numpoly.load

numpoly.load(file: Union[BinaryIO, os.PathLike], mmap_mode: Optional[str] = None, allow_pickle: bool = False, fix_imports: bool = True, encoding: str = 'ASCII') → Union[numpoly.baseclass.ndpoly, Dict[str, numpoly.baseclass.ndpoly]]

Load polynomial or pickled objects from .np{y,z} or pickled files.

Args:

file:

The file to read. File-like objects must support the seek() and read() methods. Pickled files require that the file-like object support the readline() method as well.

mmap_mode:

If not None, then memory-map the file, using the given mode (see numpy.memmap for a detailed description of the modes). A memory-mapped array is kept on disk. However, it can be accessed and sliced like any ndarray. Memory mapping is especially useful for accessing small fragments of large files without reading the entire file into memory.

allow_pickle:

Allow loading pickled object arrays stored in npy files. Reasons for disallowing pickles include security, as loading pickled data can execute arbitrary code. If pickles are disallowed, loading object arrays will fail.

fix_imports:

Only useful when loading Python 2 generated pickled files on Python 3, which includes npy/npz files containing object arrays. If fix_imports is True, pickle will try to map the old Python 2 names to the new names used in Python 3.

encoding:

What encoding to use when reading Python 2 strings. Only useful when loading Python 2 generated pickled files in Python 3, which includes npy/npz files containing object arrays. Values other than ‘latin1’, ‘ASCII’, and ‘bytes’ are not allowed, as they can corrupt numerical data.

Return:

Data stored in the file. For .npz files, the returned dictionary class must be closed to avoid leaking file descriptors.

Example:

>>> q0, q1 = numpoly.variable(2)
>>> poly = numpoly.polynomial([q0, q1-1])
>>> array = numpy.array([1, 2])
>>> numpoly.savez("/tmp/savez.npz", a=array, p=poly)
>>> numpoly.load("/tmp/savez.npz")
{'a': array([1, 2]), 'p': polynomial([q0, q1-1])}