def test_without_simcov(self):
simdict = Simulations()
meadict = Measurements()
covdict = Covariances()
# mock measurements
dtuple = DomainTuple.make((RGSpace(1), HPSpace(nside=2)))
arr_a = np.random.rand(1, 48)
comm.Bcast(arr_a, root=0)
mea = Observable(dtuple, arr_a)
meadict.append(('test', 'nan', '2', 'nan'), mea)
# mock covariance
dtuple = DomainTuple.make((RGSpace(shape=(48, 48))))
arr_c = np.random.rand(48, 48)
comm.Bcast(arr_c, root=0)
cov = Field.from_global_data(dtuple, arr_c)
covdict.append(('test', 'nan', '2', 'nan'), cov)
# mock observable with repeated single realisation
dtuple = DomainTuple.make((RGSpace(5*mpisize), HPSpace(nside=2)))
arr_b = np.random.rand(1, 48)
comm.Bcast(arr_b, root=0)
arr_ens = np.zeros((5, 48))
for i in range(len(arr_ens)):
arr_ens[i] = arr_b
sim = Observable(dtuple, arr_ens)
simdict.append(('test', 'nan', '2', 'nan'), sim)
# simplelikelihood
lh_simple = SimpleLikelihood(meadict, covdict)
rslt_simple = lh_simple(simdict)
# ensemblelikelihood
lh_ensemble = EnsembleLikelihood(meadict, covdict)
rslt_ensemble = lh_ensemble(simdict)
self.assertEqual(rslt_ensemble, rslt_simple)
def test_constructor(nside, expected):
if 'error' in expected:
with assert_raises(expected['error']):
HPSpace(nside)
else:
h = HPSpace(nside)
for key, value in expected.items():
assert_equal(getattr(h, key), value)
def test_init(self):
# initialize observable
dtuple = DomainTuple.make((RGSpace(3 * mpisize), HPSpace(nside=2)))
val = np.random.rand(3, 48)
obs = Observable(dtuple, val)
# collect local val at master
val_master = None
if mpirank == 0:
val_master = np.zeros((3 * mpisize, 48))
comm.Gather(val, val_master, root=0)
# test domain/field shape
self.assertEqual(obs.domain, dtuple)
if mpirank == 0:
self.assertEqual(obs.shape, val_master.shape)
# test function to_global_data()
raw = obs.to_global_data()
if mpirank == 0:
for i in range(len(val_master)):
self.assertListEqual(list(raw[i]), list(val_master[i]))
# test function ensemble_mean
mean = obs.ensemble_mean
if mpirank == 0:
val_mean = np.mean(val_master, axis=0)
for i in range(val_mean.size):
self.assertAlmostEqual(mean[0][i], val_mean[i])
def append(self, name, data, plain=False):
"""
:param name:
:param data: distributed
:param plain:
:return:
"""
assert (len(name) == 4)
if name in self._archive.keys(): # app
self._archive[name].append(data)
else: # new
if isinstance(data, Observable):
self._archive.update({name: data})
elif isinstance(data, Field):
self._archive.update(
{name: Observable(data.domain, data.local_data)})
elif isinstance(data, np.ndarray): # distributed data
if plain:
assert (data.shape[1] == int(name[2]))
domain = DomainTuple.make(
(RGSpace(data.shape[0] * mpisize),
RGSpace(data.shape[1])))
else:
assert (data.shape[1] == 12 * int(name[2]) * int(name[2]))
domain = DomainTuple.make(
(RGSpace(data.shape[0] * mpisize),
HPSpace(nside=int(name[2]))))
self._archive.update({name: Observable(domain, data)})
else:
raise TypeError('unsupported data type')
log.debug('observable-dict appends data %s' % str(name))
def append(self, name, data, plain=False):
"""
:param name:
:param data: distributed
:param plain:
:return:
"""
assert (len(name) == 4)
if isinstance(data, Observable):
assert (data.shape[0] == 1)
self._archive.update({name: data}) # rw
elif isinstance(data, Field):
assert (data.shape[0] == 1)
self._archive.update(
{name: Observable(data.domain, data.local_data)}) # rw
# reading from numpy array takes information only on master node
elif isinstance(data, np.ndarray):
assert (data.shape[0] == 1)
if plain:
assert (data.shape[1] == int(name[2]))
domain = DomainTuple.make(
(RGSpace(int(1)), RGSpace(data.shape[1])))
else:
assert (data.shape[1] == 12 * int(name[2]) * int(name[2]))
domain = DomainTuple.make(
(RGSpace(int(1)), HPSpace(nside=int(name[2]))))
self._archive.update({name: Observable(domain, data)}) # rw
else:
raise TypeError('unsupported data type')
log.debug('measurements-dict appends data %s' % str(name))
def test_1dinit(self):
# initialize observable
dtuple = DomainTuple.make((RGSpace(1), HPSpace(nside=2)))
val = np.random.rand(1, 48)
comm.Bcast(val, root=0)
obs = Observable(dtuple, val)
# matches val at master
raw = obs.to_global_data()
self.assertListEqual(list(raw[0]), list(val[0]))
def test_append_twice(self):
dtuple = DomainTuple.make((RGSpace(1 * mpisize), HPSpace(nside=2)))
val = np.random.rand(1, 48)
obs = Observable(dtuple, val)
# test function append with 1d array
new_data = np.random.rand(1, 48)
obs.append(new_data)
self.assertEqual(obs.shape, (2 * mpisize, 48))
obs.append(new_data)
self.assertEqual(obs.shape, (3 * mpisize, 48))
def test_append_after_replace(self):
dtuple = DomainTuple.make((RGSpace(1 * mpisize), HPSpace(nside=2)))
obs = Observable(dtuple)
self.assertTrue(obs.rw_flag)
# test function append with 1d array
new_data = np.random.rand(1, 48)
obs.append(new_data)
self.assertEqual(obs.shape, (1 * mpisize, 48))
obs.append(new_data)
self.assertEqual(obs.shape, (2 * mpisize, 48))
def test_append_with_replace(self):
dtuple = DomainTuple.make((RGSpace(1 * mpisize), HPSpace(nside=2)))
obs = Observable(dtuple)
self.assertTrue(obs.rw_flag)
# test with empty observable
dtuple = DomainTuple.make((RGSpace(8 * mpisize), HPSpace(nside=2)))
new_data = np.random.rand(8, 48)
new_obs = Observable(dtuple, new_data)
obs.append(new_obs)
raw_obs = obs.to_global_data()
# collect new val at master
new_val_master = None
if mpirank == 0:
new_val_master = np.zeros((8 * mpisize, 48))
comm.Gather(new_data, new_val_master, root=0)
# do comparison at master
if mpirank == 0:
for i in range(new_val_master.shape[0]):
self.assertListEqual(list(raw_obs[i]), list(new_val_master[i]))
def test_append_observable(self):
dtuple = DomainTuple.make((RGSpace(3 * mpisize), HPSpace(nside=2)))
val = np.random.rand(3, 48)
obs = Observable(dtuple, val)
# test function append with Observable
dtuple = DomainTuple.make((RGSpace(5 * mpisize), HPSpace(nside=2)))
new_data = np.random.rand(5, 48)
new_obs = Observable(dtuple, new_data)
obs.append(new_obs)
raw_obs = obs.to_global_data()
new_val = np.vstack([val, new_data])
# collect new val at master
new_val_master = None
if mpirank == 0:
new_val_master = np.zeros((8 * mpisize, 48))
comm.Gather(new_val, new_val_master, root=0)
# do comparison at master
if mpirank == 0:
for i in range(new_val_master.shape[0]):
self.assertListEqual(list(raw_obs[i]), list(new_val_master[i]))
def test_without_cov(self):
simdict = Simulations()
meadict = Measurements()
# mock measurements
dtuple = DomainTuple.make((RGSpace(1), HPSpace(nside=2)))
arr_a = np.random.rand(1, 48)
comm.Bcast(arr_a, root=0)
mea = Observable(dtuple, arr_a)
meadict.append(('test', 'nan', '2', 'nan'), mea)
# mock sims
dtuple = DomainTuple.make((RGSpace(3*mpisize), HPSpace(nside=2)))
arr_b = np.random.rand(3, 48)
sim = Observable(dtuple, arr_b)
simdict.append(('test', 'nan', '2', 'nan'), sim)
# no covariance
lh = SimpleLikelihood(meadict)
# calc by likelihood
rslt = lh(simdict) # feed variable value, not parameter value
# calc by hand
arr_b = sim.to_global_data() # global arr_b
diff = (np.mean(arr_b, axis=0) - arr_a)
baseline = -float(0.5)*float(np.vdot(diff, diff))
# comapre
self.assertAlmostEqual(rslt, baseline)
def test_measuredict_append_observable(self):
dtuple = DomainTuple.make((RGSpace(1), HPSpace(nside=2)))
hrr = np.random.rand(1, 48)
obs1 = Observable(dtuple, hrr)
measuredict = Measurements()
measuredict.append(('test', 'nan', '2', 'nan'),
obs1) # healpix Observable
if mpirank == 0:
self.assertListEqual(
list(measuredict[('test', 'nan', '2',
'nan')].to_global_data()[0]), list(hrr[0]))
dtuple = DomainTuple.make((RGSpace(1), RGSpace(3)))
arr = np.random.rand(1, 3)
obs2 = Observable(dtuple, arr)
measuredict.append(('test', 'nan', '3', 'nan'),
obs2) # plain Observable
if mpirank == 0:
self.assertListEqual(
list(measuredict[('test', 'nan', '3',
'nan')].to_global_data()[0]), list(arr[0]))
def append(self, name, data, plain=False):
"""
:param name:
:param data: distributed
:param plain:
:return:
"""
assert (len(name) == 4)
if isinstance(data, Observable):
assert (data.shape[0] == mpisize)
raw_data = data.local_data
for i in raw_data[0]:
assert (i == float(0) or i == float(1))
self._archive.update({name: data})
elif isinstance(data, Field):
assert (data.shape[0] == mpisize)
raw_data = data.local_data
for i in raw_data[0]:
assert (i == float(0) or i == float(1))
self._archive.update(
{name: Observable(data.domain, data.local_data)})
elif isinstance(data, np.ndarray):
assert (data.shape[0] == 1)
for i in data[0]:
assert (i == float(0) or i == float(1))
if plain:
assert (data.shape[1] == int(name[2]))
domain = DomainTuple.make(
(RGSpace(mpisize), RGSpace(data.shape[1])))
else:
assert (data.shape[1] == 12 * int(name[2]) * int(name[2]))
domain = DomainTuple.make(
(RGSpace(mpisize), HPSpace(nside=int(name[2]))))
self._archive.update({name: Observable(domain, data)})
else:
raise TypeError('unsupported data type')
log.debug('mask-dict appends data %s' % str(name))
def test_simdict_append_observable(self):
dtuple = DomainTuple.make((RGSpace(2 * mpisize), HPSpace(nside=2)))
hrr = np.random.rand(2, 48)
obs1 = Observable(dtuple, hrr)
simdict = Simulations()
simdict.append(('test', 'nan', '2', 'nan'), obs1) # healpix Observable
self.assertEqual(simdict[('test', 'nan', '2', 'nan')].shape,
(2 * mpisize, 48))
for i in range(len(hrr)):
self.assertListEqual(
list((simdict[('test', 'nan', '2', 'nan')].local_data)[i]),
list(hrr[i]))
dtuple = DomainTuple.make((RGSpace(5 * mpisize), RGSpace(3)))
arr = np.random.rand(5, 3)
obs2 = Observable(dtuple, arr)
simdict.append(('test', 'nan', '3', 'nan'), obs2,
True) # plain Observable
self.assertEqual(simdict[('test', 'nan', '3', 'nan')].shape,
(5 * mpisize, 3))
for i in range(len(arr)):
self.assertListEqual(
list((simdict[('test', 'nan', '3', 'nan')].local_data)[i]),
list(arr[i]))
def test_dvol():
assert_almost_equal(HPSpace(2).dvol, np.pi / 12)
def test_property_ret_type():
x = HPSpace(2)
assert_(isinstance(getattr(x, 'nside'), int))
