import crnt4sbml

network = crnt4sbml.CRNT("../sbml_files/DoublePhos.xml")

network.basic_report()

network.print_c_graph()

ldt = network.get_low_deficiency_approach()
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()

approach = network.get_mass_conservation_approach()

print("Decision Vector:")
print(approach.get_decision_vector())
print("")

print("Species for concentration bounds:")
print(approach.get_concentration_bounds_species())

bounds, concentration_bounds = approach.get_optimization_bounds()

params_for_global_min, obj_fun_val_for_params = approach.run_optimization(bounds=bounds,
                                                                          concentration_bounds=concentration_bounds,
                                                                          iterations=100)

multistable_param_ind, plot_specifications = approach.run_greedy_continuity_analysis(species="s4", parameters=params_for_global_min,
                                                                                     auto_parameters={'PrincipalContinuationParameter': 'C2'})

approach.generate_report()

00fe8b8e817c773bcbea2dc155d723770bea8716
<mask token>

<mask token>
network.basic_report()
network.print_c_graph()
<mask token>
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()
<mask token>
print('Decision Vector:')
print(approach.get_decision_vector())
print('')
print('Species for concentration bounds:')
print(approach.get_concentration_bounds_species())
<mask token>
approach.generate_report()

<mask token>
network = crnt4sbml.CRNT('../sbml_files/DoublePhos.xml')
network.basic_report()
network.print_c_graph()
ldt = network.get_low_deficiency_approach()
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()
approach = network.get_mass_conservation_approach()
print('Decision Vector:')
print(approach.get_decision_vector())
print('')
print('Species for concentration bounds:')
print(approach.get_concentration_bounds_species())
bounds, concentration_bounds = approach.get_optimization_bounds()
params_for_global_min, obj_fun_val_for_params = approach.run_optimization(
    bounds=bounds, concentration_bounds=concentration_bounds, iterations=100)
multistable_param_ind, plot_specifications = (approach.
    run_greedy_continuity_analysis(species='s4', parameters=
    params_for_global_min, auto_parameters={
    'PrincipalContinuationParameter': 'C2'}))
approach.generate_report()

import crnt4sbml
network = crnt4sbml.CRNT('../sbml_files/DoublePhos.xml')
network.basic_report()
network.print_c_graph()
ldt = network.get_low_deficiency_approach()
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()
approach = network.get_mass_conservation_approach()
print('Decision Vector:')
print(approach.get_decision_vector())
print('')
print('Species for concentration bounds:')
print(approach.get_concentration_bounds_species())
bounds, concentration_bounds = approach.get_optimization_bounds()
params_for_global_min, obj_fun_val_for_params = approach.run_optimization(
    bounds=bounds, concentration_bounds=concentration_bounds, iterations=100)
multistable_param_ind, plot_specifications = (approach.
    run_greedy_continuity_analysis(species='s4', parameters=
    params_for_global_min, auto_parameters={
    'PrincipalContinuationParameter': 'C2'}))
approach.generate_report()

import crnt4sbml

network = crnt4sbml.CRNT("../sbml_files/DoublePhos.xml")

network.basic_report()

network.print_c_graph()

ldt = network.get_low_deficiency_approach()
ldt.report_deficiency_zero_theorem()
ldt.report_deficiency_one_theorem()

approach = network.get_mass_conservation_approach()

print("Decision Vector:")
print(approach.get_decision_vector())
print("")

print("Species for concentration bounds:")
print(approach.get_concentration_bounds_species())

bounds, concentration_bounds = approach.get_optimization_bounds()

params_for_global_min, obj_fun_val_for_params = approach.run_optimization(bounds=bounds,
                                                                          concentration_bounds=concentration_bounds,
                                                                          iterations=100)

multistable_param_ind, plot_specifications = approach.run_greedy_continuity_analysis(species="s4", parameters=params_for_global_min,
                                                                                     auto_parameters={'PrincipalContinuationParameter': 'C2'})

approach.generate_report()
