#!/usr/bin/env python # coding: utf-8 # # 处理文本（基础） # In[1]: import matplotlib.pyplot as plt import numpy as np get_ipython().run_line_magic('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 get_ipython().run_line_magic('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()