semvec/pypi-packages huggingface dataset:
rudi 1.0.1
Small, fast and simple Python CLI image converter for CNNs.

# Rudi

If you want to train a CNN, the custom dataset must be a collection of images of the same aspect ratio, extension, etc.
This script is gonna do that for you. Rudi is a command line tool for converting and augmenting your dataset of images.

# Installation

Install `Python3` and then run the following command:
```sh
pip install rudi
```

or clone the repo firs 
```sh
git clone https://github.com/liashchynskyi/rudi
cd rudi
```
and run `python setup.py install` or `pip install .`

# Usage
![Imgur](https://i.imgur.com/KIi431Z.png)
---
* [Convert a dataset](#convert-a-dataset)
* [Dataset augmentation](#dataset-augmentation)
* [Changelog](#changelog)
* [Donate](#donate)

# Convert a dataset

For example, you have a basic tree of the root directory (the script will also work if the root containt only images without other dirs).
```
root    
в””в”Ђв”Ђв”Ђclass1
в”‚   в”‚   image_c1.png
в”‚   в”‚   image_c2.png
в”‚   в””в”Ђв”Ђв”Ђsubdirectory    
в””в”Ђв”Ђв”Ђclass2
    в”‚   image_c1.png
    в”‚   image_c2.png
```

Just run `rudi convert --help`
![Imgur](https://i.imgur.com/GAWRBja.png)

Let's convert images in current directory to `jpg` format and set new aspect ratio to `224px`.
```sh
rudi convert -t jpg --target-size=224 ./
```
Output images will be saved in `output` dir of the root.

# Dataset augmentation

Command: `rudi augment --help`
![Imgur](https://i.imgur.com/64Ijbjr.png)

There are a few supported operations:
* `flip` - random image flipping followed by `-p` option
* `rotate` - random image rotation followed by `-p`,`-mlr` and `-mrr` options
* `distortion` - random image distortion followed by `-p`,`-mg` and `-gwh` options
* `skew` - random image skewing followed by `-p` option and constant `magnitude` value of `0.7`
* `zoom` - random image zooming followed by `-p`,`-minf` and `-maxf` options

Output images will be saved in `output` dir of the root.

# Changelog

* `1.0.1`
    * Fixed problem when converting images. Now if image is placed in subdirectory
    then that subdir also will be created in `output` dir. 


# Donate
Just put a star on this repository рџЊћ Thanks!

<div align="center">
made by <a href="https://github.com/liashchynskyi">@liashchynskyi</a>
</div>
autoleastsq 1.0.1
自动化拟合多项式，并自动判断最佳次幂

## Install
```
# only for python3
# need: sqlite3

pip install autoleastsq

```

## Usage

```
# -*- coding: utf-8 -*-  

import autoleastsq

### 从文件获取数据到 list   其他数据源只要符合数据类型和对齐就行了

filename="./test.xlsx"
lists=autoleastsq.excel_col2list(filename,["a","b","result"])


################# 多元多项式回归样例

# 挑选所需要的因子  注意append 因子次序决定了拟合的公式，和后续使用时的入口次序, append 几个因子多项式就有几元，可灵活使用

factor=[]  # factor自由多个因子的二维list
factor.append(lists[0])
factor.append(lists[1])
y = lists[2]

##################### 拟合得到多项式模型

# 返回：多项式的参数列表、 幂次，并可根据新输入的因子list 进行预测

"""
支持的参数：
pows_start=1	# pows_start 起始拟合的幂次(默认为1)
loss_min=0.01		# loss 起始误差率要求（默认0.01, 达到即拟合结束）, 过程中会自动判断最可能的误差率
leastsq_mult(factor,y,pows_start,loss_min)
"""

model=autoleastsq.leastsq_mult(factor,y)

print(model.args,model.pows)  ## 最终的多项式参数 按高次向低次逐个因子排列 ; 最终的多项式参数最高幂次
print(model.func)  ##  模型公式

################# 根据拟合后结果多项式，按顺序输入得到结果

factor=[29,101]

result=model.leastsq_result(factor)

print(result)


```
BottleResource 0.0.2
Class based bottle endpoint resource plugin

# BottleResource
Class based endpoint resource plugin for bottle framework

### Usage 

```python
from bottle import Bottle

from bottle_resource import BottleResource, api, api_get, api_post, api_put, api_patch, api_delete


class DemoResource(BottleResource):

    @api('/demos')
    def get_demo_list(self):
        return {'demos': [1, 2, 3, 4, 5]}

    @api_get('/demos/<demo_id>')
    def get_demo_detail(self, demo_id):
        return {'name': 'demo', 'id': demo_id}

    @api_post('/demos')
    def create_demo(self):
        return {'status': 'ok', 'msg': 'created success'}

    @api_put('/demos/<demo_id>')
    def update_demo(self, demo_id):
        return {'status': 'ok', 'msg': 'updated success', 'id': demo_id}

    @api_patch('/demos/<demo_id>')
    def patch_demo(self, demo_id):
        return {'status': 'ok', 'msg': 'patch success', 'id': demo_id}

    @api_delete('/demos/<demo_id>')
    def delete_demo(self, demo_id):
        return {'status': 'ok', 'msg': 'delete success', 'id': demo_id}


if __name__ == '__main__':
    app = Bottle()
    app.install(DemoResource())
    app.run(host='0.0.0.0', port=8000, debug=True, reloader=True)
```
sfpy 0.6.0
softfloat and softposit in python

# sfpy
softfloat and softposit in Python
  * support for softfloat float16, float32, and float64
  * support for softposit posit8, quire8, posit16, quire16, posit32, and quire32

## Installation

On most linux distros, sfpy should work out of the box:

```
pip install sfpy
```

Binary wheels (compatible with manylinux1) are available for CPython 2.7, 3.5, 3.6, and 3.7.

Under the hood, sfpy uses Cython to create bindings for the softposit and softfloat C libraries.
These building instructions are tested on Ubuntu 18.04 - for other platforms they may need some
adaptation. The cythonized C and compiled static libraries (.a), as well as necessary headers,
are included in the source releases; installing these does not require Cython.

## Demo
```
>>> import sfpy
>>> from sfpy import *
>>> a, b = Float16(1.3), Float16(1.4)
>>> a * b - a / b
Float16(0.89208984375)
>>> sfpy.float.flag_get_inexact()
True
>>> a += b
>>> a
Float16(2.69921875)
>>>
>>> x, y = Posit16(3.0), Posit16(3)
>>> x
Posit16(3.0)
>>> x.bits
22528
>>> y
Posit16(2.9802322387695312e-08)
>>> y.bits
3
>>> x * y
Posit16(8.940696716308594e-08)
>>> acc = Posit16(0)
>>> for i in range(10000):
...   acc = acc.fma(x, y)
... 
>>> acc
Posit16(1.9073486328125e-06)
>>> acc.bits
24
>>> q = Quire16(0)
>>> for i in range(10000):
...   q.iqma(x, y)
... 
>>> q
Quire16(0.00089263916015625)
>>> q.bits
64424509440000
>>> q.to_posit()
Posit16(0.00089263916015625)
>>> q.to_posit().bits
490
```

## Building
See [BUILDING](https://github.com/billzorn/sfpy/blob/master/BUILDING.md).
my-py-tools 0.1.15
py_tools is a modern python library
Keywords: py_tools

=================================================
py_tools
=================================================

.. image:: https://readthedocs.org/projects/py_tools/badge/?version=latest
        :target: http://py_tools.readthedocs.io/en/latest/?badge=latest
        :alt: Documentation Status               

.. image:: https://img.shields.io/travis/DerThorsten/py_tools.svg
        :target: https://travis-ci.org/DerThorsten/py_tools

.. image:: https://travis-ci.org/DerThorsten/py_tools.svg?branch=master
    :target: https://travis-ci.org/DerThorsten/py_tools

.. image:: https://circleci.com/gh/DerThorsten/py_tools/tree/master.svg?style=svg
    :target: https://circleci.com/gh/DerThorsten/py_tools/tree/master

.. image:: https://dev.azure.com/derthorstenbeier/py_tools/_apis/build/status/DerThorsten.py_tools?branchName=master
    :target: https://dev.azure.com/derthorstenbeier/py_tools/_build/latest?definitionId=1&branchName=master








Features
--------

Current features include: 
  * conda ready
  * pytest unit test
  * continous integration

  * coverall code coverage
  * documentation with sphinx
  * documentation on readthedocs