utils.py
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0
| py
Geri
""" Utility function to facilitate testing. """ import os import sys import platform import re import gc import operator import warnings from functools import partial, wraps import shutil import contextlib from tempfile import mkdtemp, mkstemp from unittest.case import SkipTest from warnings import WarningMessage import pprint import sysconfig import numpy as np from numpy.core import ( intp, float32, empty, arange, array_repr, ndarray, isnat, array) from numpy import isfinite, isnan, isinf import numpy.linalg.lapack_lite from io import StringIO __all__ = [ 'assert_equal', 'assert_almost_equal', 'assert_approx_equal', 'assert_array_equal', 'assert_array_less', 'assert_string_equal', 'assert_array_almost_equal', 'assert_raises', 'build_err_msg', 'decorate_methods', 'jiffies', 'memusage', 'print_assert_equal', 'rundocs', 'runstring', 'verbose', 'measure', 'assert_', 'assert_array_almost_equal_nulp', 'assert_raises_regex', 'assert_array_max_ulp', 'assert_warns', 'assert_no_warnings', 'assert_allclose', 'IgnoreException', 'clear_and_catch_warnings', 'SkipTest', 'KnownFailureException', 'temppath', 'tempdir', 'IS_PYPY', 'HAS_REFCOUNT', "IS_WASM", 'suppress_warnings', 'assert_array_compare', 'assert_no_gc_cycles', 'break_cycles', 'HAS_LAPACK64', 'IS_PYSTON', '_OLD_PROMOTION', 'IS_MUSL', '_SUPPORTS_SVE' ] class KnownFailureException(Exception): '''Raise this exception to mark a test as a known failing test.''' pass KnownFailureTest = KnownFailureException # backwards compat verbose = 0 IS_WASM = platform.machine() in ["wasm32", "wasm64"] IS_PYPY = sys.implementation.name == 'pypy' IS_PYSTON = hasattr(sys, "pyston_version_info") HAS_REFCOUNT = getattr(sys, 'getrefcount', None) is not None and not IS_PYSTON HAS_LAPACK64 = numpy.linalg.lapack_lite._ilp64 _OLD_PROMOTION = lambda: np._get_promotion_state() == 'legacy' IS_MUSL = False # alternate way is # from packaging.tags import sys_tags # _tags = list(sys_tags()) # if 'musllinux' in _tags[0].platform: _v = sysconfig.get_config_var('HOST_GNU_TYPE') or '' if 'musl' in _v: IS_MUSL = True def assert_(val, msg=''): """ Assert that works in release mode. Accepts callable msg to allow deferring evaluation until failure. The Python built-in ``assert`` does not work when executing code in optimized mode (the ``-O`` flag) - no byte-code is generated for it. For documentation on usage, refer to the Python documentation. """ __tracebackhide__ = True # Hide traceback for py.test if not val: try: smsg = msg() except TypeError: smsg = msg raise AssertionError(smsg) if os.name == 'nt': # Code "stolen" from enthought/debug/memusage.py def GetPerformanceAttributes(object, counter, instance=None, inum=-1, format=None, machine=None): # NOTE: Many counters require 2 samples to give accurate results, # including "% Processor Time" (as by definition, at any instant, a # thread's CPU usage is either 0 or 100). To read counters like this, # you should copy this function, but keep the counter open, and call # CollectQueryData() each time you need to know. # See http://msdn.microsoft.com/library/en-us/dnperfmo/html/perfmonpt2.asp (dead link) # My older explanation for this was that the "AddCounter" process # forced the CPU to 100%, but the above makes more sense :) import win32pdh if format is None: format = win32pdh.PDH_FMT_LONG path = win32pdh.MakeCounterPath( (machine, object, instance, None, inum, counter)) hq = win32pdh.OpenQuery() try: hc = win32pdh.AddCounter(hq, path) try: win32pdh.CollectQueryData(hq) type, val = win32pdh.GetFormattedCounterValue(hc, format) return val finally: win32pdh.RemoveCounter(hc) finally: win32pdh.CloseQuery(hq) def memusage(processName="python", instance=0): # from win32pdhutil, part of the win32all package import win32pdh return GetPerformanceAttributes("Process", "Virtual Bytes", processName, instance, win32pdh.PDH_FMT_LONG, None) elif sys.platform[:5] == 'linux': def memusage(_proc_pid_stat=f'/proc/{os.getpid()}/stat'): """ Return virtual memory size in bytes of the running python. """ try: with open(_proc_pid_stat) as f: l = f.readline().split(' ') return int(l[22]) except Exception: return else: def memusage(): """ Return memory usage of running python. [Not implemented] """ raise NotImplementedError if sys.platform[:5] == 'linux': def jiffies(_proc_pid_stat=f'/proc/{os.getpid()}/stat', _load_time=[]): """ Return number of jiffies elapsed. Return number of jiffies (1/100ths of a second) that this process has been scheduled in user mode. See man 5 proc. """ import time if not _load_time: _load_time.append(time.time()) try: with open(_proc_pid_stat) as f: l = f.readline().split(' ') return int(l[13]) except Exception: return int(100*(time.time()-_load_time[0])) else: # os.getpid is not in all platforms available. # Using time is safe but inaccurate, especially when process # was suspended or sleeping. def jiffies(_load_time=[]): """ Return number of jiffies elapsed. Return number of jiffies (1/100ths of a second) that this process has been scheduled in user mode. See man 5 proc. """ import time if not _load_time: _load_time.append(time.time()) return int(100*(time.time()-_load_time[0])) def build_err_msg(arrays, err_msg, header='Items are not equal:', verbose=True, names=('ACTUAL', 'DESIRED'), precision=8): msg = ['\n' + header] if err_msg: if err_msg.find('\n') == -1 and len(err_msg) < 79-len(header): msg = [msg[0] + ' ' + err_msg] else: msg.append(err_msg) if verbose: for i, a in enumerate(arrays): if isinstance(a, ndarray): # precision argument is only needed if the objects are ndarrays r_func = partial(array_repr, precision=precision) else: r_func = repr try: r = r_func(a) except Exception as exc: r = f'[repr failed for <{type(a).__name__}>: {exc}]' if r.count('\n') > 3: r = '\n'.join(r.splitlines()[:3]) r += '...' msg.append(f' {names[i]}: {r}') return '\n'.join(msg) def assert_equal(actual, desired, err_msg='', verbose=True): """ Raises an AssertionError if two objects are not equal. Given two objects (scalars, lists, tuples, dictionaries or numpy arrays), check that all elements of these objects are equal. An exception is raised at the first conflicting values. 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. This function handles NaN comparisons as if NaN was a "normal" number. That is, AssertionError is not raised if both objects have NaNs in the same positions. This is in contrast to the IEEE standard on NaNs, which says that NaN compared to anything must return False. Parameters ---------- actual : array_like The object to check. desired : array_like The 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. Raises ------ AssertionError If actual and desired are not equal. Examples -------- >>> np.testing.assert_equal([4,5], [4,6]) Traceback (most recent call last): ... AssertionError: Items are not equal: item=1 ACTUAL: 5 DESIRED: 6 The following comparison does not raise an exception. There are NaNs in the inputs, but they are in the same positions. >>> np.testing.assert_equal(np.array([1.0, 2.0, np.nan]), [1, 2, np.nan]) """ __tracebackhide__ = True # Hide traceback for py.test if isinstance(desired, dict): if not isinstance(actual, dict): raise AssertionError(repr(type(actual))) assert_equal(len(actual), len(desired), err_msg, verbose) for k, i in desired.items(): if k not in actual: raise AssertionError(repr(k)) assert_equal(actual[k], desired[k], f'key={k!r}\n{err_msg}', verbose) return if isinstance(desired, (list, tuple)) and isinstance(actual, (list, tuple)): assert_equal(len(actual), len(desired), err_msg, verbose) for k in range(len(desired)): assert_equal(actual[k], desired[k], f'item={k!r}\n{err_msg}', verbose) return from numpy.core import ndarray, isscalar, signbit from numpy.lib import iscomplexobj, real, imag if isinstance(actual, ndarray) or isinstance(desired, ndarray): return assert_array_equal(actual, desired, err_msg, verbose) msg = build_err_msg([actual, desired], err_msg, verbose=verbose) # Handle complex numbers: separate into real/imag to handle # nan/inf/negative zero correctly # XXX: catch ValueError for subclasses of ndarray where iscomplex fail try: usecomplex = iscomplexobj(actual) or iscomplexobj(desired) except (ValueError, TypeError): usecomplex = False if usecomplex: if iscomplexobj(actual): actualr = real(actual) actuali = imag(actual) else: actualr = actual actuali = 0 if iscomplexobj(desired): desiredr = real(desired) desiredi = imag(desired) else: desiredr = desired desiredi = 0 try: assert_equal(actualr, desiredr) assert_equal(actuali, desiredi) except AssertionError: raise AssertionError(msg) # isscalar test to check cases such as [np.nan] != np.nan if isscalar(desired) != isscalar(actual): raise AssertionError(msg) try: isdesnat = isnat(desired) isactnat = isnat(actual) dtypes_match = (np.asarray(desired).dtype.type == np.asarray(actual).dtype.type) if isdesnat and isactnat: # If both are NaT (and have the same dtype -- datetime or # timedelta) they are considered equal. if dtypes_match: return else: raise AssertionError(msg) except (TypeError, ValueError, NotImplementedError): pass # Inf/nan/negative zero handling try: isdesnan = isnan(desired) isactnan = isnan(actual) if isdesnan and isactnan: return # both nan, so equal # handle signed zero specially for floats array_actual = np.asarray(actual) array_desired = np.asarray(desired) if (array_actual.dtype.char in 'Mm' or array_desired.dtype.char in 'Mm'): # version 1.18 # until this version, isnan failed for datetime64 and timedelta64. # Now it succeeds but comparison to scalar with a different type # emits a DeprecationWarning. # Avoid that by skipping the next check raise NotImplementedError('cannot compare to a scalar ' 'with a different type') if desired == 0 and actual == 0: if not signbit(desired) == signbit(actual): raise AssertionError(msg) except (TypeError, ValueError, NotImplementedError): pass try: # Explicitly use __eq__ for comparison, gh-2552 if not (desired == actual): raise AssertionError(msg) except (DeprecationWarning, FutureWarning) as e: # this handles the case when the two types are not even comparable if 'elementwise == comparison' in e.args[0]: raise AssertionError(msg) else: raise def print_assert_equal(test_string, actual, desired): """ Test if two objects are equal, and print an error message if test fails. The test is performed with ``actual == desired``. Parameters ---------- test_string : str The message supplied to AssertionError. actual : object The object to test for equality against `desired`. desired : object The expected result. Examples -------- >>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 1]) >>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 2]) Traceback (most recent call last): ... AssertionError: Test XYZ of func xyz failed ACTUAL: [0, 1] DESIRED: [0, 2] """ __tracebackhide__ = True # Hide traceback for py.test import pprint if not (actual == desired): msg = StringIO() msg.write(test_string) msg.write(' failed\nACTUAL: \n') pprint.pprint(actual, msg) msg.write('DESIRED: \n') pprint.pprint(desired, msg) raise AssertionError(msg.getvalue()) @np._no_nep50_warning() def assert_almost_equal(actual, desired, decimal=7, err_msg='', verbose=True): """ Raises an AssertionError if two items 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 that the elements of `actual` and `desired` satisfy. ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` That is a looser test than originally documented, but agrees with what the actual implementation in `assert_array_almost_equal` did up to rounding vagaries. An exception is raised at conflicting values. For ndarrays this delegates to assert_array_almost_equal Parameters ---------- actual : array_like The object to check. desired : array_like The expected object. decimal : int, optional Desired precision, default is 7. 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 -------- >>> from numpy.testing import assert_almost_equal >>> assert_almost_equal(2.3333333333333, 2.33333334) >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) Traceback (most recent call last): ... AssertionError: Arrays are not almost equal to 10 decimals ACTUAL: 2.3333333333333 DESIRED: 2.33333334 >>> assert_almost_equal(np.array([1.0,2.3333333333333]), ... np.array([1.0,2.33333334]), decimal=9) Traceback (most recent call last): ... AssertionError: Arrays are not almost equal to 9 decimals <BLANKLINE> Mismatched elements: 1 / 2 (50%) Max absolute difference: 6.66669964e-09 Max relative difference: 2.85715698e-09 x: array([1. , 2.333333333]) y: array([1. , 2.33333334]) """ __tracebackhide__ = True # Hide traceback for py.test from numpy.core import ndarray from numpy.lib import iscomplexobj, real, imag # Handle complex numbers: separate into real/imag to handle # nan/inf/negative zero correctly # XXX: catch ValueError for subclasses of ndarray where iscomplex fail try: usecomplex = iscomplexobj(actual) or iscomplexobj(desired) except ValueError: usecomplex = False def _build_err_msg(): header = ('Arrays are not almost equal to %d decimals' % decimal) return build_err_msg([actual, desired], err_msg, verbose=verbose, header=header) if usecomplex: if iscomplexobj(actual): actualr = real(actual) actuali = imag(actual) else: actualr = actual actuali = 0 if iscomplexobj(desired): desiredr = real(desired) desiredi = imag(desired) else: desiredr = desired desiredi = 0 try: assert_almost_equal(actualr, desiredr, decimal=decimal) assert_almost_equal(actuali, desiredi, decimal=decimal) except AssertionError: raise AssertionError(_build_err_msg()) if isinstance(actual, (ndarray, tuple, list)) \ or isinstance(desired, (ndarray, tuple, list)): return assert_array_almost_equal(actual, desired, decimal, err_msg) 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 (isfinite(desired) and isfinite(actual)): if isnan(desired) or isnan(actual): if not (isnan(desired) and isnan(actual)): raise AssertionError(_build_err_msg()) else: if not desired == actual: raise AssertionError(_build_err_msg()) return except (NotImplementedError, TypeError): pass if abs(desired - actual) >= np.float64(1.5 * 10.0**(-decimal)): raise AssertionError(_build_err_msg()) @np._no_nep50_warning() def assert_approx_equal(actual, desired, significant=7, err_msg='', verbose=True): """ Raises an AssertionError if two items are not equal up to significant digits. .. 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. Given two numbers, check that they are approximately equal. Approximately equal is defined as the number of significant digits that agree. Parameters ---------- actual : scalar The object to check. desired : scalar The expected object. significant : int, optional Desired precision, default is 7. 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 -------- >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, ... significant=8) >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, ... significant=8) Traceback (most recent call last): ... AssertionError: Items are not equal to 8 significant digits: ACTUAL: 1.234567e-21 DESIRED: 1.2345672e-21 the evaluated condition that raises the exception is >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) True """ __tracebackhide__ = True # Hide traceback for py.test import numpy as np (actual, desired) = map(float, (actual, desired)) if desired == actual: return # Normalized the numbers to be in range (-10.0,10.0) # scale = float(pow(10,math.floor(math.log10(0.5*(abs(desired)+abs(actual)))))) with np.errstate(invalid='ignore'): scale = 0.5*(np.abs(desired) + np.abs(actual)) scale = np.power(10, np.floor(np.log10(scale))) try: sc_desired = desired/scale except ZeroDivisionError: sc_desired = 0.0 try: sc_actual = actual/scale except ZeroDivisionError: sc_actual = 0.0 msg = build_err_msg( [actual, desired], err_msg, header='Items are not equal to %d significant digits:' % significant, verbose=verbose) 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 (isfinite(desired) and isfinite(actual)): if isnan(desired) or isnan(actual): if not (isnan(desired) and isnan(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., -(significant-1)): raise AssertionError(msg) @np._no_nep50_warning() 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 numpy.core import (array2string, isnan, inf, bool_, errstate, all, max, object_) x = np.asanyarray(x) y = np.asanyarray(y) # original array for output formatting ox, oy = x, y def isnumber(x): return x.dtype.char in '?bhilqpBHILQPefdgFDG' def istime(x): return x.dtype.char in "Mm" 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 bool_(x_id == y_id).all() != 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 isnumber(x) and isnumber(y): 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') elif istime(x) and istime(y): # If one is datetime64 and the other timedelta64 there is no point if equal_nan and x.dtype.type == y.dtype.type: flagged = func_assert_same_pos(x, y, func=isnat, hasval="NaT") 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 cond != True: n_mismatch = reduced.size - reduced.sum(dtype=intp) n_elements = flagged.size if flagged.ndim != 0 else reduced.size percent_mismatch = 100 * n_mismatch / n_elements remarks = [ 'Mismatched elements: {} / {} ({:.3g}%)'.format( n_mismatch, n_elements, percent_mismatch)] with errstate(all='ignore'): # ignore errors for non-numeric types with contextlib.suppress(TypeError): 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) if getattr(error, 'dtype', object_) == object_: remarks.append('Max absolute difference: ' + str(max_abs_error)) else: remarks.append('Max absolute difference: ' + array2string(max_abs_error)) # 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])) if getattr(error, 'dtype', object_) == object_: remarks.append('Max relative difference: ' + str(max_rel_error)) else: remarks.append('Max relative difference: ' + array2string(max_rel_error)) 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) 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. .. versionadded:: 1.24.0 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]) Assert fails with numerical imprecision with floats: >>> np.testing.assert_array_equal([1.0,np.pi,np.nan], ... [1, np.sqrt(np.pi)**2, np.nan]) Traceback (most recent call last): ... AssertionError: Arrays are not equal <BLANKLINE> Mismatched elements: 1 / 3 (33.3%) Max absolute difference: 4.4408921e-16 Max relative difference: 1.41357986e-16 x: array([1. , 3.141593, nan]) y: array([1. , 3.141593, 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: array([[3, 3, 3, 3, 3], [3, 3, 3, 3, 3]]) y: array(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 int64, float32 mismatch) x: array([2, 2, 2]) y: array([2., 2., 2.], dtype=float32) """ __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) @np._no_nep50_warning() 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: 6.e-05 Max relative difference: 2.57136612e-05 x: array([1. , 2.33333, nan]) y: array([1. , 2.33339, nan]) >>> 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: array([1. , 2.33333, nan]) y: array([1. , 2.33333, 5. ]) """ __tracebackhide__ = True # Hide traceback for py.test from numpy.core import number, float_, result_type from numpy.core.numerictypes import issubdtype from numpy.core.fromnumeric import any as npany def compare(x, y): try: if npany(isinf(x)) or npany(isinf(y)): xinfid = isinf(x) yinfid = isinf(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.) y = np.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 x is not strictly smaller than y, element-wise. 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. Max relative difference: 0.5 x: array([ 1., 1., nan]) y: array([ 1., 2., nan]) >>> 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. Max relative difference: 0.66666667 x: array([1., 4.]) y: array(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: array([1., 2., 3.]) y: array([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 runstring(astr, dict): exec(astr, dict) 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') >>> np.testing.assert_string_equal('abc', 'abcd') 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) def rundocs(filename=None, raise_on_error=True): """ Run doctests found in the given file. By default `rundocs` raises an AssertionError on failure. Parameters ---------- filename : str The path to the file for which the doctests are run. raise_on_error : bool Whether to raise an AssertionError when a doctest fails. Default is True. Notes ----- The doctests can be run by the user/developer by adding the ``doctests`` argument to the ``test()`` call. For example, to run all tests (including doctests) for `numpy.lib`: >>> np.lib.test(doctests=True) # doctest: +SKIP """ from numpy.distutils.misc_util import exec_mod_from_location import doctest if filename is None: f = sys._getframe(1) filename = f.f_globals['__file__'] name = os.path.splitext(os.path.basename(filename))[0] m = exec_mod_from_location(name, filename) tests = doctest.DocTestFinder().find(m) runner = doctest.DocTestRunner(verbose=False) msg = [] if raise_on_error: out = lambda s: msg.append(s) else: out = None for test in tests: runner.run(test, out=out) if runner.failures > 0 and raise_on_error: raise AssertionError("Some doctests failed:\n%s" % "\n".join(msg)) def check_support_sve(): """ gh-22982 """ import subprocess cmd = 'lscpu' try: output = subprocess.run(cmd, capture_output=True, text=True) return 'sve' in output.stdout except OSError: return False _SUPPORTS_SVE = check_support_sve() # # assert_raises and assert_raises_regex are taken from unittest. # import unittest class _Dummy(unittest.TestCase): def nop(self): pass _d = _Dummy('nop') def assert_raises(*args, **kwargs): """ assert_raises(exception_class, callable, *args, **kwargs) assert_raises(exception_class) Fail unless an exception of class exception_class is thrown by callable when invoked with arguments args and keyword arguments kwargs. If a different type of exception is thrown, it will not be caught, and the test case will be deemed to have suffered an error, exactly as for an unexpected exception. Alternatively, `assert_raises` can be used as a context manager: >>> from numpy.testing import assert_raises >>> with assert_raises(ZeroDivisionError): ... 1 / 0 is equivalent to >>> def div(x, y): ... return x / y >>> assert_raises(ZeroDivisionError, div, 1, 0) """ __tracebackhide__ = True # Hide traceback for py.test return _d.assertRaises(*args, **kwargs) 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 measure(code_str, times=1, label=None): """ Return elapsed time for executing code in the namespace of the caller. The supplied code string is compiled with the Python builtin ``compile``. The precision of the timing is 10 milli-seconds. If the code will execute fast on this timescale, it can be executed many times to get reasonable timing accuracy. Parameters ---------- code_str : str The code to be timed. times : int, optional The number of times the code is executed. Default is 1. The code is only compiled once. label : str, optional A label to identify `code_str` with. This is passed into ``compile`` as the second argument (for run-time error messages). Returns ------- elapsed : float Total elapsed time in seconds for executing `code_str` `times` times. Examples -------- >>> times = 10 >>> etime = np.testing.measure('for i in range(1000): np.sqrt(i**2)', times=times) >>> print("Time for a single execution : ", etime / times, "s") # doctest: +SKIP Time for a single execution : 0.005 s """ frame = sys._getframe(1) locs, globs = frame.f_locals, frame.f_globals code = compile(code_str, f'Test name: {label} ', 'exec') i = 0 elapsed = jiffies() while i < times: i += 1 exec(code, globs, locs) elapsed = jiffies() - elapsed return 0.01*elapsed 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): """ 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 import numpy as np def compare(x, y): return np.core.numeric.isclose(x, y, rtol=rtol, atol=atol, equal_nan=equal_nan) actual, desired = np.asanyarray(actual), np.asanyarray(desired) header = f'Not equal to tolerance rtol={rtol:g}, atol={atol:g}' 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) >>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x) 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))) """ __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("Arrays are not almost equal up to %g " "ULP (max difference is %g ULP)" % (maxulp, np.max(ret))) 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) 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("x and y do not have the same shape: %s - %s" % (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': inp = lambda: 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': inp1 = lambda: arange(s, dtype=dtype)[o:] inp2 = lambda: arange(s, dtype=dtype)[o:] 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" pass @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( ... 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(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*({0})\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: ver = '0.0' return ver _glibcver = _get_glibc_version() _glibc_older_than = lambda x: (_glibcver != '0.0' and _glibcver < x)
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