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added nt2cmat and cmat2nt

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master
pbrod 9 years ago
parent 3df0cf212d
commit 7f388a43fc
2 changed files with 249 additions and 16 deletions
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263
wafo/misc.py
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@ -1145,8 +1145,241 @@ def _findrfc(y, h):
return ind, ix return ind, ix
def mctp2tc(fmM, ): def nt2cmat(nt, kind=11):
raise ValueError("Not implemented yet") """
Return cycle matrix from a counting distribution.
Parameters
----------
NT: 2D array
Counting distribution. [nxn]
kind = 1: causes peaks to be projected upwards and troughs
downwards to the closest discrete level (default).
= 0: causes peaks and troughs to be projected to
the closest discrete level.
= -1: causes peaks to be projected downwards and the
troughs upwards to the closest discrete level.
Returns
-------
cmat = Cycle matrix. [nxn]
Example
--------
>>> import numpy as np
>>> cmat0 = np.round(np.triu(np.random.rand(4, 4), 1)*10)
>>> cmat0 = np.array([[ 0., 5., 6., 9.],
... [ 0., 0., 1., 7.],
... [ 0., 0., 0., 4.],
... [ 0., 0., 0., 0.]])
>>> nt = cmat2nt(cmat0)
>>> cmat = nt2cmat(nt)
>>> np.allclose(cmat, [[ 0., 5., 6., 9.],
... [ 0., 0., 1., 7.],
... [ 0., 0., 0., 4.],
... [ 0., 0., 0., 0.]])
True
See also
--------
cmat2nt
"""
n = len(nt) # Number of discrete levels
cmat = np.zeros((n, n))
if kind == 1:
I = np.r_[0:n-1]
J = np.r_[1:n]
cmat[I, J] = nt[I+1, J-1] - nt[I, J-1] - nt[I+1, J] + nt[I, J]
elif kind == 11: # same as def=1 but using for-loop
j = np.r_[1:n]
for i in range(n-1):
cmat[i, j] = nt[i+1, j-1] - nt[i, j-1] - nt[i+1, j] + nt[i, j]
elif kind == 0:
raise NotImplementedError('kind == 0 not yet implemented')
elif kind == -1:
raise NotImplementedError('kind == -1 not yet implemented')
else:
raise ValueError('kind = {}: not a valid value of kind'.format(kind))
return cmat
def cmat2nt(cmat, kind=11):
"""
CMAT2NT Calculates a counting distribution from a cycle matrix.
CALL: NT = cmat2nt(F,def);
NT = Counting distribution. [nxn]
cmat = Cycle matrix. [nxn]
kind = 1: causes peaks to be projected upwards and troughs
downwards to the closest discrete level (default).
= 0: causes peaks and troughs to be projected to
the closest discrete level.
= -1: causes peaks to be projected downwards and the
troughs upwards to the closest discrete level.
Example
-------
F = round(triu(rand(4),1)*10)
NT = cmat2nt(F)
See also
--------
nt2cmat
"""
n = len(cmat) # Number of discrete levels
nt = zeros((n, n))
if kind == 1:
nt[1:n, :n-1] = np.fliplr(np.cumsum(np.fliplr(np.cumsum(cmat[:-1,
1:])),
axis=1))
elif kind == 11: # same as def=1 but using for-loop
# j = np.r_[1:n]
for i in range(1, n):
for j in range(n-1):
nt[i, j] = np.sum(np.sum(cmat[:i, j+1:n]))
elif kind == 0:
raise NotImplementedError('kind == 0 not yet implemented')
elif kind == -1:
raise NotImplementedError('kind == -1 not yet implemented')
else:
raise ValueError('kind = {}: not a valid value of kind'.format(kind))
return nt
def mctp2tc(f_Mm, utc, param, f_mM=None):
"""
MCTP2TC Calculates frequencies for the upcrossing troughs and crests
using Markov chain of turning points.
CALL: f_tc = mctp2tc(f_Mm,utc,param);
where
f_tc = the matrix with frequences of upcrossing troughs and crests,
f_Mm = the frequency matrix for the Max2min cycles,
utc = the reference level,
param = a vector defining the discretization used to compute f_Mm,
note that f_mM has to be computed on the same grid as f_mM.
optional call: f_tc = mctp2tc(f_Mm,utc,param,f_mM)
f_mM = the frequency matrix for the min2Max cycles.
"""
raise NotImplementedError('')
# if f_mM is None:
# f_mM = np.copy(f_Mm)
#
# u = np.linspace(*param)
# udisc = np.fliplr(u)
# ntc = np.sum(udisc >= utc)
# n = len(f_Mm)
# if ntc > n-1:
# raise IndexError('index for mean-level out of range, stop')
# if param[2]-1 < ntc or ntc < 2 :
# raise ValueError('the reference level out of range, stop')
#
# # normalization of frequency matrices
#
# for i in range(n):
# rowsum = np.sum(f_Mm[i])
# if rowsum!=0:
# f_Mm[i] = f_Mm[i] /rowsum
#
# P = np.fliplr(f_Mm)
#
# Ph = np.rot90(np.fliplr(f_mM), -1)
# for i in range(n):
# rowsum = np.sum(Ph[i])
# if rowsum!=0:
# Ph[i] = Ph[i] / rowsum
#
# Ph = np.fliplr(Ph)
#
# F = np.zeros((n, n))
# F[:ntc-1,:(n-ntc)] = f_mM[:ntc-1, :(n-ntc)]
# F = cmat2nt(F)
#
# for i in range(1, ntc):
# for j in range(ntc, n-1):
#
# if i<ntc:
# Ap = P[i:ntc-1,i+1:ntc]
# Bp = Ph[i+1:ntc,i:ntc-1]
# dim_p = ntc-i
# tempp=zeros((dim_p, 1))
# I=np.eye(np.shape(Ap))
# if i==2:
# e = Ph[i+1:ntc,0]
# else:
# e = np.sum(Ph[i+1:ntc, 1:i-1], axis=1)
#
# if max(abs(e)) > 1e-10
# if dim_p == 1:
# tempp[0] = (Ap/(1-Bp*Ap)*e);
# else:
# tempp = Ap*((I-Bp*Ap)\e)
# # end
# # end
# # end
#
# if j>ntc
#
# Am=P(ntc:j-1,ntc+1:j); Bm=Ph(ntc+1:j,ntc:j-1);
# dim_m=j-ntc;
# tempm=zeros(dim_m,1);
# Im=eye(size(Am));
# if j==n-1
# em=P(ntc:j-1,n);
# else
# em=sum(P(ntc:j-1,j+1:n),2);
# end
# if max(abs(em))>1e-10
# if dim_m==1
# tempm(1,1)=(Bm/(1-Am*Bm)*em);
# else
# tempm=Bm*((Im-Am*Bm)\em);
# end
# end
# end
#
# if (j>ntc) && (i<ntc)
# F(i,n-j+1)=F(i,n-j+1)+tempp'*freqPVL(i:ntc-1,n-ntc:-1:n-j+1)*tempm;
# F(i,n-j+1)=F(i,n-j+1)+tempp'*freqPVL(i:ntc-1,n-j:-1:1)*ones(n-j,1);
# F(i,n-j+1)=F(i,n-j+1)+ones(1,i-1)*freqPVL(1:i-1,n-ntc:-1:n-j+1)*tempm;
# end
# if (j==ntc) && (i<ntc)
# F(i,n-j+1)=F(i,n-j+1)+tempp'*freqPVL(i:ntc-1,n-j:-1:1)*ones(n-j,1);
# for k=1:ntc
# F(i,n-k+1)=F(i,n-ntc+1);
# end
# end
# if (j>ntc) && (i==ntc)
# F(i,n-j+1)=F(i,n-j+1)+ones(1,i-1)*freqPVL(1:i-1,n-ntc:-1:n-j+1)*tempm;
# for k=ntc:n
# F(k,n-j+1)=F(ntc,n-j+1);
# end
# end
# end
# end
#
# return nt2cmat(F);
# fmax=max(max(F));
# contour (u,u,flipud(F),...
# fmax*[0.005 0.01 0.02 0.05 0.1 0.2 0.4 0.6 0.8])
# axis([param(1) param(2) param(1) param(2)])
# title('Crest-trough density')
# ylabel('crest'), xlabel('trough')
# axis('square')
# if mlver>1, commers, end
def mctp2rfc(fmM, fMm=None): def mctp2rfc(fmM, fMm=None):
@ -1274,20 +1507,20 @@ def mctp2rfc(fmM, fMm=None):
f_rfc[0, N - 1 - k] = min(m0, M0) f_rfc[0, N - 1 - k] = min(m0, M0)
# end # end
# %clf # clf
# %subplot(1,2,2) # subplot(1,2,2)
# %pcolor(levels(paramm),levels(paramM),flipud(f_mM)) # pcolor(levels(paramm),levels(paramM),flipud(f_mM))
# % title('Markov matrix') # title('Markov matrix')
# % ylabel('max'), xlabel('min') # ylabel('max'), xlabel('min')
# %axis([paramm(1) paramm(2) paramM(1) paramM(2)]) # axis([paramm(1) paramm(2) paramM(1) paramM(2)])
# %axis('square') # axis('square')
# #
# %subplot(1,2,1) # subplot(1,2,1)
# %pcolor(levels(paramm),levels(paramM),flipud(f_rfc)) # pcolor(levels(paramm),levels(paramM),flipud(f_rfc))
# % title('Rainflow matrix') # title('Rainflow matrix')
# % ylabel('max'), xlabel('rfc-min') # ylabel('max'), xlabel('rfc-min')
# %axis([paramm(1) paramm(2) paramM(1) paramM(2)]) # axis([paramm(1) paramm(2) paramM(1) paramM(2)])
# %axis('square') # axis('square')
return f_rfc return f_rfc

2
wafo/transform/estimation.py
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@ -400,7 +400,7 @@ class TransformEstimator(object):
>>> int(g0.dist2gauss()*100) > 90 >>> int(g0.dist2gauss()*100) > 90
True True
>>> int(g1.dist2gauss()*100) >>> int(g1.dist2gauss()*100)
66 63
>>> int(g2.dist2gauss()*100) >>> int(g2.dist2gauss()*100)
84 84

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