jax.numpy.save — JAX documentation (original) (raw)
jax.numpy.save#
jax.numpy.save(file, arr, allow_pickle=True, fix_imports=)#
Save an array to a binary file in NumPy .npy format.
Parameters:
- file (file , str, or pathlib.Path) – File or filename to which the data is saved. If file is a file-object, then the filename is unchanged. If file is a string or Path, a
.npyextension will be appended to the filename if it does not already have one. - arr (array_like) – Array data to be saved.
- allow_pickle (bool, optional) – Allow saving object arrays using Python pickles. Reasons for disallowing pickles include security (loading pickled data can execute arbitrary code) and portability (pickled objects may not be loadable on different Python installations, for example if the stored objects require libraries that are not available, and not all pickled data is compatible between different versions of Python). Default: True
- fix_imports (bool, optional) –
The fix_imports flag is deprecated and has no effect.
Deprecated since version 2.1: This flag is ignored since NumPy 1.17 and was only needed to support loading in Python 2 some files written in Python 3.
See also
Save several arrays into a .npz archive
savetxt, load
Notes
For a description of the .npy format, see numpy.lib.format.
Any data saved to the file is appended to the end of the file.
Examples
from tempfile import TemporaryFile outfile = TemporaryFile()
x = np.arange(10) np.save(outfile, x)
_ = outfile.seek(0) # Only needed to simulate closing & reopening file np.load(outfile) array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
with open('test.npy', 'wb') as f: ... np.save(f, np.array([1, 2])) ... np.save(f, np.array([1, 3])) with open('test.npy', 'rb') as f: ... a = np.load(f) ... b = np.load(f) print(a, b)