我有一个3D多边形图,并且希望在y轴上平滑该图(即,我希望它看起来像是“表面图的切片”)。
考虑此MWE(摘自here:]
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.collections import PolyCollection
import matplotlib.pyplot as plt
from matplotlib import colors as mcolors
import numpy as np
from scipy.stats import norm
fig = plt.figure()
ax = fig.gca(projection='3d')
xs = np.arange(-10, 10, 2)
verts = []
zs = [0.0, 1.0, 2.0, 3.0]
for z in zs:
ys = np.random.rand(len(xs))
ys[0], ys[-1] = 0, 0
verts.append(list(zip(xs, ys)))
poly = PolyCollection(verts, facecolors=[mcolors.to_rgba('r', alpha=0.6),
mcolors.to_rgba('g', alpha=0.6),
mcolors.to_rgba('b', alpha=0.6),
mcolors.to_rgba('y', alpha=0.6)])
poly.set_alpha(0.7)
ax.add_collection3d(poly, zs=zs, zdir='y')
ax.set_xlabel('X')
ax.set_xlim3d(-10, 10)
ax.set_ylabel('Y')
ax.set_ylim3d(-1, 4)
ax.set_zlabel('Z')
ax.set_zlim3d(0, 1)
plt.show()
现在,我想用正态分布替换这四个图(以理想地形成连续线)。
我在这里创建了发行版:
def get_xs(lwr_bound = -4, upr_bound = 4, n = 80):
""" generates the x space betwee lwr_bound and upr_bound so that it has n intermediary steps """
xs = np.arange(lwr_bound, upr_bound, (upr_bound - lwr_bound) / n) # x space -- number of points on l/r dimension
return(xs)
xs = get_xs()
dists = [1, 2, 3, 4]
def get_distribution_params(list_):
""" generates the distribution parameters (mu and sigma) for len(list_) distributions"""
mus = []
sigmas = []
for i in range(len(dists)):
mus.append(round((i + 1) + 0.1 * np.random.randint(0,10), 3))
sigmas.append(round((i + 1) * .01 * np.random.randint(0,10), 3))
return mus, sigmas
mus, sigmas = get_distribution_params(dists)
def get_distributions(list_, xs, mus, sigmas):
""" generates len(list_) normal distributions, with different mu and sigma values """
distributions = [] # distributions
for i in range(len(list_)):
x_ = xs
z_ = norm.pdf(xs, loc = mus[i], scale = sigmas[0])
distributions.append(list(zip(x_, z_)))
#print(x_[60], z_[60])
return distributions
distributions = get_distributions(list_ = dists, xs = xs, mus = mus, sigmas = sigmas)
但是将它们添加到代码中(poly = PolyCollection(distributions, ...)
和ax.add_collection3d(poly, zs=distributions, zdir='z')
会抛出ValueError
(ValueError: input operand has more dimensions than allowed by the axis remapping
),我无法解析。
使用ax.add_collection3d(poly, zs=dists, zdir='z')
而不是ax.add_collection3d(poly, zs=distributions, zdir='z')
应该可以解决此问题。
另外,您可能想要替换
def get_xs(lwr_bound = -4, upr_bound = 4, n = 80):
""" generates the x space betwee lwr_bound and upr_bound so that it has n intermediary steps """
xs = np.arange(lwr_bound, upr_bound, (upr_bound - lwr_bound) / n) # x space -- number of points on l/r dimension
return(xs)
xs = get_xs()
作者
xs = np.linspace(-4, 4, 80)
此外,我相信scale = sigmas[0]
实际上应该是该行中的scale = sigmas[i]
z_ = norm.pdf(xs, loc = mus[i], scale = sigmas[0])
[最后,我相信您应该适当地调整xlim
,ylim
和zlim
,因为您交换了绘图的y
和z
尺寸并在与参考代码进行比较时更改了比例尺。] >
错误是由于将distributions
传递到zs
而引起的。 zs
期望当verts
中的PolyCollection
具有形状MxNx2