Initial formatting with clang format file

examples/AdjointSensitivity/AdjointSensitivity.cpp

@@ -57,14 +57,13 @@
  *
  */
 
-
-#include <iostream>
 #include <iomanip>
+#include <iostream>
 
 #include <Model/PowerFlow/Bus/BusSlack.hpp>
 #include <Model/PowerFlow/Generator4/Generator4.hpp>
-#include <SystemModel.hpp>
 #include <Solver/Dynamic/Ida.hpp>
+#include <SystemModel.hpp>
 #include <Utilities/Testing.hpp>
 
 /*
@@ -112,7 +111,6 @@ int main()
     idas->configureQuadrature();
     idas->initializeQuadrature();
 
-
     idas->runSimulation(0.1, 2);
     idas->saveInitialCondition();
 
@@ -138,30 +136,29 @@ int main()
     std::cout << "\n\nCost of computing objective function:\n\n";
     idas->printFinalStats();
 
-    const double g1 = Q[0];
+    const double g1  = Q[0];
     const double eps = 2e-3;
 
     // Compute gradient of the objective function numerically
     std::vector<double> dGdp(model->size_opt());
 
-    for (unsigned i=0; i<model->size_opt(); ++i)
+    for (unsigned i = 0; i < model->size_opt(); ++i)
     {
-      model->param()[i] += eps;
-      idas->getSavedInitialCondition();
-      idas->initializeSimulation(t_init);
-      idas->initializeQuadrature();
-      idas->runSimulationQuadrature(t_final,100);
-
-      std::cout << "\n\nCost of computing derivative with respect to parameter "
-                << i << ":\n\n";
-      idas->printFinalStats();
-      double g2 = Q[0];
-
-      // restore parameter to original value
-      model->param()[i] -= eps;
-
-      // Evaluate dG/dp numerically
-      dGdp[i] = (g2 - g1)/eps;
+        model->param()[i] += eps;
+        idas->getSavedInitialCondition();
+        idas->initializeSimulation(t_init);
+        idas->initializeQuadrature();
+        idas->runSimulationQuadrature(t_final, 100);
+
+        std::cout << "\n\nCost of computing derivative with respect to parameter " << i << ":\n\n";
+        idas->printFinalStats();
+        double g2 = Q[0];
+
+        // restore parameter to original value
+        model->param()[i] -= eps;
+
+        // Evaluate dG/dp numerically
+        dGdp[i] = (g2 - g1) / eps;
     }
 
     // Compute gradient of the objective function using adjoint method
@@ -185,15 +182,15 @@ int main()
     int retval = 0;
     std::cout << "\n\nComparison of numerical and adjoint results:\n\n";
     double* neg_dGdp = idas->getAdjointIntegral();
-    for (unsigned i=0; i<model->size_opt(); ++i)
+    for (unsigned i = 0; i < model->size_opt(); ++i)
     {
-        std::cout << "dG/dp" << i << " (numerical) = " <<      dGdp[i] << "\n";
+        std::cout << "dG/dp" << i << " (numerical) = " << dGdp[i] << "\n";
         std::cout << "dG/dp" << i << " (adjoint)   = " << -neg_dGdp[i] << "\n\n";
-        if(!isEqual(dGdp[i], -neg_dGdp[i], 10*eps))
-            --retval; 
+        if (!isEqual(dGdp[i], -neg_dGdp[i], 10 * eps))
+            --retval;
     }
 
-    if(retval < 0)
+    if (retval < 0)
     {
         std::cout << "The two results differ beyond solver tolerance!\n";
     }

examples/DistributedGeneratorTest/DGTest.cpp

@@ -1,85 +1,85 @@
 
 
-#include <iostream>
-#include <iomanip>
 #include <cmath>
-#include <fstream>
 #include <filesystem>
+#include <fstream>
+#include <iomanip>
+#include <iostream>
 #include <math.h>
 
 #include <Model/PowerElectronics/DistributedGenerator/DistributedGenerator.hpp>
 
-
 /**
  * @brief Testing for the Distributed Generators outputs
- * 
- * @param argc 
- * @param argv 
- * @return int 
+ *
+ * @param argc
+ * @param argv
+ * @return int
  */
-int main(int argc, char const *argv[])
+int main(int argc, char const* argv[])
 {
-
-	ModelLib::DistributedGeneratorParameters<double, size_t> parms;
-	//Parameters from MATLAB Microgrid code for first DG
-	parms.wb_ = 2.0*M_PI*50.0;
-	parms.wc_ = 31.41;
-	parms.mp_ = 9.4e-5;
-	parms.Vn_ = 380;
-	parms.nq_ = 1.3e-3;
-	parms.F_ = 0.75;
-	parms.Kiv_ = 420.0;
-	parms.Kpv_ = 0.1;
-	parms.Kic_ = 20.0 * 1.0e3;
-	parms.Kpc_ = 15.0;
-	parms.Cf_ = 50.0e-6;
-	parms.rLf_ = 0.1;
-	parms.Lf_ = 1.35e-3;
-	parms.rLc_ = 0.03;
-	parms.Lc_ = 0.35e-3;
 
-	ModelLib::DistributedGenerator<double, size_t> *dg = new ModelLib::DistributedGenerator<double, size_t>(0, parms, true);
+    ModelLib::DistributedGeneratorParameters<double, size_t> parms;
+    // Parameters from MATLAB Microgrid code for first DG
+    parms.wb_  = 2.0 * M_PI * 50.0;
+    parms.wc_  = 31.41;
+    parms.mp_  = 9.4e-5;
+    parms.Vn_  = 380;
+    parms.nq_  = 1.3e-3;
+    parms.F_   = 0.75;
+    parms.Kiv_ = 420.0;
+    parms.Kpv_ = 0.1;
+    parms.Kic_ = 20.0 * 1.0e3;
+    parms.Kpc_ = 15.0;
+    parms.Cf_  = 50.0e-6;
+    parms.rLf_ = 0.1;
+    parms.Lf_  = 1.35e-3;
+    parms.rLc_ = 0.03;
+    parms.Lc_  = 0.35e-3;
+
+    ModelLib::DistributedGenerator<double, size_t>* dg =
+        new ModelLib::DistributedGenerator<double, size_t>(0, parms, true);
 
-	std::vector<double> t1(16,0.0);
-	std::vector<double> t2{0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0,1.1,1.2,1.3,1.4,1.5};
+    std::vector<double> t1(16, 0.0);
+    std::vector<double> t2{0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5};
 
-	dg->allocate();
+    dg->allocate();
 
-	dg->y() = t2;
-	dg->yp() = t1;
+    dg->y()  = t2;
+    dg->yp() = t1;
 
-	dg->evaluateResidual();
+    dg->evaluateResidual();
 
-	std::cout << "Output: {";
-	for (double i : dg->getResidual())
-	{
-		printf("%e ,", i);
-	}
-	std::cout << "}\n";
+    std::cout << "Output: {";
+    for (double i : dg->getResidual())
+    {
+        printf("%e ,", i);
+    }
+    std::cout << "}\n";
 
-	//Generated from matlab code with same parameters and inputs
-	std::vector<double> true_vec{3.141592277589793e+02,
-     8.941907747838389e-01,
-     1.846733023014284e+00,
-     3.141592277589793e+02,
-     1.014543000000000e+02,
-    -1.507680000000000e+01,
-     3.787993500000000e+02,
-    -1.300000000000000e+00,
-     2.899095146477517e+02,
-     2.939138495559215e+02,
-     1.507210571826699e+07,
-     1.659799832843673e+07,
-    -7.591593003913325e+03,
-    -8.376991073310774e+03,
-     3.337988298081817e+03,
-     2.684419146397466e+03};
+    // Generated from matlab code with same parameters and inputs
+    std::vector<double> true_vec{3.141592277589793e+02,
+                                 8.941907747838389e-01,
+                                 1.846733023014284e+00,
+                                 3.141592277589793e+02,
+                                 1.014543000000000e+02,
+                                 -1.507680000000000e+01,
+                                 3.787993500000000e+02,
+                                 -1.300000000000000e+00,
+                                 2.899095146477517e+02,
+                                 2.939138495559215e+02,
+                                 1.507210571826699e+07,
+                                 1.659799832843673e+07,
+                                 -7.591593003913325e+03,
+                                 -8.376991073310774e+03,
+                                 3.337988298081817e+03,
+                                 2.684419146397466e+03};
 
-	std::cout << "Test the Relative Error\n";
-	 for (size_t i = 0; i < true_vec.size(); i++)
-	 {
-		printf("%e ,\n", (true_vec[i] - dg->getResidual()[i]) / true_vec[i]);
-	 }
+    std::cout << "Test the Relative Error\n";
+    for (size_t i = 0; i < true_vec.size(); i++)
+    {
+        printf("%e ,\n", (true_vec[i] - dg->getResidual()[i]) / true_vec[i]);
+    }
 
-	return 0;
+    return 0;
 }

examples/DynamicConstrainedOpt/DynamicConstrainedOpt.cpp

@@ -57,19 +57,17 @@
  *
  */
 
-
-#include <iostream>
 #include <iomanip>
+#include <iostream>
 
+#include <IpIpoptApplication.hpp>
+#include <IpSolveStatistics.hpp>
 #include <Model/PowerFlow/Bus/BusSlack.hpp>
 #include <Model/PowerFlow/Generator2/Generator2.hpp>
-#include <SystemModel.hpp>
 #include <Solver/Dynamic/Ida.hpp>
-
-#include <IpIpoptApplication.hpp>
-#include <IpSolveStatistics.hpp>
-#include <Solver/Optimization/DynamicObjective.hpp>
 #include <Solver/Optimization/DynamicConstraint.hpp>
+#include <Solver/Optimization/DynamicObjective.hpp>
+#include <SystemModel.hpp>
 #include <Utilities/Testing.hpp>
 
 int main()
@@ -114,7 +112,7 @@ int main()
     {
         gen->V() = 0.0;
         idas->runSimulation(t_clear, 2);
-        gen->V() = 1.0;
+        gen->V()     = 1.0;
         gen->theta() = -0.01;
         idas->saveInitialCondition();
     }
@@ -131,13 +129,14 @@ int main()
     // Initialize the IpoptApplication and process the options
     Ipopt::ApplicationReturnStatus status;
     status = ipoptApp->Initialize();
-    if (status != Ipopt::Solve_Succeeded) {
+    if (status != Ipopt::Solve_Succeeded)
+    {
         std::cout << "\n\n*** Initialization failed! ***\n\n";
         return (int) status;
     }
 
     // Set solver tolerance
-    const double tol = 1e-4; 
+    const double tol = 1e-4;
 
     // Configure Ipopt application
     ipoptApp->Options()->SetStringValue("hessian_approximation", "limited-memory");
@@ -155,18 +154,19 @@ int main()
     status = ipoptApp->OptimizeTNLP(ipoptDynamicObjectiveInterface);
     std::cout << "\n\nProblem formulated as dynamic objective optimization ...\n";
 
-    if (status == Ipopt::Solve_Succeeded) {
+    if (status == Ipopt::Solve_Succeeded)
+    {
         // Print result
-        std::cout << "\nSucess:\n The problem solved in "
-                  << ipoptApp->Statistics()->IterationCount() << " iterations!\n"
+        std::cout << "\nSucess:\n The problem solved in " << ipoptApp->Statistics()->IterationCount()
+                  << " iterations!\n"
                   << " Optimal value of Pm = " << model->param()[0] << "\n"
-                  << " The final value of the objective function G(Pm) = "
-                  << ipoptApp->Statistics()->FinalObjective() << "\n\n";
+                  << " The final value of the objective function G(Pm) = " << ipoptApp->Statistics()->FinalObjective()
+                  << "\n\n";
     }
 
     // Store dynamic objective optimization results
-    double* results  = new double[model->size_opt()];
-    for(unsigned i=0; i <model->size_opt(); ++i)
+    double* results = new double[model->size_opt()];
+    for (unsigned i = 0; i < model->size_opt(); ++i)
     {
         results[i] = model->param()[i];
     }
@@ -177,34 +177,35 @@ int main()
 
     // Initialize problem
     model->param()[0] = Pm;
-    
+
     // Solve the problem
     status = ipoptApp->OptimizeTNLP(ipoptDynamicConstraintInterface);
     std::cout << "\n\nProblem formulated as dynamic constraint optimization ...\n";
 
-    if (status == Ipopt::Solve_Succeeded) {
+    if (status == Ipopt::Solve_Succeeded)
+    {
         // Print result
-        std::cout << "\nSucess:\n The problem solved in "
-                  << ipoptApp->Statistics()->IterationCount() << " iterations!\n"
+        std::cout << "\nSucess:\n The problem solved in " << ipoptApp->Statistics()->IterationCount()
+                  << " iterations!\n"
                   << " Optimal value of Pm = " << model->param()[0] << "\n"
-                  << " The final value of the objective function G(Pm) = "
-                  << ipoptApp->Statistics()->FinalObjective() << "\n\n";
+                  << " The final value of the objective function G(Pm) = " << ipoptApp->Statistics()->FinalObjective()
+                  << "\n\n";
     }
 
     // Compare results of the two optimization methods
     int retval = 0;
-    for(unsigned i=0; i <model->size_opt(); ++i)
+    for (unsigned i = 0; i < model->size_opt(); ++i)
     {
-        if(!isEqual(results[i], model->param()[i], 10*tol))
-            --retval; 
+        if (!isEqual(results[i], model->param()[i], 10 * tol))
+            --retval;
     }
 
-    if(retval < 0)
+    if (retval < 0)
     {
         std::cout << "The two results differ beyond solver tolerance!\n";
     }
 
-    delete [] results;
+    delete[] results;
     delete idas;
     delete model;
     return retval;

examples/Enzyme/Library/library.cpp

@@ -1,3 +1,4 @@
-double square(double x) {
-  return x * x;
+double square(double x)
+{
+    return x * x;
 }