"""Load polynomial or pickled objects from ``.np{y,z}`` or pickled files."""
from __future__ import annotations
from typing import BinaryIO, Dict, Optional, Union
from os import PathLike
import numpy
import numpoly
from ..baseclass import ndpoly
[docs]def load(
file: Union[BinaryIO, PathLike],
mmap_mode: Optional[str] = None,
allow_pickle: bool = False,
fix_imports: bool = True,
encoding: str = "ASCII",
) -> Union[ndpoly, Dict[str, 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])}
"""
if isinstance(file, (str, bytes, PathLike)):
with open(file, "rb") as src:
return load(
file=src,
mmap_mode=mmap_mode,
allow_pickle=allow_pickle,
fix_imports=fix_imports,
encoding=encoding,
)
out = numpy.load(
file=file,
mmap_mode=mmap_mode,
allow_pickle=allow_pickle,
fix_imports=fix_imports,
encoding=encoding,
)
if isinstance(out, numpy.lib.npyio.NpzFile): # type: ignore
out = dict(out)
for key, value in list(out.items()):
# Classical arrays untouched
if not value.dtype.names:
continue
# Structured array are assumed polynomials
del out[key]
for name in value.dtype.names:
if not name.isdigit():
length = len(name)
break
key = key.split("-")
names = tuple(key[:length])
key = "-".join(key[length:])
out[key] = numpoly.polynomial(value, names=names)
elif out.dtype.names:
names = numpy.load(
file=file,
mmap_mode=mmap_mode,
allow_pickle=allow_pickle,
fix_imports=fix_imports,
encoding=encoding,
).tolist()
out = numpoly.polynomial(out, names=names)
return out