pkpd.protocols.protocol_pkpd_ode_two_vias module

class pkpd.protocols.protocol_pkpd_ode_two_vias.ProtPKPDODETwoVias(**kwargs)[source]

Bases: pkpd.protocols.protocol_pkpd.ProtPKPD, pkpd.objects.PKPDModelBase2

Simultaneous fit of data obtained by different vias, e.g. IV and PO, but it can be any two vias and any two dosing regimes, dissolution profiles, etc. It is supposed that the PK model in both cases is the same (e.g. two monocompartments, two two-compartments, …

addSample(sample1, sample2)[source]
areParametersSignificant(lowerBound, upperBound)[source]
Parameters:and upperBound (lowerBound) – a numpy array of parameters
Returns:a list of string with “True”, “False”, “NA”, “Suspicious”
calculateParameterUnits(sample1, sample2)[source]
createFitting(prot, experiment, suffix)[source]
createOutputStep()[source]
forwardModel(parameters, x=None)[source]
getBounds()[source]
getEquation()[source]
getModelEquation()[source]
getParameterNames()[source]
keepSampleFit(fitting, prot, sampleName, x, y, yp, yplower, ypupper, prmLowerBound, prmUpperBound, optimizer2)[source]
mergeModelParameters()[source]
prepareDoseForSample(prot, sample)[source]
prepareForSampleAnalysis(sampleName)[source]
runFit(fn1, fn2)[source]
separateBounds(lowerBound, upperBound, parameterNames1, parameterNames2, parameterNames)[source]
setBounds(sample1, sample2)[source]
setConfidenceInterval(lowerBound, upperBound)[source]
setConfidenceIntervalNA()[source]
setInitialSolution(sampleName)[source]
setParameters(parameters)[source]
setXYValues(x1, y1, x2, y2)[source]
setupSample(prot, sample, prefix)[source]
setupUnderlyingProtocol(prot)[source]
splitParameterUnits()[source]
updateUnderlyingExperiments(sampleName)[source]