Python interface to map GRIB files to the NetCDF Common Data Model following the CF Convention using ecCodes.
.. image:: https://img.shields.io/pypi/v/cfgrib.svg :target: https://pypi.python.org/pypi/cfgrib/
Python interface to map GRIB files to the
Unidata's Common Data Model v4 <https://docs.unidata.ucar.edu/netcdf-java/current/userguide/common_data_model_overview.html>_
following the CF Conventions <http://cfconventions.org/>.
The high level API is designed to support a GRIB engine for xarray <http://xarray.pydata.org/>
and it is inspired by netCDF4-python <http://unidata.github.io/netcdf4-python/>_
and h5netcdf <https://github.com/shoyer/h5netcdf>.
Low level access and decoding is performed via the
ECMWF ecCodes library <https://confluence.ecmwf.int/display/ECC/> and
the eccodes python package <https://pypi.org/project/eccodes>_.
Features with development status Beta:
engine='cfgrib' option to read GRIB files with xarray,cfgrib.open_datasets,ADVANCED_USAGE.rst.Work in progress:
cfgrib utility that can convert a GRIB file to_netcdf
with a optional conversion to a specific coordinates data model,
see #40 <https://github.com/ecmwf/cfgrib/issues/40>_.xarray.Dataset's to a GRIB1 or GRIB2 file,
see the Advanced write usage section below, #18 <https://github.com/ecmwf/cfgrib/issues/18>_
and #156 <https://github.com/ecmwf/cfgrib/issues/156>_.Limitations:
gridType,
see #28 <https://github.com/ecmwf/cfgrib/issues/28>_.The easiest way to install cfgrib and all its binary dependencies is via Conda <https://conda.io/>_::
$ 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
cfgrib depends on the eccodes python package <https://pypi.org/project/eccodes>_
to access the ECMWF ecCodes binary library,
when not using conda please follow the System dependencies section there.
You may run a simple selfcheck command to ensure that your system is set up correctly::
$ python -m cfgrib selfcheck
Found: ecCodes v2.20.0.
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 <https://get.ecmwf.int/repository/test-data/cfgrib/era5-levels-members.grib>_::
$ wget https://get.ecmwf.int/repository/test-data/cfgrib/era5-levels-members.grib
Most of cfgrib users want to open a GRIB file as a xarray.Dataset and
need to have xarray installed::
$ pip install xarray
In a Python interpreter try:
.. code-block:: python
>>> import xarray as xr
>>> ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib')
>>> ds
<xarray.Dataset>
Dimensions: (number: 10, time: 4, isobaricInhPa: 2, latitude: 61,
longitude: 120)
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) 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, 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:
xarray.open_mfdataset,dask <https://dask.org/>_,dask.distributed <http://distributed.dask.org>_.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:
.. code-block:: python
>>> ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib',
... backend_kwargs={'read_keys': ['experimentVersionNumber']})
>>> ds.t.attrs['GRIB_experimentVersionNumber']
'0001'
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:
.. code-block:: python
>>> import cf2cdm
>>> ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib')
>>> cf2cdm.translate_coords(ds, cf2cdm.ECMWF)
<xarray.Dataset>
Dimensions: (number: 10, time: 4, level: 2, latitude: 61, longitude: 120)
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) 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 ... 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:
.. code-block:: python
>>> import cf2cdm
>>> cf2cdm.translate_coords(ds, cf2cdm.CDS)
<xarray.Dataset>
Dimensions: (realization: 10, forecast_reference_time: 4,
plev: 2, lat: 61, lon: 120)
Coordinates:
* realization (realization) int64 0 1 2 3 4 5 6 7 8 9
* forecast_reference_time (forecast_reference_time) datetime64[ns] 2017-01...
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: ...
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 <https://github.com/ecmwf/cfgrib/issues/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 <https://github.com/ecmwf/cfgrib/issues/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 <http://ftpprd.ncep.noaa.gov/data/nccf/com/nam/prod/>_
you can use:
.. code-block:: python
>>> xr.open_dataset('nam.t00z.awp21100.tm00.grib2', engine='cfgrib',
... backend_kwargs={'filter_by_keys': {'typeOfLevel': 'surface'}})
<xarray.Dataset>
Dimensions: (y: 65, x: 93)
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
surface float64 ...
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: y, x
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 ...
unknown (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: (y: 65, x: 93)
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
heightAboveGround float64 ...
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: y, x
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: ...
cfgrib also provides a function that automates the selection of appropriate filter_by_keys
and returns a list of all valid xarray.Dataset's in the GRIB file.
.. code-block:: python
>>> import cfgrib
>>> cfgrib.open_datasets('nam.t00z.awp21100.tm00.grib2')
[<xarray.Dataset>
Dimensions: (y: 65, x: 93)
Coordinates:
time datetime64[ns] 2018-09-17
step timedelta64[ns] 00:00:00
atmosphereSingleLayer float64 0.0
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: y, x
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, <xarray.Dataset>
Dimensions: (y: 65, x: 93)
Coordinates:
time datetime64[ns] 2018-09-17
step timedelta64[ns] 00:00:00
cloudBase float64 0.0
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: y, x
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: (y: 65, x: 93)
Coordinates:
time datetime64[ns] 2018-09-17
step timedelta64[ns] 00:00:00
cloudTop float64 0.0
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: y, x
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: (y: 65, x: 93)
Coordinates:
time datetime64[ns] 2018-09-17
step timedelta64[ns] 00:00:00
heightAboveGround float64 10.0
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: y, x
Data variables:
u10 (y, x) float32 ...
v10 (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: (y: 65, x: 93)
Coordinates:
time datetime64[ns] 2018-09-17
step timedelta64[ns] 00:00:00
heightAboveGround float64 2.0
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: y, x
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, y: 65, x: 93)
Coordinates:
time datetime64[ns] 2018-09-17
step timedelta64[ns] 00:00:00
* heightAboveGroundLayer (heightAboveGroundLayer) float64 1e+03 3e+03
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: y, x
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, y: 65, x: 93)
Coordinates:
time datetime64[ns] 2018-09-17
step timedelta64[ns] 00:00:00
* isobaricInhPa (isobaricInhPa) float64 1e+03 950.0 900.0 ... 150.0 100.0
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: y, x
Data variables:
t (isobaricInhPa, y, x) float32 ...
u (isobaricInhPa, y, x) float32 ...
v (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, y: 65, x: 93)
Coordinates:
time datetime64[ns] 2018-09-17
step timedelta64[ns] 00:00:00
* isobaricInhPa (isobaricInhPa) float64 1e+03 850.0 700.0 500.0 250.0
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: y, x
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: (y: 65, x: 93)
Coordinates:
time datetime64[ns] 2018-09-17
step timedelta64[ns] 00:00:00
isothermZero float64 0.0
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: y, x
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: (y: 65, x: 93)
Coordinates:
time datetime64[ns] 2018-09-17
step timedelta64[ns] 00:00:00
maxWind float64 0.0
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: y, x
Data variables:
pres (y, x) float32 ...
u (y, x) float32 ...
v (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: (y: 65, x: 93)
Coordinates:
time datetime64[ns] 2018-09-17
step timedelta64[ns] 00:00:00
meanSea float64 0.0
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: y, x
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, y: 65, x: 93)
Coordinates:
time datetime64[ns] 2018-09-17
step timedelta64[ns] 00:00:00
* pressureFromGroundLayer (pressureFromGroundLayer) float64 9e+03 1.8e+04
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: y, x
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, y: 65, x: 93)
Coordinates:
time datetime64[ns] 2018-09-17
step timedelta64[ns] 00:00:00
* pressureFromGroundLayer (pressureFromGroundLayer) float64 3e+03 ... 1.5e+04
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: y, x
Data variables:
t (pressureFromGroundLayer, y, x) float32 ...
u (pressureFromGroundLayer, y, x) float32 ...
v (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: (y: 65, x: 93)
Coordinates:
time datetime64[ns] 2018-09-17
step timedelta64[ns] 00:00:00
pressureFromGroundLayer float64 3e+03
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: y, x
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: (y: 65, x: 93)
Coordinates:
time datetime64[ns] 2018-09-17
step timedelta64[ns] 00:00:00
pressureFromGroundLayer float64 1.8e+04
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: y, x
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: (y: 65, x: 93)
Coordinates:
time datetime64[ns] 2018-09-17
step timedelta64[ns] 00:00:00
surface float64 0.0
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: y, x
Data variables:
unknown (y, x) float32 ...
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 ...
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: (y: 65, x: 93)
Coordinates:
time datetime64[ns] 2018-09-17
step timedelta64[ns] 00:00:00
tropopause float64 0.0
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: y, x
Data variables:
t (y, x) float32 ...
u (y, x) float32 ...
v (y, x) float32 ...
trpp (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]
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:
.. code-block:: python
>>> from cfgrib.xarray_to_grib import to_grib
>>> ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib').sel(number=0)
>>> ds
<xarray.Dataset>
Dimensions: (time: 4, isobaricInhPa: 2, latitude: 61, longitude: 120)
Coordinates:
number int64 0
* time (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00
step timedelta64[ns] ...
* isobaricInhPa (isobaricInhPa) 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 (time, isobaricInhPa, latitude, longitude) float32 ...
t (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: ...
>>> to_grib(ds, 'out1.grib', grib_keys={'edition': 2})
>>> xr.open_dataset('out1.grib', engine='cfgrib')
<xarray.Dataset>
Dimensions: (time: 4, isobaricInhPa: 2, latitude: 61, longitude: 120)
Coordinates:
number ...
* time (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00
step timedelta64[ns] ...
* isobaricInhPa (isobaricInhPa) 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 (time, isobaricInhPa, latitude, longitude) float32 ...
t (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:
.. code-block:: python
>>> 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'
>>> 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 float64 ...
* 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: ...
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:
.. code-block:: python
>>> 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
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.
By default, cfgrib caches computed geography coordinates for each record in the GRIB
file when opening a dataset, which significantly speeds up dataset creation.
This cache can theoretically grow unboundedly in memory in long-lived
applications which read many different grid types. Should it be necessary,
caching can be disabled by passing backend_kwargs=dict(cache_geo_coords=False)
to xarray.open_dataset(), cfgrib.open_dataset(), or
cfgrib.open_datasets().
============= ========================================================= Development https://github.com/ecmwf/cfgrib Download https://pypi.org/project/cfgrib User support https://stackoverflow.com/search?q=cfgrib Code quality .. image:: https://codecov.io/gh/ecmwf/cfgrib/branch/master/graph/badge.svg :target: https://codecov.io/gh/ecmwf/cfgrib :alt: Coverage status on Codecov ============= =========================================================
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 developers:
Iain Russell <https://github.com/iainrussell>_ - ECMWF <https://ecmwf.int>_Baudouin Raoult <https://github.com/b8raoult>_ - ECMWFMain contributors:
Alessandro Amici <https://github.com/alexamici>_ - B-Open <https://bopen.eu>_Aureliana Barghini <https://github.com/aurghs>_ - B-OpenLeonardo Barcaroli <https://github.com/leophys>_ - B-OpenSee also the list of contributors <https://github.com/ecmwf/cfgrib/contributors>_ who participated in this project.
Copyright 2017-2021 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.
values_dtype argument to open_dataset() to allow control over the type of numpy
array used for the values array (default is np.dtype("float32")). Usage:
ds = xr.open_dataset("data.grib", engine="cfgrib", backend_kwargs={"values_dtype": np.dtype("float64")},)
See #407 <https://github.com/ecmwf/cfgrib/pull/407>_.#401 <https://github.com/ecmwf/cfgrib/pull/401>_.Added coords_as_attributes argument to open_dataset() to allow selected dimensions
to be stored as attributes rather than dimensions, allowing more heterogeneous data
to be encoded as an xarray dataset.
See #394 <https://github.com/ecmwf/cfgrib/pull/394>_.
Added valid_month dimension if monthlyVerificationDate and validityTime are available.
See #393 <https://github.com/ecmwf/cfgrib/pull/393>_.
Added uvRelativeToGrid to list of GRIB keys read by default.
See #379 <https://github.com/ecmwf/cfgrib/pull/379>_.
Allow users to pass of list of values to filter a key by.
See #384 <https://github.com/ecmwf/cfgrib/pull/384>_.
Functionality to ignore keys when reading a grib file
See #382 <https://github.com/ecmwf/cfgrib/pull/382>_.
Preserve coordinate encoding in cfgrib.open_datasets
See #381 <https://github.com/ecmwf/cfgrib/pull/381>_.
#370 <https://github.com/ecmwf/cfgrib/pull/370>_.added automatic caching of geographic coordinates for improved performance
See #341 <https://github.com/ecmwf/cfgrib/pull/341>_.
fixed issue where to_grib() could crash if given a dataset with a single-valued dimension
See #347 <https://github.com/ecmwf/cfgrib/issues/347>_.
fixed issue where values could not be extracted when alternativeRowScanning=1 and
grid is not represented as 2D
See #358 <https://github.com/ecmwf/cfgrib/issues/358>_.
fixed issue where the grib_errors parameter was not being handled correctly.
This parameter has now been renamed to errors.
See #349 <https://github.com/ecmwf/cfgrib/issues/349>_.
dropped support for Python 3.6.
See #363 <https://github.com/ecmwf/cfgrib/issues/363>_.
cfgrib to_netcdf -v '{"dtype": "float", "scale_factor": 0.1}' -o $OUTFILE $INFILE
See #334 <https://github.com/ecmwf/cfgrib/pull/334>_.#330 <https://github.com/ecmwf/cfgrib/issues/330>_.#336 <https://github.com/ecmwf/cfgrib/issues/336>_.large reduction in memory leak
See #320 <https://github.com/ecmwf/cfgrib/pull/320/>_.
Replaced distutils.version by packaging.version and
added description and url to the xarray plugin.
See #318 <https://github.com/ecmwf/cfgrib/pull/318/>_.
#294 <https://github.com/ecmwf/cfgrib/pull/294/>_.#296 <https://github.com/ecmwf/cfgrib/pull/296/>_.#313 <https://github.com/ecmwf/cfgrib/issues/313>_.#292 <https://github.com/ecmwf/cfgrib/issues/292>_.#288 <https://github.com/ecmwf/cfgrib/pull/288/>_.#284 <https://github.com/ecmwf/cfgrib/issues/284>_.#282 <https://github.com/ecmwf/cfgrib/issues/282>_.#278 <https://github.com/ecmwf/cfgrib/issues/278>_.Fieldset similar to Metview.
See #243 <https://github.com/ecmwf/cfgrib/issues/243>_.extra_coords.
See #231 <https://github.com/ecmwf/cfgrib/issues/231>_.extra_coords return a scalar.
See #238 <https://github.com/ecmwf/cfgrib/issues/238>_.#243 <https://github.com/ecmwf/cfgrib/issues/243>_.eccodes python package <https://pypi.org/project/eccodes>_ to access
the low level ecCodes C-library, dropping all other GRIB decoding options.
See: #95 <https://github.com/ecmwf/cfgrib/issues/95>,
#14 <https://github.com/ecmwf/cfgrib/issues/14>.
#204 <https://github.com/ecmwf/cfgrib/issues/204>,
#147 <https://github.com/ecmwf/cfgrib/issues/147> and
#141 <https://github.com/ecmwf/cfgrib/issues/141>_.#142 <https://github.com/ecmwf/cfgrib/issues/142>_ and
#197 <https://github.com/ecmwf/cfgrib/issues/197>_.filter_by_keys now can select on all keys known to ecCodes without the need to
add non default ones to read_keys explicitly.
See: #187 <https://github.com/ecmwf/cfgrib/issues/187>_.engine="cfgrib" using xarray 0.18+ new backend API.
See: #216 <https://github.com/ecmwf/cfgrib/pull/216>_.#199 <https://github.com/ecmwf/cfgrib/issues/199>_.level coordinates as float in all cases, fixed issue with non-int levels.
See: #195 <https://github.com/ecmwf/cfgrib/issues/195>_.ECCODES_DIR environment variable if present. Ported from eccodes-python
by xavierabellan. See: #162 <https://github.com/ecmwf/cfgrib/issues/162>_.CFGRIB_USE_EXTERNAL_ECCODES_BINDINGS=1.ecmwflibs if present to find the ecCodes installation.indexingDate, indexingTime time coordinates.lambert_azimuthal_equal_area grids are now returned as 2D arrays.
See: #119 <https://github.com/ecmwf/cfgrib/issues/119>_.u and v components of wind (e.g. GFS, NAM, etc). This has been the single
most reported bug in cfgrib with two failed attempts at fixing it already.
Let's see if the third time's a charm. Please test!
See: #45 <https://github.com/ecmwf/cfgrib/issues/45>,
#76 <https://github.com/ecmwf/cfgrib/issues/76> and
#111 <https://github.com/ecmwf/cfgrib/issues/111>_.forecastMonth in cf2cdm.translate_coords.ensure_valid_time and the config option preferred_time_dimension that
are now better handled via time_dims.time_dims forecasts products may be represented as
('time', 'verifying_time') or ('time', 'forecastMonth').
See: #97 <https://github.com/ecmwf/cfgrib/issues/97>_.time_dims.
Forecasts products may be represented as ('time', 'step') (the default),
('time', 'valid_time') or ('valid_time', 'step').
See: #97 <https://github.com/ecmwf/cfgrib/issues/97>_.FieldIndex and the size of .idx files.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 <https://github.com/ecmwf/cfgrib/issues/89>_ and
#101 <https://github.com/ecmwf/cfgrib/issues/101>_.#91 <https://github.com/ecmwf/cfgrib/issues/91>_.indexpath in open_datasets,
See: #93 <https://github.com/ecmwf/cfgrib/issues/93>_.cfgrib.open_datasets heuristics now reads many more
heterogeneous GRIB files. The function is now a supported API.
See: #63 <https://github.com/ecmwf/cfgrib/issues/63>,
#66 <https://github.com/ecmwf/cfgrib/issues/66>,
#73 <https://github.com/ecmwf/cfgrib/issues/73>_ and
#75 <https://github.com/ecmwf/cfgrib/issues/75>_.#78 <https://github.com/ecmwf/cfgrib/issues/78>_.#69 <https://github.com/ecmwf/cfgrib/issues/69>_.#81 <https://github.com/ecmwf/cfgrib/issues/81>_.black -S -l 99.#63 <https://github.com/ecmwf/cfgrib/issues/63>_.#45 <https://github.com/ecmwf/cfgrib/issues/45>_.*.idx files must be removed.#64 <https://github.com/ecmwf/cfgrib/issues/64>_.#7 <https://github.com/ecmwf/cfgrib/issues/7>_.#5 <https://github.com/ecmwf/cfgrib/issues/5>_.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 <https://github.com/ecmwf/cfgrib/issues/54>_.#54 <https://github.com/ecmwf/cfgrib/issues/54>_.#45 <https://github.com/ecmwf/cfgrib/issues/45>_ as the fix was returning wrong data.
Now we are back to dropping all variable in a MULTI-FIELD except the first.units to avoid crashing some versions of xarray.
See: #44 <https://github.com/ecmwf/cfgrib/issues/44>_.#45 <https://github.com/ecmwf/cfgrib/issues/45>_.depthBelowLand coordinate.valid_time from a bad time-step hypercube.valid_time can index all times and steps in translate_coords.valid_time as preferred time dimension for the CDS data model.GRIB2 ecCodes template when no better option is found.
See: #39 <https://github.com/ecmwf/cfgrib/issues/39>_.cf2cdm.translate_coords on datasets with non-dimension coordinates.
See: #41 <https://github.com/ecmwf/cfgrib/issues/41>_.cfgrib script that can translate GRIB to netCDF.
See: #40 <https://github.com/ecmwf/cfgrib/issues/40>_.#32 <https://github.com/ecmwf/cfgrib/issues/32>_.CF-1.7 compliant via the Conventions global attribute.
See: #36 <https://github.com/ecmwf/cfgrib/issues/36>_.#33 <https://github.com/ecmwf/cfgrib/issues/33>_.cfgrib.to_grib to Alpha.
See: #18 <https://github.com/ecmwf/cfgrib/issues/18>_.cf2cdm.translate_coords utility function to translate the coordinates
between CF-compliant data models, defined by out_name, units and store_direction.
See: #24 <https://github.com/ecmwf/cfgrib/issues/24>_.cfgrib.__version__.
See: #31 <https://github.com/ecmwf/cfgrib/issues/31>_.#34 <https://github.com/ecmwf/cfgrib/issues/34>_.rotated_ll and rotated_gg gridType's.
See: #35 <https://github.com/ecmwf/cfgrib/issues/35>_.index_keys in a much more robust way.encode_cf option to backend_kwargs.
See: #23 <https://github.com/ecmwf/cfgrib/issues/23>_.#13 <https://github.com/ecmwf/cfgrib/issues/13>_.gridType not fully supported by the installed ecCodes
See: #27 <https://github.com/ecmwf/cfgrib/issues/27>_.#20 <https://github.com/ecmwf/cfgrib/issues/20>_.air_pressure to isobaricInhPa for consistency
with all other vertical level coordinates.
See: #25 <https://github.com/ecmwf/cfgrib/issues/25>_.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 <https://github.com/ecmwf/cfgrib/issues/21>_.FAQs
Python interface to map GRIB files to the NetCDF Common Data Model following the CF Convention using ecCodes.
We found that cfgrib demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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