weather386

weather386 is a Python package developed as part of a data science class at Brigham Young University (BYU). It provides an easy-to-use interface for accessing and visualizing weather forecast and historical data. Built with simplicity in mind, it offers several functions that allow users to quickly retrieve and analyze weather data from the National Weather Service. all times are in UTC timezone as customary with weather data

Website

https://lightningcookies.github.io/weather386/

Installation Instructions

To install weather386, you can use pip:

pip install git+https://github.com/lightningcookies/weather386.git

Usage example

Here is a basic example on how to use this package. First, import all the necessary functions from the package

from weather386.forecast import get_forecast
from weather386.history import get_history
from weather386.join_clean import join_clean
from weather386.combined_graph import combined_graph

Next, specify your latitude and longitude measurements. If you are unsure of the coordinates you want to use, https://www.latlong.net/ is a great website to use! We will use Kansas City in this example. Then use your specified latitude and longitude measurements with get_forecast() to return the weather forecast.

latitude = 39.0997
longitude = -94.5786
forecast = get_forecast(latitude,longitude)

Next, call the get_history() function with the same latitude, longitude values used to get the forecast.

history = get_history(latitude,longitude)

The forecast dataframe needs to be cleaned. Let's do that here with the join_clean() function. Out will come a nice and clean dataframe.

forecast_clean = join_clean(forecast)

We are ready to get our graphs! Simply call the combined_graph() function with your cleaned forecast dataframe and your history dataframe.

combined_graph(history, forecast_clean)

Contribution guidelines

Contributions are always welcome! If you'd like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Write your code and add tests.
4. Submit a pull request with a clear description of your changes.

Licensing information

'weather386' is licensed under the MIT License. See the LICENSE file for more details.

Contact and support

contact @Coleslaugh1 or @lightningcookies

Citations:

Zippenfenig, Patrick. Open-Meteo.com Weather API., Zenodo, 2023, doi:10.5281/ZENODO.7970649.

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023). ERA5 hourly data on single levels from 1940 to present [Data set]. ECMWF. https://doi.org/10.24381/cds.adbb2d47

Muñoz Sabater, J. (2019). ERA5-Land hourly data from 2001 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.E2161BAC

Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q. CERRA Sub-Daily Regional Reanalysis Data for Europe on Single Levels from 1984 to Present. ECMWF, 2021, doi:10.24381/CDS.622A565A.

https://api.weather.gov https://www.weather.gov/documentation/services-web-api