def test_update_cov_wsum_none(self):
__, options, parameters, data = gf.setup_mcmc()
CP = CovarianceProcedures()
CP._initialize_covariance_settings(parameters=parameters,
options=options)
theta = np.array([2., 5.])
CP._wsum = None
CP._covchain = 1
CP._meanchain = 2
CP._qcov = 0
CP._update_covariance_settings(parameter_set=theta)
CPD = CP.__dict__
self.assertEqual(CPD['_covchain'], 1, msg='_covchain unchanged.')
self.assertEqual(CPD['_meanchain'], 2, msg='_meanchain unchanged.')
def test_update_cov_wsum_not_none(self):
model, options, parameters, data = gf.setup_mcmc()
CP = CovarianceProcedures()
CP._initialize_covariance_settings(parameters=parameters,
options=options)
theta = np.array([2., 5.])
CP._wsum = 10
CP._covchain = 1
CP._meanchain = 2
CP._qcov = 0
CP._update_covariance_settings(parameter_set=theta)
CPD = CP.__dict__
self.assertEqual(CPD['_covchain'], 0, msg='_covchain = _qcov.')
self.assertTrue(np.array_equal(CPD['_meanchain'], theta),
msg='_meanchain = parameter_set.')