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Utility function to facilitate testing.
i˙˙˙˙N( t import_noset assert_equalt assert_almost_equalt assert_approx_equalt assert_array_equalt assert_array_lesst assert_string_equalt assert_array_almost_equalt
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Assert that works in release mode.
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.
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Notes
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isnan and other ufunc sometimes return a NotImplementedType object instead
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returning a TypeError object.
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exception is always raised.
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c C sC d d k l } | | } t | t i o t d n | S( s like isinf, but always raise an error if type not supported instead of
returning a TypeError object.
Notes
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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.
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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])
...
:
Items are not equal:
item=1
ACTUAL: 5
DESIRED: 6
s key=%r
%sNs
item=%r
%si˙˙˙˙( t ndarrayt isscalart signbit( t iscomplexobjt realt imagR i s, Items are not equal:
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( R t dictR R^ RU R R. t itemst listt tupleR- R Rg Rh Ri t numpy.libRj Rk Rl R R t
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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]
i˙˙˙˙Ns failed
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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)
...
:
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)
...
:
Arrays are not almost equal
(mismatch 50.0%)
x: array([ 1. , 2.33333333])
y: array([ 1. , 2.33333334])
i˙˙˙˙( Rg ( Rj Rk Rl i t decimals, Items are not equal:
ACTUAL: %s
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R Rd s Arrays are not almost equalN( R Rg Rq Rj Rk Rl Rr Rs R R Rt R Rp Ro R R R& R$ R! t roundt abs( Ru Rv R
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Raise an assertion if two items are not equal up to significant digits.
Given two numbers, check that they are approximately equal.
Approximately equal is defined as the number of significant digits
that agree.
Parameters
----------
actual : number
The object to check.
desired : number
The expected object.
significant : integer (significant=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_almost_equal: compares objects by decimals
assert_array_almost_equal: compares array_like objects by decimals
assert_equal: tests objects for equality
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)
...
:
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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Raise an assertion 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 : 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_almost_equal: test objects for equality up to precision
assert_equal: tests objects for equality
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])
...
:
AssertionError:
Arrays are not equal
(mismatch 50.0%)
x: array([ 1. , 3.14159265, NaN])
y: array([ 1. , 3.14159265, NaN])
use assert_array_almost_equal for these cases instead
>>> np.testing.assert_array_almost_equal([1.0,np.pi,np.nan],
[1, np.sqrt(np.pi)**2, np.nan], decimal=15)
Rc R Rd s Arrays are not equalN( RŠ t operatort __eq__( R" R Rc R ( ( s9 /usr/lib64/python2.6/site-packages/numpy/testing/utils.pyR g s =i c s d d k l l l d d k l d d k l f d } t | | | d | d | d d d
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Raise an assertion if two objects are not equal up to desired precision.
The test verifies identical shapes and verifies values with
abs(desired-actual) < 0.5 * 10**(-decimal)
Given two array_like objects, check that the shape is equal and all
elements of these objects are almost 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.
Parameters
----------
x : array_like
The actual object to check.
y : array_like
The desired, expected object.
decimal : integer (decimal=6)
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_almost_equal: simple version for comparing numbers
assert_array_equal: tests objects for equality
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)
...
:
AssertionError:
Arrays are not almost equal
(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)
:
ValueError:
Arrays are not almost equal
x: array([ 1. , 2.33333, NaN])
y: array([ 1. , 2.33333, 5. ])
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Raise an assertion 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])
...
:
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)
...
:
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])
...
:
Arrays are not less-ordered
(shapes (3,), (1,) mismatch)
x: array([ 1., 2., 3.])
y: array([4])
Rc R Rd s Arrays are not less-orderedN( RŠ RŞ t __lt__( R" R Rc R ( ( s9 /usr/lib64/python2.6/site-packages/numpy/testing/utils.pyR ˙ s Ac B s | | Ud S( N( ( t astrRm ( ( s9 /usr/lib64/python2.6/site-packages/numpy/testing/utils.pyR D s c
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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 "", line 1, in
...
AssertionError: Differences in strings:
- abc+ abcd? +
i˙˙˙˙Ns \As \Zi i s s - s ? s + i s Differences 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)
i˙˙˙˙Ni t __file__i R c s
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assert_raises(exception_class, callable, *args, **kwargs)
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.
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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.
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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
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Check that ufuncs don't mishandle refcount of object `1`.
Used in a few regression tests.
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robust method to compare two arrays whose amplitude is variable.
Note
----
An assertion is raised if the following condition is not met:
abs(x - y) <= nulps * spacing(max(abs(x), abs(y)))
Parameters
----------
x: array_like
first input array
y: array_like
second input array
nulp: int
max number of unit in the last place for tolerance (see Note)
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Unit in the Last Place.i˙˙˙˙Ns( Arrays are not almost equal up to %g ULP( R R RĄ R ( Rf R t maxulpR R t ret( ( s9 /usr/lib64/python2.6/site-packages/numpy/testing/utils.pyR ^ s c s5 d d k | o. i | d | } i | d | } n i | } i | } i | | } i | p i | o t d n i | d | } i | d | } | i | i j p t d | i | i f n f d } t | } t | } | | | | S( sD For each item in x and y, eeturn the number of representable floating
points between them.
Parameters
----------
x : array_like
first input array
y : array_like
second input array
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
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