def trapFit(time_days, flux_frac, period_days, phase_bkjd, duration_hrs, depth_ppm): trapFit = tf.trapezoid_fit(time_days, flux_frac, np.ones(len(time_days)), \ period_days, phase_bkjd, duration_hrs, \ depth_ppm, fitTrialN=13, fitRegion=10.0, \ errorScale=1.0, debugLevel=3, \ sampleN=15) #print trapFit return trapFit
def trapFit(time_days, flux_frac, period_days, phase_bkjd, duration_hrs, depth_ppm): trapFit = tf.trapezoid_fit( time_days, flux_frac, np.ones(len(time_days)), period_days, phase_bkjd, duration_hrs, depth_ppm, fitTrialN=13, fitRegion=10.0, errorScale=1.0, debugLevel=3, sampleN=15, ) # print trapFit return trapFit
def getSnrOfTransit(time_days, flux_frac, unc, flags, period_days, phase_bkjd, \ duration_hrs, depth_frac): """ Inputs: ------------ flux_frac (1d np array) Flux in fractional amplitude. The mean of this array should be zero for sane data. The trapezoid fit takes data with a mean of 1, the conversion is done within this function """ idx = np.isfinite(time_days) & (np.isfinite(flux_frac)) idx = idx & ~flags ioblk = tf.trapezoid_fit(time_days[idx], 1+flux_frac[idx], unc[idx], \ period_days, phase_bkjd, duration_hrs, \ 1e6*depth_frac, fitTrialN=13, fitRegion=10.0, \ errorScale=1.0, debugLevel=0, \ sampleN=15) #Taken from trapfit.py around lines 434 out = dict() out['period_days'] = period_days out['epoch_bkjd'] = ioblk.timezpt + ioblk.bestphysvals[0] out['duration_hrs'] = 24 * ioblk.bestphysvals[2] out['ingress_hrs'] = out['duration_hrs'] * ioblk.bestphysvals[3] out['depth_frac'] = ioblk.bestphysvals[1] #compute modelat all input time values subSampleN = 15 time_days[~np.isfinite(time_days)] = 0 #Hide the Nans from one_model assert (np.all(np.isfinite(time_days))) ioBlock = tf.trapezoid_model_onemodel(time_days, period_days, \ out['epoch_bkjd'], 1e6*out['depth_frac'], out['duration_hrs'], \ out['ingress_hrs'], subSampleN) out['bestFitModel'] = ioBlock.modellc - 1 #Want mean of zero out['snr'] = estimateSnr(time_days, flux_frac, flags, out['period_days'], \ out['epoch_bkjd'], out['duration_hrs'], out['depth_frac']) #out['bestFitModel'] = time_days*0 #out['snr'] = -1 return out
def getSnrOfTransit(time_days, flux_frac, unc, flags, period_days, phase_bkjd, \ duration_hrs, depth_frac): """ Inputs: ------------ flux_frac (1d np array) Flux in fractional amplitude. The mean of this array should be zero for sane data. The trapezoid fit takes data with a mean of 1, the conversion is done within this function """ idx = np.isfinite(time_days) & (np.isfinite(flux_frac)) idx = idx & ~flags ioblk = tf.trapezoid_fit(time_days[idx], 1+flux_frac[idx], unc[idx], \ period_days, phase_bkjd, duration_hrs, \ 1e6*depth_frac, fitTrialN=13, fitRegion=10.0, \ errorScale=1.0, debugLevel=0, \ sampleN=15) #Taken from trapfit.py around lines 434 out = dict() out['period_days'] = period_days out['epoch_bkjd'] = ioblk.timezpt + ioblk.bestphysvals[0] out['duration_hrs'] = 24* ioblk.bestphysvals[2] out['ingress_hrs'] = out['duration_hrs'] * ioblk.bestphysvals[3] out['depth_frac'] = ioblk.bestphysvals[1] #compute modelat all input time values subSampleN= 15 time_days[~np.isfinite(time_days)] = 0 #Hide the Nans from one_model assert(np.all(np.isfinite(time_days))) ioBlock = tf.trapezoid_model_onemodel(time_days, period_days, \ out['epoch_bkjd'], 1e6*out['depth_frac'], out['duration_hrs'], \ out['ingress_hrs'], subSampleN) out['bestFitModel'] = ioBlock.modellc - 1 #Want mean of zero out['snr'] = estimateSnr(time_days, flux_frac, flags, out['period_days'], \ out['epoch_bkjd'], out['duration_hrs'], out['depth_frac']) #out['bestFitModel'] = time_days*0 #out['snr'] = -1 return out