ensemble

Descriptive description.

Ensemble

Bases: Ensemble

Class for organizing/initializing misc. variables and simulator for an ensemble-based inversion run. Inherits the PET ensemble structure

__init__(keys_da, keys_en, sim)

Parameters:

Name	Type	Description	Default
keys_da	dict	Options for the data assimilation class daalg: spesification of the method, first the main type (e.g., "enrml"), then the solver (e.g., "gnenrml") analysis: update flavour ("approx", "full" or "subspace") energy: percent of singular values kept after SVD obsvarsave: save the observations as a file (default false) restart: restart optimization from a restart file (default false) restartsave: save a restart file after each successful iteration (defalut false) analysisdebug: specify which class variables to save to the result files truedataindex: order of the simulated data (for timeseries this is points in time) obsname: unit for truedataindex (for timeseries this is days or hours or seconds, etc.) truedata: the data, e.g., provided as a .csv file assimindex: index for the data that will be used for assimilation datatype: list with the name of the datatypes staticvar: name of the static variables dynamicvar: name of the dynamic variables datavar: data variance, e.g., provided as a .csv file	required
keys_en	dict	Options for the ensemble class ne: number of perturbations used to compute the gradient state: name of state variables passed to the .mako file prior_: the prior information the state variables, including mean, variance and variable limits NB: If keys_en is empty dict, it is assumed that the prior info is contained in keys_da. The merged dict keys_da|keys_en is what is sent to the parent class.	required
sim	callable	The forward simulator (e.g. flow)	required

check_assimindex_sequential()

Check if assim. indices is given as a 2D list as is needed in sequential updating. If not, make it a 2D list

check_assimindex_simultaneous()

Check if assim. indices is given as a 1D list as is needed in simultaneous updating. If not, make it a 2D list with one row.

compress_manager(data=None, vintage=0, aug_coeff=None)

Compress the input data using wavelets.

Parameters:

Name	Type	Description	Default
data		data to be compressed If data is None, all data (true and simulated) is re-compressed (used if leading indices are updated)	None
vintage	int	the time index for the data	0
aug_coeff	bool	False: in this case the leading indices for wavelet coefficients are computed True: in this case the leading indices are augmented using information from the ensemble None: in this case simulated data is compressed	None

local_analysis_update()

Function for updates that can be used by all algorithms. Do this once to avoid duplicate code for local analysis.

set_observations()

Generate the perturbed observed data ensemble