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处理文本（基础）

In [1]:

import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

matplotlib 对文本的支持十分完善，包括数学公式，Unicode 文字，栅格和向量化输出，文字换行，文字旋转等一系列操作。

基础文本函数

在 matplotlib.pyplot 中，基础的文本函数如下：

  • text() 在 Axes 对象的任意位置添加文本
  • xlabel() 添加 x 轴标题
  • ylabel() 添加 y 轴标题
  • title() 给 Axes 对象添加标题
  • figtext() 在 Figure 对象的任意位置添加文本
  • suptitle() 给 Figure 对象添加标题
  • anotate() 给 Axes 对象添加注释（可选择是否添加箭头标记）

In [2]:

# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
%matplotlib inline

# plt.figure() 返回一个 Figure() 对象
fig = plt.figure(figsize=(12, 9))

# 设置这个 Figure 对象的标题
# 事实上，如果我们直接调用 plt.suptitle() 函数，它会自动找到当前的 Figure 对象
fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold')

# Axes 对象表示 Figure 对象中的子图
# 这里只有一幅图像，所以使用 add_subplot(111)
ax = fig.add_subplot(111)
fig.subplots_adjust(top=0.85)

# 可以直接使用 set_xxx 的方法来设置标题
ax.set_title('axes title')
# 也可以直接调用 title()，因为会自动定位到当前的 Axes 对象
# plt.title('axes title')

ax.set_xlabel('xlabel')
ax.set_ylabel('ylabel')

# 添加文本，斜体加文本框
ax.text(3, 8, 'boxed italics text in data coords', style='italic',
        bbox={'facecolor':'red', 'alpha':0.5, 'pad':10})

# 数学公式，用 $$ 输入 Tex 公式
ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15)

# Unicode 支持
ax.text(3, 2, unicode('unicode: Institut f\374r Festk\366rperphysik', 'latin-1'))

# 颜色，对齐方式
ax.text(0.95, 0.01, 'colored text in axes coords',
        verticalalignment='bottom', horizontalalignment='right',
        transform=ax.transAxes,
        color='green', fontsize=15)

# 注释文本和箭头
ax.plot([2], [1], 'o')
ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),
            arrowprops=dict(facecolor='black', shrink=0.05))

# 设置显示范围
ax.axis([0, 10, 0, 10])

plt.show()

文本属性和布局

我们可以通过下列关键词，在文本函数中设置文本的属性：

关键词 值
alpha float
backgroundcolor any matplotlib color
bbox rectangle prop dict plus key 'pad' which is a pad in points
clip_box a matplotlib.transform.Bbox instance
clip_on [True ， False]
clip_path a Path instance and a Transform instance, a Patch
color any matplotlib color
family [ 'serif' , 'sans-serif' , 'cursive' , 'fantasy' , 'monospace' ]
fontproperties a matplotlib.font_manager.FontProperties instance
horizontalalignment or ha [ 'center' , 'right' , 'left' ]
label any string
linespacing float
multialignment ['left' , 'right' , 'center' ]
name or fontname string e.g., ['Sans' , 'Courier' , 'Helvetica' ...]
picker [None,float,boolean,callable]
position (x,y)
rotation [ angle in degrees 'vertical' , 'horizontal'
size or fontsize [ size in points , relative size, e.g., 'smaller', 'x-large' ]
style or fontstyle [ 'normal' , 'italic' , 'oblique']
text string or anything printable with '%s' conversion
transform a matplotlib.transform transformation instance
variant [ 'normal' , 'small-caps' ]
verticalalignment or va [ 'center' , 'top' , 'bottom' , 'baseline' ]
visible [True , False]
weight or fontweight [ 'normal' , 'bold' , 'heavy' , 'light' , 'ultrabold' , 'ultralight']
x float
y float
zorder any number

其中 va, ha, multialignment 可以用来控制布局。

  • horizontalalignment or ha ：x 位置参数表示的位置
  • verticalalignment or va：y 位置参数表示的位置
  • multialignment：多行位置控制

In [3]:

import matplotlib.pyplot as plt
import matplotlib.patches as patches

# build a rectangle in axes coords
left, width = .25, .5
bottom, height = .25, .5
right = left + width
top = bottom + height

fig = plt.figure(figsize=(10,7))
ax = fig.add_axes([0,0,1,1])

# axes coordinates are 0,0 is bottom left and 1,1 is upper right
p = patches.Rectangle(
    (left, bottom), width, height,
    fill=False, transform=ax.transAxes, clip_on=False
    )

ax.add_patch(p)

ax.text(left, bottom, 'left top',
        horizontalalignment='left',
        verticalalignment='top',
        transform=ax.transAxes,
        size='xx-large')

ax.text(left, bottom, 'left bottom',
        horizontalalignment='left',
        verticalalignment='bottom',
        transform=ax.transAxes,
        size='xx-large')

ax.text(right, top, 'right bottom',
        horizontalalignment='right',
        verticalalignment='bottom',
        transform=ax.transAxes,
        size='xx-large')

ax.text(right, top, 'right top',
        horizontalalignment='right',
        verticalalignment='top',
        transform=ax.transAxes,
        size='xx-large')

ax.text(right, bottom, 'center top',
        horizontalalignment='center',
        verticalalignment='top',
        transform=ax.transAxes,
        size='xx-large')

ax.text(left, 0.5*(bottom+top), 'right center',
        horizontalalignment='right',
        verticalalignment='center',
        rotation='vertical',
        transform=ax.transAxes,
        size='xx-large')

ax.text(left, 0.5*(bottom+top), 'left center',
        horizontalalignment='left',
        verticalalignment='center',
        rotation='vertical',
        transform=ax.transAxes,
        size='xx-large')

ax.text(0.5*(left+right), 0.5*(bottom+top), 'middle',
        horizontalalignment='center',
        verticalalignment='center',
        fontsize=20, color='red',
        transform=ax.transAxes)

ax.text(right, 0.5*(bottom+top), 'centered',
        horizontalalignment='center',
        verticalalignment='center',
        rotation='vertical',
        transform=ax.transAxes,
        size='xx-large')

ax.text(left, top, 'rotated\nwith newlines',
        horizontalalignment='center',
        verticalalignment='center',
        rotation=45,
        transform=ax.transAxes,
        size='xx-large')

ax.set_axis_off()
plt.show()

注释文本

text() 函数在 Axes 对象的指定位置添加文本，而 annotate() 则是对某一点添加注释文本，需要考虑两个位置：一是注释点的坐标 xy ，二是注释文本的位置坐标 xytext：

In [4]:

fig = plt.figure()
ax = fig.add_subplot(111)

t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2*np.pi*t)
line, = ax.plot(t, s, lw=2)

ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
            arrowprops=dict(facecolor='black', shrink=0.05),
            )

ax.set_ylim(-2,2)
plt.show()

在上面的例子中，两个左边使用的都是原始数据的坐标系，不过我们还可以通过 xycoords 和 textcoords 来设置坐标系（默认是 'data'）：

参数 坐标系
‘figure points’ points from the lower left corner of the figure
‘figure pixels’ pixels from the lower left corner of the figure
‘figure fraction’ 0,0 is lower left of figure and 1,1 is upper right
‘axes points’ points from lower left corner of axes
‘axes pixels’ pixels from lower left corner of axes
‘axes fraction’ 0,0 is lower left of axes and 1,1 is upper right
‘data’ use the axes data coordinate system

使用一个不同的坐标系：

In [5]:

fig = plt.figure()
ax = fig.add_subplot(111)

t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2*np.pi*t)
line, = ax.plot(t, s, lw=2)

ax.annotate('local max', xy=(3, 1),  xycoords='data',
            xytext=(0.8, 0.95), textcoords='axes fraction',
            arrowprops=dict(facecolor='black', shrink=0.05),
            horizontalalignment='right', verticalalignment='top',
            )

ax.set_ylim(-2,2)
plt.show()

极坐标系注释文本

产生极坐标系需要在 subplot 的参数中设置 polar=True：

In [6]:

fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
r = np.arange(0,1,0.001)
theta = 2*2*np.pi*r
line, = ax.plot(theta, r, color='#ee8d18', lw=3)

ind = 800
thisr, thistheta = r[ind], theta[ind]
ax.plot([thistheta], [thisr], 'o')
ax.annotate('a polar annotation',
            xy=(thistheta, thisr),  # theta, radius
            xytext=(0.05, 0.05),    # fraction, fraction
            textcoords='figure fraction',
            arrowprops=dict(facecolor='black', shrink=0.05),
            horizontalalignment='left',
            verticalalignment='bottom',
            )
plt.show()