clophfit.testing.fitter_test_utils#

Shared utilities for fitter comparison tests and benchmarks.

Classes#

TecanFitCombination

Declarative description of one Tecan fitting workflow.

Functions#

build_factorized_tecan_fit_combinations(*[, channels, ...])

Build a systematic registry across explicit Tecan benchmark factors.

build_tecan_fit_combinations(*[, base_method, ...])

Build named Tecan fit combinations for paired benchmark comparisons.

apply_tecan_combination(ds, combination)

Execute one Tecan fit combination on a fresh dataset copy.

k_from_result(fr)

Extract K value and stderr from fit result.

s_from_result(fr, which)

Extract S0 or S1 values per label if present in params.

build_fitters(*[, include_odr])

Build dictionary of fitting methods for benchmarking.

Module Contents#

class clophfit.testing.fitter_test_utils.TecanFitCombination#

Declarative description of one Tecan fitting workflow.

clophfit.testing.fitter_test_utils.build_factorized_tecan_fit_combinations(*, channels=(('1',), ('2',), ('1', '2')), prefits=('huber',), final_stages=('huber', 'odr'), weightings=('auto',), outlier_handlings=(None,))#

Build a systematic registry across explicit Tecan benchmark factors.

Parameters:

  • channels (tuple[tuple[str, Ellipsis], Ellipsis])

  • prefits (tuple[TecanFitMethod, Ellipsis])

  • final_stages (tuple[TecanFinalStage, Ellipsis])

  • weightings (tuple[TecanWeighting, Ellipsis])

  • outlier_handlings (tuple[str | None, Ellipsis])

Return type:

dict[str, TecanFitCombination]

clophfit.testing.fitter_test_utils.build_tecan_fit_combinations(*, base_method='huber', include_odr=True, include_mcmc=False, mcmc_modes=('single',))#

Build named Tecan fit combinations for paired benchmark comparisons.

Parameters:

  • base_method (TecanFitMethod)

  • include_odr (bool)

  • include_mcmc (bool)

  • mcmc_modes (tuple[str, Ellipsis])

Return type:

dict[str, TecanFitCombination]

clophfit.testing.fitter_test_utils.apply_tecan_combination(ds, combination)#

Execute one Tecan fit combination on a fresh dataset copy.

Parameters:
Return type:

clophfit.fitting.data_structures.FitResult[clophfit.fitting.data_structures.MiniT]

clophfit.testing.fitter_test_utils.k_from_result(fr)#

Extract K value and stderr from fit result.

Parameters:

fr (clophfit.fitting.data_structures.FitResult[clophfit.fitting.data_structures.MiniT])

Return type:

tuple[float | None, float | None]

clophfit.testing.fitter_test_utils.s_from_result(fr, which)#

Extract S0 or S1 values per label if present in params.

Parameters:
Return type:

dict[str, float] | None

clophfit.testing.fitter_test_utils.build_fitters(*, include_odr=True)#

Build dictionary of fitting methods for benchmarking.

Returns a registry of named fitters using the unified fit_binding_glob API with different method/reweight/remove_outliers combinations.

Parameters:

include_odr (bool) – Whether to include ODR-based fitters (requires odrpack).

Returns:

Named fitters mapping.

Return type:

dict[str, Callable[[Dataset], FitResult[MiniT]]]