【发布时间】:2022-01-27 08:37:15
【问题描述】:
我试图通过绘制所有图形并旋转表面以检查表面行为相对于 3d 空间中的分散点是否存在任何异常,从而了解表面与我的数据点的匹配程度。
问题是,当我旋转渲染来执行此操作时,绘图消失了。我怎样才能使情节持续存在?
您可以使用以下代码进行复制 - 主要取自 Python 3D polynomial surface fit, order dependent 的惊人答案。
import numpy as np
import scipy.linalg
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import itertools
def main():
# Generate Data...
numdata = 100
x = np.random.random(numdata)
y = np.random.random(numdata)
z = x**2 + y**2 + 3*x**3 + y + np.random.random(numdata)
# Fit a 3rd order, 2d polynomial
m = polyfit2d(x,y,z)
# Evaluate it on a grid...
nx, ny = 20, 20
xx, yy = np.meshgrid(np.linspace(x.min(), x.max(), nx),
np.linspace(y.min(), y.max(), ny))
zz = polyval2d(xx, yy, m)
# Plot
#plt.imshow(zz, extent=(x.min(), y.max(), x.max(), y.min()))
#plt.scatter(x, y, c=z)
#plt.show()
fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(x, y, z, color='red', zorder=0)
ax.plot_surface(xx, yy, zz, zorder=10)
ax.set_xlabel('X data')
ax.set_ylabel('Y data')
ax.set_zlabel('Z data')
plt.show()
text = "filler"
def polyfit2d(x, y, z, order=4):
ncols = (order + 1)**2
G = np.zeros((x.size, ncols))
#ij = itertools.product(range(order+1), range(order+1))
ij = xy_powers(order)
for k, (i,j) in enumerate(ij):
G[:,k] = x**i * y**j
m, _, _, _ = np.linalg.lstsq(G, z)
return m
def polyval2d(x, y, m):
order = int(np.sqrt(len(m))) - 1
#ij = itertools.product(range(order+1), range(order+1))
ij = xy_powers(order)
z = np.zeros_like(x)
for a, (i,j) in zip(m, ij):
z += a * x**i * y**j
return z
def xy_powers(order):
powers = itertools.product(range(order + 1), range(order + 1))
return [tup for tup in powers if sum(tup) <= order]
main()
【问题讨论】:
标签: python matplotlib 3d surface