684 lines
27 KiB
Python
684 lines
27 KiB
Python
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# Pytest customization
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import json
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import os
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import warnings
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import tempfile
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from contextlib import contextmanager
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from typing import Literal
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import numpy as np
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import numpy.testing as npt
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import pytest
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import hypothesis
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from scipy._lib._fpumode import get_fpu_mode
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from scipy._lib._array_api import (
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SCIPY_ARRAY_API, SCIPY_DEVICE, array_namespace, default_xp,
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is_cupy, is_dask, is_jax,
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)
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from scipy._lib._testutils import FPUModeChangeWarning
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from scipy._lib.array_api_extra.testing import patch_lazy_xp_functions
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from scipy._lib import _pep440
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try:
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from scipy_doctest.conftest import dt_config
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HAVE_SCPDT = True
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except ModuleNotFoundError:
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HAVE_SCPDT = False
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try:
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import pytest_run_parallel # noqa:F401
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PARALLEL_RUN_AVAILABLE = True
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except Exception:
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PARALLEL_RUN_AVAILABLE = False
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def pytest_configure(config):
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"""
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Add pytest markers to avoid PytestUnknownMarkWarning
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This needs to contain all markers that are SciPy-specific, as well as
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dummy fallbacks for markers defined in optional test packages.
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Note that we need both the registration here *and* in `pytest.ini`.
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"""
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config.addinivalue_line("markers",
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"slow: Tests that are very slow.")
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config.addinivalue_line("markers",
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"xslow: mark test as extremely slow (not run unless explicitly requested)")
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config.addinivalue_line("markers",
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"xfail_on_32bit: mark test as failing on 32-bit platforms")
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config.addinivalue_line("markers",
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"array_api_backends: test iterates on all array API backends")
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config.addinivalue_line("markers",
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("skip_xp_backends(backends, reason=None, np_only=False, cpu_only=False, " +
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"eager_only=False, exceptions=None): mark the desired skip configuration " +
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"for the `skip_xp_backends` fixture"))
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config.addinivalue_line("markers",
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("xfail_xp_backends(backends, reason=None, np_only=False, cpu_only=False, " +
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"eager_only=False, exceptions=None): mark the desired xfail configuration " +
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"for the `xfail_xp_backends` fixture"))
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try:
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import pytest_timeout # noqa:F401
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except Exception:
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config.addinivalue_line(
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"markers", 'timeout: mark a test for a non-default timeout')
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try:
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# This is a more reliable test of whether pytest_fail_slow is installed
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# When I uninstalled it, `import pytest_fail_slow` didn't fail!
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from pytest_fail_slow import parse_duration # type: ignore[import-not-found] # noqa:F401,E501
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except Exception:
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config.addinivalue_line(
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"markers", 'fail_slow: mark a test for a non-default timeout failure')
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if not PARALLEL_RUN_AVAILABLE:
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config.addinivalue_line(
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'markers',
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'parallel_threads(n): run the given test function in parallel '
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'using `n` threads.')
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config.addinivalue_line(
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"markers",
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"thread_unsafe: mark the test function as single-threaded",
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)
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config.addinivalue_line(
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"markers",
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"iterations(n): run the given test function `n` times in each thread",
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)
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def pytest_runtest_setup(item):
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mark = item.get_closest_marker("xslow")
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if mark is not None:
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try:
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v = int(os.environ.get('SCIPY_XSLOW', '0'))
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except ValueError:
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v = False
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if not v:
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pytest.skip("very slow test; "
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"set environment variable SCIPY_XSLOW=1 to run it")
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mark = item.get_closest_marker("xfail_on_32bit")
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if mark is not None and np.intp(0).itemsize < 8:
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pytest.xfail(f'Fails on our 32-bit test platform(s): {mark.args[0]}')
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# Older versions of threadpoolctl have an issue that may lead to this
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# warning being emitted, see gh-14441
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with npt.suppress_warnings() as sup:
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sup.filter(pytest.PytestUnraisableExceptionWarning)
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try:
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from threadpoolctl import threadpool_limits
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HAS_THREADPOOLCTL = True
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except Exception: # observed in gh-14441: (ImportError, AttributeError)
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# Optional dependency only. All exceptions are caught, for robustness
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HAS_THREADPOOLCTL = False
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if HAS_THREADPOOLCTL:
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# Set the number of openmp threads based on the number of workers
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# xdist is using to prevent oversubscription. Simplified version of what
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# sklearn does (it can rely on threadpoolctl and its builtin OpenMP helper
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# functions)
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try:
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xdist_worker_count = int(os.environ['PYTEST_XDIST_WORKER_COUNT'])
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except KeyError:
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# raises when pytest-xdist is not installed
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return
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if not os.getenv('OMP_NUM_THREADS'):
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max_openmp_threads = os.cpu_count() // 2 # use nr of physical cores
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threads_per_worker = max(max_openmp_threads // xdist_worker_count, 1)
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try:
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threadpool_limits(threads_per_worker, user_api='blas')
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except Exception:
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# May raise AttributeError for older versions of OpenBLAS.
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# Catch any error for robustness.
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return
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@pytest.fixture(scope="function", autouse=True)
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def check_fpu_mode(request):
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"""
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Check FPU mode was not changed during the test.
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"""
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old_mode = get_fpu_mode()
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yield
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new_mode = get_fpu_mode()
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if old_mode != new_mode:
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warnings.warn(f"FPU mode changed from {old_mode:#x} to {new_mode:#x} during "
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"the test",
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category=FPUModeChangeWarning, stacklevel=0)
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if not PARALLEL_RUN_AVAILABLE:
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@pytest.fixture
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def num_parallel_threads():
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return 1
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# Array API backend handling
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xp_known_backends = {'numpy', 'array_api_strict', 'torch', 'cupy', 'jax.numpy',
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'dask.array'}
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xp_available_backends = {'numpy': np}
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xp_skip_cpu_only_backends = set()
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xp_skip_eager_only_backends = set()
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if SCIPY_ARRAY_API:
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# fill the dict of backends with available libraries
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try:
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import array_api_strict
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xp_available_backends.update({'array_api_strict': array_api_strict})
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if _pep440.parse(array_api_strict.__version__) < _pep440.Version('2.3'):
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raise ImportError("array-api-strict must be >= version 2.3")
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array_api_strict.set_array_api_strict_flags(
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api_version='2024.12'
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)
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except ImportError:
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pass
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try:
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import torch # type: ignore[import-not-found]
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xp_available_backends.update({'torch': torch})
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torch.set_default_device(SCIPY_DEVICE)
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if SCIPY_DEVICE != "cpu":
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xp_skip_cpu_only_backends.add('torch')
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# default to float64 unless explicitly requested
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default = os.getenv('SCIPY_DEFAULT_DTYPE', default='float64')
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if default == 'float64':
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torch.set_default_dtype(torch.float64)
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elif default != "float32":
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raise ValueError(
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"SCIPY_DEFAULT_DTYPE env var, if set, can only be either 'float64' "
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f"or 'float32'. Got '{default}' instead."
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)
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except ImportError:
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pass
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try:
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import cupy # type: ignore[import-not-found]
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# Note: cupy disregards SCIPY_DEVICE and always runs on cuda.
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# It will fail to import if you don't have CUDA hardware and drivers.
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xp_available_backends.update({'cupy': cupy})
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xp_skip_cpu_only_backends.add('cupy')
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# this is annoying in CuPy 13.x
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warnings.filterwarnings(
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'ignore', 'cupyx.jit.rawkernel is experimental', category=FutureWarning
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)
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from cupyx.scipy import signal
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del signal
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except ImportError:
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pass
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try:
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import jax.numpy # type: ignore[import-not-found]
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xp_available_backends.update({'jax.numpy': jax.numpy})
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jax.config.update("jax_enable_x64", True)
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jax.config.update("jax_default_device", jax.devices(SCIPY_DEVICE)[0])
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if SCIPY_DEVICE != "cpu":
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xp_skip_cpu_only_backends.add('jax.numpy')
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# JAX can be eager or lazy (when wrapped in jax.jit). However it is
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# recommended by upstream devs to assume it's always lazy.
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xp_skip_eager_only_backends.add('jax.numpy')
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except ImportError:
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pass
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try:
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import dask.array as da
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xp_available_backends.update({'dask.array': da})
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# Dask can wrap around cupy. However, this is untested in scipy
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# (and will almost surely not work as delegation will misbehave).
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# Dask, strictly speaking, can be eager, in the sense that
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# __array__, __bool__ etc. are implemented and do not raise.
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# However, calling them triggers an extra computation of the whole graph
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# until that point, which is highly destructive for performance.
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xp_skip_eager_only_backends.add('dask.array')
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except ImportError:
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pass
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# by default, use all available backends
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if (
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isinstance(SCIPY_ARRAY_API, str)
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and SCIPY_ARRAY_API.lower() not in ("1", "true", "all")
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):
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SCIPY_ARRAY_API_ = json.loads(SCIPY_ARRAY_API)
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if SCIPY_ARRAY_API_ != ['all']:
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# only select a subset of backend by filtering out the dict
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try:
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xp_available_backends = {
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backend: xp_available_backends[backend]
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for backend in SCIPY_ARRAY_API_
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}
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except KeyError:
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msg = ("'--array-api-backend' must be in "
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f"{list(xp_available_backends)}; got {SCIPY_ARRAY_API_}")
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raise ValueError(msg)
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assert not set(xp_available_backends) - xp_known_backends
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xp_skip_np_only_backends = set(xp_available_backends) - {"numpy"}
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@pytest.fixture(params=[
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pytest.param(v, id=k, marks=pytest.mark.array_api_backends)
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for k, v in xp_available_backends.items()
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])
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def xp(request, monkeypatch):
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"""Run the test that uses this fixture on each available array API library.
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You can select all and only the tests that use the `xp` fixture by
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passing `-m array_api_backends` to pytest.
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You can select where individual tests run through the `@skip_xp_backends`,
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`@xfail_xp_backends`, and `@skip_xp_invalid_arg` pytest markers.
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Please read: https://docs.scipy.org/doc/scipy/dev/api-dev/array_api.html#adding-tests
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"""
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# Read all @pytest.marks.skip_xp_backends markers that decorate to the test,
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# if any, and raise pytest.skip() if the current xp is in the list.
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skip_or_xfail_xp_backends(request, "skip")
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# Read all @pytest.marks.xfail_xp_backends markers that decorate the test,
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# if any, and raise pytest.xfail() if the current xp is in the list.
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skip_or_xfail_xp_backends(request, "xfail")
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xp = request.param
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# Potentially wrap namespace with array_api_compat
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xp = array_namespace(xp.empty(0))
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if SCIPY_ARRAY_API:
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# If request.param==jax.numpy, wrap tested functions in jax.jit
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patch_lazy_xp_functions(
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xp=request.param, request=request, monkeypatch=monkeypatch
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)
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# Throughout all calls to assert_almost_equal, assert_array_almost_equal, and
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# xp_assert_* functions, test that the array namespace is xp in both the
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# expected and actual arrays. This is to detect the case where both arrays are
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# erroneously just plain numpy while xp is something else.
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with default_xp(xp):
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yield xp
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else:
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yield xp
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skip_xp_invalid_arg = pytest.mark.skipif(SCIPY_ARRAY_API,
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reason = ('Test involves masked arrays, object arrays, or other types '
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'that are not valid input when `SCIPY_ARRAY_API` is used.'))
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def _backends_kwargs_from_request(request, skip_or_xfail):
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"""A helper for {skip,xfail}_xp_backends.
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Return dict of {backend to skip/xfail: top reason to skip/xfail it}
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"""
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markers = list(request.node.iter_markers(f'{skip_or_xfail}_xp_backends'))
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reasons = {backend: [] for backend in xp_known_backends}
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for marker in markers:
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invalid_kwargs = set(marker.kwargs) - {
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"cpu_only", "np_only", "eager_only", "reason", "exceptions"}
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if invalid_kwargs:
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raise TypeError(f"Invalid kwargs: {invalid_kwargs}")
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exceptions = set(marker.kwargs.get('exceptions', []))
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invalid_exceptions = exceptions - xp_known_backends
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if (invalid_exceptions := list(exceptions - xp_known_backends)):
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raise ValueError(f"Unknown backend(s): {invalid_exceptions}; "
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f"must be a subset of {list(xp_known_backends)}")
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if marker.kwargs.get('np_only', False):
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reason = marker.kwargs.get("reason") or "do not run with non-NumPy backends"
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for backend in xp_skip_np_only_backends - exceptions:
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reasons[backend].append(reason)
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elif marker.kwargs.get('cpu_only', False):
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reason = marker.kwargs.get("reason") or (
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"no array-agnostic implementation or delegation available "
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"for this backend and device")
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for backend in xp_skip_cpu_only_backends - exceptions:
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reasons[backend].append(reason)
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elif marker.kwargs.get('eager_only', False):
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reason = marker.kwargs.get("reason") or (
|
||
|
|
"eager checks not executed on lazy backends")
|
||
|
|
for backend in xp_skip_eager_only_backends - exceptions:
|
||
|
|
reasons[backend].append(reason)
|
||
|
|
|
||
|
|
# add backends, if any
|
||
|
|
if len(marker.args) == 1:
|
||
|
|
backend = marker.args[0]
|
||
|
|
if backend not in xp_known_backends:
|
||
|
|
raise ValueError(f"Unknown backend: {backend}; "
|
||
|
|
f"must be one of {list(xp_known_backends)}")
|
||
|
|
reason = marker.kwargs.get("reason") or (
|
||
|
|
f"do not run with array API backend: {backend}")
|
||
|
|
# reason overrides the ones from cpu_only, np_only, and eager_only.
|
||
|
|
# This is regardless of order of appearence of the markers.
|
||
|
|
reasons[backend].insert(0, reason)
|
||
|
|
|
||
|
|
for kwarg in ("cpu_only", "np_only", "eager_only", "exceptions"):
|
||
|
|
if kwarg in marker.kwargs:
|
||
|
|
raise ValueError(f"{kwarg} is mutually exclusive with {backend}")
|
||
|
|
|
||
|
|
elif len(marker.args) > 1:
|
||
|
|
raise ValueError(
|
||
|
|
f"Please specify only one backend per marker: {marker.args}"
|
||
|
|
)
|
||
|
|
|
||
|
|
return {backend: backend_reasons[0]
|
||
|
|
for backend, backend_reasons in reasons.items()
|
||
|
|
if backend_reasons}
|
||
|
|
|
||
|
|
|
||
|
|
def skip_or_xfail_xp_backends(request: pytest.FixtureRequest,
|
||
|
|
skip_or_xfail: Literal['skip', 'xfail']) -> None:
|
||
|
|
"""
|
||
|
|
Helper of the `xp` fixture.
|
||
|
|
Skip or xfail based on the ``skip_xp_backends`` or ``xfail_xp_backends`` markers.
|
||
|
|
|
||
|
|
See the "Support for the array API standard" docs page for usage examples.
|
||
|
|
|
||
|
|
Usage
|
||
|
|
-----
|
||
|
|
::
|
||
|
|
skip_xp_backends = pytest.mark.skip_xp_backends
|
||
|
|
xfail_xp_backends = pytest.mark.xfail_xp_backends
|
||
|
|
...
|
||
|
|
|
||
|
|
@skip_xp_backends(backend, *, reason=None)
|
||
|
|
@skip_xp_backends(*, cpu_only=True, exceptions=(), reason=None)
|
||
|
|
@skip_xp_backends(*, eager_only=True, exceptions=(), reason=None)
|
||
|
|
@skip_xp_backends(*, np_only=True, exceptions=(), reason=None)
|
||
|
|
|
||
|
|
@xfail_xp_backends(backend, *, reason=None)
|
||
|
|
@xfail_xp_backends(*, cpu_only=True, exceptions=(), reason=None)
|
||
|
|
@xfail_xp_backends(*, eager_only=True, exceptions=(), reason=None)
|
||
|
|
@xfail_xp_backends(*, np_only=True, exceptions=(), reason=None)
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
backend : str, optional
|
||
|
|
Backend to skip/xfail, e.g. ``"torch"``.
|
||
|
|
Mutually exclusive with ``cpu_only``, ``eager_only``, and ``np_only``.
|
||
|
|
cpu_only : bool, optional
|
||
|
|
When ``True``, the test is skipped/xfailed on non-CPU devices,
|
||
|
|
minus exceptions. Mutually exclusive with ``backend``.
|
||
|
|
eager_only : bool, optional
|
||
|
|
When ``True``, the test is skipped/xfailed for lazy backends, e.g. those
|
||
|
|
with major caveats when invoking ``__array__``, ``__bool__``, ``__float__``,
|
||
|
|
or ``__complex__``, minus exceptions. Mutually exclusive with ``backend``.
|
||
|
|
np_only : bool, optional
|
||
|
|
When ``True``, the test is skipped/xfailed for all backends other
|
||
|
|
than the default NumPy backend and the exceptions.
|
||
|
|
Mutually exclusive with ``backend``. Implies ``cpu_only`` and ``eager_only``.
|
||
|
|
reason : str, optional
|
||
|
|
A reason for the skip/xfail. If omitted, a default reason is used.
|
||
|
|
exceptions : list[str], optional
|
||
|
|
A list of exceptions for use with ``cpu_only``, ``eager_only``, or ``np_only``.
|
||
|
|
This should be provided when delegation is implemented for some,
|
||
|
|
but not all, non-CPU/non-NumPy backends.
|
||
|
|
"""
|
||
|
|
if f"{skip_or_xfail}_xp_backends" not in request.keywords:
|
||
|
|
return
|
||
|
|
|
||
|
|
skip_xfail_reasons = _backends_kwargs_from_request(
|
||
|
|
request, skip_or_xfail=skip_or_xfail
|
||
|
|
)
|
||
|
|
xp = request.param
|
||
|
|
if xp.__name__ in skip_xfail_reasons:
|
||
|
|
reason = skip_xfail_reasons[xp.__name__]
|
||
|
|
assert reason # Default reason applied above
|
||
|
|
skip_or_xfail = getattr(pytest, skip_or_xfail)
|
||
|
|
skip_or_xfail(reason=reason)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.fixture
|
||
|
|
def devices(xp):
|
||
|
|
"""Fixture that returns a list of all devices for the backend, plus None.
|
||
|
|
Used to test input->output device propagation.
|
||
|
|
|
||
|
|
Usage
|
||
|
|
-----
|
||
|
|
from scipy._lib._array_api import xp_device
|
||
|
|
|
||
|
|
def test_device(xp, devices):
|
||
|
|
for d in devices:
|
||
|
|
x = xp.asarray(..., device=d)
|
||
|
|
y = f(x)
|
||
|
|
assert xp_device(y) == xp_device(x)
|
||
|
|
"""
|
||
|
|
if is_cupy(xp):
|
||
|
|
# CuPy does not support devices other than the current one
|
||
|
|
# data-apis/array-api-compat#293
|
||
|
|
pytest.xfail(reason="data-apis/array-api-compat#293")
|
||
|
|
if is_dask(xp):
|
||
|
|
# Skip dummy DASK_DEVICE from array-api-compat, which does not propagate
|
||
|
|
return ["cpu", None]
|
||
|
|
if is_jax(xp):
|
||
|
|
# The .device attribute is not accessible inside jax.jit; the consequence
|
||
|
|
# (downstream of array-api-compat hacks) is that a non-default device in
|
||
|
|
# input is not guaranteed to propagate to the output even if the scipy code
|
||
|
|
# states `device=xp_device(arg)`` in all array creation functions.
|
||
|
|
# While this issue is specific to jax.jit, it would be unnecessarily
|
||
|
|
# verbose to skip the test for each jit-capable function and run it for
|
||
|
|
# those that only support eager mode.
|
||
|
|
pytest.xfail(reason="jax-ml/jax#26000")
|
||
|
|
|
||
|
|
return xp.__array_namespace_info__().devices() + [None]
|
||
|
|
|
||
|
|
|
||
|
|
# 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/interpolate/_rbfinterp_pythran.py",
|
||
|
|
"scipy/_build_utils/tempita.py",
|
||
|
|
"scipy/_lib/array_api_compat",
|
||
|
|
"scipy/_lib/highs",
|
||
|
|
"scipy/_lib/unuran",
|
||
|
|
"scipy/_lib/_gcutils.py",
|
||
|
|
"scipy/_lib/doccer.py",
|
||
|
|
"scipy/_lib/_uarray",
|
||
|
|
"scipy/linalg/_cython_signature_generator.py",
|
||
|
|
"scipy/linalg/_generate_pyx.py",
|
||
|
|
"scipy/linalg/_linalg_pythran.py",
|
||
|
|
"scipy/linalg/_matfuncs_sqrtm_triu.py",
|
||
|
|
"scipy/ndimage/utils/generate_label_testvectors.py",
|
||
|
|
"scipy/optimize/_group_columns.py",
|
||
|
|
"scipy/optimize/_max_len_seq_inner.py",
|
||
|
|
"scipy/signal/_max_len_seq_inner.py",
|
||
|
|
"scipy/sparse/_generate_sparsetools.py",
|
||
|
|
"scipy/special/_generate_pyx.py",
|
||
|
|
"scipy/stats/_stats_pythran.py",
|
||
|
|
]
|
||
|
|
|
||
|
|
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"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
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dt_config.strict_check = True
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# ignore Matplotlib's `ax.text`:
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dt_config.stopwords.add('.text(')
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############################################################################
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