Python interface to map GRIB files to the Unidata's Common Data Model v4 following the CF Conventions. The high level API is designed to support a GRIB backend for xarray and it is inspired by netCDF4-python and h5netcdf. Low level access and decoding is performed via the ECMWF ecCodes library.

Features with development status Beta:

• read-only GRIB driver for xarray,
• reads most GRIB 1 and 2 files, for limitations see the Advanced usage section below and #13,
• supports all modern versions of Python 3.7, 3.6, 3.5 and 2.7, plus PyPy and PyPy3,
• works on most Linux distributions and MacOS, the ecCodes C-library is the only system dependency,
• PyPI package with no install time build (binds with CFFI ABI mode),
• reads the data lazily and efficiently in terms of both memory usage and disk access.

Work in progress:

• Alpha supports writing the index of a GRIB file to disk, to save a full-file scan on open, see #20.
• Pre-Alpha limited support to write carefully-crafted xarray.Dataset's to a GRIB2 file, see the Advanced write usage section below and #18,

Limitations:

• no conda package, for now, see #5,
• PyPI binary packages do not include ecCodes, see #22,
• incomplete documentation, for now,
• no Windows support, see #7,
• no support for opening multiple GRIB files, see #15,
• relies on ecCodes for the CF attributes of the data variables,
• relies on ecCodes for anything related to coordinate systems / gridType, see #28.

The package is installed from PyPI with:

$ pip install cfgrib

The Python module depends on the ECMWF ecCodes library that must be installed on the system and accessible as a shared library. Some Linux distributions ship a binary version that may be installed with the standard package manager. On Ubuntu 18.04 use the command:

$ sudo apt-get install libeccodes0

On a MacOS with HomeBrew use:

$ brew install eccodes

Or if you manage binary packages with Conda use:

$ conda install eccodes

As an alternative you may install the official source distribution by following the instructions at https://software.ecmwf.int/wiki/display/ECC/ecCodes+installation

Note that ecCodes support for the Windows operating system is experimental.

You may run a simple selfcheck command to ensure that your system is set up correctly:

$ python -m cfgrib selfcheck
Found: ecCodes v2.7.0.
Your system is ready.

First, you need a well-formed GRIB file, if you don't have one at hand you can download our ERA5 on pressure levels sample:

$ wget http://download.ecmwf.int/test-data/cfgrib/era5-levels-members.grib

You may try out the high level API in a Python interpreter:

>>> import cfgrib >>> ds = cfgrib.open_file('era5-levels-members.grib') >>> ds.attributes['GRIB_edition'] 1 >>> sorted(ds.dimensions.items()) [('isobaricInhPa', 2), ('latitude', 61), ('longitude', 120), ('number', 10), ('time', 4)] >>> sorted(ds.variables) ['isobaricInhPa', 'latitude', 'longitude', 'number', 'step', 't', 'time', 'valid_time', 'z'] >>> var = ds.variables['t'] >>> var.dimensions ('number', 'time', 'isobaricInhPa', 'latitude', 'longitude') >>> var.data[:, :, :, :, :].mean() 262.92133 >>> ds = cfgrib.open_file('era5-levels-members.grib') >>> ds.attributes['GRIB_edition'] 1 >>> sorted(ds.dimensions.items()) [('isobaricInhPa', 2), ('latitude', 61), ('longitude', 120), ('number', 10), ('time', 4)] >>> sorted(ds.variables) ['isobaricInhPa', 'latitude', 'longitude', 'number', 'step', 't', 'time', 'valid_time', 'z'] >>> var = ds.variables['t'] >>> var.dimensions ('number', 'time', 'isobaricInhPa', 'latitude', 'longitude') >>> var.data[:, :, :, :, :].mean() 262.92133

Additionally if you have xarray installed cfgrib can open a GRIB file as a xarray.Dataset:

$ pip install xarray>=0.10.9

In a Python interpreter try:

>>> ds = cfgrib.open_dataset('era5-levels-members.grib') >>> ds <xarray.Dataset> Dimensions: (isobaricInhPa: 2, latitude: 61, longitude: 120, number: 10, time: 4) 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] ... * isobaricInhPa (isobaricInhPa) int64 850 500 * 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 ... 351.0 354.0 357.0 valid_time (time) datetime64[ns] ... Data variables: z (number, time, isobaricInhPa, latitude, longitude) float32 ... t (number, time, isobaricInhPa, latitude, longitude) float32 ... Attributes: GRIB_edition: 1 GRIB_centre: ecmf GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts GRIB_subCentre: 0 history: GRIB to CDM+CF via cfgrib-0.9.../ecCodes-2...

By default cfgrib saves the index of the GRIB file to disk appending .idx to the GRIB file name. Index files are an experimental and completely optional feature, feel free to remove them and try again in case of problems. Index files saving can be disable passing adding indexpath='' to the backend_kwargs keyword argument.

Lower level APIs are not stable and should not be considered public yet. In particular the internal Python 3 ecCodes bindings are not compatible with the standard ecCodes python module.

cfgrib.Dataset and cfgrib.open_dataset can open a GRIB file only if all the messages with the same shortName can be represented as a single cfgrib.Variable hypercube. For example, a variable t cannot have both isobaricInhPa and hybrid typeOfLevel's, as this would result in multiple hypercubes for variable t. Opening a non-conformant GRIB file will fail with a ValueError: multiple values for unique key... error message, see #2.

Furthermore if different cfgrib.Variable's depend on the same coordinate, the values of the coordinate must match exactly. For example, if variables t and z share the same step coordinate, they must both have exactly the same set of steps. Opening a non-conformant GRIB file will fail with a ValueError: key present and new value is different... error message, see #13.

In most cases you can handle complex GRIB files containing heterogeneous messages by using the filter_by_keys key in backend_kwargs to select which GRIB messages belong to a well formed set of hypercubes.

For example to open US National Weather Service complex GRIB2 files you can use:

>>> cfgrib.open_dataset('nam.t00z.awp21100.tm00.grib2', ... backend_kwargs={'filter_by_keys': {'typeOfLevel': 'surface', 'stepType': 'instant'}}) <xarray.Dataset> Dimensions: (x: 93, y: 65) Coordinates: time datetime64[ns] ... step timedelta64[ns] ... surface int64 ... latitude (y, x) float64 ... longitude (y, x) float64 ... valid_time datetime64[ns] ... Dimensions without coordinates: x, y Data variables: gust (y, x) float32 ... sp (y, x) float32 ... orog (y, x) float32 ... csnow (y, x) float32 ... Attributes: GRIB_edition: 2 GRIB_centre: kwbc GRIB_centreDescription: US National Weather Service - NCEP... GRIB_subCentre: 0 history: GRIB to CDM+CF via cfgrib-0.9.../ecCodes-2... >>> cfgrib.open_dataset('nam.t00z.awp21100.tm00.grib2', ... backend_kwargs={'filter_by_keys': {'typeOfLevel': 'heightAboveGround', 'level': 2}}) <xarray.Dataset> Dimensions: (x: 93, y: 65) Coordinates: time datetime64[ns] ... step timedelta64[ns] ... heightAboveGround int64 ... latitude (y, x) float64 ... longitude (y, x) float64 ... valid_time datetime64[ns] ... Dimensions without coordinates: x, y Data variables: t2m (y, x) float32 ... r2 (y, x) float32 ... Attributes: GRIB_edition: 2 GRIB_centre: kwbc GRIB_centreDescription: US National Weather Service - NCEP... GRIB_subCentre: 0 history: GRIB to CDM+CF via cfgrib-0.9.../ecCodes-2...

cfgrib also provides an experimental function that automate the selection of appropriate filter_by_keys and returns a list of all valid xarray.Dataset's in the GRIB file (add backend_kwargs={'errors': 'ignore'} for extra robustness). The open_datasets is intended for interactive exploration of a file and it is not part of the stable API. In the future it may change or be removed altogether.

>>> from cfgrib import xarray_store >>> xarray_store.open_datasets('nam.t00z.awp21100.tm00.grib2', backend_kwargs={'errors': 'ignore'}) [<xarray.Dataset> Dimensions: (isobaricInhPa: 19, x: 93, y: 65) Coordinates: time datetime64[ns] ... step timedelta64[ns] ... * isobaricInhPa (isobaricInhPa) int64 1000 950 900 ... 150 100 latitude (y, x) float64 ... longitude (y, x) float64 ... valid_time datetime64[ns] ... Dimensions without coordinates: x, y Data variables: gh (isobaricInhPa, y, x) float32 ... t (isobaricInhPa, y, x) float32 ... r (isobaricInhPa, y, x) float32 ... w (isobaricInhPa, y, x) float32 ... u (isobaricInhPa, y, x) float32 ... Attributes: GRIB_edition: 2 GRIB_centre: kwbc GRIB_centreDescription: US National Weather Service - NCEP... GRIB_subCentre: 0 history: GRIB to CDM+CF via cfgrib-0.9.../ecCodes-2..., <xarray.Dataset> Dimensions: (x: 93, y: 65) Coordinates: time datetime64[ns] ... step timedelta64[ns] ... cloudBase int64 ... latitude (y, x) float64 ... longitude (y, x) float64 ... valid_time datetime64[ns] ... Dimensions without coordinates: x, y Data variables: pres (y, x) float32 ... gh (y, x) float32 ... Attributes: GRIB_edition: 2 GRIB_centre: kwbc GRIB_centreDescription: US National Weather Service - NCEP... GRIB_subCentre: 0 history: GRIB to CDM+CF via cfgrib-0.9.../ecCodes-2..., <xarray.Dataset> Dimensions: (x: 93, y: 65) Coordinates: time datetime64[ns] ... step timedelta64[ns] ... cloudTop int64 ... latitude (y, x) float64 ... longitude (y, x) float64 ... valid_time datetime64[ns] ... Dimensions without coordinates: x, y Data variables: pres (y, x) float32 ... gh (y, x) float32 ... t (y, x) float32 ... Attributes: GRIB_edition: 2 GRIB_centre: kwbc GRIB_centreDescription: US National Weather Service - NCEP... GRIB_subCentre: 0 history: GRIB to CDM+CF via cfgrib-0.9.../ecCodes-2..., <xarray.Dataset> Dimensions: (x: 93, y: 65) Coordinates: time datetime64[ns] ... step timedelta64[ns] ... maxWind int64 ... latitude (y, x) float64 ... longitude (y, x) float64 ... valid_time datetime64[ns] ... Dimensions without coordinates: x, y Data variables: pres (y, x) float32 ... gh (y, x) float32 ... u (y, x) float32 ... Attributes: GRIB_edition: 2 GRIB_centre: kwbc GRIB_centreDescription: US National Weather Service - NCEP... GRIB_subCentre: 0 history: GRIB to CDM+CF via cfgrib-0.9.../ecCodes-2..., <xarray.Dataset> Dimensions: (x: 93, y: 65) Coordinates: time datetime64[ns] ... step timedelta64[ns] ... isothermZero int64 ... latitude (y, x) float64 ... longitude (y, x) float64 ... valid_time datetime64[ns] ... Dimensions without coordinates: x, y Data variables: gh (y, x) float32 ... r (y, x) float32 ... Attributes: GRIB_edition: 2 GRIB_centre: kwbc GRIB_centreDescription: US National Weather Service - NCEP... GRIB_subCentre: 0 history: GRIB to CDM+CF via cfgrib-0.9.../ecCodes-2...]

Please note that write support is Pre-Alpha and highly experimental.

Only xarray.Dataset's in canonical form, that is, with the coordinates names matching exactly the cfgrib coordinates, can be saved at the moment:

>>> ds = cfgrib.open_dataset('era5-levels-members.grib') >>> ds <xarray.Dataset> Dimensions: (isobaricInhPa: 2, latitude: 61, longitude: 120, number: 10, time: 4) 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] ... * isobaricInhPa (isobaricInhPa) int64 850 500 * 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 ... 351.0 354.0 357.0 valid_time (time) datetime64[ns] ... Data variables: z (number, time, isobaricInhPa, latitude, longitude) float32 ... t (number, time, isobaricInhPa, latitude, longitude) float32 ... Attributes: GRIB_edition: 1 GRIB_centre: ecmf GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts GRIB_subCentre: 0 history: GRIB to CDM+CF via cfgrib-0.9.../ecCodes-2... >>> cfgrib.canonical_dataset_to_grib(ds, 'out1.grib', grib_keys={'edition': 2}) >>> cfgrib.open_dataset('out1.grib') <xarray.Dataset> Dimensions: (isobaricInhPa: 2, latitude: 61, longitude: 120, number: 10, time: 4) 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] ... * isobaricInhPa (isobaricInhPa) int64 850 500 * 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 ... 351.0 354.0 357.0 valid_time (time) datetime64[ns] ... Data variables: z (number, time, isobaricInhPa, latitude, longitude) float32 ... t (number, time, isobaricInhPa, latitude, longitude) float32 ... Attributes: GRIB_edition: 2 GRIB_centre: ecmf GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts GRIB_subCentre: 0 history: GRIB to CDM+CF via cfgrib-0.9.../ecCodes-2...

Per-variable GRIB keys can be set by setting the attrs variable with key prefixed by GRIB_, for example:

>>> import numpy as np >>> import xarray as xr >>> ds2 = xr.DataArray( ... np.zeros((5, 6)) + 300., ... coords=[ ... np.linspace(90., -90., 5), ... np.linspace(0., 360., 6, endpoint=False), ... ], ... dims=['latitude', 'longitude'], ... ).to_dataset(name='skin_temperature') >>> ds2.skin_temperature.attrs['GRIB_shortName'] = 'skt' >>> cfgrib.canonical_dataset_to_grib(ds2, 'out2.grib') >>> cfgrib.open_dataset('out2.grib') <xarray.Dataset> Dimensions: (latitude: 5, longitude: 6) Coordinates: time datetime64[ns] ... step timedelta64[ns] ... surface int64 ... * latitude (latitude) float64 90.0 45.0 0.0 -45.0 -90.0 * longitude (longitude) float64 0.0 60.0 120.0 180.0 240.0 300.0 valid_time datetime64[ns] ... Data variables: skt (latitude, longitude) float32 ... Attributes: GRIB_edition: 2 GRIB_centre: consensus GRIB_centreDescription: Consensus GRIB_subCentre: 0 history: GRIB to CDM+CF via cfgrib-0.9.../ecCodes-2...

The main repository is hosted on GitHub, testing, bug reports and contributions are highly welcomed and appreciated:

https://github.com/ecmwf/cfgrib

Please see the CONTRIBUTING.rst document for the best way to help.

Lead developer:

• Alessandro Amici - B-Open

Main contributors:

• Baudouin Raoult - ECMWF
• Aureliana Barghini - B-Open
• Iain Russell - ECMWF
• Leonardo Barcaroli - B-Open

See also the list of contributors who participated in this project.

Copyright 2017-2018 European Centre for Medium-Range Weather Forecasts (ECMWF).

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.