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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 Unidata’s Common Data Model v4 following the CF Conventions. The high level API is designed to support a GRIB engine 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:

  • enables the engine='cfgrib' option to read GRIB files with 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,
  • allows larger-than-memory and distributed processing via dask.

Work in progress:

  • Alpha install a cfgrib utility that can convert a GRIB file to_netcdf with a optional conversion to a specific coordinates data model, see #40.
  • Alpha supports writing the index of a GRIB file to disk, to save a full-file scan on open, see #33.
  • Alpha support writing carefully-crafted xarray.Dataset’s to a GRIB1 or GRIB2 file, see the Advanced write usage section below and #18,
  • Alpha support translating coordinates to different data models and naming conventions, #24.

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,
  • relies on ecCodes for the CF attributes of the data variables,
  • relies on ecCodes for anything related to coordinate systems / gridType, see #28.

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 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 -c conda-forge 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.

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

Read-only xarray GRIB engine

Most of cfgrib users want to open a GRIB file as a xarray.Dataset and need to have xarray>=0.11.0 installed:

$ pip install xarray>=0.11.0

In a Python interpreter try:

>>> import xarray as xr
>>> ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib')
>>> 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
    Conventions:             CF-1.7
    institution:             European Centre for Medium-Range Weather Forecasts
    history:                 ...

The cfgrib engine supports all read-only features of xarray like:

  • merge the content of several GRIB files into a single dataset using xarray.open_mfdataset,
  • work with larger-than-memory datasets with dask,
  • allow distributed processing with dask.distributed.

Dataset / Variable API

The use of xarray is not mandatory and you can access the content of a GRIB file as an hypercube with 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

GRIB index file

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

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.

Advanced usage

Translate 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 distributed with cfgrib that make it easy to translate CF compliant coordinates, like the one provided by cfgrib, to a user-defined custom data model with set out_name, units and stored_direction.

For example to translate a cfgrib styled xr.Dataset to the classic ECMWF coordinate naming conventions you can:

>>> import cf2cdm
>>> ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib')
>>> cf2cdm.translate_coords(ds, cf2cdm.ECMWF)
<xarray.Dataset>
Dimensions:     (latitude: 61, level: 2, 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] ...
  * level       (level) 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 ... 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:                 ...

To translate to the Common Data Model of the Climate Data Store use:

>>> import cf2cdm
>>> cf2cdm.translate_coords(ds, cf2cdm.CDS)
<xarray.Dataset>
Dimensions:                  (lat: 61, lon: 120, plev: 2, realization: 10, time: 4)
Coordinates:
  * realization              (realization) int64 0 1 2 3 4 5 6 7 8 9
    forecast_reference_time  (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00
    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                     (time) datetime64[ns] ...
Data variables:
    z                        (realization, time, plev, lat, lon) float32 ...
    t                        (realization, 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:                 ...

Filter heterogeneous GRIB files

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

Furthermore if different variables depend on the same coordinate, for example step, 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 passing 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:

>>> xr.open_dataset('nam.t00z.awp21100.tm00.grib2', engine='cfgrib',
...     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
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP...
    history:                 ...
>>> xr.open_dataset('nam.t00z.awp21100.tm00.grib2', engine='cfgrib',
...     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
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP...
    history:                 ...

Automatic filtering

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
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP...
    history:                 ..., <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
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP...
    history:                 ..., <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
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP...
    history:                 ..., <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
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP...
    history:                 ..., <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
    Conventions:             CF-1.7
    institution:             US National Weather Service - NCEP...
    history:                 ...]

Advanced write usage

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 = xr.open_dataset('era5-levels-members.grib', engine='cfgrib')
>>> 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
    Conventions:             CF-1.7
    institution:             European Centre for Medium-Range Weather Forecasts
    history:                 ...
>>> cfgrib.to_grib(ds, 'out1.grib', grib_keys={'edition': 2})
>>> xr.open_dataset('out1.grib', engine='cfgrib')
<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
    Conventions:             CF-1.7
    institution:             European Centre for Medium-Range Weather Forecasts
    history:                 ...

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.to_grib(ds2, 'out2.grib')
>>> xr.open_dataset('out2.grib', engine='cfgrib')
<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
    Conventions:             CF-1.7
    institution:             Consensus
    history:                 ...

Project resources

Development https://github.com/ecmwf/cfgrib
Download https://pypi.org/project/cfgrib
Code quality Build Status on Travis CI Coverage Status on Coveralls

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:

Main contributors:

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.

Changelog for cfgrib

0.9.5.2 (2019-01-24)

  • Set valid_time as preferred time dimension for the CDS data model.
  • Fall back to using the generic GRIB2 ecCodes template when no better option is found. See: #39.

0.9.5.1 (2018-12-27)

  • Fix the crash when using cf2cdm.translate_coords on datasets with non-dimension coordinates. See: #41.
  • Added a cfgrib script that can translate GRIB to netCDF. See: #40.

0.9.5 (2018-12-20)

  • Drop support for xarray versions prior to v0.11 to reduce complexity. (This is really only v0.10.9). See: #32.
  • Declare the data as CF-1.7 compliant via the Conventions global attribute. See: #36.
  • Tested larger-than-memory and distributed processing via dask and dask.distributed. See: #33.
  • Promote write support via cfgrib.to_grib to Alpha. See: #18.
  • Provide the cf2cdm.translate_coords utility function to translate the coordinates between CF-compliant data models, defined by out_name, units and store_direction. See: #24.
  • Provide cfgrib.__version__. See: #31.
  • Raise with a better error message when users attempt to open a file that is not a GRIB. See: #34.
  • Make 2D grids for rotated_ll and rotated_gg gridType’s. See: #35.

0.9.4.1 (2018-11-08)

  • Fix formatting for PyPI page.

0.9.4 (2018-11-08)

  • Saves one index file per set of index_keys in a much more robust way.
  • Refactor CF-encoding and add the new encode_cf option to backend_kwargs. See: #23.
  • Refactor error handling and the option to ignore errors (not well documented yet). See: #13.
  • Do not crash on gridType not fully supported by the installed ecCodes See: #27.
  • Several smaller bug fixes and performance improvements.

0.9.3.1 (2018-10-28)

  • Assorted README fixes, in particular advertise index file support as alpha.

0.9.3 (2018-10-28)

  • Big performance improvement: add alpha support to save to and read from disk the GRIB index produced by the full-file scan at the first open. See: #20.

0.9.2 (2018-10-22)

  • Rename coordinate air_pressure to isobaricInhPa for consistency with all other vertical level coordinates. See: #25.

0.9.1.post1 (2018-10-19)

  • Fix PyPI description.

0.9.1 (2018-10-19)

  • Change the usage of cfgrib.open_dataset to allign it with xarray.open_dataset, in particular filter_by_key must be added into the backend_kwargs dictionary. See: #21.

0.9.0 (2018-10-14)

  • Beta release with read support.

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