eit_results_list

class nrv.eit.results.eit_results_list(dt: float | None = 0.001, t_sim: float | None = None, results: list[eit_forward_results] | eit_forward_results | str = None, include_rec: bool = False)[source]

Container for multiple EIT forward simulation results, enabling batch analysis, comparison, and post-processing.

This class extends eit_forward_results to handle a list of EIT results, providing unified access to temporal, electrode, frequency, and protocol axes across all simulations. It supports adding results from files, objects, or lists, and offers methods for filtering, slicing, and statistical analysis.

Parameters:

dt (float, optional) – Time step for resampling all results (default: 0.001).
t_sim (float or None, optional) – Total simulation time. If None, inferred from first result.
results (list of eit_forward_results, eit_forward_results, or str, optional) – Initial results to add (can be a list, single object, or filename).
include_rec (bool, optional) – If True, include analytical nerve recordings in the container.

Notes

Results can be added from files, objects, or lists, and are automatically resampled to a common time axis.
Provides batch post-processing and statistical analysis tools for EIT simulations.
CAP detection and analysis methods are extended to handle multiple results.
Useful for comparing different simulation conditions, protocols, or geometries.

Examples

>>> res_list = eit_results_list(dt=0.001)
>>> res_list.add_results([res1, res2, res3])
>>> mean_v = res_list.mean(which="v_eit")
>>> cap_times = res_list.get_cap_i_t(thr=0.1)
>>> error = res_list.error(which="v_eit", i_res_ref=0)

Attributes

`eit_results_list.e_axis`
`eit_results_list.f_axis`
`eit_results_list.fail_results`
`eit_results_list.has_failed_test`
`eit_results_list.has_fem_res`
`eit_results_list.has_nerve_res`
`eit_results_list.is_multi_freqs`	Returns True if the EIT simulation was run over multiple frequencies
`eit_results_list.is_multi_patern`	Returns True if the injections protocole contains more than one patern.
`eit_results_list.n_e`	number of electrodes
`eit_results_list.n_f`	number of frequency point
`eit_results_list.n_p`	number of frequency point
`eit_results_list.n_t`	number of temporal point
`eit_results_list.r_axis`
`eit_results_list.shape`	return the shape of v_eit
`eit_results_list.t_axis`
`eit_results_list.v_eit_interp`
`eit_results_list.v_rec_interp`

Methods

`eit_results_list.__init__`([dt, t_sim, ...])
`eit_results_list.add_results`(results[, ...])
`eit_results_list.cap_duration`([alpha, ...])
`eit_results_list.clear`()
`eit_results_list.copy`()
`eit_results_list.dv_eit`([t, i_t, i_e, i_f, ...])
`eit_results_list.dv_eit_normalized`([t, i_t, ...])
`eit_results_list.dv_eit_pc`([t, i_t, i_e, ...])
`eit_results_list.error`([which, abs_err, ...])
`eit_results_list.fromkeys`(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
`eit_results_list.get`(key[, default])	Return the value for key if key is in the dictionary, else default.
`eit_results_list.get_acap_mask`([thr, ...])
`eit_results_list.get_acap_ppt`([thr, ...])
`eit_results_list.get_acap_t_ppt`([thr, ...])	Overwrite to impose i_res=None
`eit_results_list.get_acap_v_ppt`([thr, ...])
`eit_results_list.get_cap_i_t`([thr, i_res])	Return the temporal steps of caps of each results
`eit_results_list.get_cap_i_t_lim`([thr, ...])
`eit_results_list.get_cap_mask`([thr])
`eit_results_list.get_cap_res`([thr, ...])
`eit_results_list.get_dv_from_df`(data[, ...])
`eit_results_list.get_idxs`([i_res, i_t, i_e, ...])	Return a tuple containing the
`eit_results_list.get_reccap_ppt`([thr])
`eit_results_list.get_res`([which, i_res, t, ...])
`eit_results_list.i_t_duration`(i_t_start, ...)
`eit_results_list.incorporate_nerve_res`(nerve_res)
`eit_results_list.items`()
`eit_results_list.ix_`([i_t, i_e, i_f, i_p, n_t])
`eit_results_list.keys`()
`eit_results_list.load`(data)
`eit_results_list.mean`([which, i_res, t, ...])
`eit_results_list.plot`(ax[, which, t, i_t, ...])
`eit_results_list.pop`(k[,d])	If the key is not found, return the default if given; otherwise, raise a KeyError.
`eit_results_list.popitem`()	Remove and return a (key, value) pair as a 2-tuple.
`eit_results_list.res_argwhere`(to_check)
`eit_results_list.res_where`(to_check)
`eit_results_list.setdefault`(key[, default])	Insert key with a value of default if key is not in the dictionary.
`eit_results_list.std`([which, i_res, t, i_t, ...])
`eit_results_list.t`([dt, i_f])
`eit_results_list.update`([E, ]**F)	If E is present and has a .keys() method, then does: for k in E.keys(): D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
`eit_results_list.update_acap_inde_t_ppt`([...])
`eit_results_list.update_failed_results`(...)
`eit_results_list.v_0`([i_e, i_f, i_p])
`eit_results_list.v_eit`([t, i_t, i_e, i_f, i_p])
`eit_results_list.v_eit_idx`([i_t, i_e, i_f, ...])
`eit_results_list.v_rec`([t, i_e])
`eit_results_list.values`()