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 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
