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https://github.com/martibosch/meteora

:sunrise_over_mountains: Pythonic interface to access data from meteorological stations
https://github.com/martibosch/meteora

data-cubes geopandas meteorological-stations netatmo-weather-station pandas python quality-control time-series weather-station xarray

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:sunrise_over_mountains: Pythonic interface to access data from meteorological stations

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# Meteora

Pythonic interface to access observations from meteorological stations. Key features:

- easily stream meteorological observations [from multiple providers, from global (e.g., GHCNh) and regional (e.g., MetOffice) networks to citizen weather stations (e.g., Netatmo)](https://meteora.readthedocs.io/en/latest/supported-providers.html) into pandas data frames.
- user-friendly arguments to filter data by region, variables or date ranges.
- request and download caching with [requests-cache](https://github.com/requests-cache/requests-cache) and [pooch](https://github.com/fatiando/pooch) to avoid re-downloading data and help bypassing API limits.

## Overview

Meteora provides a set of provider-specific clients to get observations from meteorological stations. For instance, it can be used to stream data from [the Global Historical Climatology Network hourly (GHCNh)](https://www.ncei.noaa.gov/products/global-historical-climatology-network-hourly) into a pandas data frame:

```python
from meteora.clients import GHCNHourlyClient

region = "Davos, Switzerland"
variables = ["temperature", "precipitation", "surface_wind_speed"]
start = "12-11-2021"
end = "12-12-2021"

client = GHCNHourlyClient(region)
ts_df = client.get_ts_df(variables, start, end)
ts_df.head()
```

```
[########################################] | 100% Completed | 16.94 s
```





temperature
precipitation
surface_wind_speed


station_id
time







SZM00006784
2021-12-11 00:00:00
-4.7
0.0
5.1


2021-12-11 01:00:00
-4.8
0.0
5.1


2021-12-11 02:00:00
-4.8
0.0
3.6


2021-12-11 03:00:00
-4.8
0.0
3.6


2021-12-11 04:00:00
-4.7
0.0
3.1

We can also get the station locations using the `stations_gdf` property:

```python
import contextily as cx

ax = client.stations_gdf.plot()
cx.add_basemap(ax, crs=client.stations_gdf.crs, attribution=False)
```

![davos-stations](https://github.com/martibosch/meteora/raw/main/docs/figures/davos-stations.png)

*(C) OpenStreetMap contributors, Tiles style by Humanitarian OpenStreetMap Team hosted by OpenStreetMap France*

See [the user guide](https://meteora.readthedocs.io/en/latest/user-guide.html) for more details about the features of Meteora as well as the [list of supported providers](https://meteora.readthedocs.io/en/latest/supported-providers.html).

## Installation

The easiest way to install Meteora is with conda/mamba:

```bash
conda install -c conda-forge meteora
```

Alternatively, if [geopandas dependencies are installed correctly](https://geopandas.org/en/latest/getting_started/install.html), you can install Meteora using pip:

```bash
pip install meteora
```

## See also

Meteora intends to provide a unified way to access data from meteorological stations from multiple providers. The following libraries provide access to data from a specific provider:

- [martibosch/agrometeo-geopy](https://github.com/martibosch/agrometeo-geopy)
- [martibosch/netatmo-geopy](https://github.com/martibosch/netatmo-geopy)

Eventually these packages will be fully integrated into Meteora.

## Acknowledgements

- The logging system is based on code from [gboeing/osmnx](https://github.com/gboeing/osmnx).
- This package was created with the [martibosch/cookiecutter-geopy-package](https://github.com/martibosch/cookiecutter-geopy-package) project template.
- With the support of the École Polytechnique Fédérale de Lausanne (EPFL).