pip install geopy folium

# Read a list of California counties (you might have to adjust this based on your data)
counties = ["Alameda", "Alpine", "Amador", "Butte", "Calaveras", "Colusa", "Contra Costa", "Del Norte", "El Dorado",
            "Fresno", "Glenn", "Humboldt", "Imperial", "Inyo", "Kern", "Kings", "Lake", "Lassen", "Los Angeles",
            "Madera", "Marin", "Mariposa", "Mendocino", "Merced", "Modoc", "Mono", "Monterey", "Napa", "Nevada",
            "Orange", "Placer", "Plumas", "Riverside", "Sacramento", "San Benito", "San Bernardino", "San Diego",
            "San Francisco", "San Joaquin", "San Luis Obispo", "San Mateo", "Santa Barbara", "Santa Clara",
            "Santa Cruz", "Shasta", "Sierra", "Siskiyou", "Solano", "Sonoma", "Stanislaus", "Sutter", "Tehama",
            "Trinity", "Tulare", "Tuolumne", "Ventura", "Yolo", "Yuba"]

import pandas as pd
from geopy.geocoders import Nominatim
import folium
from geopy.exc import GeocoderTimedOut

# Function to get latitude and longitude for a given location using geopy, with retries on timeout
def get_lat_long_with_retry(location, max_retries=3):
    geolocator = Nominatim(user_agent="county_locator")
    retries = 0
    while retries < max_retries:
        try:
            location_info = geolocator.geocode(location)
            if location_info:
                return location_info.latitude, location_info.longitude
            else:
                return None
        except GeocoderTimedOut:
            retries += 1
    return None

# Extract latitude and longitude for each county
for county in counties:
    location = f"{county}, California"
    lat, lon = get_lat_long_with_retry(location)
    county_data["Latitude"].append(lat)
    county_data["Longitude"].append(lon)

# Create a DataFrame
county_df = pd.DataFrame(county_data)

# Display the DataFrame (optional)
print(county_df)

# Save it as a CSV file

county_df.to_csv('county_lat_long.csv', index=False)

# Create a map centered around California
california_map = folium.Map(location=[36.7783, -119.4179], zoom_start=6)

# Add markers for each county
for index, row in county_df.iterrows():
    folium.Marker(location=[row["Latitude"], row["Longitude"]], popup=row["County"]).add_to(california_map)

# Save the map to an HTML file or display it in any browser
california_map.save("california_counties_map.html")

import csv
import urllib.request

# Open the CSV file containing latitudes and longitudes
with open('county_lat_long.csv', 'r') as csvfile:
    reader = csv.reader(csvfile)

    # Skip the header row
    next(reader)

    # Loop through each row in the CSV file
    for row in reader:
        ID=row[0]    # County name
        lat = row[1] # Latitude is in the first column
        lon = row[2] # Longitude is in the second column

        # Construct the URL to download data for 2022 for these locations
        url = f'https://daymet.ornl.gov/single-pixel/api/data?lat={lat}&lon={lon}&years=2022'

        # Download the data and save it to a file with the county's name
        urllib.request.urlretrieve(url, '/content/CSV/{}.txt'.format(ID))

import os
import pandas as pd

# Get a list of all text files in the folder
folder_path = '/content/CSV'
text_files = [os.path.join(folder_path, file) for file in os.listdir(folder_path) if file.endswith('.txt')]

# Initialize an empty list to store all the DataFrames
dfs = []
i=1

# Loop through each text file
for text_file in text_files:
    # Read the text file into a list of strings
    with open(text_file, 'r') as f:
        data = f.read().splitlines()

    # Extract the latitude, longitude, and elevation from the first line
    lat, lon = data[0].split()[1], data[0].split()[3]
    elevation = data[3].split()[1]

    # Create a DataFrame from the remaining lines
    county_daymet_df = pd.DataFrame([line.split(',') for line in data[8:]], columns=['year', 'yday', 'dayl (s)', 'prcp (mm/day)',
                                                                       'srad (W/m^2)', 'swe (kg/m^2)', 'tmax (deg c)',
                                                                       'tmin (deg c)', 'vp (Pa)'])

    # Add columns for latitude, longitude, and elevation
    county_daymet_df['latitude'] = lat
    county_daymet_df['longitude'] = lon
    county_daymet_df['elevation'] = elevation
    file_name = os.path.basename(text_file)  # get the base name of the file
    file_name_without_ext = os.path.splitext(file_name)[0]  # get the filename without extension
    county_daymet_df['ID']= os.path.splitext(file_name_without_ext)[0]

    # Append the DataFrame to the list of DataFrames
    if i ==1:
      county_daymet_dfs=county_daymet_df
      i=i+1
    else:
      county_daymet_dfs = pd.concat([county_daymet_df,county_daymet_dfs], ignore_index=True)

# Save the big DataFrame to a CSV file
county_daymet_dfs.to_csv('county_daymet.csv', index=False)

# String to numeric
county_daymet_dfs['precip'] = pd.to_numeric(county_daymet_dfs['prcp (mm/day)'], errors='coerce')

# Calculate the summation of 'precip' for each 'ID'
summary_county_daymet = county_daymet_dfs.groupby('ID')['precip'].sum().reset_index()
summary_county_daymet.columns = ['County', 'precip_sum_mm_day']

# Read the CSV file into a DataFrame
county_df = pd.read_csv('county_lat_long.csv')

# Merge the summary_county_daymet with county_df based on 'ID'
merged_county_daymet = pd.merge(county_df, summary_county_daymet, on='County', how='left')
print(merged_county_daymet)

from branca.colormap import LinearColormap

# Create a continuous color ramp
colormap = LinearColormap(colors=['Red', 'Yellow','green'], vmin=merged_county_daymet['precip_sum_mm_day'].min(), vmax=merged_county_daymet['precip_sum_mm_day'].max())

# Create a map centered around California
california_map = folium.Map(location=[36.7783, -119.4179], zoom_start=6)

# Add markers for each county with continuous color based on values
for index, row in merged_county_daymet.iterrows():
    folium.CircleMarker(location=[row["Latitude"], row["Longitude"]],
                        radius=10,
                        popup=f"{row['County']}: {row['precip_sum_mm_day']}",
                        color=colormap(row['precip_sum_mm_day']),
                        fill=True,
                        fill_color=colormap(row['precip_sum_mm_day']),
                        fill_opacity=0.7).add_to(california_map)




# Add the colormap to the map
california_map.add_child(colormap)

# Save the map to an HTML file or display it in any browser
california_map.save("california_counties_map_continuous_point.html")

# Add markers for each county
for index, row in merged_county_daymet.iterrows():
    popup_text = f"County: {row['County']}<br>Latitude: {row['Latitude']}<br>Longitude: {row['Longitude']}<br>Precipitation (mm/year): {row['precip_sum_mm_day']}"
    folium.Marker(location=[row["Latitude"], row["Longitude"]],popup=popup_text).add_to(california_map)

# Save the map to an HTML file or display it in any browser
california_map.save("california_counties_map_continuous.html")