{
 "metadata": {
  "name": "WAFO Chapter 1"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "CHAPTER 1 demonstrates some applications of WAFO"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "CHAPTER1 gives an overview through examples some of the capabilities of WAFO. WAFO is a toolbox of Matlab routines for statistical analysis and simulation of random waves and loads. The commands are edited for fast computation.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Section 1.4 Some applications of WAFO"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Section 1.4.1 Simulation from spectrum, estimation of spectrum "
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Simulation of the sea surface from spectrum. The following code generates 200 seconds of data sampled with 10Hz from the Torsethaugen spectrum."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import wafo.spectrum.models as wsm\n",
      "S = wsm.Torsethaugen(Hm0=6, Tp=8);\n",
      "S1 = S.tospecdata()\n",
      "S1.plot()\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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NmlW2MJXnDP71L+XDdvly1V7SLDNnAhERwP/8j+xKiMiWqTZnEBQUhI0bN6Ko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\nYII=\n"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import wafo.objects as wo\n",
      "xs = S1.sim(ns=2000, dt=0.1)\n",
      "ts = wo.mat2timeseries(xs)\n",
      "ts.plot_wave('-')\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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IR6gNrGRnq7ekmzVTVuhHr0CNK8T99m2aYt+qlXmbHHF3leUu5ZaR+yARrwUx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\nfOB4/U09iIwsO9Hr6FH9HqxaxF202r2hKrbBYD1vQolLRsR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dbBwAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Estimation of spectrum "
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "A common situation is that one wants to estimate the spectrum for wave measurements. The following code simulate 20 minutes signal sampled at 4Hz and compare the spectral estimate with the original Torsethaugen spectum.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf()\n",
      "Fs = 4;  \n",
      "xs = S1.sim(ns=fix(20 * 60 * Fs), dt=1. / Fs) \n",
      "ts = wo.mat2timeseries(xs) \n",
      "Sest = ts.tospecdata(L=400)\n",
      "S1.plot()\n",
      "Sest.plot('--')\n",
      "axis([0, 3, 0, 5]) # This may depend on the simulation\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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A34dw++1NHGATiYiA/fvVjkII0RwMSgYbN26sSQBarRZFUWqaiQYOHMjAgQMp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\n3hGSAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Section 1.4.2 Probability distributions of wave characteristics."
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Probability distribution of wave trough period: WAFO gives the possibility of computing the exact probability distributions for a number of characteristics given a spectral density. In the following example we study the trough period extracted from the time series and compared with the theoretical density computed with exact spectrum, S1, and the estimated spectrum, Sest.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf()\n",
      "import wafo.misc as wm\n",
      "dtyex = S1.to_t_pdf(pdef='Tt', paramt=(0, 10, 51), nit=3)\n",
      "dtyest = Sest.to_t_pdf(pdef='Tt', paramt=(0, 10, 51), nit=3)\n",
      "\n",
      "T, index = ts.wave_periods(vh=0, pdef='d2u')\n",
      "bins = wm.good_bins(T, num_bins=25, odd=True)\n",
      "wm.plot_histgrm(T, bins=bins, normed=True)\n",
      "\n",
      "dtyex.plot()\n",
      "dtyest.plot('-.')\n",
      "axis([0, 10, 0, 0.35])\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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v0QOABdELmL13NruG7cLP1a+QAUR5eKHFCzTyeZ8ePp64qB1GCAMiIyOJjIws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\ngg==\n"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Section 1.4.3 Directional spectra"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Here are a few lines of code, which produce directional spectra with frequency independent and frequency dependent spreading."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf()\n",
      "plotflag = 1\n",
      "Nt = 101;   # number of angles\n",
      "th0 = pi / 2; # primary direction of waves\n",
      "Sp = 15;   # spreading parameter\n",
      "\n",
      "D1 = wsm.Spreading(type='cos', theta0=th0, method=None) # frequency independent\n",
      "D12 = wsm.Spreading(type='cos', theta0=0, method='mitsuyasu') # frequency dependent\n",
      "\n",
      "SD1 = D1.tospecdata2d(S1)\n",
      "SD12 = D12.tospecdata2d(S1)\n",
      "SD1.plot()\n",
      "SD12.plot()#linestyle='dashdot')\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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zcmHae7DvNAxsB96ukJIFhy5AoQH+0wPG9oEuzSp+yelcHGzcq4y3928LSydC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\nIpf6HamIVgAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "3D Simulation of the sea surface "
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "The simulations show that frequency dependent spreading leads to much more irregular surface so the orientation of waves is less transparent compared to the frequency independent case."
     ]
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Frequency independent spreading"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#plotflag = 1; iseed = 1;\n",
      "#\n",
      "#Nx = 2 ^ 8;Ny = Nx;Nt = 1;dx = 0.5; dy = dx; dt = 0.25; fftdim = 2;\n",
      "#randn('state', iseed)\n",
      "#Y1 = seasim(SD1, Nx, Ny, Nt, dx, dy, dt, fftdim, plotflag);\n",
      "#wafostamp('', '(ER)')\n",
      "#axis('fill')\n",
      "#disp('Block = 6'), pause(pstate)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Frequency dependent spreading"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#randn('state', iseed)\n",
      "#Y12 = seasim(SD12, Nx, Ny, Nt, dx, dy, dt, fftdim, plotflag);\n",
      "#wafostamp('', '(ER)')\n",
      "#axis('fill')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Estimation of directional spectrum"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "The figure is not shown in the Tutorial"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Nx = 3; Ny = 2; Nt = 2 ^ 12; dx = 10; dy = 10;dt = 0.5;\n",
      "# F = seasim(SD12, Nx, Ny, Nt, dx, dy, dt, 1, 0);  \n",
      "# Z = permute(F.Z, [3 1 2]);\n",
      "# [X, Y] = meshgrid(F.x, F.y);\n",
      "# N = Nx * Ny;\n",
      "# types = repmat(sensortypeid('n'), N, 1);\n",
      "# bfs = ones(N, 1);\n",
      "# pos = [X(:), Y(:), zeros(N, 1)];\n",
      "# h = inf;\n",
      "# nfft = 128;\n",
      "# nt = 101;\n",
      "# SDe = dat2dspec([F.t Z(:, :)], [pos types, bfs], h, nfft, nt);\n",
      "#plotspec(SDe), hold on\n",
      "#plotspec(SD12, '--'), hold off\n",
      "#disp('Block = 8'), pause(pstate)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Section 1.4.4 Fatigue, Load cycles and Markov models"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Switching Markow chain of turningpoints.\n",
      "In fatigue applications the exact sample path is not important, but only the tops and bottoms of the load, called the sequence of turning points (TP). From the turning points one can extract load cycles, from which damage calculations and fatigue life predictions can be performed.\n",
      "\n",
      "The commands below computes the intensity of rainflowcycles for the Gaussian model with spectrum S1 using the Markov approximation. \n",
      "The rainflow cycles found in the simulated load signal are shown in the figure."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#clf()\n",
      "#paramu = [-6 6 61];\n",
      "#frfc = spec2cmat(S1, [], 'rfc', [], paramu);\n",
      "#pdfplot(frfc);\n",
      "#hold on\n",
      "#tp = dat2tp(xs);\n",
      "#rfc = tp2rfc(tp);\n",
      "#plot(rfc(:, 2), rfc(:, 1), '.')\n",
      "#wafostamp('', '(ER)')\n",
      "#hold off\n",
      "#disp('Block = 9'), pause(pstate)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Section 1.4.5 Extreme value statistics"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf()\n",
      "import wafo.data as wd\n",
      "xn = wd.yura87()\n",
      "#xn = load('yura87.dat'); \n",
      "subplot(211) \n",
      "plot(xn[::30, 0] / 3600, xn[::30, 1], '.')\n",
      "title('Water level')\n",
      "ylabel('(m)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<matplotlib.text.Text at 0x730d070>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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o+ruoyFozQm1Lh8hTHWQlShYeF1Bc4ESjtpuI3GMquvy4Njo6bLeNPNMhX7DM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\nebtscv9w5SHzY3GxFTvgSoR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8UgDvvfceWlpaBj/H4/HBShUiQqEQFi9ejFmzZuG73/1u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\nQPH/x7b+U3K6ZM4AAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Formation of 5 min maxima"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "yura = xn[:85500, 1]\n",
      "yura = np.reshape(yura, (285, 300)).T\n",
      "maxyura = yura.max(axis=0)\n",
      "subplot(212)\n",
      "plot(xn[299:85500:300, 0] / 3600, maxyura, '.')\n",
      "xlabel('Time (h)')\n",
      "ylabel('(m)')\n",
      "title('Maximum 5 min water level')\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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ly2js2LHyQokKoYey8aZeeH2E608etT1ad3UAlFwS7kBrJ0saqaSl560G6XyC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\nYII=\n"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Estimation of GEV for yuramax"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf()\n",
      "import wafo.stats as ws\n",
      "phat = ws.genextreme.fit2(maxyura, method='ml')\n",
      "phat.plotfitsummary()\n",
      "show()\n",
      "#disp('Block = 11, Last block')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "c:\\pab\\workspace\\pywafo_svn\\pywafo\\src\\wafo\\stats\\estimation.py:1080: UserWarning: P-value is on the conservative side (i.e. too large) due to ties in the data!\n",
        "  warnings.warn('P-value is on the conservative side (i.e. too large) due to ties in the data!')\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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WL14s1vVC/+cvXrxYzpgVKfgoMDCQDbgTEx0doGPH/88q3r1j9jH++WceM4J0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      }
     ],
     "prompt_number": 12
    }
   ],
   "metadata": {}
  }
 ]
}