public async Task <Result <bool> > ExportToExcel(string fileDataFileName,
CoverSampleResult coverSample,
DataTable dataMatrix,
DataTable testMatrix)
{
var coverMatrix = coverSample.CoverResult;
var excelStream = ExportToStream(coverSample, dataMatrix, testMatrix);
var suffix = $"{coverSample.FileName}_SLOW_{coverSample.SLOW}_SHIGH_{coverSample.SHIGH}_LOW_{coverMatrix.DataMatrix.LOW}_HIGH_{coverMatrix.DataMatrix.HIGH}_STEP_{coverSample.STEP}_METHOD_{coverSample.SelecteMethod.Replace(' ', NewChar).ToUpper()}_PARAM_{coverSample.SelecteMethodParam}_GRADE_{coverSample.CoverResult.Grade}";
var sfdResult = await SaveFileDialog(fileDataFileName, suffix, excelStream.Value);
string fileName;
if (sfdResult.Value)
{
fileName = _newSFD.FileName;
}
else
{
return(new Result <bool>(NieZapisanoPliku));
}
return(SaveAndOpenFile(fileName));
}
public Result <bool> AddRaport(XLWorkbook wb, CoverSampleResult coverMatrix)
{
wb.Worksheets.Add(name);
IXLWorksheet ws = wb.Worksheets.Worksheet(name);
ws.Columns().Width = 17;
var lastcol = 1;
ws.Cell(1, lastcol).Style.Font.SetBold();
ws.Cell(1, lastcol).SetValue(nameof(coverMatrix.CoverResult.DataMatrix.LOW));
lastcol++;
ws.Cell(1, lastcol).Style.Font.SetBold();
ws.Cell(1, lastcol).SetValue(nameof(coverMatrix.CoverResult.DataMatrix.HIGH));
lastcol++;
ws.Cell(1, lastcol).Style.Font.SetBold();
ws.Cell(1, lastcol).SetValue(nameof(coverMatrix.CoverResult.HIGHGood));
lastcol++;
ws.Cell(1, lastcol).Style.Font.SetBold();
ws.Cell(1, lastcol).SetValue(nameof(coverMatrix.CoverResult.HIGHBad));
lastcol++;
ws.Cell(1, lastcol).Style.Font.SetBold();
ws.Cell(1, lastcol).SetValue(nameof(coverMatrix.CoverResult.LOWGood));
lastcol++;
ws.Cell(1, lastcol).Style.Font.SetBold();
ws.Cell(1, lastcol).SetValue(nameof(coverMatrix.CoverResult.LOWBad));
lastcol++;
ws.Cell(1, lastcol).Style.Font.SetBold();
ws.Cell(1, lastcol).SetValue(nameof(coverMatrix.CoverResult.Grade));
lastcol++;
ws.Cell(1, lastcol).Style.Font.SetBold();
ws.Cell(1, lastcol).SetValue(nameof(coverMatrix.CoverResult.GoodgGrade));
var row = 2;
foreach (var cov in coverMatrix.Samples.OrderByDescending(x => x.Grade))
{
lastcol = 1;
ws.Cell(row, lastcol).SetValue(cov.DataMatrix.LOW);
ws.Cell(row, ++lastcol).SetValue(cov.DataMatrix.HIGH);
ws.Cell(row, ++lastcol).SetValue(cov.HIGHGood);
ws.Cell(row, ++lastcol).SetValue(cov.HIGHBad);
ws.Cell(row, ++lastcol).SetValue(cov.LOWGood);
ws.Cell(row, ++lastcol).SetValue(cov.LOWBad);
ws.Cell(row, ++lastcol).SetValue(cov.Grade);
ws.Cell(row, ++lastcol).SetValue(cov.GoodgGrade);
row++;
}
return(new Result <bool>(true));
}
public Result <byte[]> ExportToStream(CoverSampleResult coverSample,
DataTable dataMatrix,
DataTable testMatrix)
{
var coverMatrix = coverSample.CoverResult;
using (var wb = new XLWorkbook())
{
var dataTable =
_dataObjectsConverter.ConvertToDataTable(coverMatrix.DataObjects.Concat(coverMatrix.TestObjects)
.ToArray());
var result = AddDataTableToExcel(dataTable, wb, Obiekty);
if (result.HasErrors())
{
return(new Result <byte[]>(result.Error));
}
result = _attributeWriter.AddRaport(wb, coverSample);
if (result.HasErrors())
{
return(new Result <byte[]>(result.Error));
}
result = AddDataTableToExcel(dataMatrix, wb, MacierzPredykcjiTreningowe);
if (result.HasErrors())
{
return(new Result <byte[]>(result.Error));
}
result = _attributeWriter.AddAttributes(wb, coverMatrix);
if (result.HasErrors())
{
return(new Result <byte[]>(result.Error));
}
result = AddDataTableToExcel(testMatrix, wb, MacierzPredykcjiTestowe);
if (result.HasErrors())
{
return(new Result <byte[]>(result.Error));
}
var excelStream = new MemoryStream();
wb.SaveAs(excelStream);
return(new Result <byte[]>(excelStream.ToArray()));
}
}
public async Task <Result <CoverSampleResult> > GetMatrix(
FileData fileData,
double low,
double high,
IGroupingMethod groupMethod,
double paramInput,
double step,
ProgressBarModel progressBarModel,
Task waitRun)
{
var both = GetLowsAndHighs(low, high, step).ToArray();
var lows = both.AsParallel().Select(x => x.Item1).ToArray();
var highs = both.AsParallel().Select(x => x.Item2).ToArray();
var matrices = (await ComputeForEvery2(fileData, groupMethod, paramInput, highs, lows, progressBarModel, waitRun)).OrderByDescending(y => y.Grade).ToList();
var max = matrices.First();
var testMatrixRows = _coverMatrixClassificator.Classify(fileData.TestObjects, max.AttributesCovers, max.HighZeroColumns, max.LowZeroColumns);
max.TestMatrix =
new Core.Common.Items.MatrixFeatures.Matrix(testMatrixRows);
var dataMatrix = _matrixToGridMatrix.TransformToDataTable(max.DataMatrix);
var testMatrix = _matrixToGridMatrix.TransformToDataTable(max.TestMatrix);
var result = new CoverSampleResult(max, matrices, fileData.FileName)
{
SLOW = low,
SHIGH = high,
SelecteMethod = groupMethod.MethodName,
SelecteMethodParam = paramInput,
STEP = step
};
max.DataMatrix.DataTable = dataMatrix;
max.TestMatrix.DataTable = testMatrix;
DataObject.SchemeObject = null;
return(new Result <CoverSampleResult>(result));
}