OptimalEstimationSettings

class pyarts3.arts.OptimalEstimationSettings(*args, **kwargs)

Algorithm, convergence, solver and output controls for optimal estimation.

Tip

A variable x of this group settings are valid

Workspace methods that require OptimalEstimationSettings

Overview

Method	`readxml()`	Read variable from file.
Method	`savexml()`	Saves variable to file.
Method	`validate()`	Check all controls; oemCalc and oemCalcReduced also validate before calculation.
Static Method	`fromxml()`	Create variable from file.
`int`	`cg_max_iter`	CG iteration limit; zero selects max(1000, 2 * dimension).
`float`	`cg_tolerance`	Positive relative CG residual tolerance.
`bool`	`clear_matrices`	Release Jacobian and gain when true.
`int`	`display_progress`	Print progress when 1.
`LevenbergMarquardtSettings`	`lm`	LM damping and trial controls.
`int`	`max_iter`	Maximum outer iterations.
`float`	`max_start_cost`	Maximum initial cost; infinity disables the cutoff.
`OptimalEstimationMethod`	`method`	Algorithm and linear solver.
`float`	`stop_dx`	Positive convergence tolerance.
Operator	`__eq__()`	Return self==value.
Operator	`__format__()`	`__format__(self, arg: str, /) -> str`
Operator	`__ge__()`	Return self>=value.
Operator	`__gt__()`	Return self>value.
Operator	`__hash__()`	Return hash(self).
Operator	`__init__()`	`__init__(self, *, method: pyarts3.arts.OptimalEstimationMethod = "gn", max_iter: int = 10, stop_dx: float = 0.01, max_start_cost: float = inf, cg_tolerance: float = 1e-10, cg_max_iter: int = 0, lm: pyarts3.arts.LevenbergMarquardtSettings = LevenbergMarquardtSettings(initial_damping=10, decrease_factor=2, increase_factor=2, maximum_damping=100, damping_threshold=1, convergence_damping_limit=0, maximum_trials=100), display_progress: int = 0, clear_matrices: bool = False) -> None`
Operator	`__le__()`	Return self<=value.
Operator	`__lt__()`	Return self<value.
Operator	`__ne__()`	Return self!=value.
Operator	`__repr__()`	`__repr__(self) -> str`
Operator	`__str__()`	`__str__(self) -> str`

Constructors

__init__(self) → None
__init__(self, arg: OptimalEstimationSettings) → None
__init__(self, method: str) → None
__init__(self, *, method: OptimalEstimationMethod = 'gn', max_iter: int = 10, stop_dx: float = 0.01, max_start_cost: float = inf, cg_tolerance: float = 1e-10, cg_max_iter: int = 0, lm: LevenbergMarquardtSettings = LevenbergMarquardtSettings(initial_damping=10, decrease_factor=2, increase_factor=2, maximum_damping=100, damping_threshold=1, convergence_damping_limit=0, maximum_trials=100), display_progress: int = 0, clear_matrices: bool = False) → None

Methods

readxml(self, file: str) → str

Read variable from file.

Parameters:: file (str) – A file that can be read.
Raises:: RuntimeError – For any failure to read.
Returns:: file – The file path found (may differ from input due to environment variables).
Return type:: str

savexml(self, file: str, type: str = 'ascii', clobber: bool = True) → str

Saves variable to file.

Parameters:

file (str) – The path to which the file is written. Note that several of the options might modify the name or write more files.
type (str, optional) – Type of file to save. See FileType for options. Defaults is “ascii”.
clobber (bool, optional) – Overwrite existing files or add new file with modified name? Defaults is True.

Raises:

RuntimeError – For any failure to write.

Returns:

file – The file saved. May differ from input.

Return type:

str

validate(self) → None: Check all controls; oemCalc and oemCalcReduced also validate before calculation.

Static Methods

fromxml(file: str) → OptimalEstimationSettings

Create variable from file.

Parameters:: file (str) – A file that can be read
Raises:: RuntimeError – For any failure to read.
Returns:: artstype – The variable created from the file.
Return type:: OptimalEstimationSettings

Attributes

cg_max_iter: int: CG iteration limit; zero selects max(1000, 2 * dimension).

cg_tolerance: float: Positive relative CG residual tolerance.

clear_matrices: bool: Release Jacobian and gain when true.

display_progress: int: Print progress when 1.

lm: LevenbergMarquardtSettings: LM damping and trial controls.

max_iter: int: Maximum outer iterations.

max_start_cost: float: Maximum initial cost; infinity disables the cutoff.

method: OptimalEstimationMethod: Algorithm and linear solver.

stop_dx: float: Positive convergence tolerance.

Operators

__eq__(value, /): Return self==value.

__format__(self, arg: str, /) → str

__ge__(value, /): Return self>=value.

__gt__(value, /): Return self>value.

__hash__(): Return hash(self).

__init__(self) → None
__init__(self, arg: OptimalEstimationSettings) → None
__init__(self, method: str) → None
__init__(self, *, method: OptimalEstimationMethod = 'gn', max_iter: int = 10, stop_dx: float = 0.01, max_start_cost: float = inf, cg_tolerance: float = 1e-10, cg_max_iter: int = 0, lm: LevenbergMarquardtSettings = LevenbergMarquardtSettings(initial_damping=10, decrease_factor=2, increase_factor=2, maximum_damping=100, damping_threshold=1, convergence_damping_limit=0, maximum_trials=100), display_progress: int = 0, clear_matrices: bool = False) → None

__le__(value, /): Return self<=value.

__lt__(value, /): Return self<value.

__ne__(value, /): Return self!=value.

__repr__(self) → str

__str__(self) → str