pkpd.objects module¶

class pkpd.objects.PKDepositionParameters(**args)[source]¶: Bases: pkpd.inhalation.PKDepositionParameters

class pkpd.objects.PKLung(**args)[source]¶: Bases: pkpd.inhalation.PKLung

class pkpd.objects.PKPDAllometricScale(**args)[source]¶

Bases: pwem.objects.data.EMObject

READING_MODELS = 2¶

READING_PREDICTOR = 1¶

READING_X = 3¶

READING_Y = 4¶

load(fnScale)[source]¶

write(fnScale)[source]¶

class pkpd.objects.PKPDDEOptimizer(model, fitType, goalFunction='RMSE')[source]¶

Bases: pkpd.objects.PKPDOptimizer

optimize()[source]¶

class pkpd.objects.PKPDDataSet(name, folder, files, url=None)[source]¶

Bases: object

classmethod getDataSet(name)[source]¶: This method is called every time the dataset want to be retrieved

getFile(key)[source]¶

getPath()[source]¶

class pkpd.objects.PKPDDoseResponse(**args)[source]¶

Bases: pwem.objects.data.EMObject

appendResponse(dose, response)[source]¶

findDose(dose)[source]¶

read(inputStr, isFn=False)[source]¶

write(fnOut)[source]¶

class pkpd.objects.PKPDExperiment(**args)[source]¶

Bases: pwem.objects.data.EMObject

READING_A_MEASUREMENT = 8¶

READING_DOSES = 4¶

READING_GENERAL = 1¶

READING_GROUPS = 5¶

READING_MEASUREMENTS = 7¶

READING_SAMPLES = 6¶

READING_VARIABLES = 2¶

READING_VIAS = 3¶

addLabelToSample(sampleName, varName, varDescr, varValue, rewrite=False)[source]¶

addParameterToSample(sampleName, varName, varUnits, varDescr, varValue, rewrite=False)[source]¶

addSample(sample)[source]¶

addSampleToGroup(groupName, sample)[source]¶

gather(otherExperiment)[source]¶

getDoseUnits()[source]¶

getFirstSample()[source]¶

getMeasurementVariables()[source]¶

getNonBolusDoses()[source]¶

getRange(varName)[source]¶

getSubGroup(condition)[source]¶

getSubGroupLabels(condition, labelName)[source]¶

getTimeUnits()[source]¶

getTimeVariable()[source]¶

getVarUnits(varName)[source]¶

getXYMeanValues(varNameX, varNameY)[source]¶

load(fnExperiment='', verifyIntegrity=True, fullRead=True)[source]¶

sampleSummary()[source]¶

subset(listOfSampleNames)[source]¶

write(fnExperiment, writeToExcel=True)[source]¶

writeToExcel(fnXls)[source]¶

class pkpd.objects.PKPDFitting(cls='', **args)[source]¶

Bases: pwem.objects.data.EMObject

READING_FITTING_EXPERIMENT = 1¶

READING_FITTING_MODEL = 5¶

READING_FITTING_PREDICTED = 3¶

READING_FITTING_PREDICTED_LIST = 4¶

READING_FITTING_PREDICTOR = 2¶

READING_POPULATION = 7¶

READING_POPULATION_HEADER = 6¶

READING_SAMPLEFITTINGS_BEGIN = 8¶

READING_SAMPLEFITTINGS_CONTINUE = 9¶

gather(otherFitting, experiment=None)[source]¶

getAllParameters()[source]¶

getSampleFit(sampleName)[source]¶

getStats(observations=None)[source]¶

getTimeUnits()[source]¶

isPopulation()[source]¶

load(fnFitting=None)[source]¶

loadExperiment()[source]¶

write(fnFitting, writeToExcel=True)[source]¶

writeToExcel(fnXls)[source]¶

class pkpd.objects.PKPDGroup(groupName)[source]¶

Bases: object

getSamplesString()[source]¶

class pkpd.objects.PKPDLSOptimizer(model, fitType, goalFunction='RMSE')[source]¶

Bases: pkpd.objects.PKPDOptimizer

optimize(ftol=1.49012e-08, xtol=1.49012e-08)[source]¶

setConfidenceInterval(confidenceInterval)[source]¶

class pkpd.objects.PKPDModel[source]¶

Bases: pkpd.objects.PKPDModelBase2

prepare()[source]¶

class pkpd.objects.PKPDModelBase[source]¶

Bases: object

calculateParameterUnits(sample)[source]¶

getDescription()[source]¶

getNumberOfParameters()[source]¶

getParameterDescriptions()[source]¶

getParameterNames()[source]¶

rearrange(parameters)[source]¶

setExperiment(experiment)[source]¶

setParameters(parameters)[source]¶

setXVar(x)[source]¶

setXYValues(x, y)[source]¶

setYVar(y)[source]¶

unsetExperiment()[source]¶

class pkpd.objects.PKPDModelBase2[source]¶

Bases: pkpd.objects.PKPDModelBase

areParametersSignificant(lowerBound, upperBound)[source]¶

Parameters: upperBound (lowerBound and) – a numpy array of parameters
Returns: a list of string with “True”, “False”, “NA”, “Suspicious”

areParametersValid(p)[source]¶

forwardModel(parameters, x=None)[source]¶

getBounds()[source]¶

getEquation()[source]¶

getModelEquation()[source]¶

getParameterDescriptions()[source]¶

printSetup()[source]¶

setBounds(boundsString)[source]¶

setConfidenceInterval(lowerBound, upperBound)[source]¶

setConfidenceIntervalNA()[source]¶

setSample(sample)[source]¶

class pkpd.objects.PKPDODEModel[source]¶

Bases: pkpd.objects.PKPDModelBase2

F(t, y)[source]¶

G(t, dD)[source]¶

H(y)[source]¶

forwardModel(parameters, x=None, drugSource=None)[source]¶

forwardModelByConvolution(parameters, x=None)[source]¶

getImpulseResponse(parameters, tImpulse)[source]¶

getResponseDimension()[source]¶

getStateDimension()[source]¶

imposeConstraints(yt)[source]¶

printOtherParameterization()[source]¶

setXYValues(x, y)[source]¶

class pkpd.objects.PKPDOptimizer(model, fitType, goalFunction='RMSE')[source]¶

Bases: object

evaluateQuality()[source]¶

getResiduals(parameters)[source]¶

goalRMSE(parameters)[source]¶

hugeError()[source]¶

inBounds(parameters)[source]¶

printFitting()[source]¶

class pkpd.objects.PKPDSample[source]¶

Bases: object

addMeasurement(line)[source]¶

addMeasurementColumn(varName, values)[source]¶

addMeasurementPattern(tokens)[source]¶

evaluateExpression(expression, prefix='')[source]¶

evaluateParsedExpression(parsedOperation, varList)[source]¶

getBioavailability()[source]¶

getCumulatedDose(t0, tF)[source]¶

getDescriptorValue(descriptorName)[source]¶

getDescriptorValues(descriptorNameList)[source]¶

getDoseAt(t0, dt=0.5)[source]¶

getDoseUnits()[source]¶

getNumberOfMeasurements()[source]¶

getNumberOfVariables()[source]¶

getRange(varName)[source]¶

getSampleMeasurements()[source]¶

getSampleName()[source]¶

getTimeUnits()[source]¶

getTimeVariable()[source]¶

getValues(varName)[source]¶

getVariableValues(varList)[source]¶

getXYValues(varNameX, varNameY)[source]¶

interpretDose()[source]¶

isDoseABolus()[source]¶

parseTokens(tokens, variableDict, doseDict, groupDict)[source]¶

setDescriptorValue(descriptorName, descriptorValue)[source]¶

setValues(varName, varValues)[source]¶

substituteValuesInExpression(expression, prefix='')[source]¶

class pkpd.objects.PKPDSampleFit[source]¶

Bases: object

READING_SAMPLEFITTINGS_AIC = 4¶

READING_SAMPLEFITTINGS_AICc = 5¶

READING_SAMPLEFITTINGS_BIC = 6¶

READING_SAMPLEFITTINGS_MODELEQ = 1¶

READING_SAMPLEFITTINGS_NAME = 0¶

READING_SAMPLEFITTINGS_PARAMETER_BOUNDS = 7¶

READING_SAMPLEFITTINGS_R2 = 2¶

READING_SAMPLEFITTINGS_R2ADJ = 3¶

READING_SAMPLEFITTINGS_SAMPLE_VALUES = 8¶

copyFromOptimizer(optimizer)[source]¶

getBasicInfo()[source]¶: Return a string with some basic information of the fitting.

printForPopulation(fh, observations)[source]¶

printForPopulationExcel(wb, row, observations)[source]¶

readFromLine(line)[source]¶

restartReadingState()[source]¶

class pkpd.objects.PKPDSampleFitBootstrap[source]¶

Bases: object

READING_SAMPLEFITTINGS_NAME = 0¶

READING_SAMPLEFITTINGS_PARAMETERS = 3¶

READING_SAMPLEFITTINGS_XB = 1¶

READING_SAMPLEFITTINGS_YB = 2¶

copyFromOptimizer(optimizer)[source]¶

printForPopulation(fh, observations)[source]¶

printForPopulationExcel(wb, row, observations)[source]¶

readFromLine(line)[source]¶

restartReadingState()[source]¶

class pkpd.objects.PKPDSampleMeasurement(sample, n)[source]¶

Bases: object

getValues()[source]¶

class pkpd.objects.PKPDSampleSignalAnalysis[source]¶: Bases: object

class pkpd.objects.PKPDSignalAnalysis(**args)[source]¶

Bases: pwem.objects.data.EMObject

READING_ANALYSIS_VARIABLES = 5¶

READING_EXPERIMENT = 1¶

READING_MODEL = 4¶

READING_POPULATION = 7¶

READING_POPULATION_HEADER = 6¶

READING_PREDICTED = 3¶

READING_PREDICTOR = 2¶

READING_SAMPLEANALYSIS_NAME = 8¶

READING_SAMPLEANALYSIS_PARAMETERS = 9¶

getSampleAnalysis(sampleName)[source]¶

load(fnAnalysis)[source]¶

write(fnAnalysis)[source]¶

class pkpd.objects.PKPDVariable[source]¶

Bases: object

ROLE_LABEL = 1012¶

ROLE_MEASUREMENT = 1011¶

ROLE_NAMES = {1010: 'time', 1011: 'measurement', 1012: 'label'}¶

ROLE_TIME = 1010¶

TYPE_NAMES = {1000: 'numeric', 1001: 'text'}¶

TYPE_NUMERIC = 1000¶

TYPE_TEXT = 1001¶

getLabel()[source]¶

getRoleString()[source]¶

getTypeString()[source]¶

getUnitsString()[source]¶

isLabel()[source]¶

isMeasurement()[source]¶

isNumeric()[source]¶

isTime()[source]¶

parseTokens(tokens)[source]¶

class pkpd.objects.PKPhysiologyLungParameters(**args)[source]¶: Bases: pkpd.inhalation.PKPhysiologyLungParameters

class pkpd.objects.PKSubstanceLungParameters(**args)[source]¶: Bases: pkpd.inhalation.PKSubstanceLungParameters

pkpd.objects.flattenArray(y)[source]¶

pkpd.objects.smartLog(y)[source]¶