semvec/pypi-packages huggingface dataset:
parts2kicad 0.1.4
Integrate (add) symbols, footprints and 3D models from a SamacSys Partlib zip archive (as downloaded for instance from Mouser or Farnell) into local KiCad libraries quickly and without additional steps
Requires Python: >=3.8

# parts2kicad

Extracts KiCad symbols, footprints and 3D models from SamacSys partlib zip files as you download them for instance via Mouser or Farnell.

If you have a zip file containing the component _X123_ this is achieved as follows.

* `X123.kicad_sym` (schematic symbol) → gets added to `mylibrary.kicad_sym`
* `X123.kicad_mod` (pcb footprint) → gets copied to `mylibrary.pretty/X123.kicad_mod`
* `X123.stp` (3D model) → gets copied to `mylibrary.3dshapes/X123.stp`

So that you can keep all your downloaded stuff together at one place and do not have to fiddle inside the symbol libraries by yourself. 
The path to the 3D model in the footprint file is set to a relative path of the form `../mylibrary.3dshapes/X123.stp`. 
Thus, you can move your lib freely around your hard drive. Only if you rename it, the links from footprint to 3D model break.

The script is mainly intended for Unixoid users (the official downloading tool only runs on Windows) and those who prefer command line over graphical UI.

## Installation

So far via PyPi: `pip install parts2kicad`.

## Usage

Usage is as easy as typing 
```bash
$ p2k some.zip -t mylibrary.kicad_sym
```
You can also glob over a bunch of zip archives:
```bash
$ p2k ~/downloads/*.zip -t mylibrary.kicad_sym
```

## Project Pages

* [GitHub](https://github.com/julianschick/parts2kicad)
* [PyPi](https://pypi.org/project/parts2kicad/)

## Technical Background

All in all the main ingredient is a whitespace aware S-Expression parser. It is needed
for adding Symbols to the `.kicad_sym` library files without destroying the format.
The format is normally (and also by KiCad specification) nicely indented, so
that manual editing is also possible. Footprints and 3D models are luckily
managed on file level.
crea-med 0.0.3
Agentic AutoML for Biomedical Image Analysis
Requires Python: >=3.11

# CREA

**CREA** is designed to be a **C**ollaborative Failure-aware **RE**asoning **A**gent for Biomedical AutoML.

# Quick Start

## Setup environment

We recommend using `uv` to manage the environment and dependencies.

1. **Install uv** (if you haven't already):
```bash
pip install uv
```
or using the standalone installer:
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```

2. **Create a virtual environment with Python 3.11**:
```bash
uv venv --python 3.11
source .venv/bin/activate  # On Windows use: .venv\Scripts\activate
```

3. **Install dependencies**:
```bash
uv pip install -r requirements.txt
uv pip install -e .
```

4. **Set up API keys**:
Set up your private API key for OPENAI-compatible endpoints:
```bash
export OPENAI_BASE_URL="<your base url>" # (e.g. https://api.openai.com/v1)
export OPENAI_API_KEY="<your key>"
```
## Setup knowledge base for RAG Tool
You can create the knowledge base with `create_rag_db.sh`.
For MONAI-1.4.0, you can also download from [Huggingface](https://huggingface.co/datasets/blueyo0/monai_rag_db).

You could use this command to check whether the creation succeeds.
```
python -m crea.monai_rag.query_rag_db
``` 
## Test with Example Dataset
You can download the example dataset from [Huggingface](https://huggingface.co/datasets/blueyo0/organmnist3d).
Download command:
```
hf download blueyo0/organmnist3d --local-dir dataset/organmnist3d --repo-type dataset
```
Then run the `quick_start.sh` to test:
```
bash quick_start.sh
```

# 🙏 Acknowledgement
- We thank all medical workers and dataset owners for making public datasets available to the community.
- Thanks to the open-source of the following projects, our code is developed based on their contributions:
     - [aideml](https://github.com/WecoAI/aideml)
     - [monai](https://github.com/Project-MONAI)
     - [AutoGluon](https://github.com/autogluon/autogluon)
     - [AutoKeras](https://github.com/keras-team/autokeras)
     - [Auto-SKlearn](https://github.com/automl/auto-sklearn)
     - [ML-Master](https://github.com/sjtu-sai-agents/ML-Master)
uniulm-mensaparser 1.0.1
A parser for the canteen / Mensa plans at Ulm University
Requires Python: >=3.8
Keywords: canteen, mensa, parser, studierendenwerk, ulm, university

# Uni Ulm Mensa Parser

This project contains a parser for the canteen / Mensa plans at
Ulm University that are provided on the
[Studierendenwerk Ulm website](https://studierendenwerk-ulm.de/essen-trinken/speiseplaene/).
You can see the data in action on the
[**UUlm Mensaplan website**](https://mensaplan.anter.dev/).

For an example of parsed data, see the following REST API endpoint:
[https://uulm.anter.dev/api/v1/canteens/all](https://uulm.anter.dev/api/v1/canteens/all)

## Getting Started

These instructions will give you a copy of the project up and running on
your local machine for development and testing purposes.

### Prerequisites

This project is tested and deployed with Python 3.9+. The dependencies require
Python 3.8+.

### Integration into your own project

You can install the module from PyPI:

```sh
pip install uniulm-mensaparser
```

After installing, you can use the parser like this:

```Python
from uniulm_mensaparser import get_plan, Canteen, SimpleAdapter2

# use get_plan() to get plans of all supported canteens
plan = get_plan()
print(plan)

# specify which canteen plans you want to be parsed in a set
my_canteens = {Canteen.UL_UNI_Sued}
# pass an adapter class for a different output format
plan = get_plan(my_canteens, adapter_class=SimpleAdapter2)
print(plan)
```

## Development

### Installation

If you want to extend the functionality of this library (e.g. implementing a new
Adapter or PDF parser), you can clone this repository and install the required
Python modules:

```sh
git clone https://github.com/Tanikai/uniulm_mensaparser.git
cd uniulm_mensaparser
uv sync
```

### Tooling

This project uses [uv](https://docs.astral.sh/uv/) for dependency
management, [ruff](https://docs.astral.sh/ruff/) for linting and formatting,
and [ty](https://github.com/astral-sh/ty) for type checking.

To format and lint the code, run:

```sh
uvx ruff format .
uvx ruff check . --fix
```

To run the type checks:

```sh
uv run ty check
```

To run the tests:

```sh
uv run pytest
```

### MaxManager API endpoint

The following curl command sends a request to the new endpoint. Remember to
replace the date with the current date.

```bash
curl -X POST 'https://sw-ulm-spl51.maxmanager.xyz/inc/ajax-php_konnektor.inc.php?func=make_spl&locId=1&date=2023-07-20&lang=de&startThisWeek=2023-07-17&startNextWeek=2023-07-24'
```

## Built With

- [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/) to parse the
  canteen plan from the Studierendenwerk Ulm website
- [aiohttp](https://docs.aiohttp.org/) for asynchronous HTTP requests

## Authors

- Kai Anter:
  [GitHub](https://github.com/Tanikai),
  [Mastodon](https://hachyderm.io/@Tanikai)

## License

This project is licensed under the GNU General Public License Version 3 - see
the [LICENSE](LICENSE) file for details