diff --git a/src/FiniteDifference/tests/FD_order1_unit_test.cpp b/src/FiniteDifference/tests/FD_order1_unit_test.cpp
deleted file mode 100644
index 1424389af7b76bb4f77d0ee5388106ec7dcdf03b..0000000000000000000000000000000000000000
--- a/src/FiniteDifference/tests/FD_order1_unit_test.cpp
+++ /dev/null
@@ -1,147 +0,0 @@
-//
-// 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()