cfgrib 0.9.6.1

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

Python interface to map GRIB files to the
`Unidata's Common Data Model v4 <https://www.unidata.ucar.edu/software/thredds/current/netcdf-java/CDM/>`_
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://software.ecmwf.int/wiki/display/ECC/>`_.

Features with development status **Beta**:

- enables the ``engine='cfgrib'`` option to read GRIB files with *xarray*,
- reads most GRIB 1 and 2 files, for limitations see the *Advanced usage* section below and
`#13 <https://github.com/ecmwf/cfgrib/issues/13>`_,
- supports all modern versions of Python 3.7, 3.6, 3.5 and 2.7, plus PyPy and PyPy3,
- works on *Linux*, *MacOS* and *Windows*, the *ecCodes* C-library is the only binary dependency,
- conda-forge package on all supported platforms,
- PyPI package with no install time build (binds via *CFFI* ABI mode),
- reads the data lazily and efficiently in terms of both memory usage and disk access,
- allows larger-than-memory and distributed processing via *dask*,
- supports translating coordinates to different data models and naming conventions.

Work in progress:

- **Alpha** install a ``cfgrib`` utility that can convert a GRIB file ``to_netcdf``
with a optional conversion to a specific coordinates data model,
see `#40 <https://github.com/ecmwf/cfgrib/issues/40>`_.
- **Alpha** supports writing the index of a GRIB file to disk, to save a full-file scan on open,
see `#33 <https://github.com/ecmwf/cfgrib/issues/33>`_.
- **Alpha** support writing carefully-crafted ``xarray.Dataset``'s to a GRIB1 or GRIB2 file,
see the *Advanced write usage* section below and
`#18 <https://github.com/ecmwf/cfgrib/issues/18>`_.

Limitations:

- incomplete documentation, for now,
- 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
-------------------

The Python module depends on the ECMWF *ecCodes* binary library
that must be installed on the system and accessible as a shared library.
Some Linux distributions ship a binary version that may be installed with the standard package manager.
On Ubuntu 18.04 use the command::

$ sudo apt-get install libeccodes0

On a MacOS with HomeBrew use::

$ brew install eccodes

Or if you manage binary packages with *Conda* use::

$ conda install -c conda-forge eccodes

As an alternative you may install the official source distribution
by following the instructions at
https://software.ecmwf.int/wiki/display/ECC/ecCodes+installation

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

$ python -m cfgrib selfcheck
Found: ecCodes v2.12.0.
Your system is ready.


Usage
=====

First, you need a well-formed GRIB file, if you don't have one at hand you can download our
`ERA5 on pressure levels sample <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>=0.11.0* installed::

$ pip install xarray>=0.11.0

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: (isobaricInhPa: 2, latitude: 61, longitude: 120, number: 10, time: 4)
Coordinates:
* number (number) int64 0 1 2 3 4 5 6 7 8 9
* time (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00
step timedelta64[ns] ...
* isobaricInhPa (isobaricInhPa) int64 850 500
* latitude (latitude) float64 90.0 87.0 84.0 81.0 ... -84.0 -87.0 -90.0
* longitude (longitude) float64 0.0 3.0 6.0 9.0 ... 351.0 354.0 357.0
valid_time (time) datetime64[ns] ...
Data variables:
z (number, time, isobaricInhPa, latitude, longitude) float32 ...
t (number, time, isobaricInhPa, latitude, longitude) float32 ...
Attributes:
GRIB_edition: 1
GRIB_centre: ecmf
GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts
GRIB_subCentre: 0
Conventions: CF-1.7
institution: European Centre for Medium-Range Weather Forecasts
history: ...

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

* merge the content of several GRIB files into a single dataset using ``xarray.open_mfdataset``,
* work with larger-than-memory datasets with `dask <https://dask.org/>`_,
* allow distributed processing with `dask.distributed <http://distributed.dask.org>`_.


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

>>> import cfgrib
>>> ds = cfgrib.open_file('era5-levels-members.grib')
>>> ds.attributes['GRIB_edition']
1
>>> sorted(ds.dimensions.items())
[('isobaricInhPa', 2), ('latitude', 61), ('longitude', 120), ('number', 10), ('time', 4)]
>>> sorted(ds.variables)
['isobaricInhPa', 'latitude', 'longitude', 'number', 'step', 't', 'time', 'valid_time', 'z']
>>> var = ds.variables['t']
>>> var.dimensions
('number', 'time', 'isobaricInhPa', 'latitude', 'longitude')
>>> var.data[:, :, :, :, :].mean()
262.92133
>>> ds = cfgrib.open_file('era5-levels-members.grib')
>>> ds.attributes['GRIB_edition']
1
>>> sorted(ds.dimensions.items())
[('isobaricInhPa', 2), ('latitude', 61), ('longitude', 120), ('number', 10), ('time', 4)]
>>> sorted(ds.variables)
['isobaricInhPa', 'latitude', 'longitude', 'number', 'step', 't', 'time', 'valid_time', 'z']
>>> var = ds.variables['t']
>>> var.dimensions
('number', 'time', 'isobaricInhPa', 'latitude', 'longitude')
>>> var.data[:, :, :, :, :].mean()
262.92133


GRIB index file
---------------

By default *cfgrib* saves the index of the GRIB file to disk appending ``.idx``
to the GRIB file name.
Index files are an **experimental** and completely optional feature, feel free to
remove them and try again in case of problems. Index files saving can be disable passing
adding ``indexpath=''`` to the ``backend_kwargs`` keyword argument.


Advanced usage
==============

Translate to a custom data model
--------------------------------

Contrary to netCDF the GRIB data format is not self-describing and several details of the mapping
to the *Unidata Common Data Model* are arbitrarily set by the software components decoding the format.
Details like names and units of the coordinates are particularly important because
*xarray* broadcast and selection rules depend on them.
``cf2cfm`` is a small coordinate translation module distributed with *cfgrib* that make it easy to
translate CF compliant coordinates, like the one provided by *cfgrib*, to a user-defined
custom data model with set ``out_name``, ``units`` and ``stored_direction``.

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

.. code-block: python

>>> import cf2cdm
>>> ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib')
>>> cf2cdm.translate_coords(ds, cf2cdm.ECMWF)
<xarray.Dataset>
Dimensions: (latitude: 61, level: 2, longitude: 120, number: 10, time: 4)
Coordinates:
* number (number) int64 0 1 2 3 4 5 6 7 8 9
* time (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00
step timedelta64[ns] ...
* level (level) int64 850 500
* latitude (latitude) float64 90.0 87.0 84.0 81.0 ... -84.0 -87.0 -90.0
* longitude (longitude) float64 0.0 3.0 6.0 9.0 ... 348.0 351.0 354.0 357.0
valid_time (time) datetime64[ns] ...
Data variables:
z (number, time, level, latitude, longitude) float32 ...
t (number, time, level, latitude, longitude) float32 ...
Attributes:
GRIB_edition: 1
GRIB_centre: ecmf
GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts
GRIB_subCentre: 0
Conventions: CF-1.7
institution: European Centre for Medium-Range Weather Forecasts
history: ...

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

.. code-block: python

>>> import cf2cdm
>>> cf2cdm.translate_coords(ds, cf2cdm.CDS)
<xarray.Dataset>
Dimensions: (lat: 61, lon: 120, plev: 2, realization: 10, time: 4)
Coordinates:
* realization (realization) int64 0 1 2 3 4 5 6 7 8 9
forecast_reference_time (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00
leadtime timedelta64[ns] ...
* plev (plev) float64 8.5e+04 5e+04
* lat (lat) float64 -90.0 -87.0 -84.0 ... 84.0 87.0 90.0
* lon (lon) float64 0.0 3.0 6.0 9.0 ... 351.0 354.0 357.0
* time (time) datetime64[ns] ...
Data variables:
z (realization, time, plev, lat, lon) float32 ...
t (realization, time, plev, lat, lon) float32 ...
Attributes:
GRIB_edition: 1
GRIB_centre: ecmf
GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts
GRIB_subCentre: 0
Conventions: CF-1.7
institution: European Centre for Medium-Range Weather Forecasts
history: ...


Filter heterogeneous GRIB files
-------------------------------

``cfgrib.open_file`` and ``xr.open_dataset`` can open a GRIB file only if all the messages
with the same ``shortName`` can be represented as a single hypercube.
For example, a variable ``t`` cannot have both ``isobaricInhPa`` and ``hybrid`` ``typeOfLevel``'s,
as this would result in multiple hypercubes for the same variable.
Opening a non-conformant GRIB file will fail with a ``ValueError: multiple values for unique key...``
error message, see `#2 <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', 'stepType': 'instant'}})
<xarray.Dataset>
Dimensions: (x: 93, y: 65)
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
surface int64 ...
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
gust (y, x) float32 ...
sp (y, x) float32 ...
orog (y, x) float32 ...
csnow (y, x) float32 ...
Attributes:
GRIB_edition: 2
GRIB_centre: kwbc
GRIB_centreDescription: US National Weather Service - NCEP
GRIB_subCentre: 0
Conventions: CF-1.7
institution: US National Weather Service - NCEP
history: ...
>>> xr.open_dataset('nam.t00z.awp21100.tm00.grib2', engine='cfgrib',
... backend_kwargs={'filter_by_keys': {'typeOfLevel': 'heightAboveGround', 'level': 2}})
<xarray.Dataset>
Dimensions: (x: 93, y: 65)
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
heightAboveGround int64 ...
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
t2m (y, x) float32 ...
r2 (y, x) float32 ...
Attributes:
GRIB_edition: 2
GRIB_centre: kwbc
GRIB_centreDescription: US National Weather Service - NCEP
GRIB_subCentre: 0
Conventions: CF-1.7
institution: US National Weather Service - NCEP
history: ...


Automatic filtering
-------------------

*cfgrib* also provides an **experimental function** that automate the selection of
appropriate ``filter_by_keys`` and returns a list of all valid ``xarray.Dataset``'s
in the GRIB file (add ``backend_kwargs={'errors': 'ignore'}`` for extra robustness).
The ``open_datasets`` is intended for interactive exploration of a file
and it is not part of the stable API. In the future it may change or be removed altogether.

.. code-block: python

>>> from cfgrib import xarray_store
>>> xarray_store.open_datasets('nam.t00z.awp21100.tm00.grib2', backend_kwargs={'errors': 'ignore'})
[<xarray.Dataset>
Dimensions: (x: 93, y: 65)
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
meanSea int64 ...
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
prmsl (y, x) float32 ...
mslet (y, x) float32 ...
Attributes:
GRIB_edition: 2
GRIB_centre: kwbc
GRIB_centreDescription: US National Weather Service - NCEP
GRIB_subCentre: 0
Conventions: CF-1.7
institution: US National Weather Service - NCEP
history: ..., <xarray.Dataset>
Dimensions: (x: 93, y: 65)
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
surface int64 ...
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
gust (y, x) float32 ...
sp (y, x) float32 ...
orog (y, x) float32 ...
tp (y, x) float32 ...
acpcp (y, x) float32 ...
csnow (y, x) float32 ...
cicep (y, x) float32 ...
cfrzr (y, x) float32 ...
crain (y, x) float32 ...
cape (y, x) float32 ...
cin (y, x) float32 ...
hpbl (y, x) float32 ...
Attributes:
GRIB_edition: 2
GRIB_centre: kwbc
GRIB_centreDescription: US National Weather Service - NCEP
GRIB_subCentre: 0
Conventions: CF-1.7
institution: US National Weather Service - NCEP
history: ..., <xarray.Dataset>
Dimensions: (isobaricInhPa: 19, x: 93, y: 65)
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
* isobaricInhPa (isobaricInhPa) int64 1000 950 900 ... 150 100
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
gh (isobaricInhPa, y, x) float32 ...
t (isobaricInhPa, y, x) float32 ...
r (isobaricInhPa, y, x) float32 ...
w (isobaricInhPa, y, x) float32 ...
u (isobaricInhPa, y, x) float32 ...
v (isobaricInhPa, y, x) float32 ...
Attributes:
GRIB_edition: 2
GRIB_centre: kwbc
GRIB_centreDescription: US National Weather Service - NCEP
GRIB_subCentre: 0
Conventions: CF-1.7
institution: US National Weather Service - NCEP
history: ..., <xarray.Dataset>
Dimensions: (x: 93, y: 65)
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
heightAboveGround int64 ...
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
t2m (y, x) float32 ...
r2 (y, x) float32 ...
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
history: ..., <xarray.Dataset>
Dimensions: (x: 93, y: 65)
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
level int64 ...
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
pwat (y, x) float32 ...
Attributes:
GRIB_edition: 2
GRIB_centre: kwbc
GRIB_centreDescription: US National Weather Service - NCEP
GRIB_subCentre: 0
Conventions: CF-1.7
institution: US National Weather Service - NCEP
history: ..., <xarray.Dataset>
Dimensions: (x: 93, y: 65)
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
cloudBase int64 ...
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
pres (y, x) float32 ...
gh (y, x) float32 ...
Attributes:
GRIB_edition: 2
GRIB_centre: kwbc
GRIB_centreDescription: US National Weather Service - NCEP
GRIB_subCentre: 0
Conventions: CF-1.7
institution: US National Weather Service - NCEP
history: ..., <xarray.Dataset>
Dimensions: (x: 93, y: 65)
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
cloudTop int64 ...
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
pres (y, x) float32 ...
gh (y, x) float32 ...
t (y, x) float32 ...
Attributes:
GRIB_edition: 2
GRIB_centre: kwbc
GRIB_centreDescription: US National Weather Service - NCEP
GRIB_subCentre: 0
Conventions: CF-1.7
institution: US National Weather Service - NCEP
history: ..., <xarray.Dataset>
Dimensions: (heightAboveGroundLayer: 2, x: 93, y: 65)
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
* heightAboveGroundLayer (heightAboveGroundLayer) int64 1000 3000
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
hlcy (heightAboveGroundLayer, y, x) float32 ...
Attributes:
GRIB_edition: 2
GRIB_centre: kwbc
GRIB_centreDescription: US National Weather Service - NCEP
GRIB_subCentre: 0
Conventions: CF-1.7
institution: US National Weather Service - NCEP
history: ..., <xarray.Dataset>
Dimensions: (x: 93, y: 65)
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
tropopause int64 ...
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
pres (y, x) float32 ...
t (y, x) float32 ...
u (y, x) float32 ...
v (y, x) float32 ...
Attributes:
GRIB_edition: 2
GRIB_centre: kwbc
GRIB_centreDescription: US National Weather Service - NCEP
GRIB_subCentre: 0
Conventions: CF-1.7
institution: US National Weather Service - NCEP
history: ..., <xarray.Dataset>
Dimensions: (x: 93, y: 65)
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
maxWind int64 ...
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
pres (y, x) float32 ...
gh (y, x) float32 ...
u (y, x) float32 ...
v (y, x) float32 ...
Attributes:
GRIB_edition: 2
GRIB_centre: kwbc
GRIB_centreDescription: US National Weather Service - NCEP
GRIB_subCentre: 0
Conventions: CF-1.7
institution: US National Weather Service - NCEP
history: ..., <xarray.Dataset>
Dimensions: (x: 93, y: 65)
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
isothermZero int64 ...
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
gh (y, x) float32 ...
r (y, x) float32 ...
Attributes:
GRIB_edition: 2
GRIB_centre: kwbc
GRIB_centreDescription: US National Weather Service - NCEP
GRIB_subCentre: 0
Conventions: CF-1.7
institution: US National Weather Service - NCEP
history: ..., <xarray.Dataset>
Dimensions: (pressureFromGroundLayer: 5, x: 93, y: 65)
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
* pressureFromGroundLayer (pressureFromGroundLayer) int64 3000 6000 ... 15000
latitude (y, x) float64 ...
longitude (y, x) float64 ...
valid_time datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
t (pressureFromGroundLayer, y, x) float32 ...
r (pressureFromGroundLayer, y, x) float32 ...
u (pressureFromGroundLayer, y, x) float32 ...
v (pressureFromGroundLayer, y, x) float32 ...
pli (y, x) float32 ...
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
history: ...]


Advanced write usage
====================

**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

>>> ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib')
>>> ds
<xarray.Dataset>
Dimensions: (isobaricInhPa: 2, latitude: 61, longitude: 120, number: 10, time: 4)
Coordinates:
* number (number) int64 0 1 2 3 4 5 6 7 8 9
* time (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00
step timedelta64[ns] ...
* isobaricInhPa (isobaricInhPa) int64 850 500
* latitude (latitude) float64 90.0 87.0 84.0 81.0 ... -84.0 -87.0 -90.0
* longitude (longitude) float64 0.0 3.0 6.0 9.0 ... 351.0 354.0 357.0
valid_time (time) datetime64[ns] ...
Data variables:
z (number, time, isobaricInhPa, latitude, longitude) float32 ...
t (number, time, isobaricInhPa, latitude, longitude) float32 ...
Attributes:
GRIB_edition: 1
GRIB_centre: ecmf
GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts
GRIB_subCentre: 0
Conventions: CF-1.7
institution: European Centre for Medium-Range Weather Forecasts
history: ...
>>> cfgrib.to_grib(ds, 'out1.grib', grib_keys={'edition': 2})
>>> xr.open_dataset('out1.grib', engine='cfgrib')
<xarray.Dataset>
Dimensions: (isobaricInhPa: 2, latitude: 61, longitude: 120, number: 10, time: 4)
Coordinates:
* number (number) int64 0 1 2 3 4 5 6 7 8 9
* time (time) datetime64[ns] 2017-01-01 ... 2017-01-02T12:00:00
step timedelta64[ns] ...
* isobaricInhPa (isobaricInhPa) int64 850 500
* latitude (latitude) float64 90.0 87.0 84.0 81.0 ... -84.0 -87.0 -90.0
* longitude (longitude) float64 0.0 3.0 6.0 9.0 ... 351.0 354.0 357.0
valid_time (time) datetime64[ns] ...
Data variables:
z (number, time, isobaricInhPa, latitude, longitude) float32 ...
t (number, time, isobaricInhPa, latitude, longitude) float32 ...
Attributes:
GRIB_edition: 2
GRIB_centre: ecmf
GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts
GRIB_subCentre: 0
Conventions: CF-1.7
institution: European Centre for Medium-Range Weather Forecasts
history: ...

Per-variable GRIB keys can be set by setting the ``attrs`` variable with key prefixed by ``GRIB_``,
for example:

.. 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'
>>> cfgrib.to_grib(ds2, 'out2.grib')
>>> xr.open_dataset('out2.grib', engine='cfgrib')
<xarray.Dataset>
Dimensions: (latitude: 5, longitude: 6)
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
surface int64 ...
* latitude (latitude) float64 90.0 45.0 0.0 -45.0 -90.0
* longitude (longitude) float64 0.0 60.0 120.0 180.0 240.0 300.0
valid_time datetime64[ns] ...
Data variables:
skt (latitude, longitude) float32 ...
Attributes:
GRIB_edition: 2
GRIB_centre: consensus
GRIB_centreDescription: Consensus
GRIB_subCentre: 0
Conventions: CF-1.7
institution: Consensus
history: ...


Project resources
=================

============= =========================================================
Development https://github.com/ecmwf/cfgrib
Download https://pypi.org/project/cfgrib
User support https://stackoverflow.com/search?q=cfgrib
Code quality .. image:: https://api.travis-ci.org/ecmwf/cfgrib.svg?branch=master
:target: https://travis-ci.org/ecmwf/cfgrib/branches
:alt: Build Status on Travis CI
.. image:: https://coveralls.io/repos/ecmwf/cfgrib/badge.svg?branch=master&service=github
:target: https://coveralls.io/github/ecmwf/cfgrib
:alt: Coverage Status on Coveralls
============= =========================================================


Contributing
============

The main repository is hosted on GitHub,
testing, bug reports and contributions are highly welcomed and appreciated:

https://github.com/ecmwf/cfgrib

Please see the CONTRIBUTING.rst document for the best way to help.

Lead developer:

- `Alessandro Amici <https://github.com/alexamici>`_ - `B-Open <https://bopen.eu>`_

Main contributors:

- Baudouin Raoult - `ECMWF <https://ecmwf.int>`_
- `Aureliana Barghini <https://github.com/aurghs>`_ - B-Open
- `Iain Russell <https://github.com/iainrussell>`_ - ECMWF
- `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 2017-2019 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
====================

0.9.6.1 (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.