cardiacModel2-n1/oscillatorLib.py~ at master · biophysicslab/cardiacModel2-n1 · GitHub

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import numpy as np
import pbd
import matplotlib.pyplot as plt
import numpy.random.normal


class oscillator(object):

    def __init__(self, dt, title, maxTime):

        self.dt = dt
        self.title = title

        # Preprocessing:
        self.genTime(maxTime)


    ######################################################

    def genTime(self, maxTime):
        self.t = np.arange(0, maxTime, self.dt)

    ######################################################

    def genIndices(self, events):

        ix = np.zeros(np.size(events))

        for i in range(0, np.size(events)):
            ix[i] = int(np.argmin(np.abs(events[i] - self.t)))

        return ix.astype(int)

    ######################################################

    def phaseGen(self,ix,t):
        phase = np.zeros(t.shape)
        ixDiff = np.diff(ix,n=1,axis=0)

        for ii in range(0,ix.shape[0]-1):
            #phase[ix[ii,0],0] = 0
            for jj in range(0,int(ixDiff[ii])+1):
                phase[int(ix[ii])+jj] = float(jj)/float(ixDiff[ii])

        return phase #, ixDiff


##############################################################
##############################################################
##############################################################


class cardiac(measurement):

    def __init__(self, dt, maxTime, staticRate=0.8, leakRate = 0.1, randomStd=0.1, peakEpsilon=1, actionPotentialLength=0.25, contractionDelaycouplingWindow=0.05, peakForce=1, c0 = 0, sensitivityWindow=1, peakCouplingRate=0.2, sensitivityMean = 0.2, sensitivityStd = 0.1):


        self.cellEvents = []
        # k_constant computed such that, all other factors aside, we accumulate staticRate of our accumulation
        # variable every unit time
        self.k_constant = staticRate*dt

        # For radnom component:  We want the random step to be a zero mean normal distribution
        # with a standard deviation after a unit time equivalent to randomStd
        # Considering Gaussian random walks, where each time step has standard deviation
        # k_random_std, after n timesteps, we have a normal distribution with standard
        # deviation sqrt(n)*k_random_std
        self.k_random_std = random_Std * np.sqrt(dt)

        # c_leak, the leak rate per timestep, is computed such that after n timesteps in a unit time
        # we get a reduction in integrating variable by 1-leakRate:
        self.k_leak = leakRate**(dt)

        self.k_coup = dt * peakCouplingRate / peakEpsilon

        super().__init__(dt, title, maxTime)

        self.c = np.zeros_like(self.t)
        self.c[0] = c0

        if sensitivityWindow == 1:
            self.sensitivity = lambda c: np.exp(-(c-sensitivityMean)**2/(2*sensitivityStd**2))


        ######################################################

    def stepTime(self, i, epsilon):

        self.c[i] = (1 - self.c_leak)*self.c[i-1] + self.k_constant + numpy.random.normal(0, k_random_std) + self.sensitivity(self.c[i-1]) * epsilon * self.k_coup
        if self.c[i] > 1:
            self.c[i] = 0


#################################################################
#################################################################
#################################################################

class substrate(measurement):


    def __init__(self, dt, maxTime):

        super().__init__(cellEvents, cellFreq, cellNaturalFreq, dt, startTime, title, maxTime)


    ######################################################

    def relativePhase(self, subTheta, subIx):


        if subTheta.size > self.cellTheta.size:
            subTheta = subTheta[0:self.cellTheta.size]
        elif subTheta.size < self.cellTheta.size:
            self.cellTheta = self.cellTheta[0:subTheta.size]

        dTheta = np.mod(self.cellTheta - subTheta, 1)

        minIndex = int(np.max([np.min(self.cellIx), np.min(subIx)]))
        maxIndex = int(np.min([np.max(self.cellIx), np.max(subIx)]))

        dTheta2 = dTheta[minIndex:maxIndex]
        t2 = self.t[minIndex:maxIndex]

        return t2, dTheta2


def relativePhase(self, subTheta, subIx):
    if subTheta.size > self.cellTheta.size:
        subTheta = subTheta[0:self.cellTheta.size]
    elif subTheta.size < self.cellTheta.size:
        self.cellTheta = self.cellTheta[0:subTheta.size]
    dTheta = np.mod(self.cellTheta - subTheta, 1)
    minIndex = int(np.max([np.min(self.cellIx), np.min(subIx)]))
    maxIndex = int(np.min([np.max(self.cellIx), np.max(subIx)]))
    dTheta2 = dTheta[minIndex:maxIndex]
    t2 = self.t[minIndex:maxIndex]