diff --git a/run_Fitter.py b/run_Fitter.py
index ba8037f..558895c 100644
--- a/run_Fitter.py
+++ b/run_Fitter.py
@@ -274,8 +274,9 @@ def test_effect_of_refractory_period():
     ref_periods = [0.0006, 0.001, 0.0015]
     counter = 0
     core_count = mp.cpu_count()
-    pool = mp.Pool(core_count - 1)
+
     for cell in icelldata_of_dir("./data/"):
+        pool = mp.Pool(core_count - 1)
         counter += 1
         if counter < 10:
             continue
@@ -293,6 +294,7 @@ def test_effect_of_refractory_period():
             fit_all_cells_parallel_sync([cell], all_start_parameters, pool, results_base_folder)
 
         del cell
+        del pool
 
 
 if __name__ == '__main__':
diff --git a/sam_experiments.py b/sam_experiments.py
index fb1e244..8077d75 100644
--- a/sam_experiments.py
+++ b/sam_experiments.py
@@ -9,45 +9,48 @@ from CellData import CellData
 
 def main():
     # 2012-12-13_ao fit and eod frequency:
-    parameters = {'mem_tau': 0.0133705462739553, 'tau_a': 0.06682759542588587, 'input_scaling': 60.766243690761144,
-                  'v_base': 0, 'step_size': 5e-05, 'dend_tau': 0.0008667253013050408, 'v_zero': 0, 'v_offset': -6.25,
-                  'noise_strength': 0.03337309379328535, 'a_zero': 2, 'threshold': 1, 'delta_a': 0.0726267312975076}
+    parameters = {'step_size': 5e-05, 'mem_tau': 0.009946816831208656, 'v_base': 0, 'v_zero': 0, 'threshold': 1,
+                  'v_offset': -9.375, 'input_scaling': 85.90592189374783, 'delta_a': 0.11098554500597714,
+                  'tau_a': 0.04533432159583689, 'a_zero': 2, 'noise_strength': 0.02947375332925044,
+                  'dend_tau': 0.001154822221492827, 'refractory_period': 0.0006}
     eod_freq = 658
     cell_data = CellData("./data/2012-12-13-ao-invivo-1/")
     model = LifacNoiseModel(parameters)
     mean_duration = np.mean(cell_data.get_sam_durations())
     contrasts = cell_data.get_sam_contrasts()
     spiketimes = cell_data.get_sam_spiketimes()
-    for i, m_freq in enumerate(cell_data.get_sam_delta_frequencies()):
+    delta_freqs = np.unique(cell_data.get_sam_delta_frequencies())
+    for i, m_freq in enumerate(delta_freqs):
 
-        stimulus = SAM(eod_freq, contrasts[i], m_freq)
-        prob_desnity_function_model = generate_pdf(model, stimulus, sim_length=mean_duration)
-        for spikes in spiketimes[i]:
-            prob_density_cell = spiketimes_calculate_pdf(spikes, cell_data.get_sampling_interval())
+        stimulus = SAM(eod_freq, contrasts[i]/100, m_freq)
+        v1, spikes_model = model.simulate_fast(stimulus, mean_duration)
+        prob_density_function_model = spiketimes_calculate_pdf(spikes_model, model.get_sampling_interval())
+        for spikes_cell in spiketimes[i]:
+            prob_density_cell = spiketimes_calculate_pdf(spikes_cell, cell_data.get_sampling_interval())
 
+            plt.plot(prob_density_function_model)
             plt.plot(prob_density_cell)
-            plt.plot(prob_desnity_function_model)
             plt.show()
             plt.close()
 
 
-    # __init__(carrier_frequency, contrast, modulation_frequency, start_time=0, duration=np.inf, amplitude=1)
-    mod_freqs = np.arange(-60, eod_freq*4 + 61, 10)
-    sigma_of_pdfs = []
-    for m_freq in mod_freqs:
-        print(m_freq, "max: {:.2f}".format(mod_freqs[-1]))
-        stimulus = SAM(eod_freq, 0.2, m_freq)
-
-        prob_density_function = generate_pdf(model, stimulus)
-        buffer = 0.25
-        buffer_idx = int(buffer / model.get_parameters()["step_size"])
-
-        sigma_of_pdfs.append(np.std(prob_density_function[buffer_idx:-buffer_idx]))
-
-    normed_mod_freqs = (mod_freqs + eod_freq) / eod_freq
-    plt.plot(normed_mod_freqs, sigma_of_pdfs)
-    plt.savefig("./figures/sam/test.png")
-    plt.close()
+    # # __init__(carrier_frequency, contrast, modulation_frequency, start_time=0, duration=np.inf, amplitude=1)
+    # mod_freqs = np.arange(-60, eod_freq*4 + 61, 10)
+    # sigma_of_pdfs = []
+    # for m_freq in mod_freqs:
+    #     print(m_freq, "max: {:.2f}".format(mod_freqs[-1]))
+    #     stimulus = SAM(eod_freq, 0.2, m_freq)
+    #
+    #     prob_density_function = generate_pdf(model, stimulus)
+    #     buffer = 0.25
+    #     buffer_idx = int(buffer / model.get_parameters()["step_size"])
+    #
+    #     sigma_of_pdfs.append(np.std(prob_density_function[buffer_idx:-buffer_idx]))
+    #
+    # normed_mod_freqs = (mod_freqs + eod_freq) / eod_freq
+    # plt.plot(normed_mod_freqs, sigma_of_pdfs)
+    # plt.savefig("./figures/sam/test.png")
+    # plt.close()
 
     pass