1. Axes Props
你可以控制坐标轴刻度和网格属性
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0.0, 2.0, 0.01)
s = np.sin(2 * np.pi * t)
fig, ax = plt.subplots()
ax.plot(t, s)
ax.grid(True, linestyle='-.')
ax.tick_params(labelcolor='r', labelsize='medium', width=3)
plt.show()
2. Invert Axes
通过翻转坐标轴限制的正常顺序，可以使用递减轴
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0.01, 5.0, 0.01)
s = np.exp(-t)
plt.plot(t, s)
plt.xlim(5, 0) # decreasing time
plt.xlabel('decreasing time (s)')
plt.ylabel('voltage (mV)')
plt.title('Should be growing...')
plt.grid(True)
plt.show()
3. Subplot Toolbar
Matplotlib 有一个工具栏可用于调整 suplot 间距。
import matplotlib.pyplot as plt
import numpy as np
fig, axs = plt.subplots(2, 2)
axs[0, 0].imshow(np.random.random((100, 100)))
axs[0, 1].imshow(np.random.random((100, 100)))
axs[1, 0].imshow(np.random.random((100, 100)))
axs[1, 1].imshow(np.random.random((100, 100)))
plt.subplot_tool()
plt.show()
4. Multiple Figs Demo
使用多个图形窗口和 subplots
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0.0, 2.0, 0.01)
s1 = np.sin(2*np.pi*t)
s2 = np.sin(4*np.pi*t)
创建图1
plt.figure(1)
plt.subplot(211)
plt.plot(t, s1)
plt.subplot(212)
plt.plot(t, 2*s1)
创建图2
plt.figure(2)
plt.plot(t, s2)
现在切换回图1并进行一些更改
plt.figure(1)
plt.subplot(211)
plt.plot(t, s2, 's')
ax = plt.gca()
ax.set_xticklabels([])
plt.show()
5. Multiple subplots
具有多个 subplots 的简单演示
import numpy as np
import matplotlib.pyplot as plt
x1 = np.linspace(0.0, 5.0)
x2 = np.linspace(0.0, 2.0)
y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)
y2 = np.cos(2 * np.pi * x2)
plt.subplot(2, 1, 1)
plt.plot(x1, y1, 'o-')
plt.title('A tale of 2 subplots')
plt.ylabel('Damped oscillation')
plt.subplot(2, 1, 2)
plt.plot(x2, y2, '.-')
plt.xlabel('time (s)')
plt.ylabel('Undamped')
plt.show()
6. Subplots Adjust
使用 subplots_adjust() 调整边距和 subplots 的间距。
import matplotlib.pyplot as plt
import numpy as np
# Fixing random state for reproducibility
np.random.seed(19680801)
plt.subplot(211)
plt.imshow(np.random.random((100, 100)), cmap=plt.cm.BuPu_r)
plt.subplot(212)
plt.imshow(np.random.random((100, 100)), cmap=plt.cm.BuPu_r)
plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
cax = plt.axes([0.85, 0.1, 0.075, 0.8])
plt.colorbar(cax=cax)
plt.show()
7. Geographic Projections
这显示了使用了 subplot 的4种可能的预测。Matplotlib 还支持Basemaps Toolkit 和 Cartopy 地理预测。
import matplotlib.pyplot as plt
plt.figure()
plt.subplot(111, projection="aitoff")
plt.title("Aitoff")
plt.grid(True)
plt.figure()
plt.subplot(111, projection="hammer")
plt.title("Hammer")
plt.grid(True)
plt.figure()
plt.subplot(111, projection="lambert")
plt.title("Lambert")
plt.grid(True)
plt.figure()
plt.subplot(111, projection="mollweide")
plt.title("Mollweide")
plt.grid(True)
plt.show()
8. Basic Subplot Demo
两个 subplots 演示。有关更多选项，请参阅 Subplots Demo
import numpy as np
import matplotlib.pyplot as plt
# Data for plotting
x1 = np.linspace(0.0, 5.0)
x2 = np.linspace(0.0, 2.0)
y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)
y2 = np.cos(2 * np.pi * x2)
# Create two subplots sharing y axis
fig, (ax1, ax2) = plt.subplots(2, sharey=True)
ax1.plot(x1, y1, 'ko-')
ax1.set(title='A tale of 2 subplots', ylabel='Damped oscillation')
ax2.plot(x2, y2, 'r.-')
ax2.set(xlabel='time (s)', ylabel='Undamped')
plt.show()
9. Custom Figure Class
如果要从默认图形派生，可以将自定义图形构造函数传递给图。这个简单的示例创建一个具有图形标题的图形。
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
class MyFigure(Figure):
def __init__(self, *args, **kwargs):
"""
custom kwarg figtitle is a figure title
"""
figtitle = kwargs.pop('figtitle', 'hi mom')
Figure.__init__(self, *args, **kwargs)
self.text(0.5, 0.95, figtitle, ha='center')
fig = plt.figure(FigureClass=MyFigure, figtitle='my title')
ax = fig.subplots()
ax.plot([1, 2, 3])
plt.show()
10. Shared Axis Demo
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0.01, 5.0, 0.01)
s1 = np.sin(2 * np.pi * t)
s2 = np.exp(-t)
s3 = np.sin(4 * np.pi * t)
ax1 = plt.subplot(311)
plt.plot(t, s1)
plt.setp(ax1.get_xticklabels(), fontsize=6)
# share x only
ax2 = plt.subplot(312, sharex=ax1)
plt.plot(t, s2)
# make these tick labels invisible
plt.setp(ax2.get_xticklabels(), visible=False)
# share x and y
ax3 = plt.subplot(313, sharex=ax1, sharey=ax1)
plt.plot(t, s3)
plt.xlim(0.01, 5.0)
plt.show()
11. Zooming in and out using Axes.margins
此示例演示如何使用 margins 而不是 set_xlim 和 set_ylim 。
import numpy as np
import matplotlib.pyplot as plt
def f(t):
return np.exp(-t) * np.cos(2*np.pi*t)
t1 = np.arange(0.0, 3.0, 0.01)
ax1 = plt.subplot(212)
ax1.margins(0.05) # Default margin is 0.05, value 0 means fit
ax1.plot(t1, f(t1), 'k')
ax2 = plt.subplot(221)
ax2.margins(2, 2) # Values >0.0 zoom out
ax2.plot(t1, f(t1), 'r')
ax2.set_title('Zoomed out')
ax3 = plt.subplot(222)
ax3.margins(x=0, y=-0.25) # Values in (-0.5, 0.0) zooms in to center
ax3.plot(t1, f(t1), 'g')
ax3.set_title('Zoomed in')
plt.show()
12. Creating adjacent subplots
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0.0, 2.0, 0.01)
s1 = np.sin(2 * np.pi * t)
s2 = np.exp(-t)
s3 = s1 * s2
fig, axs = plt.subplots(3, 1, sharex=True)
# Remove horizontal space between axes
fig.subplots_adjust(hspace=0)
# Plot each graph, and manually set the y tick values
axs[0].plot(t, s1)
axs[0].set_yticks(np.arange(-0.9, 1.0, 0.4))
axs[0].set_ylim(-1, 1)
axs[1].plot(t, s2)
axs[1].set_yticks(np.arange(0.1, 1.0, 0.2))
axs[1].set_ylim(0, 1)
axs[2].plot(t, s3)
axs[2].set_yticks(np.arange(-0.9, 1.0, 0.4))
axs[2].set_ylim(-1, 1)
plt.show()
13. Aligning Labels
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.gridspec as gridspec
fig = plt.figure(tight_layout=True)
gs = gridspec.GridSpec(2, 2)
ax = fig.add_subplot(gs[0, :])
ax.plot(np.arange(0, 1e6, 1000))
ax.set_ylabel('YLabel0')
ax.set_xlabel('XLabel0')
for i in range(2):
ax = fig.add_subplot(gs[1, i])
ax.plot(np.arange(1., 0., -0.1) * 2000., np.arange(1., 0., -0.1))
ax.set_ylabel('YLabel1 %d' % i)
ax.set_xlabel('XLabel1 %d' % i)
if i == 0:
for tick in ax.get_xticklabels():
tick.set_rotation(55)
fig.align_labels() # same as fig.align_xlabels(); fig.align_ylabels()
plt.show()
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