semvec/pypi-packages huggingface dataset:
multichoicelib 0.0.3
Miniscule Python Module for multiple choice menus using arrow keys
License: MIT
Requires Python: >=3.8.1

# MultiChoiceLib

MultiChoiceLib is a one-day project, meaning that it was literally written in a day. As you can imagine, it has not extensively been tested for bugs, and it does not work at all on machines which cannot run bash.

Usage:
```python
from multichoicelib import MultiChoice

query = MultiChoice("Pick one:", ["apples", "cherries"])
    .wAccentColor("\033[33m") # yellow
    .wQuestionPrefix("\033[4m") # underline, don't override
    .wAnswerPrefix("] ", True) # override params are optional and default to false
    .wSelectedPrefix(") ", True) # if false, will append instead
    .addOption("oranges")() # evaluate

print(query.index, query.value)
```

Result:

![GIF image of result](https://github.com/user-attachments/assets/824a3c3c-3f49-4f0d-89f6-991eee31702b)
> Warp terminal with Grafbase theme
pfapack-batched 0.0.3
Efficient numerical computation of the Pfaffian for dense and banded skew-symmetric matrices (batched version).
License: MIT
Requires Python: >=3.9

# `pfapack`: Efficient numerical computation of the Pfaffian for dense and banded skew-symmetric matrices

Code and algorithms are taken from [arXiv:1102.3440](https://arxiv.org/abs/1102.3440) which is authored by [Michael Wimmer](https://michaelwimmer.org/).

[![license](https://img.shields.io/github/license/basnijholt/pfapack)](https://github.com/basnijholt/pfapack/blob/master/LICENSE)
[![tests](https://github.com/basnijholt/pfapack/workflows/tests/badge.svg)](https://github.com/basnijholt/pfapack/actions?query=workflow%3Atests)
[![codecov](https://img.shields.io/codecov/c/github/basnijholt/pfapack)](https://codecov.io/gh/basnijholt/pfapack)
[![docs](https://img.shields.io/readthedocs/pfapack)](https://pfapack.readthedocs.io)
[![version](https://img.shields.io/pypi/v/pfapack)](https://pypi.org/project/pfapack/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pfapack)](https://pypi.org/project/pfapack/)

### Install
Recommended way (because it includes faster C/FORTRAN bindings)
```bash
conda install -c conda-forge pfapack
```

Alternatively use
```bash
pip install pfapack
```

## Usage
```python
from pfapack import pfaffian as pf
import numpy.matlib

A = numpy.matlib.rand(100, 100)
A = A - A.T
pfa1 = pf.pfaffian(A)
pfa2 = pf.pfaffian(A, method="H")
pfa3 = pf.pfaffian_schur(A)

print(pfa1, pfa2, pfa3)
```

If installed with `conda`, C/FORTRAN code is included with Python bindings, use it like:
```python
from pfapack.ctypes import pfaffian as cpf

pfa1 = cpf(A)
pfa2 = cpf(A, method="H")

print(pfa1, pfa2)
```

## Citing
If you have used `pfapack` in your research, please cite it using the following `bib` entry:
```
@article{wimmer2012algorithm,
  title={Efficient numerical computation of the pfaffian for dense and banded skew-symmetric matrices},
  author={Michael Wimmer},
  journal={ACM Transactions on Mathematical Software (TOMS)},
  volume={38},
  number={4},
  pages={1--17},
  year={2012},
  publisher={ACM New York, NY, USA}
}
```

## License
MIT License

## Contributions
- Bas Nijholt
- [Michael Wimmer (author of the algorithms)](https://arxiv.org/abs/1102.3440)
nb2dbpy 0.0.6
This package manages the conversion between .ipynb (Jupyter Notebook) and .py (Python script) files, which Databricks automatically performs when exporting notebooks.
License: MIT
Requires Python: <4.0,>=3.8

# nb2dbpy
A command-line tool for converting between Databricks .py script files and .ipynb notebooks. This tool was build in collaboration with 
[Yoyodyne-Data-Science](https://github.com/Yoyodyne-Data-Science), thank you for setting the foundations for this tool.

## Overview
When working with Databricks, notebooks are often exported as .py files for easier version control and collaboration. However, these scripts may not be ideal for local development in VS Code. nb2dbpy bridges this gap by enabling seamless conversion between Databricks-formatted Python scripts and Jupyter notebooks directly from the command line.

## Features
- **Bidirectional Conversion**: Convert from .py scripts to .ipynb notebooks and vice versa.
- **Command-Line Interface**: Perform conversions easily without writing additional code.
- **Databricks Compatibility**: Handles Databricks-specific syntax and comments.
- **Preserves Cell Structure**: Maintains code and markdown cells accurately during conversion.

## Installation
Clone the repository and install the required dependencies:

```
pip install nb2dbpy
```

## Usage
Use nb2dbpy from the command line to perform conversions.

#### Converting from .py to .ipynb
To convert a Databricks-exported Python script to a Jupyter notebook:

```
nb2dbpy your_python_script.py
```

This command will generate your_python_script.ipynb in the same directory.

#### Converting from .ipynb to .py
To convert a Jupyter notebook into a Databricks-compatible Python script:

```
nb2dbpy your_notebook.ipynb
```
This will create your_notebook.py, ready for import into Databricks.

#### Specifying Output Files
You can specify a custom output file name using the -o or --output option:

```
nb2dbpy input_file.py -o custom_output.ipynb
```

#### Dealing with Existing Files
If the output file already exists, the tool will find the next available filename, for example, if you already have created test.py from test.ipynb, the tool with next create test(1).py to protect against overwriting. 

## Examples
#### Example 1: Converting a Databricks Script to a Notebook
Suppose you have a file analysis.py exported from Databricks. To convert it to a Jupyter notebook:

```
nb2dbpy analysis.py
```
Open analysis.ipynb with VS Code to continue your work locally.

#### Example 2: Converting a Notebook for Databricks
You have developed a notebook experiment.ipynb and now want to run it in Databricks:

```
nb2dbpy experiment.ipynb
```
Upload the resulting experiment.py to your Databricks workspace.

## Limitations
- Advanced notebook features like interactive widgets are not supported.

### License
This project is licensed under the MIT License.

### Contributions
Contributions are welcome! Please open issues or submit pull requests for improvements and bug fixes.