def plot_comparison_dot(data: pd.DataFrame, feature: str, save_path: str):
"""
Plot dot scatter plot for screen comparison
:param data: pd.DataFrame, dataframe that contains value data for both screens
:param feature: str, plotting feature, column name
:param save_path: str, saving path
:return:
"""
feature1 = '%s1' % feature
plt.figure(figsize=(9, 6))
plt.scatter(data[feature], data[feature1], s=6, c='#A9A9A9')
screen_fit = mat.fitting_linear(data[feature].tolist(), data[feature1].tolist())
val_max = np.max(data[feature])
val_min = np.min(data[feature])
x = np.linspace(val_min*0.9, val_max*1.1, 1000)
plt.plot(x, screen_fit[2]*x+screen_fit[3], linestyle='--', label='R2=%s' % screen_fit[1])
plt.xlabel(feature)
plt.legend(loc=4, bbox_to_anchor=(0.7, 0, 0.3, 0.3))
plt.savefig('%s%s_comparison_dot.pdf' % (save_path, feature))
plt.close()
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),
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'],