Hello There

analysis_1.py

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+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 vt_spikes(dataset):
+    # 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')
+
+    # plot
+    fig = plt.figure(figsize=(5, 2.5))
+    # alternative to ax = axs[0]
+    ax = fig.add_subplot()
+    # plot vt diagram
+    ax.plot(time[time<0.1], potential[time<0.1])
+    # plot spikes into vt diagram, at max V
+    ax.scatter(spike_times[spike_times<0.1], np.ones_like(spike_times[spike_times<0.1]) * np.max(potential))
+    plt.show()
+    
+    return sam
+
+def scatter_plot(sam1):
+
+    ### plot scatter plot for one sam with all 3 stims
+    # get stim count
+    stim_count = sam1.stimulus_count
+    
+    # create colormap
+    colors = plt.cm.prism(np.linspace(0, 1, stim_count))
+    
+    # plot
+    fig = plt.figure()
+    ax = fig.add_subplot()
+    
+    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)
+        
+    ax.eventplot(stimuli, colors=colors)
+    ax.set_xlabel('Spike Times [ms]')
+    ax.set_ylabel('Loop #')
+    ax.set_yticks(range(stim_count))
+    ax.set_title('Spikes of SAM 3')
+    plt.show()
+    return stim, stim_count, time_stim
+
+# 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
+    
+
+
+'''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 metadata
+dataset = rlx.Dataset(example_file)
+
+### plot
+# timeline of whole rec
+dataset.plot_timeline()
+
+# voltage and spikes of current sam
+sam = vt_spikes(dataset)
+
+# spike times of all loops
+stim, stim_count, time_stim = scatter_plot(sam)
+
+
+'''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)
+
+# 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()
+
+eodf = stim.metadata[stim.name]['EODF'][0][0]
+df = stim.metadata['RePro-Info']['settings']['deltaf'][0][0]
+stimulus_freq = df + eodf
+
+
+### 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
+    # clean up current code (define variables outside of functions, plot spectrum in function)
+    # git
+    # tuning curve over stim intensities or over delta f?