def init_matrices(ens, mask, obs, rng):
state_size = 2
report_step = 5
meas_data = MeasData(mask)
meas_block = meas_data.addBlock("OBS", report_step, len(obs))
A = Matrix(state_size, mask.countEqual(True))
active_iens = 0
for iens, params in enumerate(ens):
if mask[iens]:
state = forward_model(params)
meas_block[0, iens] = measure(state)
A[0, active_iens] = params[0]
A[1, active_iens] = params[1]
active_iens += 1
S = meas_data.createS()
obs_data = ObsData()
obs_block = obs_data.addBlock("OBS", 1)
for iobs, obs_value in enumerate(obs):
obs_block[iobs] = obs_value
R = obs_data.createR()
dObs = obs_data.createDObs()
E = obs_data.createE(rng, meas_data.getActiveEnsSize())
D = obs_data.createD(E, S)
obs_data.scale(S, E=E, D=D, R=R, D_obs=dObs)
return (A, S, E, D, R, dObs)
def calculatePrincipalComponent(self,
fs,
local_obsdata,
truncation_or_ncomp=3):
pc = Matrix(1, 1)
pc_obs = Matrix(1, 1)
singular_values = DoubleVector()
state_map = fs.getStateMap()
ens_mask = BoolVector(False, self.ert().getEnsembleSize())
state_map.selectMatching(ens_mask, RealizationStateEnum.STATE_HAS_DATA)
active_list = ens_mask.createActiveList()
if len(ens_mask) > 0:
meas_data = MeasData(ens_mask)
obs_data = ObsData()
self.ert().getObservations().getObservationAndMeasureData(
fs, local_obsdata, active_list, meas_data, obs_data)
meas_data.deactivateZeroStdSamples(obs_data)
active_size = len(obs_data)
if active_size > 0:
S = meas_data.createS()
D_obs = obs_data.createDObs()
truncation, ncomp = self.truncationOrNumberOfComponents(
truncation_or_ncomp)
obs_data.scale(S, D_obs=D_obs)
EnkfLinalg.calculatePrincipalComponents(
S, D_obs, truncation, ncomp, pc, pc_obs, singular_values)
if self.__prior_singular_values is None:
self.__prior_singular_values = singular_values
else:
for row in range(pc.rows()):
factor = singular_values[
row] / self.__prior_singular_values[row]
pc.scaleRow(row, factor)
pc_obs.scaleRow(row, factor)
return PcaPlotData(local_obsdata.getName(), pc, pc_obs,
singular_values)
return None
def test_create(self):
ens_size = 10
ens_mask = BoolVector(default_value=True, initial_size=ens_size)
data = MeasData(ens_mask)
self.assertEqual(len(data), 0)
self.assertTrue(isinstance(data, MeasData))
block1 = data.addBlock("OBS1", 10, 5)
block2 = data.addBlock("OBS2", 27, 10)
with self.assertRaises(TypeError):
data[1.782]
with self.assertRaises(KeyError):
data["NO-this-does-not-exist"]
with self.assertRaises(IndexError):
data[2]
last0 = data[-1]
last1 = data[1]
self.assertEqual(last0, last1)
self.assertTrue("OBS1-10" in data)
self.assertTrue("OBS2-27" in data)
self.assertEqual(len(data), 2)
self.assertTrue(isinstance(block1, MeasBlock))
self.assertTrue(isinstance(block2, MeasBlock))
self.assertEqual(block1.getObsSize(), 5)
self.assertEqual(block2.getObsSize(), 10)
l = []
for b in data:
l.append(b)
self.assertEqual(len(l), 2)
self.assertEqual(l[0], block1)
self.assertEqual(l[1], block2)
with self.assertRaises(ValueError):
S = data.createS()
for iens in range(ens_size):
block1[0, iens] = 5
block2[0, iens] = 10
block2[1, iens] = 15
self.assertEqual(3, data.activeObsSize())
S = data.createS()
self.assertEqual(S.dims(), (3, ens_size))
for iens in range(ens_size):
self.assertEqual(S[0, iens], 5)
self.assertEqual(S[1, iens], 10)
self.assertEqual(S[2, iens], 15)
pfx = 'MeasData(len = '
self.assertEqual(pfx, repr(data)[:len(pfx)])