semvec/pypi-packages huggingface dataset:
gauss-cli 1.0.1
gussian elimination method package


# Gauss CLI

Gauss CLI Tool , it's a package to solve N*N equation with gauss elimination method.
## Demo with  source code

https://replit.com/@messabih-khalil/gaussCLI#main.py


## Installation

to install gauss-cli just run :

```bash
  pip install gauss-cli
```
    
## Usage/Examples

```python
from gaussCLI.gaussCli import GaussCLI
# run gauss cli
gc = GaussCLI()
```


## Screenshots

![App Screenshot](https://mir-s3-cdn-cf.behance.net/project_modules/max_1200/46ac8d157952477.63829b23dfa29.png)
![App Screenshot](https://mir-s3-cdn-cf.behance.net/project_modules/max_1200/785332157952477.63829b23e0ab9.png)
![App Screenshot](https://mir-s3-cdn-cf.behance.net/project_modules/max_1200/6f690a157952477.63829b23e1a80.png)
noisy-cifar-owaix2quzq 0.0.1
CIFAR datasets with label noise
Requires Python: >=3.8

# Noisy CIFAR-10/CIFAR-100

## Quickstart
```
from noisy_cifar import NoisyCIFAR10
from torchvision.datasets import CIFAR10
from torchvision.transforms import ToTensor

root = '/datasets/CIFAR-10'
train_dataset = NoisyCIFAR10(root, 'symmetric', 0.2, transform=ToTensor())
val_dataset = CIFAR10(root, train=False, transform=ToTensor())
```

## Install
```bash
pip install noisy-cifar-owaix2quzq
```

## Arguments
- `root`: path to the original dataset
- `noise_type`: symmetric / asymmetric / [human](http://ucsc-real.soe.ucsc.edu:1995/Home.html)
- `noise_level`:
  - (float) [0.0 ~ 1.0] for symmetric & asymmetric
  - (str) {aggregate, random1, random2, random3, worst} for human
- `random_seed`: default `0`
- `transform`: default `None`
- `target_transform`: default `None`
- `download`: default `False`
conditor 0.0.2
Project management utility.
Requires Python: >=3.9


.. _/main:

********
Conditor
********

Conditor provides an API and console tools to manage (develop, configure, build and deploy) Projects.

.. ............................................................................
.. _/main/use_cases:

Use Cases
=========

Copy file tree while formating specified file's content.

Build packages for different targets with scriptable build recipes.

Manage project development with actions.

.. ............................................................................
.. _/main/installing:

Installing
==========

Install with PyPI.

.. code-block:: console

   pip3 install conditor

.. ............................................................................
.. _/main/links:

Links
=====

`GitLab repository <https://gitlab.com/srhuerzeler/conditor>`_

`PyPI package <https://pypi.org/project/conditor>`_
turtler 0.0.3
A fun way to get started with graphics and data science in python
Requires Python: >=3.6

## Introduction

Start your Data Science journey with Turtler. Written to make learning fun!

### Getting Started

Turtler is built on top of [turtle](https://docs.python.org/3/library/turtle.html) but makes drawing easy.

#### Import

`from turtler import Turtler`



#### Initialize

`myTurtler = Turtler()`



#### Move and Draw

```
myTurtler.go(DIRECTIONS)
myTurtler.draw(DIRECTIONS)
```



DIRECTIONS is a string consisting of:-
| Character | Direction |
| --------- | --------- |
| u         | UP        |
| d         | DOWN      |
| l         | LEFT      |
| r         | RIGHT     |

EXAMPLE:-
```
directions = "uldr"
myTurtler.go(directions)
```



#### End Drawing

`myTurtler.done()`


---

### Other Methods

**Set Animation Speed**

`myTurtler.setSpeed(YOUR_SPEED)`

Options:
- fastest
- fast
- normal
- slow
- slowest

**Set Step Size**

`myTurtler.setSpeed(YOUR_SIZE)`

YOUR_SIZE is an int object
mymathpkg22 1.0.1
Simple calculator package project work
Requires Python: >=3.6

# My Math package 2022

This is a project work Python Mastery for Turing college's Data Wrangling with Python module. The purpose of this package is to practise OOP-based coding, testing and creating Python package.

The package is a simple calculator that has addition, substraction, multiplication, division, nth root of a number and reset functions. The calculator used float data-type in input and output values.

After initalizing a new calculator-object, the object will start with a value zero. All the operations are done for this value that is stored in the object.

---

## Setup

```
>>>pip install mymathpkg22
```

---

## Sample Execution

Start the calculator with importing the calculator module and initalizing the calculator-object.

```
>>>from mymathpkg22 import calculator
>>>new_calculator_object = calculator.Calculator()
```

Adding two.

```
>>> new_calculator_object.add(2)
2.0
```

Subtracting three.

```
>>> new_calculator_object.subtract(3)
-1.0
```

Dividing with two.

```
>>> new_calculator_object.divide(2)
-0.5
```
aminacadea 0.0.5
The Aminacadea library is a helper for tflite-model-maker for use in Google Colab.
Requires Python: >=3.7

# Project description
## Aminacadea
### Overview
The Aminacadea library is a dummy helper for tflite-model-maker for use in Google Colab. In Planning phase.

### Requirements
Refer to requirements.txt for dependent libraries that're needed to use the library and run the demo code.
Note that you might also need to install sndfile for Audio tasks. On Debian/Ubuntu, you can do so by sudo apt-get install libsndfile1

### Installation

```python
pip install aminacadea
```

Aminacadea depends on tflite-model-maker >=0.4.2, Pillow >=9.3.0


https://packaging.python.org/en/latest/tutorials/packaging-projects/


#### Generating distribution archives
Generate distribution packages for the package. These are archives that are uploaded to the Python Package Index and can be installed by pip.
NOTE: Run as Adminstrator
1) Make sure you have the latest version of PyPA’s build installed:
```python
python -m pip install --upgrade build
```
2) Now run this command from the same directory where pyproject.toml is located:
```python
python -m build
```
This command should output a lot of text and once completed should generate two files in the dist directory:


#### Uploading the distribution archives

3) To securely upload your project, you’ll need a PyPI API token. 
Create one at https://test.pypi.org/manage/account/#api-tokens, setting the “Scope” to “Entire account”.

4) Now that you are registered, you can use twine to upload the distribution packages. You’ll need to install Twine:
```python
python -m pip install --upgrade twine
```
CREATE .pypirc file in place it in %USERPROFILE%
5) Once installed, run Twine to upload all of the archives under dist:
```python
python -m twine upload --repository testpypi dist/*
```

6) Once installed, run Twine to upload all of the archives under dist:
```python
python -m twine upload --repository pypi dist/*
```


6) Dependencies - TODO

dependencies = [
    "tflite-model-maker>=0.1.*",
    "tflite-support>=0.1.*",
    "Pillow==7.1.*",
    "matplotlib==3.2.*",
]
nbases 1.0.0
nbases - number bases conversions
Requires Python: >=3.8
Keywords: math, logic, numbers, engineering

## nbases - number bases conversions

**nbases** package contains basically four class. These are `Decimal`,
`Binary`, `Octal`, `Hex`. Each class has some methods which provides 
conversions (like decimal to octal or hexadecimal to binary etc.).

### Installiation

+ `pip install nbases`
+ `conda install nbases`

### License

+ MIT License