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Copy pathutils.py
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2509 lines (2093 loc) · 83.2 KB
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"""
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. ])