113 lines
4.0 KiB
Python
113 lines
4.0 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 = "2018-11-13-al-invivo-1"
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# parameters for binning, smoothing and plotting
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cut_window = 20
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chirp_duration = 14 #ms
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neuronal_delay = 5 #ms
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chirp_start = int((-chirp_duration / 2 + neuronal_delay + cut_window * 2) * sampling_rate) #index
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chirp_end = int((chirp_duration / 2 + neuronal_delay + cut_window * 2) * sampling_rate) #index
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number_bins = 12
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window = 1 #ms
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time_axis = np.arange(-cut_window*2, cut_window*2, 1/sampling_rate) #steps
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spike_bins = np.arange(-cut_window*2, cut_window*2) #ms
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colors = ['k', 'k', 'k',
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'k', 'k', 'k',
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'k', 'k', 'k',
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'k', 'k', 'firebrick']
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sizes = [12, 12, 12,
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12, 12, 12,
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12, 12, 12,
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12, 12, 18]
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# differentiate between phases
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phase_vec = np.arange(0, 1 + 1 / number_bins, 1 / number_bins)
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cut_range = np.arange(-cut_window*2*sampling_rate, cut_window*2*sampling_rate, 1)
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df_phase_binary = {}
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spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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df_map = map_keys(spikes)
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for deltaf in df_map.keys():
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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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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(number_bins):
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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 40 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*2) & (spikes_to_cut < cut_window*2)]
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spikes_idx = np.round(spikes_cut*sampling_rate)
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# 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 dictionary with the correct df and phase
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if idx in df_phase_binary[deltaf].keys():
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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_binary[deltaf][idx] = binary_spikes
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csi_rates = {}
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for df in df_phase_binary.keys():
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csi_rates[df] = {}
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beat_duration = int(abs(1/df*1000)*sampling_rate) #steps
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beat_window = 0
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# beat window is at most 20 ms long, multiples of beat_duration
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while beat_window+beat_duration <= cut_window*sampling_rate:
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beat_window = beat_window+beat_duration
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for phase in df_phase_binary[df].keys():
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# csi calculation
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trials_binary = df_phase_binary[df][phase]
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train_chirp = []
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train_beat = []
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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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train_chirp.append(smoothed_trial[chirp_start:chirp_end])
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train_beat.append(smoothed_trial[chirp_start-beat_window:chirp_start])
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std_chirp = np.std(np.mean(train_chirp, axis=0))
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std_beat = np.std(np.mean(train_beat, axis=0))
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csi_spikerate = (std_chirp - std_beat) / (std_chirp + std_beat)
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csi_rates[df][phase] = np.mean(csi_spikerate)
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upper_limit = np.max(sorted(csi_rates.keys()))+30
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lower_limit = np.min(sorted(csi_rates.keys()))-30
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inch_factor = 2.54
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fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
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ax.plot([lower_limit, upper_limit], np.zeros(2), 'silver', linewidth=2, linestyle='--')
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for i, df in enumerate(sorted(csi_rates.keys())):
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for j, phase in enumerate(sorted(csi_rates[df].keys())):
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ax.plot(df, csi_rates[df][phase], 'o', color=colors[j], ms=sizes[j])
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plt.xlabel("$\Delta$f", fontsize = 22)
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plt.xticks(fontsize = 18)
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plt.ylabel("CSI", 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.savefig('CSI.png') |