4.2.1.5. SomaticFeaturesTest

class SomaticFeaturesTest(observation={}, config={}, name='Somatic features test', force_run=False, base_directory=None, show_plot=True, save_all=True, specify_data_set='')[source]

Bases: sciunit.tests.Test

Tests some somatic features under current injection of increasing amplitudes.

Parameters

  • config (dict) – dictionary loaded from a JSON file, containing the parameters of the simulation

  • observation (dict) – dictionary loaded from a JSON file, containing the experimental mean and std values for the features to be tested

  • force_run (boolean) – If True and the pickle files containing the model’s response to the simulation exists, the simulation won’t be run again, traces are loaded from the pickle file

  • base_directory (str) – Results will be saved here

  • show_plot (boolean) – If False, plots are not displayed but still saved

  • save_all (boolean) – If False, only the JSON files containing the absolute feature values, the feature error scores and the final scores, and a log file are saved, but the figures and pickle files are not.

  • specify_data_set (str) – When set to a string, output will be saved into subdirectory (within the model_name subderotory) named like this. This makes it possible to run the validation on a specific model, against different data sets, and save the results separately.

Methods Summary

analyse_traces(stimuli_list, traces_results, …)

bind_score(score, model, observation, prediction)

For the user to bind additional features to the score.

compute_score(observation, prediction[, verbose])

Implementation of sciunit.Test.score_prediction.

create_features_list(observation)

create_figs(model, traces_results, …)

create_stimuli_list()

generate_prediction(model[, verbose])

Implementation of sciunit.Test.generate_prediction.

run_stim(model, stimuli_list)

Methods Documentation

analyse_traces(stimuli_list, traces_results, features_list)[source]
bind_score(score, model, observation, prediction)[source]

For the user to bind additional features to the score.

compute_score(observation, prediction, verbose=False)[source]

Implementation of sciunit.Test.score_prediction.

create_features_list(observation)[source]
create_figs(model, traces_results, features_names, feature_results_dict, observation)[source]
create_stimuli_list()[source]
generate_prediction(model, verbose=False)[source]

Implementation of sciunit.Test.generate_prediction.

run_stim(model, stimuli_list)[source]