Python zarr.open方法代碼示例

# 需要導入模塊: import zarr [as 別名]
# 或者: from zarr import open [as 別名]
def finalise(self):
        """
        Ensures that the state of the data is flushed and writes the
        provenance for the current operation. The specified 'command' is used
        to fill the corresponding entry in the provenance dictionary.
        """
        self._check_write_modes()
        self.data.attrs[FINALISED_KEY] = True
        if self.path is not None:
            store = self.data.store
            store.close()
            logger.debug("Fixing up LMDB file size")
            with lmdb.open(self.path, subdir=False, lock=False, writemap=True) as db:
                # LMDB maps a very large amount of space by default. While this
                # doesn't do any harm, it's annoying because we can't use ls to
                # see the file sizes and the amount of RAM we're mapping can
                # look like it's very large. So, we fix this up so that the
                # map size is equal to the number of pages in use.
                num_pages = db.info()["last_pgno"]
                page_size = db.stat()["psize"]
                db.set_mapsize(num_pages * page_size)
            # Remove the lock file as we don't need it after this point.
            remove_lmdb_lockfile(self.path)
        self._open_readonly() 

# 需要導入模塊: import zarr [as 別名]
# 或者: from zarr import open [as 別名]
def test_update_coords_only(self):
        ds1 = zarr.open(TEST_CUBE_ZARR_COORDS)
        delete_list = ['geospatial_lat_max', 'geospatial_lat_min', 'geospatial_lat_units', 'geospatial_lon_max',
                       'geospatial_lon_min', 'geospatial_lon_units', 'time_coverage_end', 'time_coverage_start']
        for attr in ds1.attrs.keys():
            if attr in delete_list:
                ds1.attrs.__delitem__(attr)
        result = self.invoke_cli(['edit', TEST_CUBE_ZARR_COORDS, '-o', TEST_CUBE_ZARR_EDIT, '-C'])
        self.assertEqual(0, result.exit_code)

        ds1 = zarr.open(TEST_CUBE_ZARR_COORDS)
        ds2 = zarr.open(TEST_CUBE_ZARR_EDIT)
        for attr in delete_list:
            self.assertNotIn(attr, ds1.attrs.keys())
        self.assertEqual(ds1.__len__(), ds2.__len__())
        self.assertIn('geospatial_lat_max', ds2.attrs.keys())
        self.assertIn('geospatial_lat_min', ds2.attrs.keys())
        self.assertIn('geospatial_lat_resolution', ds2.attrs.keys())
        self.assertIn('geospatial_lat_units', ds2.attrs.keys())
        self.assertIn('geospatial_lon_max', ds2.attrs.keys())
        self.assertEqual(180.0, ds2.attrs.__getitem__('geospatial_lon_max'))
        self.assertEqual(-180.0, ds2.attrs.__getitem__('geospatial_lon_min'))
        self.assertEqual('2010-01-04T00:00:00.000000000', ds2.attrs.__getitem__('time_coverage_end'))
        self.assertEqual('2010-01-01T00:00:00.000000000', ds2.attrs.__getitem__('time_coverage_start'))
        self.assertEqual('degrees_east', ds2.attrs.__getitem__('geospatial_lon_units')) 

# 需要導入模塊: import zarr [as 別名]
# 或者: from zarr import open [as 別名]
def test_update_coords_metadata_only(self):
        ds1 = zarr.open(TEST_CUBE_ZARR_COORDS)
        delete_list = ['geospatial_lat_max', 'geospatial_lat_min', 'geospatial_lat_units', 'geospatial_lon_max',
                       'geospatial_lon_min', 'geospatial_lon_units', 'time_coverage_end', 'time_coverage_start']
        for attr in ds1.attrs.keys():
            if attr in delete_list:
                ds1.attrs.__delitem__(attr)
        edit_metadata(TEST_CUBE_ZARR_COORDS, update_coords=True, in_place=False,
                      output_path=TEST_CUBE_ZARR_EDIT, monitor=print)
        ds1 = zarr.open(TEST_CUBE_ZARR_COORDS)
        ds2 = zarr.open(TEST_CUBE_ZARR_EDIT)
        for attr in delete_list:
            self.assertNotIn(attr, ds1.attrs.keys())
        self.assertEqual(ds1.__len__(), ds2.__len__())
        self.assertIn('geospatial_lat_max', ds2.attrs.keys())
        self.assertIn('geospatial_lat_min', ds2.attrs.keys())
        self.assertIn('geospatial_lat_resolution', ds2.attrs.keys())
        self.assertIn('geospatial_lat_units', ds2.attrs.keys())
        self.assertIn('geospatial_lon_max', ds2.attrs.keys())
        self.assertEqual(180.0, ds2.attrs.__getitem__('geospatial_lon_max'))
        self.assertEqual(-180.0, ds2.attrs.__getitem__('geospatial_lon_min'))
        self.assertEqual('2010-01-04T00:00:00.000000000', ds2.attrs.__getitem__('time_coverage_end'))
        self.assertEqual('2010-01-01T00:00:00.000000000', ds2.attrs.__getitem__('time_coverage_start'))
        self.assertEqual('degrees_east', ds2.attrs.__getitem__('geospatial_lon_units')) 

# 需要導入模塊: import zarr [as 別名]
# 或者: from zarr import open [as 別名]
def test_write_csv(tmpdir):
    expected_filename = os.path.join(tmpdir, 'test.csv')
    column_names = ['column_1', 'column_2', 'column_3']
    expected_data = np.random.random((5, len(column_names)))

    # Write csv file
    io.write_csv(expected_filename, expected_data, column_names=column_names)
    assert os.path.exists(expected_filename)

    # Check csv file is as expected
    with open(expected_filename) as output_csv:
        csv.reader(output_csv, delimiter=',')
        for row_index, row in enumerate(output_csv):
            if row_index == 0:
                assert row == "column_1,column_2,column_3\n"
            else:
                output_row_data = [float(i) for i in row.split(',')]
                np.testing.assert_allclose(
                    np.array(output_row_data), expected_data[row_index - 1]
                ) 

# 需要導入模塊: import zarr [as 別名]
# 或者: from zarr import open [as 別名]
def test_zarr_multiscale():
    viewer = ViewerModel()
    multiscale = [
        np.random.random((20, 20)),
        np.random.random((10, 10)),
        np.random.random((5, 5)),
    ]
    with TemporaryDirectory(suffix='.zarr') as fout:
        root = zarr.open_group(fout, 'a')
        for i in range(len(multiscale)):
            shape = 20 // 2 ** i
            z = root.create_dataset(str(i), shape=(shape,) * 2)
            z[:] = multiscale[i]
        viewer.open(fout, multiscale=True, plugin='builtins')
        assert len(viewer.layers) == 1
        assert len(multiscale) == len(viewer.layers[0].data)
        # Note: due to lazy loading, the next line needs to happen within
        # the context manager. Alternatively, we could convert to NumPy here.
        for images, images_in in zip(multiscale, viewer.layers[0].data):
            np.testing.assert_array_equal(images, images_in) 

# 需要導入模塊: import zarr [as 別名]
# 或者: from zarr import open [as 別名]
def test_zarr_chunk_X(tmp_path):
    import zarr

    zarr_pth = Path(tmp_path) / "test.zarr"
    adata = gen_adata((100, 100), X_type=np.array)
    adata.write_zarr(zarr_pth, chunks=(10, 10))

    z = zarr.open(str(zarr_pth))  # As of v2.3.2 zarr won’t take a Path
    assert z["X"].chunks == (10, 10)
    from_zarr = ad.read_zarr(zarr_pth)
    assert_equal(from_zarr, adata)


################################
# Round-tripping scanpy datasets
################################ 

# 需要導入模塊: import zarr [as 別名]
# 或者: from zarr import open [as 別名]
def write_zarr(
    store: Union[MutableMapping, str, Path],
    adata: AnnData,
    chunks=None,
    **dataset_kwargs,
) -> None:
    if isinstance(store, Path):
        store = str(store)
    adata.strings_to_categoricals()
    if adata.raw is not None:
        adata.strings_to_categoricals(adata.raw.var)
    f = zarr.open(store, mode="w")
    if chunks is not None and not isinstance(adata.X, sparse.spmatrix):
        write_attribute(f, "X", adata.X, dict(chunks=chunks, **dataset_kwargs))
    else:
        write_attribute(f, "X", adata.X, dataset_kwargs)
    write_attribute(f, "obs", adata.obs, dataset_kwargs)
    write_attribute(f, "var", adata.var, dataset_kwargs)
    write_attribute(f, "obsm", adata.obsm, dataset_kwargs)
    write_attribute(f, "varm", adata.varm, dataset_kwargs)
    write_attribute(f, "obsp", adata.obsp, dataset_kwargs)
    write_attribute(f, "varp", adata.varp, dataset_kwargs)
    write_attribute(f, "layers", adata.layers, dataset_kwargs)
    write_attribute(f, "uns", adata.uns, dataset_kwargs)
    write_attribute(f, "raw", adata.raw, dataset_kwargs) 

# 需要導入模塊: import zarr [as 別名]
# 或者: from zarr import open [as 別名]
def read_series(dataset: zarr.Array) -> Union[np.ndarray, pd.Categorical]:
    if "categories" in dataset.attrs:
        categories = dataset.attrs["categories"]
        if isinstance(categories, str):
            categories_key = categories
            parent_name = dataset.name.rstrip(dataset.basename)
            parent = zarr.open(dataset.store)[parent_name]
            categories_dset = parent[categories_key]
            categories = categories_dset[...]
            ordered = categories_dset.attrs.get("ordered", False)
        else:
            # TODO: remove this code at some point post 0.7
            # TODO: Add tests for this
            warn(
                f"Your file {str(dataset.file.name)!r} has invalid categorical "
                "encodings due to being written from a development version of "
                "AnnData. Rewrite the file ensure you can read it in the future.",
                FutureWarning,
            )
        return pd.Categorical.from_codes(dataset[...], categories, ordered=ordered)
    else:
        return dataset[...] 

# 需要導入模塊: import zarr [as 別名]
# 或者: from zarr import open [as 別名]
def init_pretrained_wemb(self, emb_dim):
        """
            From blog.keras.io
            Initialises words embeddings with pre-trained GLOVE embeddings
        """
        embeddings_index = {}
        f = open(os.path.join(self.data_dir, 'glove.6B.') +
                 str(emb_dim) + 'd.txt')
        for line in f:
            values = line.split()
            word = values[0]
            coefs = np.asarray(values[1:], dtype=np.float32)
            embeddings_index[word] = coefs
        f.close()

        embedding_mat = np.zeros((self.q_words, emb_dim), dtype=np.float32)
        for word, i in self.q_wtoi.items():
            embedding_v = embeddings_index.get(word)
            if embedding_v is not None:
                embedding_mat[i] = embedding_v

        self.pretrained_wemb = embedding_mat 

# 需要導入模塊: import zarr [as 別名]
# 或者: from zarr import open [as 別名]
def set_platform(self, platform_dict):
        """Set the Platform group in the AZFP nc file. AZFP does not record pitch, roll, and heave.

        Parameters
        ----------
        platform_dict
            dictionary containing platform parameters
        """
        if not os.path.exists(self.file_path):
            print("netCDF file does not exist, exiting without saving Platform group...")
        elif self.format == '.nc':
            ncfile = netCDF4.Dataset(self.file_path, 'a', format='NETCDF4')
            plat = ncfile.createGroup('Platform')
            with netCDF4.Dataset(self.file_path, 'a', format='NETCDF4') as ncfile:
                [plat.setncattr(k, v) for k, v in platform_dict.items()]
        elif self.format == '.zarr' and not self.append_zarr:    # Do not save platform if appending
            zarrfile = zarr.open(self.file_path, mode='a')
            plat = zarrfile.create_group('Platform')
            for k, v in platform_dict.items():
                plat.attrs[k] = v 

# 需要導入模塊: import zarr [as 別名]
# 或者: from zarr import open [as 別名]
def _open_readonly(self):
        if self.path is not None:
            store = self._open_lmbd_readonly()
        else:
            # This happens when we finalise an in-memory container.
            store = self.data.store
        self.data = zarr.open(store=store, mode="r")
        self._check_format()
        self._mode = self.READ_MODE 

# 需要導入模塊: import zarr [as 別名]
# 或者: from zarr import open [as 別名]
def load(cls, path):
        # Try to read the file. This should raise the correct error if we have a
        # directory, missing file, permissions, etc.
        with open(path, "r"):
            pass
        self = cls.__new__(cls)
        self.mode = self.READ_MODE
        self.path = path
        self._open_readonly()
        logger.info("Loaded {}".format(self.summary()))
        return self 

# 需要導入模塊: import zarr [as 別名]
# 或者: from zarr import open [as 別名]
def test_zero_sequence_length(self):
        # Mangle a sample data file to force a zero sequence length.
        ts = msprime.simulate(10, mutation_rate=2, random_seed=5)
        with tempfile.TemporaryDirectory(prefix="tsinf_format_test") as tempdir:
            filename = os.path.join(tempdir, "samples.tmp")
            with tsinfer.SampleData(path=filename) as sample_data:
                for var in ts.variants():
                    sample_data.add_site(var.site.position, var.genotypes)
            store = zarr.LMDBStore(filename, subdir=False)
            data = zarr.open(store=store, mode="w+")
            data.attrs["sequence_length"] = 0
            store.close()
            sample_data = tsinfer.load(filename)
            self.assertEqual(sample_data.sequence_length, 0)
            self.assertRaises(ValueError, tsinfer.generate_ancestors, sample_data) 

# 需要導入模塊: import zarr [as 別名]
# 或者: from zarr import open [as 別名]
def fromzarr(path, group=None, dataset=None, chunk_size=None):
    import zarr

    if isinstance(path, zarr.Array):
        arr = path
        if isinstance(arr.store, FSMap):
            root = arr.store.root
            path, dataset = root.rsplit('/', 1)
        else:
            path = arr.store.path
            if '/' in arr.path and group is None:
                group = arr.path.rsplit('/', 1)[0]
            dataset = arr.basename
            if not dataset:
                path, dataset = path.rsplit('/', 1)
        shape = arr.shape
    elif isinstance(path, str):
        fs = get_fs(path, None)
        fs_map = FSMap(path, fs)

        if group is None and dataset is None:
            arr = zarr.open(fs_map)
            if isinstance(arr, zarr.Array):
                return fromzarr(arr, chunk_size=chunk_size)

        g = zarr.group(store=fs_map)
        arr = g[TensorFromZarr.get_path(group, dataset)]
        shape = arr.shape
    else:
        raise TypeError('`path` passed has wrong type, '
                        'expect str, or zarr.Array'
                        'got {}'.format(type(path)))

    chunk_size = chunk_size if chunk_size is not None else arr.chunks
    op = TensorFromZarr(filename=path, group=group, dataset=dataset,
                        dtype=arr.dtype)
    return op(shape, chunk_size=chunk_size, order=TensorOrder(arr.order)) 

# 需要導入模塊: import zarr [as 別名]
# 或者: from zarr import open [as 別名]
def tile(cls, op):
        import zarr

        check_chunks_unknown_shape(op.inputs, TilesError)
        in_tensor = op.input

        # create dataset
        fs = get_fs(op.path, None)
        path = op.path
        if op.group is not None:
            path += '/' + op.group
        fs_map = FSMap(path, fs)
        zarr.open(fs_map, 'w', path=op.dataset,
                  dtype=in_tensor.dtype, shape=in_tensor.shape,
                  chunks=tuple(max(ns) for ns in in_tensor.nsplits),
                  **op.zarr_options.todict())

        cum_nsplits = [[0] + np.cumsum(ns).tolist() for ns in in_tensor.nsplits]
        out_chunks = []
        for chunk in in_tensor.chunks:
            chunk_op = op.copy().reset_key()
            chunk_op._axis_offsets = \
                tuple(cs[i] for i, cs in zip(chunk.index, cum_nsplits))
            out_chunks.append(chunk_op.new_chunk([chunk], shape=(0,) * chunk.ndim,
                                                 index=chunk.index))

        new_op = op.copy()
        out = op.outputs[0]
        nsplits = tuple((0,) * len(ns) for ns in in_tensor.nsplits)
        return new_op.new_tensors(op.inputs, shape=out.shape, order=out.order,
                                  nsplits=nsplits, chunks=out_chunks)