Source code for

import numpy as np

from .coo.core import COO

[docs]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()``. 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 : COO The matrix to save to disk compressed : bool Whether to save in compressed or uncompressed mode Example -------- Store sparse matrix to disk, and load it again: >>> import os >>> import sparse >>> import numpy as np >>> dense_mat = np.array([[[0., 0.], [0., 0.70677779]], [[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 -------- load_npz scipy.sparse.save_npz scipy.sparse.load_npz numpy.savez numpy.load """ nodes = { 'data':, 'coords': matrix.coords, 'shape': matrix.shape, 'fill_value': matrix.fill_value, } if compressed: np.savez_compressed(filename, **nodes) else: np.savez(filename, **nodes)
[docs]def load_npz(filename): """ Load a sparse matrix in numpy's ``.npz`` format from disk. Note: This is not binary compatible with scipy's ``save_npz()`` output. Will only load files saved by this package. Parameters ---------- filename : file-like object, string, or pathlib.Path The file to read. File-like objects must support the ``seek()`` and ``read()`` methods. Returns ------- COO The sparse matrix at path ``filename`` Example -------- See :obj:`save_npz` for usage examples. See Also -------- save_npz scipy.sparse.save_npz scipy.sparse.load_npz numpy.savez numpy.load """ with np.load(filename) as fp: try: coords = fp['coords'] data = fp['data'] shape = tuple(fp['shape']) fill_value = fp['fill_value'][()] return COO(coords=coords, data=data, shape=shape, sorted=True, has_duplicates=False, fill_value=fill_value) except KeyError: raise RuntimeError('The file {!s} does not contain a valid sparse matrix'.format(filename))