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Cleanup gradient_bar example. #25302

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Feb 23, 2023
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Cleanup gradient_bar example. #25302

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20 changes: 6 additions & 14 deletions examples/lines_bars_and_markers/gradient_bar.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,26 +15,22 @@
A similar approach can be used to create a gradient background for an Axes.
In that case, it is helpful to use Axes coordinates (``extent=(0, 1, 0, 1),
transform=ax.transAxes``) to be independent of the data coordinates.

"""

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):
def gradient_image(ax, 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).
Expand All @@ -43,16 +39,16 @@ def gradient_image(ax, extent, direction=0.3, cmap_range=(0, 1), **kwargs):
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*.
In particular, *cmap*, *extent*, and *transform* may be useful.
"""
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)
im = ax.imshow(X, interpolation='bicubic', clim=(0, 1),
aspect='auto', **kwargs)
return im


Expand All @@ -63,11 +59,8 @@ def gradient_bar(ax, x, y, width=0.5, bottom=0):
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)
ax.set(xlim=(0, 10), ylim=(0, 1))

# background image
gradient_image(ax, direction=1, extent=(0, 1, 0, 1), transform=ax.transAxes,
Expand All @@ -77,5 +70,4 @@ def gradient_bar(ax, x, y, width=0.5, bottom=0):
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()