<h1 align="center"> OpenmeteoPy </h1> <p align="center"> <img src="https://cdn.substack.com/image/fetch/w_1360,c_limit,f_auto,q_auto:best,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Ffd0d7953-5a9d-441c-b59f-4cde244503a1_934x461.png"> </p>

Download Meteorological Data from OPEN-METEO API (https://open-meteo.com/en)

Open-Meteo collaborates with National Weather Services providing Open Data with 11 to 2 km resolution. Our high performance APIs select the best weather model for your location and provide data as a simple JSON API.

APIs are free without any API key for open-source developers and non-commercial use. You can embed them directly into your app.

What is it?

openmeteopy is a client Python wrapper library for Open-Meteo web API. It allows quick and easy consumption of Open-Meteo data from Python applications via a simple object model and in a human-friendly fashion.

You can use all Openmeteo's available APIs,which are 14 (explained in the table below).

openmeteopy runs on Python 3.6+.

Full documentation is reported here: DOCUMENTATION.

Installation (pypi)

working on it

Installation (source)

pip install git+https://github.com/m0rp43us/openmeteopy

APIs

|API |Description|Documentation |Options Class |Hourly Class|Daily Class|15 Minutes Class| |-----|--------|--------|--------|---------|---------|----------| |Weather forecast | Select your location, weather variables and start using the API.|Weather Forecast | ForecastOptions() | HourlyForecast()|DailyForecast()|-| |Historical Weather | Discover how weather has shaped our world from 1940 until now|Historical Weather | HistoricalOptions() | HourlyHistorical()|DailyHistorical()|-| |ECMWF Weather Forecast | Global High Frequency Forecasts at 0.4° resolution |ECMWF Weather Forecast |EcmwfOptions() | HourlyEcmwf()|-|-| |GFS & HRRR Forecast |Global GFS model combined with hourly HRRR updates at 3-km resolution |GFS & HRRR Forecast | GfsOptions() | HourlyGfs()|DailyGfs()|-| |MeteoFrance |Global ARPEGE model combined with high resolution AROME model| MeteoFrance | MeteoFranceOptions() | HourlyMeteoFrance()|DailyMeteoFrance()|-| |DWD ICON |Open data weather forecasts from the German weather service DWD |DWD ICON | DwdOptions() | HourlyDwd()|DailyDwd()|FifteenMinutesDwd()| |JMA |5-km high resolution forecasts for Japan, Korea, parts of China and Russia |JMA | JmaOptions() | HourlyJma()|DailyJma()|-| |MET Norway |Hourly updates & 1 km forecasts for Scandinavia| MET Norway | MetnoOptions() | HourlyMetno()|-|-| |GEM | 2.5 km high resolution forecasts for North America|GEM | GemOptions() | HourlyGem()|DailyGem()|-| |Marine Weather | Hourly wave forecasts at 5 km resolution|Marine Weather | MarineOptions() | HourlyMarine()|DailyMarine()|-| |Air Quality | Pollutants and pollen forecast in 11 km resolution|Air Quality | AirQualityOptions() | HourlyAirQuality()|-|-| |Geocoding | Search locations in any language globally|Geocoding | GeocodingOptions() | - |-|-| |Elevation | 90 meter resolution digital elevation model|Elevation | ElevationOptions() | -|-|-| |Global Flood | Simulated river discharge at 5 km resolution from 1984 up to 7 months forecast|Global Flood | FloodOptions() | - |DailyForecast()|-|

Output formats:

You can get your output in python dictionary, json string, pandas DataFrame, numpy array. Additionally, you can save APIs response to files in csv, excel or json format.

|Output | Library function | |----|-----| |dict (full server response) | client._fetch()| |dict (cleaned version) | client.get_dict()| |json string (cleaned version) |client.get_json_str()| |numpy array |client.get_numpy()| |pandas DataFrame (keys are dates,value are correspongding values) | client.get_pandas()| |save to csv (keys are dates,value are correspongding values)| save_csv(<filepath>)| |save to excel (keys are dates,value are correspongding values) | save_excel(<filepath>)| |save to json | save_json(<filepath>)|

Upcoming Changes,updates,things to do :

• Add the other 2 variables to ECWMF pressure level parameters
• remove pressure level variables from JMA (Remove inheritence from FranceMeteo)
• Add Support of Date Types (input and output)
• Add versionning and package to pypi