49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
import numpy as np
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import scipy.sparse as sparse
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import igl
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def p2e(m):
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if isinstance(m, np.ndarray):
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if m.dtype.type == np.int64:
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return igl.eigen.MatrixXi(m)
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elif m.dtype.type == np.float64:
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return igl.eigen.MatrixXd(m)
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raise TypeError("p2e only support dtype float64 or int64")
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if sparse.issparse(m):
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# convert in a dense matrix with triples
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coo = m.tocoo()
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triplets = np.vstack((coo.row, coo.col, coo.data)).T
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triples_eigen_wrapper = igl.eigen.MatrixXd(triplets)
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if m.dtype.type == np.int64:
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t = igl.eigen.SparseMatrixi()
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t.fromcoo(triples_eigen_wrapper)
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return t
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elif m.dtype.type == np.float64:
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t = igl.eigen.SparseMatrixd()
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t.fromCOO(triples_eigen_wrapper)
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return t
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raise TypeError("p2e only support numpy.array or scipy.sparse")
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def e2p(m):
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if isinstance(m, igl.eigen.MatrixXd):
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return np.array(m, dtype='float64')
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elif isinstance(m, igl.eigen.MatrixXi):
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return np.array(m, dtype='int64')
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elif isinstance(m, igl.eigen.SparseMatrixd):
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coo = np.array(m.toCOO())
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I = coo[:, 0]
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J = coo[:, 1]
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V = coo[:, 2]
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return sparse.coo_matrix((V,(I,J)), shape=(m.rows(),m.cols()), dtype='float64')
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elif isinstance(m, igl.eigen.SparseMatrixi):
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coo = np.array(m.toCOO())
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I = coo[:, 0]
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J = coo[:, 1]
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V = coo[:, 2]
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return sparse.coo_matrix((V,(I,J)), shape=(m.rows(),m.cols()), dtype='int64')
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