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 GRIB driver for xarray,
- support all modern versions of Python 3.7, 3.6, 3.5 and 2.7, plus PyPy and PyPy3,
- only system dependency is the ecCodes C-library (not the Python2-only module),
- no install time build (binds with CFFI ABI mode),
- 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,
- limited support for GRIB files containing multiple hypecubes (yet), see the Advanced Usage section below and #2, #13,
- no write support (yet),
- no support for opening multiple GRIB files (yet), see #15,
- incomplete documentation (yet),
- no Windows support, see #7,
- rely on ecCodes for the CF attributes of the data variables,
- rely on ecCodes for the
gridTypehandling.
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
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.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()
262.92133If 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 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 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 attribute...
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 keyword 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:
>>> from cfgrib.xarray_store import open_dataset
>>> open_dataset('nam.t00z.awip1200.tm00.grib2',
... filter_by_keys={'typeOfLevel': 'surface', 'stepType': 'instant'})
<xarray.Dataset>
Dimensions: (x: 614, y: 428)
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:
vis (y, x) float32 ...
gust (y, x) float32 ...
hindex (y, x) float32 ...
sp (y, x) float32 ...
orog (y, x) float32 ...
t (y, x) float32 ...
unknown (y, x) float32 ...
sdwe (y, x) float32 ...
sde (y, x) float32 ...
prate (y, x) float32 ...
sr (y, x) float32 ...
veg (y, x) float32 ...
slt (y, x) float32 ...
lsm (y, x) float32 ...
ci (y, x) float32 ...
al (y, x) float32 ...
sst (y, x) float32 ...
shtfl (y, x) float32 ...
lhtfl (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-0.8.../ecCodes-2...
>>> open_dataset('nam.t00z.awip1200.tm00.grib2',
... filter_by_keys={'typeOfLevel': 'heightAboveGround', 'topLevel': 2})
<xarray.Dataset>
Dimensions: (x: 614, y: 428)
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 ...
q (y, x) float32 ...
d2m (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-0.8.../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.