Calculated saturation value for oxygen given measured presure and corrected temperature, and salinity
platform :
glider
[586 values with dtype=float64]
eng_aa4330_O2
(sg_data_point)
float64
...
units :
micromoles/L
platform :
glider
comment :
Dissolved oxygen as reported by the instument, based on on-board calibration data, assuming optode temperature but without depth or salinity correction
instrument :
aa4330
[586 values with dtype=float64]
conductivity_raw
(sg_data_point)
float64
...
units :
S/m
comment :
Uncorrected conductivity
platform :
glider
[586 values with dtype=float64]
eng_aa4330_AirSat
(sg_data_point)
float64
...
comment :
As reported by the instrument
platform :
glider
instrument :
aa4330
[586 values with dtype=float64]
eng_pitchCtl
(sg_data_point)
float64
...
platform :
glider
[586 values with dtype=float64]
conductivity
(sg_data_point)
float64
...
units :
S/m
comment :
Conductivity corrected for anomalies
standard_name :
sea_water_electrical_conductivity
platform :
glider
[586 values with dtype=float64]
latitude_gsm
(sg_data_point)
float64
...
units :
degrees_north
comment :
Latitude of the sample based on gsm DAC
platform :
glider
[586 values with dtype=float64]
aanderaa4330_instrument_dissolved_oxygen
(sg_data_point)
float64
...
units :
micromoles/kg
instrument :
aa4330
comment :
Dissolved oxygen concentration reported from optode corrected for salinity
platform :
glider
[586 values with dtype=float64]
eng_pitchAng
(sg_data_point)
float64
...
units :
degrees
comment :
Vehicle pitch
platform :
glider
[586 values with dtype=float64]
theta
(sg_data_point)
float64
...
units :
degrees_Celsius
comment :
Potential temperature based on corrected salinity
standard_name :
sea_water_potential_temperature
platform :
glider
[586 values with dtype=float64]
eng_elaps_t_0000
(sg_data_point)
timedelta64[ns]
...
comment :
Elapsed seconds since start of mission
standard_name :
time
platform :
glider
[586 values with dtype=timedelta64[ns]]
buoyancy
(sg_data_point)
float64
...
units :
g
comment :
Buoyancy of vehicle, corrected for compression effects
Whether to trust each optode dissolved oxygen value
\n",
"
status_flag
\n",
"
float32
\n",
"
\n",
"
\n",
"
EAST_DISPLACEMENT
\n",
"
N_MEASUREMENTS
\n",
"
meters
\n",
"
Eastward displacement from hdm
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
GLIDER_HORZ_VELO_MODEL
\n",
"
N_MEASUREMENTS
\n",
"
cm/s
\n",
"
Vehicle horizontal speed based on hdm
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
GLIDER_VERT_VELO_MODEL
\n",
"
N_MEASUREMENTS
\n",
"
cm/s
\n",
"
Vehicle vertical speed based on hdm
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
GLIDE_ANGLE
\n",
"
N_MEASUREMENTS
\n",
"
cm/s
\n",
"
Glide angle based on hdm
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
GLIDE_SPEED
\n",
"
N_MEASUREMENTS
\n",
"
cm/s
\n",
"
Vehicle speed based on hdm
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
GLIDE_SPEED_QC
\n",
"
N_MEASUREMENTS
\n",
"
\n",
"
Whether to trust each hdm speed value
\n",
"
status_flag
\n",
"
float32
\n",
"
\n",
"
\n",
"
HEADING
\n",
"
N_MEASUREMENTS
\n",
"
degrees
\n",
"
Vehicle heading (magnetic)
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
LATITUDE
\n",
"
N_MEASUREMENTS
\n",
"
degrees_north
\n",
"
Latitude of the sample based on hdm DAC
\n",
"
latitude
\n",
"
float64
\n",
"
\n",
"
\n",
"
LATITUDE_GPS
\n",
"
N_MEASUREMENTS
\n",
"
degrees_north
\n",
"
\n",
"
latitude
\n",
"
float64
\n",
"
\n",
"
\n",
"
LONGITUDE
\n",
"
N_MEASUREMENTS
\n",
"
degrees_east
\n",
"
Longitude of the sample based on hdm DAC
\n",
"
longitude
\n",
"
float64
\n",
"
\n",
"
\n",
"
LONGITUDE_GPS
\n",
"
N_MEASUREMENTS
\n",
"
degrees_east
\n",
"
\n",
"
longitude
\n",
"
float64
\n",
"
\n",
"
\n",
"
NORTH_DISPLACEMENT
\n",
"
N_MEASUREMENTS
\n",
"
meters
\n",
"
Northward displacement from hdm
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
OXYSAT
\n",
"
N_MEASUREMENTS
\n",
"
micromoles/kg
\n",
"
Calculated saturation value for oxygen given measured presure and corrected temperature, and salinity
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
PHASE
\n",
"
N_MEASUREMENTS
\n",
"
\n",
"
\n",
"
\n",
"
float64
\n",
"
\n",
"
\n",
"
PHASE_QC
\n",
"
N_MEASUREMENTS
\n",
"
\n",
"
\n",
"
\n",
"
int64
\n",
"
\n",
"
\n",
"
PITCH
\n",
"
N_MEASUREMENTS
\n",
"
degrees
\n",
"
Vehicle pitch
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
PITCH_CTL
\n",
"
N_MEASUREMENTS
\n",
"
\n",
"
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
PRES
\n",
"
N_MEASUREMENTS
\n",
"
dbar
\n",
"
Uncorrected sea-water pressure at pressure sensor
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
PROFILE_NUMBER
\n",
"
N_MEASUREMENTS
\n",
"
\n",
"
\n",
"
\n",
"
float64
\n",
"
\n",
"
\n",
"
PSAL
\n",
"
N_MEASUREMENTS
\n",
"
1e-3
\n",
"
Salinity corrected for thermal-inertia effects (PSU)
\n",
"
sea_water_salinity
\n",
"
float32
\n",
"
\n",
"
\n",
"
PSAL_QC
\n",
"
N_MEASUREMENTS
\n",
"
\n",
"
Whether to trust each corrected salinity value
\n",
"
status_flag
\n",
"
float32
\n",
"
\n",
"
\n",
"
PSAL_RAW
\n",
"
N_MEASUREMENTS
\n",
"
1e-3
\n",
"
Uncorrected salinity derived from temperature_raw and conductivity_raw (PSU)
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
PSAL_RAW_QC
\n",
"
N_MEASUREMENTS
\n",
"
\n",
"
Whether to trust each raw salinity value
\n",
"
status_flag
\n",
"
float32
\n",
"
\n",
"
\n",
"
ROLL
\n",
"
N_MEASUREMENTS
\n",
"
degrees
\n",
"
Vehicle roll
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
ROLL_CTL
\n",
"
N_MEASUREMENTS
\n",
"
\n",
"
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
SIGMA_T
\n",
"
N_MEASUREMENTS
\n",
"
g/m^3
\n",
"
Sigma based on density
\n",
"
sea_water_sigma_t
\n",
"
float32
\n",
"
\n",
"
\n",
"
SIGTHETA
\n",
"
N_MEASUREMENTS
\n",
"
g/m^3
\n",
"
\n",
"
sea_water_sigma_theta
\n",
"
float32
\n",
"
\n",
"
\n",
"
TEMP
\n",
"
N_MEASUREMENTS
\n",
"
degrees_Celsius
\n",
"
Termperature (in situ) corrected for thermistor first-order lag
\n",
"
sea_water_temperature
\n",
"
float32
\n",
"
\n",
"
\n",
"
TEMP_QC
\n",
"
N_MEASUREMENTS
\n",
"
\n",
"
Whether to trust each corrected temperature value
\n",
"
status_flag
\n",
"
float32
\n",
"
\n",
"
\n",
"
TEMP_RAW
\n",
"
N_MEASUREMENTS
\n",
"
degrees_Celsius
\n",
"
Uncorrected temperature (in situ)
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
TEMP_RAW_QC
\n",
"
N_MEASUREMENTS
\n",
"
\n",
"
Whether to trust each raw temperature value
\n",
"
status_flag
\n",
"
float32
\n",
"
\n",
"
\n",
"
THETA
\n",
"
N_MEASUREMENTS
\n",
"
degrees_Celsius
\n",
"
Potential temperature based on corrected salinity
\n",
"
sea_water_potential_temperature
\n",
"
float32
\n",
"
\n",
"
\n",
"
TIME
\n",
"
N_MEASUREMENTS
\n",
"
seconds since 1970-01-01T00:00:00Z
\n",
"
Time of CTD sample in GMT epoch format
\n",
"
time
\n",
"
datetime64[ns]
\n",
"
\n",
"
\n",
"
TIME_DOXY
\n",
"
N_MEASUREMENTS
\n",
"
\n",
"
time for Aanderaa 4330 in GMT epoch format
\n",
"
time
\n",
"
datetime64[ns]
\n",
"
\n",
"
\n",
"
VBD_CC
\n",
"
N_MEASUREMENTS
\n",
"
\n",
"
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
aanderaa4330_instrument_dissolved_oxygen
\n",
"
N_MEASUREMENTS
\n",
"
micromoles/kg
\n",
"
Dissolved oxygen concentration reported from optode corrected for salinity
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
absolute_salinity
\n",
"
N_MEASUREMENTS
\n",
"
g/kg
\n",
"
Absolute salinity per TEOS-10
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
buoyancy
\n",
"
N_MEASUREMENTS
\n",
"
g
\n",
"
Buoyancy of vehicle, corrected for compression effects
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
conservative_temperature
\n",
"
N_MEASUREMENTS
\n",
"
degrees_Celsius
\n",
"
Conservative termperature per TEOS-10
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
ctd_pressure
\n",
"
N_MEASUREMENTS
\n",
"
dbar
\n",
"
Pressure at CTD thermistor
\n",
"
sea_water_pressure
\n",
"
float32
\n",
"
\n",
"
\n",
"
density_insitu
\n",
"
N_MEASUREMENTS
\n",
"
g/m^3
\n",
"
Sea water in-situ density based on pressure
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
divenum
\n",
"
N_MEASUREMENTS
\n",
"
\n",
"
\n",
"
\n",
"
int32
\n",
"
\n",
"
\n",
"
eng_GC_phase
\n",
"
N_MEASUREMENTS
\n",
"
\n",
"
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
eng_aa4330_AirSat
\n",
"
N_MEASUREMENTS
\n",
"
\n",
"
As reported by the instrument
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
eng_aa4330_CalPhase
\n",
"
N_MEASUREMENTS
\n",
"
\n",
"
As reported by the instrument
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
eng_aa4330_O2
\n",
"
N_MEASUREMENTS
\n",
"
micromoles/L
\n",
"
Dissolved oxygen as reported by the instument, based on on-board calibration data, assuming optode temperature but without depth or salinity correction
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
eng_aa4330_TCPhase
\n",
"
N_MEASUREMENTS
\n",
"
\n",
"
As reported by the instrument
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
eng_aa4330_Temp
\n",
"
N_MEASUREMENTS
\n",
"
degrees_Celsius
\n",
"
As reported by the instrument
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
eng_rec
\n",
"
N_MEASUREMENTS
\n",
"
\n",
"
\n",
"
\n",
"
float32
\n",
"
\n",
"
\n",
"
PLATFORM_MODEL
\n",
"
string
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
PLATFORM_SERIAL_NUMBER
\n",
"
string
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
TRAJECTORY
\n",
"
string
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
WMO_IDENTIFIER
\n",
"
string
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
"
\n"
],
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Print to screen a table of the variables and variable attributes\n",
"plotters.show_contents(ds_OG1,'variables')"
]
},
{
"cell_type": "markdown",
"id": "dc0a27f3",
"metadata": {},
"source": [
"### Convert mission from a local directory of basestation files\n",
"\n",
"- For local data in the directory `input_dir`\n",
"- Creates a plot of ctd_depth against ctd_time.\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "9d202485",
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-21T14:45:11.411528Z",
"iopub.status.busy": "2025-09-21T14:45:11.411260Z",
"iopub.status.idle": "2025-09-21T14:45:14.248365Z",
"shell.execute_reply": "2025-09-21T14:45:14.247896Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Loading datasets: 0%| | 0/5 [00:00, ?file/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Loading datasets: 80%|████████ | 4/5 [00:00<00:00, 39.31file/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Loading datasets: 100%|██████████| 5/5 [00:00<00:00, 39.42file/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 0%| | 0/5 [00:00, ?dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'vert_speed_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'speed_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'sound_velocity' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No conversion information found for micromoles/kg to micromoles/kg\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'north_displacement_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'longitude_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'latitude_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'horz_speed_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'glide_angle_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No conversion information found for cm s-1 to cm s-1\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_wlbb2f_VFtemp' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_tempFreq' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_sbe43_O2Freq' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_elaps_t_0000' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_elaps_t' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_depth' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_condFreq' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'east_displacement_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No conversion information found for micromoles/kg to micromoles/kg\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'depth' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'density' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'buoyancy' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 20%|██ | 1/5 [00:00<00:02, 1.97dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 40%|████ | 2/5 [00:00<00:01, 2.01dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 60%|██████ | 3/5 [00:01<00:00, 2.01dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 80%|████████ | 4/5 [00:01<00:00, 2.01dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 100%|██████████| 5/5 [00:02<00:00, 2.01dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 100%|██████████| 5/5 [00:02<00:00, 2.01dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
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"information is based on xarray Dataset\n"
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\n",
"
time_coverage_end
\n",
"
20080607T080838
\n",
"
str
\n",
"
\n",
"
\n",
"
14
\n",
"
site
\n",
"
Multiple transects of Faroe-Iceland Ridge uppe...
\n",
"
str
\n",
"
\n",
"
\n",
"
15
\n",
"
project
\n",
"
Iceland Scotland Ridge June 2008
\n",
"
str
\n",
"
\n",
"
\n",
"
16
\n",
"
contributor_name
\n",
"
Charlie Eriksen, Peter Rhines
\n",
"
str
\n",
"
\n",
"
\n",
"
17
\n",
"
contributor_role
\n",
"
PI, Principal investigator
\n",
"
str
\n",
"
\n",
"
\n",
"
18
\n",
"
contributor_role_vocabulary
\n",
"
http://vocab.nerc.ac.uk/search_nvs/W08,
\n",
"
str
\n",
"
\n",
"
\n",
"
19
\n",
"
contributor_email
\n",
"
eriksen@uw.edu,
\n",
"
str
\n",
"
\n",
"
\n",
"
20
\n",
"
contributing_institutions
\n",
"
University of Washington - School of Oceanogra...
\n",
"
str
\n",
"
\n",
"
\n",
"
21
\n",
"
contributing_institutions_vocabulary
\n",
"
https://edmo.seadatanet.org/report/1434,
\n",
"
str
\n",
"
\n",
"
\n",
"
22
\n",
"
contributing_institutions_role
\n",
"
PI,
\n",
"
str
\n",
"
\n",
"
\n",
"
23
\n",
"
contributing_institutions_role_vocabulary
\n",
"
http://vocab.nerc.ac.uk/collection/W08/current/,
\n",
"
str
\n",
"
\n",
"
\n",
"
24
\n",
"
uri
\n",
"
9e33a22e-a959-11e3-b35f-0026bb609360
\n",
"
str
\n",
"
\n",
"
\n",
"
25
\n",
"
rtqc_method
\n",
"
No QC applied
\n",
"
str
\n",
"
\n",
"
\n",
"
26
\n",
"
rtqc_method_doi
\n",
"
n/a
\n",
"
str
\n",
"
\n",
"
\n",
"
27
\n",
"
comment
\n",
"
Processing start:\\n20:13:32 11 Mar 2014 UTC: I...
\n",
"
str
\n",
"
\n",
"
\n",
"
28
\n",
"
start_date
\n",
"
20080606T180738
\n",
"
str
\n",
"
\n",
"
\n",
"
29
\n",
"
date_created
\n",
"
20140311T200332
\n",
"
str
\n",
"
\n",
"
\n",
"
30
\n",
"
featureType
\n",
"
trajectoryProfile
\n",
"
str
\n",
"
\n",
"
\n",
"
31
\n",
"
Conventions
\n",
"
CF-1.10,OG-1.0
\n",
"
str
\n",
"
\n",
"
\n",
"
32
\n",
"
date_modified
\n",
"
20250921T144514
\n",
"
str
\n",
"
\n",
"
\n",
"
33
\n",
"
disclaimer
\n",
"
Data provided AS-IS.
\n",
"
str
\n",
"
\n",
"
\n",
"
34
\n",
"
file_version
\n",
"
2.71
\n",
"
float32
\n",
"
\n",
"
\n",
"
35
\n",
"
keywords
\n",
"
Water Temperature, Conductivity, Salinity, Den...
\n",
"
str
\n",
"
\n",
"
\n",
"
36
\n",
"
keywords_vocabulary
\n",
"
NASA/GCMD Earth Science Keywords Version 6.0.0.0
\n",
"
str
\n",
"
\n",
"
\n",
"
37
\n",
"
license
\n",
"
These data may be redistributed and used witho...
\n",
"
str
\n",
"
\n",
"
\n",
"
38
\n",
"
acknowledgment
\n",
"
National Science Foundation, OCE Division, Gra...
\n",
"
str
\n",
"
\n",
"
\n",
"
39
\n",
"
contributer_email
\n",
"
null@null.com
\n",
"
str
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Attribute \\\n",
"0 title \n",
"1 id \n",
"2 platform \n",
"3 platform_vocabulary \n",
"4 naming_authority \n",
"5 institution \n",
"6 geospatial_lat_min \n",
"7 geospatial_lat_max \n",
"8 geospatial_lon_min \n",
"9 geospatial_lon_max \n",
"10 geospatial_vertical_min \n",
"11 geospatial_vertical_max \n",
"12 time_coverage_start \n",
"13 time_coverage_end \n",
"14 site \n",
"15 project \n",
"16 contributor_name \n",
"17 contributor_role \n",
"18 contributor_role_vocabulary \n",
"19 contributor_email \n",
"20 contributing_institutions \n",
"21 contributing_institutions_vocabulary \n",
"22 contributing_institutions_role \n",
"23 contributing_institutions_role_vocabulary \n",
"24 uri \n",
"25 rtqc_method \n",
"26 rtqc_method_doi \n",
"27 comment \n",
"28 start_date \n",
"29 date_created \n",
"30 featureType \n",
"31 Conventions \n",
"32 date_modified \n",
"33 disclaimer \n",
"34 file_version \n",
"35 keywords \n",
"36 keywords_vocabulary \n",
"37 license \n",
"38 acknowledgment \n",
"39 contributer_email \n",
"\n",
" Value DType \n",
"0 OceanGliders trajectory file str \n",
"1 sg005_20080606T180738_delayed str \n",
"2 sub-surface gliders str \n",
"3 https://vocab.nerc.ac.uk/collection/L06/curren... str \n",
"4 edu.washington.apl str \n",
"5 School of Oceanography\\nUniversity of Washingt... str \n",
"6 61.41231666666666 ndarray \n",
"7 61.57591666666667 ndarray \n",
"8 -8.747133333333332 ndarray \n",
"9 -8.273983333333332 ndarray \n",
"10 -0.3214989667970032 ndarray \n",
"11 845.8311973927603 ndarray \n",
"12 20080606T180256 str \n",
"13 20080607T080838 str \n",
"14 Multiple transects of Faroe-Iceland Ridge uppe... str \n",
"15 Iceland Scotland Ridge June 2008 str \n",
"16 Charlie Eriksen, Peter Rhines str \n",
"17 PI, Principal investigator str \n",
"18 http://vocab.nerc.ac.uk/search_nvs/W08, str \n",
"19 eriksen@uw.edu, str \n",
"20 University of Washington - School of Oceanogra... str \n",
"21 https://edmo.seadatanet.org/report/1434, str \n",
"22 PI, str \n",
"23 http://vocab.nerc.ac.uk/collection/W08/current/, str \n",
"24 9e33a22e-a959-11e3-b35f-0026bb609360 str \n",
"25 No QC applied str \n",
"26 n/a str \n",
"27 Processing start:\\n20:13:32 11 Mar 2014 UTC: I... str \n",
"28 20080606T180738 str \n",
"29 20140311T200332 str \n",
"30 trajectoryProfile str \n",
"31 CF-1.10,OG-1.0 str \n",
"32 20250921T144514 str \n",
"33 Data provided AS-IS. str \n",
"34 2.71 float32 \n",
"35 Water Temperature, Conductivity, Salinity, Den... str \n",
"36 NASA/GCMD Earth Science Keywords Version 6.0.0.0 str \n",
"37 These data may be redistributed and used witho... str \n",
"38 National Science Foundation, OCE Division, Gra... str \n",
"39 null@null.com str "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Specify the input directory on your local machine\n",
"input_dir = data_path + '/demo_sg005' ### chose the input directory with your data\n",
"\n",
"# Load and concatenate all datasets in the input directory\n",
"# Optionally, specify the range of profiles to load (start_profile, end_profile)\n",
"list_datasets = readers.load_basestation_files(input_dir, start_profile=1, end_profile=5)\n",
"\n",
"# Convert the list of datasets to OG1\n",
"ds_OG1, var_list = convertOG1.convert_to_OG1(list_datasets)\n",
"\n",
"# Generate a simple plot\n",
"plotters.plot_profile_depth(ds_OG1)\n",
"plotters.show_contents(ds_OG1,'attrs')"
]
},
{
"cell_type": "markdown",
"id": "fa3f9e81",
"metadata": {},
"source": [
"### Convert mission from the NCEI server (with p*nc files)\n",
"\n",
"- Data from the sg015 mission in the Labrador Sea (https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.nodc:0111844), dataset identifier gov.noaa.nodc:0111844.\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4ac290a2",
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-21T14:45:14.249952Z",
"iopub.status.busy": "2025-09-21T14:45:14.249787Z",
"iopub.status.idle": "2025-09-21T14:45:28.958719Z",
"shell.execute_reply": "2025-09-21T14:45:28.958162Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Loading datasets: 0%| | 0/19 [00:00, ?file/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Loading datasets: 21%|██ | 4/19 [00:00<00:00, 34.70file/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Loading datasets: 42%|████▏ | 8/19 [00:00<00:00, 35.19file/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading file 'p0330011_20100905.nc' from 'https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/033/20100903/p0330011_20100905.nc' to '/home/runner/.cache/seagliderOG1'.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading file 'p0330012_20100905.nc' from 'https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/033/20100903/p0330012_20100905.nc' to '/home/runner/.cache/seagliderOG1'.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Loading datasets: 63%|██████▎ | 12/19 [00:00<00:00, 14.74file/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading file 'p0330013_20100906.nc' from 'https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/033/20100903/p0330013_20100906.nc' to '/home/runner/.cache/seagliderOG1'.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading file 'p0330014_20100906.nc' from 'https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/033/20100903/p0330014_20100906.nc' to '/home/runner/.cache/seagliderOG1'.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Loading datasets: 79%|███████▉ | 15/19 [00:01<00:00, 10.39file/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading file 'p0330016_20100906.nc' from 'https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/033/20100903/p0330016_20100906.nc' to '/home/runner/.cache/seagliderOG1'.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading file 'p0330017_20100906.nc' from 'https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/033/20100903/p0330017_20100906.nc' to '/home/runner/.cache/seagliderOG1'.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Loading datasets: 89%|████████▉ | 17/19 [00:01<00:00, 8.21file/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading file 'p0330018_20100906.nc' from 'https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/033/20100903/p0330018_20100906.nc' to '/home/runner/.cache/seagliderOG1'.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading file 'p0330019_20100906.nc' from 'https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/033/20100903/p0330019_20100906.nc' to '/home/runner/.cache/seagliderOG1'.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Loading datasets: 100%|██████████| 19/19 [00:01<00:00, 7.07file/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Loading datasets: 100%|██████████| 19/19 [00:01<00:00, 9.71file/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 0%| | 0/19 [00:00, ?dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_depth' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_aa4330_Temp' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'longitude_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'speed_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'vert_speed_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_aa4330_TCPhase' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_sbect_tempFreq' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_sbect_condFreq' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_rec' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'north_displacement_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'horz_speed_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No conversion information found for micromoles/kg to micromoles/kg\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_aa4330_O2' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_aa4330_AirSat' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'latitude_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'aanderaa4330_instrument_dissolved_oxygen' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_elaps_t_0000' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'buoyancy' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'east_displacement_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'sound_velocity' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'density_insitu' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'density' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_aa4330_CalPhase' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_GC_phase' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No conversion information found for cm s-1 to cm s-1\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'conservative_temperature' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'glide_angle_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_elaps_t' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'absolute_salinity' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No conversion information found for micromoles/kg to micromoles/kg\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'ctd_pressure' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'depth' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 5%|▌ | 1/19 [00:00<00:12, 1.47dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 11%|█ | 2/19 [00:01<00:11, 1.50dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 16%|█▌ | 3/19 [00:02<00:10, 1.50dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 21%|██ | 4/19 [00:02<00:09, 1.51dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 26%|██▋ | 5/19 [00:03<00:09, 1.51dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 32%|███▏ | 6/19 [00:03<00:08, 1.51dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 37%|███▋ | 7/19 [00:04<00:07, 1.52dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 42%|████▏ | 8/19 [00:05<00:07, 1.52dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 47%|████▋ | 9/19 [00:05<00:06, 1.52dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 53%|█████▎ | 10/19 [00:06<00:05, 1.52dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 58%|█████▊ | 11/19 [00:07<00:05, 1.52dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 63%|██████▎ | 12/19 [00:07<00:04, 1.51dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 68%|██████▊ | 13/19 [00:08<00:03, 1.52dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 74%|███████▎ | 14/19 [00:09<00:03, 1.47dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 79%|███████▉ | 15/19 [00:09<00:02, 1.48dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 84%|████████▍ | 16/19 [00:10<00:02, 1.50dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 89%|████████▉ | 17/19 [00:11<00:01, 1.50dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 95%|█████████▍| 18/19 [00:11<00:00, 1.51dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 100%|██████████| 19/19 [00:12<00:00, 1.51dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 100%|██████████| 19/19 [00:12<00:00, 1.51dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# Specify the server where data are located\n",
"server = \"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/033/20100903/\"\n",
"\n",
"# Load and concatenate all datasets from the server, optionally specifying the range of profiles to load\n",
"list_datasets = readers.load_basestation_files(server, start_profile=1, end_profile=19)\n",
"\n",
"# Convert the list of datasets to OG1\n",
"ds_OG1, var_list = convertOG1.convert_to_OG1(list_datasets)"
]
},
{
"cell_type": "markdown",
"id": "54baa613",
"metadata": {},
"source": [
"## Saving data\n",
"\n",
"Due to problems with writing `xarray` datasets as netCDF when attributes are not of a specified type (`str`, `Number`, `np.ndarray`, `np.number`, `list`, `tuple`), a function was written `save_dataset`."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "3540ad10",
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-21T14:45:28.960381Z",
"iopub.status.busy": "2025-09-21T14:45:28.960230Z",
"iopub.status.idle": "2025-09-21T14:45:29.005570Z",
"shell.execute_reply": "2025-09-21T14:45:29.005072Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/runner/work/seagliderOG1/seagliderOG1/seagliderOG1/writers.py:47: UserWarning: Times can't be serialized faithfully to int64 with requested units 'seconds since 1970-01-01T00:00:00+00:00'. Resolution of 'nanoseconds' needed. Serializing times to floating point instead. Set encoding['dtype'] to integer dtype to serialize to int64. Set encoding['dtype'] to floating point dtype to silence this warning.\n",
" ds.to_netcdf(output_file, format=\"NETCDF4\")\n"
]
}
],
"source": [
"# Write the file\n",
"# This writer catches errors in data types (DType errors) when using xr.to_netcdf()\n",
"# The solution is to convert them to strings, which may be undesired behaviour\n",
"output_file = os.path.join(data_path, 'demo_test.nc')\n",
"if os.path.exists(output_file):\n",
" os.remove(output_file)\n",
"\n",
"writers.save_dataset(ds_OG1, output_file);"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "57da6f9e",
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-21T14:45:29.007083Z",
"iopub.status.busy": "2025-09-21T14:45:29.006917Z",
"iopub.status.idle": "2025-09-21T14:45:29.358867Z",
"shell.execute_reply": "2025-09-21T14:45:29.358322Z"
}
},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Load the data saved\n",
"ds1 = xr.open_dataset(output_file)\n",
"\n",
"# Generate a simple plot\n",
"#plotters.show_contents(ds_all,'attrs')\n",
"plotters.plot_depth_colored(ds1, color_by='PROFILE_NUMBER')\n"
]
},
{
"cell_type": "markdown",
"id": "7eaed738",
"metadata": {},
"source": [
"## Run multiple missions"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "12f47f0f",
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-21T14:45:29.360600Z",
"iopub.status.busy": "2025-09-21T14:45:29.360443Z",
"iopub.status.idle": "2025-09-21T14:45:29.363007Z",
"shell.execute_reply": "2025-09-21T14:45:29.362613Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'contributor_name': 'Eleanor Frajka-Williams', 'contributor_email': 'eleanorfrajka@gmail.com', 'contributor_role': 'Data scientist', 'contributor_role_vocabulary': 'http://vocab.nerc.ac.uk/search_nvs/W08', 'contributing_institutions': 'University of Hamburg - Institute of Oceanography', 'contributing_institutions_vocabulary': 'https://edmo.seadatanet.org/report/1156', 'contributing_institutions_role': 'Data scientist', 'contributing_institutions_role_vocabulary': 'http://vocab.nerc.ac.uk/search_nvs/W08'}\n"
]
}
],
"source": [
"# Add these to existing attributes - update to your details\n",
"contrib_to_append = vocabularies.contrib_to_append\n",
"print(contrib_to_append)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "f6127a51",
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-21T14:45:29.364457Z",
"iopub.status.busy": "2025-09-21T14:45:29.364309Z",
"iopub.status.idle": "2025-09-21T14:45:30.280814Z",
"shell.execute_reply": "2025-09-21T14:45:30.280345Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Loading datasets: 0%| | 0/1 [00:00, ?file/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Loading datasets: 100%|██████████| 1/1 [00:00<00:00, 34.30file/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 0%| | 0/1 [00:00, ?dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_depth' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_aa4330_Temp' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'longitude_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'speed_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'vert_speed_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_aa4330_TCPhase' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_sbect_tempFreq' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_sbect_condFreq' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_rec' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'north_displacement_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'horz_speed_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No conversion information found for micromoles/kg to micromoles/kg\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_aa4330_O2' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_aa4330_AirSat' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'latitude_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'aanderaa4330_instrument_dissolved_oxygen' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_elaps_t_0000' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'buoyancy' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'east_displacement_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'sound_velocity' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'density_insitu' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'density' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_aa4330_CalPhase' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_GC_phase' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No conversion information found for cm s-1 to cm s-1\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'conservative_temperature' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'glide_angle_gsm' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'eng_elaps_t' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'absolute_salinity' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No conversion information found for micromoles/kg to micromoles/kg\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'ctd_pressure' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Variable 'depth' not in OG1 vocabulary.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 100%|██████████| 1/1 [00:00<00:00, 1.48dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing datasets: 100%|██████████| 1/1 [00:00<00:00, 1.48dataset/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"File /home/runner/work/seagliderOG1/seagliderOG1/data/sg033_20100903T182416_delayed.nc already exists. Exiting the process.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"File /home/runner/work/seagliderOG1/seagliderOG1/data/sg033_20100903T182416_delayed.nc already exists. Exiting the process.\n"
]
}
],
"source": [
"# Specify a list of servers or local directories\n",
"input_locations = [\n",
" # Either Iceland, Faroes or RAPID/MOCHA\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/005/20090829/\", # done\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/005/20080606/\", # done\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/005/20081106/\", # done\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/012/20070831/\", # done\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/014/20080214/\", # done\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/014/20080222/\", # done\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/016/20061112/\", # done\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/016/20090605/\", # done\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/016/20071113/\", # done\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/016/20080607/\", # done\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/033/20100518/\", # done\n",
" \"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/033/20100903/\", # done\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/101/20081108/\", # done\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/101/20061112/\", # done\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/101/20070609/\", # done\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/102/20061112/\", # done\n",
" # Labrador Sea\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/015/20040924/\",\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/014/20040924/\",\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/008/20031002/\",\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/004/20031002/\",\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/016/20050406/\",\n",
" # RAPID/MOCHA\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/033/20100729/\",\n",
" #\"https://www.ncei.noaa.gov/data/oceans/glider/seaglider/uw/034/20110128/\",\n",
"]\n",
"\n",
"for input_loc in input_locations:\n",
" # Example usage\n",
" ds_all = convertOG1.process_and_save_data(input_loc, output_dir=data_path, save=True, run_quietly=True)"
]
}
],
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