diff --git a/code/plot_eodform_spikehist.py b/code/plot_eodform_spikehist.py
index 24d3590..166fc3c 100644
--- a/code/plot_eodform_spikehist.py
+++ b/code/plot_eodform_spikehist.py
@@ -22,7 +22,7 @@ spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
 interspikeintervals = np.diff(spikes)
 
 fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
-plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001), color='darkblue')
+plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001), color='royalblue')
 plt.xlabel("time [ms]", fontsize = 22)
 plt.xticks(fontsize = 18)
 plt.ylabel("number of \n interspikeintervals", fontsize = 22)
@@ -32,8 +32,13 @@ ax.spines["right"].set_visible(False)
 fig.tight_layout()
 plt.show()
 #plt.show()
+<<<<<<< HEAD
 #plt.savefig('isis.pdf')
 exit()
+=======
+plt.savefig('isis.png')
+
+>>>>>>> b9573c65638c8a716e57d75fbe550f0a1ef8859f
 
 # calculate coefficient of variation
 mu = np.mean(interspikeintervals)
@@ -85,13 +90,13 @@ plt.yticks(fontsize=18)
 ax1.spines['top'].set_visible(False)
 
 ax2 = ax1.twinx()
-ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='royalblue', alpha=0.5)
+ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='navy', alpha=0.5)
 ax2.plot(time_axis, mu_eod, color='black', lw=2)
 ax2.set_ylabel('voltage [mV]', fontsize=22)
-ax2.tick_params(axis='y', labelcolor='darkblue')
+ax2.tick_params(axis='y', labelcolor='navy')
 ax2.spines['top'].set_visible(False)
 
 plt.yticks(fontsize=18)
 fig.tight_layout()
 #plt.show()
-plt.savefig('eodform_spikehist.pdf')
\ No newline at end of file
+plt.savefig('eodform_spikehist.png')
\ No newline at end of file
diff --git a/code/repetition_firingrate.py b/code/repetition_firingrate.py
index f1f05a4..56abf88 100644
--- a/code/repetition_firingrate.py
+++ b/code/repetition_firingrate.py
@@ -8,7 +8,7 @@ from IPython import embed
 # define sampling rate and data path
 sampling_rate = 40 #kHz
 data_dir = "../data"
-dataset = "2018-11-13-aj-invivo-1"
+dataset = "2018-11-14-al-invivo-1"
 inch_factor = 2.54
 # parameters for binning, smoothing and plotting
 cut_window = 60
@@ -23,8 +23,8 @@ spike_bins = np.arange(-cut_window, cut_window+1) #ms
 
 # read data from files
 spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
-eod = read_chirp_eod(os.path.join(data_dir, dataset))
-chirp_times = read_chirp_times(os.path.join(data_dir, dataset))
+#eod = read_chirp_eod(os.path.join(data_dir, dataset))
+#chirp_times = read_chirp_times(os.path.join(data_dir, dataset))
 
 # make a delta f map for the quite more complicated keys
 df_map = map_keys(spikes)
@@ -42,12 +42,16 @@ for deltaf in df_map.keys():
     df_phase_time[deltaf] = {}
     df_phase_binary[deltaf] = {}
     for rep in df_map[deltaf]:
+        chirp_size = int(rep[-1].strip('Hz'))
+        # print(chirp_size)
+        if chirp_size == 150:
+            continue
         for phase in spikes[rep]:
             for idx in np.arange(num_bin):
                 # check the phase
                 if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
 
-                    # get spikes between 50 ms befor and after the chirp
+                    # get spikes between 60 ms before and after the chirp
                     spikes_to_cut = np.asarray(spikes[rep][phase])
                     spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window) & (spikes_to_cut < cut_window)]
                     spikes_idx = np.round(spikes_cut*sampling_rate)
@@ -98,5 +102,5 @@ for df in df_phase_time.keys():
         fig.tight_layout()
         #plt.show()
         #exit()
-        namefigure = '../figures/%s_%i_%i_firingrate.pdf' %(dataset, df, index_phase)
-        plt.savefig(namefigure)
\ No newline at end of file
+        namefigure = '../figures/%s_%i_%i_firingrate.png' %(dataset, df, index_phase)
+        plt.savefig(namefigure)
diff --git a/code/stimulus_chirp.py b/code/stimulus_chirp.py
index 5208d60..f45827c 100644
--- a/code/stimulus_chirp.py
+++ b/code/stimulus_chirp.py
@@ -40,8 +40,8 @@ ax1 = fig.add_subplot(211)
 plt.yticks(fontsize=18)
 ax2 = fig.add_subplot(212, sharex=ax1)
 plt.setp(ax1.get_xticklabels(), visible=False)
-ax1.plot(time*1000, signal, color = 'royalblue', lw = 1)
-ax2.plot(time*1000, freq, color = 'royalblue', lw = 3)
+ax1.plot(time*1000, signal, color = 'midnightblue', lw = 1)
+ax2.plot(time*1000, freq, color = 'midnightblue', lw = 3)
 
 ax1.set_ylabel("field [mV]", fontsize = 22)
 
@@ -54,4 +54,4 @@ plt.xticks(fontsize=18)
 plt.yticks(fontsize=18)
 fig.tight_layout()
 #plt.show()
-plt.savefig('stimulus_chirp.pdf')
+plt.savefig('stimulus_chirp.png')