pyemma.msm.io.save_matrix¶

pyemma.msm.io.save_matrix(filename, A, mode='default')¶

Save matrix as binary file.

Parameters:

filename (str) – Relative or absolute pathname of the output file.
A ((M, N) ndarray or sparse matrix) –

mode ({'default', 'dense', 'sparse'}) –

mode
‘default’	Use the type of A to determine the formatname.xxx (dense), name.coo.xxx (sparse)
‘dense’	Enforce conversion to a dense representationand store the corresponding ndarray
‘sparse’	Convert to sparse matrix in COO-formatand store the coordinate list as ndarray

See also

load_matrix()

Notes

(M, N) dense matrices are stored as ndarrays in numpy .npy binary format. Sparse matrices are converted to coordinate list (COO) format. The coordinate list [...,(row, col, value),...] is then stored as a (K, 3) ndarray in numpy .npy binary format. K is the number of nonzero entries in the sparse matrix.

Using the naming scheme name.npy for dense matrices and name.coo.npy for sparse matrices will allow load_matrix to automatically infer the appropriate matrix type from the given filename.

Examples

>>> from tempfile import NamedTemporaryFile
>>> from pyemma.msm.io import load_matrix, save_matrix

dense

Use temporary file with ending ‘.npy’

>>> tmpfile = NamedTemporaryFile(suffix='.npy')

Dense (3, 2) matrix

>>> A = np.array([[3, 1], [2, 1], [1, 1]])
>>> write_matrix(tmpfile.name, A)

Load from disk

>>> X = load_matrix(tmpfile.name)
>>> X
array([[ 3.,  1.],
       [ 2.,  1.],
       [ 1.,  1.]])

sparse

>>> from scipy.sparse import csr_matrix

Use temporary file with ending ‘.coo.dat’

>>> tmpfile = NamedTemporaryFile(suffix='.coo.npy')

Sparse (3, 3) matrix

>>> A = csr_matrix(np.eye(3))
>>> write_matrix(tmpfile.name, A)

Load from disk

>>> X = load_matrix(tmpfile.name)
>>> X
array([[ 1.,  0.,  0.],
       [ 0.,  1.,  0.],
       [ 0.,  0.,  1.]])