py-xl-sindy documentation

The python Xl-sindy library provides function in order to run Sparse Identification of Non linear DYnamics through lagrangian dynamics.

This documentation goes through each module and function of library.

For a better understanding of the whole framework it is recommanded to read the exemple file provided with the library

Package content :

• xlsindy.catalog module
  • CatalogCategory
    • CatalogCategory.create_solution_vector()
    • CatalogCategory.expand_catalog()
    • CatalogCategory.label()
    • CatalogCategory.separate_by_mask()
  • CatalogRepartition
    • CatalogRepartition.create_solution_vector()
    • CatalogRepartition.expand_catalog()
    • CatalogRepartition.label()
    • CatalogRepartition.reunite_solution_by_type()
    • CatalogRepartition.separate_by_mask()
    • CatalogRepartition.separate_solution_by_type()
    • CatalogRepartition.seperate_by_type()
    • CatalogRepartition.starting_index_by_type()
• xlsindy.symbolic_util module
  • augment_catalog()
  • cross_catalog()
  • generate_full_catalog()
  • generate_symbolic_matrix()
  • get_additive_equation_term()
  • sindy_create_coefficient_matrices()
• xlsindy.dynamics_modeling module
  • dynamics_function()
  • dynamics_function_RK4_env()
  • dynamics_function_fixed_external()
  • generate_acceleration_function()
  • generate_random_force()
  • optimized_force_generator()
  • run_rk45_integration()
  • sinusoidal_force_generator()
  • vectorised_acceleration_generation()
• xlsindy.euler_lagrange module
  • compute_euler_lagrange_equation()
  • create_experiment_matrix()
  • jax_create_experiment_matrix()
  • newton_from_lagrangian()
• xlsindy.optimization module
  • activated_catalog()
  • amputate_experiment_matrix()
  • bipartite_link()
  • condition_value()
  • covariance_vector()
  • hard_threshold_sparse_regression()
  • hard_threshold_sparse_regression_old()
  • lasso_regression()
  • lasso_regression_old()
  • lasso_regression_rapids()
  • normalize_experiment_matrix()
  • optimal_sampling()
  • populate_solution()
  • proximal_gradient_descent()
  • soft_threshold()
  • sparse_tradeoff_score()
  • unnormalize_experiment()
• xlsindy.render module
  • animate_double_pendulum()
  • animate_single_pendulum()
  • plot_bipartite_graph_svg()
  • plot_bipartite_graph_svg_dep()
• xlsindy.simulation module
  • combine_best_fit()
  • regression_explicite()
  • regression_implicite()
  • regression_mixed()
• xlsindy.result_formatting module
  • convert_to_lists()
  • estimate_local_volumes_emp()
  • estimate_volumes()
  • normalise_solution()
  • relative_mse()
• xlsindy.utils module
  • print_progress()
• xlsindy.sindy_utils module

Base catalog content :

• xlsindy.base_catalog package
  • Module contents
    • Classical
      • Classical.create_solution_vector()
      • Classical.expand_catalog()
      • Classical.label()
      • Classical.separate_by_mask()
    • ExternalForces
      • ExternalForces.create_solution_vector()
      • ExternalForces.expand_catalog()
      • ExternalForces.label()
      • ExternalForces.separate_by_mask()
    • Lagrange
      • Lagrange.create_solution_vector()
      • Lagrange.expand_catalog()
      • Lagrange.label()
      • Lagrange.separate_by_mask()