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# cf2cdm
Translate xarray datasets to a custom data model.

Translate xarray dataset to a custom data model
Contrary to netCDF the GRIB data format is not self-describing and several details of the mapping
to the *Unidata Common Data Model* are arbitrarily set by the software components decoding the format.
Details like names and units of the coordinates are particularly important because
*xarray* broadcast and selection rules depend on them.
`cf2cfm` is a small coordinate translation module that make it easy to
translate CF compliant coordinates to a user-defined
custom data model with set `out_name`, `units` and `stored_direction`.

## Quick Start
For example to translate a *cfgrib* styled `xr.Dataset` to the classic *ECMWF* coordinate
naming conventions you can:

```python
>>> import cf2cdm

>>> import cf2cdm
>>> ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib')
>>> cf2cdm.translate_coords(ds, cf2cdm.ECMWF)
<xarray.Dataset>
Dimensions: (number: 10, time: 4, level: 2, latitude: 61, longitude: 120)
Coordinates:
* number (number) int64 0 1 2 3 4 5 6 7 8 9
* time (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00
step timedelta64[ns] ...
* level (level) float64 850.0 500.0
* latitude (latitude) float64 90.0 87.0 84.0 81.0 ... -84.0 -87.0 -90.0
* longitude (longitude) float64 0.0 3.0 6.0 9.0 ... 348.0 351.0 354.0 357.0
valid_time (time) datetime64[ns] ...
Data variables:
z (number, time, level, latitude, longitude) float32 ...
t (number, time, level, latitude, longitude) float32 ...
Attributes:
GRIB_edition: 1
GRIB_centre: ecmf
GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts
GRIB_subCentre: 0
Conventions: CF-1.7
institution: European Centre for Medium-Range Weather Forecasts
history: ...
```

## Workflow for developers/contributors
To translate to the Common Data Model of the Climate Data Store use:

For best experience create a new conda environment (e.g. DEVELOP) with Python 3.10:
```python

>>> import cf2cdm
>>> cf2cdm.translate_coords(ds, cf2cdm.CDS)
<xarray.Dataset>
Dimensions: (realization: 10, forecast_reference_time: 4,
plev: 2, lat: 61, lon: 120)
Coordinates:
* realization (realization) int64 0 1 2 3 4 5 6 7 8 9
* forecast_reference_time (forecast_reference_time) datetime64[ns] 2017-01...
leadtime timedelta64[ns] ...
* plev (plev) float64 8.5e+04 5e+04
* lat (lat) float64 -90.0 -87.0 -84.0 ... 84.0 87.0 90.0
* lon (lon) float64 0.0 3.0 6.0 9.0 ... 351.0 354.0 357.0
time (forecast_reference_time) datetime64[ns] ...
Data variables:
z (realization, forecast_reference_time, plev, lat, lon) float32 ...
t (realization, forecast_reference_time, plev, lat, lon) float32 ...
Attributes:
GRIB_edition: 1
GRIB_centre: ecmf
GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts
GRIB_subCentre: 0
Conventions: CF-1.7
institution: European Centre for Medium-Range Weather Forecasts
history: ...
```
conda create -n DEVELOP -c conda-forge python=3.10
conda activate DEVELOP
```

Before pushing to GitHub, run the following commands:

1. Update conda environment: `make conda-env-update`
1. Install this package: `pip install -e .`
1. Sync with the latest [template](https://github.com/ecmwf-projects/cookiecutter-conda-package) (optional): `make template-update`
1. Run quality assurance checks: `make qa`
1. Run tests: `make unit-tests`
1. Run the static type checker: `make type-check`
1. Build the documentation (see [Sphinx tutorial](https://www.sphinx-doc.org/en/master/tutorial/)): `make docs-build`

## License

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