semvec/pypi-packages huggingface dataset:
bitmapy 1.0.1
Bitmap manipulation and operations.
Requires Python: >=3.13

# bitmapy

[![CI](https://github.com/oparsons22/bitmapy/actions/workflows/ci.yaml/badge.svg)](https://github.com/oparsons22/bitmapy/actions/workflows/ci.yaml)
![PyPI - Downloads](https://img.shields.io/pypi/dm/bitmapy?label=PyPI%20-%20Downloads)


A lightweight Python library for bitmap manipulation and operations.

## Features

- Create and manage bitmaps in Python
- Set, clear, and toggle bits
- Efficient bitwise operations
- Type-annotated for better development experience
- Well-tested with pytest

## Installation

You can install bitmapy using pip:

```bash
pip install bitmapy
```

Or, for local development:

```bash
git clone https://github.com/oparsons22/bitmapy.git
cd bitmapy
uv sync --all-extras --dev
```

## Usage Example

Full functionality is available when using generic `enum.IntFlag`.

```python
from enum import IntFlag, auto
from bitmapy import Bitmap

class Permission(IntFlag):
    READ = auto()
    WRITE = auto()
    EXECUTE = auto()

# Create a bitmap with no permissions
bitmap = Bitmap[Permission]()

# Check if WRITE permission is set
if bitmap.is_set(Permission.WRITE):
    print("WRITE permission is enabled.")

# Add EXECUTE permission
bitmap.set(Permission.EXECUTE)

# Remove READ permission
bitmap.clear(Permission.READ)

# Toggle WRITE permission
bitmap.toggle(Permission.WRITE)

# Get the current value
print(f"Current permissions: {bitmap.value}")
```

## Testing

To run tests:

```bash
uv run pytest
```

## Contributing

Contributions are welcome! Please open issues or submit pull requests on GitHub.

## License

This project is licensed under the MIT License. See the LICENSE file for details.
sanin 1.0.0
Synthetic anomaly injection for time series with auto baseline estimation.
Requires Python: >=3.8

# sanin
<img width="102" height="102" alt="image" src="https://github.com/user-attachments/assets/127a9f7e-0d4a-4583-a3a9-ba601e747c28" />

A lightweight package for **injecting synthetic anomalies** into time series **without** requiring prior metadata.  
It internally estimates level, trend, and seasonality, then applies user-selected anomalies.

## Local Installation

```bash
pip install -e .
```

## Uso rápido

```python
import numpy as np, pandas as pd
from sanin import AnomalyInjector, AnomalyType

# Example series
t = np.arange(1000)
y = 100 + 0.01*t + 5*np.sin(2*np.pi*t/50) + np.random.normal(0, 1, size=len(t))
s = pd.Series(y, index=pd.date_range("2024-01-01", periods=len(t), freq="min"))

inj = AnomalyInjector(random_state=123)

# 1) Positive spikes in 5 points
y1, rep1, mask1 = inj.inject(s, AnomalyType.SPIKE, severity=1.0, n_points=5, return_mask=True)

# 2) Level shift starting at a point
y2, rep2, mask2 = inj.inject(s, AnomalyType.LEVEL_SHIFT, severity=2.0, return_mask=True)

# 3) Variance change within a window
y3, rep3, mask3 = inj.inject(s, AnomalyType.VARIANCE_CHANGE, severity=1.5, return_mask=True)
```

## Anomaly Types

- `SPIKE` (point anomaly, +)
- `DROP` (point anomaly, −)
- `LEVEL_SHIFT` (step change in level)
- `VARIANCE_CHANGE` (increased noise in a window)
- `TREND_DRIFT` (slope change)
- `SEASON_AMP_CHANGE` (seasonal amplitude change in a window)
- `FLATLINE` (sensor stuck at constant value)
- `MISSING` (segment with NaNs)
- `STUCK_HIGH`, `STUCK_LOW` (saturation at high/low value)
- `BLACKOUT` (zeroing or fixed value in a window)

Each injection also returns a **report** with affected indices and resolved parameters.

## Design
- No heavy dependencies: uses `numpy`/`pandas`.
- Internal decomposition:
  - level = robust median;
  - trend = moving average over ~10% of the length;
  - seasonality = mean by phase with period estimated via autocorrelation;
  - residual = observed - (level + trend + seasonality).
- Parameters have **robust** defaults derived from the series itself.

## Tests

```bash
pytest -q
```

## License

MIT
Quadratics-Discriminant 0.0.1
The package is used to find the roots and the nature of a quadratic equation.
Requires Python: >=3.0

### This is a quadratic equation package for beginners. It is used to find the roots of a quadratic equation and identify the discriminant.
    - Feature 1: Roots of the equation.
    - Feature 2: Discriminant of the eqution and the nature of the roots.
crust-shell 0.1.3
Crust – a Python-based Linux shell with aliases and more.
Requires Python: >=3.8

![Screenshot](screenshot.png)
# 🐚 Crust Shell
[![Python](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![Platform](https://img.shields.io/badge/platform-linux-lightblue.svg)](https://kernel.org)
[![License](https://img.shields.io/badge/license-edited%20MIT-yellow.svg)]()
[![Made with ❤️](https://img.shields.io/badge/made%20with-%E2%9D%A4-red.svg)](https://github.com/mostypc123)

Crust is an interactive Linux shell written in Python, designed with a modern interface, AI integration, and enhanced tooling for common system tasks.

**Note:** I am currecntly putting crust into package managers, but it is not ready yet. Until there is an announcment, any packages made by me that are crust are not ready. 
**Edit**: Crust is available on PyPi! Use the crust-shell package to install it, as shown in the Documentation section.

## ✨ Features

- ⚙️ Custom startup hook support via `custom_commands.py`
- 🧠 AI Assistant integration using Cohere with `.question` prompt
- 🧾 Enhanced `ls`, `lsusb`, and `df -h` commands using Rich tables
- 🔧 Built-in troubleshooting interface
- 💾 Custom integration to search for packages across multiple package managers
- 📁 Git branch and repo detection in prompt
- 🪟 Venv support and styled prompt with icons
- 🧠 Neofetch context awareness for AI assistant

## 📖 Documentation
https://mostypc123.is-a.dev/crust

Installation:
```bash
pip install crust-shell
```

If using linux, --break-system-packages may be neccesarry to make pip actually install this. If you do not want to proceed with that, use pipx (not tested).

On Arch Linux, use AUR:
```bash
yay -S crust-git
```
Or with paru:
```bash
paru -S crust-git
```

## 🛡️ License

We use an edited version of the MIT license.

## 📫 Contributing

Contributions, bug reports, and ideas are welcome!  
Feel free to open an issue or pull request on GitHub.

## 🧑‍💻 Author

**Juraj Kollár**  
Creator of [XediX](https://github.com/mostypc123/XediX)