diff --git a/code/test.py b/code/test.py
index b08d495..dd49edb 100644
--- a/code/test.py
+++ b/code/test.py
@@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
 import rlxnix as rlx
 from IPython import embed
 from scipy.signal import welch
+import useful_functions as f
 
 def binary_spikes(spike_times, duration, dt):
     """
@@ -68,10 +69,10 @@ def extract_stim_data(stimulus):
     '''
     # extract metadata
     # the stim.name adjusts the first key as it changes with every stimulus
-    amplitude = stim.metadata[stim.name]['Contrast'][0][0] 
-    df = stim.metadata[stim.name]['DeltaF'][0][0]
-    eodf = round(stim.metadata[stim.name]['EODf'][0][0])
-    stim_freq = round(stim.metadata[stim.name]['Frequency'][0][0])
+    amplitude = stimulus.metadata[stimulus.name]['Contrast'][0][0] 
+    df = stimulus.metadata[stimulus.name]['DeltaF'][0][0]
+    eodf = round(stimulus.metadata[stimulus.name]['EODf'][0][0])
+    stim_freq = round(stimulus.metadata[stimulus.name]['Frequency'][0][0])
     # calculates the amplitude modulation
     amp_mod, ny_freq = AM(eodf, stim_freq)
     return amplitude, df, eodf, stim_freq, amp_mod, ny_freq
@@ -187,7 +188,7 @@ def sam_data(sam):
 #find example data
 datafolder = "../../data"
 
-example_file = datafolder + "/" + "2024-10-16-ah-invivo-1.nix"
+example_file = datafolder + "/" + "2024-10-16-ad-invivo-1.nix"
 
 data_files = glob.glob("../../data/*.nix")
 
@@ -209,41 +210,43 @@ ax.scatter(spike_times[spike_times < 0.1],
            np.ones_like(spike_times[spike_times < 0.1]) * np.max(potential)) #plot teh spike times on top
 plt.show()
 plt.close()
-# get all the stimuli
-stims = sam.stimuli
-# empty list for the spike times
-spikes = []
-#spikes2 = np.array(range(len(stims)))
-# loop over the stimuli
-for stim in stims:
-    # get the spike times
-    spike, _ = stim.trace_data('Spikes-1')
-    # append the first 100ms to spikes
-    spikes.append(spike[spike < 0.1])
-    # get stimulus duration
-    duration = stim.duration
-    ti = stim.trace_info("V-1")
-    dt = ti.sampling_interval # get the stimulus interval
-    bin_spikes = binary_spikes(spike, duration, dt) #binarize the spike_times
-    print(len(bin_spikes))
-    pot,tim= stim.trace_data("V-1") #membrane potential
-    rate = firing_rate(bin_spikes, dt = dt)
-    print(np.mean(rate))
-    fig, [ax1, ax2] = plt.subplots(1, 2,layout = 'constrained')
-    ax1.plot(tim,rate)
-    ax1.set_ylim(0,600)
-    ax1.set_xlim(0, 0.04)
-    freq, power = power_spectrum(rate, dt)
-    ax2.plot(freq,power)
-    ax2.set_xlim(0,1000)
-    plt.close()
-    if stim == stims[-1]:
-        amplitude, df, eodf, stim_freq = extract_stim_data(stim)
-        print(amplitude, df, eodf, stim_freq)
-
-# make an eventplot
-fig = plt.figure(figsize = (5, 3), layout = 'constrained')
-ax = fig.add_subplot()
-ax.eventplot(spikes, linelength = 0.8)
-ax.set_xlabel('time [ms]')
-ax.set_ylabel('loop no.')
+
+sam_amp, sam_am,sam_df, sam_eodf, sam_nyquist, sam_stim = f.sam_data(sam)
+# # get all the stimuli
+# stims = sam.stimuli
+# # empty list for the spike times
+# spikes = []
+# #spikes2 = np.array(range(len(stims)))
+# # loop over the stimuli
+# for stim in stims:
+#     # get the spike times
+#     spike, _ = stim.trace_data('Spikes-1')
+#     # append the first 100ms to spikes
+#     spikes.append(spike[spike < 0.1])
+#     # get stimulus duration
+#     duration = stim.duration
+#     ti = stim.trace_info("V-1")
+#     dt = ti.sampling_interval # get the stimulus interval
+#     bin_spikes = binary_spikes(spike, duration, dt) #binarize the spike_times
+#     print(len(bin_spikes))
+#     pot,tim= stim.trace_data("V-1") #membrane potential
+#     rate = firing_rate(bin_spikes, dt = dt)
+#     print(np.mean(rate))
+#     fig, [ax1, ax2] = plt.subplots(1, 2,layout = 'constrained')
+#     ax1.plot(tim,rate)
+#     ax1.set_ylim(0,600)
+#     ax1.set_xlim(0, 0.04)
+#     freq, power = power_spectrum(rate, dt)
+#     ax2.plot(freq,power)
+#     ax2.set_xlim(0,1000)
+#     plt.close()
+#     if stim == stims[-1]:
+#         amplitude, df, eodf, stim_freq = extract_stim_data(stim)
+#         print(amplitude, df, eodf, stim_freq)
+
+# # make an eventplot
+# fig = plt.figure(figsize = (5, 3), layout = 'constrained')
+# ax = fig.add_subplot()
+# ax.eventplot(spikes, linelength = 0.8)
+# ax.set_xlabel('time [ms]')
+# ax.set_ylabel('loop no.')