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main.py
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def main():
"""
Examples are given below. Uncomment them and their respective
imports to get started. Note that the data folder must first
be populated by the single-cell microscopy data by Granados et al.
Alternatively, jobs can be run from the command line
(see `jobs/README.md` for syntax and list of supported jobs).
"""
"""
Tutorial to get started with accessing the library.
"""
# from examples.tutorial import TutorialExamples
# tut_dh_ex = TutorialExamples.DataHandling()
# tut_dh_ex.raw_data_structure()
# tut_dh_ex.data_format()
# tut_pm_ex = TutorialExamples.PromoterModels()
# tut_pm_ex.creating_a_model()
# tut_pm_ex.randomly_generating_models()
# tut_pm_ex.visualising_models()
# tut_ts_ex = TutorialExamples.TrajectorySimulation()
# tut_ts_ex.producing_a_trajectory()
# tut_ts_ex.producing_the_probability_distribution_trajectory()
# tut_ts_ex.visualising_trajectories()
# tut_mie_ex = TutorialExamples.MIEstimation()
# tut_mie_ex.estimating_mutual_information()
# tut_mie_ex.choosing_classifiers()
# tut_mie_ex.multiprocessing_decoding()
# tut_mie_ex.experimental_trajectory_processing()
# tut_mie_ex.wrapping_into_a_pipeline()
# tut_ga_ex = TutorialExamples.GeneticAlgorithm()
# tut_ga_ex.running_evolution()
# tut_ga_ex.crossover_models()
# tut_ga_ex.mutate_models()
"""
Benchmarking simulations, classifier speeds, and multiprocessing.
"""
# from examples.benchmarking import BenchmarkingExamples
# bm_ex = BenchmarkingExamples()
# bm_ex.matrix_exponentials()
# bm_ex.trajectory_simulation()
# bm_ex.mi_estimation()
# bm_ex.mi_estimation_table()
# bm_ex.sklearn_nested_parallelism()
# bm_ex.genetic_multiprocessing_overhead()
# bm_ex.nearest_neighbours_novelty()
"""
Exploring changes in MI estimates as methodology is tweaked.
"""
# from examples.mi_trends import MITrendsExamples
# mit_ex = MITrendsExamples()
# mit_ex.mi_vs_interval()
# mit_ex.mi_distribution()
# mit_ex.max_mi_estimation()
# mit_ex.mi_vs_repeated_intervals()
"""
Visualisation of models, trajectories, activities, etc.
"""
# from examples.visualisation import VisualisationExamples
# vis_ex = VisualisationExamples()
# vis_ex.visualise_model()
# vis_ex.visualise_trajectory()
# vis_ex.visualise_activity()
# vis_ex.visualise_tf_concentration()
# vis_ex.visualise_activity_grid()
# vis_ex.visualise_tf_concentration_grid()
# vis_ex.visualise_random_model_generation()
# vis_ex.visualise_crossover()
# vis_ex.visualise_crossover_chart()
# vis_ex.visualise_crossbreeding()
"""
Optimisation of (simple and) arbitrary model weights.
"""
# from examples.optimisation import OptimisationExamples
# op_ex = OptimisationExamples()
# op_ex.grid_search_simple()
# op_ex.particle_swarm_simple()
# op_ex.particle_swarm_optimise()
"""
Analysis of genetic algorithm results.
"""
# from examples.genetic_analysis import GeneticAnalysisExamples
# ga_ex = GeneticAnalysisExamples()
# ga_ex.evaluate_models()
# ga_ex.visualise_models()
# ga_ex.examine_run_stats()
# ga_ex.evaluate_tf_presence_in_models()
if __name__ == "__main__":
main()