semvec/pypi-packages huggingface dataset:
gitgather 0.0.2
A simple tool to gather source code from a local repo or folder into a single file for LLM context windows.
Requires Python: >=3.8

# gitgather

[gitgather on PyPI](https://pypi.org/project/gitgather/)

To install `gitgather`, run the following command:

```bash
pip install gitgather
```

```bash
$ gitgather --help
usage: gitgather [-h] [--no-git] [--all] [--no-dotfiles] [-v] [--include INCLUDE] [--exclude EXCLUDE] repo_path output_file

Concatenate git repository files for LLM context analysis.

positional arguments:
  repo_path          Path to the git repository
  output_file        Output file path

options:
  -h, --help         show this help message and exit
  --no-git           Include all files, not just those tracked by git
  --all              Include all files, including hidden ones (overrides --no-dotfiles)
  --no-dotfiles      Exclude dotfiles
  -v, --verbose      Enable verbose output
  --include INCLUDE  Include files matching these patterns
  --exclude EXCLUDE  Exclude files matching these patterns
```
triqs-maxent 3.3.0
TRIQS application providing a modular Maximum Entropy progra to perform analytic continuation based on the TRIQS library (triqs.github.io)
Requires Python: >=3.7
Keywords: triqs, analytic-continuation, maximum-entropy

[![build](https://github.com/TRIQS/triqs_maxent/workflows/build/badge.svg)](https://github.com/TRIQS/triqs_maxent/actions?query=workflow%3Abuild)

# MaxEnt

The goal of this TRIQS application is to provide a modular Maximum Entropy program to perform analytic continuation.

In the spirit of TRIQS, the implementation is not intended as a monolithic package that the user interacts with via input files, but as a set of tools (i.e., functions and classes) that can be called from python.

Learn how to use this package in the [documentation](https://triqs.github.io/maxent).

## Disclaimer

   TRIQS/maxent is a new TRIQS application made public in 2018.
   We have tested the code on multiple problems and made sure
   that the unit tests cover extensive parts of the code.
   However, there is no guarantee that the code is free of bugs.
   Therefore, if you encounter any problems we kindly ask you to
   open an `issue report on github <https://github.com/triqs/maxent/issues>`_.
   Should you run benchmarks and comparison to other analytic continuation
   packages please share your results with us. Any feedback is greatly appreciated.

## Authors

- Gernot J. Kraberger, Graz University of Technology
- Manuel Zingl, CCQ, Flatiron Institute, Simons Foundation and previously Graz University of Technology
pylgrams 1.0.1
A Python-based gloss of the RaMS package in R
Requires Python: >=3.9
Keywords: GCMS, LCMS, MS, R, mass spectrometry, metabolomics, proteomics

# pylgrams
A Python language gloss of the [RaMS package](https://github.com/wkumler/RaMS) for rapid and intuitive access to mass-spectrometry data. This package parses the open source mass-spectrometry mzML and mzXML file types in Python and returns the retention time, *m/z* ratio, and intensity values to the user as a Pandas data frame. This allows for easy chromatogram extraction and visualization.

The package currently lacks much of the expanded functionality of RaMS and is currently comparable to RaMS v1.0. I don't have plans to expand this much further - for additional functionality, consider using the [rpy2 package](https://rpy2.github.io/) to port the R code directly.

## Installation

`pip install pylgrams`

## Demo

```python
import matplotlib.pyplot as plt
import seaborn as sns
import os
import pylgrams

msdata = pylgrams.grabMSdata("src/pylgrams/example_data/S30657.mzML.gz")
bet_chrom = msdata["MS1"][(msdata["MS1"]["mz"]>118.085) & (msdata["MS1"]["mz"]<118.087)]
bet_chrom = bet_chrom[(bet_chrom["rt"]>7) & (bet_chrom["rt"]<9)]
sns.relplot(bet_chrom, kind="line", x="rt", y="int")
plt.show()

bet_frags = msdata["MS2"][(msdata["MS2"]["premz"]>118.0865) & (msdata["MS2"]["premz"]<118.0867)]
plt.stem(bet_frags["fragmz"], bet_frags["int"], linefmt='-k', markerfmt='ko', basefmt=" ")
plt.show()

dir_path = "src/pylgrams/example_data/"
only_mzML_files_os = [os.path.join(dir_path, f) for f in os.listdir(dir_path) if f.endswith('.mzML.gz')]
msdata = pylgrams.grabMSdata(only_mzML_files_os)
bet_chrom = msdata["MS1"][(msdata["MS1"]["mz"]>118.085) & (msdata["MS1"]["mz"]<118.087)]
sns.relplot(bet_chrom, kind="line", x="rt", y="int", hue="filename")
plt.show()
```
lb-mail 1.1.0
Library with data models by lb services
Requires Python: >=3.6

<h1 align="center">
    <a style="color: #00bfb3"> LB Mail</a>
</h1>

<p align="left" style="color: white">🚀 
    Biblioteca responsável pelo envio de emails via integração com MAIL Gun
</p>


<h3 align="left">
    <a> Pré Requisitos (Bibliotecas): </a>
</h3>

<p style="margin: 0 0 0 5%">
    <a style="color: white" href="https://pypi.org/project/pydantic/">
        Requests (Versão -> 2.27.1) - 
    </a>
    <a style="color: white">
        Biblioteca responsável comunicação rest com a API do Mail Gun
    </a>
</p>

<p style="margin: 5%; font-size: 15px; color: white; text-align: center">
    Desenvolvido Por Lucas Barros
</p>
rngtestlib 0.0.9
rngtestlib is a comprehensive Python package enabling seamless execution of diverse random number testing suites, ensuring the statistical integrity and reliability of random number generators for various applications.
Requires Python: >=3.8

# rngtestlib

rngtestlib is a Python package designed for conducting comprehensive testing of random number generators. It provides a collection of testing suites that evaluate the statistical properties and reliability of random number sequences.

## Features

- Execute various testing suites on random number sequences.
- Assess randomness for cryptographic applications, simulations, and statistical analyses.
- Flexible architecture for easy integration into existing workflows.

## Installation

You can install rngtestlib using pip:

```bash
pip install rngtestlib