From 342914a71944b4f19cc1a3ad2921f9fece68de81 Mon Sep 17 00:00:00 2001
From: "sarah.eisele" <sarah.eisele@student.uni-tuebingen.de>
Date: Fri, 18 Oct 2024 14:59:41 +0000
Subject: [PATCH 1/8] Add analysis_1

---
 analysis_1 | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 analysis_1

diff --git a/analysis_1 b/analysis_1
new file mode 100644
index 0000000..e69de29

From 7c32c6e4b86bad2fc4c8177d78902acd72f818c1 Mon Sep 17 00:00:00 2001
From: "sarah.eisele" <sarah.eisele@student.uni-tuebingen.de>
Date: Fri, 18 Oct 2024 15:03:44 +0000
Subject: [PATCH 2/8] Upload files to "/"

---
 analysis_1.py | 154 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 154 insertions(+)
 create mode 100644 analysis_1.py

diff --git a/analysis_1.py b/analysis_1.py
new file mode 100644
index 0000000..b6d6497
--- /dev/null
+++ b/analysis_1.py
@@ -0,0 +1,154 @@
+import rlxnix as rlx
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+from scipy.signal import welch
+
+# close all currently open figures
+plt.close('all')
+
+'''FUNCTIONS'''
+def plot_vt_spikes(t, v, spike_t):
+    fig = plt.figure(figsize=(5, 2.5))
+    # alternative to ax = axs[0]
+    ax = fig.add_subplot()
+    # plot vt diagram
+    ax.plot(t[t<0.1], v[t<0.1])
+    # plot spikes into vt diagram, at max V
+    ax.scatter(spike_t[spike_t<0.1], np.ones_like(spike_t[spike_t<0.1]) * np.max(v))
+    plt.show()
+
+def scatter_plot(colormap, stimuli_list, stimulus_count):
+    '''plot scatter plot for one sam with all 3 stims'''
+    fig = plt.figure()
+    ax = fig.add_subplot()
+        
+    ax.eventplot(stimuli_list, colors=colormap)
+    ax.set_xlabel('Spike Times [ms]')
+    ax.set_ylabel('Loop #')
+    ax.set_yticks(range(stimulus_count))
+    ax.set_title('Spikes of SAM 3')
+    plt.show()
+
+# create binary array with ones for spike times
+def binary_spikes(spike_times, duration , dt):
+    '''Converts spike times to binary representation
+    Params
+    ------
+    spike_times: np.array
+        spike times
+    duration: float
+        trial duration
+    dt: float
+        temporal resolution
+    
+    Returns
+    --------
+    binary: np.array
+        The binary representation of the spike times
+    '''
+    binary = np.zeros(int(duration//dt)) # // is truncated division, returns number w/o decimals, same as np.round
+    spike_indices = np.asarray(np.round(spike_times//dt), dtype=int)
+    binary[spike_indices] = 1 
+    return binary
+
+# function to plot psth
+def firing_rates(binary_spikes, box_width=0.01, dt=0.000025):
+    box = np.ones(int(box_width // dt))
+    box /= np.sum(box * dt) # normalize box kernel w interal of 1
+    rate = np.convolve(binary_spikes, box, mode='same')
+    return rate
+
+def power_spectrum(rate, dt):
+    f, p = welch(rate, fs = 1./dt, nperseg=2**16, noverlap=2**15) 
+    # algorithm makes rounding mistakes, we want to calc many spectra and take mean of those 
+    # nperseg: length of segments in # datapoints
+    # noverlap: # datapoints that overlap in segments
+    return f, p
+    
+def power_spectrum_plot(f, p):
+    # plot power spectrum
+    fig = plt.figure()
+    ax = fig.add_subplot()
+    ax.plot(freq, power)
+    ax.set_xlabel('Frequency [Hz]')
+    ax.set_ylabel('Power [1/Hz]')
+    ax.set_xlim(0, 1000)
+    plt.show()
+
+'''IMPORT DATA'''
+datafolder = '../data' #./ wo ich gerade bin; ../ eine ebene höher; ../../ zwei ebenen höher
+
+example_file = os.path.join('..', 'data', '2024-10-16-ac-invivo-1.nix')
+
+'''EXTRACT DATA'''
+dataset = rlx.Dataset(example_file)
+
+# get sams
+sams = dataset.repro_runs('SAM')
+sam = sams[2]
+
+# get potetial over time (vt curve)
+potential, time = sam.trace_data('V-1')
+
+# get spike times
+spike_times, _ = sam.trace_data('Spikes-1')
+
+# get stim count
+stim_count = sam.stimulus_count
+
+# extract spike times of all 3 loops of current sam
+stimuli = []
+for i in range(stim_count):
+    # get stim i from sam
+    stim = sam.stimuli[i]
+    potential_stim, time_stim = stim.trace_data('V-1')
+    # get spike_times
+    spike_times_stim, _ = stim.trace_data('Spikes-1')
+    stimuli.append(spike_times_stim)
+    
+eodf = stim.metadata[stim.name]['EODF'][0][0]
+df = stim.metadata['RePro-Info']['settings']['deltaf'][0][0]
+stimulus_freq = df + eodf
+
+'''PLOT'''
+# create colormap
+colors = plt.cm.prism(np.linspace(0, 1, stim_count))
+
+# timeline of whole rec
+dataset.plot_timeline()
+
+# voltage and spikes of current sam
+plot_vt_spikes(time, potential, spike_times)
+
+# spike times of all loops
+scatter_plot(colors, stimuli, stim_count)
+
+
+'''POWER SPECTRUM'''
+# define variables for binary spikes function
+spikes, _ = stim.trace_data('Spikes-1')
+ti = stim.trace_info('V-1')
+dt = ti.sampling_interval
+duration = stim.duration
+
+### spectrum
+# vector with binary values for wholes length of stim
+binary = binary_spikes(spikes, duration, dt)
+
+# calculate firing rate 
+rate = firing_rates(binary, 0.01, dt) # box width of 10 ms
+
+# plot psth or whatever
+# plt.plot(time_stim, rate)
+# plt.show()
+
+freq, power = power_spectrum(binary, dt)
+
+power_spectrum_plot(freq, power)
+
+
+### TODO:
+    # then loop over sams/dfs, all stims, intensities
+    # when does stim start in eodf/ at which phase and how does that influence our signal --> alignment problem: egal wenn wir spectren haben
+    # we want to see peaks at phase locking to own and stim frequency, and at amp modulation frequency
\ No newline at end of file

From 72a97f18ce31d9e7a5b41d7d50a3e0ca8cc8930d Mon Sep 17 00:00:00 2001
From: mbergmann <maximilian.bergmann@student.uni-tuebingen.de>
Date: Fri, 18 Oct 2024 15:04:26 +0000
Subject: [PATCH 3/8] =?UTF-8?q?analysis=5F1=20gel=C3=B6scht?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

was empty and not needed
---
 analysis_1 | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 analysis_1

diff --git a/analysis_1 b/analysis_1
deleted file mode 100644
index e69de29..0000000

From 052210c1eadff598467ef034b447b50b512d2f61 Mon Sep 17 00:00:00 2001
From: Diana <diana.stoll@student.uni-tuebingen.de>
Date: Mon, 21 Oct 2024 12:31:36 +0000
Subject: [PATCH 4/8] Dateien nach "/" hochladen

Integrale unter Peaks berechnen
---
 GP_Code.py | 192 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 192 insertions(+)
 create mode 100644 GP_Code.py

diff --git a/GP_Code.py b/GP_Code.py
new file mode 100644
index 0000000..6ff4428
--- /dev/null
+++ b/GP_Code.py
@@ -0,0 +1,192 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Oct 17 09:23:10 2024
+
+@author: diana
+"""
+# -*- coding: utf-8 -*-
+
+import glob
+import os
+import rlxnix as rlx
+import numpy as np
+import matplotlib.pyplot as plt
+import scipy.signal as sig
+from scipy.integrate import quad
+
+
+### FUNCTIONS ###
+def binary_spikes(spike_times, duration, dt): 
+    """ Converts the spike times to a binary representation. 
+    Zeros when there is no spike, One when there is.     
+
+    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 times.
+
+    """
+    binary = np.zeros(int(np.round(duration / dt))) #Vektor, der genauso lang ist wie die stim time
+    spike_indices = np.asarray(np.round(spike_times / dt), dtype=int)
+    binary[spike_indices] = 1
+    return binary
+
+
+def firing_rate(binary_spikes, box_width, dt=0.000025):
+    """Calculate the firing rate from binary spike data.
+
+    This function computes the firing rate using a boxcar (moving average) 
+    filter of a specified width.
+
+    Parameters
+    ----------
+    binary_spikes : np.array
+        A binary array representing spike occurrences.
+    box_width : float
+        The width of the box filter in seconds.
+    dt : float, optional
+        The temporal resolution (time step) in seconds. Default is 0.000025 seconds.
+
+    Returns
+    -------
+    rate : np.array
+        An array representing the firing rate at each time step.
+    """
+    box = np.ones(int(box_width // dt))
+    box /= np.sum(box) * dt #Normalization of box kernel to an integral of 1
+    rate = np.convolve(binary_spikes, box, mode="same")
+    return rate
+
+
+def powerspectrum(rate, dt):
+    """Compute the power spectrum of a given firing rate.
+
+    This function calculates the power spectrum using the Welch method.
+
+    Parameters
+    ----------
+    rate : np.array
+        An array of firing rates.
+    dt : float
+        The temporal resolution (time step) in seconds.
+
+    Returns
+    -------
+    frequency : np.array
+        An array of frequencies corresponding to the power values.
+    power : np.array
+        An array of power spectral density values.
+    """
+    frequency, power = sig.welch(rate, fs=1/dt, nperseg=2**15, noverlap=2**14)
+    return frequency, power
+
+
+def prepare_harmonics(frequencies, categories, num_harmonics, colors):
+    points_categories = {}
+    for idx, (freq, category) in enumerate(zip(frequencies, categories)):
+        points_categories[category] = [freq * (i + 1) for i in range(num_harmonics[idx])]
+
+    points = [p for harmonics in points_categories.values() for p in harmonics]
+    color_mapping = {category: colors[idx] for idx, category in enumerate(categories)}
+
+    return points, color_mapping, points_categories
+
+
+def plot_power_spectrum_with_integrals(frequency, power, points, delta, color_mapping, points_categories):
+    """Create a figure of the power spectrum with integrals highlighted around specified points.
+
+    This function creates a plot of the power spectrum and shades areas around
+    specified harmonic points to indicate the calculated integrals.
+
+    Parameters
+    ----------
+    frequency : np.array
+        An array of frequencies corresponding to the power values.
+    power : np.array
+        An array of power spectral density values.
+    points : list
+        A list of harmonic frequencies to highlight.
+    delta : float
+        Half-width of the range for integration around each point.
+    color_mapping : dict
+        A mapping of point categories to colors.
+    points_categories : dict
+        A mapping of categories to lists of points.
+
+    Returns
+    -------
+    fig : matplotlib.figure.Figure
+        The created figure object.
+    """
+    fig, ax = plt.subplots()
+    ax.plot(frequency, power)
+    integrals = []
+
+    for point in points:
+        indices = (frequency >= point - delta) & (frequency <= point + delta)
+        integral = np.trapz(power[indices], frequency[indices])
+        integrals.append(integral)
+
+        # Get color based on point category
+        color = next((c for cat, c in color_mapping.items() if point in points_categories[cat]), 'gray')
+        ax.axvspan(point - delta, point + delta, color=color, alpha=0.3, label=f'{point:.2f} Hz')
+        print(f"Integral around {point:.2f} Hz: {integral:.5e}")
+
+    ax.set_xlim([0, 1200])
+    ax.set_xlabel('Frequency (Hz)')
+    ax.set_ylabel('Power')
+    ax.set_title('Power Spectrum with marked Integrals')
+    ax.legend()
+
+    return fig
+
+
+
+
+### Data retrieval ###
+datafolder = "../data" # Geht in der Hierarchie einen Ordern nach oben (..) und dann in den Ordner 'data'
+example_file = os.path.join("..", "data", "2024-10-16-ad-invivo-1.nix")
+dataset = rlx.Dataset(example_file)
+sams = dataset.repro_runs("SAM") 
+sam = sams[2]
+
+## Daten für Funktionen
+df = sam.metadata["RePro-Info"]["settings"]["deltaf"][0][0]
+stim = sam.stimuli[1]
+potential, time = stim.trace_data("V-1")
+spikes, _ = stim.trace_data("Spikes-1")
+duration = stim.duration
+dt = stim.trace_info("V-1").sampling_interval
+
+
+### Anwendung Functionen ### 
+b = binary_spikes(spikes, duration, dt)
+rate = firing_rate(b, box_width=0.05, dt=dt)
+frequency, power = powerspectrum(b, dt)
+
+## Important stuff 
+eodf = stim.metadata[stim.name]["EODf"][0][0]
+stimulus_frequency = eodf + df
+AM = 50 # Hz
+#print(f"EODf: {eodf}, Stimulus Frequency: {stimulus_frequency}, AM: {AM}")
+
+frequencies = [AM, eodf, stimulus_frequency]
+categories = ["AM", "EODf", "Stimulus frequency"]
+num_harmonics = [4, 2, 2]
+colors = ["green", "orange", "red"]
+delta = 2.5
+
+
+### Peaks im Powerspektrum finden ###
+points, color_mapping, points_categories = prepare_harmonics(frequencies, categories, num_harmonics, colors)
+fig = plot_power_spectrum_with_integrals(frequency, power, points, delta, color_mapping, points_categories)
+plt.show()

From b0adb74ae33d9b2ad9ba5eed6af84977d9d1a35b Mon Sep 17 00:00:00 2001
From: mbergmann <maximilian.bergmann@student.uni-tuebingen.de>
Date: Tue, 22 Oct 2024 07:14:44 +0000
Subject: [PATCH 5/8] Datei test.py nach "code" hochladen

---
 code/test.py | 101 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 101 insertions(+)
 create mode 100644 code/test.py

diff --git a/code/test.py b/code/test.py
new file mode 100644
index 0000000..f2b9a4e
--- /dev/null
+++ b/code/test.py
@@ -0,0 +1,101 @@
+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()
\ No newline at end of file

From e8d4ca90fe7c13fed6fab75c2042d64801b48994 Mon Sep 17 00:00:00 2001
From: mbergmann <maximilian.bergmann@student.uni-tuebingen.de>
Date: Tue, 22 Oct 2024 07:16:25 +0000
Subject: [PATCH 6/8] GP_code nach code verschoben

---
 GP_Code.py => code/GP_Code.py | 384 +++++++++++++++++-----------------
 1 file changed, 192 insertions(+), 192 deletions(-)
 rename GP_Code.py => code/GP_Code.py (96%)

diff --git a/GP_Code.py b/code/GP_Code.py
similarity index 96%
rename from GP_Code.py
rename to code/GP_Code.py
index 6ff4428..754715e 100644
--- a/GP_Code.py
+++ b/code/GP_Code.py
@@ -1,192 +1,192 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Oct 17 09:23:10 2024
-
-@author: diana
-"""
-# -*- coding: utf-8 -*-
-
-import glob
-import os
-import rlxnix as rlx
-import numpy as np
-import matplotlib.pyplot as plt
-import scipy.signal as sig
-from scipy.integrate import quad
-
-
-### FUNCTIONS ###
-def binary_spikes(spike_times, duration, dt): 
-    """ Converts the spike times to a binary representation. 
-    Zeros when there is no spike, One when there is.     
-
-    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 times.
-
-    """
-    binary = np.zeros(int(np.round(duration / dt))) #Vektor, der genauso lang ist wie die stim time
-    spike_indices = np.asarray(np.round(spike_times / dt), dtype=int)
-    binary[spike_indices] = 1
-    return binary
-
-
-def firing_rate(binary_spikes, box_width, dt=0.000025):
-    """Calculate the firing rate from binary spike data.
-
-    This function computes the firing rate using a boxcar (moving average) 
-    filter of a specified width.
-
-    Parameters
-    ----------
-    binary_spikes : np.array
-        A binary array representing spike occurrences.
-    box_width : float
-        The width of the box filter in seconds.
-    dt : float, optional
-        The temporal resolution (time step) in seconds. Default is 0.000025 seconds.
-
-    Returns
-    -------
-    rate : np.array
-        An array representing the firing rate at each time step.
-    """
-    box = np.ones(int(box_width // dt))
-    box /= np.sum(box) * dt #Normalization of box kernel to an integral of 1
-    rate = np.convolve(binary_spikes, box, mode="same")
-    return rate
-
-
-def powerspectrum(rate, dt):
-    """Compute the power spectrum of a given firing rate.
-
-    This function calculates the power spectrum using the Welch method.
-
-    Parameters
-    ----------
-    rate : np.array
-        An array of firing rates.
-    dt : float
-        The temporal resolution (time step) in seconds.
-
-    Returns
-    -------
-    frequency : np.array
-        An array of frequencies corresponding to the power values.
-    power : np.array
-        An array of power spectral density values.
-    """
-    frequency, power = sig.welch(rate, fs=1/dt, nperseg=2**15, noverlap=2**14)
-    return frequency, power
-
-
-def prepare_harmonics(frequencies, categories, num_harmonics, colors):
-    points_categories = {}
-    for idx, (freq, category) in enumerate(zip(frequencies, categories)):
-        points_categories[category] = [freq * (i + 1) for i in range(num_harmonics[idx])]
-
-    points = [p for harmonics in points_categories.values() for p in harmonics]
-    color_mapping = {category: colors[idx] for idx, category in enumerate(categories)}
-
-    return points, color_mapping, points_categories
-
-
-def plot_power_spectrum_with_integrals(frequency, power, points, delta, color_mapping, points_categories):
-    """Create a figure of the power spectrum with integrals highlighted around specified points.
-
-    This function creates a plot of the power spectrum and shades areas around
-    specified harmonic points to indicate the calculated integrals.
-
-    Parameters
-    ----------
-    frequency : np.array
-        An array of frequencies corresponding to the power values.
-    power : np.array
-        An array of power spectral density values.
-    points : list
-        A list of harmonic frequencies to highlight.
-    delta : float
-        Half-width of the range for integration around each point.
-    color_mapping : dict
-        A mapping of point categories to colors.
-    points_categories : dict
-        A mapping of categories to lists of points.
-
-    Returns
-    -------
-    fig : matplotlib.figure.Figure
-        The created figure object.
-    """
-    fig, ax = plt.subplots()
-    ax.plot(frequency, power)
-    integrals = []
-
-    for point in points:
-        indices = (frequency >= point - delta) & (frequency <= point + delta)
-        integral = np.trapz(power[indices], frequency[indices])
-        integrals.append(integral)
-
-        # Get color based on point category
-        color = next((c for cat, c in color_mapping.items() if point in points_categories[cat]), 'gray')
-        ax.axvspan(point - delta, point + delta, color=color, alpha=0.3, label=f'{point:.2f} Hz')
-        print(f"Integral around {point:.2f} Hz: {integral:.5e}")
-
-    ax.set_xlim([0, 1200])
-    ax.set_xlabel('Frequency (Hz)')
-    ax.set_ylabel('Power')
-    ax.set_title('Power Spectrum with marked Integrals')
-    ax.legend()
-
-    return fig
-
-
-
-
-### Data retrieval ###
-datafolder = "../data" # Geht in der Hierarchie einen Ordern nach oben (..) und dann in den Ordner 'data'
-example_file = os.path.join("..", "data", "2024-10-16-ad-invivo-1.nix")
-dataset = rlx.Dataset(example_file)
-sams = dataset.repro_runs("SAM") 
-sam = sams[2]
-
-## Daten für Funktionen
-df = sam.metadata["RePro-Info"]["settings"]["deltaf"][0][0]
-stim = sam.stimuli[1]
-potential, time = stim.trace_data("V-1")
-spikes, _ = stim.trace_data("Spikes-1")
-duration = stim.duration
-dt = stim.trace_info("V-1").sampling_interval
-
-
-### Anwendung Functionen ### 
-b = binary_spikes(spikes, duration, dt)
-rate = firing_rate(b, box_width=0.05, dt=dt)
-frequency, power = powerspectrum(b, dt)
-
-## Important stuff 
-eodf = stim.metadata[stim.name]["EODf"][0][0]
-stimulus_frequency = eodf + df
-AM = 50 # Hz
-#print(f"EODf: {eodf}, Stimulus Frequency: {stimulus_frequency}, AM: {AM}")
-
-frequencies = [AM, eodf, stimulus_frequency]
-categories = ["AM", "EODf", "Stimulus frequency"]
-num_harmonics = [4, 2, 2]
-colors = ["green", "orange", "red"]
-delta = 2.5
-
-
-### Peaks im Powerspektrum finden ###
-points, color_mapping, points_categories = prepare_harmonics(frequencies, categories, num_harmonics, colors)
-fig = plot_power_spectrum_with_integrals(frequency, power, points, delta, color_mapping, points_categories)
-plt.show()
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Oct 17 09:23:10 2024
+
+@author: diana
+"""
+# -*- coding: utf-8 -*-
+
+import glob
+import os
+import rlxnix as rlx
+import numpy as np
+import matplotlib.pyplot as plt
+import scipy.signal as sig
+from scipy.integrate import quad
+
+
+### FUNCTIONS ###
+def binary_spikes(spike_times, duration, dt): 
+    """ Converts the spike times to a binary representation. 
+    Zeros when there is no spike, One when there is.     
+
+    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 times.
+
+    """
+    binary = np.zeros(int(np.round(duration / dt))) #Vektor, der genauso lang ist wie die stim time
+    spike_indices = np.asarray(np.round(spike_times / dt), dtype=int)
+    binary[spike_indices] = 1
+    return binary
+
+
+def firing_rate(binary_spikes, box_width, dt=0.000025):
+    """Calculate the firing rate from binary spike data.
+
+    This function computes the firing rate using a boxcar (moving average) 
+    filter of a specified width.
+
+    Parameters
+    ----------
+    binary_spikes : np.array
+        A binary array representing spike occurrences.
+    box_width : float
+        The width of the box filter in seconds.
+    dt : float, optional
+        The temporal resolution (time step) in seconds. Default is 0.000025 seconds.
+
+    Returns
+    -------
+    rate : np.array
+        An array representing the firing rate at each time step.
+    """
+    box = np.ones(int(box_width // dt))
+    box /= np.sum(box) * dt #Normalization of box kernel to an integral of 1
+    rate = np.convolve(binary_spikes, box, mode="same")
+    return rate
+
+
+def powerspectrum(rate, dt):
+    """Compute the power spectrum of a given firing rate.
+
+    This function calculates the power spectrum using the Welch method.
+
+    Parameters
+    ----------
+    rate : np.array
+        An array of firing rates.
+    dt : float
+        The temporal resolution (time step) in seconds.
+
+    Returns
+    -------
+    frequency : np.array
+        An array of frequencies corresponding to the power values.
+    power : np.array
+        An array of power spectral density values.
+    """
+    frequency, power = sig.welch(rate, fs=1/dt, nperseg=2**15, noverlap=2**14)
+    return frequency, power
+
+
+def prepare_harmonics(frequencies, categories, num_harmonics, colors):
+    points_categories = {}
+    for idx, (freq, category) in enumerate(zip(frequencies, categories)):
+        points_categories[category] = [freq * (i + 1) for i in range(num_harmonics[idx])]
+
+    points = [p for harmonics in points_categories.values() for p in harmonics]
+    color_mapping = {category: colors[idx] for idx, category in enumerate(categories)}
+
+    return points, color_mapping, points_categories
+
+
+def plot_power_spectrum_with_integrals(frequency, power, points, delta, color_mapping, points_categories):
+    """Create a figure of the power spectrum with integrals highlighted around specified points.
+
+    This function creates a plot of the power spectrum and shades areas around
+    specified harmonic points to indicate the calculated integrals.
+
+    Parameters
+    ----------
+    frequency : np.array
+        An array of frequencies corresponding to the power values.
+    power : np.array
+        An array of power spectral density values.
+    points : list
+        A list of harmonic frequencies to highlight.
+    delta : float
+        Half-width of the range for integration around each point.
+    color_mapping : dict
+        A mapping of point categories to colors.
+    points_categories : dict
+        A mapping of categories to lists of points.
+
+    Returns
+    -------
+    fig : matplotlib.figure.Figure
+        The created figure object.
+    """
+    fig, ax = plt.subplots()
+    ax.plot(frequency, power)
+    integrals = []
+
+    for point in points:
+        indices = (frequency >= point - delta) & (frequency <= point + delta)
+        integral = np.trapz(power[indices], frequency[indices])
+        integrals.append(integral)
+
+        # Get color based on point category
+        color = next((c for cat, c in color_mapping.items() if point in points_categories[cat]), 'gray')
+        ax.axvspan(point - delta, point + delta, color=color, alpha=0.3, label=f'{point:.2f} Hz')
+        print(f"Integral around {point:.2f} Hz: {integral:.5e}")
+
+    ax.set_xlim([0, 1200])
+    ax.set_xlabel('Frequency (Hz)')
+    ax.set_ylabel('Power')
+    ax.set_title('Power Spectrum with marked Integrals')
+    ax.legend()
+
+    return fig
+
+
+
+
+### Data retrieval ###
+datafolder = "../data" # Geht in der Hierarchie einen Ordern nach oben (..) und dann in den Ordner 'data'
+example_file = os.path.join("..", "data", "2024-10-16-ad-invivo-1.nix")
+dataset = rlx.Dataset(example_file)
+sams = dataset.repro_runs("SAM") 
+sam = sams[2]
+
+## Daten für Funktionen
+df = sam.metadata["RePro-Info"]["settings"]["deltaf"][0][0]
+stim = sam.stimuli[1]
+potential, time = stim.trace_data("V-1")
+spikes, _ = stim.trace_data("Spikes-1")
+duration = stim.duration
+dt = stim.trace_info("V-1").sampling_interval
+
+
+### Anwendung Functionen ### 
+b = binary_spikes(spikes, duration, dt)
+rate = firing_rate(b, box_width=0.05, dt=dt)
+frequency, power = powerspectrum(b, dt)
+
+## Important stuff 
+eodf = stim.metadata[stim.name]["EODf"][0][0]
+stimulus_frequency = eodf + df
+AM = 50 # Hz
+#print(f"EODf: {eodf}, Stimulus Frequency: {stimulus_frequency}, AM: {AM}")
+
+frequencies = [AM, eodf, stimulus_frequency]
+categories = ["AM", "EODf", "Stimulus frequency"]
+num_harmonics = [4, 2, 2]
+colors = ["green", "orange", "red"]
+delta = 2.5
+
+
+### Peaks im Powerspektrum finden ###
+points, color_mapping, points_categories = prepare_harmonics(frequencies, categories, num_harmonics, colors)
+fig = plot_power_spectrum_with_integrals(frequency, power, points, delta, color_mapping, points_categories)
+plt.show()

From 7e02490a89d17a96689c3f0c0ad4919df2e09b93 Mon Sep 17 00:00:00 2001
From: mbergmann <maximilian.bergmann@student.uni-tuebingen.de>
Date: Tue, 22 Oct 2024 07:17:10 +0000
Subject: [PATCH 7/8] analysis_1.py nach code verschoben

---
 analysis_1.py => code/analysis_1.py | 306 ++++++++++++++--------------
 1 file changed, 153 insertions(+), 153 deletions(-)
 rename analysis_1.py => code/analysis_1.py (95%)

diff --git a/analysis_1.py b/code/analysis_1.py
similarity index 95%
rename from analysis_1.py
rename to code/analysis_1.py
index b6d6497..17fb46b 100644
--- a/analysis_1.py
+++ b/code/analysis_1.py
@@ -1,154 +1,154 @@
-import rlxnix as rlx
-import numpy as np
-import matplotlib.pyplot as plt
-import os
-from scipy.signal import welch
-
-# close all currently open figures
-plt.close('all')
-
-'''FUNCTIONS'''
-def plot_vt_spikes(t, v, spike_t):
-    fig = plt.figure(figsize=(5, 2.5))
-    # alternative to ax = axs[0]
-    ax = fig.add_subplot()
-    # plot vt diagram
-    ax.plot(t[t<0.1], v[t<0.1])
-    # plot spikes into vt diagram, at max V
-    ax.scatter(spike_t[spike_t<0.1], np.ones_like(spike_t[spike_t<0.1]) * np.max(v))
-    plt.show()
-
-def scatter_plot(colormap, stimuli_list, stimulus_count):
-    '''plot scatter plot for one sam with all 3 stims'''
-    fig = plt.figure()
-    ax = fig.add_subplot()
-        
-    ax.eventplot(stimuli_list, colors=colormap)
-    ax.set_xlabel('Spike Times [ms]')
-    ax.set_ylabel('Loop #')
-    ax.set_yticks(range(stimulus_count))
-    ax.set_title('Spikes of SAM 3')
-    plt.show()
-
-# create binary array with ones for spike times
-def binary_spikes(spike_times, duration , dt):
-    '''Converts spike times to binary representation
-    Params
-    ------
-    spike_times: np.array
-        spike times
-    duration: float
-        trial duration
-    dt: float
-        temporal resolution
-    
-    Returns
-    --------
-    binary: np.array
-        The binary representation of the spike times
-    '''
-    binary = np.zeros(int(duration//dt)) # // is truncated division, returns number w/o decimals, same as np.round
-    spike_indices = np.asarray(np.round(spike_times//dt), dtype=int)
-    binary[spike_indices] = 1 
-    return binary
-
-# function to plot psth
-def firing_rates(binary_spikes, box_width=0.01, dt=0.000025):
-    box = np.ones(int(box_width // dt))
-    box /= np.sum(box * dt) # normalize box kernel w interal of 1
-    rate = np.convolve(binary_spikes, box, mode='same')
-    return rate
-
-def power_spectrum(rate, dt):
-    f, p = welch(rate, fs = 1./dt, nperseg=2**16, noverlap=2**15) 
-    # algorithm makes rounding mistakes, we want to calc many spectra and take mean of those 
-    # nperseg: length of segments in # datapoints
-    # noverlap: # datapoints that overlap in segments
-    return f, p
-    
-def power_spectrum_plot(f, p):
-    # plot power spectrum
-    fig = plt.figure()
-    ax = fig.add_subplot()
-    ax.plot(freq, power)
-    ax.set_xlabel('Frequency [Hz]')
-    ax.set_ylabel('Power [1/Hz]')
-    ax.set_xlim(0, 1000)
-    plt.show()
-
-'''IMPORT DATA'''
-datafolder = '../data' #./ wo ich gerade bin; ../ eine ebene höher; ../../ zwei ebenen höher
-
-example_file = os.path.join('..', 'data', '2024-10-16-ac-invivo-1.nix')
-
-'''EXTRACT DATA'''
-dataset = rlx.Dataset(example_file)
-
-# get sams
-sams = dataset.repro_runs('SAM')
-sam = sams[2]
-
-# get potetial over time (vt curve)
-potential, time = sam.trace_data('V-1')
-
-# get spike times
-spike_times, _ = sam.trace_data('Spikes-1')
-
-# get stim count
-stim_count = sam.stimulus_count
-
-# extract spike times of all 3 loops of current sam
-stimuli = []
-for i in range(stim_count):
-    # get stim i from sam
-    stim = sam.stimuli[i]
-    potential_stim, time_stim = stim.trace_data('V-1')
-    # get spike_times
-    spike_times_stim, _ = stim.trace_data('Spikes-1')
-    stimuli.append(spike_times_stim)
-    
-eodf = stim.metadata[stim.name]['EODF'][0][0]
-df = stim.metadata['RePro-Info']['settings']['deltaf'][0][0]
-stimulus_freq = df + eodf
-
-'''PLOT'''
-# create colormap
-colors = plt.cm.prism(np.linspace(0, 1, stim_count))
-
-# timeline of whole rec
-dataset.plot_timeline()
-
-# voltage and spikes of current sam
-plot_vt_spikes(time, potential, spike_times)
-
-# spike times of all loops
-scatter_plot(colors, stimuli, stim_count)
-
-
-'''POWER SPECTRUM'''
-# define variables for binary spikes function
-spikes, _ = stim.trace_data('Spikes-1')
-ti = stim.trace_info('V-1')
-dt = ti.sampling_interval
-duration = stim.duration
-
-### spectrum
-# vector with binary values for wholes length of stim
-binary = binary_spikes(spikes, duration, dt)
-
-# calculate firing rate 
-rate = firing_rates(binary, 0.01, dt) # box width of 10 ms
-
-# plot psth or whatever
-# plt.plot(time_stim, rate)
-# plt.show()
-
-freq, power = power_spectrum(binary, dt)
-
-power_spectrum_plot(freq, power)
-
-
-### TODO:
-    # then loop over sams/dfs, all stims, intensities
-    # when does stim start in eodf/ at which phase and how does that influence our signal --> alignment problem: egal wenn wir spectren haben
+import rlxnix as rlx
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+from scipy.signal import welch
+
+# close all currently open figures
+plt.close('all')
+
+'''FUNCTIONS'''
+def plot_vt_spikes(t, v, spike_t):
+    fig = plt.figure(figsize=(5, 2.5))
+    # alternative to ax = axs[0]
+    ax = fig.add_subplot()
+    # plot vt diagram
+    ax.plot(t[t<0.1], v[t<0.1])
+    # plot spikes into vt diagram, at max V
+    ax.scatter(spike_t[spike_t<0.1], np.ones_like(spike_t[spike_t<0.1]) * np.max(v))
+    plt.show()
+
+def scatter_plot(colormap, stimuli_list, stimulus_count):
+    '''plot scatter plot for one sam with all 3 stims'''
+    fig = plt.figure()
+    ax = fig.add_subplot()
+        
+    ax.eventplot(stimuli_list, colors=colormap)
+    ax.set_xlabel('Spike Times [ms]')
+    ax.set_ylabel('Loop #')
+    ax.set_yticks(range(stimulus_count))
+    ax.set_title('Spikes of SAM 3')
+    plt.show()
+
+# create binary array with ones for spike times
+def binary_spikes(spike_times, duration , dt):
+    '''Converts spike times to binary representation
+    Params
+    ------
+    spike_times: np.array
+        spike times
+    duration: float
+        trial duration
+    dt: float
+        temporal resolution
+    
+    Returns
+    --------
+    binary: np.array
+        The binary representation of the spike times
+    '''
+    binary = np.zeros(int(duration//dt)) # // is truncated division, returns number w/o decimals, same as np.round
+    spike_indices = np.asarray(np.round(spike_times//dt), dtype=int)
+    binary[spike_indices] = 1 
+    return binary
+
+# function to plot psth
+def firing_rates(binary_spikes, box_width=0.01, dt=0.000025):
+    box = np.ones(int(box_width // dt))
+    box /= np.sum(box * dt) # normalize box kernel w interal of 1
+    rate = np.convolve(binary_spikes, box, mode='same')
+    return rate
+
+def power_spectrum(rate, dt):
+    f, p = welch(rate, fs = 1./dt, nperseg=2**16, noverlap=2**15) 
+    # algorithm makes rounding mistakes, we want to calc many spectra and take mean of those 
+    # nperseg: length of segments in # datapoints
+    # noverlap: # datapoints that overlap in segments
+    return f, p
+    
+def power_spectrum_plot(f, p):
+    # plot power spectrum
+    fig = plt.figure()
+    ax = fig.add_subplot()
+    ax.plot(freq, power)
+    ax.set_xlabel('Frequency [Hz]')
+    ax.set_ylabel('Power [1/Hz]')
+    ax.set_xlim(0, 1000)
+    plt.show()
+
+'''IMPORT DATA'''
+datafolder = '../data' #./ wo ich gerade bin; ../ eine ebene höher; ../../ zwei ebenen höher
+
+example_file = os.path.join('..', 'data', '2024-10-16-ac-invivo-1.nix')
+
+'''EXTRACT DATA'''
+dataset = rlx.Dataset(example_file)
+
+# get sams
+sams = dataset.repro_runs('SAM')
+sam = sams[2]
+
+# get potetial over time (vt curve)
+potential, time = sam.trace_data('V-1')
+
+# get spike times
+spike_times, _ = sam.trace_data('Spikes-1')
+
+# get stim count
+stim_count = sam.stimulus_count
+
+# extract spike times of all 3 loops of current sam
+stimuli = []
+for i in range(stim_count):
+    # get stim i from sam
+    stim = sam.stimuli[i]
+    potential_stim, time_stim = stim.trace_data('V-1')
+    # get spike_times
+    spike_times_stim, _ = stim.trace_data('Spikes-1')
+    stimuli.append(spike_times_stim)
+    
+eodf = stim.metadata[stim.name]['EODF'][0][0]
+df = stim.metadata['RePro-Info']['settings']['deltaf'][0][0]
+stimulus_freq = df + eodf
+
+'''PLOT'''
+# create colormap
+colors = plt.cm.prism(np.linspace(0, 1, stim_count))
+
+# timeline of whole rec
+dataset.plot_timeline()
+
+# voltage and spikes of current sam
+plot_vt_spikes(time, potential, spike_times)
+
+# spike times of all loops
+scatter_plot(colors, stimuli, stim_count)
+
+
+'''POWER SPECTRUM'''
+# define variables for binary spikes function
+spikes, _ = stim.trace_data('Spikes-1')
+ti = stim.trace_info('V-1')
+dt = ti.sampling_interval
+duration = stim.duration
+
+### spectrum
+# vector with binary values for wholes length of stim
+binary = binary_spikes(spikes, duration, dt)
+
+# calculate firing rate 
+rate = firing_rates(binary, 0.01, dt) # box width of 10 ms
+
+# plot psth or whatever
+# plt.plot(time_stim, rate)
+# plt.show()
+
+freq, power = power_spectrum(binary, dt)
+
+power_spectrum_plot(freq, power)
+
+
+### TODO:
+    # then loop over sams/dfs, all stims, intensities
+    # when does stim start in eodf/ at which phase and how does that influence our signal --> alignment problem: egal wenn wir spectren haben
     # we want to see peaks at phase locking to own and stim frequency, and at amp modulation frequency
\ No newline at end of file

From 54d10789b4bee89bbe967c3013760586435aa17b Mon Sep 17 00:00:00 2001
From: mbergmann <maximilian.bergmann@student.uni-tuebingen.de>
Date: Tue, 22 Oct 2024 07:21:37 +0000
Subject: [PATCH 8/8] test.py aktualisiert

---
 code/test.py | 230 +++++++++++++++++++++++++++++----------------------
 1 file changed, 130 insertions(+), 100 deletions(-)

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()
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