diff --git a/test.py b/test.py
index b1f1503..d72bbe7 100644
--- a/test.py
+++ b/test.py
@@ -1,68 +1,25 @@
 
 from Baseline import get_baseline_class
-from CellData import CellData
+from CellData import CellData, icelldata_of_dir
 from models.LIFACnoise import LifacNoiseModel
 from Baseline import BaselineCellData, BaselineModel
 from os import listdir
+import numpy as np
 from IPython import embed
 import pyrelacs.DataLoader as Dl
 from ModelFit import ModelFit
-
-
-
-fit = ModelFit("results/invivo_results/start_parameter_7_err_6.87/")
-
-print(fit.comparable_error())
-fit.generate_master_plot()
-
-
-
-quit()
-
-
-
-def icelldata_of_dir(base_path):
-    global COUNT
-    for item in sorted(listdir(base_path)):
-        item_path = base_path + item
-
-        try:
-            data = CellData(item_path)
-
-            yield data
-
-        except TypeError as e:
-            print(str(e))
-
-        except IndexError as e:
-            print(str(e), "\n")
-
-        except ValueError as e:
-            print(str(e), "\n")
-
-    print("Currently throw errors: {}".format(COUNT))
-
-
-for data in icelldata_of_dir("invivo_data/"):
-    v1 = data.get_base_traces(data.V1)
-
-    if len(v1) == 0:
-        embed()
-        quit()
-
-
-quit()
-
-parameter_bursty_model = {'step_size': 5e-05, 'mem_tau': 0.0066693150193490695, 'v_base': 0, 'v_zero': 0,
-                          'threshold': 1, 'v_offset': -45.703125, 'input_scaling': 172.13861987237314,
-                          'delta_a': 0.06148215166012024, 'tau_a': 0.03391674075000068, 'a_zero': 2,
-                          'noise_strength': 0.0684136549210377, 'dend_tau': 0.0013694103932013805,
-                          'refractory_period': 0.001}
-eod = 752
-model = LifacNoiseModel(parameter_bursty_model)
-baseline_model = get_baseline_class(model, 752, trials=2)
-baseline_model.get_burstiness()
-
+from FiCurve import FICurveModel
+import os
+import matplotlib.pyplot as plt
+vs = []
+for cell in icelldata_of_dir("invivo_data/"):
+    base = BaselineCellData(cell)
+
+    vs.append(base.get_burstiness())
+
+bins = np.arange(0, 1.05, 0.05)
+plt.hist(vs, bins=bins)
+plt.show()
 quit()