diff --git a/code/test.py b/code/test.py
index a0eddc9..32b704e 100644
--- a/code/test.py
+++ b/code/test.py
@@ -1,4 +1,3 @@
-
 import glob
 import pathlib
 import numpy as np
@@ -42,7 +41,6 @@ def power_spectrum(rate, dt):
     freq, power = welch(rate, fs = 1/dt, nperseg = 2**16, noverlap = 2**15)
     return freq, power
 
-
 def extract_stim_data(stimulus):
     '''
     extracts all necessary metadata for each stimulus
@@ -72,110 +70,6 @@ def extract_stim_data(stimulus):
     stim_freq = stim.metadata[stim.name]['Frequency'][0][0]
     return amplitude, df, eodf, stim_freq
 
-
-
-#find example data
-datafolder = "../data"
-
-example_file = datafolder + "/" + "2024-10-16-ad-invivo-1.nix"
-
-#load dataset
-dataset = rlx.Dataset(example_file)
-# find all sams
-sams = dataset.repro_runs('SAM')
-sam = sams[2] # our example sam
-potential,time = sam.trace_data("V-1") #membrane potential
-spike_times, _ = sam.trace_data('Spikes-1') #spike times
-df = sam.metadata['RePro-Info']['settings']['deltaf'][0][0] #find df in metadata
-amp = sam.metadata['RePro-Info']['settings']['contrast'][0][0] * 100 #find amplitude in metadata
-
-#figure for a quick plot
-fig = plt.figure(figsize = (5, 2.5))
-ax = fig.add_subplot()
-ax.plot(time[time < 0.1], potential[time < 0.1]) # plot the membrane potential in 0.1s
-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)
-
-# 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.')
-<<<<<<< HEAD
-=======
-import glob
-import pathlib
-import numpy as np
-import matplotlib.pyplot as plt
-import rlxnix as rlx
-from IPython import embed
-from scipy.signal import welch
-
-def binary_spikes(spike_times, duration, dt):
-    """
-    Converts the spike times to a binary representations
-
-    Parameters
-    ----------
-    spike_times : np.array
-        The spike times.
-    duration : float
-        The trial duration:
-    dt : float
-        The temporal resolution.
-
-    Returns
-    -------
-    binary : np.array
-        The binary representation of the spike train.
-
-    """
-    binary = np.zeros(int(np.round(duration / dt))) #create the binary array with the same length as potential
-    
-    spike_indices = np.asarray(np.round(spike_times / dt), dtype = int) # get the indices
-    binary[spike_indices] = 1 # put the indices into binary
-    return binary
-
-def firing_rate(binary_spikes, dt = 0.000025, box_width = 0.01):
-    box = np.ones(int(box_width // dt))
-    box /= np.sum(box) * dt # normalisierung des box kernels to an integral of one
-    rate = np.convolve(binary_spikes, box, mode = 'same') 
-    return rate
-
-def power_spectrum(rate, dt):
-    freq, power = welch(rate, fs = 1/dt, nperseg = 2**16, noverlap = 2**15)
-    return freq, power
-
 #find example data
 datafolder = "../data"
 
@@ -233,4 +127,3 @@ ax = fig.add_subplot()
 ax.eventplot(spikes, linelength = 0.8)
 ax.set_xlabel('time [ms]')
 ax.set_ylabel('loop no.')
-plt.show()
\ No newline at end of file