'EPSG:4326':
                exclusion_zones_gdf = exclusion_zones_gdf.to_crs('EPSG:4326')
            
            while len(valid_points) < count and attempts < max_attempts:
                # Generate a batch of points
                batch_size = min(count * 2, max_attempts - attempts)
                candidate_points = self.distribution_methods[distribution_type](
                    lat, lon, batch_size, radius
                )
                
                # Check each point against exclusion zones
                for point in candidate_points:
                    if len(valid_points) >= count:
                        break
                        
                    point_geom = Point(point[1], point[0])  # lon, lat for Point
                    
                    # Check if point intersects with any exclusion zone
                    is_excluded = False
                    for _, exclusion_zone in exclusion_zones_gdf.iterrows():
                        if point_geom.intersects(exclusion_zone.geometry):
                            is_excluded = True
                            break
                    
                    if not is_excluded:
                        valid_points.append(point)
                
                attempts += batch_size
            
            # If we couldn't generate enough valid points, warn the user
            if len(valid_points) < count:
                print(f"Warning: Could only generate {len(valid_points)} valid points out of {count} requested for location ({lat}, {lon}). Exclusion zones may be too restrictive.")
            
            return valid_points
            
        except ImportError:
            print("GeoPandas not available for exclusion zone processing. Generating points without exclusions.")
            return self.distribution_methods[distribution_type](lat, lon, count, radius)
        except Exception as e:
            print(f"Error processing exclusion zones: {str(e)}. Generating points without exclusions.")
            return self.distribution_methods[distribution_type](lat, lon, count, radius)

    def _uniform_distribution(self, lat: float, lon: float, count: int, radius: float) -> List[Tuple[float, float]]:
        """Generate points uniformly distributed within a circle"""
        points = []
        
        for _ in range(count):
            # Generate a random angle and distance
            angle = random.uniform(0, 2 * np.pi)
            # Uniform distribution needs square root to avoid clustering in center
            r = radius * np.sqrt(random.uniform(0, 1))
            
            # Convert polar coordinates to Cartesian
            x = r * np.cos(angle)
            y = r * np.sin(angle)
            
            # Convert meters to approximate degrees (this is a simplification)
            # A more accurate implementation would use proper geographic projections
            lat_offset = y / 111320  # 1 degree latitude is approximately 111320 meters
            # Longitude degrees vary with latitude, so adjust accordingly
            lon_offset = x / (111320 * np.cos(np.radians(lat)))
            
            new_lat = lat + lat_offset
            new_lon = lon + lon_offset
            
            points.append((new_lat, new_lon))
            
        return points
    
    def _normal_distribution(self, lat: float, lon: float, count: int, radius: float) -> List[Tuple[float, float]]:
        """Generate points with normal distribution (more concentrated near center)"""
        points = []
        
        # Standard deviation as a fraction of radius
        std_dev = radius / 3  # 3 sigma rule - 99.7% of points within radius
        
        for _ in range(count):
            # Generate points using normal distribution
            while True:
                # Generate x and y offsets using normal distribution
                x = np.random.normal(0, std_dev)
                y = np.random.normal(0, std_dev)
                
                # Calculate distance from center
                distance = np.sqrt(x**2 + y**2)
                
                # If point is within radius, keep it
                if distance <= radius:
                    break
            
            # Convert meters to approximate degrees
            lat_offset = y / 111320
            lon_offset = x / (111320 * np.cos(np.radians(lat)))
            
            new_lat = lat + lat_offset
            new_lon = lon + lon_offset
            
            points.append((new_lat, new_lon))
            
        return points
    
    def _exponential_decay(self, lat: float, lon: float, count: int, radius: float) -> List[Tuple[float, float]]:
        """Generate points with exponential decay from center"""
        points = []
        
        # Rate parameter - controls how quickly density decreases with distance
        rate = 3.0 / radius  # Higher value = steeper decay
        
        for _ in range(count):
            # Generate random angle
            angle = random.uniform(0, 2 * np.pi)
            
            # Generate distance with exponential distribution
            # Use rejection sampling to ensure points are within radius
            while True:
                # Generate exponential random variable
                r = random.expovariate(rate)
                if r <= radius:
                    break
            
            # Convert polar coordinates to Cartesian
            x = r * np.cos(angle)
            y = r * np.sin(angle)
            
            # Convert meters to approximate degrees
            lat_offset = y / 111320
            lon_offset = x / (111320 * np.cos(np.radians(lat)))
            
            new_lat = lat + lat_offset
            new_lon = lon + lon_offset
            
            points.append((new_lat, new_lon))
            
        return points
    
    def _distance_weighted(self, lat: float, lon: float, count: int, radius: float) -> List[Tuple[float, float]]:
        """

        Generate points with a custom distance-weighted distribution

        (more points at medium distances than at center or edge)

        """
        points = []
        
        for _ in range(count):
            # Generate random angle
            angle = random.uniform(0, 2 * np.pi)
            
            # Custom distribution - more weight at middle distances
            # Generate r² with beta distribution (concentration in middle)
            r_squared = random.betavariate(2, 2)  # Beta with alpha=beta=2 has peak in middle
            r = np.sqrt(r_squared) * radius
            
            # Convert polar coordinates to Cartesian
            x = r * np.cos(angle)
            y = r * np.sin(angle)
            
            # Convert meters to approximate degrees
            lat_offset = y / 111320
            lon_offset = x / (111320 * np.cos(np.radians(lat)))
            
            new_lat = lat + lat_offset
            new_lon = lon + lon_offset
            
            points.append((new_lat, new_lon))
            
        return points
    
    def _generate_timestamps(self, count: int, start_time: datetime.datetime, end_time: datetime.datetime) -> List[datetime.datetime]:
        """Generate uniformly distributed timestamps"""
        timestamps = []
        
        # Convert to timestamps for easier calculations
        start_ts = start_time.timestamp()
        end_ts = end_time.timestamp()
        
        for _ in range(count):
            # Generate a random timestamp between start and end
            random_ts = random.uniform(start_ts, end_ts)
            timestamp = datetime.datetime.fromtimestamp(random_ts)
            timestamps.append(timestamp)
            
        # Sort timestamps chronologically
        timestamps.sort()
            
        return timestamps

def create_visualization(input_df, output_df, show_basemap=False, exclusion_zones_gdf=None):
    """Create visualization of input and diffused points"""
    fig, ax = plt.subplots(figsize=(12, 10))
    
    # Set background color
    fig.patch.set_facecolor('white')
    ax.set_facecolor('#f8f9fa')
    
    # Define colors for different exclusion zone types
    exclusion_colors = {
        'Water': '#4FC3F7',           # Light blue
        'Park': '#66BB6A',            # Green
        'Green space': '#81C784',     # Light green
        'Forest': '#4CAF50',          # Dark green
        'Industrial/Commercial': '#90A4AE',  # Grey
        'Major road': '#FFD54F',      # Yellow
        'Other': '#FFAB91'            # Light orange
    }
    
    # If basemap is requested, convert to Web Mercator and add basemap
    if show_basemap:
        try:
            import contextily as ctx
            import geopandas as gpd
            from shapely.geometry import Point
            
            # Create GeoDataFrames for proper projection
            input_gdf = gpd.GeoDataFrame(
                input_df, 
                geometry=[Point(lon, lat) for lon, lat in zip(input_df['lon'], input_df['lat'])],
                crs='EPSG:4326'
            )
            output_gdf = gpd.GeoDataFrame(
                output_df,
                geometry=[Point(lon, lat) for lon, lat in zip(output_df['lon'], output_df['lat'])],
                crs='EPSG:4326'
            )
            
            # Convert to Web Mercator for basemap compatibility
            input_gdf_merc = input_gdf.to_crs('EPSG:3857')
            output_gdf_merc = output_gdf.to_crs('EPSG:3857')
            
            # Plot exclusion zones first (if provided) with color coding
            if exclusion_zones_gdf is not None and len(exclusion_zones_gdf) > 0:
                try:
                    exclusion_zones_merc = exclusion_zones_gdf.to_crs('EPSG:3857')
                    
                    # Group by zone type and plot with appropriate colors
                    plotted_types = set()
                    for zone_type in exclusion_zones_merc['zone_type'].unique():
                        zone_subset = exclusion_zones_merc[exclusion_zones_merc['zone_type'] == zone_type]
                        color = exclusion_colors.get(zone_type, exclusion_colors['Other'])
                        
                        # Only add label for first occurrence of each type
                        label = zone_type if zone_type not in plotted_types else None
                        if label:
                            plotted_types.add(zone_type)
                        
                        zone_subset.plot(ax=ax, color=color, alpha=0.6, edgecolor='white', 
                                       linewidth=0.5, label=label)
                        
                except Exception as e:
                    print(f"Error plotting exclusion zones: {str(e)}")
            
            # Extract coordinates for plotting
            input_x = input_gdf_merc.geometry.x
            input_y = input_gdf_merc.geometry.y
            output_x = output_gdf_merc.geometry.x
            output_y = output_gdf_merc.geometry.y
            
            # Plot diffused points first (so they appear behind source points)
            ax.scatter(output_x, output_y, 
                       alpha=0.7, color='#FF9800', s=12, label=f'Generated Points (n={len(output_df)})', 
                       edgecolors='white', linewidth=0.3)
            
            # Draw radius circles first (so they appear behind everything else)
            for idx, row in input_df.iterrows():
                radius = row.get('radius', None)
                
                if radius is not None:
                    # Convert center point to Web Mercator
                    center_point = gpd.GeoDataFrame(
                        [1], geometry=[Point(row['lon'], row['lat'])], crs='EPSG:4326'
                    ).to_crs('EPSG:3857')
                    
                    center_x = center_point.geometry.x.iloc[0]
                    center_y = center_point.geometry.y.iloc[0]
                    
                    # Draw circle (radius is already in meters, which matches Web Mercator units)
                    circle = plt.Circle((center_x, center_y), radius, 
                                       fill=False, color='#9C27B0', linestyle='--', 
                                       alpha=0.5, linewidth=2)
                    ax.add_patch(circle)
            
            # Plot source points with circles sized by count
            min_size = 100
            max_size = 800
            if len(input_df) > 1:
                size_range = input_df['count'].max() - input_df['count'].min()
                if size_range > 0:
                    sizes = min_size + (input_df['count'] - input_df['count'].min()) / size_range * (max_size - min_size)
                else:
                    sizes = [min_size] * len(input_df)
            else:
                sizes = [max_size]
            
            # Plot source points in purple
            ax.scatter(input_x, input_y, 
                        s=sizes, c='#9C27B0', alpha=0.9, 
                        edgecolors='white', linewidth=2,
                        label='Source Points (size = count)', zorder=5)
            
            # Add count labels next to source points
            for idx, row in input_df.iterrows():
                point_merc = gpd.GeoDataFrame(
                    [1], geometry=[Point(row['lon'], row['lat'])], crs='EPSG:4326'
                ).to_crs('EPSG:3857')
                
                x_merc = point_merc.geometry.x.iloc[0]
                y_merc = point_merc.geometry.y.iloc[0]
                
                ax.annotate(f'{int(row["count"])}', 
                           (x_merc, y_merc), 
                           xytext=(8, 8), textcoords='offset points',
                           fontsize=10, fontweight='bold', color='white',
                           bbox=dict(boxstyle='round,pad=0.3', facecolor='#9C27B0', alpha=0.8),
                           zorder=6)
            
            # Add basemap
            try:
                ctx.add_basemap(ax, crs='EPSG:3857', source=ctx.providers.CartoDB.Positron, alpha=0.8)
                basemap_added = True
            except Exception as e:
                print(f"Could not add basemap: {str(e)}")
                basemap_added = False
            
            # Set axis labels for Web Mercator
            ax.set_xlabel('Easting (Web Mercator)', fontsize=12)
            ax.set_ylabel('Northing (Web Mercator)', fontsize=12)
            
            # Use projected coordinates for limits
            x_coords = list(input_x) + list(output_x)
            y_coords = list(input_y) + list(output_y)
            
        except ImportError:
            print("Contextily not available for basemap. Falling back to simple plot.")
            show_basemap = False
        except Exception as e:
            print(f"Error creating basemap: {str(e)}. Falling back to simple plot.")
            show_basemap = False
    
    # Fallback to simple plot if basemap fails or is not requested
    if not show_basemap:
        # Plot exclusion zones first (if provided) with color coding
        if exclusion_zones_gdf is not None and len(exclusion_zones_gdf) > 0:
            try:
                # Ensure exclusion zones are in WGS84
                if exclusion_zones_gdf.crs != 'EPSG:4326':
                    exclusion_zones_gdf = exclusion_zones_gdf.to_crs('EPSG:4326')
                
                # Plot zones by type with appropriate colors
                plotted_types = set()
                for idx, zone in exclusion_zones_gdf.iterrows():
                    zone_type = zone.get('zone_type', 'Other')
                    color = exclusion_colors.get(zone_type, exclusion_colors['Other'])
                    
                    # Only add label for first occurrence of each type
                    label = zone_type if zone_type not in plotted_types else None
                    if label:
                        plotted_types.add(zone_type)
                    
                    if zone.geometry.geom_type == 'Polygon':
                        x, y = zone.geometry.exterior.xy
                        ax.fill(x, y, color=color, alpha=0.6, edgecolor='white', 
                               linewidth=0.5, label=label)
                    elif zone.geometry.geom_type == 'MultiPolygon':
                        for poly in zone.geometry.geoms:
                            x, y = poly.exterior.xy
                            ax.fill(x, y, color=color, alpha=0.6, edgecolor='white', 
                                   linewidth=0.5, label=label)
                            label = None  # Only label the first polygon
                            
            except Exception as e:
                print(f"Error plotting exclusion zones: {str(e)}")
        
        # Plot diffused points first (so they appear behind source points) - orange
        ax.scatter(output_df['lon'], output_df['lat'], 
                   alpha=0.7, color='#FF9800', s=12, label=f'Generated Points (n={len(output_df)})', 
                   edgecolors='white', linewidth=0.3)
        
        # Draw radius circles first (so they appear behind everything else) - purple
        for idx, row in input_df.iterrows():
            radius = row.get('radius', None)
            
            if radius is not None:
                # Approximate conversion from meters to degrees
                radius_deg_lat = radius / 111320
                radius_deg_lon = radius / (111320 * np.cos(np.radians(row['lat'])))
                
                # Use the average as an approximation
                radius_deg = (radius_deg_lat + radius_deg_lon) / 2
                
                # Draw circle in purple
                circle = plt.Circle((row['lon'], row['lat']), radius_deg, 
                                   fill=False, color='#9C27B0', linestyle='--', 
                                   alpha=0.5, linewidth=2)
                ax.add_patch(circle)
        
        # Plot source points with circles sized by count - purple
        min_size = 100
        max_size = 800
        if len(input_df) > 1:
            size_range = input_df['count'].max() - input_df['count'].min()
            if size_range > 0:
                sizes = min_size + (input_df['count'] - input_df['count'].min()) / size_range * (max_size - min_size)
            else:
                sizes = [min_size] * len(input_df)
        else:
            sizes = [max_size]
        
        # Plot source points in purple
        ax.scatter(input_df['lon'], input_df['lat'], 
                    s=sizes, c='#9C27B0', alpha=0.9, 
                    edgecolors='white', linewidth=2,
                    label='Source Points (size = count)', zorder=5)
        
        # Add count labels next to source points with purple background
        for idx, row in input_df.iterrows():
            ax.annotate(f'{int(row["count"])}', 
                       (row['lon'], row['lat']), 
                       xytext=(8, 8), textcoords='offset points',
                       fontsize=10, fontweight='bold', color='white',
                       bbox=dict(boxstyle='round,pad=0.3', facecolor='#9C27B0', alpha=0.8),
                       zorder=6)
        
        # Standard coordinate labels
        ax.set_xlabel('Longitude', fontsize=12)
        ax.set_ylabel('Latitude', fontsize=12)
        
        # Use original coordinates for limits
        x_coords = list(input_df['lon']) + list(output_df['lon'])
        y_coords = list(input_df['lat']) + list(output_df['lat'])
    
    # Common styling
    title = 'Spatial Diffusion Results'
    if show_basemap:
        title += ' (with Basemap)'
    if exclusion_zones_gdf is not None and len(exclusion_zones_gdf) > 0:
        title += ' - Exclusion Zones Applied'
    subtitle = 'Purple source points sized by count, orange generated points, dashed circles show diffusion radius'
    
    ax.set_title(f'{title}\n{subtitle}', 
                fontsize=14, fontweight='bold', pad=20)
    
    # Legend with better positioning
    legend = ax.legend(loc='upper right', bbox_to_anchor=(1, 1), 
                      frameon=True, fancybox=True, shadow=True)
    legend.get_frame().set_facecolor('white')
    legend.get_frame().set_alpha(0.9)
    
    # Add grid (lighter for basemap)
    grid_alpha = 0.2 if show_basemap else 0.3
    ax.grid(True, alpha=grid_alpha, linestyle='-', linewidth=0.5)
    
    # Make equal aspect ratio
    ax.set_aspect('equal', 'box')
    
    # Add some padding around the data
    x_margin = (max(x_coords) - min(x_coords)) * 0.1
    y_margin = (max(y_coords) - min(y_coords)) * 0.1
    
    if x_margin == 0:  # Handle case where all points have same x-coordinate
        x_margin = 1000 if show_basemap else 0.01
    if y_margin == 0:  # Handle case where all points have same y-coordinate
        y_margin = 1000 if show_basemap else 0.01
        
    ax.set_xlim(min(x_coords) - x_margin, max(x_coords) + x_margin)
    ax.set_ylim(min(y_coords) - y_margin, max(y_coords) + y_margin)
    
    # Tight layout
    plt.tight_layout()
    
    return fig

def process_csv(file_obj, distribution_type, global_radius, show_basemap, auto_exclusions, exclusion_file, include_time, time_start, time_end, seed):
    """Process input CSV and generate diffused points"""
    try:
        # Read input CSV
        df = pd.read_csv(file_obj.name)
        
        # Validate required columns
        required_cols = ['lat', 'lon', 'count']
        if not all(col in df.columns for col in required_cols):
            return None, f"Error: CSV must contain columns: {', '.join(required_cols)}"
        
        # Convert global_radius to float if provided
        if global_radius and global_radius.strip():
            try:
                global_radius = float(global_radius)
            except ValueError:
                return None, "Error: Global radius must be a number"
        else:
            global_radius = None
            # If global radius not provided, check for radius column
            if 'radius' not in df.columns:
                return None, "Error: Either provide a global radius or include a 'radius' column in the CSV"
        
        # Convert seed to int if provided
        if seed and seed.strip():
            try:
                seed = int(seed)
            except ValueError:
                return None, "Error: Seed must be an integer"
        else:
            seed = None
        
        # Process exclusion zones
        exclusion_zones_gdf = None
        
        # First, try manual file upload (takes priority)
        if exclusion_file is not None:
            try:
                import geopandas as gpd
                
                # Determine file type and read accordingly
                file_extension = os.path.splitext(exclusion_file.name)[1].lower()
                
                if file_extension in ['.geojson', '.json']:
                    exclusion_zones_gdf = gpd.read_file(exclusion_file.name)
                elif file_extension == '.gpkg':
                    exclusion_zones_gdf = gpd.read_file(exclusion_file.name)
                elif file_extension == '.shp':
                    exclusion_zones_gdf = gpd.read_file(exclusion_file.name)
                else:
                    return None, f"Error: Unsupported exclusion zone file format: {file_extension}"
                
                # Ensure CRS is set
                if exclusion_zones_gdf.crs is None:
                    exclusion_zones_gdf = exclusion_zones_gdf.set_crs('EPSG:4326')
                
                print(f"Loaded {len(exclusion_zones_gdf)} custom exclusion zones from {exclusion_file.name}")
                
            except ImportError:
                return None, "Error: GeoPandas required for exclusion zones processing"
            except Exception as e:
                return None, f"Error reading exclusion zones file: {str(e)}"
        
        # If no manual file, try automatic exclusions from OpenStreetMap
        elif auto_exclusions and len(auto_exclusions) > 0:
            try:
                # Calculate bounds around input points
                bounds = calculate_bounds_from_points(df)
                print(f"Fetching automatic exclusions for bounds: {bounds}")
                
                # Fetch OSM data
                exclusion_zones_gdf = fetch_osm_exclusion_zones(bounds, auto_exclusions)
                
                if exclusion_zones_gdf is not None:
                    print(f"Fetched {len(exclusion_zones_gdf)} exclusion zones from OpenStreetMap")
                else:
                    print("No exclusion zones found in OpenStreetMap for this area")
                    
            except Exception as e:
                print(f"Warning: Could not fetch automatic exclusions: {str(e)}")
                # Continue without exclusions rather than failing completely
                exclusion_zones_gdf = None
        
        # Process time if requested
        if include_time:
            if not time_start or not time_end:
                return None, "Error: If time distribution is enabled, both start and end times must be provided"
            try:
                time_start_dt = datetime.datetime.strptime(time_start, "%Y-%m-%d %H:%M:%S")
                time_end_dt = datetime.datetime.strptime(time_end, "%Y-%m-%d %H:%M:%S")
                if time_start_dt >= time_end_dt:
                    return None, "Error: End time must be after start time"
            except ValueError:
                return None, "Error: Invalid time format. Use YYYY-MM-DD HH:MM:SS"
        else:
            time_start_dt = None
            time_end_dt = None
        
        # Create diffuser and generate diffused points
        diffuser = SpatialDiffuser()
        result_df = diffuser.diffuse_points(
            input_data=df,
            distribution_type=distribution_type,
            global_radius=global_radius,
            time_start=time_start_dt,
            time_end=time_end_dt,
            seed=seed,
            exclusion_zones_gdf=exclusion_zones_gdf
        )
        
        # Create temporary CSV file
        temp_file = "diffused_points.csv"
        result_df.to_csv(temp_file, index=False)
        
        # Create visualization with basemap and exclusion zones
        fig = create_visualization(df, result_df, show_basemap, exclusion_zones_gdf)
        
        return fig, temp_file
        
    except Exception as e:
        return None, f"Error: {str(e)}"

def create_diffusion_interface():
    """Create Gradio interface for the spatial diffusion tool"""
    
    with gr.Blocks() as diffusion_interface:
        gr.Markdown("## 🗺️ Spatial Diffusion Tool")
        
        with gr.Row():
            with gr.Column(scale=1):
                # Move description into the left column for better space usage
                gr.Markdown("""

                ### About This Tool

                Transform aggregated geographic points with counts into individual points using spatial diffusion methods.

                

                **Input CSV Format:**

                - `lat`: Latitude of source point

                - `lon`: Longitude of source point  

                - `count`: Number of points to generate

                - `radius`: (Optional) Diffusion radius in meters

                

                **Distribution Types:**

                - **Uniform**: Equal probability throughout circle

                - **Normal**: Higher density near center

                - **Exponential Decay**: Density decreases from center

                - **Distance-Weighted**: More points at medium distances

                """)
                
                # Input controls
                input_file = gr.File(label="Input CSV File", file_types=[".csv"])
                
                # Distribution options grouped together
                gr.Markdown("### 🎯 Distribution Options")
                with gr.Row():
                    distribution = gr.Dropdown(
                        choices=["uniform", "normal", "exponential_decay", "distance_weighted"],
                        value="uniform",
                        label="Distribution Type",
                        scale=2
                    )
                    seed = gr.Textbox(
                        label="Random Seed (optional)", 
                        placeholder="e.g. 42",
                        scale=1
                    )
                
                global_radius = gr.Textbox(
                    label="Global Radius (meters)", 
                    placeholder="Only if radius column not in CSV"
                )
                
                # Temporal controls in distribution section
                with gr.Accordion("⏰ Temporal Distribution (Optional)", open=False):
                    include_time = gr.Checkbox(label="Enable Temporal Distribution", value=False)
                    with gr.Group() as time_group:
                        time_start = gr.Textbox(
                            label="Start Time", 
                            placeholder="YYYY-MM-DD HH:MM:SS"
                        )
                        time_end = gr.Textbox(
                            label="End Time", 
                            placeholder="YYYY-MM-DD HH:MM:SS"
                        )
                
                # Map and exclusion options grouped together
                gr.Markdown("### 🗺️ Map & Exclusion Options")
                show_basemap = gr.Checkbox(
                    label="Show underlying map (requires internet)", 
                    value=False
                )
                gr.Markdown("*Adds geographic context with street/satellite imagery*")
                
                # Automatic exclusion zones - no default selection
                auto_exclusions = gr.CheckboxGroup(
                    label="Auto-exclude from OpenStreetMap:",
                    choices=["Water bodies", "Parks & green spaces", "Industrial areas", "Major roads"],
                    value=[]  # No default selections
                )
                
                # Advanced manual exclusion zones
                with gr.Accordion("🔧 Advanced: Custom Exclusion Zones", open=False):
                    exclusion_file = gr.File(
                        label="Upload custom shapefile (optional)",
                        file_types=[".geojson", ".json", ".gpkg", ".shp"]
                    )
                    gr.Markdown("*Overrides automatic exclusions if provided*")
                
                process_btn = gr.Button(
                    "🎯 Generate Diffused Points", 
                    variant="primary", 
                    size="lg"
                )
            
            with gr.Column(scale=2):
                # Give more space to visualization
                plot_output = gr.Plot(
                    label="📍 Spatial Diffusion Visualization",
                    show_label=True
                )
                
                with gr.Row():
                    with gr.Column(scale=2):
                        file_output = gr.File(label="📥 Download Generated Points")
                    with gr.Column(scale=1):
                        gr.Markdown(
                            """

                            **Legend:**  

                            🟣 Source points (sized by count)  

                            🟠 Generated points  

                            ⭕ Diffusion radius  

                            🟦 Water bodies  

                            🟢 Parks & green spaces  

                            ⬜ Industrial areas  

                            🟡 Major roads

                            """
                        )
        
        # Set up event handlers
        process_btn.click(
            fn=process_csv,
            inputs=[input_file, distribution, global_radius, show_basemap, auto_exclusions, exclusion_file, include_time, time_start, time_end, seed],
            outputs=[plot_output, file_output]
        )
        
        # Show/hide time inputs based on checkbox
        include_time.change(
            fn=lambda x: gr.update(visible=x),
            inputs=[include_time],
            outputs=[time_group]
        )
    
    return diffusion_interface

if __name__ == "__main__":
    # This allows the module to be run directly for testing
    app = create_diffusion_interface()
    app.launch()