2[c'@`scdZddlmZmZmZddlZddlZddlZddlZddl Z ddl Z ddl m Z m Z ddlZddlZddlmZmZddlmZddl mZddlZddlmZmZmZmZmZmZmZdd lm Z ej!dd krDdd l"m#Z#ndd l#m#Z#d d ddddddddddddddddddd d!d"d#d$d%d&d'd(d)d*d+d,d-d.d/d0d1d2g'Z$d)e%fd3YZ&e&Z'dZ(d4ej)kZ*e+ed5e,e,k Z-d6Z.d7d8Z/d9Z0d:Z1d;Z2e d<d=d>Z3ej4d?kre,d@e,e,dAZ5dBddCZ6n5ej7dD dEkrdFej8dGZ6n dHZ6ej7dD dEkrdFej8gdIZ9n gdJZ9dKe:dLdMfdNdOZ;d7e:dPZ<dQZ=dRd7e:dSZ>dRd7e:dTZ?d7e:d7dUe:e:dVZ@d7e:dWZAdUd7e:dXZBd7e:dYZCdZZDd[ZEe,e:d\ZFd]ZGddlHZHd^eHjIfd_YZJeJd`ZKdaZLdbZMe,dcZNdde,deZOdfZPdgde:d7e:dhZQdddiZRdde,djZSe,dkZTdlZUdmZVejWe,dnZXdoZYejWe,dpZZdqZ[edrdsdtZ\d&e%fduYZ]ejWdvZ^ejWdwZ_d'e j`fdxYZad.ebfdyYZcejWe,dzZdd{ZedS(|s* Utility function to facilitate testing. i(tdivisiontabsolute_importtprint_functionN(tpartialtwraps(tmkdtemptmkstemp(tSkipTest(tWarningMessage(tfloat32temptytaranget array_reprtndarraytisnattarray(t deprecatei(tStringIOt assert_equaltassert_almost_equaltassert_approx_equaltassert_array_equaltassert_array_lesstassert_string_equaltassert_array_almost_equalt assert_raisest build_err_msgtdecorate_methodstjiffiestmemusagetprint_assert_equaltraisestrandtrundocst runstringtverbosetmeasuretassert_tassert_array_almost_equal_nulptassert_raises_regextassert_array_max_ulpt assert_warnstassert_no_warningstassert_allclosetIgnoreExceptiontclear_and_catch_warningsRtKnownFailureExceptionttemppathttempdirtIS_PYPYt HAS_REFCOUNTtsuppress_warningstassert_array_comparet_assert_valid_refcountt_gen_alignment_datatassert_no_gc_cyclescB`seZdZRS(s<Raise this exception to mark a test as a known failing test.(t__name__t __module__t__doc__(((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR.-st__pypy__t getrefcountcC`sqt}d}yddl}Wntk r5t}nX|j|krNt}n|smd|}t|n|S(s# Import nose only when needed. iiNs@Need nose >= %d.%d.%d for tests - see http://nose.readthedocs.io(iii(tTruetnoset ImportErrortFalset__versioninfo__(t nose_is_goodtminimum_nose_versionR>tmsg((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyt import_nose9s   tcC`sFt}|sBy |}Wntk r2|}nXt|ndS(sI 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. N(R=t TypeErrortAssertionError(tvalRDt__tracebackhide__tsmsg((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR%Os    cC`sDddlm}||}t|ttr@tdn|S(slike 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.i(tisnans!isnan not supported for this type(t numpy.coreRLt isinstancettypetNotImplementedRG(txRLtst((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pytgisnancs  cC`s`ddlm}m}|dd5||}t|ttrVtdnWdQX|S(slike 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.i(tisfiniteterrstatetinvalidtignores$isfinite not supported for this typeN(RMRTRURNRORPRG(RQRTRURR((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyt gisfiniteus  cC`s`ddlm}m}|dd5||}t|ttrVtdnWdQX|S(slike 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.i(tisinfRURVRWs!isinf not supported for this typeN(RMRYRURNRORPRG(RQRYRURR((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pytgisinfs  tmessagesNnumpy.testing.rand is deprecated in numpy 1.11. Use numpy.random.rand instead.cG`skddl}ddlm}m}|||}|j}x*tt|D]}|j||: {}]is...s %s: %s(tfindRaRt enumerateRNR RR treprRRuROR8tcounttjoint splitlines( tarraysterr_msgtheaderR#tnamesRRDRetatr_funcRtexc((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR s& 1 "cC`sSt}t|trt|ts?ttt|ntt|t|||x`|jD]R\}}||krtt|nt||||d||f|qkWdSt|t t frTt|t t frTtt|t|||x?t t|D]+}t||||d||f|q!WdSddl m }m}m} ddlm} m} m} t||st||rt||||St||g|d|} y| |p| |}Wntk r t}nX|r| |r;| |}| |}n |}d}| |rn| |}| |}n |}d}yt||t||Wqtk rt| qXn||||krt| nynt|}t|}|r |r dS|dkrO|dkrO| || |ksOt| qOnWntttfk rlnXyet|}t|}t|jjt|jjk}|r|r|rdSt| nWntttfk rnXy||ks t| nWn>ttfk rN}d|j dkrHt| qOnXdS( sa 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]) ... : Items are not equal: item=1 ACTUAL: 5 DESIRED: 6 s key=%r %sNs item=%r %si(R tisscalartsignbit(t iscomplexobjtrealtimagR#selementwise == comparison(!R=RNtdictRHRRORRatitemstlistttupleR`RMR RRt numpy.libRRRRRt ValueErrorR@RSRGRRRtdtypetDeprecationWarningt FutureWarningRb(tactualtdesiredRR#RJtkReR RRRRRRDt usecomplextactualrtactualitdesiredrtdesireditisdesnantisactnantisdesnattisactnatt dtypes_matchte((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR&s# )*)             $  cC`st}ddl}||kst}|j||jd|j|||jd|j||t|jndS(s 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] iNs failed ACTUAL: s DESIRED: (R=tpprintRtwriteRHtgetvalue(t test_stringRRRJRRD((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRs      ic`sTt}ddlm}ddlm}m}m} y|pJ|} Wntk rgt} nXfd} | r?|r|} | } n } d} |r|}| }n }d}y*t | |dt | |dWq?t k r;t | q?Xnt |t t fsot |t t frtSytotststrtotst | qnkst | ndSWnttfk rnXtdd krPt | ndS( sp 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) ... : 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(R (RRRc`s)d}tgdd|S(Ns*Arrays are not almost equal to %d decimalsR#R(R(R(RtdecimalRRR#(s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyt_build_err_msgs RNg?g$@(R=RMR RRRRRR@RRHRNRRRRXRSRRGtabs(RRRRR#RJR RRRRRRRRR((RRRRR#s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRsNA        c C`st}ddl}tt||f\}}||kr=dS|jddId|j||j|}|jd|j|j|}WdQXy||}Wnt k rd}nXy||} Wnt k rd} nXt ||g|dd |d |} y}t |o't |st |sBt |rlt |oWt |st | qn||kst | ndSWnttfk rnX|j|| |jd |d  krt | ndS( sU Raises an AssertionError if two items are not equal up to significant digits. .. note:: It is recommended to use one of `assert_allclose`, `assert_array_almost_equal_nulp` or `assert_array_max_ulp` instead of this function for more consistent floating point comparisons. Given two numbers, check that they are approximately equal. Approximately equal is defined as the number of significant digits that agree. Parameters ---------- actual : scalar The object to check. desired : scalar The expected object. significant : int, optional Desired precision, default is 7. err_msg : str, optional The error message to be printed in case of failure. verbose : bool, optional If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- assert_allclose: Compare two array_like objects for equality with desired relative and/or absolute precision. assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal Examples -------- >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20) >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20, significant=8) >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20, significant=8) ... : Items are not equal to 8 significant digits: ACTUAL: 1.234567e-021 DESIRED: 1.2345672000000001e-021 the evaluated condition that raises the exception is >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1) True iNRVRWg?i gRs-Items are not equal to %d significant digits:R#g$@i(R=tnumpytmaptfloatRURtpowertfloortlog10tZeroDivisionErrorRRXRSRHRGR( RRt significantRR#RJtnptscalet sc_desiredt sc_actualRD((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRKs@9   *      *ic  `st} ddlm} m} mmddlm| |dtdt}| |dtdt}d} d} | dfd }y|j dkp|j dkp|j |j k}|s't ||gd |j |j fd d d dd}t |nt}| |r| |r|ro|||d| dd}n|r"||||dfdddO}||||dfdddO}q"nW| |r"| |r"|r"|j j |j j kr"|||dtdd}q"n|jdkr^||||}}|jdkrhdSn |rhdS|||}t|tr|}dg}n$|j}|j}|j}|tkr0dd|jdt|}t ||gd|fd d d d d}t |nWnrtk rddl}|j}d|ft ||gd d d d!d}t|nXdS("Ni(RRLtinftbool_(talltcopytsubokcS`s|jjdkS(Ns?bhilqpBHILQPefdgFDG(Rtchar(RQ((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pytisnumberscS`s|jjdkS(NtMm(RR(RQ((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pytistimestnanc `s||}||}||ktkrtt||gd|dddd d}t|n|jdkr|S|jdkr|S|Sd S( sHandling nan/inf. Combine results of running func on x and y, checking that they are True at the same locations. s x and y %s location mismatch:R#RRRQtyRiN(RQR(R=RRHtndim(RQRtfuncthasvaltx_idty_idRD(RRRtnpallRR#(s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pytfunc_assert_same_poss     s (shapes %s, %s mismatch)R#RRRQRRRRc`s | kS(N((txy(R(s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pytss+infc`s | kS(N((R(R(s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRss-inftNaTidgY@is (mismatch %s%%)serror during assertion: %s %s(((RQR(RQR(RQR(R=RMRRLRRtnumpy.core.fromnumericRR@tshapeRRHRRORRtsizeRNtbooltravelttolistRRaRt tracebackt format_exc(t comparisonRQRRR#RRt equal_nant equal_infRJRRLRRRtcondRDtflaggedRItreducedtmatchRtefmt((RRRRRRR#s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR4sx"  $0     !     !     c C`s/t}ttj||d|d|dddS(s, 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]) ... : AssertionError: Arrays are not equal (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) RR#RsArrays are not equalN(R=R4toperatort__eq__(RQRRR#RJ((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR s?c `st}ddlm}mmmmddlmddl m fd}t |||d|d|dd d d S( s 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) ... : 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. ]) i(taroundtnumbertfloat_t result_typeR(t issubdtype(tanyc`s)yt|s't|rt|}t|}||kjsUtS|j|jkordknr||kS||}||}nWnttfk rnX|d}|d|dtdt}t||}|js|j }n|dd kS(Nig?RRRg?g$@( RZRR@RRGRR=RRtastype(RQRtxinfidtyinfidRtz(RRRRtnpanyRR(s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pytcompares$$  "  RR#Rs*Arrays are not almost equal to %d decimalsRN( R=RMRRRRRtnumpy.core.numerictypesRRRR4(RQRRRR#RJRR((RRRRRRRs;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRdsH(! c C`s5t}ttj||d|d|dddtdS(sF 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]) ... : 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]) RR#RsArrays are not less-orderedRN(R=R4Rt__lt__R@(RQRRR#RJ((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRs BcB`s ||UdS(N((tastrR((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR"sc C`sMt}ddl}t|ts<ttt|nt|tsfttt|ntjd|d|tj rdSt |j j |j d|j d}g}xH|r |jd}|jdrqn|jdr|g}|jd}|jdrB|j||jd}n|jd sftt|n|j||r|jd} | jdr|j| q|jd| ntjd|d d|d rqn|j|qntt|qW|sdSd d j|j} ||krIt| ndS( s 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? + iNs\As\Zis s- s? s+ isDifferences in strings: %sRF(R=tdifflibRNtstrRHRROtreRtMRtDifferRRtpopt startswithRtinserttextendRtrstrip( RRRJRtdifft diff_listtd1Rtd2td3RD((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRsL  0    "  c `sddlm}ddl}|dkrGtjd}|jd}ntjj tjj |d}|||}|j j |}|j dt}g|rfd} nd} x!|D]} |j| d| qW|jdkr|rtd d jndS( sT 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 i(tnpy_load_moduleNit__file__R#c`s j|S(N(R(ts(RD(s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRstoutsSome doctests failed: %ss (t numpy.compatRtdoctestRhtsyst _getframet f_globalstosRwtsplitexttbasenamet DocTestFinderRt DocTestRunnerR@truntfailuresRHR( tfilenametraise_on_errorRRRdtnametmtteststrunnerRttest((RDs;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR!cs"  " cG`st}|jj|S(sDecorator to check for raised exceptions. The decorated test function must raise one of the passed exceptions to pass. If you want to test many assertions about exceptions in a single test, you may want to use `assert_raises` instead. .. warning:: This decorator is nose specific, do not use it if you are using a different test framework. Parameters ---------- args : exceptions The test passes if any of the passed exceptions is raised. Raises ------ AssertionError Examples -------- Usage:: @raises(TypeError, ValueError) def test_raises_type_error(): raise TypeError("This test passes") @raises(Exception) def test_that_fails_by_passing(): pass (REttoolsR(RbR>((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRs" t_DummycB`seZdZRS(cC`sdS(N((tself((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pytnops(R8R9R+(((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR)sR+cO`st}tj||S(s 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) (R=t_dt assertRaises(RbtkwargsRJ((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRscO`s@t}tjjdkr$tj}n tj}|||||S(sY assert_raises_regex(exception_class, expected_regexp, callable, *args, **kwargs) assert_raises_regex(exception_class, expected_regexp) Fail unless an exception of class exception_class and with message that matches expected_regexp is thrown by callable when invoked with arguments args and keyword arguments kwargs. Alternatively, can be used as a context manager like `assert_raises`. Name of this function adheres to Python 3.2+ reference, but should work in all versions down to 2.6. Notes ----- .. versionadded:: 1.9.0 i(R=Rt version_infotmajorR,tassertRaisesRegextassertRaisesRegexp(texception_classtexpected_regexpRbR.RJtfuncname((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR's   c C`s|dkr%tjdtj}ntj|}|j}ddlm}g|jD]}||rZ|^qZ}x|D]}}y(t |dr|j }n |j }Wnt k rqnX|j |r|jd rt||||qqWdS(s  Apply a decorator to all methods in a class matching a regular expression. The given decorator is applied to all public methods of `cls` that are matched by the regular expression `testmatch` (``testmatch.search(methodname)``). Methods that are private, i.e. start with an underscore, are ignored. Parameters ---------- cls : class Class whose methods to decorate. decorator : function Decorator to apply to methods testmatch : compiled regexp or str, optional The regular expression. Default value is None, in which case the nose default (``re.compile(r'(?:^|[\b_\.%s-])[Tt]est' % os.sep)``) is used. If `testmatch` is a string, it is compiled to a regular expression first. s(?:^|[\\b_\\.%s-])[Tt]esti(t isfunctiontcompat_func_namet_N(RhRtcompileRtsept__dict__tinspectR6tvaluesthasattrR7R8tAttributeErrortsearchRtsetattr( tclst decoratort testmatchtcls_attrR6t_mtmethodstfunctionR5((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRs   +    ic B`sejd}|j|j}}e|d|d}d}e}x$||krm|d7}|||UqJWe|}d|S(sE 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 isTest name: %s texecig{Gz?(RRtf_localsRR9R( tcode_strttimestlabeltframetlocstglobstcodeRetelapsed((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR$-s!    c C`sts tSddl}ddl}|jdjdd}|}d}|jzRtj|}x#t dD]}|||}qrWt tj||kWd|j X~dS(sg Check that ufuncs don't mishandle refcount of object `1`. Used in a few regression tests. iNidiii'( R2R=RtgcR treshapetdisableRR<R`R%tenable( topRRStbtcRetrctjtd((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR5]s  gHz>c `st}ddlfd}j|j|}}df} t|||dt|d|d| ddS( sq 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) iNc `s(jjj||dddS(NtrtoltatolR(tcoretnumerictisclose(RQR(R^RRR](s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRs!s'Not equal to tolerance rtol=%g, atol=%gRR#RR(R=Rt asanyarrayR4R( RRR]R^RRR#RJRR((R^RRR]s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR+us- c C`st}ddl}|j|}|j|}||j|j||k||}|j|j|||ks|j|s|j|rd|}n(|jt||} d|| f}t |ndS(s 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) iNsX and Y are not equal to %d ULPs+X and Y are not equal to %d ULP (max is %g)( R=RRtspacingtwhereRRtmaxt nulp_diffRH( RQRtnulpRJRtaxtaytrefRDtmax_nulp((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR&s1 (" cC`sPt}ddl}t|||}|j||ksLtd|n|S(s 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))) iNs(Arrays are not almost equal up to %g ULP(R=RRfRRH(RRXtmaxulpRRJRtret((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR(s$  c`s.ddl|r?j|d|}j|d|}nj|}j|}j||}j|sj|rtdnj|d|}j|d|}|j|jkstd|j|jfnfd}t|}t|}||||S(sFor 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 iNRs'_nulp not implemented for complex arrays+x and y do not have the same shape: %s - %sc`s&j||d|}j|S(NR(RR(trxtrytvdtR (R(s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyt_diffHs(RRt common_typeRRRRt integer_repr(RQRRttRqRnRo((Rs;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRfs$   cC`s\|j|}|jdks?|||dk||dk>> 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 cC`sAt|j|j|_i|_tt|jd|dS(NR|(tsettuniont class_modulestmodulest_warnreg_copiestsuperR-t__init__(R*R|R((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRms cC`s_xI|jD]>}t|dr |j}|j|j|<|jq q Wtt|jS(Nt__warningregistry__( RR>RRRtclearRR-t __enter__(R*tmodtmod_reg((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRrs  cG`svtt|j|xY|jD]N}t|drE|jjn||jkr |jj|j|q q WdS(NR( RR-t__exit__RR>RRRtupdate(R*texc_infoR((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRzs (((R8R9R:RR@RRR(((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR-Bs ( cB`seZdZddZdZedd edZedd dZ edd dZ dZ d Z d Z d ZRS( s 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 RcC`sFt|_g|_|ddddhkr9tdn||_dS(NRtmoduletoncetlocationsunsupported forwarding rule.(R@t_enteredt _suppressionsRt_forwarding_rule(R*tforwarding_rule((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRs   cC`sTttdrtjdSx0|jD]%}t|dr'|jjq'q'WdS(Nt_filters_mutatedR(R>RRt _tmp_modulesRR(R*R((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyt_clear_registriess  RFcC`s|rg}nd}|jr|dkrFtjdd|d|nR|jjddd}tjdd|d|d||jj||j|j j ||t j |t j ||fn.|jj ||t j |t j ||f|S(NRtcategoryR[t.s\.t$R(RhRRtfilterwarningsR8treplaceRtaddRt_tmp_suppressionsRRR9tIR(R*RR[RR|t module_regex((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyt_filters$     ( %c C`s&|jd|d|d|dtdS(s 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. RR[RR|N(RR@(R*RR[R((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pytfiltersc C`s"|jd|d|d|dtS(si 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. RR[RR|(RR=(R*RR[R((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR|sc C`s0|jrtdntj|_tj|_|jt_t|_g|_t |_ t |_ g|_ x|j D]\}}}}}|dk r|2n|dkrtjdd|d|qz|jjddd}tjdd|d|d||j j|qzW|jt_|j|S( Ns%cannot enter suppress_warnings twice.RRR[Rs\.RR(Rt RuntimeErrorRt showwarningt _orig_showtfilterst_filtersR=RRRt _forwardedtlogRRhRR8RRt _showwarningR(R*tcattmessR8RRR((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR.s0             cG`s;|jt_|jt_|jt|_|`|`dS(N(RRRRRRR@R(R*R((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRNs     cO`s;|jdd}x|j|jdddD]\}} } } } t||r0| j|jddk r0| dkr| dk rt|||||} |jj | | j | ndS| j j |r!| dk rt|||||} |jj | | j | ndSq0q0W|j dkrp|dkr_|j ||||||n |j|dS|j dkr|j|f}nK|j dkr|j||f}n'|j dkr|j|||f}n||jkrdS|jj||dkr*|j ||||||n |j|dS(Nt use_warnmsgiiRRRR(RRhRRt issubclassRRbRRRRRRRt _orig_showmsgRR(R*R[RR!tlinenoRbR.RRR8tpatternRtrecRDt signature((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyRVsL0             c`s"tfd}|S(s_ Function decorator to apply certain suppressions to a whole function. c`s||SWdQXdS(N((RbR.(RR*(s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pytnew_funcs(R(R*RR((RR*s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyt__call__sN(R8R9R:RRtWarningRhR@RRR|RRRR(((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR3sF   4cc`s t}tsdSttjtjtj}zhx6tdD]}tjdkrFPqFqFWt dtj tj dVtj}tj }Wdtj 2tj |tj X|r|dk rd|nd}tdj||t|djd|DndS(Nidis]Unable to fully collect garbage - perhaps a __del__ method is creating more reference cycles?s when calling %sRFsXReference cycles were found{}: {} objects were collected, of which {} are shown below:{}cs`sH|]>}djt|jt|tj|jddVqdS(s {} object with id={}: {}s s N(RuROR8tidRtpformatR(t.0R((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pys s(R=R2R%RSt isenabledRUt get_debugR`tcollectRt set_debugt DEBUG_SAVEALLtgarbageRVRhRHRuRaR(R#RJtgc_debugRetn_objects_in_cyclestobjects_in_cyclesR((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyt_assert_no_gc_cycles_contexts:       cO`sK|s tS|d}|d}td|j|||WdQXdS(s3 Fail if the given callable produces any reference cycles. If called with all arguments omitted, may be used as a context manager: with assert_no_gc_cycles(): do_something() .. versionadded:: 1.15.0 Parameters ---------- func : callable The callable to test. \*args : Arguments Arguments passed to `func`. \*\*kwargs : Kwargs Keyword arguments passed to `func`. Returns ------- Nothing. The result is deliberately discarded to ensure that all cycles are found. iiR#N(RR8(RbR.R((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyR7s   (fR:t __future__RRRRRRRSRRt functoolsRRRt contextlibttempfileRRt unittest.caseRRRRMR R R R R RRtnumpy.lib.utilsRR/tioRt__all__RR.tKnownFailureTestR#RR1tgetattrRhR2RER%RSRXRZR R#RzRtplatformtgetpidRR=RRRRRR4RRRR"RR!RtunittesttTestCaseR)R,RR'RR$R5R+R&R(RfRwRstcontextmanagerRR)RR*R6R,R0R/RR-RqR3RR7(((s;/tmp/pip-build-fiC0ax/numpy/numpy/testing/_private/utils.pyts         4                 )zb  qDlI  F. (     /0  9 ?- 6   + $EA2