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save_npz

Save a sparse matrix to disk in numpy's .npz format. Note: This is not binary compatible with scipy's save_npz(). This binary format is not currently stable. Will save a file that can only be opend with this package's load_npz().

Parameters:

Name Type Description Default
filename string or file

Either the file name (string) or an open file (file-like object) where the data will be saved. If file is a string or a Path, the .npz extension will be appended to the file name if it is not already there

 required
matrix SparseArray

The matrix to save to disk

 required
compressed bool_

Whether to save in compressed or uncompressed mode

 True

Examples:

Store sparse matrix to disk, and load it again:

>>> import os
>>> import sparse
>>> import numpy as np
>>> dense_mat = np.array([[[0.0, 0.0], [0.0, 0.70677779]], [[0.0, 0.0], [0.0, 0.86522495]]])
>>> mat = sparse.COO(dense_mat)
>>> mat
<COO: shape=(2, 2, 2), dtype=float64, nnz=2, fill_value=0.0>
>>> sparse.save_npz("mat.npz", mat)
>>> loaded_mat = sparse.load_npz("mat.npz")
>>> loaded_mat
<COO: shape=(2, 2, 2), dtype=float64, nnz=2, fill_value=0.0>
>>> os.remove("mat.npz")
See Also
Source code in sparse/numba_backend/_io.py 
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def save_npz(filename, matrix, compressed=True):
    """Save a sparse matrix to disk in numpy's `.npz` format.
    Note: This is not binary compatible with scipy's `save_npz()`.
    This binary format is not currently stable. Will save a file
    that can only be opend with this package's `load_npz()`.

    Parameters
    ----------
    filename : string or file
        Either the file name (string) or an open file (file-like object)
        where the data will be saved. If file is a string or a Path, the
        `.npz` extension will be appended to the file name if it is not
        already there
    matrix : SparseArray
        The matrix to save to disk
    compressed : bool
        Whether to save in compressed or uncompressed mode

    Examples
    --------
    Store sparse matrix to disk, and load it again:

    >>> import os
    >>> import sparse
    >>> import numpy as np
    >>> dense_mat = np.array([[[0.0, 0.0], [0.0, 0.70677779]], [[0.0, 0.0], [0.0, 0.86522495]]])
    >>> mat = sparse.COO(dense_mat)
    >>> mat
    <COO: shape=(2, 2, 2), dtype=float64, nnz=2, fill_value=0.0>
    >>> sparse.save_npz("mat.npz", mat)
    >>> loaded_mat = sparse.load_npz("mat.npz")
    >>> loaded_mat
    <COO: shape=(2, 2, 2), dtype=float64, nnz=2, fill_value=0.0>
    >>> os.remove("mat.npz")

    See Also
    --------
    - [`sparse.load_npz`][]
    - [`scipy.sparse.save_npz`][]
    - [`scipy.sparse.load_npz`][]
    - [`numpy.savez`][]
    - [`numpy.load`][]

    """

    nodes = {
        "data": matrix.data,
        "shape": matrix.shape,
        "fill_value": matrix.fill_value,
    }

    if type(matrix) is COO:
        nodes["coords"] = matrix.coords
    elif type(matrix) is GCXS:
        nodes["indices"] = matrix.indices
        nodes["indptr"] = matrix.indptr
        nodes["compressed_axes"] = matrix.compressed_axes

    if compressed:
        np.savez_compressed(filename, **nodes)
    else:
        np.savez(filename, **nodes)