Solved! - Trouble creating design matrix cued switching task

Comments

• favdberg

  March 2020

  This is the code I have so far to generate the design matrix. This code runs smoothly on my computer for approx. 16 trials, but my computer can't handle the computation of a matrix with 144 trials...

  # SET-UP
  # Design matrix PRACTICE list with 8 circles/squares, 8 blue/orange items,
  # 8 color/shape cues, and 8 switch/non-switch trials
  
  
  prac_colors = 8 * ["blue"] + 8 * ["orange"] # [0]: 0 = blue, 1 = orange
  prac_shapes = 4 * ["circle"] + 4 * ["square"] + 4 * ["circle"] + 4 * ["square"] # [1]: 0 = circle, 1 = square
  prac_cue = 8 * ["color","shape"] # [2]: 0 = color, 1 = shape
  prac_design_matrix = zip(prac_colors, prac_shapes, prac_cues)
  # print prac_design_matrix
  
  
  def chain_check(prac_switch_list):
  	''' this counts consecutive indices, from the second element to the final one
  		the longest chain is then selected and its length is computed'''
  
  
  	indices = []
  	for check_count,x in enumerate(prac_switch_list):
  		if check_count == 0:
  			pass
  		else:
  			previous = prac_switch_list[check_count -1]
  			current = prac_switch_list[check_count]
  
  
  			if previous == current:
  				indices.append(check_count)
  			else:
  				pass
  	
  	longest_chain = len(max(np.split(indices, np.where(np.diff(indices) != 1)[0]+1), key=len).tolist()) + 1
  	
  	return longest_chain
  
  def draw_permutation(prac_design_matrix):
  	prac_number_of_switches = 0
  	while prac_number_of_switches != 8:
  		# generate a random permutation of the design matrix
  		prac_mixed_list = np.random.permutation(prac_design_matrix).tolist()
  		
  		# generate switch information
  		prac_switch_list = []
  
  
  		for i,x in enumerate(prac_mixed_list):    
  			if True:
  				if i == 0:
  					prac_switch_list.append("non-switch")
  				
  				if i > 0 and i < (len(prac_mixed_list)):
  					previous = prac_mixed_list[i - 1]
  					current = prac_mixed_list[i]
  					if previous[2] == current[2]:
  						prac_switch_list.append("non-switch")
  					else:
  						prac_switch_list.append("switch")
  			# print longest_chain
  		prac_number_of_switches = prac_switch_list.count('switch')
  
  
  	# add switch information to permuted matrix 
  	for trial in range(len(prac_mixed_list)):
  		prac_mixed_list[trial].append(prac_switch_list[trial])
  
  
  	return prac_mixed_list, chain_check(prac_switch_list)
  
  longest_chain = 5
  while longest_chain > 4:  
  	final_prac_mixed_list = draw_permutation(prac_design_matrix)
  	longest_chain = final_prac_mixed_list[1]
  	print(longest_chain)
  	
  # final design matrix is below  
  for trial in final_prac_mixed_list[0]:
  	print(trial)
  
  final_prac_mixed_list = final_prac_mixed_list[0]
  

  Is there a more efficient or computationally less complicated way to generate such a matrix? Hope someone can help out! Let me know if you need any other info.

  Floor
• eduard

  March 2020

  Hi Floor,

  Is this problem solved or not? I am a bit confused with the order of your postings?

  
  

  Eduard