star_oddi

Star-Oddi is a company that specializes in manufacturing and providing data loggers and sensors for oceanographic research. Their DAT files contain recorded data from various oceanographic parameters such as temperature, salinity, conductivity, and sound velocity.

DAT(path, encoding='cp1252')

Parse Star-Oddi DAT files

Parameters:

Name	Type	Description	Default
path	str	DAT file path	required
encoding	str	Encoding used. Defaults to "cp1252".	'cp1252'

Returns:

Type	Description
Dataset	xarray.Dataset: Dataset

Source code in ocean_data_parser/parsers/star_oddi.py

def DAT(path: str, encoding: str = "cp1252") -> xarray.Dataset:
    """Parse Star-Oddi DAT files

    Args:
        path (str): DAT file path
        encoding (str, optional): Encoding used. Defaults to "cp1252".

    Returns:
        xarray.Dataset: Dataset
    """

    def _standardize_attributes(item):
        item = re.sub(r"[\.\:]", "", item.strip().lower())
        return re.sub(r"\s", "_", item)

    metadata = {}
    variables = {}
    original_header = ""
    with open(path, "r", encoding=encoding) as f:
        line = "#"

        # Loop through the header lines
        while line.startswith("#"):
            line = f.readline()
            original_header += line
            if not line.startswith("#"):
                break

            _, attr, value = line.strip().split("\t", 2)
            if attr.strip() == "Axis":
                axis = line.strip().split("\t")
                name = re.search(r"(?P<name>[^\(]+)\((?P<units>.+)\)", axis[3])

                variables[_standardize_attributes(name["name"])] = {
                    "long_name": name["name"],
                    "units": name["units"],
                }
            elif attr.strip() == "Series":
                pass
            else:
                metadata[_standardize_attributes(attr)] = value.strip()

        # Check if date & time format is supported
        if (
            metadata["date_&_time"] == "1"
            and metadata["date_def"] == "dd.mm.yyyy	."
            and metadata["time_def"] == ":"
        ):
            date_format = "%d.%m.%Y\t%H:%M:%S"
            variables = {**{"time": {}}, **variables}
        elif (
            metadata["date_&_time"] == "1"
            and metadata["date_def"] == "dd-mm-yyyy\t-"
            and metadata["time_def"] == ":"
        ):
            date_format = "%d-%m-%Y\t%H:%M:%S"
            variables = {**{"time": {}}, **variables}
        elif metadata["date_&_time"] == "1":
            raise ValueError("Date & Time format is not supported")

        # TODO parse recorder info
        # TODO rename attributes to cf standard
        # TODO parse data line to review time range and n_records
        # TODO add some logging info

        # Parse data
        df = pd.read_csv(
            f,
            header=None,
            sep="\t",
            decimal=metadata.pop("decimal_point"),
            names=variables.keys(),
            parse_dates=["time"],
            date_format=date_format,
            dayfirst=True,
        )
        if "time" in df:
            df = df.set_index(["time"])

        # Parse data section of header
        n_records, start_time, end_time = metadata.pop("data").split("\t")

        if int(n_records) != len(df):
            logger.warning(
                "Length of data retrieved (=%s) does not match the expected length from the header (=%s).",
                len(df),
                int(n_records) - 1,
            )
        # Convert to xarray object and add related metadata
        ds = df.to_xarray()
        ds.attrs = {
            **DEFAULT_GLOBAL_ATTRIBUTES,
            "source": path,
            **dict(
                zip(
                    ("instrument_model", "instrument_serial_number"),
                    metadata.pop("recorder").split("\t")[1:],
                )
            ),
            **dict(
                zip(
                    ("software", "software_version"),
                    metadata.pop("version").split("\t"),
                )
            ),
            "n_records": n_records,
            "start_time": pd.to_datetime(
                start_time, format=date_format, dayfirst=True
            ).isoformat(),
            "end_time": pd.to_datetime(
                end_time, format=date_format, dayfirst=True
            ).isoformat(),
            "date_created": pd.to_datetime(
                metadata.pop("created"), format=date_format, dayfirst=True
            ).isoformat(),
            "original_file_header": original_header,
        }
        # Add variable attributes
        for var in ds:
            if var not in VARIABLES_ATTRIBUTES:
                logger.warning("Unknown variable %s", var)
            ds[var].attrs = {**variables[var], **VARIABLES_ATTRIBUTES.get(var, {})}
        return ds