Using scipy.stats.gaussian_kde with 2 dimensional data

Question

I\'m trying to use the scipy.stats.gaussian_kde class to smooth out some discrete data collected with latitude and longitude information, so it shows up as somewhat similar

Answer 1

This example seems to be what you're looking for:

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)),
                stats.norm.rvs(loc=0,scale=3,size=(2000,1)),
                axis=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)

imshow(z,aspect=x_flat.ptp()/y_flat.ptp())

Axes need fixing, obviously.

You can also do a scatter plot of the data with

scatter(rvs[:,0],rvs[:,1])