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f = lc.fold(best_period, best_t0)
inwindow = (f.time > minphase) & (f.time < maxphase)
nb = int(np.ceil(inwindow.sum()/10000))
f_source.data = {'phase': f[inwindow].time[::nb],
'flux': f[inwindow].flux[::nb]}
mf = model.model(lc.time, best_period, duration_slider.value, best_t0)
mf /= np.median(mf)
mask = ~(convolve(np.asarray(mf == np.median(mf)), Box1DKernel(2)) > 0.9)
model_lc = LightCurve(lc.time[mask], mf[mask])
model_lc_source.data = {'time': np.sort(model_lc.time),
'flux': model_lc.flux[np.argsort(model_lc.time)]}
f_model_lc = model_lc.fold(best_period, best_t0)
f_model_lc = LightCurve([-0.5], [1]).append(f_model_lc)
f_model_lc = f_model_lc.append(LightCurve([0.5], [1]))
f_model_lc_source.data = {'phase': f_model_lc.time,
'flux': f_model_lc.flux}
vertical_line.update(location=best_period)
fig_folded.title.text = 'Period: {} days \t T0: {}{}'.format(
np.round(best_period, 7),
np.round(best_t0, 7), time_format)
text_output.text = "Period: {} days, \t T0: {}{}".format(
np.round(best_period, 7),
np.round(best_t0, 7), time_format)
minpow, maxpow = bls_source.data['power'].min()*0.95, bls_source.data['power'].max()*1.05
fig_bls.y_range.start = minpow
fig_bls.y_range.end = maxpow
# Otherwise, we can just update the best_period index
minphase, maxphase = fig_folded.x_range.start, fig_folded.x_range.end
f = lc.fold(best_period, best_t0)
inwindow = (f.time > minphase) & (f.time < maxphase)
nb = int(np.ceil(inwindow.sum()/10000))
f_source.data = {'phase': f[inwindow].time[::nb],
'flux': f[inwindow].flux[::nb]}
mf = model.model(lc.time, best_period, duration_slider.value, best_t0)
mf /= np.median(mf)
mask = ~(convolve(np.asarray(mf == np.median(mf)), Box1DKernel(2)) > 0.9)
model_lc = LightCurve(lc.time[mask], mf[mask])
model_lc_source.data = {'time': np.sort(model_lc.time),
'flux': model_lc.flux[np.argsort(model_lc.time)]}
f_model_lc = model_lc.fold(best_period, best_t0)
f_model_lc = LightCurve([-0.5], [1]).append(f_model_lc)
f_model_lc = f_model_lc.append(LightCurve([0.5], [1]))
f_model_lc_source.data = {'phase': f_model_lc.time,
'flux': f_model_lc.flux}
vertical_line.update(location=best_period)
fig_folded.title.text = 'Period: {} days \t T0: {}{}'.format(
np.round(best_period, 7),
np.round(best_t0, 7), time_format)
text_output.text = "Period: {} days, \t T0: {}{}".format(
inwindow = (f.time > minphase) & (f.time < maxphase)
nb = int(np.ceil(inwindow.sum()/10000))
f_source.data = {'phase': f[inwindow].time[::nb],
'flux': f[inwindow].flux[::nb]}
mf = model.model(lc.time, best_period, duration_slider.value, best_t0)
mf /= np.median(mf)
mask = ~(convolve(np.asarray(mf == np.median(mf)), Box1DKernel(2)) > 0.9)
model_lc = LightCurve(lc.time[mask], mf[mask])
model_lc_source.data = {'time': np.sort(model_lc.time),
'flux': model_lc.flux[np.argsort(model_lc.time)]}
f_model_lc = model_lc.fold(best_period, best_t0)
f_model_lc = LightCurve([-0.5], [1]).append(f_model_lc)
f_model_lc = f_model_lc.append(LightCurve([0.5], [1]))
f_model_lc_source.data = {'phase': f_model_lc.time,
'flux': f_model_lc.flux}
vertical_line.update(location=best_period)
fig_folded.title.text = 'Period: {} days \t T0: {}{}'.format(
np.round(best_period, 7),
np.round(best_t0, 7), time_format)
text_output.text = "Period: {} days, \t T0: {}{}".format(
np.round(best_period, 7),
np.round(best_t0, 7), time_format)
half_button = Button(label="Half Period", button_type="danger", width=100)
text_output = Paragraph(text="Period: {} days, T0: {}{}".format(
np.round(best_period, 7),
np.round(best_t0, 7), time_format),
width=350, height=40)
# Set up BLS source
bls_source = prepare_bls_datasource(result, loc)
bls_help_source = prepare_bls_help_source(bls_source, npoints_slider.value)
# Set up the model LC
mf = model.model(lc.time, best_period, duration_slider.value, best_t0)
mf /= np.median(mf)
mask = ~(convolve(np.asarray(mf == np.median(mf)), Box1DKernel(2)) > 0.9)
model_lc = LightCurve(lc.time[mask], mf[mask])
model_lc = model_lc.append(LightCurve([(lc.time[0] - best_t0) + best_period/2], [1]))
model_lc = model_lc.append(LightCurve([(lc.time[0] - best_t0) + 3*best_period/2], [1]))
model_lc_source = ColumnDataSource(data=dict(
time=np.sort(model_lc.time),
flux=model_lc.flux[np.argsort(model_lc.time)]))
# Set up the LC
nb = int(np.ceil(len(lc.flux)/5000))
lc_source = prepare_lightcurve_datasource(lc[::nb])
lc_help_source = prepare_lc_help_source(lc)
# Set up folded LC
nb = int(np.ceil(len(lc.flux)/10000))
f = lc.fold(best_period, best_t0)
f_source = prepare_folded_datasource(f[::nb])
f_help_source = prepare_f_help_source(f)
lc_obj_tpf = lightcurve.LightCurve(time = self.time[q][self.cal_cadences[0]:self.cal_cadences[1]],
flux = all_corr_lc_tpf_sub[a][q][self.cal_cadences[0]:self.cal_cadences[1]])
flat_lc_tpf = lc_obj_tpf.flatten(polyorder=2, window_length=51).remove_outliers(sigma=4)
tpf_stds[a] = np.std(flat_lc_tpf.flux)
lc_obj_pc = lightcurve.LightCurve(time = self.time[q][self.cal_cadences[0]:self.cal_cadences[1]],
flux = all_corr_lc_pc_sub[a][q][self.cal_cadences[0]:self.cal_cadences[1]])
flat_lc_pc = lc_obj_pc.flatten(polyorder=2, window_length=51).remove_outliers(sigma=4)
pc_stds[a] = np.std(flat_lc_pc.flux)
if self.source_info.tc == False:
lc_2d_tpf = lightcurve.LightCurve(time = self.time[q][self.cal_cadences[0]:self.cal_cadences[1]],
flux = all_corr_lc_tpf_2d_sub[a][q][self.cal_cadences[0]:self.cal_cadences[1]])
flat_lc_2d = lc_2d_tpf.flatten(polyorder=2, window_length=51).remove_outliers(sigma=4)
stds_2d[a] = np.std(flat_lc_2d.flux)
all_corr_lc_tpf_2d_sub[a] = all_corr_lc_tpf_2d_sub[a] * np.nanmedian(all_raw_lc_tpf_2d_sub[a])
all_corr_lc_pc_sub[a] = all_corr_lc_pc_sub[a] * np.nanmedian(all_raw_lc_pc_sub[a])
all_corr_lc_tpf_sub[a] = all_corr_lc_tpf_sub[a] * np.nanmedian(all_raw_lc_tpf_sub[a])
if self.crowded_field > 0.15:
tpf_stds[ap_size > 8] = 1.0