semvec/pypi-packages huggingface dataset:
cactus-commit 4.7.0
A tool for streamlined commit messaging using AI
Requires Python: >=3.10

# Cactus: AI-Powered Commit Message Generator

Cactus is an command-line tool that leverages AI to automate and enhance the process of creating Git commit messages. By analyzing staged changes, **Cactus Automates Commits Through Uncomplicated Suggestions**.

## Demo

https://github.com/user-attachments/assets/bb3553db-78d5-42b1-888c-9e9e6a4edb77

## Features

- **AI-Powered Commit Generation**: Utilizes OpenAI's GPT models or Google's Gemini models to analyze code changes and generate appropriate commit messages.
- **Customizable Commit Grouping**: Allows specifying the number of commits to generate, intelligently grouping related changes.
- **Interactive Changelog Creation**: Generates comprehensive changelogs between specified Git commits.
- **Multi-Model Support**: Compatible with various AI models, including GPT-3.5, GPT-4, and Gemini.
- **Conventional Commits**: Adheres to the Conventional Commits standard for consistent, readable commit histories.

## Installation

Install Cactus using pip:

```shell
$ pip install cactus-commit
```

Set up your API key(s):

```shell
$ cactus setup OpenAI  # For OpenAI API
# or
$ cactus setup Gemini  # For Google Gemini API
```

## Usage

### Generate Commit Messages

Add the files you want to create commits for to the staging area:

```shell
$ git add myfile1.py myfile2.py
```
> Anything left unstaged will be ignored when generating commit messages.

Run `cactus` to generate commit messages:

```shell
$ cactus
```

If you want to force a specific number of commits, pass the number as an argument:

```shell
cactus 3 # Will generate 3 commits
```


### Create a Changelog

```sh
cactus changelog [SHA] [-p PATHSPEC]
```
- `SHA`: The starting commit hash for the changelog.

### Additional Options

- `-d, --debug`: Enable debug logging.
- `-c, --context-size`: Set the context size for git diff (default: 1).
- `-m, --model`: Specify the AI model to use (e.g., "gpt-4", "gemini-1.5-pro").

## How It Works

1. Cactus analyzes staged Git changes.
2. It uses AI to understand the context and significance of the changes.
3. Based on the analysis, it generates commit messages or changelogs.
4. Users can interactively accept, regenerate, or adjust the number of commits.
guibbon 0.4.0
Very high level GUI with an API similar to the HighGUI of OpenCV
Requires Python: >=3.10

# ![icon](https://raw.githubusercontent.com/ManuGira/Guibbon/static/docs/images/icon32.png) Guibbon with U ![icon](https://raw.githubusercontent.com/ManuGira/Guibbon/static/docs/images/icon32.png) 

### High-level GUI with an API similar to the HighGUI of OpenCV. 
**If you know how to use the GUI of OpenCV, then you already know how to use Guibbon.**

It allows to display an image and add GUI controllers such as:

 - Sliders (trackbar)
 - Buttons
 - Radio buttons
 - Check boxes
 - Color picker
 - Draggable points and polygons on the displayed image
 - Any custom widget that you write in Tkinter

### Reasons why you want to use Guibbon:

 - It's fast to learn and fast to code.
 - It's using Tkinter which is natively installed in most python distributions
 - Beside Tkinter, it only has 3 dependencies: numpy, opencv-python and pillow
 - It's less than 200 KB
 - You can create your own custom widget.
 
### Links

* Source code hosted on [GitHub](https://github.com/ManuGira/Guibbon)
* Package hosted on [PyPl](https://pypi.org/project/guibbon)
polars-timeseries 0.3.1
Polars Time Series Extension
Requires Python: <3.13,>=3.12

# Polars Time Series Extension

Welcome to the documentation for the **Polars Time Series Extension**.

---

**Documentation**: [https://drumtorben.github.io/polars-ts](https://drumtorben.github.io/polars-ts)

**Source Code**: [https://github.com/drumtorben/polars-ts](https://github.com/drumtorben/polars-ts)

---

## 📖 Overview

The **Polars Time Series Extention** offers a wide range of metrics, feature extractors, and various tools for time series forecasting.

## Installation

`pip install polars-timeseries`

## How to use

The `polars-ts` plugin is available under the namespace `pts`.
See the following example where we compute the Kaboudan metric:

```python
import polars as pl
from statsforecast import StatsForecast
from statsforecast.models import AutoETS, OptimizedTheta

import polars_ts as pts  # noqa

# Create sample dataframe with columns `unique_id`, `ds`, and `y`.
df = (
    pl.scan_parquet("https://datasets-nixtla.s3.amazonaws.com/m4-hourly.parquet")
    .filter(pl.col("unique_id").is_in(["H1", "H2", "H3"]))
    .collect()
)

# Define models
season_length = 24
models = [
    OptimizedTheta(season_length=season_length, decomposition_type="additive"),
    AutoETS(season_length=season_length),
]
sf = StatsForecast(models=models, freq=1, n_jobs=-1)

# Compute the Kaboudan metric in the `pts` namespace
res = df.pts.kaboudan(sf, block_size=200, backtesting_start=0.5, n_folds=10)
```