ocean_load

• Predict tidal values using harmonic constants

• Infer tidal values for minor constituents using major constituents

• Predicts long-period equilibrium ocean tides

Source code

pyTMD.predict.time_series(t: float | ndarray, ds: Dataset, **kwargs)

Predict tides from Dataset at times

Parameters:

t: float or np.ndarray

Days relative to 1992-01-01T00:00:00

ds: xarray.Dataset

Dataset containing tidal harmonic constants

kwargs: dict

Keyword arguments for pyTMD.constituents.arguments()

Returns:

darr: xarray.DataArray

Predicted tidal time series

pyTMD.predict.infer_minor(t: float | ndarray, ds: Dataset, **kwargs)

Infer the tidal values for minor constituents using their relation with major constituents [18, 20, 22, 66]

Parameters:

t: float or np.ndarray

Days relative to 1992-01-01T00:00:00

ds: xarray.Dataset

Dataset containing major tidal harmonic constants

deltat: float or np.ndarray, default 0.0

Time correction for converting to Ephemeris Time (days)

corrections: str, default ‘OTIS’

Use nodal corrections from OTIS/ATLAS or GOT/FES models

minor: list or None, default None

Tidal constituent IDs of minor constituents for inference

infer_long_period, bool, default True

Try to infer long period tides from constituents

raise_exception: bool, default False

Raise a ValueError if major constituents are not found

Returns:

tinfer: xr.DataArray

Tidal time series for minor constituents

pyTMD.predict._infer_short_period(t: float | ndarray, ds: Dataset, **kwargs)

Infer the tidal values for short-period minor constituents using their relation with major constituents [20, 59, 66]

For FES corrections, high precision spline coefficients are provided by pyfes [39]

Parameters:

t: float or np.ndarray

Days relative to 1992-01-01T00:00:00

ds: xarray.Dataset

Dataset containing major tidal harmonic constants

deltat: float or np.ndarray, default 0.0

Time correction for converting to Ephemeris Time (days)

corrections: str, default ‘OTIS’

Use nodal corrections from OTIS/ATLAS or GOT/FES models

minor: list or None, default None

Tidal constituent IDs of minor constituents for inference

raise_exception: bool, default False

Raise a ValueError if major constituents are not found

Returns:

tinfer: xr.DataArray

Tidal time series for minor constituents

pyTMD.predict._infer_semi_diurnal(t: float | ndarray, ds: Dataset, **kwargs)

Infer the tidal values for semi-diurnal minor constituents using their relation with major constituents [12, 49, 59]

Parameters:

t: float or np.ndarray

Days relative to 1992-01-01T00:00:00

ds: xarray.Dataset

Dataset containing major tidal harmonic constants

deltat: float or np.ndarray, default 0.0

Time correction for converting to Ephemeris Time (days)

minor: list or None, default None

Tidal constituent IDs of minor constituents for inference

method: str, default ‘linear’

Method for interpolating between major constituents

• 'linear': linear interpolation

• 'admittance': Munk-Cartwright interpolation

raise_exception: bool, default False

Raise a ValueError if major constituents are not found

Returns:

tinfer: xr.DataArray

Tidal time series for minor constituents

pyTMD.predict._infer_diurnal(t: float | ndarray, ds: Dataset, **kwargs)

Infer the tidal values for diurnal minor constituents using their relation with major constituents taking into account resonance due to free core nutation [11, 49, 60, 78]

Parameters:

t: float or np.ndarray

Days relative to 1992-01-01T00:00:00

ds: xarray.Dataset

Dataset containing major tidal harmonic constants

deltat: float or np.ndarray, default 0.0

Time correction for converting to Ephemeris Time (days)

minor: list or None, default None

Tidal constituent IDs of minor constituents for inference

method: str, default ‘linear’

Method for interpolating between major constituents

• 'linear': linear interpolation

• 'admittance': Munk-Cartwright interpolation

raise_exception: bool, default False

Raise a ValueError if major constituents are not found

Returns:

tinfer: xr.DataArray

Tidal time series for minor constituents

pyTMD.predict._infer_long_period(t: float | ndarray, ds: Dataset, **kwargs)

Infer the tidal values for long-period minor constituents using their relation with major constituents with option to take into account variations due to mantle anelasticity [11, 44, 59, 63]

Parameters:

t: float or np.ndarray

Days relative to 1992-01-01T00:00:00

ds: xarray.Dataset

Dataset containing major tidal harmonic constants

deltat: float or np.ndarray, default 0.0

Time correction for converting to Ephemeris Time (days)

minor: list or None, default None

Tidal constituent IDs of minor constituents for inference

include_anelasticity: bool, default False

Compute Love numbers taking into account mantle anelasticity

raise_exception: bool, default False

Raise a ValueError if major constituents are not found

Returns:

tinfer: xr.DataArray

Tidal time series for minor constituents

pyTMD.predict.equilibrium_tide(t: ndarray, ds: Dataset, **kwargs)

Compute the long-period equilibrium tides the summation of fifteen tidal spectral lines from Cartwright-Tayler-Edden tables [11, 12]

Parameters:

t: np.ndarray

Days relative to 1992-01-01T00:00:00

ds: xarray.Dataset

Dataset with spatial coordinates

deltat: float or np.ndarray, default 0.0

Time correction for converting to Ephemeris Time (days)

corrections: str, default ‘OTIS’

Use nodal corrections from OTIS/ATLAS or GOT/FES models

include_anelasticity: bool, default False

Compute Love numbers taking into account mantle anelasticity

constituents: list

Long-period tidal constituent IDs

Returns:

tpred: xr.DataArray

Predicted tidal time series (meters)