Hi, I want to make a program in python which runs on Google Colab which can generate a solar potential map for entire state of Gujarat in a heatmap like plot over the state's map. 
I wrote the following code but it is hardcoded, too slow, and gives a very wrong output. Can you please fix it fully, and remove all existing comments and add new comments detailing what the code does?

import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import requests
import time
from typing import Dict, List, Tuple
import logging
import json
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor
import plotly.express as px
import plotly.graph_objects as go
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from shapely.geometry import Point, Polygon
import geopandas as gpd

class NASAPowerAPI:
    def __init__(self):
        self.base_url = "https://power.larc.nasa.gov/api/temporal/daily/point"
        self.logger = self._setup_logger()

    def _setup_logger(self):
        logger = logging.getLogger(__name__)
        logger.setLevel(logging.DEBUG)
        ch = logging.StreamHandler()
        ch.setLevel(logging.INFO)
        formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
        ch.setFormatter(formatter)
        if not logger.handlers:
            logger.addHandler(ch)
        return logger

    def fetch_solar_data(self, lat: float, lon: float, start_date: str, end_date: str) -> Dict:
        parameters = [
            "ALLSKY_SFC_SW_DWN",  # Solar radiation
            "T2M",                 # Temperature at 2 meters
            "RH2M",               # Relative humidity at 2 meters
            "CLOUD_AMT",          # Cloud amount
            "ALLSKY_KT",          # Clearness index
            "WS2M"                # Wind speed at 2 meters
        ]

        params = {
            "start": start_date,
            "end": end_date,
            "latitude": lat,
            "longitude": lon,
            "community": "RE",
            "parameters": ",".join(parameters),
            "format": "JSON"
        }

        try:
            response = requests.get(self.base_url, params=params)
            response.raise_for_status()
            self.logger.debug(f"Fetched data for lat={lat}, lon={lon} from {start_date} to {end_date}")
            return response.json()
        except Exception as e:
            self.logger.error(f"Error fetching data: {str(e)}")
            return None

class SolarAnalyzer:
    def __init__(self):
        self.nasa_api = NASAPowerAPI()
        self.model = None
        self.scaler = StandardScaler()
        self.logger = logging.getLogger(__name__)

        # Define Gujarat state boundaries
        self.gujarat_bounds = {
            'min_lat': 20.1,
            'max_lat': 24.7,
            'min_lon': 68.1,
            'max_lon': 74.4
        }

        # Gujarat polygon coordinates (simplified boundary)
        self.gujarat_polygon = Polygon([
            (68.1, 23.5),  # Western tip
            (69.5, 24.7),  # Northern border
            (74.4, 24.4),  # Northeast
            (73.2, 21.5),  # Southeast
            (72.6, 20.1),  # Southern tip
            (68.1, 23.5)   # Back to start
        ])

        # Create grid points
        self.grid_resolution = 0.2  # Increased for faster processing
        self.grid_points = self._create_grid_points()

        self.constraints = {
            'temperature': {'min': -5, 'max': 55},
            'humidity': {'min': 5, 'max': 100},
            'solar_radiation': {'min': 0, 'max': 1500},
            'cloud_cover': {'min': 0, 'max': 100},
            'clearness_index': {'min': 0.1, 'max': 1.0},
            'wind_speed': {'min': 0, 'max': 50}
        }

    def _create_grid_points(self) -> List[Dict]:
        points = []
        lat_range = np.arange(
            self.gujarat_bounds['min_lat'],
            self.gujarat_bounds['max_lat'],
            self.grid_resolution
        )
        lon_range = np.arange(
            self.gujarat_bounds['min_lon'],
            self.gujarat_bounds['max_lon'],
            self.grid_resolution
        )

        for lat in lat_range:
            for lon in lon_range:
                point = Point(lon, lat)
                if self.gujarat_polygon.contains(point):
                    points.append({
                        'latitude': lat,
                        'longitude': lon,
                        'land_usage_factor': self._estimate_land_usage_factor(lat, lon)
                    })

        return points

    def _estimate_land_usage_factor(self, lat: float, lon: float) -> float:
        # Coastal areas (lower usage)
        is_coastal = (
            (lon < 69.5 and lat < 23.0) or  # Kutch coast
            (lon > 72.0 and lat < 21.5)      # Southern coast
        )

        # Urban areas (lower usage)
        major_cities = [
            (72.5714, 23.0225),  # Ahmedabad
            (72.8311, 21.1702),  # Surat
            (73.1812, 22.3072),  # Vadodara
            (70.8022, 22.3039)   # Rajkot
        ]

        is_urban = any(
            abs(lat - city[1]) < 0.3 and abs(lon - city[0]) < 0.3
            for city in major_cities
        )

        if is_urban:
            return 0.01
        elif is_coastal:
            return 0.015
        else:
            return 0.025

    def collect_data(self, start_date: str, end_date: str) -> pd.DataFrame:
        """Collect data for all grid points"""
        all_data = []

        for point in self.grid_points:
            data = self.nasa_api.fetch_solar_data(
                point['latitude'],
                point['longitude'],
                start_date,
                end_date
            )

            if data and 'properties' in data and 'parameter' in data['properties']:
                params = data['properties']['parameter']
                daily_data = []

                # Get the timespan of data
                for date, values in params['ALLSKY_SFC_SW_DWN'].items():
                    daily_data.append({
                        'date': date,
                        'latitude': point['latitude'],
                        'longitude': point['longitude'],
                        'land_usage_factor': point['land_usage_factor'],
                        'solar_radiation': params['ALLSKY_SFC_SW_DWN'].get(date),
                        'temperature': params['T2M'].get(date),
                        'humidity': params['RH2M'].get(date),
                        'cloud_cover': params['CLOUD_AMT'].get(date),
                        'clearness_index': params['ALLSKY_KT'].get(date),
                        'wind_speed': params['WS2M'].get(date)
                    })

                all_data.extend(daily_data)

            # Add a small delay to avoid hitting API rate limits
            time.sleep(1)

        return pd.DataFrame(all_data)

    def calculate_solar_power(self, df: pd.DataFrame) -> pd.Series:
        """Calculate potential solar power output"""
        panel_efficiency = 0.20
        system_losses = 0.14
        temperature_coefficient = -0.0035

        solar_radiation_wm2 = df['solar_radiation'] * 1000 / 24

        temperature_effect = 1 + temperature_coefficient * (df['temperature'] - 25)
        temperature_effect = temperature_effect.clip(0.7, 1.0)

        clearness_effect = df['clearness_index'].clip(0.1, 1.0)

        cloud_effect = 1 - (df['cloud_cover'] / 100 * 0.7)

        power_output = (solar_radiation_wm2 *
                       panel_efficiency *
                       (1 - system_losses) *
                       temperature_effect *
                       clearness_effect *
                       cloud_effect)

        power_mw_km2 = (power_output * 1_000_000 / 1_000_000) * df['land_usage_factor']

        return power_mw_km2

    def create_heatmap(self, df: pd.DataFrame):
        """Create a heatmap visualization of solar potential"""
        grid_summary = df.groupby(['latitude', 'longitude'])['total_potential'].mean().reset_index()

        fig = go.Figure()

        # Add heatmap layer
        fig.add_trace(go.Densitymapbox(
            lat=grid_summary['latitude'],
            lon=grid_summary['longitude'],
            z=grid_summary['total_potential'],
            radius=30,
            colorscale='Viridis',
            zmin=grid_summary['total_potential'].min(),
            zmax=grid_summary['total_potential'].max(),
            colorbar=dict(
                title='Solar Potential (MW/km²)',
                titleside='right'
            )
        ))

        # Add markers for major cities
        major_cities = {
            'Ahmedabad': (23.0225, 72.5714),
            'Surat': (21.1702, 72.8311),
            'Vadodara': (22.3072, 73.1812),
            'Rajkot': (22.3039, 70.8022),
            'Bhuj': (23.2419, 69.6669),
            'Gandhinagar': (23.2156, 72.6369)
        }

        fig.add_trace(go.Scattermapbox(
            lat=[coords[0] for coords in major_cities.values()],
            lon=[coords[1] for coords in major_cities.values()],
            mode='markers+text',
            marker=dict(size=10, color='red'),
            text=list(major_cities.keys()),
            textposition="top center",
            name='Major Cities'
        ))

        fig.update_layout(
            mapbox_style="carto-positron",
            mapbox=dict(
                center=dict(lat=22.5, lon=71.5),
                zoom=6
            ),
            title='Solar Power Potential Across Gujarat',
            height=800,
            width=1000,
            margin=dict(l=0, r=0, t=30, b=0)
        )

        return fig

def main():
    # Initialize the analyzer
    analyzer = SolarAnalyzer()

    # Set date range for analysis (example: last month)
    end_date = datetime.now()
    start_date = end_date - timedelta(days=30)

    # Format dates for API
    start_str = start_date.strftime('%Y%m%d')
    end_str = end_date.strftime('%Y%m%d')

    print("Collecting solar data for Gujarat...")

    # Collect data
    df = analyzer.collect_data(start_str, end_str)

    print(f"Collected data for {len(df)} points")

    # Calculate solar potential
    df['total_potential'] = analyzer.calculate_solar_power(df)

    print("Creating heatmap...")

    # Create and save heatmap
    fig = analyzer.create_heatmap(df)
    fig.write_html("gujarat_solar_potential.html")

    print("Analysis complete! Heatmap has been saved as 'gujarat_solar_potential.html'")

    # Print some statistics
    print("\nSummary Statistics:")
    print(f"Average Solar Potential: {df['total_potential'].mean():.2f} MW/km²")
    print(f"Maximum Solar Potential: {df['total_potential'].max():.2f} MW/km²")
    print(f"Minimum Solar Potential: {df['total_potential'].min():.2f} MW/km²")

if __name__ == "__main__":
    main()