semvec/pypi-packages huggingface dataset:
magicc 0.1.0
MAGICC: Metagenome-Assembled Genome Inference of Completeness and Contamination
License: MIT
Requires Python: >=3.8
Keywords: metagenomics, genome-quality, MAG, completeness, contamination, deep-learning, bioinformatics

# MAGICC

**Metagenome-Assembled Genome Inference of Completeness and Contamination**

Ultra-fast genome quality assessment using core gene k-mer profiles and deep learning.

## Installation

```bash
pip install magicc
```

Or from source:

```bash
git clone https://github.com/renmaotian/magicc.git
cd magicc
pip install -e .
```

**Note**: Git LFS is required to clone the repository (the ONNX model is ~180 MB).

### Dependencies

- Python >= 3.8
- numpy >= 1.20
- numba >= 0.53
- scipy >= 1.7
- h5py >= 3.0
- onnxruntime >= 1.10

## Usage

```bash
# Predict quality for all FASTA files in a directory (uses all CPUs by default)
magicc predict --input /path/to/genomes/ --output predictions.tsv

# Single genome
magicc predict --input genome.fasta --output predictions.tsv

# Specify threads and file extension
magicc predict --input /path/to/genomes/ --output predictions.tsv --threads 8 --extension .fa
```

### Options

```
magicc predict [OPTIONS]

Required:
  --input, -i       Path to genome FASTA file(s) or directory
  --output, -o      Output TSV file path

Optional:
  --threads, -t     Number of threads (default: 0 = all CPUs)
  --batch-size      Batch size for ONNX inference (default: 64)
  --extension, -x   Genome file extension filter (default: .fasta)
  --model           Path to ONNX model file (auto-downloads if not found)
  --quiet, -q       Suppress progress output
  --verbose, -v     Verbose debug output
```

### Output

Tab-separated file with three columns:

| genome_name | pred_completeness | pred_contamination |
|-------------|-------------------|--------------------|
| genome_001  | 95.2341           | 2.1567             |
| genome_002  | 78.4521           | 15.3421            |

- **pred_completeness**: Predicted completeness (%), range [50, 100]
- **pred_contamination**: Predicted contamination (%), range [0, 100]

## Citation

If you use MAGICC in your research, please cite:

> Tian, R. (2026). MAGICC: Ultra-fast genome quality assessment using core gene k-mer profiles and deep learning. *In preparation*.

## License

MIT License. See [LICENSE](LICENSE) for details.
toybox-3d-api 0.2.0
Python API client for ToyBox 3D printers (make.toys) via Meteor DDP
License: MIT
Requires Python: >=3.11

# toybox-api — Python Client for ToyBox 3D Printers

Async Python client for [ToyBox 3D printers](https://www.toybox.com/) via the [make.toys](https://www.make.toys) Meteor DDP protocol.

Used by the [Home Assistant integration](https://github.com/stgarrity/homeassistant-toybox).

## Features

- Meteor DDP WebSocket client (no REST — make.toys is DDP-only)
- Authentication via Meteor accounts
- Real-time printer state via DDP subscriptions (`printerStates` collection)
- Print job tracking via `toyPrints` collection
- Time remaining / elapsed / progress calculations matching make.toys web app logic

## Installation

```bash
pip install toybox-api
```

## Usage

```python
import asyncio
from toybox_api import ToyBoxClient

async def main():
    async with ToyBoxClient() as client:
        await client.connect()
        await client.authenticate("email@example.com", "password")
        await client.subscribe_to_printer_data(["printer_id"])

        data = await client.get_all_data()
        print(f"Printer: {data.printer.display_name}")
        print(f"Online: {data.printer.is_online}")
        print(f"State: {data.print_state}")

        if data.current_request:
            print(f"Printing: {data.current_request.print_name}")
            print(f"Remaining: {data.current_request.remaining_seconds}s")

asyncio.run(main())
```

## Data Model

| Class | Source | Description |
|-------|--------|-------------|
| `PrinterStatus` | `printerStates` collection | Online state, model, hardware ID, firmware |
| `PrintRequest` | `toyPrints` collection | Active/completed prints with timing data |
| `ToyBoxData` | Coordinator container | Combines printer + current/last print request |

## Status

🟡 **Beta** — Implements real Meteor DDP protocol. Needs live testing with active prints.

## Project Structure

```
ha-toybox/
├── toybox_api/
│   ├── __init__.py
│   ├── client.py        # DDP WebSocket client
│   ├── models.py        # PrinterStatus, PrintRequest, ToyBoxData
│   ├── exceptions.py    # ToyBoxError hierarchy
│   └── const.py         # DDP URLs, subscription/method names
├── pyproject.toml
├── LICENSE
└── README.md
```

## License

MIT
sardis-agent-sdk 0.1.0
Anthropic Claude Agent SDK integration for Sardis payments
License: MIT
Requires Python: >=3.11
Keywords: agent-sdk, ai-agents, anthropic, claude, mcp, payments, sardis, tool-use

# sardis-agent-sdk

Anthropic Claude Agent SDK integration for [Sardis](https://sardis.sh) payments.

## Installation

```bash
pip install sardis-agent-sdk
```

## Quick Start

```python
import anthropic
from sardis import SardisClient
from sardis_agent_sdk import SardisToolkit

sardis = SardisClient(api_key="sk_test_demo")
wallet = sardis.wallets.create(agent_id="my-agent", chain="base", currency="USDC")

toolkit = SardisToolkit(client=sardis, wallet_id=wallet.wallet_id)

# Automated agent loop
client = anthropic.Anthropic()
result = toolkit.run_agent_loop(
    client=client,
    model="claude-sonnet-4-5-20250929",
    system_prompt="You are a shopping assistant with a Sardis wallet.",
    user_message="Buy $20 of API credits from openai.com",
)
print(result["response"])
```

## Tools Provided

| Tool | Description |
|------|-------------|
| `sardis_pay` | Execute a policy-checked payment |
| `sardis_check_balance` | Check wallet balance and limits |
| `sardis_check_policy` | Dry-run a payment against policy |
| `sardis_set_policy` | Update spending policy (natural language) |
| `sardis_list_transactions` | View transaction history |
| `sardis_create_hold` | Create a temporary fund hold |

## Read-Only Mode

```python
toolkit = SardisToolkit(client=sardis, wallet_id=wallet_id, read_only=True)
# Only exposes balance, policy check, and transaction listing tools
```

## License

MIT