OTPS tide modelling function for TPXO8

otps_tides.py

import otps
import pytz

def otps_tides(lats, lons, times, timezone=None):
    """
    Model tide heights for one or more locations and times using the
    OTPS TPXO8 tidal model.
    Parameters:
    -----------
    lats, lons : numeric or list of numeric values
        One or more latitudes and longitude coordinates used to define
        the location at which to model tides.
    times : datetime.datetime or list of datetime.datetimes
        One or more `datatime.datetime` objects providing the times at
        which to model tides. By default these are assumed to be in UTC
        time; if this is not the case, use `timezone` below.
    timezone : string, optional
        If provided `datatime.datetime`s are not in UTC times, use this
        parameter to declare a timezone. E.g. to model tides for times
        expressed in local time at Darwin, Australia, provide
        `timezone='Australia/Darwin'`. Defaults to `None`, which assumes
        provided times are UTC. This is used to convert all times to UTC
        using the `pytz` module. For a full list of timezones, run:
        `import pytz; pytz.all_timezones`.
    Returns:
    --------
    tidepoints_df : pandas.DataFrame
        An `pandas.DataFrame` with a "time" index, "lat" and "lon"
        columns, and a "tide_m" column giving tide heights at each
        point location.
    """

    # Convert to list if provided as individual values
    if not isinstance(lats, list):
        lats = [lats]
    if not isinstance(lons, list):
        lons = [lons]
    if not isinstance(times, list):
        times = [times]

    # If a timezone is provided, localise the input times then
    # standardise to UTC times
    if timezone:
        times = [
            pytz.timezone(timezone).localize(time).astimezone(pytz.utc)
            for time in times
        ]

    # Create list of lat/lon/time scenarios to model tides
    observed_timepoints = [
        otps.TimePoint(lon, lat, time)
        for time in times
        for lon, lat in zip(lons, lats)
    ]

    # Model tides for each lat/lon/time
    observed_predictedtides = otps.predict_tide(observed_timepoints)

    # Output results into pandas.DataFrame
    tidepoints_df = pd.DataFrame(
        [(i.timepoint.timestamp, i.timepoint.lon, i.timepoint.lat, i.tide_m)
         for i in observed_predictedtides],
        columns=['time', 'lon', 'lat', 'tide_m'])

    return tidepoints_df.set_index('time')