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Small update

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pbrod 9 years ago
parent 02a899c0ad
commit 0d7ef88fae
1 changed files with 115 additions and 110 deletions
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wafo/objects.py
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@ -17,7 +17,8 @@ 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)
ecross, JITImport, DotDict, gravity, findrfc_astm,
detrendma)
from wafo.interpolate import stineman_interp
from wafo.containers import PlotData
from wafo.plotbackend import plotbackend
@ -37,6 +38,7 @@ from numpy import (inf, pi, zeros, ones, sqrt, where, log, exp, cos, sin,
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)
@ -1955,93 +1957,94 @@ class TimeSeries(PlotData):
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,2) = detrendma(xn(indg,2),1500);
# [g, test, cmax, irr, g2] = dat2tr(xn(indg,:),def,opt);
# xnt=xn;
# xnt(indg,:)=dat2gaus(xn(indg,:),g);
# xnt(inds,2)=NaN;
# rwin=findrwin(xnt,Lm,L);
# disp(['First reconstruction attempt, e(g-u)=', num2str(test)] )
_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
@ -2056,7 +2059,7 @@ class TimeSeries(PlotData):
#
# bias = mean(xn(:,2));
# xn(:,2)=xn(:,2)-bias; # bias correction
#
# if plotflag==2
# clf
# mind=1:min(1500,n);
@ -2091,10 +2094,12 @@ class TimeSeries(PlotData):
# pause(ptime)
# end
#
# #tobs=sqrt((param(2)-param(1))/(param(3)-1)*sum((g_old(:,2)-g(:,2)).^2))
# #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));
# 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,
@ -2107,7 +2112,8 @@ class TimeSeries(PlotData):
# 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)])
# 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,
@ -2149,7 +2155,8 @@ class TimeSeries(PlotData):
# end
#
# if plotflag==2 && length(xn)<10000,
# waveplot(xn,[xn(inds,1) muLStd ;xn(inds,1) muUStd ], 6,round(n/3000),[])
# 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])
@ -2159,22 +2166,20 @@ class TimeSeries(PlotData):
#
# return
#
# function r=findrwin(xnt,Lm,L)
# r=dat2cov(xnt,Lm);#computes ACF
# #finding where ACF is less than 2 st. deviations .
# 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 nargin<3||isempty(L)
# L=find(abs(r.R)>2*r.stdev)+1;
# if isempty(L), # pab added this check 09.10.2000
# if L is None:
# L = np.flatnonzero(np.abs(r.R) > 2 * r.stdev)
# if len(L) == 0:
# L = Lm;
# else
# L = min([floor(4/3*L(end)) Lm]);
# end
# end
# win=parzen(2*L-1);
# r.R(1:L)=win(L:2*L-1).*r.R(1:L);
# r.R(L+1:end)=0;
# return
# 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):

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