semvec/pypi-packages huggingface dataset:
co2e 0.0.1
Reserved package name for future Carbon Ops Guardrails distribution.
Requires Python: >=3.11

# co2e (Carbon Ops Guardrails placeholder)

This repository now publishes a minimal ``co2e`` package to reserve the PyPI name for the future Carbon Ops Guardrails distribution. The placeholder release ships lightweight scaffolding only; real functionality will arrive in later versions.

## What you can expect
- **Signed telemetry ledger** – every emission estimate will be ed25519 signed and anchored via Merkle proofs.
- **Energy-to-carbon estimation** – ingestion jobs will convert runtime metrics into kWh and CO₂e using grid intensity APIs.
- **Evidence exports** – repeatable audit packages with inclusion proofs and configuration snapshots.

## Repository layout
```
carbon-ops/
├── README.md
├── pyproject.toml
├── src/
│   └── carbon_ops/
│       ├── __init__.py
│       ├── config.py
│       ├── ledger.py
│       └── pipeline.py
└── tests/
    └── test_placeholder.py
```

## Quick start
1. Create and activate a Python 3.11 virtual environment.
2. Install the package in editable mode: `pip install -e ".[dev]"`.
3. Run the placeholder tests: `pytest`.

## Next steps
- Implement telemetry ingestion adapters for Vertex AI job logs.
- Flesh out the ledger module with ed25519 signing and Merkle tree anchoring.
- Integrate grid intensity providers (e.g., WattTime) and unit tests around estimation logic.

## Licensing
Choose an open-source license before publishing a production-ready release (MIT, Apache-2.0, or BUSL-1.1 are all viable depending on release strategy).
pilr-visualization 0.1.1
Interactive PILR heatmap visualization on OpenStreetMap
Requires Python: >=3.8

# PILR Visualization

Interactive PILR (Point-in-time Light Rail) heatmap visualization on OpenStreetMap using Dash and Plotly.

## Features

- Interactive OpenStreetMap-based visualization
- Time range selection via dropdowns
- Grid-based spatial aggregation (50m resolution)
- Geometric mean PILR calculation
- Real-time statistics display

## Installation

### From PyPI (once published)
```bash
pip install pilr-visualization
```

### From source
```bash
git clone https://github.com/yourusername/pilr-visualization.git
cd pilr-visualization
pip install -e .
```

## Usage

After installation, run:
```bash
pilr-viz
```

Then open your browser to `http://localhost:8051`

## Development

Install development dependencies:
```bash
pip install -e ".[dev]"
```

## Requirements

- Python 3.8+
- pandas
- numpy
- plotly
- dash

## License

MIT License