Support pixel-by-pixel alpha in imshow.

tillahoffmann · tillahoffmann · commit 8a5261d67761 · 2019-09-04T14:33:23.000+01:00
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
@@ -5504,9 +5504,11 @@ def imshow(self, X, cmap=None, norm=None, aspect=None,
             which can be set by *filterrad*. Additionally, the antigrain image
             resize filter is controlled by the parameter *filternorm*.
 
-        alpha : scalar, optional
+        alpha : [scalar | array_like], optional, default: None
             The alpha blending value, between 0 (transparent) and 1 (opaque).
-            This parameter is ignored for RGBA input data.
+            If `alpha` is an array, the alpha blending values are applied pixel
+            by pixel, and `alpha` must have the same shape as `X`. This
+            parameter is ignored for RGBA input data.
 
         vmin, vmax : scalar, optional
             When using scalar data and no explicit *norm*, *vmin* and *vmax*
diff --git a/lib/matplotlib/image.py b/lib/matplotlib/image.py
@@ -257,6 +257,7 @@ def __init__(self, ax,
         self.axes = ax
 
         self._imcache = None
+        self._array_alpha = None
 
         self.update(kwargs)
 
@@ -281,7 +282,11 @@ def set_alpha(self, alpha):
         ----------
         alpha : float
         """
-        martist.Artist.set_alpha(self, alpha)
+        if np.isscalar(alpha):
+            martist.Artist.set_alpha(self, alpha)
+        else:
+            self._array_alpha = alpha
+            martist.Artist.set_alpha(self, 1.0)
         self._imcache = None
 
     def changed(self):
@@ -487,6 +492,10 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
                 # pixel it will be between [0, 1] (such as a rotated image).
                 out_mask = np.isnan(out_alpha)
                 out_alpha[out_mask] = 1
+                # Apply the pixel-by-pixel alpha values if present
+                if self._array_alpha is not None:
+                    out_alpha *= _resample(self, self._array_alpha, out_shape,
+                                           t, resample=True)
                 # mask and run through the norm
                 output = self.norm(np.ma.masked_array(A_resampled, out_mask))
             else:
