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This Guide is old. Please contact me if there is any trouble starting the simulation.

The Python User Guide

  • The python equivalent of Ido's MatLab code is here: https://github.com/rzhu40/ASN_simulation/tree/master/Python/Ido's%20method.

  • To use the improved method please go to: https://github.com/rzhu40/ASN_simulation/tree/master/Python/multiElec.

  • Again, all-in-one simulation runner is ready as: https://github.com/rzhu40/ASN_simulation/blob/master/Python/multiElec/runMulti.py.

  • Step-by-step guide starts with importing all the libararies:

    import numpy as np
import matplotlib.pyplot as plt
from utils import *
from dataStruct import *

  • First set up simulation options:

    SimulationOptions = simulation_options__(dt = 1e-3, T = 1,
                                    interfaceElectrodes = [73, 30])

  • Load connectivity information:

    Connectivity = connectivity__(
filename = '2016-09-08-155153_asn_nw_00100_nj_00261_seed_042_avl_100.00_disp_10.00.mat')

  • Then set up electrode inputs(zeros all the time for drain signal), append to SimulationOptions.stimulus:

    SimulationOptions.stimulus = []
tempStimulus = stimulus__(biasType = 'DC', 
        TimeVector = SimulationOptions.TimeVector, 
        nanowire = SimulationOptions.interfaceElectrodes[0],
        onTime = 0, offTime = 1,
        onAmp = 1.4, offAmp = 0.005)
SimulationOptions.stimulus.append(tempStimulus)

tempStimulus = stimulus__(biasType = 'Drain', 
        TimeVector = SimulationOptions.TimeVector,
        nanowire = SimulationOptions.interfaceElectrodes[1])
SimulationOptions.stimulus.append(tempStimulus)

  • Initialize junction states:

    JunctionState = junctionState__(Connectivity.numOfJunctions)

  • Finally, one would be able to run simulation:

    this_realization = simulateNetworkPlus(SimulationOptions, Connectivity, JunctionState)

  • The simulation result returns basic network dynamics in matrices, like junction voltage, resistance, filament state, network current, etc. #For further usage

  • One will get a lot more useful information about the network, and also junction/nanowire level data by running:

    this_realization.allocateData()

  • To visualize the network, run:

    this_realization.draw()

  • By default, it returns the network distribution at time = 0. One can also do something like this:

    this_realization.draw(time = 0.9, 
                      PathHighlight = [1,2,3,4],
                      JunctionsToObserve = [6,7,8,9])