- jax.numpy.save(file, arr, allow_pickle=True, fix_imports=True)#
Save an array to a binary file in NumPy
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 Python 2 and Python 3). Default: True
fix_imports (bool, optional) – Only useful in forcing objects in object arrays on Python 3 to be pickled in a Python 2 compatible way. If fix_imports is True, pickle will try to map the new Python 3 names to the old module names used in Python 2, so that the pickle data stream is readable with Python 2.
For a description of the
Any data saved to the file is appended to the end of the file.
>>> from tempfile import TemporaryFile >>> outfile = TemporaryFile()
>>> x = np.arange(10) >>> np.save(outfile, x)
>>> _ = outfile.seek(0) # Only needed here 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) # [1 2] [1 3]