Python xarray.open_zarr方法代碼示例

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import open_zarr [as 別名]
def test_rectify_without_vars(self):
        """Test that rectify selects all variables when --var is not given."""

        # For now, specify the image geometry explicitly with --size, --point,
        # and --res to avoid triggering an "invalid y_min" ValueError when
        # ImageGeom tries to determine it automatically. Once Issue #303 has
        # been fixed, these options can be omitted.

        result = self.invoke_cli(['rectify',
                                  '--size', '508,253',
                                  '--point', '-179.5,-89.5',
                                  '--res', '0.7071067811865475',
                                  TEST_ZARR_DIR])
        self.assertEqual(0, result.exit_code)
        self.assertEqual('Opening dataset from \'test.zarr\'...\n'
                         'Rectifying...\n'
                         'Writing rectified dataset to \'out.zarr\'...\n'
                         'Done.\n',
                         result.stdout)
        self.assertTrue(os.path.isdir('out.zarr'))
        ds = xr.open_zarr('out.zarr')
        assert_cube(ds)
        self.assertIn('precipitation', ds)
        self.assertIn('temperature', ds)
        self.assertIn('soil_moisture', ds) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import open_zarr [as 別名]
def test_dry_run(self):
        result = self.invoke_cli(['prune', self.TEST_CUBE, "--dry-run"])
        self.assertEqual(0, result.exit_code)
        self.assertEqual("Opening cube from 'test.zarr'...\n"
                         "Identifying empty blocks...\n"
                         "Deleting 24 empty block file(s) for variable 'precipitation'...\n"
                         "Deleting 24 empty block file(s) for variable 'temperature'...\n"
                         "Done, 48 block file(s) deleted.\n",
                         result.stdout)
        expected_file_names = sorted(['.zarray',
                                      '.zattrs',
                                      '0.0.0', '0.0.1', '0.0.2', '0.0.3', '0.1.0', '0.1.1', '0.1.2', '0.1.3',
                                      '1.0.0', '1.0.1', '1.0.2', '1.0.3', '1.1.0', '1.1.1', '1.1.2', '1.1.3',
                                      '2.0.0', '2.0.1', '2.0.2', '2.0.3', '2.1.0', '2.1.1', '2.1.2', '2.1.3'])
        self.assertEqual(expected_file_names, sorted(os.listdir('test.zarr/precipitation')))
        self.assertEqual(expected_file_names, sorted(os.listdir('test.zarr/temperature')))
        ds = xr.open_zarr('test.zarr')
        assert_cube(ds)
        self.assertIn('precipitation', ds)
        self.assertEqual((3, 180, 360), ds.precipitation.shape)
        self.assertEqual(('time', 'lat', 'lon'), ds.precipitation.dims)
        self.assertIn('temperature', ds)
        self.assertEqual((3, 180, 360), ds.temperature.shape)
        self.assertEqual(('time', 'lat', 'lon'), ds.temperature.dims) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import open_zarr [as 別名]
def test_no_dry_run(self):
        result = self.invoke_cli(['prune', self.TEST_CUBE])
        self.assertEqual(0, result.exit_code)
        self.assertEqual("Opening cube from 'test.zarr'...\n"
                         "Identifying empty blocks...\n"
                         "Deleting 24 empty block file(s) for variable 'precipitation'...\n"
                         "Deleting 24 empty block file(s) for variable 'temperature'...\n"
                         "Done, 48 block file(s) deleted.\n",
                         result.stdout)
        expected_file_names = sorted(['.zarray', '.zattrs'])
        self.assertEqual(expected_file_names, sorted(os.listdir('test.zarr/precipitation')))
        self.assertEqual(expected_file_names, sorted(os.listdir('test.zarr/temperature')))
        ds = xr.open_zarr('test.zarr')
        assert_cube(ds)
        self.assertIn('precipitation', ds)
        self.assertEqual((3, 180, 360), ds.precipitation.shape)
        self.assertEqual(('time', 'lat', 'lon'), ds.precipitation.dims)
        self.assertIn('temperature', ds)
        self.assertEqual((3, 180, 360), ds.temperature.shape)
        self.assertEqual(('time', 'lat', 'lon'), ds.temperature.dims) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import open_zarr [as 別名]
def _write_test_cube_pyramid(cls):
        # Create bucket 'xcube-test', so it exists before we write a test pyramid
        s3_conn = boto3.client('s3')
        s3_conn.create_bucket(Bucket='xcube-test', ACL='public-read')

        # Create a test cube pyramid with just one variable "conc_chl"
        zarr_path = os.path.join(os.path.dirname(__file__), '../../examples/serve/demo/cube-1-250-250.zarr')
        base_dataset = xr.open_zarr(zarr_path)
        base_dataset = xr.Dataset(dict(conc_chl=base_dataset.conc_chl))
        ml_dataset = BaseMultiLevelDataset(base_dataset)

        # Write test cube pyramid
        write_levels(ml_dataset,
                     'https://s3.amazonaws.com/xcube-test/cube-1-250-250.levels',
                     client_kwargs=dict(provider_access_key_id='test_fake_id',
                                        provider_secret_access_key='test_fake_secret')) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import open_zarr [as 別名]
def test_insert_time_slice(self):
        self.write_cube('2019-01-02', 10)

        insert_time_slice(self.CUBE_PATH, 5, self.make_slice('2019-01-06T02:00'))
        insert_time_slice(self.CUBE_PATH, 10, self.make_slice('2019-01-10T02:00'))
        insert_time_slice(self.CUBE_PATH, 0, self.make_slice('2019-01-01T02:00'))

        cube = xr.open_zarr(self.CUBE_PATH)
        expected = np.array(['2019-01-01T14:00', '2019-01-02T12:00',
                             '2019-01-03T12:00', '2019-01-04T12:00',
                             '2019-01-05T12:00', '2019-01-06T12:00',
                             '2019-01-06T14:00', '2019-01-07T12:00',
                             '2019-01-08T12:00', '2019-01-09T12:00',
                             '2019-01-10T12:00', '2019-01-10T14:00',
                             '2019-01-11T12:00'], dtype=cube.time.dtype)
        self.assertEqual(13, cube.time.size)
        self.assertEqual(None, cube.time.chunks)
        np.testing.assert_equal(cube.time.values, expected) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import open_zarr [as 別名]
def test_update_corrupt_cube(self):
        self.write_cube('2019-01-01', 3)

        cube = xr.open_zarr(self.CUBE_PATH)
        t, y, x = cube.precipitation.shape
        new_shape = y, t, x
        t, y, x = cube.precipitation.dims
        new_dims = y, t, x
        cube['precipitation'] = xr.DataArray(cube.precipitation.values.reshape(new_shape),
                                             dims=new_dims,
                                             coords=cube.precipitation.coords)
        cube.to_zarr(self.CUBE_PATH_2)

        with self.assertRaises(ValueError) as cm:
            insert_time_slice(self.CUBE_PATH_2, 2, self.make_slice('2019-01-02T06:30'))
        self.assertEqual("dimension 'time' of variable 'precipitation' must be first dimension",
                         f"{cm.exception}") 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import open_zarr [as 別名]
def test_process_inputs_replace_multiple_zarr(self):
        status, output = gen_cube_wrapper(
            [get_inputdata_path('20170101-IFR-L4_GHRSST-SSTfnd-ODYSSEA-NWE_002-v2.0-fv1.0.nc'),
             get_inputdata_path('20170102-IFR-L4_GHRSST-SSTfnd-ODYSSEA-NWE_002-v2.0-fv1.0.nc'),
             get_inputdata_path('20170103-IFR-L4_GHRSST-SSTfnd-ODYSSEA-NWE_002-v2.0-fv1.0.nc'),
             get_inputdata_path('20170102-IFR-L4_GHRSST-SSTfnd-ODYSSEA-NWE_002-v2.0-fv1.0.nc')], 'l2c.zarr',
            no_sort_mode=True)
        self.assertEqual(True, status)
        self.assertTrue('\nstep 9 of 9: creating input slice in l2c.zarr...\n' in output)
        self.assertTrue('\nstep 9 of 9: appending input slice to l2c.zarr...\n' in output)
        self.assertTrue('\nstep 9 of 9: replacing input slice at index 1 in l2c.zarr...\n' in output)
        self.assert_cube_ok(xr.open_zarr('l2c.zarr'),
                            expected_time_dim=3,
                            expected_extra_attrs=dict(date_modified=None,
                                                      time_coverage_start='2016-12-31T12:00:00.000000000',
                                                      time_coverage_end='2017-01-03T12:00:00.000000000'))
        self.assertTrue(os.path.exists(os.path.join('l2c.zarr', '.zmetadata'))) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import open_zarr [as 別名]
def gen_index_var(dims, shape, chunks):
    # noinspection PyUnusedLocal
    def get_chunk(cube_store: ChunkStore, name: str, index: Tuple[int, ...]) -> bytes:
        data = np.zeros(cube_store.chunks, dtype=np.uint64)
        data_view = data.ravel()
        if data_view.base is not data:
            raise ValueError('view expected')
        if data_view.size < cube_store.ndim * 2:
            raise ValueError('size too small')
        for i in range(cube_store.ndim):
            j1 = cube_store.chunks[i] * index[i]
            j2 = j1 + cube_store.chunks[i]
            data_view[2 * i] = j1
            data_view[2 * i + 1] = j2
        return data.tobytes()

    store = ChunkStore(dims, shape, chunks)
    store.add_lazy_array('__index_var__', '<u8', get_chunk=get_chunk)

    ds = xr.open_zarr(store)
    return ds.__index_var__ 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import open_zarr [as 別名]
def _get_dataset_lazily(self, index: int, parameters: Dict[str, Any]) -> xr.Dataset:
        """
        Read the dataset for the level at given *index*.

        :param index: the level index
        :param parameters: keyword arguments passed to xr.open_zarr()
        :return: the dataset for the level at *index*.
        """
        ext, level_path = self._level_paths[index]
        if ext == ".link":
            with open(level_path, "r") as fp:
                level_path = fp.read()
                # if file_path is a relative path, resolve it against the levels directory
                if not os.path.isabs(level_path):
                    base_dir = os.path.dirname(self._dir_path)
                    level_path = os.path.join(base_dir, level_path)
        with measure_time(tag=f"opened local dataset {level_path} for level {index}"):
            return assert_cube(xr.open_zarr(level_path, **parameters), name=level_path) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import open_zarr [as 別名]
def open_ml_dataset_from_local_fs(path: str,
                                  data_format: str = None,
                                  ds_id: str = None,
                                  exception_type: type = ValueError,
                                  **kwargs) -> MultiLevelDataset:
    data_format = data_format or guess_ml_dataset_format(path)

    if data_format == FORMAT_NAME_NETCDF4:
        with measure_time(tag=f"opened local NetCDF dataset {path}"):
            ds = assert_cube(xr.open_dataset(path, **kwargs))
            return BaseMultiLevelDataset(ds, ds_id=ds_id)
    elif data_format == FORMAT_NAME_ZARR:
        with measure_time(tag=f"opened local zarr dataset {path}"):
            ds = assert_cube(xr.open_zarr(path, **kwargs))
            return BaseMultiLevelDataset(ds, ds_id=ds_id)
    elif data_format == FORMAT_NAME_LEVELS:
        with measure_time(tag=f"opened local levels dataset {path}"):
            return FileStorageMultiLevelDataset(path, ds_id=ds_id, zarr_kwargs=kwargs)

    raise exception_type(f'Unrecognized multi-level dataset format {data_format!r} for path {path!r}') 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import open_zarr [as 別名]
def __init__(self, url, headers, **kwargs):
        """
        Initialise local xarray, whose dask arrays contain tasks that pull data

        The matadata contains a key "internal", which is a result of running
        ``serialize_zarr_ds`` on the xarray on the server. It is a dict
        containing the metadata parts of the original dataset (i.e., the
        keys with names like ".z*"). This can be opened by xarray as-is, and
        will make a local xarray object. In ``._get_schema()``, the numpy
        parts (coordinates) are fetched and the dask-array parts (cariables)
        have their dask graphs redefined to tasks that fetch data from the
        server.
        """
        import xarray as xr
        super(RemoteXarray, self).__init__(url, headers, **kwargs)
        self._schema = None
        self._ds = xr.open_zarr(self.metadata['internal']) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import open_zarr [as 別名]
def open_minian(dpath, fname='minian', backend='netcdf', chunks=None, post_process=None):
    if backend is 'netcdf':
        fname = fname + '.nc'
        if chunks is 'auto':
            chunks = dict([(d, 'auto') for d in ds.dims])
        mpath = pjoin(dpath, fname)
        with xr.open_dataset(mpath) as ds:
            dims = ds.dims
        chunks = dict([(d, 'auto') for d in dims])
        ds = xr.open_dataset(os.path.join(dpath, fname), chunks=chunks)
        if post_process:
            ds = post_process(ds, mpath)
        return ds
    elif backend is 'zarr':
        mpath = pjoin(dpath, fname)
        dslist = [xr.open_zarr(pjoin(mpath, d)) for d in listdir(mpath) if isdir(pjoin(mpath, d))]
        ds = xr.merge(dslist)
        if chunks is 'auto':
            chunks = dict([(d, 'auto') for d in ds.dims])
        if post_process:
            ds = post_process(ds, mpath)
        return ds.chunk(chunks)
    else:
        raise NotImplementedError("backend {} not supported".format(backend)) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import open_zarr [as 別名]
def open_as_xr_dataset(self) -> xr.Dataset:
        if self.in_memory:
            chunks = None
        else:
            chunks = "auto"

        ds = xr.open_zarr(
            self.zgroup.store,
            group=self.zgroup.path,
            chunks=chunks,
            consolidated=self.consolidated,
            # disable mask (not nice with zarr default fill_value=0)
            mask_and_scale=False,
        )

        if self.in_memory:
            # lazy loading may be confusing for the default, in-memory option
            ds.load()
        else:
            # load scalar data vars (there might be many of them: model params)
            for da in ds.data_vars.values():
                if not da.dims:
                    da.load()

        return ds 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import open_zarr [as 別名]
def test_all_defaults(self):
        result = self.invoke_cli(['resample', TEST_ZARR_DIR])
        self.assertEqual(0, result.exit_code)
        self.assertEqual("Opening cube from 'test.zarr'...\n"
                         "Resampling...\n"
                         "Writing resampled cube to 'out.zarr'...\n"
                         "Done.\n",
                         result.stdout)
        self.assertTrue(os.path.isdir('out.zarr'))
        ds = xr.open_zarr('out.zarr')
        assert_cube(ds)
        self.assertIn('precipitation_mean', ds)
        self.assertIn('temperature_mean', ds)
        self.assertIn('soil_moisture_mean', ds) 

# 需要導入模塊: import xarray [as 別名]
# 或者: from xarray import open_zarr [as 別名]
def test_with_output(self):
        result = self.invoke_cli(['resample', TEST_ZARR_DIR, '--output', 'resampled.zarr'])
        self.assertEqual(0, result.exit_code)
        self.assertTrue(os.path.isdir('resampled.zarr'))
        ds = xr.open_zarr('resampled.zarr')
        assert_cube(ds)
        self.assertIn('precipitation_mean', ds)
        self.assertIn('temperature_mean', ds)
        self.assertIn('soil_moisture_mean', ds)