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| <syntaxhighlight lang="python" line>
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| # Let's modify the code to include error handling, loop safeguards, and logging to ensure that the code is more robust and less likely to crash.
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|
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| import logging
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|
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| # Setting up logging to track the execution and identify issues
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| #logging.basicConfig(filename='/FocusWobble.log', level=logging.DEBUG,
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| # format='%(asctime)s - %(levelname)s - %(message)s')
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|
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| from win32com.client import gencache
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| import SEM_API_fix as SEM_API
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| import matplotlib.pyplot as plt
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| import numpy as np
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| import time
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| import math
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| from imageio import imread
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|
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| # Imitate focus wobble
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|
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| def FocusWobble():
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| try:
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| # Get values
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| with SEM_API.SEM_API("remote") as sem:
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| wd_get = sem.GetValue("AP_WD")
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|
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| # Set wobble parameters
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| number_of_images = 15
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| number_of_overlaps = 1
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| wd_percentage_range = 0.002
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|
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| # Set image parameters
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| #store_resolution = 512 # set *3/4, DP_IMAGE_STORE
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| #file_name = "fliptest"
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|
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| # Set imaging conditions
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| with SEM_API.SEM_API("remote") as sem:
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| sem.Execute("CMD_UNFREEZE_ALL")
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| sem.Execute("CMD_SCANRATE7")
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|
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| # Loop to capture and save images
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| for i in range(number_of_images):
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| wd = wd_get * (1 + wd_percentage_range * math.sin(2 * math.pi * i / (number_of_images - number_of_overlaps)))
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| logging.info(f"Image {i}: WD={wd}")
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| try:
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| with SEM_API.SEM_API("remote") as sem:
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| s = sem.SetValue("AP_WD", wd)
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| sem.Execute("CMD_UNFREEZE_ALL")
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| time.sleep(0.5)
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| sem.Execute("CMD_FREEZE_ALL")
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|
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| retries = 0
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| max_retries = 10
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| while sem.GetState("DP_BEAM_BLANKED") == "No":
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| if retries >= max_retries:
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| logging.warning(f"Max retries reached for image {i}, exiting loop.")
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| break
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| #print(retries)
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| time.sleep(1)
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| retries += 1
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|
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| sem.Execute("CMD_TAKE_PHOTO")
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| except Exception as e:
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| logging.error(f"Error during image capture {i}: {e}")
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|
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| except Exception as e:
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| logging.critical(f"Critical error in FocusWobble: {e}")
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|
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| # Call the function
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| #FocusWobble()
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|
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| number_of_images = 25
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| wd_get = 4.5
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| wd_percentage_range= 0.002
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| number_of_overlaps = 4
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|
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| for i in range(number_of_images):
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| wd = wd_get * (1 + wd_percentage_range * math.sin(2 * math.pi * i / (number_of_images - number_of_overlaps)))
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| print(i,wd)
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|
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| </syntaxhighlight>
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