def predict_tide(t, hc, constituents, DELTAT=0.0, CORRECTIONS='OTIS'):
"""
Predict tides at a single time using harmonic constants
Parameters
----------
t: float
days relative to 1992-01-01T00:00:00
hc: complex
harmonic constant vector
constituents: list
tidal constituent IDs
DELTAT: float, default 0.0
time correction for converting to Ephemeris Time (days)
CORRECTIONS: str, default 'OTIS'
use nodal corrections from OTIS/ATLAS or GOT models
Returns
-------
ht: float
tide values reconstructed using the nodal corrections
References
----------
.. [1] Egbert and Erofeeva, "Efficient Inverse Modeling of Barotropic
Ocean Tides", Journal of Atmospheric and Oceanic Technology, (2002).
"""
#-- number of points and number of constituents
npts, nc = np.shape(hc)
#-- load the nodal corrections
#-- convert time to Modified Julian Days (MJD)
pu, pf, G = load_nodal_corrections(t + 48622.0,
constituents,
DELTAT=DELTAT,
CORRECTIONS=CORRECTIONS)
#-- allocate for output tidal elevation
ht = np.ma.zeros((npts))
ht.mask = np.zeros((npts), dtype=bool)
#-- for each constituent
for k, c in enumerate(constituents):
if CORRECTIONS in ('OTIS', 'ATLAS', 'netcdf'):
#-- load parameters for each constituent
amp, ph, omega, alpha, species = load_constituent(c)
#-- add component for constituent to output tidal elevation
th = omega * t * 86400.0 + ph + pu[0, k]
elif CORRECTIONS in ('GOT', 'FES'):
th = G[0, k] * np.pi / 180.0 + pu[0, k]
#-- sum over all tides
ht.data[:] += pf[0,k]*hc.real[:,k]*np.cos(th) - \
pf[0,k]*hc.imag[:,k]*np.sin(th)
ht.mask[:] |= (hc.real.mask[:, k] | hc.imag.mask[:, k])
#-- return the tidal elevation after removing singleton dimensions
return np.squeeze(ht)
def predict_tide(t, hc, constituents, DELTAT=0.0, CORRECTIONS='OTIS'):
"""
Predict tides at a single time using harmonic constants
Arguments
---------
t: days relative to 1992-01-01T00:00:00
hc: harmonic constant vector (complex)
constituents: tidal constituent IDs
Keyword arguments
-----------------
DELTAT: time correction for converting to Ephemeris Time (days)
CORRECTIONS: use nodal corrections from OTIS/ATLAS or GOT models
Returns
-------
ht: tide values reconstructed using the nodal corrections
"""
#-- number of points and number of constituents
npts, nc = np.shape(hc)
#-- load the nodal corrections
#-- convert time to Modified Julian Days (MJD)
pu, pf, G = load_nodal_corrections(t + 48622.0,
constituents,
DELTAT=DELTAT,
CORRECTIONS=CORRECTIONS)
#-- allocate for output tidal elevation
ht = np.ma.zeros((npts))
ht.mask = np.zeros((npts), dtype=np.bool)
#-- for each constituent
for k, c in enumerate(constituents):
if CORRECTIONS in ('OTIS', 'ATLAS', 'netcdf'):
#-- load parameters for each constituent
amp, ph, omega, alpha, species = load_constituent(c)
#-- add component for constituent to output tidal elevation
th = omega * t * 86400.0 + ph + pu[0, k]
elif CORRECTIONS in ('GOT', 'FES'):
th = G[0, k] * np.pi / 180.0 + pu[0, k]
#-- sum over all tides
ht.data[:] += pf[0,k]*hc.real[:,k]*np.cos(th) - \
pf[0,k]*hc.imag[:,k]*np.sin(th)
ht.mask[:] |= (hc.real.mask[:, k] | hc.imag.mask[:, k])
#-- return the tidal elevation after removing singleton dimensions
return np.squeeze(ht)
def predict_tidal_ts(time,hc,constituents,DELTAT=0.0,CORRECTIONS='OTIS'):
nt = len(time)
#-- load the nodal corrections
pu,pf,G = load_nodal_corrections(time + 48622.0, constituents,
DELTAT=DELTAT, CORRECTIONS=CORRECTIONS)
#-- allocate for output time series
ht = np.zeros((nt))
#-- for each constituent
for k,c in enumerate(constituents):
if CORRECTIONS in ('OTIS','ATLAS','netcdf'):
#-- load parameters for each constituent
amp,ph,omega,alpha,species = load_constituent(c)
#-- add component for constituent to output tidal time series
th = omega*time*86400.0 + ph + pu[:,k]
elif (CORRECTIONS == 'GOT'):
th = G[:,k]*np.pi/180.0 + pu[:,k]
#-- sum over all tides at location
ht += pf[:,k]*hc.real[0,k]*np.cos(th) - pf[:,k]*hc.imag[0,k]*np.sin(th)
#-- return the tidal time series
return ht