New forecasts models : AROME PI & PIAF

Features:

• Allow user to specify the directory where to store the API result before un-gribbing it #28 by @ThomasBouche in #33
• Support for Météo-France models PIAF and AROME INSTANTANE #26 by @GratienDSX in #27

Fixes:

• Tests run in 2s instead of 20s by @yoyonel in #34
• Wrong function name to fetch bulletin #29 by @TimKeff in #31
• Wrong argument in a tutorial #30 by @TimKeff in #32

0.1.1

Features :

• Raise ImportError with clear help when cfgrib not installed (#11)
• Remove deprecated property .indicators (#23)

Documentation :

• Update README.md (#20, #22)
• Details on logger usage (#25)
• Details on latitude/longitude arguments (#25)

0.1.0b1

Release v0.1.0b1 - Meteole

We are excited to announce the first release of Meteole, a Python library designed to simplify access to weather data from the Météo-France APIs. With Meteole, you can effortlessly integrate weather forecasts into your projects thanks to its powerful and intuitive features:

• Automated token management: Simplify authentication with a single application_id.
• Unified model usage: Access AROME and ARPEGE forecasts through a consistent interface.
• User-friendly parameter handling: Intuitively manage key weather forecasting parameters.
• Seamless data integration: Directly export forecasts as Pandas DataFrames.
• Vigilance bulletins: Retrieve real-time weather warnings across France.

Whether you are a data scientist, meteorologist, or developer, Meteole is the perfect tool to effortlessly integrate weather forecasts into your projects.