def test_multithread(self):
obs = ("DeltaGamma_s", "m_W")
flavio.citations.reset()
flavio.sm_covariance(obs, threads=1)
cites_singlethread = flavio.citations.set
flavio.citations.reset()
flavio.sm_covariance(obs, threads=4)
cites_multithread = flavio.citations.set
self.assertSetEqual(cites_singlethread, cites_multithread)
def test_sm_covariance(self):
o1 = Observable('test_obs 1')
o2 = Observable('test_obs 2')
def f1(wc_obj, par_dict):
return par_dict['m_b']
def f2(wc_obj, par_dict):
return par_dict['m_c']
Prediction('test_obs 1', f1)
Prediction('test_obs 2', f2)
cov_par = np.array([[0.1**2, 0.1 * 0.2 * 0.3],
[0.1 * 0.2 * 0.3, 0.2**2]])
d = flavio.statistics.probability.MultivariateNormalDistribution(
[4.2, 1.2], covariance=cov_par)
par = copy.deepcopy(flavio.parameters.default_parameters)
par.add_constraint(['m_b', 'm_c'], d)
# test serial
np.random.seed(135)
cov = flavio.sm_covariance(['test_obs 1', 'test_obs 2'],
N=1000,
par_vary='all',
par_obj=par)
npt.assert_array_almost_equal(cov, cov_par, decimal=2)
# test parallel
np.random.seed(135)
cov_parallel = flavio.sm_covariance(['test_obs 1', 'test_obs 2'],
N=1000,
par_vary='all',
par_obj=par,
threads=4)
npt.assert_array_almost_equal(cov, cov_parallel, decimal=6)
np.random.seed(135)
cov_1 = flavio.sm_covariance(['test_obs 1'],
N=1000,
par_vary='all',
par_obj=par)
# test with single observable
npt.assert_array_almost_equal(cov_1, cov[0, 0])
# test with fixed parameter
cov_f = flavio.sm_covariance(['test_obs 1', 'test_obs 2'],
N=1000,
par_vary=['m_b'],
par_obj=par)
npt.assert_array_almost_equal(cov_f, [[cov_par[0, 0], 0], [0, 0]],
decimal=3)
# delete dummy instances
Observable.del_instance('test_obs 1')
Observable.del_instance('test_obs 2')
def compute(self, N, threads):
"""Compute the covariance for `N` random values, using `threads`
CPU threads."""
return flavio.sm_covariance(obs_list=self.observables,
N=N,
par_vary=self.vary_parameters,
par_obj=self.par_obj,
threads=threads)
def compute(self, N, threads):
"""Compute the covariance for `N` random values, using `threads`
CPU threads."""
return flavio.sm_covariance(obs_list=self.observables,
N=N,
par_vary=self.vary_parameters,
par_obj=self.par_obj,
threads=threads)
def sm_covariance(self, N=1000):
"""Compute the covariance of theory predictions for the four input
observables in the SM, fixing the CKM elements."""
par_vary = [
p for p in self.par_obj.all_parameters if p not in self.ckm_par
]
return flavio.sm_covariance(self.observables,
N=N,
par_obj=self.par_obj,
par_vary=par_vary)
def test_sm_covariance(self):
o1 = Observable( 'test_obs 1' )
o2 = Observable( 'test_obs 2' )
def f1(wc_obj, par_dict):
return par_dict['m_b']
def f2(wc_obj, par_dict):
return par_dict['m_c']
Prediction('test_obs 1', f1)
Prediction('test_obs 2', f2)
cov_par = np.array([[0.1**2, 0.1*0.2*0.3], [0.1*0.2*0.3, 0.2**2]])
d = flavio.statistics.probability.MultivariateNormalDistribution([4.2, 1.2], covariance=cov_par)
par = copy.deepcopy(flavio.parameters.default_parameters)
par.add_constraint(['m_b', 'm_c'], d)
# test serial
np.random.seed(135)
cov = flavio.sm_covariance(['test_obs 1', 'test_obs 2'],
N=1000, par_vary='all', par_obj=par)
npt.assert_array_almost_equal(cov, cov_par, decimal=2)
# test parallel
np.random.seed(135)
cov_parallel = flavio.sm_covariance(['test_obs 1', 'test_obs 2'],
N=1000, par_vary='all', par_obj=par,
threads=4)
npt.assert_array_almost_equal(cov, cov_parallel, decimal=6)
np.random.seed(135)
cov_1 = flavio.sm_covariance(['test_obs 1'],
N=1000, par_vary='all', par_obj=par)
# test with single observable
npt.assert_array_almost_equal(cov_1, cov[0, 0])
# test with fixed parameter
cov_f = flavio.sm_covariance(['test_obs 1', 'test_obs 2'],
N=1000, par_vary=['m_b'], par_obj=par)
npt.assert_array_almost_equal(cov_f, [[cov_par[0, 0], 0], [0, 0]], decimal=3)
# delete dummy instances
Observable.del_instance('test_obs 1')
Observable.del_instance('test_obs 2')
def compute_sm_covariance(N=100, threads=4):
"""Compute the SM covariance for all observables."""
# all observables from all experiments at all phases
return flavio.sm_covariance(allobs, N=N, threads=threads)