matplotlib
diff --git a/‎lib/matplotlib/axes/_axes.py
Copy file name to clipboardExpand all lines: lib/matplotlib/axes/_axes.py
+1-3Lines changed: 1 addition & 3 deletions b/‎lib/matplotlib/axes/_axes.py
Copy file name to clipboardExpand all lines: lib/matplotlib/axes/_axes.py
+1-3Lines changed: 1 addition & 3 deletions
diff --git a/‎lib/matplotlib/colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/colors.py
+3-3Lines changed: 3 additions & 3 deletions b/‎lib/matplotlib/colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/colors.py
+3-3Lines changed: 3 additions & 3 deletions
diff --git a/‎lib/matplotlib/image.py
Copy file name to clipboardExpand all lines: lib/matplotlib/image.py
+2-1Lines changed: 2 additions & 1 deletion b/‎lib/matplotlib/image.py
Copy file name to clipboardExpand all lines: lib/matplotlib/image.py
+2-1Lines changed: 2 additions & 1 deletion
@@ -5122,8 +5122,7 @@ def imshow(self, X, cmap=None, norm=None, aspect=None,
 
         temp = np.asarray(X)
         if temp.ndim == 3 and isinstance(norm, mcolors.BivariateNorm):
-                    temp = norm(temp)
-                    X = cmap(temp, alpha=self.get_alpha(), bytes=True)
+                    X = norm(temp)
 
         if not self._hold:
             self.cla()
@@ -5760,7 +5759,6 @@ def pcolorfast(self, *args, **kwargs):
         if (C.ndim == 3 and isBivari):
             C = norm(C)
             nr, nc = C.shape
-            C = cmap(C, alpha=alpha, bytes=True)
         else:
             nr, nc = C.shape
         if len(args) == 1:
 
@@ -859,18 +859,18 @@ def reversed(self, name=None):
 class BivariateColormap(Colormap):
     def __init__(self, name='bivariate', N=256):
         Colormap.__init__(self, name, N)
+        self.N = self.N * self.N
 
     def _init(self):
-        red = np.linspace(0, 1, self.N)
-        green = np.linspace(0, 1, self.N)
+        red = np.linspace(0, 1, np.sqrt(self.N))
+        green = np.linspace(0, 1, np.sqrt(self.N))
         red_mesh, green_mesh = np.meshgrid(red, green)
         blue_mesh = np.zeros_like(red_mesh)
         alpha_mesh = np.ones_like(red_mesh)
         bivariate_cmap = np.dstack((red_mesh, green_mesh, blue_mesh,
                                     alpha_mesh))
         self._lut = np.vstack(bivariate_cmap)
         self._isinit = True
-        self.N = self.N * self.N
         self._set_extremes()
 
     def _resample(self, lutsize):
 
@@ -362,7 +362,8 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
                 raise ValueError("Invalid dimensions, got {}".format(A.shape))
 
             if A.ndim == 2:
-                A = self.norm(A)
+                if not isinstance(self.norm, mcolors.BivariateNorm):
+                    A = self.norm(A)
                 if A.dtype.kind == 'f':
                     # If the image is greyscale, convert to RGBA and
                     # use the extra channels for resizing the over,