107 lines
4.3 KiB
Python
107 lines
4.3 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import scipy.stats as ss
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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 = "2018-11-14-al-invivo-1"
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dataset = "2018-11-09-ad-invivo-1"
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inch_factor = 2.54
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# parameters for binning, smoothing and plotting
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cut_window = 60
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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+cut_window)*sampling_rate)
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chirp_end = int((chirp_size/2+neuronal_delay+cut_window+1)*sampling_rate)
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num_bin = 12
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window = 1 #ms
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time_axis = np.arange(-cut_window, cut_window, 1/sampling_rate) #steps
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spike_bins = np.arange(-cut_window, cut_window+1) #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 = map_keys(spikes)
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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(-cut_window*sampling_rate, cut_window*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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chirp_size = int(rep[-1].strip('Hz'))
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# print(chirp_size)
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if chirp_size == 150:
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continue
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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 60 ms before 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 > -cut_window) & (spikes_to_cut < cut_window)]
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spikes_raster = spikes_to_cut[(spikes_to_cut > -cut_window+5) & (spikes_to_cut < cut_window-5)]
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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_raster)
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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_raster]
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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 index_phase, phase in enumerate(df_phase_time[df].keys()):
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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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smoothed_spikes = smooth(plot_trials_binary, window, 1./sampling_rate)
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fig, ax = plt.subplots(2, 1, sharex=True, figsize=(18/inch_factor, 13/inch_factor))
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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[0+5*sampling_rate:-5*sampling_rate], smoothed_spikes[0+5*sampling_rate:-5*sampling_rate]*1000, color='darkblue', lw = 2)
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ax[0].set_title('$\Delta$f = %s Hz' %(df), fontsize = 18)
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ax[0].set_ylabel('Repetition', fontsize=22)
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ax[0].yaxis.set_label_coords(-0.1, 0.5)
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ax[0].set_yticks(np.arange(1, len(plot_trials)+1,2))
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ax[0].set_yticklabels(np.arange(1, len(plot_trials)+1,2), fontsize=18)
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ax[1].set_xlabel('Time [ms]', fontsize=22)
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ax[1].yaxis.set_label_coords(-0.1, 0.5)
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ax[1].set_ylabel('Firing rate [Hz]', fontsize=22)
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plt.xticks(fontsize=18)
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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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#exit()
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namefigure = '../figures/%s_%i_%i_firingrate.pdf' %(dataset, df, index_phase)
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plt.savefig(namefigure)
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