semvec/pypi-packages huggingface dataset:
cloudrender 1.3.5
An OpenGL framework for pointcloud and mesh rendering
Keywords: rendering, pointcloud, opengl, mesh

# cloudrender: an OpenGL framework for pointcloud and mesh rendering
A visualization framework capable of rendering large pointclouds, dynamic SMPL models and more. Used to visualize results in our Human POSEitioning System (HPS) project: http://virtualhumans.mpi-inf.mpg.de/hps/

## Requirements
- GPU with OpenGL 4.0 

Optionally, if you want to run included test script:
- EGL support (for headless rendering)
- ffmpeg>=2.1 with libx264 enabled and ffprobe installed (for saving to video)

## Installation
#### Step 1. Get the code
Copy the code without installation
```bash
git clone https://github.com/vguzov/cloudrender
pip install -r requirements.txt
```
or install as a package with
```
pip install cloudrender
```
#### Step 2. Get the SMPL model
- Follow install instructions at https://github.com/gulvarol/smplpytorch
- Make sure to fix the typo for male model while unpacking SMPL .pkl files: `basicmodel_m_lbs_10_207_0_v1.0.0.pkl -> basicModel_m_lbs_10_207_0_v1.0.0.pkl`

## Running test script
### test_scene_video.py
Run `download_test_assets.sh` – it will create `test_assets` folder and download everything you need for sample to work
(3D scan pointcloud, human shape and motion files, camera trajectory file)

Run `test_scene_video.py`

The following script will write a short video inside `test_assets/output.mp4` which should look similar to this:
<p align="center">
<img src="images/test_scene_video_output_example.gif" alt="output example"/>
</p>

## More data
Please check our HPS project page for more 3D scans and motion data: http://virtualhumans.mpi-inf.mpg.de/hps/

## Citation

If you find the code or data useful, please cite: 

```
@inproceedings{HPS,
    title = {Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors },
    author = {Guzov, Vladimir and Mir, Aymen and Sattler, Torsten and Pons-Moll, Gerard},
    booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {jun},
    organization = {{IEEE}},
    year = {2021},
}
```
asynciolimiter 1.2.0
Rate limiter for Async IO
Requires Python: >=3.9
Keywords: asyncio, rate limiter, throttling

# asynciolimiter
A simple yet efficient Python AsyncIO rate limiter.

[![GitHub branch checks state](https://img.shields.io/github/checks-status/bharel/asynciolimiter/master)](https://github.com/bharel/asynciolimiter/actions)
[![PyPI](https://img.shields.io/pypi/v/asynciolimiter)](https://pypi.org/project/asynciolimiter/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/asynciolimiter)](https://pypi.org/project/asynciolimiter/)
[![codecov](https://codecov.io/gh/bharel/asynciolimiter/branch/master/graph/badge.svg?token=BJBL909NH3)](https://codecov.io/gh/bharel/asynciolimiter)

## Installation
`pip install asynciolimiter`

## Sample Usage

```python
# Limit to 10 requests per 5 second (equiv to 2 requests per second)
>>> limiter = asynciolimiter.Limiter(10/5)
>>> async def main():
...     await limiter.wait() # Wait for a slot to be available.
...     pass # do stuff

>>> limiter = Limiter(1/3)
>>> async def request():
...     await limiter.wait()
...     print("Request")  # Do stuff
...
>>> async def main():
...     # Schedule 1 request every 3 seconds.
...     await asyncio.gather(*(request() for _ in range(10)))
```

## Available Limiter flavors

- `Limiter`: Limits by requests per second and takes into account CPU heavy
    tasks or other delays that can occur while the process is sleeping.
- `LeakyBucketLimiter`: Limits by requests per second according to the
    [leaky bucket algorithm](https://en.wikipedia.org/wiki/Leaky_bucket). Has a maximum capacity and an initial burst of
    requests.
- `StrictLimiter`: Limits by requests per second, without taking CPU or other
    process sleeps into account. There are no bursts and the resulting rate will
    always be a less than the set limit.

## Documentation

Full documentation available on [Read the Docs](https://asynciolimiter.readthedocs.io/en/latest/).

## License

Licensed under the MIT License.

## Contribution
See [contributing.md](CONTRIBUTING.md).
diskstat 2025.3.18.0
Visualize your disk free space without Windows Explorer
Requires Python: >=3.10

# diskstat

<a href="https://pypi.org/project/diskstat/">
    <img alt="PyPI - Version" src="https://img.shields.io/pypi/v/diskstat">
</a>

Visualize your disk free space without Windows Explorer.

## install
```bash
pip install -U diskstat
```

Then install [autohotkey](https://www.autohotkey.com/).

## Options
```bash
> diskstat.exe --help
usage: diskstat [-h] [-d] [-o]

options:
  -h, --help     show this help message and exit
  -d, --disable  disable auto start
  -o, --open     open startup folder
```

for example, if you want to start this with Windows:
```pwsh
diskstat.exe
```

to disable:
```pwsh
diskstat.exe -d
```

to launch via command line:
```pwsh
diskstat-console.exe
```

## API for testing

show: <http://127.0.0.1:12347/show>

stop: <http://127.0.0.1:12347/stop>, only useful when debugging

## Screenshots
<img src="https://raw.githubusercontent.com/soda92/diskstat/refs/heads/main/image.png" alt="demo" style="width:400px;"/>

## Features

Use 'Win+Alt+O' for shortcut key.


## Note on running from source

need to install [golang](https://go.dev/); then run `hatch_build.py`.
semantic-fera 0.0.2
FERA ontologies, type definitions, type constructors, configurations 


# Semantic FERA

#### Versions

```0.0.2``` : Intial version with only FERA ontology clusters and minimal set of corresponding type constructors.

| Feature | Element | Status | Comment |
|:--- |:--- |:--- | :--- |
| **type cluster definitions**| | complete in intial form, evolution begins|
|  |USD |✅ | complete |
|  |DUL |✅ | minmal selection |  
|  |FERA |✅ | to-be-evolved |  
|  |FERA assembly   |  | in-process |
|  |FERA machine tending   |  | in-process |
| **type defs, -constructors**| | |
|  | JointState |✅ | |
|  | ToolCenterPointState |✅ | |
| **state decorators**| | ❌ | in-process |
| **semantic-api examples**| | ❌ | in-process |


### Install

```pip3 install semantic-api```

```pip3 install semantic-fera```

### Use Example

#### Import semantic-api
```
    from semantic.api.semantic_api import semantic
    from semantic.common.utils import defineSchemaId
    from semantic.api.config import brokerIP, dbUri, batch_size, topics
```

#### Import semantic-fera
```
    from semantic.fera.fera_types.type_constructors import joint_state, tool_center_point_state, pathMap
```


#### Import semantic.fera.config

Import the default API configuration parameters from the relevant config file. The following steps will change as support for multiple users is added.  

```
    from semantic.fera.config.sweng_nvidia_macine_1 import brokerIp, schemaName, schemaVersion
```
Currently, you need to change the default logical database ```instanceName``` and possibly the ```schemaName-schemaVersion``` and follow the ```SchemaID``` steps in the insertion and query examples.

```
    # define the name of the logical database instance that you are using
    yourInstanceName: str = <your instance name>
```
See the use in the bottom of the following two examples:


#### 1 [Insertion - Narration Example](https://source.coderefinery.org/fera/semantic-fera/-/blob/main/src/doc/example-narration.md)

#### 2 [Query - Processing Example](https://source.coderefinery.org/fera/semantic-fera/-/blob/main/src/doc/example-process.md)

#### 3 [Ontologies](https://source.coderefinery.org/fera/semantic-fera/-/blob/main/src/ontologies/ontologies.md)

#### 4 [Clusters](https://source.coderefinery.org/fera/semantic-fera/-/blob/main/src/ontologies/clusters/clusters.md)

#### 5 [USD Framing](https://source.coderefinery.org/fera/semantic-fera/-/blob/main/src/doc/usd-framing.md)
payselection 1.0.3
Payselection SDK allows you to integrate work with payments for applications
Requires Python: >=3.7

## PaySelection SDK for Python

[![Latest Stable Version](https://img.shields.io/pypi/v/payselection.svg)](https://pypi.org/project/payselection/)
[![License](https://img.shields.io/pypi/l/payselection.svg)](https://github.com/Payselection/Payselection-PayApp-SDK-Python)

PaySelection SDK позволяет интегрировать работу с платежами для приложений, написанных с помощью Python

## Требования

1. Python >= 3.7
2. pip

## Установка
### C помощью pip

1. Установите pip.
2. В консоли выполните команду
```bash
pip install --upgrade payselection
```

## Важно перед началом работы

Перед использованием API **обязательно** свяжитесь с технической поддержкой PS и попросите добавить ваш IP-адрес в whitelist.  

**Как добавить IP в whitelist?**  
1. Узнайте ваш внешний IP-адрес.  
2. Отправьте запрос в техническую поддержку (support@payselection.com) с указанием вашего IP.  
3. Дождитесь подтверждения от поддержки перед началом работы.  
4. Только после подтверждения начинайте отправлять запросы.  

## Начало работы

1. Импортируйте модуль
```python
import payselection
```
2. Установите данные для конфигурации
```python
from payselection import Configuration

Configuration.configure(
    {
        'site_id': <ID сайта>,
        'secret_key': <Секретный ключ сайта>,
        'public_key': <Публичный ключ сайта>,
        'merchant_url_address': <URL-адрес сайта ТСПа>,
    }
)
```

### Полезные ссылки

[Личный кабинет](https://merchant.payselection.com/login/)

[Разработчикам](https://api.payselection.com/#section/Request-signature)

### Поддержка

По возникающим вопросам технического характера обращайтесь на support@payselection.com