Python interface to map GRIB files to the NetCDF Common Data Model following the CF Convention using ecCodes.
Project description
Python interface to map GRIB files to the NetCDF Common Data Model following the CF Conventions. The high level API is designed to support a GRIB backend for xarray and it is inspired by NetCDF-python and h5netcdf. Low level access and decoding is performed via the ECMWF ecCodes library.
Features:
provisional xarray GRIB driver,
support all modern versions of Python 3.7, 3.6, 3.5 and 2.7, plus PyPy and PyPy3,
read the data lazily and efficiently in terms of both memory usage and disk access,
map a GRIB 1 or 2 file to a set of N-dimensional variables following the NetCDF Common Data Model,
add CF Conventions attributes to known coordinate and data variables.
Limitations:
development stage: Alpha,
no write support (yet),
rely on ecCodes for the CF attributes of the data variables,
rely on ecCodes for the gridType handling.
Installation
The package is installed from PyPI with:
$ pip install cfgrib
System dependencies
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 of ecCodes 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
As an alternative you may install the official source distribution by following the ecCodes 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 self-check command to ensure that your system is set up correctly:
$ python -m cfgrib selfcheck Found: ecCodes v2.7.0. Your system is ready.
Usage
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
Dataset / Variable API
You may try out the high level API in a python interpreter:
>>> import cfgrib
>>> ds = cfgrib.Dataset.frompath('era5-levels-members.grib')
>>> ds.attributes['GRIB_edition']
1
>>> sorted(ds.dimensions.items())
[('air_pressure', 2), ('latitude', 61), ('longitude', 120), ('number', 10), ('time', 4)]
>>> sorted(ds.variables)
['air_pressure', 'latitude', 'longitude', 'number', 'step', 't', 'time', 'valid_time', 'z']
>>> var = ds.variables['t']
>>> var.dimensions
('number', 'time', 'air_pressure', 'latitude', 'longitude')
>>> var.data[:, :, :, :, :].mean()
262.92133
Provisional xarray GRIB driver
If you have xarray installed cfgrib can open a GRIB file as a xarray.Dataset:
$ pip install xarray
In a Python interpreter try:
>>> from cfgrib import xarray_store
>>> ds = xarray_store.open_dataset('era5-levels-members.grib')
>>> ds
<xarray.Dataset>
Dimensions: (air_pressure: 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 2017-01-01T12:00:00 ...
step timedelta64[ns] ...
* air_pressure (air_pressure) float64 850.0 500.0
* latitude (latitude) float64 90.0 87.0 84.0 81.0 78.0 75.0 72.0 69.0 ...
* longitude (longitude) float64 0.0 3.0 6.0 9.0 12.0 15.0 18.0 21.0 ...
valid_time (time) datetime64[ns] ...
Data variables:
z (number, time, air_pressure, latitude, longitude) float32 ...
t (number, time, air_pressure, 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-0.8.../ecCodes-2...
Lower level APIs
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.
Contributing
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.
License
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.
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