Load a sparse matrix in numpy's .npz format from disk.
Note: This is not binary compatible with scipy's save_npz()
output. This binary format is not currently stable.
Will only load files saved by this package.
Parameters:
| Name |
Type |
Description |
Default |
filename
|
file-like object, string, or pathlib.Path
|
The file to read. File-like objects must support the
seek() and read() methods.
|
required
|
Returns:
| Type |
Description |
SparseArray
|
The sparse matrix at path filename.
|
Examples:
See sparse.save_npz for usage examples.
See Also
Source code in sparse/numba_backend/_io.py
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132 | 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. This binary format is not currently stable.
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
-------
SparseArray
The sparse matrix at path `filename`.
Examples
--------
See [`sparse.save_npz`][] for usage examples.
See Also
--------
- [`sparse.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:
pass
try:
data = fp["data"]
indices = fp["indices"]
indptr = fp["indptr"]
comp_axes = fp["compressed_axes"]
shape = tuple(fp["shape"])
fill_value = fp["fill_value"][()]
return GCXS(
(data, indices, indptr),
shape=shape,
fill_value=fill_value,
compressed_axes=comp_axes,
)
except KeyError as e:
raise RuntimeError(f"The file {filename!s} does not contain a valid sparse matrix") from e
|