semvec/pypi-packages huggingface dataset:
pypardiso 0.4.7
Python interface to the Intel MKL Pardiso library to solve large sparse linear systems of equations
Requires Python: >=3.8
Keywords: pardiso, sparse solver

[![pypardiso-tests](https://github.com/haasad/PyPardisoProject/actions/workflows/tests.yaml/badge.svg?branch=master)](https://github.com/haasad/PyPardisoProject/actions/workflows/tests.yaml)
# PyPardiso

PyPardiso is a python package to solve large sparse linear systems of equations with the [Intel oneAPI Math Kernel Library PARDISO solver](https://www.intel.com/content/www/us/en/develop/documentation/onemkl-developer-reference-fortran/top/sparse-solver-routines/onemkl-pardiso-parallel-direct-sparse-solver-iface.html), a shared-memory multiprocessing parallel direct sparse solver.

PyPardiso provides the same functionality as SciPy's [scipy.sparse.linalg.spsolve](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.spsolve.html#scipy.sparse.linalg.spsolve) for solving the sparse linear system `Ax=b`. However in many cases it is significantly faster than SciPy's built-in single-threaded SuperLU solver.

PyPardiso is not a python interface to the PARDISO Solver from the [PARDISO 7.2 Solver Project](https://www.pardiso-project.org/) and it also doesn't currently support complex numbers. Check out [JuliaSparse/Pardiso.jl](https://github.com/JuliaSparse/Pardiso.jl/) for these more advanced use cases. For macOS users we recommend [scikit-umfpack](https://github.com/scikit-umfpack/scikit-umfpack) as an alternative fast solver, since MKL is not available on Apple silicon.

## Installation

PyPardiso runs on Linux and Windows. It can be installed with __conda__ or __pip__. It is recommended to install PyPardiso using a virtual environment.

conda-forge | PyPI
:---:|:---:
[![conda-forge version](https://anaconda.org/conda-forge/pypardiso/badges/version.svg)](https://anaconda.org/conda-forge/pypardiso) | [![PyPI version](https://badge.fury.io/py/pypardiso.svg)](https://pypi.org/project/pypardiso/)
`conda install -c conda-forge pypardiso` | `pip install pypardiso`


## Basic usage

How to solve the sparse linear system `Ax=b` for `x`, where `A` is a square, sparse matrix in CSR (or CSC) format and `b` is a vector (or matrix):
```python
In [1]: import pypardiso

In [2]: import numpy as np

In [3]: import scipy.sparse as sp

In [4]: A = sp.rand(10, 10, density=0.5, format='csr')

In [5]: A
Out[5]:
<10x10 sparse matrix of type '<class 'numpy.float64'>'
	with 50 stored elements in Compressed Sparse Row format>

In [6]: b = np.random.rand(10)

In [7]: x = pypardiso.spsolve(A, b)

In [8]: x
Out[8]:
array([ 0.02918389,  0.59629935,  0.33407289, -0.48788966,  3.44508841,
        0.52565687, -0.48420646,  0.22136413, -0.95464127,  0.58297397])
```
metametric 0.3.0a0
A Unified View of Evaluation Metrics for Structured Prediction
Requires Python: >=3.9

# metametric

The `metametric` Python package offers a set of tools for quickly and easily defining and implementing evaluation metrics for a variety of structured prediction tasks in natural language processing (NLP) based on the framework presented in the following paper:

> [A Unified View of Evaluation Metrics for Structured Prediction](https://arxiv.org/abs/2310.13793). Yunmo Chen, William Gantt, Tongfei Chen, Aaron Steven White, and Benjamin Van Durme. *EMNLP 2023*.

The key features of the package include:

- A decorator for automatically defining and implementing a custom metric for an arbitrary `dataclass`.
- A collection of generic components for defining arbitrary new metrics based on the framework in the paper.
- Implementations of a number of metrics for common structured prediction tasks.


To install, run:
```bash
pip install metametric
```

If you use this codebase in your work, please cite the following paper:

```tex
@inproceedings{metametric,
    title={A Unified View of Evaluation Metrics for Structured Prediction},
    author={Yunmo Chen and William Gantt and Tongfei Chen and Aaron Steven White and Benjamin {Van Durme}},
    booktitle={Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
    year={2023},
    address={Singapore},
}
```
logiclock-ipc 0.1.0
Librería de Python para Comunicación Inter-Proceso (IPC) Resiliente con limpieza garantizada.
Requires Python: >=3.8
Keywords: ipc, multiprocessing, resilience, thread-safe, cleanup

===================================================================
LOGICLOCK CORE: RESILIENT Inter-Process Communication (IPC)
===================================================================

[CORE PURPOSE]
LogicLock is an infrastructure library designed for maximum stability in 
critical Python systems. It guarantees reliable data transfer between 
isolated child processes (Guardians) and the Parent process.

[PROVEN ROBUSTNESS]
* INTEGRITY: All results are validated with an SHA-256 Checksum before use.
* RESILIENCE: Guaranteed cleanup of all resources (files/shared memory) 
  even if the Guardian process crashes unexpectedly.

[QUICK USAGE]
Use the decorator to isolate your function and 'exports.KEY' to retrieve 
the result synchronously.

<<< CODE SNIPPET >>>
from logiclock.logiclock_core import isolate_export, exports

@isolate_export(key='MY_TASK')
def heavy_job():
    return 'validated result'

result = exports.MY_TASK
<<< END CODE SNIPPET >>>

[ROBUSTNESS VERIFICATION]
To run the complete suite of tests that confirms stability and guaranteed cleanup:
python resilience_test_suite.py