def varyExpMRate(initialMutRate, endMutRate, resistanceArr, n, size,
growthRates, isDrug, isMin):
step = .01
xs = np.arange(initialMutRate, endMutRate, step)
ys = np.arange(resistanceArr[0], resistanceArr[1], 1)
results = np.zeros((len(ys), len(xs)))
for x, mRate in enumerate(xs):
mutationRateUp, mutationRateDown = main.detMutationRate(mRate, size)
for y, resVal in enumerate(ys):
if isMin:
resistances = main.expResAssign(size, n, resVal, size)
else:
resistances = main.expResAssign(size, n, 0, resVal)
eigVal = main.getEig(growthRates, resistances, mutationRateUp,
mutationRateDown, isDrug)
results[y, x] = eigVal[1]
return results, xs, ys
def tumorSizeCalcExp(sequence, mRate, size, reachMaxRes):
days = len(sequence)
k = 1
growthRates = np.full(size, .5)
resistances = main.expResAssign(size, 1.001, 0, reachMaxRes)
popArr = main.calcPopDistribution(int(size), .01)
mutationRatesUp, mutationRatesDown = main.detMutationRate(mRate, size)
resultArr = odeint(
main.calculateChange, popArr, np.arange(0, days, 1),
(growthRates, mutationRatesUp, mutationRatesDown, k, resistances,
main.periodicDrugDay(sequence, days), np.zeros(popArr.size)))
return np.sum(resultArr)
def costOfResFunction(sequence, mRate, size, maxResSwitchNumb, n):
days = len(sequence)
k = 1
resistances = main.expResAssign(size, n, 0, maxResSwitchNumb)
popArr = main.calcPopDistribution(int(size), 1)
mutationRatesUp, mutationRatesDown = main.detMutationRate(mRate, size)
growthRate = main.costOfRes(.25, resistances)
resultArr = odeint(
main.calculateChange, popArr, np.arange(0, days, 1),
(growthRate, mutationRatesUp, mutationRatesDown, k, resistances,
main.periodicDrugDay(sequence, days), np.zeros(popArr.size)))
return np.sum(resultArr)
def eigVary(initSize, endSize, mRateStart, mRateEnd, step):
sizeArr = np.arange(initSize, endSize + 1, 1)
mRateArr = np.arange(mRateStart, mRateEnd + step, step)
results = np.zeros((len(sizeArr), len(mRateArr)))
for x, mRate in enumerate(mRateArr):
for y, size in enumerate(sizeArr):
resistances = main.expResAssign(size, 1.0001, 0, size - 1)
model = main.GenModelCustRes(mRate * size, size, resistances, 1,
100, [True])
res = model.RunEig(True)
results[y, x] = res[1]
return results, mRateArr, sizeArr
def varyParametersSize(initialMutRate, endMutRate, maxSize, isDrug):
xs = np.arange(initialMutRate, endMutRate + .01, .01)
ys = np.arange(2, maxSize + 1, 1)
results = np.zeros((len(ys), len(xs)))
for x, mRate in enumerate(xs):
for y, size in enumerate(ys):
mutationRateUp, mutationRateDown = main.detMutationRate(
mRate * size, size)
growthRates = np.full(size, .5)
resistanceVal = main.expResAssign(size, 1.15, 0, size - 1)
#plotters.PlotHist(resistanceVal)
#plt.xlabel("State"); plt.ylabel("Resistance Values"); plt.title("Resistance Histogram")
#plt.show()
eigVal = main.getEig(growthRates, resistanceVal, mutationRateUp,
mutationRateDown, isDrug)
results[y, x] = eigVal[1]
return results, xs, ys
def objectiveFun(sequence):
days = 10
size = 5
k = 1
mRate = .05
growthRates = np.full(size, .5)
popArr = main.calcPopDistribution(size, .01)
resistances = main.expResAssign(size, 2, 0, size - 1)
mutationRatesUp, mutationRatesDown = main.detMutationRate(
mRate, size, True, False)
resultArr = odeint(
main.calculateChange, popArr, np.arange(0, days, 1),
(growthRates, mutationRatesUp, mutationRatesDown, k, resistances,
main.periodicDrugDay(sequence, days), np.zeros(popArr.size)))
weightAns = 0
for i in range(0, len(resistances)):
for j in range(0, len(resultArr)):
weightAns = weightAns + (resistances[i] * resultArr[j][i])
return weightAns