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