Dec 12, 2022 · Dec 12, 2022
diff --git a/examples/misc/demo_ribbon_box.py b/examples/misc/demo_ribbon_box.py
    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])

diff --git a/examples/shapes_and_collections/collections.py b/examples/shapes_and_collections/collections.py
 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
 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,
diff --git a/examples/shapes_and_collections/line_collection.py b/examples/shapes_and_collections/line_collection.py
 """
 ===============
 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
 # 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())
 # 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')
 plt.show()

 ###############################################################################
 # Inorder to efficiently plot many lines in a single setofaxes,
 #Matplotlib has the ability to add the lines all at once. Here is a
 #simple example showing how it is done.
 # Inthe following example, instead of passing a listofcolors
 #(``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 + ifori inx]
 ys = [x + i for i in x]  # Many sets of y to plot vs. x
 segs = [np.column_stack([x, y])fory inys]

 # 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')
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])

Expand Down
Original file line number	Diff line number	Diff line change
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,
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -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()

		###############################################################################
		# Inorder to efficiently plot many lines in a single setofaxes,
		#Matplotlib has the ability to add the lines all at once. Here is a
		#simple example showing how it is done.
		# Inthe following example, instead of passing a listofcolors
		#(``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 + ifori inx]
		ys = [x + i for i in x] # Many sets of y to plot vs. x
		segs = [np.column_stack([x, y])fory inys]

		# 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')
Expand Down