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462 lines
215 KiB
Plaintext
462 lines
215 KiB
Plaintext
11 years ago
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{
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"metadata": {
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"name": "WAFO Chapter 1"
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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"worksheets": [
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{
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"cells": [
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{
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"cell_type": "heading",
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"level": 1,
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"metadata": {},
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"source": [
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"CHAPTER 1 demonstrates some applications of WAFO"
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]
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},
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{
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"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"
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]
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},
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{
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"cell_type": "heading",
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"level": 2,
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"metadata": {},
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"source": [
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"Section 1.4 Some applications of WAFO"
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]
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},
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{
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"cell_type": "heading",
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"level": 3,
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"metadata": {},
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"source": [
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"Section 1.4.1 Simulation from spectrum, estimation of spectrum "
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]
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},
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{
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"Simulation of the sea surface from spectrum. The following code generates 200 seconds of data sampled with 10Hz from the Torsethaugen spectrum."
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"import wafo.spectrum.models as wsm\n",
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"S = wsm.Torsethaugen(Hm0=6, Tp=8);\n",
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"S1 = S.tospecdata()\n",
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"S1.plot()\n",
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"show()"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "display_data",
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"png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEXCAYAAABPkyhHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNfEs4JqRn58Pf39/BAcHIyEh
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|
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}
|
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|
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],
|
||
|
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"prompt_number": 5
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|
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},
|
||
|
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"import wafo.objects as wo\n",
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"xs = S1.sim(ns=2000, dt=0.1)\n",
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"ts = wo.mat2timeseries(xs)\n",
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"ts.plot_wave('-')\n",
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"show()"
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],
|
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"language": "python",
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"metadata": {},
|
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"outputs": [
|
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|
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{
|
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|
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"output_type": "display_data",
|
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|
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"png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD9CAYAAABHnDf0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8tlLBeKaN9fm7nHGlSvA3r0U
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}
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|
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],
|
||
|
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"prompt_number": 6
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},
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{
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"cell_type": "heading",
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"level": 4,
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"metadata": {},
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"source": [
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"Estimation of spectrum "
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]
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},
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{
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"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"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"clf()\n",
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"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": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEXCAYAAABPkyhHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XlcVPX6wPHPsMi+C8jmhoqiCLihhonlkhuWlqWWqNly\nb5t16/6y1ZbbZmVZN23V1Ba72mKWpi24ZqbiviGKyiIIiOzbzPn9MUEiKDPDcpiZ5/16zQs45zvn\nPMcj8/Bdj0ZRFAUhhBBWzUbtAIQQQqhPkoEQQghJBkIIISQZCCGEQJKBEEIIJBkIIYRAkoEQRpkx\nYwZPP/20QWVTU1OxsbFBp9M1WzyfffYZo0aNarbjC+shyUCoYuvWrQwePBhPT098fHyIjY1l165d\nzXrOjh078uuvvzbqGBqNBo1G00QRNd60adP46aefan62sbHh5MmTKkYkzJWd2gEI61NQUMC4ceN4\n//33mTx5MuXl5WzZsgUHB4dmPa9Go+Fqcyyrqqqws2v4V6K1z9Ns7fGJ1klqBqLFHT9+HI1Gw623\n3opGo8HR0ZERI0YQEREBwNKlS7nmmmt44IEH8PT0pEePHrX+or948SJ33nkngYGBBAcH8/TTT9dq\nivnwww8JDw/H3d2dnj17kpSUxB133MGZM2cYP348bm5uvP766zXNOJ988gkdOnRg+PDhANxyyy0E\nBATg6enJ0KFDOXz4sEHXpdPpePTRR/H19SU0NJQffvih1v6rxb106VJiY2N57LHH8Pb2pnPnzqxf\nv77mvUuXLiU0NBR3d3c6d+7M559/XrN9yJAhAFx77bUAREZG4u7uzldffUVERARr166tOU5lZSVt\n27Zl3759ht0sYT0UIVpYQUGB4uPjoyQkJCjr1q1T8vLyau1fsmSJYmdnp7z11ltKVVWVsnLlSsXD\nw0O5cOGCoiiKcuONNyr33nuvUlJSomRnZysDBgxQ3n//fUVRFOWrr75SgoKClF27dimKoignTpxQ\nTp8+rSiKonTs2FH55Zdfas5z6tQpRaPRKAkJCUpJSYlSVlZWc/6ioiKloqJCmTNnjhIVFVXznhkz\nZihPPfVUvde1aNEipXv37kpaWpqSl5enxMXFKTY2NopWq20w7iVLlij29vbKRx99pOh0OmXRokVK\nYGCgoiiKUlRUpLi7uyvHjx9XFEVRzp07pxw6dKjmfbGxsTUxaDQaJSUlpebn1157Tbn11ltrfv72\n22+V3r17G3CXhLWRZCBUceTIEWXGjBlKcHCwYmdnp8THxytZWVmKoug/4Ko/CKsNGDBAWb58uXLu\n3DnFwcFBKS0trdn3+eefK8OGDVMURVFGjhypLFy4sN5zXikZnDp16opxXrhwQdFoNEpBQYGiKFdP\nBsOGDav5cFcURdmwYYOi0WgUrVbbYNxLlixRunTpUrOvuLhY0Wg0SlZWllJUVKR4enoqq1evVkpK\nSmqds6FkkJ6erri6uiqFhYWKoijKpEmTlPnz51/xeoX1kmYioYru3buzZMkSzp49y8GDB8nIyGDO\nnDk1+4OCgmqV79ChAxkZGZw5c4bKykoCAgLw8vLCy8uLe++9l/PnzwOQlpZGaGioUbGEhITUfK/T\n6Xj88cfp0qULHh4edOrUCYCcnJwGj5OZmVnrWO3bt6/5/vTp01eNG6Bdu3Y13zs7OwNQVFSEi4sL\nK1euZPHixQQGBjJu3DiOHTtm0LUFBgZyzTXXsGrVKvLz81m/fj3Tpk0z6L3CukgHslBdWFgYCQkJ\nfPDBBzXb0tPTa5U5ffo0EyZMICQkBAcHB3Jzc7Gxqfu3TEhICCdOnKj3PFcaBXTp9s8++4w1a9bw\nyy+/0KFDB/Lz8/H29jaoUzYgIIAzZ87U/Hzp9w3F3ZCRI0cycuRIysvLefLJJ7nrrrvYvHmzQe9N\nSEjg448/prKyksGDBxMQEGD0+YXlk5qBaHHHjh3jzTffrPnAP3v2LF988QWDBg2qKZOdnc3ChQup\nrKzkf//7H0ePHmXMmDG0a9eOkSNH8sgjj1BYWIhOpyMlJaXmg3H27Nm8/vrr7NmzB0VROHHiRM2H\nsr+/PykpKVeNraioCAcHB7y9vSkuLuaJJ56otf9qSWHy5MksXLiQ9PR0Lly4wCuvvFKzLyAg4Kpx\nX012djbfffcdxcXF2Nvb4+Ligq2tbb1l67vGm266iT179rBw4UKmT5/e4PmEdZJkIFqcm5sbf/zx\nBzExMbi6ujJo0CB69+7NG2+8UVMmJiaG5ORkfH19efrpp1m9ejVeXl4ALFu2jIqKCsLDw/H29uaW\nW27h3LlzANx88808+eSTTJ06FXd3dyZOnMiFCxcAmDt3Li+++CJeXl68+eabQN3awvTp0+nQoQNB\nQUH06tWLQYMG1SpztXkGd911F6NGjSIyMpJ+/foxadKkWmWvFnd9x63+WafTsWDBAoKCgvDx8WHL\nli0sWrSo3vfNmzePhIQEvLy8WLVqFQCOjo5MnDiR1NRUJk6caNA9EtZHoxhS/xWiBS1dupSPP/6Y\nLVu2qB2KxXjhhRdITk5m2bJlaociWilV+ww6duyIu7s7tra22Nvbs3PnTjXDEcIi5eXl8cknn7B8\n+XK1QxGtmKrNRBqNhsTERJKSkiQRiBqtbckHc/bhhx/Svn17Ro8eTWxsrNrhiFZM1WaiTp06sWvX\nLnx8fNQKQQghBK2gZjB8+HD69evHhx9+qGYoQghh1VTtM9i2bRsBAQGcP3+eESNG0L1795p1VqSZ\nQAghTGNKg4+qNYPqyS++vr7cdNNNdfoNFP1yGRb5evbZZ1U79333KYDChQuWeX2Wfv/k+uTarvYy\nlWrJoKSkhMLCQgCKi4vZsGFDzaqVovksWQI//wwREXDggNrRCCFaC9WSQVZWFkOGDCEqKoqYmBjG\njRvHyJEj1QrHarz4IixbBoMGwf79akcjhGgtVOsz6NSpE3v37lXr9KqLi4tr8XMqCqSn62sFvXs3\nbzJQ4/paklyf+bLka2uMVjsDuaGnUgnjnT8PPXpATg5s2QKPPQY7dqgdlRCiKZn62SlrE1mRtDQI\nDtZ/HxEBBw9CMz6rXQhhRiQZWJG0NKh+TICnJ7RtC/LsdCEESDKwKunpf9cMoPn7DYQQ5kOSgRW5\ntGYA+mQgz0UXQoAkA6tyaZ8BSDIQQvxNkoEVubyZqGtXOHVKvXiEEK2HJAMrcnkzUUAAZGaqF48Q\novWQeQZWxM1NnxA8PPQ/a7Xg6AjFxdCmjbqxCSGahswzEFdVUKD/6u7+9zZbW/Dzg6wsdWISQrQe\nkgysRHUT0eUrg0tTkRACJBlYjctHElULDISMjJaPRwjRukgysBKXjySqJjUDIQRIMrAal48kqiY1\nAyEESDKwGldKBlIzEEKAJAOrkZ0N7drV3S7JQAgBkgysRk4O+PjU3S7NREIIkGRgNXJz9UtWX05q\nBkIIkGRgNXJz668Z+Pnp91VVtXxMQojWQ5KBFVAUyMsDb++6++zs9DUGmYUshHWTZGAFLl4EZ+cr\nrz8kTUVCCEkGVuBKTUTVpBNZCCHJwArk5NTfeVxNagZCCEkGVqChmoEkAyGEJAMrcKU5BtXatYNz\n51ouHiFE6yPJwApcaY5BNV9ffcIQQlgvSQZWoKFmorZt4fz5lotHCNH6SDKwAg11IEvNQAghycAK\nSM1ACNEQSQZWoKEOZB8fuHABdLqWi0kI0bpIMrACDXUg29uDm5s+IQghrJMkAyvQUDMRSFORENZO\nkoGFU5SGm4lAOpGFsHaSDCxccbF+ZVInp6uXk5qBENZNkoGFM6RWAFIzEMLaSTKwcA11HleTmoEQ\n1k3VZKDVaomOjmb8+PFqhmHRDOk8BqkZCGHtVE0Gb7/9NuHh4Wg0GjXDsGjGNBNJzUAI66VaMkhL\nS+PHH39k9uzZKIqiVhgWT5qJhBCGsFPrxA8//DDz58+noKDgimXmzZtX831cXBxxcXHNH5iFkQ5k\nISxbYmIiiYmJjT6OKslg7dq1
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEXCAYAAABMCOQqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X98jfX/x/HH2Xb82i/DGvvBMOwHZkyiZH4lYjGU3/Kr\npRCpJJ/PF/3wCR+VQiHJr9AnhcRCmkSzmN/DkDEzM2zGhm1n1/eP1clsO862s13n7Lzut9u55VzX\n+/2+nudoL9eu631dl0ZRFAUhhBAVmo3aAYQQQpQ9KfZCCGEFpNgLIYQVkGIvhBBWQIq9EEJYASn2\nQghhBaTYC7O0Z88efH19y2Vbp0+fpkWLFjg5ObFgwYIy396JEycICgrC2dmZ/fv3l/n2jPX777/j\n7OxMUFAQx48fVzuOMDEp9sIgb29vqlWrhpOTEy4uLjz++OMsXryYsr48o3379pw6dSpfjl27dpXJ\ntubMmUPnzp1JT09n3Lhx+dYFBATg6OiIo6MjdnZ2VK1aVf/+gw8+KNH2vvzyS3x8fLh58yZt2rQx\nxUcw2s8//4yvry/29vZ06tSJixcv6te1bduWtLQ0GjduzLJly8o1lyh7UuyFQRqNhi1btpCens7F\nixd56623mD17NqNGjSr3HGX1D8yFCxfw9/cvdN2JEye4desWt27don379ixcuFD//q233irR9lJT\nU/Hz8ytN5BK5du0affv25f333yc1NZXg4GCef/75fG00Gg3+/v5cv3693POJsiXFXhjN0dGRXr16\nsX79elasWMGJEycAuHfvHq+//jr16tWjdu3ajB07lrt37wIQGRmJp6cnH374IW5ubri7u/PVV1/p\nx9y6dSsBAQE4OTnh6enJvHnz9P28vLwAGDp0KBcvXqRXr144Ojoyd+5cevbsWeCQS/Pmzdm0aVOh\n2Tdv3kxAQAAuLi507NhR/1tDp06diIyMZNy4cTg5OXH27FmD38H9/+AsXboUf39/nJycCAgI4NCh\nQ0Z9j9nZ2Wg0mnzLunfvzsKFC/MtCwwMZOPGjUaNaYzvvvuOpk2b0rdvXypVqsSMGTM4cuQIcXFx\n+dppNBpycnJMtl1hHqTYi2Jr3bo1np6e/PbbbwC89dZbnD17liNHjnD27FkSExN555139O2Tk5NJ\nT0/n8uXLLFu2jFdeeYWbN28CMGrUKJYsWUJ6ejonTpygU6dOBba3atUq6taty5YtW7h16xZvvPEG\nw4cPZ/Xq1fo2R44c4fLlyzzzzDMF+sfFxTFo0CA++eQTrl27Ro8ePejVqxc5OTns2rVLv8eenp6O\nj4+Pwc/+d5H+3//+x8yZM1m1ahXp6en88MMP1KxZ86Hf3Y0bNzh48CD16tXLt3zQoEGsXbtW/z42\nNpaLFy8W+nkAqlevjouLS6GvOXPmFNrnxIkTBAYG6t9Xq1YNHx+fAsfn69aty8GDB7lx48ZDP4+w\nHFLsRYm4u7tz48YNFEVh6dKlfPjhh1SvXh0HBwemTp3KunXr9G21Wi3/93//h62tLd27d8fBwYHT\np08DUKlSJU6cOEF6err+5KAxevXqRVxcHOfOnQPy/kEYMGAAdnZ2BdquX7+enj170rlzZ2xtbXn9\n9de5c+cO+/bt07cp7iGiL774gilTptCqVSsAGjRoQN26dQ32+fTTT6lVqxbVq1dn2LBh+db17t2b\nw4cPk5CQAMCaNWvo27cvWq220LHS0tJITU0t9PXmm28W2icjIwMnJ6d8y5ycnLh9+3a+ZUOGDKFm\nzZrUqlWLTz75xOBnEpZDir0okUuXLlGjRg2uXbtGZmYmrVq10u9Zdu/enWvXrunb1qxZExubf/5X\nq1atmr7AbNiwga1bt+Lt7U1ISAhRUVFGbb9KlSo899xzrFq1CkVRWLduHUOHDi20bVJSUr5CrNFo\n8PLyIjExMd+y4n7+hg0bFqvP+PHjSUpKIikpqcDhJkdHR5555hn93v26desYPHhwscZ/GAcHB9LT\n0/Mtu3nzJo6OjvmWbd68mUuXLpGUlMSECRNMmkGoR4q9KLY//viDy5cv88QTT1CzZk2qVq1KbGys\nfs8yLS2tQFEpSnBwMBs3biQlJYXevXvz3HPPFdqusGI8fPhw1qxZw86dO6lWrVqRM1vc3d25cOGC\n/r2iKCQkJODh4WFUxsJ4eXk99Ph+Ydzc3Gjbti2xsbEF1g0cOJC1a9fy+++/c/fuXTp27FjkOA4O\nDvpZQQ++ipolFBAQwJEjR/TvMzIyOHfuHAEBAfnanTx5krZt2+Lm5lbszyfMlxR78VB/H+JIT09n\ny5YtDBw4kKFDhxIQEICNjQ1jxoxh4sSJpKSkAJCYmMj27dsfOm52djZr1qzh5s2b2Nra4ujoiK2t\nbaFt3dzc9Ids/ta2bVs0Gg2vv/56gcMi93vuuef48ccf2bVrF9nZ2cybN48qVarQrl27Ap/RWKNH\nj+a///0vMTExKIrC2bNn801jNKRy5cpkZ2cXWN6jRw8uXLjA9OnTGTBggMExbt++rZ8V9OCrqFlC\nffr04fjx43z33XfcvXuXmTNn0qJFCxo3bpyvXU5ODpUqVTLqswjLIcVePFSvXr1wcnKibt26/Oc/\n/2Hy5MksX75cv3727Nn4+Pjw2GOP4ezsTNeuXfPN8DB0iGT16tXUr18fZ2dnlixZwpo1awrtN3Xq\nVN577z1cXFz48MMP9cuHDRvGsWPHGDJkSJHbaNy4MatXr2b8+PG4urry448/8sMPP+Q7vl/cwzj9\n+vVj2rRpDBo0CCcnJ8LCwkhNTTWqr42NDTqdrsDySpUqERYWxs8//8ygQYOKlccYtWrVYsOGDUyb\nNo0aNWpw4MCBfOdW/paTk5PvsJuoGDTy8BJhyVatWsXSpUv59ddf1Y5itGnTpnHo0CE2b95c6All\nNeXk5BAWFoa/v3+JLxoT5kn++RYWKzMzk4ULF/Liiy+qHaVYRo8ezZ07d3B3dyc6OlrtOHpRUVHU\nqVOH9PR0i/tOxcPJnr2wSD/99BN9+/ala9eubNiwwSwOO1y8eLHAyU7IO0QUGxuLp6enCqmEyCPF\nXgghrID6u0NCCCHKnOpnh4o7C0IIIUSe4hyYMYs9e0VR5KUoTJ8+XfUM5vKS70K+C/kuDL+KyyyK\nvRBCiLIlxV4IIayAFHszEhISonYEsyHfxT/ku/iHfBclp/rUy7J8ApEoGzVqgJF3BiiSiwvI7dKF\nKLni1k4p9qLYNBoo7V+ZKcYQwpoVt3bKYRwhhLACUuyFEMIKSLEXQggrIMVeCCGsgBR7IYSwAlLs\nhRDCCkixF0IIKyDFXgghrIAUeyGEsAJS7IUQwgpIsRdCCCsgxV4IIayAFHshhLACUuyFEMIKSLEX\nQggrIMVeCCGsgBR7IYSwAlLshRDCCkixF0IIKyDFXgghrIAUeyGEsAIPLfYRERH4+vrSqFEjZs+e\nXWD9pk2bCAwMJCgoiFatWrFr1y6j+wohhCgfGkVRlKJW6nQ6mjRpws6dO/Hw8KB169asXbsWPz8/\nfZuMjAzs7e0BOHbsGH369OHs2bNG9QXQaDQYiCDMkEYDpf0rM8UYQliz4tZOg3v20dHR+Pj44O3t\njVarZcCAAWzatClfm78LPcDt27epVauW0X2FEEKUDztDKxMTE/Hy8tK/9/T0ZP/+/QXabdy4kalT\np5KUlMT27duL1RdgxowZ+j+HhIQQEhJSnM8ghBAVXmRkJJGRkSXub7DYazQaowbp3bs3vXv3Zs+e\nPQwdOpRTp04VK8T9xV4IIURBD+4Iz5w5s1j9DR7G8fDwICEhQf8+ISEBT0/PItu3b9+enJwcbty4\ngaenZ7H6CiGEKDsGi31wcDBnzpwhPj6erKws1q9fT2hoaL42586d058kiImJAaBmzZpG9RVCCFE+\nDB7GsbOzY8GCBXTr1g2dTseoUaPw8/Nj8eLFAISHh7NhwwZWrlyJVqvFwcGBdevWGewrhBCi/Bmc\nelkuAWTqpcWRqZdCqM+kUy+F
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEXCAYAAACu1P9TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npwz4t0lGRgFz5/7Mr79GsmnT
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": "iVBORw0KGgoAAAANSUhEUgAAAYAAAACOCAYAAAA4ut97AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nn0x/ImHXQ65T0PbV3Z+Jfspn
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": "iVBORw0KGgoAAAANSUhEUgAAAYAAAACdCAYAAAC9+K9OAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXr164ciRI3pl73GmTgVSU4Fh
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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",
|
||
|
|
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|
||
|
|
}
|
||
|
|
],
|
||
|
|
"prompt_number": 12
|
||
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}
|
||
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|
],
|
||
|
|
"metadata": {}
|
||
|
|
}
|
||
|
|
]
|
||
|
11 years ago
|
}
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