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Fixed bugs in cov2mmpdfreg_intfc.f

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master
Per.Andreas.Brodtkorb 14 years ago
parent 2cdadae992
commit 3d61983ed2
4 changed files with 81 additions and 70 deletions
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BIN
pywafo/src/wafo/cov2mod.pyd
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38
pywafo/src/wafo/source/mreg/cov2mmpdfreg_intfc.f
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@ -65,7 +65,7 @@ Cf2py real*8, intent(out), depend(Nu,Nv) :: UVdens
Cf2py depend(Ng) Xg
Cf2py depend(Nt,5) COV
real*8 Q0,SQ0,Q1,SQ1, U,V,VV, XL0, XL2, XL4
REAL*8 VDERI, CDER,SDER, DER, CONST1, F, HHHH,FM, VALUE
REAL*8 VDERI, CDER,SDER, DER, CONST1, F, HHHH, FM, VALUE
C INTEGER, PARAMETER :: MMAX = 5, NMAX = 101, RDIM = 10201
REAL*8, DIMENSION(NMAX) :: HHT,VT,UT,Vdd,Udd
REAL*8, DIMENSION(RDIM) :: R,R1,R2,R3
@ -118,7 +118,7 @@ c
c OBS. we are using the variables R,R1,R2 R3 as a temporary storage
C for transformation g of the process.
c
N = Nt
CALL INITLEVELS(T,HHT,Nt,NU,Nv)
C CALL INITLEVELS(Ulev,NU,Vlev,NV,T,HHT,Nt,R1,R2,NG)
IF( Tg(1) .gt. Tg(ng)) then
@ -127,8 +127,13 @@ C CALL INITLEVELS(Ulev,NU,Vlev,NV,T,HHT,Nt,R1,R2,NG)
end if
if(abs(Tg(ng)-Tg(1))*abs(Xg(ng)-Xg(1)).lt.0.01d0) then
print *,'The transformation g is singular, stop'
stop
return
end if
! do IV=1,Nt
! print *, 'Cov', COV(IV,:)
! end do
DO IV=1,Nv
V=Vlev(IV)
CALL TRANSF(NG,V,Xg,Tg,VALUE,DER)
@ -145,19 +150,19 @@ C CALL INITLEVELS(Ulev,NU,Vlev,NV,T,HHT,Nt,R1,R2,NG)
enddo
enddo
CALL COVG(XL0,XL2,XL4,COV,T,Nt)
CALL COVG(XL0,XL2,XL4,R1,R2,R3,COV,T,Nt)
Q0=XL4
IF (Q0.le.1.0D0+EPS) then
Print *,'Covariance structure is singular, stop.'
stop
return
end if
SQ0 = SQRT(Q0)
Q1 = XL0-XL2*XL2/XL4
IF (Q1.le.EPS) then
Print *,'Covariance structure is singular, stop.'
stop
return
end if
SQ1 = SQRT(Q1)
DO I=1,Nt
@ -202,7 +207,7 @@ c10 CONTINUE
C
C R1 contains Cov(X(T(I)),X'(T(J))|X'(0),X''(0),X(0))
C
R1(J+(I-1)*N)=R1(J+(I-1)*N) - COV(I,2)*(COV(J,3)/XL2)
R1(J+(I-1)*N) = R1(J+(I-1)*N) - COV(I,2)*(COV(J,3)/XL2)
1 - (B0(I)*DB0(J)/Q0) - (B1(I)*DB1(J)/Q1)
C
@ -320,16 +325,17 @@ C Here the covariance of the problem would be initiated
20 CONTINUE
enddo
! hhhh=0.0d0
! do 90 Iu=1,Nu
! do 90 Iv=1,Nv
hhhh=0.0d0
do Iu=1,Nu
do Iv=1,Nv
! WRITE(10,300) Ulev(iu),Vlev(iv),UVdens(iu,iv)
! hhhh=hhhh+UVdens(iu,iv)
! 90 continue
! if (nu.gt.1.and.nv.gt.1) then
! write(11,*) 'SumSum f_uv *du*dv='
! 1,(Ulev(2)-Ulev(1))*(Vlev(2)-Vlev(1))*hhhh
! end if
hhhh=hhhh+UVdens(iu,iv)
enddo
enddo
if (nu.gt.1.and.nv.gt.1) then
VALUE = (Ulev(2)-Ulev(1))*(Vlev(2)-Vlev(1))*hhhh
print *,'SumSum f_uv *du*dv=', VALUE
end if
C sder=sqrt(XL4-XL2*XL2/XL0)
C cder=-XL2/sqrt(XL0)

53
pywafo/src/wafo/source/mreg/intfcmod.f
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@ -132,49 +132,58 @@ C
RETURN
END FUNCTION SPLE
SUBROUTINE COVG(XL0,XL2,XL4,COV,T,N)
SUBROUTINE COVG(XL0,XL2,XL4,COV1,COV2,COV3,COV,T,N)
C
C COVG evaluates:
C Covariance function and its four derivatives for a vector T of length N
C is assumed in a vector COV; COV(1,...,N,1)=r(T), COV(1,...,N, 2)=r'(T), etc.
C The vector COV should be of the shape N x 5.
C
C COVG Returns:
C XL0,XL2,XL4 - spectral moments.
C
C Covariance function and its four derivatives for a vector T of length N.
C It is saved in a vector COV; COV(1,...,N)=r(T), COV(N+1,...,2N)=r'(T), etc.
C The vector COV should be of the length 5*N.
C
C Covariance matrices COV1=r'(T-T), COV2=r''(T-T) and COV3=r'''(T-T)
C Dimension of COV1, COV2 should be N*N.
C Dimension of COV1, COV2 should be atleast N*N.
C
! USE SIZEMOD
USE SIZEMOD
! IMPLICIT NONE
C INTEGER, PARAMETER:: NMAX = 101, RDIM = 10201
REAL*8, PARAMETER:: ZERO = 0.0d0
REAL*8, intent(inout) :: XL0,XL2,XL4
REAL*8, DIMENSION(N,5), intent(in) :: COV
REAL*8, DIMENSION(N), intent(in) :: T
REAL*8, DIMENSION(RDIM), intent(inout) :: COV1,COV2,COV3
INTEGER, intent(in) :: N
integer :: I, J, II
REAL*8 :: TT, T0
C
C COV(Y(T),Y(0)) = COV(:,1)
C
XL0 = COV(1,1)
! XL0 = SPLE(NT,ZERO,COV(:,1),T)
C
C DERIVATIVE COV(Y(T),Y(0)) = COV(:,2)
C
C 2-DERIVATIVE COV(Y(T),Y(0)) = COV(:,3)
XL2 = -COV(1,3)
! XL2 = -SPLE(NT,ZERO,COV(:,3),T)
C 3-DERIVATIVE COV(Y(T),Y(0)) = COV(:,4)
C 4-DERIVATIVE COV(Y(T),Y(0)) = COV(:,5)
XL0 = COV(1,1)
XL2 = -COV(1,3)
XL4 = COV(1,5)
! XL4 = SPLE(NT,ZERO,COV(:,5),T)
! XL0 = SPLE(NT, ZERO, COV(:,1), T)
! XL2 = -SPLE(NT, ZERO, COV(:,3), T)
! XL4 = SPLE(NT, ZERO, COV(:,5), T)
II=0
DO I=1,N
DO J=1,N
II = II+1
T0 = T(J)-T(I)
TT = ABS(T0)
COV1(II) = SPLE(N, TT, COV(:,2), T)
COV2(II) = SPLE(N, TT, COV(:,3), T)
COV3(II) = SPLE(N, TT, COV(:,4), T)
IF (T0.LT.0.0d0) then
COV1(II)=-COV1(II)
COV3(II)=-COV3(II)
endif
enddo
enddo
RETURN
END SUBROUTINE COVG
END module intfcmod

44
pywafo/src/wafo/spectrum/core.py
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@ -1148,6 +1148,13 @@ class SpecData1D(WafoData):
NIT=5, leads tp call RIND5, IAC is maximally 4-dimensional integral,
while IAC=1 leads to maximum 5 dimensional integral.
>>> import numpy as np
>>> import wafo.spectrum.models as sm
>>> Sj = sm.Jonswap(Hm0=3)
>>> w = np.linspace(0,4,256)
>>> S1 = Sj.tospecdata(w) #Make spectrum object from numerical values
>>> S = sm.SpecData1D(Sj(w),w) # Alternatively do it manually
S.to_mm_pdf()
'''
S = self.copy()
@ -1197,29 +1204,8 @@ class SpecData1D(WafoData):
dh = h[1]-h[0]
uvdens *= dh*dh
uvdens = np.rot90(uvdens,-2)
# if (defnr==0)
# f.f =fliplr(mctp2rfc(fliplr(ftmp)));%* sqrt(-R(1,6)/R(1,4))/2/pi;
# f.title ='Joint density of maximum and rainflow minimum';
# f.labx{1}='max [m]';
# f.labx{2}='rainflow min [m]';
# elseif (defnr==-1)
# %CC= normalizing constant= 1/ expected number of u-up-crossings of X
# %CC = 2*pi*sqrt(L0/L2)*exp(0.5D0*u*u/L0);
# % CC = normalizing constant = 1/ expected number of zero-up-crossings of X'
# %CC = 2*pi*sqrt(L2/L4);
# fact = sqrt(L0/L4);
#
# f.f = fliplr(mctp2tc(fliplr(ftmp*fact),utc,paramu));
# index1 = find(f.x{1}>0);
# index2 = find(f.x{2}<0);
# f.f = flipud(f.f(index2,index1));
# f.x{1} = f.x{1}(index1);
# f.x{2} = abs(flipud(f.x{2}(index2)));
# f.title ='Joint density of crest and trough';
# f.labx{1}='Crest [m]';
# f.labx{2}='Trough [m]';
# else %(defnr==1)
mmpdf = WafoData(uvdens,args=(h,h), title='Joint density of maximum and minimum',
xlab='max [m]',ylab='min [m]')
return mmpdf
@ -3287,9 +3273,19 @@ def main():
pylab.close('all')
print('done')
def test_mm_pdf():
import numpy as np
import wafo.spectrum.models as sm
Sj = sm.Jonswap(Hm0=7, Tp=11)
w = np.linspace(0,4,256)
S1 = Sj.tospecdata(w) #Make spectrum object from numerical values
S = sm.SpecData1D(Sj(w),w) # Alternatively do it manually
mm = S.to_mm_pdf()
if __name__ == '__main__':
if True: #False : #
if False : #True: #
import doctest
doctest.testmod()
else:
main()
test_mm_pdf()
#main()

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