def load_cache():
if os.path.exists(CACHE_FILE):
with open(CACHE_FILE, 'rb') as f:
try:
cache = pickle.load(f)
except Exception:
cache = {}
else:
cache = {}
return cache
def load_cache():
if os.path.exists(CACHE_FILE):
f = open(CACHE_FILE, 'rb')
try:
cache = pickle.load(f)
except Exception:
cache = {}
finally:
f.close()
else:
cache = {}
return cache
def load_cache():
if os.path.exists(CACHE_FILE):
f = open(CACHE_FILE, 'rb')
try:
cache = pickle.load(f)
except Exception:
cache = {}
finally:
f.close()
else:
cache = {}
return cache
def read_array(fp, allow_pickle=True, pickle_kwargs=None):
"""
Read an array from an NPY file.
Parameters
----------
fp : file_like object
If this is not a real file object, then this may take extra memory
and time.
allow_pickle : bool, optional
Whether to allow reading pickled data. Default: True
pickle_kwargs : dict
Additional keyword arguments to pass to pickle.load. These are only
useful when loading object arrays saved on Python 2 when using
Python 3.
Returns
-------
array : ndarray
The array from the data on disk.
Raises
------
ValueError
If the data is invalid, or allow_pickle=False and the file contains
an object array.
"""
version = read_magic(fp)
_check_version(version)
shape, fortran_order, dtype = _read_array_header(fp, version)
if len(shape) == 0:
count = 1
else:
count = numpy.multiply.reduce(shape, dtype=numpy.int64)
# Now read the actual data.
if dtype.hasobject:
# The array contained Python objects. We need to unpickle the data.
if not allow_pickle:
raise ValueError("Object arrays cannot be loaded when "
"allow_pickle=False")
if pickle_kwargs is None:
pickle_kwargs = {}
try:
array = pickle.load(fp, **pickle_kwargs)
except UnicodeError as err:
if sys.version_info[0] >= 3:
# Friendlier error message
raise UnicodeError("Unpickling a python object failed: %r\n"
"You may need to pass the encoding= option "
"to numpy.load" % (err,))
raise
else:
if isfileobj(fp):
# We can use the fast fromfile() function.
array = numpy.fromfile(fp, dtype=dtype, count=count)
else:
# This is not a real file. We have to read it the
# memory-intensive way.
# crc32 module fails on reads greater than 2 ** 32 bytes,
# breaking large reads from gzip streams. Chunk reads to
# BUFFER_SIZE bytes to avoid issue and reduce memory overhead
# of the read. In non-chunked case count < max_read_count, so
# only one read is performed.
# Use np.ndarray instead of np.empty since the latter does
# not correctly instantiate zero-width string dtypes; see
# https://github.com/numpy/numpy/pull/6430
array = numpy.ndarray(count, dtype=dtype)
if dtype.itemsize > 0:
# If dtype.itemsize == 0 then there's nothing more to read
max_read_count = BUFFER_SIZE // min(BUFFER_SIZE, dtype.itemsize)
for i in range(0, count, max_read_count):
read_count = min(max_read_count, count - i)
read_size = int(read_count * dtype.itemsize)
data = _read_bytes(fp, read_size, "array data")
array[i:i+read_count] = numpy.frombuffer(data, dtype=dtype,
count=read_count)
if fortran_order:
array.shape = shape[::-1]
array = array.transpose()
else:
array.shape = shape
return array
def read_array(fp, allow_pickle=False, pickle_kwargs=None):
"""
Read an array from an NPY file.
Parameters
----------
fp : file_like object
If this is not a real file object, then this may take extra memory
and time.
allow_pickle : bool, optional
Whether to allow writing pickled data. Default: False
.. versionchanged:: 1.16.3
Made default False in response to CVE-2019-6446.
pickle_kwargs : dict
Additional keyword arguments to pass to pickle.load. These are only
useful when loading object arrays saved on Python 2 when using
Python 3.
Returns
-------
array : ndarray
The array from the data on disk.
Raises
------
ValueError
If the data is invalid, or allow_pickle=False and the file contains
an object array.
"""
version = read_magic(fp)
_check_version(version)
shape, fortran_order, dtype = _read_array_header(fp, version)
if len(shape) == 0:
count = 1
else:
count = numpy.multiply.reduce(shape, dtype=numpy.int64)
# Now read the actual data.
if dtype.hasobject:
# The array contained Python objects. We need to unpickle the data.
if not allow_pickle:
raise ValueError("Object arrays cannot be loaded when "
"allow_pickle=False")
if pickle_kwargs is None:
pickle_kwargs = {}
try:
array = pickle.load(fp, **pickle_kwargs)
except UnicodeError as err:
if sys.version_info[0] >= 3:
# Friendlier error message
raise UnicodeError("Unpickling a python object failed: %r\n"
"You may need to pass the encoding= option "
"to numpy.load" % (err, ))
raise
else:
if isfileobj(fp):
# We can use the fast fromfile() function.
array = numpy.fromfile(fp, dtype=dtype, count=count)
else:
# This is not a real file. We have to read it the
# memory-intensive way.
# crc32 module fails on reads greater than 2 ** 32 bytes,
# breaking large reads from gzip streams. Chunk reads to
# BUFFER_SIZE bytes to avoid issue and reduce memory overhead
# of the read. In non-chunked case count < max_read_count, so
# only one read is performed.
# Use np.ndarray instead of np.empty since the latter does
# not correctly instantiate zero-width string dtypes; see
# https://github.com/numpy/numpy/pull/6430
array = numpy.ndarray(count, dtype=dtype)
if dtype.itemsize > 0:
# If dtype.itemsize == 0 then there's nothing more to read
max_read_count = BUFFER_SIZE // min(BUFFER_SIZE,
dtype.itemsize)
for i in range(0, count, max_read_count):
read_count = min(max_read_count, count - i)
read_size = int(read_count * dtype.itemsize)
data = _read_bytes(fp, read_size, "array data")
array[i:i + read_count] = numpy.frombuffer(
data, dtype=dtype, count=read_count)
if fortran_order:
array.shape = shape[::-1]
array = array.transpose()
else:
array.shape = shape
return array