arguments

Calculates the nodal corrections for tidal constituents
Originally based on Richard Ray’s ARGUMENTS fortran subroutine

Calling Sequence

import pyTMD.arguments
pu,pf,G = pyTMD.arguments.arguments(MJD, constituents,
    deltat=DELTAT, corrections=CORRECTIONS)

Source code

pyTMD.arguments.arguments(MJD: ndarray, constituents: list | ndarray, **kwargs)[source]

Calculates the nodal corrections for tidal constituents [17, 20, 43, 48]

Parameters:

MJD: np.ndarray

modified Julian day of input date

constituents: list

tidal constituent IDs

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, FES or GOT models

climate_solar_perigee: bool, default False

compute climatologically affected terms without p’

M1: str, default ‘perth5’

coefficients to use for M1 tides

'Doodson'

'Ray'

'Schureman'

'perth5'

Returns:

pu: np.ndarray: nodal angle correction
pf: np.ndarray: nodal factor correction
G: np.ndarray: phase correction in degrees

pyTMD.arguments.minor_arguments(MJD: ndarray, **kwargs)[source]

Calculates the nodal corrections for minor tidal constituents in order to infer their values [17, 19, 20, 48]

Parameters:

MJD: np.ndarray: modified Julian day of input date
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, FES or GOT models

Returns:

pu: np.ndarray: nodal angle correction
pf: np.ndarray: nodal factor correction
G: np.ndarray: phase correction in degrees

pyTMD.arguments.coefficients_table(constituents: list | tuple | ndarray | str, **kwargs)[source]

Doodson table coefficients for tidal constituents [16, 17, 43]

Parameters:

constituents: list, tuple, np.ndarray or str: tidal constituent IDs
corrections: str, default ‘OTIS’: use coefficients from OTIS, FES or GOT models
climate_solar_perigee: bool, default False: compute climatologically affected terms without p’
file: str or pathlib.Path, default `coefficients.json`: JSON file of Doodson coefficients

Returns:

coef: np.ndarray: Doodson coefficients (Cartwright numbers) for each constituent

pyTMD.arguments.doodson_number(constituents: str | list | ndarray, **kwargs)[source]

Calculates the Doodson or Cartwright number for tidal constituents [16]

Parameters:

constituents: str, list or np.ndarray

tidal constituent ID(s)

corrections: str, default ‘OTIS’

use arguments from OTIS, FES or GOT models

formalism: str, default ‘Doodson’

constituent identifier formalism

'Cartwright'

'Doodson'

'Extended'

raise_error: bool, default True

Raise exception if constituent is unsupported

Returns:

numbers: float, np.ndarray or dict: Doodson or Cartwright number for each constituent

pyTMD.arguments.nodal(n: ndarray, p: ndarray, constituents: list | tuple | ndarray | str, **kwargs)[source]

Calculates the nodal corrections for tidal constituents [17, 20, 44, 48]

Calculates factors for compound tides using recursion

Parameters:

n: np.ndarray

mean longitude of ascending lunar node (degrees)

p: np.ndarray

mean longitude of lunar perigee (degrees)

constituents: list, tuple, np.ndarray or str

tidal constituent IDs

corrections: str, default ‘OTIS’

use nodal corrections from OTIS, FES or GOT models

M1: str, default ‘perth5’

coefficients to use for M1 tides

'Doodson'

'Ray'

'Schureman'

'perth5'

Returns:

f: np.ndarray: nodal factor correction
u: np.ndarray: nodal angle correction

pyTMD.arguments.frequency(constituents: list | ndarray, **kwargs)[source]

Calculates the angular frequency for tidal constituents [44]

Parameters:

constituents: list

tidal constituent IDs

corrections: str, default ‘OTIS’

use nodal corrections from OTIS, FES or GOT models

M1: str, default ‘perth5’

coefficients to use for M1 tides

'Doodson'

'Ray'

'Schureman'

'perth5'

Returns:

omega: np.ndarray: angular frequency in radians per second

pyTMD.arguments.aliasing_period(constituents: list | ndarray, sampling: float | ndarray, **kwargs)[source]

Calculates the tidal aliasing for a repeat period

Parameters:

constituents: list

tidal constituent IDs

sampling: float

sampling repeat period in seconds

corrections: str, default ‘OTIS’

use nodal corrections from OTIS, FES or GOT models

M1: str, default ‘perth5’

coefficients to use for M1 tides

'Doodson'

'Ray'

'Schureman'

'perth5'

Returns:

period: np.ndarray: tidal aliasing period in seconds

pyTMD.arguments._arguments_table(**kwargs)[source]

Arguments table for tidal constituents [16, 17]

Parameters:

corrections: str, default ‘OTIS’: use arguments from OTIS, FES or GOT models
climate_solar_perigee: bool, default False: compute climatologically affected terms without p’

Returns:

coef: np.ndarray: Doodson coefficients (Cartwright numbers) for each constituent

pyTMD.arguments._minor_table(**kwargs)[source]

Arguments table for minor tidal constituents [16, 17]

Returns:

coef: np.ndarray: Doodson coefficients (Cartwright numbers) for each constituent

pyTMD.arguments._constituent_parameters(c: str, **kwargs)[source]

Loads parameters for a given tidal constituent [19]

Parameters:

c: str: tidal constituent ID
raise_error: bool, default False: Raise exception if constituent is unsupported

Returns:

amplitude: float: amplitude of equilibrium tide for tidal constituent (meters)
phase: float: phase of tidal constituent (radians)
omega: float: angular frequency of constituent (radians)
alpha: float: load love number of tidal constituent
species: float: spherical harmonic dependence of quadrupole potential

pyTMD.arguments._love_numbers(omega: ndarray, model: str = 'PREM')[source]

Compute the body tide Love/Shida numbers for a given frequency [30, 54, 55, 57]

Parameters:

omega: np.ndarray

angular frequency (radians per second)

model: str, default ‘PREM’

Earth model to use for Love numbers

‘1066A’

‘1066A-N’ (neutral)

‘1066A-S’ (stable)

‘PEM-C’

‘C2’

‘PREM’

Returns:

h2: float: Degree-2 Love number of vertical displacement
k2: float: Degree-2 Love number of gravitational potential
l2: float: Degree-2 Love (Shida) number of horizontal displacement

pyTMD.arguments._parse_tide_potential_table(table: str | Path)[source]

Parse tables of tide-generating potential

Parameters:

table: str or pathlib.Path: table of tide-generating potentials

Returns:

CTE: float: Cartwright-Tayler-Edden table values

pyTMD.arguments._to_constituent_id(coef: list | ndarray, **kwargs)[source]

Converts Cartwright numbers into a tidal constituent ID

Parameters:

coef: list or np.ndarray: Doodson coefficients (Cartwright numbers) for constituent
corrections: str, default ‘GOT’: use coefficients from OTIS, FES or GOT models
climate_solar_perigee: bool, default False: use climatologically affected terms without p’
arguments: int, default 7: Number of astronomical arguments to use
file: str or pathlib.Path, default `coefficients.json`: JSON file of Doodson coefficients
raise_error: bool, default True: Raise exception if constituent is unsupported

Returns:

c: str: tidal constituent ID

pyTMD.arguments._to_doodson_number(coef: list | ndarray, **kwargs)[source]

Converts Cartwright numbers into a Doodson number

Parameters:

coef: list or np.ndarray: Doodson coefficients (Cartwright numbers) for constituent
astype: type, default float: Output data type for default case
raise_error: bool, default True: Raise exception if constituent is unsupported

Returns:

DO: float or string: Doodson number for constituent

pyTMD.arguments._to_extended_doodson(coef: list | ndarray, **kwargs)[source]

Converts Cartwright numbers into an UKHO Extended Doodson number

Parameters:

coef: list or np.ndarray: Doodson coefficients (Cartwright numbers) for constituent

Returns:

XDO: string: Extended Doodson number for constituent

pyTMD.arguments._from_doodson_number(DO: str | float | ndarray, **kwargs)[source]

Converts Doodson numbers into Cartwright numbers

Parameters:

DO: str, float or np.ndarray: Doodson number for constituent

Returns:

coef: np.ndarray: Doodson coefficients (Cartwright numbers) for constituent

pyTMD.arguments._from_extended_doodson(XDO: str | str_, **kwargs)[source]

Converts UKHO Extended Doodson number into Cartwright numbers

Parameters:

XDO: string: Extended Doodson number for constituent

Returns:

coef: np.ndarray: Doodson coefficients (Cartwright numbers) for constituent