semvec/pypi-packages huggingface dataset:
mkdocs-latest-git-tag-plugin 0.1.2
MkDocs plugin to get the latest git tag from the local repository
Requires Python: >=3.8.1,<4.0.0

# MkDocs Plugin : `latest-git-tag`

[![PyPI version](https://badge.fury.io/py/mkdocs-latest-git-tag-plugin.svg)](https://badge.fury.io/py/mkdocs-latest-git-tag-plugin)

This is a simple [MkDocs](https://www.mkdocs.org/) plugin that just gets the
most recent `Tag` from the local Git repository and makes it available as a
markdown tag in the template.

The idea behind this is that GitHub uses the `Tag` to name the release and,
therefore we can use it to display the version of the documentation that
corresponds to the release.

The Tag is just returned as a string with no formatting applied. If none is
found, it returns "No Tags found"

There is a [Demo Site](https://seapagan.github.io/mkdocs-latest-git-tag-plugin/)

## Installation

Install the package with pip:

```bash
pip install mkdocs-latest-git-tag-plugin
```

or, if you are using [Poetry](https://python-poetry.org):

```bash
poetry add mkdocs-latest-git-tag-plugin --group dev
```

## Usage

Activate the plugin in your `mkdocs.yml`:

```yaml
plugins:
  - latest-git-tag
```

 > If you have no `plugins` entry in your config file yet, you'll likely also
want to add the `search` plugin. MkDocs enables it by default if  here is no
`plugins` entry set.

Then, in your template, you can use the `{{ latest_git_tag }}` variable:

```markdown
# My Project

Version: {{ latest_git_tag }}
```

The spaces around the tag are optional and it is case insensitive.

## Configuration

At this time there are no configuration options.

## License

This is released under the MIT License. See the bundled LICENSE file for more
details.

## TODO

- [ ] Add tests
optuna-worker 0.1.0
Optuna Worker
Requires Python: >=3.8

# optuna-worker

[![PyPI - Version](https://img.shields.io/pypi/v/optuna-worker.svg)](https://pypi.org/project/optuna-worker)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/optuna-worker.svg)](https://pypi.org/project/optuna-worker)
[![GitHub Super-Linter](https://github.com/Jooong/optuna-worker/actions/workflows/lint.yaml/badge.svg)](https://github.com/marketplace/actions/super-linter)
[![Codecov](https://codecov.io/gh/Jooong/optuna-worker/branch/main/graph/badge.svg)](https://codecov.io/gh/Jooong/optuna-worker)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black)

-----

`optuna-worker` is a python package to help using [optuna](https://github.com/optuna/optuna) without code modification to existing ML training project, with two assumptions.

1. You could pass hyperparameters to your trainer via cli arguments.
2. Metrics to be optimized are printed out as formatted logs during training.

A CLI named `optuna-worker` is included in this package, which runs optuna worker with a configuration yaml file.

```console
optuna-worker run $CONFIG_FILE
```

All it does is simply creating a proxy objective that

1. Creates a training subprocess, passing suggested parameters via CLI arguments.
2. Parses metric values from stdout of the training subprocess, so that they could be reported and returned.

Details of the configuration file could be found from the [documentation](./docs/configuration.md).
You can also find working example configuration files from [examples](./examples/).

## Installation

```console
pip install optuna-worker
```

## License

`optuna-worker` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
credit-card-info-generator 1.0
Credit Card Info Generator is a Python package designed to create realistic dummy credit card data. It's a handy tool for developers who need to test payment systems, verify eCommerce applications, or any situation where real credit card data is not necessary or desirable.It supports a variety of card providers and generates corresponding credit card numbers, CVVs, and expiry dates.
Requires Python: >=3.6


# 💳 Credit Card Info Generator

This is a simple, yet powerful Python 🐍 package for generating realistic dummy credit card data. It's a handy tool for developers who need to test payment systems, verify eCommerce applications, or any situation where real credit card data is not necessary or desirable.


Check out our [Credit Card Generator](https://creditcardgenerator.app/) for more information!

## 🚀 Installation

Install the package with pip using the following command:

```shell
pip install credit_card_info_generator
```

## ⚙️ Usage

```python
from credit_card_info_generator import generate_credit_card

# Generate a Visa card
card = generate_credit_card('Visa')

# Print the card details
print(card)
```

## 🏦 Supported Card Types

The package currently supports the generation of the following card types:

- American Express
- Bankcard
- China T-Union
- China UnionPay
- Diners Club enRoute
- Diners Club International
- Diners Club United States & Canada
- Discover Card
- UkrCard
- RuPay
- InterPayment
- InstaPayment
- JCB
- Mastercard
- Laser
- Maestro (UK)
- Maestro
- Dankort
- Mir
- BORICA
- NPS Pridnestrovie
- Solo
- Switch
- Troy
- Visa
- Visa Electron
- UATP
- Verve
- LankaPay
- UzCard
- Humo
- GPN

## 👥 Contributing

Pull requests are welcome! 👍 For major changes, please open an issue first to discuss what you would like to change.

## 📜 License

This project is licensed under the MIT License.
spectools-ir 1.0.1
Tools for analysis and modeling of IR spectra

# spectools-ir
[![DOI](https://zenodo.org/badge/360561194.svg)](https://zenodo.org/badge/latestdoi/360561194)

Spectools_ir is a small suite of tools designed for analysis of medium/high-resolution IR molecular astronomical spectra.  It consists
of three main sub-modules (flux_calculator, slabspec, and slab_fitter) as well as a 'utils' sub-module, with
a few additional functions.  

Spectools_ir was written with infrared medium/high-resolution molecular spectroscopy in mind.  It often assumes spectra
are in units of Jy and microns, and it uses information from the HITRAN molecular database.  Some routines are more general, but
users interested in other applications should proceed with caution.

Users are requested to let the developer know if they are using the code in spectools_ir.  The code has been
tested for only a few use cases, and users utilize at their own risk.

# Requirements
Requires internet access to utilize astroquery.hitran and access HITRAN partition function files.

Requires several standard scientific packages, as well as astropy, astroquery, corner, and emcee.

# Modules
flux_calculator is a set of python codes to compute line fluxes from an IR spectrum.

slabspec is a set of python codes to produce LTE slab model emission spectra of molecules using the HITRAN database.

slab_fitter is a set of python codes to perform MCMC slab model fits to line fluxes using "emcee" (Foreman-Mackey et al. 2013; https://github.com/dfm/emcee; https://emcee.readthedocs.io/en/stable/) with flat priors. 

utils contains some useful utility functions, including functions specific to JWST MIRI-MRS.

# Usage

Example usage can be found at https://github.com/csalyk/spectools_ir/ in docs/example.ipynb

# License
[MIT](https://choosealicense.com/licenses/mit/)
annotest 0.1
A tool to automatically generating bug-finding inputs for NN program testing.
Requires Python: >=3.6

# aNNoTest

aNNoTest is a tool (and an approach) to automatically
generating bug-finding inputs for NN program testing.
Paper [An annotation-based approach for finding bugs in
neural network 
programs](https://doi.org/10.1016/j.jss.2023.111669) by
Mohammad Rezaalipour and Carlo A. Furia explains aNNoTest
in details and provides guidelines on how to use it, effectively.


## Installation

Run the following command to install aNNoTest:

```
pip install annotest
```

We have tested aNNoTest on Python 3.6.
But it should work on Python 3.6+ as well.

## Using aNNoTest

aNNoTest is a command line tool.
After annotating your project with 
aN (aNNoTest's annotation language)
you can `cd` to your project directory
and then run aNNoTest.

```
cd path_to_python_project
```

```
annotest
```

Or you can input the project path to aNNoTest:

```
annotest path_to_python_project
```

## Examples

To see examples of using aNNoTest, see
the following repository:

[https://github.com/atom-sw/annotest-subjects](https://github.com/atom-sw/annotest-subjects)


## Citations

[aNNoTest's Journal 
Paper:](https://doi.org/10.1016/j.jss.2023.111669)

```
@article{Rezaalipour:2023,
title = {An annotation-based approach for finding bugs in neural network programs},
journal = {Journal of Systems and Software},
volume = {201},
pages = {111669},
year = {2023},
issn = {0164-1212},
doi = {https://doi.org/10.1016/j.jss.2023.111669},
url = {https://www.sciencedirect.com/science/article/pii/S016412122300064X},
author = {Mohammad Rezaalipour and Carlo A. Furia},
keywords = {Test generation, Neural networks, Debugging, Python}
}
```

# Mirrors

The current repository is a public mirror of
our internal private repository.
We have two public mirrors, which are as follows:

- https://github.com/atom-sw/annotest
- https://github.com/mohrez86/annotest