一、绘制二维函数图
1.1 绘制 函数图
代码1:
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams["font.sans-serif"]=['SimHei'] # 用于正常显示中文标签
plt.rcParams['axes.unicode_minus']=False # 用来正常显示负号
x = np.linspace(-2.5,2,256,endpoint=True) # 绘制X轴(-2.5,2)的图像
f =(np.sin(x-2))**2*(np.e)**(-x**2) # y值
plt.plot(x,f,"g-",lw=2.5,label="f(x)")
plt.title('f(x) = sin^2(x-2)e^{-x^2}函数图')
plt.legend()
plt.show()
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代码2(对于复杂函数,我们可以将其拆分成多单一函数)
分析:sin^2(x-2)e^{-x^2}分解:
sin(x1)*sin(x1)*e^x2
其中:x1 = x-2; x2 = -x^2
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
x = np.linspace(0, 2,300, endpoint=True)
x_1 = x-2
S_1 = np.sin(x_1)
S_2 = S_1**2
E_1 = -x**2
E_2 = np.exp(E_1)
f = S_2*E_2
plt.figure(figsize = ((8,6))) # 设置画布大小(可省略)
plt.plot(x,f,'b-',lw=2.5,label='f(x)=sin^2(x-2)e^{-x^2}')
plt.legend() # 显示图例
plt.show()
1.2 、绘制 sigmoid函数图:
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
x = np.linspace(-10, 10,300, endpoint=True)
E_1 = -x
E_2 = np.exp(E_1)
f = 1/(1+E_2)
plt.figure(figsize = ((8,6))) # 设置画布大小(可省略)
ax1 = plt.subplot(111)
plt.plot(x,f,'b-',lw=2.5,label='f(x)=\\frac{1}{1+e^{-x}}')
plt.legend() # 显示图例
ax1.grid(True) # 显示网格
plt.show()
1.3、绘制正态分布图
其中 s 为 , m为
import math
import matplotlib.pyplot as plt
import numpy as np
def gd(x,m,s): #其中s为sigma ,m为 mu
left=1/(math.sqrt(2*math.pi)*s)
right=math.exp(-math.pow(x-m,2)/(2*math.pow(s,2)))
return left*right
def showfigure():
plt.figure(figsize = ((8,6))) #设置画布大小(可省略)
x=np.arange(-4,5,0.1) #绘制x(-4,5)
y=[]
for i in x:
y.append(gd(i,0,1)) #m为0,s为1
plt.plot(x,y)
plt.xlim(-4.0,5.0)
plt.ylim(-0.2,0.5)
ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))
#设置并添加标签
label_f1 = "$\mu=0,\ \sigma=1$"
plt.text(2.5,0.3,label_f1,fontsize=15,verticalalignment="top",
horizontalalignment="left")
label_f2 = r"$f(x)=\frac{1}{\sqrt{2\pi}\sigma}exp(-\frac{(x-\mu)^2}{2\sigma^2})$"
plt.text(1.5,0.4,label_f2,fontsize=15,verticalalignment="top"
,horizontalalignment="left")
plt.show()
def main():
showfigure()
gd()
main()
二、绘制三维图
2.1 绘制三维螺旋图
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure(figsize = ((8,6)))
ax = fig.add_subplot(1,1,1,projection='3d')
theta = np.linspace(-4 * np.pi, 4 * np.pi, 500) # theta旋转角从-4pi到4pi,相当于两圈
z = np.linspace(0, 2, 500) # z轴从下到上,从-2到2之间画100个点
r = z # 半径设置为z大小
x = r * np.sin(theta) # x和y画圆
y = r * np.cos(theta) # x和y画圆
ax.plot(x, y, z, label='curve')
ax.legend() # 图例
plt.show()
2.2 绘制三维线性点图
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1,1,1,projection='3d')
x = np.linspace(0, 5, 10) # 在0-5之间生成10个点的向量
y = np.linspace(0, 5, 10) # 在0-5之间生成10个点的向量
z1 = x
z2 = 2*x
z3 = 3*x
ax.scatter(xx, yy, zz1, c='red', marker='o') # o型符号
ax.scatter(xx, yy, zz2, c='green', marker='^') # 三角型符号
ax.scatter(xx, yy, zz3, c='black', marker='*') # 星型符号
ax.legend() # 显示图例
plt.show()
2.3 绘制三维柱状图
import random
import matplotlib as mpl
import matplotlib.dates as mdates
from mpl_toolkits.mplot3d import Axes3D
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
mpl.rcParams['font.size'] = 10
fig = plt.figure(figsize=((8,6)))
ax = fig.add_subplot(111, projection='3d')
for z in [2011, 2012, 2013, 2014]:
xs = range(1,13)
ys = 1000 * np.random.rand(12)
color = plt.cm.Set2(random.choice(range(plt.cm.Set2.N)))
ax.bar(xs, ys, zs=z, zdir='y', color=color, alpha=0.8)
ax.xaxis.set_major_locator(mpl.ticker.FixedLocator(xs))
ax.yaxis.set_major_locator(mpl.ticker.FixedLocator(ys))
ax.set_xlabel('月份')
ax.set_ylabel('年份')
ax.set_zlabel('销售额 ')
plt.show()
2.4 绘制三维 鞍部 曲面图
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
n_angles = 1000 #曲面衔接角度(平滑度)
n_radii = 20 #鞍部半径(1:锐角,20:平滑角)
fig = plt.figure(figsize=((10,10)))
radii = np.linspace(0.125, 1.0, n_radii)
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
x = np.append(0, (radii * np.cos(angles)).flatten())
y = np.append(0, (radii * np.sin(angles)).flatten())
z = np.sin(-x * y)
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_trisurf(x, y, z,
cmap=cm.jet, #曲面颜色
linewidth=0.2)
2.5 绘制山区图
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
x,y = np.mgrid[-2:2:100j,-2:2:100j] #100j为设置曲面平滑度
z=x*np.exp(-x**2-y**2)
ax = plt.subplot(111, projection='3d')
ax.set_title('山区图');
ax.plot_surface(x,y,z,rstride=2, cstride=1, cmap=cm.jet)
ax.set_xlabel('X') #设置坐标轴标签
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.show()
鸣谢:非常感谢以下博主的帮助;
https://blog.csdn.net/sinat_36772813/article/details/77365296
https://blog.csdn.net/eddy_zheng/article/details/48713449
https://blog.csdn.net/dahunihao/article/details/77833877