Memuat Data

import os
import xarray as xr
import pandas as pd
import numpy as np
import gsw

import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.lines import Line2D
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.cm as cm

Data Model

dir_mod = '/data/local/marine-training/data/MATPEL_05/cawo_out'
paths_mod = []
for file in os.listdir(dir_mod):
    if file.endswith('nc') and 'cawo' in file:
        paths_mod.append(os.path.join(dir_mod, file))
paths_mod.sort()
ds_mod = xr.open_mfdataset(paths_mod)
ds_mod

<xarray.Dataset> Size: 306GB
Dimensions:  (date: 419, depth: 23, lat: 1201, lon: 2201)
Coordinates:
  * date     (date) datetime64[ns] 3kB 2024-02-01 2024-02-02 ... 2025-03-31
  * depth    (depth) float32 92B 0.0 -5.0 -10.0 ... -1.2e+03 -1.5e+03 -2e+03
  * lat      (lat) float32 5kB -15.0 -14.98 -14.95 -14.93 ... 14.95 14.98 15.0
  * lon      (lon) float32 9kB 90.0 90.03 90.05 90.07 ... 144.9 145.0 145.0
Data variables:
    sw_dens  (date, depth, lat, lon) float32 102GB dask.array<chunksize=(5, 4, 241, 441), meta=np.ndarray>
    sw_salt  (date, depth, lat, lon) float32 102GB dask.array<chunksize=(5, 4, 241, 441), meta=np.ndarray>
    sw_temp  (date, depth, lat, lon) float32 102GB dask.array<chunksize=(5, 4, 241, 441), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • date: 419
  • depth: 23
  • lat: 1201
  • lon: 2201
• Coordinates: (4)
  • date
    (date)
    datetime64[ns]
    2024-02-01 ... 2025-03-31

    array(['2024-02-01T00:00:00.000000000', '2024-02-02T00:00:00.000000000',
           '2024-02-03T00:00:00.000000000', ..., '2025-03-29T00:00:00.000000000',
           '2025-03-30T00:00:00.000000000', '2025-03-31T00:00:00.000000000'],
          shape=(419,), dtype='datetime64[ns]')

  • depth
    (depth)
    float32
    0.0 -5.0 -10.0 ... -1.5e+03 -2e+03

    array([    0.,    -5.,   -10.,   -15.,   -20.,   -30.,   -40.,   -50.,   -75.,
            -100.,  -125.,  -150.,  -200.,  -250.,  -300.,  -400.,  -500.,  -600.,
            -800., -1000., -1200., -1500., -2000.], dtype=float32)

  • lat
    (lat)
    float32
    -15.0 -14.98 -14.95 ... 14.98 15.0

    array([-15.   , -14.975, -14.95 , ...,  14.95 ,  14.975,  15.   ],
          shape=(1201,), dtype=float32)

  • lon
    (lon)
    float32
    90.0 90.03 90.05 ... 145.0 145.0

    array([ 90.   ,  90.025,  90.05 , ..., 144.95 , 144.975, 145.   ],
          shape=(2201,), dtype=float32)

• Data variables: (3)
  • sw_dens
    (date, depth, lat, lon)
    float32
    dask.array<chunksize=(5, 4, 241, 441), meta=np.ndarray>

    long_name :

    DENS

    standard_name :

    sea_water_density

    units :

    kg m-3

    var_desc :

    density, scalar, series

    Array Chunk Bytes 94.90 GiB 9.73 MiB Shape (419, 23, 1201, 2201) (6, 4, 241, 441) Dask graph 11850 chunks in 29 graph layers Data type float32 numpy.ndarray

  • sw_salt
    (date, depth, lat, lon)
    float32
    dask.array<chunksize=(5, 4, 241, 441), meta=np.ndarray>

    long_name :

    SALT

    standard_name :

    sea_water_salinity

    units :

    1

    var_desc :

    Ocean salinity, scalar, series

    Array Chunk Bytes 94.90 GiB 9.73 MiB Shape (419, 23, 1201, 2201) (6, 4, 241, 441) Dask graph 11850 chunks in 29 graph layers Data type float32 numpy.ndarray

  • sw_temp
    (date, depth, lat, lon)
    float32
    dask.array<chunksize=(5, 4, 241, 441), meta=np.ndarray>

    long_name :

    TEMP

    standard_name :

    sea_water_potential_temperature

    units :

    Celsius

    var_desc :

    Ocean Potential temperature, scalar, series

    Array Chunk Bytes 94.90 GiB 9.73 MiB Shape (419, 23, 1201, 2201) (6, 4, 241, 441) Dask graph 11850 chunks in 29 graph layers Data type float32 numpy.ndarray

• Indexes: (4)
  • date
    PandasIndex

    PandasIndex(DatetimeIndex(['2024-02-01', '2024-02-02', '2024-02-03', '2024-02-04',
                   '2024-02-05', '2024-02-06', '2024-02-07', '2024-02-08',
                   '2024-02-09', '2024-02-10',
                   ...
                   '2025-03-22', '2025-03-23', '2025-03-24', '2025-03-25',
                   '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29',
                   '2025-03-30', '2025-03-31'],
                  dtype='datetime64[ns]', name='date', length=419, freq=None))

  • depth
    PandasIndex

    PandasIndex(Index([    0.0,    -5.0,   -10.0,   -15.0,   -20.0,   -30.0,   -40.0,   -50.0,
             -75.0,  -100.0,  -125.0,  -150.0,  -200.0,  -250.0,  -300.0,  -400.0,
            -500.0,  -600.0,  -800.0, -1000.0, -1200.0, -1500.0, -2000.0],
          dtype='float32', name='depth'))

  • lat
    PandasIndex

    PandasIndex(Index([              -15.0, -14.975000381469727, -14.949999809265137,
           -14.925000190734863, -14.899999618530273,             -14.875,
           -14.850000381469727, -14.824999809265137, -14.800000190734863,
           -14.774999618530273,
           ...
            14.774999618530273,  14.800000190734863,  14.824999809265137,
            14.850000381469727,              14.875,  14.899999618530273,
            14.925000190734863,  14.949999809265137,  14.975000381469727,
                          15.0],
          dtype='float32', name='lat', length=1201))

  • lon
    PandasIndex

    PandasIndex(Index([              90.0,   90.0250015258789,  90.05000305175781,
            90.07499694824219,   90.0999984741211,             90.125,
             90.1500015258789,  90.17500305175781,  90.19999694824219,
             90.2249984741211,
           ...
           144.77499389648438,  144.8000030517578,  144.8249969482422,
           144.85000610351562,            144.875, 144.89999389648438,
            144.9250030517578,  144.9499969482422, 144.97500610351562,
                        145.0],
          dtype='float32', name='lon', length=2201))

• Attributes: (0)

Data Argo Float

path_argo_nc = '/data/local/marine-training/data/MATPEL_05/argo_data/nc_argo/GL_PR_PF_2902800.nc'
ds_argo = xr.open_dataset(path_argo_nc)
ds_argo

<xarray.Dataset> Size: 2MB
Dimensions:                   (TIME: 220, LATITUDE: 220, LONGITUDE: 220,
                               POSITION: 220, DEPTH: 103)
Coordinates:
  * TIME                      (TIME) datetime64[ns] 2kB 2021-01-10T05:49:54 ....
  * LATITUDE                  (LATITUDE) float32 880B 4.381 4.046 ... 4.243
  * LONGITUDE                 (LONGITUDE) float32 880B 147.0 146.8 ... 130.8
Dimensions without coordinates: POSITION, DEPTH
Data variables: (12/23)
    TIME_QC                   (TIME) float32 880B ...
    POSITION_QC               (POSITION) float32 880B ...
    DC_REFERENCE              (TIME) object 2kB ...
    DIRECTION                 (TIME) object 2kB ...
    VERTICAL_SAMPLING_SCHEME  (TIME) object 2kB ...
    PRES                      (TIME, DEPTH) float32 91kB ...
    ...                        ...
    PSAL                      (TIME, DEPTH) float64 181kB ...
    PSAL_QC                   (TIME, DEPTH) float32 91kB ...
    PSAL_ADJUSTED             (TIME, DEPTH) float64 181kB ...
    PSAL_ADJUSTED_QC          (TIME, DEPTH) float32 91kB ...
    PSAL_ADJUSTED_DM          (TIME, DEPTH) object 181kB ...
    PSAL_ADJUSTED_ERROR       (TIME, DEPTH) float64 181kB ...
Attributes: (12/49)
    data_type:                      OceanSITES vertical profile
    format_version:                 1.4
    platform_code:                  2902800
    institution:                    First Institute of Oceanography - Ministr...
    institution_edmo_code:          4640
    site_code:                       
    ...                             ...
    last_date_observation:          2025-04-05T01:30:25Z
    last_latitude_observation:      4.24300
    last_longitude_observation:     130.82900
    date_update:                    2025-04-15T09:50:18Z
    history:                        2025-04-15T09:50:18Z : Creation
    data_mode:                      M

xarray.Dataset

• Dimensions:
  • TIME: 220
  • LATITUDE: 220
  • LONGITUDE: 220
  • POSITION: 220
  • DEPTH: 103
• Coordinates: (3)
  • TIME
    (TIME)
    datetime64[ns]
    2021-01-10T05:49:54 ... 2025-04-...

    long_name :

    Time

    standard_name :

    time

    valid_min :

    -90000.0

    valid_max :

    90000.0

    uncertainty :

    comment :

    axis :

    T

    ancillary_variables :

    TIME_QC

    array(['2021-01-10T05:49:54.000000000', '2021-01-17T05:33:20.000000000',
           '2021-01-24T05:32:03.000000000', ..., '2025-03-21T21:36:20.000000000',
           '2025-03-28T23:30:57.000000000', '2025-04-05T01:30:25.000000000'],
          shape=(220,), dtype='datetime64[ns]')

  • LATITUDE
    (LATITUDE)
    float32
    4.381 4.046 3.807 ... 4.038 4.243

    standard_name :

    latitude

    long_name :

    Latitude of each location

    units :

    degree_north

    valid_min :

    -90.0

    valid_max :

    90.0

    uncertainty :

    comment :

    axis :

    Y

    ancillary_variables :

    POSITION_QC

    array([4.381, 4.046, 3.807, ..., 3.906, 4.038, 4.243],
          shape=(220,), dtype=float32)

  • LONGITUDE
    (LONGITUDE)
    float32
    147.0 146.8 146.3 ... 131.1 130.8

    standard_name :

    longitude

    long_name :

    Longitude of each location

    units :

    degree_east

    valid_min :

    -180.0

    valid_max :

    180.0

    uncertainty :

    comment :

    axis :

    X

    ancillary_variables :

    POSITION_QC

    array([147.037, 146.819, 146.317, ..., 131.383, 131.145, 130.829],
          shape=(220,), dtype=float32)

• Data variables: (23)
  • TIME_QC
    (TIME)
    float32
    ...

    long_name :

    Time quality flag

    conventions :

    Copernicus Marine In Situ reference table 2

    flag_meanings :

    no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed value_below_detection nominal_value interpolated_value missing_value

    valid_min :

    0

    valid_max :

    9

    flag_values :

    [0 1 2 3 4 5 6 7 8 9]

    [220 values with dtype=float32]

  • POSITION_QC
    (POSITION)
    float32
    ...

    long_name :

    Position quality flag

    conventions :

    Copernicus Marine In Situ reference table 2

    flag_meanings :

    no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed value_below_detection nominal_value interpolated_value missing_value

    valid_min :

    0

    valid_max :

    9

    flag_values :

    [0 1 2 3 4 5 6 7 8 9]

    [220 values with dtype=float32]

  • DC_REFERENCE
    (TIME)
    object
    ...

    long_name :

    Station/Location unique identifier in data centre

    conventions :

    Data centre convention

    [220 values with dtype=object]

  • DIRECTION
    (TIME)
    object
    ...

    long_name :

    Direction of the profiles

    conventions :

    A: ascending profile, D: descending profile

    [220 values with dtype=object]

  • VERTICAL_SAMPLING_SCHEME
    (TIME)
    object
    ...

    long_name :

    Vertical sampling scheme

    conventions :

    Argo reference table 16

    [220 values with dtype=object]

  • PRES
    (TIME, DEPTH)
    float32
    ...

    standard_name :

    sea_water_pressure

    long_name :

    Sea pressure

    units :

    dbar

    axis :

    Z

    positive :

    down

    uncertainty :

    reference :

    sea_level

    data_mode :

    R

    ancillary_variables :

    PRES_QC

    [22660 values with dtype=float32]

  • PRES_QC
    (TIME, DEPTH)
    float32
    ...

    long_name :

    Sea pressure quality flag

    conventions :

    Copernicus Marine In Situ reference table 2

    flag_meanings :

    no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed value_below_detection nominal_value interpolated_value missing_value

    valid_min :

    0

    valid_max :

    9

    flag_values :

    [0 1 2 3 4 5 6 7 8 9]

    [22660 values with dtype=float32]

  • PRES_ADJUSTED
    (TIME, DEPTH)
    float32
    ...

    standard_name :

    sea_water_pressure

    long_name :

    Sea pressure adjusted

    units :

    dbar

    data_mode :

    M

    ancillary_variables :

    PRES_ADJUSTED_QC PRES_ADJUSTED_DM PRES_ADJUSTED_ERROR

    [22660 values with dtype=float32]

  • PRES_ADJUSTED_QC
    (TIME, DEPTH)
    float32
    ...

    long_name :

    Sea pressure adjusted quality flag

    conventions :

    Copernicus Marine In Situ reference table 2

    flag_meanings :

    no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed value_below_detection nominal_value interpolated_value missing_value

    valid_min :

    0

    valid_max :

    9

    flag_values :

    [0 1 2 3 4 5 6 7 8 9]

    [22660 values with dtype=float32]

  • PRES_ADJUSTED_DM
    (TIME, DEPTH)
    object
    ...

    long_name :

    Sea pressure adjusted method of data processing

    conventions :

    Copernicus Marine In Situ reference table 1

    flag_values :

    R, A, D

    flag_meanings :

    real-time adjusted-in-real-time delayed-mode

    [22660 values with dtype=object]

  • PRES_ADJUSTED_ERROR
    (TIME, DEPTH)
    float32
    ...

    long_name :

    Sea pressure adjusted error

    units :

    dbar

    [22660 values with dtype=float32]

  • TEMP
    (TIME, DEPTH)
    float64
    ...

    standard_name :

    sea_water_temperature

    long_name :

    Sea temperature

    units :

    degrees_C

    data_mode :

    R

    ancillary_variables :

    TEMP_QC

    [22660 values with dtype=float64]

  • TEMP_QC
    (TIME, DEPTH)
    float32
    ...

    long_name :

    Sea temperature quality flag

    conventions :

    Copernicus Marine In Situ reference table 2

    flag_meanings :

    no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed value_below_detection nominal_value interpolated_value missing_value

    valid_min :

    0

    valid_max :

    9

    flag_values :

    [0 1 2 3 4 5 6 7 8 9]

    [22660 values with dtype=float32]

  • TEMP_ADJUSTED
    (TIME, DEPTH)
    float64
    ...

    standard_name :

    sea_water_temperature

    long_name :

    Sea temperature adjusted

    units :

    degrees_C

    data_mode :

    M

    ancillary_variables :

    TEMP_ADJUSTED_QC TEMP_ADJUSTED_DM TEMP_ADJUSTED_ERROR

    [22660 values with dtype=float64]

  • TEMP_ADJUSTED_QC
    (TIME, DEPTH)
    float32
    ...

    long_name :

    Sea temperature adjusted quality flag

    conventions :

    Copernicus Marine In Situ reference table 2

    flag_meanings :

    no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed value_below_detection nominal_value interpolated_value missing_value

    valid_min :

    0

    valid_max :

    9

    flag_values :

    [0 1 2 3 4 5 6 7 8 9]

    [22660 values with dtype=float32]

  • TEMP_ADJUSTED_DM
    (TIME, DEPTH)
    object
    ...

    long_name :

    Sea temperature adjusted method of data processing

    conventions :

    Copernicus Marine In Situ reference table 1

    flag_values :

    R, A, D

    flag_meanings :

    real-time adjusted-in-real-time delayed-mode

    [22660 values with dtype=object]

  • TEMP_ADJUSTED_ERROR
    (TIME, DEPTH)
    float64
    ...

    long_name :

    Sea temperature adjusted error

    units :

    degrees_C

    [22660 values with dtype=float64]

  • PSAL
    (TIME, DEPTH)
    float64
    ...

    standard_name :

    sea_water_practical_salinity

    long_name :

    Practical salinity

    units :

    0.001

    data_mode :

    R

    ancillary_variables :

    PSAL_QC

    [22660 values with dtype=float64]

  • PSAL_QC
    (TIME, DEPTH)
    float32
    ...

    long_name :

    Practical salinity quality flag

    conventions :

    Copernicus Marine In Situ reference table 2

    flag_meanings :

    no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed value_below_detection nominal_value interpolated_value missing_value

    valid_min :

    0

    valid_max :

    9

    flag_values :

    [0 1 2 3 4 5 6 7 8 9]

    [22660 values with dtype=float32]

  • PSAL_ADJUSTED
    (TIME, DEPTH)
    float64
    ...

    standard_name :

    sea_water_practical_salinity

    long_name :

    Practical salinity adjusted

    units :

    0.001

    data_mode :

    M

    ancillary_variables :

    PSAL_ADJUSTED_QC PSAL_ADJUSTED_DM PSAL_ADJUSTED_ERROR

    [22660 values with dtype=float64]

  • PSAL_ADJUSTED_QC
    (TIME, DEPTH)
    float32
    ...

    long_name :

    Practical salinity adjusted quality flag

    conventions :

    Copernicus Marine In Situ reference table 2

    flag_meanings :

    no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed value_below_detection nominal_value interpolated_value missing_value

    valid_min :

    0

    valid_max :

    9

    flag_values :

    [0 1 2 3 4 5 6 7 8 9]

    [22660 values with dtype=float32]

  • PSAL_ADJUSTED_DM
    (TIME, DEPTH)
    object
    ...

    long_name :

    Practical salinity adjusted method of data processing

    conventions :

    Copernicus Marine In Situ reference table 1

    flag_values :

    R, A, D

    flag_meanings :

    real-time adjusted-in-real-time delayed-mode

    [22660 values with dtype=object]

  • PSAL_ADJUSTED_ERROR
    (TIME, DEPTH)
    float64
    ...

    long_name :

    Practical salinity adjusted error

    units :

    0.001

    [22660 values with dtype=float64]

• Indexes: (3)
  • TIME
    PandasIndex

    PandasIndex(DatetimeIndex([          '2021-01-10 05:49:54',
                             '2021-01-17 05:33:20',
                             '2021-01-24 05:32:03',
                             '2021-01-31 05:29:49',
                             '2021-02-07 05:37:56',
                             '2021-02-14 05:29:55',
                             '2021-02-21 05:32:03',
                             '2021-02-28 05:23:03',
                   '2021-03-07 05:32:20.000000256',
                             '2021-03-14 05:38:55',
                   ...
                             '2025-01-31 07:36:29',
                             '2025-02-07 09:33:01',
                             '2025-02-14 11:25:19',
                             '2025-02-21 13:41:19',
                             '2025-02-28 15:38:13',
                             '2025-03-07 17:31:14',
                             '2025-03-14 19:44:01',
                             '2025-03-21 21:36:20',
                             '2025-03-28 23:30:57',
                             '2025-04-05 01:30:25'],
                  dtype='datetime64[ns]', name='TIME', length=220, freq=None))

  • LATITUDE
    PandasIndex

    PandasIndex(Index([  4.38100004196167,  4.046000003814697,  3.806999921798706,
            3.681999921798706,  3.566999912261963, 3.4660000801086426,
            3.359999895095825, 3.2809998989105225, 3.2709999084472656,
            3.372999906539917,
           ...
            4.248000144958496,  4.267000198364258,  4.283999919891357,
            4.361000061035156,  4.464000225067139,  4.263999938964844,
            3.933000087738037, 3.9059998989105225,  4.038000106811523,
            4.243000030517578],
          dtype='float32', name='LATITUDE', length=220))

  • LONGITUDE
    PandasIndex

    PandasIndex(Index([147.03700256347656, 146.81900024414062, 146.31700134277344,
           145.74200439453125, 145.14500427246094, 144.54800415039062,
            143.9709930419922, 143.38299560546875,  142.8800048828125,
           142.37600708007812,
           ...
           131.16900634765625, 131.46600341796875, 131.64300537109375,
            131.6300048828125, 131.68299865722656,  131.8939971923828,
            131.7030029296875, 131.38299560546875, 131.14500427246094,
           130.82899475097656],
          dtype='float32', name='LONGITUDE', length=220))

• Attributes: (49)

  data_type :

  OceanSITES vertical profile

  format_version :

  1.4

  platform_code :

  2902800

  institution :

  First Institute of Oceanography - Ministry of Natural Resources

  institution_edmo_code :

  4640

  site_code :

  wmo_platform_code :

  2902800

  coriolis_platform_code :

  2902800

  platform_name :

  P32800-20CH018

  ices_platform_code :

  source :

  drifting subsurface profiling float

  source_platform_category_code :

  46

  wmo_inst_type :

  841

  references :

  http://marine.copernicus.eu http://www.marineinsitu.eu

  comment :

  Conventions :

  CF-1.6 Copernicus-InSituTAC-FormatManual-1.42 Copernicus-InSituTAC-SRD-1.5 Copernicus-InSituTAC-ParametersList-3.2.1

  netcdf_version :

  netCDF-4 classic model

  title :

  Global Ocean - In Situ Observation Copernicus

  summary :

  naming_authority :

  Copernicus Marine In Situ

  author :

  id :

  GL_PR_PF_2902800

  cdm_data_type :

  profile

  area :

  Global Ocean

  bottom_depth :

  institution_references :

  contact :

  cmems-service@ifremer.fr

  data_assembly_center :

  Ifremer

  pi_name :

  FENG ZHOU

  distribution_statement :

  These data follow Copernicus standards; they are public and free of charge. User assumes all risk for use of data. User must display citation in any publication or product using data. User must contact PI prior to any commercial use of data.

  citation :

  These data were collected and made freely available by the Copernicus project and the programs that contribute to it.

  update_interval :

  P1D

  qc_manual :

  Recommendations for in-situ data Near Real Time Quality Control https://doi.org/10.13155/36230

  doi :

  https://doi.org/10.13155/59938 https://doi.org/10.13155/40846 https://doi.org/10.13155/53381 https://doi.org/10.13155/36230 https://doi.org/10.13155/43494

  license :

  https://marine.copernicus.eu/user-corner/service-commitments-and-licence

  geospatial_lat_min :

  1.00400

  geospatial_lat_max :

  6.68900

  geospatial_lon_min :

  128.07201

  geospatial_lon_max :

  147.03700

  geospatial_vertical_min :

  0.00

  geospatial_vertical_max :

  2021.90

  time_coverage_start :

  2021-01-10T05:49:54Z

  time_coverage_end :

  2025-04-05T01:30:25Z

  last_date_observation :

  2025-04-05T01:30:25Z

  last_latitude_observation :

  4.24300

  last_longitude_observation :

  130.82900

  date_update :

  2025-04-15T09:50:18Z

  history :

  2025-04-15T09:50:18Z : Creation

  data_mode :

  M

Data Sea Surface Drifter

# Load into dataframe
path_drifter_csv = '/data/local/marine-training/data/MATPEL_05/drifter_data/drifter_6hour_qc_f5dd_5de7_fd9d.csv'
df_drifter = pd.read_csv(path_drifter_csv)

# Display basic info and the first few rows
df_drifter

	ID	WMO	time	latitude	longitude	sst	err_sst	ve	vn	err_lat	...	deploy_lon	start_date	start_lat	start_lon	end_date	end_lat	end_lon	drogue_lost_date	DrogueType	DrogueLength
0	NaN	NaN	UTC	degrees_north	degrees_east	degree_C	degree_C	NaN	NaN	NaN	...	degrees_east	UTC	degrees_north	degrees_east	UTC	degrees_north	degrees_east	UTC	NaN	NaN
1	3.002340e+14	5102836.0	2024-02-01T00:00:00Z	11.293	131.199	28.159	0.041	-0.70395	0.12636	0.001	...	-154.965	2022-09-28T00:00:00Z	3.0	-154.98	2024-02-18T00:00:00Z	8.95	126.31	2023-01-03T00:00:00Z	NaN	6.6 m
2	3.002340e+14	5102836.0	2024-02-01T06:00:00Z	11.315	131.057	28.178	0.041	-0.67358	0.19455	0.001	...	-154.965	2022-09-28T00:00:00Z	3.0	-154.98	2024-02-18T00:00:00Z	8.95	126.31	2023-01-03T00:00:00Z	NaN	6.6 m
3	3.002340e+14	5102836.0	2024-02-01T12:00:00Z	11.369	130.932	28.082	0.041	-0.57381	0.30063	0.001	...	-154.965	2022-09-28T00:00:00Z	3.0	-154.98	2024-02-18T00:00:00Z	8.95	126.31	2023-01-03T00:00:00Z	NaN	6.6 m
4	3.002340e+14	5102836.0	2024-02-01T18:00:00Z	11.432	130.83	28.007	0.041	-0.53084	0.22220	0.001	...	-154.965	2022-09-28T00:00:00Z	3.0	-154.98	2024-02-18T00:00:00Z	8.95	126.31	2023-01-03T00:00:00Z	NaN	6.6 m
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6012	3.005341e+14	4804107.0	2024-04-13T06:00:00Z	-4.143	90.235	29.973	0.065	-0.29942	-0.16107	0.015	...	77.38	2024-01-16T00:00:00Z	-7.92	77.38	NaN	NaN	NaN	NaN	NaN	7.5 m
6013	3.005341e+14	4804107.0	2024-04-13T12:00:00Z	-4.168	90.174	29.973	0.062	-0.26649	-0.05737	0.002	...	77.38	2024-01-16T00:00:00Z	-7.92	77.38	NaN	NaN	NaN	NaN	NaN	7.5 m
6014	3.005341e+14	4804107.0	2024-04-13T18:00:00Z	-4.165	90.131	29.931	0.065	-0.22199	0.03832	0.015	...	77.38	2024-01-16T00:00:00Z	-7.92	77.38	NaN	NaN	NaN	NaN	NaN	7.5 m
6015	3.005341e+14	4804107.0	2024-04-14T00:00:00Z	-4.153	90.087	29.887	0.062	-0.31198	0.07767	0.002	...	77.38	2024-01-16T00:00:00Z	-7.92	77.38	NaN	NaN	NaN	NaN	NaN	7.5 m
6016	3.005341e+14	4804107.0	2024-04-14T06:00:00Z	-4.135	90.01	29.962	0.065	-0.48575	0.10340	0.015	...	77.38	2024-01-16T00:00:00Z	-7.92	77.38	NaN	NaN	NaN	NaN	NaN	7.5 m

6017 rows × 23 columns

Data Filtering

Argo Data

Membuat kriteria seleksi

# Menyeleksi berdasarkan QC. QC Baik -> flag: 1
ds_argo_filtered = (ds_argo
                    .where(ds_argo['PRES_ADJUSTED_QC'] == 1)
                    .where(ds_argo['TEMP_ADJUSTED_QC'] == 1))

# Menyeleksi berdasarkan waktu (disesuaikan dengan ketersediaan data model untuk keperluan verifikasi)
# serta berada di wilayah domain model

t_start = pd.to_datetime("2024-02-01")
t_end = pd.to_datetime("2025-04-01")
ext_indo = [90, 145, -15, 15]

# 1. Mengambil nilai dari dimensi
lat = ds_argo_filtered['LATITUDE'].values
lon = ds_argo_filtered['LONGITUDE'].values
tm = pd.to_datetime(ds_argo_filtered['TIME'].values)

# 2. Buat mask lokasi: hanya posisi dalam batas wilayah Indonesia
mask_pos = (lon >= ext_indo[0]) & (lon <= ext_indo[1]) & (lat >= ext_indo[2]) & (lat <= ext_indo[3])

# 3. Buat mask waktu: hanya waktu dalam rentang yang ditentukan
mask_tm = (tm >= t_start) & (tm <= t_end)

# 4. Gabungkan kedua mask (lokasi dan waktu)
mask_combined = mask_pos & mask_tm

# 5. Indeks data yang lolos seleksi
valid_indices = np.where(mask_combined)[0]
print(f"Indeks/cycle yang lolos seleksi: {valid_indices}")

Indeks/cycle yang lolos seleksi: [159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194
 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212
 213 214 215 216 217 218]

Mengambil irisan data sesuai dengan kriteria seleksi

ds_argo_filtered = ds_argo_filtered.sel(TIME=mask_combined, LATITUDE=mask_combined, LONGITUDE=mask_combined, POSITION=valid_indices)
ds_argo_filtered['POSITION'] = ('TIME', ds_argo['POSITION'].values[valid_indices])
ds_argo_filtered

<xarray.Dataset> Size: 2MB
Dimensions:                   (TIME: 60, DEPTH: 103, POSITION: 60,
                               LATITUDE: 60, LONGITUDE: 60)
Coordinates:
  * TIME                      (TIME) datetime64[ns] 480B 2024-02-05T01:30:26 ...
  * LATITUDE                  (LATITUDE) float32 240B 4.764 4.568 ... 4.038
  * LONGITUDE                 (LONGITUDE) float32 240B 130.1 129.8 ... 131.1
    POSITION                  (TIME) int64 480B 159 160 161 162 ... 216 217 218
Dimensions without coordinates: DEPTH
Data variables: (12/23)
    TIME_QC                   (TIME, DEPTH) float32 25kB 1.0 1.0 1.0 ... nan nan
    POSITION_QC               (POSITION, TIME, DEPTH) float32 1MB 1.0 ... nan
    DC_REFERENCE              (TIME, DEPTH) object 49kB b'93359759' ... nan
    DIRECTION                 (TIME, DEPTH) object 49kB b'A' b'A' ... nan nan
    VERTICAL_SAMPLING_SCHEME  (TIME, DEPTH) object 49kB b'Synthetic sampling'...
    PRES                      (TIME, DEPTH) float32 25kB 0.0 0.6 1.9 ... nan nan
    ...                        ...
    PSAL                      (TIME, DEPTH) float64 49kB 34.02 34.06 ... nan nan
    PSAL_QC                   (TIME, DEPTH) float32 25kB 1.0 1.0 1.0 ... nan nan
    PSAL_ADJUSTED             (TIME, DEPTH) float64 49kB 34.02 34.06 ... nan nan
    PSAL_ADJUSTED_QC          (TIME, DEPTH) float32 25kB 1.0 1.0 1.0 ... nan nan
    PSAL_ADJUSTED_DM          (TIME, DEPTH) object 49kB b'D' b'D' ... nan nan
    PSAL_ADJUSTED_ERROR       (TIME, DEPTH) float64 49kB 0.009 0.009 ... nan nan
Attributes: (12/49)
    data_type:                      OceanSITES vertical profile
    format_version:                 1.4
    platform_code:                  2902800
    institution:                    First Institute of Oceanography - Ministr...
    institution_edmo_code:          4640
    site_code:                       
    ...                             ...
    last_date_observation:          2025-04-05T01:30:25Z
    last_latitude_observation:      4.24300
    last_longitude_observation:     130.82900
    date_update:                    2025-04-15T09:50:18Z
    history:                        2025-04-15T09:50:18Z : Creation
    data_mode:                      M

xarray.Dataset

• Dimensions:
  • TIME: 60
  • DEPTH: 103
  • POSITION: 60
  • LATITUDE: 60
  • LONGITUDE: 60
• Coordinates: (4)
  • TIME
    (TIME)
    datetime64[ns]
    2024-02-05T01:30:26 ... 2025-03-...

    long_name :

    Time

    standard_name :

    time

    valid_min :

    -90000.0

    valid_max :

    90000.0

    uncertainty :

    comment :

    axis :

    T

    ancillary_variables :

    TIME_QC

    array(['2024-02-05T01:30:26.000000000', '2024-02-12T03:32:03.000000000',
           '2024-02-19T05:37:58.000000000', '2024-02-26T07:32:30.000000000',
           '2024-03-04T09:43:25.000000000', '2024-03-11T11:30:04.000000000',
           '2024-03-18T13:36:50.000000000', '2024-03-25T15:43:17.000000000',
           '2024-04-01T17:40:01.000000000', '2024-04-08T19:31:25.000000000',
           '2024-04-15T21:39:55.000000000', '2024-04-22T23:34:04.000000000',
           '2024-04-30T01:33:13.000000000', '2024-05-07T03:35:13.000000000',
           '2024-05-14T05:23:32.000000000', '2024-05-21T07:35:33.000000000',
           '2024-05-28T09:32:49.000000000', '2024-06-04T11:33:03.000000000',
           '2024-06-11T13:23:04.000000000', '2024-06-18T15:51:33.000000000',
           '2024-06-25T17:39:39.000000000', '2024-07-02T19:43:25.000000000',
           '2024-07-09T21:43:50.000000000', '2024-07-16T23:39:34.000000000',
           '2024-07-24T01:41:55.000000000', '2024-07-31T03:39:25.000000000',
           '2024-08-07T05:31:50.000000000', '2024-08-14T07:35:14.000000000',
           '2024-08-21T09:34:44.000000000', '2024-08-28T11:34:25.000000000',
           '2024-09-04T13:32:25.000000000', '2024-09-11T15:36:27.000000000',
           '2024-09-18T17:38:33.000000000', '2024-09-25T19:26:54.000000000',
           '2024-10-02T21:30:25.000000000', '2024-10-09T23:25:04.000000000',
           '2024-10-17T01:23:19.000000000', '2024-10-24T03:32:55.000000000',
           '2024-10-31T05:39:13.000000000', '2024-11-07T07:39:01.000000000',
           '2024-11-14T09:32:19.000000000', '2024-11-21T11:45:32.000000000',
           '2024-11-28T13:39:25.000000000', '2024-12-05T15:38:21.000000000',
           '2024-12-12T17:37:58.000000000', '2024-12-19T19:38:04.000000000',
           '2024-12-26T21:44:32.000000000', '2025-01-02T23:36:34.000000000',
           '2025-01-10T01:32:19.000000000', '2025-01-17T03:32:15.000000000',
           '2025-01-24T05:39:25.000000000', '2025-01-31T07:36:29.000000000',
           '2025-02-07T09:33:01.000000000', '2025-02-14T11:25:19.000000000',
           '2025-02-21T13:41:19.000000000', '2025-02-28T15:38:13.000000000',
           '2025-03-07T17:31:14.000000000', '2025-03-14T19:44:01.000000000',
           '2025-03-21T21:36:20.000000000', '2025-03-28T23:30:57.000000000'],
          dtype='datetime64[ns]')

  • LATITUDE
    (LATITUDE)
    float32
    4.764 4.568 4.343 ... 3.906 4.038

    standard_name :

    latitude

    long_name :

    Latitude of each location

    units :

    degree_north

    valid_min :

    -90.0

    valid_max :

    90.0

    uncertainty :

    comment :

    axis :

    Y

    ancillary_variables :

    POSITION_QC

    array([4.764, 4.568, 4.343, 4.129, 4.057, 4.039, 4.012, 4.021, 4.01 , 3.722,
           2.989, 1.569, 1.004, 2.123, 2.81 , 3.442, 3.825, 4.76 , 5.484, 5.174,
           4.335, 4.084, 4.317, 4.415, 4.258, 4.359, 4.624, 4.928, 5.047, 5.151,
           5.272, 5.288, 5.162, 5.159, 5.207, 5.49 , 5.709, 5.754, 5.688, 5.814,
           5.803, 5.47 , 4.813, 4.443, 4.373, 4.485, 4.629, 4.766, 4.683, 4.414,
           4.277, 4.248, 4.267, 4.284, 4.361, 4.464, 4.264, 3.933, 3.906, 4.038],
          dtype=float32)

  • LONGITUDE
    (LONGITUDE)
    float32
    130.1 129.8 129.5 ... 131.4 131.1

    standard_name :

    longitude

    long_name :

    Longitude of each location

    units :

    degree_east

    valid_min :

    -180.0

    valid_max :

    180.0

    uncertainty :

    comment :

    axis :

    X

    ancillary_variables :

    POSITION_QC

    array([130.078, 129.799, 129.547, 129.342, 129.331, 129.33 , 129.378, 129.56 ,
           129.317, 128.874, 129.182, 129.851, 130.826, 131.561, 131.062, 130.04 ,
           129.036, 128.316, 128.593, 129.247, 129.174, 129.106, 128.995, 128.994,
           128.995, 128.87 , 128.748, 128.562, 128.356, 128.147, 128.072, 128.181,
           128.401, 128.893, 129.572, 130.14 , 130.578, 130.824, 130.758, 130.688,
           130.861, 131.055, 131.113, 131.035, 130.997, 131.056, 131.147, 131.325,
           131.494, 131.352, 131.205, 131.169, 131.466, 131.643, 131.63 , 131.683,
           131.894, 131.703, 131.383, 131.145], dtype=float32)

  • POSITION
    (TIME)
    int64
    159 160 161 162 ... 215 216 217 218

    array([159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171,
           172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,
           185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197,
           198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210,
           211, 212, 213, 214, 215, 216, 217, 218])

• Data variables: (23)
  • TIME_QC
    (TIME, DEPTH)
    float32
    1.0 1.0 1.0 1.0 ... 1.0 nan nan nan

    long_name :

    Time quality flag

    conventions :

    Copernicus Marine In Situ reference table 2

    flag_meanings :

    no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed value_below_detection nominal_value interpolated_value missing_value

    valid_min :

    0

    valid_max :

    9

    flag_values :

    [0 1 2 3 4 5 6 7 8 9]

    array([[ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1., nan],
           [ 1.,  1.,  1., ...,  1., nan, nan],
           ...,
           [ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1., nan],
           [ 1.,  1.,  1., ..., nan, nan, nan]],
          shape=(60, 103), dtype=float32)

  • POSITION_QC
    (POSITION, TIME, DEPTH)
    float32
    1.0 1.0 1.0 1.0 ... 1.0 nan nan nan

    long_name :

    Position quality flag

    conventions :

    Copernicus Marine In Situ reference table 2

    flag_meanings :

    no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed value_below_detection nominal_value interpolated_value missing_value

    valid_min :

    0

    valid_max :

    9

    flag_values :

    [0 1 2 3 4 5 6 7 8 9]

    array([[[ 1.,  1.,  1., ..., nan, nan, nan],
            [ 1.,  1.,  1., ...,  1.,  1., nan],
            [ 1.,  1.,  1., ...,  1., nan, nan],
            ...,
            [ 1.,  1.,  1., ..., nan, nan, nan],
            [ 1.,  1.,  1., ...,  1.,  1., nan],
            [ 1.,  1.,  1., ..., nan, nan, nan]],
    
           [[ 1.,  1.,  1., ..., nan, nan, nan],
            [ 1.,  1.,  1., ...,  1.,  1., nan],
            [ 1.,  1.,  1., ...,  1., nan, nan],
            ...,
            [ 1.,  1.,  1., ..., nan, nan, nan],
            [ 1.,  1.,  1., ...,  1.,  1., nan],
            [ 1.,  1.,  1., ..., nan, nan, nan]],
    
           [[ 1.,  1.,  1., ..., nan, nan, nan],
            [ 1.,  1.,  1., ...,  1.,  1., nan],
            [ 1.,  1.,  1., ...,  1., nan, nan],
            ...,
    ...
            [ 1.,  1.,  1., ..., nan, nan, nan],
            [ 1.,  1.,  1., ...,  1.,  1., nan],
            [ 1.,  1.,  1., ..., nan, nan, nan]],
    
           [[ 1.,  1.,  1., ..., nan, nan, nan],
            [ 1.,  1.,  1., ...,  1.,  1., nan],
            [ 1.,  1.,  1., ...,  1., nan, nan],
            ...,
            [ 1.,  1.,  1., ..., nan, nan, nan],
            [ 1.,  1.,  1., ...,  1.,  1., nan],
            [ 1.,  1.,  1., ..., nan, nan, nan]],
    
           [[ 1.,  1.,  1., ..., nan, nan, nan],
            [ 1.,  1.,  1., ...,  1.,  1., nan],
            [ 1.,  1.,  1., ...,  1., nan, nan],
            ...,
            [ 1.,  1.,  1., ..., nan, nan, nan],
            [ 1.,  1.,  1., ...,  1.,  1., nan],
            [ 1.,  1.,  1., ..., nan, nan, nan]]],
          shape=(60, 60, 103), dtype=float32)

  • DC_REFERENCE
    (TIME, DEPTH)
    object
    b'93359759' b'93359759' ... nan nan

    long_name :

    Station/Location unique identifier in data centre

    conventions :

    Data centre convention

    array([[b'93359759', b'93359759', b'93359759', ..., nan, nan, nan],
           [b'93381880', b'93381880', b'93381880', ..., b'93381880',
            b'93381880', nan],
           [b'93406853', b'93406853', b'93406853', ..., b'93406853', nan,
            nan],
           ...,
           [b'95965232', b'95965232', b'95965232', ..., nan, nan, nan],
           [b'95983908', b'95983908', b'95983908', ..., b'95983908',
            b'95983908', nan],
           [b'96006661', b'96006661', b'96006661', ..., nan, nan, nan]],
          shape=(60, 103), dtype=object)

  • DIRECTION
    (TIME, DEPTH)
    object
    b'A' b'A' b'A' b'A' ... nan nan nan

    long_name :

    Direction of the profiles

    conventions :

    A: ascending profile, D: descending profile

    array([[b'A', b'A', b'A', ..., nan, nan, nan],
           [b'A', b'A', b'A', ..., b'A', b'A', nan],
           [b'A', b'A', b'A', ..., b'A', nan, nan],
           ...,
           [b'A', b'A', b'A', ..., nan, nan, nan],
           [b'A', b'A', b'A', ..., b'A', b'A', nan],
           [b'A', b'A', b'A', ..., nan, nan, nan]],
          shape=(60, 103), dtype=object)

  • VERTICAL_SAMPLING_SCHEME
    (TIME, DEPTH)
    object
    b'Synthetic sampling' ... nan

    long_name :

    Vertical sampling scheme

    conventions :

    Argo reference table 16

    array([[b'Synthetic sampling', b'Synthetic sampling',
            b'Synthetic sampling', ..., nan, nan, nan],
           [b'Synthetic sampling', b'Synthetic sampling',
            b'Synthetic sampling', ..., b'Synthetic sampling',
            b'Synthetic sampling', nan],
           [b'Synthetic sampling', b'Synthetic sampling',
            b'Synthetic sampling', ..., b'Synthetic sampling', nan, nan],
           ...,
           [b'Synthetic sampling', b'Synthetic sampling',
            b'Synthetic sampling', ..., nan, nan, nan],
           [b'Synthetic sampling', b'Synthetic sampling',
            b'Synthetic sampling', ..., b'Synthetic sampling',
            b'Synthetic sampling', nan],
           [b'Synthetic sampling', b'Synthetic sampling',
            b'Synthetic sampling', ..., nan, nan, nan]],
          shape=(60, 103), dtype=object)

  • PRES
    (TIME, DEPTH)
    float32
    0.0 0.6 1.9 3.9 ... nan nan nan

    standard_name :

    sea_water_pressure

    long_name :

    Sea pressure

    units :

    dbar

    axis :

    Z

    positive :

    down

    uncertainty :

    reference :

    sea_level

    data_mode :

    R

    ancillary_variables :

    PRES_QC

    array([[0.0000e+00, 6.0000e-01, 1.9000e+00, ...,        nan,        nan,
                   nan],
           [1.0000e-01, 1.1000e+00, 1.8000e+00, ..., 1.9632e+03, 1.9824e+03,
                   nan],
           [4.0000e-01, 1.3000e+00, 2.3000e+00, ..., 1.9828e+03,        nan,
                   nan],
           ...,
           [1.0000e-01, 6.0000e-01, 2.0000e+00, ...,        nan,        nan,
                   nan],
           [2.0000e-01, 8.0000e-01, 1.7000e+00, ..., 1.9625e+03, 1.9805e+03,
                   nan],
           [2.0000e-01, 1.1000e+00, 2.1000e+00, ...,        nan,        nan,
                   nan]], shape=(60, 103), dtype=float32)

  • PRES_QC
    (TIME, DEPTH)
    float32
    1.0 1.0 1.0 1.0 ... 1.0 nan nan nan

    long_name :

    Sea pressure quality flag

    conventions :

    Copernicus Marine In Situ reference table 2

    flag_meanings :

    no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed value_below_detection nominal_value interpolated_value missing_value

    valid_min :

    0

    valid_max :

    9

    flag_values :

    [0 1 2 3 4 5 6 7 8 9]

    array([[ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1., nan],
           [ 1.,  1.,  1., ...,  1., nan, nan],
           ...,
           [ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1., nan],
           [ 1.,  1.,  1., ..., nan, nan, nan]],
          shape=(60, 103), dtype=float32)

  • PRES_ADJUSTED
    (TIME, DEPTH)
    float32
    0.0 0.6 1.9 3.9 ... nan nan nan

    standard_name :

    sea_water_pressure

    long_name :

    Sea pressure adjusted

    units :

    dbar

    data_mode :

    M

    ancillary_variables :

    PRES_ADJUSTED_QC PRES_ADJUSTED_DM PRES_ADJUSTED_ERROR

    array([[0.0000e+00, 6.0000e-01, 1.9000e+00, ...,        nan,        nan,
                   nan],
           [1.0000e-01, 1.1000e+00, 1.8000e+00, ..., 1.9632e+03, 1.9824e+03,
                   nan],
           [4.0000e-01, 1.3000e+00, 2.3000e+00, ..., 1.9828e+03,        nan,
                   nan],
           ...,
           [1.0000e-01, 6.0000e-01, 2.0000e+00, ...,        nan,        nan,
                   nan],
           [2.0000e-01, 8.0000e-01, 1.7000e+00, ..., 1.9625e+03, 1.9805e+03,
                   nan],
           [2.0000e-01, 1.1000e+00, 2.1000e+00, ...,        nan,        nan,
                   nan]], shape=(60, 103), dtype=float32)

  • PRES_ADJUSTED_QC
    (TIME, DEPTH)
    float32
    1.0 1.0 1.0 1.0 ... 1.0 nan nan nan

    long_name :

    Sea pressure adjusted quality flag

    conventions :

    Copernicus Marine In Situ reference table 2

    flag_meanings :

    no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed value_below_detection nominal_value interpolated_value missing_value

    valid_min :

    0

    valid_max :

    9

    flag_values :

    [0 1 2 3 4 5 6 7 8 9]

    array([[ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1., nan],
           [ 1.,  1.,  1., ...,  1., nan, nan],
           ...,
           [ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1., nan],
           [ 1.,  1.,  1., ..., nan, nan, nan]],
          shape=(60, 103), dtype=float32)

  • PRES_ADJUSTED_DM
    (TIME, DEPTH)
    object
    b'D' b'D' b'D' b'D' ... nan nan nan

    long_name :

    Sea pressure adjusted method of data processing

    conventions :

    Copernicus Marine In Situ reference table 1

    flag_values :

    R, A, D

    flag_meanings :

    real-time adjusted-in-real-time delayed-mode

    array([[b'D', b'D', b'D', ..., nan, nan, nan],
           [b'D', b'D', b'D', ..., b'D', b'D', nan],
           [b'D', b'D', b'D', ..., b'D', nan, nan],
           ...,
           [b'A', b'A', b'A', ..., nan, nan, nan],
           [b'A', b'A', b'A', ..., b'A', b'A', nan],
           [b'A', b'A', b'A', ..., nan, nan, nan]],
          shape=(60, 103), dtype=object)

  • PRES_ADJUSTED_ERROR
    (TIME, DEPTH)
    float32
    2.4 2.4 2.4 2.4 ... nan nan nan nan

    long_name :

    Sea pressure adjusted error

    units :

    dbar

    array([[2.4, 2.4, 2.4, ..., nan, nan, nan],
           [2.4, 2.4, 2.4, ..., 2.4, 2.4, nan],
           [2.4, 2.4, 2.4, ..., 2.4, nan, nan],
           ...,
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan],
           [nan, nan, nan, ..., nan, nan, nan]],
          shape=(60, 103), dtype=float32)

  • TEMP
    (TIME, DEPTH)
    float64
    28.62 28.62 28.62 ... nan nan nan

    standard_name :

    sea_water_temperature

    long_name :

    Sea temperature

    units :

    degrees_C

    data_mode :

    R

    ancillary_variables :

    TEMP_QC

    array([[28.62000136, 28.62000136, 28.61900136, ...,         nan,
                    nan,         nan],
           [28.26200134, 28.26800134, 28.26900134, ...,  2.26900011,
             2.24100011,         nan],
           [28.11000134, 28.09800133, 28.06100133, ...,  2.25400011,
                    nan,         nan],
           ...,
           [29.87100142, 29.87500142, 29.87200142, ...,         nan,
                    nan,         nan],
           [30.35700144, 30.38200144, 30.38700144, ...,  2.29000011,
             2.24700011,         nan],
           [30.31100144, 30.31300144, 30.30900144, ...,         nan,
                    nan,         nan]], shape=(60, 103))

  • TEMP_QC
    (TIME, DEPTH)
    float32
    1.0 1.0 1.0 1.0 ... 1.0 nan nan nan

    long_name :

    Sea temperature quality flag

    conventions :

    Copernicus Marine In Situ reference table 2

    flag_meanings :

    no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed value_below_detection nominal_value interpolated_value missing_value

    valid_min :

    0

    valid_max :

    9

    flag_values :

    [0 1 2 3 4 5 6 7 8 9]

    array([[ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1., nan],
           [ 1.,  1.,  1., ...,  1., nan, nan],
           ...,
           [ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1., nan],
           [ 1.,  1.,  1., ..., nan, nan, nan]],
          shape=(60, 103), dtype=float32)

  • TEMP_ADJUSTED
    (TIME, DEPTH)
    float64
    28.62 28.62 28.62 ... nan nan nan

    standard_name :

    sea_water_temperature

    long_name :

    Sea temperature adjusted

    units :

    degrees_C

    data_mode :

    M

    ancillary_variables :

    TEMP_ADJUSTED_QC TEMP_ADJUSTED_DM TEMP_ADJUSTED_ERROR

    array([[28.62000136, 28.62000136, 28.61900136, ...,         nan,
                    nan,         nan],
           [28.26200134, 28.26800134, 28.26900134, ...,  2.26900011,
             2.24100011,         nan],
           [28.11000134, 28.09800133, 28.06100133, ...,  2.25400011,
                    nan,         nan],
           ...,
           [29.87100142, 29.87500142, 29.87200142, ...,         nan,
                    nan,         nan],
           [30.35700144, 30.38200144, 30.38700144, ...,  2.29000011,
             2.24700011,         nan],
           [30.31100144, 30.31300144, 30.30900144, ...,         nan,
                    nan,         nan]], shape=(60, 103))

  • TEMP_ADJUSTED_QC
    (TIME, DEPTH)
    float32
    1.0 1.0 1.0 1.0 ... 1.0 nan nan nan

    long_name :

    Sea temperature adjusted quality flag

    conventions :

    Copernicus Marine In Situ reference table 2

    flag_meanings :

    no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed value_below_detection nominal_value interpolated_value missing_value

    valid_min :

    0

    valid_max :

    9

    flag_values :

    [0 1 2 3 4 5 6 7 8 9]

    array([[ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1., nan],
           [ 1.,  1.,  1., ...,  1., nan, nan],
           ...,
           [ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1., nan],
           [ 1.,  1.,  1., ..., nan, nan, nan]],
          shape=(60, 103), dtype=float32)

  • TEMP_ADJUSTED_DM
    (TIME, DEPTH)
    object
    b'D' b'D' b'D' b'D' ... nan nan nan

    long_name :

    Sea temperature adjusted method of data processing

    conventions :

    Copernicus Marine In Situ reference table 1

    flag_values :

    R, A, D

    flag_meanings :

    real-time adjusted-in-real-time delayed-mode

    array([[b'D', b'D', b'D', ..., nan, nan, nan],
           [b'D', b'D', b'D', ..., b'D', b'D', nan],
           [b'D', b'D', b'D', ..., b'D', nan, nan],
           ...,
           [b'A', b'A', b'A', ..., nan, nan, nan],
           [b'A', b'A', b'A', ..., b'A', b'A', nan],
           [b'A', b'A', b'A', ..., nan, nan, nan]],
          shape=(60, 103), dtype=object)

  • TEMP_ADJUSTED_ERROR
    (TIME, DEPTH)
    float64
    0.001 0.001 0.001 ... nan nan nan

    long_name :

    Sea temperature adjusted error

    units :

    degrees_C

    array([[0.001, 0.001, 0.001, ...,   nan,   nan,   nan],
           [0.001, 0.001, 0.001, ..., 0.001, 0.001,   nan],
           [0.001, 0.001, 0.001, ..., 0.001,   nan,   nan],
           ...,
           [  nan,   nan,   nan, ...,   nan,   nan,   nan],
           [  nan,   nan,   nan, ...,   nan,   nan,   nan],
           [  nan,   nan,   nan, ...,   nan,   nan,   nan]], shape=(60, 103))

  • PSAL
    (TIME, DEPTH)
    float64
    34.02 34.06 34.08 ... nan nan nan

    standard_name :

    sea_water_practical_salinity

    long_name :

    Practical salinity

    units :

    0.001

    data_mode :

    R

    ancillary_variables :

    PSAL_QC

    array([[34.02400162, 34.06400162, 34.08400162, ...,         nan,
                    nan,         nan],
           [34.04300162, 34.04000162, 34.03900162, ..., 34.62800164,
            34.63000164,         nan],
           [34.14200162, 34.14800162, 34.14400162, ..., 34.62900164,
                    nan,         nan],
           ...,
           [34.39400163, 34.39000163, 34.39200163, ...,         nan,
                    nan,         nan],
           [34.34200163, 34.33200163, 34.32900163, ..., 34.63000164,
            34.63300164,         nan],
           [34.41200163, 34.40900163, 34.41100163, ...,         nan,
                    nan,         nan]], shape=(60, 103))

  • PSAL_QC
    (TIME, DEPTH)
    float32
    1.0 1.0 1.0 1.0 ... 1.0 nan nan nan

    long_name :

    Practical salinity quality flag

    conventions :

    Copernicus Marine In Situ reference table 2

    flag_meanings :

    no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed value_below_detection nominal_value interpolated_value missing_value

    valid_min :

    0

    valid_max :

    9

    flag_values :

    [0 1 2 3 4 5 6 7 8 9]

    array([[ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1., nan],
           [ 1.,  1.,  1., ...,  1., nan, nan],
           ...,
           [ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1., nan],
           [ 1.,  1.,  1., ..., nan, nan, nan]],
          shape=(60, 103), dtype=float32)

  • PSAL_ADJUSTED
    (TIME, DEPTH)
    float64
    34.02 34.06 34.08 ... nan nan nan

    standard_name :

    sea_water_practical_salinity

    long_name :

    Practical salinity adjusted

    units :

    0.001

    data_mode :

    M

    ancillary_variables :

    PSAL_ADJUSTED_QC PSAL_ADJUSTED_DM PSAL_ADJUSTED_ERROR

    array([[34.02400162, 34.06400162, 34.08400162, ...,         nan,
                    nan,         nan],
           [34.04300162, 34.04000162, 34.03900162, ..., 34.62900164,
            34.63000164,         nan],
           [34.14400162, 34.15100162, 34.14500162, ..., 34.62900164,
                    nan,         nan],
           ...,
           [34.39400163, 34.39000163, 34.39200163, ...,         nan,
                    nan,         nan],
           [34.34200163, 34.33200163, 34.32900163, ..., 34.63000164,
            34.63300164,         nan],
           [34.41200163, 34.40900163, 34.41100163, ...,         nan,
                    nan,         nan]], shape=(60, 103))

  • PSAL_ADJUSTED_QC
    (TIME, DEPTH)
    float32
    1.0 1.0 1.0 1.0 ... 1.0 nan nan nan

    long_name :

    Practical salinity adjusted quality flag

    conventions :

    Copernicus Marine In Situ reference table 2

    flag_meanings :

    no_qc_performed good_data probably_good_data bad_data_that_are_potentially_correctable bad_data value_changed value_below_detection nominal_value interpolated_value missing_value

    valid_min :

    0

    valid_max :

    9

    flag_values :

    [0 1 2 3 4 5 6 7 8 9]

    array([[ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1., nan],
           [ 1.,  1.,  1., ...,  1., nan, nan],
           ...,
           [ 1.,  1.,  1., ..., nan, nan, nan],
           [ 1.,  1.,  1., ...,  1.,  1., nan],
           [ 1.,  1.,  1., ..., nan, nan, nan]],
          shape=(60, 103), dtype=float32)

  • PSAL_ADJUSTED_DM
    (TIME, DEPTH)
    object
    b'D' b'D' b'D' b'D' ... nan nan nan

    long_name :

    Practical salinity adjusted method of data processing

    conventions :

    Copernicus Marine In Situ reference table 1

    flag_values :

    R, A, D

    flag_meanings :

    real-time adjusted-in-real-time delayed-mode

    array([[b'D', b'D', b'D', ..., nan, nan, nan],
           [b'D', b'D', b'D', ..., b'D', b'D', nan],
           [b'D', b'D', b'D', ..., b'D', nan, nan],
           ...,
           [b'A', b'A', b'A', ..., nan, nan, nan],
           [b'A', b'A', b'A', ..., b'A', b'A', nan],
           [b'A', b'A', b'A', ..., nan, nan, nan]],
          shape=(60, 103), dtype=object)

  • PSAL_ADJUSTED_ERROR
    (TIME, DEPTH)
    float64
    0.009 0.009 0.009 ... nan nan nan

    long_name :

    Practical salinity adjusted error

    units :

    0.001

    array([[0.009, 0.009, 0.009, ...,   nan,   nan,   nan],
           [0.009, 0.009, 0.009, ..., 0.009, 0.009,   nan],
           [0.009, 0.009, 0.009, ..., 0.009,   nan,   nan],
           ...,
           [  nan,   nan,   nan, ...,   nan,   nan,   nan],
           [  nan,   nan,   nan, ...,   nan,   nan,   nan],
           [  nan,   nan,   nan, ...,   nan,   nan,   nan]], shape=(60, 103))

• Indexes: (3)
  • TIME
    PandasIndex

    PandasIndex(DatetimeIndex(['2024-02-05 01:30:26', '2024-02-12 03:32:03',
                   '2024-02-19 05:37:58', '2024-02-26 07:32:30',
                   '2024-03-04 09:43:25', '2024-03-11 11:30:04',
                   '2024-03-18 13:36:50', '2024-03-25 15:43:17',
                   '2024-04-01 17:40:01', '2024-04-08 19:31:25',
                   '2024-04-15 21:39:55', '2024-04-22 23:34:04',
                   '2024-04-30 01:33:13', '2024-05-07 03:35:13',
                   '2024-05-14 05:23:32', '2024-05-21 07:35:33',
                   '2024-05-28 09:32:49', '2024-06-04 11:33:03',
                   '2024-06-11 13:23:04', '2024-06-18 15:51:33',
                   '2024-06-25 17:39:39', '2024-07-02 19:43:25',
                   '2024-07-09 21:43:50', '2024-07-16 23:39:34',
                   '2024-07-24 01:41:55', '2024-07-31 03:39:25',
                   '2024-08-07 05:31:50', '2024-08-14 07:35:14',
                   '2024-08-21 09:34:44', '2024-08-28 11:34:25',
                   '2024-09-04 13:32:25', '2024-09-11 15:36:27',
                   '2024-09-18 17:38:33', '2024-09-25 19:26:54',
                   '2024-10-02 21:30:25', '2024-10-09 23:25:04',
                   '2024-10-17 01:23:19', '2024-10-24 03:32:55',
                   '2024-10-31 05:39:13', '2024-11-07 07:39:01',
                   '2024-11-14 09:32:19', '2024-11-21 11:45:32',
                   '2024-11-28 13:39:25', '2024-12-05 15:38:21',
                   '2024-12-12 17:37:58', '2024-12-19 19:38:04',
                   '2024-12-26 21:44:32', '2025-01-02 23:36:34',
                   '2025-01-10 01:32:19', '2025-01-17 03:32:15',
                   '2025-01-24 05:39:25', '2025-01-31 07:36:29',
                   '2025-02-07 09:33:01', '2025-02-14 11:25:19',
                   '2025-02-21 13:41:19', '2025-02-28 15:38:13',
                   '2025-03-07 17:31:14', '2025-03-14 19:44:01',
                   '2025-03-21 21:36:20', '2025-03-28 23:30:57'],
                  dtype='datetime64[ns]', name='TIME', freq=None))

  • LATITUDE
    PandasIndex

    PandasIndex(Index([ 4.763999938964844,  4.567999839782715, 4.3429999351501465,
            4.129000186920166,  4.057000160217285,  4.039000034332275,
             4.01200008392334,  4.020999908447266,  4.010000228881836,
           3.7219998836517334,  2.989000082015991,  1.569000005722046,
            1.003999948501587,  2.122999906539917,  2.809999942779541,
            3.441999912261963,  3.825000047683716,  4.760000228881836,
            5.484000205993652,  5.173999786376953,  4.335000038146973,
            4.084000110626221,  4.316999912261963,  4.414999961853027,
            4.257999897003174,  4.359000205993652,  4.624000072479248,
            4.927999973297119,  5.046999931335449,  5.151000022888184,
            5.271999835968018,  5.288000106811523, 5.1620001792907715,
            5.158999919891357,  5.206999778747559,  5.489999771118164,
            5.709000110626221,  5.754000186920166,  5.688000202178955,
            5.814000129699707,  5.802999973297119,   5.46999979019165,
            4.813000202178955,  4.442999839782715,  4.373000144958496,
            4.485000133514404,  4.629000186920166,  4.765999794006348,
            4.683000087738037,  4.414000034332275, 4.2769999504089355,
            4.248000144958496,  4.267000198364258,  4.283999919891357,
            4.361000061035156,  4.464000225067139,  4.263999938964844,
            3.933000087738037, 3.9059998989105225,  4.038000106811523],
          dtype='float32', name='LATITUDE'))

  • LONGITUDE
    PandasIndex

    PandasIndex(Index([ 130.0780029296875,  129.7989959716797,  129.5469970703125,
            129.3419952392578, 129.33099365234375,  129.3300018310547,
            129.3780059814453, 129.55999755859375, 129.31700134277344,
           128.87399291992188,  129.1820068359375,  129.8509979248047,
            130.8260040283203, 131.56100463867188, 131.06199645996094,
            130.0399932861328, 129.03599548339844,  128.3159942626953,
           128.59300231933594,  129.2469940185547,  129.1739959716797,
            129.1060028076172,  128.9949951171875, 128.99400329589844,
            128.9949951171875,  128.8699951171875,  128.7480010986328,
           128.56199645996094,  128.3560028076172, 128.14700317382812,
           128.07200622558594, 128.18099975585938,  128.4010009765625,
           128.89300537109375, 129.57200622558594, 130.13999938964844,
            130.5780029296875, 130.82400512695312, 130.75799560546875,
           130.68800354003906, 130.86099243164062, 131.05499267578125,
           131.11300659179688, 131.03500366210938,  130.9969940185547,
           131.05599975585938, 131.14700317382812,  131.3249969482422,
           131.49400329589844,  131.3520050048828,  131.2050018310547,
           131.16900634765625, 131.46600341796875, 131.64300537109375,
            131.6300048828125, 131.68299865722656,  131.8939971923828,
            131.7030029296875, 131.38299560546875, 131.14500427246094],
          dtype='float32', name='LONGITUDE'))

• Attributes: (49)

  data_type :

  OceanSITES vertical profile

  format_version :

  1.4

  platform_code :

  2902800

  institution :

  First Institute of Oceanography - Ministry of Natural Resources

  institution_edmo_code :

  4640

  site_code :

  wmo_platform_code :

  2902800

  coriolis_platform_code :

  2902800

  platform_name :

  P32800-20CH018

  ices_platform_code :

  source :

  drifting subsurface profiling float

  source_platform_category_code :

  46

  wmo_inst_type :

  841

  references :

  http://marine.copernicus.eu http://www.marineinsitu.eu

  comment :

  Conventions :

  CF-1.6 Copernicus-InSituTAC-FormatManual-1.42 Copernicus-InSituTAC-SRD-1.5 Copernicus-InSituTAC-ParametersList-3.2.1

  netcdf_version :

  netCDF-4 classic model

  title :

  Global Ocean - In Situ Observation Copernicus

  summary :

  naming_authority :

  Copernicus Marine In Situ

  author :

  id :

  GL_PR_PF_2902800

  cdm_data_type :

  profile

  area :

  Global Ocean

  bottom_depth :

  institution_references :

  contact :

  cmems-service@ifremer.fr

  data_assembly_center :

  Ifremer

  pi_name :

  FENG ZHOU

  distribution_statement :

  These data follow Copernicus standards; they are public and free of charge. User assumes all risk for use of data. User must display citation in any publication or product using data. User must contact PI prior to any commercial use of data.

  citation :

  These data were collected and made freely available by the Copernicus project and the programs that contribute to it.

  update_interval :

  P1D

  qc_manual :

  Recommendations for in-situ data Near Real Time Quality Control https://doi.org/10.13155/36230

  doi :

  https://doi.org/10.13155/59938 https://doi.org/10.13155/40846 https://doi.org/10.13155/53381 https://doi.org/10.13155/36230 https://doi.org/10.13155/43494

  license :

  https://marine.copernicus.eu/user-corner/service-commitments-and-licence

  geospatial_lat_min :

  1.00400

  geospatial_lat_max :

  6.68900

  geospatial_lon_min :

  128.07201

  geospatial_lon_max :

  147.03700

  geospatial_vertical_min :

  0.00

  geospatial_vertical_max :

  2021.90

  time_coverage_start :

  2021-01-10T05:49:54Z

  time_coverage_end :

  2025-04-05T01:30:25Z

  last_date_observation :

  2025-04-05T01:30:25Z

  last_latitude_observation :

  4.24300

  last_longitude_observation :

  130.82900

  date_update :

  2025-04-15T09:50:18Z

  history :

  2025-04-15T09:50:18Z : Creation

  data_mode :

  M

Ocean Surface Drifter Data

# Data sudah terseleksi saat akuisisi, hanya perlu menyesuaikan format data dan merapihkan data tabel
# Ambil baris pertama sebagai header yang benar
new_header = np.asarray(df_drifter.columns)
df_drifter_adjs = df_drifter[1:]
df_drifter_adjs.columns = new_header

# Drop rows dengan ID, latitude, atau longitude kosong
df_drifter_adjs = df_drifter_adjs.dropna(subset=["ID", "latitude", "longitude"])

# Konversi tipe data yang diperlukan
df_drifter_adjs["ID"] = df_drifter_adjs["ID"].astype(int)
df_drifter_adjs["latitude"] = df_drifter_adjs["latitude"].astype(float)
df_drifter_adjs["longitude"] = df_drifter_adjs["longitude"].astype(float)
df_drifter_adjs["time"] = pd.to_datetime(df_drifter_adjs["time"], format="%Y-%m-%dT%H:%M:%SZ")
df_drifter_adjs["start_date"] = pd.to_datetime(df_drifter_adjs["start_date"], format="%Y-%m-%dT%H:%M:%SZ")
df_drifter_adjs["deploy_date"] = pd.to_datetime(df_drifter_adjs["deploy_date"], format="%Y-%m-%dT%H:%M:%SZ")
df_drifter_adjs["end_date"] = pd.to_datetime(df_drifter_adjs["end_date"], format="%Y-%m-%dT%H:%M:%SZ")
df_drifter_adjs["drogue_lost_date"] = pd.to_datetime(df_drifter_adjs["drogue_lost_date"], format="%Y-%m-%dT%H:%M:%SZ")

# Ambil ID unik
unique_ids = df_drifter_adjs["ID"].unique()

# Tampilkan jumlah dan sampel ID
len(unique_ids), [int(i) for i in unique_ids[:5]]

(37,
 [300234010820530,
  300234010827560,
  300234010828550,
  300234010829560,
  300234060253330])

# Menampilkan data drifter yang sudah disesuaikan
df_drifter_adjs

	ID	WMO	time	latitude	longitude	sst	err_sst	ve	vn	err_lat	...	deploy_lon	start_date	start_lat	start_lon	end_date	end_lat	end_lon	drogue_lost_date	DrogueType	DrogueLength
1	300234010820530	5102836.0	2024-02-01 00:00:00	11.293	131.199	28.159	0.041	-0.70395	0.12636	0.001	...	-154.965	2022-09-28	3.0	-154.98	2024-02-18	8.95	126.31	2023-01-03	NaN	6.6 m
2	300234010820530	5102836.0	2024-02-01 06:00:00	11.315	131.057	28.178	0.041	-0.67358	0.19455	0.001	...	-154.965	2022-09-28	3.0	-154.98	2024-02-18	8.95	126.31	2023-01-03	NaN	6.6 m
3	300234010820530	5102836.0	2024-02-01 12:00:00	11.369	130.932	28.082	0.041	-0.57381	0.30063	0.001	...	-154.965	2022-09-28	3.0	-154.98	2024-02-18	8.95	126.31	2023-01-03	NaN	6.6 m
4	300234010820530	5102836.0	2024-02-01 18:00:00	11.432	130.830	28.007	0.041	-0.53084	0.22220	0.001	...	-154.965	2022-09-28	3.0	-154.98	2024-02-18	8.95	126.31	2023-01-03	NaN	6.6 m
5	300234010820530	5102836.0	2024-02-02 00:00:00	11.455	130.722	27.968	0.041	-0.54977	-0.01695	0.001	...	-154.965	2022-09-28	3.0	-154.98	2024-02-18	8.95	126.31	2023-01-03	NaN	6.6 m
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6012	300534064403780	4804107.0	2024-04-13 06:00:00	-4.143	90.235	29.973	0.065	-0.29942	-0.16107	0.015	...	77.38	2024-01-16	-7.92	77.38	NaT	NaN	NaN	NaT	NaN	7.5 m
6013	300534064403780	4804107.0	2024-04-13 12:00:00	-4.168	90.174	29.973	0.062	-0.26649	-0.05737	0.002	...	77.38	2024-01-16	-7.92	77.38	NaT	NaN	NaN	NaT	NaN	7.5 m
6014	300534064403780	4804107.0	2024-04-13 18:00:00	-4.165	90.131	29.931	0.065	-0.22199	0.03832	0.015	...	77.38	2024-01-16	-7.92	77.38	NaT	NaN	NaN	NaT	NaN	7.5 m
6015	300534064403780	4804107.0	2024-04-14 00:00:00	-4.153	90.087	29.887	0.062	-0.31198	0.07767	0.002	...	77.38	2024-01-16	-7.92	77.38	NaT	NaN	NaN	NaT	NaN	7.5 m
6016	300534064403780	4804107.0	2024-04-14 06:00:00	-4.135	90.010	29.962	0.065	-0.48575	0.10340	0.015	...	77.38	2024-01-16	-7.92	77.38	NaT	NaN	NaN	NaT	NaN	7.5 m

6016 rows × 23 columns

Visualisasi Data

Membuat diagram Hovmoller dari data argo

Menggunakan sea water pressure sebagai layer kedalaman

# Ambil variabel penting
time = ds_argo_filtered['TIME'].values              # shape: (TIME,)
press = ds_argo_filtered['PRES_ADJUSTED'].values    # shape: (TIME, DEPTH)
temp = ds_argo_filtered['TEMP_ADJUSTED'].values     # shape: (TIME, DEPTH)

# Ulangi time sebanyak jumlah depth (103)
n_time, n_depth = press.shape
time_2d = np.repeat(time[:, np.newaxis], n_depth, axis=1)  # shape: (TIME, DEPTH)

# Flatten semua
time_flat = time_2d.flatten()
press_flat = press.flatten()
temp_flat = temp.flatten()

# Hapus NaN (masking)
valid = ~np.isnan(press_flat) & ~np.isnan(temp_flat)
time_flat = time_flat[valid]
press_flat = press_flat[valid]
temp_flat = temp_flat[valid]

# Flatten time
time_flat_datetime = np.array([pd.to_datetime(t).to_pydatetime() for t in time_flat])

# Plot
fig, ax = plt.subplots(figsize=(8, 8/(4/3)))
sc = ax.scatter(time_flat_datetime, press_flat, c=temp_flat, cmap='jet', s=60, marker='s')
ax.invert_yaxis()
plt.colorbar(sc, label="Temperature (°C)", extend='both')
ax.set_xlabel("Time")
ax.set_ylabel("Sea Water Pressure (dbar)")
ax.set_title("Argo Float Temperature vs Pressure over Time")
ax.grid(True)

# Set ticks setiap bulan
ax.xaxis.set_major_locator(mdates.MonthLocator())

# Custom formatter: tampilkan bulan, dan tahun jika Januari
def custom_date_formatter(x, pos):
    dt = mdates.num2date(x)
    if dt.month == 1:
        return f"{dt:%m}\n{dt.year}"
    else:
        return f"{dt:%m}"

ax.xaxis.set_major_formatter(plt.FuncFormatter(custom_date_formatter))

# Rotasi agar rapi
plt.setp(ax.get_xticklabels(), rotation=0, ha='center')

plt.tight_layout()
plt.show()

Menggunakan sea depth sebagai layer kedalaman

# Ekstrak variabel yang dibutuhkan dari dataset
time = ds_argo_filtered["TIME"].values                            # shape: (TIME,)
press = ds_argo_filtered["PRES_ADJUSTED"].values                  # shape: (TIME, DEPTH)
temp = ds_argo_filtered["TEMP_ADJUSTED"].values                   # shape: (TIME, DEPTH)
latitude = ds_argo_filtered["LATITUDE"].values                    # shape: (TIME,)

# Konversi tekanan ke kedalaman (dalam meter) menggunakan lintang sesuai TIME
depth = -gsw.z_from_p(press, latitude[:, np.newaxis])  # shape: (TIME, DEPTH)

# Buat time array 2D agar bisa di-flatten bareng dengan variabel lainnya
n_time, n_depth = press.shape
time_2d = np.repeat(time[:, np.newaxis], n_depth, axis=1)  # shape: (TIME, DEPTH)

# Flatten semua array
time_flat = time_2d.flatten()
depth_flat = depth.flatten()
temp_flat = temp.flatten()

# Masking untuk menghapus nilai NaN
valid = ~np.isnan(depth_flat) & ~np.isnan(temp_flat)
time_flat = time_flat[valid]
depth_flat = depth_flat[valid]
temp_flat = temp_flat[valid]

# Konversi waktu ke datetime untuk plotting
time_flat_datetime = np.array([pd.to_datetime(t).to_pydatetime() for t in time_flat])

# Plot Hovmöller diagram (Time vs Depth, colored by Temperature)
fig, ax = plt.subplots(figsize=(8, 8/(4/3)))
sc = ax.scatter(time_flat_datetime, depth_flat, c=temp_flat, cmap='jet', s=60, marker='s')
ax.invert_yaxis()
plt.colorbar(sc, label="Temperature (°C)", extend='both')
ax.set_xlabel("Time")
ax.set_ylabel("Depth (m)")
ax.set_title("Argo Float Temperature vs Depth over Time")
ax.grid(True)

# Format sumbu X agar tampil bulanan
ax.xaxis.set_major_locator(mdates.MonthLocator())

# Custom formatter untuk sumbu X
def custom_date_formatter(x, pos):
    dt = mdates.num2date(x)
    if dt.month == 1:
        return f"{dt:%m}\n{dt.year}"
    else:
        return f"{dt:%m}"

ax.xaxis.set_major_formatter(plt.FuncFormatter(custom_date_formatter))
plt.setp(ax.get_xticklabels(), rotation=0, ha='center')

plt.tight_layout()
plt.show()

Plot data model

Esktraksi data dan membuat dataframe

# Ekstrak Data model berdasarkan lat/lon/time data obs
argo_lats = ds_argo_filtered['LATITUDE'].values
argo_lons = ds_argo_filtered['LONGITUDE'].values
argo_time = pd.to_datetime(ds_argo_filtered['TIME'].values)

mod_temps_val = []

for i, dt in enumerate(argo_time):
    argLat = argo_lats[i]
    argLon = argo_lons[i]
    ds0 = ds_mod.sel(date=dt, lat=argLat, lon=argLon, method='nearest')
    mod_temps_val.append(ds0['sw_temp'].values)
    
# Buat dataframe untuk suhu pada kedalaman target dari data model
df_mod_temp = pd.DataFrame(mod_temps_val, columns=np.abs(ds_mod.depth.values))
df_mod_temp['TIME'] = argo_time
df_mod_temp.set_index('TIME', inplace=True)
df_mod_temp = df_mod_temp.transpose()
df_mod_temp.index.name = 'Depth (m)'
df_mod_temp.columns.name = 'Time'
df_mod_temp

Exception ignored in: <bound method IPythonKernel._clean_thread_parent_frames of <ipykernel.ipkernel.IPythonKernel object at 0x7f98b1ad3620>>
Traceback (most recent call last):
  File "/opt/conda/envs/ofs/lib/python3.13/site-packages/ipykernel/ipkernel.py", line 775, in _clean_thread_parent_frames
    def _clean_thread_parent_frames(
KeyboardInterrupt: 

Plot Data

# Ambil data
df_temp_sorted = df_mod_temp.sort_index(ascending=True)

# Waktu dan kedalaman
times = pd.to_datetime(df_temp_sorted.columns.values)
depths = df_temp_sorted.index.values.astype(float)

# Konversi ke edge arrays
time_edges = mdates.date2num(times)
depth_edges = depths

# Tambah 1 nilai di ujung agar jumlah edge = jumlah data + 1
def get_edges(arr):
    delta = np.diff(arr) / 2
    edges = np.concatenate([[arr[0] - delta[0]], arr[:-1] + delta, [arr[-1] + delta[-1]]])
    return edges

time_edges = get_edges(time_edges)
depth_edges = get_edges(depths)

# Data suhu
temperature_2d = df_temp_sorted.values.astype(float)

# Plot
fig, ax = plt.subplots(figsize=(11, 11/(10/6)))
pcm = ax.pcolormesh(mdates.num2date(time_edges), depth_edges, temperature_2d, cmap='jet', shading='auto')
ax.invert_yaxis()

# Format dan label
plt.colorbar(pcm, label=r"Potential Temperature ($\theta$) [°C]", extend='both')
ax.set_xlabel("Time")
ax.set_ylabel("Depth (m)")
ax.set_title("Sea Temperature from Model at Target Depths over Time")

# Format sumbu waktu
def custom_date_formatter(x, pos):
    dt = mdates.num2date(x)
    return f"{dt:%m}\n{dt.year}" if dt.month == 1 else f"{dt:%m}"

ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_major_formatter(plt.FuncFormatter(custom_date_formatter))
plt.setp(ax.get_xticklabels(), rotation=0, ha='center')

# Teks penjelas di luar plot dengan kotak
depth_note = (
    "Data hanya tersedia secara dikret pada kedalaman tertentu:\n "
    "0, 5, 10, 15, 20, 30, 40, 50, 75, 100, 125, 150, 200, 250,\n "
    "300, 400, 500, 600, 800, 1000, 1200, 1500, 2000 meter"
)

fig.text(
    0.078, -0.06, depth_note,
    ha='left', fontsize=9, color='black',
    bbox=dict(facecolor='white', edgecolor='gray', boxstyle='round,pad=0.5', alpha=0.9)
)

plt.tight_layout()
plt.show()

Memetakan data drifter

Memilih data berdasarkan ID dan data yang dibutuhkan

df_drifter2plot = df_drifter_adjs.loc[df_drifter_adjs['ID'] == 300234061473430][['ID', 'time', 'latitude', 'longitude', 'sst']]
df_drifter2plot.set_index(['time'], inplace=True)
df_drifter2plot

	ID	latitude	longitude	sst
time
2024-03-01 06:00:00	300234061473430	-14.068	144.960	30.078
2024-03-01 12:00:00	300234061473430	-14.008	144.871	29.994
2024-03-01 18:00:00	300234061473430	-13.956	144.759	29.961
2024-03-02 00:00:00	300234061473430	-13.861	144.574	29.993
2024-03-02 06:00:00	300234061473430	-13.826	144.387	30.085
2024-03-02 12:00:00	300234061473430	-13.755	144.309	30.052
2024-03-02 18:00:00	300234061473430	-13.658	144.208	29.968
2024-03-03 00:00:00	300234061473430	-13.620	144.121	30.277
2024-03-03 06:00:00	300234061473430	-13.631	144.069	30.201
2024-03-03 12:00:00	300234061473430	-13.635	144.030	29.931
2024-03-03 18:00:00	300234061473430	-13.609	143.962	29.889
2024-03-04 00:00:00	300234061473430	-13.569	143.916	29.71
2024-03-04 06:00:00	300234061473430	-13.573	143.881	29.843
2024-03-04 12:00:00	300234061473430	-13.560	143.861	29.893
2024-03-04 18:00:00	300234061473430	-13.560	143.817	29.899
2024-03-05 00:00:00	300234061473430	-13.544	143.799	29.795
2024-03-05 06:00:00	300234061473430	-13.544	143.784	30.079
2024-03-05 12:00:00	300234061473430	-13.518	143.775	29.913
2024-03-05 18:00:00	300234061473430	-13.494	143.745	29.934
2024-03-06 00:00:00	300234061473430	-13.469	143.715	29.995
2024-03-06 06:00:00	300234061473430	-13.417	143.695	30.384
2024-03-06 12:00:00	300234061473430	-13.304	143.639	30.251
2024-03-06 18:00:00	300234061473430	-13.175	143.582	29.989
2024-03-07 00:00:00	300234061473430	-13.067	143.552	29.743
2024-03-07 06:00:00	300234061473430	-13.044	143.571	29.468
2024-03-07 12:00:00	300234061473430	-12.950	143.580	29.422
2024-03-07 18:00:00	300234061473430	-12.842	143.545	29.214
2024-03-08 00:00:00	300234061473430	-12.815	143.496	29.48
2024-03-08 06:00:00	300234061473430	-12.790	143.500	30.184
2024-03-08 12:00:00	300234061473430	-12.779	143.451	30.044
2024-03-08 18:00:00	300234061473430	-12.756	143.438	29.996

Menampilkan peta

from mods import compute_extent

lons, lats = df_drifter2plot['longitude'].values, df_drifter2plot['latitude'].values
dts = pd.to_datetime(df_drifter2plot.index.values)
sst = df_drifter2plot['sst'].astype(float).values

# Final extent
exts = compute_extent(lons, lats)


# Basemap
proj = ccrs.PlateCarree()
rat=10/6
fig, ax = plt.subplots(figsize=(11, 11/rat), subplot_kw=dict(projection=proj))
ax.set_extent(exts, crs=proj)
ax.add_feature(cfeature.GSHHSFeature(scale="high", levels=[1, 2, 3, 4], facecolor="linen"), linewidth=.8)
ax.add_feature(cfeature.BORDERS, linestyle=":")


# Level SST
vmin, vmax, step = 26, 35, 0.2
levels = np.arange(vmin, vmax + step, step)

# Buat colormap dan norm untuk diskritisasi warna
cmap = cm.get_cmap('turbo', len(levels) - 1)  # colormap discrete
norm = matplotlib.colors.BoundaryNorm(boundaries=levels, ncolors=cmap.N)

# Buat keterangan start dan end untuk rute drifter
ax.plot(lons[0]+.04, lats[0]+.04, marker='*', color='k', markersize=10, transform=proj, label='Start')
ax.plot(lons[-1]+.04, lats[-1]+.04, marker='x', color='k', markersize=10, transform=proj, label='End')

# Buat scatter plot
sctmod = ax.scatter(
    lons, lats, transform=proj, alpha=1.0, marker='o',
    c=sst, s=80, cmap=cmap, norm=norm
)

# Buat colorbar
cbar = plt.colorbar(sctmod, 
                    ax=ax, 
                    orientation='horizontal', 
                    shrink=0.7, 
                    pad=0.08, 
                    extend=None)
cbar.set_label("SST (°C)")


# Gridlines (Lintang-bujur)
gl = ax.gridlines(draw_labels=True, linestyle=':', alpha=.2)
gl.top_labels = False
gl.left_labels = True
gl.right_labels = True
gl.bottom_labels = True

# Legenda
legend_elements = [
    Line2D([0], [0], lw=0, marker='*', color='k', label='Start', markersize=8),
    Line2D([0], [0], lw=0, marker='x', color='k', label='End', markersize=8)
]
ax.legend(handles=legend_elements, loc='upper right')

# Judul
ax.set_title('SST from drifter', fontsize=10)

# Menyesuaikan layout dan menampilkan plot
plt.tight_layout()
plt.show()

Plot data model

Ekstraksi data dan membuat dataframe

# Ekstrak Data model berdasarkan lat/lon/time data drifter
lons, lats = df_drifter2plot['longitude'].values, df_drifter2plot['latitude'].values
drf_time = pd.to_datetime(df_drifter2plot.index.values)

mod_temps_val = []
mod_tms = []

for i, dt in enumerate(drf_time):
    drfLat = lats[i]
    drfLon = lons[i]
    ds0 = ds_mod.sel(date=dt.strftime('%Y-%m-%d'), depth=0., lat=drfLat, lon=drfLon, method='nearest')
    mod_temps_val.append(ds0['sw_temp'].values)
    mod_tms.append(ds0.date.values)
    
# Buat dataframe untuk suhu pada kedalaman target dari data model
df_mod_temp = pd.DataFrame(
    data={
        'time': pd.to_datetime(mod_tms),
        'latitude': lats,
        'longitude': lons,
        'sst': mod_temps_val
    }
)
df_mod_temp.set_index(['time'], inplace=True)
df_mod_temp

	latitude	longitude	sst
time
2024-03-01	-14.068	144.960	30.765625
2024-03-01	-14.008	144.871	30.679688
2024-03-01	-13.956	144.759	30.625
2024-03-02	-13.861	144.574	30.34375
2024-03-02	-13.826	144.387	30.429688
2024-03-02	-13.755	144.309	30.421875
2024-03-02	-13.658	144.208	30.304688
2024-03-03	-13.620	144.121	30.328125
2024-03-03	-13.631	144.069	30.359375
2024-03-03	-13.635	144.030	30.390625
2024-03-03	-13.609	143.962	30.421875
2024-03-04	-13.569	143.916	30.539062
2024-03-04	-13.573	143.881	30.570312
2024-03-04	-13.560	143.861	30.578125
2024-03-04	-13.560	143.817	30.59375
2024-03-05	-13.544	143.799	30.414062
2024-03-05	-13.544	143.784	30.4375
2024-03-05	-13.518	143.775	30.4375
2024-03-05	-13.494	143.745	30.46875
2024-03-06	-13.469	143.715	30.5
2024-03-06	-13.417	143.695	30.546875
2024-03-06	-13.304	143.639	30.617188
2024-03-06	-13.175	143.582	30.75
2024-03-07	-13.067	143.552	30.78125
2024-03-07	-13.044	143.571	30.734375
2024-03-07	-12.950	143.580	30.789062
2024-03-07	-12.842	143.545	30.867188
2024-03-08	-12.815	143.496	30.046875
2024-03-08	-12.790	143.500	30.015625
2024-03-08	-12.779	143.451	30.015625
2024-03-08	-12.756	143.438	29.984375

Plot data

lons, lats = df_mod_temp['longitude'].values, df_mod_temp['latitude'].values
dts = pd.to_datetime(df_mod_temp.index.values)
sst = df_mod_temp['sst'].astype(float).values

# Final extent
exts = compute_extent(lons, lats)


# Basemap
proj = ccrs.PlateCarree()
rat=10/6
fig, ax = plt.subplots(figsize=(11, 11/rat), subplot_kw=dict(projection=proj))
ax.set_extent(exts, crs=proj)
ax.add_feature(cfeature.GSHHSFeature(scale="high", levels=[1, 2, 3, 4], facecolor="linen"), linewidth=.8)
ax.add_feature(cfeature.BORDERS, linestyle=":")


# Level SST
vmin, vmax, step = 26, 35, 0.2
levels = np.arange(vmin, vmax + step, step)

# Buat colormap dan norm untuk diskritisasi warna
cmap = cm.get_cmap('turbo', len(levels) - 1)  # colormap discrete
norm = matplotlib.colors.BoundaryNorm(boundaries=levels, ncolors=cmap.N)

# Buat keterangan start dan end untuk rute drifter
ax.plot(lons[0]+.04, lats[0]+.04, marker='*', color='k', markersize=10, transform=proj, label='Start')
ax.plot(lons[-1]+.04, lats[-1]+.04, marker='x', color='k', markersize=10, transform=proj, label='End')

# Buat scatter plot
sctmod = ax.scatter(
    lons, lats, transform=proj, alpha=1.0, marker='o',
    c=sst, s=80, cmap=cmap, norm=norm
)

# Buat colorbar
cbar = plt.colorbar(sctmod, 
                    ax=ax, 
                    orientation='horizontal', 
                    shrink=0.7, 
                    pad=0.08, 
                    extend=None)
cbar.set_label("SST (°C)")


# Gridlines (Lintang-bujur)
gl = ax.gridlines(draw_labels=True, linestyle=':', alpha=.2)
gl.top_labels = False
gl.left_labels = True
gl.right_labels = True
gl.bottom_labels = True

# Legenda
legend_elements = [
    Line2D([0], [0], lw=0, marker='*', color='k', label='Start', markersize=8),
    Line2D([0], [0], lw=0, marker='x', color='k', label='End', markersize=8)
]
ax.legend(handles=legend_elements, loc='upper right')

# Judul
ax.set_title('SST from model', fontsize=10)

# Menyesuaikan layout dan menampilkan plot
plt.tight_layout()
plt.show()