API Reference¶

This page lists the functions exported by the public pyPDAF __init__.py files. The top-level namespace contains the functions most users need for a PDAF3 workflow. The subpackages expose lower-level PDAF, PDAFomi, localisation, diagnostic, and compatibility routines.

The function names follow PDAF closely. See Variable naming conventions for the meaning of suffixes such as _p, _l, and _f.

Top-Level Workflow Functions ¶

Initialisation and finalisation ¶

`pyPDAF.init`(filtertype, subtype, stepnull, ...)	Initialise the PDAF system.
`pyPDAF.init_parallel`(screen, type_parallel, ...)	Initialize PDAF3's flexible parallelization layout.
`pyPDAF.init_forecast`(...)	The routine PDAF_init_forecast has to be called once at the end of the initialization of PDAF/start of DA cycles.
`pyPDAF.set_parallel`(in_comm_pdaf, ...)	Set MPI communicators and parallelisation in PDAF.
`pyPDAF.deallocate`()	Finalise the PDAF systems including freeing some of the memory used by PDAF.

Assimilation drivers ¶

`pyPDAF.assimilate`(py__collect_state_pdaf, ...)	Online ensemble filters and smoothers except for 3DVars for a single DA step using diagnoal observation error covariance matrix.
`pyPDAF.assim_offline`(py__init_dim_obs_pdaf, ...)	Offline ensemble filters and smoothers except for 3DVars for a single DA step using diagnoal observation error covariance matrix.
`pyPDAF.assimilate_local_nondiagr`(...)	Online assimilation of domain local filters for a single DA step using non-diagnoal observation error covariance matrix.
`pyPDAF.assimilate_global_nondiagr`(...)	Online assimilation of global filters except for 3DVar and stochastic EnKF for a single DA step using non-diagnoal observation error covariance matrix.
`pyPDAF.assimilate_lnetf_nondiagr`(...)	Online assimilation of LNETF for a single DA step using non-diagnoal observation error covariance matrix.
`pyPDAF.assimilate_lknetf_nondiagr`(...)	Online assimilation of LKNETF for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.assimilate_enkf_nondiagr`(...)	Online assimilation of global or Covariance localised stochastic EnKF for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.assimilate_nonlin_nondiagr`(...)	Online assimilation of global nonlinear filters for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.assim_offline_local_nondiagr`(...)	Offline assimilation of domain local filters for a single DA step using non-diagnoal observation error covariance matrix.
`pyPDAF.assim_offline_global_nondiagr`(...)	Offline assimilation of global filters except for 3DVar and stochastic EnKF for a single DA step using non-diagnoal observation error covariance matrix.
`pyPDAF.assim_offline_lnetf_nondiagr`(...)	Offline assimilation of LNETF for a single DA step using non-diagnoal observation error covariance matrix.
`pyPDAF.assim_offline_lknetf_nondiagr`(...)	Offline assimilation of LKNETF for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.assim_offline_enkf_nondiagr`(...)	Offline assimilation of global or Covariance localised stochastic EnKF for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.assim_offline_lenkf_nondiagr`(...)	Online assimilation of covariance localised stochastic EnKF for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.assim_offline_nonlin_nondiagr`(...)	Offline assimilation of global nonlinear filters for a single DA step using non-diagonal observation error covariance matrix.

Variational and hybrid drivers ¶

`pyPDAF.assimilate_3dvar_all`(...)	Online assimilation for all types of 3DVar DA for a single DA step using diagonal observation error covariance matrix.
`pyPDAF.assim_offline_3dvar_all`(...)	Offline assimilation for all types of 3DVar DA for a single DA step using diagonal observation error covariance matrix.
`pyPDAF.assimilate_3dvar_nondiagr`(...)	3DVar DA for a single DA step using non-diagnoal observation error covariance matrix.
`pyPDAF.assimilate_en3dvar_estkf_nondiagr`(...)	3DEnVar for a single DA step using non-diagnoal observation error covariance matrix.
`pyPDAF.assimilate_en3dvar_lestkf_nondiagr`(...)	3DEnVar for a single DA step where the ensemble anomaly is generated by LESTKF using non-diagonal observation error covariance matrix.
`pyPDAF.assimilate_hyb3dvar_estkf_nondiagr`(...)	Hybrid 3DEnVar for a single DA step using non-diagnoal observation error covariance matrix.
`pyPDAF.assimilate_hyb3dvar_lestkf_nondiagr`(...)	Hybrid 3DEnVar for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.assim_offline_3dvar_nondiagr`(...)	Offline 3DVar DA for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.assim_offline_en3dvar_estkf_nondiagr`(...)	Offline 3DEnVar for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.assim_offline_en3dvar_lestkf_nondiagr`(...)	Offline 3DEnVar for a single DA step where the ensemble anomaly is generated by LESTKF using non-diagonal observation error covariance matrix.
`pyPDAF.assim_offline_hyb3dvar_estkf_nondiagr`(...)	Offline hybrid 3DEnVar for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.assim_offline_hyb3dvar_lestkf_nondiagr`(...)	Offline hybrid 3DEnVar for a single DA step using non-diagonal observation error covariance matrix.

Observation generation and hooks ¶

`pyPDAF.generate_obs`(py__collect_state_pdaf, ...)	Generation of synthetic observations based on given error statistics and observation operator.
`pyPDAF.generate_obs_offline`(...)	Generation of synthetic observations based on given error statistics and observation operator in offline setup.
`pyPDAF.prepost`(collect_state, ...)	Run PDAF3 pre/post processing in an externally managed model loop.
`pyPDAF.prepost_offline`(prepoststep, outflag)	Run PDAF3 offline pre/post processing.

Top-level utilities ¶

`pyPDAF.get_fcst_info`(steps, time, doexit)	Return the number of time steps, current model time, and a flag whether the forecasting should be exited.
`pyPDAF.print_version`()	Display version information for PDAF
`pyPDAF.print_filter_types`(verbose)	Print the list of available named filter types and their IDs.
`pyPDAF.configinfo_filters`(subtype, verbose)	Print configuration info of the active filter given subtype
`pyPDAF.options_filters`(type_filter)	The overview of options for the selected DA method.
`pyPDAF.flush_fortran_stdout`()	Flush the Fortran standard output buffer.
`pyPDAF.global_except_hook`(exctype, value, ...)	Global error handler that aborts MPI jobs on uncaught exceptions.

`pyPDAF.PDAF` Utilities and Legacy Interface ¶

Core setup, execution, and information ¶

`pyPDAF.PDAF.init`(filtertype, subtype, ...)	Initialise the PDAF system.
`pyPDAF.PDAF.init_forecast`(...)	The routine PDAF_init_forecast has to be called once at the end of the initialization of PDAF/start of DA cycles.
`pyPDAF.PDAF.deallocate`()	Finalise the PDAF systems including freeing some of the memory used by PDAF.
`pyPDAF.PDAF.finalize`()	Finalize PDAF and release PDAF-internal resources.
`pyPDAF.PDAF.abort`(err)	Abort the parallel program through PDAF/MPI.
`pyPDAF.PDAF.get_state`(steps, doexit, ...)	Distribute analysis state vector to an array.
`pyPDAF.PDAF.get_fcst_info`(steps, time, doexit)	Return the number of time steps, current model time, and a flag whether the forecasting should be exited.
`pyPDAF.PDAF.force_analysis`()	Perform assimilation after this function call.
`pyPDAF.PDAF.print_version`()	Display version information for PDAF
`pyPDAF.PDAF.print_filter_types`(verbose)	Print the list of available named filter types and their IDs.
`pyPDAF.PDAF.print_da_types`(verbose)	Print the list of available named DA method types and their IDs.
`pyPDAF.PDAF.print_info`(printtype)	Print PDAF timing and memory information.
`pyPDAF.PDAF.configinfo_filters`(subtype, verbose)	Print configuration info of the active filter given subtype
`pyPDAF.PDAF.options_filters`(type_filter)	The overview of options for the selected DA method.
`pyPDAF.PDAF.flush_fortran_stdout`()	Flush the Fortran standard output buffer.

Observation gathering ¶

`pyPDAF.PDAF.gather_dim_obs_f`(dim_obs_p)	Gather the dimension of observation vector across multiple local domains/filter processors.
`pyPDAF.PDAF.gather_obs_f`(obs_p, dimobs_f)	In the local filters (LESKTF, LETKF, LSEIK, LNETF) this function returns the total observation vector from process-local observations.
`pyPDAF.PDAF.gather_obs_f2`(coords_p, nrows, ...)	In the local filters (LESKTF, LETKF, LSEIK, LNETF) this function returns the full observation coordinates from process-local observation coordinates.
`pyPDAF.PDAF.gather_obs_f_flex`(dim_obs_p, ...)	Gather full observation from processor local observation without PDAF-internal info.
`pyPDAF.PDAF.gather_obs_f2_flex`(dim_obs_p, ...)	Gather full observation coordinates from processor local observation coordinates without PDAF-internal info.

Localisation helpers ¶

`pyPDAF.PDAF.correlation_function`(ctype, ...)	The value of the chosen correlation function according to the specified length scale.
`pyPDAF.PDAF.local_weight`(wtype, rtype, ...)	Get localisation weight for given distance, cut-off radius, support radius, weighting type, and weighting function.
`pyPDAF.PDAF.local_weights`(wtype, cradius, ...)	Get a vector of localisation weights for given distances, cut-off radius, support radius, weighting type, and weighting function.

Random numbers and ensemble generation ¶

`pyPDAF.PDAF.generate_rndvec`(len, vec, ...)	Generate random perturbations with PDAF's random-vector helper.
`pyPDAF.PDAF.eofcovar`(dim, nstates, nfields, ...)	EOF analysis of an ensemble of state vectors by singular value decomposition.
`pyPDAF.PDAF.sample_ens`(dim, dim_ens, modes, ...)	Generate an ensemble from singular values and their vectors (EOF modes) of an ensemble anomaly matrix.
`pyPDAF.PDAF.reset_forget`(forget_in)	Reset the forgetting factor manually during the assimilation process.

Setters ¶

`pyPDAF.PDAF.set_comm_pdaf`(in_comm_pdaf)	Set the MPI communicator used by PDAF.
`pyPDAF.PDAF.set_debug_flag`(debugval)	Activate the debug output of the PDAF.
`pyPDAF.PDAF.set_ens_pointer`()	Return the ensemble in a numpy array.
`pyPDAF.PDAF.set_iparam`(idval, value, flag)	Set integer parameters for PDAF.
`pyPDAF.PDAF.set_memberid`(int memberid)	Set the ensemble member index to given value.
`pyPDAF.PDAF.set_offline_mode`(screen)	Activate offline mode of PDAF.
`pyPDAF.PDAF.set_rparam`(idval, value, flag)	Set floating-point parameters for PDAF.
`pyPDAF.PDAF.genobs_set_rparam`(idval, value)	Set a real-valued GENOBS parameter.
`pyPDAF.PDAF.set_seedset`(seedset_in)	Choose a seedset for the random number generator used in PDAF.
`pyPDAF.PDAF.set_seed`(seedvec)	Set PDAF's four-integer random seed vector.
`pyPDAF.PDAF.set_seedvec`(seedvec)	Set PDAF's four-integer random seed vector.
`pyPDAF.PDAF.set_smoother_ens`(maxlag)	Get a pointer to smoother ensemble.

Getters and state flags ¶

`pyPDAF.PDAF.get_assim_flag`()	Return the flag that indicates if the DA is performed in the last time step.
`pyPDAF.PDAF.get_localfilter`()	Return whether a local filter is used.
`pyPDAF.PDAF.get_local_type`()	The routine returns the information on the localization type of the selected filter.
`pyPDAF.PDAF.get_memberid`(memberid)	Return the ensemble member id on the current process.
`pyPDAF.PDAF.get_obsmemberid`(memberid)	Return the ensemble member id when observation operator is being applied.
`pyPDAF.PDAF.get_seed`()	Return PDAF's current four-integer random seed vector.
`pyPDAF.PDAF.get_seedvec`()	Return PDAF's current four-integer random seed vector.
`pyPDAF.PDAF.get_rndcount`()	Return PDAF's random-number generation counter.
`pyPDAF.PDAF.reset_fcst_flag`()	Return PDAF's forecast-time reset flag.
`pyPDAF.PDAF.get_smoother_ens`()	Return the smoothed ensemble in earlier time steps.

Incremental analysis update ¶

`pyPDAF.PDAF.iau_init`(type_iau_in, nsteps_iau_in)	Initialise parameters for incremental analysis updates, IAU.
`pyPDAF.PDAF.iau_reset`(type_iau_in, nsteps_iau_in)	Modify the IAU type and the number of IAU time steps during a run
`pyPDAF.PDAF.iau_set_pointer`()	Set a pointer to the ensemble increments array.

Diagnostics ¶

`pyPDAF.PDAF.diag_ensmean`(dim, dim_ens, ...)	Compute the ensemble mean of the state ensemble.
`pyPDAF.PDAF.diag_stddev_nompi`(dim, dim_ens, ...)	Compute ensemble standard deviation and ensemble mean without MPI.
`pyPDAF.PDAF.diag_stddev`(dim_p, dim_ens, ...)	Compute ensemble standard deviation and ensemble mean.
`pyPDAF.PDAF.diag_variance_nompi`(dim, ...)	Compute ensemble variance/standard deviation and mean without MPI.
`pyPDAF.PDAF.diag_variance`(dim_p, dim_ens, ...)	Compute ensemble variance/standard deviation and mean.
`pyPDAF.PDAF.diag_rmsd_nompi`(dim_p, statea_p, ...)	Compute the root mean squared distance between two vectors without MPI.
`pyPDAF.PDAF.diag_rmsd`(dim_p, statea_p, ...)	Compute the root mean squared distance between two vectors.
`pyPDAF.PDAF.diag_crps_mpi`(dim_p, dim_ens, ...)	Obtain a continuous rank probability score for an ensemble.
`pyPDAF.PDAF.diag_crps_nompi`(dim, dim_ens, ...)	Obtain a continuous rank probability score for an ensemble without MPI.
`pyPDAF.PDAF.diag_crps`(dim_p, dim_ens, ...)	Compute the continuous ranked probability score (CRPS).
`pyPDAF.PDAF.diag_effsample`(dim_sample, weights)	Calculating the effective sample size of a particle filter.
`pyPDAF.PDAF.diag_ensstats`(dim, dim_ens, ...)	Computing the skewness and kurtosis of the ensemble of a given element of the state vector.
`pyPDAF.PDAF.diag_compute_moments`(dim_p, ...)	Computes the mean, the unbiased variance, the unbiased skewness, and the unbiased excess kurtosis from an ensemble.
`pyPDAF.PDAF.diag_histogram`(ncall, dim, ...)	Computing the rank histogram of an ensemble.
`pyPDAF.PDAF.diag_reliability_budget`(n_times, ...)	Compute ensemble reliability budget
`pyPDAF.PDAF.diag_diffstats`(dim_p, vec1, ...)	Compare two vectors with Taylor-diagram style statistics.

`pyPDAF.PDAF3` Assimilation Interface ¶

`pyPDAF.PDAF3.init`(filtertype, subtype, ...)	Initialise the PDAF system.
`pyPDAF.PDAF3.init_parallel`(screen, ...)	Initialize PDAF3's flexible parallelization layout.
`pyPDAF.PDAF3.init_forecast`(...)	The routine PDAF_init_forecast has to be called once at the end of the initialization of PDAF/start of DA cycles.
`pyPDAF.PDAF3.set_parallel`(in_comm_pdaf, ...)	Set MPI communicators and parallelisation in PDAF.
`pyPDAF.PDAF3.assimilate`(...)	Online ensemble filters and smoothers except for 3DVars for a single DA step using diagnoal observation error covariance matrix.
`pyPDAF.PDAF3.assim_offline`(...)	Offline ensemble filters and smoothers except for 3DVars for a single DA step using diagnoal observation error covariance matrix.
`pyPDAF.PDAF3.assimilate_3dvar_all`(...)	Online assimilation for all types of 3DVar DA for a single DA step using diagonal observation error covariance matrix.
`pyPDAF.PDAF3.assim_offline_3dvar_all`(...)	Offline assimilation for all types of 3DVar DA for a single DA step using diagonal observation error covariance matrix.
`pyPDAF.PDAF3.assimilate_local_nondiagr`(...)	Online assimilation of domain local filters for a single DA step using non-diagnoal observation error covariance matrix.
`pyPDAF.PDAF3.assimilate_global_nondiagr`(...)	Online assimilation of global filters except for 3DVar and stochastic EnKF for a single DA step using non-diagnoal observation error covariance matrix.
`pyPDAF.PDAF3.assimilate_lnetf_nondiagr`(...)	Online assimilation of LNETF for a single DA step using non-diagnoal observation error covariance matrix.
`pyPDAF.PDAF3.assimilate_lknetf_nondiagr`(...)	Online assimilation of LKNETF for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.PDAF3.assimilate_enkf_nondiagr`(...)	Online assimilation of global or Covariance localised stochastic EnKF for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.PDAF3.assimilate_nonlin_nondiagr`(...)	Online assimilation of global nonlinear filters for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.PDAF3.assimilate_3dvar_nondiagr`(...)	3DVar DA for a single DA step using non-diagnoal observation error covariance matrix.
`pyPDAF.PDAF3.assimilate_en3dvar_estkf_nondiagr`(...)	3DEnVar for a single DA step using non-diagnoal observation error covariance matrix.
`pyPDAF.PDAF3.assimilate_en3dvar_lestkf_nondiagr`(...)	3DEnVar for a single DA step where the ensemble anomaly is generated by LESTKF using non-diagonal observation error covariance matrix.
`pyPDAF.PDAF3.assimilate_hyb3dvar_estkf_nondiagr`(...)	Hybrid 3DEnVar for a single DA step using non-diagnoal observation error covariance matrix.
`pyPDAF.PDAF3.assimilate_hyb3dvar_lestkf_nondiagr`(...)	Hybrid 3DEnVar for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.PDAF3.assim_offline_local_nondiagr`(...)	Offline assimilation of domain local filters for a single DA step using non-diagnoal observation error covariance matrix.
`pyPDAF.PDAF3.assim_offline_global_nondiagr`(...)	Offline assimilation of global filters except for 3DVar and stochastic EnKF for a single DA step using non-diagnoal observation error covariance matrix.
`pyPDAF.PDAF3.assim_offline_lnetf_nondiagr`(...)	Offline assimilation of LNETF for a single DA step using non-diagnoal observation error covariance matrix.
`pyPDAF.PDAF3.assim_offline_lknetf_nondiagr`(...)	Offline assimilation of LKNETF for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.PDAF3.assim_offline_enkf_nondiagr`(...)	Offline assimilation of global or Covariance localised stochastic EnKF for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.PDAF3.assim_offline_lenkf_nondiagr`(...)	Online assimilation of covariance localised stochastic EnKF for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.PDAF3.assim_offline_nonlin_nondiagr`(...)	Offline assimilation of global nonlinear filters for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.PDAF3.assim_offline_3dvar_nondiagr`(...)	Offline 3DVar DA for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.PDAF3.assim_offline_en3dvar_estkf_nondiagr`(...)	Offline 3DEnVar for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.PDAF3.assim_offline_en3dvar_lestkf_nondiagr`(...)	Offline 3DEnVar for a single DA step where the ensemble anomaly is generated by LESTKF using non-diagonal observation error covariance matrix.
`pyPDAF.PDAF3.assim_offline_hyb3dvar_estkf_nondiagr`(...)	Offline hybrid 3DEnVar for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.PDAF3.assim_offline_hyb3dvar_lestkf_nondiagr`(...)	Offline hybrid 3DEnVar for a single DA step using non-diagonal observation error covariance matrix.
`pyPDAF.PDAF3.generate_obs`(...)	Generation of synthetic observations based on given error statistics and observation operator.
`pyPDAF.PDAF3.generate_obs_offline`(...)	Generation of synthetic observations based on given error statistics and observation operator in offline setup.
`pyPDAF.PDAF3.prepost`(collect_state, ...)	Run PDAF3 pre/post processing in an externally managed model loop.
`pyPDAF.PDAF3.prepost_offline`(prepoststep, ...)	Run PDAF3 offline pre/post processing.

`pyPDAF.PDAFomi` Observation Module Interface ¶

Setup and error handling ¶

`pyPDAF.PDAFomi.init`(n_obs)	Allocating an array of obs_f derived types instances.
`pyPDAF.PDAFomi.check_error`(flag)	This function returns the value of the PDAF-OMI internal error flag.
`pyPDAF.PDAFomi.set_debug_flag`(debugval)	Activate the debug output of the PDAFomi.

Observation storage and metadata ¶

`pyPDAF.PDAFomi.gather_obs`(i_obs, dim_obs_p, ...)	Gather the dimension of a given type of observation across multiple local domains/filter processors.
`pyPDAF.PDAFomi.gather_obsstate`(i_obs, ...)	This function is used to implement custom observation operators.
`pyPDAF.PDAFomi.set_doassim`(i_obs, doassim)	Setting the doassim attribute of obs_f for i-th observation type.
`pyPDAF.PDAFomi.set_disttype`(i_obs, disttype)	Setting the observation localisation distance calculation method for i-th observation type.
`pyPDAF.PDAFomi.set_ncoord`(i_obs, ncoord)	Setting the number of spatial dimensions of observations for i-th observation type.
`pyPDAF.PDAFomi.set_obs_err_type`(i_obs, ...)	Setting the type of observation error distribution for i-th observation type.
`pyPDAF.PDAFomi.set_use_global_obs`(i_obs, ...)	Switch for only use process-local observations for i-th observation type.
`pyPDAF.PDAFomi.set_inno_omit`(i_obs, inno_omit)	Setting innovation threshold for removing observation outliers.
`pyPDAF.PDAFomi.set_inno_omit_ivar`(i_obs, ...)	Setting the inverse of observation error variance for omitted observations.
`pyPDAF.PDAFomi.set_id_obs_p`(i_obs, nrows, ...)	Setting the id_obs_p attribute of obs_f for i-th observation type.
`pyPDAF.PDAFomi.set_icoeff_p`(i_obs, nrows, ...)	Setting the observation interpolation coefficient for i-th observation type.
`pyPDAF.PDAFomi.set_domainsize`(i_obs, ncoord, ...)	Setting the domain periodicity attribute of obs_f for i-th observation type.
`pyPDAF.PDAFomi.set_name`(i_obs, obsname)	Set a name for given observation type
`pyPDAF.PDAFomi.set_searchtype`(stype, sortdir)	Select the OMI local-observation search algorithm and sort direction.

Interpolation coefficients ¶

`pyPDAF.PDAFomi.get_interp_coeff_tri`(gpc, oc, ...)	The coefficient for linear interpolation in 2D on unstructure triangular grid.
`pyPDAF.PDAFomi.get_interp_coeff_lin1d`(gpc, ...)	The coefficient for linear interpolation in 1D.
`pyPDAF.PDAFomi.get_interp_coeff_lin`(num_gp, ...)	The coefficient for linear interpolation up to 3D.
`pyPDAF.PDAFomi.get_interp_coeff_tri_vec`	get_interp_coeff_tri(gpc: np.ndarray, oc: np.ndarray, icoeff: np.ndarray) -> np.ndarray
`pyPDAF.PDAFomi.get_interp_coeff_lin1d_vec`	get_interp_coeff_lin1d(gpc: np.ndarray, oc: float, icoeff: np.ndarray) -> np.ndarray
`pyPDAF.PDAFomi.get_interp_coeff_lin_vec`	get_interp_coeff_lin(num_gp: int, n_dim: int, gpc: np.ndarray, oc: np.ndarray, icoeff: np.ndarray) -> np.ndarray

Observation operators ¶

`pyPDAF.PDAFomi.obs_op_gridpoint`(i_obs, ...)	A (partial) identity observation operator
`pyPDAF.PDAFomi.obs_op_gridavg`(i_obs, nrows, ...)	Observation operator that average values on given model grid points.
`pyPDAF.PDAFomi.obs_op_extern`(i_obs, ...)	Observation operator for given observed model state.
`pyPDAF.PDAFomi.obs_op_interp_lin`(i_obs, ...)	Observation operator that linearly interpolates model grid values to observation location.
`pyPDAF.PDAFomi.obs_op_adj_gridpoint`(i_obs, ...)	The adjoint observation operator of `pyPDAF.PDAFomi.obs_op_gridpoint()`.
`pyPDAF.PDAFomi.obs_op_adj_gridavg`(i_obs, ...)	The adjoint observation operator of `pyPDAF.PDAFomi.obs_op_gridavg()`.
`pyPDAF.PDAFomi.obs_op_adj_interp_lin`(i_obs, ...)	The adjoint observation operator of `pyPDAF.PDAFomi.obs_op_interp_lin()`.

Local observations and localisation ¶

`pyPDAF.PDAFomi.init_dim_obs_l_iso`(i_obs, ...)	Initialize the observation information corresponding to an isotropic local analysis domain.
`pyPDAF.PDAFomi.init_dim_obs_l_noniso`(i_obs, ...)	Initialize the observation information corresponding to a non-isotropic local analysis domain.
`pyPDAF.PDAFomi.init_dim_obs_l_noniso_locweights`(...)	Initialize the observation information corresponding to a non-isotropic local analysis domain.
`pyPDAF.PDAFomi.observation_localization_weights`(...)	Returns a vector of observation localisation weights.
`pyPDAF.PDAFomi.set_dim_obs_l`(i_obs, ...)	Store local observation counts for OMI local-initialization workflows.
`pyPDAF.PDAFomi.set_localization`(i_obs, ...)	Stores the isotropic localization parameters (cradius, sradius, locweight) in OMI.
`pyPDAF.PDAFomi.set_localization_noniso`(...)	Stores the non-isotropic localization parameters (cradius, sradius, locweight) in OMI.
`pyPDAF.PDAFomi.set_localize_covar_iso`(i_obs, ...)	Initialise local observation information for isotropic covariance localisation.
`pyPDAF.PDAFomi.set_localize_covar_noniso`(...)	Initialise local observation information for non-isotropic covariance localisation.
`pyPDAF.PDAFomi.set_localize_covar_noniso_locweights`(...)	Initialise local observation information for non-isotropic covariance localisation.
`pyPDAF.PDAFomi.set_domain_limits`(lim_coords)	Set the domain limits for domain decomposed local domain.
`pyPDAF.PDAFomi.get_domain_limits_unstr`(...)	Set the domain limits for unstructured domain decomposed local domain.
`pyPDAF.PDAFomi.store_obs_l_index`(i_obs, idx, ...)	Save local observation information in PDAF.
`pyPDAF.PDAFomi.store_obs_l_index_vdist`(...)	Save local observation information for 2+1D factorized localization in the vertical direction in PDAF.

Observation diagnostics ¶

`pyPDAF.PDAFomi.set_obs_diag`(diag)	Activate or deactivate the observation diagnostics.
`pyPDAF.PDAFomi.diag_dimobs`()	Observation dimension for each observation type.
`pyPDAF.PDAFomi.diag_get_hx`(id_obs)	Observed ensemble for given observation type.
`pyPDAF.PDAFomi.diag_get_hxmean`(id_obs)	Observed ensemble mean for given observation type.
`pyPDAF.PDAFomi.diag_get_ivar`(id_obs)	Inverse of observation error variance for given observation type.
`pyPDAF.PDAFomi.diag_get_obs`(id_obs)	Observation vector and corresponding coordinates for specified observation type.
`pyPDAF.PDAFomi.diag_nobstypes`(nobs)	The number of observation types that are active in an assimilation run.
`pyPDAF.PDAFomi.diag_obs_rmsd`(nobs, verbose)	Root mean squared distance between observation and obseved model state for each observation type.
`pyPDAF.PDAFomi.diag_stats`(nobs, verbose)	A selection of 6 statistics comparing the observations and the observed ensemble mean for each observation type.
`pyPDAF.PDAFomi.diag_rmsd`(nobs, verbose)	Compute RMSD between observations and observed ensemble means.
`pyPDAF.PDAFomi.diag_diffstats`(nobs, verbose)	Compare observations with observed ensemble means by observation type.
`pyPDAF.PDAFomi.diag_crps`(nobs, perturb, verbose)	Compute CRPS diagnostics between observations and observed ensembles.

`pyPDAF.PDAFlocal` Localisation Helpers ¶

`pyPDAF.PDAFlocal.set_indices`(dim_l, map)	Set index vector to map local state vector to global state vectors.
`pyPDAF.PDAFlocal.set_increment_weights`(...)	Initialises a PDAF_internal local array of increment weights.
`pyPDAF.PDAFlocal.clear_increment_weights`()	Deallocates the local increment weight vector in `pyPDAF.PDAFlocal.set_increment_weights()`.

`pyPDAF.PDAFlocalomi`¶

pyPDAF.PDAFlocalomi is exported as a namespace package, but it currently does not expose functions from its __init__.py.