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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

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<title>Line, Poly and RegularPoly Collection with autoscaling — Matplotlib 2.1.2 documentation</title>

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<h1>Line, Poly and RegularPoly Collection with autoscaling<a class="headerlink" href="#line-poly-and-regularpoly-collection-with-autoscaling" title="Permalink to this headline">¶</a></h1>

For the first two subplots, we will use spirals. Their

size will be set in plot units, not data units. Their positions

will be set in data units by using the “offsets” and “transOffset”

kwargs of the LineCollection and PolyCollection.

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)”,

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.

<div class="highlight-python"><div class="highlight"><pre>import matplotlib.pyplot as plt

from matplotlib import collections, <a href="../../api/colors_api.html#module-matplotlib.colors" title="View documentation for matplotlib.colors">colors</a>, transforms

import numpy as np

nverts = 50

npts = 100

# Make some spirals

r = <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html#numpy.arange" title="View documentation for numpy.arange">np.arange</a>(nverts)

theta = <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html#numpy.linspace" title="View documentation for numpy.linspace">np.linspace</a>(0, 2*np.pi, nverts)

xx = r * <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.sin.html#numpy.sin" title="View documentation for numpy.sin">np.sin</a>(theta)

yy = r * <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.cos.html#numpy.cos" title="View documentation for numpy.cos">np.cos</a>(theta)

spiral = list(zip(xx, yy))

# Make some offsets

# Fixing random state for reproducibility

rs = <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.RandomState.html#numpy.random.RandomState" title="View documentation for numpy.random.RandomState">np.random.RandomState</a>(19680801)

xo = rs.randn(npts)

yo = rs.randn(npts)

xyo = list(zip(xo, yo))

# Make a list of colors cycling through the default series.

<a href="../../api/colors_api.html#module-matplotlib.colors" title="View documentation for matplotlib.colors">colors</a> = [<a href="../../api/_as_gen/matplotlib.colors.to_rgba.html#matplotlib.colors.to_rgba" title="View documentation for matplotlib.colors.to_rgba">colors.to_rgba</a>(c)

for c in <a href="../../api/matplotlib_configuration_api.html#matplotlib.rcParams" title="View documentation for matplotlib.pyplot.rcParams">plt.rcParams</a>['axes.prop_cycle'].by_key()['color']]

fig, axes = <a href="../../api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="View documentation for matplotlib.pyplot.subplots">plt.subplots</a>(2, 2)

fig.subplots_adjust(top=0.92, left=0.07, right=0.97,

hspace=0.3, wspace=0.3)

((ax1, ax2), (ax3, ax4)) = axes # unpack the axes

col = <a href="../../api/collections_api.html#matplotlib.collections.LineCollection" title="View documentation for matplotlib.collections.LineCollection">collections.LineCollection</a>([spiral], offsets=xyo,

transOffset=ax1.transData)

trans = fig.dpi_scale_trans + <a href="../../api/transformations.html#matplotlib.transforms.Affine2D" title="View documentation for matplotlib.transforms.Affine2D">transforms.Affine2D</a>().scale(1.0/72.0)

col.set_transform(trans) # the points to pixels transform

# Note: the first argument to the collection initializer

# must be a list of sequences of x,y tuples; we have only

# one sequence, but we still have to put it in a list.

ax1.add_collection(col, autolim=True)

# autolim=True enables autoscaling. For collections with

# offsets like this, it is neither efficient nor accurate,

# but it is good enough to generate a plot that you can use

# as a starting point. If you know beforehand the range of

# x and y that you want to show, it is better to set them

# explicitly, leave out the autolim kwarg (or set it to False),

# and omit the 'ax1.autoscale_view()' call below.

# Make a transform for the line segments such that their size is

# given in points:

col.set_color(<a href="../../api/colors_api.html#module-matplotlib.colors" title="View documentation for matplotlib.colors">colors</a>)

ax1.autoscale_view() # See comment above, after ax1.add_collection.

ax1.set_title('LineCollection using offsets')

# The same data as above, but fill the curves.

col = <a href="../../api/collections_api.html#matplotlib.collections.PolyCollection" title="View documentation for matplotlib.collections.PolyCollection">collections.PolyCollection</a>([spiral], offsets=xyo,

transOffset=ax2.transData)

trans = <a href="../../api/transformations.html#matplotlib.transforms.Affine2D" title="View documentation for matplotlib.transforms.Affine2D">transforms.Affine2D</a>().scale(fig.dpi/72.0)

ax2.add_collection(col, autolim=True)

ax2.autoscale_view()

ax2.set_title('PolyCollection using offsets')

# 7-sided regular polygons

col = <a href="../../api/collections_api.html#matplotlib.collections.RegularPolyCollection" title="View documentation for matplotlib.collections.RegularPolyCollection">collections.RegularPolyCollection</a>(

7, sizes=np.abs(xx) * 10.0, offsets=xyo, transOffset=ax3.transData)

trans = <a href="../../api/transformations.html#matplotlib.transforms.Affine2D" title="View documentation for matplotlib.transforms.Affine2D">transforms.Affine2D</a>().scale(fig.dpi / 72.0)

ax3.add_collection(col, autolim=True)

ax3.autoscale_view()

ax3.set_title('RegularPolyCollection using offsets')

# Simulate a series of ocean current profiles, successively

# offset by 0.1 m/s so that they form what is sometimes called

# a "waterfall" plot or a "stagger" plot.

nverts = 60

ncurves = 20

offs = (0.1, 0.0)

yy = <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html#numpy.linspace" title="View documentation for numpy.linspace">np.linspace</a>(0, 2*np.pi, nverts)

ym = np.max(yy)

xx = (0.2 + (ym - yy)/ym)**2 * <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.cos.html#numpy.cos" title="View documentation for numpy.cos">np.cos</a>(yy - 0.4)*0.5

segs = []

for i in range(ncurves):

xxx = xx + 0.02*rs.randn(nverts)

curve = list(zip(xxx, yy*100))

segs.append(curve)

col = <a href="../../api/collections_api.html#matplotlib.collections.LineCollection" title="View documentation for matplotlib.collections.LineCollection">collections.LineCollection</a>(segs, offsets=offs)

ax4.add_collection(col, autolim=True)

ax4.autoscale_view()

ax4.set_title('Successive data offsets')

ax4.set_xlabel('Zonal velocity component (m/s)')

ax4.set_ylabel('Depth (m)')

# Reverse the y-axis so depth increases downward

ax4.set_ylim(ax4.get_ylim()[::-1])

<a href="../../api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="View documentation for matplotlib.pyplot.show">plt.show</a>()

</pre></div>

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