diff --git a/apteronotus_code/figure_apteronotus_gaincurve_cutofff_tau.py b/apteronotus_code/figure_apteronotus_gaincurve_cutofff_tau.py
index f734834..e24a3d7 100644
--- a/apteronotus_code/figure_apteronotus_gaincurve_cutofff_tau.py
+++ b/apteronotus_code/figure_apteronotus_gaincurve_cutofff_tau.py
@@ -7,7 +7,7 @@ from matplotlib.mlab import specgram
 import os
 from jar_functions import gain_curve_fit
 
-identifier = ['2018lepto1', '2018lepto4', '2018lepto5', '2018lepto76']
+identifier = ['2020lepto06']
 
 tau = []
 f_c = []
@@ -42,7 +42,7 @@ for ID in identifier:
     ax.set_ylabel('gain [Hz/(mV/cm)]')
     ax.set_xlabel('envelope_frequency [Hz]')
     ax.set_title('gaincurve %s' %ID)
-    plt.legend()
+    plt.legend(loc = 'lower left')
     plt.show()
 
 
diff --git a/apteronotus_code/figure_apteronotus_jar_filter_fit.py b/apteronotus_code/figure_apteronotus_jar_filter_fit.py
index 288169e..2c2d245 100644
--- a/apteronotus_code/figure_apteronotus_jar_filter_fit.py
+++ b/apteronotus_code/figure_apteronotus_jar_filter_fit.py
@@ -17,7 +17,7 @@ from jar_functions import gain_curve_fit
 def take_second(elem):      # function for taking the names out of files
     return elem[1]
 
-identifier = ['2018lepto1']
+identifier = ['2018lepto98']
 for ident in identifier:
 
     predict = []
@@ -35,16 +35,16 @@ for ident in identifier:
     currf = None
     idxlist = []
 
-    data = sorted(np.load('%s files.npy' %ident), key = take_second)      # list with filenames in it
+    data = sorted(np.load('5Hz_%s files.npy' %ident), key = take_second)      # list with filenames in it
 
     for i, d in enumerate(data):
         dd = list(d)
-        if dd[1] == '0.005':
-            jar = np.load('%s.npy' %dd)     # load data for every file name
+        if dd[1] == '0.5':
+            jar = np.load('5Hz_%s.npy' %dd)     # load data for every file name
             jm = jar - np.mean(jar)         # low-pass filtering by subtracting mean
             print(dd)
 
-            time = np.load('%s time.npy' %dd)       # time file
+            time = np.load('5Hz_%s time.npy' %dd)       # time file
             dt = time[1] - time[0]
 
             n = int(1/float(d[1])/dt)
@@ -54,10 +54,11 @@ for ident in identifier:
             sinv, sinc = curve_fit(sin_response, time, jm - cutf, [float(d[1]), 2, 0.5])        # fitting
             print('frequency, phaseshift, amplitude:', sinv)
             p = sinv[1]
-            A = np.sqrt(sinv[2] ** 2)
-            f = float(d[1])
-            if sinv[2] < 0:
+            A = sinv[2]
+            if A < 0:
                 p = p + np.pi
+                A = -A
+            f = float(d[1])
             phaseshift.append(p)
             gain.append(A)
             if f not in amfreq:
@@ -66,7 +67,7 @@ for ident in identifier:
             # jar trace
             plt.plot(time, jar, color = 'C0')
             #plt.hlines(y=np.min(jar) - 2, xmin=0, xmax=400, lw=2.5, color='r', label='stimulus duration')
-            plt.title('JAR trace 2018lepto1, AM-frequency:%sHz' % float(d[1]))
+            plt.title('JAR trace 2018lepto98, AM-frequency:%sHz, deltaf = -5Hz' % float(d[1]))
             plt.xlabel('time[s]')
             plt.ylabel('frequency[Hz]')
             plt.show()
@@ -81,7 +82,7 @@ for ident in identifier:
             # filter by running average
             plt.plot(time, jm, color = 'C0', label = 'JAR: subtracted by mean')
             plt.plot(time, jm - cutf, color = 'darkorange', label = 'JAR: subtracted by mean and step response')
-            plt.title('JAR trace spectogram 2018lepto1: subtraction of mean and step response')
+            plt.title('JAR trace spectogram 2018lepto98: subtraction of mean and step response, deltaf = -5Hz')
             plt.xlabel('time[s]')
             plt.ylabel('frequency[Hz]')
             plt.legend()
@@ -89,96 +90,12 @@ for ident in identifier:
 
             # jar trace and fit
             plt.plot(time, jm - cutf, color = 'darkorange', label = 'JAR: subtracted by mean and step response')
-            phase_gain = [(((sinv[1] % (2 * np.pi)) * 360) / (2 * np.pi)), sinv[2]]
+            phase_gain = [(((p % (2 * np.pi)) * 360) / (2 * np.pi)), A]
 
             plt.plot(time, sin_response(time, *sinv), color = 'limegreen', label='fit: phaseshift=%.2f°, gain=%.2f[Hz/(mV/cm)]' % tuple(phase_gain))
-            plt.title('JAR trace spectogram 2018lepto1 with fit')
+            plt.title('JAR trace spectogram 2018lepto98 with fit, deltaf = -5Hz')
             plt.xlabel('time[s]')
             plt.ylabel('frequency[Hz]')
             plt.legend()
             plt.show()
-
-            # root mean square
-            RMS = np.sqrt(np.mean(((jm - cutf) - sin_response(cutt, sinv[0], sinv[1], sinv[2]))**2))
-            thresh = A / np.sqrt(2)
-
-            # mean over same amfreqs for phase and gain
-            if currf is None or currf == d[1]:
-                currf = d[1]
-                idxlist.append(i)
-
-            else:  # currf != f
-                meanf = []  # lists to make mean of
-                meanp = []
-                meanrms = []
-                meanthresh = []
-                for x in idxlist:
-                    meanf.append(gain[x])
-                    meanp.append(phaseshift[x])
-                    meanrms.append(RMS)
-                    meanthresh.append(thresh)
-                meanedf = np.mean(meanf)
-                meanedp = np.mean(meanp)
-                meanedrms = np.mean(meanrms)
-                meanedthresh = np.mean(meanthresh)
-
-                mgain.append(meanedf)
-                mphaseshift.append(meanedp)
-                rootmeansquare.append(meanedrms)
-                threshold.append(meanedthresh)
-                currf = d[1]    # set back for next loop
-                idxlist = [i]
-    meanf = []
-    meanp = []
-    meanrms = []
-    meanthresh = []
-    for y in idxlist:
-        meanf.append(gain[y])
-        meanp.append(phaseshift[y])
-        meanrms.append(RMS)
-        meanthresh.append(thresh)
-    meanedf = np.mean(meanf)
-    meanedp = np.mean(meanp)
-    meanedrms = np.mean(meanrms)
-    meanedthresh = np.mean(meanthresh)
-
-    mgain.append(meanedf)
-    mphaseshift.append(meanedp)
-    rootmeansquare.append(meanedrms)
-    threshold.append(meanedthresh)
-
-    # as arrays
-    mgain_arr = np.array(mgain)
-    mphaseshift_arr = np.array(mphaseshift)
-    amfreq_arr = np.array(amfreq)
-    rootmeansquare_arr = np.array(rootmeansquare)
-    threshold_arr = np.array(threshold)
-
-    # condition needed to be fulfilled: RMS < threshold or RMS < mean(RMS)
-    idx_arr = (rootmeansquare_arr < threshold_arr) | (rootmeansquare_arr < np.mean(rootmeansquare_arr))
-
-    fig = plt.figure()
-    ax0 = fig.add_subplot(2, 1, 1)
-    ax0.plot(amfreq_arr[idx_arr], mgain_arr[idx_arr], 'o')
-    ax0.set_yscale('log')
-    ax0.set_xscale('log')
-    ax0.set_title('%s' % data[0][0])
-    ax0.set_ylabel('gain [Hz/(mV/cm)]')
-    ax0.set_xlabel('envelope_frequency [Hz]')
-    #plt.savefig('%s gain' % data[0][0])
-
-    ax1 = fig.add_subplot(2, 1, 2, sharex = ax0)
-    ax1.plot(amfreq, threshold, 'o-', label = 'threshold', color = 'b')
-    ax1.set_xscale('log')
-    ax1.plot(amfreq, rootmeansquare, 'o-', label = 'RMS', color ='orange')
-    ax1.set_xscale('log')
-    ax1.set_xlabel('envelope_frequency [Hz]')
-    ax1.set_ylabel('RMS [Hz]')
-    plt.legend()
-    pylab.show()
-
-    #np.save('phaseshift_%s' % ident, mphaseshift_arr[idx_arr])
-    #np.save('gain_%s' %ident, mgain_arr[idx_arr])
-    #np.save('amf_%s' %ident, amfreq_arr[idx_arr])
-
-embed()
\ No newline at end of file
+            embed()
diff --git a/apteronotus_code/figure_apteronotus_rms_gaincurve.py b/apteronotus_code/figure_apteronotus_rms_gaincurve.py
index 927fb57..e6c3533 100644
--- a/apteronotus_code/figure_apteronotus_rms_gaincurve.py
+++ b/apteronotus_code/figure_apteronotus_rms_gaincurve.py
@@ -17,7 +17,7 @@ from jar_functions import gain_curve_fit
 def take_second(elem):      # function for taking the names out of files
     return elem[1]
 
-identifier = ['2018lepto1']
+identifier = ['2019lepto03']
 for ident in identifier:
 
     predict = []
@@ -35,16 +35,16 @@ for ident in identifier:
     currf = None
     idxlist = []
 
-    data = sorted(np.load('%s files.npy' %ident), key = take_second)      # list with filenames in it
+    data = sorted(np.load('5Hz_%s files.npy' %ident), key = take_second)      # list with filenames in it
 
     for i, d in enumerate(data):
         dd = list(d)
 
-        jar = np.load('%s.npy' %dd)     # load data for every file name
+        jar = np.load('5Hz_%s.npy' %dd)     # load data for every file name
         jm = jar - np.mean(jar)         # low-pass filtering by subtracting mean
         print(dd)
 
-        time = np.load('%s time.npy' %dd)       # time file
+        time = np.load('5Hz_%s time.npy' %dd)       # time file
         dt = time[1] - time[0]
 
         n = int(1/float(d[1])/dt)
@@ -127,7 +127,7 @@ for ident in identifier:
     ax0.plot(amfreq_arr[idx_arr], mgain_arr[idx_arr], 'o')
     ax0.set_yscale('log')
     ax0.set_xscale('log')
-    ax0.set_title('gaincurve 2018lepto1')
+    ax0.set_title('gaincurve 2019lepto03, deltaf = -5Hz')
     ax0.set_ylabel('gain [Hz/(mV/cm)]')
     ax0.set_xlabel('envelope_frequency [Hz]')
     #plt.savefig('%s gain' % data[0][0])
diff --git a/apteronotus_code/sin_all_normal.py b/apteronotus_code/sin_all_normal.py
index f364311..f160984 100644
--- a/apteronotus_code/sin_all_normal.py
+++ b/apteronotus_code/sin_all_normal.py
@@ -7,17 +7,17 @@ from jar_functions import gain_curve_fit
 from jar_functions import avgNestedLists
 
 
-identifier = ['2018lepto1',
-              '2018lepto4',
-              '2018lepto5',
-              '2018lepto76',
+identifier = [#'2018lepto1',
+              #'2018lepto4',
+              #'2018lepto5',
+              #'2018lepto76',
               '2018lepto98',
-              '2019lepto03',
-              '2019lepto24',
-              '2019lepto27',
-              '2019lepto30',
-              '2020lepto04',
-              '2020lepto06',
+              #'2019lepto03',
+              #'2019lepto24',
+              #'2019lepto27',
+              #'2019lepto30',
+              #'2020lepto04',
+              #'2020lepto06',
               '2020lepto16',
               '2020lepto19',
               '2020lepto20'
@@ -28,14 +28,14 @@ amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
 all = []
 
 for ident in identifier:
-    data = np.load('gain_%s.npy' %ident)
+    data = np.load('5Hz_gain_%s.npy' %ident)
     all.append(data)
 
 av = avgNestedLists(all)
 
 fig = plt.figure()
 ax = fig.add_subplot(111)
-ax.plot(amf, av, 'o')
+ax.plot(amf, av, 'o', c = 'C0', label = 'gain')
 
 #plt.show()
 
@@ -45,8 +45,8 @@ fit = []
 fit_amf = []
 for ID in identifier:
     print(ID)
-    amf = np.load('amf_%s.npy' %ID)
-    gain = np.load('gain_%s.npy' %ID)
+    amf = np.load('5Hz_amf_%s.npy' %ID)
+    gain = np.load('5Hz_gain_%s.npy' %ID)
 
     sinv, sinc = curve_fit(gain_curve_fit, amf, gain)
     #print('tau:', sinv[0])
@@ -56,17 +56,19 @@ for ID in identifier:
     f_c.append(f_cutoff)
     fit.append(gain_curve_fit(amf, *sinv))
     fit_amf.append(amf)
+col = plt.cm.magma(np.linspace(0,0.8,len(fit)))
+for ff ,f in enumerate(fit):
+    ax.plot(fit_amf[ff], fit[ff], c = col[ff])
+    ax.axvline(x=f_c[ff], ymin=0, ymax=5, alpha=0.8, c = col[ff])  # colors_uniform[ff])
 
-#for ff ,f in enumerate(fit):
-#    ax.plot(fit_amf[ff], fit[ff])
 ax.set_xscale('log')
 ax.set_yscale('log')
-ax.set_title('gaincurve_average_allfish')
+ax.set_title('gain average all fish, deltaf: -5Hz')
 ax.set_ylabel('gain [Hz/(mV/cm)]')
 ax.set_xlabel('envelope_frequency [Hz]')
 ax.set_ylim(0.0008, )
-ax.plot(f_c, np.full(len(identifier), 0.0015), 'o', label = 'cutoff frequencies')
-ax.legend()
+#ax.plot(f_c, np.full(len(identifier), 0.0015), 'o', alpha = 0.5, c = 'darkorange', label = 'cutoff frequencies')
+ax.legend(loc = 'center left')
 
 plt.show()
 
diff --git a/apteronotus_code/sin_all_uniform.py b/apteronotus_code/sin_all_uniform.py
index 53bf32f..294ed44 100644
--- a/apteronotus_code/sin_all_uniform.py
+++ b/apteronotus_code/sin_all_uniform.py
@@ -9,19 +9,19 @@ import matplotlib as mpl
 from matplotlib import cm
 
 identifier_uniform = ['2018lepto1',
-              # '2018lepto4',
-              # '2018lepto5',
-              #'2018lepto76',
+              #'2018lepto4',
+              '2018lepto5',
+              '2018lepto76',
               '2018lepto98',
-              # '2019lepto03',
+              #'2019lepto03',
               '2019lepto24',
               #'2019lepto27',
-              # '2019lepto30',
-              '2020lepto04',
-              # '2020lepto06',
-              # '2020lepto16',
-              '2020lepto19',
-              # '2020lepto20'
+              #'2019lepto30',
+              #'2020lepto04',
+              '2020lepto06',
+              #'2020lepto16',
+              #'2020lepto19',
+              #'2020lepto20'
               ]
 identifier = ['2018lepto1',
               '2018lepto4',
@@ -32,10 +32,10 @@ identifier = ['2018lepto1',
               '2019lepto24',
               '2019lepto27',
               '2019lepto30',
-              '2020lepto04',
+              #'2020lepto04',
               '2020lepto06',
               '2020lepto16',
-              '2020lepto19',
+              #'2020lepto19',
               '2020lepto20'
               ]
 
@@ -56,15 +56,15 @@ new_av = avgNestedLists(new_all)
 
 fig = plt.figure()
 ax = fig.add_subplot(111)
-#ax.plot(amf, av, 'o', color = 'orange', label = 'normal')
-ax.plot(amf, new_av, 'o', label = 'uniformed')
-"""
+ax.plot(amf, av, 'o', color = 'darkorange', label = 'normal')
+ax.plot(amf, new_av, 'o', label = 'uniform')
+
 tau = []
 f_c = []
 fit = []
 fit_amf = []
 for ID in identifier:
-    #print(ID)
+    print(ID)
     amf = np.load('amf_%s.npy' %ID)
     gain = np.load('gain_%s.npy' %ID)
 
@@ -72,47 +72,46 @@ for ID in identifier:
     #print('tau:', sinv[0])
     tau.append(sinv[0])
     f_cutoff = abs(1 / (2*np.pi*sinv[0]))
-    #print('f_cutoff:', f_cutoff)
+    print('f_cutoff:', f_cutoff)
     f_c.append(f_cutoff)
     fit.append(gain_curve_fit(amf, *sinv))
     fit_amf.append(amf)
-"""
+
+# uniformed: 2018lepto5, 2018lepto1, 2018lepto76,  2018lepto98, 2020lepto06, 2019lepto24, 2020lepto4
+
 tau_uniform = []
 f_c_uniform = []
 fit_uniform = []
 fit_amf_uniform = []
 for ID in identifier_uniform:
-    #print(ID)
+    print(ID)
     amf = np.load('amf_%s.npy' %ID)
     gain = np.load('gain_%s.npy' %ID)
 
     sinv, sinc = curve_fit(gain_curve_fit, amf, gain)
-    #print('tau:', sinv[0])
+    print('tau:', sinv[0])
     tau_uniform.append(sinv[0])
     f_cutoff = abs(1 / (2*np.pi*sinv[0]))
-    #print('f_cutoff:', f_cutoff)
+    print('f_cutoff:', f_cutoff)
     f_c_uniform.append(f_cutoff)
     fit_uniform.append(gain_curve_fit(amf, *sinv))
     fit_amf_uniform.append(amf)
 
-colors_uniform = plt.cm.flag(np.linspace(0.2,0.8,len(fit_uniform)))
-#colors = plt.cm.flag(np.linspace(0.2,0.8,len(fit)))
-
-# for ff ,f in enumerate(fit):
-#     ax.plot(fit_amf[ff], fit[ff],color =  colors[ff])
-#     ax.axvline(x=f_c[ff], ymin=0, ymax=5, ls = '-', alpha = 0.5, color= colors[ff])#colors_uniform[ff])
+colors = plt.cm.flag(np.linspace(0,1,len(fit_uniform)))
 
-for ff, f in enumerate(fit_uniform):
-    ax.plot(fit_amf_uniform[ff], fit_uniform[ff], color =  colorss[ff]) #colors_uniform[ff])
-    ax.axvline(x=f_c_uniform[ff], ymin=0, ymax=5, ls = '-', alpha = 0.5, color= colorss[ff])#colors_uniform[ff])
+#for ff ,f in enumerate(fit_uniform):
+#    ax.plot(fit_amf_uniform[ff], fit_uniform[ff],color =  colorss[ff])
+#    ax.axvline(x=f_c_uniform[ff], ymin=0, ymax=5, ls = '-', alpha = 0.5, color= colorss[ff])#colors_uniform[ff])
 
 ax.set_xscale('log')
 ax.set_yscale('log')
-ax.set_title('gaincurve_average_allfish')
+ax.set_title('gain average all fish uniform')
 ax.set_ylabel('gain [Hz/(mV/cm)]')
 ax.set_xlabel('envelope_frequency [Hz]')
 ax.set_ylim(0.0008, )
-ax.legend()
+ax.plot(f_c, np.full(len(identifier), 0.0015), 'o', alpha = 0.5, c = 'darkorange', label = 'normal cutoff frequencies')
+ax.plot(f_c_uniform, np.full(len(identifier_uniform), 0.002), 'o', alpha = 0.5, c = 'C0', label = 'uniform cutoff frequencies')
+ax.legend(loc = 'center left')
 
 plt.show()
 embed()
diff --git a/eigenmannia_code/eigenmannia_jar_stacked.py b/eigenmannia_code/eigenmannia_jar_stacked.py
index ce76220..0af7db7 100644
--- a/eigenmannia_code/eigenmannia_jar_stacked.py
+++ b/eigenmannia_code/eigenmannia_jar_stacked.py
@@ -14,7 +14,8 @@ from scipy.signal import savgol_filter
 
 base_path = 'D:\\jar_project\\JAR\\eigenmannia\\deltaf'
 
-identifier = ['2013eigen13','2015eigen16', '2015eigen17', '2015eigen19', '2020eigen22','2020eigen32']
+#2015eigen8 no nix files
+identifier = ['2015eigen16', '2013eigen13','2015eigen17', '2015eigen19', '2020eigen22','2020eigen32']
 
 response = []
 deltaf = []
@@ -47,8 +48,8 @@ for ID in identifier:
         eodf = fish_f[index]
         eodf4 = eodf * 4
 
-        lim0 = eodf4 - 50
-        lim1 = eodf4 + 50
+        lim0 = eodf4 - 40
+        lim1 = eodf4 + 40
 
         df = freqs[1] - freqs[0]
         ix0 = int(np.floor(lim0/df))    # back to index
@@ -58,10 +59,17 @@ for ID in identifier:
         jar4 = freq4[np.argmax(spec4, axis=0)]      # all freqs at max specs over axis 0
 
         cut_time_jar = times[:len(jar4)]
-
-        #plt.imshow(spec4, cmap='jet', origin='lower', extent=(times[0], times[-1], lim0, lim1), aspect='auto', vmin=-80, vmax=-10)
-        #plt.plot(cut_time_jar, jar4)
+        ID_delta_f = [ID, str(delta_f[0]).split('.')[0]]
+        plt.imshow(spec4, cmap='jet', origin='lower', extent=(times[0] - 10, times[-1] - 10, lim0, lim1), aspect='auto', vmin=-80, vmax=-10)
+        plt.plot((cut_time_jar - 10), jar4, 'k', label = 'jar trace', lw = 2)
+        plt.hlines(y=lim0 + 5, xmin=0, xmax=60, lw=2.5, color='gold', label='stimulus duration')
+        plt.title('spectogram %s, deltaf: %sHz' %tuple(ID_delta_f))
+        plt.xlim(right=times[-1] - 10)
+        plt.legend()
         #plt.show()
+        delta_f_ID = [str(delta_f[0]).split('.')[0], ID]
+        plt.savefig('%sHz_specgram_jar_%s' %tuple(delta_f_ID))
+        plt.close()
 
         b = []
         for idx, i in enumerate(times):
@@ -79,8 +87,4 @@ for ID in identifier:
 
     res_df = sorted(zip(deltaf,response))
 
-    np.save('res_df_%s_new' %ID, res_df)
-
-# problem: rohdaten(data, pre_data) lassen sich auf grund ihrer 1D-array struktur nicht savgol filtern
-# diese bekomm ich nur über specgram in form von freq / time auftragen, was nicht mehr savgol gefiltert werden kann
-# jedoch könnte ich trotzdem einfach aus jar4 response herauslesen wobei dies dann weniger gefiltert wäre
\ No newline at end of file
+    #np.save('res_df_%s_new' %ID, res_df)
diff --git a/notes b/notes
index ed5ba7e..758cce8 100644
--- a/notes
+++ b/notes
@@ -1,10 +1,19 @@
+machen:
+- phaseshift (sin_all) nochmal, nicht richtiges Dings verwendet ( sinv[2]/p) und dann auch in phaseshift
+- abbildung erstellen mit custom cutoff frequencies über ganzen bereich (0.001Hz-1Hz) um hoffentlich zu zeigen dass dabei lineare
+Gerade entsteht, vergleichen mit uniformen Bereich bei Daten bei dem es sich auch eher linear verhält um zu zeigen auf was wir hinaus wollen
+- filter: zieht mean von einer amfreq periode ab wodurch alles was nicht damit in Verbindung steht herausfiltert,
+auch JAR. problematisch wird dies eher wenn JAR-Anstieg schneller abläuft als eine amfreq periode
+- wenn fit nicht funktioniert einfach weglassen, wenn sättigung vorhanden nochmal anschauen
+
+
 + figures:
     apteronotus: fundament by tims bachelor thesis, important that apteronotus only shifts his frequency up (as eigenmannia doesnt --> natalies measurements)
     + spectogram
     + jar trace out of specgram
     + filtering of jar trace: mean noise cut --> subtracting jar response over whole stimulus
     + fit and jar trace --> gain and phaseshift
-!!! + this for different am-frequencies and delta f (-15/-5Hz) --> compare gain for them
+    + this for different am-frequencies and delta f (-15/-5Hz) --> compare gain for them
     + gain curve for one or more single fish
     + fit of gain curve for cutoff frequency and tau
     + gain curve for all fish taken together
@@ -15,37 +24,30 @@
         fig_apt_gaincurve_cutoff_tau,
         sin_all_normal (without single gaincurves),
         sin_all_uniform (with gaincurves for 5Hz)
+    !!! discard 2019lepto03 -5Hz --> RMS always over threshold
+    !!! discard 2020lepto04 gain fit doesnt work
+    !!! discard 2020lepto19 gain fit doesnt work
 
     eigenmannia:
     + deltaf / response: -2Hz different, show it
     + spectogram
-    + direct to fit and jar trace --> gain and phaseshift   DURCH SIN RESPONSE SPEC JAGEN!
+    + direct to fit and jar trace 
     + gain curve for one or more single fish
-    + gain curve for all fish taken together
+    + gain curve for all fish taken together    !!! gains without filtering by RMS
     - (step response eigen)
-
+    --> fig_eig_specgram
+        fig_eig_jar_filter_fit,
+        fig_eig_rms_gaincurve,
+        fig_eig_gaincurve_cutoff_tau,
+        sin_eig_normal (without single gaincurves),
+        sin_eig_uniform (with gaincurves for 5Hz)
+    !!! discard 2015eigen8 --> RMS always over threshold
+    ??? mean_noise_cut_eigen?
     fish properties:
     + parameters
     + cutoff frequency - dominance score
-+ eigenmannia deltaf response over all fish mean
-+ phaseshift_all: wenn negativer gain in fit --> +pi rechnen, dann modulo
-- plot_eigenmannia_jar(compare res_df_%s / res_df_%s_new)
-- eigenmannia_jar:
-    - specgram auch zeigen, vorallem was auch die ausreißer bei -2 Hz betreffen
-- fish_properties:
-    - hauptsächlich auf f_c und tau konzentrieren, vor allem auch beides auftragen, gewicht/größe noch nehmen
-- step_response eigen: absolute response
-- Q10 Wert aus Formel von Jan auf base_frequenz rechnen (adjust-eodf in jar_functions)
-- sin_all_uniform - sin_all_normal (also 5Hz, let away 0.001Hz?, gain_fit): fit als spur reinlegen damit klar wird aus was gerade besteht
-
-long term:
-- extra datei mit script drin um fertige daten darzustellen, den fit-code nur zur datenverarbeitung verwenden
-- darstellung: specgram --> rausgezogene jarspur darüber --> filterung --> fit und daten zusammen dargestellt, das ganze für verschiedene frequenzen
-- unterschiedliche nffts auf anderem rechner laufen lassen evtl um unterschiede zu sehen
-
-- phase in degree: phase % (2pi) - modulo 2pi
 
+    offen: phaseshift, not working fit for some fish
 (
-- mit zu hohem RMS rauskicken: evtl nur ein trace rauskicken wenn nur da RMS zu hoch
 - 2019lepto27/30: 27 - 0.05Hz (7-27-af, erste dat mit len(dat)=1), 30 - 0.001Hz (7-30-ah mit 0.005 anstatt gewollten 0.001Hz --> fehlt)
 )
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