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Assert that works in release mode.
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Raises an AssertionError if two objects are not equal.
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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
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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]
Tr Nz failed
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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])
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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
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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)
TzArrays are not equal)r� r% r� N)r� �operator�__eq__)rL r� r� r% rF r: r: r; r s ? c sn d}ddl m}m�m�m�m� ddlm� ddlm � � ������fdd�}t
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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. ])
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issubdtype)r� c s� yt�t | ��s�t |��rrt | �}t |�}||k�� s:dS | j|j krRdkr^n n| |kS | | } || }W n ttfk
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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])
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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? +
Tr Nz\Az\Zr= z z- z? z+ � zDifferences in strings:
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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
r )�npy_load_moduleNr= �__file__F)r% c s
� � | �S )N)rz )�s)r@ r: r; �<lambda>{ � zrundocs.<locals>.<lambda>)�outzSome doctests failed:
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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)
T)rA r� r )r\ r� rF r> r: r: r; r � s c O s: d}t � }tjjdkr |jj}n|jj}|| |f|�|�S )aY
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
Tr )rA r� � version_info�majorr� r) Zassert_raises_regexp)Zexception_classZexpected_regexpr\ r� rF r> �funcnamer: r: r; r) � s
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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.
Nz(?:^|[\\b_\\.%s-])[Tt]estr )�
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<listcomp>� s z$decorate_methods.<locals>.<listcomp>�compat_func_name�_)r� �compiler� �sep�__dict__�inspectr� �values�hasattrr� r6 �AttributeError�searchr� �setattr)�clsZ decoratorZ testmatchZcls_attr�methodsZfunctionr� r: )r� r; r � s
r= c C sf t �d�}|j|j }}t| d| d�}d}t� }x ||k rR|d7 }t|||� q4W t� | }d| S )aE
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
r= zTest name: %s r� r g{�G�z�?)r� r� �f_localsr� r� r r� ) Zcode_str�timesZlabel�frameZlocsZglobs�coder_ �elapsedr: r: r; r& � s !
c C sh t sdS ddl}|�d��dd�}|}d}t�|�}xtd�D ]}| ||�}q>W tt�|�|k� ~dS )zg
Check that ufuncs don't mishandle refcount of object `1`.
Used in a few regression tests.
Tr Ni' rx r= � )r4 r� r Zreshaper� r< rZ r' )�opr� �b�cr_ Zrc�j�dr: r: r; �_assert_valid_refcount( s
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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)
Tr Nc s �j jj| |�� �d�S )N)�rtol�atolr� )ZcoreZnumericZisclose)rL r� )r r� r� r r: r; r� l s z assert_allclose.<locals>.comparez'Not equal to tolerance rtol=%g, atol=%g)r� r% r� r� )r� Z
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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)
Tr Nz X and Y are not equal to %d ULPz+X and Y are not equal to %d ULP (max is %g)) r� r� Zspacing�wherer� r� �max� nulp_diffrD )
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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)))
Tr Nz(Arrays are not almost equal up to %g ULP)r� r r� rD )r� r Zmaxulpr� rF r� Zretr: r: r; r* � s $ c s� ddl � |r*� j| |d�} � j||d�}n� �| �} � �|�}� �| |�}� �| �s^� �|�rftd��� j| |d�} � j||d�}| j|jks�td| j|jf ��� fdd�}t| �}t|�}||||�S )a� 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
r N)r� z'_nulp not implemented for complex arrayz+x and y do not have the same shape: %s - %sc s � j | | |d�}� �|�S )N)r� )r r� )�rx�ry�vdtr� )r� r: r; �_diff s znulp_diff.<locals>._diff)r� r Z common_typer� rv r� r� � integer_repr)rL r� r� �tr r r r: )r� r; r � s$
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r Zint32rX Zint64r� )rL r� r: r: r; r ' s r c @ s& e Zd ZdZdZddd�Zdd� ZdS ) �WarningMessagez�
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.
)rV �categoryr� �lineno�file�lineNc C s> t � }x | jD ]}t| ||| � qW |r4|j| _nd | _d S )N)�locals�_WARNING_DETAILSr r6 �_category_name) �selfrV r r� r! r" r# Z local_values�attrr: r: r; �__init__F s
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A context manager that copies and restores the warnings filter upon
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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.
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zWarningManager.__exit__)FN)r6 r7 r8 r9 r) r9 r: r: r: r: r; r+ V s
r+ c c sV d}t � �B}|�| �}d V t|�dksH|d k r8d| nd}td| ��W d Q R X d S )NTr z when calling %srB zNo warning raised)r5 r1 r[ rD )�
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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`.
r r= N)r� )r= r6 )r; r\ r� �funcr: r: r; r+ � s " c c s` d}t jdd��F}t �d� d V t|�dkrR| d k r>d| nd}td||f ��W d Q R X d S )NT)r1 �alwaysr z when calling %srB zGot warnings%s: %s)r, �catch_warnings� simplefilterr[ rD )r� rF ru r< r: r: r; �_assert_no_warnings_context� s
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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`.
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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
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|V W dt�|� X dS )z�Context manager to provide a temporary test folder.
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|V W dt�|� X dS )a� 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.
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� fdd� Z� fdd�Z� fdd �Z� ZS ) r/ a Context manager that resets warning registry for catching warnings
Warnings can be slippery, because, whenever a warning is triggered, Python
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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
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Context manager and decorator doing much the same as
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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
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Add a new suppressing filter or apply it if the state is entered.
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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
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Append a new recording filter or apply it if the state is entered.
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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.
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-------
log : list
A list which will be filled with all matched warnings.
Notes
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