Module `conduction.tools.meshtools`

This file is part of Conduction https://git.dias.ie/itherc/conduction/

Conduction is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.

Conduction is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with Conduction. If not, see http://www.gnu.org/licenses/.

Expand source code

"""
Copyright 2017 Ben Mather

This file is part of Conduction <https://git.dias.ie/itherc/conduction/>

Conduction is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published by
the Free Software Foundation, either version 3 of the License, or any later version.

Conduction is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License
along with Conduction.  If not, see <http://www.gnu.org/licenses/>.
"""

import numpy as np

def csr_tocoo(indptr, indices, data):
    """ Convert from CSR to COO sparse matrix format """
    d = np.diff(indptr)
    I = np.repeat(np.arange(0,d.size,dtype='int32'), d)
    return I, indices, data

def coo_tocsr(I, J, V):
    """ Convert from COO to CSR sparse matrix format """
    nnz = np.bincount(I)
    indptr = np.insert(np.cumsum(nnz),0,0)
    return indptr, J, V

def sum_duplicates(I, J, V):
    """
    Sum all duplicate entries in the matrix
    """
    order = np.lexsort((J, I))
    I, J, V = I[order], J[order], V[order]
    unique_mask = ((I[1:] != I[:-1]) |
                   (J[1:] != J[:-1]))
    unique_mask = np.append(True, unique_mask)
    unique_inds, = np.nonzero(unique_mask)
    return I[unique_mask], J[unique_mask], np.add.reduceat(V, unique_inds)

def convert_petscmat_to_scipy(mat):
    from scipy.sparse import csr_matrix
    indptr, indices, values = mat.getValuesCSR()
    return csr_matrix((values, indices, indptr))

Functions

def convert_petscmat_to_scipy(mat)

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def convert_petscmat_to_scipy(mat):
    from scipy.sparse import csr_matrix
    indptr, indices, values = mat.getValuesCSR()
    return csr_matrix((values, indices, indptr))

def coo_tocsr(I, J, V)

Convert from COO to CSR sparse matrix format

Expand source code

def coo_tocsr(I, J, V):
    """ Convert from COO to CSR sparse matrix format """
    nnz = np.bincount(I)
    indptr = np.insert(np.cumsum(nnz),0,0)
    return indptr, J, V

def csr_tocoo(indptr, indices, data)

Convert from CSR to COO sparse matrix format

Expand source code

def csr_tocoo(indptr, indices, data):
    """ Convert from CSR to COO sparse matrix format """
    d = np.diff(indptr)
    I = np.repeat(np.arange(0,d.size,dtype='int32'), d)
    return I, indices, data

def sum_duplicates(I, J, V)

Sum all duplicate entries in the matrix

Expand source code

def sum_duplicates(I, J, V):
    """
    Sum all duplicate entries in the matrix
    """
    order = np.lexsort((J, I))
    I, J, V = I[order], J[order], V[order]
    unique_mask = ((I[1:] != I[:-1]) |
                   (J[1:] != J[:-1]))
    unique_mask = np.append(True, unique_mask)
    unique_inds, = np.nonzero(unique_mask)
    return I[unique_mask], J[unique_mask], np.add.reduceat(V, unique_inds)