protected override void Context()
{
base.Context();
_normalDistribution1 = new ParameterDistributionMetaData {
Age = 20, Mean = 5, Deviation = 10
};
_normalDistribution2 = new ParameterDistributionMetaData {
Age = 10, Mean = 15, Deviation = 30
};
_normalDistributions = new List <ParameterDistributionMetaData> {
_normalDistribution1, _normalDistribution2
};
_parameter = A.Fake <IDistributedParameter>();
IParameter meanParameter = DomainHelperForSpecs.ConstantParameterWithValue(1).WithName("Mean");
IParameter deviationParameter = DomainHelperForSpecs.ConstantParameterWithValue(2).WithName("Deviation");
A.CallTo(() => _parameter.MeanParameter).Returns(meanParameter);
A.CallTo(() => _parameter.DeviationParameter).Returns(deviationParameter);
A.CallTo(() => _distrFormulaFactory.CreateNormalDistributionFormulaFor(_parameter, meanParameter, deviationParameter)).Returns(new NormalDistributionFormula());
_originData = new OriginData {
Age = 12
};
}
bool isElderlyICRPVolumeParameterIn(ParameterDistributionMetaData pd, string organ)
{
return(pd.Population.Equals(CoreConstants.Population.ICRP) &&
pd.Age >= 40 &&
pd.ParameterName.Equals("Volume") &&
pd.ContainerType.Equals(CoreConstants.ContainerType.ORGAN) &&
pd.ContainerName.Equals(organ));
}
private void scaleDistributionMetaData(ParameterDistributionMetaData parameterDistributionMeta, double hrel, double alpha)
{
var scale = Math.Pow(hrel, alpha);
parameterDistributionMeta.Mean *= scale;
if (parameterDistributionMeta.DistributionType == CoreConstants.Distribution.Normal)
{
parameterDistributionMeta.Deviation *= scale;
}
}
protected override void Context()
{
_entityPathResolver = A.Fake <IEntityPathResolver>();
_parameterFactory = A.Fake <IParameterFactory>();
_objectBaseFactory = A.Fake <IObjectBaseFactory>();
_distributedParameter = A.Fake <IDistributedParameter>();
A.CallTo(() => _parameterFactory.CreateFor(A <ParameterDistributionMetaData> ._))
.Invokes(x => _distributionMetaData = x.GetArgument <ParameterDistributionMetaData>(0)).Returns(_distributedParameter);
sut = new AdvancedParameterFactory(_entityPathResolver, _parameterFactory, _objectBaseFactory);
}
private IEnumerable <ParameterDistributionMetaData> scaleDistributions(TableFormulaParameter <IDistributedParameter> parameter, IEnumerable <ParameterDistributionMetaData> distributionsToScale)
{
//Retrieve the mean height parameter for the given origin data
var originData = parameter.OriginData;
var individualParameter = parameter.Parameter;
bool needHeightScaling = needScaling(originData, individualParameter);
if (!needHeightScaling)
{
return(distributionsToScale);
}
//retrieve the height distribution for the original individual
var heightDistributions = allDistributionsFor(_allHeightDistributionMaleParameters, _allHeightDistributionFemaleParameters, originData).ToList();
var heightDistributionParameters = distributionParametersFor(heightDistributions, originData);
double meanHeight = heightDistributionParameters.Item1;
double deviation = heightDistributionParameters.Item2;
var heigthDistributionFormula = distributionFrom(DistributionTypes.Normal, meanHeight, deviation);
double currentHeight = originData.Height.GetValueOrDefault(meanHeight);
//same height, not need to scale
if (ValueComparer.AreValuesEqual(meanHeight, currentHeight))
{
return(distributionsToScale);
}
//is used in order to retrieve the percentile
double currentPercentile = heigthDistributionFormula.CalculatePercentileForValue(currentHeight);
double alpha = individualParameter.ParentContainer.Parameter(CoreConstants.Parameter.ALLOMETRIC_SCALE_FACTOR).Value;
var currentOriginData = originData.Clone();
var scaledParameterDistributionMetaData = new List <ParameterDistributionMetaData>();
foreach (var originDistributionMetaData in distributionsToScale)
{
var distributionMetaData = ParameterDistributionMetaData.From(originDistributionMetaData);
currentOriginData.Age = originDistributionMetaData.Age;
double hrelForAge = calculateRelativeHeightForAge(heightDistributions, currentOriginData, currentPercentile);
scaleDistributionMetaData(distributionMetaData, hrelForAge, alpha);
scaledParameterDistributionMetaData.Add(distributionMetaData);
}
return(scaledParameterDistributionMetaData);
}
protected override void Context()
{
base.Context();
_normalDistribution1 = new ParameterDistributionMetaData();
_normalDistribution1.DistributionType = CoreConstants.Distribution.Normal;
_normalDistribution2 = new ParameterDistributionMetaData();
_normalDistribution2.DistributionType = CoreConstants.Distribution.Normal;
_logNormalDistribution = new ParameterDistributionMetaData();
_logNormalDistribution.DistributionType = CoreConstants.Distribution.LogNormal;
_onlyNormalDistributions = new List <ParameterDistributionMetaData> {
_normalDistribution1, _normalDistribution2
};
_mixedDistributions = new List <ParameterDistributionMetaData> {
_normalDistribution1, _logNormalDistribution
};
}
private IReadOnlyList <ParameterDistributionMetaData> scaleDistributions(TableFormulaParameter <IDistributedParameter> parameter, IReadOnlyList <ParameterDistributionMetaData> distributionsToScale)
{
//Retrieve the mean height parameter for the given origin data
var originData = parameter.OriginData;
var individualParameter = parameter.Parameter;
bool needHeightScaling = needScaling(originData, individualParameter);
if (!needHeightScaling)
{
return(distributionsToScale);
}
var(meanHeight, currentHeight, currentPercentile, heightDistributionSamples) = heightDistributionsFor(originData);
//same height, not need to scale
if (ValueComparer.AreValuesEqual(meanHeight, currentHeight))
{
return(distributionsToScale);
}
//is used in order to retrieve the percentile
double alpha = individualParameter.ParentContainer.Parameter(CoreConstants.Parameters.ALLOMETRIC_SCALE_FACTOR).Value;
var currentOriginData = originData.Clone();
var scaledParameterDistributionMetaData = new List <ParameterDistributionMetaData>();
foreach (var originDistributionMetaData in distributionsToScale)
{
var distributionMetaData = ParameterDistributionMetaData.From(originDistributionMetaData);
currentOriginData.Age.Value = originDistributionMetaData.Age;
var(mean, deviation) = heightDistributionSamples(currentOriginData);
double heightAtPercentile = valueFrom(DistributionTypes.Normal, mean, deviation, currentPercentile);
double hrelForAge = heightAtPercentile / mean;
scaleDistributionMetaData(distributionMetaData, hrelForAge, alpha);
scaledParameterDistributionMetaData.Add(distributionMetaData);
}
return(scaledParameterDistributionMetaData);
}
protected override void Context()
{
base.Context();
_discreteDistribution1 = new ParameterDistributionMetaData {
Age = 20, Mean = 5
};
_discreteDistribution2 = new ParameterDistributionMetaData {
Age = 10, Mean = 15
};
_discreteDistributions = new List <ParameterDistributionMetaData> {
_discreteDistribution1, _discreteDistribution2
};
_parameter = A.Fake <IDistributedParameter>();
var meanParameter = DomainHelperForSpecs.ConstantParameterWithValue(1).WithName("Mean");
A.CallTo(() => _parameter.MeanParameter).Returns(meanParameter);
_originData = new OriginData {
Age = 12
};
A.CallTo(() => _distrFormulaFactory.CreateDiscreteDistributionFormulaFor(_parameter, meanParameter)).Returns(new DiscreteDistributionFormula());
}
protected override void Context()
{
_compartment = A.Fake <Compartment>();
A.CallTo(() => _compartment.OrganName).Returns("MyParentOrgan");
_compartment.Name = "MyComp";
var containerPath = new ObjectPath(new[] { _compartment.OrganName, _compartment.Name }).ToString();
_containerPathRetriever = A.Fake <IEntityPathResolver>();
A.CallTo(() => _containerPathRetriever.PathFor(_compartment)).Returns(containerPath);
_originData = new OriginData();
_originData.Population = new SpeciesPopulation();
var distributionDefinition = new ParameterDistributionMetaData();
var rateDefinition = new ParameterRateMetaData();
var valueDefinition = new ParameterValueMetaData();
distributionDefinition.ParameterName = "tutu";
var distributionRepository = A.Fake <IParameterDistributionRepository>();
A.CallTo(() => distributionRepository.AllFor(containerPath)).Returns(new List <ParameterDistributionMetaData> {
distributionDefinition
});
var rateRepository = A.Fake <IParameterRateRepository>();
A.CallTo(() => rateRepository.AllFor(containerPath)).Returns(new List <ParameterRateMetaData> {
rateDefinition
});
var valueRepository = A.Fake <IParameterValueRepository>();
A.CallTo(() => valueRepository.AllFor(containerPath)).Returns(new List <ParameterValueMetaData> {
valueDefinition
});
sut = new ParameterQuery(distributionRepository, valueRepository, rateRepository, _containerPathRetriever);
}
protected override void Because()
{
_copy = ParameterDistributionMetaData.From(sut);
}
private double valueFrom(ParameterDistributionMetaData distributionMetaData, double percentile)
{
return(valueFrom(distributionMetaData.Distribution, distributionMetaData.Mean, distributionMetaData.Deviation, percentile));
}
