|
""" Utility function to facilitate testing.
"""
float32, empty, arange, array_repr, ndarray, isnat, array)
else: from StringIO 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', 'raises', 'rand', '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', 'suppress_warnings', 'assert_array_compare', '_assert_valid_refcount', '_gen_alignment_data', 'assert_no_gc_cycles', ]
'''Raise this exception to mark a test as a known failing test.'''
""" Import nose only when needed. """ nose_is_good = True minimum_nose_version = (1, 0, 0) try: import nose except ImportError: nose_is_good = False else: if nose.__versioninfo__ < minimum_nose_version: nose_is_good = False
if not nose_is_good: msg = ('Need nose >= %d.%d.%d for tests - see ' 'https://nose.readthedocs.io' % minimum_nose_version) raise ImportError(msg)
return nose
""" 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)
"""like isnan, but always raise an error if type not supported instead of returning a TypeError object.
Notes ----- isnan and other ufunc sometimes return a NotImplementedType object instead of raising any exception. This function is a wrapper to make sure an exception is always raised.
This should be removed once this problem is solved at the Ufunc level.""" from numpy.core import isnan st = isnan(x) if isinstance(st, type(NotImplemented)): raise TypeError("isnan not supported for this type") return st
"""like isfinite, but always raise an error if type not supported instead of returning a TypeError object.
Notes ----- isfinite and other ufunc sometimes return a NotImplementedType object instead of raising any exception. This function is a wrapper to make sure an exception is always raised.
This should be removed once this problem is solved at the Ufunc level.""" from numpy.core import isfinite, errstate with errstate(invalid='ignore'): st = isfinite(x) if isinstance(st, type(NotImplemented)): raise TypeError("isfinite not supported for this type") return st
"""like isinf, but always raise an error if type not supported instead of returning a TypeError object.
Notes ----- isinf and other ufunc sometimes return a NotImplementedType object instead of raising any exception. This function is a wrapper to make sure an exception is always raised.
This should be removed once this problem is solved at the Ufunc level.""" raise TypeError("isinf not supported for this type")
"Use numpy.random.rand instead.") def rand(*args): """Returns an array of random numbers with the given shape.
This only uses the standard library, so it is useful for testing purposes. """ import random from numpy.core import zeros, float64 results = zeros(args, float64) f = results.flat for i in range(len(f)): f[i] = random.random() return results
# 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)
""" Return virtual memory size in bytes of the running python.
""" try: f = open(_proc_pid_stat, 'r') l = f.readline().split(' ') f.close() return int(l[22]) except Exception: return else: def memusage(): """ Return memory usage of running python. [Not implemented]
""" raise NotImplementedError
_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: f = open(_proc_pid_stat, 'r') l = f.readline().split(' ') f.close() 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]))
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 = '[repr failed for <{}>: {}]'.format(type(a).__name__, exc) if r.count('\n') > 3: r = '\n'.join(r.splitlines()[:3]) r += '...' msg.append(' %s: %s' % (names[i], r)) return '\n'.join(msg)
""" 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.
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
""" __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], 'key=%r\n%s' % (k, 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], 'item=%r\n%s' % (k, 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)
# Inf/nan/negative zero handling try: isdesnan = gisnan(desired) isactnan = gisnan(actual) if isdesnan and isactnan: return # both nan, so equal
# handle signed zero specially for floats if desired == 0 and actual == 0: if not signbit(desired) == signbit(actual): raise AssertionError(msg)
except (TypeError, ValueError, NotImplementedError): pass
try: isdesnat = isnat(desired) isactnat = isnat(actual) dtypes_match = array(desired).dtype.type == array(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
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
""" 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())
""" 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) < 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 -------- >>> import numpy.testing as npt >>> npt.assert_almost_equal(2.3333333333333, 2.33333334) >>> npt.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
>>> npt.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 Mismatch: 50% Max absolute difference: 6.66669964e-09 Max relative difference: 2.85715698e-09 x: array([1. , 2.333333333]) y: array([1. , 2.33333334])
"""
# Handle complex numbers: separate into real/imag to handle # nan/inf/negative zero correctly # XXX: catch ValueError for subclasses of ndarray where iscomplex fail except ValueError: usecomplex = False
header = ('Arrays are not almost equal to %d decimals' % decimal) return build_err_msg([actual, desired], err_msg, verbose=verbose, header=header)
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())
or isinstance(desired, (ndarray, tuple, list)): 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(_build_err_msg()) else: if not desired == actual: raise AssertionError(_build_err_msg()) return except (NotImplementedError, TypeError): pass if abs(desired - actual) >= 1.5 * 10.0**(-decimal): raise AssertionError(_build_err_msg())
""" 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 (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., -(significant-1)): raise AssertionError(msg)
header='', precision=6, equal_nan=True, equal_inf=True):
# original array for output formating
return x.dtype.char in "Mm"
"""Handling nan/inf.
Combine results of running func on x and y, checking that they are True at the same locations.
""" # 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__ implemenations 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. 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. return bool_(x_id) return bool_(y_id) else:
msg = build_err_msg([x, y], err_msg + '\n(shapes %s, %s mismatch)' % (x.shape, y.shape), verbose=verbose, header=header, names=('x', 'y'), precision=precision) raise AssertionError(msg)
func=lambda xy: xy == +inf, hasval='+inf') 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")
# Only do the comparison if actual values are left return elif flagged: # no sense doing comparison if everything is flagged. return
cond = val reduced = [0] else:
# 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). mismatch = 100.0 * reduced.count(0) / ox.size remarks = ['Mismatch: {:.3g}%'.format(mismatch)]
with errstate(invalid='ignore', divide='ignore'): # ignore errors for non-numeric types try: error = abs(x - y) max_abs_error = error.max() 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) max_rel_error = (error / abs(y)).max() remarks.append('Max relative difference: ' + array2string(max_rel_error)) except TypeError: pass
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 = 'error during assertion:\n\n%s\n\n%s' % (efmt, header)
msg = build_err_msg([x, y], err_msg, verbose=verbose, header=header, names=('x', 'y'), precision=precision) raise ValueError(msg)
""" 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. 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.
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
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 inprecision 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 Mismatch: 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)
""" __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')
""" 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 Mismatch: 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 x and y nan location mismatch: x: array([1. , 2.33333, nan]) y: array([1. , 2.33333, 5. ])
"""
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.
z = z.astype(float_) # handle object arrays
header=('Arrays are not almost equal to %d decimals' % decimal), precision=decimal)
""" 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 Mismatch: 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 Mismatch: 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 (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)
exec(astr, dict)
""" 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(1), desired.splitlines(1))) 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 = 'Differences in strings:\n%s' % (''.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.compat import npy_load_module 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 = npy_load_module(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 raises(*args): """Decorator to check for raised exceptions.
The decorated test function must raise one of the passed exceptions to pass. If you want to test many assertions about exceptions in a single test, you may want to use `assert_raises` instead.
.. warning:: This decorator is nose specific, do not use it if you are using a different test framework.
Parameters ---------- args : exceptions The test passes if any of the passed exceptions is raised.
Raises ------ AssertionError
Examples --------
Usage::
@raises(TypeError, ValueError) def test_raises_type_error(): raise TypeError("This test passes")
@raises(Exception) def test_that_fails_by_passing(): pass
""" nose = import_nose() return nose.tools.raises(*args)
# # assert_raises and assert_raises_regex are taken from unittest. #
pass
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`.
Name of this function adheres to Python 3.2+ reference, but should work in all versions down to 2.6.
Notes ----- .. versionadded:: 1.9.0
""" __tracebackhide__ = True # Hide traceback for py.test
if sys.version_info.major >= 3: funcname = _d.assertRaisesRegex else: # Only present in Python 2.7, missing from unittest in 2.6 funcname = _d.assertRaisesRegexp
return funcname(exception_class, expected_regexp, *args, **kwargs)
""" 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
""" 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, 'Test name: %s ' % label, 'exec') i = 0 elapsed = jiffies() while i < times: i += 1 exec(code, globs, locs) elapsed = jiffies() - elapsed return 0.01*elapsed
""" Check that ufuncs don't mishandle refcount of object `1`. Used in a few regression tests. """ if not HAS_REFCOUNT: return True import numpy as np, gc
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
err_msg='', verbose=True): """ Raises an AssertionError if two objects are not equal up to desired tolerance.
The test is equivalent to ``allclose(actual, desired, rtol, atol)``. 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
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 = 'Not equal to tolerance rtol=%g, atol=%g' % (rtol, atol) assert_array_compare(compare, actual, desired, err_msg=str(err_msg), verbose=verbose, header=header, equal_nan=equal_nan)
""" 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) <= nulps * 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)
""" 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`.
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" % maxulp) return ret
"""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.
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.array(x, dtype=dtype) y = np.array(y, dtype=dtype) else: x = np.array(x) y = np.array(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)
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.array(rx-ry, dtype=vdt) return np.abs(diff)
rx = integer_repr(x) ry = integer_repr(y) return _diff(rx, ry, t)
# 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
"""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("Unsupported dtype %s" % x.dtype)
__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 = " when calling %s" % name if name is not None else "" raise AssertionError("No warning raised" + name_str)
""" 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 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_warns_context(warning_class)
func = args[0] args = args[1:] with _assert_warns_context(warning_class, name=func.__name__): return func(*args, **kwargs)
__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 = " when calling %s" % name if name is not None else "" raise AssertionError("Got warnings%s: %s" % (name_str, l))
""" 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)
""" 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')
"Ignoring this exception due to disabled feature"
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)
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)
""" 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 """
self.modules = set(modules).union(self.class_modules) self._warnreg_copies = {} super(clear_and_catch_warnings, self).__init__(record=record)
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(clear_and_catch_warnings, self).__enter__()
super(clear_and_catch_warnings, self).__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])
""" 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 """ 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
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()
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
""" 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)
""" 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)
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
warnings.showwarning = self._orig_show warnings.filters = self._filters self._clear_registries() self._entered = False del self._orig_show del self._filters
*args, **kwargs): use_warnmsg = kwargs.pop("use_warnmsg", None) 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)
""" 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
__tracebackhide__ = True # Hide traceback for py.test
# not meaningful to test if there is no refcounting if not HAS_REFCOUNT: 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 = " when calling %s" % 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 ) ) )
""" 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) |