import geopandas as gpd
import matplotlib.pyplot as plt
from geopy.geocoders import Nominatim
import requests
from bs4 import BeautifulSoup
import re
from tqdm import tqdm
from shapely.geometry import Point
import json
import pandas as pd
import numpy as np
import rasterio
import warnings
from scipy.spatial import cKDTree
from rasterio.transform import from_origin
from rasterio.features import rasterize
import os
import rasterio.mask
import matplotlib.colors as mcolors
import matplotlib.patheffects as patheffects
from pykrige.uk import UniversalKriging
from sklearn.linear_model import HuberRegressor
from scipy.stats import zscore
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler
from scipy.ndimage import gaussian_filter
from datetime import datetime
import pytz
import math

os.environ['PROJ_LIB'] = r'C:\Users\Lenovo\Documents\conda\Library\share\proj'

warnings.filterwarnings("ignore")

# Keywords to remove
keywords_to_remove = [
    r"(centrum)", "rondo", "most", "Obwodnica", "obwodnica I", "obwodnica II", "obwodnica",
    "- Zawiszyn", "OUD Węzeł", "PPO", "Węzeł", "N (wąwóz)", "S (most)", "stacja", "z pomiarów", "min", "max", "średnia"
]

def clean_station_name(station_name):
    pattern = re.compile("|".join(keywords_to_remove), re.IGNORECASE)
    cleaned_name = pattern.sub("", station_name).strip()
    return cleaned_name

def get_masovian_boundary():
    url = "https://raw.githubusercontent.com/ppatrzyk/polska-geojson/master/wojewodztwa/wojewodztwa-medium.geojson"
    # Fetch the content using requests
    response = requests.get(url)
    if response.status_code == 200:
        data = response.json()
        # Set the CRS to EPSG:4326 when creating the GeoDataFrame
        gdf = gpd.GeoDataFrame.from_features(data["features"], crs="EPSG:4326")
        masovian = gdf[gdf['nazwa'] == 'mazowieckie']
        return masovian
    else:
        print(f"Error fetching GeoJSON data: {response.status_code}")
        return None

# Global variable for Masovian boundary
masovian_boundary = get_masovian_boundary()

def is_in_masovian(lat, lon):
    point = gpd.GeoSeries([Point(lon, lat)], crs="EPSG:4326")
    masovian_geom = masovian_boundary.to_crs(epsg=2180).reset_index(drop=True)
    point_projected = point.to_crs(epsg=2180)
    return masovian_geom.contains(point_projected.buffer(100)).values[0]

def fetch_traxelektronik_data():
    url = 'https://www.traxelektronik.pl/pogoda/zbiorcza.php?RejID=10'
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')

    stations = []
    temperatures = []
    
    table_rows = soup.find_all('tr')[2:]

    for row in table_rows:
        columns = row.find_all('td')
        if len(columns) > 1:
            station = columns[0].get_text(strip=True)
            temperature = columns[1].get_text(strip=True)
            if temperature != "-":
                cleaned_station = clean_station_name(station)
                stations.append(cleaned_station)
                temperatures.append(temperature)

    print(f"Fetched {len(stations)} stations with valid temperature data from Traxelektronik.")
    return stations, temperatures

def fetch_netatmo_data():
    url = "https://api.netatmo.com/api/getpublicdata"
    params = {
        "lat_ne": 53,
        "lon_ne": 22,
        "lat_sw": 51,
        "lon_sw": 19,
        "required_data": "temperature",
        "filter": "true",
        "access_token": "66e1e80975134797710422cd|74965a60db5d205630cee60056012cf6"
    }
    
    response = requests.get(url, params=params)
    if response.status_code == 200:
        print("Successfully fetched data from Netatmo")
    else:
        print(f"Error fetching data: {response.status_code}")
    data = json.loads(response.text)
    
    stations = []
    temperatures = []
    coordinates = []
    
    for station in data.get('body', []):
        place = station.get('place', {})
        location = place.get('location')
        if location:
            lon, lat = location
            if is_in_masovian(lat, lon):
                for module, module_data in station.get('measures', {}).items():
                    if 'temperature' in module_data.get('type', []):
                        res = module_data.get('res', {})
                        if res:
                            latest_timestamp = max(res.keys())
                            temperature = res[latest_timestamp][0]
                            city = place.get('city', 'Unknown')
                            stations.append(city)
                            temperatures.append(temperature)
                            coordinates.append((lat, lon))
                            break
    
    print(f"Fetched {len(stations)} stations with valid temperature data from Netatmo in Masovian Voivodeship.")
    return stations, temperatures, coordinates

def fetch_imgw_data():
    url = "https://rafalraczynski.com.pl/imgw/dane-imgw/getJSON.php?type=table&province=14&sort=temp&order=asc"
    response = requests.get(url)
    if response.status_code == 200:
        data = response.json()
    else:
        print(f"Error fetching IMGW data: {response.status_code}")
        return [], [], []
    
    target_stations = [
        "ANDRZEJEWO", "BORKOWO", "CZARNOWO", "KAZANÓW",
        "RUSZKOWICE", "RYBIENKO", "RZĄŚNIK WŁOŚCIAŃSKI", "WIELGOLAS"
    ]

    stations = []
    temperatures = []
    station_ids = []

    for item in data:
        statName = item.get('statName')
        temp = item.get('temp')
        if statName and temp and statName.upper() in target_stations:
            stations.append(statName)
            temperatures.append(temp)
            station_ids.append(item.get('statId', 'Unknown'))

    print(f"Fetched {len(stations)} stations from IMGW data.")
    return stations, temperatures, station_ids

def get_coordinates(station_name, geolocator):
    try:
        location = geolocator.geocode(f"{station_name}, Masovian Voivodeship, Poland")
        if location:
            if is_in_masovian(location.latitude, location.longitude):
                print(f"Geocoded '{station_name}' to {location.latitude}, {location.longitude} (in Masovian).")
                return (location.latitude, location.longitude), False
            else:
                print(f"'{station_name}' is not in Masovian Voivodeship.")
                return None, False
        print(f"Failed to geocode '{station_name}'.")
        return None, False
    except Exception as e:
        print(f"Error geocoding {station_name}: {e}")
        return None, False

def clean_temperature(temp):
    cleaned_temp = re.sub(r'[↓↑\s]', '', temp)
    try:
        return float(cleaned_temp)
    except ValueError:
        return None

def remove_nearby_duplicates(trax_coords, trax_temperatures, netatmo_coords, netatmo_temperatures, distance_threshold=0.01):
    trax_points = np.array([(lon, lat) for station, (lat, lon) in trax_coords.items()])
    netatmo_points = np.array([(lon, lat) for lat, lon in netatmo_coords])
    trax_tree = cKDTree(trax_points)

    netatmo_filtered_coords = []
    netatmo_filtered_temperatures = []

    for netatmo_point, netatmo_temp in zip(netatmo_points, netatmo_temperatures):
        distance, index = trax_tree.query(netatmo_point)
        if distance > distance_threshold:
            netatmo_filtered_coords.append(tuple(netatmo_point))
            netatmo_filtered_temperatures.append(netatmo_temp)

    return netatmo_filtered_coords, netatmo_filtered_temperatures

def hillshade(array, azimuth=315, angle_altitude=45):
    # Convert angles to radians
    azimuth_rad = np.radians(azimuth)
    altitude_rad = np.radians(angle_altitude)

    # Calculate gradients in x and y directions
    x, y = np.gradient(array)

    # Calculate the slope and aspect
    slope = np.pi/2.0 - np.arctan(np.sqrt(x*x + y*y))
    aspect = np.arctan2(-x, y)

    # Calculate shaded relief
    shaded = np.sin(altitude_rad) * np.sin(slope) + np.cos(altitude_rad) * np.cos(slope) * np.cos(azimuth_rad - aspect)

    # Normalize the shaded relief
    shaded = (shaded + 1) / 2
    shaded = (shaded * 255).astype(np.uint8)

    return shaded

def gaussian_filter_nan(image, sigma):
    nan_mask = np.isnan(image)
    image_filled = np.where(nan_mask, 0, image)
    weights = np.where(nan_mask, 0, 1)
    image_filtered = gaussian_filter(image_filled, sigma=sigma, mode='nearest')
    weights_filtered = gaussian_filter(weights, sigma=sigma, mode='nearest')
    with np.errstate(divide='ignore', invalid='ignore'):
        image_final = image_filtered / weights_filtered
    image_final[nan_mask] = np.nan
    return image_final

def plot_stations_with_boundary_and_interpolation(
        trax_stations, trax_temperatures, trax_coords,
        netatmo_stations, netatmo_temperatures, netatmo_coords,
        imgw_stations, imgw_temperatures, imgw_coords):
    # Process Traxelektronik data
    trax_filtered_stations = []
    trax_filtered_temperatures = []
    trax_geometries = []

    # Filter and clean Traxelektronik data
    for station, temperature in zip(trax_stations, trax_temperatures):
        cleaned_station = clean_station_name(station)
        cleaned_temperature = clean_temperature(temperature)
        if station in trax_coords and cleaned_temperature is not None:
            lat, lon = trax_coords[station]
            trax_filtered_stations.append(cleaned_station)
            trax_filtered_temperatures.append(cleaned_temperature)
            trax_geometries.append(Point(lon, lat))

    # Create GeoDataFrame for Traxelektronik data
    trax_gdf = gpd.GeoDataFrame({
        'Station': trax_filtered_stations,
        'Temperature': trax_filtered_temperatures,
        'Source': 'Traxelektronik'
    }, geometry=trax_geometries, crs="EPSG:4326")

    # Handle Netatmo data
    netatmo_filtered_coords = []
    netatmo_filtered_temperatures = []
    netatmo_filtered_stations = []

    for station, temperature, coord in zip(netatmo_stations, netatmo_temperatures, netatmo_coords):
        if isinstance(temperature, (int, float)) and coord is not None:
            netatmo_filtered_coords.append(coord)
            netatmo_filtered_temperatures.append(temperature)
            netatmo_filtered_stations.append(station)

    if len(netatmo_filtered_coords) > 0:
        netatmo_geometries = [Point(lon, lat) for lat, lon in netatmo_filtered_coords]
        netatmo_gdf = gpd.GeoDataFrame({
            'Station': netatmo_filtered_stations,
            'Temperature': netatmo_filtered_temperatures,
            'Source': 'Netatmo'
        }, geometry=netatmo_geometries, crs="EPSG:4326")
    else:
        netatmo_gdf = gpd.GeoDataFrame(columns=['Station', 'Temperature', 'Source', 'geometry'], crs="EPSG:4326")

    # Handle IMGW data
    imgw_filtered_stations = []
    imgw_filtered_temperatures = []
    imgw_geometries = []

    # Clean and process IMGW data
    for station, temperature in zip(imgw_stations, imgw_temperatures):
        cleaned_temperature = clean_temperature(temperature)
        if station in imgw_coords and cleaned_temperature is not None:
            lat, lon = imgw_coords[station]
            imgw_filtered_stations.append(station)
            imgw_filtered_temperatures.append(cleaned_temperature)
            imgw_geometries.append(Point(lon, lat))

    # Create GeoDataFrame for IMGW data
    imgw_gdf = gpd.GeoDataFrame({
        'Station': imgw_filtered_stations,
        'Temperature': imgw_filtered_temperatures,
        'Source': 'IMGW'
    }, geometry=imgw_geometries, crs="EPSG:4326")

    # Read and clip DEM
    dem_data, dem_transform = read_and_clip_dem(masovian_boundary)

    # Read and clip Forest Cover
    forest_data, forest_transform = read_and_clip_forest(masovian_boundary)

    # Read and clip Water Bodies
    water_data, water_transform = read_and_clip_water_bodies(masovian_boundary)

    # Read ECOSTRESS data
    ecostress_data, ecostress_transform = read_and_clip_ecostress(masovian_boundary)

    # Ensure geometries are in EPSG:4326
    trax_gdf_dem_crs = trax_gdf.to_crs("EPSG:4326")
    netatmo_gdf_dem_crs = netatmo_gdf.to_crs("EPSG:4326")
    imgw_gdf_dem_crs = imgw_gdf.to_crs("EPSG:4326")

    # Get elevation at station points using dem_data and dem_transform
    trax_elevations = get_elevation_at_points(
        trax_gdf_dem_crs.geometry,
        dem_data=dem_data,
        dem_transform=dem_transform
    )
    netatmo_elevations = get_elevation_at_points(
        netatmo_gdf_dem_crs.geometry,
        dem_data=dem_data,
        dem_transform=dem_transform
    )
    imgw_elevations = get_elevation_at_points(
        imgw_gdf_dem_crs.geometry,
        dem_data=dem_data,
        dem_transform=dem_transform
    )

    # Add elevation to the GeoDataFrames
    trax_gdf['Elevation'] = trax_elevations
    netatmo_gdf['Elevation'] = netatmo_elevations
    imgw_gdf['Elevation'] = imgw_elevations

    # Get tree cover density at station points using forest_data and forest_transform
    trax_forest = get_raster_value_at_points(
        trax_gdf_dem_crs.geometry,
        raster_data=forest_data,
        raster_transform=forest_transform
    )
    netatmo_forest = get_raster_value_at_points(
        netatmo_gdf_dem_crs.geometry,
        raster_data=forest_data,
        raster_transform=forest_transform
    )
    imgw_forest = get_raster_value_at_points(
        imgw_gdf_dem_crs.geometry,
        raster_data=forest_data,
        raster_transform=forest_transform
    )

    # Add tree cover density to the GeoDataFrames
    trax_gdf['Tree_Cover'] = trax_forest
    netatmo_gdf['Tree_Cover'] = netatmo_forest
    imgw_gdf['Tree_Cover'] = imgw_forest

    # Get water body data at station points
    trax_water = get_water_body_at_points(
        trax_gdf_dem_crs.geometry,
        water_data=water_data,
        water_transform=water_transform
    )
    netatmo_water = get_water_body_at_points(
        netatmo_gdf_dem_crs.geometry,
        water_data=water_data,
        water_transform=water_transform
    )
    imgw_water = get_water_body_at_points(
        imgw_gdf_dem_crs.geometry,
        water_data=water_data,
        water_transform=water_transform
    )

    # Add water body data to the GeoDataFrames
    trax_gdf['Water_Body'] = trax_water
    netatmo_gdf['Water_Body'] = netatmo_water
    imgw_gdf['Water_Body'] = imgw_water

    # Add 'Priority' column to each GeoDataFrame
    trax_gdf['Priority'] = 2
    netatmo_gdf['Priority'] = 1
    imgw_gdf['Priority'] = 3  # Higher priority

    # Combine GeoDataFrames
    stations_gdf = gpd.GeoDataFrame(pd.concat([trax_gdf, netatmo_gdf, imgw_gdf], ignore_index=True))
    stations_gdf.crs = "EPSG:4326"
    stations_gdf = stations_gdf.dropna(subset=['Temperature', 'Elevation', 'Tree_Cover'])

    # Extract coordinates
    stations_gdf['X'] = stations_gdf.geometry.x
    stations_gdf['Y'] = stations_gdf.geometry.y

    # Prepare data for regression
    X = stations_gdf[['X', 'Y', 'Elevation', 'Tree_Cover', 'Water_Body']]
    y = stations_gdf['Temperature']

    # Normalize covariates
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)

    # Fit Random Forest Regression model
    print("Training Random Forest Regression model...")
    rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
    rf_model.fit(X_scaled, y)

    # Predict temperatures and compute residuals
    stations_gdf['Predicted_Temperature'] = rf_model.predict(X_scaled)
    stations_gdf['Residual'] = stations_gdf['Temperature'] - stations_gdf['Predicted_Temperature']

    # Outlier detection using Z-score
    stations_gdf['Residual_Z'] = zscore(stations_gdf['Residual'])
    z_threshold = 2  # Set your threshold
    stations_gdf_inliers = stations_gdf[np.abs(stations_gdf['Residual_Z']) <= z_threshold].copy()

    # Remove duplicates by prioritizing IMGW data
    data = stations_gdf_inliers.copy()

    # Sort by priority (higher priority first)
    data = data.sort_values('Priority', ascending=False)

    # Group by coordinates and keep the first record in each group (highest priority)
    data = data.groupby(['X', 'Y'], as_index=False).first()

    unique_points = data[['X', 'Y']].values
    unique_residuals = data['Residual'].values
    unique_elevation = data['Elevation'].values
    unique_tree_cover = data['Tree_Cover'].values
    unique_water_body = data['Water_Body'].values

    # Create grid in EPSG:4326 using rasterio's transform functions
    # Adjust grid_spacing as per your requirements
    grid_spacing = 0.0025  # Approximately ~5 km

    x_min, y_min, x_max, y_max = masovian_boundary.total_bounds

    # Compute the number of cols and rows with the new grid_spacing
    ncols = int(np.ceil((x_max - x_min) / grid_spacing))
    nrows = int(np.ceil((y_max - y_min) / grid_spacing))

    # Create the affine transform for the grid
    transform = from_origin(x_min, y_max, grid_spacing, grid_spacing)

    # Create arrays of row and column indices
    cols = np.arange(ncols)
    rows = np.arange(nrows)

    # Create a meshgrid of indices
    cols_indices, rows_indices = np.meshgrid(cols, rows)

    # Flatten the indices
    cols_flat = cols_indices.ravel()
    rows_flat = rows_indices.ravel()

    # Get the x and y coordinates for each grid cell center
    xs_flat, ys_flat = rasterio.transform.xy(transform, rows_flat, cols_flat)

    # Create grid dataframe
    grid_df = pd.DataFrame({
        'X': xs_flat,
        'Y': ys_flat,
        'row_idx': rows_flat,
        'col_idx': cols_flat
    })

    # Retrieve elevations from DEM
    grid_elevations = get_elevation_at_points(
        [Point(x, y) for x, y in zip(grid_df['X'], grid_df['Y'])],
        dem_data=dem_data,
        dem_transform=dem_transform
    )
    grid_df['Elevation'] = grid_elevations

    # Retrieve tree cover density from forest cover raster
    grid_forest = get_raster_value_at_points(
        [Point(x, y) for x, y in zip(grid_df['X'], grid_df['Y'])],
        raster_data=forest_data,
        raster_transform=forest_transform
    )
    grid_df['Tree_Cover'] = grid_forest

    # Retrieve water body data at grid points
    grid_water = get_water_body_at_points(
        [Point(x, y) for x, y in zip(grid_df['X'], grid_df['Y'])],
        water_data=water_data,
        water_transform=water_transform
    )
    grid_df['Water_Body'] = grid_water

    # Remove grid points with NaN values (i.e., outside data coverage)
    grid_df = grid_df.dropna(subset=['Elevation', 'Tree_Cover'])

    # Prepare data for regression prediction
    X_grid = grid_df[['X', 'Y', 'Elevation', 'Tree_Cover', 'Water_Body']]
    X_grid_scaled = scaler.transform(X_grid)
    grid_df['Predicted_Temperature'] = rf_model.predict(X_grid_scaled)

    # Set up variogram model with adjusted parameters
    variogram_model = 'spherical'
    # Adjust these parameters based on your data; you'll need to experiment with them
    variogram_parameters = {'nugget': 0.2, 'sill': 1.0, 'range': 0.05}

    # Perform Universal Kriging
    print("Performing Universal Kriging interpolation for residuals with adjusted variogram parameters...")
    uk = UniversalKriging(
        x=data['X'].values,
        y=data['Y'].values,
        z=data['Residual'].values,
        variogram_model=variogram_model,
        variogram_parameters=variogram_parameters,
        nlags=20,
        verbose=True
    )

    z, ss = uk.execute(
        'points',
        grid_df['X'].values,
        grid_df['Y'].values
    )

    grid_df['Residual'] = z
    grid_df['Kriging_Variance'] = ss

    # Final temperature estimation
    grid_df['Temperature'] = grid_df['Predicted_Temperature'] + grid_df['Residual']

    # Save a copy of the temperature before ECOSTRESS adjustments
    grid_df['Temperature_No_ECOSTRESS'] = grid_df['Temperature'].copy()

    # ECOSTRESS Adjustments Begin Here

    # Retrieve ECOSTRESS LST values at grid points
    grid_ecostress = get_raster_value_at_points(
        [Point(x, y) for x, y in zip(grid_df['X'], grid_df['Y'])],
        raster_data=ecostress_data,
        raster_transform=ecostress_transform
    )
    grid_df['ECOSTRESS_LST'] = grid_ecostress

    # Handle NaNs in ECOSTRESS_LST by filling with the mean value
    grid_df['ECOSTRESS_LST'].fillna(grid_df['ECOSTRESS_LST'].mean(), inplace=True)

    # Compute ECOSTRESS anomalies (deviation from mean)
    ecostress_mean = grid_df['ECOSTRESS_LST'].mean()
    grid_df['ECOSTRESS_Anomaly'] = grid_df['ECOSTRESS_LST'] - ecostress_mean

    # Normalize anomalies to range [-1, 1]
    ecostress_anomaly_min = grid_df['ECOSTRESS_Anomaly'].min()
    ecostress_anomaly_max = grid_df['ECOSTRESS_Anomaly'].max()
    grid_df['ECOSTRESS_Anomaly_Normalized'] = (
        (grid_df['ECOSTRESS_Anomaly'] - ecostress_anomaly_min) / (ecostress_anomaly_max - ecostress_anomaly_min) * 2 - 1
    )

    # Increase the impact of ECOSTRESS LST data
    amplification_factor = 3.0  # Increased to amplify adjustments
    grid_df['ECOSTRESS_Anomaly_Normalized'] *= amplification_factor

    # Determine the current time in CET
    cet = pytz.timezone('CET')
    now_cet = datetime.now(cet)
    hour = now_cet.hour + now_cet.minute / 60.0

    # Compute dynamic weighting centered at noon
    time_factor = ((hour - 12) % 24) / 24 * 2 * math.pi
    day_weight = (math.cos(time_factor) + 1) / 2
    night_weight = 1 - day_weight

    # Apply anomalies with dynamic weighting
    adjustment = grid_df['ECOSTRESS_Anomaly_Normalized'] * (day_weight - night_weight)

    # Prevent ECOSTRESS adjustments on water bodies
    # Set adjustments to zero where Water_Body == 1
    adjustment[grid_df['Water_Body'] == 1] = 0

    # Limit maximum adjustment to avoid over-adjusting
    max_adjustment = 5.0  # degrees Celsius
    adjustment = adjustment.clip(-max_adjustment, max_adjustment)

    # Apply adjustments to the interpolated temperatures
    grid_df['Temperature'] += adjustment

    cooling_effect = -2.0  # degrees Celsius
    grid_df.loc[grid_df['Water_Body'] == 1, 'Temperature'] += cooling_effect

    # Apply Gaussian smoothing to the interpolated temperature grid
    print("Applying Gaussian smoothing to the interpolated temperature grid...")
    # First, create a 2D array of temperatures
    grid_temperature = np.full((nrows, ncols), np.nan)
    grid_temperature[grid_df['row_idx'], grid_df['col_idx']] = grid_df['Temperature']

    # Define a function to create the temperature grid
    def create_temperature_grid(grid_df, temperature_column):
        grid_temperature = np.full((nrows, ncols), np.nan)
        grid_temperature[grid_df['row_idx'], grid_df['col_idx']] = grid_df[temperature_column].values
        return grid_temperature

    # Create temperature grids
    grid_temperature_no_ecostress = create_temperature_grid(grid_df, 'Temperature_No_ECOSTRESS')
    grid_temperature_with_ecostress = create_temperature_grid(grid_df, 'Temperature')

    # Mask the grid_temperature with the Masovian boundary
    masovian_shape = [masovian_boundary.geometry.unary_union]
    mask = rasterize(
        masovian_shape,
        out_shape=(nrows, ncols),
        transform=transform,
        fill=0,
        all_touched=True,
        default_value=1,
        dtype='uint8'
    )

    # Apply Gaussian smoothing to both temperature grids
    sigma = 1
    smoothed_temperature_no_ecostress = gaussian_filter_nan(grid_temperature_no_ecostress, sigma=sigma)
    smoothed_temperature_with_ecostress = gaussian_filter_nan(grid_temperature_with_ecostress, sigma=sigma)

    # Apply the mask
    smoothed_temperature_no_ecostress = np.ma.array(smoothed_temperature_no_ecostress, mask=mask == 0)
    smoothed_temperature_with_ecostress = np.ma.array(smoothed_temperature_with_ecostress, mask=mask == 0)

    smoothed_temperature = smoothed_temperature_with_ecostress

    # Also create grid_variance array (we'll skip smoothing variance for simplicity)
    grid_variance = np.full((nrows, ncols), np.nan)
    grid_variance[grid_df['row_idx'], grid_df['col_idx']] = grid_df['Kriging_Variance']
    grid_variance = np.ma.array(grid_variance, mask=mask == 0)

    # Compute Standard Deviation from Variance
    grid_std_dev = np.sqrt(grid_variance)
    grid_std_dev = np.ma.array(grid_std_dev, mask=mask == 0)

    # Read the custom color scale from the CSV file
    color_scale_df = pd.read_csv('input/color_scale.csv')
    color_scale_df = color_scale_df.sort_values('value')
    values = color_scale_df['value'].values
    hex_colors = color_scale_df['color'].values

    # Ensure colors start with '#'
    hex_colors = [color if color.startswith('#') else f"#{color}" for color in hex_colors]

    # Create a custom colormap
    # Normalize the values to 0..1
    vmin = values.min()
    vmax = values.max()
    normalized_values = (values - vmin) / (vmax - vmin)

    # Create a list of tuples (position, color)
    color_tuples = list(zip(normalized_values, hex_colors))

    # Create a LinearSegmentedColormap
    cmap = mcolors.LinearSegmentedColormap.from_list('custom_cmap', color_tuples)

    # Set the normalization
    norm = mcolors.Normalize(vmin=vmin, vmax=vmax)

    with rasterio.open('input/masovian_dem.tif') as dem_src:
        # Reproject Masovian boundary to DEM CRS if necessary
        if masovian_boundary.crs != dem_src.crs:
            masovian_boundary_dem_crs = masovian_boundary.to_crs(dem_src.crs)
        else:
            masovian_boundary_dem_crs = masovian_boundary

        # Clip the DEM to the Masovian boundary
        dem_plot_data, dem_plot_transform = rasterio.mask.mask(
            dem_src, masovian_boundary_dem_crs.geometry, crop=True, filled=True, nodata=np.nan
        )

        # Read the first band (assuming single-band DEM)
        dem_plot_data = dem_plot_data[0]  # Since mask returns data as (bands, rows, cols)

        # Handle NaNs in the DEM data
        dem_plot_data = np.nan_to_num(dem_plot_data, nan=np.nanmin(dem_plot_data))

        # Calculate the extent of the clipped DEM for plotting
        dem_left, dem_top = dem_plot_transform * (0, 0)
        dem_right, dem_bottom = dem_plot_transform * (dem_plot_data.shape[1], dem_plot_data.shape[0])
        dem_extent = [dem_left, dem_right, dem_bottom, dem_top]

    # Compute the extent for plotting
    def get_extent(transform, array):
        x_min = transform.c
        y_max = transform.f
        x_max = x_min + transform.a * array.shape[1]
        y_min = y_max + transform.e * array.shape[0]
        return [x_min, x_max, y_min, y_max]
        
    hs = hillshade(dem_plot_data, azimuth=315, angle_altitude=45)
        
    extent = get_extent(transform, smoothed_temperature)
    dem_extent = get_extent(dem_plot_transform, dem_plot_data)

    # Plotting Temperature Map without ECOSTRESS Adjustments
    fig1, ax1 = plt.subplots(figsize=(15, 15))

    # Set modern and minimalistic font properties with increased size
    plt.rcParams.update({
        'font.size': 10,
        'font.family': 'sans-serif',
        'font.sans-serif': ['Arial', 'Helvetica', 'DejaVu Sans']
    })

    # Plot the interpolated temperature grid without ECOSTRESS adjustments
    img1 = ax1.imshow(smoothed_temperature_no_ecostress, extent=extent, origin='upper', cmap=cmap, norm=norm, alpha=1)

    # Plot hillshade on top
    ax1.imshow(hs, extent=dem_extent, cmap='gray', origin='upper', alpha=0.3)

    # Overlay water bodies with blue color
    water_mask = np.where(water_data > 0, 1, 0)  # Adjust condition based on water_data values
    ax1.imshow(water_mask, extent=extent, origin='upper', cmap='Blues', alpha=0.5)  # Increased alpha to 0.5

    # Plot Masovian Voivodeship boundary
    masovian_boundary.boundary.plot(ax=ax1, color='black', linewidth=2)

    # Plot stations as smaller rectangles
    stations_gdf.plot(ax=ax1, color='black', marker='s', markersize=5, label='Stations')

    # Add contours for every 0.5°C value
    min_temp = np.floor(smoothed_temperature.min())
    max_temp = np.ceil(smoothed_temperature.max())
    contour_levels = np.arange(min_temp, max_temp + 0.5, 0.5)

    # Add contours
    contours1 = ax1.contour(
        smoothed_temperature_no_ecostress, levels=contour_levels, colors='black', linewidths=0.5,
        extent=extent, origin='upper'
    )
    contour_labels1 = ax1.clabel(
        contours1, inline=False, fontsize=8, fmt='%0.1f', colors='white',
        inline_spacing=4, manual=False
    )
    for txt in contour_labels1:
        txt.set_path_effects([
            patheffects.Stroke(linewidth=3, foreground='black'),
            patheffects.Normal()
        ])

    # Add colorbar and labels
    cbar1 = fig1.colorbar(img1, ax=ax1, label="Temperature (°C)")
    ticks = np.arange(np.floor(vmin / 5) * 5, np.ceil(vmax / 5) * 5 + 1, 5)
    cbar1.set_ticks(ticks)
    cbar1.set_ticklabels(ticks.astype(int))

    ax1.set_title("Temperature Interpolation without ECOSTRESS Adjustments", fontsize=18)
    ax1.legend()
    ax1.set_xlabel("Longitude")
    ax1.set_ylabel("Latitude")

    plt.savefig('output/temperature_without_ecostress.png', dpi=300, bbox_inches='tight')
    plt.show()

    print(f"Temperature interpolation plot created successfully with smoothed data.")

    # Plotting Temperature Map with ECOSTRESS Adjustments
    fig2, ax2 = plt.subplots(figsize=(15, 15))

    # Plot the interpolated temperature grid with ECOSTRESS adjustments
    img2 = ax2.imshow(smoothed_temperature_with_ecostress, extent=extent, origin='upper', cmap=cmap, norm=norm, alpha=1)

    # Plot hillshade on top
    ax2.imshow(hs, extent=dem_extent, cmap='gray', origin='upper', alpha=0.3)

    ax2.imshow(water_mask, extent=extent, origin='upper', cmap='Blues', alpha=0.5)  # Increased alpha to 0.5

    # Plot Masovian Voivodeship boundary
    masovian_boundary.boundary.plot(ax=ax2, color='black', linewidth=2)

    # Plot stations
    stations_gdf.plot(ax=ax2, color='black', marker='s', markersize=5, label='Stations')

    # Add contours
    contours2 = ax2.contour(
        smoothed_temperature_with_ecostress, levels=contour_levels, colors='black', linewidths=0.5,
        extent=extent, origin='upper'
    )
    contour_labels2 = ax2.clabel(
        contours2, inline=False, fontsize=8, fmt='%0.1f', colors='white',
        inline_spacing=4, manual=False
    )
    for txt in contour_labels2:
        txt.set_path_effects([
            patheffects.Stroke(linewidth=3, foreground='black'),
            patheffects.Normal()
        ])

    # Add colorbar and labels
    cbar2 = fig2.colorbar(img2, ax=ax2, label="Temperature (°C)")
    cbar2.set_ticks(ticks)
    cbar2.set_ticklabels(ticks.astype(int))

    ax2.set_title("Temperature Interpolation with ECOSTRESS Adjustments", fontsize=18)
    ax2.legend()
    ax2.set_xlabel("Longitude")
    ax2.set_ylabel("Latitude")

    plt.savefig('output/temperature_with_ecostress.png', dpi=300, bbox_inches='tight')
    plt.show()

    print(f"Temperature interpolation plot with ECOSTRESS adjustments created successfully.")

    # Plotting the Difference Map
    # Compute the difference between the two temperature grids
    temperature_difference = smoothed_temperature_with_ecostress - smoothed_temperature_no_ecostress

    fig3, ax3 = plt.subplots(figsize=(15, 15))

    # Use a diverging colormap to show positive and negative differences
    diff_cmap = 'seismic'
    diff_norm = mcolors.TwoSlopeNorm(vmin=-np.max(np.abs(temperature_difference)), vcenter=0, vmax=np.max(np.abs(temperature_difference)))

    # Plot the temperature difference grid
    img3 = ax3.imshow(temperature_difference, extent=extent, origin='upper', cmap=diff_cmap, norm=diff_norm, alpha=1)

    # Plot hillshade
    ax3.imshow(hs, extent=dem_extent, cmap='gray', origin='upper', alpha=0.3)

    # Overlay water bodies
    ax3.imshow(water_mask, extent=extent, origin='upper', cmap='Blues', alpha=0.5)  # Increased alpha to 0.5

    # Plot Masovian Voivodeship boundary
    masovian_boundary.boundary.plot(ax=ax3, color='black', linewidth=2)

    # Plot stations
    stations_gdf.plot(ax=ax3, color='black', marker='s', markersize=5, label='Stations')

    # Add colorbar
    cbar3 = fig3.colorbar(img3, ax=ax3, label="Temperature Difference (°C)")

    ax3.set_title("Difference Due to ECOSTRESS Adjustments", fontsize=18)
    ax3.set_xlabel("Longitude")
    ax3.set_ylabel("Latitude")

    plt.savefig('output/temperature_difference.png', dpi=300, bbox_inches='tight')
    plt.show()

    print(f"Estimated mean temperature error (standard deviation) plot created successfully.")

    return smoothed_temperature, transform

def get_elevation_at_points(geometry, dem_data=None, dem_transform=None):
    from rasterio.transform import rowcol

    if dem_data is not None and dem_transform is not None:
        elevations = []
        for point in geometry:
            x, y = point.x, point.y
            try:
                row, col = rowcol(dem_transform, x, y)
                row = int(row)
                col = int(col)
                elevation = dem_data[row, col]
                elevations.append(elevation)
            except (IndexError, ValueError):
                elevations.append(np.nan)
        return elevations
    else:
        raise ValueError("Provide dem_data and dem_transform.")

def get_interpolated_temperature_at_point(lat, lon, temperature_grid, transform):
    """
    Given a latitude and longitude, return the interpolated temperature value at that point.
    
    Parameters:
    - lat, lon: Latitude and longitude of the point.
    - temperature_grid: The interpolated temperature grid (2D numpy array).
    - transform: Affine transform for the grid.
    
    Returns:
    - Interpolated temperature value at the point, or None if point is outside the grid.
    """
    from rasterio.transform import rowcol
    
    try:
        row, col = rowcol(transform, lon, lat)  # Note that rasterio uses (x, y), so (lon, lat)
        row = int(row)
        col = int(col)
        value = temperature_grid[row, col]
        if np.ma.is_masked(value) or np.isnan(value):
            # Value is masked or NaN, meaning outside the interpolated area
            return None
        else:
            return value.item()
    except (IndexError, ValueError):
        return None  # Point is outside the grid

def get_raster_value_at_points(geometry, raster_data=None, raster_transform=None):
    from rasterio.transform import rowcol

    if raster_data is not None and raster_transform is not None:
        values = []
        for point in geometry:
            x, y = point.x, point.y
            try:
                row, col = rowcol(raster_transform, x, y)
                row = int(row)
                col = int(col)
                value = raster_data[row, col]
                values.append(value)
            except (IndexError, ValueError):
                values.append(np.nan)
        return values
    else:
        raise ValueError("Provide raster_data and raster_transform.")
    
def get_water_body_at_points(geometry, water_data=None, water_transform=None):
    from rasterio.transform import rowcol
    
    if water_data is not None and water_transform is not None:
        water_bodies = []
        for point in geometry:
            x, y = point.x, point.y
            try:
                row, col = rowcol(water_transform, x, y)
                row = int(row)
                col = int(col)
                if (0 <= row < water_data.shape[0]) and (0 <= col < water_data.shape[1]):
                    value = water_data[row, col]
                    # Adjust the condition based on your raster's water encoding
                    # For binary raster where 1 = water, 0 = land
                    water_bodies.append(1 if value else 0)
                else:
                    water_bodies.append(0)
            except (IndexError, ValueError):
                water_bodies.append(0)
        return water_bodies
    else:
        raise ValueError("Provide water_data and water_transform.")

def read_and_clip_dem(masovian_boundary):
    dem_path = 'input/masovian_dem.tif'
    with rasterio.open(dem_path) as src:
        print("Assuming DEM is in EPSG:4326; skipping reprojection.")
        dem_data = src.read(1, masked=True)
        dem_transform = src.transform
    return dem_data, dem_transform

def read_and_clip_forest(masovian_boundary):
    forest_path = 'input/forests.tif'
    with rasterio.open(forest_path) as src:
        print("Assuming forest cover raster is in EPSG:4326; skipping reprojection.")
        forest_data = src.read(1, masked=True)
        forest_data = np.where(forest_data == src.nodata, 0, forest_data)
        forest_transform = src.transform
    return forest_data, forest_transform

def read_and_clip_water_bodies(masovian_boundary):
    water_path = 'input/water_bodies.tif'
    with rasterio.open(water_path) as src:
        print("Reading and clipping water bodies raster to Masovian boundaries.")
        
        # Ensure the Masovian boundary is in the same CRS as the water raster
        if masovian_boundary.crs != src.crs:
            masovian_boundary = masovian_boundary.to_crs(src.crs)
            print("Reprojected Masovian boundary to match water body raster CRS.")
        else:
            print("Masovian boundary CRS matches water body raster CRS.")
        
        # Perform the clipping
        clipped_water, water_transform = rasterio.mask.mask(
            src, masovian_boundary.geometry, crop=True, filled=True, nodata=0
        )
        
        # Read the first band (assuming single-band raster)
        water_data = clipped_water[0]
        
        # Handle NoData values by setting them to 0 (assuming 0 represents no water)
        water_data = np.where(water_data == src.nodata, 0, water_data)
        
    return water_data, water_transform

# Add the following function to read and clip the ECOSTRESS data
def read_and_clip_ecostress(masovian_boundary):
    ecostress_path = 'input/raw_ecostress.tif'
    with rasterio.open(ecostress_path) as src:
        print("Reading ECOSTRESS LST data (assumed to be in degrees Celsius).")
        # Read data
        ecostress_data = src.read(1, masked=True)
        ecostress_data = np.where(ecostress_data == src.nodata, 0, ecostress_data)
        ecostress_transform = src.transform
    return ecostress_data, ecostress_transform

# Main execution flow
if __name__ == "__main__":
    # Fetch Traxelektronik data
    trax_stations, trax_temperatures = fetch_traxelektronik_data()

    # Geocode Traxelektronik stations
    geolocator = Nominatim(user_agent="geoapi")
    trax_coords = {}

    progress_bar = tqdm(total=len(trax_stations), desc="Geocoding Traxelektronik Stations", unit="station")
    for station in trax_stations:
        cleaned_station = clean_station_name(station)
        coords, was_cleaned = get_coordinates(cleaned_station, geolocator)
        if coords:
            trax_coords[station] = coords
        progress_bar.update(1)
    progress_bar.close()

    # Fetch Netatmo data
    netatmo_stations, netatmo_temperatures, netatmo_coords = fetch_netatmo_data()

    # Fetch IMGW data
    imgw_stations, imgw_temperatures, imgw_station_ids = fetch_imgw_data()

    # Geocode IMGW stations
    imgw_coords = {}
    progress_bar = tqdm(total=len(imgw_stations), desc="Geocoding IMGW Stations", unit="station")
    for station in imgw_stations:
        coords, was_cleaned = get_coordinates(station, geolocator)
        if coords:
            imgw_coords[station] = coords
        progress_bar.update(1)
    progress_bar.close()
    
    # After plotting, get the interpolated temperature at specific coordinates
    # Coordinates to test
    test_lat = 51.40118
    test_lon = 21.14805
    
    smoothed_temperature, transform = plot_stations_with_boundary_and_interpolation(
        trax_stations, trax_temperatures, trax_coords,
        netatmo_stations, netatmo_temperatures, netatmo_coords,
        imgw_stations, imgw_temperatures, imgw_coords
    )

    interpolated_temp = get_interpolated_temperature_at_point(
        test_lat, test_lon, smoothed_temperature, transform
    )

    if interpolated_temp is not None:
        print(f"Interpolated temperature value at {test_lat}, {test_lon} is {interpolated_temp:.2f}°C")
    else:
        print(f"Coordinates {test_lat}, {test_lon} are outside the interpolated grid or have no data.")

In that code, can you increase the warmth influence by urban heat islands for interpolated grid? Search for some additional approaches, additionally I have a population density raster (input/population.tif). You might want to use it somehow. It contains just one value: 1 to 1 and visualizes the most populated areas that have urban heat island effect. We could use it somehow in connection with the ecostress data. This raster is also already in EPSG:4326 projection, so we don't need to reproject it as we're already doing with other rasters in the code. Additionally, I want these urban heat islands to be visible in the temperature diffirence plot, together with ecostress changes data.