Python scipy.io.mmwrite() Examples

The following are 4 code examples for showing how to use scipy.io.mmwrite(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: CITE-seq-Count   Author: Hoohm   File: io.py    License: MIT License 6 votes vote down vote up
def write_to_files(sparse_matrix, top_cells, ordered_tags_map, data_type, outfolder):
    """Write the umi and read sparse matrices to file in gzipped mtx format.

    Args:
        sparse_matrix (dok_matrix): Results in a sparse matrix.
        top_cells (set): Set of cells that are selected for output.
        ordered_tags_map (dict): Tags in order with indexes as values.
        data_type (string): A string definning if the data is umi or read based.
        outfolder (string): Path to the output folder.
    """
    prefix = os.path.join(outfolder,data_type + '_count')
    os.makedirs(prefix, exist_ok=True)
    io.mmwrite(os.path.join(prefix,'matrix.mtx'),sparse_matrix)
    with gzip.open(os.path.join(prefix,'barcodes.tsv.gz'), 'wb') as barcode_file:
        for barcode in top_cells:
            barcode_file.write('{}\n'.format(barcode).encode())
    with gzip.open(os.path.join(prefix,'features.tsv.gz'), 'wb') as feature_file:
        for feature in ordered_tags_map:
            feature_file.write('{}\n'.format(feature).encode())
    with open(os.path.join(prefix,'matrix.mtx'),'rb') as mtx_in:
        with gzip.open(os.path.join(prefix,'matrix.mtx') + '.gz','wb') as mtx_gz:
            shutil.copyfileobj(mtx_in, mtx_gz)
    os.remove(os.path.join(prefix,'matrix.mtx')) 
Example 2
Project: EDeN   Author: fabriziocosta   File: util.py    License: MIT License 5 votes vote down vote up
def store_matrix(matrix='',
                 output_dir_path='',
                 out_file_name='',
                 output_format=''):
    """store_matrix."""
    if not os.path.exists(output_dir_path):
        os.mkdir(output_dir_path)
    full_out_file_name = os.path.join(output_dir_path, out_file_name)
    if output_format == "MatrixMarket":
        if len(matrix.shape) == 1:
            raise Exception(
                "'MatrixMarket' format supports only 2D dimensional array\
                and not vectors")
        else:
            io.mmwrite(full_out_file_name, matrix, precision=None)
    elif output_format == "numpy":
        np.save(full_out_file_name, matrix)
    elif output_format == "joblib":
        joblib.dump(matrix, full_out_file_name)
    elif output_format == "text":
        with open(full_out_file_name, "w") as f:
            if len(matrix.shape) == 1:
                for x in matrix:
                    f.write("%s\n" % (x))
            else:
                raise Exception(
                    "'text' format supports only mono dimensional array\
                    and not matrices")
    logger.info("Written file: %s" % full_out_file_name) 
Example 3
Project: BootEA   Author: nju-websoft   File: train_funcs.py    License: MIT License 5 votes vote down vote up
def generate_related_mat(folder, triples1, triples2, ref_ent1, ref_ent2):
    t = time.time()
    if "15" in folder:
        out_related_file = folder + "out_related_mat.npy"
        in_related_file = folder + "in_related_mat.npy"
        if os.path.exists(out_related_file):
            out_related_mat = np.load(out_related_file)
        else:
            out_related_mat = generate_out_related_mat(triples1, triples2, ref_ent1, ref_ent2)
            np.save(out_related_file, out_related_mat)
        if os.path.exists(in_related_file):
            in_related_mat = np.load(in_related_file)
        else:
            in_related_mat = generate_in_related_mat(triples1, triples2, ref_ent1, ref_ent2)
            np.save(in_related_file, in_related_mat)
        related_mat1 = out_related_mat
        # related_mat2 = out_related_mat + in_related_mat
        print("load related mat", round(time.time() - t, 2))
        return related_mat1
    else:
        out_related_file = folder + "out_related_mat.mtx"
        in_related_file = folder + "in_related_mat.mtx"
        if os.path.exists(out_related_file):
            out_related_mat = io.mmread(out_related_file)
        else:
            out_related_mat = generate_out_related_mat(triples1, triples2, ref_ent1, ref_ent2)
            io.mmwrite(out_related_file, sp.sparse.lil_matrix(out_related_mat))
        if os.path.exists(in_related_file):
            in_related_mat = io.mmread(in_related_file)
        else:
            in_related_mat = generate_in_related_mat(triples1, triples2, ref_ent1, ref_ent2)
            io.mmwrite(in_related_file, in_related_mat)
        related_mat1 = out_related_mat
        # related_mat2 = out_related_mat + in_related_mat
        print("load related mat", round(time.time() - t, 2))
        return related_mat1 
Example 4
Project: scprep   Author: KrishnaswamyLab   File: mtx.py    License: GNU General Public License v3.0 5 votes vote down vote up
def save_mtx(data, destination, cell_names=None, gene_names=None):
    """Save a mtx file
    
    Parameters
    ----------
    data : array-like, shape=[n_samples, n_features]
        Input data, saved to destination/matrix.mtx
    destination : str
        Directory in which to save the data
    cell_names : list-like, shape=[n_samples], optional (default: None)
        Cell names associated with rows, saved to destination/cell_names.tsv.
        If `data` is a pandas DataFrame and `cell_names` is None,
        these are autopopulated from `data.index`.
    gene_names : list-like, shape=[n_features], optional (default: None)
        Cell names associated with rows, saved to destination/gene_names.tsv.
        If `data` is a pandas DataFrame and `gene_names` is None,
        these are autopopulated from `data.columns`.

    Examples
    --------
    >>> import scprep
    >>> scprep.io.save_mtx(data, destination="my_data")
    >>> reload = scprep.io.load_mtx("my_data/matrix.mtx",
    ...                             cell_names="my_data/cell_names.tsv",
    ...                             gene_names="my_data/gene_names.tsv")
    """
    if isinstance(data, pd.DataFrame):
        if cell_names is None:
            cell_names = data.index
        if gene_names is None:
            gene_names = data.columns
    data = utils.to_array_or_spmatrix(data)
    data = sparse.coo_matrix(data)
    # handle ~/ and relative paths
    destination = os.path.expanduser(destination)
    if not os.path.isdir(destination):
        os.mkdir(destination)
    if cell_names is not None:
        with open(os.path.join(destination, "cell_names.tsv"), "w") as handle:
            for name in cell_names:
                handle.write("{}\n".format(name))
    if gene_names is not None:
        with open(os.path.join(destination, "gene_names.tsv"), "w") as handle:
            for name in gene_names:
                handle.write("{}\n".format(name))
    sio.mmwrite(os.path.join(destination, "matrix.mtx"), data)