semvec/pypi-packages huggingface dataset:
morpyneural 0.0.2
A small Neural Network
Requires Python: >=3.9

# Neural Network

A small Neural Network - Multilayer Perceptron made to learn how it works.

Warning : This is not a project for production use.
I'm using this project to try to publish clean code, it's part of my learning how to manage a project, make R&D, and more.

## Using Numba
I'm learning and using the library Numba to make this project. I have multiple goal :

1. Make the project in normal python, without using the Numba lib, that's the pure learning part of how to make a Neural Network
2. Make the project with Numba and use JiT to have better performance with the CPU
3. Make the project with Numba and use CUDA to improve performance again

## Genetic algorithm
I use genetic algorithm (in a very simple way i think, i'm not a mathematician xD) to make the learning part of the Neural Network.
Back Propagation is on the way too.
ohmyds 0.0.2
Python library to visualize data strcutures
Requires Python: >=3.6
Keywords: data structures,tree,graph,linked list,stack,queue,education,visualization

# oh-my-ds
Python library to pretty print data structures

# Installation

```python3
pip3 install ohmyds
```

# Usage

Right now only binary tree is supported

```python3

from ohmyds import binary_tree

root = binary_tree.create_tree([1,2,3,4,None,5,None,6,7,8])
```

For binary tree, you can do two operations right now - level order traversal and print

```python3
print(root)
```

The above command will print binary tree in a beautiful way (beauty is in the eyes of the beholder!)

```
 _1    
/  \   
3  2_  
 \   \ 
 5   4 
    / \
    7 6
```
tykit 0.1.5
A tool kit of progress bars and console logs with rich output, also has a bunch of useful functions used in work.

<!--
 * @Description: 
 * @version: 
 * @Author: TianyuYuan
 * @Date: 2021-04-02 15:42:10
 * @LastEditors: TianyuYuan
 * @LastEditTime: 2021-04-28 15:39:42
-->

# tykit (Tell You kit) ![version](https://img.shields.io/github/release/paperplane110/tykit.svg) ![rich](https://img.shields.io/badge/Powered-Rich-brightgreen.svg)

![Alt Text](./image/Kapture%202021-04-02%20at%2017.18.06.gif)

## 📜 Description

'tykit--Tell You kit', pronounced like 'ticket', is a toolkit to monitor your scripts' status easily, which has rich and pretty output for progress bar and console logs.
The tykit may support more decent output in the future

## ⚙️ Install

tykit can be easily installed with `pip` as below

```bash
# for python2
pip install tykit
# for python3
pip3 install tykit
```

## 🌟 Features

---

## 🚀 ProgressBar 进度条

for ***loop,range,multi-threading and multi-threading with multi-params***

> ___pb_range(*args)___

This function is just like the python builtin function `range()`

```python
from tykit import pb_range
from time import sleep

# use pb_range just like range()
for i in pb_range(50):
    sleep(0.001)

# or use it as a generator, with some iterabel_files
from tykit import pb_iter
some_task = lambda x: x*x
iterable_file = [x for x in range(100)]

for i in pb_iter(iterable_file):
    some_task(i)
```

![pbrange](./image/pbrange.gif)

> ___pb_multi_thread(workers:int,task,iter_files)___

This function intergrates the multi-threading with `ProgressBar`, which could show the master task's progress in a multi-threading script. The `param: workers` defined the max-worker of the multi-threading.

```python
from tykit import pb_multi_thread as pbmt

# Firstly, define your task func
task_func = lambda x: x*x*x
# Put your jobs in a iterable data structure
jobs = [x for x in range(1000)]
# run multi-threading with pb(ProgressBar)
# and save the result in a list
max_workers = 20
result = pbmt(max_workers,task_func,jobs)
print(result[10])
```

> ___pb_multi_thread_partial(workers:int,task,iter_files,**kwargs)___

This function is a higher api of ```pb_multi_thread()```, which is suitable for the multi-threading tasks with more than one parameters.

```python
from tykit import pb_multi_thread_partial as pbmtp

# define a task func with multi params
def task(x,a,b,c):
    return x+a+b+c
# Put your jobs in a iterable data structure
jobs = [x for x in range(1000)]
# run multi-threading with partial
max_workers = 20

result = pbmtp(max_workers,task,jobs,a=10,b=100,c=-20)
print(result[:10])
```

## 🛎️ rlog (Rich-log)

rlog is a module for log printing with ***rich***. It has some functions to print pretty logs and hints. The usage is simple and neat. The samples are shown below👇(for more details, you can find in [usage_of_rlog.py](https://github.com/paperplane110/tykit/blob/master/examples/usage_of_rlog.py) in examples)
![rlog_show](./image/rlog.gif)
phdictionary 0.0.4
PhDictionary. The best tool for language learning
Requires Python: >=3.6

# PhDictionary | Best tool for language learning

This tool can help you to provide better understanding of foreign word using examples, definitions and translation.

# Installation

```bash
pip install phdictionary
```

# Usage

Importing the module
```python
from phdictionary import dictionary
```
To get the definition of a word use this code
```python
word = input()
result = dictionary.get_definition(word, 2)
print(result)
```
This function returns a turple with the definition and examples in a sentence (in this case 2 examples)

To translate from English to French
```python
word = input()
result = dictionary.get_french_english(word, 2)
print(result)
```
And vice versa

```python
word = input()
result = dictionary.get_english_french(word, 2)
print(result)
```
These functions return a turple with the translation and examples in a sentence (in this case 2 examples)

To get synonyms to a word in English use this
```python
word = input()
result = dictionary.get_synonym(word, 4)
print(result)
```
This function returns a list with the synonyms (in this case 4 synonyms and by default it returns only one synonym)

To get definitions for the list of word use the code below
```python
dictionary.get_definition_from_file(open('file.txt'), 2)
```
It generates a Word document with definitions for the each word in list with 2 examples for each (by default it is 0)

To get synonyms for the list of word use the code below
```python
dictionary.get_synonyms_from_file(open('file.txt'), 3)
```
It generates a Word document with 3 synonyms to the each word in list (by default it is 1)

To get translation from French to English for each word in the list use this
```python
dictionary.get_french_english_from_file(open('file.txt'), 3)
```
It generates a Word document with translation for the each word in list with 3 examples for each (by default it is 0)


You can find all the documentation on GitHub: https://github.com/RediIVIideR/phdictionary
stox 0.5
Stock Price Prediction And Analysis
Keywords: stock price prediction,stock analysis,stock signalsstock ai

# Stox
 A Module to predict the "close price" for the next day and give "technical analysis". It 
 uses a Neural Network and the LSTM algorithm to predict the price. It uses a technical 
 indicator algorithm developed by the Stox team for technical analysis. Check out how it works [here](https://github.com/cstox/stox/blob/main/Workings.md).

## Installation
Get it from [PyPi](https://pypi.org/project/stox/):
```
pip3 install stox
```
Clone it from github:
```
git clone https://github.com/dopevog/stox.git
cd stox
python3 setup.py
```

## Usage
### Arguments:
```
    stock (str): stock ticker symbol
    output (str): 'list' or 'message' (Format Of Output)
    years (int or float): years of data to be considered
    chart (bool): generate performance plot
```

### Returns:
List: 
```
[company name, current price, predicted price, technical analysis, date (For)]
```
Message:
```
company name
current price
predicted price
technical analysis
data (for)
```

### Examples:
#### Basic
```
import stox

script = input("Stock Ticker Symbol: ")
data = stox.stox.exec(script,'list')

print(data)
```
```
$ stox> python3 main.py
$ Stock Ticker Symbol: AAPL
$ ['Apple Inc.', 125.43000030517578, 124.91, 'Bearish (Already)', '2021-05-24']
```
#### Intermediate
```
import stox
import pandas as pd

stock_list = pd.read_csv("SPX500.csv") 
df = stock_list 
number_of_stocks = 505 
x = 0
while x < number_of_stocks:
    ticker = stock_list.iloc[x]["Symbols"]
    data = stox.stox.exec(ticker,'list')
    df['Price'] = data[1] 
    df['Prediction'] = data[2]
    df['Analysis'] = data[3]
    df['DateFor'] = data[4]
    if data[2] - data[1]  >= data[1]  * 0.02:
        if data[3] == "Bullish (Starting)":
            df['Signal'] = "Buy"
        elif data[3] == "Bullish (Already)":
            df['Signal'] = "Up"
    elif data[2] - data[1]  <= data[1]  * -0.02:
        if data[3] == "Bearish (Starting)":
            df['Signal'] = "Sell"
        elif data[3] == "Bearish (Already)":
            df['Signal'] = "Down"
    else:
        df['Signal'] = "None"
    x = x+1
df.to_csv("output.csv") 
print("Done") 
```
```
$ stox> python3 main.py
$ Done
```
#### More Examples Including These Ones Can Be Found [Here](https://github.com/cstox/stox/tree/main/Examples)

### Possible Implentations
* Algorithmic Trading
* Single Stock Analysis
* Multistock Analysis
* And Much More!

## License
This Project Has Been [MIT Licensed](https://github.com/cstox/stox/blob/main/License.txt)
lookatme.contrib.qrcode 2.2.0
Adds qrcode rendering
Requires Python: >=3.6

[![Master Build Status](https://travis-ci.org/d0c-s4vage/lookatme.contrib.qrcode.svg?branch=master)](https://travis-ci.org/d0c-s4vage/lookatme.contrib.qrcode)
[![Coverage Status](https://coveralls.io/repos/github/d0c-s4vage/lookatme.contrib.qrcode/badge.svg?branch=master)](https://coveralls.io/github/d0c-s4vage/lookatme.contrib.qrcode?branch=master)
[![PyPI Statistics](https://img.shields.io/pypi/dm/lookatme.contrib.qrcode)](https://pypistats.org/packages/lookatme.contrib.qrcode)
[![Latest Release](https://img.shields.io/pypi/v/lookatme.contrib.qrcode)](https://pypi.python.org/pypi/lookatme.contrib.qrcode/)
[![Documentation Status](https://readthedocs.org/projects/lookatmecontribqrcode/badge/?version=latest)](https://lookatmecontribqrcode.readthedocs.io/en/latest/?badge=latest)

[![Twitter Follow](https://img.shields.io/twitter/follow/d0c_s4vage?style=plastic)](https://twitter.com/d0c_s4vage)

# lookatme.contrib.qrcode

This [lookatme](https://github.com/d0c-s4vage/lookatme) extension adds
QR code rendering capabilities to the code blocks.

## Installation

```bash
pip install lookatme.contrib.qrcode
```

## Usage

Add the qrcode into the extensions array in the
slide YAML header:

```markdown
---
title: A title
author: Me
date: 2019-12-04
extensions:
  - qrcode
---
```

### Basic Usage

With the extension installed and declared in the YAML header, use it in your
markdown like so:

~~~md
# A Slide

```qrcode
https://github.com/d0c-s4vage/lookatme.contrib.qrcode
```
~~~

![QR code single rendering](docs/source/_static/qrcode_single.png)

### Extended Usage

An [extended mode](https://lookatmecontribqrcode.readthedocs.io/en/latest/#qrcode-ex-codeblocks)
is also available that lets multiple columns of QR codes be rendered side-by-side:

~~~md
# A Slide

```qrcode-ex
columns:
  - data: https://github.com/d0c-s4vage/lookatme
    caption: "**lookatme** project"
  - data: https://github.com/d0c-s4vage/lookatme.contrib.qrcode
    caption: Text `lookatme.contrib.qrcode`
```
~~~

![QR code single rendering](docs/source/_static/qrcode_double.png)

### Details

[Read the documentation](https://lookatmecontribqrcode.readthedocs.io/en/latest/)
center-it 0.0.2
A library for centering python gui windows.


![Build 0.2](https://img.shields.io/badge/Build-0.2-brightgreen) ![Python-3.9.2|3.9.3|3.9.4|3.9.5](https://img.shields.io/badge/Python-3.9.2|3.9.3|3.9.4|3.9.5-blue) ![platform win|linux](https://img.shields.io/badge/platform-win%20|%20linux-red)
fasttest 1.0.1
关键字驱动自动化框架
Requires Python: >=3.6
Keywords: macaca,appium,selenium,APP自动化,WEB自动化,关键字驱动

`fasttest` 在`macaca`、`appium`、`selenium`的基础上做了一层关键字映射，在`yaml`文件上编写自动化用例，即使无代码基础的同学也已可以很快上手自动化测试

![](https://img.shields.io/badge/python-3.7-green) 

#### 我能做什么
- 支持`IDEA`、`Pycharm`插件，在`yaml`文件上写用例可智能联想关键字 --> [FastYaml](https://plugins.jetbrains.com/plugin/16600-fastyaml)
- 支持实时`debug`用例步骤，无需重复运行验证
- 支持现有关键字组合、自定义关键字，拥有无限扩展性
- 支持`PO`模式、支持`iOS`、`Android`两端共用一份用例
- 支持`if`、`while`、`for`等语法用于构造复杂场景
- 支持`CLI`命令，支持`Jenkins`持续集成
- 支持多设备并行执行

#### 演示↓↓↓
![](https://github.com/Jodeee/fasttest/blob/master/demo.gif)

更多请点击 [fasttest](https://www.yuque.com/jodeee/vt6gkg/oue9xb)
jupyter-fireworks-proxy 2021.5.0
adam@fekete.co.uk
Keywords: jupyter,Fireworks proxy,jupyterhub

# jupyter-fireworks-proxy

This package provides an easy way to launch the webgui interface of Fireworks in a Jupyter notebook environment. The Fireworks package must be fully configured with a config file stored at the `~/.fireworks/my_launchpad.yaml` location.


## Requirements

- [Python 3+](https://www.python.org/downloads/)
- [Jupyter Notebook](https://pypi.org/project/notebook/)
- [Fireworks](https://github.com/materialsproject/fireworks)


This package also contains a `fireworks_luancher` command which is also used internally to initialise a single-threaded interface for the webgui implementation of Fireworks. 

## Quick Starts

### Install jupyter-fireworks-proxy

```bash
pip install git+https://github.com/modl-uclouvain/jupyter-fireworks-proxy.git
```

### Enable jupyter-fireworks-proxy Extensions

Note: These steps might be optional, depending on the python environment that you use.

1. For Jupyter Classic, activate the `jupyter-server-proxy` extension:

```bash
jupyter serverextension enable --sys-prefix jupyter_server_proxy
```

2. For Jupyter Lab, install the `@jupyterlab/server-proxy` extension:

```bash
jupyter labextension install @jupyterlab/server-proxy
jupyter lab build
```

3. Start Jupyter Classic or Jupyter Lab

4. Click on the `Fireworks (webgui)` icon from the Jupyter Lab Launcher or the `Fireworks (webgui)` item from the `New` dropdown in Jupyter Classic.

## Credits

- [`jupyter-server-proxy`](https://github.com/jupyterhub/jupyter-server-proxy)
- [`jupyter-server-proxy` cookiecutter template](https://github.com/illumidesk/cookiecutter-jupyter-server-proxy).
wetai 0.0.1
wetAI Python utilities
Keywords: machine-learning,jupyter,neuroscience,wetai,braingeneers

# Braingeneers Python Utilities

[This package][github] is supposed to collect, as well as make installable
through Pip, all of the Python code and utilities that we develop as
part of the wetAI project.

[github]: https://www.github.com/braingeneers/wetai