pywafo/wafo/doc/tutorial_scripts/WAFO Chapter 5.ipynb

{
 "metadata": {
  "name": "WAFO Chapter 5"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Chapter 5 Extreme value analysis\n",
      "=================================\n",
      "Section 5.1 Weibull and Gumbel papers\n",
      "--------------------------------------\n",
      "Significant wave-height data on Weibull paper,\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf()\n",
      "import wafo.data as wd\n",
      "import wafo.stats as ws\n",
      "import matplotlib.pyplot as plt\n",
      "Hs = wd.atlantic()\n",
      "wei = ws.weibull_min.fit2(Hs)\n",
      "tmp = ws.probplot(Hs, wei.par, dist='weibull_min', plot=plt)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEWCAYAAABhffzLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncWg1VX8Bg0o9j/+7775jw4YN
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Significant wave-height data on Gumbel paper,"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gum = ws.gumbel_r.fit2(Hs)\n",
      "tmp = ws.probplot(Hs, gum.par, dist='gumbel_r', plot=plt)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "c:\\pab\\workspace\\pywafo_svn\\pywafo\\src\\wafo\\stats\\estimation.py:722: RuntimeWarning: invalid value encountered in sqrt\n",
        "  self.par_lower = self.par - zcrit * sqrt(pvar)\n",
        "c:\\pab\\workspace\\pywafo_svn\\pywafo\\src\\wafo\\stats\\estimation.py:723: RuntimeWarning: invalid value encountered in sqrt\n",
        "  self.par_upper = self.par + zcrit * sqrt(pvar)\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEWCAYAAABhffzLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXEb7jjbojaBhYREsXLiT7GwL
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Significant wave-height data on Normal probability  paper,\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tmp = ws.probplot(np.log(Hs), plot=plt)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEWCAYAAACOv5f1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH26y/X6ox/DPzc1l5MiRzJ8/
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Return values in the Gumbel distribution"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf()\n",
      "T=np.r_[1:100001]\n",
      "#sT=gum.par[1] - gum.par[0]*log(-log(1-1./T));\n",
      "sT = gum.isf(1./T)\n",
      "semilogx(T,sT), hold\n",
      "N=np.r_[1:len(Hs)+1]; \n",
      "Nmax=max(N);\n",
      "plot(Nmax/N,sort(Hs)[::-1],'.')\n",
      "title('Return values in the Gumbel model')\n",
      "xlabel('Return period')\n",
      "ylabel('Return value') \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.text.Text at 0x6d23570>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEeCAYAAACHXhKxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntttuA2D06NGEhoYSHh7OHXfc
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Section 5.2 Generalized Pareto and Extreme Value distributions\n",
      "----------------------------------------------------------\n",
      "Section 5.2.1 Generalized Extreme Value distribution\n",
      "-------------------------------------------------\n",
      "\n",
      "Empirical distribution of significant wave-height with estimated Generalized Extreme Value distribution"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gev = ws.genextreme.fit2(Hs)\n",
      "gev.plotesf()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAETCAYAAAAoF0GbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n43zNA9RnP2Fh8MYbzo1XRC5T
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import wafo.kdetools as wk\n",
      "wk.TKDE(Hs, L2=0.5)(output='plot').plot('g--')\n",
      "plt.hold(True)\n",
      "gev.plotepdf() "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAETCAYAAAAoF0GbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npxSEhmrTXK/r+ivP4bOHCWwa
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Analysis of yura87 wave data. \n",
      " Wave data interpolated (spline) and organized in 5-minute intervals\n",
      "Normalized to mean 0 and std = 1 to get stationary conditions. \n",
      "maximum level over each 5-minute interval analysed by GEV"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy.interpolate as si\n",
      "xn  = wd.yura87()\n",
      "XI  = np.r_[0:len(xn):0.25]\n",
      "N   = len(XI); \n",
      "N = N-np.mod(N,4*60*5); \n",
      "YI  = si.UnivariateSpline(xn[:,0].ravel(),xn[:,1].ravel(),k=3,s=0)(XI[:N])\n",
      "YI  = np.reshape(YI, (4*60*5, N/(4*60*5))); # Each column holds 5 minutes of interpolated data.\n",
      "Y5  = (YI-YI.mean(axis=0))/(YI.std(axis=0))\n",
      "Y5M = Y5.max(axis=0)\n",
      "Y5gev = ws.genextreme.fit2(Y5M,method='mps')\n",
      "Y5gev.plotesf()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAETCAYAAAAlCTHcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGaaxmF74+FioUAGCg80RWfPm
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Section 5.2.2 Generalized Pareto distribution\n",
      "-------------------------------------------\n",
      "Exceedances of significant wave-height data over level 3."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gpd3 = ws.genpareto.fit2(Hs[Hs>3],floc=3)\n",
      "gpd3.plotesf()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAETCAYAAAAoF0GbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIfROnFB3cluRxgC+5x3mokNh
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Exceedances of significant wave-height data over level 7,"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gpd7 = ws.genpareto.fit2(Hs[Hs>7],floc=7)\n",
      "gpd7.plotesf()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAETCAYAAAAoF0GbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOKechjVr2DJxFQ9e3MB/aM4q
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Simulates 100 values from the GEV distribution with parameters (0.3, 1, 2), then estimates the\n",
      "parameters using two different methods and plots the estimated distribution functions together\n",
      "with the empirical distribution.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Rgev = ws.genextreme.rvs(0.3,1,2,size=100)\n",
      "gp = ws.genextreme.fit2(Rgev,method='mps');\n",
      "gm = ws.genextreme.fit2(Rgev,method='ml');\n",
      "\n",
      "gp.plotesf()\n",
      "plt.hold(True)\n",
      "gm.plotesf('r--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAETCAYAAADd6corAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGzMbiQhQQVoOThy+3KCcxPLH
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Similarly for the GPD distribution"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Rgpd = ws.genpareto.rvs(0.4,size=100);\n",
      "gmps = ws.genpareto.fit2(Rgpd, method='mps')\n",
      "gml = ws.genpareto.fit2(Rgpd, method='ml')\n",
      "gmps.plotesf()\n",
      "plt.hold(True)\n",
      "gml.plotesf('r--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAETCAYAAADH1SqlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAhERmTCzbVkAQE4O4Okpu6MA
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Return values for the GEV distribution"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "T = logspace(1,5,10);\n",
      "sT = Y5gev.isf(1./T);\n",
      "\n",
      "\n",
      "clf\n",
      "semilogx(T,sT,T,sTlo,'r',T,sTup,'r'), hold\n",
      "N=1:length(Y5M); Nmax=max(N);\n",
      "plot(Nmax./N,sort(Y5M,'descend'),'.')\n",
      "title('Return values in the GEV model')\n",
      "xlabel('Return priod')\n",
      "ylabel('Return value') \n",
      "grid on "
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import wafo.stats as ws\n",
      "R = ws.genpareto.rvs(-0.5,size=100);\n",
      "phat = ws.genpareto.fit2(R[R>.5], -.5, scale=1, floc=0.5)\n",
      "phat.plotfitsummary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEfCAYAAABSy/GnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMuWLwMBAdu3aVeD2xMREPvro
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Better CI for phat.par[i=0] shape parameter\n",
      "Lp0 = phat.profile(i=0, pmin=-1,pmax=1)\n",
      "Lp0.plot()\n",
      "phat0_ci = Lp0.get_bounds(alpha=0.1)\n",
      "print 'phat0_ci = ', phat0_ci\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "phat0_ci =  [-0.73845586 -0.30183734]\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEXCAYAAACOFGLrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nriVVSoo+Bbh7N3z6KQQHG51I
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Better CI for phat.par[i=2] scale \n",
      "Lp2 = phat.profile(i=2,pmin=0.1,pmax=2)\n",
      "Lp2.plot()\n",
      "phat2_ci = Lp2.get_bounds(alpha=0.1)\n",
      "print 'phat2_ci = ', phat2_ci\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "phat2_ci =  [ 0.55127823  0.97075832]\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEXCAYAAACH/8KRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nitwvv0DTpnqLkt9+0+u3ypUz
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SF = 1./990\n",
      "x = phat.isf(SF)\n",
      "\n",
      "# CI for x\n",
      "Lx = phat.profile(i=2, x=x, link=phat.dist.link)\n",
      "Lx.plot()\n",
      "x_ci = Lx.get_bounds(alpha=0.2)\n",
      "print 'X_c = ', x_ci"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "X_c =  [ 1.78350616  2.09079614]\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "c:\\pab\\workspace\\pywafo_svn\\pywafo\\src\\wafo\\stats\\distributions.py:4011: RuntimeWarning: invalid value encountered in true_divide\n",
        "  return where((c != 0) & (-inf < log_sf), expm1(-c * log_sf) / c, -log_sf)\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEXCAYAAACH/8KRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nvZMULSoKDh2CjAwICoJCNrMU
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    }
   ],
   "metadata": {}
  }
 ]
}