Merge branch 'main' of https://whale.am28.uni-tuebingen.de/git/mbergmann/gpgrewe2024

code/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()

code/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

code/test.py

@@ -0,0 +1,194 @@
+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).
+    amp_mod : float
+        The current amplitude modulation.
+    ny_freq : float
+        The current nyquist frequency.
+
+    '''
+    # 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])
+    # calculates the amplitude modulation
+    amp_mod, ny_freq = AM(eodf, stim_freq)
+    return amplitude, df, eodf, stim_freq, amp_mod, ny_freq
+
+def AM(EODf, stimulus):
+    """
+    Calculates the Amplitude Modulation and Nyquist frequency
+
+    Parameters
+    ----------
+    EODf : float or int
+        The current EODf.
+    stimulus : float or int
+        The absolute frequency of the stimulus.
+
+    Returns
+    -------
+    AM : float 
+        The amplitude modulation resulting from the stimulus.
+    nyquist : float
+        The maximum frequency possible to resolve with the EODf.
+
+    """
+    nyquist = EODf * 0.5
+    AM = np.mod(stimulus, nyquist)
+    return AM, nyquist
+
+def remove_poor(files):
+    """
+    Removes poor datasets from the set of files for analysis
+
+    Parameters
+    ----------
+    files : list 
+        list of files.
+
+    Returns
+    -------
+    good_files : list
+        list of files without the ones with the label poor.
+
+    """
+    # create list for good files
+    good_files = []
+    # loop over files
+    for i in range(len(files)):
+        # print(files[i])
+        # load the file (takes some time)
+        data = rlx.Dataset(files[i])
+        # get the quality
+        quality = str.lower(data.metadata["Recording"]["Recording quality"][0][0])
+        # check the quality
+        if quality != "poor":
+            # if its good or fair add it to the good files
+            good_files.append(files[i])
+    return good_files
+
+#find example data
+datafolder = "../../data"
+
+example_file = datafolder + "/" + "2024-10-16-ah-invivo-1.nix"
+
+data_files = glob.glob("../../data/*.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)
+    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.')

code/useful_functions.py

@@ -0,0 +1,173 @@
+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 AM(EODf, stimulus):
+    """
+    Calculates the Amplitude Modulation and Nyquist frequency
+
+    Parameters
+    ----------
+    EODf : float or int
+        The current EODf.
+    stimulus : float or int
+        The absolute frequency of the stimulus.
+
+    Returns
+    -------
+    AM : float 
+        The amplitude modulation resulting from the stimulus.
+    nyquist : float
+        The maximum frequency possible to resolve with the EODf.
+
+    """
+    nyquist = EODf * 0.5
+    AM = np.mod(stimulus, nyquist)
+    return AM, nyquist
+
+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 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).
+    amp_mod : float
+        The current amplitude modulation.
+    ny_freq : float
+        The current nyquist frequency.
+
+    '''
+    # extract metadata
+    # the stim.name adjusts the first key as it changes with every stimulus
+    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
+
+def firing_rate(binary_spikes, dt = 0.000025, box_width = 0.01):
+    '''
+    Calculates the firing rate from binary spikes
+
+    Parameters
+    ----------
+    binary_spikes : np.array
+        The binary representation of the spike train.
+    dt : float, optional
+        Time difference between two datapoints. The default is 0.000025.
+    box_width : float, optional
+        Time window on which the rate should be computed on. The default is 0.01.
+
+    Returns
+    -------
+    rate : np.array
+        Array of firing rates.
+
+    '''
+    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(spike_times, duration, dt):
+    '''
+    Computes a power spectrum based on the spike times
+
+    Parameters
+    ----------
+    spike_times : np.array
+        The spike times.
+    duration : float
+        The trial duration:
+    dt : float
+        The temporal resolution.
+
+    Returns
+    -------
+    freq : np.array
+        All the frequencies of the power spectrum.
+    power : np.array
+        Power of the frequencies calculated.
+
+    '''
+    # binarizes spikes
+    binary = binary_spikes(spike_times, duration, dt)
+    # computes firing rates
+    rate = firing_rate(binary, dt = dt)
+    # creates power spectrum
+    freq, power = welch(rate, fs = 1/dt, nperseg = 2**16, noverlap = 2**15)
+    return freq, power
+
+def remove_poor(files):
+    """
+    Removes poor datasets from the set of files for analysis
+
+    Parameters
+    ----------
+    files : list 
+        list of files.
+
+    Returns
+    -------
+    good_files : list
+        list of files without the ones with the label poor.
+
+    """
+    # create list for good files
+    good_files = []
+    # loop over files
+    for i in range(len(files)):
+        # print(files[i])
+        # load the file (takes some time)
+        data = rlx.Dataset(files[i])
+        # get the quality
+        quality = str.lower(data.metadata["Recording"]["Recording quality"][0][0])
+        # check the quality
+        if quality != "poor":
+            # if its good or fair add it to the good files
+            good_files.append(files[i])
+    return good_files