diff --git a/Fitter.py b/Fitter.py
index f13d19e..4870170 100644
--- a/Fitter.py
+++ b/Fitter.py
@@ -9,6 +9,7 @@ import numpy as np
 from warnings import warn
 from scipy.optimize import minimize
 import time
+from helperFunctions import plot_errors
 
 import matplotlib.pyplot as plt
 
@@ -53,6 +54,7 @@ class Fitter:
         self.f_zero_curve_freq = np.array([])
         self.f_zero_curve_time = np.array([])
 
+        self.errors = []
 
         # self.tau_a = 0
 
@@ -125,51 +127,9 @@ class Fitter:
         error_weights = (0, 2, 2, 2, 1, 1, 1, 1, 0, 1)
         fmin = minimize(fun=self.cost_function_all,
                         args=(error_weights,), x0=x0, method="Nelder-Mead",
-                        options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 600, "maxiter": 1200})
+                        options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 600, "maxiter": 400})
 
-        return fmin, self.base_model.get_parameters()
-
-    def fit_routine_const_ref_period(self, start_parameters):
-        self.counter = 0
-        # fit only v_offset, mem_tau, input_scaling, dend_tau
-
-        x0 = np.array([start_parameters["mem_tau"], start_parameters["noise_strength"],
-                       start_parameters["input_scaling"], self.tau_a, start_parameters["delta_a"],
-                       start_parameters["dend_tau"], start_parameters["refractory_period"]])
-        initial_simplex = create_init_simples(x0, search_scale=2)
-        self.base_model.set_variable("refractory_period", start_parameters["refractory_period"])
-        # error_list = [error_bf, error_vs, error_sc, error_cv,
-        #                       error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope]
-        error_weights = (0, 1, 1, 1, 1, 1, 1, 1, 1)
-        fmin = minimize(fun=self.cost_function_without_ref_period,
-                        args=(error_weights,), x0=x0, method="Nelder-Mead",
-                        options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 200, "maxiter": 400})
-
-        return fmin, self.base_model.get_parameters()
-
-    # similar results to fit routine 1
-    def fit_routine_2(self, start_parameters):
-        self.counter = 0
-
-        x0 = np.array([start_parameters["mem_tau"], start_parameters["input_scaling"],   # mem_tau, input_scaling
-                       start_parameters["delta_a"], start_parameters["dend_tau"]])  # delta_a, dend_tau
-        initial_simplex = create_init_simples(x0, search_scale=2)
-
-        error_weights = (0, 1, 1, 1, 1, 3, 2, 3, 2)
-        fmin = minimize(fun=self.cost_function_only_adaption,
-                        args=(error_weights,), x0=x0, method="Nelder-Mead",
-                        options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 100, "maxiter": 400})
-        best_pars = fmin.x
-        x0 = np.array([best_pars[0], start_parameters["noise_strength"],
-                       best_pars[1], self.tau_a, best_pars[2],
-                       best_pars[3]])
-        initial_simplex = create_init_simples(x0, search_scale=2)
-        # error_list = [error_bf, error_vs, error_sc, error_cv,
-        #                       error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope]
-        error_weights = (0, 2, 2, 2, 2, 1, 1, 1, 1)
-        fmin = minimize(fun=self.cost_function_all,
-                        args=(error_weights,), x0=x0, method="Nelder-Mead",
-                        options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 100, "maxiter": 400})
+        plot_errors(self.errors)
 
         return fmin, self.base_model.get_parameters()
 
@@ -204,11 +164,11 @@ class Fitter:
 
         # [error_bf, error_vs, error_sc, error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope]
         error_list = self.calculate_errors(error_weights)
-        print("sum: {:.2f}, ".format(sum(error_list)))
+        # print("sum: {:.2f}, ".format(sum(error_list)))
         if sum(error_list) < self.smallest_error:
             self.smallest_error = sum(error_list)
             self.best_parameters_found = X
-
+        self.errors.append(error_list)
         return sum(error_list)
 
     def cost_function_without_ref_period(self, X, error_weights=None):