semvec/pypi-packages huggingface dataset:
pushikoo-getter-skland 0.1.0
Skland (森空岛) getter for Pushikoo
Requires Python: >=3.10

<div align="center">

# pushikoo-getter-skland

Skland (森空岛) getter for Pushikoo.

[![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)
[![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/Pushikoo/pushikoo-getter-skland/package.yml)](https://github.com/EvATive7Cloud/pushikoo-getter-skland/actions)
[![Python](https://img.shields.io/pypi/pyversions/pushikoo-getter-skland)](https://pypi.org/project/pushikoo-getter-skland)
[![PyPI version](https://badge.fury.io/py/pushikoo-getter-skland.svg)](https://pypi.org/project/pushikoo-getter-skland)
[![License](https://img.shields.io/github/license/Pushikoo/pushikoo-getter-skland.svg)](https://pypi.org/project/pushikoo-getter-skland/)

</div>
simple-repository-browser 0.10.0
A web interface to browse and search packages in any simple package repository (PEP-503), inspired by PyPI / warehouse
Requires Python: >=3.11

# simple-repository-browser

A web interface to browse and search packages in **any** simple package repository (PEP-503), inspired by PyPI / warehouse.

Built using FastAPI and the [simple-repository](https://github.com/simple-repository/simple-repository) core library.

## Demo

A tech-preview demo is available at <https://simple-repository.app.cern.ch/>.

## Usage

Install from PyPI:

```bash
python -m pip install simple-repository-browser
```

And run:

```bash
simple-repository-browser
```

(or alternatively ``python -m simple_repository_browser``)

By default, this will use the repository at PyPI (<https://pypi.org/simple/>). You can point it to a custom
repository by passing the URL to the project list endpoint (the base URL according to PEP-503):

```bash
simple-repository-browser https://my-custom-repository.example.com/foo/simple/
```

## Screenshots

Homepage:

![homepage screenshot](https://raw.githubusercontent.com/simple-repository/simple-repository-browser/main/screenshots/home.png)

Search:

![search result](https://raw.githubusercontent.com/simple-repository/simple-repository-browser/main/screenshots/search.png)

Project page:

![example project page](https://raw.githubusercontent.com/simple-repository/simple-repository-browser/main/screenshots/project.png)

## Runtime details

```simple-repository-browser``` exposes a FastAPI application, and it runs the application in a single ``uvicorn`` worker.
Metadata that is computed will be cached in the ``$XDG_CACHE_DIR/simple-repository-browser`` directory. This cache is not
intended to be shared among different repository URLs, and is unlikely to work for multiple ``simple-repository-browser``
versions. There is currently no intelligent cache invalidation for those cases.

## Development

In order to build the ``simple-repository-browser`` you will need access to npm. If not available on the development host,
you can do it in a docker container first:

```bash
docker run --rm -v $PWD:/workdir -it node bash
cd /workdir
```

Bundle the web app:

```bash
cd web
npm install --include=dev
npm run build
cd ..
```

And then the normal installation procedure applies:

```bash
python -m pip install -e .
```

You may want to disable the indexing to avoid long-running tasks during the development:

```bash
export DISABLE_REPOSITORY_INDEXING=1
```

The browser can be run with:

```bash
python -m simple_repository_browser
```
emioapi 0.2.0
An API for controlling  the motors of the EMIO robot.
Requires Python: >=3.10
Keywords: roboticsc, emio, api

# Emio API

Emio API is a simple and easy-to-use API for controling the Emio robot from [Compliance Robotics](https://compliance-robotics.com/).

The `emioapi` package is published on [PyPI](https://pypi.org/project/emioapi/).

## Installation
To install the Emio API, you can use pip:

```bash
python -m pip install emioapi
```

or to get the latest version that is unstable

```bash
python -m pip install git+https://github.com/SofaComplianceRobotics/Emio.API.git@release-main
```

## Usage
The Emio API provides the `EmioAPI` class, which can be used to control the Emio robot. The API provides methods for controlling the robot's motors and camera.
You can look at the [motors_example.py](motors_example.py) file for a simple example of how to use the API to control the motors of the Emio robot.
To control the camera, look at the [camera_example.py](exampes/camera_example.py) file or [multiprocess_camera_example.py](examples/multiprocess_camera_example.py).


Simple example thaht sets the angles of the motors to 0 radians, waits for 1 second, and then prints the status of the robot:
```python
from emioapi import emio

# Open a port to the Emio robot and configure it
emio.connectToEmioDevice()

emio.motors.angles = [0] * 4  # Set the angles of the motors to 0 radians
time.sleep(1)  # Wait for 1 second
emio.printStatus() # Print the status of the robot
emio.disconnect()  # Close the connection to the robot
```

## For Developers
The documentation is generated using [pydoc-markdown](https://pypi.org/project/pydoc-markdown/). To generate the documentation, you need to install `pydoc-markdown`:

```bash
pipx install pydoc-markdown
```

Then, you can generate the documentation in a `emioapi.md` file, by using the following command at the root of the project:

```bash
pydoc-markdown
```
dcat-ap-hub 0.1.1
A library for loading datasets and models whose metadata is provided in the DCAT-AP format.
Requires Python: >=3.11

# DCAT-AP Hub

This library enables easy downloading and loading of datasets and models whose metadata is provided in the DCAT-AP format. Currently, only JSON-LD is supported.

### How To Install

```bash
pip install dcat-ap-hub
```

### How To Load Datasets

```python
from dcat_ap_hub import Dataset

url = "https://data.europa.eu/api/hub/repo/datasets/7b715249-0c76-4592-9df6-f36b9a47f6e5.jsonld"

ds = Dataset.from_url(url)
files = ds.download(data_dir="./data")
```

### How To Load Huggingface Models

```python
from dcat_ap_hub import Dataset

url = "https://ki-daten.hlrs.de/hub/repo/datasets/6f75de8b60a9f8a2fdf7b69cbd86d9e64bcb3837.jsonld"

ds = Dataset.from_url(url)
ds.download(data_dir="./data")
model, processor, metadata = ds.load_model(model_dir="./models")
```

### How To Process Datasets If Supported

```python
from dcat_ap_hub import Dataset

url = "https://data.europa.eu/api/hub/repo/datasets/7b715249-0c76-4592-9df6-f36b9a47f6e5.jsonld"

ds = Dataset.from_url(url)
ds.download(data_dir="./data")
files = ds.process(processed_dir="./processed")
```

### Funding

This project was developed using resources from the HammerHAI project, an EU co-funded AI Factory initiative operated by the High-Performance Computing Center Stuttgart and supported by the European Commission as well as German federal and state ministries. It is funded by the European High Performance Computing Joint Undertaking under Grant Agreement No. 101234027.

<!-- ### With Custom Parsing

```python
from dcat_ap_hub import download_data, apply_parsing

json_ld_metadata = "http://localhost:8081/datasets/uci-har.jsonld"
metadata = download_data(json_ld_metadata)
df = apply_parsing(metadata)
``` -->