public double[] WindowStat(int i, int j)
{
int index_p = 0;
int new_r = 0, new_c = 0;
var stat = new double[7];
var hsv = ToDouble(i, j);
stat[0] = hsv[0];
stat[1] = hsv[1];
stat[2] = hsv[2];
double[][] window = new double[3][];
for (int c = 0; c < 3; c++)
{
window[c] = new double[9];
}
for (int r = i - 1; r <= i + 1; r++)
{
for (int c = j - 1; c <= j + 1; c++)
{
new_r = r;
new_c = c;
if (r < 0)
{
new_r = 0;
}
if (r == _nrow)
{
new_r = _nrow - 1;
}
if (c < 0)
{
new_c = 0;
}
if (c == _ncol)
{
new_c = _ncol - 1;
}
var pp = ToDouble(new_r, new_c);
window[0][index_p] = pp[0];
window[1][index_p] = pp[1];
window[2][index_p] = pp[2];
index_p++;
}
}
stat[0] = window[0][5];
stat[1] = window[1][5];
stat[2] = window[2][5];
stat[3] = MyStatisticsMath.StandardDeviation(window[0]);
stat[4] = MyStatisticsMath.StandardDeviation(window[1]);
stat[5] = MyStatisticsMath.StandardDeviation(window[2]);
stat[6] = window[1].Average();
return(stat);
}
public void Scale(float min, float max, int step, int sdNumber)
{
var vv = Metric.GetVector(0, step.ToString(), ":");
var stat = MyStatisticsMath.SimpleStatistics(vv);
float vmax = (float)(stat.Average + sdNumber * stat.StandardDeviation);
float vmin = (float)(stat.Average - sdNumber * stat.StandardDeviation);
float vdelta = vmax - vmin;
float ndelta = max - min;
float value = 0;
for (int i = 0; i < vv.Length; i++)
{
value = Metric[0, step, i];
if (value < vmin)
{
Metric[0, step, i] = min;
}
else if (value > vmax)
{
Metric[0, step, i] = max;
}
else
{
Metric[0, step, i] = min + (value - vmin) / vdelta * ndelta;
}
}
}
public StatisticsInfo GetValueStatistics(ObservationSeries series)
{
string sql = string.Format("select DateTimeUTC, DataValue from DataValues where SiteID={0} and VariableID={1} order by DateTimeUTC",
series.SiteID, series.VariableID);
var dt = ODMDB.QueryDataTable(sql).AsEnumerable();
var dates = from dr in dt select dr.Field <DateTime>("DateTimeUTC");
var values = from dr in dt select dr.Field <double>("DataValue");
var filtered = from vv in values where vv != 0 select vv;
var info = MyStatisticsMath.SimpleStatistics(filtered.ToArray());
return(info);
}
public void Scale(int step, float min, float max)
{
var vv = Metric.GetVector(0, step.ToString(), ":");
var stat = MyStatisticsMath.SimpleStatistics(vv);
float vmax = (float)stat.Max;
float vmin = (float)stat.Min;
float vdelta = vmax - vmin;
float ndelta = max - min;
for (int i = 0; i < vv.Length; i++)
{
Metric[0, step, i] = min + (Metric[0, step, i] - vmin) / vdelta * ndelta;
}
}
private void menu_trend_Click(object sender, EventArgs e)
{
var series = SelectSeries(_selectedMenuItem);
if (series != null)
{
int npt = series.Points.Count;
double rs, slope, yint;
double[] xx = new double[npt];
double[] yy = new double[npt];
for (int i = 0; i < npt; i++)
{
xx[i] = i + 1;
yy[i] = series.Points[i].YValues[0];
}
MyStatisticsMath.LinearRegression(xx, yy, 0, npt, out rs, out yint, out slope);
string trend_name = "Trend of" + series.Name;
lvStatistics.Items[4].SubItems[3].Text = string.Format("y = {0}x + {1}", slope.ToString("0.00"), yint.ToString("0.00"));
double[] yy_trend = new double[2];
if (series.XValueType == ChartValueType.Date || series.XValueType == ChartValueType.DateTime)
{
DateTime[] date_trend = new DateTime[2];
double[] xx_trend = new double[2];
xx_trend[0] = 1;
xx_trend[1] = npt;
date_trend[0] = DateTime.FromOADate(series.Points[0].XValue);
date_trend[1] = DateTime.FromOADate(series.Points[npt - 1].XValue);
yy_trend[0] = slope * xx_trend[0] + yint;
yy_trend[1] = slope * xx_trend[1] + yint;
Plot <double>(date_trend, yy_trend, trend_name, SeriesChartType.Line);
}
else
{
double[] xx_trend = new double[2];
xx_trend[0] = 1;
xx_trend[1] = npt;
yy_trend[0] = slope * xx_trend[0] + yint;
yy_trend[1] = slope * xx_trend[1] + yint;
Plot <double>(xx_trend, yy_trend, trend_name, SeriesChartType.Line);
}
}
}
public override bool Execute(DotSpatial.Data.ICancelProgressHandler cancelProgressHandler)
{
int var_indexA = 0;
var matA = Get3DMat(InputDataCube, ref var_indexA);
double prg = 0;
int count = 1;
if (matA != null)
{
int nstep = matA.Size[1];
int ncell = matA.Size[2];
var mat_out = new DataCube <float>(1, 1, ncell);
mat_out.Name = OutputDataCube;
mat_out.Variables = new string[] { "Slope" };
for (int c = 0; c < ncell; c++)
{
var vec = matA.GetVector(var_indexA, ":", c.ToString());
var dou_vec = MatrixOperation.ToDouble(vec);
var steps = new double[nstep];
double rs, slope, yint;
for (int t = 1; t < nstep; t++)
{
steps[t] = t + 1;
}
MyStatisticsMath.LinearRegression(steps, dou_vec, 0, nstep, out rs, out yint, out slope);
mat_out[0, 0, c] = (float)slope;
prg = (c + 1) * 100.0 / ncell;
if (prg > count)
{
cancelProgressHandler.Progress("Package_Tool", (int)prg, "Caculating Cell: " + (c + 1));
count++;
}
}
Workspace.Add(mat_out);
return(true);
}
else
{
return(false);
}
}
private void btnStat_Click(object sender, EventArgs e)
{
if (chart1.Series.Count != 2)
{
_MessageService.ShowWarning(this, "Two series are required!");
}
else
{
var xx = (from pt in chart1.Series[0].Points select pt.YValues[0]).ToArray();
var yy = (from pt in chart1.Series[1].Points select pt.YValues[0]).ToArray();
if (xx.Length != yy.Length)
{
return;
}
lvStatistics.Items[0].SubItems[3].Text = MyStatisticsMath.Mse(xx, yy).ToString("F3");
lvStatistics.Items[1].SubItems[3].Text = MyStatisticsMath.RMse(xx, yy).ToString("F3");
lvStatistics.Items[2].SubItems[3].Text = MyStatisticsMath.Correlation(xx, yy).ToString("F3");
lvStatistics.Items[3].SubItems[3].Text = MyStatisticsMath.NashStucliffeR(xx, yy).ToString("F3");
// lvStatistics.Items[4].SubItems[3].Text = MyStatisticsMath.GetSimilarityScore(xx, yy).ToString("F3");
}
}
public My3DMat <float> StandardDeviation(My3DMat <float> source)
{
var mat = new My3DMat <float>(source.Size[0], source.Size[1], source.Size[2]);
int nrow = source.Size[1];
int ncol = source.Size[2];
int ncol_1 = ncol - 1;
int nrow_1 = nrow - 1;
var mat_source = source.Value[0];
var vec5 = new float[5];
// the first and the last columns
for (int r = 1; r < nrow - 1; r++)
{
vec5[0] = mat_source[r - 1][0];
vec5[1] = mat_source[r - 1][1];
vec5[2] = mat_source[r][1];
vec5[3] = mat_source[r + 1][1];
vec5[4] = mat_source[r + 1][0];
mat.Value[0][r][0] = MyStatisticsMath.StandardDeviation(vec5);
vec5[0] = mat_source[r - 1][ncol_1];
vec5[1] = mat_source[r - 1][ncol_1 - 1];
vec5[2] = mat_source[r][ncol_1 - 1];
vec5[3] = mat_source[r + 1][ncol_1 - 1];
vec5[4] = mat_source[r + 1][ncol_1];
mat.Value[0][r][ncol_1] = MyStatisticsMath.StandardDeviation(vec5);
}
// the first and the last rows
for (int c = 1; c < ncol - 1; c++)
{
vec5[0] = mat_source[0][c - 1];
vec5[1] = mat_source[1][c - 1];
vec5[2] = mat_source[1][c];
vec5[3] = mat_source[1][c + 1];
vec5[4] = mat_source[0][c + 1];
mat.Value[0][0][c] = MyStatisticsMath.StandardDeviation(vec5);
vec5[0] = mat_source[nrow_1][c - 1];
vec5[1] = mat_source[nrow_1 - 1][c - 1];
vec5[2] = mat_source[nrow_1 - 1][c];
vec5[3] = mat_source[nrow_1 - 1][c + 1];
vec5[4] = mat_source[nrow_1][c + 1];
mat.Value[0][nrow_1][c] = MyStatisticsMath.StandardDeviation(vec5);
}
float[] vec8 = new float[8];
for (int r = 1; r < nrow - 1; r++)
{
for (int c = 1; c < ncol - 1; c++)
{
vec8[0] = mat_source[r - 1][c - 1];
vec8[1] = mat_source[r - 1][c];
vec8[2] = mat_source[r - 1][c + 1];
vec8[3] = mat_source[r][c - 1];
vec8[4] = mat_source[r][c + 1];
vec8[5] = mat_source[r + 1][c - 1];
vec8[6] = mat_source[r + 1][c];
vec8[7] = mat_source[r + 1][c + 1];
mat.Value[0][r][c] = MyStatisticsMath.StandardDeviation(vec8);
}
}
return(mat);
}
public My3DMat <double> StandardDeviation(My3DMat <double> source)
{
int nvar = source.Size[0];
int nrow = source.Size[1];
int ncol = source.Size[2];
var mat = new My3DMat <double>(nvar, nrow, ncol);
int ncol_1 = ncol - 1;
int nrow_1 = nrow - 1;
for (int v = 0; v < nvar; v++)
{
var mat_source = source.Value[v];
var vec5 = new double[5];
// the first and the last columns
for (int r = 1; r < nrow - 1; r++)
{
vec5[0] = mat_source[r - 1][0];
vec5[1] = mat_source[r - 1][1];
vec5[2] = mat_source[r][1];
vec5[3] = mat_source[r + 1][1];
vec5[4] = mat_source[r + 1][0];
mat.Value[v][r][0] = MyStatisticsMath.StandardDeviation(vec5);
vec5[0] = mat_source[r - 1][ncol_1];
vec5[1] = mat_source[r - 1][ncol_1 - 1];
vec5[2] = mat_source[r][ncol_1 - 1];
vec5[3] = mat_source[r + 1][ncol_1 - 1];
vec5[4] = mat_source[r + 1][ncol_1];
mat.Value[v][r][ncol_1] = MyStatisticsMath.StandardDeviation(vec5);
}
// the first and the last rows
for (int c = 1; c < ncol - 1; c++)
{
vec5[0] = mat_source[0][c - 1];
vec5[1] = mat_source[1][c - 1];
vec5[2] = mat_source[1][c];
vec5[3] = mat_source[1][c + 1];
vec5[4] = mat_source[0][c + 1];
mat.Value[v][0][c] = MyStatisticsMath.StandardDeviation(vec5);
vec5[0] = mat_source[nrow_1][c - 1];
vec5[1] = mat_source[nrow_1 - 1][c - 1];
vec5[2] = mat_source[nrow_1 - 1][c];
vec5[3] = mat_source[nrow_1 - 1][c + 1];
vec5[4] = mat_source[nrow_1][c + 1];
mat.Value[v][nrow_1][c] = MyStatisticsMath.StandardDeviation(vec5);
}
double[] vec8 = new double[8];
for (int r = 1; r < nrow - 1; r++)
{
for (int c = 1; c < ncol - 1; c++)
{
vec8[0] = mat_source[r - 1][c - 1];
vec8[1] = mat_source[r - 1][c];
vec8[2] = mat_source[r - 1][c + 1];
vec8[3] = mat_source[r][c - 1];
vec8[4] = mat_source[r][c + 1];
vec8[5] = mat_source[r + 1][c - 1];
vec8[6] = mat_source[r + 1][c];
vec8[7] = mat_source[r + 1][c + 1];
mat.Value[v][r][c] = MyStatisticsMath.StandardDeviation(vec8);
}
}
}
return(mat);
}
private void PrePro(Dictionary<int, ReachFeatureCollection> fealist, out string msg)
{
double rs = 0, slope = 0, yint = 0;
var dt = _out_sfr_layer.DataTable;
var prj = MyAppManager.Instance.CompositionContainer.GetExportedValue<IProjectService>();
msg = "";
for (int i = 0; i < _out_sfr_layer.Features.Count; i++)
{
try
{
var dr = dt.Rows[i];
var geo = _out_sfr_layer.GetFeature(i).Geometry;
if (geo.Length <= _dem_layer.CellHeight)
{
continue;
}
var npt = geo.Coordinates.Count();
int segid = int.Parse(dr[SegmentField].ToString()) + 1;
double[] dis = new double[npt];
double[] ac_dis = new double[npt];
double[] elvs = new double[npt];
double elev_av = 0;
var pt0 = geo.Coordinates[0];
var cell = _dem_layer.ProjToCell(pt0.X, pt0.Y);
double ad = 0;
dis[0] = 0;
elvs[0] = _dem_layer.Value[cell.Row, cell.Column];
for (int j = 0; j < npt; j++)
{
cell = _ad_layer.ProjToCell(geo.Coordinates[j].X, geo.Coordinates[j].Y);
ad += _ad_layer.Value[cell.Row, cell.Column];
}
ad = ad / npt;
for (int j = 1; j < npt; j++)
{
cell = _dem_layer.ProjToCell(geo.Coordinates[j].X, geo.Coordinates[j].Y);
elvs[j] = _dem_layer.Value[cell.Row, cell.Column];
dis[j] = SpatialDistance.DistanceBetween(geo.Coordinates[j - 1], geo.Coordinates[j]);
}
for (int j = 0; j < npt; j++)
{
ac_dis[j] = dis.Take(j + 1).Sum();
}
MyStatisticsMath.LinearRegression(ac_dis, elvs, 0, elvs.Length, out rs, out yint, out slope);
if (slope < 0)
{
slope = -slope;
}
else if (slope == 0)
{
slope = _minum_slope;
}
for (int j = 0; j < npt; j++)
{
elvs[j] = yint + slope * ac_dis[j];
}
elev_av = elvs.Average();
if (slope < _minum_slope)
slope = _minum_slope;
if (slope > _maximum_slope)
slope = _maximum_slope;
var rch = new ReachFeature()
{
Row = dr,
Elevation = elev_av,
Slope = slope
};
if (fealist[segid].Reaches.ContainsKey(ad))
{
ad += i * 0.001;
}
fealist[segid].Reaches.Add(ad, rch);
fealist[segid].OutSegmentID = int.Parse(dr["DSLINKNO"].ToString());
dr["Length"] = geo.Length;
}
catch (Exception ex)
{
msg += ex.Message + "\n";
}
}
}
public override bool Execute(DotSpatial.Data.ICancelProgressHandler cancelProgressHandler)
{
FileStream fs = new FileStream(_OutputFileName, FileMode.Create, FileAccess.Write);
BinaryWriter bw = new BinaryWriter(fs);
var var_index = 0;
var mat = Get3DMat(Source, ref var_index);
int progress = 0;
int nsteps = mat.Size[1];
var grid = ProjectService.Project.Model.Grid as RegularGrid;
if (grid != null)
{
float[][] lonlat = null;
if (CoornidateSystem == CS.GCS)
{
lonlat = grid.GetLonLatAxis();
}
else if (CoornidateSystem == CS.PCS)
{
lonlat = grid.GetPCSAxis();
}
var nrow = grid.RowCount;
var ncol = grid.ColumnCount;
var times = new float[nsteps];
if (mat.DateTimes != null)
{
for (int t = 0; t < nsteps; t++)
{
times[t] = (float)mat.DateTimes[t].ToOADate();
}
}
else
{
for (int t = 0; t < nsteps; t++)
{
times[t] = (float)DateTime.Now.AddDays(t).ToOADate();
}
}
bw.Write(nrow);
bw.Write(ncol);
bw.Write(nsteps);
for (int i = 0; i < ncol; i++)
{
bw.Write(lonlat[0][i]);
}
for (int i = 0; i < nrow; i++)
{
bw.Write(lonlat[1][i]);
}
for (int t = 0; t < nsteps; t++)
{
bw.Write(times[t]);
var vec = mat[var_index, t.ToString(), ":"];
bw.Write(vec.Max());
bw.Write(vec.Min());
bw.Write(vec.Average());
bw.Write(MyStatisticsMath.StandardDeviation(vec));
var mat_step = grid.ToMatrix <float>(vec, 0);
for (int r = 0; r < nrow; r++)
{
for (int c = 0; c < ncol; c++)
{
bw.Write(mat_step[r][c]);
}
}
progress = t * 100 / nsteps;
cancelProgressHandler.Progress("Package_Tool", progress, "Processing step:" + t);
}
fs.Close();
bw.Close();
return(true);
}
else
{
return(false);
}
}
