Using Pixel masks

import pylab as plt
from matplotlib.pyplot import imread
import tttrlib
import numpy as np

tttr_data = tttrlib.TTTR('../../tttr-data/imaging/pq/ht3/pq_ht3_clsm.ht3', 'HT3')
channels = (0, 1)
reading_parameter = {
    "tttr_data": tttr_data,
    "marker_frame_start": [4],
    "marker_line_start": 1,
    "marker_line_stop": 2,
    "marker_event_type": 1,
    "n_pixel_per_line": 256, # if zero n_pixel_per_line = n_lines
    "reading_routine": 'default',
    "fill": True,
    "channels": channels,
    "skip_before_first_frame_marker": True
}
clsm_image = tttrlib.CLSMImage(**reading_parameter)

n_frames, n_lines, n_pixel = clsm_image.shape
mask_template = imread("../../tttr-data/imaging/misc/clsm_mask.png").astype(np.uint8).T
mask = np.empty((n_frames, n_lines, n_pixel), dtype=np.uint8)
for i in range(n_frames):
    mask[i] = mask_template
kw = {
    "tttr_data": tttr_data,
    "mask": mask,
    "tac_coarsening": 16,
    "stack_frames": False
}
decay = clsm_image.get_decay_of_pixels(**kw)
intensity = clsm_image.intensity
intensity_stacked = intensity.sum(axis=0)

# mask small intensities to have transparency
masked_intensity_stacked = np.ma.masked_where(
    intensity_stacked < 0.9,
    intensity_stacked
)
masked_intensity = np.ma.masked_where(
    intensity < 0.9,
    intensity
)
mask = np.ma.masked_where(mask < 0.9, mask)

fig, ax = plt.subplots(nrows=1, ncols=3)
im = ax[0].imshow(intensity_stacked)
ax[0].set_title('Stacked frames')
im = ax[0].imshow(mask[0], vmin=0.1)
ax[1].set_title('Pixel selection mask')
im = ax[2].semilogy(decay.sum(axis=0), label='Mask: Stacked frames')

im = ax[1].imshow(masked_intensity[0], vmin=0.1)
ax[1].set_title('First frame')
im = ax[1].imshow(mask[0], vmin=0.1)
im = ax[2].semilogy(decay[0], label='Mask: First frame')
ax[2].legend()
plt.show()