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
from rasterio.warp import reproject, Resampling
import os
import rasterio.mask
import matplotlib.colors as mcolors
import matplotlib.patheffects as patheffects
from pykrige.uk import UniversalKriging
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
from sklearn.model_selection import GridSearchCV, cross_validate, cross_val_score
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, median_absolute_error, explained_variance_score
from sklearn.pipeline import Pipeline
from datetime import datetime
from scipy.ndimage import gaussian_filter, distance_transform_edt
import pytz
import glob
import math

os.environ['PROJ_LIB'] = r'C:\Users\Lenovo\Documents\conda\envs\temp_env\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 != "-":
                stations.append(station)  # Keep original station names
                temperatures.append(temperature)

    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|023b8e0e5e9ac24d43a8ff7b95ffc3f4"
    }
    
    response = requests.get(url, params=params)
    if response.status_code != 200:
        print(f"Error fetching Netatmo data: {response.status_code}")
        return [], [], []
    data = json.loads(response.text)
    
    stations = []
    temperatures = []
    coordinates = []
    
    # List of cities to exclude
    excluded_cities = ["Płońsk", "Łochów"]
    
    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):
                city = place.get('city', 'Unknown')
                if city in excluded_cities:
                    # Skip this station
                    continue
                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]
                            stations.append(city)
                            temperatures.append(temperature)
                            coordinates.append((lat, lon))
                            break
    
    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:
        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'))

    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):
                return (location.latitude, location.longitude), False
            else:
                return None, False
        return None, False
    except Exception as 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 read_and_process_modis_data(modis_day_folder, modis_night_folder):
    # Read and stack MODIS day data
    modis_day_files = sorted(glob.glob(os.path.join(modis_day_folder, '*.tif')))
    modis_day_arrays = []
    modis_day_transform = None
    for modis_file in modis_day_files:
        with rasterio.open(modis_file) as src:
            data = src.read(1)
            # Replace fill values with np.nan
            fill_value = src.nodata
            if fill_value is None:
                # If nodata is not set, check for common fill values
                data = np.where((data == -9999) | (data <= -1e20), np.nan, data)
            else:
                data = np.where(data == fill_value, np.nan, data)
            modis_day_arrays.append(data)
            if modis_day_transform is None:
                modis_day_transform = src.transform
    if modis_day_arrays:
        modis_day_stack = np.stack(modis_day_arrays)
        modis_day_data = np.nanmean(modis_day_stack, axis=0)
    else:
        modis_day_data = None

    # Read and stack MODIS night data
    modis_night_files = sorted(glob.glob(os.path.join(modis_night_folder, '*.tif')))
    modis_night_arrays = []
    modis_night_transform = None
    for modis_file in modis_night_files:
        with rasterio.open(modis_file) as src:
            data = src.read(1)
            # Replace fill values with np.nan
            fill_value = src.nodata
            if fill_value is None:
                data = np.where((data == -9999) | (data <= -1e20), np.nan, data)
            else:
                data = np.where(data == fill_value, np.nan, data)
            modis_night_arrays.append(data)
            if modis_night_transform is None:
                modis_night_transform = src.transform
    if modis_night_arrays:
        modis_night_stack = np.stack(modis_night_arrays)
        modis_night_data = np.nanmean(modis_night_stack, axis=0)
    else:
        modis_night_data = None

    return (modis_day_data, modis_day_transform), (modis_night_data, modis_night_transform)

def resample_modis_data(modis_data, modis_transform, dest_shape, dest_transform):
    if modis_data is None:
        return None
    else:
        # Resample to match the destination grid
        dest_modis_data = np.zeros(dest_shape, dtype=modis_data.dtype)
        reproject(
            source=modis_data,
            destination=dest_modis_data,
            src_transform=modis_transform,
            src_crs='EPSG:4326',
            dst_transform=dest_transform,
            dst_crs='EPSG:4326',
            resampling=Resampling.nearest  # Use nearest to maintain original resolution
        )
        # Replace zeros (from areas without data) with NaN
        dest_modis_data = np.where(dest_modis_data == 0, np.nan, dest_modis_data)
        return dest_modis_data

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,
        modis_day_folder, modis_night_folder):
    # Define a function to compute the cooling effect of water bodies based on the hour of the day
    def compute_water_body_cooling_effect(hour):
        """
        Computes the cooling effect of water bodies based on the hour of the day.
        Cooling effect varies between -1°C during the day to -4°C at night.
        """
        # Normalize hour to range 0 to 24
        hour = hour % 24
        # Use a cosine function to model the cooling effect
        # Peak cooling at midnight (hour=0), minimal cooling at noon (hour=12)
        cooling_effect = -3.0 - 2.0 * math.cos((hour / 24) * 2 * math.pi)
        return cooling_effect

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

    # Define daytime and nighttime hours
    day_start = 6
    day_end = 18
    is_daytime = day_start <= hour <= day_end

    # List of Traxelektronik stations to exclude during daytime
    exclude_trax_stations_daytime = [
        'Ceranów',
        'Baranów',
        'Drobin',
        'Kamion',
        'Mińsk Mazowiecki',
        'Mostówka',
        'Paplin',
        'Pilawa',
        'Stromiec',
        'Węzeł Łabiszyńska',
        'Węzeł Marki',
        'Węzeł Marynarska',
        'Wilchta',
        'Wiśniew'
    ]
    # Clean the exclude list for matching
    exclude_trax_stations_daytime_cleaned = [clean_station_name(name) for name in exclude_trax_stations_daytime]

    # Map station names to cleaned station names
    cleaned_station_dict = {station: clean_station_name(station) for station in trax_stations}

    # 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 = cleaned_station_dict[station]
        # Exclude certain stations during daytime
        if is_daytime and cleaned_station in exclude_trax_stations_daytime_cleaned:
            continue  # Skip this station
        cleaned_temperature = clean_temperature(temperature)
        if cleaned_station in trax_coords and cleaned_temperature is not None:
            lat, lon = trax_coords[cleaned_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")
    
    # Add X and Y coordinates to imgw_gdf
    imgw_gdf['X'] = imgw_gdf.geometry.x
    imgw_gdf['Y'] = imgw_gdf.geometry.y

    # 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, water_crs = read_and_clip_water_bodies(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

    # Compute distance to water bodies for station points
    water_mask_array = water_data.astype(bool)
    grid_spacing = water_transform.a  # Assuming square pixels
    distance_to_water_array = distance_transform_edt(~water_mask_array) * grid_spacing  # Distance in same units as grid_spacing

    # Get distance to water bodies at station points
    trax_distance_to_water = get_raster_value_at_points(
        trax_gdf_dem_crs.geometry,
        raster_data=distance_to_water_array,
        raster_transform=water_transform
    )
    netatmo_distance_to_water = get_raster_value_at_points(
        netatmo_gdf_dem_crs.geometry,
        raster_data=distance_to_water_array,
        raster_transform=water_transform
    )
    imgw_distance_to_water = get_raster_value_at_points(
        imgw_gdf_dem_crs.geometry,
        raster_data=distance_to_water_array,
        raster_transform=water_transform
    )

    # Add distance to water to the GeoDataFrames
    trax_gdf['Distance_to_Water'] = trax_distance_to_water
    netatmo_gdf['Distance_to_Water'] = netatmo_distance_to_water
    imgw_gdf['Distance_to_Water'] = imgw_distance_to_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', 'Distance_to_Water'])

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

    # Read ECOSTRESS data
    ecostress_data, ecostress_transform = read_and_clip_ecostress(masovian_boundary)
    # Read MODIS data
    (modis_day_data, modis_day_transform), (modis_night_data, modis_night_transform) = read_and_process_modis_data(
        modis_day_folder, modis_night_folder)

    # Adjust grid_spacing as per your requirements
    grid_spacing = 0.0025  # Approximately ~250 meters

    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)

    # Resample MODIS data to match grid
    modis_day_resampled = resample_modis_data(
        modis_day_data, modis_day_transform,
        dest_shape=(nrows, ncols), dest_transform=transform
    )
    modis_night_resampled = resample_modis_data(
        modis_night_data, modis_night_transform,
        dest_shape=(nrows, ncols), dest_transform=transform
    )

    # Decide which MODIS data to use based on time of day
    if is_daytime:
        modis_resampled = modis_day_resampled
    else:
        modis_resampled = modis_night_resampled

    # After resampling, replace any extreme negative values with np.nan
    modis_resampled = np.where(modis_resampled <= -1e20, np.nan, modis_resampled)

    # Include MODIS LST data at station points
    stations_gdf['MODIS_LST'] = get_raster_value_at_points(
        stations_gdf.geometry,
        raster_data=modis_resampled,
        raster_transform=transform
    )
    # Handle missing MODIS_LST values
    # Compute mean over finite values
    modis_lst_mean = np.nanmean(stations_gdf['MODIS_LST'])
    stations_gdf['MODIS_LST'].fillna(modis_lst_mean, inplace=True)

    # Prepare sample weights: Assign higher weight to IMGW stations
    weights_map = {'Netatmo': 1, 'Traxelektronik': 1, 'IMGW': 5}
    stations_gdf['Sample_Weight'] = stations_gdf['Source'].map(weights_map)
    sample_weights = stations_gdf['Sample_Weight'].values

    # Prepare training data
    X = stations_gdf[['X', 'Y', 'Elevation', 'Tree_Cover', 'Distance_to_Water', 'MODIS_LST']]

    # Replace infinite values with NaN
    X = X.replace([np.inf, -np.inf], np.nan)

    # Remove rows with NaN values in X
    valid_indices = ~X.isnull().any(axis=1)
    X = X[valid_indices]
    y = stations_gdf.loc[X.index, 'Temperature']
    sample_weights = sample_weights[valid_indices]

    # Proceed with scaling and model fitting
    # Training Model Progress Bar
    from tqdm import tqdm
    with tqdm(total=100, desc='Training Model', unit='%', ncols=80) as pbar:
        # Initialize and fit the scaler and polynomial features
        poly = PolynomialFeatures(degree=2, include_bias=False)
        scaler = StandardScaler()
        
        # Create a pipeline for feature engineering and scaling
        pipeline = Pipeline([
            ('poly', poly),
            ('scaler', scaler)
        ])
        X_poly_scaled = pipeline.fit_transform(X)
        pbar.update(10)

        # Define the hyperparameter grid for RandomForestRegressor
        param_grid = {
            'n_estimators': [100, 200, 300],
            'max_depth': [None, 10, 20, 30],
            'min_samples_split': [2, 5, 10],
            'min_samples_leaf': [1, 2, 4],
            'bootstrap': [True, False]
        }

        # Initialize RandomForestRegressor
        rf = RandomForestRegressor(random_state=42)

        # Initialize GridSearchCV with 5-fold cross-validation
        grid_search = GridSearchCV(
            estimator=rf,
            param_grid=param_grid,
            cv=5,
            scoring='neg_mean_absolute_error',
            n_jobs=-1,
            verbose=1
        )

        # Fit GridSearchCV with sample weights
        grid_search.fit(X_poly_scaled, y, sample_weight=sample_weights)
        pbar.update(20)

        # Retrieve the best model
        best_rf = grid_search.best_estimator_
        pbar.update(30)

        # Define the metrics to evaluate
        scoring_metrics = {
            'MAE': 'neg_mean_absolute_error',
            'MSE': 'neg_mean_squared_error',
            'RMSE': 'neg_root_mean_squared_error',
            'R2': 'r2',
            'Median_Err': 'neg_median_absolute_error',
            'Exp_Variance': 'explained_variance'
        }

        # Perform cross-validation with multiple metrics
        cv_results = cross_validate(
            best_rf,
            X_poly_scaled,
            y,
            cv=5,
            scoring=scoring_metrics,
            n_jobs=-1,
            return_train_score=False
        )
        pbar.update(10)

        # Convert negative scores to positive where necessary
        cv_metrics = {}
        for key in scoring_metrics.keys():
            if key in ['MAE', 'MSE', 'RMSE', 'Median_Err']:
                # Convert negative to positive
                cv_metrics[key] = -cv_results[f'test_{key}']
            else:
                # Keep as is (R² and Explained Variance)
                cv_metrics[key] = cv_results[f'test_{key}']

        # Calculate mean and standard deviation for each metric
        metric_summary = {}
        for metric, values in cv_metrics.items():
            mean_val = np.mean(values)
            std_val = np.std(values)
            metric_summary[metric] = (mean_val, std_val)

        # Print detailed performance metrics
        print("\nDetailed Cross-Validation Performance Metrics:")
        print("---------------------------------------------")
        for metric, (mean, std) in metric_summary.items():
            if metric in ['RMSE', 'Median_Err']:
                print(f"{metric}: {mean:.3f} ± {std:.3f}")
            else:
                print(f"{metric}: {mean:.3f} ± {std:.3f}")
        print("---------------------------------------------")
        pbar.update(10)

        # Get feature importances from the best model
        feature_importances = best_rf.feature_importances_
        # Get feature names from the pipeline
        poly_features = pipeline.named_steps['poly'].get_feature_names_out(X.columns)
        feature_names = poly_features

        # Create a DataFrame for better visualization
        feature_importances_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importances})
        feature_importances_df = feature_importances_df.sort_values('Importance', ascending=False)
        pbar.update(10)

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

    # Running Model Progress Bar
    with tqdm(total=100, desc='Running Model', unit='%', ncols=80) as pbar:
        # 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
        pbar.update(12)

        # 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
        pbar.update(12)

        # Calculate distance to water bodies for grid points
        grid_distance_to_water = get_raster_value_at_points(
            [Point(x, y) for x, y in zip(grid_df['X'], grid_df['Y'])],
            raster_data=distance_to_water_array,
            raster_transform=water_transform
        )
        grid_df['Distance_to_Water'] = grid_distance_to_water
        pbar.update(12)

        # 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
        pbar.update(12)

        # Remove grid points with NaN values (i.e., outside data coverage)
        grid_df = grid_df.dropna(subset=['Elevation', 'Tree_Cover', 'Distance_to_Water', 'Water_Body'])
        pbar.update(2)

        # Include MODIS_LST in grid_df
        grid_df['MODIS_LST'] = grid_df.apply(
            lambda row: get_raster_value_at_point(row['X'], row['Y'], modis_resampled, transform),
            axis=1
        )

        # Handle missing MODIS_LST values
        modis_lst_mean_grid = np.nanmean(grid_df['MODIS_LST'])
        grid_df['MODIS_LST'].fillna(modis_lst_mean_grid, inplace=True)
        pbar.update(10)

        # Prepare data for regression prediction
        X_grid = grid_df[['X', 'Y', 'Elevation', 'Tree_Cover', 'Distance_to_Water', 'MODIS_LST']]
        # Handle infinite values in X_grid
        X_grid = X_grid.replace([np.inf, -np.inf], np.nan)
        # Handle NaNs in X_grid
        X_grid = X_grid.fillna(method='ffill').fillna(method='bfill')

        # Ensure that all values are finite
        # Scale X_grid using the same pipeline
        X_grid_scaled = pipeline.transform(X_grid)
        pbar.update(10)

        grid_df['Predicted_Temperature'] = best_rf.predict(X_grid_scaled)
        pbar.update(10)

        # Calculate residuals at station locations
        stations_gdf['Predicted_Temperature'] = best_rf.predict(pipeline.transform(X))
        stations_gdf['Residual'] = stations_gdf['Temperature'] - stations_gdf['Predicted_Temperature']

        # Force residuals to zero at IMGW stations
        stations_gdf.loc[stations_gdf['Source'] == 'IMGW', 'Residual'] = 0

        # Adjust anomalously large residuals to reduce their impact
        lower_percentile = np.percentile(stations_gdf['Residual'], 2.5)
        upper_percentile = np.percentile(stations_gdf['Residual'], 97.5)

        # Cap the residuals
        stations_gdf['Adjusted_Residual'] = stations_gdf['Residual'].clip(lower=lower_percentile, upper=upper_percentile)

        # Remove duplicates by prioritizing IMGW data
        data = stations_gdf.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()

        # Perform Universal Kriging using Adjusted Residual
        uk = UniversalKriging(
            x=data['X'].values,
            y=data['Y'].values,
            z=data['Adjusted_Residual'].values,
            variogram_model='spherical',
            nlags=20,
            verbose=False
        )

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

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

        # Handle NaNs in residuals by filling them with zero
        grid_df['Residual'].fillna(0, inplace=True)

        # 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()

    # Topographic Cooling Effect
    max_cooling_effect = 3.0  # Maximum cooling effect in degrees Celsius
    # Normalize elevation to range from 0 to 1
    elevation_min = grid_df['Elevation'].min()
    elevation_max = grid_df['Elevation'].max()
    grid_df['Normalized_Elevation'] = (grid_df['Elevation'] - elevation_min) / (elevation_max - elevation_min)

    # Compute the topographic cooling effect
    grid_df['Topographic_Cooling'] = grid_df['Normalized_Elevation'] * max_cooling_effect * (1 - is_daytime)

    # Ensure topographic cooling is not applied over water bodies
    grid_df.loc[grid_df['Water_Body'] == 1, 'Topographic_Cooling'] = 0

    # Apply the topographic cooling effect to the temperature
    grid_df['Temperature'] -= grid_df['Topographic_Cooling']

    # ECOSTRESS Adjustments

    # Retrieve ECOSTRESS LST values at grid points (same as before)
    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 MODIS_LST values or the mean value
    grid_df['ECOSTRESS_LST'] = grid_df.apply(
        lambda row: row['ECOSTRESS_LST'] if not np.isnan(row['ECOSTRESS_LST']) else row['MODIS_LST'], axis=1
    )
    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

    # --- Begin Anomaly Adjustment for Nighttime Forested Areas ---
    if not is_daytime:
        forest_threshold = 50  # Define the threshold for forest cover (e.g., 50%)
        warm_anomaly_forest_mask = (grid_df['ECOSTRESS_Anomaly'] > 0) & (grid_df['Tree_Cover'] >= forest_threshold)
        # Invert the anomalies in forested areas with warm anomalies at night
        grid_df.loc[warm_anomaly_forest_mask, 'ECOSTRESS_Anomaly'] *= -1

    # --- End Anomaly Adjustment ---

    # 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  # Adjust as needed

    # Compute night_weight: maximum at midnight, zero at noon
    night_weight = (math.cos(((hour % 24) / 24) * 2 * math.pi) + 1) / 2  # Ranges from 1 at midnight to 0 at noon

    # Apply anomalies with the night_weight
    adjustment = grid_df['ECOSTRESS_Anomaly_Normalized'] * amplification_factor * night_weight

    # Prevent ECOSTRESS adjustments on water bodies
    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

    # Apply the variable cooling effect to water bodies based on time of day
    cooling_effect = compute_water_body_cooling_effect(hour)

    # Apply the cooling effect to grid cells identified as water bodies
    grid_df.loc[grid_df['Water_Body'] == 1, 'Temperature'] += cooling_effect

    # Create 2D arrays of temperatures
    grid_temperature_with_ecostress = np.full((nrows, ncols), np.nan)
    grid_temperature_with_ecostress[grid_df['row_idx'], grid_df['col_idx']] = grid_df['Temperature'].values

    # Apply Gaussian smoothing
    sigma = 0.2  # Adjust sigma to control the amount of smoothing
    temperature_with_ecostress_smoothed = gaussian_filter(grid_temperature_with_ecostress, sigma=sigma)

    # 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 the mask
    temperature_with_ecostress_smoothed = np.ma.array(temperature_with_ecostress_smoothed, mask=mask == 0)

    # Use the smoothed temperature grid
    temperature_with_ecostress = temperature_with_ecostress_smoothed

    # 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)

    # Extract temperatures over water bodies
    water_temperatures = grid_df.loc[grid_df['Water_Body'] == 1, 'Temperature']
    land_temperatures = grid_df.loc[grid_df['Water_Body'] == 0, 'Temperature']

    # Compare average temperatures
    avg_water_temp = water_temperatures.mean()
    avg_land_temp = land_temperatures.mean()
    print(f"Average Water Temperature: {avg_water_temp:.2f}°C")
    print(f"Average Land Temperature: {avg_land_temp:.2f}°C")
    print(f"Temperature Difference (Land - Water): {(avg_land_temp - avg_water_temp):.2f}°C")

    # 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]

    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, temperature_with_ecostress)
    dem_extent = get_extent(dem_plot_transform, dem_plot_data)

    # Read water data and get CRS
    water_data, water_transform, water_crs = read_and_clip_water_bodies(masovian_boundary)

    # Create an empty array to hold the resampled water data
    water_resampled = np.empty((nrows, ncols), dtype=water_data.dtype)

    # Perform the reprojection and resampling
    reproject(
        source=water_data,
        destination=water_resampled,
        src_transform=water_transform,
        src_crs=water_crs,
        dst_transform=transform,
        dst_crs='EPSG:4326',  # Assuming temperature grid is in EPSG:4326
        resampling=Resampling.nearest
    )

    # Now water_resampled is aligned with the temperature grid
    # Create the water mask
    water_mask = np.where(water_resampled > 0, 1, 0)

    # Define the bounding box coordinates (EPSG:4326)
    xmin, ymin, xmax, ymax = 20.188477, 51.017203, 22.061646, 51.821759

    # Compute the indices for the bounding box
    from rasterio.transform import rowcol

    # Convert coordinates to row and column indices in the temperature grid
    row_ul, col_ul = rowcol(transform, xmin, ymax)
    row_lr, col_lr = rowcol(transform, xmax, ymin)

    # Ensure indices are within valid ranges
    nrows, ncols = temperature_with_ecostress.shape
    row_ul = max(0, min(nrows - 1, row_ul))
    row_lr = max(0, min(nrows - 1, row_lr))
    col_ul = max(0, min(ncols - 1, col_ul))
    col_lr = max(0, min(ncols - 1, col_lr))

    # Ensure row_ul <= row_lr and col_ul <= col_lr
    if row_ul > row_lr:
        row_ul, row_lr = row_lr, row_ul
    if col_ul > col_lr:
        col_ul, col_lr = col_lr, col_ul

    # Extract the subset arrays
    temperature_subset = temperature_with_ecostress[row_ul:row_lr+1, col_ul:col_lr+1]
    hs_subset = hs[row_ul:row_lr+1, col_ul:col_lr+1]
    water_mask_subset = water_mask[row_ul:row_lr+1, col_ul:col_lr+1]
    mask_subset = mask[row_ul:row_lr+1, col_ul:col_lr+1]

    # Apply the Masovian mask to the subsets
    temperature_subset = np.ma.array(temperature_subset, mask=mask_subset == 0)
    hs_subset = np.ma.array(hs_subset, mask=mask_subset == 0)
    water_mask_subset = np.ma.array(water_mask_subset, mask=mask_subset == 0)

    grid_spacing_x = transform.a  # Positive grid spacing in x direction
    grid_spacing_y = transform.e  # Negative grid spacing in y direction

    new_x_min = transform.c + col_ul * grid_spacing_x
    new_y_max = transform.f + row_ul * grid_spacing_y  # grid_spacing_y is negative

    new_transform = from_origin(new_x_min, new_y_max, grid_spacing_x, -grid_spacing_y)

    # Compute the new extent
    extent_subset = get_extent(new_transform, temperature_subset)

    # Filter stations within the bounding box
    from shapely.geometry import box
    bbox_polygon = box(xmin, ymin, xmax, ymax)
    stations_in_bbox = stations_gdf[stations_gdf.geometry.within(bbox_polygon)]

    # Clip the Masovian boundary to the bounding box
    masovian_boundary_clipped = masovian_boundary.clip(bbox_polygon)

    # Plotting Temperature Map with Contours
    # Calculate the aspect ratio of the bounding box
    width = xmax - xmin
    height = ymax - ymin
    aspect_ratio = width / height

    # Set the figure size based on the aspect ratio
    desired_height_inches = 10  # You can adjust this value as desired
    desired_width_inches = desired_height_inches * aspect_ratio

    # Plotting Temperature Map with ECOSTRESS Adjustments
    fig1, ax1 = plt.subplots(figsize=(15, 15))

    # Plot the interpolated temperature grid with ECOSTRESS adjustments
    img1 = ax1.imshow(temperature_with_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)

    # ax1.imshow(water_mask, extent=extent, origin='upper', cmap='Blues', alpha=0.5)  # Increased alpha to 0.5

    # Plot Masovian Voivodeship boundary with a light, dark stroke
    masovian_boundary.boundary.plot(ax=ax1, edgecolor='#333333', linewidth=1.5)

    # Plot stations
    stations_gdf.plot(ax=ax1, color='black', marker='s', markersize=5)

    # Remove axes for a minimalistic look
    ax1.axis('off')

    # Draw a light, dark stroke around the plot extent
    from matplotlib.patches import Rectangle
    rect = Rectangle(
        (extent[0], extent[2]),  # Lower-left corner (x0, y0)
        extent[1] - extent[0],   # Width
        extent[3] - extent[2],   # Height
        linewidth=1.5,
        edgecolor='#333333',
        facecolor='none',
        transform=ax1.transData
    )
    ax1.add_patch(rect)

    # Introduce a subtle background gradient
    from matplotlib.colors import LinearSegmentedColormap
    background_cmap = LinearSegmentedColormap.from_list(
        'background_gradient', ['#ffffff', '#f0f0f0'], N=256)
    ax1.imshow(
        np.linspace(0, 1, 256).reshape(1, -1),
        cmap=background_cmap,
        extent=(extent[0], extent[1], extent[2], extent[3]),
        aspect='auto',
        alpha=0.2,
        zorder=-1  # Place it below other elements
    )

    # Adjust font properties for a modern look
    plt.rcParams.update({
        'font.size': 14,  # Base font size
        'font.family': 'sans-serif',
        'font.sans-serif': ['Arial', 'Helvetica', 'DejaVu Sans'],
        'text.color': '#333333'
    })

    # Enlarge and reposition the product name (Title)
    fig1.text(
        0.5, 0.94,  # Adjusted y-position for better placement
        "High-Resolution Mesoscale Temperature Analysis",
        fontsize=28,  # Increased font size
        fontweight='bold',
        ha='center',
        va='top',
        fontname='DejaVu Sans',
        color='#333333'
    )

    # Move the model name/resolution text closer to the product name text
    fig1.text(
        0.5, 0.91,  # Adjusted y-position to move it closer
        "Model: TempMeso v1.0 (Preview)   |   Resolution: 250 m",
        fontsize=18,  # Increased font size
        ha='center',
        va='top',
        fontname='DejaVu Sans',
        color='#666666'
    )

    # Adjust colorbar position and style
    from mpl_toolkits.axes_grid1 import make_axes_locatable

    divider = make_axes_locatable(ax1)
    cax = divider.append_axes("right", size="3%", pad=0.1)
    cbar1 = fig1.colorbar(img1, cax=cax)
    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))
    cbar1.ax.tick_params(labelsize=14)

    # Enhance colorbar aesthetics
    cbar1.outline.set_visible(False)
    cbar1.ax.yaxis.set_tick_params(width=0.5)
    cbar1.ax.tick_params(labelsize=14, colors='#333333')

    # Move the logo to the bottom right corner, directly below the colorbar
    from matplotlib.offsetbox import OffsetImage, AnnotationBbox
    import matplotlib.image as mpimg

    # Read the logo image
    logo_path = 'input/logo.png'
    logo_img = mpimg.imread(logo_path)

    # Adjust the size of the logo
    logo_zoom = 0.25
    imagebox = OffsetImage(logo_img, zoom=logo_zoom)

    # Get the position of the colorbar axes
    cbar_bbox = cax.get_position()
    cbar_x0 = cbar_bbox.x0  # Left
    cbar_x1 = cbar_bbox.x1  # Right

    # Calculate the x position for the logo (centered below the colorbar)
    logo_x = (cbar_x0 + cbar_x1) / 2
    # Set the y position as low as possible, but ensure the logo is fully visible
    logo_y = 0.02  # Adjust this value if necessary

    ab = AnnotationBbox(
        imagebox,
        xy=(logo_x, logo_y),
        xycoords='figure fraction',
        frameon=False,
        box_alignment=(0.5, 0),  # Centered horizontally, aligned at bottom
    )

    fig1.add_artist(ab)

    # Adjust the bottom margin to ensure the logo is not cut off
    plt.subplots_adjust(left=0.05, right=0.95, top=0.88, bottom=0.05)

    # Ensure the temperature grid and water mask are aligned
    if temperature_with_ecostress.shape != water_mask.shape:
        raise ValueError("Temperature grid and water mask dimensions do not match.")

    # Create a masked array for land temperatures by excluding water bodies
    temperature_land = temperature_with_ecostress.copy()
    # If temperature_with_ecostress is not already a masked array, convert it
    if not isinstance(temperature_land, np.ma.MaskedArray):
        temperature_land = np.ma.array(temperature_land)
    # Update the mask to include water bodies
    temperature_land.mask = temperature_land.mask | (water_mask == 1)

    # Calculate minimum temperature over land only
    min_temp = temperature_land.min()
    max_temp = temperature_land.max()

    # Get the indices of the min and max temperatures
    # For min_temp, use the land-only masked array
    min_indices_flat = np.ma.argmin(temperature_land)
    max_indices_flat = np.ma.argmax(temperature_land)
    min_indices = np.unravel_index(min_indices_flat, temperature_land.shape)
    max_indices = np.unravel_index(max_indices_flat, temperature_land.shape)

    # Convert grid indices to geographic coordinates
    min_lon_full, min_lat_full = rasterio.transform.xy(transform, min_indices[0], min_indices[1])
    max_lon_full, max_lat_full = rasterio.transform.xy(transform, max_indices[0], max_indices[1])

    # Plot the temperature values on the map (without markers)
    # Use white text with a thick black stroke for better visibility
    text_kwargs = dict(
        fontsize=18,  # Increased font size for clarity
        fontname='Arial',
        ha='center',
        va='center',
        color='white',
        transform=ax1.transData,
        path_effects=[patheffects.withStroke(linewidth=3, foreground='black')]
    )

    # Format temperature values with one decimal point and add text labels
    ax1.text(min_lon_full, min_lat_full, f"{min_temp:.1f}", **text_kwargs)
    ax1.text(max_lon_full, max_lat_full, f"{max_temp:.1f}", **text_kwargs)

    # Save and show the plot
    plt.savefig('output/temperature_analysis_modern.png', dpi=300)
    plt.show()

    print("Updated temperature analysis plot created successfully.")

    # Calculate the aspect ratio of the bounding box
    width = xmax - xmin
    height = ymax - ymin
    aspect_ratio = width / height

    # Set the maximum desired width
    max_width_inches = 15  # Adjust this value as desired

    # Calculate the figure height based on the aspect ratio and maximum width
    desired_width_inches = min(max_width_inches, aspect_ratio * desired_height_inches)
    desired_height_inches = desired_width_inches / aspect_ratio

    # Ensure the height doesn't exceed a certain value
    desired_height_inches = min(desired_height_inches, 10)  # Adjust as needed

    fig2, ax2 = plt.subplots(figsize=(desired_width_inches, desired_height_inches))

    # Proceed with previous plotting code, using adjusted fig2 and ax2

    # Use the subset data and extent with applied mask
    img2 = ax2.imshow(temperature_subset, extent=extent_subset, origin='upper',
                    cmap=cmap, norm=norm, alpha=1)

    # Plot hillshade on top using subset data with mask
    # ax2.imshow(hs_subset, extent=extent_subset, cmap='gray', origin='upper', alpha=0.3)

    # Overlay water bodies using subset data with mask
    # ax2.imshow(water_mask_subset, extent=extent_subset, origin='upper', cmap='Blues', alpha=0.5)

    # Plot clipped Masovian boundary with a light, dark stroke
    masovian_boundary_clipped.boundary.plot(ax=ax2, edgecolor='#333333', linewidth=1.5)

    # Plot stations within the bounding box
    stations_in_bbox.plot(ax=ax2, color='black', marker='s', markersize=5)

    # Remove axes for a minimalistic look
    ax2.axis('off')

    # Introduce a subtle background gradient using the subset extent
    ax2.imshow(
        np.linspace(0, 1, 256).reshape(1, -1),
        cmap=background_cmap,
        extent=extent_subset,
        aspect='auto',
        alpha=0.2,
        zorder=-1  # Place it below other elements
    )

    # Adjust font properties for a modern look
    plt.rcParams.update({
        'font.size': 14,
        'font.family': 'sans-serif',
        'font.sans-serif': ['Arial', 'Helvetica', 'DejaVu Sans'],
        'text.color': '#333333'
    })

    # Enlarge and reposition the product name (Title)
    fig2.text(
        0.5, 0.995,  # Moved up towards the top edge
        "High-Resolution Mesoscale Temperature Analysis",
        fontsize=12,
        fontweight='bold',
        ha='center',
        va='top',
        fontname='DejaVu Sans',
        color='#333333'
    )

    # Move the model name/resolution text closer to the product name text
    fig2.text(
        0.5, 0.970,  # Moved up slightly below the title
        "Model: HRMTA v1.1 (Preview)   |   Resolution: 250 m",
        fontsize=9,
        ha='center',
        va='top',
        fontname='DejaVu Sans',
        color='#666666'
    )

    # Add data source attribution at the bottom left corner
    fig2.text(
        0.01, 0.02,
        "Data source: IMGW-PIB (processed)",
        fontsize=12,
        ha='left',
        va='bottom',
        fontname='DejaVu Sans',
        color='#333333'
    )

    # Adjust colorbar position and style
    from mpl_toolkits.axes_grid1 import make_axes_locatable

    divider = make_axes_locatable(ax2)
    cax2 = divider.append_axes("right", size="3%", pad=0.1)
    cbar2 = fig2.colorbar(img2, cax=cax2)
    cbar2.set_ticks(ticks)
    cbar2.set_ticklabels(ticks.astype(int))

    # Import FontProperties and load the font
    from matplotlib.font_manager import FontProperties

    # Replace 'input/RobotoCondensed-Regular.ttf' with the actual path to your font file
    roboto_font_path = 'input/RobotoCondensed-Regular.ttf'
    roboto_condensed = FontProperties(fname=roboto_font_path)

    # Apply font properties to the colorbar tick labels
    for label in cbar2.ax.yaxis.get_ticklabels():
        label.set_fontproperties(roboto_condensed)

    cbar2.ax.tick_params(labelsize=14)

    # Enhance colorbar aesthetics
    cbar2.outline.set_visible(False)
    cbar2.ax.yaxis.set_tick_params(width=0.5)
    cbar2.ax.tick_params(labelsize=14, colors='#333333')

    # Move the logo to the bottom right corner, directly below the colorbar
    from matplotlib.offsetbox import OffsetImage, AnnotationBbox
    import matplotlib.image as mpimg

    # Read the logo image again for the second plot
    logo_img2 = mpimg.imread(logo_path)

    # Adjust the size of the logo
    imagebox2 = OffsetImage(logo_img2, zoom=logo_zoom)

    # Get the position of the colorbar axes
    cbar_bbox2 = cax2.get_position()
    logo_x2 = (cbar_bbox2.x0 + cbar_bbox2.x1) / 2
    logo_y2 = 0.02  # Adjust this value if necessary

    ab2 = AnnotationBbox(
        imagebox2,
        xy=(logo_x2, logo_y2),
        xycoords='figure fraction',
        frameon=False,
        box_alignment=(0.5, 0),
    )
    fig2.add_artist(ab2)

    # Adjust the bottom margin to ensure the logo and data attribution text are not cut off
    plt.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.06)

    # Ensure the temperature grid and water mask are aligned
    if temperature_with_ecostress.shape != water_mask.shape:
        raise ValueError("Temperature grid and water mask dimensions do not match.")

    # Create a masked array for land temperatures by excluding water bodies
    temperature_land = temperature_with_ecostress.copy()
    # If temperature_with_ecostress is not already a masked array, convert it
    if not isinstance(temperature_land, np.ma.MaskedArray):
        temperature_land = np.ma.array(temperature_land)
    # Update the mask to include water bodies
    temperature_land.mask = temperature_land.mask | (water_mask == 1)

    # Calculate minimum temperature over land only
    min_temp = temperature_land.min()
    # Calculate maximum temperature as before (including all areas)
    max_temp = temperature_land.max()

    # Get the indices of the min and max temperatures
    # For min_temp, use the land-only masked array
    min_indices_flat = np.ma.argmin(temperature_land)
    max_indices_flat = np.ma.argmax(temperature_land)
    min_indices = np.unravel_index(min_indices_flat, temperature_land.shape)
    max_indices = np.unravel_index(max_indices_flat, temperature_land.shape)

    # Convert grid indices to coordinates using the new_transform
    min_lon_full, min_lat_full = rasterio.transform.xy(transform, min_indices[0], min_indices[1])
    max_lon_full, max_lat_full = rasterio.transform.xy(transform, max_indices[0], max_indices[1])

    # Add contours to the plot using the subset data
    min_temp_c = np.floor(min_temp)
    max_temp_c = np.ceil(max_temp)
    contour_levels = np.arange(min_temp_c, max_temp_c + 1, 1)

    contours = ax2.contour(
        temperature_subset, levels=contour_levels, colors='black', linewidths=0.5,
        extent=extent_subset, origin='upper'
    )
    contour_labels = ax2.clabel(
        contours, inline=False, fontsize=8, fmt='%d', colors='white',
        inline_spacing=4, manual=False
    )
    for txt in contour_labels:
        txt.set_path_effects([
            patheffects.Stroke(linewidth=3, foreground='black'),
            patheffects.Normal()
        ])

    # Now add the min/max temperature labels after the contours and contour labels
    text_kwargs = dict(
        fontsize=18,
        fontname='Arial',
        ha='center',
        va='center',
        color='white',
        transform=ax2.transData,
        path_effects=[patheffects.withStroke(linewidth=3, foreground='black')]
    )

    # Format temperature values with one decimal point
    ax2.text(min_lon_full, min_lat_full, f"{min_temp:.1f}", **text_kwargs)
    ax2.text(max_lon_full, max_lat_full, f"{max_temp:.1f}", **text_kwargs)

    # Reverse geocode min and max temperature locations to find nearest village
    from geopy.geocoders import Nominatim
    geolocator = Nominatim(user_agent="geoapiExercises")

    try:
        min_location = geolocator.reverse((min_lat_full, min_lon_full), language='en')
        min_address = min_location.raw.get('address', {})
        min_village = min_address.get('village', 
                        min_address.get('town', 
                        min_address.get('city', 'Unknown Location')))
    except Exception as e:
        print(f"Error reverse geocoding min location: {e}")
        min_village = 'Unknown Location'

    try:
        max_location = geolocator.reverse((max_lat_full, max_lon_full), language='en')
        max_address = max_location.raw.get('address', {})
        max_village = max_address.get('village', 
                        max_address.get('town', 
                        max_address.get('city', 'Unknown Location')))
    except Exception as e:
        print(f"Error reverse geocoding max location: {e}")
        max_village = 'Unknown Location'

    # Create label texts
    min_label_text = f"MIN\n {min_temp:.1f}°C: {min_village}"
    max_label_text = f"MAX\n {max_temp:.1f}°C: {max_village}"

    # Define font properties
    from matplotlib.font_manager import FontProperties
    roboto_font_path = 'input/RobotoCondensed-Regular.ttf'
    roboto_condensed = FontProperties(fname=roboto_font_path)

    # Add text labels to the figure
    fig1.text(0.01, 0.95, min_label_text, ha='left', va='top', fontsize=14, fontproperties=roboto_condensed)
    fig1.text(0.99, 0.95, max_label_text, ha='right', va='top', fontsize=14, fontproperties=roboto_condensed)

    # Save and show the plot
    plt.savefig('output/temperature_analysis_with_contours2.png', dpi=300)
    plt.show()

    print("Temperature analysis plot with contours created successfully.")

    # At the end, print feature importances to confirm MODIS influence
    print("\nFeature importances from the Random Forest model:")
    print(feature_importances_df)

   # Check if 'Distance_to_Water' has significant importance
    distance_water_importance = feature_importances_df.loc[feature_importances_df['Feature'] == 'Distance_to_Water', 'Importance'].values[0]
    if distance_water_importance > 0.05:  # Threshold can be adjusted
        print(f"\n'Distance_to_Water' feature has significant influence on the model (importance: {distance_water_importance:.4f}).")
    else:
        print(f"\n'Distance_to_Water' feature has low influence on the model (importance: {distance_water_importance:.4f}).")

    return temperature_with_ecostress, 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_two_points(lat1, lon1, lat2, lon2, temperature_grid, transform):
    """
    Given two latitude and longitude pairs, return the interpolated temperature values at those points.
    
    Parameters:
    - lat1, lon1: Latitude and longitude of the first point.
    - lat2, lon2: Latitude and longitude of the second point.
    - temperature_grid: The interpolated temperature grid (2D numpy array).
    - transform: Affine transform for the grid.
    
    Returns:
    - A tuple containing interpolated temperature values at the two points,
      or None for a point if it's outside the grid or has invalid data.
    """
    import numpy as np
    from rasterio.transform import rowcol
    
    temps = []
    for lat, lon in [(lat1, lon1), (lat2, lon2)]:
        try:
            # Convert geographic coordinates to raster indices
            row, col = rowcol(transform, lon, lat)  # Note: rasterio uses (x, y), so (lon, lat)
            row = int(row)
            col = int(col)
            
            # Retrieve the value from the temperature grid
            value = temperature_grid[row, col]
            
            # Check if the value is valid
            if np.ma.is_masked(value) or np.isnan(value):
                # Value is masked or NaN, meaning outside the interpolated area
                temps.append(None)
            else:
                temps.append(value.item())
        except (IndexError, ValueError):
            # Point is outside the grid
            temps.append(None)
    return tuple(temps)

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)
                if (0 <= row < raster_data.shape[0]) and (0 <= col < raster_data.shape[1]):
                    value = raster_data[row, col]
                    # Check for NaN in raster data
                    if np.isnan(value):
                        values.append(np.nan)
                    else:
                        values.append(value)
                else:
                    values.append(np.nan)
            except (IndexError, ValueError):
                values.append(np.nan)
        return values
    else:
        raise ValueError("Provide raster_data and raster_transform.")
    
def get_raster_value_at_point(x, y, raster_data=None, raster_transform=None):
    from rasterio.transform import rowcol

    if raster_data is not None and raster_transform is not None:
        try:
            row, col = rowcol(raster_transform, x, y)
            row = int(row)
            col = int(col)
            if (0 <= row < raster_data.shape[0]) and (0 <= col < raster_data.shape[1]):
                value = raster_data[row, col]
                if np.isnan(value):
                    return np.nan
                else:
                    return value
            else:
                return np.nan
        except (IndexError, ValueError):
            return np.nan
    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:
        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:
        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:
        water_crs = src.crs  # Get the CRS of the water raster
        
        # 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)
        
        # 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, water_crs

def read_and_clip_ecostress(masovian_boundary):
    ecostress_path = 'input/raw_ecostress.tif'
    with rasterio.open(ecostress_path) as src:
        # Read data
        ecostress_data = src.read(1, masked=True)
        ecostress_data = np.where(ecostress_data == src.nodata, np.nan, ecostress_data)
        ecostress_transform = src.transform
    return ecostress_data, ecostress_transform

# Ensure to adjust your main execution block accordingly
if __name__ == "__main__":
    # Fetching Data Progress Bar
    with tqdm(total=100, desc='Fetching Data', unit='%', ncols=80) as pbar:
        # Fetch Traxelektronik data
        trax_stations, trax_temperatures = fetch_traxelektronik_data()
        pbar.update(20)

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

        for station in tqdm(trax_stations, desc="Geocoding Traxelektronik Stations", unit="station"):
            cleaned_station = clean_station_name(station)
            coords, was_cleaned = get_coordinates(cleaned_station, geolocator)
            if coords:
                trax_coords[cleaned_station] = coords  # Use cleaned station names as keys
        pbar.update(30)

        # Fetch Netatmo data
        netatmo_stations, netatmo_temperatures, netatmo_coords = fetch_netatmo_data()
        pbar.update(20)

        # Fetch IMGW data
        imgw_stations, imgw_temperatures, imgw_station_ids = fetch_imgw_data()
        pbar.update(10)

        # Geocode IMGW stations
        imgw_coords = {}
        for station in tqdm(imgw_stations, desc="Geocoding IMGW Stations", unit="station"):
            coords, was_cleaned = get_coordinates(station, geolocator)
            if coords:
                imgw_coords[station] = coords
        pbar.update(20)

    # Paths to MODIS data folders
    modis_day_folder = 'input/MODIS_day'
    modis_night_folder = 'input/MODIS_night'

    # Call the function with all required arguments
    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,
        modis_day_folder, modis_night_folder
    )

    # Coordinates to test
    test_lat1 = 51.40232
    test_lon1 = 21.14761
    test_lat2 = 51.30873
    test_lon2 = 21.13436

    # Call the modified function
    interpolated_temp1, interpolated_temp2 = get_interpolated_temperature_at_two_points(
        test_lat1, test_lon1, test_lat2, test_lon2, smoothed_temperature, transform
    )

    if interpolated_temp1 is not None:
        print(f"\nRadom [{test_lat1}, {test_lon1}]: {interpolated_temp1:.2f}°C\nMazowszany [{test_lat2}, {test_lon2}]: {interpolated_temp2:.2f}°C")
    else:
        print(f"\nCoordinates {test_lat1}, {test_lon1} are outside the interpolated grid or have no data.")

What do I do there? The model accuracy has significantly downgraded, at least at the daytime, although I haven't edited any input data nd the station amount is still the same, it is processed right. Here's the debug outputL
Number of training samples after cleaning: 336

Temperature statistics:
Min: 13.0
Max: 35.5
Mean: 15.548214285714288
Std Dev: 1.877883000322381

Feature Variations:
X: min=19.43074, max=22.717294, std=0.6387639927387609, missing_values=0
Y: min=51.077495, max=53.39866, std=0.46218698184101464, missing_values=0
Elevation: min=55.0, max=242.0, std=33.092506408691406, missing_values=0
Tree_Cover: min=0, max=90, std=21.32435734231033, missing_values=0
Distance_to_Water: min=0.0, max=0.14200352108310554, std=0.021538057262917625, missing_values=0
MODIS_LST: min=22.322002410888672, max=27.798004150390625, std=1.0476365403612204, missing_values=0

Checking for NaNs or infinite values in features:
X: NaNs=0, Infs=0
Y: NaNs=0, Infs=0
Elevation: NaNs=0, Infs=0
Tree_Cover: NaNs=0, Infs=0
Distance_to_Water: NaNs=0, Infs=0
MODIS_LST: NaNs=0, Infs=0
C:\Users\Lenovo\Documents\conda\envs\temp_env\lib\site-packages\pandas\core\frame.py:717: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.
  warnings.warn(

Outlier stations with extremely high temperatures:
C:\Users\Lenovo\Documents\conda\envs\temp_env\lib\site-packages\pandas\core\frame.py:717: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.
  warnings.warn(
           Station  Temperature          Source
1                          22.3  Traxelektronik
133        Kobyłka         22.3  Traxelektronik
183  Przywory Małe         35.5         Netatmo
Training Model:   0%|                                    | 0/100 [00:00<?, ?%/s]Fitting 5 folds for each of 1 candidates, totalling 5 fits
Training Model:  70%|██████████████████▉        | 70/100 [00:04<00:01, 16.58%/s]
Detailed Cross-Validation Performance Metrics:
---------------------------------------------
MAE: 1.314 ± 0.164
MSE: 4.183 ± 1.885
RMSE: 1.997 ± 0.440
R2: -0.305 ± 0.341
Median_Err: 1.027 ± 0.109
Exp_Variance: -0.262 ± 0.279
---------------------------------------------
Training Model:  90%|████████████████████████▎  | 90/100 [00:04<00:00, 19.71%/s]
Running Model:  48%|█████████████▍              | 48/100 [03:44<03:56,  4.54s/%]c:\Users\Lenovo\Downloads\pythonStationsData\interpolation_v1.1.py:842: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  grid_df['MODIS_LST'].fillna(modis_lst_mean_grid, inplace=True)
Running Model:  60%|████████████████▊           | 60/100 [04:07<02:24,  3.61s/%]c:\Users\Lenovo\Downloads\pythonStationsData\interpolation_v1.1.py:850: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
  X_grid = X_grid.fillna(method='ffill').fillna(method='bfill')
Running Model:  80%|██████████████████████▍     | 80/100 [04:12<00:38,  1.92s/%]C:\Users\Lenovo\Documents\conda\envs\temp_env\lib\site-packages\pandas\core\frame.py:717: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.       
  warnings.warn(
C:\Users\Lenovo\Documents\conda\envs\temp_env\lib\site-packages\pandas\core\frame.py:717: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.
  warnings.warn(
C:\Users\Lenovo\Documents\conda\envs\temp_env\lib\site-packages\pandas\core\frame.py:717: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.
  warnings.warn(
C:\Users\Lenovo\Documents\conda\envs\temp_env\lib\site-packages\pandas\core\frame.py:717: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.
  warnings.warn(
C:\Users\Lenovo\Documents\conda\envs\temp_env\lib\site-packages\pandas\core\frame.py:717: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.
  warnings.warn(
c:\Users\Lenovo\Downloads\pythonStationsData\interpolation_v1.1.py:903: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  grid_df['Residual'].fillna(0, inplace=True)
Running Model:  80%|██████████████████████▍     | 80/100 [09:42<02:25,  7.28s/%]
c:\Users\Lenovo\Downloads\pythonStationsData\interpolation_v1.1.py:941: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  grid_df['ECOSTRESS_LST'].fillna(grid_df['ECOSTRESS_LST'].mean(), inplace=True)
Average Water Temperature: 14.73°C
Average Land Temperature: 17.52°C
Temperature Difference (Land - Water): 2.79°C
C:\Users\Lenovo\Documents\conda\envs\temp_env\lib\site-packages\pandas\core\frame.py:717: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.
  warnings.warn(
C:\Users\Lenovo\Documents\conda\envs\temp_env\lib\site-packages\pandas\core\frame.py:717: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.
  warnings.warn(
C:\Users\Lenovo\Documents\conda\envs\temp_env\lib\site-packages\pandas\core\frame.py:717: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.
  warnings.warn(
C:\Users\Lenovo\Documents\conda\envs\temp_env\lib\site-packages\pandas\core\frame.py:717: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.
  warnings.warn(
Updated temperature analysis plot created successfully.
C:\Users\Lenovo\Documents\conda\envs\temp_env\lib\site-packages\pandas\core\frame.py:717: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.
  warnings.warn(
Temperature analysis plot with contours created successfully.

Feature importances from the Random Forest model:
                         Feature  Importance
19   Elevation Distance_to_Water    0.128272
11                   X MODIS_LST    0.100710
7                            X Y    0.072871
12                           Y^2    0.063740
6                            X^2    0.062891
1                              Y    0.061775
16                   Y MODIS_LST    0.060628
0                              X    0.059575
10           X Distance_to_Water    0.050855
20           Elevation MODIS_LST    0.044603
13                   Y Elevation    0.033411
26                   MODIS_LST^2    0.032846
5                      MODIS_LST    0.031993
8                    X Elevation    0.031125
25   Distance_to_Water MODIS_LST    0.027289
24           Distance_to_Water^2    0.026709
17                   Elevation^2    0.025440
15           Y Distance_to_Water    0.022526
2                      Elevation    0.022486
4              Distance_to_Water    0.022175
22  Tree_Cover Distance_to_Water    0.006193
14                  Y Tree_Cover    0.002564
9                   X Tree_Cover    0.002386
18          Elevation Tree_Cover    0.002274
3                     Tree_Cover    0.001689
21                  Tree_Cover^2    0.001629
23          Tree_Cover MODIS_LST    0.001344

'Distance_to_Water' feature has low influence on the model (importance: 0.0222).

Additionally the feature importances in forest model have significantly fallen down for some reason. Do everything what you can to restore previous temperature model interpolation accuracy in range of 0.5C