import matplotlib.pyplot as plt import numpy as np np.random.seed(19680801) 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* keyword argument. 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 gradient_image(ax, extent=(left, right, bottom, top), cmap=plt.cm.Blues_r, cmap_range=(0, 0.8)) xmin, xmax = xlim = 0, 10 ymin, ymax = ylim = 0, 1 fig, ax = plt.subplots() ax.set(xlim=xlim, ylim=ylim, autoscale_on=False) # background image gradient_image(ax, direction=1, extent=(0, 1, 0, 1), transform=ax.transAxes, cmap=plt.cm.RdYlGn, cmap_range=(0.2, 0.8), alpha=0.5) N = 10 x = np.arange(N) + 0.15 y = np.random.rand(N) gradient_bar(ax, x, y, width=0.7) ax.set_aspect('auto') plt.show()