B d0@sfddlmZddlmZddlmZddlZddlZdZdd d Z Gd d d e Z Gd dde Z dS))make_frequency_series)matched_filter_core)ArrayNzpycbc.vetoes.autochisq_TFc !Csht|} tt|} t| |} |dkr.| }|| kr:| }t||} ||j| } ||j| }| |}||d}|dkrt|d|}n@|dkrt|||}n(t|d|}t|||}t||}||}|j|j}||j|j7}d|}x8t |D]*\}}||}| |}||}||j|||j|}|ddkrj||dk| 7<|d| dkr||| dk| 8<||j|||j|}||j|}|||}|rd|}||j |||j|}||j|}||||7}|r(| | |<n | | |<q W|} |dkrP| d} |r^| d} | | |fS) a  Compute correlation (two sided) between template and data and compares with autocorrelation of the template: C(t) = IFFT(A*A/S(f)) Parameters ---------- sn: Array[complex] normalized (!) array of complex snr for the template that produced the trigger(s) being tested corr_sn : Array[complex] normalized (!) array of complex snr for the template that you want to produce a correlation chisq test for. In the [common] case that sn and corr_sn are the same, you are computing auto-correlation chisq. hautocorr: Array[complex] time domain autocorrelation for the template indices: Array[int] compute correlation chisquare at the points specified in this array, num_points: [int, optional; default=None] Number of points used for autochisq on each side, if None all points are used. stride: [int, optional; default = 1] stride for points selection for autochisq total length <= 2*num_points*stride oneside: [str, optional; default=None] whether to use one or two sided autochisquare. If None (or not provided) twosided chi-squared will be used. If given, options are 'left' or 'right', to do one-sided chi-squared on the left or right. twophase: Boolean, optional; default=True If True calculate the auto-chisq using both phases of the filter. If False only use the phase of the obtained trigger(s). maxvalued: Boolean, optional; default=False Return the largest auto-chisq at any of the points tested if True. If False, return the sum of auto-chisq at all points tested. Returns ------- autochisq: [tuple] returns autochisq values and snr corresponding to the instances of time defined by indices Nrleftrrightg?) lennpZzerosintabsrealimagZarangeappend enumeratemaxsum)!snZcorr_snZ hautocorrindicesstride num_pointsonesidetwophase maxvaluedZNsnrZachisqZnum_points_allZsnrabsZ cphi_arrayZ sphi_arrayZ start_pointZ end_pointZachisq_idx_listZachisq_idx_list_pt1Zachisq_idx_list_pt2Zhauto_corr_vecZ hauto_normZ chisq_normipindZcurr_achisq_idx_listZcphiZsphiZsnr_indzZdzZcurr_achisq_listdofrc/work/yifan.wang/ringdown/master-ringdown-env/lib/python3.7/site-packages/pycbc/vetoes/autochisq.pyautochisq_from_precomputedsh+       r!c@s$eZdZdZdddZd ddZdS) SingleDetAutoChisqzClass that handles precomputation and memory management for efficiently running the auto chisq in a single detector inspiral analysis. NFc Cs|dkrd|_d|_d|_||_||_||_||_|dk rF|jd|_||_|jr^|jd|_||_||_ |j r|dkrd}|d7}|d 7}t |||_d|_ d|_ nd |_dS) a Initialize autochisq calculation instance Parameters ----------- stride : int Number of sample points between points at which auto-chisq is calculated. num_points : int Number of sample points at which to calculate auto-chisq in each direction from the trigger onesided : optional, default=None, choices=['left','right'] If None (default), calculate auto-chisq in both directions from the trigger. If left (backwards in time) or right (forwards in time) calculate auto-chisq only in that direction. twophase : optional, default=False If False calculate auto-chisq using only the phase of the trigger. If True, compare also against the orthogonal phase. reverse_template : optional, default=False If true, time-reverse the template before calculating auto-chisq. In this case this is more of a cross-correlation chisq than auto. take_maximum_value : optional, default=False If provided, instead of adding the auto-chisq value at each sample point tested, return only the maximum value. maximal_value_dof : int, required if using take_maximum_value If using take_maximum_value the expected value is not known. This value specifies what to store in the cont_chisq_dof output. rTZ cont_chisqZcont_chisq_dofNr z+Must provide the maximal_value_dof keyword z)argument if using the take_maximum_value zoption.F) doZ column_nameZtable_dof_namerrr one_sided two_phasereverse_templatetake_maximum_value ValueError_autocor _autocor_id) selfrrZonesidedrr&r'Zmaximal_value_doferr_msgrrr __init__s2  zSingleDetAutoChisq.__init__c  Csr|jrnt|dkrnt|} t|t|f} | |jkrtd|jst| | |||d\} } } | d| d} t | dd|_ nHt| | |||d\} } } | t | |d}| |9} t | dd|_ | |_td||}|jr |d k stt| |||| |d \}} }||}n|}tg}t|}t|||j ||j|j|j|j|jd \}}} ||_|Sd S) a Calculate the auto-chisq at the specified indices. Parameters ----------- sn : Array[complex] SNR time series of the template for which auto-chisq is being computed. Provided unnormalized. indices : Array[int] List of points at which to calculate auto-chisq template : Pycbc template object The template for which we are calculating auto-chisq psd : Pycbc psd object The PSD of the data being analysed norm : float The normalization factor to apply to sn stilde : Pycbc data object, needed if using reverse-template The data being analysed. Only needed if using reverse-template, otherwise ignored low_frequency_cutoff : float The lower frequency to consider in matched-filters high_frequency_cutoff : float The upper frequency to consider in matched-filters rzCalculating autocorrelation)psdlow_frequency_cutoffhigh_frequency_cutoffg?T)copyg?z...Calculating autochisquareN)r/r0Zh_norm)rrrrr)r#r ridr*logginginfor&rrr)ZconjfloatZsigmasqAssertionErrorr arrayr!rrr$r%r'r)r+rrtemplater.ZnormZstilder/r0ZhtildekeyZPt_ZP_normZnorm_facZasnZahnrmZcorrelation_snrZ achi_listZ index_listrrrr valuessN         zSingleDetAutoChisq.values)NFFFN)NNN)__name__ __module__ __qualname____doc__r-r;rrrr r"s  9r"cs(eZdZdZfddZddZZS)SingleDetSkyMaxAutoChisqzGStub for precessing auto chisq if anyone ever wants to code it up. cstt|j||dS)N)superr@r-)r+argskwds) __class__rr r-sz!SingleDetSkyMaxAutoChisq.__init__cOs,|jr$d}|d7}|d7}t|ndSdS)Nz6Precessing single detector sky-max auto chisq has not z2been written. 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