def test_kings_point(self):
# Check that Pytides prediction in King's Point for 0000 and 0600 GMT
# on January 1 2013 are [-0.086250887498591222 2.207534179351927].
# Results have been updated to take into account commit #7d5e3c7
# 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)
heights = tide.at([datetime(2013, 1, 1, 0, 0, 0), datetime(2013, 1, 1, 6, 0, 0)])
self.assertEqual(heights[0], -0.086250887498591222)
self.assertEqual(heights[1], 2.207534179351927)
