semvec/pypi-packages huggingface dataset:
bombsquad 0.1.2
Isolate OOM-prone functions in a separate process, raise exceptions on SIGKILL or segfault
License: MIT
Requires Python: >=3.10

# bombsquad

**Isolate OOM-prone functions in a separate process, raise exceptions on SIGKILL or segfault.**

Methods that cause out-of-memory errors (OOM) or segfaults are killed by the OS immediately, crashing the interpreter without raising an exception.

To solve this problem, the `bombsquad` package provides the `@bombsquad` decorator. This runs the decorated function in a separate, isolated process. If that process is terminated by the OS (e.g. `SIGKILL`), `bombsquad` catches the exit code and raises a `NonzeroExitcode` exception in your main process.

Normal Python exceptions raised within the function are propagated back to the parent process transparently.

## Installation

```bash
pip install bombsquad
```
## Quick Start

### 1. Trapping a Crash

Protect your main application from dangerous functions.

```Python
import os
import signal
from bombsquad import bombsquad, NonzeroExitcode

@bombsquad
def risky_business():
    print("Running risky code...")
    # Simulate a hard crash (like an OOM killer)
    os.kill(os.getpid(), signal.SIGKILL)

try:
    risky_business()
except NonzeroExitcode as e:
    print(f"Caught crash! Process exited with code {e.exitcode}")
```

### 2. Propagating Exceptions

Standard exceptions work exactly as expected.

```Python
@bombsquad
def divide(a, b):
    return a / b

try:
    divide(10, 0)
except ZeroDivisionError:
    print("Caught division by zero from child process.")
```

## Advanced Configuration

The decorator accepts arguments to tune performance and safety.
```Python
@bombsquad(start_method="spawn", backend="file")
def massive_processing(data):
    ...
```

### Parameters

| Argument | Options | Default | Description |
| :--- | :--- | :--- | :--- |
| **`start_method`** | `"spawn"`, `"fork"` | `"spawn"` | How the child process is created. |
| **`backend`** | `"file"`, `"queue"` | `"file"` | How data is returned to the parent. |

### Choosing a `backend`

* **`"file"` (Default, Recommended):**
    * **Mechanism:** Serializes results to a temporary file on disk.
    * **Pros:** Supports **unlimited return sizes** (limited only by disk space).
    * **Cons:** Slower due to disk I/O.
    * **Use for:** Returning large arrays, datasets, or huge strings.

* **`"queue"`:**
    * **Mechanism:** Uses `multiprocessing.Queue` (memory pipes).
    * **Pros:** Faster (in-memory transfer).
    * **Cons:** Limited to **~4GB** payloads (Python pickle limit).
    * **Use for:** Returning small results (status codes, metrics, small dicts).

## License

MIT
shopcube 5.0.0
E-commerce solution
License: MIT
Keywords: flask, pos, management, shop, ecommerce, cms, erp, e-commerce

<div align="center">



<img src="https://github.com/Abdur-rahmaanJ/shopcube/raw/dev/assets/logo.png" width="250" />

[![First Timers Only](https://img.shields.io/badge/first--timers--only-friendly-blue.svg)](https://www.firsttimersonly.com/)

🇲🇺 🇵🇰 🇳🇬 🇮🇳 🇻🇳 🇬🇭 🇬🇧

</div>

<div align="center">

[![Discord](https://img.shields.io/badge/chat%20on-discord-green.svg)](https://discord.gg/k37Ef6w)
[![CodeQL](https://github.com/shopyo/shopcube/actions/workflows/codeql-analysis.yml/badge.svg)](https://github.com/shopyo/shopcube/actions/workflows/codeql-analysis.yml)

</div>

# shopcube

shopcube is an e-commerce solution for shops. Complete with

- cart
- wishlist
- orders
- upload by csv
- charts
- theming

If you want to contribute, go ahead, we welcome it. We follow a 100% first-timers-friendly policy. Join #shopcube on Discord if you get stuck or would just like to chat and say hi.

Powered by Shopyo, a Python web framework built on top of Flask.

## Quick Start

### Installation

Clone the repository and set up a virtual environment:

```bash
git clone https://github.com/shopyo/shopcube.git
cd shopcube
python3 -m venv venv
source venv/bin/activate
pip install --upgrade pip setuptools
pip install -e .
```

### Initialisation

To set up the database and default settings without clearing existing migrations:

```bash
cd src/shopcube
shopyo initialise --no-clear-migration
```

### Running the Application

To run the development server:

```bash
flask run
```

Access the application at http://127.0.0.1:5000

Login as administrator:
- Email: admin@domain.com
- Password: pass

The dashboard is available at http://127.0.0.1:5000/dashboard/

## Deployment

For production deployment, use a WSGI server like Gunicorn.

### Example with Gunicorn

```bash
gunicorn --bind 0.0.0.0:8000 wsgi:application
```

Ensure you have a `config.json` in your execution directory. You can copy the demo config:

```bash
cp src/shopcube/config_demo.json config.json
```

## Development

### Running Tests

```bash
cd src/shopcube
python -m pytest
```

### Useful Commands

```bash
flask flight-info
```
surfaces-cec-data 0.0.1
CEC benchmark data files for the surfaces library
License: MIT
Requires Python: >=3.8
Keywords: optimization, benchmarks, CEC, surfaces

# surfaces-cec-data

CEC benchmark data files for the [surfaces](https://github.com/SimonBlanke/Surfaces) library.

## Installation

This package is typically installed as a dependency of surfaces:

```bash
pip install surfaces[cec]
```

Or install directly:

```bash
pip install surfaces-cec-data
```

## Contents

This package provides rotation matrices, shift vectors, and shuffle indices
for CEC (Congress on Evolutionary Computation) benchmark functions:

| Suite | Dimensions | Functions |
|-------|------------|-----------|
| CEC 2013 | 2, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 | F1-F28 |
| CEC 2014 | 10, 20, 30, 50, 100 | F1-F30 |
| CEC 2017 | 2, 10, 20, 30, 50, 100 | F1-F30 |

## Usage

Once installed, the surfaces library automatically detects and uses this package:

```python
from surfaces.test_functions.cec.cec2017 import ShiftedRotatedBentCigar

func = ShiftedRotatedBentCigar(n_dim=10)
result = func([0.0] * 10)
```

## License

MIT License - See [LICENSE](https://github.com/SimonBlanke/Surfaces/blob/main/LICENSE).
surfaces-onnx-files 0.0.1
Pre-trained ONNX surrogate models for the surfaces library
License: MIT
Requires Python: >=3.8

# surfaces-onnx-files

Pre-trained ONNX surrogate models for the [surfaces](https://github.com/SimonBlanke/Surfaces) library.

## Installation

This package is typically installed as a dependency of surfaces:

```bash
pip install surfaces[surrogates]
```

Or install directly:

```bash
pip install surfaces-onnx-files
```

## Contents

This package provides pre-trained ONNX models for fast surrogate evaluation of machine learning test functions:

| Model | Description |
|-------|-------------|
| k_neighbors_regressor | KNN regressor surrogate |
| k_neighbors_classifier | KNN classifier surrogate |
| gradient_boosting_regressor | Gradient boosting regressor surrogate |

## Usage

Once installed, the surfaces library automatically detects and uses this package:

```python
from surfaces import load_surrogate

surrogate = load_surrogate("k_neighbors_regressor")
if surrogate:
    result = surrogate.predict({"n_neighbors": 5, "leaf_size": 30, ...})
```

## License

MIT License - See [LICENSE](https://github.com/SimonBlanke/Surfaces/blob/main/LICENSE).
toolkit_cmm 0.0.1.9
Toolkit for personal use
License: MIT
Requires Python: >=3.8
Keywords: toolkit

# python3.8+ Toolkit for personal use

## Install

```bash
pip install toolkit_cmm
```

## Quick Start

```py
from toolkit_cmm.helloworld import hello_world;
print(hello_world())
```

## Docs

<https://cromarmot.github.io/pytoolkit>