Merge remote-tracking branch 'tubgitlab/lynn_illusions' into dev_refa…

…ctor

Mainly auto-formatting, but also use utils.plot_stim

stimuli/illusions/cornsweet.py

@@ -2,67 +2,75 @@
 
 
 def cornsweet(
-    size=(10, 10),
-    ppd=10,
-    contrast=0.5,
-    ramp_width=2,
+    size=(10.0, 10.0),
+    ppd=10.0,
+    vmax=1.0,
+    vmin=0.0,
+    vtarget=0.5,
+    ramp_width=2.0,
     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
-    L = L_mean +/- (1 - X / w) ** a * L_mean * C/2 for the ramp and
-    L = L_mean for the area beyond the ramp.
+    Left side:
+    v = vtarget + (1 - X / w) ** a * (vmax-vtarget) for the ramp and v = vtarget beyond.
+    Right side:
+    v = vtarget - (1 - X / w) ** a * (vmin-vtarget) for the ramp and v = vtarget beyond.
     X is the distance to the edge, w is the width of the ramp, a is a variable
-    determining the steepness of the ramp, and C is the contrast at the edge,
-    defined as C = (L_max-L_min) / L_mean.
+    determining the steepness of the ramp, vtarget is the luminance of the targets and
+    vmax/vmin are the max/min luminances.
 
     Parameters
     ----------
     size : tuple of 2 numbers
-           the size of the matrix in degrees of visual angle
-    ppd : number
-          the number of pixels in one degree of visual angle
-    contrast : float, in [0,1]
-               the contrast at the Cornsweet edge, defined as Michelson contrast
-               (max_luminance - min_luminance) / (max_luminance + min_luminance)
-    ramp_width : number (optional)
-                 the width of the luminance ramp in degrees of visual angle.
-                 Default is 3.
-    exponent : number (optional)
-               Determines the steepness of the ramp. Default is 2.75. An
-               exponent value of 0 leads to a stimulus with uniform flanks.
-    mean_lum : number
-               The mean luminance of the stimulus, i.e. the value outside of
-               the ramp area.
+        size in degrees of visual angle
+    ppd : float
+        number of pixels in one degree of visual angle
+    vmax : float
+        maximum luminance value
+    vmin : float
+        minimum luminance value
+    vtarget : float
+        luminance value of targets (=plateaus)
+    ramp_width : float
+        width of luminance ramp in degrees of visual angle
+    exponent : float
+        determines steepness of ramp (default is 2.75. 1 would be linear)
 
     Returns
     -------
-    Dictionary with img: ndarray (2D) and empty mask
+    Dictionary with img: ndarray (2D) and mask
 
     References
     ----------
-    The formula and default values are taken from Boyaci, H., Fang, F., Murray,
+    The formula and default values are adapted from Boyaci, H., Fang, F., Murray,
     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)]
-    img = np.ones(size) * mean_lum
+    ramp_width = int(ramp_width * ppd)
+    img = np.ones(size) * vtarget
+    mask = np.zeros(size)
+
+    # Create ramp profiles individually for left and right side
     dist = np.arange(size[1] / 2.0)
-    dist = dist / (ramp_width * ppd)
+    dist = dist / ramp_width
     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
+    profile1 = (1.0 - dist) ** exponent * (vmax - vtarget)
+    profile2 = (1.0 - dist) ** exponent * (vmin - vtarget)
+    img[:, : int(size[1] / 2.0)] += profile1[::-1]
+    img[:, size[1] // 2 :] += profile2
+
+    # Generate the target mask
+    mask[:, 0 : int(size[1] / 2.0 - ramp_width - 1)] = 1
+    mask[:, int(size[1] / 2.0 + ramp_width + 1) : :] = 2
     return {"img": img, "mask": mask}
 
 
 if __name__ == "__main__":
-    import matplotlib.pyplot as plt
+    from stimuli.utils import plot_stim
 
     stim = cornsweet()
-    plt.imshow(stim, cmap="gray")
-    plt.show()
+    plot_stim(stim, mask=True)