Matrix Market

The Matrix Market exchange format is a text-based file format described by NIST. Matrix Market supports both a coordinate format for sparse matrices and an array format for dense matrices. The scipy.io module provides the scipy.io.mmread and scipy.io.mmwrite functions to read and write data in Matrix Market format, respectively. These functions work with either numpy.ndarray or scipy.sparse.coo_matrix objects depending on whether the data is in array or coordinate format. These functions can be combined with those of NetworkX’s convert_matrix module to read and write Graphs in Matrix Market format.

Examples

Reading and writing graphs using Matrix Market’s array format for dense matrices:

>>> import scipy as sp
>>> import scipy.io  # for mmread() and mmwrite()
>>> import io  # Use BytesIO as a stand-in for a Python file object
>>> fh = io.BytesIO()

>>> G = nx.complete_graph(5)
>>> a = nx.to_numpy_array(G)
>>> print(a)
[[0. 1. 1. 1. 1.]
 [1. 0. 1. 1. 1.]
 [1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0.]]

>>> # Write to file in Matrix Market array format
>>> sp.io.mmwrite(fh, a)
>>> print(fh.getvalue().decode('utf-8'))  # file contents
%%MatrixMarket matrix array real symmetric
%
5 5
0.0000000000000000e+00
1.0000000000000000e+00
1.0000000000000000e+00
1.0000000000000000e+00
1.0000000000000000e+00
0.0000000000000000e+00
1.0000000000000000e+00
1.0000000000000000e+00
1.0000000000000000e+00
0.0000000000000000e+00
1.0000000000000000e+00
1.0000000000000000e+00
0.0000000000000000e+00
1.0000000000000000e+00
0.0000000000000000e+00

>>> # Read from file
>>> fh.seek(0)
>>> H = nx.from_numpy_array(sp.io.mmread(fh))
>>> H.edges() == G.edges()
True

Reading and writing graphs using Matrix Market’s coordinate format for sparse matrices:

>>> import scipy as sp
>>> import scipy.io  # for mmread() and mmwrite()
>>> import io  # Use BytesIO as a stand-in for a Python file object
>>> fh = io.BytesIO()

>>> G = nx.path_graph(5)
>>> m = nx.to_scipy_sparse_matrix(G)
>>> print(m)
  (0, 1)        1
  (1, 0)        1
  (1, 2)        1
  (2, 1)        1
  (2, 3)        1
  (3, 2)        1
  (3, 4)        1
  (4, 3)        1

>>> sp.io.mmwrite(fh, m)
>>> print(fh.getvalue().decode('utf-8'))  # file contents
%%MatrixMarket matrix coordinate integer symmetric
%
5 5 4
2 1 1
3 2 1
4 3 1
5 4 1

>>> # Read from file
>>> fh.seek(0)
>>> H = nx.from_scipy_sparse_matrix(sp.io.mmread(fh))
>>> H.edges() == G.edges()
True