B d8@s\dZddlZddlmZddlmZddlmZddlm Z ddl m Z Gdd d e Z dS) z[Chisq based on sine-gaussian tiles. See https://arxiv.org/abs/1709.08974 for a discussion. N)apply_fseries_time_shift)sigma) sinegauss)SingleDetPowerChisq)rankingc@sDeZdZdZdejiZd ddZeddZ e d d Z d d Z dS)SingleDetSGChisqzpClass that handles precomputation and memory management for efficiently running the sine-Gaussian chisq Zsg_chisqrNc Cs|dk rd|_||_||_i|_xf|D]V}|d\}}|jd|fdgd}|jd|t} x| D]} ||j| <qjWq&Wnd|_dS) a Create sine-Gaussian Chisq Calculator Parameters ---------- bank: pycbc.waveform.TemplateBank The template bank that will be processed. num_bins: str The string determining the number of power chisq bins snr_threshold: float The threshold to calculate the sine-Gaussian chisq chisq_locations: list of strs List of strings which detail where to place a sine-Gaussian. The format is 'region-boolean:q1-offset1,q2-offset2'. The offset is relative to the end frequency of the approximant. The region is a boolean expression such as 'mtotal>40' indicating which templates to apply this set of sine-Gaussians to. NT:)relser template_hashF) donum_bins snr_thresholdparamssplittableZparse_boolargsastypebool) selfbankr rZchisq_locationsdescrregionvaluesmaskhasheshra/work/yifan.wang/ringdown/master-ringdown-env/lib/python3.7/site-packages/pycbc/vetoes/sgchisq.py__init__s  zSingleDetSGChisq.__init__cCs0|d}|jdtdd|jdtddddS) NzSine-Gaussian Chisqz--sgchisq-snr-thresholdz%Minimum SNR threshold to use SG chisq)typehelpz--sgchisq-locations+aFrequency offsets and quality factors of the sine-Gaussians to use, format 'region-boolean:q1-offset1,q2-offset2'. Offset is relative to the end frequency of the approximant. Region is a boolean expression selecting templates to apply the sine-Gaussians to, ex. 'mtotal>40')rnargsr )add_argument_group add_argumentfloatstr)parsergrouprrrinsert_option_group5s   z$SingleDetSGChisq.insert_option_groupcCs||||j|jS)N)Zsgchisq_snr_thresholdZsgchisq_locations)clsargsrZ chisq_binsrrrfrom_cliAszSingleDetSGChisq.from_clic $ Csl|js dS|jj|jkr&tt|S|j|jjd} |||} tt|} xt|D]\} } t | |}t ||| || }||j krq`t|dd}d||j }t|j|j }t|j||| }t|| }d}d| | <| d| d }| d||j }t||j d }d}x| D]}|d \}}t|t|}}||}t||j ||}||}||krtt|St||j }t||j } td|||t||j |j tj}!t|!|||d }"|!|| ||| }#|#d |!j |"9}#| | t |#d7<|d7}q6W|dkrTd| | <q`| | |<q`W| S)a] Calculate sine-Gaussian chisq Parameters ---------- stilde: pycbc.types.Frequencyseries The overwhitened strain template: pycbc.types.Frequencyseries The waveform template being analyzed psd: pycbc.types.Frequencyseries The power spectral density of the data snrv: numpy.ndarray The peak unnormalized complex SNR values snr_norm: float The normalization factor for the snr bchisq: numpy.ndarray The Bruce Allen power chisq values for these triggers bchisq_dof: numpy.ndarray The degrees of freedom of the Bruce chisq indics: numpy.ndarray The indices of the snr peaks. Returns ------- chisq: Array Chisq values, one for each sample index N,r g?2rg?-)psdZlow_frequency_cutoffZhigh_frequency_cutoffg@g@)r rr numpyZoneslenrZcached_chisq_bins enumerateabsrZnewsnrrZdelta_fintZf_lowerr%epochrmaxrZfd_sine_gaussianrZ complex64rsum)$rZstildetemplater3ZsnrvZsnr_normZbchisqZ bchisq_dofindicesrZbinsZchisqiZsnrviZsnrZnsnrNdtZkmintimeZ stilde_shiftZqwindowZfstepZfpeakZfstopZdofrqoffsetZfcenZflowZfhighZkmaxZgtemZgsigmaZgsnrrrrrGs^         zSingleDetSGChisq.values)rNN) __name__ __module__ __qualname____doc__r4Zfloat32Zreturnsr staticmethodr) classmethodr,rrrrrr s  r) rGr4Zpycbc.waveform.utilsrZ pycbc.filterrZpycbc.waveformrZpycbc.vetoes.chisqrZ pycbc.eventsrrrrrrs