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Fixed a bug in the extrapolate method of LevelCrossings class.

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master
per.andreas.brodtkorb 14 years ago
parent bd7218621c
commit 578470f36a
1 changed files with 137 additions and 120 deletions
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pywafo/src/wafo/objects.py
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@ -27,8 +27,8 @@ from scipy.special import ndtr as cdfnorm, ndtri as invnorm
import warnings import warnings
import numpy as np import numpy as np
from numpy import (inf, pi, zeros, ones, sqrt, where, log, exp, sin, arcsin, mod, finfo, interp, #@UnresolvedImport from numpy import (inf, pi, zeros, ones, sqrt, where, log, exp, cos, sin, arcsin, mod, finfo, interp, #@UnresolvedImport
linspace, arange, sort, all, abs, vstack, hstack, atleast_1d, #@UnresolvedImport linspace, arange, sort, all, abs, vstack, hstack, atleast_1d, sign, expm1, #@UnresolvedImport
finfo, polyfit, r_, nonzero, cumsum, ravel, size, isnan, nan, ceil, diff, array) #@UnresolvedImport finfo, polyfit, r_, nonzero, cumsum, ravel, size, isnan, nan, ceil, diff, array) #@UnresolvedImport
from numpy.fft import fft from numpy.fft import fft
from numpy.random import randn from numpy.random import randn
@ -111,7 +111,7 @@ class LevelCrossings(WafoData):
y = cmax * exp(-x ** 2 / 2.0) y = cmax * exp(-x ** 2 / 2.0)
self.children = [WafoData(y, self.args)] self.children = [WafoData(y, self.args)]
def extrapolate(self, u_min=None, u_max=None, method='ml',dist='genpar', plotflag=0): def extrapolate(self, u_min=None, u_max=None, method='ml', dist='genpar', plotflag=0):
''' '''
Returns an extrapolated level crossing spectrum Returns an extrapolated level crossing spectrum
@ -127,7 +127,7 @@ class LevelCrossings(WafoData):
defining distribution function. Options are: defining distribution function. Options are:
genpareto : Generalized Pareto distribution (GPD) genpareto : Generalized Pareto distribution (GPD)
expon : Exponential distribution (GPD with k=0) expon : Exponential distribution (GPD with k=0)
rayleigh : Rayleigh distribution rayleigh : truncated Rayleigh distribution
plotflag : scalar integer plotflag : scalar integer
1: Diagnostic plots. (default) 1: Diagnostic plots. (default)
0: Don't plot diagnostic plots. 0: Don't plot diagnostic plots.
@ -139,29 +139,43 @@ class LevelCrossings(WafoData):
Est = Estimated parameters. [struct array] Est = Estimated parameters. [struct array]
Extrapolates the level crossing spectrum (LC) for high and for low levels. Extrapolates the level crossing spectrum (LC) for high and for low levels.
The tails of the LC is fited to a survival function of a GPD. The tails of the LC is fitted to a survival function of a GPD.
H(x) = (1-k*x/s)^(1/k) (GPD) H(x) = (1-k*x/s)^(1/k) (GPD)
The use of GPD is motivated by POT methods in extreme value theory. The use of GPD is motivated by POT methods in extreme value theory.
For k=0 the GPD is the exponential distribution For k=0 the GPD is the exponential distribution
H(x) = exp(-x/s), k=0 (expon) H(x) = exp(-x/s), k=0 (expon)
The tails with the survival function of a Rayleigh distribution. The tails with the survival function of a truncated Rayleigh distribution.
H(x) = exp(-((x+x0).^2-x0^2)/s^2) (rayleigh) H(x) = exp(-((x+x0).^2-x0^2)/s^2) (rayleigh)
where x0 is the value where the LC has its maximum. where x0 is the distance from the truncation level to where the LC has its maximum.
The method 'gpd' uses the GPD. We recommend the use of 'gpd,ml'. The method 'gpd' uses the GPD. We recommend the use of 'gpd,ml'.
The method 'exp' uses the Exp. The method 'exp' uses the Exp.
The method 'ray' uses Ray, and should be used if the load is a Gaussian process. The method 'ray' uses Ray, and should be used if the load is a Gaussian process.
Example: Example
S = jonswap; -------
x = spec2sdat(S,100000,0.1,[],'random'); >>> import wafo.data
lc = dat2lc(x); s = std(x(:,2)); >>> import wafo.objects as wo
[lcEst,Est] = extralc(lc,s*[-2 2]); >>> x = wafo.data.sea()
[lcEst,Est] = extralc(lc,s*[-2 2],'exp,ml'); >>> ts = wo.mat2timeseries(x)
[lcEst,Est] = extralc(lc,s*[-2 2],'ray,ml');
>>> tp = ts.turning_points()
>>> mm = tp.cycle_pairs()
>>> lc = mm.level_crossings()
>>> s = x[:,1].std()
>>> lc_gpd = lc.extrapolate(-2*s, 2*s)
>>> lc_exp = lc.extrapolate(-2*s, 2*s, dist='expon')
>>> lc_ray = lc.extrapolate(-2*s, 2*s, dist='rayleigh')
lc_gpd.plot()
lc_exp.plot()
lc_ray.plot()
See also
--------
cmat2extralc, rfmextrapolate, lc2rfmextreme, extralc, fitgenpar See also
--------
cmat2extralc, rfmextrapolate, lc2rfmextreme, extralc, fitgenpar
References References
---------- ----------
@ -178,141 +192,131 @@ class LevelCrossings(WafoData):
if u_min is None or u_max is None: if u_min is None or u_max is None:
fraction = sqrt(c_max) fraction = sqrt(c_max)
i = np.flatnonzero(self.data>fraction) i = np.flatnonzero(self.data > fraction)
if u_min is None : if u_min is None :
u_min = self.args[i.min()] u_min = self.args[i.min()]
if u_max is None: if u_max is None:
u_max = self.args[i.max()] u_max = self.args[i.max()]
lcf, lcx = self.data, self.args lcf, lcx = self.data, self.args
# Extrapolate LC for high levels # Extrapolate LC for high levels
[lcEst.High,Est.High] = self._extrapolate(lcx,lcf,u_max,u_max-lc_max,method, dist); [lc_High, phat_high] = self._extrapolate(lcx, lcf, u_max, u_max - lc_max, method, dist);
# #
# # Extrapolate LC for low levels # # Extrapolate LC for low levels
# [lcEst1, phat_low] = self._extrapolate(-lcx[::-1], lcf[::-1], -u_min, lc_max - u_min, method, dist)
# lc1 = [-flipud(lc(:,1)) flipud(lc(:,2))]; lc_Low = lcEst1[::-1, :] #[-lcEst1[::-1, 0], lcEst1[::-1, 1::]]
# lc_Low[:,0] *= -1
# [lcEst1,Est1] = extrapolate(lc1,-u(1),method,lc_max-u(1));
# lcEst.Low = [-flipud(lcEst1(:,1)) flipud(lcEst1(:,2:end))];
# Est.Low = Est1; # Est.Low = Est1;
# #
# if plotflag if plotflag:
# semilogx(lc(:,2),lc(:,1),lcEst.High(:,2),lcEst.High(:,1),lcEst.Low(:,2),lcEst.Low(:,1)) plotbackend.semilogx(lcf, lcx,
# end lc_High[:, 1], lc_High[:, 0],
# lc_Low[:, 1], lc_Low[:, 0])
i_mask = (u_min<lcx) & (lcx<u_max)
f = np.hstack((lc_Low[:,1],lcf[i_mask],lc_High[:, 1]))
x = np.hstack((lc_Low[:,0],lcx[i_mask],lc_High[:, 0]))
lc_out = LevelCrossings(f,x, sigma=self.sigma, mean=self.mean)
lc_out.phat_high = phat_high
lc_out.phat_low = phat_low
return lc_out
## ##
def _extrapolate(self, lcx,lcf,u,offset,method,dist): def _extrapolate(self, lcx, lcf, u, offset, method, dist):
# Extrapolate the level crossing spectra for high levels # Extrapolate the level crossing spectra for high levels
method = method.lower() method = method.lower()
dist = dist.lower() dist = dist.lower()
# Excedences over level u # Excedences over level u
Iu = lcx>u; Iu = lcx > u
lcx1, lcf1 = lcx[Iu], lcf[Iu] lcx1, lcf1 = lcx[Iu], lcf[Iu]
lcf2,lcx2 = _make_increasing(lcf1[::-1],lcx1[::-1]) lcf2, lcx2 = self._make_increasing(lcf1[::-1], lcx1[::-1])
nim1 = 0 nim1 = 0
x=[] x = []
for k, ni in enumerate(lcf2.tolist()): for xk, ni in zip(lcx2.tolist(),lcf2.tolist()):
nk = ni - nim1 x.append(ones(ni - nim1) * xk)
x.append(ones(nk)*lcx2[k]) nim1 = ni
#end
x = np.hstack(x)-u
x = np.hstack(x) - u
df = 0.01
xF = np.arange(0.0, 4 + df / 2, df)
lcu = np.interp(u, lcx, lcf) + 1
# Estimate tail # Estimate tail
if dist.startswith('gen'):
if dist=='genpareto':
genpareto = distributions.genpareto genpareto = distributions.genpareto
phat = genpareto.fit2(x, floc=0, method=method) phat = genpareto.fit2(x, floc=0, method=method)
df = 0.01
xF = np.arange(0.0,4+df/2,df)
F = phat.cdf(xF) F = phat.cdf(xF)
lcu = np.interp(u,lcx,lcf)+1
covar = phat.par_cov[::2,::2] covar = phat.par_cov[::2, ::2]
# Calculate 90 # confidence region, an ellipse, for (k,s) # Calculate 90 # confidence region, an ellipse, for (k,s)
B, D = np.linalg.eig(covar); D, B = np.linalg.eig(covar);
b = phat.par[::2] b = phat.par[::2]
if b[0]>0: if b[0] > 0:
upperlimit = u + b[1]/b[0] phat.upperlimit = u + b[1] / b[0]
r = sqrt(-2*log(1-90/100)); # 90 # confidence sphere r = sqrt(-2 * log(1 - 90 / 100)) # 90 # confidence sphere
Nc = 16+1; Nc = 16 + 1
ang = linspace(0,2*pi,Nc); ang = linspace(0, 2 * pi, Nc)
c0 = np.vstack((r*sqrt(D[0,0])*sin(ang), r*sqrt(D[1,1])*cos(ang))) # 90# Circle c0 = np.vstack((r * sqrt(D[0]) * sin(ang), r * sqrt(D[1]) * cos(ang))) # 90# Circle
# plot(c0(1,:),c0(2,:)) # plot(c0(1,:),c0(2,:))
c1 = B*c0+b*ones((1,len(c0))); # Transform to ellipse for (k,s) c1 = np.dot(B, c0) + b[:,None] #* ones((1, len(c0))) # Transform to ellipse for (k,s)
# plot(c1(1,:),c1(2,:)), hold on # plot(c1(1,:),c1(2,:)), hold on
# Calculate conf.int for lcu # Calculate conf.int for lcu
# Assumtion: lcu is Poisson distributed # Assumtion: lcu is Poisson distributed
# Poissin distr. approximated by normal when calculating conf. int. # Poissin distr. approximated by normal when calculating conf. int.
dXX = 1.64*sqrt(lcu); # 90 # quantile for lcu dXX = 1.64 * sqrt(lcu) # 90 # quantile for lcu
lcEstCu = zeros((len(xF),Nc)); lcEstCu = zeros((len(xF), Nc))
lcEstCl = lcEstCu; lcEstCl = zeros((len(xF), Nc))
for i in range(Nc): for i in range(Nc):
k=c1[0,i] k = c1[0, i]
s=c1[2,i] s = c1[1, i]
F2 = genpareto.cdf(xF,k,scale=s) F2 = genpareto.cdf(xF, k, scale=s)
lcEstCu[:,i] = (lcu+dXX)*(1-F2); lcEstCu[:, i] = (lcu + dXX) * (1 - F2)
lcEstCl[:,i] = (lcu-dXX)*(1-F2); lcEstCl[:, i] = (lcu - dXX) * (1 - F2)
#end #end
lcEstCI = [min(lcEstCl), max(lcEstCu)] lcEst = np.vstack((xF + u, lcu * (1 - F),
lcEstCl.min(axis=1), lcEstCu.max(axis=1))).T
lcEst = [xF+u, lcu*(1-F), lcEstCI]; elif dist.startswith('exp'):
expon = distributions.expon
elif dist=='expon': phat = expon.fit2(x, floc=0, method=method)
n = len(x) F = phat.cdf(xF)
s = mean(x); lcEst = np.vstack((xF + u, lcu * (1 - F))).T
cov = s/n;
Est.s = s;
Est.cov = cov;
xF = np.arange(0.0,4+df/2,df) #xF = (0.0:0.01:4)';
F = -expm1(-xF/s)
lcu = np.interp(u,lcx,lcf)+1
lcEst = [xF+u, Est.lcu*(1-F)]
elif dist== 'ray':
n = len(x)
Sx = sum((x+offset)**2-offset**2)
s = sqrt(Sx/n); # Shape parameter
cov = NaN;
xF = np.arange(0.0,4+df/2,df) #xF = (0.0:0.01:4)';
F = -expm1(-((xF+offset)**2-offset**2)/s**2);
lcu = np.interp(u,lcx,lcf,u)+1
lcEst = [xF+u, Est.lcu*(1-F)];
elif dist.startswith('ray') or dist.startswith('trun'):
phat = distributions.truncrayleigh.fit2(x, floc=0, method=method)
F = phat.cdf(xF)
if False:
n = len(x)
Sx = sum((x + offset) ** 2 - offset ** 2)
s = sqrt(Sx / n); # Shape parameter
F = -np.expm1(-((xF + offset) ** 2 - offset ** 2) / s ** 2)
lcEst = np.vstack((xF + u, lcu * (1 - F))).T
else: else:
raise ValueError() raise ValueError()
return lcEst, phat
## End extrapolate ## End extrapolate
def _make_increasing(f, t=None): def _make_increasing(self, f, t=None):
# Makes the signal f strictly increasing. # Makes the signal f strictly increasing.
n = len(f) n = len(f)
if t is None: if t is None:
t = np.arange(n) t = np.arange(n)
ff = [f[0],] ff = [f[0], ]
tt = [t[0],] tt = [t[0], ]
for i in xrange(1,n): for i in xrange(1, n):
if f[i]>ff[-1]: if f[i] > ff[-1]:
ff.append(f[i]) ff.append(f[i])
tt.append(t[i]) tt.append(t[i])
return np.asarray(ff), np.asarray(tt) return np.asarray(ff), np.asarray(tt)
def sim(self, ns, alpha): def sim(self, ns, alpha):
""" """
@ -605,7 +609,7 @@ class LevelCrossings(WafoData):
lc22 = hstack((0, cumtrapz(lc2, lc1) + cor1)) lc22 = hstack((0, cumtrapz(lc2, lc1) + cor1))
if self.intensity: if self.intensity:
lc22 = (lc22 + 0.5/ncr) / (lc22[-1] + cor2 + 1./ncr) lc22 = (lc22 + 0.5 / ncr) / (lc22[-1] + cor2 + 1. / ncr)
else: else:
lc22 = (lc22 + 0.5) / (lc22[-1] + cor2 + 1) lc22 = (lc22 + 0.5) / (lc22[-1] + cor2 + 1)
@ -646,7 +650,19 @@ class LevelCrossings(WafoData):
g2.plot() g2.plot()
return g, g2 return g, g2
def test_levelcrossings_extrapolate():
import wafo.data
#import wafo.objects as wo
x = wafo.data.sea()
ts = mat2timeseries(x)
tp = ts.turning_points()
mm = tp.cycle_pairs()
lc = mm.level_crossings()
s = x[:,1].std()
lc_gpd = lc.extrapolate(-2*s, 2*s, dist='rayleigh')
class CyclePairs(WafoData): class CyclePairs(WafoData):
''' '''
Container class for Cycle Pairs data objects in WAFO Container class for Cycle Pairs data objects in WAFO
@ -817,9 +833,9 @@ class CyclePairs(WafoData):
dcount = cumsum(extr[1, 0:nx]) - extr[3, 0:nx] dcount = cumsum(extr[1, 0:nx]) - extr[3, 0:nx]
elif defnr == 3: ## This are upcrossings + minima + maxima elif defnr == 3: ## This are upcrossings + minima + maxima
dcount = cumsum(extr[1, 0:nx]) + extr[2, 0:nx] dcount = cumsum(extr[1, 0:nx]) + extr[2, 0:nx]
ylab='Count' ylab = 'Count'
if intensity: if intensity:
dcount = dcount/self.time dcount = dcount / self.time
ylab = 'Intensity [count/sec]' ylab = 'Intensity [count/sec]'
return LevelCrossings(dcount, levels, mean=self.mean, sigma=self.sigma, ylab=ylab, intensity=intensity) return LevelCrossings(dcount, levels, mean=self.mean, sigma=self.sigma, ylab=ylab, intensity=intensity)
@ -935,7 +951,7 @@ class TurningPoints(WafoData):
SurvivalCycleCount SurvivalCycleCount
""" """
if h>0: if h > 0:
ind = findrfc(self.data, h, method=method) ind = findrfc(self.data, h, method=method)
data = self.data[ind] data = self.data[ind]
else: else:
@ -957,9 +973,9 @@ class TurningPoints(WafoData):
M = data[iM:-1:2] M = data[iM:-1:2]
m = data[iM + 1::2] m = data[iM + 1::2]
time = self.args[-1]-self.args[0] time = self.args[-1] - self.args[0]
return CyclePairs(M, m, kind=kind, mean=self.mean, sigma=self.sigma, return CyclePairs(M, m, kind=kind, mean=self.mean, sigma=self.sigma,
time=time) time=time)
def cycle_astm(self): def cycle_astm(self):
@ -2548,7 +2564,7 @@ class TransferFunction(object):
ind = np.flatnonzero(1 - np.isfinite(Hw)) ind = np.flatnonzero(1 - np.isfinite(Hw))
Hw.flat[ind] = 0 Hw.flat[ind] = 0
sgn = sign(Hw); sgn = np.sign(Hw);
k0 = np.flatnonzero(sgn < 0) k0 = np.flatnonzero(sgn < 0)
if len(k0): # make sure Hw>=0 ie. transfer negative signs to Gwt if len(k0): # make sure Hw>=0 ie. transfer negative signs to Gwt
Gwt[:, k0] = -Gwt[:, k0] Gwt[:, k0] = -Gwt[:, k0]
@ -2826,9 +2842,10 @@ def main():
sensortype(range(21)) sensortype(range(21))
if __name__ == '__main__': if __name__ == '__main__':
if True: #False : # test_levelcrossings_extrapolate()
import doctest # if True: #False : #
doctest.testmod() # import doctest
else: # doctest.testmod()
main() # else:
# main()

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