1、二维正态分布绘制
1、描述
绘制三维图像-二元类正态分布(mu,sigma^2)=(0,1)。
在显示图像时,有下面的一下参数信息可以配置,比如:maker的描述信息:
# marker description
# ”.” point
# ”,” pixel
# “o” circle
# “v” triangle_down
# “^” triangle_up
# “<” triangle_left
# “>” triangle_right
# “1” tri_down
# “2” tri_up
# “3” tri_left
# “4” tri_right
# “8” octagon
# “s” square
# “p” pentagon
# “*” star
# “h” hexagon1
# “H” hexagon2
# “+” plus
# “x” x
# “D” diamond
# “d” thin_diamond
# “|” vline
# “_” hline
# TICKLEFT tickleft
# TICKRIGHT tickright
# TICKUP tickup
# TICKDOWN tickdown
# CARETLEFT caretleft
# CARETRIGHT caretright
# CARETUP caretup
# CARETDOWN caretdown
着色方案的参数设置:
cmaps = [('Perceptually Uniform Sequential',
['viridis', 'inferno', 'plasma', 'magma']),
('Sequential', ['Blues', 'BuGn', 'BuPu',
'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd',
'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu',
'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']),
('Sequential (2)', ['afmhot', 'autumn', 'bone', 'cool',
'copper', 'gist_heat', 'gray', 'hot',
'pink', 'spring', 'summer', 'winter']),
('Diverging', ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr',
'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral',
'seismic']),
('Qualitative', ['Accent', 'Dark2', 'Paired', 'Pastel1',
'Pastel2', 'Set1', 'Set2', 'Set3']),
('Miscellaneous', ['gist_earth', 'terrain', 'ocean', 'gist_stern',
'brg', 'CMRmap', 'cubehelix',
'gnuplot', 'gnuplot2', 'gist_ncar',
'nipy_spectral', 'jet', 'rainbow',
'gist_rainbow', 'hsv', 'flag', 'prism'])]
2、代码
# @Time : 2020/12/8 17:31
# @Description : 绘制三维图像-二元类正态分布(mu,sigma^2)=(0,1)
import math
import numpy as np
import matplotlib as mpl
from matplotlib import cm
import matplotlib.pyplot as plt
x, y = np.ogrid[-3:3:100j, -3:3:100j]
# 下面的两行代码和上面代码等价
# u = np.linspace(-3, 3, 101)
# x, y = np.meshgrid(u, u)
# x * y 乘到下面的函数中,会有正负交替图像的出现。
z = x * y * np.exp(-(x ** 2 + y ** 2) / 2) / math.sqrt(2 * math.pi)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# cmap着色方案
# rstride=5, cstride=5:横轴纵轴有101个点我们每5个取一个点。数值越小,图形显示越密集。
ax.plot_surface(x, y, z, rstride=5, cstride=5, cmap=cm.Accent, linewidth=0.5)
plt.show()
结果展示: