import numpy as np
from matplotlib import pyplot as plt
#設(shè)置橫坐標(biāo)取值、確定函數(shù)表達(dá)式
x = np.arange(1, 11)
y = 2 * x + 5
#添加標(biāo)題、橫坐標(biāo)、縱坐標(biāo)標(biāo)簽
plt.title("Matplotlib demo")
plt.xlabel("x axis caption")
plt.ylabel("y axis caption")
#畫函數(shù)圖像
plt.plot(x, y)
plt.show()

如果需要函數(shù)圖像以圓點(diǎn)呈現(xiàn)，而不是用線呈現(xiàn)，則需要將
plt.plot(x,y)改為plt.plot(x,y,'ob')

import numpy as np
from matplotlib import pyplot as plt
#設(shè)置橫坐標(biāo)取值、確定函數(shù)表達(dá)式
x = np.arange(0, 3*np.pi,0.1)
y = np.sin(x)
#添加標(biāo)題、橫坐標(biāo)、縱坐標(biāo)標(biāo)簽
plt.title("y=sin(x)")
plt.xlabel("x axis caption")
plt.ylabel("y axis caption")
#畫函數(shù)圖像
plt.plot(x, y)
plt.show()

余弦函數(shù)只需將
y = np.sin(x) 改為 y = np.cos(x)即可

# ReLU和Sigmoid激活函數(shù)示意圖
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
#設(shè)置圖片大小
plt.figure(figsize=(6, 4))
# x是1維數(shù)組，數(shù)組大小是從-10. 到10.的實(shí)數(shù)，每隔0.1取一個(gè)點(diǎn)
x = np.arange(-10, 10, 0.1)
# 計(jì)算 Sigmoid函數(shù)
s = (np.exp(x)-np.exp(-x))/(np.exp(x)+np.exp(-x))

# 計(jì)算ReLU函數(shù)
y = np.clip(x, a_min = 0., a_max = None)
#########################################################
# 以下部分為畫圖程序
# 設(shè)置兩個(gè)子圖窗口，將Sigmoid的函數(shù)圖像畫在上邊
f = plt.subplot(2,1,1)
# 畫出函數(shù)曲線
plt.plot(x, s, color='r')
# 添加文字說明
plt.text(-5., 0.9, r'$y=sigmoid(x)$', fontsize=13)
# 設(shè)置坐標(biāo)軸格式
currentAxis=plt.gca()
currentAxis.xaxis.set_label_text('x', fontsize=15)
currentAxis.yaxis.set_label_text('y', fontsize=15)
# 將ReLU的函數(shù)圖像畫在下邊
f = plt.subplot(2,1,2)
# 畫出函數(shù)曲線
plt.plot(x, y, color='g')
# 添加文字說明
plt.text(-3.0, 9, r'$y=ReLU(x)$', fontsize=13)
# 設(shè)置坐標(biāo)軸格式
currentAxis=plt.gca()
currentAxis.xaxis.set_label_text('x', fontsize=15)
currentAxis.yaxis.set_label_text('y', fontsize=15)
plt.show()

from matplotlib import pyplot as plt
import numpy as np
n = 256  #n可以理解為曲線的平滑程度
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()

from matplotlib import pyplot as plt
#數(shù)據(jù)組1
x =  [5,8,11]  #橫坐標(biāo)
y =  [12,16,6] #對應(yīng)值
#數(shù)據(jù)組2
x2 =  [6,9,12] #橫坐標(biāo)
y2 =  [6,15,7] #對應(yīng)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()

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()

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) #等高線模型
n = 256  #n可以理解為曲線的平滑程度
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()

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()

from matplotlib import pyplot as plt
import numpy as np
n = 20  #n決定了有多少個(gè)分區(qū)，該代碼顯示有20個(gè)分區(qū)
Z = np.random.uniform(0,1,n)
plt.pie(Z)
plt.show()

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()

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()