Gaurav Kumar Raghav
43 linhas
1.4 KiB
Python
43 linhas
1.4 KiB
Python
import csv
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from collections import defaultdict
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import numpy as np
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from scipy.signal import *
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from numpy.fft import *
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from scipy import *
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from pylab import *
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import pywt
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fout_data = open("train.csv",'a')
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vec = []
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chan = ['Fp1','AF3','F3','F7','FC5','FC1','C3','T7','CP5','CP1','P3','P7','PO3','O1','Oz','Pz','Fp2','AF4','Fz','F4','F8','FC6','FC2','Cz','C4','T8','CP6','CP2','P4','P8','PO4','O2']
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columns = defaultdict(list) # each value in each column is appended to a list
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with open("features_raw.csv") as f:
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reader = csv.DictReader(f) # read rows into a dictionary format
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for row in reader: # read a row as {column1: value1, column2: value2,...}
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for (k,v) in row.items(): # go over each column name and value
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columns[k].append(v) # append the value into the appropriate list
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# based on column name k
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for i in chan:
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x = np.array(columns[i]).astype(np.float)
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coeffs = pywt.wavedec(x, 'db4', level=6)
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cA6, cD6, cD5,cD4,cD3,cD2,cD1 = coeffs
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cD5 = np.std(cD5)
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cD4 = np.std(cD4)
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cD3 = np.std(cD3)
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cD2 = np.std(cD2)
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cD1 = np.std(cD1)
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if i =="O2":
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fout_data.write(str(cD5)+",")
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fout_data.write(str(cD4)+",")
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fout_data.write(str(cD3)+",")
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fout_data.write(str(cD2)+",")
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fout_data.write(str(cD1))
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else:
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fout_data.write(str(cD5)+",")
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fout_data.write(str(cD4)+",")
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fout_data.write(str(cD3)+",")
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fout_data.write(str(cD2)+",")
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fout_data.write(str(cD1)+",")
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fout_data.write("\n")
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fout_data.close()
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