rxnfit.expdata_fit module

Estimate unknown rate constants from experimental concentration trajectories.

Minimizers in scipy.optimize drive repeated ODE integrations through rxnfit.solver_backend.solve_ode(); see there for LSODA / numbalsoda behavior and SciPy fallbacks.

class rxnfit.expdata_fit.ExpDataFit(builded_rxnode, df_list, t_range, method='RK45', rtol=1e-06, df_names=None)

Bases: object

High-level façade over pooled least-squares fits and post-fit replay.

Internally drives solve_fit_model_multi() and exposes helpers compatible with RxnODEsolver.

get_fitted_rate_const_dict(result=None)

Map symbolic names to optimized numeric rates.

Parameters:

result – External OptimizeResult or None to reuse cached output.

Returns:

Plain dict mergeable back into builded_rxnode.rate_consts_dict.

Raises:

RuntimeError – Missing prior optimization state.

get_solver_config_args(dataset_index=0)

Build kwargs for SolverConfig post-fit.

Parameters:

dataset_index – Which pooled dataset supplies y0/t0.

Returns:

Mapping with y0, t_span, integrator knobs, plus optional callable rate hooks for time-dependent kinetics.

Raises:

RuntimeError – If run_fit() has not finished and stored state, or (when kinetics are time-dependent) _result is missing before building the evaluator.

plot_fitted_solution(expdata_df: DataFrame | List[DataFrame] | None = None, plot_datasets: List[str] | None = None, species: List[str] | None = None, subplot_layout: tuple | None = None)

Replay best parameters with overlays mirroring plot_time_course_solutions.

Parameters:

  • expdata_df – Optional replacement overlay tables; defaults to inputs.

  • plot_datasets – Subset selector against df_names.

  • species – Restrict plotted columns; None keeps all.

  • subplot_layout – Manual subplot grid sizing.

Returns:

None

Raises:

  • RuntimeError – Missing fit artifacts.

  • ValueError – Unknown dataset labels or dataframe shape mismatches.

run_fit(p0, opt_method='L-BFGS-B', bounds=None, verbose=True, use_log_fit=False, lower_bound=None)

Optimize rate constants via scipy.optimize.minimize().

Parameters:

  • p0 – Sequence aligned with symbolic keys or explicit name→value mapping. Log-space mode rejects non-positive entries.

  • opt_method – SciPy method string forwarded unchanged.

  • bounds – Box constraints for linear mode; superseded under log-fit.

  • verbose – Toggle progress printing.

  • use_log_fit – Optimize log(k) internally while exposing linear k.

  • lower_bound – Optional shared positivity floor (mode dependent).

Returns:

(OptimizeResult-like, param_info, metrics_dict).

Raises:

ValueError – Invalid seeds, bounds, or incompatible log guesses.

to_dataframe_list(time_column_name='time')

Materialize every replayed trajectory via _ode_result_to_dataframe().

Parameters:

time_column_name – Leading column label for timestamps.

Returns:

Parallel list mixing pandas.DataFrame entries and None holes.

Raises:

RuntimeError – Missing optimized state.

rxnfit.expdata_fit.run_fit_multi(builded_rxnode, df_list, p0, t_range=None, method='RK45', rtol=1e-06, df_names=None, opt_method='L-BFGS-B', bounds=None, verbose=True, use_log_fit=False, lower_bound=None)

Stateful-free shortcut constructing ExpDataFit then run_fit().

When t_range is omitted it becomes the min/max of the first dataframe’s time column.

Parameters:

  • builded_rxnode – Reaction scaffold.

  • df_list – Experimental bundle.

  • p0 – Seeds understood by ExpDataFit.run_fit().

  • t_range – Optional manual span tuple.

  • method – Forwarded to the constructor.

  • rtol – Forwarded to the constructor.

  • df_names – Forwarded to the constructor.

  • opt_method – Forwarded to run_fit().

  • bounds – Forwarded to run_fit().

  • verbose – Forwarded to run_fit().

  • use_log_fit – Forwarded to run_fit().

  • lower_bound – Forwarded to run_fit().

Returns:

Same triple as ExpDataFit.run_fit().

Raises:

ValueError – Validation errors bubbled up from pooling or SciPy phases.

rxnfit.expdata_fit.solve_fit_model(builded_rxnode, fixed_initial_values, t_span, method='RK45', rtol=1e-06)

Build f(t, *params) that predicts species trajectories during fitting.

Bare symbolic rate constants (Symbol) are optimized via params; every other rate_consts_dict entry (numbers, expressions, etc.) stays in the merged constants dict passed to the RHS. fixed_initial_values are fixed ICs.

Parameters:

  • builded_rxnode – Configured reaction network.

  • fixed_initial_values – Species IC vector aligned with function_names.

  • t_span – Global integration window covering every experimental time.

  • method – Passed through to solve_ode().

  • rtol – Relative tolerance for integrations.

Returns:

functools.partial wrapping _eval_ode_fit() plus a param_info dict attached as attribute param_info.

Raises:

ValueError – Wrong IC length versus species count.

rxnfit.expdata_fit.solve_fit_model_multi(builded_rxnode, df_list, t_span, method='RK45', rtol=1e-06, df_names=None)

Prepare multi-experiment residuals sharing one kinetic parameter vector.

Each DataFrame yields its own (y0, t0); integrations stop at t_span[1].

Parameters:

  • builded_rxnode – Configured reaction network.

  • df_list – Homogeneous experimental tables (time + species columns).

  • t_span – Global window enclosing every observation time.

  • method – Passed to solve_ode().

  • rtol – Relative integration tolerance.

  • df_names – Optional labels; see _resolve_df_names().

Returns:

(residual_func, param_info, fit_ctx) where residual_func(params) returns the summed squared error and fit_ctx backs plotting helpers.

Raises:

ValueError – Empty df_list or invalid column layout.