diff --git a/GeneralRelativity/Interpolation.py b/GeneralRelativity/Interpolation.py
index 0586340..971b038 100644
--- a/GeneralRelativity/Interpolation.py
+++ b/GeneralRelativity/Interpolation.py
@@ -327,7 +327,6 @@ def get_postion(self, tensor: torch.Tensor) -> torch.Tensor:
                         self.relative_positions,
                     ):
                         result = 0
-
                         # Writing results to the interpolated array
                         ind = 2 * (index_for_input_array - (ghosts - 1)) + (
                             relative_index
@@ -455,60 +454,3 @@ def sinusoidal_function(x, y, z):
     # print_grid_lay_out()
     interpolation = interp(6, 3, 25, align_grids_with_lower_dim_values=True)
     interpolation.plot_grid_position()
-
-    for centering in [True, False]:
-        tol = 1e-10
-        channels = 25
-        interpolation = interp(
-            num_points=6,
-            max_degree=3,
-            num_channels=channels,
-            learnable=False,
-            align_grids_with_lower_dim_values=centering,
-        )
-        length = 10
-        dx = 0.01
-
-        # Initializing a tensor of random values to represent the grid
-        x = torch.rand(2, channels, length, length, length)
-
-        # Preparing input positions for the sinusoidal function
-        input_positions = torch.zeros(length, length, length, 3)
-        for i in range(x.shape[2]):
-            for j in range(x.shape[3]):
-                for k in range(x.shape[4]):
-                    input_positions[i, j, k] = torch.tensor([i, j, k])
-                    pos = dx * np.array([i, j, k])
-                    x[:, :, i, j, k] = sinusoidal_function(*pos)
-
-        # Perform interpolation and measure time taken
-        time1 = time.time()
-        interpolated, _ = interpolation.non_vector_implementation(x)
-        print(f"Time taken for interpolation: {(time.time() - time1):.2f} sec")
-        positions = interpolation.get_postion(x)
-
-        # Preparing ground truth for comparison
-        ghosts = int(math.ceil(6 / 2))
-        shape = x.shape
-        ground_truth = torch.zeros(
-            shape[0],
-            shape[1],
-            (shape[2] - 2 * ghosts) * 2 + 2,
-            (shape[3] - 2 * ghosts) * 2 + 2,
-            (shape[4] - 2 * ghosts) * 2 + 2,
-        )
-
-        # Applying sinusoidal function to the interpolated positions
-
-        shape = ground_truth.shape
-        # Perform interpolation
-        for i in range(shape[2]):
-            for j in range(shape[3]):
-                for k in range(shape[4]):
-                    pos = dx * (positions[i, j, k])
-                    ground_truth[:, :, i, j, k] = sinusoidal_function(*pos)
-
-        plt.plot(ground_truth[0, 0, 4, :, 4] - interpolated[0, 0, 4, :, 4])
-        plt.savefig(f"interpolation_results{centering}.png")
-        plt.close()
-        print(torch.mean(torch.abs(interpolated[0, 0, ...] - ground_truth[0, 0, ...])))