''' Created on 8. mai 2014 @author: pab ''' from __future__ import absolute_import from .core import TrData from .models import TrHermite, TrOchi, TrLinear from ..stats import edf, skew, kurtosis from ..interpolate import SmoothSpline from scipy.special import ndtri as invnorm from scipy.integrate import cumtrapz import warnings import numpy as np floatinfo = np.finfo(float) class TransformEstimator(object): ''' Estimate transformation, g, from ovserved data. Assumption: a Gaussian process, Y, is related to the non-Gaussian process, X, by Y = g(X). Parameters ---------- method : string estimation method. Options are: 'nonlinear' : smoothed crossing intensity (default) 'mnonlinear': smoothed marginal cumulative distribution 'hermite' : cubic Hermite polynomial 'ochi' : exponential function 'linear' : identity. chkDer : bool False: No check on the derivative of the transform. True: Check if transform have positive derivative csm, gsm : real scalars defines the smoothing of the logarithm of crossing intensity and the transformation g, respectively. Valid values must be 0<=csm,gsm<=1. (default csm=0.9, gsm=0.05) Smaller values gives smoother functions. param : vector (default (-5, 5, 513)) defines the region of variation of the data X. If X(t) is likely to cross levels higher than 5 standard deviations then the vector param has to be modified. For example if X(t) is unlikely to cross a level of 7 standard deviations one can use param = (-7, 7, 513). crossdef : string Crossing definition used in the crossing spectrum: 'u' or 1: only upcrossings 'uM' or 2: upcrossings and Maxima (default) 'umM' or 3: upcrossings, minima, and Maxima. 'um' or 4: upcrossings and minima. plotflag : int 0 no plotting (Default) 1 plots empirical and smoothed g(u) and the theoretical for a Gaussian model. 2 monitor the development of the estimation Delay : real scalar Delay time for each plot when PLOTFLAG==2. linextrap: int 0 use a regular smoothing spline 1 use a smoothing spline with a constraint on the ends to ensure linear extrapolation outside the range of the data. (default) cvar: real scalar Variances for the the crossing intensity. (default 1) gvar: real scalar Variances for the empirical transformation, g. (default 1) ne : int Number of extremes (maxima & minima) to remove from the estimation of the transformation. This makes the estimation more robust against outliers. (default 7) ntr : int Maximum length of empirical crossing intensity or CDF. The empirical crossing intensity or CDF is interpolated linearly before smoothing if their lengths exceeds Ntr. A reasonable NTR will significantly speed up the estimation for long time series without loosing any accuracy. NTR should be chosen greater than PARAM(3). (default 10000) multip : Bool False: the data in columns belong to the same seastate (default). True: the data in columns are from separate seastates. ''' def __init__(self, method='nonlinear', chkder=True, plotflag=False, csm=.95, gsm=.05, param=(-5, 5, 513), delay=2, ntr=10000, linextrap=True, ne=7, cvar=1, gvar=1, multip=False, crossdef='uM', monitor=False): self.method = method self.chkder = chkder self.plotflag = plotflag self.csm = csm self.gsm = gsm self.param = param self.delay = delay self.ntr = ntr self.linextrap = linextrap self.ne = ne self.cvar = cvar self.gvar = gvar self.multip = multip self.crossdef = crossdef def _check_tr(self, tr, tr_raw): eps = floatinfo.eps x = tr.args mean = tr.mean sigma = tr.sigma for ix in range(5): dy = np.diff(tr.data) if (dy <= 0).any(): dy[dy > 0] = eps gvar = -(np.hstack((dy, 0)) + np.hstack((0, dy))) / 2 + eps pp_tr = SmoothSpline(tr_raw.args, tr_raw.data, p=1, lin_extrap=self.linextrap, var=ix * gvar) tr = TrData(pp_tr(x), x, mean=mean, sigma=sigma) else: break else: msg = ''' The estimated transfer function, g, is not a strictly increasing function. The transfer function is possibly not sufficiently smoothed. ''' warnings.warn(msg) return tr def _trdata_lc(self, level_crossings, mean=None, sigma=None): ''' Estimate transformation, g, from observed crossing intensity. Assumption: a Gaussian process, Y, is related to the non-Gaussian process, X, by Y = g(X). Parameters ---------- mean, sigma : real scalars mean and standard deviation of the process **options : csm, gsm : real scalars defines the smoothing of the crossing intensity and the transformation g. Valid values must be 0<=csm,gsm<=1. (default csm = 0.9 gsm=0.05) Smaller values gives smoother functions. param : vector which defines the region of variation of the data X. (default [-5, 5, 513]). monitor : bool if true monitor development of estimation linextrap : bool if true use a smoothing spline with a constraint on the ends to ensure linear extrapolation outside the range of data. (default) otherwise use a regular smoothing spline cvar, gvar : real scalars Variances for the crossing intensity and the empirical transformation, g. (default 1) ne : scalar integer Number of extremes (maxima & minima) to remove from the estimation of the transformation. This makes the estimation more robust against outliers. (default 7) ntr : scalar integer Maximum length of empirical crossing intensity. The empirical crossing intensity is interpolated linearly before smoothing if the length exceeds ntr. A reasonable NTR (eg. 1000) will significantly speed up the estimation for long time series without loosing any accuracy. NTR should be chosen greater than PARAM(3). (default inf) Returns ------- gs, ge : TrData objects smoothed and empirical estimate of the transformation g. Notes ----- The empirical crossing intensity is usually very irregular. More than one local maximum of the empirical crossing intensity may cause poor fit of the transformation. In such case one should use a smaller value of GSM or set a larger variance for GVAR. If X(t) is likely to cross levels higher than 5 standard deviations then the vector param has to be modified. For example if X(t) is unlikely to cross a level of 7 standard deviations one can use param = [-7 7 513]. Example ------- >>> import wafo.spectrum.models as sm >>> import wafo.transform.models as tm >>> from wafo.objects import mat2timeseries >>> Hs = 7.0 >>> Sj = sm.Jonswap(Hm0=Hs) >>> S = Sj.tospecdata() #Make spectrum object from numerical values >>> S.tr = tm.TrOchi(mean=0, skew=0.16, kurt=0, ... sigma=Hs/4, ysigma=Hs/4) >>> xs = S.sim(ns=2**16, iseed=10) >>> ts = mat2timeseries(xs) >>> tp = ts.turning_points() >>> mm = tp.cycle_pairs() >>> lc = mm.level_crossings() >>> g0, g0emp = lc.trdata(monitor=False) # Monitor the development >>> g1, g1emp = lc.trdata(gvar=0.5 ) # Equal weight on all points >>> g2, g2emp = lc.trdata(gvar=[3.5, 0.5, 3.5]) # Less weight on ends >>> int(S.tr.dist2gauss()*100) 141 >>> int(g0emp.dist2gauss()*100) 380995 >>> int(g0.dist2gauss()*100) 143 >>> int(g1.dist2gauss()*100) 162 >>> int(g2.dist2gauss()*100) 120 g0.plot() # Check the fit. See also -------- troptset, dat2tr, trplot, findcross, smooth NB! the transformated data will be N(0,1) Reference --------- Rychlik , I., Johannesson, P., and Leadbetter, M.R. (1997) "Modelling and statistical analysis of ocean wavedata using a transformed Gaussian process", Marine structures, Design, Construction and Safety, Vol 10, pp 13--47 ''' if mean is None: mean = level_crossings.mean if sigma is None: sigma = level_crossings.sigma lc1, lc2 = level_crossings.args, level_crossings.data intensity = level_crossings.intensity Ne = self.ne ncr = len(lc2) if ncr > self.ntr and self.ntr > 0: x0 = np.linspace(lc1[Ne], lc1[-1 - Ne], self.ntr) lc1, lc2 = x0, np.interp(x0, lc1, lc2) Ne = 0 Ner = self.ne ncr = self.ntr else: Ner = 0 ng = len(np.atleast_1d(self.gvar)) if ng == 1: gvar = self.gvar * np.ones(ncr) else: gvar = np.interp(np.linspace(0, 1, ncr), np.linspace(0, 1, ng), self.gvar) uu = np.linspace(*self.param) g1 = sigma * uu + mean if Ner > 0: # Compute correction factors cor1 = np.trapz(lc2[0:Ner + 1], lc1[0:Ner + 1]) cor2 = np.trapz(lc2[-Ner - 1::], lc1[-Ner - 1::]) else: cor1 = 0 cor2 = 0 lc22 = np.hstack((0, cumtrapz(lc2, lc1) + cor1)) if intensity: lc22 = (lc22 + 0.5 / ncr) / (lc22[-1] + cor2 + 1. / ncr) else: lc22 = (lc22 + 0.5) / (lc22[-1] + cor2 + 1) lc11 = (lc1 - mean) / sigma lc22 = invnorm(lc22) # - ymean g2 = TrData(lc22.copy(), lc1.copy(), mean=mean, sigma=sigma) g2.setplotter('step') # NB! the smooth function does not always extrapolate well outside the # edges causing poor estimate of g # We may alleviate this problem by: forcing the extrapolation # to be linear outside the edges or choosing a lower value for csm2. inds = slice(Ne, ncr - Ne) # indices to points we are smoothing over slc22 = SmoothSpline(lc11[inds], lc22[inds], self.gsm, self.linextrap, gvar[inds])(uu) g = TrData(slc22.copy(), g1.copy(), mean=mean, sigma=sigma) if self.chkder: tr_raw = TrData(lc22[inds], lc11[inds], mean=mean, sigma=sigma) g = self._check_tr(g, tr_raw) if self.plotflag > 0: g.plot() g2.plot() return g, g2 def _trdata_cdf(self, data): ''' Estimate transformation, g, from observed marginal CDF. Assumption: a Gaussian process, Y, is related to the non-Gaussian process, X, by Y = g(X). Parameters ---------- options = options structure defining how the smoothing is done. (See troptset for default values) Returns ------- tr, tr_emp = smoothed and empirical estimate of the transformation g. The empirical CDF is usually very irregular. More than one local maximum of the empirical CDF may cause poor fit of the transformation. In such case one should use a smaller value of GSM or set a larger variance for GVAR. If X(t) is likely to cross levels higher than 5 standard deviations then the vector param has to be modified. For example if X(t) is unlikely to cross a level of 7 standard deviations one can use param = [-7 7 513]. ''' mean = data.mean() sigma = data.std() cdf = edf(data.ravel()) Ne = self.ne nd = len(cdf.data) if nd > self.ntr and self.ntr > 0: x0 = np.linspace(cdf.args[Ne], cdf.args[nd - 1 - Ne], self.ntr) cdf.data = np.interp(x0, cdf.args, cdf.data) cdf.args = x0 Ne = 0 uu = np.linspace(*self.param) ncr = len(cdf.data) ng = len(np.atleast_1d(self.gvar)) if ng == 1: gvar = self.gvar * np.ones(ncr) else: self.gvar = np.atleast_1d(self.gvar) gvar = np.interp(np.linspace(0, 1, ncr), np.linspace(0, 1, ng), self.gvar.ravel()) ind = np.flatnonzero(np.diff(cdf.args) > 0) # remove equal points nd = len(ind) ind1 = ind[Ne:nd - Ne] tmp = invnorm(cdf.data[ind]) x = sigma * uu + mean pp_tr = SmoothSpline(cdf.args[ind1], tmp[Ne:nd - Ne], p=self.gsm, lin_extrap=self.linextrap, var=gvar[ind1]) tr = TrData(pp_tr(x), x, mean=mean, sigma=sigma) tr_emp = TrData(tmp, cdf.args[ind], mean=mean, sigma=sigma) tr_emp.setplotter('step') if self.chkder: tr_raw = TrData(tmp[Ne:nd - Ne], cdf.args[ind1], mean=mean, sigma=sigma) tr = self._check_tr(tr, tr_raw) if self.plotflag > 0: tr.plot() tr_emp.plot() return tr, tr_emp def trdata(self, timeseries): ''' Returns ------- tr, tr_emp : TrData objects with the smoothed and empirical transformation, respectively. TRDATA estimates the transformation in a transformed Gaussian model. Assumption: a Gaussian process, Y, is related to the non-Gaussian process, X, by Y = g(X). The empirical crossing intensity is usually very irregular. More than one local maximum of the empirical crossing intensity may cause poor fit of the transformation. In such case one should use a smaller value of CSM. In order to check the effect of smoothing it is recomended to also plot g and g2 in the same plot or plot the smoothed g against an interpolated version of g (when CSM=GSM=1). Example ------- >>> import wafo.spectrum.models as sm >>> import wafo.transform.models as tm >>> from wafo.objects import mat2timeseries >>> Hs = 7.0 >>> Sj = sm.Jonswap(Hm0=Hs) >>> S = Sj.tospecdata() #Make spectrum object from numerical values >>> S.tr = tm.TrOchi(mean=0, skew=0.16, kurt=0, ... sigma=Hs/4, ysigma=Hs/4) >>> xs = S.sim(ns=2**16, iseed=10) >>> ts = mat2timeseries(xs) >>> g0, g0emp = ts.trdata(monitor=False) >>> g1, g1emp = ts.trdata(method='m', gvar=0.5 ) >>> g2, g2emp = ts.trdata(method='n', gvar=[3.5, 0.5, 3.5]) >>> int(S.tr.dist2gauss()*100) 141 >>> int(g0emp.dist2gauss()*100) 217949 >>> int(g0.dist2gauss()*100) 93 >>> int(g1.dist2gauss()*100) 66 >>> int(g2.dist2gauss()*100) 84 See also -------- LevelCrossings.trdata wafo.transform.models References ---------- Rychlik, I. , Johannesson, P and Leadbetter, M. R. (1997) "Modelling and statistical analysis of ocean wavedata using transformed Gaussian process." Marine structures, Design, Construction and Safety, Vol. 10, No. 1, pp 13--47 Brodtkorb, P, Myrhaug, D, and Rue, H (1999) "Joint distribution of wave height and crest velocity from reconstructed data" in Proceedings of 9th ISOPE Conference, Vol III, pp 66-73 ''' data = np.atleast_1d(timeseries.data) ma = data.mean() sa = data.std() method = self.method[0] if method == 'l': return TrLinear(mean=ma, sigma=sa), TrLinear(mean=ma, sigma=sa) if method == 'n': tp = timeseries.turning_points() mM = tp.cycle_pairs() lc = mM.level_crossings(self.crossdef) return self._trdata_lc(lc) elif method == 'm': return self._trdata_cdf(data) elif method == 'h': ga1 = skew(data) ga2 = kurtosis(data, fisher=True) # kurt(xx(n+1:end))-3; up = min(4 * (4 * ga1 / 3) ** 2, 13) lo = (ga1 ** 2) * 3 / 2 kurt1 = min(up, max(ga2, lo)) + 3 return TrHermite(mean=ma, var=sa ** 2, skew=ga1, kurt=kurt1) elif method[0] == 'o': ga1 = skew(data) return TrOchi(mean=ma, var=sa ** 2, skew=ga1) __call__ = trdata