Hi @jacobbieker, can I take this one?

Yep! That would be great

Hey @jacobbieker,
I've added code to fetch hourly data from different NWPs via the OpenMeteo API. Any quick thoughts on how we can compare these NWPs effectively? Also, could you check out the code?

import openmeteo_requests  # Importing required libraries
import requests_cache
import pandas as pd
from retry_requests import retry

class WeatherDataFetcher:
    BASE_URL = "https://api.open-meteo.com/v1/"  # Base URL for OpenMeteo API

    def __init__(self):
        # Initialize the WeatherDataFetcher class
        # Setup the Open-Meteo API client with cache and retry on error
        cache_session = requests_cache.CachedSession('.cache', expire_after=3600)
        retry_session = retry(cache_session, retries=5, backoff_factor=0.2)
        self.openmeteo = openmeteo_requests.Client(session=retry_session)

    def fetch_weather_data(self, NWP, params):
        # Fetch weather data from OpenMeteo API for the specified model (NWP) and parameters
        url = f"https://api.open-meteo.com/v1/{NWP}"  # Construct API URL
        try:
            responses = self.openmeteo.weather_api(url, params=params)  # Get weather data
            return responses[0]  # Return the first response (assuming only one location)
        except openmeteo_requests.OpenMeteoRequestsError as e:
            # Handle OpenMeteoRequestsError exceptions
            if 'No data is available for this location' in str(e):
                print(f"Error: No data available for the location for model '{NWP}'.")
            else:
                print(f"Error: {e}")
            return None

    def process_hourly_data(self, response):
        # Process hourly data from OpenMeteo API response
        # Extract hourly data from the response
        hourly = response.Hourly()

        # Extract variables
        hourly_variables = {
            "temperature_2m": hourly.Variables(0).ValuesAsNumpy(),
            "relative_humidity_2m": hourly.Variables(1).ValuesAsNumpy(),
            "precipitation": hourly.Variables(2).ValuesAsNumpy(),
            "cloud_cover": hourly.Variables(3).ValuesAsNumpy()
        }

        # Extract time information
        time_range = pd.date_range(
            start=pd.to_datetime(hourly.Time(), unit="s", utc=True),
            end=pd.to_datetime(hourly.TimeEnd(), unit="s", utc=True),
            freq=pd.Timedelta(seconds=hourly.Interval()),
            inclusive="left"
        )

        # Create a dictionary for hourly data
        hourly_data = {"date": time_range}

        # Assign each variable to the corresponding key in the dictionary
        for variable_name, variable_values in hourly_variables.items():
            hourly_data[variable_name] = variable_values

        # Create a DataFrame from the dictionary
        hourly_dataframe = pd.DataFrame(data=hourly_data)
        return hourly_dataframe

    def print_location_info(self, response):
        # Print location information from OpenMeteo API response
        print(f"Coordinates {response.Latitude()}°N {response.Longitude()}°E")
        print(f"Elevation {response.Elevation()} m asl")
        print(f"Timezone {response.Timezone()} {response.TimezoneAbbreviation()}")
        print(f"Timezone difference to GMT+0 {response.UtcOffsetSeconds()} s")


def main():
    # Main function to demonstrate usage of WeatherDataFetcher class
    fetcher = WeatherDataFetcher()  # Create instance of WeatherDataFetcher

    # Specify parameters for weather data fetch
    NWP = "gfs"  # Choose NWP model
    params = {
        "latitude": 40.77,  # Latitude of the location
        "longitude": -73.91,  # Longitude of the location
        "hourly": ["temperature_2m", "relative_humidity_2m", "precipitation", "cloud_cover"],  # Variables to fetch
        "start_date": "2023-12-21",  # Start date for data
        "end_date": "2024-03-15"  # End date for data
    }

    # Fetch weather data for specified model and parameters
    response = fetcher.fetch_weather_data(NWP, params)

    # Print location information
    fetcher.print_location_info(response)

    # Process and print hourly data
    gfs_dataframe = fetcher.process_hourly_data(response)
    print(gfs_dataframe)


if __name__ == "__main__":
    main()  # Call main function if script is executed directly

Hey @jacobbieker,
I've added code to fetch hourly data from different NWPs via the OpenMeteo API. Any quick thoughts on how we can compare these NWPs effectively? Also, could you check out the code?

import openmeteo_requests  # Importing required libraries
import requests_cache
import pandas as pd
from retry_requests import retry

class WeatherDataFetcher:
    BASE_URL = "https://api.open-meteo.com/v1/"  # Base URL for OpenMeteo API

    def __init__(self):
        # Initialize the WeatherDataFetcher class
        # Setup the Open-Meteo API client with cache and retry on error
        cache_session = requests_cache.CachedSession('.cache', expire_after=3600)
        retry_session = retry(cache_session, retries=5, backoff_factor=0.2)
        self.openmeteo = openmeteo_requests.Client(session=retry_session)

    def fetch_weather_data(self, NWP, params):
        # Fetch weather data from OpenMeteo API for the specified model (NWP) and parameters
        url = f"https://api.open-meteo.com/v1/{NWP}"  # Construct API URL
        try:
            responses = self.openmeteo.weather_api(url, params=params)  # Get weather data
            return responses[0]  # Return the first response (assuming only one location)
        except openmeteo_requests.OpenMeteoRequestsError as e:
            # Handle OpenMeteoRequestsError exceptions
            if 'No data is available for this location' in str(e):
                print(f"Error: No data available for the location for model '{NWP}'.")
            else:
                print(f"Error: {e}")
            return None

    def process_hourly_data(self, response):
        # Process hourly data from OpenMeteo API response
        # Extract hourly data from the response
        hourly = response.Hourly()

        # Extract variables
        hourly_variables = {
            "temperature_2m": hourly.Variables(0).ValuesAsNumpy(),
            "relative_humidity_2m": hourly.Variables(1).ValuesAsNumpy(),
            "precipitation": hourly.Variables(2).ValuesAsNumpy(),
            "cloud_cover": hourly.Variables(3).ValuesAsNumpy()
        }

        # Extract time information
        time_range = pd.date_range(
            start=pd.to_datetime(hourly.Time(), unit="s", utc=True),
            end=pd.to_datetime(hourly.TimeEnd(), unit="s", utc=True),
            freq=pd.Timedelta(seconds=hourly.Interval()),
            inclusive="left"
        )

        # Create a dictionary for hourly data
        hourly_data = {"date": time_range}

        # Assign each variable to the corresponding key in the dictionary
        for variable_name, variable_values in hourly_variables.items():
            hourly_data[variable_name] = variable_values

        # Create a DataFrame from the dictionary
        hourly_dataframe = pd.DataFrame(data=hourly_data)
        return hourly_dataframe

    def print_location_info(self, response):
        # Print location information from OpenMeteo API response
        print(f"Coordinates {response.Latitude()}°N {response.Longitude()}°E")
        print(f"Elevation {response.Elevation()} m asl")
        print(f"Timezone {response.Timezone()} {response.TimezoneAbbreviation()}")
        print(f"Timezone difference to GMT+0 {response.UtcOffsetSeconds()} s")


def main():
    # Main function to demonstrate usage of WeatherDataFetcher class
    fetcher = WeatherDataFetcher()  # Create instance of WeatherDataFetcher

    # Specify parameters for weather data fetch
    NWP = "gfs"  # Choose NWP model
    params = {
        "latitude": 40.77,  # Latitude of the location
        "longitude": -73.91,  # Longitude of the location
        "hourly": ["temperature_2m", "relative_humidity_2m", "precipitation", "cloud_cover"],  # Variables to fetch
        "start_date": "2023-12-21",  # Start date for data
        "end_date": "2024-03-15"  # End date for data
    }

    # Fetch weather data for specified model and parameters
    response = fetcher.fetch_weather_data(NWP, params)

    # Print location information
    fetcher.print_location_info(response)

    # Process and print hourly data
    gfs_dataframe = fetcher.process_hourly_data(response)
    print(gfs_dataframe)


if __name__ == "__main__":
    main()  # Call main function if script is executed directly

Hi,

Overall looks good although a bit hard to review here, could you open a pull request instead to add the code? I can give better feedback that way.