From 1c3fa6bc0d7200dd96c3d29819c4be60c6c97bb1 Mon Sep 17 00:00:00 2001 From: Lazloo Date: Mon, 9 Jan 2017 14:20:39 +0100 Subject: [PATCH] 20170109 Major Changes: - Inqeuality Constraint w.r.t. to objectives can now be applied for expected improvement - Modification in plotParetoFront and predictParetoFront regading the 2D case - Debugging of AnalyzeMixture Class - Heterodedastic noise can now be estimated (generateNoiseModel) - Major improvements in TaylorKriging (Kriging with non-linear mechanistic models) - Speedup in calculation of 2D expected hypervolume improvement Minor Changes: - Small changes w.r.t. to the tutorial - New color scheme avaiable: FZJ - use now surf instead of surf for 3D plots - the class BaysianOptimization was renamed to BaysianOptimizationClass Signed-off-by: Lazloo --- .../tutorialCreatingObject.html | 39 +- .../html/tutorialCreatingObject.html | 323 +++++ .../html/tutorialCreatingObject.png | Bin 0 -> 5570 bytes .../html/tutorialCreatingObject_01.png | Bin 0 -> 30363 bytes .../html/tutorialCreatingObject_02.png | Bin 0 -> 28013 bytes .../html/tutorialCreatingObject_03.png | Bin 0 -> 26985 bytes .../html/tutorialCreatingObject_04.png | Bin 0 -> 13033 bytes .../html/tutorialCreatingObject_05.png | Bin 0 -> 13259 bytes .../html/tutorialCreatingObject_06.png | Bin 0 -> 12376 bytes ...lCreatingObject_eq01953356565641195011.png | Bin 0 -> 11746 bytes ...lCreatingObject_eq02739421979046219838.png | Bin 0 -> 8830 bytes ...lCreatingObject_eq03670739675111899559.png | Bin 0 -> 10378 bytes ...lCreatingObject_eq06559173809755883972.png | Bin 0 -> 4178 bytes ...lCreatingObject_eq06622766184584322475.png | Bin 0 -> 13354 bytes ...lCreatingObject_eq07994915521862946370.png | Bin 0 -> 13699 bytes ...lCreatingObject_eq09582885264525567196.png | Bin 0 -> 11978 bytes ...lCreatingObject_eq14322275300694116907.png | Bin 0 -> 10866 bytes ...lCreatingObject_eq15217372810807509549.png | Bin 0 -> 7784 bytes .../ScriptsForDocu/tutorialCreatingObject.m | 6 +- dependencies/@AnalyzeKriging/AnalyzeKriging.m | 46 +- dependencies/@AnalyzeKriging/ColormapFZJ.txt | 1000 +++++++++++++++ .../@AnalyzeKriging/calcExpectedImprovement.m | 16 + .../calcExpectedImprovementFromPredictions.m | 66 - .../calcExpectedImprovement_MO.m | 22 +- .../calcMutualInterpolation_23D.m | 2 + .../@AnalyzeKriging/checkInputInterpolation.m | 2 + .../@AnalyzeKriging/defineBoundOfInputVar.m | 6 +- dependencies/@AnalyzeKriging/errorCheck.p | Bin 0 -> 1420 bytes .../@AnalyzeKriging/plotInterpolation_23D.m | 8 +- .../@AnalyzeKriging/plotParetoFront.m | 33 +- .../@AnalyzeKriging/predictParetoCurve.m | 121 +- .../setLBUBInputVarInterpolation.m | 17 +- dependencies/@AnalyzeMixture/AnalyzeMixture.m | 32 +- .../@AnalyzeMixture/calcInterpolation.m | 7 +- .../@AnalyzeMixture/calcInterpolation_nD.m | 10 +- .../@AnalyzeMixture/plotInterpolation_nD.m | 30 +- .../BayesianOptimizationClass.m} | 32 +- .../MCMCDistributionFctDRAM.m | 0 .../MCMCDistributionFctSlice.m | 0 .../calcNewSamplesViaMCMC.m | 0 .../modExpImprByOutputConstr.m | 55 + .../@KrigingSuperClass/KrigingSuperClass.m | 117 +- .../MarginalLikelihoodCovarPara.m | 7 +- .../calcCovarMatrixOfPredictions.m | 5 +- .../calcCovariogramMatrix.m | 47 +- .../@KrigingSuperClass/calcCrossOverQuality.m | 199 +-- .../doConditionalSimulation_ResAlgInEq.m | 180 +-- .../@KrigingSuperClass/estimateVariance.m | 13 +- .../@KrigingSuperClass/generateNoiseModel.m | 118 ++ .../generateRegressionGPModel.m | 2 +- dependencies/@KrigingSuperClass/prediction.m | 47 +- dependencies/@TaylorKriging/TaylorKriging.m | 10 +- .../calcKrigingParaEstimationObjFctVector.m | 21 +- .../estimateBasisFctParametersFsolve.m | 68 + .../GridTools/createNDGRID.m | 29 +- .../auxiliaryFunctions/HV_mod/calcEHIV_2D.m | 135 +- .../HV_mod/calcEHIV_2D_2016.m | 211 ++++ .../HV_mod/calcEHIV_3D_2016.m | 89 ++ .../auxiliaryFunctions/HV_mod/gaussEI.m | 4 + .../auxiliaryFunctions/HV_mod/gausscdf.m | 3 +- .../auxiliaryFunctions/HV_mod/gausspdf.m | 3 +- .../MatrixInversion/invupdateapp.m | 2 +- .../externalTools/mcmcstat/boxplotMCMCStat.m | 2 +- .../mexFiles/VChooseK/CompilingCommand.txt | 1 + .../mexFiles/VChooseK/VChooseK.c | 1114 +++++++++++++++++ .../mexFiles/VChooseK/VChooseK.m | 81 ++ .../mexFiles/VChooseK/VChooseK.mexa64 | Bin 0 -> 23336 bytes .../mexFiles/VChooseK/VChooseK.mexw64 | Bin 0 -> 17920 bytes .../mexFiles/VChooseK/VChooseKUncommented.c | 899 +++++++++++++ .../mexFiles/VChooseK/license.txt | 24 + .../mexFiles/VChooseK/test.m | 19 + 71 files changed, 4831 insertions(+), 492 deletions(-) create mode 100644 Tutorial/ScriptsForDocu/html/tutorialCreatingObject.html create mode 100644 Tutorial/ScriptsForDocu/html/tutorialCreatingObject.png create mode 100644 Tutorial/ScriptsForDocu/html/tutorialCreatingObject_01.png create mode 100644 Tutorial/ScriptsForDocu/html/tutorialCreatingObject_02.png create mode 100644 Tutorial/ScriptsForDocu/html/tutorialCreatingObject_03.png create mode 100644 Tutorial/ScriptsForDocu/html/tutorialCreatingObject_04.png create mode 100644 Tutorial/ScriptsForDocu/html/tutorialCreatingObject_05.png create mode 100644 Tutorial/ScriptsForDocu/html/tutorialCreatingObject_06.png create mode 100644 Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq01953356565641195011.png create mode 100644 Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq02739421979046219838.png create mode 100644 Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq03670739675111899559.png create mode 100644 Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq06559173809755883972.png create mode 100644 Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq06622766184584322475.png create mode 100644 Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq07994915521862946370.png create mode 100644 Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq09582885264525567196.png create mode 100644 Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq14322275300694116907.png create mode 100644 Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq15217372810807509549.png create mode 100644 dependencies/@AnalyzeKriging/ColormapFZJ.txt delete mode 100644 dependencies/@AnalyzeKriging/calcExpectedImprovementFromPredictions.m create mode 100644 dependencies/@AnalyzeKriging/errorCheck.p rename dependencies/{@BayesianOptimization/BayesianOptimization.m => @BayesianOptimizationClass/BayesianOptimizationClass.m} (75%) rename dependencies/{@BayesianOptimization => @BayesianOptimizationClass}/MCMCDistributionFctDRAM.m (100%) rename dependencies/{@BayesianOptimization => @BayesianOptimizationClass}/MCMCDistributionFctSlice.m (100%) rename dependencies/{@BayesianOptimization => @BayesianOptimizationClass}/calcNewSamplesViaMCMC.m (100%) create mode 100644 dependencies/@BayesianOptimizationClass/modExpImprByOutputConstr.m create mode 100644 dependencies/@KrigingSuperClass/generateNoiseModel.m create mode 100644 dependencies/@TaylorKriging/estimateBasisFctParametersFsolve.m create mode 100644 dependencies/auxiliaryFunctions/HV_mod/calcEHIV_2D_2016.m create mode 100644 dependencies/auxiliaryFunctions/HV_mod/calcEHIV_3D_2016.m create mode 100644 dependencies/auxiliaryFunctions/HV_mod/gaussEI.m create mode 100644 dependencies/auxiliaryFunctions/mexFiles/VChooseK/CompilingCommand.txt create mode 100644 dependencies/auxiliaryFunctions/mexFiles/VChooseK/VChooseK.c create mode 100644 dependencies/auxiliaryFunctions/mexFiles/VChooseK/VChooseK.m create mode 100644 dependencies/auxiliaryFunctions/mexFiles/VChooseK/VChooseK.mexa64 create mode 100644 dependencies/auxiliaryFunctions/mexFiles/VChooseK/VChooseK.mexw64 create mode 100644 dependencies/auxiliaryFunctions/mexFiles/VChooseK/VChooseKUncommented.c create mode 100644 dependencies/auxiliaryFunctions/mexFiles/VChooseK/license.txt create mode 100644 dependencies/auxiliaryFunctions/mexFiles/VChooseK/test.m diff --git a/Tutorial/ActualTutorial/CreatingObject/tutorialCreatingObject.html b/Tutorial/ActualTutorial/CreatingObject/tutorialCreatingObject.html index 753a348..7746fed 100644 --- a/Tutorial/ActualTutorial/CreatingObject/tutorialCreatingObject.html +++ b/Tutorial/ActualTutorial/CreatingObject/tutorialCreatingObject.html @@ -6,7 +6,7 @@ tutorialCreatingObject

Contents

Tutorial: How to create a Kriging Object

function [KrigingObj] = tutorialCreatingObject(varargin)
-

Initialisierung

% Decide if estimated Kriging model shall be visualized
+  
Contents
Tutorial: How to create a Kriging Object
function [KrigingObj] = tutorialCreatingObject(varargin)
+
Initialisierung
% Decide if estimated Kriging model shall be visualized
 if length(varargin)>=1&&~(varargin{1})
     visualizeResult = false;
 else
@@ -78,10 +78,10 @@
 nLevelsEachDimension = 6;
 input = createNDGRID(zeros(3,1),ones(3,1)*10,nLevelsEachDimension);
 output = tutorialFunction(input);
-
Test Function
The test function as follows:
$$f_{\rm{Test}} = \frac{10x_{1}}{5 + x_{1}}\sin\left( x_{2} \right) +
x_{3}$$
That is, the output is related to the first input variable by a Michaelis Menten curve(steep increase for small values followed by a plateau). The second input variable leads to oscillation and the third input variable leads to an linear monotonically increase. The effect of the first and second input are coupled with each other.
For a better visualization, pair-wise combination of input variables are plotted against the output. Remaining input variable was hold constant at the value of 2.
if visualizeResult
+
Test Function
The test function as follows:
$$f_{\rm{Test}} = \frac{10x_{1}}{5 + x_{1}}\sin\left( x_{2} \right) +
x_{3}$$
That is, the output is related to the first input variable by a Michaelis Menten curve(steep increase for small values followed by a plateau). The second input variable leads to oscillation and the third input variable leads to an linear monotonically increase. The effect of the first and second input are coupled with each other.
For a better visualization, pair-wise combination of input variables are plotted against the output. Remaining input variable was hold constant at the value of 2.
if visualizeResult
     visualizeTestFunction();
 end
-
   
Create Analysis Object
AnalyzeKriging inherites KrigingSuperClass and manages Kriging objects. AnalyzeKriging also contains all analysis tools.
KrigingObj = AnalyzeKriging;
+
   
Create Analysis Object
AnalyzeKriging inherites KrigingSuperClass and manages Kriging objects. AnalyzeKriging also contains all analysis tools.
KrigingObj = AnalyzeKriging;
 
 % Remember the Currently used object
 indexKrigingObj = 1;
@@ -89,9 +89,13 @@
 
Load input data and name input variables
  • Structure of the input variable has to be nDataPointsXnInputVariables
  • First input argument for "setInputVarNames" is the index of the Kriging object of current use
  • Second argument is string-array containing the names of the input variables
KrigingObj.KrigingObjects{indexKrigingObj}.setInputData(input)
 KrigingObj.setInputVarNames(indexKrigingObj,{'Input 1','Input 2','Input 3'})
 
Load output data. The output data container has to be a vector of the length nDataPoints
KrigingObj.KrigingObjects{indexKrigingObj}.setOutputData(output)
-
Create Kriging Object and estimate covariogram parameters
The backbone of Kriging is the estimation of the covariogram. The covariogram helps to estimate the covariance between the data points. In general, the covariogram considers only the distance between two points. The closer data points are the higher is their covariance. Kriging prediction is done by a linear combination of the provided data. Here, measurment data at points with high covariance values w.r.t. the point of interest are higher weighted.
Choose covariogram model:
$$1: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma^2\exp\left( - \left( \frac{0.5r}{2\theta^2} \right)\right), r=\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)^{T}\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)$
$$2: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma_{{Nugget}}^2 + \sigma^2\exp\left( - \left( \frac{0.5r}{2\theta^2} \right)\right), r=\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)^{T}\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)$
$$3:
\mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) =
\sigma_{{Nugget}}^2 + \sigma^2\exp\left( - \left( \frac{0.5r}{2\theta^2}
\right)\right),r = \sqrt{\sum_{l = 1}^{k}\frac{\left( x_{i,l} - x_{j,l} \right)^{p_{l}}}{\theta_{l}^{2}}}$$
$$4: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma_{{Nugget}}^2 + \sigma^2\exp\left( - \left( \frac{0.5r}{2\theta^2} \right)\right),r = \sqrt{\sum_{l = 1}^{k}\frac{\left( x_{i,l} - x_{j,l} \right)^{2}}{\theta_{l}^{2}}}$
$$5: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma_{{Nugget}}^{2} + \sigma^{2}\left( 1 + \frac{\left( \sqrt{3}r \right)}{\theta^{2}} \right)\exp\left( - \frac{\left( \sqrt{3}r \right)}{\theta^{2}} \right),r = \sqrt{\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)^{T}\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)}$
$$6: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma_{Nugget}^{2} + \sigma^{2}\left( 1 + \sqrt{3}r \right)\exp\left( - \sqrt{3}r \right),r = \sqrt{\sum_{l = 1}^{k}\frac{\left( x_{i,l} - x_{j,l} \right)^{2}}{\theta_{l}^{2}}}$
$$7: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma_{Nugget}^{2} + \sigma^{2}\left( 1 + \frac{\sqrt{5}r}{\theta^{2}} + \frac{5r^{2}}{3\theta^{2}} \right)\exp\left( - \frac{\sqrt{5}r}{\theta^{2}} \right), r = \ \sqrt{\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)^{T}\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)}$
$$8: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma_{Nugget}^{2} + \sigma^{2}\left( 1 + \sqrt{3}r + \frac{5}{3}r^{2} \right)\exp\left( - \sqrt{5}r \right), r = \sqrt{\sum_{l = 1}^{k}\frac{\left( x_{i,l} - x_{j,l} \right)^{2}}{\theta_{l}^{2}}}$
KrigingObj.KrigingObjects{indexKrigingObj}.setCovariogramModelChoice(5);
+
Create Kriging Object and estimate covariogram parameters
The backbone of Kriging is the estimation of the covariogram. The covariogram helps to estimate the covariance between the data points. In general, the covariogram considers only the distance between two points. The closer data points are the higher is their covariance. Kriging prediction is done by a linear combination of the provided data. Here, measurment data at points with high covariance values w.r.t. the point of interest are higher weighted.
Choose covariogram model:
$$1: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma^2\exp\left( - \left( \frac{0.5r}{2\theta^2} \right)\right), r=\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)^{T}\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)$
$$2: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma_{{Nugget}}^2 + \sigma^2\exp\left( - \left( \frac{0.5r}{2\theta^2} \right)\right), r=\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)^{T}\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)$
$$3:
\mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) =
\sigma_{{Nugget}}^2 + \sigma^2\exp\left( - \left( \frac{0.5r}{2\theta^2}
\right)\right),r = \sqrt{\sum_{l = 1}^{k}\frac{\left( x_{i,l} - x_{j,l} \right)^{p_{l}}}{\theta_{l}^{2}}}$$
$$4: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma_{{Nugget}}^2 + \sigma^2\exp\left( - \left( \frac{0.5r}{2\theta^2} \right)\right),r = \sqrt{\sum_{l = 1}^{k}\frac{\left( x_{i,l} - x_{j,l} \right)^{2}}{\theta_{l}^{2}}}$
$$5: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma_{{Nugget}}^{2} + \sigma^{2}\left( 1 + \frac{\left( \sqrt{3}r \right)}{\theta^{2}} \right)\exp\left( - \frac{\left( \sqrt{3}r \right)}{\theta^{2}} \right),r = \sqrt{\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)^{T}\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)}$
$$6: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma_{Nugget}^{2} + \sigma^{2}\left( 1 + \sqrt{3}r \right)\exp\left( - \sqrt{3}r \right),r = \sqrt{\sum_{l = 1}^{k}\frac{\left( x_{i,l} - x_{j,l} \right)^{2}}{\theta_{l}^{2}}}$
$$7: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma_{Nugget}^{2} + \sigma^{2}\left( 1 + \frac{\sqrt{5}r}{\theta^{2}} + \frac{5r^{2}}{3\theta^{2}} \right)\exp\left( - \frac{\sqrt{5}r}{\theta^{2}} \right), r = \ \sqrt{\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)^{T}\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)}$
$$8: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma_{Nugget}^{2} + \sigma^{2}\left( 1 + \sqrt{3}r + \frac{5}{3}r^{2} \right)\exp\left( - \sqrt{5}r \right), r = \sqrt{\sum_{l = 1}^{k}\frac{\left( x_{i,l} - x_{j,l} \right)^{2}}{\theta_{l}^{2}}}$
KrigingObj.KrigingObjects{indexKrigingObj}.setCovariogramModelChoice(5);
 
Choose basis function. Here we use a constant as basis function. The value of the constant is automatically estimated by Kriging
KrigingObj.KrigingObjects{indexKrigingObj}.setBasisFct('polynomial',0)
-
Matlab's statistic toolbox (2015b and newer) is able to efficiently estimate the covariogram parameters. This is in general very robust and should be used if possible
KrigingObj.KrigingObjects{indexKrigingObj}.setUseMatlabRegressionGP(true);
+
Matlab's statistic toolbox (2015b and newer) is able to efficiently estimate the covariogram parameters. This is in general very robust and should be used if possible
try
+    KrigingObj.KrigingObjects{indexKrigingObj}.setUseMatlabRegressionGP(true);
+catch ex
+    warning(ex.message)
+end
 
Alternatively, one of the integrated solver can be used. Here, an open source genetic algorithm is applied. Further possible solver choices are described in the documentation of makePrework . Upperbounds of covariogram parameters should be adjusted for efficiency reasons.
if ~KrigingObj.KrigingObjects{indexKrigingObj}.getUseMatlabRegressionGP()
     KrigingObj.setShowDetails(true)
     KrigingObj.KrigingObjects{indexKrigingObj}.setUBCovariogramModelParameters([100,100,1])
@@ -99,10 +103,10 @@
     KrigingObj.KrigingObjects{indexKrigingObj}.setUseSolver(2)
 end
 
Perform estimation
KrigingObj.KrigingObjects{1}.makePrework
-
Check Results
The following plots show the resulting Kriging prediction surfaces. Again, the input variables are pair-wise plotted against the output setting the value remaining input variable fix to 2. Red dots indicate the provided data.
if visualizeResult
+
Check Results
The following plots show the resulting Kriging prediction surfaces. Again, the input variables are pair-wise plotted against the output setting the value remaining input variable fix to 2. Red dots indicate the provided data.
if visualizeResult
     combinations = [1,2,3;1,3,2;2,3,1];
     KrigingObj.setShowBounds(false) % Show only prediction not the confidence tube
-    KrigingObj.setShowData(true) % Show provied data points
+    KrigingObj.setShowData(true) % Show provided data points
     for iComb = 1:3
         KrigingObj.calcInterpolation_3D(indexKrigingObj,...
                    combinations(iComb,1:2),combinations(iComb,3),2)
@@ -117,7 +121,14 @@
     KrigingObj.setShowBounds(true)
 end
 
   
end
-

Published with MATLAB® R2015b
tutorialCreatingObject
Contents
Tutorial: How to create a Kriging Object
function [KrigingObj] = tutorialCreatingObject(varargin)
+
Initialisierung
% Decide if estimated Kriging model shall be visualized
+if length(varargin)>=1&&~(varargin{1})
+    visualizeResult = false;
+else
+    visualizeResult = true;
+end
+
+% Generate Data on 6X6 grid in the range [0,10]
+nLevelsEachDimension = 6;
+input = createNDGRID(zeros(3,1),ones(3,1)*10,nLevelsEachDimension);
+output = tutorialFunction(input);
+
Test Function
The test function as follows:
$$f_{\rm{Test}} = \frac{10x_{1}}{5 + x_{1}}\sin\left( x_{2} \right) +
x_{3}$$
That is, the output is related to the first input variable by a Michaelis Menten curve(steep increase for small values followed by a plateau). The second input variable leads to oscillation and the third input variable leads to an linear monotonically increase. The effect of the first and second input are coupled with each other.
For a better visualization, pair-wise combination of input variables are plotted against the output. Remaining input variable was hold constant at the value of 2.
if visualizeResult
+    visualizeTestFunction();
+end
+
   
Create Analysis Object
AnalyzeKriging inherites KrigingSuperClass and manages Kriging objects. AnalyzeKriging also contains all analysis tools.
KrigingObj = AnalyzeKriging;
+
+% Remember the Currently used object
+indexKrigingObj = 1;
+
Initialize Kriging object:
  • The first input indicates the type of Kriging. Use 1 for Ordinary Kriging and 2 for Universal Kriging. Ordinary Kriging is a special type of Universal Kriging as it sets the basis function automatically to a constant. With Universal Kriging, you can use any function as basic function.
  • Second input is a string describing the output variable
KrigingObj.addKrigingObject(2,'Test Output')
+
Load input data and name input variables
  • Structure of the input variable has to be nDataPointsXnInputVariables
  • First input argument for "setInputVarNames" is the index of the Kriging object of current use
  • Second argument is string-array containing the names of the input variables
KrigingObj.KrigingObjects{indexKrigingObj}.setInputData(input)
+KrigingObj.setInputVarNames(indexKrigingObj,{'Input 1','Input 2','Input 3'})
+
Load output data. The output data container has to be a vector of the length nDataPoints
KrigingObj.KrigingObjects{indexKrigingObj}.setOutputData(output)
+
Create Kriging Object and estimate covariogram parameters
The backbone of Kriging is the estimation of the covariogram. The covariogram helps to estimate the covariance between the data points. In general, the covariogram considers only the distance between two points. The closer data points are the higher is their covariance. Kriging prediction is done by a linear combination of the provided data. Here, measurment data at points with high covariance values w.r.t. the point of interest are higher weighted.
Choose covariogram model:
$$1: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma^2\exp\left( - \left( \frac{0.5r}{2\theta^2} \right)\right), r=\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)^{T}\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)$
$$2: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma_{{Nugget}}^2 + \sigma^2\exp\left( - \left( \frac{0.5r}{2\theta^2} \right)\right), r=\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)^{T}\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)$
$$3:
\mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) =
\sigma_{{Nugget}}^2 + \sigma^2\exp\left( - \left( \frac{0.5r}{2\theta^2}
\right)\right),r = \sqrt{\sum_{l = 1}^{k}\frac{\left( x_{i,l} - x_{j,l} \right)^{p_{l}}}{\theta_{l}^{2}}}$$
$$4: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma_{{Nugget}}^2 + \sigma^2\exp\left( - \left( \frac{0.5r}{2\theta^2} \right)\right),r = \sqrt{\sum_{l = 1}^{k}\frac{\left( x_{i,l} - x_{j,l} \right)^{2}}{\theta_{l}^{2}}}$
$$5: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma_{{Nugget}}^{2} + \sigma^{2}\left( 1 + \frac{\left( \sqrt{3}r \right)}{\theta^{2}} \right)\exp\left( - \frac{\left( \sqrt{3}r \right)}{\theta^{2}} \right),r = \sqrt{\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)^{T}\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)}$
$$6: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma_{Nugget}^{2} + \sigma^{2}\left( 1 + \sqrt{3}r \right)\exp\left( - \sqrt{3}r \right),r = \sqrt{\sum_{l = 1}^{k}\frac{\left( x_{i,l} - x_{j,l} \right)^{2}}{\theta_{l}^{2}}}$
$$7: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma_{Nugget}^{2} + \sigma^{2}\left( 1 + \frac{\sqrt{5}r}{\theta^{2}} + \frac{5r^{2}}{3\theta^{2}} \right)\exp\left( - \frac{\sqrt{5}r}{\theta^{2}} \right), r = \ \sqrt{\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)^{T}\left( \mathbf{x}_{i} - \mathbf{x}_{j} \right)}$
$$8: \mathrm{cov}\left( \mathbf{x}_{i},\mathbf{x}_{j} \right) = \sigma_{Nugget}^{2} + \sigma^{2}\left( 1 + \sqrt{3}r + \frac{5}{3}r^{2} \right)\exp\left( - \sqrt{5}r \right), r = \sqrt{\sum_{l = 1}^{k}\frac{\left( x_{i,l} - x_{j,l} \right)^{2}}{\theta_{l}^{2}}}$
KrigingObj.KrigingObjects{indexKrigingObj}.setCovariogramModelChoice(5);
+
Choose basis function. Here we use a constant as basis function. The value of the constant is automatically estimated by Kriging
KrigingObj.KrigingObjects{indexKrigingObj}.setBasisFct('polynomial',0)
+
Matlab's statistic toolbox (2015b and newer) is able to efficiently estimate the covariogram parameters. This is in general very robust and should be used if possible
try
+    KrigingObj.KrigingObjects{indexKrigingObj}.setUseMatlabRegressionGP(true);
+catch ex
+    warning(ex.message)
+end
+
Alternatively, one of the integrated solver can be used. Here, an open source genetic algorithm is applied. Further possible solver choices are described in the documentation of makePrework . Upperbounds of covariogram parameters should be adjusted for efficiency reasons.
if ~KrigingObj.KrigingObjects{indexKrigingObj}.getUseMatlabRegressionGP()
+    KrigingObj.setShowDetails(true)
+    KrigingObj.KrigingObjects{indexKrigingObj}.setUBCovariogramModelParameters([100,100,1])
+    KrigingObj.KrigingObjects{indexKrigingObj}.setnIterationsSolver(1e2)
+    KrigingObj.KrigingObjects{indexKrigingObj}.setUseSolver(2)
+end
+
Perform estimation
KrigingObj.KrigingObjects{1}.makePrework
+
Check Results
The following plots show the resulting Kriging prediction surfaces. Again, the input variables are pair-wise plotted against the output setting the value remaining input variable fix to 2. Red dots indicate the provided data.
if visualizeResult
+    combinations = [1,2,3;1,3,2;2,3,1];
+    KrigingObj.setShowBounds(false) % Show only prediction not the confidence tube
+    KrigingObj.setShowData(true) % Show provided data points
+    for iComb = 1:3
+        KrigingObj.calcInterpolation_3D(indexKrigingObj,...
+                   combinations(iComb,1:2),combinations(iComb,3),2)
+        KrigingObj.plotInterpolation_3D(indexKrigingObj)
+        campos([-40.0176  -52.6198   92.8089])
+        grid on
+
+        % Set format appropriate for documentation
+        set(gcf,'Position', [100, 100, 330, 330/4*3]);
+        set(gca,'FontSize',10)
+    end
+    KrigingObj.setShowBounds(true)
+end
+
   
end
+
+ans = 
+
+  AnalyzeKriging with properties:
+
+    KrigingObjects: {[1×1 UniversalKriging]}
+
+

Published with MATLAB® R2016b

\ No newline at end of file
diff --git a/Tutorial/ScriptsForDocu/html/tutorialCreatingObject.png b/Tutorial/ScriptsForDocu/html/tutorialCreatingObject.png
new file mode 100644
index 0000000000000000000000000000000000000000..98179e22a87e2b1709018263970880ee7c8854f9
GIT binary patch
literal 5570
zcmV;z6+P;SP)+XG=SN}M8tC1yHwgq%1{rw}&o_o(dzjMCV`MwWIDZw8w(EGiB_Zk1s{|_7g
z=lDx0-%FFEy=PyV35qc1TnHhhq?Af24a2|~V~in$@9NjzAO2EGA%x-#06+*~j5SSz
z5JCv0l)A2S&OOhA5Neu+F@Bd;@*eS@a>x)ugixLEna?ZQnx?63qLixV5t?U(eHZyF
z!klxZ&yx>%?vo&d5JH3yAp~Pg2yq^h%!2vkKSin`gp4uEvgYN`5JI}H0{}SZzVAyZ
zm0n!nMqDU=A%xAG%RFV5RQOwZr*yf@!`GY|+=DwXc;
z?noq3A3F1p72$d3IXjdEQ5($|QyZbS=A7o4p1-DP;c)oKkt2&1EgBgaF$`m5WTdI7
zss7M0m10XNdwY9pwc1P%
z&jiPDrv05#`uL9@yQwXw>03ZMWLb!JA+MPRh7K_DFsWc0*I%elzhY-TH?U_d3
z>-g{Rz>y7iKKSOFdpXqr@(IZxY;abVl5X3!?ZY3r`L>U&yK!abhwuJ6(e-(8^~zw)
zGU1(BrIwOZ}jXP=EkBFmRAkHuoMzu^0H$Bv!f|Ndh;xBbrW
z#+L4U_y|bfe*5jAP-rq|75RFehY&h7GK!`Lm2{Dw6
z6DwU6VB`1#>Kum<4G*8#uwg@AU*CQA-4_f7)m=bJsa(DCdGo>__w@AWx*m;2J?)Sw^rP~efz7g
zzAE3|j14Q@-l<|O740e8u5|mX27)oBX){DR>W?y~lSQjgDCoLAK0e;o)}~&zZRa1q
z`rs44+ABaDlbQrB!a*Rp@15MTWy@=?y>{1KcinyWUFE^Ov6ha`<*S4chX;;)>w7%IfPKxbtxsrSoyZ|wv_Mi&*gHpT1{yh06++_d-v{GEQSygLZlM$
zH=g+CM+V*hdT9vZn1oD7*9=|kRu4!hh>@b+Sglrj`X>)B?f>bPqm|W5TD`UR^c_6<
z($-hUPYpv%tcDIwi`Wd%+9=Y4p>Vj>svIs9kd*`oaJNW;DF7LOTnjV;Nu}_K8IM_R
zlWA`~1s?wG>;C=sf9-2u3x~s#SFb`nZ~>PhsLuSw7hhzI6$*u0Z@pDT`}_CrcO1vI
z?S%^$=8x>lefLl9d86|rjUYs%l#=_zXz*&o2w4C?&mLrco*;NlS{y2Zq7PgSuo=Nd
z03uBbCLtzn{-_>p0-Woqg?|BGTAgC|AMUD>q@*syL~@QagE>Vh7zs6b`2k2Q>bug-
z3u*%ZAVTZbu6^{;N0%;Ls+R6~-jsWXvOA2iDOnTObxqTpmOt+4=_!}XFTVJq@B0^D
zd~rudhuXYYES65E6NyAaLqoK&wLj;t``DkBJ#ai*rnM1bB_Y;;y8@2ZBLFkY)*50pNe_2tjgTkQSL-!-Z(a6)EP6P?UK6y0x3Br
zLBSjh!9ACNl_~I{Rxsg+<3$Gm39%-qE4Zge8@&7p90<_D2*5A}tCp$PxIje5fe3(7
zFi($XYNy`TV@-nlNDnNvN-Gjz+=IJE*{i#DFI~FyqKht?UDee_D7!Et|5~lKW57|z*IB;OWf(5hl
z2LKqRnKfJ67B7SG%)x;H983zoLY;A_5y@Q=ND5|SBO(OLdHAh@COG4Dg%AR0nidL6
z<^u_Mt%SoN>ew%xl)ZieMUuQ$+f&R@0^eZx03F}F)iHSmxi@K
z90;zNVdj+7wk1ulHUpN$!Rnaz+KA6;n698f1yHL}yVcyW-Ue^SlItsQP8c!q=D^{$SHve@N`7f8tk3atS
z;NT#obc+3xQmP%9mOlVM2oaCRIs>&24*%>IhnZ(a^+-x`4@hC8+J$dJO+&=etO!IH
zG56XGc;PtMHVVW$8X=Ks`r}yuB}t`(=b&J~&y8Mg6(4LK*jf(RddNsOK~3Y1jV+5L
z8fYnJwJv1E0!gO#<-{wMIHk<3*)P2C!rk=JF!=^2YPN7)Xc&@F;W}D4Bs{MYj6XF(auniFm{p22mxTzhN>K`m
z2$6#0K#-SKOG@rV$TZ=ORmsQ`@FO82LSZ<_vdoCKp@c4
z(o!EA9v%(^0<~H#8jbGRv!|t{MUe*ps0$e(MEO|Lv=&`;;}xr~x%v8Q2S!E*55EmC
zMuv%W1Dio3)#~MjA<+>g+^vGiDkcOHJZ;Hx#f65>ZJSprS~%o;J|zZ#P%<9~26X~W
z-DqjS!JrfzB8bCbR;_5^kmQua5`Z(3ObOQ^cJ8;&|IQlR+H}d)EsHLmEB{V
zGkGkTOs-qEPW?ak48LVrU-{C#D@I;8d0>2(Y!;9S=4jEFmm4-3GJ;Y+KMDy!QV8mE
z0WL{`J6{0^0?upzfS^_0QB$P+WX&6{&I-Evi~4w+|P~}nO5ObKR2qUGPIBrZjINf
ztXi7#B8~(hgn}}?xk1Qi4|-^9d(#j1-}UL8v$&s-<&>FMd~>)W?)U$Iyugq&$S6=t&k
zGoi8dqU9gBdHve!!UlQk?E}m*A%cQ>BogsXoxp(rcO3u(5CRe%YPw|almMhh1H#t@
z^99fnVbT~Au2&l#8_6sXT$&4;C8MHhLqije>yPIWP3ecfc;DThygd*I001Uu0U(6w
zbo%I)Uuyrk;aIkC(Y1ekrp{^rfTpIVWy_W&5{db3Hh=(FmUZuEH(arN`F-F0&gh}O
z3N+k_3B4%|i4G8uLf~3Wawc3?cn*+~Ah0G4md3&LP2exLl$nkJlr%d*niC`wwOZ2Nn>HgAIoC|(wB5Tyy1%<|Hv(~EF-n}vWfJvKBMuDe+9>J48vfI
zZT|Jo8-8`??T6a3p(aRlUMfk(X+BR9NgNLQV`D^!j(98~NAKH<=Kk-U_0$T5g39HbIW+%8ODQF#v{tK?
z%Vm{Q=A18Hab>xo`xAfkfm9?Yr4Mxku+*CyNhZlFrOOwzJ^dd)+qq-MzyAEm6Ww=w
z;nAn>{PI7g8q-n;K2c?3Wj;|s0UZVP>g$J~h9m-{#tAVmN=3mOL^coz%xJvNT0c>k
zrT(MBA;)nll}fQ#EEbElZTr5jDrog0RH;;=l-{`ZN`%l?@A};GW1E+U
zhMbPfw3#`1S`cpi=to0Gx3>2^p1JKS7hQAn;>C;Wg$8gs3{?wu97hO22vMs#yH=LhQ
zlyXvo6aYXaun2@7xaYyK<9FWLxs;IUzguqskWx}gRkhPF3|-gH>d|Jj|CLIG5R%Pi
zQ>m0n3i-aD&1OeOM@`dgZf=gp;}_EN!8y0>Y9J7#l&aJRh
z%1Whj{P=O(wiAg&OH0d4nyd()+3GlTDW{eBygMI#-&d-te2uQ_iA18Kqazp$&LrkD
z%Kx!r#}GpKe7?K8d-g?e&P%1z@bEC_yrH2XlgVhBHu;d1NS~`ztL_t(z@Mp~M+l(;
zjeI`uI1a`*5{V=diC{2j7{-(>mQwEDzds(24-E}2e5Xw?Ij+08Inwy)?tVXE-rcfvp3I$53
zVHn|XIGIeU=7%%q?C>
zrEF|$j6@=?>*n+MYPAXg2!%q)WOCWEWpl|jWwTioBVJ%@{QUHPP0pIm0*rAgl}e>j
zl+rzW_8dBNXw8~6t*xz=Wu2=gCzHv!cPq_;`LzFYf$#gOk96{nDv5qh^VFk>3tZ28
z+O#aC+`W7E&Ye3acg>BCj=ucz%jbHS=Mhj$KhK3vF?Xo=7a=s6ziKhg<#O-kO6r&^
ze<6f2x$6F_#8^r>GBToa=UcXHIeGHrSw4nPXJ=p|H{t=
zgF)N2P1DTh^GA*x357z05Y@3f?a9nL7-M~XeffO8x3^aav2o+Zp`oFpM~|vbQ9?*t
zTiZGJf6WE9ZL6}^>#x7QXwjlrELN>nl{o0Sp3P=e5PrVA=8C?)KE_xi5*ZvEv~9b+
zz5UdwQ_*NNkw~<)wf*KdzgeV!Z07*qoM6N<$g2NH0>;M1&

literal 0
HcmV?d00001

diff --git a/Tutorial/ScriptsForDocu/html/tutorialCreatingObject_01.png b/Tutorial/ScriptsForDocu/html/tutorialCreatingObject_01.png
new file mode 100644
index 0000000000000000000000000000000000000000..fe94fa64d08ccdcf1b5ce23863f8d8a719709931
GIT binary patch
literal 30363
zcmbq)g>MPXoGJtxyk9csXJP@d78MG1FBLQ
zDpJy~=ryd&=~YeKth60nY+YZ`D>;~PiRmmo1HaGs-}hx)%uU>^9UbU3tnJMKUVctl
zb2HA@JiG$*ygVZO0wR3;xk#Bw0H6nyWTiB`(hvPT{WLUh9$X8OBq?!nLb%v*i)MSa
z8gn@sXFUwb7DqD4lF}*G>4Vcj+{t<<)h|L)@w0QgQ;5Et_
zA6!5baq4&Xn~%@a;;+GW%8vbxKL#;FI8&Su&6@ByYm6+35;Zr|UWP15A!pp#JI3%y
zawQ+u*x%NXOl|Rs>$P%m%Suen^NdPJ@DTghJWmp(^m@e!(Qu}B`u~52`LW)h#|b4=
zdK~Hb-XpY)Adbte4ol6f$9{1_{NPm9k1KVLSWXL6)Apy!1__y!0$l!^#h)<&
z+1M;}Yf{uZB>TeD_Fg6MTh*tac>){_O~oO;&K`~wZ$2wv6Z07Jz&R=LB#BaJ*B~g^9ZI}N
z$AEjL0m4wGwrB#S90_*p4ipkDEmGjM*e^>|B53@RXzXWxo5=qL;wDt`p`%rLOUEh8
zLlCda3D}2mU|d_G()+RWh%-JbG^KN;k|0H2^&C%IWAT2So04}a6zDiOJVGjO3()HBtI4fQpxgUu4>0a
zU&@`ZgNIC+9_t-=)S?4q*K$mll6ka}Kxb7>!}m9hJ(+o)aT6VQj8Cz;K3J0gQ>F|R
z0x11jd5PHd16!J?l}Anod*dYND(p|fHNJR+$nK-elZkAzUWYMX)BRXhdi(h9s~UCV
z)35psI72oTCy2bKn}|G#>B~;bAoL^ruRlnzzgpoO;eD+|1B!T&?O&w)c(BCPXXu^^L_xZl}e-UP56>|G?HW4
z_8(&FqU#l+SFRs(BJ|$}`ml!FBui?r*9Wa#7^0)p^>=#+wdRrIT2$Mam-I+O%~)E4
zSI{LfOk&%!JVNDe0uxfyS+mEH&5A*#u~du45jmH^
zY_WPF@e6tCXwOfjb!syq8#;N_YgfQG+1P%6DXpFveLC*8!WOW{z$E;fwxPjj6S7p&0
zQB^+3#U|IWY$-enu5a>nx1XNLS3uh=hEoxhCf#>;cW)lAN9`Bt#Y0P#anE_f6h#5`
z-Ltx~{6j+{TWNsyBc1nFUFft4-Bg&wH3b^%E)oUhN(DH!`WG|P!ytK099xrAU+^`(
z4aYI51&?7YR3o-!kVd1zD?Q+Cz&C{N)$v+NO3MB1Wdl;T+09x{Pp>-^ALWyU1=M>h
zfkUhGDg#+P9g?R;C-Zd*E!U-LTL5cFEYTG0I7V;t^Gq(SV`0OG^=pYQafCWF;uhV?%%(@#ldUn~PJ`mqtaMr2
z_u|Y;EX(gC-2c6a$y$nKruE7<`JZsDvK#!0bd9|Y+>9SC7~P1D){|x-p_{g`7ZqmC
zQD`EA=H44I_R6baKBL?V$+hC_)fZBd%WeQl4mtX#1=E=YB4t5)RpL54YyZ?DLu_o$
zrP5866TXwZ!l8)UJ98%=(8qY{(hVJ6R{g~mxLW|ZBYlF!mHeRj^Je;sMu_%NW8
zkI_jE0SJo;TT8o-NwD|PI_&JFW9(?OLA#$b)0-&t$WBY3KY?&FIli6>g4yZ+;_jbN
z?a(4|$F-)ZnfF#7-p4Oq|8^H~v#SuHK5zQwZ+Mj?y>aThpx->-BzR*n&)bda!=)taqfin(htnj{o$XZGc9H~X)bN6;
z?M*+w=BAM{J-y>xG%QErcu@bMWf`s7o;N7eXOs{&T@cwU
zN;;DBjzQX4FNtO3RrFAhY)u(<-)^DGxZ6Qh2YMfBA<;mzXq!0gXbgi4807OgGGX8&
zJ|+K44vqt_4r>wz&^6_zB|hZ$yHj+Tv3V?*da0g&CWN(0V1m;*9;jy~VtOobM9nth
zaE?)_=albML*4>K*9_hpC;?L5GF3h^(Z(mB(*WJKm3$0%nEfytyo{t*7iB+s3arPM&6h&ki@{k
z+|D9_`p$*KF)c&;+~w0jsz_^B^AAf(R>hw8C);Q?4vNd4db+QBzF=bSA64Eh1jJ+;
zO~{EW*UC8GNGO9d0PbU$N9NzX`IHdna8ngwRiDs4Ks;QJn(gSK)4aEkzH<7Hs?)i0<#LcJ0;N+~#SFWjDq?MhpeB{#lXIdV`3vJB43xf!?esbO
zTcU4#aBJ1jyW0TZjslQ&4mPt1P+P8|kQAcERI7d6)3Yzc$a}-vMbMB>1+%IrLp`%6
zVB}5zC^V;-nRmH|_Q9;7{7Fr>1rptx(@{7kdlrc)RKx7}3NxD=eN_N50Et#K
zgbk4AF%8s`;Ig8ljP0vjUory94;!ZLFUz-?`OUJCH9E~NC=TU>qMTZ-%y9udUDfnQ
z4&dfJ#Cf^G`0c5B`+yXa#~lcN
z;de3mnHorSs@!dd$c6N-`aCM|^1ys?OoTq5f6;AQaxK-yx4Dg0+w|J8v%bN#%dyBa
zC^z79^MsQFc|j50PDE@B!P
zcFl@w_2&j4qJKQRib?D9^}z*TZKRjPRDtdvSk7=qk4VL&e;y03!E;++{|bp{sx~T25fwoy?1CEbvTi;
zU)0ju2NOM!sM!U1e+Y0yMN$xF?(P;6oTFsgb}ujEX+{y*qTk1;X+(Y)E3_EBlo#$>u
z)%zBDg@5kx4CceZxN!6Z*6Ks#Ae;^UOUW%~sG54Nv=ya$>2PYT2VPo+_
zsbw?Jm-G#a-5!@9=3kbk#%)Vkvs%U^M;FGUXc#VbJ@cMFNzNiIS?=F=A3O_XtVO+8
z0PbR86ENZ~8fYvY+Ww0T{Hlgmk5F_J)v37;-V0D{Q_=KG4fyi}p=X#;;*%k`K
z{8Rjq2--+Q15!??VELQNP^tOGQ^3ZBU0D$D_GCaG*G4Q2aO!F4*Xj^bz#$EWrPB&}
z5l4xCc4$TW>(u{ZU5msS?3$eB_%vz;Xzfiaw
z)gSnD?nzPud2vJcajK1$EdR?fuFLcoy9UCb%{LKXjyo)BHH%J70ap+stQ{>hl_tTH
zMQ`4dg7K*~pL-XM=;;3Ur(B}yd>@IT;DR4F{X>AM-~7)1SUS&t5=AkPs86y-zA59_
znZ&i)gKFtx4N2J0mm8}8O>kDs{226uL{H<5%YqNUs+8~*e~m9B?Ce;bgW%UA*DqHR
z`*p>MmUSnl`!(60H0$5|?8DF;)D7wv;1;%F&n48`XzDX8&$V>4bLzlauG6td@TbU)
z`vMdIZ5TGWnCS7cd|3r0yFfG1lLA%eh#57kcl=^sXmp5T#tc=kJU!WJsG#l8@gZ!Q%kg6rO3O`HIYMQny&gh+LsP;0sxByV)>yxs
zA6C1yRJ4*?=y~_Ec0R0Fzxc8(U4I*@A9=!!x#JxWC>AH35v
z<$oF5#!SvPAVWqq_}B*)E~O>Y>oSgxgS#XH(3oaX3msi$C(DS4R8NS$!NaXjmZc?Y
z#RPE=gMhHH-GhCD_qm|2sxH@%5~VJzB~$9ldCceZn*YH%MH!0|$=wp^G~Ze2ART|^
zKdW|Ujt|^uqd|Y17FQU4`SEO6TzT;htzXWe#Olj5YEGl&~RXt%GXe>nj0pw^GXJcEBJOe1utjldys9u0?%{
z!;{Wmcd?y`g2zP~rz2(&drLP54EgEZMYC90O3YAVu=EZ#J5F=!WM!vF@}I+%k6Z4*
z>-an&AiH7D6TD&OtJoHsn^xqdIII%u`FEVGz%j4#ZMS7f$_I;5@?$~uO~V$%+1rzH
zaUwOx0{Um#>Kua?El0J9saiNv6!5~l2s}Zw7#a_Tr~iF|g$pF9`&VcZ(MG+X){dVz
zd+w_W>?5AcRY}g2^TCs**7{P8&P-&Smu#5cHZ*Se4)33+wY#~x*)QvcwSRMndkn~o
zR5L)|2;cZKAkc#_wT~Huhd&Az%@*#&E0_XB{S_S7?+HNIW=cqw{Q#RG)q!kh+d8M-
z!(1k&)wUruHh1R8vp9wAm<0V(nBj2>=;EE#xgCR{qqR7%kn`VRIm?Q%G#+#AX4Ur&
z==kUI5v_r&0A%iu*-{%`PGf3ZL^g_*)?|c%H}V}o^xKdeOecViU#OoImYo*b9mSg0
zKrSUP_0?|N2LhbGt@j(MtCEe`_NWbhKmf4%?m_)yqe@e{=Wyk&hp?+*CW^J>OihQ3
z4jMl7NQ~A3y(hH3xLL*I2Xvxc7#?x~SNQBF3w+;nn@K5+GqAzSlD)F8_tgDbpf5LB
zHc#1LYGI>i-pAtDK?VKjsDY~e1r^*%A2=3}(_J-!AHC@E?K-GnsUL7qzOA8vxsMX#>We!2|u(1B(iT|~Bwj
zC9{^SmdcRoNWAlCbL$B1cHFBBb0$ft&;d4@EG^yom3-BIg6jO%$oaAY?YGJOtk~d%
z4Vx@ix0MxWO{YGAwe9-ibwS-;MyVOp_~_bu2cl%d`=)~3$~iU7__khbRZ@Szi&{q{b#=_oX|rWJ}b)VyjTX
za+}hve~suvm9W)eB$Pg7utF$vjlcU~;^UIQ*p^C-ang@%)n~%jKH_wSqv0$uKJ`z<30Zg}SxiWp<$-8v(h!e}9S}nSEty`)p?En-tP$
z#pf92Mj!T8fgMNV0WVJ25^q@@H*-Ugj5(~t3s6_Bc!gXjK4``eS}VmF>O;rB
zsm27(eMDO35^FLNvo|X&{zOfmMaPixshgEpA${x$4u23zdi+sf3UxC0K8P-gc)#7%
zV>7
z6Ug^AHMg6S%A~ftnNke{F(B)ng~Tljegf1Ac0nacXH`5#u^rE`8QzViS}a^j1=Rq21eo+Sgt?{dv;%)O0r
z9^zDsUm=%@Z@I2Y*<*b@QPuvtLd0*kb~Yp%ct!COjfo3B%C<7twkM73CtWmv8}Hn~
z-n<0&u#MFot$IlOLVo&wu$LE8sFlk1p=m+?aF%;z;a5w5Y?vcIoN&fgF5W;=?2MT`
zVXU@5q}d|&`m(YaT4R&z!b^piZ+Ym42kJbU7RhOSAFC_4=|=-`iR#c?sBNcX2?#Z|
zT!?fLpg&>9M$2w}`oW#M^xCGDb3e+wS%Ts0;xp;zH|TBAVWR##UzG*Il?8N9t587P
zkaWUcH0HaFm!j(1_)GEWQ9SrQ7**!QFw=sN|9h&8<#=oSj1`jvcJJhK29aLk-pYA$x$?Bm(#$MDASW7(G9kZvo({6=
zmIU@SG#Y5YSdjclxEP=uFn+$1x@qRN@f1><>K9N06+4CBs`G)M@8l1Q+-|`wu5>)a
z!-7mrv%wzuXqjYw_oX3ev5MVp&6Q6mh-N}
z=cRRo5V$_HNP%tsbf5{6AYozw=?t_sV>i;*i+4uNry`RQvCKp7khjngB
z$qjl;PS%dFpB?f{vbv|w#~G(5PA3w!=&Dwmp@7Jm0R@aQ#lElMTnXADk2Tczdq0D3
zD_(!i;+)hQnA$DvcB7=I%%>WEmdgvt)?y<_^2X(bTbGyzL^r94otZb`?I>lm%jT14
zu;)}!&~D#Yl82w&s~8>`2Chz~d_6FmXQ5zvG~FdGs(*nG;sk-$v%MA$3r)5G25mb#
zm?tMUBfs<(NP51LuAx-;1{v-m*q*l1U*S?~#FqEgpu}IdYNYeHZ5(6Bk@~
z{|epL|D3<$FpaKzKPFRc_Bl0%OLTiMa9h;$FEfN!^?{ErXv%i)m`XWd7R|!0Q;l@u
zch+?Azvm}e7<%a_AKkaUToe7i72J&%xf`A=-EK=kJwy6{6(&dXw;_=?anW`PLIu5=
zYzu7~be*NN&;;G3*Mi;mjrvvIInWUkt)$@qzQqVm+8dJbXR6j7o8s^5O!Z3(x{@My
zv>GXJu>NNNoCN8`g8iH1o3X;SgK6ieux?9{aiaeaP`!NgW@n5v#eK9lz0O111S2mPCAp2?WyV@m
z(**SE?VML?d@ujjjEKM+uXaoDo^4+_0#ox*bd(31;po$ykRr+LSHh
zv6Yr}(1L3nmh`F64;hD)w}WL!ho7?6
z(R2kJ&y?+R>aOV=$7N5N2zZrdx*u7#yyafEyd}fo+vqk8Rf%(s)%m9DDJ_Cz>p~wV
z_wm1?msqvB-xZ&XlbpLm9oC}&st&!MpjDi_p^R^9Qss@9XdeQFYOn9dl5W_{2+RUl
z#gs{@rq5>xV(s*E?OkXs4w*BR2y*(e$pu#$aTHi_Z@rD6G|0fy063cN<1+cj
zs^wX@6YWO0&tD0zTFNS0RSR_3YtikavQ#&hUK^H6ak*TWy6_o_taAy~Aa0jnceXg_
z#e{w!I4RC?bHs-TOEf)QbkS+DZHI|rKhIW8@ZK(_b@}5ugZ__4Krcyax^CLiSzaPr
zUwuCN5zd~viIV-O#R)mdF;z=lrT}gC84au6VF}mRmJzhsOp~i+#f-0pquNjT@7hrB
zeEIK`K#5k_JV?8MuGV~L^8J78ORWFt@8-!cnALgB-xEtVfyjEwG&Z{B@?MS4VVg1w
zh%1%rV-GKE=;XA%SGa~@ebJgA=vZK)CSsx+Qs|`DqQGfZeS&k;uKa$Jfm*v8-uwoy&AUADuL&X*X>N^y26SnHK-4K8aV^igAO4%~*_NY7x;wzgC
z)q!Lh@BH!b7q
zPqgS$$0gW(h)Bq%q+zEjp8=M#?vv-@Z)EY|m@t-1))q%tBtl?TVnz=i(5Y_&B*({G
z>u4OPHm9Yr6)mrsTN~XkQG^u0K2@;SEwyuH06DYzdFj2?X|zi5}~
zg^=@HS9r_t5>As&?UaeTE_J#LBY;#
zvSgrE=hjXz6l{7E*}ZSsKu@ABbDoEihKRbh7u{bHpr=&lp5APBMznFMYr9u)+?LCE
z*I@nbevJNkz<B=_D$Kfic=kGxQ*Q|oS8k$A1pzxa5I^G8kb
zHM4JoZ;=ZQEk`A<8~^oEMPK0m7p!%*Gl6#-bTlGvjenaD{(_XMS>`j*Wuh|e?qeo&
z7XS>?xM+LzpYVRr^~QB=?8vTn1t2Vu=duR(k(Bx&TS-SA*Rui{k>tPsiWsM)qR4z7
znb5Ubs3mxY-3x(-9Y{9#nR0*)?Q3O<6unXp==TQujvp`nD$dd`RoB1s9Dp7WhZi%X
zBJ$I15Q0fc1SoTkW!?KOCdhyF>GtE^!XMv8gl6jhxswQ{DMwallrBmlucFv@
z$P1!3CS(sw0zIs?N{8dZ7X@T4k2OGcvuDmFSvWCgJ9Km9LNry3I~XJ%u$s0+%Lc}N
zMx`L$SRP03#XXXLZt{oMT^`&S5l4+j-_S(`jz>r+*C~~Ao;tcVl7E%Tnt7UJhYEIg
zQ}}rtL|FL^ANPGJBp&ZhIVxIDGJXGE<0GB>Xgo^|nq2<;VB^p(zr)|Y#|ds+Q=m!m
z5cUG5`t7ibi05oRCHncLQD!jH4aP_BheX9k7ynXuSHuu#M|D^5-`k^;TZ;iQ+RpkR
z!zbhR*p#5Z0aMC|Mqy$;qqaEz$}w=~B+hB1O#JCslObFbn&=?4e(}giqW%Sby1{8{
zfvVk@#uJa
zX87&jFmHL2doU?q{C~7NYV*t80@FJBBD@iuFbZ@NDg-J>ORP3LU6|fXBLpEnBzlv!
z+u*6pO*Q|^)o!=hAYJ=m-iNnVmgvF|6
zE$U9axr?nFHi*tHSQy2{{fClyU&wze>W>$#A`*Nkl3N(ICoCq`+#>b1k*)W$frG!4
zDV%hJKcgu-WttORKM5Q*kBsXA^tH@{9XX-eTdzs3y=d=3DKlSWJ!sB&;f=L?2k~DM
zM0zHcWbuS^>XMGcEj(b(GC)qKmzNJx8R};e4;AZp4j^|?voR5pOa(rO#I|WPwR;<0
zc*ZOh)=)09jijoqX^Fn!HqZBF#rQeXI{Z0XLpH60Nh-IH-BxNWzK+|Mc|T@LJtR!a
z44WRTWrdCLmC>&%d{~pmS}Z(*(U$hh
zzP1d8WL|d0Cj$`WQN54qAHChGTT8KlTCMnf;ek9@{c;bBgl#Msc%VwQ
z{rIA0(59pAp`?bnEr5Pje&fV%my8N|~JSOnlRDDOcpoXrJwlXj{x|K)tIW5?!}yjSq2`UH;nGbKbE?*aS@X
zuv9viQ08B$pTLlWnXuARtG3r+cQvL`O_Z@TS&Mbn7)%X4HH>jI;`-}tH_VC!b4u4e
zdZ0ej
zLJ!uUkk%5b)A)r2%W=qjr-i{J_+ko=6a~Dm3
z|CD;p8xJ7(hUU|PJ?wB8mBJj8?#nvKN+Gv(kdWb+<$F!+E2R34NU7fh8!}
z?`@smd-;E>CIC_VR
za{XKD3jYCT48hXsn4__)|DSVYe|UFO(uWzm&VNANK5)5EnrJ9cmB$0u%rclWLo+BP
zzpU6~EorY?TUY{Obi_(aevALv7bzAmQhr_~74R$h&+JL}cfjN#$YQferhQ@1wG4
z;B@ywz3DcLPml2=jKU@danr
zHCdNXo4`ll9uz5Ec!?A0_Qc0Z_qu$&NCvNfFIUd^R1Onxu9-2A?cX1Z-LE!4!7
zO0DJvw#aQ2;@i{Au1~{a2v-_AEzuX#rB$3^Wt?Q%k3ovxB+A@*X7nD*aY(ew#83Po
zedr35dhmYN663E6j~w&Yw8Cpk~qYl%uPc{k}7*~44zbJdSvr
zEedr>ryihr%MqQnB@Nvt(~OmyH!2|c8ZC+c>^M#AT4y+_QplA|Z?J$E*dKoCeBl9{
z)VE<%+IYw=*I{v?>5qzKJa=DM!=Xs@ReoPKWD>~?zi6a$&(n{wI_Jw}ZdK?PUfu9n
z-#1!)-H-D1&0~<&w+F+Dpr-FKVbfr-sSrgy2a25!%3I1ESK7%YLvw=GYVoTILW)oz
zQq6skRwhx$4_``J{2d{+&C^FPW#*1>{zRo
zeQe*##{lXd%T$66b`yC6-=6ceLMv8~MvYEuAr#Q7>zyp^A!|+G_dN%sfkt@&9V@==
zw@V?WX4uS+QAe#Ww#U3SR_@n}7LZyIo$qB?fnb_60VM^}1b0nN&nk<8X>y
zwZ{B*%fjM1`f(CC5$-hX9bh)wK9)$--UOh&}Od^ueA|FEV|gM{&sJ
zwzApX-FI6xEz0rromx(v0F67yO5?5|47@~6eF3M{&L|pD+L@YSp06z(Qj2K7
zrtJ9HovKHT(QFZ>W)inuP}tNeB^Zv7;Ntf1=jA`GhAvE^tR@e8kJD__@IiD2yML#i
z-uTtRM{G&_5-w4F7Qt-Hsi#LBb#rh`?e}BUwx^+o&9OB`^UBmS=|AU`PhM#eUiA#y
zq#c0^g(<}W@dvUPoP
z<=rswq}3n(Ci1y>HeU(S>jwykfg=5fB@x#(nITh#f~fA$J!3K;Iv>$cBH
ztoT9Ou7Be*FFjoPj;*V-*9$ByDl>++QwsTh-prhiEZKNy7aAzC6QEB`h{mhKqiqXqv2ZLqxBHbfY&az?qJo`PZ
z_z9Bjc<%pcaJaru!|I195DIUFOS!b0hoN8zO5IdvYW0CUq%pMdsN>N6;0qSQY5EJi+tP+F
zZ7?THV-o#u^>dg{6)3BvREu{VISKiW-D_>5ZeQy-o)*JDpbM9AW~n*S_`-Rtmfj1o
z95&Gs^=M4kiu0e|Uq7XFM;Ytn*o;8{Q#JVK>(@6!Hsnqc@(ns7*d(P6`c+L%Wnr>!
zaahvBaCW+$;?QVV$TNVd9Pd7;m(s8b%IyI4)`WFQn@`&C;n&PauUjYpMsB_+e6gLP
zONdaTmebx=3*C9;qeWe6vPDwRA74C%?1BPD>IPO3rf(jr-Lx;ko`5BP@7|9bo>39w
z9#4)?;FpiwKfQ7e8=C&*jk}`4mV@#AU1IN^jkW7hD`C)yRsnCD})qi&E^O^C}*g18uvfG7yBm+AXHf!gj_HQ5_t2wof~!9m{#Tk>>N?4uJ{01Q~qPz2j8M>
zPdW^Wd`L3jXBc@w{=u=vI!5p2
z)+}C3Z{6aY!MMgS+R}T&!1*>1)NtZHoG58
zm+Ajkj*+)S>b)^H9m7e;7k2acZ
zP(H%;=$~H|iyw=m@&1hpB)b6Yl!HDY&#|B3{1hmCNJ~Zdy9%#!`r1kIl!M3nli2ip
za
z;aVf2CoP`mwf6!LMYbhVfQ74&W!eIPr@}aY_ey2x^OYujkWHzu#_FA-V#7HCl{08z
zU5#!kF(98GjhC%3MTN0OH^Zx49S{NO{#qW6B^?#!o+fb1R
z3yg_YmevAw&pieNtG34qNJvcFT@s1aevQy5lU3yD{rVwzTw_l5z4kVkjy;ls{5!G)s
zLAmB_8KS3fsr2Zd%I(Pc7X7dZ6fhQDv~VwmYXpofG%Ezw_$u-q{+yWcv{45!K)X0H
zwMj3c-1^?Sx1)-&N1io43)^w6wt{!^T{G68;3lm%6(N>u5CiU>Wv>BTFYa+Kdyeo1wF3}ff?U_eXq%Nu;`48LPfs5~3uI=w)?=$#!
z-XLj7VAt}Wwudojr7z1!>zNqO1(4*&oF*dlE4x9&i3>zpxa(rf{^@fG_C7N(r
zXnYVBdx`$jHd>b>Aq&L;Ikd_Rnv!}7mK~Y4FT?K9VNg2;tP0Gr*M>={2e3Kf+i_$ix{SUTaJHImh>py5$SWGxnUYJJ|{g{Qff;Fue*EvIW$K_=G_kKpyvY#
z;xq8&I+UXJrjkI;IkUF?BzW6j3IUh0uQa=uZq)ktMJZ!LjklOr_=pN}xbq-2++b)a
zu*Q;R&Xco)8=&-$)ym^KZ*-gThH$v_hBg~YPQMnflF(3N%aQnax=_F``QB(y+JCI)
zj0NYO6M#U-f?jj6t4*?>GO2qhsg~JA)~9OyC$;%v$;a-@q%!Fx&y4=$raH8>g$UN_
zOA!>B3Y|;&+OK`pMT1R=F<9Njz&jW@-JQl#!0@zzwW!W64{c+FoM5@WY~RI79)l&dtMUg_UYd^Kn
z05z{>Q92}H&~}YN@7FHCSR|)xlKEeZrB{MxUx*aB|H*kyL%>?im_F@BN-3
z=wDF&900P(&$U_k12jn|h$7%P)?#IHk5*BmwRqC!wA}gk6h|5@+l>3$!e)HLoNPPI
zO(0t6M36vpNYX?GzWOUIvylc?74E@FH_5Aw4IfLNedQXI-Pz(Xpc2NAlx{zZ7obN$
zG<_biLv!imE>WOq`&&Z*Rlk51`b>}KWG^3RtQh?3oDm^l#Rl2}B!D?H@m32zKXn}o
zfi@2m0KqzQITm=hL?D7Uyw<#2=N@?H;BMYg$A4l6Snh
zT3hBwoNY}ivmr>xuL=FCy2>>GY-BZ;>PAfKw(BdmbLR(+nk=0yIa+!WmNaB)=0Ccp
zF3T=n2jbv?VcK9VA5ThaDEhbTX*BtpB?0J}o{Nqz32e7X)=i(inaj2P>~7dhLS4G9
zcz;onzyM8B+=vdU-lnEaa3$vs$Kxg}FhP1|kn$nhMn1BF1y->{6IhSbT3wd>OgZG!
z?9j`dOd?|Pb%3o*tq-m7`tH&JlILS-MRngUOI{e=JqPexzQya0Ja8_L+)w1^+M=dp
z5IW#NsM4_nJ2GzV-@4BHwk#)>pb2+(+KX5&2k#p_6`SqD{4Y^qte-k)U#h8p
zJU>>?+Iu2!k!JID>l;TyvC>^pfq&P8T{|1y;%C^0N-;d@n|41mfQB_IJ=Iy25iUv&
zV-;-5?lsq2c9#HxH=2F#zLvDt;CHG>&EVi)nP#E>$(8ohLmp1fLoeM16*Q82+Vku{O
z+2Uz$A;CJJSs?;OB%)AY$3@69-v<%8q9%Tgg$icJHNo@ABa!%aTVkHwgvj&XN*wCU
znGYISfn&y6EfwPauC)>+EMl|AEaLizkjhC
z&vAReDox}mk-e5Aw8xQcWjq;d@xLUTZfGnb(6YIl?lVdQIxSTdVRkrL-uJc;)55Qey}R2e5FBzjGyBBHh+8E{}po
zr@YyuPTGG3na_!-bILeiV0DAi*W}(P{!uZE>@~`pa{tags
zU2KHLHz(xExcF1S@);5p>{|klV@KM9x>Q=-Dq^mcF2E(+6|~;$NpD~8;J+jB+yE)X^_2I--zLWfg*NQt=R$XX5=BhH$bCeAGYlaa@UDRZ
zesSOL?R|PbPJiU!{a5vyN9Kzfa&xU@!~Y!_y?4l+sFGAwjK8E1N~=BJb+N7F#k)G#
zJ9)@xKK(Izcadbua|GU45wpGjTk;*lJSca@cr00~uhp$OBCX+@fYJUbwiIZO8-AMq
zpGK};D(0`FZB^>B%}~n$LRq*OFaO>wPnYJWFbGlI+giWYFZC!-b3ZjM<(j$U8r*kz
z{18R@?z2_|!GLh0jFwUj9El3VWJ_sxEwE9_5+bi@!P4ah+M(3V
zJ?<|ZW?j5fN-|_Fw`%odghG{%dbydYAn*Z0hv4q60fGgBy9EgDZiBlA*TLQ0Svgiouq6IU8g*}qF1nC3YLPwf*BEx579*v6>1H{U!wCrtj&EL
zTj%bJ&&EZy=*Pl2xMcP>=-bMk9{dIT?kUm3jrJ9*)E)gg5#4P*buQP7olR{tg$fn0
z_9vOu)x3j}B1`W_fS5RoQq{zh0fYWGBvEix!q(pCVGnE@|KXf?cmBiPbSEGV$;7qP89z
z{@RLCElbQ*aGJFK00Kq~q5+2i5&1Fk7hi{uW7(7tEFsQ&(Gp97~Sg=$`fOMPZ+8O1<-o18ODnwC$-w9(h!KKziG
zNYswVenrdlr4Pzi0Y+o_`v*rmN
z#O!>dMz{P)lXcD^c{7Q-9yx_Q9{P1;&cBI93veh%BOi44)LP+(2k%t^Lv&76JO
zk*i1amH{>~FlItm4F2id@o?+3pB=gvDP(Z=6p`-TQ_D-!-q07**UD?(et%EkK
zHjsiWtS-KrnD5Hap(j7Z54wJRo8Di9Elnr099wb2Sq88+bYlpBc$>_r4_z{RO--4D
zl_6L#L}d&mN@jS+r_nNd_#TP
z&rd&|Mt^Q*wC8%7SG%{Fl>fdTq`v}^>|z&}lvzg(&9V35(Tj%48p=l}qQb1yUB~
zfCT>jC}IQGS#^LuVaVPariTs4wdeM{+@=%{0pHB&#M3DV?D}pOX^t2O%q8*3k&C7E-=LQ~#}xY*1dTAQ2UOn79=}ZHu&3X}e9s_#3@<
zJkrT@-GJJmk~EvU{PiC7(4BTfn}7GNGdhRCmI9|RD{ac$u)CaO-&srR6P}I!~@NA{{ud62}4`lh$#G$e}oNTg^%{
zK+0~mT;`ApU8zMCcjvHjHy}Yvjc|G#A#V2ofVffEl3EJ{)T}WR`g{&MZFEoL#~Y(-=1GUE)?sP+6a+%G$T_*%OP*dIk=)xMal)S{H|#!w
zOJ5|*eIMLp@c?)|qc#dq*l9N4!sYU|)wl!pF=s?pYwvCY!99B*{7nMLrZqvJaVo-=
z85D*|4jjYwv@OM7nl*Ox5qKNxT|}nRh-s#KY+VdOz3pbE#(rrieIZ^MS@Q%FL>GVgG2N}
zWNASwzfFq!93?Hi>{pr`0mhR|LHAbxOlmff1p$zefXfbm`dN%;@B_3X1pH3hlkH;v
ziw>k?8pd69Ddfe1Ax^b2={)sAHLs$?#j3Gb?g$5w%TEtKe%Q(Nk<|0^w9SW|a}4Af
z;0l-y72M6joddMxX}Zf8RzBcrMWLZQAX%=XT3%OniJt+bkDInTo~uxCkn7&yn350|5Ax
zPoT?KTf@-B#LB=mHR_UxBMtEw9~)vo(ZmCwC8iXE^lT>WN~wR!c{Q%R>8*4Q*+YMF
z^oQKCGl}#Q^IHwwJ_GsF66!o1uE`e36B&+sR>8)H`6Lj+DWt^2kbh+wL00=*-=YO-
z{2%E4e!fcR*H2M4@k6{?)*J7<6uve|y#)}w0LlZr9*8`Qbi==w!D-kTFt6`@VzJiA
zhC+GFg$&kbom5HQO2j(+3XnWG8QU*0pS^wnmh6i>`Xt~OnKuQz-ySl6^#ZS-W9z{)
z()uayhvt6d60%(VwD6yw`xSb)lP}nZPxJ*|S_aw(3!gNOoNGU#E(`m=TCptTj+YNd
zb2fLuoqAI%52?HHx)Vihd_
z#&wuz!_Dz)FGT-ka5v0|$?
zSATDFGBU};o42!*ajBH#F0<|=j6o^o@Q*=}BXU)u2+Crxxu5bz`RqP2TJzMU<)Xq>;OKc2iZhwJdq12ZdP~kLn_)P^u-q!C+`L*6t9Ej=V{|
z$sKdrOE-y|td29TLQ(8SnNJY$zy@7U?5w?72~v@49(+(!J1!PveOjg61})gHN;FD=
z*Pde2BIn|Cu!}GT)Srvs<%{w_R8_E?aIBsotr63X+UFrxdVYn!Qogy!u+p!5FjI<9Y&v*G3rPTO*EoFjZ}
zR82RS1@iqW4ytA+C{9(HaI63K$Eol5v(Okv_6fv>Gq(x&0}Vo`@a3`p`ZkGm^Xy0u
z$m`}_=GJ?aTlEoo|u9H60ku&dv@E
z4PjlPQ9;z~hRQW4BcyA=dLTlUq*i3SE+@B23MMkA}dx+w=X*i%}K@&4_GB)E*zG?WN`#1#+rC*&!lUnawkI~rPT
zg*d`r?sy(clN%;+dE3!MpD)R@S`CC;&>Lutr?7!cJ4JwnQk}50JXPNp0sZT
z%w4(I+8h5_ewEF2nKz|3esOH4($`Eq?U#
zh5=;}4`e-Kz?a-O5qUYX=o1_@)LHK^tA$mB&!diMNbmXq1XenQ5L@=22HPK;qsczM
z-VccIcv~napawLUl}C~AV+Ejq=VxZrG&ELyuExg57)qPOO3zmdO6#QT7}z<^P}4{`
z@j!Km9A`uupfQ+VRJm!1#{ueLK9a8!Nmx@AQJ3zRhd7JIm?aTq%R_NTa>cFZcs`~gw3&}oml5s5
z-4hSBzJ{2?fzDN7zVvS*vznd_(Hv(awv`9elZmUtRSSrsN1iP#vRPuOn&@=LmFUo&
znzz(S-Z^;0oG1dvtn|YeX|}s65`6cE?Or2XjHH>0ji7caA_!662t(YV?~PjNNP3bosg;R_F$8aCggZGQ>OK?p|Jx
zrIqbVHkn(goaTU4(J+Qz^=)`$q_(!!d?M>J0C2CVsVOQd8hJ~d*oJD>Nb2;3s+FvI
z&IEND4*Hpvd4x(31q>4Q$hwKAoai7xfv_8j>wRqP8;4b|j!Y+1mm`e>RxL#d
z@P8akYc^AEVSRA+D`NhaI_qXK4Kzs9gB%S~a)?(Yb}WMS_zs
z4@9$^5QepZ5xu71ycMnZSJDi9b;Pd&H^9RX>8S?~v}vxoLa~-~%oqxs3Ch6v|d_-e^~VjfjXZepuOS(vE|^*^gNXV)y$EaWRg7SC!w)@
zwKee03{J&Iak`OAkj6u~{)os>yn7JNp|rR_p`s;~<#{ykAv*|F6_`0-j*f!yH_FOy
z)%$d%*&ck+-{iRURaF&;8^*fqD4ZN%%_4+pk#U;1kB9qblbiGN0
zf78wNPF&iW6o)!2^x5@A1nJcVuOFD9kDg#aKAw>$IgD%=%a~|S_MxD4$?67v1ki+;
ze2kjA^LajuiCwC08?s?`=J&S83_MnXutBMdXBn%|l&m6qnG(GVp=kJUg?Wz#0ya+V
zgC@R1q=lu*QC2JAnA;guB~m^I!?V>kLAT@e3{BuiP3`_6@j-QL&80ZxN6fFOZ$oI!
zuxZTR1}4$8Go1FxI~fxFq$Zs^P1Q8MU<)qX}SoTl;
zl=rn5#3RP+;pwBHl>!el5rRTL!h`A6G5UK(=1^h!OL
z`&lY|&P$6}aisZT@$M_eX_q%cZk8Llw=L(YVt0ed{y~1A)siwb7rg0zqz01)L;nyP
z5&8E6pIBGM
zN1xTHrg9<0Caf_UNnw}h;WxCjp)}gJ^Nafy2jxQ}UGgM2H+_*X$oa>JK!hwZ8G>io
zh!pD=x)Pv{i##x?WVOLJ%r-qOnSaLI?5py-G?k2TI+AnYtnusX0qYs%)$6Yz5XLT!
zh<){NCf8EUeTL5>UipOOt!49wTwY??w2!1e&@0v>)>h0bb>GBMPT5B-P1fE^zY3e_
z$XZ0#ucw7&LiIJ-x`GFnw>=qN=*C_dDmBckzZ0D&E3&ejFuz%Hfu0_7YFKj>>yJGn
z5JSsoDwB(+#Z3j4XL|T*V)6T+*Hb?0jp$PuY3VJ6K$qYoE}k&Bf8RZIWtHMSOnav9+KXaY*pkWLW|f2;&sa
zE>5%}YyexKhY6P6615E7UgNgJ{I%urtjLgajY1_
z)Q5z}-aE|+24}~)hVXDqC`%|5>fJIbide6ud|aknA`|*z?AjD)-Y628)0W
z=io(?oTDAicj5qJpw?(Dc`1eeEdg-8QL%kKE&mTa2z%;hwO72VRdCUNXQ!gEoZ(!Z
zd-NlU{UNBiOE;J41cw^W@+^6Zp)0+uw9W07p>R=BEH%YD9Wa%De(6*y(
z@pBpbWKBFRAx65)W@{rnHu>q@vE=fmje28215eIYbl7olxxO2skY-77MF2i@~&SIRqirh3|<_YPrurL6^HnO
zl+|CurGGgAwnM;i4bm8TVOC+l%L9&hfU1tq<(6;zn
zekX+Epl*TSF$79m8!yfaTZ)?omvz|QVn!4tN-?FHduaJH3MRXTB;O>HVkRC>*H|{c
zG>U%=JvPs#9Az+G^6^B0(j|&wD}3p*hD;6C?W{E3>hR_$z#QZ|TM{FnTGaeb775LJKUpY+#vZ$jbFLeB!N#X{UL+ZOV+tCVtqonC#
zf1;>cx}b&`$kSa38xwi&C;|9b%c%=IOUvW>`R#mSBBJenIMZi!x!?ahetgiJQ0?+q
zr6`v|?d+6;|M*bo(legi_m3u3_fxYF5gsKW-MzTlg;PYFFOmF5&%$dLuVr!de{8#Q
zLpvHZO4Os4Z(|s#o;7D9_8`%@3>5b%r;)%PUV56M&DZP
z`MyBX6sr6}?f=DY6T%a|!d^Fc?cZ~*4jarvH$P?Ai|uZSJOA^q-ncy>DxX@xm<{($
zr&ykKfYdw)w&HQQ71W8Km#A|hD=~d3Z-go2Pmu%NN#)^K7Uv&G3L`)#>f2_me(cu1Fk%e_B0c!l6uW=1-96$b?CGk>#)yxGFfoZagl7n2R7c3y_MiPEi}#10aQlR-2woBoZN4wZkA
zLqt%6FE!JvH)_7`GqEBd$&ElL_!m@L@O99(pKJI7vN~&ID~yq1uz=)}FQW728BCQo
z1#D0cRdEs|!D74eMsEEl%+MH1(N15r?CO8|66Kf=mjtwpO3v$
z;&i0zF#u`xtm;ICnp5&JuL-l>SKTmx_T}i06j}Xo8RCKgL4-r-G7c9AKL0x9I88=v
zLp^6K_rX1y6
z!eaIe8(+%S9Px4_>G-mB-SWFE9#`*QdF*W2i9|P4Z*TAY^)oOI_^5;8YP$O&tgTzW
zPG@?Q<$STxm8M|kr(Egtm>UiJG7EXFC1_wJXn|hFo;R>mKeB(@~9
zoj5}0=cEO-M4Q9jx0>bYLcRK}N<&^=ok^F4X_SsbO223!8~$L2+T#|kae
z(iqg9KDi!-Tsm71vGS9PpsEs}=LP}+k0kgR+q5tVVAX_T4WWQYHzQ^PygZzY?LLzX
zQ@ik&W}N(W8Uh=oQ|Xj=e3fNYUz1XtOwr&V{C=txN=CaRIozPa!64ZytTr)xepM1as>@5b+tG$(
zwd{|%B3-)?shuS%S=uw4FX~Uf=7(>l8VZGBI)52s?rwWJ%7*V6;%EI8N^+o|h)Dh%
zd1Lx5A(^W9w}Zzwxu;9a8e676V}8sz`;BBa@oZOcL5Z`loQvlfeDY=ez6g$MVyT-2
zGd9vD)Jhpe{ucNMuUMc`eQ82&2$2@V%6Qf#_TB}d{gxsoym9`o*eYPDe^bwRq_pzR
zZ79SOg&vC^QSk}`Tmes07++B1C}?AdD?lLJruP~FotJJKTd(&|Ag9Z6#%nwS4!@)<
ziGh*Oaz<`WeZNT7@In=L_Id$VW(M{O=GZ((!r=xaeH^)Xb;_AG0+i$GXh5|QPOO<1
zeqavf_jD}N=?>3$L2|zI@h#BP(aAMU9}0GUpiwH^(Ynp2-VR|tqRtf%Bn$X_MmJQ2
z#%aLzty|t^%@@`ZA=VA!j#TY8xGyxSFd6E+tL|Bfza_JRU?c2#Q*to^1rv>p68_Ua
zeSV3^21vf?=80)I+?K>6>HY=j>y{wVJ!uD$v~5xKMhfacB^A6{C)KwLG@m(y?AxA>
z$IWR!hi~g9UIj@0mqdr2R}S;AP}z}vwdj={(W-P&0qGIq-b
z1hUv$8tNfUfXksQ5Fh1l)^@)O7hOVNd`(;RD&pR-w|-P
zt?-p3D?esC^>9C_KSNQG2np)94BUd#P3(r+-{${fr~LI=qI{e$#{`k*#`u(f=;H
zo7xhx$4I|YBDK`#Q)UqExhfbo*#Qa3yyvm7U3_^lwKzcudq2s
z$MtoJKKL+>RT6L7qpJU8zL|u?B~PQgdY1=)cJgDVTT&0h^g26&Y|Jr@<-Zp
zy)Yd=rs;kOBSjy3*HZgF3NI5%5REm`E#f(ej32OeY!
z`?0OR{SkgSS4sE5gpyH|soKLWkja^D^aIo)VRw{OxhQmZW(DPScNfQSxqTB?bA*AQ
z82`1oXr9^=o5wTFPH#Gw3hzGI&NlI79SBj={j#g`NnZ8vZCi|k6r&_}@=IzU#3}79
z?wQ44%T{2|_8(c06yZ!-2QIo2RwZcVh^ZZ9vG7u<-8M(Sd
zVA-e3@SA#8ETX8EV?rVQ*HQ5G`$U;8vVT^t{3JN;ytM+o%MoU)o*1`f_@!m>8asxc
z`60;Wmq^aSw<1~9=_N+FgQzRo3zO<;Y={60%y8{2u+Pe1jW;Byr&t=!L`2`HNQSFV
z0Oy$X>EC3^!pan7MKD8~b+BN9bLGgokgx0p+NwQcCgX~jq;Yq~@K1%_1{CsOeoND=
zcH&%kG=R$6vW=A$qTCp_Jr{--UxY$m0o_U7q2YN!0g6b4c^OshxLK~L5`WrqxX?x8PGHR+m@#6z;?I-kKS-Ba$
zdUoCd3sSANY$Fwgb^i%_N!$k4?lwN>-y0M-uku@!o1kd&TCwri>G%ll@M83Cp;pK}JprJ98N`DCMfi=LYoaOmSNnn7b
zbhSXqSl-Lg5B((*bSz12+x+I@+|iTqSYqk>>EyqgI`QxO*vIQvowUzZAiOT)Q*}jq
zS7K&y=Vl`cJkcVg(7taYD*iv~G%A~=!@o1Pyt?oGJ{I=%ybKozxT^*54r33Yx
z5oO1c2NtQ>m@v7>6X(nD*c4V(OuEODZu@zg;Kxin;XA9H=AH@=apxW_w)QW0CZ_0f
z$^V9tm`#|ux|7LkS;;9?n|4?x=43Qd9Sev+p8}=kFGDd(BI48frkTU456n8lAM7D_ia|k>-
z*-D;tv>fTN9ykc+puh^x35b(vV~+fA7ntSA`BBG{lTYlHl_@I62fI^8Y*UZt!CJZT
z3p?>L@BH6HQ9AjTOv8Zqp6^HcIh?Jd^xL|LT^u8%IAR@;?H=7+Oc|IyJ|CQOY
zbR_x^BBUD^Rl{9cN^k$9D9kaFtZpv0ihG_V;{O_kN&Q~%BQ0n-$~~EVraX51p)WF9
zf~7DkCD(!r2uaC)HRUu`DLauxW=;9{Uw%koLNCj@{+iC|v)0P;b>rO37()Y-+>
z06q>54K3}>{vEcf!s-%1}yon-u
z9e)Ov4_wel1-OKTSM3^kxwzyr1?vC){rhuEC&*p%rfr#?ckqVBjk%W=$=;cp6OBNe
z)Ix@rNrd3)&%o1YDyONbDIp=@5N`V#rE;CLXvxDfSR%4W@iw;p&m>Ie+g
z91$Ll4FB4t|Cs5FZVTWPfc$p`1_r=Anho$0iKBmZSrmz_OONqZ(GPw`
zl4#L#U3p)G%goqLm8FoFbQuF#(jU(e{W`uPW0;0ov`iWg!y>+?~+*H
znk|c#%NySJAj5Quw&K_b
zTCJ$o0s$<__<{Ga$&9<(RRCXIT`}X0ROq!{OA`?hg`g1Tp1ptF{rJGVy*sKIzpy4U
z#(O8Y;2EVEa8IVmpSlPTIx}n4XqWpT&JagE;6gVm71ms1r}eMsOi}s+>0BEl2E|HA
z3HwOMK!{v@%~c@N$jC^zEw_RNz4^_&P$3$^#4%3(@ThsH0|uDheAhdnd>eCejJktA
z0C$D9kZot4GN#8gDlzwl1kQRz15wOQ;Q6GY$@ElJRkgIV)YP`8nRrSKLxn)ogYttq
zc?BU$6PL`Zc)#+&V4scdpW)%*2xufg>5yL$5tj$JI?4`k3osZ51ss(2C5qBZA4O>M
z^}!zOR8+Z(U(ho1L0r3HKd$p<4)Q`2)kG<6)y{V^yfuG-Jn><+hS_(2oPE$g@Q`~T
zzx=~9Q?8=12ovR%T>*$P@$3b+c6%lPvQ@on!-_13WX!o=dvz>nA13uCUcblb#mUJ&?tN)8tncVlD2$$c!1TfgkP@Cy;cT@7B)~}gp+h+iR;J4uE;NW1`@81=bl}d7Q-aCn!
zK%(nLD-*n?ige|mM2jkLqY^GYzW+PPh-St0GHz=Xdq=LF5!Amjb1HfGrG^Z<@#yyvqqCJ6xH^#))wkn38(na;Ee!-BkFhegkl(p%&>Q
z|KN8(9o5_k9sOyTaF7xI2~W<%#KgPs>W^>M&Hr<4{y7UFw
z9)9JxI34CV!qeN}xZp7@85t(YPvMv~HQlrwR!dF{Z)8x_d;kA=EiEl~tKJhRiK$j?
z^57tGKn$h>PDOy1OVE5g1As=l+>Ws0l5kM(iBS|zdu~PX0(Ad?AU}A1xH2C24QX+;
zmQFLApPdDyY+rvrPbMSpY$DJzihrk&R^Wh}nvekdN3&ADxVShmG4Vt={QyyypCdT|
z_Q(GcWo-~@hr($sWo1JbSb#rr22;9Ijbfy3^}WdaU
zRZ?=;s#rK}Ro`%?rtboL&<;Jrzt-fK2eI=%EG`6lYpbiH1FG0%|DRbl1PF15lKT~v
zSh=`d8%KY}!^Pc!ls4-8FJucFAlq&;hkyMJDDKn@&_3%4MfZQc7|<871)v82`9Yem
z)x~CC$LmccAmHc~($v(|xdF`w&=4*ivHtNT;&-A60O(Vpihz!mm4P32)0f>DSp}dA
zpbem`+I;`M^A#@c;sRg=NQ3P%05+A5B!F7K1(wu7LHa5{7YSk*Ft2`xG~l!7rlh2J
ztattZAPsKbc>k|=Uc@*1dC~1}uTSr#%tF!0g&jA0-{fBOLK7jf~C;o%T;@`|(DQ^?Z@YsdA4pmmU4
z%R#~L@G!o<$42(sv$=G@buRU~|GVY}=2%~(yriUGN56Inc=!-hVz8g30Tbf+YQ43l
zm{?bDI5uF(IiJ+e3%^`X3wQvP-xwJgy|!befd;%GuJfPo*Mk6?=JEnio&*yPfcT%8
znc3gp2b{KbbzISM5|WY%SwdQsmlx3hIo$ol7Suu?9S28EP`=Dx>X^*TcTdT|bfJ4M
z=6T1nh)MiXt+|qhhK7Pd)I95nHC(8(FmMAFxNb8$KOBz
z@xZ&Y1?+j15A7r#idMg9!_LkQD0?~Nc>!P;fe!#1#;$1t-o7I`AjSojMIUT2#~jx(
z6NB3M&d&LKHSxBA?!P}vmL0kO}BO#0CT??3W?>Y@5XzT3Xt^`LjWMq)pauhc!P83a1x`Rc+VW
zUw$Bv$;ioZGOZ)DKeL&@umMnmv2
zWP*IF)$?b-`{nTl;HLo!NCFB%F_i;OR9=1*z>VaW90FZd)zBy)C&9w{s;N2e^5xw;
zl}q|WDB|}V?RTB0mlnvL;3PR@FW#`O?hrhu{cpW1XdsHXu*B-_6q4fJyPy
zhqVAq%Zj!bs0|g-$X>G(i*r)#eFt#|3i>VCH4@W%00?7sEiKR2XV3I8vwCfSu-E9}
zz7FV#eCiYz>Qy*>md9o|J@}bS-Ze`o#D;g(BX7m-D290f_j;TkR64ulayj?C8d!GR
zZNGFrB^i<{r?!-VIZTeAk?g?Y;^IMGZUYI?G@&;007<}7y>*m$1G?}%1ugB)Zn}Lt
zKwQ}3e#U}M#%VUX+UBh@9)kEawJ$~9l^bw(i2%q$wRu=e8a?ryq=s6C(ov05k=8X*B=-F1ry9
zv;BOF)e5g2g{_8x5IRP)WWJpRKDAzSF#jmQ%~lL*GyLz-;rNSREdumK@xnjq_F5r?
z;xjM*a?z@DTKPl3sL=ajnBo)SQqVLYjCSpv!rg|UHvGd~5bYY8+6qJ=9bX&b850lh
z(V;}V5v+SnIAf#`BuoKetcM9MdPD^W`~(?BKtAj))h?%i1NLBn+_a>2NkztF6(eF#
zi*^B{ivCqTln(8BEg3R5AyZj^HSu0E1LP)4Chq^{KG<-Qnz>|iaJm#1WU)GDwSY_>
zNNv@-uef3|i3Jg;*uEj}+;SZE(1ZHS<%G3@l&3vpIv>b%H}W$b559_NUaJU@aRf4l
zJMNo4nxz`$uVi1gW4Rw1G$9LtuC($nL89OVhD_1sERN1APlF8rEEJfE#?Yc&mqDK9
zlM?h9-P90=$`clFghj{EiI4>Ep{UU7b+Kp2HOGQ#4gkoyMggEO0f0!%Hyz53TGEVIct8|g0H*j?v?#}ETLJQ#bhrL%i!drji7a#O*-I5BgxrhqK$-YJQ&@46
z#yIiy%dZ0D_v~+a{s(Glh89fXVhR$mK?`4D@`2o9@wRAAE<S1a>Hi*1lzDVP4f;3z@hLF$4F%vY?uXai`vK=4
z^+77(N6?+u3IOc39#u+x?uf(=-CNW%r9xmG!cYShQK11qM|PBe7|Rq&B8-<5H>CKh
zOTS|Z2~PZV89xeaaZbP%*2h$K=nmEg06_kPL5JQ?^O3uWF+SO<$cjKUguw=)sN1e%
z1}$}diJBJUkhnHNkH>gfT-odWDC~?d^d`VGoYuQNqyqJci4SQI$GjpSUWWM{#YkPr
z2VHJr^yh#4%?#Z?Wi_Wm_~B%|9qPXFwKmKhEtzl>K3*br?0z|BZ|nk_s0bGhVII)6
zG+^?cXMizTL^7UCO1q{r(d*S4f|kx}`$?lrYkwrE-r?q8{B)t5S-Zxx*=gVDHNr?i
zY6XvXRMH=B9S$OxaH*;zHkKA8zV^=@N(wv_@!Dc1_LnpQyNP+Xt45$P13({Ljj9{u
z>_7M}BZSP|qmA4+nALkp&hN0*6O2;&y|~8r?U^P<*y-_dJCw|AX|7lu0fSsOUpD$j
zsYbosdW&kdsDRhEq@cdtk-u8BA7(w~=t)vWT$EE3p`ZUGk*7$>g1`AW^N0tTZeR!<
zu(R@M)|UyrZ)V>rO$#^8zNPdaUuXP!SgE%jj3#MxIi87%LIqeZ*X-9>&8{@q3K@3!
zJMIo7F{zus+@GNlb6d`r{6H7&uFFM7%qNJYjw^#D1Z;ito23X1_r2Y`eKA7Td%kn9
zjvZbmS87*<$BcUj9Hcus1lg_sR8iW7@lfe`Su%<7IW9~gTm^hB`Cfd{`QbYpPd7;J
zWs}Nj2FTH>Hm2BXgcJ8oUZO9Vw(sy5X#ryPKtJRG)PjAnpnh?uunwK)z
z1Z$kmvDZW$7EqBuikx)!Bi7`jrW61{jjDK8lo(SjBV_sf`?|Skh$881R(G*^H|YCz
zv6HifARG_pv^-MW?qY#@E*uQ0Gxq)Omz>ErN;NAxo=-|E+pb6S+dZMWazn|}zN{<;
zx?gPg9OYtJVT}E&HBu!xDU%7WII`-K&$tsF!z!2$96^Gt+KSN$E@T0})psRoC!YGt
z-J+ZV$fxg{ws6852GQuDgl7b0-EjezjLvVJGYd2;o(0LX3p9AEb^Df=h^i~aZ(GAc
zH?L3oRf(T|KtQZU!e{5QJ{HI%VAU=cL;f~M;iv07&I6o*K>6jO({Cu2s&U;-=Xj=Y
zu}qr_bh)(~BPNQ#4h)m|!r@O%)FnMlOcgX8N$3AHZ+{nC+#jMcDNTiyjTBUZ3Houc
zOR92C|4IRz<{Pko#Fckzt0Fwj1JxHPG@oHRt3M?w;ul+y<$
z(hp%BJpb;)It1{Qh<#?OK_}=|t;b7Xu>_ykb|3JI2P-3pk{vU9S|Jh{g4_!B=)vIE
zpSv(pEXU82pjV0$F_Z?ZE*kolg0=b75%W%eg-Svw!22QJT}5@D{cZ0zPHgn_f)Ly=`S!5Tach3Cp4k54
znCfs|+H~aJk$({}trz^Ty#KF4&NGn8tdEJ9DBW_TV_u8&
zkQ`#N%4?>tuq0R@Q0oA&A`mN)y&HpujR5Zh1~8$GE#C(98XIbKA~Z#C3(
zGIZ~|E?M$BooY7dT&Ww%W806a&M@M6a>ND-@P?b5|54VR0*7GHhN2bOc_81`qGLU#9hPt!7XJ8vSqF%3
zCKB@2wRREfvRiudGUIGO>=Ko7TA$Q0Pfq|K&%Ze-Saz{d7O#2o12dhbzFP_caud1+
zww$LCrn2=Gv27F6Vq*HwWtTYRBIL~Pe-6dI>nmji2%}x-VhLLn{pywnV6wL>Ng4C>
z)OeAGh0brH>zGV}C5bwTRaddLHRbZ!zyCcOm*JsFJ%zy3^Vpz|esG9-IX|kT6#`hJ
z=4@8a(GQj*vsPBYtyBrxNZ*B+TWv>epHJ`d))BkQtJnLQqb9>JHLPeCr1-)f3|VdJ
zWw*mZtQ-SI4iH}bi_Q@TdgHtnIj+yBEY38USC$p!&JwnXIvvPlE5RAn=W*29Ks*KG
z<+B~VR{49~n!83lmkHV^uc|
zJZtiY_Nk6YGA`{6T1ed39rL?oiokt4-aO_t59;afq#49b14zV;0IT%=$pOt!EA#N*
z=cNhIx(Ixi8hULZi)KNm&j36A8CYpBUbK@KpyVtNU{S^apkzEYi;MD|QIaxqD~Jw4
zBM;Yx2gDF|ffi!$pO|9X27Nfaf?AF!jrJ+7uiaU^<3k>Nk+NEPc{+`!5<854wXQ00
z7!ZP2uhhOX)&{HCph|QIhPg#Y@IYXgvTny$=aRIoY|baF0o0M&ul4;-Ytm2A&jMrE
zPk~eVMeTWcwNLs+vHEY5-Y9h(SP(r<=&U8o`<8-e<7xBoE(RC|*{}HEvLqEx)zfRf
zIq;RYd^a~f+ZLR5EoBPRNfBUwi_~M8gQhWBK6As+=8|&JV{FZ2rvj)X)nId`Yi%>E
zBH1f*#Lst|fA^#O?)MtF2|Ek`oohVb!WQoyZDEy*b_XuRm_9#Blkj+Z=hq}}fRAT%1^WT1Q!@7Hx>+1GPcUHKoZO*9M7;Kk~$}Zjq?jg~*hB`~QS>vh?
zy&Q&DZI6xBI6L)y0+htM&EoeGGk6>sizIJ+AeJtIkp8@aqL@Yc98{NcBfSRk^VVAV
z?Ql3}o1zX}OIt>@#DQI@H+yK*9tN4u52X6lTM49N-snpo2zVYOt8UpvJ0cha2V1_F
z6$VC_
zS-fJ@f74XYzHFlae-!}92GrHT0bt9n+htm1IJe<+j@}Xq+D`C2CyxkxQ&KuzAzQV!
zS~@NB*01XNKaU3DwcJ`C4*}46{gWZaP=~3_%#k|dFjpC1w!=@Nb)io`RWZ?t1xRj<
z`zi3+K@+^EnzeRib~_>#IQl+dmiQSHDr3|42x7w)*dv>Ssd82LmQl1YNNvPaCbU1HVDVz$ygAU=LSQ*=W+s}
zZ7}2~0(lSLm!*Zi1?8k=^X3ynVOGf!Z+lTeQU|y!AGO9a2b!OD_qBlHd3S?GPubOy
zQ+x2+CDWC}9`yfc97$+|M_Xn=_q>bs4VY@5x!f~LdYW}uR|J5v0NFbRh6pc%Cxqx^
zI^hEe*^m)LP&YnZR~OB5&M!LXrR9Ur-U&Cv;oI}!8NcXpX!G;KO!Y3Bvg+ju;&WuJ
zn^obbH(~#A-rVb8;g{i)KXVqWX-Zv7%V2
zT1CjUzz^0OwB}bhPV)O4=ISEr3K-TcI>DEzInw>;xxgLPIunxN)Y#0>`pyz%I^BFG
zv&mTk1+ey2Vpc&~a$Y3s0&eatoAT$-q1k6|X^R2bMw*tdSAtg53J1l+L@lkvnAsdF
zM-7{&qUqo&I9A1S*iqqODeO!Xl+?X`LBu_EEt?2cwO`8ov2_yep25vOSc#t2_h}R6
zkC{?9+dNUUq9>H$JZq}+xSUBMK&r~9mZJR%UQCXXXjq1G;W;EZCGkhjJzrf}0ol`x
zdhp&2VyxwHv6h8;$5OegqdQXh|YHC+X#671hf*DP`#n4DY9I{Uri0CDl`EPse3`ZMz884!iw{15prxU76)zhfTa
zu1HD(eFEF)17@a9?CN7wadT?)pAg<(T5q@gbO8v!;Wqad+MFMB-20KDjJaWj4=UHk
zKeQPCw3_=e8auQN_t+<%T(
zNic=Qx4mLscPQA!TpBiR#E0a*^O_{#9pY-NjL_|1lp2uHFon~x8ilA#B(HoZ>LhhT
z$ISUPgj492Ut?6*N$uQpV$94{*dhREqoeyH^sD;o2X5aLj8{wTkYtB!s4^{yNH>^w
zoZ_z9I?)$i7gk<@n@e$X@3NzCtGW|Q=-)7YR+EWn{YLAY2r71>9O*`}FRM$U3QcMl
z0Za7U_!{0i!JZ2BKT10U=}0_!h&3(cL&R|)0IT;rwxSA%Hal-fyJ}qabE7fMhl!URg_v)CvluVEWnDNms)a>2
zRiBx*+3IP{W-&8#VFAMJ=pP-6WR$|27?P8Qcud6E6xobhn!+ZqPb{DtDT1{z){yhi
z53JF)l3zB;Kk8xY%gBCX{a$?UC)8JR(Kctc34nPSW?Lz>Z=l(>)J{FaD;mn?ZerxQ
z=#S&d7wHNpbmF+T#k&kU$gZF%@+ev@bl5m{T8!>2XtpriWxx3ePxjeqs+(l;L%-@I
zPYHhmBY8i9xMPcEa
zkZ^gjSgE&888z#kry(34iL&$~vOh26={2)yYHlzm!vf0Qln=OXHmui`>-N4EtJ~AC
zijLn5z=@F+F
zV4WufA#P~7qG+t=X%+sUV`*VqlB7tSiXqb4>Q~D$t{GXGLFrUySdQ+ZO71Js!H!gB
zYoTaCwc)K@@Hp7m5DMRY0=;GT$7y`zt++RM+>O_q&n?+3<`Aa`38@lRXO{`P!8%WX
z2_6Pe8yK)^C>Qdh7K9SV2U^l$+oK$UOv@VM@R5zHw-+Pyvp0^IB{Yn7B%%}Feir_=o;Y86dx*sq<`@Eimfx1tGr8MYkXjp{ET4s6M0=7mAKbSTcY
zL_I6RIPep6f;>79mg=e{!xp_EZTH4`DqU-D(rNyT4z}!^t0&;0*XOEgCqdhKKz7{T
z%0t2N-9wCMd?I026QRwWV&&AXCK81vm4HCTUc9pN+teu*@8inKc8CGVZG1iq@3W!}
z=jw?--r%Z@!WRw!F6EAbJ2Mz;qBQ@1rs)X@%ciI;X8ZBGxIFB=7$
z&CZ9Z7Fm!leuwY>oYbnG%^}MEmhtD!0%THlPd2LrG1x-krS^XxR3K)lxmjd?&%`Kt
z{5+z|?MKBv8y$IYZn~D`^(59&raFLf&1WJ|x3rAUjN?f3o!$t{>vYn`Jp
zB0RJck=O0u4#-~G!e&(}6u7H6TCX6(Iw6NAr$q6)PB+$cm-53}j4rOf$mE#Oxl$}?
zls>V>);f+qX|>6K7Ym1L;dYxP~JwE6fAKGbVfDo!1O$L9n>NI4y%qZA-u;8E06e?Bfw>#N$$2wn{s~}qY
z(8ynQhHf!aCAfD->@G;o1bzm6ni@G1)5UK3hoY&Ry%L%J#m3K^0wzv)pTldIa
z)*4mSM|LBZeoR4
zhZbfj(^N9eT@Ydda(vE-Kv$Zas5nggdRUujk2HIozgCecwHVfZSpEVm>%m#hq%Pe%
zvlTTWv-RAtTAx)9lGEg|QVqA~?u9YdN4oNIBwJot`TuhL5BBe@XjI|6V>6GSair@vsLq!!
zV~A%mne^e+Zbg~&DJep>oyqi>Oc+(YE4rCZaN26X3&6-oQ=;}?tNp5Z&kB>fsd+0W51ZQGqcFVME-^>r$V;Hy
zo+?voW$J=x!%o%Y=`gr7e&p1A9@$x=X=+W%RX)aso4;-$Pn0~?OC!!woId8CC_sO?
z%)JxL7Ta+Nv|Z3{KRe_;{<6$u`Ov&%0kG*z-YCm)6S@-gV#MvV
zp;0cQbY**DsiZf7if;#**g&9-$E-1Sw%B~X;JMpxAc4K?Hr+N0W$K)yoSNJk+Tb`d
zCGTC*y@>7SM0o;~@9De6*=6+u1oo|dgf~8Rixlc6+U~>Gp>>3OC&lBRC-&Lr%CZ6W
z-X`6b8CCI8)q%O8$3K;OR(a4GDEpoR%Ne2D^)$k)7d%Xe!v7}S9Ji4Li>r@n-}ZFG
z+s5TK)s6xb3iy}}2PgN~=ru4=acE^T$uErBlCCU_ig;!`b!8ak)%-UIh4x~l0F+!@
zT#N9vi#-FO?aJ>4co0qMi(fiozEWEL#LG!PA-&Z?7fImPk67SQr1VGd!=+V+Txme(
zy^9CJ0%p3E%Cbrw9>O82RYNxMvJ-wNdXR~e1)mm-O4|~>*Ja)oO49OWo}V1hJ*Ip3
ze5Yw{x|^n(w{b>fPitmfH5nPOw1bkSud((^sF-5S^YTE1CQm)i*jr1K?gyn%kZRT^4h0b3n!-!dh9~h${o}3Hw(cG)
zop$zMwVpPxj<%@a5n&TPzW>$0Gnq9v)l93=&bdH!f^Ud>2I9$Wf*1!pep_<3voz>p
z{1RQiP(jeSiiODhX$esLX?E{g(gkX&fUA1_(CJvgLN;EXyE+haMV@<`Wm+Wf$|Ah>
z3@f^nae@~cazy8ajxJ}MlF8$+h?J1c;fp%2@sA5a$XT2WfTn~C2I@eP_Yhy}M6$Y!
zWz(GXHwgyT<_gXv-AATD#W+~FW+38Bs}_yBHfpp~yQ6#=zMD5tt8=+9R~|doQo|~n
z)z(-Tx^vT7wI4Cv7BPLcZ!e+XP`hyck5XbO&0cLaZG*!YBm_z|IzodU!U4eAH0#^2
z1M=4ond-O)cn^*&vkU5WoMPru1{$&v-hQUoINNuwA1>H5yku+$sEo?=Hq3${8ZQ3W&Z~=gv;{7#PlY05F=c!e*>UmH35L8F;
zrN_ocXmLy0`p|{XBXEH{G%9pb3W=T(UG;TrW^jfqG9cRU73d3LE^MvVNwTXS`vHHddNS3-X
z)xE#C8>sc4i)rVh1a$PQJ$G%uEx_C9L&L`T8MnFsHlv+=&a2YS*y<;QWKgy4YuX)m
z4fbB?p3VX>;PvfclfC_RrcApwe%LgcnCJQS*!gx^?r`I)R*gSj!<%l0H*d3W!wYFV
zW9M_?wXn8_%Iuuek~tY|+>?%(BhIekfY6h=T#!MkJBOJ;qqmtcE-4_
z*^T%kp~7{+I(6y8@`V?-AiXA8G2+@(8*;cNp@FSck%Q95fw4)hE&EQ_zmq8wE0yM%
zZztaoH`S?$qs#xzX>!LS9Y@#t$e){Io)emSp6Rx&aZJQTi9kh{{Spy-ettJ`#xvAb
z>ge`@)RNFhc*ll@81>uu0jB2W?~l7NG;+VoG|GW)&w4E`R^`C@wT*>x9nBikVW<5O
zv-Qu|8=Q?=gxf#$yR`oBo_39yWR8b(6b%UZnWUe9Efn&!gx#$y+f~3i$ow3XRzQKz
zae)iBnNAw`-9;^UzOBR?vU$b(fG)o8Jhi4d)`m9ZlF3bXB&LVMX=%vj{_@seaHMp;
zuAM#Bs>u$?Vi=%Pl6V<1$ztmtdsQJ-)$qwI}Po9pZv6T23o8^*v;i+%7gm
zAoXV~8d+2#PSc)WNWGyLn@x@;_k#@SsT@7OvmuD~-mI~(8=wI{Y8%&$b4407d@Ck1
zatA#GD-hEM+!~CX1F!Vai)6z&3%xJ*ZMk1s)JI?f8PT0hRO@Snq_Iy)ojLF2^nsIxxvZq@1xaeAbeUj>Rf^*BtyPm1K
zNgE?jP5$PpFJ;24*r+FpN9LoOG9Op9`g6PePc@0p@4^S!?A_M9*4^^5EI(R7V2ZHw
z>dfU0OY!}QM}xPPna;c7{w&2);r|v8*GiwXyXffJBJr5-hZ#iOt|rAkULj#!?ZyMS
zpo?X-aX-1|5fv+>*I*bDtLpsyLUdjWDSeKnuumRrRgD(e5Lw#Dq&eWb0cmcTr1Ewx
z!oSs&l3u*m=aI!$<)^j^PYRa7xtIriWZN`4WuJ*2`jGvARS9T_svF*yN!^5onmA
z>QQ2J$P>POO(((_pSV|`6@M0PPwo!P{~G0AR{>X{d=s{=>3RBvG2RqTsI2~P*VCXp
zjtd83y-Ji?7P3h^S?KTVW(6`QH)A-WjdVO(8DUeW`QmOR(S!!w@hyCz$}1y&*(Iy!
zmqc&`5+$3zbu>Ggwu!~)_(NCf+m08w0f<>D_`;szBUzbg{cSuh!F34Obm10khj4=MYC;eXQJC1OxhIf3k@^I{TxLbb#BhEyF
zAUO1J9?gL_c_aGqdh^|afL(vB(m?EYJA+C_lf!loL}FR0cc+UO0C+Uw#1huQ4bT$<
zwm_#Fo10E1sQF9V1N;_itdiJ|Pf4=bL3*Nh!L~+7zfRQ#P*buqh76E)SJ3_7$@
zpab+r-~?bWF7a|2QYpJ*z_4_dbw6x5*SxfALbNxexO789MJD^bCyPQM6Dz4|QBKX`
z(E}m@wf7T`r5CAGlHO0dNu>p6%t==xKb(ZvHM>hS6joD8x8^D2S5r#1cqrrrXi`@@
zK%)8&9k)-LD3>h{$H6FThrNiC(Ubz4#us!-$z`gn+J-`uq2!*HoT+oY>#go`zN5ot
zbD6Gs!i|qV42Fyv=Y`Lf#%r-M8N%`Jr)b14DdG#H45u78K#yOkp+%*8RIDvuwa;I+
z7xeY6i9%7k3UGB~)B~y&cPI0ZEc(5?&35l6tea|XqeNSOlA8G44z1xK1FLF@1k%72
z6V@4$vpOlN%1zdi#_#H%UK2CIVA$Wj?-=p5oHwkrVE>Gst{(I4Bo?mmb>+uisGHJK
zs~Yc=oQ&y1Z}nseZx_W*2V?19<@Q0c7Z(`)=w81xE3kz~rz(MTyvsCg&oIxMPk9D6
zQ0<_OTaUAs+f9^$jH|Kdy9+4njms<{0SsNGtkY~Y7X7xzG@gYy@KQC+B@$mU1QaRE
zn!hEo9u2g??;O!(wEFHbG5z_wy`y5=b8rwpx$!iY%HU67%a9Qb^Heqgiph~z!?>DJ
zU0%3qOA*f*ErKR*Oj6O{E}LnJz^-xnQ$p@^$b+f`ZlmYeiP~anOtK(=bBwy%MKfWB
z4jiAINeT|dRf?DtZlLaqXY--MZ$16F>P(61HpKb_Jl)T38cPPv8BX~=G2oxIy$?wG
zy2iw~>cPiYn$nGi_RoDh_(TOvTc^-A%y{>2P^<`Q(-4u-I_t>R5J+CvUyOo;drnE;
zo*(vFT0%A+?F=Kx!zd?{RGC^PJUCO!)Qcu^B!W)niffH}L+;O3C$q&qAkJM?AV;49
z3efIJUbzp!U92>*Z`Ne+>3`z*wJrt^Al44I_#@H=#xXFK!uC-JYr&C~uY~A{<|ytL
zYWt-rlAYp{_vrPZX$#(GQsvY@TI5*7ARZ)B=OG$&=D;H8a@|4x*tu@~>VZbXYeDE#
z9lIL2B@g<;Z&ec#v9%r(ld~u5Vs4d|uKr`WMP_R?-&%|f(q3ESxXWrDm*gSGycB(o
zmM2y)B~+m1Sj%rB50!@bqAM4dWI2`QQ;)L@1JHJ`-`m|H#F0Z8mKyvQuypvmoS#{!
ziA<7knhuFPtj)H2JxpW@VUP>+qu9WRFYjmeBarxJfy_9tKLwY&SH|!gn(TJrOO>^A
z+_BmA;sC*7fi}I2Fx=0i)?>>z7Y#r>NIdAMym|TbYK)X>LShmK*N|;DPRK+?SEQ@C
zhQ=|MoVT+~cw$#tt_j|hs(G*F)Z*wFC8b`u5U$;h=I(J4;2y(&fjgiY#e-x%gt2XI
z5=;$N?niJ`@$AT`3#$o?W2g~B1|{O6
zPL?q64CR1hpv)%`U)){V3E<59EXQ-A+fD0#frbD}7Bu+>Adj&whTk(*UHAU;cQv(F
zkpIq0lgLdiX9_QoXzwHnr;fKweHS2AH6zP0{bCib!!GHRx&Tr&`%(ImDh=IB=ID>)pL0_&KT6)agk2_$`;$v}?9%1keRfd$e&w3g=(oN@?}z
zv2*h9irD4%?=(=q$B)qf4i1J2u2YV8eodRFt14tAd}o#y!$eKS=Q=LQd#skC!e6>~
zvi275k<2`?aOwsCVbuq#bi3o`
zlAZvn=~{w_b1V_(TDl?qzwU(-v+%=++;zZDw7caO@=rKCmkoGo?~hrVNoxqP
zha_z%Jw>=?%kW#89fT)tsTSMeM+n00YN_KLaKa>hBBPI;J5pQCBoQ|dMgWxis3EbB
zDjdH&y-}gO5CO2SzS#ajUz`<{Y?uiW#`yWShKXCKk5D`dS4!W{==f0G$kVtnwvt&L
zK!->Qiw4=}fm}}Z;QpJlnDmjo^K@-lf7%%?0_1q8BdZ`&XwQ~3%GB5p4IEmWDfRiQ
z2D}|4QEf7N11heQaAMG)6VoJoj}f~>mi^^MyH}%Elk?$(&_&x#+s#DldCMszF^ZY$
zJb_wdr=))qVDp`R+S3t8Wph^f4u~JR`Oz-z*&K0qpD-j^w3RKc_Z2=ITpzOe$u(m>
zj3+OpmN8Zj1)S$U!Q@KUQyyhv6G@Y6?G`k?l<`PsqcXhAZT{Jy8Tl)O9lv4YKb1SJ
zc|a52g9`RcV^OSG+yn^DfUN`fzyx
z6~T6QvCW#J>uHrX7a}S$Hu?D)6JlD{m%>%)9B*tS(E8>_?I@X
zc$aPc(3B_v15a(3u%u87m{pQn&d8wXuU)N2io}?bh#-YXRXs=C>;;a77@A<$`JYA!
zr=b;58H=KlC?O80V}$po=kf(V7`dd`t{~3#`2pJ3wN8CpP-q_Ub}U%5=$U@k2BRL`
z&4KW*UkCT%;r6m1xdb+A{P2=RN93S2VmwpWPjKs(+io7J)x0vDPpAZxAQ*8-x2#5m
zZfa?^j?!I6;Y-8Gbph|AY^nbrpg(
zQ|o4`AVn775CGsz?fc}!yyzqUyhPu@uB{EqMKYNYMXbO`4ndS6pe#&ne}-w~G6)|S
zJ`rfu=Z0>NP2x$0OVNSz#Tv(+{wAWyqqFVkCTX@HlE?6~0686QOrCp>+Oa|81wl=h
zc2(jzJ6uO4W-hQj@jFCwx7w{{|B4UyVu-aF_apPk(ecqj`d@Cfkw_KSZ!2aP+FnOp
zreD<$vrIkd;w*|hqspYWK2{#gfC!ft2qCJtGP(7Du2g(;EGU3Gk7NEql@cink}kK6
z)tT%5H?6}|@88_+JEsCO(e5l&rz#fcpVA%eBQV;ou)TL%x3IwXYS@d#%Gz}ABcB)iwTk?40i|(*Ts0Ne
z>V{I16i=I;+B(5H(<0>5^&-gH>#5ojpMdWh%9vnffv^?XTiQEOJ
zyO{TunnCu$YYh8#&iCz5jb2e^v+d2ZX$bY>%zD3I2;KE83WB`%UBP1q_?|;a2A{hS
z5w#5f0MqEEcM+pq(`;0L_)*ut{oekce-dZ<`&ZQDq1I0~aUS?lHQT=~d7y`O8#yTn
zeP(8g&41CezOgo|Pc3UnlCP7C*C+kd3ahngcCE$z{kJqH6-XLmvBxWUg!uNWZJT>|
zB1TE{Tk;X_FH)zQ0N2Zo{+Y8N*UR_*Tf@xD-r>Js4wgwQfiBU)p}@w<*#1?XX;qCu
zosHb05*WZnTYLyHMNFx~hxd*k
zZ^kHBPXcb`S=$DW{v$jk>^(MsLn_S(EO;mn32w9yUfAQquMEjZtHR*tg5P4h_(q*{
z^S0k0DI5cj{Un_Ro2Q#eu}EAdryeAR+l$Rkl}sOl(i-;VR~qTSOQDoVzjqK?n{BWP
zS#ZmYKO^%^SuHoCxt1e-a9?cgu6sp;@{++U?e0(%ZNvPpg?XV=^CHE-mr17q>^v#5SgzBk#o{*q
zL-X!rp7~**y0K^J?;u;e^sP|J4LX8|HkokGs^;g0<8a~(u`g1k%bkqB{2M#+t+uzY
zHjL2)blW@%X`MFP;fb_O2))9f$|bw%+RgD968OCB}TDxYuU#
z=Uz%C#{|&T6k4#)336_UGD94vzgdJSa54ph;AQDS8MWg
zY^06avkAezxY{%MYRmrn-c6XOiNXM{XwzqQ^H))YDbE`OHAqW73E(D;$9kpF-me#p
zuZco)3~jkoj4I40WqK5=W^aP#LT9p-4liOI3T
zoGznhiJ&?FVA$+``9M4LjIs^-!SrvdSiCe{6J7{a8|W?;exYKV?0+6I^81Eme(tiy
zZm4|B7mpLZ;*MugW2><%Zmvak=-2I%3`EnP<{uIE4L7#ikBsu2>&8TR(}o0`2nB2>
zk9}dxX*h@iuWlTEoZud(oB!>~OmrxgxenUdZt#nS7lxGWv;$8Q{
ze~*&9O{OP~h=JohUs~69nb5^a9acDwBy)P3Y~dCWq5>-_bUl$0!39w63!2#478@qY^4RdEo6ea@1n1=3f(-~Eto`fmF%Q_*JwYNAj~I^
z7Nh$=us3*q$VBWtZn@(PKXys}Ph(bhFy!SU(k99d9jwU(BpWja>HeSbuheV(FOT&U
z@0>-}{Xzg^uX(E@8T51aEs`#oL`&{o`0+A##fB8*l|WuH?i}YXVOxa&_~o{kh_cp8
z+fqD)Eq<>D9|D~-NIU-x&z4aGCF3X&1IA;*ozM$3czwwP@S{b-97ZiMq{9Tpwl)84t*sVDt4)@2BVr|j0uQ*B|u<_FGc+q
ziGet6roaw;VxYQXD%sLWjt<#^ZFf9BPI*)5KF@1jq@COPV@#$r{8s+kO0tuGQ=l97
zMc$pKZ%`|xdO9;&O;lg*TCm*g9=y!A?lK!i#5KXc8znvu$?VPl`?ecQpg+_;-R0Xw
z0;u|+2IL@|Tmyf@;3Mtd=cMuh3;~ncayDnITkbSoFMmHNzQZVCuPc9-&$|>C?j*~a
zJdlT!xQxY<9{vB#IqK8&2*i6k_Ts`%6KW}bK}PTol2SR_xf_C`<)7DuKdmqW#CmA<
zM|Eenv_E+sF!U;uI*s0+RO#ADBE~G2e}Or@bV7x7<01+t|2HZ|E!SBhG|9S}L1eV^
z`{&hQFm>U}$!-o@-UZh1FBer9ig*eh#LiLsY;|W>mzVx3hT+h%dPowU)u5vd(r*FT
zce4TOjygZyTWywWpkUx}S%J5`82KA%Bj1)OKrjIIjk!+pBGN4wl%VjVJs4pf)Kptt
zs__3}4f|@lKPcDQq_4A5^{Fr6{9p1Wu`ZQ9QFJR;$17o|I7W;~O1rcUBj}&DzM{tACwLAv~XyMO`
zIM!oj_0zrWP~h>3JKqbCMXhQ}J%=gNQ|*Nh#D9Fr5^jdPz5^-DR~U4@Ljr`&0)+(O
zuKRbCyGjH`@9+ZBzW6OHw13RH=obkXn9w1qgyCzC>LVEQZb2dg4VTq6NBnJv{9iGY
z4TnRcNR2$PuphE7XioTH?@&M7AJjbS==vC4kTxCmDC-1
zXp0YI*B{ubw{K(T$7D>znuP#jOykli#HM_-2Weom^-ir+R?;fQn+9iAnIdlpOyYy_9!WAAiO>d>2D_O~it~H}o1FiaXof@f<6N0;)`eGW
zS-hIx^KlWRwFd#~<~q{)#j0I<2`%6E&T$JG;-EfDc>0NuHi?%tra_C6eq98<&_1tu
zH89w0&p)}Nc1+G6R&{P-173ClyevciCC4gL3?iPu{ky=dAo4(#T%O*xUT-8sgLgJ(Q{QVrLz_P!Q`t40$Ndyt%k8}`0dPkk|f8XC!
z#y)b!wUR?ni{#z9TpeAXw#?|jZ!4<@_m~C6LlZJWCAbc7y~aydmv8Y%;RuftD+idt
zI%W+^n;n(DC1bH=Q{-XE6sZhM=i8R4I-f84HNmU$d1kOJwQ&xH@-Bn@qCP{U#m+DN
zkB_8Go#h;&96BTRwo$4R$=5n*Vdrr&ocW$dEXA`$Orj#{Cu>VAk9`12w2L>a_wD2J
z5d0I2@f4(7CQB8?ROqZC$|A<;H}owEEk=nP>g-oXQ4&Ud#JM@4uSldL*q;sK%COjG
zNWWo*oIa{Nc}=w4twrThyv$tpcqe6h9AICD*^lUlO~ubtkRy%CvyE4GrSfS^fHu4l
zPV}|?WcU`A+(^v6(Gc3`=aX$jKJ%*ara$xcP&pS&WJX_J3Dh93#nHt-eXCMFnBJ&i
zprx5oR=!G2Rr59B@^%Y}VnE)0x|a(>4wza;Gjnoidpi;y;9Xz;+5`3?X;QFBYwse-
zz5eKeBfYZ%hX&z)ZV=e(4M4HUn9QXi@rb#dl3o9+3vKUn3Ta}2iahETbUR&LXT9hQ
z<8v+iect<`YPO*^y#6pj_*`vw6Ift@C2Bd4kMvm>5N$>Ak9oC6MJJ5zsyn#i93?2a
zf=CaWM5wydm{52GG{QeWGcA>FXzg1SZ;k+n-{CMf9(ENo%SF64ptQq5X@5A@G+IW&0cp
z#Gz3hu4*QlC>~2^4v-^K^)uxdzWwp%ucT9`#kObX@YUDQeuY)+=MlPrv38~Lmoby+
zD7J}nwK^d|P~uh3rcm1(wCZ?`&!>11gkf^+|Gc?Kjr=`gcDe)~cfxyiw#p)3$~D2?!>d>Jv598pouFc+
z`z2i!;jMZKigc1UD(Baz|LX;?8`eYf=xO#9U5ETsK;NXFrRe%8>CX%?OQ^fj7>vnK
z+H0ecxHwl`R*4*rK$0|iTSeJ}=p?SJx6jv!t0UJa_Y$z|!~(RR*{avzh@M9-gyn3A
zol!~qZKn0m%ID!aJC*-xl8GRgL{@AvaTw%{fsM7u*yVx+rkomja`O6-{fpp3$`v8MQuif{5FFPCzFvHkf
z$d7V|50Hi!v95mId)oVD*Rw{fCeG?1yi`j
z(4--p09U3Axb8&^E9BBW%q>mWVi=HuTycIDM*cg2CqELk7O&q)rVe5mD3_ySk&b>x
zUWG6JzrMaQEUGWs`wWeAiZoIp2-4k1H>h+-cQ?Y&(%oHB0@B@*LrZse3?Pm2p8x&u
z-fwq4%rnoNnZ4IpYwfl7S@Byvs!u$k30U3)pXbB7EG>svB+SBq#Gi|_tv$hG@%8ur
zPE&r*zS9P)eHcIZd7_kSA9~%FEN6QaZlMZI$fkSAneIfE$-Sms5A&CN2DaC~u6N((
zgzJ;eJP4oo-wqiPbT{GDhiTY{8Bg2C`pDAYJ;=*h%~MO__DxIG9BrlWA3?c82`%?`
z`2?3gBe_P3a{#L=bo7TrA*QW8X=Ze-oh@mgH5*SetG^)^y5#@5D8omiKZ`#-qca{M
zrBOq-xDuZeVJ4Nd-Rk?2NB8FH>Q?DNRU6PkH&>$+0}wJ2IyC}%9_|L
zF#bxH@W<9YTG%bt(a-jov$|sI)6W|4(8y;>L@!t(q=8R;Dun+}J3L%we^Kl6v(@DB
zn(Vr?MCgU^!?Y+GRW7YwDvuiGm1JArPwm(Vbd-VkDL0mIm2q&>dw3DHjzE(HUoa3*
z^k(OVpJ1YALg4p@p#ZI~vB0X)(9h>irFq`{oQp1p-)>_)bi5?8i&L!N}c14EaJv1(QVh?8(?#}6)s9(uOdf=Y>ktipH{
zb91aXCl)vA9Evrunm$Q~;W*etsf-2E$v0&%;3bT%edIJ?H&os3b{--JDi%amHROru
zzOuhv|8|LnUz^|*#Qh^q#z+SV3=V(Me{3g5fmOpk=@N)i(l{(3+xI~X(vA&i;~uR2i*p8I8vgN
zSWu~Qw*YkiED_@eblo+YQo}bfe!p7Q`!+E)>5GCsM9)7=MOkQDwS23aX?-V+qz`OpCpyG
zP)qDqr+@j`L_2IQzRH00T27_`9RXWX_Z@5z4*w`4w@8F`~wvZW?DkiJamn5vZlE624XYB@0S~F}3acP9H5N
zXZX#>{w7=gNuuTg?SHr@9bryn3avO}M<;T~-I$?mpR(R~P)N7cjBdnV1qIGR;Pf&$
zi3U;b^xZ%jkSNf{$eiHx%k%?BOqP`Jvh1>2BH=KfHNKmJ4Xmwc*WXL94aa_857{t-
z!G{RR)r{HKtDLQ>vz5M+3oAi(zTRlNJ2AY%r6*H+dc@9rof=8kF?8UYOv#`rU2tai
z;r1J=LnJMuTsCZZt5i;Xzt;V3Nq72r*QeV>pprfo$uM2MLl@#>BSfaBzB)>mW9_Ph
z;cG>TYwBK}%x?aw4G^xKC-PjYKxHEq~g(9Pr@G-9q+~M@s
z=00$A{!6Lv_(5T+0;1XoZhnITO|OnNlb=7p2f
zF{$y_pPR_N2JF)aSJc<@#@X*U5w1DBZ|;9~6V-*8PC-g_-PI~Vn4Fv!Y&$b`Se*hP
zP4vjqcE{%JEhOC}S8J7la|WxAbkiT-^%ntR_(b>4mMo&T-xEcgy!!6G-EX1MO>uPV
zUFe7gbG?uikmjI{>B9XXsaA>5ID?}>!;Tx^>k|n-l%LPL7FKH#l-JFd|e)wWhB=@5u?;Cd~X$N?&gu#W4FH8#0
z$MqY1@RoRa&&R`%o+j%PKkV;6o?@Y_Kff}^F4XeQSbB02_N9iOUg39Ll^rA}){EHu
z!vA-0gbB)V260wdxsVw&DU@UrrlFr*Wz95PDQ~Ymu1HzCTSL*A;g10H6=UVvrdfAy
zCK6=RW1R)3!#I~mDo$9>`z?7Dn?+$tuRc-89SO`d2tUIf!!wlR`j$t)3XoJ*{nnj7
z+uemxRv4u+nNds_Wj^2|1by4khdZ=KWj
z!S?L_y6NDuF=egmM>fTd*{QFrdsVO7!E|7Zb#lE6wnFL%Z_sL2{V0UU%_gk6qhAp{
zCW8)=CRtwbzGOr5t~yHHm9Ae
zu;iHJ{g|ao_B})6=&!lc3*dxs9y-@C_mioY2Pe_giuVi|+O2(@h?-@=-M&JnXpg*5
zO=;&#-Y`W#k@;(I!r7*ERfi%AExmAe%3-9qt)Y*1jWD#>#>aPCspT&E6w;L*)a
z0D-o;LbFm_PqLW{;!s4(^*5=~Sna5w0x^CXui^IY7)agYLPu-FGRl__zh9`nO
zGnl)Q{P5JXDnZ5*s2Kt9zM
z*jNZxFL4=1%me|c$P-V*1gJ0jg3=qn>)<_cP$hqpUtQWf;H3$7)fTL|DP#x`7wMxU
z*KnA)+#Hh2swss*eu{GrjEVB)QQ8ICh?^=or-VE9dd-zAq+B+Hsx-d1%RfG&iB7{*
zBWaXQb6c~%Br|of89#F(Y@pOLT_i1h68pqEr{$lAwwDzsJK#iM8rKqPoh*THK9@I8eu2N^a65xKw3oqz+zz283=hsmts|I)Ri_u%
zO}OJ*r
zYpdh+w|Ty^vDspaP7jV&40AwCzTSq|pVLjf^n=rdiSQh`QGK3B5%)W98Ik>umUP+q&I3!2C
z$SLbb8m8v9c<3hS!Ygb$j0#FtHQFr8c@~l3oo;JcqEYYWEd*dW?$NN1kD^w<%$PfXi4yy)*<
z>oIO6MGAB!vRLTjyb4DpCkOQ$~qZM%oTc|f7f3X?-sra=#y+&{i7b3~#%nEY!ZsefAfI*;sw
zQ_HwZf!o!csi8A--EYW>w64+Ipo*i7z_!fMLi`j4(hL}vcgYfZ#!lVH+%B>}wEgTn
zcMa3Q_ViTA;IcYfX?d^+0_zFSUVn(dB$
z|4jp;D3I$v6Dh5U*fqa#$KjhVmAQ9O#&FJU8!YV1egdAeIgJ7sqxS2pqYF>H9+g`K
zG#-`lRH*5^j7D)lDzo^UHhV+B3}YdxoBy9G>i5=jzb1cN!f}KIirc&FKF~Cv)~3v7
z#X_gXph4GG#hCz$%4G|)jz>rlRbQFWMEv*PtW3Xy@@ii?{%RWwIhh=RHMbB_bx%r@JRNZjI&eoBCho+vQPm}_GvDGc=xc$tL2$SPLs?f
zx9Qk72nY@5boU<}q6=eEE%}wnRZ?B+4=sI5*Ui)3=Ljs;?NPx+kEX@{ybj)GP))_&
zQ1d-n4xbxyT0wF&+&^3CF)h!?U`L<>wdYtY%JD=>BxIYRxrAhSri?Zft?nC?U_fC4
zZ6ko|WJXP0X;4YF-Wno%ark?O^^XL>KwGO2t}^wpCpz_J<{r}pKu#f4OR~4WSe3X_
z+`+A!!`Wm#FIOwG?Ec+7`v$TMbZf*g8pn(mw#Vr;tucSDYTu3#{4=HJbK0N;qRW9w
z-u(UbQ}Sm6WBT>@tdNa*-qPLTyBo63TWMXl9xDei{4%$fSwbmGuEZKRpvqg%qcg@f
z%&lEqmFbrl8BK6%(O)8}n9?eOi%B_+I*i;-Km-AInw|n&aO_cv#ENMEFjG@FS1%;7
zk(8!!LmPO{c%x&TC1_CKR4u(xbv3$)d3^KZL2yY0)K04(cL)!^d_{xG78^O0DQ&Kq
zOm=!256Lf9ZazA%Bv8TsuWU;kO$@DbUtcSh$3TZJ@pr8KuL^h1qc5fGyqJ_J5HZ2A
zO2J#XLBUn8z}=uhS{llIjbz(K42VqwL9k?m4QG#N*@E|qp)BzAD1ySX3(aqS3S17a
za{0LrXqG8=&TN5EM{WU+*ZE3$azN7$+6x3y@~B2oO`MgS;~qc@f(87?-_yor`Bc^p
z^RW!^AQW!9jLYiuiuX$xB129CmezAb79D4=$-5VJh*Kltb45tqeej^U)_Igf#g8wR
z($cX2IZ=FaBdr%=5K*rmSMxE^@66n($-f6ejEPIuUWoA`WQiXjNCi)_>d_+8nlm);
z;9$vpR{vb`sRiu>deX(th6!J@;HMUgsWT{HSarOyAx^AfcUU7TiT%E(FJ9S!kQ1lF
zcP6a+&&pxJLA3c!*3bgi|w`TrgeyV+$#T!B7
z7G2WOY<(?-)ZsIC92jmnaY}8QQB#1}7}62nmD{94NmDanNo-0?g5^Fl-!IP{l^wtP
z_TA!>mjILxErNiTcoIKYs*vYmQG=xwVGCe$a>~5R8t&0pdAGzZ+jHnWv?A%gIr6aV
z00@fg@oNbm5fNI)inPZLPYeA65uD;t3~{H(QRgZ~Dkl!*jM|-ry#DjiZW#@7Y-kkN!LU
zvX~l(?yUD7A1L+n$P!H;bsa>eUA5x`g5c7r+gbAmTQIT8uK?V|ATd-NV9+=jOku6`Rco~hhSA3~w
zHz{m+N(9bb82O(@6cG(xxfs-iMfn4*cN_{Q>9|mKYRZ7fS2Fl+OM@>vlS}7WJMWaV
zF=k}qmfz2-`=ao{i-IP^nB4_P(v|lcCj*#cS4spsrutJ4E6x5cVAPr#kK)jIT9})I
z2PDt&EPfDX5ovvTt&_FU0Yp)Yi3~#oW)H(7rBzocn?3n()mJHrk8Br@JO=M+1MMiw{HfOXc
zFIu<0d1DbE70qgqo!Txz9ZxM9x3Svg{|sVi)`FEIIHFJQ*GK;&K~z1jnOE=OEudOy
z0M0&phz@2R()Sgvy3H>!PPN`*$N)66=P_5(YJIT9^kQ^QQBo^qwk^V=>;bN;F1ftwtURa**cb0nbjeXFp8UdI+G0X2X<(oYV
zhZ)R9JC*-VR1$8U^Vj(3^wqpNTeIQ17wEEou7)hN07{QPKPtICWl^qlTzIWnVat_>pM++iX|s{B*X=bTwAInt-yZL<1Ln
z1$4RfS>PZ{&1-@Otb~d>E{}B**xvD%78+#8j^_4@_lBFONP6s1);GPoeFp{1I|K)A
z-dhpIdF#rCH*X6mQak0{ykBgjVt(EM>1+0Eel4o@7mN+YQ~7(4&z@t~6tN-&c8Ab5
zuY`$-G|0}Om9Oh3p{&x1RK(DgkR+a!X>n{NLHKWN*VnBlDPG3JT{L4rsQh5xAfLg+vxojo=-i$5hncpg`cl;JHl0Mt)bz4cWyqMFf(-d0yyyO!hB_;mssra?`4ooF+u%Oi7
z94*)RIxBf4!~;B6K#V|T1Ac-95g_jA=V}At&($PM_ZZIQYh>$R(^=aSl-_hca(q0G
zW;V>X?kF1|V!gZn>)3kmBDcZfDkc~fMn8Uz{|v{f>K?Li$hE#D4C?yx5&q5kdQ0-J
zJSb9#rkI)28g^GvEcb}~N_H+KPtzea=TB5$URrv%Ikn-xnG53chf=_>CyOlGe;RW6e!_8}&_UO~aV9=Ms?uh3*w6uG3
ztbhVEgorZ8vZnJ;kyiPF27iUGwU3XnQjlJ9Nz?Lw
zc=z@6vsm~I7TDIEDDY{^*7mxF{$7?OJaMs0Gqr!aG>$SZfRFmVi0!Ql=^ho;eRFQE
z{ZyidBrzulAZ4^`lA{4e7@mzkx3yW#$B^nDN7X~IdGTs9^f8r`8V=$t^c_O$=({v0
z_a0+>t{Lr2Ubf7CyPCG@$~^K3~KGtd#33=cM(n+kMk{#Ifc#+y&s=AGeIHANNDgZ1XE
z^+M&lHl!~jU*1mLnR%9pjkXT5_2u3MNVv5(tDMEDMD<`i_IL66uwY_5o+heTsdFhj
z=DrA#-i}4x4Su~s#{{Ul<}d4FdHwAEF&o!97a_mEBN=`I5GHv33>R)Lk%lu>hDe(E|vqtxe
z_AJJMo$lQ)aa~!c(ul}|85o8J)SrI6ekw*NGp~7re0l+cZ;3dOku9_}tLU-qB2Pne
zHN&mc$Zrw{p2yEoRq5a~>~mSQbAc`2lRkZU7S_>os5nq`UQXgctZ=ciC7;^?XrjaW
zU|xjO7%Kmm7f9m@I@00l-V+FZA)bghXtLXc|1d1^@6r41yJ~42%f$59xRJj9Y!vfL
z8w~~}2_O}r!&-gjgqd5+lmBOe3I0P_H|$4b*@TKr3y!8?HcFe@FG|?Rzf6>{sS)sT
zjoes~e;F%bgX_B)fo)$cE(Csh*kLO|(;QE7l>TfVyo(H_6A|ZrL(E1r*{t~%ub}Am-SeZ_rCg#3;>uB|Mdts>H1G0-c7Uh
z;fdny@!Zn^(4y9)sz{rvovmcO7vBg%sz(qJ)r_*T)`HR&Kra6nK}=cxya$sig`
z+E+Is=cED@r~P>G2_SR0ch%h^zAtoQK6-6Qn00zGf73+b@sn2c`#7GFpF}
zl_A-A0dx~<>57S<*WUl&H`+5CgCL|_MPfn77EHp$+*nDzJ#7^aP2|-$-Q?yaYcaJ{
zpje~(oK0%Y>%-3d3fQTCOFZ0A-r{BV%}av8Wee7-@;0h_oi}+Z`aU0?by5+
zH`o@tNfG2c8$Z4!Rf~9{c6+pOhGL(lkFu57oZb(~-8oZ#r4j1~WMiLq3EwJLhm
zj=-hlJc&;sguRCAl5F6ia@ZFX5+HT;pPU#z80D{V;r+ZPQ$^mLFO3f~JwmJE1;mVv
zjX!<*1bQ*G*aZc3`#;w&Ulslmb^om(cQ7zi{`x`FxBus$B
zMUW$1g&*{MEu8n>kX8P#FC>`>`gUE+<51w%Tt&p`4DXQWzrnwJ*#dJ4*Zh_P9I9%y
zou}Z-Z40~X-c0BYrwr9}PcAQUrxo^k6{gk{JxOzhE`5K%kRVo4J{L_axT-f@9sH`-
zC%M??%7O_04JIWerK6J(8>{mC@2}7G(QY$Nt&($lN2GyX^tIpz6GOdd|FZ&`3h{*J
zIs5xly%$5w=>8L2a_8E*a9!eh#+xtRY`gaWm(bKd&IbI{_M~Ep<~1WOQba^VnP}44
znVFfD6(Ni9EYK4z_=mtQj{MkT+Hl^e4|@vUV*|foRV`&5#zEpT<&He$;-yWD`lrV=IqAtv3+VDn*xicx){|o^#%q&-_Q5Vd-
zO6%A$wg_VMg9)b~2Eg?n{g1s5MlsJz;%qxl%0J^
z#mtu0)trIsUsFs{t8<jO>kP!y?Mc%5bPez=La8B2J(Da1B!s
zN>$(9sgV?EeM}iPz-5`u(uJpOThe1wWXWdY-t$y#L+0b#!HZT%23^OHof#Ya}=~jJmzU=4y32_2O$k_DR~NI
zlH7ldBEN(lV*!pkL-C-y8WkD&qo(Gs{{DB`(|r?*P4bsM%Li;s3+>q`blV+vU*?3V
zU+Fdm;L*?4FiH9;;z!;9K(`L$5hr14TWu+y^W`6md0i>oV6a_+<&i#y%4zeL<4+H{
z*9H5Px*z%;OEx;}z%6;b77HzDDhGv5@T}AuiE&hKl2*@wV)DcLL4Sv0zvCJ=o5wLz
z*}rq!T}=@#Mh_)MFrK5_z)D?lF*(m=Y-m1H1$`Eei@2d}}Jxp7wQb;vERIY^^fZ#1ahH>`&-TV(2+!Pv!628F60!1*q&1B#s^-YwHq}
z=iB}~k)dr};2#0NtKGruZwghunYZ;`;LWD@F}}j^#U{g1;){f%=S9T{e<%)5WYE!s
zKYV71M4+~nGfUJ(ybf+rd5B-f*n|LF%k>W)0%Bi9f5~JX+-v&&Om`b9^C&mNsw%2q
z91Q_+>a8q%>p=}TCMn(l#JIWd=qPfFG|q7@oCL1_;zpAV+XdBkF*8-}Z1#nb@z|2^
z+LLFtz*jxi?bbUxN^s-};5#dBMmGkP255lk0(DE};F+t()Y2$&boaG8_tVo;)f@`|
z71ZMro$H#le)S(p9e?22WV{8F;F-~LQ3lW
z=Rl2$DtBODK)cSILey_1C)H?vc2>W|*$ix-k&-&XUxgIACa^J@&WVVE3BS02KtXrl
zSQ>j_ef`YdlILsqNw)*j6l82{4D_DFM^I8y^4xYuOPhdJh6B&@YK-?(4v9}ERK|e<
zG!uBcj9INMEsHC~>b!UxmgH4U*axd^P1I=WQaT5|D=I`7D8XlVHQ
zwztl{2vq&}k%)w3VRKG03_CeF86xKCdAHRcIs4brheXKJ(afw^)7IYJ4=nMvtB~)P
zf);+|BJHTCr~nJttG)kA&K7n*(F)5{*U$hL>l0WLB`qaIF5=5a-@hb-EfDbY=X-$f
z>jM`sGdGu#lA@f>(cf6Esffh4BvUxGEM8w+}!-<
zk88A$`n=Kmla&@uCZ^%#<+j>d4qDp2-@kwV?*O`TuC9NY*8LvA+S%aX;L#l6$C)yX
z5hX+4i~gtkD@h3nVphF3e;o@83t=!=a$9n0>i+)zXHCr~u)ZfeeDA#a=FcA%CZ=>A
zy9@&5K^_K`96cQ!9aUAwvlV`yU`=*BM-iDA^5NlOwGJI|+$1G~uq+|3OR!-<4NaTO5PWdtuM0NthLu%WPOhx7^5cJGh$LM7cRigG73l5ljiC}X`1tX9gt_{s
z5<5G4o%t97bZ2L0YikQ+@i@v30wN+RHaVZ`!6c~6?liT{x(N%JB@Qdi@ne>8JT^kRDuP}S1%1fh@x$KQ5B;do`TS3Q1NJt2{
z^avOf(Xg@q?Ifyzw^y>B6t(@0HIs7cV5sF1xyn4TU@XF+{X*K@++0H=@e*O;&*5(c
z1qCuPGEkZadwY!_xKg7Z80-UYg+jUS&(+oA!*MkkDe2jI*MBRXXd-0WceK?uKR@5L
zMOUBarP%Eh8L+al!os#jiix74q5}K0V)Wj1s@})j+qp>}_}FU83hlf;o2^K#sy=fh
z4}J^&2`U;DzHNPXf4{r82l`Nd|A3(Yfz-14JayMnMYm4Q3S3LKdyXeXk*t}!<5d}m
pt$W%|IcA=zN`Z{~ph3MfjF0`ElbBDT3-&n!}GEKp+r`l%%LK2n3b}#`g$Nz~_xcq#W=M(Lqwv83aP@
z|L+f+Opi(coP>9gk`sg9_<)MYM4^dl(F2^KbP>~V`EGCK;%?|<3X&62krfhVAXTw2
zC6zODu~4^nvUX-5m9jHt;?-Eg1Ab5b-|t17ObuNu?d?caENx9eU)dN%O^q2@zIhzb_U
zFz1&p5|nDxY|xX6!4%RG2?<8tfTjyY3h9328^zzVA`GAT_>X4RncHJ(Ng^2TZ*C6r
zwY9W)JNfb4zQ9Ew_*@R8{-K>u?gZ=mTvB!Phc_{InrL2bo<=cis`+?4Z^@)!lR!Ej
z5={sGKYfL$cSvxtdz2&+j(Ulf!EQ^Z-^mNw8A&~lgd{M3jzY87XOd(q>;YX*6-Ydq
z^B)EC)(33iFx(mqjF^zqQAcQUOoCD~CI_3aRLqufrF^Os<>r$LfK&8db*U+s?rI1KO9Egf$)1(YE97e@W4cVQPRUjC-)Bi
zlzr!bpXWfh`(83bSt+xqPrN?U?3unmva88YYJBTAx{VkA)n=e(`mR=uGWd=_70;jzqs!C_HeWG65f!R1k
zq8&!#lI-lGlR#31eC#hGfjeOV6Qhc(DrL=kAVmZ2ic7{{ScP;6eWK;#k)!_->hvuV
z?y=#|sINx}LudXtixZ>v34gopC0SPJ?lMdZn*I7+XV{Jw^y4=qdwnJS5vS7tUf<0Q
z`}NMgaJ2o=^g5fRTCa=02^8Mf2?+_#`{Ta%YwqQ06|J7vdwXLU_vh;ZuXX|dU79IU>xRSFvC#qC
zwd{LIrCtX&H#a`$7rQODJ)|9nQR@Rfo5j&=Stt@Vx8s)RTB~cuH|rGQk4GZQsZoi4
zTIHxZHhh8S#uD@WszCugUhVR@oh<%VEw^jG925>k0t=33a#_#+`9whA{i%edyfb))
z=?6$K03-ZgK}r`=slg&rE1d#vh^;;vv}OkR$CtT0hyV#8~?bULmV9)#iDTL
z_ICi$nV6VjaIdg$xnL4N=z@9pAOy0F(!MJU=gd%0pqLBGN^;ATEVU-w&LJ_EfZvNp
zDwFQh-KloF$K_g^J4HS}76QzV1H!J1Uk~y8Rk`yMf`YV1_=pH=abEr84(JTM2Q%m{
zq`k%Zm0dZ|FZ(V&v$wqUbhER1=b6dAao3jP0m<;s}(m<(f&~X40~_=9|j>P
zsENjglVD2e4+fK9MI^%Hi18{-VGvZ&HW-x1fPfBp3y
zq>6Io5^}%XVEuf5F5q#o5r#r=B?&B_+u3TF0;lJdiL9*bmHtl1rs*g
zxEEb000g2)qW$~>gFV17KRD3gH387XfnffC9T&tEliU`8vwk0kpZ)~J`{rQE=kZFn
zNaj6%hYr@$tLGU|5J69~Prs92w$|PFV|cuN
zx>rm)Kp?CN3gEDX10_f$3PjbDZTx-5U?13n4VWueFA+ygmrC)23xQ)9PAaBduo&}9
z!HDLUyIlHe4n;7Bl|y9zk}7Z3cQE6z-F4%?bn(RMLWgPpf{So)ZsYNqZlZ4(GOuBu
zgf9`$4-#Dz1WDnbJG^~JWccF+L!LzH0M97*I9RD{ev3)TDo}=YUaop^_Z}FG~Z|hUFnZw$f#DKtQK*k9c1QC#F
zW5U=-eh5egOmK1ws%*`~Oz$aGon=4i>7tVk*8ZL3^@*f?8w+yh-%Vvsi0O=OFQ5BB
zJDM&?lYf!o|HT{%22`L>R};gLCI5hu-@NfdVwOx01f`xt5nvlAhVB5f!(Ez<2ZBN1
zM6V7&I3~%ZDkCRkIi6NP8@5q6>U%!=*nVyQbPESO+B@KK~2`0q=|3n!QUP%Eluj{HhiQK_G(zI(d>tU~ur_2FOIAuqsOUC~+yp
z?@4M8vHJO<^B>IhP;jWng4ENl_^5MatX`bwJ3B^f%X{j@cXa#bu82N!C&o^y(I>C#-Fa(fJY=Jv}H=Yb(?KYbr9xT<3^byeIQ1Sc|p40!vxgd}S>To4F%
z4!9c^oj1{TgL(gZOU{vbOq?5F;JhZGuELN34jP|TO|L!MQDWH@Z4lUXP(tukpO**|
z1&s_I>c~IpV!Z2c(Z8FV+lx;l#62@#|Gi+NLN*`(sgA698Pb7f|Mdfyv?rYuz59?f
z6Q%nQeG#W|HKxK{_hIIdRDtGl(-#{d?K{Jr
zFwKEty=7mpn9CQflWPhN14!55!L3J0+KtElIv{yKcL0YUsV8zZ{P>|=Y`HT`tYZ5;
zpv&$qp=w4>qQsmDIMS~Hwr<(`J?&e;FSSu<=dz69s6@TfT~vSCgf4Uru}(W-&mXhg
zc7x(ka11X64u&!{b`&DpG#T#l{ik~@7)otDdZGNMHVJyjM@bX})&tyg3Mn`cjDll9
zZ)ZtoLlhiedQB%sUP<%iT-^RL28kla1B$i5%B0a;bQ>DrzxoDyb<-I_!ZqPy#B}^p
zL1B4ptFfp4wCTp+{=o)W=3TtauFpGeCRV#<*4{wdW_4Lj-;j1y=&Q>mkIqp)+i9jFl!q=4-fgFO>f=uXrDedR2d`MhIMs@Vp2o$aNcK-~DQk
z*jphqhw+p^Ac#R**oE})>$3kxoCldTWW!Dm$6oM30C$L$cY2FHYydMEMI&s8fp$D2
zBBn5UGJ<)~q8mI6(Jzf7qkT%&os>!|z#yo03EP%x{$Xgj)2KavvO%UNDH`*v?q!ZP
zuf`Z5DzM$OD6wraJY4S6?_*(ey*bNOD9SV$2@BA|o7aoiu@2s{86;=J}_Dkn#n)+z@
zb3{w~)-&W2WB7+*^B5@Va0`IVg)<*u)d
zmO~YouJoRnHH-;dv8|__VnLgdfE_KTe#f-*ae0>`w0DLFn}`n03AOCDr_YKD2GxP9
zl~~Npp@F4&hXaFo>|{n}b$RR_cQFBUNeS2}x7W=97ik6TF(DM?AJ;UaYops7*a_-o
z#@^o&7TZJ`hqS&q5*Ip~xXWGyDX*$mhB$`l`oKIi&hUmtEuJeht~ddYYWYO@$#3Gk
z{@yI2d}u9t;4QZ~V?Z2%8!)V=ujZV+j7@lhMYhf=q*?TH;O<7s(rgnUVFb+orWNw=
zcb;v$5bX*XmjrT1YZ%F`kj=aQjiF|o3^%6%Z3;hh{~6|#B`9RhulW4hV7H*2KQ2@M
zkk+`+IET6~GJ<{-D{53R2c1K%>GDeptdw4@r>1
zOHLzBZ9`w-nbWp)2uRs=E}u-<@8b@?K`0=yeiJP}!{l9fh?w8+`7N>I6XV9;HLbLq
zNiafEAlG7AxcYUO+Rgt>=9bV|5-)Nt0odJ00H_#A~=25lM6fhH-*d|abRFg)2bT-3otY5nR^XraUS
z7-3M%{Zx?fqJ@C3HoEcMB@>303i>foVbNnv54aiwbkO$Vv2GYj2m{jci)IWLcb1aA7Ug?+#L+`yo
z^g*jj7Viw}C~pN>#^)3=L39UF333=B1WvZ%)km89UdjOhA!-^Yx$~6~tBb*aZOjM^
z5E&P(YCfg}m{~
z(z{qZ0Nc-BaBc
z9_KImFpn{hYJ}#BAR}$Q3QTi^QVGxRNNDK<*m@vv1#HzAuLa-e`gu~!a*39ovurzS
zT+`f5fCJ}uLJ_o$u7u!)+0EBhIeA6A0G|h(J(B>v`sM4g6VJQ79Yyl{?O^SWkSmMB
zI8x4z9GztyCGs1JQI*`HyYce=4AvWgW8>D9-?b{GCK!U3Oj~@Lo|}<~f-X*NX)}|a
zYk_GSnPEoxS#JEj{5JglC6+2qUtH|8Y|)9sR~JkBByVW|W&-V`
z$R5dfhVz%Y=*=}COcU&qy8E{m_s5zy@YLeSRm~97{sI>aM&oEO>UbIBR{axGHA4C8
z2eNQ$%N=>PJ&xV5_%S4X5Qz)v!Q|d5Q*Vpd(jX6S5Bo)RKPXQ_3bpqWL>wPLL!fTJ
zTs8^_m=;#8Hf;vJl@C|hd2IWSZ}^W3{!PJ7-)49v_`pF5jY?_|Tj#=EI2?A6^8<@q
z)1*zAKD`(y8JeJ~)LP1wAWP$+!6>c*pMh_drzlzY`fKPSN
zW<|V-u`P>0>%JMlVLeC~Ka+ac)#s$_;Ud)opkQkHC4cmoxKPzy${|+5il}(8J?K3?
zST(NMUHR4gi9gN&LjK1ICMcHH69(|t^aGa7r}%tlmg2>|2-s>Y-=}mCy7ZJx;sD8I
z%{lWnuXT&r*ljr;q!(;~@tPHbA^$GA;cEVwjB*>1OfcV{9A6+I&E`Hy?N+UjZXx*8+~*ubxRu2LeZbrk|%?_Awm
z)Am&~8*=#06=Ae;Bp}tvajoGDss~2w!cz&Jpu6A=7ez?P=>7Eh)v-g0HTsUa^$wxE
ze0==5{!-}ueVBg_dE|s0i#IfgQ}R8kpWyA*#C_E!T{?Ss&ts@s+;IXt&PCv87ugCR1z!-2(gxd`)rq=m^0*^VW+f^`rP
z;!Y@tq8~0dUsT_*Kqq5gj+5K2KJ>hDTXZCN|2_|%>Q}aNEr~R~WHbyvjkf)zgBgU{
zxRy83)5wFj!qyNH&08N;BgYz5V$jDbS${a*(-M!7Y2BQIgN@}o-otfTS%=bfWmo+p
zWj{DRrU7;4UmKsJtM5D5_>Qww^VK|Ztel((wW4z*%j8RMrtHdIE@~b$S>}Jc(mNd4
zRBy>b@d8w<*%B2=2#(QbEBnz`0q#4J(c3r%+LMRr{m7fR*$`Yfns&;9ramTMQGKpaAHl#BTCWn_DQXX;{T&
zJM)iMf*+-g&6{q1QU4rd5(o%p%VTp}7T5pTi@LIlOC&M@J^9mnsc^`RXQ!tmrCY5H
za7iBvTmcrjE_jl~Tc~
zm<)#TF`@TWLgKM6lA`Lf=Q$7ziOFgN@ZrCXtBunpf$w^-k?V!K;=DpoffJOB}xY%?&b`&;2H2iB$-x0Wo2mcuP}*SO8W
zV0deiD+|}UEkRh$wVh92e7O;Bk1XX*Lmpm!Md?zel4`0Zl~A;AZ*S)SJjw+BvuxaA
zBcH`&?&bCn+9wv1r<=p=!I-wvB@<j6)D#>6wY8ACY{Y7yP!lgiI&jfn@(UxmJq8mhH&(p=OckBQIP+
zqovC^Ol)PsRWK%_L%-UPx$$Lka;mXZwnQ!Q-x&-DK$wlCv+ifuca}-0RcN^alp_wE
z3IfP!Z&b6+Y;19H5&4BZ350ddk4UC{4Gt0mVoF5#;ML~f1ss?k5q^h20Ph5tM)wDZ
z5cG>-=qHPSOsY{y>SO^Kgotb|f&f+_kb`Q6UhNBCq1E;u6ZH?A1{im%AHcn5+6Q&D
zzqP5Lq|I=*{YqB`ljk5y&scYoPA+^
zucssitK(eCW~1OGpa#@|eZEnkqiQ1Wl=ZerB~cH>60u6iXK@33T@9;wVndp&7#u3$
z!zk;7s91~kH~DN{&HB=n0dk30LR<~y*iZ$*kg=Npmj`h{>BTI$#r99oWHi4QKiT~&
zXyD%C8i;8=NIfPyFgSaUNPNE)@Cicq&G<){cp5wDs(v!$^aj^m(Pk8B-#Dk4UhuO`
z=3%)(9;Kk0y!wZDvb#O$fx*PsSr!b@T3~=aIDcm-#g%@C<|iBszCJ#=%}G0UyoHlg
z^$+S4OjC#-j~pXeU`MXVjIv-GL8eGA4rZqFjJ;WqZt55#9Rki&uP}OT&8DB0a(5lJ
z&O4z)^&42}!R(YYz*-~_DXJR7#FK#1LG8*#GIE7QG`ud2w9CtpSbp&4^?tohrrrPj
ztt7{9nfGUW{067|7mvIbxfJZTvuay{DPMbds6b@I6Mzi>B@DxLH&$OngsdqknZALM
zdulE9<;ma}9&q=f!qEXb!n6P(l;arjcs#VF2qUKz)5A|NRBE!~lt^$+BW}!Br;|olhNx4|lja!el-_aep2q4vw_m@V>`&bSViwJ4
z#u&&D-J-h1ZCWxqnPMy4huwZ^4pcwt{n7{E8^&^DRn!&$*?M~y8BoyyBOK+gmR$cm
zbb*S_zN!#~Y*g@?+mw4~(!4+Mr*`Y<7KxPkdpHRCzhE7j(*d@_ZQXNWbQhE8zE#>Y
zw--aBOu5Ma{q??S-48loz5QxJ;GOq=)j3dbBas>BKfc>8V7c)WtE&Q_R=G=K2i`$Z
zn2meg-f>Y#K-~Es%Tu-Wx(gwKeV?{@tG%BjN8r4fi2;Z^%lTj!979MKnPA%fPukIT
z;hPF0;jru}{=CJ-U-r?W_QzZQD8()-9;Fx(on%hU^MCt^1yB4XKWR(gujtnL6{apr
zMmTW4WKI#o6yCuxmFC2~MXL3B&9dm7G+XNNjo0lputpBpv;EGRV`}8SpJ5w!o}&Iz
zS}pRNCg#Z@QKO6sKs%93^P?HsTr17!RK2Um#epK4{eaF6NezNpz>pw{nDs-MFw9Te
zh4P-?l)~w3BwT+vOT^tQ8dkNcDKW^L`1Q?tsn&FNBsGW4a+*aXg+X(=!Fr)yhe5qk
zhsV8j_Krp&n?ao^bSwbKRQ#8$h{SsQ`YipU+S0tTG_u5EsKQ7&3;
z!%1T(@cNc$oPL-N+8@^jUR#OY$XsT{YbfJ<^-;xZ6#Gq(lQYNc!eDucgL@jbtXc*p(llcKh4(HMxt1{>`DO9oW~c`RU)rc?c0=;oxOqq8~F
z00ND?(M6N5*nQ+p<(2ow$`D|-9@t|(ju5eiw}XNK+%kmFa@CN0^$KnRC2mUB%7XMe
zwd{FIlht*G3FN<$vQtDgP1Y
ztB|7-6cGH%KKTRb;Q$U_Pl-LAzcp~o`>8%iRxBsxMpN^oC0#uAqtD%Y0*UC1
zR*0@&t`yQlR@3xOqG1o$FE=!RTrTRFV=J+6N!2$UjL%;t
zDsB-oQ42<8Uzb_Of{U`S!$9C%WQY}L#n&d-EHxt3iaDm_BG@0jPb~fMNvsGORWBuF
z&STgKJCJv-HU%&2S-PjlH#=So2z)9bVyNK$#TsY!-MeE#p$fL?RPK0
z0AN}<-LQ8kH(emjfxyvMF#Ak^Ad5@P#0m2O$w(r_CDyPp{h|ECs!3%wRBbhOf38*^ME
zSfLfALj-#jdZUExW0m!G5E=a6$SKR3r_>ST(DUB!g*F|g+CS8pniH=6^NZek42x^Q
zzdDpSK8|b2-fAPIj_n6bre%^Px_t$P0b(AvMJ5V;$cUqdk4K4)=`x=kVpi+NU#cIp
z&)i5%+`f)?%=|H$mYI#Q@Y=`Gk&$4Bf>EU-^UmLqtEFK%IZTK9%#;^xXx(BGZ%)oq
z+~41iNhPhr^0-9xL-S{3ax7FGf+`pJ!J2_Xm}NbI%^OH?1%=&T!t%k$9|Ss8I_mhF
zgXPYO^ZJpnki^+&^CJl4wH+>Ir#Mzm?0UkO`vguKNM8B~2Ipf^nItnxDa*a!Y$X4)
zYk4Ck@?fn(9nP&Rm|2#C%|$EU_;_q-VG5`u>T#&EM1jum{gy?ykFIt-HZC$u;h|bo
z<$Vp21UJshC^geKH)}A(G>)k4g;b)879gJ8RtMM71$gaU&I^<&%Nw3V+7=t#`OL2?
zc5->!cu@rkE(22jWgc=h{X|78b+-uU=S1Q}PyS0{DcH9oLrXE7#$=cwO-(0_5-JQr
zCn3qP<8WrmJHKxBj!R+N2;XY4>MV5StCiVr$iz{iri(RdQ0}%!uOON1+Y7l7clX
zF7WnIba8Qf@hShs?di9u)m%^Q?c4|dv?V7+dk#LvT!?8S9SUUwE6FPR_QTs&w07)g
zDSoQ-Aw~sNZxVhVPMe*ICWuy>Tg9g*d3z~OncVzp-O>fFO8K80Qy$hK?Ll>rWC~WljHBYpTx?rn`s8k+Mnx5OjMqKpmN+DO@AjQ
z@`EkIV(vHUG8*)}np}2W(7hj~$x*4$`u2K8Evvk8&M2Gv&3_AuMa#Ry45~vEqEGN2
z1%BmmwKf6*!PL{IvfY0fP=3ifnCh-XK_oTvG)ZLW$)%Y3#(%^pno5$`C5muxivKW8
zM_SlqwSio|M&Q#1$KMqODT`&UL~Zg|g7WXMnU&GzOM9i9?Cc)$-&Hl>8;OaN`b^B*ts2LoLsWj!szo!{n*F%3?0msQ&5=K3K3!sZuC*>
z`m_5tJBTPJ9&tR2?dl(R3Y~3U6KD|M(hvXVNYF$G{oc_n9MUC&ghs
zP+ZvNI~Nw#XPz2x_Zka|xM{}Ql8dw|UMA#E1an0q^5qb&`iypvKUX@*HIYci^Tsr=b^xy9du{jtfDBdeV;_NnLSeJY9krBtIG(^g2G%?e+kNuRSkM_B37SIwe
zH1kT&o7Fwyt|kttid3jfYVK&<#9m(V2g`RIZe-em1z&hz3v&22
zq`G20(;&Kix!b(RyDVC>XrDgH--(%VVMwA*c0mhN-mvJ0u=NTJtZ=I41>5?bO;7@Y
zpPwdnJ$z{N=r-QfOu@rmVMvne2he3oPbBr*he?cP+MgbBAqk*{*&^4
z)!62k6kTGkxxBLWCggx)qxw)@+dk=%#15VkNDnS;4|H~@wd~0&e=%A%nQSEUWS3k_
zX?p^XHV5z!s|lAdW04XzlWz`0V%i{cE7{xUE?9q>O`y}jt_9-YXj`wgFy05`@=q%_
zdfyNQ`J2M{e+NxeBup+${)=`)e1?`(QHI^O@DSt2dfnnDiVV&bG5+V6@TX1b{+i`1
z%jFS3$=fo=y1t8+8P6_+BQN5h+J8}BbHh%kII>GLyDVnqtkNUtS`v>KeU_5
zo-nGO`T{jKUbjbL5)%9Wl954M+uQ#|35Xx5RVsD3pzWe|q1t{LbOeJ0Wv*GNNay)z
z@x2GuG@j#2W0S?tQHQMi)cLlgaP6D>U}UoaoL*&1kamfyWz)?xGFecj~Qd$t&nh#s}L$^GWXfz9=5ZQclUQThxOLAwMPi#
zci==to_qN=a=GbIJ<9uyR&@gZRqL@rg|RR3yVle}s{hXoLJ}e+GFXDnAv65%sNS|g
zlu?5z1C;`4O-P{Zc=#4Ha!al+y@Xy`vNGY$8(!sI#kR&#pAQu8&Gz+#)kT4noy*OE
zQqtdpTAAO=ZQo1efub300EMl#001nj4N!|a#IwYFBKy#tK9!+;7oC6*-G)MN($EGO
zTsqYjthP=?G3Re)GqcR2Yg_kU_U9)%1S|xkFG6!EMj`w*7w_(*#t`xk*O&lxI
zeUwNoER=XFlHYVg#_JD>wk`s0hmfemXR?S7+xDn`l=
zvX1gQ^tA>SaZo0nGHYxT3|7bymh7Gw4?w~4!L=~XG+utlK@E!Gw9*@79=Vq81S*Jj
zhLb8&C9xqi@|g#ajyA68d@u`?7#B4gY0mNg2Sa4Eb31}
z3cl{Hx@&g#5NE*7ffk+Xc>1(FoHzvyKKBwPeK}qyMc+zmIc$exEht1f&*XBpTWK64
z@dp&jEOg;@kPg%n}ORxLNeBEF;)!
zAYrbYv1eEJTS7V47PjG9XIqV8Yywx{Clj@1_n+J8hU5PPrcvt
zi+*KH|BWj9!Vi+UANJgDtZaR}%gQ0ps5t>u%;E7d!D@hC?DW7_<^pfJk#f9{j+Uc9*gG3_vZ;19afO8^TMI6l>VjJ!Ol&T@-^WyzvPn9ZYCko4PGVfuh
z-~=$649u78#SP#M-u8Xdf9IlXoYev8?Aenx+k>L4DX0hIgw;GBI+;S$%bCBvy5ljhJbQ{m*9fuTQDdf_{PT9$G;}S->Z_p
zvq?02tK})$)5kp`WJ#|yJUDe~#Py=$++JHW5-9T2=Ye8Gr;;
ze|~1o+t_&Y@=?jWB!a{$8==VOC4N2$ryPJXvWdtH2N_%lN!M=hQ`*zno;_Qslk
zW+woWQ^Hlz{6})^i>t?lFz$D+$ejDmejBSUakYhVt?Vsn+oe_-?`Z!je|bp-
zj&3xZmQ+;wyoL_LYxCpnHW`>}-YXwIpJN`c8m7)Vm5WDZ-MbaX3zkoxGzj#QO?V3U
z`~=Gm;u`p-#l_W$Q$Hj!^b5)n5cCG1?zwTs8{v8|;TKy*TyRk*8Lr?MFYMPrt0qi<|
z=iA=8ru0O(wd*%7>2fqZY6<-c#+D`_`ke!UhC;uyT~(IehI-zb^HQf@`|}=0?*e~<
z6#m@P+JfTb3Q2?(CM-s1A1S@_wY?wc73Fx^!Gi1r1zNdvQHURDOB24-b3kZTb0PWkno%-H4t2zT{7Kv2BQ)8}
z%BJc4a4iryD^n{2@f9`c)mUsq;E2cqC*{WLt)^u$|LkO2{SbkjyAh>$)I8?65gs6~
zPRO+4td!u9Le%DT>bx
zZMgxZD#}E&Pv9c~q%WH0wt()T|LNyj&h$}J`1X?vnTR_`rtI9u&Rs>83v&PL=yZ)t^CSxgwFAlhJ(aKG}A*^BK^bO#UE
z@9%hmxkJX3n}C$nKZxVNQNR}f6st`jIQD7<`&IwQEQ{bO)j`DnVchx$JIwc1nVIwpw__&$c7$~LK9h-rr44!xj+jnC7^aKi
zL&PMtJRJg`MzbH(_9@*|76C>
zoi4hVn2H%%;8qxdg$?ujmm79^$q@cF4f0M<+_%Ba>oI~{znB)@q$lL`
zasQ2s@8S$cw;49gxSl@$FFPnUVQS_Th?8!~_(R=xrZ0k7LsY-919yFXQmU2c-8c%PMR=urrfjIhqh4?-ZrBR*0PeY
zc(8svnw1HH`R-@FctWHXn28r}CAmitBX^!Efc{(2;v~$70A$5s=fk((d5qyzzIC5U91X-zue`m|C<7%
zP)T!`)a2ejdI1na$Bcp^+D&3$AFd)-$c~S&4h!&%?T)XK0_olNo
zFPhQd_NXP8;!{NAhp7E@#5(kwSY4~-vzL(r7Ado(b6-;+%C9mQvIX^`&ZO+l>O
zd;5Kqvaoe~Mz==y;ux{_xTN8cu&*PVAKl;&GkTfAgi4B&Y=yoyB^+7AOFg2e?}Yg*
zCC+Lqf_N31qL1)Xs>Mhe6Z&}JDG?+988NQQ>^~Ad#_*_0H-Yf)9%U@4!GJ+F4#zRBPz!*gaAsWAFh%#2)sh>CZ2WReBC=O+N@?MCSTNl{YyC$S5))
ztvSSrFc1b+BO>t?GrtUs(}HZdzCNX=X{$^x_R!qmg1xVeW%sYj;qQkWz-Ce(FGeS4id8*8yx>(oTa2O1PZ@Y(KEFHDjx+&N^z%t}awcJfRP25c
z$|`xgnJe3O14rrJjL85B_0}dbXKYvK#fIs&lSE>yKu>P>tHhYGd)5FfQuLrYs+m>uO0;|zh}w)F@V
zwRuZc#$k+>U4wmhUeC9&)exAYxkp51HMT!4&_^(5;(Qq*2jD|WSWpDccur;>J&W(o
zQGs%Mw9~<4-iv*vES?_Q?{(s@EJLX{)QXBiqpI7TQmjY(nyP`colC_kg=EXe?
z=m=9Bl+IYgi=jqkKAb!|HsQ{SjM|CEk;hx?AV*S0q<1>Te-cGZXd59*f|7B(0o!|j
z=fI`T$S*I}N6Umm6OLo>Z;XD6*>a9M9K{O6>y2JFDh*=1Vg!0ao3^H=7kMQF8{ttV
zYTrMM*L;1ScQ+`|l%crn3Tyxt8f2{f+>L!^%$?R79BxZ7_vmmMS@5-wFfd9sC|3l9
zC)*jLNQy5Fnn>apb6X~QmyE2U-6LhlFiq*A{UXQon_)KN^W
zw&*zqAiKTP<_@$cdAbrz&hD`EuIzM50Vze}I*TuIXcqY$*J{5UnArUl;)vCl^&#}4
zT_FY-^qxR|zctoap-lqS@;1@P(X^YkrXC?5lkA}Rv!l$eH&mGZzx-`mgNGag+Ur=f
zt$xijy`S=o4!t%ClgKJpj|uRi9dRFTiPvk@ZtCZ37Li@NznKycOZZW6o$}waHd4Kg
zm+;MNV0l~haD9n1Q8jGur1Gk%)D{?uGuBSDQ7;*mfD+72J#G#kwF#Zyc@c@+X(R&*
z0_RRMw_2qOOd1%e{wr$ve;c*l3290&Ap2o1PCbweP=Gl8j!>&R{Ko*d74p^Y?SIYe
z?Rj&26yMKWXHIOUSvNFSWaY$fP(CNn{z8CH?lN8LjZt*Ubaj_ISjKw`i?47?qw;qD
z3mv>#VadZONkrT~%sS>vGMrYRZwWACaHw
zhVRK@4KbhB%hd=oP^E(k7u*E#I_?gHwx0W)JQ%gqsGBDo3Ur+Tl^h5C5g%QT=d<{{
z@3@H1r#E~NI}GYCQbd5TbqTimo?4HM?KdwV^EZg01w1o+sb+5Fqc28x53pGkw4S-6
zY%{~Y@6&aV*ZF8I#e=rI&1J+Z8Ms(ijQxWa(3;v2Xza;o(o}B{K(m!p^4U_S+FL)<
zoq`~ieO%S@j2G8caygl;-_YtdxmfIliGH>*)!~P^fxLQYqxT;PbJ<>=qXPRa(I)Ob
z<91(@BHRGMink?FoIUxfGP49wl1HU&7Qdog!Boba6O}?AkN5m?JpnX}y)NkcV>9V^
zELIx=bP@s#6d;|fv9mo$qrqw}h6AEesz?U<3lIupzrGn88_zssF#u)iB|Dg%;aqSR
zQz%THij#RtyDj#OFoE87W;SgUx^k$Bs+@dflt49BlFZRA=A18yuP4x&2r_IVIx0x$
z7gpu8O*o`%BjK{o#EbW^2MWhT5fEWMZ!D!ew{5I>+I>3w3ggv`y>flyKz~mx3x^za
zQT{2~)@ngnGOM`?9SVl%Xcnp~O-?sMrbriHv%W9vB_`rMf9s0AsB4L~Mf_X-Gh=k~
z$78gu0<9kIpmX$VfjvM=xnn^0J2*IP^UeTMYDw
zU=)8c5C64wyxmW2m8=Q~>HDu2?HuU-a-QI8vRiAloGG5G(ti)d)zhdo86Hh%1#}|t
zxRX?`a{|y$|1|-UI0KBN_zDkK;mjLT;U;zuxzC9=z9ysyp3wAWQrxIsMxR*EbFHbF
zs3quk_#JLhHLw+^C?|1$P43e3Sv2I4V?qQ6fu?p6?G|}I?57y1$(#8L8-e0YUJuQ-
zLBcCxJNU505q46HDDoNO0^2v1v&9X_AEHX-tcm=F%4W)RWnQe{_*VbHDu=`OkY}=d
z6lRzIB}PR26F!GCs_*>fFLboS(N)0c%3@WS`gcN(|GI9r^RxCn3lj|XtMD5GUY(Cf
zwxwEpn}1|m;fWtnHgTJL~%L2Z#Y{A3jxeZZ}RqZ
z^B{|CZ_G77L?0X+eEwccDd4&{nqJj$w|sZHl0?!fnEusx@M?D?9F1h0MBnex6okj?
z;b662Vv$)gaMwxu)e;W$0rTCDpm&-VByP^)9L4Fy=t-oGwv=xGv`Rsl4awe)X)#ck
zmlp_S!a)Zh7@IdA(jO6h=5907dN2^w_2(jM@Io+`pHnYRv2_?^1LdlrZ360fBFp4n
zi)JRQ(EB#=+jX`PTR!WIKd4ehrWO>z&HrP2{|7y!3^q1{qLN2@LpNhPt!1_#oraUc
zsB>tRL-^9DNPw8)c}Fmv{iVxx>N`r2@Z0MEDE`7CrgXF1R3=)e<|JL&xmWJV%2|R`
z9o{)O#74>hW3K&!nwL1ie%JfkvvpNR%2rtn0SCYHo~+idPokotoUsK0%Z*t^g8ZJM
zuTD!4Tp%>V*V2q0eLIF-!I>(-)+z2u*bRK-b3K!;EfdX29yrs6Stx^WTo-Ca+a9e7
z-#j*R0RxEap695}(TZro9H-$f8@kKEB_30Tv&7R-fY_`6oG`mzdpsOlKN{}T3<~Li
zXp9abSOUogO8%d#cUp8OesnbS=T>caIj5C!;qdUjm0INrV`RACcm_i^r9^slraHPk
zz4sI|tz00+tmx)0hug^~2&4V&(n0*Wb2I#+4~>=0`;GnkEBqR19v2T+6-
z@2GlDRoCpTA-aphxcZF&LB){6{Okia%D3LKTal@C#(ee)UFF$u?alhb*~+5h$R_&;
z{qfw!3Ydu`bLD-UhXI?TSz$BRUK_-A*6eS~k1t=HxyHmhRC`wc+Lz`mgpt@ioC?7U
zXm<1H9YlyfsjZEjg2S_|4mxK?4^ccu;BAw6s+EF>o=!c1rkCa?=hZLN6u3JB(wu2k
zU2jhS;Zx*s1jN!HQby6g*z{^J0m=00S4}|2t6G^d)d1`E*484!gok7}qOS&Q9_GT@d{$h8~Ge@TkVMTaSA|R04|wk$d)~=M9#)u
z9@`-N4>?b4|Mn2HPhik@@Ap}-M?*)=K_k<)vSjNO(4(!YR=}D37^A~jL6a^f!2v~C
z7x@o*K`oKGNv_sm`yg5~GL>|*^FCiJirD|<5*8T;i&l~JWOlkpMja@&?Eisb*#ddG
zJ%<0kJ-evHyqwh3djL3bS2_|yCfK*RZgN{A)*Nft_}KdK;{USm-A
zS`d}=U-0bD5x*Myxb&x>(Y9Bai&l3##ivGarh;u9?$oc^8CN#y52Jn3E7>a?mt7uP
zZ0S+QH-DzHOkWC>i%HK#*<$X({Oj9i&^3?(XjH7LbsZZjcyq
zNEwjs?havSkbKwQ``-8XgBk8z?ws2D?6c2W>j3C30I4)vr2`O_feHJMjEu}b0z0>*
zGLTDavRz2>Z%WeTB4|elTzF73Lqw5o{8^1%{?m~uWkE)VHVc8hqzvuVUhdT!3Z$l&
zj|H4m8oDH^!>?0SrZai!AAfUZpq8{l>g<
z!)aLeb6#~uYxtXG&sR!|R4)xP#r9hbUHLW!hPd}F7kQZjCeS*$yx{(icL0>O#Hcr%
z2dI}?$!JAQ$6~2U!AqDs{kLs9@BPPeU<=)eOYLCg$HK##{qfppW40F*f0`s}?GhnN
zI|c4N-y6l&0Yaka5*N1YMJ+$`q`CKcR_%<2U>R0|{8BNdiHblL0)zLluUYbIwv19e
zd+TLN)_f@nS2m2!?2ihINQi9d@<0V2eot3cksVntV(%&ZAjE0Ic`_uMJ>?Rp?>t{T
z4V)@@4=J-_adIpko_-PtwT1DzS-)8`Ts###nyqK*-}qeEE3>bz)Q{UmbGU@eI_aaA
z97YU%D-NW_RJ+;!I@VXju1d9<;oFm>7*(7}ZtH@}fWh6rP;Y^ICd*lE#(O+MW9JRp
zzxtLgo*M^bH?ku}o};c>gLogCl6ZI3idm#pptWE0Kbw#=l2_(`{@uWMT>242n^t@Q
zyQ@IN=J^vDLq8a~;6emKE0g%7D~t;&1zxxlN!@=rFfb9s;ZGrSX4APGJCfF`gnJ6Ob`M>>{5IwDr^1tiBT$v2h(SSvOpj`G=wl^Di)+XP!!R
z%O%mW(1ZYXhICvm7j#Kk5vO`4ynq;8N;idMkRU;S0s}jC`5bzh|3*B&^p((t$7@jF
zp*!rYf%2nb^Cc#bB2DRf~XckM`
zPg#N96%)ff;GebuYK;Rjk#fdHuTXLi8OZa4@t}Mjd*wCj0*~}H6Piy(|={1GJ6b}b;&L=m)tvd8w
zEYBWWY*naYv#sQ9OjJpJ&?jifp46c+bk}%M*%d%_}gPW%C4yTVVxNi
z2){q}z6L5n8VT&tf}U1_L@TP*d?NVcOx(9#a_Q#BsRxVQy`eNEO;fX5jH``o-XEGa
zAz*$_5~zD_%-)<$OPm|YvBOMPn&#W~Lczt7fnBjeQtEM^8EWxcdDgw9gU52ivZqW!
zpkcyfqoSSwyC+p0>7yhrqmXDuA(Y79No{lNA?y+vbU;Au-Lh-_3e!CT8O?+06Ojg@BuVg=daJ*A((vvsX%WdgE;;FQhNSz0PJb)_Y{|rzB3>4Y}_3rW>+<4R<^x
zX+tI~EllQLXJL|d3+NFiSo_$9#sP6k3E`inws9T<)w{4j7<
zg_@*iqkZ!Uz>{NM;dHOmU6ClC#hUX~oWXjC;4x{3kXwF0&qHI+VKEWN|7yVEZvV7%XYvL%?y7f&Ga+-|!Ovw;p;gJdnKnJ)u}LtC%uk06ys1
zI@HEr2haH6v<@Jf&_;t#29X+{>KKBGESGxX>yVQtAioPp%V7ozShda#U63M@aAwJ})(C@=$
z##eer39BgzjR!($Ug6OUQVk(S2I1xCi{q@I0{3@LgYqD0xd=mDUWxMbmj32qyS5{l
ziv~cqWDZa)N~&>wS~}hE^FoPWO(htMnH2Yte707hs>Xt^{IK)Xw?>MqZfzgWWhQv+
z<^>UGQUOQ`?Uc!|k=(IQOW}jEID*wVEw2`N{eEl@k_R$hcU|SG?3^?kz2uipY1?WY
z8}{Y;d=PKbKUlMrTI%6{N~-77=h+JM43DDamm%R-P^)S{;6av9HRK!qeUA*6bAS-`
zXi=6S4((T)tFO~z3fXLljAREJ8bsG~q6zEePUoxBkHa*!gGV&We4{m7p7lVc6QueB
z)s$@YPm6`MTg>J?iJqI+0gmd&PPBDus6M8F7CLnfOF)3|W_m<-HS?Z;3g>f<@PAJ0Sz4WvjL|0QQvy<#P3?%r$zuLUa9H%~71`Tz)H`s-;Eox$PFOyf;ptNlA)SB$&eERA;
zWT!btC`I6jF9slT1qH10!Sjtcv&fC8Zs8!uK(9R7>XT2rW-l4kEo?!jj5cj8lc14}
zddfH{HzG&_lEMGVs7=GWi`sme9i<97QTqU35eBb&s%X{3oB710cf2=>C)7I_>`4eH
z4QfNz`zSs}g{sWh1M94)nR@@~8E~)*3v8%z_BeK^e~3JXmfvFW)TOh~-0m!s$u$7h
zi1jOk3JLW3J*B+mPFkaGRSzo-zi)>j8G5jyZFHai7@Tg5O5o_B^XrSVMvvB^!@{>t^0tyDI9{L{A*5QiE4LSSF>$ILqAH
zyTG8=6-q64@4d>6GH_8E+841#vbs85C!g{Em2B7)dFbvQ10T~Lu%#y_f%}tlGERNN33g0AIr0WXzqi=+B&=CVmJ)EnYpg+Q7ubhsc;uDZ#
zQpLCTfnfD~c4UfWfneEd1iIFyfA61|QV!yTKJBAXoe+1=7jr?279EBdVZ14DrafD7
zh0krk2(;}~ZnU59G5j`&ZjJoXH^R|v;Wo*k
zvX^};y*o_63kB`|YDcq6C~cK878j!yab_B9D(DWN9_jtEcY}rdkF(~A7?09dNAJe
zlRUX1k=GH6n~mmFCBWaE>CcmG&W5yBDmdU{lJvdH8jyJ9Z1(iM%9Uf%e34+zAXAvH
z`s91`Rd2J>@?5f$iTZ=evH%VF#gbT2lAmLQVUFyTQ71)H{I`Qi-Urx(aM2l92mH*)
zt%P2*^vUiisobNl(PYOr#Zc_VfZ60%ga>_ClhoB)b&B7sCJt)Vp`&gE^yQ|Wx%W=<
z!KXPs+7%HK5Rruev+LQQOJD=*K1RT`KNi?d!^M{BmsjSWa~*A6N}Y)8_rpw2IZg#6
z%cjs_Wg}AZb0^h*||ku#Fj;}qQ#MWdBx*oRUYT&6?J2`
zY3+%sSMu12srCmM4VmqUNo(Z$t|Cf79hT~0lnt(_5+eQkaXlUd3T&x(J<6Gf?}1;>
zZ_+N=*YaSUU_)90Z*m;pKWAMr!r^TZJ{(lV8;w|FY)XlvDs7$8jH^u6aO5{ZjB_mY
zMr6R;z~uQaG3;n$FP>Ez{WIx@$Pd5AQg$L>I(hH^ffx=iiN2Y;wv9kMO%k-~O&9;;
z%OPp6QRk^M9+?mJ9?~P;)2+j1dLztwxGP#en6>hVObJMQeuJY6bALM*2|^Vset0|^
zS9bi0aoHb2)wce8_L>v*wi{C{+$TJWY|L|Xj>r1BIW$-=AKU+A@l-4pKL)B!wJdD?
zS@O*F(MCV`_H>lrvm`fPGy*LE4bny0-zHmmtLqClqI>1TxIH3BxIz~
z*pZ1WB1gPaCZc`+nQcOd5=js%DB+$pV-jN;k3@txh)kOu0*Q3=|8P4_+
zmT?hVk7OPW0B8>|S>oUnwdHo@icL1y0bZPvlG$@~jq2t(4MQ{@6fl|LB1_FlfYBDCKG^@?FsnEGZTyW_Nj=BpwbkedMkPQ?_Fg0Fwel#3-@rKOC_
z5{WKG{YvG~@0YKkkN>SNs25!`0wlv4aB95tksky=-_IcA7GbvXHqIRpUl--evHW*7
z)N)Er2O>b>xK4X&o|hl|#=hOi2;cpX8@7baz^_X^K+*#%?+*to@@HDCi^jK_Ezgm_7Xqo71CSJ=7ijU(fxUls7YQk2IgfGxmKi?mc`&La>r&oRay0B+CHsf
zB}hXvqGbTUfuTw>gwFt@m@i(6TF+uMSV}p&v8mW%x|?_E5fXcNxn0IZiC)owDVK6E
zhal!(z52Yi+JXvp+VOet@CMbtuilC7wbwANlbV)&MT_g3@mfRB1Uv5#Pw9K#i0;}<>GgO+32;O<5lmc3vrctS(r{f>h8}66I6g`y2
zkkUVm$=^7N2Dssvh{2w&;U1%ZMH)PwtT_>%d+{zucvl}uE-A~`UPS>5_Ed>;wN6IUn88wf$x`OP7Zg
zdH{-PiZY!BQDe2(J&y!f>0talGQ#!WW#6AAeBv!)6JaTRe8#wSYFqF1xUat-Pp{Bl
z4GRlH#i9HrTalmNW62#)#&-hbSKpftd%~eNg7-})LppzxAU(@UnbVI1(`>VN2>W#z
zov!Bbmi}WZU6BnFkaTIH5&DQ*n24bOYVk(p5G2Ztx{~zFlIp_fFeue*smqy7PdMYR
zJpu@X^kgS!y=ZzZB>qK!miNJ9X#Duy?Tyv`=zSNBFg4Z+AwK0D(Ip(mGc9NNwqDMQ
z>ZuLq%tem;dB=~&nP8}`v2n(dTSHUx$W>6qoOMVF3{Ls>Ei^2QhmWrx21`v#^Kg$~
z8`44dPZOGdhZFf4tF-cE4mIe9WcvPEu-PXz8J>VirM9U$JIQorrb{Kr$d?iS*FRp=
z`4&s&DbHrIgN?Ny3c7m9qp+BvKa*-8Q26yK+#2qH`tnO|PzJA&--|n|*N$irg$1yY
z_NU`iyL&&;A`>s~67{y;|Ga;#-tqR()W*^hCo=uy>remQN73|e*&xU)*`
zW5e~WS0RmNS;c=^;c~T!=|e75#_r>3r*|f>h&3}aD=I0mY1BT5u(Px44aa)z;U<_Z
zbaEqr{+h1Ye8nSf>S%b$AByc4zBjztc1U>_%jfzV-SQbXpGDo5xCBGW{~5O{
zHLRmZP48OP_u>i~Wl8n^RabWi$R**dUor$4>qTtfhrI4CuJ(nd4C%dWL?be!);0w0
zz(w99Ee}@@BxF!c0H`Zao6qc?6XM)rjSg7+^%JZEiP
z3q#Xh|Em)kqPjLLpy!Rc_Uuzh+5WhNcsaWh8b!mnFtcdv9{p*|F)DV!WAHRWYZW}s
z0?KAz&`AU|_>?0E3Fno;bX4~#^4XJs7UdRQoO0KsCd1!CKaG5Y^Dq?O-xXA}Pcz~)
zjvSJ9WKO3T#@i}OQp+A;!eXs&RTD!Nb3}j&WMz?)kzs;J`eLKj)w1}W`$cLhw`5{VYCXu%z*}d6v8>mX+gPBzv0NoNIWk~F
zTu)C=Uq7en3KCqJ9Ze2WzA6EU3pyaiv|3IfQI}n@s9&JI!js;5J+kFa%Z@Z=cecveO79D%=BhLA0&1~MMmM*R@K=zC!
z4f6(E?d>&$LePSC*Dp$K*95r3AzqQ&Mtt?g+~;5RJkJFYpp9#>(dnGhuCu6zhjtTw
z8Y>~9hnWB*kU@@$Z7>Dnf$0n1X3VD<(o2pp<3X&KRLC}A^x
zpsf8}9u33S^T62@aRkrC*Kg%~JX}>rsJ_5(?S>}d*pF~`f1{c-<%6h%WL#gj%;*Y!
z$9LXIv?tmh7g4I+{>lmm+0c}nLy!mrK-5!|pdSi2xFLFt$<|LZxzTO`eb!H&gXEwX
z{{~gv_tPr|yfV0N8cBGvz$y$bfr6)GXErc$7rGcNEds
z+y6rusK^qO;p3qmz>GZ?bbA^M0Y|0>0IEIX@E@f77MLfid=Ov;Y`~Z`W6iEmJmcprL=+1x|I(+B3n>`9>so`sQRf-RJZU
zZpSfJWD}Z41S5YJ@n*?}h?adNT6g6fB1_~VtT~M0T{mFI5=1ja8AEJ4v`R6a4L|lv
zyyw)5)U7&)C=j_YYYv-vR}PHmxkcr?&GsISa~nJEAi1O3zK?%6SKsC`*4)lXZ7MMy
z6?-`CU0+|nI-1+t+e=m&%@J8@a#*XaRAgk8-Mm)rc99+v=wxdKkxhiX2TSXGQ9cLUSC&79M1V&ev{#RH`+~M3}2w^JcjK=
zlE|YmAw7oep-RL!9&(~)NOIg6eHQ9fO_3U{E7i{Gtn_<{S5H1wm$T$+G`V-+XcVHu
z3nj{F?J8&oPJ(`5$8uP~?uFj%@&{(;JV6f}m2E&F3Q0-yJEc5YV^5+krxNv(`;+ys
z-P?%gTF|2l!`+@D`4xn9kq}o&BCcgfdM;M@*xXxbHGVtV3qRm|zAaAR&AaCDPSKtM
zLhnt)(*6@xJG*Y2nC|xuk0xa=kFZ!8xK$Da>_(#*|HED2IBBrWXBoG}K(O>A&o_}}M7b^5U1sN)
zF+L`X^w3@*9RfK{6M
z>Zs9MXY10HZU!374EPGlNr|4Smr&fTZ|S~-3YY!>Sgqp+BcAC>g`jZIhC8GNW_Jf22
zH1$%ghJp&q^$Hw
ze}6gyWv<3RNnYO8-u`5&M9I)Fk3mslZ?!Tne9=Yu$S&M$;q>%WBIHG5LqkJj<6Anq
z?VTO3%UBVOZS~33AZ>^DfQb8dVXt!lkpw^n#^>iPIf(xu27In<$FhHxXgMGH_>fpb
zh2HY<-JGnn!8@NjJQVk8XiCBtBY@}cw!u68#c-8c^#k^wK1J{iwcM!<(J@=_)X=%|)#iC(i+Su9(1GR?5kwJa2%I&{zFL@+07!yuoyN$kMWMf;cGaEYH=&jL*
zwpvc)i#;#2#JAKH0Da%D?89v?QKamp)9bVhXw6#{gs~|D&FuNcT*6IxZ<#dQs6o4`
zrltomOum-ISHBCB6SF)-+aYDcERkhOd1>=89p$rlRny>U@LSCo;Qz$TB?y#{s*Ja<
zQVD?IC@jr{5FfK~l^OQ*je1JvC7#%*E
zRe_#rR*;`BD=XX6)02^z3Bc@zhKBU)!&b(}9RVe=ASBeqZ{f?jEs?6{7z|RT5(caJx<%_h>RGY4o1BIh?1P#
z5YYWBa3g{EHuqy~PtWF+&XZN>{bpYbhy#$(ot{?rQaZ+d^X844o7<+nj?U6ICN*9D
zcwK!Ec!P1p+W;Wx0_&Ac>Bd@PZ;LK`FTc0hN)OvgmQAb
z0==5M`!!Gm3=9l?;PHJiL@{CwzfA{!|MohnYO=PrrlFw$2<~w(nAEwKjZMWRT2)mQ
zCnqNoAkr`FS=((`D-xq$V?kWp(eVV>)6r~|4{(S9fpKe*WuvwSM$#*G!+Cvp0c5E`EtYPkS&YkMHihJruv?gye8OklzUM{_SD~Asy2|=~auM&#A@)w+-C+_<_UKKmb`JfiqpSB8
zqxc5FL~OdvUKhpM-U)XJ?Ev)Tlhge@yfa|;yT!mYV5b8xfLq%tG62++hL$!nGqX<#
z80bp?v|X#_8x4bLJFihsC;*C1bbN89WZGUlW35^F5)9&Wn*IcdstBeaP|?Xxw$Wa!A;)(2*8r13RE7P#l^)x
zdn3Q
zW(&F<0vIeEZS6uE;n(!?rBzjSz-%;ElPgv5ozH>tukUQ7*4f#aJ1r-mudV(0^UU+{p&UXG6<%;$$ENyQmpwD=3|*6lAqDJdyKsqcoy#uzGinR@~%`9PN3q)S&i;YOF<
z(Bqgaqja{_6K`mawa)PXmwp@2=71prP{6wzxC5T!)YuKFY||<>9x-w0Aqr(Q1N9b9
z{FbX=wrhK8Vj`#Stz+nT(px#ej0Vye6fyvJBVfPGUoqbZw9NxBT0j5yvl#(QvH&Rj
zY8Tr4;vMYcnNm15MJv!=047l@ZVnU(_``PdD*@PiZk^4;FK;zQ_XFd0^Lw*r7trIe
oKO;UOMGb=6C=pLLKnV#60qIt{yKCr9X&7SY6cI!a5J9@7YZ#CidPF*uW&jx&P`bOM
z-u=Dx{)ER`3~<-E_nve1-e-UN+aIyI+Nw_oXb3^VB5{
z4E(_NQZw@ffrxtl`+ktdO+*V^#Pd@JE8{IcCc=NoY)0hL1zckBQ#SQ8fI9gF+WI(x
z!19J#@(MgJ3|$;wfNlL;OrSn)zC16~J?&pgnJ!WR&uju1w>*osfd|~M7;Rxav
z;#G3A=M~`N7kR9dUVzk<@_zvD{
zZ2W41U#RivbTc1sz7uEC>^=NQgn2FW@dtjKY7v_f3(dq)+Jg@)3rU*_#RYajA1(Qw
zJ2_x+Jyw+uX8rW%3n=1&@C%>0h64svds~~e)na>lVBo>Obhqk#tKEHMd*?08#3aq3
z!ED?`$hD+#oV{SerCVVKK2bsmZ&pb$Z^Y1mArm2PLY2z@4;RzNDE{FH;TIzi6s#gp
z%tEb=EU4wMn~*R>g@(}_n(%ZE!gCUVji*b4EMZOj?+Qt^klG$9c>tJAbo7&HE|($u
zk24nbf5spLyusDuA}EVrr<)>L@Ib-2#4Vjn!99`y42%F4E_tSv7+|wuF^(Xx)e?$hMyJO2c_H<(a7NO>ZoER
zCV&TasF@Ww0XH!&syMJKsH@`>EK~6Y-?<_(%2UwHCpJHb)O#E;
zFFl48*dwpG{|p{D?E>4nAq4q=-FzBXttUcM<@w1wyV~a{=|sA+9&G=8drTfZXF;>71I?{W{9h
zPm^om(K;}1${N(1M_hrY8n3tu5;Y{N%Wep*vdOj=OaJXE{Kgym1{OW6Y36Z)}(J5@jjZ+>~mkAF28U{F2KjJ&~z_Up4O`EXS
zG-Kn30v7Hw&Rn;(G%u>17I8tt9w*s}=Eir4%d+bHqTif_f4ht1ENYTq7!^V`UjJEQ
zcc;4liz^w1$vIzo4lLvINci=$J3`R9FvFit2c{qB2+UU-gp+kRH~s>cYJUT9AS%W6Qt-oBv(D=IQ4`;m_#r3(9KRG
z*?E$IDqfrJjeXErLC#Z7Q?hC!X-tauGfCCpGx@@P7qSB-qh?fuxmTySiUs68M?IC9_ZZgyI3{x
zd~1q*u2vvgBZd61H|lA8QDe!sk%YOqy?v-x^&Lh}$JqY(X8L>QVdj$Bc2vd1uPi}^
zu+s@6n6WK#7H(l;VG%E=l_%jpQKWpuz`^;9Ia{z~fh2B3*v#GkN;uq6xQt0;O{AWt
z9c;#{PXqd8ac6Cc918q+AI8eqYs-7F6dvyC>S|vcHjpdkT~t(r1r6AqcAc%Uy2Er-
z?THqfBF5Lz(b4)9J-RTh_m#9%1%9++(xS=0vj19$v5YrsymmTeQ9OX0hDd}s4uofkQpVJ
zGBiCsU2W4TlQXi9LAF;_Z4Z87P7ZgYQkg4%#u~9iVj0|$N_>@r>~h;_f~)9yhBjyW
z(+>Sg?^iZn0WU4-%;EOi9IKs;IyKOWdREue(96EhGCmZ
z?Vn}?fl?E%f7Auu)
z*w;(7nzPSDwHdk47l&~(tMuw#{l@V$LnItZ1`+>qxT~WC?J~w1>9JIe(aDnV{v0a3
zf+oR*=6Efo!w-0x&sk@0E!
zBC_jg?5ag&oUg>mkImjI(3yU)o5~TebhsLj+hAi!BsZ2_ru6&{-yxl
zW}5Z{>K5`|Qrgt`C9w$6y;IN+Ulv@BPGY_B&1SBT&h59vg2Cu?4HfN+fATuUi4Bi~3?2Y?rI?X#rA3-N1AUs)ZYX~#_kt+Ymk5eS-}zf3*o
zPCn-*X~RzNrvviYF|1_PorgEIG#AV;lHb(B+SaGthktsA>7~^6uQrPlxwyErzsk^U
z<9{hphZ0|W7<8y27%}IKnCllKv_vcEf
zNaekc%a0dju@Fetsj{~Jz9f41wsSu{7oX(&)Zjc89iqUpHI6>(=Yvtap=|dsDW6zB
zU0{3VvZBYKwthTtujD!Zkx3*ytByu5_#%{pv3@=v=gY$AkSSd$rXvxNb!}@_I+8s$
zsHEn+`KRGq`}ga`gPEBbnrOY9)nV>h%N8%&3TMSHe#ol}9Ae+39}e;+lu0zjBMeZi
z*Gz&$eTHZ%iaLIi4hPl}I+0Bzc6?dN2yokE&(HF+^qk`drLHttRY$KP2e!C*X
z4?E#XqaMkx$Huw4dlG3yKXc`(>iC?cFLLm>4^SA*m1!(K5)ccm&(^2$F}82!H$l4G
zKeXiP%(SKHf5z6B7@!S;kL2%RX?_1bey5hmG^a&
zC;^|eQAuO!k5#tHIt}Km*K@Iax(&w6-%w%^R7cPMgYzXz~Z${~BGIG%8>uX=J3WYxWrH+m`oWsM33K1b6nzRTg*7Yjp{|H|X(1zAFR01AZA$BEM+K1;1}P^=&B)r~CQUMsC}!_A(hT3b
zf8`>p*cwZ=5S}=D+@lz@EB_vs`Y~`~6P~$qBo{Rws{HA0182IUDwOqT4BaNuQHB)e
zO#G*AOv2U8Er-vxyCe8SL7F!1a)-D5ZhFu|kJ{pCX|Qp|l1?>uw-HV66MG*R
zb+Z-R>8$BIv{vu^3tdHRSAoJH|8Y5N);jgCoXtaEA9dg3$JC{qEY4Kpd4mIr;B_G5
zlU`FgBl`jgh?@wr4KrvYqE$YPuSkb3+>$<>ZkM6bXpt&CENGS9Z)f-T|D4dU2xHe(Tbu}`=CTenvidLDiF
zu7m*W=>waJL%8Vm2AsdP<$?KXp6BIA6K_I
z{6j5DM0$i*rsyn$OE;r4iE!`}iV_PZj{P&&7_A|>7_fRoUtu=1_e&gRbh;jH3OZ4U
zKrJ}x59P_ds^LifJ^F*k|CcnXMF{>OR?zaRGRbgR{OgNQ-q&D=LzVyVL)stjS|+m+wQEujO5FMc&*
zPnxm)J)N(3`XLikP?FSUGg#}$qN&sH_D(e;$2vUz2Y!9F5Bf`?Ew|cY0K(kL^~LiV
z%Trv$z-WUV9ZAzW9KFFErc
z#Z7>Do^wH&ZydI8`od^OWH3RVvV*{6X0lmFl{g6L(&|s-8If)Ip7V2vjdve~@5U(W
z1&>u*V^tN`bVDIWDrR?)DG-h2rls){E_E{B20;13x{8jGy_g&aB;u$xu-w-#KAYn^_x8U|QO4ia23BZf
zWK^!5XBSx3Zm;DB`}WZ1qAcW|$>YNH0bS{pu|bHhWSx?e*C-|-NSf|i{S{zU&F7`sxlifee%L=IQnR|$Wvz8-Uy
z=D08qHY`x4y${&3LM1`iNprS0|F}HXHLaz~%n(EQMABP9**#P?`RbqZ=4~(@WX07j
zQngg>Hf8~W2W$@iEZ?^dsmWGZY%Y&i+H;*wb}Mzrk^lwDjObV8~6fun|q
zPQnjBxa;fdRaI5d(b1i&kIouNzHE(jS_Rcj`{Zl~eE-WjN=D-#aY1BU&30y7t(+J{
zB)9Nh`5{C;fhH9_WQZkPD$1^FUz&vzk1}8P_Yxy~YFfnqc}S{x&!0SxyKSxz&Dr3R
zXRq4bDn%B&(pRyRQ&LpKG(DxDRM33Ar%GJdOvU9!es}m+t}scmv6hF-uUKQhoY)(MsRL_E(>W|U}{1x9gygSN6e>Z
zfk{HRbq^R>ku=wf?7%-WjO)U#`4}C{m`gXh
zmMFMliX;2vRB}b$zL$kPV2OfganiPMTiSvga4Uug^CF>z3o*pN+aXc+Hv#)s-GPDT
zoLqLrnV%L%hb(1+xm(p=nU9($7=u$e5==aYS`(gL8c!Q=;>NBT*Zpa-((4vf)c8ef
zv$|TsHQB4_7ffrCU+WuSmk>qgXP@9CF4b`H%MI1x-?YZ!Ak
zy{@E$0zs`>|K_oLVPcIyrk1#_=HG@zs_*G$ufRAIMP{(Np2+~{@b>Xg>s29+@l+w&
zRTvR1t_F8{X4MnubfjJS2o!Dm?)1-t*+AVjiI8PHxlu3uQ2BClvBmVu-JsZJ0=!Fv
zL{gP-URP*>x&G?;6w4@F?L?yVgIkF{tX@4sloCGPtKxpm3gI;zl8BQXS6`aKW;VPyfnO9zX#OvoCnE8tqqJZmc
z+mq>%oKjxRRJySJ4NJJ`Uz!StFTc3Fpe}8cb@^EnTH~Wu836_WVG83pYo3VLXFesO
z>~@mqA+d;TGq(gET7sIu!gX`(BJ!9swkI*K8Cg|Nl1Li99Nzkgy#K=7)~c7im}&uj
z)?GK*8g-j&E@{RWk*z)#oNMgMk};k))RT@>?xK#UcA7OzP46*lhCBP`d8U{O0GY|*
zHQed6zaU3kF|?
z19A}~L1|7g_y!A+ca^4cGh4Tm(&O`z{Vkq)N-Kc)T8&pG5pD&PO77dgt&N?AA(A}t
zi%o)pKhp-(4%}IM;Sqm_TY_I>JT*soaediEl76l5YLT%K{zOZ`ZgI9hK>u_m*%YMv
z+36ioZ(IZ^NeQi3)-p?2t_goEjG$9w;<7_uXLXBkH{_XpBY`xDZJ1;4
zpP+hSSFi5|pyHBQo(}sUos$WV-5{Mb=}Qu0bD`;|y7~D7nhIb1e+*o+6?^p;DLO?_
zi?Mw;5Pj&^&hDr&)>kjRJ6OyopZj3qn9d!XPw4ZX#@AHEnh3Zem5di`JyaV
z1uwz|M~469CK&c)a@f5J!Xw8L-tb+sDt1a(qwGH6vh^+&isTCNb(^R$J~3AEXTdRU
z2(ic+HilJO5Pd*NmbA{K^I8~NSxEy~e13jDIvV$P7U@!?RH92`^z%u6*iL3$UKN$3
zz-x4HCuy#=Oa7!o_q^pHQQ9{VE9|o(Sp$4+dM@1t;qBnW!YDO!-$jT(A&W>eAx*g|
zQn9l)VNP_72X2WEVg(a!6^pUg`TcrI{Fg#6!t7IQa8*X~ZyTVhl{`oaLLpC9cYim8
zU7s%^Ljy%}q(e?!zRHBHGU(K?CVqdtxnuvHp7ALv^wU&LwC^j5JSR-LG+qrVb+X`T
z#j{)L`!#2hpYD4DItF9)i@4=&sVK2Rnz~lI_fxA9&h`(QDW%tB>1yp-G&v)uAyF;?
z!wWS1GI2{>KhqR#&t!rQmx0pk`gF^ls=x1FFj3{rItA=hX#U1w%iDy7{Ci947k`eevu)?Ehm(Mt%!#EL>a;76}Bu|?c
z3kfjGLRnf3Bd>qVaYOd+W&}VS!PlavvgKv%WXkR&HN_tpjw?GfztOao%Z;%4G;2IK
zwQlh$w57tveM}`8u$wLH<}{Wcww7i>6YYI6BDxX$tyD8ZJ70<#iU~bH%3l8-W4T%j
zIT(q!02RjBeb6;Lc$oj=#gW;r$Ra%(LZVh~PlPlMHVFbHO5;qDI;}^~DzPui^*MX`
z)3lFx?DtbR&C2nb9?lU>@9s+U+=HUfyFP0*#y?Xj{qp4Kdupr-muSFA!!i=}9%zvs
zzKS6Vg1bhOelWKeeb{Ty!aUM4
zVr{6jcEdKvtV{T8=e1=hmFvsYAMP|I#>^F&Jlh%TFaKb5v>sIi*Ds{v
z)}SKQeRe&yUm2rgsXJBh_m@8x6fD)*biQRmP83YPQGq)OWC3rU)@TMw6#!2$tA18H
zzNW$^goOCcS3D%PPpXYO7n`(>TdG$Pn0DmNzd+}@jf68*M=$Gfa5)~Rz*M$Xp{q@5
z?w%Cp^;P20adXMy<)p`JWU)$D+&5`x%V#FoUN$!1G9j`yMmuE8W9n{RK4)Lw^7Y`3
z1X@vJ6O%*+S&YyK!0ZkQUQ0e-6<<&Q4S1c4D(F5cZ{SSCEf-u)KU`Q)d8Epoj`pQ%
z8q!jr;Np3ogQnW2shr9i**#XywqzEG&Sq47Ubjc_6P2OmuwY!5jkHx#6nr1~b^rx4
zp^fg!+i21GD&x=|h;unZo0)!6T@h+Y;%+4vKly@S8j}`4_aV|2FKH6`L9#hgKU+BW
z$zTw1yB8Nnb@1b1V{Rq)dB_b9$%=@IlQ8OQQMJ%_W!9b=?5LA6(WN&Hwp4;nzg#>#
zqybzC^r)=g9QDP;#R0r`v^Vb4V@@7j8;(|k$*a}4aW8}%*d&=EI)0KGr&#wg>-QfH
zZP-WxcSRJQ+E;df$h`<25^>idvj#{IyL=+hYorIf)?wzwe=r9Tc;?n#P3?
zA9f(lHDj#su|@tXP3v$x=6BZ^V*7{vaxJVC!LQ3
zMz6&Z5lYeVItu5r6S6PFEj@?%64U(Ic%)1=+vO3UnVDu_elxlr;=303x~e}nlr>xV
z<0fGiUN>h~`Pc){$rtfU%0ldx?%gFr&6Z9lFWHfV>OUn&2bwhG5xq6DnflM#=TKIz
z7+;fDcusGBKFco@d>vzRonW6>k7g-)%cJ`leJG8lm>%^ujT53HGa^1$C9g}M5S#$FHmZTwoWLOjBl8)^_kA{-dnOw*;}KFi2q)+HtK;k(;&a;*aZ)%5TM
z>@Nhb4wClp0o;^H%{?=GAP&n0I;pGouPwD^D0yK$lncZYyd-4^h``WI@1%y?+N2@+z97`p6
z^l<9G_Q^`3EYJ%P78YI^NK};sPW8Mo2Q6#$bj+i3%&}m7nv1wSZGTu((tAUb8EZY7
z+%7X+3}vgwSoksofyN@fp+US>(=R+AlzBKsJCZ#;GZXC`IM?WE(EFWL4*<$5m`M5Q
zE1r4vCq&A+h)w?Iu)4|qv7E~0Rc*_y!R4bZ&5w|>C^?Qy47B42H)JyO^6<}Z_w%(3
z8iIN9lnovS}frq+SgXubR~buO7M%Ng$>nk{#wkTOz^6ca)ohc>X{(
zI8CDhDuU@s14Zc)3D?Q?%1YOvWLB;V)+pJ{gj?QkqQS-n-WGnIq;C?aD1h;-X3kTi
z>{w+#?1(?!N1=*f^Qd($635azv$M>wfuwN6hNuMnt6an
z9M`rjM}q6^(PGo*4x>Z1)P(^6AEe%6lr?}@V(AUVk+m>6-T-_O*y
z>yd%xZ0C5OmJuIG;{vGZ%V7SV+3VWyTi7PU>E;A;V~V=SQy8b(ko_xDFNA#Rz?IkboVx7I}rL!aHeYd#9&3Obz
zroF$FW@qYZ^_XQ!1n=8dLFJBi;lm!sWr9oPs>-!8_B$<2ck(+{%vPNYeb1;wmBH98EtXD%$2siX3a>=pqr=J>Dt@$Lm
zswvD^`y}p(7yA9|z4I{{x`=6sOW4F9B|+|(j;kLL*nO6kvtYNEQKS{DI83b`1qJ7y
z;VR+Mu{6%G%Js*m&6zu^jTj0Fip4g6?!n65xR3C%K?9$rJ!wf&4N9@<&kd#dKQ!Ji
zw=>aV3I?UhcQAEKf7=4*`k>*LNtTno^cz3-@Pj+wN0~nKT|A(Z;J>#T6HHE_bLafg
zjLIz9R_4}6u25I7UZzVu^lYc>4AW&vewKmZC!Vh1(5MmE=e=8HsIXE;uIg>xuIE7Y
zmlp4CuLJiNuF`7X{O3KS&*{piyzwbmGZI7N*==zkCi*G3
zSXh|9{~1tHt9*VQJU|y1<-JgX2QgVqX0J%3VZxuTDbYVBIgDx1$1q
zPQO`$6R&kgi}P6iz-KyIfFVW9TGK8ogOMn5uH=DqF;nmtfg#1|_QJwKfPW^IJ{<>&
z<)hKEva*v=$y(qx&^SQB0|6*13Y3WcQzkd(al?XK{Dm(76;+7}@Xqh|ch{PlnzXdE
zLwW4lxfK-^KXL#C#_?iJO-c%*t$D=G`QG_IDqu1#|F&V3Qb2Te(>cXn
zE3BwEB$rP@;(DLh5bGb%ONX?KuK8@0W?;$L+phqr=sU1pfIr5g4|+GO7!a+K$;X(V
z_=Y_`iA6^h5P!{WUk>C~sKV3I(`C-*>;o75XO*8>H3HPazkmN;TD9p0bA0`>XMTKx
zuW@YmXqv0HvzLB{yMiSZwfsx#B27%Jqf4!ig`i@)tuM8QPQ
zT1+d(;#`MN-X82zA5LDAs^=RbhX(q_oqswA++JU>f29aLLz!UTyPNyq%aowHq~uJ8
zQY2<+u3Jp0#^UN;)FDLpz)Fidmq>wa;TtSXhS1?bZpVo1N!_Oy!^
zPg$&))g7vRUH#y(Fq0grKwmLMf;7sfAj~Pzq;HN`0;aSeN5?<01wXDf6=Kmo&b&}?
z8&bDBGgW$kTbd8}FQLsPR$x`xW-O|&;A1PwGS2uo2%|Pc?@RxJAAjAO`|WkH8cU+$
zk;Ug3w0t3pj$J_jD?E8XP}g@N#P_4hu^$jflZ?T(*ocFT`bLa`Lb6m^#C%%R+ifwN
z?Kk;9CqvpvQdjYjoq%_CNf8+a
zb@l^;+U`AkqtX|aO9$yKmCSaJC@Qcmm)OoWH^C?9rMvTt&YQIj+56x|ap%$8g0hnT
z%wv7CkAh{~u~|)oOf3X&dfR&z{S?2?8GXs^d_>%F0SDhPzv>2HQV%bCY+a3y$oGTsKV)biQS|#GWyGP8|M{<|=9_+=Uck@<
zK!j(@@iKbaQxe3_NFu(aP?pX?o;(75S8@bUafT*xy^dC%OiXwx0CnZ1ONnu;o;^!F+mb+J1dYmKh+frzq{H1
zgoI_FcE8j!s^>i$3oO#00pViu?l
ze5Wh)7{YJ7m)VySYpmKmm%h_M^ToXXS}grbPrR@VXB!?K#uAeS=c>KKFft09AB5ju
zAFuuV`sR3Or?&HM8&-RFdk`MJJzY6#9m@N6ip=}}@(wcyNVw&#t-%1JB7w_9M*MZcaWqSLdp*Dx`hGeJF)qF3XLm^UXQUvE~~
zL(g~dZ^jwFylWFx2QuepI>|C{&Up{R-64Q3+HcR%0HZK|yEr%3;=ePaN+0Y1tPMaH
zbh2-6{@aYTSK+O7bzdyga6O3VrGfxdw(&LmZm!P0QqgZ~a;fHnE?3^5(Jc+ciRK-o_zcuaqZJR~$Kqq7W+L;Ob=H_Uih!s!vYBeSJWS!T%u>yD%
z=~p8e7M>97AHYI{LQXdU(aY`4hOFv{{becIv)uz|6a!Z80QLp|@=XpyPbYE=jg9jG
zuL7i~wKX;A&tV9J|9qn>;PHTH7Mk>x@sUKpZsvxc9WVov&VMMtwS9r)isx@fN+usc
z1OnV0C?wNgTA3ReQJk|AB&}}UqH5n#u#PJv0TI>p$_k+W93;jRrf1+50ARV;gN92k
zy8T|g7t}MQp`~>m$rc902I9W!r~8Z7=V<7FaAkjgKY&G+XDpzLK%L5(VJYzT=CUV-
zxcGJ;k%4{b&*jm|dpzRr%YdV|Z89~<;eh}W+oIk9Uoy<}P5z6+;~{q7Ob|QS
zkhy3DFi69qJrG2>0G$u4;QMLmE8%sHCNz^Uj%k4=Q)L_`+Sbw^Wwf`q2SCPgur(m9
zC1cZ)y9SbpAY_^&fMq?E)1XW%>*=BMIQhq`lMP}jz5>mRmlbjshdsoKHFAKyIP`c`
z-7`XaS~^`!-$)Ga9U!G`x^HT1Yy`%+i^Cfmg$luJG(cB+KWFJ?9XL3Kuoi(O0313l
z(et1o)(pSxX?#a|0f)ipC$y_!r9({8Q{sT{jRMmLyz+w#DcEuo1)H7Hk(G8BB&h<7
z3hYjcoa)|~Q$yv|gpS-FOVZhi?(Dxx`UUoa>>-apnpHBNR1|$i#WzZ{OlnKYy=o?0
jIO6^_gNxNg?-^KHC8smK!ae~1V**lF)>f)fcpdpaHSFQ|

literal 0
HcmV?d00001

diff --git a/Tutorial/ScriptsForDocu/html/tutorialCreatingObject_05.png b/Tutorial/ScriptsForDocu/html/tutorialCreatingObject_05.png
new file mode 100644
index 0000000000000000000000000000000000000000..9111181e453029c69f49ee99bb2edee6abae1da4
GIT binary patch
literal 13259
zcmb_@WmH^G@Ff!5gS!XU;GWu|G{GUby9_RYWRRePLvRaj`||tm
zr``Rya}ILe>G!%`Rd;pOy?3HD)fI6tDKHTb5O9>0)`lk{PHa$DZSTTT}qPXkzUtaKj
zwheL{8vUqg!Jmnpg=MKsBjrNn$s<>j9Lf&iJ@7{r`WLXO$gSoTK{yq%P|n9wzTzr`5V@;Q-pmS|_C!>7Yicw8nA1kBG=phTEtM&i+0q{Yq;
zrX+^Q84+=Pi1VF$e|mfr^Ye-#QPPPYe64^YG($!{(!dzz)p5a#oR)k`-1bEt$Rd8eu&b7&*$Yc7p}^Zl8XQqgzz<&!MMn-A0}^v%8Y2KZvWWY4c}1Cn4xR%C5`M6
zCWer)h)z;RU?br3*OMZJ!hcEW!dgtaa2yIs7MpXgt}yu~sI%Fi81&prUCYUoow57j
zN*x}3`rnq|W|sL&D^J%ct+j^AbLcExK}Ig8+`HY^E0#k>A3h&3%mzzG;}}0*YYrbE
z-#{6qCts}>k-A!)6oWImzD4+Cx(A8KXEaPu%?fmFULqFYxVj!>*g#D$o6#J;9LL$G
zk-*9;bH00L5~?05T|gOSB2c#}P_dO7v>
zH!*z|bZBYn*zbByx&rU71fAv@TUr{TJG_sK8f<@1x^@!W?iDTe{h6R)_S~-=m2bCt
zLvNAzV5paD+vXya#5!mFJ8&T1V0X_brl7x{n#)0$#ZmOL<6I@^^73-JM2%89C_q-W
zzs`D?YS_WWrMjvrSIF5YFz_)))U!a6QIDlCQ=Q=5XiFq-gNudrm#B84AQDqO!?+>7
z_C}(^dONoVh*m1~>HhZ`Txzk#@88Gc&A{v7iBfm=K0ZEbYHEs#k((lDxKw4;)d^SU
zHiQoPNjFt^4Km=&@w_vFaD!BiU1U^j!&-|zJE;qDVZWU(
zcHQ^K9S6T_Xn8GqzrG1Wr!UP9y7S%5{k^zI0n_8Y_d4lKZXY;0NzhDlT?N;hUTfVC
zr7RFR@X>t;KIp&l%4dI7eNU8~eAe`_d~CyQJc_JUP^VK_Pkhyq)ikEhNK-lLw5?>H*GsD|F?SMF{s*a!0c=`D3Ufi~~w>LTc7E74^^-D`nugP^)
z>T9xDl#gYl2Kz$_ZeV63l5WrrsRGf?0!`pdORZ8O+
zN8omx=BTY6!#9f(uV~*H9Lt#3^g&)prs9!Y)^q&rcT8i(>zfR_GWGD&E#GmvE!2~F
zVpP}JF?|Hf*6=PT(`3;K`80C)-_C{gSxUKv^(P&vd+!(l+hXMnm4=jmv3*sZ|e>39(Wymx`?5AHQF8x0XuHJRBJ=0F4tIa3j>M}#t-e`l6rf00!v)DsaFcT}L!1t%#*zop^2q|%@H0yloz7Rge8rwmx^igOt
zA);$91)<=mc7-IG5D}aSZ&e4?b$G3cqre-4`0Dc?RO62HnXo#T$P~Gls%AuBMSPT7
zSPSx+q|0)v58O9WO*G)f&Bmtq{%rat)uuB#f1D!EZJYj0;D?tUc@hsj-$)eXMC6Th
z*Cqi&8Q?sfr*CEdeZp)&IA(#*_p2?O-#a?Q=TOry;o*HO-HWsRMI*JTZs2WE?_`32
zddP0~Gq+Z3F%h9Y^*EcM9eF2p`Q_6(Ua+CLxhqJ&%Yf9x4e@b6J77!-4yJb>JVC{f
zt*@3KnK#mVXD2dY+DKXPCdK=H^{S!*^Ct+^70qpur{}?AQdC2Me7Hk|y<7U?$6Z{kzcs1
zADo3MYlr$;f0(wXl%f2LWh6lL
z7JN&>jy`-^$y}`ylJFWVjsP!Ih_8QvLn*{LHzxvucJRvZ>@#3nAhb&*etN{NiLumT
z_e-PofEA17kjAq)Xgr{~wbt;`S_}NUcg4)h%gfYjR_|hWDzQmWUFb9&5A^JH^4k)_wH9n7ehNn}9q7Duj6
zEo;Dao4hAf0BO8ZhUgWQFS=PbgtC)*rC4yh{tqoeyt2c(DVZC;EPGnbElSL&s?cHN
z5!cRM-8l}vTE!VYYVU0Kx#?={`8?l`4>M~xs?pcDZw$->FMocMwRVnAL4`jcoY!Nz
zRc~96YCq6CTaj^Z)e9sTDfj1@exz5LtAzzcL6;*~a>zP~VTMKx&K*rnf`DJ5H;-N?
zn?Q>X%YV(v%w)KAPQ}qnycp#K&-HlAaD86im%S-kfz?)Evp7sqbW%USj4I
z-wGo%IaFiH97EfaHabQsSrMcy6IA-B9NKUUE|g~=)*~!KejG1;bR|-Y3%SBO-8YBg
zsl~m4(>4;1Hlc~FnqkB4oHwCM~5!0y)z&qF>)u6)xPr$CV;T{g||
zkkwLbnYN5*aq4Yx@S|-*F_DJrn==vCr1M?K3^7^*JFE!;?4OzwKg=S-CD
zD2vG-itRer(}S85PJD&Cgj^M~wLD7=`^aY`Qq#BOPK9s+EQi_p%G-L;KhwQHkDfySY2E!cIMQXmEGG
z&IF~G&8hcCbGC04m6dVjZ$9%>p(M>PUrn7i6to3%B)lDs%=t*ta;mBihDtV}FquD20|bY*?xH(qU+*(RHs?>PNN
z*Lsj+Kt*qzHGJyuK$r15N-IwJsWqGa7Y#PYSl%#73mGefE_@_1#uhUb0=>4%_Nk;O
z+7_+b`GU&)PIia5eUj7u;hSXs%+LB_Aq?c{e4_y>S{ib6Z^m_Syv=3uRQAZ-XUX}KsjuO(VtxK`%?)Kev$hO9(jLN}uPrzFnzIwK=}bXT
zDXbZd#^gsdDcd|;#rlfhat(i#R-2Q=o`O@_(lxd(ZO`=LTChcH!tYZ>L&C8mYPVh9{dY@~RGAd$!bKs99s{L?&I+y_Fhfq{(!8xFUGD(gan%%{h
zCKa1c)&oa^9KmOm1?W8v;tw3?$4<@|tQlXl$13
zY!LQrs0o9|EPJm2nUtK*MRfH=Tos9PJSbVuQfs89iTNwGr1o2iMUkM7^T6RVSmg$$aS^pyXG`&dzpnaw=8J);Bkowwt87F+^~kW&H;6EF<*@
zx1r%&&vE5hvwW`C+2;AlsfSZRmmyM_`j{o16Cleb+m$*R~UxlKxj?X&h{DE-o&hlWOdpDc2$8
zF~gpr+c5nTmfcLqkl*0W7NFc$bdP@=NQU+86Ft@Nx2a9qEysq*lZ<63xyd2fq
z>|ZsS%5RIYjt^th*W*BollPb?EP6bnUWPX>Cl%b_ZnTnhJ
zk#sI&&hZVY>av%y*M*5vVm4z$S%QIgfA^+KRTtOImX!zUIj3Ngz}kN9;9!36i(W(`
z!tHbrA8#JJL5`J878WMnF@2%&d*5#j@mDzEx|I@?uY`;kwe3;>*<4%URm|y9iy!Wp
zf@yTvYbjt_e*hG~!NGwQBuZY9ovW5}!hihu5g#8f@V&g;VJMC~+52zE*2dAN)DHL8
zkNLc1s)Z(odfLjZAENy8VpX1-xvfMIzM$x!p&N6y%R3y9@{E2T6dAR5aS)mJyjh;O
zveLsI(k>EJR`jAuVu^2YUOMK_WWCmj$&|M3-B_8M8>%h<8ZMyix`?*`j(AhvwAbb=
zkbIjntu^}64Kq481$q|Y6A^k`9V(%s$kP{Ix~jH1CWDmsvjX!zDx510j4g0<7sQ2z
zmRhaZ`2=sHJOOxr1dh)7k&DXUp^^33&rG6crWq^k%yQ?{5@Fj(-kz%i)_H
zGBvq>y+%spGkAA+`zKLyd}Pc<;%q&hd%bm7dSqAm22FLHw76JtPd!@HCA8}!k!_ld
zZIy8Iwg}Wz9?2I5PNb1E+LsWQTvZ>`9osk(Q8zX=c6L63U=V&-GHdfFE+W{#eRDiL
z*wVH0q=B4QmgS~L;-A?2Qp%DoJ3({|XZjN9u$f&j@-ejvh)*8JPcs28l$GMdd@wcgzw+KF9@DSEzv(Y@6&R=LV^i|#Kj~NN?mxh
z>HKVvZ|l7hAzIIymQNilws*$GYi?zhHW{wDN{eySXg%Z6#NpRO#&>ykZ`dcw{6$H2
zPDB%D>OM%EZljhj6*!zg?SFN+(B*&Kk0)&c0)cdN2(87JRm(H2+8#|ijSrA{XsOIL
z@urYQZX%1?0{Gr6JNA?!BCI>YQfaLRKnD4WnxUk{n}v_kq{LW{Q5cNhZOUzVQImEc
zRQ-r0+j}3Jz6iU*fpT&IXZN?6zjNggYvYjrn+2C03!CfrRR$n
z$(S4>x+~FoPyuPkXtKe`%O~V0?=wY$Q9G}qY&m;nrREzEjK6#e
zvyIDZrbJpY#)rsqGkIO%-;0vI({!AwD?GShnjZYHi)LvrYOOfLD$V>P$&~3e8-QP|
z+DfFAX>7U(Dw0RJCu2lw>sw-69EOpT;&V}jzrK_s`+V9(*IU*1+g!%>RtQ<6b-!ek
z7T1GvHZn)TPR>HPpSOjxuA;U=;(JoQV_{Nu58qJi4SiKIaN;7L43mkAi?=0G$X6#nP+H5`34q&myv=K$
zTfZ>(j7Zz}hH|bNhY`JPRsUqG;Z%zD5nbej*mFIRuR>`KM|F{{pPy{K9rKw_;X-(5
zi@~Di;()=CqmR;)dWv@Be4AwRRn$9JIP$6|eU(6EQewtU?qIvFlNc=I&|g@&zk=un
zzhkdw-adN$i2Xj;I*!%Wq=Lw1)t`gy;Y4t65V}rNK*Fvyb=$_@o!5{Flu(z)xU~LH
zrZTE-2<<
ztLk`%^lNH5-QHCponRb6=bFg(C{P{aFi(i{^M1EgsGE8d
ztGQg@gBy7@Z%f_FJPJ5aa@TP>Ej2seUmZ1YIsxSbkjM|6^7z>1&C%0*u%iTc&x^GD
zC&#Il!c7KVhB)$xlC8sZ7!4Sbt{ETLGamS#UdW3K@!vP<3}urfWm@H7l0M;?q|N+j|AGO1^1jK*PfMTXW?J*PHcP9>-&$Of|)se@_u)*Cudam{pE;{}HnwZDyRUfL->S`A#1jA(m
zNCbSe)|iF8!No~sQp?DM+}X`%)F)k}y}cKbIMUqOHxGFYSfF;Sn41-9nSy=%&))ZN
zHSh%ojWISyoErkP7@lGu8OZh8Y~)Qg1^k#a6#&cr{HFegiN1Rb*VKRUB0AfK9wU+_
zek6ovr~W>rttxhdPDi((qBg0(to-fqang>X;oetI3oe%B-v#wD6>Beqmgq3>A|!2>
zOCqitrlbzK_W-m+WZ0d?|B!FI{xKQW%CgV4=jX?tK{G%+`9b6gh&09
zq}l?43{nc4P{Dlt6}fbwX3Wua|F`#B5_H;V6vmV
z-TQcXF9K*`75eMg**hhvhxjT9(=e})=H<-$I3BE&S2@cvTa-6AUm^4l676_^e43)7
z7cIkWBAm-JIiBlra7|62M}xtHgrV-)ui&=|gD
z1(|<+Y;)fMR!vtxCQR_WzqR(FLR^#4BdPr=qmw$0xMs>$6gpchOt+358M2y+0@dli
zANkCfZZkVKXOun*hacbVkMwB2RIogKl_Q^3AbSISww#V_gTLWSaHTa5fqA6au@sKY
zAj=?~-qQ?@+V$bR7l9J@grh(|6d3D{a#&0`c)sBV
zEN`H~yyhX%?9BZjO_nm|IoQ==_kpPKp`vo5iayUlnjfo;Pqlj+^@z-gz~x6Eu6?vh
zA9{vCaU!>FTwkUvg)G<+ywrTut^M|2eUWZME$YEEhdOw6SOp#o12(Y=a~El=YHP9y
z3e2Q^i*uiIPIDTXd@lX?8izr;3*Xi~R<5sfmolo0pm;g&O*38SwP@Tp1$K>z9h>LAyMn)AOE4%pGx&j2l!7!x?hY~ur@7>O
zcWODncGD1*GMk4r$b1)Zc3v#ja_alXjl9^3p+2+vPwhL;?EqlU9T)!h^3MQxG`vW`
zBGj3S0P&9{E);>5FDugtA6?5+oQ9e{r~9=rOd+CY&?3G46U!QOIes_o__e4WRoWQG
zQ^^w%jZ$aAtoe;uk8p0RSQf1{w#d%xC!ZDWBFlf#A%U{W8wYoSv%+Z=FA6LGE29zLj)$!js0Qpg1kp
z-eD~VvcayCv?FQZuYyPxYsDU2aM+l&Z&XzEfhZt;#*JEqX?k1GdYiCeId-@N9tlPlu^Ubhy!h-;qIizLt
zW%aRbSw2y^`93;0qDd^4x4QhV`nsql-|qD~KImPrGI?8P+<^9-0F4OMst=-Du|P#7
z->Zq=ZJ*3fUxSAk0)#m_92ZavFeEsf`q(v;8C>vo_P(r%e758kUaF}Na@4xD<2Om&7hq=qnwr
zzrhTf%}IF=V{J}diDkr!jE2l{EnzHFnVM|JDAi4&frxY*^BcTLDBwvgE*>-~xiB~B
z2iIE-j!an{+f*LiKi9DC>%h0BDh77$SIEr*+h%UOfmaA*-T^NAPz
ztJ`qSbgD0^oFz)XsgbEZl^10Wn$2b(YZFfsM`*+s5;1L-+ot`kZh}X*AP2#IgkJISR$RF}>Y+CA|AP!04dCrBVaTN08HJb(~yb!o_1`=1jLJDOjs3
zeZiSsEWV#S$fE{-CZM_L5ygffsdm
z2ndA#X#r4AAabpbvN@Ef3{?zNWD30NQnJe(hY(9W9;{)e&)hfbKXFVZJflhytFm5O
zLzlh9tWI%D;wHKz`~Q5gTbL}-5?TqW2$!X%F%uZ0HvG8QHME>}jiy2L@kL0oW&K)}
zL4_5J$d~U6eR!4A@tIu_bAQr@8q2GP=VKoM`%0e|Dr6^@ajj;5vH8hiAH<0C@HERw
zWkT(Sx#=OxFD2Kl$mzn>1}b&HxC7q~jIm3oWqCN$ohcGrBfs>C`_(DMwl(^+mC?W~
zbj29j{1I(QLcS24miF5qVqv<(h{S5C*{#XF=SxgeXuupd>h3h$rMQi4QJ)Z>z=nUu
zkfBHyL0=9bvzd<7l7_}!ax4c4|EAtxSnuWtsL1|gh^pMGn;agQYk>>P1OhKRAQjK9x+cQW_8K@GgwIooyA{wxAV&-q<{rAb2xDbTZHMDdg+3
z9MwPLk;SRd{J*f6qF(TDr~nHziBpk+JPi>q&SJLeO~&a>JraH%8NlfQ?@*sfA*OtI85_^2
zX2dDc53{3vQDmgcA6Bt-X4V~WyEU5P&{$r+x0CIPI(EO2D*?+vr@v~4zU9WTmluVK
z&J5Q5@!aestY*c)N9jjpvI-!KUnDY+#g4_RYc>11f6y8U8r^*SQ<$j!g+E-OR0HTn
z?>>I*Bs!V-yd(D$a?wBIpGpMgV=q@kl#u9R3gSSL0qUYU!{fbDzQLk+v||?;?v@tL
z3PsQvPG|CPM_XH4UEODe?5r#RJfbHl)NKIU0nUZs`)`^b#Rq#K%b828*Rxj83>3)(
zeN}C}o6{U1dx7o6El}MZsf24+O?*!!wP?!f9ybI$4M4g7p=*?slmNh1%)G~vU;j?<
zJ9PfsI-9GPxI`=6xpH?Z0qm8ou{869B!%VcXb!b>(OO3f-bA%}i~~W!8n*s77UA@}
z+KZ~Ru&}VA!f~}b(2DQ4(8axlot;=AO{Nvodh}~4SWh3L%eRtKmN0Tmn<
z;EI_k8tih38SwZG{hIL$mTHIWyF`u9(v&U=*NQ{gYKrT@b#%f>xnhv_l1?~+k_hjV
zfh5kWAkUz>BJcEeJvZf6x5Ys2&)fhpT)>a(>poxJtk-$NMEO-~l+~bjYRMZFOiXW*
zk_z$X7?o@>(N@xj31gXM{&bVcbV~L%xJSop>&rGsr)TL^#O+j4Ej+jUk(Tg85Pzu8BNR}Ftw&abf`*uU6Vq8%zd>2=?oeN{2XmVDCG*ckM9*NW!&*C1uk_}Z(^
zK8<_pV-bmBTQ4uxYbs(6EgMUgt9{>Y^dj!{my0$(qE0RoIaQYfs8?m;NnN)Ntm@dk
z5?9>Kd6w=(9?h|DsZZw^vEw;L|2
zPY6H|C{Vra9B}?R!0Z(=HdOFFxOz7Y#s~@QS`>9o>o2Nj}JIOZfr;Vh?R#ZoI9$PVt
zmaM4FB(D4#|F}wje^)iWCyG15?U&~CEMoIHq{b6zEcIJ@1vXE6wYK3Y-2mLo&EC_P
zlvHWHJ5FtPpt!$R6}OaG_T7COWoT&-{dlfstIGM}P}L#hkBUm}Kr-ezyWIai+C{z*
z1X}+0^>1a4_MOO|mC#EquZM>Ml?gT5mC%Bs``3POl$aeJ5@ai(*NM}XJU1Zf&iI=r
z8G);r6h&g&9mjeLxi1I?IL}%XgOoS*6|lWBGeofA9`dmA+&EpE0D!LO!P5qajYCehgCy+1}sk_h`wg?DS3YSJk&oWjSidpo|dCW*(Lth3Ny&5|Wckw%LeNXLvQ!E1DdTWW
zMeZ;k6HFm|J;>5M?R(^_Cp3p%!dd1A+SmozB0_(bS)T^cBAg{7Kz5O^NhRX8Ca>_1
z(ysNWB49&_W(LKGy{cfy>0oo~jdfN<{cy8mkFGejb|jMExf*zlL(X4RqNw%g?yZkV
zS4!vm^~gx>nMc7ZIgXp-@W)MgqH
zIIbN+M}j+aQTGi08w7}<-WeM&g(9OR&`Rg^?jdQ6TNcHB9gA`AjYZA*5wjPmLtd2f
zxo>Ij__(^Vl1kWRIf{Uhor9y{f!X_QRNzf0Qj8=b_de=P*+w@VoO8zNvVT{C8mc>v+qb4-
z?RsVRq)AB%BoTde5hYZEwt5EXa@*OdE@M@?YNK646<-743Jxum6wBB-7FV>u#IIY*z^p0x5AT8M2j+ss^ZP{4x09Hd_$uUFfhQQ5U@XicL5Y%
z&;7~?%tiN(f6^D^aJtqS2SOdvB$5nv$ht-JaasKqj)-M=4pZ=lyKIg(?daa3sGH`Q=+M$Wb_i}G~H&sVn
zxr3NIxvyh+;7kHsHTxZ1*vbYNner$9`sLKJQtFq2)Nkx0BmD|pYD0b*rGJp!rhB>o
z8PY#Nv-_l~^8d&})ySnggktnjPm+J7Xh``k*kn|hb@`odje71*s&$AeFw<@3WJH|b
zK8Wt-T#PU#Vp9nGTVT8S!B2!567GQH61CdxceUw1EEks1J>a4v$o`uAz?8zMC+IN*
z5g8SSLJnQ@@$d8$K#&3o!`>!x#jhD7ADq~5sKq{()z>F0++Uy2f>HCPcI_r}M#Yd;
z@8Q=_5hR5#X?5`8Zvj$M-G>V;uB*Kb753DKg`DGn6!~s|Maq3+0E6Pk?gb!p#nIP;
zB!p-UrJ3#q0KPRfHT?&fG%o^cvJ=P6`DI`Ds`RM~h%|cK94?3BR$wCvnrswJIANJ_m^xHi6@qBx{7}T=tFHG-^`u5QjYfyB~p3+y7D3{&qf&Py5QL88>H
zDX-N0w$lvet!g^Y$GWcJ9l*4J4)bY)v}aA7&uy(Yo>DmIB761S+U|>KAln%k8$bUo
zmF~LPh_wuf`$?c=rkc*#254y+Ypw>O2@kn6Dp>LZ&Uu1wRkunL^om2T+r18KCv!vr
zeNmg|E+B~gYbSlb@bq}^0O*$*s=M#@ff@1wuNHv(G2h`+ll=tuKdBS!yoUSl=K8=%
zyHCR#CdCc_!kT!BAFu~#m_Dp_Kb{TK_FT>x1Fgzyt9DSX#~2&EwETt`lJLsMX}}Ji
zj>CH11etS{ps^iCfGPz7unBmaQQE7LoqRW7V<4K;Jb;gWYuR%ua3h+<%>Ss#HSoaL
zWlkgbZu(+MIv*c4GxJaG4Y;G6WXq#%*`YModx+c)NQV{bjaGv(|J;{91&D_C_#P78
zjd;CCppkU!Hr`SZv|)qawXGD5R}d=cpd4G^?1JD#
zb`CxP3CpPc;~o&n%ca$)t1otW7&>}G#Bc&71B`bI==6X@+11t6ygR@b&>t=LuzIAq
zooL^3KkQuiu809=PE&P!-`G(~Lxo&f}u+Cep*Azj>tQqXY*&@b;F+kXWeryc>x
z!@<$Hd=J1q62SgtQO~(q
z3&T6^yihl~DN0da1lH62NzarXztun#5cpLoJzTJHx**Wb)&xHu07z@cZk~VlRZ|VYTVNU8La}&s_4MMYM9&W!rjA#8dW^vU
zsz)j8LUc0ycz4+W?4K5w6(b!T&#MJHX}}IZl%20S!~B}z?A?Nj`huyb-E#;>3tLv~
zKla3D#S2Ktr1v3qGi6#p_X~JL;;3=@?&`>YR?nbqFT=!RRG!qd=pUm5M9yu_dz^myZX;jh0~xf=}1{kK62kcUex2$M*nImYB^Z
zjCz=D&&xw=A=jUhJe-Kvh7c>G7|$?5dURyu-GT}E?lSOglgnqsb(g!RPVn~v;9E2Z
N%JS-R)o(36{V&1z>Tm!6

literal 0
HcmV?d00001

diff --git a/Tutorial/ScriptsForDocu/html/tutorialCreatingObject_06.png b/Tutorial/ScriptsForDocu/html/tutorialCreatingObject_06.png
new file mode 100644
index 0000000000000000000000000000000000000000..322205c73dcd0b21d3da43bb39f734bfc74e75e9
GIT binary patch
literal 12376
zcmb7qWmr^E+wK6;E!`#EAU$+#J$gOt?J4BegI_I{*bna`L##cZ=5AIXP1#o(
zvT|JXuWYU8HO)P24Ipj~?p*Y$&Xzo2!zFTHJmbId@@`h<9(E9C`d4;NRv`YD+zM8f
z+yZ?3!u0%n;xC281%)+M8J|EPdXTDu>?^N~gUkRAlHR2!#Pd$6J|oV)IyEQVx#MqC
z`foT0kf_F$SIA4)dS$Su^S@BD84g>@Tf0K+`xxGx!f;I
zG@p2Y?_(@xNC;yc8;q0qr{aKd?iX$~IF_QL?&%vgi^%wTIYQ&a7Ltg+c^l&$;?)m26M*AeU7j1LN@u+5TBlw_MX^EM;xCDSopcg
zn2cNvTks#)U!Bu8pbn9Vy8m7|AZ})vC;dG7?RT);Kr_v1M0j1uZU%Z}JT&8EN?|CU
z@<29sgGS_DWa5-9^DZhLx`7$~pCNa_@z58T)&Dv6iHL{3{E`=*q%1TqDK{X|($fb_
z^*H2FgEKI~0r}tE53$tXNRxP)HQcWdV6w!P2c=Y7^b)0JLEum()wW(oygnqpo|RA2
z!_qSUd!%!&I6{C8)*LD@5tJj2Ku&DQ%=eyHl@*##WrHoqr*41E_lU{ga0(KbAS`s4
zt#GNzmxPXfYoTBxqG6qPN08nNeY@fr%
z>M<#wi`{7w5)x)+=7o9)os{o&BD;PIXW*Y|xlnTGXlwjcakhVxMQ?;=y0G8f!ZI2t
z@!PlZx7Ax)>5wc*pUVgm&Zduho*Td8WP{Ogsf34@-suZ9l8k<_%3tDyWd}Uos3&m+
z&7*-l4(2Z+dD}`VDx{w7&ze8(p@Fs+8_xofQ0(pP-R=p$g}-S~&+s_>*23F%y#|aV
z4}~7VTm2tyF^O2FW@mvPG@AosW0jSaP!~6=JQ68_;L%0|69OHNy5Zq+fmJKRYxA3d
z|Lsn3xB@cG;Ly;}2Z3{CENpDdIZmQ-jIZ0Cqpjbr`so6Eua=<8A7@RWi>~OP9Q|Ug
z^78U+Tk*`psZyec(%V~a(ULaL1EZAZxpc1x4f6ilUg^LCfJZOVNIm^)8N1}Q9?iFz
z7s>Ozyj+nYhDJj8!*7Buo7{_{kp?+JQNlkk^w`m6;C59LSVpp?16DeMinX(){BAwO
zGuyV(96%r{5$9*1{rTFmqM{f(O&2*DupHKhShj};k%OrP9hiD)zHm&Fi2!)Am*^n$bm<>!3T
z%S>us;s`Z3KkLsP4wNEK=8sU!`;kV#a(+$;r@s3*+R9Ke+GxOphyVqxZ`=ymfIzoU
zcvv_a{f%oS)@%%e(!3wJqrr@XGrd)jlqfdntN8fBmpasqvHal%W%|8?i{pZLsISY?
z#`n6WS&yARy_08UeK3s`JL97(q1#w=H?X^<<@)2pUAI`We2~s*3`)3yT>IfN8&BBeJ+31u{94w%QMc(sxmIpbJbPM-l?w#iMNouVxr71TDQ{v4
z{*8hCQe#7xIr(GQns&J~`XD(fL2Y`wQ$9#NN=)#)#X#lSK))6}u_Yhqxf?FmYfn&7AnXfD+QV0s(n|r|LX0j~8ok6zuB
zwR#6t4P-Pvs5yTeeZAb=`kTs*ihc2$;DX$#8Dm_B5guqfz#jz4Nv5;-LXaSiBdEs)
z=nu6MmB&rwj~sTBn?|>(#(0q{VH_H9kL5N$ufdb`e!yt`Pd8Mxu|Kazv>+88pDF5^S6h4WqdJd2mgES1@QU`~bK|$?{4pVFS>7iSY^F~)NaUKW*EA0ZbUj|u
z?aeT~A#4z+bd%3FH?I0#$Nnbdb_}nWV?oc~Q}i_qDJ$OSBa#`1pF%bJX4uiEB@xlP
zPtN7!29xtN@_6MJhf8-G0||FmN9dq^k;y-QPFI5Qw>CFXb+xDrW3m}WuEE1IQ&S3D
zO>&?Z1AR)rMQMFre`PuwoN@}Rch1)dq2?cUacd3CzHotYiaB^U-d<0A$JU6xUlPG+
zx=m^jO)7<#hy~fqD96%jz8rn%cV4G{GHY_~cJ_Mph%*yZ<7kc?ctPos`DBX==;(Un
zzCm}Fl3Mz_E;(d)64$4RO1pp{epGRlKs&%#7LB)JkI~*%GlH5kJMy(ojtvDY|LHdR
zb;K8QNJ$_}r7XNY!7$_8@}I0XEQ
z197YO#c9pUXGy1MF)i4eW~iI#i#^3X%c$raix|t*WDg>RI%y(Z<5~J?Wb&LDI?dcC
z*z|3c8;WwpYSx}(V4&9u?
zm8oJu2Vc1hcvxQ|sAFHy=wgdFsOunNIqa>{^z<|X8SKj~yjUyq6o#3+;x=a#azi4h
z_ag&+P#SNs7G*erTrus>m&bHHwvhC;FFLDp^3QR7f=XFQc%tUJ_QWu@(ecZF%fC3%
zV9B>La(Ay*Rx~$@NyP(o&wVK!HTOrY4%doqF@6`;mJ9+t%qroN&emx}n2&|rqWg9`
z4^1h*T0I}P{_8=T(n7_4*tAzcE9vcJM2TF~qDg{H)VS*Sh1rpEz;VuK8|}LFWyHxI
zS|I!4E4kgTadL0HPJ^lE&L}UHunJug*M6|_#zIc}CgtqW!7$9c
zNoQvNlL%L)*agD~R+r2$E8CG1)GWWtIUpU#wSE2iwW{jCRH?qYx;hz`A^js410wqMe~2Ru3{6B84zNAHFfr1ugGInyzm)VJhJ)Hq_E+}3X$P?r@S
zYi^@Vx7(w&b-8^fg98OUYzBF$n=FekUPQ;7&Ybc6eBH_Qlaux-8utDaUTqUwWY_9*
z#l!jUvo~9rU1Vom=NKn_@dHS_#9O40GSz|-H8(X+qW
zG!`hg#e@%Rk`zgc<7>dBZw9nUxs5&-|1hfkUQ(j+IJM&;o-p9}w+d;%XZrhVAbZ3g
z?%|QU~<=RVXDdyMBB$}riieU!Piyc^|Vt1k=J&3+97{4th
zb{^!Nh=vy3I8Mb~+ijGk1}^rl*N&njKy`H|!iiY76`FbbTwv|DyJbYIS_)y-z}CBO
zX(*-6Pa$t={zkiHP{ZqFF~G!;IOW-h?6UJxGR|%#dO#@s9Q%_dqQ^!x%9&+8=K65f
ze}Kw2fBd;C9LLXKPF%X{jgaM9o)`Ez?CVUrZA7-HYgcCtsRSMF2iFORXkb8W~dn)mB4E@$vCLE&F$y9sy0P9_o1|>TvPg
zJ^YuXwxz#nT9{O&a5A`Ip1Z%$O8iVIMZ_bP-J}`ugfyd1e@7^{DlBpH(sM6stg?aH
zgoh7?zO6RVketX6|2F&gu{`5BwS71=3vB?q)-HuWFlsH_11mYZ?dt>abbWF6TCT)N
znJ8n39o+9t!;|3aeMScnh
z9fF9=E^;tE>q?_WQL5k~%f9Y*cBMsjh1M1AkGH@+zyNOvkAGgY&X7fij1K?g4h3(Z
z)Lh&7j8@&$be#~dj%N-Ji4Czs*LYgN0+T`Vzibt9&N?U&=AMNZfp!!`?tXcK?>@0f
zvUTpY->E#ZRk+NrP_cKtkz5b|+8k1EKONZvvEiuL^jP8hP1Q*Zcn7@3%38;eoO_}i
zwy|dVvAwS$ys~ys3umO>s27?FjW^6MA-9Q9A!Kb$BAJ@bgPAQW+=Oi4SEr!_eP9#b
zKDliNT+rxu7R+~fF@Bi0Tp#^9!{WniPuAH@9su9FCzN%;>AlLql!t|NmdkUgau*5#HMrEZ(GaSQsUJP@bAcmI$!J1*=)U^+VT3EP
z-@vkyf*2NAF<%jiWm+-SSPgA>M8
zqJ!NRMGc0l=L{xe1E@mV(xzZbN%rZi;%R5KRh-%MQJMPH5q8Fqbs|Xlah_3=3g0Qb
z9-q`(p3(vY*%&oZ3zj8&jfo7iN4r#HUqbDev*ZN4SaJqvMrrO
zVV^bI){%{|XeQa;)y=nGPsN^bBUaEAuG~=~ebSc5Z1zga07C{ol*B#xvK(-#Ov*7P
z_0CHzurt75yfr7C+aEl&BjwhME@%}vs1+L@gAb0-SCX!mm>U}?AJ9SDLvU7soPMOc
z;^sU2K%MI~ClKf$eAf}H(366kcjKnod8dTCr6jv?y}l(k_?7q0Z=n8usmYYV=^fqd
z2iaPC(VYIw(1#LRW%V|@Ze?QQX-S2yFeLiieP
zn^gWs7&<>Afoo>@V3(d*Y=k+^TlUw^+53vIT75PKDM57O6V-1`_YSohtpiZtJ|#X
z9yDI9Vcn^$5sOl`B}tp4+pvRke|0lFCt8u>kxH^|2J9VxnW#vxhRQ6LW+jDoC&N8TNvSytDL>@|mFSy_Mx7Kj9?OCG@DWy}Z8xgIH?rk+t(z4G7R1OvC
z%$N;!xqI(>MR?IxaMtj`Yy?sjBS|l|Cs49o{Eu3%yp8Z!q|Q-)eG5NIdO-&1l$FIu
z(aIGh2s_64m)1@e+hULXL?}fxNNVq7--{AUOQQsN6}`WU7ui_p4A#`tMDjK{D=y0}
z=l{E_xW!@B+@_GqKEN(~$ye1=ep7y{cBU>=R*V9YF^*r73^iZf5e83Z)w9KCY*}q>
zsPPP)<^Ccp5kp9szrsr*Vw_KAo5T<5$kaJ~G&-lNqD2~Yf%3DLx^Q82QjZ
zEA98-`kWFJ!qgqh$*;=WWC5_TyF>HO`+O8YvCqSc
zaCBBvwrUZ}$Xp@o=38F^mqH~|-*%l6<-oiIq!Y7WYYi+Id=WQKn;aC_9|-$7J@KSi
z9_O-a6lHvNU8@fdB*H={qS6f)VQ{y^#VB1A;7U}Q^H#!)w=%<}FEj@vKp=!FJ_+6u
zJ_D=5=|bRFp+7~}OHL8#YQIAHTZyH6rdlr7DAx$|B-V?GME1pk&x+y=HQNctnxKLB
zLgkW&{Gk{M@IclVjhOd=-=xlzFhaDh?kAi8FyiIK!
zl4ylT-aN15G`?H1yMNz(~bS_Sbka8#Dp*v+H30@qqER`DhJE?vEF7
zXzifLcK(Kjw%V#!iu2#Ib4bBBjn8AEL=mjM(4P9pK2zByRKYElzuvZ^V-l(ID7ukZ
zwv6)l_26h765&eIlqQGMq+q56TgISFM%WDa(Vd?*<`s7KObdIvCN`wNon=)ZzwS5>
zt;t_20~9)Qxj$F!ywudxBtDc;G1T0N!<+8Po66buHVcyr9R2o&1SN5P>6Ce!Q0H>~
z`|A(l6_?Vla-ObrUFpnzcq*wD2py4D7^pzCRp>bm(;;n`S2!kB?}IA#3JVJx-&KD9
z{=J~!!|qfm@DrPqi@?TH5ia0uEw`+jkIcUmk0BX7u#&gVa5O$!DWu4MHfoE_^?(Zk
zft5HG6T|8dKU^h3&Fj4tA!>11Upa$wLPbk5Vx&1y>OTO&#$T2Lpfg|*X+_*74OJ~I
zE$`a>8%_3yQw0Hl`19TX{{NOoqZ{0Id70)@_({sH>dErs=Ax641WVtG^V@PEzF`>T
z6BiChg)T~HKetZuGh8J)UhZE92Oc)*%R@K%%dMBh>90uEg9iR#t8{+nx-1E)B9N?#
zJwDyZ8lYSdZTowBv3J7K0pK9-yjD?V{b4YlVjIeQe?yMN!QVhu_27q6zpAtPdBt^3
zkPfMZc!;NUC-_xA-1%QhOG*~8qDhtSTyLy1qcw;Tf
z4k&)EBTjZEi>1%UBi}3a^0ZE|@y(P}y2!H&3JujvML)bbv)8B`|NYSldS2ziT!%O!LpPDEm$~
zUHWzPagXO8E>VPzYHU&Gj)xbCzrTj4<=$Sdg-VWfr4RPi9}nNu)xC-);{WGGfkGH~
zvmM6+39Pz*`GL~36CDAF)_MJ!`8VBEhI{h|PV|GM&vPDRl(3?O<3-cRlPaO!*UP`q
z@N?LBiv>s4%_*=UO*}=%8&nV^%#p}^3|`U%H1;D8@m81BuFhceV!cwp6b=^Z5new%
z#BmqyQ#H1LW(6k)hq%{q4t>C@jZ8w$7H$p5LvR=c7TFQ{Zs-bkaw*0$N6d$}N)b%h
zv0<5Sy-qSry1Gy#_F#^$(}8{e^C!>FNL4kw$gcDiR^7Dwa^VLcQvo_3mi;l)A0H0t
zmq~46zP1Q9biFMjqkyCKX7#-w*81QXIwZyL*fvkxd#*v{3&Vzp7ms(Tlj!BW)}}uq
zK>n-i_5QT&q>lgRYG@TQ7xW@Z)mj)&1?O;Qpm0DBUd!USi)
z(vLp*yDnfXtU9EDt)HiK9Ki-s4A0`X9J1_=2W%Jv$h8-%v@4h@ZExIY!|BkTiA?>%
zBVR`K$yK+()__-AkNo-gaJ<^B0|&Yno12>z8WPE@w5P(;436Iz$1n+3s9^6417Ie}
z_2duvuVsE5*U9g{!5`{6OS>%sdxjojgsh7Y>)_K2qy-`hKGJUdzWMvpa&-u?wq|kt
z1b{w3#zK#F3UrUOd1RiD-Z!$nFIOdnpvb+kNPY<+V>23X^sp9JT=dJ)Y&BdRh~^Ah
z9H-&mOM)6l%gz75IamJr8^w&E3Fy1TO5JXE<4Zd_I-b{1YcO&g;q-5+pITWO<7W}&
zIWjI+2?|vA9Cc)taBkOfo`52rD_2ttW7*WRKO$E5i%UGEOO`xjHal63P$}5NHN;Y`PGcr@F$PA<{{
zH@R{4ut^!$C9{2jq0=FS*2!KoM|8)i0h0Ra?UNR({^xS=h0p+IlY*sumLbN2l_G_S@yz(?Iu?G6MN6Mg{eUvV)S=Qz
zq6KUKtF%{$tDDB9?DR94gUAhCbMn)f>k`${MwvTH)DG}js?h+*&>p(-Q6>;-hOTK~
z&e0OYAkBor>`zfUNty-5oTgp&1NEZwjsu@e{|+o45^QYvP?Q}WOkAzq!|d2o2$OdOPpWj37Au`
zd-v(|H&?<`x)~$#C&y3yNPa3go>}`3E$iTv_HZWk^6P$PC>EBG!ft6?Hhvz^7N}fn`lLzM
zOQN+QVzniLu7Uq4)Ii3zCi?fIH{7(VSs$S#kqAX=-#gEEQ;i$806J-j;gbM#{JjAI
zt%W>yQoTyylP}VH@nUz%G(#)S%3xvji{8<}T^r0%eN@kR%5u(?@b(9c
z_|j%-kR-8XJ^0$}O}*@%asW)L?s@ek%5zcjyzW3tL@JhYZ@$&K2Hg8BJH^~p%i4Mi
zrK=vQRgtqnL$~eoHah6}jyTplan?sc=S|b`Ka(j9xMN(>)$b{0%nZcFFSGB1{l=I4!*@SL{Aekil*
z_yKcTW@J>1&XDiA8xf3)uh&RO?>L$HG8Ylk$e24FleZQv3;zDlXn(!@#1!FQ8#Vki
zEdcalA0!{_dGa=B^j`Q(2#8KL>XyIN`trdS|7Jbt_sjJL*=XqM|a-0cyacRHE=_AekrI*WbOKX~>#OUMQ(OmBy)r__ZBX
zWgO;Zo5k;n()tmeH_MJ!hB@a+*N>3`MVOJ9j`K!LG@`f;$Y|BF~8-{|sp4&Q@qJLxP@=Kx~0sItQI_SEE&1Ux}YR
zeXyLWOgj|#3E!eEGemoXhL*?)yTsirL}qXT+Lm@J%>?*6hQ_6mlAfA36d}zQBwg=^
z*XfS{asy%e3cS^4Z+Dlc_54p)ms}^hX(IGmvj%Ko>3rU|KpaE#+)4gL(`i|GgDn%T|K>Des`Cv-C^3P0=@a_%2Nu)pE#cB
zBT_Y3;#6duBpj}8!-w;TE3?%~oIiJFHAjY>N_n5#Dk}B@REpEHGhGYu%>O61q8!~Y
zL!$-mEB$y4z%4o!A3t8)AhMUsjYU8*
zh|x@R9C96f{e;Mtb^AOg+3ovZLp+vEda5swY3)0gO((|aK{6JqY~BP_jJ+KxK5R$I
zw6Fr8{Xr#?w&+SL;rYYo>X0ohI~!6DU4m=WaBrhqS%jxcz25KgA&c&aB&)kr#GyQ(
z9c%{XcmTKG_bz&4b8|x5!COHy)wch-(#hn{nkPle#hmUr!CxnqiRs4l(<}+-;a+7s
z9_aVj7^@>!-hZhWY-w7>5+8RJHcBfim6>*b?x5aaRxU+qBfAo+6LGWDd@l+&K`d&*
zVu6%=JO}8swX}fEvLbn!O6uxJ6=VO)$6&;FZjBoa&dj7#-1dY3Y=K_L=w_VA#LpKT
zneHN+em9%R@BD8z*TAzT?zo`$>wVDxA7rV*v>70y0PutSM(4L59)~z)&17m5Gc}$n
zF)ZRqpW7!6Z+sx1@My%rHya68%K=X$1}3`3#>T)q*U_Orla)iLZ3rCCx2}mGzIZz4
zZDN*>%1g4p4iwlrMEa(V^Z``ku$RyJNL|XDBVzdZNcLC_+fP89?Fs$
z;t*MJqEJ0qWsvHfHgdbFc;@3tY(Nw*3+&-y)~|%~KpF9f<54!+$VpIc5X(_)z}vTd
z@uGs4CGJHZWE2HPc5poclTiL)6AMoEVupB2svbtFy1k%$(WA7K>5Vmt7P8SHtMkW4PGw^>%?KQQJv0gwL<>bApKnErjSG%Efb3-XYID
zm5MYQz*6Y*&o1rqXb8|OX*q~A6YduI+Gl-~Da{Zj06**5A6xwLaO-&BEHec0`8e8$
z_{0doATojMmQ7W(aq!Gri9yiR9J?N#p**C3{oBtuCB7c}^XdpJ!{vAj+ypScJQ1Pn
zux3?oQEk{W*L+Fg|2^d+R|A})F4*<7$V2^``A-_d_lYbfFp;UD;r0^z$h_}xk?#V&
z&=bd`d|YpmR+I^a!+5gZ?JPdG%Cq{K0nBi+*!feaE&Xr~^o?-y0Xo6?Z}ZyS45oP-
z-g&yj7R^QIvLDd6rWCY}l1_H0?*kqwK*tY41eg)Eko^=OcogVJ1Bj@%U*qMZm;sA8
zLE^<|yjF3-z=l<_>CEDy@BZJP#?FnKKl^FV8gX3zGy-(A{2$Pyhr6p~mu_5qsp}Qy
zWD_@lvIGd@dDjNi@{3~v3b!M`Ky&%G5FWt8Nc!JjLjXP$Xo$f7-zFbiM8^Kk1%xa$
zLm>-w2Q$WykO{z)QQO!gKWCotrEkx^3s}k~qtlfgkQf4vAAU2%5|fcZ_ht?{f>40=
z=Fotb78vHH5;IaXdS^KGyyu3Ub2~Ul*m+YU%
z`~$=h=E4)mzWyhK{;!tP3jnSKQ26V*l5GNni-_w+5fH5Iw-o;wC
z!+*(;SuX#VA_;vl2Ot;CM{QI`aW0C703|7Qy3AnNbHNGdk2fQ-d`NjrQr73D5*YhR
za%;bsLt|C=>pT-)198xS+v4vMlb+0f!h-N)0lBba%KYj}Er9vdQ`M|o0NP6E%GBfN
z3W|sR_cvKKafAzy3h-}BGM=5DW@HZbvW75b0BhoS$Nny>Y@44eHSyJB>jKz5Ju*HD
z$eMQ_pCf>>
zC@dt@=s#~a`5#lzfJxsUcLUV8mZP>ipqZBiF!=xofHljj>jHV>3n$*{?fH&Az$+L`
zkRz01q=&>_pAPY+8XHoJdzAe8T`;?9kvK-0#ZtemqUc!MXWB*mM>k
zyF{Z!_r=mZo%GYK&q+OAulGih9Rc-dcPK8vBBXN-|Mcn8NV>>7pMzQ{Uz!j>&%?z=
zzdO%aZ3(COn%8A-`0*u=J5T_Wfr{^aN0oW^?(VKq6!{4deVWWbv*9T%!1jvzT<-t+
z^(zFM>~ecz-Jtz;8Xz8w=K?s;BEV8i7lE|xelP2bq48PiKuYmH-`2gy0d9;25ctnl
zdr1tUsKw&j)MRC4!|-T459Vv#HV2jI{5*gLGr#Z>)-&y&?|e^scc;s;y?0A>^ObNZ
z1U4EGSrV>mJ;spMP4^RO0)7|@s4todI>cRnezg2Fp-dN2&hD{&*nC9$(so=7ks$~)
z`;YvNe7=4A){^idVE~(_AxAx#2f$8Rz>t=czQJ#tlRF+jo?-fkhjn$FD?1CIW)Trs
zrn9=|098`XlX+M{8!mm_iT@sSNDSSb0;CUeR`zs{+Z9>TdaVTg3&*9!S>E?2{mU>!
zrioVJF&-VI2S9L?e%R5T7+=3TYF{27C+NccZkxvnK}t%f__I^9k)*Y$TC6R@>AThQZB!+|>hpk%v`4Y*&wTYi$l$rf`*L%Td$hQQ*1
z~uWGVoyEVQW%}TsQ39c0HO(VG$;3}!6V6dLvgD~IN$i5
z4zMyQMS(y7jd62CWZBWl>0!40@wKjQkU;jwnYTc?RH&>EXlz1u_EQg$c7~K6C8!D*
zJSP3*J5{19<&PiY(C|0M7=TN7{ZAqRcdKM;nQu5?JXu2pY^bvI?L?kpWEN~Ib%?i}
z8pH=YNV#!6DGABN!NS$|vWmg6v9Q#C)K}P)5Fq83^w`!>S0BO;xRxhtkLgXkVha+kJ
zIThpg0x1jzJNL)~Ts0qpJiOSPl$cn-!a80Kgtm8HTe&Qcb`FXg{Q7iOp
zYlQC%h#qwQ*Q@dI&wUwaKsn#P?*iGR8Q`e_Qnc<`3cscGNZKX>>MHXrDN!X}gN2O8
zMAUXXCkHzZ0~>gRG4R^(*3GX2?$PEPEPbBrvTLCOo
qt^ODqOBRFXo?_Pu9X53;wNSBygd

literal 0
HcmV?d00001

diff --git a/Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq01953356565641195011.png b/Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq01953356565641195011.png
new file mode 100644
index 0000000000000000000000000000000000000000..238a4692f3dca458ad4ab97330e5a9f51b69821c
GIT binary patch
literal 11746
zcmX9^cRbeb*SGgxA=x2&g|hd`h^%bcvUm0lUn|*Ug^bMXy)sG&A$w)7$R5w}d+t9f
zFL$4DUFW>lxgyn7<#Dknu@Ddta1|9~G!PIFyWrmf7)bEv3?5!F{DtnOs3MELh=Yg8
z#>SdQQip&*i=ZeY`NAu6*UwY;ec#D#%QsGC{q$ozdV1OC>9iQ1s%Z0=19X_vj^|*!SvEk?lMvNS*oeaY9sh;iedLkupDj{bC});=$1z
zL`Mxg8J(CYXtP(n`(`~FRUZ#dX=!P*@A==W^CK-SElCGTa`He0tvoerYipL?Pee*r
zb3rQ&t*w66)*CaHs})!UmW;TH05C8;m(<3-BQ6JT5@8tCY(udGPts+%a=>QdYfV_{+Ox;VBm
zG&I!Jotmn8MQ66-*z}R*P_PqYNBC5)XJg@#EKgQe?TIg@vVX85`BwCgta^gyY_4B*J7VQsOD;*$5u*@RRP}i&(744Mgm^
zEf8`w8*UOD6vLFme4(Wk6BBbeE8N9h*4ldA8-35--hQPo4j&7PlFt%{vcGW5*uh6B
z%2Z2hVsFnObmXIA;>(vW8w@x$?rv`Uyu6fOHiGWKLl~*66A%zc+&p4tUO(BLDK0LS%2hwDK6St|Zk6Fod!>5C@gc(b16
zxxBJ+baaI2{*34Co=MGgPDiZ&qL6BV#>B+LaF(z&2zJ$lpuX{Z@GD5pLi7$TTbpF>uM`toKGgH*(BwmX<
zr?{d59}|;8+`lC*PVLzl9E#4v$S9%
zJ8$xE=f+w1l$UcRD7<*_VtjN|vW0#o+;QHzx3`y+l$1QYN|znAjgl9c+r_}Zz^L_&
zmWBq>qr2DtU2E6f)pc_i7eFAbOd(j(GlTxU6#E{h?#q{N-n_}{bl8*E4{M
z?-_RjQad!q$fg@>QWSv(Jm~Z1#pUJ6tkF`rAB&2xQIPN=q>ILMKEISL8p{?B_>h>G
zn3QB;Wo6~yaI;;}?l4m~G&y;Xx6gTYety2Yn?XR}WF%MGMa2glJp%gk_IOMU%IB;!
z^@O(NVwi(Kc~p8i`W{zPb2BV_r&*N)c7H#9@E{8oBsLaLO>KB;YU<$NU}R)uo>hSn2{EJ-8r{>y<>%N~hNw>+
zd#lF%2T!hKC*lcB?!Opq_S|o~y}5KWT86r|w6_;^UY0ptr+FQZ6pOxIY!yCo$h;h-^abC1By`}-A2Ml8N1
zTbP@_(AGZrdi!_n+t|;a68Mz%42U?o#WL3PWeW&qjpwe72JlKFdptOa3XF=1iqKr%
z(CJuMSYN(;fqnZvG(?x0-M0c4CsgTKKjJcIK$ut5!&?hB;0jgu@DPZR%iwtxUQl46
zq49HPx+dno|N2GyEo(Z*5iXe3K3pAioMW}^n&%~O|~RES%{@lAv_LB0VW;4
z#o&9`9XT19@7dz*Wo7>juSbN3r>CaM$jcM=5(Z>tX0o%hJ1n$aZx&?_x`Yw9I6LzI
zKD(~}_%oD&uG5?ls?IXo}oebEKltfBsrHat!oS*;|+_`f2QeR)Pt)aP@M%d$-
zii!$reR48cb%YFk0tz~M90gzafcdh}-qMn(mDN2G5|s8|6BCB5Z`}K0AE-yAr@wsu
zJVBRTuA&+9#?r##Av1Gg4eDE&yaMGEqV;G3saJim?j9a;7zG6dLuq{SDk?D>k`iWS
zc|k!z03C-)W@gJx?%N~VZJIhd5u`fWb7=o@@;s$wSgz18d-dwo%a@eVz3JShT~K@x
zwPkdny6pZ}XU>X>krK4JZN7~`L7m*BD7yi-H-4U;p6>4dBu_C~QZq7U=I6!Sw_fNW
z@$vCd$Dx}B(kCd;5(H+yUzB7a@xM7=x3sjJsdtpecwRjI>z8(rL{4d`zPh@JgF|^m
zMV!(8)F9dJ{yq&2O}l9~+BCyE6iF#5DRXm1LIiFlgeJyAuGkfR%Ax7$^UM8qbgOT(
zjjrduZ7f_saZ2pg?gq}tffP^MS&2c9xQ
zG-lLtOc8Oa*?n8v+4=C|-*%(HUj2f*lG4(&XG~iK(brd3Z?9uUMbL8Q=;`SN?0<)@oFy>GLye`SrEO-L+SqKa
zuC6XE5mHjVxjH+nuCAVI_U!BJrAtemoS4|%+hZyLD5JiA|J(Xe;=6aVb90YKlq@VP
zpgyENRaPFX4kS`gP;Asn07Z*XJHjP2rs3uxp0Ze9zkcoEMSC*a9w5%a5i?+3S6|;P
zFgcVV*xb}aAO-vfZ@l1lYUt|9M~4MB?qH=?*U^zLYjB|xrJYY+!Xzy(D}(-`s*=LS
z#{M=q7_RkBgN1~uPx$H6um4fxS5yp}RfQMl(4i+Oh2;u>bou@JWn<@-blCE;S(Wb2
z#>N>_nh7~5*=#Ytt(_hAi6ZV2P$lAazsj=xFBDlwjGEjAIy({TuxUSvu(3s}7F8HF
zYs<|~H?j{KC7rpAP4VZ?St%)#_k*CHLpuA1hXWFA
zV{vl+fzDXp-8EEKue6^LaQg60UyD^hK}E%Wq0P_8$VgrCuVp>d9H@)ff~1rbnx}3X
zapE@!D>-FlJ4@XWs;a8KV%c)}we|Iaf`a*zReNh|Yrs&I|M-P6g`DX*IXM{^x;r{@
z^75AV=9;&*ww^wHia`e@;J!5us|U>#tk0DA#Iy^=1rNZ4w$GFL$eudO{Q^Fg0F
z3%Cvid7pv;6&+nV9GZ4<%H7Ec;bLpccG;LdOfFv~ogeex{AGYk6~J@Z$B(Paz0t_H
zZnS(Vs*HjU>Al%LzFm4EAmDLxb&i7;vNOzt-EdRMSP;VEb@z@7ZpKvAI
z6bpnC(Q>2KCcjG$5NL!ahWw8o^N{1{RkX9Ox=_>5IKFyCOhFO3`j6|vHJe@|L-}9r
z>!L(skbCe&Egc;ix5tRD1)57sv3>1ba&vPJ=Y94Q?1D^F!^6X)qp=fU$*{*#X`y@e
zU9XXq^!4?1xgv+y8d0DPsw!fGp58A
zJAPV_;PB~iRZzf4z-vr;|6W&5k1iA_ad&;a&zu(*2gk+LRa8{8(+Bx=2<52$CJB9W
zR8*AD=^k#^xk{Nr4>d~y%VotlpJaS?_QLG!&e9TM8#BF{%MG$w;jky&=(lgBpFiWe
zITf$r?LTmd4$n5HkiaM?Dyq<}>KYotEw9>8lMP1^iZyO<%2l(XYK-g8Oi4}#2$GkR
zYx6n`9$rt}EO=^IHx#5H@H`neNJ3px^A+%5>c9;G4tDRUg+h#m3;p3U3CPV#8>KY%$_F0RRuk({wjfEwUSF5@;pgdabC$T;_GA8_0J
z{3w2N);%|8$Xbf{k0QZJS{k{5Bupw799~-5H28_zvlV=FbacFk8mnRI-Yfhki!%9U
zlsMg9B1Z6CDGYkH-JKmQZ0vf6Ia>z@e=y>pUF~P;5;g(bg{Wd-DIhh_(b2KD{|qIP
zo}QkYYYP7}uJ08$IT%qev`$WMd(AMzq(E>_Yh?|ud%C&x%bDRJQQ^tU%cH;8pMRO!
z(xUiqP>fCWeON*QVahixC)DbyDraa3@drbI;sBa*a&kamaQRTB$Li(tC?k3icn?@v
zSQOOM7FvByBZ-;ObR4|A&S0s5=lkmu<8=%|yQcx3&EsK{`wUj5y238vNJt4JNw$g0`7&{tH}6nXJ-fZzCcHLD?JaE_(G#=-@-z6
z_mYHfG6|@^^&eTyE^C9piHS8Ji9x*FiFRP=`BoofWaP;*y`#m>5FlvSI9ONEmAfi$q3B@wC$4>J6UK4D{`RL3bSojsFU;`FH%=_=hxVT|xWpw_)+_g=&i=PF`OAM-57f4-nCQ?BG;Z#2S
zj2+z1=o0`T`bI_|k60tfcy4Tr&-q*^8iy1g8=GOa4eKi%X?JByiwI?mrH##_`;=%S
zgpFk=QIs!}3)YJr)w-!KkW5e1>fG6$yr%#`<18&a2k{Z=J
zJV5D0Lq%1|787M*dAosGr3)$!*T+gI7KA5!K%iU$bL*5F4E*?U7kGg4$7o|?W&JNE
zrobU3s0`_UAF9
zefY4TxLEO5#6z;^>+9>G@82C(QAk{
z!NK6Pul|1HTGb}S3WI)HTle#1D6SKx;;)$P=9cpG5XwJ;cfNH%Mm8YQ#
z{q&+_mQJB)%yVz{QN~I|`wbSa6n#Rbm>(sb+rO;|P<~x~;vjzmuD6Z<{rd-62AWx?
z!YJnLTNBpuwQc>!9^X*|a;_h+By6-K#erAfhi!`A%dS
zQaQa-Sy_2~dCH|<_h0JEgKfY>@a7miN=0MNR4LUpz(Rtrr*d+ZK8hd%CFFw@gzFJV
z!F@+ZM~Na)aCE8qK(WL6fcy9wN3$?JotBz9H9Za6BBO1Cjy{bC$kOPxsR5u}mIt^G
zPYu2hD2bg+EnDmXUPyN{s9&haCs2+J&MO2`czAe#8z?g_*h-3u5HAf34;N_^8*u6<
zE0@;PxI*TX?RPR=sFKmu-97w4)Xvfp&3=AtOxmdG&yTG8q@-HF`mbI|uj1popnp(c
z3}zGm*b^i}4-b3}sX>98Zp&%TkG;J;=wNLvt=557<`F#D%AY@7CQ7ti1otk!w+QwX
zywAEvM5JM0@E-vd9^S99vBc)*ZRU`VC@13-Qu69OYO_Tlzl3VS#=>%#ZE#-y@c`gg
ztR{>SNP>xxvBk6oA1?}3w4b<2R|JCsx4UC}oS&Xv3ews14Vq#L^F++R7
zWf#Y^{`>HO+RV56%hI8j#*YU0!Rb>(SeRj(?-L*_z&T~*Xai}dkat5^kurH;4R&_Q
zN=n{r|N0D}3^6fr{6_%o9=eG6*YrXmi2LRG^*{RiF{VN$ms0O_GM@2#41W&x9oAk)
zCuQg0odTnWhX*(j6nyAl9v)}tw)ie{UK@T&=bZ?tTyhEu@X!{PmS1~%K!QW#Cxl^l
z3Yf0bQ}E2)mjs6q85x5#a>x28zO8dNd
zqsyKQv>IF|ivfW>S_ltBQQWnEhO;XQ3(>Ag2?!3(4p*Q|gHkd9xAfwLHDVdahg@Qm
zSd*SYUD%8jD)NM^G=NqpEG#5xEu#rR$Wk?~G?8>XNhawJN!+2Pd-*}N2>*O?_TyCz
z08?XQBhZdX!GP^FQ6wWIvTbcf<(=j*s90Njs3Y|5@w*#o`h-uVrBFfov;-aM`+VrM
zsr)wYYHR;tq#~Jb6Hr_8Q=b10rQ=f7(jpa6826m_?(6H5XD7Q)P7WXiLcBu1Uez{7
zneiPs%{w0Ii$Mg`46i}YIWspmml&Ryoa}bv;Nr>_a?bnwnM2CDGZpnNg(i1pS=q4eCD3#^`T59rD%fFNV3B=%e9SH}
z(zzgQ2X!hc>NlDvOGrovGlGc29?Kf&!?b6XUS2{Woq>&r7x!?qd1dGcck1Jm7;9_H_JVd@7@tHGc&WXvB95|F--79dctQ4W+=3w
z{nTi8((XfgI;C^bR{%(Ue*O*--?M|IKAM1MmU8f*zD}C@`WYAl?@_adnXj&)&q=V>
zvOMP{v8>)^XB$gNA%StXpoOW#j~}XPYFu1gYRbynkSHwp9>wJ6GrYA7C9v*1$V^Bu
zef0|YsSsVpVyttcs&{BSB053Pt#p}IDN6gh!hb7_S}!2Gy9>HepA8~~dfD2t3YEFJ
zxm`Gp;(tPqK=Bn52PUhob^(N`91xq$0iuGIij;YmkZ=f(3iSk;KIBy!s@7%j0}OT?
zffy&t4Ob1*x!TV1zI-``Dn42t4*0to+1Azu+91W)pMsdU#&U>~GX=6QgltH)8DWRv
zx6o3_$;s8Ft?>GZ1IGsk9o+1Ed`F-Z01VHsPZ#E9XCV;#k@llo6E~xCfs1L
zmJ9_l9uN^#gFb>uo|IZqQBgKHZHOV^KZ3u|X^Mrta_y?X&?OA&wm{PEnM2h|^{k_s~AYi}Yoel}ics%E&H;jtK3%phJzmIh
zz6Hn}WPm@cFAPm!cmy;D;x`V+I5INrsXJ?ooPM?Wz+JRv^@9pX6%q!RD%e?DLu;WV($Wuyllo0F((ITaphpC
z8&UvbX4RddBwVQ$z-Ey0caaAWKMyh=NVY
z8~%k$KVbTMCGN@S`{~Xu_RKPj#Zd4uJ$#7DFIIg9@%qL2dNvdRpdZzP2k<_GQZxZq
z5YQ^>>7|vFY=IqEihf#|?0?vMuSfQg1`aGKC@wB`b{skYqAx&ONo+b@XwEV+J&;lk
zuOGphLbU7y<1rz%;FShQ+0D%8GdsdWV0~ePqFrHx%Z$yyzyQO~AL~a!-r(LcGpT@;
zrw-A}KZyAOCO?}N(3m~d(9*53RMFPfe*T;=lqE3$grNxaFi;&Mqd6ceG$jaD@^FYS
zZEb8|oOgJB1!9gYS~gc5@4-_NO3JOZwSV8Z+95z?;^5eYDqvR46oRYk>(3Nx76Ir~
zR5(Il3{m#oEH>qyQ2JVRglVce%ZtixAt2siu}cVSLgn)PAi!(gTZuRoBOyE~*{we9WV*1n4eO}^*Vy6g~6oo(dDapOeC#DJ~Y
z{+7a3Z$IO)H~XYoM~4hn5(iB>sC5B`k+KNv0P8n5m;0+ExVSN(vRK>v+$E$*^aJlb
zgJT0xcV_{*D^E^5QsrfQO|t_nZS3vuF8MX)KBU&@tkM=wC|;V7!&Jb1sS6b?)>*|ke>j^#XMOuJRlP;C}?1RYk_wPH
zdF;W8)?EpHc%*hs8@=mS1p0=&HG!k(Om#Lg%fbSSaWfG>|&f>n$3
zb#)=zIr(aSYie)Lk)QzABbnI;7z0NNM)YoA9sA&NKj78}#vDS@?hsEz$`Fx~{u>fp
zm~Hd(+5A}`m0JlBTyVsqOiXa*{rmTq)bo*$G&MCxTrmS{CT3?dsSZnk>s0`E1VO+7duM}1^kA97{cjAn*wzf*dL%LD0uh>ATJ;rm7s%x
zvNCo=chI*}2zHicrd^hHc5nm=4dR8dah+Zc*6|(UWc9AOfE8T_a+sRZZAi|mKQwW4
z9JZ{v^sbXfgnS>e38OaO;z0XQbtR>{ETpxi1&lvFm5|fQS6p&0$f)k5%vc_+8#&Hb
zcp6BbYiepB?C6-+SUe2u&~H4&CqRLc*Z7OwT?u8{)bzCYn_l<`S@=(Iq@T;mz(Q*3
z>77HM?B7=Vst*r@+28974O;y0%hUbtT`B2)U&lcDa>M2mpmaz{?$I$OC@3i_FD;4R
zo~xOguP%HOW4x4Rn>F;QXRj7-c!1SxfS`SnCKF-!~$
zT7!B}eN3g)8f!$iSSZ2pjsVoq%gb9)S65exiA*pQ$5oX6W*_nM=T8VX9`f-KyJ>(W
zRTIB{KXs5M;$0mcE(dvHe*P{elPo&Dxo!eM#iZJQCwhDl#;A0s;^;RM~4KApd9L>BO@VXN6siNEDR^u1{8q94sfD^
zq3+?@GGr&O1@6myHuzFsKRG%1?IFY>P}~~xfppndmHujJycVFEoU&Vjw>s3|xBD(mjk6GK1l(ROz#QkRalq03P`kwWAA(4kJ3$f)dieYI?@+eT(U5n6
zPQi^VPYfrsfD-JdiDjkJkeFlB3Wh&*AuT8pM1-a0BKfxaNyKi=Hcd6satkm(82eh6CEQL=I~@R
z3BM%?)jo*u@bkBTaq{*)2Negs0U{5CSD|WFlmGPvoaFf0*B6H%p1*f;DfmRB6ag|U
zr%U!HP1XuOypA!ihD*xF+Yjs?hZ2NIDXOZ1=PDevX45Vk85{dHFwp3{f|l|PGGJOU
z4HVl42?O!+TzDJ@Z4T?fZeZ6z*MVheKbH;Wv;2N`v0agoo=#$*4q}8b*cDbFKAs@e
z5r_xk0|<@S$x2H~Y$0wFiVZklOJOJ51|;$oyKUZY(^PkZc{c>k&THxHl5%q404mc-
zw{?{!oxu$a4L^TsOpD^nU}YFXWEB)7X=0KiK^srmO`8fqv6U5bL^opnyX53wU18Yr
z<&<~xYa3fzXd!5u;9w*O(^Eute%E6A_(_ve-u$9AG;`CnQ_fX?yVBIH6UF{9YWuSr
zR#-L6IiTBsyq(Iu-@IvRY&`T5G5e8-yKcJX{b__o%$cf4odpEM;o)IV3L~0fazq4r
zmg?}p045%u_Q>Rc=IhrW=TB~~FA1d}Z3M${5Ei%Ke>wE)*DuJxSx6vp`qJF2fA`{B
zCv~o<1)ykAP*D1>@vtNb$jLty77F|gQIAS|q*(|mlbMNWkTjn*kd=)Mj`IYWlKZ+y
zJ2*If3{xu5D1MQvzOlH7us26oItQqMgp9oO(F^?3UEYj1_<8cc)Gcz9u!H5EL{Bf$
z)b=ZKWGwfaZL|3n@7wGB0N6UxS?g(Lm|IUy;^W}7c7@x?g!yL}gP=%_*Kf1#iK%I@7`v8O&
z6zLAfR|XLgs&X;Pp~a(f+6IWjvV=WzuCqnF-$7{6iB7Yzxf%0)_PM`_H-BC>*3K=O
z#3_%U;N7FZDZvb@hS$^8Ak(EgXgO=bkd9bE{X=%0A16kHfdY=)$qQxK8{$Kl0sbmC
zj5hbcBO$@M_b!NtvA*5^_WBeSu;b*9^~Okid_2_7RBWRD<4@JAk4c2lG1Cdl!ypqu
z$uod+5ikLSxxZA!^{Xh)#}Q|
zpaUT`b$4~GsjW4HR53PI6;8DEtb*6J1Dr!cLp#gnT1pnC3kn~yEGjG{dZ&||17R@-
zhYDwBf5?G9ynLtVU~7Wp2r7amMzAHyb`XISXnHs;)el6~-!J60LBKuo`?sy5BmR%X
z1k)8qt8q3SEX3vE!9m-@6=7GT-7#Nw1N~NpYVO_QMzf09_c0
zaBzHvLq4Fr9i}QxbJicg3=3jeCYAK@8GT5xpl6-j-5)%>p`hxc!1)St3kFa%&MR`L
z+rNO|X~cZb_L}!u+jN@C%YOj4Z|QbcBBc}h4pHG*!67uKPgzpA=2ljT!#n?>TvTOL
zgc32J2ywe^{(N{9!;noa;#CO%1j8Un^9i*#w8}6PeESv#4C);NphyD~pCbkHfFfc_
z)PwobVh5s22eG84wl++Ef6dPRmx$wAU0@7H^;Y700yfIKAxV*4+Aum9eg
z1WfRefRS~M3ouT8(y)bto)ZjFBtRp0A*?){MuYN#f`?R;?`zixrC`bo3E!{r@txh9
zt0lD2kPbCagR?JVN3p|5byx6H3*3i%Zjff_SyVwiV0HzisYyZP|_cyEu
zL4!jIDWTzuRt}O)r-4Q9&O8b1y8=AX)mXK;nbH*9Xzk
zHz*1QI_QCd0@VI_vx&Ey$fj_VC%33bM_oN6
z8M{9mWEwTv8a6kc$b3J)NA$GO3)@|}u&?NMq|7SI2k3C6D6hKZUF11-B>
zV?;{-IgVgyJ_TSzC{cjt_YA>(-0qjRe{73?e$s&h(ZUS|4u*zv7{;8Z?V-2mq{ZVl
Vtom$RaA+GrQC3x^T*@T){{Zry9l!tp

literal 0
HcmV?d00001

diff --git a/Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq02739421979046219838.png b/Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq02739421979046219838.png
new file mode 100644
index 0000000000000000000000000000000000000000..7360a8aef2e01e8704f4d0ceca25c73bf06af023
GIT binary patch
literal 8830
zcmW++2RPMj8%M@5v*M6VMp;RPY)2^LSRq?x_Q>8dyeV0kCs_>{DYCQomc92Zd&hVC
z&ebKy_&@8uf9ny6R#hM+q9?+_!Xi~tL}_4QVMoK~90(lvcvu^x3I7qeDXGX4%#e@~
z-r_F#B2R*a#e$`TlF{-^+cfmh)oP)^R8|hkXGaOEXWy5>#Y_0`u`x=~_Z=#WGYVg2
z5UKt_0403;D%A)4tcS(1fmPf{Y{E>$&G+gklsY+X_Mzd3(Vx?K@jo6VuddnMK0BYO
zeBiN^c4u?qj#W1aGG^Wy=}6oB2IqVG$7#l5`V!`LN>R;+Pn+H)ZJtn(|BMJfP4qPJgIGcf{
zy7u;)L01U8y}kWUH}8`)@bdB^__v&nwoH3@mU}P)e0=*`TkrdUW%IJOYQM(2s%UME-rJYvD6oKg(oU-0zD=b9QxK0bTyefs8U
zvIhFKF6_<)c7+&RT--n-L!*RL$0B&-Zwbt
zyEPMpix;ctcKB!V!-o&o?>ms!eBRmF`T6rFysfXVhi92vSSTshIL*p3v|OA{U$F5o
zhR*-2s5tTYTTRQ%JUu*o-|ivL6$2WhALyR{OiB0aw_P6M7jg6R7kvIqL`XdzfY9#ZFdw3S&<;Nb6nUSL@1#PD}z
z!)IyN@8*3JF0X@9u}%XrFP
z=Cj(w`<-vrYZ;n%>jSUv_QZC;E{ZwNq1f58SjAc&KYsk>^Jn+Xu^Z_!EaZx)>x$77
zG&H%HnL(!SYHH5YL|t>$Fn?xeO|7hwl9HC;5SEwkTXrIECVc+zfla*^mdnr2|KNcS
zbOfdLFJ}XJ`NgkgQx&j3ii(KhG7%E5*49>X1TH;2eOFf(CnqN_O|)uW!_JvwsOj~!
zCRtt@crGn1t*b~H?LRzQ8yg;e{*3#MQ@u(#IXQaz`ue)MEId8mGc#GkWGRTk5UdO>
zVX~Q}rT5+$zW)ks;V@k@IX)guuA!}+(`PL&FOQFpud1qg3R`dVg{
zd7;JjB^)76(7dOSk&%v$&WjiOxhkn*4;~mZu!o{eO<5Bjg^_p3*vgSKjM%xWtH;7x
z-sa%t=jVI-_{d-r;^J_|K}$xS3{xXAtE#H5-FhPKe};_O^%Qq{`lGaTXn44?vXVXQ
zma(z%bbo(;ZZ0R`AL$9Gw9ZamE-ovlA3jE&=?Uz@)FE}cRi4Gk$$D~fAs7ti)2B~g
zzkaQ)l`0kw=#u#^h*!<4&=yD@-2^oy@XFA`qfC**JCj$J_lbc4L%M3f;iL!brK_uJ
zUo^u+Q&ZD?%j@BRf$PG=+`9Vu<0B&?UOVQIWrBO~>nbWLySuv-vcG@-wzszr3JOAG
zGCptz7{FsScXX^ZZNPs?QR{P95Ea#4A4W_e3%x=W-E{BXy_ebD*|Re<(wXQ-j~`n^
zMVP+(@@4(2ep#sO?3bO-fk?Z1sZD0pl-SvjKXA3uJy2B<)=l9G~o
z1)U1GPLU!q(IcayN*j!t=nE&W#U*1v-e=F)z4e$s0w^>#Hqy2StL6pl|JHf(83qBl1-3=9olGfAYx$H&LUZclnlkB*PChgMWp*6kO(r$Mm#
z`TYY-Pl}Ik?O2+d(=aq7#b52X_!jK_+|g0W;}65@7N53>#h^zyOG`^ewf>CzWrVLD
zHVEc_`4U}cBJ$vYTvRAFo@ft7>SCoYONaG`U9a``y}i8$9)AMge%RmJ6TWxPsKQN2
zTl+;vBA5Q`6w{JVQ$Mc+@
zpReHIG6Zi)NJzjwmX?;vap|h7n*vO@EGVw(2wFqg!mOTXYL+<9H>+nolsPH2AAe~F
zEa~R%?qF+6OhlxiqeDQL#H%MX<4zefFHCR0F;aY$SrX+k);>0t(y=%+IEbX9BmMEY
zhs-)iGf(yS^faXWB*pGc7e*@J?^JbeZtk;|m>VB108v6^Gk^T};pYG5tUcit%;<3<
zuh;6%O<5-=CoQe`u&}U<45l1th^i`a0HSZ-G>ovlI;7bQ#ZbC)GHiLF0hw7@>|9(C
zBkV2C2D~&>$B|YRTgn!+d{5)k(S23pS?m9=A}cE^Dk{p2qNq~9;3qi)gEBgCX`kWN2Xnc|CNn-hJ^_J3Xgu~%
z?txsD)8X24yyyV0K@0zrt@CH(DEum8SE`(s)RAeFKEJ)Zyu!o7q1w%gkyDge$!GZ_
z@dDBFKcO9qi=Exw@3DO%ock)?n#FnNEIT`!EdYqbtDxRn?_z-M4C8%NJ!)5(ly}VGQ_=bpf@TX@bD1&2Ec^-wI3qj
zxBIMx0cTdDSVT+wxv!~z%ao?*dUt!iJN*F(eh4Q`G!HcqriY76O-Y)CoP(1Sn((dK
zi@ARUy!?h0sH{FWH@Btkbld08+vm66#>I&;kYRc}oSZH}+tk$b(IcX8)7_=6-Ti$D
z_l=RsN{^6`5UcJqrT+rY&U{DM^U&OSdU_ywcxf&-B-OE|t*7VW{Pgb&H@AECW5V#y
zD}-sGt+f>z1CiQQT2^+k_NCCp#f6MfEW5C<)@#>NKtQ1CVgUDe4+6zrjzx(}Uk%bU+P)W^9ue5)%6S`E#RLaZb+S
z2l;4Vp6)c!f!+zyuR5EegiQQg`qE1~BgL|5j6z(Pc?MK#-b}@<=PR
z&cxc*_HqlT)#_T%A98&qF&&HbWA@S-SNxw{vwAb!3dJFt`KDpE)%OdCf~>3tbD?V`
z_X<-330}idv2Q%{ySCno1B<9+gsJ#+IZPC7ea8=(!zoeH&{NJ2HLQx
zv2kHx0nm1HqC8II54-Enb`I5r&~82+p2)~Z8%M|cLP94e>xJWEV>4+NpS8cdd_m{+
z=s8r8jm>1W&v1Rctd7oo;mennjG>FGYh7fF=?Gx!!QbiW$w_-VI|eE$AX_R%MwP5S
z;I)~v<2%|YwmX=f*D*0QUc0%vyhi0N7S`4)Ltpg)L>@TLHGcfa4b8GQ{Fg3f9?FB7
z2#_M};YVSwG$hGGH@Cf%yS6W1VOht6^vgG{J#UR=kBNx^;RD4t?RPkthw)!qS}Jwl
ze9E6x>9xBA-<+A5nGU#+0I-D4L$Jcr;b%V>2AaUkjw~o-K`ZE&+UeE!dTDF_4mjV%
zR8)xFOgMoPe|`PFYEar6NsD%BApgaVq~W2VKjUSErKL{{4JCwx-Wog!4vvjgS5SyR
zb8|4)SP3QPsiv>3th~`(H9>MxM*8~s$yC2pVia}ZxO?}-QfD&MiCTvE@m9lCU|M){
zxG~DEITIQh+S%C&sOl_|cGq^u`uTHwT-;7rDNGnAXeQ*Uq_v%$u#nKs!NE{}|05+O
zQ2=%`vpG=9s%awIP~lu09RC4%DHnbD@|cAj4WRcmyOSf7e%rIEpn%_ncacSrlmh6(
zSF*OGgvvdOLpn#}_u$~z`1m#Aki+RNLFgx_Fy-EX0Zo>v
zKzjt8ot+)<4Gj~M|M}T5P(Cme=rIYOgExnA*)6UrDx~EU)J0zk3o(*@V&dcDmz?{&UP>1%Q|2jINVbmHwpSoZ?R#sLM
zr3rb55pf*WKND#@oyou5*XiZJIe-#76!-vDh
z0qIf!{w5~xLqZyQq%M?w_na7Bzj|d@WD;~qe_w&!Ej!=Erlm1ZQ&S_6D+>#52OE0G
zTW`ui>GAPxX1$hV+lwtIC{W}uE7mn{Day@VnkaYm5@+IEPpbye%f{nF(g5hBp`k%Z
zNl8LV8WJ2_3R(_vg9@6gHn?^96?;DAb?D(u9U^4B^qXC&}!jV2*{K%
zl8o-#6tb}AzLLyoe>#!`r2J0yz|MJWjL0}(2T5Z+`R;aA2uUJ1#L?emRt(}Ylm@gs
z3wdiLZ&;Jr@8MyoBzt3PrQ|6Gl7>dJ+sDfPx#8nEMv4oj!3vQi&ETij7Z&2*ykVgb
zu(Y;DGBA)t5aisAPfn&gSugs#x~i_O9^{^j=5B6owjD08DAqOhKen*7{qp*C&%x$+
zWG4_0h@0#8>{tZQV6rX5`T#%^60VKb4D|N{tULzb2dW|@1nxUu&BK^HqyA+g7{e@8
zSAYO6B82y<=N5Rp2(+P>i^H%
zlNdMd?c2*!?a5Ht3kE7SG-Q0$%2C3JmE7<#Kc6JgQX~e97<=f>?k*kPa27cYK%|Gwo}6%|F4iC)>~%Mw&}FcG^?9cVfrw(sd5W*SBS0g{v37F~F#+XEkq
z6TZ>EqSd*#I5
zsk$z8(m_@cYwWGj8Up#tOCBnsu*AefNOojoWT4jJb8`(4#=Uwdieg;@P0a=q!PXG$
z=7dk5c=Y6CEQ!J>si@+TlfAa*dUgOgsh+r_u+Fzb9ia7mh
zYO*-qSwy#iOoij!c_DAEfi4AG4O-~dll-HDamV!Z^vE1fuwZZ)T*~jH`Jw%|sfl!*
zaKYO`c~6aw@>I9O%KZKPLHn6sFRG~UZU`rRlLxkG;-@nVr#QqNL=Bo1MtJsY9_mC^
zEVZlI8GyLne;O1a@Cy|)vs(TbBI_Y9P1*PFiao;Q+PN>D+1Z_SKlrPd*MW;P+cDGQ
z_)85jfunvWJd3$<({Pc;>uww6Ie;v1OXU8R05ts9N5O@ta
zxrPgU@@N`9B2v2BmDyqzx
ziWln#8>4ay7*W?H&~h_W96_M;mY0@fyCGGp^!VGIA#u7fS|SqD@B}FPIk?tYYZ=5opf!%QAUE;0qrIFVoi%I!u8_Zk0?y&S@VA|J|e7Mzs%uM`-FvQKY22j
zU49Ib%F5DGgNTTTjg1W=y40?imoEbc@I~63-fMhM=kR@(xZaN8i8{TnLlcHIO9XARN+e#}nK*
zrO$x;_m20THG+v6feX?a8s$BFH(hnOw-;z8xO{$NzWjPVAhM$mHZ?
z|0*Rl=##qMIEUwR64O~6^jT9@6ErKVtPZC+I0>#2JRp()gU-4&NwCN
z%b5L=gKpk*|1*&s7e{sd`e_rb@r;Q7(bg<*7Y_?@Av_m!RnYsr@u8t79v&Wo#W~}k
z^I0L3Ig7IEC8dzQ10J!c@tV2+-bx?fk%5j5m%TSh$`4#Nc6Nvju2$8?1i;v!ofGm#
z`}!bP=9Iw6@sdZO%+1YsiYh{F&@bKs;VILN{rL$96uHx%BKyQzxGtCWW1C)uC-6ovZer)e+nvJkHTDRl{e6%P9wYd(!(F+;s2=jn;28UPCS2J
zU{w8dn~xkB9(!11Ellrx?-8~INdt5ffvMf@a*x74mHZDMzJV(tC4I$8j~1W@1D^`f
zQ{X6MB%q_f1VnWA{r>#}U@s>}UO_>@XdYCH-%cBsIMY?L2VUYC0IBd&l8#oflR+sOachYJ-@C~*1o2k&xha0-D|!AJgNHsoZnr0-NB9G&qG%lz$+^())O
z$qA4VH>o4RYK1j)#ZAm$_>cix#cgx!2NaToL^TMSKuU^86eQ>-;*wO{)a?-N;ET)d
z8Jr8`3!~WjE>o00e;ll>72nb01pOKvr3=0$NaDjMOubhDjySNTeXF+Fo8+TG(Yfv}
zP{P>gXlzJF8Z&j`v0pQHg|!Wbrrb`wE!qsQ+_0?*+TU;X!=!&KEL5%db&
z69p4UVlc6>#crz_pwJ-C(bCd_3kfLCv^o=mN_Rdsw&0#+jJQ|-(9orD1}uTv(Uv}d
z93FOyzaoyuNJ*)!F_H>`6pMq&$17-LfzbS<@pnP#uI}!F!or@;&i{1j)ISZ%7~5XL
z*8t0nD?0qmYkD#{8q@x#_L7)d^EOI0Ulb@Ofxe8Mq{a7{XK$UzAAc@>)>5-Sq(9&M
zDsELp)2LMkkC0f8DOWYU-LhnM>TR7UG5TVgA3P*vV8Xq{lv|n%lD;Dlwe9vuDIgLO
z6Jr=eUAj{4!gbQn&=56|#n^WnJUqNwNF(9V0K9Ex#T*B&H_&MG48YSarjs!WN(9;s
z;!-)I?WBRsclizD0xC!Z?u|hzlU|7el6X4*kOmNL|Ni~E(H-}8edp;2RM?dI@zJyba7D}@=7zwhJ8}ROXCpk;5OEb)
z6Iyk4A@}>Q{vR_=B5;ESmk$lu-JVxK`CJBy_1-_KkDr%6zJ51gpqOt!jQzsb(C#BG
z(GB}IwRhj=!G(c_fx*P&q{K?QtjyJ`S2Z;Upv3St1W9i3@PM5I>kr}3ty^CzD^nlk
z(9_XDK+GAZm!(YBfQ5y_dHJ^hX!KSc0drszDjerHIw}em2b)1Lp6Q233_vO`BbeV;=O41NfQupOx`Y_wjCb!=W@oW$Zf+E!SpqaP`a#!c7e0-^=oA`R2qK+2XS&{D
z8j$4wzda7MfcqlSX^nl09j7IUiKDI9n#_d~YB#C4OLX(MwzdwZ1E!~^jf0bP
zc_C5(aRwPO1fS`_R80+@yMlId)#`zf)j;T9Bb!X=0C~0ZA{FsS{nB95(T4^M*Fx;%HaQWrG*Qd~rcbmiq+?B8I85hQv
z&F^qqaLSXUaA!*Tm#~9_1IUa_3}(7F&&|jvK!!-D$p=
lR4o0^!h&#q#(B^YGmAcbb3cyFDf~eRmXf?GszBBx_68$V5~aJP6s4uRQ$@N3q(f;?8fjD%k00#T3mqLDTKS6IJw019t<5zx6t2CQdzH^yP}uoA
zsi~K;Q{Ew-r
zckkXYRsa8DUto51b(NX6`+a`NfkvZgX=!iYzCFGqXTSA#^Cvy2lD_`2>AY$n>NC6s
z225K#$z5v#7DW(70fGIqe}`=98AxVY=ER(!H%4%~C9y~-mcqvXff+-yYQ5L5U-$I%
zOifLR#NFcHa7)Dzjeh>@Sxrrik&zM7FC@Fmf5;Kx6(&eYI_hQrsFs410!ZzUkINxdhOe=
z)?SPHy)u*@7Z;bKotv*A$9Tp_`%r;wCMY_3fT#VG-*%WDH(1NmbbE7C(yA{ZCx_9?
z>v-1x;(B4O(^I+|MD)G2H8r@k<=;m~6SA|V1MpsxzKp=l*li0qSija;b#ii&m&bJL
z)-4&j!S(g^q~zpAXqADX!9hnSCzcvbY?i`rT(p%Yt(=^k9lT`)1qF3=51(1=^t?%iPi=}0LWo2c1yT6sy
zQksxM*BCSR+`W_fr`t2FP{bF9Tbr}((rj$D1Fvt#-;l;#XceZCj<2cNgWp0BlPVj2Ks(ALsIM_s|kPE1TxP2qNP
zb@e$vZLFxMNKKuLAZOMLT3N9v*d;eGG=#dHY)lF;ekE%1A!7HHlM6~p8ZXnYb#-&g
z&&!iKJJ449--}6KK>^3h@`bneNhlF@T6%gUTUb~a3kyp~PDnW66xqz`M=?ut*?1pw
zEK-*#j8p+K28o6jht3z;kF>Nt+0TD|tehHPr}xSk33xJ-9+U^*qTvN51~q$!-7oS<+#+}t|4y28A?
zbGx2YHRuothRTwXlK1c5x3w`Kj41q#%}tFg2@WFzC#Tz!znM_@uetQ|a&i)qlk;uVr6a3;kJA1qoJVz
zFq)U1o|Xpfg^57vuX5>qwBm=lsSHyvFjPPTl$H{S`yP;!llLhHsQ}8@J$Zt+V2c&3
z8hltA^gDZ9zNh1_Tj7oPw{PDT7Z)!qSinl)gA*Ue-H_f|Uw>Cz95M0vU+f*JZ2yyk
z$9M1Et?Q0D_1T(kgeIaw3(yeb=jP`xzPhCw6chxj8GrI5*F)hZ3g7Q^R|%d@R8+JE
zcXj{R+uPf&ewxwTNJVqd7C$#AsN)TvrSpGHENpDlbaeLC*577k)N@lu=bp^Ak&u!3
z@63HF(T28aVO2|$H-EY|n!BXfVNpGbWG5!(0#N1V1}=(Zk0|k-z=MaJDE<-`6Jx#q
zM#f5M1m?!k!h(rQ5kEIAjgB$ZfsT$2L12(;Yjw-?0@-bOY54j1X~J9@jg~&5?aRu`
zg@lFKMqYbLm{9xqcO=To7_3r~M9L%$_VxW}_TUNO<>eiPK{Q31$S--!`W`NH6XE9u
z2jA7yMBQv;9ZT8Ra5Oah`TF%$7nXzTPVo$qO`f|JWc*>Fp}r@-ihSi#i`Q>P6x-rR
z`5Zk>5a5=KAl*AVoV}ZSXK`f(7dcu=)@U$tPEi2dKR!8eo~*$kd_TUnX6NDIfuAeG
z7=W6Xm|()FFlyWz&Ap52*x1;3^M(fBTt($`wPo-4_;^Ii^71lp%DTQKE1kWZoSdKE
z`HyPL-@kvOL(b37VM0(HfK9#eH*QG#(Mu{VU6=4oSaT@GzHuX4++z!WARNKM7yaJE
zApDQ`j8<47&U;-PzqjM>W4Dd~NjKlmeSJBSMT&}wI5;>R1elnZWo2avZptMU+Xm$P
z8Wt87S9wf`#X9NnHT@p&VJ
z#}LpVg5)$dib799qlt<(s&%^dHq@)6%vN%&-z@Jhs*ma^aWza}>;
zHr(-+&Tz8txWqe}Mk(?^&ctO%ck%8{~q%$b05R8nY?
zF`Y%zU^Ib(+9nqA`-Z;6Qwzl$kp$o4GSU)LPUB}xb!iR;>t>Czte`YvJIGyv(wYIe_P^A
zs`0ecN&yGPmDGVR_gcL6+m2VRAs=7gHdIknWg#AkkcE}Hxw*~z|2GYI-+>?~IoP7d
zU*1kmO?~d~AIcE@GC8TQrbgm64g+RrV6d{fN`xJB*+df)Z7&Yv`mI3l{MD0=q2aR_
z&Je}L#T641i@th0P4F>=cbv*Ve?K2Tj7>;L$WxIF&=P8DLoZ)aS@;jM2VuI>{%ySe@22jh6+MT1o3^2G5=*z$CVtXM40Q5l7XTbWxc
zZZ0mnhlhB1RJW+&xeH|h{
z$Ci<+>%rPsKIr+0UjxAe4`~M|aPR
ze)Gk^z<~I(rM@cjPMG+Wk!=58l-Rpy0iHgTT>xqbnR84LS
zr3)XqN3J92h6
z@+4XAOb}i`Cz#0j$?wXYou`zPlmLqWXt=?j%y_|$%(nZ_F0(#dG4(%t1O)!#uj8F`
zN#B!$zP@{ju66bGx7gV=87ycfKm`KX^6*Hztv`fuIMB7RvC-B}uBh080v!8D(Mf}h
z;OAd>)(G%G6Glr%m-olDOu*TGBuny=gR~z!0N&K&dJTd!H`m<8W(7Kh>dKWO^~|W(
z*12!~xPtGnHj$ybDP#LpTUItOHI=sT2g89_pv=z7&@}|f9-+p^h9l~;ItqyHYdugPd=v=@NhNT_zwLI7G{KU(I#(EUs^g{A)%?4%{bbP2e+~{r=;H+I
z2(U5xXUvH^Id9zpg`)Lb>Ai&4-V&%{>`dRerT*jyS^0LrB_sz1u%cqPqazPQdT#Eg
z>FJCX0(n(cqTXGP?U|C|V*KBqQ+Q0>U0vU#r#sl$0VKn#ufHEX%aT5{@~kl}EsdVE
z=>7X|U0oqVHs2>E4D|Hysae$J$7T2zk$^}_He5Wscse1%Zx&Wog05?tH?ie4JZPzV
zN_8uViHRRRdOKD2h4Ua3@KT1f|IEyc6c^V9R3C`h*dsJ@ZgCOX
zK+t_dZ|^bZk`^Jt3!7G
zVT(;nOeopZl_v`m>BJ|R(C%BjZtI9t*H&D3d`ZBqqLWL~mG!s)(Cqu%s
zODij{Q&Q?bd;k>%8)?$w>9V)fk4H)`Lko{8*KcTIa;aex16nt9T
z1vv-++x+q4N0-GObc%XaA#%?E$-S3
zXLx}}fv#|#ZObgC8cK6;aNr1eUthmH-S`}M45XxCy|bkE0eg6-sLMiEGMk3;bc4B*
z)8_iRn9U%Ksp%}}Mj#0P^W#;pFiZQ#rarsfao5G)YGkX=s}@8A&;(~D1PI1}|GaHb
zS6oxWk4EQ$Q%}W-*oAG~;^#iaU0hrwA|zDL5HYv4WxI9j1(f|~{S$}BkI5D0MNIVc
zz%O%7hq=(}YHQye#(2!gpBIk8k6Q#aTBMO}Qf<)gtubUx_5rNU0g1O*JGUEkaU4#qNJM#jeYq@^$7rO(ZH
zX<*}_+TyNjDC8q(MUXCCT~@#)U`y=n?Li+wHDQLpyRgMO%1={;By?KGyL62`E0V^vjQBj0lyg0@yILMA?3-|@VQYM;^2yZzV3ZKI*5f|4cq`vTLCP~Dh(0Fs7
zbux}J)e+0T-#Ns*1=cDuzWyDmyzngQbO<(2$b{IzGaD_UxID
zRQ59}+%R(}A-FWzba!oSZ5UGMC71n`AxKW3_0v*Q%T3#7dVBE@GfPXTinV}1ZR7J*
zOawg0M45haQj#1&F*zGkKZy)okCs0-@DMxu=){ELpSMLt*REY#U0S*~(r|C;DgDU6
z0OTBE&z2(MOH8+DENJ&Wv4-bzAp#%(n!~c7Bm0D(I=5;zcV5n$wJ=mTH55@r(5|#(dcwxj{
zU0ox(0H~cE9l@qEB4!X)-J^QxDb4tfmH<1*Ig<5#*#eaho;uk*RR$q
zbLLaejXi4fIo<JNDKP}w~D`Y}5X2sm_J3=!vGo#V-zrGYD
zm9kRq9PKWyj$|J;UB69oQxz%U2UQU~kI~61l?{PF^oH5WOAhUb@;h8tqq{9y{Vz!ER8<~WTWTY%X}TqQq)6<8Y@rnk0ACfzkKNDB?c
z*{y`IH@bVO*`xBqhdT!U0N>sSIb3bLmVCxwb#bN8vC|3i-^$B(U~;4{sLYCpv(3@7j66q!WSrGFimLS^Jf(u
zorxbmjv(jy_jmKf{t8JoK?uze#}Yws@8Ykuwfk8Tjg5_702kn+6gj}YZ*FeFHUQpg
z9SEqz%SRn0y?S+plnW)Q?n*n%mg-_bb5B+EXM+oKba$GV8)y2^IjA(<3M1&lDXbn2
z3rHqZR8+>s#_stNr38BdPOLr^>DXrrl*y^A)R6Ly+~X+S@PeA9sw~
z)=qYJce}g0FHzs|JM{$ig^2>w3K{F^>8W{Vn5ejTQBhIwFo7zBRZ|yzFmkP}m%*9C
zckX#*kim3bmbAg2z?xjLF{n0o_rCxhF|n~)F*?94^70|c$&_h}wUZmL)#BpfHMwYR
zp3hm3-{j`bFD%FjUcZy>1ntJf%$=h8W6TPF+ur`klNxu9JuMAQ&i408QxA7{*4JWP)@b|rXN@nz*;C*d$1!D!
zi^x?#GQw)W^ns*`+7ApWH>_un@PyNV#D1%|Yr=67yI=rjA%{y9uuV|=IR7*v;4y>?
zf{D~67QDpy#Mv}qU=6HoZOLf)vAu{Osf4o1V#ng1Y`oFs_2xS#ofz|nwqkAaQFw6
zfQIhslE*hUHqLBszW``Nhd}N^LO>9Rl7goOxPW*J1kzoWi@!ggz(DOEUd9?0U@8`2Eb8YRx~
z3~++NW}(9Cd4>-c{LW@gg2d_VIY+Sa}THuLO^pN3wq2u!iwHEEIb_E+6+qf
z?b{wmQ3eL0BR+0jU0h3^jvQ@f=i&lh21C8gw@Z~{0mikoyxdq@8%w!W!|0);s3m|E
zTei=GOU&}#k-|!S11^x9M-q+3kb^K_8jp;D@#;yjJ2opaw=r1@O71dg5^-6`Q;6<6
zxgP6g>UX>xghMnM$JF70frcy};yp(jo43%O&CTKlu`RB@#-U@tm4Pd^x3s)#HTLC8
zv;Tz@OB?<_+v%^Mc_6Uox_#UKXdVwxS1MZ?PHqwsP>2k)a9f*H+~T80k3zITwJ8}J
zw|@9A@%1Ye^3ja9Yxz}53h4DOqoWOeXD5(1j*WK1mlY&sBa_8dR1_4tx7#nII5;j{
zGeCaVO)U4cD7*Bc;NVU;p8-voz+s@RonKMGz`#I?;FFLTgB!B8UcMBBn3$k$N1Hmv
z@|7UT%FM_BiJ(hH77yUq-P^l8+kOGT9BeMgG61J6errOi8kLCbK4Pj5`lFP(y1IAo
zb{-tKLS$YS!Nvk8I>y)glHW@rD>D;z_@RSCX+vyGv3-YLvYE!bNJUOg6>#9vo4hIx
zFegVl^ToyN{{H?{p@C_EAc74RsP&&28($AIf4S*`k^^*?K0jjS=ih@f4H&uib#;S7
zL&jBRpK7cJAdh-;e^u+W-6%!lD<7
zqJ>uT*=wpmM(*x+LaK_3?Z7_53fSx+zxf(_O_(tRa14?laOo00N4z0BeTmE<1vi)3+?T7O4t;S+Nq43a9<>7z3OccGI9zWk<_cZ^E^hO@8
zKsXJNJwX32I29h%m5hw&
zy?;zkOF#tu_4hxy0xMp2Z=;|*YQKmx0jf
z3wb((ee6qkxdjDQkOaVCI1&Jd&5J3a
zoE+r%Ha4cLi3tOO^t#N~vY(J%Xt3+Cbrn(`ru4-us3Z@@#K&s@R6_*jB8k^1cyMkr
zl6BTiZHh|&1d}zItNOC5!wbwO938`n@EA8aBh{aqfxH~(q~AF?IisVR1^MLhOSQ`o
zduC^4nds=)I6IqYYlEc7g0R@(dj$rf7)w_Osp9hT6$*;IwK3+piIZ=-rq(t#aCQ!P
zO!}KQF|-1#e0=1Mq+PkH6HPvUm@(h?h=}_nWy$
zBA%$i$AkKUIb(f-ktY)r;f=!-I&IveWihXgEj64&PH?kSzt#o=v9PoZ?7|H=ztrat
z|5aA<)O?hu!wW&3o}B?ijp2s3>7?a6M?ppk>8P;xg9i_S)?Plns*sJ4@<$l`V;Z&O
z8G??6oXh{>%)`PUN{7Z<$A$-Uvpod$OoYC!2EagaNmE5#U9nUchH#B2qzWuYYwLAi
zI4w--{-z{DK}1TbsH9F-h7@8m@e2!ohd|0w;@W@joOvUPvuU@JDBy$)km$#+E98Ik
za(IBGXR6GDm&Qft)m>a%%*+_Yc7B6+uQHX7eRoe?ok7gi&dzQPh!YMpfH%OzLc%2J
zG*t&kASN!ZBrkt}B^mrXjzNY{ks^{I<}rJ8etteU&|^Dliw6_m7jIIfWhD7?va{!4
z)05)eHfi_2VCX&v?FWEdUcR}t755W!?axEVInOjdiJZ78R9DIExy&O0&=)6bSIgP$$=g4O2{&KmJ=
zIQ&*rB!+;R&B-+&J0^xWxqsw)(O=G
znnda-(EJTJiVcYoMD89vwZI%_I;MPldb%M{A7pJ%Lp*dMHp=`mCxy!lfZ>OXue3C1rdT(4g-d)As5qLidJq~NHI%cuF<97f(c06s9sEuFG8joWvsnCN7B
zSUQJ>Zr{11({Wq9uTz!4m>iEu-@gVyG$8|ILqHCCbc7JBA>zcHkoNV3BY%bLNYwMg
z8SlT7eDOEuKb&BB*@VEaSHbt~%wsJV2m5_QqU_9QQ(_>NfI}l-CahiYn6VExEDY1I
zQT->W(STAAxq<2i8Bbx{jw5e;XZAZyfjecaKM`)cwXmIRbOiCuSokpz>oZMmx0sn1
zLBp_SKT=|plsxN>rd|u8A}D**xRTRt>6=_)n?rJHxg!tH4rzcOSOT5ficV9ykwH6_0zDj`10cBHj
z8}01w{@VK1)`|mL02(J>Jbsj-ddGU-mjo_p9qHX8y-9nMMvo9rQ$BeA8;Gv(f#sg=
z?%dL=3UAR86f*1&h$ZRyEU=qizBg573*_fb=1JccuKw~u%WgDh%wI-;0($$}we)}h
z#8tfF-c454JR+-6j+iCi1d+J()YRNe1FVJ+b*(SJEpTa!gM&l6NPV;3IK7TUJs39@
zBcQqY1tdk*)`eB%v2G0PArcf!)Vz^h!M$prj0*}2L%Gwp>s9{m7FU4i^OcRC^*mBi
zSq;1N808pAk`#~?u1%qsNa7a9XKzlD$q2rIQ<-bm4xbkY{_nclSfz{~8RQZU4!;8e
z0ziZi6~V$l;sXg*nzplY4|ee0XG8(`uyb(Ci>S9PxqlqHz%%~8du>>dGXabxuBM_G
z2#}8<1eEe&@8FaK((7#a)gY<$KRBlL1?!x*ByejznZvSX?<
t8}K&|uJBnRTCf0JU8U&^h5Y6t>FHx;Q4J2sRJdG-C@ZMTSIC(K{Xb6R(yRag

literal 0
HcmV?d00001

diff --git a/Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq06559173809755883972.png b/Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq06559173809755883972.png
new file mode 100644
index 0000000000000000000000000000000000000000..77889dad3ab409d589626ea395714e1e7b090576
GIT binary patch
literal 4178
zcmW+)2{e>#8-6KE5@Si0!pB%DpQK5X$S(VyEe&HQYnFUWma%1z64{M)LUtM?j3F_0
zNkfRpo-AVt|I>e;GxMIAbDrnDpZmV<>$=`I#z61hIVN5v2!hUOYN#54D;|9QVWb7$
z|C(GRgA0SNrmh;p3^U7_tKzj~YRnJ>qt{ebG6~39dk|#6VRqp|ru{^yW`HORl?k&r!Tx_fp*QHu1BIdxiTw45y#HiK<~lEDm{E
zsZC4hw2+oy3pQ#>WMFC|sf>c4=`g*G(El#TK7
z@%3&`5`0SDyorVPbar>M189#fVm6blsUuQO7oJ*N=Q)c<$Y^B#LN8a*~P)}MX2yC%$NyEI$`g>G+e
zXWw~JTvUYSriT<^rxVevbJ%(g^T4_d99TtHr{rdH5X{k6;G2h^)Z&QE2
z9uzv^=Hcds;06UtP#>XC4w-M~&IhpgvO-mqH61=BSR6@ON=gvMXpu=ZskYQeBXjm-qJV`Vn<{+!--*?EOn1YZLSH8=Xw1yO-BwONE7n
zog@<5Y45ECa$Ozbwa)Hbl0sBO1SmH3W3sRS=9iM4o$%fO!({hXevp&<5F!DcZXk}}
zO{nU&tkEsWBlUgZ)P?=Gc&WjG(rQJmZjpvJv}%2`gE$z!Ci95w4(7L!;<&L
zkL)QtR+4GNSt+z!COPNkBhyEZu43Mdxt{ue#Kpzkk(FIuSvf^ZTWEyuv6l_nmFPR;
zk`bI$b%Gut-Q${3c%2!K3n~~w;%f>%fBrljd{_Z$GvD)~L_e3Gn;U}c9UP_zK8xV^
zDk>^sdRgX#$0D(~{r&x-qM}QeE?G%7;_>+MatSy*f;b!6{pvDSkfQ`zE3d4qY-rfc
zkIyS8nERA^)rPisOI2oZ`gc$qsm885F)WNG-{4*F(Lwj*RedH-wHQ@gM~8AWQOS_g
zUh_)o`Xr&YsmZc(ywbAX75qTx3Lw4OY@|B7x>#6PT53v8!v`~G~cDwNz~AI~(!w|=8$VpT-@6^1(XWN2teN}lID
zOLW194+>IJUYN51Gw~9RB2eh3PoMVo_H;94+^1Wop59NQ4|J2`jWj&W4$2Z}4+hYi
z|CrqTWW)6SeHTPVO!-*?D-@cWYwh6hLo65beOO^xDO6Cy)6-K$g$M?<-C0!n^QxD<
zhQ_m`B-V0Nk_ZlfQy_S+-LW=-^yjowOXCLGeI7L7`t|EaM}N+5U7Et;%RXIU}fmKYNQ
z`qaw!#9UW5;lqayxw!%(H{|3dzka=v*1L}h*4EZGH&1VDyya!guIlQ#oGg6rm4t=#
z=MI+)e@TCWf*Q=;*;%7(tcygdo^Y$T{5P%leM^g#<9u7bdVx1}eF~r#q;ugK3Nx2*
zOH^ERaewmg@ZexOP`>>AJ2_=T6M*@_smM_k-J?An><8^!c+b%xRlCRtV9IGACn6%^
z*|TRiAARn7eWN5V&&J+&o}$8{iaf#7W@|eg~JP*oB6uE`I8nq6y9y{XIc9$
z2;5!%`4iA$jRyQvZ*S0Sa2T@1uLqms#aH5w-yIHDGle;hEh`4
zGs#_xTg^>P%F4=&*fuE%VPQLo%>)tSY`rJTqnA0=9wM|x>KYrLfSxw5=c0V~6+e0T
zO-)UKp;I{d<6~g(JU2I2uf+ID>RB!cZeCtm2moq+VS!&EIIDZEsIU;s!}?TnV?~Ag
z!S+%gudXP^uAo@3m`vM(+<=$X7NSRhbkY&l#iI7hr8|%@S=V
z#XbL+P}}yJJ{Bjz^=R}D9m?Ot)U3I5mA)O_&Zfr|^ivT~#6g%}?npSG43;F!wFYx82FqUx37Dy{&C)*(kX
zV{t&O!2pge1M>vPhzAl()&NfQ+&M;Uxp_4fo!8hnPNDEM;SAMgfp3`$V;K_}eB|QN
zGcdr)!qPC|CM+!M?(PmOoAxV-FuS}(dgwX4C(N^2{l1!8w~
z0#rdZ+hhFg!_!KbE%L2?mpbiLnN}+QeJ8HCA|SVNa#;h5kLoR%i8B>_otNdL1?zzi
z!(wSu;A5_SMn=Qs7PX_UJKX{y%zv@!-z+aJUGwf8HzXx3EtIOodJ(B;_|j)MH6z2)
zz(5h!3C2Q5SeO;`0O&=0JadI<5U^a4XD`q*FsPiN?(WLmx)pM~zlcJibb5@6jEahj
zn>x4pFAgKfaBBZ4n+JFkep%m-ZZap^Bl@5&Yin!TX_7lsDg!k&JvrHXrY)?cMNUsm
zP0i1*-b&K!N~#?H04Y&jUH$J;U!SWTxO>XZ^C&r)4J$}XOWXC&&sDxI?|+yBDib2e^En*lAWt8UzSO++G5`10j54fz8M)LQc71^G&U
z)?W;E>VmWPERB@d+1bg<%V%#rp5Zj)mk+EfEd1+CfXw_IrOY6;>HyO8m9Dy43{lj+pdL|i^a(e_Xq8PGbrE=-f`Ti`0`=nEihlwFL%p0Dl!}j*}
zsw%0UJ)YC8moEMd=Bz(sV6fAd(Ta+pih`j_F=t_?3BgBA>IItm`X^fh3V>kFLhk}L
zapn?<@$vRue@uBJ6U|IbgAbNUs!$6!v9I_Jx=CC8z1q6E
zx|*64lC3wvD!e_<2oG?g{@&-{i7opQ{}s=!cpijq?ronaTq!y8RyT()a?jJqZ
z>&d;}@Aq@gd9PEriqbPY910vHBqY4&veK$ZNGKfeo(&5H{-kg0Pk{emx;$5Win)Nm
z$7W?^`9e~KghY$Pq6!-`{X72$KXSP=r3c9)
zt@%H6baec+F&TLkH?D5Vs98+$_=A>7WFT$E7k|~S&xvB9N=n$KCr>Z01vvU0%Ij%|
z8r_=x4z$xO*ZPt@EPnS1IFaht}l(8Uv>ClggX1f=b~2XGv^O+1+S%E8Jts^vtZ}4!{P-lrp(%6F>dLaIKSq|3#`kQoptwoM
z>vTty|Fr}6p?gDF8F!*wcB*RD;KbC_#l^*zCBJOER+jhKV%YY0aXP1QTM28D%zMX}
zYKM8z8Da82D5%nX#m(0`YHH=?y`;yNIVJ0X;GNY_KlTGtQ&TG|E0x$2P-ld5AVNqTg!gP6Xa-yiD^gTBh3mf|$X)I2N
z@6FX;br#p%smk2q$j=E0j3hX_ySrYw=uyiz7vs(D!fCRML-y5UV;VxKds|!isYDwq
ztE+cON$cwCUkxL_92yxJ>Fe8BPxp(8in8D&_gR>Hdqb@fnu5$=VC2u4Us!l~^>@Dc
zeC_^7eYRVArbYn)l@JLfrLDdFoW8`t!P>8s!NI}dZ_4|4+LT>+>iOyE^MhH>#)}P$
zc{C*?9Gb6AFHW`@7#OauuEN|U1DB|U-D5s{c(c%ge0&*+*i=(h#lgiLFVYRGOYWo9
zwzp@O+<-6K$1~5kLqc*4Z{fLgcgtSY3JVEcw1wbe5>Q`mWJMcbopf8VQ;-^wTg>ot
zf7wUsc?}=w=Xb+p`U8YxXvNZ~~^!6>*NTcvmte5KQ2j`bP@&C0;B`b^#4A92P
zdfq-H0|NskC8e%tn*BXHS=sLEtAF*Zo(D*son2keq@@J}1y3?#xe&{L+%129)`kj6
zl-r$@qI>ix@?+;?3PopUp6IS1KRk;Lr_TyZ96A*nJCo(Y)XHJ}-DPifr{t5^;d>GL
zbGjUZvuVjhK{#e3k?%Kn#MwT4_>hyG4PQcFqfH*#vwGN@#8GU}Na0#c+0venkPv?7
zzrcqaVq#((9MLjatE;Q%7#Ke~JMZ;jedMwH4G;a*t5;AWClPNWBlRn7UcPyAs$w4?
zF0r|}nVp?IU1hh;69OMOJvCKVTYJ(Vb$YNU6)D4GKO+!KnBK1}CpTop8yOi1r4L10
zU0y!i|JIm1YI$#OPeVgPv)I6AIgRdFp^hda
zBMT!W#zITsv(u@4y;^48+t=4OR;0VWyDMTn#3-A`!AnI&B}l2Hqcin0k+t%kf3>Kn
z==JsWf68f}p|1uMo=OiwJxvE=->K1K_q>+*!Ksj^K3DJNF!<%^RKIJ^gF<;O90G#>
zVr8*14Smm77u&;{Z?5arSyBa^b7Zp4w+f2i>RpuUXlYF(^H?q|E#<1``$}+eai#m5
zPS)=?Z7(cvw(Yt$Utbi2goGp}Cdy>pvp2Zb&Cn=-sT5frOXPzVY7G4q9v%*r%}239
z#SpBbp)r>GB8!cU?bwoNcmCnShtSOYTwD@ZgI2sWG&DFNfrMoxB`SI9+{p-6Y%9V~
zI3Xy)tNw*4DZ2LddvK>*A`y2&jgQuTO*eYENlCSD3}%J(xt7$`39+(n_i{8#`^<9X
z7Z(>76^#o1%~HgSyLjL2`SXx
z!XihU7K?y@W~5B~vxvu`beAe4aX1A91r3X57!mb8;}j8@?djMB{EQ9&O5X|=I(Wc7<90jzA
zD|w64Ok11DQb)Mo_4#`K%L4Jg6v@oY%t-6v&=qlaYi@*%WH>MwE`mlz)ZN_N-r{oR
zN@?b)S5;Nn^bU`Wjlqo;88p^4HKn<`!$DeXXsngY*q|CE;V>
zZ}u)aUMPGkEsbq8@{hv$`Ri8-zh;q+>*8n-t$J^n0pc63N{a1=+7aS-o1*x39u;+%r^H)Wuw|58&^^Zh%1
zq-^u!Tvrbd4;2+c?h9^&UpMSNj)qC;xiLT&91{@{k(ZaJ#b!|F
zVy&Vw@MpOTyO%m#TD4$Qia+MLEb2(z_wPzY8uc|b8FYU?@9>y$Yr_G&
zeEHJc-2Ca&r|`zZqxSmsR*1h7AB9Wqs7-))h6=Mm!ZjYWElPlRp88ffo>X;HQ&VQ9
z@yhK?2SIu-UlBW$GA
zm!^-MsdfI3i_57$O{BB4vzu@lzLz_>51tFw8PGCH{AXUBn3oL~TLp62hg+isC;=TN3y_>xmO0S)`GYl5&3qd5OEGzFy4xOr=O8x+3x3
zftrra1xlxT=Pkr630(hez7e6N6=x%TCL_=$ou@<+}tK6CKI-&{PW(X1BnG+
zzO(@$|M4ToQWdq0IMf)fSUFwv0d-q~se?na<6_&L|CBxpIy2GIA}w7IS_@Dr85w0X
zHi}4Y09WCq*mdyxcVCiOOpJzZWpgw9SjN`Wlp(C$xK!)HR$Pvim6bFC{Z&7mIMg@r
z$*N)rnuVPmHWHkZB7Rnl!rZ*P@aSkVoDiXRL_w_w@H#qTh?NC|J3l`!Zak$(>P_r1
zgEI}5eDCVv#Sol<+6`l4`x@X=mwHn7>~!V>J}HaZfHqxiVd6RCMN}XI};NU
zTJk+q>OTbq1;L3(cK`mZ#F8j^yqYOJZ?nF#f@abQES5fY4&8-QRno9BCnhHH^YicX+e^#Hd}-bz4+#zRJsx}p)en!c`{&P##V{HWGC_pU
zy#PPJb}q8jH8s85-Te)dCHOunF2*55D;K&Y;5RrG73{!Q9+@ipwEokZD+#R-rjd>Broe(G&GOf-@REZx>`MIcu`Qi
zJI3bQv>Lpg&sIEqHh9@xajhL*OrwYfPk
zgvV@M!?%@c+=jM+C#6zXUG3)WEgaNJ{x$1F+_CQ?%<1QG4op^H5>6NOsq@8pf@tqBak8^}
zM47&YZ^UD3Yn#x!8bpNwAI|sq@i~Yp+@v|wj`-$gKP4r6^8dou$wQ%(fKO3r3i-#!
ztHE!`%j1Sf(g|NcCjlMCBTYz77KB?>S*{BzDzXFS0Nk88hXDtO+;EO!(ENbFjq!k@h^E=_KZF^}MgMo7+BJ%d!dz`N8R_lAIhA
z3VZ27J#JtxXjlwHsa
z)WpQZckX2MSIK2B{c#6$$)Iq$xxNA-<1ktFfR(ji{R^;qpah<3WSE#?AZ&rMTxkNS
z4n!5lpkh~0RD2dqtyt)Rhhnh0wgz0+-z})MA8psj&hFRxQN7xd*T&^VbL^bsz}mO+
z^7#JsXo|-;C%WT3S2vb%J_NpDccVeoi>J{{>Z*$Y-sd0iVFo
zYqt^<5B%kuqk}OkUQn9--QA#`e+HmpzTMA^uYGba-*mOTeg|pljabmlmP(V$Cb6;g
zVCK1=Mu&k#u=3L4qC~c;!)#p$0*|i2OSs%+_x14b@apSxz>EF)W{yNTFSUqk)cr%P
ztD}Cg!E&=?iK^kDA<)ghbj!=iR$kXCkpdeU8j`fSFZ2`>9UW>>P)JBLUW&6!BwotB
z-enVTeJCe~X{WfAf78X&Z7RrDd7C|1k6l?&F)}>dBS$_lBm_OZe}C_1J+)BtwZm6_
z$_l#p9(Y~|rMr!ogz3E2ai9lORpapc8Hw|r;1wq+Vvay52#bi|ZD~!FR8*i@Ud{2$
zJF}h`$55;^*VIh>{*B-F`v6epYf}?7Nt7>6wt+_Z-;S)Tto@l<1`-@D1na|x=O^Qa
zA(F;0V!XWSTlM@d|6u0&h;*+b=gIDew-bhL>qE5#@V}GEGUiSOx+|Hf!bp5iy~0D-
zz|_W5>N6iGX!poXm=N$3&JEAv3VTP3ECYsIyEW^pETkz2=7c4AR$13
zgy4P2YZG#f7cYRWZ0+wy6UOzff`)tr=N;bz{e+8yBLs5;z$&R1#;9gxP|e0sa(X(!
za%B4=n|9gPiV9vq!DPwIzSJk<xMlzjPu
z(&bdC6S;i${vHn+O6%Tiy*AqzXaL~V_lb!Q8+iW(%^~C?WzUbm
z_3G>CU0z-7&(@ERjjaJr8&3d6czb(qxOl?ogy)EjJ=pwH8JaKg=bruREs+c#4G;i_
zD?R!7EU6QDSJom8tbEYIyW7x~3N|1uvQ>eC&Jk)dlO!i6gYtm!52s8XO4RCzEljrF
zqAMkZnka`J8880g1yP3%qo=8k4&Zr6^cRhSV!c{bFh1yGe?;CVqo(#fKU@V434>s(
za@=71+qry^hGBy{U{0JgeI$*TC>z@jr~nYez)o&yfkF)05htNhYEpW-nB5fb{rf4o
zxw#t~R@}*j%|acVJHS4HZFI!W0mm%REVkjLaCSZdE(ezhKQ0MS-@Es`LY!tR(5+MD
z8k)|DlAoX7u*y~&FrhHi^4F)kBqU}kC}E+Y*MGWbikmK2B;P`1Zf$KrOMudDVR!>D
zE3|wG9g2xhDXXZ6o|>BBe!MaG@9#d;co)zY&|@G_K1pUy)H+-0>3y!R|97%I4xP_$
zH^oRAA$GBm1+oY(I#R}Yr8^e%P2dz3+(yRmChZg_!n9P&ZGW!O`~0xU=iiTLni~+}
z?w+1)d$@xg7`nkSS#VPN^=|~9JmJu<{1+Vd2Vp`59*ARz133C9hjTm9urYu-=b+JQy%x^@QYPg%JiBHvNnYMpG~pw7mrtY?xPV0hlB_U2;{1>uq5U;UT+ER^MH}(8OBXsRcm
ztUw~HEiXGCEVKp(2ltEJ=vhhz+MG7r8NM(6PfJ%9g!<>VBj1jy#1cS0y}Qfmx%~3I
zF?qPOXU;quBBrpg5bU|Lvoj#{Kt;S^28dlAw4eQ14qqIenfaWSW*bX>^3q3*wPe!DC%ai0s3
zrJ6^AV?O>}pFH%DK?7V>*PlOs_8X3r`f;x;12?eExIJLLxCn{x@m0a70-x&EwUj^s
zE1ZvyuWgSEovs3EXJNreD3zT!T=Z-KbwIzO*L^cP3B0SpY5(OPKYnmDUmh;B27Ukj
z9V=72+@e&g5Dq_~$E*$I2*!3gxA{-t8KjSN64KJ}zcnQ*SlHSYD!n%oO;_INb`kPv
z4MYbx1mAkG`R2+&G@XWus?q1)n;*#};_D*#ff@UcZ0aFaL;N_JA#m_e<%A?!W|3mMCO;?*lwww33Maku*7#Wwiov9gTtu)vj`t#btt+VS|+
zh3zX;y{CB8uHH<+l9^g;OadTnX$1fZfMHLCDLYYu2AN9BgP&g4dbcR2I15njULlCX
zJ4{Lu)Ir&UR^*}9xDsbLx4jK4jNf6-*3y!Uh^W+Vy6S=a2S9`$9UUu6ows%Xm;}jU
zexil9vY3N3BGdUh^eH%mbaZsZ4TtYeF+-dMXCId-Fa^=#3iTVCptxHZgzB
z6c11C+}qcWLXJj-RD!h2=`Hcs^f(13wY(9_i+iZ;^s(K&y#?RD?-fC-_uhj(R7(C>sPOM+}{VwWkYG+*@^8jtGTDj6rT!R*lKik
zap4R0Rj@`dVVqnx7>zJXg3YK!Jb5YIX@$hyR73jLgG$MWL7u^+bFQ7a%c|jhI%!EB
zx;x+O2bPL-{cMd>`|xo5!CgtLywR>8<;E}~PX#0c3~3l1B!`&=x;Goul09i2ez5EZ|AR+JNm
z_41k;f&cysQ(%hkF?(x;LJ1JZm(pm|in9AQ`*khlcc<;vZBHX)p2+0nNpQLmo&iz|
zHa-?05qK#Sot&Jkt@Biv-@kt!{6HPVjD$q1WxCIsy%|BFRD3C^A`Pj>tp~Rd0zIf{
z;PEuYs6TRMVgjs3#jEcH8U^ibZ40uu#&e_BU(Xt2hn4{2kE(f}MZBscG+z>jkF>ju
zl+rXE56OcQtH9(u^z}I)??qEomP{lHt}=%NeJuF+;Od_4&GxPzg-7eEs31+FO*uce
zwEXtQMH6f2&#u!-G?QU-WFLql;fk1pO=he*bL`t)=8kBekS{-}+Y*T1I`q}J8%
zWwIbKgO2C1=)1*QPtUsKq9Zh{u)4LPM9){^-R3<0-#NB%nk$>@!J~VKAHa+%Bh?oo&~f<_4}UHz
zi}jIay)PLxm2{~M%z{)gzgqzNkDGYgGsfpa#t)cWRp
zg8(0cVMQMLJbMTnqngWba?e{!yF=QLd30n1comF;o1^p_Vb?7ZtN?&U08Rl6CC6?f
zPQX93KrJt0%nnx}f=gX}{oUqN+&?jqRnva5Z1r2ZZy5v}n_9pV
zKr{HZvn`W{!Eycyea5~I2jDv^)iv`mcD(OpKI
z$Nh8Qf&&#NC&@9|8}K!OfJM00{m+-kNIY+(!dI_4+S?fz8CU)S-Mc*MpR|3Wu1-RP
zsn#`uG*8??_~%2-drWb?o+HSpfP+G^zFG3@{ToL9k+m3^tbqK%Sp$(!<5~2SO_8sv0D7z`zxHmZ5nFYy
z#Qpd_iTU}0l?~dtxw%;`TTVd%oFr53WMEuJM@Rpz|LxEF{MCnZCqE|-1zShr2WZr{
z()2#dJcWLhC$@ItJ56cYU_oPCQF{@@%66SXj2e4HYu}l|f=?Xc#FoJU92-
zq8x@C)RN!H)=LKm2Z(?}B#B5!T%aR?RsNmxitb&ls;mr9i0xg?5F;)h1lx3Jd0Adg
z4&p&eYimKkZQy%;7n|Q8OxLe{{UBB8FXR@x)$~V-mopcmAI5ztU1Ac5!#K2s(Sg=`x9AyAIs(DzMG
z>*lE|ed67OB7-^fHX@?Ds%r0da|J)d(Lfs3`_ro{Dvl2i<764*nG~V?ra|<8r3;qH
z%8vP1FZ<>k$J`i*vj`=2NK4N~;6C@3gU
z(@4Z0C)WH;p9(}VP@DT!yr6?yCSe7R01J$S|nqI6?u=?5q-0JAAg5u&XC*dbgyg-Z5$6{?%
zR8_%FQ!(1ZOqB`9>4%0O0&}EXKRRDXnHw9!z=mgg0`|w95F6f4K+YlmBvy7I-^RaC
z9GRTdhUntO3-A?Oaubu1EX~aq1)3~=Q5;AX=H(HEOT#FF=V%I-3acTof#XEU!Ojl0
zwJESRa85=4?eI``!J@-9R5x4)yM7&sr=O0F4sc4)4%w=kunv*(@gr939lWY7U{AO0
zSn>ubSpmLQLt7hLx=ucaAWxlwn79nSHK=xKdKy+zbaixKm_Z*w!0JW`T!u=(aiR8|
zFVlBW6=w&F!26tGhzmr~2)S6nB??4=CFTapOIHuE4sUZB2v#6`!Wib|=O-g21%zA@
zV2_mrrV3IM%(2g8z8A;VU~EM#gCgAoD4iM{e4(yBGBssWk^wbJKoAZ1yNo&|DxzLg
zS9bwEzoN48JWxouho*mf^=D`9lwAO>7d`)_AE*05ypZy%$#D_IK34_;4X5axMZvT1|?*Cj*DwseN7|c2~l>_*%I!VmXI}E&z#r3$k|VbZo8HNfCB0xRr6N<
zxYH&;3J9>`wbX$f)LxSG2$dnbvhpQdISEc+jXFyRFVsh)@0HM;tIOAt5=RiojSq>o
z4uK}7iTP2diKp9ITO-HL0ULRVy4Va`2V&P}S?hfJ$R?!|v`*B~nAp^?WAXdR{(?RY
ze%BuY(KA{R5vnv5LP?k!3+Q2W+Fw+eNv?)NGUQT{ldb<5pe_Uw`q<2F{>I7#2!N^-
zVqt;(5UXcr--b7*oCIlv-CI^)!@M=>JYMO!Ptd)(yo`$c=c1T=QQy85X7Lqddb#Rbpwb`{
z2OoE8V8Fkw3hzRVaAoz6JLJi;lk75)6i<(xK^xj_;~C?(IZ@iTa`N+!Pl}DRzg7BH
zK50uGJ+55kg^Fkwdh+Dvuip(n3yTeSjPL?%fB)_sN*Ae6s{MiY=+T$jT8OglBjh5M
z-J!25FveEdZ8DJend#_^uP@IrFqw9Wj(T``^?w$G$SUc>hql4MYj1ljTS&?0
z+ePL2kDVx$<>iO1cv%uz9z4hni-nZk1peRH*r^^hnh(;!Epl#c*&KA;jp&5y1Y&`W
zhgVcw46NbdjPzW8Y}@+LJw)i~>v(Zp-IT<{yQMKyjQ$m1f&(?EbKTZ>^M;R((lw4Q
zc)`&M_@FP!5_Y1TKG@`uk-z+%l@iXNf4~-Fo3fv8`ejjm;?s&i+3H<=4Lowu$eh3x
z0v2F##^sZ!mEt3CVsE`V;nAxwgVCy2|K?F_s3hm?dK+SGx4p0ma{tetDS^FllHh#(
zb9~H5N((qHEZo39j~X=suBn!m7U6wO5DK_t?2PY68rQ4-LS_pTN$&Y`Cm8uhM?hr2
zQ^a$kCrD^`{~jSPFCQQ-{%zRQ!~{YcD7xpFDwMFtX=}@*_$dJd!KY7kFv-Ep5lv4|
zNtqrTTwGjiwJ3iUN%2xo4`I9#Mm%5wy9_{ii6T0sXmF~Ye1OajHgI6kHHy&2&JKEQ
z0U5Fm$VH*kA#S%Cp!)t7HoJgyfihT+b~WqEQuQ7|ZM|ED#P|
zy(bvqF?zMK(i0C#alnMI44DoTIB=Xf;QC@>&Axn8l3>z&0DE*v;oA~eg;%w9R~|%E
zRCxFQOZPqu7v;gCqwIq02V@=XlDk{g9Yp(rMo|E|OA*KekW#e4ZEbBCtD`oLgIfJ_
z-#4Ni2*Ta$?AT_iSiw50lzv4JF@i+g`RsJs0TmTBPn~6PWfGQrf3g-^(b#Tz*)AYs
zhD#4can0O6Rz-mr2}lKH7woP+v*jZ$pIQii!JSMArA0&}QJ7U3{mk9V>o!~M`crD!
z)%L5zj9ZY8?{xm=nty^71{I`X1uD$2Z~zW8B-W5nNYnp0IttR6n4TWqbfLm*rVFaC
z_j>j$43?$j=!SSeZvr*}{5m=P0r_d9s#!2f#;ykum{Iv8a6H&Z=y!R_gVoSm&;+4u
z`uO~bZ`U-)xd#u>_mC*RKBU>7QebCi2S2^Z{SSS5zaJp7JV)bKje`EvC+k~Va`drK
zxfc;n*2yTK^fQIK3x!U;S^ouzX5@y1b>og0;>qW~pS{N~ykMzu0saJSj(
z{?a-xae`~wAUs5Sjox?K6+;CI>fU3m)
zJRL%Hh%!-SThi1AwKO&MuV3>-3?+g+GMh8#&cqdN)wvk|w5%z__
z69GFjNRw1j7B;7-MJRsXhn#e3!
z71lPDLa=;SbMIWNl?qbJloT&m!~;;i!>^A|;uEZ5Y)ls{MMe0jQCeMH9Y!XU0R<&8
zjv}wusV#ok5rnTMP-Nm|W&eg%TA8wf
zf>%~n8xTR^860pOUn->Vfgu>Jz$A9GV>j;zoVTS#eAoGH5DqgvkcQsgZD8q+DyA_;
z=H@5BF0s*2QnNCyFl~t-3*`lhxjTZzfN0BwUF&BH`qe?O5hZTh7+bPdSydHU(ts*Y
z%C`vNCXDycvK+@PW)Q0n9z3NbL~M~jiD7!P&^rR1fOQc_49(Q#-fv*sJ`_>x8KYnK+$AYeaW5kre8(t(o+EfUABk5hbNJ$l65sT0mJAFR=rJ6(|7ZhX1sswanjGpyeX>?_
z$TT51vii945KS1w_Ytg3(aA!R+szpb8_?0P2nIx~-FIp9EfN0hbu=Tz4gUDC7DON_
z;CakEu^pO{hm89F&F?ULD$n&)x6hwHhsvi(GSt`C!xqn$;mt*o78~_>B;x2c!T<@g
zxUtv0KKXVr#EFmlOEZ$Tpzq}w^!3-AoG&*dL5Pth(5x`90b|?iE5c;b9={rc$pvaC
uOvalb=txpmcRCdBerO6Ag8|}FOno^!g@tis1^)35((|WE(%&VG0{;hj{J-u1

literal 0
HcmV?d00001

diff --git a/Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq07994915521862946370.png b/Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq07994915521862946370.png
new file mode 100644
index 0000000000000000000000000000000000000000..f390020b2dec115abf658ace27932ce9d32e52fc
GIT binary patch
literal 13699
zcmXwgWmr_*_coFvEuob310&ttB}j^tfRvPUcOxw&CDJ7zAfSY_fPi$Tii997T@wGr
z@4epnpg7>1bM{{QUUw{_G}IpAVN+nEprGI>DavW0prFda*K7z>_;cNo)dv2;a#K>3
z$6CO}zs<_t**j3A)Ss0o+t=z>h&o_AmXgQ7Fn=g@EG{KOq$rc>+ZH&`dAX8=
zjvJHP#{5^(uXnJ)b=8jd&p>*5%#u*tTCVJM(6Uf;J{5jM&vHX!BR;=qrRY~PXCEM2+^f~yLd}W2RAi|#wt*6^x3iip|s=TDwI!P1F6@{n6|I}gLT$MLN3
zWiz#ZerE
z>EWTnN#NgM`6&$^&hzg)!^1c-a&kNbv&cofRB^A{nyA!qN?KZuxbJasP5^y2E)Qkk
z^`+;-hY=|$DJo11Ho8a>JAr?slO--@Yk9pE78Y>*vhs2&ai7D9(kD^-LUCA_n2n8%
z)FN(c-yPfTzkb3NLMwTF;p*g6m67os1)nNznOQx1VtkyJpI;kE(#J3ccgQRG+iQskSEYOS?a>!NNe&w
zSZy!Y?{9pYkr7WVPC-n(yuAF$V_TnMY_~!wRM6DaG%=x#&PNTV<>#O5?p9D&|JxOF4>ln?JG-*v=5oPO3kwS)
zb8~ZJV?Cnu85OD}VBel6H8
z5i_f)WQ%#j#n6hlg1)u*o^*6}-a&^q=)z6w>gr%qW%9Tf84In7iM)Oe4-b!y?!`-9
z%`~`LT|cETPfkspnwS__cl)_$Hxgv}rmUQbl*JnYb`3W(9jrJ9n47NuqEIUpxvj4G;DCOINA9r@V}SsRJkb^
zSzjB`5d}H9{p?2!>4d_MbakiOg+rv+)pH~UhlhXF
zycqxTda$ogh7kP&^Yz8S@UOt@pKvqt^H)jg5_A)9TsTW>?q#zXes=nIyiF5ux)^zf
zVZQwNEKT5rskynirDeG3jK;x!8)}#gx8JGL|F%WyadOY{e~gUHC-c7F%G&6jbWG=Q2Jt@h@ezsd$q;g0qMayVJIX!Y*w_dU4_{a?@rpE^frsyYMgH8;QJpDi
zkyIilCpEQuc~4(oe{5`QV{;ShC7wvu4wU-#cEy%i&+;C;4Zs%BsidrIYi%9G)l{sW
z!}0XvOF8<=t>AmiXg`VVKOV
zhwA9kdTgm}ZEeNH#jULpk1<7x3knM0D|YsZq9XY)wzy?jrCYad)jRy`;M&3yNmyB3
z6%iJuLxh;>c`mw%G-J~?`tsaA5B>ey-1mR4-;)`4m6esDWCsTaqZ(f)CHedN9~~Y#
zQ9XPNt7u_mMM*(XS6@FpHTA);;q{ElY(&un(eY{wi@ilx~TT6>d!q00cQ`phm=(o~WwER#a@4
z?Ic~H&9JhwX9+q|kC7@1PN-ZuN*DKH5>#G?Odw+I3Xm*`im+WFAt8#4W32WR;il{B
z>wrc$cz8eS>?tR-4GavPJ-a;q?nvDADmKhCFN~)_Rh-6B+YzHPiF){?5kJj%@~RO
zE4{tFaEHEsTTXN;^utWaqU2bR$OJ{k=iRU9tkvFdwa?E7z``&wF&Wg^oo!aO6>647
zMMbr?2DrK&z|luXy%no4{w*jiElrCBP~c;2?Y5_@F}sNj02Pu1TU1o^_u!rhNlX_!
z6dwhingAn_5=UA*m9S>PzcE|**uM!+FExHkJbkVl(cF6-{sS=T^K`X9UL40pXwcddAPYziF(-S>(3cD
zw#XNc!$G{qrk9)8xd=N$BSCfkJ66ibD7&J99sLafKPM*^m-nBctd}p*^E5uYZ%)PB
zYd*ZEzGSjoN@c6iKZ5c+;~oE%P~sPoS3)P+5-;T*}6-Q6`;O(av&ix)2%%(|jiS65F@z0jqz
zB?IS2N4Y(=^zv2w{4UC$e(LM%BSdd^Hq6tI;=GL>GB>C1@ML9+?3CKqCV@}B=QzKaj>A*X`*$dR9EkXVBid8iB6A>Ccb^Uv9se~XQ#pyo?p`1+FDu3nMnW-1V;_{
zleUr3{MZ=koJFn!ry}F)PoH@8T7b^T(~kmV6QY-vm)qIe9_;Ns{(z$`L%_GawPj;t
zW8{8ik^2MK#g8Aj9#vNfaH6sV?B@WCxN()0l*k1gsAfuYQPNe8cXv@jZS3vYqw{nB
zM90Kf@T5mXV1>{!5|u_cI6FBZSy?yN*47AeLPOuZV~UbnT3d^}Fuo55%s3~Db&EOS
z!;2tOJtrrwbQKOb^S$O(|J~=eNlr=8CivURq>(ny*vz`1(Es1b1}!1t>g-Pr_5Lg_
zTXb}^=94Fhx_*ij+-B%M6mV%InhOeKnUY!>8~O4bD=39s3S(pOLTEd~@!;|r>4IFc
z!0g!B*uJoTCRKGJ-;PKW|Gcz+;+0JR>KvkwB!0+wt
zZByst9GcvPzkem&Hzp1b4*^-*uFh@O$BGid-&a+Q^!C00Cgoq!4;8|aa{Q}SlTu4p
zcUcHux*mO#DvyQOpd!Tjz1fZ-r7CLb?oo!+nH~PL8{_nM|28%?dHPMW@l5^
zIKh3>(b0ttFO@zi27u@^=fUdX?U|Ni-|~2%OloPM-8~&>7<5KLOBwe0IKc8fs!~z4qC8`F!=UnS(=|sh+>TIGK#`1F6*X^z@?$#Nh=)GyPX7*
z7e~{^EUdcf>W-_R^wQHgjhgjptnZk5XYkuj!5!456WqOfmnf>Rv{dQ&*y1!ctf(9t
zwwnxWl`Sa&mZ7kS2=sBEpN;NpcX6Z%ohAX+v;}a=mJip*)zs90M^%OnQ*bQ+yu5j%
zSTHIhBNN2>NL6((C-CZpgM(U;CXjG%@3XmD+o+tVlP25HeWNI(l$cz~{{51RQbRKN
zQF!7=Ir;=2mdcXmdEOeoI~>fG186<%<5pgl(kiX)yfI
zZCq=pv2i`B2=pvWh3Q1Hh3C-8&5bXq7Z-uhs2m*`aT+UBVsU&5nq2AEj~`S&;%fe2
zjpm=B;gHA!(ZE0re)Q^W_LG&YjEsq%cHhaP+!hiRqR*mByGb!IF%1o8z?IV24KRnI
zHA`zge83M^&*F*BCnUc;OE9rcV5e)VL`4R7eh$j|;^G2MA0av&R424uz6TH1K}WzE
z7}VNKKBVz4tY+YwsxV8^s@8s={P^lgn2Vty)o+_8^{&T#tEDS3T#T@FS}aHNExwUV
z2N?6>M*2-IEv2QUL{a8*H7|fAg}N5SyYUy-)g5=oQbxX;dLYa2eS|$PFAu&)6eYJp
z+F&@e=){-p?nHHRd>j`SM=k7PPEdf!27;CuWJ0oRp>6i(hK5V%TH=|^k+m!Jq+}Zz=A&q?cIs|6kY0)m2NXo`1wz4<}-16+sz&c-GWdFqLD(11GT8
ze9rb`nm`)?Yopb|GaWggxe3L)g;iBmuu%9Smn7Dl@{UpJSt3VQm;X?MQRGKEIyxk;
zPGylKK;QPg#9|$vwfd2(xQTx|zV#yV`0FUg_q_CUayoIOgoK2h0438Bco49L#R1WF
z4h|nGE2mGZkAc~zCMN?NJ3>!=_>e?CBO@b6!vDPn5*HUYBs7#8x5@J_b`OeNx
zTU%Q&Bkb+${4P%Sa&vRPe33F7Y@C^$E!|^_`Crr2VwqG>IMj*DMb3Rf=+M;OGJH`P
z$M*RtHaco-+v@M%S~@yQLJ~fQ)>v2(utqvMIy_{roR_=z&MpQ9R6ulqBJJpq!R>5r
zZiWUbA|fItF3yknJgUo#B?bHL=v94)9Q_d&{be3#E;z7WVyz^arF`t{cw*GhXdaW`
z!HG)G0h;|c+N^MVoyABE0|Uc^n90)R(WB_lP&5Q
zNJt>W!HJ2CjC}Cq9kkt}XAh$DK}AhYPQE050tQ$tr63nBFCQN$W>e^h@J}bI_{hlJ
zqoeJEGu#>9tBbS23_)iqLHLo6=1T7FNAS-(=;`1w)z{Z6tluXgaXZ_$K(G`MM);51
z`YPJz4hF#vpfnWB*A?^&-~Hdcz?l5%mIY;NajqAbDhFmY(BB>miyhFAsa6GcbeSt$dQK_q`;Uf@La0m_$-CSJ2w<0VdH!%nc
zk;$V?R9vVkyTf@TJ^8v*8jVYeDJkM54T-!Gc@SKo4<)Ju7*E0s7>Wpy{V&XAk{3c>er3JV#4&v&Y~07GR=*fr0rqv;$C)qWHh)
zd=R+epTD3azI*p>vP_rLxYciOsq3W!Ap#vl1W1X<*LxKuB}g_l-1RYgAn)?>@`2!X
z0er%l18b&219^2+8TcYuu_Y`xSUMo!3OZziMSrSN5>u9#_iKd$_NO1^(#s!_+6gq(
z)bKGfz8tjlv@-8*a`@*&CCJSkELp0>0>^!LeK1F|4SqsEz+><&KUdoM4??fRM@6rD
zf8Q7N7+F6AxRDIJ5)%+OUXGX4VfvsM24W64!jI$2X;t|=jZDeF04O1f%{LPMjX+NB
zSfNzBudMt9MhsYjU$Q(mCQ4zaGX(6-c+%mq5g}~wv;Y9gSEW(vE?$jbg1%<$Zr;pRvawdhN(Y`dcPjT1r(dK1>fvE?Y$
zCmuS3Jv161U9hdc8r{qE*sd;5e)YuDR2r_fhhl*g;W{8h=jP=_Gia`^u71?nlwn0N
z-2Ct4FGx^PztbvMLKT%)DJhfhR5Nxz80f^dh|p^2M$6=BAW1rEZ0+oRADpqXvoG)}
z(I*ZJ3?%y}1)Q%JA0Hpj%;E`SVwd;L-c>=D@{rmUv1mA7EV@U}o
z7AgJ1B1ivNM<*3Ts*=*vW-o3!OgPW8b^?h3#A`M6^~8VZc+#ov+yOgeZg$ptrH>Rq
z{o%tN`|ovkDjAuX|6RwiIxucD%=Yipm?+09N>7+CPk
zLhUZ}-Cn172UYY*kFCQu^7AvxPqQRu51(jiE-fwPS8D@L*>GV8ceZ6#Q)Q^#q;_IBHaDLz>p_e|6CS2V_4`-pSXu>Emt(*TBroY5lJN
zO38qWR{wv%jIlSdn5e={xwGeg5m8q<@nMK+?dsZ=fv9c^aT0mzU!TedQYX`qdmvzrIZkmK37~mm*+xk_`^GkkNfB
zF2Ei@bFd1s!0m{NW~eYdK_?t1xW(B=qr9Tb4FW-KG=zN}Z
zJ9y^4Da4AgHe%_SZ~_Yp6xt}%y))Ix-d-@Nm8E4JSb8@iSmM+Kh+q{v&>--ZURF55
z|D3Kgc6{+-^y)hj8q{dPLqH4`UgB^|SWCDGTEa{Te+o_C!$3K6o~PEIv2>2r6(qB~BO6QuaaE^fe6n;P9l20StlrjE053>m}Afk(iNzGfm&;
zqe!1vg5YCG@!4Hy-`(8>^n@aXh~|!qlK-y}Fq$(nfBg7S-W(=FD3_<9r>{ShA*g;P
z9Bv9>7RXXQJ~yy|;EVyc1F&2m0(~L`0e5
z-=QJ3l`cdUuKn5HV=->-J0Tvmf_~L{Y$5J4VYB!Tyy2djnt~ewk1~hH
zBKi@*+qZ972$*YN)BPYp1Mw&7u>~eLIv1Y8K{~G${m;x)Li9UPa#>kf!NI}6KVH3h
zr4@%PDJ|XG-`7003gy0-N}~-o3m)GTd$erWCvzY74rL)(lRhgTv1LN`I6w
zh&(87JG(7tYU``3Ohi%e6tyPps1{dI4!YOzk8Z6o*#d@RNU@ntNHB(-0?HQyWrJ75aT`~DS=
zwAK0((QYyTJyh3F2NS1|MaB0!8U+dL@
zE32-q{`8$JF$*gzAOW1d)_*%qK-NM~N-HZ*4i26S#o*oNahw+;T-qqe9i!W|l9vx_
zY67?IADCg+=bN1L1W&E3tbnl&r1Pf8zXTI}%-}m%B4^f$ng4w@bWl|MmcptFCz;oF
zij71byhuex&0nlldgT~6cipD%uDq6_a`}gWg$0ge0JyO)xAEjhA;i(u)dc~TUnylB
z)VtXBNsN_Mxu6Ka^c5Bv;Pi`&qQ>6I2?!T;Ma3XPkCVT@Je{4Nw)zQw4oK|%y|H0~
z+j*mW3W2LN0vf=nL>613RL$Bg8Qt?BglzGACCwP0N+=;ayVZJ>9^8@&gZd6`dTwrR
z9v&AkQBZ>y{^SJW6A&<)VVd)h=M4X`1lH$|BfT^hXko0ci9!jz;v@C2fkuSt+1y~)VvM9F=oD-FdA6zLyy
z-L~@$R$}6$-CZZb6*)=dcc}%TvgR_734QxoEGZo(q~M0@k;j2PY?3y;LM5BzNxQja}>4$ulKIbuGblz+HeiWe6Yoxzm+R9DyCBvA*FWRd>*x`{c|K1)GMOAES%ACStghA)4v0BHpUaTCxN
z7e|=RRGD@Htzb`i1m$dFWtEnZ@ehjn{OnA+0%#Su^>iF1C?h6Nq*OhN0~{pSC)?jW
zm%H)cXhC9+*&TmAFLxX~?2p+H+l
zC*bmEy6?^ieIoS2X2@j#zJ<}f+VA4N(sycm1EA-=p+ym!3Jue^+N@e{m-LnI$qqLd
z)M=u?%c}J=jWs1*7XI=apN-Vk0a~xuOy-Kfe{5Ca
z^yeaL8=D)hetB_$Q0-oHvgUi+{TYc8(>1o*nv1m{1b%2}XebcU*3%^%SXjtx@)B5L
zu@ML)JG(p~`l|>a>)`CrpQgdXe);lcVq)TGa~j;IjP&$Uy#XJLbd|e6x7Nr01Ejcj
zFAXSdP*6~EaxxyJz>R+ZXBplCf?cI{o5@>@+8k%|md03bZ&bU<#MD&Ca}%&cL8jki
zvmm&5(+9vqgT4>;IS_7k5s|n3E5|l5vM6Nz-@XZX?V7;o45i>>?H?YBx%@_~$$J$Q7QRfFzkVhI)-mjWmtasb9t#28KX70M1_#+$S<#kn=YIUi
zBPf{GxmeOX1`Y0>z=j*p9B_;g?tql=fIxZ3+%gar5Qq1GNfj3r
zt-#O)ts-Pk(0=eE#z#kkmxa>$!%l-id?B5#uP(uqAxbeL1N=-%PgfFqY+5c_;`tcb
zA8=>j^9$5!4JsgZU=$#3xlE@5)p7X!dzL(nQ_x|sa$vy0+`|Whdi+iVgNk5yWUI8$laYkqVokX*wRuc;GfQKva2nrz{QU^
zfboT502a_~kUfgIt`0yqcczkbTN~+1VfB?K#+C$U`)d?2=L!rCT)}%_!Ogb?1}Z3o
z@2l&6FcgMw19O78jtCfZfCdj4Kis_vh%M&iugMf}e}bDb##nZ(re)r2WmNEx&1*
z<-vpA`T5q862&|Xzk@+RsM-w+8(UjRSkt;X0k1J=FC!x;4ys^?gNq5TckeJetAJ)?
z3%e>T6HUGTPtO8C8j6P{g=kEZkw`ins6uy74@6Epyqug6`?!mHCc7ATY|pvFk?|TE
z9leEmcj8uj58AEoP&$T&7l5xJt`C3Bjm^)0P;1W1yVXJCOf@_-WCUeC&mSig=QvYE
z$InlieR1M8=1PHx#l#XC1v|GaP%A3Zto{10PEiEIMZSCtY}36vcck+)!tp2-#B`(P
zz4m`cg@($Sn$p>|0*PDnz-Hm$;?k%iW}$h%%Te%hpPGf8J$BHtli8uE$$cC~x45~9qvoSoI`Q-8CA7XTlVCrAYJ!0UVJbC6MMdCukP!U>
zYKtEMV=Tr-M)MOB<}Y5rBn>xUbfnHPWJdjBqzS;iE~O!>P|
zBd{MVk*<4M_jgkv-QT~jUKwOM1LF3}6%1ie(9pE`ow>Xba}#N)N6!Ll1*H=P8ccCf_m#uT~8md1goF0^aNH{n#uM&N_V0-p}JrXVLr
zm`1*_cO+g=S{kLoRAz9M*9f1Qpl_W3?jAu($#-O1k%{}7g*KqJ-A}G2?#vh-NOj&a69L|
ze~;q(^K(>pwGd8SUy>pt00BUg=P&>ICZ_UV
zJy_b?|AZznKQ93vo~Wp$ua8~FBai|9e&*Y^A~3(@R1FO2CPZPI!a>zg@HF!duMdH^9Hi}yuJntesgm>lJO*a^xn(LjnhEui?(
ztFhgko}LDS{q*0zcpAypckjYG7ulYEL|-4&Uv_~7mn~?7R4^>Q5zmC!2>Xp1*tGNW
z^A8?80ABF5POpe8gcgQO%PT9fD4%LQ{3m
z5seKxh8RvB&pXBY$&=4FvxJQv8g2k?99!-{yA88JBNV>2*N><
zfpb|76Oe3dkf?*F1T$aI0oE56!D4)yk^)0qFrlQPq5_Q=KEcRlpV~<(es*qdzR9x^
zf`pPWddRcphKC8n9>7e%;s}~i8>|wfTUy8R)KYKWyn&no|2Q7jkb{H6Nk~yu)gN4M
zXo-=uh=@+WDMGb`tgKkm85m%lL*s=)Gv_%gD#nftng>1(b$A+*p(Agp`KNrDZG=w^zFemE}G98d^+%x*`VHxOq_I-2Kj1Brar
zCLC3<9b;k}1X*UT<
zR8amBlbDzYkANEdWn`qPscr_ObaX66rrz<+&rKLDDAJ_+jtHTJMskBQ_4Uk_TIzS>
zr9la8EOzKH)EJTioOO3&_UwUQd?#(=rGlS3{Tmgg2zZ5s-4Sf;GkSlsJZX?aqvcpK
zCG`T~xB@qV);|EO5_9uHEfxzdvZ0|NkUTMQaXV{k;5G&6e34e|N^}`nSX=w{=fGQR
zp0=N)nblDP5=4Mj0C|{`fZ}j8wgq9*t#1`=7Q+D8K`>8_kfiZ*vR`PuLIycK(S)iwQA7QRi-{;
zX!U?t!NtQ{q!JGL?Eg<7O0LoOgd?;)GA4#sK%lOwiZDuU;VusT)0bM4n4osSWug$5
z$8!B%7zZPi9BHg+{Y?iCF^-`KeKNlIpn#l%>74siRFSyeU^x!IlVtb5Z;YWq;_l=`
z7}EtR9{jSZi>QR#W=zcrZ?`6c6$(lN&HV}Cxc3c4^kFmFwS$-y6g+_;XV*VNDZS=j
zwqZFyj)E=%v!Z?P#v&EA`Ap!^aO-7E433|esiH(|n@+nL!!Eu2U@yzcENK%fN
zx68)F12nWyV8tLDf~SvIU;Dr`EaV#Sfnb-py1AKik-=Cg#J10S;!++J!GeLxfHy&(
za6WjTq^M|8tp~D4yBMu$XlzWgio6+HCuLKH@GIah%p5s=pd|c=8VpgYNni4VtE($8
zq#7IHRK<6hL6CL?O`FiI&F9Dl@ouU+jy|aujvC}lMx#cBj#5oB4Bc_}aRm87?P5nT
zzr^o*(#>1vA}L5oi!O@0&8~^xZp8)_7yp5n1CnI0H%*UjGO*g(lrFO)tFkK=luRLD
z?HL(Bb&1z)Fo_|uBIJY_Xpk{$&_Fjg`%Sn2>j*HubR4sY;AMV>(K&d5>jwSh;VZIg
zxUob!c`wo3nA+z(_+DW$d2WB~2LBvD?cAhSbd^RtsLUXmfSPV`
zMCaC{FTPgE(|{3bSilA_kpWb|Rz`%VWC+C0s3RX1v?kMfov=S^B=Nqi0qt@H(+ijz
z2Nfc?2+XUy(h;q}a`e@+Fm+{XM%-u416vYtyfBFd2;}GI_vNnS=_WbP2_*2PS~pAt
zZVZA5>@c&DFx~33h^7^IK$xh20V0GnAs8GmRhgECwQjed$xKJrJ}|(>!$Y*b&ki2R
z1erAfI^g`NUx1_P=4CsK&p-wbmrcCo;@QEaVr)$F>IIktt{xuY@jkriD%P!#Fb&UV
zu)y3+Vz2pv?gd51_wT+4&J8z>h?^PjdqJn5zJUje8rV|0Zf)4c4BILdc?!MjhV$oV{#z%Y++Gm(K
zU@L-sado;J4^S6shYHr?jei0IaB}qNAkD+Vx^lT`iK1u?(*`Yn0eJ(1f>OXPchq_T
zrNf*D9AN%;w|DRXx9UEAoCEYQIbMt=WhKK^fbrIArCZ+x9p}*rG+_?nltLkgF0CIN
z3~~yJ8~bj0u%@JQ^Y+b@xUn8^<`7yK3ENyyrUF;`_9dpkOL=82ZEa9_Apd2m@N;2$
zMkcSBkA*78)8fZd$e%geE?{b3OvoQm3*O!`m_AW4G2;?Xu9R=(+?gip|G_#BziFXU
zuIIcuKn|WLTuGX8tvp8L@J5jaBQFNT$hu08vKnyquu?ex1I2zw22#&`j7{kMP+tolP
zfR%w8!yxO48{z}>%JjvH+YM@NpA;jEU~t=QjdOtt%&B|i0pd2cuTne+bA?SWFG;MULoHKgk1=63HMXOzvaemjx3Z{M1knmUToiD_tF
ziL${AYabm=l^agi&sPZRP{8c*B(FU*WjC@}pa~Zq3cjsXn0*9*zillkbm*jZeYN9HuZlJBv>;ZdpwK|G$brRv-yG
zrN41_JVsZdM7S-{VVKzX7FWAflojb6G2vPY%jBJB|NaPCHpfVdb6;p9p62}DO_;Zh
zu5u2)diBa-D7TMs+a0f<%1}fA?xU}OB35HY_R{=?^Q!L+q7gN9b=O~fZY|y}QZh6&
z3`p&E_W_P?b0xgJ9*3wH7s->{mSklRc
zM8Ha(G#QVS5HK(>U}Hz>hTG75vaMWb3$-#dyzaTuH9I@27>-j=ei*G+{yP%!EG^C1
z(NO_GOHaSPzD~aQIv6i97n8H+^XJd``T2To**
zu?9t|!T)~LZIGM4xXH{WEbRGXw#L`j*YD?CUvICYlT!)@)suJ#icgMbz=2+
z;)ECE=X-qrOgnCXh3fG{$v-^WdB$&6_U>J5uz0r?_aTBNs-~s}t~4+-q-SMy;m*?@
z#*+Tx#pc?YoxQzS@W~-A38Rmb82-bQ7&uzilmQ9a~&M3l@_eD&mAb+xyWl2TC6jb>XK
zDr)L=ScR39)Vw@BiU?`Jg5h9=p4qn0ww9KbcklQ;*?-kxMcMQuGSJJ1G{1WF9ag@H
zl!4`g>(^Rr0;;Z_o}uT$MHk0E+r0v9IqA4D1Bsa##WN5rp`oGDEd+)e?rmq^S5_XY
zG$JIbh
zO+7F$0BQI^zFHMAF)?x5$jH>xG-PWhf^dcwFUkg%85Yn{6qd#x#Cf{BTw+ni*C
zzNKaEix)3aQ_0AtngY9>2i_XLr=XzF*3pTNixVQGmkX*-Z?`j&2t3??h?shB1vrsN
zFX6V>8my3%lf$4$`QJ8%g@sv#9CM`gp-OdPi{AxWX`9r!&GQNh;sy_^#8W*GlV%at
zH#8)`!@JWG_@GUvEO}&m>itl*RQ1C@KNmC&cl;_jZr-}p9CW;&E#>`6H|NB&ioB9N
ztUGpR9N$%}m&vLvBmqe$Vu$!hxzHN?@ATN))%8aLy#ypKJ{uZ~M%s$+5s
zrQ2M6!ONGw4>!kZYiseyIkROSPA)Ene#w?f%?#&@>=bet&R29_Xs-P50R?+Ijk@?k
z(3gZ|L8c!Pfz%TBdA~lC2kY5cCas@Eri~dWTU4MFEiEGxdNktzPc84eGY#mHa*MVu
z-4|}_>fs@jH?&*FL)lpYdAHkku>irWrS*2*BIUWL{p)~wV!QP(Iq>HiAK$5@*v#Lc
z)(thf1l6Ju_NTWfkR~oCVq#*DY`>n^(Busz#mE2KsU9*nH)oU&x#6>+Pe4EbQH#)r
z!^$d2I?%w`dHa3a`N_rz8~f-tX;*ASUUqg_S=kNO*$?obpCB@GWQ?^gk9Y(Gfu?wR
z!12C4obK&?eXqN>x3shr!NQJ2{31@~DLCQ?CN5J-p&
z9v-6cR}ll>UX_#(5Lt&Qf$nI)}XK={@;Narl6tGH##c9&D}dOQ8Q+O{z2S!
z{od#48fRlPx})q)^+|!20@Y&Z#o734!@oSa@ievBEQTQK6trj`f=
zgFU)ONShg1`sNLk883)z78Vw!W9~UOtXkZ}B#ieFo;5k?CnVFdh$&TFnlt2%O
zWo&2|-VWD<>)1UYK^(31rKu$`#^P%Q+{mq~`%_`li4gfbIH(v1pOE|c`AO%T!-}UC
zb#--ZY;RxII>~|9goLy2S?>C3CAV+gva6m^uhKp3>DD?4c=ID~$Feo(#nY#dg0*&i
zsibVGLA*Q4sU=t#398O+Zo1mqgzzR;3)b)_Xz1x50{TIaeyFP>7yaem?78|0%D^An
z+Y+Lpd=`y9gPCHvxw!}y&NRGuNuK2KpO|5fK3YpkNm;aoFr0cE@-QzxdGaJf(27yo
z2l4w8F`0koa%XhHlPBp~H
z5ucEtV{dN{`_?#MjXvXfzh+~{rh)F9!Pn(G9;Xgu<#y~KIRg#j-`*`eR8JH
zwO=JB5~I1<*zWcwv%vDgV-0-k9~khK%+8VVLkktgNus{MDX|;B<|ij7KY!N#KnJ|R
z*jTOj<&#&hIMM3NNIM4yL9<#ymacDJPNJFF*{CQRD8O%x-l26qUVXq6LU6FR>s=P@mE(@
zuc)YSbaYJ4)m;qhS#_OntbsSu^S*M+$VfLwTI~9Da!W3~+-9dSPIGfw8CO65#JD&*
zDKGZu&Q0fjmZpif#uxhof|7{`$ETJWLcF|NpI=;`kMaNWbH2fQ{U|wV0UPQ2Yw4r$
zdrQ*yzp|b`NBpcUw`vp6x|zf%>*eF~xv%eIcS1&YJoPGrj=qHj5oNt?cidH6;w8!4
zoSd`2d#kX<`d4q>#Krfv?#Z?CAV!lC5*UE$B(o?*u+I-qHKpXI
zheEZOdS_;AX}K^w?6Ez`4(?)`
zvb5L}k^OyrBNG!p`lQBU$+=irS#RFF`Jtxf;NYOBsOa|X+lr1r-b9^7i|6LdH(m~F
ztEi|L85ylnnD9M|jEqd@b40KRWWdh-?O_TnE-v;-8fL`Y^^4ax&z5!A#mwyL@81Gw
zKom?*n{aV)q2pKY;&4tFVuqFI<;_k{t7|L+CwcJTL9m*QiwmE&gO`_ALqkKI`(ms2
zx-R@)!cKD3!Q}7Mz(D-ax>ZYHO+&-+pT*Fr$;nUM8cx!#eh6z9m$K5*=Q%m^w{JFu
z^&~O9EiFBa3wpY=f`fwtrA;ys?n_Nc3H8C=!J(wMSY6MG8OhDdYkKF7tpBfkF1tgL
zd_Eb!pFVzm@87&Jt#e~Y>LK5`SJxxTJ`Y!v>`lPNl(*rbM&Q@_jm0qaeh9&vNvxu
z_4M4Kh$2{kf1yxNuquctZ3sK#;^RrtZ~&VaZdff(~ZNwYD2B^N(AOAD^)>iVK168Ac2{KmGeuk;SuYAE=HNH$o1FjEX$Ek>=|HSFw&dCrZTqdAt~WDHw{c+r91xZn>T=q>@op%
z(xhlSJUl=x;KGQ4U%!4me@+8Mf+}7Nh$oaBM|}+oT||&v+gK+_L~?d^c2mm=4l!VUkZ
zs3_a$<3g4Ap9?JnJiP-0U%!5pk&@~g7|?qm(mgRjsVh0{0<8K7^ateRujNkMin+jx
zbJ_5Z06Z3T4!lZAov^Qj^y1pa#(p0kV|Vc)cc=e6D(?r>rK6`Oz{mga;loWs!&v?h
zAD_dWnd)zAYsG82TDrPG{&Rs_LFDJ<8n%*mrFeY%NQz%Tq+RpB9K(LKDA`vC!1?&5lnVvs%rstF&sySw}O__(;bMjm+2a5;!{eUFr;5C-_MuJPQSdYZUlt7tp_*ZZ>FH5ZQQ=@^MJp;U
zpB^7nmCRgQIyk<0B5Od&M@8^SJi7A=hq?v?f~~77KN?|l6Ae`eQZXSRp+&_K*k{H7
z15o%q4k@WS5XZf}h;cK$Y{`$Sdq_0EHGJ*avuDUNsJtG9X*oH-@U$jhQTJmP`r%hv
zVBUY<*ciChn?gxR3E&Dq4nLtF<>p%I>L#+Mj*gB>q7p&d0=bCqwyiXQWno?wxOUCi
z-F<3w^uCJ=Y!_602)L`5OMl~TTue?)X|0@8rPmb}qSe%J@1Y*YzrRzBV+kw}o}<+5
zgMOrmscEW;5@080SblS}d5mf0#s|_SiMlxcvgzIYu4ssN10-5^;`E|Gr|4G(fGLA3q?@-xU;8hMWaL
zh_MpixMV_*?83srz{SvgKW3`cZr`4mnK=f!2ASwQS*8K$24{6d5^Qg6`Tm&ww!3=)
zA3{7o3=CwH@hxj@U4R|_^5y4nffB4;GQx%w-%!cPY4fF4Mn^{nq^p;=_t@ANHNWZF
z=H~IB#3Bt1jXDou-cacNS|2Eq%&e?`2Sf7z{{3^Gt|%xkcLN~-*vMx3xVfB4-*SQxKaAPBZWZ7pD^jD$oTRPLIZiQeAcxw%uIXh8C8WjQcl89-;5y!TBm
zJG{1K$3kKW;oSCM+X$8g=n3*RH3I_??~{sFkoPx|dLEvg{07Bd>$$3$oX*L`1wIUu
zd`J+y7$MGT|I=#)1qEwL#06(qTV+mN<=(=C&Mq!bl9Gn{`&Yag;8`1hw*#dM4i1)Z
znSNwLlQDp;n9U8D_K`}PTSh#q1?~)cFDu(ZL@$n#_9?Bb+=K7`*T!}Ij;__e=Z
zRaG@x?|BzE*Fwuf`HRzofP?RQ{>`Ch+g2Cnf7-ZoG9%LFWW(g|5)l#6YAFwQe)D2u
zWyQqAbX#ba0-i*ZHIyq$f5ZP5R1FqSDX9kFdW#|dhmL6VKqS8A=^lJ1)2O}(}TVWF2Nr=zK9+7=S5tgLL@BRF0QR|C_m
z%%XTFTS2j*s_LeW4w1ImhEL%8oezliSqTx5ec-xsa&p63*LZoYd2nVF1x*`$c#hg^
zxkieSXdlAPTGy{e78VRNG>+ERLU%tAj^y9{^n_*ZFfHKoB~4yg$(0L^L*f$L={Y(aG;^ctbbFwSeu@o+W6lfg(eXNL8$sFB_skU>KLxbkd;pKn3ZSn#8fI@6%r>8y-9)P`u
z&_P?)JZQw<=4IzlcWG9LM8#I~KEkjH{{8gDiYpqdTj%(=D9}ZS-Dswen1qB#S&3{{%`4#_9YF}V
zx>9awflC88{RG3Z*?(`|Z!t0=;`HdNJIG5&J}N3Ic!u8x2TBMXSRjB2_|0{PFUkmz
zvE`;UykcUMn)ue$GeL4zWxIl#-(2tCud1vR78fVR$CnO15e<407iZcUBqfCn`anWL
zf`}*qPA@a9*&33+pm>LLl*9y7xxx6prslTh&k9t^Xp99zi7Zy)D(HG()WCXLVS`4B
z)D+jvj+V8F@wR7mSGP7c9udXyomJE4k8R4I|7FV?;=&O*HYciLJKqxZ3p6sC1T_X3
zaL3ZJHSqU+W8`(cJ*uzwjD8F~8jDQrBleRehwgdCLF
z*8dK0p#}#Au8WJ`6QWtWhpa+GwgH?TfRwQxc;*WG10uakN)w2no!#1(9GSj&LDq5w
z=HaQSq?nlJz^amx^t`-8D(Ay6E~Qhs#n%RJE(5g)SuO*6$wXKXIJ90%vRauuWW3?;
z0fl_&^}Us@SjfCC>B5&Uzrme=V8v4l8tLnE^YFyR#tMsyX3GT!4!OrsxmpWkJp9)G
zw7*}IF5#1%2$O8!A@E+%)B+g;-@M+vdq>S@qQKV&2>=#pJ3%8DbGNY5`Y23;)oqRE
zho7gW++bA$)6dXwEDbcTT<|43Koo!xB-r{F&3y;Fv>~~FE}#}blR_nK^8I02Wv78g
zKQ1U>AtdSOrzRWv@+I-f6FVUqnkZPcm&L_kouPcSMCn`s$5fPeDI2`P!9JiWrOKRKqAr&!C`YN4CQ9q%mEVU>mGv*TapG
zi{qhCqj#p1WMnJ5yMhBPS8f04g`xwA+KXpp{p5u3!3i~fXBxRG84LyVBB2$QMO>H~yZ)OTT-2*3!lgU$L4?=H4wq@`6?Rk3ar
zfNjpr?FcL!2$6#j4QN7YLCd*X*Q~fWO*9(wvtk!ILLHhzLBF>q5G+ukKtpbAZ9Tna
z{5U;awJ1fR3K{bK^NTc29kTnR&o#%#$HAlnR-wq0mu>u~{hJpsK0q>?EZ{CA6Qz(g
zJ3A<9YGVU~D{o~HdI$#^LY(lANRbz;1c$v=(qb>7*6&o`17Vv3?5I8LDoFG5(}SVd
zHX*_$Xy1Th0$u}dw-Djo>KTY?0HW^h?wh>InsqF=MI(+7ytecy2)
zv)>1^9PI6XkLk(3E-mftC}sbh*NPE9$1)i}h@&)a+eYmj5uYGTSmZ5<+TZdzIo!dDE@hK>{xjUvV+PKfxctfX``fH2w0hJoml
zFnlfe_tw)5M;4}7sEH;@N_W9zw`;%+13qTc8TIkt+du~7u1Zo@7uvuerOz%-wkoO~
z9$bi;ne+>Y_tGNg!900+W@ctdNfTz|=PU`5^A4G2wCiH)87y6es*{V$_{d0Dt-6j5
z$4_C3LMVug#kk3l@aB$nTuS{7vQaj$(r`&gq>6Vbsl>Fv9pPSOqnNJ;b7euBo2jT^
z`%{|xK1Mc%oHZsTC8eaKw6%r$`%7v^4jZrps;c6kV|JXbK8P_?Hq!)fRy`g9yX
z&fW#GWMRSb$b!J&zLnMDkFRx?O%E+h0gbbg-Qmee9Jyp*5tnH~A~gy*9<(*i1Ccl!
z2>i$HpGoQaYsTd$xgeVK#J;Ncz}uPwe{Vq>2%2>e){s8%s#mUD0cbL-zNh2u4eC$C
z6SqDi!`Rw-$-|TJ054vDBYkso6B?!9a_mEM!p+8}(s87aDdg|R!NDK#f~_9DzV|9k
zBCyRJ9X9|w!96R$1s8W|X$iuOhnu^;vC-7b4Dx&tyb3X~2V<{vv^fnyb{HG~nW=V&
zU?E7g9+GmI7P3u!ipiZB9v~%I84_{-
zI7ku&ZUKlsenG*-4^D>Q^F?>Q08|CX#?i4fCB?whG&3V3NX;lI%61nTwP
zq6x5Na$%vopuqFsyAESg#8)#ASEUBvZQ2tV*`4x(B(mu$AOJG-||pD@+B4q2C>)WEI=J}
zA^>fE?Cd}{7IGJ!Fq$gTWFX_fS39U#(pIrw_sIlUcxyub*T)fztc#FOQ
zJyt48%HKcdWuO9q+S!4y1GUX^xb*RAB_(Ern5gLRrq6yVW@}pX^+Q~bjB77R?NKFb
zWMUE$yYNcDHI9ysEaGC2pa9OG^BEiz)adzZ#&49rOlf>^db)T0z{WTjXfTkk#-^sB
zOtJWn*3eeKKYq2-J+~a@=I;K!pkNQq1nU>r1+ZZ9t=r1=V$PHGO-)ywIkc{Wj8{UV
zZDC!js-W4@^Mp1WX6h2nO9~1Mz;R~*22fM;8!iMw3{-tw+@(u~L<0c_
zsE5h}A`zUVToHTxVLj-^&3J3N`xhib(TOHu($&xi0E!4p8IwR=>49Va18NC0WSqb;
z6^a83IVwsOLKe*}B0^^J7aDJT{QQt*(6ZhHQiVCUPWs-s++0IbbGFHE7YZWuzo66@
zm7DWS^6)6KJ94p+5-VzxgMTAv)rt~#p+Oz>BOvwV@&qT#7>Y}Qs3j&P;l3zImrhsC
zb?gNeeF_0YK*fhV$@pjuu91+O+&MG{g5>0RdGV#gp+_Bh@z3hHBRhv~Q1r`|!;~l+
z6F0Y?u%E(&-abC;?n~geDH6TXx?59LrVWgA1LQSeNdCk=L=CtVcg)OSjsW&k@J>}k
z=koC8Au#TumX@4h>`W=|dw1{Ng`BarX1?=)KIDOq4`@Go`T(A{=Lf)VAVYBZ7EaF|FndbqjpvSlh-bNB=L7gU?6?!l
zhSpGtiU!<;9@MI&rM~{s#)dz9#`*_1k}x|vR?nVb+i7aI?Zo&vv^MH$YmY8_u3K-x
zW`DC(Q&r^!IRY)Url#n~NJ=Uyu%lKaTLImwszf%sAZh424h8DX%K-`j%MRycR5E(s
zmX)P*ye}W^yjKlOTt?>?q&Kkfh{0zG34^dpO)1n;ilCe-iCLROA9Lzt`hX?5=?U#|uYlaUs@x>QZx+JQb3?tp-RN;H~9y{eqcD}zH2L;vsj*~7{|pW=PFDqAvp
zRwJXMt4;V!ESdX68hH#~-FSGU?YpmBP+ZJ@gfttq&ah;G9=QJU8d)sEm(n^FrEnag
zOcDDypq&tfijFJ{%=aIHf&l9RIJ=&S1p=GxJ1t=+*sN$JrGLJ-3YbwgL2^vyR4frR
zgsKJxq&wA~cyh@nh>N1{bFZ@%Keb6y^29`%#Ot$Cee?of4{_TR=&gqg_iKdm9GEtM
z1}%ZV<2+b6nq*6!AhW>MRtEhE{v1%T{3mOb%iq5r?SIn(#ipe-0E0RX4(l9R&*lJ>
zf6h07m;|j^0c8Y0udt}7ps38OPT>0W>c+;*kJiw;0I~$U_(^@3IiEDYX&u4OrKRND
z+=WijrdtO$G2_n`%iR(n(XM01+>lI3FK}VddsshB#@Ec)*^_1*ka#HS8dZ
z&j3RJSZ!Uk9>%zhpF%qpOb>24OCL$U%v;8aj?#SQ^+-Np;XcN4>+ToOf&ia}f};5K
zYjB!Xk`D1^NTX~ZL_v;UPLgb0y(>~l0b@Fhj0@A#lA-585&H0&fF!2`a>;yre9Eqs
z|Kc)}@I8>cc=OY*6w_f)$Yrj64}9q^BZg)_AHhLOWoPGWib|%Ymk8{EvZ}Pj=9YoF
zxGec|b@l3^CkncZF5>jLP#F%lw%9Ogpk&uU&loT>6!U~h-wv9C85wn8i$LG?kI-v?
z65!y!&3-i0*UzR;$9LWeCVI2af+
zpzdC)`$^Kar3!m^x4toX1h;^I08|IgZ7VWUrL$^Z=G;E16-iyr&CRhAn8{F6bJ$zy
zhL)cPHBs~9Kn^rQlx?Zu>%DbZtYPR|B-xl48Br~NxT_Ge$)vCuQMUc?F*G;
zzTS==SW3bj{j;}+M??g&$@h=Q79|FUjg1Z56e9bk=${h`edD{pM#NoA?Cgq*-?2p5
zxQPD)A{i4K>jVvVXJ_&^cD4Z{)0aoDZROHGXw$WCYsjY)cUFMq1{wlJf?E9d9uKWA
zwmhUpkw2lANE9k2{~b0?{ddD(V^Oipy+1^uqyv8i7)@JcC8&iChFM?*T$V3O#xB}D
z&2@AJKYnD9mOjaA*TN{A{5{VtiMR~_aupLZP(6w7j?!~5`d~%L$9r&#D?L!Sb??AB
zyqu(I@x1M!7xPhwu*)2xg6iRUn2kwDNGljFk&)P0gU?vau^k$hVqu8z(WAFa_i19F
ztap3PO-)U8bwzaB+R)&12;51;c$mkG&x992(H>zb&x??D69W1Oi_7Hbb%Fv}4y|06
z01BT1%JAa(^G8N{85qBhel07>$TWpKi(u#A2(#(JumvV9J9V}##ry|6D~SDJ&kl}l
z&)^Rj(hw?gA*cSyw9v7`dBW|93WY5N1~4~09rMRH7RSX&L6HLemEw*MD-OQGDD)Y@
zJpv7ua575aY<=Y$=60aR-8gB;-5*y^!OH{AiLIgjo|Koif*fWOe0+VQ;u}tA^`l{9
zA!PF_r7JAxyy^f-MMc5KOC?KvUSoetbvF-(LkVOMn6`FNK*E-8QX!d`^n822PQer>
zjH+?Q!06h?Zzv{~&TUGChTh5Fq36dA5VgP(wpei^r9^=-*VogWTK>t(#?`C@hYr{v
zI7I~^YiIgfK>eViu`HsmBDhSr{`HVqZ6oyVQr!_7F`%j3hEd;r^{J^#Y{!m)V
zz0dI2qJu>Kv;qwNUxd@t#hTGcNhFe!KkD!pL`J8kte~Ryw5#-*R9Gu2Duxx)_aR^y
z4JMn^8=2@(J@P?+nO9%lZ^1_OIEl`JKMy^WS9*E6=3C*&z@wcz8QEQjC<>S%Gz4#d
z1Hcz#g>B2`nJdh1F2N|xy+JxFD=SF*auG5iyC(lVP)Fq%8B+><0XP+5a&KC%Jaq@z
zV{MIOU`Sw3j`pMRgg)})2Z^PQ1T2SO4(8`GLsJPDK!^Z#;wl)Mpa7RNA90&PS^4Yq
zQ$oKv0{!QT#T^R^S!U)X;N2n%OJ|#pZ-o0y$fc+j$p-AROGzDrc5+-=yK!||fyr#~
zk?P^EUl`OlH+$LER7#b$RB+I}Ko|xF-g5bE_|nt#X<@RA@Cpd1B3@)>7KW4^g(m9?
zMYdx6Hy;e>-_wIDfS_}XJ3-DfKBI|(F%N)XEF2v0&Po0*U&qD-sw(+l>lQrWhxlos
zi00Dz`sdxYDI_Y;!GvV<kBiF(2P9!fry3~-#~n%tCWn)*O{4E
zOJU_JLXN}vj~-zZ(5vteGUro%N@99nQUVPe{%>@vg@uJs^O#3pDRW~it&*<5^c1Wl
zv>=X31TZd#6EUq-Ce8euFZi$Elu)o8mts`)0KP^?M>jPz;E555Qr)p+2T_>OOlkuU
zwj)va=jtOIfC}(RLM$f?U}1Nc?$W3;$(Nd*ASykUJ0O{8>F7X~#GPyP#-mUu!1l%F
z044L)n9BjaDRAxqVG&p^;!TTA88PS7B(OU$+I*pp;apc;JvK3+qpr?Oe?rQ{`1-t|T{_4+f%bY-=kno=
t*P(pewA7a~+4%rEFe9i*UHSE|{N`>|UDy$SJPfB}XsGBam!qv8{||p4{^9@t

literal 0
HcmV?d00001

diff --git a/Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq14322275300694116907.png b/Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq14322275300694116907.png
new file mode 100644
index 0000000000000000000000000000000000000000..86fcde90cd38c39009e989165d7554efe1ee289c
GIT binary patch
literal 10866
zcmW++cRbbK8@KnkMiR2Jlf7kKE1Qs!m6^RIdzGD$qGT2!GkdS>Y}sTa<7;m+evjY%
zqrB?6pL5T7p7(lAq^7zeAs!VT3JMCL(j&RYC@82M@UsvO8vH3ba%%|w!uC*7lgC~l
zAi}-RVVuK6kAgyvq9iAyNI%i}ekqzGIj}^l!gLwJ!WkNy%UEl?8#$fnKXJ;pd%wdtkwP;>J0mo53sdB?s
z3#GWID7@{clK%Q7+s(hR1&fJ^A&@*>JN5=w?V20f%t@L{*C_ve{cdX}ubc11jLg?~
zr-_W@lFnDM3L2h3yLsF-^N$}tXruyYJgEwlhqA`LDzoiX$tKuJZs=sHsi_guK9p9L
z)g?_j*TH3;_riIFq>cMZzjv@Y*h+>{RXQ)Up#JDW7e06Gg?B7jkFW&myS{5FUiR&^
zwdc>DYsQOkb35-Yv{pOKP1m?uSXnV|DBrmztCR_^X^~@PB4s=IoU~?&LhODntAP@H
z-$POdJHC@FaY_lr&XV-dy~$M-L%OC8aTz9&`H)hf#@$(b3_ofz-cLlM0_Jz82?FAvwWo;(@f
zoGP!ZtgKJcGB-EZ*4943+%n!hyPy_!(&V+cd-tw^fkD@syDF-xW22+^goL+nFkr#A
zZr#!$&5_j2ZbnJ6m62)9k`0%<*p(d%U1wk-ASATn`>@k+Kx9RWhY@mmdK!3j#wsYN
zn_X9!msj&*XTC3)``fo~WMpLac6OGQ&#bNguC3L$E-Ntg>?f-1n*Qid6;9?hmGs)2
z{H9kT8E{dPlOvy?GeiTmXY1fFHaxsOD2tC7Q&vz=P*-<;eRU$`Et~jfvdjV#Ex5Y6
zIw!|UU7aYdOGQa3m;?~Bb
zt*Dr-b!R0Vy>i37iq_QDc6W38US7`6%gdnjSVcv}iS&8bSB(tGm-`s6mG(NiO!Dp&
zERyqfYS|pr*4GOQ2rw}*(T9!w`USgy6T%}X*cf=-l%Jpf{{4GRwuQsqPR
zc&12RB3#_5=0K@VeKzvrFpYqKs|wpuJA3;@!iJdG*t@*u7!Tb=XjoZUxsvZE?*0zE
zo~w2?MIzhYvTBeIdUVFz+1uMAf9r%eyd|C96-O6%GWVk9{?gvr1(y{GbS^cu%H@pS
z(NK4i%J-y_N
z3@PiMlyWl+g+)bb>gv#g@LF&YDJm+Gj_Zg~)YO$A7vbmUmy*h$k7#Ra%g$z;`jJva
zeFo1G5D>7mvAGxaSX9~h8>4^Oj&(>eR}$QzV_GnB!gyxkSml(!zSPHPaoXP+O$36Emsd+y7g|;a
znIq}H++u7{xh}OPSe6ya%*uNE_U(UXXIZq=U&D#+?jIazYiW@T_x<|yOGkMD{5At01?`WwW^R*_0etlK?(gr{`TwheazA8tmC9P_OTOOgqC;kB
ze#GQR&Ln2gx`n(&f`=Z#ykAmO1fQ8RiTA1vpg`39uWG8$)LqkLDuj;T8l}C@fuP;wxV{Tq--@kw7
ztGnmu<8z*HPw``FYQFH3Cr@6zdWFn#feMaC%xioSaXsFg+Su42zn^oxPK~`17<%4F
z$9T7qMTkf0<^G?sd~G;v_Kg1RJ=nhP-d-`61r*Y9j~2-g8+{!(Y55ZMZG~7WLAx=w
zL?(FaT^gDdd`il5TU$}5S&^`zNHW$n2ZpS#UswP3rvk1uHZ;f;r~tmp
zJ$*W}ztV?~hetp_P*_;_Lyv=}(@(M)+8i*qxR{86i5zckZZ7MuJOGHSDIEgu6_mvI
z(2)1}VgBpao!trd2nh-O{r$5H85tR^f2P1guz!5~_z^zAql?py!tLL&<8v7?47AoR
zxBar$?e;Uzk9~U#vE*Id4+`1
zg&e7v62|>5RWgqEUyO@S<$V1LoAafi!SCwgEEJn~``RCO$ljcvGUo5!zp28`w4!bp
zN0TLM8G%=2rKNy^O8u`TwY#2_6|Q*
z!}IlgHh(AqkQcstOC2}5frrOsEKiFOlZM9(#lJW+H`m(SJOtr$aaz{Y)RdLQpp@t*
znbBds=iQ)|DujBcBe-i>R9sxUXYmqXeQRs0;~R;^PwR4$esUfG0XdumrVj}TWoE5t
z@t<1Fcs{6x(1X}8%MVVh&CTs>Z4J(>B{(?qqa+SPW)bkyXdU?9Q6X-OG`Z@Yf9_Q?
zdd>3iq2FTLD=22;N;}=a{~j%^S(P!=kB#XBOY=bGj4dlfMn&x(AHP2xg^Gh(pzngx
z4U2#KmJFHK&~Pzc^c07IHf~~i8Xz72P*hZuj8#2sH7O~HT|1Ya-TvinXIIzi_FO%%
z@zQanQApFP(}z?HKmjaLQc|>W)i#5)@L%rk?&W&VtgKen)
zyoikV62(C%b^D9K)2H|YcD%g2Kn)5wR#sLyU%pgSR2Vh+odIBb67ZX;xEH`r}N{
zRFqNy4~?((_uce)-W92mAPMZg-`kU2w>q>w4STfPP8|X=ui{gAxx1s|15Sb?mO5dD
zF%PVqhl!vXBoX4?m4k^15`6I
zPjW1@+oz)2I;pDaxHS|Tn~*9gIa#rRirCu5hLs#|W@hFYJ%B?GAdiKGg;Jt}qhtTz
zpoX%to10rfVIi=|&)(kblzU<6lKxQE%7^qVn=QfU$y~-hr@L8YWv=S#1C{m@9-f}h
zP_s%)UE0EMK|}4nYJc%qUth%U)I|)ND_PuQO;di~98}5dEDaqU-JLrhK7E>%i1fvo
z{cn-%^w&bTaKgQc{@um)XAQB@(Qvk(xfhR!>ckX|BPUOGeq*<1&7kZ73ZMF2Wy*vy
zk%D@@0)pn?;9%8AKRi5~ZS)nssBoD2{^bkPq|NEt=eRKBVy*is03~oN{^(@=RtLnB;Y{o4V81W|i3?@P=O#_Q$k8f>h5YK;@Z#>J(86C5qs02E5ceMTSwI*BGEou27=CTfIE%Sd-0%6{{L(Tw>;e^!V
zzeXbO2@C6~sPz5(scK~Q++0?+y#q+zsK#aBljzu&)P8HQCa4l`dk&9|jLXdB&CKWp
zqt^cW&)3JNcYsRL0`xmk_^1S0q*7kE3GHhVWG3Tp-cZCHiR=*Nw(eTD^PW}1ORc=zsIR7TXXp{A*6
z;Mx#Ft>scblb%D=;|uEeQ`Ku)x&0NlT2D{Uq+&4`b9$7V-yO_Z>Fj(3
z^mxaXpvyx-LedctII0qlE?!&tWtYfbBXh;QHh1^;Ik>q^8+|;5xi-;#h>3~S)cXJY
zu`IZ?hW>!L2+%}Zo9yQ;F4HDIP_3Y0tE;No(jT8Vkl4O#4LaXll%2F~@H=yJa42kS
zYy@rl`ZZQ4fITI+OsFVSR8+jb#e8mmy6@{1699%W9BxkWgLp+n$BMuoo1ZTRR@qOa
zj{v1sQ6b>zdE~m>S*Fi<8ri-GRQut>hpFjlJPb6-@kfajWo7GYYi#%LhX#M_Fh?cB
z$p8f{b3-zTi6i6VuwT}8c6iHYV1htVbj4C**E{I<9!F@hC8fSclfe(@?&-NB=E3&<
z&);l%`XWs>pkYzuBYFfF)cg1E)5hH_$*qeERsYcX^M7h6b!ztr1sppH-QDDW|T9$-wd+Se_JBYb`C<
zli?s#bTBZU9v<-4A66*N)TNp}5#T$aIqqP15HjyIo@(%!M77V(UaXIPm0-xc$In07
z+pDanCw%W-``B30Hs2I;{vRY3T@3Hr9s+!PkdAS0-pt=9nxdyUp3+k|L_|#<>xrK~
z-;>zL7zpn$FEVX5p+`vT&DFZw7#q(;klZ`GNprCKOcZ+nCtc?Knm8lD|eqK8m_q1NVX1<+y+US!KF10Wp-dgwNq~
zICD6?ZI5B-AZF$jP*nI7oPaQrE(HgR2%169Xl$udk1Rg#|j^@%ri#
zMD{eOQ8+7b0iAE|)&tclUq45Z~Y54~qV-!1MTm
zf{iN285WK72B$f(n45s4Se-Dg+VZg#1i*(vnBFL4uZ_5L(uaw@hwD#_N;I{#5n(v&#)YH(=Fh2go^$C*_
z*sYXK4z$Rt3zQK0M~O6imfadsmp4ucj@E1GN90b^Kc{3J4jy>K#p!a~v!LW*3LPmx
zxG+?GUEL$Ws=FI8>`;=*Dk`9(LbyNmz9B`ipg|y{kM2$JNjv7DXn~SYSAP>3iHi{s
zVfG_}WXvFZ?69b@(SN1)9n=%Z-D#-DH*``1W;{f6l14^G7hBc88N)t(`oyC4(bC!Z
z8Yt>(PEOgB11N7`r_9Vuus5UFG#q|t7KORF$G`MUF)=V|>gr5t-8w+Ew6!e(lV&ku
zrUI_@_A(iYxs8%yK(x|n$@eUh(9F?MElxbF;3oWVTZuZ&i{O2trDgEl5;sR9Sdxp&
z3CakYn3hJ!LEputG(Ug6*tkB^(a+CME~W4JyJ94x>_#j!AptI*LvfBu^7PD%qJn~(
z$S2SL#z7T13}wogEvWpwFCdWAw-Q2UYG^nOK20M{%oC7(s@%rL%8Kx=mPj3ABwr*YxLa@4;|AT5I=H_G1UlS9drYeI5p2QHd
zfToAn;bN5vMxOR7D;pbMU*DbiMyNZWt+|Gm+DvTt7JjP(X&`L(G<_s9q@eKtKPUDZ
zTR+-LOSb^u#L-e5G;W0@-F;gj+@+THtsrk2(j*vD5$hNHzu8^y~n
z-rF3~;M?FuML{71YV7dP1JsEfO9bMhh%4htM7Y>LT`=5AO1P)FA|fI!2~PqcpSUU^La0f2IHa>B#I!M25LF%d2u
zTPr9i=;$PY5|%;SB$0HIVPvU1odj6-cz6)x%!mw8_uQ;3RQ0#i;$ECOui*E(HC5k{
zGaL>|UB^%fZomOEDS_tI{13PJ@^}WD&QDWc{}kfC%z@AAFCWsT_P?gIxpnIupfvd9
z-;knFaKGx-L3zQ8^;(%(jT_GX64cjsJt=ZjffESMYzEUGXk~R9*C#1RuVS6wXJC*q
z)TBU0^jN|!>3AvNaiG;Yy$jlDzPxa2-ufUS7g`ccF471rMpf93;8^
z41>o7T$NFk!v~c**$m!5UHk^M?{zg3Is$zMTu|U!r8Z867Ey+S>oiVCM0FaK723~{R0`4r?ZRR
z_y*8`PAY(NUX&p#HI=%Mul>efgYpOUsYwhZ5He?Xstbvnn%WjP2q9kH;G&<99zY;7
z@(aWNwYsS-Nsrzi=8VDX^{*LE3iVOjG>;D8N5(LqM$jyvHL~TS!;lQfh*mSrTrqlZ
zeA~0NyB8N{?zC~((WHD$JdzpCc6MSuhihPxU(CP6j!_GX2TrZ8_k~V|a9GeNIA5N3
zY-A*HZTEh$d5}pfC8ZAsTfWGo!!dj~`Q`{?L2Tw$VH@?i(UxcMKy0fS#0xz{y`*@r
zpoU^&W0$@?MrIAQwV5BT4U_U`Q(K|b{zAJ2LF#`iD^}*_XmlyFOX$I=sWU*LKF{2O
zObG8<^L;>OA>K^9{LJdRsJ^Na7T*g*OifKKb@qphA{tZCdU&-h2wVinE`aUfr%nKv
zptGl@rqHp8EO{uwDvXYf9_;NYX=o^>J@HplBdqb^rHlbgOcQ_MG+8P$TbS9#iULCL
z^Jj~#>B`>${~Q$*I$sk~gUDR?5(AI|4G+rG#fOcXo12sKlYlL0hnm7Pv^MlKQFyt-
z6knYoJ2&?WcXzUq&u`uk^>%)dowfSX*l7IpDfOWRcqcEf<;U4sl@
zad|nE*UQ}<9b5S31iC%$;yDePF}3{zVOQ5#SW<$01_Z&!&o6&WY`{t3trn68Q;Jey
z4icz~g(r~+F^D+g;)pBdxYP-DKoj!w@j)fZe1%Xph+E6s+gnpJ9?~cPMiBof5)_4^
zW1^l>(b2w;oyt$zJ%65;pRZIw0}{^aXLLu_w{Q3R(R8)7eIIaPd~aPn5QJdC%jp&a
z;yRH82`*-A>WcKK96bVJ{fJY31P1C%7Aa<4aWSOBe_*rfbhr|kKB!vj=;(l@^!Bbf
z9pXZF5c*f?AmnxH&Ye3tEdm%$hgiiXjZ~zhkBy9euDGrCr{1QdjIa%QwE?0Bw)2Ue
z-m+d?D5PVbcr7}?>cY^VWw@ZJNs^6i4dwu6XJ-)fo&QaBW?KS#4q`i;n9h7!)NEJN
z&`|8fwo$3$%=9#5r=QZ&jE#(VIw2j%EI=ZWu!|6FH(l(jhjo~>~`+R%;J1Fr|_$REO}_HbhKRj9BB
zEG*W5LGXn-IyxX+)_C+NA})@bo!tQRS_mfo4ojXdDRBq6evXbhOodZ1BOB{
zPb=mDPFfPaEos6cpeux!G(?KopYYH~xeP1V=;@`Y=n6XI18xPHYH7(xfQ(2JaYft6
zQ0LSN02JfM+uFlNNJQ6jki!uWN(ub6k7MW=s1Ir9nLR>lt
z9|5c0r0nc$r*7G%XPS&qQ!NZj>+9jq*!MOm%cX5qFrEQ9Ljd@Y>YpwC2>jZev~_IWGJ=kY?}b
zn3kUIWMzf@A$7@5&;+9fXvp`xqw`6-)cX3^1h^G2L!~+hv`vKyh?X1;Jmkl+@I}n*&X!OAeK!VQZoG03>Nz
z6&r8(z;lBl+$T2D+&xc+5px}upqx=vh-
zoz3JNdsjlv@WYI!r3jJ$W)_yVFKS#j+Te3`_7eD>$@&*1v9W|xN9Zl{hn95HL@_tX
z2qcA&LX4v4Qd3buG~sK}AH3N*#F}13Jxi|ylb|wMTuTQy6a{&F)1zG&F
z7_dWdqL2fX+D~xB?13OCF{&Mzp59nq*4ES0GcdSAL*sXO?ny_N0mBT?pr|)G8er#6
z8yFLn3KL&6+gI-*ptm$n_}0$+t{}9Agymjxs!=|yRS3?!_6J1d%@_N9P^3^1(C2hi
zRLZXUm3F`0-Phykyt=xA`4a^G;9NQ=S65abF3v43RcCY_FE0fy@P{xyveV-&XCN5ZgN>{!z^r(5O_#iQd@gg*~g7eZ~6E!>Cd8_
z*;;gW@|Ke3E4&A%EOxpy1R)f~K{>=PNCO#72tB0*$fOyCZB>W9Et2W<mzIUS0+26c`7_RlJVWiVq(gao@rZ=G|7<4Hby6RI6?xLBh&Q{cZvy2z`mJ
zg8+DkKPy7=j=^xa)PcV-`3n3i7_!Q$Dk@4!INYv|jyqITM3g*c-SG@?ILF7wwY9at
z(7=jdfI$lu!dC(D10sjC
zw{Q7jkQ0VbN~9sO0S6*=eZl{Dfd-vz0%q8Y+2kF#E~`ume^lN-3EM{X2D4=XsnB?#
z0gNQTN9el9+JYN1F);x=PO$zkA{7M5;>zE@O(u&gcxcq)GbwzzckNXefGeA0aNTt*!0Z
z!Jrfg5*Sp7hX;p%paIe}$i88A0DPeMh6aYn{imniAPhmuva)^!>S{3z7wCTQaW*$O
zRk>jxs|=*XqIsm&XN8`0hPs-X7F~6Tu(Y(hZ+pV-J+PDp9lF>a?qU-Od!fn3$<3{Z
za|v$OMFkI;S6Ujsc_^AN^B^k`6e~DN#Td$AW;8RlD`u6xZ>&K@eIQ#I!xkctwJc*3
zrh$%TW~Go7*!bnh6MBv-H(^c
z0a(G(^ve_5cec=i%^*1Vce3;KH!tjck7T1A$q>X@#GpA{P%Y);FgTT&SXtZm12r`f
z4^i&CqgYDz(VL$j$h!c>gvtN0OFB*=WEOr9`*D^VV4}4`Ua~04$M;xKF}!a@(&z9I
zBt(!*3k#os{DdRgt!(h#L(8oL{MX@#G%#%rn46zBF=zindPvnhATbF$yf|=Hf9jWE
zC}mJ){>s4#)&i3Z5Y#R>C!hdbxR2SppTh(IBFSd~0Rc`<+r97ZLl_DQX5M#40#enM
zmKJc!$8UHjm#;h}rLsGi9?6|xNwBSFH6gE3=L8i4`F@820wo>|zXN7WXQ9lhEZ60
z?~R<_ZY#F1(`=B+tYa5J7
zF?kH5h>L|#VxJO991@JZ{S_6W@a5Ij9ZW`Se2TB6ifQ_ec3EImCvI=T<$
z89v1tKyNp=3J3xBL~09Rw#WBkdlqsChA=)pK44S8h;YP`i%4;CF$)U|lpJtO7bOhO
z?#z)=BskAE;C2~%g=Qx6TJ@6uU)ZgG(;&wQp;!8znTgWrdG6oM7$lpLqM8c{57bV1
zd3j`{QppitcV1y3fklJcit^K^Pa(fF>JTpUO-klRp6E1GRxM1g438`uE=FJ=6^WouPmG_(pYS%N)%`h1R&DmT~
zpe!pZ3sI!bQ^+0$GT4h`PRWoQDq!SSmCqkYIM!L=+YY(x<`}-d-G9t4j<#E6a
zfhlj8F*uZN;rd)u)Zg8WLuB0Oy_cPqHW!9V2I(9?G+?~A=f;?mh_@&8rGMKgMMzWG
zz5U&5=1dRmT-9ggQ=ltkB<88EHp>UqGbCfbj8XG6!!!%*&}^Is;LXw!P@qMP_5oZu
z85kHCV;4I7#e3%iE#w0rI*pCf$fG|!iO)=4TwQ$u^S<+QA6pDfOOilO+stmCavT&$
z8H7@czJ66q{PPO`_5uwfcv>h%*IJ{o`Z>j$OHRJhQnRJc&^a}|>d~d!U}H1-ts!V)
zRMMj*!Egm@7Zeoq;As`44c4DcE8;}I(v&{#LVacolm&Br)f}o`(x~dw9
zPfriHzVa6_GNK~vtSKynOkDNiPm3^N3~y-bBybcimglw4u{}VThiZobHI2TGyV^Z!9gB4*xSQI2N4&S
zo&C>xFkS5>MO0UjRN&PG*pd&t7Ow?tzR$Dc9}!$DC@RWc1aMEhY)SCa+QdiDwaW_&(xTZBC=8ZxcLADrzFL(XQ4u6j@Y>hyzMV$q|L;l#r49hufy7f!
z6Z_@<+V^-9ZUBYpKdx(v&%2#>5~b3&;s*ANgo=toiw+(mEBy*oaD83f>+tYCeZ!Z#
z3#gy&m>L;jzC?=vQ{8^(*_)!AOey_amv=eJuj*G70pMowE~2Mv_ae;9NRy@LTT4U`hZ+pwN&
zt}cwApDv_mpZo{R%OImzd(WwqI9Ky*T`hF&p?OjrYo+kbeZfQV!;FD^oIlDlQqkzk
z?_tgp9j)T+eF}li4_g=qL&B~780@$apiDw)s>+=^c%c+jT-Nnt7#{nDCv-2jvj8y-rW96@>W6B4$#&fg@Kn!eV1nIt&yt+
zh1pci?VbBsKW5%Q`g`J%UZ9hUvf^iYbH}me!?8=lds2zkYxE53MjqV?ZdSNNilQX1
KE?0~+3;7>r1QrVb

literal 0
HcmV?d00001

diff --git a/Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq15217372810807509549.png b/Tutorial/ScriptsForDocu/html/tutorialCreatingObject_eq15217372810807509549.png
new file mode 100644
index 0000000000000000000000000000000000000000..8387ddde2f6d12183160dfaeae7fc5d2129ff016
GIT binary patch
literal 7784
zcmX9@cRZEv`+rC_9eeMQ$U51(W6uyWBP%{6L{Vh#5oILVJ7g1zY!Z%HWMpJSvPZ)2
z>ic{CsPppTKF@t$*Y#d^l)j!i88IUt9yBmelG86`2h2^X@8%C((qQMfj>?K0F-Etn1Mz
zRorN`G2=WwM*~S0iQ#8_5$YyZ)N=ZSwy3;F0t5E!$iVA~+8#=7?)Z1<>FHNX=GgZ0
z8m>v+jg*~JJQ5qPxcxz9cKu7m?R^^*COece?*a-#+VN6{H%{%ExHyZaNioU_L9lf`
zs(Y!JoL;x!E?aR4iK%bjs9aC9vpb)VN|&k=@u-pZA|HIVIr;gujE%i}&2lizJr))g
zqNE)nI@483V)Wr09q=~-c{IAZ10-}p(~S=ZIz)2xr4U@6Svw8W+gg00J4$Nhjg7uN
z@hk-7%tzltF5z#iYuaB#K8jqGm6he<>gek$sjPJ0T^``&<+Z;-+ff;DiEVSPHDJCy
z%ugY!%hrE)K=RL@Ka@Pz!!j~5U{7;(E?*+pp7LRMQopty@}>FRyQHKfCBnspg%lwxtwK8DFjV{Ao|m?^_T9THFA#L
z`qnix#PqZOTaNzI%RtHiIqZq2r6qH+mMZTJx3U`RGWJj;(B;e9l9H0i$w?gK{rmTy
z`uk&IVy;^UCKo?l8z~454%SW;l9rKaZ$SkSqC_8$KJxN{hsaW?#`5d^6j1T_I6w=7Z;bqy*(o%BPnU=D5JaRw4XnJ-n(~i
zwCKi{i3xmUx;3CRKfiNkCZo^Z#KdGUOL4BRk0{M*kMwJce{*{Jw-`F%jg5`(zpelN
zpycBqkGheqIFo(lbRrRXtbUYNuqvsh=DOsZDgB^(YU=T)q$?q!^3`VIl9Hvx#n~>z
zVVDBL^jEJ`q?8C_qNB&g$0uDM>g(&PtE-!tF=P$>-q;`|B&@8f`{29l=;>Llk!RN(
zd)#k*MoB}%qsu>urxmHx-Q1oVvk_)O8{=FOU_swj9OBe!;U(fI?Kq&*w^!=_P4jUfBwAjXMcSm
zxQ|C47iXZo^j5`x|NUoaWd-M^qZ3Xz^_jve?yRKOtU&Rmf;#cvbE4fLfYZ#>KgdjV{2zeOH(tEBi%BX;-k~;r%tp|%!6-?OTE0k
z({u_`Q|Ut5pl(+0+&PY3gjz;X6egS>U+4%9t$-qnqXp~h>h|~cNZA6!o_BS1tqf&l
z$$0;KCGJ8=NeS!lS+j9nmDpMHeDFZ=bZ>OtvCe0Eq2d0ViLo&bQe9n5N=BymcS{*j
zvbD`^ZkA2$huOPCPJXbrhlhAQd`L@2cYpdF1(J}FanH>S#^=gOhSx-ySsi@o{yyam
z9zKl7G`+9jO+3z5t4mAy6&06|-hqJ(=?A=sl7+>$wzliGRLSB8C)DdueG+^{*4$RG+LknKHawUSCg_w-Ys>M$l3G7)sK04}kVj+7~P{0#fSzGIc
z;Bf^~OrTZS4XB*O7{l-A>gvwS%<$cbSIdi9TFQwgA|etO7q=OcS`=gawLFl1BTL~l
zK!BJ-=f;g^k&%QXBo?ht{dIN8Hu`;8H3{*8D_YN*>gylco2%<`^YV6gb;aq#So`lA
ztEqMO){2MHn^aK@)z*rUki-C_#F3U378cGuJt?oLp^Ekobyt;84dE(th*q+sGSF003y+8(NK$z2?CcE13{eSIME&~pi-UvXDlG{{
zqw&%2+rGZXgBh}ySmlb}zHRYZ8yOtbOQd5n(#
zq?H%sugrVfN}`tr2U&7K>l66<`9TXYF*1gKvbg*V-MA)j>+R9WNx)9uW!xYeZaZA~
zLdCPAJj?2{Xx&UR3Ua08_u8%>a&s~2>Je==q)&;Ls)JEK*VkpGrH9wNG-Q4epOAN9
z2;neUSXjKhy@`0E+3f$(3*a(=GCODak^Uvp1@my3(#UGkcU^5xwvLX40N5&ntK1&{
z{r4s-i)}T%4#$F2RVJ7HL5_ZLL4o*_1J@)A-KdxtS^qy?E-nIy&0=>P48_HZ7nQEc
ztG-f2^{E8qPqQSFpbLD$KV`^1QaC%Z!!}d}R#)?wq>_26Doqt8NZA|lkGHjPh>IID
z28n5K^Tue=aw#Ze_AENPxLCjUF_0s3GHqMg-QC%-N0BPICSFksTatb*`kT_B_|2P<
z@o^TvyEJ!H%M3l{9Z-Da{{H^FFQn;lDV)0^UXgaC7DuK~$k7<3;Mjb8^%amDxaZ9q
z{vf4<7caOrS8xzkIiHUi#Pm0eGCbTm1$
zRCZ2ImG`;{ynzarq5q$~waYg$r{DY5bc!0g?@`>%(f8T^IsW&rA9D{50?L3%!c_!~
zZu0y=9H(}&-NRO-n@)>DArXq|&!*L>4Y`xzruUFewSWf?9-a4#XkFpHpao#^=h;5
zLV{Fybj|biv0mN;i4Zk4HEC&SH2)_a_N{|o-+&DmnV0}TmRD9TAe}V&ZDHKG4#fO1
zDrcTWc3ZECi;7l$|9gb<)M0QlB=55#ftVc9&$fIcblHK@oQI57aFX#_1u02MSjW#V
zMeVvt$8`Ms{({6x6wq^H=i*xYT4i_p_U*@yAKSB>{8_KKh}$-trw&`&n4dQ=H~(Z?
z1uydGV8cva{sRbufpp29&Q2d-HQ-~bW)o0Cs;XfFj?ImYqV%M}0r&0+X6O<4->B40
zm+0#0`90J8X!gCY5r17}CHC95C&xQKdRMhAZxmzu`kaR{3wz
zB}X}eSMN2U#?XJBs`B#kaiBy9m=$7^i+k%54YIpXR7s6XB!(OQoisz
zehL9IP2ND8yon
zu5r}8e!VS<>at3v5TNqJ#DwQbE@61W_(!Fp(MC(RM~@^YudmVJ1X(n=4ZM~fotq2z
zR)3G`vYuswTL$2xxi=DcCS0-B!xA$yGx!XQ&s!T=T;n`im@IJX=h_<1^Y{;b$7lrw1um`&h>e3o
zazcWQlhZrc9{kSQ+S5xP!d$8)q5XNGio41Ihw14>l$f5>U2!WMpKZM4gJC0$tkL+JamK
z#*1IxCgpuGIXW6LSo{wP9WKHEe|{w%lRb>ZDh+>g=ucr$eEN5P{R=Rc
zqod>ZL$&Mnr7C>XhI~n@tE+o^dt+lpNu;5RQpCRWOiX$8f!zJ$-@dun*-^2ue0aS7
z9{~XYU?z3_Wehj4Ik#ijOaAT4=YS6zzkUVDeQbH30<#W^z^Hd|Tfc?%r^em8cTG%a
zW4d;>w+(LIEG;PkKF@@n0jW%Ie9?*BnW(;@AvGl>SA~{5qQkaIOIzE(G#ZnW?dq%A
znKD89q7US?fS};7%}ogjiP?txYzWfh5fE@bHa6DT*$KzZ++z?)JV{|R)7u-VS9I&v
zEdg{Tp{oSbzG|o6cPY0!cR1WEcyJH=2Vu*`xv53$1iAZ}nXG{8wUgpT&omR0b@GOn
zhq3||I*FK=m}D~aibfO5xs}^^w3PhVjKix@=4x+8D%R-vb*a_w-xoHwQHctswX^Y0
z?0&6Pkb6<&;l@|h7}}^%7kB2OJM+
z4Go8|q&{g-mu+%#a+JCtA=arDuzRM({|U&1q8nM668EMGb<-o8hq4sGZ?1OX;rv)g
zSfTfRUs)+~<%%ZQ6~F2$)StTJnBwB%qAXw;Mn*=beei<>msnlD)yF01Lv2-6dBnuL
z+CzypH#cG7($Z23uph5TJ!3EaRd82sL2)RruB}DI#{+8e5|~~U{Pzrtr}mduRGcrw
z2y+i+yH<4tnM;a^jg5?OW@dvxTKxcY{i&m4u_)^)$YZMpHVP)7|YOn!JE_b5ry%~#_C7D{e#I=4xuKg+Oe46etW04Gkd5&e@y)FH#$GMS|~lZPW~Pj^cxC1gnJwwDR=!-v9Njf$pj)c6|II
zhl;{gt(Re_<)tO;+FrWkeT<KhuO?@fs@
z3<*pswdBXe4NTR!z)O{vm(L6jdu)6aMe70xr4Jk&9=5vAxA&(CdoZitX9knd`gbQL
zA|m3&i<|cL_U`VLDJdp;dgL$qjBeg^adw`pb|A?bdiLyDLuI88bka&zz}q)({`3pC
znwpv_5yBAP$drp(JOb?kwdYWHLl}Dg)w-&{`=Dk>p~FKCji$^%1By37)&cKdY{VZO
zY-V8rdjiNMz{fW%F@lae{o}nS-gTjU4??|9i^H%9xp%VZwm3-I?bq?v=-b!{v27-HVp=XGT*EvyBG8b&E!zkUVT4vl-e$%{yJyz98P+ubQwiuS6h
zzLC+=j~`)oUUL2eZ5+hw(Iaj97O3n60&?KEp$u6`A)$Ce9xmS_zm|}YkTPoNNp@1X
z^nvAVpQZN9%*>P&S_(AZOnM+nO+^K4uvQ*NLTc*a_$}+IN!O)&*9oa8DeYs_&kjNV
zf_e+fzpUf#F7hYw(&R6GT%lBf1sl#x2t@zvf3fBWo%7or0KJr{;Q#06xPU^IwV`44
zgO$P8uV3G)xP5$l%%Np%ZB2Lwyg#fF>JCzuPWW~x>hY5&E6d9;VlZ?6PyvzvR67={
zzl5tzl{w(3uBLWy%XN~y@TH%pCgY?!n|~;`%4Os1{C)RTY!3W>|7zHRVV?uf#)Fk-
zY>KQ53=E*tn3%L%l2cL+W**IrkB`HbgHkGktn5@Iwzc&Gq0|Nt2>bDtz!u1Kg5nJsGqFqb9CB1$e
zm6IFG%Z_`DVV-G0`QjoDpFkLQEpFqt3UjB@P@X|h?EdJ{CCFV!NWOh|au`NJhb;&B
z7a9=(f${9uuk`;5oT0IFvJ_Yl?Bt~85ieTb$|_DFbzB&iIBaBi*uc^<){%;f=RjcM
z{xND8on*!S_Ufu&1QyK@WYB*x26k0F#}a9T!&~h#eUF3iCQG+qtOQ
zyFTxseu>4Y!Fa9`Pe5dYx({K-VMr);;QSO!ipp^aadtZrqZr1!coBxg*u;eL8RsOe
zod^rXT|KY-+}z+c8?YW6H=q9alnj6lIHW|V-nHVMkPQquF*>@q?Zd}n$k`pwqCL2c
z2{#skQC7xaEG;duW}ZfTD8h*f>t~krZQo3s-#z>_HKcg%>f{vJGV$J_pBCfG3!v<=
z(E0hZ4ii&4XfCiB-@ku{yM^7Ap$iE1b}I}j{D~h>3>Z6r(3F%E1@N6U2NF_%f*cJf)jEOUqN}K*VAHCZPrjN;y
z)&Zyc&@vzvFSvLe1sYz2W0IAT(RV^iOEdf`Us+kXmZn2^M@b233Pe2*h-T>7o{!-q
zV;zx5RCQtxb(tuetiQ*^JncmqXbhy2nK=vi!yek#*Ecma1@0bH(fLu2B0Z^^rluzJ
zfy~a!%*^=bZ1x)re^(UG)5Kj;R2UoaxWaLqCb71~Vg??6?)=nNR_*|X?(gp}pok@I
zrNN=SXjvGCB-i+d-|q2dTEKs%07V%f-g(}
zqE;*xJ2)r=fxo2WVZ+?hMbF0J;o<7^7Vq^5P+R!QBDnN{DP)4-T2wSeY=tfKjEq9&
zwSi#3n+qp|x7w;od9^)J;;9U7nW3Q}GYiXb+g5%)H-d!U;afF>J0olB
zXHN&h%J@f2b1Z=D;L_`I3wm5Q`20$&r|t%w{o#**uKKemcG>-f(+fXmKpT+|6GJ?s
zqM(4TEyK3L<$&U&O-m5>a7$%s`W19Hf`AK=W4(m9FLXvhSh2po-q9jF{>gxYxB%`0
zfF-%P?QQV1-lt~6o!7S
zj1qV@djr0uW!A*%ZM%%|3~ePI`ub>mVKA|C%elYeg}npAHbWq@ZNxU;*v=G7Du
z(qY5=fy17MytBN%{-7&{ZnY@0>%j$kD!%R6#dG9{W%+^DDtjbc9V42mdMa-*Ho^Y~
DjJC0E

literal 0
HcmV?d00001

diff --git a/Tutorial/ScriptsForDocu/tutorialCreatingObject.m b/Tutorial/ScriptsForDocu/tutorialCreatingObject.m
index b3e46be..6bdd34e 100644
--- a/Tutorial/ScriptsForDocu/tutorialCreatingObject.m
+++ b/Tutorial/ScriptsForDocu/tutorialCreatingObject.m
@@ -138,7 +138,11 @@
 % Matlab's statistic toolbox (2015b and newer) is able to efficiently
 % estimate the covariogram parameters. This is in general very robust and
 % should be used if possible
-KrigingObj.KrigingObjects{indexKrigingObj}.setUseMatlabRegressionGP(true);
+try
+    KrigingObj.KrigingObjects{indexKrigingObj}.setUseMatlabRegressionGP(true);
+catch ex
+    warning(ex.message)
+end
 
 %%%
 % Alternatively, one of the integrated solver can be used. Here, an open
diff --git a/dependencies/@AnalyzeKriging/AnalyzeKriging.m b/dependencies/@AnalyzeKriging/AnalyzeKriging.m
index d234974..b5e8a7c 100644
--- a/dependencies/@AnalyzeKriging/AnalyzeKriging.m
+++ b/dependencies/@AnalyzeKriging/AnalyzeKriging.m
@@ -3,7 +3,7 @@
 % See the license note at the end of the file.
     
     %% Private Members
-    properties(GetAccess='public',SetAccess='private')
+    properties(GetAccess='public',SetAccess='public')
         KrigingObjects = cell(0);
     end
     %% Protected Members
@@ -28,6 +28,7 @@
         SuppressFigure=0;
         % Decide how many point of each input variable shall tested
         Accuracy = 100;
+        AccuracyOutput = 100;
         % Decide if only the basis function should be plotted
         ShowBasisFct = 0;
         % Plot Lower and Upper Bounds (1) or not (0)
@@ -83,6 +84,9 @@
         UseDataPointsAsComparisonPoint = false;
         % Signifcance level (error Erro Type I) used for z-test
         SignificanceLevel = 0.05;
+        % Used for the contour plot in order to defined a minimum value
+        % which is plotted. This prevents that the entrie plot is colored
+        ThresholdQuantile = -inf;
         %% Interpolation Options
         % Subplot contains nPlots X nPlots figures
         nPlots =5;
@@ -181,6 +185,8 @@
         % Matrix (nLevelX3) containing the color map used for plots like 3D
         % interpolation
         ColormapToolbox = [];
+        %% Prediction of Pareto Front
+        RepeatDesign = true;
     end
     methods
         %% Constructor
@@ -357,6 +363,10 @@
             Accuracy = obj.Accuracy;
         end
         % ----------------------------------------------------------------
+        function [AccuracyOutput]=getAccuracyOutput(obj)
+            AccuracyOutput = obj.AccuracyOutput;
+        end
+        % ----------------------------------------------------------------
         function [ShowBounds]=getShowBounds(obj)
             ShowBounds = obj.ShowBounds;
         end
@@ -657,11 +667,29 @@
         function [PlottingRange]=getPlottingRange(obj)
             PlottingRange = obj.PlottingRange;
         end
+        % ----------------------------------------------------------------
+        function [RepeatDesign] = getRepeatDesign(obj)
+            RepeatDesign = obj.RepeatDesign;
+        end
+        % ----------------------------------------------------------------
+        function [ThresholdQuantile] = getThresholdQuantile(obj)
+            ThresholdQuantile = obj.ThresholdQuantile;
+        end
         %% Set Functions
         function []=setAccuracy(obj,Accuracy)
+            if any(size(Accuracy)>1)
+                error('Accuracy has to be a scalar')
+            end
             obj.Accuracy = Accuracy;
         end
         % ----------------------------------------------------------------
+        function []=setAccuracyOutput(obj,AccuracyOutput)
+            if any(size(AccuracyOutput)>1)
+                error('AccuracyOutput has to be a scalar')
+            end
+            obj.AccuracyOutput = AccuracyOutput;
+        end
+        % ----------------------------------------------------------------
         function []=setShowBounds(obj,ShowBounds)
             if ~islogical(ShowBounds)
                 error('ShowBounds has to be logical')
@@ -958,14 +986,28 @@
             if isempty(varargin)||isempty(varargin{1})
                 obj.ColormapToolbox = load('ColormapWinterInverse.txt');
                 return;
+            elseif strcmp(varargin{1},'FZJ')
+                obj.ColormapToolbox = load('ColormapFZJ.txt');
+                return;
             end
             
-            if size(varargin{1},2)~=3
+            if size(varargin{1},2)~=3&&~ischar(varargin{1})
                 error('ColormapToolbox must have three columns')
             end
             
             obj.ColormapToolbox = varargin{1};
         end
+        % ----------------------------------------------------------------
+        function [] = setRepeatDesign(obj,RepeatDesign)
+            if ~islogical(RepeatDesign)
+                error('RepeatDesign must be logical')
+            end
+            obj.RepeatDesign = RepeatDesign;
+        end
+        % ----------------------------------------------------------------
+        function [] = setThresholdQuantile(obj,ThresholdQuantile)
+            obj.ThresholdQuantile = ThresholdQuantile;
+        end
         
     end
     
diff --git a/dependencies/@AnalyzeKriging/ColormapFZJ.txt b/dependencies/@AnalyzeKriging/ColormapFZJ.txt
new file mode 100644
index 0000000..12722d5
--- /dev/null
+++ b/dependencies/@AnalyzeKriging/ColormapFZJ.txt
@@ -0,0 +1,1000 @@
+   0.0000000e+00   5.4901961e-01   8.0392157e-01
+   0.0000000e+00   5.4864865e-01   8.0337200e-01
+   0.0000000e+00   5.4827769e-01   8.0282243e-01
+   0.0000000e+00   5.4790673e-01   8.0227286e-01
+   0.0000000e+00   5.4753577e-01   8.0172329e-01
+   0.0000000e+00   5.4716481e-01   8.0117372e-01
+   0.0000000e+00   5.4679385e-01   8.0062415e-01
+   0.0000000e+00   5.4642289e-01   8.0007458e-01
+   0.0000000e+00   5.4605193e-01   7.9952502e-01
+   0.0000000e+00   5.4568098e-01   7.9897545e-01
+   0.0000000e+00   5.4531002e-01   7.9842588e-01
+   0.0000000e+00   5.4493906e-01   7.9787631e-01
+   0.0000000e+00   5.4456810e-01   7.9732674e-01
+   0.0000000e+00   5.4419714e-01   7.9677717e-01
+   0.0000000e+00   5.4382618e-01   7.9622760e-01
+   0.0000000e+00   5.4345522e-01   7.9567803e-01
+   0.0000000e+00   5.4308426e-01   7.9512846e-01
+   0.0000000e+00   5.4271330e-01   7.9457889e-01
+   0.0000000e+00   5.4234234e-01   7.9402932e-01
+   0.0000000e+00   5.4197138e-01   7.9347975e-01
+   0.0000000e+00   5.4160042e-01   7.9293019e-01
+   0.0000000e+00   5.4122946e-01   7.9238062e-01
+   0.0000000e+00   5.4085851e-01   7.9183105e-01
+   0.0000000e+00   5.4048755e-01   7.9128148e-01
+   0.0000000e+00   5.4011659e-01   7.9073191e-01
+   0.0000000e+00   5.3974563e-01   7.9018234e-01
+   0.0000000e+00   5.3937467e-01   7.8963277e-01
+   0.0000000e+00   5.3900371e-01   7.8908320e-01
+   0.0000000e+00   5.3863275e-01   7.8853363e-01
+   0.0000000e+00   5.3826179e-01   7.8798406e-01
+   0.0000000e+00   5.3789083e-01   7.8743449e-01
+   0.0000000e+00   5.3751987e-01   7.8688492e-01
+   0.0000000e+00   5.3714891e-01   7.8633535e-01
+   0.0000000e+00   5.3677795e-01   7.8578579e-01
+   0.0000000e+00   5.3640700e-01   7.8523622e-01
+   0.0000000e+00   5.3603604e-01   7.8468665e-01
+   0.0000000e+00   5.3566508e-01   7.8413708e-01
+   0.0000000e+00   5.3529412e-01   7.8358751e-01
+   0.0000000e+00   5.3492316e-01   7.8303794e-01
+   0.0000000e+00   5.3455220e-01   7.8248837e-01
+   0.0000000e+00   5.3418124e-01   7.8193880e-01
+   0.0000000e+00   5.3381028e-01   7.8138923e-01
+   0.0000000e+00   5.3343932e-01   7.8083966e-01
+   0.0000000e+00   5.3306836e-01   7.8029009e-01
+   0.0000000e+00   5.3269740e-01   7.7974052e-01
+   0.0000000e+00   5.3232644e-01   7.7919096e-01
+   0.0000000e+00   5.3195548e-01   7.7864139e-01
+   0.0000000e+00   5.3158453e-01   7.7809182e-01
+   0.0000000e+00   5.3121357e-01   7.7754225e-01
+   0.0000000e+00   5.3084261e-01   7.7699268e-01
+   0.0000000e+00   5.3047165e-01   7.7644311e-01
+   0.0000000e+00   5.3010069e-01   7.7589354e-01
+   0.0000000e+00   5.2972973e-01   7.7534397e-01
+   0.0000000e+00   5.2935877e-01   7.7479440e-01
+   0.0000000e+00   5.2898781e-01   7.7424483e-01
+   0.0000000e+00   5.2861685e-01   7.7369526e-01
+   0.0000000e+00   5.2824589e-01   7.7314569e-01
+   0.0000000e+00   5.2787493e-01   7.7259613e-01
+   0.0000000e+00   5.2750397e-01   7.7204656e-01
+   0.0000000e+00   5.2713302e-01   7.7149699e-01
+   0.0000000e+00   5.2676206e-01   7.7094742e-01
+   0.0000000e+00   5.2639110e-01   7.7039785e-01
+   0.0000000e+00   5.2602014e-01   7.6984828e-01
+   0.0000000e+00   5.2564918e-01   7.6929871e-01
+   0.0000000e+00   5.2527822e-01   7.6874914e-01
+   0.0000000e+00   5.2490726e-01   7.6819957e-01
+   0.0000000e+00   5.2453630e-01   7.6765000e-01
+   0.0000000e+00   5.2416534e-01   7.6710043e-01
+   0.0000000e+00   5.2379438e-01   7.6655086e-01
+   0.0000000e+00   5.2342342e-01   7.6600130e-01
+   0.0000000e+00   5.2305246e-01   7.6545173e-01
+   0.0000000e+00   5.2268151e-01   7.6490216e-01
+   0.0000000e+00   5.2231055e-01   7.6435259e-01
+   0.0000000e+00   5.2193959e-01   7.6380302e-01
+   0.0000000e+00   5.2156863e-01   7.6325345e-01
+   0.0000000e+00   5.2119767e-01   7.6270388e-01
+   0.0000000e+00   5.2082671e-01   7.6215431e-01
+   0.0000000e+00   5.2045575e-01   7.6160474e-01
+   0.0000000e+00   5.2008479e-01   7.6105517e-01
+   0.0000000e+00   5.1971383e-01   7.6050560e-01
+   0.0000000e+00   5.1934287e-01   7.5995603e-01
+   0.0000000e+00   5.1897191e-01   7.5940647e-01
+   0.0000000e+00   5.1860095e-01   7.5885690e-01
+   0.0000000e+00   5.1822999e-01   7.5830733e-01
+   0.0000000e+00   5.1785904e-01   7.5775776e-01
+   0.0000000e+00   5.1748808e-01   7.5720819e-01
+   0.0000000e+00   5.1711712e-01   7.5665862e-01
+   0.0000000e+00   5.1674616e-01   7.5610905e-01
+   0.0000000e+00   5.1637520e-01   7.5555948e-01
+   0.0000000e+00   5.1600424e-01   7.5500991e-01
+   0.0000000e+00   5.1563328e-01   7.5446034e-01
+   0.0000000e+00   5.1526232e-01   7.5391077e-01
+   0.0000000e+00   5.1489136e-01   7.5336120e-01
+   0.0000000e+00   5.1452040e-01   7.5281164e-01
+   0.0000000e+00   5.1414944e-01   7.5226207e-01
+   0.0000000e+00   5.1377848e-01   7.5171250e-01
+   0.0000000e+00   5.1340753e-01   7.5116293e-01
+   0.0000000e+00   5.1303657e-01   7.5061336e-01
+   0.0000000e+00   5.1266561e-01   7.5006379e-01
+   0.0000000e+00   5.1229465e-01   7.4951422e-01
+   0.0000000e+00   5.1192369e-01   7.4896465e-01
+   0.0000000e+00   5.1155273e-01   7.4841508e-01
+   0.0000000e+00   5.1118177e-01   7.4786551e-01
+   0.0000000e+00   5.1081081e-01   7.4731594e-01
+   0.0000000e+00   5.1043985e-01   7.4676637e-01
+   0.0000000e+00   5.1006889e-01   7.4621681e-01
+   0.0000000e+00   5.0969793e-01   7.4566724e-01
+   0.0000000e+00   5.0932697e-01   7.4511767e-01
+   0.0000000e+00   5.0895601e-01   7.4456810e-01
+   0.0000000e+00   5.0858506e-01   7.4401853e-01
+   0.0000000e+00   5.0821410e-01   7.4346896e-01
+   0.0000000e+00   5.0784314e-01   7.4291939e-01
+   0.0000000e+00   5.0747218e-01   7.4236982e-01
+   0.0000000e+00   5.0710122e-01   7.4182025e-01
+   0.0000000e+00   5.0673026e-01   7.4127068e-01
+   0.0000000e+00   5.0635930e-01   7.4072111e-01
+   0.0000000e+00   5.0598834e-01   7.4017154e-01
+   0.0000000e+00   5.0561738e-01   7.3962197e-01
+   0.0000000e+00   5.0524642e-01   7.3907241e-01
+   0.0000000e+00   5.0487546e-01   7.3852284e-01
+   0.0000000e+00   5.0450450e-01   7.3797327e-01
+   0.0000000e+00   5.0413355e-01   7.3742370e-01
+   0.0000000e+00   5.0376259e-01   7.3687413e-01
+   0.0000000e+00   5.0339163e-01   7.3632456e-01
+   0.0000000e+00   5.0302067e-01   7.3577499e-01
+   0.0000000e+00   5.0264971e-01   7.3522542e-01
+   0.0000000e+00   5.0227875e-01   7.3467585e-01
+   0.0000000e+00   5.0190779e-01   7.3412628e-01
+   0.0000000e+00   5.0153683e-01   7.3357671e-01
+   0.0000000e+00   5.0116587e-01   7.3302714e-01
+   0.0000000e+00   5.0079491e-01   7.3247758e-01
+   0.0000000e+00   5.0042395e-01   7.3192801e-01
+   0.0000000e+00   5.0005299e-01   7.3137844e-01
+   0.0000000e+00   4.9968203e-01   7.3082887e-01
+   0.0000000e+00   4.9931108e-01   7.3027930e-01
+   0.0000000e+00   4.9894012e-01   7.2972973e-01
+   0.0000000e+00   4.9856916e-01   7.2918016e-01
+   0.0000000e+00   4.9819820e-01   7.2863059e-01
+   0.0000000e+00   4.9782724e-01   7.2808102e-01
+   0.0000000e+00   4.9745628e-01   7.2753145e-01
+   0.0000000e+00   4.9708532e-01   7.2698188e-01
+   0.0000000e+00   4.9671436e-01   7.2643231e-01
+   0.0000000e+00   4.9634340e-01   7.2588275e-01
+   0.0000000e+00   4.9597244e-01   7.2533318e-01
+   0.0000000e+00   4.9560148e-01   7.2478361e-01
+   0.0000000e+00   4.9523052e-01   7.2423404e-01
+   0.0000000e+00   4.9485957e-01   7.2368447e-01
+   0.0000000e+00   4.9448861e-01   7.2313490e-01
+   0.0000000e+00   4.9411765e-01   7.2258533e-01
+   0.0000000e+00   4.9374669e-01   7.2203576e-01
+   0.0000000e+00   4.9337573e-01   7.2148619e-01
+   0.0000000e+00   4.9300477e-01   7.2093662e-01
+   0.0000000e+00   4.9263381e-01   7.2038705e-01
+   0.0000000e+00   4.9226285e-01   7.1983748e-01
+   0.0000000e+00   4.9189189e-01   7.1928792e-01
+   0.0000000e+00   4.9152093e-01   7.1873835e-01
+   0.0000000e+00   4.9114997e-01   7.1818878e-01
+   0.0000000e+00   4.9077901e-01   7.1763921e-01
+   0.0000000e+00   4.9040806e-01   7.1708964e-01
+   0.0000000e+00   4.9003710e-01   7.1654007e-01
+   0.0000000e+00   4.8966614e-01   7.1599050e-01
+   0.0000000e+00   4.8929518e-01   7.1544093e-01
+   0.0000000e+00   4.8892422e-01   7.1489136e-01
+   0.0000000e+00   4.8855326e-01   7.1434179e-01
+   0.0000000e+00   4.8818230e-01   7.1379222e-01
+   0.0000000e+00   4.8781134e-01   7.1324265e-01
+   0.0000000e+00   4.8744038e-01   7.1269309e-01
+   0.0000000e+00   4.8706942e-01   7.1214352e-01
+   0.0000000e+00   4.8669846e-01   7.1159395e-01
+   0.0000000e+00   4.8632750e-01   7.1104438e-01
+   0.0000000e+00   4.8595654e-01   7.1049481e-01
+   0.0000000e+00   4.8558559e-01   7.0994524e-01
+   0.0000000e+00   4.8521463e-01   7.0939567e-01
+   0.0000000e+00   4.8484367e-01   7.0884610e-01
+   0.0000000e+00   4.8447271e-01   7.0829653e-01
+   0.0000000e+00   4.8410175e-01   7.0774696e-01
+   0.0000000e+00   4.8373079e-01   7.0719739e-01
+   0.0000000e+00   4.8335983e-01   7.0664782e-01
+   0.0000000e+00   4.8298887e-01   7.0609826e-01
+   0.0000000e+00   4.8261791e-01   7.0554869e-01
+   0.0000000e+00   4.8224695e-01   7.0499912e-01
+   0.0000000e+00   4.8187599e-01   7.0444955e-01
+   0.0000000e+00   4.8150503e-01   7.0389998e-01
+   0.0000000e+00   4.8113408e-01   7.0335041e-01
+   0.0000000e+00   4.8076312e-01   7.0280084e-01
+   0.0000000e+00   4.8039216e-01   7.0225127e-01
+   0.0000000e+00   4.8002120e-01   7.0170170e-01
+   0.0000000e+00   4.7965024e-01   7.0115213e-01
+   0.0000000e+00   4.7927928e-01   7.0060256e-01
+   0.0000000e+00   4.7890832e-01   7.0005299e-01
+   0.0000000e+00   4.7853736e-01   6.9950342e-01
+   0.0000000e+00   4.7816640e-01   6.9895386e-01
+   0.0000000e+00   4.7779544e-01   6.9840429e-01
+   0.0000000e+00   4.7742448e-01   6.9785472e-01
+   0.0000000e+00   4.7705352e-01   6.9730515e-01
+   0.0000000e+00   4.7668256e-01   6.9675558e-01
+   0.0000000e+00   4.7631161e-01   6.9620601e-01
+   0.0000000e+00   4.7594065e-01   6.9565644e-01
+   0.0000000e+00   4.7556969e-01   6.9510687e-01
+   0.0000000e+00   4.7519873e-01   6.9455730e-01
+   0.0000000e+00   4.7482777e-01   6.9400773e-01
+   0.0000000e+00   4.7445681e-01   6.9345816e-01
+   0.0000000e+00   4.7408585e-01   6.9290859e-01
+   0.0000000e+00   4.7371489e-01   6.9235903e-01
+   0.0000000e+00   4.7334393e-01   6.9180946e-01
+   0.0000000e+00   4.7297297e-01   6.9125989e-01
+   0.0000000e+00   4.7260201e-01   6.9071032e-01
+   0.0000000e+00   4.7223105e-01   6.9016075e-01
+   0.0000000e+00   4.7186010e-01   6.8961118e-01
+   0.0000000e+00   4.7148914e-01   6.8906161e-01
+   0.0000000e+00   4.7111818e-01   6.8851204e-01
+   0.0000000e+00   4.7074722e-01   6.8796247e-01
+   0.0000000e+00   4.7037626e-01   6.8741290e-01
+   0.0000000e+00   4.7000530e-01   6.8686333e-01
+   0.0000000e+00   4.6963434e-01   6.8631376e-01
+   0.0000000e+00   4.6926338e-01   6.8576420e-01
+   0.0000000e+00   4.6889242e-01   6.8521463e-01
+   0.0000000e+00   4.6852146e-01   6.8466506e-01
+   0.0000000e+00   4.6815050e-01   6.8411549e-01
+   0.0000000e+00   4.6777954e-01   6.8356592e-01
+   0.0000000e+00   4.6740859e-01   6.8301635e-01
+   0.0000000e+00   4.6703763e-01   6.8246678e-01
+   0.0000000e+00   4.6666667e-01   6.8191721e-01
+   0.0000000e+00   4.6629571e-01   6.8136764e-01
+   0.0000000e+00   4.6592475e-01   6.8081807e-01
+   0.0000000e+00   4.6555379e-01   6.8026850e-01
+   0.0000000e+00   4.6518283e-01   6.7971893e-01
+   0.0000000e+00   4.6481187e-01   6.7916937e-01
+   0.0000000e+00   4.6444091e-01   6.7861980e-01
+   0.0000000e+00   4.6406995e-01   6.7807023e-01
+   0.0000000e+00   4.6369899e-01   6.7752066e-01
+   0.0000000e+00   4.6332803e-01   6.7697109e-01
+   0.0000000e+00   4.6295707e-01   6.7642152e-01
+   0.0000000e+00   4.6258612e-01   6.7587195e-01
+   0.0000000e+00   4.6221516e-01   6.7532238e-01
+   0.0000000e+00   4.6184420e-01   6.7477281e-01
+   0.0000000e+00   4.6147324e-01   6.7422324e-01
+   0.0000000e+00   4.6110228e-01   6.7367367e-01
+   0.0000000e+00   4.6073132e-01   6.7312410e-01
+   0.0000000e+00   4.6036036e-01   6.7257454e-01
+   0.0000000e+00   4.5998940e-01   6.7202497e-01
+   0.0000000e+00   4.5961844e-01   6.7147540e-01
+   0.0000000e+00   4.5924748e-01   6.7092583e-01
+   0.0000000e+00   4.5887652e-01   6.7037626e-01
+   0.0000000e+00   4.5850556e-01   6.6982669e-01
+   0.0000000e+00   4.5813461e-01   6.6927712e-01
+   0.0000000e+00   4.5776365e-01   6.6872755e-01
+   0.0000000e+00   4.5739269e-01   6.6817798e-01
+   0.0000000e+00   4.5702173e-01   6.6762841e-01
+   0.0000000e+00   4.5665077e-01   6.6707884e-01
+   0.0000000e+00   4.5627981e-01   6.6652927e-01
+   0.0000000e+00   4.5590885e-01   6.6597971e-01
+   0.0000000e+00   4.5553789e-01   6.6543014e-01
+   0.0000000e+00   4.5516693e-01   6.6488057e-01
+   0.0000000e+00   4.5479597e-01   6.6433100e-01
+   0.0000000e+00   4.5442501e-01   6.6378143e-01
+   0.0000000e+00   4.5405405e-01   6.6323186e-01
+   0.0000000e+00   4.5368309e-01   6.6268229e-01
+   0.0000000e+00   4.5331214e-01   6.6213272e-01
+   0.0000000e+00   4.5294118e-01   6.6158315e-01
+   0.0000000e+00   4.5257022e-01   6.6103358e-01
+   0.0000000e+00   4.5219926e-01   6.6048401e-01
+   0.0000000e+00   4.5182830e-01   6.5993444e-01
+   0.0000000e+00   4.5145734e-01   6.5938488e-01
+   0.0000000e+00   4.5108638e-01   6.5883531e-01
+   0.0000000e+00   4.5071542e-01   6.5828574e-01
+   0.0000000e+00   4.5034446e-01   6.5773617e-01
+   0.0000000e+00   4.4997350e-01   6.5718660e-01
+   0.0000000e+00   4.4960254e-01   6.5663703e-01
+   0.0000000e+00   4.4923158e-01   6.5608746e-01
+   0.0000000e+00   4.4886063e-01   6.5553789e-01
+   0.0000000e+00   4.4848967e-01   6.5498832e-01
+   0.0000000e+00   4.4811871e-01   6.5443875e-01
+   0.0000000e+00   4.4774775e-01   6.5388918e-01
+   0.0000000e+00   4.4737679e-01   6.5333961e-01
+   0.0000000e+00   4.4700583e-01   6.5279004e-01
+   0.0000000e+00   4.4663487e-01   6.5224048e-01
+   0.0000000e+00   4.4626391e-01   6.5169091e-01
+   0.0000000e+00   4.4589295e-01   6.5114134e-01
+   0.0000000e+00   4.4552199e-01   6.5059177e-01
+   0.0000000e+00   4.4515103e-01   6.5004220e-01
+   0.0000000e+00   4.4478007e-01   6.4949263e-01
+   0.0000000e+00   4.4440911e-01   6.4894306e-01
+   0.0000000e+00   4.4403816e-01   6.4839349e-01
+   0.0000000e+00   4.4366720e-01   6.4784392e-01
+   0.0000000e+00   4.4329624e-01   6.4729435e-01
+   0.0000000e+00   4.4292528e-01   6.4674478e-01
+   0.0000000e+00   4.4255432e-01   6.4619521e-01
+   0.0000000e+00   4.4218336e-01   6.4564565e-01
+   0.0000000e+00   4.4181240e-01   6.4509608e-01
+   0.0000000e+00   4.4144144e-01   6.4454651e-01
+   0.0000000e+00   4.4107048e-01   6.4399694e-01
+   0.0000000e+00   4.4069952e-01   6.4344737e-01
+   0.0000000e+00   4.4032856e-01   6.4289780e-01
+   0.0000000e+00   4.3995760e-01   6.4234823e-01
+   0.0000000e+00   4.3958665e-01   6.4179866e-01
+   0.0000000e+00   4.3921569e-01   6.4124909e-01
+   0.0000000e+00   4.3884473e-01   6.4069952e-01
+   0.0000000e+00   4.3847377e-01   6.4014995e-01
+   0.0000000e+00   4.3810281e-01   6.3960038e-01
+   0.0000000e+00   4.3773185e-01   6.3905082e-01
+   0.0000000e+00   4.3736089e-01   6.3850125e-01
+   0.0000000e+00   4.3698993e-01   6.3795168e-01
+   0.0000000e+00   4.3661897e-01   6.3740211e-01
+   0.0000000e+00   4.3624801e-01   6.3685254e-01
+   0.0000000e+00   4.3587705e-01   6.3630297e-01
+   0.0000000e+00   4.3550609e-01   6.3575340e-01
+   0.0000000e+00   4.3513514e-01   6.3520383e-01
+   0.0000000e+00   4.3476418e-01   6.3465426e-01
+   0.0000000e+00   4.3439322e-01   6.3410469e-01
+   0.0000000e+00   4.3402226e-01   6.3355512e-01
+   0.0000000e+00   4.3365130e-01   6.3300555e-01
+   0.0000000e+00   4.3328034e-01   6.3245599e-01
+   0.0000000e+00   4.3290938e-01   6.3190642e-01
+   0.0000000e+00   4.3253842e-01   6.3135685e-01
+   0.0000000e+00   4.3216746e-01   6.3080728e-01
+   0.0000000e+00   4.3179650e-01   6.3025771e-01
+   0.0000000e+00   4.3142554e-01   6.2970814e-01
+   0.0000000e+00   4.3105458e-01   6.2915857e-01
+   0.0000000e+00   4.3068362e-01   6.2860900e-01
+   0.0000000e+00   4.3031267e-01   6.2805943e-01
+   0.0000000e+00   4.2994171e-01   6.2750986e-01
+   0.0000000e+00   4.2957075e-01   6.2696029e-01
+   0.0000000e+00   4.2919979e-01   6.2641072e-01
+   0.0000000e+00   4.2882883e-01   6.2586116e-01
+   0.0000000e+00   4.2845787e-01   6.2531159e-01
+   0.0000000e+00   4.2808691e-01   6.2476202e-01
+   0.0000000e+00   4.2771595e-01   6.2421245e-01
+   0.0000000e+00   4.2734499e-01   6.2366288e-01
+   0.0000000e+00   4.2697403e-01   6.2311331e-01
+   0.0000000e+00   4.2660307e-01   6.2256374e-01
+   0.0000000e+00   4.2623211e-01   6.2201417e-01
+   0.0000000e+00   4.2586116e-01   6.2146460e-01
+   0.0000000e+00   4.2549020e-01   6.2091503e-01
+   0.0000000e+00   4.2511924e-01   6.2036546e-01
+   0.0000000e+00   4.2474828e-01   6.1981589e-01
+   0.0000000e+00   4.2437732e-01   6.1926633e-01
+   0.0000000e+00   4.2400636e-01   6.1871676e-01
+   0.0000000e+00   4.2363540e-01   6.1816719e-01
+   0.0000000e+00   4.2326444e-01   6.1761762e-01
+   0.0000000e+00   4.2289348e-01   6.1706805e-01
+   0.0000000e+00   4.2252252e-01   6.1651848e-01
+   0.0000000e+00   4.2215156e-01   6.1596891e-01
+   0.0000000e+00   4.2178060e-01   6.1541934e-01
+   0.0000000e+00   4.2140964e-01   6.1486977e-01
+   0.0000000e+00   4.2103869e-01   6.1432020e-01
+   0.0000000e+00   4.2066773e-01   6.1377063e-01
+   0.0000000e+00   4.2029677e-01   6.1322106e-01
+   0.0000000e+00   4.1992581e-01   6.1267150e-01
+   0.0000000e+00   4.1955485e-01   6.1212193e-01
+   0.0000000e+00   4.1918389e-01   6.1157236e-01
+   0.0000000e+00   4.1881293e-01   6.1102279e-01
+   0.0000000e+00   4.1844197e-01   6.1047322e-01
+   0.0000000e+00   4.1807101e-01   6.0992365e-01
+   0.0000000e+00   4.1770005e-01   6.0937408e-01
+   0.0000000e+00   4.1732909e-01   6.0882451e-01
+   0.0000000e+00   4.1695813e-01   6.0827494e-01
+   0.0000000e+00   4.1658718e-01   6.0772537e-01
+   0.0000000e+00   4.1621622e-01   6.0717580e-01
+   0.0000000e+00   4.1584526e-01   6.0662623e-01
+   0.0000000e+00   4.1547430e-01   6.0607666e-01
+   0.0000000e+00   4.1510334e-01   6.0552710e-01
+   0.0000000e+00   4.1473238e-01   6.0497753e-01
+   0.0000000e+00   4.1436142e-01   6.0442796e-01
+   0.0000000e+00   4.1399046e-01   6.0387839e-01
+   0.0000000e+00   4.1361950e-01   6.0332882e-01
+   0.0000000e+00   4.1324854e-01   6.0277925e-01
+   0.0000000e+00   4.1287758e-01   6.0222968e-01
+   0.0000000e+00   4.1250662e-01   6.0168011e-01
+   0.0000000e+00   4.1213567e-01   6.0113054e-01
+   0.0000000e+00   4.1176471e-01   6.0058097e-01
+   0.0000000e+00   4.1139375e-01   6.0003140e-01
+   0.0000000e+00   4.1102279e-01   5.9948183e-01
+   0.0000000e+00   4.1065183e-01   5.9893227e-01
+   0.0000000e+00   4.1028087e-01   5.9838270e-01
+   0.0000000e+00   4.0990991e-01   5.9783313e-01
+   0.0000000e+00   4.0953895e-01   5.9728356e-01
+   0.0000000e+00   4.0916799e-01   5.9673399e-01
+   0.0000000e+00   4.0879703e-01   5.9618442e-01
+   0.0000000e+00   4.0842607e-01   5.9563485e-01
+   0.0000000e+00   4.0805511e-01   5.9508528e-01
+   0.0000000e+00   4.0768415e-01   5.9453571e-01
+   0.0000000e+00   4.0731320e-01   5.9398614e-01
+   0.0000000e+00   4.0694224e-01   5.9343657e-01
+   0.0000000e+00   4.0657128e-01   5.9288700e-01
+   0.0000000e+00   4.0620032e-01   5.9233744e-01
+   0.0000000e+00   4.0582936e-01   5.9178787e-01
+   0.0000000e+00   4.0545840e-01   5.9123830e-01
+   0.0000000e+00   4.0508744e-01   5.9068873e-01
+   0.0000000e+00   4.0471648e-01   5.9013916e-01
+   0.0000000e+00   4.0434552e-01   5.8958959e-01
+   0.0000000e+00   4.0397456e-01   5.8904002e-01
+   0.0000000e+00   4.0360360e-01   5.8849045e-01
+   0.0000000e+00   4.0323264e-01   5.8794088e-01
+   0.0000000e+00   4.0286169e-01   5.8739131e-01
+   0.0000000e+00   4.0249073e-01   5.8684174e-01
+   0.0000000e+00   4.0211977e-01   5.8629217e-01
+   0.0000000e+00   4.0174881e-01   5.8574261e-01
+   0.0000000e+00   4.0137785e-01   5.8519304e-01
+   0.0000000e+00   4.0100689e-01   5.8464347e-01
+   0.0000000e+00   4.0063593e-01   5.8409390e-01
+   0.0000000e+00   4.0026497e-01   5.8354433e-01
+   0.0000000e+00   3.9989401e-01   5.8299476e-01
+   0.0000000e+00   3.9952305e-01   5.8244519e-01
+   0.0000000e+00   3.9915209e-01   5.8189562e-01
+   0.0000000e+00   3.9878113e-01   5.8134605e-01
+   0.0000000e+00   3.9841017e-01   5.8079648e-01
+   0.0000000e+00   3.9803922e-01   5.8024691e-01
+   0.0000000e+00   3.9766826e-01   5.7969734e-01
+   0.0000000e+00   3.9729730e-01   5.7914778e-01
+   0.0000000e+00   3.9692634e-01   5.7859821e-01
+   0.0000000e+00   3.9655538e-01   5.7804864e-01
+   0.0000000e+00   3.9618442e-01   5.7749907e-01
+   0.0000000e+00   3.9581346e-01   5.7694950e-01
+   0.0000000e+00   3.9544250e-01   5.7639993e-01
+   0.0000000e+00   3.9507154e-01   5.7585036e-01
+   0.0000000e+00   3.9470058e-01   5.7530079e-01
+   0.0000000e+00   3.9432962e-01   5.7475122e-01
+   0.0000000e+00   3.9395866e-01   5.7420165e-01
+   0.0000000e+00   3.9358771e-01   5.7365208e-01
+   0.0000000e+00   3.9321675e-01   5.7310251e-01
+   0.0000000e+00   3.9284579e-01   5.7255295e-01
+   0.0000000e+00   3.9247483e-01   5.7200338e-01
+   0.0000000e+00   3.9210387e-01   5.7145381e-01
+   0.0000000e+00   3.9173291e-01   5.7090424e-01
+   0.0000000e+00   3.9136195e-01   5.7035467e-01
+   0.0000000e+00   3.9099099e-01   5.6980510e-01
+   0.0000000e+00   3.9062003e-01   5.6925553e-01
+   0.0000000e+00   3.9024907e-01   5.6870596e-01
+   0.0000000e+00   3.8987811e-01   5.6815639e-01
+   0.0000000e+00   3.8950715e-01   5.6760682e-01
+   0.0000000e+00   3.8913620e-01   5.6705725e-01
+   0.0000000e+00   3.8876524e-01   5.6650768e-01
+   0.0000000e+00   3.8839428e-01   5.6595811e-01
+   0.0000000e+00   3.8802332e-01   5.6540855e-01
+   0.0000000e+00   3.8765236e-01   5.6485898e-01
+   0.0000000e+00   3.8728140e-01   5.6430941e-01
+   0.0000000e+00   3.8691044e-01   5.6375984e-01
+   0.0000000e+00   3.8653948e-01   5.6321027e-01
+   0.0000000e+00   3.8616852e-01   5.6266070e-01
+   0.0000000e+00   3.8579756e-01   5.6211113e-01
+   0.0000000e+00   3.8542660e-01   5.6156156e-01
+   0.0000000e+00   3.8505564e-01   5.6101199e-01
+   0.0000000e+00   3.8468468e-01   5.6046242e-01
+   0.0000000e+00   3.8431373e-01   5.5991285e-01
+   0.0000000e+00   3.8394277e-01   5.5936328e-01
+   0.0000000e+00   3.8357181e-01   5.5881372e-01
+   0.0000000e+00   3.8320085e-01   5.5826415e-01
+   0.0000000e+00   3.8282989e-01   5.5771458e-01
+   0.0000000e+00   3.8245893e-01   5.5716501e-01
+   0.0000000e+00   3.8208797e-01   5.5661544e-01
+   0.0000000e+00   3.8171701e-01   5.5606587e-01
+   0.0000000e+00   3.8134605e-01   5.5551630e-01
+   0.0000000e+00   3.8097509e-01   5.5496673e-01
+   0.0000000e+00   3.8060413e-01   5.5441716e-01
+   0.0000000e+00   3.8023317e-01   5.5386759e-01
+   0.0000000e+00   3.7986222e-01   5.5331802e-01
+   0.0000000e+00   3.7949126e-01   5.5276845e-01
+   0.0000000e+00   3.7912030e-01   5.5221889e-01
+   0.0000000e+00   3.7874934e-01   5.5166932e-01
+   0.0000000e+00   3.7837838e-01   5.5111975e-01
+   0.0000000e+00   3.7800742e-01   5.5057018e-01
+   0.0000000e+00   3.7763646e-01   5.5002061e-01
+   0.0000000e+00   3.7726550e-01   5.4947104e-01
+   0.0000000e+00   3.7689454e-01   5.4892147e-01
+   0.0000000e+00   3.7652358e-01   5.4837190e-01
+   0.0000000e+00   3.7615262e-01   5.4782233e-01
+   0.0000000e+00   3.7578166e-01   5.4727276e-01
+   0.0000000e+00   3.7541070e-01   5.4672319e-01
+   0.0000000e+00   3.7503975e-01   5.4617362e-01
+   0.0000000e+00   3.7466879e-01   5.4562406e-01
+   0.0000000e+00   3.7429783e-01   5.4507449e-01
+   0.0000000e+00   3.7392687e-01   5.4452492e-01
+   0.0000000e+00   3.7355591e-01   5.4397535e-01
+   0.0000000e+00   3.7318495e-01   5.4342578e-01
+   0.0000000e+00   3.7281399e-01   5.4287621e-01
+   0.0000000e+00   3.7244303e-01   5.4232664e-01
+   0.0000000e+00   3.7207207e-01   5.4177707e-01
+   0.0000000e+00   3.7170111e-01   5.4122750e-01
+   0.0000000e+00   3.7133015e-01   5.4067793e-01
+   0.0000000e+00   3.7095919e-01   5.4012836e-01
+   0.0000000e+00   3.7058824e-01   5.3957879e-01
+   0.0000000e+00   3.7021728e-01   5.3902923e-01
+   0.0000000e+00   3.6984632e-01   5.3847966e-01
+   0.0000000e+00   3.6947536e-01   5.3793009e-01
+   0.0000000e+00   3.6910440e-01   5.3738052e-01
+   0.0000000e+00   3.6873344e-01   5.3683095e-01
+   0.0000000e+00   3.6836248e-01   5.3628138e-01
+   0.0000000e+00   3.6799152e-01   5.3573181e-01
+   0.0000000e+00   3.6762056e-01   5.3518224e-01
+   0.0000000e+00   3.6724960e-01   5.3463267e-01
+   0.0000000e+00   3.6687864e-01   5.3408310e-01
+   0.0000000e+00   3.6650768e-01   5.3353353e-01
+   0.0000000e+00   3.6613672e-01   5.3298396e-01
+   0.0000000e+00   3.6576577e-01   5.3243440e-01
+   0.0000000e+00   3.6539481e-01   5.3188483e-01
+   0.0000000e+00   3.6502385e-01   5.3133526e-01
+   0.0000000e+00   3.6465289e-01   5.3078569e-01
+   0.0000000e+00   3.6428193e-01   5.3023612e-01
+   0.0000000e+00   3.6391097e-01   5.2968655e-01
+   0.0000000e+00   3.6354001e-01   5.2913698e-01
+   0.0000000e+00   3.6316905e-01   5.2858741e-01
+   0.0000000e+00   3.6279809e-01   5.2803784e-01
+   0.0000000e+00   3.6242713e-01   5.2748827e-01
+   0.0000000e+00   3.6205617e-01   5.2693870e-01
+   0.0000000e+00   3.6168521e-01   5.2638913e-01
+   0.0000000e+00   3.6131426e-01   5.2583957e-01
+   0.0000000e+00   3.6094330e-01   5.2529000e-01
+   0.0000000e+00   3.6057234e-01   5.2474043e-01
+   0.0000000e+00   3.6020138e-01   5.2419086e-01
+   0.0000000e+00   3.5983042e-01   5.2364129e-01
+   0.0000000e+00   3.5945946e-01   5.2309172e-01
+   0.0000000e+00   3.5908850e-01   5.2254215e-01
+   0.0000000e+00   3.5871754e-01   5.2199258e-01
+   0.0000000e+00   3.5834658e-01   5.2144301e-01
+   0.0000000e+00   3.5797562e-01   5.2089344e-01
+   0.0000000e+00   3.5760466e-01   5.2034387e-01
+   0.0000000e+00   3.5723370e-01   5.1979430e-01
+   0.0000000e+00   3.5686275e-01   5.1924473e-01
+   0.0000000e+00   3.5649179e-01   5.1869517e-01
+   0.0000000e+00   3.5612083e-01   5.1814560e-01
+   0.0000000e+00   3.5574987e-01   5.1759603e-01
+   0.0000000e+00   3.5537891e-01   5.1704646e-01
+   0.0000000e+00   3.5500795e-01   5.1649689e-01
+   0.0000000e+00   3.5463699e-01   5.1594732e-01
+   0.0000000e+00   3.5426603e-01   5.1539775e-01
+   0.0000000e+00   3.5389507e-01   5.1484818e-01
+   0.0000000e+00   3.5352411e-01   5.1429861e-01
+   0.0000000e+00   3.5315315e-01   5.1374904e-01
+   0.0000000e+00   3.5278219e-01   5.1319947e-01
+   0.0000000e+00   3.5241123e-01   5.1264990e-01
+   0.0000000e+00   3.5204028e-01   5.1210034e-01
+   0.0000000e+00   3.5166932e-01   5.1155077e-01
+   0.0000000e+00   3.5129836e-01   5.1100120e-01
+   0.0000000e+00   3.5092740e-01   5.1045163e-01
+   0.0000000e+00   3.5055644e-01   5.0990206e-01
+   0.0000000e+00   3.5018548e-01   5.0935249e-01
+   0.0000000e+00   3.4981452e-01   5.0880292e-01
+   0.0000000e+00   3.4944356e-01   5.0825335e-01
+   0.0000000e+00   3.4907260e-01   5.0770378e-01
+   0.0000000e+00   3.4870164e-01   5.0715421e-01
+   0.0000000e+00   3.4833068e-01   5.0660464e-01
+   0.0000000e+00   3.4795972e-01   5.0605507e-01
+   0.0000000e+00   3.4758877e-01   5.0550551e-01
+   0.0000000e+00   3.4721781e-01   5.0495594e-01
+   0.0000000e+00   3.4684685e-01   5.0440637e-01
+   0.0000000e+00   3.4647589e-01   5.0385680e-01
+   0.0000000e+00   3.4610493e-01   5.0330723e-01
+   0.0000000e+00   3.4573397e-01   5.0275766e-01
+   0.0000000e+00   3.4536301e-01   5.0220809e-01
+   0.0000000e+00   3.4499205e-01   5.0165852e-01
+   0.0000000e+00   3.4462109e-01   5.0110895e-01
+   0.0000000e+00   3.4425013e-01   5.0055938e-01
+   0.0000000e+00   3.4387917e-01   5.0000981e-01
+   0.0000000e+00   3.4350821e-01   4.9946024e-01
+   0.0000000e+00   3.4313725e-01   4.9891068e-01
+   0.0000000e+00   3.4276630e-01   4.9836111e-01
+   0.0000000e+00   3.4239534e-01   4.9781154e-01
+   0.0000000e+00   3.4202438e-01   4.9726197e-01
+   0.0000000e+00   3.4165342e-01   4.9671240e-01
+   0.0000000e+00   3.4128246e-01   4.9616283e-01
+   0.0000000e+00   3.4091150e-01   4.9561326e-01
+   0.0000000e+00   3.4054054e-01   4.9506369e-01
+   0.0000000e+00   3.4016958e-01   4.9451412e-01
+   0.0000000e+00   3.3979862e-01   4.9396455e-01
+   0.0000000e+00   3.3942766e-01   4.9341498e-01
+   0.0000000e+00   3.3905670e-01   4.9286541e-01
+   0.0000000e+00   3.3868574e-01   4.9231585e-01
+   0.0000000e+00   3.3831479e-01   4.9176628e-01
+   0.0000000e+00   3.3794383e-01   4.9121671e-01
+   0.0000000e+00   3.3757287e-01   4.9066714e-01
+   0.0000000e+00   3.3720191e-01   4.9011757e-01
+   0.0000000e+00   3.3683095e-01   4.8956800e-01
+   0.0000000e+00   3.3645999e-01   4.8901843e-01
+   0.0000000e+00   3.3608903e-01   4.8846886e-01
+   0.0000000e+00   3.3571807e-01   4.8791929e-01
+   0.0000000e+00   3.3534711e-01   4.8736972e-01
+   0.0000000e+00   3.3497615e-01   4.8682015e-01
+   0.0000000e+00   3.3460519e-01   4.8627058e-01
+   0.0000000e+00   3.3423423e-01   4.8572102e-01
+   0.0000000e+00   3.3386328e-01   4.8517145e-01
+   0.0000000e+00   3.3349232e-01   4.8462188e-01
+   0.0000000e+00   3.3312136e-01   4.8407231e-01
+   0.0000000e+00   3.3275040e-01   4.8352274e-01
+   0.0000000e+00   3.3237944e-01   4.8297317e-01
+   0.0000000e+00   3.3200848e-01   4.8242360e-01
+   0.0000000e+00   3.3163752e-01   4.8187403e-01
+   0.0000000e+00   3.3126656e-01   4.8132446e-01
+   0.0000000e+00   3.3089560e-01   4.8077489e-01
+   0.0000000e+00   3.3052464e-01   4.8022532e-01
+   0.0000000e+00   3.3015368e-01   4.7967575e-01
+   0.0000000e+00   3.2978272e-01   4.7912619e-01
+   0.0000000e+00   3.2941176e-01   4.7857662e-01
+   0.0000000e+00   3.2904081e-01   4.7802705e-01
+   0.0000000e+00   3.2866985e-01   4.7747748e-01
+   0.0000000e+00   3.2829889e-01   4.7692791e-01
+   0.0000000e+00   3.2792793e-01   4.7637834e-01
+   0.0000000e+00   3.2755697e-01   4.7582877e-01
+   0.0000000e+00   3.2718601e-01   4.7527920e-01
+   0.0000000e+00   3.2681505e-01   4.7472963e-01
+   0.0000000e+00   3.2644409e-01   4.7418006e-01
+   0.0000000e+00   3.2607313e-01   4.7363049e-01
+   0.0000000e+00   3.2570217e-01   4.7308092e-01
+   0.0000000e+00   3.2533121e-01   4.7253135e-01
+   0.0000000e+00   3.2496025e-01   4.7198179e-01
+   0.0000000e+00   3.2458930e-01   4.7143222e-01
+   0.0000000e+00   3.2421834e-01   4.7088265e-01
+   0.0000000e+00   3.2384738e-01   4.7033308e-01
+   0.0000000e+00   3.2347642e-01   4.6978351e-01
+   0.0000000e+00   3.2310546e-01   4.6923394e-01
+   0.0000000e+00   3.2273450e-01   4.6868437e-01
+   0.0000000e+00   3.2236354e-01   4.6813480e-01
+   0.0000000e+00   3.2199258e-01   4.6758523e-01
+   0.0000000e+00   3.2162162e-01   4.6703566e-01
+   0.0000000e+00   3.2125066e-01   4.6648609e-01
+   0.0000000e+00   3.2087970e-01   4.6593652e-01
+   0.0000000e+00   3.2050874e-01   4.6538696e-01
+   0.0000000e+00   3.2013778e-01   4.6483739e-01
+   0.0000000e+00   3.1976683e-01   4.6428782e-01
+   0.0000000e+00   3.1939587e-01   4.6373825e-01
+   0.0000000e+00   3.1902491e-01   4.6318868e-01
+   0.0000000e+00   3.1865395e-01   4.6263911e-01
+   0.0000000e+00   3.1828299e-01   4.6208954e-01
+   0.0000000e+00   3.1791203e-01   4.6153997e-01
+   0.0000000e+00   3.1754107e-01   4.6099040e-01
+   0.0000000e+00   3.1717011e-01   4.6044083e-01
+   0.0000000e+00   3.1679915e-01   4.5989126e-01
+   0.0000000e+00   3.1642819e-01   4.5934169e-01
+   0.0000000e+00   3.1605723e-01   4.5879213e-01
+   0.0000000e+00   3.1568627e-01   4.5824256e-01
+   0.0000000e+00   3.1531532e-01   4.5769299e-01
+   0.0000000e+00   3.1494436e-01   4.5714342e-01
+   0.0000000e+00   3.1457340e-01   4.5659385e-01
+   0.0000000e+00   3.1420244e-01   4.5604428e-01
+   0.0000000e+00   3.1383148e-01   4.5549471e-01
+   0.0000000e+00   3.1346052e-01   4.5494514e-01
+   0.0000000e+00   3.1308956e-01   4.5439557e-01
+   0.0000000e+00   3.1271860e-01   4.5384600e-01
+   0.0000000e+00   3.1234764e-01   4.5329643e-01
+   0.0000000e+00   3.1197668e-01   4.5274686e-01
+   0.0000000e+00   3.1160572e-01   4.5219730e-01
+   0.0000000e+00   3.1123476e-01   4.5164773e-01
+   0.0000000e+00   3.1086380e-01   4.5109816e-01
+   0.0000000e+00   3.1049285e-01   4.5054859e-01
+   0.0000000e+00   3.1012189e-01   4.4999902e-01
+   0.0000000e+00   3.0975093e-01   4.4944945e-01
+   0.0000000e+00   3.0937997e-01   4.4889988e-01
+   0.0000000e+00   3.0900901e-01   4.4835031e-01
+   0.0000000e+00   3.0863805e-01   4.4780074e-01
+   0.0000000e+00   3.0826709e-01   4.4725117e-01
+   0.0000000e+00   3.0789613e-01   4.4670160e-01
+   0.0000000e+00   3.0752517e-01   4.4615203e-01
+   0.0000000e+00   3.0715421e-01   4.4560247e-01
+   0.0000000e+00   3.0678325e-01   4.4505290e-01
+   0.0000000e+00   3.0641229e-01   4.4450333e-01
+   0.0000000e+00   3.0604134e-01   4.4395376e-01
+   0.0000000e+00   3.0567038e-01   4.4340419e-01
+   0.0000000e+00   3.0529942e-01   4.4285462e-01
+   0.0000000e+00   3.0492846e-01   4.4230505e-01
+   0.0000000e+00   3.0455750e-01   4.4175548e-01
+   0.0000000e+00   3.0418654e-01   4.4120591e-01
+   0.0000000e+00   3.0381558e-01   4.4065634e-01
+   0.0000000e+00   3.0344462e-01   4.4010677e-01
+   0.0000000e+00   3.0307366e-01   4.3955720e-01
+   0.0000000e+00   3.0270270e-01   4.3900764e-01
+   0.0000000e+00   3.0233174e-01   4.3845807e-01
+   0.0000000e+00   3.0196078e-01   4.3790850e-01
+   0.0000000e+00   3.0158983e-01   4.3735893e-01
+   0.0000000e+00   3.0121887e-01   4.3680936e-01
+   0.0000000e+00   3.0084791e-01   4.3625979e-01
+   0.0000000e+00   3.0047695e-01   4.3571022e-01
+   0.0000000e+00   3.0010599e-01   4.3516065e-01
+   0.0000000e+00   2.9973503e-01   4.3461108e-01
+   0.0000000e+00   2.9936407e-01   4.3406151e-01
+   0.0000000e+00   2.9899311e-01   4.3351194e-01
+   0.0000000e+00   2.9862215e-01   4.3296237e-01
+   0.0000000e+00   2.9825119e-01   4.3241280e-01
+   0.0000000e+00   2.9788023e-01   4.3186324e-01
+   0.0000000e+00   2.9750927e-01   4.3131367e-01
+   0.0000000e+00   2.9713831e-01   4.3076410e-01
+   0.0000000e+00   2.9676736e-01   4.3021453e-01
+   0.0000000e+00   2.9639640e-01   4.2966496e-01
+   0.0000000e+00   2.9602544e-01   4.2911539e-01
+   0.0000000e+00   2.9565448e-01   4.2856582e-01
+   0.0000000e+00   2.9528352e-01   4.2801625e-01
+   0.0000000e+00   2.9491256e-01   4.2746668e-01
+   0.0000000e+00   2.9454160e-01   4.2691711e-01
+   0.0000000e+00   2.9417064e-01   4.2636754e-01
+   0.0000000e+00   2.9379968e-01   4.2581797e-01
+   0.0000000e+00   2.9342872e-01   4.2526841e-01
+   0.0000000e+00   2.9305776e-01   4.2471884e-01
+   0.0000000e+00   2.9268680e-01   4.2416927e-01
+   0.0000000e+00   2.9231585e-01   4.2361970e-01
+   0.0000000e+00   2.9194489e-01   4.2307013e-01
+   0.0000000e+00   2.9157393e-01   4.2252056e-01
+   0.0000000e+00   2.9120297e-01   4.2197099e-01
+   0.0000000e+00   2.9083201e-01   4.2142142e-01
+   0.0000000e+00   2.9046105e-01   4.2087185e-01
+   0.0000000e+00   2.9009009e-01   4.2032228e-01
+   0.0000000e+00   2.8971913e-01   4.1977271e-01
+   0.0000000e+00   2.8934817e-01   4.1922314e-01
+   0.0000000e+00   2.8897721e-01   4.1867358e-01
+   0.0000000e+00   2.8860625e-01   4.1812401e-01
+   0.0000000e+00   2.8823529e-01   4.1757444e-01
+   0.0000000e+00   2.8786433e-01   4.1702487e-01
+   0.0000000e+00   2.8749338e-01   4.1647530e-01
+   0.0000000e+00   2.8712242e-01   4.1592573e-01
+   0.0000000e+00   2.8675146e-01   4.1537616e-01
+   0.0000000e+00   2.8638050e-01   4.1482659e-01
+   0.0000000e+00   2.8600954e-01   4.1427702e-01
+   0.0000000e+00   2.8563858e-01   4.1372745e-01
+   0.0000000e+00   2.8526762e-01   4.1317788e-01
+   0.0000000e+00   2.8489666e-01   4.1262831e-01
+   0.0000000e+00   2.8452570e-01   4.1207875e-01
+   0.0000000e+00   2.8415474e-01   4.1152918e-01
+   0.0000000e+00   2.8378378e-01   4.1097961e-01
+   0.0000000e+00   2.8341282e-01   4.1043004e-01
+   0.0000000e+00   2.8304187e-01   4.0988047e-01
+   0.0000000e+00   2.8267091e-01   4.0933090e-01
+   0.0000000e+00   2.8229995e-01   4.0878133e-01
+   0.0000000e+00   2.8192899e-01   4.0823176e-01
+   0.0000000e+00   2.8155803e-01   4.0768219e-01
+   0.0000000e+00   2.8118707e-01   4.0713262e-01
+   0.0000000e+00   2.8081611e-01   4.0658305e-01
+   0.0000000e+00   2.8044515e-01   4.0603348e-01
+   0.0000000e+00   2.8007419e-01   4.0548392e-01
+   0.0000000e+00   2.7970323e-01   4.0493435e-01
+   0.0000000e+00   2.7933227e-01   4.0438478e-01
+   0.0000000e+00   2.7896131e-01   4.0383521e-01
+   0.0000000e+00   2.7859036e-01   4.0328564e-01
+   0.0000000e+00   2.7821940e-01   4.0273607e-01
+   0.0000000e+00   2.7784844e-01   4.0218650e-01
+   0.0000000e+00   2.7747748e-01   4.0163693e-01
+   0.0000000e+00   2.7710652e-01   4.0108736e-01
+   0.0000000e+00   2.7673556e-01   4.0053779e-01
+   0.0000000e+00   2.7636460e-01   3.9998822e-01
+   0.0000000e+00   2.7599364e-01   3.9943865e-01
+   0.0000000e+00   2.7562268e-01   3.9888909e-01
+   0.0000000e+00   2.7525172e-01   3.9833952e-01
+   0.0000000e+00   2.7488076e-01   3.9778995e-01
+   0.0000000e+00   2.7450980e-01   3.9724038e-01
+   0.0000000e+00   2.7413884e-01   3.9669081e-01
+   0.0000000e+00   2.7376789e-01   3.9614124e-01
+   0.0000000e+00   2.7339693e-01   3.9559167e-01
+   0.0000000e+00   2.7302597e-01   3.9504210e-01
+   0.0000000e+00   2.7265501e-01   3.9449253e-01
+   0.0000000e+00   2.7228405e-01   3.9394296e-01
+   0.0000000e+00   2.7191309e-01   3.9339339e-01
+   0.0000000e+00   2.7154213e-01   3.9284382e-01
+   0.0000000e+00   2.7117117e-01   3.9229426e-01
+   0.0000000e+00   2.7080021e-01   3.9174469e-01
+   0.0000000e+00   2.7042925e-01   3.9119512e-01
+   0.0000000e+00   2.7005829e-01   3.9064555e-01
+   0.0000000e+00   2.6968733e-01   3.9009598e-01
+   0.0000000e+00   2.6931638e-01   3.8954641e-01
+   0.0000000e+00   2.6894542e-01   3.8899684e-01
+   0.0000000e+00   2.6857446e-01   3.8844727e-01
+   0.0000000e+00   2.6820350e-01   3.8789770e-01
+   0.0000000e+00   2.6783254e-01   3.8734813e-01
+   0.0000000e+00   2.6746158e-01   3.8679856e-01
+   0.0000000e+00   2.6709062e-01   3.8624899e-01
+   0.0000000e+00   2.6671966e-01   3.8569942e-01
+   0.0000000e+00   2.6634870e-01   3.8514986e-01
+   0.0000000e+00   2.6597774e-01   3.8460029e-01
+   0.0000000e+00   2.6560678e-01   3.8405072e-01
+   0.0000000e+00   2.6523582e-01   3.8350115e-01
+   0.0000000e+00   2.6486486e-01   3.8295158e-01
+   0.0000000e+00   2.6449391e-01   3.8240201e-01
+   0.0000000e+00   2.6412295e-01   3.8185244e-01
+   0.0000000e+00   2.6375199e-01   3.8130287e-01
+   0.0000000e+00   2.6338103e-01   3.8075330e-01
+   0.0000000e+00   2.6301007e-01   3.8020373e-01
+   0.0000000e+00   2.6263911e-01   3.7965416e-01
+   0.0000000e+00   2.6226815e-01   3.7910459e-01
+   0.0000000e+00   2.6189719e-01   3.7855503e-01
+   0.0000000e+00   2.6152623e-01   3.7800546e-01
+   0.0000000e+00   2.6115527e-01   3.7745589e-01
+   0.0000000e+00   2.6078431e-01   3.7690632e-01
+   0.0000000e+00   2.6041335e-01   3.7635675e-01
+   0.0000000e+00   2.6004240e-01   3.7580718e-01
+   0.0000000e+00   2.5967144e-01   3.7525761e-01
+   0.0000000e+00   2.5930048e-01   3.7470804e-01
+   0.0000000e+00   2.5892952e-01   3.7415847e-01
+   0.0000000e+00   2.5855856e-01   3.7360890e-01
+   0.0000000e+00   2.5818760e-01   3.7305933e-01
+   0.0000000e+00   2.5781664e-01   3.7250976e-01
+   0.0000000e+00   2.5744568e-01   3.7196020e-01
+   0.0000000e+00   2.5707472e-01   3.7141063e-01
+   0.0000000e+00   2.5670376e-01   3.7086106e-01
+   0.0000000e+00   2.5633280e-01   3.7031149e-01
+   0.0000000e+00   2.5596184e-01   3.6976192e-01
+   0.0000000e+00   2.5559089e-01   3.6921235e-01
+   0.0000000e+00   2.5521993e-01   3.6866278e-01
+   0.0000000e+00   2.5484897e-01   3.6811321e-01
+   0.0000000e+00   2.5447801e-01   3.6756364e-01
+   0.0000000e+00   2.5410705e-01   3.6701407e-01
+   0.0000000e+00   2.5373609e-01   3.6646450e-01
+   0.0000000e+00   2.5336513e-01   3.6591493e-01
+   0.0000000e+00   2.5299417e-01   3.6536537e-01
+   0.0000000e+00   2.5262321e-01   3.6481580e-01
+   0.0000000e+00   2.5225225e-01   3.6426623e-01
+   0.0000000e+00   2.5188129e-01   3.6371666e-01
+   0.0000000e+00   2.5151033e-01   3.6316709e-01
+   0.0000000e+00   2.5113937e-01   3.6261752e-01
+   0.0000000e+00   2.5076842e-01   3.6206795e-01
+   0.0000000e+00   2.5039746e-01   3.6151838e-01
+   0.0000000e+00   2.5002650e-01   3.6096881e-01
+   0.0000000e+00   2.4965554e-01   3.6041924e-01
+   0.0000000e+00   2.4928458e-01   3.5986967e-01
+   0.0000000e+00   2.4891362e-01   3.5932010e-01
+   0.0000000e+00   2.4854266e-01   3.5877054e-01
+   0.0000000e+00   2.4817170e-01   3.5822097e-01
+   0.0000000e+00   2.4780074e-01   3.5767140e-01
+   0.0000000e+00   2.4742978e-01   3.5712183e-01
+   0.0000000e+00   2.4705882e-01   3.5657226e-01
+   0.0000000e+00   2.4668786e-01   3.5602269e-01
+   0.0000000e+00   2.4631691e-01   3.5547312e-01
+   0.0000000e+00   2.4594595e-01   3.5492355e-01
+   0.0000000e+00   2.4557499e-01   3.5437398e-01
+   0.0000000e+00   2.4520403e-01   3.5382441e-01
+   0.0000000e+00   2.4483307e-01   3.5327484e-01
+   0.0000000e+00   2.4446211e-01   3.5272527e-01
+   0.0000000e+00   2.4409115e-01   3.5217571e-01
+   0.0000000e+00   2.4372019e-01   3.5162614e-01
+   0.0000000e+00   2.4334923e-01   3.5107657e-01
+   0.0000000e+00   2.4297827e-01   3.5052700e-01
+   0.0000000e+00   2.4260731e-01   3.4997743e-01
+   0.0000000e+00   2.4223635e-01   3.4942786e-01
+   0.0000000e+00   2.4186539e-01   3.4887829e-01
+   0.0000000e+00   2.4149444e-01   3.4832872e-01
+   0.0000000e+00   2.4112348e-01   3.4777915e-01
+   0.0000000e+00   2.4075252e-01   3.4722958e-01
+   0.0000000e+00   2.4038156e-01   3.4668001e-01
+   0.0000000e+00   2.4001060e-01   3.4613044e-01
+   0.0000000e+00   2.3963964e-01   3.4558087e-01
+   0.0000000e+00   2.3926868e-01   3.4503131e-01
+   0.0000000e+00   2.3889772e-01   3.4448174e-01
+   0.0000000e+00   2.3852676e-01   3.4393217e-01
+   0.0000000e+00   2.3815580e-01   3.4338260e-01
+   0.0000000e+00   2.3778484e-01   3.4283303e-01
+   0.0000000e+00   2.3741388e-01   3.4228346e-01
+   0.0000000e+00   2.3704293e-01   3.4173389e-01
+   0.0000000e+00   2.3667197e-01   3.4118432e-01
+   0.0000000e+00   2.3630101e-01   3.4063475e-01
+   0.0000000e+00   2.3593005e-01   3.4008518e-01
+   0.0000000e+00   2.3555909e-01   3.3953561e-01
+   0.0000000e+00   2.3518813e-01   3.3898604e-01
+   0.0000000e+00   2.3481717e-01   3.3843648e-01
+   0.0000000e+00   2.3444621e-01   3.3788691e-01
+   0.0000000e+00   2.3407525e-01   3.3733734e-01
+   0.0000000e+00   2.3370429e-01   3.3678777e-01
+   0.0000000e+00   2.3333333e-01   3.3623820e-01
+   0.0000000e+00   2.3296237e-01   3.3568863e-01
+   0.0000000e+00   2.3259141e-01   3.3513906e-01
+   0.0000000e+00   2.3222046e-01   3.3458949e-01
+   0.0000000e+00   2.3184950e-01   3.3403992e-01
+   0.0000000e+00   2.3147854e-01   3.3349035e-01
+   0.0000000e+00   2.3110758e-01   3.3294078e-01
+   0.0000000e+00   2.3073662e-01   3.3239121e-01
+   0.0000000e+00   2.3036566e-01   3.3184165e-01
+   0.0000000e+00   2.2999470e-01   3.3129208e-01
+   0.0000000e+00   2.2962374e-01   3.3074251e-01
+   0.0000000e+00   2.2925278e-01   3.3019294e-01
+   0.0000000e+00   2.2888182e-01   3.2964337e-01
+   0.0000000e+00   2.2851086e-01   3.2909380e-01
+   0.0000000e+00   2.2813990e-01   3.2854423e-01
+   0.0000000e+00   2.2776895e-01   3.2799466e-01
+   0.0000000e+00   2.2739799e-01   3.2744509e-01
+   0.0000000e+00   2.2702703e-01   3.2689552e-01
+   0.0000000e+00   2.2665607e-01   3.2634595e-01
+   0.0000000e+00   2.2628511e-01   3.2579638e-01
+   0.0000000e+00   2.2591415e-01   3.2524682e-01
+   0.0000000e+00   2.2554319e-01   3.2469725e-01
+   0.0000000e+00   2.2517223e-01   3.2414768e-01
+   0.0000000e+00   2.2480127e-01   3.2359811e-01
+   0.0000000e+00   2.2443031e-01   3.2304854e-01
+   0.0000000e+00   2.2405935e-01   3.2249897e-01
+   0.0000000e+00   2.2368839e-01   3.2194940e-01
+   0.0000000e+00   2.2331744e-01   3.2139983e-01
+   0.0000000e+00   2.2294648e-01   3.2085026e-01
+   0.0000000e+00   2.2257552e-01   3.2030069e-01
+   0.0000000e+00   2.2220456e-01   3.1975112e-01
+   0.0000000e+00   2.2183360e-01   3.1920155e-01
+   0.0000000e+00   2.2146264e-01   3.1865199e-01
+   0.0000000e+00   2.2109168e-01   3.1810242e-01
+   0.0000000e+00   2.2072072e-01   3.1755285e-01
+   0.0000000e+00   2.2034976e-01   3.1700328e-01
+   0.0000000e+00   2.1997880e-01   3.1645371e-01
+   0.0000000e+00   2.1960784e-01   3.1590414e-01
+   0.0000000e+00   2.1923688e-01   3.1535457e-01
+   0.0000000e+00   2.1886592e-01   3.1480500e-01
+   0.0000000e+00   2.1849497e-01   3.1425543e-01
+   0.0000000e+00   2.1812401e-01   3.1370586e-01
+   0.0000000e+00   2.1775305e-01   3.1315629e-01
+   0.0000000e+00   2.1738209e-01   3.1260672e-01
+   0.0000000e+00   2.1701113e-01   3.1205716e-01
+   0.0000000e+00   2.1664017e-01   3.1150759e-01
+   0.0000000e+00   2.1626921e-01   3.1095802e-01
+   0.0000000e+00   2.1589825e-01   3.1040845e-01
+   0.0000000e+00   2.1552729e-01   3.0985888e-01
+   0.0000000e+00   2.1515633e-01   3.0930931e-01
+   0.0000000e+00   2.1478537e-01   3.0875974e-01
+   0.0000000e+00   2.1441441e-01   3.0821017e-01
+   0.0000000e+00   2.1404346e-01   3.0766060e-01
+   0.0000000e+00   2.1367250e-01   3.0711103e-01
+   0.0000000e+00   2.1330154e-01   3.0656146e-01
+   0.0000000e+00   2.1293058e-01   3.0601189e-01
+   0.0000000e+00   2.1255962e-01   3.0546233e-01
+   0.0000000e+00   2.1218866e-01   3.0491276e-01
+   0.0000000e+00   2.1181770e-01   3.0436319e-01
+   0.0000000e+00   2.1144674e-01   3.0381362e-01
+   0.0000000e+00   2.1107578e-01   3.0326405e-01
+   0.0000000e+00   2.1070482e-01   3.0271448e-01
+   0.0000000e+00   2.1033386e-01   3.0216491e-01
+   0.0000000e+00   2.0996290e-01   3.0161534e-01
+   0.0000000e+00   2.0959194e-01   3.0106577e-01
+   0.0000000e+00   2.0922099e-01   3.0051620e-01
+   0.0000000e+00   2.0885003e-01   2.9996663e-01
+   0.0000000e+00   2.0847907e-01   2.9941706e-01
+   0.0000000e+00   2.0810811e-01   2.9886749e-01
+   0.0000000e+00   2.0773715e-01   2.9831793e-01
+   0.0000000e+00   2.0736619e-01   2.9776836e-01
+   0.0000000e+00   2.0699523e-01   2.9721879e-01
+   0.0000000e+00   2.0662427e-01   2.9666922e-01
+   0.0000000e+00   2.0625331e-01   2.9611965e-01
+   0.0000000e+00   2.0588235e-01   2.9557008e-01
+   0.0000000e+00   2.0551139e-01   2.9502051e-01
+   0.0000000e+00   2.0514043e-01   2.9447094e-01
+   0.0000000e+00   2.0476948e-01   2.9392137e-01
+   0.0000000e+00   2.0439852e-01   2.9337180e-01
+   0.0000000e+00   2.0402756e-01   2.9282223e-01
+   0.0000000e+00   2.0365660e-01   2.9227266e-01
+   0.0000000e+00   2.0328564e-01   2.9172310e-01
+   0.0000000e+00   2.0291468e-01   2.9117353e-01
+   0.0000000e+00   2.0254372e-01   2.9062396e-01
+   0.0000000e+00   2.0217276e-01   2.9007439e-01
+   0.0000000e+00   2.0180180e-01   2.8952482e-01
+   0.0000000e+00   2.0143084e-01   2.8897525e-01
+   0.0000000e+00   2.0105988e-01   2.8842568e-01
+   0.0000000e+00   2.0068892e-01   2.8787611e-01
+   0.0000000e+00   2.0031797e-01   2.8732654e-01
+   0.0000000e+00   1.9994701e-01   2.8677697e-01
+   0.0000000e+00   1.9957605e-01   2.8622740e-01
+   0.0000000e+00   1.9920509e-01   2.8567783e-01
+   0.0000000e+00   1.9883413e-01   2.8512827e-01
+   0.0000000e+00   1.9846317e-01   2.8457870e-01
+   0.0000000e+00   1.9809221e-01   2.8402913e-01
+   0.0000000e+00   1.9772125e-01   2.8347956e-01
+   0.0000000e+00   1.9735029e-01   2.8292999e-01
+   0.0000000e+00   1.9697933e-01   2.8238042e-01
+   0.0000000e+00   1.9660837e-01   2.8183085e-01
+   0.0000000e+00   1.9623741e-01   2.8128128e-01
+   0.0000000e+00   1.9586645e-01   2.8073171e-01
+   0.0000000e+00   1.9549550e-01   2.8018214e-01
+   0.0000000e+00   1.9512454e-01   2.7963257e-01
+   0.0000000e+00   1.9475358e-01   2.7908300e-01
+   0.0000000e+00   1.9438262e-01   2.7853344e-01
+   0.0000000e+00   1.9401166e-01   2.7798387e-01
+   0.0000000e+00   1.9364070e-01   2.7743430e-01
+   0.0000000e+00   1.9326974e-01   2.7688473e-01
+   0.0000000e+00   1.9289878e-01   2.7633516e-01
+   0.0000000e+00   1.9252782e-01   2.7578559e-01
+   0.0000000e+00   1.9215686e-01   2.7523602e-01
+   0.0000000e+00   1.9178590e-01   2.7468645e-01
+   0.0000000e+00   1.9141494e-01   2.7413688e-01
+   0.0000000e+00   1.9104399e-01   2.7358731e-01
+   0.0000000e+00   1.9067303e-01   2.7303774e-01
+   0.0000000e+00   1.9030207e-01   2.7248817e-01
+   0.0000000e+00   1.8993111e-01   2.7193861e-01
+   0.0000000e+00   1.8956015e-01   2.7138904e-01
+   0.0000000e+00   1.8918919e-01   2.7083947e-01
+   0.0000000e+00   1.8881823e-01   2.7028990e-01
+   0.0000000e+00   1.8844727e-01   2.6974033e-01
+   0.0000000e+00   1.8807631e-01   2.6919076e-01
+   0.0000000e+00   1.8770535e-01   2.6864119e-01
+   0.0000000e+00   1.8733439e-01   2.6809162e-01
+   0.0000000e+00   1.8696343e-01   2.6754205e-01
+   0.0000000e+00   1.8659247e-01   2.6699248e-01
+   0.0000000e+00   1.8622152e-01   2.6644291e-01
+   0.0000000e+00   1.8585056e-01   2.6589334e-01
+   0.0000000e+00   1.8547960e-01   2.6534378e-01
+   0.0000000e+00   1.8510864e-01   2.6479421e-01
+   0.0000000e+00   1.8473768e-01   2.6424464e-01
+   0.0000000e+00   1.8436672e-01   2.6369507e-01
+   0.0000000e+00   1.8399576e-01   2.6314550e-01
+   0.0000000e+00   1.8362480e-01   2.6259593e-01
+   0.0000000e+00   1.8325384e-01   2.6204636e-01
+   0.0000000e+00   1.8288288e-01   2.6149679e-01
+   0.0000000e+00   1.8251192e-01   2.6094722e-01
+   0.0000000e+00   1.8214096e-01   2.6039765e-01
+   0.0000000e+00   1.8177001e-01   2.5984808e-01
+   0.0000000e+00   1.8139905e-01   2.5929851e-01
+   0.0000000e+00   1.8102809e-01   2.5874895e-01
+   0.0000000e+00   1.8065713e-01   2.5819938e-01
+   0.0000000e+00   1.8028617e-01   2.5764981e-01
+   0.0000000e+00   1.7991521e-01   2.5710024e-01
+   0.0000000e+00   1.7954425e-01   2.5655067e-01
+   0.0000000e+00   1.7917329e-01   2.5600110e-01
+   0.0000000e+00   1.7880233e-01   2.5545153e-01
+   0.0000000e+00   1.7843137e-01   2.5490196e-01
diff --git a/dependencies/@AnalyzeKriging/calcExpectedImprovement.m b/dependencies/@AnalyzeKriging/calcExpectedImprovement.m
index 133456e..6066290 100644
--- a/dependencies/@AnalyzeKriging/calcExpectedImprovement.m
+++ b/dependencies/@AnalyzeKriging/calcExpectedImprovement.m
@@ -19,6 +19,15 @@
 % For further optional settings and more details about the calculation of
 % expected improvement see "calcExpectedImprovementMainPart()"
 %
+% Note: For more information about how to introduces fuzzy inequality
+% constraints for the output see documentation for
+% "modExpImprByOutputConstr". For fuzzy constraints the probability is
+% calculated that these constraints hold. If fuzzy constraints are
+% available than the modified expected improvement value is a compormise
+% between achieving high improvement and not violating the constraints.
+% This should be prefered for Markov Chain Monte Charlo Design of
+% Experiment instead of hard constraints.
+% 
 % Copyright 2014-2016: Lars Freier, Eric von Lieres
 % See the license note at the end of the file.
 
@@ -56,6 +65,13 @@
     ExpectedImprovement = ExpectedImprovement';
 end
 
+% Include Inequality constraint distribution into final expected
+% improvement
+if isprop(obj,'InequalityConstraintOutputDistribution')&&~isempty(obj.getInequalityConstraintOutputDistribution)
+    predictionProto = -obj.MinMax(KrigingObjectIndex)*prediction(:,1);
+    ExpectedImprovement = obj.modExpImprByOutputConstr(ExpectedImprovement,predictionProto,prediction(:,2));
+end
+
 end
 % =============================================================================
 %  KriKit - Kriging toolKit
diff --git a/dependencies/@AnalyzeKriging/calcExpectedImprovementFromPredictions.m b/dependencies/@AnalyzeKriging/calcExpectedImprovementFromPredictions.m
deleted file mode 100644
index bd89bac..0000000
--- a/dependencies/@AnalyzeKriging/calcExpectedImprovementFromPredictions.m
+++ /dev/null
@@ -1,66 +0,0 @@
-function [ExpectedImprovement]=calcExpectedImprovementFromPredictions(obj,varargin)
-% [ExpectedImprovement]=calcExpectedImprovement(obj,KrigingObjectIndex,predictions)
-% KrigingObjectIndex ... indicates the which Kriging Objective
-% is considered
-% prediction ... nPoints X 1 matrix containing Kriging predictions
-% 
-% Copyright 2014-2016: Lars Freier, Eric von Lieres
-% See the license note at the end of the file.
-
-    KrigingObjectIndex = varargin{1};
-    if length(KrigingObjectIndex)~=1
-        error('KrigingObjectIndex must be a scalar')
-    end
-    
-    % Make Prediction
-    prediction = varargin{2};
-    prediction(:,1) = -obj.MinMax(1)*prediction(:,1);
-
-    % Calculate the expected improvements at the particular sample
-    % locations
-        % Best Measurement so far
-    %             minOutput = min(obj.MinMax(1)*obj.KrigingObjects{KrigingObjectIndex}.getOutputData);
-     outputData = -obj.MinMax(KrigingObjectIndex)*obj.KrigingObjects{KrigingObjectIndex}.getOutputData;
-    [~,indexMin] = min(outputData);
-    bestOutput = outputData(indexMin);
-        % Argument for cumulative distribution function
-    u = (bestOutput - prediction(:,1))./prediction(:,2);
-        % Calculate expected improvement
-            % if statistic toolbox is installed, the following command can be used:
-            % ExpectedImprovement = (minOutput - prediction(:,1)).*normcdf(u,0,1) + prediction(:,2).*normpdf(u,0,1);
-            % ExpectedImprovement = (minOutput - prediction(:,1)).*(1/2+1/2*erf(u/sqrt(2)))+...
-            %                         prediction(:,2)./sqrt(2*pi).*exp(-u.^2/2);
-   switch obj.DegreeOfExpectedImprovement
-        case 0
-            ExpectedImprovement = Phi(u);
-        case 1
-            ExpectedImprovement = prediction(:,2).*(u.*Phi(u)+phi(u));
-        case 2
-            ExpectedImprovement = prediction(:,2).^2.*((u.^2+1).*Phi(u)  +u.*phi(u));
-        case 3
-            ExpectedImprovement = prediction(:,2).^3.*((u.^3+3*u).*Phi(u) + (2+u.^2).*phi(u));
-       otherwise
-            [ExpectedImprovement] = calcFinalExpImprovement(obj,prediction,u);
-    end
-
-    %% Nested Function
-    function [cumProb]=Phi(x)
-        cumProb = (1/2+1/2*erf(x/sqrt(2)));
-    end
-    function [prob]=phi(x)
-        prob = 1./sqrt(2*pi).*exp(-x.^2/2);
-    end
-
-end
-% =============================================================================
-%  KriKit - Kriging toolKit
-%  
-%  Copyright 2014-2016: Lars Freier(1), Eric von Lieres(1)
-%                                      
-%    (1)Forschungszentrum Juelich GmbH, IBG-1, Juelich, Germany.
-%  
-%  All rights reserved. This program and the accompanying materials
-%  are made available under the terms of the GNU Public License v3.0 (or, at
-%  your option, any later version) which accompanies this distribution, and
-%  is available at http://www.gnu.org/licenses/gpl.html
-% =============================================================================
diff --git a/dependencies/@AnalyzeKriging/calcExpectedImprovement_MO.m b/dependencies/@AnalyzeKriging/calcExpectedImprovement_MO.m
index faa5b4a..da01e84 100644
--- a/dependencies/@AnalyzeKriging/calcExpectedImprovement_MO.m
+++ b/dependencies/@AnalyzeKriging/calcExpectedImprovement_MO.m
@@ -22,13 +22,13 @@
 %           [boolConstraintHolds]=inequalityConstraintOutput(estimationMean,estimationSD)
 %           With 
 %           estimationMean ... kriging prediction at the point of interest
-%                              given in "estimationPoints" [nPointsX1]
+%                              given in "estimationPoints" [nPointsXnObj]
 %           estimationSD ... kriging prediction error at the point of
 %                            interest, given in "estimationPoints"
 %                            [nPointsX1] 
 %           boolConstraintHolds ... boolean output vector containing true
 %                                   if the point hold the constraint
-%                                   [nPointsX1] 
+%                                   [nPointsXnObj] 
 %
 % - DegreeOfExpectedImprovement ... exploration factor for adjusting the
 %                   trade-off between exploitation and exploration. 
@@ -42,7 +42,17 @@
 % You can get: 
 %
 % Note: Before Running "calcExpectedImprovement_MO" you should calculate
-% the run "determineParetoSet()"
+% the run "determineParetoSet()". 
+% 
+% Note: For more information about how to introduces fuzzy inequality
+% constraints for the output see documentation for
+% "modExpImprByOutputConstr". For fuzzy constraints the probability is
+% calculated that these constraints hold. If fuzzy constraints are
+% available than the modified expected improvement value is a compormise
+% between achieving high improvement and not violating the constraints.
+% This should be prefered for Markov Chain Monte Charlo Design of
+% Experiment instead of hard constraints (like
+% "InequalityConstraintOutputHandle" )
 %
 % Copyright 2014-2016: Lars Freier, Eric von Lieres
 % See the license note at the end of the file.
@@ -138,6 +148,12 @@
         end
 end
 
+% Include Inequality constraint distribution into final expected
+% improvement
+if isprop(obj,'InequalityConstraintOutputDistribution')&&~isempty(obj.getInequalityConstraintOutputDistribution)
+    estimationMeanProto = bsxfun(@times,estimationMean,-obj.MinMax(objectiveIndices));
+    ExpectedImprovement = obj.modExpImprByOutputConstr(ExpectedImprovement,estimationMeanProto,estimationSD);
+end
 
 end
 
diff --git a/dependencies/@AnalyzeKriging/calcMutualInterpolation_23D.m b/dependencies/@AnalyzeKriging/calcMutualInterpolation_23D.m
index c20df0b..659efe7 100644
--- a/dependencies/@AnalyzeKriging/calcMutualInterpolation_23D.m
+++ b/dependencies/@AnalyzeKriging/calcMutualInterpolation_23D.m
@@ -106,9 +106,11 @@
         RemainingIndices = varargin{3};
         RemainingValues = varargin{4};
         if length(unique([varargin{2},RemainingIndices]))~=nInputData
+            errorCheck();
             error('Entries in "[InputVar1,InputVar2]" and "RemainingIndices" must be unique')
         end
         if (size(RemainingValues,1)*size(RemainingValues,2))~=nInputData-(dimensionInterpolation-1)
+            errorCheck();
             error('RemainingValues must be a vector with length %i',nInputData-(dimensionInterpolation-1))
         end
 
diff --git a/dependencies/@AnalyzeKriging/checkInputInterpolation.m b/dependencies/@AnalyzeKriging/checkInputInterpolation.m
index 3ce4ab1..4cad6b9 100644
--- a/dependencies/@AnalyzeKriging/checkInputInterpolation.m
+++ b/dependencies/@AnalyzeKriging/checkInputInterpolation.m
@@ -58,9 +58,11 @@
     RemainingIndices = varargin{3};
     RemainingValues = varargin{4};
     if length(unique([varargin{2},RemainingIndices]))~=obj.KrigingObjects{KrigingObjectIndex(1)}.getnInputVar
+        errorCheck();
         error('Entries in "[InputVar1,InputVar2]" and "RemainingIndices" must be unique')
     end
     if dimensionInterpolation>2&&(size(RemainingValues,1)*size(RemainingValues,2))~=obj.KrigingObjects{KrigingObjectIndex(1)}.getnInputVar-2
+        errorCheck();
         error('RemainingValues must be a vector with length %i',obj.KrigingObjects{KrigingObjectIndex(1)}.getnInputVar-2)
     end
 
diff --git a/dependencies/@AnalyzeKriging/defineBoundOfInputVar.m b/dependencies/@AnalyzeKriging/defineBoundOfInputVar.m
index 17c1158..cd45d0c 100644
--- a/dependencies/@AnalyzeKriging/defineBoundOfInputVar.m
+++ b/dependencies/@AnalyzeKriging/defineBoundOfInputVar.m
@@ -19,9 +19,9 @@
             obj.UBInputVarInterpolation{KrigingObjectIndex(iKriging)} = max(obj.KrigingObjects{KrigingObjectIndex(iKriging)}.getInputData);
         end
         
-        if sum(obj.LBInputVarInterpolation{KrigingObjectIndex(iKriging)}>=obj.UBInputVarInterpolation{KrigingObjectIndex(iKriging)})>=1
-            error('One of the upper bounds is smaller or equal to the associated lower bound. ')
-        end
+%         if sum(obj.LBInputVarInterpolation{KrigingObjectIndex(iKriging)}>=obj.UBInputVarInterpolation{KrigingObjectIndex(iKriging)})>=1
+%             error('One of the upper bounds is smaller or equal to the associated lower bound. ')
+%         end
     end
 end
 % =============================================================================
diff --git a/dependencies/@AnalyzeKriging/errorCheck.p b/dependencies/@AnalyzeKriging/errorCheck.p
new file mode 100644
index 0000000000000000000000000000000000000000..b9631ecda54b334ca02bd457a3def6736df217d6
GIT binary patch
literal 1420
zcmV;71#|j#FflGLFm^C7E-)|v1&|!2Pd~8$04}xw00nFS07m91_+^WRBFvs#O(6nB
zvf=CaQfn9G#v$Xn9@ht^pb(N)^NJQD@eSx*uFT%eV#RU#|ExQDs84!rnVOLFe88of
z6V;mfu>v#m=E8QfvxY|&c=&`vPb-I?W9bt6qH%Czr#>7ghbeVQU%@D1WY?T;VusqJ
zjT3$X1*vfahrC|+;Y(7uao#Z-M_~Jj+crsI0K^a9Ex-@nGuwj1O%9VqH=q(G>^{@)
zDu)Y-=*wmGbzs?pYQq%sRRyt4X7C@y7!Duq>Q;wh)hp
zYS$i?`)g`-&F*;QIJ*Or#OH2m%rraXV~Bsd%U;#XjTw0fcbPYaeKDA4kD
zEd_m%B=fPqH(qFN62E7Jttyi*%lqCmV_kkpEzawNf1yXRKSBs!GUv4S>=z^-H!SH*l~0kAsPr^2?gi8z7b+P%?Tou5hdsJ@Kq{+(^UjUaa+H#y@X
zMFtphzf_Pw$e&|pdNag5`%ui-6ggObfue6bK`!;~^}>|XE2V=r1~m4UTJ
zP-Om3Ez^|!(}DMD_sV6#&)Cd;nkEObq!;G<;erI_ORmmG=kUUt6gc0V_NB|>{uqNQ
zP{Kn2uVAiYdHusm9its!e#kRFX!L_qTs&fU1S&rMMDVZ{O{cQBe?$Tp;;x
z@Yemo?u~jf+6XA;uEa(}%YBj<@axEEzUCgR9mU0rxZpxASJY+F)G*pO5_c&-3Y
z_liDlK$KsnE9ruz?)4+?B$F?RrsB)EKec6vtpyU|xPD~X0BIiW#zN)UvLuS|aQc>Y
z7kbH{P1c@dV>R}`QT*H(%!cP_}Z8UYDcBQ*(Najv6shrE?Ngk#{a
z*#p6x&Kmwu9uESoJ3N^Jd$*S}@eD$lQ0$9rJ^+JogjEF-m-HAX|CF%fE^7Ufqv0I1wtRrilSTRIHu?Oh(;v*HJ)B#>u$norgLLMYjO)NjEn5
z3P9OO((yv#Xh3u}37R-#udOcm=}>`hqr`%tu|0iAc718-pzx+GoN1JloV?RLP;1do
z2UX=j$MY~{c$A$A*(5qbFDJbuhT0dtWAcG1?i)W5saw>nPVy0%zv|Tx@2=K`IDI0WjgeE
zTpF5s?HOs!jc$;+E$)38Sl+CgT|SKiSP5DG*@{I8XL0PQi@4xBEq#MMmY)Ex=p)j(
aZfb?l<;p5R9eo5hd%pndEM=>O+!^&Yxy9B1

literal 0
HcmV?d00001

diff --git a/dependencies/@AnalyzeKriging/plotInterpolation_23D.m b/dependencies/@AnalyzeKriging/plotInterpolation_23D.m
index 729edb4..3c6a0d5 100644
--- a/dependencies/@AnalyzeKriging/plotInterpolation_23D.m
+++ b/dependencies/@AnalyzeKriging/plotInterpolation_23D.m
@@ -98,7 +98,7 @@
 
 % Set Colormap
 colormap(gcf,obj.ColormapToolbox);
-
+grid on
 
 %% Nested Functions
 % -------------------------------------------------------------------------
@@ -173,7 +173,7 @@
                          unique(obj.KrigingPrediction_Interpolation3D{KrigingObjectIndex,2}(:,inputIndices(2))),...
                          reshape(output,AccuracyTemp,AccuracyTemp)')
             else
-                mesh(unique(obj.KrigingPrediction_Interpolation3D{KrigingObjectIndex,2}(:,inputIndices(1))),...
+                surf(unique(obj.KrigingPrediction_Interpolation3D{KrigingObjectIndex,2}(:,inputIndices(1))),...
                          unique(obj.KrigingPrediction_Interpolation3D{KrigingObjectIndex,2}(:,inputIndices(2))),...
                          reshape(output,AccuracyTemp,AccuracyTemp)')
             end
@@ -196,12 +196,12 @@
                      UB,'-.','Color',[0.3 0.3 0.3]);
             case 3
                 % LB
-                 mesh(unique(obj.KrigingPrediction_Interpolation3D{KrigingObjectIndex,2}(:,obj.KrigingPrediction_Interpolation3D{KrigingObjectIndex,3}(1))),...
+                 surf(unique(obj.KrigingPrediction_Interpolation3D{KrigingObjectIndex,2}(:,obj.KrigingPrediction_Interpolation3D{KrigingObjectIndex,3}(1))),...
                  unique(obj.KrigingPrediction_Interpolation3D{KrigingObjectIndex,2}(:,obj.KrigingPrediction_Interpolation3D{KrigingObjectIndex,3}(2))),...
                  reshape(LB,AccuracyTemp,AccuracyTemp)')
 
                  % UB
-                 mesh(unique(obj.KrigingPrediction_Interpolation3D{KrigingObjectIndex,2}(:,obj.KrigingPrediction_Interpolation3D{KrigingObjectIndex,3}(1))),...
+                 surf(unique(obj.KrigingPrediction_Interpolation3D{KrigingObjectIndex,2}(:,obj.KrigingPrediction_Interpolation3D{KrigingObjectIndex,3}(1))),...
                  unique(obj.KrigingPrediction_Interpolation3D{KrigingObjectIndex,2}(:,obj.KrigingPrediction_Interpolation3D{KrigingObjectIndex,3}(2))),...
                  reshape(UB,AccuracyTemp,AccuracyTemp)')
             otherwise
diff --git a/dependencies/@AnalyzeKriging/plotParetoFront.m b/dependencies/@AnalyzeKriging/plotParetoFront.m
index 19d390d..bdcb315 100644
--- a/dependencies/@AnalyzeKriging/plotParetoFront.m
+++ b/dependencies/@AnalyzeKriging/plotParetoFront.m
@@ -1,5 +1,7 @@
 function [] = plotParetoFront(obj,varargin)
 % [] = plotParetoFront(krigingObjVector,expectedParetoCurve,deviationParetoCurve,pCover,gridOriginal);
+% This function plots the results produced by "predictParetoCurve"
+%
 % Input: 
 % - krigingObjVector(1XnObjectives): contains indices of kriging objects
 % - expectedParetoCurve(nParetoPointsXnObjectives): matrix contains
@@ -44,21 +46,29 @@
     gridOriginal = varargin{5};
     
     nObj = length(krigingObjVector);
-    nGridPointsOutput = round(size(gridOriginal,1)^(1/nObj));
+%     nGridPointsOutput = round(size(gridOriginal,1)^(1/nObj));
     
     % Determine Unique Grid
     uniqueGridMatrix = calcUniqueGridMatrix();
 
     % Find Quantiles in order to define the confidence tube
-    [gridToLow,gridToBig]=determinConfidenceBounds;
+    if obj.getShowBounds
+        [gridToLow,gridToBig]=determinConfidenceBounds;
+    end
     
     switch nObj
         case 2
+            levelVec = unique(deviationParetoCurve(deviationParetoCurve>obj.getThresholdQuantile));
+            
             figure()
             hold on
-            contourf(uniqueGridMatrix(:,1),uniqueGridMatrix(:,2),reshape(deviationParetoCurve,nGridPointsOutput,nGridPointsOutput)');
-            hExpected = plot(expectedParetoCurve(:,1),expectedParetoCurve(:,2),'k.','MarkerSize',15);
-            
+            contourf(uniqueGridMatrix{1},...
+                    uniqueGridMatrix{2},...
+                    reshape(deviationParetoCurve,length(uniqueGridMatrix{1}),length(uniqueGridMatrix{2}))',...
+                    levelVec);
+            [expectedParetoCurveXSort,idx] = sort(expectedParetoCurve(:,1));
+            hExpected = plot(expectedParetoCurveXSort,expectedParetoCurve(idx,2),'k-','LineWidth',2);
+
             if obj.getShowBounds
                 % sort in ascend w.r.t to the first objective but descend
                 % order w.r.t to the second objective
@@ -149,14 +159,14 @@
 
 %% Nested
     function [uniqueGridMatrix] = calcUniqueGridMatrix()
-        uniqueGridMatrix = zeros(nGridPointsOutput,nObj);
+        uniqueGridMatrix = cell(nObj,1);
         % Bring Data Set in correct order
         for iObjNested=1:nObj
             krigingIndexNested = krigingObjVector(iObjNested);
             if obj.getMinMax(krigingIndexNested)==1
-                uniqueGridMatrix(:,iObjNested)=sort(unique(gridOriginal(1:end,krigingIndexNested)),'ascend');
+                uniqueGridMatrix{iObjNested}=sort(unique(gridOriginal(1:end,krigingIndexNested)),'ascend');
             else
-                uniqueGridMatrix(:,iObjNested)=sort(unique(gridOriginal(1:end,krigingIndexNested)),'descend');
+                uniqueGridMatrix{iObjNested}=sort(unique(gridOriginal(1:end,krigingIndexNested)),'descend');
             end
         end
     end
@@ -170,8 +180,11 @@
         
         % Choose all points outside off and at the broder off desired
         % confidence region
-        qToLow = pCover<=quantileValueLB;
-        qToBig = pCover>=quantileValueUB;
+        qToLow = pCover(end:-1:1)<=quantileValueLB;
+        qToBig = pCover(end:-1:1)>=quantileValueUB;
+        if ~any(qToLow)||~any(qToBig)
+            error('No point in the solution space fits the quantile contraints. This can be solved by adjusting number of simulations or by adjusting the outputConstraint for the "predictParetoCurve" function. You can also deactivate "ShowBounds"')
+        end
         
         % Define confidence borders
         gridToLow = determineParetoSet_Mex(bsxfun(@times,gridOriginal(qToLow,:),obj.MinMax(krigingObjVector)));
diff --git a/dependencies/@AnalyzeKriging/predictParetoCurve.m b/dependencies/@AnalyzeKriging/predictParetoCurve.m
index 5baa953..76b0420 100644
--- a/dependencies/@AnalyzeKriging/predictParetoCurve.m
+++ b/dependencies/@AnalyzeKriging/predictParetoCurve.m
@@ -1,5 +1,5 @@
 function [expectedParetoCurve,deviationParetoCurve,pCover,gridOutput] = predictParetoCurve(obj,varargin)
-% [expectedParetoCurve,deviationParetoCurve,pCover,gridOutput] = predictParetoCurve(krigingObjVector,nRealizations,nSampleLocations,nGridPointEachDirection,inEqualityCellArray)
+% [expectedParetoCurve,deviationParetoCurve,pCover,gridOutput] = predictParetoCurve(krigingObjVector,nRealizations,nSampleLocations,nGridPointEachDirection,inEqualityCellArray,OutputConstraints)
 %
 % This function estimated the expected pareto curve pareto based on
 % conditional simulation results. Theory is described in detail in is
@@ -57,6 +57,9 @@
 %                           out a (nSamplePointsX1)-boolean vector. Where
 %                           each entry indicates if the associated sample
 %                           point is valid (true) or not (false).
+% - OutputConstraints ... a matrix (nKrigingObjectsX2) containing the
+%                         feasible lower and upperbound for the output
+%                         space which shall be investigated
 % Output: 
 % - expectedParetoCurve ... objective matrix containing the Pareto points
 %                           (nParetoPointsXnKrigingObjects)
@@ -69,9 +72,13 @@
 % You can set:
 % - setShowData ... Scan solution space also w.r.t. to the given output
 %   data. This might needed if you want to plot the expected Pareto Curve
-%   together with the data
+%   together with the data. Caution: This might lead to a ncombinatorical
+%   explosion!
 % - LB(UB)InputVarInterpolation ... restict the input space for the
 %                                   conditional simulation
+%  - RepeatDesign ... If "false" than a different lattin hypercube design
+%                     is used for each realization (more roust but more
+%                     expensive)
 %
 % You can get: - 
 %
@@ -96,11 +103,19 @@
     nGridPointEachDirection = 1e6^(1/nObjects);
     inequalityConstraintInput = {};
 end
-if length(varargin)>5&&~isempty(varargin{6})
-    inEqualityCellArray = varargin{6};
+if length(varargin)>4&&~isempty(varargin{5})
+    inEqualityCellArray = varargin{5};
 else
     inEqualityCellArray = {};
 end
+if length(varargin)>5&&~isempty(varargin{6})
+    OutputConstraints = varargin{6};
+else
+    OutputConstraints = ones(nObjects,2);
+    OutputConstraints(:,1) = -inf;
+    OutputConstraints(:,2) = inf;
+end
+
 
 % If inequalityConstraintInput is given, nSampleLocations might change
 defineBoundOfInputVar(obj,krigingObjVector(krigingObjVector(1)));
@@ -146,7 +161,7 @@
 
     paretoSets{iRealization} = determineParetoSet_Mex(...
             bsxfun(@times,realizationCurveMaxtrix(:,indexSet),-obj.MinMax(krigingObjVector)) );
-
+        
     % Save Pareto Set in Matrix
     totalParetoSet = [totalParetoSet;paretoSets{iRealization}];
     nMemberPratoCurve(iRealization) = size(paretoSets{iRealization},1);
@@ -161,7 +176,12 @@
 [rangeToCheck] = determineRangeForGrid;
 
 % Create Grid
-gridX = createNDGRID(rangeToCheck(:,1),rangeToCheck(:,2),nGridPointEachDirection);
+if obj.ShowData
+    gridX = createNDGRID(rangeToCheck(:,1),rangeToCheck(:,2),nGridPointEachDirection,totalParetoSet);
+else
+    gridX = createNDGRID(rangeToCheck(:,1),rangeToCheck(:,2),nGridPointEachDirection);
+end
+
 nGridPoints = size(gridX,1);
 
 % Actual Calculation
@@ -217,7 +237,8 @@
               expectedParetoCurve,...
               pCover);
  deviationParetoCurve(1:end) = deviationParetoCurve(end:-1:1);
-
+ pCover(1:end) = pCover(end:-1:1);
+ 
 %% #### Convert everything back to the original space #### 
 expectedParetoCurve = bsxfun(@times,expectedParetoCurve,-obj.MinMax(krigingObjVector));
 gridOutput = bsxfun(@times,gridX,-obj.MinMax(krigingObjVector));
@@ -229,7 +250,7 @@
 function [rangeToCheck] = determineRangeForGrid()
     
     % Initialization
-    rangeToCheck = [];
+    rangeToCheck = nan(nObjects,2);
     
     % Check provided sample points
     if obj.getShowData
@@ -247,11 +268,17 @@
                 rangeToCheck(iKrigingObj,:) = [min(paretoSets{iRealizationNested}(:,iKrigingObj)),...
                                                max(paretoSets{iRealizationNested}(:,iKrigingObj))];
             else
-                rangeToCheck(iKrigingObj,:) = [min([rangeToCheck(iKrigingObj,1),rangeToCheck(iKrigingObj,2),min(paretoSets{iRealizationNested}(:,iKrigingObj))]),...
-                                    max([rangeToCheck(iKrigingObj,1),rangeToCheck(iKrigingObj,2),min(paretoSets{iRealizationNested}(:,iKrigingObj))])];
+                
+                pointsToCheck = [rangeToCheck(iKrigingObj,1),...
+                                 rangeToCheck(iKrigingObj,2),...
+                                 (paretoSets{iRealizationNested}(:,iKrigingObj))'];
+                rangeToCheck(iKrigingObj,:) = ...
+                    [min(pointsToCheck),...
+                     max(pointsToCheck)];
             end
         end
     end
+    
 
     % Check if scale of data prediction are similar
     if obj.getShowData
@@ -267,6 +294,16 @@
         end
     end
     
+    if ~isempty(OutputConstraints)
+        if ~all(size(rangeToCheck)==size(OutputConstraints))
+            error('OutputConstraints should be of Size nKrigingObjectsX2')
+        end
+        rangeToCheckProto = bsxfun(@times,(-obj.MinMax(krigingObjVector)'),OutputConstraints);
+        rangeToCheckProto = sort(rangeToCheckProto,2); % Guaranty that LBis first column
+        
+        rangeToCheck(~isinf(rangeToCheckProto)) = rangeToCheckProto(~isinf(rangeToCheckProto));
+        
+    end
 end
 
 % ----------------------------------------------------------
@@ -274,16 +311,46 @@
     epsilon = 1e-10;
     lowerLevel = 0;
     upperLevel = 1;
+    deltaNew = inf;
+    deltaOld = inf;
+    HV_QuantileOld = inf;
+    upperLevelOld = upperLevel;
+    lowerLevelOld = lowerLevel;
+    
+    firstTime = true;
     while upperLevel-lowerLevel>epsilon
+        if ~firstTime
+            deltaOld = deltaNew;
+            HV_QuantileOld = HV_Quantile;
+        end
+        
         pointsSelected = pCover>=(upperLevel+lowerLevel)/2;
-        HV_Quantile = Hypervolume_MEX(gridX(pointsSelected,:),bsxfun(@times,obj.ReferencePointHyperVolume(krigingObjVector),-obj.MinMax(krigingObjVector)));
-
-        if HV_Quantileobj.getnKrigingObjects
+        error('KrigingObjectIndex must be a scalar between 1 and %i',obj.getnKrigingObjects)
+    end
+    
     % A row vector is needed
     if size(LBUBInputVarInterpolation,1)==obj.KrigingObjects{KrigingObjectIndex}.getnInputVar&&size(LBUBInputVarInterpolation,2)==1
         LBUBInputVarInterpolation = LBUBInputVarInterpolation';
@@ -20,17 +25,17 @@
         error('LB/UBInputVarInterpolation has to be of dimension 1x%i',obj.KrigingObjects{KrigingObjectIndex}.getnInputVar)
     end
 
-    % Check Input
-    if length(KrigingObjectIndex)~=1||KrigingObjectIndex<1||KrigingObjectIndex>obj.getnKrigingObjects
-        error('KrigingObjectIndex must be a scalar between 1 and %i',obj.getnKrigingObjects)
-    end
+    
 
     switch LBorUB
         case -1
             obj.LBInputVarInterpolation{KrigingObjectIndex} = LBUBInputVarInterpolation;
         case 1
-            if sum(obj.LBInputVarInterpolation{KrigingObjectIndex}>=LBUBInputVarInterpolation)>=1
-                error('One of the upper bounds is smaller or equal to the associated lower bound. Please define the lower bound first')
+            if any(obj.LBInputVarInterpolation{KrigingObjectIndex}>LBUBInputVarInterpolation)
+                error('One of the upper bounds is smaller than the associated lower bound. Please define the lower bound first')
+            end
+            if all(obj.LBInputVarInterpolation{KrigingObjectIndex}==LBUBInputVarInterpolation)
+                error('All upper bounds is smaller or equal to the associated lower bound. Please define the lower bound first')
             end
             obj.UBInputVarInterpolation{KrigingObjectIndex} = LBUBInputVarInterpolation;
         otherwise
diff --git a/dependencies/@AnalyzeMixture/AnalyzeMixture.m b/dependencies/@AnalyzeMixture/AnalyzeMixture.m
index c4238d0..0cbd679 100644
--- a/dependencies/@AnalyzeMixture/AnalyzeMixture.m
+++ b/dependencies/@AnalyzeMixture/AnalyzeMixture.m
@@ -14,7 +14,7 @@
     
     %% Private Members
     properties(GetAccess='private',SetAccess='private')
-        
+        PartOfMixture = [];
     end
     
     %% Protected Members
@@ -25,6 +25,7 @@
         %% Constructor
         function obj = AnalyzeMixture()
             obj@AnalyzeKriging();
+            
         end
         %% Copy Operator for a shallow copy
         % ----------------------------------------------------------------
@@ -176,6 +177,35 @@
         [] = labelPlots_23D(obj,varargin);
         % ----------------------------------------------------------------
         [] = plotInterpolation_23D(obj,varargin)
+        
+        
+        
+        
+        
+        %% Get
+        % ----------------------------------------------------------------
+        function [PartOfMixture] = getPartOfMixture(obj)
+            PartOfMixture = obj.PartOfMixture;
+        end
+        
+        %% Set
+        % ----------------------------------------------------------------
+        function [] = setPartOfMixture(obj,varargin)
+            PartOfMixture = varargin{1};
+            idxObj = varargin{2};
+            if mod(idxObj,1)~=0 || length(idxObj)~=1 || idxObj>obj.getnKrigingObjects
+                error('Second input (idxObj) has to be an integer')
+            end
+            if length(PartOfMixture)~=obj.KrigingObjects{idxObj}.getnInputVar || ...
+                    (size(PartOfMixture,1)*size(PartOfMixture,2))~=obj.KrigingObjects{idxObj}.getnInputVar
+                error('PartOfMixture has to be a vector of 1XninputVar')
+            end
+            if ~islogical(PartOfMixture)
+                error('PartOfMixture must contain only logical entries')
+            end
+            obj.PartOfMixture = PartOfMixture;
+        end
+%         PartOfMixture
     end
     
 end
diff --git a/dependencies/@AnalyzeMixture/calcInterpolation.m b/dependencies/@AnalyzeMixture/calcInterpolation.m
index 54e3c13..055bc33 100644
--- a/dependencies/@AnalyzeMixture/calcInterpolation.m
+++ b/dependencies/@AnalyzeMixture/calcInterpolation.m
@@ -47,7 +47,7 @@
     nUnique = size(uniqueRows,1);
     iIndex = 0;
     for iUnique = 1:nUnique
-        concVec = linspace(0,1-sum(uniqueRows(iUnique,:),2),obj.getAccuracy);
+        concVec = linspace(0,1-sum(uniqueRows(iUnique,obj.PartOfMixture),2),obj.getAccuracy);
         for iAccuary=1:obj.getAccuracy
             indexStart = iIndex+(iAccuary-1)*obj.getAccuracy^(2-iComponent) + 1;
             indexEnd = iIndex+(iAccuary)*obj.getAccuracy^(2-iComponent);
@@ -56,9 +56,12 @@
         iIndex = iIndex+obj.getAccuracy^(iComponent-1);
     end
 end
+% Input = createNDGRID([0,0],[1,1],obj.getAccuracy);
 
 % Use definition of mixtrue: Sum of input variables has to be 100%
-indicesOthers = setdiff(1:obj.KrigingObjects{KrigingObjectIndex}.getnInputVar,inputVarIndices(3));
+idxAll = 1:obj.KrigingObjects{KrigingObjectIndex}.getnInputVar;
+idxDoNotMod = [inputVarIndices(3),idxAll(~obj.PartOfMixture)];
+indicesOthers = setdiff(1:obj.KrigingObjects{KrigingObjectIndex}.getnInputVar,idxDoNotMod);
 Input(:,inputVarIndices(3)) = 1- sum(Input(:,indicesOthers),2);
 Input = unique(Input,'rows');
 
diff --git a/dependencies/@AnalyzeMixture/calcInterpolation_nD.m b/dependencies/@AnalyzeMixture/calcInterpolation_nD.m
index 187a001..b51566e 100644
--- a/dependencies/@AnalyzeMixture/calcInterpolation_nD.m
+++ b/dependencies/@AnalyzeMixture/calcInterpolation_nD.m
@@ -140,16 +140,20 @@
             nUnique = size(uniqueRows,1);
             iIndex = 0;
             for iUnique = 1:nUnique
-                concVec = linspace(0,1-sum(uniqueRows(iUnique,:),2),obj.getAccuracy);
+                concVec = linspace(0,1-sum(uniqueRows(iUnique,obj.PartOfMixture),2),obj.getAccuracy);
                 for iAccuary=1:obj.getAccuracy
-                    InputMatrix(iIndex+(iAccuary-1)*obj.getAccuracy^(nInputVarConti-1-iComponent)+startIndex + 1:iIndex+(iAccuary)*obj.getAccuracy^(nInputVarConti-1-iComponent)+startIndex,inputVarIndices(iComponent)) = concVec(iAccuary);
+                    InputMatrix(iIndex+(iAccuary-1)*obj.getAccuracy^(nInputVarConti-1-iComponent)+startIndex + 1:iIndex+(iAccuary)*obj.getAccuracy^(nInputVarConti-1-iComponent)+startIndex,inputVarIndices(iComponent)) = ...
+                        concVec(iAccuary);
                 end
                 iIndex = iIndex+obj.getAccuracy^(iComponent-1);
             end
         end
         
         % Use definition of mixtrue: Sum of input variables has to be 100%
-        remainintVar = setdiff(1:obj.KrigingObjects{KrigingObjectIndex}.getnInputVar,inputVarIndices(3));
+        
+        idxAll = 1:obj.KrigingObjects{KrigingObjectIndex}.getnInputVar;
+        idxDoNotMod = [inputVarIndices(3),idxAll(~obj.PartOfMixture)];
+        remainintVar = setdiff(1:obj.KrigingObjects{KrigingObjectIndex}.getnInputVar,idxDoNotMod);
         InputMatrix(startIndex+1:endIndex,inputVarIndices(3)) = 1- sum(InputMatrix(startIndex+1:endIndex,remainintVar),2);
 
         threshold = [1-1e-10,1+1e-10];
diff --git a/dependencies/@AnalyzeMixture/plotInterpolation_nD.m b/dependencies/@AnalyzeMixture/plotInterpolation_nD.m
index 6af04d1..0fb760c 100644
--- a/dependencies/@AnalyzeMixture/plotInterpolation_nD.m
+++ b/dependencies/@AnalyzeMixture/plotInterpolation_nD.m
@@ -44,7 +44,7 @@
 % variables
 InputMatrixProto = obj.KrigingPrediction_InterpolationnD{KrigingObjectIndex,4};
 nRows = size(InputMatrixProto,2);
-
+nCol = obj.getnPlots+obj.getShowColorBar;
 %% Actual Plotting
 figure();
 hold on
@@ -53,7 +53,8 @@
 for iPlots1=1:nRows
     for iPlots2=1:obj.getnPlots
         createContourPlot(iPlots1,iPlots2);
-        set(gca,'PlotBoxAspectRatio',[1,1,1])
+        set(gca,'PlotBoxAspectRatio',[1,1,1],'XTick',[],'YTick',[],'Visible','off')
+        
     end
 end
 
@@ -66,15 +67,19 @@
 end
 
 % Title
-subplot(nRows,obj.getnPlots+1,ceil(obj.getnPlots/2))
-title(obj.getKrigingObjectNames{KrigingObjectIndex},'FontSize',20)
+subplot(nRows,nCol,ceil(obj.getnPlots/2))
+title(sprintf('%s\n',obj.getKrigingObjectNames{KrigingObjectIndex}),'FontSize',20)
 
 % Colorbar
-subplot(obj.getnPlots,obj.getnPlots+1,obj.getnPlots+1:obj.getnPlots+1:obj.getnPlots*(obj.getnPlots+1))
+
+if obj.getShowColorBar
+    subplot(obj.getnPlots,obj.getnPlots+1,obj.getnPlots+1:obj.getnPlots+1:obj.getnPlots*(obj.getnPlots+1))
+end
 if obj.getNormColors==1
     caxis([minEstimation,maxEstimation])
 end
 if obj.getShowColorBar==1&&obj.getNormColors==1
+    
     colorbar
 end
 
@@ -95,7 +100,7 @@
     indexSubPlot = (iRow-1)*(obj.getnPlots+1)+iPlots2;
     
     % Plot
-    subplot(nRows,obj.getnPlots+1,indexSubPlot);
+    subplot(nRows,nCol,indexSubPlot);
     hold on
     [uniqueRow,indexRows]=unique(InputMatrix(validIndicesSub,:),'rows');
     ternpcolor(uniqueRow(:,inputVarIndices(iRow,1)),...
@@ -110,7 +115,7 @@
     else
         stringInputVar = obj.getInputVarNames(KrigingObjectIndex(1));
         xString = sprintf('\n\n\n%s=%g\n',stringInputVar{inputVarIndices(iRow,1)},max(uniqueRow(:,inputVarIndices(iRow,1))) );
-        yString = sprintf('%s=%g',stringInputVar{inputVarIndices(iRow,2)},max(uniqueRow(:,inputVarIndices(iRow,2))) );
+        yString = sprintf('%s=%g\n\n',stringInputVar{inputVarIndices(iRow,2)},max(uniqueRow(:,inputVarIndices(iRow,2))) );
         zString = sprintf('\n\n\n\t\t                       %s=%g\t\t                       ',stringInputVar{inputVarIndices(iRow,3)},max(uniqueRow(:,inputVarIndices(iRow,3))) );
     end
     ternlabel(zString,yString,xString);
@@ -128,7 +133,12 @@
             plotSamples(Data,iRow,iPlots2)
         end
     end
-
+    
+%     ternpcolor(uniqueRow(:,inputVarIndices(iRow,1)),...
+%                uniqueRow(:,inputVarIndices(iRow,2)),...
+%                uniqueRow(:,inputVarIndices(iRow,3)),...
+%                 Estimation(validIndicesSub(indexRows)))
+    
 end
 % -------------------------------------------------------------
 function []=plotSamples(Data,iRow,iPlots2)
@@ -160,12 +170,14 @@
             Data(r,obj.KrigingPrediction_InterpolationnD{KrigingObjectIndex,3}(iRow,2)),...
             Data(r,obj.KrigingPrediction_InterpolationnD{KrigingObjectIndex,3}(iRow,3)),...
             'ko','MarkerFaceColor',[255/255 102/255 0/255]);
+    lineObj = findobj(gca,'type','line');
+    lineObj.ZData=(ones(1,size(lineObj.XData,2))*max(Estimation));
 end
 % -------------------------------------------------------------
 function []=labelPlotxAxis(iRow,iPlots2)
 %     subplot(obj.getnPlots,obj.getnPlots+1,(obj.getnPlots-1)*(obj.getnPlots+1)+iPlots1)
     indexSubPlot = (iRow-1)*(obj.getnPlots+1)+iPlots2;
-    subplot(nRows,obj.getnPlots+1,indexSubPlot)
+    subplot(nRows,nCol,indexSubPlot)
     
     % Use default names of input variables when user did not provide any
     if isempty(obj.getInputVarNames(KrigingObjectIndex))
diff --git a/dependencies/@BayesianOptimization/BayesianOptimization.m b/dependencies/@BayesianOptimizationClass/BayesianOptimizationClass.m
similarity index 75%
rename from dependencies/@BayesianOptimization/BayesianOptimization.m
rename to dependencies/@BayesianOptimizationClass/BayesianOptimizationClass.m
index 237a5a1..b2054e9 100644
--- a/dependencies/@BayesianOptimization/BayesianOptimization.m
+++ b/dependencies/@BayesianOptimizationClass/BayesianOptimizationClass.m
@@ -1,4 +1,4 @@
-classdef BayesianOptimization2015b and Statistic Toolbox)
         UseMatlabRegressionGP = false;
         GPR_Model = [];
-        
+        %% Noise Modeling
+        KriKitObjNoise = [];
+        ThresholdSolver = 1e-6;
     end
     
     methods
@@ -317,6 +321,10 @@
         solveLeastSquareCovariogramGA2(obj);
         % Generate RGP model for further Kriging estimation
         generateRegressionGPModel(obj);
+        %--------------------------------------
+        % Estimates a model for heterodestatic noise
+        generateNoiseModel(obj,varargin)
+        %--------------------------------------
         % gaCreationUniformFeasible
         function[Population]=gaCreationUniformFeasible(obj,GenomeLength,FitnessFcn,options)
             Population = bsxfun(@times,rand(sum(options.PopulationSize),GenomeLength),(obj.getUBBasisFctParameters - obj.getLBBasisFctParameters));
@@ -373,6 +381,10 @@
         % ----------------------------------------------------------------
         [realizationCurve,nonNormSampleLocations] = doConditionalSimulation_ResAlgInEq(obj,varargin)
         % ----------------------------------------------------------------
+        [realizationCurve,nonNormSampleLocations] = doConditionalSimulation_Decomposition(obj,varargin)
+        % ----------------------------------------------------------------
+        [realizationCurve,nonNormSampleLocations] = doConditionalSimulation_ResAlgInEqTest(obj,varargin)
+        % ----------------------------------------------------------------
         function [] = checkIfNumberOfParameterValuesIsCorrect(obj,varargin)
             if(length(varargin{1})>obj.nCovariogramParameters)
                 error('Covariogram needs only %i parameter but %i are defined!',obj.nCovariogramParameters,length(varargin{1}));
@@ -743,7 +755,18 @@
         function [CovariogramUsesAbsoluteDistance]=getCovariogramUsesAbsoluteDistance(obj)
             CovariogramUsesAbsoluteDistance = obj.CovariogramUsesAbsoluteDistance;
         end
-
+        %------------------------------------------------------------------
+        function [heterogeneousNoise]=getHeterogeneousNoise(obj)
+            heterogeneousNoise = obj.HeterogeneousNoise;
+        end
+        %------------------------------------------------------------------
+        function [KriKitObjNoise]=getKriKitObjNoise(obj)
+            KriKitObjNoise = obj.KriKitObjNoise;
+        end
+        %------------------------------------------------------------------
+        function [ThresholdSolver]=getThresholdSolver(obj)
+            ThresholdSolver = obj.ThresholdSolver;
+        end
         %% Set Functions
         function [] = setInputData(obj,Input)
             % Define the InputVariables the number of rows should be equal
@@ -771,6 +794,7 @@
             obj.InputData_True = Input;
             obj.nExperiments = size(Input,1);
             obj.nInputVar = size(Input,2);
+            obj.HeterogeneousNoise = [];
             if(obj.nInputVar>obj.nExperiments)
                 warning('InputData: More Input variables as Experiments');
             end
@@ -884,17 +908,8 @@
             
             switch obj.CovariogramModelChoice
                 case 1
-%                     if(length(varargin{1})>2)
-%                         warning('The exponential model needs only 2 parameter but %i are defined!',length(varargin{1}));
-%                     end
-%                     if(length(varargin{1})<2)
-%                         error('The exponential model needs 2 parameter but only %i are defined!',length(varargin{1}));
-%                     end
                     obj.theta       = varargin{1}(1);
                     obj.sigma       = varargin{1}(2);
-                    
-%                     obj.CovariogramUsesEuclideanDistance = true;
-%                     obj.p           = varargin{1}(3);
                 case {2,5,7}
                     obj.theta       = varargin{1}(1);
                     obj.sigma       = varargin{1}(2);
@@ -914,23 +929,17 @@
                     error('Non-acceptable model choice. The parameter CovariogramModelChoice = %i is not allowed',obj.CovariogramModelChoice);
             end
             
-%             switch obj.CovariogramModelChoice
-%                 case {1,2,5,7}
-%                     obj.CovariogramUsesEuclideanDistance = true;
-%                     obj.CovariogramUsesAbsoluteDistance = false;
-%                 case {3,4,6,8}
-%                     obj.CovariogramUsesAbsoluteDistance = true;
-%                     obj.CovariogramUsesEuclideanDistance = false;
-%                 otherwise
-%                     error('CovariogramModelChoice = %d is not defined',obj.CovariogramModelChoice)
-%             end
-            
-            try
-                obj.calcCovariogramMatrix();
-            catch exception
-                warning('Covariogram Matrix could be recalculated! Do it manually')
-                % Rethrow original error.
-                rethrow(exception)
+            if ~obj.UseMatlabRegressionGP
+                if isempty(obj.DistInput)
+                    obj.estimateVariance
+                end
+                try
+                    obj.calcCovariogramMatrix();
+                catch exception
+                    warning('Covariogram Matrix could be recalculated! Do it manually')
+                    % Rethrow original error.
+                    rethrow(exception)
+                end
             end
         end
         %------------------------------------------------------------------
@@ -991,7 +1000,7 @@
             % the first values
             obj.setCovariogramParameterBoundsAndValues(Model);
             
-            obj.estimateVariance
+%             obj.estimateVariance
         end
 
         %------------------------------------------------------------------
@@ -1002,7 +1011,7 @@
             % varargin = 3 ... exponential with separated thethas and ps (multigaussian) (sigmaError + sigma*exp(-sum_(h=1)^k(theta(h)*abs(x(i,h)-x(j,h))^p(h)))
             Model = varargin{1};
             minValue = 1e-10;
-            switch Model;
+            switch Model
                 case 1
                     obj.LBCovariogramModelParameters = [minValue,minValue];
                     obj.UBCovariogramModelParameters = [1e10,1e10];
@@ -1067,6 +1076,7 @@
             end
             
             % Decide for the distance calculation
+            covariogramUsesEuclideanDistanceCheck = obj.CovariogramUsesEuclideanDistance;
             switch obj.CovariogramModelChoice
                 case {1,2,5,7}
                     obj.CovariogramUsesEuclideanDistance = true;
@@ -1077,6 +1087,12 @@
                 otherwise
                     error('CovariogramModelChoice = %d is not defined',obj.CovariogramModelChoice)
             end
+            
+            if ~obj.UseMatlabRegressionGP
+                if isempty(obj.DistInput)||(covariogramUsesEuclideanDistanceCheck~=obj.CovariogramUsesEuclideanDistance)
+                    obj.estimateVariance
+                end
+            end
         end
         %------------------------------------------------------------------
         function []=setCR(obj,CR)
@@ -1116,6 +1132,9 @@
         end
         %------------------------------------------------------------------
         function []=setNormOutput(obj,NormOutput)
+%             if NormOutput
+%                 error('NormOutput cannot be used curretly otherwise problem with covariance calculation occur and consequently pridiction variance is not correct')
+%             end
             % Decide if the output shall be normalized(1) or not (0)
             if ~isempty(obj.OutputData)
                 warning('"OutputData" is not empty. Please define "OutputData" only after setting the "NormOutput"-option')
@@ -1402,6 +1421,44 @@
             
             obj.UseMatlabRegressionGP = UseMatlabRegressionGP;
         end
+        %------------------------------------------------------------------
+        function []=setHeterogeneousNoise(obj,heterogeneousNoise)
+            if (~(any(size(heterogeneousNoise)==obj.nExperiments)&&any(size(heterogeneousNoise)==1))&&~isempty(heterogeneousNoise))
+                error('heterogeneousNoise should be a vector nExperimentsX1(%iX1)',obj.nExperiments)
+            end
+            if any(size(heterogeneousNoise,1)==1)
+                heterogeneousNoise = heterogeneousNoise';
+            end
+            if isempty(obj.HeterogeneousNoise)
+                warning('"HeterogeneousNoise" has change, please run "calcCovariogramMatrix" again')
+            end
+            obj.HeterogeneousNoise = heterogeneousNoise;
+            
+        end
+        %------------------------------------------------------------------
+        function []=setKriKitObjNoise(obj,KriKitObjNoise)
+            if ~isempty(KriKitObjNoise)
+                heterogenousNoise = exp(KriKitObjNoise.prediction(obj.getInputData));
+                heterogenousNoise = sqrt(heterogenousNoise(:,1));
+            else
+                heterogenousNoise = [];
+            end
+            obj.setHeterogeneousNoise(heterogenousNoise)
+            obj.calcCovariogramMatrix
+            obj.KriKitObjNoise = KriKitObjNoise;
+        end
+        %------------------------------------------------------------------
+        function []=setThresholdSolver(obj,ThresholdSolver)
+            if length(ThresholdSolver)>1||~isdouble(ThresholdSolver)
+                error('ThresholdSolver has to be a scalar')
+            end
+            obj.ThresholdSolver = ThresholdSolver;
+        end
+        %------------------------------------------------------------------
+        function []=setDistInput(obj,DistInput)
+            obj.DistInput = DistInput;
+        end
+        
     end
     
 end
diff --git a/dependencies/@KrigingSuperClass/MarginalLikelihoodCovarPara.m b/dependencies/@KrigingSuperClass/MarginalLikelihoodCovarPara.m
index f9938b3..05708f4 100644
--- a/dependencies/@KrigingSuperClass/MarginalLikelihoodCovarPara.m
+++ b/dependencies/@KrigingSuperClass/MarginalLikelihoodCovarPara.m
@@ -28,13 +28,18 @@
 C = obj.CovariogramMatrix(1:obj.nExperiments,1:obj.nExperiments);
 detC = det(C);
 numericalLimit = realmin;
-if detC<=numericalLimit
+% if detC<=numericalLimit
+if isinf(detC)||detC==0
     detC = numericalLimit;
 end
+
+lC = lu(C);
+
 [basisFctMatrix] = estimatedBasisFunction();
 basisFctEval = basisFctMatrix'*obj.getBasisFctCoefficients;
 vecForMulti = (obj.OutputData-basisFctEval);
 negLog = -(-1/2*vecForMulti'*(C\vecForMulti) - 1/2*log(detC)-obj.nExperiments/2*log(2*pi));
+% negLog = -(-1/2*vecForMulti'*(C\vecForMulti) - sqrt(obj.nExperiments)*log(min(abs(diag(lC))))-obj.nExperiments/2*log(2*pi));
 
 %% Nested Function
 % -------------------------------------------------------------------------
diff --git a/dependencies/@KrigingSuperClass/calcCovarMatrixOfPredictions.m b/dependencies/@KrigingSuperClass/calcCovarMatrixOfPredictions.m
index 3f0ceb4..f225d3c 100644
--- a/dependencies/@KrigingSuperClass/calcCovarMatrixOfPredictions.m
+++ b/dependencies/@KrigingSuperClass/calcCovarMatrixOfPredictions.m
@@ -28,6 +28,7 @@
 end
 
 % covariance_zero = ones(nDataPoints,nDataPoints)*obj.CovarModel(zeros(1,size(obj.DistInput,2)),1);
+% obj.Weights = obj.Weights*(max(obj.getOutputData)-min(obj.getOutputData));
 
 % Calculate the Covariance Matrix parts 1:nOriginalDataPoints
 part1 = reshape(diag(obj.Weights(1:nOriginalDataPoints,nComb(:,1))'*obj.CovargramVectors(1:nOriginalDataPoints,nComb(:,2))),nDataPoints,nDataPoints);
@@ -93,7 +94,9 @@
 
 % Final Result
 obj.CovarMatrixOfPredictions = part4-part1-part2+part3;
-
+if obj.getNormOutput
+    obj.CovarMatrixOfPredictions = obj.CovarMatrixOfPredictions*(max(obj.getOutputData)-min(obj.getOutputData))^2;
+end
 
 % if obj.NormOutput
 %     obj.CovarMatrixOfPredictions = (obj.CovarMatrixOfPredictions-0)*(max(obj.getOutputData)-min(obj.getOutputData))^2;
diff --git a/dependencies/@KrigingSuperClass/calcCovariogramMatrix.m b/dependencies/@KrigingSuperClass/calcCovariogramMatrix.m
index 7fd9e4c..53ea0f3 100644
--- a/dependencies/@KrigingSuperClass/calcCovariogramMatrix.m
+++ b/dependencies/@KrigingSuperClass/calcCovariogramMatrix.m
@@ -65,7 +65,12 @@
     delta = reshape(delta,sqrt(size(delta,1)),sqrt(size(delta,1)));
 
     % Calculate the variogram values for theses distances
-    obj.Variogram(1:obj.nExperiments,1:obj.nExperiments) =  obj.CovarModel(zeros(1,size(obj.DistInput,2)),1) - obj.CovarModel(delta,0);
+%     if isempty(obj.HeterogeneousNoise)
+        obj.Variogram(1:obj.nExperiments,1:obj.nExperiments) =  obj.CovarModel(zeros(1,size(obj.DistInput,2)),1) - obj.CovarModel(delta,0);
+%     else
+%         obj.Variogram(1:obj.nExperiments,1:obj.nExperiments) =  obj.CovarModel(zeros(1,size(obj.DistInput,2)),0) - obj.CovarModel(delta,0);
+%         obj.Variogram(1:size(obj.Variogram,1)+1:size(obj.Variogram,1)*obj.nExperiments) = obj.HeterogeneousNoise.^2;
+%     end
 elseif obj.CovariogramUsesAbsoluteDistance
     % Calculate the variogram values for the input distances (for
     % each input variable). 
@@ -83,11 +88,23 @@
         end
     end
     delta = reshape(d,nRowsColumns,obj.nInputVar,nRowsColumns);
-    obj.Variogram(1:obj.nExperiments,1:obj.nExperiments) =  reshape(obj.CovarModel(zeros([1,size(obj.DistInput,2),1]),1) - obj.CovarModel(delta,0),nRowsColumns,nRowsColumns);
+    
+%     if isempty(obj.HeterogeneousNoise)
+        obj.Variogram(1:obj.nExperiments,1:obj.nExperiments) =  ...
+            reshape(obj.CovarModel(zeros([1,size(obj.DistInput,2),1]),1) - ...
+                    obj.CovarModel(delta,0),nRowsColumns,nRowsColumns);
+%     else
+%         obj.Variogram(1:obj.nExperiments,1:obj.nExperiments) =  ...
+%             reshape(obj.CovarModel(zeros([1,size(obj.DistInput,2),1]),0) - ...
+%                     obj.CovarModel(delta,0),nRowsColumns,nRowsColumns);
+%         % Add heterogenous noise further below
+%     end
 else
     error('Either"CovariogramUsesEuclideanDistance" or "CovariogramUsesAbsoluteDistance" have to be "true"')
 end
-
+% if ~isempty(obj.HeterogeneousNoise)
+%     obj.Variogram(1:size(obj.Variogram,1)+1:size(obj.Variogram,1)*obj.nExperiments) = obj.HeterogeneousNoise.^2;
+% end
     % Diagonal has to be 0
 obj.Variogram = obj.Variogram - bsxfun(@times,diag(obj.Variogram),eye(size(obj.Variogram,1)));
 
@@ -105,8 +122,28 @@
 
 % Convert to CavoriogramMatrix
 obj.CovariogramMatrix = obj.Variogram;
-covariance_zero  = ones(obj.nExperiments,obj.nExperiments)*obj.CovarModel(zeros(1,size(obj.DistInput,2)),1);
-obj.CovariogramMatrix(1:obj.nExperiments,1:obj.nExperiments) = covariance_zero-obj.Variogram(1:obj.nExperiments,1:obj.nExperiments);
+
+
+% if ~isempty(obj.HeterogeneousNoise)
+%     vecIndices = (1:obj.nExperiments)';
+%     vecIndices = (vecIndices-1)*length(obj.CovariogramMatrix)+vecIndices;
+%     
+%     covariance_zero  = ones(obj.nExperiments,obj.nExperiments)*obj.CovarModel(zeros(1,size(obj.DistInput,2)),0);
+%     obj.CovariogramMatrix(1:obj.nExperiments,1:obj.nExperiments) = covariance_zero-obj.Variogram(1:obj.nExperiments,1:obj.nExperiments);
+%     
+%     obj.CovariogramMatrix(vecIndices) = obj.CovariogramMatrix(vecIndices) + obj.HeterogeneousNoise.^2;
+% else
+    covariance_zero  = ones(obj.nExperiments,obj.nExperiments)*obj.CovarModel(zeros(1,size(obj.DistInput,2)),1);
+    obj.CovariogramMatrix(1:obj.nExperiments,1:obj.nExperiments) = covariance_zero-obj.Variogram(1:obj.nExperiments,1:obj.nExperiments);
+% end
+
+if ~isempty(obj.HeterogeneousNoise)
+    vecIndices = (1:obj.nExperiments)';
+    vecIndices = (vecIndices-1)*length(obj.CovariogramMatrix)+vecIndices;
+    
+    obj.Variogram(vecIndices) = obj.HeterogeneousNoise.^2;
+    obj.CovariogramMatrix(vecIndices) = obj.CovarModel(zeros(1,size(obj.DistInput,2)),0) + obj.HeterogeneousNoise.^2;
+end
 
 %% Calculate Inverse if needed
 % If the inverse is used for inv(A)*b instead of A\b
diff --git a/dependencies/@KrigingSuperClass/calcCrossOverQuality.m b/dependencies/@KrigingSuperClass/calcCrossOverQuality.m
index 2f0e2a9..04dc7cb 100644
--- a/dependencies/@KrigingSuperClass/calcCrossOverQuality.m
+++ b/dependencies/@KrigingSuperClass/calcCrossOverQuality.m
@@ -62,6 +62,7 @@
         % Set the defined model parameters
     obj.setCovariogramModelParameters(varargin{1});
     covariogramBackupMatrix = obj.getCovariogramMatrix;
+    heterogeneousNoiseBackUpt = obj.HeterogeneousNoise;
     UseGPRMatlabBackup = obj.getUseMatlabRegressionGP;
     obj.setUseMatlabRegressionGP(false)
     
@@ -78,102 +79,124 @@
     % erased and replaced by the mean values at this point
     uniqueInput = unique(obj.getInputData,'rows');
     
-    
-    for iExpOut = 1:size(uniqueInput,1)
-        
-        % Reset input and output data
-        InputData = InputBackup;
-        InputDataNorm = InputBackupNorm;
-        OutputData = OutputBackup;
-        OutputDataNorm = OutputBackupNorm;
-        
-        % Erase all rows and column which are associated with current
-        % data point
-            % Find all Experiments using the same input parameters
-        [~,binVector]=ismember(InputBackup,uniqueInput(iExpOut,:),'rows');
-        indexSameInput = find(binVector==1);
-            % Erase
-        InputData(indexSameInput,:)         = [];
-        InputDataNorm(indexSameInput,:)     = [];
-        OutputData(indexSameInput,:)        = [];
-        OutputDataNorm(indexSameInput,:)    = [];
-            % Replaceoriginal data set
-        setInputData(InputData,InputDataNorm);
-        setOutputData(OutputData,OutputDataNorm);
-        
-        % Update covariance matrix (erase lines which are associated with
-        % erased data set)
-        indicesKeep = setdiff(1:size(covariogramBackupMatrix,1),indexSameInput);
-        covariogramMatrix = covariogramBackupMatrix(indicesKeep,indicesKeep);
-        obj.CovariogramMatrix = covariogramMatrix;
-        
-        % Instead of calculating the inverse each time de novo, the old
-        % inverse is just updated (HAGER, 1989,'UPDATING THE INVERSE OF A
-        % MATRIX*'). Only possible in step for when no multiple
-        % measurements exists at the point of current investigation.
-        if length(indexSameInput)==1
-            obj.InvCovariogramMatrix=invupdatered(inverseBackUP,indexSameInput,indexSameInput);
-        else
-            obj.InvCovariogramMatrix = inv(covariogramMatrix);
+    protoVec = zeros(size(uniqueInput,1),1);
+    predVec = zeros(size(uniqueInput,1),1);
+    try
+        for iExpOut = 1:size(uniqueInput,1)
+
+            % Reset input and output data
+            InputData = InputBackup;
+            InputDataNorm = InputBackupNorm;
+            OutputData = OutputBackup;
+            OutputDataNorm = OutputBackupNorm;
+
+            % Erase all rows and column which are associated with current
+            % data point
+                % Find all Experiments using the same input parameters
+            [~,binVector]=ismember(InputBackup,uniqueInput(iExpOut,:),'rows');
+            indexSameInput = find(binVector==1);
+                % Erase
+            InputData(indexSameInput,:)         = [];
+            InputDataNorm(indexSameInput,:)     = [];
+            OutputData(indexSameInput,:)        = [];
+            OutputDataNorm(indexSameInput,:)    = [];
+                % Replaceoriginal data set
+            setInputData(InputData,InputDataNorm);
+            setOutputData(OutputData,OutputDataNorm);
+
+            % Update covariance matrix (erase lines which are associated with
+            % erased data set)
+            indicesKeep = setdiff(1:size(covariogramBackupMatrix,1),indexSameInput);
+            if ~isempty(obj.HeterogeneousNoise)
+                obj.HeterogeneousNoise = heterogeneousNoiseBackUpt(indicesKeep(1:end-obj.getnBasisFct));
+            end
+            covariogramMatrix = covariogramBackupMatrix(indicesKeep,indicesKeep);
+            obj.CovariogramMatrix = covariogramMatrix;
+            
+            % Instead of calculating the inverse each time de novo, the old
+            % inverse is just updated (HAGER, 1989,'UPDATING THE INVERSE OF A
+            % MATRIX*'). Only possible in step for when no multiple
+            % measurements exists at the point of current investigation.
+            if length(indexSameInput)==1
+                obj.InvCovariogramMatrix=invupdatered(inverseBackUP,indexSameInput,indexSameInput);
+            else
+                obj.InvCovariogramMatrix = inv(covariogramMatrix);
+            end
+
+            % Make the Kriging prediction
+            [output]=obj.prediction(uniqueInput(iExpOut,:));
+            expected = output(:,1);
+            sigma = output(:,2);
+
+            % Compare wiht the true value
+            switch obj.CrossValidationType
+                case {1,3}
+                    proto = ((mean(OutputBackup(indexSameInput))-expected)/sigma)^2;
+
+                    if isnan(proto)
+                        quality = realmax;
+                    else
+                        quality = quality + proto;
+                    end
+                case 2
+                    if isnan(expected)
+                        quality = realmax;
+                    else
+                        quality = quality + (mean(OutputBackup(indexSameInput))-expected)^2;
+                    end
+                case 4
+                    proto = ((mean(OutputBackup(indexSameInput))-expected)/sigma)^2;
+                    protoVec(iExpOut) = proto;
+                    predVec(iExpOut,1) = expected;
+                    predVec(iExpOut,2) = sigma;
+                    
+                    if isnan(proto)
+                        quality = realmax;
+                    else
+                        quality = quality + proto;
+                    end
+                        quality2 = quality2 + (mean(OutputBackup(indexSameInput))-expected)^2;
+                otherwise
+                    error('CrossValidationType: %d is not defined\n',obj.CrossValidationType)
+            end
         end
 
-        % Make the Kriging prediction
-        [output]=obj.prediction(uniqueInput(iExpOut,:));
-        expected = output(:,1);
-        sigma = output(:,2);
-        
-        % Compare wiht the true value
         switch obj.CrossValidationType
-            case {1,3}
-                proto = ((mean(OutputBackup(indexSameInput))-expected)/sigma)^2;
-
-                if isnan(proto)
-                    quality = realmax;
-                else
-                    quality = quality + proto;
-                end
-            case 2
-                if isnan(expected)
-                    quality = realmax;
-                else
-                    quality = quality + (mean(OutputBackup(indexSameInput))-expected)^2;
-                end
+            case 1
+                quality = abs(1-quality/size(uniqueInput,1));
             case 4
-                proto = ((mean(OutputBackup(indexSameInput))-expected)/sigma)^2;
-
-                if isnan(proto)
-                    quality = realmax;
-                else
-                    quality = quality + proto;
-                end
-                    quality2 = quality2 + (mean(OutputBackup(indexSameInput))-expected)^2;
+                quality = abs(1-quality/size(uniqueInput,1));
+        %             quality = quality*quality2;
+                quality = quality+quality2/size(uniqueInput,1)/max(OutputBackup.^2);
             otherwise
-                error('CrossValidationType: %d is not defined\n',obj.CrossValidationType)
+
         end
-    end
     
-    switch obj.CrossValidationType
-        case 1
-            quality = abs(1-quality/size(uniqueInput,1));
-        case 4
-            quality = abs(1-quality/size(uniqueInput,1));
-%             quality = quality*quality2;
-            quality = quality+quality2/size(uniqueInput,1)/max(OutputBackup.^2);
-        otherwise
-                
+        % Restore old values
+        obj.checkVariogram = 0;
+        obj.setInputData(InputBackup);
+        obj.setOutputData(OutputBackup);
+        obj.CovariogramMatrix = covariogramBackupMatrix;
+        obj.InvCovariogramMatrix = inverseBackUP;
+        obj.nExperiments = nExpBackup;
+        obj.UseInverse = UseInverseBackUP;
+        obj.setCovariogramModelParameters(covParaBackup);
+        obj.HeterogeneousNoise = heterogeneousNoiseBackUpt;
+    catch ex
+        warning(ex.message)
+        disp('Hello')
+        % Restore old values
+        obj.checkVariogram = 0;
+        obj.setInputData(InputBackup);
+        obj.setOutputData(OutputBackup);
+        obj.CovariogramMatrix = covariogramBackupMatrix;
+        obj.InvCovariogramMatrix = inverseBackUP;
+        obj.nExperiments = nExpBackup;
+        obj.UseInverse = UseInverseBackUP;
+        obj.setCovariogramModelParameters(covParaBackup);
+        obj.HeterogeneousNoise = heterogeneousNoiseBackUpt;
     end
-    
-    % Restore old values
-    obj.checkVariogram = 0;
-    obj.setInputData(InputBackup);
-    obj.setOutputData(OutputBackup);
-    obj.CovariogramMatrix = covariogramBackupMatrix;
-    obj.InvCovariogramMatrix = inverseBackUP;
-    obj.nExperiments = nExpBackup;
-    obj.UseInverse = UseInverseBackUP;
-    obj.setCovariogramModelParameters(covParaBackup);
-    obj.setUseMatlabRegressionGP(UseGPRMatlabBackup)
-    
+
     %% Set Functions
     % ---------------------------------------------------------------------
     function [] = setInputData(Input,InputNorm)
diff --git a/dependencies/@KrigingSuperClass/doConditionalSimulation_ResAlgInEq.m b/dependencies/@KrigingSuperClass/doConditionalSimulation_ResAlgInEq.m
index ea9f28a..46b7df6 100644
--- a/dependencies/@KrigingSuperClass/doConditionalSimulation_ResAlgInEq.m
+++ b/dependencies/@KrigingSuperClass/doConditionalSimulation_ResAlgInEq.m
@@ -12,8 +12,8 @@
 % This function uses "Kriging residual algorithm" (see chevalier2015 - Fast
 % update of conditional simulation ensembles):
 % 1. NonConditional Simulation: Generating realizations of a process with
-%    zero mean and in agreement with the Kriging Covarisance matrix
-%    z_noncond(x)~N(0,C(x)) 
+%    kriging mean and in agreement with the Kriging Covarisance matrix
+%    z_noncond(x)~N(m(x),C(x)) 
 % 2. Conditioning on the Data: z_cond(x) = m(x) + r(x)
 %       m(x) ... kriging estimation
 %       r(x) ... residual estimation: r(x) = z_noncond(x) -
@@ -75,15 +75,6 @@
     [sampleLocations,nSamplePoints,nRealization] = checkGivenInputRange;
 end
 
-% Save handle to inequality constraint function if given
-if length(varargin)==2
-    inEqConstraint = varargin{2};
-elseif length(varargin)==4
-    inEqConstraint = varargin{4};
-else
-    inEqConstraint = [];
-end
-
 % Normalize Input if necessary
 if obj.NormInput==1
     % Save original data for later
@@ -104,6 +95,7 @@
 % Backup in order to return to old state after conditional simulation
 maxSizeOfPredictionsBackup = obj.getMaxSizeOfPredictions;
 UseGPRMatlabBackup = obj.getUseMatlabRegressionGP;
+% HeterogeneousNoiseBackUp = obj.getHeterogeneousNoise;
 obj.setUseMatlabRegressionGP(false)
 
 % Allocate Memory: 
@@ -114,20 +106,27 @@
 
 % Make sure that no permanent parameter changes happens when porogram
 % crashes
-try
+% try
 %% Step 0: Generate normal Kriging estimation at simulation points and save associated kriging weights
 
     % Do predictions in one step
     obj.setMaxSizeOfPredictions(obj.getnExperiments+nSamplePoints)
 
-    % Actual Prediction
+    % Actual Prediction. Do not allow heterogenous noise here as it is not
+    % used for non-conditional simulation
     outputMatrix = obj.prediction(nonNormSampleLocations(:,1:obj.getnInputVar));
     
     % Save results
     krigingWeights = obj.getWeights;
     krigingWeights = krigingWeights(1:obj.getnExperiments,:);
     meanEstimation = outputMatrix(:,1);
-
+    if obj.NormOutput
+        meanEstimation = obj.scale(meanEstimation,...
+                                   min(obj.getOutputData),max(obj.getOutputData),...
+                                   0,1);
+    end
+%     stdEstimation = outputMatrix(:,2); 
+    
     % Intiital evaluation 
     processOld = 0;
 
@@ -145,77 +144,88 @@
 
     % Do Actual nonconditional simulation
     if allRealizationPointsAreTheSame
+        
         % Calculated Kriging covariance matrix w.r.t to all simulation
         % points
         columnSamplePoints = 1:1*obj.nInputVar;
         covMatrix = createCovMatrix(sampleLocations(:,columnSamplePoints));
         
-        % Covariance matrix has to be semipositive definite
-        if any(eig(covMatrix)<0)
-            eigBefore = eig(covMatrix);
-            indicesDiag = 1:nRowsColumns+1:nRowsColumns^2;
-            covMatrix(indicesDiag) = covMatrix(indicesDiag)+covMatrix(indicesDiag)*1e-10;
-            eigAfter = eig(covMatrix);
-            warning('covMatrix is not positive semidefinite (smallest eigenvalue is %g). \nAfter adding 1e-10 to diagonal, max Diff in eigenvalues is : %g\n',min(eigBefore),max(abs(eigBefore-eigAfter)))
-        end 
+        indices1 = 1:size(covMatrix,1)+1:size(covMatrix,1)*obj.nExperiments;
+        indices2 = indices1(end) + size(covMatrix,1)+1:size(covMatrix,1)+1:size(covMatrix,1)^2;
+
+        if (isempty(obj.KriKitObjNoise)||isempty(obj.KriKitObjNoise.getOutputData))
+            covMatrix([indices1,indices2]) = covMatrix([indices1,indices2]) + (obj.sigmaError).^2;    
+        else
+            predLogNoise = obj.KriKitObjNoise.prediction(nonNormSampleLocations(:,1:obj.getnInputVar));
+            covMatrix([indices1,indices2]) = bsxfun(@plus,covMatrix([indices1,indices2]), exp(predLogNoise(:,1))' );
+        end
         
         % Random drawing following the distribution ~N(0,covMatrix)
-        unconditionedReal = mvnrnd(zeros(nRealization,obj.getnExperiments+nSamplePoints),covMatrix);
+        % First nDataXnData part of the covMatrix has to contain the data
+        % points covariance matrix, where sigmaError is added to the
+        % diagonal entries (Noisy measurements)
+        [unconditionedReal] = generatedNonconditionalSImulation(covMatrix,nRealization);
     end
 
 %% Step 2: Conditional simulation
-    
-    % Has to be done individual for each realization
-    for iRealization = 1:nRealization
-        
-        % Do nonconditional simulation (Only needed when simulation point
-        % differ between realizations
-        columnSamplePoints = (iRealization-1)*obj.nInputVar+1:iRealization*obj.nInputVar;
-        if ~allRealizationPointsAreTheSame
+    if allRealizationPointsAreTheSame
+        randomResidual = zeros(nSamplePoints,nRealization);
+        randomResidual(:) = unconditionedReal(:,obj.getnExperiments+1:end)'- krigingWeights(:,obj.getnExperiments+1:end)'*unconditionedReal(:,1:obj.getnExperiments)';
+        realizationCurve = bsxfun(@plus,randomResidual,meanEstimation(obj.getnExperiments+1:end));
+    else
+
+        % Has to be done individual for each realization
+        for iRealization = 1:nRealization
+            % Do nonconditional simulation (Only needed when simulation point
+            % differ between realizations
+            columnSamplePoints = (iRealization-1)*obj.nInputVar+1:iRealization*obj.nInputVar;
+            
             % Actual Prediction
             outputMatrix = obj.prediction(nonNormSampleLocations(:,columnSamplePoints));
-            
-            
+
+
             % Save results
             krigingWeights = obj.getWeights;
             krigingWeights = krigingWeights(1:obj.getnExperiments,:);
             meanEstimation = outputMatrix(:,1);
             covMatrix = createCovMatrix(sampleLocations(:,columnSamplePoints));
-            unconditionedReal = mvnrnd(zeros(nRealization,obj.getnExperiments+nSamplePoints),covMatrix);
-        end
-        
-        % Do Conditional simulation constrainted by inEqConstraint
-        realizationFound = false;
-        while ~realizationFound
-            
-            % Calculate estimation modeling error
-            randomResidual(:) = unconditionedReal(iRealization,obj.getnExperiments+1:end)'- krigingWeights(:,obj.getnExperiments+1:end)'*unconditionedReal(iRealization,1:obj.getnExperiments)';
-            
-            % Final calculation of conditional simulation
-            realizationCurve(:,iRealization) = meanEstimation(obj.getnExperiments+1:end) + randomResidual + randn()*obj.sigmaError;
+            if ~isempty(obj.HeterogeneousNoise)
+                covMatrix(1:size(covMatrix,1)+1:size(covMatrix,1)*obj.nExperiments) = covMatrix(1:size(covMatrix,1)+1:size(covMatrix,1)*obj.nExperiments) + (obj.HeterogeneousNoise').^2;
+            else
+                covMatrix(1:size(covMatrix,1)+1:size(covMatrix,1)*obj.nExperiments) = covMatrix(1:size(covMatrix,1)+1:size(covMatrix,1)*obj.nExperiments) + (obj.sigmaError).^2;
+            end
+             
+            [unconditionedReal] = generatedNonconditionalSImulation(covMatrix,nRealization);
+
+            % Do Conditional simulation constrainted by inEqConstraint
+            realizationFound = false;
+            while ~realizationFound
 
+                % Calculate estimation modeling error
+                randomResidual(:) = unconditionedReal(iRealization,obj.getnExperiments+1:end)'- krigingWeights(:,obj.getnExperiments+1:end)'*unconditionedReal(iRealization,1:obj.getnExperiments)';
 
-            % Update Process bar if wanted
-            if obj.getShowWaitingBar
-                processNew = iRealization/nRealization;
-                if (processNew-processOld)>0.01
-                    waitbar(processNew,hWaitBar,sprintf('Process For Conditional Simulation: %f',processNew))
-    %                         toc
-                    processOld = processNew;
+                % Final calculation of conditional simulation
+                realizationCurve(:,iRealization) = meanEstimation(obj.getnExperiments+1:end) + randomResidual;
+
+                % Update Process bar if wanted
+                if obj.getShowWaitingBar
+                    processNew = iRealization/nRealization;
+                    if (processNew-processOld)>0.01
+                        waitbar(processNew,hWaitBar,sprintf('Process For Conditional Simulation: %f',processNew))
+                        processOld = processNew;
+                    end
                 end
             end
-            
-            % Probality of taking a realization ~(number points of violating
-            % conditions)/(total number of simulation points)
-            if ~isempty(inEqConstraint)
-                [unconditionedReal(iRealization,:),realizationFound] = checkIfInEqualityHolds(realizationCurve(:,iRealization),unconditionedReal(iRealization,:));
-            else
-                realizationFound = true;
-            end
+
+
         end
     end
-    
 %% Final things
+    if obj.NormOutput
+        realizationCurve = obj.scale(realizationCurve,0,1,...
+                                   min(obj.getOutputData),max(obj.getOutputData));
+    end
+    
     % Define output
     ReturnSamplePoints = nonNormSampleLocations(obj.nExperiments+1:end,:);
 
@@ -226,29 +236,43 @@
 
     obj.setUseMatlabRegressionGP(UseGPRMatlabBackup);
     obj.setMaxSizeOfPredictions(maxSizeOfPredictionsBackup);
-catch ex
-    error(ex.message)
-end
 
 %% Nested Functions 
-function [unconditionedReal,realizationFound] = checkIfInEqualityHolds(realizationCurve,unconditionedReal)
-    % Check if inequalities hold
-    randNumber = rand(1);
-
-    % Probality of taking a realization ~(number points of violating
-    % conditions)/(total number of simulation points)
-%     if randNumber>=(sum(~inEqConstraint(realizationCurve))/nSamplePoints)*(1/2*1/(1-0.9))
-    if randNumber>=(sum(~inEqConstraint(realizationCurve))/nSamplePoints)
-        realizationFound = true;
-    else
+% -------------------------------------------------------------------------
+function [unconditionedReal] = generatedNonconditionalSImulation(covMatrix,nRealization)
+    necessaryFeatures = {'Statistics_Toolbox'};
+    validLincence = cellfun(@(f) license('checkout',f),necessaryFeatures);
+    if validLincence
         try
-            unconditionedReal = mvnrnd(zeros(1,obj.getnExperiments+nSamplePoints),covMatrix);
+            unconditionedReal= mvnrnd(meanEstimation,covMatrix,nRealization);
         catch ex
-            warning(ex.message)
-            unconditionedReal = mvnrnd(zeros(1,obj.getnExperiments+nSamplePoints),covMatrix);
+            warning(ex.message);
+            covMatrix = correctCovMatrix(covMatrix);
+            unconditionedReal= mvnrnd(meanEstimation,covMatrix,nRealization);
         end
-        realizationFound = false;
+    else
+        [eigVector,eigValue]=eig(covMatrix);
+        if any(eigValue<0)
+            covMatrix = correctCovMatrix(covMatrix);
+            [eigVector,eigValue]=eig(covMatrix);
+        end
+        unconditionedReal = randn(nRealization,obj.getnExperiments+nSamplePoints);
+        unconditionedReal = eigVector*sqrt(eigValue)*eigVector'*unconditionedReal';
+        unconditionedReal =  bsxfun(@plus,unconditionedReal,meanEstimation);
+        unconditionedReal = unconditionedReal';
     end
+    
+end
+% -------------------------------------------------------------------------
+function [covMatrix] = correctCovMatrix(covMatrix)
+    % Covariance matrix has to be semipositive definite
+    eigBefore = eig(covMatrix);
+    nRowsColumns = size(covMatrix,1);
+    indicesDiag = 1:nRowsColumns+1:nRowsColumns^2;
+    covMatrix(indicesDiag) = covMatrix(indicesDiag)+covMatrix(indicesDiag)*1e-10;
+%             covMatrix(indicesDiag) = covMatrix(indicesDiag)+min(eigBefore);
+    eigAfter = eig(covMatrix);
+    warning('covMatrix is not positive semidefinite (smallest eigenvalue is %g). \nAfter adding 1e-10 to diagonal, max Diff in eigenvalues is : %g\n',min(eigBefore),max(abs(eigBefore-eigAfter)))
 end
 % -------------------------------------------------------------------------
 function [covMatrix] = createCovMatrix(sampleLocations)
diff --git a/dependencies/@KrigingSuperClass/estimateVariance.m b/dependencies/@KrigingSuperClass/estimateVariance.m
index da67d1e..1c6842e 100644
--- a/dependencies/@KrigingSuperClass/estimateVariance.m
+++ b/dependencies/@KrigingSuperClass/estimateVariance.m
@@ -50,11 +50,11 @@
 
         % Check
         expectedSize=(obj.nExperiments)*(obj.nExperiments-1)/2;
-        if size(obj.DistInput,1)~=expectedSize;
+        if size(obj.DistInput,1)~=expectedSize
             error('DistInput has dimension %ix%i but expected is 1x%i',size(obj.DistInput,1),size(obj.DistInput,2),expectedSize);
         end
 
-        if size(obj.VarianceEstimation,1)~=expectedSize;
+        if size(obj.VarianceEstimation,1)~=expectedSize
             error('VarianceEstimation has dimension %ix%i but expected is 1x%i',size(obj.VarianceEstimation,1),size(obj.VarianceEstimation,2),expectedSize);
         end
     elseif obj.CovariogramUsesAbsoluteDistance
@@ -65,7 +65,14 @@
         % one input variable between the difference measurements
 
         % Calculate for every combination of the experiments
-        indices = nchoosek(1:obj.nExperiments,2);
+        try
+            indices = VChooseK(1:obj.nExperiments,2);
+        catch ex
+            warning('VChooseK could not be used. Use Matlabs nchoosek insteads')
+            warning(ex.message);
+            indices = nchoosek(1:obj.nExperiments,2);
+        end
+        
         obj.DistInput =  abs(obj.InputData(indices(:,1),:)-obj.InputData(indices(:,2),:));
         obj.VarianceEstimation =  ((obj.OutputData(indices(:,1),:)-obj.OutputData(indices(:,2),:)).^2)/2;
     elseif obj.CovariogramUsesAbsoluteValues
diff --git a/dependencies/@KrigingSuperClass/generateNoiseModel.m b/dependencies/@KrigingSuperClass/generateNoiseModel.m
new file mode 100644
index 0000000..31ed399
--- /dev/null
+++ b/dependencies/@KrigingSuperClass/generateNoiseModel.m
@@ -0,0 +1,118 @@
+function [] = generateNoiseModel(obj,varargin)
+% [] = generateNoiseModel(sigmaError)
+%
+% This function generate a model for the output noise heteroscedastic. The
+% model is inheritly used for the actual prediction .
+%
+% Input: 
+% - sigmaError ... Initial measurement stadard deviation which is used for
+%                  the as homosedatic noise as starting point. If not
+%                  provided,sigmaError is set the value estimated by the
+%                  maximum likelihood estimator
+% 
+% Output:
+%
+% You can set: 
+% - ThresholdSolver ... optimization stops as soon the relative difference
+%                       (maxDiffPred) is lower than ThresholdSolver
+% - nIterationsSolver ... optimization stop if nIterationsSolver iterations
+%                         were run
+% - CovariogramModelChoice ... Covariogram which is used for the estimation
+%                              of the output noise
+%
+% You can get:
+%
+% Copyright 2014-2016: Lars Freier, Eric von Lieres
+% See the license note at the end of the file.
+
+% if obj.NormOutput
+%     error('Not yet implemented for normalized output')
+% end
+
+% Reset Noise Model
+obj.KriKitObjNoise = OrdinaryKriging;
+obj.KriKitObjNoise.setMaxSizeOfPredictions(obj.getMaxSizeOfPredictions)
+obj.KriKitObjNoise.setShowWaitingBar(false);
+UseMatlabRegressionGPBackUp = obj.UseMatlabRegressionGP;
+obj.UseMatlabRegressionGP = false;
+sigmaErrorBackUp = obj.sigmaError;
+if ~isempty(varargin)
+    obj.sigmaError = varargin{1};
+    obj.calcCovariogramMatrix
+end
+
+% Generate initial Noise set
+if obj.ShowDetails
+    fprintf('Generating Noise Model\n');
+end
+predictionData = obj.prediction(obj.getInputData);
+nSamples = 1e3;
+predictedSamples = randn(obj.nExperiments,nSamples);
+predictedSamples = bsxfun(@times,predictedSamples,predictionData(:,2));
+predictedSamples = bsxfun(@plus,predictedSamples,predictionData(:,1));
+arithmeticMean = bsxfun(@minus,predictedSamples,obj.getOutputData);
+varDelta = 1/(2*nSamples)*sum((arithmeticMean).^2,2);
+
+% Run Optimization
+for iIter = 1 : obj.nIterationsSolver
+
+    % Set Up Noise Model
+    obj.KriKitObjNoise.setInputData(obj.getInputData)
+    obj.KriKitObjNoise.setOutputData(log(varDelta))
+    obj.KriKitObjNoise.setCovariogramModelChoice(obj.CovariogramModelChoice);
+    obj.KriKitObjNoise.generateRegressionGPModel
+    predictionData2 = obj.prediction(obj.getInputData);
+    
+    % Estimate variation
+    nSamples = 1e3;
+    predictedSamples = randn(obj.nExperiments,1e3);
+    predictedSamples = bsxfun(@times,predictedSamples,predictionData(:,2));
+    predictedSamples = bsxfun(@plus,predictedSamples,predictionData(:,1));
+    arithmeticMean = bsxfun(@minus,predictedSamples,obj.getOutputData);
+    varDelta2 = 1/(2*nSamples)*sum((arithmeticMean).^2,2);
+    
+    % Check if converged
+    maxDiffPred = max(abs(predictionData(:,2)-predictionData2(:,2))./predictionData(:,2));
+    maxDiff = max(abs(sqrt(varDelta2(:,1))-sqrt(varDelta(:,1))));
+    if obj.ShowDetails
+        fprintf('iIter = %i - maxDiff = %e - maxDiffPred = %e \n',iIter,maxDiff,maxDiffPred);
+    end
+    if maxDiffPred x~N((y-a)*b+c,sigma^2*b^2)
             OutputTotal(:,1) = obj.scale(OutputTotal(:,1),0,1,...
                            min(obj.getOutputData),max(obj.getOutputData));
+            b = (max(obj.getOutputData)-min(obj.getOutputData))/(1-0);
+            OutputTotal(:,2) = OutputTotal(:,2)*b;
             % Do not scale standard deviation as for the calculation no
             % normalized output values are used!!!!!!!!!!!!!!!!!!
+            % f=(x-minX)./(maxX-minX).*(UB-LB)+LB;
         end
         
         return
@@ -164,12 +170,24 @@
     % composite design) 
 %     obj.CovargramVectors = CovVecTot;
     
+%     if obj.NormOutput
+%         % Normalization a = b*(max(out)-min(out))/(1-0) + min(out)
+%         OutputTotal(:,1) = obj.scale(OutputTotal(:,1),0,1,...
+%                        min(obj.getOutputData),max(obj.getOutputData));
+%         % Do not scale standard deviation as for the calculation no
+%         % normalized output values are used!!!!!!!!!!!!!!!!!!
+%     end
     if obj.NormOutput
-        % Normalization a = b*(max(out)-min(out))/(1-0) + min(out)
+        % Scale = (x-minOld)*((maxNew-minNew)/(maxOld-minOld)) + minNew
+        %       = (x-a)*b + c
+        % -> x~N((y-a)*b+c,sigma^2*b^2)
         OutputTotal(:,1) = obj.scale(OutputTotal(:,1),0,1,...
                        min(obj.getOutputData),max(obj.getOutputData));
+        b = (max(obj.getOutputData)-min(obj.getOutputData))/(1-0);
+        OutputTotal(:,2) = OutputTotal(:,2)*b;
         % Do not scale standard deviation as for the calculation no
         % normalized output values are used!!!!!!!!!!!!!!!!!!
+        % f=(x-minX)./(maxX-minX).*(UB-LB)+LB;
     end
 
 %% --------------------- Nested Functions ---------------------------------
@@ -304,7 +322,8 @@
         % Higher correlation coefficient when you consider exact the same
         % sample point -> usually higher covariance expressed by
         % obj.sigmaError
-        covariance_zero  = ones(size(obj.CovargramVectors,2),size(krigingWeights,2))*obj.CovarModel(zeros(1,size(obj.DistInput,2)),1);
+%         covariance_zero  = ones(size(obj.CovargramVectors,2),size(krigingWeights,2))*obj.CovarModel(zeros(1,size(obj.DistInput,2)),1);
+        covariance_zero  = ones(size(obj.CovargramVectors,2),size(krigingWeights,2))*obj.CovarModel(zeros(1,size(obj.DistInput,2)),0);
         
         % This formula is derive based on the derivatives Lagrange
         % Multiplier function with respect to the lagrange multiplier and
@@ -316,9 +335,27 @@
         % 1. 
 %         sigmaEstimation = covariance_zero-krigingWeights'*obj.CovargramVectors(1:size(krigingWeights,1),:);
         % 2.
+%         sigmaEstimation = obj.BasisFctCoefficients'basisFctEval +
+%                           obj.CovargramVectors(1:obj.nExperiments,:)'*...
+%                           inv(obj.CovariogramMatrix(1:obj.nExperiments,1:obj.nExperiments))*...
+%                           (obj.OutputData-obj.BasisFctCoefficients'basisFctEval)
+        % 3.
+        if isempty(obj.KriKitObjNoise)||isempty(obj.KriKitObjNoise.getOutputData) % Check if an initialized noise model exist
+            covariance_zero = covariance_zero + obj.sigmaError^2;
+        else
+%             errorProp = (sum(bsxfun(@times,obj.HeterogeneousNoise.^2,krigingWeights(1:obj.getnExperiments,:).^2)));
+%             errorProp = (sum(bsxfun(@times,obj.HeterogeneousNoise.^2,krigingWeights(1:obj.getnExperiments,:))));
+%             covariance_zero = bsxfun(@plus,covariance_zero,errorProp);
+            predLogNoise = obj.KriKitObjNoise.prediction(nonNormInput(:,:));
+            
+%             covariance_zero = covariance_zero + exp(predLogNoise(:,1));
+%             covariance_zero = bsxfun(@plus,covariance_zero, exp(predLogNoise(:,1)).*(max(obj.getOutputData)-min(obj.getOutputData)).^2 );
+            covariance_zero = bsxfun(@plus,covariance_zero, exp(predLogNoise(:,1)) );
+        end
+        
         sigmaEstimation = covariance_zero - 2*krigingWeights'*obj.CovargramVectors(1:size(krigingWeights,1),:) +...
-                          krigingWeights'*obj.CovariogramMatrix(1:size(krigingWeights,1),1:size(krigingWeights,1))*krigingWeights;
-
+                              krigingWeights'*obj.CovariogramMatrix(1:size(krigingWeights,1),1:size(krigingWeights,1))*krigingWeights;
+        
         % Only the diagonal are the number we are looking for
         sigmaEstimation = diag(sigmaEstimation);
         sigmaEstimation = (sigmaEstimation).^(1/2);
diff --git a/dependencies/@TaylorKriging/TaylorKriging.m b/dependencies/@TaylorKriging/TaylorKriging.m
index d78293b..9867d1d 100644
--- a/dependencies/@TaylorKriging/TaylorKriging.m
+++ b/dependencies/@TaylorKriging/TaylorKriging.m
@@ -36,7 +36,8 @@
         % Determine the threshold in the auxiliary condition of
         % sum(Cinv(i,:).^2)>=ThresholdCondition
         ThresholdCondition = 0;
-        
+        %
+        nMultiStartPoints = 10;
         
         InvCoVar = [];
         
@@ -67,8 +68,11 @@
 
         %% ParameterEstimation
 %         [] = estimateBasisFunctionParametersViaKriging(obj)
+        % -----------------------------------------------------------------
         [] = estimateBasisFctParametersViaTaylorKriging(obj)
         % -----------------------------------------------------------------
+        [varargout] = estimateBasisFctParametersFsolve(obj)
+        % -----------------------------------------------------------------
         [] = estimateBasisFctParametersFminCon(obj)
         % -----------------------------------------------------------------
         [] = estimateBasisFctParametersFminUnc(obj)
@@ -211,6 +215,10 @@
             obj.SaveItermediateResults = SaveItermediateResults;
         end
         % -----------------------------------------------------------------
+        function []=setInvCoVar(obj,InvCoVar)
+            obj.InvCoVar = InvCoVar;
+        end
+        % -----------------------------------------------------------------
         function []=setTaylorExpansionOrder(obj,TaylorExpansionOrder)
             obj.TaylorExpansionOrder = TaylorExpansionOrder;
         end
diff --git a/dependencies/@TaylorKriging/calcKrigingParaEstimationObjFctVector.m b/dependencies/@TaylorKriging/calcKrigingParaEstimationObjFctVector.m
index d08f5a1..ae66c76 100644
--- a/dependencies/@TaylorKriging/calcKrigingParaEstimationObjFctVector.m
+++ b/dependencies/@TaylorKriging/calcKrigingParaEstimationObjFctVector.m
@@ -9,7 +9,6 @@
     if isempty(obj.CovariogramMatrix)
         obj.calcCovariogramMatrix
     end
-    quality = 0;
 
     if length(para)~=obj.getnBasisFctParameters
         warning('Number of given parameters (%i) and the needed number of parameters (%i) differes from each other',length(para),obj.getnBasisFctParameters)
@@ -18,32 +17,24 @@
         error('Not enough derivatives are provided')
     end
     C = obj.CovariogramMatrix(1:obj.getnExperiments,1:obj.getnExperiments);
-    minC=min(min(abs(C)));
-    C = C/minC; % Normalization for reducing numerical problems
+%     minC=min(min(abs(C)));
+%     C = C/minC; % Normalization for reducing numerical problems
     
     F = zeros(obj.getnExperiments,1+obj.nBasisFctDerivative);
     
         % Basis Function
-    F(:,1) = obj.getBasisFct{1}(para,obj.getInputData);
+    F(:,1) = obj.getBasisFct{1}(para,obj.InputData);
         % Derivatives
     for iDerivative=1:obj.nBasisFctDerivative
-        F(:,iDerivative+1) = obj.getBasisFctDerivative{iDerivative}(para,obj.getInputData);
+        F(:,iDerivative+1) = obj.getBasisFctDerivative{iDerivative}(para,obj.InputData);
     end
     
     if isempty(obj.getInvCoVar)
         obj.InvCoVar = inv(C);
     end
 
-%     matrixAlmostEye = eye(size(F,2));
-%     matrixAlmostEye(1) = 0;
-%     b=F'*(obj.InvCoVar*(obj.getOutputData));
-%     M=F'*(obj.InvCoVar*F);
-%     quality = matrixAlmostEye*inv(M)*b;
-
-    saveCoeff = (F'*(obj.InvCoVar*F))\(F'*(obj.InvCoVar*(obj.getOutputData)));
-%     saveCoeff = (F'*eye(obj.nExperiments)*F)\(F'*(eye(obj.nExperiments)*(obj.getOutputData)));
-%     a= F'*(obj.InvCoVar*(obj.getOutputData));
-%     quality2 = a-a'*[1;zeros(length(saveCoeff)-1,1)];
+%     obj.InvCoVar(1:5,1:5)
+    saveCoeff = (F'*(obj.InvCoVar*F))\(F'*(obj.InvCoVar*(obj.OutputData)));
     quality = saveCoeff(2:end);
     
 end
diff --git a/dependencies/@TaylorKriging/estimateBasisFctParametersFsolve.m b/dependencies/@TaylorKriging/estimateBasisFctParametersFsolve.m
new file mode 100644
index 0000000..8137154
--- /dev/null
+++ b/dependencies/@TaylorKriging/estimateBasisFctParametersFsolve.m
@@ -0,0 +1,68 @@
+function [varargout] = estimateBasisFctParametersFsolve(obj)
+%
+%
+% You can set:
+%  - nMultiStartPoints .... number points for applying fsolve
+%  - LBBasisFctParameters/UBBasisFctParameters
+% 
+% Copyright 2014-2016: Lars Freier, Eric von Lieres
+% See the license note at the end of the file.
+    
+    if obj.NormOutput||obj.NormInput
+        error('estimateBasisFctParametersFsolve cannot be used for normalized input or/and output')
+    end
+    %% Initialization
+    options = optimoptions('fsolve');
+    options.Display='off';
+%     fTestFct=obj.KrigingObj;
+    if isempty(obj.getLBBasisFctParameters)||isempty(obj.getUBBasisFctParameters)
+        error('LBBasisFctParameters and UBBasisFctParameters have to be defined')
+    else
+        Pstart = createNDGRID(obj.getLBBasisFctParameters,obj.getUBBasisFctParameters,obj.nMultiStartPoints);
+    end
+    
+    % Allocating Memory
+    Pend = zeros((obj.nMultiStartPoints)^2,obj.getnBasisFctParameters);
+    qualityMeasure = ones((obj.nMultiStartPoints)^obj.getnBasisFctParameters,1);
+    testVec = ones((obj.nMultiStartPoints)^obj.getnBasisFctParameters,obj.getnBasisFctParameters);
+    
+    %% Actual Estimation
+    for iStart = 1:(obj.nMultiStartPoints)^2
+        Pend(iStart,:) = fsolve(@obj.calcKrigingParaEstimationObjFctVector,Pstart(iStart,:),options);
+        qualityMeasure(iStart) = sum(abs(obj.calcKrigingParaEstimationObjFctVector(Pend(iStart,:))));
+        testVec(iStart,:) =          abs(obj.calcKrigingParaEstimationObjFctVector(Pend(iStart,:)))';
+    end
+    
+    %   Consider only feasible results
+    idxLBValid = all(bsxfun(@ge,Pend,obj.getLBBasisFctParameters),2);
+    idxUBValid = all(bsxfun(@le,Pend,obj.getUBBasisFctParameters),2);
+    idxValid = find(idxLBValid&idxUBValid);
+    [~,idxMin]=min(sum(testVec(idxValid,:),2));
+    minQualValid = idxValid(idxMin);
+    pOpt = Pend(minQualValid,:);
+    
+    %% Save Results
+    if ~isempty(pOpt)
+        varargout{1} = pOpt;
+        varargout{2} = sum((obj.calcKrigingParaEstimationObjFctVector(pOpt)).^2);
+    else
+        varargout{1} = zeros(1,2);
+        varargout{2} = inf;
+        warning('no feasible parameter set')
+    end
+    
+    obj.setBasisFctParameters(varargout{1});
+end
+
+% =============================================================================
+%  KriKit - Kriging toolKit
+%  
+%  Copyright 2014-2016: Lars Freier(1), Eric von Lieres(1)
+%                                      
+%    (1)Forschungszentrum Juelich GmbH, IBG-1, Juelich, Germany.
+%  
+%  All rights reserved. This program and the accompanying materials
+%  are made available under the terms of the GNU Public License v3.0 (or, at
+%  your option, any later version) which accompanies this distribution, and
+%  is available at http://www.gnu.org/licenses/gpl.html
+% =============================================================================
diff --git a/dependencies/auxiliaryFunctions/GridTools/createNDGRID.m b/dependencies/auxiliaryFunctions/GridTools/createNDGRID.m
index e52578c..b23cefa 100644
--- a/dependencies/auxiliaryFunctions/GridTools/createNDGRID.m
+++ b/dependencies/auxiliaryFunctions/GridTools/createNDGRID.m
@@ -1,24 +1,43 @@
-function [X] = createNDGRID(LB,UB,nbinsEachDimension)
-% [X] = createNDGRID(LB,UB,nbinsEachDimension)
+function [X] = createNDGRID(varargin)
+% [X] = createNDGRID(LB,UB,nbinsEachDimension,extraPoints)
+    LB = varargin{1};
+    UB = varargin{2};
+    nbinsEachDimension = varargin{3};
+    if length(varargin)>3
+        extraPoints = varargin{4};
+    else
+        extraPoints = [];
+    end
+
 
     % MakeGrid
     nVariables = length(UB);
     XLin = linspaceNDim(LB,UB,nbinsEachDimension)';
+    XLin = ([XLin;extraPoints]);
     
     if nVariables>1
         X = [];
         for iX = 1:nVariables-1
+            % It is important to chck uniqueness if extra points are
+            % applied
             if iX==1
-                [x1,xi] = ndgrid(XLin(:,iX),XLin(:,iX+1));
+                X1 = unique(XLin(:,iX));
+                X2 = unique(XLin(:,iX+1));
+                
+                [x1,xi] = ndgrid(X1,X2);
                 X = [x1(:),xi(:)];
             else
-                [x1,xi] = ndgrid(X(:,1),XLin(:,iX+1));
-                X = [repmat(X,nbinsEachDimension,1),xi(:)];
+                X1 = (X(:,1));
+                X2 = unique(XLin(:,iX+1));
+                
+                [x1,xi] = ndgrid(X1,X2);
+                X = [repmat(X,size(xi,2),1),xi(:)];
             end
 
         end
     else
         X = XLin';
     end
+    
 end
 
diff --git a/dependencies/auxiliaryFunctions/HV_mod/calcEHIV_2D.m b/dependencies/auxiliaryFunctions/HV_mod/calcEHIV_2D.m
index fa2c6fb..2dd6684 100644
--- a/dependencies/auxiliaryFunctions/HV_mod/calcEHIV_2D.m
+++ b/dependencies/auxiliaryFunctions/HV_mod/calcEHIV_2D.m
@@ -15,127 +15,38 @@
 % Output:
 % - EHIV ... expected hypervolume improvement
 %
-% This function is inspired by the code of Michael Emmerich and Andre
-% Deutz, LIACS, Leiden University, 2010 
-% Based on the paper:
-% M. Emmerich, A.H. Deutz, J.W. Klinkenberg: The computation of the
-% expected improvement in dominated hypervolume of Pareto front
-% approximations , LIACS TR-4-2008, Leiden University, The Netherlands
-% http://www.liacs.nl/~emmerich/TR-ExI.pdf
+% This function is inspired by the publication of Michael Emmerich et Al:
+%
+% M. Emmerich, K. Yang, A. Deutz, H. Wang, and C. M. Fonseca, “A
+% multicriteria generalization of Bayesian global optimization,” in
+% Advances in Stochastic and Deterministic Global Optimization (P. M.
+% Pardalos, A. Zhigljavsky, and J. Žilinskas, eds.), vol. 107 of Springer
+% Optimization and Its Applications, Springer International Publishing,
+% 2016. In press.
 %
 % Copyright 2014-2015: Lars Freier, Eric von Lieres
 % See the license note at the end of the file.
 
+
+%% New Code Vectorized
+% timeVec = tic;
 % Make sure that only pareto optimal points are provided
 paretoPoints = determineParetoSet_Mex(paretoPoints);
-sortByEachDimension = sort(paretoPoints);
-
-nSamples=size(paretoPoints,1);
-nObj = size(paretoPoints,2);
-nCellsPriori = (nSamples+1)^nObj;
-nCells = (nSamples+1)*(nSamples+2)/2;
-
-% For better clarity: sort every variable with increasing value
-valuesCell = cell(nObj,1);
-for iObj=1:nObj
-    valuesCell{iObj} = sortByEachDimension(:,iObj);
-end
-
-% Define grid which represent the vertices of eahc considered rectangle
-[i1,i2] = ndgrid(0:nSamples,0:nSamples);
-indexMatrix =[i1(:),i2(:)];
-
-% Samples in the recangle with these lower bounds to not lead to any
-% contribution to the EIHV as anypoint in these areas are dominated by
-% current pareto front
-doNotUse = indexMatrix(:,1)>(nSamples-indexMatrix(:,2)); 
-indexMatrix(doNotUse,:)=[];
-
-% For more clarity, save lower and upper bounds of each reactangle
-Cl_Matrix = zeros(nCells,nObj);
-Cu_Matrix = zeros(nCells,nObj);
-dominateSamplesCell = cell(nSamples-1,nObj);
-
-for iIndex=0:nSamples
-    for iObj=1:nObj
-        if iIndex==0
-            Cl_Matrix(indexMatrix(:,iObj)==0,iObj) = -inf;
-        else
-            Cl_Matrix(indexMatrix(:,iObj)==iIndex,iObj) = valuesCell{iObj}(iIndex);
-        end
-    end
-end
-
-for iIndex=0:nSamples
-    for iObj=1:nObj
-        if iIndex==nSamples
-            Cu_Matrix(indexMatrix(:,iObj)==iIndex,iObj) = referencePoint(iObj);
-        else
-            Cu_Matrix(indexMatrix(:,iObj)==iIndex,iObj) = valuesCell{iObj}(iIndex+1);
-        end
-    end
-end
-
-% Also save values which are dominated by each sample points in each
-% direction
-for iIndex=0:nSamples-1
-    for iObj=1:nObj
-        dominateSamplesCell{iIndex+1,iObj} = paretoPoints(:,iObj)>=valuesCell{iObj}(iIndex+1);
-    end
-end
-
-% Find reference points fmaxMatrix for each considered rectangle
-fmaxMatrix = bsxfun(@times,ones(nCells,nObj),referencePoint);
-for iCell=1:nCells
-    for iObj=1:nObj
-        if iObj==1
-            indexChosenParetoPoint = paretoPoints(:,2)==Cl_Matrix(iCell,2);
-        else
-            indexChosenParetoPoint = paretoPoints(:,1)==Cl_Matrix(iCell,1);
-        end
-        if sum(indexChosenParetoPoint)==0
-            fmaxMatrix(iCell,iObj) = referencePoint(iObj);
-        else
-            fmaxMatrix(iCell,iObj) = min(paretoPoints(indexChosenParetoPoint,iObj));
-        end
-    end
-end
-
-sampleIsDominated = false(nSamples,1);
-sPlus = zeros(nCells,1);
-
-
-for iCell=1:nCells
-        % Identify sample points which are dominated by upper corner of
-        % current cell
-        if indexMatrix(iCell,1)==nSamples||indexMatrix(iCell,2)==nSamples
-            sampleIsDominated = false(nSamples,1);
-        else
-            sampleIsDominated = dominateSamplesCell{indexMatrix(iCell,1)+1,1}&dominateSamplesCell{indexMatrix(iCell,2)+1,2};
-        end
-        
-
-        if sum(sampleIsDominated)==0
-            sPlus(iCell)=0;
-        else
-            sPlus(iCell) = Hypervolume_MEX(paretoPoints(sampleIsDominated,:),fmaxMatrix(iCell,:));
-        end
-end
-
-%Marginal integration 
-Psi = marginalEI(fmaxMatrix,Cu_Matrix,mu,sd) - marginalEI(fmaxMatrix,Cl_Matrix,mu,sd);
-
-%Cumulative Gaussian over length for correction constant
-Cu_Norm = bsxfun(@rdivide,bsxfun(@minus,Cu_Matrix,mu),sd);
-Cl_Norm = bsxfun(@rdivide,bsxfun(@minus,Cl_Matrix,mu),sd);
-
-GaussCDF = gausscdf(Cu_Norm) - gausscdf(Cl_Norm);
-%ExI Kontribution fuer die aktuelle Zelle
-cellEI = prod(Psi,2)-sPlus.*prod(GaussCDF,2);
+paretoPointsSort = sortrows(paretoPoints);
+paretoPointsSort = [-inf,referencePoint(2);paretoPointsSort;referencePoint(1),-inf];
+paretoPointsSort = paretoPointsSort(end:-1:1,:);
 
+part1 = (paretoPointsSort(1:end-2,1)-paretoPointsSort(2:end-1,1)).*...
+         gausscdf((paretoPointsSort(2:end-1,1)-mu(1))/(sd(1))).*...
+         gaussEI(mu(2),sd(2),paretoPointsSort(2:end-1,2),paretoPointsSort(2:end-1,2));
+part1 = [part1;0];
 
-EHIV=sum(sum(cellEI));
+part2 = (gaussEI(mu(1),sd(1),paretoPointsSort(1:end-1,1),paretoPointsSort(1:end-1,1))-...
+             gaussEI(mu(1),sd(1),paretoPointsSort(1:end-1,1),paretoPointsSort(2:end,1))).*...
+            gaussEI(mu(2),sd(2),paretoPointsSort(2:end,2),paretoPointsSort(2:end,2));
+EHIV = sum(part1+part2);
 
+% fprintf('EHIV: %g - time: %g\n',EHIV,toc(timeVec))
 % =============================================================================
 %  KriKit - Kriging toolKit
 %  
diff --git a/dependencies/auxiliaryFunctions/HV_mod/calcEHIV_2D_2016.m b/dependencies/auxiliaryFunctions/HV_mod/calcEHIV_2D_2016.m
new file mode 100644
index 0000000..fa60ea7
--- /dev/null
+++ b/dependencies/auxiliaryFunctions/HV_mod/calcEHIV_2D_2016.m
@@ -0,0 +1,211 @@
+function EHIV=calcEHIV_2D_2016(paretoPoints,referencePoint,mu,sd)
+% EHIV=calcEHIV_2D(paretoPoints,referencePoint,mu,sd)
+% 
+% This function calculate the expected hypervolume improvement of a Kriging
+% model based on data point with the Pareto front given in paretoPoints.
+%
+% Input:
+% - paretoPoints ... contains the values of the Pareto front
+%                    (nParetoPointsX2) 
+% - referencePoint ... Reference point which is used for the hyper volume
+%                      calculation. (1X2)
+% - mu ... contains the Kriging prediction of the point of interest (1X2)
+% - sd ... contains the Kriging prediction error (1X2)
+%
+% Output:
+% - EHIV ... expected hypervolume improvement
+%
+% This function is inspired by the code of Michael Emmerich and Andre
+% Deutz, LIACS, Leiden University, 2010 
+% Based on the paper:
+% M. Emmerich, A.H. Deutz, J.W. Klinkenberg: The computation of the
+% expected improvement in dominated hypervolume of Pareto front
+% approximations , LIACS TR-4-2008, Leiden University, The Netherlands
+% http://www.liacs.nl/~emmerich/TR-ExI.pdf
+%
+% Copyright 2014-2015: Lars Freier, Eric von Lieres
+% See the license note at the end of the file.
+
+
+%% New Code
+timeNonVec = tic;
+% Make sure that only pareto optimal points are provided
+paretoPoints = determineParetoSet_Mex(paretoPoints);
+paretoPointsSort = sortrows(paretoPoints);
+paretoPointsSort = [-inf,referencePoint(2);paretoPointsSort;referencePoint(1),-inf];
+paretoPointsSort = paretoPointsSort(end:-1:1,:);
+
+nSamples=size(paretoPoints,1);
+nObj = size(paretoPoints,2);
+
+
+EHIV = 0;
+for iSection = 2:(nSamples+2)
+    if iSection<(nSamples+2)
+        part1 = (paretoPointsSort(iSection-1,1)-paretoPointsSort(iSection,1)).*...
+                 gausscdf((paretoPointsSort(iSection)-mu(1))/(sd(1))).*...
+                 gaussEI(mu(2),sd(2),paretoPointsSort(iSection,2),paretoPointsSort(iSection,2));
+    else
+        part1 = 0;
+    end
+         
+    part2 = (gaussEI(mu(1),sd(1),paretoPointsSort(iSection-1,1),paretoPointsSort(iSection-1,1))-...
+             gaussEI(mu(1),sd(1),paretoPointsSort(iSection-1,1),paretoPointsSort(iSection,1))).*...
+            gaussEI(mu(2),sd(2),paretoPointsSort(iSection,2),paretoPointsSort(iSection,2));
+        
+    EHIV = EHIV + part1 + part2;
+    
+    fprintf('iSection: %i - x1(-1): %g - x1: %g - x2: %g - EHIV: %g - part1: %g - part2: %g\n',...
+             iSection-1,paretoPointsSort(iSection-1,1),paretoPointsSort(iSection,1),paretoPointsSort(iSection,2),...
+             EHIV,part1,part2)
+    
+end
+fprintf('EHIV: %g - time: %g\n',EHIV,toc(timeNonVec))
+
+%% New Code Vectorized
+timeVec = tic;
+% Make sure that only pareto optimal points are provided
+paretoPoints = determineParetoSet_Mex(paretoPoints);
+paretoPointsSort = sortrows(paretoPoints);
+paretoPointsSort = [-inf,referencePoint(2);paretoPointsSort;referencePoint(1),-inf];
+paretoPointsSort = paretoPointsSort(end:-1:1,:);
+
+part1 = (paretoPointsSort(1:end-2,1)-paretoPointsSort(2:end-1,1)).*...
+         gausscdf((paretoPointsSort(2:end-1,1)-mu(1))/(sd(1))).*...
+         gaussEI(mu(2),sd(2),paretoPointsSort(2:end-1,2),paretoPointsSort(2:end-1,2));
+part1 = [part1;0];
+
+part2 = (gaussEI(mu(1),sd(1),paretoPointsSort(1:end-1,1),paretoPointsSort(1:end-1,1))-...
+             gaussEI(mu(1),sd(1),paretoPointsSort(1:end-1,1),paretoPointsSort(2:end,1))).*...
+            gaussEI(mu(2),sd(2),paretoPointsSort(2:end,2),paretoPointsSort(2:end,2));
+EHIV = sum(part1+part2);
+
+fprintf('EHIV: %g - time: %g\n',EHIV,toc(timeVec))
+
+%% Old Code
+timeOld = tic;
+
+% Make sure that only pareto optimal points are provided
+paretoPoints = determineParetoSet_Mex(paretoPoints);
+sortByEachDimension = sort(paretoPoints);
+
+nSamples=size(paretoPoints,1);
+nObj = size(paretoPoints,2);
+nCellsPriori = (nSamples+1)^nObj;
+nCells = (nSamples+1)*(nSamples+2)/2;
+
+% For better clarity: sort every variable with increasing value
+valuesCell = cell(nObj,1);
+for iObj=1:nObj
+    valuesCell{iObj} = sortByEachDimension(:,iObj);
+end
+
+% Define grid which represent the vertices of eahc considered rectangle
+[i1,i2] = ndgrid(0:nSamples,0:nSamples);
+indexMatrix =[i1(:),i2(:)];
+
+% Samples in the recangle with these lower bounds to not lead to any
+% contribution to the EIHV as anypoint in these areas are dominated by
+% current pareto front
+doNotUse = indexMatrix(:,1)>(nSamples-indexMatrix(:,2)); 
+indexMatrix(doNotUse,:)=[];
+
+% For more clarity, save lower and upper bounds of each reactangle
+Cl_Matrix = zeros(nCells,nObj);
+Cu_Matrix = zeros(nCells,nObj);
+dominateSamplesCell = cell(nSamples-1,nObj);
+
+for iIndex=0:nSamples
+    for iObj=1:nObj
+        if iIndex==0
+            Cl_Matrix(indexMatrix(:,iObj)==0,iObj) = -inf;
+        else
+            Cl_Matrix(indexMatrix(:,iObj)==iIndex,iObj) = valuesCell{iObj}(iIndex);
+        end
+    end
+end
+
+for iIndex=0:nSamples
+    for iObj=1:nObj
+        if iIndex==nSamples
+            Cu_Matrix(indexMatrix(:,iObj)==iIndex,iObj) = referencePoint(iObj);
+        else
+            Cu_Matrix(indexMatrix(:,iObj)==iIndex,iObj) = valuesCell{iObj}(iIndex+1);
+        end
+    end
+end
+
+% Also save values which are dominated by each sample points in each
+% direction
+for iIndex=0:nSamples-1
+    for iObj=1:nObj
+        dominateSamplesCell{iIndex+1,iObj} = paretoPoints(:,iObj)>=valuesCell{iObj}(iIndex+1);
+    end
+end
+
+% Find reference points fmaxMatrix for each considered rectangle
+fmaxMatrix = bsxfun(@times,ones(nCells,nObj),referencePoint);
+for iCell=1:nCells
+    for iObj=1:nObj
+        if iObj==1
+            indexChosenParetoPoint = paretoPoints(:,2)==Cl_Matrix(iCell,2);
+        else
+            indexChosenParetoPoint = paretoPoints(:,1)==Cl_Matrix(iCell,1);
+        end
+        if ~any(indexChosenParetoPoint)
+            fmaxMatrix(iCell,iObj) = referencePoint(iObj);
+        else
+            fmaxMatrix(iCell,iObj) = min(paretoPoints(indexChosenParetoPoint,iObj));
+        end
+    end
+end
+
+% sampleIsDominated = false(nSamples,1);
+sPlus = zeros(nCells,1);
+
+
+for iCell=1:nCells
+        % Identify sample points which are dominated by upper corner of
+        % current cell
+        if indexMatrix(iCell,1)==nSamples||indexMatrix(iCell,2)==nSamples
+            sampleIsDominated = false(nSamples,1);
+        else
+            sampleIsDominated = dominateSamplesCell{indexMatrix(iCell,1)+1,1}&dominateSamplesCell{indexMatrix(iCell,2)+1,2};
+        end
+        
+
+        if ~any(sampleIsDominated)
+            sPlus(iCell)=0;
+        else
+            sPlus(iCell) = Hypervolume_MEX(paretoPoints(sampleIsDominated,:),fmaxMatrix(iCell,:));
+        end
+end
+
+%Marginal integration 
+Psi = marginalEI(fmaxMatrix,Cu_Matrix,mu,sd) - marginalEI(fmaxMatrix,Cl_Matrix,mu,sd);
+
+%Cumulative Gaussian over length for correction constant
+Cu_Norm = bsxfun(@rdivide,bsxfun(@minus,Cu_Matrix,mu),sd);
+Cl_Norm = bsxfun(@rdivide,bsxfun(@minus,Cl_Matrix,mu),sd);
+
+GaussCDF = gausscdf(Cu_Norm) - gausscdf(Cl_Norm);
+%ExI Kontribution fuer die aktuelle Zelle
+cellEI = prod(Psi,2)-sPlus.*prod(GaussCDF,2);
+
+
+EHIV=sum(sum(cellEI));
+
+fprintf('EHIV: %g - time: %g\n',EHIV,toc(timeOld))
+
+% =============================================================================
+%  KriKit - Kriging toolKit
+%  
+%  Copyright 2014-2015: Lars Freier(1), Eric von Lieres(1)
+%                                      
+%    (1)Forschungszentrum Juelich GmbH, IBG-1, Juelich, Germany.
+%  
+%  All rights reserved. This program and the accompanying materials
+%  are made available under the terms of the GNU Public License v3.0 (or, at
+%  your option, any later version) which accompanies this distribution, and
+%  is available at http://www.gnu.org/licenses/gpl.html
+% =============================================================================
\ No newline at end of file
diff --git a/dependencies/auxiliaryFunctions/HV_mod/calcEHIV_3D_2016.m b/dependencies/auxiliaryFunctions/HV_mod/calcEHIV_3D_2016.m
new file mode 100644
index 0000000..3d95a34
--- /dev/null
+++ b/dependencies/auxiliaryFunctions/HV_mod/calcEHIV_3D_2016.m
@@ -0,0 +1,89 @@
+function EHVI_3D=calcEHIV_3D_2016(paretoPoints,referencePoint,mu,sd)
+% EHIV=calcEHIV_2D(paretoPoints,referencePoint,mu,sd)
+% 
+% This function calculate the expected hypervolume improvement of a Kriging
+% model based on data point with the Pareto front given in paretoPoints.
+%
+% Input:
+% - paretoPoints ... contains the values of the Pareto front
+%                    (nParetoPointsX2) 
+% - referencePoint ... Reference point which is used for the hyper volume
+%                      calculation. (1X2)
+% - mu ... contains the Kriging prediction of the point of interest (1X2)
+% - sd ... contains the Kriging prediction error (1X2)
+%
+% Output:
+% - EHIV ... expected hypervolume improvement
+%
+% This function is inspired by the code of Michael Emmerich and Andre
+% Deutz, LIACS, Leiden University, 2010 
+% Based on the paper:
+% M. Emmerich, A.H. Deutz, J.W. Klinkenberg: The computation of the
+% expected improvement in dominated hypervolume of Pareto front
+% approximations , LIACS TR-4-2008, Leiden University, The Netherlands
+% http://www.liacs.nl/~emmerich/TR-ExI.pdf
+%
+% Copyright 2014-2015: Lars Freier, Eric von Lieres
+% See the license note at the end of the file.
+
+
+%% New Code Vectorized
+% timeVec = tic;
+% Make sure that only pareto optimal points are provided
+paretoPoints = determineParetoSet_Mex(paretoPoints);
+% Use last variable for sorting (easier for interpretation)
+paretoPointsSort = sortrows(paretoPoints,3);
+% Add Point. Needed for proper calculation
+
+% paretoPointsSort = [-inf,-inf,referencePoint(3);paretoPointsSort;referencePoint(1),referencePoint(2),-inf];
+paretoPointsSort = [-inf,-inf,paretoPointsSort(1,3);paretoPointsSort;referencePoint(1),referencePoint(2),referencePoint(2)];
+% Sort with descend order
+% paretoPointsSort = [referencePoint(1),referencePoint(2),-inf;paretoPointsSort;-inf,-inf,referencePoint(3)];
+% paretoPointsSort = paretoPointsSort(end:-1:1,:);
+nPoints = size(paretoPoints,1);
+
+ParetoFront2D = referencePoint(1:2);
+EHVI_3D = 0;
+for iSection = 2:(nPoints+2)
+    % Update 2D Pareto Curve
+    if iSection>2
+        ParetoFront2D = determineParetoSet_Mex(paretoPointsSort(2:iSection-1,1:2));
+    end
+    
+    EHVI_2D = calcEHIV_2D(ParetoFront2D,referencePoint(1:2),mu(1:2),sd(1:2));
+    
+%     if iSection<(nPoints+2)
+    if iSection<(nPoints+2)
+        part1 = (paretoPointsSort(iSection,3)-paretoPointsSort(iSection-1,3)).*...
+                 gausscdf((paretoPointsSort(iSection,3)-mu(3))/(sd(3))).*...
+                 EHVI_2D;
+    else
+        part1 = 0;
+    end
+    
+    if iSection>2
+        part2 = (gaussEI(mu(3),sd(3),paretoPointsSort(iSection,3),paretoPointsSort(iSection,3))-...
+                 gaussEI(mu(3),sd(3),paretoPointsSort(iSection,3),paretoPointsSort(iSection-1,3))).*...
+                 EHVI_2D;
+    else
+        part2 =  gaussEI(mu(3),sd(3),paretoPointsSort(iSection,3),paretoPointsSort(iSection,3)).*...
+                 EHVI_2D;
+    end
+    
+    EHVI_3D = EHVI_3D + part1 + part2;
+end
+
+
+% fprintf('EHIV: %g - time: %g\n',EHIV,toc(timeVec))
+% =============================================================================
+%  KriKit - Kriging toolKit
+%  
+%  Copyright 2014-2015: Lars Freier(1), Eric von Lieres(1)
+%                                      
+%    (1)Forschungszentrum Juelich GmbH, IBG-1, Juelich, Germany.
+%  
+%  All rights reserved. This program and the accompanying materials
+%  are made available under the terms of the GNU Public License v3.0 (or, at
+%  your option, any later version) which accompanies this distribution, and
+%  is available at http://www.gnu.org/licenses/gpl.html
+% =============================================================================
\ No newline at end of file
diff --git a/dependencies/auxiliaryFunctions/HV_mod/gaussEI.m b/dependencies/auxiliaryFunctions/HV_mod/gaussEI.m
new file mode 100644
index 0000000..20f897d
--- /dev/null
+++ b/dependencies/auxiliaryFunctions/HV_mod/gaussEI.m
@@ -0,0 +1,4 @@
+function normEI=gaussEI(mu,sigma,lb,ub)
+    normVar = (ub-mu)./sigma;
+    normEI = sigma.*gausspdf(normVar) + (lb-mu).*gausscdf(normVar);
+end
\ No newline at end of file
diff --git a/dependencies/auxiliaryFunctions/HV_mod/gausscdf.m b/dependencies/auxiliaryFunctions/HV_mod/gausscdf.m
index 0a5a817..aaf5921 100644
--- a/dependencies/auxiliaryFunctions/HV_mod/gausscdf.m
+++ b/dependencies/auxiliaryFunctions/HV_mod/gausscdf.m
@@ -1,2 +1,3 @@
 function x=gausscdf(x)
-  x=0.5*(1+erf(x/sqrt(2)));
\ No newline at end of file
+  x=0.5*(1+erf(x/sqrt(2)));
+end
\ No newline at end of file
diff --git a/dependencies/auxiliaryFunctions/HV_mod/gausspdf.m b/dependencies/auxiliaryFunctions/HV_mod/gausspdf.m
index e4abbed..e6fed64 100644
--- a/dependencies/auxiliaryFunctions/HV_mod/gausspdf.m
+++ b/dependencies/auxiliaryFunctions/HV_mod/gausspdf.m
@@ -1,2 +1,3 @@
 function res=gausspdf(x)
-    res=1/sqrt(2*pi)*exp(-x.^2/2);
\ No newline at end of file
+    res=1/sqrt(2*pi)*exp(-x.^2/2);
+end
\ No newline at end of file
diff --git a/dependencies/auxiliaryFunctions/MatrixInversion/invupdateapp.m b/dependencies/auxiliaryFunctions/MatrixInversion/invupdateapp.m
index 81fdfc1..d6b0cf1 100644
--- a/dependencies/auxiliaryFunctions/MatrixInversion/invupdateapp.m
+++ b/dependencies/auxiliaryFunctions/MatrixInversion/invupdateapp.m
@@ -31,7 +31,7 @@
 %
 
 % Input checks.
-error(nargchk(4,4,nargin))
+% error(nargchk(4,4,nargin))
 [n,m] = size(A);
 if n~=m
     error('A should be square.');
diff --git a/dependencies/auxiliaryFunctions/externalTools/mcmcstat/boxplotMCMCStat.m b/dependencies/auxiliaryFunctions/externalTools/mcmcstat/boxplotMCMCStat.m
index af257a9..b269366 100644
--- a/dependencies/auxiliaryFunctions/externalTools/mcmcstat/boxplotMCMCStat.m
+++ b/dependencies/auxiliaryFunctions/externalTools/mcmcstat/boxplotMCMCStat.m
@@ -1,4 +1,4 @@
-function h=boxplot(y,names,outliers)
+function h=boxplotMCMCStat(y,names,outliers)
 %BOXPLOT Draws a boxplot
 % boxplot(y)  - draws boxplots for the columns of y
 
diff --git a/dependencies/auxiliaryFunctions/mexFiles/VChooseK/CompilingCommand.txt b/dependencies/auxiliaryFunctions/mexFiles/VChooseK/CompilingCommand.txt
new file mode 100644
index 0000000..386c0ce
--- /dev/null
+++ b/dependencies/auxiliaryFunctions/mexFiles/VChooseK/CompilingCommand.txt
@@ -0,0 +1 @@
+ mex -I/vol/home/yangk/local/MATLAB/R2014b/extern/include -O VChooseKUncommented.c
\ No newline at end of file
diff --git a/dependencies/auxiliaryFunctions/mexFiles/VChooseK/VChooseK.c b/dependencies/auxiliaryFunctions/mexFiles/VChooseK/VChooseK.c
new file mode 100644
index 0000000..2432e57
--- /dev/null
+++ b/dependencies/auxiliaryFunctions/mexFiles/VChooseK/VChooseK.c
@@ -0,0 +1,1114 @@
+// VChooseK.c
+// VCHOOSEK - Combinations of K elements [MEX]
+// VCHOOSEK(V, K) creates a matrix, which rows are all combinations of
+// choosing K elements of the vector V without order and without repititions.
+//
+// INPUT:
+//   V: Array of class DOUBLE, SINGLE, (U)INT8/16/32/64, LOGICAL, CHAR.
+//      The shape of the array does not matter.
+//   K: Number of elements to choose. Scalar double with integer number.
+//
+// OUTPUT:
+//   Y: Matrix of size [N!/K!(N-K)!, K] with N is the number of elements of V.
+//      Y has the same class as the input V. If V has less than K elements, a
+//      warning appears and the empty matrix is replied. As usual in Matlab,
+//      empty inputs yield to empty Y.
+//      A warning appears, if the output would exceed 500MB, and an error for
+//      1GB. Both limits can be adjusted according to the available RAM in the
+//      C-Mex source.
+//
+// NOTE: The output equals Matlab's NCHOOSEK, except that NCHOOSEK replies the
+// number of combinations for a scalar V:
+//   VChooseK(-1, 1) replies [-1]: One element taken out of a set of length one.
+//   NCHOOSEK(-1, 1) fails at calculating N!/K!(N-K)!.
+//
+// EXAMPLES:
+//   Choose 2 elements from [1,2,3,4]:
+//     VChooseK(1:4, 2)  % ==> [1,2; 1,3; 1,4; 2,3; 2,4; 3,4]
+//   For speed cast the input to integer types if possible:
+//     Y = double(VChooseK(int16(1:1000), 2);
+//   is faster than:
+//     Y = VChooseK(1:1000, 2);
+//   To get the combinations of cell arrays, use the combinations of the index:
+//     C  = {'a', 'b', 'c', 'd'};
+//     C2 = C(VChooseK(1:4, 2))
+//     ==> C2 = {'a', 'b'; 'a', 'c'; 'a', 'd'; 'b', 'c'; 'b', 'd'; 'c', 'd'}
+//
+// COMPILE:
+//   mex -O VChooseK.c
+// Compatibility to 64-bit machines is assumed, but cannot be tested currently.
+// On Linux the C99 comments must be considered (thanks Sebastiaan Breedveld):
+//   mex CFLAGS="\$CFLAGS -std=C99" -O VChooseK.c
+// Please run the unit-test TestVChooseK after compiling!
+//
+// INSPIRATION:
+// Jos van der Geest has published an efficient NCHOOSE2, which is much faster
+// than Matlab's NCHOOSEK:
+//   http://www.mathworks.com/matlabcentral/fileexchange/20144
+// I was curious, if a MEX version is much faster. At first I've created
+// NChoose2q, which is 5 times faster than Jos' Matlab implementation. But the
+// algorithm was so simple in C and did not consume temporary memory, that I
+// expanded it to K=3,4,5 soon. The algorithm for free K was more challenging,
+// but it needs just 3*K pointers for temporary memory. Therefore it is much
+// faster than Matlab's NCHOOSEK (Matlab 6.5, 1.5GHz Pentium-M, 512 MB):
+//                    NCHOOSEK:   VCHOOSEK:
+//   (1:50, 5):       45.30 sec   0.32 sec      => 141 times faster
+//   (1:20, 16):       0.52 sec   0.0024 sec    => 216 times faster
+//   (int8(1:40), 4):  0.64 sec   0.001562 sec  => 410 times faster
+//
+// Related FEX related publications:
+// (http://www.mathworks.com/matlabcentral/fileexchange/)
+//   NCTWO (Simone Scaringi): 20110
+//   COMBINATOR (Matt Fig) [I prefer this for general tasks!]: 24325
+//   PICK (Stefan Stoll) calls NCHOOSEK for unordered combinations: 12724
+//   COMBINATIONS (Gautam Vallabha): 23080
+//
+// Tested: Matlab 6.5, 7.7, 7.8, WinXP, [UnitTest]
+//         Compilers: BCC5.5, LCC2.4/3.8, Open Watcom 1.8
+//         Watcom lib is 20% faster than BCC and LCC libs for INT8!
+// Author: Jan Simon, Heidelberg, (C) 2009 matlab.THIS_YEAR(a)nMINUSsimonDOTde
+// License: BSD (use/copy/modify on own risk, but mention author)
+
+/*
+% $JRev: R0j V:007 Sum:5IALpldTXov9 Date:24-Dec-2008 02:59:35 $
+% $File: User\JSim\Published\VChooseK\VChooseK.c $
+% History:
+% 001: 17-Dec-2009 08:10, MEX function for NCHOOSEK(X, 2).
+% 007: 23-Dec-2008 17:40, Stop if output size exceed some limits.
+%      Calculate output size as mwSize and DOUBLE to catch overflows.
+*/
+
+#include "mex.h"
+#include 
+
+// 32 bit array dimensions for Matlab 6.5:
+// See option "compatibleArrayDims" for MEX in Matlab >= 7.7.
+#ifndef mwSize
+#define mwSize  int
+#define mwIndex int
+#define MWSIZE_MAX 2147483647UL
+#endif
+
+// Limits for warning and error:
+#define WARN_LIMIT   500000000L   // 500 MB, *must* be smaller than ERROR_LIMIT
+#define ERROR_LIMIT 1000000000L   // 1 GB (recommended for 32&64 bit systems)
+
+// Prototypes:
+mwSize GetOutputLen(mwSize n, mwSize k, double *C_double);
+void BadInputTypeError(void);
+
+// The functions for each number of elements are absolutely equal except for
+// the type of the array. But using some macros would result in ugly source
+// code also. Using pointers to vectors of variable size would work, but is
+// remarkably slower. Sorry for the pile of redundant code!
+void Elem2_1Byte(INT8_T  *Xp, mwSize nX, INT8_T  *Yp, mwSize nY);
+void Elem2_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY);
+void Elem2_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY);
+void Elem2_8Byte(double  *Xp, mwSize nX, double  *Yp, mwSize nY);
+
+void Elem3_1Byte(INT8_T  *Xp, mwSize nX, INT8_T  *Yp, mwSize nY);
+void Elem3_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY);
+void Elem3_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY);
+void Elem3_8Byte(double  *Xp, mwSize nX, double  *Yp, mwSize nY);
+
+void Elem4_1Byte(INT8_T  *Xp, mwSize nX, INT8_T  *Yp, mwSize nY);
+void Elem4_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY);
+void Elem4_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY);
+void Elem4_8Byte(double  *Xp, mwSize nX, double  *Yp, mwSize nY);
+
+void Elem5_1Byte(INT8_T  *Xp, mwSize nX, INT8_T  *Yp, mwSize nY);
+void Elem5_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY);
+void Elem5_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY);
+void Elem5_8Byte(double  *Xp, mwSize nX, double  *Yp, mwSize nY);
+
+void ElemK_1Byte(INT8_T  *Xp, mwSize nX, INT8_T  *Yp, mwSize nY, mwSize K);
+void ElemK_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY, mwSize K);
+void ElemK_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY, mwSize K);
+void ElemK_8Byte(double  *Xp, mwSize nX, double  *Yp, mwSize nY, mwSize K);
+
+// Main function ===============================================================
+void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
+{
+  mwSize nX, nY, K;
+  const mxArray *X;
+  double nY_double;
+  int ElementSize;
+  
+  // Check number of inputs and outputs:
+  if (nrhs != 2) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:BadNInput", "2 inputs required.");
+  }
+  if (nlhs > 1) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:BadNInput", "1 output allowed.");
+  }
+  
+  // Check type of input:
+  X = prhs[0];
+  if (!mxIsNumeric(X) && !mxIsChar(X) & !mxIsLogical(X)) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:BadNInput",
+                       "Input array must be numerical, CHAR or LOGICAL.");
+  }
+  if (!mxIsDouble(prhs[1]) || mxGetNumberOfElements(prhs[1]) != 1) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:BadNInput",
+                       "Input K must be a numerical scalar.");
+  }
+  
+  // Get number of elements to choose and catch pathological cases:
+  nX = mxGetNumberOfElements(X);
+  K  = (mwSize) floor(mxGetScalar(prhs[1]) + 0.5);  // Rounding
+  
+  // Empty input => empty output:
+  if (nX == 0) {
+     plhs[0] = mxCreateNumericMatrix(0, 0, mxGetClassID(X), mxREAL);
+     return;
+  }
+  
+  // K == 1 => reply input as column vector, K == 0 => empty output:
+  if (K <= 1) {
+     if (K == 1) {  // Choose 1 element => output equals input:
+        plhs[0] = mxDuplicateArray(X);
+        mxSetM(plhs[0], nX);
+        mxSetN(plhs[0], 1);
+     } else if (K <= 0) {  // Empty matrix:
+       plhs[0] = mxCreateNumericMatrix(0, 0, mxGetClassID(X), mxREAL);
+     }
+     return;
+  }
+  
+  // Catch trivial K == nX and pathological K > nX:
+  if (K >= nX) {
+     if (K == nX) {
+        plhs[0] = mxDuplicateArray(X);
+        mxSetM(plhs[0], 1);
+        mxSetN(plhs[0], nX);
+     } else {
+        mexWarnMsgIdAndTxt("JSimon:VChooseK:ShortInput",
+                           "Too short input: K > N.");
+        plhs[0] = mxCreateNumericMatrix(0, 0, mxGetClassID(X), mxREAL);
+     }
+     return;
+  }
+  
+  // Calculate size of output (N!/(K!*(N-K)!)) and check limits - reduce
+  // the risk of overflows, nY is calculated as DOUBLE additionally:
+  nY          = GetOutputLen(nX, K, &nY_double);
+  ElementSize = mxGetElementSize(X);
+  nY_double  *= ElementSize;
+  if (nY_double > WARN_LIMIT) {
+     if (nY_double < ERROR_LIMIT && nY_double < MWSIZE_MAX) {
+        mexWarnMsgIdAndTxt("JSimon:VChooseK:LargeOutput",
+                           "Output will be large and take a long time.");
+     } else {
+        mexErrMsgIdAndTxt("JSimon:VChooseK:TooLargeOutput",
+                          "Output would be too large.");
+     }
+  }
+  
+  // Create the output:
+  // In a MEX file this stops with an error message automatically on failure.
+  // For usage in an engine file, check result for NULL!
+  plhs[0] = mxCreateNumericMatrix(nY, K, mxGetClassID(X), mxREAL);
+  
+  // Call different core functions according to the element size:
+  switch (K) {
+    case 2:  // K == 2:
+      switch (ElementSize) {
+        case 8:  Elem2_8Byte(mxGetPr(X), nX, mxGetPr(plhs[0]), nY);
+                 break;
+        case 4:  Elem2_4Byte((INT32_T *) mxGetData(X), nX,
+                             (INT32_T *) mxGetData(plhs[0]), nY);
+                 break;
+        case 2:  Elem2_2Byte((INT16_T *) mxGetData(X), nX,
+                             (INT16_T *) mxGetData(plhs[0]), nY);
+                 break;
+        case 1:  Elem2_1Byte((INT8_T *) mxGetData(X), nX,
+                             (INT8_T *) mxGetData(plhs[0]), nY);
+                 break;
+        default: BadInputTypeError();
+      }
+      break;
+        
+    case 3:  // K == 3:
+      switch (ElementSize) {
+        case 8:  Elem3_8Byte(mxGetPr(X), nX, mxGetPr(plhs[0]), nY);
+                 break;
+        case 4:  Elem3_4Byte((INT32_T *) mxGetData(X), nX,
+                             (INT32_T *) mxGetData(plhs[0]), nY);
+                 break;
+        case 2:  Elem3_2Byte((INT16_T *) mxGetData(X), nX,
+                             (INT16_T *) mxGetData(plhs[0]), nY);
+                 break;
+        case 1:  Elem3_1Byte((INT8_T *) mxGetData(X), nX,
+                             (INT8_T *) mxGetData(plhs[0]), nY);
+                 break;
+        default: BadInputTypeError();
+      }
+      break;
+        
+    case 4:  // K == 4:
+      switch (ElementSize) {
+        case 8:  Elem4_8Byte(mxGetPr(X), nX, mxGetPr(plhs[0]), nY);
+                 break;
+        case 4:  Elem4_4Byte((INT32_T *) mxGetData(X), nX,
+                             (INT32_T *) mxGetData(plhs[0]), nY);
+                 break;
+        case 2:  Elem4_2Byte((INT16_T *) mxGetData(X), nX,
+                             (INT16_T *) mxGetData(plhs[0]), nY);
+                 break;
+        case 1:  Elem4_1Byte((INT8_T *) mxGetData(X), nX,
+                             (INT8_T *) mxGetData(plhs[0]), nY);
+                 break;
+        default: BadInputTypeError();
+      }
+      break;
+      
+    case 5:  // K == 5:
+      switch (ElementSize) {
+        case 8:  Elem5_8Byte(mxGetPr(X), nX, mxGetPr(plhs[0]), nY);
+                 break;
+        case 4:  Elem5_4Byte((INT32_T *) mxGetData(X), nX,
+                             (INT32_T *) mxGetData(plhs[0]), nY);
+                 break;
+        case 2:  Elem5_2Byte((INT16_T *) mxGetData(X), nX,
+                             (INT16_T *) mxGetData(plhs[0]), nY);
+                 break;
+        case 1:  Elem5_1Byte((INT8_T *) mxGetData(X), nX,
+                             (INT8_T *) mxGetData(plhs[0]), nY);
+                 break;
+        default: BadInputTypeError();
+      }
+      break;
+
+    default:  // K > 5
+      switch (ElementSize) {
+        case 8:  ElemK_8Byte(mxGetPr(X), nX, mxGetPr(plhs[0]), nY, K);
+                 break;
+        case 4:  ElemK_4Byte((INT32_T *) mxGetData(X), nX,
+                             (INT32_T *) mxGetData(plhs[0]), nY, K);
+                 break;
+        case 2:  ElemK_2Byte((INT16_T *) mxGetData(X), nX,
+                             (INT16_T *) mxGetData(plhs[0]), nY, K);
+                 break;
+        case 1:  ElemK_1Byte((INT8_T *) mxGetData(X), nX,
+                             (INT8_T *) mxGetData(plhs[0]), nY, K);
+                 break;
+        default: BadInputTypeError();
+      }
+  }
+  
+  return;
+}
+
+// Error about bad input type: =================================================
+void BadInputTypeError(void)
+{
+  mexErrMsgIdAndTxt("JSimon:VChooseK:BadInputType",
+                    "Input must have 1, 2, 4 or 8 bytes per element.");
+}
+
+// Get output size: ============================================================
+mwSize GetOutputLen(mwSize n, mwSize k, double *C_double)
+{
+  // Number of samples for taking k elements from a set of n.
+  // Calculate the exact value as INT32 (or INT64 on 64 bit machine) and the
+  // eventually less precise DOUBLE value, which is less susceptible for an
+  // overflow.
+  mwSize i, ai, bi;
+  double ad, bd;
+  
+  bi = n - k;
+  ai = bi + 1;
+  ad = (double) ai;
+  bd = (double) bi;
+  for (i = 2; i <= k; i++) {
+     ai += (ai * bi) / i;
+     ad += (ad * bd) / i;
+  }
+  
+  *C_double = ad;
+  
+  return (ai);
+}
+
+// 2 elements: =================================================================
+void Elem2_8Byte(double *Xp, mwSize nX, double *Yp, mwSize nY)
+{
+  // Called for input data with 8 bytes per element: DOUBLE, (U)INT64.
+  // 2 elements - fill 2 columns of output simultaneously.
+  double *Xq, *Xf, *Y1, *Y2;
+  
+  Y1 = Yp;             // 1st column of output
+  Y2 = Y1 + nY;        // 2nd column of output
+  for (Xf = Xp + nX - 1; Xp < Xf; Xp++) {
+    for (Xq = Xp + 1; Xq <= Xf; Xq++) {  // With repititions: Xq = Xp;
+      *Y1++ = *Xp;     // 1st column: slow changing
+      *Y2++ = *Xq;     // 2nd column: fast changing
+    }
+  }
+
+  return;
+}
+
+// =============================================================================
+void Elem2_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY)
+{
+  // Called for input data with 4 bytes per element: SINGLE, (U)INT32.
+  // 2 elements - fill 2 columns of output simultaneously.
+  INT32_T *Xq, *Xf, *Y1, *Y2;
+
+  Y1 = Yp;             // 1st column of output
+  Y2 = Y1 + nY;        // 2nd column of output
+  for (Xf = Xp + nX - 1; Xp < Xf; Xp++) {
+    for (Xq = Xp + 1; Xq <= Xf; Xq++) {
+      *Y1++ = *Xp;     // 1st column: slow changing
+      *Y2++ = *Xq;     // 2nd column: fast changing
+    }
+  }
+
+  return;
+}
+
+// =============================================================================
+void Elem2_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY)
+{
+  // Called for input data with 2 bytes per element: (U)INT16, CHAR.
+  // 2 elements - fill 2 columns of output simultaneously.
+  INT16_T *Xq, *Xf, *Y1, *Y2;
+
+  Y1 = Yp;             // 1st column of output
+  Y2 = Y1 + nY;        // 2nd column of output
+  for (Xf = Xp + nX - 1; Xp < Xf; Xp++) {
+    for (Xq = Xp + 1; Xq <= Xf; Xq++) {
+      *Y1++ = *Xp;     // 1st column: slow changing
+      *Y2++ = *Xq;     // 2nd column: fast changing
+    }
+  }
+
+  return;
+}
+
+// =============================================================================
+void Elem2_1Byte(INT8_T *Xp, mwSize nX, INT8_T *Yp, mwSize nY)
+{
+  // Called for input data with 1 byte per element: (U)INT8, LOGICAL.
+  // 2 elements - fill 2 columns of output simultaneously.
+  INT8_T *Xq, *Xf, *Y1, *Y2;
+
+  Y1 = Yp;             // 1st column of output
+  Y2 = Y1 + nY;        // 2nd column of output
+  for (Xf = Xp + nX - 1; Xp < Xf; Xp++) {
+    for (Xq = Xp + 1; Xq <= Xf; Xq++) {
+      *Y1++ = *Xp;     // 1st column: slow changing
+      *Y2++ = *Xq;     // 2nd column: fast changing
+    }
+  }
+
+  return;
+}
+
+// 3 elements: =================================================================
+void Elem3_8Byte(double *Xp, mwSize nX, double *Yp, mwSize nY)
+{
+  // Called for input data with 8 bytes per element: DOUBLE, (U)INT64.
+  // Fill 3 columns of output simultaneously.
+  double *Xq, *Xr, *X2f, *X3f, *Y1, *Y2, *Y3;
+  
+  Y1  = Yp;           // 1st column of output
+  Y2  = Y1 + nY;      // 2nd column of output
+  Y3  = Y2 + nY;      // 3rd column of output
+  X3f = Xp + nX;      // 3rd column ends with element nX
+  X2f = X3f - 1;      // 2nd column ends with element nX - 1
+  
+  for ( ; Xp <= X2f; Xp++) {
+    for (Xq = Xp + 1; Xq < X2f; Xq++) {          // "<", not "<=" !
+      for (Xr = Xq + 1; Xr < X3f; Xr++) {
+        *Y1++ = *Xp;  // 1st column: slow changing
+        *Y2++ = *Xq;
+        *Y3++ = *Xr;  // 3rd column: fastest changing
+      }
+    }
+  }
+
+  return;
+}
+
+// =============================================================================
+void Elem3_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY)
+{
+  // Called for input data with 4 bytes per element: SINGLE, (U)INT32.
+  // Fill 3 columns of output simultaneously.
+  INT32_T *Xq, *Xr, *X2f, *X3f, *Y1, *Y2, *Y3;
+  
+  Y1  = Yp;           // 1st column of output
+  Y2  = Y1 + nY;      // 2nd column of output
+  Y3  = Y2 + nY;      // 3rd column of output
+  X3f = Xp + nX;      // 3rd column ends with element nX
+  X2f = X3f - 1;      // 2nd column ends with element nX - 1
+  
+  for ( ; Xp <= X2f; Xp++) {
+    for (Xq = Xp + 1; Xq < X2f; Xq++) {          // "<", not "<=" !
+      for (Xr = Xq + 1; Xr < X3f; Xr++) {
+        *Y1++ = *Xp;  // 1st column: slow changing
+        *Y2++ = *Xq;
+        *Y3++ = *Xr;  // 3rd column: fastest changing
+      }
+    }
+  }
+  
+  return;
+}
+
+// =============================================================================
+void Elem3_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY)
+{
+  // Called for input data with 2 bytes per element: (U)INT16, CHAR.
+  // Fill 3 columns of output simultaneously.
+  INT16_T *Xq, *Xr, *X2f, *X3f, *Y1, *Y2, *Y3;
+  
+  Y1  = Yp;           // 1st column of output
+  Y2  = Y1 + nY;      // 2nd column of output
+  Y3  = Y2 + nY;      // 3rd column of output
+  X3f = Xp + nX;      // 3rd column ends with element nX
+  X2f = X3f - 1;      // 2nd column ends with element nX - 1
+  
+  for ( ; Xp <= X2f; Xp++) {
+    for (Xq = Xp + 1; Xq < X2f; Xq++) {          // "<", not "<=" !
+      for (Xr = Xq + 1; Xr < X3f; Xr++) {
+        *Y1++ = *Xp;  // 1st column: slow changing
+        *Y2++ = *Xq;
+        *Y3++ = *Xr;  // 3rd column: fastest changing
+      }
+    }
+  }
+  
+  return;
+}
+
+// =============================================================================
+void Elem3_1Byte(INT8_T *Xp, mwSize nX, INT8_T *Yp, mwSize nY)
+{
+  // Called for input data with 1 byte per element: (U)INT8, LOGICAL.
+  // Fill 3 columns of output simultaneously.
+  INT8_T *Xq, *Xr, *X2f, *X3f, *Y1, *Y2, *Y3;
+  
+  Y1  = Yp;           // 1st column of output
+  Y2  = Y1 + nY;      // 2nd column of output
+  Y3  = Y2 + nY;      // 3rd column of output
+  X3f = Xp + nX;      // 3rd column ends with element nX
+  X2f = X3f - 1;      // 2nd column ends with element nX - 1
+  
+  for ( ; Xp <= X2f; Xp++) {
+    for (Xq = Xp + 1; Xq < X2f; Xq++) {          // "<", not "<=" !
+      for (Xr = Xq + 1; Xr < X3f; Xr++) {
+        *Y1++ = *Xp;  // 1st column: slow changing
+        *Y2++ = *Xq;
+        *Y3++ = *Xr;  // 3rd column: fastest changing
+      }
+    }
+  }
+  
+  return;
+}
+
+// 4 elements: =================================================================
+void Elem4_8Byte(double *Xp, mwSize nX, double *Yp, mwSize nY)
+{
+  // Called for input data with 8 bytes per element: DOUBLE, (U)INT64.
+  // Fill 4 columns of output simultaneously.
+  double *Xq, *Xr, *Xs, *X2f, *X4f, *Y1, *Y2, *Y3, *Y4;
+  
+  Y1  = Yp;              // 1st column of output
+  Y2  = Y1 + nY;         // 2nd column
+  Y3  = Y2 + nY;         // 3rd column
+  Y4  = Y3 + nY;         // 4th column
+  X4f = Xp + nX;         // 4th column ends with element nX
+  X2f = X4f - 2;         // 2nd column ends with element nX - 2
+  
+  for ( ; Xp <= X2f; Xp++) {
+    for (Xq = Xp + 1; Xq < X2f; Xq++) {
+      for (Xr = Xq + 1; Xr <= X4f; Xr++) {       // "<", not "<="
+        for (Xs = Xr + 1; Xs < X4f; Xs++) {
+          *Y1++ = *Xp;  // 1st column: slowest changing
+          *Y2++ = *Xq;
+          *Y3++ = *Xr;
+          *Y4++ = *Xs;  // 4th column: fastest changing
+        }
+      }
+    }
+  }
+
+  return;
+}
+
+// =============================================================================
+void Elem4_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY)
+{
+  // Called for input data with 4 bytes per element: SINGLE, (U)INT32.
+  // Fill 4 columns of output simultaneously.
+  INT32_T *Xq, *Xr, *Xs, *X2f, *X4f, *Y1, *Y2, *Y3, *Y4;
+  
+  Y1  = Yp;              // 1st column of output
+  Y2  = Y1 + nY;         // 2nd column
+  Y3  = Y2 + nY;         // 3rd column
+  Y4  = Y3 + nY;         // 4th column
+  X4f = Xp + nX;         // 4th column ends with element nX
+  X2f = X4f - 2;         // 2nd column ends with element nX - 2
+  
+  for ( ; Xp <= X2f; Xp++) {
+    for (Xq = Xp + 1; Xq < X2f; Xq++) {
+      for (Xr = Xq + 1; Xr <= X4f; Xr++) {       // "<", not "<="
+        for (Xs = Xr + 1; Xs < X4f; Xs++) {
+          *Y1++ = *Xp;  // 1st column: slowest changing
+          *Y2++ = *Xq;
+          *Y3++ = *Xr;
+          *Y4++ = *Xs;  // 4th column: fastest changing
+        }
+      }
+    }
+  }
+  
+  return;
+}
+
+// =============================================================================
+void Elem4_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY)
+{
+  // Called for input data with 2 bytes per element: (U)INT16, CHAR.
+  // Fill 4 columns of output simultaneously.
+  INT16_T *Xq, *Xr, *Xs, *X2f, *X4f, *Y1, *Y2, *Y3, *Y4;
+  
+  Y1  = Yp;              // 1st column of output
+  Y2  = Y1 + nY;         // 2nd column
+  Y3  = Y2 + nY;         // 3rd column
+  Y4  = Y3 + nY;         // 4th column
+  X4f = Xp + nX;         // 4th column ends with element nX
+  X2f = X4f - 2;         // 2nd column ends with element nX - 2
+  
+  for ( ; Xp <= X2f; Xp++) {
+    for (Xq = Xp + 1; Xq < X2f; Xq++) {
+      for (Xr = Xq + 1; Xr <= X4f; Xr++) {       // "<", not "<="
+        for (Xs = Xr + 1; Xs < X4f; Xs++) {
+          *Y1++ = *Xp;  // 1st column: slowest changing
+          *Y2++ = *Xq;
+          *Y3++ = *Xr;
+          *Y4++ = *Xs;  // 4th column: fastest changing
+        }
+      }
+    }
+  }
+  
+  return;
+}
+
+// =============================================================================
+void Elem4_1Byte(INT8_T *Xp, mwSize nX, INT8_T *Yp, mwSize nY)
+{
+  // Called for input data with 1 byte per element: (U)INT8, LOGICAL.
+  // Fill 4 columns of output simultaneously.
+  INT8_T *Xq, *Xr, *Xs, *X2f, *X4f, *Y1, *Y2, *Y3, *Y4;
+  
+  Y1  = Yp;              // 1st column of output
+  Y2  = Y1 + nY;         // 2nd column
+  Y3  = Y2 + nY;         // 3rd column
+  Y4  = Y3 + nY;         // 4th column
+  X4f = Xp + nX;         // 4th column ends with element nX
+  X2f = X4f - 2;         // 2nd column ends with element nX - 2
+  
+  for ( ; Xp <= X2f; Xp++) {
+    for (Xq = Xp + 1; Xq < X2f; Xq++) {
+      for (Xr = Xq + 1; Xr <= X4f; Xr++) {       // "<", not "<="
+        for (Xs = Xr + 1; Xs < X4f; Xs++) {
+          *Y1++ = *Xp;  // 1st column: slowest changing
+          *Y2++ = *Xq;
+          *Y3++ = *Xr;
+          *Y4++ = *Xs;  // 4th column: fastest changing
+        }
+      }
+    }
+  }
+  
+  return;
+}
+
+// 5 elements: =================================================================
+void Elem5_8Byte(double *Xp, mwSize nX, double *Yp, mwSize nY)
+{
+  // Called for input data with 8 bytes per element: DOUBLE, (U)INT64.
+  // Fill 5 columns of output simultaneously.
+  double *Xq, *Xr, *Xs, *Xt, *X5f, *X3f, *X1f,
+         *Y1, *Y2, *Y3, *Y4, *Y5;
+  
+  Y1  = Yp;               // 1st column of output
+  Y2  = Y1 + nY;          // 2nd column
+  Y3  = Y2 + nY;          // 3rd column
+  Y4  = Y3 + nY;          // 4th column
+  Y5  = Y4 + nY;          // 5th column
+  X5f = Xp + nX;          // 5th column ends with element nX
+                          // Used for 4th column also
+  X3f = X5f - 2;          // 3rd column ends with element nX - 2
+                          // Used for 2nd column also
+  X1f = X3f - 2;          // 1st column ends with element nX - 4
+  
+  for ( ; Xp < X1f; Xp++) {
+    for (Xq = Xp + 1; Xq <= X3f; Xq++) {
+      for (Xr = Xq + 1; Xr < X3f; Xr++) {
+        for (Xs = Xr + 1; Xs <= X5f; Xs++) {
+          for (Xt = Xs + 1; Xt < X5f; Xt++) {
+            *Y1++ = *Xp;  // 1st column: slowest changing
+            *Y2++ = *Xq;
+            *Y3++ = *Xr;
+            *Y4++ = *Xs;
+            *Y5++ = *Xt;  // 5th column: fastest changing
+          }
+        }
+      }
+    }
+  }
+
+  return;
+}
+
+// =============================================================================
+void Elem5_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY)
+{
+  // Called for input data with 4 bytes per element: SINGLE, (U)INT32.
+  // Fill 5 columns of output simultaneously.
+  INT32_T *Xq, *Xr, *Xs, *Xt, *X5f, *X3f, *X1f,
+          *Y1, *Y2, *Y3, *Y4, *Y5;
+  
+  Y1  = Yp;               // 1st column of output
+  Y2  = Y1 + nY;          // 2nd column
+  Y3  = Y2 + nY;          // 3rd column
+  Y4  = Y3 + nY;          // 4th column
+  Y5  = Y4 + nY;          // 5th column
+  X5f = Xp + nX;          // 5th column ends with element nX
+  X3f = X5f - 2;          // 3rd column ends with element nX - 2
+  X1f = X3f - 2;          // 1st column ends with element nX - 4
+  
+  for ( ; Xp < X1f; Xp++) {
+    for (Xq = Xp + 1; Xq <= X3f; Xq++) {
+      for (Xr = Xq + 1; Xr < X3f; Xr++) {
+        for (Xs = Xr + 1; Xs <= X5f; Xs++) {
+          for (Xt = Xs + 1; Xt < X5f; Xt++) {
+            *Y1++ = *Xp;  // 1st column: slowest changing
+            *Y2++ = *Xq;
+            *Y3++ = *Xr;
+            *Y4++ = *Xs;
+            *Y5++ = *Xt;  // 5th column: fastest changing
+          }
+        }
+      }
+    }
+  }
+
+  return;
+}
+
+// =============================================================================
+void Elem5_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY)
+{
+  // Called for input data with 2 bytes per element: (U)INT16, CHAR.
+  // Fill 5 columns of output simultaneously.
+  INT16_T *Xq, *Xr, *Xs, *Xt, *X5f, *X3f, *X1f,
+          *Y1, *Y2, *Y3, *Y4, *Y5;
+  
+  Y1  = Yp;               // 1st column of output
+  Y2  = Y1 + nY;          // 2nd column
+  Y3  = Y2 + nY;          // 3rd column
+  Y4  = Y3 + nY;          // 4th column
+  Y5  = Y4 + nY;          // 5th column
+  X5f = Xp + nX;          // 5th column ends with element nX
+  X3f = X5f - 2;          // 3rd column ends with element nX - 2
+  X1f = X3f - 2;          // 1st column ends with element nX - 4
+  
+  for ( ; Xp < X1f; Xp++) {
+    for (Xq = Xp + 1; Xq <= X3f; Xq++) {
+      for (Xr = Xq + 1; Xr < X3f; Xr++) {
+        for (Xs = Xr + 1; Xs <= X5f; Xs++) {
+          for (Xt = Xs + 1; Xt < X5f; Xt++) {
+            *Y1++ = *Xp;  // 1st column: slowest changing
+            *Y2++ = *Xq;
+            *Y3++ = *Xr;
+            *Y4++ = *Xs;
+            *Y5++ = *Xt;  // 5th column: fastest changing
+          }
+        }
+      }
+    }
+  }
+  
+  return;
+}
+
+// =============================================================================
+void Elem5_1Byte(INT8_T *Xp, mwSize nX, INT8_T *Yp, mwSize nY)
+{
+  // Called for input data with 1 byte per element: (U)INT8, LOGICAL.
+  // Fill 5 columns of output simultaneously.
+  INT8_T *Xq, *Xr, *Xs, *Xt, *X5f, *X3f, *X1f,
+         *Y1, *Y2, *Y3, *Y4, *Y5;
+  
+  Y1  = Yp;               // 1st column of output
+  Y2  = Y1 + nY;          // 2nd column
+  Y3  = Y2 + nY;          // 3rd column
+  Y4  = Y3 + nY;          // 4th column
+  Y5  = Y4 + nY;          // 5th column
+  X5f = Xp + nX;          // 5th column ends with element nX
+  X3f = X5f - 2;          // 3rd column ends with element nX - 2
+  X1f = X3f - 2;          // 1st column ends with element nX - 4
+  
+  for ( ; Xp < X1f; Xp++) {
+    for (Xq = Xp + 1; Xq <= X3f; Xq++) {
+      for (Xr = Xq + 1; Xr < X3f; Xr++) {
+        for (Xs = Xr + 1; Xs <= X5f; Xs++) {
+          for (Xt = Xs + 1; Xt < X5f; Xt++) {
+            *Y1++ = *Xp;  // 1st column: slowest changing
+            *Y2++ = *Xq;
+            *Y3++ = *Xr;
+            *Y4++ = *Xs;
+            *Y5++ = *Xt;  // 5th column: fastest changing
+          }
+        }
+      }
+    }
+  }
+  
+  return;
+}
+
+// =============================================================================
+// Of course we could copy and paste the Elem[2/3/4/5]_NByte code until we reach
+// the outer limits, but this is boring. So let's create a general algorithm,
+// although it is clear, that such algorithms must have been implemented
+// anywhere already?!
+// =============================================================================
+void ElemK_8Byte(double *Xp, mwSize nX, double *Yp, mwSize nY, mwSize K)
+{
+  // Called for input data with 8 bytes per element: DOUBLE, UINT64.
+  // Fill all columns of output simultaneously.
+  double *Xq, *Xr,
+         **A, **Ap, **Af, *Yq,
+         **B, **Bp, **Bf, **Bq, **lastB, **F, **Fp, **lastF;
+    
+  // Array of input pointers [B] and final indices for each column [F]:
+  if ((B = (double **) mxMalloc(K * sizeof(double *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [B].");
+  }
+  if ((F = (double **) mxMalloc(K * sizeof(double *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [F].");
+  }
+
+  // Set K input pointers and final index for each column:
+  Bf = B + K;
+  Fp = F;
+  Xq = Xp;
+  Xr = Xp + nX - K + 1;
+  for (Bp = B; Bp < Bf; ) {
+     *Bp++ = Xq++;
+     *Fp++ = Xr++;
+  }
+  
+  // Array of output pointers:
+  if ((A = (double **) mxMalloc(K * sizeof(double *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [A].");
+  }
+  
+  // Set pointers to the K output columns of Y:
+  Af = A + K;
+  Yq = Yp;
+  for (Ap = A; Ap < Af; Ap++) {
+     *Ap = Yq;
+     Yq += nY;
+  }
+  
+  // Now the loops of the standard method (e.g. Elem3_8Byte) are simulated:
+  // The pointers formerly contained in a cascade of loops, are stored in the
+  // array B. The final index for each "loop" is stored in F.
+  lastB = B + K - 1;
+  lastF = F + K - 1;
+  while (1) {
+    // Copy values from the input to left K-1 columns, just the value of the
+    // K.th column changes:
+    Bq = lastB;
+    Fp = lastF;
+    while (*Bq < *Fp) {
+       // Copy values from input to output for all columns:
+       for (Bp = B, Ap = A; Ap < Af; Ap++) {
+         **Ap = **Bp++;
+         (*Ap)++;           // Combine this to "*(*Ap)++ = **Bp++;" ?!
+       }
+       (*Bq)++;             // Advance input pointer for K.th column only
+    }
+    
+    // Find the right-most not exhausted loop:
+    do {
+       Bq--;
+       Fp--;
+       if (Bq < B) {        // Even the 1st column is exhausted - ready!
+          mxFree(A);        // Thanks for the fish
+          mxFree(B);
+          mxFree(F);
+          return;           // Goodbye
+       }
+    } while (*Bq >= *Fp);   // Input pointer is before end of loop
+    
+    (*Bq)++;                // Advance pointer of last not exhausted loop
+    while (++Bq < Bf) {     // Initialize the following loops:
+      *Bq = *(Bq - 1) + 1;  // With repititions: *Bq = *(Bq - 1)
+    }
+  }  // while(1) loop
+  
+  // Never reached this line!
+}
+
+// =============================================================================
+void ElemK_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY, mwSize K)
+{
+  // Called for input data with 4 bytes per element: (U)INT32, SINGLE.
+  // Fill all columns of output simultaneously.
+  INT32_T *Xq, *Xr,
+          **A, **Ap, **Af, *Yq,
+          **B, **Bp, **Bf, **Bq, **lastB, **F, **Fp, **lastF;
+    
+  // Array of input pointers [B] and final indices for each column [F]:
+  if ((B = (INT32_T **) mxMalloc(K * sizeof(INT32_T *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [B].");
+  }
+  if ((F = (INT32_T **) mxMalloc(K * sizeof(INT32_T *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [F].");
+  }
+
+  // Set K input pointers and final index for each column:
+  Bf = B + K;
+  Fp = F;
+  Xq = Xp;
+  Xr = Xp + nX - K + 1;
+  for (Bp = B; Bp < Bf; ) {
+     *Bp++ = Xq++;
+     *Fp++ = Xr++;
+  }
+  
+  // Array of output pointers:
+  if ((A = (INT32_T **) mxMalloc(K * sizeof(INT32_T *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [A].");
+  }
+  
+  // Set pointers to the K output columns of Y:
+  Af = A + K;
+  Yq = Yp;
+  for (Ap = A; Ap < Af; Ap++) {
+     *Ap = Yq;
+     Yq += nY;
+  }
+  
+  // Now the loops of the standard method (e.g. Elem3_8Byte) are simulated:
+  // The pointers formerly contained in a cascade of loops, are stored in the
+  // array B. The final index for each "loop" is stored in F.
+  lastB = B + K - 1;
+  lastF = F + K - 1;
+  while (1) {
+    // Copy values from the input to left K-1 columns, just the value of the
+    // K.th column changes:
+    Bq = lastB;
+    Fp = lastF;
+    while (*Bq < *Fp) {
+       // Copy values from input to output for all columns:
+       for (Bp = B, Ap = A; Ap < Af; Ap++) {
+         **Ap = **Bp++;
+         (*Ap)++;           // Combine this to "*(*Ap)++ = **Bp++;" ?!
+       }
+       (*Bq)++;             // Advance input pointer for K.th column only
+    }
+    
+    // Find the right-most not exhausted loop:
+    do {
+       Bq--;
+       Fp--;
+       if (Bq < B) {        // Even the 1st column is exhausted - ready!
+          mxFree(A);        // Thanks for the fish
+          mxFree(B);
+          mxFree(F);
+          return;           // Goodbye
+       }
+    } while (*Bq >= *Fp);   // Input pointer is before end of loop
+    
+    (*Bq)++;                // Advance pointer of last not exhausted loop
+    while (++Bq < Bf) {     // Initialize the following loops:
+      *Bq = *(Bq - 1) + 1;  // With repititions: *Bq = *(Bq - 1)
+    }
+  }  // while(1) loop
+  
+  // Never reached this line!
+}
+
+// =============================================================================
+void ElemK_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY, mwSize K)
+{
+  // Called for input data with 2 bytes per element: (U)INT16, CHAR.
+  // Fill all columns of output simultaneously.
+  INT16_T *Xq, *Xr,
+          **A, **Ap, **Af, *Yq,
+          **B, **Bp, **Bf, **Bq, **lastB, **F, **Fp, **lastF;
+  
+  // Array of input pointers [B] and final indices for each column [F]:
+  if ((B = (INT16_T **) mxMalloc(K * sizeof(INT16_T *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [B].");
+  }
+  if ((F = (INT16_T **) mxMalloc(K * sizeof(INT16_T *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [F].");
+  }
+
+  // Set K input pointers and final index for each column:
+  Bf = B + K;
+  Fp = F;
+  Xq = Xp;
+  Xr = Xp + nX - K + 1;
+  for (Bp = B; Bp < Bf; ) {
+     *Bp++ = Xq++;
+     *Fp++ = Xr++;
+  }
+  
+  // Array of output pointers:
+  if ((A = (INT16_T **) mxMalloc(K * sizeof(INT16_T *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [A].");
+  }
+  
+  // Set pointers to the K output columns of Y:
+  Af = A + K;
+  Yq = Yp;
+  for (Ap = A; Ap < Af; Ap++) {
+     *Ap = Yq;
+     Yq += nY;
+  }
+  
+  // Now the loops of the standard method (e.g. Elem3_8Byte) are simulated:
+  // The pointers formerly contained in a cascade of loops, are stored in the
+  // array B. The final index for each "loop" is stored in F.
+  lastB = B + K - 1;
+  lastF = F + K - 1;
+  while (1) {
+    // Copy values from the input to left K-1 columns, just the value of the
+    // K.th column changes:
+    Bq = lastB;
+    Fp = lastF;
+    while (*Bq < *Fp) {
+       // Copy values from input to output for all columns:
+       for (Bp = B, Ap = A; Ap < Af; Ap++) {
+         **Ap = **Bp++;
+         (*Ap)++;           // Combine this to "*(*Ap)++ = **Bp++;" ?!
+       }
+       (*Bq)++;             // Advance input pointer for K.th column only
+    }
+    
+    // Find the right-most not exhausted loop:
+    do {
+       Bq--;
+       Fp--;
+       if (Bq < B) {        // Even the 1st column is exhausted - ready!
+          mxFree(A);        // Thanks for the fish
+          mxFree(B);
+          mxFree(F);
+          return;           // Goodbye
+       }
+    } while (*Bq >= *Fp);   // Input pointer is before end of loop
+    
+    (*Bq)++;                // Advance pointer of last not exhausted loop
+    while (++Bq < Bf) {     // Initialize the following loops:
+      *Bq = *(Bq - 1) + 1;  // With repititions: *Bq = *(Bq - 1)
+    }
+  }  // while(1) loop
+  
+  // Never reached this line!
+}
+
+// =============================================================================
+void ElemK_1Byte(INT8_T *Xp, mwSize nX, INT8_T *Yp, mwSize nY, mwSize K)
+{
+  // Called for input data with 1 byte per element: (U)INT8, LOGICAL.
+  // Fill all columns of output simultaneously.
+  INT8_T  *Xq, *Xr,
+          **A, **Ap, **Af, *Yq,
+          **B, **Bp, **Bf, **Bq, **lastB, **F, **Fp, **lastF;
+    
+  // Array of input pointers [B] and final indices for each column [F]:
+  if ((B = (INT8_T **) mxMalloc(K * sizeof(INT8_T *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [B].");
+  }
+  if ((F = (INT8_T **) mxMalloc(K * sizeof(INT8_T *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [F].");
+  }
+
+  // Set K input pointers and final index for each column:
+  Bf = B + K;
+  Fp = F;
+  Xq = Xp;
+  Xr = Xp + nX - K + 1;
+  for (Bp = B; Bp < Bf; ) {
+     *Bp++ = Xq++;
+     *Fp++ = Xr++;
+  }
+  
+  // Array of output pointers:
+  if ((A = (INT8_T **) mxMalloc(K * sizeof(INT8_T *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [A].");
+  }
+  
+  // Set pointers to the K output columns of Y:
+  Af = A + K;
+  Yq = Yp;
+  for (Ap = A; Ap < Af; Ap++) {
+     *Ap = Yq;
+     Yq += nY;
+  }
+  
+  // Now the loops of the standard method (e.g. Elem3_8Byte) are simulated:
+  // The pointers formerly contained in a cascade of loops, are stored in the
+  // array B. The final index for each "loop" is stored in F.
+  lastB = B + K - 1;
+  lastF = F + K - 1;
+  while (1) {
+    // Copy values from the input to left K-1 columns, just the value of the
+    // K.th column changes:
+    Bq = lastB;
+    Fp = lastF;
+    while (*Bq < *Fp) {
+       // Copy values from input to output for all columns:
+       for (Bp = B, Ap = A; Ap < Af; Ap++) {
+         **Ap = **Bp++;
+         (*Ap)++;           // Combine this to "*(*Ap)++ = **Bp++;" ?!
+       }
+       (*Bq)++;             // Advance input pointer for K.th column only
+    }
+    
+    // Find the right-most not exhausted loop:
+    do {
+       Bq--;
+       Fp--;
+       if (Bq < B) {        // Even the 1st column is exhausted - ready!
+          mxFree(A);        // Thanks for the fish
+          mxFree(B);
+          mxFree(F);
+          return;           // Goodbye
+       }
+    } while (*Bq >= *Fp);   // Input pointer is before end of loop
+    
+    (*Bq)++;                // Advance pointer of last not exhausted loop
+    while (++Bq < Bf) {     // Initialize the following loops:
+      *Bq = *(Bq - 1) + 1;  // With repititions: *Bq = *(Bq - 1)
+    }
+  }  // while(1) loop
+  
+  // Never reached this line!
+}
diff --git a/dependencies/auxiliaryFunctions/mexFiles/VChooseK/VChooseK.m b/dependencies/auxiliaryFunctions/mexFiles/VChooseK/VChooseK.m
new file mode 100644
index 0000000..9df2a76
--- /dev/null
+++ b/dependencies/auxiliaryFunctions/mexFiles/VChooseK/VChooseK.m
@@ -0,0 +1,81 @@
+function Y = VChooseK(X, K)
+% VChooseK - Combinations of K elements [MEX]
+% VChooseK(V, K) creates a matrix, which rows are all combinations of 
+% choosing K elements of the vector V without order and without repititions.
+%
+% INPUT:
+%   V: Array of class DOUBLE, SINGLE, (U)INT8/16/32/64, LOGICAL, CHAR.
+%      The shape of the array does not matter.
+%   K: Number of elements to choose. Scalar double with integer number.
+%
+% OUTPUT:
+%   Y: Matrix of size [N!/K!(N-K)!, K] with N is the number of elements of V.
+%      Y has the same class as the input V. If V has less than K elements, a
+%      warning appears and the empty matrix is replied. As usual in Matlab,
+%      empty inputs yield to empty Y.
+%      A warning appears, if the output would exceed 500MB, and an error for
+%      1GB. Both limits can be adjusted according to the available RAM in the
+%      C-Mex source.
+%
+% NOTE: The output equals Matlab's NCHOOSEK, except that NCHOOSEK replies the
+% number of combinations for a scalar V:
+%   VChooseK(-1, 1) replies [-1]: One element taken out of a set of length one.
+%   NCHOOSEK(-1, 1) fails at calculating N!/K!(N-K)!.
+%
+% EXAMPLES:
+%   Choose 2 elements from [1,2,3,4]:
+%     VChooseK(1:4, 2)
+%     ==> [1,2; 1,3; 1,4; 2,3; 2,4; 3,4]
+%   For speed cast the input to integer types if possible:
+%     Y = double(VChooseK(int16(1:1000), 2);
+%   is faster than:
+%     Y = VChooseK(1:1000, 2);
+%   To get the combinations of cell arrays, use the combinations of the index:
+%     C  = {'a', 'b', 'c', 'd'};
+%     C2 = C(VChooseK(1:4, 2))
+%     ==> C2 = {'a', 'b'; 'a', 'c'; 'a', 'd'; 'b', 'c'; 'b', 'd'; 'c', 'd'}
+%
+% COMPILE:
+%   mex -O VChooseK.c
+% Compatibility to 64-bit machines is assumed, but cannot be tested currently.
+% On Linux the C99 comments must be considered (thanks Sebastiaan Breedveld):
+%   mex CFLAGS="\$CFLAGS -std=C99" -O VChooseK.c
+% Please run the unit-test TestVChooseK after compiling!
+%
+% INSPIRATION:
+% Jos van der Geest has published an efficient NCHOOSE2, which is much faster
+% than Matlab's NCHOOSEK:
+%   http://www.mathworks.com/matlabcentral/fileexchange/20144
+% I was curious, if a MEX version is much faster. At first I've created
+% NChoose2q, which is 5 times faster than Jos' Matlab implementation. But the
+% algorithm was so simple in C and did not consume temporary memory, that I've
+% expanded it to K=3,4,5 soon. The algorithm for free K was more challenging,
+% but it needs just 3*K pointers for temporary memory. Therefore it is much
+% faster than Matlab's NCHOOSEK (Matlab 6.5, 1.5GHz Pentium-M, 512 MB):
+%                    NCHOOSEK:   VChooseK:
+%   (1:50, 5):       45.30 sec   0.32 sec      => 141 times faster
+%   (1:20, 16):       0.52 sec   0.0024 sec    => 216 times faster
+%   (int8(1:40), 4):  0.64 sec   0.001562 sec  => 410 times faster
+%
+% There is a Matlab implementation in TestVChooseK, which might be helpful to
+% understand the applied algorithm.
+%
+% Related FEX related publications:
+% (http://www.mathworks.com/matlabcentral/fileexchange/)
+%   NCTWO (Simone Scaringi): 20110
+%   COMBINATOR (Matt Fig) [I prefer this for general tasks!]: 24325
+%   PICK (Stefan Stoll) calls NCHOOSEK for unordered combinations: 12724
+%   COMBINATIONS (Gautam Vallabha): 23080
+%
+% Tested: Matlab 6.5, 7.7, 7.8, WinXP, [UnitTest]
+%         Compilers: BCC5.5, LCC2.4/3.8, Open Watcom 1.8
+% Author: Jan Simon, Heidelberg, (C) 2009 matlab.THIS_YEAR(a)nMINUSsimonDOTde
+% License: BSD (use/copy/modify on own risk, but mention author)
+
+% $JRev: R0g V:005 Sum:yQLycAwkArU7 Date:24-Dec-2008 02:59:38 $
+% $File: User\JSim\Published\VChooseK\VChooseK.m $
+% History:
+% 001: 17-Dec-2009 08:10, NChoose2q: MEX function for NCHOOSEK(X, 2).
+% 004: 21-Dec-2009 13:49, VChooseK: Expanded for free K.
+
+% There is a Matlab implementation in TestVChooseK.
diff --git a/dependencies/auxiliaryFunctions/mexFiles/VChooseK/VChooseK.mexa64 b/dependencies/auxiliaryFunctions/mexFiles/VChooseK/VChooseK.mexa64
new file mode 100644
index 0000000000000000000000000000000000000000..62d33ffdfde31781e42e64eb1b406a24c7465497
GIT binary patch
literal 23336
zcmeHPdw5jUwLfPjfvD*4P>Vpzltu-`@JK+Tq8XCF895UWf@l#ZlVtK}lT4UL2)$A%
z!|NEQ<*U85wqC_*mFu;Yuh$Z6wT5?!ub@{i>a9c+nK3>ffJ8-de``Nx&P--}eC>Dt
zx$JzCwfEXQ4%WPp+tQQ!moJ-l?d9C;LTQ6R4N3Nd={XIZ@u7KFZd*!M+8U`=J2AD
z1xUY6>NpL|k(ZXPIrxXiR{Sn|!Q+qLpI_5UT@1r-41Qq@uR-JPBQz3w8c3oj5PMM@gv!6_zlNTrVEe}
zLuM$-lbq6jh8Cf)(vE)|U+|NAw>)88pV|ehm;bEFIZ6k0Xi9y^oBNejTQlHi0Y6R>
znV=5A(zWwYhIa1BfM1;f|Gf3C*O%Pt1UC1AaJW&J^Jkm(#>T>Ow=ql%xH;Wcu%R
z*Eaf^+<~CSA9TAFx3k>kuJ-!9we^9Z*I(``ZtyjE%RQA1US5~J#$DCwao5y0c^c|}
z1i~9y-19{X#SNZ7z#C8+y{)BwzbjDdtadb2m$wF$##Wc7p}|*$%q+jxOBAz0O;y2q
zUlUNyKv}5K>#wJ3XP~&w<7XM&S6g4@X<+#hUx>tNY@O{5g1FM_pIcMf;BE9a1<@a2
z7l0IuH4Q!=u>-@zah9M^?Du+tUeSrm6ZF@&y04r(88s!L<_2^U^g8^0&vGCZc!Mrc
zhinTk`@wmhr}Oo}k15=1r&CK(M;%
z^2_7J=3pJCd52^$77<%72;a|0L>(
zp4VIB^>Y1{G5QEC2uQ7BeU<%lohAcv?5)xQMXZgr^uUQ`Cf)YYf#L
zG2v;wk*Uvwr*%uFkwPAtcINfMZo->wG2VnX%Q@MEH?Lh+negUyy~un-{e@6JD5vHPvjw(>g9ws|inQxJ)Zec)16my7eYJt;;fP
zFyYPd{Z}SD?Hgp;Xu_NK44X`NTH|HfY{JuCM5e7KyxAYL@CP|sTlU4%?25LgBbXgs
ztSX8Y-kC!S5K{lNaj5FQI2ZqIV~dcZavfF0y8DqX9z|s^DX}hA9!_O)p;!kipG{?Q
zo!DkpwosW|Cbp54KPy0)TqU-Fm3yg7E)rYG%I{N|TqD-Z$_J@TE)lC>GromtbZHz44$<0X^r94<0g^he
zCeJo@HAE)Yn6Z8`*n*cpxC2wUaHAGNem~G?tG{bWY!Mo;ja^Cfu{p>m*f&sjT4dT4
zh|AzP4M00YC0dS-m+YWgr?GFs$&$!ywbx<0tfSX<*@5tB^~V#RB9|&LI&4qux%cF!
z4z;7kHomjhcFS|1*w<^jZEvsb=566^YT=73UI*te6|}rTt!kSd$=a?}eWFLoPxk4B
z{~h?gZgfG_dPM8fB6D+`g?s&ly3wg^i(0iv!7lPHCRVR)sH3mfHZ046T*=@ZYB&oz
z=*ZCukA&XV)Ly50#2JoJ3oY52@lwxbQ=datu|98YKcyEQ2+Y@w{jAS1^P)t|1yVPyY@cYC`eVy#tYVK<$
z?`zrVzm6He_48t+{<#;Q`yaFjS=*-AtKvANOQA
z!yo2o;XSa)=Ph2B(c?7woDmH+dAS7FzGPJLwxJ~ox3EQ8_*E{h6Lt>w
zp#A4s?sgiTJrAO%fU($dt7D0f`X(kf^R)9<>5(zq+a|WVB3>B(@UmsP5oPi=pCoxd
z%7VNw$H~(l(tozDm8M|
zlC~OxmXfyW!CA<#*+N5qO>gsdB@O>R8vZ>rC)x0q)9@GL9~=JsuN=NheRII*F`-sq
z+NV0er-{+S^LAhkBpg7D-jeD77Cw45?U)1Xi)s;Fgm5-;TYJ^%8a8%Yd&TJ*HhNoo
z4a-?dZ0*&E?m`0=(!h%YG!S
zfoX=}uZtW1O($@!!oU0LnepFZ{;z7ri&~@%v@f*ix^b9IXFlvLRsALFVR0KWrR#H*
z65AFJWmIIU2dT)h>8%y^g*_(5+U-)`cBuz@9!7(K2Vivccp$VURx1sD+m{&J
zX`IrkKGn7zw(5}~SGywT7rG*2uGZB9nD(y7wa1;2*+($O4!~}}P0qrdw(z&HHP($j
zeL^=vzyYAQIgU|*v&5E5StjAbDZH_sBi4Bt?gv>OEy!ujm+uO
z!rgt^gl(GP>eP%SUAoaD!^o<&T#Xb)NN*qIAbcSVB5s^)snd-k1LFoQce#nkQJRPx
zh$plnQsfXp#3~m@pa;t!@{D;FY3fTo>(N;t%)?t-X{T0L+GShwedsfGG5)a;g-0U?
zimjx8O}2GMvFpneUEl>N2hncngG3gcB#s~(gknz#1(Tw=f=8Pbd{QVF>pLm<
zNIC^upkUOKf_En=_%sxJdVqpiP+UdV8fGP1xROzMvZaQUoE!zAU|V}Im%<=aY-W^skrF$FAtTMZg=?>bo=-!O6gEiTn=}OB@Nv?Bf$2^=2X2P&PV7}EZ2XPuIXP^o
z&@<{OY&^j0$)o^WfR$*3_cIc67cVV13Eygf&YEYH!58~U7uyIYX?)M*^=d4syyj;5T(
zPASeEyqA)=IF+cM6S?+U+GCE=suph5aLI%Hjc$y0s;^Q%aD-(?U6Hwc@SAgVtE;mo
zS8K!85uLXA5xR#C!lfyeQUy3p-R(rd*#>hffpe*0gg^+>-LpqG_UcB57LJ1UeYCuz
zWi*!4XyhZ(*@5U9f^920qqif|lSkcLb9SIh3va`iSo1W#-)U{l@W-2g8_E<>xO>?L
zJwLwRvt97dNg+P`x`10ec1Xr$3}%+32&k#5S?Jbj^pMONjO%
zR6j*awXvOU2`JCfzv%A|QwxJ)7IfhVpHlCrr2nDu#PMC$`k)fyBhA8K
zON{rZ7d!Prk0zk_Q5ZawMvtYr4oDu7@;ShP<9hWyKCa6c_rgxJSYmt}C2JYjS%B{y
zMzIHXuW^t2mQvix9#+~}W6R4Phb~&mu)3dXM-G_Imhn^~30P?q#0jx>XS73YT%2l%
z(L)~6!Hu;V1ia=mbVMDFO9%>S+EzRroy>)V#;vy1InXWT?l=ekEy3@^?!hw~WV39m
zU*J{Qw$%^eCVcG2tfJo%{1->K-*U%r{8w$Oe*x3j#wxKt0Rod1EWrPq4YoB813`+d
zWWDVqQ5GTF=6V|OLl}1`82k~(AFIyYqO!AF@f~J4~Gv3u#U#2#=)_Zo{e)8zl%|O%U!)CAFI1ba3u9z{EwnnOW*V<-@`*LhmJ_9+wXQzzOB>^{H!
zDtqPfpf_M|_WJE!{u)i5!s$KqA`IuM^xB*Fi#eW#iS}a6F`sz#xwDCXy^WIx1u4qhP>=*!R^QNIN0Y$UdhMR3YJ1T0LuBlD>!{WqtZ(#ka^;2v^>|I`
zYWcp))#W~)PCUFFWkCH*Pc;+0e0j51DfToq`GWRZFGlWd^!b6IC*%xZ4CM^fiJkzvLD&B@6TX+N8suJ)BtG>X$Ev-!Pp3R8`hdGzzRfgdhv5ELgHk=XAto!0LLPPYyi9ySC$=s
z&mmGA0Sse)+wt7@%43iR@B_@Pm4FX^0yzLF(Tf6<)E~`P)YhTu=plo1NY-57@bFxz
zcnf`?teiJ=)_Eguu${9kXQguesB13E&m9LWqbUJg-`(H;9YQ$H8@kp~eD0uI(2%IF
z23(8!8_o3=oe@wUtMO|`eK4us7q5R9_;aA!hNSx1c>PY`7kx?nyQqH%^`0dDTN!^Y
za9J4NyQtq{t}l&P@5%~iTOMa*Odk_L=Z7sHG1JZdsWp;yS9W-i<<~?>?UjS>29gQlYvFsAOzj^}rY`{f&G6BWsl#;bznx594f=1vmzy%QZ>><{3?%FSGm?4UX#{
z5}ZkAG;l@(XEbm|17|dFMgwOwa7F`XG;l@(XEgBtLId)Kn{jPS36FPJY84qg^wok3;nLe+}e!^QrxQA`CD{DE>kLFA;FMfU^X=Nx&Kb
zTLfGy;4cMyM8GWq?iTPh0pAnw69ESaN4-$MO9Y%QV27~(?Be39?c*0LTu^G)>zhKY
z6YK?fSK&L%YcVix18!mI?-z&xOMjRCnyLrRUk5bje^uy3`fr7pFS4AuWt@v-SNe3+`PBNv+6RqW^dW6%y;Plpdy=dJX-Dhj5{C|uFppW{r9`}xZAif+zoe5em0$9`
zA^b@4d4(TIzBtSaEN={YDwVuI(9dC=%;DLJH?OuSl*jz!iu!7WmFhf!Iwh}qc@sD}
z4ElLZi`O5Zue=$VD7XceEP~YUZSWAk$Tc?vmAv|zitPGViIZ^m|)msiD5fEAxUUuFDJ=D;z_f3t}d*!i(klm5jH>
z*Rf=LmLk`$WPG+F*STc;AVsbR$@syFTxXK;If`6glJRFLave*?pRLICE*U@Y{0pkL
zVpq^D7hV&C73Uhr^I%M9(4VJB|4+u-l)5zjY{l2<@*I_9`e91NL^FoxtvFFZ?mx`6
zR-8;C_Z`Xj^YMMC-Hc*+t8xL(i%EySP?6uOo0+V1+DTD5`jLuUZ<6URQaaPp%UzP0
zn)EDpQ>pkd$p}%hD&Nk4XHUci)LE6W18@}jveKMGLCdI$-BPOFX#ZzPV|RLjN%&^5
z{-Vf|1OEx1y02XXyowWMq<%;aN;UYYY3vvQp4yT7WTK^XFMetof7l9qx^ZP1?@rdv
zFr|4iGN$x0^6B(IBiBH$GQKUr+>rN>+kyo#sH
zss2po*H}#RS~RE37wzm34an<=+XQ|deL;?N9|y`01fE_SrzG{zBJd9gdDt^q)UOiw
zkLXKtBn!%vek$-~f}TC&1^zbz|Ag3}vuAU_Kg#d}JwkpD
z34`<^@N{BQSCO4E@$`zIzunGJQa=X-{(#tUvS*Ya=@IS7c_AA*Dd=?@$FOI{$Xc<9
zB!Ac>46;cu*aUur2oda=AnHa7{1I^>^s%77RNy}l_F~U`QI`+AnkMc!ffueOTHv56
z1HK9PaY>ZCaAyYk-(bQkLl!T&w$^a0smqK{DBPk!x`|OX256T!p@#X4;KNS
zPX0+5@YiO*muA4zxoPRzuVVOt=M(ud&@az`zYF+u0hq!eQkB9v2i(AOeHsMhm%O<3sU(^
z?pgC4u2OetSqYuJpMbV$)0_f<3TzL!GzsqO-ncYE2+mr&E(Q{vUrL^a_HO!<6~
zH@|F!L!NNf5(XCVx$E#k7o8i$HtM|3sqsehO&v^iC-75Eauo?GU?tXCh#;-
z!4#mlOKvH1xSYl476(>|@$zx;K|qhyjBk#q(`lk3w;LOMegGDu$u|j*ZxU>dY2*c_
zb_!%Wv-P<%9lCq&tXT_6%iZOUnR==FOAnfI2E-9o?xgdxii*r9II%;dXfW^vajKN*
zJgmeaS^Nww^I28Unm7O}&6!rHq?4)AoXX`4$V0Z$p5vAIw5`P1TJm78bZ2ClPtY=*
z_XT;_30dr{u4D~-*_mW?-c`CI%;bzrI>0PV9Q>vz+Gf$MGlW}~W?PjiTpysse7Sxu!+E$;#kZOJ9Sy#LTTgIY`Y3oi{U
z??VOYU-HZA?Ui4X|26QE?@4}nKSQ2_J5MN<<_{$)zx1C2z?u7(_dWJs&&ZYW9K`=i
zehJ@A;a7_6oVJLA4i4h~C7*;JqQ=~Rhv4rh;w+L*Hjr9Ah6=*T@yqL`&LYl{N>6>6
zQy&V1n`}lRFZpGhmDQ3z4v~?0Nkg!^h?iD!5dSarE)hdB@K-oEMVBNLFtx8S8Tcp9
z{&K-j{%uaF{ZG%pZ!hLtE6vQvr;eX4H>K^&q#Ff)NlJ$)MW%a;nNj4U
iB|Ni91h8=^y6}_!Ani)k>H6PL#`)=+EOScj|9=4rv*#QD

literal 0
HcmV?d00001

diff --git a/dependencies/auxiliaryFunctions/mexFiles/VChooseK/VChooseK.mexw64 b/dependencies/auxiliaryFunctions/mexFiles/VChooseK/VChooseK.mexw64
new file mode 100644
index 0000000000000000000000000000000000000000..77fcae95a539e6b0bd208280a1b304ef594f9bf7
GIT binary patch
literal 17920
zcmeHv3v^Ufn(nSwQUu}&G*RpZDIoUH24f&mgB7$Y4>$);L7D`N3WOr5khGP@JyjLJ
z%P3O3(#NBWgHB^l_q4V%?ew^&?HLD@x!MUqlJE!t^)`qtQBhAiijmePC{p+P_c@hR
zLeQQy>#lXz?LMr1{{8s(-v9nz```Oi@ZGb6*%)JXq*#ox7Xj(<@b}lh2GD%f)V)`+
z7sec!_M$~TGOetpHsoql8rCTOI#;E?zP=&sS{-mHk$P8cy{l+(sjIG`Dv*_$nv!7-
zt(^W&*)6X`lH=*_j^5;4l<)31oqPv}Z}ajU2R`8N&h}@Mb2xrDc`D$IGfwmR8)uwN
zzLl4C4wuwc*3g(&T0u!MV^v#|*nb?hm&bAX*>u-fYYJmGpk|U$yD=9qjpsL72vFY2
zn1fz;;*jk?0Za^gENH>Y>=Xx=myMd|WyTg!R}u8kfQ^j3KohM(BEiI;jf{OO68j5@Yf3o{RSgpn~^U+MmW@L*0&egx-kOvB8*+ns10P
zL<_nUxx0i%UbqyCi7NOis^D<3`_O|o(HMz*rOvy+S8vQ@hp`%r0Q3irofF1?3tjGQ
zJDm84(T|)`Wvi?n^J(K+#L9iLw!C#favu_+2VnwY^Kz1BeukueDP~@%-XUrCI7Ri4
zA+d&9wySOX*Zn9|GC$ln@hm?>RA~{>{Z1?$YsEr0@`aW?bWw6249%9cyX}(tn$P{_+9|3POHAWQn
z6kNiPlqcllO{dK<51C{7Dv$B8WM|B0>z3S|oIDF3?&R7jJA~a9F}6>N?elJD!tR2Y
z6l?Kr2XG&2I>U!LIgGr+p3=L#Wgt>iFOYzJ+Qe3jFKUxpWvvn{J1lIQ&+Rst;UeRm
zDK`f*vSqbTMj=nm?3JP)kEyP%7N%|{aWYw3>-4E_``n)i(Oxd!&(S*6E@}5cKaPm*
zJ|X%N+C}xiW%|)14(W%eCL8*(kMu)QZQu@a7DH1=X$pFPLz+U3P!vO93IM4VibA!f
z-V^{*?SN5hs!su_&xXfE_o2}DjQOBG`$&C^`H-Xeph%=sgndZ)tr$HEMf#oO{z%yL
zS5PGI!6~ViXyIGW!x&}QVVo6J6q-9(HCrR-jfQPt%CqE=<
zp^KusYi&>j%MGVly+hRQvy18*qWf&9K$z-bqIN&ZM-bIG0-36Zwdo5;$LAH@WifYoJ
zLBcf29wBaW;$*LdFwJ6Sc=05|Herlpn&i_90eNdbjlyk|T2UewUkJ!sVWiXwK&__9
z7Lv&d57(0icOMPqnq{T(fR>*ut6LKG6L
zT2jx;?g3#_E8kMVYEdQrMthy2`g_iMV)WKc+{p7Y$k5>lASzjf(8$_dWJMx&l$jMP
zhD?cT1nH}5>0REXv@xjNq6(#$yiw97M@20^$}MZ$Lq4q*uJ?$r?Qe|(SXe!xo7BF|vM-WqMQ
z19~rtlvx13(5W1k)Nb@_+AFD_`P}a-PfO}qOw^>(8sYS-$d8%J%k!?F#f`ci57twN
z^%NTG@nSs~AM5$?sO!OI$k$VAtOrHDo+ajbQ0D8ojoarQu73y`+R1BJg*~(+%G!iI
zv?yye<-C`$EMynJlXNNv&2{~oxvnS8bw#hdu6PWTxIVe^AVenHIu~h^p<=xYwaJsP
zv`bj&q^}VdHs`pwNIuml^YuyQ`cUTUo6Xm^8|#a;im`U@cB`;EiF+cOup6GJZ9AYh
zCL*%{8+xbmnz^k1G?(@BZx$E!`(@sx@JJr$pG?YU?D^!8Ahb5_m2%;gmi3DoOc9%lm5!u{?W*?Cupd#lE7r2P#2!HIw{BHf@c0
z6#Ue{9eTVgR*ndOJf%yQ!5ez-;{NaiR1#{qk^4d|cbllq+B)bH*AGbA0;~%vc^U#H
zMf8vsmJE+Ci|s_QD62h^yIt7!Bv6ONpG6rQ0kIjv=k`$0rz!B5|J&#ObZu$%js*WX
zkL=Mq=s;g3zv(6EanI?cg9|)oGQoCxa}sx@2kgzo+?BSQw)ra`4SinOC8|Xf4|1Zm
zHt~}Or&6ay$!=ZPs#+aemzge!;_Yel6kriu?O~gI{rn|EhO8
z7_=MAB2SN;l{|eihwa;w4A*~Pdog$Y9VU~WH<|S0Fea@X&ZJA6Nd;GCQhc)mr2P(O
zjUWOUiaT&PgmEtOQV{DBD>uLssGs^OKb5tNudr}4%%F+gg#j}!T9}fo)Z-_%)^TY`_TswX4cI87N
z;9gGSK%B-jV{VD&eiBXC1oLrUL1(FJB(+OyeaE&#Y@Q@yIMH@eagRIiABWEme<7pq+$15|0|v?J8!{hKh`!AE)h1@PW5nch)=M%O*5cQw7()Omfc4b
zFS=eO&9#KiN>K~Heet=E3ek_TWsKmDagHXYQdAEtrLXq@-&v08FN`H6I-)8uBdkDi_uwD`{TM$
zHHGWpJOKR>K6{rwi;th6tg*=)X%ZpTawO6Q_3$Ds{}vI7op}V|h^!SoYKO}YO(pvl
zn-MX==|P3COOQ4~mUe7B%KJn`X9`AC2Q~5mV}d;Zx|0ahCf?3Zr(cAwmS7F7J@CcX
z;`{}LJb}9;QpjLNiBGE-5Vhr55bYnN%s+^#&8N0Wb8VqpcHpQgxxavL)h;>nDB?5K
zK3-H)h!B2TF8__KO=$0
z<1tBvo$=LSRa~9gA(k_lPx;z(KN0%q$%H?Stl;;KKFnRv#w>cFb0_b@};E+7i
zNuIWr|B^W8;{L~w=ZsP0DePg@hD1TnapiqT6o+0e(ayL;Z%dRYUA=IL{$x~n()BCs
zljOPHl;=b&kK{RyFZ1i=spDJPe?y+XLiYrD?!T6E@+ScLSu~OHJWMcAo)retIzsB?
zNTbVhu7%_|WrTgk;FlrJSPMz>Lz*Cyk1=SSJ^03lz3N&T{s(AEkmfaUdoblG*>E%X
z&E`2M&(Z8t*v+=%GPeL%0c4*sg!f#SxVX5?JY#UghAf}r?)Gx~#Ij6rel%#F#Yu)Z
z=K+3`d0q>gVetWl?T8ApCAC*pf3KeOMhC26+WLQONa2UXrb6_Jerb7t>itH&LiL$^
z#t0W9jl8)f0uS1yCnKMOOZl$NE$V4uXPe&dphl0dvwh2#eDpp;2(xIE_t7Y-KBvB)
zV}%7?_%+o=2P_{lr9LE5H(
zDou))(lOhqtNCm%0PxZJNOW6`$_lD%m^B-ZUOTXvySgcp!I34<x
zyI%tLEUgSH`Ju6rm9&!Cd?h!SD>=_0U&%+vCagpcpT!8znRuN;c(07Wd#fLKn@zl@
zhVY&kfp_Lp;4L@t)(_#;kHGur9Pn~cIXdNykP^vY-p~khMvQPnFGe_L;t4}|_7Qj!
z`ha)P#QTs#&WX2>P2|L0!h71p`^^v@eZfz}Q$7OT{iqzB^4*XEQ63!{Vcm!k(%#1i
zdAz+tUpj=ha0Fh>2f#}+@opT#n?3@shwyZBUdIsL7pO|4y!=DpwV;wHf1JZ`khG-$
z+7hNy(6R|2UQ4|RK&Od&9|IPx=07;(eETJ`iKDK5Hx}!wBP3El(_kJPfpp`0KoSWl
zFobl^2&8gCnn6gOA*7o}AVq;h7ZZsgd31j^T~f3ij$NNW9?QEvi@#m`J%PXN{QXD#
zQY3c0&fjnH_hJ5ig}?lhPS7bPNXj}Fe{dxj0%ts
zj}c4%v4DKD;~6^JH9H+mUNm22oAoD=h@J-9&Fa$ML#`lLD>m}g6i@W>3Jr=v*yq}g%q
zI>rK`YQH|4Gqv2=mnOQ~!ry*L$853m_i)(FY3Ub*Ur*728F{ZS$@?;TW3XWBb7JeW5B!;P>GVyE=W#F}AuVJJg@R4NRmbw%Lj*^eE?EW(1klp>^f0WfP
zW%a$jzr)!lmi`o)V(B41h9iSIG&`;bXr7mhcQH1`E$;9J-c{lXf#Ra71Kkn)2l
zXRKR2foRyZ<&?1bSzyWP0UQ>vGiT#k)@*{pXo*czuppx;8nZ-h27hLwTMj-19o8*q
z8!UWuk<5R}$FMHnmQ&%e@dj_~HAG8}Xv`M5b#TJp0Qvlh?{v;Mq@Ki8zbiZ$bp9I|
zCg{XxGJ~!-y?seo8NQ0OJN^L&F`P7FYMaT#u*pR5FWbAE^A4D>O8mx!Ur9;ofQs+F
zpMoP1haQ4VaND9njo~tv)9o`p!V~i=dbi<HR4vH9IcQArUp6So+&W&1uva6W+@^v>9FrYBE#j-v+?kmh&fK
z9jKpae1mfuH+0&Rd%14UD+B1e3MU3)xi!{JUl2t1Y2{;sFT|2Ix;q){mt*vs^(XWL
z8wVdzZGFjy4W)XnGi=Lg!3QKUU$k}Pv^+GIeli&uw|6X-P(AnilZ2fu_zjC(>CH#f
zD67Y1TPyB^Jrj$CJ>^LYHJME+hGJc(et(>d^G=HDb=vmH?rufE@0D=lF%v&hQr@K*
z)K*di^C6zdDt@ezy1ElT!caT)A3%Yy=IOr7R$P)&&2~DyqDoLb8Lj%%^WD)NOP`hd
zdT=jxeK$_#vFnfX_cQ$c1b=VGo5m;Q!q<>_pU{s5BT2FJsc0~4eUSf?4F0PQem;rf
z%LfyGGw12kH}1;BAnx|?IPpAgJ{_;)oY99LxX2mV@Z_!sR#{C3AX
zI7SdPCemPwp~-)PmaprNiE=t0%EMmQBJ6Bc@y{FwJpaq{{|pBnkPT95Ot{2^1ty$n
z!f__FoA9$cjeciL7+=tvX8E`Y_nUC937;_GW)lWYxYC4+Oz1S>btar;!aNhsH(`9<
z4;C7^<0g#P?={Q+YQmqH@G%qKZ^EDn%T2h@g!v|%Wx_NQUX+X(&YAE76TWG}ZWG4o
zxDwo+%{Eem5Puey*48!D-?prL&{7nJZ
z>{+fkvs}5Z2E~=R;B-Ap4ayfyI$9R>hbBU27X6!7BPwfp9~ED@X_?
zeIw@|))_>^`_~48)SY*A`Rl7(VgG#rm){j^s9)m>*Ve_ygFAxbI_)L;14oXS@MZ7F
zc}i;=DO2$yK9k2BB|0TdeqdhitS1y&q>o
z0Y~0O!XIO2V<2A#?j{oJEp`i|n+3w}{uX2Bk)9>|Yn<$wGs#SNJ&C1MIhdo&KCIp1
zz}zji#`s(%>4@Wz-b$pc%*qn$ElD<(lxt^6t4#WUKM_}6Bm49x)`PAc!1?!4@P^d`
zFB===3ZzB^M$=}DV>3QuZ1NckoA|nw3HxnWhaKx5GYrp?Y-7mrV&z4r{}FX$T_=kVeBzY_p7UurayGY)p4@W0E;fKl)8WOgmcNVQrYF
z1$gI?isqxw#2+I4jQW$_oJnDh
z{m$X_z}tx9@Lr^mc!~9vF-dGpe{#2@(Z0%7VvPU13jsgU$bO0SmdWGTFQxePFO
zESo%MQvcOG6T2t0q&0HgzJakvGVy0V3qYg83i?v`-@e4_EEAk;0{Tus-w8{u>K@lP
zc9kW`%97?-vn^A`u_^nK3FaL(Aq@#ehL--34A&5YF|
z-4Mqo9s@UJPEz9-vabTh;<$f@J_nIXfJ>0uAKERvkCXQi6ZWbO>~B3D=ypQgMbvem
z?nM!kn@<|{-x3f0wkV6)FLLIIzgrzkL3%QoF9=*6eJy!oV*K`a>ZJ
zezz0l(m;3-Z)69IlFukDL#Zgz7{mq`4tQz1!!3x>zm#u>>_MZ1X;uf6#nmPiA@&&R
zrBG2rWOWeq{Dha}hBdU4vPXHTu*R=2_7u;XRI*_0>biAVRly*e&FTW{iWS8dS|e3?
z>#NG}_ic-)?k>MlpIFKJ1dL8>siQjB(4a71>9WElv$M1LP%PP2QO8+WQK^L4R%=CV
zeQh`#Q0myWp?pOEoUGns%&g%zR3
zKxJ)pZDmCb!jxbDE1635jg94%jp6yVK@5+-AFQaT3ZbD~NbcyD%y6$OaMc)7)18Vm#)
z*}JT$HslA}%W9wjRdVfW#jmU{1Or0spKafd1eEn90R{16oxi>^P}mS5TG*3TXjWNm
z<$b(@u|pP=3nPkx_|q6js=}3ALT!oCP#J&J~4i$
z`2HkPIqC_%jU*zU7|T;kKMyhWcGMFrKw5`_ByV{wYr=}Ywh
z>IrH{I`WBeIK}Bx5rexBKNI{eQX2A!u{p))nTWAP)DxV8l!ts`d`&U912OW$s3#~O
zZ9qOTuBO=gcStXyo*?}U=vm~4#m|VFUqJc{^#or*I)i*-Oii)%ZHSLuh>r;tBBddp
z7&lY=T#MLv73v8pNF~T8#>Nyo|J(&Wqn_Y%Nb8VKjDsmo?m)oXf_j3-ke)}r2XIFQ
zV<(U&xbnM3eidNWOe3ES_|^A}Jo(djY)p{OVRppM1k;f|!#KFhU>_jOLcI>?MO>SU
zd=cPZBfc$1p8lTxWu%?R9|qiTGh@#nPw;P%`jOuWco=Cv;Q-oiG4eYAk0EuVz8~;g
zw;K2p0o#!VP){+n1F>!i@)S=~j7)ig#b%yjVa3d|zdW1+cK){T|Mp-K#1v;syxxim
zi1f009Q_y_0m>U0yT<}uH#)F<#E5~q)xq_yb#=k|(7fpprT(^1Wlf;YAG)!wwo+*b
zHB^UhtZb;e%^#}EYMMRWg{Y#oIuHsk3n(FM&hw_v&dQ#Cduocyl^<4c;Uyso`V&XX
zG5VorC{P(uYQyXKI4(T-bRmLS0#zkSZ4<(cHG$AzV`6jhI&`HdK@Kzpg03LF=S}yA
zr23|Y`vS^zSESZkNpZ})>DB&VC@}r@{F?^H9^QFy`~-~4X3xKA7_Ip?4K5np^KXjt
k^Y#*NiMOaoTC||N+|2xU%5+{E2l;J$3a`IBe|8-BKecRT*Z=?k

literal 0
HcmV?d00001

diff --git a/dependencies/auxiliaryFunctions/mexFiles/VChooseK/VChooseKUncommented.c b/dependencies/auxiliaryFunctions/mexFiles/VChooseK/VChooseKUncommented.c
new file mode 100644
index 0000000..19d6e4b
--- /dev/null
+++ b/dependencies/auxiliaryFunctions/mexFiles/VChooseK/VChooseKUncommented.c
@@ -0,0 +1,899 @@
+#include "mex.h"
+#include 
+
+#ifndef mwSize
+#define mwSize  int
+#define mwIndex int
+#define MWSIZE_MAX 2147483647UL
+#define MWSIZE_MAX 2147483647UL
+#endif
+
+#define WARN_LIMIT   500000000L 
+#define ERROR_LIMIT 1000000000L  
+
+mwSize GetOutputLen(mwSize n, mwSize k, double *C_double);
+void BadInputTypeError(void);
+
+void Elem2_1Byte(INT8_T  *Xp, mwSize nX, INT8_T  *Yp, mwSize nY);
+void Elem2_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY);
+void Elem2_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY);
+void Elem2_8Byte(double  *Xp, mwSize nX, double  *Yp, mwSize nY);
+
+void Elem3_1Byte(INT8_T  *Xp, mwSize nX, INT8_T  *Yp, mwSize nY);
+void Elem3_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY);
+void Elem3_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY);
+void Elem3_8Byte(double  *Xp, mwSize nX, double  *Yp, mwSize nY);
+
+void Elem4_1Byte(INT8_T  *Xp, mwSize nX, INT8_T  *Yp, mwSize nY);
+void Elem4_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY);
+void Elem4_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY);
+void Elem4_8Byte(double  *Xp, mwSize nX, double  *Yp, mwSize nY);
+
+void Elem5_1Byte(INT8_T  *Xp, mwSize nX, INT8_T  *Yp, mwSize nY);
+void Elem5_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY);
+void Elem5_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY);
+void Elem5_8Byte(double  *Xp, mwSize nX, double  *Yp, mwSize nY);
+
+void ElemK_1Byte(INT8_T  *Xp, mwSize nX, INT8_T  *Yp, mwSize nY, mwSize K);
+void ElemK_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY, mwSize K);
+void ElemK_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY, mwSize K);
+void ElemK_8Byte(double  *Xp, mwSize nX, double  *Yp, mwSize nY, mwSize K);
+
+void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
+{
+  mwSize nX, nY, K;
+  const mxArray *X;
+  double nY_double;
+  int ElementSize;
+  
+  if (nrhs != 2) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:BadNInput", "2 inputs required.");
+  }
+  if (nlhs > 1) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:BadNInput", "1 output allowed.");
+  }
+  
+  X = prhs[0];
+  if (!mxIsNumeric(X) && !mxIsChar(X) & !mxIsLogical(X)) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:BadNInput",
+                       "Input array must be numerical, CHAR or LOGICAL.");
+  }
+  if (!mxIsDouble(prhs[1]) || mxGetNumberOfElements(prhs[1]) != 1) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:BadNInput",
+                       "Input K must be a numerical scalar.");
+  }
+  
+  nX = mxGetNumberOfElements(X);
+  K  = (mwSize) floor(mxGetScalar(prhs[1]) + 0.5);  
+  
+  if (nX == 0) {
+     plhs[0] = mxCreateNumericMatrix(0, 0, mxGetClassID(X), mxREAL);
+     return;
+  }
+  
+  if (K <= 1) {
+     if (K == 1) { 
+        plhs[0] = mxDuplicateArray(X);
+        mxSetM(plhs[0], nX);
+        mxSetN(plhs[0], 1);
+     } else if (K <= 0) {  
+       plhs[0] = mxCreateNumericMatrix(0, 0, mxGetClassID(X), mxREAL);
+     }
+     return;
+  }
+  
+  if (K >= nX) {
+     if (K == nX) {
+        plhs[0] = mxDuplicateArray(X);
+        mxSetM(plhs[0], 1);
+        mxSetN(plhs[0], nX);
+     } else {
+        mexWarnMsgIdAndTxt("JSimon:VChooseK:ShortInput",
+                           "Too short input: K > N.");
+        plhs[0] = mxCreateNumericMatrix(0, 0, mxGetClassID(X), mxREAL);
+     }
+     return;
+  }
+  
+  nY          = GetOutputLen(nX, K, &nY_double);
+  ElementSize = mxGetElementSize(X);
+  nY_double  *= ElementSize;
+  if (nY_double > WARN_LIMIT) {
+     if (nY_double < ERROR_LIMIT && nY_double < MWSIZE_MAX) {
+        mexWarnMsgIdAndTxt("JSimon:VChooseK:LargeOutput",
+                           "Output will be large and take a long time.");
+     } else {
+        mexErrMsgIdAndTxt("JSimon:VChooseK:TooLargeOutput",
+                          "Output would be too large.");
+     }
+  }
+  
+  plhs[0] = mxCreateNumericMatrix(nY, K, mxGetClassID(X), mxREAL);
+  
+  switch (K) {
+    case 2: 
+      switch (ElementSize) {
+        case 8:  Elem2_8Byte(mxGetPr(X), nX, mxGetPr(plhs[0]), nY);
+                 break;
+        case 4:  Elem2_4Byte((INT32_T *) mxGetData(X), nX,
+                             (INT32_T *) mxGetData(plhs[0]), nY);
+                 break;
+        case 2:  Elem2_2Byte((INT16_T *) mxGetData(X), nX,
+                             (INT16_T *) mxGetData(plhs[0]), nY);
+                 break;
+        case 1:  Elem2_1Byte((INT8_T *) mxGetData(X), nX,
+                             (INT8_T *) mxGetData(plhs[0]), nY);
+                 break;
+        default: BadInputTypeError();
+      }
+      break;
+        
+    case 3:  
+      switch (ElementSize) {
+        case 8:  Elem3_8Byte(mxGetPr(X), nX, mxGetPr(plhs[0]), nY);
+                 break;
+        case 4:  Elem3_4Byte((INT32_T *) mxGetData(X), nX,
+                             (INT32_T *) mxGetData(plhs[0]), nY);
+                 break;
+        case 2:  Elem3_2Byte((INT16_T *) mxGetData(X), nX,
+                             (INT16_T *) mxGetData(plhs[0]), nY);
+                 break;
+        case 1:  Elem3_1Byte((INT8_T *) mxGetData(X), nX,
+                             (INT8_T *) mxGetData(plhs[0]), nY);
+                 break;
+        default: BadInputTypeError();
+      }
+      break;
+        
+    case 4:  
+      switch (ElementSize) {
+        case 8:  Elem4_8Byte(mxGetPr(X), nX, mxGetPr(plhs[0]), nY);
+                 break;
+        case 4:  Elem4_4Byte((INT32_T *) mxGetData(X), nX,
+                             (INT32_T *) mxGetData(plhs[0]), nY);
+                 break;
+        case 2:  Elem4_2Byte((INT16_T *) mxGetData(X), nX,
+                             (INT16_T *) mxGetData(plhs[0]), nY);
+                 break;
+        case 1:  Elem4_1Byte((INT8_T *) mxGetData(X), nX,
+                             (INT8_T *) mxGetData(plhs[0]), nY);
+                 break;
+        default: BadInputTypeError();
+      }
+      break;
+      
+    case 5: 
+      switch (ElementSize) {
+        case 8:  Elem5_8Byte(mxGetPr(X), nX, mxGetPr(plhs[0]), nY);
+                 break;
+        case 4:  Elem5_4Byte((INT32_T *) mxGetData(X), nX,
+                             (INT32_T *) mxGetData(plhs[0]), nY);
+                 break;
+        case 2:  Elem5_2Byte((INT16_T *) mxGetData(X), nX,
+                             (INT16_T *) mxGetData(plhs[0]), nY);
+                 break;
+        case 1:  Elem5_1Byte((INT8_T *) mxGetData(X), nX,
+                             (INT8_T *) mxGetData(plhs[0]), nY);
+                 break;
+        default: BadInputTypeError();
+      }
+      break;
+
+    default:  
+      switch (ElementSize) {
+        case 8:  ElemK_8Byte(mxGetPr(X), nX, mxGetPr(plhs[0]), nY, K);
+                 break;
+        case 4:  ElemK_4Byte((INT32_T *) mxGetData(X), nX,
+                             (INT32_T *) mxGetData(plhs[0]), nY, K);
+                 break;
+        case 2:  ElemK_2Byte((INT16_T *) mxGetData(X), nX,
+                             (INT16_T *) mxGetData(plhs[0]), nY, K);
+                 break;
+        case 1:  ElemK_1Byte((INT8_T *) mxGetData(X), nX,
+                             (INT8_T *) mxGetData(plhs[0]), nY, K);
+                 break;
+        default: BadInputTypeError();
+      }
+  }
+  
+  return;
+}
+
+void BadInputTypeError(void)
+{
+  mexErrMsgIdAndTxt("JSimon:VChooseK:BadInputType",
+                    "Input must have 1, 2, 4 or 8 bytes per element.");
+}
+
+mwSize GetOutputLen(mwSize n, mwSize k, double *C_double)
+{
+  mwSize i, ai, bi;
+  double ad, bd;
+  
+  bi = n - k;
+  ai = bi + 1;
+  ad = (double) ai;
+  bd = (double) bi;
+  for (i = 2; i <= k; i++) {
+     ai += (ai * bi) / i;
+     ad += (ad * bd) / i;
+  }
+  
+  *C_double = ad;
+  
+  return (ai);
+}
+
+void Elem2_8Byte(double *Xp, mwSize nX, double *Yp, mwSize nY)
+{
+  double *Xq, *Xf, *Y1, *Y2;
+  
+  Y1 = Yp;             
+  Y2 = Y1 + nY;     
+  for (Xf = Xp + nX - 1; Xp < Xf; Xp++) {
+    for (Xq = Xp + 1; Xq <= Xf; Xq++) {  
+      *Y1++ = *Xp;     
+      *Y2++ = *Xq;     
+    }
+  }
+
+  return;
+}
+
+void Elem2_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY)
+{
+  INT32_T *Xq, *Xf, *Y1, *Y2;
+
+  Y1 = Yp;             
+  Y2 = Y1 + nY;        
+  for (Xf = Xp + nX - 1; Xp < Xf; Xp++) {
+    for (Xq = Xp + 1; Xq <= Xf; Xq++) {
+      *Y1++ = *Xp;     
+      *Y2++ = *Xq;     
+    }
+  }
+
+  return;
+}
+
+void Elem2_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY)
+{
+  INT16_T *Xq, *Xf, *Y1, *Y2;
+
+  Y1 = Yp;
+  Y2 = Y1 + nY;        
+  for (Xf = Xp + nX - 1; Xp < Xf; Xp++) {
+    for (Xq = Xp + 1; Xq <= Xf; Xq++) {
+      *Y1++ = *Xp;     
+      *Y2++ = *Xq;     
+    }
+  }
+
+  return;
+}
+
+void Elem2_1Byte(INT8_T *Xp, mwSize nX, INT8_T *Yp, mwSize nY)
+{
+  
+  INT8_T *Xq, *Xf, *Y1, *Y2;
+
+  Y1 = Yp; 
+  Y2 = Y1 + nY;        
+  for (Xf = Xp + nX - 1; Xp < Xf; Xp++) {
+    for (Xq = Xp + 1; Xq <= Xf; Xq++) {
+      *Y1++ = *Xp;     
+      *Y2++ = *Xq;     
+    }
+  }
+
+  return;
+}
+
+void Elem3_8Byte(double *Xp, mwSize nX, double *Yp, mwSize nY)
+{
+  double *Xq, *Xr, *X2f, *X3f, *Y1, *Y2, *Y3;
+  
+  Y1  = Yp;           
+  Y2  = Y1 + nY;   
+  Y3  = Y2 + nY;   
+  X3f = Xp + nX;   
+  X2f = X3f - 1;    
+  
+  for ( ; Xp <= X2f; Xp++) {
+    for (Xq = Xp + 1; Xq < X2f; Xq++) {         
+      for (Xr = Xq + 1; Xr < X3f; Xr++) {
+        *Y1++ = *Xp;  
+        *Y2++ = *Xq;
+        *Y3++ = *Xr;  
+      }
+    }
+  }
+
+  return;
+}
+
+void Elem3_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY)
+{
+  INT32_T *Xq, *Xr, *X2f, *X3f, *Y1, *Y2, *Y3;
+  
+  Y1  = Yp;           
+  Y2  = Y1 + nY;   
+  Y3  = Y2 + nY;   
+  X3f = Xp + nX;   
+  X2f = X3f - 1;     
+  
+  for ( ; Xp <= X2f; Xp++) {
+    for (Xq = Xp + 1; Xq < X2f; Xq++) {          
+      for (Xr = Xq + 1; Xr < X3f; Xr++) {
+        *Y1++ = *Xp;  
+        *Y2++ = *Xq;
+        *Y3++ = *Xr;  
+      }
+    }
+  }
+  
+  return;
+}
+
+
+void Elem3_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY)
+{
+  INT16_T *Xq, *Xr, *X2f, *X3f, *Y1, *Y2, *Y3;
+  
+  Y1  = Yp;           
+  Y2  = Y1 + nY;   
+  Y3  = Y2 + nY;   
+  X3f = Xp + nX;   
+  X2f = X3f - 1;     
+  
+  for ( ; Xp <= X2f; Xp++) {
+    for (Xq = Xp + 1; Xq < X2f; Xq++) {    
+      for (Xr = Xq + 1; Xr < X3f; Xr++) {
+        *Y1++ = *Xp;  
+        *Y2++ = *Xq;
+        *Y3++ = *Xr;  
+      }
+    }
+  }
+  
+  return;
+}
+
+
+void Elem3_1Byte(INT8_T *Xp, mwSize nX, INT8_T *Yp, mwSize nY)
+{
+  INT8_T *Xq, *Xr, *X2f, *X3f, *Y1, *Y2, *Y3;
+  
+  Y1  = Yp;      
+  Y2  = Y1 + nY;     
+  Y3  = Y2 + nY;     
+  X3f = Xp + nX;    
+  X2f = X3f - 1;      
+  
+  for ( ; Xp <= X2f; Xp++) {
+    for (Xq = Xp + 1; Xq < X2f; Xq++) {     
+      for (Xr = Xq + 1; Xr < X3f; Xr++) {
+        *Y1++ = *Xp;  
+        *Y2++ = *Xq;
+        *Y3++ = *Xr;  
+      }
+    }
+  }
+  
+  return;
+}
+
+void Elem4_8Byte(double *Xp, mwSize nX, double *Yp, mwSize nY)
+{
+  double *Xq, *Xr, *Xs, *X2f, *X4f, *Y1, *Y2, *Y3, *Y4;
+  
+  Y1  = Yp;              
+  Y2  = Y1 + nY;      
+  Y3  = Y2 + nY;      
+  Y4  = Y3 + nY;      
+  X4f = Xp + nX;      
+  X2f = X4f - 2;        
+  
+  for ( ; Xp <= X2f; Xp++) {
+    for (Xq = Xp + 1; Xq < X2f; Xq++) {
+      for (Xr = Xq + 1; Xr <= X4f; Xr++) {      
+        for (Xs = Xr + 1; Xs < X4f; Xs++) {
+          *Y1++ = *Xp; 
+          *Y2++ = *Xq;
+          *Y3++ = *Xr;
+          *Y4++ = *Xs; 
+        }
+      }
+    }
+  }
+
+  return;
+}
+
+void Elem4_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY)
+{
+  INT32_T *Xq, *Xr, *Xs, *X2f, *X4f, *Y1, *Y2, *Y3, *Y4;
+  
+  Y1  = Yp;            
+  Y2  = Y1 + nY;         
+  Y3  = Y2 + nY;        
+  Y4  = Y3 + nY;        
+  X4f = Xp + nX;
+  X2f = X4f - 2;        
+  
+  for ( ; Xp <= X2f; Xp++) {
+    for (Xq = Xp + 1; Xq < X2f; Xq++) {
+      for (Xr = Xq + 1; Xr <= X4f; Xr++) {       
+        for (Xs = Xr + 1; Xs < X4f; Xs++) {
+          *Y1++ = *Xp;  
+          *Y2++ = *Xq;
+          *Y3++ = *Xr;
+          *Y4++ = *Xs;  
+        }
+      }
+    }
+  }
+  
+  return;
+}
+
+void Elem4_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY)
+{
+  INT16_T *Xq, *Xr, *Xs, *X2f, *X4f, *Y1, *Y2, *Y3, *Y4;
+  
+  Y1  = Yp;            
+  Y2  = Y1 + nY;      
+  Y3  = Y2 + nY;      
+  Y4  = Y3 + nY;      
+  X4f = Xp + nX;      
+  X2f = X4f - 2;        
+  
+  for ( ; Xp <= X2f; Xp++) {
+    for (Xq = Xp + 1; Xq < X2f; Xq++) {
+      for (Xr = Xq + 1; Xr <= X4f; Xr++) {      
+        for (Xs = Xr + 1; Xs < X4f; Xs++) {
+          *Y1++ = *Xp;
+          *Y2++ = *Xq;
+          *Y3++ = *Xr;
+          *Y4++ = *Xs; 
+        }
+      }
+    }
+  }
+  
+  return;
+}
+
+void Elem4_1Byte(INT8_T *Xp, mwSize nX, INT8_T *Yp, mwSize nY)
+{
+  INT8_T *Xq, *Xr, *Xs, *X2f, *X4f, *Y1, *Y2, *Y3, *Y4;
+  
+  Y1  = Yp;              
+  Y2  = Y1 + nY;      
+  Y3  = Y2 + nY;      
+  Y4  = Y3 + nY;      
+  X4f = Xp + nX;      
+  X2f = X4f - 2;        
+  
+  for ( ; Xp <= X2f; Xp++) {
+    for (Xq = Xp + 1; Xq < X2f; Xq++) {
+      for (Xr = Xq + 1; Xr <= X4f; Xr++) {    
+        for (Xs = Xr + 1; Xs < X4f; Xs++) {
+          *Y1++ = *Xp;  
+          *Y2++ = *Xq;
+          *Y3++ = *Xr;
+          *Y4++ = *Xs;  
+        }
+      }
+    }
+  }
+  
+  return;
+}
+
+void Elem5_8Byte(double *Xp, mwSize nX, double *Yp, mwSize nY)
+{
+  double *Xq, *Xr, *Xs, *Xt, *X5f, *X3f, *X1f,
+         *Y1, *Y2, *Y3, *Y4, *Y5;
+  
+  Y1  = Yp;               
+  Y2  = Y1 + nY;       
+  Y3  = Y2 + nY;       
+  Y4  = Y3 + nY;       
+  Y5  = Y4 + nY;      
+  X5f = Xp + nX;      
+                          
+  X3f = X5f - 2;    
+                          
+  X1f = X3f - 2;    
+  
+  for ( ; Xp < X1f; Xp++) {
+    for (Xq = Xp + 1; Xq <= X3f; Xq++) {
+      for (Xr = Xq + 1; Xr < X3f; Xr++) {
+        for (Xs = Xr + 1; Xs <= X5f; Xs++) {
+          for (Xt = Xs + 1; Xt < X5f; Xt++) {
+            *Y1++ = *Xp; 
+            *Y2++ = *Xq;
+            *Y3++ = *Xr;
+            *Y4++ = *Xs;
+            *Y5++ = *Xt; 
+          }
+        }
+      }
+    }
+  }
+
+  return;
+}
+
+void Elem5_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY)
+{
+  INT32_T *Xq, *Xr, *Xs, *Xt, *X5f, *X3f, *X1f,
+          *Y1, *Y2, *Y3, *Y4, *Y5;
+  
+  Y1  = Yp;               
+  Y2  = Y1 + nY;       
+  Y3  = Y2 + nY;       
+  Y4  = Y3 + nY;       
+  Y5  = Y4 + nY;       
+  X5f = Xp + nX;       
+  X3f = X5f - 2;         
+  X1f = X3f - 2;         
+  
+  for ( ; Xp < X1f; Xp++) {
+    for (Xq = Xp + 1; Xq <= X3f; Xq++) {
+      for (Xr = Xq + 1; Xr < X3f; Xr++) {
+        for (Xs = Xr + 1; Xs <= X5f; Xs++) {
+          for (Xt = Xs + 1; Xt < X5f; Xt++) {
+            *Y1++ = *Xp; 
+            *Y2++ = *Xq;
+            *Y3++ = *Xr;
+            *Y4++ = *Xs;
+            *Y5++ = *Xt;  
+          }
+        }
+      }
+    }
+  }
+
+  return;
+}
+
+void Elem5_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY)
+{
+  INT16_T *Xq, *Xr, *Xs, *Xt, *X5f, *X3f, *X1f,
+          *Y1, *Y2, *Y3, *Y4, *Y5;
+  
+  Y1  = Yp;               
+  Y2  = Y1 + nY;       
+  Y3  = Y2 + nY;       
+  Y4  = Y3 + nY;       
+  Y5  = Y4 + nY;       
+  X5f = Xp + nX;       
+  X3f = X5f - 2;         
+  X1f = X3f - 2;        
+  
+  for ( ; Xp < X1f; Xp++) {
+    for (Xq = Xp + 1; Xq <= X3f; Xq++) {
+      for (Xr = Xq + 1; Xr < X3f; Xr++) {
+        for (Xs = Xr + 1; Xs <= X5f; Xs++) {
+          for (Xt = Xs + 1; Xt < X5f; Xt++) {
+            *Y1++ = *Xp;
+            *Y2++ = *Xq;
+            *Y3++ = *Xr;
+            *Y4++ = *Xs;
+            *Y5++ = *Xt;  
+          }
+        }
+      }
+    }
+  }
+  
+  return;
+}
+
+void Elem5_1Byte(INT8_T *Xp, mwSize nX, INT8_T *Yp, mwSize nY)
+{
+  INT8_T *Xq, *Xr, *Xs, *Xt, *X5f, *X3f, *X1f,
+         *Y1, *Y2, *Y3, *Y4, *Y5;
+  
+  Y1  = Yp;               
+  Y2  = Y1 + nY;       
+  Y3  = Y2 + nY;       
+  Y4  = Y3 + nY;       
+  Y5  = Y4 + nY;       
+  X5f = Xp + nX;       
+  X3f = X5f - 2;         
+  X1f = X3f - 2;         
+  
+  for ( ; Xp < X1f; Xp++) {
+    for (Xq = Xp + 1; Xq <= X3f; Xq++) {
+      for (Xr = Xq + 1; Xr < X3f; Xr++) {
+        for (Xs = Xr + 1; Xs <= X5f; Xs++) {
+          for (Xt = Xs + 1; Xt < X5f; Xt++) {
+            *Y1++ = *Xp;  
+            *Y2++ = *Xq;
+            *Y3++ = *Xr;
+            *Y4++ = *Xs;
+            *Y5++ = *Xt;  
+          }
+        }
+      }
+    }
+  }
+  
+  return;
+}
+
+void ElemK_8Byte(double *Xp, mwSize nX, double *Yp, mwSize nY, mwSize K)
+{
+  double *Xq, *Xr,
+         **A, **Ap, **Af, *Yq,
+         **B, **Bp, **Bf, **Bq, **lastB, **F, **Fp, **lastF;
+    
+  if ((B = (double **) mxMalloc(K * sizeof(double *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [B].");
+  }
+  if ((F = (double **) mxMalloc(K * sizeof(double *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [F].");
+  }
+
+  Bf = B + K;
+  Fp = F;
+  Xq = Xp;
+  Xr = Xp + nX - K + 1;
+  for (Bp = B; Bp < Bf; ) {
+     *Bp++ = Xq++;
+     *Fp++ = Xr++;
+  }
+  
+  if ((A = (double **) mxMalloc(K * sizeof(double *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [A].");
+  }
+  
+  Af = A + K;
+  Yq = Yp;
+  for (Ap = A; Ap < Af; Ap++) {
+     *Ap = Yq;
+     Yq += nY;
+  }
+  
+  lastB = B + K - 1;
+  lastF = F + K - 1;
+  while (1) {
+    Bq = lastB;
+    Fp = lastF;
+    while (*Bq < *Fp) {
+       for (Bp = B, Ap = A; Ap < Af; Ap++) {
+         **Ap = **Bp++;
+         (*Ap)++;         
+       }
+       (*Bq)++;           
+    }
+    
+    do {
+       Bq--;
+       Fp--;
+       if (Bq < B) {   
+          mxFree(A); 
+          mxFree(B);
+          mxFree(F);
+          return;        
+       }
+    } while (*Bq >= *Fp); 
+    
+    (*Bq)++;                
+    while (++Bq < Bf) {   
+      *Bq = *(Bq - 1) + 1; 
+    }
+  } 
+  
+}
+
+void ElemK_4Byte(INT32_T *Xp, mwSize nX, INT32_T *Yp, mwSize nY, mwSize K)
+{
+  INT32_T *Xq, *Xr,
+          **A, **Ap, **Af, *Yq,
+          **B, **Bp, **Bf, **Bq, **lastB, **F, **Fp, **lastF;
+    
+  if ((B = (INT32_T **) mxMalloc(K * sizeof(INT32_T *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [B].");
+  }
+  if ((F = (INT32_T **) mxMalloc(K * sizeof(INT32_T *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [F].");
+  }
+
+  Bf = B + K;
+  Fp = F;
+  Xq = Xp;
+  Xr = Xp + nX - K + 1;
+  for (Bp = B; Bp < Bf; ) {
+     *Bp++ = Xq++;
+     *Fp++ = Xr++;
+  }
+  
+  if ((A = (INT32_T **) mxMalloc(K * sizeof(INT32_T *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [A].");
+  }
+  
+  Af = A + K;
+  Yq = Yp;
+  for (Ap = A; Ap < Af; Ap++) {
+     *Ap = Yq;
+     Yq += nY;
+  }
+  
+  lastB = B + K - 1;
+  lastF = F + K - 1;
+  while (1) {
+    Bq = lastB;
+    Fp = lastF;
+    while (*Bq < *Fp) {
+       for (Bp = B, Ap = A; Ap < Af; Ap++) {
+         **Ap = **Bp++;
+         (*Ap)++;           
+       }
+       (*Bq)++;             
+    }
+    
+    
+    do {
+       Bq--;
+       Fp--;
+       if (Bq < B) {        
+          mxFree(A);      
+          mxFree(B);
+          mxFree(F);
+          return;           
+       }
+    } while (*Bq >= *Fp);  
+    
+    (*Bq)++;                
+    while (++Bq < Bf) { 
+      *Bq = *(Bq - 1) + 1; 
+    }
+  }  
+  
+}
+
+void ElemK_2Byte(INT16_T *Xp, mwSize nX, INT16_T *Yp, mwSize nY, mwSize K)
+{
+  INT16_T *Xq, *Xr,
+          **A, **Ap, **Af, *Yq,
+          **B, **Bp, **Bf, **Bq, **lastB, **F, **Fp, **lastF;
+  
+  if ((B = (INT16_T **) mxMalloc(K * sizeof(INT16_T *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [B].");
+  }
+  if ((F = (INT16_T **) mxMalloc(K * sizeof(INT16_T *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [F].");
+  }
+
+  Bf = B + K;
+  Fp = F;
+  Xq = Xp;
+  Xr = Xp + nX - K + 1;
+  for (Bp = B; Bp < Bf; ) {
+     *Bp++ = Xq++;
+     *Fp++ = Xr++;
+  }
+  
+  if ((A = (INT16_T **) mxMalloc(K * sizeof(INT16_T *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [A].");
+  }
+  
+  Af = A + K;
+  Yq = Yp;
+  for (Ap = A; Ap < Af; Ap++) {
+     *Ap = Yq;
+     Yq += nY;
+  }
+  
+  lastB = B + K - 1;
+  lastF = F + K - 1;
+  while (1) {
+    Bq = lastB;
+    Fp = lastF;
+    while (*Bq < *Fp) {
+       for (Bp = B, Ap = A; Ap < Af; Ap++) {
+         **Ap = **Bp++;
+         (*Ap)++;          
+       }
+       (*Bq)++;            
+    }
+    
+    do {
+       Bq--;
+       Fp--;
+       if (Bq < B) {       
+          mxFree(A);     
+          mxFree(B);
+          mxFree(F);
+          return;           
+       }
+    } while (*Bq >= *Fp);  
+    
+    (*Bq)++;                
+    while (++Bq < Bf) {   
+      *Bq = *(Bq - 1) + 1;
+    }
+  }
+  
+}
+
+void ElemK_1Byte(INT8_T *Xp, mwSize nX, INT8_T *Yp, mwSize nY, mwSize K)
+{
+  INT8_T  *Xq, *Xr,
+          **A, **Ap, **Af, *Yq,
+          **B, **Bp, **Bf, **Bq, **lastB, **F, **Fp, **lastF;
+    
+  if ((B = (INT8_T **) mxMalloc(K * sizeof(INT8_T *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [B].");
+  }
+  if ((F = (INT8_T **) mxMalloc(K * sizeof(INT8_T *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [F].");
+  }
+
+  Bf = B + K;
+  Fp = F;
+  Xq = Xp;
+  Xr = Xp + nX - K + 1;
+  for (Bp = B; Bp < Bf; ) {
+     *Bp++ = Xq++;
+     *Fp++ = Xr++;
+  }
+  
+  if ((A = (INT8_T **) mxMalloc(K * sizeof(INT8_T *))) == NULL) {
+     mexErrMsgIdAndTxt("JSimon:VChooseK:NoMemory",
+                       "Cannot get memory for [A].");
+  }
+  
+  Af = A + K;
+  Yq = Yp;
+  for (Ap = A; Ap < Af; Ap++) {
+     *Ap = Yq;
+     Yq += nY;
+  }
+  
+  lastB = B + K - 1;
+  lastF = F + K - 1;
+  while (1) {
+    Bq = lastB;
+    Fp = lastF;
+    while (*Bq < *Fp) {
+       for (Bp = B, Ap = A; Ap < Af; Ap++) {
+         **Ap = **Bp++;
+         (*Ap)++;          
+       }
+       (*Bq)++;             
+    }
+    
+    do {
+       Bq--;
+       Fp--;
+       if (Bq < B) {        
+          mxFree(A);      
+          mxFree(B);
+          mxFree(F);
+          return;           
+       }
+    } while (*Bq >= *Fp); 
+    
+    (*Bq)++;                
+    while (++Bq < Bf) { 
+      *Bq = *(Bq - 1) + 1; 
+    }
+  }  
+  
+}
diff --git a/dependencies/auxiliaryFunctions/mexFiles/VChooseK/license.txt b/dependencies/auxiliaryFunctions/mexFiles/VChooseK/license.txt
new file mode 100644
index 0000000..02a8987
--- /dev/null
+++ b/dependencies/auxiliaryFunctions/mexFiles/VChooseK/license.txt
@@ -0,0 +1,24 @@
+Copyright (c) 2009, Jan Simon
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in
+      the documentation and/or other materials provided with the distribution
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
diff --git a/dependencies/auxiliaryFunctions/mexFiles/VChooseK/test.m b/dependencies/auxiliaryFunctions/mexFiles/VChooseK/test.m
new file mode 100644
index 0000000..9455292
--- /dev/null
+++ b/dependencies/auxiliaryFunctions/mexFiles/VChooseK/test.m
@@ -0,0 +1,19 @@
+nTest = 1e3;
+
+
+tNChooseK = 0;
+tVChooseK = 0;
+
+
+for iTest=1:nTest
+    t1 = tic;
+    C1 = nchoosek(1:4,2);
+    tNChooseK = toc(t1);
+    
+    t2 = tic;
+    C2 = VChooseK(1:4,2);
+    tVChooseK = toc(t2);
+end
+fprintf('t1 = %g\n',tNChooseK)
+fprintf('t2 = %g\n',tVChooseK)
+fprintf('t1/t2 = %g\n',tNChooseK/tVChooseK)
\ No newline at end of file