In [1]:
%matplotlib inline
In [1]:
import numpy as np, pandas as pd
import matplotlib.pyplot as plot
from scipy import signal
In [35]:
resp = pd.read_csv('sampleData/download.csv', header=None)
resp.columns = ['time', 'resp', 'tone']
In [36]:
bci = pd.read_csv('sampleData/OpenBCI-RAW-2016-12-30_18-07-39.txt', sep=', ', skiprows=4, header=None)
bci.columns = ['tix'] + [str(i) for i in range(1,9)] + ['a', 'b', 'c', 'time']
/home/mike/ve/ml/lib/python3.5/site-packages/ipykernel/__main__.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.
if __name__ == '__main__':
In [37]:
resp.sort_values('time', inplace=True)
In [38]:
t0 = bci.loc[0, 'time']
tmid_bci = bci.loc[len(bci) // 2, 'time']
tmid_resp = resp.loc[len(resp) // 2, 'time']
delta_t = tmid_resp-tmid_bci
print(tmid_bci, tmid_resp, delta_t, t0)
resp['time'] += tmid_bci-tmid_resp # stupid hack to get the times to line up right
resp['time'] -= t0
bci['time'] -= t0
1.48313927979e+12 1484171949901 1032670109.0 1.48313927268e+12
In [43]:
tonef = resp['tone'].replace({'C4':1, 'C5':2, 'C3': 0})
resp['tonef'] = pd.Series(tonef, dtype='float64')
# resp['tonef'].dtype
In [44]:
resp.dtypes
Out[44]:
time float64
resp int64
tone object
tonef float64
dtype: object
In [45]:
resp
Out[45]:
time
resp
tone
tonef
0
6050.0
0
C4
1.0
1
6444.0
0
C4
1.0
2
6844.0
0
C4
1.0
7
7114.0
2
0
0.0
3
7244.0
0
C4
1.0
8
7366.0
2
0
0.0
9
7608.0
2
0
0.0
4
7644.0
0
C5
2.0
10
7836.0
1
0
0.0
5
8044.0
0
C4
1.0
11
8187.0
1
0
0.0
12
8406.0
1
0
0.0
6
8444.0
0
C3
0.0
13
8761.0
2
0
0.0
In [46]:
len(bci)
Out[46]:
3563
In [48]:
resp['time'] = pd.Series(resp['time'], dtype='int64') # Converting to int to allow for indexing safely
bci['time'] = pd.Series(bci['time'], dtype='int64')
In [49]:
bci
Out[49]:
tix
1
2
3
4
5
6
7
8
a
b
c
time
0
0
-120122.36
-113415.72
-114035.63
-117027.27
-136190.16
-148000.78
-155031.11
-131657.89
0.01
0.10
1.03
0
1
1
-120122.92
-113416.37
-114035.91
-117027.36
-136190.20
-148000.81
-155031.06
-131657.52
0.00
0.00
0.00
4
2
2
-120119.12
-113412.79
-114034.35
-117025.66
-136188.48
-147998.63
-155028.94
-131653.63
0.00
0.00
0.00
8
3
3
-120115.72
-113409.64
-114032.02
-117023.45
-136186.20
-147996.28
-155026.94
-131651.13
0.00
0.00
0.00
12
4
4
-120121.53
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-131657.11
0.00
0.00
0.00
16
5
5
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0.00
0.00
0.00
19
6
6
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0.00
0.00
0.00
24
7
7
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0.01
0.12
1.03
27
8
8
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0.00
0.00
0.00
32
9
9
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0.00
0.00
0.00
37
10
10
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-131651.34
0.00
0.00
0.00
41
11
11
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0.00
0.00
0.00
43
12
12
-120117.11
-113412.70
-114034.73
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-136187.88
-147998.72
-155030.52
-131653.02
0.00
0.00
0.00
47
13
13
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0.00
0.00
0.00
51
14
14
-120118.27
-113414.02
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-147999.03
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0.00
0.00
0.00
55
15
15
-120113.40
-113409.48
-114032.72
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-136185.44
-147995.97
-155028.08
-131648.55
0.00
0.00
0.00
59
16
16
-120116.05
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-114034.88
-117026.33
-136187.77
-147998.34
-155031.06
-131652.13
0.00
0.00
0.00
64
17
17
-120121.94
-113418.11
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-136191.61
-148002.73
-155035.66
-131657.56
0.00
0.12
1.03
68
18
18
-120120.26
-113416.57
-114037.70
-117029.23
-136190.11
-148001.28
-155034.02
-131655.13
0.00
0.00
0.00
71
19
19
-120115.86
-113412.61
-114035.83
-117027.23
-136188.09
-147998.81
-155031.52
-131651.08
0.00
0.00
0.00
76
20
20
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-113413.58
-114036.70
-117028.19
-136189.16
-147999.95
-155033.11
-131652.95
0.00
0.00
0.00
79
21
21
-120120.44
-113417.40
-114038.62
-117030.24
-136191.08
-148002.36
-155035.73
-131656.55
0.00
0.00
0.00
83
22
22
-120121.31
-113418.31
-114039.63
-117030.91
-136191.61
-148002.97
-155036.19
-131656.38
0.00
0.00
0.00
88
23
23
-120117.40
-113414.87
-114038.24
-117029.26
-136189.98
-148000.84
-155034.02
-131652.48
0.00
0.00
0.00
92
24
24
-120115.88
-113413.73
-114037.52
-117028.74
-136189.33
-148000.23
-155033.73
-131652.03
0.00
0.00
0.00
95
25
25
-120120.86
-113418.60
-114040.66
-117032.14
-136192.45
-148003.91
-155037.78
-131657.06
0.00
0.00
0.00
99
26
26
-120120.77
-113418.60
-114040.02
-117031.52
-136191.66
-148003.19
-155036.95
-131656.02
0.00
0.00
0.00
103
27
27
-120116.52
-113414.70
-114038.04
-117029.20
-136189.44
-148000.48
-155034.23
-131651.59
0.00
0.12
1.03
108
28
28
-120114.25
-113412.55
-114036.88
-117028.12
-136188.33
-147999.14
-155033.27
-131650.23
0.00
0.00
0.00
112
29
29
-120118.47
-113416.91
-114039.42
-117030.87
-136190.84
-148002.23
-155036.70
-131654.77
0.00
0.00
0.00
116
...
...
...
...
...
...
...
...
...
...
...
...
...
...
3533
205
-119732.52
-113638.08
-114496.49
-117481.19
-136267.94
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0.00
0.00
0.00
14115
3534
206
-119733.75
-113639.53
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-117482.98
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0.00
0.00
0.00
14119
3535
207
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0.00
0.00
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14123
3536
208
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0.00
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0.00
14127
3537
209
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0.00
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14131
3538
210
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0.00
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14134
3539
211
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0.00
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14138
3540
212
-119738.47
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0.00
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0.00
14144
3541
213
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0.00
0.11
1.03
14148
3542
214
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0.00
0.00
0.00
14151
3543
215
-119735.38
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0.00
0.00
0.00
14155
3544
216
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14159
3545
217
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0.00
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14161
3546
218
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0.00
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14166
3547
219
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0.00
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14170
3548
220
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14175
3549
221
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0.00
0.00
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14180
3550
222
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0.00
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14182
3551
223
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0.01
0.10
1.03
14186
3552
224
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0.00
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14190
3553
225
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3554
226
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3555
227
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3556
228
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3557
229
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230
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3559
231
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3560
232
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3561
233
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3562
234
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14231
3563 rows × 13 columns
In [50]:
mid = len(bci) // 2
bci2 = bci.iloc[mid-100:mid+100]
In [51]:
# bci2.merge(resp, 'outer', on='time', sort=True)
In [52]:
# bci.merge(resp, 'outer', on='time', sort=True).to_csv('testmerge.csv')
mframe = pd.merge_asof(bci, resp, on='time', allow_exact_matches=False)
In [53]:
mframe
Out[53]:
tix
1
2
3
4
5
6
7
8
a
b
c
time
resp
tone
tonef
0
0
-120122.36
-113415.72
-114035.63
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-136190.16
-148000.78
-155031.11
-131657.89
0.01
0.10
1.03
0
NaN
NaN
NaN
1
1
-120122.92
-113416.37
-114035.91
-117027.36
-136190.20
-148000.81
-155031.06
-131657.52
0.00
0.00
0.00
4
NaN
NaN
NaN
2
2
-120119.12
-113412.79
-114034.35
-117025.66
-136188.48
-147998.63
-155028.94
-131653.63
0.00
0.00
0.00
8
NaN
NaN
NaN
3
3
-120115.72
-113409.64
-114032.02
-117023.45
-136186.20
-147996.28
-155026.94
-131651.13
0.00
0.00
0.00
12
NaN
NaN
NaN
4
4
-120121.53
-113415.52
-114036.18
-117027.72
-136190.31
-148001.00
-155031.83
-131657.11
0.00
0.00
0.00
16
NaN
NaN
NaN
5
5
-120124.88
-113418.91
-114038.69
-117030.06
-136192.45
-148003.45
-155034.17
-131659.50
0.00
0.00
0.00
19
NaN
NaN
NaN
6
6
-120117.47
-113411.83
-114033.43
-117024.61
-136186.97
-147997.47
-155028.16
-131651.81
0.00
0.00
0.00
24
NaN
NaN
NaN
7
7
-120113.48
-113408.26
-114031.09
-117022.48
-136184.84
-147994.97
-155025.97
-131648.81
0.01
0.12
1.03
27
NaN
NaN
NaN
8
8
-120118.41
-113413.24
-114034.55
-117026.17
-136188.38
-147999.03
-155030.33
-131654.23
0.00
0.00
0.00
32
NaN
NaN
NaN
9
9
-120119.88
-113414.83
-114034.80
-117026.33
-136188.44
-147999.41
-155030.70
-131654.92
0.00
0.00
0.00
37
NaN
NaN
NaN
10
10
-120116.82
-113412.01
-114033.43
-117024.61
-136186.73
-147997.25
-155028.55
-131651.34
0.00
0.00
0.00
41
NaN
NaN
NaN
11
11
-120113.94
-113409.41
-114032.54
-117023.78
-136185.73
-147995.91
-155027.52
-131649.11
0.00
0.00
0.00
43
NaN
NaN
NaN
12
12
-120117.11
-113412.70
-114034.73
-117026.20
-136187.88
-147998.72
-155030.52
-131653.02
0.00
0.00
0.00
47
NaN
NaN
NaN
13
13
-120121.83
-113417.35
-114037.77
-117029.28
-136190.81
-148002.05
-155033.97
-131657.22
0.00
0.00
0.00
51
NaN
NaN
NaN
14
14
-120118.27
-113414.02
-114035.20
-117026.60
-136188.03
-147999.03
-155030.94
-131652.94
0.00
0.00
0.00
55
NaN
NaN
NaN
15
15
-120113.40
-113409.48
-114032.72
-117023.94
-136185.44
-147995.97
-155028.08
-131648.55
0.00
0.00
0.00
59
NaN
NaN
NaN
16
16
-120116.05
-113412.32
-114034.88
-117026.33
-136187.77
-147998.34
-155031.06
-131652.13
0.00
0.00
0.00
64
NaN
NaN
NaN
17
17
-120121.94
-113418.11
-114038.93
-117030.49
-136191.61
-148002.73
-155035.66
-131657.56
0.00
0.12
1.03
68
NaN
NaN
NaN
18
18
-120120.26
-113416.57
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-117029.23
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3563 rows × 16 columns
In [58]:
mframe['dtone'] = mframe['tonef'].diff().fillna(0)
mframe['oddball'] = mframe['dtone'] == 2
mframe.drop(['a', 'b', 'c', 'dtone'], axis=1, inplace=True)
In [59]:
mframe.to_csv('testmerge.csv')
print(mframe.shape, bci.shape)
(3563, 14) (3563, 13)
In [57]:
resp['tone'].dtype
Out[57]:
dtype('O')
In [72]:
import os
orig = os.getcwd()
In [73]:
os.getcwd()
os.path.dirname(os.getcwd())
os.path.split(os.getcwd())
Out[73]:
('/home/mike/w/bci_erp', 'python')
In [74]:
os.chdir('..')
In [75]:
os.path.split(os.getcwd())
Out[75]:
('/home/mike/w', 'bci_erp')
In [2]:
a = 1
def foo(val):
print(val)
foo(val+1)
In [3]:
foo(1)
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---------------------------------------------------------------------------
RecursionError Traceback (most recent call last)
<ipython-input-3-9e45007b2b59> in <module>()
----> 1 foo(1)
<ipython-input-2-28b9dc9bee6c> in foo(val)
2 def foo(val):
3 print(val)
----> 4 foo(val+1)
... last 1 frames repeated, from the frame below ...
<ipython-input-2-28b9dc9bee6c> in foo(val)
2 def foo(val):
3 print(val)
----> 4 foo(val+1)
RecursionError: maximum recursion depth exceeded in comparison
In [4]:
import os, sys
od = os.getcwd()
In [5]:
os.chdir('')
Out[5]:
'/home/mike/w/bci_erp/python'
In [8]:
import antigravity
In [ ]:
Content source: prefrontalvortex/bci_erp
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