def _dump(self, file, protocol=2):
if hasattr(file, "write"):
ctx = contextlib_nullcontext(file)
else:
ctx = open(os_fspath(file), "wb")
with ctx as f:
pickle.dump(self, f, protocol=protocol)
def write_array(fp,
array,
version=None,
allow_pickle=True,
pickle_kwargs=None):
"""
Write an array to an NPY file, including a header.
If the array is neither C-contiguous nor Fortran-contiguous AND the
file_like object is not a real file object, this function will have to
copy data in memory.
Parameters
----------
fp : file_like object
An open, writable file object, or similar object with a
``.write()`` method.
array : ndarray
The array to write to disk.
version : (int, int) or None, optional
The version number of the format. None means use the oldest
supported version that is able to store the data. Default: None
allow_pickle : bool, optional
Whether to allow writing pickled data. Default: True
pickle_kwargs : dict, optional
Additional keyword arguments to pass to pickle.dump, excluding
'protocol'. These are only useful when pickling objects in object
arrays on Python 3 to Python 2 compatible format.
Raises
------
ValueError
If the array cannot be persisted. This includes the case of
allow_pickle=False and array being an object array.
Various other errors
If the array contains Python objects as part of its dtype, the
process of pickling them may raise various errors if the objects
are not picklable.
"""
_check_version(version)
_write_array_header(fp, header_data_from_array_1_0(array), version)
if array.itemsize == 0:
buffersize = 0
else:
# Set buffer size to 16 MiB to hide the Python loop overhead.
buffersize = max(16 * 1024**2 // array.itemsize, 1)
if array.dtype.hasobject:
# We contain Python objects so we cannot write out the data
# directly. Instead, we will pickle it out
if not allow_pickle:
raise ValueError("Object arrays cannot be saved when "
"allow_pickle=False")
if pickle_kwargs is None:
pickle_kwargs = {}
pickle.dump(array, fp, protocol=3, **pickle_kwargs)
elif array.flags.f_contiguous and not array.flags.c_contiguous:
if isfileobj(fp):
array.T.tofile(fp)
else:
for chunk in numpy.nditer(
array,
flags=['external_loop', 'buffered', 'zerosize_ok'],
buffersize=buffersize,
order='F'):
fp.write(chunk.tobytes('C'))
else:
if isfileobj(fp):
array.tofile(fp)
else:
for chunk in numpy.nditer(
array,
flags=['external_loop', 'buffered', 'zerosize_ok'],
buffersize=buffersize,
order='C'):
fp.write(chunk.tobytes('C'))
def save_cache(cache):
with open(CACHE_FILE + '.new', 'wb') as f:
pickle.dump(cache, f)
os.rename(CACHE_FILE + '.new', CACHE_FILE)
def write_array(fp, array, version=None, allow_pickle=True, pickle_kwargs=None):
"""
Write an array to an NPY file, including a header.
If the array is neither C-contiguous nor Fortran-contiguous AND the
file_like object is not a real file object, this function will have to
copy data in memory.
Parameters
----------
fp : file_like object
An open, writable file object, or similar object with a
``.write()`` method.
array : ndarray
The array to write to disk.
version : (int, int) or None, optional
The version number of the format. None means use the oldest
supported version that is able to store the data. Default: None
allow_pickle : bool, optional
Whether to allow writing pickled data. Default: True
pickle_kwargs : dict, optional
Additional keyword arguments to pass to pickle.dump, excluding
'protocol'. These are only useful when pickling objects in object
arrays on Python 3 to Python 2 compatible format.
Raises
------
ValueError
If the array cannot be persisted. This includes the case of
allow_pickle=False and array being an object array.
Various other errors
If the array contains Python objects as part of its dtype, the
process of pickling them may raise various errors if the objects
are not picklable.
"""
_check_version(version)
used_ver = _write_array_header(fp, header_data_from_array_1_0(array),
version)
# this warning can be removed when 1.9 has aged enough
if version != (2, 0) and used_ver == (2, 0):
warnings.warn("Stored array in format 2.0. It can only be"
"read by NumPy >= 1.9", UserWarning, stacklevel=2)
if array.itemsize == 0:
buffersize = 0
else:
# Set buffer size to 16 MiB to hide the Python loop overhead.
buffersize = max(16 * 1024 ** 2 // array.itemsize, 1)
if array.dtype.hasobject:
# We contain Python objects so we cannot write out the data
# directly. Instead, we will pickle it out with version 2 of the
# pickle protocol.
if not allow_pickle:
raise ValueError("Object arrays cannot be saved when "
"allow_pickle=False")
if pickle_kwargs is None:
pickle_kwargs = {}
pickle.dump(array, fp, protocol=2, **pickle_kwargs)
elif array.flags.f_contiguous and not array.flags.c_contiguous:
if isfileobj(fp):
array.T.tofile(fp)
else:
for chunk in numpy.nditer(
array, flags=['external_loop', 'buffered', 'zerosize_ok'],
buffersize=buffersize, order='F'):
fp.write(chunk.tobytes('C'))
else:
if isfileobj(fp):
array.tofile(fp)
else:
for chunk in numpy.nditer(
array, flags=['external_loop', 'buffered', 'zerosize_ok'],
buffersize=buffersize, order='C'):
fp.write(chunk.tobytes('C'))
