cfgrib: A 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**:
- 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 3.9, 3.8, 3.7 and PyPy3,
- the 0.9.6.x series with support for Python 2 will stay active and receive critical bugfixes,
- works wherever *eccodes-python* does: *Linux*, *MacOS* and *Windows*
- conda-forge package on all supported platforms,
- reads the data lazily and efficiently in terms of both memory usage and disk access,
- allows larger-than-memory and distributed processing via *xarray* and *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,
- accepts objects implementing a generic *Fieldset* interface as described in `ADVANCED_USAGE.rst`.
Work in progress:
- **Beta** install a ``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>`_.
- **Alpha/Broken** support writing carefully-crafted ``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:
- relies on *ecCodes* for the CF attributes of the data variables,
- relies on *ecCodes* for anything related to coordinate systems / ``gridType``,
see `#28 <https://github.com/ecmwf/cfgrib/issues/28>`_.
Installation
============
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
Binary dependencies
-------------------
*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.
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 <http://download.ecmwf.int/test-data/cfgrib/era5-levels-members.grib>`_::
$ 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* 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:
* merge the content of several GRIB files into a single dataset using ``xarray.open_mfdataset``,
* work with larger-than-memory datasets with `dask <https://dask.org/>`_,
* allow distributed processing with `dask.distributed <http://distributed.dask.org>`_.
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:
.. code-block: python
>>> 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:
.. 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: ...
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 <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 ...
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: (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: ...
Automatic filtering
-------------------
*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:
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: (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 ]
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:
.. 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: ...
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:
.. 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
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.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
============= =========================================================
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 developers:
- `Iain Russell <https://github.com/iainrussell>`_ - `ECMWF <https://ecmwf.int>`_
- `Baudouin Raoult <https://github.com/b8raoult>`_ - ECMWF
Main contributors:
- `Alessandro Amici <https://github.com/alexamici>`_ - `B-Open <https://bopen.eu>`_
- `Aureliana Barghini <https://github.com/aurghs>`_ - B-Open
- `Leonardo Barcaroli <https://github.com/leophys>`_ - B-Open
See also the list of `contributors <https://github.com/ecmwf/cfgrib/contributors>`_ who participated in this project.
License
=======
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.
Changelog for cfgrib
====================
0.9.10.4 (2023-05-19)
---------------------
- added --var-encoding-json (or -v) option to the to_netcdf tool, e.g.
``cfgrib to_netcdf -v '{"dtype": "float", "scale_factor": 0.1}' -o $OUTFILE $INFILE``
See `#334 <https://github.com/ecmwf/cfgrib/pull/334>`_.
- fix issue where xarrays derived from Gaussian grids did not have the correct
geometry when written back out as GRIB
See `#330 <https://github.com/ecmwf/cfgrib/issues/330>`_.
- fix issue where open_datasets() could merge different GRIB fields
that have the same data values
See `#336 <https://github.com/ecmwf/cfgrib/issues/336>`_.
0.9.10.3 (2022-11-24)
---------------------
- 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/>`_.
0.9.10.2 (2022-10-04)
---------------------
- added --netcdf_kwargs_json option to 'cfgrib to_netcdf'
See `#294 <https://github.com/ecmwf/cfgrib/pull/294/>`_.
- fixed support for GRIB files with alternativeRowScanning=1
See `#296 <https://github.com/ecmwf/cfgrib/pull/296/>`_.
- fixed support for missing values
See `#313 <https://github.com/ecmwf/cfgrib/issues/313>`_.
0.9.10.1 (2022-03-16)
---------------------
- Fix failure to read index files.
See `#292 <https://github.com/ecmwf/cfgrib/issues/292>`_.
- Allow backend kwargs to be provided in the to_netcdf executable,
either via a json format string, or a path to a json file via -b.
See `#288 <https://github.com/ecmwf/cfgrib/pull/288/>`_.
- Fixed issue where the use of relpath() could cause a problem on Windows.
See `#284 <https://github.com/ecmwf/cfgrib/issues/284>`_.
- Fix passing of pathlib.Path.
See `#282 <https://github.com/ecmwf/cfgrib/issues/282>`_.
- Fixed issue where writing an ensemble number into a GRIB file caused an error.
See `#278 <https://github.com/ecmwf/cfgrib/issues/278>`_.
0.9.10.0 (2022-01-31)
---------------------
- Big internal refactor to add support for a generic ``Fieldset`` similar to Metview.
See `#243 <https://github.com/ecmwf/cfgrib/issues/243>`_.
0.9.9.1 (2021-09-29)
--------------------
- Fix the plugin interface that was missing ``extra_coords``.
See `#231 <https://github.com/ecmwf/cfgrib/issues/231>`_.
- Fix the crash when ``extra_coords`` return a scalar.
See `#238 <https://github.com/ecmwf/cfgrib/issues/238>`_.
- Improve type-hints.
Needed by `#243 <https://github.com/ecmwf/cfgrib/issues/243>`_.
0.9.9.0 (2021-04-09)
--------------------
- Depend on the ECMWF `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>`_.
- Many performance improvements during the generation of the index and during data access.
See: `#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>`_.
- Include support for `engine="cfgrib"` using *xarray* 0.18+ new backend API.
See: `#216 <https://github.com/ecmwf/cfgrib/pull/216>`_.
- Fixed issue where could not load a GRIB message that has only one grid point.
See: `#199 <https://github.com/ecmwf/cfgrib/issues/199>`_.
- Decode ``level`` coordinates as float in all cases, fixed issue with non-int levels.
See: `#195 <https://github.com/ecmwf/cfgrib/issues/195>`_.
0.9.8.5 (2020-11-11)
--------------------
- Simpler and clearer messages in the event of errors.
- Use `ECCODES_DIR` environment variable if present. Ported from *eccodes-python*
by xavierabellan. See: `#162 <https://github.com/ecmwf/cfgrib/issues/162>`_.
- Fix using current ecCodes bindings when setting `CFGRIB_USE_EXTERNAL_ECCODES_BINDINGS=1`.
0.9.8.4 (2020-08-03)
--------------------
- Use `ecmwflibs` if present to find the *ecCodes* installation.
0.9.8.3 (2020-06-25)
--------------------
- Added support for ``indexingDate``, ``indexingTime`` time coordinates.
- ``lambert_azimuthal_equal_area`` grids are now returned as 2D arrays.
See: `#119 <https://github.com/ecmwf/cfgrib/issues/119>`_.
0.9.8.2 (2020-05-22)
--------------------
- Add support for MULTI-FIELD messages used in some GRIB products to store
``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>`_.
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 <https://github.com/ecmwf/cfgrib/issues/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 <https://github.com/ecmwf/cfgrib/issues/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 <https://github.com/ecmwf/cfgrib/issues/89>`_ and
`#101 <https://github.com/ecmwf/cfgrib/issues/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 <https://github.com/ecmwf/cfgrib/issues/91>`_.
- Honor setting ``indexpath`` in ``open_datasets``,
See: `#93 <https://github.com/ecmwf/cfgrib/issues/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 <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>`_.
- Fix conda dependencies on Python 2 only package,
See: `#78 <https://github.com/ecmwf/cfgrib/issues/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 <https://github.com/ecmwf/cfgrib/issues/69>`_.
- Sync internal ecCodes bindings API to the one in eccodes-python.
See: `#81 <https://github.com/ecmwf/cfgrib/issues/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 <https://github.com/ecmwf/cfgrib/issues/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 <https://github.com/ecmwf/cfgrib/issues/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 <https://github.com/ecmwf/cfgrib/issues/64>`_.
0.9.6 (2019-02-26)
------------------
- Add support for *Windows* by installing *ecCodes* via *conda*.
See: `#7 <https://github.com/ecmwf/cfgrib/issues/7>`_.
- Added *conda-forge* package.
See: `#5 <https://github.com/ecmwf/cfgrib/issues/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 <https://github.com/ecmwf/cfgrib/issues/54>`_.
- Fixed a serious bug in variable naming that could drop or at worse mix the values of variables.
Again see: `#54 <https://github.com/ecmwf/cfgrib/issues/54>`_.
- Re-opened `#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.
0.9.5.6 (2019-02-04)
--------------------
- Do not set explicit timezone in ``units`` to avoid crashing some versions of *xarray*.
See: `#44 <https://github.com/ecmwf/cfgrib/issues/44>`_.
0.9.5.5 (2019-02-02)
--------------------
- Enable ecCodes implicit MULTI-FIELD support by default, needed for NAM Products by NCEP.
See: `#45 <https://github.com/ecmwf/cfgrib/issues/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 <https://github.com/ecmwf/cfgrib/issues/39>`_.
0.9.5.1 (2018-12-27)
--------------------
- Fix the crash when using ``cf2cdm.translate_coords`` on datasets with non-dimension coordinates.
See: `#41 <https://github.com/ecmwf/cfgrib/issues/41>`_.
- Added a ``cfgrib`` script that can translate GRIB to netCDF.
See: `#40 <https://github.com/ecmwf/cfgrib/issues/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 <https://github.com/ecmwf/cfgrib/issues/32>`_.
- Declare the data as ``CF-1.7`` compliant via the ``Conventions`` global attribute.
See: `#36 <https://github.com/ecmwf/cfgrib/issues/36>`_.
- Tested larger-than-memory and distributed processing via *dask* and *dask.distributed*.
See: `#33 <https://github.com/ecmwf/cfgrib/issues/33>`_.
- Promote write support via ``cfgrib.to_grib`` to **Alpha**.
See: `#18 <https://github.com/ecmwf/cfgrib/issues/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 <https://github.com/ecmwf/cfgrib/issues/24>`_.
- Provide ``cfgrib.__version__``.
See: `#31 <https://github.com/ecmwf/cfgrib/issues/31>`_.
- Raise with a better error message when users attempt to open a file that is not a GRIB.
See: `#34 <https://github.com/ecmwf/cfgrib/issues/34>`_.
- Make 2D grids for ``rotated_ll`` and ``rotated_gg`` ``gridType``'s.
See: `#35 <https://github.com/ecmwf/cfgrib/issues/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 <https://github.com/ecmwf/cfgrib/issues/23>`_.
- Refactor error handling and the option to ignore errors (not well documented yet).
See: `#13 <https://github.com/ecmwf/cfgrib/issues/13>`_.
- Do not crash on ``gridType`` not fully supported by the installed *ecCodes*
See: `#27 <https://github.com/ecmwf/cfgrib/issues/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 <https://github.com/ecmwf/cfgrib/issues/20>`_.
0.9.2 (2018-10-22)
------------------
- Rename coordinate ``air_pressure`` to ``isobaricInhPa`` for consistency
with all other vertical ``level`` coordinates.
See: `#25 <https://github.com/ecmwf/cfgrib/issues/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 <https://github.com/ecmwf/cfgrib/issues/21>`_.
0.9.0 (2018-10-14)
------------------
- Beta release with read support.
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"description": "cfgrib: A Python interface to map GRIB files to the NetCDF Common Data Model following the CF Convention using ecCodes\n======================================================================================================================\n\n.. image:: https://img.shields.io/pypi/v/cfgrib.svg\n :target: https://pypi.python.org/pypi/cfgrib/\n\nPython interface to map GRIB files to the\n`Unidata's Common Data Model v4 <https://docs.unidata.ucar.edu/netcdf-java/current/userguide/common_data_model_overview.html>`_\nfollowing the `CF Conventions <http://cfconventions.org/>`_.\nThe high level API is designed to support a GRIB engine for `xarray <http://xarray.pydata.org/>`_\nand it is inspired by `netCDF4-python <http://unidata.github.io/netcdf4-python/>`_\nand `h5netcdf <https://github.com/shoyer/h5netcdf>`_.\nLow level access and decoding is performed via the\n`ECMWF ecCodes library <https://confluence.ecmwf.int/display/ECC/>`_ and\nthe `eccodes python package <https://pypi.org/project/eccodes>`_.\n\nFeatures with development status **Beta**:\n\n- enables the ``engine='cfgrib'`` option to read GRIB files with *xarray*,\n- reads most GRIB 1 and 2 files including heterogeneous ones with ``cfgrib.open_datasets``,\n- supports all modern versions of Python 3.9, 3.8, 3.7 and PyPy3,\n- the 0.9.6.x series with support for Python 2 will stay active and receive critical bugfixes,\n- works wherever *eccodes-python* does: *Linux*, *MacOS* and *Windows*\n- conda-forge package on all supported platforms,\n- reads the data lazily and efficiently in terms of both memory usage and disk access,\n- allows larger-than-memory and distributed processing via *xarray* and *dask*,\n- supports translating coordinates to different data models and naming conventions,\n- supports writing the index of a GRIB file to disk, to save a full-file scan on open,\n- accepts objects implementing a generic *Fieldset* interface as described in `ADVANCED_USAGE.rst`.\n\nWork in progress:\n\n- **Beta** install a ``cfgrib`` utility that can convert a GRIB file ``to_netcdf``\n with a optional conversion to a specific coordinates data model,\n see `#40 <https://github.com/ecmwf/cfgrib/issues/40>`_.\n- **Alpha/Broken** support writing carefully-crafted ``xarray.Dataset``'s to a GRIB1 or GRIB2 file,\n see the *Advanced write usage* section below, `#18 <https://github.com/ecmwf/cfgrib/issues/18>`_\n and `#156 <https://github.com/ecmwf/cfgrib/issues/156>`_.\n\nLimitations:\n\n- relies on *ecCodes* for the CF attributes of the data variables,\n- relies on *ecCodes* for anything related to coordinate systems / ``gridType``,\n see `#28 <https://github.com/ecmwf/cfgrib/issues/28>`_.\n\n\nInstallation\n============\n\nThe easiest way to install *cfgrib* and all its binary dependencies is via `Conda <https://conda.io/>`_::\n\n $ conda install -c conda-forge cfgrib\n\nalternatively, if you install the binary dependencies yourself, you can install the\nPython package from *PyPI* with::\n\n $ pip install cfgrib\n\n\nBinary dependencies\n-------------------\n\n*cfgrib* depends on the `eccodes python package <https://pypi.org/project/eccodes>`_\nto access the ECMWF *ecCodes* binary library,\nwhen not using *conda* please follow the *System dependencies* section there.\n\nYou may run a simple selfcheck command to ensure that your system is set up correctly::\n\n $ python -m cfgrib selfcheck\n Found: ecCodes v2.20.0.\n Your system is ready.\n\n\nUsage\n=====\n\nFirst, you need a well-formed GRIB file, if you don't have one at hand you can download our\n`ERA5 on pressure levels sample <http://download.ecmwf.int/test-data/cfgrib/era5-levels-members.grib>`_::\n\n $ wget http://download.ecmwf.int/test-data/cfgrib/era5-levels-members.grib\n\n\nRead-only *xarray* GRIB engine\n------------------------------\n\nMost of *cfgrib* users want to open a GRIB file as a ``xarray.Dataset`` and\nneed to have *xarray* installed::\n\n $ pip install xarray\n\nIn a Python interpreter try:\n\n.. code-block: python\n\n>>> import xarray as xr\n>>> ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib')\n>>> ds\n<xarray.Dataset>\nDimensions: (number: 10, time: 4, isobaricInhPa: 2, latitude: 61, longitude: 120)\nCoordinates:\n * number (number) int64 0 1 2 3 4 5 6 7 8 9\n * time (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00\n step timedelta64[ns] ...\n * isobaricInhPa (isobaricInhPa) float64 850.0 500.0\n * latitude (latitude) float64 90.0 87.0 84.0 81.0 ... -84.0 -87.0 -90.0\n * longitude (longitude) float64 0.0 3.0 6.0 9.0 ... 351.0 354.0 357.0\n valid_time (time) datetime64[ns] ...\nData variables:\n z (number, time, isobaricInhPa, latitude, longitude) float32 ...\n t (number, time, isobaricInhPa, latitude, longitude) float32 ...\nAttributes:\n GRIB_edition: 1\n GRIB_centre: ecmf\n GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: European Centre for Medium-Range Weather Forecasts\n history: ...\n\nThe *cfgrib* ``engine`` supports all read-only features of *xarray* like:\n\n* merge the content of several GRIB files into a single dataset using ``xarray.open_mfdataset``,\n* work with larger-than-memory datasets with `dask <https://dask.org/>`_,\n* allow distributed processing with `dask.distributed <http://distributed.dask.org>`_.\n\n\nRead arbitrary GRIB keys\n------------------------\n\nBy default *cfgrib* reads a limited set of ecCodes recognised *keys* from the GRIB files\nand exposes them as ``Dataset`` or ``DataArray`` attributes with the ``GRIB_`` prefix.\nIt is possible to have *cfgrib* read additional keys to the attributes by adding the\n``read_keys`` dictionary key to the ``backend_kwargs`` with values the list of desired GRIB keys:\n\n.. code-block: python\n\n>>> ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib',\n... backend_kwargs={'read_keys': ['experimentVersionNumber']})\n>>> ds.t.attrs['GRIB_experimentVersionNumber']\n'0001'\n\n\nTranslate to a custom data model\n--------------------------------\n\nContrary to netCDF the GRIB data format is not self-describing and several details of the mapping\nto the *Unidata Common Data Model* are arbitrarily set by the software components decoding the format.\nDetails like names and units of the coordinates are particularly important because\n*xarray* broadcast and selection rules depend on them.\n``cf2cfm`` is a small coordinate translation module distributed with *cfgrib* that make it easy to\ntranslate CF compliant coordinates, like the one provided by *cfgrib*, to a user-defined\ncustom data model with set ``out_name``, ``units`` and ``stored_direction``.\n\nFor example to translate a *cfgrib* styled ``xr.Dataset`` to the classic *ECMWF* coordinate\nnaming conventions you can:\n\n.. code-block: python\n\n>>> import cf2cdm\n>>> ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib')\n>>> cf2cdm.translate_coords(ds, cf2cdm.ECMWF)\n<xarray.Dataset>\nDimensions: (number: 10, time: 4, level: 2, latitude: 61, longitude: 120)\nCoordinates:\n * number (number) int64 0 1 2 3 4 5 6 7 8 9\n * time (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00\n step timedelta64[ns] ...\n * level (level) float64 850.0 500.0\n * latitude (latitude) float64 90.0 87.0 84.0 81.0 ... -84.0 -87.0 -90.0\n * longitude (longitude) float64 0.0 3.0 6.0 9.0 ... 348.0 351.0 354.0 357.0\n valid_time (time) datetime64[ns] ...\nData variables:\n z (number, time, level, latitude, longitude) float32 ...\n t (number, time, level, latitude, longitude) float32 ...\nAttributes:\n GRIB_edition: 1\n GRIB_centre: ecmf\n GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: European Centre for Medium-Range Weather Forecasts\n history: ...\n\nTo translate to the Common Data Model of the Climate Data Store use:\n\n.. code-block: python\n\n>>> import cf2cdm\n>>> cf2cdm.translate_coords(ds, cf2cdm.CDS)\n<xarray.Dataset>\nDimensions: (realization: 10, forecast_reference_time: 4, plev: 2, lat: 61, lon: 120)\nCoordinates:\n * realization (realization) int64 0 1 2 3 4 5 6 7 8 9\n * forecast_reference_time (forecast_reference_time) datetime64[ns] 2017-01...\n leadtime timedelta64[ns] ...\n * plev (plev) float64 8.5e+04 5e+04\n * lat (lat) float64 -90.0 -87.0 -84.0 ... 84.0 87.0 90.0\n * lon (lon) float64 0.0 3.0 6.0 9.0 ... 351.0 354.0 357.0\n time (forecast_reference_time) datetime64[ns] ...\nData variables:\n z (realization, forecast_reference_time, plev, lat, lon) float32 ...\n t (realization, forecast_reference_time, plev, lat, lon) float32 ...\nAttributes:\n GRIB_edition: 1\n GRIB_centre: ecmf\n GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: European Centre for Medium-Range Weather Forecasts\n history: ...\n\n\nFilter heterogeneous GRIB files\n-------------------------------\n\n``xr.open_dataset`` can open a GRIB file only if all the messages\nwith the same ``shortName`` can be represented as a single hypercube.\nFor example, a variable ``t`` cannot have both ``isobaricInhPa`` and ``hybrid`` ``typeOfLevel``'s,\nas this would result in multiple hypercubes for the same variable.\nOpening a non-conformant GRIB file will fail with a ``ValueError: multiple values for unique key...``\nerror message, see `#2 <https://github.com/ecmwf/cfgrib/issues/2>`_.\n\nFurthermore if different variables depend on the same coordinate, for example ``step``,\nthe values of the coordinate must match exactly.\nFor example, if variables ``t`` and ``z`` share the same ``step`` coordinate,\nthey must both have exactly the same set of steps.\nOpening a non-conformant GRIB file will fail with a ``ValueError: key present and new value is different...``\nerror message, see `#13 <https://github.com/ecmwf/cfgrib/issues/13>`_.\n\nIn most cases you can handle complex GRIB files containing heterogeneous messages by passing\nthe ``filter_by_keys`` key in ``backend_kwargs`` to select which GRIB messages belong to a\nwell formed set of hypercubes.\n\nFor example to open\n`US National Weather Service complex GRIB2 files <http://ftpprd.ncep.noaa.gov/data/nccf/com/nam/prod/>`_\nyou can use:\n\n.. code-block: python\n\n>>> xr.open_dataset('nam.t00z.awp21100.tm00.grib2', engine='cfgrib',\n... backend_kwargs={'filter_by_keys': {'typeOfLevel': 'surface'}})\n<xarray.Dataset>\nDimensions: (y: 65, x: 93)\nCoordinates:\n time datetime64[ns] ...\n step timedelta64[ns] ...\n surface float64 ...\n latitude (y, x) float64 ...\n longitude (y, x) float64 ...\n valid_time datetime64[ns] ...\nDimensions without coordinates: y, x\nData variables:\n gust (y, x) float32 ...\n sp (y, x) float32 ...\n orog (y, x) float32 ...\n tp (y, x) float32 ...\n acpcp (y, x) float32 ...\n csnow (y, x) float32 ...\n cicep (y, x) float32 ...\n cfrzr (y, x) float32 ...\n crain (y, x) float32 ...\n cape (y, x) float32 ...\n cin (y, x) float32 ...\n hpbl (y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP...\n history: ...\n>>> xr.open_dataset('nam.t00z.awp21100.tm00.grib2', engine='cfgrib',\n... backend_kwargs={'filter_by_keys': {'typeOfLevel': 'heightAboveGround', 'level': 2}})\n<xarray.Dataset>\nDimensions: (y: 65, x: 93)\nCoordinates:\n time datetime64[ns] ...\n step timedelta64[ns] ...\n heightAboveGround float64 ...\n latitude (y, x) float64 ...\n longitude (y, x) float64 ...\n valid_time datetime64[ns] ...\nDimensions without coordinates: y, x\nData variables:\n t2m (y, x) float32 ...\n r2 (y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP...\n history: ...\n\n\nAutomatic filtering\n-------------------\n\n*cfgrib* also provides a function that automates the selection of appropriate ``filter_by_keys``\nand returns a list of all valid ``xarray.Dataset``'s in the GRIB file.\n\n.. code-block: python\n\n>>> import cfgrib\n>>> cfgrib.open_datasets('nam.t00z.awp21100.tm00.grib2')\n[<xarray.Dataset>\nDimensions: (y: 65, x: 93)\nCoordinates:\n time datetime64[ns] 2018-09-17\n step timedelta64[ns] 00:00:00\n atmosphereSingleLayer float64 0.0\n latitude (y, x) float64 ...\n longitude (y, x) float64 ...\n valid_time datetime64[ns] ...\nDimensions without coordinates: y, x\nData variables:\n pwat (y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP , <xarray.Dataset>\nDimensions: (y: 65, x: 93)\nCoordinates:\n time datetime64[ns] 2018-09-17\n step timedelta64[ns] 00:00:00\n cloudBase float64 0.0\n latitude (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29\n longitude (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6\n valid_time datetime64[ns] 2018-09-17\nDimensions without coordinates: y, x\nData variables:\n pres (y, x) float32 ...\n gh (y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP , <xarray.Dataset>\nDimensions: (y: 65, x: 93)\nCoordinates:\n time datetime64[ns] 2018-09-17\n step timedelta64[ns] 00:00:00\n cloudTop float64 0.0\n latitude (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29\n longitude (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6\n valid_time datetime64[ns] 2018-09-17\nDimensions without coordinates: y, x\nData variables:\n pres (y, x) float32 ...\n t (y, x) float32 ...\n gh (y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP , <xarray.Dataset>\nDimensions: (y: 65, x: 93)\nCoordinates:\n time datetime64[ns] 2018-09-17\n step timedelta64[ns] 00:00:00\n heightAboveGround float64 10.0\n latitude (y, x) float64 ...\n longitude (y, x) float64 ...\n valid_time datetime64[ns] ...\nDimensions without coordinates: y, x\nData variables:\n u10 (y, x) float32 ...\n v10 (y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP , <xarray.Dataset>\nDimensions: (y: 65, x: 93)\nCoordinates:\n time datetime64[ns] 2018-09-17\n step timedelta64[ns] 00:00:00\n heightAboveGround float64 2.0\n latitude (y, x) float64 12.19 12.39 12.58 ... 57.68 57.49 57.29\n longitude (y, x) float64 226.5 227.2 227.9 ... 308.5 309.6 310.6\n valid_time datetime64[ns] 2018-09-17\nDimensions without coordinates: y, x\nData variables:\n t2m (y, x) float32 ...\n r2 (y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP , <xarray.Dataset>\nDimensions: (heightAboveGroundLayer: 2, y: 65, x: 93)\nCoordinates:\n time datetime64[ns] 2018-09-17\n step timedelta64[ns] 00:00:00\n * heightAboveGroundLayer (heightAboveGroundLayer) float64 1e+03 3e+03\n latitude (y, x) float64 ...\n longitude (y, x) float64 ...\n valid_time datetime64[ns] ...\nDimensions without coordinates: y, x\nData variables:\n hlcy (heightAboveGroundLayer, y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP , <xarray.Dataset>\nDimensions: (isobaricInhPa: 19, y: 65, x: 93)\nCoordinates:\n time datetime64[ns] 2018-09-17\n step timedelta64[ns] 00:00:00\n * isobaricInhPa (isobaricInhPa) float64 1e+03 950.0 900.0 ... 150.0 100.0\n latitude (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29\n longitude (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6\n valid_time datetime64[ns] 2018-09-17\nDimensions without coordinates: y, x\nData variables:\n t (isobaricInhPa, y, x) float32 ...\n u (isobaricInhPa, y, x) float32 ...\n v (isobaricInhPa, y, x) float32 ...\n w (isobaricInhPa, y, x) float32 ...\n gh (isobaricInhPa, y, x) float32 ...\n r (isobaricInhPa, y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP , <xarray.Dataset>\nDimensions: (isobaricInhPa: 5, y: 65, x: 93)\nCoordinates:\n time datetime64[ns] 2018-09-17\n step timedelta64[ns] 00:00:00\n * isobaricInhPa (isobaricInhPa) float64 1e+03 850.0 700.0 500.0 250.0\n latitude (y, x) float64 ...\n longitude (y, x) float64 ...\n valid_time datetime64[ns] ...\nDimensions without coordinates: y, x\nData variables:\n absv (isobaricInhPa, y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP , <xarray.Dataset>\nDimensions: (y: 65, x: 93)\nCoordinates:\n time datetime64[ns] 2018-09-17\n step timedelta64[ns] 00:00:00\n isothermZero float64 0.0\n latitude (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29\n longitude (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6\n valid_time datetime64[ns] 2018-09-17\nDimensions without coordinates: y, x\nData variables:\n gh (y, x) float32 ...\n r (y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP , <xarray.Dataset>\nDimensions: (y: 65, x: 93)\nCoordinates:\n time datetime64[ns] 2018-09-17\n step timedelta64[ns] 00:00:00\n maxWind float64 0.0\n latitude (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29\n longitude (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6\n valid_time datetime64[ns] 2018-09-17\nDimensions without coordinates: y, x\nData variables:\n pres (y, x) float32 ...\n u (y, x) float32 ...\n v (y, x) float32 ...\n gh (y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP , <xarray.Dataset>\nDimensions: (y: 65, x: 93)\nCoordinates:\n time datetime64[ns] 2018-09-17\n step timedelta64[ns] 00:00:00\n meanSea float64 0.0\n latitude (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29\n longitude (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6\n valid_time datetime64[ns] 2018-09-17\nDimensions without coordinates: y, x\nData variables:\n prmsl (y, x) float32 ...\n mslet (y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP , <xarray.Dataset>\nDimensions: (pressureFromGroundLayer: 2, y: 65, x: 93)\nCoordinates:\n time datetime64[ns] 2018-09-17\n step timedelta64[ns] 00:00:00\n * pressureFromGroundLayer (pressureFromGroundLayer) float64 9e+03 1.8e+04\n latitude (y, x) float64 12.19 12.39 12.58 ... 57.49 57.29\n longitude (y, x) float64 226.5 227.2 227.9 ... 309.6 310.6\n valid_time datetime64[ns] 2018-09-17\nDimensions without coordinates: y, x\nData variables:\n cape (pressureFromGroundLayer, y, x) float32 ...\n cin (pressureFromGroundLayer, y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP , <xarray.Dataset>\nDimensions: (pressureFromGroundLayer: 5, y: 65, x: 93)\nCoordinates:\n time datetime64[ns] 2018-09-17\n step timedelta64[ns] 00:00:00\n * pressureFromGroundLayer (pressureFromGroundLayer) float64 3e+03 ... 1.5e+04\n latitude (y, x) float64 12.19 12.39 12.58 ... 57.49 57.29\n longitude (y, x) float64 226.5 227.2 227.9 ... 309.6 310.6\n valid_time datetime64[ns] 2018-09-17\nDimensions without coordinates: y, x\nData variables:\n t (pressureFromGroundLayer, y, x) float32 ...\n u (pressureFromGroundLayer, y, x) float32 ...\n v (pressureFromGroundLayer, y, x) float32 ...\n r (pressureFromGroundLayer, y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP , <xarray.Dataset>\nDimensions: (y: 65, x: 93)\nCoordinates:\n time datetime64[ns] 2018-09-17\n step timedelta64[ns] 00:00:00\n pressureFromGroundLayer float64 3e+03\n latitude (y, x) float64 ...\n longitude (y, x) float64 ...\n valid_time datetime64[ns] ...\nDimensions without coordinates: y, x\nData variables:\n pli (y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP , <xarray.Dataset>\nDimensions: (y: 65, x: 93)\nCoordinates:\n time datetime64[ns] 2018-09-17\n step timedelta64[ns] 00:00:00\n pressureFromGroundLayer float64 1.8e+04\n latitude (y, x) float64 ...\n longitude (y, x) float64 ...\n valid_time datetime64[ns] ...\nDimensions without coordinates: y, x\nData variables:\n 4lftx (y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP , <xarray.Dataset>\nDimensions: (y: 65, x: 93)\nCoordinates:\n time datetime64[ns] 2018-09-17\n step timedelta64[ns] 00:00:00\n surface float64 0.0\n latitude (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29\n longitude (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6\n valid_time datetime64[ns] 2018-09-17\nDimensions without coordinates: y, x\nData variables:\n cape (y, x) float32 ...\n sp (y, x) float32 ...\n acpcp (y, x) float32 ...\n cin (y, x) float32 ...\n orog (y, x) float32 ...\n tp (y, x) float32 ...\n crain (y, x) float32 ...\n cfrzr (y, x) float32 ...\n cicep (y, x) float32 ...\n csnow (y, x) float32 ...\n gust (y, x) float32 ...\n hpbl (y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP , <xarray.Dataset>\nDimensions: (y: 65, x: 93)\nCoordinates:\n time datetime64[ns] 2018-09-17\n step timedelta64[ns] 00:00:00\n tropopause float64 0.0\n latitude (y, x) float64 12.19 12.39 12.58 12.77 ... 57.68 57.49 57.29\n longitude (y, x) float64 226.5 227.2 227.9 228.7 ... 308.5 309.6 310.6\n valid_time datetime64[ns] 2018-09-17\nDimensions without coordinates: y, x\nData variables:\n t (y, x) float32 ...\n u (y, x) float32 ...\n v (y, x) float32 ...\n trpp (y, x) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: kwbc\n GRIB_centreDescription: US National Weather Service - NCEP...\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: US National Weather Service - NCEP ]\n\n\nAdvanced usage\n==============\n\nWrite support\n=============\n\n**Please note that write support is Alpha.**\nOnly ``xarray.Dataset``'s in *canonical* form,\nthat is, with the coordinates names matching exactly the *cfgrib* coordinates,\ncan be saved at the moment:\n\n.. code-block: python\n\n>>> from cfgrib.xarray_to_grib import to_grib\n>>> ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib').sel(number=0)\n>>> ds\n<xarray.Dataset>\nDimensions: (time: 4, isobaricInhPa: 2, latitude: 61, longitude: 120)\nCoordinates:\n number int64 0\n * time (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00\n step timedelta64[ns] ...\n * isobaricInhPa (isobaricInhPa) float64 850.0 500.0\n * latitude (latitude) float64 90.0 87.0 84.0 81.0 ... -84.0 -87.0 -90.0\n * longitude (longitude) float64 0.0 3.0 6.0 9.0 ... 351.0 354.0 357.0\n valid_time (time) datetime64[ns] ...\nData variables:\n z (time, isobaricInhPa, latitude, longitude) float32 ...\n t (time, isobaricInhPa, latitude, longitude) float32 ...\nAttributes:\n GRIB_edition: 1\n GRIB_centre: ecmf\n GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: European Centre for Medium-Range Weather Forecasts\n history: ...\n>>> to_grib(ds, 'out1.grib', grib_keys={'edition': 2})\n>>> xr.open_dataset('out1.grib', engine='cfgrib')\n<xarray.Dataset>\nDimensions: (time: 4, isobaricInhPa: 2, latitude: 61, longitude: 120)\nCoordinates:\n number ...\n * time (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00\n step timedelta64[ns] ...\n * isobaricInhPa (isobaricInhPa) float64 850.0 500.0\n * latitude (latitude) float64 90.0 87.0 84.0 81.0 ... -84.0 -87.0 -90.0\n * longitude (longitude) float64 0.0 3.0 6.0 9.0 ... 351.0 354.0 357.0\n valid_time (time) datetime64[ns] ...\nData variables:\n z (time, isobaricInhPa, latitude, longitude) float32 ...\n t (time, isobaricInhPa, latitude, longitude) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: ecmf\n GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: European Centre for Medium-Range Weather Forecasts\n history: ...\n\nPer-variable GRIB keys can be set by setting the ``attrs`` variable with key prefixed by ``GRIB_``,\nfor example:\n\n.. code-block: python\n\n>>> import numpy as np\n>>> import xarray as xr\n>>> ds2 = xr.DataArray(\n... np.zeros((5, 6)) + 300.,\n... coords=[\n... np.linspace(90., -90., 5),\n... np.linspace(0., 360., 6, endpoint=False),\n... ],\n... dims=['latitude', 'longitude'],\n... ).to_dataset(name='skin_temperature')\n>>> ds2.skin_temperature.attrs['GRIB_shortName'] = 'skt'\n>>> to_grib(ds2, 'out2.grib')\n>>> xr.open_dataset('out2.grib', engine='cfgrib')\n<xarray.Dataset>\nDimensions: (latitude: 5, longitude: 6)\nCoordinates:\n time datetime64[ns] ...\n step timedelta64[ns] ...\n surface float64 ...\n * latitude (latitude) float64 90.0 45.0 0.0 -45.0 -90.0\n * longitude (longitude) float64 0.0 60.0 120.0 180.0 240.0 300.0\n valid_time datetime64[ns] ...\nData variables:\n skt (latitude, longitude) float32 ...\nAttributes:\n GRIB_edition: 2\n GRIB_centre: consensus\n GRIB_centreDescription: Consensus\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: Consensus\n history: ...\n\nDataset / Variable API\n----------------------\n\nThe use of *xarray* is not mandatory and you can access the content of a GRIB file as\nan hypercube with the high level API in a Python interpreter:\n\n.. code-block: python\n\n>>> ds = cfgrib.open_file('era5-levels-members.grib')\n>>> ds.attributes['GRIB_edition']\n1\n>>> sorted(ds.dimensions.items())\n[('isobaricInhPa', 2), ('latitude', 61), ('longitude', 120), ('number', 10), ('time', 4)]\n>>> sorted(ds.variables)\n['isobaricInhPa', 'latitude', 'longitude', 'number', 'step', 't', 'time', 'valid_time', 'z']\n>>> var = ds.variables['t']\n>>> var.dimensions\n('number', 'time', 'isobaricInhPa', 'latitude', 'longitude')\n>>> var.data[:, :, :, :, :].mean()\n262.92133\n>>> ds = cfgrib.open_file('era5-levels-members.grib')\n>>> ds.attributes['GRIB_edition']\n1\n>>> sorted(ds.dimensions.items())\n[('isobaricInhPa', 2), ('latitude', 61), ('longitude', 120), ('number', 10), ('time', 4)]\n>>> sorted(ds.variables)\n['isobaricInhPa', 'latitude', 'longitude', 'number', 'step', 't', 'time', 'valid_time', 'z']\n>>> var = ds.variables['t']\n>>> var.dimensions\n('number', 'time', 'isobaricInhPa', 'latitude', 'longitude')\n>>> var.data[:, :, :, :, :].mean()\n262.92133\n\n\nGRIB index file\n---------------\n\nBy default *cfgrib* saves the index of the GRIB file to disk appending ``.idx``\nto the GRIB file name.\nIndex files are an **experimental** and completely optional feature, feel free to\nremove them and try again in case of problems. Index files saving can be disable passing\nadding ``indexpath=''`` to the ``backend_kwargs`` keyword argument.\n\n\nProject resources\n=================\n\n============= =========================================================\nDevelopment https://github.com/ecmwf/cfgrib\nDownload https://pypi.org/project/cfgrib\nUser support https://stackoverflow.com/search?q=cfgrib\nCode quality .. image:: https://codecov.io/gh/ecmwf/cfgrib/branch/master/graph/badge.svg\n :target: https://codecov.io/gh/ecmwf/cfgrib\n :alt: Coverage status on Codecov\n============= =========================================================\n\n\nContributing\n============\n\nThe main repository is hosted on GitHub,\ntesting, bug reports and contributions are highly welcomed and appreciated:\n\nhttps://github.com/ecmwf/cfgrib\n\nPlease see the CONTRIBUTING.rst document for the best way to help.\n\nLead developers:\n\n- `Iain Russell <https://github.com/iainrussell>`_ - `ECMWF <https://ecmwf.int>`_\n- `Baudouin Raoult <https://github.com/b8raoult>`_ - ECMWF\n\nMain contributors:\n\n- `Alessandro Amici <https://github.com/alexamici>`_ - `B-Open <https://bopen.eu>`_\n- `Aureliana Barghini <https://github.com/aurghs>`_ - B-Open\n- `Leonardo Barcaroli <https://github.com/leophys>`_ - B-Open\n\nSee also the list of `contributors <https://github.com/ecmwf/cfgrib/contributors>`_ who participated in this project.\n\n\nLicense\n=======\n\nCopyright 2017-2021 European Centre for Medium-Range Weather Forecasts (ECMWF).\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0.\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\nChangelog for cfgrib\n====================\n\n0.9.10.4 (2023-05-19)\n---------------------\n\n- added --var-encoding-json (or -v) option to the to_netcdf tool, e.g.\n ``cfgrib to_netcdf -v '{\"dtype\": \"float\", \"scale_factor\": 0.1}' -o $OUTFILE $INFILE``\n See `#334 <https://github.com/ecmwf/cfgrib/pull/334>`_.\n- fix issue where xarrays derived from Gaussian grids did not have the correct\n geometry when written back out as GRIB\n See `#330 <https://github.com/ecmwf/cfgrib/issues/330>`_.\n- fix issue where open_datasets() could merge different GRIB fields\n that have the same data values\n See `#336 <https://github.com/ecmwf/cfgrib/issues/336>`_.\n\n0.9.10.3 (2022-11-24)\n---------------------\n\n- large reduction in memory leak\n See `#320 <https://github.com/ecmwf/cfgrib/pull/320/>`_.\n\n- Replaced ``distutils.version`` by ``packaging.version`` and\n added description and url to the xarray plugin.\n See `#318 <https://github.com/ecmwf/cfgrib/pull/318/>`_.\n\n\n0.9.10.2 (2022-10-04)\n---------------------\n\n- added --netcdf_kwargs_json option to 'cfgrib to_netcdf'\n See `#294 <https://github.com/ecmwf/cfgrib/pull/294/>`_.\n- fixed support for GRIB files with alternativeRowScanning=1\n See `#296 <https://github.com/ecmwf/cfgrib/pull/296/>`_.\n- fixed support for missing values\n See `#313 <https://github.com/ecmwf/cfgrib/issues/313>`_.\n\n\n0.9.10.1 (2022-03-16)\n---------------------\n\n- Fix failure to read index files.\n See `#292 <https://github.com/ecmwf/cfgrib/issues/292>`_.\n- Allow backend kwargs to be provided in the to_netcdf executable,\n either via a json format string, or a path to a json file via -b.\n See `#288 <https://github.com/ecmwf/cfgrib/pull/288/>`_.\n- Fixed issue where the use of relpath() could cause a problem on Windows.\n See `#284 <https://github.com/ecmwf/cfgrib/issues/284>`_.\n- Fix passing of pathlib.Path.\n See `#282 <https://github.com/ecmwf/cfgrib/issues/282>`_.\n- Fixed issue where writing an ensemble number into a GRIB file caused an error.\n See `#278 <https://github.com/ecmwf/cfgrib/issues/278>`_.\n\n\n0.9.10.0 (2022-01-31)\n---------------------\n\n- Big internal refactor to add support for a generic ``Fieldset`` similar to Metview.\n See `#243 <https://github.com/ecmwf/cfgrib/issues/243>`_.\n\n\n0.9.9.1 (2021-09-29)\n--------------------\n\n- Fix the plugin interface that was missing ``extra_coords``.\n See `#231 <https://github.com/ecmwf/cfgrib/issues/231>`_.\n- Fix the crash when ``extra_coords`` return a scalar.\n See `#238 <https://github.com/ecmwf/cfgrib/issues/238>`_.\n- Improve type-hints.\n Needed by `#243 <https://github.com/ecmwf/cfgrib/issues/243>`_.\n\n\n0.9.9.0 (2021-04-09)\n--------------------\n\n- Depend on the ECMWF `eccodes python package <https://pypi.org/project/eccodes>`_ to access\n the low level ecCodes C-library, dropping all other GRIB decoding options.\n See: `#95 <https://github.com/ecmwf/cfgrib/issues/95>`_,\n `#14 <https://github.com/ecmwf/cfgrib/issues/14>`_.\n `#204 <https://github.com/ecmwf/cfgrib/issues/204>`_,\n `#147 <https://github.com/ecmwf/cfgrib/issues/147>`_ and\n `#141 <https://github.com/ecmwf/cfgrib/issues/141>`_.\n- Many performance improvements during the generation of the index and during data access.\n See: `#142 <https://github.com/ecmwf/cfgrib/issues/142>`_ and\n `#197 <https://github.com/ecmwf/cfgrib/issues/197>`_.\n- ``filter_by_keys`` now can select on all keys known to *ecCodes* without the need to\n add non default ones to ``read_keys`` explicitly.\n See: `#187 <https://github.com/ecmwf/cfgrib/issues/187>`_.\n- Include support for `engine=\"cfgrib\"` using *xarray* 0.18+ new backend API.\n See: `#216 <https://github.com/ecmwf/cfgrib/pull/216>`_.\n- Fixed issue where could not load a GRIB message that has only one grid point.\n See: `#199 <https://github.com/ecmwf/cfgrib/issues/199>`_.\n- Decode ``level`` coordinates as float in all cases, fixed issue with non-int levels.\n See: `#195 <https://github.com/ecmwf/cfgrib/issues/195>`_.\n\n\n0.9.8.5 (2020-11-11)\n--------------------\n\n- Simpler and clearer messages in the event of errors.\n- Use `ECCODES_DIR` environment variable if present. Ported from *eccodes-python*\n by xavierabellan. See: `#162 <https://github.com/ecmwf/cfgrib/issues/162>`_.\n- Fix using current ecCodes bindings when setting `CFGRIB_USE_EXTERNAL_ECCODES_BINDINGS=1`.\n\n\n0.9.8.4 (2020-08-03)\n--------------------\n\n- Use `ecmwflibs` if present to find the *ecCodes* installation.\n\n\n0.9.8.3 (2020-06-25)\n--------------------\n\n- Added support for ``indexingDate``, ``indexingTime`` time coordinates.\n- ``lambert_azimuthal_equal_area`` grids are now returned as 2D arrays.\n See: `#119 <https://github.com/ecmwf/cfgrib/issues/119>`_.\n\n\n0.9.8.2 (2020-05-22)\n--------------------\n\n- Add support for MULTI-FIELD messages used in some GRIB products to store\n ``u`` and ``v`` components of wind (e.g. GFS, NAM, etc). This has been the single\n most reported bug in *cfgrib* with two failed attempts at fixing it already.\n Let's see if the third time's a charm. Please test!\n See: `#45 <https://github.com/ecmwf/cfgrib/issues/45>`_,\n `#76 <https://github.com/ecmwf/cfgrib/issues/76>`_ and\n `#111 <https://github.com/ecmwf/cfgrib/issues/111>`_.\n\n\n0.9.8.1 (2020-03-13)\n--------------------\n\n- Always open GRIB files in binary mode, by @b8raoult\n\n\n0.9.8.0 (2020-03-12)\n--------------------\n\n- Add support of experimental pyeccodes low-level driver by @b8raoult\n\n\n0.9.7.7 (2020-01-24)\n--------------------\n\n- Add support for `forecastMonth` in `cf2cdm.translate_coords`.\n\n\n0.9.7.6 (2019-12-05)\n--------------------\n\n- Fix the README.\n\n\n0.9.7.5 (2019-12-05)\n--------------------\n\n- Deprecate ``ensure_valid_time`` and the config option ``preferred_time_dimension`` that\n are now better handled via ``time_dims``.\n\n\n0.9.7.4 (2019-11-22)\n--------------------\n\n- Add more options to ``time_dims`` forecasts products may be represented as\n ``('time', 'verifying_time')`` or ``('time', 'forecastMonth')``.\n See: `#97 <https://github.com/ecmwf/cfgrib/issues/97>`_.\n\n\n0.9.7.3 (2019-11-04)\n--------------------\n\n- Add support for selecting the time coordinates to use as dimensions via ``time_dims``.\n Forecasts products may be represented as ``('time', 'step')`` (the default),\n ``('time', 'valid_time')`` or ``('valid_time', 'step')``.\n See: `#97 <https://github.com/ecmwf/cfgrib/issues/97>`_.\n- Reduce the in-memory footprint of the ``FieldIndex`` and the size of ``.idx`` files.\n\n\n0.9.7.2 (2019-09-24)\n--------------------\n\n- Add support to read additional keys from the GRIB files via ``read_keys``, they\n appear in the variable ``attrs`` and you can ``filter_by_keys`` on them.\n This is a general solution for all issues where users know the name of the additional keys\n they are interested in.\n See: `#89 <https://github.com/ecmwf/cfgrib/issues/89>`_ and\n `#101 <https://github.com/ecmwf/cfgrib/issues/101>`_.\n\n\n0.9.7.1 (2019-07-08)\n--------------------\n\n- Fix a bytes-in-the-place-of-str bug when attempting to write a GRIB on Windows.\n See: `#91 <https://github.com/ecmwf/cfgrib/issues/91>`_.\n- Honor setting ``indexpath`` in ``open_datasets``,\n See: `#93 <https://github.com/ecmwf/cfgrib/issues/93>`_.\n\n\n0.9.7 (2019-05-27)\n------------------\n\n- Much improved ``cfgrib.open_datasets`` heuristics now reads many more\n heterogeneous GRIB files. The function is now a supported API.\n See: `#63 <https://github.com/ecmwf/cfgrib/issues/63>`_,\n `#66 <https://github.com/ecmwf/cfgrib/issues/66>`_,\n `#73 <https://github.com/ecmwf/cfgrib/issues/73>`_ and\n `#75 <https://github.com/ecmwf/cfgrib/issues/75>`_.\n- Fix conda dependencies on Python 2 only package,\n See: `#78 <https://github.com/ecmwf/cfgrib/issues/78>`_.\n\n\n0.9.7rc1 (2019-05-14)\n---------------------\n\n- Drop support for Python 2, in line with *xarray* 0.12.0.\n The 0.9.6.x series will be supported long term for Python 2 users.\n See: `#69 <https://github.com/ecmwf/cfgrib/issues/69>`_.\n- Sync internal ecCodes bindings API to the one in eccodes-python.\n See: `#81 <https://github.com/ecmwf/cfgrib/issues/81>`_.\n- Source code has been formatted with ``black -S -l 99``.\n- Added initial support for spectral coordinates.\n\n\n0.9.6.2 (2019-04-15)\n--------------------\n\n- Improve merging of variables into a dataset.\n See: `#63 <https://github.com/ecmwf/cfgrib/issues/63>`_.\n\n\n0.9.6.1.post1 (2019-03-17)\n--------------------------\n\n- Fix an issue in the README format.\n\n\n0.9.6.1 (2019-03-17)\n--------------------\n\n- Fixed (for real) MULTI-FIELD messages,\n See: `#45 <https://github.com/ecmwf/cfgrib/issues/45>`_.\n- Added a protocol version to the index file. Old ``*.idx`` files must be removed.\n\n\n0.9.6.post1 (2019-03-07)\n------------------------\n\n- Fix an important typo in the README. See: `#64 <https://github.com/ecmwf/cfgrib/issues/64>`_.\n\n\n0.9.6 (2019-02-26)\n------------------\n\n- Add support for *Windows* by installing *ecCodes* via *conda*.\n See: `#7 <https://github.com/ecmwf/cfgrib/issues/7>`_.\n- Added *conda-forge* package.\n See: `#5 <https://github.com/ecmwf/cfgrib/issues/5>`_.\n\n\n0.9.5.7 (2019-02-24)\n--------------------\n\n- Fixed a serious bug in the computation of the suggested ``filter_by_keys`` for non-cubic\n GRIB files. As a result ``cfgrib.xarray_store.open_datasets`` was not finding all the\n variables in the files.\n See: `#54 <https://github.com/ecmwf/cfgrib/issues/54>`_.\n- Fixed a serious bug in variable naming that could drop or at worse mix the values of variables.\n Again see: `#54 <https://github.com/ecmwf/cfgrib/issues/54>`_.\n- Re-opened `#45 <https://github.com/ecmwf/cfgrib/issues/45>`_ as the fix was returning wrong data.\n Now we are back to dropping all variable in a MULTI-FIELD except the first.\n\n\n0.9.5.6 (2019-02-04)\n--------------------\n\n- Do not set explicit timezone in ``units`` to avoid crashing some versions of *xarray*.\n See: `#44 <https://github.com/ecmwf/cfgrib/issues/44>`_.\n\n\n0.9.5.5 (2019-02-02)\n--------------------\n\n- Enable ecCodes implicit MULTI-FIELD support by default, needed for NAM Products by NCEP.\n See: `#45 <https://github.com/ecmwf/cfgrib/issues/45>`_.\n- Added support for ``depthBelowLand`` coordinate.\n\n\n0.9.5.4 (2019-01-25)\n--------------------\n\n- Add support for building ``valid_time`` from a bad ``time-step`` hypercube.\n\n\n0.9.5.3 (2019-01-25)\n--------------------\n\n- Also convert is ``valid_time`` can index all times and steps in ``translate_coords``.\n\n\n0.9.5.2 (2019-01-24)\n--------------------\n\n- Set ``valid_time`` as preferred time dimension for the CDS data model.\n- Fall back to using the generic ``GRIB2`` *ecCodes* template when no better option is found.\n See: `#39 <https://github.com/ecmwf/cfgrib/issues/39>`_.\n\n\n0.9.5.1 (2018-12-27)\n--------------------\n\n- Fix the crash when using ``cf2cdm.translate_coords`` on datasets with non-dimension coordinates.\n See: `#41 <https://github.com/ecmwf/cfgrib/issues/41>`_.\n- Added a ``cfgrib`` script that can translate GRIB to netCDF.\n See: `#40 <https://github.com/ecmwf/cfgrib/issues/40>`_.\n\n\n0.9.5 (2018-12-20)\n------------------\n\n- Drop support for *xarray* versions prior to *v0.11* to reduce complexity.\n (This is really only v0.10.9).\n See: `#32 <https://github.com/ecmwf/cfgrib/issues/32>`_.\n- Declare the data as ``CF-1.7`` compliant via the ``Conventions`` global attribute.\n See: `#36 <https://github.com/ecmwf/cfgrib/issues/36>`_.\n- Tested larger-than-memory and distributed processing via *dask* and *dask.distributed*.\n See: `#33 <https://github.com/ecmwf/cfgrib/issues/33>`_.\n- Promote write support via ``cfgrib.to_grib`` to **Alpha**.\n See: `#18 <https://github.com/ecmwf/cfgrib/issues/18>`_.\n- Provide the ``cf2cdm.translate_coords`` utility function to translate the coordinates\n between CF-compliant data models, defined by ``out_name``, ``units`` and ``store_direction``.\n See: `#24 <https://github.com/ecmwf/cfgrib/issues/24>`_.\n- Provide ``cfgrib.__version__``.\n See: `#31 <https://github.com/ecmwf/cfgrib/issues/31>`_.\n- Raise with a better error message when users attempt to open a file that is not a GRIB.\n See: `#34 <https://github.com/ecmwf/cfgrib/issues/34>`_.\n- Make 2D grids for ``rotated_ll`` and ``rotated_gg`` ``gridType``'s.\n See: `#35 <https://github.com/ecmwf/cfgrib/issues/35>`_.\n\n\n0.9.4.1 (2018-11-08)\n--------------------\n\n- Fix formatting for PyPI page.\n\n\n0.9.4 (2018-11-08)\n------------------\n\n- Saves one index file per set of ``index_keys`` in a much more robust way.\n- Refactor CF-encoding and add the new ``encode_cf`` option to ``backend_kwargs``.\n See: `#23 <https://github.com/ecmwf/cfgrib/issues/23>`_.\n- Refactor error handling and the option to ignore errors (not well documented yet).\n See: `#13 <https://github.com/ecmwf/cfgrib/issues/13>`_.\n- Do not crash on ``gridType`` not fully supported by the installed *ecCodes*\n See: `#27 <https://github.com/ecmwf/cfgrib/issues/27>`_.\n- Several smaller bug fixes and performance improvements.\n\n\n0.9.3.1 (2018-10-28)\n--------------------\n\n- Assorted README fixes, in particular advertise index file support as alpha.\n\n\n0.9.3 (2018-10-28)\n------------------\n\n- Big performance improvement: add alpha support to save to and read from disk\n the GRIB index produced by the full-file scan at the first open.\n See: `#20 <https://github.com/ecmwf/cfgrib/issues/20>`_.\n\n\n0.9.2 (2018-10-22)\n------------------\n\n- Rename coordinate ``air_pressure`` to ``isobaricInhPa`` for consistency\n with all other vertical ``level`` coordinates.\n See: `#25 <https://github.com/ecmwf/cfgrib/issues/25>`_.\n\n\n0.9.1.post1 (2018-10-19)\n------------------------\n\n- Fix PyPI description.\n\n\n0.9.1 (2018-10-19)\n------------------\n\n- Change the usage of ``cfgrib.open_dataset`` to allign it with ``xarray.open_dataset``,\n in particular ``filter_by_key`` must be added into the ``backend_kwargs`` dictionary.\n See: `#21 <https://github.com/ecmwf/cfgrib/issues/21>`_.\n\n0.9.0 (2018-10-14)\n------------------\n\n- Beta release with read support.\n\n\n",
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