semvec/pypi-packages huggingface dataset:
g3 0.5.0
An AI-powered CLI tool to help you write better commit messages and PRs.
Requires Python: >=3.11,<4.0

# G3
All-in-one CLI to commit your work to Github [[Presentation](https://docs.google.com/presentation/d/1BZN4cfeGYR9U4UjXF6wH_-x9l8DwGOgJRZr7UDKXiMQ/), [Design](https://docs.google.com/document/d/1b3SkLVnrB_P-lYsD2Kv-fO-oUHciovQbTQbKsNswHtA)]

[![Watch the video](docs/g.png)](https://youtu.be/dTKCxIoPC54)

## Install
```bash
pip install g3
```

#### Alias

You can, optionally, create the `alias g=g3` so that you execute simply `g commit` and `g pr`.

## Configuration

```bash
g3 configure
```

You will be asked to enter:
- your Github token 
- your open-ai key
- the openai model you want to use
- the temperature which will be used to generate the commit messages and PR descriptions
- the openai api version
- the tone which will be used in the commit messages and PR descriptions
- the commit message max characters
- the PR description max words


## Usage

### Commit

```bash
g3 commit
```

#### Options:
- --tone: The tone to use
- --jira: The jira ticket(s) to reference
- --include: A phrase you want to include
- --edit: The hash of the commit you want to rephrase

### PR

```bash
g3 pr
```

#### Options:
- --tone: The tone to use
- --jira: The jira ticket(s) to reference
- --include: A phrase you want to include
- --edit: The number of the PR you want to rephrase

## Development

The project requires `Python 3.11` and [Poetry](https://python-poetry.org/docs/#installation) for dependency management. 

Optionally configure poetry to create the virtual environment within the project as follows:
```shell script
poetry config virtualenvs.in-project true
```

### Build

Now install the project, along with its development dependencies, in a local virtual environment as follows:

```shell
poetry install
```
You may enable the virtual environment, so that you run modules without the `poetry run` prefix, as follows:
```
source `poetry env info -p`/bin/activate
```
or simply as follows:
```
poetry shell
```

### Contribution

You are expected to enable pre-commit hooks so that you get your code auto-sanitized before being committed.
* mypy:   Static type checker of variables and functions based on [PEP 484](https://peps.python.org/pep-0484/) 
* isort:  Optimizes imports
* black:  Opinionated code formatter based on [PEP 8](https://peps.python.org/pep-0008/) 
* flake8: Improves code style and quality based on [PEP 8](https://peps.python.org/pep-0008/)

Install pre-commit before starting to contribute to the project as follows:
```
pre-commit install
```
KKMeans 0.1.0
https://github.com/bauxn/kernel-kmeans
Requires Python: >=3.9

# Kernel K-Means

Implementation of a Kernel K-Means clustering framework in Python. Utilizes Cython to generate efficient C code. <br>
<https://github.com/bauxn/kernel-kmeans> 
## Installation
Installation via Pip does not allow OpenMP. To use OpenMP, see github installation.
#### Pip
Ensure pip is updated, then run: 

> pip install KKMeans

#### Github

Install via:
> git clone https://github.com/bauxn/kernel-kmeans

Then open the project folder and run
> pip install .

Enabling OpenMP: There is a clearly marked line in setup.py that contains the compiler arguments. Before installing, these need 
to be edited so that they contain whichever command your compiler uses to enable OpenMP. There already is a outcommented line which contains the correct arguments (and some additional ones for efficiency) for the msvc compiler.

## Basic Usage

> from KKMeans import KKMeans <br><br>
> kkm = KKMeans(n_clusters=3, kernel="rbf")<br>
> kkm.fit(data)<br>
> print(kkm.labels_)  # shows label for each datapoint <br>
> print(kkm.quality_)  # print quality (default is inertia) of clustering <br><br>
> predictions = kkm.predict(data_to_predict)  # returns labels of points in data_to_predict<br> 


KKMeans also contains the modules _kernels_ (provides functionality to build kernel matrices / calculate kernels), _elkan_ and _lloyd_ which allow to calculate single iterations of the respective algorithms and _quality_, which contains functionality to calculate the silhouette coefficient.
For more elaborate usage consult the thesis on github or the docstrings.

## Limitations
As the computations happen C, in extreme cases overflows and other datatype errors may occur. Critical points are:

| values | datatype |
| --- | --- |
|kernel_matrix| double | 
|n_clusters| long|
|cluster_sizes| long|
|labels|long|

1. The kernel matrix consists of the results of the kernel function, which usually is applied pairwise on the dataset. So ensuring the results are able to fit in a **double** is necessary. 
2. Ensure the number of clusters fits in a long.
3. Ensure the number of points fits in a long.
qrcode-img 1.0.8
Generation QR Code with IMG
Requires Python: >=3.6

## Генерация QR Code по картинке

### https://pypi.org/project/qrcode-img/

 
## Пример работы

```
from path import Path
from qrcode_img import QRCode

text = 'Hello'

path_to_download = Path().joinpath("example", "11.png")
path_to_save = Path().joinpath("example", "1example.png")


qrcode = QRCode(text)

byte_io = qrcode.gen_qr_code(path_to_download)

print(byte_io)

qrcode.save_qr_code(path_to_save)
```
tmath 0.4
Only for demo purpose add, sub, mul, div and squ

Project description

The project was started in 2023 by Tushali Devikar for mathematical function.

Installation
Dependencies
tmath requires:

Python (>= 3.8)

User installation
The easiest way to install tmath is using pip:

pip install -U tmath

USAGE
Quickstart

import tmath
# For Checking Version
print(tmath.__version__)

# The dir() function can be used on all modules
print(dir(tmath))

# For addition
tmath.add(1,2)

# For substraction
tmath.sub(1,2)

# For Multiplication
tmath.mul(1,2)

# For division
tmath.div(1,2)

# For square
tmath.squ(2)

# Use this way also
from tmath import add
print(add(1,2))


Development
The tmath goals are to be helpful, welcoming, and effective. The Development Guide has detailed information about contributing code, documentation, tests, and more. I am included some basic information in this README.

Project History
The project was started in 2023 by Tushali Devikar for mathematical function.

Help and Support