percolate/share/jugfile.py
def bond_run(perc_graph_result, seed, ps, convolution_factors_tasks):
    """
    Perform a single run (realization) over all microstates and return the
    canonical cluster statistics
    """
    microcanonical_statistics = percolate.hpc.bond_microcanonical_statistics(
        seed=seed, **perc_graph_result
    )

    # initialize statistics array
    canonical_statistics = np.empty(
        ps.size,
        dtype=percolate.hpc.canonical_statistics_dtype(
            spanning_cluster=SPANNING_CLUSTER,
        )
    )

    # loop over all p's and convolve canonical statistics
    # http://docs.scipy.org/doc/numpy/reference/arrays.nditer.html#modifying-array-values
    for row, convolution_factors_task in zip(
        np.nditer(canonical_statistics, op_flags=['writeonly']),
        convolution_factors_tasks,
    ):
        # load task result
        # http://jug.readthedocs.org/en/latest/api.html#jug.Task.load
        assert not convolution_factors_task.is_loaded()
        convolution_factors_task.load()
        # fetch task result
        my_convolution_factors = convolution_factors_task.result

        # convolve to canonical statistics
        row[...] = percolate.hpc.bond_canonical_statistics(
            microcanonical_statistics=microcanonical_statistics,
            convolution_factors=my_convolution_factors,
            spanning_cluster=SPANNING_CLUSTER,
        )
        # explicitly unload task to save memory
        # http://jug.readthedocs.org/en/latest/api.html#jug.Task.unload
        convolution_factors_task.unload()

    # initialize canonical averages for reduce
    ret = percolate.hpc.bond_initialize_canonical_averages(
        canonical_statistics=canonical_statistics,
        spanning_cluster=SPANNING_CLUSTER,
    )

    return ret
def bond_run(perc_graph_result, seed, ps, convolution_factors_tasks):
    """
    Perform a single run (realization) over all microstates and return the
    canonical cluster statistics
    """
    microcanonical_statistics = percolate.hpc.bond_microcanonical_statistics(
        seed=seed, **perc_graph_result
    )

    # initialize statistics array
    canonical_statistics = np.empty(
        ps.size,
        dtype=percolate.hpc.canonical_statistics_dtype(
            spanning_cluster=SPANNING_CLUSTER,
        )
    )

    # loop over all p's and convolve canonical statistics
    # http://docs.scipy.org/doc/numpy/reference/arrays.nditer.html#modifying-array-values
    for row, convolution_factors_task in zip(
        np.nditer(canonical_statistics, op_flags=['writeonly']),
        convolution_factors_tasks,
    ):
        # load task result
        # http://jug.readthedocs.org/en/latest/api.html#jug.Task.load
        assert not convolution_factors_task.is_loaded()
        convolution_factors_task.load()
        # fetch task result
        my_convolution_factors = convolution_factors_task.result

        # convolve to canonical statistics
        row[...] = percolate.hpc.bond_canonical_statistics(
            microcanonical_statistics=microcanonical_statistics,
            convolution_factors=my_convolution_factors,
            spanning_cluster=SPANNING_CLUSTER,
        )
        # explicitly unload task to save memory
        # http://jug.readthedocs.org/en/latest/api.html#jug.Task.unload
        convolution_factors_task.unload()

    # initialize canonical averages for reduce
    ret = percolate.hpc.bond_initialize_canonical_averages(
        canonical_statistics=canonical_statistics,
        spanning_cluster=SPANNING_CLUSTER,
    )

    return ret
def bond_run ( perc_graph_result , seed , ps , convolution_factors_tasks ) : microcanonical_statistics = percolate . hpc . bond_microcanonical_statistics ( seed = seed , * * perc_graph_result ) # initialize statistics array canonical_statistics = np . empty ( ps . size , dtype = percolate . hpc . canonical_statistics_dtype ( spanning_cluster = SPANNING_CLUSTER , ) ) # loop over all p's and convolve canonical statistics # http://docs.scipy.org/doc/numpy/reference/arrays.nditer.html#modifying-array-values for row , convolution_factors_task in zip ( np . nditer ( canonical_statistics , op_flags = [ 'writeonly' ] ) , convolution_factors_tasks , ) : # load task result # http://jug.readthedocs.org/en/latest/api.html#jug.Task.load assert not convolution_factors_task . is_loaded ( ) convolution_factors_task . load ( ) # fetch task result my_convolution_factors = convolution_factors_task . result # convolve to canonical statistics row [ ... ] = percolate . hpc . bond_canonical_statistics ( microcanonical_statistics = microcanonical_statistics , convolution_factors = my_convolution_factors , spanning_cluster = SPANNING_CLUSTER , ) # explicitly unload task to save memory # http://jug.readthedocs.org/en/latest/api.html#jug.Task.unload convolution_factors_task . unload ( ) # initialize canonical averages for reduce ret = percolate . hpc . bond_initialize_canonical_averages ( canonical_statistics = canonical_statistics , spanning_cluster = SPANNING_CLUSTER , ) return ret
Perform a single run (realization) over all microstates and return the
    canonical cluster statistics
Perform a single run ( realization ) over all microstates and return the canonical cluster statistics