python gaussian，gaussian2

import numpy as np

import matplotlib.pyplot as plt

import mpl_toolkits.axisartist as axisartist

from mpl_toolkits.mplot3d import Axes3D #画三维图不可少

from matplotlib import cm  #cm 是colormap的简写

#定义坐标轴函数

def setup_axes(fig, rect):

    ax = axisartist.Subplot(fig, rect)

    fig.add_axes(ax)

    ax.set_ylim(-4, 4)

    #自定义刻度

#    ax.set_yticks([-10, 0,9])

    ax.set_xlim(-4,4)

    ax.axis[:].set_visible(False)

    #第2条线，即y轴,经过x=0的点

    ax.axis["y"] = ax.new_floating_axis(1, 0)

    ax.axis["y"].set_axisline_style("-|>", size=1.5)

#    第一条线，x轴，经过y=0的点

    ax.axis["x"] = ax.new_floating_axis(0, 0)

    ax.axis["x"].set_axisline_style("-|>", size=1.5)

    return(ax)

# 1_dimension gaussian function

def gaussian(x,mu,sigma):

    f_x = 1/(sigma*np.sqrt(2*np.pi))*np.exp(-np.power(x-mu, 2.)/(2*np.power(sigma,2.)))

    return(f_x)

# 2_dimension gaussian function

def gaussian_2(x,y,mu_x,mu_y,sigma_x,sigma_y):

    f_x_y = 1/(sigma_x*sigma_y*(np.sqrt(2*np.pi))**2)*np.exp(-np.power\

              (x-mu_x, 2.)/(2*np.power(sigma_x,2.))-np.power(y-mu_y, 2.)/\

              (2*np.power(sigma_y,2.)))

    return(f_x_y)

#设置画布

# fig = plt.figure(figsize=(8, 8)) #建议可以直接plt.figure()不定义大小

# ax1 = setup_axes(fig, 111)

# ax1.axis["x"].set_axis_direction("bottom")

# ax1.axis['y'].set_axis_direction('right')

# #在已经定义好的画布上加入高斯函数

x_values = np.linspace(-5,5,2000)

y_values = np.linspace(-5,5,2000)

X,Y = np.meshgrid(x_values,y_values)

mu_x,mu_y,sigma_x,sigma_y = 0,0,0.8,0.8

#F_x_y = gaussian_2(X,Y,mu_x,mu_y,sigma_x,sigma_y)

F_x_y = gaussian(X,mu_x,sigma_x)

#显示2d等高线图,画100条线

# plt.contour(X,Y,F_x_y,100)

# fig.show()

#显示三维图

fig = plt.figure()

ax = plt.gca(projection='3d')

ax.plot_surface(X,Y,F_x_y,cmap='jet')

#显示3d等高线图

ax.contour3D(X,Y,F_x_y,50,cmap='jet')

fig.show()

=======================二维========================

import numpy as np

import matplotlib.pyplot as plt

import mpl_toolkits.axisartist as axisartist

from mpl_toolkits.mplot3d import Axes3D #画三维图不可少

from matplotlib import cm  #cm 是colormap的简写

#定义坐标轴函数

def setup_axes(fig, rect):

    ax = axisartist.Subplot(fig, rect)

    fig.add_axes(ax)

    ax.set_ylim(-4, 4)

    #自定义刻度

#    ax.set_yticks([-10, 0,9])

    ax.set_xlim(-4,4)

    ax.axis[:].set_visible(False)

    #第2条线，即y轴,经过x=0的点

    ax.axis["y"] = ax.new_floating_axis(1, 0)

    ax.axis["y"].set_axisline_style("-|>", size=1.5)

#    第一条线，x轴，经过y=0的点

    ax.axis["x"] = ax.new_floating_axis(0, 0)

    ax.axis["x"].set_axisline_style("-|>", size=1.5)

    return(ax)

# 1_dimension gaussian function

def gaussian(x,mu,sigma):

    f_x = 1/(sigma*np.sqrt(2*np.pi))*np.exp(-np.power(x-mu, 2.)/(2*np.power(sigma,2.)))

    return(f_x)

# 2_dimension gaussian function

def gaussian_2(x,y,mu_x,mu_y,sigma_x,sigma_y):

    f_x_y = 1/(sigma_x*sigma_y*(np.sqrt(2*np.pi))**2)*np.exp(-np.power\

              (x-mu_x, 2.)/(2*np.power(sigma_x,2.))-np.power(y-mu_y, 2.)/\

              (2*np.power(sigma_y,2.)))

    return(f_x_y)

#设置画布

fig = plt.figure(figsize=(8, 8)) #建议可以直接plt.figure()不定义大小

ax1 = setup_axes(fig, 111)

ax1.axis["x"].set_axis_direction("bottom")

ax1.axis['y'].set_axis_direction('right')

# #在已经定义好的画布上加入高斯函数

x_values = np.linspace(-5,5,2000)

y_values = np.linspace(-5,5,2000)

X,Y = np.meshgrid(x_values,y_values)

mu_x,mu_y,sigma_x,sigma_y = 0,0,0.8,0.8

F_x_y = gaussian_2(X,Y,mu_x,mu_y,sigma_x,sigma_y)

#F_x_y = gaussian(X,mu_x,sigma_x)

#显示2d等高线图,画100条线

plt.contour(X,Y,F_x_y,100)

fig.show()

圆形

矩形：

import numpy as np

import matplotlib.pyplot as plt

import mpl_toolkits.axisartist as axisartist

from mpl_toolkits.mplot3d import Axes3D #画三维图不可少

from matplotlib import cm  #cm 是colormap的简写

#定义坐标轴函数

def setup_axes(fig, rect):

    ax = axisartist.Subplot(fig, rect)

    fig.add_axes(ax)

    ax.set_ylim(-4, 4)

    #自定义刻度

#    ax.set_yticks([-10, 0,9])

    ax.set_xlim(-4,4)

    ax.axis[:].set_visible(False)

	#第2条线，即y轴,经过x=0的点

    ax.axis["y"] = ax.new_floating_axis(1, 0)

    ax.axis["y"].set_axisline_style("-|>", size=1.5)

#    第一条线，x轴，经过y=0的点

    ax.axis["x"] = ax.new_floating_axis(0, 0)

    ax.axis["x"].set_axisline_style("-|>", size=1.5)

    return(ax)

# 1_dimension gaussian function

def gaussian(x,mu,sigma):

    f_x = 1/(sigma*np.sqrt(2*np.pi))*np.exp(-np.power(x-mu, 2.)/(2*np.power(sigma,2.)))

    return(f_x)

# 2_dimension gaussian function

def gaussian_2(x,y,mu_x,mu_y,sigma_x,sigma_y):

    f_x_y = 1/(sigma_x*sigma_y*(np.sqrt(2*np.pi))**2)*np.exp(-np.power\

              (x-mu_x, 2.)/(2*np.power(sigma_x,2.))-np.power(y-mu_y, 2.)/\

              (2*np.power(sigma_y,2.)))

    return(f_x_y)

#设置画布

fig = plt.figure(figsize=(8, 8)) #建议可以直接plt.figure()不定义大小

ax1 = setup_axes(fig, 111)

ax1.axis["x"].set_axis_direction("bottom")

ax1.axis['y'].set_axis_direction('right')

# #在已经定义好的画布上加入高斯函数

x_values = np.linspace(-5,5,2000)

y_values = np.linspace(-5,5,2000)

X,Y = np.meshgrid(x_values,y_values)

mu_x,mu_y,sigma_x,sigma_y = 0,0,0.8,0.8

#F_x_y = gaussian_2(X,Y,mu_x,mu_y,sigma_x,sigma_y)

F_x_y = gaussian(X,mu_x,sigma_x)

#显示2d等高线图,画100条线

plt.contour(X,Y,F_x_y,100)

fig.show()

　　

球

from mpl_toolkits.mplot3d import Axes3D

import matplotlib.pyplot as plt

import numpy as np

fig = plt.figure()

ax = fig.add_subplot(111, projection='3d')

u = np.linspace(0, 2 * np.pi, 100)

v = np.linspace(0, np.pi, 100)

x = 10 * np.outer(np.cos(u), np.sin(v))

y = 10 * np.outer(np.sin(u), np.sin(v))

z = 10 * np.outer(np.ones(np.size(u)), np.cos(v))

ax.plot_surface(x, y, z,  rstride=4, cstride=4, color='b')

plt.show()