OSCR

Back-illumination Phase Imaging Enables Nanoscale Drift Stabilization in Non-transparent Biological Tissues

Code ↔ Paper

3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 3 matches
  1. [1] § Results and discussion › Stabilization in fixed brain slices ↔ Transmission_microscopeControl/Autofocus_ui_v1.3.py, lines 612–691 · score 0.72 · cross correlation curve, numerical correction, xy drift, CCC, SIFT, fitted
  2. [2] § Methods › Autofocusing procedure ↔ Transmission_microscopeControl/Autofocus_ui_v1.3.py, lines 552–611 · score 0.61 · XY correction, Microscope control, GPU, 1.5 s, SIFT, autofocusing
  3. [3] § Methods › Oblique back-illumination optical setup ↔ LEDcontrol/LEDdisplay2.3_2.4.py, lines 35–66 · score 0.60 · Micro Manager, Hamamatsu, KURO, hardware, triggering, exposure

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 955 lines · 46 KB · no license · 2 matches

  1. # -*- coding: utf-8 -*-
  2. """
  3. Created on Tue Apr 16 15:54:00 2024
  5. @author: hmanko
  6. """
  8. #!/usr/bin/env python3
  9. # -*- coding: utf-8 -*-
  10. """
  11. Created on Thu Jun 22 18:17:01 2023
  13. @author: hannamanko
  15. """
  17. """
  18. The gui created in order to control Nikon Ti microscope with Phasics camera. This version of GUI was created using QtDesigner
  19. (can be started using 'qt5-tools designer' from the command promt).
  23. The soft was developed to be started on windows system, if you are going to use it on any
  24. other system it will be required to change the way how the path for saving data is defined
  25. in all the functions.
  26. Ex: for windows before the file name separated by '\\' need to be written, as it was done in this code
  29. The lines where changes could be required are marked as ############ ***
  30. #### *** !!!
  32. """
  33. ### importing all the required libraries
  34. import pymmcore_plus
  35. import numpy as np
  36. import cv2
  37. import os.path
  38. from pymmcore_plus import CMMCorePlus
  39. import useq
  40. from useq import MDAEvent, MDASequence
  41. from pymmcore_plus.mda import MDAEngine
  42. import matplotlib.pyplot as plt
  43. from IPython import get_ipython
  44. from numpy import median
  47. import pymmcore
  48. import pandas as pd
  49. import numpy as np
  50. import napari
  51. import os.path
  52. import matplotlib
  53. #matplotlib.use("Qt5Agg")
  54. import matplotlib.pyplot as plt
  55. from tifffile import imread, imwrite
  56. from skimage import io
  57. import sys
  58. from matplotlib.widgets import Button
  59. import warnings
  60. from qtpy.QtWidgets import QApplication, QWidget, QLineEdit, QLabel,QPushButton, QProgressBar, QMessageBox, QCheckBox
  61. from qtpy.QtGui import QFont
  62. from qtpy import QtCore
  63. from matplotlib.widgets import RectangleSelector
  64. np.seterr(divide='ignore', invalid='ignore')
  65. from IPython import get_ipython
  66. #get_ipython().run_line_magic('matplotlib', 'inline')
  67. import glob
  68. import time
  69. from silx.image import sift
  70. import silx
  71. import math
  72. from numpy import where
  73. import datetime
  74. from math import sqrt
  76. import glob
  77. import os
  78. import keyboard
  79. from scipy.optimize import curve_fit
  80. import cv2
  84. from PyQt5 import QtWidgets, uic
  85. from pymmcore_plus import CMMCorePlus
  86. from qtpy.QtWidgets import QApplication, QGroupBox, QHBoxLayout, QBoxLayout, QWidget,QGridLayout
  88. from pymmcore_widgets import ExposureWidget
  89. get_ipython().run_line_magic('matplotlib', 'qt5')
  92. ############ ****************
  93. ######### *** !!! ***
  95. # Here I am addind the folder with required functions file and then importing *-everything from this file
  96. # if you want to specify the function that will be imported you need to write the function name intead of *
  97. sys.path.append("Z:\Hanna\CODE")
  98. from functions import *
  100. ############ ****************
  101. ######### *** !!! ***
  102. pathMM = "C:\Program Files\Micro-Manager-2.0_NB" ## path to Micromanager folder on the computer
  103. config = "Phasics_dis_coller_Ni.cfg" ## name of configuration file. Need to be created in MicroManager
  104. # before starting this soft, NikonTi and Phasics camera need to be in devices
  106. #pymmcore.CMMCore().getAPIVersionInfo()
  107. mmc = CMMCorePlus() ## initialisation of MicroManager core
  108. mmc.setDeviceAdapterSearchPaths([pathMM]) ## looking for device adapters
  109. mmc.loadSystemConfiguration(os.path.join(pathMM, config)) # Loading configuration
  111. ############ ****************
  112. ######### *** !!! ***
  113. ## As in our study we used QLSI module i.e. Phasics (Andor) camera, there is the requirement to turn off the sensor cooling
  114. print('The cooling of camera sensor is :', mmc.getProperty("Andor sCMOS Camera", "SensorCooling")) ##checking if the cooler is off
  115. mmc.waitForDevice('TIDiaLamp') ## The lamp of Nikon Ti can take time to turn on so we need to wait a bit
  117. mmc.setAutoShutter(False) ##
  119. ############ ****************
  120. ######### *** !!! ***
  121. lamp = str('TIDiaLamp')
  122. #mmc.getProperty(lamp, "ComputerControl")
  123. mmc.setProperty(lamp, "ComputerControl", "On") ### preparing the lamp of Nikon microscope
  124. mmc.setProperty(lamp, "Intensity", 4)
  125. mmc.setProperty(lamp, "State", 1)
  128. Zstage = mmc.getFocusDevice() # giving the name to the zdrive
  129. exposure = mmc.getExposure()
  130. pos0 = mmc.getPosition(Zstage) # getting current position
  131. mmc.setROI(500,500 ,1500, 1500) #for Phasics camera it is required to make the roi
  133. mmc.snapImage() # The camera gets the image to have it in the bufffer for further use
  135. #######
  136. ## In this part I define all the functios that will be used in the gui
  137. viewer = napari.Viewer() ## opening napari viewer
  139. exposure = 10 ## the deaful value of exposure time
  141. center1_x,center1_y,center2_x,center2_y = 1005, 320, 1170, 1003 # the positions of harmonics of the fourier transform of 1500*1500 image
  143. #textBrow.append("Initialization Done")
  145. global pathth
  147. def start_live(): ## function to start live\ show images in real time
  148. textBrow.append("Live is running, to stop/close the window press 'q'")
  149. while (True):
  150. mmc.snapImage()
  151. frame = mmc.getImage()
  152. gray = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
  153. gray = cv2.resize(gray, (700, 700)) ## to resize the window (otherwise can be bigger than screen)
  154. cv2.imshow('frame', gray)
  155. cv2.setWindowTitle("frame", "Pres q to quit")
  156. if cv2.waitKey(1) & 0xFF == ord('q'):
  157. break
  158. cv2.destroyAllWindows()
  160. def set_exposure(): ## The function to set new exposure time from input field in the gui
  161. expos = int(lineExp.text()) ## reading the value from the input field line
  162. mmc.setExposure(expos) ## seting exposure time
  163. global exposure
  164. exposure = mmc.getExposure() ## reading exposure from MicroManager to be sure thet it was set
  165. textBrow.append("Exposure time was set to : "+str(exposure))
  166. return exposure
  168. def set_lamp_int(): # the function to set lamp intensity to value entered into corresponding line in gui
  169. global ints # intensity value set to be global. Allows to access it from other functions
  170. ints = int(lineLamp.text()) # reading value from the input line
  171. mmc.setProperty(lamp, "Intensity", ints) # the intensity set to lamp
  172. textBrow.append("Nikon lamp intensity was set to "+str(ints))
  173. return ints
  175. def Lamp_on(): ## the function to turn On the microscope Lamp
  176. mmc.setProperty(lamp, "State", 1)
  178. def Lamp_off(): ## the function to turn Off microscope Lamp
  179. mmc.setProperty(lamp, "State", 0)
  182. def set_z_pos(): # the function to move z drive
  183. pos0 = mmc.getPosition(Zstage) # getting current position
  184. pos = np.float32(lineZpos.text()) # reading desired value of movement from gui line
  185. print("pos = ", pos)
  186. mmc.setPosition(Zstage, pos+pos0)
  187. mmc.waitForDevice(Zstage)
  188. textBrow.append("the position of z-drive is set to = "+str(mmc.getPosition(Zstage)))
  191. global R
  192. def reference_image():
  193. global reference
  194. start = time.time()
  195. textBrow.append("Starting refererence aquisition")
  196. progressBar.resetFormat() ## progress bar shows nothing at the begining
  197. try: ### To check if path to folder was defined
  198. pathth
  199. except NameError: # in case if path was not defined before it will be read from line in the gui
  200. pathth = linePath.text()
  201. if os.path.isdir(pathth) == False: ## if path do not exist the error message will pop up
  202. textBrow.append("Path does not exists, try again")
  203. return
  204. else:
  205. with open(pathth + '\\METADATA.txt', 'a') as file: ## opening the Metadata file, if file do not exists it will be created automaticaly
  206. file.write('Ref recorded at: %s\n Exposure: %s\n Lamp: %s\n' %(datetime.datetime.now(), exposure, ints))
  207. im =[]
  208. n_of_im = int(lineRef.text()) ## reading the number of images to acquire from corresponding input field
  209. c = 100/n_of_im
  210. mmc.startSequenceAcquisition(n_of_im, 0, True)
  211. i=0
  212. while mmc.isSequenceRunning():
  213. if mmc.getRemainingImageCount() != 0:
  214. image_and_MD_raw = mmc.popNextImageAndMD(fix = False)
  215. image_and_MD = np.asarray([image_and_MD_raw[0], image_and_MD_raw[1].json()], dtype=object)
  216. im.append(image_and_MD[0])
  217. progressBar.setValue(int(i*c))
  218. i=i+1
  219. ref = np.mean(np.asarray(im), axis=0)
  220. viewer.add_image(ref, name='Reference image')
  221. R = ref.shape[0] // 6
  222. #start = time.time()
  223. xm, ym = np.meshgrid(np.arange(ref.shape[1]), np.arange(ref.shape[0]))
  224. mask_x = np.sqrt((xm - center1_x)**2 + (ym - center1_y)**2) <= R
  225. mask_y = np.sqrt((xm - center2_x)**2 + (ym - center2_y)**2) <= R
  226. fourier_ref = fft.fftshift(fft.fft2(ref, norm="ortho")).astype(np.complex64)
  227. Ix_ref = fft.ifft2(fftpack.fftshift((fourier_ref) * mask_x))
  228. Iy_ref = fft.ifft2(fftpack.fftshift((fourier_ref) * mask_y))
  229. reference = np.stack((ref, mask_x, mask_y, Ix_ref, Iy_ref))
  230. np.save(file_name_check(pathth + '\\ref.npy'), reference.reshape(5*ref.shape[0], ref.shape[1]))
  231. textBrow.append('The obtained images were saved under the name ref.tif')
  233. #return image_ref
  234. textBrow.append('time:' +str(time.time()-start))
  235. with open(pathth + '\\METADATA.txt', 'a') as file:
  236. file.write('The number of images avaraged for reference: %s\n' %n_of_im)
  237. textBrow.append("Done")
  238. return
  241. def stack_acquisition(): # the function to perform the acqusition without autofocusing
  242. progressBar.resetFormat()
  243. try:
  244. pathth
  245. except NameError:
  246. pathth = linePath.text()
  247. if os.path.isdir(pathth) == False:
  248. textBrow.append("Path does not exists, try again")
  249. return
  250. else:
  251. im =[]
  252. stack = []
  253. nIm = int(line_acq.text())
  254. if line_nIm.text() =='':
  255. nImAv = 10
  256. else:
  257. nImAv = int(line_nIm.text())
  258. c = 100/(nIm*nImAv)
  259. mmc.startSequenceAcquisition(nIm*nImAv, 0, True)
  260. i=0
  261. while mmc.isSequenceRunning():
  262. if mmc.getRemainingImageCount() != 0:
  263. image_and_MD_raw = mmc.popNextImageAndMD(fix = False)
  264. image_and_MD = np.asarray([image_and_MD_raw[0], image_and_MD_raw[1].json()], dtype=object)
  265. im.append(image_and_MD[0])
  266. i=i+1
  267. progressBar.setValue(int(i*c))
  268. if keyboard.is_pressed('q'): # Check if 'q' is pressed
  269. textBrow.append("Interrupted by user")
  270. break
  271. if keyboard.is_pressed('s'): # Check if 'q' is pressed
  272. textBrow.append("Interrupted by user, the data will be saved")
  273. images = np.asarray(im)
  274. Stack = np.asarray([np.mean(images[i:i+nImAv], axis=0) for i in range(0, len(images), nImAv)])
  275. textBrow.append("Saving the data...")
  276. save_path = file_name_check(pathth + '\\Stack.tif')
  277. _, tail = os.path.split(save_path)
  278. imwrite(save_path, Stack.astype(np.float16))
  279. textBrow.append("Done")
  280. break
  281. images = np.asarray(im)
  282. Stack = np.asarray([np.mean(images[i:i+nImAv], axis=0) for i in range(0, len(images), nImAv)])
  283. progressBar.setFormat("Saving")
  284. save_path = file_name_check(pathth + '\\Stack.tif')
  285. _, tail = os.path.split(save_path)
  286. imwrite(save_path, Stack.astype(np.float16))
  287. textBrow.append('The obtained images were saved under the name ' +srt(tail))
  288. textBrow.append("Done")
  290. #global zInt
  291. def zStack():
  292. global reference
  293. progressBar.resetFormat()
  294. start = time.time()
  295. try: ### To check if path to folder was defined
  296. pathth
  297. except NameError: # in case if path was not defined before the path will be read from line in the gui
  298. pathth = linePath.text()
  299. if os.path.isdir(pathth) == False: ## if path do not exist the error message will pop up
  300. textBrow.append("Path does not exists, try again")
  301. return
  302. else:
  303. if 'reference' not in globals():
  304. print('reading zGradients from disk')
  305. list_of_files = glob.glob(os.path.normpath(pathth+ '\\*'))
  306. refpath = os.path.normpath(max([file for file in list_of_files if 'ref' in file], key=os.path.getmtime))
  307. reference = np.load(refpath)
  308. reference = reference.reshape(5, int(reference.shape[0]/5), reference.shape[1])
  309. ref = reference[0].real
  310. Zstage = mmc.getFocusDevice()
  311. if line_start.text() =='':
  312. start_pos = -1.5
  313. else:
  314. start_pos = np.float32(line_start.text())
  315. if line_stop.text() =='':
  316. stop_pos = 1.5
  317. else:
  318. stop_pos = np.float32(line_stop.text())
  319. if line_step.text() =='':
  320. step = 0.05
  321. else:
  322. step = np.float32(line_step.text())
  323. pos0 = mmc.getPosition(Zstage)
  324. print("0 position : ", pos0)
  325. if line_nIm.text() =='':
  326. nIm = 10
  327. else:
  328. nIm = int(line_nIm.text())
  329. start_pos = start_pos+pos0
  330. stop_pos = stop_pos + pos0 #+ step
  331. print("start:", start_pos, "stop: ", stop_pos, "step :", step)
  332. mmc.snapImage()
  333. length = len(np.arange(start_pos, stop_pos, step))
  334. print(length)
  335. c = 100/length
  336. frame = 0
  337. posR = []
  338. stack=[]
  339. with open(pathth + '\\METADATA.txt', 'a') as file:
  340. file.write('zStack recorded at %s\n zStack \n start:%s\n end: %s\n step %s\n N of averaged images %s\n Exposure: %s\n Lamp: %s\n' %(datetime.datetime.now(), start_pos, stop_pos, step, nIm, exposure, ints))
  341. for pos in np.arange(start_pos, stop_pos, step):
  342. textBrow.append("pos = "+str(pos))
  343. mmc.setPosition(Zstage, pos)
  344. mmc.waitForDevice(Zstage)
  345. mmc.startSequenceAcquisition(nIm, 0, True)
  346. if keyboard.is_pressed('q'): # Check if 'q' is pressed
  347. textBrow.append("Interrupted by user")
  348. break
  349. while mmc.isSequenceRunning():
  350. if mmc.getRemainingImageCount() != 0:
  351. image_and_MD_raw = mmc.popNextImageAndMD(fix = False)
  352. image_and_MD = np.asarray([image_and_MD_raw[0], image_and_MD_raw[1].json()], dtype=object)
  353. stack.append(image_and_MD[0])
  354. textBrow.append("pos read = "+str(mmc.getPosition(Zstage)))
  355. posR.append(mmc.getPosition(Zstage))
  356. frame = frame + 1
  357. progressBar.setValue(int(frame*c))
  358. textBrow.append('Saving, it took:'+str(time.time()-start))
  359. z_Stack = np.asarray([np.mean(stack[i:i+nIm], axis=0) for i in range(0, len(stack), nIm)])
  360. #viewer.add_image(z_Stack, name = 'z Stack')
  361. global zGrads, zInt
  362. zGrads = grad_diff(z_Stack, reference[1], reference[2],reference[3],reference[4])
  363. viewer.add_image(zGrads, name = 'gradients z Stack')
  364. mmc.setPosition(Zstage, pos0)
  365. np.save(file_name_check(pathth + '\\zStackRaw.npy'), z_Stack.astype(np.float16).reshape(z_Stack.shape[0]*z_Stack.shape[1], z_Stack.shape[2]))
  366. zInt = intensity_image(z_Stack, ref, choose_coord=False)
  367. np.savetxt(file_name_check(pathth + '\\z_posRead.txt'), posR)
  368. textBrow.append('The obtained images were saved')
  369. progressBar.setFormat("Done")
  370. mmc.setPosition(Zstage, pos0)
  371. #viewer.add_image(phase_image(z_Stack[int(len(z_Stack)/2)], ref, choose_coord=False), name = 'Phase z Stack')
  372. print('Done', 'pos now = ', pos0, 'it took: ', time.time()-start )
  373. return zGrads
  375. def start_live_grad(): ## function to start live\ show images in real time
  376. try:
  377. reference
  378. except NameError:
  379. try:
  380. ref = imread(pathth+'\\ref.tif' )
  381. except:
  382. msgBox = QMessageBox()
  383. msgBox.setIcon(QMessageBox.Information)
  384. msgBox.setText("There is no reference")
  385. msgBox.setStandardButtons(QMessageBox.Ok)
  386. msgBox.exec_()
  387. while (True):
  388. mmc.snapImage()
  389. frame = mmc.getImage()
  390. diff = grad_diff(frame, mask_x, mask_y,Ix_ref,Iy_ref )
  391. gray = cv2.cvtColor(diff, cv2.COLOR_GRAY2BGR)
  392. gray = cv2.resize(gray, (1000, 1000)) ## to resize the window (otherwise can be bigger than screen)
  393. cv2.imshow('frame', gray)
  394. cv2.setWindowTitle("frame", "Pres q to quit")
  395. mmc.sleep(exposure)
  396. if cv2.waitKey(1) & 0xFF == ord('q'):
  397. break
  398. cv2.destroyAllWindows()
  400. def check_max():
  401. mmc.snapImage()
  402. image = mmc.getImage()
  403. textBrow.append("The maaximum value on the image is "+str(image.max()))
  406. def test_z_stack():
  407. global reference
  408. progressBar.resetFormat()
  409. start = time.time()
  410. textBrow.append("Starting zStack acquisition")
  411. try: ### To check if path to folder was defined
  412. pathth
  413. except NameError: # in case if path was not defined before the path will be read from line in the gui
  414. pathth = linePath.text()
  415. if os.path.isdir(pathth) == False: ## if path do not exist the error message will pop up
  416. textBrow.append("Path does not exists, try again")
  417. return
  418. else:
  419. if 'reference' not in globals():
  420. textBrow.append('reading ref from disk')
  421. list_of_files = glob.glob(os.path.normpath(pathth+ '\\*'))
  422. refpath = os.path.normpath(max([file for file in list_of_files if 'ref' in file], key=os.path.getmtime))
  423. reference = np.load(refpath)
  424. reference = reference.reshape(5, int(reference.shape[0]/5), reference.shape[1])
  425. ref = reference[0].real
  426. Zstage = mmc.getFocusDevice()
  427. if line_start.text() =='': # if the value is not specified in GUI, the default one will be used
  428. start_pos = -1.5
  429. else:
  430. start_pos = np.float32(line_start.text())
  431. if line_stop.text() =='':
  432. stop_pos = 1.5
  433. else:
  434. stop_pos = np.float32(line_stop.text())
  435. if line_step.text() =='':
  436. step = 0.05
  437. else:
  438. step = np.float32(line_step.text())
  439. pos0 = mmc.getPosition(Zstage)
  440. textBrow.append("0 position : "+str(pos0))
  441. if line_nIm.text() =='':
  442. nIm = 8
  443. else:
  444. nIm = int(line_nIm.text())
  445. start_pos = start_pos+pos0
  446. stop_pos = stop_pos + pos0
  447. textBrow.append("start:"+str(start_pos)+"stop: "+str(stop_pos)+ "step :"+str(step))
  448. mmc.snapImage()
  449. length = len(np.arange(start_pos, stop_pos, step))
  450. textBrow.append(str(length)+"of zStack")
  451. c = 100/length
  452. frame = 0
  453. posR = []
  454. stack=[]
  455. with open(pathth + '\\METADATA.txt', 'a') as file:
  456. file.write('zStack recorded at %s\n zStack \n start:%s\n end: %s\n step %s\n N of averaged images %s\n Exposure: %s\n Lamp: %s\n' %(datetime.datetime.now(), start_pos, stop_pos, step, nIm, exposure, ints))
  457. pos_list = []
  458. pos=start_pos
  459. rpos = pos-pos0
  460. while rpos < stop_pos-pos0:
  461. pos_list.append(pos)
  462. if (rpos>-0.4) and (rpos <0.4):
  463. print('step = 0.05:' , pos)
  464. pos=pos+step
  465. elif (rpos<-0.4)or(rpos>0.4):
  466. print('step = 0.2', pos)
  467. pos=pos+0.2
  468. elif (rpos<-0.4)or(rpos>0.4):
  469. print('step = 0.2', pos)
  470. pos=pos+0.2
  471. rpos=pos-pos0
  472. if keyboard.is_pressed('q'): # Check if 'q' is pressed
  473. textBrow.append("Interrupted by user")
  474. break
  475. textBrow.append("pos = "+str(pos))
  476. mmc.setPosition(Zstage, pos)
  477. mmc.waitForDevice(Zstage)
  478. mmc.startSequenceAcquisition(nIm, 0, True)
  479. while mmc.isSequenceRunning():
  480. if mmc.getRemainingImageCount() != 0:
  481. image_and_MD_raw = mmc.popNextImageAndMD(fix = False)
  482. image_and_MD = np.asarray([image_and_MD_raw[0], image_and_MD_raw[1].json()], dtype=object)
  483. stack.append(image_and_MD[0])
  484. textBrow.append("pos read = "+str(mmc.getPosition(Zstage)))
  485. posR.append(mmc.getPosition(Zstage))
  486. frame = frame + 1
  487. progressBar.setValue(int(frame*c))
  488. if keyboard.is_pressed('q'): # Check if 'q' is pressed
  489. textBrow.append("Interrupted by user")
  490. break
  491. z_Stack = np.asarray([np.mean(stack[i:i+nIm], axis=0) for i in range(0, len(stack), nIm)])
  492. mmc.setPosition(Zstage, pos0)
  493. ##################################### To Rytov/Intensity part
  494. textBrow.append('acquired'+str(time.time()-start))
  495. #zInt = intensity_image(z_Stack, ref, choose_coord=False)
  496. global zGrads, zInt
  497. zGrads = grad_diff(z_Stack, reference[1], reference[2],reference[3],reference[4])
  498. viewer.add_image(zGrads, name = 'grqdients z Stack')
  499. textBrow.append('stacks done'+str( time.time()-start))
  500. textBrow.append("Saving the data ...")
  501. mmc.setPosition(Zstage, pos0)
  502. np.save(file_name_check(pathth + '\\zStackRaw.npy'), z_Stack.astype(np.float16).reshape(z_Stack.shape[0]*z_Stack.shape[1], z_Stack.shape[2]))
  503. zInt = intensity_image(z_Stack, ref, choose_coord=False)
  504. np.savetxt(file_name_check(pathth + '\\z_posRead.txt'), posR)
  505. textBrow.append('Done, pos now = '+str(pos0)+'time = '+str(time.time()-start))
  506. msgBox = QMessageBox()
  507. msgBox.setText('The obtained images were saved')
  508. msgBox.exec_()
  509. progressBar.setFormat("Done")
  510. mmc.setPosition(Zstage, pos0)
  511. viewer.add_image(phase_image(z_Stack[int(len(z_Stack)/2)], ref, choose_coord=False), name = 'Example of phase image from z Stack')
  515. def acquisition_calib_grad(): ## the function to perform autofocusing with gradient images
  516. progressBar.resetFormat() ##
  517. textBrow.append("Starting acquisition with autofocusing using Gradient images") #message to GUI window
  518. try: ### To check if path to folder was defined
  519. pathth
  520. except NameError: # in case if path was not defined before the path will be read from line in the gui
  521. pathth = linePath.text()
  522. if os.path.isdir(pathth) == False: ## if path do not exist the error message is shown
  523. textBrow.append("Path does not exists, try again") # message to GUI wimdow
  524. list_of_files = glob.glob(os.path.normpath(pathth+ '\\*')) # list of files in folder
  525. refpath = os.path.normpath(max([file for file in list_of_files if 'ref' in file], key=os.path.getmtime)) # looking for the last file with name containing 'ref'
  526. reference = np.load(refpath) # saving path to last ref file
  527. Reference = reference.reshape(5, int(reference.shape[0]/5), reference.shape[1]) # geting ref image frm ref file
  528. ref = Reference[0].real # .real because ref can be saved in complex format
  529. mask_x = Reference[1]
  530. mask_y = Reference[2]
  531. Ix_ref = Reference[3]
  532. Iy_ref = Reference[4]
  533. Gradpath = os.path.normpath(max([file for file in list_of_files if 'zStack' in file], key=os.path.getmtime)) # geting path to last gradients zStack
  534. _, tail = os.path.split(Gradpath)
  535. if 'zGrads' not in globals(): # checking if Gradients z Stack was saved in global values, if no read from disk
  536. textBrow.append('reading zGradients from disk')
  537. zImage = np.load(Gradpath)
  538. zImage = zImage.reshape(int(zImage.shape[0]/ref.shape[0]), ref.shape[0], ref.shape[1])
  539. zImage = grad_diff(zImage, Reference[1], Reference[2],Reference[3],Reference[4])
  540. else:
  541. zImage = zGrads
  542. textBrow.append('The start position'+str(mmc.getPosition(Zstage)))
  543. nIm = int(lineAcqCalibNbI.text()) # getting the numebr of images to acquire from GUI
  544. nImAv = 8 #int(line_nImAcq.text())
  545. if lineFitDegree.text() =='': # if degree was not specified it will be set to 12
  546. degree = 12# = 10
  547. else:
  548. degree = int(lineFitDegree.text()) # the values will be used if specified in GUI
  549. frequency = int(lineAcqCalibFr.text()) # getting frequency of refocusing from GUI
  550. Stack = np.zeros((nIm, int(mmc.getImage().shape[0]), int(mmc.getImage().shape[1]))) # empty array where images will be added
  552. posPath = os.path.normpath(max([file for file in list_of_files if '_posRead' in file], key=os.path.getmtime))
  553. poslist = np.loadtxt(posPath)
  554. cfr = 1
  555. drft = []
  556. posN=[]
  557. micPos=[]
  558. poslist = poslist-poslist.min()
  559. x = poslist-poslist[int(len(poslist)/2)]
  560. xx = np.arange(x.min(), x.max(), 0.001)
  561. frame = int(len(zImage)/2)
  562. sift_ocl = silx.image.sift.SiftPlan(template=zImage[frame], devicetype="GPU") # zImage[frame] will be used as template for xy correction
  563. keypoints = sift_ocl(zImage[frame]) #looking for keypoints on the image
  564. mp = sift.MatchPlan()
  565. # creating the figure where the calculated drift values will be displayed
  566. plt.ion()
  567. xg = np.linspace(0, nIm, nIm)
  568. yg = np.arange(-1.5, 1.5, 3/nIm)
  569. fig = plt.figure()
  570. ax = fig.add_subplot(111)
  571. line1, = ax.plot(xg, yg, 'b+:')
  572. plt.xlabel("Refocusing step")
  573. plt.ylabel("Drift, μm")
  574. plt.title("Updating plot...")
  575. yyg = np.zeros(nIm)
  576. yyg[:]= np.nan
  577. yygg = np.zeros(nIm)
  578. yygg[:]= np.nan
  579. k=0
  580. # creating the figure where the values read from z-drive will be displayed
  581. plt.ion()
  582. fig2 = plt.figure()
  583. ax2 = fig2.add_subplot(111)
  584. line2, = ax2.plot(x, np.linspace(0, 0.0005, len(zImage)), 'b+:')
  585. line3, = ax2.plot(x, np.linspace(0, 0.0005, len(zImage)), 'r')
  586. plt.xlabel("Refocusing step")
  587. plt.ylabel("CCC, μm")
  588. plt.title("Updating plot...")
  589. progressBar.setValue(0)
  590. c = 100/nIm
  591. for i in range(0, nIm): # starting the main loop of acquisition with autofocusing
  592. if keyboard.is_pressed('q'): # Check if 'q' is pressed
  593. textBrow.append("Interrupted by user")
  594. break
  595. if keyboard.is_pressed('s'): # Check if 's' is pressed if yes the process is interupted while saving the data
  596. textBrow.append("Interrupted by user, the data will be saved")
  597. np.savetxt(file_name_check(pathth+'/detectedDrift_grad_interupted_z'+tail[-5]+'.txt'), pd.concat([pd.DataFrame(drft), pd.DataFrame(micPos)], axis=1))
  598. progressBar.setFormat("Done, interupted")
  599. np.save(file_name_check(pathth + '\\Stack_autofocus_grad_interupted_z'+tail[-5]+'.npy'), Stack.astype(np.float16).reshape(Stack.shape[0]*Stack.shape[1], Stack.shape[2]))
  600. textBrow.append("Done")
  601. break
  602. mmc.snapImage() # getting the image from camera
  603. stack = np.zeros((nImAv, int(mmc.getImage().shape[0]), int(mmc.getImage().shape[1]))) # images will be stored to stack
  604. for n in range(0, nImAv): # getting 10 images to avarage
  605. mmc.snapImage()
  606. stack[n] = mmc.getImage()
  607. st = np.mean(stack, axis=0) # getting mean value of images
  608. Stack[i] = st # mean image stored in Stack
  609. ###############################
  610. if (i % frequency == 0) == True: # when the frequency matches the value specified in GUI do autofocusing
  611. if lineFitDegree.text() =='': # checking if degree of fit was changed in GUI
  612. degree = 12# = 10
  613. else:
  614. degree = int(lineFitDegree.text())
  615. corrcoef = []
  616. pos = mmc.getPosition(Zstage) # current position of z-drive
  617. start = time.time() # getting current time
  618. imm = grad_diff(Stack[i], mask_x, mask_y,Ix_ref,Iy_ref) # calculation of gradient image
  619. ### Shift correction
  620. if keyboard.is_pressed('q'): # Check if 'q' is pressed
  621. textBrow.append("Interrupted by user")
  622. break
  623. try:
  624. im_keypoints = sift_ocl(imm.astype(np.float32)) # looking for keyposint on the new image
  625. match = mp(keypoints, im_keypoints) # looking for maching keyposint
  626. sa = silx.image.sift.LinearAlign(zImage[frame], devicetype="GPU") # xy-drift numerical correction
  627. im = sa.align(imm, shift_only=True)
  628. except: # if something went wrong the process is interupted and data is saved
  629. np.savetxt(file_name_check(pathth+'/detectedDrift_grad_err1_z'+tail[-5]+'.txt'), pd.concat([pd.DataFrame(drft), pd.DataFrame(micPos)], axis=1))
  630. textBrow.append("Done, data saved after xy-drfit correction didn't work")
  631. imwrite(file_name_check(pathth + '\\Stack_autofocus_grad_err1_z'+tail[-5]+'.tif', Stack.astype(np.float16)))
  632. with open(pathth + '\\METADATA.txt', 'a') as file:
  633. file.write('Aqqusition with autofocus recorded FAIL xy correction at %s\n N of images %s \n frequency:%s\n Exposure: %s\n Lamp: %s\n degree: %s\n ' %(datetime.datetime.now(), nIm, frequency, exposure, ints, degree))
  634. textBrow.append('xy correction failed, data was saved')
  635. break
  636. ######################
  637. for f in range(0, len(zImage)): # loop to run through all images in zStack and calculate cross-correlation coeficients (CCC)
  638. try:
  639. corrcoef.append(ncc(im[200:1300, 200:1300], zImage[f,200:1300, 200:1300])) ##
  640. except: # if something went wrong interupt with saving data
  641. np.savetxt(file_name_check(pathth+'/detectedDrift_grad_err2_z'+tail[-5]+'.txt'), pd.concat([pd.DataFrame(drft), pd.DataFrame(micPos)], axis=1))
  642. textBrow.append("Done, data saved after xy-drfit correction didn't work")
  643. imwrite(file_name_check(pathth + '\\Stack_autofocus_grad_err2_z'+tail[-5]+'.tif'), Stack.astype(np.float16))
  644. with open(pathth + '\\METADATA.txt', 'a') as file:
  645. file.write('Aqqusition with autofocus recorded FAIL correlation at %s\n N of images %s \n frequency:%s\n Exposure: %s\n Lamp: %s\n degree: %s\n ' %(datetime.datetime.now(), nIm, frequency, exposure, ints, degree))
  646. textBrow.append('correlation failed, data was saved')
  647. break
  648. y = np.array(corrcoef)
  649. model2 = np.poly1d(np.polyfit(x, y, degree)) # fitting the cross-correlation curve
  650. drft.append(xx[where(model2(xx) == model2(xx).max())]) # the calculated drift stored into 'drft' list
  651. i=i+1
  652. yyg[i] = drft[-1][0] # updayting the value on the figure
  653. line1.set_ydata(yyg)
  654. k = k+1
  655. fig.canvas.draw()
  656. fig.canvas.flush_events()
  657. ax2.set_ylim(y.min()-y.min()*0.1, y.max()+y.max()*0.1)
  658. line2.set_ydata(y)
  659. line3.set_ydata(model2(x))
  660. fig2.canvas.draw()
  661. fig2.canvas.flush_events()
  662. textBrow.append('newvalue'+str(drft[-1])) # message to GIU with the last value of drift calculated
  663. if keyboard.is_pressed('q'): # Check if 'q' is pressed
  664. textBrow.append("Interrupted by user")
  665. break
  666. micPos.append(mmc.getPosition(Zstage)) # reading current position of z-drive and storing into list
  667. if keyboard.is_pressed('s'): # Check if 'q' is pressed
  668. textBrow.append("Interrupted by user, the data will be saved")
  669. np.savetxt(file_name_check(pathth+'/detectedDrift_grad_interupted_z'+tail[-5]+'.txt'), pd.concat([pd.DataFrame(drft), pd.DataFrame(micPos)], axis=1))
  670. progressBar.setFormat("Done, interupted")
  671. np.save(file_name_check(pathth + '\\Stack_autofocus_grad_interupted_z'+tail[-5]+'.npy'), Stack.astype(np.float16).reshape(Stack.shape[0]*Stack.shape[1], Stack.shape[2]))
  672. break
  673. if (np.abs(drft[-1]) < 0.010): # if the drift was smaller then 10 nm corresction is not performed to avoid werd behavious of z-drive
  674. textBrow.append('drift was smaller then 10 nm no correction is done')
  675. continue
  676. mmc.setPosition(Zstage, pos-drft[-1][0]) # the new position (corrected one) is set to z-drive
  677. mmc.waitForDevice(Zstage) # to ensure that z-drive finisehd movement
  678. cfr = cfr+1
  679. textBrow.append("calibration, detected drift = "+str(drft[-1])+'the drift was = '+str(pos-drft[-1])+'microscope reply = '+str(mmc.getPosition(Zstage))+'it took '+str(time.time()-start)+'to refocus')
  680. progressBar.setValue(int(i*c))
  681. plt.figure() # after acquisition is finished the last cross-correlation curve and fit are displayed
  682. plt.plot(xx, model2(xx))
  683. plt.plot(x, y)
  684. plt.figure()
  685. plt.plot(micPos)
  686. plt.show()
  687. np.savetxt(file_name_check(pathth+'/detectedDrift_grad_z'+tail[-5]+'.txt'), pd.concat([pd.DataFrame(drft), pd.DataFrame(micPos)], axis=1))
  688. textBrow.append("Done")
  689. np.save(file_name_check(pathth + '\\Stack_autofocus_grad_z'+tail[-5]+'.npy'), Stack.astype(np.float16).reshape(Stack.shape[0]*Stack.shape[1], Stack.shape[2]))
  690. with open(pathth + '\\METADATA.txt', 'a') as file:
  691. file.write('Aqqusition with autofocus recorded at %s\n N of images %s \n frequency:%s\n Exposure: %s\n Lamp: %s\n degree: %s\n' %(datetime.datetime.now(), nIm, frequency, exposure, ints, degree))
  695. def acquisition_calib_int():
  696. progressBar.resetFormat()
  697. textBrow.append("Starting acquisition with active autofocusing using Intensity images")
  698. try: ### To check if path to folder was defined
  699. pathth
  700. except NameError: # in case if path was not defined before the path will be read from line in the gui
  701. pathth = linePath.text()
  702. if os.path.isdir(pathth) == False: ## if path do not exist the error message will pop up
  703. textBrow.append("Path does not exists, try again")
  704. list_of_files = glob.glob(os.path.normpath(pathth+ '\\*'))
  705. if 'reference' not in globals():
  706. textBrow.append('reading reference from drive')
  707. refpath = os.path.normpath(max([file for file in list_of_files if 'ref' in file], key=os.path.getmtime))
  708. reference = np.load(refpath)
  709. reference = reference.reshape(5, int(reference.shape[0]/5), reference.shape[1])
  710. else:
  711. reference
  712. Reference = reference
  713. ref = Reference[0].real
  714. mask_x = Reference[1]
  715. mask_y = Reference[2]
  716. Ix_ref = Reference[3]
  717. Iy_ref = Reference[4]
  718. Gradpath = os.path.normpath(max([file for file in list_of_files if 'zStack' in file], key=os.path.getmtime))
  719. _, tail = os.path.split(Gradpath)
  720. if 'zInt' not in globals():
  721. textBrow.append('reading zGradients from disk')
  722. zImage = np.load(Gradpath)
  723. zImage = zImage.reshape(int(zImage.shape[0]/ref.shape[0]), ref.shape[0], ref.shape[1])
  724. zImage = intensity_image(zImage, ref, choose_coord=False)
  725. else:
  726. zImage = zInt
  727. textBrow.append('The start position'+str(mmc.getPosition(Zstage)))
  728. nIm = int(lineAcqCalibNbI.text())
  729. nImAv = 10
  730. if lineFitDegree.text() =='':
  731. degree = 12# = 10
  732. else:
  733. degree = int(lineFitDegree.text())
  734. frequency = int(lineAcqCalibFr.text())
  735. Stack = np.zeros((nIm, int(mmc.getImage().shape[0]), int(mmc.getImage().shape[1])))
  737. posPath = os.path.normpath(max([file for file in list_of_files if '_posRead' in file], key=os.path.getmtime))
  738. poslist = np.loadtxt(posPath)
  739. cfr = 1
  740. drft = []
  741. posN=[]
  742. micPos=[]
  743. poslist = poslist-poslist.min()
  744. x = poslist-poslist[int(len(poslist)/2)]
  745. xx = np.arange(x.min(), x.max(), 0.001)
  746. frame = int(len(zImage)/2)
  747. sift_ocl = silx.image.sift.SiftPlan(template=zImage[frame], devicetype="GPU")
  748. keypoints = sift_ocl(zImage[frame])
  749. mp = sift.MatchPlan()
  751. plt.ion()
  752. xg = np.linspace(0, nIm, nIm)
  753. yg = np.arange(-1.5, 1.5, 3/nIm)
  754. fig = plt.figure()
  755. ax = fig.add_subplot(111)
  756. line1, = ax.plot(xg, yg, 'b+:')
  757. plt.xlabel("Refocusing step")
  758. plt.ylabel("Drift, μm")
  759. plt.title("Updating plot...")
  760. yyg = np.zeros(nIm)
  761. yyg[:]= np.nan
  762. yygg = np.zeros(nIm)
  763. yygg[:]= np.nan
  764. k=0
  766. plt.ion()
  767. fig2 = plt.figure()
  768. ax2 = fig2.add_subplot(111)
  769. line2, = ax2.plot(x, np.linspace(0, 0.0005, len(zImage)), 'b+:')
  770. line3, = ax2.plot(x, np.linspace(0, 0.0005, len(zImage)), 'r')
  771. plt.xlabel("Refocusing step")
  772. plt.ylabel("CCC, μm")
  773. plt.title("Updating plot...")
  774. progressBar.setValue(0)
  775. c = 100/nIm
  776. for i in range(0, nIm):
  777. if keyboard.is_pressed('q'): # Check if 'q' is pressed
  778. textBrow.append("Interrupted by user")
  779. break
  780. mmc.snapImage()
  781. stack = np.zeros((nImAv, int(mmc.getImage().shape[0]), int(mmc.getImage().shape[1])))
  782. for n in range(0, nImAv):
  783. mmc.snapImage()
  784. stack[n] = mmc.getImage()
  785. st = np.mean(stack, axis=0)
  786. Stack[i] = st
  787. ###############################
  788. if (i % frequency == 0) == True:
  789. ###############################
  790. if lineFitDegree.text() =='':
  791. degree = 12
  792. else:
  793. degree = int(lineFitDegree.text())
  794. corrcoef = []
  795. pos = mmc.getPosition(Zstage)
  796. start = time.time()
  797. imageInt = intensity_image(Stack[i], ref, choose_coord = False)
  798. ### Shift correction
  799. try:
  800. im_keypoints = sift_ocl(imageInt.astype(np.float32))
  801. match = mp(keypoints, im_keypoints)
  802. sa = silx.image.sift.LinearAlign(zImage[frame], devicetype="GPU")
  803. im = sa.align(imageInt, shift_only=True)
  804. except:
  805. np.savetxt(file_name_check(pathth+'/detectedDrift_int_err1_z'+tail[-5]+'.txt'), pd.concat([pd.DataFrame(drft), pd.DataFrame(micPos)], axis=1))
  806. imwrite(file_name_check(pathth + '\\Stack_autofocus_int_err1_z'+tail[-5]+'.tif', Stack.astype(np.float16)))
  807. with open(pathth + '\\METADATA.txt', 'a') as file:
  808. file.write('Finished with xy drift correction error, aqusition with autofocus recorded at %s\n N of images %s \n frequency:%s\n Exposure: %s\n Lamp: %s\n degree: %s\n ' %(datetime.datetime.now(), nIm, frequency, exposure, ints, degree))
  809. textBrow.append('xy correction failed, data was saved')
  810. break
  811. ######################
  812. for f in range(0, len(zImage)):
  813. try:
  814. corrcoef.append(ncc(im[200:1300, 200:1300], zImage[f,200:1300, 200:1300]))
  815. except:
  816. np.savetxt(file_name_check(pathth+'/detectedDrift_grad_err2_z'+tail[-5]+'.txt'), pd.concat([pd.DataFrame(drft), pd.DataFrame(micPos)], axis=1))
  817. textBrow.append("Done, data saved after xy-drfit correction didn't work")
  818. imwrite(file_name_check(pathth + '\\Stack_autofocus_grad_err2_z'+tail[-5]+'.tif'), Stack.astype(np.float16))
  819. with open(pathth + '\\METADATA.txt', 'a') as file:
  820. file.write('Aqqusition with autofocus recorded FAIL correlation at %s\n N of images %s \n frequency:%s\n Exposure: %s\n Lamp: %s\n degree: %s\n ' %(datetime.datetime.now(), nIm, frequency, exposure, ints, degree))
  821. textBrow.append('xy correction failed, data was saved')
  822. break
  823. y = np.array(corrcoef)
  824. model2 = np.poly1d(np.polyfit(x, y, degree))
  825. drft.append(xx[where(model2(xx) == model2(xx).max())])
  826. i=i+1
  827. yyg[i] = drft[-1][0]
  828. line1.set_ydata(yyg)
  829. k = k+1
  830. fig.canvas.draw()
  831. fig.canvas.flush_events()
  832. ax2.set_ylim(y.min()-y.min()*0.1, y.max()+y.max()*0.1)
  833. line2.set_ydata(y)
  834. line3.set_ydata(model2(x))
  835. fig2.canvas.draw()
  836. fig2.canvas.flush_events()
  837. if keyboard.is_pressed('q'): # Check if 'q' is pressed
  838. textBrow.append("Interrupted by user")
  839. break
  840. textBrow.append('newvalue'+str(drft[-1]))
  841. micPos.append(mmc.getPosition(Zstage))
  842. if keyboard.is_pressed('s'): # Check if 'q' is pressed
  843. textBrow.append("Interrupted by user, the data will be saved")
  844. np.savetxt(file_name_check(pathth+'/detectedDrift_int_interupted_z'+tail[-5]+'.txt'), pd.concat([pd.DataFrame(drft), pd.DataFrame(micPos)], axis=1))
  845. progressBar.setFormat("Done, interupted")
  846. np.save(file_name_check(pathth + '\\Stack_autofocus_int_interupted_z'+tail[-5]+'.npy'), Stack.astype(np.float16).reshape(Stack.shape[0]*Stack.shape[1], Stack.shape[2]))
  847. break
  848. if (np.abs(drft[-1]) < 0.010):
  849. print('drift was smaller then 10 nm')
  850. continue
  851. mmc.setPosition(Zstage, pos-drft[-1][0])
  852. mmc.waitForDevice(Zstage)
  853. cfr = cfr+1
  854. textBrow.append("calibration, detected drift = "+str(drft[-1])+'the drift was = '+str(pos-drft[-1])+'microscope reply = '+str(mmc.getPosition(Zstage))+'it took '+str(time.time()-start)+'to refocus')
  855. progressBar.setValue(int(i*c))
  856. plt.figure()
  857. #plt.plot(xx, model2(xx))
  858. plt.plot(x, y)
  859. plt.figure()
  860. plt.plot(micPos)
  861. plt.show()
  862. np.savetxt(file_name_check(pathth+'/detectedDrift_int_z'+tail[-5]+'.txt'), pd.concat([pd.DataFrame(drft), pd.DataFrame(micPos)], axis=1))
  863. textBrow.append("Done")
  864. np.save(file_name_check(pathth + '\\Stack_autofocus_int_z'+tail[-5]+'.npy'), Stack.astype(np.float16).reshape(Stack.shape[0]*Stack.shape[1], Stack.shape[2]))
  865. with open(pathth + '\\METADATA.txt', 'a') as file:
  866. file.write('Aqqusition with autofocus recorded at %s\n N of images %s \n frequency:%s\n Exposure: %s\n Lamp: %s\n degree: %s\n ' %(datetime.datetime.now(), nIm, frequency, exposure, ints, degree))
  869. ################################################################################################
  870. ################################################################################################
  871. ######################## Main window created by QtDesigner ################################
  872. ################################################################################################
  875. class Ui(QtWidgets.QMainWindow):
  876. def __init__(self):
  877. super(Ui, self).__init__()
  878. ui = uic.loadUi('Z:/Hanna/CODE/Autofocus_fArtcl/Autofocus.ui', self)
  879. global lineRef,lineLamp,line_name,lineExp,line_start,line_stop,line_step,linePath,line_nImAcq, lineZpos,lineDegree ## The global names are defined in order
  880. global line_nIm,progressBar,line_acq,linePhase,lineRoi,lineCalib,labelRefIm,lineRefIm,lineAcqCalibNbI,lineAcqCalibFr,lineFitDegree # be able to use it outside the window function
  881. global checkBoxIntsave,textBrow
  882. lineFitDegree = ui.fitDegree
  883. lineRef = ui.lineRef
  884. lineLamp = ui.lineLamp
  885. line_nImAcq = ui.line_nImAcq
  886. #line_name = ui.line_name
  887. lineExp = ui.lineExp
  888. line_start = ui.line_start
  889. line_stop = ui.line_stop
  890. line_step = ui.line_step
  891. linePath = ui.line_path
  892. line_nIm = ui.line_nIm
  893. progressBar = ui.progressBar
  894. lineAcqCalibNbI = ui.lineAcqCalibNbI
  895. lineAcqCalibFr = ui.lineAcqCalibFr
  896. line_acq = ui.line_acq
  897. lineZpos = ui.lineZpos
  898. #checkBoxIntsave = ui.checkBoxIntsave
  899. #checkBoxRAWsave = ui.checkBoxRAWsave
  901. textBrow = ui.fuckingText
  903. #checkBoxIntsave.stateChanged.connect(checkbox_saveInt)
  904. #checkBoxRAWsave.stateChanged.connect(checkbox_saveRAW)
  906. buttonMaxV = ui.buttonMaxV
  907. buttonMaxV.clicked.connect(check_max)
  909. buttonRef = ui.buttonRef
  910. buttonRef.clicked.connect(reference_image)
  912. buttonExp = ui.buttonExp
  913. buttonExp.clicked.connect(set_exposure)
  915. buttonLampOn = ui.LampOn
  916. buttonLampOn.clicked.connect(Lamp_on)
  918. buttonLampOff = ui.LampOff
  919. buttonLampOff.clicked.connect(Lamp_off)
  921. buttonLamp = ui.buttonLamp
  922. buttonLamp.clicked.connect(set_lamp_int)
  924. buttonZpos = ui.buttonZpos
  925. buttonZpos.clicked.connect(set_z_pos)
  927. buttonZ = ui.buttonZ
  928. buttonZ.clicked.connect(zStack)
  930. buttonAcq = ui.buttonAcq
  931. buttonAcq.clicked.connect(stack_acquisition)
  933. buttonAcqCalib_int = ui.buttonAcqCalib_int
  934. buttonAcqCalib_int.clicked.connect(acquisition_calib_int)
  936. buttonAcqCalib_grads = ui.buttonAcqCalib_grads
  937. buttonAcqCalib_grads.clicked.connect(acquisition_calib_grad)
  938. ######## tests
  939. buttonZ_stepping = ui.buttonZ_stepping
  940. buttonZ_stepping.clicked.connect(test_z_stack)
  942. buttonLive = ui.startLive
  943. buttonLive.clicked.connect(start_live)
  945. self.show()
  946. app = QtWidgets.QApplication(sys.argv)
  947. window = Ui()
  948. window.show()
  949. app.exec_()
  952. ################################################################################################
  953. ################################################################################################

Autofocus_ui_v1.3.py at commit 64141b1, no license · at the source

Overview

Authors: Hanna Manko1,2, Marc Tondusson1,2, Adeline Boyreau1,2, Morgane Meras3, Stéphane Bancelin1,2, Laurent Groc3, Laurent Cognet1,2
ORCID iDs: Laurent Cognet
  1. Laboratoire Photonique Numérique et Nanosciences, Université de Bordeaux, 33400, Talence, France
  2. LP2N, Institut d’Optique Graduate School, CNRS UMR 5298, 33400, Talence, France
  3. Interdisciplinary Institute for Neuroscience, CNRS, Univ. Bordeaux, 33076, Bordeaux, France
Institutions: Laboratoire Photonique, Numérique et Nanosciences (France); Université de Bordeaux (France); Institut d’Optique Graduate School (France); Institut Interdisciplinaire de Neuroscience (France)
Journal: bioRxiv (preprint)
Dates: published online 14 March 2026
Type: Preprint
License: CC BY-NC
Identifiers: DOI 10.64898/2026.03.12.711239 · OpenAlex W7135371821
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality)
Methods: Spectral & time-frequency, fMRI & imaging
Keywords: Microscope stabilization, deep tissue imaging, oblique back-illumination, phase contrast imaging, single-particle tracking, super-resolution microscopy
Topic: Digital Holography and Microscopy (Atomic and Molecular Physics, and Optics, Physics and Astronomy), according to OpenAlex
Funding: European Research Council (951294)
Citations: not cited yet (Europe PMC); 36 references in the paper

Abstract

High-resolution optical microscopy enables nanoscale investigation of molecular structures but is challenged by sample drift during long acquisitions, particularly in thick biological tissues where trans-illumination is unpractical. Precise stabilization at the nanoscale is critical for high-resolution imaging techniques like localization microscopy and single particle tracking. Here we introduce a method combining homogenized differential phase contrast imaging with cross-correlation-based analysis to achieve automated, precise 3D drift correction applicable under oblique back-illumination. We demonstrate its effectiveness in fixed and live organotypic brain slices, maintaining focus within tens of nanometers and enabling high-quality nanoscale mapping of extracellular structures based on single particle tracking. Furthermore, we illustrate its application to opaque liver tissues combined with near-infrared single particle tracking. Our label-free approach provides a versatile solution for stabilizing optical microscopes in thick non-transparent tissues, facilitating extended high-resolution imaging across increasingly complex biological samples.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

hmanko/hDPC_autofocus

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 64141b18ee1b913536e088b1da777666fc3ceb28, 27 March 2026
Languages: Python (3)
Size: 8 files, 3 scripts
Software Heritage: not archived
Found in: “Data and Software Availability”
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: Matplotlib (3 files), NumPy (3 files), tifffile (3 files), pandas (2 files), scikit-image (2 files), SciPy (2 files), CuPy (1 file), napari (1 file), OpenCV (1 file), Pillow (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
4 files
At the source: github.com/hmanko/hDPC_autofocus.git

The paper's code and data availability statement is in the Data section.

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 3 scripts, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

No dataset and no data link were found in the paper.

Data and Software Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

The custom python-based software and the user interface developed during this study together with user manual are freely available at https://github.com/hmanko/hDPC_autofocus.git.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, journal, dates, 7 authors, 6 keywords, 1 funder, 32 references.

Cite

This paper

Manko, H., Tondusson, M., Boyreau, A., Meras, M., Bancelin, S., Groc, L., & Cognet, L. (2026). Back-illumination Phase Imaging Enables Nanoscale Drift Stabilization in Non-transparent Biological Tissues. bioRxiv (preprint). https://doi.org/10.64898/2026.03.12.711239

BibTeX

@article{manko2026back,
author = {Manko, Hanna and Tondusson, Marc and Boyreau, Adeline and Meras, Morgane and Bancelin, Stéphane and Groc, Laurent and Cognet, Laurent},
title = {{Back-illumination Phase Imaging Enables Nanoscale Drift Stabilization in Non-transparent Biological Tissues}},
journal = {bioRxiv (preprint)},
year = {2026},
month = mar,
publisher = {bioRxiv},
issn = {2692-8205},
doi = {10.64898/2026.03.12.711239},
url = {https://doi.org/10.64898/2026.03.12.711239}
}

RIS

TY - JOUR
AU - Manko, Hanna
AU - Tondusson, Marc
AU - Boyreau, Adeline
AU - Meras, Morgane
AU - Bancelin, Stéphane
AU - Groc, Laurent
AU - Cognet, Laurent
TI - Back-illumination Phase Imaging Enables Nanoscale Drift Stabilization in Non-transparent Biological Tissues
T2 - bioRxiv (preprint)
J2 - bioRxiv
PY - 2026
DA - 2026/03/14
SN - 2692-8205
PB - bioRxiv
DO - 10.64898/2026.03.12.711239
UR - https://doi.org/10.64898/2026.03.12.711239
ER -

CSL-JSON

{
"id": "10.64898/2026.03.12.711239",
"type": "article",
"title": "Back-illumination Phase Imaging Enables Nanoscale Drift Stabilization in Non-transparent Biological Tissues",
"container-title": "bioRxiv (preprint)",
"author": [
{
"family": "Manko",
"given": "Hanna"
},
{
"family": "Tondusson",
"given": "Marc"
},
{
"family": "Boyreau",
"given": "Adeline"
},
{
"family": "Meras",
"given": "Morgane"
},
{
"family": "Bancelin",
"given": "Stéphane"
},
{
"family": "Groc",
"given": "Laurent"
},
{
"family": "Cognet",
"given": "Laurent"
}
],
"container-title-short": "bioRxiv",
"DOI": "10.64898/2026.03.12.711239",
"ISSN": "2692-8205",
"publisher": "bioRxiv",
"URL": "https://doi.org/10.64898/2026.03.12.711239",
"issued": {
"date-parts": [
[
2026,
3,
14
]
]
}
}

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