matplotlib pyplot tutorial

pyplot tutorial

• matplotlib.pyplot is a collection of command style functions that make matplotlib work like MATLAB

• each pyplot function makes some change to a figure

  • create a figure

  • create a plotting area in a figure

  • plot some lines in a plotting area

  • decorate the plot with labels

  • etc…

• matplotlib.pyplot is stateful

  • it that it keeps track of the current figure and plotting area

  • and the plotting functions are directed to the current axes

code

1. draw line

        import matplotlib.pyplot as plt
        plt.plot([1, 2, 3, 4])
        plt.ylabel('some numbers')
        plt.show()
   

   

   • if you provide a single list or array to the plot() command, matplotlib assumes it is a sequence of y values

   • and automatically generates x values for you

   • the default x vector has same length as y but start with 0

   • hence the x data are [0, 1, 2, 3]

2. plot() is a versatile command and will take an arbitrary number of arguments

   • to plot x versus y you can issue the command

       plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
     

3. for every x,y pair of arguments there is an optional third argument

   • which is the format string

     • indicates the color

     • and line type of the plot

   • the letters and symbols of the format string are from MATLAB

   • default format string is b-, which is a solid blue line

       import matplotlib.pyplot as plt
       plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')
       plt.axis([0, 6, 0, 20])
       plt.show()
     

   

   • the axis() command in the example above takes a list of [xmin, xmax, ymin, ymax]

   • and specifies the viewport of the axes

4. all sequences are converted to numpy arrays internally

        import numpy as np
        import matplotlib.pyplot as plt
        # evenly sampled time at 200ms intervals
        t = np.arange(0., 5., 0.2)
        # red dashes, blue squares and green triangles
        plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')
        plt.show()
   

   

controlling line properties

1. line have many attributes that you can set

   1. linewidth

   2. dash style

   3. antialiased

2. there are several ways to set line properties

   1. use keyword args

       plt.plot(x, y, linewidth=2.0)
      

   2. use the setter methods of the Line2D instance .plot returns a list of lines

       line1, line2 = plot(x1, y1, x2, y2)
      
       line, = plt.plot(x, y, '-')
       # turn off antialising
       line.set_antialiased(False)
      

   3. use the setp() command

       lines = plt.plot(x1, y1, x2, y2)
       # use keyword args
       plt.setp(lines, color='r', linewidth=2.0)
       # or MATLAB style string values pairs
       plt.setp(lines, 'color', 'r', 'linewidth', 2.0)
      

Line2D properties

working with multiple figures and axes

• MATLAB and pyplot have the concept of the current figure and the current axes

• all plotting commands apply to the current axes

• the function gca() returns the current axes (a matplotlib.axes.Axes instance)

• and gcf() returns the current figure (matplotlib.figure.Figure instance)

        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, 5.0, 0.1)
        t1 = np.arange(0.0, 5.0, 0.02)
  
        plt.figure(1)
        plt.subplot(211)
        plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')
  
        plt.subplot(212)
        plt.plot(t2, np.cos(2 * np.pi * t2), 'r--')
        plt.show()
  

  

• you can create multiple figures by using multiple figure() calls with an increasing figure number

        import matplotlib.pyplot as plt
        plt.figure(1)                  # the first figure
        plt.subplot(211)               # the first subplot in the first figure
        plt.plot([1, 2, 3])
        plt.subplot(212)               # the second subplot in the first figure
        plt.plot([4, 5, 6])
  
        plt.figure(2)                  # the second figure
        plt.plot([4, 5, 6])            # creates a subplot(111) by default
  
        plt.figure(1)                  # figure 1 current; subplot(212) still current
        plt.subplot(211)               # make subplot(211) in figure 1 current
        plt.title('easy as 1, 2, 3')   # subplot 211 title
  

• you can clear the current figure with clf() and the current axes with cla()

• a figure is not completely released until the figure is explicitly closed with close()

working with text

• the text() command can be used to add text in an arbitrary location

• the xlabel(), ylabel() and title() are used to add text in the indicated locations

        import numpy as np
        import matplotlib.pyplot as plt
  
        mu, sigma = 100, 15
        x = mu + sigma * np.random.randn(10000)
  
        # the histogram of the data
        n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)
  
  
        plt.xlabel('Smarts')
        plt.ylabel('Probability')
        plt.title('Histogram of IQ')
        plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
        plt.axis([40, 160, 0, 0.03])
        plt.grid(True)
        plt.show()
  

  

• all the text() commands return an matplotlib.text.Text instance

        t = plt.xlabel('my data', fontsize=14, color='red')
  

  • you can customize the properties by passing keyword arguments into the text functions or using setp()

annotating text

• annotate() method provides helper functionality to make annotation ease

• there 2 points to consider

  • the location being annotated represented by the argument xy

  • the location of the text xytext

      import numpy as np
      import matplotlib.pyplot as plt
    
      ax = plt.subplot(111)
    
      t = np.arange(0.0, 5.0, 0.01)
      s = np.cos(2*np.pi*t)
      line, = plt.plot(t, s, lw=2)
    
      plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
                  arrowprops=dict(facecolor='black', shrink=0.05),
                  )
    
      plt.ylim(-2,2)
      plt.show()
    

  

  • see annotating text and annotating axes

  • more examples can be found in pylab-examples-annotation-demo