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 __call__(self, field_list):
"""
Runs the simulator over a Fields list
Parameters
----------
field_list : list
List of imagine.Field object which must include all the `required_field_types`
Returns
-------
sims : imagine.Simulations
A Simulations object containing all the specified mock data
"""
sims = Simulations()
self.register_ensemble_size(field_list)
for i in range(self._ensemble_size):
# Prepares all fields
self.prepare_fields(field_list, i)
for key in self.observables:
sim = self.simulate(key=key,
coords_dict=self.output_coords[key],
realization_id=i,
output_units=self.output_units[key])
sims.append(name=key,
data=sim[np.newaxis, :].to(self.output_units[key]),
coords=self.output_coords[key],
otype=self.output_type[key])
return sims
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_simdict_append_array_twice(self):
arr = np.random.rand(2, 3)
simdict = Simulations()
simdict.append(name=('test', None, 3, None), data=arr,
otype='plain') # plain array
assert simdict[('test', None, 3, None)].shape == (2 * mpisize, 3)
simdict.append(name=('test', None, 3, None), data=arr,
otype='plain') # plain array
assert simdict[('test', None, 3, None)].shape == (4 * mpisize, 3)
def test_simdict_append_array(self):
arr = np.random.rand(2, 3)
simdict = Simulations()
simdict.append(name=('test', None, 3, None), data=arr,
otype='plain') # plain array
assert simdict[('test', None, 3, None)].shape == (2 * mpisize, 3)
assert np.allclose(simdict[('test', None, 3, None)].data, arr)
hrr = np.random.rand(3, 48)
simdict.append(name=('test', None, 2, None), data=hrr,
otype='HEALPix') # healpix array
assert simdict[('test', None, 2, None)].shape == (3 * mpisize, 48)
assert np.allclose(simdict[('test', None, 2, None)].data, hrr)
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)