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Change","sub_category":"Climate Data Processing and Analysis","monthly_downloads":817166,"total_dependent_repos":224,"total_dependent_packages":60,"readme":"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 \u003chttps://docs.unidata.ucar.edu/netcdf-java/current/userguide/common_data_model_overview.html\u003e`_\nfollowing the `CF Conventions \u003chttp://cfconventions.org/\u003e`_.\nThe high level API is designed to support a GRIB engine for `xarray \u003chttp://xarray.pydata.org/\u003e`_\nand it is inspired by `netCDF4-python \u003chttp://unidata.github.io/netcdf4-python/\u003e`_\nand `h5netcdf \u003chttps://github.com/shoyer/h5netcdf\u003e`_.\nLow level access and decoding is performed via the\n`ECMWF ecCodes library \u003chttps://confluence.ecmwf.int/display/ECC/\u003e`_ and\nthe `eccodes python package \u003chttps://pypi.org/project/eccodes\u003e`_.\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 \u003chttps://github.com/ecmwf/cfgrib/issues/40\u003e`_.\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 \u003chttps://github.com/ecmwf/cfgrib/issues/18\u003e`_\n  and `#156 \u003chttps://github.com/ecmwf/cfgrib/issues/156\u003e`_.\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 \u003chttps://github.com/ecmwf/cfgrib/issues/28\u003e`_.\n\n\nInstallation\n============\n\nThe easiest way to install *cfgrib* and all its binary dependencies is via `Conda \u003chttps://conda.io/\u003e`_::\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 \u003chttps://pypi.org/project/eccodes\u003e`_\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 \u003chttps://sites.ecmwf.int/repository/earthkit-data/test-data/era5-levels-members.grib\u003e`_::\n\n    $ wget https://sites.ecmwf.int/repository/earthkit-data/test-data/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    \u003e\u003e\u003e import xarray as xr\n    \u003e\u003e\u003e ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib')\n    \u003e\u003e\u003e ds\n    \u003cxarray.Dataset\u003e\n    Dimensions:        (number: 10, time: 4, isobaricInhPa: 2, latitude: 61,\n                        longitude: 120)\n    Coordinates:\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] ...\n    Data variables:\n        z              (number, time, isobaricInhPa, latitude, longitude) float32 ...\n        t              (number, time, isobaricInhPa, latitude, longitude) float32 ...\n    Attributes:\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 \u003chttps://dask.org/\u003e`_,\n* allow distributed processing with `dask.distributed \u003chttp://distributed.dask.org\u003e`_.\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    \u003e\u003e\u003e ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib',\n    ...                      backend_kwargs={'read_keys': ['experimentVersionNumber']})\n    \u003e\u003e\u003e 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    \u003e\u003e\u003e import cf2cdm\n    \u003e\u003e\u003e ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib')\n    \u003e\u003e\u003e cf2cdm.translate_coords(ds, cf2cdm.ECMWF)\n    \u003cxarray.Dataset\u003e\n    Dimensions:     (number: 10, time: 4, level: 2, latitude: 61, longitude: 120)\n    Coordinates:\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] ...\n    Data variables:\n        z           (number, time, level, latitude, longitude) float32 ...\n        t           (number, time, level, latitude, longitude) float32 ...\n    Attributes:\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    \u003e\u003e\u003e import cf2cdm\n    \u003e\u003e\u003e cf2cdm.translate_coords(ds, cf2cdm.CDS)\n    \u003cxarray.Dataset\u003e\n    Dimensions:                  (realization: 10, forecast_reference_time: 4,\n                                plev: 2, lat: 61, lon: 120)\n    Coordinates:\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] ...\n    Data variables:\n        z                        (realization, forecast_reference_time, plev, lat, lon) float32 ...\n        t                        (realization, forecast_reference_time, plev, lat, lon) float32 ...\n    Attributes:\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 \u003chttps://github.com/ecmwf/cfgrib/issues/2\u003e`_.\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 \u003chttps://github.com/ecmwf/cfgrib/issues/13\u003e`_.\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 \u003chttp://ftpprd.ncep.noaa.gov/data/nccf/com/nam/prod/\u003e`_\nyou can use:\n\n.. code-block:: python\n\n    \u003e\u003e\u003e xr.open_dataset('nam.t00z.awp21100.tm00.grib2', engine='cfgrib',\n    ...     backend_kwargs={'filter_by_keys': {'typeOfLevel': 'surface'}})\n    \u003cxarray.Dataset\u003e\n    Dimensions:     (y: 65, x: 93)\n    Coordinates:\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] ...\n    Dimensions without coordinates: y, x\n    Data 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        unknown     (y, x) float32 ...\n    Attributes:\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    \u003e\u003e\u003e xr.open_dataset('nam.t00z.awp21100.tm00.grib2', engine='cfgrib',\n    ...     backend_kwargs={'filter_by_keys': {'typeOfLevel': 'heightAboveGround', 'level': 2}})\n    \u003cxarray.Dataset\u003e\n    Dimensions:            (y: 65, x: 93)\n    Coordinates:\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] ...\n    Dimensions without coordinates: y, x\n    Data variables:\n        t2m                (y, x) float32 ...\n        r2                 (y, x) float32 ...\n    Attributes:\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    \u003e\u003e\u003e import cfgrib\n    \u003e\u003e\u003e cfgrib.open_datasets('nam.t00z.awp21100.tm00.grib2')\n    [\u003cxarray.Dataset\u003e\n    Dimensions:                (y: 65, x: 93)\n    Coordinates:\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] ...\n    Dimensions without coordinates: y, x\n    Data variables:\n        pwat                   (y, x) float32 ...\n    Attributes:\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, \u003cxarray.Dataset\u003e\n    Dimensions:     (y: 65, x: 93)\n    Coordinates:\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\n    Dimensions without coordinates: y, x\n    Data variables:\n        pres        (y, x) float32 ...\n        gh          (y, x) float32 ...\n    Attributes:\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, \u003cxarray.Dataset\u003e\n    Dimensions:     (y: 65, x: 93)\n    Coordinates:\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\n    Dimensions without coordinates: y, x\n    Data variables:\n        pres        (y, x) float32 ...\n        t           (y, x) float32 ...\n        gh          (y, x) float32 ...\n    Attributes:\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, \u003cxarray.Dataset\u003e\n    Dimensions:            (y: 65, x: 93)\n    Coordinates:\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] ...\n    Dimensions without coordinates: y, x\n    Data variables:\n        u10                (y, x) float32 ...\n        v10                (y, x) float32 ...\n    Attributes:\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, \u003cxarray.Dataset\u003e\n    Dimensions:            (y: 65, x: 93)\n    Coordinates:\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\n    Dimensions without coordinates: y, x\n    Data variables:\n        t2m                (y, x) float32 ...\n        r2                 (y, x) float32 ...\n    Attributes:\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, \u003cxarray.Dataset\u003e\n    Dimensions:                 (heightAboveGroundLayer: 2, y: 65, x: 93)\n    Coordinates:\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] ...\n    Dimensions without coordinates: y, x\n    Data variables:\n        hlcy                    (heightAboveGroundLayer, y, x) float32 ...\n    Attributes:\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, \u003cxarray.Dataset\u003e\n    Dimensions:        (isobaricInhPa: 19, y: 65, x: 93)\n    Coordinates:\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\n    Dimensions without coordinates: y, x\n    Data 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 ...\n    Attributes:\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, \u003cxarray.Dataset\u003e\n    Dimensions:        (isobaricInhPa: 5, y: 65, x: 93)\n    Coordinates:\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] ...\n    Dimensions without coordinates: y, x\n    Data variables:\n        absv           (isobaricInhPa, y, x) float32 ...\n    Attributes:\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, \u003cxarray.Dataset\u003e\n    Dimensions:       (y: 65, x: 93)\n    Coordinates:\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\n    Dimensions without coordinates: y, x\n    Data variables:\n        gh            (y, x) float32 ...\n        r             (y, x) float32 ...\n    Attributes:\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, \u003cxarray.Dataset\u003e\n    Dimensions:     (y: 65, x: 93)\n    Coordinates:\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\n    Dimensions without coordinates: y, x\n    Data variables:\n        pres        (y, x) float32 ...\n        u           (y, x) float32 ...\n        v           (y, x) float32 ...\n        gh          (y, x) float32 ...\n    Attributes:\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, \u003cxarray.Dataset\u003e\n    Dimensions:     (y: 65, x: 93)\n    Coordinates:\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\n    Dimensions without coordinates: y, x\n    Data variables:\n        prmsl       (y, x) float32 ...\n        mslet       (y, x) float32 ...\n    Attributes:\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, \u003cxarray.Dataset\u003e\n    Dimensions:                  (pressureFromGroundLayer: 2, y: 65, x: 93)\n    Coordinates:\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\n    Dimensions without coordinates: y, x\n    Data variables:\n        cape                     (pressureFromGroundLayer, y, x) float32 ...\n        cin                      (pressureFromGroundLayer, y, x) float32 ...\n    Attributes:\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, \u003cxarray.Dataset\u003e\n    Dimensions:                  (pressureFromGroundLayer: 5, y: 65, x: 93)\n    Coordinates:\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\n    Dimensions without coordinates: y, x\n    Data 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 ...\n    Attributes:\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, \u003cxarray.Dataset\u003e\n    Dimensions:                  (y: 65, x: 93)\n    Coordinates:\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] ...\n    Dimensions without coordinates: y, x\n    Data variables:\n        pli                      (y, x) float32 ...\n    Attributes:\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, \u003cxarray.Dataset\u003e\n    Dimensions:                  (y: 65, x: 93)\n    Coordinates:\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] ...\n    Dimensions without coordinates: y, x\n    Data variables:\n        4lftx                    (y, x) float32 ...\n    Attributes:\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, \u003cxarray.Dataset\u003e\n    Dimensions:     (y: 65, x: 93)\n    Coordinates:\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\n    Dimensions without coordinates: y, x\n    Data variables:\n        unknown     (y, x) float32 ...\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    Attributes:\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, \u003cxarray.Dataset\u003e\n    Dimensions:     (y: 65, x: 93)\n    Coordinates:\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\n    Dimensions without coordinates: y, x\n    Data variables:\n        t           (y, x) float32 ...\n        u           (y, x) float32 ...\n        v           (y, x) float32 ...\n        trpp        (y, x) float32 ...\n    Attributes:\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    \u003e\u003e\u003e from cfgrib.xarray_to_grib import to_grib\n    \u003e\u003e\u003e ds = xr.open_dataset('era5-levels-members.grib', engine='cfgrib').sel(number=0)\n    \u003e\u003e\u003e ds\n    \u003cxarray.Dataset\u003e\n    Dimensions:        (time: 4, isobaricInhPa: 2, latitude: 61, longitude: 120)\n    Coordinates:\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] ...\n    Data variables:\n        z              (time, isobaricInhPa, latitude, longitude) float32 ...\n        t              (time, isobaricInhPa, latitude, longitude) float32 ...\n    Attributes:\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    \u003e\u003e\u003e to_grib(ds, 'out1.grib', grib_keys={'edition': 2})\n    \u003e\u003e\u003e xr.open_dataset('out1.grib', engine='cfgrib')\n    \u003cxarray.Dataset\u003e\n    Dimensions:        (time: 4, isobaricInhPa: 2, latitude: 61, longitude: 120)\n    Coordinates:\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] ...\n    Data variables:\n        z              (time, isobaricInhPa, latitude, longitude) float32 ...\n        t              (time, isobaricInhPa, latitude, longitude) float32 ...\n    Attributes:\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    \u003e\u003e\u003e import numpy as np\n    \u003e\u003e\u003e import xarray as xr\n    \u003e\u003e\u003e 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    \u003e\u003e\u003e ds2.skin_temperature.attrs['GRIB_shortName'] = 'skt'\n    \u003e\u003e\u003e to_grib(ds2, 'out2.grib')\n    \u003e\u003e\u003e xr.open_dataset('out2.grib', engine='cfgrib')\n    \u003cxarray.Dataset\u003e\n    Dimensions:     (latitude: 5, longitude: 6)\n    Coordinates:\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] ...\n    Data variables:\n        skt         (latitude, longitude) float32 ...\n    Attributes:\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    \u003e\u003e\u003e ds = cfgrib.open_file('era5-levels-members.grib')\n    \u003e\u003e\u003e ds.attributes['GRIB_edition']\n    1\n    \u003e\u003e\u003e sorted(ds.dimensions.items())\n    [('isobaricInhPa', 2), ('latitude', 61), ('longitude', 120), ('number', 10), ('time', 4)]\n    \u003e\u003e\u003e sorted(ds.variables)\n    ['isobaricInhPa', 'latitude', 'longitude', 'number', 'step', 't', 'time', 'valid_time', 'z']\n    \u003e\u003e\u003e var = ds.variables['t']\n    \u003e\u003e\u003e var.dimensions\n    ('number', 'time', 'isobaricInhPa', 'latitude', 'longitude')\n    \u003e\u003e\u003e var.data[:, :, :, :, :].mean()\n    262.92133\n    \u003e\u003e\u003e ds = cfgrib.open_file('era5-levels-members.grib')\n    \u003e\u003e\u003e ds.attributes['GRIB_edition']\n    1\n    \u003e\u003e\u003e sorted(ds.dimensions.items())\n    [('isobaricInhPa', 2), ('latitude', 61), ('longitude', 120), ('number', 10), ('time', 4)]\n    \u003e\u003e\u003e sorted(ds.variables)\n    ['isobaricInhPa', 'latitude', 'longitude', 'number', 'step', 't', 'time', 'valid_time', 'z']\n    \u003e\u003e\u003e var = ds.variables['t']\n    \u003e\u003e\u003e var.dimensions\n    ('number', 'time', 'isobaricInhPa', 'latitude', 'longitude')\n    \u003e\u003e\u003e var.data[:, :, :, :, :].mean()\n    262.92133\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\nGeographic Coordinate Caching\n-----------------------------\n\nBy default, *cfgrib* caches computed geography coordinates for each record in the GRIB\nfile when opening a dataset, which significantly speeds up dataset creation.\nThis cache can theoretically grow unboundedly in memory in long-lived\napplications which read many different grid types. Should it be necessary,\ncaching can be disabled by passing `backend_kwargs=dict(cache_geo_coords=False)`\nto `xarray.open_dataset()`, `cfgrib.open_dataset()`, or\n`cfgrib.open_datasets()`.\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 \u003chttps://github.com/iainrussell\u003e`_ - `ECMWF \u003chttps://ecmwf.int\u003e`_\n- `Baudouin Raoult \u003chttps://github.com/b8raoult\u003e`_ - ECMWF\n\nMain contributors:\n\n- `Alessandro Amici \u003chttps://github.com/alexamici\u003e`_ - `B-Open \u003chttps://bopen.eu\u003e`_\n- `Aureliana Barghini \u003chttps://github.com/aurghs\u003e`_ - B-Open\n- `Leonardo Barcaroli \u003chttps://github.com/leophys\u003e`_ - B-Open\n\nSee also the list of `contributors \u003chttps://github.com/ecmwf/cfgrib/contributors\u003e`_ 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","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["weather","bufr","earth-science","closember","odb","atmospheric-science","convolutional-neural-network","climate","wmo","netcdf4"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/899","html_url":"https://ost.ecosyste.ms/projects/899"}