[SciPy-user] scipy.stats.gaussian_kde for 2d kernel density estimation

Dave dave.hirschfeld@gmail....
Wed Jul 23 08:12:54 CDT 2008

massimo sandal <massimo.sandal <at> unibo.it> writes:

> Hi,
> I can't figure out how to do bivariate kernel density estimation with 
> the scipy.stats.gaussian_kde module .1D evaluation seems working, but 2D 
> evaluation escapes me.
> I have two vectors representing x and y coordinates of points:
> xvect=[72.11,81.52,66.46,52.34,81.12,76.83,...]
> yvect=[26.91,17.39,28.84,15.05,10.21,26.42,...]
> The problem is: how do I build the grid to evaluate the points? 

Hopefully the example below will help...


import numpy as np
import scipy.stats as stats
from matplotlib.pyplot import imshow

# Create some dummy data
rvs = np.append(stats.norm.rvs(loc=2,scale=1,size=(2000,1)),

kde = stats.kde.gaussian_kde(rvs.T)

# Regular grid to evaluate kde upon
x_flat = np.r_[rvs[:,0].min():rvs[:,0].max():128j]
y_flat = np.r_[rvs[:,1].min():rvs[:,1].max():128j]
x,y = np.meshgrid(x_flat,y_flat)
grid_coords = np.append(x.reshape(-1,1),y.reshape(-1,1),axis=1)

z = kde(grid_coords.T)
z = z.reshape(128,128)


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