How to use the xarray.open_mfdataset function in xarray

To help you get started, we’ve selected a few xarray examples, based on popular ways it is used in public projects.

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github CCI-Tools / cate / cate / core / View on Github external
# The file size is fine
        return xr.open_mfdataset(paths, concat_dim=concat_dim, **kwargs)

    divisor = sqrt(n_chunks)

    # Chunking will pretty much 'always' be 2x2, very rarely 3x3 or 4x4. 5x5
    # would imply an uncompressed single file of ~6GB! All expected grids
    # should be divisible by 2,3 and 4.
    if not (n_lat % divisor == 0) or not (n_lon % divisor == 0):
        raise ValueError("Can't find a good chunking strategy for the given"
                         "data source. Are lat/lon coordinates divisible by "

    chunks = {lat: n_lat // divisor, lon: n_lon // divisor}

    return xr.open_mfdataset(paths, concat_dim=concat_dim, chunks=chunks, **kwargs)
github recipy / recipy / integration_test / packages / View on Github external
def open_mfdataset_glob(self):
        Use xarray.open_mfdataset to read multiple netcdf files with a glob
        pattern = os.path.join(self.data_dir, "*")
github CCI-Tools / cate / ect / core / View on Github external
def open_xarray_dataset(paths, preprocess=True, chunks=None, **kwargs) -> xr.Dataset:
    Adapted version of the xarray 'open_mfdataset' function.
    if isinstance(paths, str):
        paths = sorted(glob(paths))
    if not paths:
        raise IOError('no files to open')

    if not preprocess:
        return xr.open_mfdataset(paths, concat_dim='time')

    # open all datasets
    lock = xr.backends.api._default_lock(paths[0], None)
    # TODO (forman, 20160601): align with chunking from netcdf metadata attribute

    datasets = []
    engine = 'netcdf4'
    for p in paths:
        datasets.append(xr.open_dataset(p, engine=engine, decode_cf=False, chunks=chunks or {}, lock=lock, **kwargs))

    preprocessed_datasets = []
    file_objs = []
    for ds in datasets:
        pds = _preprocess_datasets(ds)
        if pds is None:
github jhamman / xsd / scripts / View on Github external
ds_obs_1var[v].attrs = ds_obs[v].attrs
            if i:
                ds_obs_1var[v].encoding['_FillValue'] = None
        for v in ds_obs_1var:
            for attr in attrs_to_delete:
                if attr in ds_obs_1var[v].attrs:
                    del ds_obs_1var[v].attrs[attr]

        if 'time' in ds_obs_1var['xv'].dims:
            ds_obs_1var['xv'] = ds_obs_1var['xv'].isel(time=0)
            ds_obs_1var['yv'] = ds_obs_1var['yv'].isel(time=0)


        da_train = xr.open_mfdataset(
            train_fname.format(gcm_var=gcm_var), chunks=chunks,
            combine='by_coords', data_vars='minimal')[gcm_var].pipe(resample, time_bounds)
        da_predict = xr.open_mfdataset(
            predict_fname.format(gcm_var=gcm_var), chunks=chunks,
            combine='by_coords', data_vars='minimal')[gcm_var].pipe(resample, predict_time_bounds)
        print('da_train', da_train)
        print('da_predict', da_predict)

        anoms[obs_var] = bcsd(ds_obs_1var, da_train.to_dataset(name=obs_var),
        out[obs_var] = disagg(ds_obs_daily[obs_var], anoms[obs_var],
        for var in ['xv', 'yv']:
            if var not in out.coords:
                out[var] = ds_obs_1var[var]
github dcs4cop / xcube / xcube / genl3 / View on Github external
def _read_dataset(input_file):
    input_file_name = os.path.basename(input_file)
    if os.path.isdir(input_file):
        if input_file_name.endswith('.zarr'):
            ds = xr.open_zarr(input_file)
            ds = xr.open_mfdataset(glob.glob(os.path.join(input_file, '**', '*.nc'), recursive=True))
        if input_file_name.endswith(''):
            ds = xr.open_zarr(input_file)
            ds = xr.open_dataset(input_file)
    return ds
github noaa-oar-arl / MONET / MONET / View on Github external
def open_metcro2d(self, f):
        from glob import glob
        from numpy import sort
            if type(f) == str:
                self.metcrofnames = sort(array(glob(f)))
                print self.metcrofnames
                self.metcrofnames = sort(array(f))
                print self.metcrofnames
            if self.metcrofnames.shape[0] >= 1:
                self.metcro2d = xr.open_mfdataset(self.metcrofnames.tolist(), concat_dim='TSTEP')
            self.metcrokeys = self.metcro2d.keys()
            if self.grid is not None:
                self.metcro2d =	self.metcro2d.assign(latitude=self.grid.LAT.squeeze())
                self.metcro2d =	self.metcor2d.assign(longitude=self.grid.LON.squeeze())
                self.metcro2d =	self.metcro2d.set_coords(['latitude','longitude'])
            print 'METCRO2D Files Not Found'
github deeplycloudy / glmtools / glmtools / io / View on Github external
def open_glm_time_series(filenames, chunks=None):
    """ Convenience function for combining individual 1-min GLM gridded imagery
    files into a single xarray.Dataset with a time dimension.
    Creates an index on the time dimension.
    The time dimension will be in the order in which the files are listed
    due to the behavior of combine='nested' in open_mfdataset.
    Adjusts the time_coverage_start and time_coverage_end metadata.
    # Need to fix time_coverage_start and _end in concat dataset
    starts = [t for t in gen_file_times(filenames)]
    ends = [t for t in gen_file_times(filenames, time_attr='time_coverage_end')]
    d = xr.open_mfdataset(filenames, concat_dim='time', chunks=chunks, combine='nested')
    d['time'] = starts
    d = d.set_index({'time':'time'})
    d = d.set_coords('time')
    d.attrs['time_coverage_start'] = pd.Timestamp(min(starts)).isoformat()
    d.attrs['time_coverage_end'] = pd.Timestamp(max(ends)).isoformat()

    return d
github MPAS-Dev / MPAS-Analysis / mpas_analysis / shared / climatology / View on Github external
ds = ds.where(ds.month == month, drop=True)
                ds = ds.mean(dim='Time')
                write_netcdf(ds, climatologyFileName)
            outFileName = parentTask.get_file_name(season=season)
  'computing climatology {}'.format(
            fileNames = []
            weights = []
            for month in constants.monthDictionary[season]:
                monthName = constants.abrevMonthNames[month-1]

            with xarray.open_mfdataset(fileNames, concat_dim='weight',
                                       chunks={'nCells': chunkSize},
                                       decode_cf=False, decode_times=False,
                                       preprocess=_preprocess) as ds:
                ds.coords['weight'] = ('weight', weights)
                ds = ((ds.weight*ds).sum(dim='weight') /
                write_netcdf(ds, outFileName)
github monocongo / climate_indices / scripts / View on Github external
second_file = netcdf_temp
        keeper_data_vars = [var_name_precip, var_name_temp]

    elif netcdf_pet is not None:

        second_file = netcdf_pet
        keeper_data_vars = [var_name_precip, var_name_pet]

        message = "SPEI requires either PET or temperature to compute PET, but neither input file was provided."
        raise ValueError(message)

    # open the precipitation and secondary input NetCDFs as an xarray DataSet object
    dataset = xr.open_mfdataset([netcdf_precip, second_file])  # , chunks={'lat': 10})

    # trim out all data variables from the dataset except precipitation and temperature
    for var in dataset.data_vars:
        if var not in keeper_data_vars:
            dataset = dataset.drop(var)

    # get the initial year of the data
    data_start_year = int(str(dataset['time'].values[0])[0:4])

    # get the scale increment for use in later log messages
    if arguments.periodicity is compute.Periodicity.daily:
        scale_increment = 'day'
        pet_method = "Hargreaves"
    elif arguments.periodicity is compute.Periodicity.monthly:
        scale_increment = 'month'
        pet_method = "Thornthwaite"
github pydata / xarray / asv_bench / benchmarks / View on Github external
def time_load_dataset_netcdf4(self):
        xr.open_mfdataset(self.filenames_list, engine="netcdf4").load()