OpenMC/openmc/deplete/pool.py

"""Dedicated module containing depletion function

Provided to avoid some circular imports
"""
from itertools import repeat, starmap
from multiprocessing import Pool

import numpy as np
from scipy.sparse import hstack, vstack

from openmc.mpi import comm
from .._sparse_compat import block_array, csc_array

# Configurable switch that enables / disables the use of
# multiprocessing routines during depletion
USE_MULTIPROCESSING = True

# Allow user to override the number of worker processes to use for depletion
# calculations
NUM_PROCESSES = None

def _distribute(items):
    """Distribute items across MPI communicator

    Parameters
    ----------
    items : list
        List of items of distribute

    Returns
    -------
    list
        Items assigned to process that called

    """
    min_size, extra = divmod(len(items), comm.size)
    j = 0
    for i in range(comm.size):
        chunk_size = min_size + int(i < extra)
        if comm.rank == i:
            return items[j:j + chunk_size]
        j += chunk_size


def _add_external_source(
    matrices, n, chain, external_source_rates, current_timestep
):
    """Augment depletion matrices and nuclide vectors with external sources."""
    sources = map(chain.form_ext_source_term, repeat(external_source_rates),
                  repeat(current_timestep), external_source_rates.local_mats)
    matrices = [
        hstack([matrix, source])
        for matrix, source in zip(matrices, sources)
    ]
    n_solve = [arr.copy() for arr in n]

    # Homogenize the augmented matrices and nuclide vectors
    for i, matrix in enumerate(matrices):
        if matrix.shape[0] + 1 == matrix.shape[1]:
            matrices[i] = vstack(
                [matrix, csc_array((1, matrix.shape[1]))])
            n_solve[i] = np.append(n_solve[i], 1.0)

    return matrices, n_solve


def deplete(func, chain, n, rates, dt, current_timestep=None, matrix_func=None,
            transfer_rates=None, external_source_rates=None, substeps=1,
            *matrix_args):
    """Deplete materials using given reaction rates for a specified time

    Parameters
    ----------
    func : callable
        Function to use to get new compositions. Expected to have the signature
        ``func(A, n0, t, substeps=1) -> n1``.
    chain : openmc.deplete.Chain
        Depletion chain
    n : list of numpy.ndarray
        List of atom number arrays for each material. Each array in the list
        contains the number of [atom] of each nuclide.
    rates : openmc.deplete.ReactionRates
        Reaction rates (from transport operator)
    dt : float
        Time in [s] to deplete for
    current_timestep : int
        Current timestep index
    matrix_func : callable, optional
        Function to form the depletion matrix after calling ``matrix_func(chain,
        rates, fission_yields)``, where ``fission_yields = {parent: {product:
        yield_frac}}`` Expected to return the depletion matrix required by
        ``func``
    transfer_rates : openmc.deplete.TransferRates, Optional
        Transfer rates for continuous removal/feed.

        .. versionadded:: 0.14.0
    external_source_rates : openmc.deplete.ExternalSourceRates, Optional
        External source rates for continuous removal/feed.

        .. versionadded:: 0.15.3
    substeps : int, optional
        Number of substeps to pass to solvers that support substepping.
    matrix_args: Any, optional
        Additional arguments passed to matrix_func

    Returns
    -------
    n_result : list of numpy.ndarray
        Updated list of atom number arrays for each material. Each array in the
        list contains the number of [atom] of each nuclide.

    """
    fission_yields = chain.fission_yields
    if len(fission_yields) == 1:
        fission_yields = repeat(fission_yields[0])
    elif len(fission_yields) != len(n):
        raise ValueError(
            "Number of material fission yield distributions {} is not "
            "equal to the number of compositions {}".format(
                len(fission_yields), len(n)))

    if matrix_func is None:
        matrices = map(chain.form_matrix, rates, fission_yields)
    else:
        matrices = map(matrix_func, repeat(chain), rates, fission_yields,
                       *matrix_args)

    # Determine if transfer rates or external source rates are active
    transfer_active = transfer_rates is not None and \
        current_timestep in transfer_rates.external_timesteps
    external_active = external_source_rates is not None and \
        current_timestep in external_source_rates.external_timesteps

    n_solve = n
    if transfer_active:
        # Calculate transfer rate terms as diagonal matrices
        transfers = map(chain.form_rr_term, repeat(transfer_rates),
                        repeat(current_timestep), transfer_rates.local_mats)

        # Subtract transfer rate terms from Bateman matrices
        matrices = [matrix - transfer for (matrix, transfer) in zip(matrices,
                                                                    transfers)]

        if transfer_rates.redox:
            for mat_idx, mat_id in enumerate(transfer_rates.local_mats):
                if mat_id in transfer_rates.redox:
                    matrices[mat_idx] = chain.add_redox_term(matrices[mat_idx],
                                                transfer_rates.redox[mat_id][0],
                                                transfer_rates.redox[mat_id][1])

        # Add external sources if present
        if external_active:
            matrices, n_solve = _add_external_source(
                matrices, n, chain, external_source_rates, current_timestep)

        # Set transfer rate terms with destination material if present
        if current_timestep in transfer_rates.index_transfer:
            # Gather all on comm.rank 0
            matrices = comm.gather(matrices)
            n = comm.gather(n_solve)

            if comm.rank == 0:
                # Expand lists
                matrices = [elm for matrix in matrices for elm in matrix]
                n = [n_elm for n_mat in n for  n_elm in n_mat]

                # Calculate transfer rate terms as diagonal matrices
                transfer_pair = {}
                for mat_pair in dict.fromkeys(transfer_rates.index_transfer[current_timestep]):
                    transfer_matrix = chain.form_rr_term(transfer_rates,
                                                         current_timestep,
                                                         mat_pair)
                    # check if destination material has a redox control
                    if mat_pair[0] in transfer_rates.redox:
                        transfer_matrix = chain.add_redox_term(transfer_matrix,
                                          transfer_rates.redox[mat_pair[0]][0],
                                          transfer_rates.redox[mat_pair[0]][1])
                    # Add external source rates if present
                    if external_active:
                        if len(external_source_rates.get_components(mat_pair[0], current_timestep)) > 0:
                            transfer_matrix = vstack([transfer_matrix,
                                csc_array((1, transfer_matrix.shape[1]))])
                        if len(external_source_rates.get_components(mat_pair[1], current_timestep)) > 0:
                            transfer_matrix = hstack([transfer_matrix,
                                csc_array((transfer_matrix.shape[0], 1))])
                    transfer_pair[mat_pair] = transfer_matrix

                # Combine all matrices together in a single block matrix of matrices
                # to be solved on one rank
                n_rows = n_cols = len(transfer_rates.burnable_mats)
                rows = []
                for row in range(n_rows):
                    cols = []
                    for col in range(n_cols):
                        mat_pair = (transfer_rates.burnable_mats[row],
                                    transfer_rates.burnable_mats[col])
                        if row == col:
                            # Fill the diagonals with the Bateman matrices
                            cols.append(matrices[row])
                        elif mat_pair in transfer_rates.index_transfer[current_timestep]:
                            # Fill the off-diagonals with the transfer pair matrices
                            cols.append(transfer_pair[mat_pair])
                        else:
                            cols.append(None)

                    rows.append(cols)
                matrix = block_array(rows)

                # Concatenate vectors of nuclides in one
                n_multi = np.concatenate(n)
                n_result = func(matrix, n_multi, dt, substeps)

                # Split back the nuclide vector result into the original form
                n_result = np.split(n_result, np.cumsum([len(i) for i in n])[:-1])
            else:
                n_result = None

            # Broadcast result to other MPI ranks and then distribute
            n_result = comm.bcast(n_result)
            n_result = _distribute(n_result)

            # Remove extra values based on the materials local to each rank
            if external_active:
                external_source_rates.reformat_nuclide_vectors(n_result)

            return n_result

    # If only external source rates are present
    elif external_active:
        matrices, n_solve = _add_external_source(
            matrices, n, chain, external_source_rates, current_timestep)

    inputs = zip(matrices, n_solve, repeat(dt), repeat(substeps))

    if USE_MULTIPROCESSING:
        with Pool(NUM_PROCESSES) as pool:
            n_result = list(pool.starmap(func, inputs))
    else:
        n_result = list(starmap(func, inputs))

    # Remove extra value at the end of the nuclide vectors if external source rates are present
    if external_active:
        external_source_rates.reformat_nuclide_vectors(n_result)

    return n_result