class ProtPKPDDissolutionIVIVCJoin(ProtPKPD): """ Join several IVIVCs into a single one. The strategy is to compute the average of all the plots involved in the IVIVC process: 1) tvivo -> tvitro; 2) tvitro -> Adissol; 3) Adissol->FabsPredicted. The plot tvivo-Fabs comes after the IVIVC process, while the plot tvivo-FabsOrig is the observed one in the input files. These two plots need not be exactly the same. """ _label = 'dissol ivivc join avg' #--------------------------- DEFINE param functions -------------------------------------------- def _defineParams(self, form): form.addSection('Input') form.addParam( 'inputIVIVCs', params.MultiPointerParam, label="IVIVCs Fabs", pointerClass='PKPDExperiment', help= 'Choose experiments with IVIV correlations (only the Fabs experiments)' ) #--------------------------- INSERT steps functions -------------------------------------------- def _insertAllSteps(self): self._insertFunctionStep('calculateAllIvIvC') self._insertFunctionStep('createOutputStep') #--------------------------- STEPS functions -------------------------------------------- def calculateAllIvIvC(self): L1 = [] L2 = [] L3 = [] L4 = [] L5 = [] for ptrExperiment in self.inputIVIVCs: experiment = PKPDExperiment() experiment.load(ptrExperiment.get().fnPKPD.get()) x, y = experiment.getXYMeanValues("tvivo", "tvitroReinterpolated") L1.append((x, y)) x, y = experiment.getXYMeanValues("tvivo", "Fabs") L2.append((x, y)) x, y = experiment.getXYMeanValues("AdissolReinterpolated", "FabsPredicted") L3.append((x, y)) x, y = experiment.getXYMeanValues("FabsPredicted", "Fabs") L4.append((x, y)) x, y = experiment.getXYMeanValues("tvitroReinterpolated", "AdissolReinterpolated") L5.append((x, y)) tvivo1, tvitroReinterpolatedY = computeXYmean(L1, common=True) tvivoOrig, FabsOrig = computeXYmean(L2, common=True) AdissolReinterpolatedX, FabsPredictedY = computeXYmean(L3, common=True) FabsPredictedX, Fabs = computeXYmean(L4, common=True) tvitroReinterpolatedX, AdissolReinterpolatedY = computeXYmean( L5, common=True) x, y = twoWayUniqueFloatValues(tvivo1, tvitroReinterpolatedY) Bt = InterpolatedUnivariateSpline(x, y, k=1) x, y = twoWayUniqueFloatValues(tvitroReinterpolatedX, AdissolReinterpolatedY) BtA = InterpolatedUnivariateSpline(x, y, k=1) x, y = twoWayUniqueFloatValues(tvivoOrig, FabsOrig) BtF = InterpolatedUnivariateSpline(x, y, k=1) x, y = twoWayUniqueFloatValues(AdissolReinterpolatedX, FabsPredictedY) BAF = InterpolatedUnivariateSpline(x, y, k=1) x, y = twoWayUniqueFloatValues(FabsPredictedX, Fabs) BFF = InterpolatedUnivariateSpline(x, y, k=1) vtvitroReinterpolated = np.zeros(len(tvivo1)) vAdissolReinterpolated = np.zeros(len(tvivo1)) vFabs = np.zeros(len(tvivo1)) vFabsPredicted = np.zeros(len(tvivo1)) vFabsOrig = np.zeros(len(tvivo1)) for i in range(len(tvivo1)): tvivoi = tvivo1[i] vtvitroReinterpolated[i] = Bt(tvivoi) vAdissolReinterpolated[i] = BtA(vtvitroReinterpolated[i]) vFabsPredicted[i] = BAF(vAdissolReinterpolated[i]) vFabs[i] = BFF(vFabsPredicted[i]) vFabsOrig[i] = BtF(tvivoi) tvitroReinterpolatedVar = experiment.variables["tvitroReinterpolated"] AdissolReinterpolatedVar = experiment.variables[ "AdissolReinterpolated"] tvivoVar = experiment.variables["tvivo"] FabsOrigVar = copy.copy(experiment.variables["Fabs"]) FabsOrigVar.varName = "FabsOriginal" FabsVar = experiment.variables["Fabs"] FabsVar.comment += ". After IVIVC: tvivo->tvitro->Adissol->Fabs " FabsPredictedVar = experiment.variables["FabsPredicted"] self.outputExperimentFabsSingle = PKPDExperiment() self.outputExperimentFabsSingle.variables[ tvitroReinterpolatedVar.varName] = tvitroReinterpolatedVar self.outputExperimentFabsSingle.variables[ AdissolReinterpolatedVar.varName] = AdissolReinterpolatedVar self.outputExperimentFabsSingle.variables[tvivoVar.varName] = tvivoVar self.outputExperimentFabsSingle.variables[FabsVar.varName] = FabsVar self.outputExperimentFabsSingle.variables[ FabsPredictedVar.varName] = FabsPredictedVar self.outputExperimentFabsSingle.variables[ FabsOrigVar.varName] = FabsOrigVar self.outputExperimentFabsSingle.general[ "title"] = "In-vitro In-vivo correlation" self.outputExperimentFabsSingle.general[ "comment"] = "Fabs vs Predicted Fabs" sampleName = "jointIVIVC" newSampleFabsSingle = PKPDSample() newSampleFabsSingle.sampleName = sampleName newSampleFabsSingle.variableDictPtr = self.outputExperimentFabsSingle.variables newSampleFabsSingle.descriptors = {} newSampleFabsSingle.addMeasurementColumn("tvitroReinterpolated", vtvitroReinterpolated) newSampleFabsSingle.addMeasurementColumn("AdissolReinterpolated", vAdissolReinterpolated) newSampleFabsSingle.addMeasurementColumn("tvivo", tvivo1) newSampleFabsSingle.addMeasurementColumn("FabsPredicted", vFabsPredicted) newSampleFabsSingle.addMeasurementColumn("Fabs", vFabs) newSampleFabsSingle.addMeasurementColumn("FabsOriginal", vFabsOrig) self.outputExperimentFabsSingle.samples[ sampleName] = newSampleFabsSingle self.outputExperimentFabsSingle.addLabelToSample( sampleName, "from", "individual---vesel", "AvgVivo---AvgVitro") self.outputExperimentFabsSingle.write( self._getPath("experimentFabsSingle.pkpd")) def createOutputStep(self): self._defineOutputs( outputExperimentFabsSingle=self.outputExperimentFabsSingle) for ptrExperiment in self.inputIVIVCs: self._defineSourceRelation(ptrExperiment.get(), self.outputExperimentFabsSingle) def _validate(self): retval = [] for ptrExperiment in self.inputIVIVCs: if not "experimentFabs" in ptrExperiment.get().fnPKPD.get(): retval.append("You can only take Fabs files") return retval def _summary(self): return []
class ProtPKPDDissolutionLevyPlotJoin(ProtPKPD): """ Join several Levy plots into a single one. The strategy is to compute the average of all the plots involved in the Levy plot process: 1) tvivo -> tvitro """ _label = 'dissol levyplot join avg' #--------------------------- DEFINE param functions -------------------------------------------- def _defineParams(self, form): form.addSection('Input') form.addParam( 'inputLevyplots', params.MultiPointerParam, label="Levy plots", pointerClass='PKPDExperiment', help= 'Choose experiments with IVIV correlations (only the Fabs experiments)' ) #--------------------------- INSERT steps functions -------------------------------------------- def _insertAllSteps(self): self._insertFunctionStep('calculateAllLevy') self._insertFunctionStep('createOutputStep') #--------------------------- STEPS functions -------------------------------------------- def calculateAllLevy(self): L1 = [] for ptrExperiment in self.inputLevyplots: experiment = PKPDExperiment() experiment.load(ptrExperiment.get().fnPKPD.get()) x, y = experiment.getXYMeanValues("tvivo", "tvitro") L1.append((x, y)) tvivo, tvitro = computeXYmean(L1, common=True) x, y = twoWayUniqueFloatValues(tvivo, tvitro) Bt = InterpolatedUnivariateSpline(x, y, k=1) vtvitroReinterpolated = np.zeros(len(tvivo)) for i in range(len(tvivo)): tvivoi = tvivo[i] vtvitroReinterpolated[i] = Bt(tvivoi) tvitroReinterpolatedVar = experiment.variables["tvitro"] tvivoVar = experiment.variables["tvivo"] self.outputExperimentSingle = PKPDExperiment() self.outputExperimentSingle.variables[ tvitroReinterpolatedVar.varName] = tvitroReinterpolatedVar self.outputExperimentSingle.variables[tvivoVar.varName] = tvivoVar self.outputExperimentSingle.general["title"] = "Levy plot" self.outputExperimentSingle.general["comment"] = "tvitro vs tvivo" sampleName = "jointLevyPlot" newSampleSingle = PKPDSample() newSampleSingle.sampleName = sampleName newSampleSingle.variableDictPtr = self.outputExperimentSingle.variables newSampleSingle.descriptors = {} newSampleSingle.addMeasurementColumn("tvitro", vtvitroReinterpolated) newSampleSingle.addMeasurementColumn("tvivo", tvivo) self.outputExperimentSingle.samples[sampleName] = newSampleSingle self.outputExperimentSingle.addLabelToSample(sampleName, "from", "individual---vesel", "meanVivo---meanVitro") self.outputExperimentSingle.write(self._getPath("experiment.pkpd")) def createOutputStep(self): self._defineOutputs(outputExperiment=self.outputExperimentSingle) for ptrExperiment in self.inputLevyplots: self._defineSourceRelation(ptrExperiment.get(), self.outputExperimentSingle) def _summary(self): return []