553 lines
22 KiB
Python
553 lines
22 KiB
Python
# Pytest customization
|
|
import json
|
|
import os
|
|
import warnings
|
|
import tempfile
|
|
from contextlib import contextmanager
|
|
|
|
import numpy as np
|
|
import numpy.testing as npt
|
|
import pytest
|
|
import hypothesis
|
|
|
|
from scipy._lib._fpumode import get_fpu_mode
|
|
from scipy._lib._testutils import FPUModeChangeWarning
|
|
from scipy._lib._array_api import SCIPY_ARRAY_API, SCIPY_DEVICE
|
|
from scipy._lib import _pep440
|
|
|
|
try:
|
|
from scipy_doctest.conftest import dt_config
|
|
HAVE_SCPDT = True
|
|
except ModuleNotFoundError:
|
|
HAVE_SCPDT = False
|
|
|
|
try:
|
|
import pytest_run_parallel # noqa:F401
|
|
PARALLEL_RUN_AVAILABLE = True
|
|
except Exception:
|
|
PARALLEL_RUN_AVAILABLE = False
|
|
|
|
|
|
def pytest_configure(config):
|
|
config.addinivalue_line("markers",
|
|
"slow: Tests that are very slow.")
|
|
config.addinivalue_line("markers",
|
|
"xslow: mark test as extremely slow (not run unless explicitly requested)")
|
|
config.addinivalue_line("markers",
|
|
"xfail_on_32bit: mark test as failing on 32-bit platforms")
|
|
try:
|
|
import pytest_timeout # noqa:F401
|
|
except Exception:
|
|
config.addinivalue_line(
|
|
"markers", 'timeout: mark a test for a non-default timeout')
|
|
try:
|
|
# This is a more reliable test of whether pytest_fail_slow is installed
|
|
# When I uninstalled it, `import pytest_fail_slow` didn't fail!
|
|
from pytest_fail_slow import parse_duration # type: ignore[import-not-found] # noqa:F401,E501
|
|
except Exception:
|
|
config.addinivalue_line(
|
|
"markers", 'fail_slow: mark a test for a non-default timeout failure')
|
|
config.addinivalue_line("markers",
|
|
"skip_xp_backends(backends, reason=None, np_only=False, cpu_only=False, "
|
|
"exceptions=None): "
|
|
"mark the desired skip configuration for the `skip_xp_backends` fixture.")
|
|
config.addinivalue_line("markers",
|
|
"xfail_xp_backends(backends, reason=None, np_only=False, cpu_only=False, "
|
|
"exceptions=None): "
|
|
"mark the desired xfail configuration for the `xfail_xp_backends` fixture.")
|
|
if not PARALLEL_RUN_AVAILABLE:
|
|
config.addinivalue_line(
|
|
'markers',
|
|
'parallel_threads(n): run the given test function in parallel '
|
|
'using `n` threads.')
|
|
config.addinivalue_line(
|
|
"markers",
|
|
"thread_unsafe: mark the test function as single-threaded",
|
|
)
|
|
config.addinivalue_line(
|
|
"markers",
|
|
"iterations(n): run the given test function `n` times in each thread",
|
|
)
|
|
|
|
|
|
def pytest_runtest_setup(item):
|
|
mark = item.get_closest_marker("xslow")
|
|
if mark is not None:
|
|
try:
|
|
v = int(os.environ.get('SCIPY_XSLOW', '0'))
|
|
except ValueError:
|
|
v = False
|
|
if not v:
|
|
pytest.skip("very slow test; "
|
|
"set environment variable SCIPY_XSLOW=1 to run it")
|
|
mark = item.get_closest_marker("xfail_on_32bit")
|
|
if mark is not None and np.intp(0).itemsize < 8:
|
|
pytest.xfail(f'Fails on our 32-bit test platform(s): {mark.args[0]}')
|
|
|
|
# Older versions of threadpoolctl have an issue that may lead to this
|
|
# warning being emitted, see gh-14441
|
|
with npt.suppress_warnings() as sup:
|
|
sup.filter(pytest.PytestUnraisableExceptionWarning)
|
|
|
|
try:
|
|
from threadpoolctl import threadpool_limits
|
|
|
|
HAS_THREADPOOLCTL = True
|
|
except Exception: # observed in gh-14441: (ImportError, AttributeError)
|
|
# Optional dependency only. All exceptions are caught, for robustness
|
|
HAS_THREADPOOLCTL = False
|
|
|
|
if HAS_THREADPOOLCTL:
|
|
# Set the number of openmp threads based on the number of workers
|
|
# xdist is using to prevent oversubscription. Simplified version of what
|
|
# sklearn does (it can rely on threadpoolctl and its builtin OpenMP helper
|
|
# functions)
|
|
try:
|
|
xdist_worker_count = int(os.environ['PYTEST_XDIST_WORKER_COUNT'])
|
|
except KeyError:
|
|
# raises when pytest-xdist is not installed
|
|
return
|
|
|
|
if not os.getenv('OMP_NUM_THREADS'):
|
|
max_openmp_threads = os.cpu_count() // 2 # use nr of physical cores
|
|
threads_per_worker = max(max_openmp_threads // xdist_worker_count, 1)
|
|
try:
|
|
threadpool_limits(threads_per_worker, user_api='blas')
|
|
except Exception:
|
|
# May raise AttributeError for older versions of OpenBLAS.
|
|
# Catch any error for robustness.
|
|
return
|
|
|
|
|
|
@pytest.fixture(scope="function", autouse=True)
|
|
def check_fpu_mode(request):
|
|
"""
|
|
Check FPU mode was not changed during the test.
|
|
"""
|
|
old_mode = get_fpu_mode()
|
|
yield
|
|
new_mode = get_fpu_mode()
|
|
|
|
if old_mode != new_mode:
|
|
warnings.warn(f"FPU mode changed from {old_mode:#x} to {new_mode:#x} during "
|
|
"the test",
|
|
category=FPUModeChangeWarning, stacklevel=0)
|
|
|
|
|
|
if not PARALLEL_RUN_AVAILABLE:
|
|
@pytest.fixture
|
|
def num_parallel_threads():
|
|
return 1
|
|
|
|
|
|
# Array API backend handling
|
|
xp_available_backends = {'numpy': np}
|
|
|
|
if SCIPY_ARRAY_API and isinstance(SCIPY_ARRAY_API, str):
|
|
# fill the dict of backends with available libraries
|
|
try:
|
|
import array_api_strict
|
|
xp_available_backends.update({'array_api_strict': array_api_strict})
|
|
if _pep440.parse(array_api_strict.__version__) < _pep440.Version('2.0'):
|
|
raise ImportError("array-api-strict must be >= version 2.0")
|
|
array_api_strict.set_array_api_strict_flags(
|
|
api_version='2023.12'
|
|
)
|
|
except ImportError:
|
|
pass
|
|
|
|
try:
|
|
import torch # type: ignore[import-not-found]
|
|
xp_available_backends.update({'torch': torch})
|
|
# can use `mps` or `cpu`
|
|
torch.set_default_device(SCIPY_DEVICE)
|
|
except ImportError:
|
|
pass
|
|
|
|
try:
|
|
import cupy # type: ignore[import-not-found]
|
|
xp_available_backends.update({'cupy': cupy})
|
|
except ImportError:
|
|
pass
|
|
|
|
try:
|
|
import jax.numpy # type: ignore[import-not-found]
|
|
xp_available_backends.update({'jax.numpy': jax.numpy})
|
|
jax.config.update("jax_enable_x64", True)
|
|
jax.config.update("jax_default_device", jax.devices(SCIPY_DEVICE)[0])
|
|
except ImportError:
|
|
pass
|
|
|
|
# by default, use all available backends
|
|
if SCIPY_ARRAY_API.lower() not in ("1", "true"):
|
|
SCIPY_ARRAY_API_ = json.loads(SCIPY_ARRAY_API)
|
|
|
|
if 'all' in SCIPY_ARRAY_API_:
|
|
pass # same as True
|
|
else:
|
|
# only select a subset of backend by filtering out the dict
|
|
try:
|
|
xp_available_backends = {
|
|
backend: xp_available_backends[backend]
|
|
for backend in SCIPY_ARRAY_API_
|
|
}
|
|
except KeyError:
|
|
msg = f"'--array-api-backend' must be in {xp_available_backends.keys()}"
|
|
raise ValueError(msg)
|
|
|
|
if 'cupy' in xp_available_backends:
|
|
SCIPY_DEVICE = 'cuda'
|
|
|
|
array_api_compatible = pytest.mark.parametrize("xp", xp_available_backends.values())
|
|
|
|
skip_xp_invalid_arg = pytest.mark.skipif(SCIPY_ARRAY_API,
|
|
reason = ('Test involves masked arrays, object arrays, or other types '
|
|
'that are not valid input when `SCIPY_ARRAY_API` is used.'))
|
|
|
|
|
|
def _backends_kwargs_from_request(request, skip_or_xfail):
|
|
"""A helper for {skip,xfail}_xp_backends"""
|
|
# do not allow multiple backends
|
|
args_ = request.keywords[f'{skip_or_xfail}_xp_backends'].args
|
|
if len(args_) > 1:
|
|
# np_only / cpu_only has args=(), otherwise it's ('numpy',)
|
|
# and we do not allow ('numpy', 'cupy')
|
|
raise ValueError(f"multiple backends: {args_}")
|
|
|
|
markers = list(request.node.iter_markers(f'{skip_or_xfail}_xp_backends'))
|
|
backends = []
|
|
kwargs = {}
|
|
for marker in markers:
|
|
if marker.kwargs.get('np_only'):
|
|
kwargs['np_only'] = True
|
|
kwargs['exceptions'] = marker.kwargs.get('exceptions', [])
|
|
elif marker.kwargs.get('cpu_only'):
|
|
if not kwargs.get('np_only'):
|
|
# if np_only is given, it is certainly cpu only
|
|
kwargs['cpu_only'] = True
|
|
kwargs['exceptions'] = marker.kwargs.get('exceptions', [])
|
|
|
|
# add backends, if any
|
|
if len(marker.args) > 0:
|
|
backend = marker.args[0] # was a tuple, ('numpy',) etc
|
|
backends.append(backend)
|
|
kwargs.update(**{backend: marker.kwargs})
|
|
|
|
return backends, kwargs
|
|
|
|
|
|
@pytest.fixture
|
|
def skip_xp_backends(xp, request):
|
|
"""skip_xp_backends(backend=None, reason=None, np_only=False, cpu_only=False, exceptions=None)
|
|
|
|
Skip a decorated test for the provided backend, or skip a category of backends.
|
|
|
|
See ``skip_or_xfail_backends`` docstring for details. Note that, contrary to
|
|
``skip_or_xfail_backends``, the ``backend`` and ``reason`` arguments are optional
|
|
single strings: this function only skips a single backend at a time.
|
|
To skip multiple backends, provide multiple decorators.
|
|
""" # noqa: E501
|
|
if "skip_xp_backends" not in request.keywords:
|
|
return
|
|
|
|
backends, kwargs = _backends_kwargs_from_request(request, skip_or_xfail='skip')
|
|
skip_or_xfail_xp_backends(xp, backends, kwargs, skip_or_xfail='skip')
|
|
|
|
|
|
@pytest.fixture
|
|
def xfail_xp_backends(xp, request):
|
|
"""xfail_xp_backends(backend=None, reason=None, np_only=False, cpu_only=False, exceptions=None)
|
|
|
|
xfail a decorated test for the provided backend, or xfail a category of backends.
|
|
|
|
See ``skip_or_xfail_backends`` docstring for details. Note that, contrary to
|
|
``skip_or_xfail_backends``, the ``backend`` and ``reason`` arguments are optional
|
|
single strings: this function only xfails a single backend at a time.
|
|
To xfail multiple backends, provide multiple decorators.
|
|
""" # noqa: E501
|
|
if "xfail_xp_backends" not in request.keywords:
|
|
return
|
|
backends, kwargs = _backends_kwargs_from_request(request, skip_or_xfail='xfail')
|
|
skip_or_xfail_xp_backends(xp, backends, kwargs, skip_or_xfail='xfail')
|
|
|
|
|
|
def skip_or_xfail_xp_backends(xp, backends, kwargs, skip_or_xfail='skip'):
|
|
"""
|
|
Skip based on the ``skip_xp_backends`` or ``xfail_xp_backends`` marker.
|
|
|
|
See the "Support for the array API standard" docs page for usage examples.
|
|
|
|
Parameters
|
|
----------
|
|
backends : tuple
|
|
Backends to skip/xfail, e.g. ``("array_api_strict", "torch")``.
|
|
These are overriden when ``np_only`` is ``True``, and are not
|
|
necessary to provide for non-CPU backends when ``cpu_only`` is ``True``.
|
|
For a custom reason to apply, you should pass a dict ``{'reason': '...'}``
|
|
to a keyword matching the name of the backend.
|
|
reason : str, optional
|
|
A reason for the skip/xfail in the case of ``np_only=True``.
|
|
If unprovided, a default reason is used. Note that it is not possible
|
|
to specify a custom reason with ``cpu_only``.
|
|
np_only : bool, optional
|
|
When ``True``, the test is skipped/xfailed for all backends other
|
|
than the default NumPy backend. There is no need to provide
|
|
any ``backends`` in this case. To specify a reason, pass a
|
|
value to ``reason``. Default: ``False``.
|
|
cpu_only : bool, optional
|
|
When ``True``, the test is skipped/xfailed on non-CPU devices.
|
|
There is no need to provide any ``backends`` in this case,
|
|
but any ``backends`` will also be skipped on the CPU.
|
|
Default: ``False``.
|
|
exceptions : list, optional
|
|
A list of exceptions for use with ``cpu_only`` or ``np_only``.
|
|
This should be provided when delegation is implemented for some,
|
|
but not all, non-CPU/non-NumPy backends.
|
|
skip_or_xfail : str
|
|
``'skip'`` to skip, ``'xfail'`` to xfail.
|
|
"""
|
|
skip_or_xfail = getattr(pytest, skip_or_xfail)
|
|
np_only = kwargs.get("np_only", False)
|
|
cpu_only = kwargs.get("cpu_only", False)
|
|
exceptions = kwargs.get("exceptions", [])
|
|
|
|
if reasons := kwargs.get("reasons"):
|
|
raise ValueError(f"provide a single `reason=` kwarg; got {reasons=} instead")
|
|
|
|
# input validation
|
|
if np_only and cpu_only:
|
|
# np_only is a stricter subset of cpu_only
|
|
cpu_only = False
|
|
if exceptions and not (cpu_only or np_only):
|
|
raise ValueError("`exceptions` is only valid alongside `cpu_only` or `np_only`")
|
|
|
|
if np_only:
|
|
reason = kwargs.get("reason", "do not run with non-NumPy backends.")
|
|
if not isinstance(reason, str) and len(reason) > 1:
|
|
raise ValueError("please provide a singleton `reason` "
|
|
"when using `np_only`")
|
|
if xp.__name__ != 'numpy' and xp.__name__ not in exceptions:
|
|
skip_or_xfail(reason=reason)
|
|
return
|
|
if cpu_only:
|
|
reason = ("no array-agnostic implementation or delegation available "
|
|
"for this backend and device")
|
|
exceptions = [] if exceptions is None else exceptions
|
|
if SCIPY_ARRAY_API and SCIPY_DEVICE != 'cpu':
|
|
if xp.__name__ == 'cupy' and 'cupy' not in exceptions:
|
|
skip_or_xfail(reason=reason)
|
|
elif xp.__name__ == 'torch' and 'torch' not in exceptions:
|
|
if 'cpu' not in xp.empty(0).device.type:
|
|
skip_or_xfail(reason=reason)
|
|
elif xp.__name__ == 'jax.numpy' and 'jax.numpy' not in exceptions:
|
|
for d in xp.empty(0).devices():
|
|
if 'cpu' not in d.device_kind:
|
|
skip_or_xfail(reason=reason)
|
|
|
|
if backends is not None:
|
|
for i, backend in enumerate(backends):
|
|
if xp.__name__ == backend:
|
|
reason = kwargs[backend].get('reason')
|
|
if not reason:
|
|
reason = f"do not run with array API backend: {backend}"
|
|
|
|
skip_or_xfail(reason=reason)
|
|
|
|
|
|
# Following the approach of NumPy's conftest.py...
|
|
# Use a known and persistent tmpdir for hypothesis' caches, which
|
|
# can be automatically cleared by the OS or user.
|
|
hypothesis.configuration.set_hypothesis_home_dir(
|
|
os.path.join(tempfile.gettempdir(), ".hypothesis")
|
|
)
|
|
|
|
# We register two custom profiles for SciPy - for details see
|
|
# https://hypothesis.readthedocs.io/en/latest/settings.html
|
|
# The first is designed for our own CI runs; the latter also
|
|
# forces determinism and is designed for use via scipy.test()
|
|
hypothesis.settings.register_profile(
|
|
name="nondeterministic", deadline=None, print_blob=True,
|
|
)
|
|
hypothesis.settings.register_profile(
|
|
name="deterministic",
|
|
deadline=None, print_blob=True, database=None, derandomize=True,
|
|
suppress_health_check=list(hypothesis.HealthCheck),
|
|
)
|
|
|
|
# Profile is currently set by environment variable `SCIPY_HYPOTHESIS_PROFILE`
|
|
# In the future, it would be good to work the choice into dev.py.
|
|
SCIPY_HYPOTHESIS_PROFILE = os.environ.get("SCIPY_HYPOTHESIS_PROFILE",
|
|
"deterministic")
|
|
hypothesis.settings.load_profile(SCIPY_HYPOTHESIS_PROFILE)
|
|
|
|
|
|
############################################################################
|
|
# doctesting stuff
|
|
|
|
if HAVE_SCPDT:
|
|
|
|
# FIXME: populate the dict once
|
|
@contextmanager
|
|
def warnings_errors_and_rng(test=None):
|
|
"""Temporarily turn (almost) all warnings to errors.
|
|
|
|
Filter out known warnings which we allow.
|
|
"""
|
|
known_warnings = dict()
|
|
|
|
# these functions are known to emit "divide by zero" RuntimeWarnings
|
|
divide_by_zero = [
|
|
'scipy.linalg.norm', 'scipy.ndimage.center_of_mass',
|
|
]
|
|
for name in divide_by_zero:
|
|
known_warnings[name] = dict(category=RuntimeWarning,
|
|
message='divide by zero')
|
|
|
|
# Deprecated stuff in scipy.signal and elsewhere
|
|
deprecated = [
|
|
'scipy.signal.cwt', 'scipy.signal.morlet', 'scipy.signal.morlet2',
|
|
'scipy.signal.ricker',
|
|
'scipy.integrate.simpson',
|
|
'scipy.interpolate.interp2d',
|
|
'scipy.linalg.kron',
|
|
]
|
|
for name in deprecated:
|
|
known_warnings[name] = dict(category=DeprecationWarning)
|
|
|
|
from scipy import integrate
|
|
# the functions are known to emit IntegrationWarnings
|
|
integration_w = ['scipy.special.ellip_normal',
|
|
'scipy.special.ellip_harm_2',
|
|
]
|
|
for name in integration_w:
|
|
known_warnings[name] = dict(category=integrate.IntegrationWarning,
|
|
message='The occurrence of roundoff')
|
|
|
|
# scipy.stats deliberately emits UserWarnings sometimes
|
|
user_w = ['scipy.stats.anderson_ksamp', 'scipy.stats.kurtosistest',
|
|
'scipy.stats.normaltest', 'scipy.sparse.linalg.norm']
|
|
for name in user_w:
|
|
known_warnings[name] = dict(category=UserWarning)
|
|
|
|
# additional one-off warnings to filter
|
|
dct = {
|
|
'scipy.sparse.linalg.norm':
|
|
dict(category=UserWarning, message="Exited at iteration"),
|
|
# tutorials
|
|
'linalg.rst':
|
|
dict(message='the matrix subclass is not',
|
|
category=PendingDeprecationWarning),
|
|
'stats.rst':
|
|
dict(message='The maximum number of subdivisions',
|
|
category=integrate.IntegrationWarning),
|
|
}
|
|
known_warnings.update(dct)
|
|
|
|
# these legitimately emit warnings in examples
|
|
legit = set('scipy.signal.normalize')
|
|
|
|
# Now, the meat of the matter: filter warnings,
|
|
# also control the random seed for each doctest.
|
|
|
|
# XXX: this matches the refguide-check behavior, but is a tad strange:
|
|
# makes sure that the seed the old-fashioned np.random* methods is
|
|
# *NOT* reproducible but the new-style `default_rng()` *IS* repoducible.
|
|
# Should these two be either both repro or both not repro?
|
|
|
|
from scipy._lib._util import _fixed_default_rng
|
|
import numpy as np
|
|
with _fixed_default_rng():
|
|
np.random.seed(None)
|
|
with warnings.catch_warnings():
|
|
if test and test.name in known_warnings:
|
|
warnings.filterwarnings('ignore',
|
|
**known_warnings[test.name])
|
|
yield
|
|
elif test and test.name in legit:
|
|
yield
|
|
else:
|
|
warnings.simplefilter('error', Warning)
|
|
yield
|
|
|
|
dt_config.user_context_mgr = warnings_errors_and_rng
|
|
dt_config.skiplist = set([
|
|
'scipy.linalg.LinAlgError', # comes from numpy
|
|
'scipy.fftpack.fftshift', # fftpack stuff is also from numpy
|
|
'scipy.fftpack.ifftshift',
|
|
'scipy.fftpack.fftfreq',
|
|
'scipy.special.sinc', # sinc is from numpy
|
|
'scipy.optimize.show_options', # does not have much to doctest
|
|
'scipy.signal.normalize', # manipulates warnings (XXX temp skip)
|
|
'scipy.sparse.linalg.norm', # XXX temp skip
|
|
# these below test things which inherit from np.ndarray
|
|
# cross-ref https://github.com/numpy/numpy/issues/28019
|
|
'scipy.io.matlab.MatlabObject.strides',
|
|
'scipy.io.matlab.MatlabObject.dtype',
|
|
'scipy.io.matlab.MatlabOpaque.dtype',
|
|
'scipy.io.matlab.MatlabOpaque.strides',
|
|
'scipy.io.matlab.MatlabFunction.strides',
|
|
'scipy.io.matlab.MatlabFunction.dtype'
|
|
])
|
|
|
|
# these are affected by NumPy 2.0 scalar repr: rely on string comparison
|
|
if np.__version__ < "2":
|
|
dt_config.skiplist.update(set([
|
|
'scipy.io.hb_read',
|
|
'scipy.io.hb_write',
|
|
'scipy.sparse.csgraph.connected_components',
|
|
'scipy.sparse.csgraph.depth_first_order',
|
|
'scipy.sparse.csgraph.shortest_path',
|
|
'scipy.sparse.csgraph.floyd_warshall',
|
|
'scipy.sparse.csgraph.dijkstra',
|
|
'scipy.sparse.csgraph.bellman_ford',
|
|
'scipy.sparse.csgraph.johnson',
|
|
'scipy.sparse.csgraph.yen',
|
|
'scipy.sparse.csgraph.breadth_first_order',
|
|
'scipy.sparse.csgraph.reverse_cuthill_mckee',
|
|
'scipy.sparse.csgraph.structural_rank',
|
|
'scipy.sparse.csgraph.construct_dist_matrix',
|
|
'scipy.sparse.csgraph.reconstruct_path',
|
|
'scipy.ndimage.value_indices',
|
|
'scipy.stats.mstats.describe',
|
|
]))
|
|
|
|
# help pytest collection a bit: these names are either private
|
|
# (distributions), or just do not need doctesting.
|
|
dt_config.pytest_extra_ignore = [
|
|
"scipy.stats.distributions",
|
|
"scipy.optimize.cython_optimize",
|
|
"scipy.test",
|
|
"scipy.show_config",
|
|
# equivalent to "pytest --ignore=path/to/file"
|
|
"scipy/special/_precompute",
|
|
"scipy/interpolate/_interpnd_info.py",
|
|
"scipy/_lib/array_api_compat",
|
|
"scipy/_lib/highs",
|
|
"scipy/_lib/unuran",
|
|
"scipy/_lib/_gcutils.py",
|
|
"scipy/_lib/doccer.py",
|
|
"scipy/_lib/_uarray",
|
|
]
|
|
|
|
dt_config.pytest_extra_xfail = {
|
|
# name: reason
|
|
"ND_regular_grid.rst": "ReST parser limitation",
|
|
"extrapolation_examples.rst": "ReST parser limitation",
|
|
"sampling_pinv.rst": "__cinit__ unexpected argument",
|
|
"sampling_srou.rst": "nan in scalar_power",
|
|
"probability_distributions.rst": "integration warning",
|
|
}
|
|
|
|
# tutorials
|
|
dt_config.pseudocode = set(['integrate.nquad(func,'])
|
|
dt_config.local_resources = {
|
|
'io.rst': [
|
|
"octave_a.mat",
|
|
"octave_cells.mat",
|
|
"octave_struct.mat"
|
|
]
|
|
}
|
|
|
|
dt_config.strict_check = True
|
|
############################################################################
|