added fourier (not finished)

added TurningPoints.rainflow_filter
+translated some parts of chapter4.py

pywafo/.pydevproject

@@ -3,7 +3,7 @@
 
 <pydev_project>
 <pydev_pathproperty name="org.python.pydev.PROJECT_SOURCE_PATH">
-<path>/pywafo/src</path>
+<path>/google_pywafo/src</path>
 </pydev_pathproperty>
 <pydev_property name="org.python.pydev.PYTHON_PROJECT_VERSION">python 2.6</pydev_property>
 <pydev_property name="org.python.pydev.PYTHON_PROJECT_INTERPRETER">Default</pydev_property>

pywafo/src/wafo/doc/tutorial_scripts/chapter4.py

@@ -1,64 +1,77 @@
-%% CHAPTER4 contains the commands used in Chapter 4 of the tutorial
+import numpy as np
-%
+from scipy import *
-% CALL:  Chapter4
+from pylab import *
-% 
+
-% Some of the commands are edited for fast computation. 
+#! CHAPTER4 contains the commands used in Chapter 4 of the tutorial
-% Each set of commands is followed by a 'pause' command.
+#!=================================================================
-% 
+#!
-% This routine also can print the figures; 
+#! CALL:  Chapter4
-% For printing the figures on directory ../bilder/ edit the file and put  
+#! 
-%     printing=1;
+#! Some of the commands are edited for fast computation. 
-
+#! Each set of commands is followed by a 'pause' command.
-% Tested on Matlab 5.3
+#! 
-% History
+#! This routine also can print the figures; 
-% Revised pab sept2005
+#! For printing the figures on directory ../bilder/ edit the file and put  
-%  Added sections -> easier to evaluate using cellmode evaluation.
+#!     printing=1;
-% revised pab Feb2004
+
-% updated call to lc2sdat
+#! Tested on Matlab 5.3
-% Created by GL July 13, 2000
+#! History
-% from commands used in Chapter 4
+#! Revised pab sept2005
-%
+#!  Added sections -> easier to evaluate using cellmode evaluation.
+#! revised pab Feb2004
+#! updated call to lc2sdat
+#! Created by GL July 13, 2000
+#! from commands used in Chapter 4
+#!
  
-%% Chapter 4 Fatigue load analysis and rain-flow cycles
+#! Chapter 4 Fatigue load analysis and rain-flow cycles
-
+#!------------------------------------------------------
-pstate = 'off';
 
 printing=0;
-%set(0,'DefaultAxesFontSize',15')
-
-%% Section 4.3.1 Crossing intensity
-xx_sea = load('sea.dat'); 
-tp_sea = dat2tp(xx_sea);
-lc_sea = tp2lc(tp_sea);
-T_sea = xx_sea(end,1)-xx_sea(1,1);
-lc_sea(:,2) = lc_sea(:,2)/T_sea;
-clf
-subplot(221), plot(lc_sea(:,1),lc_sea(:,2)) 
-title('Crossing intensity, (u, \mu(u))')
-subplot(222), semilogx(lc_sea(:,2),lc_sea(:,1)) 
-title('Crossing intensity, (log \mu(u), u)')
-wafostamp([],'(ER)')
-disp('Block 1'), pause(pstate)
 
-m_sea = mean(xx_sea(:,2));
+
-f0_sea = interp1(lc_sea(:,1),lc_sea(:,2),m_sea,'linear')
+#! Section 4.3.1 Crossing intensity
-extr_sea = length(tp_sea)/(2*T_sea);
+#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+import wafo.data as wd
+import wafo.objects as wo
+
+xx_sea = wd.sea()  
+ts = wo.mat2timeseries(xx_sea)
+tp = ts.turning_points()
+mM = tp.cycle_pairs(kind='min2max')
+lc = mM.level_crossings()
+lci = lc.copy()
+T_sea = lci.args[-1]-lci.args[0]
+lci.data = lci.data/T_sea
+lci.labels.ylab='Crossing intensity'
+subplot(2,2,1)
+lci.plot()
+subplot(2,2,2)
+lci.setplotter(plotmethod='step')
+show() 
+ 
+ 
+m_sea = ts.data.mean() 
+f0_sea = interp(m_sea, lci.args,lci.data)
+extr_sea = len(tp.data)/(2*T_sea)
 alfa_sea = f0_sea/extr_sea
-disp('Block 2'),pause(pstate)
+print('alfa = %g ' % alfa_sea )
 
-%% Section 4.3.2 Extraction of rainflow cycles
+#! Section 4.3.2 Extraction of rainflow cycles
-%% Min-max and rainflow cycle plots
+#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-RFC_sea=tp2rfc(tp_sea);
+#!#! Min-max and rainflow cycle plots
+
+mM_rfc=tp.cycle_pairs(h=0.3)
 mM_sea=tp2mm(tp_sea);
 clf
-subplot(122), ccplot(mM_sea);
+subplot(122), 
+mM.plot() 
 title('min-max cycle count')
-subplot(121), ccplot(RFC_sea);
+subplot(121), 
+mM_rfc.plot()
 title('Rainflow cycle count')
-wafostamp([],'(ER)')
-disp('Block 3'),pause(pstate)
 
-%% Min-max and rainflow cycle distributions
+#!#! Min-max and rainflow cycle distributions
 ampmM_sea=cc2amp(mM_sea);
 ampRFC_sea=cc2amp(RFC_sea);
 clf
@@ -69,8 +82,8 @@ title('Rainflow amplitude distribution')
 wafostamp([],'(ER)')
 disp('Block 4'),pause(pstate)
 
-%% Section 4.3.3 Simulation of rainflow cycles
+#!#! Section 4.3.3 Simulation of rainflow cycles
-%% Simulation of cycles in a Markov model
+#!#! Simulation of cycles in a Markov model
 n=41; param_m=[-1 1 n]; param_D=[1 n n];
 u_markov=levels(param_m);
 G_markov=mktestmat(param_m,[-0.2 0.2],0.15,1);
@@ -84,8 +97,8 @@ wafostamp([],'(ER)')
 disp('Block 5'),pause(pstate)
 
 
-%% Rainflow cycles in a transformed Gaussian model
+#!#! Rainflow cycles in a transformed Gaussian model
-%% Hermite transformed wave data and rainflow filtered turning points, h = 0.2.
+#!#! Hermite transformed wave data and rainflow filtered turning points, h = 0.2.
 me = mean(xx_sea(:,2));
 sa = std(xx_sea(:,2));
 Hm0_sea = 4*sa;
@@ -98,10 +111,10 @@ param_h = [-1.5 2 51];
 spec_norm = spec;
 spec_norm.S = spec_norm.S/sa^2;
 xx_herm = spec2sdat(spec_norm,[2^15 1],0.1);
-% ????? PJ, JR 11-Apr-2001
+#! ????? PJ, JR 11-Apr-2001
-% NOTE, in the simulation program spec2sdat
+#! NOTE, in the simulation program spec2sdat
-%the spectrum must be normalized to variance 1 
+#!the spectrum must be normalized to variance 1 
-% ?????
+#! ?????
 h = 0.2;
 [dtp,u_herm,xx_herm_1]=dat2dtp(param_h,xx_herm,h);
 clf
@@ -112,7 +125,7 @@ title('Rainflow filtered wave data')
 wafostamp([],'(ER)')
 disp('Block 6'),pause(pstate)
 
-%% Rainflow cycles and rainflow filtered rainflow cycles in the transformed Gaussian process.
+#!#! Rainflow cycles and rainflow filtered rainflow cycles in the transformed Gaussian process.
 tp_herm=dat2tp(xx_herm);
 RFC_herm=tp2rfc(tp_herm);
 mM_herm=tp2mm(tp_herm);
@@ -129,7 +142,7 @@ end
 wafostamp([],'(ER)')
 disp('Block 7'),pause(pstate)
 
-%% Section 4.3.4 Calculating the rainflow matrix
+#!#! Section 4.3.4 Calculating the rainflow matrix
 
 
 Grfc_markov=mctp2rfm({G_markov []});
@@ -139,13 +152,13 @@ subplot(122), cmatplot(u_markov,u_markov,Grfc_markov), axis('square')
 wafostamp([],'(ER)')
 disp('Block 8'),pause(pstate)
 
-%% 
+#!#! 
 clf
 cmatplot(u_markov,u_markov,{G_markov Grfc_markov},3) 
 wafostamp([],'(ER)')
 disp('Block 9'),pause(pstate)	
 
-%% Min-max-matrix and theoretical rainflow matrix for test Markov sequence.
+#!#! Min-max-matrix and theoretical rainflow matrix for test Markov sequence.
 cmatplot(u_markov,u_markov,{G_markov Grfc_markov},4)
 subplot(121), axis('square'), title('min2max transition matrix')
 subplot(122), axis('square'), title('Rainflow matrix')
@@ -154,7 +167,7 @@ end
 wafostamp([],'(ER)')
 disp('Block 10'),pause(pstate)
 
-%% Observed and theoretical rainflow matrix for test Markov sequence.
+#!#! Observed and theoretical rainflow matrix for test Markov sequence.
 n=length(u_markov);
 Frfc_markov=dtp2rfm(xxD_markov,n);
 clf
@@ -166,7 +179,7 @@ end
 wafostamp([],'(ER)')
 disp('Block 11'),pause(pstate)
 
-%% Smoothed observed and calculated rainflow matrix for test Markov sequence.
+#!#! Smoothed observed and calculated rainflow matrix for test Markov sequence.
 tp_markov=dat2tp(xx_markov);
 RFC_markov=tp2rfc(tp_markov);
 h=1;
@@ -180,16 +193,16 @@ end
 wafostamp([],'(ER)')
 disp('Block 12'),pause(pstate)
 
-%% Rainflow matrix from spectrum
+#!#! Rainflow matrix from spectrum
 clf
-%GmM3_herm=spec2mmtpdf(spec,[],'Mm',[],[],2);
+#!GmM3_herm=spec2mmtpdf(spec,[],'Mm',[],[],2);
 GmM3_herm=spec2cmat(spec,[],'Mm',[],param_h,2);
 pdfplot(GmM3_herm)
 wafostamp([],'(ER)')
 disp('Block 13'),pause(pstate)
 
 
-%% Min-max matrix and theoretical rainflow matrix for Hermite-transformed Gaussian waves.
+#!#! Min-max matrix and theoretical rainflow matrix for Hermite-transformed Gaussian waves.
 Grfc_herm=mctp2rfm({GmM3_herm.f []});
 u_herm=levels(param_h);
 clf
@@ -201,7 +214,7 @@ end
 wafostamp([],'(ER)')
 disp('Block 14'),pause(pstate)
 
-%%
+#!#!
 clf
 Grfc_direct_herm=spec2cmat(spec,[],'rfc',[],[],2);
 subplot(121), pdfplot(GmM3_herm), axis('square'), hold on
@@ -212,8 +225,8 @@ wafostamp([],'(ER)')
 disp('Block 15'),pause(pstate)
 
 
-%% Observed smoothed and theoretical min-max matrix, 
+#!#! Observed smoothed and theoretical min-max matrix, 
-%% (and observed smoothed and theoretical rainflow matrix for Hermite-transformed Gaussian waves).
+#!#! (and observed smoothed and theoretical rainflow matrix for Hermite-transformed Gaussian waves).
 tp_herm=dat2tp(xx_herm);
 RFC_herm=tp2rfc(tp_herm);
 mM_herm=tp2mm(tp_herm);
@@ -233,10 +246,10 @@ end
 wafostamp([],'(ER)')
 disp('Block 16'),pause(pstate)
    
-%% Section 4.3.5 Simulation from crossings and rainflow structure
+#!#! Section 4.3.5 Simulation from crossings and rainflow structure
 
-%% Crossing spectrum (smooth curve) and obtained spectrum (wiggled curve)
+#!#! Crossing spectrum (smooth curve) and obtained spectrum (wiggled curve)
-%% for simulated process with irregularity factor 0.25.
+#!#! for simulated process with irregularity factor 0.25.
 clf
 cross_herm=dat2lc(xx_herm);
 alpha1=0.25;
@@ -258,8 +271,8 @@ end
 wafostamp([],'(ER)')
 disp('Block 16'),pause(pstate)
 
-%% Crossing spectrum (smooth curve) and obtained spectrum (wiggled curve)
+#!#! Crossing spectrum (smooth curve) and obtained spectrum (wiggled curve)
-%% for simulated process with irregularity factor 0.75.
+#!#! for simulated process with irregularity factor 0.75.
 xx_herm_sim2=lc2sdat(cross_herm,500,alpha2);
 cross_herm_sim2=dat2lc(xx_herm_sim2);
 subplot(211)
@@ -277,15 +290,15 @@ end
 wafostamp([],'(ER)')
 disp('Block 17'),pause(pstate)
 
-%% Section 4.4 Fatigue damage and fatigue life distribution
+#!#! Section 4.4 Fatigue damage and fatigue life distribution
-%% Section 4.4.1 Introduction
+#!#! Section 4.4.1 Introduction
 beta=3.2; gam=5.5E-10; T_sea=xx_sea(end,1)-xx_sea(1,1);
 d_beta=cc2dam(RFC_sea,beta)/T_sea;
-time_fail=1/gam/d_beta/3600      %in hours of the specific storm
+time_fail=1/gam/d_beta/3600      #!in hours of the specific storm
 disp('Block 18'),pause(pstate)
 
-%% Section 4.4.2 Level crossings
+#!#! Section 4.4.2 Level crossings
-%% Crossing intensity as calculated from the Markov matrix (solid curve) and from the observed rainflow matrix (dashed curve).
+#!#! Crossing intensity as calculated from the Markov matrix (solid curve) and from the observed rainflow matrix (dashed curve).
 clf
 mu_markov=cmat2lc(param_m,Grfc_markov);
 muObs_markov=cmat2lc(param_m,Frfc_markov/(T_markov/2));
@@ -297,8 +310,8 @@ end
 wafostamp([],'(ER)')
 disp('Block 19'),pause(pstate)
 
-%% Section 4.4.3 Damage
+#!#! Section 4.4.3 Damage
-%% Distribution of damage from different RFC cycles, from calculated theoretical and from observed rainflow matrix.
+#!#! Distribution of damage from different RFC cycles, from calculated theoretical and from observed rainflow matrix.
 beta = 4;
 Dam_markov = cmat2dam(param_m,Grfc_markov,beta)
 DamObs1_markov = cc2dam(RFC_markov,beta)/(T_markov/2)
@@ -318,24 +331,24 @@ wafostamp([],'(ER)')
 disp('Block 21'),pause(pstate)
 
 
-%%
+#!#!
-%Damplus_markov = lc2dplus(mu_markov,beta)
+#!Damplus_markov = lc2dplus(mu_markov,beta)
 pause(pstate)
 
-%% Section 4.4.4 Estimation of S-N curve
+#!#! Section 4.4.4 Estimation of S-N curve
 
-%% Load SN-data and plot in log-log scale.
+#!#! Load SN-data and plot in log-log scale.
 SN = load('sn.dat');
 s = SN(:,1);
 N = SN(:,2);
 clf
 loglog(N,s,'o'), axis([0 14e5 10 30])
-%if (printing==1), print -deps ../bilder/fatigue_?.eps end
+#!if (printing==1), print -deps ../bilder/fatigue_?.eps end
 wafostamp([],'(ER)')
 disp('Block 22'),pause(pstate)
 
 
-%% Check of S-N-model on normal probability paper.
+#!#! Check of S-N-model on normal probability paper.
 
 normplot(reshape(log(N),8,5))
 if (printing==1), print -deps ../bilder/fatigue_17.eps 
@@ -343,7 +356,7 @@ end
 wafostamp([],'(ER)')
 disp('Block 23'),pause(pstate)
 
-%% Estimation of S-N-model on linear  scale.
+#!#! Estimation of S-N-model on linear  scale.
 clf
 [e0,beta0,s20] = snplot(s,N,12);
 title('S-N-data with estimated N(s)','FontSize',20)
@@ -353,7 +366,7 @@ end
 wafostamp([],'(ER)')
 disp('Block 24'),pause(pstate)
 
-%% Estimation of S-N-model on log-log scale.
+#!#! Estimation of S-N-model on log-log scale.
 clf
 [e0,beta0,s20] = snplot(s,N,14);
 title('S-N-data with estimated N(s)','FontSize',20)
@@ -363,8 +376,8 @@ end
 wafostamp([],'(ER)')
 disp('Block 25'),pause(pstate)
 
-%% Section 4.4.5 From S-N curve to fatigue life distribution
+#!#! Section 4.4.5 From S-N curve to fatigue life distribution
-%% Damage intensity as function of $\beta$
+#!#! Damage intensity as function of $\beta$
 beta = 3:0.1:8;
 DRFC = cc2dam(RFC_sea,beta);
 dRFC = DRFC/T_sea;
@@ -375,7 +388,7 @@ end
 wafostamp([],'(ER)')
 disp('Block 26'),pause(pstate)
 
-%% Fatigue life distribution with sea load.
+#!#! Fatigue life distribution with sea load.
 dam0 = cc2dam(RFC_sea,beta0)/T_sea;
 [t0,F0] = ftf(e0,dam0,s20,0.5,1);
 [t1,F1] = ftf(e0,dam0,s20,0,1);

pywafo/src/wafo/misc.py

@@ -1,12 +1,12 @@
 '''
-Misc lsdkfalsdflasdfl
+Misc 
 '''
 from __future__ import division
 
 import sys
 import fractions
 import numpy as np
-from numpy import (abs, amax, any, logical_and, arange, linspace, atleast_1d,
+from numpy import (abs, amax, any, logical_and, arange, linspace, atleast_1d,atleast_2d,
                    array, asarray, broadcast_arrays, ceil, floor, frexp, hypot,
                    sqrt, arctan2, sin, cos, exp, log, mod, diff, empty_like,
                    finfo, inf, pi, interp, isnan, isscalar, zeros, ones,
@@ -16,6 +16,7 @@ import types
 import warnings
 from wafo import plotbackend
 
+
 try:
     import wafo.c_library as clib
 except:
@@ -31,6 +32,7 @@ __all__ = ['is_numlike','JITImport', 'DotDict', 'Bunch', 'printf', 'sub_dict_sel
     'discretize', 'discretize2', 'pol2cart', 'cart2pol',  'meshgrid', 'ndgrid', 
     'trangood', 'tranproc', 'histgrm', 'num2pistr']
 
+
 def is_numlike(obj):
     'return true if *obj* looks like a number'
     try:
@@ -2039,6 +2041,173 @@ def num2pistr(x, n=3):
         xtxt = format % x
     return xtxt     
 
+def fourier(x,t,T=None,M=None,N=None, method='trapz'):
+    '''
+    Returns Fourier coefficients.
+    
+      CALL:  [a,b] = fourier(t,x,T,M);
+    
+        
+    Parameters
+    ----------
+    x : array-like
+        vector or matrix of row vectors with data points shape p x n.
+    t : array-like
+        vector with n values indexed from 1 to N.
+    T : real scalar
+        primitive period of signal, i.e., smallest period. (default T = t[-1]-t[0] 
+    M : scalar integer
+        defines no of harmonics desired (default M = N)
+    N : scalar integer 
+        no of data points (default len(t))
+    
+    Returns
+    -------
+    a,b  = Fourier coefficients size M x P
+    
+    FOURIER finds the coefficients for a Fourier series representation
+    of the signal x(t) (given in digital form).  It is assumed the signal
+    is periodic over T.  N is the number of data points, and M-1 is the
+    number of coefficients.
+    
+    The signal can be estimated by using M-1 harmonics by:
+                        M-1
+     x[i] = 0.5*a[0] + sum (a[n]*c[n,i] + b[n]*s[n,i])
+                       n=1
+    where
+       c[n,i] = cos(2*pi*(n-1)*t[i]/T)
+       s[n,i] = sin(2*pi*(n-1)*t[i]/T)
+    
+    Note that a[0] is the "dc value".
+    Remaining values are a[1], a[2], ... , a[M-1].
+     
+    Example
+    -------
+    >>> t = linspace(0,4*T) 
+    >>> x = sin(t);
+    >>> a, b = fourier(t, x, T=2*pi, M=5)
+    
+    See also
+    --------
+    fft
+    '''
+    x = np.atleast_2d(x)
+    p, n = x.shape
+    if t is None:
+        t = np.arange(n)
+    else:
+        t = np.atleast_1d(t)
+    
+    n = len(t) if n is None else n
+    m = n if n is none else m
+    T = t[-1]-t[0] if T is None else T
+    
+     
+    
+#    switch 0
+#      case -1,
+#           % Define the vectors for computing the Fourier coefficients
+#      %
+#      a = zeros(M,P);
+#      b = zeros(M,P);
+#      a(1,:) = simpson(x);
+#    
+#      %
+#      % Compute M-1 more coefficients
+#      tmp  = 2*pi*t(:,ones(1,P))/T;
+#      % tmp =  2*pi*(0:N-1).'/(N-1); 
+#      for n1 = 1:M-1,
+#        n = n1+1;
+#        a(n,:) = simpson(x.*cos(n1*tmp));
+#        b(n,:) = simpson(x.*sin(n1*tmp));
+#      end
+#      
+#      a = 2*a/N;
+#      b = 2*b/N;
+#      
+#      case 0,
+#         %
+#      a = zeros(M,P);
+#      b = zeros(M,P);
+#      a(1,:) = trapz(t,x);
+#    
+#      %
+#      % Compute M-1 more coefficients
+#      tmp  = 2*pi*t(:,ones(1,P))/T;
+#      % tmp =  2*pi*(0:N-1).'/(N-1); 
+#      for n1 = 1:M-1,
+#        n = n1+1;
+#        a(n,:) = trapz(t,x.*cos(n1*tmp));
+#        b(n,:) = trapz(t,x.*sin(n1*tmp));
+#      end
+#      a = a/pi;
+#      b = b/pi;
+#      
+#      case 1,
+#      % Define the vectors for computing the Fourier coefficients
+#      %
+#      a = zeros(M,P);
+#      b = zeros(M,P);
+#      
+#      %
+#      % Compute the dc-level (the a(0) component).
+#      %
+#      % Note: the index has to begin with "1".
+#      %
+#      
+#      a(1,:) = sum(x);
+#    
+#      %
+#      % Compute M-1 more coefficients
+#      tmp  = 2*pi*t(:,ones(1,P))/T;
+#      % tmp =  2*pi*(0:N-1).'/(N-1); 
+#      for n1 = 1:M-1,
+#        n = n1+1;
+#        a(n,:) = sum(x.*cos(n1*tmp));
+#        b(n,:) = sum(x.*sin(n1*tmp));
+#      end
+#      a = 2*a/N;
+#      b = 2*b/N;
+#    case 2,
+#       % Define the vectors for computing the Fourier coefficients
+#      %
+#      a = zeros(M,P);
+#      b = zeros(M,P);
+#      a(1,:) = trapz(x);
+#    
+#      %
+#      % Compute M-1 more coefficients
+#      tmp  = 2*pi*t(:,ones(1,P))/T;
+#      % tmp =  2*pi*(0:N-1).'/(N-1); 
+#      for n1 = 1:M-1,
+#        n = n1+1;
+#        a(n,:) = trapz(x.*cos(n1*tmp));
+#        b(n,:) = trapz(x.*sin(n1*tmp));
+#      end
+#      
+#      a = 2*a/N;
+#      b = 2*b/N;
+#    case 3
+#      % Alternative:  faster for large M, but gives different results than above.
+#      nper = diff(t([1 end]))/T; %No of periods given
+#      if nper == round(nper), 
+#        N1 = N/nper;
+#      else
+#        N1 = N;
+#      end
+#    
+#      % Fourier coefficients by fft
+#      Fcof1 = 2*ifft(x(1:N1,:),[],1);
+#      Pcor = [1; exp(sqrt(-1)*(1:M-1).'*t(1))]; % correction term to get
+#                                                  % the correct integration limits
+#      Fcof = Fcof1(1:M,:).*Pcor(:,ones(1,P));
+#      a = real(Fcof(1:M,:));
+#      b = imag(Fcof(1:M,:));
+#    end
+#    return
+
+ 
+
 def _test_find_cross():
     t = findcross([0, 0, 1, -1, 1], 0)
     

pywafo/src/wafo/objects.py

@@ -16,7 +16,7 @@ from __future__ import division
 from wafo.transform.core import TrData
 from wafo.transform.models import TrHermite, TrOchi, TrLinear
 from wafo.stats import edf
-from wafo.misc import (nextpow2, findtp, findtc, findcross,
+from wafo.misc import (nextpow2, findtp, findrfc, findtc, findcross,
                        ecross, JITImport, DotDict, gravity)
 from wafodata import WafoData
 from wafo.interpolate import SmoothSpline
@@ -632,7 +632,50 @@ class TurningPoints(WafoData):
             self.args = ravel(self.args)
         self.data = ravel(self.data)
 
-    def cycle_pairs(self, kind='min2max'):
+    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)
+        >>> 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
@@ -660,7 +703,12 @@ class TurningPoints(WafoData):
         TurningPoints
         SurvivalCycleCount
         """
-        if self.data[0] > self.data[1]:
+        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:
@@ -670,12 +718,12 @@ class TurningPoints(WafoData):
         # Extract min-max and max-min cycle pairs
         #n = len(self.data)
         if kind.lower().startswith('min2max'):
-            m = self.data[im:-1:2]
+            m = data[im:-1:2]
-            M = self.data[im + 1::2]
+            M = data[im + 1::2]
         else:
             kind = 'max2min'
-            M = self.data[iM:-1:2]
+            M = data[iM:-1:2]
-            m = self.data[iM + 1::2]
+            m = data[iM + 1::2]
         return CyclePairs(M, m, kind=kind, mean=self.mean, sigma=self.sigma)
 
 def mat2timeseries(x):