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) ndarrays, or a dense ndarray.

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

pip install fast_matrix_market

conda install fast_matrix_market

Compared to SciPy 1.12

As of version 1.12, scipy.io.mmread and scipy.io.mmread are based on fast_matrix_market. If those methods suit your needs then there is no need to use this package.

The following are extra features supported by the stand-alone FMM:

Directly write CSC/CSR matrices with no COO intermediary.
Vector files
Read 1D vector files. scipy.io.mmread() throws a ValueError.
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.
Deprecation Warning: this type is going away in future versions of NumPy and SciPy.

FMM also ships wheels for PyPy and for some older Python versions only supported by older versions of SciPy.

Compared to classic scipy.io.mmread (version <1.12)

The fast_matrix_market.mmread() and mmwrite() methods are direct replacements for scipy.io.mmread and mmwrite.
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.
See comparison with SciPy 1.12.

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'}

Controlling parallelism

All methods other than read_header and mminfo accept a parallelism parameter that controls the number of threads used. Default parallelism is equal to the core count of the system.

mat = fmm.mmread("matrix.mtx", parallelism=2)  # will use 2 threads

Alternatively, use threadpoolctl:

with threadpoolctl.threadpool_limits(limits=2):
    mat = fmm.mmread("matrix.mtx")  # will use 2 threads

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

Dependencies of fast-matrix-market (3).
Dependency	Optional
pytest	true
scipy	true
threadpoolctl	true

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fast-matrix-market

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