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j ƒkr1d t | j ƒks„t ‚nJ| j| | } tdt ƒ| d<| j| | }t | |ddƒdS(Ngš™™™™™é?g{®Gáz„?i(R-R.RPR,R1RRiiRQR3R4R!gü©ñÒMb€?(R R6R%RR9RSRTRUR:RVRWR=R<R(R,R-R.R/RPRRRRR@RBRARXtP_fasttP_slow((sOlib/python2.7/site-packages/astropy/stats/lombscargle/tests/test_lombscargle.pyttest_fast_approximations®s& 3tshapecCs€|\}}}tjtj|ƒƒ}tjd|jdƒ|_t||dtƒj|d|ƒ}|j |ks|t ‚dS(NiR.R,( R tasarraytzerosR6tsizetflatRR<R:R]R=(R,R]RRRRR(tPLS((sOlib/python2.7/site-packages/astropy/stats/lombscargle/tests/test_lombscargle.pyttest_output_shapesÒs $c Csö|\}}}|tj}|tj}tjdddƒ}tjtƒ)}t||dt ƒj |d|ƒWdQXt |j ƒj dƒs–t‚tjtƒ-}t|||dt ƒj ||jƒWdQXt |j ƒj dƒsòt‚dS(Ngà?gø?i R.R,s!Units of frequency not equivalentsUnits of dy not equivalent(RtsecondR8R tlinspaceRSRTRURR<R:RVRWt startswithR=R5(R,RRRRR@RX((sOlib/python2.7/site-packages/astropy/stats/lombscargle/tests/test_lombscargle.pyttest_errors_on_unit_mismatchÜs  (,t with_errorcCsX|\}}}|tj}tj|dƒ} |tj} tj| dƒ} t|| ƒt| | ƒ|r}|tj}nd}t|| |ƒjƒ\} } t| | |ƒjƒ\}}| jdtjksÛt ‚|jdtj ks÷t ‚t| |ƒt| |ƒt|| |ƒj |ƒ}t| | |ƒj | ƒ}t||ƒdS(Nthourt millimetergð?( RR7tQuantitytmeterRR%Rt autopowerR5R=RiR:(RR.R-R1RhRRRtt_daytt_hourty_metert y_millimetertfreq_daytP1t freq_hourtP2tP3tP4((sOlib/python2.7/site-packages/astropy/stats/lombscargle/tests/test_lombscargle.pyttest_unit_conversionsñs&      R(g@c Csþtjjdƒ}d|jdƒ}d|jdƒ}tj|ƒ}|r^||d7}n||dtjdtj|||dƒ7}|rÀ|tj}|tj }|tj}nt ||dt d|ƒ}|j ||ƒ}t ||ƒdS( Nii i(iiiR-R.(R R R Rt zeros_likeRRRR7R8RR<R R( R.R0R(RRtparamsRRBty_fit((sOlib/python2.7/site-packages/astropy/stats/lombscargle/tests/test_lombscargle.pyt test_models2  tt_unittfrequency_unitty_unitc CsŽ|\}}}|d }d}||}||}||}||}||}t|||ƒ} | j||ƒ} | j|ksŠt‚dS(Nigð?(RR R5R=( RR}R~RRRRtt_fitR@RBR{((sOlib/python2.7/site-packages/astropy/stats/lombscargle/tests/test_lombscargle.pyttest_model_units_match0s      c CsP|\}}}d}|d }|tj}|tj}|tj}d|j}tjtƒ#}t||ƒj|ddƒWdQXt |j ƒj dƒs¥t ‚tjtƒ&}t||ƒjddg|ƒWdQXt |j ƒj dƒsút ‚tjtƒ#}t|||ƒj||ƒWdQXt |j ƒj dƒsLt ‚dS( Ngð?iR@s!Units of frequency not equivalentiisUnits of t not equivalentsUnits of dy not equivalent( RRdR8R5RSRTRURR RVRWRfR=(RRRRR@R€RX((sOlib/python2.7/site-packages/astropy/stats/lombscargle/tests/test_lombscargle.pyttest_model_units_mismatchCs      "%"c Cs“|\}}}t|||ƒ}tddddddddƒ}|j|}|j|ƒ}|j|\}} t||ƒt|| ƒdS(NR iRiRR(RR9R%R$R:RmR( RRRRRBtkwargstfreq1tpower1tfreq2tpower2((sOlib/python2.7/site-packages/astropy/stats/lombscargle/tests/test_lombscargle.pyttest_autopower]s iècCs`tjj|ƒ}d|j|ƒ}d|d|j|ƒ}||j|ƒ}|||fS(sGenerate null hypothesis dataidgà?i(R R R RR(RRRRRR((sOlib/python2.7/site-packages/astropy/stats/lombscargle/tests/test_lombscargle.pyt null_datajs c CsH|\}}}t|ƒ}t|||ƒ}|jd|ddƒ\}}tjd|jƒdƒ} | d| d} | d d| } t| |d|ƒ} t| |d|ƒ} t| tj | ƒ| d d d d ƒtj |d dt ƒ\}}d|d|d }t||d|ƒ} t|| d dd d| dƒdS(NR1Ri(iièiiÿÿÿÿgà?RRgñh㈵øä>R!g:Œ0âŽyE>itnormedgš™™™™™©?( R&RRmR ReR"t_lombscargle_pdft_lombscargle_cdfRtdifft histogramtTrue(R‰R1RRRRRBR(R:tztdztz_midtpdftcdfthisttbinst midpoints((sOlib/python2.7/site-packages/astropy/stats/lombscargle/tests/test_lombscargle.pyttest_distributionts &cCsÕ||dkrtdƒ‚n||}|dkrCtj| ƒS|dkrkd|d|d|dS|dkr“d|d|d|dS|d kr¼d|tjd||ƒStd j|ƒƒ‚d S( sâProbability density function for Lomb-Scargle periodogram Compute the expected probability density function of the periodogram for the null hypothesis - i.e. data consisting of Gaussian noise. Parameters ---------- z : array-like the periodogram value N : int the number of data points from which the periodogram was computed normalization : string The periodogram normalization. Must be one of ['standard', 'model', 'log', 'psd'] dH, dK : integers (optional) The number of parameters in the null hypothesis and the model Returns ------- pdf : np.ndarray The expected probability density function Notes ----- For normalization='psd', the distribution can only be computed for periodograms constructed with errors specified. All expressions used here are adapted from Table 1 of Baluev 2008 [1]_. References ---------- .. [1] Baluev, R.V. MNRAS 385, 1279 (2008) isDegrees of freedom != 2RRgà?igà¿R R s%normalization='{0}' is not recognizedN(tNotImplementedErrorR texpRUtformat(RRR1tdHtdKtNk((sOlib/python2.7/site-packages/astropy/stats/lombscargle/tests/test_lombscargle.pyR‹s!      cCsÅ||dkrtdƒ‚n||}|dkrGdtj| ƒS|dkrgdd|d|S|dkr‡dd|d|S|d kr¬dtjd||ƒStd j|ƒƒ‚d S( sàCumulative distribution for the Lomb-Scargle periodogram Compute the expected cumulative distribution of the periodogram for the null hypothesis - i.e. data consisting of Gaussian noise. Parameters ---------- z : array-like the periodogram value N : int the number of data points from which the periodogram was computed normalization : string The periodogram normalization. Must be one of ['standard', 'model', 'log', 'psd'] dH, dK : integers (optional) The number of parameters in the null hypothesis and the model Returns ------- cdf : np.ndarray The expected cumulative distribution function Notes ----- For normalization='psd', the distribution can only be computed for periodograms constructed with errors specified. All expressions used here are adapted from Table 1 of Baluev 2008 [1]_. References ---------- .. [1] Baluev, R.V. MNRAS 385, 1279 (2008) isDegrees of freedom != 2RiRgà¿R gà?R s%normalization='{0}' is not recognizedN(R™R RšRUR›(RRR1RœRRž((sOlib/python2.7/site-packages/astropy/stats/lombscargle/tests/test_lombscargle.pyRŒÂs!      ((i(i(i(ii(-RStnumpyR t numpy.testingRtRt tests.helperRRtavailable_methodst ALL_METHODSR,tALL_METHODS_NO_AUTOR;tNTERMS_METHODStNORMALIZATIONStfixtureRtmarkt parametrizeR%R+RR<RERORYR\RcRgRxR|RdR7tHzR8tjanskyRR‚RˆR‰R˜R‹RŒ(((sOlib/python2.7/site-packages/astropy/stats/lombscargle/tests/test_lombscargle.pytst   %%%$ --$*#-3 0 !-%*"0   2