semvec/pypi-packages huggingface dataset:
torado 0.0.2
Routes your traffic through TOR Network
Requires Python: >=3.7

# Torado
A script that routes all your machine traffic through TOR Network. It changes IP every 5 minutes (change it in your needs)

## There are 2 versions. 'Torado.py' is for everything and it changes IP every 5 seconds. You just have to setup proxy. The 2nd version 'torado_browseronly.py'is only for browsers without setting up something. Ofcourse you can set also the 2nd version to work for everything too

#### Now either you setup your machine with SOCK5 proxy 127.0.0.1:9050 or you setup proxy in a software (from settigns as usually)
#### Note that you may need to run scrip with admin privilages

### For more informations visit Torado's github page from [here](https://github.com/th3knv/Torado/)
stockman 0.0.2
A股量化数据工具
Requires Python: <=3.11

![header.png](https://cdn.wenanzhe.com/img/68747470733a2f2f7a332e617831782e636f6d2f323032312f30372f30322f5236496832382e6a7067.jfif)

# Stockman

友好的A股量化数据包，支持tushare等多数据渠道（其他渠道开发中）收集，自带常用和非常用数据指标（开发中）

----

## 一、使用方式

### 1、生成配置文件清单

```python
# example_generate_config_pystock.py

import stockman

stock = stockman.Stock()
stock.generate_config()
```

上述代码将会在项目当前目录生成 <b>sample.yaml</b> 配置文件， 请根据实际情况进行填写

```yaml
DBCONFIG: # MYSQL数据库配置
  host: 127.0.0.1                   # MYSQL数据库主机地址
  dbname: stock_db                  # MYSQL数据库名称，需要自己创建该名称数据库
  username: root                    # MYSQL数据库用户名
  password: stock_db                # MYSQL数据库密码
  port: 3306                        # MYSQL数据库端口
  charset: utf8                     # MYSQL数据库默认字符集
  prefix: stockman_                 # MYSQL数据库表前缀
APIS: # 数据渠道配置
  Tushare: # Tushare 数据渠道
    Params: # Tushare 数据渠道配置
      token: ''                     # Tushare 数据渠道TOKEN，请于Tushare个人中心获取
    Permissions: # Tushare 数据功能权限配置
      stock_list: true              # 是否使用Tushare渠道进行股票列表数据权限获取
      stock_daily: true             # 是否使用Tushare渠道进行股票日线数据权限获取
      stock_trade_cal: true         # 是否使用Tushare渠道进行股票交易日历数据获取
```

### 2、自动安装数据表

```python
# example_install_pystock.py

import stockman

with open("sample.yaml", "r") as f:
    res = f.read()
stock = stockman.Stock(res)
stock.install()
```

上述代码将会根据配置文件 <b>sample.yaml</b> 的数据库部分配置，生成各项数据表

### 3、设置接口调度频率限制

本功能主要用于开源API或者未来主动采集数据时拉取数据的速度限制，如tushare的日线数据接口调用限制为 100次/60秒

```python
# example_set_func_times_limit_pystock.py

import stockman

with open("sample.yaml", "r") as f:
    res = f.read()
stock = stockman.Stock(res)
stock.set_times_limit(feature="tushare.stock_daily_sub", limit_time=(100, 60))
```

`set_times_limit` 方法接收 `feature` 与 `limit_time` 两个参数，其中 `feature` 参数的组成结构为 `{api_name}.{func_name}` , `limit_time`
参数为一个包含两个值的元组，依次为 <b>次数</b> 、 <b>时间（单位为秒）</b>，指代的频率为 `次数/时间（单位为秒）`

目前feature支持调节频率的方法为

| 接口名称          | api_name | func_name       | 默认值    |
|---------------|----------|-----------------|--------|
| Tushare日线数据接口 | tushare  | stock_daily_sub | 100/60 |


### 4、初始化数据

```python
# example_init_data_pystock.py

import stockman

with open("sample.yaml", "r") as f:
    res = f.read()
stock = stockman.Stock(res)
stock.init()
```

上述代码将会自动依次根据配置文件 <b>sample.yaml</b> 中的数据渠道部分中的权限配置去导入历史数据，目前包含有交易日历、股票列表、历史日K线数据等。

### 5、主动更新数据

```python
# example_update_pystock.py

import stockman

with open("sample.yaml", "r") as f:
    res = f.read()
stock = stockman.Stock(res)


# 更新日K线数据
def update_daily_k_data():
    stock.update_daily_k_data()

# 更新股票列表数据
def update_stock_list_data():
    stock.update_stock_list_data()


if __name__ == '__main__':
    update_daily_k_data()
    update_stock_list_data()
```
ThesisHNSC 0.0.6
test file
Keywords: python,Head and neck cancer,Diagnosis,Single cell,Genomics


# HNSCPredict :- A Tool for Identification of HNSCC from Single Cell Genome using Deep Learning

A computational approach tool to predict Head and Neck Cancer affected patients from their single cell RNA seq data.


## Introduction

Head and neck cancer, which encompasses a range of malignancies affecting the respiratory tract and upper digestive tract and also the seventh most common cancer in the world. 

This tool aims to use Artifical Neural Network (Deep Learning) model to classify Normal Control(NC) patients and Head and Neck Cancer (HNSCC) patients from
their single cell RNA seq data. The tool takes 10x single cell genomics data as input and predicts whether the patient is diseased or healthy with the help of highly trained model.

An excellent feature selection method called mRMR (Minimum Redundancy Maximum Relevance) was used to find out top 100 features which act as promising biomarkers
in classification and prediction of Normal and Diseased patients.

Please read/cite the content about the AlzScPredict for complete information including algorithm behind the approach.
## Installation

Install my-project with pip

```bash
  pip install HNSCPred
```
    
You if previously installed please update the python package to the latest version using the command below

```bash
  pip3 install --upgrade HNSCPred
```
## Usage/Examples

After installation of the HNSCPred package in your python enviornment. Import the library using the below code.


```python
import HNSCPred
```
The HNSCPred comes with 1 inbuilt modules . 

- Predict
Please import all 3 modules in your python enviornment using the code below.

```python
from HNSCPred import preprocessing
from HNSCPred import Model_Selection
from HNSCPred import PredictionModule

```

After importing all the important pre requisites. You can follow the demo below for your case.


```python
import pandas as pd
df = pd.read_csv("Your file path here")

#Takes single cell data in form of dataframe with cells in rows and features in columns. Returns preprocessed dataframe.
processed = preprocessing.preprocess_data(df)

# Load the model with the code below. This takes no arguments.
my_model = Model_Selection.load_model()

# Prediction:- Execute the code below to get the output. It takes 2 arguments i.e 
# preloaded model and the processed dataframe with 35 features.

PredictionModule.predict_patient(my_model,processed)

```

## Used By

This project is used by the following companies:

- Company 1
- Company 2


## Authors

- Aman Srivastava.
- Akanksha Jarwal. 
- [Prof. G.P.S. Raghava](https://www.github.com/octokatherine)

## Acknowledgements

 - [Awesome Readme Templates](https://awesomeopensource.com/project/elangosundar/awesome-README-templates)
 - [Awesome README](https://github.com/matiassingers/awesome-readme)
 - [How to write a Good readme](https://bulldogjob.com/news/449-how-to-write-a-good-readme-for-your-github-project)