51 lines
1.9 KiB
Python
51 lines
1.9 KiB
Python
import numpy as np
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from IPython import embed
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from scipy.signal import convolve
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import matplotlib.mlab as mlab
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def avgNestedLists(nested_vals):
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"""
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Averages a 2-D array and returns a 1-D array of all of the columns
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averaged together, regardless of their dimensions.
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"""
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output = []
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maximum = 0
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for lst in nested_vals:
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if len(lst) > maximum:
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maximum = len(lst)
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for index in range(maximum): # Go through each index of longest list
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temp = []
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for lst in nested_vals: # Go through each list
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if index < len(lst): # If not an index error
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temp.append(lst[index])
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output.append(np.nanmean(temp))
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return output
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def gaussian_convolve(spike_train, fxn, sampling_rate):
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all_convolve_spikes = []
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trial_length = int((spike_train[-1] - spike_train[0]) * sampling_rate)
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spike_train = spike_train - spike_train[0] # changing spike train to start at 0 (subtracting baseline)
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trial_time = np.arange(0, (trial_length + 1), 1)
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trial_bool = np.zeros(len(trial_time))
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# Boolean list in length of trial length, where 1 means spike happened, 0 means no spike
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spike_indx = (spike_train * sampling_rate).astype(np.int)
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trial_bool[spike_indx] = 1
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# trial_bool = trial_bool[30000:(len(trial_bool)-30000)]
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convolve_spikes = np.asarray(
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[convolve(trial_bool, fxn, mode='valid')]) # convolve gaussian with boolean spike list
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all_convolve_spikes.append(convolve_spikes[0, :])
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avg_convolve_spikes = avgNestedLists(all_convolve_spikes)
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return avg_convolve_spikes
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def fourier_psd(avg_convolve_spikes, sampling_rate):
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p, freq = mlab.psd(avg_convolve_spikes, NFFT=sampling_rate * 3, noverlap=sampling_rate * 1.5,
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Fs=sampling_rate,
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detrend=mlab.detrend_mean)
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std_four = np.std(freq[5:])
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mn_four = np.mean(freq)
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return p, freq, std_four, mn_four
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