63 lines
2.2 KiB
Python
63 lines
2.2 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 read_baseline_data import *
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from utility import *
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from IPython import embed
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# define data path and important parameters
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data_dir = "../data"
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sampling_rate = 40 #kHz
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cut_window = 100
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cut_range = np.arange(-cut_window * sampling_rate, 0, 1)
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window = 1
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#dataset = "2018-11-13-ad-invivo-1"
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#dataset = "2018-11-13-aj-invivo-1"
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#dataset = "2018-11-13-ak-invivo-1" #al
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#dataset = "2018-11-14-ad-invivo-1"
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dataset = "2018-11-20-af-invivo-1"
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base_spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
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base_spikes = base_spikes[1000:2000]
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spikerate = len(base_spikes) / base_spikes[-1]
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print(spikerate)
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# read spikes during chirp stimulation
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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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rates = {}
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# iterate over df
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for deltaf in df_map.keys():
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rates[deltaf] = {}
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beat_duration = int(abs(1 / deltaf) * 1000)
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beat_window = 0
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while beat_window + beat_duration <= cut_window/2:
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beat_window = beat_window + beat_duration
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for x, repetition in enumerate(df_map[deltaf]):
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for phase in spikes[repetition]:
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# get spikes some ms before the chirp first chirp
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spikes_to_cut = np.asarray(spikes[repetition][phase])
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spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window) & (spikes_to_cut < 0)]
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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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smoothed_data = smooth(binary_spikes, window, 1 / sampling_rate)
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#train = smoothed_data[window*sampling_rate:beat_window*sampling_rate+window*sampling_rate]
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modulation = np.std(smoothed_data)
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rates[deltaf][x] = modulation
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break
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fig, ax = plt.subplots()
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for i, df in enumerate(sorted(rates.keys())):
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for j, rep in enumerate(rates[df].keys()):
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if j == 15:
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farbe = 'royalblue'
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gro = 18
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else:
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farbe = 'k'
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gro = 12
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ax.plot(df, rates[df][rep], marker='o', color=farbe, ms=gro)
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fig.tight_layout()
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plt.show()
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