B dY-@sldZddlZddlmZddlmZddfdd Zd d Zd d ZdddZ ddZ dddZ ddZ dS)zC This module contains utilities for calculating search sensitivity N)chirp_distance) bin_utilscCs|jfS)N)distance)simr^/work/yifan.wang/ringdown/master-ringdown-env/lib/python3.7/site-packages/pycbc/sensitivity.py r cst|fdd|Dfdd|Dtjt|d}t|jd|jj j}tjt||d}||fS)z Calculate search efficiency in the given ndbins. The first dimension of ndbins must be bins over injected distance. sim_to_bins_function must map an object to a tuple indexing the ndbins. csg|]}|qSr)Z incnumerator).0r)binssim_to_bins_functionrr sz5compute_search_efficiency_in_bins..csg|]}|qSr)Zincdenominator)r r)r r rrrs)arrayr) rZ BinnedRatiosZ regularize BinnedArrayNDBinsrationumpysqrtr denominator)foundtotalndbinsr effZerr_arrerrr)r r r!compute_search_efficiency_in_binss rc Cst||||\}}|d|d}|d}tt|dd}tt|dd} t|j j dtj |d|||_ t dtj |d|j j |dj dd| _ || fS) a/ Calculate search sensitive volume by integrating efficiency in distance bins No cosmological corrections are applied: flat space is assumed. The first dimension of ndbins must be bins over injected distance. sim_to_bins_function must maps an object to a tuple indexing the ndbins. rrNg@)Zaxis)rupperlowerZcentresrrrrZtrapzrTpirsum) rrrr rrZdxrvolerrorsrrrcompute_search_volume_in_bins$s &.r'cCsn|ddtjd}||ddtjd|}t||k||d}||ddtjd}|||fS)z Return the distance and standard deviation upper and lower bounds Parameters ---------- vol: float vol_err: float Returns ------- dist: float ehigh: float elow: float g@g@gUUUUUU?r)rr"where)r%vol_errdistZehighdeltaZelowrrrvolume_to_distance_with_errors@s r,c CsNddddd|} ddddd|} |d krtd } t||f} | } |d k r`|| | d}qt||}n6|d kr|d k r|}qt||}n td |dtj|d}|d kr|| }|| }n8|d kr|| || }|| || }n td|t||f}t|d|f}t|}|d krJt|}n|d krht|| | d}||}|d krt|}n|d kr|dkr|d k r|t| | d}ntt|t|}t||}t|dtt| | |}nt| | | }t|d|||d}||}|||d}||fS)a Compute sensitive volume and standard error via direct Monte Carlo integral Injections should be made over a range of distances such that sensitive volume due to signals closer than D_min is negligible, and efficiency at distances above D_max is negligible TODO : Replace this function by Collin's formula given in Usman et al. ? OR get that coded as a new function? Parameters ----------- found_d: numpy.ndarray The distances of found injections missed_d: numpy.ndarray The distances of missed injections found_mchirp: numpy.ndarray Chirp mass of found injections missed_mchirp: numpy.ndarray Chirp mass of missed injections distribution_param: string Parameter D of the injections used to generate a distribution over distance, may be 'distance', 'chirp_distance'. distribution: string form of the distribution over the parameter, may be 'log' (uniform in log D) 'uniform' (uniform in D) 'distancesquared' (uniform in D**2) 'volume' (uniform in D**3) limits_param: string Parameter Dlim specifying limits inside which injections were made may be 'distance', 'chirp distance' min_param: float minimum value of Dlim at which injections were made; only used for log distribution, then if None the minimum actually injected value will be used max_param: float maximum value of Dlim out to which injections were made; if None the maximum actually injected value will be used Returns -------- volume: float Volume estimate volume_error: float The standard error in the volume g@g@g?g)loguniformZdistancesquaredvolumeg?g?g@rg0f?Nrz %s is not a recognized parametergUUUUUU?z)%s is not a recognized distance parameterrr-g?) r concatenatemaxNotImplementedErrorr"r#lenminr-)found_dmissed_d found_mchirp missed_mchirpdistribution_param distribution limits_param min_param max_paramZd_powerZ mchirp_powerZmchirp_standard_bnsZ all_mchirpZ max_mchirpZ max_distanceZmontecarlo_vtotZ found_weightsZmissed_weightsZ all_weightsZmc_weight_samplesZmc_sumZmc_normZ mc_prefactorZNinjZ min_distanceZlograngeZmc_sample_variancer%r)rrrvolume_montecarloVsx2            r>c CsD|dks t|dkstt||} t||} t| | ||d|d|| S)Nrr)AssertionErrorrr>) r5r6r7r8r9r:r;r<r=Z found_dchirpZ missed_dchirprrrchirp_volume_montecarlos    r@cCsfdd}t||g}ttt|t||tdddg}t||||\}}|jd|jdfS)a| Compute the sensitive volume using a distance binned efficiency estimate Parameters ----------- f_dist: numpy.ndarray The distances of found injections m_dist: numpy.ndarray The distances of missed injections Returns -------- volume: float Volume estimate volume_error: float The standard error in the volume cSs|dfS)Nrr)rrrr sims_to_bin sz(volume_binned_pylal..sims_to_bingrr) rr0rrZ LinearBinsr4r1r'r)f_distm_distr rBrrr%Zverrrrrvolume_binned_pylals rEc Cs||t||g}|}||}d}d}d}xtt|D]}|t|dkr^P||d||d}||} ||t|kr|dtj||d| 7}|dtj||d| d7}|}qHW|d}||fS)aq Compute the sensitive volume using sum over spherical shells. Parameters ----------- f_dist: numpy.ndarray The distances of found injections m_dist: numpy.ndarray The distances of missed injections Returns -------- volume: float Volume estimate volume_error: float The standard error in the volume rrrrg@g?)sortrr0Zargsortranger3r") rCrDZ distancesZ dist_sortinglowr%r)ihighZ bin_widthrrr volume_shells&"rK)NN)rA) __doc__rZpycbc.conversionsrrrr'r,r>r@rErKrrrrs