def test_weibull_i_cdf(weibull_shape, weibull_loc, weibull_scale):
x = np.linspace(0, 1)
ref_cdf = sts.weibull_min.ppf(x, weibull_shape(None), weibull_loc(None),
weibull_scale(None))
dist = WeibullDistribution(weibull_shape, weibull_loc, weibull_scale)
my_cdf = dist.i_cdf(x, x, (None, None, None))
assert np.allclose(ref_cdf, my_cdf)
def test_weibull_draw_sample(weibull_number, weibull_shape, weibull_loc,
weibull_scale):
ref_points = weibull_number
dist = WeibullDistribution(weibull_shape, weibull_loc, weibull_scale)
my_points = dist.draw_sample(weibull_number)
my_points = my_points.size
assert ref_points == my_points
def test_IForm3d(self): # TODO what does this test do
"""
Creating Contour example
"""
#define dependency tuple
dep1 = (None, None, None)
dep2 = (0, None, 0)
dep3 = (0, None, 0)
#define parameters
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
mu = FunctionParam(0.1000, 1.489, 0.1901, "f1")
sigma = FunctionParam(0.0400, 0.1748, -0.2243, "f2")
#del shape, loc, scale
#create distributions
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(mu=mu, sigma=sigma)
dist3 = LognormalDistribution(mu=mu, sigma=sigma)
distributions = [dist1, dist2, dist3]
dependencies = [dep1, dep2, dep3]
mul_dist = MultivariateDistribution(distributions, dependencies)
test_contour_IForm = IFormContour(mul_dist, 50, 3, 400)
def test_IForm3d(self): # TODO what does this test do
"""
3-dimensional IFORM contour.
"""
# Define dependency tuple.
dep1 = (None, None, None)
dep2 = (0, None, 0)
dep3 = (0, None, 0)
# Define parameters.
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
mu = FunctionParam('power3', 0.1000, 1.489, 0.1901)
sigma = FunctionParam('exp3', 0.0400, 0.1748, -0.2243)
#del shape, loc, scale
# Create distributions.
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(mu=mu, sigma=sigma)
dist3 = LognormalDistribution(mu=mu, sigma=sigma)
distributions = [dist1, dist2, dist3]
dependencies = [dep1, dep2, dep3]
mul_dist = MultivariateDistribution(distributions, dependencies)
test_contour_IForm = IFormContour(mul_dist, 50, 3, 400)
def _setup(self,
dep1=(None, None, None),
dep2=(0, None, 0),
par1=(ConstantParam(1.471), ConstantParam(0.8888),
ConstantParam(2.776)),
par2=(FunctionParam('exp3', 0.0400, 0.1748, -0.2243), None,
FunctionParam('power3', 0.1, 1.489, 0.1901))
):
"""
Creating contour.
"""
# Define dependency tuple.
self.dep1 = dep1
self.dep2 = dep2
# Define parameters.
self.par1 = par1
self.par2 = par2
# Create distributions.
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(sigma=par2[0], mu=par2[2])
distributions = [dist1, dist2]
dependencies = [dep1, dep2]
mul_dist = MultivariateDistribution(distributions, dependencies)
# Calculate contour
dsc = DirectSamplingContour(mul_dist, 10000000, 25, 6, 6)
test_contour_dsc = dsc.direct_sampling_contour()
return test_contour_dsc
def test_distribution_loc_None(self):
"""
Tests if loc is set to default when it has value 'None'.
"""
# Define parameters.
shape = ConstantParam(0.8888)
loc = None
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
rv_values = [0.8, 1, 8]
dependencies = (0, 1, 1)
dist = WeibullDistribution(*par1)
loc_test = dist._get_parameter_values(rv_values, dependencies)[1]
self.assertEqual(loc_test, 0)
def test_multivariate_draw_sample(self):
"""
Create an example MultivariateDistribution (Vanem2012 model).
"""
# Define dependency tuple.
dep1 = (None, None, None)
dep2 = (0, None, 0)
# Define parameters.
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
shape = FunctionParam('exp3', 0.0400, 0.1748, -0.2243)
loc = None
scale = FunctionParam('power3', 0.1, 1.489, 0.1901)
par2 = (shape, loc, scale)
del shape, loc, scale
# Create distributions.
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(*par2)
distributions = [dist1, dist2]
dependencies = [dep1, dep2]
ref_points = 99119
mul_var_dist = MultivariateDistribution(distributions, dependencies)
my_points = mul_var_dist.draw_sample(ref_points)
my_points0 = my_points[0].size
my_points1 = my_points[1].size
assert ref_points == my_points0
assert ref_points == my_points1
def test_weibull_param_out_of_bounds(weibull_param_name):
dist = WeibullDistribution()
setattr(dist, weibull_param_name, ConstantParam(-np.inf))
with pytest.raises(ValueError):
dist.cdf([0, 100], [0, 100], (None, None, None))
dist = WeibullDistribution()
setattr(dist, weibull_param_name, ConstantParam(np.inf))
with pytest.raises(ValueError):
dist.cdf([0, 100], [0, 100], (None, None, None))
def setup_mul_dist(probabilistic_model: ProbabilisticModel):
"""
Generates a MultiVariateDistribution from a ProbabilisticModel.
MultiVariateDistribution objects are used to perform the statistical
computations in the viroconcom package. ProbabilisticModel objects are used
in the viroconweb package to be saved in the data base.
Parameters
----------
probabilistic_model : ProbabilisticModel,
The probabilistic model, which should be converted.
Returns
-------
mutivar_distribution : MultivariateDistribution,
The object, which can be used in the viroconcom package.
"""
distributions_model = DistributionModel.objects.filter(
probabilistic_model=probabilistic_model)
distributions = []
dependencies = []
for dist in distributions_model:
dependency = []
parameters = []
parameters_model = ParameterModel.objects.filter(distribution=dist)
for param in parameters_model:
dependency.append(adjust(param.dependency))
if adjust(param.function) is not None:
parameters.append(
FunctionParam(float(param.x0), float(param.x1),
float(param.x2), param.function))
else:
parameters.append(ConstantParam(float(param.x0)))
dependencies.append(dependency)
if dist.distribution == 'Normal':
distributions.append(NormalDistribution(*parameters))
elif dist.distribution == 'Weibull':
distributions.append(WeibullDistribution(*parameters))
elif dist.distribution == 'Lognormal_SigmaMu':
distributions.append(
LognormalDistribution(sigma=parameters[0], mu=parameters[2]))
elif dist.distribution == 'KernelDensity':
distributions.append(KernelDensityDistribution(*parameters))
else:
raise KeyError('{} is not a matching distribution'.format(
dist.distribution))
mutivar_distribution = MultivariateDistribution(distributions,
dependencies)
return mutivar_distribution
def test_HDC3d_WLN(self):
dep1 = (None, None, None)
dep2 = (0, None, 0)
dep3 = (None, 0, 0)
#define parameters
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
shape = None
loc = FunctionParam(4, 10, 0.02, "f1")
scale = FunctionParam(0.1, 0.02, -0.1, "f2")
par2 = (shape, loc, scale)
mu = FunctionParam(0.1, 1.5, 0.2, "f1")
sigma = FunctionParam(0.1, 0.2, -0.2, "f2")
#create distributions
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(mu=mu, sigma=sigma)
dist3 = NormalDistribution(*par2)
distributions = [dist1, dist2, dist3]
dependencies = [dep1, dep2, dep3]
mul_dist = MultivariateDistribution(distributions, dependencies)
del mu, sigma
#del dist1, dist2, par1, par2, dep1, dep2, dependencies, distributions
#calc contour
n_years = 50
limits = [(0, 20), (0, 20), (0, 20)]
deltas = [0.5, 0.5, 0.05]
test_contour_HDC = HighestDensityContour(mul_dist, n_years, 3, limits,
deltas)
finaldt3 = pd.DataFrame({
'x': test_contour_HDC.coordinates[0][0],
'y': test_contour_HDC.coordinates[0][1],
'z': test_contour_HDC.coordinates[0][2]
})
matlab3 = pd.read_csv(testfiles_path + "/hdc3d_wln.csv",
names=['x', 'y', 'z'])
result3 = pd.read_csv(testfiles_path + "/HDC3dWLN_coordinates.csv")
for m, n in [(m, n) for m in result3.index for n in result3.columns]:
self.assertAlmostEqual(result3.loc[m, n],
finaldt3.loc[m, n],
places=8)
def test_draw_sample_distribution(self):
"""
Create an example MultivariateDistribution (Vanem2012 model).
"""
# Define dependency tuple.
dep1 = (None, None, None)
dep2 = (0, None, 0)
# Define parameters.
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
shape = FunctionParam('exp3', 0.0400, 0.1748, -0.2243)
loc = None
scale = FunctionParam('power3', 0.1, 1.489, 0.1901)
par2 = (shape, loc, scale)
del shape, loc, scale
# Create distributions.
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(*par2)
distributions = [dist1, dist2]
dependencies = [dep1, dep2]
points = 1000000
mul_var_dist = MultivariateDistribution(distributions, dependencies)
my_points = mul_var_dist.draw_sample(points)
#Fit the sample
# Describe the distribution that should be fitted to the sample.
dist_description_0 = {
'name': 'Weibull',
'dependency': (None, None, None),
'width_of_intervals': 2
}
dist_description_1 = {
'name': 'Lognormal',
'dependency': (0, None, 0),
'functions': ('exp3', None, 'power3')
}
my_fit = Fit([my_points[0], my_points[1]],
[dist_description_0, dist_description_1])
print(my_fit.mul_var_dist.distributions[0].shape(0))
print(mul_var_dist.distributions[0].shape(0))
assert np.round(my_fit.mul_var_dist.distributions[0].shape(0),
2) == np.round(mul_var_dist.distributions[0].shape(0),
2)
def test_HDC2d_WL(self):
"""
2-d HDC with Weibull and Lognormal distribution.
The used probabilistic model is described in Vanem and Bitner-Gregersen
(2012), DOI: 10.1016/j.apor.2012.05.006
"""
#define dependency tuple
dep1 = (None, None, None)
dep2 = (0, None, 0)
#define parameters
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
mu = FunctionParam(0.1000, 1.489, 0.1901, 'power3')
sigma = FunctionParam(0.0400, 0.1748, -0.2243, 'exp3')
#del shape, loc, scale
#create distributions
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(mu=mu, sigma=sigma)
distributions = [dist1, dist2]
dependencies = [dep1, dep2]
mul_dist = MultivariateDistribution(distributions, dependencies)
#del dist1, dist2, par1, par2, dep1, dep2, dependencies, distributions
#calc contour
n_years = 50
limits = [(0, 20), (0, 18)]
deltas = [0.1, 0.1]
test_contour_HDC = HighestDensityContour(mul_dist, n_years, 3, limits,
deltas)
finaldt0 = pd.DataFrame({
'x': test_contour_HDC.coordinates[0][0],
'y': test_contour_HDC.coordinates[0][1]
})
result0 = pd.read_csv(testfiles_path + "/HDC2dWL_coordinates.csv")
for g, h in [(g, h) for g in result0.index for h in result0.columns]:
self.assertAlmostEqual(result0.loc[g, h],
finaldt0.loc[g, h],
places=8)
def test_HDC3d_WLL(self):
"""
Creating Contour example for 3-d HDC with Weibull, Lognormal and
Lognormal distribution
"""
dep1 = (None, None, None)
dep2 = (0, None, 0)
dep3 = (0, None, 0)
#define parameters
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
mu = FunctionParam(0.1000, 1.489, 0.1901, "f1")
sigma = FunctionParam(0.0400, 0.1748, -0.2243, "f2")
#del shape, loc, scale
#create distributions
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(mu=mu, sigma=sigma)
dist3 = LognormalDistribution(mu=mu, sigma=sigma)
distributions = [dist1, dist2, dist3]
dependencies = [dep1, dep2, dep3]
mul_dist = MultivariateDistribution(distributions, dependencies)
#del dist1, dist2, par1, par2, dep1, dep2, dependencies, distributions
#calc contour
n_years = 50
limits = [(0, 20), (0, 18), (0, 18)]
deltas = [1, 1, 1]
test_contour_HDC = HighestDensityContour(mul_dist, n_years, 3, limits,
deltas)
finaldt = pd.DataFrame({
'x': test_contour_HDC.coordinates[0][0],
'y': test_contour_HDC.coordinates[0][1],
'z': test_contour_HDC.coordinates[0][2]
})
result = pd.read_csv(testfiles_path + "/HDC3dWLL_coordinates.csv")
for i, j in [(i, j) for i in result.index for j in result.columns]:
self.assertAlmostEqual(result.loc[i, j],
finaldt.loc[i, j],
places=8)
def test_HDC2d_WN(self):
"""
Creating Contour example
"""
#define dependency tuple
dep1 = (None, None, None)
dep2 = (None, 0, 0)
#define parameters
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
shape = None
loc = FunctionParam(4, 10, 0.02, "f1")
scale = FunctionParam(0.1, 0.02, -0.1, "f2")
par2 = (shape, loc, scale)
#del shape, loc, scale
#create distributions
dist1 = WeibullDistribution(*par1)
dist2 = NormalDistribution(*par2)
distributions = [dist1, dist2]
dependencies = [dep1, dep2]
mul_dist = MultivariateDistribution(distributions, dependencies)
#del dist1, dist2, par1, par2, dep1, dep2, dependencies, distributions
#calc contour
n_years = 50
limits = [(0, 20), (0, 20)]
deltas = [0.05, 0.01]
test_contour_HDC = HighestDensityContour(mul_dist, n_years, 3, limits,
deltas)
finaldt2 = pd.DataFrame({
'x': test_contour_HDC.coordinates[0][0],
'y': test_contour_HDC.coordinates[0][1]
})
result2 = pd.read_csv(testfiles_path + "/HDC2dWN_coordinates.csv")
for k, l in [(k, l) for k in result2.index for l in result2.columns]:
self.assertAlmostEqual(result2.loc[k, l],
finaldt2.loc[k, l],
places=8)
def test_IForm2d_WL(self):
"""
2-d IFORM contour.
The used probabilistic model is described in Vanem and Bitner-Gregersen
(2012), DOI: 10.1016/j.apor.2012.05.006
"""
# Define dependency tuple
dep1 = (None, None, None)
dep2 = (0, None, 0)
# Define parameters
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
mu = FunctionParam(0.1000, 1.489, 0.1901, "power3")
sigma = FunctionParam(0.0400, 0.1748, -0.2243, "exp3")
# Create distributions
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(mu=mu, sigma=sigma)
distributions = [dist1, dist2]
dependencies = [dep1, dep2]
mul_dist = MultivariateDistribution(distributions, dependencies)
test_contour_IForm = IFormContour(mul_dist, 50, 3, 50)
calculated_coordinates = pd.DataFrame({
'x':
test_contour_IForm.coordinates[0][0],
'y':
test_contour_IForm.coordinates[0][1]
})
#calculated_coordinates.to_csv('save_this_file.csv', sep=',', header=['x', 'y'], index=False)
true_coordinates = pd.read_csv(testfiles_path +
"/IForm2dWL_coordinates.csv")
for o, p in [(o, p) for o in true_coordinates.index
for p in true_coordinates.columns]:
self.assertAlmostEqual(calculated_coordinates.loc[o, p],
true_coordinates.loc[o, p],
places=8)
def test_IForm2d_WN(self):
"""
2-d IFORM contour.
"""
# Define dependency tuple.
dep1 = (None, None, None)
dep2 = (None, 0, 0)
# Define parameters.
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
shape = None
loc = FunctionParam(7, 1.489, 0.1901, "power3")
scale = FunctionParam(1.5, 0.1748, -0.2243, "exp3")
par2 = (shape, loc, scale)
# Create distributions.
dist1 = WeibullDistribution(*par1)
dist2 = NormalDistribution(*par2)
distributions = [dist1, dist2]
dependencies = [dep1, dep2]
mul_dist = MultivariateDistribution(distributions, dependencies)
test_contour_IForm = IFormContour(mul_dist, 50, 3, 50)
calculated_coordinates = pd.DataFrame({
'x':
test_contour_IForm.coordinates[0][0],
'y':
test_contour_IForm.coordinates[0][1]
})
true_coordinates = pd.read_csv(testfiles_path +
"/IForm2dWN_coordinates.csv")
for r, s in [(r, s) for r in true_coordinates.index
for s in true_coordinates.columns]:
self.assertAlmostEqual(calculated_coordinates.loc[r, s],
true_coordinates.loc[r, s],
places=8)
def test_IForm2d_WN(self):
"""
Creating Contour example
"""
#define dependency tuple
dep1 = (None, None, None)
dep2 = (None, 0, 0)
#define parameters
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
shape = None
loc = FunctionParam(7, 1.489, 0.1901, "f1")
scale = FunctionParam(1.5, 0.1748, -0.2243, "f2")
par2 = (shape, loc, scale)
#del shape, loc, scale
#create distributions
dist1 = WeibullDistribution(*par1)
dist2 = NormalDistribution(*par2)
distributions = [dist1, dist2]
dependencies = [dep1, dep2]
mul_dist = MultivariateDistribution(distributions, dependencies)
test_contour_IForm = IFormContour(mul_dist, 50, 3, 400)
finaldt5 = pd.DataFrame({
'x': test_contour_IForm.coordinates[0][0],
'y': test_contour_IForm.coordinates[0][1]
})
result5 = pd.read_csv(testfiles_path + "/IForm2dWN_coordinates.csv")
for r, s in [(r, s) for r in result5.index for s in result5.columns]:
self.assertAlmostEqual(result5.loc[r, s],
finaldt5.loc[r, s],
places=8)
def test_HDC4d_WLLL(self):
"""
Creating Contour example for 4-d HDC with Weibull, Lognormal,
Lognormal and Lognormal distribution
"""
#define dependency tuple
dep1 = (None, None, None)
dep2 = (0, None, 0)
dep3 = (0, None, 0)
dep4 = (0, None, 0)
#define parameters
shape = ConstantParam(2.776)
loc = ConstantParam(1.471)
scale = ConstantParam(0.8888)
par1 = (shape, loc, scale)
mu = FunctionParam(0.1000, 1.489, 0.1901, "f1")
sigma = FunctionParam(0.0400, 0.1748, -0.2243, "f2")
#create distributions
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(mu=mu, sigma=sigma)
dist3 = LognormalDistribution(mu=mu, sigma=sigma)
dist4 = LognormalDistribution(mu=mu, sigma=sigma)
distributions = [dist1, dist2, dist3, dist4]
dependencies = [dep1, dep2, dep3, dep4]
mul_dist = MultivariateDistribution(distributions, dependencies)
#del dist1, dist2, par1, par2, dep1, dep2, dependencies, distributions
#calc contour
n_years = 50
limits = [(0, 20), (0, 18), (0, 18), (0, 18)]
deltas = [1, 1, 1, 1]
test_contour_HDC = HighestDensityContour(mul_dist, n_years, 3, limits,
deltas)
def _setup(self,
limits=[(0, 20), (0, 20)],
deltas=[0.05, 0.05],
n_years = 25,
dep1=(None, None, None),
dep2=(0, None, 0),
par1=(ConstantParam(1.471), ConstantParam(0.8888),
ConstantParam(2.776)),
par2=(FunctionParam('exp3', 0.0400, 0.1748, -0.2243), None,
FunctionParam('power3', 0.1, 1.489, 0.1901))
):
"""
Creating a contour (same as in DOI: 10.1016/j.coastaleng.2017.03.002).
"""
self.limits = limits
self.deltas = deltas
self.n_years = n_years
# Define dependency tuple.
self.dep1 = dep1
self.dep2 = dep2
# Define parameters.
self.par1 = par1
self.par2 = par2
# Create distributions.
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(*par2)
distributions = [dist1, dist2]
dependencies = [dep1, dep2]
mul_dist = MultivariateDistribution(distributions, dependencies)
# Compute contour.
test_contour_HDC = HighestDensityContour(mul_dist, n_years, 3,
limits, deltas)
return test_contour_HDC
def test_HDC4d_WLLL(self):
"""
Contour example for a 4-dimensinal HDC with Weibull, Lognormal,
Lognormal and Lognormal distribution.
"""
# Define dependency tuple.
dep1 = (None, None, None)
dep2 = (0, None, 0)
dep3 = (0, None, 0)
dep4 = (0, None, 0)
# Define parameters.
shape = ConstantParam(2.776)
loc = ConstantParam(1.471)
scale = ConstantParam(0.8888)
par1 = (shape, loc, scale)
mu = FunctionParam('power3', 0.1000, 1.489, 0.1901)
sigma = FunctionParam('exp3', 0.0400, 0.1748, -0.2243)
# Create distributions.
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(mu=mu, sigma=sigma)
dist3 = LognormalDistribution(mu=mu, sigma=sigma)
dist4 = LognormalDistribution(mu=mu, sigma=sigma)
distributions = [dist1, dist2, dist3, dist4]
dependencies = [dep1, dep2, dep3, dep4]
mul_dist = MultivariateDistribution(distributions, dependencies)
# Compute contour.
n_years = 50
limits = [(0, 20), (0, 18), (0, 18), (0, 18)]
deltas = [1, 1, 1, 1]
test_contour_HDC = HighestDensityContour(mul_dist, n_years, 3,
limits, deltas)
def test_plot_contour_without_sample(self):
"""
Plots a contour in the most basic way.
"""
# Define dependency tuple.
dep1 = (None, None, None)
dep2 = (0, None, 0)
# Define parameters.
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
mu = FunctionParam('power3', 0.1000, 1.489, 0.1901)
sigma = FunctionParam('exp3', 0.0400, 0.1748, -0.2243)
# Create distributions.
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(mu=mu, sigma=sigma)
distributions = [dist1, dist2]
dependencies = [dep1, dep2]
mul_dist = MultivariateDistribution(distributions, dependencies)
test_contour_IForm = IFormContour(mul_dist, 50, 3, 50)
contour_hs = test_contour_IForm.coordinates[0][0]
contour_tz = test_contour_IForm.coordinates[0][1]
fig = plt.figure()
ax = fig.add_subplot(111)
plot_contour(contour_hs, contour_tz, ax)
#plt.show()
x_plot, y_plot = ax.lines[0].get_xydata().T
self.assertAlmostEqual(y_plot[0], contour_tz[0], delta=0.001)
def test_IForm2d_WL(self):
"""
Creating Contour example
"""
#define dependency tuple
dep1 = (None, None, None)
dep2 = (0, None, 0)
#define parameters
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
mu = FunctionParam(0.1000, 1.489, 0.1901, "f1")
sigma = FunctionParam(0.0400, 0.1748, -0.2243, "f2")
#create distributions
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(mu=mu, sigma=sigma)
distributions = [dist1, dist2]
dependencies = [dep1, dep2]
mul_dist = MultivariateDistribution(distributions, dependencies)
test_contour_IForm = IFormContour(mul_dist, 50, 3, 400)
finaldt4 = pd.DataFrame({
'x': test_contour_IForm.coordinates[0][0],
'y': test_contour_IForm.coordinates[0][1]
})
result4 = pd.read_csv(testfiles_path + "/IForm2dWL_coordinates.csv")
for o, p in [(o, p) for o in result4.index for p in result4.columns]:
self.assertAlmostEqual(result4.loc[o, p],
finaldt4.loc[o, p],
places=8)
def _setup(self,
limits=[(0, 20), (0, 20)],
deltas=[0.05, 0.05],
n_years=25,
dep1=(None, None, None),
dep2=(0, None, 0),
par1=(ConstantParam(1.471), ConstantParam(0.8888),
ConstantParam(2.776)),
par2=(FunctionParam(0.0400, 0.1748, -0.2243, "f2"), None,
FunctionParam(0.1, 1.489, 0.1901, "f1"))):
"""
Creating Contour example
"""
self.limits = limits
self.deltas = deltas
self.n_years = n_years
#define dependency tuple
self.dep1 = dep1
self.dep2 = dep2
#define parameters
self.par1 = par1
self.par2 = par2
#create distributions
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(*par2)
distributions = [dist1, dist2]
dependencies = [dep1, dep2]
mul_dist = MultivariateDistribution(distributions, dependencies)
#calc contour
test_contour_HDC = HighestDensityContour(mul_dist, n_years, 3, limits,
deltas)
return test_contour_HDC
def test_check_parameter_value(self):
"""
Tests if the right exception is raised when the given parameters are
not in the valid range of numbers.
"""
shape = None
loc = ConstantParam(0.8888)
scale = ConstantParam(-2.776)
par1 = (shape, loc, scale)
dist = WeibullDistribution(*par1)
with self.assertRaises(ValueError):
dist._check_parameter_value(2, -2.776)
with self.assertRaises(ValueError):
dist._check_parameter_value(2, np.inf)
from viroconcom.params import ConstantParam, FunctionParam
from viroconcom.distributions import WeibullDistribution, LognormalDistribution, \
MultivariateDistribution
from viroconcom.contours import IFormContour, ISormContour, HighestDensityContour
import matplotlib.pyplot as plt
# Define the multivariate distribution given in the paper by Vanem and
# Bitner-Gregersen (2012; doi: 10.1016/j.apor.2012.05.006)
shape = ConstantParam(1.471)
loc = ConstantParam(0.889)
scale = ConstantParam(2.776)
dist0 = WeibullDistribution(shape, loc, scale)
dep0 = (None, None, None) # All three parameters are independent.
my_sigma = FunctionParam('exp3', 0.040, 0.175, -0.224)
my_mu = FunctionParam('power3', 0.100, 1.489, 0.190)
dist1 = LognormalDistribution(sigma=my_sigma, mu=my_mu)
dep1 = (0, None, 0) # Parameter one and three depend on dist0.
distributions = [dist0, dist1]
dependencies = [dep0, dep1]
mul_dist = MultivariateDistribution(distributions, dependencies)
# Compute an IFORM, an ISORM and a highest density contour.
return_period = 50 # In years
sea_state_duration = 6 # In hours
iform_contour = IFormContour(mul_dist, return_period, sea_state_duration, 100)
isorm_contour = ISormContour(mul_dist, return_period, sea_state_duration, 100)
limits = [(0, 20), (0, 20)] # Limits of the computational domain
deltas = [0.005, 0.005] # Dimensions of the grid cells
hdens_contour = HighestDensityContour(
mul_dist, return_period, sea_state_duration, limits, deltas)
# Define a hs - steepness bivariate model
dist_description_hs = {
'name': 'Weibull_Exp'
} # Order: shape, loc, scale, shape2
dist_description_s = {'name': 'Weibull_3p'}
from scipy.stats import weibull_min
from viroconcom.distributions import WeibullDistribution
from viroconcom.distributions import ExponentiatedWeibullDistribution
from viroconcom.distributions import MultivariateDistribution
from viroconcom.params import FunctionParam
params = weibull_min.fit(steepness, floc=0.005)
my_loc = FunctionParam('poly1', 0.0015, 0.002, None)
dist_s = WeibullDistribution(shape=params[0], loc=my_loc, scale=params[2])
dist_hs = ExponentiatedWeibullDistribution()
dist_hs.fit(hs)
joint_dist = MultivariateDistribution(distributions=[dist_hs, dist_s],
dependencies=[(None, None, None, None),
(None, 0, None)])
# Fit the model to the data.
#fit = Fit((hs, steepness),
# (dist_description_hs, dist_description_s))
#joint_dist = fit.mul_var_dist
trs = [1, 50, 250]
fms = np.empty(shape=(3, 1))
for i, tr in enumerate(trs):
HDC = HighestDensityContour(joint_dist,
class MultivariateDistributionTest(unittest.TestCase):
"""
Create an example MultivariateDistribution (Vanem2012 model).
"""
# Define dependency tuple.
dep1 = (None, 0, None)
dep2 = (0, None, 0)
# Define parameters.
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
shape = FunctionParam(0.0400, 0.1748, -0.2243, "exp3")
loc = None
scale = FunctionParam(0.1, 1.489, 0.1901, "power3")
par2 = (shape, loc, scale)
del shape, loc, scale
# Create distributions.
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(*par2)
distributions = [dist1, dist2]
dependencies = [dep1, dep2]
def test_add_distribution_err_msg(self):
"""
Tests if the right exception is raised when distribution1 has a
dependency.
"""
with self.assertRaises(ValueError):
MultivariateDistribution(self.distributions, self.dependencies)
def test_add_distribution_iter(self):
"""
Tests if an exception is raised by the function add_distribution when
distributions isn't iterable but dependencies is and the other way around.
"""
distributions = 1
with self.assertRaises(ValueError):
MultivariateDistribution(distributions, self.dependencies)
dependencies = 0
with self.assertRaises(ValueError):
MultivariateDistribution(self.distributions, dependencies)
def test_add_distribution_length(self):
"""
Tests if an exception is raised when distributions and dependencies
are of unequal length.
"""
dep3 = (0, None, None)
dependencies = [self.dep1, self.dep2, dep3]
with self.assertRaises(ValueError):
MultivariateDistribution(self.distributions, dependencies)
def test_add_distribution_dependencies_length(self):
"""
Tests if an exception is raised when a tuple in dependencies
has not length 3.
"""
dep1 = (None, None)
dependencies = [dep1, self.dep2]
with self.assertRaises(ValueError):
MultivariateDistribution(self.distributions, dependencies)
def test_add_distribution_dependencies_value(self):
"""
Tests if an exception is raised when dependencies has an invalid value.
"""
dep1 = (-3, None, None)
dependencies = [dep1, self.dep2]
with self.assertRaises(ValueError):
MultivariateDistribution(self.distributions, dependencies)
def test_add_distribution_not_iterable(self):
"""
Tests the function when both distributions and dependencies
are not iterable.
"""
distributions = 1
dependencies = 2
with self.assertRaises(ValueError):
MultivariateDistribution(distributions, dependencies)
def test_latex_representation(self):
"""
Tests if the latex representation is correct.
"""
dep1 = (None, None, None)
dep2 = (0, None, 0)
dependencies = [dep1, dep2]
m = MultivariateDistribution(self.distributions, dependencies)
computed_latex = m.latex_repr(['Hs', 'Tp'])
correct_latex = \
['\\text{ joint PDF: }',
'f(h_{s},t_{p})=f_{H_{s}}(h_{s})f_{T_{p}|H_{s}}(t_{p}|h_{s})',
'',
'1\\text{. variable, }H_{s}: ',
'f_{H_{s}}(h_{s})=\\dfrac{\\beta_{h_{s}}}{\\alpha_{h_{s}}}'
'\\left(\\dfrac{h_{s}-\\gamma_{h_{s}}}{\\alpha_{h_{s}}}'
'\\right)^{\\beta_{h_{s}}-1}\\exp\\left[-\\left(\\dfrac{h_{s}-'
'\\gamma_{h_{s}}}{\\alpha_{h_{s}}}\\right)^{\\beta_{h_{s}}}\\right]',
'\\quad\\text{ with }\\alpha_{h_{s}}=2.776,',
'\\quad\\qquad\\;\\; \\beta_{h_{s}}=1.471,',
'\\quad\\qquad\\;\\; \\gamma_{h_{s}}=0.8888.',
'',
'2\\text{. variable, }T_{p}: ',
'f_{T_{p}|H_{s}}(t_{p}|h_{s})=\\dfrac{1}{t_{p}\\tilde{\\sigma}_'
'{t_{p}}\\sqrt{2\\pi}}\\exp\\left[-\\dfrac{(\\ln t_{p}-\\tilde{'
'\\mu}_{t_{p}})^2}{2\\tilde{\\sigma}_{t_{p}}^2}\\right]',
'\\quad\\text{ with }\\exp{\\tilde{\\mu}}_{t_{p}}='
'0.1+1.489h_{s}^{0.1901},',
'\\quad\\qquad\\;\\; \\tilde{\\sigma}_{t_{p}}='
'0.04+0.1748e^{-0.2243h_{s}}.']
assert (computed_latex, correct_latex)
class MultivariateDistributionTest(unittest.TestCase):
"""
Create an example MultivariateDistribution (Vanem2012 model).
"""
# Define dependency tuple.
dep1 = (None, 0, None)
dep2 = (0, None, 0)
# Define parameters.
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
shape = FunctionParam('exp3', 0.0400, 0.1748, -0.2243)
loc = None
scale = FunctionParam('power3', 0.1, 1.489, 0.1901)
par2 = (shape, loc, scale)
del shape, loc, scale
# Create distributions.
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(*par2)
distributions = [dist1, dist2]
dependencies = [dep1, dep2]
def test_add_distribution_err_msg(self):
"""
Tests if the right exception is raised when distribution1 has a
dependency.
"""
with self.assertRaises(ValueError):
MultivariateDistribution(self.distributions, self.dependencies)
def test_multivariate_draw_sample(self):
"""
Create an example MultivariateDistribution (Vanem2012 model).
"""
# Define dependency tuple.
dep1 = (None, None, None)
dep2 = (0, None, 0)
# Define parameters.
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
shape = FunctionParam('exp3', 0.0400, 0.1748, -0.2243)
loc = None
scale = FunctionParam('power3', 0.1, 1.489, 0.1901)
par2 = (shape, loc, scale)
del shape, loc, scale
# Create distributions.
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(*par2)
distributions = [dist1, dist2]
dependencies = [dep1, dep2]
ref_points = 99119
mul_var_dist = MultivariateDistribution(distributions, dependencies)
my_points = mul_var_dist.draw_sample(ref_points)
my_points0 = my_points[0].size
my_points1 = my_points[1].size
assert ref_points == my_points0
assert ref_points == my_points1
def test_draw_sample_distribution(self):
"""
Create an example MultivariateDistribution (Vanem2012 model).
"""
# Define dependency tuple.
dep1 = (None, None, None)
dep2 = (0, None, 0)
# Define parameters.
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
shape = FunctionParam('exp3', 0.0400, 0.1748, -0.2243)
loc = None
scale = FunctionParam('power3', 0.1, 1.489, 0.1901)
par2 = (shape, loc, scale)
del shape, loc, scale
# Create distributions.
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(*par2)
distributions = [dist1, dist2]
dependencies = [dep1, dep2]
points = 1000000
mul_var_dist = MultivariateDistribution(distributions, dependencies)
my_points = mul_var_dist.draw_sample(points)
#Fit the sample
# Describe the distribution that should be fitted to the sample.
dist_description_0 = {
'name': 'Weibull',
'dependency': (None, None, None),
'width_of_intervals': 2
}
dist_description_1 = {
'name': 'Lognormal',
'dependency': (0, None, 0),
'functions': ('exp3', None, 'power3')
}
my_fit = Fit([my_points[0], my_points[1]],
[dist_description_0, dist_description_1])
print(my_fit.mul_var_dist.distributions[0].shape(0))
print(mul_var_dist.distributions[0].shape(0))
assert np.round(my_fit.mul_var_dist.distributions[0].shape(0),
2) == np.round(mul_var_dist.distributions[0].shape(0),
2)
def test_add_distribution_iter(self):
"""
Tests if an exception is raised by the function add_distribution when
distributions isn't iterable but dependencies is and the other way around.
"""
distributions = 1
with self.assertRaises(ValueError):
MultivariateDistribution(distributions, self.dependencies)
dependencies = 0
with self.assertRaises(ValueError):
MultivariateDistribution(self.distributions, dependencies)
def test_add_distribution_length(self):
"""
Tests if an exception is raised when distributions and dependencies
are of unequal length.
"""
dep3 = (0, None, None)
dependencies = [self.dep1, self.dep2, dep3]
with self.assertRaises(ValueError):
MultivariateDistribution(self.distributions, dependencies)
def test_add_distribution_dependencies_length(self):
"""
Tests if an exception is raised when a tuple in dependencies
has not length 3.
"""
dep1 = (None, None)
dependencies = [dep1, self.dep2]
with self.assertRaises(ValueError):
MultivariateDistribution(self.distributions, dependencies)
def test_add_distribution_dependencies_value(self):
"""
Tests if an exception is raised when dependencies has an invalid value.
"""
dep1 = (-3, None, None)
dependencies = [dep1, self.dep2]
with self.assertRaises(ValueError):
MultivariateDistribution(self.distributions, dependencies)
def test_add_distribution_not_iterable(self):
"""
Tests the function when both distributions and dependencies
are not iterable.
"""
distributions = 1
dependencies = 2
with self.assertRaises(ValueError):
MultivariateDistribution(distributions, dependencies)
def test_latex_representation(self):
"""
Tests if the latex representation is correct.
"""
dep1 = (None, None, None)
dep2 = (0, None, 0)
dependencies = [dep1, dep2]
m = MultivariateDistribution(self.distributions, dependencies)
computed_latex = m.latex_repr(['Hs', 'Tp'])
correct_latex = \
['\\text{ joint PDF: }',
'f(h_{s},t_{p})=f_{H_{s}}(h_{s})f_{T_{p}|H_{s}}(t_{p}|h_{s})',
'',
'1\\text{. variable, }H_{s}: ',
'f_{H_{s}}(h_{s})=\\dfrac{\\beta_{h_{s}}}{\\alpha_{h_{s}}}'
'\\left(\\dfrac{h_{s}-\\gamma_{h_{s}}}{\\alpha_{h_{s}}}'
'\\right)^{\\beta_{h_{s}}-1}\\exp\\left[-\\left(\\dfrac{h_{s}-'
'\\gamma_{h_{s}}}{\\alpha_{h_{s}}}\\right)^{\\beta_{h_{s}}}\\right]',
'\\quad\\text{ with }\\alpha_{h_{s}}=2.776,',
'\\quad\\qquad\\;\\; \\beta_{h_{s}}=1.471,',
'\\quad\\qquad\\;\\; \\gamma_{h_{s}}=0.8888.',
'',
'2\\text{. variable, }T_{p}: ',
'f_{T_{p}|H_{s}}(t_{p}|h_{s})=\\dfrac{1}{t_{p}\\tilde{\\sigma}_'
'{t_{p}}\\sqrt{2\\pi}}\\exp\\left[-\\dfrac{(\\ln t_{p}-\\tilde{'
'\\mu}_{t_{p}})^2}{2\\tilde{\\sigma}_{t_{p}}^2}\\right]',
'\\quad\\text{ with }\\exp{\\tilde{\\mu}}_{t_{p}}='
'0.1+1.489h_{s}^{0.1901},',
'\\quad\\qquad\\;\\; \\tilde{\\sigma}_{t_{p}}='
'0.04+0.1748e^{-0.2243h_{s}}.']
assert computed_latex, correct_latex
