77 lines
2.0 KiB
Python
77 lines
2.0 KiB
Python
import os
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import numpy as np
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from IPython import embed
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import matplotlib.pyplot as plt
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import nix_helpers as nh
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from jar_functions import get_time_zeros
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from jar_functions import parse_dataset
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from jar_functions import mean_traces
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from jar_functions import mean_noise_cut_eigen
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from jar_functions import adjust_eodf
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base_path = 'D:\\jar_project\\JAR\\sin'
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identifier = ['2018lepto1',
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'2018lepto4',
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'2018lepto5',
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'2018lepto76',
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'2018lepto98',
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'2019lepto03',
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'2019lepto24',
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'2019lepto27',
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'2019lepto30',
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'2020lepto04',
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'2020lepto06',
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'2020lepto16',
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'2020lepto19',
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'2020lepto20'
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]
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eod = []
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for ID in identifier:
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base = []
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for dataset in os.listdir(os.path.join(base_path, ID)):
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if dataset == 'prerecordings':
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continue
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datapath = os.path.join(base_path, ID, dataset, 'beats-eod.dat')
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print(datapath)
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try:
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o = open(datapath)
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except IOError:
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continue
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frequency, time, amplitude, eodf, deltaf, stimulusf, duration, pause = parse_dataset(datapath)
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dm = np.mean(duration)
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pm = np.mean(pause)
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timespan = dm + pm
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start = np.mean([t[0] for t in time])
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stop = np.mean([t[-1] for t in time])
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mf, tnew = mean_traces(start, stop, timespan, frequency, time) # maybe fixed timespan/sampling rate
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cf, ct = mean_noise_cut_eigen(mf, tnew, 1250)
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f = []
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for idx, i in enumerate(ct):
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if i > -45 and i < -5:
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f.append(cf[idx])
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ff = np.mean(f)
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base.append(ff)
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#plt.plot(ct, cf)
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#plt.show()
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base_eod = np.mean(base)
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print(ID)
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print(base_eod)
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eod.append(base_eod)
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temp = np.load('temperature.npy')
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eod_temp = zip(eod, temp)
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Q10_eod = []
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for et in eod_temp:
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Q10 = adjust_eodf(et[0], et[1])
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Q10_eod.append(Q10)
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embed()
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