semvec/pypi-packages huggingface dataset:
VoigtFit 3.22
Voigt Profile Fitting in Python
Requires Python: >=3.8
Keywords: voigtfit, absorption, analysis, voigt, spectroscopy, fitting


========
VoigtFit
========

Absorption line fitting implemented in Python.

If you use this software, please cite my paper on `arXiv <http://arxiv.org/abs/1803.01187>`_.
Please let me know that you're using VoigtFit by filling in this `short form <https://forms.gle/exPEsrPoyfB4Us7w9>`_.
This way I can keep you updated with critical updates.


Installation
============

Dependencies
------------

Python version >3.6 (tested on 3.6 - 3.9 so far).

VoigtFit depends on ``matplotlib``, ``numpy==1.20.3``, ``scipy``, ``h5py``, ``astropy``, ``lmfit``, and ``numba==0.55.0``.
You can install these using your favorite Python package manager such as
`conda <http://conda.pydata.org/docs/>`_ or pip_.

If you encounter issues with the installation of ``h5py`` make sure that you have the HDF5 library installed. If you use Homebrew as a package manager you can try this fix [following `stackoverflow <https://stackoverflow.com/questions/66741778/how-to-install-h5py-needed-for-keras-on-macos-with-m1>`_]::

    brew install hdf5
    export HDF5_DIR="$(brew --prefix hdf5)"
    pip install --no-binary=h5py h5py

If you encounter issues with the installation of ``numba`` (happened for me on Mac M1 architecture), try the following::

    arch -arm64 brew install llvm@11
    LLVM_CONFIG="/opt/homebrew/Cellar/llvm@11/11.1.0_4/bin/llvm-config" arch -arm64 pip install llvmlite

before installing `numba`.


Using pip
---------

The easiest way to install the most recent stable version of ``VoigtFit`` is
using pip_::

    pip install VoigtFit

|

If you encounter the following AttributeError when attempting to install via pip:

  AttributeError: 'NoneType' object has no attribute 'splitlines'

Try running pip with the ``--upgrade`` option::

    pip install --upgrade VoigtFit


From source
-----------

Alternatively, you can get the latest version of the source by cloning the git
repository::

    git clone https://github.com/jkrogager/VoigtFit.git

Once you've downloaded the source, you can navigate into the root source
directory and run::

    python setup.py install


If you encounter any problems, do not hesitate to raise an issue here.


Further documentation and how to use it
---------------------------------------

Check out the documentation_ for instructions of installation and use.

.. _pip: http://www.pip-installer.org/
.. _documentation: http://VoigtFit.readthedocs.io
sagar-neuron-gpt2 0.1.9
A GPT-2 implementation for training and inference in pytorch
Requires Python: >=3.8
Keywords: gpt2, machine learning, nlp


##  How to Run

###  Language & Framework

* **Language**: Python
* **Framework**: PyTorch

---

###  Installation

Make sure you have Python installed, then run the following commands to set up the environment:

```bash
## Installation

To install the package without ML dependencies:
pip install sagar-neuron-gpt2

Then you should install
pip install torch
pip install tiktoken
pip install numpy


To include ML dependencies (torch and tiktoken):
pip install sagar-neuron-gpt2[ml]
pip install numpy



```

---

###  Train the Model

To train and save your GPT-2 model weights, run the following:

```python
from sagar_nueron_gpt2.TrainAndSaveGptWeights import Execute

exe = Execute()
exe.execute()
```

---

###  Inference from Trained Model

To run inference using the model you trained:

```python
from sagar_nueron_gpt2.inference_model import Inferencing

exe = Inferencing()
exe.inference()
```

---
gpu-stress 1.0.0
Command-line utility for stressing GPUs using PyTorch.
Requires Python: >=3.12
Keywords: command-line, utility

gpu-stress
==========


[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
&nbsp;
[![Version](https://img.shields.io/github/v/release/purduercac/gpu-stress?sort=semver)](https://github.com/purduercac/gpu-stress)
&nbsp;
[![Python Version](https://img.shields.io/badge/Python-3.12+-blue.svg)](https://www.python.org/downloads)


A simple command-line program to stress Nvidia GPU devices.
We use [PyTorch](https://pytorch.org) to multiply two large matrices repeatedly for some period of time.


Install
-------

The well-known [uv](https://docs.astral.sh/uv/) utility is the best way to install this program.

```sh
uv tool install gpu-stress
```

Usage
-----

Use `gpu-stress --help` to get usage and help on options.
Use something like the following to stress the GPU for 1 minute.

```sh
gpu-stress -t 1m
```
hidden-text 0.1.0
Cross-platform terminal password input with masked characters
Requires Python: >=3.6

# hidden_text

A simple, cross-platform Python module to securely accept password input in the terminal with masked characters (`*`).

## Features

- Supports Windows, Linux, macOS
- Masks password characters with asterisks
- Works in real-time in the terminal

## Example

```python
from hidden_text import hidden_text

password = hidden_text("Enter your password: ")
print("You entered:", password)