semvec/pypi-packages huggingface dataset:
jsonthrift 0.1.4
a package for serializing json to/from thrift
License: MIT

jsonthrift is implemented for serializing and deserializing json to/from thrift.

Usage
-----

     >>> transport = 'TBufferedTransport'
     >>> protocol = 'TBinaryProtocol'
     >>> thrift_file = 'tutorial.thrift'
     >>> service = 'Calculator'
     >>> method = 'add'
     >>> params = {'num1': 1, 'num2': 3}

     >>> jt = JsonThrift(transport, protocol, thrift_file, service)
     >>> msg = jt.pack_request(method, params, 1)
     >>> response = send_and_recv(msg) # send msg to server, receive response
     >>> result = jt.unpack_response(response)[4]
     >>> print result
     {'success': 4}

     >>> method = 'calculate'
     >>> params = {'logid': 1, 'w':{'op': 4, 'num1': 1, 'num2': 0}}
     >>> msg = jt.pack_request(method, params, 2)
     >>> response = send_and_recv(msg) # send msg to server, receive response
     >>> result = jt.unpack_response(response)[4]
     >>> print result
     {'ouch': {'whatOp': 4, 'why': 'Cannot divide by 0'}}

     >>> # unpack_response returns a tuple, which contains:
     >>> #   message size(TFramedTransport)/None(TBufferedTransport),
     >>> #   message type, will be 2 for thrift REPLY message
     >>> #   method
     >>> #   sequence id
     >>> #   return value
hornet 0.1.1
An approach to stacking configurations made up of nested dictionaries
License: MIT
Keywords: config configuration nested stacked json yaml dict dictionary

``hornet``
==========

**WARNING** This library is currently under development and is not functional.

The hornet library aims to provide a relatively simple interface for working
with stacking nested configurations from multiple sources.  These sources may be
JSON files, YAML files, specially formatted environment variables, databases, or
anything else that you can dream up.

Basic Usage
===========

TODO

Features
========

TODO

Documentation
=============

TODO

Installation
============

The recommended way to install is using ``pip`` or ``conda``::

    pip install hornet
    conda install hornet

Or it can be installed from source using ``setuptools``::

    python setup.py install

Bugs and issues
===============

If you find a bug, let me know at https://github.com/bheklilr/hornet/issues.  Or
if you feel really motivated, write the fix and submit a pull request.

License
=======

I'm using MIT because that's what I'm most familiar with, but really I don't
care who uses this software or for what reason.  I make no claim to its accuracy
or reliability, use at your own risk.  This is just a side project for me, so
don't expect too much.  If you need it under a different license, just shoot me
an email and I'll work with you on how to make that happen.
atproject 1.1
John Hoffman's fork of the original bittorrent, we do some modify and use it as torrent download library
License: MIT

# Atproject

Becuase this package in under development, so we have not upload it to PyPI yet. When this project is good enough,

we will upload it to PyPI, at that time, user can use "pip install academictorrents" install this module.

# How to Install:
  in termimal copy the following command:
  
  (1) if you have administrator permission:
      
      sudo pip install git+https://github.com/liuyifei0226/atproject
      
  (2) If you do not have administrator permission， please use:
      
      pip install git+https://github.com/liuyifei0226/atproject --user
      
# How to use:
  (1) After installing the Academictorrents, enter python in terminal, get into python commond line:
  
      python
      
  and you'll see
  
      >>>
      
  This means you're in python now.
  
  (2) Import academictorrents in your python. 
  For your convinience, you can import it as at for short:
  
      import academictorrents as at
    
  (3) Download the file. 
  Use the command at.get(" ") to download the file you need. Here you should pay attention that your current path should be higher than the folder you save your .torrent file. For example, if you put your .torrent file in the folder /Users/ABC/Downloads, you should at Downloads or ABC to download the file.
  
      >>> at.get("path/torrentname.torrent")
      
 When you see something like
 
      saving:         devinberg.com_471592PB.pdf (0.2 MB)
      percent done:   0.0
      time left:      
      download to:    /Users/ChunjieXu/devinberg.com_471592PB.pdf
      download rate:  0.0 kB/s
      upload rate:    0.0 kB/s
      share rating:   0.000   (0.0 MB up / 0.0 MB down)
      seed status:    1 seen now, plus 0.000 distributed copies
      peer status:    1 seen now, 0.0% done at 0.0 kB/s

      saving:         devinberg.com_471592PB.pdf (0.2 MB)
      percent done:   86.4
      time left:      complete!
      download to:    /Users/ChunjieXu/devinberg.com_471592PB.pdf
      download rate:  29.6 kB/s
      upload rate:    0.0 kB/s
      share rating:   0.000   (0.0 MB up / 0.2 MB down)
      seed status:    4 seen now, plus 0.000 distributed copies
      peer status:    0 seen now, 0.0% done at 0.0 kB/s

      saving:         devinberg.com_471592PB.pdf (0.2 MB)
      percent done:   100
      time left:      Download Succeeded!
      download to:    /Users/ChunjieXu/devinberg.com_471592PB.pdf
      download rate:  
      upload rate:    0.0 kB/s
      share rating:   0.000   (0.0 MB up / 0.2 MB down)
      seed status:    4 seen now, plus 0.000 distributed copies
      peer status:    0 seen now, 0.0% done at 0.0 kB/s
      
 Congratulations! This means your academic torrents works perfect. You can Press "Control+C" to quit and get back to python.
XsCrypto 2.1.0
A CTFCrypto Tools project
License: MIT
Keywords: XsCrypto setuptools development

A CTF Crypto Tools
=======================
古典密码：栅栏，凯撒，仿射，摩斯，维吉尼亚
现代密码：DES，AES，RES(Common Modulus Attack，Wiener Attack，Low-Exponentv Attack)
进制转换，数字与字符间转换
kaftools 0.1.1
Small extensible package for Kernel Adaptive Filtering (KAF) methods.
License: MIT
Keywords: kernel adaptive filters kaf

**Warning: this is a side-project in progress so many bugs could arise. Please raise an issue if this happens.**

# Kernel Adaptive Filtering for Python
[![Build status](https://ci.appveyor.com/api/projects/status/otkhkwrimf3e4vg5?svg=true)](https://ci.appveyor.com/project/Canas/kaftools) [![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://raw.githubusercontent.com/Canas/kaftools/master/LICENSE)

This package implements several Kernel Adaptive Filtering algorithms for research purposes. It aims to be easily extendable.

# Requirements
- Python 3.4+
- NumPy
- SciPy
- (Optional) Matplotlib

# Features
## Adaptive Kernel Filters
- Kernel Least Mean Squares (KLMS) - `KlmsFilter`
- Exogenous Kernel Least Mean Squares (KLMS-X) - `KlmsxFilter`
- Kernel Recursive Least Squares (KRLS) - `KrlsFilter`

## Sparsification Criteria
- Novelty (KLMS)
- Approximate Linear Dependency (KLRS)

## Additional Features
- Delayed input support (KLMS)
- Adaptive kernel parameter learning (KLMS)

For a more visual comparison, check the [latest features sheet](https://docs.google.com/spreadsheets/d/1kvBNAqDSgNGBTcXqMDN7j_dpp949peH_-F1GYVP29y8/edit?usp=sharing).

# Quickstart
Let's do a simple example using a KLMS Filter over given input and target arrays:
```
from kaftools.filters import KlmsFilter
from kaftools.kernels import GaussianKernel

klms = KlmsFilter(input, target)
klms.fit(learning_rate=0.1, kernel=GaussianKernel(sigma=0.1))
```

And that's it!