semvec/pypi-packages huggingface dataset:
sungrow-http-config 0.2.1
A package to manipulate settings on Sungrow Inverters
Requires Python: >=3.9

# Sungrow HTTP Config

This is a package to configure parameters on Sungrow inverters that are not typically available over standard interfaces. It supports both the Sungrow Local Access HTTP API and direct Modbus TCP communication.

It currently supports setting or unsetting an export limit, which is useful if on a variable-rate tariff that can go negative (i.e., charging you for exports at times there's oversupply in the grid), such as [Amber Electric](http://amber.com.au/) in Australia.

## Usage

```python
from sungrow_http_config import SungrowHttpConfig

# Using HTTP mode (default)
config = SungrowHttpConfig(host="192.168.1.100")

# Using direct Modbus TCP mode
config_modbus = SungrowHttpConfig(host="192.168.1.100", mode="modbus")

# Connect to the inverter
config.connect()

# Set export limit to 1kW (100 dekawatts)
config.setExportLimit(100)

# Get current export limit
limit = config.getCurrentExportLimit()
print(f"Current export limit: {limit/100}kW")

# Remove export limit
config.unsetExportLimit()
```

## Notes

- Please note that a 0kW export limit is equivalent to unlimited. To set as close to zero as possible, use 0.01kW (1 dekawatt).
- The reaction time of the inverter to changes in loads can be a little slow, which could result in more use of grid power than anticipated if a new load is switched on. Depending on your differential in price between imports and exports, you may want to configure a bigger export "buffer" to mitigate this.
- When using Modbus mode, the default port is 502 and the default unit ID is 1. These can be configured when initialising the class.

## Communication Modes

### HTTP Mode
Uses the Sungrow Local Access HTTP API via a WiNet-S dongle connected to the inverter. This is the default mode and requires the inverter to be connected to a WiNet-S dongle.

### Modbus Mode
Communicates directly with the inverter using Modbus TCP on port 502. This mode doesn't require a WiNet-S dongle but requires the inverter to be accessible via TCP/IP.

**WARNING**
This package has only been tested on a limited number of inverters, and the protocol is not well understood. Using it with anything else could damage your equipment, void your warranty, cause physical damage, or violate your agreement with your network operator.

Do not use this package if you have an export limit normally configured.
citylearn 2.5.0
An open source Farama Foundation Gymnasium environment for benchmarking distributed energy resource control algorithms to provide energy flexibility in a district of buildings.
Requires Python: >=3.7.7

# CityLearn
CityLearn is an open source Farama Foundation Gymnasium environment for the implementation of Multi-Agent Reinforcement Learning (RL) for building energy coordination and demand response in cities. A major challenge for RL in demand response is the ability to compare algorithm performance. Thus, CityLearn facilitates and standardizes the evaluation of RL agents such that different algorithms can be easily compared with each other.

![Demand-response](https://github.com/intelligent-environments-lab/CityLearn/blob/master/assets/images/dr.jpg)

## Environment Overview

CityLearn includes energy models of buildings and distributed energy resources (DER) including air-to-water heat pumps, electric heaters and batteries. A collection of building energy models makes up a virtual district (a.k.a neighborhood or community). In each building, space cooling, space heating and domestic hot water end-use loads may be independently satisfied through air-to-water heat pumps. Alternatively, space heating and domestic hot water loads can be satisfied through electric heaters.

![Citylearn](https://github.com/intelligent-environments-lab/CityLearn/blob/master/assets/images/environment.jpg)

## Installation
Install latest release in PyPi with `pip`:
```console
pip install CityLearn
```

## Documentation
Refer to the [docs](https://intelligent-environments-lab.github.io/CityLearn/).

## CityLearn UI

CityLearn UI is a visual dashboard for exploring simulation data generated by the CityLearn framework. It was developed to simplify the analysis of results from smart energy communities, district energy coordination, demand response (among other applications), allowing users to visually inspect building-level components, compare simulation KPIs, and create simulation schemas with ease.

The interface is available in two options:

* Web app: https://citylearnui.netlify.app/ (free hosted version — not recommended for sensitive/personal data)
* Open-source code: https://github.com/Soft-CPS-Research-Group/citylearn-ui

You can check a tutorial at the official CityLearn [website](https://intelligent-environments-lab.github.io/CityLearn/ui.html), in the CityLearn UI repository [README](https://github.com/Soft-CPS-Research-Group/citylearn-ui), or at the help [tooltip of the oficial webapp](https://citylearn-ui.netlify.app/admin/help).

**Compatibility:** This version of the UI currently supports CityLearn v2.5.0 simulation data.

**Developed by:** José, a member of the [SoftCPS](https://www2.isep.ipp.pt/softcps/), Software for Cyber-Physical Systems research group (ISEP, Portugal) in collaboration with the Intelligent Environments Lab, University of Texas at Austin.
a3 0.2.2
Foundational scaffolding for the A4 AI workflow orchestrator
Requires Python: >=3.10

# A4

Foundational scaffolding for the A4 AI workflow orchestrator. This repository hosts the CLI and SDK skeleton along with shared infrastructure (configuration, dependency injection, logging) that other phases will extend.

## Getting Started

1. Create a virtual environment with Python 3.10+.
2. Install the package in editable mode:
   ```bash
   pip install -e .[dev]
   ```
3. Run diagnostics:
   ```bash
   a4 --health
   a4 --version
   ```

## Highlights

- **Provider Fallbacks**: Configure multiple AI providers with automatic failover in `a4.yaml` (`ai.fallback_providers`).
- **Resumable Drafting**: Retry interrupted drafting runs with `a4 draft generate --resume`; progress is checkpointed under `.a4/state/`.
- **Usage Analytics**: Telemetry-aware logging writes to `.a4/metrics/`. Inspect summaries with `a4 observe usage` or disable tracking via `features.telemetry=false`.

## Project Structure

- `a4/` - Python package with CLI, configuration loader, and core utilities.
- `.a4/` - Workspace artifacts generated at runtime (plans, specs, drafts, validation).
- `docs/` - Architectural, product, and delivery documentation.
- `tests/` - Automated tests covering core scaffolding.

See `docs/` for detailed design and roadmap information.

## Documentation

- docs/CLI_HANDBOOK.md - CLI usage guide with full command reference.
- docs/SDK_QUICKSTART.md - Programmatic examples for integrating the SDK.
- docs/ACCEPTANCE_REPORT.md - Pilot testing summary and sign-off details.
- docs/POST_LAUNCH_SUPPORT.md - Support rotation and telemetry hand-off plan.
- docs/SYSTEM_ARCHITECTURE.md - Architecture blueprint with release and operations view.