semvec/pypi-packages huggingface dataset:
qpiai-opt-api 1.0.6
QpiAI-Opt-API is a an interface python package to access QpiAI-Opt solvers on cloud
Requires Python: >=3.8

# qpiai-opt-api

qpiai-opt-api provides a python package to interface with the qpiai-opt solver on the cloud to solve NP-Hard Combinatorial problems.

QpiAI-Opt is a high-level optimization library to solve NP-Hard (combinatorial) problems. We provide access to our solvers through QpiAI-Opt API to access four base class problems (that have real-world applications):
1) Maximum Cut

2) Minimum Vertex Cover

3) Maximum Independent Set

4) Number Partition


QpiAI-Opt API also provides a **QUBO design engine** to express a general QUBO problem and solve it with our solver.

# Installation steps

1. Install the latest version of the project from: https://pypi.org/project/qpiai-opt-api/1.0.1/
You can use the following command to install.
    
    `pip install qpiai-opt-api==1.0.1`

2. Install the dependencies from requirements.txt

    `pip install networkx`

    `pip install requests`

    

3. You are ready to access the solvers using the url and access token. Refer to the below tutorial to get started.

4. In order to use the wrappers for dwave and qiskit support, install the following dependencies.
    `pip install qiskit`

    `pip install qiskit[optimization]`

    `pip install 'qiskit-optimization[cplex]`

    `pip install dimod`

    `pip install dwave-neal`

# What's new?

We have added dwave and qiskit wrappers to solve the qubo problem on both the platforms, besides qpiai_opt's solver. Refer to documentaion for more info.

You can also refer to the below turorials to get started with qpiai_opt and its wrappers to solve on supported platforms.

# Tutorials

Refer to [QpiAI-API tutorials](https://gitlab.qpiai.tech/qpiquantum/optimisation/qpiai-opt-api/-/tree/main/tutorials)




MIT License

Copyright (c) 2022 Sagar B Dollin, QpiAI India Private Limited

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
voxface 2024.7.23
Fast MRI face voxelator
Requires Python: <4,>=3.8
Keywords: neuroimaging MRI Nifti deface deidentify

# Voxface

Fast voxelation of the face in 3D structural MRI data combining spline downsampling with nearest-neighbor upsampling to
deidentify the face while retaining some signal intensity to guide whole-head registration.

## Installation

### Latest Version
2024.7.23

### GitHub Installation

1. Clone this branch to your local system
   ```
   % git clone https://github.com/jmtyszka/voxface.git
   ```
2. Install to your local Python 3 environment
   ```
   % cd voxface
   % [sudo] python3 setup.py install
   ```
   
### PyPI Installation

1. Install the latest Python 3 version of *voxface* from PyPI
    ```
    % [sudo] pip3 install voxface
    ```

### Typical Performance
Face voxelation of a typical 1 mm isotropic T1w image takes 5 - 6 seconds on a 3.2 GHz 6-Core Intel Core i7 Mac Mini.
podcast-transcript-convert 0.1.2
Convert podcast transcripts from HTML, SRT, WebVtt, Podlove etc into PodcastIndex JSON.
Requires Python: >=3.11
Keywords: convert, pci, podcast, podcastindex, podlove, srt, transcripts, vtt, webvtt

# podcast-transcript-convert

[![PyPI](https://img.shields.io/pypi/v/podcast-transcript-convert.svg)](https://pypi.org/project/podcast-transcript-convert/)
[![Lint and Test](https://github.com/hbmartin/podcast-transcript-convert/actions/workflows/lint.yml/badge.svg)](https://github.com/hbmartin/podcast-transcript-tools/actions/workflows/lint.yml)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
[![Code style: black](https://img.shields.io/badge/🐧️-black-000000.svg)](https://github.com/psf/black)
[![Checked with pytype](https://img.shields.io/badge/🦆-pytype-437f30.svg)](https://google.github.io/pytype/)
[![twitter](https://img.shields.io/badge/@hmartin-00aced.svg?logo=twitter&logoColor=black)](https://twitter.com/hmartin)

<img src=".idea/icon.svg" width="100" align="right">

Convert podcast transcripts from HTML, SRT, WebVtt, Podlove etc into [PodcastIndex JSON](https://github.com/Podcastindex-org/podcast-namespace/blob/main/transcripts/transcripts.md).

## Installation

It is recommended to use [pipx](https://pipx.pypa.io/stable/) to install and run the CLI tool. If you wish to use the library, you can install with `pip` instead.

```bash
brew install pipx
pipx install podcast-transcript-convert
```

If you've already installed the package and wish to upgrade:

```bash
pipx upgrade podcast-transcript-convert
```

## Usage
Run the conversion app on your transcripts directory.

```bash
transcript2json transcripts/ converted/
```
You can then inspect the output JSON files in the `converted/` directory.

## Library Usage
```python
from podcast_transcript_convert.convert import bulk_convert

bulk_convert("transctipts_dir/", "converted_dir/")
```

Individual file type converters are in the `converters` package. You can use them directly if you know the file type.

You can use `file_typing.identify_file_type(file)` to determine the file type of a transcript file.


## Development

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

```bash
git clone git@github.com:hbmartin/podcast-transcript-convert.git
cd podcast-transcript-convert
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
# Replace with the actual path to your transcript files
python -m podcast_transcript_convert ~/Downloads/overcast-to-sqlite/archive/transcripts converted/
```

### Code Formatting

This project is linted with [ruff](https://docs.astral.sh/ruff/) and uses [Black](https://github.com/ambv/black) code formatting.


## Authors
- [Harold Martin](https://www.linkedin.com/in/harold-martin-98526971/) - harold.martin at gmail
- Icon courtesy of [Vecteezy.com](https://www.vecteezy.com)
openet-landsat-nrt 0.0.9
Earth Engine near realtime Landsat functions
Requires Python: >=3.8

# OpenET - Landsat Near Realtime (NRT) Algorithms

Generate synthetic level 2 (surface reflectance) images for any Landsat 8 collection 2 tier 1 TOA image.

## Example

```
image_id = 'LANDSAT/LC08/C02/T1_RT_TOA/LC08_043034_20230710'
sr_image = openet.nrt.landsat8(ee.Image(image_id))
```
org-todo-send 0.1.2
A CLI tool which sends TODOs from an Org file to people they have been assigned to via the Signal REST API
Requires Python: <4.0,>=3.11

#+TITLE: org-todo-send
I made this little utility to facilitate keeping track of tasks. I take meeting notes in an org file, and action items get a TODO headline tagged with the person it's assigned to. This script reads an org file and sends each person all of their TODOs in a neatly formatted message via Signal.

This package is subject to change as my needs shift and is provided as is.

* Installation
This package is available on [[https://pypi.org/project/org-todo-send/][pypi]].

* Usage
- See [[file:usage.md][usage.md]]
- The expected TOML file is simply a list of names with the number associated. [[file:tests/recipients_test.toml][See the test toml for an example.]]
- This package relies on the [[https://github.com/bbernhard/signal-cli-rest-api][Signal REST API]].
* Contributing
My development environment for this project relies on using a nix flake and poetry, facilitated by devenv. To get set up, you'll need nix package manager, and direnv with nix-direnv installed. Once you have those, entering the project directory should automatically activate the flake and install everything, giving you a local environment with all of the dependencies provided.
* Features to add
- [ ] message styling
- [ ] tests
- [ ] automated testing and deployment with actions
- [ ] deadlines and timestamps in message