Get total rain for a location given 3 different stations

rain_amount.py

import requests
from lxml import html
import re
from datetime import datetime, timedelta
from math import radians, sin, cos, sqrt, atan2

# Function to calculate distance between two GPS coordinates (Haversine formula)
def haversine(lat1, lon1, lat2, lon2):
    R = 6371.0  # Earth radius in kilometers
    lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])

    dlat = lat2 - lat1
    dlon = lon2 - lon1
    a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
    c = 2 * atan2(sqrt(a), sqrt(1 - a))
    distance = R * c
    return distance

# Function to get the precipitation value for a date and location
def get_precipitation(url):
    response = requests.get(url)
    if response.status_code == 200:
        tree = html.fromstring(response.content)
        precipitation_value = tree.xpath('//*[@id="resum-diari"]/div/table/tbody/tr[5]/td/text()')
        if precipitation_value:
            try:
                # Extract and return the numeric value
                return float(re.findall(r'\d+\.\d+', precipitation_value[0])[0])
            except IndexError:
                print(f"Error extracting value from URL: {url}")
                return 0.0
    else:
        print(f"Error in GET request: {response.status_code} for URL: {url}")
    return 0.0

# Coordinates of stations and El Catllar
stations = {
    "Torredembarra": {"coords": (41.145, 1.402), "code": "DK"},
    "Nulles": {"coords": (41.263, 1.327), "code": "VY"},
    "Tarragona": {"coords": (41.118, 1.244), "code": "XE"}
}
el_catllar_coords = (41.222, 1.346)

# Generate URLs for each day of the last six weeks
url_base = "https://www.meteo.cat/observacions/xema/dades?codi="
num_weeks = 6
days_per_week = 7

# Function to generate URLs for a specific location
def generate_urls_for_location(code):
    weeks = [[] for _ in range(num_weeks)]
    for week in range(num_weeks):
        for day in range(days_per_week):
            date = (datetime.now() - timedelta(weeks=week, days=day)).strftime('%Y-%m-%dT00:00Z')
            weeks[week].append(url_base + code + "&dia=" + date)
    return weeks

# Get the accumulated precipitation per week for each station
for station_name, station_data in stations.items():
    print(f"Processing station: {station_name}")
    weeks = generate_urls_for_location(station_data["code"])
    precipitation_per_week = []

    for idx, week in enumerate(weeks):
        precipitations = [get_precipitation(url) for url in week]
        precipitation_per_week.append(precipitations)

    station_data["precipitation_per_week"] = [sum(week) for week in precipitation_per_week]

# Calculate distances from El Catllar to each station
for station_name, station_data in stations.items():
    lat, lon = station_data["coords"]
    station_data["distance_to_catllar"] = haversine(el_catllar_coords[0], el_catllar_coords[1], lat, lon)

# Function to perform Inverse Distance Weighting (IDW)
def idw_precipitation(precipitation_values, distances):
    inv_distances = [1 / d if d != 0 else 0 for d in distances]a
    weighted_precipitations = [p * inv_d for p, inv_d in zip(precipitation_values, inv_distances)]
    total_weight = sum(inv_distances)
    return sum(weighted_precipitations) / total_weight if total_weight > 0 else 0

# Estimate precipitation in El Catllar using IDW
precipitation_catllar_per_week = []

for week_idx in range(num_weeks):
    precipitations = [stations[station]["precipitation_per_week"][week_idx] for station in stations]
    distances = [stations[station]["distance_to_catllar"] for station in stations]
    estimated_precipitation = idw_precipitation(precipitations, distances)
    precipitation_catllar_per_week.append(estimated_precipitation)

# Display the estimated rainfall for El Catllar
for week_idx, total_precipitation in enumerate(precipitation_catllar_per_week):
    print(f"Estimated total precipitation {week_idx} week(s) ago for El Catllar: {total_precipitation:.2f} mm")