source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Views/3.4/MenuItems/ExportSymbolicSolutionToExcelMenuItem.cs @ 9906

Last change on this file since 9906 was 9906, checked in by mkommend, 6 years ago

#1730: Added progress bar to excel export.

File size: 20.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Collections.Generic;
24using System.ComponentModel;
25using System.IO;
26using System.Linq;
27using System.Windows.Forms;
28using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Views;
29using HeuristicLab.MainForm;
30using HeuristicLab.MainForm.WindowsForms;
31using HeuristicLab.Optimizer;
32using OfficeOpenXml;
33using OfficeOpenXml.Drawing.Chart;
34
35namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Views {
36  public class ExportSymbolicSolutionToExcelMenuItem : MainForm.WindowsForms.MenuItem, IOptimizerUserInterfaceItemProvider {
37    private const string TRAININGSTART = "TrainingStart";
38    private const string TRAININGEND = "TrainingEnd";
39    private const string TESTSTART = "TestStart";
40    private const string TESTEND = "TestEnd";
41
42    public override string Name {
43      get { return "Export Symbolic Solution To Excel"; }
44    }
45    public override IEnumerable<string> Structure {
46      get { return new string[] { "&Data Analysis" }; }
47    }
48    public override int Position {
49      get { return 5200; }
50    }
51    public override string ToolTipText {
52      get { return "Create excel file of symbolic data analysis solutions."; }
53    }
54
55    protected override void OnToolStripItemSet(EventArgs e) {
56      base.OnToolStripItemSet(e);
57      ToolStripItem.Enabled = false;
58      var menuItem = ToolStripItem.OwnerItem as ToolStripMenuItem;
59      if (menuItem != null)
60        menuItem.DropDownOpening += menuItem_DropDownOpening;
61    }
62
63    private void menuItem_DropDownOpening(object sender, EventArgs e) {
64      IContentView activeView = MainFormManager.MainForm.ActiveView as IContentView;
65      Control control = activeView as Control;
66      activeView = control.GetNestedControls((c) => c.Visible)
67        .OfType<IContentView>().FirstOrDefault(v => v.Content is ISymbolicDataAnalysisSolution && v.Content is IRegressionSolution);
68      ToolStripItem.Enabled = activeView != null;
69    }
70
71    public override void Execute() {
72      IContentView activeView = MainFormManager.MainForm.ActiveView as IContentView;
73      Control control = activeView as Control;
74      activeView = control.GetNestedControls((c) => c.Visible)
75        .OfType<IContentView>().First(v => v.Content is ISymbolicDataAnalysisSolution && v.Content is IRegressionSolution);
76      var solution = (ISymbolicDataAnalysisSolution)activeView.Content;
77      var formatter = new SymbolicDataAnalysisExpressionExcelFormatter();
78      var formula = formatter.Format(solution.Model.SymbolicExpressionTree, solution.ProblemData.Dataset);
79      control = (Control)activeView;
80
81
82      SaveFileDialog saveFileDialog = new SaveFileDialog();
83      saveFileDialog.Filter = "Excel Workbook|*.xlsx";
84      saveFileDialog.Title = "Save an Excel File";
85      if (saveFileDialog.ShowDialog() == DialogResult.OK) {
86        string fileName = saveFileDialog.FileName;
87        using (BackgroundWorker bg = new BackgroundWorker()) {
88          MainFormManager.GetMainForm<MainForm.WindowsForms.MainForm>().AddOperationProgressToView(control, "Exportion solution to " + fileName + ".");
89          bg.DoWork += (b, e) => ExportChart(fileName, solution, formula);
90          bg.RunWorkerCompleted += (o, e) => MainFormManager.GetMainForm<MainForm.WindowsForms.MainForm>().RemoveOperationProgressFromView(control);
91          bg.RunWorkerAsync();
92        }
93      }
94    }
95
96    private void ExportChart(string fileName, ISymbolicDataAnalysisSolution solution, string formula) {
97      FileInfo newFile = new FileInfo(fileName);
98      if (newFile.Exists) {
99        newFile.Delete();
100        newFile = new FileInfo(fileName);
101      }
102      var formulaParts = formula.Split(new string[] { Environment.NewLine }, StringSplitOptions.None);
103
104      using (ExcelPackage package = new ExcelPackage(newFile)) {
105        ExcelWorksheet modelWorksheet = package.Workbook.Worksheets.Add("Model");
106        FormatModelSheet(modelWorksheet, solution, formulaParts);
107
108        ExcelWorksheet datasetWorksheet = package.Workbook.Worksheets.Add("Dataset");
109        WriteDatasetToExcel(datasetWorksheet, solution.ProblemData);
110
111        ExcelWorksheet inputsWorksheet = package.Workbook.Worksheets.Add("Inputs");
112        WriteInputSheet(inputsWorksheet, datasetWorksheet, formulaParts.Skip(2), solution.ProblemData.Dataset);
113
114        if (solution is IRegressionSolution) {
115          ExcelWorksheet estimatedWorksheet = package.Workbook.Worksheets.Add("Estimated Values");
116          WriteEstimatedWorksheet(estimatedWorksheet, datasetWorksheet, formulaParts, solution as IRegressionSolution);
117
118          ExcelWorksheet chartsWorksheet = package.Workbook.Worksheets.Add("Charts");
119          AddCharts(chartsWorksheet);
120        }
121        package.Workbook.Properties.Title = "Excel Export";
122        package.Workbook.Properties.Author = "HEAL";
123        package.Workbook.Properties.Comments = "Excel export of a symbolic data analysis solution from HeuristicLab";
124
125        package.Save();
126      }
127    }
128
129    private void FormatModelSheet(ExcelWorksheet modelWorksheet, ISymbolicDataAnalysisSolution solution, IEnumerable<string> formulaParts) {
130      int row = 1;
131      modelWorksheet.Cells[row, 1].Value = "Model";
132      modelWorksheet.Cells[row, 2].Value = solution.Name;
133
134      foreach (var part in formulaParts) {
135        modelWorksheet.Cells[row, 4].Value = part;
136        row++;
137      }
138
139      row = 2;
140      modelWorksheet.Cells[row, 1].Value = "Model Depth";
141      modelWorksheet.Cells[row, 2].Value = solution.Model.SymbolicExpressionTree.Depth;
142      row++;
143
144      modelWorksheet.Cells[row, 1].Value = "Model Length";
145      modelWorksheet.Cells[row, 2].Value = solution.Model.SymbolicExpressionTree.Length;
146      row += 2;
147
148      modelWorksheet.Cells[row, 1].Value = "Estimation Limits Lower";
149      modelWorksheet.Cells[row, 2].Value = solution.Model.LowerEstimationLimit;
150      modelWorksheet.Names.Add("EstimationLimitLower", modelWorksheet.Cells[row, 2]);
151      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000";
152      row++;
153
154      modelWorksheet.Cells[row, 1].Value = "Estimation Limits Upper";
155      modelWorksheet.Cells[row, 2].Value = solution.Model.UpperEstimationLimit;
156      modelWorksheet.Names.Add("EstimationLimitUpper", modelWorksheet.Cells[row, 2]);
157      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000";
158      row += 2;
159
160      modelWorksheet.Cells[row, 1].Value = "Trainings Partition Start";
161      modelWorksheet.Cells[row, 2].Value = solution.ProblemData.TrainingPartition.Start;
162      modelWorksheet.Names.Add(TRAININGSTART, modelWorksheet.Cells[row, 2]);
163      row++;
164
165      modelWorksheet.Cells[row, 1].Value = "Trainings Partition End";
166      modelWorksheet.Cells[row, 2].Value = solution.ProblemData.TrainingPartition.End;
167      modelWorksheet.Names.Add(TRAININGEND, modelWorksheet.Cells[row, 2]);
168      row++;
169
170      modelWorksheet.Cells[row, 1].Value = "Test Partition Start";
171      modelWorksheet.Cells[row, 2].Value = solution.ProblemData.TestPartition.Start;
172      modelWorksheet.Names.Add(TESTSTART, modelWorksheet.Cells[row, 2]);
173      row++;
174
175      modelWorksheet.Cells[row, 1].Value = "Test Partition End";
176      modelWorksheet.Cells[row, 2].Value = solution.ProblemData.TestPartition.End;
177      modelWorksheet.Names.Add(TESTEND, modelWorksheet.Cells[row, 2]);
178      row += 2;
179
180      string excelTrainingTarget = Indirect("B", true);
181      string excelTrainingEstimated = Indirect("C", true);
182      string excelTrainingAbsoluteError = Indirect("D", true);
183      string excelTrainingRelativeError = Indirect("E", true);
184      string excelTrainingMeanError = Indirect("F", true);
185      string excelTrainingMSE = Indirect("G", true);
186
187      string excelTestTarget = Indirect("B", false);
188      string excelTestEstimated = Indirect("C", false);
189      string excelTestAbsoluteError = Indirect("D", false);
190      string excelTestRelativeError = Indirect("E", false);
191      string excelTestMeanError = Indirect("F", false);
192      string excelTestMSE = Indirect("G", false);
193
194      modelWorksheet.Cells[row, 1].Value = "Pearson's R² (training)";
195      modelWorksheet.Cells[row, 2].Formula = string.Format("POWER(PEARSON({0},{1}),2)", excelTrainingTarget, excelTrainingEstimated);
196      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000";
197      row++;
198
199      modelWorksheet.Cells[row, 1].Value = "Pearson's R² (test)";
200      modelWorksheet.Cells[row, 2].Formula = string.Format("POWER(PEARSON({0},{1}),2)", excelTestTarget, excelTestEstimated);
201      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000";
202      row++;
203
204      modelWorksheet.Cells[row, 1].Value = "Mean Squared Error (training)";
205      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTrainingMSE);
206      modelWorksheet.Names.Add("TrainingMSE", modelWorksheet.Cells[row, 2]);
207      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000";
208      row++;
209
210      modelWorksheet.Cells[row, 1].Value = "Mean Squared Error (test)";
211      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTestMSE);
212      modelWorksheet.Names.Add("TestMSE", modelWorksheet.Cells[row, 2]);
213      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000";
214      row++;
215
216      modelWorksheet.Cells[row, 1].Value = "Mean absolute error (training)";
217      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTrainingAbsoluteError);
218      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000";
219      row++;
220
221      modelWorksheet.Cells[row, 1].Value = "Mean absolute error (test)";
222      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTestAbsoluteError);
223      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000";
224      row++;
225
226      modelWorksheet.Cells[row, 1].Value = "Mean error (training)";
227      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTrainingMeanError);
228      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000";
229      row++;
230
231      modelWorksheet.Cells[row, 1].Value = "Mean error (test)";
232      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTestMeanError);
233      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000";
234      row++;
235
236      modelWorksheet.Cells[row, 1].Value = "Average relative error (training)";
237      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTrainingRelativeError);
238      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.00%";
239      row++;
240
241      modelWorksheet.Cells[row, 1].Value = "Average relative error (test)";
242      modelWorksheet.Cells[row, 2].Formula = string.Format("AVERAGE({0})", excelTestRelativeError);
243      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.00%";
244      row++;
245
246      modelWorksheet.Cells[row, 1].Value = "Normalized Mean Squared error (training)";
247      modelWorksheet.Cells[row, 2].Formula = string.Format("TrainingMSE / VAR({0})", excelTrainingTarget);
248      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000";
249      row++;
250
251      modelWorksheet.Cells[row, 1].Value = "Normalized Mean Squared error  (test)";
252      modelWorksheet.Cells[row, 2].Formula = string.Format("TestMSE / VAR({0})", excelTestTarget);
253      modelWorksheet.Cells[row, 2].Style.Numberformat.Format = "0.000";
254
255      modelWorksheet.Cells["A1:B" + row].AutoFitColumns();
256
257      AddModelTreePicture(modelWorksheet, solution.Model);
258    }
259
260    private string Indirect(string column, bool training) {
261      if (training) {
262        return string.Format("INDIRECT(\"'Estimated Values'!{0}\"&{1}+2&\":{0}\"&{2}+1)", column, TRAININGSTART, TRAININGEND);
263      } else {
264        return string.Format("INDIRECT(\"'Estimated Values'!{0}\"&{1}+2&\":{0}\"&{2}+1)", column, TESTSTART, TESTEND);
265      }
266    }
267
268    private void AddCharts(ExcelWorksheet chartsWorksheet) {
269      chartsWorksheet.Names.AddFormula("AllId", "OFFSET('Estimated Values'!$A$1,1,0, COUNTA('Estimated Values'!$A:$A)-1)");
270      chartsWorksheet.Names.AddFormula("AllTarget", "OFFSET('Estimated Values'!$B$1,1,0, COUNTA('Estimated Values'!$B:$B)-1)");
271      chartsWorksheet.Names.AddFormula("AllEstimated", "OFFSET('Estimated Values'!$C$1,1,0, COUNTA('Estimated Values'!$C:$C)-1)");
272      chartsWorksheet.Names.AddFormula("TrainingId", "OFFSET('Estimated Values'!$A$1,Model!TrainingStart + 1,0, Model!TrainingEnd - Model!TrainingStart)");
273      chartsWorksheet.Names.AddFormula("TrainingTarget", "OFFSET('Estimated Values'!$B$1,Model!TrainingStart + 1,0, Model!TrainingEnd - Model!TrainingStart)");
274      chartsWorksheet.Names.AddFormula("TrainingEstimated", "OFFSET('Estimated Values'!$C$1,Model!TrainingStart + 1,0, Model!TrainingEnd - Model!TrainingStart)");
275      chartsWorksheet.Names.AddFormula("TestId", "OFFSET('Estimated Values'!$A$1,Model!TestStart + 1,0, Model!TestEnd - Model!TestStart)");
276      chartsWorksheet.Names.AddFormula("TestTarget", "OFFSET('Estimated Values'!$B$1,Model!TestStart + 1,0, Model!TestEnd - Model!TestStart)");
277      chartsWorksheet.Names.AddFormula("TestEstimated", "OFFSET('Estimated Values'!$C$1,Model!TestStart + 1,0, Model!TestEnd - Model!TestStart)");
278
279      var scatterPlot = chartsWorksheet.Drawings.AddChart("scatterPlot", eChartType.XYScatter);
280      scatterPlot.SetSize(800, 400);
281      scatterPlot.SetPosition(0, 0);
282      scatterPlot.Title.Text = "Scatter Plot";
283      var seriesAll = scatterPlot.Series.Add("AllTarget", "AllEstimated");
284      seriesAll.Header = "All";
285      var seriesTraining = scatterPlot.Series.Add("TrainingTarget", "TrainingEstimated");
286      seriesTraining.Header = "Training";
287      var seriesTest = scatterPlot.Series.Add("TestTarget", "TestEstimated");
288      seriesTest.Header = "Test";
289
290      var lineChart = chartsWorksheet.Drawings.AddChart("lineChart", eChartType.XYScatterLinesNoMarkers);
291      lineChart.SetSize(800, 400);
292      lineChart.SetPosition(400, 0);
293      lineChart.Title.Text = "LineChart";
294      var lineTarget = lineChart.Series.Add("AllTarget", "AllId");
295      lineTarget.Header = "Target";
296      var lineAll = lineChart.Series.Add("AllEstimated", "AllId");
297      lineAll.Header = "All";
298      var lineTraining = lineChart.Series.Add("TrainingEstimated", "TrainingId");
299      lineTraining.Header = "Training";
300      var lineTest = lineChart.Series.Add("TestEstimated", "TestId");
301      lineTest.Header = "Test";
302    }
303
304    private void AddModelTreePicture(ExcelWorksheet modelWorksheet, ISymbolicDataAnalysisModel model) {
305      SymbolicExpressionTreeChart modelTreePicture = new SymbolicExpressionTreeChart();
306      modelTreePicture.Tree = model.SymbolicExpressionTree;
307      string tmpFilename = Path.GetTempFileName();
308      modelTreePicture.Width = 1000;
309      modelTreePicture.Height = 500;
310      modelTreePicture.SaveImageAsEmf(tmpFilename);
311
312      FileInfo fi = new FileInfo(tmpFilename);
313      var excelModelTreePic = modelWorksheet.Drawings.AddPicture("ModelTree", fi);
314      excelModelTreePic.SetSize(50);
315      excelModelTreePic.SetPosition(2, 0, 6, 0);
316    }
317
318    private void WriteEstimatedWorksheet(ExcelWorksheet estimatedWorksheet, ExcelWorksheet datasetWorksheet, string[] formulaParts, IRegressionSolution solution) {
319      string preparedFormula = PrepareFormula(formulaParts);
320      int rows = solution.ProblemData.Dataset.Rows;
321      estimatedWorksheet.Cells[1, 1].Value = "Id";
322      estimatedWorksheet.Cells[1, 2].Value = "Target Variable";
323      estimatedWorksheet.Cells[1, 3].Value = "Estimated Values";
324      estimatedWorksheet.Cells[1, 4].Value = "Absolute Error";
325      estimatedWorksheet.Cells[1, 5].Value = "Relative Error";
326      estimatedWorksheet.Cells[1, 6].Value = "Error";
327      estimatedWorksheet.Cells[1, 7].Value = "Squared Error";
328      estimatedWorksheet.Cells[1, 9].Value = "Unbounded Estimated Values";
329      estimatedWorksheet.Cells[1, 10].Value = "Bounded Estimated Values";
330
331      estimatedWorksheet.Cells[1, 1, 1, 10].AutoFitColumns();
332
333      int targetIndex = solution.ProblemData.Dataset.VariableNames.ToList().FindIndex(x => x.Equals(solution.ProblemData.TargetVariable)) + 1;
334      for (int i = 0; i < rows; i++) {
335        estimatedWorksheet.Cells[i + 2, 1].Value = i;
336        estimatedWorksheet.Cells[i + 2, 2].Formula = datasetWorksheet.Cells[i + 2, targetIndex].FullAddress;
337        estimatedWorksheet.Cells[i + 2, 9].Formula = string.Format(preparedFormula, i + 2);
338      }
339      estimatedWorksheet.Cells["B2:B" + (rows + 1)].Style.Numberformat.Format = "0.000";
340
341      estimatedWorksheet.Cells["C2:C" + (rows + 1)].Formula = "J2";
342      estimatedWorksheet.Cells["C2:C" + (rows + 1)].Style.Numberformat.Format = "0.000";
343      estimatedWorksheet.Cells["D2:D" + (rows + 1)].Formula = "ABS(B2 - C2)";
344      estimatedWorksheet.Cells["D2:D" + (rows + 1)].Style.Numberformat.Format = "0.000";
345      estimatedWorksheet.Cells["E2:E" + (rows + 1)].Formula = "ABS(D2 / B2)";
346      estimatedWorksheet.Cells["E2:E" + (rows + 1)].Style.Numberformat.Format = "0.000";
347      estimatedWorksheet.Cells["F2:F" + (rows + 1)].Formula = "C2 - B2";
348      estimatedWorksheet.Cells["F2:F" + (rows + 1)].Style.Numberformat.Format = "0.000";
349      estimatedWorksheet.Cells["G2:G" + (rows + 1)].Formula = "POWER(F2, 2)";
350      estimatedWorksheet.Cells["G2:G" + (rows + 1)].Style.Numberformat.Format = "0.000";
351
352      estimatedWorksheet.Cells["I2:I" + (rows + 1)].Style.Numberformat.Format = "0.000";
353      estimatedWorksheet.Cells["J2:J" + (rows + 1)].Formula = "IFERROR(IF(I2 > Model!EstimationLimitUpper, Model!EstimationLimitUpper, IF(I2 < Model!EstimationLimitLower, Model!EstimationLimitLower, I2)), AVERAGE(Model!EstimationLimitLower, Model!EstimationLimitUpper))";
354      estimatedWorksheet.Cells["J2:J" + (rows + 1)].Style.Numberformat.Format = "0.000";
355    }
356
357    private string PrepareFormula(string[] formulaParts) {
358      string preparedFormula = formulaParts[0];
359      foreach (var part in formulaParts.Skip(2)) {
360        var varMap = part.Split(new string[] { " = " }, StringSplitOptions.None);
361        var columnName = "$" + varMap[1] + "1";
362        preparedFormula = preparedFormula.Replace(columnName, "Inputs!$" + varMap[1] + "{0}");   //{0} will be replaced later with the row number
363      }
364      return preparedFormula;
365    }
366
367    private void WriteInputSheet(ExcelWorksheet inputsWorksheet, ExcelWorksheet datasetWorksheet, IEnumerable<string> list, Dataset dataset) {
368      //remark the performance of EPPlus drops dramatically
369      //if the data is not written row wise (from left to right) due the internal indices used.
370      var variableNames = dataset.VariableNames.Select((v, i) => new { variable = v, index = i + 1 }).ToDictionary(v => v.variable, v => v.index);
371      var nameMapping = list.Select(x => x.Split('=')[0].Trim()).ToArray();
372
373      for (int row = 1; row <= dataset.Rows + 1; row++) {
374        for (int column = 1; column < nameMapping.Length + 1; column++) {
375          int variableIndex = variableNames[nameMapping[column - 1]];
376          inputsWorksheet.Cells[row, column].Formula = datasetWorksheet.Cells[row, variableIndex].FullAddress;
377        }
378      }
379    }
380
381    private void WriteDatasetToExcel(ExcelWorksheet datasetWorksheet, IDataAnalysisProblemData problemData) {
382      //remark the performance of EPPlus drops dramatically
383      //if the data is not written row wise (from left to right) due the internal indices used.
384      Dataset dataset = problemData.Dataset;
385      var variableNames = dataset.VariableNames.ToList();
386      var doubleVariables = new HashSet<string>(dataset.DoubleVariables);
387
388      for (int col = 1; col <= variableNames.Count; col++)
389        datasetWorksheet.Cells[1, col].Value = variableNames[col - 1];
390
391      for (int row = 0; row < dataset.Rows; row++) {
392        for (int col = 0; col < variableNames.Count; col++) {
393          if (doubleVariables.Contains(variableNames[col]))
394            datasetWorksheet.Cells[row + 2, col + 1].Value = dataset.GetDoubleValue(variableNames[col], row);
395          else
396            datasetWorksheet.Cells[row + 2, col + 1].Value = dataset.GetValue(row, col);
397        }
398      }
399    }
400  }
401}
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