def test_without_simcov(self):
simdict = Simulations()
meadict = Measurements()
covdict = Covariances()
# mock measurements
arr_a = np.random.rand(1, 4*mpisize)
comm.Bcast(arr_a, root=0)
mea = Observable(arr_a, 'measured')
meadict.append(name=('test', None, 4*mpisize, None),
data=mea, otype='plain')
# mock covariance
arr_c = np.random.rand(4, 4*mpisize)
cov = Observable(arr_c, 'covariance')
covdict.append(name=('test', None, 4*mpisize, None),
cov_data=cov)
# mock observable with repeated single realisation
arr_b = np.random.rand(1, 4*mpisize)
comm.Bcast(arr_b, root=0)
arr_ens = np.zeros((2, 4*mpisize))
for i in range(len(arr_ens)):
arr_ens[i] = arr_b
sim = Observable(arr_ens, 'simulated')
simdict.append(name=('test', None, 4*mpisize, None),
data=sim, otype='plain')
# simplelikelihood
lh_simple = SimpleLikelihood(meadict, covdict)
rslt_simple = lh_simple(simdict)
# ensemblelikelihood
lh_ensemble = EnsembleLikelihood(meadict, covdict)
rslt_ensemble = lh_ensemble(simdict)
assert rslt_ensemble == rslt_simple
def test_diag(self):
simdict = Simulations()
meadict = Measurements()
covdict = Covariances()
# mock measurements
arr_a = np.random.rand(1, 40)
mea = Observable(arr_a, 'measured')
meadict.append(name=('test', None, 40, None),
data=mea, otype='plain')
# mock (diagonal) covariance
arr_var = np.random.rand(40)
cov = Observable(np.diag(arr_var), 'covariance')
covdict.append(name=('test', None, 40, None),
cov_data=cov)
# mock observable
arr_ens = np.random.rand(10, 40)
sim = Observable(arr_ens, 'simulated')
simdict.append(name=('test', None, 40, None),
data=sim, otype='plain')
# ensemblelikelihood + diagonal_covcov
lh_ens = EnsembleLikelihood(meadict, covdict, cov_func=diagonal_mcov)
result_ens = lh_ens(simdict)
# EnsembleLikelihoodDiagonal
lh_diag = EnsembleLikelihoodDiagonal(meadict, covdict)
result_diag = lh_diag(simdict)
assert np.allclose(result_diag, result_ens)
def test_with_cov(self):
simdict = Simulations()
meadict = Measurements()
covdict = Covariances()
# mock measurements
arr_a = np.random.rand(1, 4*mpisize)
comm.Bcast(arr_a, root=0)
mea = Observable(arr_a, 'measured')
meadict.append(name=('test', None, 4*mpisize, None),
data=mea, otype='plain')
# mock sims
arr_b = np.random.rand(5, 4*mpisize)
sim = Observable(arr_b, 'simulated')
simdict.append(name=('test', None, 4*mpisize, None),
data=sim, otype='plain')
# mock covariance
arr_c = np.random.rand(4, 4*mpisize)
cov = Observable(arr_c, 'covariance')
covdict.append(name=('test', None, 4*mpisize, None),
cov_data=cov)
# with covariance
lh = SimpleLikelihood(meadict, covdict)
# calc by likelihood
rslt = lh(simdict) # feed variable value, not parameter value
# calc by hand
full_b = np.vstack(comm.allgather(arr_b)) # global arr_b
diff = (np.mean(full_b, axis=0) - arr_a)
full_cov = np.vstack(comm.allgather(arr_c)) # global covariance
(sign, logdet) = np.linalg.slogdet(full_cov*2.*np.pi)
baseline = -0.5*(np.vdot(diff, np.linalg.solve(full_cov, diff.T))+sign*logdet)
assert np.allclose(rslt, baseline)
def test_without_cov(self):
simdict = Simulations()
meadict = Measurements()
# mock measurements
arr_a = np.random.rand(1, 12*mpisize**2)
comm.Bcast(arr_a, root=0)
mea = Observable(arr_a, 'measured')
meadict.append(name=('test', None, mpisize, None),
data=mea, otype='HEALPix')
# mock observable with repeated single realisation
arr_b = np.random.rand(1, 12*mpisize**2)
comm.Bcast(arr_b, root=0)
if not mpirank:
arr_ens = np.zeros((3, 12*mpisize**2))
else:
arr_ens = np.zeros((2, 12*mpisize**2))
for i in range(len(arr_ens)):
arr_ens[i] = arr_b
sim = Observable(arr_ens, 'simulated')
simdict.append(name=('test', None, mpisize, None),
data=sim, otype='HEALPix')
# simplelikelihood
lh_simple = SimpleLikelihood(meadict)
rslt_simple = lh_simple(simdict)
# ensemblelikelihood
lh_ensemble = EnsembleLikelihood(meadict)
rslt_ensemble = lh_ensemble(simdict)
assert rslt_ensemble == rslt_simple
def test_with_trace_approximation(self):
simdict = Simulations()
meadict = Measurements()
covdict = Covariances()
# mock measurements
arr_a = np.random.rand(1, 4*mpisize)
comm.Bcast(arr_a, root=0)
mea = Observable(arr_a, 'measured')
meadict.append(name=('test', None, 4*mpisize, None),
data=mea, otype='plain')
# mock covariance (NB for the trace approximation to work, the data
# covariance needs to be diagonal)
arr_c = np.diag(np.random.rand(4))
cov = Observable(arr_c, 'covariance')
covdict.append(name=('test', None, 4*mpisize, None),
cov_data=cov)
# mock observable with repeated single realisation
arr_b = np.random.rand(1, 4*mpisize)
comm.Bcast(arr_b, root=0)
arr_ens = np.zeros((2, 4*mpisize))
for i in range(len(arr_ens)):
arr_ens[i] = arr_b
sim = Observable(arr_ens, 'simulated')
simdict.append(name=('test', None, 4*mpisize, None),
data=sim, otype='plain')
# simplelikelihood
lh_simple = SimpleLikelihood(meadict, covdict)
result_simple = lh_simple(simdict)
# ensemblelikelihood
lh_ensemble = EnsembleLikelihood(meadict, covdict,
use_trace_approximation=True)
result_ensemble = lh_ensemble(simdict)
assert result_ensemble == result_simple
def test_covdict_append_observable(self):
cov = Observable(np.random.rand(2, 2 * mpisize), 'covariance')
covdict = Covariances()
covdict.append(name=('test', None, 2 * mpisize, None),
cov_data=cov) # plain covariance
assert np.allclose(covdict[('test', None, 2 * mpisize, None)].data,
cov.data)
cov = Observable(np.random.rand(12 * mpisize, 12 * mpisize * mpisize),
'covariance')
covdict.append(name=('test', None, mpisize, None),
cov_data=cov) # healpix covariance
assert np.allclose(covdict[('test', None, mpisize, None)].data,
cov.data)
def test_append_with_rewrite(self):
if not mpirank:
arr = np.random.rand(2, 128)
else:
arr = np.random.rand(1, 128)
test_obs = Observable(arr, 'simulated')
test_obs.rw_flag = True
brr = np.random.rand(1, 128)
test_obs.append(brr)
global_shape = test_obs.shape
globalrr = test_obs.global_data
if not mpirank:
assert global_shape == globalrr.shape
assert np.allclose(test_obs.data[0], brr[0])
def test_append_ndarray(self):
if not mpirank:
arr = np.random.rand(2, 128)
else:
arr = np.random.rand(1, 128)
test_obs = Observable(arr, 'simulated')
brr = np.random.rand(1, 128)
test_obs.append(brr)
global_shape = test_obs.shape
globalrr = test_obs.global_data
if not mpirank:
assert global_shape == globalrr.shape
fullrr = np.vstack([arr, brr])
assert np.allclose(fullrr, test_obs.data)
def test_simdict_append_observable(self):
hrr = np.random.rand(2, 48)
obs1 = Observable(hrr, 'simulated')
simdict = Simulations()
simdict.append(name=('test', None, 2, None),
data=obs1,
otype='HEALPix') # healpix Observable
assert simdict[('test', None, 2, None)].shape == (2 * mpisize, 48)
assert np.allclose(simdict[('test', None, 2, None)].data, hrr)
arr = np.random.rand(5, 3)
obs2 = Observable(arr, 'simulated')
simdict.append(name=('test', None, 3, None), data=obs2,
otype='plain') # plain Observable
assert simdict[('test', None, 3, None)].shape == (5 * mpisize, 3)
assert np.allclose(simdict[('test', None, 3, None)].data, arr)
def test_measuredict_append_observable(self):
hrr = np.random.rand(1, 48)
obs1 = Observable(hrr, 'measured')
measuredict = Measurements()
measuredict.append(name=('test', None, 2, None),
data=obs1) # healpix Observable
assert np.allclose(measuredict[('test', None, 2, None)].data[0],
hrr[0])
arr = np.random.rand(1, 3)
obs2 = Observable(arr, 'measured')
measuredict.append(name=('test', None, 3, None),
data=obs2,
otype='plain') # plain Observable
assert np.allclose(measuredict[('test', None, 3, None)].data[0],
arr[0])
def test_init_covariance(self):
arr = np.random.rand(1, mpisize)
test_obs = Observable(arr, 'covariance')
assert test_obs.dtype == 'covariance'
assert test_obs.shape == (mpisize, mpisize)
assert np.allclose(arr[0], test_obs.data[0])
assert test_obs.size == mpisize
def test_init_measure(self):
arr = np.random.rand(1, 128)
test_obs = Observable(arr, 'measured')
assert test_obs.dtype == 'measured'
assert test_obs.shape == (mpisize, 128)
assert np.allclose(arr[0], test_obs.data[0])
assert np.allclose(arr[0], test_obs.ensemble_mean[0])
def test_append_obs(self):
if not mpirank:
arr = np.random.rand(2, 128)
else:
arr = np.random.rand(1, 128)
test_obs = Observable(arr, 'simulated')
if not mpirank:
brr = np.random.rand(1, 128)
else:
brr = np.random.rand(1, 128)
test_obs2 = Observable(brr, 'simulated')
test_obs2.append(test_obs)
global_shape = test_obs2.shape
globalrr = test_obs2.global_data
if not mpirank:
assert global_shape == globalrr.shape
fullrr = np.vstack([arr, brr])
assert np.alltrue(np.isin(test_obs2.data, fullrr))
def _initialize_observable_dict_helper(self, observable_dict,
magnetic_field, observable):
ensemble_space = magnetic_field.domain[0]
for component in observable.component_names:
# It is important to initialize the Observables with an explicit
# value. Otherwise the d2o will not instantaneuosly be created
# (c.f. lazy object creation).
observable_dict[component] = Observable(
val=0,
domain=(ensemble_space, self._hpSpace),
distribution_strategy='equal')
return observable_dict
def test_without_cov(self):
simdict = Simulations()
meadict = Measurements()
# mock measurements
arr_a = np.random.rand(1, 48)
comm.Bcast(arr_a, root=0)
mea = Observable(arr_a, 'measured')
meadict.append(name=('test', None, 2, None), data=mea, otype='HEALPix')
# mock sims
arr_b = np.random.rand(3, 48)
sim = Observable(arr_b, 'simulated')
simdict.append(name=('test', None, 2, None), data=sim, otype='HEALPix')
# no covariance
lh = SimpleLikelihood(meadict)
# calc by likelihood
rslt = lh(simdict) # feed variable value, not parameter value
# calc by hand
full_b = np.vstack(comm.allgather(arr_b)) # global arr_b
diff = (np.mean(full_b, axis=0) - arr_a)
baseline = -float(0.5)*float(np.vdot(diff, diff))
# comapre
assert np.allclose(rslt, baseline)
def test_append_after_rewrite(self):
arr = np.random.rand(1, 128)
test_obs = Observable(arr, 'simulated')
if not mpirank:
brr = np.random.rand(2, 128)
else:
brr = np.random.rand(1, 128)
test_obs.rw_flag = True
test_obs.append(brr)
crr = np.random.rand(1, 128)
# rw_flag must have be switched off
test_obs.append(crr)
global_shape = test_obs.shape
globalrr = test_obs.global_data
if not mpirank:
assert global_shape == globalrr.shape
fullrr = np.vstack([brr, crr])
assert np.alltrue(np.isin(test_obs.data, fullrr))
