matplotlib
diff --git a/‎examples/lines_bars_and_markers/gradient_bar.py
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+58-12Lines changed: 58 additions & 12 deletions b/‎examples/lines_bars_and_markers/gradient_bar.py
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+58-12Lines changed: 58 additions & 12 deletions
@@ -1,7 +1,21 @@
 """
-============
-Gradient Bar
-============
+========================
+Bar chart with gradients
+========================
+
+Matplotlib does not natively support gradients. However, we can emulate a
+gradient-filled rectangle by an `.AxesImage` of the right size and coloring.
+
+In particular, we use a colormap to generate the actual colors. It is then
+sufficient to define the underlying values on the corners of the image and
+let bicubic interpolation fill out the area. We define the gradient direction
+by a unit vector *v*. The values at the corners are then obtained by the
+lengths of the projections of the corner vectors on *v*.
+
+A similar approach can be used to create a gradient background for an axes.
+In that case, it is helpful to uses Axes coordinates
+(`extent=(0, 1, 0, 1), transform=ax.transAxes`) to be independent of the data
+coordinates.
 
 """
 import matplotlib.pyplot as plt
@@ -10,12 +24,44 @@
 np.random.seed(19680801)
 
 
-def gbar(ax, x, y, width=0.5, bottom=0):
-    X = [[.6, .6], [.7, .7]]
+def gradient_image(ax, extent, direction=0.3, cmap_range=(0, 1), **kwargs):
+    """
+    Draw a gradient image based on a colormap.
+
+    Parameters
+    ----------
+    ax : Axes
+        The axes to draw on.
+    extent
+        The extent of the image as (xmin, xmax, ymin, ymax).
+        By default, this is in Axes coordinates but may be
+        changed using the *transform* kwarg.
+    direction : float
+        The direction of the gradient. This is a number in
+        range 0 (=vertical) to 1 (=horizontal).
+    cmap_range : float, float
+        The fraction (cmin, cmax) of the colormap that should be
+        used for the gradient, where the complete colormap is (0, 1).
+    **kwargs
+        Other parameters are passed on to `.Axes.imshow()`.
+        In particular useful is *cmap*.
+    """
+    phi = direction * np.pi / 2
+    v = np.array([np.cos(phi), np.sin(phi)])
+    X = np.array([[v @ [1, 0], v @ [1, 1]],
+                  [v @ [0, 0], v @ [0, 1]]])
+    a, b = cmap_range
+    X = a + (b - a) / X.max() * X
+    im = ax.imshow(X, extent=extent, interpolation='bicubic',
+                   vmin=0, vmax=1, **kwargs)
+    return im
+
+
+def gradient_bar(ax, x, y, width=0.5, bottom=0):
     for left, top in zip(x, y):
         right = left + width
-        ax.imshow(X, interpolation='bicubic', cmap=plt.cm.Blues,
-                  extent=(left, right, bottom, top), alpha=1)
+        gradient_image(ax, extent=(left, right, bottom, top),
+                       cmap=plt.cm.Blues_r, cmap_range=(0, 0.8))
 
 
 xmin, xmax = xlim = 0, 10
@@ -24,13 +70,13 @@ def gbar(ax, x, y, width=0.5, bottom=0):
 fig, ax = plt.subplots()
 ax.set(xlim=xlim, ylim=ylim, autoscale_on=False)
 
-X = [[.6, .6], [.7, .7]]
-ax.imshow(X, interpolation='bicubic', cmap=plt.cm.copper,
-          extent=(xmin, xmax, ymin, ymax), alpha=1)
+# background image
+gradient_image(ax, direction=0, extent=(0, 1, 0, 1), transform=ax.transAxes,
+               cmap=plt.cm.Oranges, cmap_range=(0.1, 0.6))
 
 N = 10
-x = np.arange(N) + 0.25
+x = np.arange(N) + 0.15
 y = np.random.rand(N)
-gbar(ax, x, y, width=0.7)
+gradient_bar(ax, x, y, width=0.7)
 ax.set_aspect('auto')
 plt.show()