问stats.gaussian_kde方法是如何计算pdf的？

EN

import numpy as np
import scipy.stats
from scipy import stats

def getDistribution1(data):
    kernel = stats.gaussian_kde(data,bw_method=0.06)
    class rv(stats.rv_continuous):
        def _rvs(self, *x, **y):
            return kernel.resample(int(self._size)) #random variates
        def _cdf(self, x):
            return kernel.integrate_box_1d(0,max(x)) #Integrate pdf between two bounds (-inf to x here!)
        def _pdf(self, x):
            return kernel.evaluate(x)  #Evaluate the estimated pdf on a provided set of points
    return rv(name='kdedist')

test_data = np.random.random(100) # random test data 
distribution_data = getDistribution1(test_data)
pdf_data = distribution_data.pdf(test_data) # the pdf of the data