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Dec 12, 2022
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Cleanup a few examples. #24705

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2 changes: 1 addition & 1 deletion examples/misc/demo_ribbon_box.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@ class RibbonBox:
nx = original_image.shape[1]

def __init__(self, color):
rgb = mcolors.to_rgba(color)[:3]
rgb = mcolors.to_rgb(color)
self.im = np.dstack(
[self.b_and_h - self.color * (1 - np.array(rgb)), self.alpha])

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7 changes: 3 additions & 4 deletions examples/shapes_and_collections/collections.py
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Expand Up @@ -11,14 +11,14 @@
The third subplot will make regular polygons, with the same
type of scaling and positioning as in the first two.

The last subplot illustrates the use of "offsets=(xo, yo)",
The last subplot illustrates the use of ``offsets=(xo, yo)``,
that is, a single tuple instead of a list of tuples, to generate
successively offset curves, with the offset given in data
units. This behavior is available only for the LineCollection.
"""

import matplotlib.pyplot as plt
from matplotlib import collections, colors, transforms
from matplotlib import collections, transforms
import numpy as np

nverts = 50
Expand All @@ -38,8 +38,7 @@
xyo = rs.randn(npts, 2)

# Make a list of colors cycling through the default series.
colors = [colors.to_rgba(c)
for c in plt.rcParams['axes.prop_cycle'].by_key()['color']]
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
fig.subplots_adjust(top=0.92, left=0.07, right=0.97,
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43 changes: 13 additions & 30 deletions examples/shapes_and_collections/line_collection.py
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@@ -1,23 +1,17 @@
"""
===============
Line Collection
===============
=============================================
Plotting multiple lines with a LineCollection
=============================================

Plotting lines with Matplotlib.

`~matplotlib.collections.LineCollection` allows one to plot multiple
lines on a figure. Below we show off some of its properties.
Matplotlib can efficiently draw multiple lines at once using a
`~.LineCollection`, as showcased below.
"""

import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from matplotlib import colors as mcolors

import numpy as np

# In order to efficiently plot many lines in a single set of axes,
# Matplotlib has the ability to add the lines all at once. Here is a
# simple example showing how it is done.

x = np.arange(100)
# Here are many sets of y to plot vs. x
Expand All @@ -30,7 +24,7 @@
# Mask some values to test masked array support:
segs = np.ma.masked_where((segs > 50) & (segs < 60), segs)

# We need to set the plot limits.
# We need to set the plot limits, they will not autoscale
fig, ax = plt.subplots()
ax.set_xlim(x.min(), x.max())
ax.set_ylim(ys.min(), ys.max())
Expand All @@ -41,8 +35,7 @@
# onoffseq is an even length tuple of on and off ink in points. If linestyle
# is omitted, 'solid' is used.
# See `matplotlib.collections.LineCollection` for more information.
colors = [mcolors.to_rgba(c)
for c in plt.rcParams['axes.prop_cycle'].by_key()['color']]
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']

line_segments = LineCollection(segs, linewidths=(0.5, 1, 1.5, 2),
colors=colors, linestyle='solid')
Expand All @@ -51,32 +44,22 @@
plt.show()

###############################################################################
# In order to efficiently plot many lines in a single set of axes,
# Matplotlib has the ability to add the lines all at once. Here is a
# simple example showing how it is done.
# In the following example, instead of passing a list of colors
# (``colors=colors``), we pass an array of values (``array=x``) that get
# colormapped.

N = 50
x = np.arange(N)
# Here are many sets of y to plot vs. x
ys = [x + i for i in x]
ys = [x + i for i in x] # Many sets of y to plot vs. x
segs = [np.column_stack([x, y]) for y in ys]

# We need to set the plot limits, they will not autoscale
fig, ax = plt.subplots()
ax.set_xlim(np.min(x), np.max(x))
ax.set_ylim(np.min(ys), np.max(ys))

# colors is sequence of rgba tuples
# linestyle is a string or dash tuple. Legal string values are
# solid|dashed|dashdot|dotted. The dash tuple is (offset, onoffseq)
# where onoffseq is an even length tuple of on and off ink in points.
# If linestyle is omitted, 'solid' is used
# See `matplotlib.collections.LineCollection` for more information

# Make a sequence of (x, y) pairs.
line_segments = LineCollection([np.column_stack([x, y]) for y in ys],
line_segments = LineCollection(segs, array=x,
linewidths=(0.5, 1, 1.5, 2),
linestyles='solid')
line_segments.set_array(x)
ax.add_collection(line_segments)
axcb = fig.colorbar(line_segments)
axcb.set_label('Line Number')
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