diff --git a/experiment/wood-bilayer/GridAccuracy_Test.py b/experiment/wood-bilayer/GridAccuracy_Test.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e6c125f3b1f32d4678f0458eba465b798d4d155
--- /dev/null
+++ b/experiment/wood-bilayer/GridAccuracy_Test.py
@@ -0,0 +1,96 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.colors as colors
+import codecs
+import re
+import json
+
+
+
+# # Test result_5
+# four_5 = np.array([0.60150376, 0.87218045, 1.23308271, 1.5037594,  1.71428571, 2.46616541
+#  ,2.79699248])
+
+# five_5 = np.array([0.56112224, 0.84168337, 1.19238477, 1.43286573, 1.63326653, 2.36472946,
+#  2.68537074])
+
+# experiment_5 = np.array([0.357615902,0.376287785,0.851008627,0.904475291,1.039744708,1.346405241,1.566568558]) # curvature kappa from Experiment]
+
+
+
+# # Test result_0
+# four_0 = np.array([1.29323308, 1.83458647, 2.40601504, 2.76691729, 3.03759398, 3.81954887, 4.03007519])
+
+
+# five_0 = np.array([1.28128128, 1.7967968,  2.36236236, 2.71271271, 2.97297297, 3.73373373, 3.96396396])
+# experiment_0 = np.array([1.140351217, 1.691038688, 2.243918105, 2.595732726, 2.945361006,4.001528043, 4.312080261]) # curvature kappa from Experiment]
+
+
+
+
+
+gridLevel4 = [
+np.array([1.30260521, 1.83366733, 2.41482966, 2.76553106, 3.03607214, 3.81763527, 4.04809619]), # Dataset 0
+np.array([1.29258517, 1.81362725, 2.39478958, 2.74549098, 3.01603206, 3.83767535, 4.08817635]), # Dataset 1
+np.array([1.20240481, 1.73346693, 2.3246493,  2.68537074, 2.95591182, 3.74749499, 3.97795591]), # Dataset 2
+np.array([0.87174349, 1.28256513, 1.76352705, 2.0741483,  2.31462926, 3.05611222,3.28657315]), # Dataset 3
+np.array([0.61122244, 0.90180361, 1.25250501, 1.48296593, 1.66332665, 2.26452906, 2.48496994]), # Dataset 4
+# np.array([0.54108216, 0.80160321, 1.14228457, 1.37274549, 1.56312625, 2.26452906, 2.5751503 ]), # Dataset 5 (curvature of global minimizer)
+np.array([0.42084168336673344, 0.6312625250501002, 0.8817635270541082, 1.0521042084168337, 1.1923847695390781, 1.6933867735470942, 1.9138276553106213]), # Dataset 5 (curvature of local minimizer)
+]
+
+gridLevel5 = [
+np.array([1.282565130260521, 1.7935871743486973, 2.3647294589178354, 2.7054108216432864, 2.975951903807615, 3.7374749498997994, 3.967935871743487]), # Dataset 0
+np.array([1.282565130260521, 1.8036072144288577, 2.3847695390781563, 2.7354709418837673, 3.006012024048096, 3.817635270541082, 4.06813627254509]), # Dataset 1
+np.array([1.1923847695390781, 1.723446893787575, 2.314629258517034, 2.6753507014028055, 2.9458917835671343, 3.727454909819639, 3.9579158316633265]), # Dataset 2
+np.array([0.8717434869739479, 1.2725450901803608, 1.753507014028056, 2.064128256513026, 2.294589178356713, 3.036072144288577, 3.2665330661322645]), # Dataset 3
+np.array([0.6012024048096192, 0.8917835671342685, 1.2324649298597194, 1.4629258517034067, 1.6332665330661322, 2.224448897795591, 2.444889779559118]), # Dataset 4
+# np.array([0.561122244488978, 0.8416833667334669, 1.1923847695390781, 1.4328657314629258, 1.6332665330661322, 2.3647294589178354, 2.685370741482966]), # Dataset 5 # Dataset 5 (curvature of global minimizer)
+np.array([0.4108216432865731, 0.6112224448897795, 0.8617234468937875, 1.032064128256513, 1.1623246492985972, 1.653306613226453, 1.8637274549098195]), # Dataset 5 # Dataset 5 (curvature of local minimizer)
+]
+
+experiment = [
+np.array([1.140351217, 1.691038688, 2.243918105, 2.595732726, 2.945361006,4.001528043, 4.312080261]),  # Dataset 0
+np.array([1.02915975,1.573720805,2.407706364,2.790518802,3.173814476,4.187433094,4.511739072]),        # Dataset 1
+np.array([1.058078122, 1.544624544, 2.317033799, 2.686043143, 2.967694189, 3.913528418, 4.262750825]), # Dataset 2
+np.array([0.789078472,1.1299263,1.738136936,2.159520896,2.370047499,3.088299431,3.18097558]), # Dataset 3
+np.array([0.577989364,0.829007544,1.094211707,1.325332511,1.400455154,1.832325697,2.047483977]), # Dataset 4
+np.array([0.357615902,0.376287785,0.851008627,0.904475291,1.039744708,1.346405241,1.566568558]), # Dataset 5
+]
+
+
+# test0 = [
+#     np.array([1, 2, 3])
+# ]
+
+# test1 = [
+#     np.array([2, 2, 2])
+# ]
+
+# print('TEST:', test1[0]-test0[0])
+# print('TEST2:', (test1[0]-test0[0])/test1[0])
+
+
+for i in range(0,6):
+    print("------------------")
+    print("Dataset_" + str(i))
+    print("------------------")
+    print('i:', i)
+    print('relative Error to experiment (gridLevel5):', abs(gridLevel5[i] - experiment[i])/experiment[i])
+    print('relative Error to experiment (gridLevel4):', abs((gridLevel4[i] - experiment[i]))/experiment[i])
+    print('difference in curvature  (gridLevel4-gridLevel5):', gridLevel4[i]-gridLevel5[i])
+    print('relative Error grid Levels: |level5 - level4|/level5):', abs((gridLevel5[i] - gridLevel4[i]))/gridLevel5[i])
+
+
+# print('difference (four_0-experiment_0):', four_0-experiment_0)
+
+# print('difference (four_0-five_0):', four_0-five_0)
+
+# # print('rel. error:', (four-five)/five )
+
+# print('rel Error (gLevel5):', (five_0 - experiment_0)/experiment_0)
+# print('rel Error (gLevel4):', (four_0 - experiment_0)/experiment_0)
+
+
+# print('rel Error (gLevel5):', (five_5 - experiment_5)/experiment_5)
+# print('rel Error (gLevel4):', (four_5 - experiment_5)/experiment_5)
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