How to use the NOAA's published Harmonic Constituents in Python with Pytides

An effort has been made to ensure Pytides uses the same harmonic constituents as NOAA. Whilst there may be some slight differences, these don't appear to have a significant effect. In this example, we will make a prediction using the NOAA constituents published for King's Point, NY. These can be found here (I've used GMT and Metres). The important thing to take from this is how to initialise a Pytides Tide class without decomposing/fitting previous tidal data.

from datetime import datetime
from pytides.tide import Tide
import pytides.constituent as cons
import numpy as np

#These are the NOAA constituents, in the order presented on their website.
constituents = [c for c in cons.noaa if c != cons._Z0]

#Phases and amplitudes (relative to GMT and in degrees and metres)
published_phases = [115.7,140.7,92.6,192,145.5,220.6,159.9,202.8,152.3,117.2,92,0,0,69.7,224.5,141.7,121.9,
228.4,252.1,0,60.1,135.5,0,0,204.5,212.2,112.3,141.8,249.1,211.1,75.1,181.4,140.4,202.4,141.8,155,160.9]

published_amplitudes = [1.142,0.189,0.241,0.1,0.036,0.066,0.08,0.01,0.004,0.022,0.052,0,0,0.03,0.007,0.025,0.009,
0.005,0.008,0,0.024,0.065,0,0,0.004,0.017,0.015,0.002,0.002,0.032,0.003,0.007,0.07,0.009,0.053,0.007,0.008]

#We can add a constant offset (e.g. for a different datum, we will use relative to MLLW):
MTL = 5.113
MLLW = 3.928
offset = MTL - MLLW
constituents.append(cons._Z0)
published_phases.append(0)
published_amplitudes.append(offset)

#Build the model.
assert(len(constituents) == len(published_phases) == len(published_amplitudes))
model = np.zeros(len(constituents), dtype = Tide.dtype)
model['constituent'] = constituents
model['amplitude'] = published_amplitudes
model['phase'] = published_phases

tide = Tide(model = model, radians = False)

print tide.at([datetime(2013,1,1,0,0,0), datetime(2013,1,1,6,0,0)])

Running the program yields [-0.08631255 2.20748666], whilst the actual NOAA prediction for 0000 and 0600 GMT on January 1 2013 are [-0.081, 2.206].