diff --git a/code/test.py b/code/test.py
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+++ b/code/test.py
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+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"
+
+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.')
+plt.show()
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