clophfit.fitting.bayes#

Bayesian (PyMC) fitting utilities and pipelines.

Functions#

create_x_true(xc, x_errc, n_xerr[, lower_nsd])

Create latent variables for x-values with uncertainty.

create_parameter_priors(params, n_sd[, key, ctr_name, ...])

Create PyMC parameter prior distributions from lmfit Parameters.

rename_keys(data)

Rename dictionary keys coming from multi-trace into base names.

process_trace(trace, p_names, ds, n_xerr)

Process the trace to extract parameter estimates and update datasets.

extract_fit(key, ctr, trace_df, ds)

Compute individual dataset fit from a multi-well trace summary.

x_true_from_trace_df(trace_df)

Extract x_true from an ArviZ summary DataFrame.

fit_binding_pymc(ds_or_fr[, n_sd, n_xerr, ye_scaling, ...])

Analyze multi-label titration datasets using PyMC (single model).

fit_binding_pymc2(ds_or_fr[, n_sd, n_xerr, n_samples])

Analyze multi-label titration datasets using PyMC with separate ye_mag per label.

fit_binding_pymc_compare(fr, buffer_sd, *[, ...])

Fits a Bayesian binding model with two different noise models for comparison.

closest_point_on_curve(f, x_obs, y_obs)

Find the closest point on the model curve.

fit_binding_pymc_odr(fr[, n_sd, xe_scaling, ...])

Bayesian ODR-like modeling of x and y errors.

weighted_stats(values, stderr)

Weighted mean and stderr for control priors.

fit_binding_pymc_multi(results, scheme[, n_sd, ...])

Multi-well PyMC with shared K per control group and per-label noise.

fit_binding_pymc_multi2(results, scheme, bg_err[, ...])

Multi-well PyMC with heteroscedastic noise combining buffer and signal.

plot_ppc_well(trace, key[, labels, figsize])

Draw posterior predictive samples for a particular well (and all its labels).

compare_posteriors(trace, results)

Print posterior mean ± 95 % C.I.

fit_pymc_hierarchical(results, scheme, bg_err[, n_sd, ...])

Analyze multiple titrations with a hierarchical Bayesian model.

Module Contents#

clophfit.fitting.bayes.create_x_true(xc, x_errc, n_xerr, lower_nsd=2.5)#

Create latent variables for x-values with uncertainty.

Returns a PyMC Deterministic variable when in a Model context with uncertainty, or a numpy array when there’s no uncertainty or no active Model.

Parameters:

  • xc (clophfit.clophfit_types.ArrayF)

  • x_errc (clophfit.clophfit_types.ArrayF)

  • n_xerr (float)

  • lower_nsd (float)

Return type:

clophfit.clophfit_types.ArrayF | pymc.Deterministic

clophfit.fitting.bayes.create_parameter_priors(params, n_sd, key='', ctr_name='', default_sigma=0.001)#

Create PyMC parameter prior distributions from lmfit Parameters.

Parameters:

  • params (Parameters) – lmfit Parameters to convert to PyMC priors.

  • n_sd (float) – Scaling factor for parameter standard errors.

  • key (str) – Optional suffix to add to parameter names.

  • ctr_name (str) – If specified, skip creating K prior (shared from control group).

  • default_sigma (float) – Default sigma when stderr is not available (default: 1e-3).

Returns:

Dictionary of PyMC distribution objects.

Return type:

dict[str, pm.Distribution]

clophfit.fitting.bayes.rename_keys(data)#

Rename dictionary keys coming from multi-trace into base names.

Parameters:

data (dict[str, Any])

Return type:

dict[str, Any]

clophfit.fitting.bayes.process_trace(trace, p_names, ds, n_xerr)#

Process the trace to extract parameter estimates and update datasets.

Parameters:

  • trace (az.InferenceData) – The posterior samples from PyMC sampling.

  • p_names (KeysView[str]) – Parameter names.

  • ds (Dataset) – The dataset containing titration data.

  • n_xerr (float) – Scaling factor for x_errc.

Returns:

The updated fit result with extracted parameter values and datasets. Residuals are WEIGHTED (weight * (obs - pred)) where weight = 1/y_err, computed using posterior mean parameter estimates.

Return type:

FitResult[az.InferenceData]

Raises:

TypeError – If az.summary does not return a DataFrame.

clophfit.fitting.bayes.extract_fit(key, ctr, trace_df, ds)#

Compute individual dataset fit from a multi-well trace summary.

Parameters:
Return type:

clophfit.fitting.data_structures.FitResult[arviz.InferenceData]

clophfit.fitting.bayes.x_true_from_trace_df(trace_df)#

Extract x_true from an ArviZ summary DataFrame.

Parameters:

trace_df (pandas.DataFrame)

Return type:

clophfit.fitting.data_structures.DataArray

clophfit.fitting.bayes.fit_binding_pymc(ds_or_fr, n_sd=10.0, n_xerr=1.0, ye_scaling=1.0, n_samples=2000)#

Analyze multi-label titration datasets using PyMC (single model).

Parameters:

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

  • n_sd (float) – Number of standard deviations for parameter priors.

  • n_xerr (float) – Scaling factor for x-error.

  • ye_scaling (float) – Scaling factor for y-error magnitude prior.

  • n_samples (int) – Number of MCMC samples.

Returns:

Bayesian fitting results.

Return type:

FitResult[az.InferenceData]

clophfit.fitting.bayes.fit_binding_pymc2(ds_or_fr, n_sd=10.0, n_xerr=1.0, n_samples=2000)#

Analyze multi-label titration datasets using PyMC with separate ye_mag per label.

Parameters:

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

  • n_sd (float) – Number of standard deviations for parameter priors.

  • n_xerr (float) – Scaling factor for x-error.

  • n_samples (int) – Number of MCMC samples.

Returns:

Bayesian fitting results with per-label error scaling.

Return type:

FitResult[az.InferenceData]

clophfit.fitting.bayes.fit_binding_pymc_compare(fr, buffer_sd, *, learn_separate_y_mag=False, n_sd=10.0, n_xerr=1.0, n_samples=2000)#

Fits a Bayesian binding model with two different noise models for comparison.

Parameters:

  • fr (FitResult[MiniT]) – The fit result from a previous run, providing initial parameters and dataset.

  • buffer_sd (dict[str, float]) – bg_err

  • learn_separate_y_mag (bool) – If True, learns a unique noise scaling factor for each dataset label. If False, learns a single scaling factor for all pre-weighted data.

  • n_sd (float) – Prior width for parameters in create_parameter_priors.

  • n_xerr (float) – Scaling factor for x_errc in create_x_true.

  • n_samples (int) – Number of MCMC samples to draw.

Returns:

The posterior samples from PyMC for the specified noise model.

Return type:

az.InferenceData

clophfit.fitting.bayes.closest_point_on_curve(f, x_obs, y_obs)#

Find the closest point on the model curve.

Parameters:

  • f (clophfit.clophfit_types.FloatFunc)

  • x_obs (float)

  • y_obs (float)

Return type:

float

clophfit.fitting.bayes.fit_binding_pymc_odr(fr, n_sd=10.0, xe_scaling=1.0, ye_scaling=10.0, n_samples=2000)#

Bayesian ODR-like modeling of x and y errors.

Parameters:
Return type:

arviz.InferenceData | pymc.backends.base.MultiTrace

clophfit.fitting.bayes.weighted_stats(values, stderr)#

Weighted mean and stderr for control priors.

Parameters:

  • values (dict[str, list[float]])

  • stderr (dict[str, list[float]])

Return type:

dict[str, tuple[float, float]]

clophfit.fitting.bayes.fit_binding_pymc_multi(results, scheme, n_sd=5.0, n_xerr=1.0, ye_scaling=1.0, n_samples=2000)#

Multi-well PyMC with shared K per control group and per-label noise.

Parameters:
Return type:

arviz.InferenceData

clophfit.fitting.bayes.fit_binding_pymc_multi2(results, scheme, bg_err, n_sd=5.0, n_xerr=1.0, n_samples=2000)#

Multi-well PyMC with heteroscedastic noise combining buffer and signal.

Parameters:

  • results (dict[str, clophfit.fitting.data_structures.FitResult[clophfit.fitting.data_structures.MiniT]])

  • scheme (clophfit.prtecan.PlateScheme)

  • bg_err (dict[int, clophfit.clophfit_types.ArrayF])

  • n_sd (float)

  • n_xerr (float)

  • n_samples (int)

Return type:

arviz.InferenceData

clophfit.fitting.bayes.plot_ppc_well(trace, key, labels=None, figsize=(8, 4))#

Draw posterior predictive samples for a particular well (and all its labels).

The returned figure can be displayed with matplotlib.

Parameters:

  • trace (az.InferenceData) – Trace produced by fit_binding_pymc_advanced.

  • key (str) – Well identifier (e.g. ‘A01’).

  • labels (list[str] | None) – Names of the bands to show. If None the function will automatically look for all variables starting with 'y_' that contain this key.

  • figsize (tuple[float, float]) – size?

Returns:

Plot

Return type:

figure.Figure

clophfit.fitting.bayes.compare_posteriors(trace, results)#

Print posterior mean ± 95 % C.I.

For the K parameter for each well, and juxtapose it with the deterministic K (from fit_binding_pymc).

Parameters:

  • trace (az.InferenceData) – Output of fit_binding_pymc_advanced.

  • results (dict[str, FitResult[MiniT]]) – Deterministic fits produced by the old pipeline.

Return type:

None

clophfit.fitting.bayes.fit_pymc_hierarchical(results, scheme, bg_err, n_sd=5.0, n_xerr=1.0, n_samples=2000)#

Analyze multiple titrations with a hierarchical Bayesian model.

This model shares information about the dissociation constant ‘K’ among wells belonging to the same control group, leading to more robust estimates.

Parameters:

  • results (dict[str, FitResult[MiniT]]) – A dictionary mapping well IDs to their initial FitResult from a prior fit_lm run.

  • scheme (PlateScheme) – The plate scheme defining control groups.

  • bg_err (dict[int, ArrayF]) – Background error for each signal band.

  • n_sd (float) – The number of standard deviations for the prior width of S0/S1.

  • n_xerr (float) – Scaling factor for x-value uncertainties.

  • n_samples (int) – Number of MCMC samples.

Returns:

The PyMC trace containing the posterior distributions.

Return type:

az.InferenceData

Raises:

ValueError – With invalid dataset.