102 lines
3.0 KiB
Python
102 lines
3.0 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from read_baseline_data import *
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from NixFrame import *
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from utility import *
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from IPython import embed
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# plot and data values
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inch_factor = 2.54
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data_dir = '../data'
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#dataset = '2018-11-09-ad-invivo-1'
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dataset = '2018-11-14-al-invivo-1'
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# read eod and time of baseline
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time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
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# read spikes during baseline activity
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spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
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# calculate interpike intervals and plot them
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interspikeintervals = np.diff(spikes)
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fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
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plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001), color='royalblue')
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plt.xlabel("time [ms]", fontsize = 22)
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plt.xticks(fontsize = 18)
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plt.ylabel("number of \n interspikeintervals", fontsize = 22)
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plt.yticks(fontsize = 18)
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ax.spines["top"].set_visible(False)
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ax.spines["right"].set_visible(False)
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fig.tight_layout()
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plt.show()
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#plt.show()
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<<<<<<< HEAD
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#plt.savefig('isis.pdf')
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exit()
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=======
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plt.savefig('isis.png')
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>>>>>>> b9573c65638c8a716e57d75fbe550f0a1ef8859f
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# calculate coefficient of variation
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mu = np.mean(interspikeintervals)
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sigma = np.std(interspikeintervals)
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cv = sigma/mu
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print(cv)
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# calculate eod times and indices by zero crossings
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threshold = 0
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shift_eod = np.roll(eod, 1)
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eod_times = time[(eod >= threshold) & (shift_eod < threshold)]
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sampling_rate = 40000.0
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eod_idx = eod_times*sampling_rate
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# align eods and spikes to eods
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max_cut = int(np.max(np.diff(eod_idx)))
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eod_cuts = np.zeros([len(eod_idx)-1, max_cut])
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spike_times = []
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eod_durations = []
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for i, idx in enumerate(eod_idx[:-1]):
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eod_cut = eod[int(idx):int(eod_idx[i+1])]
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eod_cuts[i, :len(eod_cut)] = eod_cut
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eod_cuts[i, len(eod_cut):] = np.nan
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time_cut = time[int(idx):int(eod_idx[i+1])]
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spike_cut = spikes[(spikes > time_cut[0]) & (spikes < time_cut[-1])]
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spike_time = spike_cut - time_cut[0]
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if len(spike_time) > 0:
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spike_times.append(spike_time[:][0]*1000)
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eod_durations.append(len(eod_cut)/sampling_rate*1000)
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# calculate vector strength
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vs = vector_strength(spike_times, eod_durations)
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# determine means and stds of eod for plot
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# determine time axis
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mu_eod = np.nanmean(eod_cuts, axis=0)
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std_eod = np.nanstd(eod_cuts, axis=0)*3
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time_axis = np.arange(max_cut)/sampling_rate*1000
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# plot eod form and spike histogram
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fig, ax1 = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
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ax1.hist(spike_times, color='firebrick')
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ax1.set_xlabel('time [ms]', fontsize=22)
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ax1.set_ylabel('number', fontsize=22)
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ax1.tick_params(axis='y', labelcolor='firebrick')
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plt.xticks(fontsize=18)
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plt.yticks(fontsize=18)
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ax1.spines['top'].set_visible(False)
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ax2 = ax1.twinx()
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ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='navy', alpha=0.5)
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ax2.plot(time_axis, mu_eod, color='black', lw=2)
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ax2.set_ylabel('voltage [mV]', fontsize=22)
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ax2.tick_params(axis='y', labelcolor='navy')
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ax2.spines['top'].set_visible(False)
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plt.yticks(fontsize=18)
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fig.tight_layout()
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#plt.show()
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plt.savefig('eodform_spikehist.png') |