2391 lines
75 KiB
Python
2391 lines
75 KiB
Python
# mypy: ignore-errors
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"""
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Utility function to facilitate testing.
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"""
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import contextlib
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import gc
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import operator
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import os
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import platform
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import pprint
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import re
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import shutil
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import sys
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import warnings
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from functools import wraps
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from io import StringIO
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from tempfile import mkdtemp, mkstemp
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from warnings import WarningMessage
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import torch._numpy as np
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from torch._numpy import arange, asarray as asanyarray, empty, float32, intp, ndarray
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__all__ = [
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"assert_equal",
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"assert_almost_equal",
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"assert_approx_equal",
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"assert_array_equal",
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"assert_array_less",
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"assert_string_equal",
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"assert_",
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"assert_array_almost_equal",
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"build_err_msg",
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"decorate_methods",
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"print_assert_equal",
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"verbose",
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"assert_",
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"assert_array_almost_equal_nulp",
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"assert_raises_regex",
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"assert_array_max_ulp",
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"assert_warns",
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"assert_no_warnings",
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"assert_allclose",
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"IgnoreException",
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"clear_and_catch_warnings",
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"temppath",
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"tempdir",
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"IS_PYPY",
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"HAS_REFCOUNT",
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"IS_WASM",
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"suppress_warnings",
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"assert_array_compare",
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"assert_no_gc_cycles",
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"break_cycles",
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"IS_PYSTON",
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]
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verbose = 0
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IS_WASM = platform.machine() in ["wasm32", "wasm64"]
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IS_PYPY = sys.implementation.name == "pypy"
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IS_PYSTON = hasattr(sys, "pyston_version_info")
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HAS_REFCOUNT = getattr(sys, "getrefcount", None) is not None and not IS_PYSTON
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def assert_(val, msg=""):
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"""
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Assert that works in release mode.
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Accepts callable msg to allow deferring evaluation until failure.
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The Python built-in ``assert`` does not work when executing code in
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optimized mode (the ``-O`` flag) - no byte-code is generated for it.
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For documentation on usage, refer to the Python documentation.
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"""
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__tracebackhide__ = True # Hide traceback for py.test
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if not val:
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try:
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smsg = msg()
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except TypeError:
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smsg = msg
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raise AssertionError(smsg)
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def gisnan(x):
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return np.isnan(x)
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def gisfinite(x):
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return np.isfinite(x)
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def gisinf(x):
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return np.isinf(x)
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def build_err_msg(
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arrays,
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err_msg,
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header="Items are not equal:",
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verbose=True,
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names=("ACTUAL", "DESIRED"),
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precision=8,
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):
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msg = ["\n" + header]
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if err_msg:
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if err_msg.find("\n") == -1 and len(err_msg) < 79 - len(header):
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msg = [msg[0] + " " + err_msg]
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else:
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msg.append(err_msg)
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if verbose:
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for i, a in enumerate(arrays):
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if isinstance(a, ndarray):
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# precision argument is only needed if the objects are ndarrays
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# r_func = partial(array_repr, precision=precision)
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r_func = ndarray.__repr__
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else:
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r_func = repr
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try:
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r = r_func(a)
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except Exception as exc:
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r = f"[repr failed for <{type(a).__name__}>: {exc}]"
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if r.count("\n") > 3:
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r = "\n".join(r.splitlines()[:3])
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r += "..."
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msg.append(f" {names[i]}: {r}")
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return "\n".join(msg)
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def assert_equal(actual, desired, err_msg="", verbose=True):
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"""
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Raises an AssertionError if two objects are not equal.
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Given two objects (scalars, lists, tuples, dictionaries or numpy arrays),
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check that all elements of these objects are equal. An exception is raised
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at the first conflicting values.
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When one of `actual` and `desired` is a scalar and the other is array_like,
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the function checks that each element of the array_like object is equal to
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the scalar.
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This function handles NaN comparisons as if NaN was a "normal" number.
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That is, AssertionError is not raised if both objects have NaNs in the same
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positions. This is in contrast to the IEEE standard on NaNs, which says
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that NaN compared to anything must return False.
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Parameters
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----------
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actual : array_like
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The object to check.
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desired : array_like
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The expected object.
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err_msg : str, optional
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The error message to be printed in case of failure.
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verbose : bool, optional
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If True, the conflicting values are appended to the error message.
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Raises
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------
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AssertionError
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If actual and desired are not equal.
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Examples
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--------
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>>> np.testing.assert_equal([4,5], [4,6])
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Traceback (most recent call last):
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...
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AssertionError:
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Items are not equal:
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item=1
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ACTUAL: 5
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DESIRED: 6
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The following comparison does not raise an exception. There are NaNs
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in the inputs, but they are in the same positions.
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>>> np.testing.assert_equal(np.array([1.0, 2.0, np.nan]), [1, 2, np.nan])
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"""
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__tracebackhide__ = True # Hide traceback for py.test
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num_nones = sum([actual is None, desired is None])
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if num_nones == 1:
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raise AssertionError(f"Not equal: {actual} != {desired}")
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elif num_nones == 2:
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return True
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# else, carry on
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if isinstance(actual, np.DType) or isinstance(desired, np.DType):
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result = actual == desired
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if not result:
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raise AssertionError(f"Not equal: {actual} != {desired}")
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else:
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return True
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if isinstance(desired, str) and isinstance(actual, str):
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assert actual == desired
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return
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if isinstance(desired, dict):
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if not isinstance(actual, dict):
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raise AssertionError(repr(type(actual)))
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assert_equal(len(actual), len(desired), err_msg, verbose)
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for k in desired.keys():
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if k not in actual:
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raise AssertionError(repr(k))
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assert_equal(actual[k], desired[k], f"key={k!r}\n{err_msg}", verbose)
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return
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if isinstance(desired, (list, tuple)) and isinstance(actual, (list, tuple)):
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assert_equal(len(actual), len(desired), err_msg, verbose)
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for k in range(len(desired)):
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assert_equal(actual[k], desired[k], f"item={k!r}\n{err_msg}", verbose)
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return
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from torch._numpy import imag, iscomplexobj, isscalar, ndarray, real, signbit
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if isinstance(actual, ndarray) or isinstance(desired, ndarray):
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return assert_array_equal(actual, desired, err_msg, verbose)
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msg = build_err_msg([actual, desired], err_msg, verbose=verbose)
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# Handle complex numbers: separate into real/imag to handle
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# nan/inf/negative zero correctly
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# XXX: catch ValueError for subclasses of ndarray where iscomplex fail
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try:
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usecomplex = iscomplexobj(actual) or iscomplexobj(desired)
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except (ValueError, TypeError):
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usecomplex = False
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if usecomplex:
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if iscomplexobj(actual):
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actualr = real(actual)
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actuali = imag(actual)
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else:
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actualr = actual
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actuali = 0
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if iscomplexobj(desired):
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desiredr = real(desired)
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desiredi = imag(desired)
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else:
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desiredr = desired
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desiredi = 0
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try:
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assert_equal(actualr, desiredr)
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assert_equal(actuali, desiredi)
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except AssertionError:
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raise AssertionError(msg) # noqa: TRY200
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# isscalar test to check cases such as [np.nan] != np.nan
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if isscalar(desired) != isscalar(actual):
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raise AssertionError(msg)
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# Inf/nan/negative zero handling
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try:
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isdesnan = gisnan(desired)
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isactnan = gisnan(actual)
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if isdesnan and isactnan:
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return # both nan, so equal
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# handle signed zero specially for floats
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array_actual = np.asarray(actual)
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array_desired = np.asarray(desired)
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if desired == 0 and actual == 0:
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if not signbit(desired) == signbit(actual):
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raise AssertionError(msg)
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except (TypeError, ValueError, NotImplementedError):
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pass
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try:
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# Explicitly use __eq__ for comparison, gh-2552
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if not (desired == actual):
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raise AssertionError(msg)
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except (DeprecationWarning, FutureWarning) as e:
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# this handles the case when the two types are not even comparable
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if "elementwise == comparison" in e.args[0]:
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raise AssertionError(msg) # noqa: TRY200
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else:
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raise
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def print_assert_equal(test_string, actual, desired):
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"""
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Test if two objects are equal, and print an error message if test fails.
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The test is performed with ``actual == desired``.
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Parameters
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----------
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test_string : str
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The message supplied to AssertionError.
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actual : object
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The object to test for equality against `desired`.
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desired : object
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The expected result.
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Examples
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--------
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>>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 1]) # doctest: +SKIP
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>>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 2]) # doctest: +SKIP
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Traceback (most recent call last):
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...
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AssertionError: Test XYZ of func xyz failed
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ACTUAL:
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[0, 1]
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DESIRED:
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[0, 2]
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"""
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__tracebackhide__ = True # Hide traceback for py.test
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import pprint
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if not (actual == desired):
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msg = StringIO()
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msg.write(test_string)
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msg.write(" failed\nACTUAL: \n")
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pprint.pprint(actual, msg)
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msg.write("DESIRED: \n")
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pprint.pprint(desired, msg)
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raise AssertionError(msg.getvalue())
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def assert_almost_equal(actual, desired, decimal=7, err_msg="", verbose=True):
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"""
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Raises an AssertionError if two items are not equal up to desired
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precision.
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.. note:: It is recommended to use one of `assert_allclose`,
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`assert_array_almost_equal_nulp` or `assert_array_max_ulp`
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instead of this function for more consistent floating point
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comparisons.
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The test verifies that the elements of `actual` and `desired` satisfy.
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``abs(desired-actual) < float64(1.5 * 10**(-decimal))``
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That is a looser test than originally documented, but agrees with what the
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actual implementation in `assert_array_almost_equal` did up to rounding
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vagaries. An exception is raised at conflicting values. For ndarrays this
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delegates to assert_array_almost_equal
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Parameters
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----------
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actual : array_like
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The object to check.
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desired : array_like
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The expected object.
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decimal : int, optional
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Desired precision, default is 7.
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err_msg : str, optional
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The error message to be printed in case of failure.
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verbose : bool, optional
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If True, the conflicting values are appended to the error message.
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Raises
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------
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AssertionError
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If actual and desired are not equal up to specified precision.
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See Also
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--------
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assert_allclose: Compare two array_like objects for equality with desired
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relative and/or absolute precision.
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assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
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Examples
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--------
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>>> from torch._numpy.testing import assert_almost_equal
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>>> assert_almost_equal(2.3333333333333, 2.33333334)
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>>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
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Traceback (most recent call last):
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...
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AssertionError:
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Arrays are not almost equal to 10 decimals
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ACTUAL: 2.3333333333333
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DESIRED: 2.33333334
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>>> assert_almost_equal(np.array([1.0,2.3333333333333]),
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... np.array([1.0,2.33333334]), decimal=9)
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Traceback (most recent call last):
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...
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AssertionError:
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Arrays are not almost equal to 9 decimals
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<BLANKLINE>
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Mismatched elements: 1 / 2 (50%)
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Max absolute difference: 6.666699636781459e-09
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Max relative difference: 2.8571569790287484e-09
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x: torch.ndarray([1.0000, 2.3333], dtype=float64)
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y: torch.ndarray([1.0000, 2.3333], dtype=float64)
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"""
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__tracebackhide__ = True # Hide traceback for py.test
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from torch._numpy import imag, iscomplexobj, ndarray, real
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# Handle complex numbers: separate into real/imag to handle
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# nan/inf/negative zero correctly
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# XXX: catch ValueError for subclasses of ndarray where iscomplex fail
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try:
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usecomplex = iscomplexobj(actual) or iscomplexobj(desired)
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except ValueError:
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usecomplex = False
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def _build_err_msg():
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header = "Arrays are not almost equal to %d decimals" % decimal
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return build_err_msg([actual, desired], err_msg, verbose=verbose, header=header)
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if usecomplex:
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if iscomplexobj(actual):
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actualr = real(actual)
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actuali = imag(actual)
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else:
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actualr = actual
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actuali = 0
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if iscomplexobj(desired):
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desiredr = real(desired)
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desiredi = imag(desired)
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else:
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desiredr = desired
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desiredi = 0
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try:
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assert_almost_equal(actualr, desiredr, decimal=decimal)
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assert_almost_equal(actuali, desiredi, decimal=decimal)
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except AssertionError:
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raise AssertionError(_build_err_msg()) # noqa: TRY200
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if isinstance(actual, (ndarray, tuple, list)) or isinstance(
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desired, (ndarray, tuple, list)
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):
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return assert_array_almost_equal(actual, desired, decimal, err_msg)
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try:
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# If one of desired/actual is not finite, handle it specially here:
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# check that both are nan if any is a nan, and test for equality
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# otherwise
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if not (gisfinite(desired) and gisfinite(actual)):
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if gisnan(desired) or gisnan(actual):
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if not (gisnan(desired) and gisnan(actual)):
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raise AssertionError(_build_err_msg())
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else:
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if not desired == actual:
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raise AssertionError(_build_err_msg())
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return
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except (NotImplementedError, TypeError):
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pass
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if abs(desired - actual) >= np.float64(1.5 * 10.0 ** (-decimal)):
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raise AssertionError(_build_err_msg())
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|
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def assert_approx_equal(actual, desired, significant=7, err_msg="", verbose=True):
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"""
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Raises an AssertionError if two items are not equal up to significant
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digits.
|
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|
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.. note:: It is recommended to use one of `assert_allclose`,
|
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`assert_array_almost_equal_nulp` or `assert_array_max_ulp`
|
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instead of this function for more consistent floating point
|
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comparisons.
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Given two numbers, check that they are approximately equal.
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Approximately equal is defined as the number of significant digits
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that agree.
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Parameters
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----------
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actual : scalar
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The object to check.
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desired : scalar
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The expected object.
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significant : int, optional
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Desired precision, default is 7.
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err_msg : str, optional
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The error message to be printed in case of failure.
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verbose : bool, optional
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If True, the conflicting values are appended to the error message.
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|
|
Raises
|
|
------
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AssertionError
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If actual and desired are not equal up to specified precision.
|
|
|
|
See Also
|
|
--------
|
|
assert_allclose: Compare two array_like objects for equality with desired
|
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relative and/or absolute precision.
|
|
assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
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|
|
|
Examples
|
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--------
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>>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) # doctest: +SKIP
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>>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, # doctest: +SKIP
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... significant=8)
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>>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, # doctest: +SKIP
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... significant=8)
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Traceback (most recent call last):
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...
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AssertionError:
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Items are not equal to 8 significant digits:
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ACTUAL: 1.234567e-21
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DESIRED: 1.2345672e-21
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the evaluated condition that raises the exception is
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|
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>>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1)
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True
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"""
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
import numpy as np
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|
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(actual, desired) = map(float, (actual, desired))
|
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if desired == actual:
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return
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# Normalized the numbers to be in range (-10.0,10.0)
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# scale = float(pow(10,math.floor(math.log10(0.5*(abs(desired)+abs(actual))))))
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scale = 0.5 * (np.abs(desired) + np.abs(actual))
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scale = np.power(10, np.floor(np.log10(scale)))
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try:
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sc_desired = desired / scale
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except ZeroDivisionError:
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sc_desired = 0.0
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try:
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sc_actual = actual / scale
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except ZeroDivisionError:
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sc_actual = 0.0
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msg = build_err_msg(
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[actual, desired],
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err_msg,
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header="Items are not equal to %d significant digits:" % significant,
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verbose=verbose,
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)
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try:
|
|
# If one of desired/actual is not finite, handle it specially here:
|
|
# check that both are nan if any is a nan, and test for equality
|
|
# otherwise
|
|
if not (gisfinite(desired) and gisfinite(actual)):
|
|
if gisnan(desired) or gisnan(actual):
|
|
if not (gisnan(desired) and gisnan(actual)):
|
|
raise AssertionError(msg)
|
|
else:
|
|
if not desired == actual:
|
|
raise AssertionError(msg)
|
|
return
|
|
except (TypeError, NotImplementedError):
|
|
pass
|
|
if np.abs(sc_desired - sc_actual) >= np.power(10.0, -(significant - 1)):
|
|
raise AssertionError(msg)
|
|
|
|
|
|
def assert_array_compare(
|
|
comparison,
|
|
x,
|
|
y,
|
|
err_msg="",
|
|
verbose=True,
|
|
header="",
|
|
precision=6,
|
|
equal_nan=True,
|
|
equal_inf=True,
|
|
*,
|
|
strict=False,
|
|
):
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
from torch._numpy import all, array, asarray, bool_, inf, isnan, max
|
|
|
|
x = asarray(x)
|
|
y = asarray(y)
|
|
|
|
def array2string(a):
|
|
return str(a)
|
|
|
|
# original array for output formatting
|
|
ox, oy = x, y
|
|
|
|
def func_assert_same_pos(x, y, func=isnan, hasval="nan"):
|
|
"""Handling nan/inf.
|
|
|
|
Combine results of running func on x and y, checking that they are True
|
|
at the same locations.
|
|
|
|
"""
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
x_id = func(x)
|
|
y_id = func(y)
|
|
# We include work-arounds here to handle three types of slightly
|
|
# pathological ndarray subclasses:
|
|
# (1) all() on `masked` array scalars can return masked arrays, so we
|
|
# use != True
|
|
# (2) __eq__ on some ndarray subclasses returns Python booleans
|
|
# instead of element-wise comparisons, so we cast to bool_() and
|
|
# use isinstance(..., bool) checks
|
|
# (3) subclasses with bare-bones __array_function__ implementations may
|
|
# not implement np.all(), so favor using the .all() method
|
|
# We are not committed to supporting such subclasses, but it's nice to
|
|
# support them if possible.
|
|
if (x_id == y_id).all().item() is not True:
|
|
msg = build_err_msg(
|
|
[x, y],
|
|
err_msg + "\nx and y %s location mismatch:" % (hasval),
|
|
verbose=verbose,
|
|
header=header,
|
|
names=("x", "y"),
|
|
precision=precision,
|
|
)
|
|
raise AssertionError(msg)
|
|
# If there is a scalar, then here we know the array has the same
|
|
# flag as it everywhere, so we should return the scalar flag.
|
|
if isinstance(x_id, bool) or x_id.ndim == 0:
|
|
return bool_(x_id)
|
|
elif isinstance(y_id, bool) or y_id.ndim == 0:
|
|
return bool_(y_id)
|
|
else:
|
|
return y_id
|
|
|
|
try:
|
|
if strict:
|
|
cond = x.shape == y.shape and x.dtype == y.dtype
|
|
else:
|
|
cond = (x.shape == () or y.shape == ()) or x.shape == y.shape
|
|
if not cond:
|
|
if x.shape != y.shape:
|
|
reason = f"\n(shapes {x.shape}, {y.shape} mismatch)"
|
|
else:
|
|
reason = f"\n(dtypes {x.dtype}, {y.dtype} mismatch)"
|
|
msg = build_err_msg(
|
|
[x, y],
|
|
err_msg + reason,
|
|
verbose=verbose,
|
|
header=header,
|
|
names=("x", "y"),
|
|
precision=precision,
|
|
)
|
|
raise AssertionError(msg)
|
|
|
|
flagged = bool_(False)
|
|
|
|
if equal_nan:
|
|
flagged = func_assert_same_pos(x, y, func=isnan, hasval="nan")
|
|
|
|
if equal_inf:
|
|
flagged |= func_assert_same_pos(
|
|
x, y, func=lambda xy: xy == +inf, hasval="+inf"
|
|
)
|
|
flagged |= func_assert_same_pos(
|
|
x, y, func=lambda xy: xy == -inf, hasval="-inf"
|
|
)
|
|
|
|
if flagged.ndim > 0:
|
|
x, y = x[~flagged], y[~flagged]
|
|
# Only do the comparison if actual values are left
|
|
if x.size == 0:
|
|
return
|
|
elif flagged:
|
|
# no sense doing comparison if everything is flagged.
|
|
return
|
|
|
|
val = comparison(x, y)
|
|
|
|
if isinstance(val, bool):
|
|
cond = val
|
|
reduced = array([val])
|
|
else:
|
|
reduced = val.ravel()
|
|
cond = reduced.all()
|
|
|
|
# The below comparison is a hack to ensure that fully masked
|
|
# results, for which val.ravel().all() returns np.ma.masked,
|
|
# do not trigger a failure (np.ma.masked != True evaluates as
|
|
# np.ma.masked, which is falsy).
|
|
if not cond:
|
|
n_mismatch = reduced.size - int(reduced.sum(dtype=intp))
|
|
n_elements = flagged.size if flagged.ndim != 0 else reduced.size
|
|
percent_mismatch = 100 * n_mismatch / n_elements
|
|
remarks = [
|
|
f"Mismatched elements: {n_mismatch} / {n_elements} ({percent_mismatch:.3g}%)"
|
|
]
|
|
|
|
# with errstate(all='ignore'):
|
|
# ignore errors for non-numeric types
|
|
with contextlib.suppress(TypeError, RuntimeError):
|
|
error = abs(x - y)
|
|
if np.issubdtype(x.dtype, np.unsignedinteger):
|
|
error2 = abs(y - x)
|
|
np.minimum(error, error2, out=error)
|
|
max_abs_error = max(error)
|
|
remarks.append(
|
|
"Max absolute difference: " + array2string(max_abs_error.item())
|
|
)
|
|
|
|
# note: this definition of relative error matches that one
|
|
# used by assert_allclose (found in np.isclose)
|
|
# Filter values where the divisor would be zero
|
|
nonzero = bool_(y != 0)
|
|
if all(~nonzero):
|
|
max_rel_error = array(inf)
|
|
else:
|
|
max_rel_error = max(error[nonzero] / abs(y[nonzero]))
|
|
remarks.append(
|
|
"Max relative difference: " + array2string(max_rel_error.item())
|
|
)
|
|
|
|
err_msg += "\n" + "\n".join(remarks)
|
|
msg = build_err_msg(
|
|
[ox, oy],
|
|
err_msg,
|
|
verbose=verbose,
|
|
header=header,
|
|
names=("x", "y"),
|
|
precision=precision,
|
|
)
|
|
raise AssertionError(msg)
|
|
except ValueError:
|
|
import traceback
|
|
|
|
efmt = traceback.format_exc()
|
|
header = f"error during assertion:\n\n{efmt}\n\n{header}"
|
|
|
|
msg = build_err_msg(
|
|
[x, y],
|
|
err_msg,
|
|
verbose=verbose,
|
|
header=header,
|
|
names=("x", "y"),
|
|
precision=precision,
|
|
)
|
|
raise ValueError(msg) # noqa: TRY200
|
|
|
|
|
|
def assert_array_equal(x, y, err_msg="", verbose=True, *, strict=False):
|
|
"""
|
|
Raises an AssertionError if two array_like objects are not equal.
|
|
|
|
Given two array_like objects, check that the shape is equal and all
|
|
elements of these objects are equal (but see the Notes for the special
|
|
handling of a scalar). An exception is raised at shape mismatch or
|
|
conflicting values. In contrast to the standard usage in numpy, NaNs
|
|
are compared like numbers, no assertion is raised if both objects have
|
|
NaNs in the same positions.
|
|
|
|
The usual caution for verifying equality with floating point numbers is
|
|
advised.
|
|
|
|
Parameters
|
|
----------
|
|
x : array_like
|
|
The actual object to check.
|
|
y : array_like
|
|
The desired, expected object.
|
|
err_msg : str, optional
|
|
The error message to be printed in case of failure.
|
|
verbose : bool, optional
|
|
If True, the conflicting values are appended to the error message.
|
|
strict : bool, optional
|
|
If True, raise an AssertionError when either the shape or the data
|
|
type of the array_like objects does not match. The special
|
|
handling for scalars mentioned in the Notes section is disabled.
|
|
|
|
Raises
|
|
------
|
|
AssertionError
|
|
If actual and desired objects are not equal.
|
|
|
|
See Also
|
|
--------
|
|
assert_allclose: Compare two array_like objects for equality with desired
|
|
relative and/or absolute precision.
|
|
assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
|
|
|
|
Notes
|
|
-----
|
|
When one of `x` and `y` is a scalar and the other is array_like, the
|
|
function checks that each element of the array_like object is equal to
|
|
the scalar. This behaviour can be disabled with the `strict` parameter.
|
|
|
|
Examples
|
|
--------
|
|
The first assert does not raise an exception:
|
|
|
|
>>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
|
|
... [np.exp(0),2.33333, np.nan])
|
|
|
|
Use `assert_allclose` or one of the nulp (number of floating point values)
|
|
functions for these cases instead:
|
|
|
|
>>> np.testing.assert_allclose([1.0,np.pi,np.nan],
|
|
... [1, np.sqrt(np.pi)**2, np.nan],
|
|
... rtol=1e-10, atol=0)
|
|
|
|
As mentioned in the Notes section, `assert_array_equal` has special
|
|
handling for scalars. Here the test checks that each value in `x` is 3:
|
|
|
|
>>> x = np.full((2, 5), fill_value=3)
|
|
>>> np.testing.assert_array_equal(x, 3)
|
|
|
|
Use `strict` to raise an AssertionError when comparing a scalar with an
|
|
array:
|
|
|
|
>>> np.testing.assert_array_equal(x, 3, strict=True)
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError:
|
|
Arrays are not equal
|
|
<BLANKLINE>
|
|
(shapes (2, 5), () mismatch)
|
|
x: torch.ndarray([[3, 3, 3, 3, 3],
|
|
[3, 3, 3, 3, 3]])
|
|
y: torch.ndarray(3)
|
|
|
|
The `strict` parameter also ensures that the array data types match:
|
|
|
|
>>> x = np.array([2, 2, 2])
|
|
>>> y = np.array([2., 2., 2.], dtype=np.float32)
|
|
>>> np.testing.assert_array_equal(x, y, strict=True)
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError:
|
|
Arrays are not equal
|
|
<BLANKLINE>
|
|
(dtypes dtype("int64"), dtype("float32") mismatch)
|
|
x: torch.ndarray([2, 2, 2])
|
|
y: torch.ndarray([2., 2., 2.])
|
|
"""
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
assert_array_compare(
|
|
operator.__eq__,
|
|
x,
|
|
y,
|
|
err_msg=err_msg,
|
|
verbose=verbose,
|
|
header="Arrays are not equal",
|
|
strict=strict,
|
|
)
|
|
|
|
|
|
def assert_array_almost_equal(x, y, decimal=6, err_msg="", verbose=True):
|
|
"""
|
|
Raises an AssertionError if two objects are not equal up to desired
|
|
precision.
|
|
|
|
.. note:: It is recommended to use one of `assert_allclose`,
|
|
`assert_array_almost_equal_nulp` or `assert_array_max_ulp`
|
|
instead of this function for more consistent floating point
|
|
comparisons.
|
|
|
|
The test verifies identical shapes and that the elements of ``actual`` and
|
|
``desired`` satisfy.
|
|
|
|
``abs(desired-actual) < 1.5 * 10**(-decimal)``
|
|
|
|
That is a looser test than originally documented, but agrees with what the
|
|
actual implementation did up to rounding vagaries. An exception is raised
|
|
at shape mismatch or conflicting values. In contrast to the standard usage
|
|
in numpy, NaNs are compared like numbers, no assertion is raised if both
|
|
objects have NaNs in the same positions.
|
|
|
|
Parameters
|
|
----------
|
|
x : array_like
|
|
The actual object to check.
|
|
y : array_like
|
|
The desired, expected object.
|
|
decimal : int, optional
|
|
Desired precision, default is 6.
|
|
err_msg : str, optional
|
|
The error message to be printed in case of failure.
|
|
verbose : bool, optional
|
|
If True, the conflicting values are appended to the error message.
|
|
|
|
Raises
|
|
------
|
|
AssertionError
|
|
If actual and desired are not equal up to specified precision.
|
|
|
|
See Also
|
|
--------
|
|
assert_allclose: Compare two array_like objects for equality with desired
|
|
relative and/or absolute precision.
|
|
assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
|
|
|
|
Examples
|
|
--------
|
|
the first assert does not raise an exception
|
|
|
|
>>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan],
|
|
... [1.0,2.333,np.nan])
|
|
|
|
>>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
|
|
... [1.0,2.33339,np.nan], decimal=5)
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError:
|
|
Arrays are not almost equal to 5 decimals
|
|
<BLANKLINE>
|
|
Mismatched elements: 1 / 3 (33.3%)
|
|
Max absolute difference: 5.999999999994898e-05
|
|
Max relative difference: 2.5713661239633743e-05
|
|
x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64)
|
|
y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64)
|
|
|
|
>>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
|
|
... [1.0,2.33333, 5], decimal=5)
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError:
|
|
Arrays are not almost equal to 5 decimals
|
|
<BLANKLINE>
|
|
x and y nan location mismatch:
|
|
x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64)
|
|
y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64)
|
|
|
|
"""
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
from torch._numpy import any as npany, float_, issubdtype, number, result_type
|
|
|
|
def compare(x, y):
|
|
try:
|
|
if npany(gisinf(x)) or npany(gisinf(y)):
|
|
xinfid = gisinf(x)
|
|
yinfid = gisinf(y)
|
|
if not (xinfid == yinfid).all():
|
|
return False
|
|
# if one item, x and y is +- inf
|
|
if x.size == y.size == 1:
|
|
return x == y
|
|
x = x[~xinfid]
|
|
y = y[~yinfid]
|
|
except (TypeError, NotImplementedError):
|
|
pass
|
|
|
|
# make sure y is an inexact type to avoid abs(MIN_INT); will cause
|
|
# casting of x later.
|
|
dtype = result_type(y, 1.0)
|
|
y = asanyarray(y, dtype)
|
|
z = abs(x - y)
|
|
|
|
if not issubdtype(z.dtype, number):
|
|
z = z.astype(float_) # handle object arrays
|
|
|
|
return z < 1.5 * 10.0 ** (-decimal)
|
|
|
|
assert_array_compare(
|
|
compare,
|
|
x,
|
|
y,
|
|
err_msg=err_msg,
|
|
verbose=verbose,
|
|
header=("Arrays are not almost equal to %d decimals" % decimal),
|
|
precision=decimal,
|
|
)
|
|
|
|
|
|
def assert_array_less(x, y, err_msg="", verbose=True):
|
|
"""
|
|
Raises an AssertionError if two array_like objects are not ordered by less
|
|
than.
|
|
|
|
Given two array_like objects, check that the shape is equal and all
|
|
elements of the first object are strictly smaller than those of the
|
|
second object. An exception is raised at shape mismatch or incorrectly
|
|
ordered values. Shape mismatch does not raise if an object has zero
|
|
dimension. In contrast to the standard usage in numpy, NaNs are
|
|
compared, no assertion is raised if both objects have NaNs in the same
|
|
positions.
|
|
|
|
|
|
|
|
Parameters
|
|
----------
|
|
x : array_like
|
|
The smaller object to check.
|
|
y : array_like
|
|
The larger object to compare.
|
|
err_msg : string
|
|
The error message to be printed in case of failure.
|
|
verbose : bool
|
|
If True, the conflicting values are appended to the error message.
|
|
|
|
Raises
|
|
------
|
|
AssertionError
|
|
If actual and desired objects are not equal.
|
|
|
|
See Also
|
|
--------
|
|
assert_array_equal: tests objects for equality
|
|
assert_array_almost_equal: test objects for equality up to precision
|
|
|
|
|
|
|
|
Examples
|
|
--------
|
|
>>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1.1, 2.0, np.nan])
|
|
>>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1, 2.0, np.nan])
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError:
|
|
Arrays are not less-ordered
|
|
<BLANKLINE>
|
|
Mismatched elements: 1 / 3 (33.3%)
|
|
Max absolute difference: 1.0
|
|
Max relative difference: 0.5
|
|
x: torch.ndarray([1., 1., nan], dtype=float64)
|
|
y: torch.ndarray([1., 2., nan], dtype=float64)
|
|
|
|
>>> np.testing.assert_array_less([1.0, 4.0], 3)
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError:
|
|
Arrays are not less-ordered
|
|
<BLANKLINE>
|
|
Mismatched elements: 1 / 2 (50%)
|
|
Max absolute difference: 2.0
|
|
Max relative difference: 0.6666666666666666
|
|
x: torch.ndarray([1., 4.], dtype=float64)
|
|
y: torch.ndarray(3)
|
|
|
|
>>> np.testing.assert_array_less([1.0, 2.0, 3.0], [4])
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError:
|
|
Arrays are not less-ordered
|
|
<BLANKLINE>
|
|
(shapes (3,), (1,) mismatch)
|
|
x: torch.ndarray([1., 2., 3.], dtype=float64)
|
|
y: torch.ndarray([4])
|
|
|
|
"""
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
assert_array_compare(
|
|
operator.__lt__,
|
|
x,
|
|
y,
|
|
err_msg=err_msg,
|
|
verbose=verbose,
|
|
header="Arrays are not less-ordered",
|
|
equal_inf=False,
|
|
)
|
|
|
|
|
|
def assert_string_equal(actual, desired):
|
|
"""
|
|
Test if two strings are equal.
|
|
|
|
If the given strings are equal, `assert_string_equal` does nothing.
|
|
If they are not equal, an AssertionError is raised, and the diff
|
|
between the strings is shown.
|
|
|
|
Parameters
|
|
----------
|
|
actual : str
|
|
The string to test for equality against the expected string.
|
|
desired : str
|
|
The expected string.
|
|
|
|
Examples
|
|
--------
|
|
>>> np.testing.assert_string_equal('abc', 'abc') # doctest: +SKIP
|
|
>>> np.testing.assert_string_equal('abc', 'abcd') # doctest: +SKIP
|
|
Traceback (most recent call last):
|
|
File "<stdin>", line 1, in <module>
|
|
...
|
|
AssertionError: Differences in strings:
|
|
- abc+ abcd? +
|
|
|
|
"""
|
|
# delay import of difflib to reduce startup time
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
import difflib
|
|
|
|
if not isinstance(actual, str):
|
|
raise AssertionError(repr(type(actual)))
|
|
if not isinstance(desired, str):
|
|
raise AssertionError(repr(type(desired)))
|
|
if desired == actual:
|
|
return
|
|
|
|
diff = list(
|
|
difflib.Differ().compare(actual.splitlines(True), desired.splitlines(True))
|
|
)
|
|
diff_list = []
|
|
while diff:
|
|
d1 = diff.pop(0)
|
|
if d1.startswith(" "):
|
|
continue
|
|
if d1.startswith("- "):
|
|
l = [d1]
|
|
d2 = diff.pop(0)
|
|
if d2.startswith("? "):
|
|
l.append(d2)
|
|
d2 = diff.pop(0)
|
|
if not d2.startswith("+ "):
|
|
raise AssertionError(repr(d2))
|
|
l.append(d2)
|
|
if diff:
|
|
d3 = diff.pop(0)
|
|
if d3.startswith("? "):
|
|
l.append(d3)
|
|
else:
|
|
diff.insert(0, d3)
|
|
if d2[2:] == d1[2:]:
|
|
continue
|
|
diff_list.extend(l)
|
|
continue
|
|
raise AssertionError(repr(d1))
|
|
if not diff_list:
|
|
return
|
|
msg = f"Differences in strings:\n{''.join(diff_list).rstrip()}"
|
|
if actual != desired:
|
|
raise AssertionError(msg)
|
|
|
|
|
|
import unittest
|
|
|
|
|
|
class _Dummy(unittest.TestCase):
|
|
def nop(self):
|
|
pass
|
|
|
|
|
|
_d = _Dummy("nop")
|
|
|
|
|
|
def assert_raises_regex(exception_class, expected_regexp, *args, **kwargs):
|
|
"""
|
|
assert_raises_regex(exception_class, expected_regexp, callable, *args,
|
|
**kwargs)
|
|
assert_raises_regex(exception_class, expected_regexp)
|
|
|
|
Fail unless an exception of class exception_class and with message that
|
|
matches expected_regexp is thrown by callable when invoked with arguments
|
|
args and keyword arguments kwargs.
|
|
|
|
Alternatively, can be used as a context manager like `assert_raises`.
|
|
|
|
Notes
|
|
-----
|
|
.. versionadded:: 1.9.0
|
|
|
|
"""
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
return _d.assertRaisesRegex(exception_class, expected_regexp, *args, **kwargs)
|
|
|
|
|
|
def decorate_methods(cls, decorator, testmatch=None):
|
|
"""
|
|
Apply a decorator to all methods in a class matching a regular expression.
|
|
|
|
The given decorator is applied to all public methods of `cls` that are
|
|
matched by the regular expression `testmatch`
|
|
(``testmatch.search(methodname)``). Methods that are private, i.e. start
|
|
with an underscore, are ignored.
|
|
|
|
Parameters
|
|
----------
|
|
cls : class
|
|
Class whose methods to decorate.
|
|
decorator : function
|
|
Decorator to apply to methods
|
|
testmatch : compiled regexp or str, optional
|
|
The regular expression. Default value is None, in which case the
|
|
nose default (``re.compile(r'(?:^|[\\b_\\.%s-])[Tt]est' % os.sep)``)
|
|
is used.
|
|
If `testmatch` is a string, it is compiled to a regular expression
|
|
first.
|
|
|
|
"""
|
|
if testmatch is None:
|
|
testmatch = re.compile(r"(?:^|[\\b_\\.%s-])[Tt]est" % os.sep)
|
|
else:
|
|
testmatch = re.compile(testmatch)
|
|
cls_attr = cls.__dict__
|
|
|
|
# delayed import to reduce startup time
|
|
from inspect import isfunction
|
|
|
|
methods = [_m for _m in cls_attr.values() if isfunction(_m)]
|
|
for function in methods:
|
|
try:
|
|
if hasattr(function, "compat_func_name"):
|
|
funcname = function.compat_func_name
|
|
else:
|
|
funcname = function.__name__
|
|
except AttributeError:
|
|
# not a function
|
|
continue
|
|
if testmatch.search(funcname) and not funcname.startswith("_"):
|
|
setattr(cls, funcname, decorator(function))
|
|
return
|
|
|
|
|
|
def _assert_valid_refcount(op):
|
|
"""
|
|
Check that ufuncs don't mishandle refcount of object `1`.
|
|
Used in a few regression tests.
|
|
"""
|
|
if not HAS_REFCOUNT:
|
|
return True
|
|
|
|
import gc
|
|
|
|
import numpy as np
|
|
|
|
b = np.arange(100 * 100).reshape(100, 100)
|
|
c = b
|
|
i = 1
|
|
|
|
gc.disable()
|
|
try:
|
|
rc = sys.getrefcount(i)
|
|
for j in range(15):
|
|
d = op(b, c)
|
|
assert_(sys.getrefcount(i) >= rc)
|
|
finally:
|
|
gc.enable()
|
|
del d # for pyflakes
|
|
|
|
|
|
def assert_allclose(
|
|
actual,
|
|
desired,
|
|
rtol=1e-7,
|
|
atol=0,
|
|
equal_nan=True,
|
|
err_msg="",
|
|
verbose=True,
|
|
check_dtype=False,
|
|
):
|
|
"""
|
|
Raises an AssertionError if two objects are not equal up to desired
|
|
tolerance.
|
|
|
|
Given two array_like objects, check that their shapes and all elements
|
|
are equal (but see the Notes for the special handling of a scalar). An
|
|
exception is raised if the shapes mismatch or any values conflict. In
|
|
contrast to the standard usage in numpy, NaNs are compared like numbers,
|
|
no assertion is raised if both objects have NaNs in the same positions.
|
|
|
|
The test is equivalent to ``allclose(actual, desired, rtol, atol)`` (note
|
|
that ``allclose`` has different default values). It compares the difference
|
|
between `actual` and `desired` to ``atol + rtol * abs(desired)``.
|
|
|
|
.. versionadded:: 1.5.0
|
|
|
|
Parameters
|
|
----------
|
|
actual : array_like
|
|
Array obtained.
|
|
desired : array_like
|
|
Array desired.
|
|
rtol : float, optional
|
|
Relative tolerance.
|
|
atol : float, optional
|
|
Absolute tolerance.
|
|
equal_nan : bool, optional.
|
|
If True, NaNs will compare equal.
|
|
err_msg : str, optional
|
|
The error message to be printed in case of failure.
|
|
verbose : bool, optional
|
|
If True, the conflicting values are appended to the error message.
|
|
|
|
Raises
|
|
------
|
|
AssertionError
|
|
If actual and desired are not equal up to specified precision.
|
|
|
|
See Also
|
|
--------
|
|
assert_array_almost_equal_nulp, assert_array_max_ulp
|
|
|
|
Notes
|
|
-----
|
|
When one of `actual` and `desired` is a scalar and the other is
|
|
array_like, the function checks that each element of the array_like
|
|
object is equal to the scalar.
|
|
|
|
Examples
|
|
--------
|
|
>>> x = [1e-5, 1e-3, 1e-1]
|
|
>>> y = np.arccos(np.cos(x))
|
|
>>> np.testing.assert_allclose(x, y, rtol=1e-5, atol=0)
|
|
|
|
"""
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
|
|
def compare(x, y):
|
|
return np.isclose(x, y, rtol=rtol, atol=atol, equal_nan=equal_nan)
|
|
|
|
actual, desired = asanyarray(actual), asanyarray(desired)
|
|
header = f"Not equal to tolerance rtol={rtol:g}, atol={atol:g}"
|
|
|
|
if check_dtype:
|
|
assert actual.dtype == desired.dtype
|
|
|
|
assert_array_compare(
|
|
compare,
|
|
actual,
|
|
desired,
|
|
err_msg=str(err_msg),
|
|
verbose=verbose,
|
|
header=header,
|
|
equal_nan=equal_nan,
|
|
)
|
|
|
|
|
|
def assert_array_almost_equal_nulp(x, y, nulp=1):
|
|
"""
|
|
Compare two arrays relatively to their spacing.
|
|
|
|
This is a relatively robust method to compare two arrays whose amplitude
|
|
is variable.
|
|
|
|
Parameters
|
|
----------
|
|
x, y : array_like
|
|
Input arrays.
|
|
nulp : int, optional
|
|
The maximum number of unit in the last place for tolerance (see Notes).
|
|
Default is 1.
|
|
|
|
Returns
|
|
-------
|
|
None
|
|
|
|
Raises
|
|
------
|
|
AssertionError
|
|
If the spacing between `x` and `y` for one or more elements is larger
|
|
than `nulp`.
|
|
|
|
See Also
|
|
--------
|
|
assert_array_max_ulp : Check that all items of arrays differ in at most
|
|
N Units in the Last Place.
|
|
spacing : Return the distance between x and the nearest adjacent number.
|
|
|
|
Notes
|
|
-----
|
|
An assertion is raised if the following condition is not met::
|
|
|
|
abs(x - y) <= nulp * spacing(maximum(abs(x), abs(y)))
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.array([1., 1e-10, 1e-20])
|
|
>>> eps = np.finfo(x.dtype).eps
|
|
>>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x) # doctest: +SKIP
|
|
|
|
>>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x) # doctest: +SKIP
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError: X and Y are not equal to 1 ULP (max is 2)
|
|
|
|
"""
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
import numpy as np
|
|
|
|
ax = np.abs(x)
|
|
ay = np.abs(y)
|
|
ref = nulp * np.spacing(np.where(ax > ay, ax, ay))
|
|
if not np.all(np.abs(x - y) <= ref):
|
|
if np.iscomplexobj(x) or np.iscomplexobj(y):
|
|
msg = "X and Y are not equal to %d ULP" % nulp
|
|
else:
|
|
max_nulp = np.max(nulp_diff(x, y))
|
|
msg = "X and Y are not equal to %d ULP (max is %g)" % (nulp, max_nulp)
|
|
raise AssertionError(msg)
|
|
|
|
|
|
def assert_array_max_ulp(a, b, maxulp=1, dtype=None):
|
|
"""
|
|
Check that all items of arrays differ in at most N Units in the Last Place.
|
|
|
|
Parameters
|
|
----------
|
|
a, b : array_like
|
|
Input arrays to be compared.
|
|
maxulp : int, optional
|
|
The maximum number of units in the last place that elements of `a` and
|
|
`b` can differ. Default is 1.
|
|
dtype : dtype, optional
|
|
Data-type to convert `a` and `b` to if given. Default is None.
|
|
|
|
Returns
|
|
-------
|
|
ret : ndarray
|
|
Array containing number of representable floating point numbers between
|
|
items in `a` and `b`.
|
|
|
|
Raises
|
|
------
|
|
AssertionError
|
|
If one or more elements differ by more than `maxulp`.
|
|
|
|
Notes
|
|
-----
|
|
For computing the ULP difference, this API does not differentiate between
|
|
various representations of NAN (ULP difference between 0x7fc00000 and 0xffc00000
|
|
is zero).
|
|
|
|
See Also
|
|
--------
|
|
assert_array_almost_equal_nulp : Compare two arrays relatively to their
|
|
spacing.
|
|
|
|
Examples
|
|
--------
|
|
>>> a = np.linspace(0., 1., 100)
|
|
>>> res = np.testing.assert_array_max_ulp(a, np.arcsin(np.sin(a))) # doctest: +SKIP
|
|
|
|
"""
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
import numpy as np
|
|
|
|
ret = nulp_diff(a, b, dtype)
|
|
if not np.all(ret <= maxulp):
|
|
raise AssertionError(
|
|
f"Arrays are not almost equal up to {maxulp:g} "
|
|
f"ULP (max difference is {np.max(ret):g} ULP)"
|
|
)
|
|
return ret
|
|
|
|
|
|
def nulp_diff(x, y, dtype=None):
|
|
"""For each item in x and y, return the number of representable floating
|
|
points between them.
|
|
|
|
Parameters
|
|
----------
|
|
x : array_like
|
|
first input array
|
|
y : array_like
|
|
second input array
|
|
dtype : dtype, optional
|
|
Data-type to convert `x` and `y` to if given. Default is None.
|
|
|
|
Returns
|
|
-------
|
|
nulp : array_like
|
|
number of representable floating point numbers between each item in x
|
|
and y.
|
|
|
|
Notes
|
|
-----
|
|
For computing the ULP difference, this API does not differentiate between
|
|
various representations of NAN (ULP difference between 0x7fc00000 and 0xffc00000
|
|
is zero).
|
|
|
|
Examples
|
|
--------
|
|
# By definition, epsilon is the smallest number such as 1 + eps != 1, so
|
|
# there should be exactly one ULP between 1 and 1 + eps
|
|
>>> nulp_diff(1, 1 + np.finfo(x.dtype).eps) # doctest: +SKIP
|
|
1.0
|
|
"""
|
|
import numpy as np
|
|
|
|
if dtype:
|
|
x = np.asarray(x, dtype=dtype)
|
|
y = np.asarray(y, dtype=dtype)
|
|
else:
|
|
x = np.asarray(x)
|
|
y = np.asarray(y)
|
|
|
|
t = np.common_type(x, y)
|
|
if np.iscomplexobj(x) or np.iscomplexobj(y):
|
|
raise NotImplementedError("_nulp not implemented for complex array")
|
|
|
|
x = np.array([x], dtype=t)
|
|
y = np.array([y], dtype=t)
|
|
|
|
x[np.isnan(x)] = np.nan
|
|
y[np.isnan(y)] = np.nan
|
|
|
|
if not x.shape == y.shape:
|
|
raise ValueError(f"x and y do not have the same shape: {x.shape} - {y.shape}")
|
|
|
|
def _diff(rx, ry, vdt):
|
|
diff = np.asarray(rx - ry, dtype=vdt)
|
|
return np.abs(diff)
|
|
|
|
rx = integer_repr(x)
|
|
ry = integer_repr(y)
|
|
return _diff(rx, ry, t)
|
|
|
|
|
|
def _integer_repr(x, vdt, comp):
|
|
# Reinterpret binary representation of the float as sign-magnitude:
|
|
# take into account two-complement representation
|
|
# See also
|
|
# https://randomascii.wordpress.com/2012/02/25/comparing-floating-point-numbers-2012-edition/
|
|
rx = x.view(vdt)
|
|
if not (rx.size == 1):
|
|
rx[rx < 0] = comp - rx[rx < 0]
|
|
else:
|
|
if rx < 0:
|
|
rx = comp - rx
|
|
|
|
return rx
|
|
|
|
|
|
def integer_repr(x):
|
|
"""Return the signed-magnitude interpretation of the binary representation
|
|
of x."""
|
|
import numpy as np
|
|
|
|
if x.dtype == np.float16:
|
|
return _integer_repr(x, np.int16, np.int16(-(2**15)))
|
|
elif x.dtype == np.float32:
|
|
return _integer_repr(x, np.int32, np.int32(-(2**31)))
|
|
elif x.dtype == np.float64:
|
|
return _integer_repr(x, np.int64, np.int64(-(2**63)))
|
|
else:
|
|
raise ValueError(f"Unsupported dtype {x.dtype}")
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def _assert_warns_context(warning_class, name=None):
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
with suppress_warnings() as sup:
|
|
l = sup.record(warning_class)
|
|
yield
|
|
if not len(l) > 0:
|
|
name_str = f" when calling {name}" if name is not None else ""
|
|
raise AssertionError("No warning raised" + name_str)
|
|
|
|
|
|
def assert_warns(warning_class, *args, **kwargs):
|
|
"""
|
|
Fail unless the given callable throws the specified warning.
|
|
|
|
A warning of class warning_class should be thrown by the callable when
|
|
invoked with arguments args and keyword arguments kwargs.
|
|
If a different type of warning is thrown, it will not be caught.
|
|
|
|
If called with all arguments other than the warning class omitted, may be
|
|
used as a context manager:
|
|
|
|
with assert_warns(SomeWarning):
|
|
do_something()
|
|
|
|
The ability to be used as a context manager is new in NumPy v1.11.0.
|
|
|
|
.. versionadded:: 1.4.0
|
|
|
|
Parameters
|
|
----------
|
|
warning_class : class
|
|
The class defining the warning that `func` is expected to throw.
|
|
func : callable, optional
|
|
Callable to test
|
|
*args : Arguments
|
|
Arguments for `func`.
|
|
**kwargs : Kwargs
|
|
Keyword arguments for `func`.
|
|
|
|
Returns
|
|
-------
|
|
The value returned by `func`.
|
|
|
|
Examples
|
|
--------
|
|
>>> import warnings
|
|
>>> def deprecated_func(num):
|
|
... warnings.warn("Please upgrade", DeprecationWarning)
|
|
... return num*num
|
|
>>> with np.testing.assert_warns(DeprecationWarning):
|
|
... assert deprecated_func(4) == 16
|
|
>>> # or passing a func
|
|
>>> ret = np.testing.assert_warns(DeprecationWarning, deprecated_func, 4)
|
|
>>> assert ret == 16
|
|
"""
|
|
if not args:
|
|
return _assert_warns_context(warning_class)
|
|
|
|
func = args[0]
|
|
args = args[1:]
|
|
with _assert_warns_context(warning_class, name=func.__name__):
|
|
return func(*args, **kwargs)
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def _assert_no_warnings_context(name=None):
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
with warnings.catch_warnings(record=True) as l:
|
|
warnings.simplefilter("always")
|
|
yield
|
|
if len(l) > 0:
|
|
name_str = f" when calling {name}" if name is not None else ""
|
|
raise AssertionError(f"Got warnings{name_str}: {l}")
|
|
|
|
|
|
def assert_no_warnings(*args, **kwargs):
|
|
"""
|
|
Fail if the given callable produces any warnings.
|
|
|
|
If called with all arguments omitted, may be used as a context manager:
|
|
|
|
with assert_no_warnings():
|
|
do_something()
|
|
|
|
The ability to be used as a context manager is new in NumPy v1.11.0.
|
|
|
|
.. versionadded:: 1.7.0
|
|
|
|
Parameters
|
|
----------
|
|
func : callable
|
|
The callable to test.
|
|
\\*args : Arguments
|
|
Arguments passed to `func`.
|
|
\\*\\*kwargs : Kwargs
|
|
Keyword arguments passed to `func`.
|
|
|
|
Returns
|
|
-------
|
|
The value returned by `func`.
|
|
|
|
"""
|
|
if not args:
|
|
return _assert_no_warnings_context()
|
|
|
|
func = args[0]
|
|
args = args[1:]
|
|
with _assert_no_warnings_context(name=func.__name__):
|
|
return func(*args, **kwargs)
|
|
|
|
|
|
def _gen_alignment_data(dtype=float32, type="binary", max_size=24):
|
|
"""
|
|
generator producing data with different alignment and offsets
|
|
to test simd vectorization
|
|
|
|
Parameters
|
|
----------
|
|
dtype : dtype
|
|
data type to produce
|
|
type : string
|
|
'unary': create data for unary operations, creates one input
|
|
and output array
|
|
'binary': create data for unary operations, creates two input
|
|
and output array
|
|
max_size : integer
|
|
maximum size of data to produce
|
|
|
|
Returns
|
|
-------
|
|
if type is 'unary' yields one output, one input array and a message
|
|
containing information on the data
|
|
if type is 'binary' yields one output array, two input array and a message
|
|
containing information on the data
|
|
|
|
"""
|
|
ufmt = "unary offset=(%d, %d), size=%d, dtype=%r, %s"
|
|
bfmt = "binary offset=(%d, %d, %d), size=%d, dtype=%r, %s"
|
|
for o in range(3):
|
|
for s in range(o + 2, max(o + 3, max_size)):
|
|
if type == "unary":
|
|
|
|
def inp():
|
|
return arange(s, dtype=dtype)[o:]
|
|
|
|
out = empty((s,), dtype=dtype)[o:]
|
|
yield out, inp(), ufmt % (o, o, s, dtype, "out of place")
|
|
d = inp()
|
|
yield d, d, ufmt % (o, o, s, dtype, "in place")
|
|
yield out[1:], inp()[:-1], ufmt % (
|
|
o + 1,
|
|
o,
|
|
s - 1,
|
|
dtype,
|
|
"out of place",
|
|
)
|
|
yield out[:-1], inp()[1:], ufmt % (
|
|
o,
|
|
o + 1,
|
|
s - 1,
|
|
dtype,
|
|
"out of place",
|
|
)
|
|
yield inp()[:-1], inp()[1:], ufmt % (o, o + 1, s - 1, dtype, "aliased")
|
|
yield inp()[1:], inp()[:-1], ufmt % (o + 1, o, s - 1, dtype, "aliased")
|
|
if type == "binary":
|
|
|
|
def inp1():
|
|
return arange(s, dtype=dtype)[o:]
|
|
|
|
inp2 = inp1
|
|
out = empty((s,), dtype=dtype)[o:]
|
|
yield out, inp1(), inp2(), bfmt % (o, o, o, s, dtype, "out of place")
|
|
d = inp1()
|
|
yield d, d, inp2(), bfmt % (o, o, o, s, dtype, "in place1")
|
|
d = inp2()
|
|
yield d, inp1(), d, bfmt % (o, o, o, s, dtype, "in place2")
|
|
yield out[1:], inp1()[:-1], inp2()[:-1], bfmt % (
|
|
o + 1,
|
|
o,
|
|
o,
|
|
s - 1,
|
|
dtype,
|
|
"out of place",
|
|
)
|
|
yield out[:-1], inp1()[1:], inp2()[:-1], bfmt % (
|
|
o,
|
|
o + 1,
|
|
o,
|
|
s - 1,
|
|
dtype,
|
|
"out of place",
|
|
)
|
|
yield out[:-1], inp1()[:-1], inp2()[1:], bfmt % (
|
|
o,
|
|
o,
|
|
o + 1,
|
|
s - 1,
|
|
dtype,
|
|
"out of place",
|
|
)
|
|
yield inp1()[1:], inp1()[:-1], inp2()[:-1], bfmt % (
|
|
o + 1,
|
|
o,
|
|
o,
|
|
s - 1,
|
|
dtype,
|
|
"aliased",
|
|
)
|
|
yield inp1()[:-1], inp1()[1:], inp2()[:-1], bfmt % (
|
|
o,
|
|
o + 1,
|
|
o,
|
|
s - 1,
|
|
dtype,
|
|
"aliased",
|
|
)
|
|
yield inp1()[:-1], inp1()[:-1], inp2()[1:], bfmt % (
|
|
o,
|
|
o,
|
|
o + 1,
|
|
s - 1,
|
|
dtype,
|
|
"aliased",
|
|
)
|
|
|
|
|
|
class IgnoreException(Exception):
|
|
"Ignoring this exception due to disabled feature"
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def tempdir(*args, **kwargs):
|
|
"""Context manager to provide a temporary test folder.
|
|
|
|
All arguments are passed as this to the underlying tempfile.mkdtemp
|
|
function.
|
|
|
|
"""
|
|
tmpdir = mkdtemp(*args, **kwargs)
|
|
try:
|
|
yield tmpdir
|
|
finally:
|
|
shutil.rmtree(tmpdir)
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def temppath(*args, **kwargs):
|
|
"""Context manager for temporary files.
|
|
|
|
Context manager that returns the path to a closed temporary file. Its
|
|
parameters are the same as for tempfile.mkstemp and are passed directly
|
|
to that function. The underlying file is removed when the context is
|
|
exited, so it should be closed at that time.
|
|
|
|
Windows does not allow a temporary file to be opened if it is already
|
|
open, so the underlying file must be closed after opening before it
|
|
can be opened again.
|
|
|
|
"""
|
|
fd, path = mkstemp(*args, **kwargs)
|
|
os.close(fd)
|
|
try:
|
|
yield path
|
|
finally:
|
|
os.remove(path)
|
|
|
|
|
|
class clear_and_catch_warnings(warnings.catch_warnings):
|
|
"""Context manager that resets warning registry for catching warnings
|
|
|
|
Warnings can be slippery, because, whenever a warning is triggered, Python
|
|
adds a ``__warningregistry__`` member to the *calling* module. This makes
|
|
it impossible to retrigger the warning in this module, whatever you put in
|
|
the warnings filters. This context manager accepts a sequence of `modules`
|
|
as a keyword argument to its constructor and:
|
|
|
|
* stores and removes any ``__warningregistry__`` entries in given `modules`
|
|
on entry;
|
|
* resets ``__warningregistry__`` to its previous state on exit.
|
|
|
|
This makes it possible to trigger any warning afresh inside the context
|
|
manager without disturbing the state of warnings outside.
|
|
|
|
For compatibility with Python 3.0, please consider all arguments to be
|
|
keyword-only.
|
|
|
|
Parameters
|
|
----------
|
|
record : bool, optional
|
|
Specifies whether warnings should be captured by a custom
|
|
implementation of ``warnings.showwarning()`` and be appended to a list
|
|
returned by the context manager. Otherwise None is returned by the
|
|
context manager. The objects appended to the list are arguments whose
|
|
attributes mirror the arguments to ``showwarning()``.
|
|
modules : sequence, optional
|
|
Sequence of modules for which to reset warnings registry on entry and
|
|
restore on exit. To work correctly, all 'ignore' filters should
|
|
filter by one of these modules.
|
|
|
|
Examples
|
|
--------
|
|
>>> import warnings
|
|
>>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP
|
|
... modules=[np.core.fromnumeric]):
|
|
... warnings.simplefilter('always')
|
|
... warnings.filterwarnings('ignore', module='np.core.fromnumeric')
|
|
... # do something that raises a warning but ignore those in
|
|
... # np.core.fromnumeric
|
|
"""
|
|
|
|
class_modules = ()
|
|
|
|
def __init__(self, record=False, modules=()):
|
|
self.modules = set(modules).union(self.class_modules)
|
|
self._warnreg_copies = {}
|
|
super().__init__(record=record)
|
|
|
|
def __enter__(self):
|
|
for mod in self.modules:
|
|
if hasattr(mod, "__warningregistry__"):
|
|
mod_reg = mod.__warningregistry__
|
|
self._warnreg_copies[mod] = mod_reg.copy()
|
|
mod_reg.clear()
|
|
return super().__enter__()
|
|
|
|
def __exit__(self, *exc_info):
|
|
super().__exit__(*exc_info)
|
|
for mod in self.modules:
|
|
if hasattr(mod, "__warningregistry__"):
|
|
mod.__warningregistry__.clear()
|
|
if mod in self._warnreg_copies:
|
|
mod.__warningregistry__.update(self._warnreg_copies[mod])
|
|
|
|
|
|
class suppress_warnings:
|
|
"""
|
|
Context manager and decorator doing much the same as
|
|
``warnings.catch_warnings``.
|
|
|
|
However, it also provides a filter mechanism to work around
|
|
https://bugs.python.org/issue4180.
|
|
|
|
This bug causes Python before 3.4 to not reliably show warnings again
|
|
after they have been ignored once (even within catch_warnings). It
|
|
means that no "ignore" filter can be used easily, since following
|
|
tests might need to see the warning. Additionally it allows easier
|
|
specificity for testing warnings and can be nested.
|
|
|
|
Parameters
|
|
----------
|
|
forwarding_rule : str, optional
|
|
One of "always", "once", "module", or "location". Analogous to
|
|
the usual warnings module filter mode, it is useful to reduce
|
|
noise mostly on the outmost level. Unsuppressed and unrecorded
|
|
warnings will be forwarded based on this rule. Defaults to "always".
|
|
"location" is equivalent to the warnings "default", match by exact
|
|
location the warning warning originated from.
|
|
|
|
Notes
|
|
-----
|
|
Filters added inside the context manager will be discarded again
|
|
when leaving it. Upon entering all filters defined outside a
|
|
context will be applied automatically.
|
|
|
|
When a recording filter is added, matching warnings are stored in the
|
|
``log`` attribute as well as in the list returned by ``record``.
|
|
|
|
If filters are added and the ``module`` keyword is given, the
|
|
warning registry of this module will additionally be cleared when
|
|
applying it, entering the context, or exiting it. This could cause
|
|
warnings to appear a second time after leaving the context if they
|
|
were configured to be printed once (default) and were already
|
|
printed before the context was entered.
|
|
|
|
Nesting this context manager will work as expected when the
|
|
forwarding rule is "always" (default). Unfiltered and unrecorded
|
|
warnings will be passed out and be matched by the outer level.
|
|
On the outmost level they will be printed (or caught by another
|
|
warnings context). The forwarding rule argument can modify this
|
|
behaviour.
|
|
|
|
Like ``catch_warnings`` this context manager is not threadsafe.
|
|
|
|
Examples
|
|
--------
|
|
|
|
With a context manager::
|
|
|
|
with np.testing.suppress_warnings() as sup:
|
|
sup.filter(DeprecationWarning, "Some text")
|
|
sup.filter(module=np.ma.core)
|
|
log = sup.record(FutureWarning, "Does this occur?")
|
|
command_giving_warnings()
|
|
# The FutureWarning was given once, the filtered warnings were
|
|
# ignored. All other warnings abide outside settings (may be
|
|
# printed/error)
|
|
assert_(len(log) == 1)
|
|
assert_(len(sup.log) == 1) # also stored in log attribute
|
|
|
|
Or as a decorator::
|
|
|
|
sup = np.testing.suppress_warnings()
|
|
sup.filter(module=np.ma.core) # module must match exactly
|
|
@sup
|
|
def some_function():
|
|
# do something which causes a warning in np.ma.core
|
|
pass
|
|
"""
|
|
|
|
def __init__(self, forwarding_rule="always"):
|
|
self._entered = False
|
|
|
|
# Suppressions are either instance or defined inside one with block:
|
|
self._suppressions = []
|
|
|
|
if forwarding_rule not in {"always", "module", "once", "location"}:
|
|
raise ValueError("unsupported forwarding rule.")
|
|
self._forwarding_rule = forwarding_rule
|
|
|
|
def _clear_registries(self):
|
|
if hasattr(warnings, "_filters_mutated"):
|
|
# clearing the registry should not be necessary on new pythons,
|
|
# instead the filters should be mutated.
|
|
warnings._filters_mutated()
|
|
return
|
|
# Simply clear the registry, this should normally be harmless,
|
|
# note that on new pythons it would be invalidated anyway.
|
|
for module in self._tmp_modules:
|
|
if hasattr(module, "__warningregistry__"):
|
|
module.__warningregistry__.clear()
|
|
|
|
def _filter(self, category=Warning, message="", module=None, record=False):
|
|
if record:
|
|
record = [] # The log where to store warnings
|
|
else:
|
|
record = None
|
|
if self._entered:
|
|
if module is None:
|
|
warnings.filterwarnings("always", category=category, message=message)
|
|
else:
|
|
module_regex = module.__name__.replace(".", r"\.") + "$"
|
|
warnings.filterwarnings(
|
|
"always", category=category, message=message, module=module_regex
|
|
)
|
|
self._tmp_modules.add(module)
|
|
self._clear_registries()
|
|
|
|
self._tmp_suppressions.append(
|
|
(category, message, re.compile(message, re.I), module, record)
|
|
)
|
|
else:
|
|
self._suppressions.append(
|
|
(category, message, re.compile(message, re.I), module, record)
|
|
)
|
|
|
|
return record
|
|
|
|
def filter(self, category=Warning, message="", module=None):
|
|
"""
|
|
Add a new suppressing filter or apply it if the state is entered.
|
|
|
|
Parameters
|
|
----------
|
|
category : class, optional
|
|
Warning class to filter
|
|
message : string, optional
|
|
Regular expression matching the warning message.
|
|
module : module, optional
|
|
Module to filter for. Note that the module (and its file)
|
|
must match exactly and cannot be a submodule. This may make
|
|
it unreliable for external modules.
|
|
|
|
Notes
|
|
-----
|
|
When added within a context, filters are only added inside
|
|
the context and will be forgotten when the context is exited.
|
|
"""
|
|
self._filter(category=category, message=message, module=module, record=False)
|
|
|
|
def record(self, category=Warning, message="", module=None):
|
|
"""
|
|
Append a new recording filter or apply it if the state is entered.
|
|
|
|
All warnings matching will be appended to the ``log`` attribute.
|
|
|
|
Parameters
|
|
----------
|
|
category : class, optional
|
|
Warning class to filter
|
|
message : string, optional
|
|
Regular expression matching the warning message.
|
|
module : module, optional
|
|
Module to filter for. Note that the module (and its file)
|
|
must match exactly and cannot be a submodule. This may make
|
|
it unreliable for external modules.
|
|
|
|
Returns
|
|
-------
|
|
log : list
|
|
A list which will be filled with all matched warnings.
|
|
|
|
Notes
|
|
-----
|
|
When added within a context, filters are only added inside
|
|
the context and will be forgotten when the context is exited.
|
|
"""
|
|
return self._filter(
|
|
category=category, message=message, module=module, record=True
|
|
)
|
|
|
|
def __enter__(self):
|
|
if self._entered:
|
|
raise RuntimeError("cannot enter suppress_warnings twice.")
|
|
|
|
self._orig_show = warnings.showwarning
|
|
self._filters = warnings.filters
|
|
warnings.filters = self._filters[:]
|
|
|
|
self._entered = True
|
|
self._tmp_suppressions = []
|
|
self._tmp_modules = set()
|
|
self._forwarded = set()
|
|
|
|
self.log = [] # reset global log (no need to keep same list)
|
|
|
|
for cat, mess, _, mod, log in self._suppressions:
|
|
if log is not None:
|
|
del log[:] # clear the log
|
|
if mod is None:
|
|
warnings.filterwarnings("always", category=cat, message=mess)
|
|
else:
|
|
module_regex = mod.__name__.replace(".", r"\.") + "$"
|
|
warnings.filterwarnings(
|
|
"always", category=cat, message=mess, module=module_regex
|
|
)
|
|
self._tmp_modules.add(mod)
|
|
warnings.showwarning = self._showwarning
|
|
self._clear_registries()
|
|
|
|
return self
|
|
|
|
def __exit__(self, *exc_info):
|
|
warnings.showwarning = self._orig_show
|
|
warnings.filters = self._filters
|
|
self._clear_registries()
|
|
self._entered = False
|
|
del self._orig_show
|
|
del self._filters
|
|
|
|
def _showwarning(
|
|
self, message, category, filename, lineno, *args, use_warnmsg=None, **kwargs
|
|
):
|
|
for cat, _, pattern, mod, rec in (self._suppressions + self._tmp_suppressions)[
|
|
::-1
|
|
]:
|
|
if issubclass(category, cat) and pattern.match(message.args[0]) is not None:
|
|
if mod is None:
|
|
# Message and category match, either recorded or ignored
|
|
if rec is not None:
|
|
msg = WarningMessage(
|
|
message, category, filename, lineno, **kwargs
|
|
)
|
|
self.log.append(msg)
|
|
rec.append(msg)
|
|
return
|
|
# Use startswith, because warnings strips the c or o from
|
|
# .pyc/.pyo files.
|
|
elif mod.__file__.startswith(filename):
|
|
# The message and module (filename) match
|
|
if rec is not None:
|
|
msg = WarningMessage(
|
|
message, category, filename, lineno, **kwargs
|
|
)
|
|
self.log.append(msg)
|
|
rec.append(msg)
|
|
return
|
|
|
|
# There is no filter in place, so pass to the outside handler
|
|
# unless we should only pass it once
|
|
if self._forwarding_rule == "always":
|
|
if use_warnmsg is None:
|
|
self._orig_show(message, category, filename, lineno, *args, **kwargs)
|
|
else:
|
|
self._orig_showmsg(use_warnmsg)
|
|
return
|
|
|
|
if self._forwarding_rule == "once":
|
|
signature = (message.args, category)
|
|
elif self._forwarding_rule == "module":
|
|
signature = (message.args, category, filename)
|
|
elif self._forwarding_rule == "location":
|
|
signature = (message.args, category, filename, lineno)
|
|
|
|
if signature in self._forwarded:
|
|
return
|
|
self._forwarded.add(signature)
|
|
if use_warnmsg is None:
|
|
self._orig_show(message, category, filename, lineno, *args, **kwargs)
|
|
else:
|
|
self._orig_showmsg(use_warnmsg)
|
|
|
|
def __call__(self, func):
|
|
"""
|
|
Function decorator to apply certain suppressions to a whole
|
|
function.
|
|
"""
|
|
|
|
@wraps(func)
|
|
def new_func(*args, **kwargs):
|
|
with self:
|
|
return func(*args, **kwargs)
|
|
|
|
return new_func
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def _assert_no_gc_cycles_context(name=None):
|
|
__tracebackhide__ = True # Hide traceback for py.test
|
|
|
|
# not meaningful to test if there is no refcounting
|
|
if not HAS_REFCOUNT:
|
|
yield
|
|
return
|
|
|
|
assert_(gc.isenabled())
|
|
gc.disable()
|
|
gc_debug = gc.get_debug()
|
|
try:
|
|
for i in range(100):
|
|
if gc.collect() == 0:
|
|
break
|
|
else:
|
|
raise RuntimeError(
|
|
"Unable to fully collect garbage - perhaps a __del__ method "
|
|
"is creating more reference cycles?"
|
|
)
|
|
|
|
gc.set_debug(gc.DEBUG_SAVEALL)
|
|
yield
|
|
# gc.collect returns the number of unreachable objects in cycles that
|
|
# were found -- we are checking that no cycles were created in the context
|
|
n_objects_in_cycles = gc.collect()
|
|
objects_in_cycles = gc.garbage[:]
|
|
finally:
|
|
del gc.garbage[:]
|
|
gc.set_debug(gc_debug)
|
|
gc.enable()
|
|
|
|
if n_objects_in_cycles:
|
|
name_str = f" when calling {name}" if name is not None else ""
|
|
raise AssertionError(
|
|
"Reference cycles were found{}: {} objects were collected, "
|
|
"of which {} are shown below:{}".format(
|
|
name_str,
|
|
n_objects_in_cycles,
|
|
len(objects_in_cycles),
|
|
"".join(
|
|
"\n {} object with id={}:\n {}".format(
|
|
type(o).__name__,
|
|
id(o),
|
|
pprint.pformat(o).replace("\n", "\n "),
|
|
)
|
|
for o in objects_in_cycles
|
|
),
|
|
)
|
|
)
|
|
|
|
|
|
def assert_no_gc_cycles(*args, **kwargs):
|
|
"""
|
|
Fail if the given callable produces any reference cycles.
|
|
|
|
If called with all arguments omitted, may be used as a context manager:
|
|
|
|
with assert_no_gc_cycles():
|
|
do_something()
|
|
|
|
.. versionadded:: 1.15.0
|
|
|
|
Parameters
|
|
----------
|
|
func : callable
|
|
The callable to test.
|
|
\\*args : Arguments
|
|
Arguments passed to `func`.
|
|
\\*\\*kwargs : Kwargs
|
|
Keyword arguments passed to `func`.
|
|
|
|
Returns
|
|
-------
|
|
Nothing. The result is deliberately discarded to ensure that all cycles
|
|
are found.
|
|
|
|
"""
|
|
if not args:
|
|
return _assert_no_gc_cycles_context()
|
|
|
|
func = args[0]
|
|
args = args[1:]
|
|
with _assert_no_gc_cycles_context(name=func.__name__):
|
|
func(*args, **kwargs)
|
|
|
|
|
|
def break_cycles():
|
|
"""
|
|
Break reference cycles by calling gc.collect
|
|
Objects can call other objects' methods (for instance, another object's
|
|
__del__) inside their own __del__. On PyPy, the interpreter only runs
|
|
between calls to gc.collect, so multiple calls are needed to completely
|
|
release all cycles.
|
|
"""
|
|
|
|
gc.collect()
|
|
if IS_PYPY:
|
|
# a few more, just to make sure all the finalizers are called
|
|
gc.collect()
|
|
gc.collect()
|
|
gc.collect()
|
|
gc.collect()
|
|
|
|
|
|
def requires_memory(free_bytes):
|
|
"""Decorator to skip a test if not enough memory is available"""
|
|
import pytest
|
|
|
|
def decorator(func):
|
|
@wraps(func)
|
|
def wrapper(*a, **kw):
|
|
msg = check_free_memory(free_bytes)
|
|
if msg is not None:
|
|
pytest.skip(msg)
|
|
|
|
try:
|
|
return func(*a, **kw)
|
|
except MemoryError:
|
|
# Probably ran out of memory regardless: don't regard as failure
|
|
pytest.xfail("MemoryError raised")
|
|
|
|
return wrapper
|
|
|
|
return decorator
|
|
|
|
|
|
def check_free_memory(free_bytes):
|
|
"""
|
|
Check whether `free_bytes` amount of memory is currently free.
|
|
Returns: None if enough memory available, otherwise error message
|
|
"""
|
|
env_var = "NPY_AVAILABLE_MEM"
|
|
env_value = os.environ.get(env_var)
|
|
if env_value is not None:
|
|
try:
|
|
mem_free = _parse_size(env_value)
|
|
except ValueError as exc:
|
|
raise ValueError( # noqa: TRY200
|
|
f"Invalid environment variable {env_var}: {exc}"
|
|
)
|
|
|
|
msg = (
|
|
f"{free_bytes/1e9} GB memory required, but environment variable "
|
|
f"NPY_AVAILABLE_MEM={env_value} set"
|
|
)
|
|
else:
|
|
mem_free = _get_mem_available()
|
|
|
|
if mem_free is None:
|
|
msg = (
|
|
"Could not determine available memory; set NPY_AVAILABLE_MEM "
|
|
"environment variable (e.g. NPY_AVAILABLE_MEM=16GB) to run "
|
|
"the test."
|
|
)
|
|
mem_free = -1
|
|
else:
|
|
msg = (
|
|
f"{free_bytes/1e9} GB memory required, but {mem_free/1e9} GB available"
|
|
)
|
|
|
|
return msg if mem_free < free_bytes else None
|
|
|
|
|
|
def _parse_size(size_str):
|
|
"""Convert memory size strings ('12 GB' etc.) to float"""
|
|
suffixes = {
|
|
"": 1,
|
|
"b": 1,
|
|
"k": 1000,
|
|
"m": 1000**2,
|
|
"g": 1000**3,
|
|
"t": 1000**4,
|
|
"kb": 1000,
|
|
"mb": 1000**2,
|
|
"gb": 1000**3,
|
|
"tb": 1000**4,
|
|
"kib": 1024,
|
|
"mib": 1024**2,
|
|
"gib": 1024**3,
|
|
"tib": 1024**4,
|
|
}
|
|
|
|
size_re = re.compile(
|
|
r"^\s*(\d+|\d+\.\d+)\s*({})\s*$".format("|".join(suffixes.keys())), re.I
|
|
)
|
|
|
|
m = size_re.match(size_str.lower())
|
|
if not m or m.group(2) not in suffixes:
|
|
raise ValueError(f"value {size_str!r} not a valid size")
|
|
return int(float(m.group(1)) * suffixes[m.group(2)])
|
|
|
|
|
|
def _get_mem_available():
|
|
"""Return available memory in bytes, or None if unknown."""
|
|
try:
|
|
import psutil
|
|
|
|
return psutil.virtual_memory().available
|
|
except (ImportError, AttributeError):
|
|
pass
|
|
|
|
if sys.platform.startswith("linux"):
|
|
info = {}
|
|
with open("/proc/meminfo") as f:
|
|
for line in f:
|
|
p = line.split()
|
|
info[p[0].strip(":").lower()] = int(p[1]) * 1024
|
|
|
|
if "memavailable" in info:
|
|
# Linux >= 3.14
|
|
return info["memavailable"]
|
|
else:
|
|
return info["memfree"] + info["cached"]
|
|
|
|
return None
|
|
|
|
|
|
def _no_tracing(func):
|
|
"""
|
|
Decorator to temporarily turn off tracing for the duration of a test.
|
|
Needed in tests that check refcounting, otherwise the tracing itself
|
|
influences the refcounts
|
|
"""
|
|
if not hasattr(sys, "gettrace"):
|
|
return func
|
|
else:
|
|
|
|
@wraps(func)
|
|
def wrapper(*args, **kwargs):
|
|
original_trace = sys.gettrace()
|
|
try:
|
|
sys.settrace(None)
|
|
return func(*args, **kwargs)
|
|
finally:
|
|
sys.settrace(original_trace)
|
|
|
|
return wrapper
|
|
|
|
|
|
def _get_glibc_version():
|
|
try:
|
|
ver = os.confstr("CS_GNU_LIBC_VERSION").rsplit(" ")[1]
|
|
except Exception as inst:
|
|
ver = "0.0"
|
|
|
|
return ver
|
|
|
|
|
|
_glibcver = _get_glibc_version()
|
|
|
|
|
|
def _glibc_older_than(x):
|
|
return _glibcver != "0.0" and _glibcver < x
|