kernel-density

I have a large dataset of (x,y,z) protein positions and would like to plot areas of high occupancy as a heatmap. Ideally the output should look similiar to the volumetric visualisation below, but I'm not sure how to achieve this with matplotlib. My initial idea was to display my positions as a 3D scatter plot and color their density via a KDE. I coded this up as follows with test data: import numpy as np from scipy import stats import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D mu, sigma = 0, 0.1 x = np.random.normal(mu, sigma, 1000) y = np.random.normal(mu, sigma, 1000) z

I have a x,y distribution of points for which I obtain the KDE through scipy.stats.gaussian_kde . This is my code and how the output looks (the x,y data can be obtained from here ): import numpy as np from scipy import stats # Obtain data from file. data = np.loadtxt('data.dat', unpack=True) m1, m2 = data[0], data[1] xmin, xmax = min(m1), max(m1) ymin, ymax = min(m2), max(m2) # Perform a kernel density estimate (KDE) on the data x, y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j] positions = np.vstack([x.ravel(), y.ravel()]) values = np.vstack([m1, m2]) kernel = stats.gaussian_kde(values) f = np