danh
/
pywafo
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

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

Browse Source
master
per.andreas.brodtkorb 14 years ago
parent afd3c87e22
commit 8ff8b2183e
1 changed files with 122 additions and 122 deletions
Show Stats Download Patch File Download Diff File

226
pywafo/src/wafo/misc.py
Unescape Escape View File

@ -6,10 +6,10 @@ from __future__ import division
import sys
import fractions
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,
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)
from scipy.special import gammaln
from scipy.integrate import trapz, simps
@ -25,7 +25,7 @@ except:
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',
'findextrema', 'findpeaks', 'findrfc', 'rfcfilter', 'findtp', 'findtc',
'findoutliers', 'common_shape', 'argsreduce',
@ -437,7 +437,7 @@ def findpeaks(data, n=2, min_h=None, min_p=0.0):
smax = S.max()
if min_h is None:
smin = S.min()
min_h = 0.05*(smax-smin)
min_h = 0.05 * (smax - smin)
ndim = S.ndim
S = np.atleast_2d(S)
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
ind = np.atleast_1d(S[iy].argmax())
if ndim>1:
if iy==0:
ind2 = np.flatnonzero(S[iy,ind]>S[iy+1,ind])
elif iy==nrows-1:
ind2 = np.flatnonzero(S[iy,ind]>S[iy-1,ind])
if ndim > 1:
if iy == 0:
ind2 = np.flatnonzero(S[iy, ind] > S[iy + 1, ind])
elif iy == nrows - 1:
ind2 = np.flatnonzero(S[iy, ind] > S[iy - 1, ind])
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):
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 []
if len(ind)==0:
if len(ind) == 0:
return []
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.
nmax = min(n,len(ind))
nmax = min(n, len(ind))
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
ind = ind[(S.take(ind) > min_p*smax)]
ind = ind[(S.take(ind) > min_p * smax)]
return ind
@ -508,7 +508,7 @@ def findrfc_astm(tp):
# 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
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
return sig_rfc
@ -642,114 +642,121 @@ def findrfc(tp, hmin=0.0, method='clib'):
ind, ix = clib.findrfc(y, hmin)
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
computes f_rfc = f_mM + F_mct(f_mM).
CALL: f_rfc = mctp2rfc(f_mM);
where
f_rfc = the rainflow matrix,
Parameters
----------
f_mM = the min2max Markov matrix,
f_Mm = the max2min 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:
f_Mm=f_mM
# if nargin<3
# paramm=[-1, 1 ,length(f_mM)];
# paramM=paramm;
# end
#
# if nargin<4
# paramM=paramm;
# end
f_mM = np.atleast_1d(f_mM)
f_Mm = f_mM.copy()
else:
f_mM, f_Mm = np.atleast_1d(f_mM, f_Mm)
N = max(f_mM.shape)
f_max = sum(f_mM,axis=1)
f_min = sum(f_mM, axis=0)
f_rfc = zeros((N,N))
f_rfc[N-1,0]=f_max[N-1]
f_rfc[1,N-1]=f_min[N-1]
for k in range(2,N-1):
for i in range(1,k-1):
AA = f_mM[N-k+1:N-k+i-1, k-i+1:k-1]
AA1 = f_Mm[N-k+1:N-k+i-1, k-i+1:k-1]
RAA = f_rfc[N-k+1:N-k+i-1, k-i+1:k-1]
nA = max(AA.shape);
MA = f_max[N-k+1:N-k+i-1]
mA = f_min[k-i+1:k-1]
f_max = np.sum(f_mM, axis=1)
f_min = np.sum(f_mM, axis=0)
f_rfc = zeros((N, N))
f_rfc[N - 2, 0] = f_max[N - 2]
f_rfc[0, N - 2] = f_min[N - 2]
for k in range(2, N - 1):
for i in range(1, k):
AA = f_mM[N - 1 - k:N - 1 - k + i, k - i:k]
AA1 = f_Mm[N - 1 - k:N - 1 - k + i, k - i:k]
RAA = f_rfc[N - 1 - k:N - 1 - k + i, k - i:k]
nA = max(AA.shape)
MA = f_max[N - 1 - k:N - 1 - k + i]
mA = f_min[k - i:k]
SA = AA.sum()
SRA = RAA.sum()
DRFC = SA-SRA;
NT = min(mA[0]-sum(RAA[:,1]),MA[0]-sum(RAA[1,:])) # ?? check
NT = max(NT,0) # ??check
DRFC = SA - SRA
NT = min(mA[0] - sum(RAA[:, 0]), MA[0] - sum(RAA[0, :])) # ?? check
NT = max(NT, 0) # ??check
if NT>1e-6*max(MA[0],mA[0]):
NN = MA-sum(AA,axis=1) # T
e = (mA-sum(AA, axis=0)) # T
if NT > 1e-6 * max(MA[0], mA[0]):
NN = MA - np.sum(AA, axis=1) # T
e = (mA - np.sum(AA, axis=0)) # T
e = np.flipud(e)
PmM = np.rot90(AA)
PmM = np.rot90(AA.copy())
for j in range(nA):
norm=mA[nA-j+1]
if norm!=0:
PmM[j,:] = PmM[j,:]/norm
e[j] = e[j]/norm
norm = mA[nA - 1 - j]
if norm != 0:
PmM[j, :] = PmM[j, :] / norm
e[j] = e[j] / norm
#end
#end
fx=0.0;
if max(abs(e))>1e-6 and max(abs(NN))>1e-6*max(MA[0],mA[0]):
PMm=AA1;
fx = 0.0;
if max(abs(e)) > 1e-6 and max(abs(NN)) > 1e-6 * max(MA[0], mA[0]):
PMm = AA1.copy()
for j in range(nA):
norm=MA(j);
if norm!=0:
PMm[j,:]=PMm[j,:]/norm;
norm = MA[j]
if norm != 0:
PMm[j, :] = PMm[j, :] / norm;
#end
#end
PMm=fliplr(PMm)
PMm = np.fliplr(PMm)
A=PMm; B=PmM;
I=eye(A.shape)
A = PMm
B = PmM
if nA==1:
fx=NN*(A/(1-B*A)*e)
if nA == 1:
fx = NN * (A / (1 - B * A) * e)
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
f_rfc[N-k+1,k-i+1] = fx+DRFC
f_rfc[N - 1 - k, k - i] = fx + DRFC
# check2=[ DRFC fx]
# pause
else:
f_rfc[N-k+1,k-i+1]=0.;
f_rfc[N - 1 - k, k - i] = 0.0
#end
#end
m0 = max(0,f_min[0]-sum(f_rfc[N-k+2:N,1]));
M0 = max(0,Max(N-k+1)-sum(f_rfc[N-k+1,2:k]));
f_rfc[N-k+1,1] = min(m0,M0)
m0 = max(0, f_min[0] - np.sum(f_rfc[N - k + 1:N, 0]))
M0 = max(0, f_max[N - 1 - k] - np.sum(f_rfc[N - 1 - k, 1:k]))
f_rfc[N - 1 - k, 0] = min(m0, M0)
#% n_loops_left=N-k+1
#end
for k in range(1,N):
M0 = max(0,f_max[0]-sum(f_rfc[1,N-k+2:N]));
m0 = max(0,f_min[N-k+1]-sum(f_rfc[2:k,N-k+1]));
f_rfc[1,N-k+1] = min(m0,M0)
for k in range(1, N):
M0 = max(0, f_max[0] - np.sum(f_rfc[0, N - k:N]));
m0 = max(0, f_min[N - 1 - k] - np.sum(f_rfc[1:k+1, N - 1 - k]));
f_rfc[0, N - 1 - k] = min(m0, M0)
#end
# %clf
@ -767,14 +774,7 @@ def mctp2rfc(f_mM,f_Mm=None):
# %axis([paramm(1) paramm(2) paramM(1) paramM(2)])
# %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
mn = (np.floor(mn / 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
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'
'''
frac = fractions.Fraction.from_float(x/pi).limit_denominator(10000000)
frac = fractions.Fraction.from_float(x / pi).limit_denominator(10000000)
num = frac.numerator
den = frac.denominator
if (den<10) and (num<10) and (num!=0):
dtxt = '' if abs(den)==1 else '/%d' % den
if abs(num)==1: # % numerator
ntxt='-' if num==-1 else ''
if (den < 10) and (num < 10) and (num != 0):
dtxt = '' if abs(den) == 1 else '/%d' % den
if abs(num) == 1: # % numerator
ntxt = '-' if num == -1 else ''
else:
ntxt = '%d' % num
xtxt= ntxt+r'\pi'+dtxt
xtxt = ntxt + r'\pi' + dtxt
else:
format = '%0.' +'%dg' % n
format = '%0.' + '%dg' % n
xtxt = format % x
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.
@ -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
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'):
intfun = trapz
@ -2348,20 +2348,20 @@ def fourier(data,t=None,T=None,m=None,n=None, method='trapz'):
intfun = simps
# Define the vectors for computing the Fourier coefficients
t.shape = (1,-1)
a = zeros((m,p))
b = zeros((m,p))
a[0] = intfun(x,t, axis=-1)
t.shape = (1, -1)
a = zeros((m, p))
b = zeros((m, p))
a[0] = intfun(x, t, axis= -1)
# Compute M-1 more coefficients
tmp = 2*pi*t/T
tmp = 2 * pi * t / T
#% tmp = 2*pi*(0:N-1).'/(N-1);
for i in range(1,m):
a[i] = intfun(x*cos(i*tmp),t, axis=-1)
b[i] = intfun(x*sin(i*tmp),t, axis=-1)
for i in range(1, m):
a[i] = intfun(x * cos(i * tmp), t, axis= -1)
b[i] = intfun(x * sin(i * tmp), t, axis= -1)
a = a/pi
b = b/pi
a = a / pi
b = b / pi
# Alternative: faster for large M, but gives different results than above.
# nper = diff(t([1 end]))/T; %No of periods given

Write Preview
Loading…
Cancel
Save