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1023 lines
36 KiB
Python
1023 lines
36 KiB
Python
10 years ago
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from numpy import (r_, minimum, maximum, atleast_1d, atleast_2d, mod, ones,
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10 years ago
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floor, random, eye, nonzero, where, repeat, sqrt, exp, inf,
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10 years ago
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diag, zeros, sin, arcsin, nan)
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from numpy import triu
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10 years ago
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from scipy.special import ndtr as cdfnorm, ndtri as invnorm
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from scipy.special import erfc
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from wafo import mvn
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import numpy as np
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import wafo.mvnprdmod as mvnprdmod
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import wafo.rindmod as rindmod
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import warnings
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from wafo.misc import common_shape
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__all__ = ['Rind', 'rindmod', 'mvnprdmod', 'mvn', 'cdflomax', 'prbnormtndpc',
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'prbnormndpc', 'prbnormnd', 'cdfnorm2d', 'prbnorm2d', 'cdfnorm',
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'invnorm', 'test_docstring']
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class Rind(object):
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'''
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RIND Computes multivariate normal expectations
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Parameters
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----------
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S : array-like, shape Ntdc x Ntdc
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Covariance matrix of X=[Xt,Xd,Xc] (Ntdc = Nt+Nd+Nc)
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m : array-like, size Ntdc
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expectation of X=[Xt,Xd,Xc]
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Blo, Bup : array-like, shape Mb x Nb
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Lower and upper barriers used to compute the integration limits,
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Hlo and Hup, respectively.
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indI : array-like, length Ni
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vector of indices to the different barriers in the indicator function.
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(NB! restriction indI(1)=-1, indI(NI)=Nt+Nd, Ni = Nb+1)
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(default indI = 0:Nt+Nd)
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xc : values to condition on (default xc = zeros(0,1)), size Nc x Nx
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Nt : size of Xt (default Nt = Ntdc - Nc)
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Returns
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-------
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val: ndarray, size Nx
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expectation/density as explained below
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err, terr : ndarray, size Nx
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estimated sampling error and estimated truncation error, respectively.
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(err is with 99 confidence level)
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Notes
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-----
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RIND computes multivariate normal expectations, i.e.,
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E[Jacobian*Indicator|Condition ]*f_{Xc}(xc(:,ix))
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where
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"Indicator" = I{ Hlo(i) < X(i) < Hup(i), i = 1:N_t+N_d }
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"Jacobian" = J(X(Nt+1),...,X(Nt+Nd+Nc)), special case is
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"Jacobian" = |X(Nt+1)*...*X(Nt+Nd)|=|Xd(1)*Xd(2)..Xd(Nd)|
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"condition" = Xc=xc(:,ix), ix=1,...,Nx.
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X = [Xt, Xd, Xc], a stochastic vector of Multivariate Gaussian
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variables where Xt,Xd and Xc have the length Nt,Nd and Nc, respectively
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(Recommended limitations Nx,Nt<=100, Nd<=6 and Nc<=10)
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Multivariate probability is computed if Nd = 0.
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If Mb<Nc+1 then Blo[Mb:Nc+1,:] is assumed to be zero.
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The relation to the integration limits Hlo and Hup are as follows
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Hlo(i) = Blo(1,j)+Blo(2:Mb,j).T*xc(1:Mb-1,ix),
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Hup(i) = Bup(1,j)+Bup(2:Mb,j).T*xc(1:Mb-1,ix),
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where i=indI(j-1)+1:indI(j), j=2:NI, ix=1:Nx
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NOTE : RIND is only using upper triangular part of covariance matrix, S
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(except for method=0).
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Examples
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--------
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Compute Prob{Xi<-1.2} for i=1:5 where Xi are zero mean Gaussian with
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Cov(Xi,Xj) = 0.3 for i~=j and
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Cov(Xi,Xi) = 1 otherwise
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>>> import wafo.gaussian as wg
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>>> n = 5
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>>> Blo =-np.inf; Bup=-1.2; indI=[-1, n-1] # Barriers
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>>> m = np.zeros(n); rho = 0.3;
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>>> Sc =(np.ones((n,n))-np.eye(n))*rho+np.eye(n)
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>>> rind = wg.Rind()
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>>> E0, err0, terr0 = rind(Sc,m,Blo,Bup,indI) # exact prob. 0.001946
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>>> A = np.repeat(Blo,n); B = np.repeat(Bup,n) # Integration limits
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>>> E1 = rind(np.triu(Sc),m,A,B) #same as E0
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Compute expectation E( abs(X1*X2*...*X5) )
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>>> xc = np.zeros((0,1))
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>>> infinity = 37
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>>> dev = np.sqrt(np.diag(Sc)) # std
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>>> ind = np.nonzero(indI[1:])[0]
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>>> Bup, Blo = np.atleast_2d(Bup,Blo)
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>>> Bup[0,ind] = np.minimum(Bup[0,ind] , infinity*dev[indI[ind+1]])
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>>> Blo[0,ind] = np.maximum(Blo[0,ind] ,-infinity*dev[indI[ind+1]])
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10 years ago
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>>> np.allclose(rind(Sc,m,Blo,Bup,indI, xc, nt=0),
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... ([0.05494076], [ 0.00083066], [ 1.00000000e-10]), rtol=1e-3)
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True
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10 years ago
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Compute expectation E( X1^{+}*X2^{+} ) with random
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correlation coefficient,Cov(X1,X2) = rho2.
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>>> m2 = [0, 0]; rho2 = np.random.rand(1)
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>>> Sc2 = [[1, rho2], [rho2 ,1]]
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>>> Blo2 = 0; Bup2 = np.inf; indI2 = [-1, 1]
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>>> rind2 = wg.Rind(method=1)
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10 years ago
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>>> def g2(x):
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... return (x*(np.pi/2+np.arcsin(x))+np.sqrt(1-x**2))/(2*np.pi)
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10 years ago
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>>> E2 = g2(rho2) # exact value
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>>> E3 = rind(Sc2,m2,Blo2,Bup2,indI2,nt=0)
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>>> E4 = rind2(Sc2,m2,Blo2,Bup2,indI2,nt=0)
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>>> E5 = rind2(Sc2,m2,Blo2,Bup2,indI2,nt=0,abseps=1e-4)
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See also
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--------
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prbnormnd, prbnormndpc
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References
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----------
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Podgorski et al. (2000)
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"Exact distributions for apparent waves in irregular seas"
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Ocean Engineering, Vol 27, no 1, pp979-1016.
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P. A. Brodtkorb (2004),
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Numerical evaluation of multinormal expectations
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In Lund university report series
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and in the Dr.Ing thesis:
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The probability of Occurrence of dangerous Wave Situations at Sea.
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Dr.Ing thesis, Norwegian University of Science and Technolgy, NTNU,
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Trondheim, Norway.
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Per A. Brodtkorb (2006)
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"Evaluating Nearly Singular Multinormal Expectations with Application to
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Wave Distributions",
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Methodology And Computing In Applied Probability, Volume 8, Number 1,
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pp. 65-91(27)
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'''
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def __init__(self, **kwds):
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'''
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Parameters
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----------
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method : integer, optional
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defining the integration method
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0 Integrate by Gauss-Legendre quadrature (Podgorski et al. 1999)
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1 Integrate by SADAPT for Ndim<9 and by KRBVRC otherwise
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2 Integrate by SADAPT for Ndim<20 and by KRBVRC otherwise
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3 Integrate by KRBVRC by Genz (1993) (Fast Ndim<101) (default)
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4 Integrate by KROBOV by Genz (1992) (Fast Ndim<101)
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5 Integrate by RCRUDE by Genz (1992) (Slow Ndim<1001)
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6 Integrate by SOBNIED (Fast Ndim<1041)
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7 Integrate by DKBVRC by Genz (2003) (Fast Ndim<1001)
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xcscale : real scalar, optional
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scales the conditinal probability density, i.e.,
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f_{Xc} = exp(-0.5*Xc*inv(Sxc)*Xc + XcScale) (default XcScale=0)
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abseps, releps : real scalars, optional
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absolute and relative error tolerance.
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(default abseps=0, releps=1e-3)
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coveps : real scalar, optional
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error tolerance in Cholesky factorization (default 1e-13)
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maxpts, minpts : scalar integers, optional
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maximum and minimum number of function values allowed. The
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parameter, maxpts, can be used to limit the time. A sensible
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strategy is to start with MAXPTS = 1000*N, and then increase MAXPTS
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if ERROR is too large.
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(Only for METHOD~=0) (default maxpts=40000, minpts=0)
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seed : scalar integer, optional
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seed to the random generator used in the integrations
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(Only for METHOD~=0)(default floor(rand*1e9))
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nit : scalar integer, optional
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maximum number of Xt variables to integrate. This parameter can be
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used to limit the time. If NIT is less than the rank of the
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covariance matrix, the returned result is a upper bound for the
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true value of the integral. (default 1000)
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xcutoff : real scalar, optional
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cut off value where the marginal normal distribution is truncated.
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(Depends on requested accuracy. A value between 4 and 5 is
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reasonable.)
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xsplit : real scalar
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parameter controlling performance of quadrature integration:
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if Hup>=xCutOff AND Hlo<-XSPLIT OR
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Hup>=XSPLIT AND Hlo<=-xCutOff then
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do a different integration to increase speed
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in rind2 and rindnit. This give slightly different results
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if XSPILT>=xCutOff => do the same integration always
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(Only for METHOD==0)(default XSPLIT = 1.5)
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quadno : scalar integer
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Quadrature formulae number used in integration of Xd variables.
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This number implicitly determines number of nodes
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used. (Only for METHOD==0)
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speed : scalar integer
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defines accuracy of calculations by choosing different parameters,
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possible values: 1,2...,9 (9 fastest, default []).
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If not speed is None the parameters, ABSEPS, RELEPS, COVEPS,
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XCUTOFF, MAXPTS and QUADNO will be set according to
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INITOPTIONS.
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nc1c2 : scalar integer, optional
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number of times to use the regression equation to restrict
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integration area. Nc1c2 = 1,2 is recommended. (default 2)
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(note: works only for method >0)
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'''
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self.method = 3
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self.xcscale = 0
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self.abseps = 0
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self.releps = 1e-3,
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self.coveps = 1e-10
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self.maxpts = 40000
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self.minpts = 0
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self.seed = None
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self.nit = 1000,
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self.xcutoff = None
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self.xsplit = 1.5
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self.quadno = 2
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self.speed = None
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self.nc1c2 = 2
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self.__dict__.update(**kwds)
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self.initialize(self.speed)
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self.set_constants()
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def initialize(self, speed=None):
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'''
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Initializes member variables according to speed.
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Parameter
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---------
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speed : scalar integer
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defining accuracy of calculations.
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Valid numbers: 1,2,...,10
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(1=slowest and most accurate,10=fastest, but less accuracy)
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Member variables initialized according to speed:
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-----------------------------------------------
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speed : Integer defining accuracy of calculations.
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abseps : Absolute error tolerance.
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releps : Relative error tolerance.
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covep : Error tolerance in Cholesky factorization.
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xcutoff : Truncation limit of the normal CDF
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maxpts : Maximum number of function values allowed.
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quadno : Quadrature formulae used in integration of Xd(i)
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implicitly determining # nodes
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'''
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if speed is None:
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return
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self.speed = min(max(speed, 1), 13)
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self.maxpts = 10000
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self.quadno = r_[1:4] + (10 - min(speed, 9)) + (speed == 1)
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if speed in (11, 12, 13):
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self.abseps = 1e-1
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elif speed == 10:
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self.abseps = 1e-2
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elif speed in (7, 8, 9):
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self.abseps = 1e-2
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elif speed in (4, 5, 6):
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self.maxpts = 20000
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self.abseps = 1e-3
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elif speed in (1, 2, 3):
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self.maxpts = 30000
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self.abseps = 1e-4
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if speed < 12:
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tmp = max(abs(11 - abs(speed)), 1)
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expon = mod(tmp + 1, 3) + 1
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self.coveps = self.abseps * ((1.0e-1) ** expon)
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elif speed < 13:
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self.coveps = 0.1
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else:
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self.coveps = 0.5
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self.releps = min(self.abseps, 1.0e-2)
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if self.method == 0:
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# This gives approximately the same accuracy as when using
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# RINDDND and RINDNIT
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# xCutOff= MIN(MAX(xCutOff+0.5d0,4.d0),5.d0)
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self.abseps = self.abseps * 1.0e-1
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trunc_error = 0.05 * max(0, self.abseps)
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self.xcutoff = max(min(abs(invnorm(trunc_error)), 7), 1.2)
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self.abseps = max(self.abseps - trunc_error, 0)
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def set_constants(self):
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if self.xcutoff is None:
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trunc_error = 0.1 * self.abseps
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self.nc1c2 = max(1, self.nc1c2)
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xcut = abs(invnorm(trunc_error / (self.nc1c2 * 2)))
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self.xcutoff = max(min(xcut, 8.5), 1.2)
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# self.abseps = max(self.abseps- truncError,0);
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# self.releps = max(self.releps- truncError,0);
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if self.method > 0:
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names = ['method', 'xcscale', 'abseps', 'releps', 'coveps',
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'maxpts', 'minpts', 'nit', 'xcutoff', 'nc1c2', 'quadno',
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'xsplit']
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constants = [getattr(self, name) for name in names]
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constants[0] = mod(constants[0], 10)
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rindmod.set_constants(*constants) # @UndefinedVariable
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def __call__(self, cov, m, ab, bb, indI=None, xc=None, nt=None, **kwds):
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if any(kwds):
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self.__dict__.update(**kwds)
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self.set_constants()
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if xc is None:
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xc = zeros((0, 1))
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BIG, Blo, Bup, xc = atleast_2d(cov, ab, bb, xc)
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Blo = Blo.copy()
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Bup = Bup.copy()
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Ntdc = BIG.shape[0]
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Nc = xc.shape[0]
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if nt is None:
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nt = Ntdc - Nc
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unused_Mb, Nb = Blo.shape
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Nd = Ntdc - nt - Nc
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Ntd = nt + Nd
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if indI is None:
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if Nb != Ntd:
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raise ValueError('Inconsistent size of Blo and Bup')
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|
|
indI = r_[-1:Ntd]
|
||
|
|
|
||
|
|
Ex, indI = atleast_1d(m, indI)
|
||
|
|
if self.seed is None:
|
||
|
|
seed = int(floor(random.rand(1) * 1e10)) # @UndefinedVariable
|
||
|
|
else:
|
||
|
|
seed = int(self.seed)
|
||
|
|
|
||
|
|
# INFIN = INTEGER, array of integration limits flags: size 1 x Nb
|
||
|
|
# if INFIN(I) < 0, Ith limits are (-infinity, infinity);
|
||
|
|
# if INFIN(I) = 0, Ith limits are (-infinity, Hup(I)];
|
||
|
|
# if INFIN(I) = 1, Ith limits are [Hlo(I), infinity);
|
||
|
|
# if INFIN(I) = 2, Ith limits are [Hlo(I), Hup(I)].
|
||
|
|
infinity = 37
|
||
|
|
dev = sqrt(diag(BIG)) # std
|
||
|
|
ind = nonzero(indI[1:] > -1)[0]
|
||
|
|
infin = repeat(2, len(indI) - 1)
|
||
|
|
infin[ind] = (2 - (Bup[0, ind] > infinity * dev[indI[ind + 1]])
|
||
|
|
- 2 * (Blo[0, ind] < -infinity * dev[indI[ind + 1]]))
|
||
|
|
|
||
|
|
Bup[0, ind] = minimum(Bup[0, ind], infinity * dev[indI[ind + 1]])
|
||
|
|
Blo[0, ind] = maximum(Blo[0, ind], -infinity * dev[indI[ind + 1]])
|
||
|
|
ind2 = indI + 1
|
||
|
|
|
||
|
|
return rindmod.rind(BIG, Ex, xc, nt, ind2, Blo, Bup, infin, seed) # @UndefinedVariable @IgnorePep8
|
||
|
|
|
||
|
|
|
||
|
|
def test_rind():
|
||
|
|
''' Small test function
|
||
|
|
'''
|
||
|
|
n = 5
|
||
|
|
Blo = -inf
|
||
|
|
Bup = -1.2
|
||
|
|
indI = [-1, n - 1] # Barriers
|
||
|
10 years ago
|
# A = np.repeat(Blo, n)
|
||
|
|
# B = np.repeat(Bup, n) # Integration limits
|
||
|
10 years ago
|
m = zeros(n)
|
||
|
|
rho = 0.3
|
||
|
|
Sc = (ones((n, n)) - eye(n)) * rho + eye(n)
|
||
|
|
rind = Rind()
|
||
|
|
E0 = rind(Sc, m, Blo, Bup, indI) # exact prob. 0.001946 A)
|
||
|
|
print(E0)
|
||
|
|
|
||
|
|
A = repeat(Blo, n)
|
||
|
|
B = repeat(Bup, n) # Integration limits
|
||
|
|
_E1 = rind(triu(Sc), m, A, B) # same as E0
|
||
|
|
|
||
|
|
xc = zeros((0, 1))
|
||
|
|
infinity = 37
|
||
|
|
dev = sqrt(diag(Sc)) # std
|
||
|
|
ind = nonzero(indI[1:])[0]
|
||
|
|
Bup, Blo = atleast_2d(Bup, Blo)
|
||
|
|
Bup[0, ind] = minimum(Bup[0, ind], infinity * dev[indI[ind + 1]])
|
||
|
|
Blo[0, ind] = maximum(Blo[0, ind], -infinity * dev[indI[ind + 1]])
|
||
|
|
_E3 = rind(Sc, m, Blo, Bup, indI, xc, nt=1)
|
||
|
|
|
||
|
|
|
||
|
|
def cdflomax(x, alpha, m0):
|
||
|
|
'''
|
||
|
|
Return CDF for local maxima for a zero-mean Gaussian process
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
x : array-like
|
||
|
|
evaluation points
|
||
|
|
alpha, m0 : real scalars
|
||
|
|
irregularity factor and zero-order spectral moment (variance of the
|
||
|
|
process), respectively.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
prb : ndarray
|
||
|
|
distribution function evaluated at x
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
The cdf is calculated from an explicit expression involving the
|
||
|
|
standard-normal cdf. This relation is sometimes written as a convolution
|
||
|
|
|
||
|
|
M = sqrt(m0)*( sqrt(1-a^2)*Z + a*R )
|
||
|
|
|
||
|
|
where M denotes local maximum, Z is a standard normal r.v.,
|
||
|
|
R is a standard Rayleigh r.v., and "=" means equality in distribution.
|
||
|
|
|
||
|
|
Note that all local maxima of the process are considered, not
|
||
|
|
only crests of waves.
|
||
|
|
|
||
|
|
Example
|
||
|
|
-------
|
||
|
|
>>> import pylab
|
||
|
|
>>> import wafo.gaussian as wg
|
||
|
|
>>> import wafo.spectrum.models as wsm
|
||
|
|
>>> import wafo.objects as wo
|
||
|
|
>>> import wafo.stats as ws
|
||
|
|
>>> S = wsm.Jonswap(Hm0=10).tospecdata();
|
||
|
|
>>> xs = S.sim(10000)
|
||
|
|
>>> ts = wo.mat2timeseries(xs)
|
||
|
|
>>> tp = ts.turning_points()
|
||
|
|
>>> mM = tp.cycle_pairs()
|
||
|
|
>>> m0 = S.moment(1)[0]
|
||
|
|
>>> alpha = S.characteristic('alpha')[0]
|
||
|
|
>>> x = np.linspace(-10,10,200);
|
||
|
|
>>> mcdf = ws.edf(mM.data)
|
||
|
|
>>> h = mcdf.plot(), pylab.plot(x,wg.cdflomax(x,alpha,m0))
|
||
|
|
|
||
|
|
See also
|
||
|
|
--------
|
||
|
|
spec2mom, spec2bw
|
||
|
|
'''
|
||
|
|
c1 = 1.0 / (sqrt(1 - alpha ** 2)) * x / sqrt(m0)
|
||
|
|
c2 = alpha * c1
|
||
|
|
return cdfnorm(c1) - alpha * exp(-x ** 2 / 2 / m0) * cdfnorm(c2)
|
||
|
|
|
||
|
|
|
||
|
|
def prbnormtndpc(rho, a, b, D=None, df=0, abseps=1e-4, IERC=0, HNC=0.24):
|
||
|
|
'''
|
||
|
|
Return Multivariate normal or T probability with product correlation.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
rho : array-like
|
||
|
|
vector of coefficients defining the correlation coefficient by:
|
||
|
|
correlation(I,J) = rho[i]*rho[j]) for J!=I
|
||
|
|
where -1 < rho[i] < 1
|
||
|
|
a,b : array-like
|
||
|
|
vector of lower and upper integration limits, respectively.
|
||
|
|
Note: any values greater the 37 in magnitude, are considered as
|
||
|
|
infinite values.
|
||
|
|
D : array-like
|
||
|
|
vector of means (default zeros(size(rho)))
|
||
|
|
df = Degrees of freedom, NDF<=0 gives normal probabilities (default)
|
||
|
|
abseps = absolute error tolerance. (default 1e-4)
|
||
|
|
IERC = 1 if strict error control based on fourth derivative
|
||
|
|
0 if error control based on halving the intervals (default)
|
||
|
|
HNC = start interval width of simpson rule (default 0.24)
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
value = estimated value for the integral
|
||
|
|
bound = bound on the error of the approximation
|
||
|
|
inform = INTEGER, termination status parameter:
|
||
|
|
0, if normal completion with ERROR < EPS;
|
||
|
|
1, if N > 1000 or N < 1.
|
||
|
|
2, IF any abs(rho)>=1
|
||
|
|
4, if ANY(b(I)<=A(i))
|
||
|
|
5, if number of terms exceeds maximum number of evaluation points
|
||
|
|
6, if fault accurs in normal subroutines
|
||
|
|
7, if subintervals are too narrow or too many
|
||
|
|
8, if bounds exceeds abseps
|
||
|
|
|
||
|
|
PRBNORMTNDPC calculates multivariate normal or student T probability
|
||
|
|
with product correlation structure for rectangular regions.
|
||
|
|
The accuracy is as best around single precision, i.e., about 1e-7.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
--------
|
||
|
|
>>> import wafo.gaussian as wg
|
||
|
|
>>> rho2 = np.random.rand(2)
|
||
|
|
>>> a2 = np.zeros(2)
|
||
|
|
>>> b2 = np.repeat(np.inf,2)
|
||
|
|
>>> [val2,err2, ift2] = wg.prbnormtndpc(rho2,a2,b2)
|
||
|
10 years ago
|
>>> def g2(x):
|
||
|
|
... return 0.25+np.arcsin(x[0]*x[1])/(2*np.pi)
|
||
|
10 years ago
|
>>> E2 = g2(rho2) # exact value
|
||
|
|
>>> np.abs(E2-val2)<err2
|
||
|
|
True
|
||
|
|
|
||
|
|
>>> rho3 = np.random.rand(3)
|
||
|
10 years ago
|
>>> a3 = np.zeros(3)
|
||
|
|
>>> b3 = np.repeat(inf,3)
|
||
|
|
>>> [val3, err3, ift3] = wg.prbnormtndpc(rho3,a3,b3)
|
||
|
|
>>> def g3(x):
|
||
|
|
... return 0.5-sum(np.sort(np.arccos([x[0]*x[1],
|
||
|
|
... x[0]*x[2],x[1]*x[2]])))/(4*np.pi)
|
||
|
10 years ago
|
>>> E3 = g3(rho3) # Exact value
|
||
|
10 years ago
|
>>> np.abs(E3-val3) < 5 * err2
|
||
|
10 years ago
|
True
|
||
|
|
|
||
|
|
|
||
|
|
See also
|
||
|
|
--------
|
||
|
|
prbnormndpc, prbnormnd, Rind
|
||
|
|
|
||
|
|
Reference
|
||
|
|
---------
|
||
|
|
Charles Dunnett (1989)
|
||
|
|
"Multivariate normal probability integrals with product correlation
|
||
|
|
structure", Applied statistics, Vol 38,No3, (Algorithm AS 251)
|
||
|
|
'''
|
||
|
|
|
||
|
|
if D is None:
|
||
|
|
D = zeros(len(rho))
|
||
|
|
# Make sure integration limits are finite
|
||
|
|
A = np.clip(a - D, -100, 100)
|
||
|
|
B = np.clip(b - D, -100, 100)
|
||
|
|
|
||
|
|
return mvnprdmod.prbnormtndpc(rho, A, B, df, abseps, IERC, HNC) # @UndefinedVariable @IgnorePep8
|
||
|
|
|
||
|
|
|
||
|
|
def prbnormndpc(rho, a, b, abserr=1e-4, relerr=1e-4, usesimpson=True,
|
||
|
|
usebreakpoints=False):
|
||
|
|
'''
|
||
|
|
Return Multivariate Normal probabilities with product correlation
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
rho = vector defining the correlation structure, i.e.,
|
||
|
|
corr(Xi,Xj) = rho(i)*rho(j) for i~=j
|
||
|
|
= 1 for i==j
|
||
|
|
-1 <= rho <= 1
|
||
|
|
a,b = lower and upper integration limits respectively.
|
||
|
|
tol = requested absolute tolerance
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
value = value of integral
|
||
|
|
error = estimated absolute error
|
||
|
|
|
||
|
|
PRBNORMNDPC calculates multivariate normal probability
|
||
|
|
with product correlation structure for rectangular regions.
|
||
|
|
The accuracy is up to almost double precision, i.e., about 1e-14.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
-------
|
||
|
|
>>> import wafo.gaussian as wg
|
||
|
|
>>> rho2 = np.random.rand(2)
|
||
|
|
>>> a2 = np.zeros(2)
|
||
|
|
>>> b2 = np.repeat(np.inf,2)
|
||
|
|
>>> [val2,err2, ift2] = wg.prbnormndpc(rho2,a2,b2)
|
||
|
10 years ago
|
>>> g2 = lambda x : 0.25+np.arcsin(x[0]*x[1])/(2*np.pi)
|
||
|
10 years ago
|
>>> E2 = g2(rho2) #% exact value
|
||
|
|
>>> np.abs(E2-val2)<err2
|
||
|
|
True
|
||
|
|
|
||
|
|
>>> rho3 = np.random.rand(3)
|
||
|
|
>>> a3 = np.zeros(3)
|
||
|
|
>>> b3 = np.repeat(inf,3)
|
||
|
|
>>> [val3,err3, ift3] = wg.prbnormndpc(rho3,a3,b3)
|
||
|
10 years ago
|
>>> def g3(x):
|
||
|
|
... return 0.5-sum(np.sort(np.arccos([x[0]*x[1],
|
||
|
|
... x[0]*x[2],x[1]*x[2]])))/(4*np.pi)
|
||
|
10 years ago
|
>>> E3 = g3(rho3) # Exact value
|
||
|
|
>>> np.abs(E3-val3)<err2
|
||
|
|
True
|
||
|
|
|
||
|
|
See also
|
||
|
|
--------
|
||
|
|
prbnormtndpc, prbnormnd, Rind
|
||
|
|
|
||
|
|
Reference
|
||
|
|
---------
|
||
|
|
P. A. Brodtkorb (2004),
|
||
|
|
"Evaluating multinormal probabilites with product correlation structure."
|
||
|
|
In Lund university report series
|
||
|
|
and in the Dr.Ing thesis:
|
||
|
|
"The probability of Occurrence of dangerous Wave Situations at Sea."
|
||
|
|
Dr.Ing thesis, Norwegian University of Science and Technolgy, NTNU,
|
||
|
|
Trondheim, Norway.
|
||
|
|
|
||
|
|
'''
|
||
|
|
# Call fortran implementation
|
||
|
|
val, err, ier = mvnprdmod.prbnormndpc(rho, a, b, abserr, relerr, usebreakpoints, usesimpson) # @UndefinedVariable @IgnorePep8
|
||
|
|
|
||
|
|
if ier > 0:
|
||
|
|
warnings.warn('Abnormal termination ier = %d\n\n%s' %
|
||
|
|
(ier, _ERRORMESSAGE[ier]))
|
||
|
|
return val, err, ier
|
||
|
|
|
||
|
|
_ERRORMESSAGE = {}
|
||
|
|
_ERRORMESSAGE[0] = ''
|
||
|
|
_ERRORMESSAGE[1] = '''
|
||
|
|
Maximum number of subdivisions allowed has been achieved. one can allow
|
||
|
|
more subdivisions by increasing the value of limit (and taking the
|
||
|
|
according dimension adjustments into account). however, if this yields
|
||
|
|
no improvement it is advised to analyze the integrand in order to
|
||
|
|
determine the integration difficulties. if the position of a local
|
||
|
|
difficulty can be determined (i.e. singularity discontinuity within
|
||
|
|
the interval), it should be supplied to the routine as an element of
|
||
|
|
the vector points. If necessary an appropriate special-purpose
|
||
|
|
integrator must be used, which is designed for handling the type of
|
||
|
|
difficulty involved.
|
||
|
|
'''
|
||
|
|
_ERRORMESSAGE[2] = '''
|
||
|
|
the occurrence of roundoff error is detected, which prevents the requested
|
||
|
|
tolerance from being achieved. The error may be under-estimated.'''
|
||
|
|
|
||
|
|
_ERRORMESSAGE[3] = '''
|
||
|
|
Extremely bad integrand behaviour occurs at some points of the integration
|
||
|
|
interval.'''
|
||
|
|
_ERRORMESSAGE[4] = '''
|
||
|
|
The algorithm does not converge. Roundoff error is detected in the
|
||
|
|
extrapolation table. It is presumed that the requested tolerance cannot be
|
||
|
|
achieved, and that the returned result is the best which can be obtained.
|
||
|
|
'''
|
||
|
|
_ERRORMESSAGE[5] = '''
|
||
|
|
The integral is probably divergent, or slowly convergent.
|
||
|
|
It must be noted that divergence can occur with any other value of ier>0.
|
||
|
|
'''
|
||
|
|
_ERRORMESSAGE[6] = '''the input is invalid because:
|
||
|
|
1) npts2 < 2
|
||
|
|
2) break points are specified outside the integration range
|
||
|
|
3) (epsabs<=0 and epsrel<max(50*rel.mach.acc.,0.5d-28))
|
||
|
|
4) limit < npts2.'''
|
||
|
|
|
||
|
|
|
||
|
|
def prbnormnd(correl, a, b, abseps=1e-4, releps=1e-3, maxpts=None, method=0):
|
||
|
|
'''
|
||
|
|
Multivariate Normal probability by Genz' algorithm.
|
||
|
|
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
CORREL = Positive semidefinite correlation matrix
|
||
|
|
A = vector of lower integration limits.
|
||
|
|
B = vector of upper integration limits.
|
||
|
|
ABSEPS = absolute error tolerance.
|
||
|
|
RELEPS = relative error tolerance.
|
||
|
|
MAXPTS = maximum number of function values allowed. This
|
||
|
|
parameter can be used to limit the time. A sensible strategy is to
|
||
|
|
start with MAXPTS = 1000*N, and then increase MAXPTS if ERROR is too
|
||
|
|
large.
|
||
|
|
METHOD = integer defining the integration method
|
||
|
|
-1 KRBVRC randomized Korobov rules for the first 20 variables,
|
||
|
|
randomized Richtmeyer rules for the rest, NMAX = 500
|
||
|
|
0 KRBVRC, NMAX = 100 (default)
|
||
|
|
1 SADAPT Subregion Adaptive integration method, NMAX = 20
|
||
|
|
2 KROBOV Randomized KOROBOV rules, NMAX = 100
|
||
|
|
3 RCRUDE Crude Monte-Carlo Algorithm with simple
|
||
|
|
antithetic variates and weighted results on restart
|
||
|
|
4 SPHMVN Monte-Carlo algorithm by Deak (1980), NMAX = 100
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
VALUE REAL estimated value for the integral
|
||
|
|
ERROR REAL estimated absolute error, with 99% confidence level.
|
||
|
|
INFORM INTEGER, termination status parameter:
|
||
|
|
if INFORM = 0, normal completion with ERROR < EPS;
|
||
|
|
if INFORM = 1, completion with ERROR > EPS and MAXPTS
|
||
|
|
function vaules used; increase MAXPTS to
|
||
|
|
decrease ERROR;
|
||
|
|
if INFORM = 2, N > NMAX or N < 1. where NMAX depends on the
|
||
|
|
integration method
|
||
|
|
Example
|
||
|
|
-------
|
||
|
|
Compute the probability that X1<0,X2<0,X3<0,X4<0,X5<0,
|
||
|
|
Xi are zero-mean Gaussian variables with variances one
|
||
|
|
and correlations Cov(X(i),X(j))=0.3:
|
||
|
|
indI=[0 5], and barriers B_lo=[-inf 0], B_lo=[0 inf]
|
||
|
|
gives H_lo = [-inf -inf -inf -inf -inf] H_lo = [0 0 0 0 0]
|
||
|
|
|
||
|
|
>>> Et = 0.001946 # # exact prob.
|
||
|
|
>>> n = 5; nt = n
|
||
|
|
>>> Blo =-np.inf; Bup=0; indI=[-1, n-1] # Barriers
|
||
|
|
>>> m = 1.2*np.ones(n); rho = 0.3;
|
||
|
|
>>> Sc =(np.ones((n,n))-np.eye(n))*rho+np.eye(n)
|
||
|
|
>>> rind = Rind()
|
||
|
|
>>> E0, err0, terr0 = rind(Sc,m,Blo,Bup,indI, nt=nt)
|
||
|
|
|
||
|
|
>>> A = np.repeat(Blo,n)
|
||
|
|
>>> B = np.repeat(Bup,n)-m
|
||
|
|
>>> [val,err,inform] = prbnormnd(Sc,A,B);[val, err, inform]
|
||
|
|
[0.0019456719705212067, 1.0059406844578488e-05, 0]
|
||
|
|
|
||
|
|
>>> np.abs(val-Et)< err0+terr0
|
||
|
|
array([ True], dtype=bool)
|
||
|
|
>>> 'val = %2.5f' % val
|
||
|
|
'val = 0.00195'
|
||
|
|
|
||
|
|
See also
|
||
|
|
--------
|
||
|
|
prbnormndpc, Rind
|
||
|
|
'''
|
||
|
|
|
||
|
|
m, n = correl.shape
|
||
|
|
Na = len(a)
|
||
|
|
Nb = len(b)
|
||
|
|
if (m != n or m != Na or m != Nb):
|
||
|
|
raise ValueError('Size of input is inconsistent!')
|
||
|
|
|
||
|
|
if maxpts is None:
|
||
|
|
maxpts = 1000 * n
|
||
|
|
|
||
|
|
maxpts = max(round(maxpts), 10 * n)
|
||
|
|
|
||
|
|
# % array of correlation coefficients; the correlation
|
||
|
|
# % coefficient in row I column J of the correlation matrix
|
||
|
|
# % should be stored in CORREL( J + ((I-2)*(I-1))/2 ), for J < I.
|
||
|
|
# % The correlation matrix must be positive semidefinite.
|
||
|
|
|
||
|
|
D = np.diag(correl)
|
||
|
|
if (any(D != 1)):
|
||
|
|
raise ValueError('This is not a correlation matrix')
|
||
|
|
|
||
|
|
# Make sure integration limits are finite
|
||
|
|
A = np.clip(a, -100, 100)
|
||
|
|
B = np.clip(b, -100, 100)
|
||
|
|
ix = np.where(np.triu(np.ones((m, m)), 1) != 0)
|
||
|
|
L = correl[ix].ravel() # % return only off diagonal elements
|
||
|
|
|
||
|
|
infinity = 37
|
||
|
|
infin = np.repeat(2, n) - (B > infinity) - 2 * (A < -infinity)
|
||
|
|
|
||
|
|
err, val, inform = mvn.mvndst(A, B, infin, L, maxpts, abseps, releps) # @UndefinedVariable @IgnorePep8
|
||
|
|
|
||
|
|
return val, err, inform
|
||
|
|
|
||
|
|
# CALL the mexroutine
|
||
|
|
# t0 = clock;
|
||
|
|
# if ((method==0) && (n<=100)),
|
||
|
|
# %NMAX = 100
|
||
|
|
# [value, err,inform] = mexmvnprb(L,A,B,abseps,releps,maxpts);
|
||
|
|
# elseif ( (method<0) || ((method<=0) && (n>100)) ),
|
||
|
|
# % NMAX = 500
|
||
|
|
# [value, err,inform] = mexmvnprb2(L,A,B,abseps,releps,maxpts);
|
||
|
|
# else
|
||
|
|
# [value, err,inform] = mexGenzMvnPrb(L,A,B,abseps,releps,maxpts,method);
|
||
|
|
# end
|
||
|
|
# exTime = etime(clock,t0);
|
||
|
|
# '
|
||
|
|
|
||
|
|
# gauss legendre points and weights, n = 6
|
||
|
|
_W6 = [0.1713244923791705e+00, 0.3607615730481384e+00, 0.4679139345726904e+00]
|
||
|
|
_X6 = [-0.9324695142031522e+00, -
|
||
|
|
0.6612093864662647e+00, -0.2386191860831970e+00]
|
||
|
|
# gauss legendre points and weights, n = 12
|
||
|
|
_W12 = [0.4717533638651177e-01, 0.1069393259953183e+00, 0.1600783285433464e+00,
|
||
|
|
0.2031674267230659e+00, 0.2334925365383547e+00, 0.2491470458134029e+00]
|
||
|
|
_X12 = [-0.9815606342467191e+00, -0.9041172563704750e+00,
|
||
|
|
-0.7699026741943050e+00,
|
||
|
|
- 0.5873179542866171e+00, -0.3678314989981802e+00,
|
||
|
|
-0.1252334085114692e+00]
|
||
|
|
# gauss legendre points and weights, n = 20
|
||
|
|
_W20 = [0.1761400713915212e-01, 0.4060142980038694e-01,
|
||
|
|
0.6267204833410906e-01, 0.8327674157670475e-01,
|
||
|
|
0.1019301198172404e+00, 0.1181945319615184e+00,
|
||
|
|
0.1316886384491766e+00, 0.1420961093183821e+00,
|
||
|
|
0.1491729864726037e+00, 0.1527533871307259e+00]
|
||
|
|
_X20 = [-0.9931285991850949e+00, -0.9639719272779138e+00,
|
||
|
|
- 0.9122344282513259e+00, -0.8391169718222188e+00,
|
||
|
|
- 0.7463319064601508e+00, -0.6360536807265150e+00,
|
||
|
|
- 0.5108670019508271e+00, -0.3737060887154196e+00,
|
||
|
|
- 0.2277858511416451e+00, -0.7652652113349733e-01]
|
||
|
|
|
||
|
|
|
||
|
|
def cdfnorm2d(b1, b2, r):
|
||
|
|
'''
|
||
|
|
Returnc Bivariate Normal cumulative distribution function
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
|
||
|
|
b1, b2 : array-like
|
||
|
|
upper integration limits
|
||
|
|
r : real scalar
|
||
|
|
correlation coefficient (-1 <= r <= 1).
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
bvn : ndarray
|
||
|
|
distribution function evaluated at b1, b2.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
CDFNORM2D computes bivariate normal probabilities, i.e., the probability
|
||
|
|
Prob(X1 <= B1 and X2 <= B2) with an absolute error less than 1e-15.
|
||
|
|
|
||
|
|
This function is based on the method described by Drezner, z and
|
||
|
|
G.O. Wesolowsky, (1989), with major modifications for double precision,
|
||
|
|
and for |r| close to 1.
|
||
|
|
|
||
|
|
Example
|
||
|
|
-------
|
||
|
|
>>> import wafo.gaussian as wg
|
||
|
|
>>> x = np.linspace(-5,5,20)
|
||
|
|
>>> [B1,B2] = np.meshgrid(x, x)
|
||
|
|
>>> r = 0.3;
|
||
|
|
>>> F = wg.cdfnorm2d(B1,B2,r)
|
||
|
|
|
||
|
|
surf(x,x,F)
|
||
|
|
|
||
|
|
See also
|
||
|
|
--------
|
||
|
|
cdfnorm
|
||
|
|
|
||
|
|
Reference
|
||
|
|
---------
|
||
|
|
Drezner, z and g.o. Wesolowsky, (1989),
|
||
|
|
"On the computation of the bivariate normal integral",
|
||
|
|
Journal of statist. comput. simul. 35, pp. 101-107,
|
||
|
|
'''
|
||
|
|
# Translated into Python
|
||
|
|
# Per A. Brodtkorb
|
||
|
|
#
|
||
|
|
# Original code
|
||
|
|
# by alan genz
|
||
|
|
# department of mathematics
|
||
|
|
# washington state university
|
||
|
|
# pullman, wa 99164-3113
|
||
|
|
# email : alangenz@wsu.edu
|
||
|
|
|
||
|
|
cshape = common_shape(b1, b2, r, shape=[1, ])
|
||
|
|
one = ones(cshape)
|
||
|
|
|
||
|
|
h, k, r = (-b1 * one).ravel(), (-b2 * one).ravel(), (r * one).ravel()
|
||
|
|
|
||
|
|
bvn = where(abs(r) > 1, nan, 0.0)
|
||
|
|
|
||
|
|
two = 2.e0
|
||
|
|
twopi = 6.283185307179586e0
|
||
|
|
|
||
|
|
hk = h * k
|
||
|
|
|
||
|
|
k0, = nonzero(abs(r) < 0.925e0)
|
||
|
|
if len(k0) > 0:
|
||
|
|
hs = (h[k0] ** 2 + k[k0] ** 2) / two
|
||
|
|
asr = arcsin(r[k0])
|
||
|
|
k1, = nonzero(r[k0] >= 0.75)
|
||
|
|
if len(k1) > 0:
|
||
|
|
k01 = k0[k1]
|
||
|
|
for i in range(10):
|
||
|
|
for sign in - 1, 1:
|
||
|
|
sn = sin(asr[k1] * (sign * _X20[i] + 1) / 2)
|
||
|
|
bvn[k01] = bvn[k01] + _W20[i] * \
|
||
|
|
exp((sn * hk[k01] - hs[k1]) / (1 - sn * sn))
|
||
|
|
|
||
|
|
k1, = nonzero((0.3 <= r[k0]) & (r[k0] < 0.75))
|
||
|
|
if len(k1) > 0:
|
||
|
|
k01 = k0[k1]
|
||
|
|
for i in range(6):
|
||
|
|
for sign in - 1, 1:
|
||
|
|
sn = sin(asr[k1] * (sign * _X12[i] + 1) / 2)
|
||
|
|
bvn[k01] = bvn[k01] + _W12[i] * \
|
||
|
|
exp((sn * hk[k01] - hs[k1]) / (1 - sn * sn))
|
||
|
|
|
||
|
|
k1, = nonzero(r[k0] < 0.3)
|
||
|
|
if len(k1) > 0:
|
||
|
|
k01 = k0[k1]
|
||
|
|
for i in range(3):
|
||
|
|
for sign in - 1, 1:
|
||
|
|
sn = sin(asr[k1] * (sign * _X6[i] + 1) / 2)
|
||
|
|
bvn[k01] = bvn[k01] + _W6[i] * \
|
||
|
|
exp((sn * hk[k01] - hs[k1]) / (1 - sn * sn))
|
||
|
|
|
||
|
|
bvn[k0] *= asr / (two * twopi)
|
||
|
|
bvn[k0] += fi(-h[k0]) * fi(-k[k0])
|
||
|
|
|
||
|
|
k1, = nonzero((0.925 <= abs(r)) & (abs(r) <= 1))
|
||
|
|
if len(k1) > 0:
|
||
|
|
k2, = nonzero(r[k1] < 0)
|
||
|
|
if len(k2) > 0:
|
||
|
|
k12 = k1[k2]
|
||
|
|
k[k12] = -k[k12]
|
||
|
|
hk[k12] = -hk[k12]
|
||
|
|
|
||
|
|
k3, = nonzero(abs(r[k1]) < 1)
|
||
|
|
if len(k3) > 0:
|
||
|
|
k13 = k1[k3]
|
||
|
|
a2 = (1 - r[k13]) * (1 + r[k13])
|
||
|
|
a = sqrt(a2)
|
||
|
|
b = abs(h[k13] - k[k13])
|
||
|
|
bs = b * b
|
||
|
|
c = (4.e0 - hk[k13]) / 8.e0
|
||
|
|
d = (12.e0 - hk[k13]) / 16.e0
|
||
|
|
asr = -(bs / a2 + hk[k13]) / 2.e0
|
||
|
|
k4, = nonzero(asr > -100.e0)
|
||
|
|
if len(k4) > 0:
|
||
|
|
bvn[k13[k4]] = (a[k4] * exp(asr[k4]) *
|
||
|
|
(1 - c[k4] * (bs[k4] - a2[k4]) *
|
||
|
|
(1 - d[k4] * bs[k4] / 5) / 3 +
|
||
|
|
c[k4] * d[k4] * a2[k4] ** 2 / 5))
|
||
|
|
|
||
|
|
k5, = nonzero(hk[k13] < 100.e0)
|
||
|
|
if len(k5) > 0:
|
||
|
|
# b = sqrt(bs);
|
||
|
|
k135 = k13[k5]
|
||
|
|
bvn[k135] = bvn[k135] - exp(-hk[k135] / 2) * sqrt(twopi) * fi(-b[k5] / a[k5]) * \
|
||
|
|
b[k5] * (1 - c[k5] * bs[k5] * (1 - d[k5] * bs[k5] / 5) / 3)
|
||
|
|
|
||
|
|
a /= two
|
||
|
|
for i in range(10):
|
||
|
|
for sign in - 1, 1:
|
||
|
|
xs = (a * (sign * _X20[i] + 1)) ** 2
|
||
|
|
rs = sqrt(1 - xs)
|
||
|
|
asr = -(bs / xs + hk[k13]) / 2
|
||
|
|
k6, = nonzero(asr > -100.e0)
|
||
|
|
if len(k6) > 0:
|
||
|
|
k136 = k13[k6]
|
||
|
|
bvn[k136] += (a[k6] * _W20[i] * exp(asr[k6]) *
|
||
|
|
(exp(-hk[k136] * (1 - rs[k6]) /
|
||
|
|
(2 * (1 + rs[k6]))) / rs[k6] -
|
||
|
|
(1 + c[k6] * xs[k6] *
|
||
|
|
(1 + d[k6] * xs[k6]))))
|
||
|
|
|
||
|
|
bvn[k3] = -bvn[k3] / twopi
|
||
|
|
|
||
|
|
k7, = nonzero(r[k1] > 0)
|
||
|
|
if len(k7):
|
||
|
|
k17 = k1[k7]
|
||
|
|
bvn[k17] += fi(-np.maximum(h[k17], k[k17]))
|
||
|
|
|
||
|
|
k8, = nonzero(r[k1] < 0)
|
||
|
|
if len(k8) > 0:
|
||
|
|
k18 = k1[k8]
|
||
|
|
bvn[k18] = -bvn[k18] + np.maximum(0, fi(-h[k18]) - fi(-k[k18]))
|
||
|
|
|
||
|
|
bvn.shape = cshape
|
||
|
|
return bvn
|
||
|
|
|
||
|
|
|
||
|
|
def fi(x):
|
||
|
|
return 0.5 * (erfc((-x) / sqrt(2)))
|
||
|
|
|
||
|
|
|
||
|
|
def prbnorm2d(a, b, r):
|
||
|
|
'''
|
||
|
|
Returns Bivariate Normal probability
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
---------
|
||
|
|
a, b : array-like, size==2
|
||
|
|
vector with lower and upper integration limits, respectively.
|
||
|
|
r : real scalar
|
||
|
|
correlation coefficient
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
prb : real scalar
|
||
|
|
computed probability Prob(A[0] <= X1 <= B[0] and A[1] <= X2 <= B[1])
|
||
|
|
with an absolute error less than 1e-15.
|
||
|
|
|
||
|
|
Example
|
||
|
|
-------
|
||
|
|
>>> import wafo.gaussian as wg
|
||
|
|
>>> a = [-1, -2]
|
||
|
|
>>> b = [1, 1]
|
||
|
|
>>> r = 0.3
|
||
|
|
>>> wg.prbnorm2d(a,b,r)
|
||
|
|
array([ 0.56659121])
|
||
|
|
|
||
|
|
See also
|
||
|
|
--------
|
||
|
|
cdfnorm2d,
|
||
|
|
cdfnorm,
|
||
|
|
prbnormndpc
|
||
|
|
'''
|
||
|
|
infinity = 37
|
||
|
|
lower = np.asarray(a)
|
||
|
|
upper = np.asarray(b)
|
||
|
|
if np.all((lower <= -infinity) & (infinity <= upper)):
|
||
|
|
return 1.0
|
||
|
|
if (lower >= upper).any():
|
||
|
|
return 0.0
|
||
|
|
correl = r
|
||
|
|
infin = np.repeat(2, 2) - (upper > infinity) - 2 * (lower < -infinity)
|
||
|
|
|
||
|
|
if np.all(infin == 2):
|
||
|
|
return (bvd(lower[0], lower[1], correl)
|
||
|
|
- bvd(upper[0], lower[1], correl)
|
||
|
|
- bvd(lower[0], upper[1], correl)
|
||
|
|
+ bvd(upper[0], upper[1], correl))
|
||
|
|
elif (infin[0] == 2 and infin[1] == 1):
|
||
|
|
return (bvd(lower[0], lower[1], correl) -
|
||
|
|
bvd(upper[0], lower[1], correl))
|
||
|
|
elif (infin[0] == 1 and infin[1] == 2):
|
||
|
|
return (bvd(lower[0], lower[1], correl) -
|
||
|
|
bvd(lower[0], upper[1], correl))
|
||
|
|
elif (infin[0] == 2 and infin[1] == 0):
|
||
|
|
return (bvd(-upper[0], -upper[1], correl) -
|
||
|
|
bvd(-lower[0], -upper[1], correl))
|
||
|
|
elif (infin[0] == 0 and infin[1] == 2):
|
||
|
|
return (bvd(-upper[0], -upper[1], correl) -
|
||
|
|
bvd(-upper[0], -lower[1], correl))
|
||
|
|
elif (infin[0] == 1 and infin[1] == 0):
|
||
|
|
return bvd(lower[0], -upper[1], -correl)
|
||
|
|
elif (infin[0] == 0 and infin[1] == 1):
|
||
|
|
return bvd(-upper[0], lower[1], -correl)
|
||
|
|
elif (infin[0] == 1 and infin[1] == 1):
|
||
|
|
return bvd(lower[0], lower[1], correl)
|
||
|
|
elif (infin[0] == 0 and infin[1] == 0):
|
||
|
|
return bvd(-upper[0], -upper[1], correl)
|
||
|
|
return 1
|
||
|
|
|
||
|
|
|
||
|
|
def bvd(lo, up, r):
|
||
|
|
return cdfnorm2d(-lo, -up, r)
|
||
|
|
|
||
|
|
|
||
|
|
def test_docstrings():
|
||
|
|
import doctest
|
||
|
|
doctest.testmod()
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
test_docstrings()
|
||
|
|
# if __name__ == '__main__':
|
||
|
|
# if False: #True: #
|
||
|
|
# test_rind()
|
||
|
|
# else:
|
||
|
|
# import doctest
|
||
|
|
# doctest.testmod()
|