Ocean Science Minor Cruise data¶

    Jody Klymak  jklymak@uvic.ca

This directory contains data from the various Ocean Science Minor cruises since they started in 2007. Every Eos 314 cruise CTD data is represented here. From 2007-2019, most Eos/Bio 311 and Eos 312 CTD data sets are here as well.

The bulk of the data management has been devoted to CTD data - there is sporadic data from other sources.

20XXcruise directories¶

These contain the data collected for that year. Inside these directories, there is typically one or more cruises. Eg 2007cruises/200701, 2007cruises/200705, 2007cruises/200707 each represent a cruise, with the cruise name being yyyymm, y being the year, and m being the month of the year.

In 2022 this naming convention changed a bit: 2022cruises/20220924 with the last two digits being the day of the month.

Inside these directories are a ctd directory that typically looks like:

In [1]:
!ls 2007cruises/200705/ctd/

20070523_s1.csv     20070523_s2.csv     20070524_S3.csv     20070524_s8.csv
20070523_s1.hex     20070523_s2.hex     20070524_S3.hex     20070524_s8.hex
20070523_s1.mat     20070523_s2.mat     20070524_S3.mat     20070524_s8.mat
20070523_s12.csv    20070523_s2_1m.csv  20070524_S3_1m.csv  20070524_s8_1m.csv
20070523_s12.hex    20070523_s4.csv     20070524_s5.csv     CtdGrid.mat
20070523_s12.mat    20070523_s4.hex     20070524_s5.hex     CtdGrid.nc
20070523_s12_1m.csv 20070523_s4.mat     20070524_s5.mat
20070523_s1_1m.csv  20070523_s4_1m.csv  20070524_s5_1m.csv

So 20070523_s12.mat was collected on 2007-05-23 at station id S12.

This data is "raw", in that it has not been binned verticaly. 1-m vertical bins are very useful, and are provided in CtdGrid.mat and CtdGrid.nc. The mat file is a Matlab file, and the .nc file is a netCDF file.

Note the cruise directories are not reprocessed, and the data in the files may vary from cruise to cruise as the processing changed. If you are going to use gridded data, it is recommended you do so as described next.

CtdCruiseGrids directory¶

This simply contains all the 1-m cruise grids, named by the cruise name. These grid files should all have the same variables, and be in the same units (if they are not let Jody know: jklymak@uvic.ca)

In [22]:
!ls CtdCruiseGrids/

200701.mat     201001.nc      201207.mat     201610.nc      202009.mat
200701.nc      201005.mat     201207.nc      201709.mat     202009.nc
200705.mat     201005.nc      201301a.mat    201709.nc      202110.mat
200705.nc      201006.mat     201301a.nc     201709b.mat    202110.nc
200707.mat     201006.nc      201301b.mat    201709b.nc     20220924.mat
200707.nc      201007.mat     201301b.nc     201710.mat     20220924.nc
200801.mat     201007.nc      201406.mat     201710.nc      20220928.mat
200801.nc      201101.mat     201406.nc      201809a.mat    20220928.nc
200805.mat     201101.nc      201407.mat     201809a.nc     20230923.mat
200805.nc      201105.mat     201407.nc      201809b.mat    20230923.nc
200807.mat     201105.nc      201407b.mat    201809b.nc     20230927.mat
200807.nc      201106.mat     201407b.nc     201901.mat     20230927.nc
200807b.mat    201106.nc      201509a.mat    201901.nc      20240918.mat
200807b.nc     201107.mat     201509a.nc     201909a.mat    20240918.nc
200901.mat     201107.nc      201510.mat     201909a.nc     20240925.mat
200901.nc      201201.mat     201510.nc      201909b.mat    20240925.nc
200905.mat     201201.nc      201609a.mat    201909b.nc     AllCruises.mat
200905.nc      201205.mat     201609a.nc     202001.mat     AllCruises.nc
200907.mat     201205.nc      201609b.mat    202001.nc
200907.nc      201206.mat     201609b.nc     202003.mat
201001.mat     201206.nc      201610.mat     202003.nc
In [4]:
import xarray as xr
import numpy as np

with xr.open_dataset('CtdCruiseGrids/201709.nc') as ds:
    display(ds)

<xarray.Dataset>
Dimensions:  (depths: 324, time: 16)
Coordinates:
  * depths   (depths) float64 0.5 1.5 2.5 3.5 4.5 ... 320.5 321.5 322.5 323.5
  * time     (time) datetime64[ns] 2017-09-27T19:44:00 ... 2017-09-27T16:47:59
Data variables: (12/13)
    temp     (depths, time) float64 ...
    cond     (depths, time) float64 ...
    sal      (depths, time) float64 ...
    pden     (depths, time) float64 ...
    O2       (depths, time) float64 ...
    O2sat    (depths, time) float64 ...
    ...       ...
    Par      (depths, time) float64 ...
    lat      (time) float64 ...
    lon      (time) float64 ...
    id       (time) object ...
    alongx   (time) float64 ...
    serial   (time) object ...
xarray.Dataset
    • depths: 324
    • time: 16
    • depths
      (depths)
      float64
      0.5 1.5 2.5 ... 321.5 322.5 323.5
      array([  0.5,   1.5,   2.5, ..., 321.5, 322.5, 323.5])
    • time
      (time)
      datetime64[ns]
      2017-09-27T19:44:00 ... 2017-09-...
      array(['2017-09-27T19:44:00.000000000', '2017-09-26T09:48:00.000000000',
       '2017-09-26T12:20:00.000000000', '2017-09-28T17:10:00.000000000',
       '2017-10-01T21:06:00.000000000', '2017-10-01T22:50:00.000000000',
       '2017-09-30T16:10:00.000000000', '2017-09-28T20:32:59.000000000',
       '2017-09-30T17:37:00.000000000', '2017-10-01T17:28:00.000000000',
       '2017-10-02T19:55:00.000000000', '2017-10-02T17:42:00.000000000',
       '2017-10-01T16:02:59.000000000', '2017-09-30T22:47:59.000000000',
       '2017-09-30T20:45:00.000000000', '2017-09-27T16:47:59.000000000'],
      dtype='datetime64[ns]')
    • temp
      (depths, time)
      float64
      ...
      units :
      deg C
      longname :
      Temperature [deg C]
      [5184 values with dtype=float64]
    • cond
      (depths, time)
      float64
      ...
      units :
      S/m
      longname :
      conductivity [S/m]
      [5184 values with dtype=float64]
    • sal
      (depths, time)
      float64
      ...
      units :
      psu
      longname :
      practical salinity
      [5184 values with dtype=float64]
    • pden
      (depths, time)
      float64
      ...
      units :
      kg m-3
      longname :
      potential density [kg/m^3]
      [5184 values with dtype=float64]
    • O2
      (depths, time)
      float64
      ...
      units :
      umol/kg
      longname :
      O_2 concentration [umol/kg]
      [5184 values with dtype=float64]
    • O2sat
      (depths, time)
      float64
      ...
      [5184 values with dtype=float64]
    • Flu
      (depths, time)
      float64
      ...
      units :
      ?
      longname :
      flurometry
      [5184 values with dtype=float64]
    • Par
      (depths, time)
      float64
      ...
      units :
      ?
      longname :
      Photo-active Radiometery
      [5184 values with dtype=float64]
    • lat
      (time)
      float64
      ...
      units :
      deg N
      longname :
      latitude
      [16 values with dtype=float64]
    • lon
      (time)
      float64
      ...
      units :
      deg E
      longname :
      longitude
      [16 values with dtype=float64]
    • id
      (time)
      object
      ...
      description :
      Station name
      [16 values with dtype=object]
    • alongx
      (time)
      float64
      ...
      units :
      km
      longname :
      distance along thalweg [km]
      [16 values with dtype=float64]
    • serial
      (time)
      object
      ...
      description :
      serial number of CTD
      [16 values with dtype=object]
    • depths
      PandasIndex
      PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
       ...
       314.5, 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5],
      dtype='float64', name='depths', length=324))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2017-09-27 19:44:00', '2017-09-26 09:48:00',
               '2017-09-26 12:20:00', '2017-09-28 17:10:00',
               '2017-10-01 21:06:00', '2017-10-01 22:50:00',
               '2017-09-30 16:10:00', '2017-09-28 20:32:59',
               '2017-09-30 17:37:00', '2017-10-01 17:28:00',
               '2017-10-02 19:55:00', '2017-10-02 17:42:00',
               '2017-10-01 16:02:59', '2017-09-30 22:47:59',
               '2017-09-30 20:45:00', '2017-09-27 16:47:59'],
              dtype='datetime64[ns]', name='time', freq=None))

The above shows what is inside a typical cruise grid; this uses python, but Matlab structures will be very similar. The grid has 324 depths in 1-m vertical bins, and this cruise had 16 stations, and hence 16 times. Variables like temp, sal O2 etc are then gridded onto the grid, and NaN are placed where there is no data.

In 2022 the CTD manufacturer was changed from Seabird CTD to a pair of RBR CTDs , so the files are slightly different:

In [5]:
with xr.open_dataset('CtdCruiseGrids/20220924.nc') as ds:
    display(ds)

<xarray.Dataset>
Dimensions:  (depths: 324, time: 12)
Coordinates:
  * depths   (depths) float64 0.5 1.5 2.5 3.5 4.5 ... 320.5 321.5 322.5 323.5
    cast     (time) int64 ...
  * time     (time) datetime64[ns] 2022-09-24T23:46:00.875000064 ... 2022-09-...
Data variables: (12/14)
    pres     (depths, time) float64 ...
    temp     (depths, time) float64 ...
    cond     (depths, time) float64 ...
    Flu      (depths, time) float64 ...
    O2sat    (depths, time) float64 ...
    sal      (depths, time) float64 ...
    ...       ...
    id       (time) object ...
    lon      (time) float64 ...
    lat      (time) float64 ...
    alongx   (time) float64 ...
    acrossx  (time) float64 ...
    O2       (depths, time) float64 ...
xarray.Dataset
    • depths: 324
    • time: 12
    • depths
      (depths)
      float64
      0.5 1.5 2.5 ... 321.5 322.5 323.5
      array([  0.5,   1.5,   2.5, ..., 321.5, 322.5, 323.5])
    • cast
      (time)
      int64
      ...
      [12 values with dtype=int64]
    • time
      (time)
      datetime64[ns]
      2022-09-24T23:46:00.875000064 .....
      array(['2022-09-24T23:46:00.875000064', '2022-09-24T23:27:24.750000128',
       '2022-09-24T23:10:36.500000000', '2022-09-24T22:56:51.500000000',
       '2022-09-24T22:41:17.500000000', '2022-09-24T22:29:23.375000064',
       '2022-09-24T22:10:36.249999872', '2022-09-24T18:38:07.875000064',
       '2022-09-24T18:20:08.750000128', '2022-09-24T17:59:46.624999936',
       '2022-09-24T17:35:48.124999936', '2022-09-24T17:06:33.875000064'],
      dtype='datetime64[ns]')
    • pres
      (depths, time)
      float64
      ...
      units :
      dbar
      derived :
      True
      long_name :
      Sea pressure
      [3888 values with dtype=float64]
    • temp
      (depths, time)
      float64
      ...
      units :
      °C
      derived :
      False
      long_name :
      Temperature
      [3888 values with dtype=float64]
    • cond
      (depths, time)
      float64
      ...
      units :
      mS/cm
      derived :
      False
      long_name :
      Conductivity
      [3888 values with dtype=float64]
    • Flu
      (depths, time)
      float64
      ...
      units :
      µg/l
      derived :
      False
      long_name :
      Chlorophyll a
      [3888 values with dtype=float64]
    • O2sat
      (depths, time)
      float64
      ...
      units :
      %
      derived :
      False
      long_name :
      Dissolved Oâ‚‚ saturation
      [3888 values with dtype=float64]
    • sal
      (depths, time)
      float64
      ...
      units :
      PSU
      derived :
      True
      long_name :
      Salinity
      [3888 values with dtype=float64]
    • Par
      (depths, time)
      float64
      ...
      units :
      µMol/m²/s
      derived :
      False
      long_name :
      PAR
      [3888 values with dtype=float64]
    • pden
      (depths, time)
      float64
      ...
      [3888 values with dtype=float64]
    • id
      (time)
      object
      ...
      [12 values with dtype=object]
    • lon
      (time)
      float64
      ...
      [12 values with dtype=float64]
    • lat
      (time)
      float64
      ...
      [12 values with dtype=float64]
    • alongx
      (time)
      float64
      ...
      units :
      dist from S4 [km]
      [12 values with dtype=float64]
    • acrossx
      (time)
      float64
      ...
      units :
      dist Thalweg [km]
      [12 values with dtype=float64]
    • O2
      (depths, time)
      float64
      ...
      units :
      umol/kg
      longname :
      O_2 concentration [umol/kg]
      [3888 values with dtype=float64]
    • depths
      PandasIndex
      PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
       ...
       314.5, 315.5, 316.5, 317.5, 318.5, 319.5, 320.5, 321.5, 322.5, 323.5],
      dtype='float64', name='depths', length=324))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2022-09-24 23:46:00.875000064',
               '2022-09-24 23:27:24.750000128',
                  '2022-09-24 23:10:36.500000',
                  '2022-09-24 22:56:51.500000',
                  '2022-09-24 22:41:17.500000',
               '2022-09-24 22:29:23.375000064',
               '2022-09-24 22:10:36.249999872',
               '2022-09-24 18:38:07.875000064',
               '2022-09-24 18:20:08.750000128',
               '2022-09-24 17:59:46.624999936',
               '2022-09-24 17:35:48.124999936',
               '2022-09-24 17:06:33.875000064'],
              dtype='datetime64[ns]', name='time', freq=None))

CtdStationGrids¶

Similarly, it is often useful to look at time series of stations. These are stored in CtdStationGrids/

In [20]:
!ls CtdStationGrids/

A1.mat    H1.mat    J35.mat   J6.mat    S12.mat   S25.mat   S45.mat   S8.mat
A1.nc     H1.nc     J35.nc    J6.nc     S12.nc    S25.nc    S45.nc    S8.nc
A15.mat   H2.mat    J4.mat    PB2.mat   S1225.mat S3.mat    S475.mat  S9.mat
A15.nc    H2.nc     J4.nc     PB2.nc    S1225.nc  S3.nc     S475.nc   S9.nc
A2.mat    H3.mat    J45.mat   PB4.mat   S125.mat  S35.mat   S5.mat    test.mat
A2.nc     H3.nc     J45.nc    PB4.nc    S125.nc   S35.nc    S5.nc     test.nc
A3.mat    J1.mat    J48.mat   S1.mat    S15.mat   S4.mat    S55.mat
A3.nc     J1.nc     J48.nc    S1.nc     S15.nc    S4.nc     S55.nc
A4.mat    J2.mat    J5.mat    S10.mat   S16.mat   S425.mat  S6.mat
A4.nc     J2.nc     J5.nc     S10.nc    S16.nc    S425.nc   S6.nc
A5.mat    J3.mat    J55.mat   S11.mat   S2.mat    S425W.mat S7.mat
A5.nc     J3.nc     J55.nc    S11.nc    S2.nc     S425W.nc  S7.nc

All the data¶

It is also useful to just have all the data in a single file. This can be found in CtdCruiseGrids/AllCruises.nc and CtdCruiseGrids/AllCruises.mat.

In [6]:
with xr.open_dataset('CtdCruiseGrids/AllCruises.nc') as ds:
    display(ds)

<xarray.Dataset>
Dimensions:      (depths: 335, time: 486)
Coordinates:
  * depths       (depths) float64 0.5 1.5 2.5 3.5 ... 331.5 332.5 333.5 334.5
  * time         (time) datetime64[ns] 2007-01-22T11:53:49 ... 2024-09-25T22:...
    cast         (time) int64 ...
Data variables: (12/18)
    temp         (depths, time) float64 ...
    cond         (depths, time) float64 ...
    sal          (depths, time) float64 ...
    pden         (depths, time) float64 ...
    O2           (depths, time) float64 ...
    O2sat        (depths, time) float64 ...
    ...           ...
    cruise       (time) object ...
    alongx       (time) float64 ...
    cond0        (depths, time) float64 ...
    pres         (depths, time) float64 ...
    acrossx      (time) float64 ...
    water_depth  (time) float64 ...
xarray.Dataset
    • depths: 335
    • time: 486
    • depths
      (depths)
      float64
      0.5 1.5 2.5 ... 332.5 333.5 334.5
      array([  0.5,   1.5,   2.5, ..., 332.5, 333.5, 334.5])
    • time
      (time)
      datetime64[ns]
      2007-01-22T11:53:49 ... 2024-09-...
      array(['2007-01-22T11:53:49.000000000', '2007-01-22T12:29:32.000000000',
       '2007-01-22T14:13:40.000000000', ..., '2024-09-25T22:18:18.875000064',
       '2024-09-25T22:35:58.124999936', '2024-09-25T22:56:09.500000000'],
      dtype='datetime64[ns]')
    • cast
      (time)
      int64
      ...
      [486 values with dtype=int64]
    • temp
      (depths, time)
      float64
      ...
      units :
      deg C
      longname :
      Temperature [deg C]
      [162810 values with dtype=float64]
    • cond
      (depths, time)
      float64
      ...
      units :
      S/m
      longname :
      conductivity [S/m]
      [162810 values with dtype=float64]
    • sal
      (depths, time)
      float64
      ...
      units :
      psu
      longname :
      practical salinity
      [162810 values with dtype=float64]
    • pden
      (depths, time)
      float64
      ...
      units :
      kg m-3
      longname :
      potential density [kg/m^3]
      [162810 values with dtype=float64]
    • O2
      (depths, time)
      float64
      ...
      units :
      umol/kg
      longname :
      O_2 concentration [umol/kg]
      [162810 values with dtype=float64]
    • O2sat
      (depths, time)
      float64
      ...
      units :
      1
      longname :
      O_2 % saturation
      [162810 values with dtype=float64]
    • Flu
      (depths, time)
      float64
      ...
      units :
      ?
      longname :
      flurometry
      [162810 values with dtype=float64]
    • Par
      (depths, time)
      float64
      ...
      units :
      ?
      longname :
      Photo-active Radiometery
      [162810 values with dtype=float64]
    • lat
      (time)
      float64
      ...
      units :
      deg N
      longname :
      latitude
      [486 values with dtype=float64]
    • lon
      (time)
      float64
      ...
      units :
      deg E
      longname :
      longitude
      [486 values with dtype=float64]
    • id
      (time)
      object
      ...
      description :
      Station name
      [486 values with dtype=object]
    • serial
      (time)
      object
      ...
      description :
      serial number of CTD
      [486 values with dtype=object]
    • cruise
      (time)
      object
      ...
      [486 values with dtype=object]
    • alongx
      (time)
      float64
      ...
      units :
      km
      longname :
      distance along thalweg [km]
      [486 values with dtype=float64]
    • cond0
      (depths, time)
      float64
      ...
      units :
      S/m
      [162810 values with dtype=float64]
    • pres
      (depths, time)
      float64
      ...
      units :
      dbar
      [162810 values with dtype=float64]
    • acrossx
      (time)
      float64
      ...
      units :
      dist from S4 [km]
      [486 values with dtype=float64]
    • water_depth
      (time)
      float64
      ...
      [486 values with dtype=float64]
    • depths
      PandasIndex
      PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
       ...
       325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5, 333.5, 334.5],
      dtype='float64', name='depths', length=335))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex([          '2007-01-22 11:53:49',
                         '2007-01-22 12:29:32',
                         '2007-01-22 14:13:40',
                         '2007-01-25 10:24:49',
                         '2007-01-24 14:19:29',
                         '2007-01-23 14:55:44',
                         '2007-01-24 12:17:30',
                         '2007-01-24 10:40:55',
                         '2007-05-23 09:04:11',
                         '2007-05-23 10:04:39',
               ...
               '2024-09-25 18:31:12.624999936',
               '2024-09-25 18:51:27.124999936',
               '2024-09-25 20:54:15.750000128',
               '2024-09-25 21:16:46.624999936',
               '2024-09-25 21:32:45.750000128',
                         '2024-09-25 21:52:49',
               '2024-09-25 22:06:38.124999936',
               '2024-09-25 22:18:18.875000064',
               '2024-09-25 22:35:58.124999936',
                  '2024-09-25 22:56:09.500000'],
              dtype='datetime64[ns]', name='time', length=486, freq=None))

To help narrow down the cruise that may be interested in, this data set has a "cruise" variable that can be searched on. In python, for instance you could get all the data from the 20220924 cruise using where:

In [7]:
with xr.open_dataset('CtdCruiseGrids/AllCruises.nc') as ds:
    ds = ds.where(ds.cruise == '20220924', drop=True)
    display(ds)

<xarray.Dataset>
Dimensions:      (depths: 335, time: 12)
Coordinates:
  * depths       (depths) float64 0.5 1.5 2.5 3.5 ... 331.5 332.5 333.5 334.5
  * time         (time) datetime64[ns] 2022-09-24T23:46:00.875000064 ... 2022...
    cast         (time) int64 11 10 9 8 7 6 5 4 3 2 1 0
Data variables: (12/18)
    temp         (depths, time) float64 15.17 15.0 14.64 14.35 ... nan nan nan
    cond         (depths, time) float64 35.36 35.26 34.9 34.66 ... nan nan nan
    sal          (depths, time) float64 28.09 28.12 28.07 28.05 ... nan nan nan
    pden         (depths, time) float64 1.021e+03 1.021e+03 ... nan nan
    O2           (depths, time) float64 325.1 285.2 302.5 285.7 ... nan nan nan
    O2sat        (depths, time) float64 123.1 107.7 113.3 106.3 ... nan nan nan
    ...           ...
    cruise       (time) object '20220924' '20220924' ... '20220924' '20220924'
    alongx       (time) float64 -0.438 1.842 3.474 5.485 ... 21.73 26.31 29.02
    cond0        (depths, time) float64 nan nan nan nan nan ... nan nan nan nan
    pres         (depths, time) float64 0.9396 0.8128 0.8714 ... nan nan nan
    acrossx      (time) float64 0.1403 0.294 0.5684 ... 0.05532 0.09388 0.08632
    water_depth  (time) float64 nan nan nan nan nan nan nan nan nan nan nan nan
xarray.Dataset
    • depths: 335
    • time: 12
    • depths
      (depths)
      float64
      0.5 1.5 2.5 ... 332.5 333.5 334.5
      array([  0.5,   1.5,   2.5, ..., 332.5, 333.5, 334.5])
    • time
      (time)
      datetime64[ns]
      2022-09-24T23:46:00.875000064 .....
      array(['2022-09-24T23:46:00.875000064', '2022-09-24T23:27:24.750000128',
       '2022-09-24T23:10:36.500000000', '2022-09-24T22:56:51.500000000',
       '2022-09-24T22:41:17.500000000', '2022-09-24T22:29:23.375000064',
       '2022-09-24T22:10:36.249999872', '2022-09-24T18:38:07.875000064',
       '2022-09-24T18:20:08.750000128', '2022-09-24T17:59:46.624999936',
       '2022-09-24T17:35:48.124999936', '2022-09-24T17:06:33.875000064'],
      dtype='datetime64[ns]')
    • cast
      (time)
      int64
      11 10 9 8 7 6 5 4 3 2 1 0
      array([11, 10,  9,  8,  7,  6,  5,  4,  3,  2,  1,  0])
    • temp
      (depths, time)
      float64
      15.17 15.0 14.64 ... nan nan nan
      units :
      deg C
      longname :
      Temperature [deg C]
      array([[15.16766357, 15.00306447, 14.63601481, ..., 11.89400991,
        12.57474518,         nan],
       [15.00248477, 14.77207031, 14.66712007, ..., 11.92218475,
        12.53092839, 11.95966034],
       [14.95297648, 14.30854034, 14.64167223, ..., 11.79826864,
        12.4707498 , 11.89019237],
       ...,
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan]])
    • cond
      (depths, time)
      float64
      35.36 35.26 34.9 ... nan nan nan
      units :
      S/m
      longname :
      conductivity [S/m]
      array([[35.36153875, 35.26207066, 34.90322469, ..., 33.92854277,
        33.87472439,         nan],
       [35.24832414, 34.96544971, 34.91781453, ..., 33.93243313,
        33.85390472, 33.68156033],
       [35.20129038, 34.61907268, 34.89440938, ..., 33.93773433,
        33.8276154 , 33.66903058],
       ...,
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan]])
    • sal
      (depths, time)
      float64
      28.09 28.12 28.07 ... nan nan nan
      units :
      psu
      longname :
      practical salinity
      array([[28.08951024, 28.12018389, 28.06532529, ..., 29.22796567,
        28.6514101 ,         nan],
       [28.10782704, 28.02265453, 28.05570094, ..., 29.20930939,
        28.66500502, 28.94103842],
       [28.10142225, 28.0472095 , 28.05283797, ..., 29.31147875,
        28.68581087, 28.98265491],
       ...,
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan]])
    • pden
      (depths, time)
      float64
      1.021e+03 1.021e+03 ... nan nan
      units :
      kg m-3
      longname :
      potential density [kg/m^3]
      array([[1020.61685495, 1020.67412308, 1020.70783594, ..., 1022.12964929,
        1021.55972628,           nan],
       [1020.66526184, 1020.64689412, 1020.69411325, ..., 1022.11017268,
        1021.57831978, 1021.8955596 ],
       [1020.67057775, 1020.76002673, 1020.69712242, ..., 1022.21145775,
        1021.60548424, 1021.94022065],
       ...,
       [          nan,           nan,           nan, ...,           nan,
                  nan,           nan],
       [          nan,           nan,           nan, ...,           nan,
                  nan,           nan],
       [          nan,           nan,           nan, ...,           nan,
                  nan,           nan]])
    • O2
      (depths, time)
      float64
      325.1 285.2 302.5 ... nan nan nan
      units :
      umol/kg
      longname :
      O_2 concentration [umol/kg]
      array([[325.1457821 , 285.19775613, 302.48871605, ..., 167.20663928,
        180.52144768,          nan],
       [331.29826436, 300.8778791 , 300.97921595, ..., 161.84961347,
        177.50714782, 156.30952715],
       [334.46792552, 310.37931786, 300.74832861, ..., 160.22043542,
        178.29011308, 154.37579825],
       ...,
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan],
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan],
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan]])
    • O2sat
      (depths, time)
      float64
      123.1 107.7 113.3 ... nan nan nan
      units :
      1
      longname :
      O_2 % saturation
      array([[123.11834499, 107.650216  , 113.28359782, ...,  59.54046377,
         64.98991394,          nan],
       [125.04028642, 112.96583557, 112.7836638 , ...,  57.6610672 ,
         63.85058124,  55.63866577],
       [126.10406291, 115.44408607, 112.63627062, ...,  56.96512713,
         64.05838192,  54.8826276 ],
       ...,
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan],
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan],
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan]])
    • Flu
      (depths, time)
      float64
      6.029 3.576 4.385 ... nan nan nan
      units :
      ?
      longname :
      flurometry
      array([[6.02927072, 3.57607863, 4.38474121, ..., 0.89861043, 1.35571289,
               nan],
       [7.28204346, 3.58753052, 3.56193615, ..., 0.89621887, 1.22281588,
        1.05584717],
       [4.88196208, 3.78649139, 4.48506245, ..., 0.95077951, 1.37735524,
        1.05559944],
       ...,
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan]])
    • Par
      (depths, time)
      float64
      63.15 76.88 64.96 ... nan nan nan
      units :
      ?
      longname :
      Photo-active Radiometery
      array([[ 63.15401786,  76.88357205,  64.95725911, ..., 729.26546224,
        663.62426758,          nan],
       [ 47.04767167,  57.3402832 ,  47.19986979, ..., 571.52202148,
        694.82883864, 147.40625   ],
       [ 29.51005859,  39.9329834 ,  32.6196803 , ..., 279.92845517,
        443.4553079 , 118.8510455 ],
       ...,
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan],
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan],
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan]])
    • lat
      (time)
      float64
      48.64 48.66 48.67 ... 48.73 48.71
      units :
      deg N
      longname :
      latitude
      array([48.63887   , 48.65949825, 48.674199  , 48.6923185 , 48.708085  ,
       48.71513994, 48.727564  , 48.74315937, 48.761889  , 48.7604618 ,
       48.73019182, 48.71460733])
    • lon
      (time)
      float64
      -123.5 -123.5 ... -123.3 -123.2
      units :
      deg E
      longname :
      longitude
      array([-123.501729  , -123.503242  , -123.5065275 , -123.498954  ,
       -123.4732195 , -123.45596983, -123.418052  , -123.39327863,
       -123.36515275, -123.3185792 , -123.27630559, -123.24825949])
    • id
      (time)
      object
      'S4' 'S4.25' 'S4.5' ... 'A2' 'H1'
      description :
      Station name
      array(['S4', 'S4.25', 'S4.5', 'S12', 'S12.5', 'S5', 'S5.5', 'S8', 'A5',
       'A3', 'A2', 'H1'], dtype=object)
    • serial
      (time)
      object
      '' '' '' '' '' '' '' '' '' '' '' ''
      description :
      serial number of CTD
      array(['', '', '', '', '', '', '', '', '', '', '', ''], dtype=object)
    • cruise
      (time)
      object
      '20220924' ... '20220924'
      array(['20220924', '20220924', '20220924', '20220924', '20220924',
       '20220924', '20220924', '20220924', '20220924', '20220924',
       '20220924', '20220924'], dtype=object)
    • alongx
      (time)
      float64
      -0.438 1.842 3.474 ... 26.31 29.02
      units :
      km
      longname :
      distance along thalweg [km]
      array([-0.4380438 ,  1.84218422,  3.47434743,  5.48454845,  8.05880588,
        9.5409541 , 12.63126313, 15.14551455, 18.02580258, 21.73417342,
       26.31263126, 29.01890189])
    • cond0
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      S/m
      array([[nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       ...,
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan]])
    • pres
      (depths, time)
      float64
      0.9396 0.8128 0.8714 ... nan nan
      units :
      dbar
      array([[0.93959672, 0.81278748, 0.87137852, ..., 0.75998918, 0.91217327,
               nan],
       [1.44953191, 1.49592819, 1.49074881, ..., 1.53608766, 1.51102653,
        1.50157366],
       [2.49781405, 2.52293837, 2.50519657, ..., 2.52088583, 2.48001923,
        2.53719958],
       ...,
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan]])
    • acrossx
      (time)
      float64
      0.1403 0.294 ... 0.09388 0.08632
      units :
      dist from S4 [km]
      array([0.14032179, 0.29403658, 0.56841646, 0.16513225, 0.14303654,
       0.07205183, 0.08495222, 0.03281367, 0.28155732, 0.0553167 ,
       0.09388133, 0.08631505])
    • water_depth
      (time)
      float64
      nan nan nan nan ... nan nan nan nan
      array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])
    • depths
      PandasIndex
      PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
       ...
       325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5, 333.5, 334.5],
      dtype='float64', name='depths', length=335))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['2022-09-24 23:46:00.875000064',
               '2022-09-24 23:27:24.750000128',
                  '2022-09-24 23:10:36.500000',
                  '2022-09-24 22:56:51.500000',
                  '2022-09-24 22:41:17.500000',
               '2022-09-24 22:29:23.375000064',
               '2022-09-24 22:10:36.249999872',
               '2022-09-24 18:38:07.875000064',
               '2022-09-24 18:20:08.750000128',
               '2022-09-24 17:59:46.624999936',
               '2022-09-24 17:35:48.124999936',
               '2022-09-24 17:06:33.875000064'],
              dtype='datetime64[ns]', name='time', freq=None))

Similarly if you wanted a single station of data, you can search on id:

In [8]:
with xr.open_dataset('CtdCruiseGrids/AllCruises.nc') as ds:
    ds = ds.where(ds.id == 'S4', drop=True)
    display(ds)

<xarray.Dataset>
Dimensions:      (depths: 335, time: 42)
Coordinates:
  * depths       (depths) float64 0.5 1.5 2.5 3.5 ... 331.5 332.5 333.5 334.5
  * time         (time) datetime64[ns] 2007-01-25T10:24:49 ... 2024-09-25T22:...
    cast         (time) int64 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 11 11 11 10 12 13
Data variables: (12/18)
    temp         (depths, time) float64 nan nan nan nan nan ... nan nan nan nan
    cond         (depths, time) float64 nan nan nan nan nan ... nan nan nan nan
    sal          (depths, time) float64 nan nan nan nan nan ... nan nan nan nan
    pden         (depths, time) float64 nan nan nan nan nan ... nan nan nan nan
    O2           (depths, time) float64 nan nan nan nan nan ... nan nan nan nan
    O2sat        (depths, time) float64 nan nan nan nan nan ... nan nan nan nan
    ...           ...
    cruise       (time) object 200701 200705 200707 ... 20240918 20240925
    alongx       (time) float64 nan nan nan nan ... -0.5341 -0.5341 -0.45 -0.474
    cond0        (depths, time) float64 nan nan nan nan nan ... nan nan nan nan
    pres         (depths, time) float64 nan nan nan nan nan ... nan nan nan nan
    acrossx      (time) float64 nan nan nan nan ... 0.1401 0.1401 0.1548 0.1408
    water_depth  (time) float64 nan nan nan nan nan ... nan nan nan 200.0 200.0
xarray.Dataset
    • depths: 335
    • time: 42
    • depths
      (depths)
      float64
      0.5 1.5 2.5 ... 332.5 333.5 334.5
      array([  0.5,   1.5,   2.5, ..., 332.5, 333.5, 334.5])
    • time
      (time)
      datetime64[ns]
      2007-01-25T10:24:49 ... 2024-09-...
      array(['2007-01-25T10:24:49.000000000', '2007-05-23T06:28:20.000000000',
       '2007-07-11T06:31:59.000000000', '2008-01-21T23:40:44.000000000',
       '2008-05-22T18:26:03.000000000', '2008-07-08T21:04:51.000000000',
       '2008-07-29T23:57:54.000000000', '2009-01-21T22:13:24.000000000',
       '2009-05-20T09:34:11.000000000', '2009-07-09T15:12:53.000000000',
       '2010-01-26T18:02:43.000000000', '2010-06-09T11:19:10.000000000',
       '2010-07-14T11:19:25.000000000', '2011-01-25T14:03:14.000000000',
       '2011-06-10T20:34:23.000000000', '2011-07-07T20:43:19.000000000',
       '2012-06-12T13:09:56.000000000', '2012-07-13T12:17:00.000000000',
       '2014-06-13T18:24:23.000000000', '2014-07-08T22:52:12.000000000',
       '2014-07-30T19:45:55.000000000', '2015-09-16T21:52:57.000000000',
       '2016-09-21T20:21:14.000000000', '2016-09-29T15:45:44.000000000',
       '2017-09-28T17:10:00.000000000', '2017-09-29T23:10:00.000000000',
       '2017-10-04T22:25:00.000000000', '2018-09-19T21:40:00.000000000',
       '2018-09-22T21:55:00.000000000', '2019-09-27T17:37:01.820648825',
       '2019-09-23T22:05:00.000000000', '2019-09-30T14:18:33.166666566',
       '2020-01-25T09:33:00.000000000', '2020-02-28T11:26:00.000000000',
       '2020-02-29T11:15:00.000000000', '2020-09-30T12:53:00.000000000',
       '2022-09-24T23:46:00.875000064', '2022-09-28T23:19:09.500000000',
       '2023-09-23T15:43:51.624999936', '2023-09-27T15:27:56.500000000',
       '2024-09-18T23:18:56.750000128', '2024-09-25T22:56:09.500000000'],
      dtype='datetime64[ns]')
    • cast
      (time)
      int64
      0 0 0 0 0 0 0 ... 11 11 11 10 12 13
      array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0, 11, 11, 11, 10, 12, 13])
    • temp
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      deg C
      longname :
      Temperature [deg C]
      array([[        nan,         nan,         nan, ..., 13.79092117,
        16.62237794, 14.17031726],
       [        nan,         nan,         nan, ..., 13.78977797,
        16.58763566, 14.02203613],
       [        nan,         nan,         nan, ..., 13.77928431,
        16.39043172, 13.50763957],
       ...,
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan]])
    • cond
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      S/m
      longname :
      conductivity [S/m]
      array([[        nan,         nan,         nan, ..., 36.20827375,
        38.0694004 , 36.29706422],
       [        nan,         nan,         nan, ..., 36.21124967,
        38.06185864, 36.19696106],
       [        nan,         nan,         nan, ..., 36.20872453,
        37.91244035, 35.89567711],
       ...,
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan]])
    • sal
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      psu
      longname :
      practical salinity
      array([[        nan,         nan,         nan, ..., 29.87822424,
        29.39005611, 29.66611955],
       [        nan,         nan,         nan, ..., 29.88146093,
        29.40881108, 29.68883415],
       [        nan,         nan,         nan, ..., 29.88694786,
        29.42473938, 29.8108446 ],
       ...,
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan]])
    • pden
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      kg m-3
      longname :
      potential density [kg/m^3]
      array([[          nan,           nan,           nan, ..., 1022.27456742,
        1021.29938438, 1022.03488212],
       [          nan,           nan,           nan, ..., 1022.27732316,
        1021.32158205, 1022.08202918],
       [          nan,           nan,           nan, ..., 1022.28366843,
        1021.37780601, 1022.2784949 ],
       ...,
       [          nan,           nan,           nan, ...,           nan,
                  nan,           nan],
       [          nan,           nan,           nan, ...,           nan,
                  nan,           nan],
       [          nan,           nan,           nan, ...,           nan,
                  nan,           nan]])
    • O2
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      umol/kg
      longname :
      O_2 concentration [umol/kg]
      array([[         nan,          nan,          nan, ..., 299.76098582,
        340.19413414, 294.9286985 ],
       [         nan,          nan,          nan, ..., 300.72204305,
        339.58730373, 297.58979108],
       [         nan,          nan,          nan, ..., 305.69521461,
        340.13073092, 299.50797142],
       ...,
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan],
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan],
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan]])
    • O2sat
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      1
      longname :
      O_2 % saturation
      array([[         nan,          nan,          nan, ..., 111.55791364,
        133.68836652, 110.48048311],
       [         nan,          nan,          nan, ..., 111.91515774,
        133.37322407, 111.15177979],
       [         nan,          nan,          nan, ..., 113.74500948,
        133.07765997, 110.76206752],
       ...,
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan],
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan],
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan]])
    • Flu
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      ?
      longname :
      flurometry
      array([[        nan,         nan,         nan, ...,  9.71827044,
         0.69759887,  4.09920547],
       [        nan,         nan,         nan, ..., 10.81130642,
         0.74037417,  9.35284912],
       [        nan,         nan,         nan, ..., 11.87920065,
         0.96227155, 19.66575114],
       ...,
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan]])
    • Par
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      ?
      longname :
      Photo-active Radiometery
      array([[         nan,          nan,          nan, ..., 282.08475167,
        505.43611   , 169.73482738],
       [         nan,          nan,          nan, ..., 172.18462457,
        338.15984123, 122.16910156],
       [         nan,          nan,          nan, ..., 110.87942325,
        223.67066592,  80.09151786],
       ...,
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan],
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan],
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan]])
    • lat
      (time)
      float64
      48.64 48.64 48.64 ... 48.64 48.64
      units :
      deg N
      longname :
      latitude
      array([48.6401    , 48.63796667, 48.63796667, 48.6408    , 48.63733333,
       48.64043333, 48.64205   , 48.64058333, 48.63836667, 48.63781667,
       48.63838333, 48.63753333, 48.63638333, 48.63785   , 48.63763333,
       48.63835   , 48.63798333, 48.63845   , 48.63778333, 48.64046667,
       48.63805   , 48.64133333, 48.6398    , 48.61856667, 48.61783333,
       48.64283333, 48.6418    , 48.64216667, 48.64158333, 48.6402799 ,
       48.64118333, 48.64256646, 48.64135   , 48.63643333, 48.63825   ,
       48.63853333, 48.63887   , 48.638195  , 48.638     , 48.638     ,
       48.638753  , 48.638565  ])
    • lon
      (time)
      float64
      -123.5 -123.5 ... -123.5 -123.5
      units :
      deg E
      longname :
      longitude
      array([-123.5009    , -123.50171667, -123.50171667, -123.50233333,
       -123.50181667, -123.5021    , -123.50108333, -123.50136667,
       -123.4997    , -123.50005   , -123.5       , -123.5006    ,
       -123.4996    , -123.50055   , -123.50076667, -123.50031667,
       -123.50061667, -123.49801667, -123.50065   , -123.50268333,
       -123.50023333, -123.49978333, -123.50027   , -123.49963333,
       -123.50116667, -123.49616667, -123.49946667, -123.50048333,
       -123.50006667, -123.50347636, -123.50315   , -123.50110509,
       -123.5028    , -123.503     , -123.50145   , -123.50173333,
       -123.501729  , -123.501707  , -123.50166667, -123.50166667,
       -123.501919  , -123.501714  ])
    • id
      (time)
      object
      'S4' 'S4' 'S4' ... 'S4' 'S4' 'S4'
      description :
      Station name
      array(['S4', 'S4', 'S4', 'S4', 'S4', 'S4', 'S4', 'S4', 'S4', 'S4', 'S4',
       'S4', 'S4', 'S4', 'S4', 'S4', 'S4', 'S4', 'S4', 'S4', 'S4', 'S4',
       'S4', 'S4', 'S4', 'S4', 'S4', 'S4', 'S4', 'S4', 'S4', 'S4', 'S4',
       'S4', 'S4', 'S4', 'S4', 'S4', 'S4', 'S4', 'S4', 'S4'], dtype=object)
    • serial
      (time)
      object
      seabird seabird seabird ...
      description :
      serial number of CTD
      array([array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('', dtype=object),
       array('', dtype=object), array('', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('', dtype=object), array('', dtype=object),
       array('', dtype=object), array('', dtype=object),
       array('', dtype=object), array('', dtype=object)], dtype=object)
    • cruise
      (time)
      object
      200701 200705 ... 20240918 20240925
      array([array('200701', dtype=object), array('200705', dtype=object),
       array('200707', dtype=object), array('200801', dtype=object),
       array('200805', dtype=object), array('200807', dtype=object),
       array('200807b', dtype=object), array('200901', dtype=object),
       array('200905', dtype=object), array('200907', dtype=object),
       array('201001', dtype=object), array('201006', dtype=object),
       array('201007', dtype=object), array('201101', dtype=object),
       array('201106', dtype=object), array('201107', dtype=object),
       array('201206', dtype=object), array('201207', dtype=object),
       array('201406', dtype=object), array('201407', dtype=object),
       array('201407b', dtype=object), array('201509a', dtype=object),
       array('201609a', dtype=object), array('201609b', dtype=object),
       array('201709', dtype=object), array('201709b', dtype=object),
       array('201710', dtype=object), array('201809a', dtype=object),
       array('201809b', dtype=object), array('201901', dtype=object),
       array('201909a', dtype=object), array('201909b', dtype=object),
       array('202001', dtype=object), array('202003', dtype=object),
       array('202003', dtype=object), array('202009', dtype=object),
       array('20220924', dtype=object), array('20220928', dtype=object),
       array('20230923', dtype=object), array('20230927', dtype=object),
       array('20240918', dtype=object), array('20240925', dtype=object)],
      dtype=object)
    • alongx
      (time)
      float64
      nan nan nan ... -0.45 -0.474
      units :
      km
      longname :
      distance along thalweg [km]
      array([            nan,             nan,             nan,             nan,
                   nan,             nan,             nan,             nan,
                   nan,             nan,             nan,             nan,
                   nan,             nan,             nan,             nan,
                   nan,             nan,  0.00000000e+00,  0.00000000e+00,
                   nan,  0.00000000e+00,  1.51155116e+04,  1.27572757e+04,
       -2.77227723e+00,  0.00000000e+00, -1.20012001e-01, -7.80078008e-02,
       -1.44014401e-01, -2.94029403e-01, -1.92019202e-01, -3.60036004e-02,
       -1.74017402e-01, -7.02070207e-01, -5.10051005e-01, -4.74047405e-01,
       -4.38043804e-01, -5.10051005e-01, -5.34053405e-01, -5.34053405e-01,
       -4.50045005e-01, -4.74047405e-01])
    • cond0
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      S/m
      array([[nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       ...,
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan]])
    • pres
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      dbar
      array([[       nan,        nan,        nan, ..., 0.49719261, 0.70173735,
        0.6793953 ],
       [       nan,        nan,        nan, ..., 1.52458885, 1.43756623,
        1.47363705],
       [       nan,        nan,        nan, ..., 2.53495856, 2.52466288,
        2.52598699],
       ...,
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan]])
    • acrossx
      (time)
      float64
      nan nan nan ... 0.1548 0.1408
      units :
      dist from S4 [km]
      array([       nan,        nan,        nan,        nan,        nan,
              nan,        nan,        nan,        nan,        nan,
              nan,        nan,        nan,        nan,        nan,
              nan,        nan,        nan,        nan,        nan,
              nan,        nan,        nan,        nan,        nan,
              nan,        nan,        nan,        nan, 0.26780293,
       0.24588682, 0.09892961,        nan,        nan,        nan,
              nan, 0.14032179, 0.14213488, 0.14014435, 0.14014435,
       0.15484538, 0.14078941])
    • water_depth
      (time)
      float64
      nan nan nan nan ... nan 200.0 200.0
      array([ nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,
        nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,
        nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,
        nan,  nan,  nan,  nan,  nan,  nan,  nan, 200., 200.])
    • depths
      PandasIndex
      PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
       ...
       325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5, 333.5, 334.5],
      dtype='float64', name='depths', length=335))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex([          '2007-01-25 10:24:49',
                         '2007-05-23 06:28:20',
                         '2007-07-11 06:31:59',
                         '2008-01-21 23:40:44',
                         '2008-05-22 18:26:03',
                         '2008-07-08 21:04:51',
                         '2008-07-29 23:57:54',
                         '2009-01-21 22:13:24',
                         '2009-05-20 09:34:11',
                         '2009-07-09 15:12:53',
                         '2010-01-26 18:02:43',
                         '2010-06-09 11:19:10',
                         '2010-07-14 11:19:25',
                         '2011-01-25 14:03:14',
                         '2011-06-10 20:34:23',
                         '2011-07-07 20:43:19',
                         '2012-06-12 13:09:56',
                         '2012-07-13 12:17:00',
                         '2014-06-13 18:24:23',
                         '2014-07-08 22:52:12',
                         '2014-07-30 19:45:55',
                         '2015-09-16 21:52:57',
                         '2016-09-21 20:21:14',
                         '2016-09-29 15:45:44',
                         '2017-09-28 17:10:00',
                         '2017-09-29 23:10:00',
                         '2017-10-04 22:25:00',
                         '2018-09-19 21:40:00',
                         '2018-09-22 21:55:00',
               '2019-09-27 17:37:01.820648825',
                         '2019-09-23 22:05:00',
               '2019-09-30 14:18:33.166666566',
                         '2020-01-25 09:33:00',
                         '2020-02-28 11:26:00',
                         '2020-02-29 11:15:00',
                         '2020-09-30 12:53:00',
               '2022-09-24 23:46:00.875000064',
                  '2022-09-28 23:19:09.500000',
               '2023-09-23 15:43:51.624999936',
                  '2023-09-27 15:27:56.500000',
               '2024-09-18 23:18:56.750000128',
                  '2024-09-25 22:56:09.500000'],
              dtype='datetime64[ns]', name='time', freq=None))

However, be aware that some of the station ids are written inconsistently. For instance "A1" and "H1" are almost the same, and sometimes are used interchangeably, or even "H1/A1". In that case it may be a good idea to use the alongx variable:

In [18]:
with xr.open_dataset('CtdCruiseGrids/AllCruises.nc') as ds:
    # get H1 along x:

    alongxH1 = ds.where(ds.id=='A1', drop=True).alongx[0]
    ds = ds.where((ds.alongx>alongxH1-1), drop=True)
    ds = ds.where((ds.alongx<alongxH1+1), drop=True)
    
    display(ds)

<xarray.Dataset>
Dimensions:      (depths: 335, time: 22)
Coordinates:
  * depths       (depths) float64 0.5 1.5 2.5 3.5 ... 331.5 332.5 333.5 334.5
  * time         (time) datetime64[ns] 2014-07-08T15:58:04 ... 2024-09-25T16:...
    cast         (time) int64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Data variables: (12/18)
    temp         (depths, time) float64 nan nan nan nan nan ... nan nan nan nan
    cond         (depths, time) float64 nan nan nan nan nan ... nan nan nan nan
    sal          (depths, time) float64 nan nan nan nan nan ... nan nan nan nan
    pden         (depths, time) float64 nan nan nan nan nan ... nan nan nan nan
    O2           (depths, time) float64 nan nan nan nan nan ... nan nan nan nan
    O2sat        (depths, time) float64 nan nan nan nan nan ... nan nan nan nan
    ...           ...
    cruise       (time) object 201407 201509a 201509a ... 20230927 20240925
    alongx       (time) float64 29.13 29.95 28.13 29.15 ... 29.16 29.16 29.44
    cond0        (depths, time) float64 nan nan nan nan nan ... nan nan nan nan
    pres         (depths, time) float64 nan nan nan nan nan ... nan nan nan nan
    acrossx      (time) float64 nan nan nan nan ... 0.3589 0.9371 0.9371 0.1794
    water_depth  (time) float64 nan nan nan nan nan ... nan nan nan nan 312.0
xarray.Dataset
    • depths: 335
    • time: 22
    • depths
      (depths)
      float64
      0.5 1.5 2.5 ... 332.5 333.5 334.5
      array([  0.5,   1.5,   2.5, ..., 332.5, 333.5, 334.5])
    • time
      (time)
      datetime64[ns]
      2014-07-08T15:58:04 ... 2024-09-...
      array(['2014-07-08T15:58:04.000000000', '2015-09-16T16:48:22.000000000',
       '2015-09-16T17:28:30.000000000', '2015-10-01T21:20:33.000000000',
       '2016-10-05T15:16:40.000000000', '2017-09-30T22:47:59.000000000',
       '2017-09-29T16:57:00.000000000', '2017-10-04T16:23:00.000000000',
       '2018-09-19T16:42:00.000000000', '2018-09-22T16:36:59.000000000',
       '2018-09-22T17:43:00.000000000', '2019-09-23T17:46:00.000000000',
       '2019-09-30T09:47:52.500000000', '2020-09-30T08:30:00.000000000',
       '2021-10-04T12:12:00.000000000', '2021-10-04T12:20:59.000000000',
       '2021-10-04T12:55:00.000000000', '2022-09-24T17:06:33.875000064',
       '2022-09-28T16:31:47.249999872', '2023-09-23T10:58:51.249999872',
       '2023-09-27T11:01:13.124999936', '2024-09-25T16:43:15.375000064'],
      dtype='datetime64[ns]')
    • cast
      (time)
      int64
      0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0
      array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
    • temp
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      deg C
      longname :
      Temperature [deg C]
      array([[        nan,         nan,         nan, ..., 12.58183011,
        11.10329482, 11.45620479],
       [        nan,         nan,         nan, ..., 12.5707016 ,
        11.10268707, 11.47004022],
       [        nan,         nan,         nan, ..., 12.54851205,
        11.05529785, 11.392772  ],
       ...,
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan]])
    • cond
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      S/m
      longname :
      conductivity [S/m]
      array([[        nan,         nan,         nan, ..., 34.34822204,
        34.82908765, 34.74626364],
       [        nan,         nan,         nan, ..., 34.33699385,
        34.82894707, 34.74424786],
       [        nan,         nan,         nan, ..., 34.33571625,
        34.82832122, 34.76780723],
       ...,
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan]])
    • sal
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      psu
      longname :
      practical salinity
      array([[        nan,         nan,         nan, ..., 29.0902749 ,
        30.73905866, 30.36617337],
       [        nan,         nan,         nan, ..., 29.08792973,
        30.73902483, 30.35242059],
       [        nan,         nan,         nan, ..., 29.10353688,
        30.77770567, 30.43841721],
       ...,
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan]])
    • pden
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      kg m-3
      longname :
      potential density [kg/m^3]
      array([[          nan,           nan,           nan, ..., 1021.89793392,
        1023.44176402, 1023.09033485],
       [          nan,           nan,           nan, ..., 1021.89820441,
        1023.44186341, 1023.07723972],
       [          nan,           nan,           nan, ..., 1021.91440456,
        1023.48015643, 1023.15764415],
       ...,
       [          nan,           nan,           nan, ...,           nan,
                  nan,           nan],
       [          nan,           nan,           nan, ...,           nan,
                  nan,           nan],
       [          nan,           nan,           nan, ...,           nan,
                  nan,           nan]])
    • O2
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      umol/kg
      longname :
      O_2 concentration [umol/kg]
      array([[         nan,          nan,          nan, ..., 190.40868407,
        149.58309082, 169.87526892],
       [         nan,          nan,          nan, ..., 189.10670398,
        148.62699508, 170.20160301],
       [         nan,          nan,          nan, ..., 187.59210523,
        147.20559176, 171.00833546],
       ...,
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan],
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan],
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan]])
    • O2sat
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      1
      longname :
      O_2 % saturation
      array([[        nan,         nan,         nan, ..., 68.74755721,
        52.86145917, 60.35184709],
       [        nan,         nan,         nan, ..., 68.2603302 ,
        52.5228775 , 60.48064507],
       [        nan,         nan,         nan, ..., 67.68826948,
        51.97965431, 60.69864789],
       ...,
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan],
       [        nan,         nan,         nan, ...,         nan,
                nan,         nan]])
    • Flu
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      ?
      longname :
      flurometry
      array([[       nan,        nan,        nan, ..., 0.98199463, 0.40109432,
        1.27639912],
       [       nan,        nan,        nan, ..., 1.35035706, 0.47210693,
        1.34337362],
       [       nan,        nan,        nan, ..., 1.55829293, 0.53277206,
        1.18490062],
       ...,
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan]])
    • Par
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      ?
      longname :
      Photo-active Radiometery
      array([[         nan,          nan,          nan, ..., 280.61796431,
        315.93000345,  75.75068132],
       [         nan,          nan,          nan, ..., 183.84200033,
        257.71958008,  53.06700304],
       [         nan,          nan,          nan, ..., 130.95539202,
        206.30181885,  39.47774012],
       ...,
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan],
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan],
       [         nan,          nan,          nan, ...,          nan,
                 nan,          nan]])
    • lat
      (time)
      float64
      48.72 48.72 48.72 ... 48.72 48.71
      units :
      deg N
      longname :
      latitude
      array([48.7174    , 48.71956667, 48.72053333, 48.71796667, 48.73798   ,
       48.74996667, 48.72238333, 48.72301667, 48.71671667, 48.71366667,
       48.7146    , 48.71935   , 48.72008333, 48.71938333, 48.71588333,
       48.71491667, 48.71771667, 48.71460733, 48.714989  , 48.72008333,
       48.72008333, 48.710647  ])
    • lon
      (time)
      float64
      -123.2 -123.2 ... -123.2 -123.2
      units :
      deg E
      longname :
      longitude
      array([-123.24886667, -123.23583333, -123.26053333, -123.23848333,
       -123.28733   , -123.13486667, -123.23525   , -123.24378333,
       -123.249     , -123.24853333, -123.24563333, -123.24196667,
       -123.23991667, -123.24276667, -123.24403333, -123.24156667,
       -123.23791667, -123.24825949, -123.242663  , -123.23991667,
       -123.23991667, -123.24409851])
    • id
      (time)
      object
      'A1' 'A1' 'A1.5' ... 'A1' 'A1' 'H1'
      description :
      Station name
      array(['A1', 'A1', 'A1.5', 'H1b', '', 'S6', 'A1', 'A1', 'A1', 'A1', 'A1b',
       'A1', 'A1', 'A1', 'H1/A1', 'H1', 'A1', 'H1', 'H1', 'A1', 'A1',
       'H1'], dtype=object)
    • serial
      (time)
      object
      seabird seabird seabird ...
      description :
      serial number of CTD
      array([array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('', dtype=object),
       array('', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('seabird', dtype=object),
       array('seabird', dtype=object), array('', dtype=object),
       array('', dtype=object), array('', dtype=object),
       array('', dtype=object), array('', dtype=object)], dtype=object)
    • cruise
      (time)
      object
      201407 201509a ... 20240925
      array([array('201407', dtype=object), array('201509a', dtype=object),
       array('201509a', dtype=object), array('201510', dtype=object),
       array('201610', dtype=object), array('201709', dtype=object),
       array('201709b', dtype=object), array('201710', dtype=object),
       array('201809a', dtype=object), array('201809b', dtype=object),
       array('201809b', dtype=object), array('201909a', dtype=object),
       array('201909b', dtype=object), array('202009', dtype=object),
       array('202110', dtype=object), array('202110', dtype=object),
       array('202110', dtype=object), array('20220924', dtype=object),
       array('20220928', dtype=object), array('20230923', dtype=object),
       array('20230927', dtype=object), array('20240925', dtype=object)],
      dtype=object)
    • alongx
      (time)
      float64
      29.13 29.95 28.13 ... 29.16 29.44
      units :
      km
      longname :
      distance along thalweg [km]
      array([29.1321846 , 29.94875912, 28.13481477, 29.15424691, 29.69467903,
       29.33093309, 29.29492949, 28.74287429, 28.83888389, 29.06090609,
       29.17491749, 29.08490849, 29.16291629, 29.03690369, 29.18691869,
       29.32493249, 29.32493249, 29.01890189, 29.32493249, 29.16291629,
       29.16291629, 29.44494449])
    • cond0
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      S/m
      array([[nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       ...,
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan]])
    • pres
      (depths, time)
      float64
      nan nan nan nan ... nan nan nan nan
      units :
      dbar
      array([[       nan,        nan,        nan, ..., 0.48370561, 0.57262179,
        0.34624601],
       [       nan,        nan,        nan, ..., 1.54683836, 1.57330289,
        1.49520731],
       [       nan,        nan,        nan, ..., 2.47211579, 2.54947686,
        2.51620612],
       ...,
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan]])
    • acrossx
      (time)
      float64
      nan nan nan ... 0.9371 0.1794
      units :
      dist from S4 [km]
      array([       nan,        nan,        nan,        nan,        nan,
              nan,        nan,        nan,        nan,        nan,
              nan, 0.783479  , 0.93709242,        nan,        nan,
              nan,        nan, 0.08631505, 0.35890793, 0.93709242,
       0.93709242, 0.17939627])
    • water_depth
      (time)
      float64
      nan nan nan nan ... nan nan 312.0
      array([ nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,
        nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan, 312.])
    • depths
      PandasIndex
      PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
       ...
       325.5, 326.5, 327.5, 328.5, 329.5, 330.5, 331.5, 332.5, 333.5, 334.5],
      dtype='float64', name='depths', length=335))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex([          '2014-07-08 15:58:04',
                         '2015-09-16 16:48:22',
                         '2015-09-16 17:28:30',
                         '2015-10-01 21:20:33',
                         '2016-10-05 15:16:40',
                         '2017-09-30 22:47:59',
                         '2017-09-29 16:57:00',
                         '2017-10-04 16:23:00',
                         '2018-09-19 16:42:00',
                         '2018-09-22 16:36:59',
                         '2018-09-22 17:43:00',
                         '2019-09-23 17:46:00',
                  '2019-09-30 09:47:52.500000',
                         '2020-09-30 08:30:00',
                         '2021-10-04 12:12:00',
                         '2021-10-04 12:20:59',
                         '2021-10-04 12:55:00',
               '2022-09-24 17:06:33.875000064',
               '2022-09-28 16:31:47.249999872',
               '2023-09-23 10:58:51.249999872',
               '2023-09-27 11:01:13.124999936',
               '2024-09-25 16:43:15.375000064'],
              dtype='datetime64[ns]', name='time', freq=None))
In [ ]: