AverageOverCell.py

Go to the documentation of this file.

# This file is part of the Peano project. For conditions of distribution and
# use, please see the copyright notice at www.peano-framework.org
from .Filter import Filter
from tarch.plotter.api.input.Patch import Patch
 
 
class AverageOverCell(Filter):
    """
    Run over all cells. Each cell holds a patch with dof x dof (2d) or
    dof x dof x dof (3d) cells. Each cell hosts unknown unknowns. This
    filter reduces each patch to a 1x1 or 1x1x1 outcome, i.e. a point
    cloud.
    """
 
    def __init__(self, verbose=False):
        Filter.__init__(self, True, False, verbose)
        pass
 
    def render(
        self,
        cell_data,
        dof,
        dimension,
        unknowns,
        is_data_associated_to_cell,
        description,
        mapping,
    ):
        """
        Overwrite this one for the particular filter.
        """
        result_cell_data = []
 
        for patch in cell_data:
            average_values = [0] * unknowns
 
            entries_per_cell = dof**dimension
 
            for i in range(0, entries_per_cell):
                for j in range(0, unknowns):
                    average_values[j] = (
                        average_values[j] + patch.values[i * unknowns + j]
                    )
            for j in range(0, unknowns):
                average_values[j] = average_values[j] / entries_per_cell
 
            new_patch = Patch(
                patch.offset, patch.size, average_values, patch.subdomain_number
            )
            result_cell_data.append(new_patch)
 
        if self.verbose and len(result_cell_data) == 0:
            print("Snapshot has been empty")
        elif self.verbose:
            print(
                "Replaced "
                + str(len(result_cell_data))
                + " patches with one voxel with average values"
            )
 
        mapping = []
        if dimension == 2:
            mapping = [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0]
        else:
            mapping = [
                0.0,
                0.0,
                0.0,
                1.0,
                0.0,
                0.0,
                0.0,
                1.0,
                0.0,
                1.0,
                1.0,
                0.0,
                0.0,
                0.0,
                1.0,
                1.0,
                0.0,
                1.0,
                0.0,
                1.0,
                1.0,
                1.0,
                1.0,
                1.0,
            ]
        return result_cell_data, 1, dimension, unknowns, True, description, mapping