# --------------------------------------------------
# FRAP CURVE ANALYSIS from bleach spots
# --------------------------------------------------
print("### Image analysis: FRAP curve calculation ...")
# create control spots mask
ctrl_nucleoli = ~nucleoli_pd.index.isin(log_pd['nucleoli'].tolist())
ctrl_x = nucleoli_pd[ctrl_nucleoli]['x'].astype(int).tolist()
ctrl_y = nucleoli_pd[ctrl_nucleoli]['y'].astype(int).tolist()
ctrl_spots = ana.analysis_mask(ctrl_x, ctrl_y, pix, num_dilation)
num_ctrl_spots = obj.object_count(ctrl_spots)
pointer_pd['num_ctrl_spots'] = [num_ctrl_spots] * len(pointer_pd)
# get raw intensities for bleach spots and control spots
pointer_pd['raw_int'] = ana.get_intensity(bleach_spots, pixels_tseries)
ctrl_spots_int_tseries = ana.get_intensity(ctrl_spots, pixels_tseries)
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(
# FRAP CURVE ANALYSIS from bleach spots
# --------------------------------------------------
print("### Image analysis: FRAP curve calculation ...")
# create control spots mask
ctrl_organelle = ~organelle_pd.index.isin(
log_pd['%s' % analyze_organelle].tolist())
ctrl_x = organelle_pd[ctrl_organelle]['x'].astype(int).tolist()
ctrl_y = organelle_pd[ctrl_organelle]['y'].astype(int).tolist()
ctrl_spots, ctrl_pd = ble.get_spots(ctrl_y, ctrl_x, pix, num_dilation)
ctrl_pd.columns = ['x', 'y', 'ctrl_spots']
num_ctrl_spots = obj.object_count(ctrl_spots)
pointer_pd['num_ctrl_spots'] = [num_ctrl_spots] * len(pointer_pd)
# get raw intensities for bleach spots and control spots
pointer_pd['raw_int'] = ana.get_intensity(
label(bleach_spots, connectivity=1), pixels_tseries)
ctrl_pd['raw_int'] = ana.get_intensity(
label(ctrl_spots, connectivity=1), pixels_tseries)
ctrl_pd['pos'] = [pos] * num_ctrl_spots
# 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),
round_x = [round(num) for num in organelle_pd['x']]
round_y = [round(num) for num in organelle_pd['y']]
organelle_pd['nuclear'] = obj.points_in_objects(label_nuclear, round_y,
round_x)
# nuclear pd dataset
nuclear_pd = nuclear_analysis(label_nuclear, organelle_pd, pos)
# calculate nuclear background intensity
# calculate nuclear without nucleoli region
label_nuclear_bg = label_nuclear.copy()
label_nuclear_bg[organelle_before_filter == 1] = 0
# get label_nuclear_bg_pd
label_nuclear_bg_pd = pd.DataFrame(
regionprops_table(label_nuclear_bg, properties=['label']))
label_nuclear_bg_pd['mean_intensity'] = ana.get_intensity(
label_nuclear_bg, pixels_tseries)
# assign nuclear background intensity to corresponding nuclear
nuclear_pd = dat.copy_based_on_index(nuclear_pd, label_nuclear_bg_pd,
'nuclear', 'label',
['nuclear_bg_int'],
['mean_intensity'])
# assign nuclear background intensity to corresponding nucleoli
organelle_pd = dat.copy_based_on_index(organelle_pd, nuclear_pd, 'nuclear',
'nuclear', ['nuclear_bg_int'],
['nuclear_bg_int'])
data_log['num_nucleoli_in_nuclei'] = [
len(organelle_pd[organelle_pd['nuclear'] != 0])
]
print("Found %d out of %d nucleoli within nuclei." %
(data_log['num_nucleoli_in_nuclei'][0], obj.object_count(organelle)))