8000 Improve Gradient bar example by timhoffm · Pull Request #14057 · matplotlib/matplotlib · GitHub
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May 4, 2019
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Improve Gradient bar example #14057

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70 changes: 58 additions & 12 deletions examples/lines_bars_and_markers/gradient_bar.py
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Original file line number Diff line number Diff line change
@@ -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
Expand All @@ -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).
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Just passing the angle (in degrees or in radians, up to you) seems simpler (in particular to explain)?

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It would be the angle only for square images. In general, it's a (nonlinear monotoneous) parametrization of the angle due to the distortion. I did not bother to untangle the transformation from extent; and I didn't want to raise false expectations.

If you want to make real angles work, you are welcome to provide a PR. OTOH, maybe one does not want real angles - probably depends on the application. 0.5 is now always a gradient along the diagonal of the image.

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Good point, let's just leave it as it is.

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]]])
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Just a style/judgment thing.

I think this is

np.dot(v, [[[1, 0], [1, 1]], [[0, 0], [1, 1]]]

or alternatively, just don't bother with dot products at all

s = np.sin(phi)
c = np.cos(phi)
X = [[s, s+c], [0, c]]

(either fully vectorize the thing, or don't vectorize it at all.)

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I've chosen this notation intentionally to illustrate the projection of the corners on v.

The upper does not produce the same result (I assume it's some axis ordering, but didn't investigate). The lower does work, but obscures what the calculation actually does.

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Ah, I didn't see it as a projection; indeed, that makes things clearer.

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
Expand All @@ -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()
0