Python loompy.connect方法代碼示例

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import connect [as 別名]
def load_exp_matrix_as_loom(fname,
                            return_sparse=False,
                            attribute_name_cell_id: str = ATTRIBUTE_NAME_CELL_IDENTIFIER,
                            attribute_name_gene: str = ATTRIBUTE_NAME_GENE) -> pd.DataFrame:
    """
    Load expression matrix from loom file.

    :param fname: The name of the loom file to load.
    :return: A 2-dimensional dataframe (rows = cells x columns = genes).
    """
    if return_sparse:
        with lp.connect(fname,mode='r',validate=False) as ds:
            ex_mtx = ds.layers[''].sparse().T.tocsc()
            genes = pd.Series(ds.ra[attribute_name_gene])
            cells = ds.ca[attribute_name_cell_id]
        return ex_mtx, genes, cells

    else:
        with lp.connect(fname,mode='r',validate=False) as ds:
            # The orientation of the loom file is always:
            #   - Columns represent cells or aggregates of cells
            # 	- Rows represent genes
            return pd.DataFrame(data=ds[:, :],
                                index=ds.ra[attribute_name_gene],
                                columns=ds.ca[attribute_name_cell_id]).T 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import connect [as 別名]
def __init__(self, loom_filepath: str) -> None:
        self.loom_filepath = loom_filepath
        ds = loompy.connect(self.loom_filepath)
        self.S = ds.layer["spliced"][:, :]
        self.U = ds.layer["unspliced"][:, :]
        self.A = ds.layer["ambiguous"][:, :]
        self.ca = dict(ds.col_attrs.items())
        self.ra = dict(ds.row_attrs.items())
        ds.close()

        self.initial_cell_size = self.S.sum(0)
        self.initial_Ucell_size = self.U.sum(0)

        try:
            if np.mean(self.ca["_Valid"]) < 1:
                logging.warn(f"fraction of _Valid cells is {np.mean(self.ca['_Valid'])} but all will be taken in consideration")
        except KeyError:
            pass
            # logging.debug("The file did not specify the _Valid column attribute") 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import connect [as 別名]
def __enter__(self) -> Any:
		"""
		Context manager, to support "with loompy.connect(..)" construct
		"""
		return self 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import connect [as 別名]
def load_loom_file(self, partial_md5_hash: str, file_path: str, abs_file_path: str, mode: str = "r"):
        try:
            loom_connection = lp.connect(abs_file_path, mode=mode, validate=False)
        except KeyError as e:
            logger.error(e)
            os.remove(file_path)
            logger.warning(f"Deleting malformed loom {file_path}")
            return None
        return self.add_loom(
            partial_md5_hash=partial_md5_hash,
            file_path=file_path,
            abs_file_path=abs_file_path,
            loom_connection=loom_connection,
        ) 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import connect [as 別名]
def reload_raw(self, substitute: bool=False) -> None:
        """Reload raw data as it was before filtering steps

        Arguments
        ---------
        substitute: bool=False
            if True `S, U, A, ca, ra` will be all overwritten
            if False `S, U, A, ca, ra` will be loaded separately as `raw_S, raw_U, raw_A, raw_ca, raw_ra`
        """
        if substitute:
            ds = loompy.connect(self.loom_filepath)
            self.S = ds.layer["spliced"][:, :]
            self.U = ds.layer["unspliced"][:, :]
            self.A = ds.layer["ambiguous"][:, :]
            self.initial_cell_size = self.S.sum(0)
            self.initial_Ucell_size = self.U.sum(0)
            self.ca = dict(ds.col_attrs.items())
            self.ra = dict(ds.row_attrs.items())
            ds.close()
        else:
            ds = loompy.connect(self.loom_filepath)
            self.raw_S = ds.layer["spliced"][:, :]
            self.raw_U = ds.layer["unspliced"][:, :]
            self.raw_A = ds.layer["ambiguous"][:, :]
            self.raw_initial_cell_size = self.raw_S.sum(0)
            self.raw_initial_Ucell_size = self.raw_U.sum(0)
            self.raw_ca = dict(ds.col_attrs.items())
            self.raw_ra = dict(ds.row_attrs.items())
            ds.close() 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import connect [as 別名]
def load_loom(filename):
    """Load data from a loom file

    Parameters
    ----------
    filename: str
        file to load

    Returns
    -------
    coo : coo_matrix
        cell x gene sparse count matrix
    genes : Dataframe
        Dataframe of gene attributes.  Attributes are ordered so
        Accession and Gene are the first columns, if those attributs are
        present
    """
    import loompy
    # load the loom file
    with loompy.connect(filename) as ds:
        loom_genes = pd.DataFrame(dict(ds.ra.items()))
        loom_coo = ds.sparse().T

    # order gene attributes so Accession and Gene are the first two columns,
    # if they are present
    first_cols = []
    for colname in ['Accession', 'Gene']:
        if colname in loom_genes.columns:
            first_cols.append(colname)
    rest_cols = loom_genes.columns.difference(first_cols).tolist()
    loom_genes = loom_genes[first_cols + rest_cols]

    return loom_coo,loom_genes 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import connect [as 別名]
def validate(self, path: str, strictness: str = "speconly") -> bool:
		"""
		Validate a file for conformance to the Loom specification

		Args:
			path: 			Full path to the file to be validated
			strictness:		"speconly" or "conventions"

		Remarks:
			In "speconly" mode, conformance is assessed relative to the file format specification
			at http://linnarssonlab.org/loompy/format/. In "conventions" mode, conformance is additionally
			assessed relative to attribute name and data type conventions given at http://linnarssonlab.org/loompy/conventions/.
		"""
		valid1 = True
		with h5py.File(path, mode="r") as f:
			if self.version == None:
				self.version = get_loom_spec_version(f)
			valid1 = self.validate_spec(f)
			if not valid1:
				self.errors.append("For help, see http://linnarssonlab.org/loompy/format/")

		valid2 = True
		if strictness == "conventions":
			with loompy.connect(path, mode="r") as ds:
				valid2 = self.validate_conventions(ds)
				if not valid2:
					self.errors.append("For help, see http://linnarssonlab.org/loompy/conventions/")

		return valid1 and valid2 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import connect [as 別名]
def __exit__(self, type: Any, value: Any, traceback: Any) -> None:
		"""
		Context manager, to support "with loompy.connect(..)" construct
		"""
		if self.shape[0] == 0 or self.shape[1] == 0:
			raise ValueError("Newly created loom file must be filled with data before leaving the 'with' statement")
		if not self.closed:
			self.close(True) 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import connect [as 別名]
def create_append(filename: str, layers: Union[np.ndarray, Dict[str, np.ndarray], loompy.LayerManager], row_attrs: Dict[str, np.ndarray], col_attrs: Dict[str, np.ndarray], *, file_attrs: Dict[str, str] = None, fill_values: Dict[str, np.ndarray] = None) -> None:
	"""
	**DEPRECATED** - Use `new` instead; see https://github.com/linnarsson-lab/loompy/issues/42
	"""
	deprecated("'create_append' is deprecated. See https://github.com/linnarsson-lab/loompy/issues/42")
	if os.path.exists(filename):
		with connect(filename) as ds:
			ds.add_columns(layers, col_attrs, fill_values=fill_values)
	else:
		create(filename, layers, row_attrs, col_attrs, file_attrs=file_attrs) 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import connect [as 別名]
def new(filename: str, *, file_attrs: Optional[Dict[str, str]] = None) -> LoomConnection:
	"""
	Create an empty Loom file, and return it as a context manager.
	"""
	if filename.startswith("~/"):
		filename = os.path.expanduser(filename)
	if file_attrs is None:
		file_attrs = {}

	# Create the file (empty).
	# Yes, this might cause an exception, which we prefer to send to the caller
	f = h5py.File(name=filename, mode='w')
	f.create_group('/attrs')  # v3.0.0
	f.create_group('/layers')
	f.create_group('/row_attrs')
	f.create_group('/col_attrs')
	f.create_group('/row_graphs')
	f.create_group('/col_graphs')
	f.flush()
	f.close()

	ds = connect(filename, validate=False)
	for vals in file_attrs:
		if file_attrs[vals] is None:
			ds.attrs[vals] = "None"
		else:
			ds.attrs[vals] = file_attrs[vals]
	# store creation date
	ds.attrs['CreationDate'] = timestamp()
	ds.attrs["LOOM_SPEC_VERSION"] = loompy.loom_spec_version
	return ds 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import connect [as 別名]
def connect(filename: str, mode: str = 'r+', *, validate: bool = True, spec_version: str = "3.0.0") -> LoomConnection:
	"""
	Establish a connection to a .loom file.

	Args:
		filename:		Path to the Loom file to open
		mode:			Read/write mode, 'r+' (read/write) or 'r' (read-only), defaults to 'r+'
		validate:		Validate the file structure against the Loom file format specification
		spec_version:	The loom file spec version to validate against (e.g. "2.0.1" or "old")
	Returns:
		A LoomConnection instance.

	Remarks:
		This function should typically be used as a context manager (i.e. inside a ``with``-block):

		.. highlight:: python
		.. code-block:: python

			import loompy
			with loompy.connect("mydata.loom") as ds:
				print(ds.ca.keys())

		This ensures that the file will be closed automatically when the context block ends

		Note: if validation is requested, an exception is raised if validation fails.
	"""
	return LoomConnection(filename, mode, validate=validate) 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import connect [as 別名]
def test_scan_with_default_ordering(self) -> None:
        with loompy.connect(self.file.name) as ds:
            for axis in [0, 1]:
                _, _, view = next(iter(ds.scan(axis=axis)))
                no_ordering_data = view[:, :]

                _, _, view = next(iter(ds.scan(axis=axis, key="key")))
                original_ordering_data = view[:, :]

        np.testing.assert_almost_equal(no_ordering_data, original_ordering_data,
                                       err_msg="Default ordering should same as in file") 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import connect [as 別名]
def test_new() -> None:
	with loompy.new("test.loom") as ds:
		m = np.zeros((20, 100))
		ra = {"Gene": [x for x in "ABCDEFGHILJKLMNOPQRS"]}
		ca = {"Cell": np.arange(100)}
		ds.add_columns(m, ca, row_attrs=ra)
		ds.add_columns(m, ca, row_attrs=ra)
	with loompy.connect("test.loom") as ds:
		assert(ds.shape == (20, 200)) 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import connect [as 別名]
def test_sparse() -> None:
	G = 1000
	C = 100
	S = sparse.eye(G, C)

	loompy.create('test.loom', S, {'g_id': np.arange(G)}, {'c_id': np.arange(C)})
	with loompy.connect("test.loom") as ds:
		ds["layer"] = S
		assert(np.all(ds[:, :] == S.toarray()))
		assert(np.all(ds.sparse().data == S.tocoo().data))
		assert(np.all(ds.layers["layer"][:, :] == S.toarray()))
		assert(np.all(ds.layers["layer"].sparse().data == S.tocoo().data)) 

# 需要導入模塊: import loompy [as 別名]
# 或者: from loompy import connect [as 別名]
def populate(self):
        logger.info("Loading smFISH dataset")
        ds = loompy.connect(os.path.join(self.save_path, self.filenames[0]))
        gene_names = ds.ra["Gene"].astype(np.str)

        labels = ds.ca["ClusterID"].reshape(-1, 1)
        tmp_cell_types = np.asarray(ds.ca["ClusterName"])

        u_labels, u_index = np.unique(labels.ravel(), return_index=True)
        cell_types = ["" for _ in range(max(u_labels) + 1)]
        for i, index in zip(u_labels, u_index):
            cell_types[i] = tmp_cell_types[index]
        cell_types = np.asarray(cell_types, dtype=np.str)

        x_coord, y_coord = ds.ca["X"], ds.ca["Y"]
        x_coord = x_coord.reshape((-1, 1))
        y_coord = y_coord.reshape((-1, 1))
        data = ds[:, :].T
        self.populate_from_data(
            X=data,
            labels=labels,
            gene_names=gene_names,
            cell_types=cell_types,
            cell_attributes_dict={"x_coord": x_coord, "y_coord": y_coord},
            remap_attributes=False,
        )
        major_clusters = dict(
            [
                ((3, 2), "Astrocytes"),
                ((7, 26), "Endothelials"),
                ((18, 17, 14, 19, 15, 16, 20), "Inhibitory"),
                ((29, 28), "Microglias"),
                ((32, 33, 30, 22, 21), "Oligodendrocytes"),
                ((9, 8, 10, 6, 5, 4, 12, 1, 13), "Pyramidals"),
            ]
        )
        if self.use_high_level_cluster:
            self.map_cell_types(major_clusters)
            self.filter_cell_types(
                [
                    "Astrocytes",
                    "Endothelials",
                    "Inhibitory",
                    "Microglias",
                    "Oligodendrocytes",
                    "Pyramidals",
                ]
            )

        self.remap_categorical_attributes()