semvec/pypi-packages huggingface dataset:
py-deploy 1.0.1
A simple deployer cmd tool
Requires Python: >=3.6

# Python application deployer

This command line script exists for fast deployment to remote server.
Was mostly created for test purposes and might have some issues.

## Used languages/packages

Python3.6, [click](https://click.palletsprojects.com/en/7.x/),
[paramiko](http://www.paramiko.org/) and [scp](https://pypi.org/project/scp/).

## Installation and usage

To install simply run `pip install py_deploy`

You can also install it from git: `pip install git+https://github.com/velykanov/py_deploy.git`

To deploy your application to the server simply run `py-deploy -t /some/dir/ -h remote_host`.
This command would deploy current directory to remote server.

Here's the full list of options:
```shell script
-i, --identity-file TEXT  SSH identity file
-p, --password            Defines if password must be passed
-P, --port TEXT           SSH target port
-s, --source TEXT         Source  (default: '.')
-t, --target TEXT         Target  [required]
-h, --host TEXT           Host IP address or name  [required]
-u, --username TEXT       Remote username
-e, --exclude TEXT        Dirs names to exclude  (default: ['tests', '.git', '__pycache__'])
-v, --verbose
--version                 Show the version and exit.
--help                    Show this message and exit.
```

### Please note!

This tool won't work if your python versions are mismatched
(for instance your app is written using Python3.8 including
some specific features and you're running this tool using Python3.6).

## Changelog

| Version | Changes |
|:--------|:--------|
| 1.0.1 | Added ssh password support. Split target into separate arguments. |
dnassembly 0.1.10
Digest and assemble DNA
Keywords: DNA,assembly,cloning,plasmid,digest,restriction,enzyme,molecular,biology

# DNAssembly

*Yet another DNA manipulation package, except this one does exactly what I want*

DNAssembly is a framework that lets you do your cloning as you would in the wet lab, but on your computer. In place of
colorless liquids in test tubes, DNAssembly will take your plasmid files in your favorite format (Genbank, Snapgene)
and run them through an assembly protocol to produce fully annotated cloning products. All input sequences are verified
to avoid common cloning pitfalls.

DNAssembly is initially implemented to perform Modular Golden Gate Cloning as described in "A Highly Characterized Yeast
Toolkit for Modular, Multipart Assembly" (Lee et al. 2013).
hyperdrive-quant 0.32.0
An algorithmic trading platform
Requires Python: >=3.7

| <img src="https://raw.githubusercontent.com/suchak1/hyperdrive/master/img/1.png" width="75" /> | _hyperdrive_: Robinhood analytics and algorithmic trading |
| ---------------------------------------------------------------------------------------------- | --------------------------------------------------------- |


![Build Pipeline](https://github.com/suchak1/hyperdrive/workflows/Build%20Pipeline/badge.svg) ![Dev Pipeline](https://github.com/suchak1/hyperdrive/workflows/Dev%20Pipeline/badge.svg) ![New Release](https://github.com/suchak1/hyperdrive/workflows/New%20Release/badge.svg)

_hyperdrive_ is a project to obtain stock data, create trading strategies, test against historical data (backtesting), and deploy strategies for algorithmic trading.

## Getting Started

### Prerequisites

You will need Python 3.8+ and a Robinhood account.

Place your credentials in a file named `.env` in the project root directory.
Follow this structure:

```
RH_USERNAME=...
RH_PASSWORD=...
RH_2FA=...
IEXCLOUD=...
```

### Installation

To install the necessary packages, run

```
pip install -r requirements.txt
```

## Use

### Making Scripts

To make a script, create a new .py file in the `scripts/` dir with the following code:

```
import sys
sys.path.append('src')
from Algotrader import HyperDrive  # noqa autopep8

drive = HyperDrive()
```

## Features:

- [x] Broker authentication
- [ ] Automated data storage
- [ ] Backtesting engine
- [ ] Monte Carlo simulations
- [ ] Plotting and technical analysis
- [ ] Model training
- [ ] Strategy definition (start with buy and hold)
- [ ] Buy and sell functionality
- [ ] Live trading
- [ ] Documentation

Check out the [Roadmap](https://github.com/suchak1/hyperdrive/projects/2) for progress
...

### Auth

Using Robinhood 2FA, we can simply provide our MFA one-time password in the `.env` file to login to Robinhood (via `pyotp`).

### Data

- [ ] Price and Volume
  - [x] ![Symbols](https://github.com/suchak1/hyperdrive/workflows/Symbols/badge.svg)
  - [x] ![OHLC](https://github.com/suchak1/hyperdrive/workflows/OHLC/badge.svg)
  - [x] ![Intraday](https://github.com/suchak1/hyperdrive/workflows/Intraday/badge.svg)
- [x] Actions
  - [x] ![Dividends](https://github.com/suchak1/hyperdrive/workflows/Dividends/badge.svg)
  - [x] ![Splits](https://github.com/suchak1/hyperdrive/workflows/Splits/badge.svg)
  - [ ] Mergers
  - [ ] Buybacks
- [ ] Sentiment
  - [ ] News Sentiment
  - [x] ![Social Sentiment](<https://github.com/suchak1/hyperdrive/workflows/Social%20Sentiment%20(1)/badge.svg>)
  - [ ] [Institutional Sentiment](http://www.aaii.com/files/surveys/sentiment.xls)
  - [ ] Analyst Recommendations
- [ ] Company / Micro
  - [ ] Profile (Sector, # of Employees)
  - [ ] Earnings
  - [ ] Cash Flow
  - [ ] CEO Compensation
- [ ] Government / Macro

  - [ ] Unemployment Rate
  - [ ] Real GDP
  - [ ] US Recession Probabilities

- [ ] Market
  - [ ] General Volatility (VIX)
  - [ ] Sector Performance

---
adapay-merchant 1.2.0
adapay_merchant version of adapay sdk

Adapay Python SDK
===================================

 Adapay-Merchant Python SDK。

安装
-----
如果已下载至本地：
pip install local_sdk_path

远程下载并安装：
pip install adapay-merchant

文档
--------------------
相关文档请参考官方提供的线下文件
slidingwindow-generator 1.0.6
Generate Sliding Window from Time Series Dataset
Requires Python: >=3.6

# SlidingWindowGenerator
- based on tensorflow v2.3.0
    - use [timeseries_dataset_from_array function](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/timeseries_dataset_from_array) which was introduced in tf v2.3.0 
- This module converts time series data from dataframe type to sliding window type
    - to use as input in RNN based layer
- This module was based on [tensorflow official docs](https://www.tensorflow.org/tutorials/structured_data/time_series#data_windowing), just aggregate some functions and add small tuning to use it more efficiently.
    - to make it possible to control batch_size, sequence_stride_size and shuffle more freely.
- Module is in `./src/sliding_window_generator.py` and the example of how to use it is in `./src/example.ipynb`
grpc-client 0.0.2
grpc client for python
Requires Python: >=3.6

#Python GRPC Client

A simple GRPC client to easy use.

##Requirements:
```
grpcio
protobuf
```

##How to use:

```
from grpc_client.bbase_client import BaseClient

def CallbackFunction(response):
    return response

client = BaseClient(
    host='Host',
    port='Port',
    stub_class=GrpcStubClass,
    call_func='stub_function_name',
    data={'request': GrpcRequestFunction},
    response_callback=CallbackFunction
).execute()
```
sliding-window-generator 1.0.0
Generate Sliding Window from Time Series Dataset
Requires Python: >=3.6

# SlidingWindowGenerator
- based on tensorflow v2.3.0
    - use [timeseries_dataset_from_array function](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/timeseries_dataset_from_array) which was introduced in tf v2.3.0 
- This module converts time series data from dataframe type to sliding window type
    - to use as input in RNN based layer
- This module was based on [tensorflow official docs](https://www.tensorflow.org/tutorials/structured_data/time_series#data_windowing), just aggregate some functions and add small tuning to use it more efficiently.
    - to make it possible to control batch_size, sequence_stride_size and shuffle more freely.
- Module is in `./src/sliding_window_generator.py` and the example of how to use it is in `./src/example.ipynb`
jsonpath-kv 0.0.8
Gets the value in the JSON data

# **jsonpath-kv**
    pip install jsonpath-kv
    from jsonpath_kv import jsonvalue
## 正常的dict
    user_dict = {
        "sites":
            [
                { "name":"runoob" , "url":"www.runoob.com" },
                { "name":"google" , "url":"www.google.com" },
                { "name":"weibo" , "url":"www.weibo.com" }
            ]
    }
    r = jsonvalue.get(user_dict,'url')
    print(r)
    output: ['www.runoob.com', 'www.google.com', 'www.weibo.com']
## json字符串
    user_json = '{ "sites": [ { "name":"runoob" , "url":"www.runoob.com" }, { "name":"google" , "url":"www.google.com" }, { "name":"weibo" , "url":"www.weibo.com" } ] }'
    r = jsonvalue.get(user_dict,'url')
    print(r)
    output: ['runoob', 'google', 'weibo']
## 当所取key在字符串包裹里时
    user_json = { "sites": '[ { "name":"runoob" , "url":"www.runoob.com" }, { "name":"google" , "url":"www.google.com" }, { "name":"weibo" , "url":"www.weibo.com" } ]'}
    r = jsonvalue.get(user_dict,'url')
    print(r)
    output: ['runoob', 'google', 'weibo']
## 当所取key在字符串包裹里时
    user_dict = {"sites": "{ \"name\":\"google\" , \"url\":\"www.google.com\" }"}
    r = jsonvalue.get(user_dict,'name')
    print(r)
    output: ['google']
## key不存在时返回false
    user_dict = {"sites": "{ \"name\":\"google\" , \"url\":\"www.google.com\" }"}
    r = jsonvalue.get(user_dict, 'address')
    print(r)
    output: False