fast-matrix-market 1.4.5

Fast and full-featured Matrix Market file I/O

Fast and full-featured Matrix Market file I/O package for Python.

Fastest way to read and write any Matrix Market .mtx file into a SciPy sparse matrix, sparse coordinate (triplet) arrays, or dense ndarray.

Implemented as a Python binding of the C++ fast_matrix_market library.

pip install fast_matrix_market

Compared to scipy.io.mmread()

The fast_matrix_market.mmread() and mmwrite() methods are direct replacements for their respective SciPy versions. Compared to SciPy v1.10.0:

• Significant performance boost

  

  The bytes in the plot refer to MatrixMarket file length. All cores on the system are used by default, use the parallelism argument to override. SciPy's routines are single-threaded.

• 64-bit indices, but only if the matrix dimensions require it.

  scipy.io.mmread() crashes on large matrices (dimensions > 2³¹) because it uses 32-bit indices on most platforms.

• Directly write CSC/CSR matrices with no COO intermediary.

• longdouble
  Read and write longdouble/longcomplex values for more floating-point precision on platforms that support it (e.g. 80-bit floats).

  Just pass long_type=True argument to any read method to use longdouble arrays. SciPy can write longdouble matrices but reads use double precision.

  Note: Many platforms do not offer any precision greater than double even if the longdouble type exists. On those platforms longdouble == double so check your Numpy for support. As of writing only Linux tends to have longdouble > double.

• Vector files
  Read 1D vector files. scipy.io.mmread() throws a ValueError.

Differences

• scipy.io.mmwrite() will search the matrix for symmetry if the symmetry argument is not specified. This is a very slow process that significantly impacts writing time for all matrices, including non-symmetric ones. It can be disabled by setting symmetry="general", but that is easily forgotten. fast_matrix_market.mmwrite() only looks for symmetries if the find_symmetry=True argument is passed.

Usage

import fast_matrix_market as fmm

Read as scipy sparse matrix

>>> a = fmm.mmread("eye3.mtx")
>>> a
<3x3 sparse matrix of type '<class 'numpy.float64'>'
        with 3 stored elements in COOrdinate format>
>>> print(a)
(0, 0)	1.0
(1, 1)	1.0
(2, 2)	1.0

Read as raw coordinate/triplet arrays

>>> (data, (rows, cols)), shape = fmm.read_coo("eye3.mtx")
>>> rows, cols, data
(array([0, 1, 2], dtype=int32), array([0, 1, 2], dtype=int32), array([1., 1., 1.]))

Read as dense ndarray

>>> a = fmm.read_array("eye3.mtx")
>>> a
array([[1., 0., 0.],
       [0., 1., 0.],
       [0., 0., 1.]])

Write any of the above to a file

>>> fmm.mmwrite("matrix_out.mtx", a)

Write to streams (read from streams too)

>>> bio = io.BytesIO()
>>> fmm.mmwrite(bio, a)

Read only the header

>>> header = fmm.read_header("eye3.mtx")
header(shape=(3, 3), nnz=3, comment="3-by-3 identity matrix", object="matrix", format="coordinate", field="real", symmetry="general")

>>> header.shape
(3, 3)

>>> header.to_dict()
{'shape': (3, 3), 'nnz': 3, 'comment': '3-by-3 identity matrix', 'object': 'matrix', 'format': 'coordinate', 'field': 'real', 'symmetry': 'general'}

Note: SciPy is only a runtime dependency for the mmread and mmwrite methods. All others depend only on NumPy.

Quick way to try

Replace scipy.io.mmread with fast_matrix_market.mmread to quickly see if your scripts would benefit from a refactor:

import scipy.io
import fast_matrix_market as fmm

scipy.io.mmread = fmm.mmread
scipy.io.mmwrite = fmm.mmwrite

Dependencies

• No dependencies to read/write MatrixMarket headers (i.e. read_header(), mminfo()).
• numpy to read/write arrays (i.e. read_array() and read_coo()). SciPy is not required.
• scipy to read/write scipy.sparse sparse matrices (i.e. read_scipy() and mmread()).

Neither numpy nor scipy are listed as package dependencies, and those packages are imported only by the methods that need them. This means that you may use read_coo() without having SciPy installed.

Development

This Python binding is implemented using pybind11 and built with scikit-build-core.

All code is in the python/ directory. If you make any changes simply install the package directory to build it:

pip install python/ -v