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frontierphysics-pr44-evidence

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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")
E ValueError: Object arrays cannot be loaded when allow_pickle=False
/usr/local/lib/python3.12/site-packages/numpy/lib/format.py:822: ValueError
=================================== FAILURES ===================================
________________ test_public_schema_and_frozen_input_integrity _________________
def test_public_schema_and_frozen_input_integrity() -> None:
assert hashlib.sha256(PUBLIC_CONFIG_PATH.read_bytes()).hexdigest() == PUBLIC_CONFIG_SHA256
> result, arrays = validate_result_and_archive(PUBLIC_CONFIG_PATH, RESULT_PATH, SAMPLES_PATH)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
/verifier/test_outputs.py:199:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/verifier/test_outputs.py:168: in validate_result_and_archive
arrays = load_archive(archive_path)
^^^^^^^^^^^^^^^^^^^^^^^^^^
/verifier/test_outputs.py:77: in load_archive
return {name: np.array(archive[name], copy=True) for name in archive.files}
^^^^^^^^^^^^^
/usr/local/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py:258: in __getitem__
return format.read_array(bytes,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
fp = <zipfile.ZipExtFile name='model_names.npy' mode='r'>, allow_pickle = False
pickle_kwargs = {'encoding': 'ASCII', 'fix_imports': True}
def read_array(fp, allow_pickle=False, pickle_kwargs=None, *,
max_header_size=_MAX_HEADER_SIZE):
"""
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.
max_header_size : int, optional
Maximum allowed size of the header. Large headers may not be safe
to load securely and thus require explicitly passing a larger value.
See :py:func:`ast.literal_eval()` for details.
This option is ignored when `allow_pickle` is passed. In that case
the file is by definition trusted and the limit is unnecessary.
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.
"""
if allow_pickle:
# Effectively ignore max_header_size, since `allow_pickle` indicates
# that the input is fully trusted.
max_header_size = 2**64
version = read_magic(fp)
_check_version(version)
shape, fortran_order, dtype = _read_array_header(
fp, version, max_header_size=max_header_size)
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")
E ValueError: Object arrays cannot be loaded when allow_pickle=False
/usr/local/lib/python3.12/site-packages/numpy/lib/format.py:822: ValueError
______________ test_npz_physical_ranges_histograms_and_replicates ______________