diff --git a/helperFunctions.py b/helperFunctions.py
index 65b404a..903b793 100644
--- a/helperFunctions.py
+++ b/helperFunctions.py
@@ -4,6 +4,7 @@ from thunderfish.eventdetection import detect_peaks, threshold_crossing_times, t
 from scipy.optimize import curve_fit
 import functions as fu
 from numba import jit
+import matplotlib.pyplot as plt
 
 
 def fit_clipped_line(x, y):
@@ -193,7 +194,8 @@ def calculate_mean_of_frequency_traces(trial_time_traces, trial_frequency_traces
     latest_start = max(starts)
     earliest_end = min(ends)
 
-    shortened_time = np.arange(latest_start, earliest_end, sampling_interval)
+    length = int(round((earliest_end - latest_start) / sampling_interval))
+    shortened_time = (np.arange(0, length) * sampling_interval) + latest_start
 
     shortened_freqs = []
     for i in range(len(trial_frequency_traces)):
@@ -204,6 +206,21 @@ def calculate_mean_of_frequency_traces(trial_time_traces, trial_frequency_traces
 
     mean_freq = [sum(e) / len(e) for e in zip(*shortened_freqs)]
 
+    # for i in range(len(trial_time_traces)):
+    #     if i > 5:
+    #         break
+    #     plt.plot(trial_time_traces[i], trial_frequency_traces[i])
+    #
+    # plt.plot(shortened_time, mean_freq, color="black")
+    # plt.show()
+    # plt.close()
+
+
+    # if len(mean_freq) == len(shortened_time):
+    #     print("time and freq trace worked out.")
+    # else:
+    #     print("time and freq trace were different length. time- freq:" + str(len(shortened_time)-len(mean_freq)))
+
     return shortened_time, mean_freq
 
 
@@ -437,12 +454,11 @@ def __vector_strength__(relative_spike_times: np.ndarray, eod_durations: np.ndar
 
 
 def detect_f_zero_in_frequency_trace(time, frequency, stimulus_start, sampling_interval, peak_buffer_percent=0.05, buffer=0.025):
-    stimulus_start = stimulus_start - time[0]  # time start is generally != 0 and != delay
 
-    freq_before = frequency[int(buffer/sampling_interval):int((stimulus_start - buffer) / sampling_interval)]
+    freq_before = frequency[int(time[0]+buffer/sampling_interval):int((stimulus_start - time[0] - buffer) / sampling_interval)]
 
     if len(freq_before) < 3:
-        print("mäh")
+        print("Length of reference frequency before the stimulus too short (< 3 points)")
         return 0
 
     min_before = min(freq_before)
@@ -450,8 +466,7 @@ def detect_f_zero_in_frequency_trace(time, frequency, stimulus_start, sampling_i
     mean_before = np.mean(freq_before)
 
     # time where the f-zero is searched in
-    start_idx = int((stimulus_start-0.1*buffer) / sampling_interval)
-    end_idx = int((stimulus_start + buffer) / sampling_interval)
+    start_idx, end_idx = time_window_detect_f_zero(time[0], stimulus_start, sampling_interval, buffer)
 
     min_during_start_of_stim = min(frequency[start_idx:end_idx])
     max_during_start_of_stim = max(frequency[start_idx:end_idx])
@@ -480,22 +495,39 @@ def detect_f_zero_in_frequency_trace(time, frequency, stimulus_start, sampling_i
     return f_zero
 
 
+def time_window_detect_f_zero(time_start, stimulus_start, sampling_interval, buffer=0.025):
+    stimulus_start = stimulus_start - time_start
+    start_idx = int((stimulus_start - 0.5 * buffer) / sampling_interval)
+    end_idx = int((stimulus_start + buffer) / sampling_interval)
+    return start_idx, end_idx
+
+
 def detect_f_infinity_in_freq_trace(time, frequency, stimulus_start, stimulus_duration, sampling_interval, length=0.1, buffer=0.025):
-    stimulus_end_time = stimulus_start + stimulus_duration - time[0]
+    start_idx, end_idx = time_window_detect_f_infinity(time[0], stimulus_start, stimulus_duration, sampling_interval, length, buffer)
+    return np.mean(frequency[start_idx:end_idx])
+
+
+def time_window_detect_f_infinity(time_start, stimulus_start, stimulus_duration, sampling_interval, length=0.1, buffer=0.025):
+    stimulus_end_time = stimulus_start + stimulus_duration - time_start
 
     start_idx = int((stimulus_end_time - length - buffer) / sampling_interval)
     end_idx = int((stimulus_end_time - buffer) / sampling_interval)
-    return np.mean(frequency[start_idx:end_idx])
+    return start_idx, end_idx
 
 
 def detect_f_baseline_in_freq_trace(time, frequency, stimulus_start, sampling_interval, buffer=0.025):
-        stim_start = stimulus_start - time[0]
+    start_idx, end_idx = time_window_detect_f_baseline(time[0], stimulus_start, sampling_interval, buffer)
+    f_baseline = np.mean(frequency[start_idx:end_idx])
+
+    return f_baseline
+
 
-        if stim_start < 0.1:
-            warn("FICurve:__calculate_f_baseline__(): Quite short delay at the start.")
+def time_window_detect_f_baseline(time_start, stimulus_start, sampling_interval, buffer=0.025):
+    stim_start = stimulus_start - time_start
 
-        start_idx = int(buffer/sampling_interval)
-        end_idx = int((stim_start-buffer)/sampling_interval)
-        f_baseline = np.mean(frequency[start_idx:end_idx])
+    if stim_start < 0.1:
+        warn("FICurve:__calculate_f_baseline__(): Quite short delay at the start.")
 
-        return f_baseline
\ No newline at end of file
+    start_idx = int(buffer / sampling_interval)
+    end_idx = int((stim_start - buffer) / sampling_interval)
+    return start_idx, end_idx