diff --git a/jar_functions.py b/jar_functions.py
index 0c73330..4edb410 100644
--- a/jar_functions.py
+++ b/jar_functions.py
@@ -2,6 +2,15 @@ import os   #compability with windows
 from IPython import embed
 import numpy as np
 
+def step_response(t, a1, a2, tau1, tau2):
+    r_step = (a1*(1 - np.exp(-t/tau1))) + (a2*(1 - np.exp(-t/tau2)))
+    r_step[t<0] = 0
+    return r_step
+
+def sin_response(t, f, p, A):
+    r_sin = A*sin(2*np.pi*t*f + p)
+    return r_sin
+
 def parse_dataset(dataset_name):
     assert(os.path.exists(dataset_name))        #see if data exists
     f = open(dataset_name, 'r')                 #open data we gave in
@@ -100,12 +109,6 @@ def mean_noise_cut(frequencies, time, n):
         cutt.append(t)
     return cutf, cutt
 
-
-def step_response(t, a1, a2, tau1, tau2):
-    r_step = (a1*(1 - np.exp(-t/tau1))) + (a2*(1 - np.exp(-t/tau2)))
-    r_step[t<0] = 0
-    return r_step
-
 def norm_function(cf_arr, ct_arr, onset_point, offset_point):
     onset_end = onset_point - 10
     offset_start = offset_point - 10
diff --git a/second_try.py b/second_try.py
deleted file mode 100644
index 9d0de30..0000000
--- a/second_try.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import matplotlib.pyplot as plt
-import os
-import glob
-import IPython
-import numpy as np
-from IPython import embed
-from scipy.optimize import curve_fit
-from jar_functions import parse_dataset
-from jar_functions import parse_infodataset
-from jar_functions import mean_traces
-from jar_functions import mean_noise_cut
-from jar_functions import norm_function
-from jar_functions import step_response
-from jar_functions import sort_values
-
-#nicht: 19-aa, 22-ae, 22-ad (?)
-datasets = [#(os.path.join('D:\\jar_project\\JAR\\2020-06-19-aa\\beats-eod.dat')),   #-5Hz delta f, horrible fit
-            #(os.path.join('D:\\jar_project\\JAR\\2020-06-19-ab\\beats-eod.dat')),   #-5Hz delta f, bad fit
-            #(os.path.join('D:\\jar_project\\JAR\\2020-06-22-aa\\beats-eod.dat')),   #-5Hz delta f, bad fit
-            #(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ab\\beats-eod.dat')),   #-5Hz delta f, bad fit
-            (os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat')),   #-15Hz delta f, good fit
-            #(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ad\\beats-eod.dat')),   #-15Hz delta f, horrible fit
-            #(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ae\\beats-eod.dat')),   #-15Hz delta f, maxfev way to high so horrible
-            (os.path.join('D:\\jar_project\\JAR\\2020-06-22-af\\beats-eod.dat'))]   #-15Hz delta f, good fit
-
-#np.array(sorted(glob.glob('D:\\jar_project\\JAR\\2020*\\beats-eod.dat')))
-
-infodatasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\info.dat')),
-                (os.path.join('D:\\jar_project\\JAR\\2020-06-22-af\\info.dat'))]
-
-
-time_all = []
-freq_all = []
-
-ID = []
-
-for infodataset in infodatasets:
-    i = parse_infodataset(infodataset)
-    identifier = i[0]
-    ID.append(identifier)
-
-
-for idx, dataset in enumerate(datasets):
-    #input of the function
-    frequency, time, amplitude, eodf, deltaf, stimulusf, duration, pause = parse_dataset(dataset)
-    dm = np.mean(duration)
-    pm = np.mean(pause)
-    timespan = dm + pm
-    start = (time[0][0] + time[1][0]) / 2
-    stop = (time[0][-1] + time[1][-1]) / 2
-    mf , tnew = mean_traces(start, stop, timespan, frequency, time) # maybe fixed timespan/sampling rate
-
-    #for i in range(len(mf)):
-
-    cf, ct = mean_noise_cut(mf, tnew, n=1250)
-
-    cf_arr = np.array(cf)
-    ct_arr = np.array(ct)
-
-    norm = norm_function(cf_arr, ct_arr, onset_point = dm - dm, offset_point = dm) #dm-dm funktioniert nur wenn onset = 0 sec
-
-    freq_all.append(norm.tolist())
-    time_all.append(ct_arr)
-
-    plt.plot(ct_arr, norm, color = 'grey', label='fish=%s' % ID[idx])
-
-    # fit function
-    sv, sc = curve_fit(step_response, ct_arr[ct_arr < dm], norm[ct_arr < dm]) #step_values and step_cov
-
-    # sorted a and tau
-    values = sort_values(sv)
-
-    '''
-    plt.plot(ct_arr[ct_arr < 100], step_response(ct_arr, *sv)[ct_arr < 100], color='orange',
-             label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values))
-    '''
-
-    print('fish: a1, a2, tau1, tau2', values)
-
-# average over all fish
-mf_all , tnew_all = mean_traces(start, stop, timespan, freq_all, time_all)
-
-plt.plot(tnew_all, mf_all, color = 'b', label = 'average', ls = 'dashed')
-
-# fit for average
-sv_all, sc_all = curve_fit(step_response, tnew_all[tnew_all < dm], mf_all[tnew_all < dm]) #step_values and step_cov
-
-values_all = sort_values(sv_all)
-
-plt.plot(tnew_all[tnew_all < 100], step_response(tnew_all, *sv_all)[tnew_all < 100], color='orange',
-         label='average_fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values_all))
-
-print('average: a1, a2, tau1, tau2', values_all)
-
-const_line = plt.axhline(y = 0.632)
-stimulus_duration = plt.hlines(y = -0.25, xmin = 0, xmax = 100, color = 'r', label = 'stimulus_duration')
-base_line = plt.axhline(y = 0, color = 'black', ls = 'dotted', linewidth = '1')
-
-plt.xlim([-10,220])
-plt.xlabel('time [s]')
-plt.ylabel('rel. JAR magnitude')
-plt.title('relative JAR')
-plt.savefig('relative JAR')
-plt.legend(loc = 'lower right')
-plt.show()
-embed()
-
-# Fragen:
-# wie offset point wenn nicht start bei 0 sec? über zeitdatenpunkt? oder einfach immer bei 0 onset..?
-# wie zip ich ID liste mit plot (für eine for schleife) zusammen?
-# welche Stimulusintesität?
-# start/stop/timespan ok?
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