dfo.nafc

The Fisheries and Oceans Canada - Newfoundland and Labrador Region - North Atlantic Fisheries Centre (NAFC) is a research facility located in St. John's, Newfoundland and Labrador.

pcnv(path, rename_variables=True, generate_extra_variables=True, global_attributes=None, encoding='UTF-8', encoding_errors='strict', match_metqa_table=False)

DFO NAFC pcnv file format parser

The pcnv format essentially a seabird cnv file format with NAFC specific inputs within the manual section.

Parameters:

Name	Type	Description	Default
path	Path	pcvn file path	required
rename_variables	bool	Rename variables to DFO BODC names. Defaults to True.	True
generate_extra_variables	bool	Generate extra vocabulary variables. Defaults to True.	True
global_attributes	dict	Global attributes to add to the dataset.	None
match_metqa_table	bool	Match metqa table to the file if available within same directory. Defaults to True.	False

Returns:

Type	Description
Dataset	xr.Dataset

Source code in ocean_data_parser/parsers/dfo/nafc.py

def pcnv(
    path: Path,
    rename_variables: bool = True,
    generate_extra_variables: bool = True,
    global_attributes: dict = None,
    encoding: str = "UTF-8",
    encoding_errors: str = "strict",
    match_metqa_table: bool = False,
) -> xr.Dataset:
    """DFO NAFC pcnv file format parser

    The pcnv format  essentially a seabird cnv file format
    with NAFC specific inputs within the manual section.

    Args:
        path (Path): pcvn file path
        rename_variables (bool, optional): Rename variables to
            DFO BODC names. Defaults to True.
        generate_extra_variables (bool, optional): Generate extra
            vocabulary variables. Defaults to True.
        global_attributes (dict, optional): Global attributes to add to the dataset.
        match_metqa_table (bool, optional): Match metqa table to the file if
            available within same directory. Defaults to True.

    Returns:
        xr.Dataset
    """

    def _pop_attribute_from(names: list):
        """Pop attribute from dataset"""
        for name in names:
            if name in ds.attrs:
                return ds.attrs.pop(name)

        if "comment" not in names[0].lower():
            logger.error("No matching attribute found in {}", names)

    def get_vocabulary(**kwargs):
        return p_file_vocabulary.query(
            " and ".join(f"{key} == '{value}'" for key, value in kwargs.items())
        ).to_dict(orient="records")

    path = Path(path)
    ds = seabird.cnv(
        path,
        encoding=encoding,
        encoding_errors=encoding_errors,
        xml_parsing_error_level="WARNING",
    )

    # Map global attributes
    ship_trip_seq_station = re.search(
        r"(?P<dfo_nafc_platform_name>\w{3})(?P<trip>\d{3})_(?P<year>\d{4})_(?P<stn>\d{3})",
        ds.attrs.pop("vessel_trip_seq_stn", ""),
    )
    if not ship_trip_seq_station:
        logger.error(
            "Unable to parse ship_trip_seq_station from VESEL/TRIP/SEQ STN= {}",
            ds.attrs.get("vessel_trip_seq_stn", ""),
        )
    ds.attrs.update(
        {
            "dfo_nafc_platform_name": ship_trip_seq_station["dfo_nafc_platform_name"],
            **_get_platform_by_nafc_platform_name(
                ship_trip_seq_station["dfo_nafc_platform_name"]
            ),
            "trip": _int(ship_trip_seq_station["trip"]),
            "year": _int(ship_trip_seq_station["year"]),
            "station": _int(ship_trip_seq_station["stn"]),
            "time": pd.to_datetime(ds.attrs.pop("date_time"), utc=True),
            "latitude": _parse_lat_lon(
                _pop_attribute_from(
                    ["latitude", "latitude_xx_xx.xx", "latitude_xx_xx.xx_n"]
                )
            ),
            "longitude": _parse_lat_lon(
                _pop_attribute_from(
                    ["longitude", "longitude_xx_xx.xx", "longitude _xx_xx.xx_w"]
                )
            ),
            "sounder_depth": ds.attrs.pop("sounding_depth_m", None),
            "instrument": ds.attrs.pop("probe_type", None),
            "set_number": _int(ds.attrs.pop("xbt_number", None))
            or _int(ds.attrs.pop("ctd_number", None)),
            "format": ds.attrs.pop("format", None),
            "commment": _pop_attribute_from(["comments", "comments_14_char"]),
            # "trip_tag": ds.attrs.pop("trip_tag", None),
            "vnet": ds.attrs.pop("vnet", None),
            "do2": ds.attrs.pop("do2", None),
            "bottles": _int(ds.attrs.pop("bottles", None)),
            **_add_metqa_info_to_pcvn(path, match_metqa_table),
            **(global_attributes or {}),
        }
    )

    # Move coordinates to variables
    coords = ["time", "latitude", "longitude"]
    p_file_vocabulary
    for coord in coords:
        if coord in ds.attrs:
            ds[coord] = ds.attrs[coord]
            ds[coord].attrs = get_vocabulary(variable_name=coord)[0]

    ds = ds.set_coords([coord for coord in coords if coord in ds])

    # Map variable attributes to pfile vocabulary via long_name
    variables_new_name = {}
    for variable in ds.variables:
        if variable in coords:
            continue
        variable_attributes = get_vocabulary(
            long_name=ds[variable].attrs.get("long_name", variable)
        )
        if not variable_attributes:
            logger.warning("Missing vocabulary for variable={}", variable)
            continue

        if rename_variables and variable_attributes[-1].get("variable_name"):
            variables_new_name[variable] = variable_attributes[-1].pop("variable_name")

        ds[variable].attrs.update(variable_attributes[-1])
        if not generate_extra_variables:
            continue

        for extra in variable_attributes[0:-1]:
            new_var = extra.pop("variable_name", variable)
            if new_var == "depth" and "depSM" in ds.variables:
                continue
            logger.debug("Generate extra variable={}", new_var)
            ds[new_var] = (ds[variable].dims, ds[variable].data, extra)

    if rename_variables:
        logger.debug("Rename variables to NAFC standard: {}", variables_new_name)
        ds = ds.rename(variables_new_name)

    ds = standardize_dataset(ds)

    return ds

pfile(file, encoding='UTF-8', rename_variables=True, generate_extra_variables=True, encoding_errors='strict')

Parse DFO NAFC oceanography p-file format

Parameters:

Name	Type	Description	Default
file	str	file path	required
encoding	str	file encoding. Defaults to "UTF-8".	'UTF-8'
rename_variables	bool	Rename variables to BODC standard. Defaults to True.	True
generate_extra_variables	bool	Generate extra BODC mapping variables. Defaults to True.	True

Raises:

Type	Description
TypeError	File provided isn't a p file.

Returns:

Type	Description
Dataset	xr.Dataset: Parser dataset

Source code in ocean_data_parser/parsers/dfo/nafc.py

def pfile(
    file: str,
    encoding: str = "UTF-8",
    rename_variables: bool = True,
    generate_extra_variables: bool = True,
    encoding_errors: str = "strict",
) -> xr.Dataset:
    """Parse DFO NAFC oceanography p-file format

    Args:
        file (str): file path
        encoding (str, optional): file encoding. Defaults to "UTF-8".
        rename_variables (bool, optional): Rename variables to BODC
            standard. Defaults to True.
        generate_extra_variables (bool, optional): Generate extra
            BODC mapping variables. Defaults to True.

    Raises:
        TypeError: File provided isn't a p file.

    Returns:
        xr.Dataset: Parser dataset
    """

    def _parse_ship_trip_stn():
        """Review if the ship,trip,stn string is the same
        accorss the 3 metadata rows and the file name."""
        ship_trip_stn = [line[:8] for line in metadata_lines[1:] if line.strip()] + [
            file.stem
        ]
        if len(set(ship_trip_stn)) != 1:
            logger.warning(
                "Ship,trip,station isn't consistent: {}. "
                "The file name will be considered = {}.",
                set(ship_trip_stn),
                file.stem,
            )
        return dict(
            ship_code=file.stem[:2],
            trip=int(file.stem[2:5]),
            station=int(file.stem[5:8]),
        )

    file = Path(file)
    line = None
    header = {}
    section = None
    with open(file, encoding=encoding, errors=encoding_errors) as file_handle:
        # Read the four first lines to extract the information
        original_header = [file_handle.readline() for _ in range(4)]
        metadata_lines = original_header[:4]
        while file_handle:
            previous_line, line = line, file_handle.readline()
            if "-- DATA --" in line:
                break
            original_header += [line]

            # search section specific
            new_section = re.search(r"-- ([\w\s]+) -->", line)
            section = new_section[1] if new_section else section
            if section and "-- END --" in line:
                section = None
            if section is None:
                continue
            elif section not in header:
                header[section] = []

            header[section] += [line]

        # Define each fields width based on the column names
        names = re.findall(r"\w+", previous_line)
        if len(names) != len(set(names)):
            # Rename duplicated names
            logger.warning("Column names aren't unique: {}", names)
            duplicated_names = []
            for index, name in enumerate(names):
                if names.count(name) > 1:
                    new_name = f"{name}{names[:index].count(name)}"
                    logger.warning("Rename {} to {}", name, new_name)
                    duplicated_names += [new_name]
                else:
                    duplicated_names += [name]

            names = duplicated_names

        # Read data section
        # TODO confirm that 5+12 character width is constant
        ds = pd.read_csv(
            file_handle,
            sep=r"\s+",
            engine="python",
            names=names,
            dtype={name: _get_dtype(name) for name in names},
            encoding_errors=encoding_errors,
        ).to_xarray()

    if len(ds.index) == 0:
        logger.error("No data found in file")

    # Review datatypes
    if any([dtype is object for _, dtype in ds.dtypes.items()]):
        logger.warning(
            "Some columns dtype=object suggest the file data wasn't correctely parsed."
        )

    # Review metadata
    if metadata_lines[0] == "NAFC_Y2K_HEADER":
        raise TypeError(
            "File header doesn't contain pfile first line 'NAFC_Y2K_HEADER'"
        )

    # Convert dataframe to an xarray and populate information
    ds.attrs.update(
        {
            "id": metadata_lines[1][:8],
            **global_attributes,
            **_parse_ship_trip_stn(),
            **(_parse_pfile_header_line1(metadata_lines[1]) or {}),
            **(_parse_pfile_header_line2(metadata_lines[2]) or {}),
            **(_parse_pfile_header_line3(metadata_lines[3]) or {}),
            "history": header.get("HISTORY"),
        }
    )
    ds.attrs["original_header"] = "\n".join(original_header)
    ds.attrs["history"] = _pfile_history_to_cf(header.get("HISTORY"))
    ds.attrs.update(_get_platform_by_nafc_platform_code(ds.attrs.get("ship_code", {})))

    # Move coordinates to variables:
    coords = ["time", "latitude", "longitude"]
    for coord in coords:
        if coord in ds.attrs:
            ds[coord] = ds.attrs[coord]
    ds = ds.set_coords([coord for coord in coords if coord in ds])

    # Populate variable attributes base on vocabulary
    variables_span = _parse_channel_stats(header.get("CHANNEL STATS"))
    extra_vocabulary_variables = []
    for var in ds.variables:
        ds[var].attrs.update(variables_span.get(var, {}))
        variable_attributes = _get_pfile_variable_vocabulary(
            var, ds.attrs.get("instrument")
        )
        if not variable_attributes:
            continue

        ds[var].attrs.update(variable_attributes[0])
        for extra in variable_attributes[1:]:
            extra_vocabulary_variables += [
                [
                    extra.pop("variable_name", var),
                    ds[var],
                    extra,
                ]
            ]

    # Rename variables
    if rename_variables:
        ds = ds.rename(
            {
                var: ds[var].attrs.pop("variable_name")
                for var in ds.variables
                if "variable_name" in ds[var].attrs
                and ds[var].attrs["variable_name"]
                and ds[var].attrs["variable_name"] != var
            }
        )

    # Generate extra variables
    if generate_extra_variables:
        for name, var, attrs in extra_vocabulary_variables:
            if name in ds:
                logger.info(
                    (
                        "Extra variable is already in dataset and will be ignored. "
                        "name={}, attrs={} is already in dataset and will be ignored"
                    ),
                    var.name,
                    attrs,
                )
                continue
            apply_func = attrs.pop("apply_func", None)
            new_data = (
                eval(
                    apply_func,
                    {},
                    {
                        "gsw": gsw,
                        **{
                            ds[variable].attrs.get("legacy_p_code", variable): ds[
                                variable
                            ]
                            for variable in ds.variables
                        },
                    },
                )
                if apply_func not in (None, np.nan)
                else var
            )
            ds.attrs["history"] += (
                f"\n{pd.Timestamp.now()} - Generated variable {name} = {apply_func}"
            )
            attrs["source"] = f"Generated variable {name} = {apply_func}"
            ds[name] = (var.dims, new_data.data, {**var.attrs, **attrs})

    # standardize
    ds = standardize_dataset(ds)

    return ds

p-files vocabulary

legacy_p_code	accepted_instruments	apply_func	seabird_name	variable_name	long_name	units	sdn_uom_urn	sdn_uom_name	instrument	comments	sdn_parameter_urn	sdn_parameter_name	bodc_alternative_label	definition	standard_name	coverage_content_type
cond			c0S/m	CNDCST01	Conductivity	S/m	SDN:P06::UECA	Siemens per metre		1st sensor	SDN:P01::CNDCST01	Electrical conductivity of the water body by CTD	CTDCond		sea_water_electrical_conductivity	physicalMeasurement
cond2			c1S/m	CNDCST02	Conductivity, 2	S/m	SDN:P06::UECA	Siemens per metre		2nd sensor	SDN:P01::CNDCST02	Electrical conductivity of the water body by CTD (sensor 2)	CTDCond2		sea_water_electrical_conductivity	physicalMeasurement
flor				CPHLPR01	Fluorescence	mg/m^3	SDN:P06::UMMC	Milligrams per cubic metre		1st sensor	SDN:P01::CPHLPR01	Concentration of chlorophyll-a {chl-a CAS 479-61-8} per unit volume of the water body [particulate >unknown phase] by in-situ chlorophyll fluorometer	chl-a_water_ISfluor	In-situ fluorometer with either manufacturer, laboratory or sample calibration applied.	mass_concentration_of_chlorophyll_a_in_sea_water	physicalMeasurement
flor	WET Labs ECO-AFL/FL		flECO-AFL		Fluorescence, Wet Labs Eco-Afl/Fl	mg/m^3	SDN:P06::UMMC	Milligrams per cubic metre	WET Labs ECO-AFL/FL	1st sensor					mass_concentration_of_chlorophyll_a_in_sea_water	physicalMeasurement
flor	Chelsea Aqua 3 Chl Con		flC		Fluorescence, Chelsea Aqua 3 Chl Con	mg/m^3	SDN:P06::UGPL	Micrograms per litre	Chelsea Aqua 3 Chl Con	1st sensor					mass_concentration_of_chlorophyll_a_in_sea_water	physicalMeasurement
oxy			sbeox0ML/L	DOXYSU01	Oxygen, SBE 43	ml/l	SDN:P06::UMLL	Millilitres per litre	SBE 43	1st sensor	SDN:P01::DOXYSU01	Concentration of oxygen {O2 CAS 7782-44-7} per unit volume of the water body [dissolved plus reactive particulate phase] by Sea-Bird SBE 43 sensor and no calibration against sample data	WC_dissO2_uncalib	Sea-Bird SBE 43 oxygen sensor with no field calibration against sample data	volume_fraction_of_oxygen_in_sea_water	physicalMeasurement
oxy			sbeox0ML/L	DOXYZZ01	Oxygen, SBE 43	ml/l	SDN:P06::UMLL	Millilitres per litre	SBE 43	1st sensor	SDN:P01::DOXYZZ01	Concentration of oxygen {O2 CAS 7782-44-7} per unit volume of the water body [dissolved plus reactive particulate phase] by in-situ sensor	WC_dissO2_IS	Unspecified type of oxygen sensor with no information on whether it has been calibrated against sample data	volume_fraction_of_oxygen_in_sea_water	physicalMeasurement
oxy2			sbeox1ML/L	DOXYSU02	Oxygen, SBE 43, 2	ml/l	SDN:P06::UMLL	Millilitres per litre	SBE 43	2nd sensor	SDN:P01::DOXYSU02	Concentration of oxygen {O2 CAS 7782-44-7} per unit volume of the water body [dissolved plus reactive particulate phase] by Sea-Bird SBE 43 sensor (second sensor) and no calibration against sample data	WC_dissO2_uncalib2	Secondary Sea-Bird SBE 43 oxygen sensor with no field calibration against sample data	volume_fraction_of_oxygen_in_sea_water	physicalMeasurement
oxy2			sbeox1ML/L	DOXYZZ02	Oxygen, SBE 43, 2	ml/l	SDN:P06::UMLL	Millilitres per litre	SBE 43	2nd sensor	SDN:P01::DOXYZZ02	Concentration of oxygen {O2 CAS 7782-44-7} per unit volume of the water body [dissolved plus reactive particulate phase] by in-situ sensor	WC_dissO2_IS_2	Unspecified type of oxygen sensor with no information on whether it has been calibrated against sample data	volume_fraction_of_oxygen_in_sea_water	physicalMeasurement
oxypsat			oxsatML/L	OXYSZZ01	Oxygen Saturation, Weiss	ml/l	SDN:P06::UMLL	Millilitres per litre	Weiss		SDN:P01::OXYSZZ01	Saturation of oxygen {O2 CAS 7782-44-7} in the water body [dissolved plus reactive particulate phase]	O2Sat	Computed (algorithm/data source unspecified)		physicalMeasurement
sbeox0V			sbeox0V		Oxygen Raw, SBE 43	V	SDN:P06::UVLT	Volts	SBE 43	1st sensor						physicalMeasurement
sbeox1V			sbeox1V		Oxygen Raw, SBE 43, 2	V	SDN:P06::UVLT	Volts	SBE 43	2nd sensor						physicalMeasurement
par	light			par	DWIRRXUD	Par/Irradiance, Biospherical/Licor	µeinsteins/s/m^2		downwelling_photon_spherical_irradiance_in_sea_water	Biospherical/Licor	Biospherical, Licor, or Chelsea sensor; 1st sensor	SDN:P01::DWIRRXUD	Downwelling vector irradiance as energy of electromagnetic radiation (PAR wavelengths) in the water body by cosine-collector radiometer	SubSurDWVectPAR		downwelling_photon_spherical_irradiance_in_sea_water
par	Biospherical/Licor		par	DWIRRXUD	Par/Irradiance, Biospherical/Licor	µeinsteins/s/m^2		downwelling_photon_spherical_irradiance_in_sea_water	Biospherical/Licor	Biospherical, Licor, or Chelsea sensor; 1st sensor	SDN:P01::DWIRRXUD	Downwelling vector irradiance as energy of electromagnetic radiation (PAR wavelengths) in the water body by cosine-collector radiometer	SubSurDWVectPAR		downwelling_photon_spherical_irradiance_in_sea_water	physicalMeasurement
par	Biospherical/Satlantic		par	DWIRRXUD	PAR/Logarithmic, Satlantic	µeinsteins/s/m^2		downwelling_photon_spherical_irradiance_in_sea_water	Biospherical/Licor	Biospherical, Licor, or Chelsea sensor; 1st sensor	SDN:P01::DWIRRXUD	Downwelling vector irradiance as energy of electromagnetic radiation (PAR wavelengths) in the water body by cosine-collector radiometer	SubSurDWVectPAR		downwelling_photon_spherical_irradiance_in_sea_water	physicalMeasurement
ph			ph	PHXXZZXX	pH		SDN:P06::UUPH	pH units			SDN:P01::PHXXZZXX	pH (unspecified scale) of the water body	pH	Minus the log of the hydrogen ion concentration (moles per litre) in the water column.	sea_water_ph_reported_on_total_scale	physicalMeasurement
pres			prDM	PRESPR01	Pressure, Strain Gauge	dbar	SDN:P06::UPBL	Decibar	Digiquartz	Digiquartz pressure sensor	SDN:P01::PRESPR01	Pressure (spatial coordinate) exerted by the water body by profiling pressure sensor and correction to read zero at sea level	Pres_Z	The force per unit area exerted by the water column on a mobile sensor located in the water column.	sea_water_pressure	physicalMeasurement
pres		-gsw.z_from_p(pres,latitude)	depth	depth	Depth [salt water]	m	SDN:P06::ULAA	Metres							depth	coordinate
depth			depth	depth	Depth [salt water]	m	SDN:P06::ULAA	Metres							depth	coordinate
sal			sal00	PSALST01	Salinity, Practical	PSU	SDN:P06::UUUU	Dimensionless		1st sensor	SDN:P01::PSALST01	Practical salinity of the water body by CTD and computation using UNESCO 1983 algorithm	P_sal_CTD	This is the preferred term for this definition. Alternative term PSALST02 is included to cover cases where there are two sensors of the same type contributing to the data set and referential integrity considerations prevent a usage of a single code.	sea_water_practical_salinity	physicalMeasurement
sal2			sal11	PSALST02	Salinity, Practical, 2	PSU	SDN:P06::UUUU	Dimensionless		2nd sensor	SDN:P01::PSALST02	Practical salinity of the water body by CTD (second sensor) and computation using UNESCO 1983 algorithm	P_sal_CTD2	This is the alternative term for this definition. Only use to cover cases where there are two sensors of the same type contributing to the data set and referential integrity considerations prevent a usage of a single code.	sea_water_practical_salinity	physicalMeasurement
scan			scan	scan	Scan Count											physicalMeasurement
sig			sigma-t00	SIGTEQST	Density	kg/m^3	SDN:P06::UKMC	Kilograms per cubic metre		1st sensor	SDN:P01::SIGTEQST	Sigma-T of the water body by computation from salinity and temperature using UNESCO algorithm	Sigma-T	Computed by UNESCO SVAN function using in-situ temperature	sea_water_sigma_t	physicalMeasurement
sig2			sigma-t11	SIGTEQST2	Density 2 [sigma-t]	kg/m^3	SDN:P06::UKMC	Kilograms per cubic metre		2nd sensor	SDN:P01::SIGTEQST	Sigma-T of the water body by computation from salinity and temperature using UNESCO algorithm	Sigma-T	Computed by UNESCO SVAN function using in-situ temperature	sea_water_sigma_t	physicalMeasurement
temp			t090C	TEMPS901	Temperature [ITS-90]	degrees_celsius	SDN:P06::UPAA	Degrees Celsius		1st sensor	SDN:P01::TEMPS901	Temperature (ITS-90) of the water body by CTD or STD	CTDTmp90		sea_water_temperature	physicalMeasurement
temp2			t190C	TEMPS902	Temperature, 2	degrees_celsius	SDN:P06::UPAA	Degrees Celsius		2nd sensor	SDN:P01::TEMPS902	Temperature (ITS-90) of the water body by CTD or STD (second sensor)	CTDTmp90_2		sea_water_temperature	physicalMeasurement
tra			CStarAt0	ATTNZS01	Beam Attenuation, Wet Labs C-Star	1/m	SDN:P06::UPRM	per metre	WET Labs C-Star	1st sensor	SDN:P01::ATTNZS01	Attenuation (red light wavelength) per unit length of the water body by WET Labs transmissometer and calibration to read zero in clear water	RedPWCorr	WET Labs transmissometer calibrated to zero in clear water	volume_beam_attenuation_coefficient_of_radiative_flux_in_sea_water	physicalMeasurement
trp	trans			CStarTr0	OPTCPS01	Beam Transmission, Wet Labs C-Star	%	SDN:P06::UPCT	Percent	WET Labs C-Star	1st sensor	SDN:P01::OPTCPS01	Transmittance (red light wavelength) per unit length of the water body	TransmRedLight
wet			wetCDOM	CCOMD002	Fluorescence, Wet Labs CDOM	mg/m^3	SDN:P06::UMMC	Milligrams per cubic metre	WET Labs CDOM	1st sensor	SDN:P01::CCOMD002	Concentration of coloured dissolved organic matter {CDOM Gelbstoff} per unit volume of the water body [dissolved plus reactive particulate phase] by in-situ WET Labs FDOM ECO fluorometer	CDOM_fluor_WETLabs	Quantity or mass of the specified analyte in a unit volume of an unfiltered sample of fresh or salt water.	concentration_of_colored_dissolved_organic_matter_in_sea_water_expressed_as_equivalent_mass_fraction_of_quinine_sulfate_dihydrate	physicalMeasurement
sigt				SIGTEQST	Sigma-T	kg/m^3	SDN:P06::UKMC	Kilograms per cubic metre			SDN:P01::SIGTEQST	Sigma-T of the water body by computation from salinity and temperature using UNESCO algorithm	Sigma-T	Computed by UNESCO SVAN function using in-situ temperature	sea_water_sigma_t	physicalMeasurement
sigmat				SIGTEQST	Sigma-T	kg/m^3	SDN:P06::UKMC	Kilograms per cubic metre			SDN:P01::SIGTEQST	Sigma-T of the water body by computation from salinity and temperature using UNESCO algorithm	Sigma-T	Computed by UNESCO SVAN function using in-situ temperature	sea_water_sigma_t	physicalMeasurement
time				time	Time										time	coordinate
latitude				latitude	Latitude	degrees_north	SDN:P06::DEGN	Degrees north							latitude	coordinate
longitude				longitude	Longitude	degrees_east	SDN:P06::DEGN	Degrees east							longitude	coordinate
index				index	index											auxiliaryInformation
flag				flag	Flag											qualityInformation