def getWindRose():
"""Gets a chaospy distribution,
which is initialized with a distribution class I created
and extended by it.
"""
amalia_wind_rose = amaliaWindRose()
# amalia_wind_rose = amaliaWindRoseRaw() # Using this option needs updating
# amalia_wind_rose = amaliaWindRoseRaw01()
# Set the necessary functions to construct a chaospy distribution
windRose = cp.construct(
cdf=lambda self, x: amalia_wind_rose.cdf(x),
bnd=lambda self: amalia_wind_rose.bnd(),
pdf=lambda self, x: amalia_wind_rose.pdf(x),
str=lambda self: amalia_wind_rose.str()
)
windrose_dist = windRose()
# print windrose_dist
# print windrose_dist.pdf(180)
# print windrose_dist.pdf(365)
# print windrose_dist.range()
# Dynamically add method
if amalia_wind_rose.str() == 'Amalia windrose':
windrose_dist.get_zero_probability_region = amalia_wind_rose.get_zero_probability_region
return windrose_dist
def getWeibull():
my_weibull = myWeibull()
# Set the necessary functions to construct a chaospy distribution
Weibull = cp.construct(
cdf=lambda self, x: my_weibull.cdf(x),
bnd=lambda self: my_weibull.bnd(),
pdf=lambda self, x: my_weibull.pdf(x),
mom=lambda self, k: my_weibull.mom(k),
str=lambda self: my_weibull.str()
)
weibull_dist = Weibull()
return weibull_dist
def getWeibull():
# my_weibull = myWeibull()
my_weibull = TruncatedWeibull()
# my_weibull = TruncatedWeibull01()
# Set the necessary functions to construct a chaospy distribution
Weibull = cp.construct(
cdf=lambda self, x: my_weibull.cdf(x),
bnd=lambda self: my_weibull.bnd(),
pdf=lambda self, x: my_weibull.pdf(x),
# mom=lambda self, k: my_weibull.mom(k),
str=lambda self: my_weibull.str()
)
weibull_dist = Weibull()
# Dynamically add method
weibull_dist.get_truncation_value = my_weibull.get_truncation_value
return weibull_dist
def getWindRose():
amalia_wind_rose = amaliaWindRose()
# amalia_wind_rose = amaliaWindRoseRaw() # Using this option needs updating
# Set the necessary functions to construct a chaospy distribution
windRose = cp.construct(
cdf=lambda self, x: amalia_wind_rose.cdf(x),
bnd=lambda self: amalia_wind_rose.bnd(),
pdf=lambda self, x: amalia_wind_rose.pdf(x),
str=lambda self: amalia_wind_rose.str()
)
windrose_dist = windRose()
# print windrose_dist
# print windrose_dist.pdf(180)
# print windrose_dist.pdf(365)
# print windrose_dist.range()
return windrose_dist
integral = sum(f(nodes[0])*weights) return integral def pdf(self, x): return kernel.pdf(x) def cdf(self, x): return kernel.integrate_box1d(min(samples), x) def bnd(self): return min(samples), max(samples) if __name__ == '__main__': quad_deg = 4 # standard approach dist_real = cp.Normal() nodes_std, weights_std = cp.generate_quadrature(quad_deg, dist_real, rule = "G") integral_std = eval_integral(test_function, nodes_std, weights_std) # convoluted approach samples = random.normal(0, 1, size=100000) kernel = gaussian_kde(samples) Dist = cp.construct(pdf=pdf, cdf=cdf, bnd=bnd) dist_approx = Dist() nodes_approx, weights_approx = cp.generate_quadrature(2*quad_deg, dist_approx, rule="G") integral_approx = eval_integral(test_function, nodes_approx, weights_approx) print integral_std print integral_approx
