Metadata-Version: 2.1
Name: py-polar-codes
Version: 1.2.0
Summary: A package for polar codes in Python.
Home-page: https://github.com/mcba1n/polar-codes
Author: Brendon McBain
Author-email: brendon.mcbain9@gmail.com
License: UNKNOWN
Description: # Polar Codes in Python
        
        A library written in Python3 for Polar Codes, a capacity-achieving channel coding technique used in 5G. The library includes functions for construction, encoding, decoding, and simulation of polar codes. In addition, it supports puncturing and shortening.
        
        It provides:
         - a systematic and non-systemic encoder.
         - non-recursive implementations of the successive cancellation decoder (SCD).
         - mothercode construction of polar codes using Bhattacharyya Bounds or Gaussian Approximation
         - support for puncturing and shortening.
         - Bit-Reversal Shortening (BRS), Wang-Liu Shortening (WLS), and Bioglio-Gabry-Land (BGL) shortening constructions.
         - an AWGN channel with BPSK modulation.
         - an easy-to-use Graphical User Interface (GUI)
         
        Documentation:
         - [Main reference (pdf)](https://github.com/mcba1n/polar-codes/blob/master/main_reference_v1.0.pdf)
         - [Quick reference (website)](https://mcba1n.github.io/polar-codes-docs/)
         - Introduction to polar codes, shortening, and the library: http://www.youtube.com/watch?v=v47rn77RAxM
         
        ## Getting Started
        
        1. Install the package with `pip install py-polar-codes` from https://pypi.org/project/py-polar-codes/.
        2. Install matplotlib from https://matplotlib.org/users/installing.html.
        3. Install numpy from https://docs.scipy.org/doc/numpy/user/install.html.
        4. Run test.py using a Python3 compiler. If the program runs successfully, the library is ready to use. Make sure the compiler has writing access to directory "root/data", where simulation data will be saved by default.
        5. Call `GUI()` to start the GUI.
        
        ## Examples
        ### Mothercode Encoding & Decoding
        An example of encoding and decoding over an AWGN channel for a (256,100) non-systematic mothercode, using Bhattacharyya Bounds for construction and SCD for decoding.
        For systematic encoding and decoding, replace `Encode(myPC)` with `Encode(myPC, 'systematic_encode')` and `Decode(myPC)` with `Decode(myPC, 'systematic_scd')`.
        
        ```python
           import numpy as np
           from polarcodes import *
        
            # initialise polar code
            myPC = PolarCode(256, 100)
            myPC.construction_type = 'bb'
            
            # mothercode construction
            design_SNR  = 5.0
            Construct(myPC, design_SNR)
            print(myPC, "\n\n")
            
            # set message
            my_message = np.random.randint(2, size=myPC.K)
            myPC.set_message(my_message)
            print("The message is:", my_message)
            
            # encode message
            Encode(myPC)
            print("The coded message is:", myPC.u)
            
            # transmit the codeword
            AWGN(myPC, design_SNR)
            print("The log-likelihoods are:", myPC.likelihoods)
            
            # decode the received codeword
            Decode(myPC)
            print("The decoded message is:", myPC.message_received)
        ```
        
        ### Shortened Code Construction
        An example of constructing a shortened polar code with Bit-Reversal Shortening (BRS) algorithm.
        The shortening parameters are set by the tuple `shorten_params`, the third argument of `PolarCode`, and is defined by:
        - Puncturing type: `shorten` or `punct`.
        - Puncturing algorithm: `brs`, `wls`, or `bgl`.
        - Puncturing set (for manual puncturing): `List`
        - Overcapable set (for manual puncturing): `List`
        - Update reliabilities after puncturing (or use mothercode reliabilities): `True` or `False`.
        
        
        ```python
           import numpy as np
           from polarcodes import *
        
            # initialise shortened polar code
            shorten_params = ('shorten', 'brs', None, None, False)
            myPC = PolarCode(200, 100, shorten_params)
            
            # construction
            design_SNR  = 5.0
            Shorten(myPC, design_SNR)
            print(myPC, "\n\n")
        ```
        
        ### Simulation & Plotting
        A script to simulate a defined polar code, save the data to directory "/data", and then display the result in a *matplotlib* figure.
        
        ```python
            # simulate polar code 
            myPC.simulate(save_to='data/pc_sim', Eb_No_vec=np.arange(1,5), manual_const_flag=True)
            
            # plot the frame error rate
            myPC.plot(['pc_sim'], 'data/')
        ```
        
        The simulation will save your PolarCode object in a JSON file, for example:
        ```JSON
        {
            "N": 64,
            "n": 6,
            "K": 32,
            "frozen": [
                22, 38, 49, 26, 42, 3, 28, 50, 5,44,9, 52, 6, 17, 10, 33, 56, 18, 12, 34, 20, 36, 1, 24, 40, 48, 2, 4, 8, 16, 32, 0
            ],
            "construction_type": "bb",
            "punct_flag": false,
            "punct_type": "",
            "punct_set": [],
            "source_set": [],
            "punct_algorithm": "",
            "update_frozen_flag": [],
            "BER": [
                0.09709375, 0.03740625, 0.00815625, 0.0010184612211221122
            ],
            "FER": [
                0.313, 0.126, 0.03,0.004125412541254125
            ],
            "SNR": [
                1, 2, 3, 4
            ]
        }
        ```
        
        ### Graphical User Interface
        An example of using the GUI to simulate and plot a specified polar code. Note: if "manual construction" is ticked, the user is required to input the frozen bits and the shortened bits.
        <br/><img src="gui_example.PNG" width="500">
        
        *This is a final year project created by Brendon McBain under the supervision of Dr Harish Vangala at Monash University.*
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
