semvec/pypi-packages huggingface dataset:
phys-benchmark 3.7
Benchamrking MD on PHYS MPU cluster
Requires Python: >=3.7

Only works on PHYS cluster & tests up to 4 nodes!

Need the topology file in the directory where you execute the script. Benchmark results will be printed under filename Benchmark_Perfomance.png in the same directory where script was executed. Check progress by executing squeue -n bingo in terminal (when no jobs are running, results should be available)

NEED TO LOAD ANACONDA/3/2020.2 ON PHYS CLUSTER PRIOR TO USING LIB

Need to login to cluster with ssh-o ServerAliveInterval=300 login@mpi because when terminal disconnects from cluster, process stops (wip).

Execute the following:

module load anaconda/3/2020.02 

python3

from phys_benchmark import num_nodes

num_nodes( < # of nodes you want to test -- 4 nodes max > )

exit()
curefit 0.2.0
Linear regression of data sets with correlated and uncorrelated uncertainties.
Requires Python: >=3.8

# CUREFit linear regression

[![pypi](https://img.shields.io/pypi/v/curefit?color=informational)](https://pypi.org/project/curefit/)
[![tests](https://github.com/galactic-forensics/CUREFit/actions/workflows/package_testing.yml/badge.svg)](https://github.com/galactic-forensics/CUREFit/actions/workflows/package_testing.yml)
[![codecov](https://codecov.io/gh/galactic-forensics/CUREFit/branch/main/graph/badge.svg?token=C8KN5UE831)](https://codecov.io/gh/galactic-forensics/CUREFit)
[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/galactic-forensics/CUREFit/main.svg)](https://results.pre-commit.ci/latest/github/galactic-forensics/CUREFit/main)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

The goal of this project is to provide a python package
that allows to calculate linear regressions on data sets with uncertainties.
Linear regressions can be performed for correlated and uncorrelated uncertainties.
The calculations follow the methodology published by
[Mahon (1996)](https://doi.org/10.1080/00206819709465336).
Typographical errors that were made in that work have been corrected.
A method to allow calculating a linear regression through a fixed point,
avoiding previously made errors,
is also provided.

## Installation

The package can be installed from `pypi` via:

```
pip install curefit
```

## Usage

Below is an example on how to use the package.

```python
>>> import numpy as np
>>> from curefit import LinReg

>>> # some data
>>> xdata = np.array([1, 2, 3.1, 4.9])
>>> ydata = np.array([1.1, 1.9, 3, 5.5])

>>> # some uncertainty and correlation
>>> xunc = 0.05 * xdata
>>> yunc = 0.073 * ydata
>>> rho = np.zeros_like(xdata) + 0.5

>>> # do regression
>>> my_reg = LinReg(xdata, xunc, ydata, yunc, rho)

>>> # print out the parameters and their uncertainties
>>> my_reg.slope
(0.9983617286559998, 0.0684389236571533)
>>> my_reg.intercept
(0.05545339135826666, 0.11811730191506546)
>>> my_reg.mswd
2.5105964767246842
```

Detailed example on how to use the class for fitting and plotting the results
can be found
[in this Jupyter notebook](https://github.com/galactic-forensics/CUREFit/tree/main/examples).


## Development & Contributing

If you would like to contribute,
clone the GitHub repo and then install the package locally from within the folder via:

```
pip install -e .[dev]
```

If you also want to install the full test environment,
run:

```
pip install -e .[dev,test]
```

Code auto formatting is implemented using
[`pre-commit`](https://pre-commit.com/) hooks.
Full testing of the package can be done with
[`nox`](https://nox.thea.codes/en/stable/index.html).

Please feel free to raise issues on GitHub
and open pull requests if you have a feature to be added.
Tests and adequate docstrings should be provided along with your new code.