semvec/pypi-packages huggingface dataset:
libvinput 1.4
Python interface for libvinput

![libvinput banner](meta/libvinput.png)


Cross-platform, simple C keyboard hook and emulator with mouse support in the
works.

Building and running demo
-------------------------

Note: On Linux, you need `x11`, `xcb-xtest` and `libxdo` development headers
installed.

Note (part 2): On Windows, you might want to use something like MSYS2 to
compile. Does not require any additional dependencies.

###### Linux
```
make wordlogger
./wordlogger
```

###### Windows
```
make wordlogger.exe
./wordlogger.exe
```

Documentation
-------------

All the documentation can be found inside the `libvinput.h` file.

License
-------

This software is licensed under the AGPLv3 license, more info in the LICENSE
file.
detokenizer 1.0.0rc8
Python command line for property injection.

# detokenizer

detokenizer of configuration files. Inject properties in source code before
deployment.

## Usage

This parameters are positional parameters.

|Parameters|Description|
|:---|:---|
|source|source file, all tokens in this file will be replaced with properties|
|target|target file, output after the replacement|
|config|config files, this fils is a yaml file defining the delimiters for the token|
|properties|properties file, this file contains the dictionary of properties which shall be injected into the source file|

the following command run the tool on the test data contained in this repo:

```
python3 -m detokenizer \
  ./test/data/source_folder/config.properties.php \
  ./build/config.php \
  ./test/data/config.yaml \
  ./test/data/default.properties
```

## Example

Source file:

```
<?php

ini_set('memory_limit', '${packaging.memory_limit}');

$ENVIRONMENT_NAME = '${packaging.environment_name}';

#region URL
$site_URL = '${packaging.site_URL}';
#endregion
...
```

Properties file:
```
packaging.memory_limit=1024
packaging.environment_name=default
packaging.site_URL=

packaging.host=193.168.1.12
packaging.port=3000
packaging.db=db_ms_12
...
```

Produced target:

```
<?php

ini_set('memory_limit', '1024');

$ENVIRONMENT_NAME = 'default';

#region URL
$site_URL = '';
#endregion
...
```
auto-blur 0.0.1
Auto blur lib
Requires Python: >=3.8

# AutoBlur

### 🛠 Tool
<img src="https://img.shields.io/badge/Python-3766AB?style=flat-square&logo=Python&logoColor=white"/> <img src="https://img.shields.io/badge/TensorFlow-FF6F00?style=flat-square&logo=TensorFlow&logoColor=white"/> <img src="https://img.shields.io/badge/PyTorch-EE4C2C?style=flat-square&logo=PyTorch&logoColor=white"/> <img src="https://img.shields.io/badge/VSCode-007ACC?style=flat-square&logo=Visual Studio Code&logoColor=white"/> <img src="https://img.shields.io/badge/tkinter-47A248?style=flat-square&logo=Python&logoColor=white"/> 

### 개발환경
💻 M1 Pro Mac (Sonoma)

## 프로젝트 소개

AutoBlur는 영상 편집의 편의와 영상에 나온 사람들의 초상권을 보호하는 프로그램입니다.

PyQt와 Yolov8을 사용하여 구현하였으며 사용 모델은 아래 레퍼지토리에서 사용했습니다.

- [yolov8-face🏠](https://github.com/akanametov/yolov8-face)

-> model 디렉터리에 모델을 다운로드해야 합니다. 


## 사용 방법

앱을 실행하면 이러한 창이 생깁니다.
<div align="center">
   <img src="image.png">

</div>
- Open Video는 사용자가 원하는 영상 파일을 열어 자동으로 얼굴을 인식하고, 모자이크 처리를 진행합니다. 

- DownLoad Video는 클릭 시 유튜브 링크를 입력하는 창이 나오고, 반환하면 저장할 위치를 입력 받고 영상을 저장합니다. ( 유튜브에서 다운로드를 할 수 없는 영상도 있으니 참고 바랍니다. )

- 앱을 실행하면 .temp 디렉터리와 BluredVideo가 생성되며, 프로그램의 결과물이 BluredVideo에 저장됩니다. .temp는 각 영상을 임시로 파일이 저장되었다가 소리가 합쳐진 다음 자동으로 삭제됩니다. ( 이 두 디렉터리는 앱 실행중에 삭제하면 안됩니다. )

<br>

```
git clone https://github.com/codernoah404/AutoBlur.git
```

사용중인 가상환경을 활성화한 후 라이브러리를 설치하고, 앱을 실행시킵니다.

```
pip install -r requirements.txt
python src/app.py
```
dacutil 0.4.4
Function Tools For Data Analytic
Requires Python: <4.0,>=3.9

# Data Analytic Utils

## Install

```bash
pip install dacutil
```
or dev
```bash
pip install git+https://github.com/attapon-th/dacutil@main
```

## Usage

```python
from dacutil import (
    datediff,
    get_config,
    check_mod11,
    df_strip,
    worker,
    Addict
)
```

# Functions

## `get_config`
support file type:
- [x] ini
- [x] json
- [x] yaml
- [x] toml
  
### Example 
```python
from dacutil import get_config, Addict

# get config from file
config: Addict = get_config("path/to/file.ini")
# or
config: Addict  = get_config("file://path/to/file.ini")

# get config from url
config: Addict  = get_config("http://example.com/file.json")
# or
config: Addict  = get_config("https://example.com/file.json")
```
PyFlume 0.8.7
Package to integrate with Flume Sensor

# Flume API Integration
## Overview
The Flume API Integration provides a comprehensive set of classes and methods to interact with various Flume endpoints. This integration allows developers to retrieve and manage notifications, usage alerts, devices, leak alerts, data, and authentication within the Flume environment.

## Retrieve API Key
You can find your Client ID and Client Secret under "API Access" on the [settings page](https://portal.flumewater.com/settings). These credentials are essential for interacting with the Flume API.

## Modules
Below are the details of each module, each documented in its corresponding file:

### Notifications
Retrieve notifications from the Flume API, including filtering based on the read status.
- [Read the Notifications documentation](https://github.com/ChrisMandich/PyFlume/blob/master/docs/notifications.md)

### Usage Alerts
Manage and retrieve usage alert notifications from the Flume API.
- [Read the Usage Alerts documentation](https://github.com/ChrisMandich/PyFlume/blob/master/docs/usage.md)

### Devices
Retrieve information related to Flume devices, including their list from the API.
- [Read the Devices documentation](https://github.com/ChrisMandich/PyFlume/blob/master/docs/devices.md)

### Leak Alerts
Manage and retrieve leak notifications from the Flume API.
- [Read the Leak Alerts documentation](https://github.com/ChrisMandich/PyFlume/blob/master/docs/leak.md)

### Data Retrieval
Retrieve and update data from the Flume API, working with authentication and various data endpoints.
- [Read the Data Retrieval documentation](https://github.com/ChrisMandich/PyFlume/blob/master/docs/data.md)

### Authentication
Authentication module to handle tokens and user credentials within the Flume environment.
- [Read the Authentication documentation](https://github.com/ChrisMandich/PyFlume/blob/master/docs/auth.md)

## Getting Started
To get started with the Flume API Integration, refer to the individual documentation files for each module. They provide detailed information on dependencies, initialization, methods, and example usage.

For any questions or additional support, please refer to the official Flume API documentation or contact the development team.

## Contributing
Feel free to contribute to the codebase by opening issues, submitting pull requests, or suggesting improvements.
sICTA-chg 0.0.1
A marker-based cell type annotation method that combines the self-training strategy with pseudo-labeling and the nonlinear association capturing capability of Transformer.
Requires Python: >=3.8

# sICTA: Interpretable Cell Type Annotation based on self-training

The full description of sICTA and its application on published single cell RNA-seq datasets are available.

Download archive with preprocessed data at: https://drive.google.com/drive/folders/1jbqSxacL_IDIZ4uPjq220C9Kv024m9eL.

The repository includes detailed installation instructions and requirements, scripts and demos.


## 1 The workflow of sICTA.

![](Flow.jpg)

**(a)** Combining cell expression and marker gene specificity to generate pseudo-labels. **(b)**  The downstream Transformer classifiers are first pre-trained based on cell type probability distributions (pseudo-labels), followed by iterative refinement of the classifiers through a self-training framework until convergence. The sICTA takes the a priori knowledge from the biological domain and uses masked learnable embeddings to transform the input data ($G$ genes) into $k$ input tokens representing each gene set (GS) and a class token (CLS).
## 2 Requirements

+ Linux/UNIX/Windows system
+ Python == 3.8.6
+ torch == 1.12.1
+ scanpy == 1.9.1

<!-- Topic_gene_embedding -->

## 3 Usage

### Data format

sICTA requires cell-gene matrix and cell type marker information to be entered in `.h5ad` object format.

### Training

```bash
python main.py
```

We provide default data for users to understand and debug sICTA code.


## Reference

If you use `sICTA` in your work, please cite