Deleted unit test for FD_order1 because no longer needed.

src/FiniteDifference/tests/FD_order1_unit_test.cpp

-//
-// Created by jstark on 16.06.21.
-//
-
-#define BOOST_TEST_DYN_LINK
-#include <boost/test/unit_test.hpp>
-
-// Include header files for testing
-#include "level_set/redistancing_Sussman/tests/l_norms/LNorms.hpp"
-#include "Gaussian.hpp"
-#include "FiniteDifference/Upwind_gradient.hpp"
-#include "level_set/redistancing_Sussman/HelpFunctions.hpp"
-
-BOOST_AUTO_TEST_SUITE(FDOrder1TestSuite)
-	const size_t Field              = 0;
-	const size_t AnalyticalGradient = 1;
-	const size_t NumericalGradient  = 2;
-	const size_t Error              = 3;
-	
-	// 32, 0.573022, 1.33305
-	// 64, 0.288525, 1
-	//128, 0.171455, 1
-	double l2_norms [] = {0.573022, 0.288525, 0.171455};
-	
-	const double EPSILON = std::numeric_limits<double>::epsilon();
-	
-	BOOST_AUTO_TEST_CASE(Forward_difference_1D_test)
-	{
-		const size_t grid_dim  = 1;
-		
-		const double box_lower = -1.0;
-		const double box_upper = 1.0;
-		Box<grid_dim, double> box({box_lower}, {box_upper});
-		Ghost<grid_dim, long int> ghost(3);
-		typedef aggregate<double, Point<grid_dim, double>, Point<grid_dim, double>, double> props;
-		typedef grid_dist_id<grid_dim, double, props> grid_in_type;
-		
-		double mu = 0.5 * (box_upper - abs(box_lower));
-		double sigma = 0.1 * (box_upper - box_lower);
-		
-		int count = 0;
-		for (size_t N = 32; N <= 128; N *= 2, ++count)
-		{
-			const size_t sz[grid_dim] = {N};
-			grid_in_type g_dist(sz, box, ghost);
-			
-			g_dist.setPropNames({"Field", "AnalyticalGradient", "NumericalGradient", "Error"});
-			
-			auto gdom = g_dist.getDomainGhostIterator();
-			while (gdom.isNext())
-			{
-				auto key = gdom.get();
-				Point<grid_dim, double> p = g_dist.getPos(key);
-				// Initialize grid and ghost with gaussian function
-				g_dist.getProp<Field>(key) = gaussian(p, mu, sigma);
-				++gdom;
-			}
-			
-			auto dom = g_dist.getDomainIterator();
-			while (dom.isNext())
-			{
-				auto key = dom.get();
-				Point<grid_dim, double> p = g_dist.getPos(key);
-				
-				for (int d = 0; d < grid_dim; d++)
-				{
-					// Analytical gradient
-					g_dist.getProp<AnalyticalGradient>(key)[d] = hermite_polynomial(p.get(d), sigma, 1) * g_dist.getProp<Field>(key);
-				    // 1st order Finite Difference gradient
-					g_dist.getProp<NumericalGradient>(key)[d]  = FD_forward<Field>(g_dist, key, d);
-				}
-				++dom;
-			}
-			
-			// Get the error between analytical and numerical solution
-//			get_absolute_error<NumericalGradient, AnalyticalGradient, Error>(g_dist);
-			get_relative_error<NumericalGradient, AnalyticalGradient, Error>(g_dist);
-			
-			L_norms lNorms;
-			lNorms = get_l_norms_grid<Error>(g_dist);
-			BOOST_CHECK_MESSAGE(lNorms.l2   < l2_norms[count] + 0.00001 + EPSILON, "Checking L2-norm ENO");
-//			write_lnorms_to_file(N, lNorms, "l_norms_FDfwd", "./");
-			std::cout << N << ", " << lNorms.l2 << ", " << lNorms.linf << std::endl;
-//			if (N==128) g_dist.write("grid_gaussian_FDfwd_N" + std::to_string(N), FORMAT_BINARY);
-		}
-	}
-	BOOST_AUTO_TEST_CASE(Backward_difference_1D_test)
-	{
-		const size_t grid_dim  = 1;
-		
-		const double box_lower = -1.0;
-		const double box_upper = 1.0;
-		Box<grid_dim, double> box({box_lower}, {box_upper});
-		Ghost<grid_dim, long int> ghost(3);
-		typedef aggregate<double, Point<grid_dim, double>, Point<grid_dim, double>, double> props;
-		typedef grid_dist_id<grid_dim, double, props> grid_in_type;
-		
-		double mu = 0.5 * (box_upper - abs(box_lower));
-		double sigma = 0.1 * (box_upper - box_lower);
-		
-		int count = 0;
-		for (size_t N = 32; N <= 128; N *= 2, ++count)
-		{
-			const size_t sz[grid_dim] = {N};
-			grid_in_type g_dist(sz, box, ghost);
-			
-			g_dist.setPropNames({"Field", "AnalyticalGradient", "NumericalGradient", "Error"});
-			
-			auto gdom = g_dist.getDomainGhostIterator();
-			while (gdom.isNext())
-			{
-				auto key = gdom.get();
-				Point<grid_dim, double> p = g_dist.getPos(key);
-				// Initialize grid and ghost with gaussian function
-				g_dist.getProp<Field>(key) = gaussian(p, mu, sigma);
-				++gdom;
-			}
-			
-			auto dom = g_dist.getDomainIterator();
-			while (dom.isNext())
-			{
-				auto key = dom.get();
-				Point<grid_dim, double> p = g_dist.getPos(key);
-				
-				for (int d = 0; d < grid_dim; d++)
-				{
-					// Analytical gradient
-					g_dist.getProp<AnalyticalGradient>(key)[d] = hermite_polynomial(p.get(d), sigma, 1) * g_dist.getProp<Field>(key);
-					// 1st order Finite Difference gradient
-					g_dist.getProp<NumericalGradient>(key)[d]  = FD_backward<Field>(g_dist, key, d);
-				}
-				++dom;
-			}
-			
-			// Get the error between analytical and numerical solution
-//			get_absolute_error<NumericalGradient, AnalyticalGradient, Error>(g_dist);
-			get_relative_error<NumericalGradient, AnalyticalGradient, Error>(g_dist);
-			
-			L_norms lNorms;
-			lNorms = get_l_norms_grid<Error>(g_dist);
-			BOOST_CHECK_MESSAGE(lNorms.l2   < l2_norms[count] + 0.00001 + EPSILON, "Checking L2-norm ENO");
-//			write_lnorms_to_file(N, lNorms, "l_norms_FDbwd", "./");
-			std::cout << N << ", " << lNorms.l2 << ", " << lNorms.linf << std::endl;
-//			if (N==128) g_dist.write("grid_gaussian_FDbwd_N" + std::to_string(N), FORMAT_BINARY);
-		}
-	}
-BOOST_AUTO_TEST_SUITE_END()