Example

import numpy as np
import matplotlib.pyplot as plt

from ensnest.model import Model
from ensnest import mpNestedSampler
from ensnest import stdplots

class AckleyModel(Model):

    def set_parameters(self):
        self.names=['x','y']
        self.bounds=[[-5,5],[-5,5]]

    @Model.varenv
    def log_likelihood(self,var):
        partial_1 = -20.*np.exp(-.2*np.sqrt(0.5*(var['x']**2 + var['y']**2)))
        partial_2 = -np.exp(0.5*(np.cos(2*np.pi*var['x']) + np.cos(2*np.pi*var['y'])))
        offset = np.e + 20.
        return np.log(partial_1 + partial_2 + offset)

    @Model.auto_bound
    def log_prior(self,var):
        return 0

M = AckleyModel()
ns = mpNestedSampler(M, nlive=500, evosteps=200, filename='ackley', load_old=False)

ns.run()

stdplots.XLplot(ns)
stdplots.scat3D(ns)

# plt.plot(ns.logX, np.exp(ns.logX + ns.logL), color='k')
# for nns in ns.nested_samplers:
#     plt.plot(nns.logX, np.exp(nns.logX + nns.logL))


plt.show()