[](https://pypi.org/project/fast-matrix-market/)
[](https://anaconda.org/conda-forge/fast_matrix_market)
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) `ndarray`s, or a dense `ndarray`.
Implemented as a Python binding of the C++ [fast_matrix_market](https://github.com/alugowski/fast_matrix_market) library.
```shell
pip install fast_matrix_market
```
```shell
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 > 2<sup>31</sup>) 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
```python
import fast_matrix_market as fmm
```
#### Read as scipy sparse matrix
```python
>>> 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
```python
>>> (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
```python
>>> 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
```python
>>> fmm.mmwrite("matrix_out.mtx", a)
```
#### Write to streams (read from streams too)
```python
>>> bio = io.BytesIO()
>>> fmm.mmwrite(bio, a)
```
#### Read only the header
```python
>>> 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.
```python
mat = fmm.mmread("matrix.mtx", parallelism=2) # will use 2 threads
```
Alternatively, use [threadpoolctl](https://pypi.org/project/threadpoolctl/):
```python
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:
```python
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](https://pybind11.readthedocs.io) and built with [scikit-build-core](https://github.com/scikit-build/scikit-build-core).
All code is in the [python/](https://github.com/alugowski/fast_matrix_market/tree/main/python) directory. If you make any changes simply install the package directory to build it:
```shell
pip install python/ -v
```
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If those methods suit your needs then there is no need to use this package.\n\nThe following are extra features supported by the stand-alone FMM:\n\n* **Directly write CSC/CSR matrices** with no COO intermediary.\n\n* **Vector files** \n Read 1D vector files. `scipy.io.mmread()` throws a `ValueError`.\n\n* **longdouble** \n Read and write `longdouble`/`longcomplex` values for more floating-point precision on platforms that support it (e.g. 80-bit floats).\n\n Just pass `long_type=True` argument to any read method to use `longdouble` arrays. SciPy can write `longdouble` matrices but reads use `double` precision.\n\n **Note:** Many platforms do not offer any precision greater than `double` even if the `longdouble` type exists.\n On those platforms `longdouble == double` so check your NumPy for support. As of writing only Linux tends to have `longdouble > double`.\n **Deprecation Warning:** this type is going away in future versions of NumPy and SciPy.\n\nFMM also ships wheels for PyPy and for some older Python versions only supported by older versions of SciPy.\n\n# Compared to classic scipy.io.mmread (version <1.12)\n\nThe `fast_matrix_market.mmread()` and `mmwrite()` methods are direct replacements for `scipy.io.mmread` and `mmwrite`.\nCompared to SciPy v1.10.0:\n\n* **Significant performance boost**\n\n \n \n\n 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.\n\n* **64-bit indices**, but only if the matrix dimensions require it.\n\n `scipy.io.mmread()` crashes on large matrices (dimensions > 2<sup>31</sup>) because it uses 32-bit indices on most platforms.\n\n* See comparison with SciPy 1.12.\n\n### Differences\n\n* `scipy.io.mmwrite()` will search the matrix for symmetry if the `symmetry` argument is not specified.\n This is a very slow process that significantly impacts writing time for all matrices, including non-symmetric ones.\n It can be disabled by setting `symmetry=\"general\"`, but that is easily forgotten.\n `fast_matrix_market.mmwrite()` only looks for symmetries if the `find_symmetry=True` argument is passed.\n\n# Usage\n```python\nimport fast_matrix_market as fmm\n```\n\n#### Read as scipy sparse matrix\n```python\n>>> a = fmm.mmread(\"eye3.mtx\")\n>>> a\n<3x3 sparse matrix of type '<class 'numpy.float64'>'\n with 3 stored elements in COOrdinate format>\n>>> print(a)\n(0, 0)\t1.0\n(1, 1)\t1.0\n(2, 2)\t1.0\n```\n#### Read as raw coordinate/triplet arrays\n```python\n>>> (data, (rows, cols)), shape = fmm.read_coo(\"eye3.mtx\")\n>>> rows, cols, data\n(array([0, 1, 2], dtype=int32), array([0, 1, 2], dtype=int32), array([1., 1., 1.]))\n```\n#### Read as dense ndarray\n```python\n>>> a = fmm.read_array(\"eye3.mtx\")\n>>> a\narray([[1., 0., 0.],\n [0., 1., 0.],\n [0., 0., 1.]])\n```\n#### Write any of the above to a file\n```python\n>>> fmm.mmwrite(\"matrix_out.mtx\", a)\n```\n#### Write to streams (read from streams too)\n```python\n>>> bio = io.BytesIO()\n>>> fmm.mmwrite(bio, a)\n```\n#### Read only the header\n```python\n>>> header = fmm.read_header(\"eye3.mtx\")\nheader(shape=(3, 3), nnz=3, comment=\"3-by-3 identity matrix\", object=\"matrix\", format=\"coordinate\", field=\"real\", symmetry=\"general\")\n\n>>> header.shape\n(3, 3)\n\n>>> header.to_dict()\n{'shape': (3, 3), 'nnz': 3, 'comment': '3-by-3 identity matrix', 'object': 'matrix', 'format': 'coordinate', 'field': 'real', 'symmetry': 'general'}\n```\n\n#### Controlling parallelism\n\nAll 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.\n```python\nmat = fmm.mmread(\"matrix.mtx\", parallelism=2) # will use 2 threads\n```\n\nAlternatively, use [threadpoolctl](https://pypi.org/project/threadpoolctl/):\n```python\nwith threadpoolctl.threadpool_limits(limits=2):\n mat = fmm.mmread(\"matrix.mtx\") # will use 2 threads\n```\n\n# Quick way to try\n\nReplace `scipy.io.mmread` with `fast_matrix_market.mmread` to quickly see if your scripts would benefit from a refactor:\n\n```python\nimport scipy.io\nimport fast_matrix_market as fmm\n\nscipy.io.mmread = fmm.mmread\nscipy.io.mmwrite = fmm.mmwrite\n```\n\n\n# Dependencies\n\n* No dependencies to read/write MatrixMarket headers (i.e. `read_header()`, `mminfo()`).\n* `numpy` to read/write arrays (i.e. `read_array()` and `read_coo()`). 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