diff --git a/RelationAdaptionVariables.py b/RelationAdaptionVariables.py
index 3768b4b..998df93 100644
--- a/RelationAdaptionVariables.py
+++ b/RelationAdaptionVariables.py
@@ -10,15 +10,22 @@ import functions as fu
 
 
 def main():
-    lifac_model = LIFACModel({"delta_a": 0})
-    stimulus_strengths = np.arange(20, 32, 1)
+    values = np.arange(2, 18, 1)
+    parameter = "threshold"
+    for value in values:
+        lifac_model = LIFACModel({"delta_a": 0})
+        lifac_model.set_variable(parameter, value)
+        stimulus_strengths = np.arange(50, 60, 1)
 
-    line_vars, boltzmann_vars = find_fitting_boltzmann(lifac_model, stimulus_strengths)
-    find_relation(line_vars, boltzmann_vars)
+        line_vars, boltzmann_vars = find_fitting_boltzmann(lifac_model, stimulus_strengths)
+        relation = find_relation(lifac_model, line_vars, boltzmann_vars, stimulus_strengths)
+
+        print("threshold:", value)
+        print(relation)
 
 
 def find_fitting_boltzmann(lifac_model, stimulus_strengths):
-    # Requieres a lifac model with adaption delta_a = 0, so just the base is fit
+    # Requires a lifac model with adaption delta_a = 0, so just the base is fit
     frequencies = []
 
     duration = 0.2
@@ -44,26 +51,23 @@ def find_fitting_boltzmann(lifac_model, stimulus_strengths):
     return popt, popt2
 
 
-def find_relation(line_vars, boltzmann_vars, stimulus_strengths):
+def find_relation(lifac, line_vars, boltzmann_vars, stimulus_strengths, use_line=True):
     # boltzmann_vars = [2.00728705e+02, 1.09905953e-12, 1.03639686e-01, 2.55002788e+01]
     # line_vars = [5.10369405, -29.79774806]
     # example values for base lifac (15.1.20) and stimulus 20-32
 
-    stimulus_step_size = 2
-    stimulus_range = np.arange(20, 32, stimulus_step_size)
-
+    duration = 0.1
 
-    duration = 0.2
-
-    lifac_adaption_strength_range = np.arange(0, 3, 0.5)
+    lifac_adaption_strength_range = np.arange(0, 3.1, 0.5)
     firerate_adaption_variables = []
     for lifac_adaption_strength in lifac_adaption_strength_range:
         print(lifac_adaption_strength)
-        lifac = LIFACModel({"delta_a": lifac_adaption_strength, "tau_a": 20})
+        lifac.set_variable("delta_a", lifac_adaption_strength)
+        lifac.set_variable("tau_a", 10)
 
         adapted_frequencies = []
-        for stim in stimulus_range:
-            print("stim:", stim)
+        for stim in stimulus_strengths:
+            #print("stim:", stim)
             stimulus = StepStimulus(0, duration, stim)
             lifac.simulate(stimulus, duration)
             spiketimes = lifac.get_spiketimes()
@@ -75,16 +79,18 @@ def find_relation(line_vars, boltzmann_vars, stimulus_strengths):
         for i in range(len(adapted_frequencies)):
             goal_adapted_freq = adapted_frequencies[i]
 
-            # stimulus_strength_after_adaption = fu.inverse_full_boltzmann(goal_adapted_freq,
-            #                                                              boltzmann_vars[0],
-            #                                                              boltzmann_vars[1],
-            #                                                              boltzmann_vars[2],
-            #                                                              boltzmann_vars[3],)
-
-            # assume fitted line as basis of the fire-rate model:
-            stimulus_strength_after_adaption = fu.inverse_line(goal_adapted_freq, line_vars[0], line_vars[1])
+            if use_line:
+                # assume fitted linear firing rate as basis of the fire-rate model:
+                stimulus_strength_after_adaption = fu.inverse_line(goal_adapted_freq, line_vars[0], line_vars[1])
+            else:
+                # assume fitted boltzmann firing rate as basis of the fire-rate model:
+                stimulus_strength_after_adaption = fu.inverse_full_boltzmann(goal_adapted_freq,
+                                                                             boltzmann_vars[0],
+                                                                             boltzmann_vars[1],
+                                                                             boltzmann_vars[2],
+                                                                             boltzmann_vars[3],)
 
-            adaption_strength = stimulus_range[i] - stimulus_strength_after_adaption
+            adaption_strength = stimulus_strengths[i] - stimulus_strength_after_adaption
 
             firerate_adaption = adaption_strength / goal_adapted_freq
             firerate_adaption_strengths.append(firerate_adaption)
@@ -95,7 +101,7 @@ def find_relation(line_vars, boltzmann_vars, stimulus_strengths):
         # plt.show()
 
     for i in range(len(lifac_adaption_strength_range)):
-        plt.plot([lifac_adaption_strength_range[i]+p*0.01 for p in range(len(stimulus_range))], firerate_adaption_variables[i])
+        plt.plot([lifac_adaption_strength_range[i]+p*0.01 for p in range(len(stimulus_strengths))], firerate_adaption_variables[i])
 
     mean_firerate_adaption_value = [np.mean(strengths) for strengths in firerate_adaption_variables]
 
@@ -103,11 +109,12 @@ def find_relation(line_vars, boltzmann_vars, stimulus_strengths):
     plt.title("Relation of adaption strength variables:\n Small 'subplots' value for different stimulus strength")
     plt.xlabel("lifac adaption strength: delta_a")
     plt.ylabel("firerate adaption strength: alpha")
-    plt.savefig("figures/adaption_relation_stimulus_strength_stepsize_0-0001.png")
+    plt.savefig("figures/adaption_relation_threshold_" + str(lifac.get_parameters()["threshold"]) + ".png")
     plt.close()
     popt, pcov = curve_fit(fu.line, lifac_adaption_strength_range, mean_firerate_adaption_value)
 
-    print(popt)
+    # print(popt)
+    return popt
 
 
 def test_firerate_model( boltzmann_vars):