Question

我想绘制概率密度函数z=f(x,y)。我找到了在Color matplotlib plot_surface command with surface gradient

中绘制冲浪的代码

但我不知道如何将z值转换为grid，以便我可以绘制它示例代码和我的修改如下。

import numpy as np
import matplotlib.pyplot as plt
from sklearn import mixture
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm

%matplotlib inline

n_samples = 1000

# generate random sample, two components
np.random.seed(0)
shifted_gaussian = np.random.randn(n_samples, 2) + np.array([20, 5])
sample = shifted_gaussian

# fit a Gaussian Mixture Model with two components
clf = mixture.GMM(n_components=3, covariance_type='full')
clf.fit(sample)

# Plot it
fig = plt.figure()
ax = fig.gca(projection='3d')
X = np.arange(-5, 5, .25)
Y = np.arange(-5, 5, .25)
X, Y = np.meshgrid(X, Y)
## In example Code, the z is generate by grid
# R = np.sqrt(X**2 + Y**2)
# Z = np.sin(R)

# In my case,
# for each point [x,y], the probability value is
# z = clf.score([x,y])
# but How can I generate a grid Z?

Gx, Gy = np.gradient(Z) # gradients with respect to x and y
G = (Gx**2+Gy**2)**.5  # gradient magnitude
N = G/G.max()  # normalize 0..1
surf = ax.plot_surface(
    X, Y, Z, rstride=1, cstride=1,
    facecolors=cm.jet(N),
    linewidth=0, antialiased=False, shade=False)
plt.show()

绘制z的原始方法是通过网格生成。但就我而言，拟合模型不能以grid-like样式返回结果，所以问题是我如何生成grid-style z值并绘制它？

Answer 1

如果我理解正确，你基本上有一个函数z在列表中采用两个标量值x,y并返回另一个标量z_val。换句话说z_val = z([x,y])，对吗？

如果是这种情况，您可以执行以下操作（请注意，这不是考虑到效率，而是注重可读性）：

from itertools import product

X = np.arange(15) # or whatever values for x
Y = np.arange(5)  # or whatever values for y
N, M = len(X), len(Y)
Z = np.zeros((N, M))
for i, (x,y) in enumerate(product(X,Y)):
    Z[np.unravel_index(i, (N,M))] = z([x,y])

如果您想使用plot_surface，请按照以下步骤操作：

X, Y = np.meshgrid(X, Y)
ax.plot_surface(X, Y, Z.T)

Python＆amp; Matplotlib：如何创建一个meshgrid来绘制冲浪？

1 个答案: