def solve(self, objId1):
self.experiment = self.readExperiment(self.inputExperiment.get().fnPKPD)
# Create output object
self.outputExperiment = None
timeRange = self.experiment.getRange(self.timeVar.get())
for sampleName, sample in self.experiment.samples.items():
# Get t, A
t=np.asarray(sample.getValues(self.timeVar.get()),dtype=np.float64)
A=np.asarray(sample.getValues(self.amountVar.get()),dtype=np.float64)
if t[0]>0:
t=np.insert(t,0,0) # Add (0,0) to the profile
A=np.insert(A,0,0)
t, A = uniqueFloatValues(t, A)
if self.resampleT.get()>0:
B = InterpolatedUnivariateSpline(t, A, k=1)
t = np.arange(np.min(t),np.max(t)+self.resampleT.get(),self.resampleT.get())
A = B(t)
# Find C and Cp
C, Cp = self.calculateConcentrations2(t,A)
if self.outputExperiment is None:
self.outputExperiment = PKPDExperiment()
tvar = PKPDVariable()
tvar.varName = "t"
tvar.varType = PKPDVariable.TYPE_NUMERIC
tvar.role = PKPDVariable.ROLE_TIME
tvar.units = createUnit(self.experiment.getTimeUnits().unit)
Cvar = PKPDVariable()
Cvar.varName = "C"
Cvar.varType = PKPDVariable.TYPE_NUMERIC
Cvar.role = PKPDVariable.ROLE_MEASUREMENT
Lunits = createUnit("L")
Cvar.units = createUnit(divideUnits(self.experiment.getVarUnits(self.amountVar.get()),Lunits.unit))
Cvar.comment = "Concentration central compartment"
Cpvar = PKPDVariable()
Cpvar.varName = "Cp"
Cpvar.varType = PKPDVariable.TYPE_NUMERIC
Cpvar.role = PKPDVariable.ROLE_MEASUREMENT
Cpvar.units = createUnit(divideUnits(self.experiment.getVarUnits(self.amountVar.get()),Lunits.unit))
Cpvar.comment = "Concentration peripheral compartment"
self.outputExperiment.variables[tvar.varName] = tvar
self.outputExperiment.variables[Cvar.varName] = Cvar
self.outputExperiment.variables[Cpvar.varName] = Cpvar
self.outputExperiment.general["title"]="Inverse Loo-Riegelman"
self.outputExperiment.general["comment"]=""
self.addSample(sampleName,t,C,Cp)
self.outputExperiment.write(self._getPath("experiment.pkpd"))
def calculateParameterUnits(self, sample):
tunits = self.experiment.getVarUnits(self.xName)
Cunits = self.experiment.getVarUnits(self.yName)
Dunits = sample.getDoseUnits()
AUCunits = multiplyUnits(tunits, Cunits)
AUMCunits = multiplyUnits(tunits, AUCunits)
Vunits = divideUnits(Dunits, Cunits)
CLunits = divideUnits(Vunits, tunits)
self.parameterUnits = [
AUCunits, AUCunits, AUMCunits, AUMCunits, tunits, Vunits, Vunits,
CLunits, CLunits, tunits
]
def calculateParameterUnits(self, sample):
yunits = self.experiment.getVarUnits(self.yName)
xunits = self.experiment.getVarUnits(self.xName)
sunits = divideUnits(yunits, xunits)
if self.allowTlag:
self.parameterUnits = [xunits, sunits]
else:
self.parameterUnits = [sunits]
return self.parameterUnits
def calculateParameterUnits(self, sample):
yunits = self.experiment.getVarUnits(self.yName)
xunits = self.experiment.getVarUnits(self.xName)
sunits = divideUnits(yunits, xunits)
self.parameterUnits = []
for i in range(0, self.N + 1):
deg = self.N - i
if deg == 0:
self.parameterUnits.append(yunits)
elif deg == 1:
self.parameterUnits.append(sunits)
else:
self.parameterUnits.append(PKPDUnit.UNIT_NONE)
return self.parameterUnits
def runSimulate(self, Nsimulations, confidenceInterval, doses):
if self.odeSource.get() == self.SRC_ODE:
self.protODE = self.inputODE.get()
if hasattr(self.protODE, "outputExperiment"):
self.experiment = self.readExperiment(
self.protODE.outputExperiment.fnPKPD)
elif hasattr(self.protODE, "outputExperiment1"):
self.experiment = self.readExperiment(
self.protODE.outputExperiment1.fnPKPD)
else:
raise Exception(
"Cannot find an outputExperiment in the input ODE")
if self.paramsSource.get() == ProtPKPDODESimulate.PRM_POPULATION:
self.fitting = self.readFitting(
self.inputPopulation.get().fnFitting,
cls="PKPDSampleFitBootstrap")
elif self.paramsSource.get() == ProtPKPDODESimulate.PRM_FITTING:
self.fitting = self.readFitting(
self.inputFitting.get().fnFitting)
else:
# User defined or experiment
if hasattr(self.protODE, "outputFitting"):
self.fitting = self.readFitting(
self.protODE.outputFitting.fnFitting)
elif hasattr(self.protODE, "outputFitting1"):
self.fitting = self.readFitting(
self.protODE.outputFitting1.fnFitting)
self.varNameX = self.fitting.predictor.varName
if type(self.fitting.predicted) != list:
self.varNameY = self.fitting.predicted.varName
else:
self.varNameY = [var.varName for var in self.fitting.predicted]
tunits = self.experiment.getTimeUnits().unit
else:
self.varNameX = 't'
self.varNameY = 'C'
self.fitting = None
self.experiment = None
self.protODE = None
tunits = unitFromString(self.timeUnits.get())
# Create drug source
self.clearGroupParameters()
self.createDrugSource()
# Create output object
self.outputExperiment = PKPDExperiment()
self.outputExperiment.general["title"] = "Simulated ODE response"
self.outputExperiment.general["comment"] = "Simulated ODE response"
# Create the predictor variable
tvar = PKPDVariable()
tvar.varName = "t"
tvar.varType = PKPDVariable.TYPE_NUMERIC
tvar.role = PKPDVariable.ROLE_TIME
tvar.units = createUnit(tunits)
self.outputExperiment.variables[self.varNameX] = tvar
# Vias
if self.odeSource.get() == self.SRC_ODE:
self.outputExperiment.vias = self.experiment.vias
else:
# "[ViaName]; [ViaType]; [tlag]; [bioavailability]"
viaPrmList = [
token for token in self.viaPrm.get().strip().split(',')
]
if self.viaType.get() == self.VIATYPE_IV:
tokens = [
"Intravenous", "iv", "tlag=0 min", "bioavailability=1"
]
elif self.viaType.get() == self.VIATYPE_EV0:
tokens = ["Oral", "ev0"] + [
"tlag=" + viaPrmList[-2].strip() + " " +
self.timeUnits.get()
] + ["bioavailability=" + viaPrmList[-1].strip()]
elif self.viaType.get() == self.VIATYPE_EV1:
tokens = ["Oral", "ev1"] + [
"tlag=" + viaPrmList[-2].strip() + " " +
self.timeUnits.get()
] + ["bioavailability=" + viaPrmList[-1].strip()]
vianame = tokens[0]
self.outputExperiment.vias[vianame] = PKPDVia(
ptrExperiment=self.outputExperiment)
self.outputExperiment.vias[vianame].parseTokens(tokens)
# Read the doses
dunits = PKPDUnit.UNIT_NONE
for line in self.doses.get().replace('\n', ';;').split(';;'):
tokens = line.split(';')
if len(tokens) < 5:
print("Skipping dose: ", line)
continue
dosename = tokens[0].strip()
self.outputExperiment.doses[dosename] = PKPDDose()
self.outputExperiment.doses[dosename].parseTokens(
tokens, self.outputExperiment.vias)
dunits = self.outputExperiment.doses[dosename].getDoseUnits()
# Create predicted variables
if self.odeSource.get() == self.SRC_ODE:
if type(self.fitting.predicted) != list:
self.outputExperiment.variables[
self.varNameY] = self.experiment.variables[self.varNameY]
else:
for varName in self.varNameY:
self.outputExperiment.variables[
varName] = self.experiment.variables[varName]
else:
Cvar = PKPDVariable()
Cvar.varName = "C"
Cvar.varType = PKPDVariable.TYPE_NUMERIC
Cvar.role = PKPDVariable.ROLE_MEASUREMENT
Cvar.units = createUnit(
divideUnits(dunits.unit,
unitFromString(self.volumeUnits.get())))
self.outputExperiment.variables[self.varNameY] = Cvar
# Setup model
if self.odeSource.get() == self.SRC_ODE:
self.model = self.protODE.createModel()
if hasattr(self.protODE, "deltaT"):
self.model.deltaT = self.protODE.deltaT.get()
else:
if self.pkType.get() == self.PKTYPE_COMP1:
self.model = PK_Monocompartment()
elif self.pkType.get() == self.PKTYPE_COMP2:
self.model = PK_Twocompartment()
elif self.pkType.get() == self.PKTYPE_COMP2CLCLINT:
self.model = PK_TwocompartmentsClintCl()
self.model.setExperiment(self.outputExperiment)
self.model.setXVar(self.varNameX)
self.model.setYVar(self.varNameY)
Nsamples = int(
math.ceil((self.tF.get() - self.t0.get()) / self.model.deltaT)) + 1
self.model.x = [
self.t0.get() + i * self.model.deltaT for i in range(0, Nsamples)
]
self.modelList.append(self.model)
auxSample = PKPDSample()
auxSample.descriptors = {}
auxSample.doseDictPtr = self.outputExperiment.doses
auxSample.variableDictPtr = self.outputExperiment.variables
auxSample.doseList = self.outputExperiment.doses.keys()
auxSample.interpretDose()
self.drugSource.setDoses(auxSample.parsedDoseList,
self.t0.get() - 10,
self.tF.get() + 10)
self.model.drugSource = self.drugSource
# Check units
# Dunits = self.outputExperiment.doses[dosename].dunits
# Cunits = self.experiment.variables[self.varNameY].units
# Process user parameters
if self.odeSource.get() == self.SRC_ODE:
if self.paramsSource == ProtPKPDODESimulate.PRM_POPULATION:
Nsimulations = self.Nsimulations.get()
elif self.paramsSource == ProtPKPDODESimulate.PRM_USER_DEFINED:
lines = self.prmUser.get().strip().replace('\n',
';;').split(';;')
Nsimulations = len(lines)
prmUser = []
for line in lines:
tokens = line.strip().split(',')
prmUser.append([float(token) for token in tokens])
elif self.paramsSource == ProtPKPDODESimulate.PRM_FITTING:
Nsimulations = len(self.fitting.sampleFits)
else:
self.inputExperiment = self.readExperiment(
self.inputExperiment.get().fnPKPD)
Nsimulations = len(self.inputExperiment.samples)
inputSampleNames = self.inputExperiment.samples.keys()
else:
lines = self.prmUser.get().strip().replace('\n', ';;').split(';;')
Nsimulations = len(lines)
prmUser = []
for line in lines:
tokens = line.strip().split(',')
prmUser.append([float(token) for token in tokens])
# Simulate the different responses
simulationsX = self.model.x
simulationsY = np.zeros(
(Nsimulations, len(simulationsX), self.getResponseDimension()))
AUCarray = np.zeros(Nsimulations)
AUMCarray = np.zeros(Nsimulations)
MRTarray = np.zeros(Nsimulations)
CminArray = np.zeros(Nsimulations)
CmaxArray = np.zeros(Nsimulations)
CavgArray = np.zeros(Nsimulations)
CtauArray = np.zeros(Nsimulations)
TminArray = np.zeros(Nsimulations)
TmaxArray = np.zeros(Nsimulations)
TtauArray = np.zeros(Nsimulations)
fluctuationArray = np.zeros(Nsimulations)
percentageAccumulationArray = np.zeros(Nsimulations)
wb = openpyxl.Workbook()
wb.active.title = "Simulations"
for i in range(0, Nsimulations):
self.setTimeRange(None)
if self.odeSource.get() == self.SRC_ODE:
if self.paramsSource == ProtPKPDODESimulate.PRM_POPULATION:
# Take parameters randomly from the population
nfit = int(random.uniform(0, len(self.fitting.sampleFits)))
sampleFit = self.fitting.sampleFits[nfit]
nprm = int(random.uniform(0,
sampleFit.parameters.shape[0]))
parameters = sampleFit.parameters[nprm, :]
self.fromSample = "Population %d" % nprm
elif self.paramsSource == ProtPKPDODESimulate.PRM_USER_DEFINED:
parameters = np.asarray(prmUser[i], np.double)
self.fromSample = "User defined"
elif self.paramsSource == ProtPKPDODESimulate.PRM_FITTING:
parameters = self.fitting.sampleFits[i].parameters
self.fromSample = self.fitting.sampleFits[i].sampleName
else:
parameters = []
self.fromSample = inputSampleNames[i]
sample = self.inputExperiment.samples[self.fromSample]
for prmName in self.fitting.modelParameters:
parameters.append(
float(sample.getDescriptorValue(prmName)))
else:
parameters = np.asarray(viaPrmList[:-2] + prmUser[i],
np.double)
self.fromSample = "User defined"
print("From sample name: %s" % self.fromSample)
print("Simulated sample %d: %s" % (i, str(parameters)))
# Prepare source and this object
self.drugSource.setDoses(auxSample.parsedDoseList, self.model.t0,
self.model.tF)
if self.protODE is not None:
self.protODE.configureSource(self.drugSource)
self.model.drugSource = self.drugSource
parameterNames = self.getParameterNames(
) # Necessary to count the number of source and PK parameters
# Prepare the model
self.setParameters(parameters)
y = self.forwardModel(parameters,
[simulationsX] * self.getResponseDimension())
# Create AUC, AUMC, MRT variables and units
if i == 0:
if type(self.varNameY) != list:
self.Cunits = self.outputExperiment.variables[
self.varNameY].units
else:
self.Cunits = self.outputExperiment.variables[
self.varNameY[0]].units
self.AUCunits = multiplyUnits(tvar.units.unit,
self.Cunits.unit)
self.AUMCunits = multiplyUnits(tvar.units.unit, self.AUCunits)
if self.addStats or self.addIndividuals:
fromvar = PKPDVariable()
fromvar.varName = "FromSample"
fromvar.varType = PKPDVariable.TYPE_TEXT
fromvar.role = PKPDVariable.ROLE_LABEL
fromvar.units = createUnit("none")
AUCvar = PKPDVariable()
AUCvar.varName = "AUC0t"
AUCvar.varType = PKPDVariable.TYPE_NUMERIC
AUCvar.role = PKPDVariable.ROLE_LABEL
AUCvar.units = createUnit(strUnit(self.AUCunits))
AUMCvar = PKPDVariable()
AUMCvar.varName = "AUMC0t"
AUMCvar.varType = PKPDVariable.TYPE_NUMERIC
AUMCvar.role = PKPDVariable.ROLE_LABEL
AUMCvar.units = createUnit(strUnit(self.AUMCunits))
MRTvar = PKPDVariable()
MRTvar.varName = "MRT"
MRTvar.varType = PKPDVariable.TYPE_NUMERIC
MRTvar.role = PKPDVariable.ROLE_LABEL
MRTvar.units = createUnit(
self.outputExperiment.getTimeUnits().unit)
Cmaxvar = PKPDVariable()
Cmaxvar.varName = "Cmax"
Cmaxvar.varType = PKPDVariable.TYPE_NUMERIC
Cmaxvar.role = PKPDVariable.ROLE_LABEL
Cmaxvar.units = createUnit(strUnit(self.Cunits.unit))
Tmaxvar = PKPDVariable()
Tmaxvar.varName = "Tmax"
Tmaxvar.varType = PKPDVariable.TYPE_NUMERIC
Tmaxvar.role = PKPDVariable.ROLE_LABEL
Tmaxvar.units = createUnit(
self.outputExperiment.getTimeUnits().unit)
Cminvar = PKPDVariable()
Cminvar.varName = "Cmin"
Cminvar.varType = PKPDVariable.TYPE_NUMERIC
Cminvar.role = PKPDVariable.ROLE_LABEL
Cminvar.units = createUnit(strUnit(self.Cunits.unit))
Tminvar = PKPDVariable()
Tminvar.varName = "Tmin"
Tminvar.varType = PKPDVariable.TYPE_NUMERIC
Tminvar.role = PKPDVariable.ROLE_LABEL
Tminvar.units = createUnit(
self.outputExperiment.getTimeUnits().unit)
Cavgvar = PKPDVariable()
Cavgvar.varName = "Cavg"
Cavgvar.varType = PKPDVariable.TYPE_NUMERIC
Cavgvar.role = PKPDVariable.ROLE_LABEL
Cavgvar.units = createUnit(strUnit(self.Cunits.unit))
self.outputExperiment.variables["FromSample"] = fromvar
self.outputExperiment.variables["AUC0t"] = AUCvar
self.outputExperiment.variables["AUMC0t"] = AUMCvar
self.outputExperiment.variables["MRT"] = MRTvar
self.outputExperiment.variables["Cmax"] = Cmaxvar
self.outputExperiment.variables["Tmax"] = Tmaxvar
self.outputExperiment.variables["Cmin"] = Cminvar
self.outputExperiment.variables["Tmin"] = Tminvar
self.outputExperiment.variables["Cavg"] = Cavgvar
excelWriteRow([
"simulationName", "fromSample", "doseNumber",
"AUC [%s]" % strUnit(self.AUCunits),
"AUMC [%s]" % strUnit(self.AUMCunits),
"Cmin [%s]" % strUnit(self.Cunits.unit),
"Cavg [%s]" % strUnit(self.Cunits.unit),
"Cmax [%s]" % strUnit(self.Cunits.unit),
"Ctau [%s]" % strUnit(self.Cunits.unit),
"Tmin [%s]" %
strUnit(self.outputExperiment.getTimeUnits().unit),
"Tmax [%s]" %
strUnit(self.outputExperiment.getTimeUnits().unit),
"Ttau [%s]" %
strUnit(self.outputExperiment.getTimeUnits().unit)
],
wb,
1,
bold=True)
wbRow = 2
# Evaluate AUC, AUMC and MRT in the last full period
AUClist, AUMClist, Cminlist, Cavglist, Cmaxlist, Ctaulist, Tmaxlist, Tminlist, Ttaulist = self.NCA(
self.model.x, y[0])
for doseNo in range(0, len(AUClist)):
excelWriteRow([
"Simulation_%d" % i, self.fromSample, doseNo,
AUClist[doseNo], AUMClist[doseNo], Cminlist[doseNo],
Cavglist[doseNo], Cmaxlist[doseNo], Ctaulist[doseNo],
Tminlist[doseNo], Tmaxlist[doseNo], Ttaulist[doseNo]
], wb, wbRow)
wbRow += 1
# Keep results
for j in range(self.getResponseDimension()):
simulationsY[i, :, j] = y[j]
AUCarray[i] = self.AUC0t
AUMCarray[i] = self.AUMC0t
MRTarray[i] = self.MRT
CminArray[i] = self.Cmin
CmaxArray[i] = self.Cmax
CavgArray[i] = self.Cavg
CtauArray[i] = self.Ctau
TminArray[i] = self.Tmin
TmaxArray[i] = self.Tmax
TtauArray[i] = self.Ttau
fluctuationArray[i] = self.fluctuation
percentageAccumulationArray[i] = self.percentageAccumulation
if self.addIndividuals or self.paramsSource != ProtPKPDODESimulate.PRM_POPULATION:
self.addSample("Simulation_%d" % i, dosename, simulationsX,
y[0])
# Report NCA statistics
fhSummary = open(self._getPath("summary.txt"), "w")
alpha_2 = (100 - self.confidenceLevel.get()) / 2
limits = np.percentile(AUCarray, [alpha_2, 100 - alpha_2])
self.doublePrint(
fhSummary, "AUC %f%% confidence interval=[%f,%f] [%s] mean=%f" %
(self.confidenceLevel.get(), limits[0], limits[1],
strUnit(self.AUCunits), np.mean(AUCarray)))
limits = np.percentile(AUMCarray, [alpha_2, 100 - alpha_2])
self.doublePrint(
fhSummary, "AUMC %f%% confidence interval=[%f,%f] [%s] mean=%f" %
(self.confidenceLevel.get(), limits[0], limits[1],
strUnit(self.AUMCunits), np.mean(AUMCarray)))
limits = np.percentile(MRTarray, [alpha_2, 100 - alpha_2])
self.doublePrint(
fhSummary, "MRT %f%% confidence interval=[%f,%f] [%s] mean=%f" %
(self.confidenceLevel.get(), limits[0], limits[1],
strUnit(self.outputExperiment.getTimeUnits().unit),
np.mean(MRTarray)))
limits = np.percentile(CminArray, [alpha_2, 100 - alpha_2])
self.doublePrint(
fhSummary, "Cmin %f%% confidence interval=[%f,%f] [%s] mean=%f" %
(self.confidenceLevel.get(), limits[0], limits[1],
strUnit(self.Cunits.unit), np.mean(CminArray)))
limits = np.percentile(CmaxArray, [alpha_2, 100 - alpha_2])
self.doublePrint(
fhSummary, "Cmax %f%% confidence interval=[%f,%f] [%s] mean=%f" %
(self.confidenceLevel.get(), limits[0], limits[1],
strUnit(self.Cunits.unit), np.mean(CmaxArray)))
limits = np.percentile(CavgArray, [alpha_2, 100 - alpha_2])
self.doublePrint(
fhSummary, "Cavg %f%% confidence interval=[%f,%f] [%s] mean=%f" %
(self.confidenceLevel.get(), limits[0], limits[1],
strUnit(self.Cunits.unit), np.mean(CavgArray)))
limits = np.percentile(CtauArray, [alpha_2, 100 - alpha_2])
self.doublePrint(
fhSummary, "Ctau %f%% confidence interval=[%f,%f] [%s] mean=%f" %
(self.confidenceLevel.get(), limits[0], limits[1],
strUnit(self.Cunits.unit), np.mean(CtauArray)))
limits = np.percentile(TminArray, [alpha_2, 100 - alpha_2])
self.doublePrint(
fhSummary, "Tmin %f%% confidence interval=[%f,%f] [%s] mean=%f" %
(self.confidenceLevel.get(), limits[0], limits[1],
strUnit(self.outputExperiment.getTimeUnits().unit),
np.mean(TminArray)))
limits = np.percentile(TmaxArray, [alpha_2, 100 - alpha_2])
self.doublePrint(
fhSummary, "Tmax %f%% confidence interval=[%f,%f] [%s] mean=%f" %
(self.confidenceLevel.get(), limits[0], limits[1],
strUnit(self.outputExperiment.getTimeUnits().unit),
np.mean(TmaxArray)))
limits = np.percentile(TtauArray, [alpha_2, 100 - alpha_2])
self.doublePrint(
fhSummary, "Ttau %f%% confidence interval=[%f,%f] [%s] mean=%f" %
(self.confidenceLevel.get(), limits[0], limits[1],
strUnit(self.outputExperiment.getTimeUnits().unit),
np.mean(TtauArray)))
aux = fluctuationArray[~np.isnan(fluctuationArray)]
if len(aux) > 0:
limits = np.percentile(aux, [alpha_2, 100 - alpha_2])
self.doublePrint(
fhSummary,
"Fluctuation %f%% confidence interval=[%f,%f] [%%] mean=%f" %
(self.confidenceLevel.get(), limits[0] * 100, limits[1] * 100,
np.mean(aux) * 100))
aux = percentageAccumulationArray[~np.isnan(percentageAccumulationArray
)]
limits = np.percentile(aux, [alpha_2, 100 - alpha_2])
self.doublePrint(
fhSummary,
"Accum(1) %f%% confidence interval=[%f,%f] [%%] mean=%f" %
(self.confidenceLevel.get(), limits[0] * 100, limits[1] * 100,
np.mean(aux) * 100))
fhSummary.close()
# Calculate statistics
if self.addStats and self.odeSource == 0 and self.paramsSource == 0:
if self.paramsSource != ProtPKPDODESimulate.PRM_USER_DEFINED:
limits = np.percentile(simulationsY, [alpha_2, 100 - alpha_2],
axis=0)
print("Lower limit NCA")
self.NCA(simulationsX, limits[0])
self.fromSample = "LowerLimit"
self.addSample("LowerLimit", dosename, simulationsX, limits[0])
print("Mean profile NCA")
if self.getResponseDimension() == 1:
mu = np.mean(simulationsY, axis=0)
self.NCA(simulationsX, mu)
else:
mu = []
for j in range(self.getResponseDimension()):
mu.append(np.mean(simulationsY[:, :, j], axis=0))
self.NCA(simulationsX, mu[0])
self.fromSample = "Mean"
self.addSample("Mean", dosename, simulationsX, mu)
if self.paramsSource != ProtPKPDODESimulate.PRM_USER_DEFINED:
print("Upper limit NCA")
self.NCA(simulationsX, limits[1])
self.fromSample = "UpperLimit"
self.addSample("UpperLimit", dosename, simulationsX, limits[1])
self.outputExperiment.write(self._getPath("experiment.pkpd"))
wb.save(self._getPath("nca.xlsx"))