public cFeedBackMessage Run()
{
if (base.Start() == false)
{
base.FeedBackMessage.IsSucceed = false;
return base.FeedBackMessage;
}
#region parameters initilization
object _firstValue = base.ListProperties.FindByName("Kernel size");
int KernelSize = 0;
if (_firstValue == null)
{
base.GenerateError("Kernel size not found !");
return base.FeedBackMessage;
}
try
{
cProperty TmpProp = (cProperty)_firstValue;
KernelSize = (int)TmpProp.GetValue();
}
catch (Exception)
{
base.GenerateError("Kernel size cast didn't work");
return base.FeedBackMessage;
}
_firstValue = base.ListProperties.FindByName("Image width");
int ImWidth = 0;
if (_firstValue == null)
{
base.GenerateError("Image width not found !");
return base.FeedBackMessage;
}
try
{
cProperty TmpProp = (cProperty)_firstValue;
ImWidth = (int)TmpProp.GetValue();
}
catch (Exception)
{
base.GenerateError("Image width cast didn't work");
return base.FeedBackMessage;
}
_firstValue = base.ListProperties.FindByName("Image height");
int ImHeight = 0;
if (_firstValue == null)
{
base.GenerateError("Image height not found !");
return base.FeedBackMessage;
}
try
{
cProperty TmpProp = (cProperty)_firstValue;
ImHeight = (int)TmpProp.GetValue();
}
catch (Exception)
{
base.GenerateError("Image height cast didn't work");
return base.FeedBackMessage;
}
_firstValue = base.ListProperties.FindByName("Image depth");
int ImDepth = 0;
if (_firstValue == null)
{
base.GenerateError("Image depth not found !");
return base.FeedBackMessage;
}
try
{
cProperty TmpProp = (cProperty)_firstValue;
ImDepth = (int)TmpProp.GetValue();
}
catch (Exception)
{
base.GenerateError("Image depth cast didn't work");
return base.FeedBackMessage;
}
#endregion
if (this.Input[0].Count == 2) ImDepth = 1;
if (this.Input[0].Count == 1)
{
ImDepth = 1;
ImHeight = 1;
}
this.Output = new cImage(ImWidth, ImHeight, ImDepth, this.Input.Count);
cImageDrawKernel GK = new cImageDrawKernel();
GK.sigma_x = KernelSize;
GK.sigma_y = KernelSize;
GK.sigma_z = KernelSize;
GK.Run();
cImage K = GK.GetOutPut();
for (int IdxChannel = 0; IdxChannel < this.Input.Count; IdxChannel++)
{
cExtendedTable CurrentTable = this.Input[IdxChannel];
cExtendedList ListVolumes = new cExtendedList();
double MaxX = CurrentTable[0].Max();
double MinX = CurrentTable[0].Min();
double MaxY = CurrentTable[1].Max();
double MinY = CurrentTable[1].Min();
double MaxZ = 0;
double MinZ = 0;
if (CurrentTable.Count>2)
{
MaxZ = CurrentTable[2].Max();
MinZ = CurrentTable[2].Min();
}
this.Output.SingleChannelImage[IdxChannel].Resolution.X = (MaxX - MinX) / (double)ImWidth;
this.Output.SingleChannelImage[IdxChannel].Resolution.Y = (MaxY - MinY) / (double)ImHeight;
this.Output.SingleChannelImage[IdxChannel].Resolution.Z = (MaxZ - MinZ) / (double)ImDepth;
if (CurrentTable.Count > 2)
{
for (int j = 0; j < CurrentTable[0].Count; j++)
{
double TmpValueX = (ImWidth * (CurrentTable[0][j] - MinX)) / (MaxX - MinX) - K.Width / 2;
double TmpValueY = (ImHeight * (CurrentTable[1][j] - MinY)) / (MaxY - MinY) - K.Height / 2;
double TmpValueZ = (ImDepth * (CurrentTable[2][j] - MinZ)) / (MaxZ - MinZ) - K.Depth / 2;
this.Output.AddInto(K, (int)TmpValueX, (int)TmpValueY, (int)TmpValueZ, IdxChannel);
}
}
else
{
for (int j = 0; j < CurrentTable[0].Count; j++)
{
double TmpValueX = (ImWidth * (CurrentTable[0][j] - MinX)) / (MaxX - MinX) - K.Width / 2;
double TmpValueY = ImHeight - (ImHeight * (CurrentTable[1][j] - MinY)) / (MaxY - MinY) - K.Height / 2;
this.Output.AddInto(K, (int)TmpValueX, (int)TmpValueY, 0, IdxChannel);
}
}
cListExtendedTable TablesForDensity = new cListExtendedTable(this.Output);
for (int idxChannel = 0; idxChannel < this.Output.GetNumChannels(); idxChannel++)
{
this.Output.SingleChannelImage[idxChannel].Name = this.Input.Name;//cGlobalInfo.ListCellularPhenotypes[idxChannel].Name;
float Sum = 1;
if (IsNormalized)
{
Sum = (float)TablesForDensity[idxChannel].Sum();
if (Sum <= 0.0) continue;
for (int i = 0; i < this.Output.SingleChannelImage[idxChannel].Data.Length; i++)
{
this.Output.SingleChannelImage[idxChannel].Data[i] = (10000000 * this.Output.SingleChannelImage[idxChannel].Data[i]) / Sum;
}
}
//else
//{
// for (int i = 0; i < this.Output.SingleChannelImage[idxChannel].Data.Length; i++)
// {
// // if (ClassPopulations[idxChannel] > 0)
// this.Output.SingleChannelImage[idxChannel].Data[i] = this.Output.SingleChannelImage[idxChannel].Data[i];
// }
//}
}
}
this.Output.SingleChannelImage[0].UpDateMax();
this.Output.SingleChannelImage[0].UpDateMin();
base.End();
return FeedBackMessage;
}
private void buttonProcess_Click(object sender, EventArgs e)
{
ImageWidth = (int)numericUpDownMapWidth.Value;
ImageHeight = (int)numericUpDownMapHeight.Value;
if (this.radioButton2D.Checked) ImageDepth = 1;
else
ImageDepth = (int)numericUpDownMapDepth.Value;
//GlobalInfo.ListCellularPhenotypes[(int)MachineLearning.Classes[j]]
cImageDrawKernel GK = new cImageDrawKernel();
GK.sigma_x = (int)numericUpDownKernelSize.Value;
GK.sigma_y = (int)numericUpDownKernelSize.Value;
GK.sigma_z = (int)numericUpDownKernelSize.Value;
GK.Run();
cImage K = GK.GetOutPut();
if (Parent.comboBoxAxeY.SelectedIndex == -1) return;
if (Parent.comboBoxVolume.SelectedIndex == -1) return;
cExtendedList ListVolumes = new cExtendedList();
ListX.Clear();
ListY.Clear();
ListZ.Clear();
cExtendedTable TableForDataToBeAnalyzed = Parent.GetActiveSignatures(false);
for (int j = 0; j < TableForDataToBeAnalyzed[0].Count; j++)
{
ListX.Add(TableForDataToBeAnalyzed[Parent.comboBoxAxeX.SelectedIndex][j]);
ListY.Add(TableForDataToBeAnalyzed[Parent.comboBoxAxeY.SelectedIndex][j]);
}
//MaxX = ListX.Max();
//MinX = ListX.Min();
//MaxY = ListY.Max();
//MinY = ListY.Min();
MaxX = Parent.chartForPoints.ChartAreas[0].AxisX.Maximum;
MinX = Parent.chartForPoints.ChartAreas[0].AxisX.Minimum;
MaxY = Parent.chartForPoints.ChartAreas[0].AxisY.Maximum;
MinY = Parent.chartForPoints.ChartAreas[0].AxisY.Minimum;
//MaxY = Parent.MaxY;
//MinY = Parent.MinY;
if (this.radioButton3D.Checked)
{
for (int j = 0; j < TableForDataToBeAnalyzed[0].Count; j++)
{
ListZ.Add(TableForDataToBeAnalyzed[Parent.comboBoxVolume.SelectedIndex][j]);
}
MaxZ = ListZ.Max();
MinZ = ListZ.Min();
}
DensityMaps = new cImage(ImageWidth, ImageHeight, ImageDepth, cGlobalInfo.ListCellularPhenotypes.Count);
int[] ClassPopulations = new int[cGlobalInfo.ListCellularPhenotypes.Count];
if (this.radioButton3D.Checked)
{
int Idx = 0;
for (int j = 0; j < Parent.InputTable[0].Count; j++)
{
if (Parent.PanelPhenotypeSelection.GetListSelectedClass()[(int)Parent.MachineLearning.Classes[j]])
{
double TmpValueX = (ImageWidth * (ListX[Idx] - MinX)) / (MaxX - MinX) - K.Width / 2;
double TmpValueY = ImageHeight - (ImageHeight * (ListY[Idx] - MinY)) / (MaxY - MinY) - K.Height / 2;
double TmpValueZ = ImageDepth - (ImageDepth * (ListZ[Idx] - MinZ)) / (MaxZ - MinZ) - K.Depth / 2;
DensityMaps.AddInto(K, (int)TmpValueX, (int)TmpValueY, (int)TmpValueZ, (int)Parent.MachineLearning.Classes[j]);
ClassPopulations[(int)Parent.MachineLearning.Classes[j]]++;
Idx++;
}
}
}
else
{
int Idx = 0;
for (int j = 0; j < Parent.InputTable[0].Count; j++)
{
if (Parent.PanelPhenotypeSelection.GetListSelectedClass()[(int)Parent.MachineLearning.Classes[j]])
{
double TmpValueX = (ImageWidth * (ListX[Idx] - MinX)) / (MaxX - MinX) - K.Width / 2;
double TmpValueY = ImageHeight - (ImageHeight * (ListY[Idx] - MinY)) / (MaxY - MinY) - K.Height / 2;
DensityMaps.AddInto(K, (int)TmpValueX, (int)TmpValueY, 0, (int)Parent.MachineLearning.Classes[j]);
ClassPopulations[(int)Parent.MachineLearning.Classes[j]]++;
Idx++;
}
}
}
cListExtendedTable TablesForDensity = new cListExtendedTable(DensityMaps);
for (int idxChannel = 0; idxChannel < DensityMaps.GetNumChannels(); idxChannel++)
{
DensityMaps.SingleChannelImage[idxChannel].Name = cGlobalInfo.ListCellularPhenotypes[idxChannel].Name;
float Sum = 1;
if (checkBoxNormalized.Checked)
{
Sum = (float)TablesForDensity[idxChannel].Sum();
if (Sum <= 0.0) continue;
for (int i = 0; i < DensityMaps.SingleChannelImage[idxChannel].Data.Length; i++)
{
//if (ClassPopulations[idxChannel] > 0)
DensityMaps.SingleChannelImage[idxChannel].Data[i] = (100 * DensityMaps.SingleChannelImage[idxChannel].Data[i]) / Sum;
}
}
else
{
for (int i = 0; i < DensityMaps.SingleChannelImage[idxChannel].Data.Length; i++)
{
// if (ClassPopulations[idxChannel] > 0)
DensityMaps.SingleChannelImage[idxChannel].Data[i] = DensityMaps.SingleChannelImage[idxChannel].Data[i];
}
}
}
cDisplaySingleImage IV = new cDisplaySingleImage();
DensityMaps.Name = "Density Maps. K["+ this.numericUpDownKernelSize.Value +"]";
if (this.checkBoxModifyColors.Checked)
IV.ListLinearMaxColor = new List<Color>();
int RealIdxChannel = 0;
for (int idxChannel = 0; idxChannel < ClassPopulations.Length; idxChannel++)
{
if (ClassPopulations[idxChannel] == 0)
DensityMaps.SingleChannelImage.RemoveAt(RealIdxChannel--);
else
{
if (this.checkBoxModifyColors.Checked)
IV.ListLinearMaxColor.Add(cGlobalInfo.ListCellularPhenotypes[idxChannel].ColourForDisplay);
}
RealIdxChannel++;
}
IV.SetInputData(DensityMaps);
IV.Run();
this.buttonApply.Enabled = true;
}
