def boot_to_posterior(values, weights):
q = [0.5 - 0.5 * 0.999999426697, 0.5 + 0.5 * 0.999999426697]
span = _quantile(values, q, weights=weights)
s = 0.02
bins = int(round(10. / 0.02))
n, b = np.histogram(values, bins=bins, weights=weights,range=np.sort(span))
n = norm_kde(n, 20.)
x0 = 0.5 * (b[1:] + b[:-1])
y0 = n
return x0, y0 / np.trapz(y0,x0)
def Get_posterior(sample, logwt, logz):
weight = np.exp(logwt - logz[-1])
q = [0.5 - 0.5 * 0.999999426697, 0.5 + 0.5 * 0.999999426697]
span = _quantile(sample.T, q, weights=weight)
s = 0.02
bins = int(round(10. / 0.02))
n, b = np.histogram(sample, bins=bins, weights=weight, range=np.sort(span))
n = norm_kde(n, 10.)
x0 = 0.5 * (b[1:] + b[:-1])
y0 = n
return x0, y0 / np.trapz(y0, x0)