filter-stations 0.8.1

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 [e-mail].

• 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 1-to-10 day forecasts (both hourly and daily)
• SeasonalForecaster: Connects to Open-Meteo API to pull seasonal forecasts.
• 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, SeasonalForecaster

# 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)
seasonal = SeasonalForecaster(auth_session=auth)

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

# Seasonal Forecasting Example

# 4. See what is available
print(seasonal.list_all_models())

# 5. Pull 6 months of ECMWF AIFS and Standard IFS data simultaneously
data = seasonal.get_forecast(
    lat=-0.41,
    lon=36.95,
    models=["ecmwf_seasonal_ensemble_mean_seamless"], 
    forecast_months=6,
    daily=['temperature_2m_max',
            'temperature_2m_mean',
            'temperature_2m_min',
            'precipitation_sum',
            'rain_sum',
            'pressure_msl_min',
            'pressure_msl_mean',
            'relative_humidity_2m_min',
            'relative_humidity_2m_mean',
            'relative_humidity_2m_max',
            'dew_point_2m_max',
            'dew_point_2m_mean',
            'dew_point_2m_min',
            'surface_pressure_min',
            'surface_pressure_mean',
            'surface_pressure_max',
            'cloud_cover_min',
            'cloud_cover_mean',
            'cloud_cover_max' ]
)

# 6. View the AIFS DataFrame (first model in the list)
aifs_df = data[0]["daily_df"]
print(aifs_df)

seasonal.list_api_variables()

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/}},
}