removed old bullseye, changed illusion name and doc

stimuli/illusions/bullseye.py

@@ -1,82 +1,8 @@
-import numpy as np
-from stimuli.illusions.rings import ring_pattern, ring_single
+from stimuli.illusions.rings import ring
 from stimuli.utils import plot_stim
 
 
-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.
-
-    Parameters
-    ----------
-    ppd : int
-        pixels per degree (visual angle)
-    n_rings : int
-        the number of rings
-    ring_width : float
-        width per ring in degrees visual angle
-    target_pos_l : int
-        specify the target index in the left ring
-    target_pos_r : int
-        specify the target index in the right ring
-    padding : (float, float, float, float)
-        4-valued tuple specifying padding (top, bottom, left, right) in degrees visual angle
-    back : float
-        background value
-    rings : float
-        value for grid cells
-    target : float
-        value for target
-
-    Returns
-    -------
-    A stimulus object
-    """
-    stim1 = ring_pattern(
-        ppd=ppd,
-        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(
-        ppd=ppd,
-        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"]))
-    # Increase target mask values to differentiate from single-stimulus targets:
-    stim2["mask"][stim2["mask"] != 0] += 1
-    mask = np.hstack((stim1["mask"], stim2["mask"]))
-
-    return {"img": img, "mask": mask}
-
-
-def bullseye_single(
+def bullseye(
     ppd=10,
     n_rings=8,
     target_idx=0,
@@ -107,9 +33,9 @@ def bullseye_single(
 
     Returns
     -------
-    A stimulus object
+    A stimulus dictionary with the stimulus ['img'] and target mask ['mask']
     """
-    stim = ring_single(
+    stim = ring(
         ppd=ppd,
         n_rings=n_rings,
         target_idx=target_idx,
@@ -125,5 +51,5 @@ def bullseye_single(
 if __name__ == "__main__":
     import matplotlib.pyplot as plt
 
-    stim = bullseye_single()
+    stim = bullseye()
     plot_stim(stim, mask=True)