pyrtid.inverse#

Provide the inverse reactive transport model and solver as well as executors.

The following functionalities are directly provided on module-level.

Classes#

Different executors are provided (scipy, stochopy, pyesmda, pypcga).

Provide interfaces to various inverse problem solvers.

Sub module providing regularization tools.

PyRTID invsere sub module providing regularization tools.

Sub module providing an adjoint operator and the associated gradients.

`Observable`(state_variable, node_indices, ...)	Class representing observations data within time at a defined location.
`Observables`	alias of `Union`[`Observable`, `Sequence`[`Observable`]]
`StateVariable`(value[, names, module, ...])	Type of observable existing.
`get_observables_uncertainties_as_1d_vector`(...)	Return the uncertainties of all observables as a 1D vector.
`get_observables_values_as_1d_vector`(observables)	Return the values of all given observables as a 1D vector.
`get_predictions_matching_observations`(model, ...)	Return the 1D vector of predictions matching the observations.
`get_sorted_observable_times`(obs[, max_time])	Get the observation times sorted in ascending order.
`get_sorted_observable_uncertainties`(obs[, ...])	Get the observation uncertainties sorted by ascending corresponding times.
`get_sorted_observable_values`(obs[, max_time])	Get the observation values sorted by ascending corresponding times.
`get_values_matching_node_indices`(...)	Return the values for the given node_indices with shape.
`get_adjoint_sources_for_obs`(model, obs, n_obs)	Get the adjoint sources for a given observable instance.
`eval_model_loss_ls`(model, observables[, ...])	Return the least-square loss function of the model for the given observations.
`update_perturbation_values`(observables, pvals)	Update the perturbation values for the given observables instances.

`eval_loss_ls`(d_obs, d_calc, x_sigma)	Return the objective function with regard to x.
`eval_model_loss_function`(model, observables, ...)	_summary_
`eval_model_loss_ls`(model, observables[, ...])	Return the least-square loss function of the model for the given observations.
`get_theoretical_noise_level`(observables[, n_std])	Get the theoretical noise level in the solution.

Sub module providing preconditioners and parametrization tools.