124 lines
4.3 KiB
Python
124 lines
4.3 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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from read_chirp_data import *
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from utility import *
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from IPython import embed
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# define sampling rate and data path
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sampling_rate = 40 #kHz
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data_dir = "../data"
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dataset = "2018-11-09-ad-invivo-1"
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# parameters for binning, smoothing and plotting
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chirp_size = 14 #ms
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neuronal_delay = 5 #ms
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chirp_start = int((-chirp_size/2+neuronal_delay+50)*sampling_rate)
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chirp_end = int((chirp_size/2+neuronal_delay+51)*sampling_rate)
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num_bin = 12
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window = 1 #ms
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time_axis = np.arange(-50, 50, 1/sampling_rate) #steps
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spike_bins = np.arange(-50, 51) #ms
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# read data from files
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spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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eod = read_chirp_eod(os.path.join(data_dir, dataset))
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chirp_times = read_chirp_times(os.path.join(data_dir, dataset))
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# make a delta f map for the quite more complicated keys
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df_map = {}
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for k in spikes.keys():
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df = k[1]
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if df in df_map.keys():
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df_map[df].append(k)
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else:
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df_map[df] = [k]
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# differentiate between phases
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phase_vec = np.arange(0, 1+1/num_bin, 1/num_bin)
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cut_range = np.arange(-50*sampling_rate, 50*sampling_rate, 1)
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# make dictionaries for spiketimes
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df_phase_time = {}
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df_phase_binary = {}
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# iterate over delta f, repetition, phases and a single chirp
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for deltaf in df_map.keys():
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df_phase_time[deltaf] = {}
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df_phase_binary[deltaf] = {}
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for rep in df_map[deltaf]:
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for phase in spikes[rep]:
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for idx in np.arange(num_bin):
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# check the phase
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if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
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# get spikes between 50 ms befor and after the chirp
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spikes_to_cut = np.asarray(spikes[rep][phase])
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spikes_cut = spikes_to_cut[(spikes_to_cut > -50) & (spikes_to_cut < 50)]
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spikes_idx = np.round(spikes_cut*sampling_rate)
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# also save as binary, 0 no spike, 1 spike
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binary_spikes = np.isin(cut_range, spikes_idx)*1
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# add the spikes to the dictionaries with the correct df and phase
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if idx in df_phase_time[deltaf].keys():
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df_phase_time[deltaf][idx].append(spikes_cut)
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df_phase_binary[deltaf][idx] = np.vstack((df_phase_binary[deltaf][idx], binary_spikes))
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else:
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df_phase_time[deltaf][idx] = [spikes_cut]
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df_phase_binary[deltaf][idx] = binary_spikes
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# for plotting and calculating iterate over delta f and phases
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for df in df_phase_time.keys():
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for phase in df_phase_time[df].keys():
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trials_binary = df_phase_binary[df][phase]
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sr_chirp = np.zeros(len(trials_binary))
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sr_beat = np.zeros(len(trials_binary))
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for i, trial in enumerate(trials_binary):
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smoothed_trial = smooth(trial, window, 1/sampling_rate)
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sr_chirp[i] = np.mean(smoothed_trial[chirp_start:chirp_end])
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sr_beat[i] = np.mean(smoothed_trial[0:chirp_start])
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for rate_chirp in sr_chirp:
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for rate_beat in sr_beat:
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r = np.corrcoef(rate_chirp, rate_beat)
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print(r)
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embed()
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exit()
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#csi = (spikerate_chirp-spikerate_befor)/(spikerate_chirp+spikerate_befor)
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# plot
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#plot_trials = df_phase_time[df][phase]
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#plot_trials_binary = np.mean(df_phase_binary[df][phase], axis=0)
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# calculation
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#overall_spikerate = (np.sum(plot_trials_binary)/len(plot_trials_binary))*sampling_rate*1000
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'''
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smoothed_spikes = smooth(plot_trials_binary, window, 1./sampling_rate)
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fig, ax = plt.subplots(2, 1, sharex=True)
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for i, trial in enumerate(plot_trials):
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ax[0].scatter(trial, np.ones(len(trial))+i, marker='|', color='k')
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ax[1].plot(time_axis, smoothed_spikes*1000)
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ax[0].set_title(df)
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ax[0].set_ylabel('repetition', fontsize=12)
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ax[1].set_xlabel('time [ms]', fontsize=12)
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ax[1].set_ylabel('firing rate [Hz]', fontsize=12)
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plt.show()
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'''
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'''
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for trial in range(len(trials_binary)):
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spike_rate = np.zeros(len(spike_bins)-1)
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for idx in range(len(spike_bins)-1):
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bin_start = spike_bins[idx]*sampling_rate
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bin_end = spike_bins[idx+1]*sampling_rate
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spike_rate[idx] = np.sum(trials_binary[trial][bin_start:bin_end])
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embed()
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exit()
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''' |