semvec/pypi-packages huggingface dataset:
pynter-defects 1.3.14
Tools for atomistic calculations, provides features for point-defect calculations

# pynter
Tools for atomistic calculations. Uses the [`pymatgen`](https://pymatgen.org) package. The main features are designed to systematically run, analyse and plot DFT point defect calculations with VASP. 

It can be installed with the Python Package Index (PyPI) by running:
```sh
pip install pynter-defects
```

To configure settings for High-Performance Computer job submissions and for VASP calculations, run:
```sh
pynter configure
```

Check-out the tutorials in the [`pynter-tutorials`](https://github.com/lorenzo-villa-hub/pynter-tutorials) repository. For an introduction consult this notebook:
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lorenzo-villa-hub/pynter-tutorials/blob/master/notebooks/intro.ipynb)
python-magi 0.0.4
Python-MAGI: A TensorFlow Probability-Powered Upgraded Implementation of MAnifold-Constrained Gaussian Process Inference (MAGI)
Requires Python: >=3.8
Keywords: dynamical systems, inverse problems, monte carlo

# Python-MAGI
A TensorFlow Probability-Powered Upgraded Implementation of [Manifold-Constrained Gaussian Process Inference (MAGI)](https://www.pnas.org/doi/10.1073/pnas.2020397118).

This package is an upgraded version of the MAGI algorithm developed by [Yang et al. (2020)](https://www.pnas.org/doi/10.1073/pnas.2020397118) for solving the ordinary differential equation (ODE) dynamical system inverse problem. Compared to the [original MAGI implementation](https://www.jstatsoft.org/article/view/v109i04), this version is based in TensorFlow Probability and includes higher-performance routines for completely-missing components and GPU-accelerated MCMC sampling. The package is designed to be essentially hyperparameter-tuning-free, as it uses Hoffman & Gelman's [No U-Turn Sampler](https://sites.stat.columbia.edu/gelman/research/published/nuts.pdf) with Dual-Averaging Stepsize Adaptation, all natively-implemented in TensorFlow Probability.

The main `MAGI` class was written to roughly mirror `scikit-learn` grammar for user-friendliness, with some minor modifications. Please see our [vignette](https://github.com/skbwu/Python-MAGI/blob/main/vignette.ipynb) for a quick start guide.

**High-Level Description of Core Functions:** please see the complete source code at `magi.py` for full details.
- `__init__`: constructor where the governing ODE equations `f_vec`, (potentially partially-observed) timesteps `ts_obs` and noisy observations `X_obs`, and computational settings (e.g., number of components in system, bandmatrix approximations) are specified to create a `MAGI` object.
- `initial_fit`: fits the initial Matern kernel hyperparameters for the ODE inverse problem. Can take user-specified subsets of hyperparameters, too, and automatically accounts for missing observations and/or entirely-missing components in the provided noisy observed data.
- `predict`: after receiving and/or fitting Matern kernel hyperparameters to the provided data, returns samples from the posterior distribution of the entire system along with diagnostics.

Please contact [skylerw@stanford.edu](skylerw@stanford.edu) if you have any comments, suggestions, or concerns.
bluelight-license-info 0.1.0
Shows license info for BluelightAI software.
Requires Python: >=3.8

# bluelight-license-info

A script to provide information about a [BluelightAI](https://bluelightai.com) software license.

Usage:

```
> bluelight-license-info --license-key LICENSE_KEY --license-file LICENSE_FILE

BluelightAI License Information
===============================
License file: PATH_TO_LICENSE_FILE
License key: LICENSE_KEY

Product: Cobalt
Product code: 8358-0

License start date: 2025-05-01 00:00
License end date: 2026-05-01 00:00

Site Name: Example, Inc.
Site ID: 0001
Site Contact: John Example
Site Contact Email: example@example.com
```

Start and end dates are presented in the local time zone.

To extract license information from a license file, you must provide both the
license key and the path to the license file. If only a key is provided, it will
query the license server for information about the associated license.
tempest-fmt 1.0.2
A tiny language-independent text templater for python

# tempest
Syntax-independent text templating library

Easily embed literal python code into any file.

Features:
- Configurable open/close delimiters
- Works with any language
- It's just more python

# Big Scary Security Warning
This library will execute arbitrary code contained in the template. **Do not allow untrusted templates to be executed.**

## Install

`pip install tempest-fmt`

## Example

### Template
```md
# This is a markdown file

Hello my name is {name=}

My friends are:
{for f in friends:}
    - {f=}

{divisor = 2}
{for i in range(5):}
    {if i % divisor == 0:}
        Did you know {i=} is divisible by {divisor=}

I can even evaluate stuff: {len(name)=}
```

### Python Implementation
```py
import tempest

t = tempest.parse_template_file("myTemplate.md", "{", "}")

values = {
    "name": "Jimmy",
    "friends": ["Carl", "Steve", "Greg"],
}

with open("output.md", mode='w') as f:
    t.generate(f, value)
```

### Output file
```md
# This is a markdown file

Hello my name is Jimmy

My friends are:
- Carl
- Steve
- Greg

Did you know 0 is divisible by 2
Did you know 2 is divisible by 2
Did you know 4 is divisible by 2

I can even evaluate stuff: 5
```

## Syntax specifics

**For simplicity, we will assume the delimiters to be `{ }`**  
All python statements will be contained within the delimiters, 
any text outside will be considered raw text.

Statements of the form `{<expr>=}` will be evaluated as `str(<expr>)` and directly inserted into the text.  
All other statements will be considered logical statements. Such statements **must** follow indentation rules for python and should have no raw text on the line. Any statements following will have indentation stripped, this includes raw text (extra whitespace after the expected indentation is left as-is).

**Indentation is important**  
*Leading spaces replaced with dots for this example*

```md
{for x in range(3):}
....- {x}

My next thing
```
Will produce
```md
- 0
- 1
- 2

My next thing
```

Compared to
```md
{for x in range(3):}
....- {x}
....

My next thing
```
Will produce
```md
- 0

- 1

- 2

My next thing
```
whisper-gui 0.1.0
A GUI application for Whisper speech recognition
Requires Python: >=3.8

# whisper-gui
 Basic GUI for openai-whisper

A simple graphical user interface for OpenAI's Whisper speech recognition system.

## Features
- Convert video files to audio
- Transcribe audio files using Whisper
- Support for multiple languages
- Drag & drop support
- Save transcription settings

## Installation

1. Install ffmpeg (required for audio conversion):
   ```bash
   # Ubuntu/Debian
   sudo apt install ffmpeg

   # macOS
   brew install ffmpeg

   # Windows
   # Download from https://ffmpeg.org/download.html or use winget / choco
   winget install ffmpeg
   ```

2. Install whisper-gui:
   ```bash
   pip install openai-whisper 
   # or pip install git+https://github.com/openai/whisper.git 
   pip install PySide6
   pip install whisper-gui
   ```

## Usage

1. Launch the application:
   ```bash
   whisper-gui
   ```

2. Either:
   - Open a video file and convert it to audio
   - Open an audio file directly
   
3. Select language and model size
4. Click "Transcribe" to generate text from speech