semvec/pypi-packages huggingface dataset:
LocalSolarWatt 0.8.2
python api wrapper for solar watt device api
Requires Python: >=3.13
Keywords: IOT, Solar, Local

# LocalSolarWatt

A python3 library to access the data of solar watt devices via local api  
Disclaimer: This library uses an unofficial local api and could therefore break at any point

# Overview

The package uses the kiwigrid api provided by your solar watt energy manager device.  
Data available:

- Real time power values from inverter, AC sensor and battery (if exists)
- Long term energy statistics

This library does some preprocessing by extracting relevant fields and grouping them by device class.  
For now this mostly includes data I deem usefully for the integration with home assistant.

Note: Polling the energy manager with a very high interval often could lead to reliability issues (untested).

# Supported devices

As I only have access to one installation and the api is as far as I'm aware not documented by SolarWatt, I can not
guarantee that this library works with your setup.  
If you encounter issues with your concrete setup, feel free to open a new issue.  
The raw json data is available via `http://YOUR_DEVICE_IP/rest/kiwigrid/wizard/devices`  
If you can provide the raw json dump, I can try to add support for your device.  
**Important Note**: This data does contain some sensitive information (especially for the "Location" device), so make
sure to remove any sensitive data before sharing.

## Installation

```
pip install LocalSolarWatt
```

## Usage

```
from local_solar_watt import EnergyManagerApi, EnergyManagerVersion

# create a new api object
energy_api = EnergyManagerApi(EnergyManagerVersion.CLASSIC, "<host_or_ip>")

# create a new api object for new energy manager flex
energy_api = EnergyManagerApi(EnergyManagerVersion.FLEX, "<host_or_ip>")

# optional, change work units to Wh instead of kWh
api = EnergyManagerApi(EnergyManagerVersion.CLASSIC, "<host_or_ip>", work_unit=WorkUnits.Wh)

# with custom logger
my_logger = logging.getLogger("my_logger")
energy_api = EnergyManagerApi(EnergyManagerVersion.CLASSIC, "<host_or_ip>", logger=my_logger)

# test the connection (returns bool based on success)
status = api.test_connection()

# pull data from the device (returns only selected power values, grouped by device class)
print(api.fetch_data())
```

# Development:

Project uses uv for dependency management.

Rudimentary unit tests are included in the `tests` folder.
These use static data from j́son files and do not require a connection to the device.
comrak 0.0.12
Python bindings for the Comrak Rust library, a fast CommonMark/GFM parser
Requires Python: >=3.9
Keywords: markdown, html, rust, commonmark, gfm, parser

# comrak

<!-- [![downloads](https://static.pepy.tech/badge/comrak/month)](https://pepy.tech/project/comrak) -->
[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)
[![pdm-managed](https://img.shields.io/badge/pdm-managed-blueviolet)](https://pdm.fming.dev)
[![PyPI](https://img.shields.io/pypi/v/comrak.svg)](https://pypi.org/project/comrak)
[![Supported Python versions](https://img.shields.io/pypi/pyversions/comrak.svg)](https://pypi.org/project/comrak)
[![License](https://img.shields.io/pypi/l/comrak.svg)](https://pypi.python.org/pypi/comrak)
[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/lmmx/comrak/master.svg)](https://results.pre-commit.ci/latest/github/lmmx/comrak/master)

Python bindings for the [Comrak Rust library](https://crates.io/crates/comrak), a fast CommonMark/GFM parser

## Installation

```bash
pip install comrak
```

### Requirements

- Python 3.9+

## Features

Fast Markdown to HTML parser in Rust, shipped for Python via PyO3.

### Options

All options are exposed in a simple manner:

```py
>>> import comrak
>>> opts = comrak.ExtensionOptions()
>>> comrak.render_markdown("foo :smile:", extension_options=opts)
'<p>foo :smile:</p>\n'
>>> opts.shortcodes = True
>>> comrak.render_markdown("foo :smile:", extension_options=opts)
'<p>foo 😄</p>\n'
```

Refer to the [Comrak docs](https://docs.rs/comrak/latest/comrak/struct.Options.html) for all available options.

## Benchmarks

Tested with small (8 lines) and medium (1200 lines) markdown strings

- vs. [markdown](https://pypi.org/project/markdown): 15x faster (S/M)
- vs. [markdown2](https://pypi.org/project/markdown2): 20x (S) - 60x (M) faster

## Contributing

Maintained by [lmmx](https://github.com/lmmx). Contributions welcome!

1. **Issues & Discussions**: Please open a GitHub issue or discussion for bugs, feature requests, or questions.
2. **Pull Requests**: PRs are welcome!
   - Install the dev extra (e.g. with [uv](https://docs.astral.sh/uv/): `uv pip install -e .[dev]`)
   - Run tests (when available) and include updates to docs or examples if relevant.
   - If reporting a bug, please include the version and the error message/traceback if available.

## License

Licensed under the 2-Clause BSD License. See [LICENSE](https://github.com/lmmx/comrak/blob/master/LICENSE) for all the details.
huscy.project-design 1.1.0
Defining a project design for projects in a research context.

# huscy.project_design

![PyPi Version](https://img.shields.io/pypi/v/huscy-project_design.svg)
![PyPi Status](https://img.shields.io/pypi/status/huscy-project_design)
![PyPI Downloads](https://img.shields.io/pypi/dm/huscy-project_design)
![PyPI License](https://img.shields.io/pypi/l/huscy-project_design?color=yellow)
![Python Versions](https://img.shields.io/pypi/pyversions/huscy-project_design.svg)
![Django Versions](https://img.shields.io/pypi/djversions/huscy-project_design)


Huscy is a free open-source software solution for managing study participants in the context of human sciences.
The software is deliberately implemented in a generic manner to appeal to a wide user base, while considering all relevant aspects of data protection.
The strictly modular software architecture allows for easy integration of new requirements, enabling the software to be customized to individual needs at any time.



## Requirements

- Python 3.9+
- A supported version of Django

In this project, Django versions 4.2, 5.1 and 5.2 are tested using tox.



## Installation

To install `husy.project_design` simply run:

    pip install huscy.project_design

Add required apps to `INSTALLED_APPS` in your `settings.py`:

```python
INSTALLED_APPS = (
    ...

    'huscy.project_design.apps.HuscyApp',
    'huscy.projects.apps.HuscyApp',
    'huscy.users.apps.HuscyApp',
)
```

Hook the urls from this app into your global `urls.py`:

```python
urlpatterns = [
    ...
    path('', include('huscy.project_design.urls')),
]
```



## Development

Install PostgreSQL and create a database user called `huscy` and a database called `huscy`.

    sudo -u postgres createdb huscy
    sudo -u postgres createuser -d huscy
    sudo -u postgres psql -c "GRANT ALL PRIVILEGES ON DATABASE huscy TO huscy;"
    sudo -u postgres psql -c "ALTER USER huscy WITH PASSWORD '123';"

Check out the repository and start your virtual environment (if necessary).

Install dependencies:

    make install

Create database tables:

    make migrate

Run tests to see if everything works fine:

    make test
dash-aggrid-scales 0.4.0
Plotly color scales to apply to columns in an AgGrid
Requires Python: >=3.10

# Dash AgGrid Scales

Plotly color scales for columns in `dash-ag-grid`

This package provides three main scales to highlight values in AgGrid columns. For consistency, the names use the same conventions use by the `plotly.colors` module:

- sequential
- qualitative
- bar

![](grid_with_color_scales.png)

## Installation

```bash
pip install dash-aggrid-scales
```

## Hello World

```python
import dash_aggrid_scales as das
from dash import Dash, html
from dash_ag_grid import AgGrid
import plotly.express as px
medals = px.data.medals_long().assign(negative=list(range(-5, 4)))

app = Dash()

app.layout = html.Div([
    AgGrid(
        rowData=medals.to_dict("records"),
        columnSize="sizeToFit",
        style={"height": 500},
        columnDefs=[
            {
                "field": "nation",
                "headerName": "nation (qualitative)",
                "cellStyle": {"styleConditions": das.qualitative(medals["nation"])},
            },
            {
                "field": "medal",
                "headerName": "medal (qualitative with dict)",
                "cellStyle": {"styleConditions": das.qualitative(
                    medals["medal"], 
                    {"gold": "gold", "silver": "silver", "bronze": "#cd7f32"}
                )},
            },
            {
                "field": "count",
                "headerName": "count (sequential)",
                "cellStyle": {"styleConditions": das.sequential(medals["count"])},
            },
            {
                "field": "count",
                "headerName": "count (bar)",
                "cellStyle": {"styleConditions": das.bar(medals["count"])},
            },
            {
                "field": "negative",
                "headerName": "random +ve & -ve values (bar)",
                "cellStyle": {"styleConditions": das.bar(medals["negative"], "teal")},
            },
        ],
    )
])

if __name__ == "__main__":
    app.run(debug=True)

```

The resulting app will display an `AgGrid` as follows:

![](dash-aggrid-scales-sample-app.png)
lumecode-ai 1.0.0
AI-powered coding agent with multi-model LLM support
Requires Python: >=3.8

<div align="center">

# 🔮 Lumecode

**AI-powered coding agent with multi-model LLM support**

[![PyPI version](https://badge.fury.io/py/lumecode.svg)](https://badge.fury.io/py/lumecode)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)

</div>

---

## Installation

```bash
pip install lumecode
```

## Usage

```bash
# Start interactive TUI
lumecode

# Show help
lumecode --help
```

## Configuration

Set your LLM provider API key:

```bash
# OpenAI
export LUMECODE_PROVIDER=openai
export LUMECODE_API_KEY=sk-...

# Anthropic
export LUMECODE_PROVIDER=anthropic
export LUMECODE_API_KEY=sk-ant-...

# Ollama (local)
export LUMECODE_PROVIDER=ollama
```

## Features

- 🤖 7 specialized AI agents
- 🔗 6 LLM providers (OpenAI, Anthropic, Gemini, Groq, Ollama, DeepSeek)
- 🎯 Interactive TUI with parallel execution
- 📊 Smart code analysis

## Links

- [GitHub Repository](https://github.com/anonymus-netizien/lumecode)
- [Documentation](https://github.com/anonymus-netizien/lumecode#readme)
- [Releases](https://github.com/anonymus-netizien/lumecode/releases)

## License

MIT License - see [LICENSE](https://github.com/anonymus-netizien/lumecode/blob/main/LICENSE)