device_manager

##device_manager dir

device.py

function list:

1. device_selection
2. cuda_number

a decorator device_selection is defined, if cuda is used, this function will perform operation on the correct device

device_seltecion(device,provide_backend:bool)

if a function is decortated by this decorator, args is a,b,c,d

if provided_backend is True, the following is performed:

func(backend,a,b,c,d)

The backend is set in the decorator, is device is 'cuda', the backend will be set to cupy, and if device is 'cpu', the backend will be set to numpy

Examples:

from ocsim import device_selection


def normalize(signal, device):
    # define the real function
    @device_selection(device, provide_backend=True)
    # real_func(*args,**kwargs)
    # if provid_backend:
    #   func(backend,*args,**kwargs)
    # else:
    #   func(*args,**kwargs)
    def normailize_(backend, args):
        # do sth to signal
        return 2

    normailize_(1)  # real_func(1)----> normailize(backend,1)

core dir:

1. constl.py
2. Signal.py

constl.py

#TO BE ADDED

Signal.py

Two classes are defined here:

Signal: The baseclass of all Signal

QamSignal: The class represents QamSignal

SignalSetting: define the paprameters of the class QamSignal

define a QamSignal object

from ocsim import QamSignal, SignalSetting

signal_setting = SignalSetting(
    center_freq=193.1e12, sps_in_fiber=4,
    device='cuda:0', symbol_rate=35e9,
    sps_dsp=2
)

signal = QamSignal(signal_setting=signal_setting)

signal.normalize()  # inplace normalize the signal
signal.to('cuda:1')  # Move signal to NVIDIA GRAPHIC CARD
signal.power()  # print the signal power in W and dBm

Tranceiver dir

dsp.py

Define the function at Tx and Rx

Tx: rrc pulse shaping

Rx: CDC, matched_filter, LMS_PLL and superscalaer

instruments.py

Define the electrical and optical instruments, at current state, Laser and Mux are implemented

numba_backend.py

Define the low-level lms_pll function using numba LLVM compiler to speed up

TODO: implement the CPP backend for LMS_PLL