Linien-, Poly- und RegularPoly-Sammlung mit automatischer Skalierung #

Für die ersten beiden Nebenhandlungen verwenden wir Spiralen. Ihre Größe wird in Ploteinheiten festgelegt, nicht in Dateneinheiten. Ihre Positionen werden in Dateneinheiten festgelegt, indem die Schlüsselwortargumente offsets und offset_transform von LineCollection und verwendet werden PolyCollection.

Der dritte Teilplot erstellt regelmäßige Polygone mit der gleichen Art der Skalierung und Positionierung wie in den ersten beiden.

Der letzte Teilplot veranschaulicht die Verwendung von "offsets=(xo, yo)", d. h. eines einzelnen Tupels anstelle einer Liste von Tupeln, um nacheinander versetzte Kurven zu erzeugen, wobei der Versatz in Dateneinheiten angegeben ist. Dieses Verhalten ist nur für LineCollection verfügbar.

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

nverts = 50
npts = 100

# Make some spirals
r = np.arange(nverts)
theta = np.linspace(0, 2*np.pi, nverts)
xx = r * np.sin(theta)
yy = r * np.cos(theta)
spiral = np.column_stack([xx, yy])

# Fixing random state for reproducibility
rs = np.random.RandomState(19680801)

# Make some offsets
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']]

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
fig.subplots_adjust(top=0.92, left=0.07, right=0.97,
                    hspace=0.3, wspace=0.3)


col = collections.LineCollection(
    [spiral], offsets=xyo, offset_transform=ax1.transData)
trans = fig.dpi_scale_trans + transforms.Affine2D().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* keyword argument (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(colors)

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 = collections.PolyCollection(
    [spiral], offsets=xyo, offset_transform=ax2.transData)
trans = transforms.Affine2D().scale(fig.dpi/72.0)
col.set_transform(trans)  # the points to pixels transform
ax2.add_collection(col, autolim=True)
col.set_color(colors)


ax2.autoscale_view()
ax2.set_title('PolyCollection using offsets')

# 7-sided regular polygons

col = collections.RegularPolyCollection(
    7, sizes=np.abs(xx) * 10.0, offsets=xyo, offset_transform=ax3.transData)
trans = transforms.Affine2D().scale(fig.dpi / 72.0)
col.set_transform(trans)  # the points to pixels transform
ax3.add_collection(col, autolim=True)
col.set_color(colors)
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 = np.linspace(0, 2*np.pi, nverts)
ym = np.max(yy)
xx = (0.2 + (ym - yy) / ym) ** 2 * np.cos(yy - 0.4) * 0.5
segs = []
for i in range(ncurves):
    xxx = xx + 0.02*rs.randn(nverts)
    curve = np.column_stack([xxx, yy * 100])
    segs.append(curve)

col = collections.LineCollection(segs, offsets=offs)
ax4.add_collection(col, autolim=True)
col.set_color(colors)
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])


plt.show()

LineCollection mit Offsets, PolyCollection mit Offsets, RegularPolyCollection mit Offsets, aufeinander folgende Daten-Offsets

Verweise

In diesem Beispiel wird die Verwendung der folgenden Funktionen, Methoden, Klassen und Module gezeigt:

Galerie generiert von Sphinx-Gallery