semvec/pypi-packages huggingface dataset:
ronchang 0.1.3
Hands on toolkits
License: MIT
Requires Python: >=3.6

# RonChang
### What is this?
This is my personal hands-on develop toolkits.
|name|popurse|
|-|-|
|Timer|colorful and expandable timer|

### How to install?
```bash
pip install ronchang
```

## 1. Timer
#### - import
```python
from ronchang import Timer
Timer(
    interval,  # unit: seconds
    start=None,
    is_error=False,
    exception=None,
    **kwargs
)
```
#### - demonstration
```python
from ronchang import Demo
Demo.Timer()
```

## 2. Slicer
#### - import
```python
from ronchang import Slicer
Slicer.list()
Slicer.dict()
Slicer.number()
```
#### - demonstration
```python
from ronchang import Demo
Demo.Slicer()
```

## 3. Bar
#### - import
```python
from ronchang import Bar
Bar(count, amount, desc=f'{count} of {amount}', info='INFO')
```
#### - demonstration
```python
from ronchang import Demo
Demo.Bar()
```
If you like my work, please consider buying me a coffee or [PayPal](https://paypal.me/RonDevStudio?locale.x=zh_TW)
Thanks for your support! Cheers! 🎉
<a href="https://www.buymeacoffee.com/ronchang" target="_blank"><img src="https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png" alt="Buy Me A Coffee" style="height: 41px !important;width: 174px !important;box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;-webkit-box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;" align="right"></a>
qmdesc 1.0.6
A qm descriptor prediction package
License: MIT
Requires Python: >=3.6
Keywords: chemistry,machine learning,QM descriptors

# qmdesc

[![GitHub license](https://img.shields.io/github/license/yanfeiguan/qmdesc)](https://github.com/yanfeiguan/qmdesc/blob/master/LICENSE)
[![Documentation Status](https://readthedocs.org/projects/qmdesc/badge/?version=latest)](https://qmdesc.readthedocs.io/en/latest/?badge=latest)
[![PyPI version](https://badge.fury.io/py/qmdesc.svg)](https://badge.fury.io/py/qmdesc)


A trained multitask constraint message passing neural networks 
for QM atomic/bond property predictions as described in the paper 
[Regio-Selectivity Prediction with a Machine-Learned Reaction Representation and On-the-Fly Quantum Mechanical Descriptors](https://doi.org/10.26434/chemrxiv.12907316.v1).

QM descriptors under B3LYP/def2svp level of theory that can be predicted with this model:
1. Hirshfeld partial charge
2. Neucleuphilic Fukui indices
3. Electrophilic Fukui indices
4. NMR shielding constants
5. Bond lengths
6. Bond orders

**Documentation:** Documentation of qmdesc is available at https://qmdesc.readthedocs.io/en/latest/index.html.

## Requirements

* RDKit

### Installation
For all installations, we recommend using conda to get the necessary rdkit dependency:
```console
conda install -c rdkit rdkit
pip install qmdesc
```

Or from envrioment.yml 
```console
conda create --name qmdesc --file environment.yml
```
imageZIP 3.1
Archive (encrypt) files and directories to image file
License: MIT
Requires Python: >=3

# imageZIP

This is a small python package to archive (encrypt) files and directories into a single image file. 

This project is made just for fun, it has no practical use, output "ZIP" image will always have the same or greater file size than it's content. Also, this is not easy-to-scan like QR codes.

Algorithm is pretty easy. If folder to zip is chosen, it will iterate through all folders and files inside it. If folder is found, it will write relative path of folder in bytes and add special folder delimiter at the end. If file is found, it will write it's relative path and name in bytes, add special file delimiter and then write all it's content in bytes with additional second file delimiter. When bytes array is ready to go, it will build an image, where individual pixels representing particular byte number.

## Example

![demo image](.github/demo_zip.png)

This is an "imageZIP archive", containing this whole "imageZIP" repository.
 
It was created with ```imageZIP.zip("some path/imageZIP/", scale=4)```.

You can actually download this image and unzip it with ```imageZIP.unzip("some path/demo_zip.png", scale=4)```, it will create "imageZIP" folder with all repository files.

## How to use

To download this package, you can use ```pip install imageZIP```.

Then go to python terminal and write ```import imageZIP```.

Then you can use 2 available functions:

- ```imageZIP.zip(path: str, scale: int = 1, color_mode: str = 'heat')```;
- ```imageZIP.unzip(path: str, scale: int = 1, color_mode: str = 'heat')```.

Where:
- ```path``` - path (```str```) to a single file or whole directory to "zip", or image file to "unzip";
- ```scale```* - scale (```int```) of input or output image file. Default: ```1```;
- ```color_mode```* - color mode (```str```) of input or output image file. Default: ```"heat"```.

> *not necessary attributes, which can be omitted.

#### Notes:

- For ```zip``` and ```unzip``` you have to provide same ```scale``` and ```color_mode``` settings;
- Available ```color_mode```s are: ```bw```, ```heat```, ```heat_toxic```, ```rainbow```, ```red```, ```blue```;
- ```scale``` is have to be ```int >= 1```;
- ```path``` can be absolute or relative;
- If a good rectangle image can't be created, output image will be a square with some empty pixels at the end.