semvec/pypi-packages huggingface dataset:
sanic-gzip 0.4.1
Add compress to Sanic response as decorator
Requires Python: >=3.6

# sanic-gzip
A Sanic plugin to manage compression as a decorator

## Installation

Install with `pip`:

`pip install sanic-gzip`

## Usage

Usage is as simple as a decorator before your function

```python
from sanic import Sanic
from sanic_gzip import compress

app = Sanic(__name__)
compress = Compress()

@app.get("/logs")
@compress.compress()
async def my_verbose_function(request):
```

The current version supports both gzip and deflate algorithms.

## Options

Config options are to be setted as init argument:

* Compression min. size
* Compression level
* MIME types impacted
* Number of threadused for compression
pyGL-SukyLaplante 1.1
A user friendly (I guess) Lib for games
Requires Python: >=3.10.1

pyGL is an Python Graphical Librairy for **python**
You can do anything you want with it 

**DOCUMENTATION**
-
- Import the lib
- After do this method : 
> app = Window("App name", width, height)
- Do a *while* loop
- After use the method 
>app.clearScreen()
- You can set your max FPS by doing 
>app.setMaxFPS(maxFPS)
- You can add a background color, you need todo 
>app.setBackgroundColor(color)
- At the end you need todo an app.loop()

**DRAWING/Position**
-
- In this Engine you have Vector2 and Transform; contains two **Vector2** Object, One is Position and the other is Scale
- Drawing a basic line is Simple :
> app.DrawLine(pos1 : Vector2, pos 2 : Vector2, color, linewidth, outline)
- Drawing a you have two methods; fast and slow
- SLOW :
> app.drawRect(Transform, color)
- FAST :
> app.fdr(x,y,w,h,c)

**MOVING A TRANSFORM/SCALING**
-
- you can use Transfom for Moving and Scaling or just Moving by a presise axis
- Moving
> transform.Translate(movX,movY)
> 
> //Recommended : do an transform.Update()
- Scaling
> transform.Scale(scaleX,scaleY)
> 
> //Recommended : do an transform.Update()
- Fixed Movement / Scaling
> transform.x or y = VALUE **OR** tranform.position.x or y = VAULE
> 
> transform.w or h = VALUE **OR** transform.scale.x or y = VALUE

# Constant usefull
Object -> an array stock all of the transform of the objects (width, height, x, y)

#Drawing Images
- To print images you need to get them / load
- Loading
> Image = app.LoadImage(Path)
- Print the Image
> app.DisplayImage(Image, x, y)
dejavu-gi 0.1.3
ctypes bindings to graph isomorphism solver dejavu
Requires Python: >=3.6

# Bindings to graph isomorphism solver dejavu

This package provides a basic ctypes wrapper to the graph isomorphism solver `dejavu` (https://www.mathematik.tu-darmstadt.de/dejavu). The goal of the package is to provide easy-to-use, quick access to the main functionality of `dejavu`. For performance critical software, consider using the C++ version of `dejavu` directly. 

# Features

The package exposes methods of the probabilistic graph isomorphism solver `dejavu`. The main features include easy access to a probabilistic graph isomorphism test, probabilistic computation of graph automorphisms (AKA symmetries), color refinement (AKA 1-WL) and random walks of IR trees.

# Quickstart

Once installed using pip, the package can simply be imported using `import dejavu_gi`. 

Lets assume we want to compute the symmetries of the 5-cycle. Graphs in the package are represented using the number of vertices `n` (in this case 5) and an (undirected) edgelist, in this case `[[0, 1], [1, 2], [2, 3], [3, 4], [4, 0]]`. We can then simply compute the symmetries as follows.
```
import dejavu_gi

group = dejavu_gi.get_automorphisms(5, [[0, 1], [1, 2], [2, 3], [3, 4], [4, 0]])
print(group)
```
The above code outputs the following representation of the automorphism group:
```
{'generators': [[4, 3, 2, 1, 0], [2, 3, 4, 0, 1]], 'base': [2, 4], 'size': 10.0}
```
`generators` is a generating set of the automorphism group, whereas `base` is a base and `size` is the order of the automorphism group. Note that this computation is probabilistic. More precisely, the solver is only guaranteed to return all the symmetries with some bounded error probability (which can be set using `err`). For more precise information on this, see the code documentation.

If we want to test two graphs for isomorphism, we can do so using:
```
import dejavu_gi

is_iso1 = dejavu_gi.are_isomorphic(5, [[0, 1], [1, 2], [2, 3], [3, 4], [4, 0]], 5, [[1, 0], [1, 2], [3, 2], [3, 4], [4, 0]])
is_iso2 = dejavu_gi.are_isomorphic(5, [[0, 1], [1, 2], [2, 3], [3, 4], [4, 0]], 5, [[1, 0], [1, 2], [3, 2], [3, 4], [4, 1]])
print(is_iso1)
print(is_iso2)
```
The output for the above would be:
```
0
0
```

We can also use the color refinement of `dejavu`, using the following code:
```
colors = dejavu_gi.color_refinement(3, [[0, 1],[0, 2]])
print(colors)
```
This returns a mapping from vertices to colors, in the above example this is:
```
[2, 0, 0]
```
autodc 1.0.6
Auto DC

# AutoDC
## Automated data-centric processing

This repository is the official Python implementation of position paper "AutoDC: Automated data-centric processing". The implementation will continue being updated in the coming months.

![image](Fig_1.png)

AutoDC is a framework to enable domain experts to automatically and systematically improve datasets without much coding requirement and manual process, the idea similar with AutoML (automated machine learning). 

By using the AutoML system, such as Google Cloud AutoML, domain experts only need to bring in the input data, and AutoML takes care of the manual ML processes, then produces output predictions, along with user-defined evaluation metrics. With a similar idea, AutoDC is designed for domain experts to bring in a labeled dataset, such as annotated images, to the system; AutoDC takes care of the manual data improvement processes, and produces the improved dataset, by automatically correcting the incorrect labels (with user feedbacks), detecting edge cases, and augmenting edge cases.

## Citation

Zac Yung-Chun Liu, Shoumik Roychowdhury, Scott Tarlow, Akash Nair, Shweta Badhe, and Tejas Shah. AutoDC: Automated data-centric processing, NeurIPS 2021: DCAI workshop, [arXiv: 2111.12548](https://arxiv.org/abs/2111.12548).
csv-db-package 0.0.3
This package is found useful for those who wants to modify their CSV file without using database.It creates a local server that having a functionality of uploading a csv file and then perform crud operations through browser itself.
Requires Python: >=3.7

# csv_db_package
Package having the local server module to upload a csv file and perform CRUD operations on the data shown using different features through database.

## Installation
pip install csv_db_package

---
### STEPS TO INSTALL & MODIFY THE PROJECT 
-Activate your python venv
-Install the requirements given:
    python
    pip install -r requirements.txt
    
Mysql is used as a database engine here.
-Set env variables
  <br/>`$env:HOST= your hostname for mysql`
  <br/>`$env:USER= your username`
  <br/>`$env:PASSWORD= your password`

-Run server inside directory by using command
python fileserver.py

-example table

| First column | Second column |                                    |
|--------------|---------------|------------------------------------|
| Content Cell | Content Cell  | [update](#update "Goto heading-1") |
| Content Cell | Content Cell  | [update](#update "Goto heading-1") |


___
© 2022 Ankita Liya

This repository is licensed under the MIT license. See LICENSE for details.
spycalc 0.1.3
extensibly scriptable graphical calculator

# spycalc
Extensibly scriptable calculator

this repository is a complete redesign of my previous project, [nscalc](https://github.com/Krayfighter/nscalc)


## Supported Platforms

spycalc is written in pure python, so in theory it should run on
any system, but it is only tested on Windows 10, and Linux (arch)


# Installation


## Cross-Platform

This is the prefered way of installing if you do not really know what you are doing.
This also assumes that you have no prerequisites like python, pip, git, or any other dev tools


### Windows

The first step here is to install python from the microsoft store.

It is recommended that you install python 3.10

![search for python](https://raw.githubusercontent.com/Krayfighter/spycalc/main/images/ms_store_1.png)

click the "Get" button, this will then show, and "Install" button, click that button to install python

![install python](https://raw.githubusercontent.com/Krayfighter/spycalc/main/images/ms_store_2.png)

you may now close that window

the next step is to install spycalc via python's package manager, pip. If you do know what that means, that is ok, just make sure to read the instructions carefully.

Now, type "power" into the "type here to search" bar on the bottom left of your screen, and run powershell as administrator

![launch powershell](https://raw.githubusercontent.com/Krayfighter/spycalc/main/images/ms_pwshl_0.png)

![powershell](https://raw.githubusercontent.com/Krayfighter/spycalc/main/images/ms_pwshl_1.png)

It will bring up a blue screen with white text.
copy the following text and paste it into the
powershell window, and press enter

```powershell
pip install spycalc
```

this will run a command that is pretty intuitive.
it tells 'pip' to 'install' 'spycalc'

![pip](https://raw.githubusercontent.com/Krayfighter/spycalc/main/images/ms_pwshl_2.png "copy/pasting the command")

![command output](https://raw.githubusercontent.com/Krayfighter/spycalc/main/images/ms_pwshl_3.png)

Now, you have install spycalc on your system.

The Final step is to add a shortcut. In the powershell window run the following command
```powershell
py -m spycalc --setup
```

it should now be runnable from the start menu by typing spycalc into "type here to search"



### Linux


#### arch

sudo pacman -S python3

python3 -m pip install spycalc

python3 -m spycalc --setup

#### deb+

sudo apt install python3

python3 -m pip install spycalc

python3 -m spycalc --setup


## Development Mode

This is recommended for those who intend to extend, or fork spycalc

copy the git repository

```bash
git clone https://github.com/Krayfighter/spycalc.git
cd spycalc
./setup.sh
```

This may need work in the future but works for now
exlog-pkg-yrom1 0.0.10
exception logging decorator
Requires Python: >=3.6

# exception-logging-decorator
The following function:
```py
from typing import NoReturn

from src.exlog.exceptlog import exception_logger

log = exception_logger("./tests/log.log", "US/Eastern")


@log
def no_return() -> NoReturn:
    """Test function."""
    raise Exception("DO NOT PASS GO DO NOT COLLECT $200.")


if __name__ == "__main__":
    try:
        no_return()
    except:
        pass
```
Logs the following exception in `log.log`.
```
ERROR:root:no_return
  raised Exception: DO NOT PASS GO DO NOT COLLECT $200.
    called with args = (), kwargs = {}
    at 2022-01-04 13:42:45.247324-05:00
Traceback (most recent call last):
  File "/home/ryan/pypi-packaging-tut/src/exlog/exceptlog.py", line 65, in wrapper
    output = function(*args, **kwargs)
  File "/home/ryan/pypi-packaging-tut/./tests/test.py", line 21, in no_return
    raise Exception("DO NOT PASS GO DO NOT COLLECT $200.")
Exception: DO NOT PASS GO DO NOT COLLECT $200.
```