1.4. Matplotlib：绘图¶

Nicolas Rougier, Mike Müller, Gaël Varoquaux

In [1]: %matplotlib

%matplotlib inline

import matplotlib.pyplot as plt

import numpy as np

X = np.linspace(-np.pi, np.pi, 256)
C, S = np.cos(X), np.sin(X)

$ ipython --matplotlib

IPython 0.13 -- An enhanced Interactive Python.
?       -> Introduction to IPython's features.
%magic  -> Information about IPython's 'magic' % functions.
help    -> Python's own help system.
object? -> Details about 'object'. ?object also works, ?? prints more.

$ python plot_exercise_1.py

import numpy as np
import matplotlib.pyplot as plt

X = np.linspace(-np.pi, np.pi, 256)
C, S = np.cos(X), np.sin(X)

plt.plot(X, C)
plt.plot(X, S)

plt.show()

import numpy as np
import matplotlib.pyplot as plt

# Create a figure of size 8x6 inches, 80 dots per inch
plt.figure(figsize=(8, 6), dpi=80)

# Create a new subplot from a grid of 1x1
plt.subplot(1, 1, 1)

X = np.linspace(-np.pi, np.pi, 256)
C, S = np.cos(X), np.sin(X)

# Plot cosine with a blue continuous line of width 1 (pixels)
plt.plot(X, C, color="blue", linewidth=1.0, linestyle="-")

# Plot sine with a green continuous line of width 1 (pixels)
plt.plot(X, S, color="green", linewidth=1.0, linestyle="-")

# Set x limits
plt.xlim(-4.0, 4.0)

# Set x ticks
plt.xticks(np.linspace(-4, 4, 9))

# Set y limits
plt.ylim(-1.0, 1.0)

# Set y ticks
plt.yticks(np.linspace(-1, 1, 5))

# Save figure using 72 dots per inch
# plt.savefig("exercise_2.png", dpi=72)

# Show result on screen
plt.show()

...
plt.figure(figsize=(10, 6), dpi=80)
plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-")
plt.plot(X, S, color="red",  linewidth=2.5, linestyle="-")
...

...
plt.xlim(X.min() * 1.1, X.max() * 1.1)
plt.ylim(C.min() * 1.1, C.max() * 1.1)
...

...
plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])
plt.yticks([-1, 0, +1])
...

...
plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
          [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$'])

plt.yticks([-1, 0, +1],
          [r'$-1$', r'$0$', r'$+1$'])
...

...
ax = plt.gca()  # gca stands for 'get current axis'
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))
...

...
plt.plot(X, C, color="blue", linewidth=2.5, linestyle="-", label="cosine")
plt.plot(X, S, color="red",  linewidth=2.5, linestyle="-", label="sine")

plt.legend(loc='upper left')
...

...

t = 2 * np.pi / 3
plt.plot([t, t], [0, np.cos(t)], color='blue', linewidth=2.5, linestyle="--")
plt.scatter([t, ], [np.cos(t), ], 50, color='blue')

plt.annotate(r'$cos(\frac{2\pi}{3})=-\frac{1}{2}$',
             xy=(t, np.cos(t)), xycoords='data',
             xytext=(-90, -50), textcoords='offset points', fontsize=16,
             arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

plt.plot([t, t],[0, np.sin(t)], color='red', linewidth=2.5, linestyle="--")
plt.scatter([t, ],[np.sin(t), ], 50, color='red')

plt.annotate(r'$sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$',
             xy=(t, np.sin(t)), xycoords='data',
             xytext=(+10, +30), textcoords='offset points', fontsize=16,
             arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))
...

...
for label in ax.get_xticklabels() + ax.get_yticklabels():
    label.set_fontsize(16)
    label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.65))
...

plt.close(1)     # Closes figure 1

ax = plt.gca()
ax.xaxis.set_major_locator(eval(locator))

n = 256
X = np.linspace(-np.pi, np.pi, n)
Y = np.sin(2 * X)

plt.plot(X, Y + 1, color='blue', alpha=1.00)
plt.plot(X, Y - 1, color='blue', alpha=1.00)

n = 1024
rng = np.random.default_rng()
X = rng.normal(0,1,n)
Y = rng.normal(0,1,n)

plt.scatter(X,Y)

n = 12
X = np.arange(n)
rng = np.random.default_rng()
Y1 = (1 - X / float(n)) * rng.uniform(0.5, 1.0, n)
Y2 = (1 - X / float(n)) * rng.uniform(0.5, 1.0, n)

plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')
plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')

for x, y in zip(X, Y1):
    plt.text(x + 0.4, y + 0.05, '%.2f' % y, ha='center', va='bottom')

plt.ylim(-1.25, +1.25)

def f(x, y):
    return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 -y ** 2)

n = 256
x = np.linspace(-3, 3, n)
y = np.linspace(-3, 3, n)
X, Y = np.meshgrid(x, y)

plt.contourf(X, Y, f(X, Y), 8, alpha=.75, cmap='jet')
C = plt.contour(X, Y, f(X, Y), 8, colors='black', linewidth=.5)

def f(x, y):
    return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)

n = 10
x = np.linspace(-3, 3, 4 * n)
y = np.linspace(-3, 3, 3 * n)
X, Y = np.meshgrid(x, y)
plt.imshow(f(X, Y))

rng = np.random.default_rng()
Z = rng.uniform(0, 1, 20)
plt.pie(Z)

n = 8
X, Y = np.mgrid[0:n, 0:n]
plt.quiver(X, Y)

axes = plt.gca()
axes.set_xlim(0, 4)
axes.set_ylim(0, 3)
axes.set_xticklabels([])
axes.set_yticklabels([])

plt.subplot(2, 2, 1)
plt.subplot(2, 2, 3)
plt.subplot(2, 2, 4)

plt.axes([0, 0, 1, 1])

N = 20
theta = np.arange(0., 2 * np.pi, 2 * np.pi / N)
rng = np.random.default_rng()
radii = 10 * rng.random(N)
width = np.pi / 4 * rng.random(N)
bars = plt.bar(theta, radii, width=width, bottom=0.0)

for r, bar in zip(radii, bars):
    bar.set_facecolor(plt.cm.jet(r / 10.))
    bar.set_alpha(0.5)

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = Axes3D(fig)
X = np.arange(-4, 4, 0.25)
Y = np.arange(-4, 4, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)

ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='hot')

>>> import matplotlib.pyplot as plt
>>> help(plt.plot)
Help on function plot in module matplotlib.pyplot:

plot(*args: ...) -> 'list[Line2D]'
    Plot y versus x as lines and/or markers.

    Call signatures::

        plot([x], y, [fmt], *, data=None, **kwargs)
        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)
...

参数	默认值	描述
`num`	`1`	图形编号
`figsize`	`figure.figsize`	图形大小，以英寸为单位（宽度、高度）
`dpi`	`figure.dpi`	每英寸点数的分辨率
`facecolor`	`figure.facecolor`	绘图背景颜色
`edgecolor`	`figure.edgecolor`	绘图背景周围边缘的颜色
`frameon`	`True`	是否绘制图形框架

属性	描述	外观
alpha (或 a)	0-1 范围内的 alpha 透明度
antialiased	True 或 False - 使用抗锯齿渲染
color (或 c)	matplotlib 颜色参数
linestyle (或 ls)	参见线属性
linewidth (或 lw)	浮点数，线宽，以磅为单位
solid_capstyle	实线端点样式
solid_joinstyle	实线连接样式
dash_capstyle	虚线端点样式
dash_joinstyle	虚线连接样式
标记	参见标记
标记边框宽度 (mew)	标记符号周围的线宽
标记边框颜色 (mec)	使用标记时的边框颜色
标记填充颜色 (mfc)	使用标记时的填充颜色
标记大小 (ms)	标记的大小，以点为单位

1.4. Matplotlib：绘图¶

1.4.1.1. IPython、Jupyter 和 Matplotlib 模式¶

1.4.1.2. pyplot¶

1.4.2.1. 使用默认设置绘图¶

1.4.2.2. 实例化默认值¶

1.4.2.3. 更改颜色和线条宽度¶

1.4.2.4. 设置限制¶

1.4.2.5. 设置刻度¶

1.4.2.6. 设置刻度标签¶

1.4.2.7. 移动脊柱¶

1.4.2.8. 添加图例¶

1.4.2.9. 注释一些点¶

1.4.2.10. 魔鬼藏在细节中¶

1.4.3.1. 图形¶

1.4.3.2. 子图¶

1.4.3.3. 轴¶

1.4.3.4. 刻度¶

刻度定位器¶

1.4.4.1. 常规绘图¶

1.4.4.2. 散点图¶

1.4.4.3. 条形图¶

1.4.4.4. 等高线图¶

1.4.4.5. Imshow¶

1.4.4.6. 饼图¶

1.4.4.7. 矢量图¶

1.4.4.8. 网格¶

1.4.4.9. 多图¶

1.4.4.10. 极坐标轴¶

1.4.4.11. 3D 图形¶

1.4.4.12. 文本¶

1.4.5.1. 教程¶

1.4.5.2. Matplotlib 文档¶

1.4.5.3. 代码文档¶

1.4.5.4. 图库¶

1.4.5.5. 邮件列表¶

1.4.6.1. 线属性¶

1.4.6.2. 线型¶

1.4.6.3. 标记¶

1.4.6.4. 颜色图¶

1.4.7.1. Matplotlib 代码示例¶

1.4.7.2. 本章练习的代码¶

1.4.7.3. 演示选项选择的示例¶

1.4.7.4. 生成带标题的摘要图形的代码¶