Source code for assembly

"""
Assemble a series of sparse matrices using two gathering matrices. For
more information about assembling routines, see, for example,
`[F. Cuvelier, C. Japhet and G. Scarella, An efficient way to assemble
finite element matrices in vector languages, 2016]
<http://doi.org/10.1007/s10543-015-0587-4>`_.


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:download:`[assembly.py]</contents/LIBRARY/ptc/mathischeap_ptc/assembly.py>`.
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"""

import numpy as np
from scipy import sparse as spspa


[docs] def assemble(Ms, Gr, Gc=None): """A (not very fast) routine for assembling matrices and vectors. :param Ms: The matrices/vectors to be assembled. ``Ms[i]`` refers to the ith (for example, in element No. i) matrix/vector. The matrices/vectors need to be sparse matrices/vectors. A sparse vector is a csc_matrix of shape :math:`(n, 1)`. :param Gr: The row gathering matrix. :type Gr: np.array :param Gc: (default: ``None``) The column gathering matrix. When it is ``None``, it means we are assembling vectors. Therefore we will only need the row gathering matrix, ``Gr``. :type Gc: None, np.array :return: A csc matrix representing the assembled matrix/vector. :example: >>> from crazy_mesh import CrazyMeshGlobalNumbering >>> from scipy import sparse as spspa >>> K = 2 >>> N = 3 >>> GM = CrazyMeshGlobalNumbering(K, N) >>> GM_F = GM.FP >>> GM_V = GM.VP >>> Ms = [spspa.random(N**3, 3*(N+1)*N**2, 0.1, 'csc') ... for _ in range(K**3)] # generate a series of sparse matrices >>> M = assemble(Ms, GM_V, GM_F) # assemble matrices >>> M.shape (216, 756) >>> Vs = [spspa.random(N**3, 1, 0.5, 'csc') ... for _ in range(K**3)] # generate a series of sparse vectors >>> V = assemble(Vs, GM_V) >>> V.shape (216, 1) """ if Gc is None: # we are assembling vectors DEP = int(np.max(Gr) + 1) ROW = list() DAT = list() for i, Vi in enumerate(Ms): assert Vi.__class__.__name__ == 'csc_matrix' and \ np.shape(Vi)[1] == 1, f"V[{i}] is not a sparse vector." indices = Vi.indices data = Vi.data ROW.extend(Gr[i][indices]) DAT.extend(data) return spspa.csc_matrix((DAT, ROW, [0, len(ROW)]), shape=(DEP, 1)) else: DEP = int(np.max(Gr) + 1) WID = int(np.max(Gc) + 1) ROW = list() COL = list() DAT = list() for i, Mi in enumerate(Ms): assert Mi.__class__.__name__ in ('csc_matrix', 'csr_matrix') indices = Mi.indices indptr = Mi.indptr data = Mi.data nums = np.diff(indptr) if Mi.__class__.__name__ == 'csc_matrix': for j, num in enumerate(nums): idx = indices[indptr[j]:indptr[j + 1]] ROW.extend(Gr[i][idx]) COL.extend([Gc[i][j], ] * num) elif Mi.__class__.__name__ == 'csr_matrix': for j, num in enumerate(nums): idx = indices[indptr[j]:indptr[j + 1]] ROW.extend([Gr[i][j], ] * num) COL.extend(Gc[i][idx]) else: raise Exception("I can not handle %r." % Mi) DAT.extend(data) return spspa.csc_matrix((DAT, (ROW, COL)),shape=(DEP, WID))
if __name__ == '__main__': import doctest doctest.testmod()