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pywafo/wafo/objects.py

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# Name: module1
# Purpose:
#
# Author: pab
#
# Created: 16.09.2008
# Copyright: (c) pab 2008
# Licence: <your licence>
# !/usr/bin/env python
from __future__ import absolute_import, division
from wafo.transform.core import TrData
from wafo.transform.estimation import TransformEstimator
from wafo.stats import distributions
from wafo.misc import (nextpow2, findtp, findrfc, findtc, findcross,
ecross, JITImport, DotDict, gravity, findrfc_astm,
detrendma)
from wafo.interpolate import stineman_interp
from wafo.containers import PlotData
from wafo.plotbackend import plotbackend as plt
from scipy.integrate import trapz
from scipy.signal import welch, lfilter
from scipy.signal.windows import get_window # @UnusedImport
from scipy import special
from scipy.interpolate.interpolate import interp1d
from scipy.special import ndtr as cdfnorm
import warnings
import numpy as np
from numpy import (inf, pi, zeros, ones, sqrt, where, log, exp, cos, sin,
arcsin, mod,
linspace, arange, sort, all, abs, vstack, hstack,
atleast_1d, finfo, polyfit, r_, nonzero,
cumsum, ravel, isnan, ceil, diff, array)
from numpy.fft import fft # @UnusedImport
from numpy.random import randn
from matplotlib.mlab import psd, detrend_mean
from scipy.signal.windows import parzen
floatinfo = finfo(float)
_wafocov = JITImport('wafo.covariance')
_wafocov_estimation = JITImport('wafo.covariance.estimation')
_wafospec = JITImport('wafo.spectrum')
__all__ = ['TimeSeries', 'LevelCrossings', 'CyclePairs', 'TurningPoints',
'sensortypeid', 'sensortype']
def _invchi2(q, df):
return special.chdtri(df, q)
class LevelCrossings(PlotData):
'''
Container class for Level crossing data objects in WAFO
Member variables
----------------
data : array-like
number of upcrossings or upcrossingintensity
args : array-like
crossing levels
Examples
--------
>>> import wafo.data as wd
>>> import wafo.objects as wo
>>> x = wd.sea()
>>> ts = wo.mat2timeseries(x)
>>> tp = ts.turning_points()
>>> mm = tp.cycle_pairs()
>>> lc = mm.level_crossings()
>>> np.allclose(lc.data[:5], [ 0., 1., 2., 2., 3.])
True
>>> m, s = lc.estimate_mean_and_stdev()
>>> np.allclose([m, s], (0.033974280952584639, 0.48177752818956326))
True
>>> np.allclose((lc.mean, lc.sigma),
... (1.5440875692709283e-09, 0.47295493383306714))
True
>>> h2 = lc.plot()
'''
def __init__(self, *args, **kwds):
options = dict(title='Level crossing spectrum',
xlab='Levels', ylab='Count',
plotmethod='semilogy',
plot_args=['b'],
plot_args_children=['r--'])
options.update(**kwds)
super(LevelCrossings, self).__init__(*args, **options)
self.intensity = kwds.get('intensity', False)
self.sigma = kwds.get('sigma')
self.mean = kwds.get('mean')
# self.setplotter(plotmethod='step')
if self.data is not None:
i_cmax = self.data.argmax()
if self.sigma is None or self.mean is None:
mean, sigma = self.estimate_mean_and_stdev(i_cmax)
if self.sigma is None:
# estimated standard deviation of x
self.sigma = sigma
if self.mean is None:
self.mean = mean
cmax = self.data[i_cmax]
x = (self.args - self.mean) / self.sigma
y = cmax * exp(-x ** 2 / 2.0)
self.children = [PlotData(y, self.args)]
def estimate_mean_and_stdev(self, i_cmax=None):
"""
Return mean and standard deviation of process x estimated from crossing
"""
if i_cmax is None:
i_cmax = self.data.argmax()
logcros = where(self.data == 0.0, inf, -log(self.data))
logcmin = logcros[i_cmax]
logcros = sqrt(2 * abs(logcros - logcmin))
logcros[0:i_cmax + 1] = 2 * logcros[i_cmax] - logcros[0:i_cmax + 1]
ncr = 10
# least square fit
p = polyfit(self.args[ncr:-ncr], logcros[ncr:-ncr], 1)
sigma = 1.0 / p[0]
mean = -p[1] / p[0] # self.args[i_cmax]
return mean, sigma
def extrapolate(self, u_min=None, u_max=None, method='ml', dist='genpar',
plotflag=0):
'''
Returns an extrapolated level crossing spectrum
Parameters
-----------
u_min, u_max : real scalars
extrapolate below u_min and above u_max.
method : string
describing the method of estimation. Options are:
'ml' : Maximum Likelihood method (default)
'mps': Maximum Product Spacing method
dist : string
defining distribution function. Options are:
genpareto : Generalized Pareto distribution (GPD)
expon : Exponential distribution (GPD with k=0)
rayleigh : truncated Rayleigh distribution
plotflag : scalar integer
1: Diagnostic plots.
0: Don't plot diagnostic plots. (default)
Returns
-------
lc : LevelCrossing object
with the estimated level crossing spectrum
Est = Estimated parameters. [struct array]
Extrapolates the level crossing spectrum (LC) for high and for low
levels.
The tails of the LC is fitted to a survival function of a GPD.
H(x) = (1-k*x/s)^(1/k) (GPD)
The use of GPD is motivated by POT methods in extreme value theory.
For k=0 the GPD is the exponential distribution
H(x) = exp(-x/s), k=0 (expon)
The tails with the survival function of a truncated Rayleigh
distribution.
H(x) = exp(-((x+x0)**2-x0^2)/s**2) (rayleigh)
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 'exp' uses the Exp.
The method 'ray' uses Ray, and should be used if the load is a
Gaussian process.
Example
-------
>>> import wafo.data as wd
>>> import wafo.objects as wo
>>> x = wd.sea()
>>> ts = wo.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)
>>> lc_exp = lc.extrapolate(-2*s, 2*s, dist='expon')
>>> lc_ray = lc.extrapolate(-2*s, 2*s, dist='rayleigh')
>>> n = 3
>>> np.allclose([lc_gpd.data[:n], lc_gpd.data[-n:]],
... [[ 0., 0., 0.], [ 0., 0., 0.]])
True
>>> np.allclose([lc_exp.data[:n], lc_exp.data[-n:]],
... [[ 6.51864195e-12, 7.02339889e-12, 7.56724060e-12],
... [ 1.01040335e-05, 9.70417448e-06, 9.32013956e-06]])
True
>>> np.allclose([lc_ray.data[:n], lc_ray.data[-n:]],
... [[ 1.78925398e-37, 2.61098785e-37, 3.80712964e-37],
... [ 1.28140956e-13, 1.11668143e-13, 9.72878135e-14]])
True
>>> h0 = lc.plot()
>>> h1 = lc_gpd.plot()
>>> h2 = lc_exp.plot()
>>> h3 = lc_ray.plot()
See also
--------
cmat2extralc, rfmextrapolate, lc2rfmextreme, extralc, fitgenpar
References
----------
Johannesson, P., and Thomas, J-.J. (2000):
Extrapolation of Rainflow Matrices.
Preprint 2000:82, Mathematical statistics, Chalmers, pp. 18.
'''
i_max = self.data.argmax()
c_max = self.data[i_max]
lc_max = self.args[i_max]
if u_min is None or u_max is None:
fraction = sqrt(c_max)
i = np.flatnonzero(self.data > fraction)
if u_min is None:
u_min = self.args[i.min()]
if u_max is None:
u_max = self.args[i.max()]
lcf, lcx = self.data, self.args
# Extrapolate LC for high levels
lc_High, phat_high = self._extrapolate(lcx, lcf, u_max, u_max - lc_max,
method, dist)
# Extrapolate LC for low levels
lcEst1, phat_low = self._extrapolate(-lcx[::-1], lcf[::-1], -u_min,
lc_max - u_min, method, dist)
lc_Low = lcEst1[::-1, :] # [-lcEst1[::-1, 0], lcEst1[::-1, 1::]]
lc_Low[:, 0] *= -1
if plotflag:
plt.semilogx(lcf, lcx, 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):
# Extrapolate the level crossing spectra for high levels
method = method.lower()
dist = dist.lower()
# Excedences over level u
Iu = lcx > u
lcx1, lcf1 = lcx[Iu], lcf[Iu]
lcf2, lcx2 = self._make_increasing(lcf1[::-1], lcx1[::-1])
nim1 = 0
x = []
for xk, ni in zip(lcx2.tolist(), lcf2.tolist()):
ni = int(ni)
x.append(ones(ni - nim1) * xk)
nim1 = ni
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
if dist.startswith('gen'):
genpareto = distributions.genpareto
phat = genpareto.fit2(x, floc=0, method=method)
SF = phat.sf(xF)
covar = phat.par_cov[::2, ::2]
# Calculate 90 # confidence region, an ellipse, for (k,s)
D, B = np.linalg.eig(covar)
b = phat.par[::2]
if b[0] > 0:
phat.upperlimit = u + b[1] / b[0]
r = sqrt(-2 * log(1 - 90 / 100)) # 90 # confidence sphere
Nc = 16 + 1
ang = linspace(0, 2 * pi, Nc)
# 90% Circle
c0 = np.vstack(
(r * sqrt(D[0]) * sin(ang), r * sqrt(D[1]) * cos(ang)))
# plot(c0(1,:),c0(2,:))
# * ones((1, len(c0))) # Transform to ellipse for (k,s)
c1 = np.dot(B, c0) + b[:, None]
# plot(c1(1,:),c1(2,:)), hold on
# Calculate conf.int for lcu
# Assumtion: lcu is Poisson distributed
# Poissin distr. approximated by normal when calculating conf. int.
dXX = 1.64 * sqrt(lcu) # 90 # quantile for lcu
lcEstCu = zeros((len(xF), Nc))
lcEstCl = zeros((len(xF), Nc))
for i in range(Nc):
k = c1[0, i]
s = c1[1, i]
SF2 = genpareto.sf(xF, k, scale=s)
lcEstCu[:, i] = (lcu + dXX) * (SF2)
lcEstCl[:, i] = (lcu - dXX) * (SF2)
# end
lcEst = np.vstack((xF + u, lcu * (SF),
lcEstCl.min(axis=1), lcEstCu.max(axis=1))).T
elif dist.startswith('exp'):
expon = distributions.expon
phat = expon.fit2(x, floc=0, method=method)
SF = phat.sf(xF)
lcEst = np.vstack((xF + u, lcu * (SF))).T
elif dist.startswith('ray') or dist.startswith('trun'):
phat = distributions.truncrayleigh.fit2(x, floc=0, method=method)
SF = phat.sf(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 * (SF))).T
else:
raise NotImplementedError('Unknown distribution {}'.format(dist))
return lcEst, phat
# End extrapolate
def _make_increasing(self, f, t=None):
# Makes the signal f strictly increasing.
n = len(f)
if t is None:
t = np.arange(n)
ff = [f[0], ]
tt = [t[0], ]
for i in range(1, n):
if f[i] > ff[-1]:
ff.append(f[i])
tt.append(t[i])
return np.asarray(ff), np.asarray(tt)
def sim(self, ns, alpha):
"""
Simulates process with given irregularity factor and crossing spectrum
Parameters
----------
ns : scalar, integer
number of sample points.
alpha : real scalar
irregularity factor, 0<alpha<1, small alpha gives
irregular process.
Returns
--------
ts : timeseries object
with times and values of the simulated process.
Example
-------
>>> import wafo.spectrum.models as sm
>>> from wafo.objects import mat2timeseries
>>> Sj = sm.Jonswap(Hm0=7)
>>> S = Sj.tospecdata() #Make spectrum object from numerical values
>>> alpha = S.characteristic('alpha')[0]
>>> n = 10000
>>> xs = S.sim(ns=n)
>>> ts = mat2timeseries(xs)
>>> tp = ts.turning_points()
>>> mm = tp.cycle_pairs()
>>> lc = mm.level_crossings()
>>> xs2 = lc.sim(n,alpha)
>>> ts2 = mat2timeseries(xs2)
>>> Se = ts2.tospecdata(L=324)
>>> alpha2 = Se.characteristic('alpha')[0]
>>> np.round(alpha2*10)
array([ 7.])
>>> np.abs(alpha-alpha2)<0.03
array([ True], dtype=bool)
>>> lc2 = ts2.turning_points().cycle_pairs().level_crossings()
>>> import pylab as plt
>>> h0 = S.plot('b')
>>> h1 = Se.plot('r')
>>> h = plt.subplot(211)
>>> h2 = lc2.plot()
>>> h = plt.subplot(212)
>>> h0 = lc.plot()
"""
# TODO: add a good example
f = linspace(0, 0.49999, 1000)
rho_st = 2. * sin(f * pi) ** 2 - 1.
tmp = alpha * arcsin(sqrt((1. + rho_st) / 2))
tmp = sin(tmp) ** 2
a2 = (tmp - rho_st) / (1 - tmp)
y = vstack((a2 + rho_st, 1 - a2)).min(axis=0)
maxidx = y.argmax()
# [maximum,maxidx]=max(y)
rho_st = rho_st[maxidx]
a2 = a2[maxidx]
a1 = 2. * rho_st + a2 - 1.
r0 = 1.
r1 = -a1 / (1. + a2)
r2 = (a1 ** 2 - a2 - a2 ** 2) / (1 + a2)
sigma2 = r0 + a1 * r1 + a2 * r2
# randn = np.random.randn
e = randn(ns) * sqrt(sigma2)
e[:2] = 0.0
L0 = randn(1)
L0 = hstack((L0, r1 * L0 + sqrt(1 - r2 ** 2) * randn(1)))
# Simulate the process, starting in L0
z0 = lfilter([1, a1, a2], ones(1), L0)
L, unused_zf = lfilter(ones(1), [1, a1, a2], e, axis=0, zi=z0)
epsilon = 1.01
min_L = min(L)
max_L = max(L)
maxi = max(abs(r_[min_L, max_L])) * epsilon
mini = -maxi
nu = 101
u = linspace(mini, maxi, nu)
G = cdfnorm(u) # (1 + erf(u / sqrt(2))) / 2
G = G * (1 - G)
x = linspace(0, r1, 100)
factor1 = 1. / sqrt(1 - x ** 2)
factor2 = 1. / (1 + x)
integral = zeros(u.shape, dtype=float)
for i in range(nu):
y = factor1 * exp(-u[i] * u[i] * factor2)
integral[i] = trapz(y, x)
# end
G = G - integral / (2 * pi)
G = G / max(G)
Z = ((u >= 0) * 2 - 1) * sqrt(-2 * log(G))
sumcr = trapz(self.data, self.args)
lc = self.data / sumcr
lc1 = self.args
mcr = trapz(lc1 * lc, lc1) if self.mean is None else self.mean
if self.sigma is None:
scr = sqrt(trapz(lc1 ** 2 * lc, lc1) - mcr ** 2)
else:
scr = self.sigma
lc2 = LevelCrossings(lc, lc1, mean=mcr, sigma=scr, intensity=True)
g = lc2.trdata()[0]
f = g.gauss2dat(Z)
G = TrData(f, u)
process = G.dat2gauss(L)
return np.vstack((arange(len(process)), process)).T
#
#
# Check the result without reference to getrfc:
# LCe = dat2lc(process)
# max(lc(:,2))
# max(LCe(:,2))
#
# clf
# plot(lc(:,1),lc(:,2)/max(lc(:,2)))
# hold on
# plot(LCe(:,1),LCe(:,2)/max(LCe(:,2)),'-.')
# title('Relative crossing intensity')
#
# %% Plot made by the function funplot_4, JE 970707
# %param = [min(process(:,2)) max(process(:,2)) 100]
# %plot(lc(:,1),lc(:,2)/max(lc(:,2)))
# %hold on
# %plot(levels(param),mu/max(mu),'--')
# %hold off
# %title('Crossing intensity')
# %watstamp
#
# % Temporarily
# %funplot_4(lc,param,mu)
def trdata(self, mean=None, sigma=None, **options):
'''
Estimate transformation, g, from observed crossing intensity, version2.
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(plotflag=0)
>>> 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
'''
estimate = TransformEstimator(**options)
return estimate._trdata_lc(self, mean, sigma)
class CycleMatrix(PlotData):
"""
Container class for Cycle Matrix data objects in WAFO
"""
def __init__(self, *args, **kwds):
self.kind = kwds.pop('kind', 'min2max')
self.sigma = kwds.pop('sigma', None)
self.mean = kwds.pop('mean', None)
self.time = kwds.pop('time', 1)
options = dict(title=self.kind + ' cycle matrix',
xlab='min', ylab='max',
plot_args=['b.'])
options.update(**kwds)
super(CycleMatrix, self).__init__(*args, **options)
class CyclePairs(PlotData):
'''
Container class for Cycle Pairs data objects in WAFO
Member variables
----------------
data : array_like
args : vector for 1D
Examples
--------
>>> import wafo.data
>>> import wafo.objects as wo
>>> x = wafo.data.sea()
>>> ts = wo.mat2timeseries(x)
>>> tp = ts.turning_points()
>>> mM = tp.cycle_pairs(kind='min2max')
>>> np.allclose(mM.data[:5],
... [ 0.83950546, -0.02049454, -0.04049454, 0.25950546, -0.08049454])
True
>>> np.allclose(mM.args[:5],
... [-1.2004945 , -0.09049454, -0.09049454, -0.16049454, -0.43049454])
True
>>> Mm = tp.cycle_pairs(kind='max2min')
>>> np.allclose(Mm.data[:5],
... [ 0.83950546, -0.02049454, -0.04049454, 0.25950546, -0.08049454])
True
>>> np.allclose(Mm.args[:5],
... [-0.09049454, -0.09049454, -0.16049454, -0.43049454, -0.21049454])
True
>>> h1 = mM.plot(marker='x')
'''
def __init__(self, *args, **kwds):
self.kind = kwds.pop('kind', 'min2max')
self.sigma = kwds.pop('sigma', None)
self.mean = kwds.pop('mean', None)
self.time = kwds.pop('time', 1)
options = dict(title=self.kind + ' cycle pairs',
xlab='min', ylab='max',
plot_args=['b.'])
options.update(**kwds)
super(CyclePairs, self).__init__(*args, **options)
def amplitudes(self):
return (self.data - self.args) / 2.
def damage(self, beta, K=1):
"""
Calculates the total Palmgren-Miner damage of cycle pairs.
Parameters
----------
beta : array-like, size m
Beta-values, material parameter.
K : scalar, optional
K-value, material parameter.
Returns
-------
D : ndarray, size m
Damage.
Notes
-----
The damage is calculated according to
D[i] = sum ( K * a**beta[i] ), with a = (max-min)/2
Examples
--------
>>> import wafo
>>> from matplotlib import pyplot as plt
>>> ts = wafo.objects.mat2timeseries(wafo.data.sea())
>>> tp = ts.turning_points()
>>> mm = tp.cycle_pairs()
>>> bv = range(3,9)
>>> D = mm.damage(beta=bv)
>>> np.allclose(D, [ 138.5238799 , 117.56050788, 108.99265423,
... 107.86681126, 112.3791076 , 122.08375071])
True
>>> h = mm.plot(marker='.')
>>> h = plt.plot(bv, D, 'x-')
See also
--------
SurvivalCycleCount
"""
amp = abs(self.amplitudes())
return atleast_1d([K * np.sum(amp ** betai) for betai in beta])
def get_minima_and_maxima(self):
index, = nonzero(self.args <= self.data)
if index.size == 0:
index, = nonzero(self.args >= self.data)
M = self.args[index]
m = self.data[index]
else:
m = self.args[index]
M = self.data[index]
return m, M
def level_crossings(self, kind='uM', intensity=False):
""" Return level crossing spectrum from a cycle count.
Parameters
----------
kind : int or string
defining crossing type, options are
0,'u' : only upcrossings.
1,'uM' : upcrossings and maxima (default).
2,'umM': upcrossings, minima, and maxima.
3,'um' : upcrossings and minima.
intensity : bool
True if level crossing intensity spectrum
False if level crossing count spectrum
Return
------
lc : level crossing object
with levels and number of upcrossings.
Calculates the number of upcrossings from a cycle pairs, e.g.
min2Max cycles or rainflow cycles.
Example:
--------
>>> import wafo
>>> ts = wafo.objects.mat2timeseries(wafo.data.sea())
>>> tp = ts.turning_points()
>>> mm = tp.cycle_pairs()
>>> lc = mm.level_crossings()
h = mm.plot(marker='.')
h2 = lc.plot()
See also
--------
TurningPoints
LevelCrossings
"""
defnr = dict(u=0, uM=1, umM=2, um=3).get(kind, kind)
if defnr not in [1, 2, 3, 4]:
raise ValueError('kind must be one of (1, 2, 3, 4, "u", "uM",'
' "umM", "um"). Got kind = {}'.format(kind))
m, M = self.get_minima_and_maxima()
ncc = len(m)
minima = vstack((m, ones(ncc), zeros(ncc), ones(ncc)))
maxima = vstack((M, -ones(ncc), ones(ncc), zeros(ncc)))
extremes = hstack((maxima, minima))
index = extremes[0].argsort()
extremes = extremes[:, index]
ii = 0
n = extremes.shape[1]
extr = zeros((4, n))
extr[:, 0] = extremes[:, 0]
for i in range(1, n):
if extremes[0, i] == extr[0, ii]:
extr[1:4, ii] = extr[1:4, ii] + extremes[1:4, i]
else:
ii += 1
extr[:, ii] = extremes[:, i]
nx = extr[0].argmax() + 1
levels = extr[0, 0:nx]
if defnr == 2: # This are upcrossings + maxima
dcount = cumsum(extr[1, 0:nx]) + extr[2, 0:nx] - extr[3, 0:nx]
elif defnr == 4: # This are upcrossings + minima
dcount = cumsum(extr[1, 0:nx])
dcount[nx - 1] = dcount[nx - 2]
elif defnr == 1: # This are only upcrossings
dcount = cumsum(extr[1, 0:nx]) - extr[3, 0:nx]
elif defnr == 3: # This are upcrossings + minima + maxima
dcount = cumsum(extr[1, 0:nx]) + extr[2, 0:nx]
ylab = 'Count'
if intensity:
dcount = dcount / self.time
ylab = 'Intensity [count/sec]'
return LevelCrossings(dcount, levels, mean=self.mean, sigma=self.sigma,
ylab=ylab, intensity=intensity)
def _smoothcmat(F, method=1, h=None, NOsubzero=0, alpha=0.5):
"""
SMOOTHCMAT Smooth a cycle matrix using (adaptive) kernel smoothing
CALL: Fsmooth = smoothcmat(F,method);
Fsmooth = smoothcmat(F,method,[],NOsubzero);
Fsmooth = smoothcmat(F,2,h,NOsubzero,alpha);
Input:
F = Cycle matrix. [nxn]
method = 1: Kernel estimator (constant bandwidth). (Default)
2: Adaptiv kernel estimator (local bandwidth).
h = Bandwidth (Optional, Default='automatic choice')
NOsubzero = Number of subdiagonals that are zero
(Optional, Default = 0, only the diagonal is zero)
alpha = Parameter for method (2) (Optional, Default=0.5).
A number between 0 and 1.
alpha=0 implies constant bandwidth (method 1).
alpha=1 implies most varying bandwidth.
Output:
F = Smoothed cycle matrix. [nxn]
h = Selected bandwidth.
See also
cc2cmat, tp2rfc, tp2mm, dat2tp
"""
aut_h = h is None
if method not in [1, 2]:
raise ValueError('Input argument "method" should be 1 or 2')
n = len(F) # Size of matrix
N = np.sum(F) # Total number of cycles
Fsmooth = np.zeros((n, n))
if method == 1 or method == 2: # Kernel estimator
d = 2 # 2-dim
x = np.arange(n)
I, J = np.meshgrid(x, x)
# Choosing bandwidth
# This choice is optimal if the sample is from a normal distr.
# The normal bandwidth usualy oversmooths,
# therefore we choose a slightly smaller bandwidth
if aut_h == 1:
h_norm = smoothcmat_hnorm(F, NOsubzero)
h = 0.7 * h_norm # Don't oversmooth
# h0 = N^(-1/(d+4));
# FF = F+F';
# mean_F = sum(sum(FF).*(1:n))/N/2;
# s2 = sum(sum(FF).*((1:n)-mean_F).^2)/N/2;
# s = sqrt(s2); % Mean of std in each direction
# h_norm = s*h0; % Optimal for Normal distr.
# h = h_norm; % Test
# endif
# Calculating kernel estimate
# Kernel: 2-dim normal density function
for i in range(n - 1):
for j in range(i + 1, n):
if F[i, j] != 0:
F1 = exp(-1 / (2 * h**2) * ((I - i)**2 + (J - j)**2)) # Gaussian kernel
F1 = F1 + F1.T # Mirror kernel in diagonal
F1 = np.triu(F1, 1 + NOsubzero) # Set to zero below and on diagonal
F1 = F[i, j] * F1 / np.sum(F1) # Normalize
Fsmooth = Fsmooth + F1
# endif
# endfor
# endfor
# endif method 1 or 2
if method == 2:
Fpilot = Fsmooth / N
Fsmooth = np.zeros(n, n)
[I1, I2] = find(F > 0)
logg = 0
for i in range(len(I1)): # =1:length(I1):
logg = logg + F(I1[i], I2[i]) * log(Fpilot(I1[i], I2[i]))
# endfor
g = np.exp(logg / N)
_lamda = (Fpilot / g)**(-alpha)
for i in range(n - 1): # = 1:n-1
for j in range(i + 1, n): # = i+1:n
if F[i, j] != 0:
hi = h * _lamda[i, j]
F1 = np.exp(-1 / (2 * hi**2) * ((I - i)**2 + (J - j)**2)) # Gaussian kernel
F1 = F1 + F1.T # Mirror kernel in diagonal
F1 = np.triu(F1, 1 + NOsubzero) # Set to zero below and on diagonal
F1 = F[i, j] * F1 / np.sum(F1) # Normalize
Fsmooth = Fsmooth + F1
# endif
# endfor
# endfor
# endif method 2
return Fsmooth,h
def cycle_matrix(self, param=(), ddef=1, method=0, h=None, NOsubzero=0, alpha=0.5):
"""CC2CMAT Calculates the cycle count matrix from a cycle count.
using (0) Histogram, (1) Kernel smoothing, (2) Kernel smoothing.
CALL: [F,h] = cc2cmat(param,cc,ddef,method,h,NOsubzero,alpha);
Input:
param = Parameter vector, [a b n], defines the grid.
cc = Cycle count with minima in column 1 and maxima in column 2. [nx2]
ddef = 1: causes peaks to be projected upwards and troughs
downwards to the closest discrete level (default).
= 0: causes peaks and troughs to be projected
the closest discrete level.
= -1: causes peaks to be projected downwards and the
troughs upwards to the closest discrete level.
method = 0: Histogram. (Default)
1: Kernel estimator (constant bandwidth).
2: Adaptiv kernel estimator (local bandwidth).
h = Bandwidth (Optional, Default='automatic choice')
NOsubzero = Number of subdiagonals that are set to zero
(Optional, Default = 0, only the diagonal is zero)
alpha = Parameter for method (2) (Optional, Default=0.5).
A number between 0 and 1.
alpha=0 implies constant bandwidth (method 1).
alpha=1 implies most varying bandwidth.
Output:
F = Estimated cycle matrix.
h = Selected bandwidth.
See also
dcc2cmat, cc2dcc, smoothcmat
"""
if not 0 <= method <= 2:
raise ValueError('Input argument "method" should be 0, 1 or 2')
u = np.linspace(*param) # Discretization levels
n = param[2] # size of matrix
# Compute Histogram
dcp = self._discretize_cycle_pairs(param, ddef)
F = self._dcp2cmat(dcp, n)
# Smooth by using Kernel estimator ?
#if method >= 1:
# F, h = smoothcmat(F,method, h, NOsubzero, alpha)
return CycleMatrix(F, u, u)
def _dcp2cmat(self, dcp, n):
"""
DCP2CMAT Calculates the cycle matrix for a discrete cycle pairs.
CALL: F = dcc2cmat(dcc,n);
F = Cycle matrix
dcc = a two column matrix with a discrete cycle count.
n = Number of discrete levels.
The discrete cycle count takes values from 1 to n.
A cycle count is transformed into a discrete cycle count by
using the function CC2DCC.
See also cc2cmat, cc2dcc, cmatplot
"""
F = np.zeros((n, n));
cp1, cp2 = dcp
for i, j in zip(cp1, cp2):
F[i, j] += 1
return F
def _discretize_cycle_pairs(self, param, ddef=1):
"""
Discretize a cycle pairs.
Parameters
----------
param = the parameter matrix.
ddef = 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
-------
dcc = a two column matrix with discrete classes.
Example:
x = load('sea.dat');
tp = dat2tp(x);
rfc = tp2rfc(tp);
param = [-2, 2, 41];
dcc = cc2dcc(param,rfc);
u = levels(param);
Frfc = dcc2cmat(dcc,param(3));
cmatplot(u,u,{Frfc}, 4);
close all;
See also cc2cmat, dcc2cmat, dcc2cc
"""
cp1, cp2 = np.copy(self.args), np.copy(self.data)
# Make so that minima is in first column
ix = np.flatnonzero(cp1 > cp2)
if np.any(ix):
cp1[ix], cp2[ix] = cp2[ix], cp1[ix]
# Make discretization
a, b, n = param
delta = (b - a) / (n - 1) # Discretization step
cp1 = (cp1 - a) / delta + 1
cp2 = (cp2 - a) / delta + 1
if ddef == 0:
cp1 = np.clip(np.round(cp1), 0, n - 1)
cp2 = np.clip(np.round(cp2), 0, n - 1)
elif ddef == +1:
cp1 = np.clip(np.floor(cp1), 0, n - 2)
cp2 = np.clip(np.ceil(cp2), 1, n - 1)
elif ddef == -1:
cp1 = np.clip(np.ceil(cp1), 1, n - 1)
cp2 = np.clip(np.floor(cp2), 0, n - 2)
else:
raise ValueError('Undefined discretization definition, ddef = {}'.format(ddef))
if np.any(ix):
cp1[ix], cp2[ix] = cp2[ix], cp1[ix]
return np.asarray(cp1, type=int), np.asarray(cp2, type=int)
class TurningPoints(PlotData):
'''
Container class for Turning Points data objects in WAFO
Member variables
----------------
data : array_like
args : vector for 1D
Examples
--------
>>> import wafo.data
>>> import wafo.objects as wo
>>> x = wafo.data.sea()
>>> ts = wo.mat2timeseries(x)
>>> tp = ts.turning_points()
>>> np.allclose(tp.data[:5],
... [-1.2004945 , 0.83950546, -0.09049454, -0.02049454, -0.09049454])
True
h1 = tp.plot(marker='x')
'''
def __init__(self, *args, **kwds):
self.name_ = kwds.pop('name', 'WAFO TurningPoints Object')
self.sigma = kwds.pop('sigma', None)
self.mean = kwds.pop('mean', None)
options = dict(title='Turning points')
options.update(**kwds)
super(TurningPoints, self).__init__(*args, **options)
if not any(self.args):
n = len(self.data)
self.args = range(0, n)
else:
self.args = ravel(self.args)
self.data = ravel(self.data)
def rainflow_filter(self, h=0.0, method='clib'):
'''
Return rainflow filtered turning points (tp).
Parameters
----------
h : scalar
a threshold
if h<=0, then tp is a sequence of turning points (default)
if h>0, then all rainflow cycles with height smaller than
h are removed.
Returns
-------
tp : TurningPoints object
with times and turning points.
Example:
>>> import wafo.data
>>> x = wafo.data.sea()
>>> x1 = x[:200,:]
>>> ts1 = mat2timeseries(x1)
>>> tp = ts1.turning_points(wavetype='Mw')
>>> tph = tp.rainflow_filter(h=0.3)
>>> np.allclose(tph.data[:5],
... [-0.16049454, 0.25950546, -0.43049454, -0.08049454, -0.42049454])
True
>>> np.allclose(tph.args[:5],
... [ 7.05, 7.8 , 9.8 , 11.8 , 12.8 ])
True
>>> hs = ts1.plot()
>>> hp = tp.plot('ro')
>>> hph = tph.plot('k.')
See also
---------
findcross,
findrfc
findtp
'''
ind = findrfc(self.data, max(h, 0.0), method)
try:
t = self.args[ind]
except:
t = ind
mean = self.mean
sigma = self.sigma
return TurningPoints(self.data[ind], t, mean=mean, sigma=sigma)
def cycle_pairs(self, h=0, kind='min2max', method='clib'):
""" Return min2Max or Max2min cycle pairs from turning points
Parameters
----------
kind : string
type of cycles to return options are 'min2max' or 'max2min'
method : string
specify which library to use
'clib' for wafo's c_library
'None' for wafo's Python functions
Return
------
mm : cycles object
with min2Max or Max2min cycle pairs.
Example
-------
>>> import wafo
>>> x = wafo.data.sea()
>>> ts = wafo.objects.mat2timeseries(x)
>>> tp = ts.turning_points()
>>> mM = tp.cycle_pairs()
>>> np.allclose(mM.data[:5], [ 0.83950546, -0.02049454, -0.04049454,
... 0.25950546, -0.08049454])
True
>>> h = mM.plot(marker='x')
See also
--------
TurningPoints
SurvivalCycleCount
"""
if h > 0:
ind = findrfc(self.data, h, method=method)
data = self.data[ind]
else:
data = self.data
if data[0] > data[1]:
im = 1
iM = 0
else:
im = 0
iM = 1
# Extract min-max and max-min cycle pairs
if kind.lower().startswith('min2max'):
m = data[im:-1:2]
M = data[im + 1::2]
else:
kind = 'max2min'
M = data[iM:-1:2]
m = data[iM + 1::2]
time = self.args[-1] - self.args[0]
return CyclePairs(M, m, kind=kind, mean=self.mean, sigma=self.sigma,
time=time)
def cycle_astm(self):
"""
Rainflow counted cycles according to Nieslony's ASTM implementation
Parameters
----------
Returns
-------
sig_rfc : array-like
array of shape (n,3) with:
sig_rfc[:,0] Cycles amplitude
sig_rfc[:,1] Cycles mean value
sig_rfc[:,2] Cycle type, half (=0.5) or full (=1.0)
References
----------
Adam Nieslony, "Determination of fragments of multiaxial service
loading strongly influencing the fatigue of machine components",
Mechanical Systems and Signal Processing 23, no. 8 (2009): 2712-2721.
and is based on the following standard:
ASTM E 1049-85 (Reapproved 1997), Standard practices for cycle counting
in fatigue analysis, in: Annual Book of ASTM Standards,
vol. 03.01, ASTM, Philadelphia, 1999, pp. 710-718.
Copyright (c) 1999-2002 by Adam Nieslony
Ported to Python by David Verelst
Example
-------
>>> import wafo
>>> x = wafo.data.sea()
>>> sig_ts = wafo.objects.mat2timeseries(x)
>>> sig_tp = sig_ts.turning_points(h=0, wavetype='astm')
>>> sig_cp = sig_tp.cycle_astm()
"""
# output of Nieslony's algorithm is organised differently with
# respect to wafo's approach
# TODO: integrate ASTM method into the CyclyPairs class?
return findrfc_astm(self.data)
def mat2timeseries(x):
"""
Convert 2D arrays to TimeSeries object
assuming 1st column is time and the remaining columns contain data.
"""
return TimeSeries(x[:, 1::], x[:, 0].ravel())
class TimeSeries(PlotData):
'''
Container class for 1D TimeSeries data objects in WAFO
Member variables
----------------
data : array_like
args : vector for 1D, list of vectors for 2D, 3D, ...
sensortypes : list of integers or strings
sensor type for time series (default ['n'] : Surface elevation)
see sensortype for more options
position : vector of size 3
instrument position relative to the coordinate system
Examples
--------
>>> import wafo.data
>>> import wafo.objects as wo
>>> x = wafo.data.sea()
>>> ts = wo.mat2timeseries(x)
>>> rf = ts.tocovdata(lag=150)
>>> S = ts.tospecdata()
>>> tp = ts.turning_points()
>>> mm = tp.cycle_pairs()
>>> lc = mm.level_crossings()
h = rf.plot()
h1 = mm.plot(marker='x')
h2 = lc.plot()
'''
def __init__(self, *args, **kwds):
self.name_ = kwds.pop('name', 'WAFO TimeSeries Object')
self.sensortypes = kwds.pop('sensortypes', ['n', ])
self.position = kwds.pop('position', [zeros(3), ])
super(TimeSeries, self).__init__(*args, **kwds)
if not any(self.args):
n = len(self.data)
self.args = range(0, n)
def sampling_period(self):
'''
Returns sampling interval
Returns
-------
dt : scalar
sampling interval, unit:
[s] if lagtype=='t'
[m] otherwise
See also
'''
t_vec = self.args
dt1 = t_vec[1] - t_vec[0]
n = len(t_vec) - 1
t = t_vec[-1] - t_vec[0]
dt = t / n
if abs(dt - dt1) > 1e-10:
warnings.warn('Data is not uniformly sampled!')
return dt
def tocovdata(self, lag=None, tr=None, detrend=detrend_mean,
window='boxcar', flag='biased', norm=False, dt=None):
'''
Return auto covariance function from data.
Parameters
----------
lag : scalar, int
maximum time-lag for which the ACF is estimated. (Default lag=n-1)
flag : string, 'biased' or 'unbiased'
If 'unbiased' scales the raw correlation by 1/(n-abs(k)),
where k is the index into the result, otherwise scales the raw
cross-correlation by 1/n. (default)
norm : bool
True if normalize output to one
dt : scalar
time-step between data points (default see sampling_period).
Return
-------
R : CovData1D object
with attributes:
data : ACF vector length L+1
args : time lags length L+1
sigma : estimated large lag standard deviation of the estimate
assuming x is a Gaussian process:
if R(k)=0 for all lags k>q then an approximation
of the variance for large samples due to Bartlett
var(R(k))=1/N*(R(0)^2+2*R(1)^2+2*R(2)^2+ ..+2*R(q)^2)
for k>q and where N=length(x). Special case is
white noise where it equals R(0)^2/N for k>0
norm : bool
If false indicating that R is not normalized
Example:
--------
>>> import wafo.data
>>> import wafo.objects as wo
>>> x = wafo.data.sea()
>>> ts = wo.mat2timeseries(x)
>>> acf = ts.tocovdata(150)
>>> np.allclose(acf.data[:3], [ 0.22368637, 0.20838473, 0.17110733])
True
h = acf.plot()
'''
estimate_cov = _wafocov_estimation.CovarianceEstimator(
lag=lag, tr=tr, detrend=detrend, window=window, flag=flag,
norm=norm, dt=dt)
return estimate_cov(self)
def _get_bandwidth_and_dof(self, wname, n, L, dt, ftype='w'):
'''Returns bandwidth (rad/sec) and degrees of freedom
used in chi^2 distribution
'''
if isinstance(wname, tuple):
wname = wname[0]
dof = int(dict(parzen=3.71, hanning=2.67,
bartlett=3).get(wname, np.nan) * n/L)
Be = dict(parzen=1.33, hanning=1,
bartlett=1.33).get(wname, np.nan) * 2 * pi / (L*dt)
if ftype == 'f':
Be = Be / (2 * pi) # bandwidth in Hz
return Be, dof
def tospecdata(self, L=None, tr=None, method='cov', detrend=detrend_mean,
window='parzen', noverlap=0, ftype='w', alpha=None):
'''
Estimate one-sided spectral density from data.
Parameters
----------
L : scalar integer
maximum lag size of the window function. As L decreases the
estimate becomes smoother and Bw increases. If we want to resolve
peaks in S which is Bf (Hz or rad/sec) apart then Bw < Bf. If no
value is given the lag size is set to be the lag where the auto
correlation is less than 2 standard deviations. (maximum 300)
tr : transformation object
the transformation assuming that x is a sample of a transformed
Gaussian process. If g is None then x is a sample of a Gaussian
process (Default)
method : string
defining estimation method. Options are
'cov' : Frequency smoothing using the window function
on the estimated autocovariance function. (default)
'psd' : Welch's averaged periodogram method with no overlapping
batches
detrend : function
defining detrending performed on the signal before estimation.
(default detrend_mean)
window : vector of length NFFT or function
To create window vectors see numpy.blackman, numpy.hamming,
numpy.bartlett, scipy.signal, scipy.signal.get_window etc.
noverlap : scalar int
gives the length of the overlap between segments.
ftype : character
defining frequency type: 'w' or 'f' (default 'w')
Returns
---------
spec : SpecData1D object
Example
-------
>>> import wafo.data as wd
>>> import wafo.objects as wo
>>> x = wd.sea()
>>> ts = wo.mat2timeseries(x)
>>> S0 = ts.tospecdata(method='psd', L=150)
>>> np.allclose(S0.data[21:25],
... [0.1948925209459276, 0.19124901618176282, 0.1705625876220829, 0.1471870958122376],
... rtol=1e-2)
True
>>> S = ts.tospecdata(L=150)
>>> np.allclose(S.data[21:25],
... [0.13991863694982026, 0.15264493584526717, 0.160156678854338, 0.1622894414741913],
... rtol=1e-2)
True
>>> h = S.plot()
See also
--------
dat2tr, dat2cov
References:
-----------
Georg Lindgren and Holger Rootzen (1986)
"Stationara stokastiska processer", pp 173--176.
Gareth Janacek and Louise Swift (1993)
"TIME SERIES forecasting, simulation, applications",
pp 75--76 and 261--268
Emanuel Parzen (1962),
"Stochastic Processes", HOLDEN-DAY,
pp 66--103
'''
nugget = 1e-12
rate = 2 # interpolationrate for frequency
dt = self.sampling_period()
yy = self.data.ravel()
if tr is not None:
yy = tr.dat2gauss(yy)
yy = detrend(yy) if hasattr(detrend, '__call__') else yy
n = len(yy)
estimate_L = L is None
if method == 'cov' or estimate_L:
tsy = TimeSeries(yy, self.args)
R = tsy.tocovdata(lag=L, window=window)
L = len(R.data) - 1
if method == 'cov':
# add a nugget effect to ensure that round off errors
# do not result in negative spectral estimates
spec = R.tospecdata(rate=rate, nugget=nugget)
L = min(L, n - 1)
if method == 'psd':
nfft = 2 ** nextpow2(L)
pad_to = rate * nfft # Interpolate the spectrum with rate
f, S = welch(yy, fs=1.0 / dt, window=window, nperseg=nfft,
noverlap=noverlap, nfft=pad_to, detrend=detrend,
return_onesided=True, scaling='density', axis=-1)
# S, f = psd(yy, Fs=1. / dt, NFFT=nfft, detrend=detrend,
# window=win, noverlap=noverlap, pad_to=pad_to,
# scale_by_freq=True)
fact = 2.0 * pi
w = fact * f
spec = _wafospec.SpecData1D(S / fact, w)
elif method == 'cov':
pass
else:
raise ValueError('Unknown method (%s)' % method)
Be, dof = self._get_bandwidth_and_dof(window, n, L, dt, ftype)
spec.Bw = Be
if alpha is not None:
# Confidence interval constants
spec.CI = [dof / _invchi2(1 - alpha / 2, dof),
dof / _invchi2(alpha / 2, dof)]
spec.tr = tr
spec.L = L
spec.norm = False
spec.note = 'method=%s' % method
return spec
def trdata(self, method='nonlinear', **options):
'''
Estimate transformation, g, from data.
Parameters
----------
method : string defining transform based on:
'nonlinear' : smoothed crossing intensity (default)
'mnonlinear': smoothed marginal distribution
'hermite' : cubic Hermite polynomial
'ochi' : exponential function
'linear' : identity.
options : keyword with the following fields:
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 see lc2tr)
which defines the region of variation of the data x.
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
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)
gvar: real scalar
Variances for the empirical transformation, g. (default 1)
ne - Number of extremes (maxima & minima) to remove from the
estimation of the transformation. This makes the
estimation more robust against outliers. (default 7)
ntr - 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 1000)
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).
If x is likely to cross levels higher than 5 standard deviations
then the vector param has to be modified. For example if x 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)
>>> g0, g0emp = ts.trdata(plotflag=0)
>>> g1, g1emp = ts.trdata(method='mnonlinear', gvar=0.5 )
>>> g2, g2emp = ts.trdata(method='nonlinear', gvar=[3.5, 0.5, 3.5])
>>> 100 < S.tr.dist2gauss()*100 < 200
True
>>> 2000 < g0emp.dist2gauss() < 4000
True
>>> 80 < g0.dist2gauss()*100 < 150
True
>>> 50 < g1.dist2gauss()*100 < 100
True
>>> 70 < g2.dist2gauss()*100 < 140
True
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
'''
estimate = TransformEstimator(method=method, **options)
return estimate.trdata(self)
def turning_points(self, h=0.0, wavetype=None):
'''
Return turning points (tp) from data, optionally rainflowfiltered.
Parameters
----------
h : scalar
a threshold
if h<=0, then tp is a sequence of turning points (default)
if h>0, then all rainflow cycles with height smaller than
h are removed.
wavetype : string
defines the type of wave. Possible options are
'astm' 'mw' 'Mw' or 'none'.
If None all rainflow filtered min and max
will be returned, otherwise only the rainflow filtered
min and max, which define a wave according to the
wave definition, will be returned.
'astm' forces to have the first data point of the load history as
the first turning point. To be used in combination with
TurningPoints.cycle_astm()
Returns
-------
tp : TurningPoints object
with times and turning points.
Example:
>>> import wafo.data
>>> x = wafo.data.sea()
>>> x1 = x[:200,:]
>>> ts1 = mat2timeseries(x1)
>>> tp = ts1.turning_points(wavetype='Mw')
>>> tph = ts1.turning_points(h=0.3,wavetype='Mw')
>>> np.allclose(tph.data[:3], [ 0.83950546, -0.16049454, 0.25950546])
True
hs = ts1.plot()
hp = tp.plot('ro')
hph = tph.plot('k.')
See also
---------
findcross,
findrfc
findtp
'''
ind = findtp(self.data, max(h, 0.0), wavetype)
try:
t = self.args[ind]
except:
t = ind
mean = self.data.mean()
sigma = self.data.std()
return TurningPoints(self.data[ind], t, mean=mean, sigma=sigma)
def trough_crest(self, v=None, wavetype=None):
"""
Return trough and crest turning points
Parameters
-----------
v : scalar
reference level (default v = mean of x).
wavetype : string
defines the type of wave. Possible options are
'dw', 'uw', 'tw', 'cw' or None.
If None indices to all troughs and crests will be returned,
otherwise only the paired ones will be returned
according to the wavedefinition.
Returns
--------
tc : TurningPoints object
with trough and crest turningpoints
"""
ind = findtc(self.data, v, wavetype)[0]
try:
t = self.args[ind]
except:
t = ind
mean = self.data.mean()
sigma = self.data.std()
return TurningPoints(self.data[ind], t, mean=mean, sigma=sigma)
def wave_parameters(self, rate=1):
'''
Returns several wave parameters from data.
Parameters
----------
rate : scalar integer
interpolation rate. Interpolates with spline if greater than one.
Returns
-------
parameters : dict
wave parameters such as
Ac, At : Crest and trough amplitude, respectively
Tcf, Tcb : Crest front and crest (rear) back period, respectively
Hu, Hd : zero-up- and down-crossing wave height, respectively.
Tu, Td : zero-up- and down-crossing wave period, respectively.
The definition of g, Ac,At, Tcf, etc. are given in gravity and
wafo.definitions.
Example
-------
>>> import wafo.data as wd
>>> import wafo.objects as wo
>>> x = wd.sea()
>>> ts = wo.mat2timeseries(x)
>>> wp = ts.wave_parameters()
>>> true_wp = {'Ac':[ 0.25950546, 0.34950546],
... 'At': [ 0.16049454, 0.43049454],
... 'Hu': [ 0.69, 0.86],
... 'Hd': [ 0.42, 0.78],
... 'Tu': [ 6.10295202, 3.36978685],
... 'Td': [ 3.84377468, 6.35707656],
... 'Tcf': [ 0.42656819, 0.57361617],
... 'Tcb': [ 0.93355982, 1.04063638]}
>>> for name in ['Ac', 'At', 'Hu', 'Hd', 'Tu', 'Td', 'Tcf', 'Tcb']:
... np.allclose(wp[name][:2], true_wp[name])
True
True
True
True
True
True
True
True
import pylab as plt
h = plt.plot(wp['Td'],wp['Hd'],'.')
h = plt.xlabel('Td [s]')
h = plt.ylabel('Hd [m]')
See also
--------
wafo.definitions
'''
dT = self.sampling_period()/np.maximum(rate, 1)
xi, ti = self._interpolate(rate)
tc_ind, z_ind = findtc(xi, v=0, kind='tw')
tc_a = xi[tc_ind]
tc_t = ti[tc_ind]
Ac = tc_a[1::2] # crest amplitude
At = -tc_a[0::2] # trough amplitude
Hu = Ac + At[1:]
Hd = Ac + At[:-1]
tu = ecross(ti, xi, z_ind[1::2], v=0)
Tu = diff(tu) # Period zero-upcrossing waves
td = ecross(ti, xi, z_ind[::2], v=0)
Td = diff(td) # Period zero-downcrossing waves
Tcf = tc_t[1::2] - tu[:-1]
Tcf[(Tcf == 0)] = dT # avoiding division by zero
Tcb = td[1:] - tc_t[1::2]
Tcb[(Tcb == 0)] = dT # avoiding division by zero
return dict(Ac=Ac, At=At, Hu=Hu, Hd=Hd, Tu=Tu, Td=Td, Tcf=Tcf, Tcb=Tcb)
def wave_height_steepness(self, kind='Vcf', rate=1, g=None):
'''
Returns waveheights and steepnesses from data.
Parameters
----------
rate : scalar integer
interpolation rate. Interpolates with spline if greater than one.
kind : scalar integer (default 1)
0 max(Vcf, Vcb) and corresponding wave height Hd or Hu in H
1 crest front (rise) speed (Vcf) in S and wave height Hd in H.
-1 crest back (fall) speed (Vcb) in S and waveheight Hu in H.
2 crest front steepness in S and the wave height Hd in H.
-2 crest back steepness in S and the wave height Hu in H.
3 total wave steepness in S and the wave height Hd in H
for zero-downcrossing waves.
-3 total wave steepness in S and the wave height Hu in H.
for zero-upcrossing waves.
Returns
-------
S, H = Steepness and the corresponding wave height according to kind
The parameters are calculated as follows:
Crest front speed (velocity) = Vcf = Ac/Tcf
Crest back speed (velocity) = Vcb = Ac/Tcb
Crest front steepness = 2*pi*Ac./Td/Tcf/g
Crest back steepness = 2*pi*Ac./Tu/Tcb/g
Total wave steepness (zero-downcrossing wave) = 2*pi*Hd./Td.^2/g
Total wave steepness (zero-upcrossing wave) = 2*pi*Hu./Tu.^2/g
The definition of g, Ac,At, Tcf, etc. are given in gravity and
wafo.definitions.
Example
-------
>>> import wafo.data as wd
>>> import wafo.objects as wo
>>> x = wd.sea()
>>> ts = wo.mat2timeseries(x)
>>> true_SH = [
... [[ 0.01186982, 0.04852534], [ 0.69, 0.86]],
... [[ 0.02918363, 0.06385979], [ 0.69, 0.86]],
... [[ 0.27797411, 0.33585743], [ 0.69, 0.86]],
... [[ 0.60835634, 0.60930197], [ 0.42, 0.78]],
... [[ 0.60835634, 0.60930197], [ 0.42, 0.78]],
... [[ 0.10140867, 0.06141156], [ 0.42, 0.78]],
... [[ 0.01821413, 0.01236672], [ 0.42, 0.78]]]
>>> for i in range(-3,4):
... S, H = ts.wave_height_steepness(kind=i)
... np.allclose((S[:2],H[:2]), true_SH[i+3])
True
True
True
True
True
True
True
import pylab as plt
h = plt.plot(S,H,'.')
h = plt.xlabel('S')
h = plt.ylabel('Hd [m]')
See also
--------
wafo.definitions
'''
dT = self.sampling_period() / np.maximum(rate, 1)
if g is None:
g = gravity() # acceleration of gravity
xi, ti = self._interpolate(rate)
tc_ind, z_ind = findtc(xi, v=0, kind='tw')
tc_a = xi[tc_ind]
tc_t = ti[tc_ind]
Ac = tc_a[1::2] # crest amplitude
At = -tc_a[0::2] # trough amplitude
defnr = dict(maxVcfVcb=0, Vcf=1, Vcb=-1, Scf=2, Scb=-2, StHd=3,
StHu=-3).get(kind, kind)
if 0 <= defnr <= 2:
# time between zero-upcrossing and crest [s]
tu = ecross(ti, xi, z_ind[1:-1:2], v=0)
Tcf = tc_t[1::2] - tu
Tcf[(Tcf == 0)] = dT # avoiding division by zero
if -2 <= defnr <= 0:
# time between crest and zero-downcrossing [s]
td = ecross(ti, xi, z_ind[2::2], v=0)
Tcb = td - tc_t[1::2]
Tcb[(Tcb == 0)] = dT
if defnr == 0:
# max(Vcf, Vcr) and the corresponding wave height Hd or Hu in H
Hu = Ac + At[1:]
Hd = Ac + At[:-1]
T = np.where(Tcf < Tcb, Tcf, Tcb)
S = Ac / T
H = np.where(Tcf < Tcb, Hd, Hu)
elif defnr == 1: # extracting crest front velocity [m/s] and
# Zero-downcrossing wave height [m]
H = Ac + At[:-1] # Hd
S = Ac / Tcf
elif defnr == -1: # extracting crest rear velocity [m/s] and
# Zero-upcrossing wave height [m]
H = Ac + At[1:] # Hu
S = Ac / Tcb
# crest front steepness in S and the wave height Hd in H.
elif defnr == 2:
H = Ac + At[:-1] # Hd
Td = diff(ecross(ti, xi, z_ind[::2], v=0))
S = 2 * pi * Ac / Td / Tcf / g
# crest back steepness in S and the wave height Hu in H.
elif defnr == -2:
H = Ac + At[1:]
Tu = diff(ecross(ti, xi, z_ind[1::2], v=0))
S = 2 * pi * Ac / Tu / Tcb / g
elif defnr == 3: # total steepness in S and the wave height Hd in H
# for zero-downcrossing waves.
H = Ac + At[:-1]
# Period zero-downcrossing waves
Td = diff(ecross(ti, xi, z_ind[::2], v=0))
S = 2 * pi * H / Td ** 2 / g
# total steepness in S and the wave height Hu in H for
elif defnr == -3:
# zero-upcrossing waves.
H = Ac + At[1:]
# Period zero-upcrossing waves
Tu = diff(ecross(ti, xi, z_ind[1::2], v=0))
S = 2 * pi * H / Tu ** 2 / g
return S, H
@staticmethod
def _default_index(x, vh, wdef, pdef):
if pdef in ('m2m', 'm2M', 'M2m', 'M2M'):
index = findtp(x, vh, wdef)
elif pdef in ('u2u', 'u2d', 'd2u', 'd2d'):
index = findcross(x, vh, wdef)
elif pdef in ('t2t', 't2c', 'c2t', 'c2c'):
index = findtc(x, vh, wdef)[0]
elif pdef in ('d2t', 't2u', 'u2c', 'c2d', 'all'):
index, v_ind = findtc(x, vh, wdef)
# sorting crossings and tp in sequence
index = sort(r_[index, v_ind])
else:
raise ValueError('Unknown pdef option! {}'.format(str(pdef)))
return index
def _get_start_index(self, pdef, down_crossing_or_max):
if down_crossing_or_max:
if pdef in ('d2t', 'M2m', 'c2t', 'd2u', 'M2M', 'c2c', 'd2d',
'all'):
start = 1
elif pdef in ('t2u', 'm2M', 't2c', 'u2d', 'm2m', 't2t', 'u2u'):
start = 2
elif pdef in ('u2c'):
start = 3
elif pdef in ('c2d'):
start = 4
else:
raise ValueError('Unknown pdef option!')
# else first is up-crossing or min
elif pdef in ('all', 'u2c', 'm2M', 't2c', 'u2d', 'm2m', 't2t', 'u2u'):
start = 0
elif pdef in ('c2d', 'M2m', 'c2t', 'd2u', 'M2M', 'c2c', 'd2d'):
start = 1
elif pdef in ('d2t'):
start = 2
elif pdef in ('t2u'):
start = 3
else:
raise ValueError('Unknown pdef option!')
return start
def _get_step(self, pdef):
# determine the steps between wanted periods
if pdef in ('d2t', 't2u', 'u2c', 'c2d'):
step = 4
elif pdef in ('all'):
step = 1 # secret option!
else:
step = 2
return step
def _interpolate(self, rate):
if rate > 1: # interpolate with spline
n = ceil(self.data.size * rate)
ti = linspace(self.args[0], self.args[-1], n)
x = stineman_interp(ti, self.args, self.data.ravel())
# xi = interp1d(self.args, self.data.ravel(), kind='cubic')(ti)
else:
x = self.data.ravel()
ti = self.args
return x, ti
def wave_periods(self, vh=None, pdef='d2d', wdef=None, index=None, rate=1):
"""
Return sequence of wave periods/lengths from data.
Parameters
----------
vh : scalar
reference level ( default v=mean(x(:,2)) ) or
rainflow filtering height (default h=0)
pdef : string
defining type of waveperiod (wavelength) returned:
Level v separated 't2c', 'c2t', 't2t' or 'c2c' -waveperiod.
Level v 'd2d', 'u2u', 'd2u' or 'u2d' -waveperiod.
Rain flow filtered (with height greater than h)
'm2M', 'M2m', 'm2m' or 'M2M' -waveperiod.
Explanation to the abbreviations:
M=Max, m=min, d=down-crossing, u=up-crossing ,
t=trough and c=crest.
Thus 'd2d' means period between a down-crossing to the
next down-crossing and 'u2c' means period between a
u-crossing to the following crest.
wdef : string
defining type of wave. Possible options are
'mw','Mw','dw', 'uw', 'tw', 'cw' or None.
If wdef is None all troughs and crests will be used,
otherwise only the troughs and crests which define a
wave according to the wavedefinition are used.
index : vector
index sequence of one of the following :
-level v-crossings (indices to "du" are required to
calculate 'd2d', 'd2u', 'u2d' or 'u2u' waveperiods)
-level v separated trough and crest turningpoints
(indices to 'tc' are required to calculate
't2t', 't2c', 'c2t' or 'c2c' waveperiods)
-level v crossings and level v separated trough and
crest turningpoints (indices to "dutc" are
required to calculate t2u, u2c, c2d or d2t
waveperiods)
-rainflow filtered turningpoints with minimum rfc height h
(indices to "mMtc" are required to calculate
'm2m', 'm2M', 'M2m' or 'M2M' waveperiods)
rate : scalar
interpolation rate. If rate larger than one, then x is
interpolated before extrating T
Returns
--------
T : vector
sequence of waveperiods (or wavelengths).
index : vector
of indices
Example:
--------
Histogram of crest2crest waveperiods
>>> import wafo.data as wd
>>> import wafo.objects as wo
>>> import pylab as plb
>>> x = wd.sea()
>>> ts = wo.mat2timeseries(x[0:400,:])
>>> T, ix = ts.wave_periods(vh=0.0, pdef='c2c')
>>> np.allclose(T[:3], [-0.27, -0.08, 0.32])
True
h = plb.hist(T)
See also:
--------
findtp,
findtc,
findcross, perioddef
"""
x, ti = self._interpolate(rate)
if vh is None:
if pdef[0] in ('m', 'M'):
vh = 0
print(' The minimum rfc height, h, is set to: %g' % vh)
else:
vh = x.mean()
print(' The level l is set to: %g' % vh)
if index is None:
index = self._default_index(x, vh, wdef, pdef)
down_crossing_or_max = (x[index[0]] > x[index[1]])
start = self._get_start_index(pdef, down_crossing_or_max)
step = self._get_step(pdef)
# determine the distance between min2min, t2t etc..
if pdef in ('m2m', 't2t', 'u2u', 'M2M', 'c2c', 'd2d'):
dist = 2
else:
dist = 1
nn = len(index)
if pdef[0] in ('u', 'd'):
t0 = ecross(ti, x, index[start:(nn - dist):step], vh)
else: # min, Max, trough, crest or all crossings wanted
t0 = x[index[start:(nn - dist):step]]
if pdef[2] in ('u', 'd'):
t1 = ecross(ti, x, index[(start + dist):nn:step], vh)
else: # min, Max, trough, crest or all crossings wanted
t1 = x[index[(start + dist):nn:step]]
T = t1 - t0
return T, index
def reconstruct(self, inds=None, Nsim=20, L=None, def_='nonlinear',
**options):
'''
function [y,g,g2,test,tobs,mu1o, mu1oStd] = reconstruct(x,)
RECONSTRUCT reconstruct the spurious/missing points of timeseries
CALL: [y,g,g2,test,tobs,mu1o,mu1oStd]=
reconstruct(x,inds,Nsim,L,def,options)
Returns
-------
y = reconstructed signal
g,g2 = smoothed and empirical transformation, respectively
test, tobs = test observator int(g(u)-u)^2 du and
int(g_new(u)-g_old(u))^2 du,
respectively, where int limits is given by param in lc2tr.
Test is a measure of departure from the Gaussian model for
the data. Tobs is a measure of the convergence of the
estimation of g.
mu1o = expected surface elevation of the Gaussian model process.
mu1o_std = standarddeviation of mu1o.
Parameters
----------
x : 2 column timeseries
first column sampling times [sec]
second column surface elevation [m]
inds : integer array
indices to spurious points of x
Nsim = the maximum # of iterations before we stop
L = lag size of the Parzen window function.
If no value is given the lag size is set to
be the lag where the auto correlation is less than
2 standard deviations. (maximum 200)
def :
'nonlinear' : transform from smoothed crossing intensity (default)
'mnonlinear': transform from smoothed marginal distribution
'linear' : identity.
options = options structure defining how the estimation of g is
done, see troptset.
In order to reconstruct the data a transformed Gaussian random process
is used for modelling and simulation of the missing/removed data
conditioned on the other known observations.
Estimates of standarddeviations of y is obtained by a call to tranproc
Std = tranproc(mu1o+/-mu1oStd,fliplr(g));
See also
--------
troptset, findoutliers, cov2csdat, dat2cov, dat2tr, detrendma
Reference
---------
Brodtkorb, P, Myrhaug, D, and Rue, H (2001)
"Joint distribution of wave height and wave crest velocity from
reconstructed data with application to ringing"
Int. Journal of Offshore and Polar Engineering, Vol 11, No. 1,
pp 23--32
Brodtkorb, P, Myrhaug, D, and Rue, H (1999)
"Joint distribution of wave height and wave crest velocity from
reconstructed data
in Proceedings of 9th ISOPE Conference, Vol III, pp 66-73
'''
opt = DotDict(chkder=True, plotflag=False, csm=0.9, gsm=.05,
param=(-5, 5, 513), delay=2, linextrap=True, ntr=10000,
ne=7, gvar=1)
opt.update(options)
_xn = self.data.copy().ravel()
# n = len(xn)
#
# if n < 2:
# raise ValueError('The vector must have more than 2 elements!')
#
# param = opt.param
# plotflags = dict(none=0, off=0, final=1, iter=2)
# plotflag = plotflags.get(opt.plotflag, opt.plotflag)
#
# olddef = def_
# method = 'approx'
# ptime = opt.delay # pause for ptime sec if plotflag=2
#
# expect1 = 1 # first reconstruction by expectation? 1=yes 0=no
# expect = 1 # reconstruct by expectation? 1=yes 0=no
# tol = 0.001 # absolute tolerance of e(g_new-g_old)
#
# cmvmax = 100
# # if number of consecutive missing values (cmv) are longer they
# # are not used in estimation of g, due to the fact that the
# # conditional expectation approaches zero as the length to
# # the closest known points increases, see below in the for loop
# dT = self.sampling_period()
#
# Lm = np.minimum([n, 200, int(200/dT)]) # Lagmax 200 seconds
# if L is not None:
# Lm = max(L, Lm)
# # Lma: size of the moving average window used for detrending the
# # reconstructed signal
# Lma = 1500
# if inds is not None:
# xn[inds] = np.nan
#
# inds = isnan(xn)
# if not inds.any():
# raise ValueError('No spurious data given')
#
# endpos = np.diff(inds)
# strtpos = np.flatnonzero(endpos > 0)
# endpos = np.flatnonzero(endpos < 0)
#
# indg = np.flatnonzero(1-inds) # indices to good points
# inds = np.flatnonzero(inds) # indices to spurious points
#
# indNaN = [] # indices to points omitted in the covariance estimation
# indr = np.arange(n) # indices to point used in the estimation of g
#
# # Finding more than cmvmax consecutive spurios points.
# # They will not be used in the estimation of g and are thus removed
# # from indr.
#
# if strtpos.size > 0 and (endpos.size == 0 or
# endpos[-1] < strtpos[-1]):
# if (n - strtpos[-1]) > cmvmax:
# indNaN = indr[strtpos[-1]+1:n]
# indr = indr[:strtpos[-1]+1]
# strtpos = strtpos[:-1]
#
# if endpos.size > 0 and (strtpos.size == 0 or endpos[0] < strtpos[0]):
# if endpos[0] > cmvmax:
# indNaN = np.hstack((indNaN, indr[:endpos[0]]))
# indr = indr[endpos[0]:]
#
# strtpos = strtpos-endpos[0]
# endpos = endpos-endpos[0]
# endpos = endpos[1:]
#
# for ix in range(len(strtpos)-1, -1, -1):
# if (endpos[ix]-strtpos[ix] > cmvmax):
# indNaN = np.hstack((indNaN, indr[strtpos[ix]+1:endpos[ix]]))
# # remove this when estimating the transform
# del indr[strtpos[ix]+1:endpos[ix]]
#
# if len(indr) < 0.1*n:
# raise ValueError('Not possible to reconstruct signal')
#
# if indNaN.any():
# indNaN = np.sort(indNaN)
#
# # initial reconstruction attempt
# xn[indg, 1] = detrendma(xn[indg, 1], 1500)
# g, test, cmax, irr, g2 = dat2tr(xn[indg, :], def_, opt)
# xnt = xn.copy()
# xnt[indg,:] = dat2gaus(xn[indg,:], g)
# xnt[inds, 1] = np.nan
# rwin = findrwin(xnt, Lm, L)
# print('First reconstruction attempt, e(g-u) = {}'.format(test))
# # old simcgauss
# [samp ,mu1o, mu1oStd] = cov2csdat(xnt(:,2),rwin,1,method,inds);
# if expect1,# reconstruction by expectation
# xnt(inds,2) =mu1o;
# else
# xnt(inds,2) =samp;
# end
# xn=gaus2dat(xnt,g);
# xn(:,2)=detrendma(xn(:,2),Lma); # detrends the signal with a moving
# # average of size Lma
# g_old=g;
#
# bias = mean(xn(:,2));
# xn(:,2)=xn(:,2)-bias; # bias correction
#
# if plotflag==2
# clf
# mind=1:min(1500,n);
# waveplot(xn(mind,:),x(inds(mind),:), 6,1)
# subplot(111)
# pause(ptime)
# end
#
# test0=0;
# for ix=1:Nsim,
# # if 0,#ix==2,
# # rwin=findrwin(xn,Lm,L);
# # xs=cov2sdat(rwin,[n 100 dT]);
# # [g0 test0 cmax irr g2] = dat2tr(xs,def,opt);
# # [test0 ind0]=sort(test0);
# # end
# if 1, #test>test0(end-5),
# # 95# sure the data comes from a non-Gaussian process
# def = olddef; #Non Gaussian process
# else
# def = 'linear'; # Gaussian process
# end
# # used for isope article
# # indr =[1:27000 30000:39000];
# # Too many consecutive missing values will influence the
# # estimation of g. By default do not use consecutive missing
# # values if there are more than cmvmax.
#
# [g test cmax irr g2] = dat2tr(xn(indr,:),def,opt);
# if plotflag==2,
# pause(ptime)
# end
#
# #tobs=sqrt((param(2)-param(1))/(param(3)-1)*
# sum((g_old(:,2)-g(:,2)).^2))
# # new call
# tobs=sqrt((param(2)-param(1))/(param(3)-1)
# *sum((g(:,2)-interp1(g_old(:,1)-bias, g_old(:,2),g(:,1),
# 'spline')).^2));
#
# if ix>1
# if tol>tobs2 && tol>tobs,
# break, #estimation of g converged break out of for loop
# end
# end
#
# tobs2=tobs;
#
# xnt=dat2gaus(xn,g);
# if ~isempty(indNaN), xnt(indNaN,2)=NaN; end
# rwin=findrwin(xnt,Lm,L);
# disp(['Simulation nr: ', int2str(ix), ' of ' num2str(Nsim),
# ' e(g-g_old)=', num2str(tobs), ', e(g-u)=', num2str(test)])
# [samp ,mu1o, mu1oStd] = cov2csdat(xnt(:,2),rwin,1,method,inds);
#
# if expect,
# xnt(inds,2) =mu1o;
# else
# xnt(inds,2) =samp;
# end
#
# xn=gaus2dat(xnt,g);
# if ix<Nsim
# bias=mean(xn(:,2));
# xn(:,2) = (xn(:,2)-bias); # bias correction
# end
# g_old=g;# saving the last transform
# if plotflag==2
# waveplot(xn(mind,:),x(inds(mind),:),6,1,[])
# subplot(111)
# pause(ptime)
# end
# end # for loop
#
# if 1, #test>test0(end-5)
# xnt=dat2gaus(xn,g);
# [samp ,mu1o, mu1oStd] = cov2csdat(xnt(:,2),rwin,1,method,inds);
# xnt(inds,2) =samp;
# xn=gaus2dat(xnt,g);
# bias=mean(xn(:,2));
# xn(:,2) = (xn(:,2)-bias); # bias correction
# g(:,1)=g(:,1)-bias;
# g2(:,1)=g2(:,1)-bias;
# gn=trangood(g);
#
# #mu1o=mu1o-tranproc(bias,gn);
# muUStd=tranproc(mu1o+2*mu1oStd,fliplr(gn));#
# muLStd=tranproc(mu1o-2*mu1oStd,fliplr(gn));#
# else
# muLStd=mu1o-2*mu1oStd;
# muUStd=mu1o+2*mu1oStd;
# end
#
# if plotflag==2 && length(xn)<10000,
# waveplot(xn,[xn(inds,1) muLStd ;xn(inds,1) muUStd ],
# 6,round(n/3000),[])
# legend('reconstructed','2 stdev')
# #axis([770 850 -1 1])
# #axis([1300 1325 -1 1])
# end
# y=xn;
# toc
#
# return
#
# def findrwin(xnt, Lm, L=None):
# r = dat2cov(xnt, Lm) # computes ACF
# # finding where ACF is less than 2 st. deviations .
# # in order to find a better L value
# if L is None:
# L = np.flatnonzero(np.abs(r.R) > 2 * r.stdev)
# if len(L) == 0:
# L = Lm;
# else:
# L = min(np.floor(4/3*(L[-1] + 1), Lm)
# win = parzen(2 * L - 1)
# r.R[:L] = win[L:2*L-1] * r.R[:L]
# r.R[L:] = 0
# return r
def plot_wave(self, sym1='k.', ts=None, sym2='k+', nfig=None, nsub=None,
sigma=None, vfact=3):
'''
Plots the surface elevation of timeseries.
Parameters
----------
sym1, sym2 : string
plot symbol and color for data and ts, respectively
(see PLOT) (default 'k.' and 'k+')
ts : TimeSeries or TurningPoints object
to overplot data. default zero-separated troughs and crests.
nsub : scalar integer
Number of subplots in each figure. By default nsub is such that
there are about 20 mean down crossing waves in each subplot.
If nfig is not given and nsub is larger than 6 then nsub is
changed to nsub=min(6,ceil(nsub/nfig))
nfig : scalar integer
Number of figures. By default nfig=ceil(Nsub/6).
sigma : real scalar
standard deviation of data.
vfact : real scalar
how large in stdev the vertical scale should be (default 3)
Example
-------
Plot x1 with red lines and mark troughs and crests with blue circles.
>>> import wafo
>>> x = wafo.data.sea()
>>> ts150 = wafo.objects.mat2timeseries(x[:150,:])
>>> h = ts150.plot_wave('r-', sym2='bo')
See also
--------
findtc, plot
'''
nw = 20
tn = self.args
xn = self.data.ravel()
indmiss = isnan(xn) # indices to missing points
indg = where(1 - indmiss)[0]
if ts is None:
tc_ix = findtc(xn[indg], 0, 'tw')[0]
xn2 = xn[tc_ix]
tn2 = tn[tc_ix]
else:
xn2 = ts.data
tn2 = ts.args
if sigma is None:
sigma = xn[indg].std()
if nsub is None:
# about Nw mdc waves in each plot
nsub = int(len(xn2) / (2 * nw)) + 1
if nfig is None:
nfig = int(ceil(nsub / 6))
nsub = min(6, int(ceil(nsub / nfig)))
n = len(xn)
Ns = int(n / (nfig * nsub))
ind = r_[0:Ns]
if all(xn >= 0):
vscale = [0, 2 * sigma * vfact] # @UnusedVariable
else:
vscale = array([-1, 1]) * vfact * sigma # @UnusedVariable
XlblTxt = 'Time [sec]'
dT = 1
timespan = tn[ind[-1]] - tn[ind[0]]
if abs(timespan) > 18000: # more than 5 hours
dT = 1 / (60 * 60)
XlblTxt = 'Time (hours)'
elif abs(timespan) > 300: # more than 5 minutes
dT = 1 / 60
XlblTxt = 'Time (minutes)'
if np.max(abs(xn[indg])) > 5 * sigma:
XlblTxt = XlblTxt + ' (Spurious data since max > 5 std.)'
plot = plt.plot
subplot = plt.subplot
figs = []
for unused_iz in range(nfig):
figs.append(plt.figure())
plt.title('Surface elevation from mean water level (MWL).')
for ix in range(nsub):
if nsub > 1:
subplot(nsub, 1, ix)
h_scale = array([tn[ind[0]], tn[ind[-1]]])
ind2 = where((h_scale[0] <= tn2) & (tn2 <= h_scale[1]))[0]
plot(tn[ind] * dT, xn[ind], sym1)
if len(ind2) > 0:
plot(tn2[ind2] * dT, xn2[ind2], sym2)
plot(h_scale * dT, [0, 0], 'k-')
# plt.axis([h_scale*dT, v_scale])
for iy in [-2, 2]:
plot(h_scale * dT, iy * sigma * ones(2), ':')
ind = ind + Ns
plt.xlabel(XlblTxt)
return figs
def plot_sp_wave(self, wave_idx_, *args, **kwds):
"""
Plot specified wave(s) from timeseries
Parameters
----------
wave_idx : integer vector
of indices to waves we want to plot, i.e., wave numbers.
tz_idx : integer vector
of indices to the beginning, middle and end of
defining wave, i.e. for zero-downcrossing waves, indices to
zerocrossings (default trough2trough wave)
Examples
--------
Plot waves nr. 6,7,8 and waves nr. 12,13,...,17
>>> import wafo
>>> x = wafo.data.sea()
>>> ts = wafo.objects.mat2timeseries(x[0:500,...])
>>> h = ts.plot_sp_wave(np.r_[6:9,12:18])
See also
--------
plot_wave, findtc
"""
wave_idx = atleast_1d(wave_idx_).flatten()
tz_idx = kwds.pop('tz_idx', None)
if tz_idx is None:
# finding trough to trough waves
unused_tc_ind, tz_idx = findtc(self.data, 0, 'tw')
dw = nonzero(abs(diff(wave_idx)) > 1)[0]
Nsub = dw.size + 1
Nwp = zeros(Nsub, dtype=int)
if Nsub > 1:
dw = dw + 1
Nwp[Nsub - 1] = wave_idx[-1] - wave_idx[dw[-1]] + 1
wave_idx[dw[-1] + 1:] = -2
for ix in range(Nsub - 2, 1, -2):
# of waves pr subplot
Nwp[ix] = wave_idx[dw[ix] - 1] - wave_idx[dw[ix - 1]] + 1
wave_idx[dw[ix - 1] + 1:dw[ix]] = -2
Nwp[0] = wave_idx[dw[0] - 1] - wave_idx[0] + 1
wave_idx[1:dw[0]] = -2
wave_idx = wave_idx[wave_idx > -1]
else:
Nwp[0] = wave_idx[-1] - wave_idx[0] + 1
Nsub = min(6, Nsub)
Nfig = int(ceil(Nsub / 6))
Nsub = min(6, int(ceil(Nsub / Nfig)))
figs = []
for unused_iy in range(Nfig):
figs.append(plt.figure())
for ix in range(Nsub):
plt.subplot(Nsub, 1, mod(ix, Nsub) + 1)
ind = r_[tz_idx[2 * wave_idx[ix] - 1]:tz_idx[
2 * wave_idx[ix] + 2 * Nwp[ix] - 1]]
# indices to wave
plt.plot(self.args[ind], self.data[ind], *args, **kwds)
plt.hold('on')
xi = [self.args[ind[0]], self.args[ind[-1]]]
plt.plot(xi, [0, 0])
if Nwp[ix] == 1:
plt.ylabel('Wave %d' % wave_idx[ix])
else:
plt.ylabel(
'Wave %d - %d' % (wave_idx[ix],
wave_idx[ix] + Nwp[ix] - 1))
plt.xlabel('Time [sec]')
# wafostamp
return figs
def test_docstrings():
import doctest
print('Testing docstrings in %s' % __file__)
doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
if __name__ == '__main__':
test_docstrings()
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