diff --git a/code/test.py b/code/test.py
index f2b9a4e..9274e87 100644
--- a/code/test.py
+++ b/code/test.py
@@ -1,101 +1,131 @@
-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.')
+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
+
+def extract_stim_data(stimulus):
+ '''
+ extracts all necessary metadata for each stimulus
+
+ Parameters
+ ----------
+ stimulus : Stimulus object or rlxnix.base.repro module
+ The stimulus from which the data is needed.
+
+ Returns
+ -------
+ amplitude : float
+ The relative signal amplitude in percent.
+ df : float
+ Distance of the stimulus to the current EODf.
+ eodf : float
+ Current EODf.
+ stim_freq : float
+ The total stimulus frequency (EODF+df).
+
+ '''
+ # 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 = stim.metadata[stim.name]['EODf'][0][0]
+ 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.')
plt.show()
\ No newline at end of file