semvec/pypi-packages huggingface dataset:
pyserialui 1.0.0
SerialUI druid support and examples

# PySerialUI

Support and examples for Python SerialUI callbacks, overrides and functionality.

### SerialUI, Device Druid and Builder

SerialUI provides a user-defined set of menus, commands, inputs, tracked states and data views through a serial channel (e.g. USB or bluetooth LE serial), for embedded and Linux devices (Arduino, Raspberry Pi, etc).

It also includes a programatic API to allow clients, such as [device druid](https://devicedruid.com/), to create and manage the SerialUI menu items while providing any desirable interface such as a GUI.

The [Device Druid Builder](https://devicedruid.com/builder/) is the easiest way to define the SerialUI menu structure and will generate projects that use the SerialUI library and implement your layout.

### Python

On embedded devices, functionality is implemented by fleshing out the various callbacks in C++.

On Linux hosts, such as [Raspberry Pi](https://www.raspberrypi.org/) or [Ubuntu](https://ubuntu.com/), Python may be used instead thanks to the built-in CPython support and (optionally) this library.

### Copyright and License
PySerialUI is 
Copyright (C) 2019 Pat Deegan, [psychogenic.com](https://psychogenic.com/)
and is released under the terms of the LGPLv3.  See the LICENSE file for details.

More information is available through the [device druid](https://devicedruid.com/) and [inductive-kickback](https://inductive-kickback.com/) websites.
flywheel-bids-tools 0.0.12
Tools for extracting, editing, and uploading BIDS data from and to Flywheel

# bids-on-flywheel
A walkthrough on making sure your data is BIDS-compliant on flywheel and how to upload it.

See the [wiki](https://github.com/PennBBL/bids-on-flywheel/wiki) for more, or get started right away with command-line tools:
```
pip install flywheel-bids-tools
```
git-repo-master 0.1.3
Small package to automate actions with git repo on Azure DevOps

# git-repo-master

Simple application to manage your Azure DevOps git repository

## Installation

``` bash
git clone https://github.com/mustafmst/git-repo-master.git
python git-repo-master/setup.py install
```

## Usage

```bash
$ grm -h

usage: __main__.py [-h] [-organization ORGANIZATION] [-project PROJECT]
                   [-repository REPOSITORY [REPOSITORY ...]] [-token TOKEN]
                   [-action {lock-branch,unlock-branch,create-pull-request}]
                   [-branch BRANCH] [-source SOURCE] [-target TARGET]
                   [-title TITLE] [-description DESCRIPTION]

optional arguments:
  -h, --help            show this help message and exit

All arguments:
  -organization ORGANIZATION
                        Azure DevOps organization
  -project PROJECT      Azure DevOps project in given organization
  -repository REPOSITORY [REPOSITORY ...]
                        Repo for action
  -token TOKEN          Personal access token for Azure DevOps
  -action {lock-branch,unlock-branch,create-pull-request}
                        Action to perform

Branch lock and unlock actions arguments:
  -branch BRANCH        Branch to perform action on

Create pull request action arguments:
  -source SOURCE        Branch name from which action has to be done
  -target TARGET        Branch name to which action has to be done
  -title TITLE          Pull request title
  -description DESCRIPTION
                        Pull request description

```
binarypipe 0.1.1
Import binary data such as images, audio in ready to feed format for ML models with just one line of code

# BinaryPipe

binarypipe provides easy pipeline to use different binary format files in different Machine Larning models.

#### Installation

```bash
pip install binarypipe
```

### ImagePipe

imagepipe provides utilities to use image datasets in ML models

* import imagepipe

```python
from binarypipe import imagepipe as ip
```

* Load single image file

```python
# Load image
img= ip.load("path/image.jpg")

# Load in certain size
img= ip.load("path/image.jpg", size= (400, 600))
```

* Load all images of folder as dataset

```python
X, y= ip.load_dataset("folder_path/", 0)
# X= (?, width, height, channel)
# y is label 
```

* Load multiple class images 

```python
X, y= ip.load_datasets("folder_path/", )
```