filter-stations 0.8.0

A secure, unified Python interface for African climate data, integrating TAHMO station data and gridded datasets (IMERG, CHIRPS, ERA5, TAMSAT), and medium-to-seasonal weather models

Documentation

You can find the documentation for the project by following this link
https://filter-stations.readthedocs.io/en/latest/

Getting Started

All methods require an API key and secret, which can be obtained by contacting TAHMO.

• AuthManager: The new secure gateway. Log in once with your credentials to automatically unlock access to all downstream data loaders
• RainLoader: class is used to get our DSAIL unified weather dataset from HuggingFace (See the documentation for more information on this)
  
• MediumForecaster: Connects to the Google Weather API to pull and flatten highly accurate 1-to-10 day forecasts (both hourly and daily)
• The RetrieveData class is used to retrieve data from the TAHMO API endpoints.
  
• The Kieni class is used to get weather data for stations 100km around Kieni from the central point.with water level data.
  

Example

from filter_stations import AuthManager, MediumForecaster, RetrieveData

# 1. Authenticate once securely
auth = AuthManager(email="[e-mail]", password="your_password")

# 2. Initialize any tool you need seamlessly
weather = MediumForecaster(auth_session=auth)
tahmo = RetrieveData(auth_session=auth)

# 3. Pull your data!
nyeri_forecast = weather.get_weather_api_forecast(lat=-0.41, lon=36.95, days=7)

To get started on the module test it out on Google Colab

For earlier versions <= v0.6.2 use the link below for documentation

https://filter-stations.netlify.app/

Citations

If you use this package in your research, please cite it using the following BibTeX entry:

@misc{filter-stations,
  author = {Austin Kaburia},
  title = {filter-stations},
  year = {2024},
  publisher = {Python Package Index},
  journal = {PyPI},
  howpublished = {\url{https://pypi.org/project/filter-stations/}},
}