Autoformat

stimuli/illusions/benary_cross.py

@@ -40,8 +40,12 @@ def benarys_cross(
     A stimulus object
     """
 
-
-    cross_top_px, cross_bottom_px, cross_left_px, cross_right_px = degrees_to_pixels(cross_size,ppd)
+    (
+        cross_top_px,
+        cross_bottom_px,
+        cross_left_px,
+        cross_right_px,
+    ) = degrees_to_pixels(cross_size, ppd)
     cross_thickness_px = degrees_to_pixels(cross_thickness, ppd)
     target_size_px = degrees_to_pixels(target_size, ppd)
 
@@ -56,25 +60,28 @@ def benarys_cross(
     img[:, x_edge_left:x_edge_right] = cross
     img[y_edge_top:y_edge_bottom, :] = cross
 
-
     tpos1y = y_edge_top - target_size_px
     tpos1x = x_edge_left - target_size_px
     tpos2y = y_edge_top
     tpos2x = -target_size_px
-    img[tpos1y:tpos1y + target_size_px, tpos1x:tpos1x + target_size_px] = target
-    img[tpos2y:tpos2y + target_size_px, tpos2x:] = target
+    img[
+        tpos1y : tpos1y + target_size_px, tpos1x : tpos1x + target_size_px
+    ] = target
+    img[tpos2y : tpos2y + target_size_px, tpos2x:] = target
 
-    mask[tpos1y:tpos1y + target_size_px, tpos1x:tpos1x + target_size_px] = 1
-    mask[tpos2y:tpos2y + target_size_px, tpos2x:] = 2
+    mask[
+        tpos1y : tpos1y + target_size_px, tpos1x : tpos1x + target_size_px
+    ] = 1
+    mask[tpos2y : tpos2y + target_size_px, tpos2x:] = 2
 
     img = pad_img(img, padding, ppd, back)
     mask = pad_img(mask, padding, ppd, 0)
 
-
     return {"img": img, "mask": mask}
 
 
 if __name__ == "__main__":
     import matplotlib.pyplot as plt
+
     stim = benarys_cross()
     plot_stim(stim, mask=True)

stimuli/illusions/bullseye.py

@@ -1,9 +1,19 @@
 import numpy as np
 from stimuli.illusions.rings import ring_pattern
-from stimuli.utils import degrees_to_pixels, pad_img
 from stimuli.Stimulus import Stimulus
 
-def bullseye_illusion(ppd=10, n_rings=8, ring_width=.5, target_pos_l=0, target_pos_r=0, padding=(1.0,1.0,1.0,1.0), back=0., rings=1., target=.5):
+
+def bullseye_illusion(
+    ppd=10,
+    n_rings=8,
+    ring_width=0.5,
+    target_pos_l=0,
+    target_pos_r=0,
+    padding=(1.0, 1.0, 1.0, 1.0),
+    back=0.0,
+    rings=1.0,
+    target=0.5,
+):
     """
     Bullseye Illusion.
     Two ring patterns (see ring_pattern func), with target in centre and one ring pattern inverted.
@@ -33,15 +43,33 @@ def bullseye_illusion(ppd=10, n_rings=8, ring_width=.5, target_pos_l=0, target_p
     -------
     A stimulus object
     """
-    stim1 = ring_pattern(n_rings=n_rings, target_pos_l=target_pos_l, ring_width=ring_width, padding=padding,
-                        back=back, rings=rings, target=target, invert_rings=False, double=False)
-    stim2 = ring_pattern(n_rings=n_rings, target_pos_l=target_pos_r, ring_width=ring_width, padding=padding,
-                        back=back, rings=rings, target=target, invert_rings=True, double=False)
+    stim1 = ring_pattern(
+        n_rings=n_rings,
+        target_pos_l=target_pos_l,
+        ring_width=ring_width,
+        padding=padding,
+        back=back,
+        rings=rings,
+        target=target,
+        invert_rings=False,
+        double=False,
+    )
+    stim2 = ring_pattern(
+        n_rings=n_rings,
+        target_pos_l=target_pos_r,
+        ring_width=ring_width,
+        padding=padding,
+        back=back,
+        rings=rings,
+        target=target,
+        invert_rings=True,
+        double=False,
+    )
 
-    img = np.hstack((stim1['img'], stim2['img']))
+    img = np.hstack((stim1["img"], stim2["img"]))
     # Increase target mask values to differentiate from single-stimulus targets:
-    stim2['mask'][stim2['mask'] != 0] += 1
-    mask = np.hstack((stim1['mask'], stim2['mask']))
+    stim2["mask"][stim2["mask"] != 0] += 1
+    mask = np.hstack((stim1["mask"], stim2["mask"]))
 
     return {"img": img, "mask": mask}
 
@@ -75,8 +103,9 @@ def RHS2007_bullseye_thick():
     )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     import matplotlib.pyplot as plt
+
     stim = bullseye_illusion()
-    plt.imshow(stim.img, cmap='gray')
+    plt.imshow(stim.img, cmap="gray")
     plt.show()

stimuli/illusions/checkerboard_contrast_contrast.py

@@ -13,7 +13,7 @@ def checkerboard_contrast_contrast_effect(
     check2=2.0,
     tau=0.5,
     alpha=0.5,
-    limit_mask_vals=True
+    limit_mask_vals=True,
 ):
     """
     Contrast-contrast effect on checkerboard with square transparency layer.
@@ -54,7 +54,7 @@ def checkerboard_contrast_contrast_effect(
 
     idx = np.zeros((n_checks, n_checks), dtype=bool)
     tpos = (n_checks - target_length) // 2
-    idx[tpos:tpos + target_length, tpos:tpos + target_length] = True
+    idx[tpos : tpos + target_length, tpos : tpos + target_length] = True
     arr1[idx] = alpha * arr1[idx] + (1 - alpha) * tau
     mask_id = 0
     for i, _ in enumerate(idx):
@@ -71,14 +71,22 @@ def checkerboard_contrast_contrast_effect(
     mask_arr2 = mask_arr1.copy()
     mask_arr2[mask_arr2 != 0] += mask_id
 
-    img1 = np.repeat(np.repeat(arr1, check_size_px, axis=0), check_size_px, axis=1)
+    img1 = np.repeat(
+        np.repeat(arr1, check_size_px, axis=0), check_size_px, axis=1
+    )
     img1 = pad_img(img1, padding, ppd, tau)
-    img2 = np.repeat(np.repeat(arr2, check_size_px, axis=0), check_size_px, axis=1)
+    img2 = np.repeat(
+        np.repeat(arr2, check_size_px, axis=0), check_size_px, axis=1
+    )
     img2 = pad_img(img2, padding, ppd, tau)
 
-    mask1 = np.repeat(np.repeat(mask_arr1, check_size_px, axis=0), check_size_px, axis=1)
+    mask1 = np.repeat(
+        np.repeat(mask_arr1, check_size_px, axis=0), check_size_px, axis=1
+    )
     mask1 = pad_img(mask1, padding, ppd, 0)
-    mask2 = np.repeat(np.repeat(mask_arr2, check_size_px, axis=0), check_size_px, axis=1)
+    mask2 = np.repeat(
+        np.repeat(mask_arr2, check_size_px, axis=0), check_size_px, axis=1
+    )
     mask2 = pad_img(mask2, padding, ppd, 0)
 
     img = np.hstack([img1, img2])
@@ -90,5 +98,6 @@ def checkerboard_contrast_contrast_effect(
 
 if __name__ == "__main__":
     import matplotlib.pyplot as plt
+
     stim = checkerboard_contrast_contrast_effect()
     plot_stim(stim, mask=True)

stimuli/illusions/checkerboard_sbc.py

@@ -1,15 +1,18 @@
 import numpy as np
-from stimuli.utils import (
-    degrees_to_pixels,
-    pad_img,
-    pad_img_to_shape,
-    plot_stim,
-)
+from stimuli.utils import degrees_to_pixels, pad_img_to_shape, plot_stim
 from stimuli.Stimulus import Stimulus
-import matplotlib.pyplot as plt
 
-def checkerboard_contrast(ppd=10, board_shape=(8,8), check_size=1.0, targets_coords=((3, 2), (5, 5)), extend_targets=False,
-                         check1=0., check2=1., target=.5):
+
+def checkerboard_contrast(
+    ppd=10,
+    board_shape=(8, 8),
+    check_size=1.0,
+    targets_coords=((3, 2), (5, 5)),
+    extend_targets=False,
+    check1=0.0,
+    check2=1.0,
+    target=0.5,
+):
     # TODO: targets_coords used to be coord1 and coords2, now multiple targets are allowed. This should be changed in all places calling this function
     """
     Checkerboard Contrast
@@ -53,13 +56,13 @@ def checkerboard_contrast(ppd=10, board_shape=(8,8), check_size=1.0, targets_coo
 
     for i, coords in enumerate(targets_coords):
         arr[coords] = target
-        mask[coords] = i+1
+        mask[coords] = i + 1
 
     if extend_targets:
         for i, coords in enumerate(targets_coords):
             for idx in [(-1, 0), (0, 1), (1, 0), (0, -1)]:
                 arr[coords[0] + idx[0], coords[1] + idx[1]] = target
-                mask[coords[0] + idx[0], coords[1] + idx[1]] = i+1
+                mask[coords[0] + idx[0], coords[1] + idx[1]] = i + 1
 
     img = np.repeat(
         np.repeat(arr, check_size_px, axis=0), check_size_px, axis=1
@@ -93,8 +96,8 @@ def RHS2007_Checkerboard016():
         target=target,
     )
 
-    img = pad_img_to_shape(stim['img'], (1024, 1024), val=target)
-    mask = pad_img_to_shape(stim['mask'], (1024, 1024), val=0)
+    img = pad_img_to_shape(stim["img"], (1024, 1024), val=target)
+    mask = pad_img_to_shape(stim["mask"], (1024, 1024), val=0)
 
     return {"img": img, "mask": mask}
 
@@ -120,8 +123,8 @@ def RHS2007_Checkerboard0938():
         check2=check2,
         target=target,
     )
-    img = pad_img_to_shape(stim['img'], (1024, 1024), val=target)
-    mask = pad_img_to_shape(stim['mask'], (1024, 1024), val=0)
+    img = pad_img_to_shape(stim["img"], (1024, 1024), val=target)
+    mask = pad_img_to_shape(stim["mask"], (1024, 1024), val=0)
 
     return {"img": img, "mask": mask}
 
@@ -147,8 +150,8 @@ def RHS2007_Checkerboard209():
         check2=check2,
         target=target,
     )
-    img = pad_img_to_shape(stim['img'], (1024, 1024), val=target)
-    mask = pad_img_to_shape(stim['mask'], (1024, 1024), val=0)
+    img = pad_img_to_shape(stim["img"], (1024, 1024), val=target)
+    mask = pad_img_to_shape(stim["mask"], (1024, 1024), val=0)
 
     return {"img": img, "mask": mask}
 

stimuli/illusions/cornsweet.py

@@ -2,9 +2,15 @@
 from stimuli.Stimulus import Stimulus
 
 
-def cornsweet(size=(10,10), ppd=10, contrast=0.5, ramp_width=2, exponent=2.75,
-              mean_lum=.5):
-    #TODO: the parameters aren't analogous to the other stimuli
+def cornsweet(
+    size=(10, 10),
+    ppd=10,
+    contrast=0.5,
+    ramp_width=2,
+    exponent=2.75,
+    mean_lum=0.5,
+):
+    # TODO: the parameters aren't analogous to the other stimuli
     """
     Create a matrix containing a rectangular Cornsweet edge stimulus.
     The 2D luminance profile of the stimulus is defined as
@@ -43,20 +49,21 @@ def cornsweet(size=(10,10), ppd=10, contrast=0.5, ramp_width=2, exponent=2.75,
     S.O., Kersten, D. (2007). Responses to Lightness Variations in Early Human
     Visual Cortex. Current Biology 17, 989-993.
     """
-    size = [int(size[0]*ppd), int(size[1]*ppd)]
+    size = [int(size[0] * ppd), int(size[1] * ppd)]
     img = np.ones(size) * mean_lum
-    dist = np.arange(size[1] / 2.)
-    dist = dist / (ramp_width*ppd)
-    dist[dist > 1.] = 1.
-    profile = (1. - dist) ** exponent * mean_lum * contrast
-    img[:, :int(np.ceil(size[1]/2.))] += profile[::-1]
-    img[:, size[1] // 2:] -= profile
+    dist = np.arange(size[1] / 2.0)
+    dist = dist / (ramp_width * ppd)
+    dist[dist > 1.0] = 1.0
+    profile = (1.0 - dist) ** exponent * mean_lum * contrast
+    img[:, : int(np.ceil(size[1] / 2.0))] += profile[::-1]
+    img[:, size[1] // 2 :] -= profile
     mask = None
     return {"img": img, "mask": mask}
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     import matplotlib.pyplot as plt
+
     stim = cornsweet()
-    plt.imshow(stim, cmap='gray')
+    plt.imshow(stim, cmap="gray")
     plt.show()