def tidal_model(tide_station_name='Fort_Point'):
""" This function gives a model of the predicted tides. To get tides at a
specific time, use the command:
tide.at([datetime(year,month,day,hour,minute)])
Input:
tide_station_name - Name of the tide station for which we want the
tidal model
Output:
tide_model - Model of the tides using NOAA's Harmonic Constituents
"""
# Determine which station was inputed - to make easier, we are converting
# all inputs to a lowercase string
ts = str(tide_station_name).lower()
if (ts == 'fort_point' or ts == 'fort point' or ts == '8423898'):
tide_station = 'Fort_Point'
elif (ts == 'wells' or ts == 'wells, me' or ts == '8419317'):
tide_station = 'Wells'
elif (ts == 'boston' or ts == 'boston, ma' or ts == '8443970'):
tide_station = 'Boston'
else:
print 'ERROR: Invalid Tide Station. Create a new file for {}.'.format(
tide_station_name)
return None
station_filename = '{}/{}.txt'.format(tide_station, tide_station)
station_info = parse_file(station_filename)
# 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 meters)
published_amplitudes = station_info[0][1]
published_phases = station_info[1][1]
# We can add a constant offset (e.g. for a different datum, we will use
# relative to MLLW):
# MTL = station_info[4][1]
MSL = station_info[5][1]
MLLW = station_info[6][1]
offset = MSL - 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)
return tide
def build_tide_models(tide_model_file):
"""Build models for all stations.
:returns: Dict -- {'station_id': model, ...}
"""
# We try to read the pre fitted tidal models
# if they don't exists we create them
# this model for all the stations
# is very tiny, so run locally
# if the database of station data is updated
# to re-fit the model.
logAudit(severity=7,
actor="bf-tideprediction",
action="buildingTideModels",
actee=tide_model_file,
message="Building tide models from file %s" % tide_model_file)
try:
with open(tide_model_file, 'rb') as tm:
tide_models = dill.load(tm)
except IOError:
tide_models = {}
for station in all_stations():
# station is like (1, )
station = station[0]
data = station_data(station)
model = build_tide_model(data)
if model is not None:
# see below, model is pickelable
# Tide is not.
tide_models[station] = model
else:
tide_models[station] = None
# Store data for later
with open(tide_model_file, 'wb') as tm:
dill.dump(tide_models, tm, protocol=dill.HIGHEST_PROTOCOL)
# we are doing it like this because the instantiated Tide
# is apparently not pickelable even using dill, also
# we can't build a model if v is None.
return {
k: Tide(model=v, radians=False) if v is not None else v
for (k, v) in tide_models.iteritems()
}
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)
