cfgrib 0.9.0

Python interface to map GRIB files to the NetCDF Common Data Model following the CF Convention using ecCodes.

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 with development status Beta:

• read-only GRIB driver for xarray,

• support reading most GRIB 1 and 2 files, for limitations see the Advanced usage section below and #2, #13,

• support all modern versions of Python 3.7, 3.6, 3.5 and 2.7, plus PyPy and PyPy3,

• support most Linux distributions and MacOS,

• 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.

Work in progress (development status Pre-Alpha):

• limited support to write carefully-crafted xarray.Dataset’s to a GRIB2 file, see the Advanced write usage section below and #18,

• opening a GRIB file requires a full-file scan and the result is not saved to disk, see #20.

Limitations:

• target is correctness, not performance, for now,

• incomplete documentation, for now,

• no Windows support, see #7,

• no support for opening multiple GRIB files, see #15,

• 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 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.

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.open_file('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
>>> ds = cfgrib.open_file('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

Read-only xarray GRIB driver

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

$ pip install xarray

In a Python interpreter try:

>>> ds = cfgrib.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 ... 2017-01-02T12:00:00
    step          timedelta64[ns] ...
  * air_pressure  (air_pressure) float64 850.0 500.0
  * latitude      (latitude) float64 90.0 87.0 84.0 81.0 ... -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, 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-0.9.../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.

Advanced usage

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 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:

>>> cfgrib.open_dataset('nam.t00z.awp21100.tm00.grib2',
...              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',
...              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. 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')
[<xarray.Dataset>
Dimensions:       (air_pressure: 19, x: 93, y: 65)
Coordinates:
    time          datetime64[ns] ...
    step          timedelta64[ns] ...
  * air_pressure  (air_pressure) float64 1e+03 950.0 900.0 ... 200.0 150.0 100.0
    latitude      (y, x) float64 ...
    longitude     (y, x) float64 ...
    valid_time    datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
    gh            (air_pressure, y, x) float32 ...
    t             (air_pressure, y, x) float32 ...
    r             (air_pressure, y, x) float32 ...
    w             (air_pressure, y, x) float32 ...
    u             (air_pressure, 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...]

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 = cfgrib.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 ... 2017-01-02T12:00:00
    step          timedelta64[ns] ...
  * air_pressure  (air_pressure) float64 850.0 500.0
  * latitude      (latitude) float64 90.0 87.0 84.0 81.0 ... -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, 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-0.9.../ecCodes-2...
>>> cfgrib.canonical_dataset_to_grib(ds, 'out1.grib', grib_keys={'centre': 'ecmf'})
>>> cfgrib.open_dataset('out1.grib')
<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 ... 2017-01-02T12:00:00
    step          timedelta64[ns] ...
  * air_pressure  (air_pressure) float64 850.0 500.0
  * latitude      (latitude) float64 90.0 87.0 84.0 81.0 ... -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, air_pressure, latitude, longitude) float32 ...
    t             (number, time, air_pressure, 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...

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.