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Simulations-API

pyadm1.simulation.simulator.Simulator

Handles ADM1 simulation runs with various configurations.

This class provides high-level interfaces for running ADM1 simulations, including single runs and multi-scenario optimization for substrate feed determination.

Attributes:

Name Type Description
adm1 ADM1

ADM1 model instance
solver ODESolver

ODE solver instance
Example

simulator = Simulator(adm1) result = simulator.simulate_AD_plant([0, 10], initial_state)

Source code in pyadm1/simulation/simulator.py 
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class Simulator:
    """
    Handles ADM1 simulation runs with various configurations.

    This class provides high-level interfaces for running ADM1 simulations,
    including single runs and multi-scenario optimization for substrate feed
    determination.

    Attributes:
        adm1: ADM1 model instance
        solver: ODE solver instance

    Example:
        >>> simulator = Simulator(adm1)
        >>> result = simulator.simulate_AD_plant([0, 10], initial_state)
    """

    def __init__(self, adm1: ADM1, solver: ODESolver = None) -> None:
        """
        Initialize simulator with ADM1 model instance.

        Args:
            adm1: ADM1 model instance
            solver: Optional ODE solver. If None, creates default BDF solver
        """
        self._adm1 = adm1
        self._solver = solver or create_solver(method="BDF", rtol=1e-6, atol=1e-8)

    def determine_best_feed_by_n_sims(
        self,
        state_zero: List[float],
        Q: List[float],
        Qch4sp: float,
        feeding_freq: int,
        n: int = 13,
    ) -> Tuple[float, float, List[float], float, float, List[float], float, float, float, float]:
        """
        Determine optimal substrate feed by running n simulations.

        Runs n simulations with varying substrate feed rates around Q and
        returns the feed rate yielding methane production closest to setpoint.

        The first simulation uses Q, the 2nd and 3rd use Q ± 1.5 m³/d,
        and remaining simulations use random variations.

        Args:
            state_zero: Initial ADM1 state vector (37 elements)
            Q: Initial volumetric flow rates [m³/d], e.g. [15, 10, 0, ...]
            Qch4sp: Methane flow rate setpoint [m³/d]
            feeding_freq: Feeding frequency [hours]
            n: Number of simulations to run (default: 13, minimum: 3)

        Returns:
            Tuple containing:
                - Q_Gas_7d_best: Best biogas production after 7 days [m³/d]
                - Q_CH4_7d_best: Best methane production after 7 days [m³/d]
                - Qbest: Best substrate feed rates [m³/d]
                - Q_Gas_7d_initial: Initial biogas production after 7 days [m³/d]
                - Q_CH4_7d_initial: Initial methane production after 7 days [m³/d]
                - Q_initial: Initial substrate feed rates [m³/d]
                - q_gas_best_2d: Best biogas after feeding_freq/24 days [m³/d]
                - q_ch4_best_2d: Best methane after feeding_freq/24 days [m³/d]
                - q_gas_2d: Initial biogas after feeding_freq/24 days [m³/d]
                - q_ch4_2d: Initial methane after feeding_freq/24 days [m³/d]

        Example:
            >>> result = simulator.determine_best_feed_by_n_sims(
            ...     state, [15, 10, 0, 0, 0, 0, 0, 0, 0, 0], 900, 48, n=13
            ... )
            >>> Q_best = result[2]
        """
        if n < 3:
            raise ValueError("n must be at least 3")

        Q_CH4_7d = [0.0] * n
        Q_Gas_7d = [0.0] * n

        # Generate substrate feed mixtures
        Qnew = self._adm1.feedstock.get_substrate_feed_mixtures(Q, n)

        # Run n simulations
        for i, q in enumerate(Qnew):
            Q_Gas_7d[i], Q_CH4_7d[i] = self._simulate_without_saving_state([0, 7], state_zero, q)

        # Find scenario closest to methane setpoint
        ii = np.argmin([np.sum((q - Qch4sp) ** 2) for q in Q_CH4_7d])
        Qbest = Qnew[ii]

        # Simulate with initial Q for feeding_freq/24 days
        q_gas_2d, q_ch4_2d = self._simulate_without_saving_state([0, feeding_freq / 24], state_zero, Qnew[0])

        # Simulate with best Q for feeding_freq/24 days
        q_gas_best_2d, q_ch4_best_2d = self._simulate_without_saving_state([0, feeding_freq / 24], state_zero, Qbest)

        return (
            Q_Gas_7d[ii][-1] if hasattr(Q_Gas_7d[ii], "__iter__") else Q_Gas_7d[ii],
            Q_CH4_7d[ii][-1] if hasattr(Q_CH4_7d[ii], "__iter__") else Q_CH4_7d[ii],
            Qbest,
            Q_Gas_7d[0][-1] if hasattr(Q_Gas_7d[0], "__iter__") else Q_Gas_7d[0],
            Q_CH4_7d[0][-1] if hasattr(Q_CH4_7d[0], "__iter__") else Q_CH4_7d[0],
            Qnew[0],
            q_gas_best_2d[-1] if hasattr(q_gas_best_2d, "__iter__") else q_gas_best_2d,
            q_ch4_best_2d[-1] if hasattr(q_ch4_best_2d, "__iter__") else q_ch4_best_2d,
            q_gas_2d[-1] if hasattr(q_gas_2d, "__iter__") else q_gas_2d,
            q_ch4_2d[-1] if hasattr(q_ch4_2d, "__iter__") else q_ch4_2d,
        )

    def simulate_AD_plant(self, tstep: List[float], state_zero: List[float]) -> List[float]:
        """
        Simulate ADM1 for specified time span and return final state.

        This is the main simulation method that integrates the ADM1 ODEs
        and tracks process values for operator information.

        Args:
            tstep: Time span [t_start, t_end] in days
            state_zero: Initial ADM1 state vector (37 elements)

        Returns:
            Final ADM1 state vector after simulation (37 elements)

        Example:
            >>> final_state = simulator.simulate_AD_plant([0, 1], initial_state)
            >>> print(f"Final pH: {final_state[...])
        """
        final_state = self._simulate_and_return_final_state(tstep, state_zero)

        # Print process parameters for monitoring
        self._adm1.print_params_at_current_state(final_state)

        return final_state

    def _simulate_without_saving_state(
        self, tstep: List[float], state_zero: List[float], Q: List[float]
    ) -> Tuple[float, float]:
        """
        Run simulation without saving final state.

        Used internally for optimization scenarios where we only need
        gas production values.

        Args:
            tstep: Time span [t_start, t_end] in days
            state_zero: Initial ADM1 state vector
            Q: Volumetric flow rates [m³/d]

        Returns:
            Tuple of (q_gas, q_ch4) - biogas and methane production rates [m³/d]
        """
        # Create ADM1 input stream
        self._adm1.create_influent(Q, 0)

        # Integrate ODEs
        result = self._solver.solve(fun=self._adm1.ADM1_ODE, t_span=tstep, y0=state_zero)

        # Extract final gas phase state
        final_state = result.y[:, -1]
        pi_Sh2, pi_Sch4, pi_Sco2, pTOTAL = final_state[33:37]

        # Calculate gas production rates
        q_gas, q_ch4, q_co2, p_gas = self._adm1.calc_gas(pi_Sh2, pi_Sch4, pi_Sco2, pTOTAL)

        return q_gas, q_ch4

    def _simulate_and_return_final_state(self, tstep: List[float], state_zero: List[float]) -> List[float]:
        """
        Simulate and return final state vector.

        Args:
            tstep: Time span [t_start, t_end] in days
            state_zero: Initial ADM1 state vector

        Returns:
            Final ADM1 state vector (37 elements)
        """
        # Integrate ODEs using solver
        result = self._solver.solve(fun=self._adm1.ADM1_ODE, t_span=tstep, y0=state_zero)

        # Extract final state
        final_state = result.y[:, -1].tolist()

        return final_state

    @property
    def adm1(self) -> ADM1:
        """ADM1 model instance."""
        return self._adm1

    @property
    def solver(self) -> ODESolver:
        """ODE solver instance."""
        return self._solver

Attributes

adm1 property

ADM1 model instance.

solver property

ODE solver instance.

Functions

__init__(adm1, solver=None)

Initialize simulator with ADM1 model instance.

Parameters:

Name Type Description Default
adm1 ADM1

ADM1 model instance

 required
solver ODESolver

Optional ODE solver. If None, creates default BDF solver

 None
Source code in pyadm1/simulation/simulator.py 
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def __init__(self, adm1: ADM1, solver: ODESolver = None) -> None:
    """
    Initialize simulator with ADM1 model instance.

    Args:
        adm1: ADM1 model instance
        solver: Optional ODE solver. If None, creates default BDF solver
    """
    self._adm1 = adm1
    self._solver = solver or create_solver(method="BDF", rtol=1e-6, atol=1e-8)

determine_best_feed_by_n_sims(state_zero, Q, Qch4sp, feeding_freq, n=13)

Determine optimal substrate feed by running n simulations.

Runs n simulations with varying substrate feed rates around Q and returns the feed rate yielding methane production closest to setpoint.

The first simulation uses Q, the 2nd and 3rd use Q ± 1.5 m³/d, and remaining simulations use random variations.

Parameters:

Name Type Description Default
state_zero List[float]

Initial ADM1 state vector (37 elements)

 required
Q List[float]

Initial volumetric flow rates [m³/d], e.g. [15, 10, 0, ...]

 required
Qch4sp float

Methane flow rate setpoint [m³/d]

 required
feeding_freq int

Feeding frequency [hours]

 required
n int

Number of simulations to run (default: 13, minimum: 3)

 13

Returns:

Type Description
Tuple[float, float, List[float], float, float, List[float], float, float, float, float]

Tuple containing: - Q_Gas_7d_best: Best biogas production after 7 days [m³/d] - Q_CH4_7d_best: Best methane production after 7 days [m³/d] - Qbest: Best substrate feed rates [m³/d] - Q_Gas_7d_initial: Initial biogas production after 7 days [m³/d] - Q_CH4_7d_initial: Initial methane production after 7 days [m³/d] - Q_initial: Initial substrate feed rates [m³/d] - q_gas_best_2d: Best biogas after feeding_freq/24 days [m³/d] - q_ch4_best_2d: Best methane after feeding_freq/24 days [m³/d] - q_gas_2d: Initial biogas after feeding_freq/24 days [m³/d] - q_ch4_2d: Initial methane after feeding_freq/24 days [m³/d]
Example

result = simulator.determine_best_feed_by_n_sims( ... state, [15, 10, 0, 0, 0, 0, 0, 0, 0, 0], 900, 48, n=13 ... ) Q_best = result[2]

Source code in pyadm1/simulation/simulator.py 
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def determine_best_feed_by_n_sims(
    self,
    state_zero: List[float],
    Q: List[float],
    Qch4sp: float,
    feeding_freq: int,
    n: int = 13,
) -> Tuple[float, float, List[float], float, float, List[float], float, float, float, float]:
    """
    Determine optimal substrate feed by running n simulations.

    Runs n simulations with varying substrate feed rates around Q and
    returns the feed rate yielding methane production closest to setpoint.

    The first simulation uses Q, the 2nd and 3rd use Q ± 1.5 m³/d,
    and remaining simulations use random variations.

    Args:
        state_zero: Initial ADM1 state vector (37 elements)
        Q: Initial volumetric flow rates [m³/d], e.g. [15, 10, 0, ...]
        Qch4sp: Methane flow rate setpoint [m³/d]
        feeding_freq: Feeding frequency [hours]
        n: Number of simulations to run (default: 13, minimum: 3)

    Returns:
        Tuple containing:
            - Q_Gas_7d_best: Best biogas production after 7 days [m³/d]
            - Q_CH4_7d_best: Best methane production after 7 days [m³/d]
            - Qbest: Best substrate feed rates [m³/d]
            - Q_Gas_7d_initial: Initial biogas production after 7 days [m³/d]
            - Q_CH4_7d_initial: Initial methane production after 7 days [m³/d]
            - Q_initial: Initial substrate feed rates [m³/d]
            - q_gas_best_2d: Best biogas after feeding_freq/24 days [m³/d]
            - q_ch4_best_2d: Best methane after feeding_freq/24 days [m³/d]
            - q_gas_2d: Initial biogas after feeding_freq/24 days [m³/d]
            - q_ch4_2d: Initial methane after feeding_freq/24 days [m³/d]

    Example:
        >>> result = simulator.determine_best_feed_by_n_sims(
        ...     state, [15, 10, 0, 0, 0, 0, 0, 0, 0, 0], 900, 48, n=13
        ... )
        >>> Q_best = result[2]
    """
    if n < 3:
        raise ValueError("n must be at least 3")

    Q_CH4_7d = [0.0] * n
    Q_Gas_7d = [0.0] * n

    # Generate substrate feed mixtures
    Qnew = self._adm1.feedstock.get_substrate_feed_mixtures(Q, n)

    # Run n simulations
    for i, q in enumerate(Qnew):
        Q_Gas_7d[i], Q_CH4_7d[i] = self._simulate_without_saving_state([0, 7], state_zero, q)

    # Find scenario closest to methane setpoint
    ii = np.argmin([np.sum((q - Qch4sp) ** 2) for q in Q_CH4_7d])
    Qbest = Qnew[ii]

    # Simulate with initial Q for feeding_freq/24 days
    q_gas_2d, q_ch4_2d = self._simulate_without_saving_state([0, feeding_freq / 24], state_zero, Qnew[0])

    # Simulate with best Q for feeding_freq/24 days
    q_gas_best_2d, q_ch4_best_2d = self._simulate_without_saving_state([0, feeding_freq / 24], state_zero, Qbest)

    return (
        Q_Gas_7d[ii][-1] if hasattr(Q_Gas_7d[ii], "__iter__") else Q_Gas_7d[ii],
        Q_CH4_7d[ii][-1] if hasattr(Q_CH4_7d[ii], "__iter__") else Q_CH4_7d[ii],
        Qbest,
        Q_Gas_7d[0][-1] if hasattr(Q_Gas_7d[0], "__iter__") else Q_Gas_7d[0],
        Q_CH4_7d[0][-1] if hasattr(Q_CH4_7d[0], "__iter__") else Q_CH4_7d[0],
        Qnew[0],
        q_gas_best_2d[-1] if hasattr(q_gas_best_2d, "__iter__") else q_gas_best_2d,
        q_ch4_best_2d[-1] if hasattr(q_ch4_best_2d, "__iter__") else q_ch4_best_2d,
        q_gas_2d[-1] if hasattr(q_gas_2d, "__iter__") else q_gas_2d,
        q_ch4_2d[-1] if hasattr(q_ch4_2d, "__iter__") else q_ch4_2d,
    )

simulate_AD_plant(tstep, state_zero)

Simulate ADM1 for specified time span and return final state.

This is the main simulation method that integrates the ADM1 ODEs and tracks process values for operator information.

Parameters:

Name Type Description Default
tstep List[float]

Time span [t_start, t_end] in days

 required
state_zero List[float]

Initial ADM1 state vector (37 elements)

 required

Returns:

Type Description
List[float]

Final ADM1 state vector after simulation (37 elements)
Example

final_state = simulator.simulate_AD_plant([0, 1], initial_state) print(f"Final pH: {final_state[...])

Source code in pyadm1/simulation/simulator.py 
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def simulate_AD_plant(self, tstep: List[float], state_zero: List[float]) -> List[float]:
    """
    Simulate ADM1 for specified time span and return final state.

    This is the main simulation method that integrates the ADM1 ODEs
    and tracks process values for operator information.

    Args:
        tstep: Time span [t_start, t_end] in days
        state_zero: Initial ADM1 state vector (37 elements)

    Returns:
        Final ADM1 state vector after simulation (37 elements)

    Example:
        >>> final_state = simulator.simulate_AD_plant([0, 1], initial_state)
        >>> print(f"Final pH: {final_state[...])
    """
    final_state = self._simulate_and_return_final_state(tstep, state_zero)

    # Print process parameters for monitoring
    self._adm1.print_params_at_current_state(final_state)

    return final_state

pyadm1.simulation.parallel.ParallelSimulator

Parallel simulator for running multiple ADM1 scenarios concurrently.

Uses multiprocessing to distribute scenarios across CPU cores for efficient parameter sweeps, sensitivity analysis, and Monte Carlo simulations.

Attributes:

Name Type Description
adm1

Base ADM1 model instance (will be copied for each worker)
n_workers

Number of parallel worker processes
verbose

Enable progress reporting
Example

parallel = ParallelSimulator(adm1, n_workers=4, verbose=True) results = parallel.run_scenarios(scenarios, duration=30)

Source code in pyadm1/simulation/parallel.py 
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class ParallelSimulator:
    """
    Parallel simulator for running multiple ADM1 scenarios concurrently.

    Uses multiprocessing to distribute scenarios across CPU cores for efficient
    parameter sweeps, sensitivity analysis, and Monte Carlo simulations.

    Attributes:
        adm1: Base ADM1 model instance (will be copied for each worker)
        n_workers: Number of parallel worker processes
        verbose: Enable progress reporting

    Example:
        >>> parallel = ParallelSimulator(adm1, n_workers=4, verbose=True)
        >>> results = parallel.run_scenarios(scenarios, duration=30)
    """

    def __init__(self, adm1: "ADM1", n_workers: Optional[int] = None, verbose: bool = True):
        """
        Initialize parallel simulator.

        Args:
            adm1: ADM1 model instance
            n_workers: Number of worker processes (default: CPU count - 1)
            verbose: Enable progress output
        """
        self.adm1 = adm1
        self.n_workers = n_workers or max(1, mp.cpu_count() - 1)
        self.verbose = verbose

    def run_scenarios(
        self,
        scenarios: List[Dict[str, Any]],
        duration: float,
        initial_state: List[float],
        dt: float = 1.0 / 24.0,
        compute_metrics: bool = True,
        save_time_series: bool = False,
    ) -> List[ScenarioResult]:
        """
        Run multiple simulation scenarios in parallel.

        Each scenario is a dictionary containing parameter values and substrate
        feed rates. The simulator will run all scenarios concurrently and
        collect results.

        Args:
            scenarios: List of scenario dictionaries with parameters
            duration: Simulation duration [days]
            initial_state: Initial ADM1 state vector
            dt: Time step [days]
            compute_metrics: Calculate performance metrics
            save_time_series: Save full time series data

        Returns:
            List of ScenarioResult objects

        Example:
            >>> scenarios = [
            ...     {"k_dis": 0.5, "Q": [15, 10, 0, 0, 0, 0, 0, 0, 0, 0]},
            ...     {"k_dis": 0.6, "Q": [20, 10, 0, 0, 0, 0, 0, 0, 0, 0]},
            ... ]
            >>> results = parallel.run_scenarios(scenarios, duration=30, initial_state=state)
        """
        if self.verbose:
            print(f"Starting parallel simulation with {len(scenarios)} scenarios")
            print(f"Using {self.n_workers} worker processes")

        start_time = time.time()

        # Create worker function with fixed parameters
        worker_func = partial(
            _run_single_scenario,
            adm1_config=self._serialize_adm1(),
            duration=duration,
            initial_state=initial_state,
            dt=dt,
            compute_metrics=compute_metrics,
            save_time_series=save_time_series,
            verbose=self.verbose,
        )

        # Add scenario IDs
        scenarios_with_ids = [(i, scenario) for i, scenario in enumerate(scenarios)]

        # Avoid spawning a process pool for trivial workloads. This also prevents
        # pythonnet/.NET deadlocks seen in some CI environments with forked workers.
        use_sequential = self.n_workers <= 1 or len(scenarios_with_ids) <= 1

        if use_sequential:
            results = []
            for i, scenario_data in enumerate(scenarios_with_ids):
                results.append(worker_func(scenario_data))
                if self.verbose and ((i + 1) % 10 == 0 or (i + 1) == len(scenarios)):
                    print(f"  Completed {i + 1}/{len(scenarios)} scenarios")
        else:
            ctx = _get_mp_context()
            # Run scenarios in parallel
            # Keep workers alive across multiple scenarios to avoid repeated
            # python startup / pythonnet initialization overhead.
            with ctx.Pool(processes=self.n_workers) as pool:
                if self.verbose:
                    # Use imap for progress tracking
                    results = []
                    for i, result in enumerate(pool.imap(worker_func, scenarios_with_ids)):
                        results.append(result)
                        if (i + 1) % 10 == 0 or (i + 1) == len(scenarios):
                            print(f"  Completed {i + 1}/{len(scenarios)} scenarios")
                else:
                    results = pool.map(worker_func, scenarios_with_ids)

        elapsed_time = time.time() - start_time

        if self.verbose:
            n_success = sum(1 for r in results if r.success)
            print("\nSimulation complete:")
            print(f"  Total scenarios: {len(scenarios)}")
            print(f"  Successful: {n_success}")
            print(f"  Failed: {len(scenarios) - n_success}")
            print(f"  Total time: {elapsed_time:.1f} seconds")
            print(f"  Average time per scenario: {elapsed_time / len(scenarios):.2f} seconds")

        return results

    def parameter_sweep(
        self,
        config: ParameterSweepConfig,
        duration: float,
        initial_state: List[float],
        **kwargs: Any,
    ) -> List[ScenarioResult]:
        """
        Run parameter sweep for a single parameter.

        Tests multiple values of one parameter while keeping others fixed.

        Args:
            config: ParameterSweepConfig with parameter and values
            duration: Simulation duration [days]
            initial_state: Initial ADM1 state vector
            **kwargs: Additional arguments for run_scenarios

        Returns:
            List of ScenarioResult objects

        Example:
            >>> config = ParameterSweepConfig(
            ...     parameter_name="k_dis",
            ...     values=[0.3, 0.4, 0.5, 0.6, 0.7],
            ...     other_params={"Q": [15, 10, 0, 0, 0, 0, 0, 0, 0, 0]}
            ... )
            >>> results = parallel.parameter_sweep(config, duration=30, initial_state=state)
        """
        # Generate scenarios
        scenarios = []
        for value in config.values:
            scenario = config.other_params.copy()
            scenario[config.parameter_name] = value
            scenarios.append(scenario)

        if self.verbose:
            print(f"Parameter sweep: {config.parameter_name}")
            print(f"  Values: {config.values}")

        return self.run_scenarios(scenarios, duration, initial_state, **kwargs)

    def multi_parameter_sweep(
        self,
        parameter_configs: Dict[str, List[float]],
        duration: float,
        initial_state: List[float],
        fixed_params: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> List[ScenarioResult]:
        """
        Run multi-parameter sweep (full factorial design).

        Tests all combinations of provided parameter values.

        Args:
            parameter_configs: Dict mapping parameter names to value lists
            duration: Simulation duration [days]
            initial_state: Initial ADM1 state vector
            fixed_params: Parameters to keep fixed
            **kwargs: Additional arguments for run_scenarios

        Returns:
            List of ScenarioResult objects

        Example:
            >>> parameter_configs = {
            ...     "k_dis": [0.4, 0.5, 0.6],
            ...     "Y_su": [0.09, 0.10, 0.11]
            ... }
            >>> results = parallel.multi_parameter_sweep(
            ...     parameter_configs,
            ...     duration=30,
            ...     initial_state=state,
            ...     fixed_params={"Q": [15, 10, 0, 0, 0, 0, 0, 0, 0, 0]}
            ... )
        """
        fixed_params = fixed_params or {}

        # Generate all combinations using recursive approach
        param_names = list(parameter_configs.keys())
        param_values = [parameter_configs[name] for name in param_names]

        scenarios = []
        for combination in self._generate_combinations(param_values):
            scenario = fixed_params.copy()
            for name, value in zip(param_names, combination):
                scenario[name] = value
            scenarios.append(scenario)

        if self.verbose:
            print("Multi-parameter sweep:")
            for name, values in parameter_configs.items():
                print(f"  {name}: {len(values)} values")
            print(f"  Total combinations: {len(scenarios)}")

        return self.run_scenarios(scenarios, duration, initial_state, **kwargs)

    def monte_carlo(
        self,
        config: MonteCarloConfig,
        duration: float,
        initial_state: List[float],
        **kwargs: Any,
    ) -> List[ScenarioResult]:
        """
        Run Monte Carlo simulation with parameter uncertainty.

        Samples parameters from normal distributions and runs multiple scenarios
        to quantify uncertainty in predictions.

        Args:
            config: MonteCarloConfig with distributions and sample count
            duration: Simulation duration [days]
            initial_state: Initial ADM1 state vector
            **kwargs: Additional arguments for run_scenarios

        Returns:
            List of ScenarioResult objects

        Example:
            >>> config = MonteCarloConfig(
            ...     n_samples=100,
            ...     parameter_distributions={
            ...         "k_dis": (0.5, 0.05),  # mean=0.5, std=0.05
            ...         "Y_su": (0.10, 0.01)
            ...     },
            ...     fixed_params={"Q": [15, 10, 0, 0, 0, 0, 0, 0, 0, 0]},
            ...     seed=42
            ... )
            >>> results = parallel.monte_carlo(config, duration=30, initial_state=state)
        """
        # Set random seed for reproducibility
        if config.seed is not None:
            np.random.seed(config.seed)

        # Generate scenarios
        scenarios = []
        for i in range(config.n_samples):
            scenario = config.fixed_params.copy()

            # Sample each parameter from its distribution
            for param_name, (mean, std) in config.parameter_distributions.items():
                value = np.random.normal(mean, std)
                scenario[param_name] = value

            scenarios.append(scenario)

        if self.verbose:
            print("Monte Carlo simulation:")
            print(f"  Samples: {config.n_samples}")
            print("  Parameters with uncertainty:")
            for param_name, (mean, std) in config.parameter_distributions.items():
                print(f"    {param_name}: N({mean}, {std}²)")

        return self.run_scenarios(scenarios, duration, initial_state, **kwargs)

    def summarize_results(self, results: List[ScenarioResult], metrics: Optional[List[str]] = None) -> Dict[str, Any]:
        """
        Summarize results from multiple scenarios.

        Computes summary statistics for each metric across all successful scenarios.

        Args:
            results: List of ScenarioResult objects
            metrics: List of metric names to summarize (default: all)

        Returns:
            Dictionary with summary statistics

        Example:
            ```python
            summary = parallel.summarize_results(results)
            ch4_stats = summary['metrics']['Q_ch4']
            print(f"Mean CH4: {ch4_stats['avg']:.1f} m³/d")
            ```
        """
        successful = [r for r in results if r.success]
        n_scenarios = len(results)
        n_successful = len(successful)

        summary = {
            "n_scenarios": n_scenarios,
            "n_successful": n_successful,
            "n_failed": n_scenarios - n_successful,
            "success_rate": n_successful / n_scenarios if n_scenarios > 0 else 0,
            "metrics": {},
        }

        if not successful:
            if n_scenarios > 0:
                summary["error"] = "No successful scenarios"
            else:
                summary["error"] = "No scenarios to summarize"
            return summary

        # Determine which metrics to summarize
        if metrics is None:
            # Use all metrics from first result
            metrics = list(successful[0].metrics.keys())

        # Compute statistics for each metric
        for metric_name in metrics:
            values = [r.metrics.get(metric_name, np.nan) for r in successful]
            values = [v for v in values if not np.isnan(v)]

            if values:
                summary["metrics"][metric_name] = {
                    "mean": np.mean(values),
                    "std": np.std(values),
                    "min": np.min(values),
                    "max": np.max(values),
                    "median": np.median(values),
                    "q25": np.percentile(values, 25),
                    "q75": np.percentile(values, 75),
                }

        return summary

    def _serialize_adm1(self) -> Dict[str, Any]:
        """
        Serialize ADM1 model for passing to worker processes.

        Returns:
            Dictionary with ADM1 configuration
        """
        return {
            "V_liq": self.adm1.V_liq,
            "V_gas": self.adm1._V_gas,
            "T_ad": self.adm1._T_ad,
            "feedstock_config": {
                "feeding_freq": 48,  # Default from feedstock
            },
        }

    @staticmethod
    def _generate_combinations(value_lists: List[List[Any]]) -> List[Tuple]:
        """
        Generate all combinations from lists of values (Cartesian product).

        Args:
            value_lists: List of lists containing values

        Returns:
            List of tuples with all combinations
        """
        if not value_lists:
            return [()]

        result = []
        for value in value_lists[0]:
            for rest in ParallelSimulator._generate_combinations(value_lists[1:]):
                result.append((value,) + rest)

        return result

Functions

__init__(adm1, n_workers=None, verbose=True)

Initialize parallel simulator.

Parameters:

Name Type Description Default
adm1 ADM1

ADM1 model instance

 required
n_workers Optional[int]

Number of worker processes (default: CPU count - 1)

 None
verbose bool

Enable progress output

 True
Source code in pyadm1/simulation/parallel.py 
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def __init__(self, adm1: "ADM1", n_workers: Optional[int] = None, verbose: bool = True):
    """
    Initialize parallel simulator.

    Args:
        adm1: ADM1 model instance
        n_workers: Number of worker processes (default: CPU count - 1)
        verbose: Enable progress output
    """
    self.adm1 = adm1
    self.n_workers = n_workers or max(1, mp.cpu_count() - 1)
    self.verbose = verbose

monte_carlo(config, duration, initial_state, **kwargs)

Run Monte Carlo simulation with parameter uncertainty.

Samples parameters from normal distributions and runs multiple scenarios to quantify uncertainty in predictions.

Parameters:

Name Type Description Default
config MonteCarloConfig

MonteCarloConfig with distributions and sample count

 required
duration float

Simulation duration [days]

 required
initial_state List[float]

Initial ADM1 state vector

 required
**kwargs Any

Additional arguments for run_scenarios

 {}

Returns:

Type Description
List[ScenarioResult]

List of ScenarioResult objects
Example

config = MonteCarloConfig( ... n_samples=100, ... parameter_distributions={ ... "k_dis": (0.5, 0.05), # mean=0.5, std=0.05 ... "Y_su": (0.10, 0.01) ... }, ... fixed_params={"Q": [15, 10, 0, 0, 0, 0, 0, 0, 0, 0]}, ... seed=42 ... ) results = parallel.monte_carlo(config, duration=30, initial_state=state)

Source code in pyadm1/simulation/parallel.py 
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def monte_carlo(
    self,
    config: MonteCarloConfig,
    duration: float,
    initial_state: List[float],
    **kwargs: Any,
) -> List[ScenarioResult]:
    """
    Run Monte Carlo simulation with parameter uncertainty.

    Samples parameters from normal distributions and runs multiple scenarios
    to quantify uncertainty in predictions.

    Args:
        config: MonteCarloConfig with distributions and sample count
        duration: Simulation duration [days]
        initial_state: Initial ADM1 state vector
        **kwargs: Additional arguments for run_scenarios

    Returns:
        List of ScenarioResult objects

    Example:
        >>> config = MonteCarloConfig(
        ...     n_samples=100,
        ...     parameter_distributions={
        ...         "k_dis": (0.5, 0.05),  # mean=0.5, std=0.05
        ...         "Y_su": (0.10, 0.01)
        ...     },
        ...     fixed_params={"Q": [15, 10, 0, 0, 0, 0, 0, 0, 0, 0]},
        ...     seed=42
        ... )
        >>> results = parallel.monte_carlo(config, duration=30, initial_state=state)
    """
    # Set random seed for reproducibility
    if config.seed is not None:
        np.random.seed(config.seed)

    # Generate scenarios
    scenarios = []
    for i in range(config.n_samples):
        scenario = config.fixed_params.copy()

        # Sample each parameter from its distribution
        for param_name, (mean, std) in config.parameter_distributions.items():
            value = np.random.normal(mean, std)
            scenario[param_name] = value

        scenarios.append(scenario)

    if self.verbose:
        print("Monte Carlo simulation:")
        print(f"  Samples: {config.n_samples}")
        print("  Parameters with uncertainty:")
        for param_name, (mean, std) in config.parameter_distributions.items():
            print(f"    {param_name}: N({mean}, {std}²)")

    return self.run_scenarios(scenarios, duration, initial_state, **kwargs)

multi_parameter_sweep(parameter_configs, duration, initial_state, fixed_params=None, **kwargs)

Run multi-parameter sweep (full factorial design).

Tests all combinations of provided parameter values.

Parameters:

Name Type Description Default
parameter_configs Dict[str, List[float]]

Dict mapping parameter names to value lists

 required
duration float

Simulation duration [days]

 required
initial_state List[float]

Initial ADM1 state vector

 required
fixed_params Optional[Dict[str, Any]]

Parameters to keep fixed

 None
**kwargs Any

Additional arguments for run_scenarios

 {}

Returns:

Type Description
List[ScenarioResult]

List of ScenarioResult objects
Example

parameter_configs = { ... "k_dis": [0.4, 0.5, 0.6], ... "Y_su": [0.09, 0.10, 0.11] ... } results = parallel.multi_parameter_sweep( ... parameter_configs, ... duration=30, ... initial_state=state, ... fixed_params={"Q": [15, 10, 0, 0, 0, 0, 0, 0, 0, 0]} ... )

Source code in pyadm1/simulation/parallel.py 
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def multi_parameter_sweep(
    self,
    parameter_configs: Dict[str, List[float]],
    duration: float,
    initial_state: List[float],
    fixed_params: Optional[Dict[str, Any]] = None,
    **kwargs: Any,
) -> List[ScenarioResult]:
    """
    Run multi-parameter sweep (full factorial design).

    Tests all combinations of provided parameter values.

    Args:
        parameter_configs: Dict mapping parameter names to value lists
        duration: Simulation duration [days]
        initial_state: Initial ADM1 state vector
        fixed_params: Parameters to keep fixed
        **kwargs: Additional arguments for run_scenarios

    Returns:
        List of ScenarioResult objects

    Example:
        >>> parameter_configs = {
        ...     "k_dis": [0.4, 0.5, 0.6],
        ...     "Y_su": [0.09, 0.10, 0.11]
        ... }
        >>> results = parallel.multi_parameter_sweep(
        ...     parameter_configs,
        ...     duration=30,
        ...     initial_state=state,
        ...     fixed_params={"Q": [15, 10, 0, 0, 0, 0, 0, 0, 0, 0]}
        ... )
    """
    fixed_params = fixed_params or {}

    # Generate all combinations using recursive approach
    param_names = list(parameter_configs.keys())
    param_values = [parameter_configs[name] for name in param_names]

    scenarios = []
    for combination in self._generate_combinations(param_values):
        scenario = fixed_params.copy()
        for name, value in zip(param_names, combination):
            scenario[name] = value
        scenarios.append(scenario)

    if self.verbose:
        print("Multi-parameter sweep:")
        for name, values in parameter_configs.items():
            print(f"  {name}: {len(values)} values")
        print(f"  Total combinations: {len(scenarios)}")

    return self.run_scenarios(scenarios, duration, initial_state, **kwargs)

parameter_sweep(config, duration, initial_state, **kwargs)

Run parameter sweep for a single parameter.

Tests multiple values of one parameter while keeping others fixed.

Parameters:

Name Type Description Default
config ParameterSweepConfig

ParameterSweepConfig with parameter and values

 required
duration float

Simulation duration [days]

 required
initial_state List[float]

Initial ADM1 state vector

 required
**kwargs Any

Additional arguments for run_scenarios

 {}

Returns:

Type Description
List[ScenarioResult]

List of ScenarioResult objects
Example

config = ParameterSweepConfig( ... parameter_name="k_dis", ... values=[0.3, 0.4, 0.5, 0.6, 0.7], ... other_params={"Q": [15, 10, 0, 0, 0, 0, 0, 0, 0, 0]} ... ) results = parallel.parameter_sweep(config, duration=30, initial_state=state)

Source code in pyadm1/simulation/parallel.py 
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def parameter_sweep(
    self,
    config: ParameterSweepConfig,
    duration: float,
    initial_state: List[float],
    **kwargs: Any,
) -> List[ScenarioResult]:
    """
    Run parameter sweep for a single parameter.

    Tests multiple values of one parameter while keeping others fixed.

    Args:
        config: ParameterSweepConfig with parameter and values
        duration: Simulation duration [days]
        initial_state: Initial ADM1 state vector
        **kwargs: Additional arguments for run_scenarios

    Returns:
        List of ScenarioResult objects

    Example:
        >>> config = ParameterSweepConfig(
        ...     parameter_name="k_dis",
        ...     values=[0.3, 0.4, 0.5, 0.6, 0.7],
        ...     other_params={"Q": [15, 10, 0, 0, 0, 0, 0, 0, 0, 0]}
        ... )
        >>> results = parallel.parameter_sweep(config, duration=30, initial_state=state)
    """
    # Generate scenarios
    scenarios = []
    for value in config.values:
        scenario = config.other_params.copy()
        scenario[config.parameter_name] = value
        scenarios.append(scenario)

    if self.verbose:
        print(f"Parameter sweep: {config.parameter_name}")
        print(f"  Values: {config.values}")

    return self.run_scenarios(scenarios, duration, initial_state, **kwargs)

run_scenarios(scenarios, duration, initial_state, dt=1.0 / 24.0, compute_metrics=True, save_time_series=False)

Run multiple simulation scenarios in parallel.

Each scenario is a dictionary containing parameter values and substrate feed rates. The simulator will run all scenarios concurrently and collect results.

Parameters:

Name Type Description Default
scenarios List[Dict[str, Any]]

List of scenario dictionaries with parameters

 required
duration float

Simulation duration [days]

 required
initial_state List[float]

Initial ADM1 state vector

 required
dt float

Time step [days]

 1.0 / 24.0
compute_metrics bool

Calculate performance metrics

 True
save_time_series bool

Save full time series data

 False

Returns:

Type Description
List[ScenarioResult]

List of ScenarioResult objects
Example

scenarios = [ ... {"k_dis": 0.5, "Q": [15, 10, 0, 0, 0, 0, 0, 0, 0, 0]}, ... {"k_dis": 0.6, "Q": [20, 10, 0, 0, 0, 0, 0, 0, 0, 0]}, ... ] results = parallel.run_scenarios(scenarios, duration=30, initial_state=state)

Source code in pyadm1/simulation/parallel.py 
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def run_scenarios(
    self,
    scenarios: List[Dict[str, Any]],
    duration: float,
    initial_state: List[float],
    dt: float = 1.0 / 24.0,
    compute_metrics: bool = True,
    save_time_series: bool = False,
) -> List[ScenarioResult]:
    """
    Run multiple simulation scenarios in parallel.

    Each scenario is a dictionary containing parameter values and substrate
    feed rates. The simulator will run all scenarios concurrently and
    collect results.

    Args:
        scenarios: List of scenario dictionaries with parameters
        duration: Simulation duration [days]
        initial_state: Initial ADM1 state vector
        dt: Time step [days]
        compute_metrics: Calculate performance metrics
        save_time_series: Save full time series data

    Returns:
        List of ScenarioResult objects

    Example:
        >>> scenarios = [
        ...     {"k_dis": 0.5, "Q": [15, 10, 0, 0, 0, 0, 0, 0, 0, 0]},
        ...     {"k_dis": 0.6, "Q": [20, 10, 0, 0, 0, 0, 0, 0, 0, 0]},
        ... ]
        >>> results = parallel.run_scenarios(scenarios, duration=30, initial_state=state)
    """
    if self.verbose:
        print(f"Starting parallel simulation with {len(scenarios)} scenarios")
        print(f"Using {self.n_workers} worker processes")

    start_time = time.time()

    # Create worker function with fixed parameters
    worker_func = partial(
        _run_single_scenario,
        adm1_config=self._serialize_adm1(),
        duration=duration,
        initial_state=initial_state,
        dt=dt,
        compute_metrics=compute_metrics,
        save_time_series=save_time_series,
        verbose=self.verbose,
    )

    # Add scenario IDs
    scenarios_with_ids = [(i, scenario) for i, scenario in enumerate(scenarios)]

    # Avoid spawning a process pool for trivial workloads. This also prevents
    # pythonnet/.NET deadlocks seen in some CI environments with forked workers.
    use_sequential = self.n_workers <= 1 or len(scenarios_with_ids) <= 1

    if use_sequential:
        results = []
        for i, scenario_data in enumerate(scenarios_with_ids):
            results.append(worker_func(scenario_data))
            if self.verbose and ((i + 1) % 10 == 0 or (i + 1) == len(scenarios)):
                print(f"  Completed {i + 1}/{len(scenarios)} scenarios")
    else:
        ctx = _get_mp_context()
        # Run scenarios in parallel
        # Keep workers alive across multiple scenarios to avoid repeated
        # python startup / pythonnet initialization overhead.
        with ctx.Pool(processes=self.n_workers) as pool:
            if self.verbose:
                # Use imap for progress tracking
                results = []
                for i, result in enumerate(pool.imap(worker_func, scenarios_with_ids)):
                    results.append(result)
                    if (i + 1) % 10 == 0 or (i + 1) == len(scenarios):
                        print(f"  Completed {i + 1}/{len(scenarios)} scenarios")
            else:
                results = pool.map(worker_func, scenarios_with_ids)

    elapsed_time = time.time() - start_time

    if self.verbose:
        n_success = sum(1 for r in results if r.success)
        print("\nSimulation complete:")
        print(f"  Total scenarios: {len(scenarios)}")
        print(f"  Successful: {n_success}")
        print(f"  Failed: {len(scenarios) - n_success}")
        print(f"  Total time: {elapsed_time:.1f} seconds")
        print(f"  Average time per scenario: {elapsed_time / len(scenarios):.2f} seconds")

    return results

summarize_results(results, metrics=None)

Summarize results from multiple scenarios.

Computes summary statistics for each metric across all successful scenarios.

Parameters:

Name Type Description Default
results List[ScenarioResult]

List of ScenarioResult objects

 required
metrics Optional[List[str]]

List of metric names to summarize (default: all)

 None

Returns:

Type Description
Dict[str, Any]

Dictionary with summary statistics
Example 
summary = parallel.summarize_results(results)
ch4_stats = summary['metrics']['Q_ch4']
print(f"Mean CH4: {ch4_stats['avg']:.1f} m³/d")
Source code in pyadm1/simulation/parallel.py 
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def summarize_results(self, results: List[ScenarioResult], metrics: Optional[List[str]] = None) -> Dict[str, Any]:
    """
    Summarize results from multiple scenarios.

    Computes summary statistics for each metric across all successful scenarios.

    Args:
        results: List of ScenarioResult objects
        metrics: List of metric names to summarize (default: all)

    Returns:
        Dictionary with summary statistics

    Example:
        ```python
        summary = parallel.summarize_results(results)
        ch4_stats = summary['metrics']['Q_ch4']
        print(f"Mean CH4: {ch4_stats['avg']:.1f} m³/d")
        ```
    """
    successful = [r for r in results if r.success]
    n_scenarios = len(results)
    n_successful = len(successful)

    summary = {
        "n_scenarios": n_scenarios,
        "n_successful": n_successful,
        "n_failed": n_scenarios - n_successful,
        "success_rate": n_successful / n_scenarios if n_scenarios > 0 else 0,
        "metrics": {},
    }

    if not successful:
        if n_scenarios > 0:
            summary["error"] = "No successful scenarios"
        else:
            summary["error"] = "No scenarios to summarize"
        return summary

    # Determine which metrics to summarize
    if metrics is None:
        # Use all metrics from first result
        metrics = list(successful[0].metrics.keys())

    # Compute statistics for each metric
    for metric_name in metrics:
        values = [r.metrics.get(metric_name, np.nan) for r in successful]
        values = [v for v in values if not np.isnan(v)]

        if values:
            summary["metrics"][metric_name] = {
                "mean": np.mean(values),
                "std": np.std(values),
                "min": np.min(values),
                "max": np.max(values),
                "median": np.median(values),
                "q25": np.percentile(values, 25),
                "q75": np.percentile(values, 75),
            }

    return summary