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fast-matrix-market-1.5.1
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مشاهده بیشترتوضیحات
Fast and full-featured Matrix Market file I/O
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fast-matrix-market-1.5.1 |
| نام | fast-matrix-market |
| نسخه کتابخانه | 1.5.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Adam Lugowski |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/alugowski/fast_matrix_market |
| آدرس اینترنتی | https://pypi.org/project/fast-matrix-market/ |
| مجوز | - |
[](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) arrays, or 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.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 > 2<sup>31</sup>) 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
```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'}
```
**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:
```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
```
نیازمندی
| مقدار | نام |
|---|---|
| xtr | pytest; |
| xtr | scipy; |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl fast-matrix-market-1.5.1:
pip install fast-matrix-market-1.5.1.whl
نصب پکیج tar.gz fast-matrix-market-1.5.1:
pip install fast-matrix-market-1.5.1.tar.gz