semvec/pypi-packages huggingface dataset:
seven-reaper 0.1.1
7DT image-specific Tractor photometry pipeline
Requires Python: >=3.10

# seven-reaper

7DT image-specific Tractor photometry pipeline.
This distribution is licensed under MIT.

## Quick start

1) Install Tractor's prerequisites (see Tractor docs):

2) Install Tractor (from source or your preferred method).

3) Create a YAML config (see `seven_reaper/data/sample_config.yaml`).

4) Run the full pipeline:

```bash
seven-reaper run --config /path/to/config.yaml
```

## Commands

- `run`: full pipeline (load → ePSF → patches → patch inputs → patch runs → merge)
- `run-epsf`: build ePSF only
- `build-patches`: build patch definitions only
- `build-patch-inputs`: build patch inputs only
- `run-patches`: run Tractor on patch inputs
- `merge`: merge patch results into final catalog

## Notes

- The package preserves the reference behavior; changes are limited to packaging and path handling.
- Tractor and `astrometry.net` must be installed separately.
- Python prerequisites like `numpy` and `scipy` are included in dependencies; optional extras (eg `fitsio`, `emcee`) are included for safety.
- Input images are provided via a text file (one FITS path per line).
- Parameter mapping reference: `docs/CONFIG_MAPPING.md`.
jao-auction-api 0.1.0
Client and utilities for the JAO Auction Data API
Requires Python: >=3.9
Keywords: jao, auction, api, energy, market

# JAO Auction Data API Package

This package provides a small, focused Python client for the JAO Auction Data API, plus helpers to parse auctions, bids, and curtailments and plot a professional bid stack chart.

## Features
- Authenticated REST client for `getcorridors`, `gethorizons`, `getauctions`, `getbids`, `getcurtailment`
- Parsing utilities for auction IDs and corridor codes
- Offered-capacity detection in auction/bid payloads
- Bid stack visualization (cumulative step curve)
- Enum helpers for corridors, horizons, and curtailment reasons
- Curtailment stacked-bar visualization by reason

## Install
```bash
pip install jao-auction-api
```

## Quick Start
```python
from jao_auction_api import (
    JaoClient,
    load_env,
    extract_auction_ids,
    extract_corridor_codes,
    plot_bid_stack_step,
    plot_curtailment_stacked_bar,
    corridor_enum,
)

load_env()  # reads .env into environment variables if present
client = JaoClient()

auctions = client.get_auctions(
    corridor="FR-DE",
    fromdate="YYYY-MM-DD-HH:MM:SS",
    todate="YYYY-MM-DD-HH:MM:SS",
)
auction_ids = extract_auction_ids(auctions)

bids = client.get_bids(auction_ids[0])
fig, ax = plot_bid_stack_step(bids)

corridors = client.get_corridors()
corridor_codes = extract_corridor_codes(corridors)
Corridor = corridor_enum(corridor_codes)

curtailments = client.get_curtailment(
    corridor=Corridor.FR_DE.value,
    fromdate="YYYY-MM-DD-HH:MM:SS",
    todate="YYYY-MM-DD-HH:MM:SS",
)
fig2, ax2 = plot_curtailment_stacked_bar(curtailments)
```

## Environment
Set `JAO_API_KEY` in your environment or `.env` file.
group-genie 0.0.3
Group Genie
Requires Python: <3.15,>=3.11

# Group Genie

## Introduction

Group Genie enables single-user AI agents to participate in group chat conversations without requiring modification to the agents themselves. While many AI agents excel at responding to direct queries from individual users, they typically cannot handle multi-party conversations where relevant information emerges from complex exchanges between multiple participants. Group Genie solves this by combining Group Sense's intelligent pattern detection with a flexible agent integration layer. Agents can be based on any technology stack (framework, API, etc.) and integrated through a simple agent interface, with default implementations provided for Pydantic AI and the OpenAI Agents SDK.

## Documentation

- [Installation](https://gradion-ai.github.io/group-genie/installation/): Installation instructions and API key setup
- [Tutorial](https://gradion-ai.github.io/group-genie/tutorial/): Complete walkthrough of building a group chat agent
- [Integration](https://gradion-ai.github.io/group-genie/integration/): Integrating Group Genie into applications
- [API Documentation](https://gradion-ai.github.io/group-genie/api/message/): Complete API reference

## LLM-optimized documentation

For AI assistants and LLM-based tools, optimized documentation formats are available:

- [llms.txt](https://gradion-ai.github.io/group-genie/llms.txt): Concise documentation suitable for LLM context windows
- [llms-full.txt](https://gradion-ai.github.io/group-genie/llms-full.txt): Complete documentation with full API details

## Development

For development setup, see [DEVELOPMENT.md](DEVELOPMENT.md)