updated rings and bullseye to only create single stim and let paper-p…

…ys do the stacking and padding

stimuli/illusions/bullseye.py

@@ -1,5 +1,6 @@
 import numpy as np
-from stimuli.illusions.rings import ring_pattern
+from stimuli.illusions.rings import ring_pattern, ring_single
+from stimuli.utils import plot_stim
 
 
 def bullseye_illusion(
@@ -75,9 +76,54 @@ def bullseye_illusion(
     return {"img": img, "mask": mask}
 
 
+def bullseye_single(
+    ppd=10,
+    n_rings=8,
+    target_idx=0,
+    ring_width=0.5,
+    vring1=1.0,
+    vring2=0.0,
+    vtarget=0.5
+):
+    """
+    Bullseye Illusion.
+
+    Parameters
+    ----------
+    ppd : int
+        pixels per degree (visual angle)
+    n_rings : int
+        the number of rings
+    target_idx : int or tuple or list
+        indices of target ring(s)
+    ring_width : float
+        width per ring in degrees visual angle
+    vring1 : float
+        value for even rings
+    vring2 : float
+        value for odd rings
+    vtarget : float
+        value for target
+
+    Returns
+    -------
+    A stimulus object
+    """
+    stim = ring_single(
+        ppd=ppd,
+        n_rings=n_rings,
+        target_idx=target_idx,
+        ring_width=ring_width,
+        vring1=vring1,
+        vring2=vring2,
+        vtarget=vtarget
+    )
+
+    return {"img": stim['img'], "mask": stim['mask']}
+
+
 if __name__ == "__main__":
     import matplotlib.pyplot as plt
 
-    stim = bullseye_illusion()
-    plt.imshow(stim.img, cmap="gray")
-    plt.show()
+    stim = bullseye_single()
+    plot_stim(stim, mask=True)

stimuli/illusions/rings.py

@@ -116,8 +116,86 @@ def ring_pattern(
     return {"img": img, "mask": mask}
 
 
+def ring_single(
+    ppd=10,
+    n_rings=8,
+    target_idx=4,
+    ring_width=0.5,
+    vring1=1.0,
+    vring2=0.0,
+    vtarget=0.5
+):
+    """
+    Ring Pattern White's-like illusion
+
+    Parameters
+    ----------
+    ppd : int
+        pixels per degree (visual angle)
+    n_rings : int
+        the number of rings
+    target_idx : int or tuple or list
+        indices of target ring(s)
+    ring_width : float
+        width per ring in degrees visual angle
+    vring1 : float
+        value for even rings
+    vring2 : float
+        value for odd rings
+    vtarget : float
+        value for target
+
+    Returns
+    -------
+    A stimulus dictionary with the stimulus ['img'] and target mask ['mask']
+    """
+
+    ring_width_px = degrees_to_pixels(ring_width, ppd)
+
+    # calculate Minkowski-p=inf distances from centre
+    x = np.arange(0, n_rings)
+    x = np.hstack([np.flip(x), x])
+    radii = np.maximum(np.abs(x[np.newaxis]), np.abs(x[:, np.newaxis]))
+
+    # build array representing rings
+    arr = np.ones((n_rings * 2, n_rings * 2)) * vring1
+    mask_arr = np.zeros((n_rings * 2, n_rings * 2))
+    arr[radii % 2 == 1] = vring2
+    if isinstance(target_idx, int):
+        target_idx = [target_idx]
+    elif isinstance(target_idx, tuple):
+        target_idx = list(target_idx)
+    target_idx = list(np.array(target_idx))
+    for idx in target_idx:
+        arr[radii == idx] = vtarget
+        mask_arr[radii == idx] = 1
+
+    # build image from array
+    img = np.repeat(
+        np.repeat(arr, ring_width_px, axis=0), ring_width_px, axis=1
+    )
+    mask = np.repeat(
+        np.repeat(mask_arr, ring_width_px, axis=0), ring_width_px, axis=1
+    )
+
+    y_c, x_c = img.shape
+    y_c //= 2
+    x_c //= 2
+
+    row_c = img[y_c, :]
+    row_c_mask = mask[y_c, :]
+    img = np.insert(img, y_c, row_c, axis=0)
+    mask = np.insert(mask, y_c, row_c_mask, axis=0)
+
+    col_c = img[:, x_c]
+    col_c_mask = mask[:, x_c]
+    img = np.insert(img, x_c, col_c, axis=1)
+    mask = np.insert(mask, x_c, col_c_mask, axis=1)
+    return {"img": img, "mask": mask}
+
+
 if __name__ == "__main__":
     import matplotlib.pyplot as plt
 
-    stim = ring_pattern()
+    stim = ring_single()
     plot_stim(stim, mask=True)

stimuli/papers/RHS2007.py

@@ -625,36 +625,89 @@ def todorovic_benary3_4():
 
 
 def bullseye_thin(ppd=PPD):
-    # The parameters are mostly guessed
-    stim = stimuli.illusions.bullseye.bullseye_illusion(
-        n_rings=8,
-        ring_width=1,
-        back=1.0,
-        rings=9.0,
-        target=5.0,
-    )
-    shape = degrees_to_pixels(VISEXTENT, ppd)
-    stim["img"] = pad_img_to_shape(stim["img"], shape, val=0.5)
-    stim["mask"] = pad_img_to_shape(stim["mask"], shape, val=0)
-
-    return stim
+    v1, v2, v3 = 1., 0.5, 0.
+    shape_ind = degrees_to_pixels(np.array(VISEXTENT)/2., ppd)
+    shape_all = degrees_to_pixels(VISEXTENT, ppd)
+    stim1 = stimuli.illusions.bullseye.bullseye_single(
+            ppd=ppd,
+            n_rings=8,
+            target_idx=(0, 1, 2, 3),
+            ring_width=0.1,
+            vring1=v1,
+            vring2=v3,
+            vtarget=v2,
+            )
+    stim2 = stimuli.illusions.bullseye.bullseye_single(
+            ppd=ppd,
+            n_rings=8,
+            target_idx=(0, 1, 2, 3),
+            ring_width=0.1,
+            vring1=v3,
+            vring2=v1,
+            vtarget=v2,
+            )
+
+    # Individual padding
+    img1 = pad_img_to_shape(stim1['img'], shape_ind, v2)
+    mask1 = pad_img_to_shape(stim1['mask'], shape_ind, 0)
+    img2 = pad_img_to_shape(stim2['img'], shape_ind, v2)
+    mask2 = pad_img_to_shape(stim2['mask'], shape_ind, 0)
+
+    # Increase target index of right stimulus half
+    mask2 = mask2 + 1
+    mask2[mask2 == 1] = 0
+
+    # Stacking
+    img = np.hstack([img1, img2])
+    mask = np.hstack([mask1, mask2])
+
+    # Full padding
+    img = pad_img_to_shape(img, shape_all, v2)
+    mask = pad_img_to_shape(mask, shape_all, 0)
+    return {"img": img, "mask": mask}
 
 
 def bullseye_thick(ppd=PPD):
-    # The parameters are mostly guessed
-    stim = stimuli.illusions.bullseye.bullseye_illusion(
-        n_rings=8,
-        ring_width=1,
-        back=1.0,
-        rings=9.0,
-        target=5.0,
-    )
-
-    shape = degrees_to_pixels(VISEXTENT, ppd)
-    stim["img"] = pad_img_to_shape(stim["img"], shape, val=0.5)
-    stim["mask"] = pad_img_to_shape(stim["mask"], shape, val=0)
-
-    return stim
+    v1, v2, v3 = 1., 0.5, 0.
+    shape_ind = degrees_to_pixels(np.array(VISEXTENT)/2., ppd)
+    shape_all = degrees_to_pixels(VISEXTENT, ppd)
+    stim1 = stimuli.illusions.bullseye.bullseye_single(
+            ppd=ppd,
+            n_rings=6,
+            target_idx=(0, 1),
+            ring_width=0.2,
+            vring1=v1,
+            vring2=v3,
+            vtarget=v2,
+            )
+    stim2 = stimuli.illusions.bullseye.bullseye_single(
+            ppd=ppd,
+            n_rings=6,
+            target_idx=(0, 1),
+            ring_width=0.2,
+            vring1=v3,
+            vring2=v1,
+            vtarget=v2,
+            )
+
+    # Individual padding
+    img1 = pad_img_to_shape(stim1['img'], shape_ind, v2)
+    mask1 = pad_img_to_shape(stim1['mask'], shape_ind, 0)
+    img2 = pad_img_to_shape(stim2['img'], shape_ind, v2)
+    mask2 = pad_img_to_shape(stim2['mask'], shape_ind, 0)
+
+    # Increase target index of right stimulus half
+    mask2 = mask2 + 1
+    mask2[mask2 == 1] = 0
+
+    # Stacking
+    img = np.hstack([img1, img2])
+    mask = np.hstack([mask1, mask2])
+
+    # Full padding
+    img = pad_img_to_shape(img, shape_all, v2)
+    mask = pad_img_to_shape(mask, shape_all, 0)
+    return {"img": img, "mask": mask}
 
 
 if __name__ == "__main__":

stimuli/papers/domijan2015.py

@@ -84,17 +84,39 @@ def rings(ppd=PPD):
 
 
 def bullseye(ppd=PPD):
-    return illusions.bullseye.bullseye_illusion(
-        ppd=ppd,
-        n_rings=8,
-        ring_width=0.5,
-        target_pos_l=0,
-        target_pos_r=0,
-        padding=(0.9, 1.0, 0.9, 1.0),
-        back=1.0,
-        rings=9.0,
-        target=5.0,
-    )
+    v1, v2, v3 = 1., 5., 9.
+    pad = (0.9, 1.0, 0.9, 1.0)
+    stim1 = illusions.bullseye.bullseye_single(
+            ppd=ppd,
+            n_rings=8,
+            target_idx=0,
+            ring_width=0.5,
+            vring1=v1,
+            vring2=v3,
+            vtarget=v2,
+            )
+    stim2 = illusions.bullseye.bullseye_single(
+            ppd=ppd,
+            n_rings=8,
+            target_idx=0,
+            ring_width=0.5,
+            vring1=v3,
+            vring2=v1,
+            vtarget=v2,
+            )
+
+    # Padding
+    img1, mask1 = pad_img(stim1['img'], pad, ppd, v1), pad_img(stim1['mask'], pad, ppd, 0)
+    img2, mask2 = pad_img(stim2['img'], pad, ppd, v1), pad_img(stim2['mask'], pad, ppd, 0)
+
+    # Increase target index of right stimulus half
+    mask2 = mask2 + 1
+    mask2[mask2 == 1] = 0
+
+    # Stacking
+    img_stacked = np.hstack([img1, img2])
+    mask_stacked = np.hstack([mask1, mask2])
+    return {"img": img_stacked, "mask": mask_stacked}
 
 
 def simultaneous_brightness_contrast(ppd=PPD):