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
Exemple #2
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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
Exemple #4
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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
Exemple #5
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	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