Notebook

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import xarray as xr

Loading Argo netCDF file¶

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pres_first_prof = data['PRES'].values[0,:]
temp_first_prof = data['TEMP'].values[0,:]

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plt.plot(temp_first_prof,pres_first_prof)
plt.gca().invert_yaxis()

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data['PRES'].attrs['long_name']

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'Sea water pressure, equals 0 at sea-level'

Loading, processing, and plotting NB Palmer¶

shipboard CNV file

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filepath = 'dnbp2211_001.cnv'

# Identify header line number (= line_idx - 1)
file_object = open(filepath,'r')
all_lines = file_object.readlines()
file_object.close()
header_names = []
for line_idx, line in enumerate(all_lines):
  if '*END*' in line:
    break
  elif ' name' in line:
    header_names.append(line.split('= ')[1].split(':')[0])
header_names.append('')
# print('First line of data after header:',line_idx, line)

# Extract header names
for line_idx, line in enumerate(all_lines):
  if ' name' in line:
    header_names.append(line.split('= ')[1].split(':')[0])
header_names.append('')

pd.read_csv(filepath,header=None,skiprows=line_idx+1,delim_whitespace=True)

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	0	1	2	3	4	5	6	7	8	9	...	15	16	17	18	19	20	21	22	23	24
0	3.0	2.986	0.6714	28.697590	0.6729	28.699145	7.8876	7.9709	0.3025	97.2691	...	105.930	694.320	16665	1027.1163	1027.1160	33.8013	33.8016	7.93803	1	0.0
1	4.0	3.961	0.6706	28.698075	0.6731	28.700361	7.8880	7.9623	0.3049	97.6027	...	103.870	695.620	16696	1027.1214	1027.1215	33.8021	33.8024	7.93815	27	0.0
2	5.0	4.953	0.6693	28.697519	0.6709	28.699846	7.8909	7.9627	0.3116	97.7276	...	104.170	696.479	16716	1027.1264	1027.1274	33.8022	33.8036	7.93841	19	0.0
3	6.0	5.945	0.6608	28.691212	0.6632	28.693173	7.8883	7.9726	0.3036	97.9193	...	103.450	713.080	17115	1027.1322	1027.1321	33.8027	33.8027	7.94012	40	0.0
4	7.0	6.935	0.6685	28.698019	0.6711	28.700008	7.8871	7.9641	0.2961	97.9622	...	103.960	720.073	17283	1027.1362	1027.1358	33.8026	33.8023	7.93856	30	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2024	2027.0	1998.408	1.3263	30.845606	1.3290	30.847386	4.2602	4.3357	-0.0203	100.1124	...	39.696	3345.650	80297	1037.1768	1037.1759	34.7218	34.7212	7.75619	34	0.0
2025	2028.0	1999.389	1.3256	30.845380	1.3281	30.847227	4.2630	4.3362	-0.0177	100.1098	...	39.665	3348.430	80363	1037.1814	1037.1807	34.7218	34.7213	7.75633	36	0.0
2026	2029.0	2000.371	1.3249	30.845152	1.3273	30.846828	4.2646	4.3354	-0.0198	100.1064	...	40.152	3356.138	80548	1037.1859	1037.1852	34.7217	34.7212	7.75646	33	0.0
2027	2030.0	2001.352	1.3245	30.845188	1.3271	30.846991	4.2660	4.3372	-0.0184	100.1108	...	40.492	3384.184	81221	1037.1905	1037.1897	34.7217	34.7212	7.75654	40	0.0
2028	2031.0	2002.333	1.3241	30.845491	1.3269	30.847048	4.2608	4.3330	-0.0123	100.1082	...	38.314	3463.743	83131	1037.1952	1037.1941	34.7220	34.7210	7.75661	27	0.0

2029 rows × 25 columns

Loading, processing, and plotting float EDF file¶

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filepath = '/content/1122.001.edf.txt'

# Identify header line number (= line_idx - 1)
file_object = open(filepath,'r')
all_lines = file_object.readlines()
for line_idx, line in enumerate(all_lines):
  if line[0] != '$':
    break
print('First line of data after header:',line_idx, line)

# Extract column names
col_names = all_lines[line_idx-1].split()
print('Header column names:',col_names,'\n')

# Load data
data = pd.read_csv(filepath,comment='$',delim_whitespace=True,na_values='*',
                   header=None,names=col_names[2:])
display(data)

First line of data after header: 91      2.30   10.366   34.405   17.786   0.874579   406  1457   168   -0.902169         nan  10.366  26.4249  273.6  284.8   10.333    6.2   32

Header column names: ['$', 'T', 'p', 't', 's', 'O2Phase', 'O2tV', 'fl', 'bb', 'cd', 'pH-Vrs', 'pH-Vk', 'theta', 'sigma', 'o2sat', '[O2]', 'T63', 'speed', 'n']

	p	t	s	O2Phase	O2tV	fl	bb	cd	pH-Vrs	pH-Vk	theta	sigma	o2sat	[O2]	T63	speed	n
0	2.3	10.366	34.405	17.786	0.874579	406	1457	168	-0.902169	NaN	10.366	26.4249	273.6	284.8	10.333	6.2	32
1	4.0	10.247	34.407	17.779	0.877397	449	1414	175	-0.901938	NaN	10.247	26.4471	274.3	286.2	10.229	4.5	44
2	6.0	10.171	34.408	17.776	0.879281	582	1473	173	-0.901731	NaN	10.170	26.4610	274.8	287.1	10.159	5.1	39
3	7.9	10.141	34.408	17.784	0.880097	644	1505	172	-0.901716	NaN	10.140	26.4662	274.9	287.1	10.128	5.4	37
4	10.0	10.118	34.409	17.794	0.880719	946	1590	175	-0.901667	NaN	10.117	26.4709	275.1	287.0	10.105	5.9	34
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
496	1299.8	3.092	34.401	24.207	1.087143	64	343	219	-0.918275	-0.828354	2.999	27.4070	321.4	177.2	3.089	NaN	1
497	1349.4	3.005	34.424	24.404	1.089901	63	343	220	-0.918504	-0.828408	2.909	27.4336	322.0	174.0	3.002	NaN	1
498	1399.7	2.921	34.451	24.573	1.092556	63	345	221	-0.918678	-0.828381	2.822	27.4629	322.5	171.4	2.919	NaN	1
499	1449.3	2.857	34.471	24.694	1.094618	65	342	225	-0.918760	-0.828328	2.755	27.4849	322.9	169.6	2.854	NaN	1
500	1499.4	2.768	34.496	24.096	1.097324	63	345	228	-0.918283	-0.827827	2.663	27.5129	323.5	183.2	2.770	NaN	1

501 rows × 17 columns

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plt.plot(data['t'],data['p'],color='k')
plt.gca().invert_yaxis()
plt.grid()
plt.xlabel('Temperature (°C)')
plt.ylabel('Pressure (dbar)');

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