def test_it(self):
state_size = 10
with ErtTestContext("update", self.config_file) as tc:
analysis = self.createAnalysisModule()
ert = tc.getErt()
obs = ert.getObservations()
local_obsdata = obs.getAllActiveLocalObsdata()
fs = ert.getEnkfFsManager().getCurrentFileSystem()
state = EnkfStateType.FORECAST
mask = BoolVector(initial_size=ert.getEnsembleSize(),
default_value=True)
meas_data = MeasData(mask)
obs_data = ObsData()
obs.getObservationAndMeasureData(fs, local_obsdata, state,
mask.createActiveList(),
meas_data, obs_data)
update(self.rng, mask, analysis, ert, meas_data, obs_data,
state_size)
mask[0] = False
mask[4] = False
meas_data = MeasData(mask)
obs_data = ObsData()
obs.getObservationAndMeasureData(fs, local_obsdata, state,
mask.createActiveList(),
meas_data, obs_data)
update(self.rng, mask, analysis, ert, meas_data, obs_data,
state_size)
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 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):
obs_data = ObsData()
obs_size = 10
block = obs_data.addBlock("OBS", obs_size)
self.assertTrue(isinstance(block, ObsBlock))
block[0] = (10, 10)
block[1] = (12, 12)
D = obs_data.createDObs()
self.assertTrue(isinstance(D, Matrix))
self.assertEqual(D.dims(), (2, 1))
self.assertEqual(D[0, 0], 10)
self.assertEqual(D[1, 0], 12)
obs_data.scaleMatrix(D)
self.assertEqual(D[0, 0], 1)
self.assertEqual(D[1, 0], 1)
R = obs_data.createR()
self.assertEqual((2, 2), R.dims())
with self.assertRaises(IndexError):
obs_data[10]
v, s = obs_data[0]
self.assertEqual(v, 10)
self.assertEqual(s, 10)
v, s = obs_data[1]
self.assertEqual(v, 12)
self.assertEqual(s, 12)
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 = BoolVector.createActiveList(ens_mask)
if len(active_list) > 0:
state = EnkfStateType.FORECAST
meas_data = MeasData(active_list)
obs_data = ObsData()
self.ert().getObservations().getObservationAndMeasureData(fs, local_obsdata, state, active_list, meas_data, obs_data)
meas_data.deactivateZeroStdSamples(obs_data)
active_size = len(obs_data)
if active_size > 0:
S = meas_data.createS(active_size)
D_obs = obs_data.createDobs(active_size)
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):
obs_data = ObsData()
obs_size = 10
block = obs_data.addBlock("OBS" , obs_size)
self.assertTrue( isinstance( block , ObsBlock ))
block[0] = (10,10)
block[1] = (12,12)
D = obs_data.createDObs()
self.assertTrue( isinstance(D , Matrix ))
self.assertEqual( D.dims() , (2,1))
self.assertEqual( D[0,0] , 10 )
self.assertEqual( D[1,0] , 12 )
obs_data.scaleMatrix( D )
self.assertEqual( D[0,0] , 1 )
self.assertEqual( D[1,0] , 1 )
R = obs_data.createR()
self.assertEqual( (2,2) , R.dims() )
with self.assertRaises(IndexError):
obs_data[10]
v,s = obs_data[0]
self.assertEqual( v , 10 )
self.assertEqual( s , 10 )
v,s = obs_data[1]
self.assertEqual( v , 12 )
self.assertEqual( s , 12 )
def test_scale_obs(self):
with ErtTestContext("obs_test", self.config_file) as test_context:
ert = test_context.getErt()
obs = ert.getObservations()
obs1 = obs["WWCT:OP_1"].getNode(50)
obs2 = obs["WWCT:OP_1_50"].getNode(50)
self.assertEqual(obs1.getStandardDeviation(),
obs2.getStandardDeviation())
std0 = obs1.getStandardDeviation()
local_obsdata = LocalObsdata("obs", obs)
node1 = local_obsdata.addNode("WWCT:OP_1")
node2 = local_obsdata.addNode("WWCT:OP_1_50")
node1.addTimeStep(50)
node2.addTimeStep(50)
mask = BoolVector(default_value=True)
mask[2] = True
meas_data = MeasData(mask)
obs_data = ObsData()
fs = ert.getEnkfFsManager().getCurrentFileSystem()
active_list = IntVector()
active_list.initRange(0, 2, 1)
obs.getObservationAndMeasureData(fs, local_obsdata,
EnkfStateType.FORECAST,
active_list, meas_data, obs_data)
self.assertEqual(2, len(obs_data))
v1 = obs_data[0]
v2 = obs_data[1]
self.assertEqual(v1[1], std0)
self.assertEqual(v2[1], std0)
meas_data = MeasData(mask)
obs_data = ObsData(10)
obs.getObservationAndMeasureData(fs, local_obsdata,
EnkfStateType.FORECAST,
active_list, meas_data, obs_data)
self.assertEqual(2, len(obs_data))
v1 = obs_data[0]
v2 = obs_data[1]
self.assertEqual(v1[1], std0 * 10)
self.assertEqual(v2[1], std0 * 10)
actl = ActiveList()
obs1.updateStdScaling(10, actl)
obs2.updateStdScaling(20, actl)
meas_data = MeasData(mask)
obs_data = ObsData()
obs.getObservationAndMeasureData(fs, local_obsdata,
EnkfStateType.FORECAST,
active_list, meas_data, obs_data)
self.assertEqual(2, len(obs_data))
v1 = obs_data[0]
v2 = obs_data[1]
self.assertEqual(v1[1], std0 * 10)
self.assertEqual(v2[1], std0 * 20)