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()
