semvec/pypi-packages huggingface dataset:
dbbasic-queue 1.0.0
TSV-based job queue for async tasks
Requires Python: >=3.6
Keywords: queue, job queue, background jobs, async, tsv, cron, worker

# dbbasic-queue

TSV-based job queue for async tasks. Simple, reliable background job processing.

## Philosophy

> "Store work, not workers. Queue jobs, not processes."

Background jobs are **actual work to be done**, not temporary state. Unlike sessions (which are ephemeral authentication), jobs need persistent storage, retry logic, and failure handling.

## Features

- **Simple**: ~50 lines of core code
- **Reliable**: Retry logic with exponential backoff
- **Debuggable**: Plain text TSV, inspect with cat/grep
- **Unix-Compatible**: Cron-based workers, no daemon required
- **Foundation-First**: Built on dbbasic-tsv

## Installation

```bash
pip install dbbasic-queue
```

## Quick Start

### 1. Queue a job

```python
from dbbasic_queue import enqueue

# Queue an email to be sent
job_id = enqueue('send_email', {
    'to': 'user@example.com',
    'subject': 'Welcome',
    'body': 'Thanks for signing up!'
})
```

### 2. Create a worker

```python
# workers/queue_worker.py
from dbbasic_queue import process_jobs

def send_email_handler(payload):
    """Send email via SMTP"""
    # ... send email logic
    return {'sent_at': time.time()}

if __name__ == '__main__':
    handlers = {
        'send_email': send_email_handler,
    }
    process_jobs(handlers, max_attempts=3)
```

### 3. Set up cron

```bash
# Run worker every minute
* * * * * cd /app && python3 workers/queue_worker.py >> /var/log/queue.log 2>&1
```

## API Reference

### `enqueue(job_type, payload, run_at=None)`

Add job to queue.

- **job_type** (str): Job handler name
- **payload** (dict): Job parameters
- **run_at** (int, optional): Unix timestamp to run job (default: now)
- **Returns**: job_id (str)

### `process_jobs(handlers, max_attempts=3)`

Process pending jobs (run by worker).

- **handlers** (dict): Map of job_type → handler function
- **max_attempts** (int): Max retry attempts before marking failed

### `get_job(job_id)`

Get job status and details.

- **job_id** (str): Job identifier
- **Returns**: job (dict) or None

### `cancel_job(job_id)`

Cancel pending job.

- **job_id** (str): Job identifier
- **Returns**: bool (True if cancelled)

## Storage Format

Jobs are stored in a single TSV file: `data/queue.tsv`

```
id  type    payload status  created_at  run_at  attempts    error   result
```

## Performance

- Enqueue job: 0.1ms
- Process job: 0.5ms
- Perfect for single-server apps with < 10K jobs

## When to Use

✅ Single-server applications
✅ < 10,000 queued jobs
✅ Background email sending
✅ Video processing
✅ Report generation

## When to Graduate to Redis/Celery

- 100K+ queued jobs
- Multiple worker servers
- Sub-second job pickup required

## License

MIT

## Full Specification

See http://dbbasic.com/queue-spec for complete specification.
hcr-nn 1.0.4
Package implementing Hierarchical Correlation Reconstruction Methodologies

# HCR-NN Library

![Python version](https://img.shields.io/badge/python-3.9%2B-blue)
![License](https://img.shields.io/badge/license-MIT-green)

A professional, modular Python library for **Hierarchical Correlation Reconstruction with Neural Networks (HCR-NN)**.

---

##  What Is HCR-NN?

HCR-NN enables efficient density estimation and conditional inference on multivariate data using principled statistical modeling wrapped in modern neural network constructs. It is designed for both research and application:

- Model **joint and conditional densities**, e.g., \(ρ(u_1, u_2)\) and \(E[u_1\mid u_2]\).  
- Normalize raw data using empirical or Gaussian CDFs (`CDFNorm`).  
- Approximate densities via **basis expansions** (polynomial, cosine, KDE).  
- Build, train, and visualize **conditional models** with `HCRCond2D`.  
- Integrate with aurhus-propositional modeling workflows & PyTorch pipelines.

---

## ​ Highlights

- **Precise probabilistic modeling** with neural network flexibility.  
- **Modular design**: swap between normalization, basis, model easily.  
- **Reproducible example notebooks** (`examples/`) that run in CI and output artifacts.  
- **Optimized density utilities** for direct density inference and conditional expectation.

---

<!-- ##  Quick Start

```bash
git clone https://github.com/mateuszwalo/HCR-NN-Library.git
cd HCR-NN-Library
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt  # includes torch, numpy, pandas, pytest, etc.
``` -->

## Example Workflow: Train and Visualize

```bash
jupyter notebook example_implementation/01_hcrnn_end_to_end.ipynb
```

This notebook demonstrates loading data, normalization, training HCRCond2D, and visualizing 𝐸[𝑢1∣𝑢2]E[u1∣u2].

## Documentation

Getting started & API reference: see docs/guide.md.

In-code documentation: each module in hcr_nn/ is documented via docstrings.

Further reading: the foundational theory is detailed in [the HCR‑NN paper (arXiv:2405.05097)].

## License

This project is available under the MIT License. See LICENSE for details.

## Acknowledgments

Developed by contributors from AIntern:

****Mateusz Walo****

****Felicja Warno****

****Adrian Przybysz****

****Karol Rochalski****

****Zuzanna Sieńko****

****Under the supervision of PhD Jarek Duda author HCR-NN theory****

Inspired by HCR Theory: Refer to the original paper (arXiv:2405.05097) for theoretical foundations (normalization, orthonormal basis, propagation).


## Contributing

We welcome contributions of any kind:

Fork the repo and create a branch for your feature or fix.

Add clear documentation and tests (preferably notebooks or pytest) — all examples should run in CI.

Open a Pull Request with a clear description of your changes.

Check the test suite `pytest -q` in CI for regression safety.
icrn 0.1.0
Indexed Chemical Reaction Networks in a differentiable, tensor-based framework
Requires Python: >=3.9

# icrn

All interfaces are considered to be subject to change.

Tutorial Notebooks
- [Getting started with icrn](https://colab.research.google.com/drive/13-3sszv0Pl0mvFRh8zHOK2oKn5dBteZx?usp=sharing)
- [Reaction Diffusion with icrn](https://colab.research.google.com/drive/1DacnTGoGNVpuGdc-yRuHE2z47cNPHbHy?usp=share_link)
- [Training Parameters with icrn](https://colab.research.google.com/drive/1KDA67p3XToZwTD6Soe9eHtf1nq36I0OH?usp=sharing)

The [DNA31 Examples](https://colab.research.google.com/drive/1ioMWwmCQ8ulOcybkvNppgLtMFgGeCscy) for exploring DNA31 examples.
jks-system 1.0.3
JKS Measurement database system
Requires Python: >=3.6

# JKS measurement database system
[![DOI](https://zenodo.org/badge/881044249.svg)](https://doi.org/10.5281/zenodo.14017592)

Install using

> python3 -m pip install jks-system

# Citation
You can cite usage of the system as
```
@software{christoph_lehner_2024_14017593,
  author       = {Christoph Lehner},
  title        = {{JKS measurement database, 2024-10}},
  month        = 10,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {2024-10},
  doi          = {10.5281/zenodo.14017593},
  url          = {https://doi.org/10.5281/zenodo.14017593}
}
```