/// <summary>
/// 計算穩定狀態下的參數組(單一觀測值)，使用一般的 Variance-Covariance Matrix 去計算，而非 Sullivan-Woodall
/// </summary>
/// <param name="data">欲使用的資料表</param>
/// <returns></returns>
public TsquareParameters Execute(Algebra.Matrix <double> data, Mtb.Project proj)
{
//確認基本資訊能否計算
int p = data.ColumnCount;
int m = data.RowCount;
if (m - p <= 0)
{
throw new Exception(string.Format("樣本數不可低於{0}，請重新確認。", p));
}
//組合管制界線指令並計算 Phase1 的 limit
double ucl;
StringBuilder cmnd = new StringBuilder();
cmnd.AppendLine("invCdf 0.9973 k1;");
cmnd.AppendFormat("Beta {0} {1}.\r\n", (double)p / 2, (double)(m - p - 1) / 2);
cmnd.AppendFormat("let k1=(({0}-1)**2)/{0}*k1\r\n", m);
try
{
Mtb.Worksheet ws = proj.Worksheets.Add(1);
string fpath = Mtblib.Tools.MtbTools.BuildTemporaryMacro("mymacro.mtb", cmnd.ToString());
proj.ExecuteCommand(string.Format("Exec \"{0}\" 1", fpath), ws);
ucl = ws.Constants.Item("k1").GetData();
ws.Delete();
proj.Commands.Delete();
}
catch (Exception e)
{
throw new Exception(e.Message);
}
double miss = Mtblib.Tools.MtbTools.MISSINGVALUE;
var M = Algebra.Matrix <double> .Build;
//計算 Mean vector & Covariance matrix
IEnumerable <Algebra.Vector <double> > cols = data.EnumerateColumns();
var mean = M.DenseOfColumnMajor(1, data.ColumnCount, cols.Select(x => x.Where(o => o < miss).Average()));
var subData = M.DenseOfRows((data.EnumerateRows().Where(x => x.All(y => y < miss)).ToArray()));
var diff = subData - M.Dense(subData.RowCount, 1, (i, j) => 1).Multiply(mean);
var cov = diff.Transpose().Multiply(diff) / (subData.RowCount - 1);
var invS = cov.Inverse();
TsquareParameters tsquarePara = new TsquareParameters()
{
Mean = mean,
Covariance = cov,
SampleSize = m,
SubgroupSize = 1
};
//計算出資料的Tsquare value
List <double> t2 = new List <double>();
for (int i = 0; i < data.RowCount; i++)
{
if (data.Row(i).ToArray().All(x => x < miss))
{
t2.Add(Tool.CalculateTSquare(data.Row(i).ToRowMatrix(), mean, invS));
}
else
{
t2.Add(miss);
}
}
if (t2.Any(x => x >= ucl && x < miss))
{
int[] oocRow = t2.Select((x, i) => new { Tsquare = x, Index = i }).Where(x => x.Tsquare >= ucl && x.Tsquare < miss).Select(x => x.Index).OrderByDescending(x => x).ToArray();
subData = data.Clone();
//逐列移除OOC的資料列
for (int i = 0; i < oocRow.Length; i++)
{
subData = subData.RemoveRow(oocRow[i]);
}
tsquarePara = Execute(subData, proj);
}
return(tsquarePara);
}
public override void Execute(Mtb.Project proj)
{
// 將資料匯入 Minitab
if (_rawdata == null || _rawdata.Rows.Count == 0)
{
return;
}
_rptLst = new List <IRptOutput>(); //重新建立一個分析結果列舉
Mtb.Worksheet ws = proj.Worksheets.Add(1); //新增工作表
Mtblib.Tools.MtbTools.InsertDataTableToMtbWs(_rawdata, ws); //匯入資料至 Minitab
List <string> varnames = new List <string>();
List <Mtb.Column> varCols = new List <Mtb.Column>(); //變數的欄位集合
for (int i = 0; i < _rawdata.Columns.Count; i++)
{
DataColumn col = _rawdata.Columns[i];
switch (col.ColumnName)
{
case "TIMESTAMP":
case "CHART_PARA_INDEX":
break;
default:
varnames.Add(col.ColumnName);
varCols.Add(ws.Columns.Item(col.ColumnName));
break;
}
}
foreach (var col in varCols)
{
if ((int)col.DataType == 3 || col.MissingCount == col.RowCount)
{
throw new ArgumentNullException(string.Format("[{0}]查無資料-多變量管制圖", col.Name));
}
}
StringBuilder cmnd = new StringBuilder();
List <TsquareParameters> tmpParaList = new List <TsquareParameters>(); //用於計算 decomposition
string[] colIds = Mtblib.Tools.MtbTools.CreateVariableStrArray(ws, 4, Mtblib.Tools.MtbVarType.Column); //Plot point, CL, UCL and Test column
// 指定繪圖時需要的欄位變數
Mtb.Column
pplotCol = ws.Columns.Item(colIds[0]),
clCol = ws.Columns.Item(colIds[1]),
uclCol = ws.Columns.Item(colIds[2]),
oocCol = ws.Columns.Item(colIds[3]),
timeCol = ws.Columns.Item("TIMESTAMP");
// 計算 T2 plot points
if (_parameters == null || _parameters.Count == 0)
{
//自己算的時候需要的變數和子命令
#region Phase I
cmnd.AppendLine("macro");
cmnd.AppendLine("myt2calculator x.1-x.p;");
cmnd.AppendLine("variance cov ssiz;");
cmnd.AppendLine("meanvect location; ");
cmnd.AppendLine("ppoint t2;");
cmnd.AppendLine("climit cl ucl;");
cmnd.AppendLine("test ooc;");
cmnd.AppendLine("siglevel alpha.");
cmnd.AppendLine("mcolumn x.1-x.p t2");
cmnd.AppendLine("mcolumn loc.1-loc.p xx.1-xx.p tmp.1-tmp.3 ooc ucl cl");
cmnd.AppendLine("mconstant ssiz m alpha conf a1 a2 kk");
cmnd.AppendLine("mmatrix location diff cov tdiff invCov");
cmnd.AppendLine("default alpha = 0.0027");
cmnd.AppendLine("rnmiss x.1-x.p tmp.1");
cmnd.AppendLine("copy x.1-x.p xx.1-xx.p;");
cmnd.AppendLine("exclude;");
cmnd.AppendFormat("where \"tmp.1>0\".\r\n");
cmnd.AppendLine("cova xx.1-xx.p cov"); // Get Covariance matrix
cmnd.AppendLine("let ssiz = n(xx.1)"); // Get the sample size of covariance calculation, ALSO..THAT IS THE NUMBER OF NONMISSING OBSERVATIONS
cmnd.AppendLine("stat x.1-x.p;");
cmnd.AppendLine("mean loc.1-loc.p."); //Get mean vector.
/*
* Check if there is enough nonmissing observation to calculate control limit under
* Tsquare command (m>p+1, where m=#observation, include missing obs, p=#items)
* If no, you still need to draw plot points on graph..
* The trick is we given parameters (Mean & Covariance) and given a fake sample size, then
* you get t-sq value, becasue we don't need the CL & UCL from Minitab.
* (WE USE REGULAR COVARIANCE INSTEAD OF COVARIANCE BY Sullivan & Woodall)
*
*/
cmnd.AppendLine("if(ssiz < p+1)");
cmnd.AppendLine("let kk = p+1");
cmnd.AppendLine("else");
cmnd.AppendLine("copy ssiz kk\r\n");
cmnd.AppendLine("endif");
cmnd.AppendLine("tsquare x.1-x.p 1;");
cmnd.AppendLine("mu loc.1-loc.p;");
cmnd.AppendLine("sigma cov;");
cmnd.AppendLine("number kk;");
cmnd.AppendLine("sampsize tmp.1;"); //Get subgroup size.
cmnd.AppendLine("ppoint t2."); //Get Tsquare value.
cmnd.AppendLine("copy loc.1-loc.p location"); //Copy column to mean vector
cmnd.AppendLine("let m = sum(tmp.1)"); //Get the actual sample size (include missing observations)
cmnd.AppendLine("if (m <= p+1)"); // Check if there is enough obs to calc contril limit...
cmnd.AppendLine("let cl[m]=miss()"); //no center line
cmnd.AppendLine("let ucl[m]=miss()"); //no control limit
cmnd.AppendLine("set ooc"); //no ooc
cmnd.AppendLine("(0)m");
cmnd.AppendLine("end");
cmnd.AppendLine("else");
cmnd.AppendLine("let conf = 1-alpha");
cmnd.AppendLine("let a1 = p/2");
cmnd.AppendLine("let a2 = (m-p-1)/2");
// Calculate UCL
cmnd.AppendLine("set tmp.1");
cmnd.AppendLine("(conf)m");
cmnd.AppendLine("end");
cmnd.AppendLine("invcdf tmp.1 ucl;");
cmnd.AppendLine(" beta a1 a2.");
cmnd.AppendLine("let ucl = (m-1)**2/m*ucl"); //Get upper control limit
// Calculate CL
cmnd.AppendLine("set tmp.1");
cmnd.AppendLine("(0.5)m");
cmnd.AppendLine("end");
cmnd.AppendLine("invcdf tmp.1 cl;");
cmnd.AppendLine(" beta a1 a2.");
cmnd.AppendLine("let cl = (m-1)**2/m*cl"); //Get center line
cmnd.AppendLine("let ooc = if(t2>ucl and t2<>MISS(),1,0)"); //Get OOC info
cmnd.AppendLine("endif");
//刪除所有圖形
cmnd.AppendLine("gmana;");
cmnd.AppendLine("all;");
cmnd.AppendLine("close;");
cmnd.AppendLine("nopr.");
cmnd.AppendLine("endmacro");
string macPath = Mtblib.Tools.MtbTools.BuildTemporaryMacro("mytsquare.mac", cmnd.ToString());
string[] matIds = Mtblib.Tools.MtbTools.CreateVariableStrArray(ws, 2, Mtblib.Tools.MtbVarType.Matrix); //紀錄Mean vecot & Covariance matrix
string[] constIds = Mtblib.Tools.MtbTools.CreateVariableStrArray(ws, 1, Mtblib.Tools.MtbVarType.Constant); // Sample size of Covariance matrix
cmnd.Clear();
cmnd.AppendLine("notitle");
cmnd.AppendLine("brief 0");
cmnd.AppendFormat("%\"{0}\" {1};\r\n", macPath, string.Join(" &\r\n", varCols.Select(x => x.SynthesizedName)));
cmnd.AppendFormat("variance {0} {1};\r\n", matIds[0], constIds[0]);
cmnd.AppendFormat("meanvect {0};\r\n", matIds[1]);
cmnd.AppendFormat("ppoint {0};\r\n", pplotCol.SynthesizedName);
cmnd.AppendFormat("climit {0} {1};\r\n", clCol.SynthesizedName, uclCol.SynthesizedName);
cmnd.AppendFormat("test {0}.\r\n", oocCol.SynthesizedName);
string path = Mtblib.Tools.MtbTools.BuildTemporaryMacro("mymacro.mtb", cmnd.ToString());
proj.ExecuteCommand(string.Format("exec \"{0}\" 1", path), ws);
#endregion
//取得參數組
TsquareParameters tmpPara = new TsquareParameters();
Mtb.Matrix mat;
mat = ws.Matrices.Item(matIds[1]);
tmpPara.Mean = LinearAlgebra.Matrix <double> .Build.DenseOfColumnMajor(1, mat.ColumnCount, mat.GetData());
mat = ws.Matrices.Item(matIds[0]);
tmpPara.Covariance = LinearAlgebra.Matrix <double> .Build.DenseOfColumnMajor(mat.RowCount, mat.ColumnCount, mat.GetData());
tmpPara.SampleSize = ws.Constants.Item(constIds[0]).GetData();
tmpPara.SubgroupSize = 1;
tmpPara.ParaID = null;
tmpParaList.Add(tmpPara);
}
else
{
//有指定參數的時候
#region Phase II
int nParas = ws.Columns.Item("CHART_PARA_INDEX").GetNumDistinctRows(); //宣告多組參數組
cmnd.AppendLine("macro");
cmnd.AppendLine("myt2calculator x.1-x.p;");
cmnd.AppendFormat("variance cov.1-cov.{0} ssiz.1-ssiz.{0};\r\n", nParas);
cmnd.AppendFormat("meanvect mvect.1-mvect.{0};\r\n", nParas);
cmnd.AppendLine("ppoint t2;");
cmnd.AppendLine("climit cl ucl;");
cmnd.AppendLine("test ooc;");
cmnd.AppendLine("siglevel alpha.");
cmnd.AppendLine("mcolumn x.1-x.p xx.1-xx.p xxx.1-xxx.p loc.1-loc.p");
cmnd.AppendLine("mcolumn tmp t2 ucl cl ooc tmpT2 tmpUCL tmpCL tmpOOC");
cmnd.AppendFormat("mmatrix mvect.1-mvect.{0} cov.1-cov.{0}\r\n", nParas);
cmnd.AppendFormat("mconstant m histm alpha conf a1 a2 ssiz.1-ssiz.{0} kk\r\n", nParas);
cmnd.AppendLine("default alpha = 0.0027");
var _chartparaindex = _rawdata.AsEnumerable()
.Select(x => x.Field <string>("CHART_PARA_INDEX")).ToArray();
var _distinctIds = _chartparaindex.Distinct().ToArray();
for (int i = 0; i < _distinctIds.Length; i++) //對每個參數與對應的資料計算 Tsquare 和管制界限
{
string id = _distinctIds[i];
cmnd.AppendLine("copy x.1-x.p xx.1-xx.p;");
cmnd.AppendLine("include;");
cmnd.AppendFormat("rows {0}.\r\n",
string.Join(" &\r\n",
_chartparaindex.Select((x, rowid) => new { Value = x, RowId = rowid + 1 })
.Where(x => x.Value == id).Select(x => x.RowId).ToArray()));
#region Set parameter information into Minitab macro
TsquareParameters para = new TsquareParameters();
para.Mean = null; para.Covariance = null; para.SampleSize = MISSINGVALUE; para.SubgroupSize = 1;
if (id != null)
{
para = _parameters.Where(x => x.ParaID == id).First();
}
if (para.Mean != null) //把已知的 Mean vector 寫入 Minitab
{
cmnd.AppendLine("read loc.1-loc.p");
cmnd.AppendFormat("{0}\r\n", string.Join(" &\r\n", para.Mean.Enumerate()));
cmnd.AppendLine("end");
}
else
{
cmnd.AppendLine("stat xx.1-xx.p;");
cmnd.AppendLine("mean loc.1-loc.p.");
}
if (para.Covariance != null) //把已知的 Covariance 寫入 Minitab
{
cmnd.AppendFormat("read {1} {2} cov.{0} \r\n", i + 1, para.Covariance.RowCount, para.Covariance.ColumnCount);
List <LinearAlgebra.Vector <double> > valuesByRow = para.Covariance.EnumerateRows().ToList();
for (int r = 0; r < para.Covariance.RowCount; r++)
{
cmnd.AppendFormat("{0}\r\n", string.Join(" &\r\n", valuesByRow[r]));
}
cmnd.AppendLine("end");
cmnd.AppendFormat("let ssiz.{0}={1}\r\n", i + 1, para.SampleSize);
}
else
{
cmnd.AppendLine("rnmiss xx.1-xx.p tmp");
cmnd.AppendLine("copy xx.1-xx.p xxx.1-xxx.p;");
cmnd.AppendLine("exclud;");
cmnd.AppendFormat("where \"tmp>0\".\r\n");
cmnd.AppendFormat("cova xxx.1-xxx.p cov.{0}\r\n", i + 1);
cmnd.AppendFormat("let ssiz.{0} = count(xxx.1)\r\n", i + 1);
}
#endregion
//Tsquare command
cmnd.AppendFormat("if(ssiz.{0} < p+1)\r\n", i + 1);
cmnd.AppendLine("let kk = p+1");
cmnd.AppendLine("else");
cmnd.AppendFormat("copy ssiz.{0} kk\r\n", i + 1);
cmnd.AppendLine("endif");
if (id == null && _chartparaindex.Where(x => x == id).Count() == 1) //如果只有一個觀測值且沒有參數設定
{
cmnd.AppendLine("let tmp=1");
cmnd.AppendLine("let tmpT2=miss()");
}
else
{
cmnd.AppendFormat("tsquare xx.1-xx.p {0};\r\n", para.SubgroupSize);
cmnd.AppendLine(" mu loc.1-loc.p;");
cmnd.AppendFormat(" sigma cov.{0};\r\n", i + 1);
cmnd.AppendLine(" number kk;");
cmnd.AppendLine("sampsize tmp;"); //Get subgroup size.
cmnd.AppendLine("ppoint tmpT2."); //Get Tsquare value.
}
cmnd.AppendFormat("copy loc.1-loc.p mvect.{0}\r\n", i + 1); //Copy column to mean vector
/*
* Get the value of m which use to calculate UCL, CL.
* In phase II, m is the sample size of the data used to calculate historical
* covariance matrix.
* In phase I, m is the number of observations.
*
*/
cmnd.AppendLine("let m = sum(tmp)"); //Get the actual sample size (include missing data)
if (para.Covariance != null)
{
cmnd.AppendFormat("let histm = ssiz.{0}\r\n", i + 1);
}
else
{
cmnd.AppendLine("copy m histm");
}
// Calculate control limits
if (id == null && _chartparaindex.Where(x => x == id).Count() == 1)
{
cmnd.AppendLine("let tmpUCL=miss()");
cmnd.AppendLine("let tmpCL=miss()");
}
else
{
cmnd.AppendLine("let conf = 1-alpha");
cmnd.AppendLine("let a1 = p");
cmnd.AppendLine("let a2 = histm");
#region Get UCL
cmnd.AppendLine("set tmp");
cmnd.AppendLine("(conf)m");
cmnd.AppendLine("end");
if (id == null) //phase I case
{
cmnd.AppendLine("invcdf tmp tmpUCL;");
cmnd.AppendLine(" beta a1 a2.");
cmnd.AppendLine("let tmpUCL = (m-1)**2/m*tmpUCL"); //Get upper control limit
}
else //phase II case
{
cmnd.AppendLine("invcdf tmp tmpUCL;");
cmnd.AppendLine(" f a1 a2.");
cmnd.AppendLine("let tmpUCL = p*(histm+1)*(histm-1)/histm/(histm-p)*tmpUCL"); //Get upper control limit
}
#endregion
#region Get CL
cmnd.AppendLine("set tmp");
cmnd.AppendLine("(0.5)m");
cmnd.AppendLine("end");
if (id == null)
{
cmnd.AppendLine("invcdf tmp tmpCL;");
cmnd.AppendLine(" beta a1 a2.");
cmnd.AppendLine("let tmpCL = (m-1)**2/m*tmpCL"); //Get upper control limit
}
else
{
cmnd.AppendLine("invcdf tmp tmpCL;");
cmnd.AppendLine(" f a1 a2.");
cmnd.AppendLine("let tmpCL = p*(histm+1)*(histm-1)/histm/(histm-p)*tmpCL"); //Get center line
}
#endregion
}
cmnd.AppendLine("let tmpOOC = if(tmpT2>tmpUCL AND tmpT2<>MISS(),1,0)"); //Get OOC info
if (i == 0)
{
cmnd.AppendLine("copy tmpT2 tmpUCL tmpCL tmpOOC t2 ucl cl ooc");
}
else
{
cmnd.AppendLine("stack (t2 ucl cl ooc) (tmpT2 tmpUCL tmpCL tmpOOC) (t2 ucl cl ooc)");
}
}
//刪除所有圖形
cmnd.AppendLine("gmana;");
cmnd.AppendLine("all;");
cmnd.AppendLine("close;");
cmnd.AppendLine("nopr.");
cmnd.AppendLine("endmacro");
string macPath = Mtblib.Tools.MtbTools.BuildTemporaryMacro("mytsquare.mac", cmnd.ToString());
string[] matMeanIds = Mtblib.Tools.MtbTools.CreateVariableStrArray(ws, nParas, Mtblib.Tools.MtbVarType.Matrix);
string[] matCovIds = Mtblib.Tools.MtbTools.CreateVariableStrArray(ws, nParas, Mtblib.Tools.MtbVarType.Matrix);
string[] constIds = Mtblib.Tools.MtbTools.CreateVariableStrArray(ws, 1 * nParas, Mtblib.Tools.MtbVarType.Constant);
cmnd.Clear();
cmnd.AppendLine("notitle");
cmnd.AppendLine("brief 0");
cmnd.AppendFormat("%\"{0}\" {1};\r\n", macPath, string.Join(" &\r\n", varCols.Select(x => x.SynthesizedName)));
cmnd.AppendFormat("variance {0} {1};\r\n", string.Join(" &\r\n", matCovIds), string.Join(" &\r\n", constIds));
cmnd.AppendFormat("meanvect {0};\r\n", string.Join(" &\r\n", matMeanIds));
cmnd.AppendFormat("ppoint {0};\r\n", pplotCol.SynthesizedName);
cmnd.AppendFormat("climit {0} {1};\r\n", clCol.SynthesizedName, uclCol.SynthesizedName);
cmnd.AppendFormat("test {0}.\r\n", oocCol.SynthesizedName);
string path = Mtblib.Tools.MtbTools.BuildTemporaryMacro("mymacro.mtb", cmnd.ToString());
proj.ExecuteCommand(string.Format("exec \"{0}\" 1", path), ws);
#endregion
//取得參數組
TsquareParameters tmpPara;
Mtb.Matrix mat;
for (int i = 0; i < _distinctIds.Length; i++)
{
tmpPara = new TsquareParameters();
tmpPara.ParaID = _distinctIds[i];
mat = ws.Matrices.Item(matMeanIds[i]);
tmpPara.Mean = LinearAlgebra.Matrix <double> .Build.DenseOfColumnMajor(mat.RowCount, mat.ColumnCount, mat.GetData());
mat = ws.Matrices.Item(matCovIds[i]);
tmpPara.Covariance = LinearAlgebra.Matrix <double> .Build.DenseOfColumnMajor(mat.RowCount, mat.ColumnCount, mat.GetData());
tmpPara.SampleSize = ws.Constants.Item(constIds[i]).GetData();
tmpParaList.Add(tmpPara);
}
}
//繪圖
//會使用 TIMESTAMP, PPOINT, CL, UCL, TEST
#region 繪製 T2 Control Chart (TSPLOT)
string gpath = System.IO.Path.Combine(Environment.GetEnvironmentVariable("tmp"), "Minitab", string.Format("tsquare_{0}.jpg", _rawdata.TableName));
cmnd.Clear();
cmnd.AppendFormat("fdate {0};\r\n", timeCol.SynthesizedName);
cmnd.AppendLine("format(dtyyyy-MM-dd hh:mm).");
cmnd.AppendFormat("tsplot {0} {1} {2};\r\n", pplotCol.SynthesizedName, uclCol.SynthesizedName, clCol.SynthesizedName);
cmnd.AppendFormat("gsave \"{0}\";\r\n", gpath);
cmnd.AppendLine("repl;");
cmnd.AppendLine("jpeg;");
cmnd.AppendLine("over;");
cmnd.AppendFormat("symb {0};\r\n", oocCol.SynthesizedName);
cmnd.AppendLine("type &");
double[] oocData = oocCol.GetData();
if (oocData.Any(x => x == 0))
{
cmnd.AppendLine("6 &");
}
if (oocData.Any(x => x == 1))
{
cmnd.AppendLine("12 &");
}
cmnd.AppendLine("0 0 0 0;");
cmnd.AppendLine("size 1;");
cmnd.AppendLine("color &");
if (oocData.Any(x => x == 0))
{
cmnd.AppendLine("1 &"); //r17 color 64
}
if (oocData.Any(x => x == 1))
{
cmnd.AppendLine("2 &");
}
cmnd.AppendLine(";");
cmnd.AppendLine("conn;");
cmnd.AppendLine("type 1 1 1;");
cmnd.AppendLine("color 1 2 120;"); //r17 conn:64 cl: 9, climit:8
//cmnd.AppendLine("graph;");
//cmnd.AppendLine("color 22;");
cmnd.AppendLine("nole;");
cmnd.AppendFormat("stamp {0};\r\n", timeCol.SynthesizedName);
cmnd.AppendLine("scale 1;");
cmnd.AppendFormat("tick 1:{0}/{1};\r\n", pplotCol.RowCount,
pplotCol.RowCount > 35 ? Math.Ceiling((double)pplotCol.RowCount / 35) : 1);
cmnd.AppendLine("axla 1;");
cmnd.AppendLine("adis 0;");
cmnd.AppendLine("axla 2;");
cmnd.AppendLine("adis 0;");
string ttlString = string.Join(",", varnames);
if (ttlString.Length > 23)
{
ttlString = ttlString.Substring(0, 20) + "...";
}
cmnd.AppendLine("graph 8 4;");
cmnd.AppendFormat("title \"T2管制圖 {0}\";\r\n", ttlString);
cmnd.AppendFormat("footn \"更新時間: {0}\";\r\n", DateTime.Now);
cmnd.AppendFormat("ZTag \"{0}\";\r\n", "_T2CHART");
cmnd.AppendLine(".");
//刪除所有圖形
cmnd.AppendLine("gmana;");
cmnd.AppendLine("all;");
cmnd.AppendLine("close;");
cmnd.AppendLine("nopr.");
string t2MacroPath = Mtblib.Tools.MtbTools.BuildTemporaryMacro("myt2macro.mtb", cmnd.ToString());
proj.ExecuteCommand(string.Format("exec \"{0}\" 1", t2MacroPath), ws);
#endregion
//將檔案轉為二進位陣列
this.Contents.Add(new RptOutput()
{
OType = MtbOType.GRAPH,
OutputInByteArr = File.ReadAllBytes(gpath)
});
//計算 Decomposition
if (oocData.Any(x => x == 1))
{
DataTable tmpDataTable = _rawdata.Copy();
tmpDataTable.Columns.Add("OOC", typeof(int));
for (int r = 0; r < tmpDataTable.Rows.Count; r++)
{
DataRow dr = tmpDataTable.Rows[r];
dr["OOC"] = oocData[r];
}
//建立 Decomposition 的表格
DataTable decoTable = new DataTable();
decoTable.Columns.Add("TIMESTAMP", typeof(DateTime));
foreach (var item in varnames)
{
decoTable.Columns.Add(item, typeof(double));
}
//將OOC的項目值取出
var subData = tmpDataTable.Select("OOC=1").CopyToDataTable();
var oocParaSet = subData.AsEnumerable().Select(dr => dr.Field <string>("CHART_PARA_INDEX")).Distinct().ToArray(); //OOC 的參數組有哪些
foreach (var item in tmpParaList)
{
if (oocParaSet.Contains(item.ParaID)) //該參數組的觀測值是OOC
{
//把對應的觀測值轉換成 List<double[]>，其中每個 double[] 是每個 row 的數據
var subsubData = subData.Select(string.Format("CHART_PARA_INDEX {0}", item.ParaID == null ? "IS NULL" : "='" + item.ParaID + "'"))
.CopyToDataTable();
var obsArray = subsubData.DefaultView.ToTable(false, varnames.ToArray())
.AsEnumerable().Select(x => x.ItemArray.Select(o => Convert.ToDouble(o)).ToArray()).ToList();
LinearAlgebra.Matrix <double> obs = LinearAlgebra.Matrix <double> .Build.DenseOfRowArrays(obsArray);
LinearAlgebra.Matrix <double> t2deco = Tool.T2Decomposition(obs, item.Mean, item.Covariance);
var t2decoByRow = t2deco.EnumerateRows().ToArray();
for (int r = 0; r < t2decoByRow.Count(); r++)
{
DataRow dr = decoTable.NewRow();
object[] o = new object[1 + t2deco.ColumnCount];
t2decoByRow[r].ToArray().CopyTo(o, 1);
o[0] = subsubData.Rows[r].Field <DateTime>("TIMESTAMP");
decoTable.Rows.Add(o);
}
}
}
cmnd.Clear();
//LinearAlgebra.Matrix<double> obs = LinearAlgebra.Matrix<double>.Build.DenseOfRowArrays(subData);
this.Contents.Add(new RptOutput()
{
OType = MtbOType.TABLE,
OutputInByteArr = Tool.ConvertDataSetToByteArray(decoTable)
});
}
else // 沒有 OOC 就不做
{
this.Contents.Add(new RptOutput()
{
OType = MtbOType.TABLE,
OutputInByteArr = null
});
}
//Console.ReadKey();
}
