Current File : //proc/thread-self/root/opt/alt/python37/lib64/python3.7/site-packages/numpy/testing/utils.py
"""
Utility function to facilitate testing.
"""
from __future__ import division, absolute_import, print_function
import os
import sys
import re
import operator
import warnings
from functools import partial, wraps
import shutil
import contextlib
from tempfile import mkdtemp, mkstemp
from unittest.case import SkipTest
from numpy.core import(
float32, empty, arange, array_repr, ndarray, isnat, array)
from numpy.lib.utils import deprecate
if sys.version_info[0] >= 3:
from io import StringIO
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'
]
class KnownFailureException(Exception):
'''Raise this exception to mark a test as a known failing test.'''
pass
KnownFailureTest = KnownFailureException # backwards compat
verbose = 0
IS_PYPY = '__pypy__' in sys.modules
HAS_REFCOUNT = getattr(sys, 'getrefcount', None) is not None
def import_nose():
""" 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 '
'http://nose.readthedocs.io' %
minimum_nose_version)
raise ImportError(msg)
return nose
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)
def gisnan(x):
"""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
def gisfinite(x):
"""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
def gisinf(x):
"""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."""
from numpy.core import isinf, errstate
with errstate(invalid='ignore'):
st = isinf(x)
if isinstance(st, type(NotImplemented)):
raise TypeError("isinf not supported for this type")
return st
@deprecate(message="numpy.testing.rand is deprecated in numpy 1.11. "
"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
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
# 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='/proc/%s/stat' % (os.getpid())):
"""
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:
return
else:
def memusage():
"""
Return memory usage of running python. [Not implemented]
"""
raise NotImplementedError
if sys.platform[:5] == 'linux':
def jiffies(_proc_pid_stat='/proc/%s/stat' % (os.getpid()),
_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:
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 = '[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)
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.
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])
...
<type 'exceptions.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:
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:
# 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
try:
# If both are NaT (and have the same dtype -- datetime or timedelta)
# they are considered equal.
if (isnat(desired) == isnat(actual) and
array(desired).dtype.type == array(actual).dtype.type):
return
else:
raise AssertionError(msg)
# If TypeError or ValueError raised while using isnan and co, just handle
# as before
except (TypeError, ValueError, NotImplementedError):
pass
# Explicitly use __eq__ for comparison, ticket #2552
if not (desired == actual):
raise AssertionError(msg)
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())
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) < 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)
...
<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
# 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 abs(desired - actual) >= 1.5 * 10.0**(-decimal):
raise AssertionError(_build_err_msg())
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)
...
<type 'exceptions.AssertionError'>:
Items are not equal to 8 significant digits:
ACTUAL: 1.234567e-021
DESIRED: 1.2345672000000001e-021
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)
def assert_array_compare(comparison, x, y, err_msg='', verbose=True,
header='', precision=6, equal_nan=True,
equal_inf=True):
__tracebackhide__ = True # Hide traceback for py.test
from numpy.core import array, isnan, isinf, any, inf
x = array(x, copy=False, subok=True)
y = array(y, copy=False, subok=True)
def isnumber(x):
return x.dtype.char in '?bhilqpBHILQPefdgFDG'
def istime(x):
return x.dtype.char in "Mm"
def chk_same_position(x_id, y_id, hasval='nan'):
"""Handling nan/inf: check that x and y have the nan/inf at the same
locations."""
try:
assert_array_equal(x_id, y_id)
except AssertionError:
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)
try:
cond = (x.shape == () or y.shape == ()) or x.shape == y.shape
if not cond:
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)
if isnumber(x) and isnumber(y):
has_nan = has_inf = False
if equal_nan:
x_isnan, y_isnan = isnan(x), isnan(y)
# Validate that NaNs are in the same place
has_nan = any(x_isnan) or any(y_isnan)
if has_nan:
chk_same_position(x_isnan, y_isnan, hasval='nan')
if equal_inf:
x_isinf, y_isinf = isinf(x), isinf(y)
# Validate that infinite values are in the same place
has_inf = any(x_isinf) or any(y_isinf)
if has_inf:
# Check +inf and -inf separately, since they are different
chk_same_position(x == +inf, y == +inf, hasval='+inf')
chk_same_position(x == -inf, y == -inf, hasval='-inf')
if has_nan and has_inf:
x = x[~(x_isnan | x_isinf)]
y = y[~(y_isnan | y_isinf)]
elif has_nan:
x = x[~x_isnan]
y = y[~y_isnan]
elif has_inf:
x = x[~x_isinf]
y = y[~y_isinf]
# Only do the comparison if actual values are left
if x.size == 0:
return
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:
x_isnat, y_isnat = isnat(x), isnat(y)
if any(x_isnat) or any(y_isnat):
chk_same_position(x_isnat, y_isnat, hasval="NaT")
if any(x_isnat) or any(y_isnat):
x = x[~x_isnat]
y = y[~y_isnat]
val = comparison(x, y)
if isinstance(val, bool):
cond = val
reduced = [0]
else:
reduced = val.ravel()
cond = reduced.all()
reduced = reduced.tolist()
if not cond:
match = 100-100.0*reduced.count(1)/len(reduced)
msg = build_err_msg([x, y],
err_msg
+ '\n(mismatch %s%%)' % (match,),
verbose=verbose, header=header,
names=('x', 'y'), precision=precision)
if not cond:
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)
def assert_array_equal(x, y, err_msg='', verbose=True):
"""
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])
...
<type 'exceptions.ValueError'>:
AssertionError:
Arrays are not equal
<BLANKLINE>
(mismatch 50.0%)
x: array([ 1. , 3.14159265, NaN])
y: array([ 1. , 3.14159265, 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')
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)
...
<type 'exceptions.AssertionError'>:
AssertionError:
Arrays are not almost equal
<BLANKLINE>
(mismatch 50.0%)
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)
<type 'exceptions.ValueError'>:
ValueError:
Arrays are not almost equal
x: array([ 1. , 2.33333, NaN])
y: array([ 1. , 2.33333, 5. ])
"""
__tracebackhide__ = True # Hide traceback for py.test
from numpy.core import around, number, float_, result_type, array
from numpy.core.numerictypes import issubdtype
from numpy.core.fromnumeric import any as npany
def compare(x, y):
try:
if npany(gisinf(x)) or npany( gisinf(y)):
xinfid = gisinf(x)
yinfid = gisinf(y)
if not (xinfid == yinfid).all():
return False
# if one item, x and y is +- inf
if x.size == y.size == 1:
return x == y
x = x[~xinfid]
y = y[~yinfid]
except (TypeError, NotImplementedError):
pass
# make sure y is an inexact type to avoid abs(MIN_INT); will cause
# casting of x later.
dtype = result_type(y, 1.)
y = array(y, dtype=dtype, copy=False, subok=True)
z = abs(x - y)
if not issubdtype(z.dtype, number):
z = z.astype(float_) # handle object arrays
return z < 1.5 * 10.0**(-decimal)
assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,
header=('Arrays are not almost equal to %d decimals' % decimal),
precision=decimal)
def assert_array_less(x, y, err_msg='', verbose=True):
"""
Raises an AssertionError if two array_like objects are not ordered by less
than.
Given two array_like objects, check that the shape is equal and all
elements of the first object are strictly smaller than those of the
second object. An exception is raised at shape mismatch or incorrectly
ordered values. Shape mismatch does not raise if an object has zero
dimension. In contrast to the standard usage in numpy, NaNs are
compared, no assertion is raised if both objects have NaNs in the same
positions.
Parameters
----------
x : array_like
The smaller object to check.
y : array_like
The larger object to compare.
err_msg : string
The error message to be printed in case of failure.
verbose : bool
If True, the conflicting values are appended to the error message.
Raises
------
AssertionError
If actual and desired objects are not equal.
See Also
--------
assert_array_equal: tests objects for equality
assert_array_almost_equal: test objects for equality up to precision
Examples
--------
>>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1.1, 2.0, np.nan])
>>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1, 2.0, np.nan])
...
<type 'exceptions.ValueError'>:
Arrays are not less-ordered
(mismatch 50.0%)
x: array([ 1., 1., NaN])
y: array([ 1., 2., NaN])
>>> np.testing.assert_array_less([1.0, 4.0], 3)
...
<type 'exceptions.ValueError'>:
Arrays are not less-ordered
(mismatch 50.0%)
x: array([ 1., 4.])
y: array(3)
>>> np.testing.assert_array_less([1.0, 2.0, 3.0], [4])
...
<type 'exceptions.ValueError'>:
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)
def runstring(astr, dict):
exec(astr, dict)
def assert_string_equal(actual, desired):
"""
Test if two strings are equal.
If the given strings are equal, `assert_string_equal` does nothing.
If they are not equal, an AssertionError is raised, and the diff
between the strings is shown.
Parameters
----------
actual : str
The string to test for equality against the expected string.
desired : str
The expected string.
Examples
--------
>>> np.testing.assert_string_equal('abc', 'abc')
>>> np.testing.assert_string_equal('abc', 'abcd')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
...
AssertionError: Differences in strings:
- abc+ abcd? +
"""
# delay import of difflib to reduce startup time
__tracebackhide__ = True # Hide traceback for py.test
import difflib
if not isinstance(actual, str):
raise AssertionError(repr(type(actual)))
if not isinstance(desired, str):
raise AssertionError(repr(type(desired)))
if re.match(r'\A'+desired+r'\Z', actual, re.M):
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 re.match(r'\A'+d2[2:]+r'\Z', 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,**kwargs):
nose = import_nose()
return nose.tools.raises(*args,**kwargs)
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
nose = import_nose()
return nose.tools.assert_raises(*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
nose = import_nose()
if sys.version_info.major >= 3:
funcname = nose.tools.assert_raises_regex
else:
# Only present in Python 2.7, missing from unittest in 2.6
funcname = nose.tools.assert_raises_regexp
return funcname(exception_class, expected_regexp, *args, **kwargs)
def decorate_methods(cls, decorator, testmatch=None):
"""
Apply a decorator to all methods in a class matching a regular expression.
The given decorator is applied to all public methods of `cls` that are
matched by the regular expression `testmatch`
(``testmatch.search(methodname)``). Methods that are private, i.e. start
with an underscore, are ignored.
Parameters
----------
cls : class
Class whose methods to decorate.
decorator : function
Decorator to apply to methods
testmatch : compiled regexp or str, optional
The regular expression. Default value is None, in which case the
nose default (``re.compile(r'(?:^|[\\b_\\.%s-])[Tt]est' % os.sep)``)
is used.
If `testmatch` is a string, it is compiled to a regular expression
first.
"""
if testmatch is None:
testmatch = re.compile(r'(?:^|[\\b_\\.%s-])[Tt]est' % os.sep)
else:
testmatch = re.compile(testmatch)
cls_attr = cls.__dict__
# delayed import to reduce startup time
from inspect import isfunction
methods = [_m for _m in cls_attr.values() if isfunction(_m)]
for function in methods:
try:
if hasattr(function, 'compat_func_name'):
funcname = function.compat_func_name
else:
funcname = function.__name__
except AttributeError:
# not a function
continue
if testmatch.search(funcname) and not funcname.startswith('_'):
setattr(cls, funcname, decorator(function))
return
def measure(code_str,times=1,label=None):
"""
Return elapsed time for executing code in the namespace of the caller.
The supplied code string is compiled with the Python builtin ``compile``.
The precision of the timing is 10 milli-seconds. If the code will execute
fast on this timescale, it can be executed many times to get reasonable
timing accuracy.
Parameters
----------
code_str : str
The code to be timed.
times : int, optional
The number of times the code is executed. Default is 1. The code is
only compiled once.
label : str, optional
A label to identify `code_str` with. This is passed into ``compile``
as the second argument (for run-time error messages).
Returns
-------
elapsed : float
Total elapsed time in seconds for executing `code_str` `times` times.
Examples
--------
>>> etime = np.testing.measure('for i in range(1000): np.sqrt(i**2)',
... times=times)
>>> print("Time for a single execution : ", etime / times, "s")
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
def _assert_valid_refcount(op):
"""
Check that ufuncs don't mishandle refcount of object `1`.
Used in a few regression tests.
"""
if not HAS_REFCOUNT:
return True
import numpy as np
b = np.arange(100*100).reshape(100, 100)
c = b
i = 1
rc = sys.getrefcount(i)
for j in range(15):
d = op(b, c)
assert_(sys.getrefcount(i) >= rc)
del d # for pyflakes
def assert_allclose(actual, desired, rtol=1e-7, atol=0, equal_nan=True,
err_msg='', verbose=True):
"""
Raises an AssertionError if two objects are not equal up to desired
tolerance.
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))
>>> 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)
def assert_array_almost_equal_nulp(x, y, nulp=1):
"""
Compare two arrays relatively to their spacing.
This is a relatively robust method to compare two arrays whose amplitude
is variable.
Parameters
----------
x, y : array_like
Input arrays.
nulp : int, optional
The maximum number of unit in the last place for tolerance (see Notes).
Default is 1.
Returns
-------
None
Raises
------
AssertionError
If the spacing between `x` and `y` for one or more elements is larger
than `nulp`.
See Also
--------
assert_array_max_ulp : Check that all items of arrays differ in at most
N Units in the Last Place.
spacing : Return the distance between x and the nearest adjacent number.
Notes
-----
An assertion is raised if the following condition is not met::
abs(x - y) <= 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)
def assert_array_max_ulp(a, b, maxulp=1, dtype=None):
"""
Check that all items of arrays differ in at most N Units in the Last Place.
Parameters
----------
a, b : array_like
Input arrays to be compared.
maxulp : int, optional
The maximum number of units in the last place that elements of `a` and
`b` can differ. Default is 1.
dtype : dtype, optional
Data-type to convert `a` and `b` to if given. Default is None.
Returns
-------
ret : ndarray
Array containing number of representable floating point numbers between
items in `a` and `b`.
Raises
------
AssertionError
If one or more elements differ by more than `maxulp`.
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
def nulp_diff(x, y, dtype=None):
"""For each item in x and y, return the number of representable floating
points between them.
Parameters
----------
x : array_like
first input array
y : array_like
second input array
dtype : dtype, optional
Data-type to convert `x` and `y` to if given. Default is None.
Returns
-------
nulp : array_like
number of representable floating point numbers between each item in x
and y.
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)
def _integer_repr(x, vdt, comp):
# Reinterpret binary representation of the float as sign-magnitude:
# take into account two-complement representation
# See also
# http://www.cygnus-software.com/papers/comparingfloats/comparingfloats.htm
rx = x.view(vdt)
if not (rx.size == 1):
rx[rx < 0] = comp - rx[rx < 0]
else:
if rx < 0:
rx = comp - rx
return rx
def integer_repr(x):
"""Return the signed-magnitude interpretation of the binary representation of
x."""
import numpy as np
if x.dtype == np.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)
# The following two classes are copied from python 2.6 warnings module (context
# manager)
class WarningMessage(object):
"""
Holds the result of a single showwarning() call.
Deprecated in 1.8.0
Notes
-----
`WarningMessage` is copied from the Python 2.6 warnings module,
so it can be used in NumPy with older Python versions.
"""
_WARNING_DETAILS = ("message", "category", "filename", "lineno", "file",
"line")
def __init__(self, message, category, filename, lineno, file=None,
line=None):
local_values = locals()
for attr in self._WARNING_DETAILS:
setattr(self, attr, local_values[attr])
if category:
self._category_name = category.__name__
else:
self._category_name = None
def __str__(self):
return ("{message : %r, category : %r, filename : %r, lineno : %s, "
"line : %r}" % (self.message, self._category_name,
self.filename, self.lineno, self.line))
class WarningManager(object):
"""
A context manager that copies and restores the warnings filter upon
exiting the context.
The 'record' argument 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()``.
The 'module' argument is to specify an alternative module to the module
named 'warnings' and imported under that name. This argument is only useful
when testing the warnings module itself.
Deprecated in 1.8.0
Notes
-----
`WarningManager` is a copy of the ``catch_warnings`` context manager
from the Python 2.6 warnings module, with slight modifications.
It is copied so it can be used in NumPy with older Python versions.
"""
def __init__(self, record=False, module=None):
self._record = record
if module is None:
self._module = sys.modules['warnings']
else:
self._module = module
self._entered = False
def __enter__(self):
if self._entered:
raise RuntimeError("Cannot enter %r twice" % self)
self._entered = True
self._filters = self._module.filters
self._module.filters = self._filters[:]
self._showwarning = self._module.showwarning
if self._record:
log = []
def showwarning(*args, **kwargs):
log.append(WarningMessage(*args, **kwargs))
self._module.showwarning = showwarning
return log
else:
return None
def __exit__(self):
if not self._entered:
raise RuntimeError("Cannot exit %r without entering first" % self)
self._module.filters = self._filters
self._module.showwarning = self._showwarning
@contextlib.contextmanager
def _assert_warns_context(warning_class, name=None):
__tracebackhide__ = True # Hide traceback for py.test
with suppress_warnings() as sup:
l = sup.record(warning_class)
yield
if not len(l) > 0:
name_str = " when calling %s" % name if name is not None else ""
raise AssertionError("No warning raised" + name_str)
def assert_warns(warning_class, *args, **kwargs):
"""
Fail unless the given callable throws the specified warning.
A warning of class warning_class should be thrown by the callable when
invoked with arguments args and keyword arguments kwargs.
If a different type of warning is thrown, it will not be caught.
If called with all arguments other than the warning class omitted, may be
used as a context manager:
with assert_warns(SomeWarning):
do_something()
The ability to be used as a context manager is new in NumPy v1.11.0.
.. versionadded:: 1.4.0
Parameters
----------
warning_class : class
The class defining the warning that `func` is expected to throw.
func : callable
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)
@contextlib.contextmanager
def _assert_no_warnings_context(name=None):
__tracebackhide__ = True # Hide traceback for py.test
with warnings.catch_warnings(record=True) as l:
warnings.simplefilter('always')
yield
if len(l) > 0:
name_str = " when calling %s" % name if name is not None else ""
raise AssertionError("Got warnings%s: %s" % (name_str, l))
def assert_no_warnings(*args, **kwargs):
"""
Fail if the given callable produces any warnings.
If called with all arguments omitted, may be used as a context manager:
with assert_no_warnings():
do_something()
The ability to be used as a context manager is new in NumPy v1.11.0.
.. versionadded:: 1.7.0
Parameters
----------
func : callable
The callable to test.
\\*args : Arguments
Arguments passed to `func`.
\\*\\*kwargs : Kwargs
Keyword arguments passed to `func`.
Returns
-------
The value returned by `func`.
"""
if not args:
return _assert_no_warnings_context()
func = args[0]
args = args[1:]
with _assert_no_warnings_context(name=func.__name__):
return func(*args, **kwargs)
def _gen_alignment_data(dtype=float32, type='binary', max_size=24):
"""
generator producing data with different alignment and offsets
to test simd vectorization
Parameters
----------
dtype : dtype
data type to produce
type : string
'unary': create data for unary operations, creates one input
and output array
'binary': create data for unary operations, creates two input
and output array
max_size : integer
maximum size of data to produce
Returns
-------
if type is 'unary' yields one output, one input array and a message
containing information on the data
if type is 'binary' yields one output array, two input array and a message
containing information on the data
"""
ufmt = 'unary offset=(%d, %d), size=%d, dtype=%r, %s'
bfmt = 'binary offset=(%d, %d, %d), size=%d, dtype=%r, %s'
for o in range(3):
for s in range(o + 2, max(o + 3, max_size)):
if type == 'unary':
inp = lambda: arange(s, dtype=dtype)[o:]
out = empty((s,), dtype=dtype)[o:]
yield out, inp(), ufmt % (o, o, s, dtype, 'out of place')
d = inp()
yield d, d, ufmt % (o, o, s, dtype, 'in place')
yield out[1:], inp()[:-1], ufmt % \
(o + 1, o, s - 1, dtype, 'out of place')
yield out[:-1], inp()[1:], ufmt % \
(o, o + 1, s - 1, dtype, 'out of place')
yield inp()[:-1], inp()[1:], ufmt % \
(o, o + 1, s - 1, dtype, 'aliased')
yield inp()[1:], inp()[:-1], ufmt % \
(o + 1, o, s - 1, dtype, 'aliased')
if type == 'binary':
inp1 = lambda: arange(s, dtype=dtype)[o:]
inp2 = lambda: arange(s, dtype=dtype)[o:]
out = empty((s,), dtype=dtype)[o:]
yield out, inp1(), inp2(), bfmt % \
(o, o, o, s, dtype, 'out of place')
d = inp1()
yield d, d, inp2(), bfmt % \
(o, o, o, s, dtype, 'in place1')
d = inp2()
yield d, inp1(), d, bfmt % \
(o, o, o, s, dtype, 'in place2')
yield out[1:], inp1()[:-1], inp2()[:-1], bfmt % \
(o + 1, o, o, s - 1, dtype, 'out of place')
yield out[:-1], inp1()[1:], inp2()[:-1], bfmt % \
(o, o + 1, o, s - 1, dtype, 'out of place')
yield out[:-1], inp1()[:-1], inp2()[1:], bfmt % \
(o, o, o + 1, s - 1, dtype, 'out of place')
yield inp1()[1:], inp1()[:-1], inp2()[:-1], bfmt % \
(o + 1, o, o, s - 1, dtype, 'aliased')
yield inp1()[:-1], inp1()[1:], inp2()[:-1], bfmt % \
(o, o + 1, o, s - 1, dtype, 'aliased')
yield inp1()[:-1], inp1()[:-1], inp2()[1:], bfmt % \
(o, o, o + 1, s - 1, dtype, 'aliased')
class IgnoreException(Exception):
"Ignoring this exception due to disabled feature"
@contextlib.contextmanager
def tempdir(*args, **kwargs):
"""Context manager to provide a temporary test folder.
All arguments are passed as this to the underlying tempfile.mkdtemp
function.
"""
tmpdir = mkdtemp(*args, **kwargs)
try:
yield tmpdir
finally:
shutil.rmtree(tmpdir)
@contextlib.contextmanager
def temppath(*args, **kwargs):
"""Context manager for temporary files.
Context manager that returns the path to a closed temporary file. Its
parameters are the same as for tempfile.mkstemp and are passed directly
to that function. The underlying file is removed when the context is
exited, so it should be closed at that time.
Windows does not allow a temporary file to be opened if it is already
open, so the underlying file must be closed after opening before it
can be opened again.
"""
fd, path = mkstemp(*args, **kwargs)
os.close(fd)
try:
yield path
finally:
os.remove(path)
class clear_and_catch_warnings(warnings.catch_warnings):
""" Context manager that resets warning registry for catching warnings
Warnings can be slippery, because, whenever a warning is triggered, Python
adds a ``__warningregistry__`` member to the *calling* module. This makes
it impossible to retrigger the warning in this module, whatever you put in
the warnings filters. This context manager accepts a sequence of `modules`
as a keyword argument to its constructor and:
* stores and removes any ``__warningregistry__`` entries in given `modules`
on entry;
* resets ``__warningregistry__`` to its previous state on exit.
This makes it possible to trigger any warning afresh inside the context
manager without disturbing the state of warnings outside.
For compatibility with Python 3.0, please consider all arguments to be
keyword-only.
Parameters
----------
record : bool, optional
Specifies whether warnings should be captured by a custom
implementation of ``warnings.showwarning()`` and be appended to a list
returned by the context manager. Otherwise None is returned by the
context manager. The objects appended to the list are arguments whose
attributes mirror the arguments to ``showwarning()``.
modules : sequence, optional
Sequence of modules for which to reset warnings registry on entry and
restore on exit. To work correctly, all 'ignore' filters should
filter by one of these modules.
Examples
--------
>>> import warnings
>>> with clear_and_catch_warnings(modules=[np.core.fromnumeric]):
... warnings.simplefilter('always')
... warnings.filterwarnings('ignore', module='np.core.fromnumeric')
... # do something that raises a warning but ignore those in
... # np.core.fromnumeric
"""
class_modules = ()
def __init__(self, record=False, modules=()):
self.modules = set(modules).union(self.class_modules)
self._warnreg_copies = {}
super(clear_and_catch_warnings, self).__init__(record=record)
def __enter__(self):
for mod in self.modules:
if hasattr(mod, '__warningregistry__'):
mod_reg = mod.__warningregistry__
self._warnreg_copies[mod] = mod_reg.copy()
mod_reg.clear()
return super(clear_and_catch_warnings, self).__enter__()
def __exit__(self, *exc_info):
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])
class suppress_warnings(object):
"""
Context manager and decorator doing much the same as
``warnings.catch_warnings``.
However, it also provides a filter mechanism to work around
http://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 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 = suppress_warnings()
>>> sup.filter(module=np.ma.core) # module must match exact
>>> @sup
>>> def some_function():
... # do something which causes a warning in np.ma.core
... pass
"""
def __init__(self, forwarding_rule="always"):
self._entered = False
# Suppressions are either instance or defined inside one with block:
self._suppressions = []
if forwarding_rule not in {"always", "module", "once", "location"}:
raise ValueError("unsupported forwarding rule.")
self._forwarding_rule = forwarding_rule
def _clear_registries(self):
if hasattr(warnings, "_filters_mutated"):
# clearing the registry should not be necessary on new pythons,
# instead the filters should be mutated.
warnings._filters_mutated()
return
# Simply clear the registry, this should normally be harmless,
# note that on new pythons it would be invalidated anyway.
for module in self._tmp_modules:
if hasattr(module, "__warningregistry__"):
module.__warningregistry__.clear()
def _filter(self, category=Warning, message="", module=None, record=False):
if record:
record = [] # The log where to store warnings
else:
record = None
if self._entered:
if module is None:
warnings.filterwarnings(
"always", category=category, message=message)
else:
module_regex = module.__name__.replace('.', r'\.') + '$'
warnings.filterwarnings(
"always", category=category, message=message,
module=module_regex)
self._tmp_modules.add(module)
self._clear_registries()
self._tmp_suppressions.append(
(category, message, re.compile(message, re.I), module, record))
else:
self._suppressions.append(
(category, message, re.compile(message, re.I), module, record))
return record
def filter(self, category=Warning, message="", module=None):
"""
Add a new suppressing filter or apply it if the state is entered.
Parameters
----------
category : class, optional
Warning class to filter
message : string, optional
Regular expression matching the warning message.
module : module, optional
Module to filter for. Note that the module (and its file)
must match exactly and cannot be a submodule. This may make
it unreliable for external modules.
Notes
-----
When added within a context, filters are only added inside
the context and will be forgotten when the context is exited.
"""
self._filter(category=category, message=message, module=module,
record=False)
def record(self, category=Warning, message="", module=None):
"""
Append a new recording filter or apply it if the state is entered.
All warnings matching will be appended to the ``log`` attribute.
Parameters
----------
category : class, optional
Warning class to filter
message : string, optional
Regular expression matching the warning message.
module : module, optional
Module to filter for. Note that the module (and its file)
must match exactly and cannot be a submodule. This may make
it unreliable for external modules.
Returns
-------
log : list
A list which will be filled with all matched warnings.
Notes
-----
When added within a context, filters are only added inside
the context and will be forgotten when the context is exited.
"""
return self._filter(category=category, message=message, module=module,
record=True)
def __enter__(self):
if self._entered:
raise RuntimeError("cannot enter suppress_warnings twice.")
self._orig_show = warnings.showwarning
self._filters = warnings.filters
warnings.filters = self._filters[:]
self._entered = True
self._tmp_suppressions = []
self._tmp_modules = set()
self._forwarded = set()
self.log = [] # reset global log (no need to keep same list)
for cat, mess, _, mod, log in self._suppressions:
if log is not None:
del log[:] # clear the log
if mod is None:
warnings.filterwarnings(
"always", category=cat, message=mess)
else:
module_regex = mod.__name__.replace('.', r'\.') + '$'
warnings.filterwarnings(
"always", category=cat, message=mess,
module=module_regex)
self._tmp_modules.add(mod)
warnings.showwarning = self._showwarning
self._clear_registries()
return self
def __exit__(self, *exc_info):
warnings.showwarning = self._orig_show
warnings.filters = self._filters
self._clear_registries()
self._entered = False
del self._orig_show
del self._filters
def _showwarning(self, message, category, filename, lineno,
*args, **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)
def __call__(self, func):
"""
Function decorator to apply certain suppressions to a whole
function.
"""
@wraps(func)
def new_func(*args, **kwargs):
with self:
return func(*args, **kwargs)
return new_func
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