# 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)):
ctrl_int = ctrl_pd['bg_cor_int'][i]
print(max(ctrl_int))
print(min(ctrl_int))
if (max(ctrl_int) - min(ctrl_int)) / max(ctrl_int) > 0.4:
filter_ctrl.append(0)
print(0)
else:
filter_ctrl.append(1)
print(1)
ctrl_pd['filter'] = filter_ctrl
# 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_ft = ble.filter_ctrl(ctrl_pd)
pointer_pd['num_ctrl_spots_ft'] = [len(ctrl_pd_ft)] * len(pointer_pd)
data_log['num_ctrl_spots'] = len(ctrl_pd_ft)
print("### Image analysis: photobleaching correction ...")
# photobleaching factor calculation
if len(ctrl_pd_ft) != 0:
pointer_pd = ble.frap_pb_correction(pointer_pd, ctrl_pd_ft)
# normalize frap curve and measure mobile fraction and t-half based on curve itself
frap_pd = ble.frap_analysis(pointer_pd, max_t,
acquire_time_tseries, real_time,
# 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))
if analyze_organelle == 'nucleoli':
pointer_nuclear_bg_pd = pointer_pd[
pointer_pd['nuclear_bg_int'] != 'na'].copy().reset_index()
pointer_nuclear_bg_pd['bg_cor_int'] = ana.bg_correction(
pointer_nuclear_bg_pd['raw_int'],
pointer_nuclear_bg_pd['nuclear_bg_int'])
ctrl_nuclear_bg_pd = ctrl_pd[
ctrl_pd['nuclear_bg_int'] != 'na'].copy().reset_index()
ctrl_nuclear_bg_pd['bg_cor_int'] = ana.bg_correction(
ctrl_nuclear_bg_pd['raw_int'], ctrl_nuclear_bg_pd['nuclear_bg_int'])
# filter control traces