cfgrib

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

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Project links

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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 including heterogeneous ones with cfgrib.open_datasets,
supports all modern versions of Python 3.7, 3.6, 3.5 and PyPy3,
the 0.9.6.x series with support for Python 2 will stay active and receive critical bugfixes,
works on Linux, MacOS and Windows, the ecCodes C-library is the only binary dependency,
conda-forge package on all supported platforms,
PyPI package with no install time build (binds via 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,
supports translating coordinates to different data models and naming conventions,
supports writing the index of a GRIB file to disk, to save a full-file scan on open.

Work in progress:

Alpha limited support for MULTI-FIELD messages, e.g. u-v components, see #76.
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 support writing carefully-crafted xarray.Dataset’s to a GRIB1 or GRIB2 file, see the Advanced write usage section below and #18.

Limitations:

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 easiest way to install cfgrib and all its binary dependencies is via Conda:

$ conda install -c conda-forge cfgrib

alternatively, if you install the binary dependencies yourself, you can install the Python package from PyPI with:

$ pip install cfgrib

Binary dependencies

The Python module depends on the ECMWF ecCodes binary 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

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

$ python -m cfgrib selfcheck
Found: ecCodes v2.12.0.
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>=0.12.0 installed:

$ pip install xarray>=0.12.0

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.

Read arbitrary GRIB keys

By default cfgrib reads a limited set of ecCodes recognised keys from the GRIB files and exposes them as Dataset or DataArray attributes with the GRIB_ prefix. It is possible to have cfgrib read additional keys to the attributes by adding the read_keys dictionary key to the backend_kwargs with values the list of desired GRIB keys:

>>> ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib',
...                      backend_kwargs={'read_keys': ['experimentVersionNumber']})
>>> ds.t.attrs['GRIB_experimentVersionNumber']
'0001'

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:                  (forecast_reference_time: 4, lat: 61, lon: 120, plev: 2, realization: 10)
Coordinates:
  * realization              (realization) int64 0 1 2 3 4 5 6 7 8 9
  * forecast_reference_time  (forecast_reference_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                     (forecast_reference_time) datetime64[ns] ...
Data variables:
    z                        (realization, forecast_reference_time, plev, lat, lon) float32 ...
    t                        (realization, forecast_reference_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

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'}})
<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 ...
    tp          (y, x) float32 ...
    acpcp       (y, x) float32 ...
    csnow       (y, x) float32 ...
    cicep       (y, x) float32 ...
    cfrzr       (y, x) float32 ...
    crain       (y, x) float32 ...
    cape        (y, x) float32 ...
    cin         (y, x) float32 ...
    hpbl        (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 a function that automate the selection of appropriate filter_by_keys and returns a list of all valid xarray.Dataset’s in the GRIB file.

>>> import cfgrib
>>> cfgrib.open_datasets('nam.t00z.awp21100.tm00.grib2')
[<xarray.Dataset>
Dimensions:     (x: 93, y: 65)
Coordinates:
    time        datetime64[ns] 2018-09-17
    step        timedelta64[ns] 00:00:00
    cloudBase   int64 0
    latitude    (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29
    longitude   (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6
    valid_time  datetime64[ns] 2018-09-17
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 , <xarray.Dataset>
Dimensions:     (x: 93, y: 65)
Coordinates:
    time        datetime64[ns] 2018-09-17
    step        timedelta64[ns] 00:00:00
    cloudTop    int64 0
    latitude    (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29
    longitude   (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6
    valid_time  datetime64[ns] 2018-09-17
Dimensions without coordinates: x, y
Data variables:
    pres        (y, x) float32 ...
    t           (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 , <xarray.Dataset>
Dimensions:            (x: 93, y: 65)
Coordinates:
    time               datetime64[ns] 2018-09-17
    step               timedelta64[ns] 00:00:00
    heightAboveGround  int64 10
    latitude           (y, x) float64 ...
    longitude          (y, x) float64 ...
    valid_time         datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
    u10                (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 , <xarray.Dataset>
Dimensions:            (x: 93, y: 65)
Coordinates:
    time               datetime64[ns] 2018-09-17
    step               timedelta64[ns] 00:00:00
    heightAboveGround  int64 2
    latitude           (y, x) float64 12.19 12.39 12.58 ... 57.68 57.49 57.29
    longitude          (y, x) float64 226.5 227.2 227.9 ... 308.5 309.6 310.6
    valid_time         datetime64[ns] 2018-09-17
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 , <xarray.Dataset>
Dimensions:                 (heightAboveGroundLayer: 2, x: 93, y: 65)
Coordinates:
    time                    datetime64[ns] 2018-09-17
    step                    timedelta64[ns] 00:00:00
  * heightAboveGroundLayer  (heightAboveGroundLayer) int64 1000 3000
    latitude                (y, x) float64 ...
    longitude               (y, x) float64 ...
    valid_time              datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
    hlcy                    (heightAboveGroundLayer, 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 , <xarray.Dataset>
Dimensions:        (isobaricInhPa: 19, x: 93, y: 65)
Coordinates:
    time           datetime64[ns] 2018-09-17
    step           timedelta64[ns] 00:00:00
  * isobaricInhPa  (isobaricInhPa) int64 1000 950 900 850 ... 250 200 150 100
    latitude       (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29
    longitude      (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6
    valid_time     datetime64[ns] 2018-09-17
Dimensions without coordinates: x, y
Data variables:
    t              (isobaricInhPa, y, x) float32 ...
    u              (isobaricInhPa, y, x) float32 ...
    w              (isobaricInhPa, y, x) float32 ...
    gh             (isobaricInhPa, y, x) float32 ...
    r              (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 , <xarray.Dataset>
Dimensions:        (isobaricInhPa: 5, x: 93, y: 65)
Coordinates:
    time           datetime64[ns] 2018-09-17
    step           timedelta64[ns] 00:00:00
  * isobaricInhPa  (isobaricInhPa) int64 1000 850 700 500 250
    latitude       (y, x) float64 ...
    longitude      (y, x) float64 ...
    valid_time     datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
    absv           (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 , <xarray.Dataset>
Dimensions:       (x: 93, y: 65)
Coordinates:
    time          datetime64[ns] 2018-09-17
    step          timedelta64[ns] 00:00:00
    isothermZero  int64 0
    latitude      (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29
    longitude     (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6
    valid_time    datetime64[ns] 2018-09-17
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 , <xarray.Dataset>
Dimensions:     (x: 93, y: 65)
Coordinates:
    time        datetime64[ns] 2018-09-17
    step        timedelta64[ns] 00:00:00
    maxWind     int64 0
    latitude    (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29
    longitude   (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6
    valid_time  datetime64[ns] 2018-09-17
Dimensions without coordinates: x, y
Data variables:
    pres        (y, x) float32 ...
    u           (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 , <xarray.Dataset>
Dimensions:     (x: 93, y: 65)
Coordinates:
    time        datetime64[ns] 2018-09-17
    step        timedelta64[ns] 00:00:00
    meanSea     int64 0
    latitude    (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29
    longitude   (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6
    valid_time  datetime64[ns] 2018-09-17
Dimensions without coordinates: x, y
Data variables:
    prmsl       (y, x) float32 ...
    mslet       (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 , <xarray.Dataset>
Dimensions:                  (pressureFromGroundLayer: 2, x: 93, y: 65)
Coordinates:
    time                     datetime64[ns] 2018-09-17
    step                     timedelta64[ns] 00:00:00
  * pressureFromGroundLayer  (pressureFromGroundLayer) int64 9000 18000
    latitude                 (y, x) float64 12.19 12.39 12.58 ... 57.49 57.29
    longitude                (y, x) float64 226.5 227.2 227.9 ... 309.6 310.6
    valid_time               datetime64[ns] 2018-09-17
Dimensions without coordinates: x, y
Data variables:
    cape                     (pressureFromGroundLayer, y, x) float32 ...
    cin                      (pressureFromGroundLayer, 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 , <xarray.Dataset>
Dimensions:                  (pressureFromGroundLayer: 5, x: 93, y: 65)
Coordinates:
    time                     datetime64[ns] 2018-09-17
    step                     timedelta64[ns] 00:00:00
  * pressureFromGroundLayer  (pressureFromGroundLayer) int64 3000 6000 ... 15000
    latitude                 (y, x) float64 12.19 12.39 12.58 ... 57.49 57.29
    longitude                (y, x) float64 226.5 227.2 227.9 ... 309.6 310.6
    valid_time               datetime64[ns] 2018-09-17
Dimensions without coordinates: x, y
Data variables:
    t                        (pressureFromGroundLayer, y, x) float32 ...
    u                        (pressureFromGroundLayer, y, x) float32 ...
    r                        (pressureFromGroundLayer, 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 , <xarray.Dataset>
Dimensions:                  (x: 93, y: 65)
Coordinates:
    time                     datetime64[ns] 2018-09-17
    step                     timedelta64[ns] 00:00:00
    pressureFromGroundLayer  int64 3000
    latitude                 (y, x) float64 ...
    longitude                (y, x) float64 ...
    valid_time               datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
    pli                      (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 , <xarray.Dataset>
Dimensions:                  (x: 93, y: 65)
Coordinates:
    time                     datetime64[ns] 2018-09-17
    step                     timedelta64[ns] 00:00:00
    pressureFromGroundLayer  int64 18000
    latitude                 (y, x) float64 ...
    longitude                (y, x) float64 ...
    valid_time               datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
    4lftx                    (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 , <xarray.Dataset>
Dimensions:     (x: 93, y: 65)
Coordinates:
    time        datetime64[ns] 2018-09-17
    step        timedelta64[ns] 00:00:00
    surface     int64 0
    latitude    (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29
    longitude   (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6
    valid_time  datetime64[ns] 2018-09-17
Dimensions without coordinates: x, y
Data variables:
    cape        (y, x) float32 ...
    sp          (y, x) float32 ...
    acpcp       (y, x) float32 ...
    cin         (y, x) float32 ...
    orog        (y, x) float32 ...
    tp          (y, x) float32 ...
    crain       (y, x) float32 ...
    cfrzr       (y, x) float32 ...
    cicep       (y, x) float32 ...
    csnow       (y, x) float32 ...
    gust        (y, x) float32 ...
    hpbl        (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 , <xarray.Dataset>
Dimensions:     (x: 93, y: 65)
Coordinates:
    time        datetime64[ns] 2018-09-17
    step        timedelta64[ns] 00:00:00
    tropopause  int64 0
    latitude    (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29
    longitude   (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6
    valid_time  datetime64[ns] 2018-09-17
Dimensions without coordinates: x, y
Data variables:
    pres        (y, x) float32 ...
    t           (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 , <xarray.Dataset>
Dimensions:     (x: 93, y: 65)
Coordinates:
    time        datetime64[ns] 2018-09-17
    step        timedelta64[ns] 00:00:00
    level       int64 0
    latitude    (y, x) float64 ...
    longitude   (y, x) float64 ...
    valid_time  datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
    pwat        (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 ]

Advanced usage

Write support

Please note that write support is Alpha. 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:                 ...

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:

>>> 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.

Project resources

Development	https://github.com/ecmwf/cfgrib
Download	https://pypi-hypernode.com/project/cfgrib
User support	https://stackoverflow.com/search?q=cfgrib
Code quality

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

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

0.9.8.1 (2020-03-13)

Always open GRIB files in binary mode, by @b8raoult

0.9.8.0 (2020-03-12)

Add support of experimental pyeccodes low-level driver by @b8raoult

0.9.7.7 (2020-01-24)

Add support for forecastMonth in cf2cdm.translate_coords.

0.9.7.6 (2019-12-05)

Fix the README.

0.9.7.5 (2019-12-05)

Deprecate ensure_valid_time and the config option preferred_time_dimension that are now better handled via time_dims.

0.9.7.4 (2019-11-22)

Add more options to time_dims forecasts products may be represented as ('time', 'verifying_time') or ('time', 'forecastMonth'). See: #97.

0.9.7.3 (2019-11-04)

Add support for selecting the time coordinates to use as dimensions via time_dims. Forecasts products may be represented as ('time', 'step') (the default), ('time', 'valid_time') or ('valid_time', 'step'). See: #97.
Reduce the in-memory footprint of the FieldIndex and the size of .idx files.

0.9.7.2 (2019-09-24)

Add support to read additional keys from the GRIB files via read_keys, they appear in the variable attrs and you can filter_by_keys on them. This is a general solution for all issues where users know the name of the additional keys they are interested in. See: #89 and #101.

0.9.7.1 (2019-07-08)

Fix a bytes-in-the-place-of-str bug when attempting to write a GRIB on Windows. See: #91.
Honor setting indexpath in open_datasets, See: #93.

0.9.7 (2019-05-27)

Much improved cfgrib.open_datasets heuristics now reads many more heterogeneous GRIB files. The function is now a supported API. See: #63, #66, #73 and #75.
Fix conda dependencies on Python 2 only package, See: #78.

0.9.7rc1 (2019-05-14)

Drop support for Python 2, in line with xarray 0.12.0. The 0.9.6.x series will be supported long term for Python 2 users. See: #69.
Sync internal ecCodes bindings API to the one in eccodes-python. See: #81.
Source code has been formatted with black -S -l 99.
Added initial support for spectral coordinates.

0.9.6.2 (2019-04-15)

Improve merging of variables into a dataset. See: #63.

0.9.6.1.post1 (2019-03-17)

Fix an issue in the README format.

0.9.6.1 (2019-03-17)

Fixed (for real) MULTI-FIELD messages, See: #45.
Added a protocol version to the index file. Old *.idx files must be removed.

0.9.6.post1 (2019-03-07)

Fix an important typo in the README. See: #64.

0.9.6 (2019-02-26)

Add support for Windows by installing ecCodes via conda. See: #7.
Added conda-forge package. See: #5.

0.9.5.7 (2019-02-24)

Fixed a serious bug in the computation of the suggested filter_by_keys for non-cubic GRIB files. As a result cfgrib.xarray_store.open_datasets was not finding all the variables in the files. See: #54.
Fixed a serious bug in variable naming that could drop or at worse mix the values of variables. Again see: #54.
Re-opened #45 as the fix was returning wrong data. Now we are back to dropping all variable in a MULTI-FIELD except the first.

0.9.5.6 (2019-02-04)

Do not set explicit timezone in units to avoid crashing some versions of xarray. See: #44.

0.9.5.5 (2019-02-02)

Enable ecCodes implicit MULTI-FIELD support by default, needed for NAM Products by NCEP. See: #45.
Added support for depthBelowLand coordinate.

0.9.5.4 (2019-01-25)

Add support for building valid_time from a bad time-step hypercube.

0.9.5.3 (2019-01-25)

Also convert is valid_time can index all times and steps in translate_coords.

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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cfgrib 0.9.8.1

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Project description

Installation

Binary dependencies

Usage

Read-only xarray GRIB engine

Read arbitrary GRIB keys

Translate to a custom data model

Filter heterogeneous GRIB files

Automatic filtering

Advanced usage

Write support

Dataset / Variable API

GRIB index file

Project resources

Contributing

License

Changelog for cfgrib

0.9.8.1 (2020-03-13)

0.9.8.0 (2020-03-12)

0.9.7.7 (2020-01-24)

0.9.7.6 (2019-12-05)

0.9.7.5 (2019-12-05)

0.9.7.4 (2019-11-22)

0.9.7.3 (2019-11-04)

0.9.7.2 (2019-09-24)

0.9.7.1 (2019-07-08)

0.9.7 (2019-05-27)

0.9.7rc1 (2019-05-14)

0.9.6.2 (2019-04-15)

0.9.6.1.post1 (2019-03-17)

0.9.6.1 (2019-03-17)

0.9.6.post1 (2019-03-07)

0.9.6 (2019-02-26)

0.9.5.7 (2019-02-24)

0.9.5.6 (2019-02-04)

0.9.5.5 (2019-02-02)

0.9.5.4 (2019-01-25)

0.9.5.3 (2019-01-25)

0.9.5.2 (2019-01-24)

0.9.5.1 (2018-12-27)

0.9.5 (2018-12-20)

0.9.4.1 (2018-11-08)

0.9.4 (2018-11-08)

0.9.3.1 (2018-10-28)

0.9.3 (2018-10-28)

0.9.2 (2018-10-22)

0.9.1.post1 (2018-10-19)

0.9.1 (2018-10-19)

0.9.0 (2018-10-14)

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