semvec/pypi-packages huggingface dataset:
scrunch 0.18.17
Pythonic scripting library for cleaning data in Crunch
Requires Python: >=2.7

# Pythonic scripting library for cleaning data in `Crunch <http://crunch.io/>`__
        
        To learn more, including how to install the library, see the
        [Overview](https://github.com/Crunch-io/scrunch/wiki/Overview) wiki
        page.
        
        Once you have it installed, to get started using `scrunch` to work with data in
        Crunch, see the [User Guide](https://github.com/Crunch-io/scrunch/wiki/User-Reference).
immersim 1.1.0
Immersive Simulations for Jupyter Notebooks
Requires Python: >=3.10

# Immersive Simulations for Jupyter Notebooks
Enables seamless, minimalistic variable and namespace storage:
  - Shelve storage for per-variable storage and retrieval
  - Quick-Save and Quick-Load of notebook namespace
  - Enable checkpoints so that cells store their starting and ending states

Useful for post-simulation analysis, or ensuring that data
which hasn't been explicitly saved remains entirely available.
billiez 0.2.2
NVIDIA API wrapper for AI text generation
Requires Python: >=3.6

```markdown
# billiez

Python package for interacting with NVIDIA's AI API

## Installation

```bash
pip install billiez
```

## Usage

### Basic Usage
```python
import billiez

# Default usage
billiez()

# Generate AI response with default model
billiez(prompt="Tell me about AI")

```

### Advanced Usage
```python
import billiez

# Custom model and parameters
response = billiez(
    model="meta/llama3-70b-instruct",
    prompt="Explain quantum computing",
    temperature=0.7,
    max_tokens=1024,
    stream=False
)
```

## Parameters

- `model` (str): AI model to use (default: "deepseek-ai/deepseek-r1-distill-qwen-7b")
- `prompt` (str): Your query for the AI (returns Craftguy info if None)
- `temperature` (float): Controls randomness (0.0-1.0)
- `top_p` (float): Controls diversity (0.0-1.0) 
- `max_tokens` (int): Maximum length of response
- `stream` (bool): Stream response if True

## Requirements

- Python 3.6+
- `openai` package
adaptivesolveplanner 0.1.3.dev0
Adaptive tournament planning engine
Requires Python: >=3.11
Keywords: adaptive, planner, planning, solver

# AdaptiveSolvePlanner

AdaptiveSolvePlanner is a production-oriented adaptive tournament planner and solver. It supports sequential and process-parallel search over retention/decay parameters to produce "nice" tournament round plans (groups, qualifiers, wildcards) while honoring configurable limits.

[![Python >=3.11](https://img.shields.io/badge/python-%3E%3D3.11-blue)](https://www.python.org/)
[![License: GPL-3.0](https://img.shields.io/badge/license-GPL--3.0-blue.svg)](LICENSE)

## Quickstart

To install from PyPI:

```bash
python -m pip install adaptivesolveplanner
```

To install latest from GitHub:

```bash
pip install git+https://github.com/Smoki-Cool/adaptivesolveplanner.git
```

## Example:

```python
from adaptive_solve_planner import plan

p = plan(start_guess=100, finals_target=12, rounds_desired=5, strictness="generous",
         prefer_wc=True, allow_wc_down=True, top_k_per_round=12, max_nodes=300000, time_limit=12.0)
print(p["_meta"])
for r in p["rows"]:
    print(r)
```

## API

Two primary entry points:
* `plan(...)` — sequential single-process planner
* `parallel_plan(...)` — process-parallel planner with auto-tuning

Both return a dict with:
* `rows` — per-round dicts
* `final_total` — final number of players
* `_meta` — diagnostic metadata
* `_score` — score used for selecting best plan

See `examples/` for runnable samples.

## Configuration

`PlannerConfig` centralizes defaults and can be overridden from environment variables using the `ASP_` prefix (e.g. `ASP_WC_PCT=0.12`). See `adaptive_solve_planner/config.py`.

## CLI

If you installed the package with the console script, run:

```bash
adaptivesolveplanner --help
```

## Development

Create a virtualenv, install dev requirements, run tests:

```bash
python -m venv .venv
source .venv/bin/activate
pip install -U pip
pip install build twine pytest
python -m pytest
```

## Contributing

See `CONTRIBUTING.md`.

## License

This project is licensed under the GNU General Public License v3.0 — see the `LICENSE` file.