first version of plotting all stimuli and masks

stimuli/illusions/plot_murray_stimuli_and_masks.py

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+import numpy as np
+from os.path import dirname, join as pjoin
+import scipy.io as sio
+import matplotlib.pyplot as plt
+
+data_dir = pjoin(dirname(sio.__file__), 'matlab', 'ablage') 
+mat_fname = pjoin(data_dir, 'grid_stimuli.mat')
+mat_content = sio.loadmat(mat_fname)  
+
+def argyle():
+    argyle = mat_content['argyle']
+    argyle = np.array(((argyle[0])[0])[0])
+    #print(argyle)
+    return argyle 
+
+def argyle_control():
+    argyle_control = mat_content['argyle_control']
+    argyle_control = np.array(((argyle_control[0])[0])[0])
+    #print(argyle_control)
+    return argyle_control 
+
+def argyle_long():
+    argyle_long = mat_content['argyle_long']
+    argyle_long = np.array(((argyle_long[0])[0])[0])
+    #print(argyle_long)
+    return argyle_long 
+
+def snake():
+    snake = mat_content['snake']
+    snake = np.array(((snake[0])[0])[0])
+    #print(snake)
+    return snake 
+
+def snake_control():
+    snake_control = mat_content['snake_control']
+    snake_control = np.array(((snake_control[0])[0])[0])
+    #print(snake_control)
+    return snake_control 
+
+def koffka_adelson():
+    koffka_adelson = mat_content['koffka_adelson']
+    koffka_adelson = np.array(((koffka_adelson[0])[0])[0])
+    #print(koffka_adelson)
+    return koffka_adelson 
+
+def koffka_broken():
+    koffka_broken = mat_content['koffka_broken']
+    koffka_broken = np.array(((koffka_adelson[0])[0])[0])
+    #print(koffka_broken)
+    return koffka_broken 
+
+def koffka_connected():
+    koffka_connected = mat_content['koffka_connected']
+    koffka_connected = np.array(((koffka_connected[0])[0])[0])
+    #print(koffka_connected)
+    return koffka_connected
+
+def checkassim():
+    checkassim = mat_content['checkassim']
+    checkassim = np.array(((checkassim[0])[0])[0])
+    #print(checkassim)
+    return checkassim
+
+def simcon():
+    simcon = mat_content['simcon']
+    simcon = np.array(((simcon[0])[0])[0])
+    #print(simcon)
+    return simcon
+
+def simcon_articulated():
+    simcon_articulated = mat_content['simcon_articulated']
+    simcon_articulated = np.array(((simcon_articulated[0])[0])[0])
+    #print(simcon_articulated)
+    return simcon_articulated
+
+def white():
+    white = mat_content['white']
+    white = np.array(((white[0])[0])[0])
+    #print(white)
+    return white
+
+
+def get_mask(arr_list, ppd, mask):
+    target1 = arr_list[0]
+    target2 = arr_list[1]
+    y = target1[0]
+    x = target1[1]
+    mask[y][x] = 1
+    y = target1[2]
+    x = target1[3]
+    mask[y][x] = 1
+
+    x_diff = target1[3] - target1[1] +1
+    for i in range(x_diff):
+        y_diff = target1[2] - target1[0] +1
+        for j in range(y_diff):
+            y = target1[0]
+            x = target1[1]
+            mask[y+j][x+i] = 1  
+
+    y = target2[0]
+    x = target2[1]
+    mask[y][x] = 2
+    y = target2[2]
+    x = target2[3]
+    mask[y][x] = 2
+
+    x_diff = target2[3] - target2[1] +1
+    for i in range(x_diff):
+        y_diff = target2[2] - target2[0] +1
+        for j in range(y_diff):
+            y = target2[0]
+            x = target2[1]
+            mask[y+j][x+i] = 2
+
+    plt.imshow(mask)
+    return(mask)
+
+
+def ini_matrix(ppd):
+    a = []
+    for i in range(ppd):
+        a.append(np.zeros(ppd))
+    return(a)
+
+def ini_matrix_2(ppd):
+    a = []
+    for i in range(ppd):
+        a.append(np.zeros(25))
+    return(a)
+
+
+def get_mask_argyle():
+   a = mat_content['argyle']
+   t = np.array((((a[0])[0])[1])[0])
+   t2 = np.array((((a[0])[0])[2])[0])
+   arr_list = [t-int(ppd/16),t2-int(ppd/16)]
+   #arr_list = [t-1,t2-1]
+   mask = np.array(ini_matrix(ppd))   
+   return (get_mask(arr_list, ppd, mask))
+
+def get_mask_argyle_control():
+   a = mat_content['argyle_control']
+   t = np.array((((a[0])[0])[1])[0])
+   t2 = np.array((((a[0])[0])[2])[0])
+   arr_list = [t-1,t2-1] 
+   mask = np.array(ini_matrix(ppd))
+   return(get_mask(arr_list, ppd, mask))
+
+def get_mask_argyle_long():
+   a = mat_content['argyle_long']
+   t = np.array((((a[0])[0])[1])[0])
+   t2 = np.array((((a[0])[0])[2])[0])
+   arr_list = [t-1,t2-1]
+   mask = np.array(ini_matrix_2(ppd))
+   return(get_mask(arr_list, ppd, mask))
+
+def get_mask_snake():
+   a = mat_content['snake']
+   t = np.array((((a[0])[0])[1])[0])
+   t2 = np.array((((a[0])[0])[2])[0])
+   arr_list = [t-1,t2-1] 
+   mask = np.array(ini_matrix(ppd))
+   return(get_mask(arr_list, ppd, mask))
+
+def get_mask_snake_control():
+   a = mat_content['snake_control']
+   t = np.array((((a[0])[0])[1])[0])
+   t2 = np.array((((a[0])[0])[2])[0])
+   arr_list = [t-1,t2-1]
+   mask = np.array(ini_matrix(ppd))
+   return(get_mask(arr_list, ppd, mask))
+
+def get_mask_koffka_adelson():
+   a = mat_content['koffka_adelson']
+   t = np.array((((a[0])[0])[1])[0])
+   t2 = np.array((((a[0])[0])[2])[0])
+   arr_list = [t-1,t2-1] 
+   mask = np.array(ini_matrix(ppd))
+   get_mask(arr_list, ppd, mask)
+
+def get_mask_koffka_broken():
+   a = mat_content['koffka_broken']
+   t = np.array((((a[0])[0])[1])[0])
+   t2 = np.array((((a[0])[0])[2])[0])
+   arr_list = [t-1,t2-1] 
+   mask = np.array(ini_matrix(ppd))
+   return(get_mask(arr_list, ppd, mask))
+
+def get_mask_koffka_connected():
+   a = mat_content['koffka_connected']
+   t = np.array((((a[0])[0])[1])[0])
+   t2 = np.array((((a[0])[0])[2])[0])
+   arr_list = [t-1,t2-1] 
+   mask = np.array(ini_matrix(ppd))
+   return(get_mask(arr_list, ppd, mask))
+
+def get_mask_checkassim():
+   a = mat_content['checkassim']
+   t = np.array((((a[0])[0])[1])[0])
+   t2 = np.array((((a[0])[0])[2])[0])
+   arr_list = [t-1,t2-1]
+   mask = np.array(ini_matrix(ppd))
+   return(get_mask(arr_list, ppd, mask))
+
+def get_mask_simcon():
+   a = mat_content['simcon']
+   t = np.array((((a[0])[0])[1])[0])
+   t2 = np.array((((a[0])[0])[2])[0])
+   arr_list = [t-1,t2-1]
+   mask = np.array(ini_matrix(ppd))
+   return(get_mask(arr_list, ppd, mask))
+
+def get_mask_simcon_articulated():
+   a = mat_content['simcon_articulated']
+   t = np.array((((a[0])[0])[1])[0])
+   t2 = np.array((((a[0])[0])[2])[0])
+   arr_list = [t-1,t2-1]
+   mask = np.array(ini_matrix(ppd))
+   return(get_mask(arr_list, ppd, mask))
+
+def get_mask_white():
+   a = mat_content['white']
+   t = np.array((((a[0])[0])[1])[0])
+   t2 = np.array((((a[0])[0])[2])[0])
+   arr_list = [t-1,t2-1]
+   mask = np.array(ini_matrix(ppd))
+   return(get_mask(arr_list, ppd, mask))
+
+if __name__ == '__main__':
+
+    ppd = 16
+
+    # test argyle illusion
+    plt.subplot(1,2,1)
+    plt.imshow(argyle(), cmap='gray')
+    plt.subplot(1,2,2)
+    plt.imshow(get_mask_argyle(), cmap='gray')
+