def test_reflective(self):
model = models.Stockdon2006(Hs=4, Tp=11, beta=0.1)
assert model.R2 == approx(2.54, abs=0.01)
assert model.setup == approx(0.96, abs=0.01)
assert model.sig == approx(1.65, abs=0.01)
assert model.sinc == approx(2.06, abs=0.01)
assert model.swash == approx(2.64, abs=0.01)
def test_list_input(self):
model = models.Stockdon2006(Hs=[1, 2], Lp=[100, 200], beta=[0.05, 0.1])
assert model.R2 == approx((0.59, 1.84), abs=0.1)
assert model.setup == approx((0.17, 0.70), abs=0.1)
assert model.sig == approx((0.6, 1.2), abs=0.1)
assert model.sinc == approx((0.37, 1.5), abs=0.1)
assert model.swash == approx((0.71, 1.92), abs=0.01)
def test_dissipative(self):
model = models.Stockdon2006(Hs=4, Tp=11, beta=0.001)
assert model.R2 == approx(1.18, abs=0.01)
assert model.setup == approx(0.0096, abs=0.01)
assert model.sig == approx(1.65, abs=0.01)
assert model.sinc == approx(0.02, abs=0.01)
assert model.swash == approx(1.65, abs=0.01)
def load_random_sto06(
seed=12345,
t_start=datetime(2000, 1, 1),
t_end=datetime(2000, 1, 2),
dt=timedelta(hours=1),
hs_range=(1, 3),
tp_range=(6, 10),
beta_range=(0.08, 0.1),
noise_std=0.3,
):
"""
Loads a randomly generated wave runup dataframe.
This function returns a randomly generated :obj:`pandas.DataFrame` containing wave
parameters and noisey runup values calculated using the Stockdon et al (2006) runup
model. This random data is intended to be used to demonstrate runup analysis
without having to use actual data
Args:
seed (:obj:`int`): Seed the random number generator
t_start (:obj:`datetime.datetime`): Start time of the dataframe
t_end (:obj:`datetime.datetime`): End time of the dataframe
dt (:obj:`datetime.timedelta`): Time interval of the dataframe
hs_range (:obj:`tuple`): Range (`min`, `max`) of the significant wave height
tp_range (:obj:`tuple`): Range (`min`, `max`) of the peak wave period
beta_range (:obj:`tuple`): Range (`min`, `max`) of the nearshore slope
noise_std (:obj:`float`): Standard deviation (in meters) of the wave runup
statistics.
Returns:
A :obj:`pandas.DataFrame`
Examples:
Get
>>> from py_wave_runup import datasets
>>> df = datasets.load_random_sto06()
>>> df.head()
hs tp beta ... swash sig sinc
2000-01-01 00:00:00 3.000000 6.000000 0.098110 ... 1.171474 0.717714 0.894084
2000-01-01 01:00:00 2.850109 6.128432 0.099052 ... 1.378190 0.919351 1.104072
2000-01-01 02:00:00 2.865462 6.461471 0.099766 ... 1.154970 0.664189 0.866796
2000-01-01 03:00:00 2.942888 6.750824 0.100000 ... 1.223142 0.701520 0.918580
2000-01-01 04:00:00 2.906808 6.937742 0.099069 ... 2.001251 1.476527 1.687905
<BLANKLINE>
[5 rows x 8 columns]
"""
# Create the time index for our dataframe
index = pd.date_range(start=t_start, end=t_end, freq=dt)
df = pd.DataFrame(index=index)
# Create some random noise
random.seed(seed)
np.random.seed(seed)
pn = PerlinNoise(num_octaves=7, persistence=0.5)
# Generate input parameters. Note that we offset the noise value by the length of
# the index to avoid having the same timeseries shape for each parameter.
l = len(index)
hs = minmax_scale([pn.noise(i) for i, _ in enumerate(index)], hs_range)
tp = minmax_scale([pn.noise(i + l) for i, _ in enumerate(index)], tp_range)
beta = minmax_scale([pn.noise(i + 2 * l) for i, _ in enumerate(index)],
beta_range)
# Generate wave parameters based on Stockdon
model = models.Stockdon2006(Hs=hs, Tp=tp, beta=beta)
# Generate additional noise
noise = np.random.normal(0, noise_std, l)
# Combine into a dataframe
df["hs"] = hs
df["tp"] = tp
df["beta"] = beta
df["r2"] = model.R2 + noise
df["setup"] = model.setup + noise
df["swash"] = model.swash + noise
df["sig"] = model.sig + noise
df["sinc"] = model.sinc + noise
return df
def test_different_list_input(self):
with raises(ValueError):
models.Stockdon2006(Hs=[1, 2], Lp=[100, 200], beta=[0.1])
def test_no_wave_length(self):
with raises(ValueError):
models.Stockdon2006(Hs=1, beta=0.1)
def test_depth_and_wavelength(self):
model = models.Stockdon2006(Hs=4, Lp=200, h=15, beta=0.05)
assert model.R2 == approx(1.69, 0.1)
def test_depth_and_period(self):
model = models.Stockdon2006(Hs=4, Tp=12, h=15, beta=0.05)
assert model.R2 == approx(1.00, 0.1)
def test_wave_length(self):
model = models.Stockdon2006(Hs=4, Lp=200, beta=0.05)
assert model.R2 == approx(1.69, 0.1)