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Added cdfnorm2d and prbnorm2d + tests

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Per.Andreas.Brodtkorb 15 years ago
parent d70f75a3cf
commit e1ed205bd2
2 changed files with 358 additions and 53 deletions
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pywafo/src/wafo/gaussian.py
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@ -1,12 +1,14 @@
import warnings from numpy import r_, minimum, maximum, atleast_1d, atleast_2d, mod, ones, floor, \
random, eye, nonzero, where, repeat, sqrt, exp, inf, diag, zeros, sin, arcsin, nan #@UnresolvedImport
from numpy import triu #@UnresolvedImport
from scipy.special import ndtr as cdfnorm, ndtri as invnorm
from wafo import mvn
import numpy as np import numpy as np
from numpy import (r_, minimum, maximum, atleast_1d, atleast_2d, mod, zeros, #@UnresolvedImport
ones, floor, random, eye, nonzero, repeat, sqrt, exp, inf, diag, triu) #@UnresolvedImport
from scipy.special import ndtri as invnorm
from scipy.special import ndtr as cdfnorm
import wafo.rindmod as rindmod
import wafo.mvnprdmod as mvnprdmod import wafo.mvnprdmod as mvnprdmod
from wafo import mvn import wafo.rindmod as rindmod
import warnings
from wafo.misc import common_shape
from scipy.stats.stats import erfc
class Rind(object): class Rind(object):
''' '''
@ -22,7 +24,7 @@ class Rind(object):
Lower and upper barriers used to compute the integration limits, Hlo and Hup, respectively. Lower and upper barriers used to compute the integration limits, Hlo and Hup, respectively.
indI : array-like, length Ni indI : array-like, length Ni
vector of indices to the different barriers in the indicator function. vector of indices to the different barriers in the indicator function.
(NB! restriction indI(1)=0, indI(NI)=Nt+Nd, Ni = Nb+1) (NB! restriction indI(1)=-1, indI(NI)=Nt+Nd, Ni = Nb+1)
(default indI = 0:Nt+Nd) (default indI = 0:Nt+Nd)
xc : values to condition on (default xc = zeros(0,1)), size Nc x Nx xc : values to condition on (default xc = zeros(0,1)), size Nc x Nx
Nt : size of Xt (default Nt = Ntdc - Nc) Nt : size of Xt (default Nt = Ntdc - Nc)
@ -361,7 +363,7 @@ def test_rind():
Blo[0, ind] = maximum(Blo[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) E3 = rind(Sc, m, Blo, Bup, indI, xc, nt=1)
def cdflomax(x,alpha,m0): def cdflomax(x, alpha, m0):
''' '''
Return CDF for local maxima for a zero-mean Gaussian process Return CDF for local maxima for a zero-mean Gaussian process
@ -412,11 +414,11 @@ def cdflomax(x,alpha,m0):
-------- --------
spec2mom, spec2bw spec2mom, spec2bw
''' '''
c1 = 1.0/(sqrt(1-alpha**2))*x/sqrt(m0) c1 = 1.0 / (sqrt(1 - alpha ** 2)) * x / sqrt(m0)
c2 = alpha*c1 c2 = alpha * c1
return cdfnorm(c1)-alpha*exp(-x**2/2/m0)*cdfnorm(c2) 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): 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 structure. Return Multivariate normal or T probability with product correlation structure.
@ -490,11 +492,11 @@ def prbnormtndpc(rho,a,b,D=None,df=0,abseps=1e-4,IERC=0,HNC=0.24):
if D is None: if D is None:
D = zeros(len(rho)) D = zeros(len(rho))
# Make sure integration limits are finite # Make sure integration limits are finite
A = np.clip(a-D,-100,100) A = np.clip(a - D, -100, 100)
B = np.clip(b-D,-100,100) B = np.clip(b - D, -100, 100)
return mvnprdmod.prbnormtndpc(rho,A,B,df,abseps,IERC,HNC) return mvnprdmod.prbnormtndpc(rho, A, B, df, abseps, IERC, HNC)
def prbnormndpc(rho,a,b,abserr=1e-4,relerr=1e-4,usesimpson=True, usebreakpoints=False): def prbnormndpc(rho, a, b, abserr=1e-4, relerr=1e-4, usesimpson=True, usebreakpoints=False):
''' '''
Return Multivariate Normal probabilities with product correlation Return Multivariate Normal probabilities with product correlation
@ -552,15 +554,15 @@ def prbnormndpc(rho,a,b,abserr=1e-4,relerr=1e-4,usesimpson=True, usebreakpoints=
''' '''
# Call fortran implementation # Call fortran implementation
val,err,ier = mvnprdmod.prbnormndpc(rho,a,b,abserr,relerr,usebreakpoints,usesimpson); val, err, ier = mvnprdmod.prbnormndpc(rho, a, b, abserr, relerr, usebreakpoints, usesimpson);
if ier>0: if ier > 0:
warnings.warn('Abnormal termination ier = %d\n\n%s' % (ier,_ERRORMESSAGE[ier])) warnings.warn('Abnormal termination ier = %d\n\n%s' % (ier, _ERRORMESSAGE[ier]))
return val, err, ier return val, err, ier
_ERRORMESSAGE = {} _ERRORMESSAGE = {}
_ERRORMESSAGE[0] = '' _ERRORMESSAGE[0] = ''
_ERRORMESSAGE[1] =''' _ERRORMESSAGE[1] = '''
Maximum number of subdivisions allowed has been achieved. one can allow Maximum number of subdivisions allowed has been achieved. one can allow
more subdivisions by increasing the value of limit (and taking the more subdivisions by increasing the value of limit (and taking the
according dimension adjustments into account). however, if this yields according dimension adjustments into account). however, if this yields
@ -571,27 +573,27 @@ _ERRORMESSAGE[1] ='''
the vector points. If necessary an appropriate special-purpose integrator the vector points. If necessary an appropriate special-purpose integrator
must be used, which is designed for handling the type of difficulty involved. must be used, which is designed for handling the type of difficulty involved.
''' '''
_ERRORMESSAGE[2] =''' _ERRORMESSAGE[2] = '''
the occurrence of roundoff error is detected, which prevents the requested the occurrence of roundoff error is detected, which prevents the requested
tolerance from being achieved. The error may be under-estimated.''' tolerance from being achieved. The error may be under-estimated.'''
_ERRORMESSAGE[3] =''' _ERRORMESSAGE[3] = '''
Extremely bad integrand behaviour occurs at some points of the integration interval.''' Extremely bad integrand behaviour occurs at some points of the integration interval.'''
_ERRORMESSAGE[4] =''' _ERRORMESSAGE[4] = '''
The algorithm does not converge. Roundoff error is detected in the extrapolation table. 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 It is presumed that the requested tolerance cannot be achieved, and that
the returned result is the best which can be obtained.''' the returned result is the best which can be obtained.'''
_ERRORMESSAGE[5] =''' _ERRORMESSAGE[5] = '''
The integral is probably divergent, or slowly convergent. The integral is probably divergent, or slowly convergent.
It must be noted that divergence can occur with any other value of ier>0.''' It must be noted that divergence can occur with any other value of ier>0.'''
_ERRORMESSAGE[6] ='''the input is invalid because: _ERRORMESSAGE[6] = '''the input is invalid because:
1) npts2 < 2 1) npts2 < 2
2) break points are specified outside the integration range 2) break points are specified outside the integration range
3) (epsabs<=0 and epsrel<max(50*rel.mach.acc.,0.5d-28)) 3) (epsabs<=0 and epsrel<max(50*rel.mach.acc.,0.5d-28))
4) limit < npts2.''' 4) limit < npts2.'''
def prbnormnd(correl,a,b,abseps=1e-4,releps=1e-3,maxpts=None,method=0): def prbnormnd(correl, a, b, abseps=1e-4, releps=1e-3, maxpts=None, method=0):
''' '''
Multivariate Normal probability by Genz' algorithm. Multivariate Normal probability by Genz' algorithm.
@ -628,19 +630,30 @@ def prbnormnd(correl,a,b,abseps=1e-4,releps=1e-3,maxpts=None,method=0):
decrease ERROR; decrease ERROR;
if INFORM = 2, N > NMAX or N < 1. where NMAX depends on the if INFORM = 2, N > NMAX or N < 1. where NMAX depends on the
integration method integration method
Example:% Compute the probability that X1<0,X2<0,X3<0,X4<0,X5<0, Example
% Xi are zero-mean Gaussian variables with variances one -------
% and correlations Cov(X(i),X(j))=0.3: Compute the probability that X1<0,X2<0,X3<0,X4<0,X5<0,
% indI=[0 5], and barriers B_lo=[-inf 0], B_lo=[0 inf] Xi are zero-mean Gaussian variables with variances one
% gives H_lo = [-inf -inf -inf -inf -inf] H_lo = [0 0 0 0 0] and correlations Cov(X(i),X(j))=0.3:
indI=[0 5], and barriers B_lo=[-inf 0], B_lo=[0 inf]
N = 5; rho=0.3; NIT=3; Nt=N; indI=[0 N]; gives H_lo = [-inf -inf -inf -inf -inf] H_lo = [0 0 0 0 0]
B_lo=-10; B_up=0; m=1.2*ones(N,1);
Sc=(ones(N)-eye(N))*rho+eye(N); >>> Et = 0.001946 # # exact prob.
E = rind(Sc,m,B_lo,B_up,indI,[],Nt) % exact prob. 0.00195 >>> n = 5; nt = n
A = [-inf -inf -inf -inf -inf], >>> Blo =-np.inf; Bup=0; indI=[-1, n-1] # Barriers
B = [0 0 0 0 0]-m' >>> m = 1.2*np.ones(n); rho = 0.3;
[val,err,inform] = prbnormnd(Sc,A,B); >>> 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
>>> np.abs(val-Et)< err0+terr0
array([ True], dtype=bool)
>>> 'val = %2.6f' % val
'val = 0.001945'
See also See also
-------- --------
@ -648,16 +661,16 @@ def prbnormnd(correl,a,b,abseps=1e-4,releps=1e-3,maxpts=None,method=0):
''' '''
m,n = correl.shape m, n = correl.shape
Na = len(a) Na = len(a)
Nb = len(b) Nb = len(b)
if (m!=n or m!=Na or m!=Nb): if (m != n or m != Na or m != Nb):
raise ValueError('Size of input is inconsistent!') raise ValueError('Size of input is inconsistent!')
if maxpts is None: if maxpts is None:
maxpts = 1000*n maxpts = 1000 * n
maxpts = max(round(maxpts),10*n); maxpts = max(round(maxpts), 10 * n);
# % array of correlation coefficients; the correlation # % array of correlation coefficients; the correlation
# % coefficient in row I column J of the correlation matrix # % coefficient in row I column J of the correlation matrix
@ -665,16 +678,21 @@ def prbnormnd(correl,a,b,abseps=1e-4,releps=1e-3,maxpts=None,method=0):
# % The correlation matrix must be positive semidefinite. # % The correlation matrix must be positive semidefinite.
D = np.diag(correl) D = np.diag(correl)
if (any(D!=1)): if (any(D != 1)):
raise ValueError('This is not a correlation matrix') raise ValueError('This is not a correlation matrix')
# Make sure integration limits are finite # Make sure integration limits are finite
A = np.clip(a,-100,100) A = np.clip(a, -100, 100)
B = np.clip(b,-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
#L = correl((triu(ones(m),1)~=0)); % return only off diagonal elements infinity = 37
return mvn.mvnun(A,B, correl,maxpts, abseps, releps) infin = np.repeat(2, n) - (B > infinity) - 2 * (A < -infinity)
err, val, inform = mvn.mvndst(A, B, infin, L, maxpts, abseps, releps)
return val, err, inform
#CALL the mexroutine #CALL the mexroutine
# t0 = clock; # t0 = clock;
@ -688,7 +706,253 @@ def prbnormnd(correl,a,b,abseps=1e-4,releps=1e-3,maxpts=None,method=0):
# [value, err,inform] = mexGenzMvnPrb(L,A,B,abseps,releps,maxpts,method); # [value, err,inform] = mexGenzMvnPrb(L,A,B,abseps,releps,maxpts,method);
# end # end
# exTime = etime(clock,t0); # 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
-------
>>> x = np.linspace(-5,5,20)
>>> [B1,B2] = np.meshgrid(x, x)
>>> r = 0.3;
>>> F = 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
-------
>>> a = [-1, -2]
>>> b = [1, 1]
>>> r = 0.3
>>> prbnorm2d(a,b,r)
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)
if __name__ == '__main__': if __name__ == '__main__':
if False: #True: # if False: #True: #

45
pywafo/src/wafo/test/test_gaussian.py
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@ -5,7 +5,7 @@ Created on 17. juli 2010
''' '''
import numpy as np import numpy as np
from numpy import pi, inf from numpy import pi, inf
from wafo.gaussian import Rind, prbnormtndpc, prbnormndpc from wafo.gaussian import Rind, prbnormtndpc, prbnormndpc, prbnormnd, cdfnorm2d, prbnorm2d
def test_rind(): def test_rind():
''' '''
@ -83,7 +83,7 @@ def test_prbnormtndpc():
>>> [val3,err3, ift3] = prbnormtndpc(rho3,a3,b3) >>> [val3,err3, ift3] = prbnormtndpc(rho3,a3,b3)
>>> g3 = lambda x : 0.5-sum(np.sort(np.arccos([x[0]*x[1],x[0]*x[2],x[1]*x[2]])))/(4*pi) >>> g3 = lambda x : 0.5-sum(np.sort(np.arccos([x[0]*x[1],x[0]*x[2],x[1]*x[2]])))/(4*pi)
>>> E3 = g3(rho3) # Exact value >>> E3 = g3(rho3) # Exact value
>>> np.abs(E3-val3)<err2 >>> np.abs(E3-val3)<err3
True True
''' '''
def test_prbnormndpc(): def test_prbnormndpc():
@ -106,6 +106,47 @@ def test_prbnormndpc():
>>> np.abs(E3-val3)<err2 >>> np.abs(E3-val3)<err2
True True
''' '''
def test_prbnormnd():
'''
>>> 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 = %2.6f' % val
'val = 0.001945'
'''
def test_cdfnorm2d():
'''
>>> x = np.linspace(-3,3,3)
>>> [b1,b2] = np.meshgrid(x,x)
>>> r = 0.3
>>> cdfnorm2d(b1,b2,r)
array([[ 2.38515157e-05, 1.14504149e-03, 1.34987703e-03],
[ 1.14504149e-03, 2.98493342e-01, 4.99795143e-01],
[ 1.34987703e-03, 4.99795143e-01, 9.97324055e-01]])
'''
def test_prbnorm2d():
'''
>>> a = [-1, -2]
>>> b = [1, 1]
>>> r = 0.3
>>> prbnorm2d(a,b,r)
array([ 0.56659121])
'''
if __name__ == '__main__': if __name__ == '__main__':
import doctest import doctest
doctest.testmod() doctest.testmod()
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