73 lines
2.7 KiB
Python
73 lines
2.7 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 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 = 40
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cut_range = np.arange(-cut_window * sampling_rate, 0, 1)
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window = 1
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'''
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# norm: -150, 150, 300
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data = ["2018-11-13-aa-invivo-1", "2018-11-13-ac-invivo-1","2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1",
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"2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1"]
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# norm: -50
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data = ["2018-11-20-aa-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1","2018-11-20-ad-invivo-1",
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"2018-11-20-ae-invivo-1", "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1",
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"2018-11-20-ai-invivo-1"]
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'''
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data = ["2018-11-14-aa-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-af-invivo-1",
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"2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-ai-invivo-1", "2018-11-14-ak-invivo-1",
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"2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1"]
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rates = {}
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for dataset in data:
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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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print(dataset)
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for df in df_map.keys():
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'''
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if df == 50:
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pass
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else:
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continue
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'''
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print(df)
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rep_rates = []
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beat_duration = int(abs(1 / df) * 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 rep in df_map[df]:
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for phase in spikes[rep]:
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# get spikes 40 ms before the chirp first 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 < 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:beat_window+window]
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rep_rates.append(np.std(train))
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break
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df_rate = np.mean(rep_rates)
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if df in rates.keys():
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rates[df].append(df_rate)
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else:
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rates[df] = [df_rate]
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fig, ax = plt.subplots()
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for i, k in enumerate(sorted(rates.keys())):
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ax.plot(np.ones(len(rates[k]))*k, rates[k], 'o')
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#ax.legend(sorted(rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
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fig.tight_layout()
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plt.show()
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