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Resolved issue 6: mctp2rfc is now working for the example given

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master
per.andreas.brodtkorb 14 years ago
parent afd3c87e22
commit 8ff8b2183e
1 changed files with 122 additions and 122 deletions
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pywafo/src/wafo/misc.py
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@ -6,10 +6,10 @@ from __future__ import division
import sys import sys
import fractions import fractions
import numpy as np import numpy as np
from numpy import (abs, amax, any, logical_and, arange, linspace, atleast_1d,atleast_2d, from numpy import (abs, amax, any, logical_and, arange, linspace, atleast_1d, atleast_2d,
array, asarray, broadcast_arrays, ceil, floor, frexp, hypot, array, asarray, broadcast_arrays, ceil, floor, frexp, hypot,
sqrt, arctan2, sin, cos, exp, log, mod, diff, empty_like, sqrt, arctan2, sin, cos, exp, log, mod, diff, empty_like,
finfo, inf, pi, interp, isnan, isscalar, zeros, ones, finfo, inf, pi, interp, isnan, isscalar, zeros, ones, linalg,
r_, sign, unique, hstack, vstack, nonzero, where, extract) r_, sign, unique, hstack, vstack, nonzero, where, extract)
from scipy.special import gammaln from scipy.special import gammaln
from scipy.integrate import trapz, simps from scipy.integrate import trapz, simps
@ -25,12 +25,12 @@ except:
floatinfo = finfo(float) floatinfo = finfo(float)
__all__ = ['is_numlike','JITImport', 'DotDict', 'Bunch', 'printf', 'sub_dict_select', __all__ = ['is_numlike', 'JITImport', 'DotDict', 'Bunch', 'printf', 'sub_dict_select',
'parse_kwargs', 'detrendma', 'ecross', 'findcross', 'parse_kwargs', 'detrendma', 'ecross', 'findcross',
'findextrema', 'findpeaks', 'findrfc', 'rfcfilter', 'findtp', 'findtc', 'findextrema', 'findpeaks', 'findrfc', 'rfcfilter', 'findtp', 'findtc',
'findoutliers', 'common_shape', 'argsreduce', 'findoutliers', 'common_shape', 'argsreduce',
'stirlerr', 'getshipchar', 'betaloge', 'gravity', 'nextpow2', 'stirlerr', 'getshipchar', 'betaloge', 'gravity', 'nextpow2',
'discretize', 'pol2cart', 'cart2pol', 'meshgrid', 'ndgrid', 'discretize', 'pol2cart', 'cart2pol', 'meshgrid', 'ndgrid',
'trangood', 'tranproc', 'plot_histgrm', 'num2pistr', 'test_docstrings'] 'trangood', 'tranproc', 'plot_histgrm', 'num2pistr', 'test_docstrings']
@ -437,7 +437,7 @@ def findpeaks(data, n=2, min_h=None, min_p=0.0):
smax = S.max() smax = S.max()
if min_h is None: if min_h is None:
smin = S.min() smin = S.min()
min_h = 0.05*(smax-smin) min_h = 0.05 * (smax - smin)
ndim = S.ndim ndim = S.ndim
S = np.atleast_2d(S) S = np.atleast_2d(S)
nrows, mcols = S.shape nrows, mcols = S.shape
@ -453,20 +453,20 @@ def findpeaks(data, n=2, min_h=None, min_p=0.0):
else: # % did not find any , try maximum else: # % did not find any , try maximum
ind = np.atleast_1d(S[iy].argmax()) ind = np.atleast_1d(S[iy].argmax())
if ndim>1: if ndim > 1:
if iy==0: if iy == 0:
ind2 = np.flatnonzero(S[iy,ind]>S[iy+1,ind]) ind2 = np.flatnonzero(S[iy, ind] > S[iy + 1, ind])
elif iy==nrows-1: elif iy == nrows - 1:
ind2 = np.flatnonzero(S[iy,ind]>S[iy-1,ind]) ind2 = np.flatnonzero(S[iy, ind] > S[iy - 1, ind])
else: else:
ind2 = np.flatnonzero((S[iy,ind]>S[iy-1,ind]) & (S[iy,ind]>S[iy+1,ind])) ind2 = np.flatnonzero((S[iy, ind] > S[iy - 1, ind]) & (S[iy, ind] > S[iy + 1, ind]))
if len(ind2): if len(ind2):
indP.append((ind[ind2] + iy*mcols)) indP.append((ind[ind2] + iy * mcols))
if ndim>1: if ndim > 1:
ind = np.hstack(indP) if len(indP) else [] ind = np.hstack(indP) if len(indP) else []
if len(ind)==0: if len(ind) == 0:
return [] return []
peaks = S.take(ind) peaks = S.take(ind)
@ -474,11 +474,11 @@ def findpeaks(data, n=2, min_h=None, min_p=0.0):
# keeping only the Np most significant peak frequencies. # keeping only the Np most significant peak frequencies.
nmax = min(n,len(ind)) nmax = min(n, len(ind))
ind = ind[ind2[:nmax]] ind = ind[ind2[:nmax]]
if (min_p >0 ) : if (min_p > 0) :
# Keeping only peaks larger than min_p percent relative to the maximum peak # Keeping only peaks larger than min_p percent relative to the maximum peak
ind = ind[(S.take(ind) > min_p*smax)] ind = ind[(S.take(ind) > min_p * smax)]
return ind return ind
@ -508,7 +508,7 @@ def findrfc_astm(tp):
# the sig_rfc was constructed too big in rainflow.rf3, so # the sig_rfc was constructed too big in rainflow.rf3, so
# reduce the sig_rfc array as done originally by a matlab mex c function # reduce the sig_rfc array as done originally by a matlab mex c function
n = len(sig_rfc) n = len(sig_rfc)
sig_rfc = sig_rfc.__getslice__(0,n-cnr[0]) sig_rfc = sig_rfc.__getslice__(0, n - cnr[0])
# sig_rfc holds the actual rainflow counted cycles, not the indices # sig_rfc holds the actual rainflow counted cycles, not the indices
return sig_rfc return sig_rfc
@ -642,114 +642,121 @@ def findrfc(tp, hmin=0.0, method='clib'):
ind, ix = clib.findrfc(y, hmin) ind, ix = clib.findrfc(y, hmin)
return np.sort(ind[:ix]) return np.sort(ind[:ix])
def mctp2rfc(f_mM,f_Mm=None): def mctp2rfc(f_mM, f_Mm=None):
''' '''
Return Rainflow matrix given a Markov matrix of a Markov chain of turning points Return Rainflow matrix given a Markov matrix of a Markov chain of turning points
computes f_rfc = f_mM + F_mct(f_mM). computes f_rfc = f_mM + F_mct(f_mM).
CALL: f_rfc = mctp2rfc(f_mM); Parameters
----------
where f_mM = the min2max Markov matrix,
f_Mm = the max2min Markov matrix,
f_rfc = the rainflow matrix,
f_mM = the min2max Markov matrix,
Further optional input arguments; Returns
-------
f_rfc = the rainflow matrix,
CALL: f_rfc = mctp2rfc(f_mM,f_Mm,paramm,paramM); Example:
-------
>>> fmM = np.array([[ 0.0183, 0.0160, 0.0002, 0.0000, 0],
... [0.0178, 0.5405, 0.0952, 0, 0],
... [0.0002, 0.0813, 0, 0, 0],
... [0.0000, 0, 0, 0, 0],
... [ 0, 0, 0, 0, 0]])
>>> mctp2rfc(fmM)
array([[ 2.66998090e-02, 7.79970042e-03, 4.90607697e-07,
0.00000000e+00, 0.00000000e+00],
[ 9.59962873e-03, 5.48500862e-01, 9.53995094e-02,
0.00000000e+00, 0.00000000e+00],
[ 5.62297379e-07, 8.14994377e-02, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00]])
f_Mm = the max2min Markov matrix,
paramm = the parameter matrix defining discretization of minimas,
paramM = the parameter matrix defining discretization of maximas,
''' '''
# TODO: Check this: paramm and paramM are never used?????
if f_Mm is None: if f_Mm is None:
f_Mm=f_mM f_mM = np.atleast_1d(f_mM)
f_Mm = f_mM.copy()
# if nargin<3 else:
# paramm=[-1, 1 ,length(f_mM)]; f_mM, f_Mm = np.atleast_1d(f_mM, f_Mm)
# paramM=paramm;
# end
#
# if nargin<4
# paramM=paramm;
# end
f_mM, f_Mm = np.atleast_1d(f_mM, f_Mm)
N = max(f_mM.shape) N = max(f_mM.shape)
f_max = sum(f_mM,axis=1) f_max = np.sum(f_mM, axis=1)
f_min = sum(f_mM, axis=0) f_min = np.sum(f_mM, axis=0)
f_rfc = zeros((N,N)) f_rfc = zeros((N, N))
f_rfc[N-1,0]=f_max[N-1] f_rfc[N - 2, 0] = f_max[N - 2]
f_rfc[1,N-1]=f_min[N-1] f_rfc[0, N - 2] = f_min[N - 2]
for k in range(2,N-1): for k in range(2, N - 1):
for i in range(1,k-1): for i in range(1, k):
AA = f_mM[N-k+1:N-k+i-1, k-i+1:k-1] AA = f_mM[N - 1 - k:N - 1 - k + i, k - i:k]
AA1 = f_Mm[N-k+1:N-k+i-1, k-i+1:k-1] AA1 = f_Mm[N - 1 - k:N - 1 - k + i, k - i:k]
RAA = f_rfc[N-k+1:N-k+i-1, k-i+1:k-1] RAA = f_rfc[N - 1 - k:N - 1 - k + i, k - i:k]
nA = max(AA.shape); nA = max(AA.shape)
MA = f_max[N-k+1:N-k+i-1] MA = f_max[N - 1 - k:N - 1 - k + i]
mA = f_min[k-i+1:k-1] mA = f_min[k - i:k]
SA = AA.sum() SA = AA.sum()
SRA = RAA.sum() SRA = RAA.sum()
DRFC = SA-SRA; DRFC = SA - SRA
NT = min(mA[0]-sum(RAA[:,1]),MA[0]-sum(RAA[1,:])) # ?? check NT = min(mA[0] - sum(RAA[:, 0]), MA[0] - sum(RAA[0, :])) # ?? check
NT = max(NT,0) # ??check NT = max(NT, 0) # ??check
if NT>1e-6*max(MA[0],mA[0]): if NT > 1e-6 * max(MA[0], mA[0]):
NN = MA-sum(AA,axis=1) # T NN = MA - np.sum(AA, axis=1) # T
e = (mA-sum(AA, axis=0)) # T e = (mA - np.sum(AA, axis=0)) # T
e = np.flipud(e) e = np.flipud(e)
PmM = np.rot90(AA) PmM = np.rot90(AA.copy())
for j in range(nA): for j in range(nA):
norm=mA[nA-j+1] norm = mA[nA - 1 - j]
if norm!=0: if norm != 0:
PmM[j,:] = PmM[j,:]/norm PmM[j, :] = PmM[j, :] / norm
e[j] = e[j]/norm e[j] = e[j] / norm
#end #end
#end #end
fx=0.0; fx = 0.0;
if max(abs(e))>1e-6 and max(abs(NN))>1e-6*max(MA[0],mA[0]): if max(abs(e)) > 1e-6 and max(abs(NN)) > 1e-6 * max(MA[0], mA[0]):
PMm=AA1; PMm = AA1.copy()
for j in range(nA): for j in range(nA):
norm=MA(j); norm = MA[j]
if norm!=0: if norm != 0:
PMm[j,:]=PMm[j,:]/norm; PMm[j, :] = PMm[j, :] / norm;
#end #end
#end #end
PMm=fliplr(PMm) PMm = np.fliplr(PMm)
A=PMm; B=PmM; A = PMm
I=eye(A.shape) B = PmM
if nA==1: if nA == 1:
fx=NN*(A/(1-B*A)*e) fx = NN * (A / (1 - B * A) * e)
else: else:
fx=NN*(A*((I-B*A)\e)) #least squares rh = np.eye(A.shape[0]) - np.dot(B, A)
fx = np.dot(NN, np.dot(A, linalg.solve(rh, e))) #least squares
#end #end
#end #end
f_rfc[N - 1 - k, k - i] = fx + DRFC
f_rfc[N-k+1,k-i+1] = fx+DRFC
# check2=[ DRFC fx] # check2=[ DRFC fx]
# pause # pause
else: else:
f_rfc[N-k+1,k-i+1]=0.; f_rfc[N - 1 - k, k - i] = 0.0
#end #end
#end #end
m0 = max(0,f_min[0]-sum(f_rfc[N-k+2:N,1])); m0 = max(0, f_min[0] - np.sum(f_rfc[N - k + 1:N, 0]))
M0 = max(0,Max(N-k+1)-sum(f_rfc[N-k+1,2:k])); M0 = max(0, f_max[N - 1 - k] - np.sum(f_rfc[N - 1 - k, 1:k]))
f_rfc[N-k+1,1] = min(m0,M0) f_rfc[N - 1 - k, 0] = min(m0, M0)
#% n_loops_left=N-k+1 #% n_loops_left=N-k+1
#end #end
for k in range(1,N): for k in range(1, N):
M0 = max(0,f_max[0]-sum(f_rfc[1,N-k+2:N])); M0 = max(0, f_max[0] - np.sum(f_rfc[0, N - k:N]));
m0 = max(0,f_min[N-k+1]-sum(f_rfc[2:k,N-k+1])); m0 = max(0, f_min[N - 1 - k] - np.sum(f_rfc[1:k+1, N - 1 - k]));
f_rfc[1,N-k+1] = min(m0,M0) f_rfc[0, N - 1 - k] = min(m0, M0)
#end #end
# %clf # %clf
@ -767,14 +774,7 @@ def mctp2rfc(f_mM,f_Mm=None):
# %axis([paramm(1) paramm(2) paramM(1) paramM(2)]) # %axis([paramm(1) paramm(2) paramM(1) paramM(2)])
# %axis('square') # %axis('square')
return f_frfc return f_rfc
@ -2173,7 +2173,7 @@ def good_bins(data=None, range=None, num_bins=None, num_data=None, odd=False, lo
d = m * 10 ** e d = m * 10 ** e
mn = (np.floor(mn / d) - loose) * d - odd * d / 2 mn = (np.floor(mn / d) - loose) * d - odd * d / 2
mx = (np.ceil(mx / d) + loose) * d + odd * d / 2 mx = (np.ceil(mx / d) + loose) * d + odd * d / 2
limits = np.arange(mn, mx+d/2, d) limits = np.arange(mn, mx + d / 2, d)
return limits return limits
def plot_histgrm(data, bins=None, range=None, normed=False, weights=None, lintype='b-'): def plot_histgrm(data, bins=None, range=None, normed=False, weights=None, lintype='b-'):
@ -2262,22 +2262,22 @@ def num2pistr(x, n=3):
'3\\pi/4' '3\\pi/4'
''' '''
frac = fractions.Fraction.from_float(x/pi).limit_denominator(10000000) frac = fractions.Fraction.from_float(x / pi).limit_denominator(10000000)
num = frac.numerator num = frac.numerator
den = frac.denominator den = frac.denominator
if (den<10) and (num<10) and (num!=0): if (den < 10) and (num < 10) and (num != 0):
dtxt = '' if abs(den)==1 else '/%d' % den dtxt = '' if abs(den) == 1 else '/%d' % den
if abs(num)==1: # % numerator if abs(num) == 1: # % numerator
ntxt='-' if num==-1 else '' ntxt = '-' if num == -1 else ''
else: else:
ntxt = '%d' % num ntxt = '%d' % num
xtxt= ntxt+r'\pi'+dtxt xtxt = ntxt + r'\pi' + dtxt
else: else:
format = '%0.' +'%dg' % n format = '%0.' + '%dg' % n
xtxt = format % x xtxt = format % x
return xtxt return xtxt
def fourier(data,t=None,T=None,m=None,n=None, method='trapz'): def fourier(data, t=None, T=None, m=None, n=None, method='trapz'):
''' '''
Returns Fourier coefficients. Returns Fourier coefficients.
@ -2340,7 +2340,7 @@ def fourier(data,t=None,T=None,m=None,n=None, method='trapz'):
n = len(t) if n is None else n n = len(t) if n is None else n
m = n if n is None else m m = n if n is None else m
T = t[-1]-t[0] if T is None else T T = t[-1] - t[0] if T is None else T
if method.startswith('trapz'): if method.startswith('trapz'):
intfun = trapz intfun = trapz
@ -2348,20 +2348,20 @@ def fourier(data,t=None,T=None,m=None,n=None, method='trapz'):
intfun = simps intfun = simps
# Define the vectors for computing the Fourier coefficients # Define the vectors for computing the Fourier coefficients
t.shape = (1,-1) t.shape = (1, -1)
a = zeros((m,p)) a = zeros((m, p))
b = zeros((m,p)) b = zeros((m, p))
a[0] = intfun(x,t, axis=-1) a[0] = intfun(x, t, axis= -1)
# Compute M-1 more coefficients # Compute M-1 more coefficients
tmp = 2*pi*t/T tmp = 2 * pi * t / T
#% tmp = 2*pi*(0:N-1).'/(N-1); #% tmp = 2*pi*(0:N-1).'/(N-1);
for i in range(1,m): for i in range(1, m):
a[i] = intfun(x*cos(i*tmp),t, axis=-1) a[i] = intfun(x * cos(i * tmp), t, axis= -1)
b[i] = intfun(x*sin(i*tmp),t, axis=-1) b[i] = intfun(x * sin(i * tmp), t, axis= -1)
a = a/pi a = a / pi
b = b/pi b = b / pi
# Alternative: faster for large M, but gives different results than above. # Alternative: faster for large M, but gives different results than above.
# nper = diff(t([1 end]))/T; %No of periods given # nper = diff(t([1 end]))/T; %No of periods given

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