In [1]:
# if you would like to perform the analysis using the source code, please add the path as shown below:
import sys
sys.path.append('..')
# if you have installed the pysofi package using 'pip install pysofi' you can skip the two lines of codes above.
import numpy as np
import matplotlib.pyplot as plt
from pysofi import reconstruction
from pysofi import (filtering, masks, finterp)
import tifffile as tiff
%matplotlib inline
%load_ext autoreload
%autoreload 2
In [2]:
# define a function to display the cumulant images up to 7th order
def check_cumulants(k_frame):
# gamma display reconstructions
fig, axs = plt.subplots(2,3,figsize=(40,30))
for i in range(2,8):
row = (i - 2) // 3
ax = axs[row,i-2-row*3]
img = ax.imshow((2*(k_frame[i]>0)-1)*abs(k_frame[i])**0.9**(i+1))
ax.tick_params(axis='x', labelsize= 30)
ax.tick_params(axis='y', labelsize= 30)
ax.set_title(f'Order = {i}', fontsize=35)
cb = plt.colorbar(img, ax=ax)
cb.ax.tick_params(labelsize=30)
cb.ax.yaxis.get_offset_text().set_fontsize(30)
In [3]:
# define the file path and file name fo rthe testing dataset
filepath = '../sampledata/simulations/convergence_demo'
filename = 'nobleach_frame20000_3.tif'
In [4]:
# calcualte the cumulant images using frames in the range of 0 to 1000 (1000 frames in total)
m_frame1000 = reconstruction.calc_block_moments(filepath, filename, 7, frames=[0,1000])
k_frame1000 = reconstruction.calc_cumulants_from_moments(m_frame1000)
# visualize the calculate cumulants using frames in the range of 0 to 1000 (1000 frames in total)
check_cumulants(k_frame1000)
[==============================] 100.0%
In [5]:
# calcualte the cumulant images using frames in the range of 0 to 2000 (2000 frames in total)
m_frame2000 = reconstruction.calc_block_moments(filepath, filename, 7, frames=[0,2000])
k_frame2000 = reconstruction.calc_cumulants_from_moments(m_frame2000)
# visualize the calculate cumulants using frames in the range of 0 to 2000 (2000 frames in total)
check_cumulants(k_frame2000)
[==============================] 100.0%
In [6]:
# calcualte the cumulant images using frames in the range of 0 to 3000 (3000 frames in total)
m_frame3000 = reconstruction.calc_block_moments(filepath, filename, 7, frames=[0,3000])
k_frame3000 = reconstruction.calc_cumulants_from_moments(m_frame3000)
# visualize the calculate cumulants using frames in the range of 0 to 3000 (3000 frames in total)
check_cumulants(k_frame3000)
[==============================] 100.0%
In [7]:
# save all cumulants
from numpy import save
all_cumulants = []
frame_lst = list(range(1000, 4000, 1000))
for i in range(3):
x = eval(compile("k_frame" + str(frame_lst[i]), "<string>", "eval"))
all_cumulants.append(x)
save('../sampledata/simulations/convergence_demo/frame1000_3000.npy', all_cumulants)
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