private Boolean SaveMatrix(String fileName, Double[,] matrix)
{
try
{
StreamWriter sw = File.CreateText(fileName);
for (Int32 y = 0; y < matrix.GetLength(1); y++)
{
for (Int32 x = 0; x < matrix.GetLength(0); x++)
{
sw.Write(matrix[x, y].ToString(System.Globalization.CultureInfo.InvariantCulture));
sw.Write(" ");
}
sw.Write(Environment.NewLine);
}
sw.Close();
}
catch
{
MessageBox.Show("Error while saving the matrix to a file! Please check the path!", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return false;
}
return true;
}
static void Output(Double[,] a)
{
for (Int32 i = 0; i < a.GetLength(0); i++)
{
for (Int32 j = 0; j < a.GetLength(1); j++)
{
Console.Write(a[i, j].ToString() + ",");
}
Console.WriteLine();
}
}
public static Term[,] matrix(Double[,] m)
{
int rows = m.GetLength(0);
int cols = m.GetLength(1);
Term[,] r = new Term[rows, cols];
for (int row = 0; row < rows; row++)
for (int col = 0; col < cols; col++)
r[row, col] = m[row, col];
return r;
}
public XLMatrix(Double[,] arr)
:this(arr.GetLength(0), arr.GetLength(1))
{
var roCount = arr.GetLength(0);
var coCount = arr.GetLength(1);
for (int ro = 0; ro < roCount; ro++)
{
for (int co = 0; co < coCount; co++)
{
mat[ro, co] = arr[ro, co];
}
}
}
public static void Main()
{
// define cost matrix
Double INF = Double.PositiveInfinity;
/* Double[,] costMatrix = new Double[,]
{
{ INF, 2.0, 4.0, 22.0, 2.0, INF},
{ 2.0, INF, 8.0, 15.0, 13.0, 10.0},
{ 4.0, 8.0, INF, 5.0, 2.0, INF},
{22.0, 15.0, 5.0, INF, 11.0, 12.0},
{ 2.0, 13.0, 2.0, 11.0, INF, 14.0},
{ INF, 10.0, INF, 12.0, 14.0, INF},
};
*/
Double[,] costMatrix = new Double[,]
{
{ INF, 2.0, INF, INF, 7.0},
{ 2.0, INF, 1.0, 3.0, INF},
{ INF, 1.0, INF, 1.0, 5.0},
{ INF, 3.0, 1.0, INF, INF},
{ 7.0, INF, 5.0, INF, INF},
};
costMatrix = new Double[,]
{
{ INF, 20.0, INF, 70.0},
{ 20.0, INF, 40.0, INF},
{ INF, 40.0, INF,50.0},
{ 70.0, INF, 50.0,INF},
};
// create prototype chromosome
PermutationChromosome prototype = new PermutationChromosome(0, costMatrix.GetLength(0) - 1);
// set prototype's parameters
prototype.CrossOverStrategy = new PermutationChromosome.CycleCrossOverStrategy();
prototype.RandomGenerator = new ThreadSafeRandomGenerator();
prototype.MutationStrategy = new PermutationChromosome.InsertMutationStrategy();
prototype.Fitness = new TSPFitness(costMatrix);
// stop condition, keep reference for selecting the leader
NoChangeStopCondion stopCondition = new NoChangeStopCondion(10);
// create population
DefaultPopulation population = new DefaultPopulation(prototype, 40);
// set population's parameters
population.SelectionStrategy
= new StochasticUniversalSamplingStrategy(new FixedSizeStrategy(40), new ThreadSafeRandomGenerator());
population.RandomGenerator = new ThreadSafeRandomGenerator();
population.StopCondition = stopCondition;
// perform util the stop condition returns false
while (population.NextGeneration())
;
// print the result
System.Console.WriteLine("Best fitness: " + (1.0 / stopCondition.Leader.Evaluate()));
System.Console.WriteLine(stopCondition.Leader.ToString());
System.Console.ReadKey();
}
/* Takes a graph as input an adjacency matrix (see top for details) and a starting node */
public BellmanFord(Double[,] G,List<Edge> edge, int s)
{
int u, v;
/* Check graph format and that the graph actually contains something */
if (G.GetLength(0) < 1 || G.GetLength(0) != G.GetLength(1))
{
throw new ArgumentException("Graph error, wrong format or no nodes to compute");
}
int len = G.GetLength(0);
/* foreach(Vertex v in vertices)
{
if (v.ident == s) v.dist = 0;
else {v.dist = int.MaxValue; v.pre = null;}
} */
Initialize(s, len);
// for (i = 1; i <= sizeof(vertices) - 1; i++);
while (queue > 0)
{
queue = queue - 1;
/* Find the nodes that u connects to and perform relax */
foreach (Edge uv in edge)
{ u = uv.from;
v = uv.to;
/* Edge exists, relax the edge */
if (dist[u] + G[u, v] < dist[v])
{
dist[v] = dist[u] + G[u, v];
path[v] = u;
}
}
}
/* Checks for edges with negative weight */
foreach (Edge vu in edge)
{
u = vu.from;
v = vu.to;
if (G[u,v] < 0) throw new ArgumentException("Graph contains negative edge(s)");
// if (dist[u] + G[u, v] < dist[v]) throw new ArgumentException("Graph contains negative edge(s)");
}
}
public static string printMatrix(Double[,] matrix)
{
int rowLength = matrix.GetLength(0);
int colLength = matrix.GetLength(1);
StringBuilder builder = new StringBuilder();
for (int i = 0; i < rowLength; i++) {
for (int j = 0; j < colLength; j++) {
Console.Write(string.Format("{0} ", matrix[i, j]));
builder.Append(string.Format("{0} ", matrix[i, j]));
}
Console.Write(Environment.NewLine + Environment.NewLine);
builder.Append(Environment.NewLine + Environment.NewLine);
}
builder.Append("\n\n");
return builder.ToString();
}
private void WriteAccuracy(Double[,] result)
{
var p_size = result.GetLength(0);
var d_size = result.GetLength(1);
using (var writer = new StreamWriter("../Accuracy.cs"))
{
writer.WriteLine("namespace CloudBall.Engines.Toothless");
writer.WriteLine("{");
writer.WriteLine(" public partial class Statistics");
writer.WriteLine(" {");
writer.WriteLine(" private static readonly float[,] Accuracy = new float[,]{");
for (var p = 0; p < p_size; p++)
{
writer.Write("{{ {0:0.000}f /* {1:00.0}° */", result[p, 0], ToAngle(result[p, 0]));
for (var d = 1; d < d_size; d++)
{
writer.Write(", {0:0.000}f /* {1:00.0}° */", result[p, d], ToAngle(result[p, d]));
}
writer.WriteLine(" },");
}
writer.WriteLine("};}}");
}
}
/// <summary>
/// Prints the provided multi-dimentional array to a csv file with the given filename
/// </summary>
/// <param name='data'>
/// Data.
/// </param>
/// <param name='fileName'>
/// File name.
/// </param>
public static void PrintArrayToFile(Double[][] data, String fileName)
{
using (StreamWriter stream = File.CreateText(fileName))
{
for (Int32 x = 0; x < data.GetLength(0); x++)
{
for (Int32 y = 0; y < data[x].GetLength(0); y++)
{
if (y + 1 < data[x].GetLength(0)) stream.Write(data[x][y] + ",");
else stream.Write(data[x][y]);
}
stream.WriteLine("");
}
}
}
public static String PrintArrayToString(Double[][] data)
{
StringBuilder theString = new StringBuilder();
for (Int32 x = 0; x < data.GetLength(0); x++)
{
for (Int32 y = 0; y < data[x].GetLength(0); y++)
{
if (y + 1 < data[x].GetLength(0)) theString.Append(data[x][y] + ",");
else theString.Append(data[x][y]);
}
theString.AppendLine();
}
return theString.ToString();
}
public static bool IsEqual(this Int16[][] a, Double[][] b, Double atol = 0, Double rtol = 0)
{
if (a == null && b == null)
return true;
if (a == null ^ b == null)
return false;
int[] la = a.GetLength(true);
int[] lb = b.GetLength(true);
if (la.Length != lb.Length)
return false;
for (int i = 0; i < la.Length; i++)
if (la[i] != lb[i])
return false;
if (rtol > 0)
{
for (int i = 0; i < a.Length; i++)
for (int j = 0; j < a[i].Length; j++)
{
var A = a[i][j];
var B = b[i][j];
if (A == B)
continue;
if (Double.IsNaN(B))
return false;
if (Double.IsInfinity(B))
return false;
var C = A;
var D = B;
var delta = Math.Abs(C - D);
if (C == 0)
{
if (delta <= rtol)
continue;
}
else if (D == 0)
{
if (delta <= rtol)
continue;
}
if (delta <= Math.Abs(C) * rtol)
continue;
return false;
}
}
else if (atol > 0)
{
for (int i = 0; i < a.Length; i++)
for (int j = 0; j < a[i].Length; j++)
{
var A = a[i][j];
var B = b[i][j];
if (A == B)
continue;
if (Double.IsNaN(B))
return false;
if (Double.IsInfinity(B))
return false;
var C = A;
var D = B;
if (Math.Abs(C - D) <= atol)
continue;
return false;
}
}
else
{
for (int i = 0; i < a.Length; i++)
for (int j = 0; j < a[i].Length; j++)
{
var A = a[i][j];
var B = b[i][j];
if (Double.IsNaN(B))
return false;
if (Double.IsInfinity(B))
return false;
if (A != B)
return false;
}
}
return true;
}
public static bool IsEqual(this sbyte[,] a, Double[,] b, Double atol = 0, Double rtol = 0)
{
if (a == null && b == null)
return true;
if (a == null ^ b == null)
return false;
int[] la = a.GetLength(true);
int[] lb = b.GetLength(true);
if (la.Length != lb.Length)
return false;
for (int i = 0; i < la.Length; i++)
if (la[i] != lb[i])
return false;
unsafe
{
fixed (sbyte* ptrA = a)
fixed (Double* ptrB = b)
{
if (rtol > 0)
{
for (int i = 0; i < a.Length; i++)
{
var A = ptrA[i];
var B = ptrB[i];
if (A == B)
continue;
if (Double.IsNaN(B))
return false;
if (Double.IsInfinity(B))
return false;
var C = A;
var D = B;
var delta = Math.Abs(C - D);
if (C == 0)
{
if (delta <= rtol)
continue;
}
else if (D == 0)
{
if (delta <= rtol)
continue;
}
if (delta <= Math.Abs(C) * rtol)
continue;
return false;
}
}
else if (atol > 0)
{
for (int i = 0; i < a.Length; i++)
{
var A = ptrA[i];
var B = ptrB[i];
if (A == B)
continue;
if (Double.IsNaN(B))
return false;
if (Double.IsInfinity(B))
return false;
var C = A;
var D = B;
if (Math.Abs(C - D) <= atol)
continue;
return false;
}
}
else
{
for (int i = 0; i < a.Length; i++)
{
var A = ptrA[i];
var B = ptrB[i];
if (Double.IsNaN(B))
return false;
if (Double.IsInfinity(B))
return false;
if (A != B)
return false;
}
}
}
}
return true;
}
public static bool IsEqual(this Decimal[,] a, Double[][] b, Decimal atol = 0, Decimal rtol = 0)
{
if (a == null && b == null)
return true;
if (a == null ^ b == null)
return false;
int[] la = a.GetLength(true);
int[] lb = b.GetLength(true);
if (la.Length != lb.Length)
return false;
for (int i = 0; i < la.Length; i++)
if (la[i] != lb[i])
return false;
if (rtol > 0)
{
for (int i = 0; i < b.Length; i++)
for (int j = 0; j < b[i].Length; j++)
{
var A = a[i, j];
var B = b[i][j];
if (Double.IsNaN(B))
return false;
if (Double.IsInfinity(B))
return false;
decimal C = (decimal)A;
decimal D = (decimal)B;
if (C == D)
continue;
var delta = Math.Abs(C - D);
if (C == 0)
{
if (delta <= rtol)
continue;
}
else if (D == 0)
{
if (delta <= rtol)
continue;
}
if (delta <= Math.Abs(C) * rtol)
continue;
return false;
}
}
else if (atol > 0)
{
for (int i = 0; i < b.Length; i++)
for (int j = 0; j < b[i].Length; j++)
{
var A = a[i, j];
var B = b[i][j];
if (Double.IsNaN(B))
return false;
if (Double.IsInfinity(B))
return false;
decimal C = (decimal)A;
decimal D = (decimal)B;
if (C == D)
continue;
if (Math.Abs(C - D) <= atol)
continue;
return false;
}
}
else
{
for (int i = 0; i < b.Length; i++)
for (int j = 0; j < b[i].Length; j++)
{
var A = (decimal)a[i, j];
var B = (decimal)b[i][j];
if (A != B)
return false;
}
}
return true;
}
public static void Map3D(MappingMode mappingMode, Double[, ,] array, ImplicitModuleBase module, MappingRanges ranges)
{
var width = array.GetLength(0);
var height = array.GetLength(1);
var depth = array.GetLength(2);
for (var x = 0; x < width; ++x)
{
for (var y = 0; y < height; ++y)
{
for (var z = 0; z < depth; ++z)
{
var p = x / (Double)width;
var q = y / (Double)height;
var r = z / (Double)depth;
Double nx;
Double ny;
Double nz;
Double nw;
Double nu;
Double dx;
Double dy;
Double dz;
var val = 0.0;
switch (mappingMode)
{
case MappingMode.SeamlessNone:
dx = ranges.MapX1 - ranges.MapX0;
dy = ranges.MapY1 - ranges.MapY0;
dz = ranges.MapZ1 - ranges.MapZ0;
nx = ranges.MapX0 + p * dx;
ny = ranges.MapY0 + q * dy;
nz = ranges.MapZ0 + r * dz;
val = module.Get(nx, ny, nz);
break;
case MappingMode.SeamlessX:
dx = ranges.LoopX1 - ranges.LoopX0;
dy = ranges.MapY1 - ranges.MapY0;
dz = ranges.MapZ1 - ranges.MapZ0;
p = p * (ranges.MapX1 - ranges.MapX0) / (ranges.LoopX1 - ranges.LoopX0);
nx = ranges.LoopX0 + Math.Cos(p * PI2) * dx / PI2;
ny = ranges.LoopX0 + Math.Sin(p * PI2) * dx / PI2;
nz = ranges.MapY0 + q * dy;
nw = ranges.MapZ0 + depth * dz;
val = module.Get(nx, ny, nz, nw);
break;
case MappingMode.SeamlessY:
dx = ranges.MapX1 - ranges.MapX0;
dy = ranges.LoopY1 - ranges.LoopY0;
dz = ranges.MapZ1 - ranges.MapZ0;
q = q * (ranges.MapY1 - ranges.MapY0) / (ranges.LoopY1 - ranges.LoopY0);
nx = ranges.MapX0 + p * dx;
ny = ranges.LoopY0 + Math.Cos(q * PI2) * dy / PI2;
nz = ranges.LoopY0 + Math.Sin(q * PI2) * dy / PI2;
nw = ranges.MapZ0 + r * dz;
val = module.Get(nx, ny, nz, nw);
break;
case MappingMode.SeamlessZ:
dx = ranges.MapX1 - ranges.MapX0;
dy = ranges.MapY1 - ranges.MapY0;
dz = ranges.LoopZ1 - ranges.LoopZ0;
r = r * (ranges.MapZ1 - ranges.MapZ0) / (ranges.LoopZ1 - ranges.LoopZ0);
nx = ranges.MapX0 + p * dx;
ny = ranges.MapY0 + q * dy;
nz = ranges.LoopZ0 + Math.Cos(r * PI2) * dz / PI2;
nw = ranges.LoopZ0 + Math.Sin(r * PI2) * dz / PI2;
val = module.Get(nx, ny, nz, nw);
break;
case MappingMode.SeamlessXY:
dx = ranges.LoopX1 - ranges.LoopX0;
dy = ranges.LoopY1 - ranges.LoopY0;
dz = ranges.MapZ1 - ranges.MapZ0;
p = p * (ranges.MapX1 - ranges.MapX0) / (ranges.LoopX1 - ranges.LoopX0);
q = q * (ranges.MapY1 - ranges.MapY0) / (ranges.LoopY1 - ranges.LoopY0);
nx = ranges.LoopX0 + Math.Cos(p * PI2) * dx / PI2;
ny = ranges.LoopX0 + Math.Sin(p * PI2) * dx / PI2;
nz = ranges.LoopY0 + Math.Cos(q * PI2) * dy / PI2;
nw = ranges.LoopY0 + Math.Sin(q * PI2) * dy / PI2;
nu = ranges.MapZ0 + r * dz;
val = module.Get(nx, ny, nz, nw, nu, 0);
break;
case MappingMode.SeamlessXZ:
dx = ranges.LoopX1 - ranges.LoopX0;
dy = ranges.MapY1 - ranges.MapY0;
dz = ranges.LoopZ1 - ranges.LoopZ0;
p = p * (ranges.MapX1 - ranges.MapX0) / (ranges.LoopX1 - ranges.LoopX0);
r = r * (ranges.MapZ1 - ranges.MapZ0) / (ranges.LoopZ1 - ranges.LoopZ0);
nx = ranges.LoopX0 + Math.Cos(p * PI2) * dx / PI2;
ny = ranges.LoopX0 + Math.Sin(p * PI2) * dx / PI2;
nz = ranges.MapY0 + q * dy;
nw = ranges.LoopZ0 + Math.Cos(r * PI2) * dz / PI2;
nu = ranges.LoopZ0 + Math.Sin(r * PI2) * dz / PI2;
val = module.Get(nx, ny, nz, nw, nu, 0);
break;
case MappingMode.SeamlessYZ:
dx = ranges.MapX1 - ranges.MapX0;
dy = ranges.LoopY1 - ranges.LoopY0;
dz = ranges.LoopZ1 - ranges.LoopZ0;
q = q * (ranges.MapY1 - ranges.MapY0) / (ranges.LoopY1 - ranges.LoopY0);
r = r * (ranges.MapZ1 - ranges.MapZ0) / (ranges.LoopZ1 - ranges.LoopZ0);
nx = ranges.MapX0 + p * dx;
ny = ranges.LoopY0 + Math.Cos(q * PI2) * dy / PI2;
nz = ranges.LoopY0 + Math.Sin(q * PI2) * dy / PI2;
nw = ranges.LoopZ0 + Math.Cos(r * PI2) * dz / PI2;
nu = ranges.LoopZ0 + Math.Sin(r * PI2) * dz / PI2;
val = module.Get(nx, ny, nz, nw, nu, 0);
break;
case MappingMode.SeamlessXYZ:
dx = ranges.LoopX1 - ranges.LoopX0;
dy = ranges.LoopY1 - ranges.LoopY0;
dz = ranges.LoopZ1 - ranges.LoopZ0;
p = p * (ranges.MapX1 - ranges.MapX0) / (ranges.LoopX1 - ranges.LoopX0);
q = q * (ranges.MapY1 - ranges.MapY0) / (ranges.LoopY1 - ranges.LoopY0);
r = r * (ranges.MapZ1 - ranges.MapZ0) / (ranges.LoopZ1 - ranges.LoopZ0);
nx = ranges.LoopX0 + Math.Cos(p * PI2) * dx / PI2;
ny = ranges.LoopX0 + Math.Sin(p * PI2) * dx / PI2;
nz = ranges.LoopY0 + Math.Cos(q * PI2) * dy / PI2;
nw = ranges.LoopY0 + Math.Sin(q * PI2) * dy / PI2;
nu = ranges.LoopZ0 + Math.Cos(r * PI2) * dz / PI2;
Double nv = ranges.LoopZ0 + Math.Sin(r * PI2) * dz / PI2;
val = module.Get(nx, ny, nz, nw, nu, nv);
break;
}
array[x, y, z] = val;
}
}
}
}
public static Double StandardDeviation(Double[,] numbers, Int32 columnIndex)
{
if (numbers == null)
throw new ArgumentNullException("numbers");
Int32 length = numbers.GetLength(0);
var row = new Double[length];
for (Int32 i = 0; i < length; i++)
row[i] = numbers[i, columnIndex];
return StandardDeviation(row);
}
/// <summary>
/// Constructs a new matrix based on the given two dimensional array.
/// </summary>
/// <param name="values">The values which set the dimensions and starting values of the matrix.</param>
public MatrixValue(Double[,] values)
: this(values.GetLength(0), values.GetLength(1))
{
for (var j = 0; j < _dimY; j++)
{
for (var i = 0; i < _dimX; i++)
{
if (values[j, i] != 0.0)
{
this[j + 1, i + 1] = new ScalarValue(values[j, i]);
}
}
}
}
private Double[][] Desnormalizar(Double[][] vectoresEncontrados)
{
Double[][] vectorDesnormalizado = new Double[vectoresEncontrados.GetLength(0)][];
Double[][] maxYmin = csvtoMatrix("maxYmin");
int alto = vectoresEncontrados.Length;
for (int i = 0; i < alto; i++)
{
Double[] fila = new Double[10];
for (int j = 0; j < 10; j++)
{
fila[j] = (vectoresEncontrados[i][j] * (maxYmin[0][j] - maxYmin[1][j])) + maxYmin[1][j];
}
vectorDesnormalizado[i] = fila;
}
return vectorDesnormalizado;
}
/// <summary>
/// Wstawia dane do kontrolki je wyświetlającej.
/// </summary>
private void InsertData(Double[,] dane)
{
StringBuilder builder = new StringBuilder();
dataListBox.Items.Clear();
for (Int32 i = 0; i < dane.GetLength(0); i++)
{
for (Int32 j = 0; j < dane.GetLength(1); j++)
{
Double d = dane[i, j];
if (Double.IsInfinity(d))
builder.Append("INF");
else
builder.Append(d);
builder.Append('\t');
}
dataListBox.Items.Add(builder.ToString());
builder.Clear();
}
}
private void WriteAccuracy(Double[,] result)
{
var p_size = result.GetLength(0);
var d_size = result.GetLength(1);
using (var writer = new StreamWriter("../Accuracy.cs"))
{
writer.WriteLine("{");
for (var p = 0; p < p_size; p++)
{
writer.Write("{{ {0:0.000}f /* {1:00.0}° */", result[p, 0], ToAngle(result[p, 0]));
for (var d = 1; d < d_size; d++)
{
writer.Write(", {0:0.000}f /* {1:00.0}° */", result[p, d], ToAngle(result[p, d]));
}
writer.WriteLine(" },");
}
writer.WriteLine("};");
}
}
private Double[,] InverseDCT(Double[,] input)
{
double sqrtOfLength = System.Math.Sqrt(input.Length);
int N = input.GetLength(0);
Double[,] coefficientsMatrix = this.quantizationMatrixForStandardJPEG();
Double[,] output = new Double[N, N];
for (int x = 0; x <= N - 1; x++)
{
for (int y = 0; y <= N - 1; y++)
{
double sum = 0.0;
for (int u = 0; u <= N - 1; u++)
{
for (int v = 0; v <= N - 1; v++)
{
sum += coefficientsMatrix[u, v] * input[u, v] * System.Math.Cos(((2.0 * x + 1.0) / (2.0 * N)) * u * System.Math.PI) * System.Math.Cos(((2.0 * y + 1.0) / (2.0 * N)) * v * System.Math.PI);
}
};
output[x, y] = System.Math.Round(sum);
}
}
return output;
}
private Double[,] v; // right singular vectors
#endregion Fields
#region Constructors
/// <summary>Constructs a new singular value decomposition.</summary>
/// <param name="value">
/// The matrix to be decomposed.</param>
public SingularValueDecomposition(Double[,] value)
{
if (value == null)
{
throw new ArgumentNullException("value", "Matrix cannot be null.");
}
//m should not less than n
Double[,] a;
m = value.GetLength(0); // rows
n = value.GetLength(1); // cols
if (m < n)
{
throw new System.Exception("rows should not less than cols in value matrix");
}
// Proceed anyway
a = (Double[,])value.Clone();
int nu = System.Math.Min(m, n);
int ni = System.Math.Min(m + 1, n);
s = new Double[ni];
u = new Double[m, nu];
v = new Double[n, n];
Double[] e = new Double[n];
Double[] work = new Double[m];
// Will store ordered sequence of indices after sorting.
si = new int[ni]; for (int i = 0; i < ni; i++) si[i] = i;
// Reduce A to bidiagonal form, storing the diagonal elements in s and the super-diagonal elements in e.
int nct = System.Math.Min(m - 1, n);
int nrt = System.Math.Max(0, System.Math.Min(n - 2, m));
for (int k = 0; k < System.Math.Max(nct, nrt); k++)
{
if (k < nct)
{
// Compute the transformation for the k-th column and place the k-th diagonal in s[k].
// Compute 2-norm of k-th column without under/overflow.
s[k] = 0;
for (int i = k; i < m; i++)
{
s[k] = Hypotenuse(s[k], a[i, k]);
}
if (s[k] != 0)
{
if (a[k, k] < 0)
s[k] = -s[k];
for (int i = k; i < m; i++)
a[i, k] /= s[k];
a[k, k] += 1;
}
s[k] = -s[k];
}
for (int j = k + 1; j < n; j++)
{
if ((k < nct) & (s[k] != 0))
{
// Apply the transformation.
Double t = 0;
for (int i = k; i < m; i++)
{
t += a[i, k] * a[i, j];
}
t = -t / a[k, k];
for (int i = k; i < m; i++)
{
a[i, j] += t * a[i, k];
}
}
// Place the k-th row of A into e for the subsequent calculation of the row transformation.
e[j] = a[k, j];
}
if (k < nct)
{
// Place the transformation in U for subsequent back
// multiplication.
for (int i = k; i < m; i++)
u[i, k] = a[i, k];
}
if (k < nrt)
{
// Compute the k-th row transformation and place the k-th super-diagonal in e[k].
// Compute 2-norm without under/overflow.
e[k] = 0;
for (int i = k + 1; i < n; i++)
e[k] = Hypotenuse(e[k], e[i]);
if (e[k] != 0)
{
if (e[k + 1] < 0)
e[k] = -e[k];
for (int i = k + 1; i < n; i++)
e[i] /= e[k];
e[k + 1] += 1;
}
e[k] = -e[k];
if ((k + 1 < m) & (e[k] != 0))
{
// Apply the transformation.
for (int i = k + 1; i < m; i++)
work[i] = 0;
int k1 = k + 1;
for (int i = k1; i < m; i++)
{
for (int j = k1; j < n; j++)
{
work[i] += e[j] * a[i, j];
}
}
for (int j = k1; j < n; j++)
{
Double t = -e[j] / e[k1];
for (int i = k1; i < m; i++)
{
a[i, j] += t * work[i];
}
}
}
// Place the transformation in V for subsequent back multiplication.
for (int i = k + 1; i < n; i++)
v[i, k] = e[i];
}
}
// Set up the final bidiagonal matrix or order p.
int p = System.Math.Min(n, m + 1);
if (nct < n) s[nct] = a[nct, nct];
if (m < p) s[p - 1] = 0;
if (nrt + 1 < p) e[nrt] = a[nrt, p - 1];
e[p - 1] = 0;
//generate U.
for (int j = nct; j < nu; j++)
{
for (int i = 0; i < m; i++)
u[i, j] = 0;
u[j, j] = 1;
}
for (int k = nct - 1; k >= 0; k--)
{
if (s[k] != 0)
{
for (int j = k + 1; j < nu; j++)
{
Double t = 0;
for (int i = k; i < m; i++)
{
t += u[i, k] * u[i, j];
}
t = -t / u[k, k];
for (int i = k; i < m; i++)
{
u[i, j] += t * u[i, k];
}
}
for (int i = k; i < m; i++)
{
u[i, k] = -1.0 * u[i, k];
}
u[k, k] = 1 + u[k, k];
for (int i = 0; i < k - 1; i++)
u[i, k] = 0;
}
else
{
for (int i = 0; i < m; i++)
u[i, k] = 0;
u[k, k] = 1;
}
}
//generate V.
for (int k = n - 1; k >= 0; k--)
{
if ((k < nrt) & (e[k] != 0))
{
// TODO: The following is a pseudo correction to make SVD
// work on matrices with n > m (less rows than columns).
// For the proper correction, compute the decomposition of the
// transpose of A and swap the left and right eigenvectors
// Original line:
// for (int j = k + 1; j < nu; j++)
// Pseudo correction:
// for (int j = k + 1; j < n; j++)
for (int j = k + 1; j < n; j++) // pseudo-correction
{
Double t = 0;
for (int i = k + 1; i < n; i++)
{
t += v[i, k] * v[i, j];
}
t = -t / v[k + 1, k];
for (int i = k + 1; i < n; i++)
{
v[i, j] += t * v[i, k];
}
}
}
for (int i = 0; i < n; i++)
{
v[i, k] = 0;
}
v[k, k] = 1;
}
// Main iteration loop for the singular values.
int pp = p - 1;
int iter = 0;
while (p > 0)
{
int k, kase;
// Here is where a test for too many iterations would go.
// This section of the program inspects for
// negligible elements in the s and e arrays. On
// completion the variables kase and k are set as follows.
// kase = 1 if s(p) and e[k-1] are negligible and k<p
// kase = 2 if s(k) is negligible and k<p
// kase = 3 if e[k-1] is negligible, k<p, and
// s(k), ..., s(p) are not negligible (qr step).
// kase = 4 if e(p-1) is negligible (convergence).
for (k = p - 2; k >= -1; k--)
{
if (k == -1)
break;
if (System.Math.Abs(e[k]) <=
tiny + eps * (System.Math.Abs(s[k]) + System.Math.Abs(s[k + 1])))
{
e[k] = 0;
break;
}
}
if (k == p - 2)
{
kase = 4;
}
else
{
int ks;
for (ks = p - 1; ks >= k; ks--)
{
if (ks == k)
break;
Double t = (ks != p ? System.Math.Abs(e[ks]) : 0) +
(ks != k + 1 ? System.Math.Abs(e[ks - 1]) : 0);
if (System.Math.Abs(s[ks]) <= tiny + eps * t)
{
s[ks] = 0;
break;
}
}
if (ks == k)
kase = 3;
else if (ks == p - 1)
kase = 1;
else
{
kase = 2;
k = ks;
}
}
k++;
// Perform the task indicated by kase.
switch (kase)
{
// Deflate negligible s(p).
case 1:
{
Double f = e[p - 2];
e[p - 2] = 0;
for (int j = p - 2; j >= k; j--)
{
Double t = Hypotenuse(s[j], f);
Double cs = s[j] / t;
Double sn = f / t;
s[j] = t;
if (j != k)
{
f = -sn * e[j - 1];
e[j - 1] = cs * e[j - 1];
}
for (int i = 0; i < n; i++)
{
t = cs * v[i, j] + sn * v[i, p - 1];
v[i, p - 1] = -sn * v[i, j] + cs * v[i, p - 1];
v[i, j] = t;
}
}
}
break;
// Split at negligible s(k).
case 2:
{
Double f = e[k - 1];
e[k - 1] = 0;
for (int j = k; j < p; j++)
{
Double t = Hypotenuse(s[j], f);
Double cs = s[j] / t;
Double sn = f / t;
s[j] = t;
f = -sn * e[j];
e[j] = cs * e[j];
for (int i = 0; i < m; i++)
{
t = cs * u[i, j] + sn * u[i, k - 1];
u[i, k - 1] = -sn * u[i, j] + cs * u[i, k - 1];
u[i, j] = t;
}
}
}
break;
// Perform one qr step.
case 3:
{
// Calculate the shift.
Double scale = System.Math.Max(System.Math.Max(System.Math.Max(System.Math.Max(System.Math.Abs(s[p - 1]), System.Math.Abs(s[p - 2])), System.Math.Abs(e[p - 2])), System.Math.Abs(s[k])), System.Math.Abs(e[k]));
Double sp = s[p - 1] / scale;
Double spm1 = s[p - 2] / scale;
Double epm1 = e[p - 2] / scale;
Double sk = s[k] / scale;
Double ek = e[k] / scale;
Double b = ((spm1 + sp) * (spm1 - sp) + epm1 * epm1) / 2;
Double c = (sp * epm1) * (sp * epm1);
double shift = 0;
if ((b != 0) | (c != 0))
{
if (b < 0)
shift = -System.Math.Sqrt(b * b + c);
else
shift = System.Math.Sqrt(b * b + c);
shift = c / (b + shift);
}
Double f = (sk + sp) * (sk - sp) + (Double)shift;
Double g = sk * ek;
// Chase zeros.
for (int j = k; j < p - 1; j++)
{
Double t = Hypotenuse(f, g);
Double cs = f / t;
Double sn = g / t;
if (j != k) e[j - 1] = t;
f = cs * s[j] + sn * e[j];
e[j] = cs * e[j] - sn * s[j];
g = sn * s[j + 1];
s[j + 1] = cs * s[j + 1];
for (int i = 0; i < n; i++)
{
/*t = cs * v[i, j] + sn * v[i, j + 1];
v[i, j + 1] = -sn * v[i, j] + cs * v[i, j + 1];
v[i, j] = t;*/
Double vij = v[i, j]; // *vj;
Double vij1 = v[i, j + 1]; // *vj1;
t = cs * vij + sn * vij1;
v[i, j + 1] = -sn * vij + cs * vij1;
v[i, j] = t;
}
t = Hypotenuse(f, g);
cs = f / t;
sn = g / t;
s[j] = t;
f = cs * e[j] + sn * s[j + 1];
s[j + 1] = -sn * e[j] + cs * s[j + 1];
g = sn * e[j + 1];
e[j + 1] = cs * e[j + 1];
if (j < m - 1)
{
for (int i = 0; i < m; i++)
{
/* t = cs * u[i, j] + sn * u[i, j + 1];
u[i, j + 1] = -sn * u[i, j] + cs * u[i, j + 1];
u[i, j] = t;*/
Double uij = u[i, j]; // *uj;
Double uij1 = u[i, j + 1]; // *uj1;
t = cs * uij + sn * uij1;
u[i, j + 1] = -sn * uij + cs * uij1;
u[i, j] = t;
}
}
}
e[p - 2] = f;
iter = iter + 1;
}
break;
// Convergence.
case 4:
{
// Make the singular values positive.
if (s[k] <= 0)
{
s[k] = (s[k] < 0 ? -s[k] : 0);
for (int i = 0; i <= pp; i++)
v[i, k] = -v[i, k];
}
// Order the singular values.
while (k < pp)
{
if (s[k] >= s[k + 1])
break;
Double t = s[k];
s[k] = s[k + 1];
s[k + 1] = t;
int ti = si[k];
si[k] = si[k + 1];
si[k + 1] = ti;
if (k < n - 1)
{
for (int i = 0; i < n; i++)
{
t = v[i, k + 1];
v[i, k + 1] = v[i, k];
v[i, k] = t;
}
}
if (k < m - 1)
{
for (int i = 0; i < m; i++)
{
t = u[i, k + 1];
u[i, k + 1] = u[i, k];
u[i, k] = t;
}
}
k++;
}
iter = 0;
p--;
}
break;
}
}
}
public double[,] spreadsmatrix(DataTable initdata, double injcharge, double widthcharge)
{
//Declare ?D-String array
//here we start from second row as ColumnNames are stored in first row
double[,] initarray = new Double[initdata.Rows.Count, initdata.Columns.Count-1];
double[,] resultarray = new Double[initdata.Columns.Count-1, initdata.Columns.Count - 1];
//Now save DataTable values in array,
for (int row = 0; row < initdata.Rows.Count; row++)
{
for (int col = 1; col < initdata.Columns.Count; col++)
{
initarray[row, col-1] = Math.Round((double)initdata.Rows[(row)][col], 6);
}
}
//find the discounted spreads for injection in month i and withdraw in month j
// Each value shown in the table represents the discounted revenue the owner of the facility would receive by
//injecting one unit of gas and withdrawing it at a later time, inclusive of injection costs and withdrawal costs...
for (int i = 0; i < resultarray.GetLength(0); i++)
{
int ij = i;
for (int j = initarray.GetLength(1); j > i; j--)
{
resultarray[i, ij] = Math.Round(((initarray[0, j - 1] + widthcharge) - (initarray[0, i] - injcharge)), 5);
ij++;
}
}
return resultarray;
}
/// <summary>
/// 求解t时刻位于隐藏状态Si的概率矩阵
/// </summary>
/// <param name="ALPHA">前向算法局部概率</param>
/// <param name="BETA">后向算法局部概率</param>
/// <param name="GAMMA">输出：各时刻位于各隐藏状态的概率矩阵</param>
private void ComputeGamma(Double[,] ALPHA, Double[,] BETA, ref Double[,] GAMMA)
{
Int32 T = ALPHA.GetLength(0);
if (GAMMA == null) GAMMA = new Double[T, N];
for (Int32 t = 0; t < T; t++)
{
Double Denominator = 0;
for (Int32 i = 0; i < N; i++)
{
GAMMA[t, i] = ALPHA[t, i] * BETA[t, i];
Denominator += GAMMA[t, i];
}
for (Int32 i = 0; i < N; i++)
{
GAMMA[t, i] /= Denominator; // 保证各时刻的概率总和等于1
}
}
}
/// <summary>
/// Zapisuje macierz danych z pliku.
/// </summary>
private void SaveFile(String sciezka, Double[,] matrix)
{
using (StreamWriter stream = new StreamWriter(sciezka))
{
for (Int32 i = 0; i < matrix.GetLength(0); ++i)
{
for (Int32 j = 0; j < matrix.GetLength(1); ++j)
{
stream.Write(Double.IsInfinity(matrix[i, j]) ? "INF" : matrix[i, j].ToString());
stream.Write("; ");
}
stream.WriteLine();
}
}
}
/// <summary>
/// Construct an eigenvalue decomposition.</summary>
///
/// <param name="value">
/// The matrix to be decomposed.</param>
/// <param name="assumeSymmetric">
/// Defines if the matrix should be assumed as being symmetric
/// regardless if it is or not. Default is <see langword="false"/>.</param>
/// <param name="inPlace">
/// Pass <see langword="true"/> to perform the decomposition in place. The matrix
/// <paramref name="value"/> will be destroyed in the process, resulting in less
/// memory comsumption.</param>
/// <param name="sort">
/// Pass <see langword="true"/> to sort the eigenvalues and eigenvectors at the end
/// of the decomposition.</param>
///
public EigenvalueDecomposition(Double[,] value, bool assumeSymmetric,
bool inPlace = false, bool sort = false)
{
if (value == null)
throw new ArgumentNullException("value", "Matrix cannot be null.");
if (value.GetLength(0) != value.GetLength(1))
throw new ArgumentException("Matrix is not a square matrix.", "value");
n = value.GetLength(1);
V = new Double[n, n];
d = new Double[n];
e = new Double[n];
this.symmetric = assumeSymmetric;
if (this.symmetric)
{
V = inPlace ? value : (Double[,])value.Clone();
// Tridiagonalize.
this.tred2();
// Diagonalize.
this.tql2();
}
else
{
H = inPlace ? value : (Double[,])value.Clone();
ort = new Double[n];
// Reduce to Hessenberg form.
this.orthes();
// Reduce Hessenberg to real Schur form.
this.hqr2();
}
if (sort)
{
// Sort eigenvalues and vectors in descending order
var idx = Vector.Range(n);
Array.Sort(idx, (i, j) =>
{
if (Math.Abs(d[i]) == Math.Abs(d[j]))
return -Math.Abs(e[i]).CompareTo(Math.Abs(e[j]));
return -Math.Abs(d[i]).CompareTo(Math.Abs(d[j]));
});
this.d = this.d.Get(idx);
this.e = this.e.Get(idx);
this.V = this.V.Get(null, idx);
}
}
public static void Map2DNoZ(MappingMode mappingMode, Double[,] array, ImplicitModuleBase module, MappingRanges ranges)
{
var width = array.GetLength(0);
var height = array.GetLength(1);
for (var x = 0; x < width; ++x)
{
for (var y = 0; y < height; ++y)
{
var p = x / (double)width;
var q = y / (double)height;
double nx;
double ny;
double nz;
double dx;
double dy;
var val = 0.00;
switch (mappingMode)
{
case MappingMode.SeamlessNone:
nx = ranges.MapX0 + p*(ranges.MapX1 - ranges.MapX0);
ny = ranges.MapY0 + q*(ranges.MapY1 - ranges.MapY0);
val = module.Get(nx, ny);
break;
case MappingMode.SeamlessX:
dx = ranges.LoopX1 - ranges.LoopX0;
dy = ranges.MapY1 - ranges.MapY0;
p = p*(ranges.MapX1 - ranges.MapX0)/(ranges.LoopX1 - ranges.LoopX0);
nx = ranges.LoopX0 + Math.Cos(p*PI2)*dx/PI2;
ny = ranges.LoopX0 + Math.Sin(p*PI2)*dx/PI2;
nz = ranges.MapY0 + q*dy;
val = module.Get(nx, ny, nz);
break;
case MappingMode.SeamlessY:
dx = ranges.MapX1 - ranges.MapX0;
dy = ranges.LoopY1 - ranges.LoopY0;
q = q*(ranges.MapY1 - ranges.MapY0)/(ranges.LoopY1 - ranges.LoopY0);
nx = ranges.MapX0 + p*dx;
ny = ranges.LoopY0 + Math.Cos(q*PI2)*dy/PI2;
nz = ranges.LoopY0 + Math.Sin(q*PI2)*dy/PI2;
val = module.Get(nx, ny, nz);
break;
case MappingMode.SeamlessXY:
dx = ranges.LoopX1 - ranges.LoopX0;
dy = ranges.LoopY1 - ranges.LoopY0;
p = p*(ranges.MapX1 - ranges.MapX0)/(ranges.LoopX1 - ranges.LoopX0);
q = q*(ranges.MapY1 - ranges.MapY0)/(ranges.LoopY1 - ranges.LoopY0);
nx = ranges.LoopX0 + Math.Cos(p*PI2)*dx/PI2;
ny = ranges.LoopX0 + Math.Sin(p*PI2)*dx/PI2;
nz = ranges.LoopY0 + Math.Cos(q*PI2)*dy/PI2;
double nw = ranges.LoopY0 + Math.Sin(q*PI2)*dy/PI2;
val = module.Get(nx, ny, nz, nw);
break;
}
array[x, y] = val;
}
}
}
static Term[,] array(Double[] m)
{
int rows = m.GetLength(0);
Term[,] r = new Term[rows, 1];
for (int row = 0; row < rows; row++)
r[row, 0] = m[row];
return r;
}
public static void Map2D(MappingMode mappingMode, Double[,] array, ImplicitModuleBase module, MappingRanges ranges, Double z)
{
var width = array.GetLength(0);
var height = array.GetLength(1);
for (var x = 0; x < width; ++x)
{
for (var y = 0; y < height; ++y)
{
var p = x / (double)width;
var q = y / (double)height;
double r;
double nx;
double ny;
double nz;
double nw;
double nu;
double dx;
double dy;
double dz;
var val = 0.00;
switch (mappingMode)
{
case MappingMode.SeamlessNone:
nx = ranges.MapX0 + p*(ranges.MapX1 - ranges.MapX0);
ny = ranges.MapY0 + q*(ranges.MapY1 - ranges.MapY0);
nz = z;
val = module.Get(nx, ny, nz);
break;
case MappingMode.SeamlessX:
dx = ranges.LoopX1 - ranges.LoopX0;
dy = ranges.MapY1 - ranges.MapY0;
p = p*(ranges.MapX1 - ranges.MapX0)/(ranges.LoopX1 - ranges.LoopX0);
nx = ranges.LoopX0 + Math.Cos(p*PI2)*dx/PI2;
ny = ranges.LoopX0 + Math.Sin(p*PI2)*dx/PI2;
nz = ranges.MapY0 + q*dy;
nw = z;
val = module.Get(nx, ny, nz, nw);
break;
case MappingMode.SeamlessY:
dx = ranges.MapX1 - ranges.MapX0;
dy = ranges.LoopY1 - ranges.LoopY0;
q = q*(ranges.MapY1 - ranges.MapY0)/(ranges.LoopY1 - ranges.LoopY0);
nx = ranges.MapX0 + p*dx;
ny = ranges.LoopY0 + Math.Cos(q*PI2)*dy/PI2;
nz = ranges.LoopY0 + Math.Sin(q*PI2)*dy/PI2;
nw = z;
val = module.Get(nx, ny, nz, nw);
break;
case MappingMode.SeamlessZ:
dx = ranges.MapX1 - ranges.MapX0;
dy = ranges.MapY1 - ranges.MapY0;
dz = ranges.LoopZ1 - ranges.LoopZ0;
nx = ranges.MapX0 + p*dx;
ny = ranges.MapY0 + p*dy;
r = (z - ranges.MapZ0)/(ranges.MapZ1 - ranges.MapZ0);
var zval = r*(ranges.MapZ1 - ranges.MapZ0)/(ranges.LoopZ1 - ranges.LoopZ0);
nz = ranges.LoopZ0 + Math.Cos(zval*PI2)*dz/PI2;
nw = ranges.LoopZ0 + Math.Sin(zval*PI2)*dz/PI2;
val = module.Get(nx, ny, nz, nw);
break;
case MappingMode.SeamlessXY:
dx = ranges.LoopX1 - ranges.LoopX0;
dy = ranges.LoopY1 - ranges.LoopY0;
p = p*(ranges.MapX1 - ranges.MapX0)/(ranges.LoopX1 - ranges.LoopX0);
q = q*(ranges.MapY1 - ranges.MapY0)/(ranges.LoopY1 - ranges.LoopY0);
nx = ranges.LoopX0 + Math.Cos(p*PI2)*dx/PI2;
ny = ranges.LoopX0 + Math.Sin(p*PI2)*dx/PI2;
nz = ranges.LoopY0 + Math.Cos(q*PI2)*dy/PI2;
nw = ranges.LoopY0 + Math.Sin(q*PI2)*dy/PI2;
nu = z;
val = module.Get(nx, ny, nz, nw, nu, 0);
break;
case MappingMode.SeamlessXZ:
dx = ranges.LoopX1 - ranges.LoopX0;
dy = ranges.MapY1 - ranges.MapY0;
dz = ranges.LoopZ1 - ranges.LoopZ0;
r = (z - ranges.MapZ0)/(ranges.MapZ1 - ranges.MapZ0);
var xzval = r*(ranges.MapX1 - ranges.MapZ0)/(ranges.LoopZ1 - ranges.LoopZ0);
p = p*(ranges.MapX1 - ranges.MapX0)/(ranges.LoopX1 - ranges.LoopX0);
nx = ranges.LoopX0 + Math.Cos(p*PI2)*dx/PI2;
ny = ranges.LoopX0 + Math.Sin(p*PI2)*dx/PI2;
nz = ranges.MapY0 + q*dy;
nw = ranges.LoopZ0 + Math.Cos(xzval*PI2)*dz/PI2;
nu = ranges.LoopZ0 + Math.Sin(xzval*PI2)*dz/PI2;
val = module.Get(nx, ny, nz, nw, nu, 0);
break;
case MappingMode.SeamlessYZ:
dx = ranges.MapX1 - ranges.MapX0;
dy = ranges.LoopY1 - ranges.LoopY0;
dz = ranges.LoopZ1 - ranges.LoopZ0;
r = (z - ranges.MapZ0)/(ranges.MapZ1 - ranges.MapZ0);
var yzval = r*(ranges.MapZ1 - ranges.MapZ0)/(ranges.LoopZ1 - ranges.LoopZ0);
q = q*(ranges.MapY1 - ranges.MapY0)/(ranges.LoopY1 - ranges.LoopY0);
nx = ranges.MapX0 + p*dx;
ny = ranges.LoopY0 + Math.Cos(q*PI2)*dy/PI2;
nz = ranges.LoopY0 + Math.Sin(q*PI2)*dy/PI2;
nw = ranges.LoopZ0 + Math.Cos(yzval*PI2)*dz/PI2;
nu = ranges.LoopZ0 + Math.Sin(yzval*PI2)*dz/PI2;
val = module.Get(nx, ny, nz, nw, nu, 0);
break;
case MappingMode.SeamlessXYZ:
dx = ranges.LoopX1 - ranges.LoopX0;
dy = ranges.LoopY1 - ranges.LoopY0;
dz = ranges.LoopZ1 - ranges.LoopZ0;
p = p*(ranges.MapX1 - ranges.MapX0)/(ranges.LoopX1 - ranges.LoopX0);
q = q*(ranges.MapY1 - ranges.MapY0)/(ranges.LoopY1 - ranges.LoopY0);
r = (z - ranges.MapZ0)/(ranges.MapZ1 - ranges.MapZ0);
var xyzval = r*(ranges.MapZ1 - ranges.MapZ0)/(ranges.LoopZ1 - ranges.LoopZ0);
nx = ranges.LoopX0 + Math.Cos(p*PI2)*dx/PI2;
ny = ranges.LoopX0 + Math.Sin(p*PI2)*dx/PI2;
nz = ranges.LoopY0 + Math.Cos(q*PI2)*dy/PI2;
nw = ranges.LoopY0 + Math.Sin(q*PI2)*dy/PI2;
nu = ranges.LoopZ0 + Math.Cos(xyzval*PI2)*dz/PI2;
double nv = ranges.LoopZ0 + Math.Sin(xyzval*PI2)*dz/PI2;
val = module.Get(nx, ny, nz, nw, nu, nv);
break;
}
array[x, y] = val;
}
}
}
/// <summary>
/// Construct an eigenvalue decomposition.</summary>
/// <param name="value">
/// The matrix to be decomposed.</param>
/// <param name="assumeSymmetric">
/// Defines if the matrix should be assumed as being symmetric
/// regardless if it is or not. Default is <see langword="false"/>.</param>
/// <param name="inPlace">
/// Pass <see langword="true"/> to perform the decomposition in place. The matrix
/// <paramref name="value"/> will be destroyed in the process, resulting in less
/// memory comsumption.</param>
public EigenvalueDecomposition(Double[,] value, bool assumeSymmetric, bool inPlace)
{
if (value == null)
{
throw new ArgumentNullException("value", "Matrix cannot be null.");
}
if (value.GetLength(0) != value.GetLength(1))
{
throw new ArgumentException("Matrix is not a square matrix.", "value");
}
n = value.GetLength(1);
V = new Double[n, n];
d = new Double[n];
e = new Double[n];
this.symmetric = assumeSymmetric;
if (this.symmetric)
{
V = inPlace ? value : (Double[,])value.Clone();
// Tridiagonalize.
this.tred2();
// Diagonalize.
this.tql2();
}
else
{
H = inPlace ? value : (Double[,])value.Clone();
ort = new Double[n];
// Reduce to Hessenberg form.
this.orthes();
// Reduce Hessenberg to real Schur form.
this.hqr2();
}
}
// Method to set up the recording side of neurorighter
private bool NRAcquisitionSetup()
{
lock (this)
{
if (!taskRunning)
{
try
{
this.Cursor = Cursors.WaitCursor;
if (switch_record.Value)
{
// Create file name
if (filenameBase == null) //user hasn't specified a file
button_BrowseOutputFile_Click(null, null); //call file selection routine
if (filenameBase == null) //this happens if the user pressed cancel for the dialog
{
MessageBox.Show("An output file must be selected before recording."); //display an error message
this.Cursor = Cursors.Default;
return true;
}
// If the user is just doing repeated recordings
if (checkbox_repeatRecord.Checked || Properties.Settings.Default.useFidTimeStamp)
{
DateTime nowDate = DateTime.Now;//Get current time (local to computer);
string datePrefix = nowDate.ToString("'-'yyyy'-'MM'-'dd'-'HH'-'mm'-'ss");
filenameBase = originalNameBase + datePrefix;
}
// Look for old files with same name
string[] matchFiles;
try
{
matchFiles = Directory.GetFiles(currentSaveDir, currentSaveFile + "*");
}
catch
{
matchFiles = new string[0];
}
if (matchFiles.Length > 0)
{
DialogResult dr = MessageBox.Show("File " + filenameBase + " exists. Overwrite?",
"NeuroRighter Warning", MessageBoxButtons.YesNoCancel, MessageBoxIcon.Warning);
if (dr == DialogResult.No)
button_BrowseOutputFile_Click(null, null); //call file selection routine
else if (dr == DialogResult.Cancel)
{
this.Cursor = Cursors.Default;
return true;
}
}
// Set file base name + number of channels
recordingSettings.SetFID(filenameBase);
recordingSettings.SetNumElectrodes(numChannels);
}
// Find out how many devs and channels/dev we are going to need
int numDevices = (numChannels > 32 ? Properties.Settings.Default.AnalogInDevice.Count : 1);
numChannelsPerDev = (numChannels < 32 ? numChannels : 32);
// Set spike buffer lengths
spikeBufferLength = Convert.ToInt32(Properties.Settings.Default.ADCPollingPeriodSec * Properties.Settings.Default.RawSampleFrequency);
lfpBufferLength = Convert.ToInt32(Properties.Settings.Default.ADCPollingPeriodSec * Properties.Settings.Default.LFPSampleFrequency);
// Create spike aquisition task list
spikeTask = new List<Task>(numDevices);
Properties.Settings.Default.numSpikeTasks = numDevices;
NRAIChannelCollection spikeAqSet = new NRAIChannelCollection(numDevices, numChannelsPerDev);
spikeAqSet.SetupSpikeCollection(ref spikeTask);
// Check audio and video properties
if (Properties.Settings.Default.UseSingleChannelPlayback)
spikeOutTask = new Task("spikeOutTask"); //For audio output
if (checkBox_video.Checked) //NB: This can't be checked unless video is enabled (no need to check properties)
triggerTask = new Task("triggerTask");
// Set MUA sample rate
double muaSamplingRate = spikeSamplingRate / MUA_DOWNSAMPLE_FACTOR;
//Add LFP channels, if configured
if (Properties.Settings.Default.SeparateLFPBoard && Properties.Settings.Default.UseLFPs)
{
lfpTask = new Task("lfpTask");
for (int i = 0; i < Properties.Settings.Default.NumChannels; ++i)
lfpTask.AIChannels.CreateVoltageChannel(Properties.Settings.Default.LFPDevice + "/ai" + i.ToString(), "",
AITerminalConfiguration.Nrse, -10.0, 10.0, AIVoltageUnits.Volts);
setGain(lfpTask, Properties.Settings.Default.LFPgain);
lfpTask.Control(TaskAction.Verify);
}
//Add EEG channels, if configured
if (Properties.Settings.Default.UseEEG)
{
eegTask = new Task("eegTask");
for (int i = 0; i < Properties.Settings.Default.EEGNumChannels; ++i)
eegTask.AIChannels.CreateVoltageChannel(Properties.Settings.Default.EEGDevice + "/ai" +
(i).ToString(), "", AITerminalConfiguration.Nrse, -10.0, 10.0, AIVoltageUnits.Volts);
setGain(eegTask, (double)Properties.Settings.Default.EEGGain);
eegTask.Control(TaskAction.Verify);
eegSamplingRate = Properties.Settings.Default.EEGSamplingRate;
}
//Add channel to control Cineplex, if configured
if (checkBox_video.Checked)
triggerTask.DOChannels.CreateChannel(Properties.Settings.Default.CineplexDevice + "/Port0/line0:7", "",
ChannelLineGrouping.OneChannelForAllLines);
//Change gain based on comboBox values (1-100)
for (int i = 0; i < spikeTask.Count; ++i)
setGain(spikeTask[i], Properties.Settings.Default.A2Dgain);
//Verify the Tasks
for (int i = 0; i < spikeTask.Count; ++i)
spikeTask[i].Control(TaskAction.Verify);
//if (Properties.Settings.Default.UseSingleChannelPlayback)
// spikeOutTask.Control(TaskAction.Verify);
//Get sampling rates, set to private variables
spikeSamplingRate = Properties.Settings.Default.RawSampleFrequency;
lfpSamplingRate = Properties.Settings.Default.LFPSampleFrequency;
//Version with videoTask as master clock
if (Properties.Settings.Default.UseCineplex)
{
for (int i = 0; i < spikeTask.Count; ++i)
{
spikeTask[i].Timing.ReferenceClockSource = videoTask.Timing.ReferenceClockSource;
spikeTask[i].Timing.ReferenceClockRate = videoTask.Timing.ReferenceClockRate;
}
}
else
{
string masterclock = "/" + Properties.Settings.Default.AnalogInDevice[0].ToString() + "/10MhzRefClock";//"OnboardClock";//
if (!Properties.Settings.Default.UseStimulator)
{
//Deal with non M-series devices (these can't use "ReferenceClockSource"
Device analogInDevice = DaqSystem.Local.LoadDevice(Properties.Settings.Default.AnalogInDevice[0]);
if (analogInDevice.ProductCategory == ProductCategory.MSeriesDaq || analogInDevice.ProductCategory == ProductCategory.XSeriesDaq)
spikeTask[0].Timing.ReferenceClockSource = masterclock; //This will be the master clock
}
else
{
spikeTask[0].Timing.ReferenceClockSource = masterclock;//stimPulseTask.Timing.ReferenceClockSource;
spikeTask[0].Timing.ReferenceClockRate = 10000000.0; //stimPulseTask.Timing.ReferenceClockRate;
}
for (int i = 1; i < spikeTask.Count; ++i) //Set other analog in tasks to master clock
{
spikeTask[i].Timing.ReferenceClockSource = spikeTask[0].Timing.ReferenceClockSource;
spikeTask[i].Timing.ReferenceClockRate = spikeTask[0].Timing.ReferenceClockRate;
}
}
spikeTask[0].Timing.ConfigureSampleClock("", spikeSamplingRate,
SampleClockActiveEdge.Rising, SampleQuantityMode.ContinuousSamples, Convert.ToInt32(Properties.Settings.Default.RawSampleFrequency / 2));
for (int i = 1; i < spikeTask.Count; ++i)
{
//Pipe ai dev0's sample clock to slave devices
spikeTask[i].Timing.ConfigureSampleClock("/" + Properties.Settings.Default.AnalogInDevice[0] + "/ai/SampleClock", spikeSamplingRate,
SampleClockActiveEdge.Rising, SampleQuantityMode.ContinuousSamples, Convert.ToInt32(Properties.Settings.Default.RawSampleFrequency / 2));
//Trigger off of ai dev0's trigger
spikeTask[i].Triggers.StartTrigger.ConfigureDigitalEdgeTrigger("/" + Properties.Settings.Default.AnalogInDevice[0] +
"/ai/StartTrigger", DigitalEdgeStartTriggerEdge.Rising);
// Manually allocate buffer memory
//spikeTask[i].Stream.Buffer.InputBufferSize = DAQ_BUFFER_SIZE_SAMPLES;
}
if (Properties.Settings.Default.SeparateLFPBoard && Properties.Settings.Default.UseLFPs)
{
lfpTask.Timing.ReferenceClockSource = spikeTask[0].Timing.ReferenceClockSource;
lfpTask.Timing.ReferenceClockRate = spikeTask[0].Timing.ReferenceClockRate;
lfpTask.Timing.ConfigureSampleClock("", lfpSamplingRate,
SampleClockActiveEdge.Rising, SampleQuantityMode.ContinuousSamples, Convert.ToInt32(Properties.Settings.Default.LFPSampleFrequency / 2));
// Manually allocate buffer memory
//lfpTask.Stream.Buffer.InputBufferSize = DAQ_BUFFER_SIZE_SAMPLES;
}
else
{
Properties.Settings.Default.numLFPTasks = Properties.Settings.Default.numSpikeTasks;
}
if (Properties.Settings.Default.UseEEG)
{
eegTask.Timing.ReferenceClockSource = spikeTask[0].Timing.ReferenceClockSource;
eegTask.Timing.ReferenceClockRate = spikeTask[0].Timing.ReferenceClockRate;
eegTask.Timing.ConfigureSampleClock("", eegSamplingRate,
SampleClockActiveEdge.Rising, SampleQuantityMode.ContinuousSamples, Convert.ToInt32(Convert.ToDouble(Properties.Settings.Default.EEGSamplingRate) / 2));
// Manually allocate buffer memory
//eegTask.Stream.Buffer.InputBufferSize = DAQ_BUFFER_SIZE_SAMPLES;
}
if (Properties.Settings.Default.UseCineplex)
{
if (checkBox_video.Checked)
{
triggerTask.Timing.ConfigureSampleClock("/" + Properties.Settings.Default.AnalogInDevice[0] + "/ai/SampleClock",
spikeSamplingRate, SampleClockActiveEdge.Rising, SampleQuantityMode.FiniteSamples,
3);
}
if (Properties.Settings.Default.SeparateLFPBoard && Properties.Settings.Default.UseLFPs)
{
lfpTask.Triggers.StartTrigger.ConfigureDigitalEdgeTrigger("/" +
Properties.Settings.Default.AnalogInDevice[0] + "/ai/StartTrigger",
DigitalEdgeStartTriggerEdge.Rising);
}
if (Properties.Settings.Default.UseEEG)
{
eegTask.Triggers.StartTrigger.ConfigureDigitalEdgeTrigger("/" +
Properties.Settings.Default.AnalogInDevice[0] + "/ai/StartTrigger",
DigitalEdgeStartTriggerEdge.Rising);
}
}
if (Properties.Settings.Default.UseStimulator && Properties.Settings.Default.RecordStimTimes)
{
try
{
numStimReads = new List<int>(numDevices);
for (int i = 0; i < spikeTask.Count; ++i)
numStimReads.Add(0);
stimTimeTask = new Task("stimTimeTask");
stimTimeTask.AIChannels.CreateVoltageChannel(Properties.Settings.Default.StimInfoDevice + "/ai16", "",
AITerminalConfiguration.Nrse, -10.0, 10.0, AIVoltageUnits.Volts);
stimTimeTask.AIChannels.CreateVoltageChannel(Properties.Settings.Default.StimInfoDevice + "/ai0", "", AITerminalConfiguration.Nrse,
-10.0, 10.0, AIVoltageUnits.Volts); //For triggers
// Pipe the spikeTasks sample clock to PFI14 on the stim board
DaqSystem.Local.ConnectTerminals(spikeTask[0].Timing.ReferenceClockSource,
"/" + Properties.Settings.Default.StimulatorDevice.ToString() + "/PFI0");
if (isNormalRecording)
stimTimeTask.Timing.ReferenceClockSource = "/" + Properties.Settings.Default.StimulatorDevice.ToString() + "/PFI0";
else
stimTimeTask.Timing.ReferenceClockSource = spikeTask[0].Timing.ReferenceClockSource;
stimTimeTask.Timing.ReferenceClockRate = spikeTask[0].Timing.ReferenceClockRate;
stimTimeTask.Timing.ConfigureSampleClock("", spikeSamplingRate,
SampleClockActiveEdge.Rising, SampleQuantityMode.ContinuousSamples, Convert.ToInt32(Properties.Settings.Default.RawSampleFrequency / 2));
stimTimeTask.Triggers.StartTrigger.ConfigureDigitalEdgeTrigger(
"/" + Properties.Settings.Default.AnalogInDevice[0] + "/ai/StartTrigger", DigitalEdgeStartTriggerEdge.Rising);
stimTimeTask.Control(TaskAction.Verify);
// stim Timing Channel settings object
StringCollection stimTimePhysChan = new StringCollection();
for (int i = 0; i < stimTimeTask.AIChannels.Count; ++i)
{
stimTimePhysChan.Add(stimTimeTask.AIChannels[i].PhysicalName);
}
// Write down the indicies corresponding to the portion of this task that will
// actually record stimulus infromation instead of aux analog input
stimTimeChanSet = new NRAIChannelCollection(stimTimePhysChan);
int[] stimTimeChannels = new int[] { 0, 1 };
stimTimeChanSet.SetupNumericalChannelOnly(stimTimeChannels);
// Manually allocate buffer memory
//stimTimeTask.Stream.Buffer.InputBufferSize = DAQ_BUFFER_SIZE_SAMPLES;
Console.WriteLine("NRAcquisitionSetup complete");
}
catch (Exception e)
{
MessageBox.Show(e.Message);
}
}
//Setup scaling coefficients (to convert digital values to voltages)
scalingCoeffsSpikes = new List<double[]>(spikeTask.Count);
for (int i = 0; i < spikeTask.Count; ++i)
scalingCoeffsSpikes.Add(spikeTask[0].AIChannels[0].DeviceScalingCoefficients);
if (Properties.Settings.Default.SeparateLFPBoard)
scalingCoeffsLFPs = lfpTask.AIChannels[0].DeviceScalingCoefficients;
if (Properties.Settings.Default.UseEEG)
scalingCoeffsEEG = eegTask.AIChannels[0].DeviceScalingCoefficients;
// Setup auxiliary recording tasks
if (Properties.Settings.Default.useAuxAnalogInput)
{
// Set up the aux channel set
auxChanSet = new NRAIChannelCollection(Properties.Settings.Default.auxAnalogInChan);
if (Properties.Settings.Default.auxAnalogInDev == Properties.Settings.Default.StimInfoDevice
&& Properties.Settings.Default.RecordStimTimes)
{
// In this case we are recording both stimulus times and aux analog input times on the same
// DAQ, so we need to just make the auxAnInTask reference the stimulus timing task
twoAITasksOnSingleBoard = true;
auxAnInTask = stimTimeTask;
auxChanSet.SetupAuxCollection(ref auxAnInTask);
}
else
{
// In this case there is no conflict for AI, so we can create a dedicated task for aux analog input
twoAITasksOnSingleBoard = false;
auxAnInTask = new Task("AuxiliaryAnalogInput");
auxChanSet.SetupAuxCollection(ref auxAnInTask);
auxAnInTask.Timing.ReferenceClockSource = spikeTask[0].Timing.ReferenceClockSource;
auxAnInTask.Timing.ReferenceClockRate = spikeTask[0].Timing.ReferenceClockRate;
//Pipe ai dev0's sample clock to slave devices
auxAnInTask.Timing.ConfigureSampleClock("", spikeSamplingRate,
SampleClockActiveEdge.Rising, SampleQuantityMode.ContinuousSamples, Convert.ToInt32(Properties.Settings.Default.RawSampleFrequency / 2));
auxAnInTask.Triggers.StartTrigger.ConfigureDigitalEdgeTrigger("/Dev1/ai/StartTrigger", DigitalEdgeStartTriggerEdge.Rising);
// Manually allocate buffer memory
// auxAnInTask.Stream.Buffer.InputBufferSize = DAQ_BUFFER_SIZE_SAMPLES;
// Create space for the buffer
auxAnData = new double[auxChanSet.numericalChannels.Length, spikeBufferLength];
}
}
if (Properties.Settings.Default.useAuxDigitalInput)
{
auxDigInTask = new Task("AuxiliaryDigitalInput");
auxDigInTask.DIChannels.CreateChannel(Properties.Settings.Default.auxDigitalInPort,
"Auxiliary Digitial In", ChannelLineGrouping.OneChannelForAllLines);
auxDigInTask.Timing.ConfigureSampleClock("", spikeSamplingRate,
SampleClockActiveEdge.Rising, SampleQuantityMode.ContinuousSamples, Convert.ToInt32(Properties.Settings.Default.RawSampleFrequency / 2));
auxDigInTask.Timing.SampleClockSource = spikeTask[0].Timing.SampleClockTerminal;
// Manually allocate buffer memory
// auxDigInTask.Stream.Buffer.InputBufferSize = DAQ_BUFFER_SIZE_SAMPLES;
}
#region Setup_Plotting
numSnipsDisplayed = (int)numericUpDown_NumSnipsDisplayed.Value;
#region PlotData_Buffers
//***********************
//Make PlotData buffers
//***********************
int downsample, numRows, numCols;
const double spikeplotlength = 0.25; //in seconds
switch (Properties.Settings.Default.NumChannels)
{
case 16:
numRows = numCols = 4;
downsample = 10;
break;
case 32:
numRows = numCols = 6;
downsample = 15;
break;
case 64:
numRows = numCols = 8;
downsample = 20; //if this gets really small, LFP data won't plot
break;
default:
numRows = numCols = 4;
downsample = 5;
break;
}
//Create plot colormap
NRBrainbow = (64).GenerateBrainbow();
NRSnipBrainbow = (64).GenerateSnipBrainbow();
NRUnitBrainbow = (64).GenerateUnitBrainbow();
//Initialize graphs
if (spikeGraph != null) { spikeGraph.Dispose(); spikeGraph = null; }
spikeGraph = new GridGraph();
int samplesPerPlot = (int)(Math.Ceiling(Properties.Settings.Default.ADCPollingPeriodSec * spikeSamplingRate / downsample) * (spikeplotlength / Properties.Settings.Default.ADCPollingPeriodSec));
spikeGraph.setup(numRows, numCols, samplesPerPlot, false, 1 / 4.0, spikeTask[0].AIChannels.All.RangeHigh * 2.0);
spikeGraph.setMinMax(0, (float)(samplesPerPlot * numCols) - 1,
(float)(spikeTask[0].AIChannels.All.RangeLow * (numRows * 2 - 1)), (float)(spikeTask[0].AIChannels.All.RangeHigh));
spikeGraph.Dock = DockStyle.Fill;
spikeGraph.Parent = tabPage_spikes;
if (Properties.Settings.Default.UseLFPs)
{
if (lfpGraph != null) { lfpGraph.Dispose(); lfpGraph = null; }
lfpGraph = new RowGraph();
lfpGraph.setup(numChannels, (int)((Math.Ceiling(Properties.Settings.Default.ADCPollingPeriodSec * lfpSamplingRate / downsample) * (5 / Properties.Settings.Default.ADCPollingPeriodSec))),
5.0, spikeTask[0].AIChannels.All.RangeHigh * 2.0);
if (Properties.Settings.Default.SeparateLFPBoard)
lfpGraph.setMinMax(0, 5 * (int)(Math.Ceiling(Properties.Settings.Default.ADCPollingPeriodSec * lfpSamplingRate / downsample) / Properties.Settings.Default.ADCPollingPeriodSec) - 1,
(float)(lfpTask.AIChannels.All.RangeLow * (numChannels * 2 - 1)), (float)(lfpTask.AIChannels.All.RangeHigh));
else
lfpGraph.setMinMax(0, 5 * (int)(Math.Ceiling(Properties.Settings.Default.ADCPollingPeriodSec * lfpSamplingRate / downsample) / Properties.Settings.Default.ADCPollingPeriodSec) - 1,
(float)(spikeTask[0].AIChannels.All.RangeLow * (numChannels * 2 - 1)), (float)(spikeTask[0].AIChannels.All.RangeHigh));
lfpGraph.Dock = DockStyle.Fill;
lfpGraph.Parent = tabPage_LFPs;
}
if (Properties.Settings.Default.ProcessMUA)
{
if (muaGraph != null) { muaGraph.Dispose(); muaGraph = null; }
muaGraph = new RowGraph();
muaGraph.setup(numChannels, (int)((Math.Ceiling(Properties.Settings.Default.ADCPollingPeriodSec * muaSamplingRate / downsample) * (5 / Properties.Settings.Default.ADCPollingPeriodSec))),
5.0, spikeTask[0].AIChannels.All.RangeHigh * 2.0);
muaGraph.setMinMax(0, 5 * (int)(Math.Ceiling(Properties.Settings.Default.ADCPollingPeriodSec * muaSamplingRate / downsample) / Properties.Settings.Default.ADCPollingPeriodSec) - 1,
(float)(spikeTask[0].AIChannels.All.RangeLow * (numChannels * 2 - 1)), (float)(spikeTask[0].AIChannels.All.RangeHigh));
muaGraph.Dock = DockStyle.Fill;
muaGraph.Parent = tabPage_MUA;
muaPlotData = new PlotDataRows(numChannels, downsample, (int)(muaSamplingRate * 5), muaSamplingRate,
(float)spikeTask[0].AIChannels.All.RangeHigh * 2F, 0.5, 5, Properties.Settings.Default.ADCPollingPeriodSec);
//muaPlotData.setGain(Properties.Settings.Default.LFPDisplayGain);
//muaGraph.setDisplayGain(Properties.Settings.Default.LFPDisplayGain);
muaPlotData.dataAcquired += new PlotData.dataAcquiredHandler(muaPlotData_dataAcquired);
}
if (Properties.Settings.Default.UseEEG)
{
if (eegGraph != null) { eegGraph.Dispose(); eegGraph = null; }
eegGraph = new RowGraph();
eegGraph.setup(Properties.Settings.Default.EEGNumChannels, (int)((Math.Ceiling(Properties.Settings.Default.ADCPollingPeriodSec * eegSamplingRate / downsample) * (5 / Properties.Settings.Default.ADCPollingPeriodSec))),
5.0, eegTask.AIChannels.All.RangeHigh * 2.0);
eegGraph.setMinMax(0, 5 * (int)(Math.Ceiling(Properties.Settings.Default.ADCPollingPeriodSec * eegSamplingRate / downsample) / Properties.Settings.Default.ADCPollingPeriodSec) - 1,
(float)(eegTask.AIChannels.All.RangeLow * (Properties.Settings.Default.EEGNumChannels * 2 - 1)), (float)(eegTask.AIChannels.All.RangeHigh));
eegGraph.Dock = DockStyle.Fill;
eegGraph.Parent = tabPage_EEG;
}
resetSpkWfm(); //Take care of spike waveform graph
double ampdec = (1 / Properties.Settings.Default.PreAmpGain);
spikePlotData = new PlotDataGrid(numChannels, downsample, (int)(spikeSamplingRate), spikeSamplingRate,
(float)(spikeTask[0].AIChannels.All.RangeHigh * 2.0), numRows, numCols, spikeplotlength,
Properties.Settings.Default.ChannelMapping, Properties.Settings.Default.ADCPollingPeriodSec);
spikePlotData.dataAcquired += new PlotData.dataAcquiredHandler(spikePlotData_dataAcquired);
spikePlotData.setGain(Properties.Settings.Default.SpikeDisplayGain);
spikeGraph.setDisplayGain(Properties.Settings.Default.SpikeDisplayGain);
if (Properties.Settings.Default.UseLFPs)
{
if (Properties.Settings.Default.SeparateLFPBoard)
lfpPlotData = new PlotDataRows(numChannels, downsample, (int)(lfpSamplingRate * 5), lfpSamplingRate,
(float)lfpTask.AIChannels.All.RangeHigh * 2F, 0.5, 5, Properties.Settings.Default.ADCPollingPeriodSec);
else lfpPlotData = new PlotDataRows(numChannels, downsample, (int)(lfpSamplingRate * 5), lfpSamplingRate,
(float)spikeTask[0].AIChannels.All.RangeHigh * 2F, 0.5, 5, Properties.Settings.Default.ADCPollingPeriodSec);
lfpPlotData.setGain(Properties.Settings.Default.LFPDisplayGain);
lfpGraph.setDisplayGain(Properties.Settings.Default.LFPDisplayGain);
lfpPlotData.dataAcquired += new PlotData.dataAcquiredHandler(lfpPlotData_dataAcquired);
}
waveformPlotData = new EventPlotData(numChannels, spikeDet.NumPre + spikeDet.NumPost + 1, (float)(spikeTask[0].AIChannels.All.RangeHigh * 2F),
numRows, numCols, numSnipsDisplayed, Properties.Settings.Default.ChannelMapping);
waveformPlotData.setGain(Properties.Settings.Default.SpkWfmDisplayGain);
spkWfmGraph.setDisplayGain(Properties.Settings.Default.SpkWfmDisplayGain);
waveformPlotData.dataAcquired += new EventPlotData.dataAcquiredHandler(waveformPlotData_dataAcquired);
waveformPlotData.start();
#endregion
if (Properties.Settings.Default.UseEEG)
{
eegPlotData = new PlotDataRows(Properties.Settings.Default.EEGNumChannels, downsample, (int)(eegSamplingRate * 5), eegSamplingRate,
(float)eegTask.AIChannels.All.RangeHigh * 2F, 0.5, 5, Properties.Settings.Default.ADCPollingPeriodSec);
eegPlotData.setGain(Properties.Settings.Default.EEGDisplayGain);
eegGraph.setDisplayGain(Properties.Settings.Default.EEGDisplayGain);
eegPlotData.dataAcquired += new PlotData.dataAcquiredHandler(eegPlotData_dataAcquired);
}
if (Properties.Settings.Default.useAuxAnalogInput)
{
// Remove existing plots
for (int i = scatterGraph_AuxAnalogData.Plots.Count-1; i > 0; --i)
{
scatterGraph_AuxAnalogData.Plots.RemoveAt(i);
}
// Initialize the aux data scatter graph with a plot for each aux Analog channel
for (int i = 0; i < Properties.Settings.Default.auxAnalogInChan.Count-1; ++i)
{
ScatterPlot p = new ScatterPlot();
scatterGraph_AuxAnalogData.Plots.Add(p);
}
// Initialize the controller
auxInputGraphController = new ScatterGraphController(ref scatterGraph_AuxAnalogData);
// Make history selector reflect current limits on input
//slide_AnalogDispMaxVoltage.Range = new Range(0.05, 10);
//slide_AnalogDispWidth.Range = new Range(2*Properties.Settings.Default.ADCPollingPeriodSec, Properties.Settings.Default.datSrvBufferSizeSec);
}
#endregion
#region Setup_Filters
//Setup filters, based on user's input
resetSpikeFilter();
if (Properties.Settings.Default.UseLFPs) resetLFPFilter();
resetEEGFilter();
muaFilter = new Filters.MUAFilter(
numChannels, spikeSamplingRate, spikeBufferLength,
Properties.Settings.Default.MUAHighCutHz,
Properties.Settings.Default.MUAFilterOrder,
MUA_DOWNSAMPLE_FACTOR,
Properties.Settings.Default.ADCPollingPeriodSec);
#endregion
#region Setup_DataStorage
//Initialize data storing matrices
// numChannels = Properties.Settings.Default.NumChannels;
numSpikeReads = new int[spikeTask.Count];
filtSpikeData = new rawType[numChannels][];
if (Properties.Settings.Default.UseLFPs)
{
filtLFPData = new rawType[numChannels][];
finalLFPData = new rawType[numChannels][];
for (int i = 0; i < filtSpikeData.GetLength(0); ++i)
{
if (Properties.Settings.Default.SeparateLFPBoard)
filtLFPData[i] = new rawType[lfpBufferLength];
else
filtLFPData[i] = new rawType[spikeBufferLength];
}
}
if (Properties.Settings.Default.ProcessMUA)
{
muaData = new double[numChannels][];
for (int c = 0; c < numChannels; ++c)
muaData[c] = new double[spikeBufferLength / MUA_DOWNSAMPLE_FACTOR];
}
if (Properties.Settings.Default.UseEEG)
{
filtEEGData = new double[Properties.Settings.Default.EEGNumChannels][];
for (int i = 0; i < filtEEGData.GetLength(0); ++i)
{
filtEEGData[i] = new double[eegBufferLength];
}
}
for (int i = 0; i < filtSpikeData.GetLength(0); ++i)
{
filtSpikeData[i] = new rawType[spikeBufferLength];
if (Properties.Settings.Default.UseLFPs)
finalLFPData[i] = new rawType[lfpBufferLength];
}
if (Properties.Settings.Default.UseStimulator)
{
stimDataBuffer = new double[STIM_BUFFER_LENGTH];
stimJump = (double)spikeSamplingRate * 0.0001; //num. indices in 100 us of data
}
stimIndices = new List<StimTick>(5);
//if devices refresh rate is reset, need to reset SALPA
if (checkBox_SALPA.Checked)
resetSALPA();
if (spikeDet != null && isNormalRecording)
setSpikeDetector();
if (spikeDet.spikeDetector == null)
setSpikeDetector();
#endregion
#region Verify Tasks
if (Properties.Settings.Default.UseStimulator && Properties.Settings.Default.RecordStimTimes)
stimTimeTask.Control(TaskAction.Verify);
if (Properties.Settings.Default.UseEEG)
eegTask.Control(TaskAction.Verify);
if (Properties.Settings.Default.SeparateLFPBoard && Properties.Settings.Default.UseLFPs)
lfpTask.Control(TaskAction.Verify);
if (checkBox_video.Checked)
triggerTask.Control(TaskAction.Verify);
for (int i = 0; i < spikeTask.Count; ++i)
spikeTask[i].Control(TaskAction.Verify);
if (Properties.Settings.Default.useAuxAnalogInput)
auxAnInTask.Control(TaskAction.Verify);
if (Properties.Settings.Default.useAuxDigitalInput)
auxDigInTask.Control(TaskAction.Verify);
#endregion
SetupFileWriting();
//Set callbacks for data acq.
spikeReader = new List<AnalogMultiChannelReader>(spikeTask.Count);
for (int i = 0; i < spikeTask.Count; ++i)
{
spikeReader.Add(new AnalogMultiChannelReader(spikeTask[i].Stream));
spikeReader[i].SynchronizeCallbacks = true;
}
//if (Properties.Settings.Default.UseSingleChannelPlayback)
// spikeOutWriter = new AnalogSingleChannelWriter(spikeOutTask.Stream);
if (checkBox_video.Checked)
triggerWriter = new DigitalSingleChannelWriter(triggerTask.Stream);
//if (Properties.Settings.Default.UseSingleChannelPlayback)
// spikeOutWriter.SynchronizeCallbacks = false; //These don't use UI, so they don't need to be synched
spikeCallback = new AsyncCallback(AnalogInCallback_spikes);
if (Properties.Settings.Default.UseStimulator && Properties.Settings.Default.RecordStimTimes)
{
stimTimeReader = new AnalogMultiChannelReader(stimTimeTask.Stream);
}
if (Properties.Settings.Default.SeparateLFPBoard && Properties.Settings.Default.UseLFPs)
{
lfpReader = new AnalogUnscaledReader(lfpTask.Stream);
lfpReader.SynchronizeCallbacks = true;
lfpCallback = new AsyncCallback(AnalogInCallback_LFPs);
}
if (Properties.Settings.Default.UseEEG)
{
eegReader = new AnalogUnscaledReader(eegTask.Stream);
eegReader.SynchronizeCallbacks = true;
eegCallback = new AsyncCallback(AnalogInCallback_EEG);
}
if (Properties.Settings.Default.useAuxAnalogInput)
{
auxAnReader = new AnalogMultiChannelReader(auxAnInTask.Stream);
auxAnReader.SynchronizeCallbacks = true;
auxAnCallback = new AsyncCallback(AnalogInCallback_AuxAn);
}
if (Properties.Settings.Default.useAuxDigitalInput)
{
auxDigReader = new DigitalSingleChannelReader(auxDigInTask.Stream);
auxDigReader.SynchronizeCallbacks = true;
auxDigCallback = new AsyncCallback(AnalogInCallback_AuxDig);
}
//Setup background workers for data processing
bwSpikes = new List<BackgroundWorker>(spikeTask.Count);
bwIsRunning = new bool[spikeTask.Count];
for (int i = 0; i < spikeTask.Count; ++i)
{
bwSpikes.Add(new BackgroundWorker());
bwSpikes[i].DoWork += new DoWorkEventHandler(bwSpikes_DoWork);
bwSpikes[i].RunWorkerCompleted += new RunWorkerCompletedEventHandler(bwSpikes_RunWorkerCompleted);
bwSpikes[i].WorkerSupportsCancellation = true;
}
//Make persistent buffers for spikeData
spikeData = new List<AnalogWaveform<double>[]>(spikeReader.Count);
for (int i = 0; i < spikeReader.Count; ++i)
{
spikeData.Add(new AnalogWaveform<double>[numChannelsPerDev]);
for (int j = 0; j < numChannelsPerDev; ++j)
spikeData[i][j] = new AnalogWaveform<double>(spikeBufferLength);
}
//Make channel playback task
if (Properties.Settings.Default.UseSingleChannelPlayback)
BNCOutput = new ChannelOutput(spikeSamplingRate, 0.1, Properties.Settings.Default.ADCPollingPeriodSec, spikeTask[0],
Properties.Settings.Default.SingleChannelPlaybackDevice, 0);
}
catch (Exception exception)
{
//Display Errors
this.Cursor = Cursors.Default;
MessageBox.Show(exception.Message);
reset();
}
// Set up the DataSrv object. This is an object that publishes a nice large data history
// for use in closed loop control and other things
if (datSrv != null)
datSrv = null;
datSrv = new DataSrv(
Properties.Settings.Default.datSrvBufferSizeSec,
checkBox_SALPA.Checked,
SALPA_WIDTH,
checkBox_spikesFilter.Checked,
spikeDet.spikeDetectionLag
);
// Set the number of units if appropriate
if (spikeDet.spikeSorter != null)
datSrv.SetNumberOfUnits(spikeDet.spikeSorter.totalNumberOfUnits, spikeDet.spikeSorter.unit2Channel);
Debugger = new Logger();
Debugger.GrabTimer(spikeTask[0]);
//Send debug output to the user's application data folder
Debugger.SetPath(Path.Combine(Properties.Settings.Default.neurorighterAppDataPath, "neurorighter-log.txt"));
//Tell neuroRighter that the tasks now exist
taskRunning = true;
}
else
{
Console.WriteLine("NRAcquisitionSetup was called while a task was running, and therefore setup did not execute. Perhaps this should have thrown an error");
}
}
//update gui at the end
// Modify the UI, so user doesn't try running multiple instances of tasks
spikeDet.numPreSamples.Enabled = false;
spikeDet.numPostSamples.Enabled = false;
settingsToolStripMenuItem.Enabled = false;
button_Train.Enabled = false;
button_SetRecordingStreams.Enabled = false;
switch_record.Enabled = false;
//processingSettingsToolStripMenuItem.Enabled = false;
button_startStimFromFile.Enabled = false;
button_startClosedLoopStim.Enabled = false;
checkBox_SALPA.Enabled = false;
//numericUpDown_NumSnipsDisplayed.Enabled = false;
button_startClosedLoopStim.Enabled = false;
button_scaleUp.Enabled = true;
button_scaleDown.Enabled = true;
button_scaleReset.Enabled = true;
// Disable spike detector saving while running
spikeDet.DisableFileMenu();
Console.WriteLine("NRAcquisitionSetup successfully executed");
this.Cursor = Cursors.Default;
return false;
}