(*\c@`sdddlmZmZmZddlmZddlZddlZddlZddl Z ddl Z ddl m Z ddl mZmZmZmZddlmZmZmZmZddlmZddlmZejdd krd Zn d Zd ZeeZd Z ee Z!dZ"ee"Z#dZ$dZ%dZ&dZ'dZ(dZ)dZ*dZ+dZ,dZ,dZ-dZ.dZ/dZ0dZ1dZ2ddZ4d Z5d!Z6d"Z7d#Z8d$Z9d%Z:ed&Z;d'Z<ed(Z=d)Z>ed*Z?d+Z@ed,ZAd-ZBd.ZCd/ZDd0ZEd1ZFd2ZGd3ZHed4ZId5ZJd6ZKd7ZLd8ZMd9ZNd:ZOd;ZPd<ZQd=ZRd>ZSd?ZTd@ZUdAZVdBZWdCZXdDZYdEZZdFZ[e\dGkr`ddlZej]ndS(Hi(tdivisiontabsolute_importtprint_function(t namedtupleN(t update_config(tNumpyDocStringt FunctionDoctClassDoct ParseError(tSphinxDocStringtSphinxClassDoctSphinxFunctionDoctget_doc_object(traises(twarnsicC`s|S(N((ts((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttcC`s t|dS(Ntunicode_escape(tunicode(R((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyRRs numpy.multivariate_normal(mean, cov, shape=None, spam=None) Draw values from a multivariate normal distribution with specified mean and covariance. The multivariate normal or Gaussian distribution is a generalisation of the one-dimensional normal distribution to higher dimensions. Parameters ---------- mean : (N,) ndarray Mean of the N-dimensional distribution. .. math:: (1+2+3)/3 cov : (N, N) ndarray Covariance matrix of the distribution. shape : tuple of ints Given a shape of, for example, (m,n,k), m*n*k samples are generated, and packed in an m-by-n-by-k arrangement. Because each sample is N-dimensional, the output shape is (m,n,k,N). Returns ------- out : ndarray The drawn samples, arranged according to `shape`. If the shape given is (m,n,...), then the shape of `out` is (m,n,...,N). In other words, each entry ``out[i,j,...,:]`` is an N-dimensional value drawn from the distribution. list of str This is not a real return value. It exists to test anonymous return values. no_description Other Parameters ---------------- spam : parrot A parrot off its mortal coil. Raises ------ RuntimeError Some error Warns ----- RuntimeWarning Some warning Warnings -------- Certain warnings apply. Notes ----- Instead of specifying the full covariance matrix, popular approximations include: - Spherical covariance (`cov` is a multiple of the identity matrix) - Diagonal covariance (`cov` has non-negative elements only on the diagonal) This geometrical property can be seen in two dimensions by plotting generated data-points: >>> mean = [0,0] >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis >>> x,y = multivariate_normal(mean,cov,5000).T >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show() Note that the covariance matrix must be symmetric and non-negative definite. References ---------- .. [1] A. Papoulis, "Probability, Random Variables, and Stochastic Processes," 3rd ed., McGraw-Hill Companies, 1991 .. [2] R.O. Duda, P.E. Hart, and D.G. Stork, "Pattern Classification," 2nd ed., Wiley, 2001. See Also -------- some, other, funcs otherfunc : relationship Examples -------- >>> mean = (1,2) >>> cov = [[1,0],[1,0]] >>> x = multivariate_normal(mean,cov,(3,3)) >>> print x.shape (3, 3, 2) The following is probably true, given that 0.6 is roughly twice the standard deviation: >>> print list( (x[0,0,:] - mean) < 0.6 ) [True, True] .. index:: random :refguide: random;distributions, random;gauss s Test generator Yields ------ a : int The number of apples. b : int The number of bananas. int The number of unknowns. s Test generator Yields ------ a : int The number of apples. Receives -------- b : int The number of bananas. c : int The number of oranges. cC`s6tdjdsttdjds2tdS(Nt Signaturesnumpy.multivariate_normal(s spam=None)(tdoct startswithtAssertionErrortendswith(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_signaturescC`s>tddjdsttddjds:tdS(NtSummaryis Draw valuesis covariance.(RRRR(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt test_summaryscC`s!tddjdstdS(NsExtended SummaryisThe multivariate normal(RRR(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_extended_summaryscC`sttddkstgtdD]\}}}|^q'}tdt|dddgDspttdd\}}}|dkst|d jd sttdd d d d kstdS( Nt Parametersics`s!|]\}}||kVqdS(N((t.0tatb((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pys stmeantcovtshapeis(N, N) ndarrayisCovariance matrixis (1+2+3)/3(tlenRRtalltzipR(tnt_tnamestargtarg_typetdesc((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_parameterss&.cC`sttddkstgtdD]\}}}|^q'dgksQttdd\}}}|dkszt|djdstdS(NsOther ParametersitspamitparrotsA parrot off its mortal coil(R$RRR(R'R(R*R+R,((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_other_parameterss 5cC`sQttddksttdd\}}}|dksEt|dksWt|djdspt|djdsttdd \}}}|d kst|d kst|djd st|djd sttdd\}}}|d kst|dks1td j|j sMtdS(NtReturnsiitouttndarraysThe drawn samplesis distribution.iRs list of strsThis is not a realsanonymous return values.itno_description(R$RRRRtjointstrip(R*R+R,((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt test_returnsscC`std}t|dks"td d dg}xt||D]t\\}}}\}}}||ksqt||kst|d jd st|d j|sAtqAWdS(NtYieldsiRtintsapples.R sbananas.Rs unknowns.is The number of(RR9sapples.(R R9sbananas.(RR9s unknowns.(t doc_yieldsR$RR&RR(tsectionttruthR*R+R,targ_t arg_type_tend((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt test_yieldss  .cC`std}t|dks"td d g}xt||D]t\\}}}\}}}||ksnt||kst|djd st|dj|s>tq>WdS( NtReceivesiR R9sbananas.tcsoranges.is The number of(R R9sbananas.(RBR9soranges.(tdoc_sentR$RR&RR(R;R<R*R+R,R=R>R?((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt test_sents  .cC`sd}ttt|dS(Ns Test having returns and yields. Returns ------- int The number of apples. Yields ------ a : int The number of apples. b : int The number of bananas. (t assert_raisest ValueErrorR(tdoc_text((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_returnyieldscC`sd}ttt|dS(Ns Test having returns and yields. Returns ------- int The number of apples. Yields ------ a : int The number of apples. b : int The number of bananas. (RERFR(RG((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyRHscC`sd}ttt|dtfdY}d}yt|Wn=tk r}dt|ksdt|kstnXyt|Wn=tk r}dt|ksdt|kstnXdS( Nsy Test having a section Notes twice Notes ----- See the next note for more information Notes ----- That should break... tDummycB`s eZdZdZdZRS(s Dummy class. Notes ----- First note. Notes ----- Second note. cS`sdS(sSpam Spam spam.N((tselfRR ((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyR.AscS`sdS(sCheese No cheese.N((RJRBtd((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pythamEs(t__name__t __module__t__doc__R.RL(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyRI4s  cS`sdS(s Dummy function. Notes ----- First note. Notes ----- Second note. N((R*((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt dummy_funcIRs!test_section_twice..Dummystest_docscrape.Dummys&test_section_twice..dummy_funcsfunction dummy_func(RERFRtobjectR tstrRR (RGRIRPte((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_section_twice%s  cC`sZtddjdsttddjds:tttddksVtdS(NtNotesitInsteadis definite.i(RRRRR$(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt test_notesescC`s>tddjdsttddjds:tdS(Nt Referencesis..is2001.(RRRR(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_referenceskscC`s>tddjdsttddjds:tdS(NtExamplesis>>>isTrue](RRRR(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt test_examplespscC`sZtdddkstttddks6tttdddksVtdS(Ntindextdefaulttrandomitrefguide(RRR$(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt test_indexuscC`sCtjdd|}tjdd|}tjdd|}|S(s1Remove leading, trailing and multiple blank liness^\s*\nRs\n\s*$s\n\s*\ns\n\n(tretsub(R((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt_strip_blank_lines{scC`stj|}tj|}gt|jdD]}|j^q4| }gt|jdD]}|j^qf| }t|t|kstx;tt||D]$\}\}}||kstqWdS(Ns ( ttextwraptdedentRctsplittrstripR$Rt enumerateR&(RR tn_linestltiitaatbb((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pytline_by_line_compares22(cC`stttddS(Ns numpy.multivariate_normal(mean, cov, shape=None, spam=None) Draw values from a multivariate normal distribution with specified mean and covariance. The multivariate normal or Gaussian distribution is a generalisation of the one-dimensional normal distribution to higher dimensions. Parameters ---------- mean : (N,) ndarray Mean of the N-dimensional distribution. .. math:: (1+2+3)/3 cov : (N, N) ndarray Covariance matrix of the distribution. shape : tuple of ints Given a shape of, for example, (m,n,k), m*n*k samples are generated, and packed in an m-by-n-by-k arrangement. Because each sample is N-dimensional, the output shape is (m,n,k,N). Returns ------- out : ndarray The drawn samples, arranged according to `shape`. If the shape given is (m,n,...), then the shape of `out` is (m,n,...,N). In other words, each entry ``out[i,j,...,:]`` is an N-dimensional value drawn from the distribution. list of str This is not a real return value. It exists to test anonymous return values. no_description Other Parameters ---------------- spam : parrot A parrot off its mortal coil. Raises ------ RuntimeError Some error Warns ----- RuntimeWarning Some warning Warnings -------- Certain warnings apply. See Also -------- `some`_, `other`_, `funcs`_ .. `otherfunc`_ relationship Notes ----- Instead of specifying the full covariance matrix, popular approximations include: - Spherical covariance (`cov` is a multiple of the identity matrix) - Diagonal covariance (`cov` has non-negative elements only on the diagonal) This geometrical property can be seen in two dimensions by plotting generated data-points: >>> mean = [0,0] >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis >>> x,y = multivariate_normal(mean,cov,5000).T >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show() Note that the covariance matrix must be symmetric and non-negative definite. References ---------- .. [1] A. Papoulis, "Probability, Random Variables, and Stochastic Processes," 3rd ed., McGraw-Hill Companies, 1991 .. [2] R.O. Duda, P.E. Hart, and D.G. Stork, "Pattern Classification," 2nd ed., Wiley, 2001. Examples -------- >>> mean = (1,2) >>> cov = [[1,0],[1,0]] >>> x = multivariate_normal(mean,cov,(3,3)) >>> print x.shape (3, 3, 2) The following is probably true, given that 0.6 is roughly twice the standard deviation: >>> print list( (x[0,0,:] - mean) < 0.6 ) [True, True] .. index:: random :refguide: random;distributions, random;gauss(RnRRR(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_strs kcC`stttddS(NsTest generator Yields ------ a : int The number of apples. b : int The number of bananas. int The number of unknowns. (RnRRR:(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_yield_strs cC`stttddS(NsTest generator Yields ------ a : int The number of apples. Receives -------- b : int The number of bananas. c : int The number of oranges. (RnRRRC(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_receives_strs cC`s^dttdkstdttdks<tdttdksZtdS(NR\sTest idx s%Test idx .. index :: random s*Test idx .. index :: foo (RRRR(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_no_index_in_str s    cC`s#tt}tt|ddS(Ns} .. index:: random single: random;distributions, random;gauss Draw values from a multivariate normal distribution with specified mean and covariance. The multivariate normal or Gaussian distribution is a generalisation of the one-dimensional normal distribution to higher dimensions. :Parameters: **mean** : (N,) ndarray Mean of the N-dimensional distribution. .. math:: (1+2+3)/3 **cov** : (N, N) ndarray Covariance matrix of the distribution. **shape** : tuple of ints Given a shape of, for example, (m,n,k), m*n*k samples are generated, and packed in an m-by-n-by-k arrangement. Because each sample is N-dimensional, the output shape is (m,n,k,N). :Returns: **out** : ndarray The drawn samples, arranged according to `shape`. If the shape given is (m,n,...), then the shape of `out` is (m,n,...,N). In other words, each entry ``out[i,j,...,:]`` is an N-dimensional value drawn from the distribution. list of str This is not a real return value. It exists to test anonymous return values. no_description .. :Other Parameters: **spam** : parrot A parrot off its mortal coil. :Raises: RuntimeError Some error :Warns: RuntimeWarning Some warning .. warning:: Certain warnings apply. .. seealso:: :obj:`some`, :obj:`other`, :obj:`funcs` .. :obj:`otherfunc` relationship .. rubric:: Notes Instead of specifying the full covariance matrix, popular approximations include: - Spherical covariance (`cov` is a multiple of the identity matrix) - Diagonal covariance (`cov` has non-negative elements only on the diagonal) This geometrical property can be seen in two dimensions by plotting generated data-points: >>> mean = [0,0] >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis >>> x,y = multivariate_normal(mean,cov,5000).T >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show() Note that the covariance matrix must be symmetric and non-negative definite. .. rubric:: References .. [1] A. Papoulis, "Probability, Random Variables, and Stochastic Processes," 3rd ed., McGraw-Hill Companies, 1991 .. [2] R.O. Duda, P.E. Hart, and D.G. Stork, "Pattern Classification," 2nd ed., Wiley, 2001. .. only:: latex [1]_, [2]_ .. rubric:: Examples >>> mean = (1,2) >>> cov = [[1,0],[1,0]] >>> x = multivariate_normal(mean,cov,(3,3)) >>> print x.shape (3, 3, 2) The following is probably true, given that 0.6 is roughly twice the standard deviation: >>> print list( (x[0,0,:] - mean) < 0.6 ) [True, True] (R tdoc_txtRnRR(t sphinx_doc((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_sphinx_str0s  scC`s#tt}tt|ddS(NsTest generator :Yields: **a** : int The number of apples. **b** : int The number of bananas. int The number of unknowns. (R tdoc_yields_txtRnRR(Rt((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_sphinx_yields_strs  s Returns array of indices of the maximum values of along the given axis. Parameters ---------- a : {array_like} Array to look in. axis : {None, integer} If None, the index is into the flattened array, otherwise along the specified axiscC`s ttddkstdS(NRi(R$tdoc2R(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt,test_parameters_without_extended_descriptionssB my_signature(*params, **kwds) Return this and that. cC`s]ttjdd}|dks+td}td|}|ddksYtdS(Ns is my_signature(\*params, \*\*kwds)c[`sdS(N((RR tkwargs((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pytmy_funcstfuncRsmy_func(a, b, \*\*kwargs)(RRtdoc3RfRR(t signatureR{tfdoc((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_escape_starss  sJa.conj() Return an array with all complex-valued elements conjugated.cC`stdgkstdS(NsExtended Summary(tdoc4R(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_empty_extended_summaryss a.something() Raises ------ LinAlgException If array is singular. Warns ----- SomeWarning If needed cC`spttddksttdd}|jdks?t|jdksTt|jdgksltdS(NtRaisesiiRtLinAlgExceptionsIf array is singular.(R$tdoc5RtnamettypeR,(tparam((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt test_raisess cC`spttddksttdd}|jdks?t|jdksTt|jdgksltdS(NtWarnsiiRt SomeWarnings If needed(R$RRRRR,(R((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt test_warnss cC`s6td}t|ddks(tx|dD]\}}x|D]\}}|d"kr| stt||gnK|d#kr|stt||gn|stt||g|d kr|dkstn|d ks|dkr|dkstnf|dkr:|dkstnE|d$kr[|dkstn$|dkstt||g|dkr|dgks*tqF|dkr|ddgks*tqF|dkr|dd gks*tqF|d%krF|d!gks*tt|d!ggqFqFWq3WdS(&Ns z(x,theta) See Also -------- func_a, func_b, func_c func_d : some equivalent func foo.func_e : some other func over multiple lines func_f, func_g, :meth:`func_h`, func_j, func_k func_f1, func_g1, :meth:`func_h1`, func_j1 func_f2, func_g2, :meth:`func_h2`, func_j2 : description of multiple :obj:`baz.obj_q` :obj:`~baz.obj_r` :class:`class_j`: fubar foobar sSee Alsoi tfunc_atfunc_btfunc_ctfunc_ftfunc_gtfunc_htfunc_jtfunc_ks baz.obj_qtfunc_f1tfunc_g1tfunc_h1tfunc_j1s ~baz.obj_rtfunc_f2tfunc_g2tfunc_h2tfunc_j2tmethtobjtclass_jtclasstfunc_dssome equivalent funcs foo.func_essome other func oversmultiple linestfubartfoobarsdescription of multiple(RRRRRRRRs baz.obj_qRRRRs ~baz.obj_r(RRRR(RR(RRRR(RR$RRRtNone(tdoc6tfuncsR,R|trole((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt test_see_alsos<  " !   $    cC`sVd}tt}t|WdQXtd}t|j}||ksRtdS(Ns@ z(x,theta) See Also -------- :func:`~foo` sk:func:`~foo` is not a item name in '\n z(x,theta)\n\n See Also\n --------\n :func:`~foo`\n '(RERRRRtvalueR(ttextterrts1ts2((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_see_also_parse_error=s  cC`sqdtfdY}|}tt|dd}d|ksItd|ks[td|ksmtdS(NRIcB`seZdZRS(s See Also -------- func_a, func_b func_c : some relationship goes here func_d (RMRNRO(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyRIOsRR|s:func:`func_a`, :func:`func_b`s some relationships:func:`func_d`(RQRRRR(RIRR((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_see_also_printNs  cC`s6tjdttddtd}WdQXdS(NterrortmatchsEUnexpected comma or period after function list at index 43 of line .*s z(x,theta) See Also -------- func_f2, func_g2, :meth:`func_h2`, func_j2, : description of multiple :class:`class_j`: fubar foobar (twarningstfilterwarningst assert_warnstWarningR(R((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt$test_see_also_trailing_comma_warningas  c C`sd}dtfdY}tjdt[}tjddtt|t|dksftdt |d j kstWdQXtjdtt}tjddtt |t|dkstd t |d j ks d t |d j ks tWdQXdS( NsS Test having an unknown section Mope ---- This should be ignored and warned about t BadSectioncB`seZdZRS(sbClass with bad section. Nope ---- This class has a nope section. (RMRNRO(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyRystrecordtalwaysRisUnknown section Mopeis7test_docscrape.test_unknown_section..BadSectionstest_docscrape.BadSection( RQRtcatch_warningstTrueRt UserWarningRR$RRRtmessageR (RGRtw((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_unknown_sectionps  % s. Doc starts on second line. cC`s!tddjdstdS(NRis Doc starts(tdoc7RR(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_empty_first_linescC`sttddS(Ns Parameters ----------(RRR (((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_no_summaryscC`sGtd}t|ddts)t|dddksCtdS(Ns öäöäöäöäöåååå öäöäöäööäååå Parameters ---------- ååå : äää ööö Returns ------- ååå : ööö äää Risöäöäöäöäöåååå(R t isinstanceRRR(R((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt test_unicodes cC`stdt}tdd|}dt|ksEtt|tdd|}dt|ks{tt|tdd|}t|jddkstt|dS(Nt use_plotss| Examples -------- >>> import matplotlib.pyplot as plt >>> plt.plot([1,2,3],[4,5,6]) >>> plt.show() tconfigsplot::s Examples -------- >>> from matplotlib import pyplot as plt >>> plt.plot([1,2,3],[4,5,6]) >>> plt.show() s Examples -------- .. plot:: import matplotlib.pyplot as plt plt.plot([1,2,3],[4,5,6]) plt.show() i(tdictRR RRRtcount(tcfgR((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_plot_exampless$$cC`s8tdt}tdd|}tt|ddS(Ntuse_blockquotess Parameters ---------- abc : def ghi jkl mno Returns ------- ABC : DEF GHI JKL MNO Rs :Parameters: **abc** : def ghi **jkl** mno :Returns: **ABC** : DEF GHI JKL MNO (RRR RnRR(RR((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_use_blockquotess  c C`sdtfdY}xttfD] }||dtdt}dt|ksnt|t|fdt|kst|t|fdt|kst|t|fdt|kst|t|fd t|kst|t|f||dtdt}dt|ks[t|t|fdt|kst|t|fdt|kst|t|fdt|kst|t|f|tkr d t|ks0tt|q#d t|ks#tt|q#Wd |fd Y}xttfD]}||dtdtd t}dt|kst|t|fdt|kst|t|fdt|kst|t|fdt|ks&t|t|fdt|ksPt|t|f|tkrd t|kstt|n$d t|kstt|||dtdtd t}dt|kst|t|fdt|kst|t|fdt|ksFt|t|fdt|kspt|t|fdt|kst|t|f|tkrd t|kstt|qWd t|ksWtt|qWWdS(NRIcB`sEeZdZdZdZedZdefdYZRS(s Dummy class. cS`sdS(sSpam Spam spam.N((RJRR ((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyR.scS`sdS(sCheese No cheese.N((RJRBRK((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyRL scS`sdS(sSpammity indexgffffff?((RJ((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pytspammity st IgnorablecB`seZdZRS(slocal class, to be ignored(RMRNRO(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyRs( RMRNROR.RLtpropertyRRQR(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyRIs   Rtshow_class_memberstMethodsR.RLRsSpammity indexs.. autosummary::tSubDummycB`s eZdZdZdZRS(s+ Subclass of Dummy class. cS`sdS(s'Cheese No cheese. Overloaded Dummy.hamN((RJRBRK((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyRL.scS`sdS(s Bar No barN((RJRR ((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pytbar2s(RMRNRORLR(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyR)s tshow_inherited_class_membersR(RQRR RtFalseRRRR(RItclsRR((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_class_memberssL********* '(  ***** '$ ***** 'cC`s,td}|djdks(tdS(Ns$ z(x1, x2) z(a, theta) Rs z(a, theta)(RR6R(R((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_duplicate_signatureRs sV Foo Parameters ---------- f : callable ``f(t, y, *f_args)`` Aaa. jac : callable ``jac(t, y, *jac_args)`` Bbb. Attributes ---------- t : float Current time. y : ndarray Current variable values. * hello * world an_attribute : float The docstring is printed instead no_docstring : str But a description no_docstring2 : str multiline_sentence midword_period no_period Methods ------- a b c Examples -------- For usage examples, see `ode`. cC`s&tdt}tt|ddS(NsZ Foo Parameters ---------- f : callable ``f(t, y, *f_args)`` Aaa. jac : callable ``jac(t, y, *jac_args)`` Bbb. Examples -------- For usage examples, see `ode`. Attributes ---------- t : float Current time. y : ndarray Current variable values. * hello * world an_attribute : float The docstring is printed instead no_docstring : str But a description no_docstring2 : str multiline_sentence midword_period no_period Methods ------- a b c (RRt class_doc_txtRnRR(R((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_class_members_docs 'cC`s9dddY}t|t}tt|ddS(NtFoocB`sbeZedZedZedZedZedZedZRS(cS`sdS(sTest attributeN(R(RJ((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt an_attributescS`sdS(N(R(RJ((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt no_docstringscS`sdS(N(R(RJ((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt no_docstring2scS`sdS(s8This is a sentence. It spans multiple lines.N(R(RJ((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pytmultiline_sentencescS`sdS(sThe sentence for numpy.org.N(R(RJ((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pytmidword_periodscS`sdS(sThis does not have a period so we truncate its summary to the first linebreak Apparently. N(R(RJ((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt no_periods( RMRNRRRRRRR(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyRs s Foo :Parameters: **f** : callable ``f(t, y, *f_args)`` Aaa. **jac** : callable ``jac(t, y, *jac_args)`` Bbb. .. rubric:: Examples For usage examples, see `ode`. :Attributes: **t** : float Current time. **y** : ndarray Current variable values. * hello * world :obj:`an_attribute ` : float Test attribute **no_docstring** : str But a description **no_docstring2** : str .. :obj:`multiline_sentence ` This is a sentence. :obj:`midword_period ` The sentence for numpy.org. :obj:`no_period ` This does not have a period .. rubric:: Methods ===== ========== **a** **b** **c** ===== ========== ((R RRnRR(RR((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_class_members_doc_sphinxs" 5cC`sdddY}d}|tt|ks7tdtt|ksUtd}tdt}|tt|d|kstdtt|d|kstdS( NRcB`seZdZedZRS(s Class docstring. Attributes ---------- an_attribute Another description that is not used. cS`sdS(sTest attributeN(R(RJ((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyRs(RMRNRORR(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyRs sK:Attributes: :obj:`an_attribute ` Test attributesAnother descriptionsF.. rubric:: Attributes .. autosummary:: :toctree: an_attributetattributes_as_param_listR((RRR RRR(Rtattr_doct attr_doc2R((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt$test_class_attributes_as_member_lists$cC`s<tdtditjdd6}tt|ddS(NRs{{examples}} {{parameters}}ttemplates .. rubric:: Examples For usage examples, see `ode`. :Parameters: **f** : callable ``f(t, y, *f_args)`` Aaa. **jac** : callable ``jac(t, y, *jac_args)`` Bbb. (R RRtjinja2tTemplateRnRR(R((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_templated_sections7s  c`sWdtfdYddfdY}t|}dt|ksStdS(NtSpecialPropertycB`s)eZdddZdZdZRS(iRcS`s||_||_dS(N(taxisRO(RJRR((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt__init__Ps cS`s%|dkr|S|jj|jSdS(N(Rt_datataxesR(RJRR((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt__get__Ts cS`s|j|j|dS(N(t _set_axisR(RJRR((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt__set__[s(RMRNRRR(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyRNs RIc`seZddZRS(Rstest attribute(RMRNtattr((R(s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyRI^sstest attribute((RQR RRR(RIR((Rs<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_nonstandard_propertyKs cC`s2t}tdd|}tt|ddS(Ns Parameters ---------- param1 : int First parameter *args : tuple Arguments **kwargs : dict Keyword arguments Rs :Parameters: **param1** : int First parameter **\*args** : tuple Arguments **\*\*kwargs** : dict Keyword arguments (RR RnRR(RR((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_args_and_kwargsfs    cC`sDtdtdt}ttdd|}tt|dddS(NRRs. A top section before .. autoclass:: str RsE A top section before .. autoclass:: str .. rubric:: Methods i(RRR RRRn(RR((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyttest_autoclasss    se Test xref in Parameters, Other Parameters and Returns Parameters ---------- p1 : int Integer value p2 : float, optional Integer value Other Parameters ---------------- p3 : list[int] List of integers p4 : :class:`pandas.DataFrame` A dataframe p5 : sequence of `int` A sequence Returns ------- out : array Numerical return value s Test xref in Parameters, Other Parameters and Returns :Parameters: **p1** : :class:`python:int` Integer value **p2** : :class:`python:float`, optional Integer value :Returns: **out** : :obj:`array ` Numerical return value :Other Parameters: **p3** : :class:`python:list`\[:class:`python:int`] List of integers **p4** : :class:`pandas.DataFrame` A dataframe **p5** : :obj:`python:sequence` of `int` A sequence c C`sidd6}tdd|}tdd|}t|dddh}ttdtdtd |d |}tt|tdS( Ns:obj:`python:sequence`tsequencetnumpydoc_xref_aliasesRtofR]toptionaltxref_param_typet xref_aliasest xref_ignore( RRR t xref_doc_txtRRRnRRtxref_doc_txt_expected(RRtappRR((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyt test_xrefs   t__main__(^t __future__RRRt collectionsRRatsysRdRRtnumpydoc.numpydocRtnumpydoc.docscrapeRRRRtnumpydoc.docscrape_sphinxR R R R tpytestR RERRt version_infotsixuRsRRvR:t doc_sent_txtRCRRRR-R0R7R@RDRHRTRWRYR[R`RcRRnRoRpRqRrRuRwRxRyR}RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRMtmain(((s<lib/python2.7/site-packages/numpydoc/tests/test_docscrape.pyts     ""  n           @      r    x           7    !     ! 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