Introduction to Computer Science and Programming Using Python(7)

 
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Week Seven: Plotting

Plotting

  1. Visualization of data

    import pylab as plt
   
mySamples = []
myLinear = []
myQuadratic = []
myCubic = []
myExponential = []
   
for i in range(0,30):
    mySamples.append(i)
    myLinear.append(i)
    myQuadratic.append(i**2)
    myCubic.append(i**3)
    myExponential.append(1.5**i)
       
# first trial   
plt.plot(mySamples, myLinear)
plt.plot(mySamples, myQuadratic)
plt.plot(mySamples, myCubic)
plt.plot(mySamples, myExponential)

    plotting

  2. Different Plots

    import pylab as plt
   
mySamples = []
myLinear = []
myQuadratic = []
myCubic = []
myExponential = []
   
for i in range(0,30):
    mySamples.append(i)
    myLinear.append(i)
    myQuadratic.append(i**2)
    myCubic.append(i**3)
    myExponential.append(1.5**i)
       
# second trial   
plt.figure('lin')
plt.plot(mySamples, myLinear)
plt.figure('quad')
plt.plot(mySamples, myQuadratic)
plt.figure('cube')
plt.plot(mySamples, myCubic)
plt.figure('expo')
plt.plot(mySamples, myExponential)

    different plots

  3. label & title & clf

    • label:横纵轴标签

    • title:表格标题

      import pylab as plt
     
mySamples = []
myLinear = []
myQuadratic = []
myCubic = []
myExponential = []
     
for i in range(0,30):
    mySamples.append(i)
    myLinear.append(i)
    myQuadratic.append(i**2)
    myCubic.append(i**3)
    myExponential.append(1.5**i)
         
# second trial   
plt.figure('lin')
plt.plot(mySamples, myLinear)
plt.figure('quad')
plt.plot(mySamples, myQuadratic)
plt.figure('cube')
plt.plot(mySamples, myCubic)
plt.figure('expo')
plt.plot(mySamples, myExponential)
     
plt.figure('lin')
plt.xlabel('Sample Points')
plt.ylabel('myLinear')
plt.title('Linear')
     
plt.figure('quad')
plt.xlabel('Sample Points')
plt.ylabel('myQuad')
plt.title('Quad')

      title

    • clf:清除表格信息 ```python import pylab as plt mySamples = [] myLinear = [] myQuadratic = [] myCubic = [] myExponential = []

      for i in range(0,30): mySamples.append(i) myLinear.append(i) myQuadratic.append(i2) myCubic.append(i3) myExponential.append(1.5**i)

      plt.figure('lin')
plt.plot(mySamples, myLinear)
plt.figure('quad')
plt.plot(mySamples, myQuadratic)
plt.figure('cube')
plt.plot(mySamples, myCubic)
plt.figure('expo')
plt.plot(mySamples, myExponential)
   
plt.figure('lin')
plt.xlabel('Sample Points')
plt.ylabel('myLinear')
plt.title('Linear')
   
plt.figure('quad')
plt.xlabel('Sample Points')
plt.ylabel('myQuad')
plt.title('Quad')
   
plt.figure('quad')
plt.clf() #清除表格信息

    ```

    clf

  4. lim & Overlaying & Legend

    • lim:限制横纵轴参数

      import pylab as plt
     
mySamples = []
myLinear = []
myQuadratic = []
myCubic = []
myExponential = []
     
for i in range(0,30):
    mySamples.append(i)
    myLinear.append(i)
    myQuadratic.append(i**2)
    myCubic.append(i**3)
    myExponential.append(1.5**i)
         
# second trial   
plt.figure('lin')
plt.plot(mySamples, myLinear)
plt.figure('quad')
plt.plot(mySamples, myQuadratic)
plt.figure('cube')
plt.plot(mySamples, myCubic)
plt.figure('expo')
plt.plot(mySamples, myExponential)
     
plt.figure('lin')
plt.xlabel('Sample Points')
plt.ylabel('myLinear')
plt.title('Linear')
     
plt.figure('quad')
plt.xlabel('Sample Points')
plt.ylabel('myQuad')
plt.title('Quad')
     
plt.figure('quad')
plt.clf()
plt.ylim(0,1000)
plt.plot(mySamples, myQuadratic)

      ylim

    • overlaying:比较两个图表

      import pylab as plt
     
mySamples = []
myLinear = []
myQuadratic = []
myCubic = []
myExponential = []
     
for i in range(0,30):
    mySamples.append(i)
    myLinear.append(i)
    myQuadratic.append(i**2)
    myCubic.append(i**3)
    myExponential.append(1.5**i)
         
# second trial   
plt.figure('lin')
plt.plot(mySamples, myLinear)
plt.figure('quad')
plt.plot(mySamples, myQuadratic)
plt.figure('cube')
plt.plot(mySamples, myCubic)
plt.figure('expo')
plt.plot(mySamples, myExponential)
     
plt.figure('lin')
plt.xlabel('Sample Points')
plt.ylabel('myLinear')
plt.title('Linear')
     
plt.figure('quad')
plt.xlabel('Sample Points')
plt.ylabel('myQuad')
plt.title('Quad')
     
plt.figure('quad')
plt.clf()
plt.ylim(0,1000)
plt.plot(mySamples, myQuadratic)
     
plt.figure('lin quad')
plt.clf()
plt.plot(mySamples, myLinear)
plt.plot(mySamples, myQuadratic)
plt.title('lin vs. quad')

      lin vs. quad

    • legend:图例

      import pylab as plt
     
mySamples = []
myLinear = []
myQuadratic = []
myCubic = []
myExponential = []
     
for i in range(0,30):
    mySamples.append(i)
    myLinear.append(i)
    myQuadratic.append(i**2)
    myCubic.append(i**3)
    myExponential.append(1.5**i)
         
# second trial   
plt.figure('lin')
plt.plot(mySamples, myLinear)
plt.figure('quad')
plt.plot(mySamples, myQuadratic)
plt.figure('cube')
plt.plot(mySamples, myCubic)
plt.figure('expo')
plt.plot(mySamples, myExponential)
     
plt.figure('lin')
plt.xlabel('Sample Points')
plt.ylabel('myLinear')
plt.title('Linear')
     
plt.figure('quad')
plt.xlabel('Sample Points')
plt.ylabel('myQuad')
plt.title('Quad')
     
plt.figure('quad')
plt.clf()
plt.ylim(0,1000)
plt.plot(mySamples, myQuadratic)
     
plt.figure('lin quad')
plt.clf()
plt.plot(mySamples, myLinear)
plt.plot(mySamples, myQuadratic)
plt.title('lin vs. quad')
     
plt.figure('lin quad')
plt.clf()
plt.plot(mySamples, myLinear, label = 'linear')
plt.plot(mySamples, myQuadratic, label = 'Quadratic')
plt.legend(loc = 'upper left')

      legend

  5. Changing Data Display

    • color & style:

      • -:line
      • o:circle
      • ^:triange
      • --:dash

    • linewidth:线宽

      import pylab as plt
     
mySamples = []
myLinear = []
myQuadratic = []
myCubic = []
myExponential = []
     
for i in range(0,30):
    mySamples.append(i)
    myLinear.append(i)
    myQuadratic.append(i**2)
    myCubic.append(i**3)
    myExponential.append(1.5**i)
         
     
plt.figure('lin quad')
plt.clf()
plt.plot(mySamples, myLinear, 'b-', label = 'linear', linewidth = 4.0)
plt.plot(mySamples, myQuadratic, 'r--', label = 'quadratic', linewidth = 1.0)

      color & width

    • subplot

      subplot

      import pylab as plt
     
mySamples = []
myLinear = []
myQuadratic = []
myCubic = []
myExponential = []
     
for i in range(0,30):
    mySamples.append(i)
    myLinear.append(i)
    myQuadratic.append(i**2)
    myCubic.append(i**3)
    myExponential.append(1.5**i)
         
     
plt.figure('lin quad')
plt.clf()
plt.subplot(211)
plt.ylim(0,900)
plt.plot(mySamples, myLinear, 'g-', label = 'linear', linewidth = 2.0)
plt.subplot(212)
plt.ylim(0,1000)
plt.plot(mySamples, myQuadratic, 'r--', label = 'quadratic', linewidth = 1.0)

      subplot

    • scale

      import pylab as plt
     
mySamples = []
myLinear = []
myQuadratic = []
myCubic = []
myExponential = []
     
for i in range(0,30):
    mySamples.append(i)
    myLinear.append(i)
    myQuadratic.append(i**2)
    myCubic.append(i**3)
    myExponential.append(1.5**i)
         
     
plt.figure('cube exp log')
plt.clf()
plt.plot(mySamples, myCubic, 'g--', label = 'cubic', linewidth = 2.0)
plt.plot(mySamples, myExponential, 'r', label = 'exponential', linewidth = 4.0)
plt.yscale('log')
plt.legend()
plt.title('Cubic vs. Exponential')
     
plt.figure('cube exp linear')
plt.clf()
plt.plot(mySamples, myCubic, 'g--', label = 'cubic', linewidth = 2.0)
plt.plot(mySamples, myExponential, 'r', label = 'exponential', linewidth = 4.0)
plt.legend()
plt.title('Cubic vs. Exponential')

      scale log

  6. An Example—Retirement Saving

    1. compound interest

      import pylab as plt
      
def retirement(monthly, rate, terms):
    base = [0]
    value = [0]
    mrate = rate/12
    for i in range(terms):
        base += [i]
        value += [value[-1] * (1 + mrate) + monthly]
    return base, value

    2. display retirement

      def displayretirement(monthlies, rate, terms):
   plt.figure('retirement')
   plt.clf
   for i in monthlies:
       xvalue,yvalue = retirement(i, rate, terms)
       plt.plot(xvalue,yvalue, label = "retire:" + str(i))
       plt.legend(loc='upper left')
             
displayretirement([400, 600, 800], 0.03, 30*12)

    3. display retirement rates

      def displayretirementrate(monthly, rates, terms):
    plt.figure('retirement rate')
    plt.clf
    for i in rates:
        xvalue,yvalue = retirement(monthly, i, terms)
        plt.plot(xvalue,yvalue, label = "retirerate:" + str(i))
        plt.legend(loc = 'upper left')
             
displayretirementrate(600, [0.03, 0.04, 0.05], 30*12)

    4. display both retirement

      def displaybothretirement(monthlies, rates, terms):
    plt.figure('retirement monthlies rate')
    plt.clf
    plt.xlim(25*12, 30*12)
    monthlabels = ['r', 'b', 'g','k']
    ratelabels = ['-', 'o', '--']
    for i in range(len(monthlies)):
        month = monthlies[i]
        monthlabel = monthlabels[i % len(monthlies)] #循环赋值
        for j in range(len(rates)):
            rate = rates[j]
            ratelabel = ratelabels[j % len(ratelabels)] #循环赋值
            xvalue,yvalue = retirement(month, rate, terms)
            plt.plot(xvalue,yvalue, monthlabel+ratelabel,  label = "retire:" + str(month) + ':' +str(rate) ) #赋值线段属性
            plt.legend(loc = 'upper left')
      
      
displaybothretirement([400, 600, 800, 1000], [0.03, 0.04, 0.05], 30*12)

  7. Course Summary

    • Topics covered

      • topics

    • Computational thinking

      • The process

      • 3 As

        abstraction automation algorithms

      • Think recursively!!!

    • One more thing

      Good Luck