Numpy之matplotlib

客官°小女子只卖身不卖艺 2022-11-25 10:14 196阅读 0赞

Matplotlib是Python的绘图库，它与Numpy一起使用  
**【引例】**  
画出y=2\*x+5的函数图像

import numpy as np
    from matplotlib import pyplot as plt
    
    #设置横坐标取值、确定函数表达式
    x = np.arange(1, 11)
    y = 2 * x + 5
    #添加标题、横坐标、纵坐标标签
    plt.title("Matplotlib demo")
    plt.xlabel("x axis caption")
    plt.ylabel("y axis caption")
    #画函数图像
    plt.plot(x, y)
    plt.show()

【运行结果】  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2dldHNfcw_size_16_color_FFFFFF_t_70]

如果需要函数图像以圆点呈现，而不是用线呈现，则需要将
    
    plt.plot(x,y)改为plt.plot(x,y,'ob')

【运行结果】  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2dldHNfcw_size_16_color_FFFFFF_t_70 1]

#### 正弦波 y=sin(x) ####

import numpy as np
    from matplotlib import pyplot as plt
    
    #设置横坐标取值、确定函数表达式
    x = np.arange(0, 3*np.pi,0.1)
    y = np.sin(x)
    #添加标题、横坐标、纵坐标标签
    plt.title("y=sin(x)")
    plt.xlabel("x axis caption")
    plt.ylabel("y axis caption")
    #画函数图像
    plt.plot(x, y)
    plt.show()

【运行结果】  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2dldHNfcw_size_16_color_FFFFFF_t_70 2]

余弦函数只需将
    y = np.sin(x) 改为 y = np.cos(x)即可

#### subplot()函数 ####

应用subplot（）函数，在同一图中绘制不同的东西

# ReLU和Sigmoid激活函数示意图
    import numpy as np
    import matplotlib.pyplot as plt
    import matplotlib.patches as patches
    
    #设置图片大小
    plt.figure(figsize=(6, 4))
    
    # x是1维数组，数组大小是从-10. 到10.的实数，每隔0.1取一个点
    x = np.arange(-10, 10, 0.1)
    # 计算 Sigmoid函数
    s = (np.exp(x)-np.exp(-x))/(np.exp(x)+np.exp(-x))
    
    
    # 计算ReLU函数
    y = np.clip(x, a_min = 0., a_max = None)
    
    #########################################################
    # 以下部分为画图程序
    
    # 设置两个子图窗口，将Sigmoid的函数图像画在上边
    f = plt.subplot(2,1,1)
    # 画出函数曲线
    plt.plot(x, s, color='r')
    # 添加文字说明
    plt.text(-5., 0.9, r'$y=sigmoid(x)$', fontsize=13)
    # 设置坐标轴格式
    currentAxis=plt.gca()
    currentAxis.xaxis.set_label_text('x', fontsize=15)
    currentAxis.yaxis.set_label_text('y', fontsize=15)
    
    # 将ReLU的函数图像画在下边
    f = plt.subplot(2,1,2)
    # 画出函数曲线
    plt.plot(x, y, color='g')
    # 添加文字说明
    plt.text(-3.0, 9, r'$y=ReLU(x)$', fontsize=13)
    # 设置坐标轴格式
    currentAxis=plt.gca()
    currentAxis.xaxis.set_label_text('x', fontsize=15)
    currentAxis.yaxis.set_label_text('y', fontsize=15)
    
    plt.show()

【运行结果】  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2dldHNfcw_size_16_color_FFFFFF_t_70 3]

#### 普通图 ####

from matplotlib import pyplot as plt
    import numpy as np
    n = 256  #n可以理解为曲线的平滑程度
    X = np.linspace(-np.pi,np.pi,n,endpoint=True)
    Y = np.sin(2*X)
    
    plt.plot(X,Y+1,color='red',alpha=1.00)
    plt.plot(X,Y-1,color='blue',alpha=1.00)
    plt.show()

【运行结果】  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2dldHNfcw_size_16_color_FFFFFF_t_70 4]

#### 条形图 ####

from matplotlib import pyplot as plt
    #数据组1
    x =  [5,8,11]  #横坐标
    y =  [12,16,6] #对应值
    #数据组2
    x2 =  [6,9,12] #横坐标
    y2 =  [6,15,7] #对应值
    plt.bar(x, y, color = 'r', align =  'center')
    plt.bar(x2, y2, color =  'g', align =  'center')
    plt.title('Bar graph')
    plt.ylabel('Y axis')
    plt.xlabel('X axis')
    plt.show()

【运行结果】  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2dldHNfcw_size_16_color_FFFFFF_t_70 5]

#### 散点图 ####

from matplotlib import pyplot as plt
    import numpy as np
    
    n = 1024
    X = np.random.normal(0,30,n)
    Y = np.random.normal(0,30,n)
    
    plt.title('scale:30')
    plt.scatter(X,Y,color='red')
    plt.show()

【运行结果】以下分别是方差分别为5，10，15，20，25，30的散点图  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2dldHNfcw_size_16_color_FFFFFF_t_70 6]

#### 等高线图 ####

from matplotlib import pyplot as plt
    import numpy as np
    
    def f(x,y):return(1-x/2+x**5+y**3)*np.exp(-x**2-y**2) #等高线模型
    
    n = 256  #n可以理解为曲线的平滑程度
    x = np.linspace(-3,3,n)
    y = np.linspace(-3,3,n)
    X,Y = np.meshgrid(x,y)
    
    plt.contourf(X,Y,f(X,Y),8,alpha=.75,cmap='jet')
    C = plt.contour(X,Y,f(X,Y),8,color='black',linewidth=.5)
    plt.show()

【运行结果】  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2dldHNfcw_size_16_color_FFFFFF_t_70 7]

#### 灰度图 ####

from matplotlib import pyplot as plt
    import numpy as np
    
    def f(x,y):return(1-x/2+x**5+y**3)*np.exp(-x**2-y**2)
    
    n = 10
    x = np.linspace(-3,3,4*n)
    y = np.linspace(-3,3,4*n)
    X,Y = np.meshgrid(x,y)
    
    plt.imshow(f(X,Y))
    plt.show()

【运行结果】  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2dldHNfcw_size_16_color_FFFFFF_t_70 8]

#### 饼状图 ####

from matplotlib import pyplot as plt
    import numpy as np
    
    n = 20  #n决定了有多少个分区，该代码显示有20个分区
    Z = np.random.uniform(0,1,n)
    plt.pie(Z)
    plt.show()

【运行结果】  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2dldHNfcw_size_16_color_FFFFFF_t_70 9]

#### 量场图 ####

from matplotlib import pyplot as plt
    import numpy as np
    
    n = 8
    X,Y = np.mgrid[0:n,0:n]
    plt.quiver(X,Y,color="red")
    plt.show()

【运行结果】  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2dldHNfcw_size_16_color_FFFFFF_t_70 10]

#### 极轴图 ####

from matplotlib import pyplot as plt
    import numpy as np
    
    plt.figure(figsize=(4,4))
    ax1 = plt.subplot(111,projection='polar')
    ax1.set_title('spot fish')
    ax1.set_rlim(0,12)
    
    data = np.random.randint(1,10,10)
    theta = np.arange(0,2*np.pi,2*np.pi/10)
    
    bar = ax1.bar(theta,data,alpha=0.5)
    for r,bar in zip(data,bar):
        bar.set_facecolor(plt.cm.jet(r/10.))
    plt.show()

【运行结果】  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2dldHNfcw_size_16_color_FFFFFF_t_70 11]

#### 3D图 ####

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