isofit.configs.sections.inversion_config

Classes

`InversionConfig`	Inversion configuration.
`McmcConfig`	MCMC inversion configuration.
`LeastSquaresConfig`	Least squares config parameters.

Module Contents

class InversionConfig(sub_configdic: dict = None)[source]

Bases: isofit.configs.base_config.BaseConfigSection

Inversion configuration.

_windows_type = [][source]

windows = None[source]

inversion retrieval windows to operate over.

Type:: List[List[float]]

_cressie_map_confidence_type[source]

cressie_map_confidence = False[source]

N. Cressie [ASA 2018] suggests an alternate definition of S_hat for more statistically-consistent posterior confidence estimation, this flag runs in this mode

Type:: bool

_mcmc_type[source]

mcmc[source]: MCMC parameters, only used if mode = mcmc.

_integration_grid_type[source]

integration_grid[source]: Grid of inversion points to execute if mode=’grid’. Either fixed, or starting points, depending on self.fixed_inversion_grid

_priors_in_initial_guess_type[source]

priors_in_initial_guess = True[source]: Boolean to inidicate the use of surface priors outside of the inversion windows during the intial guess.

_inversion_grid_as_preseed_type[source]

inversion_grid_as_preseed = False[source]: Parameter indicating whether to treat the inversion grid as: (True) - a series of seeds for the optimization (variable by the optimization algorithm). (False) - a set of fixed points (not variable by the optimization algorithm)

_least_squares_params_type[source]

least_squares_params[source]: Least squares parameters for core inversion solve. See https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.least_squares.html for details.

_check_config_validity() → List[str][source]

class McmcConfig(sub_configdic: dict = None)[source]

Bases: isofit.configs.base_config.BaseConfigSection

MCMC inversion configuration.

_iterations_type[source]

iterations = 10000[source]

Number of MCMC iterations to run.

Type:: int

_burnin_type[source]

burnin = 200[source]

_regularizer_type[source]

regularizer = 0.001[source]

_proposal_scaling_type[source]

proposal_scaling = 0.01[source]

_verbose_type[source]

verbose = True[source]

_restart_every_type[source]

restart_every = 2000[source]

_check_config_validity() → List[str][source]

class LeastSquaresConfig(sub_configdic: dict = None)[source]

Bases: isofit.configs.base_config.BaseConfigSection

Least squares config parameters.

_method_type[source]

method = 'trf'[source]

Optimzation method to use. Default ‘trf’.

Type:: str

_max_nfev_type[source]

max_nfev = 20[source]

Maximum number of function evaluations before the termination. If None (default), the value is chosen automatically. Default 20.

Type:: int

_xtol_type[source]

xtol = None[source]

Tolerance for termination by the change of the independent variables. Default is None, which disables termination from this criteria.

Type:: float

_ftol_type[source]

ftol = 0.01[source]

Tolerance for termination by the change of the cost function. Default is 0.01

Type:: float

_gtol_type[source]

gtol = None[source]

Tolerance for termination by the norm of the gradient. Default is None, which disables termination from this criteria.

Type:: float

_tr_solver_type[source]

tr_solver = 'lsmr'[source]

Method for solving trust-region subproblems, relevant only for ‘trf’ and ‘dogbox’ methods. Options are None, ‘exact’, or ‘lsmr’

Type:: str

_check_config_validity() → List[str][source]