private SummaryStatisticsModel GetModel(IDataset dataset)
{
var model = new SummaryStatisticsModel(dataset)
{
ByCategoriesAndOverall = false,
CoefficientOfVariation = false,
ConfidenceInterval = true,
FirstCatFactor = "Cat4",
FourthCatFactor = "Cat456",
Mean = true,
MedianAndQuartiles = false,
MinAndMax = false,
N = true,
NormalProbabilityPlot = false,
Responses = new System.Collections.Generic.List <string>
{
"Resp 2",
"Resp8"
},
SecondCatFactor = "Cat5",
Significance = 95,
StandardDeviation = true,
StandardErrorOfMean = false,
ThirdCatFactor = "Cat6",
Transformation = "None",
Variance = false
};
return(model);
}
public async Task SS19()
{
string testName = "SS19";
//Arrange
HttpClient client = _factory.CreateClient();
SummaryStatisticsModel model = new SummaryStatisticsModel();
model.DatasetID = _factory.SheetNames.Single(x => x.Value == "Summary Statistics").Key;
model.Responses = new string[] { "Resp1" };
model.Mean = false;
model.N = false;
model.StandardDeviation = false;
model.Variance = false;
model.StandardErrorOfMean = false;
model.MinAndMax = false;
model.MedianAndQuartiles = false;
model.CoefficientOfVariation = false;
model.NormalProbabilityPlot = false;
model.CoefficientOfVariation = false;
model.ByCategoriesAndOverall = false;
model.ConfidenceInterval = true;
//Act
HttpResponseMessage response = await client.PostAsync("Analyses/SummaryStatistics", new FormUrlEncodedContent(model.ToKeyValue()));
IEnumerable <string> errors = await Helpers.ExtractErrors(response);
//Assert
Assert.Contains("You have not selected anything to output!", errors);
Helpers.SaveOutput("SummaryStatistics", testName, errors);
}
public async Task SS22()
{
string testName = "SS22";
//Arrange
HttpClient client = _factory.CreateClient();
SummaryStatisticsModel model = new SummaryStatisticsModel();
model.DatasetID = _factory.SheetNames.Single(x => x.Value == "Summary Statistics").Key;
model.Responses = new string[] { "Resp 2", "Resp8", "Resp9" };
model.Transformation = "Log10";
model.Mean = true;
model.N = true;
model.StandardDeviation = true;
model.Variance = true;
model.StandardErrorOfMean = true;
model.MinAndMax = true;
model.MedianAndQuartiles = true;
model.CoefficientOfVariation = true;
model.NormalProbabilityPlot = true;
model.CoefficientOfVariation = true;
model.ByCategoriesAndOverall = true;
model.ConfidenceInterval = true;
//Act
StatsOutput statsOutput = await Helpers.SubmitAnalysis(client, "SummaryStatistics", new FormUrlEncodedContent(model.ToKeyValue()));
Helpers.SaveTestOutput("SummaryStatistics", model, testName, statsOutput);
//Assert
string expectedHtml = File.ReadAllText(Path.Combine("ExpectedResults", "SummaryStatistics", testName + ".html"));
Assert.Equal(Helpers.RemoveAllImageNodes(expectedHtml), Helpers.RemoveAllImageNodes(statsOutput.HtmlResults));
}
public void ScriptFileName_ReturnsCorrectString()
{
//Arrange
System.Threading.Thread.CurrentThread.CurrentCulture = CultureInfo.InvariantCulture;
SummaryStatisticsModel sut = new SummaryStatisticsModel();
//Act
string result = sut.ScriptFileName;
//Assert
Assert.Equal("SummaryStatistics", result);
}
public void GetCommandLineArguments_ReturnsCorrectString()
{
//Arrange
System.Threading.Thread.CurrentThread.CurrentCulture = CultureInfo.InvariantCulture;
SummaryStatisticsModel sut = GetModel(GetDataset());
//Act
string result = sut.GetCommandLineArguments();
//Assert
Assert.Equal("Respivs_sp_ivs2,Resp8 None Cat4 Cat5 Cat6 Cat456 Y Y Y N N N N N Y 95 N N", result);
}
protected override ValidationResult IsValid(object value, ValidationContext validationContext)
{
SummaryStatisticsModel model = (SummaryStatisticsModel)validationContext.ObjectInstance;
if (model.Significance < 1 || model.Significance > 100)
{
return(new ValidationResult("You have selected a confidence limit that is less than 1. Note that this value should be entered as a percentage and not a fraction."));
}
else
{
return(ValidationResult.Success);
}
}
public async Task SS28()
{
string testName = "SS28";
//Arrange
HttpClient client = _factory.CreateClient();
SummaryStatisticsModel model = new SummaryStatisticsModel();
model.DatasetID = _factory.SheetNames.Single(x => x.Value == "Summary Statistics").Key;
model.Responses = new string[] { "Resp 2", "Resp3", "Resp6" };
model.FirstCatFactor = "Cat6";
model.Transformation = "ArcSine";
model.Mean = true;
model.N = true;
model.StandardDeviation = true;
model.Variance = true;
model.StandardErrorOfMean = true;
model.MinAndMax = true;
model.MedianAndQuartiles = true;
model.CoefficientOfVariation = true;
model.NormalProbabilityPlot = true;
model.CoefficientOfVariation = true;
model.ByCategoriesAndOverall = true;
model.ConfidenceInterval = true;
model.Significance = 95;
//Act
HttpResponseMessage response = await client.PostAsync("Analyses/SummaryStatistics", new FormUrlEncodedContent(model.ToKeyValue()));
IEnumerable <string> warnings = await Helpers.ExtractWarnings(response);
//Assert
Assert.Contains("You have ArcSine transformed the Resp3 variable. Unfortunately some of the Resp3 values are <0 or >1. These values have been ignored in the analysis as it is not possible to transform them.", warnings);
Helpers.SaveOutput("SummaryStatistics", testName, warnings);
//Act - ignore warnings
var modelIgnoreWarnings = model.ToKeyValue();
modelIgnoreWarnings.Add("ignoreWarnings", "true");
StatsOutput statsOutput = await Helpers.SubmitAnalysis(client, "SummaryStatistics", new FormUrlEncodedContent(modelIgnoreWarnings));
Helpers.SaveTestOutput("SummaryStatistics", model, testName, statsOutput);
//Assert
string expectedHtml = File.ReadAllText(Path.Combine("ExpectedResults", "SummaryStatistics", testName + ".html"));
Assert.Equal(Helpers.RemoveAllImageNodes(expectedHtml), Helpers.RemoveAllImageNodes(statsOutput.HtmlResults));
}
public void TransformationsList_ReturnsCorrectList()
{
//Arrange
System.Threading.Thread.CurrentThread.CurrentCulture = CultureInfo.InvariantCulture;
SummaryStatisticsModel sut = new SummaryStatisticsModel();
//Act
IEnumerable <string> result = sut.TransformationsList;
//Assert
Assert.IsAssignableFrom <IEnumerable <string> >(result);
Assert.Equal(new List <string>()
{
"None", "Log10", "Loge", "Square Root", "ArcSine"
}, result);
}
public async Task SS10()
{
string testName = "SS10";
//Arrange
HttpClient client = _factory.CreateClient();
SummaryStatisticsModel model = new SummaryStatisticsModel();
model.DatasetID = _factory.SheetNames.Single(x => x.Value == "Summary Statistics").Key;
model.Responses = new string[] { "Resp1", "Resp 2" };
//Act
HttpResponseMessage response = await client.PostAsync("Analyses/SummaryStatistics", new FormUrlEncodedContent(model.ToKeyValue()));
IEnumerable <string> errors = await Helpers.ExtractErrors(response);
//Assert
Assert.Contains("The Response (Resp1) contains non-numeric data that cannot be processed. Please check the data and make sure it was entered correctly.", errors);
Helpers.SaveOutput("SummaryStatistics", testName, errors);
}
public async Task SS2()
{
string testName = "SS2";
//Arrange
HttpClient client = _factory.CreateClient();
SummaryStatisticsModel model = new SummaryStatisticsModel();
model.DatasetID = _factory.SheetNames.Single(x => x.Value == "Summary Statistics").Key;
model.Responses = new string[] { "Resp 2" };
model.FirstCatFactor = "Cat1";
//Act
HttpResponseMessage response = await client.PostAsync("Analyses/SummaryStatistics", new FormUrlEncodedContent(model.ToKeyValue()));
IEnumerable <string> errors = await Helpers.ExtractErrors(response);
//Assert
Assert.Contains("The categorisation factor (Cat1) contains missing data where there are observations present in the Response. Please check the input data and make sure the data was entered correctly.", errors);
Helpers.SaveOutput("SummaryStatistics", testName, errors);
}
public async Task SS17()
{
string testName = "SS17";
//Arrange
HttpClient client = _factory.CreateClient();
SummaryStatisticsModel model = new SummaryStatisticsModel();
model.DatasetID = _factory.SheetNames.Single(x => x.Value == "Summary Statistics").Key;
model.Responses = new string[] { "Resp4", "Resp 2" };
model.Transformation = "Loge";
//Act
HttpResponseMessage response = await client.PostAsync("Analyses/SummaryStatistics", new FormUrlEncodedContent(model.ToKeyValue()));
IEnumerable <string> warnings = await Helpers.ExtractWarnings(response);
//Assert
Assert.Contains("You have Loge transformed the Resp4 variable. Unfortunately some of the Resp4 values are zero and/or negative. These values have been ignored in the analysis as it is not possible to transform them.", warnings);
Helpers.SaveOutput("SummaryStatistics", testName, warnings);
}
public async Task SS14()
{
string testName = "SS14";
//Arrange
HttpClient client = _factory.CreateClient();
SummaryStatisticsModel model = new SummaryStatisticsModel();
model.DatasetID = _factory.SheetNames.Single(x => x.Value == "Summary Statistics").Key;
model.Responses = new string[] { "Resp 2" };
model.Significance = 0;
//Act
HttpResponseMessage response = await client.PostAsync("Analyses/SummaryStatistics", new FormUrlEncodedContent(model.ToKeyValue()));
IEnumerable <string> errors = await Helpers.ExtractErrors(response);
//Assert
Assert.Contains("You have selected a confidence limit that is less than 1. Note that this value should be entered as a percentage and not a fraction.", errors);
Helpers.SaveOutput("SummaryStatistics", testName, errors);
}
public void ExportData_ReturnsCorrectStringArray()
{
//Arrange
System.Threading.Thread.CurrentThread.CurrentCulture = CultureInfo.InvariantCulture;
Mock <IDataset> mockDataset = new Mock <IDataset>();
mockDataset.Setup(x => x.DatasetID).Returns(It.IsAny <int>);
mockDataset.Setup(x => x.DatasetToDataTable()).Returns(GetTestDataTable());
SummaryStatisticsModel sut = GetModel(mockDataset.Object);
//Act
string[] result = sut.ExportData();
//Assert
Assert.Equal("Respivs_sp_ivs2,Resp8,Cat4,Cat5,Cat6,Cat456,catfact", result[0]);
Assert.Equal(34, result.Count());
Assert.StartsWith("0.939350659,0.90541248", result[33]);
//catfactor check
Assert.EndsWith("A 2 2 A_ 2_ 2", result[33]);
}
public async Task SS4()
{
string testName = "SS4";
//Arrange
HttpClient client = _factory.CreateClient();
SummaryStatisticsModel model = new SummaryStatisticsModel();
model.DatasetID = _factory.SheetNames.Single(x => x.Value == "Summary Statistics").Key;
model.Responses = new string[] { "Resp 2" };
model.FirstCatFactor = "Cat3";
//Act
HttpResponseMessage response = await client.PostAsync("Analyses/SummaryStatistics", new FormUrlEncodedContent(model.ToKeyValue()));
IEnumerable <string> errors = await Helpers.ExtractErrors(response);
//Assert
Assert.Contains("There are no observations recorded on the levels of the categorisation factor (Cat3). Please amend the dataset prior to running the analysis.", errors);
Helpers.SaveOutput("SummaryStatistics", testName, errors);
}
public async Task SS20()
{
string testName = "SS20";
//Arrange
HttpClient client = _factory.CreateClient();
SummaryStatisticsModel model = new SummaryStatisticsModel();
model.DatasetID = _factory.SheetNames.Single(x => x.Value == "Summary Statistics").Key;
model.Responses = new string[] { "Resp 2", "Resp10" };
model.FirstCatFactor = "Cat4";
//Act
HttpResponseMessage response = await client.PostAsync("Analyses/SummaryStatistics", new FormUrlEncodedContent(model.ToKeyValue()));
IEnumerable <string> warnings = await Helpers.ExtractWarnings(response);
//Assert
Assert.Contains("The Response (Resp10) contains missing data. Any rows of the dataset that contain missing responses will be excluded prior to the analysis.", warnings);
Helpers.SaveOutput("SummaryStatistics", testName, warnings);
}
public async Task SS12()
{
string testName = "SS12";
//Arrange
HttpClient client = _factory.CreateClient();
SummaryStatisticsModel model = new SummaryStatisticsModel();
model.DatasetID = _factory.SheetNames.Single(x => x.Value == "Summary Statistics").Key;
model.Responses = new string[] { "Resp 2" };
model.FirstCatFactor = "Cat2";
model.SecondCatFactor = "Cat4";
//Act
HttpResponseMessage response = await client.PostAsync("Analyses/SummaryStatistics", new FormUrlEncodedContent(model.ToKeyValue()));
IEnumerable <string> errors = await Helpers.ExtractErrors(response);
//Assert
Assert.Contains("There is no replication in one or more of the levels of the categorisation factor (Cat2). Please select another factor.", errors);
Helpers.SaveOutput("SummaryStatistics", testName, errors);
}
public SummaryStatisticsValidator(SummaryStatisticsModel ss)
: base(ss.DataTable)
{
ssVariables = ss;
}
public void GetArguments_ReturnsCorrectArguments()
{
//Arrange
System.Threading.Thread.CurrentThread.CurrentCulture = CultureInfo.InvariantCulture;
SummaryStatisticsModel sut = GetModel(GetDataset());
//Act
List <Argument> result = sut.GetArguments().ToList();
//Assert
var responses = result.Single(x => x.Name == "Responses");
Assert.Equal("Resp 2,Resp8", responses.Value);
var firstCatFactor = result.Single(x => x.Name == "FirstCatFactor");
Assert.Equal("Cat4", firstCatFactor.Value);
var secondCatFactor = result.Single(x => x.Name == "SecondCatFactor");
Assert.Equal("Cat5", secondCatFactor.Value);
var thirdCatFactor = result.Single(x => x.Name == "ThirdCatFactor");
Assert.Equal("Cat6", thirdCatFactor.Value);
var fourthCatFactor = result.Single(x => x.Name == "FourthCatFactor");
Assert.Equal("Cat456", fourthCatFactor.Value);
var mean = result.Single(x => x.Name == "Mean");
Assert.Equal("True", mean.Value);
var n = result.Single(x => x.Name == "N");
Assert.Equal("True", n.Value);
var medianAndQuartiles = result.Single(x => x.Name == "MedianAndQuartiles");
Assert.Equal("False", medianAndQuartiles.Value);
var minAndMax = result.Single(x => x.Name == "MinAndMax");
Assert.Equal("False", minAndMax.Value);
var normalProbabilityPlot = result.Single(x => x.Name == "NormalProbabilityPlot");
Assert.Equal("False", normalProbabilityPlot.Value);
var significance = result.Single(x => x.Name == "Significance");
Assert.Equal("95", significance.Value);
var standardDeviation = result.Single(x => x.Name == "StandardDeviation");
Assert.Equal("True", standardDeviation.Value);
var standardErrorOfMean = result.Single(x => x.Name == "StandardErrorOfMean");
Assert.Equal("False", standardErrorOfMean.Value);
var transformation = result.Single(x => x.Name == "Transformation");
Assert.Equal("None", transformation.Value);
var variance = result.Single(x => x.Name == "Variance");
Assert.Equal("False", variance.Value);
var coefficientOfVariation = result.Single(x => x.Name == "CoefficientOfVariation");
Assert.Equal("False", coefficientOfVariation.Value);
var confidenceInterval = result.Single(x => x.Name == "ConfidenceInterval");
Assert.Equal("True", confidenceInterval.Value);
var byCategoriesAndOverall = result.Single(x => x.Name == "ByCategoriesAndOverall");
Assert.Equal("False", byCategoriesAndOverall.Value);
}
public void LoadArguments_ReturnsCorrectArguments()
{
//Arrange
System.Threading.Thread.CurrentThread.CurrentCulture = CultureInfo.InvariantCulture;
SummaryStatisticsModel sut = new SummaryStatisticsModel(GetDataset());
List <Argument> arguments = new List <Argument>();
arguments.Add(new Argument {
Name = "Responses", Value = "Resp1,Resp 2"
});
arguments.Add(new Argument {
Name = "FirstCatFactor", Value = "Cat1"
});
arguments.Add(new Argument {
Name = "SecondCatFactor", Value = "Cat2"
});
arguments.Add(new Argument {
Name = "ThirdCatFactor", Value = "Cat3"
});
arguments.Add(new Argument {
Name = "FourthCatFactor", Value = "Cat4"
});
arguments.Add(new Argument {
Name = "Transformation", Value = "Log10"
});
arguments.Add(new Argument {
Name = "ByCategoriesAndOverall", Value = "True"
});
arguments.Add(new Argument {
Name = "CoefficientOfVariation", Value = "False"
});
arguments.Add(new Argument {
Name = "ConfidenceInterval", Value = "True"
});
arguments.Add(new Argument {
Name = "Mean", Value = "False"
});
arguments.Add(new Argument {
Name = "MedianAndQuartiles", Value = "True"
});
arguments.Add(new Argument {
Name = "MinAndMax", Value = "False"
});
arguments.Add(new Argument {
Name = "N", Value = "True"
});
arguments.Add(new Argument {
Name = "NormalProbabilityPlot", Value = "False"
});
arguments.Add(new Argument {
Name = "Significance", Value = "90"
});
arguments.Add(new Argument {
Name = "StandardDeviation", Value = "True"
});
arguments.Add(new Argument {
Name = "StandardErrorOfMean", Value = "False"
});
arguments.Add(new Argument {
Name = "Variance", Value = "True"
});
Assert.Equal(18, arguments.Count);
//Act
sut.LoadArguments(arguments);
//Assert
Assert.Equal(new List <string>()
{
"Resp1", "Resp 2"
}, sut.Responses);
Assert.Equal("Cat1", sut.FirstCatFactor);
Assert.Equal("Cat2", sut.SecondCatFactor);
Assert.Equal("Cat3", sut.ThirdCatFactor);
Assert.Equal("Cat4", sut.FourthCatFactor);
Assert.Equal("Log10", sut.Transformation);
Assert.True(sut.ByCategoriesAndOverall);
Assert.False(sut.CoefficientOfVariation);
Assert.True(sut.ConfidenceInterval);
Assert.False(sut.Mean);
Assert.True(sut.MedianAndQuartiles);
Assert.False(sut.MinAndMax);
Assert.True(sut.N);
Assert.False(sut.NormalProbabilityPlot);
Assert.Equal(90, sut.Significance);
Assert.True(sut.StandardDeviation);
Assert.False(sut.StandardErrorOfMean);
Assert.True(sut.Variance);
}
public async Task <IActionResult> SummaryStatistics(SummaryStatisticsModel model, bool ignoreWarnings)
{
return(await RunAnalysis(model, ignoreWarnings));
}
