def frap_pb_correction(pointer_pd: pd.DataFrame, ctrl_pd: pd.DataFrame):
"""
Photobleaching correction for FRAP
:param pointer_pd: pd.DataFrame, dataframe of all the bleach spots
:param ctrl_pd: pd.DataFrame, dataframe of control spots
:return:
"""
# calculate photobleaching factor
pb_factor = ana.get_pb_factor(ctrl_pd['bg_cor_int'])
pointer_pd['pb_factor'] = [pb_factor] * len(pointer_pd)
# pb_factor fitting with single exponential decay
pb_fit = mat.fitting_single_exp_decay(np.arange(0, len(pb_factor), 1),
pb_factor)
pointer_pd = dat.add_columns(pointer_pd, [
'pb_single_exp_decay_fit', 'pb_single_exp_decay_r2',
'pb_single_exp_decay_a', 'pb_single_exp_decay_b',
'pb_single_exp_decay_c'
], [[pb_fit[0]] * len(pointer_pd), [pb_fit[1]] * len(pointer_pd),
[pb_fit[2]] * len(pointer_pd), [pb_fit[3]] * len(pointer_pd),
[pb_fit[4]] * len(pointer_pd)])
# photobleaching correction
if np.isnan(pb_fit[2]):
pb = pb_factor
else:
pb = pb_fit[0]
pointer_pd['mean_int'] = ana.pb_correction(pointer_pd['bg_cor_int'], pb)
return pointer_pd
# link ctrl spots with corresponding organelle
ctrl_pd['%s' % analyze_organelle] = obj.points_in_objects(
label_organelle, ctrl_pd['x'], ctrl_pd['y'])
print("### Image analysis: background correction ...")
# background intensity measurement
bg_int_tseries = ana.get_bg_int(pixels_tseries)
pointer_pd['bg_int'] = [bg_int_tseries] * len(pointer_pd)
# background intensity fitting
bg_fit = mat.fitting_linear(np.arange(0, len(bg_int_tseries), 1),
bg_int_tseries)
pointer_pd = dat.add_columns(
pointer_pd,
['bg_linear_fit', 'bg_linear_r2', 'bg_linear_a', 'bg_linear_b'],
[[bg_fit[0]] * len(pointer_pd), [bg_fit[1]] * len(pointer_pd),
[bg_fit[2]] * len(pointer_pd), [bg_fit[3]] * len(pointer_pd)])
# background correction
# use original measurement if fitting does not exist
if np.isnan(bg_fit[2]):
bg = bg_int_tseries
else:
bg = bg_fit[0]
pointer_pd['bg_cor_int'] = ana.bg_correction(pointer_pd['raw_int'],
[bg] * len(pointer_pd))
ctrl_pd['bg_cor_int'] = ana.bg_correction(ctrl_pd['raw_int'],
[bg] * len(ctrl_pd))
# filter control traces
ctrl_pd = pd.DataFrame({
'ctrl_spots': np.arange(0, num_ctrl_spots, 1),
'raw_int': ctrl_spots_int_tseries
})
print("### Image analysis: background correction ...")
# background intensity measurement
bg_int_tseries = ana.get_bg_int(pixels_tseries)
pointer_pd['bg_int'] = [bg_int_tseries] * len(pointer_pd)
# background intensity fitting
bg_fit = mat.fitting_linear(np.arange(0, len(bg_int_tseries), 1),
bg_int_tseries)
pointer_pd = dat.add_columns(
pointer_pd,
['bg_linear_fit', 'bg_linear_r2', 'bg_linear_a', 'bg_linear_b'],
[[bg_fit[0]] * len(pointer_pd), [bg_fit[1]] * len(pointer_pd),
[bg_fit[2]] * len(pointer_pd), [bg_fit[3]] * len(pointer_pd)])
# background correction
# use original measurement if fitting does not exist
if np.isnan(bg_fit[2]):
bg = bg_int_tseries
else:
bg = bg_fit[0]
pointer_pd['bg_cor_int'] = ana.bg_correction(pointer_pd['raw_int'], bg)
ctrl_pd['bg_cor_int'] = ana.bg_correction(ctrl_pd['raw_int'], bg)
# filter control traces
filter_ctrl = []
for i in range(len(ctrl_pd)):