def assert_equal(actual,desired,err_msg='',verbose=True):
"""
Raise an assertion if two objects are not equal.
Given two objects (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 : list, tuple, dict or ndarray
The object to check.
desired : list, tuple, dict or ndarray
The expected object.
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 are not equal.
Examples
--------
>>> np.testing.assert_equal([4,5], [4,6])
...
<type 'exceptions.AssertionError'>:
Items are not equal:
item=1
ACTUAL: 5
DESIRED: 6
"""
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:
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)
# Inf/nan/negative zero handling
try:
# isscalar test to check cases such as [np.nan] != np.nan
if isscalar(desired) != isscalar(actual):
raise AssertionError(msg)
# 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)):
isdesnan = gisnan(desired)
isactnan = gisnan(actual)
if isdesnan or isactnan:
if not (isdesnan and isactnan):
raise AssertionError(msg)
else:
if not desired == actual:
raise AssertionError(msg)
return
elif desired == 0 and actual == 0:
if not signbit(desired) == signbit(actual):
raise AssertionError(msg)
# If TypeError or ValueError raised while using isnan and co, just handle
# as before
except (TypeError, ValueError, NotImplementedError):
pass
if desired != actual :
raise AssertionError(msg)
def assert_almost_equal(actual,desired,decimal=7,err_msg='',verbose=True):
"""
Raise an assertion if two items are not equal up to desired precision.
The test is equivalent to abs(desired-actual) < 0.5 * 10**(-decimal)
Given two objects (numbers or ndarrays), check that all elements of these
objects are almost equal. An exception is raised at conflicting values.
For ndarrays this delegates to assert_array_almost_equal
Parameters
----------
actual : number or ndarray
The object to check.
desired : number or ndarray
The expected object.
decimal : integer (decimal=7)
desired precision
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 are not equal up to specified precision.
See Also
--------
assert_array_almost_equal: compares array_like objects
assert_equal: tests objects for equality
Examples
--------
>>> import numpy.testing as npt
>>> npt.assert_almost_equal(2.3333333333333, 2.33333334)
>>> npt.assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
...
<type 'exceptions.AssertionError'>:
Items are not equal:
ACTUAL: 2.3333333333333002
DESIRED: 2.3333333399999998
>>> npt.assert_almost_equal(np.array([1.0,2.3333333333333]),
\t\t\tnp.array([1.0,2.33333334]), decimal=9)
...
<type 'exceptions.AssertionError'>:
Arrays are not almost equal
<BLANKLINE>
(mismatch 50.0%)
x: array([ 1. , 2.33333333])
y: array([ 1. , 2.33333334])
"""
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
msg = build_err_msg([actual, desired], err_msg, verbose=verbose,
header='Arrays are not almost equal')
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(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 (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 (NotImplementedError, TypeError):
pass
if round(abs(desired - actual),decimal) != 0 :
raise AssertionError(msg)
def assert_almost_equal(actual,desired,decimal=7,err_msg='',verbose=True):
"""
Raise an assertion if two items are not equal up to desired precision.
The test is equivalent to abs(desired-actual) < 0.5 * 10**(-decimal)
Given two objects (numbers or ndarrays), check that all elements of these
objects are almost equal. An exception is raised at conflicting values.
For ndarrays this delegates to assert_array_almost_equal
Parameters
----------
actual : number or ndarray
The object to check.
desired : number or ndarray
The expected object.
decimal : integer (decimal=7)
desired precision
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 are not equal up to specified precision.
See Also
--------
assert_array_almost_equal: compares array_like objects
assert_equal: tests objects for equality
Examples
--------
>>> npt.assert_almost_equal(2.3333333333333, 2.33333334)
>>> npt.assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
...
<type 'exceptions.AssertionError'>:
Items are not equal:
ACTUAL: 2.3333333333333002
DESIRED: 2.3333333399999998
>>> npt.assert_almost_equal(np.array([1.0,2.3333333333333]),
\t\t\tnp.array([1.0,2.33333334]), decimal=9)
...
<type 'exceptions.AssertionError'>:
Arrays are not almost equal
<BLANKLINE>
(mismatch 50.0%)
x: array([ 1. , 2.33333333])
y: array([ 1. , 2.33333334])
"""
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
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("Items are not equal:\n" \
"ACTUAL: %s\n" \
"DESIRED: %s\n" % (str(actual), str(desired)))
if isinstance(actual, (ndarray, tuple, list)) \
or isinstance(desired, (ndarray, tuple, list)):
return assert_array_almost_equal(actual, desired, decimal, err_msg)
msg = build_err_msg([actual, desired], err_msg, verbose=verbose,
header='Arrays are not almost equal')
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:
pass
if round(abs(desired - actual),decimal) != 0 :
raise AssertionError(msg)
def assert_equal(actual,desired,err_msg='',verbose=True):
"""
Raise an assertion if two objects are not equal.
Given two objects (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 : list, tuple, dict or ndarray
The object to check.
desired : list, tuple, dict or ndarray
The expected object.
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 are not equal.
Examples
--------
>>> np.testing.assert_equal([4,5], [4,6])
...
<type 'exceptions.AssertionError'>:
Items are not equal:
item=1
ACTUAL: 5
DESIRED: 6
"""
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:
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("Items are not equal:\n" \
"ACTUAL: %s\n" \
"DESIRED: %s\n" % (str(actual), str(desired)))
# Inf/nan/negative zero handling
try:
# isscalar test to check cases such as [np.nan] != np.nan
if isscalar(desired) != isscalar(actual):
raise AssertionError(msg)
# 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)):
isdesnan = gisnan(desired)
isactnan = gisnan(actual)
if isdesnan or isactnan:
if not (isdesnan and isactnan):
raise AssertionError(msg)
else:
if not desired == actual:
raise AssertionError(msg)
return
elif desired == 0 and actual == 0:
if not signbit(desired) == signbit(actual):
raise AssertionError(msg)
# If TypeError or ValueError raised while using isnan and co, just handle
# as before
except TypeError:
pass
except ValueError:
pass
if desired != actual :
raise AssertionError(msg)
def assert_almost_equal(actual,desired,decimal=7,err_msg='',verbose=True):
"""
Raise an assertion 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 is equivalent to ``abs(desired-actual) < 0.5 * 10**(-decimal)``.
Given two objects (numbers or ndarrays), check that all elements of these
objects are almost equal. 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)
...
<type 'exceptions.AssertionError'>:
Items are not equal:
ACTUAL: 2.3333333333333002
DESIRED: 2.3333333399999998
>>> npt.assert_almost_equal(np.array([1.0,2.3333333333333]),
... np.array([1.0,2.33333334]), decimal=9)
...
<type 'exceptions.AssertionError'>:
Arrays are not almost equal
<BLANKLINE>
(mismatch 50.0%)
x: array([ 1. , 2.33333333])
y: array([ 1. , 2.33333334])
"""
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
msg = build_err_msg([actual, desired], err_msg, verbose=verbose,
header=('Arrays are not almost equal to %d decimals' % decimal))
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(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 (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 (NotImplementedError, TypeError):
pass
if round(abs(desired - actual),decimal) != 0 :
raise AssertionError(msg)
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 is equivalent to ``abs(desired-actual) < 0.5 * 10**(-decimal)``.
Given two objects (numbers or ndarrays), check that all elements of these
objects are almost equal. 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)
...
<type 'exceptions.AssertionError'>:
Items are not equal:
ACTUAL: 2.3333333333333002
DESIRED: 2.3333333399999998
>>> npt.assert_almost_equal(np.array([1.0,2.3333333333333]),
... np.array([1.0,2.33333334]), decimal=9)
...
<type 'exceptions.AssertionError'>:
Arrays are not almost equal
<BLANKLINE>
(mismatch 50.0%)
x: array([ 1. , 2.33333333])
y: array([ 1. , 2.33333334])
"""
__tracebackhide__ = True # Hide traceback for py.test
from numpy.core import ndarray
from numpy.lib import iscomplexobj, real, imag
from numpy.testing.utils import (assert_array_almost_equal,
build_err_msg, gisfinite, gisnan)
# 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 (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 round(abs(desired - actual), decimal) != 0:
raise AssertionError(_build_err_msg())
def assert_almost_equal(actual,
desired,
rtol=1e-7,
atol=0,
ignored_values=None,
err_msg='',
verbose=True):
"""
Function imported from numpy.testing: assert_equal. Version 16.2.
Two key modifications compared to the original implementation:
1) allow comparison of numbers with a tolerance on the difference (other functions
in numpy that allow a tolerance as an argument do not support comparison
between dictionaries).
2) allow to skip the explicit comparison of some attributes in dictionaries.
Raises an AssertionError if two objects are not equal within the required tolerances.
Given two objects (scalars, lists, tuples, dictionaries or numpy arrays),
check that all elements of these objects are almost equal. An exception is raised
at the first conflicting values.
Comparison for numerical values is performed with assert_allclose
Args:
actual: the object to check.
desired : the expected object.
rtol (float): relative tolerance.
atol (float): absolute tolerance.
ignored_values (list): if a comparison between two dictionaries, keywords contained
in this list will not be compared (the key should still exist in both the
dictionaries though).
err_msg (str): 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 are not equal.
"""
__tracebackhide__ = True # Hide traceback for py.test
if isinstance(desired, dict):
if not isinstance(actual, dict):
raise AssertionError(repr(type(actual)))
assert_almost_equal(len(actual), len(desired), rtol, atol,
ignored_values, err_msg, verbose)
if ignored_values is None:
ignored_values = []
for k, i in desired.items():
if k not in actual:
raise AssertionError(repr(k))
# don't check nested values if the key belong to the list to skip
if k in ignored_values:
continue
assert_almost_equal(actual[k], desired[k], rtol, atol,
ignored_values, 'key=%r\n%s' % (k, err_msg),
verbose)
return
if isinstance(desired,
(list, tuple)) and isinstance(actual, (list, tuple)):
assert_almost_equal(len(actual), len(desired), rtol, atol,
ignored_values, err_msg, verbose)
for k in range(len(desired)):
assert_almost_equal(actual[k], desired[k], rtol, atol,
ignored_values, '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 np.testing.assert_allclose(actual,
desired,
rtol=rtol,
atol=atol,
err_msg=err_msg,
verbose=verbose)
msg = np.testing.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:
np.testing.assert_allclose(actualr, desiredr, rtol=rtol, atol=atol)
np.testing.assert_allclose(actuali, desiredi, rtol=rtol, atol=atol)
except AssertionError:
raise AssertionError(msg)
# isscalar test to check cases such as [np.nan] != np.nan
if isscalar(desired) != isscalar(actual):
raise AssertionError(msg)
a_is_number = isinstance(actual, numbers.Number)
d_is_number = isinstance(desired, numbers.Number)
if a_is_number != d_is_number:
raise AssertionError(msg)
if a_is_number:
if np.isclose(actual, desired, rtol=rtol, atol=atol):
return
else:
raise AssertionError(msg)
# Inf/nan/negative zero handling
try:
isdesnan = gisnan(desired)
isactnan = gisnan(actual)
if isdesnan and isactnan:
return # both nan, so equal
# allow 0.0 and -0.0 to match
if desired == 0 and actual == 0:
return
# if not signbit(desired) == signbit(actual):
# raise AssertionError(msg)
except (TypeError, ValueError, NotImplementedError):
pass
try:
isdesnat = np.core.isnat(desired)
isactnat = np.core.isnat(actual)
dtypes_match = np.array(desired).dtype.type == np.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:
# First check if they are equal with ==. If not Use assert allclose instead of __eq__
if not (desired == actual):
raise AssertionError(msg)
# try:
# np.testing.assert_allclose(actual, desired, rtol=rtol, atol=atol)
# except (AssertionError, TypeError):
# 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
