diff --git a/run_Fitter.py b/run_Fitter.py
index 4893a22..fd378c8 100644
--- a/run_Fitter.py
+++ b/run_Fitter.py
@@ -1,12 +1,14 @@
 
 from models.LIFACnoise import LifacNoiseModel
-from CellData import icelldata_of_dir
+from CellData import icelldata_of_dir, CellData
 from Baseline import get_baseline_class
 from FiCurve import get_fi_curve_class
 from Fitter import Fitter
 
+import numpy as np
 import time
 import os
+import copy
 
 import multiprocessing as mp
 
@@ -14,17 +16,54 @@ import multiprocessing as mp
 SAVE_PATH_PREFIX = ""
 FIT_ROUTINE = ""
 
+
 def main():
-    count = 0
-    for data in icelldata_of_dir("./data/"):
-        count += 1
-        # if count <= 3:
-        #     continue
 
-        fit_cell_parrallel(data, [p for p in iget_start_parameters()])
+    test_effect_of_refractory_period()
+
+    quit()
+    cells = [data for data in icelldata_of_dir("./data/")]
+
+    start_parameter = [p for p in iget_start_parameters()]
+
+    fit_all_cells_parallel_sync(cells, start_parameter, )
+
+
+def qfit_cell_base(parameter):
+    # parameter = (cell_data, start_parameter_index, start_parameter, results_base_folder)
+    time1 = time.time()
+    fitter = Fitter()
+    fitter.set_data_reference_values(parameter[0])
+    fmin, res_par = fitter.fit_routine_const_ref_period(parameter[2])
+
+    cell_data = parameter[0]
+    cell_path = os.path.split(cell_data.get_data_path())[-1]
+
+    error = fitter.calculate_errors(model=LifacNoiseModel(res_par))
+    save_path = parameter[3] + "/" + cell_path + "/start_parameter_{:}_err_{:.2f}/".format(parameter[1], sum(error))
+    save_fitting_run_info(parameter[0], res_par, parameter[2], plot=True, save_path=save_path)
+    time2 = time.time()
+
+    print("Time taken for " + cell_path +
+          "\n and start parameters ({:}): {:.2f}s thread time".format(parameter[1]+1, time2 - time1) +
+          "\n error: {:.2f}".format(sum(error)))
+
+
+def fit_all_cells_parallel_sync(cells, start_parameters, results_base_folder):
+    parameter = []
+    for cell in cells:
+        for i, s_pars in enumerate(start_parameters):
+            parameter.append((cell, i, s_pars, results_base_folder))
+
+    core_count = mp.cpu_count()
+    pool = mp.Pool(core_count - 1)
+    time1 = time.time()
+    pool.map(fit_cell_base, parameter)
+    time2 = time.time()
+    print("Time taken for all cells and start parameters ({:}): {:.2f}s".format(len(parameter), time2 - time1))
 
 
-def fit_cell_parrallel(cell_data, start_parameters):
+def fit_cell_parallel(cell_data, start_parameters):
     cell_path = os.path.basename(cell_data.get_data_path())
     print(cell_path)
     core_count = mp.cpu_count()
@@ -71,7 +110,7 @@ def test_fit_routines():
         for i, routine_results in enumerate(best):
             res_file.write(names[i])
             for cell_best in routine_results:
-                    res_file.write("," + str(cell_best))
+                res_file.write("," + str(cell_best))
 
 
 def find_best_run(cell_path):
@@ -89,7 +128,7 @@ def iget_start_parameters():
     # expand by tau_a, delta_a ?
 
     mem_tau_list = [0.01]
-    input_scaling_list = [40, 60]
+    input_scaling_list = [60]
     noise_strength_list = [0.03]  # [0.02, 0.06]
     dend_tau_list = [0.001, 0.002]
     delta_a_list = [0.035, 0.065]
@@ -233,5 +272,18 @@ def save_fitting_run_info(cell_data, parameters, start_parameters, plot=False, s
         model_ficurve.plot_fi_curve_comparision(data_fi_curve, model_ficurve, save_path)
 
 
+def test_effect_of_refractory_period():
+    ref_periods = np.arange(0.0006, 0.001, 0.0015)
+
+    cells = [c for c in icelldata_of_dir("./data/")]
+    start_parameters_base = [p for p in iget_start_parameters()]
+    for ref_period in ref_periods:
+        results_base_folder = "./test_routines/ref_period_{:.3f}/".format(ref_period)
+        all_start_parameters = copy.deepcopy(start_parameters_base)
+
+        for par_set in all_start_parameters:
+            par_set["refractory_period"] = ref_period
+        fit_all_cells_parallel_sync(cells, all_start_parameters, results_base_folder)
+
 if __name__ == '__main__':
     main()