diff --git a/Fitter.py b/Fitter.py
index 70248c6..e755cab 100644
--- a/Fitter.py
+++ b/Fitter.py
@@ -227,6 +227,9 @@ class Fitter:
         # time1 = time.time()
         v_offset = test_model.find_v_offset(self.baseline_freq, base_stimulus)
         self.base_model.set_variable("v_offset", v_offset)
+
+
+
         # time2 = time.time()
         # print("time taken for finding v_offset: {:.2f}s".format(time2-time1))
 
diff --git a/models/LIFACnoise.py b/models/LIFACnoise.py
index 87526bf..3bc6c42 100644
--- a/models/LIFACnoise.py
+++ b/models/LIFACnoise.py
@@ -184,7 +184,7 @@ class LifacNoiseModel(AbstractModel):
 
     def find_v_offset(self, goal_baseline_frequency, base_stimulus, threshold=2, border=50000):
         test_model = self.get_model_copy()
-        simulation_length = 9
+        simulation_length = 6
 
         v_search_step_size = 100
 
@@ -214,7 +214,7 @@ def binary_search_base_freq(model: LifacNoiseModel, base_stimulus, goal_frequenc
         counter += 1
         middle = upper_bound - (upper_bound - lower_bound) / 2
         frequency = test_v_offset(model, middle, base_stimulus, simulation_length)
-
+        # print("offset: {:.1f}, freq: {:.0f}".format(middle, frequency))
         # print('{:.1f}, {:.1f}, {:.1f}, {:.1f} vs {:.1f} '.format(lower_bound, middle, upper_bound, frequency, goal_frequency))
 
         if abs(frequency - goal_frequency) < threshold:
@@ -231,6 +231,7 @@ def binary_search_base_freq(model: LifacNoiseModel, base_stimulus, goal_frequenc
         if abs(upper_bound - lower_bound) < 0.0001:
             print("v_offset search stopped. bounds converged! freq: {:.2f}, bounds: {:.0f}"
                   .format(frequency, lower_bound))
+            # print(model.parameters)
             warn("Search was stopped. Upper and lower bounds converged without finding a value closer than threshold!")
             return middle
 
@@ -239,8 +240,11 @@ def test_v_offset(model: LifacNoiseModel, v_offset, base_stimulus, simulation_le
     model.set_variable("v_offset", v_offset)
     try:
         v, spiketimes = model.simulate(base_stimulus, simulation_length)
+        # if len(spiketimes) > 0:
+            # print("sim length", simulation_length, "last spike",  max(spiketimes), "num of spikes:", len(spiketimes))
+        rel_spikes = [s for s in spiketimes if s > simulation_length / 3]
 
-        freq = hF.mean_freq_of_spiketimes_after_time_x(spiketimes, simulation_length / 3)
+        return len(rel_spikes) / (2/3 * simulation_length)
     except ZeroDivisionError:
         print("divide by zero!")
         freq = 0
diff --git a/test.py b/test.py
index 87e3524..b49f1ae 100644
--- a/test.py
+++ b/test.py
@@ -39,14 +39,18 @@ eodf = fit.get_cell_data().get_eod_frequency()
 baseline = BaselineModel(model, eod_frequency=eodf)
 print("model BF: {:.2f}".format(baseline.get_baseline_frequency()))
 
-model_no_dend = model.get_model_copy()
-model_no_dend.parameters["dend_tau"] = 0
-model_no_dend.parameters["input_scaling"] = 20
+model_no_dend = LifacNoiseModel({'step_size': 5e-05, 'mem_tau': 0.0018888888888888887, 'v_base': 0, 'v_zero': 0, 'threshold': 1, 'v_offset': -175.9718894958496, 'input_scaling': 364.44444444444446, 'delta_a': 0.03, 'tau_a': 0.1511111111111111, 'a_zero': 4.402004449113477, 'noise_strength': 0, 'dend_tau': 5e-05, 'refractory_period': 0.0007944444444444448}
+)
 
 baseline = BaselineModel(model_no_dend, eod_frequency=eodf)
-print("no dend BF: {:.2f}".format(baseline.get_baseline_frequency()))
-baseline.plot_interspike_interval_histogram()
 
-v1, spikes = model_no_dend.simulate(SinusoidalStepStimulus(eodf, 0, 0), 2)
-plt.plot(v1)
-plt.show()
\ No newline at end of file
+print("no dend BF: {:.2f}".format(baseline.get_baseline_frequency()))
+# baseline.plot_interspike_interval_histogram()
+
+for i in range(10):
+    v1, spikes = model.simulate(SinusoidalStepStimulus(eodf, 0, 0), 30)
+    print("last spikes:", max(spikes))
+    print(len(spikes) / 30)
+    time, freq = hF.calculate_time_and_frequency_trace(spikes, 5e-05)
+    plt.plot(time, freq)
+    plt.show()
\ No newline at end of file