含有带有很少误差条的数据全精度数据图。

import numpy as np
import matplotlib.pyplot as plt

# example data
x = np.arange(0.1, 4, 0.1)
y1 = np.exp(-1.0 * x)
y2 = np.exp(-0.5 * x)

# example variable error bar values
y1err = 0.1 + 0.1 * np.sqrt(x)
y2err = 0.1 + 0.1 * np.sqrt(x/2)


# Now switch to a more OO interface to exercise more features.
fig, (ax_l, ax_c, ax_r) = plt.subplots(nrows=1, ncols=3,
                                       sharex=True, figsize=(12, 6))

ax_l.set_title('all errorbars')
ax_l.errorbar(x, y1, yerr=y1err)
ax_l.errorbar(x, y2, yerr=y2err)

ax_c.set_title('only every 6th errorbar')
ax_c.errorbar(x, y1, yerr=y1err, errorevery=6)
ax_c.errorbar(x, y2, yerr=y2err, errorevery=6)

ax_r.set_title('second series shifted by 3')
ax_r.errorbar(x, y1, yerr=y1err, errorevery=(0, 6))
ax_r.errorbar(x, y2, yerr=y2err, errorevery=(3, 6))

fig.suptitle('Errorbar subsampling for better appearance')
plt.show()
import matplotlib.pyplot as plt
from matplotlib.collections import EventCollection
import numpy as np

# Fixing random state for reproducibility
np.random.seed(19680801)

# create random data
xdata = np.random.random([2, 10])

# split the data into two parts
xdata1 = xdata[0, :]
xdata2 = xdata[1, :]

# sort the data so it makes clean curves
xdata1.sort()
xdata2.sort()

# create some y data points
ydata1 = xdata1 ** 2
ydata2 = 1 - xdata2 ** 3

# plot the data
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(xdata1, ydata1, color='tab:blue')
ax.plot(xdata2, ydata2, color='tab:orange')

# create the events marking the x data points
xevents1 = EventCollection(xdata1, color='tab:blue', linelength=0.05)
xevents2 = EventCollection(xdata2, color='tab:orange', linelength=0.05)

# create the events marking the y data points
yevents1 = EventCollection(ydata1, color='tab:blue', linelength=0.05,
                           orientation='vertical')
yevents2 = EventCollection(ydata2, color='tab:orange', linelength=0.05,
                           orientation='vertical')

# add the events to the axis
ax.add_collection(xevents1)
ax.add_collection(xevents2)
ax.add_collection(yevents1)
ax.add_collection(yevents2)

# set the limits
ax.set_xlim([0, 1])
ax.set_ylim([0, 1])

ax.set_title('line plot with data points')

# display the plot
plt.show()