###
#
# Test of convergence diagnostics
#
###


from numpy.testing import assert_equal, assert_array_equal, TestCase
import unittest
import numpy as np
import pymc
import pymc.examples.weibull_fit as model
import os,  warnings


S = pymc.MCMC(model, 'ram')
S.sample(10000, 2000)
#a = S.a.trace()
#b = S.b.trace()

DIR = 'testresults'

class test_geweke(TestCase):

    def test_simple(self):
        scores = pymc.geweke(S, intervals=20)
        a_scores = scores['a']
        assert_equal(len(a_scores), 20)

        # If the model has converged, 95% the scores should lie
        # within 2 standard deviations of zero, under standard normal model
        assert(sum(np.abs(np.array(a_scores)[:,1]) > 1.96) < 2)

        # Plot diagnostics (if plotting is available)
        try:
            from pymc.Matplot import geweke_plot as plot
            plot(scores,  path=DIR, verbose=0)
        except ImportError:
            pass

class test_raftery_lewis(TestCase):

    def test_simple(self):

        nmin, kthin, nburn, nprec, kmind = pymc.raftery_lewis(S.a, 0.5, .05, verbose=0)

        # nmin should approximately be the same as nprec/kmind
        assert(0.8 < (float(nprec)/kmind) / nmin < 1.2)

if __name__ == "__main__":
    warnings.simplefilter('ignore', FutureWarning)
    import nose
    C =nose.config.Config(verbosity=1)
    nose.runmodule(config=C)
