253 lines
7.3 KiB
Python
253 lines
7.3 KiB
Python
import glob
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import pathlib
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import numpy as np
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import matplotlib.pyplot as plt
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import rlxnix as rlx
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from IPython import embed
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from scipy.signal import welch
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import useful_functions as f
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def binary_spikes(spike_times, duration, dt):
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"""
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Converts the spike times to a binary representations
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Parameters
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----------
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spike_times : np.array
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The spike times.
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duration : float
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The trial duration:
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dt : float
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The temporal resolution.
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Returns
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-------
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binary : np.array
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The binary representation of the spike train.
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"""
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binary = np.zeros(int(np.round(duration / dt))) #create the binary array with the same length as potential
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spike_indices = np.asarray(np.round(spike_times / dt), dtype = int) # get the indices
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binary[spike_indices] = 1 # put the indices into binary
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return binary
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def firing_rate(binary_spikes, dt = 0.000025, box_width = 0.01):
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box = np.ones(int(box_width // dt))
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box /= np.sum(box) * dt # normalisierung des box kernels to an integral of one
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rate = np.convolve(binary_spikes, box, mode = 'same')
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return rate
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def power_spectrum(rate, dt):
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freq, power = welch(rate, fs = 1/dt, nperseg = 2**16, noverlap = 2**15)
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return freq, power
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def extract_stim_data(stimulus):
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'''
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extracts all necessary metadata for each stimulus
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Parameters
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----------
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stimulus : Stimulus object or rlxnix.base.repro module
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The stimulus from which the data is needed.
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Returns
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-------
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amplitude : float
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The relative signal amplitude in percent.
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df : float
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Distance of the stimulus to the current EODf.
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eodf : float
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Current EODf.
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stim_freq : float
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The total stimulus frequency (EODF+df).
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amp_mod : float
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The current amplitude modulation.
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ny_freq : float
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The current nyquist frequency.
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'''
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# extract metadata
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# the stim.name adjusts the first key as it changes with every stimulus
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amplitude = stimulus.metadata[stimulus.name]['Contrast'][0][0]
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df = stimulus.metadata[stimulus.name]['DeltaF'][0][0]
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eodf = round(stimulus.metadata[stimulus.name]['EODf'][0][0])
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stim_freq = round(stimulus.metadata[stimulus.name]['Frequency'][0][0])
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# calculates the amplitude modulation
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amp_mod, ny_freq = AM(eodf, stim_freq)
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return amplitude, df, eodf, stim_freq, amp_mod, ny_freq
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def AM(EODf, stimulus):
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"""
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Calculates the Amplitude Modulation and Nyquist frequency
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Parameters
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----------
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EODf : float or int
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The current EODf.
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stimulus : float or int
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The absolute frequency of the stimulus.
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Returns
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-------
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AM : float
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The amplitude modulation resulting from the stimulus.
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nyquist : float
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The maximum frequency possible to resolve with the EODf.
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"""
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nyquist = EODf * 0.5
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AM = np.mod(stimulus, nyquist)
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return AM, nyquist
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def remove_poor(files):
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"""
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Removes poor datasets from the set of files for analysis
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Parameters
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----------
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files : list
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list of files.
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Returns
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-------
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good_files : list
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list of files without the ones with the label poor.
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"""
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# create list for good files
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good_files = []
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# loop over files
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for i in range(len(files)):
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# print(files[i])
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# load the file (takes some time)
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data = rlx.Dataset(files[i])
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# get the quality
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quality = str.lower(data.metadata["Recording"]["Recording quality"][0][0])
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# check the quality
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if quality != "poor":
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# if its good or fair add it to the good files
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good_files.append(files[i])
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return good_files
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def sam_data(sam):
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'''
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Gets metadata for each SAM
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Parameters
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----------
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sam : ReproRun object
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The sam the metdata should be extracted from.
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Returns
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-------
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sam_amp : float
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amplitude in percent, relative to the fish amplitude.
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sam_am : float
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Amplitude modulation frequency.
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sam_df : float
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Difference from the stimulus to the current fish eodf.
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sam_eodf : float
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The current EODf.
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sam_nyquist : float
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The Nyquist frequency of the EODf.
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sam_stim : float
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The stimulus frequency.
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'''
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# create lists for the values we want
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amplitudes = []
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dfs = []
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eodfs = []
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stim_freqs = []
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amp_mods = []
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ny_freqs = []
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# get the stimuli
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stimuli = sam.stimuli
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# loop over the stimuli
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for stim in stimuli:
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amplitude, df, eodf, stim_freq, amp_mod, ny_freq = extract_stim_data(stim)
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amplitudes.append(amplitude)
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dfs.append(df)
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eodfs.append(eodf)
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stim_freqs.append(stim_freq)
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amp_mods.append(amp_mod)
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ny_freqs.append(ny_freq)
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# get the means
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sam_amp = np.mean(amplitudes)
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sam_am = np.mean(amp_mods)
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sam_df = np.mean(dfs)
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sam_eodf = np.mean(eodfs)
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sam_nyquist = np.mean(ny_freqs)
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sam_stim = np.mean(stim_freqs)
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return sam_amp, sam_am,sam_df, sam_eodf, sam_nyquist, sam_stim
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#find example data
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datafolder = "../../data"
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example_file = datafolder + "/" + "2024-10-16-ad-invivo-1.nix"
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data_files = glob.glob("../../data/*.nix")
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#load dataset
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dataset = rlx.Dataset(example_file)
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# find all sams
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sams = dataset.repro_runs('SAM')
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sam = sams[2] # our example sam
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potential,time = sam.trace_data("V-1") #membrane potential
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spike_times, _ = sam.trace_data('Spikes-1') #spike times
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df = sam.metadata['RePro-Info']['settings']['deltaf'][0][0] #find df in metadata
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amp = sam.metadata['RePro-Info']['settings']['contrast'][0][0] * 100 #find amplitude in metadata
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#figure for a quick plot
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fig = plt.figure(figsize = (5, 2.5))
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ax = fig.add_subplot()
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ax.plot(time[time < 0.1], potential[time < 0.1]) # plot the membrane potential in 0.1s
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ax.scatter(spike_times[spike_times < 0.1],
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np.ones_like(spike_times[spike_times < 0.1]) * np.max(potential)) #plot teh spike times on top
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plt.show()
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plt.close()
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sam_amp, sam_am,sam_df, sam_eodf, sam_nyquist, sam_stim = f.sam_data(sam)
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# # get all the stimuli
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# stims = sam.stimuli
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# # empty list for the spike times
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# spikes = []
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# #spikes2 = np.array(range(len(stims)))
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# # loop over the stimuli
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# for stim in stims:
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# # get the spike times
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# spike, _ = stim.trace_data('Spikes-1')
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# # append the first 100ms to spikes
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# spikes.append(spike[spike < 0.1])
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# # get stimulus duration
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# duration = stim.duration
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# ti = stim.trace_info("V-1")
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# dt = ti.sampling_interval # get the stimulus interval
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# bin_spikes = binary_spikes(spike, duration, dt) #binarize the spike_times
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# print(len(bin_spikes))
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# pot,tim= stim.trace_data("V-1") #membrane potential
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# rate = firing_rate(bin_spikes, dt = dt)
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# print(np.mean(rate))
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# fig, [ax1, ax2] = plt.subplots(1, 2,layout = 'constrained')
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# ax1.plot(tim,rate)
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# ax1.set_ylim(0,600)
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# ax1.set_xlim(0, 0.04)
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# freq, power = power_spectrum(rate, dt)
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# ax2.plot(freq,power)
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# ax2.set_xlim(0,1000)
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# plt.close()
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# if stim == stims[-1]:
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# amplitude, df, eodf, stim_freq = extract_stim_data(stim)
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# print(amplitude, df, eodf, stim_freq)
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# # make an eventplot
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# fig = plt.figure(figsize = (5, 3), layout = 'constrained')
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# ax = fig.add_subplot()
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# ax.eventplot(spikes, linelength = 0.8)
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# ax.set_xlabel('time [ms]')
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# ax.set_ylabel('loop no.')
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