CODEWITHSUNDEEP
pythonmatplotlibkernel-density
Dude-N
Dude-N

Reputation: 13

3D data contour ploting using a kde

I have two Arrays of positional Data (X,Y) and a corresponding 1D Array of Integers (Z) that weighs the positional Data. So my Data set looks like that:

X = [ 507, 1100, 1105, 1080, 378, 398, 373]
Y = [1047,  838,  821,  838, 644, 644, 659]
Z = [ 300,   55,   15,   15,  55,  15,  15]

I want to use that Data to create a KDE thats equivalent to a KDE that gets only X and Y as input but gets the X and Y values Z times. To apply that KDE to a np.mgrid to create a contourplot.

I already got it working by just iterating over the arrays in a FOR Loop and adding Z times X and Y, but that looks to me like a rather inelegant Solution and I hope you can help me to find a better way of doing this.

Upvotes: 1

Views: 1252

Answers (1)

JohanC
JohanC

Reputation: 80329

You could use the weights= parameter of scipy.stats.gaussian_kde:

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
import numpy as np
from scipy import stats

X = [ 507, 1100, 1105, 1080, 378, 398, 373]
Y = [1047,  838,  821,  838, 644, 644, 659]
Z = [ 300,   55,   15,   15,  55,  15,  15]

kernel = stats.gaussian_kde(np.array([X, Y]), weights=Z)

fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
xs, ys = np.mgrid[0:1500:30j, 0:1500:30j]
zs = kernel(np.array([xs.ravel(), ys.ravel()])).reshape(xs.shape)
ax.plot_surface(xs, ys, zs, cmap="hot_r", lw=0.5, rstride=1, cstride=1, ec='k')
plt.show()

resulting plot

Upvotes: 3

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