Matplotlib之基本做图

Refer to 莫烦Python之Matplotlib

快速查看某种图的实现，https://matplotlib.org/gallery.html#widgets

1. Installation 安装

numpy is required before installing the ‘matplotlib’

On unix-like system, directly install these package(module) through pip command

# python2
$ pip install numpy, matplotlib
# python3
$ pip3 install numpy, matplotlib

Personally, managing the python modules through Conda is highly recommended.

$ conda info -e
$ anaconda search conda numpy

2. Basic Operations 基本画图

• Grammar

  • similar to the function plot in MatLab
  • 7 buttons are listed below the image, which provide som functions: the Subplot configuration Tool , Zoom Im, Reset, Move Save etc.

• Coding Example

  # import the modules
  import matplotlib.pyplot as plt
  import numpy as np
  # set up the variables
  x = np.linspace(-1, 1, 50)
  y = x ** 2
  # plot it
  plt.plot(x, y)
  plt.show()
  

3. Figure 画多个图, 不同线性

• draw multiple figures

  • pyplot.figure([num, figsize=(longth, width), ])

• plot multiple lines on a figure

  • invoke multiple pyplot.plot(x,xx,) functions with different styles, (color, marker, size, line style)

    1 import numpy as np
    2 import matplotlib.pyplot as plt
    3
    4 # data preparation
    5 x = np.linspace(-1, 1, 50)
    6 y1 = 2 * x + 1
    7 y2 = x ** 2
    8 y3 = np.sin(x)
    9
   11 # draw data
   12 plt.figure(figsize=(5,5))
   13 plt.plot(x, y1, '-.r', linewidth=2, markersize=3)
   14 plt.plot(x, y2)
   15
   16 plt.figure(num=5, figsize=[5,7])
   17 plt.plot(x, y3, color='y', linestyle='-.', linewidth=1.0)
   18
   19 plt.show()
  

4. Axis Setting 坐标轴

• range

  • pyplot.xlim((x_min, x_max))
  • pyplot.ylim((y_min, y_max))
  • ex: pyplot.ylim((0, max(y) * 1.1))

• label

  • pyplot.xlabel(str)
  • pyplot.ylabel(str)
  • Ex: pyplot.ylabel("Amplitute")

• self-defined ticks

  • pyplot.xticks(lst)
  • pyplot.yticks(lst)
  • Ex1: pyplot.xticks(np.linspace(-1, 2, 5))
  • Ex2: pyplot.yticks([-1, 0, 1, 2,], [r"$so\ bad$", "normal", r"$good$", r"$exce\beta$"])

• position of axis

  • get handle of axis: ax = pyplot.gca()
  • access four axis: ax.spines["top",'bottom','left','right'] , xxx.set_color('none'), xxx.set_position(('data', data_value))
  • set defualt x,y axis: ax.xaxis.set_ticks_position('left') ax.yaxis.set_ticks_position()

  import matplotlib.pyplot as plt
  import numpy as np
    
  # data
  x = np.linspace(-3, 3, 50)
  y1 = x ** 2
  y2 = 2 * x + 1
    
  # basic plot
  plt.figure()
  plt.plot(x, y2)
  plt.plot(x, y1, color='red', linewidth=1.0, linestyle="--")
    
  # set label and range
  plt.xlim((-1, 2))
  plt.ylim((-2, 3))
  plt.xlabel("Axis x (m)")
  plt.ylabel("Axis Y ")
    
  # set self-defined ticks
  plt.xticks(np.linspace(-1, 2, 5))
  plt.yticks([-2, -1.5, -1, 1.22, 3],
            [r'$so\ bad$', r'$bad$', r'$pretty\ normal$', "good", r"$very\ good$"])
    
  # set axis color and position
  ax = plt.gca() # gca "get current axis"
  ax.spines["right"].set_color('none')
  ax.spines["top"].set_color('none')
  ax.spines["bottom"].set_position(("data", 0)) # outward, axes(percentage)
  ax.spines["left"].set_position(("data", 0 ))
  ax.xaxis.set_ticks_position("bottom")
  ax.yaxis.set_ticks_position("left")
  plt.show()
  

5. Legend 图例

• simply

  1. set the attribute label when invoking the plot function

     pyplot.plot(x, y, label="This is first line")

  2. call the legend func

     pyplot.legend()

• personalize legend (advanced)

  • loc: “upper right”, “lower left”, “best”
  • handles : pass in the lines
  • labels: list of strings

  line1, = pyplot.plot(x, y1) # note the comma
  line2, = pyplot.plot(x, y2)
    
  pyplot.legend(handles=[line1, line2], labels=["aaa", "bbb"], loc="best")
  

6. Annotation 注释

• two ways

  • pyplot.annotations()
  • pyplot.text()

• coding

  import matplotlib.pyplot as plt
  import numpy as np
  
  x = np.linspace(-3, 3, 50)
  y = 2 * x + 1
  
  plt.figure(num=1, figsize=(8,5),)
  plt.plot(x, y)
  
  ax=plt.gca()
  ax.spines["top"].set_color("none")
  ax.spines["right"].set_color("none")
  ax.xaxis.set_ticks_position("bottom")
  ax.yaxis.set_ticks_position("left")
  ax.spines["bottom"].set_position(("data", 0))
  ax.spines["left"].set_position(("data", 0 ))
  
  # add notations
  x0 = 1
  y0 = 2 * x0 + 1
  plt.scatter(x0, y0, s=50, color="b")
  plt.plot([x0, x0], [0, y0], 'k--', linewidth=2.5)
  
  # method 1
  plt.annotate(r"$2x+1=%s$" % y0, xy=(x0,y0), xycoords="data", xytext=(+30, -30), textcoords="offset points", fontsize=16, arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))
  
  # method 2
  plt.text(-3.7, 3, r"$Some\ text\ goes\  Here.\ \mu\ \sigma\ \alpha$", fontdict={"size":16, "color":'r'})
  
  plt.show()
  

7. Tick 设置

• Access & Set tick labels

  • ax = plt.gca()
  • ax.get_xticklabels() / ax.get_yticklabels()
  • xxx.set_fontsize()
  • xxx.set_bbox()

• examples：

    1 import matplotlib.pyplot as plt
    2 import numpy as np
    3
    4 x = np.linspace(-3, 3, 50)
    5 y = 0.1 * x
    6
    7 plt.figure()
    8 plt.plot(x, y, linewidth=10)
    9
   10 ax=plt.gca()
   11 ax.spines["top"].set_color("none")
   12 ax.spines["right"].set_color("none")
   13 ax.xaxis.set_ticks_position("bottom")
   14 ax.yaxis.set_ticks_position("left")
   15 ax.spines["bottom"].set_position(("data", 0))
   16 ax.spines["left"].set_position(("data", 0 ))
   17
   18 for label in ax.get_xticklabels() + ax.get_yticklabels():
   19   label.set_fontsize(12)
   20   label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.7))
   21
   22 plt.show()
  

8. Subplot 多图合一

• similar to what we did in MatLab, using subplot

  import matplotlib.pyplot as plt
    
  plt.figure(figsize=(10,10))
    
  plt.subplot(2,2,1)
  plt.plot([0,1], [0,1])
  plt.title('figure 1')
    
  plt.subplot(2,2,2)
  plt.plot([1,0], [0.5,1])
  plt.title('figure 2')
    
  plt.subplot(223)
  plt.plot([1,0], [0,1])
  plt.title('figure 3')
    
  plt.subplot(224)
  plt.plot([0,1], [0.5,1])
  plt.title('figure 4')
    
  plt.figure(figsize=(10,5))
  plt.subplot(2,1,1)
  plt.plot([0,1], [0,1])
    
  plt.subplot(2,3,4)
  plt.plot([0,1], [0,1])
    
  plt.subplot(2,3,5)
  plt.plot([0,1], [0,1])
    
  plt.subplot(236)
  plt.plot([0,1], [0,1])
    
  plt.show()
  

9. Grid 分格显示

• 3 methods

  • subplot2grid

    import matplotlib.pyplot as plt
    import matplotlib.gridspec as gridspec
        
    plt.figure(figsize=(10,10))
    # (3,3)-> shape of the whole grid
    # (0,0)-> specify where this plot begins
    # by default, colspan = rowspan = 1
    ax1 = plt.subplot2grid((3,3), (0,0), colspan=3, rowspan=1)
    ax1.plot([1,0],[0,1])
    ax1.set_title("figure 1")
        
    ax2 = plt.subplot2grid((3,3), (1,0), colspan=2)
    ax2.plot([1,0],[1,1])
    ax2.set_title("figure 2")
        
    ax3 = plt.subplot2grid((3,3), (1,2), rowspan=2)
    ax3.plot([1,1],[0,1])
    ax3.set_title("figure 3")
        
    ax4 = plt.subplot2grid((3,3), (2,0)) 
    ax4.scatter(0,0, s=30, color='r')
    ax4.set_title("figure 4")
        
    ax5 = plt.subplot2grid((3,3), (2,1))
    ax5.scatter(0,0, s=60, color='b')
    ax5.set_title("figure 5")
        
    plt.show()
    

  • gridspec (import matplotlib.gridspec as gridspec)

    import matplotlib.pyplot as plt
    import matplotlib.gridspec as gridspec
        
    plt.figure(figsize=(10,10))
    gs = gridspec.GridSpec(3,3)
        
        
    ax1 = plt.subplot(gs[0,:])
    ax1.plot([1,0],[0,1])
    ax1.set_title("figure 1")
        
    ax2 = plt.subplot(gs[1,:2])
    ax2.plot([1,0],[1,1])
    ax2.set_title("figure 2")
        
    ax3 = plt.subplot(gs[1:,2])
    ax3.plot([1,1],[0,1])
    ax3.set_title("figure 3")
        
    ax4 = plt.subplot(gs[2,0])
    #ax4 = plt.subplot(gs[-1,0])
    ax4.scatter(0,0, s=30, color='r')
    ax4.set_title("figure 4")
        
    ax5 = plt.subplot(gs[2,1])
    #ax5 = plt.subplot(gs[-1,-2])
    ax5.scatter(0,0, s=60, color='b')
    ax5.set_title("figure 5")
        
    plt.show()
    

  • easy to define structure (subplots)

    import matplotlib.pyplot as plt
    import matplotlib.gridspec as gridspec
        
    f, ((ax11, ax12), (ax21, ax22)) = plt.subplots(2,2, sharex=True, sharey=True)
    ax11.scatter([0,1,2], [0, 1, 2])
        
    plt.tight_layout()
    plt.show()
    

10. Second Axis 次坐标轴

• twinx()

  import matplotlib.pyplot as plt
  import numpy as np
    
  x = np.arange(0, 10, 0.1)
  y1 = 0.05 * x ** 2
  y2 = -1 * y1
    
  fig, ax1 = plt.subplots()
  ax2 = ax1.twinx()
  ax1.plot(x,y1, 'g-')
  ax2.plot(x,y2, 'b-.')
    
  ax1.set_xlabel("XX")
  ax1.set_ylabel("YY1", color='g')
  ax2.set_ylabel("YY2", color='b')
    
  plt.show()