pyfli.analysis.load_results

Load saved PyFLI fitting sessions and inject derived analysis results.

This module belongs to pyfli.analysis and is part of PyFLI post-processing, diagnostics, statistical comparison, and result-loading utilities for fitted FLI/FLIM datasets. Public API includes functions load_session_arrays(), scan_session_results(), load_fitting_results(), save_laguerre_result(), inject_phasor_result(), and add_mean_lifetime().

Functions

add_mean_lifetime(all_datasets)	Add mean lifetime.
inject_phasor_result(tau_map_ns, ...[, label])	Inject phasor result.
load_fitting_results(save_dir, experiments)	Load fitting results using a user-defined filename → label mapping.
load_session_arrays(save_dir)	Load clean_decay, clean_irf, and final_mask from a pf_Analysis session directory.
save_laguerre_result(saver, lag_results, ...)	Save laguerre result.
scan_session_results(save_dir)	Scan session results.

load_session_arrays(save_dir)

Load clean_decay, clean_irf, and final_mask from a pf_Analysis session directory.

Returns:

• decay (np.ndarray  (H, W, T))

• irf (np.ndarray  (H, W, T))

• mask (np.ndarray  (H, W)  bool)

Parameters:

save_dir (str)

Return type:

tuple[Any, …]

scan_session_results(save_dir)

Scan session results.

Parameters:

save_dir (str) – Directory where outputs are saved.

Returns:

Session result arrays discovered from the output folder.

Return type:

np.ndarray

load_fitting_results(save_dir, experiments)

Load fitting results using a user-defined filename → label mapping.

Parameters:

• save_dir (str) – Path to the pf_Analysis session folder.

• experiments (dict[str, str]) –

  Maps each .npy filename to a short display label. You control exactly which results are loaded and in what order. Mix any model types freely (NLSF, MLE, Laguerre, FBI, etc.).

  Example — mono-exponential, CPU only:

  experiments = {
      'CPU_NLSF_least_squares_mono-exponential.npy': 'NLSF',
      'CPU_MLE_poisson_mono-exponential.npy':        'MLE',
      'Laguerre Results_mono-exponential.npy':       'Laguerre',
  }
  

  Example — bi-exponential, selective:

  experiments = {
      'CPU_NLSF_least_squares_bi-exponential.npy': 'NLSF-bi',
      'GPU_MLE_poisson_bi-exponential.npy':        'MLE-GPU-bi',
  }
  

Returns:

• all_datasets (list[dict]   parameter maps  (tau_map, alpha1_map, ))

• all_fitset (list[dict]   TR maps         (fit_map, residual_map))

• names (list[str]    labels matching each entry, in dict order)

Return type:

tuple[Any, …]

save_laguerre_result(saver, lag_results, model_type)

Save laguerre result.

Parameters:

• saver (Any) – Optional saver used to persist messages or figures.

• lag_results (np.ndarray) – Laguerre deconvolution results written into the saver.

• model_type (str) – FLI/FLIM model family, such as mono- or bi-exponential.

Returns:

No object is returned; the function save laguerre result.

Return type:

None

inject_phasor_result(tau_map_ns, all_datasets, all_fitset, names, label='Phasor')

Inject phasor result.

Parameters:

• tau_map_ns (np.ndarray) – Lifetime map in nanoseconds.

• all_datasets (np.ndarray) – Collection of fitted datasets to classify, compare, or summarize.

• all_fitset (np.ndarray) – Collection of fit-result dictionaries used for comparison or plotting.

• names (Any) – Dataset names used in summaries and plots.

• label (str) – Display label assigned to the data or plot element.

Returns:

No object is returned; the function inject phasor result.

Return type:

None

add_mean_lifetime(all_datasets)

Add mean lifetime.

Parameters:

all_datasets (np.ndarray) – Collection of fitted datasets to classify, compare, or summarize.

Returns:

No object is returned; the function add mean lifetime.

Return type:

None