In [1]:
%matplotlib inline
from pathlib import Path
import pandas as pd
import ctd
path = Path('..', 'tests', 'data')
In [2]:
cast = ctd.from_edf(path.joinpath('XBT.EDF.gz'))
ax = cast['temperature'].plot_cast()
ax.axis([20, 24, 19, 0]);
In [3]:
cast = ctd.from_fsi(path.joinpath('FSI.txt.gz'))
downcast, upcast = cast.split()
ax = downcast['TEMP'].plot_cast()
ax.grid(True)
In [4]:
cast = ctd.from_cnv(path.joinpath('CTD_big.cnv.bz2'))
downcast, upcast = cast.split()
ax = downcast['t090C'].plot_cast()
ax.grid(True)
In [5]:
from ctd import rosette_summary
ros = rosette_summary(path.joinpath('CTD', 'g01l01s01.ros'))
ros = ros.groupby(ros.index).mean()
ros
Out[5]:
altM
bat
bpos
c0S/m
dz/dtM
wetCDOM
latitude
longitude
sbeox0Mm/Kg
sbeox1Mm/Kg
...
timeS
v0
v1
v2
v3
v4
v5
sbeox0V
flag
pressure
nbf
1
8.478571
0.171437
1
3.424277
0.105204
4.017227
28.248180
-89.256480
168.191714
142.890857
...
1579.458327
1.664078
1.578265
0.424000
4.472133
0.056218
0.232686
1.664078
False
835.662429
2
66.331224
0.081443
2
3.451780
-0.080184
4.033410
28.248029
-89.257220
159.894163
135.617918
...
1774.625000
1.620106
1.536220
3.316561
4.573853
0.051178
0.233322
1.620106
False
806.290082
3
98.710408
0.057733
3
3.507171
-0.128571
3.888237
28.247920
-89.258180
145.460980
123.346531
...
1991.500000
1.549616
1.471447
4.935324
4.601027
0.052941
0.227516
1.549616
False
705.723367
4
98.725510
0.052141
4
3.608956
0.024898
3.891386
28.247880
-89.259296
129.973571
110.019102
...
2231.375000
1.476616
1.403033
4.936231
4.607461
0.050094
0.227639
1.476616
False
604.714939
5
98.730000
0.051008
5
3.697932
-0.027122
3.735594
28.248020
-89.260240
122.336388
103.630857
...
2443.250000
1.452165
1.381265
4.936500
4.608810
0.049329
0.221429
1.452165
False
503.967776
6
98.719388
0.051835
6
3.856994
0.094367
3.734400
28.248205
-89.261400
121.758939
103.323898
...
2683.250000
1.495259
1.422049
4.935863
4.607824
0.043996
0.221367
1.495259
False
404.474163
7
98.689184
0.052108
7
4.116017
0.204959
3.554333
28.248220
-89.262620
128.523776
109.458286
...
2940.791673
1.622308
1.541096
4.934467
4.607498
0.037233
0.214171
1.622308
False
303.768020
8
98.634898
0.050173
8
4.470728
-0.023490
3.386757
28.248180
-89.263730
149.198265
127.546388
...
3162.750000
1.908180
1.805661
4.931796
4.609765
0.028345
0.207449
1.908180
False
201.675061
9
98.631837
0.052755
9
4.613940
-0.023694
3.276531
28.248140
-89.264458
147.471286
126.537571
...
3303.625000
1.935986
1.834457
4.931673
4.606733
0.023469
0.203067
1.935986
False
151.205347
10
98.613265
0.065596
10
4.941123
0.052041
3.507029
28.248100
-89.265098
154.761102
133.424122
...
3429.583327
2.102939
1.990384
4.930596
4.591998
0.026229
0.212314
2.102939
False
100.727000
11
98.571020
0.093890
11
5.405148
0.086796
2.568824
28.248060
-89.265682
226.836898
198.537694
...
3545.916673
3.052133
2.889500
4.928661
4.559645
0.060233
0.174763
3.052133
False
51.274918
12
98.570204
0.093537
12
5.912824
-0.023755
1.950820
28.248040
-89.266280
200.325694
176.624469
...
3668.125000
2.957757
2.803282
4.928612
4.560043
1.418735
0.150004
2.957757
False
1.016061
12 rows × 30 columns
In [6]:
bottles = ctd.from_btl(path.joinpath('btl', 'bottletest.btl'))
bottles.head()
Out[6]:
Bottle
Date
DepSM
PrDM
T090C
T190C
C0S/m
C1S/m
Sal00
Sal11
...
Sigma-�11
FlECO-AFL
CStarTr0
CStarAt0
Sbeox0Mm/Kg
Par
Spar
Cpar
Scan
Statistic
0
1
2013-06-27 21:23:18
31.638
31.875
9.1594
9.1600
3.627258
3.626589
33.9171
33.9096
...
26.2393
0.1277
11.6390
8.6035
118.458
0.441550
1860.800
0.023731
18147
avg
1
1
2013-06-27 21:23:18
0.206
0.208
0.0253
0.0265
0.002050
0.002150
0.0027
0.0031
...
0.0066
0.0224
0.1272
0.0437
1.770
0.002367
16.188
0.000266
14
sdev
2
1
2013-06-27 21:23:18
31.320
31.555
9.1330
9.1326
3.625157
3.624497
33.9130
33.9024
...
26.2277
0.1025
11.3524
8.5231
115.722
0.432660
1812.700
0.023224
18123
min
3
1
2013-06-27 21:23:18
31.982
32.222
9.2073
9.2061
3.631310
3.630471
33.9206
33.9139
...
26.2471
0.1538
11.8746
8.7030
121.180
0.444400
1871.400
0.024516
18171
max
4
2
2013-06-27 21:23:20
31.162
31.396
9.2179
9.2178
3.632233
3.631494
33.9134
33.9058
...
26.2271
0.1589
11.3333
8.7104
115.429
0.429650
1859.100
0.023113
18186
avg
5 rows × 21 columns
Content source: pyoceans/python-ctd
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