In [107]:
%matplotlib inline
import serial
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
filepath = "raw.tsv"
In [108]:
listfil = os.listdir("/dev/")
filt = lambda k: 'ttyACM' in k
acm_list = filter(filt, listfil)
ard_path = "/dev/%s" % (list(acm_list)[0])
In [109]:
ser = serial.Serial(ard_path, 9600)
with open(filepath, "wb") as fil:
while True:
fil.write(ser.readline())
KeyboardInterruptTraceback (most recent call last)
<ipython-input-109-03627012af02> in <module>()
2 with open(filepath, "wb") as fil:
3 while True:
----> 4 fil.write(ser.readline())
/usr/local/lib/python3.5/dist-packages/serial/serialposix.py in read(self, size)
479 try:
480 start_time = time.time()
--> 481 ready, _, _ = select.select([self.fd], [], [], timeout)
482 # If select was used with a timeout, and the timeout occurs, it
483 # returns with empty lists -> thus abort read operation.
KeyboardInterrupt:
In [101]:
raw = pd.read_csv(filepath, delimiter="\t", names=('t', 'p', 'u', 'tbmp', 'tdht', 'none'))[1:]
x = raw.t / 1e6
In [102]:
plt.plot(x, raw.u,'-')
plt.show()
In [103]:
plt.plot(x, raw.tbmp,'-')
plt.plot(x, raw.tdht, '-')
plt.show()
In [49]:
raw
Out[49]:
t
p
u
tbmp
tdht
none
1
431632508
934.96
58.4
22.10
22.5
NaN
2
433939172
934.97
58.5
22.10
22.6
NaN
3
436246772
934.89
58.4
22.09
22.6
NaN
4
438553464
934.99
58.3
22.11
22.6
NaN
5
440860196
934.95
58.3
22.11
22.6
NaN
6
443166900
934.91
58.3
22.11
22.6
NaN
7
445473608
934.96
58.3
22.12
22.6
NaN
8
447780240
934.92
58.1
22.12
22.6
NaN
9
450086856
934.88
58.1
22.12
22.6
NaN
10
452393464
934.85
58.2
22.11
22.6
NaN
11
454700100
934.87
58.2
22.14
22.6
NaN
12
457006716
934.93
58.2
22.16
22.6
NaN
13
459313428
934.88
58.3
22.16
22.6
NaN
14
461620132
934.87
58.3
22.15
22.6
NaN
15
463926840
934.87
58.3
22.16
22.6
NaN
16
466234584
934.85
58.3
22.16
22.6
NaN
17
468541204
934.89
58.1
22.17
22.6
NaN
18
470847820
934.97
58.1
22.17
22.6
NaN
19
473154444
934.91
58.1
22.16
22.6
NaN
20
475461072
934.94
58.1
22.16
22.6
NaN
21
477767604
934.91
58.0
22.17
22.6
NaN
22
480074132
934.87
58.0
22.15
22.6
NaN
23
482380784
934.88
57.9
22.17
22.6
NaN
24
484687456
934.96
57.9
22.17
22.6
NaN
25
486995056
934.92
57.8
22.17
22.6
NaN
26
489301620
934.92
57.8
22.16
22.6
NaN
27
491609212
934.89
57.8
22.16
22.6
NaN
28
493915780
934.91
57.8
22.17
22.6
NaN
29
496223372
934.91
57.8
22.17
22.6
NaN
30
498529944
934.88
57.8
22.17
22.6
NaN
...
...
...
...
...
...
...
42
526212236
934.85
58.1
22.23
22.6
NaN
43
528518748
934.93
58.0
22.24
22.6
NaN
44
530826372
934.82
58.0
22.23
22.7
NaN
45
533132924
934.98
57.7
22.26
22.6
NaN
46
535440616
934.88
57.8
22.24
22.7
NaN
47
537747096
934.94
57.6
22.25
22.6
NaN
48
540054744
934.93
57.9
22.26
22.7
NaN
49
542361340
934.90
58.0
22.26
22.7
NaN
50
544667940
934.89
58.0
22.25
22.7
NaN
51
546974548
934.92
57.9
22.25
22.7
NaN
52
549281212
934.92
57.8
22.25
22.7
NaN
53
551587888
934.87
57.8
22.25
22.7
NaN
54
553894496
934.95
57.9
22.27
22.7
NaN
55
556202136
934.91
57.9
22.27
22.7
NaN
56
558508744
934.89
57.9
22.25
22.7
NaN
57
560815408
934.98
57.8
22.28
22.7
NaN
58
563122072
934.90
57.8
22.27
22.7
NaN
59
565428748
934.88
57.8
22.27
22.7
NaN
60
567735416
934.96
57.8
22.28
22.7
NaN
61
570042076
934.90
57.8
22.28
22.7
NaN
62
572348740
934.89
57.8
22.28
22.7
NaN
63
574655356
935.01
57.9
22.30
22.7
NaN
64
576961980
934.88
57.9
22.30
22.7
NaN
65
579268580
934.95
57.9
22.30
22.7
NaN
66
581575240
934.88
57.8
22.30
22.7
NaN
67
583882924
934.91
57.8
22.30
22.7
NaN
68
586189572
934.93
57.8
22.33
22.7
NaN
69
588496188
934.80
57.9
22.31
22.7
NaN
70
590802796
934.88
57.9
22.31
22.7
NaN
71
593109416
934.90
57.9
22.32
22.7
NaN
71 rows × 6 columns
In [ ]:
Content source: danilolessa/playground
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