Representation of a kernel-density estimate using Gaussian kernels.
dataset : (# of dims, # of data)-array
datapoints to estimate from
kde.evaluate(points) : array
evaluate the estimated pdf on a provided set of points kde(points) : array same as kde.evaluate(points) kde.integrate_gaussian(mean, cov) : float multiply pdf with a specified Gaussian and integrate over the whole domain kde.integrate_box_1d(low, high) : float integrate pdf (1D only) between two bounds kde.integrate_box(low_bounds, high_bounds) : float integrate pdf over a rectangular space between low_bounds and high_bounds kde.integrate_kde(other_kde) : float integrate two kernel density estimates multiplied together
evaluate the estimated pdf on a provided set of points
same as kde.evaluate(points)
multiply pdf with a specified Gaussian and integrate over the whole domain
integrate pdf (1D only) between two bounds
integrate pdf over a rectangular space between low_bounds and high_bounds
integrate two kernel density estimates multiplied together
scipy.stats.ppcc_plot
Image Array Manipulation and Convolution (scipy.stsci)