scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
This can be instantiated in several ways:
Notes
coo_matrix(D)
with a dense matrix D
coo_matrix(S)
with another sparse matrix S (equivalent to S.tocoo())
coo_matrix((M, N), [dtype])
to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype=’d’.
coo_matrix((data, ij), [shape=(M, N)])
The arguments ‘data’ and ‘ij’ represent three arrays:
1. data[:] the entries of the matrix, in any order
2. ij[0][:] the row indices of the matrix entries
3. ij[1][:] the column indices of the matrix entries
Where A[ij[0][k], ij[1][k] = data[k]. When shape is not specified, it is inferred from
the index arrays
Advantages of the COO format
• facilitates fast conversion among sparse formats
• permits duplicate entries (see example)
• very fast conversion to and from CSR/CSC formats
Disadvantages of the COO format
Intended Usage
Examples
• does not directly support:
– arithmetic operations
– slicing
• COO is a fast format for constructing sparse matrices
• Once a matrix has been constructed, convert to CSR or CSC format for fast arithmetic and matrix
vector operations
• By default when converting to CSR or CSC format, duplicate (i,j) entries will be summed together.
This facilitates efficient construction of finite element matrices and the like. (see example)
>>> from scipy.sparse import *
>>> from scipy import *
>>> coo_matrix( (3,4), dtype=int8 ).todense()
376 Chapter 3. Reference