clophfit.fitting.odr#

Orthogonal Distance Regression (ODR) utilities and fitting pipeline.

Functions#

format_estimate(value, error[, significant_digit_limit])

Format the value and its associated error into "{value} ± {error}" string.

generalized_combined_model(pars, x, dataset_lengths, ...)

Handle multiple datasets with different lengths and masks.

fit_binding_odr(ds_or_fr, *[, reweight, ...])

Analyze multi-label titration datasets using ODR.

outlier(output, *[, threshold, plot_z_scores])

Identify outliers.

Module Contents#

clophfit.fitting.odr.format_estimate(value, error, significant_digit_limit=5)#

Format the value and its associated error into “{value} ± {error}” string.

Parameters:

  • value (float)

  • error (float)

  • significant_digit_limit (int)

Return type:

str

clophfit.fitting.odr.generalized_combined_model(pars, x, dataset_lengths, *, is_ph)#

Handle multiple datasets with different lengths and masks.

Parameters:

  • pars (clophfit.clophfit_types.ArrayF | list[float])

  • x (clophfit.clophfit_types.ArrayF)

  • dataset_lengths (list[int])

  • is_ph (bool)

Return type:

clophfit.clophfit_types.ArrayF

clophfit.fitting.odr.fit_binding_odr(ds_or_fr, *, reweight=False, remove_outliers=None, max_iter=15, tol=0.1)#

Analyze multi-label titration datasets using ODR.

Parameters:

  • ds_or_fr (Dataset | FitResult[MiniT]) – Either a Dataset (will run initial LS fit) or a FitResult with initial params.

  • reweight (bool) – Whether to perform iterative reweighting (recursive ODR).

  • remove_outliers (str | None) – Outlier removal configuration, e.g., “zscore:2.5” or “zscore:2.5:5”. If set, performs recursive ODR and masks outliers iteratively.

  • max_iter (int) – Maximum number of iterations for recursive ODR.

  • tol (float) – Convergence tolerance for residual variance.

Returns:

ODR fitting results. Residuals are WEIGHTED by the ORIGINAL y_err (before ODR modified it), making them comparable to LM residuals.

Return type:

FitResult[odrpack.OdrResult]

clophfit.fitting.odr.outlier(output, *, threshold=2.0, plot_z_scores=False)#

Identify outliers.

Parameters:

  • output (odrpack.OdrResult)

  • threshold (float)

  • plot_z_scores (bool)

Return type:

clophfit.clophfit_types.ArrayMask