main_comparison.py
def generate_calls(
    n: Union[int, list],
    Date,
    n_shooting: tuple,
    dynamics_types: list,
    ode_solver: list,
    nstep: int,
    n_threads: int,
    out_path_raw: str,
    model_str: str,
    extra_obj: bool,
) -> list:
    """
    Generate the list of calls to be used in multiprocessing

    Parameters
    ----------
    n : Union[int, list]
        The int of list of irand to be run
    Date : str
        The date of the run
    n_shooting : tuple
        The number of shooting points for each phase
    dynamics_types : list
        The list of dynamics types to be run such as MillerDynamics.EXPLICIT, MillerDynamics.IMPLICIT, MillerDynamics.EXPLICIT_IMPLICIT
    ode_solver : list
        The list of OdeSolver to be run such as OdeSolver.RK4, OdeSolver.RK2
    nstep : int
        The number of intermediate steps between two shooting points
    n_threads : int
        The number of threads to be used
    out_path_raw : str
        The path to store the raw results
    model_str : str
        The path to the bioMod model
    extra_obj : bool
        Whether to run with the extra objective or not (minimizing extra controls for implicit formulations)

    Returns
    -------
    calls: list
        The list of calls to be run
    """
    if isinstance(n, int):
        rand_loop = range(n)
    else:
        rand_loop = n
    calls = []
    for i, dynamics_type in enumerate(dynamics_types):
        for i_rand in rand_loop:  # Should be 100
            calls.append(
                [
                    Date,
                    i_rand,
                    n_shooting,
                    dynamics_type,
                    ode_solver[i],
                    nstep,
                    n_threads,
                    out_path_raw,
                    model_str,
                    extra_obj,
                ]
            )
    return calls

    Generate the list of calls to be used in multiprocessing

    Parameters
    ----------
    n : Union[int, list]
        The int of list of irand to be run
    Date : str
        The date of the run
    n_shooting : tuple
        The number of shooting points for each phase
    dynamics_types : list
        The list of dynamics types to be run such as MillerDynamics.EXPLICIT, MillerDynamics.IMPLICIT, MillerDynamics.EXPLICIT_IMPLICIT
    ode_solver : list
        The list of OdeSolver to be run such as OdeSolver.RK4, OdeSolver.RK2
    nstep : int
        The number of intermediate steps between two shooting points
    n_threads : int
        The number of threads to be used
    out_path_raw : str
        The path to store the raw results
    model_str : str
        The path to the bioMod model
    extra_obj : bool
        Whether to run with the extra objective or not (minimizing extra controls for implicit formulations)

    Returns
    -------
    calls: list
        The list of calls to be run
    
Generate the list of calls to be used in multiprocessing
Parameters



Returns

list
The list of calls to be run
Generate the list of calls to be used in multiprocessing Parameters Returns list The list of calls to be run
def generate_calls(
    n: Union[int, list],
    Date,
    n_shooting: tuple,
    dynamics_types: list,
    ode_solver: list,
    nstep: int,
    n_threads: int,
    out_path_raw: str,
    model_str: str,
    extra_obj: bool,
) -> list:
    if isinstance(n, int):
        rand_loop = range(n)
    else:
        rand_loop = n
    calls = []
    for i, dynamics_type in enumerate(dynamics_types):
        for i_rand in rand_loop:  
            calls.append(
                [
                    Date,
                    i_rand,
                    n_shooting,
                    dynamics_type,
                    ode_solver[i],
                    nstep,
                    n_threads,
                    out_path_raw,
                    model_str,
                    extra_obj,
                ]
            )
    return calls
def generate_calls ( n : Union [ int , list ] , Date , n_shooting : tuple , dynamics_types : list , ode_solver : list , nstep : int , n_threads : int , out_path_raw : str , model_str : str , extra_obj : bool , ) -> list : if isinstance ( n , int ) : rand_loop = range ( n ) else : rand_loop = n calls = [ ] for i , dynamics_type in enumerate ( dynamics_types ) : for i_rand in rand_loop : calls . append ( [ Date , i_rand , n_shooting , dynamics_type , ode_solver [ i ] , nstep , n_threads , out_path_raw , model_str , extra_obj , ] ) return calls
Generate the list of calls to be used in multiprocessing
Parameters
Generate the list of calls to be used in multiprocessing Parameters
"""
    Generate the list of calls to be used in multiprocessing

    Parameters
    ----------
    n : Union[int, list]
        The int of list of irand to be run
    Date : str
        The date of the run
    n_shooting : tuple
        The number of shooting points for each phase
    dynamics_types : list
        The list of dynamics types to be run such as MillerDynamics.EXPLICIT, MillerDynamics.IMPLICIT, MillerDynamics.EXPLICIT_IMPLICIT
    ode_solver : list
        The list of OdeSolver to be run such as OdeSolver.RK4, OdeSolver.RK2
    nstep : int
        The number of intermediate steps between two shooting points
    n_threads : int
        The number of threads to be used
    out_path_raw : str
        The path to store the raw results
    model_str : str
        The path to the bioMod model
    extra_obj : bool
        Whether to run with the extra objective or not (minimizing extra controls for implicit formulations)

    Returns
    -------
    calls: list
        The list of calls to be run
    """ # Should be 100
n
Union[int, list]
Date
n_shooting
tuple
dynamics_types
list
ode_solver
list
nstep
int
n_threads
int
out_path_raw
str
model_str
str
extra_obj
bool