diff --git a/pywafo/src/wafo/doc/tutorial_scripts/WAFO Chapter 1.ipynb b/pywafo/src/wafo/doc/tutorial_scripts/WAFO Chapter 1.ipynb index 040b042..e5aa752 100644 --- a/pywafo/src/wafo/doc/tutorial_scripts/WAFO Chapter 1.ipynb +++ b/pywafo/src/wafo/doc/tutorial_scripts/WAFO Chapter 1.ipynb @@ -1,298 +1,462 @@ -{ - "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\nS = wsm.Torsethaugen(Hm0=6, Tp=8);\nS1 = S.tospecdata()\nS1.plot()\nshow()", - "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\nxs = S1.sim(ns=2000, dt=0.1)\nts = wo.mat2timeseries(xs)\nts.plot_wave('-')\nshow()", - "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()\nFs = 4; \nxs = S1.sim(ns=fix(20 * 60 * Fs), dt=1. / Fs) \nts = wo.mat2timeseries(xs) \nSest = ts.tospecdata(L=400)\nS1.plot()\nSest.plot('--')\naxis([0, 3, 0, 5]) # This may depend on the simulation\nshow()", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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A34dw++1NHGATiYiA/fvVjkII0RwMSgYbN26sSQBarRZFUWqaiQYOHMjAgQMp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\nDBgwgMDAQACioqI4deoUbm5uBAQE0Ld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- } - ], - "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()\nimport wafo.misc as wm\ndtyex = S1.to_t_pdf(pdef='Tt', paramt=(0, 10, 51), nit=3)\ndtyest = Sest.to_t_pdf(pdef='Tt', paramt=(0, 10, 51), nit=3)\n\nT, index = ts.wave_periods(vh=0, pdef='d2u')\nbins = wm.good_bins(T, num_bins=25, odd=True)\nwm.plot_histgrm(T, bins=bins, normed=True)\n\ndtyex.plot()\ndtyest.plot('-.')\naxis([0, 10, 0, 0.35])\nshow()", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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v0QOABdELmL13NruG7cLP1a+QAUR5eKHFCzTyeZ8ePp64qB1GCAMiIyOJjIws\ncX+Dxb60D8nYu3cvderUISUlha5du+Lr60v79gX3YO8v9mYnLQ3GjgXWFr4+JweuXYPatfMvnzED\nnJ0LNFcUhVl7ZrH88HL2jNiDd3VvUycWxeBq70r3Rt1ZfXQ149uMVzuOEEV6cEd45syZxepv8DCO\nh4cHCQkJ+vcJCQl4enoaPXidOnWAvEM9ffr0ITrazOc1KwrodPmXVaoEL75YsO2VKzB8eF6RnzWr\n4HoXF7B54Ou1u8sbO95g3Yl1UujNyIstX2RJzBLrPuQoKjyDxT44OJgzZ84QHx9PVlYW69evJzQ0\ntNC2D/6gZGZmcuvWLQAyMjLYvn07zZo1M1FsE7tzB774Apo1g82b86+rVg06dizYJz097zBNTAx8\n8onB4a9mXGVm5EyY6M3p66fZ/cJu6jjWMeEHEKUR4h3CvZx7/H7pd4iLg5s31Y4khMk9dJ79tm3b\nmDhxIjqdjlGjRjF16lQWL14MQHh4OFeuXKF169akp6djY2ODo6MjsbGxXL16lbCwMABycnIYPHgw\nU6dOLRjAHGbjzJ0Lu3fDpEnQqVOhU1ZKMvvjxNUTfBT1ERtObqC/f3+WjpqIctW/VFHNYRaLOYxh\nigz3++++/3L86nG+2lMTevWCinDeSFRoxa2dclGVkYwpLoqicCn9EtGJ0SyNWcqR5CO8HPwyLwW/\nhKu9q1lMezSHQm2KMUxd7FMyUmj0aSPOv3oel6pyqlaYPyn2ZeTB4pKr5HL62mkOXTmU90o6xOEr\nh7G1sSWodhDPBzzPwGYDqWJXpcgxTJGjvPubyximLvYAAzcMpJ1nOzlRKyyCFPsyotFA+t1b7Phz\nB1vitvDjmR+x19rTsk5LgmoHEVQniKDaQQaPxVeUImkOY5RFsf/l/C9MiJjA0ZeOlnommhBlTR44\nboxvv807CTdq1EObXki7wA9xP8CQH3D/cB9tPdvSq3Ev/vXkv2jg0qAcwory8veJ2qhLUXl3Hc3M\nBFtbqFxZ7WhClJp1FvvWreGvmUJFiUmK4Z3d77A3YS/PNHoGDr7I5S++xbGyYzmFFA9T2p1vFxe4\nceP+8TS82OpFFh9cnFfsX34ZWrSAiRNLtyEhzIAcxnnAwcsHmbl7JgeTDjLl8SmMaTmGqtqqZnHo\nwhRjmEMGU4xRVhnynag9ewm6doUzZ8BR/pEX5kUeOF5CBy4foNfaXjy77lm6NujK2fFnmdBmAlW1\nVdWOJspNrqjbAAAd70lEQVSRq70rT/s8zeqjq/Ouuxg8GE6fVjuWEKVmPXv2ubkFr2gFsnRZDP1+\nKHsv7uWtJ95idMvR+WbQ/JNT/b1ZU4xhDhlMMUZZZpATtcISyJ59Ya5cgaZNITs732JFURi1eRR3\nc+5ydsJZxj06rtBCL6zL/SdqhagorKPYz58PnTvDA7dhfnvX25y9cZa1fddKkRd695+oFaKiqPiz\ncXJzISICvv8+3+JFfyziu5PfsXfkXqppq6kUTpir4YHDafRpIxLTE/Fw8shbeO+eTMMUFqvi79nb\n2MDBg+DtrV+08dRG3vv1PbYN3katarXUyybMlqu9K28+/ib9/tePezn38u6G2rKlPOREWCzrOUH7\nl30J+3h23bNEDI6glXsro/uZw0lJU4xhDhlMMYapMhhukAvP9YXbdeDHRThwi9vkn4L54Fx9IcqL\nnKA14PS104StD2Nl75XFKvSi4jL4CMZcG26uWEHjp3/my5jl3FIcC7RJTVX7EwhhHKvYs69RA1Kz\nr8CodrD733B4RInGUXtv1hRjmEMGU4xRnhliU2Lp8FWHQn8bLIt79AhhDNmz/9umTbB8OZC39/Xs\n0pd4O3QgyqERVv2QbVF8/q7+fP7M5/T9pi/XMq+pHUeIEqm4xb5ZMwgKyvuz114OXTnEv5/8t7qZ\nhMXq69+XAU0HMODbAeRcvgQjRkBWltqxhDBaxS32DRpAixZ5v+Y89QbvdnxX5tKLUnmv03toNBr+\ndeJTuH4d3npL7UhCGK3iFvu/bDy1EbQZDG42WO0owsLZ2dixtu9a1p1Yz+apYfDTT7iTqHYsIYxS\noU/Q5uTm0HRRU07P/xjlzNOlGsscTkqaYgxzyGCKMdTMcPDyQbqv6c6BkVHUq9VAzukIVcgJ2gMH\n9D/By2KW5V39eLabyqFERdLKvRWTHpvEqK3heXPxhbAAFavYnz4NzzwDd+6QkZXBzN0zmdNlDiB3\nLhSm9cbjb3A76zYEf6Z2FCGMYhHFvkaNvF+5H/Z633clc68OQ2NfDYeuH5L0eweCPeTiKWF6djZ2\nrOi9AkJmcOb6GcjIgP371Y4lRJEsotinpho3F37aLAfeODqM5NtXqfnMfM4tfV+Op4oy07hmY9j9\nfwzfOBzdqZOwfr3akYQo0kOLfUREBL6+vjRq1IjZs2cXWH/q1Cnatm1LlSpVmDdvXrH6mtzUqdCs\nGe/++i6Dmw+WB4KLsvfHK1Sxq8LcOzvhww/VTiNEkQzOxtHpdDRp0oSdO3fi4eFB69atWbt2LX5+\nfvo2KSkpXLhwgY0bN+Li4sLkyZON7gvGnVEuzqyJczfO0eaLNpx85SSu9q7F7m+KDOY8hjlkMMUY\n5pDh7zHiUy8QvDSYXcN20cytWekGFMJIJp2NEx0djY+PD97e3mi1WgYMGMCmTZvytXF1dSU4OBjt\nAw8GMaZvWZi2axqTHpukL/RClDVvl3pc+3oOzWcMRWOXZdT5pftfNWqo/QmENTD48JLExES8vLz0\n7z09Pdlv5Emo4vSdMWOG/s8hISGEhIQYtY0HHU0+yp6Le1gWuqxE/YUoibxzRi/w7Lrvad7/Hd7r\n9B78+CPs2QP/+c9D76Usj7kVxoiMjCQyMrLE/Q0W+9I8bLk4fe8v9iXy6qswciRfJi1ndMvR2Fey\nL914QhSTRqNhSa8ltPi8Bb0a96JN27Z555CqVYP/+z+144kK4MEd4ZkzZxarv8HDOB4eHiQkJOjf\nJyQk4OnpadTApelbbIMHk1XPk6+Pfc2w5sPKZhtCPERth9p89sxnDNgwgNSqGtixA44cgcxMtaMJ\nYbjYBwcHc+bMGeLj48nKymL9+vWEhoYW2vbBEwXF6Vtqjz7K1it78HP1o2GNhmWzDSGM0MevD719\nezN843ByH3GFDRvy9u6FUJnBYm9nZ8eCBQvo1q0b/v7+PP/88/j5+bF48WIWL14MwJUrV/Dy8uKj\njz7ivffeo27duty+fbvIvmXlq8Nf8ULgC2U2vhDGmt1lNimZKfx333/VjiKEnkXcCO1hU+SuZlyl\n8aeNSZiUgGNlxwLrzWmantpjmEMGU4xhDhkMjZFwM4HWS1vzTf9veLLek/+s0OnA1tbkOYT1sa4b\noaWkgKLw9bGvedb32UILvRBq8HL24qveXzFowyCSbyfnLczKgjZt8u6FL0Q5s9xiryjQrh0cOSKH\ncIRZetrnaUYGjWTQd4PQ5eqgUqW8x2XWrKl2NGGFLLfYR0eDjQ2H3RTS7qbRwbuD2omEKGB6h+lo\n0DBj94y8BR4equYR1styi/3p0xAezldHVjAscBg2Gsv9KKLisrWxZU3YGpYfWs62M9vUjiOsmEWf\noM3SZeH5oSe/j/rd4JRLcz+ZV55jmEMGU4xhDhmKM8aeC3vo979+HBhzAC/nv64sz82FHTvQPN1N\nTtCKYrOqE7Rbz2zFt5avzK0XZq99vfZMemwSg78bTE5uTt7CW7dg3DhGIrf3EGXPoov9V4e/4oUW\nL6gdQwijvPn4m1SyrcR7v76Xt8DZGbZsYRZvw8GD6oYTFZ7FHsZ52Nz6h/Uvfs6KMYY5ZDDFGOaQ\noSRjJN1KouWSlqzvt14//95Xc4pT2T5gZ/BWVULkU/EP48TEwLff8vWxrwltEipz64VFqeNYh2Wh\nyxjy3RCuZ+bNtz+NrxR6UeYsr9jb2kLlyqw4skIO4QiL1KNRD/r592PU5lHF2jMTojQsr9gHBnK4\ntRc37twgxDtE7TRClMh/Ov+HhPQEPjvwWcGVx47J/ROEyVlesQdWyNx6YeEq21Vmbd+1TI+cDm5H\n/1mRmwtTpkBionrhRIVkcdVSl6uT+9aLCqFxzcbMe2oe9BtAZvZf97y3sYGtW6Gsnv0grJbFFft9\nCfuo41CHRjUbqR1FiFIb2nwoJLVk/LbxcvxelCnLKfa5udCrF1uPfMuzvs+qnUYIk9BoNPDjIo5c\nOcLLW18mV8lVO5KooCyn2B86hHL2LP+L/5HeTXqrnUYI07nnxK7huziZcpKh3w8lW5f9z7rMTBgz\nBtLS1MsnKgTLKfbbt3OjfTDZudm0qN1C7TRCmJRTZSe2Dd5G+r10wr4J4072nbwVVauCvT107w63\nb6sbUlg0yyn2o0ez+ml3nm3ybN6vvkJUMFW1Vfnuue9wquxE9zXdSb+XnneJ7kcfQbNmeQ8wF6KE\nLKfYu7qy5vovPNtEjteLiktrq2VVn1X4u/rTeWVnrmVeyyv4ixdDnz5qxxMWzGKu0U5MT+TsjbP5\nn+cpRAWR/5dVG2AhdJ6G65EnYeUOuFX0Q09cXODGjbJOKCydxRT7zac306NRD7S2WrWjCGFyBWdd\naoBZzN3rwgKfdmwZuIVmbs0KdtBokKOawhgWcRinKplsPL2R3r4yC0dYlzcef4MPOn9A55Wd2fnn\nzn9WLFkCs2erF0xYHPO/xXFSEmc8nqDVe1dJnHy5RHe5tNTb4ZbFGOaQwRRjmEOG8hxjz4U99P9f\nf2Z1nsXIoJFw8ybk5EDNmibJICyPyW9xHBERga+vL40aNWJ2EXsSEyZMoFGjRgQGBnLo0CH9cm9v\nb5o3b05QUBCPPvqo0aHyqVMHf/+ZtPd+Um5nLKxW+3rt2f3Cbt7f8z7/2vUvFCcnqFlT7VjCghgs\n9jqdjnHjxhEREUFsbCxr167l5MmT+dps3bqVs2fPcubMGZYsWcLYsWP16zQaDZGRkRw6dIjo6OgS\nh8zx/VFm4Qir16RWE6JGRfHz+Z8Z8v0Q7uXcy98gJ0edYMIiGCz20dHR+Pj44O3tjVarZcCAAWza\ntClfm82bNzN8+HAA2rRpQ1paGsnJyfr1pT1KlKXLAp8IQpuElmocISoCV3tXdg3bxb2cezy1+imS\nb//1s3bvHgQFwbZt6gYUZsvgbJzExES8vLz07z09Pdm/f/9D2yQmJuLm5oZGo6FLly7Y2toSHh7O\nmDFjCt3OjBkz9H8OCQkhJCRE/z4yPhKu+VLboXYxPpYQFVdVbVW+6f8N0yOn0+yzZtByFrmVRmLz\n+efQrx/MnAkvvqh2TGFikZGRREZGlri/wWJv7JWqRe29//bbb7i7u5OSkkLXrl3x9fWlffv2Bdrd\nX+zzOX6c7bHfwCmZhSPE/Ww0Nrzb8V36+fWjRdyLdPhqBYt7Lsb/99/hvt+sRcXx4I7wzJkzi9Xf\n4GEcDw8PEhIS9O8TEhLwfOA+2w+2uXTpEh4eeReAuLu7A+Dq6kqfPn2KfdxeGTmSC5GbpNgLUYTA\n2oGwbB8Dmw6kw1cd+NefX3CnZXO1YwkzZLDYBwcHc+bMGeLj48nKymL9+vWEhuY/dh4aGsrKlSsB\niIqKonr16ri5uZGZmcmtW7cAyMjIYPv27TRr1qzANop0/Tq6U7GcalwDrjcp5scSwoootrzc+mWO\nvHSEuOtxNPusWf45+cnJcOeOevmEWTB4GMfOzo4FCxbQrVs3dDodo0aNws/Pj8WLFwMQHh5Ojx49\n2Lp1Kz4+Ptjb27N8+XIArly5QlhYGAA5OTkMHjyYp556yvhkycns69GcZ5o+yfESfjghrIm7ozvf\n9P+GH+N+ZPTm0TxZ70nmPTUP1+XLwckJXn5Z7YhCRWZ9UVWzz5qxpOcS2tVtKxfgmGgMc8hgijHM\nIYO5jFFY/9tZt5keOZ3VR1czp8schjUfisbGIi6YF0Yq7kVVZlvsz904xxPLnyDxtURsbWykMJho\nDHPIYIoxzCGDuYxhqP/Bywd5ccuLVK9Snc+f+fyfx3nm5uY971ZYLJNfQauWTac30atxL2w0ZhtR\nCLPXyr0V+0fvp2ejnrRd1pb3f30/79qVuXPzpmneN7lCVGxmW0m3xG2hV+NeascQwuLZ2dgxqe0k\nDr54kN8v/U7TRU1ZE1KT3AD/vAuxjstZMWtglodx7nyxmDanXmPfrGQcKjnIr/wmHMMcMphiDHPI\nYC5jFKe/oihExkcyc/dMLqVfYpZPOH26TkCrrVzyAEIVxT2MY5b3sz919QRN3VvgUMlB7ShCWATj\n72mvATrmvert5vkO78D2z6h28G1SI4dRybZSiTPUqAGpqSXuDsiDWMqSWR7GWRKYTavHwtSOIYTF\nUJQSvOI7oKz4mT2vrSSz/jc0/rQx86Pmc2/SBHjgHljGSE0tYY77XqX9x0IUzeyKvaIoRJyN4Gmf\np9WOIoRVeKLuE7BqO+v6rSMqMYpm9it47e4mjl+VY/kVidkV+7jrceTk5uDv6q92FCGsymOej7G2\n71p2TzlF9dredFvdjZCvQvg29luy793Jm64pLJbZnaD9OOpjYlNiWdJryX1t5GSeqcYwhwymGMMc\nMpjLGGWVIVuXzfenvmdB9AKa7D7B7J1Q6ZVXcQgfl3eAvpxyiMJZ9kVVp0+zYVI3NPM+JMwv7L42\n5vnDYIljmEMGU4xhDhnMZYzyyHAk6TBbVv0bn3U/obRsif/7S2julv+Ga+bwXVgTi76oKuunbWRc\nTaRz/c5qRxFC3CewTgumvfkDnfYkcm5YL7qv6U7HFR3ZeGojulwdZGerHVE8hFnt2V95+gm+8Erh\nX0tPP9DG/Pd8LGUMc8hgijHMIYO5jKFGhixdFhtiNzB//3wS0xKImXebJlWWceVsaKmmb8qevfEs\nep79V8/Ww75eJ7VjCGGVjJ+rD1AJGJj3co2lQcP/cTtgHm7/HUO3ht0IbRJKjwbdqF7JCbTasgks\nisWs9uybLGjCur7rCKoT9EAby9vzMdcxzCGDKcYwhwzmMoY5ZPh7jKRbV/jh9A9sjttMVuTPfPN1\nDpdDWlErfBKuPZ8rlxzWwmJP0P6Z+iftlrXj8uTLBW5+VpF+GNQewxwymGIMc8hgLmOYQ4bCxsjI\nyuDX39Zw7esviEk9wd6nA+jt25vevr3xq+VHYY89lWJvPIst9ov+WMT+xP2s6L2ikDYV84dBjTHM\nIYMpxjCHDOYyhjlkeNgY2bpsfr3wKxtPb2TjqY1UtavK9Dh36rQKwb/fS9R2qG2yHNbCMot9bi6h\na0MZ1HwwA5oOKKRNxf9hKK8xzCGDKcYwhwzmMoY5ZCjOGIqicOjKIU5v+pJf7p7k23uHcLV3pUO9\nDiz915MkfeFB7Sat8p6uJYpkkcX+XnQUUQMeJ+BYMjWr1SykjXX9MJTlGOaQwRRjmEMGcxnDHDKU\nZoxcJZdjycfYfWE3r364m2/jfqR7bBZptatzaMm7PPrEc7jau5YuXAVkkcUe7x04t3+Lm6sOFNnO\nmn8YTDmGOWQwxRjmkMFcxjCHDKYcQ5eby7FLMRz/eS3/08TyS9I+6levT6f6nWhVpxUd5m8i+53p\n1HFrSBW7KqXboAWzyGL/+vbXcdA6MD1kehFt5IfBVGOYQwZTjGEOGcxlDHPIUJZjZOuyOXD5AD+f\n/5kTV44RtOUgi4KyScq4glNlJzydPKlr785bq+KhoQ9VAwKpPmgEXs51sbWxLV0gM2aRxb7poqZ8\n0esL2ni2KaKN/DCYagxzyGCKMcwhg7mMYQ4Z1BgjV8klJSOFS+mXSLz2J1W+/gblzBm4mszoMDuu\nZV6jfvX6+NTwwdumBr23/snxl/vxiP0j+ldtezdqVqtV6Mwgc2eRxb7m7Jokv55c5L/C8sNgujHM\nIYMpxjCHDOYyhjlkMKcx/paZncm5G+c4l3qO1OQLuP74C9s71uVqxlX9q/L5BH75NJ2U6pU436AG\nX0/pTl3nung5eeHu6E5trQu172mp5dMcra15XRxm8mIfERHBxIkT0el0jB49milTphRoM2HCBLZt\n20a1atX46quvCAoKMrqvRqNh8LrnWP38egMfyjp+GCIjIwkJCSnTHBXlu7CUz2GKMaztuzAsEggp\ncm2xn3SlKGSkXCY5LoaU5PMcq1+NhPQELt68SNKtJKqdvcD4ded5arAOp8pOuNm74ebghl9GNZ6L\nSEBTvTpZ9etxvf8z1KpWi1rVavGI/SPU0jpTSQfY2xcjTPGY9HYJOp2OcePGsXPnTjw8PGjdujWh\noaH4+fnp22zdupWzZ89y5swZ9u/fz9ixY4mKijKq799GJrsX4yNWXMYUe2sh38U/rO27MFS/ZsyI\nZMaMkCLXF/tojEaD/SMeNHjEgwZAoQeSZ8A9JZfrmddJzkgm+XYyty+exebcDnQ3rpOWfIHvT33P\ntcxrpGSkkJKZgndcCrN2aQifUB9Xe1cesX+EGlVr0ORqLj3WxYCzE5m+Plx7oT/OlZ3RaDTkKrlw\n+za2N9LIdHclV8nFRmODQyUHnCo74VjZEafKTjhUcihw4akxDBb76OhofHx88Pb2BmDAgAFs2rQp\nX8HevHkzw4cPB6BNmzakpaVx5coVzp8//9C+fwvoO7bYwYUQorzYaGxwtXfF1d6Vpo80hQadISRc\nv77/A+1zlVzS7qbxQ0YKVzOukpKZwo07N7hnk0DsozfITUslJTOOrdGfcvPuTRQUbDW2BJy/TZ9f\nrjBnjD+2NrbocnXczrpN49hk3luVSKKtwq91c3k9rPjP5zZY7BMTE/Hy8tK/9/T0ZP/+/Q9tk5iY\nyOXLlx/a929uHo2LHVwIIcyVjcaGGlVr5O3N12ryz4qWQM9/3o4von+Bh7LeuQPjLsPduzTR2jGo\nnjtObxfvojODxd7YM9SlPcdrzHZKe7LcFCfby2OMmTNnlnmOivJdWMrnMMUY8l38ozy+i4rIYLH3\n8PAgISFB/z4hIQFPT0+DbS5duoSnpyfZ2dkP7Qul/4dCCCHEwxk8yh8cHMyZM2eIj48nKyuL9evX\nExoamq9NaGgoK1euBCAqKorq1avj5uZmVF8hhBDlw+CevZ2dHQsWLKBbt27odDpGjRqFn58fixcv\nBiA8PJwePXqwdetWfHx8sLe3Z/ny5Qb7CiGEUIGiom3btilNmjRRfHx8lA8++EDNKKq6ePGiEhIS\novj7+ysBAQHK/Pnz1Y6kqpycHKVFixZKz5491Y6iutTUVKVv376Kr6+v4ufnp/z+++9qR1LNrFmz\nFH9/f6Vp06bKwIEDlbt376odqdyMGDFCeeSRR5SmTZvql12/fl3p0qWL0qhRI6Vr165KamqqwTFU\ne+D43/PwIyIiiI2NZe3atZw8eVKtOKrSarV89NFHnDhxgqioKBYuXGi13wXA/Pnz8ff3t8hL2E3t\n1VdfpUePHpw8eZKjR49a7W/H8fHxLF26lJiYGI4dO4ZOp2PdunVqxyo3I0aMICIiIt+yDz74gK5d\nuxIXF0fnzp354IMPDI6hWrG/fw6/VqvVz8O3RrVr16ZFixYAODg44Ofnx+XLl1VOpY5Lly6xdetW\nRo8ebfUn72/evMmePXsYOXIkkHdo1NnZWeVU6nByckKr1ZKZmUlOTg6ZmZl4eHioHavctG/fHhcX\nl3zL7r/Gafjw4WzcuNHgGKoV+6Lm51u7+Ph4Dh06RJs2hd8UrqKbNGkSc+fOxcZGtf81zcb58+dx\ndXVlxIgRtGzZkjFjxpCZmal2LFXUqFG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- } - ], - "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()\nplotflag = 1\nNt = 101; # number of angles\nth0 = pi / 2; # primary direction of waves\nSp = 15; # spreading parameter\n\nD1 = wsm.Spreading(type='cos', theta0=th0, method=None) # frequency independent\nD12 = wsm.Spreading(type='cos', theta0=0, method='mitsuyasu') # frequency dependent\n\nSD1 = D1.tospecdata2d(S1)\nSD12 = D12.tospecdata2d(S1)\nSD1.plot()\nSD12.plot()#linestyle='dashdot')\nshow()", - "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.\nIn 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\nThe commands below computes the intensity of rainflowcycles for the Gaussian model with spectrum S1 using the Markov approximation. \nThe 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()\nimport wafo.data as wd\nxn = wd.yura87()\n#xn = load('yura87.dat'); \nsubplot(211) \nplot(xn[::30, 0] / 3600, xn[::30, 1], '.')\ntitle('Water level')\nylabel('(m)')", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "pyout", - "prompt_number": 10, - "text": "" - }, - { - "output_type": "display_data", - "png": 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o+ruoyFozQm1Lh8hTHWQlShYeF1Bc4ESjtpuI3GMquvy4Njo6bLeNPNMhX7DM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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]\nyura = np.reshape(yura, (285, 300)).T\nmaxyura = yura.max(axis=0)\nsubplot(212)\nplot(xn[299:85500:300, 0] / 3600, maxyura, '.')\nxlabel('Time (h)')\nylabel('(m)')\ntitle('Maximum 5 min water level')\nshow()", - "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()\nimport wafo.stats as ws\nphat = ws.genextreme.fit2(maxyura, method='ml')\nphat.plotfitsummary()\nshow()\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\n7MhE732jsQOZmZmwtraGoaEhANEjs0WJD7p9+zZ8fHwAAO/fv8f58+ehqqqKvn37SvguJEh4ONCp\nk+Tk2dszs9YMyYlkkTLC/GeHDx8u0jF5IIL6LBWQlUWkpvb/OIv+/cucPHaMSF+fKDJSbvrJk4iI\nCAoPD6fw8HCxCheJGx80evRoOn78uMBzCmXbNjZEt24RkeixD0LbbdvGxlHIEXHtS+hm9v379/l+\nLy4u5uXnZ6m56Oj8v1yFs/OXJWMuAwdineshZHUdhMWtTiM7Wy4qyo1z587xzVI9PT0RUjaJVgVU\nFB+0fft2XuGiGse//wKvXgFOTpKV+2VWhffvJSuXRTpUNIIsX76cNDQ0SFlZmTQ0NHgfXV1dmlcm\nulKeAKCAgAAKDw+Xtyo1kqwsosGDmX+/pmNHIhfcoNcwIH/tvQLb1FacnJwoPDycAgICeG9eX9dT\nkTaVPJqy5cgRIm9v3q8Sm1Fw26xfXzW9WKqFuPYltMLd/PnzeXmeFA2F8zWvRXh5AefPAy3wGBfQ\nAzvrz0B8h+k4eJCZjdRGtm7dii1btiAlJQWWlpYAmBm1mZkZ2rVrhwMHDshMF4Wx7enTASMjYO5c\nAKJXpROlHYcDkPN3TBoBFpki8TgK7gPDpbi4WOwdc5aax8GDgKEhkARr9KofjVH5m+FwfiUmTJC3\nZtJj2LBhOHPmDPr164ezZ8/izJkzAJgNaFkOEgpFbCyTjlhafPgA3LkjPfksEkHoQBEWFgYvLy+8\nevUK9+/fR5s2bZDDFsGp9ejoAI8eMUF6xm1M0RGRmFh3L4Y9CYRnR4KXF2rd3oW2tjbMzc0xZswY\nmJmZwdzcHADQoEED7N27V77KyYOCAiZvvTTT0o8a9dUGGYtCIsr61KFDh6hBgwZkampK0dHR4i6H\nSQ0R1WepJty9jOwn/9LT+va0Aj8TUEqDB8tbM+nQvn17mjRpEuXl5REA6tOnDw0YMECmOiiEbcfG\nEjk68h2S+B7F06eMh92X0rMsskFc+xI6o3jy5Ak2bNiAAQMGwNTUFPv37xc7g6w0YVN4SB9uVLd2\ns0b4pXU4uuMiNqnORFYm1bpZBQBERkaCiHhR1r6+vjh+/LictZIDcXFA69bS7cPSEmjZ8v+1LlgU\nEqEDRd++fbFkyRLs2LEDkZGRaNasGVxdXWWhm0hwA+5YZMP2Yw0wtEEYWhXF4PuwHzFxfO1LU56V\nlYXMzExWx2bIAAAgAElEQVSenaenpyvGxrKskfb+BBc/P2DPHun3w1JlhA4UcXFx6Nq1K9NYSQmz\nZs3CPzKqkHbq1ClMmDABPj4+uHTpkkz6ZKkcHR2guZsOuuESnFXuY3j0BPTuVVqrZhZt2rRBjx49\ncOHCBQBMfqZ27drJWSs5IIsZBQAMGgRERzM58FkUEqHusW/evMGCBQuQkZGB0NBQPHz4EDExMRg7\ndqysdER2djZmz56NXbt28R1XGBfCb4zsbGDCBCDzRT4CY3sgAc6IHrQBR/9WnNQu1aFsnieujUVF\nRaEDN0JRBsjdtt+9Y/J9ZWYyNa95eknWPVYUdHXZxMaSRuLusaNHj0b37t3x6tUrAECzZs3wxx9/\niKXUmDFjYGBgAHt7e77johZ5WbZsGfz9/cXqk0V6cPcs1HTrozfOoYt6LP4ymldrigs0bNgQS5cu\n5WWNTU5OFsvTT5hdnzp1Co6OjnB2dkarVq1w5coViekuMeLiADc3vkFCGvCSyERFg2xsQaVUpoAv\n88nKkqoKLKIgbLe7VatWRER8NbMdv/KEEEZUVBTFx8fzRbcWFxeTpaUlpaamUmFhIS8vzl9//UXT\np0+njIwMKi0tpblz59Lly5cFyhVBfRYpwvOGevaByN6eKDBQ3ipJhMGDB9PKlSvJxsaGAFBeXh45\nODiIdG1Fdl2WvLw83s93794lS0vLcnLkbtsLFhAtXFjusMS9nriUlhJZWhLFxVW5TxbREde+hL4u\naGho4MOHD7zfY2Njoa2tLdZg5OHhAV1dXb5jFRV5GTFiBP744w8YGRlh48aNCAsLw7Fjx2purpxa\nDM8bykIPuHQJOHQI+P33KsubMAHw9ITcYzRSUlIwb948XjnU+mJU/hOleFFZeXl5eWjYsKFkFJck\ncXGy2cjmwuEwKYzZTW2FRGia8bVr18Lb2xvPnj1D27Zt8e7dOxw7dqzaHYtS5OWnn37CTz/9VKkc\nhSnu8q1jYMBUQevQAVBXB378UWwRT54wpQ8AZtDgFlqSJREREXj79i0WLFiAN182V1NSUlDn6+Lj\nFSCKXQPAP//8g/nz5+P169e8MsNfIzfbLi0Fbt6U7UABMMF3Tk7AunVA3bqy7buWI/XCRa1atUJk\nZCSSkpIAAC1atICqqmqVO+QiyZoW7AChIBgbM4NFx45AvXrAmDFiXa6uzvzr6grs2CEF/UTA09MT\nQUFBmDt3Lm9fonPnztgj4puuqHbdv39/9O/fH9HR0RgxYgTv+SqL3AoXPX4MNGgAyLpUa5MmQKtW\nTO1ebnZZFokg9cJFADOdTktLQ3FxMeK/JPAaOXKkWB19jShFXkRBYaqAsTCYmzPLUJ06MYOFr6/I\nlx48yMwkduyQb+LB7t2747vvvkNsbCy8vb1x+/ZtkZeHxLVrDw8PFBcX48OHD2jQoEG1dZcIsnKL\nFYSfH5PSgx0oFAqhexTDhw/HnDlzcO3aNdy6dQs3b97EzZs3q92xi4sLkpOTkZaWhsLCQhw5cqRK\nVb7YyGwFpHlzpvb2jBlM3VUR4e55SGKQqO5+R3h4OE6cOAEAePjwIaKiokS6ThS7TklJ4bkmcl+8\nFGaQAGQXaCeI/v2BW7eYGt0sCoPQGcXt27fx8OHDai0V+fr6IjIyEh8+fECTJk2wZMkS+Pn58Yq8\nlJSUYOzYsWjZsmWV+2BRMOzsgJAQoGdPZmbRs6dMu6/Ofse8efOwd+9eaGpqAgB+/7JBL0ocRdni\nRWXtmuuMMXHiRBw/fhx//fUXVFVVoaGhgcOHD4t3c9ImLo6pay0P6tVjMlHu2wf88ot8dGAph9CA\nu8GDB2P9+vUwMjKSlU4iI/egJBbhxMQA/foxf6lluI/Erafh6gpcvCjeLKV58+a4d+8e6tSpIzcb\nk5tt5+UxjgmZmYCADXyJ16MQ1CYuDhg+nBntORyR+2QRHXHtS+iM4t27d7CxsYGbmxvP80OUQvOy\ngpvrid3MVlDatGEGiSFDgFOnmN9lQHX2OywtLREWFoYbN25IRzlF5vZtwN5e4CAhM9zcAFVV4No1\noH17+enBwkPojIK7/l92BOJwOOjYsaPUlRMGO6OoQYSGAiNHMv9+9528tamUAQMG4M6dO+jSpQt2\n7twJf39/cDgcbNiwQWY6yM22V60CXr8G/vxT4GmZzCgAJh7n0SNg9252RiEFJJ7Cw9PTE+bm5igq\nKoKnpyfc3Nzg7OxcLSUlCbuZXUPo2ZN5vffyAu7fl7c2ldK3b18MHjwYb9++BcC4iLdq1UrOWskI\nWQfaVcSoUYwjRJlgXxb5IXRGsWPHDuzcuROZmZlISUnBkydPMHnyZISFhclKxwphZxQ1kEOHgNmz\ngfBwxjtKQfn06RPS09PRsmXLb2ePgoiJhbl2DbCwqEAvGc0oAGawsLUFZ95cdkYhYSQ+o9i8eTOu\nXr0KLS0tAMxGH/dNSxFgZxQ1DF9fYOlSoGtXIC1N3toI5PTp07C2tkbrL7EECQkJVXLdrnG8fAkU\nFzOxMIrA1KnAli3y1oIFImxm16lThy99QXFxsUSjqqsLG3BXAxkzBvj8GejSBYiKYt5i5cCECYxj\njbo6s/nN3fQODAzE3bt30alTJyQmJsLZ2RnPnj2Ti44yJTaWCbRTlOfbxQVo3Bh4Lm9FWITOKDp2\n7Ijly5fj06dPuHTpEgYPHgxvb29Z6MZSm/nxR2DSJGaw+PdfuajAjbU4f54ZNLioqqpC5ytXKSUp\np9tWCBRlf6IsQnK9scgGoda/cuVK6Ovrw97eHtu3b4eXlxeWLVsmC91Egl16qsHMmcMsRXXrJpdN\ny4pyS9na2mLhwoW8GixTp05F27ZtZa6fzJFnRHZFDBzI/PvggXz1+NaRRG5zeVHD1WchYuoQzJlD\n5OJClJ0t06659TSysviP5+Xl0fz586lVq1YEgH755Rf6/PmzyHLPnz9PLVq0ICsrK1q5cmW58/v3\n7ycHBweyt7entm3b0p07d8q1kbltFxSQLj58VTJI8EcUxK5HIazd+PGiNWYRCXHtS6jXk729fbkd\ncm1tbbi6umLhwoVyzVHDej3VEoiYjcuEBCZHlIaGvDXiIa6NlZSUoEWLFrh8+TKMjY3h6uqKQ4cO\n8aWniYmJgY2NDbS1tREaGorAwEDExsZWq99qc/MmOG6u1fdUEqOdWLJ09ZhZRePGwi9gEYrEI7N7\n9uwJFRUVDBs2DESEw4cP49OnTzAwMMDo0aNx5syZainMwgIOB9iwARg3jongPn0aUBEpsbFU8Pb2\n5nuQ+vbtCy0tLbi6umLixImoW0mthLKFiwDwCheVHSjalIlOd3d3x8uXL6VzI+IQEwPAVd5aVMzw\n4UwQYCUlk1mkh9Cn8fLly0hISOD97uDgAGdnZyQkJJSrgc3CUmWUlIDt24HevYFp04BNm+TmfWNh\nYYH379/D19cXZ8+ehaamJjQ1NfHkyROMHz8e+/btq/BaUQsXcQkKCoKXl5fAczItXBQTA0CBN45n\nzWIi+ufPl28O+hqK1AsXlZSUIC4uDu5fNrlu3LiB0tJS5mI5vvVxYXM91SJUVYG//wbatQPWrwem\nT5eLGtevX8eaNWt4D9aBAwfg4uKCW7duwdbWttJrxXEdDw8Px+7du3Ht2jWB52Xq+v3V0pfCYWbG\nvERs2cJmla0CUi9cFBQUBD8/P+Tl5QEANDU1ERQUhPz8fPz888/iaSsF2DiKWoa2NnDuHNC2LRMd\n3K+fzFXIz8+HhYUFPD09sXjxYjx//hz5+fkAwKujXRGiFi66e/cuxo8fj9DQ0HL15GXOmzfAx4/y\n1UEUfvmFKbX744+MnbDIDlF3vbOysijra/cQOSOG+iw1jRs3iBo2JLp1S+Zdnzt3jpo0aUIdO3Yk\nANSkSRM6c+YM5eXl0bp16yq9tqioiJo2bUqpqalUUFBAjo6O9PDhQ742z58/J0tLS4qJialQjkxt\n++RJol69JO+pJA1Zo0cTLVok2oUsFSKufVXYOjg4mIqKiiq8sKCggHbv3i1WZ+Lw6NEjmjRpEg0e\nPJh27dolsA07UNRyTpwgMjYmev5c5l1//vyZEhISCAB9+vRJrGtDQkKoefPmZGlpSStWrCAiom3b\nttG2bduIiGjs2LGkp6dHTk5O5OTkRK6uruVkyNS2Z88mWrKkZgwUqalEenpEb9+KdjGLQMS1rwrd\nYzdt2oSgoCBYW1vDxcUFjRs3BhHhzZs3uHXrFh4/fozx48djypQpUp3xlJaWwsfHB0cFlChj3WO/\nAdauBfbuBa5eBb7kG5MWERER5fa6vrax8PBwdOrUSap6COpXqri7A6tXg+PZUXHdY8u28/dn6mWs\nXSv8YhaBiGtflcZREBGuXbuGq1evIj09HQBgZmaG9u3bo23btiJt3I0ZMwbnzp1Do0aNcO/ePd7x\n0NBQTJ8+HSUlJRg3bhzmzZtX7tozZ85gy5YtGD9+PAYMGFBeeXagqP0QAZMnA8+fA2fOSNVtdvbs\n2YiKikLXrl15L0dt27bFsWPHcOvWLVy+fBmdOnXC6tWrpaYDF5nZdl4eYGgIvH8PTr26NWOgeP2a\nKbV75w4gYP+HRTgSHSgkQXR0NDQ0NDBy5EjeQFFRUNKtW7cQHx+POXPm8JVe7devH06dOlVeeXag\n+DYoLgb69GE2t7dskarbbG5uLk6dOoVr167h+fPnOH/+PCZNmoT27dujX79+0JBRMKDMbPviRWDZ\nMiAqSrp/3CUta8ECID2dqa3NIjYKN1AAQFpaGry9vXkDRUxMDBYvXozQ0FAATD4pAHxeVJGRkThx\n4gT+++8/tGzZEtMFuEqyA8U3RE4O4zbr5wfMnCmzbmt9zeyFC5l/ly2rWQNFXh7QsiVw+DBjFyxi\nIfHIbGkgSlBSx44dRSq3KtOgJBb5oaXFuM22aQM0bQr07y+VbqobmFTjiIoCFi2StxZCKT+J1ADw\nAvhSUltXF8jMlLFS3xByGSgkXc+CHSC+EUxNgVOngF69mLVpFxeJd8G1pW9iwPjvPyA+nhl8FRyB\nL79EQMeOgI8POD9K16nmW0foQPHgwQNERUUhLS0NHA4H5ubm8PDwEBqhWhmiBiWxsJTDxQXYuZMJ\nxIuJYQYPlqoREwPY2ipUEkax4HCAbduYwQLsQCFNKqxHsW/fPri5uWH27Nl48+YNmjZtCnNzc7x+\n/RqzZ8+Gq6sr9u/fX6VOXVxckJycjLS0NBQWFuLIkSPfRqlJFsnQvz+T+6d3b2bvQsJkZ2cjLi4O\nN2/eBMB46H2sCZHL4nL+PDM7q8nY2AAzZjA/l5TIV5faTEUBFuvXr6ecnJwKAzA+fvxI69evFxqo\n4ePjQ40bNyY1NTUyMTHhBekJCkoSl0rUZ6ntlJYSTZ5M1KMHUSWBoeIQFRVF3t7eZG9vTyNHjqSf\nf/6ZANDIkSPJ3t6evL29KTo6WiJ9CUMmtm1rSxQXV6ZP4ZfIPeBOEMXFTJulS0UTyCK5yOzKKCgo\nqMplEgcABQQEUHh4uLxVYZEHRUVEPXsSTZzIDBzVZMaMGfTkyRMiIgoPD6eAgAC+ByopKYlmzJgh\nVI6wwkWPHj2i1q1bU506dWjNmjUCZUh9oEhLI9LXJyopKdOn8MsUcqDgtjEwILp2TTSh3zji2pdQ\n99iOHTtiz549sLCwAMBkjx03bhzu3r0r1ZmOKLDusSzIyQHatwdGjWKWoySMNAoXvXv3Ds+fP8c/\n//wDXV1dzBKgtyRtW08PyMoSrW2NcY8V1OafU0zG4YQENhW5EMS1L6E1s3/55Rf06tULmzdvxi+/\n/IKJEydiz5491dFRorA1s79xuG6zf/wBnDwpEZHDhw/H2bNnea7XaWlp6Ny5s0jXli1cpKqqyitc\nVBZ9fX24uLhAVVVVIvoKIytLQEFT776gQ4f5jtV4+vVj9q0mTKglN6Q4CPV66tGjB7Zu3Ypu3bpB\nX18fCQkJMDQ0lIVuIsGmGWdBkyaM22zPnozbrGv1KrV5eHhg1qxZWLduHQCge/fuWCtiXiFxCxdV\nhtRihP77D4iIABTohU9i/P47k4p8xQomepsFgAwKFy1duhRHjhxBdHQ07t69i44dO2Lt2rXo06dP\nlTtlYZE4rVoBu3YxHlHXrzOFbqrIxIkTYWNjw5tFREZGorGItZolGSMktZegqCjAwYFZk6pt1KvH\nlNJt3ZqpWeHvL2+NFILqFi4SuvT04cMH3Lx5E23atMHEiRNx8eJFrF+/XmxFpQW79MTCo18/YM4c\nZvmhGu6s+/btg6+vL89l28vLC4mJiSJdWyNihI4fB7y95a2F9GjcGIiMBDZuZGYV7DJU9anKjnmp\nBDxMJEEV1WepzZSWEk2ZQtStG1FhYZVE9OvXj/79918iYmwsLi6OHB0dRbpWlMJFXAICAmTi9cQn\nKj+fSFeX6OXLytuJIqua7aQu6907Ind3pthRFW2htiKufVXY2s/Pj27cuFHhhbGxsTR69GixOpM0\n7EDBIpCiIqJevYgmTKi22yzXxv777z+RrxFWuOj169dkYmJCWlpapKOjQ02aNKHc3FyB/UoCPlEH\nDjCxJ8LaiSKrmu1kIisvj8jLi7GHvDzROvwGENe+KnSPvXfvHn7//XfExsaiRYsWfIWLkpKS0LZt\nW8yePRt2dnaymvyUg8PhICAggM31xFKe3Fwmh5G/PzBpkkiXBAYGYvLkyTAwMOBt/i1evJjnRvj6\n9Wts27ZN7PXdqiBJ91g+99Ju3YBx44ChQytvJ4qsaraTmayiImDiROD+fcZDTl9feKe1HImnGS8o\nKEBCQgKeP38ODocDMzMzODo6om7dutVWtrqwcRQslZKczKSgPnVKpMR3Z8+exdq1a1FYWIjvvvsO\njRs3xoIFC+Dv74/4+HjUqVMHs2fPhpeXl9RVl8pAkZ4OODsDGRmAgOdXof64S1oWEZMl9+hRIDSU\nyUD8DSOxgSI9PR2mCp5wjR0oWIRy5gwwZQpw6xZgYCDSJS9evMC1a9eQnp6OefPm4fDhw2jXrp1M\nN6WlMlAsWcJUh9u6tfJ2osgStU8ZyhIFXfX/kKljyXhGtWol2kW1ELHtq6I1KScnJ97PAwYMEGs9\nS1ZUoj4Ly//59VeiDh2EbmgOHz6ciIj++OMP3jF52Zgk+wWIWZ83MCC6f7/ydqLIErVPRZV1/DiT\nvuTnn5nN/W8Qce1LqHssADx79qwqg5ZMYN1jWYQSEMCk0p47t9Jmt2/fxqtXr7B7926cPn2aV8c9\nMzMTmTW9Ks6ffwKdOjFpxb91BgwA7t4FUlMBe3tmOaq0VN5aKTQVLj05OzsjISGh3M+KBLv0xCIy\nWVlMxPaSJcCwYQKbbNiwAVu3bsWzZ894NdvT0tJgbm4ODocj0xcmiS89NWgIxMUBlpaVt6vBS09V\nknXpErN3kZsL/PorMGgQoKwsXFANR2J7FMrKylBXVwcAfP78GfXq1ePrJEcKdQDEhR0oWMTi7l2g\nSxcgLIyJTK6ASZMmYdu2bQBqR81sDgegmbMAIWlIaswf92rKEoau+n/IfPGpdkauf0HiXk/yJD8/\nH56enggMDETv3r3LnWcHChaxOXiQeXO8eZMptCyEGj9QBAWBM24s6NNnJr1FpX0q7h93mcgiAiIj\nwenkCdLWAezsAEdH5qXCwYFZpqqp1QC/QuLZY+XJ6tWrMVSAv7c0kPQ+hyTlsbIkKGvYMKBPH2D4\n8Nq/Ln3sGLBwIYAIoYOE6ERISI4CyuJwAE9PRlZaGrB0KdC8ObNkN3Uq0KgRYGXF7HEEBjLZilNS\nKrUjRX1GxEXqA8WYMWNgYGAAe3t7vuOhoaGwtrZGs2bNsGrVqnLXXbp0CTY2NtCXUXAMO1B8Q7J+\n/51Zk16yRGJ9chFm1wDw008/oVmzZnB0dJTe3t/798DMmUy5U0X7g1wTZOnoMJv/06YBu3cz7tU5\nOcDZs0ywYlERc7xzZyb5YNu2TGDnli3A1au8Er2K+oyIi9DssdXFz88PU6dOxciRI3nHSkpK4O/v\nz1fcpW/fvrh16xbi4+MxZ84cREZGIj8/Hw8fPkS9evXg5eUl0cycLN8wqqqMp4urK+DmBkgogK4i\nuy5btCgkJARPnz5FcnIy4uLiMHnyZMTGxla5z4qLEjUEkA44A8A/VZbPUgYVFcDamvmUXenIygLu\n3WP2wBITgb17gQcPmAjwOnWYGYetLTP41K3LfOrVE/yzjGqUiIvUBwoPDw+kpaXxHStb3AUAr7jL\nzz//jBEjRgAAli1bBgDYu3cv9PX12UGCRbIYGjJBVxKsrVKRXZcdKE6fPo1Ro0YBANzd3ZGdnY1/\n//0XBiIGA34NtyhRZbCPjviItOmtC2RmfvmhQwfmw6WkBHj2DKvs/sLipcJnrrrIRKbylz0zQQNI\n3bpYFd8VMsgeIxjJhG9UTmpqKtnZ2fF+//vvv2ncuHG83/ft20f+/v5iywXAftiP1D+iIopd9+nT\nh66VqevcpUsXunXrFmvb7EfmH3GQ+oxCEJKaHRDr8cSiQIhq11/braDrWNtmUSTk4vVUI4q7sLCI\niSh2/XWbly9fwtjYWGY6srBUBbkMFC4uLkhOTkZaWhoKCwtx5MgRXjUxFpaaiih23bdvX/z1118A\ngNjYWOjo6FR5f4KFRVZIfenJ19cXkZGR+PDhA5o0aYIlS5bAz88PmzZtQo8ePVBSUoKxY8fybfix\nsNREVFRUBNr19u3bATC1uL28vBASEgIrKyvUr18fwcHBctaahUUExNrRkCN+fn7UqFEjvk3xo0eP\nko2NDSkpKdHt27erJWv27NlkbW1NDg4O9P3331N2dnaVZS1cuJAcHBzI0dGROnfuTOnp6VWWxWXN\nmjXE4XDow4cPVZYVEBBAxsbG5OTkRE5OTnT+/Plq6bVhwwaytrYmW1tbmjt3bpVlDR06lKeTubk5\nX+ZicWXFxcWRq6srOTk5kYuLS6VVGoXJSkxMpNatW5O9vT15e3tTTk6OSLLEhbVt1rZFkSVP264x\nA0VUVBTFx8fz3eyjR48oKSmJPD09xXqYBMm6ePEilZSUEBHRvHnzaN68eSLJWrRoEbVp04ZPVtkv\n3crKitq3b19lvYiI0tPTydXVlZSVlUV+mATJCgwMpLVr14p0vTBZV65coa5du1Lhl9Tdb9++5bvm\n+fPnpKGhwauv3rFjR9q1a1eF98hl1qxZtHTpUpH14nA41Lx5c96xjh07UmhoKBExJUk9PT2rfI8u\nLi4UFRVFRES7d++mRYsWiSRLXORp2/v376fu3btXKKtVq1ZkZGTEO1bWtjds2EBjx44VSa/169eT\ngYGBQNvu0aMHmZubS8S2U1NTicPh8O5XXFkcDof2798v0La//q44HA6lpKQQEdGkSZNo3LhxErXt\nr2V17NiRZs2aRe3bt5e5bSt0Co+yeHh4QPer3DzW1tZo3ry5yDLMzc2hrq4OLy8vdO/eHS9evOAl\nN+zWrRuUlJivw93dHS9fvhRJ5pIlS3Dw4EG+Y5qamryfS0tL+X6vDEH3CAAzZ87ExIkTK72WiPD7\n77+jefPmUFdXx/Dhw7Fr1y6UfpVegKrgTSNIr61bt2L+/PlQ/RIg5OrqiitXrvDOm5qaIjc3l+fR\nw+FwwOFwKrxHrm5Hjx6Fr68v71haWhqUlJSgqakJTU1NWFhY8CKePTw8yslo3LgxPn78CADIzs7m\nbRTv2bNHYPvK7jE5OZl3TdeuXXH8+PEKr68Ooto21341NTVhaGiIESNGlEvOKUhWZbb9ww8/4MKF\nCxXqpaqqyueVVdaW8/Ly0LBhQ5Hu0cHBgadDWWbOnInVq1cjLS0Npqam0NTUhImJCWbNmlXOdiu7\nR0Bytn3gwAE+2+Zmh6jsu9q6dSt27twJXV1d5Ofno0mTJuV0+9q2KyIwMBCdO3eGh4cHHj58iHbt\n2iE2NhaNGzfGp0+fAPDbdkV4enoiKChIIrZdYwYKScDhcHD27Fnk5uYiJCQEBQUFvMC+suzevbva\n5S4XLFgAU1NTvH79ulqyTp06BRMTE1hWkh4aYNJC7Ny5E/v27UNeXh7Onz+Pa9eu8XnYAMDGjRvh\n6OiIsWPHIjs7u8p6JScnIyoqCq1bt4anpyeKioqq7dIZHR0NAwMDgff68eNH5Obm4tChQ1iyZAku\nXrwoUMbKlSsxa9YsmJqaYs6cOfjtt9+qrI+trS1OnToFAPj777/LfZeypqz93rlzB/fu3RNov5Uh\nSdveu3cvfv755yrL4dq2w5dMvlFRUcjNzUVYWBgOHjyInTt3lrumuLi4QnkbN25Er169QETVsu20\ntDQ+275161aVZXGpzLa/hsPhwNfXF/fv30fLli3Rvn17DBgwACtXrsThw4dx48YNkWy7MndtcW37\nmxooyqKvrw8NDQ08ePCAdyw2Nhampqa4cOECVq9ejcjISN65PXv2wNLSElpaWmjatClvFrFnzx4M\nGTKE1+7SpUuwtrbG5s2b0a9fPxgaGuLw4cMAmDcFbuQ58P+3Ze6bU3BwMLp164aHDx/C0tISmzZt\nwooVK7C4TDimoD/GycnJ2Lp1Kw4ePAh3d3coKSnBxsYGW7duRV5eHi9HzMWLF7Fw4UIkJiaicePG\n6Nu3L99b9rRp02BqagptbW24uLjg6tWrvHN//vkn0tPTMWrUKGhpaeHRo0dISkpCbGws1NXV8erV\nK3h7e0NTUxNr1qwpd29fk5WVBRsbG+jp6aFnz55IT0/HoUOHMKyCWhFcWrduDVtbW9y/f7/cuY8f\nP6J169bIycmBkpISPDw84Ofnh0ePHmHy5MmIiYmBpqYm9ERMH717925s2bIFLi4uyMvLg5qamkjX\nyQIDAwN07969nP22bdsWjo6OePr0aTn7bdCgAUJDQ7Fw4UI++y1rA1z71dHRwdSpU/nsjWu/y5cv\nR3p6Ovr16wddXV0++7WxsYGWlhYsLS2xY8eOCvX/9OlThbbdokULeHh44MGDB3j+/DmUlJSwe/du\nmACEKGAAACAASURBVJmZoWvXriAibNy4EUlJSTAwMMCoUaMwfPhwpKamIiQkBADjXWZsbAwjIyOs\nLZNe/caNG2jTpg10dXVhZGSEqVOnoqioiE+37Oxs/Pnnn0hJSYGpqSkGDx4s8Lsqy+jRo7Fo0SJ8\n/vwZaWlpePXqFTQ1NaGlpYXXr1+jS5cu6N+/P699fHw8GjVqhJKSknKyiNkSAMD8sR85ciTevHmD\nkSNH4ocffoCbmxv++OMPjBkzBtevX4erqyt0dHTg5uaGmJgYAMxgHh0dDX9/f2hqaiIwMJCvD3Ft\n+5sbKLj/Aa9fv0Zubi7c3d0BABkZGejWrRs0NDSQk5ODNWvWYODAgfjw4QPy8/Mxbdo0hIaGIicn\nBzExMXBycion+/379xg4cCBWrFiBDx8+wNLSEi9evEBqaioA4QFZBgYG2L17N2xsbBAcHIy5c+ci\nOTkZjo6O8PX1RUlJCVq1aoW3b9/yXRcWFoYmTZrAxcWF73jjxo2hrq6Oy5cvAwDU1NSgpKQEDoeD\ncePGlSvE4+bmhjt37iArKwvDhg3D4MGDUVhYyDufm5sLX19ffPz4EcbGxrh79y4AJn+RiooKDh48\niNzcXMyePbvS+7x06RLevXuHkydP4v379/Dw8ICPjw9OnjxZYbZg7sNz7do1PHjwAM7OzuXaTJ06\nFe/fv8erV68QGRmJ+Ph4XLt2DS1btsS2bdvQpk0b5ObmilytrkWLFrhw4QJu3boFHx8fkd4GpQ3X\nfl++fInQ0FA+++3Tpw9+/fVX3LlzB4aGhnz2O2XKFJiZmSE3N1cs+719+zbv/Nf2+7Xrr4GBAc6d\nO4ecnBwEBwdjxowZFSY9TElJQVpaGhwdHWFhYcGT9/btWzx8+BDR0dF8/8dRUVF4/PgxQkNDERwc\njBMnTsDCwgLPnj1DXl4eFi9ezFve5HA4uH//Pp4+fYqLFy9i1apVCAsLA8B4pq1fvx4fPnxATEwM\nwsLCsH//fj7dCgoKcODAAcTHxyMuLg75+fn48OFDpf8v3H7r1asHc3NzGBkZITc3Fzk5OdDX14eS\nkhKUyxRE2rdvH3x9ffmOCaK0tBR79uyBqakpEhIS0OpLne9BgwYhLi4OvXv3xvTp05GZmYmZM2ei\nd+/eyMrKwvLly+Hh4YHNmzcjNze33EAhrm3XmoFClGUPIkL//v2hpaWFdu3aQU1NDQsXLgQALFq0\nCMrKyoiKikLdunXRtWtXuLi44Ny5c+BwOFBSUsK9e/fw+fNnGBgYwMbGppz8kJAQ2NnZwd7eHsrK\nypg+fTo0NDRgamoqko5eXl68tc0OHTqgR48eCAwMRGpqKg4dOgRlZWXem0hZ3r9/D8MKchapqKjg\n/fv3AMD3R//kyZPlgsF++OEH6OrqQklJCTNnzkRBQQGSkpJ45+vXr4+ePXuCw+FgxIgRePLkCQDg\nyZMnICJoaWlVen9cDhw4AH19fbRo0QJKSkqYP38+4uPjYWFhwass9zUNGzZEgwYNMH78eKxatQqd\nOnXiO19SUoIjR46gWbNmuHXrFszMzNC7d2/eOnNVlsXevXsHgHlYly1bhsmTJ4stQxJwdS9rv6am\nprC0tOTZ7/79++Hl5YWePXsCADQ0NHj2e+nSJRQUFOCnn34CEQm13wEDBvDst2z2ZiJCbm4u7/dL\nly7xXe/l5cX7o9+hQwd0794d0dHRAu/J3t4e//77L1JTU3kvUrm5ubC2tkbfvn0xfvx4+Pn58e49\nMDAQ9erVQ926dXHgwAGMGzcOampqqF+/Pn777TccPnyYb/bavn171KtXD3Z2dvDz88OhQ4cAAN99\n9x3c3NygpKQEMzMzTJgwAXFxcXy6DR8+HDdu3ECTJk3g6+uLnJwcNGjQQNh/U4U2dvnyZVhZWeHs\n2bMAGFs9fPgw3+rC1xw9ehSOjo5ISkpCQkICTp48CSsrK97zeOXKFTRs2BAtWrTADz/8ACUlJfj4\n+MDa2hqnT58WqpPYti3StrkC4OPjQ40bNyZVVVUyMTGhoKAgOnnyJJmYmFDdunXJwMCAevbsWakM\nc3NzCgsLIx8fH9LT0yMA1KhRIwoKCiItLS3icDikrKxMysrKpKamRhoaGrRq1SoiIrpw4QJ169aN\ndHR0qHfv3vT48WMiInJ3dydVVVVSVVUlLS0tcnFxoYEDB5KdnR05OjqSnp4e/fHHH0TEuPANHz6c\np8/XHhodOnQgVVVVAkAcDodUVFTo119/JSKi8PDwCr2etm7dSmZmZgK/Lw6HQ5qamhQUFEQGBgZk\nbGxMDg4O1K9fP1q/fj2fR9bvv/9OLVu2JG1tbdLR0SElJSW6cuUK+fj4kIaGBikpKZGJiQnt3r2b\nnjx5QgDI1taWvvvuOzI0NKSwsLAK783T05OCgoLIx8eHVFRUePeorq5OOjo6pKKiItDTrDIvFh8f\nHwJAKioqvHuNjo4mNzc3cnR0JGtrazI1NSUiouDg4Eq9zwTZ1/r166l58+bUvHlzmj9/foXXVhdR\nbZtrv0REkZGRpKWlRXFxcURENHnyZKpbty6pqqoSh8Phfb+DBg0iKysr0tfXJ01NTVJWViYzMzOe\n/Zb9Xn777TcaPHgwn16qqqqkrKxMJiYm1LdvXzI1NeXZdo8ePfj+b0JCQsjd3Z309PRIR0eH1NTU\nePbbuXNnUlJSIjU1NZ4NlQWAQO8u7v9/cXEx75iWlhbp6uryvq9t27YRALK2tiZra2sCQKmpqbz2\nmzZtol69ehERUVJSEvXu3ZsMDQ1JS0uL96xz9eJwOHTnzh0aPnw42dn9r70zD2vq2tr4GyZFxQFR\ni6KCgEZmBERQBAdErRNqFXAeoFqnWmu19uuVtk69xVqlg7ZX1KJSxaG0Cta2gIoD4FBQrIgWFK0o\nIlZwaBjW9wfmmJAEEnIgCezf8/Bozjl77XVy3mRlT2s7kLW1NffZqq4hyVlPM2fOJDs7OzI3NycD\nAwPS19fn7nHmzJn05ZdfUrt27Sg3N5cSEhKoV69eCvWwevVqsrS0lNJEVFQUpaenk5WVFbVs2ZL6\n9etHixcvlnpe4me2bt06IpL+zKmrbZ0JFHwg+UEjIvrggw+4KWbr16+n0NDQWm28ePGCli1bRj4+\nPkQkLZ5du3ZRv379uGsrKyu5B0NU9UU8fvx47vzZs2e5D9qLFy/I2NiYDh48yH0oxo0bx01bS0pK\nIgsLC7k+ZWdnk56ensy86tu3b1OzZs0oJSWFiIhef/112rJlC3d+/fr1nO8nT56kjh070pUrV7jz\n7dq1496v2oKclZWVUoGCiCggIID27t2r8D2WpLbpjuIPa3l5ORkZGdHVq1e5c9u2baNBgwYREdHO\nnTuVnqasrTRW/RJJf+lKIu/5DxkyhL7++mvudXZ2NhkaGlJFRQV3vTgQEhG99957XLLGwYMH0/Ll\ny6m0tJSIiDZt2iTz5S+eXk1E9PXXX9PQoUNl3qvqPs+cOZO71+TkZLn3GhYWRp988glNmTKF1q5d\nq/C9CA8Pl/qsSSLpQ3R0NPXt21fqvJeXF+3atYuIiAYNGsQ9O3VpNF1PdeHtt99GWloaUlNTMXXq\nVPz88884fvw4Kioq8OLFCyQnJ+Pu3bt48OAB4uLi8PTpUxgaGqJly5Zy+xZHjhyJrKwsHD58GOXl\n5diyZQsKCgq48y4uLjh58iTy8/Pxzz//SM1aEIlEEIlEMDMzg56eHhISEhTO7KlOz549MW/ePEyZ\nMgWpqamoqKhAVlYWJkyYgFGjRqF///5c/YcOHcLz589x48YNbN++net3LikpgYGBAczMzCASifDx\nxx+rtC96p06dcPPmTaWunTdvHtatW4erV68CqBqEjo2NVboueejr62PSpEn44IMPUFpailu3bmHT\npk2YOnUq59+dO3dkBi51mcaiX1UJDg7Gpk2bkJeXh9LSUqxatQpBQUFS02/XrFmD58+fIysrCzt3\n7uTGvkpLS2FiYoIWLVrg2rVr+Oabb2TsR0RE4PHjx8jPz8eWLVuU2mWTJAagO3XqhKKiIpnPz/Tp\n07Fjxw789NNPNXY7kZLdpCNGjMD169cRExOD8vJy7Nu3D9euXcOoUaM4P5T9TNZGkw4UZmZmmDFj\nBj799FNYWFggLi4O69atQ8eOHdGtWzds3LgRRITKykps2rQJXbp0Qfv27XHq1ClOYOJBLLG92NhY\nrFy5EmZmZrhx4wYGDBjA1Td06FBMnjwZTk5O8PDwwOjRo7myJiYm2LJlCyZNmgRTU1PExMRg7Nix\nUv7WNBj+5ZdfYu7cuZg6dSpatmwJR0dHODk5Sa3xWLp0KYyMjNCpUyfMmjWL+xIFgOHDh2P48OHo\n2bMnLC0tYWxszI2tVL9Pef68//77WLNmDdq1a4fPP/+8Rn/HjRuHFStWICgoCG3atIGjo6PC+em1\n3bfkucjISLRs2RI9evSAj48PpkyZglmzZgEAhgwZAnt7e7z22msyYzy6SmPSryrnZs+ejWnTpmHg\nwIHo0aMHWrRogcjISKnrfX19YWNjg6FDh2L58uUYOnQogKogsHfvXrRu3RphYWEICgqSsT927Fi4\nubnB1dUVo0aNwpw5c2Teq+p+SZ4TCoUIDg5Gjx49YGpqygXb/v37Q09PD25ubjLrLKrfr6L3Q/Jc\n+/btceTIEWzcuBFmZmaIiIjAkSNHuFl9S5YswYEDB2Bqaoq3335bYX3KICBlwxdDpwgPD0dMTAzO\nnj2r9HRQBoNRvwwdOhQhISGYPXu2pl1RCa1oUcjbVzsjIwNeXl5wcnLCmDFjpGZbMGonPDwcixcv\nlpnRwahfFO0RL0mD7JnN0DrS09Nx8eJFpbqytA5eRjrURJN5dpoCaWlp5OTkRC9evKDS0lKyt7en\nrKwsTbvVKKktl9XRo0e5GTjnzp0jT0/PhnSv0aEr2p4+fTq1adOGG2jWNbSm6ykvLw+jR4/G5cuX\nAQBt27blluHn5+dj+PDhUqtQGarx4Ycf4sWLF3j+/Dm6du2KFStWaNqlRkt1LUsyb948DBo0iPtV\nKRQKceLECbYnhRowbdc/GtkKVRnEuUjGjh2rMBcJX1uqNkXUydHT1ODzt9Tdu3elBjItLCxw584d\nmUDBtF13mLaVQxVda8UYhTyUzUVCL6elKfu3evVqlcvU5a8h6mksdWjzvdQH1e0qCgr1/R7Vx3uu\n7T5q6z17trqCuzBHMPYAWA0nJ0Jxcf35pyq8tSgeP36Ms2fPIi8vDwKBAJaWlvDy8kKbNm3qZE+c\niwSoShFx9OhRvlxlMGpEUssAcOzYMbW0LAnbM5tRnX4mWThU6o93EYEYhEAgCMeJE0Dbtpr27BVq\ntyhOnTqFMWPGYODAgfjhhx9w+/Zt5OXlISYmBj4+PhgzZoxUFlJl0ZY8O4ymgzwtA1Bby5KwPbMZ\nUmRJBwkAmDdPu4IEAPVnPS1dupSuX7+u8Hx2djYtXbq0Rht1zUVSF/eTkpJULlMXGqKexlJHjfWI\nREQFBfVbx0vkaVlSY3XV8tatW2nr1q3cNQsWLCBra2tycnJSuHudPG3z/Szq49lqu49adc9XrhCZ\nm9NU/T0EEAFEsbENc8+qfndqzaynuiAQCOrU38bQEcrKgMmTgS5dAImVtw2JpjTGtN3IycoC/P2x\nDBH4tiQEpaVAQgLwMvlvvaOqvngbzC4oKMCcOXO4NMdXr17F9u3b+TLPaGqIg0R5ORAR0aBVMy0z\n6pWsLDxw9kfIvQh8fq8qSADAy0whWglvgWLmzJkYNmwY/v77bwCAra0tNm3axJd5RlNCMkjExgLN\nmjVo9UzLjHrjZUtildGrMQkxcvaS0hp4CxQPHz7E5MmTuayUhoaGMDDQ2mUaDG1Fw0ECYFpm1BMv\ngwQiIrCHpIOErS2wZ4+G/FIC3gJFq1atpLYLPHfuHC/TCRlNCC0IEgDTMqMekAgSYckheLnxIgCg\nY0cgLU0LZzpJovaQ+kvOnz9PXl5e1Lp1a/Ly8iIbGxv6448/lCo7a9Ys6tixo1R+nNTUVPLw8CAX\nFxdyd3eX2ZSHqG6znhhaikhEFBhINHo00YsXGnVFUssAVNIyXzBtNyJezm6iPXsoNJTIyIi4WU5t\n2xIVFze8S6rqi1c1ikQiunLlCl2+fJlEIpHS5eQlUvP19eV2moqPj+d28pKEfZgaCVoUJMSItQxA\nJS3zBdN2I+FlkPhu0B7S138VIMR/I0dqxi1V9cVbx2t5eTni4+ORl5eH8vJy/PLLLxAIBHjnnXdq\nLevj48OtghVjbm6Of/75B0DVSlm2erWRoiXdTZJIahkAtmzZorSWGQwOie6mxXNDUFEhfdrAQLvH\nJSThLVCMHj0axsbGcHR0lNqSsK5s2LABAwYMwLvvvovKykqcPXtW7nXh4eHc//38/ODn56d23YwG\nQguDRHJyMkJDQ2FgYMCtmC4Vz19kMJRFIkgIPw7B8+fSp/X0gIsXtXxcQhK+mjKOjo5qlc/NzZXq\nehoyZAgdOnSIiIj279/PbXAuCY/uMxoaLexuEiOpZU1pjGlbh5EYk+jVS7a7ycyMKC9Psy6qqi/e\nZj0NGzasxn2PVSUtLQ2BgYEAgIkTJyItLY032wwNo4UtCUn41jKjCVFtdtONG9Kn/fyAwkKge3eN\neFdneAsU3t7eCAwMRPPmzWFiYgITExO0bt26zvZsbGxw4sQJAEBiYiJ69uzJl6sMTaLlQQKQ1jIA\ntbXMaCJIBAmEhODIEUiNSwiFwOHDmnNPLfhqynTv3p0yMjKooqJC5bLVE6lFRUVReno69e3bl5yd\nnalfv3508eJFmXI8us9oCLS4u0kSSS2rqrGEhATq1asX2djY0IYNG2TOFxYWUkBAADk7O5O9vT3t\n2LFDrh2mbR1DoruJiGS6nDp00Mw0WEWoqi/e1Ojj40Pl5eV8mVMK9mHSIXQkSBBJa1kVjZWXl5O1\ntTXl5uaSSCQiZ2dnunr1qtQ1q1evppUrVxJRVdAwNTWlsrIyGVtM2zpEtSBBRNSmjXSgGDtWg/7J\nQVV98TbrycrKCoMGDcKIESO43ejYlEIGAJ3obpJEUssAsHHjRqW0nJaWBhsbG1haWgIAgoKCEBcX\nh969e3PXmJubIzMzEwDw5MkTtG/fnqUH0WWqdTcBQFgY8OTJq0uEQmDnTs24xxe8BgorKyuIRCKI\nRCK+zDJ0HR0LEoC0lgHlp8fK2w87NTVV6prQ0FAMHjwYnTt3RklJCfbv38+f44yGRU6QAIDdu6va\nEWJ69dKhabAK4C1QiNczPH36FC1btuTLLEOX4SFIhIUB168DLVoAe/c2zAdOUsvh4eFYvXq1UuUU\n7X0tybp16+Di4oLk5GTcvHkT/v7+yMjIgImJiUI/ALZGSOuQEySEQiA7W/oyQ0PtaE0kJycjOTm5\n7gb46vM6ffo09e7dmywsLIiI6I8//qD58+crVVZerqfJkyeTi4sLubi4kKWlJbm4uMiU49F9Bt/w\nNCbh6/uqn/eNN/hzryYktQxAaS2fPXuWAgICuNfr1q2TGdAeMWIEpaSkcK8HDx5M6enpMraYtrWY\narmb5KXmEP9lZmraWfmoqi/e1Ojh4UG3bt2S+kK3s7NTqqy8XE+SLFu2jD755BOZ4+zDpKXwOHA9\nYkTVB87Do+FmjUhqWawxZbRcVlZGPXr0oNzcXPr333/lDmYvXbqUwsPDiYiooKCAunTpQkVFRTK2\nmLa1lGoD18bGioNEQoKGfa0BVfXF2zoKAOjWrZvUa2UH6Xx8fNCuXTu554gI+/fvR3BwsNr+MRoA\nnsck9u4F3ngDOH68Yft566JlAwMDfPnllwgICICdnR0mT56M3r17Y9u2bdi2bRsAYNWqVTh//jyc\nnZ0xdOhQ/Pe//4WpqWm93AODZ6otpjMwgExqDgAQCICUlIbb1rQh4G2Molu3bjh9+jQAQCQSYcuW\nLVKzPerKqVOn0KlTJ1hbW6tti1HP1MPAddu2QEOP90pqGQAiIiKU1vKIESO42VJi3nzzTe7/ZmZm\n+Pnnn/lxlNFwVMvdVH0sAgBMTQEvr6rBbF0fvK4Ob4Fi69atWLx4Me7evYsuXbpg2LBh+Oqrr9S2\nGxMTg5CQEIXn2YCflqCDs5vkkZycjB49emDevHn466+/AACXLl3iRcsMHeVlkFiGCHw+Rf53UUJC\n42pBVEfwsr9KLcrLyzFjxgzsUSNnbl5eHkaPHo3Lly9L2bWwsMDFixfRuXNnmTICgQA8uM9Ql0YS\nJABZLWtKY0zbWoJkkLjXeIKEqvriZYzCwMAAt27dwr///suHOY7ffvsNvXv3lhskGFpCIwoSQP1p\nmaGD1BIkzMyAvDzdCxJ1gdcFdwMGDMCYMWPQokULAMqvzA4ODsaJEydQVFSErl274uOPP8asWbOw\nb98+NoitzfAYJDSxXkIRkloGlF+ZzWhEvAwS/xNGYFOybJBISQH699eAXxqCt0BhbW0Na2trVFZW\nqrzRS0xMjNzjO3bs4MM1Rn3Ac0vi+nXgZbJghIU1/AC2JJJaBtjGRU0OiSDx5okQqVXWBgbAjRu6\nlyZcXXgLFHZ2dpg0aZLUMZaeoJFSD91NLxuh8PAAvv1WbXNqIall8cpspuUmgkSQWHA6BC9/K3A0\nxSAB8DSYDQCurq64dOlSrcf4hA34aQAlgkRdupEeP64q9+23mp9aKKlbscbqW8vVYdrWABJTYDu/\nG4J796RPZ2YCjo6acY1vVNWX2i2KhIQExMfH4+7du1i8eDFXeUlJCQwNDdU1z9AmlGxJ1KUbSRPr\nJaqjSMszZ85kWm7sSLQk5k0PkdpwSCAAMjIaT5CoC2rPeurcuTPc3NzQvHlzuLm5wc3NDe7u7hgz\nZgzbTrIxoUJ3kzZ1I6mCPC0DYFpu5IyyysLfDv4IuReB0CTpIAEAI0Y07SABgL+EMrdv35Y5du3a\ntVrLyUsISES0ZcsWEgqFZG9vT++9957csjy6z1BAaCjRYB8RpXQKJNFw5XI3FRdXJfDTph29VEFS\ny2KNKaNlPmHabiCuXKG7MKdg7JGbr0ko1F0d14Sq+uJNjT179qQffviBiIgqKyspIiKChEJhreXk\nJQRMTEykoUOHkkgkIiKiBw8eyC3LPkz1z2AfER1EIMVhNAWP1+6d6fhCUssAlNYynzBtNwBXrtB9\nfcVBwsencQYJIg3ucJecnIywsDAcOHAA9+/fh1AoRHp6eq3lfHx8kJeXJ3Xsm2++wfvvv8/1C3fo\n0IEvNxkqMH9uGd4+NxmEcqx1jsUv23V7MZ2ySGoZALKzs5XSMkOHyMrCA2d/vF0RgRi8WidhaFiV\nsyk1tWnOblIEb9ljzc3NERAQgDNnziAvLw8zZ85Eq1at6mQrJycHJ0+eRL9+/eDn54fz588rvDY8\nPJz7U2tjDoYU8+eWYeSuyaCycryBWJhbNtP4bKSGIDk5Gdu2bUNFRQU3LqGKlo8dOwahUAhbW1t8\n+umnCutwdXWFg4MDy02mCbKycM9RNkj4+wMiEVBQwIKEDHw1ZYYMGUJTp06l4uJiyszMJA8PD1q2\nbJlSZXNzc6W6nhwcHGjx4sVERJSWlkZWVlZyy/HoPuMl4jGJnw2rupuM8ILatWu8TXB5SGoZgNJa\nLi8vJ2tra8rNzSWRSCR3P4ri4mKys7Oj/Px8IiIqLCyUa4tpu374z0T5YxImJk1L46rqi7cWxYIF\nCxAdHY22bdvC0dERZ86cQZs2bepky8LCAuPHjwcAeHh4QE9PD0VFRXy5ylBAWBhwaF8ZFpyajMqX\nLYmW7Zrh0iXNr21oSCS1DEBpLaelpcHGxgaWlpYwNDREUFAQ4uLipK7Zu3cvJkyYAAsLCwBVaccZ\nDURWFuYf8se7kG5JtGoFXL7ctDSuKryNUQQGBuLUqVO4ceMGZs2aheLiYkyZMqVOtsaNG4fExET4\n+vri+vXrEIlEaN++PV+uMuQgDhLfPpkMA5RjbutYjBzcDDt2NL0PkKSWASit5bt376Jr167cawsL\nC6Smpkpdk5OTg7KyMgwaNAglJSVYsmQJpk2bJtceS6HPIy/HJN6plA4SXl5AfHzj17jW7Jm9evVq\nGjVqFNna2hIR0Z07d8jb27vWckFBQWRubk5GRkZkYWFBUVFRJBKJaOrUqeTg4EB9+vShpKQkuWV5\ndL/JERpatR/1iBFVTW7J2U0tDV5QXp6mPdQckloGoLSWDxw4QHPnzuVeR0dH08KFC6WuWbBgAXl5\nedGzZ8/o4cOHZGtrS9evX5exxbTNIwpmN0lsXd7kUFVfvLUoDh8+jEuXLnGLlLp06YKSkpJayylK\nCBgdHc2Xa4xqiFdL//NP1ev5c8vw8fXJKEI5wtrGIuuPZk16MK+uWu7SpQvy8/O51/n5+VwXk5iu\nXbvCzMwMxsbGMDY2xsCBA5GRkQFbW1t+b4IBoRDQz87Cr5DtbvL3b1rZX9WFtzGKZs2aQU/vlbmn\nT5/yZZrBM0eOvAoSZm3KsOvfyejrWo4fxsfiWm7TDhJA3bXs7u6OnJwc5OXlQSQSYd++fVyqcjFj\nx45FSkoKKioq8OzZM6SmpsLOzo5X/xlVP4aMcuQHCRMTzaeL0TV4a1G88cYbePPNN/H48WN8++23\niIqKwty5c/kyz+CR+/er/jVAGQ4ZToaRoBz4MRZ7dXzTIb6Q1DIADBkyRCktGxgY4Msvv0RAQAAq\nKiowZ84c9O7dG9u2bQNQtXe2UCjE8OHD4eTkBD09PYSGhrJAwTM1tSTYwHXd4C17LAAcP34cx48f\nBwAEBATA39+fL9NyYRk2VUcoBLKzq4LEPkxGq2blGPaP7u9MxzdiLW/cuBHHjx+vdy1Xh2m7bigK\nEkZGgJ8fsG8fCxKA6vriNVA0NOzDpDzi1N8nTwL6VBUkDFAO5+xYdO/JgoQi2J7ZuoH4B5AdpIOE\nQADk5rIFdNXRyJ7ZAHDw4EHY2tqidevWMDExgYmJCVq3bq1U2dmzZ6NTp05wlEjRGB4eDgsLBS6t\nnwAAIABJREFUC7i6usLV1RXHjh3jy9UmR1gYsGtXVepvySDx5UAWJOQhqWUAKmmZ0bCEhVVlK1YU\nJDIyWJDgA95aFNbW1jhy5Ah69+6tctlTp06hVatWmD59Oi5fvgwA+Oijj2BiYlLjPsXsV1fttG37\nauBa3N1kgHJ8ZB+L31OaRloOVZHUMmtRaDctWgDPn8sGiVatgCtXWJBQhMZaFK+99lqdggRQlRiw\nXbt2MsfZB0V9qgeJdq3KET2aBYmaUEfLjIZBKKzaUEhekBg8GMjPZ0GCT3ib9eTu7o7Jkydj3Lhx\nMDIyAlAVtcSpOOpCZGQkvv/+e7i7u2Pjxo1cSgVJ2OpVxQiFVf9KtiRcb8QithPrblJEcnIyKioq\n4ODggF69egGo6opSV8sM/pBsJUsGifuDQ1B8kA1W1we8dT3NnDmzyqBAIHV8x44dSpXPy8vD6NGj\nua6nBw8ecOnFP/zwQ9y7dw/bt2+XKsOa54qpPrvJAOXYNiQWR39jQaI2JLW8c+dO7rWyWuYDpm1Z\nwsKA3burWhGAdJCYnhCC4cM1658uobOznqoHCmXOsQ+TfOQFiVktY3HzDutuUhU2RqEdhIUB27cD\nlZVVr8VBYjki8FZKCFtlrSIaG6PIzs7GkCFDYG9vDwDIzMzEmjVr6mzv3r173P8PHz4sNSOKIR9x\nv231IPEGYnExiwUJZeFbywz1EAqB776TDRL/6xWBr4pZkGgQ6p5WShofHx86d+4cubi4EFHVdqh2\ndnZKlRUnBjQ0NCQLCwvavn07TZs2jRwdHcnJyYnGjh1LBQUFMuV4dF/n6dXrVbIzA7xK8Ndc8IIy\nMzXtnW4hqWUAKmmZL5i2q5DUNUBkh6r9JK6t3qNp13QaVfXF22D2s2fP4Onpyb0WCATcVqa1IS8x\n4OzZs/lyrUmQnV31r2RLYn77WFy7wHI3qYo6WmbwQ1gYEBUFVFS8OiZuSZSsjkCv8BDFhRm8w1ug\n6NChA5e/HwAOHDgAc3NzvswzakDe7KZmcbG4O4YNXNcFpmXNIjmrSYw4SJRviECvFSxINDh8NWVu\n3LhBgwcPpubNm5O5uTl5e3tTbm4uX+blwqP7Ok317iYjvNC0SzqNpJYBqKTlhIQE6tWrF9nY2NCG\nDRsUXpeWlkb6+vp08OBBueebqrardzWJu5v+FpjTg82su4kvVNUX77Oenj59isrKSpiYmPBpVi5s\nZkjV4HX1gevElGZsgI8Hnj59ilatWimtsYqKCvTq1Qu//fYbunTpAg8PD8TExMgs3quoqIC/vz9a\ntGiBWbNmYcKECTK2mqK2w8KA//2vKjyIsUMWThj5w/jLCLQMZS0JvlBVX7x1PW3cuFFmDUWbNm3g\n5uYGFxcXheVmz56No0ePomPHjjLTXzdu3Ijly5fj4cOHMDU15cvVRoNQKBskPvyEBQl1qa7lzz//\nXCktS+6ZDYDbM7t6oIiMjMTEiRORnp5eL/7rKtevSweJ/m2zcLK5P/Q2RgAhLEhoEt4CxYULF3D+\n/HmMHj0aRISjR4/C0dERW7duxcSJE7FixQq55WbNmoVFixZh+vTpUsfz8/Px66+/ojsbiZWLUAjc\nzJYOEiI0w//9n6Y9030ktQwA27ZtU0rLyuyZfffuXcTFxSExMRHp6ekyP64kaUpZB6qPS4zoloUj\nIhYk+EJr9sweMGAAlZSUcK9LSkrIx8eHnj59SkKhsMayubm55ODgIHVs4sSJlJGRQZaWllRUVCS3\nHI/u6xRt2siOSQBECQma9qxxIKllAEprWZk9sydOnEjnzp0jIqIZM2bQgQMH5NpqStqWNwX2np45\n0R42JlFfqKov3loUhYWFXI4nADA0NMT9+/fRokULNG/eXCVbcXFxsLCwgJOTE1/uNSqe/iPbkvjk\nE7AUBjxRVy0rs2f2hQsXEBQUBAB4+PAhEhISYGhoKLNlalOgekoO4NXsJv1NrCWhTfAWKKZMmQJP\nT0+MGzcORISff/4ZISEhePr0qUpbPT579gzr1q3Dr7/+yh2jGgZdmlLzHAAMBbJBYtUqsC4nnkhO\nToa5uTm6d+/OJQX09vZWSsuSe2Z37twZ+/btk1kj9Ndff3H/nzVrFkaPHt1kg4RkSg7gVZAw3ByB\nDotZkNAq+GzOpKWl0aZNm+iLL76g9PR0pctJdj1lZmZSx44dydLSkiwtLcnAwIC6d+9O9+/flynH\ns/taj7zupm7dNO1V40SsZQAqaTk+Pp569uxJ1tbWtG7dOiIi2rp1K23dulXm2pkzZzbJ6bGhoUR6\nerLdTfcNzKn0W9bd1BCoqi+1p8eWlJTUOhW2tmtqSghoZWWFCxcuyJ311JSmEJq1KcO3T6RbEgBQ\nXMzSKvOFPJ1W15gyeueDxqxt8WZDYtgU2IanwZMCBgYGYsGCBTh+/DgePXrEHX/06BF++eUXzJ8/\nH4GBgQrLBwcHw9vbG9evX0fXrl1lUjnXNCukqTB/rvwgkZLCggSfqKtlRu20bSsbJC6194fZDhYk\ntBleFtwlJiZi7969OH36NP7++28AQOfOnTFgwABMmTKl3sYNGvOvLo6yMhwykg0Sq1YBa9dq2LdG\nSHUtP3nyBEKhsN61XJ3GqG1x+nsx4iBhtIUNXDc0OrsfRV1ojB8mKRQEiXHjgMOHNexbE4HtR6E+\nihL8XTD1R/NIFiQ0AQsUjQUFQQKQXr3KqF9YoFCP6q0IgAUJbUBjGxcxeKSGIJGSokG/GAwVYEGi\n8cAChbZRQ5D45BOwPE4MnSE3V/o1CxK6i9oL7iRnh8hDmWR+8hIDfvjhh/jpp58gEAjQvn177Ny5\nUyqPTqOkhiDxzjtsUV19o0jL4uMsMaXyhIUBItGr1/bIwqUO/jD8ggUJXUTtMQpLS8sap7DmVv9Z\nIYdTp06hVatWmD59OhcoJOerR0ZGIiMjA//73/+kyjWWflwAQFkZfm4xGVQuP0hs3KhB35oI8rSc\nl5fHZYNVRst8ocvart7l5KSfhQvt/WHA0nJoDQ2eZjwvL09dE/Dx8ZGxI7moqbS0FGZmZmrXo7WU\nleFws8nQJxYkNIk8LQsEggYNELpOWBggsTkg7JCFX4gFCV2Ht1xPlZWV2LNnD3Jzc/Gf//wHt2/f\nRkFBAfr27Vtnmx988AGio6PRokULnDt3Tu41Op/rqYYg8dZbLEg0NMnJyUhKSkJmZiYeP34MALxo\nuSlQPVU4S/DXeOBteuy8efOgp6eHxMREXLt2DY8ePcKwYcNw/vx5pcrXlMZjw4YNyM7OlrtqW1eb\n5wBqDRJffaVB35owklrOzs5GUVGRSlrmA13TdlgY8N13r16zBH/ajcamx6ampuLrr7+GsbExgKqB\nv7KyMl5sh4SENL7dwGrpbmJBQnOoq+Vjx45BKBTC1tYWn376qcz5PXv2wNnZGU5OTujfvz8yMzN5\n810TiDPBimFBovHBW6AwMjJChcTSy8LCQujp1d18Tk4O9/+4uDi4urqq5Z82MX9u1ewmeUHik09Y\nd5OmUUfLFRUVWLhwIY4dO4arV68iJiYGf/75p9Q1PXr0wMmTJ5GZmYkPP/wQYWFhvPrfkFRPF86C\nRCOlbklqZYmOjqbRo0dT586d6f333ydbW1vat2+fUmWDgoLI3NycDA0NycLCgrZv304TJkwgBwcH\ncnZ2pvHjxzeKNOOhoUTN9GRThYv/vv5a0x4yiKS1DEAlLZ85c4YCAgK41+vXr6f169crvP7Ro0fU\npUsXmeO6om1fX+lU4XdhTg82s1Th2o6q+uJtMHvq1Klwc3PD77//DgByN5VXRPXNXYCqtRWNibZt\n5e9MBwACAXDqFFtMpy1IannRokUqaVmZfbMl2b59O0aOHCn3nLZP1JAcvBa3JEpWR6AXa0loHeru\nma32YHb1RUpic+L56PW5SElXBvxqChI7dwIzZmjWP0YV8rRsZmaGoqIiAMpp+eDBgzh27Bi+ezmy\nu3v3bqSmpiIyMlLm2qSkJCxYsACnT59Gu3btpM5pu7blBYndzhF47w8WJHSBBl9H0adPH67S27dv\nc4IvLi5G9+7dm/QcdPGewGXPZYOEmRlw/jzQvbumvWSIUaRlMzMzpbWszL7ZAJCZmYnQ0FAcO3ZM\nJkhoO2FhskHiiy4RWJXMgkSjha8+r7lz59LRo0e51/Hx8RQaGsqXebnw6D7vtGlT1W9bfftSfX2i\nzExNe8eoCUktA1BJy2VlZdSjRw/Kzc2lf//9l5ydnenq1atS19y6dYusra3p7NmzCu1oq7YltzEV\nj0n8x2YPFRdr2jOGKqiqL97UaG9vr9QxPtHmD5O8IOHjQ+wDpQNI6lasMVW0XNu+2XPmzCFTU1Ny\ncXEhFxcX8vDwkLGhrdo2NpYOEmGt2MC1LqKqvnhbcDds2DAMHDgQU6dOBRFh7969OHnyJH755Zda\ny8pLCrh8+XIcOXIERkZGsLa2xo4dO9CmTRupctraj9u5M1B471V30xTDWFzJaca6mXQESS1bWVlh\nzZo1SmuZL7RR2+IcTuLupncRgfV5IUzXOojGFtzFxMTgwYMHCAwMxPjx4/HgwQO5s5nkMWvWLBw7\ndkzq2LBhw5CVlYWMjAz07NkT69ev58vVeuefh6+CxKyWsch/wIKELiGpZQAqabmxEhYGXL8uHSQW\npLAg0VTgfYe7kpISANJJ/ZShphQehw8fxsGDB7F7926p49r4q6t6FtihI5vh6FFNO8WoCyUlJWjd\nunWT3+EuLAzYtQuwEb0KEvf8QpCUpGnPGHWlwWc9ibl8+TKmT5/OTSXs0KEDdu3aBQcHB7VtR0VF\nITg4WO45bZprPn9uGYLjJsNAUI5xiIW9azPs2aMxdxh1IDk5Gfv27cOPP/6IZ8+eAQDc3Nx407Iu\ncuSIdJA42joEt9ie7U0LvgZH+vXrR4mJidzrpKQk8vLyUrp8bm4uOTg4yBxfs2YNjR8/Xm4ZHt3n\nhTHehbQJS8gIL8jCgg1c6yqSWgagspb5QFu0HRpKZP9y4DoYe8jAgCgvT9NeMdRFVX3x1qJ49uwZ\nBg0axL328/PD06dP1bK5c+dOxMfHc6u9tZ2yNmZYii/g4QEcP161KImhe9SHlnWV64ezcPxlSyIG\nIRg5jK39aYrwNphtZWWFTz75BHl5ecjNzcWaNWvQo0ePOts7duwYPvvsM8TFxaF58+Z8uVmv7N0L\nvPEGCxK6jqSWAaitZZ0lKwt7H74KEq1bg3WlNlF4CxRRUVF48OABxo8fjwkTJqCwsBBRUVFKlQ0O\nDoa3tzeys7PRtWtXREVFYdGiRSgtLYW/vz9cXV3x1ltv8eVqvdG2LbB/PwsSuo6klgGopOVGQ1YW\nHji/ChIAMGAA03ZThfdZTw2JNs0MYTRONKUxjWo7KwuP+/pjwbMI7H0ZJAwMgMJCFigaCw0+62n0\n6NEKKxUIBPjpp5/UrYLBaBAUaVl8vEloWRwknr8KEgAwaBALEk0ZtVsUHTp0gIWFBYKDg+Hp6QlA\nOoOsr6+v+l4qgLUoGHwiT8t+fn5ISkqqdy1XRyPafhkkFv8bgeiKV0FCXx94+JAFisaEqvpSO1CU\nl5fj119/RUxMDC5fvozXX38dwcHBsLe3V8esUrBAweATeVpeu3Zt0+h6ysoC/P0x82EEdpVJZ4HN\nzAQcHRvOFUb9o7K+eJiSy/HixQvasWMHtW/fniIjI5UuN2vWLOrYsaPUOor9+/eTnZ0d6enp0YUL\nF+SWq4v7SUlJKpepCw1RT2Opo6HqUaUOsZYBqKTlhIQE6tWrF9nY2NCGDRvkXrNo0SKysbEhJycn\nunjxotxr5Gmb7/eIs3flCpG5Oc0x3iO14yKgeqbjevNRS+3Vh82GsKfqdycvs55evHiBgwcPYurU\nqfjqq6+wZMkSLk+OMsjL9eTo6IjDhw9j4MCBfLjIoc4uT9pWT2Opo6HqUaaO6loGoLSWldkvOz4+\nHjdu3EBOTg6+/fZbzJ8/n1f/VSE5OZlrScx9HIHtz9VvSdSLj1psrz5saqM9tQezp02bhqysLIwc\nORL/+c9/4FiHNqqPjw83Z12MUChU1zUGQyXkaVkgEKBLly5KlU9LS4ONjQ0sLS0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- } - ], - "prompt_number": 12 - }, - { - "cell_type": "code", - "collapsed": false, - "input": "", - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] +{ + "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\nI6iggEZ13bub95kwAdi40bl1lJysXNyNRiPmzDHh0iVyoYkzfbX+bkrcMkosd2dB1aIiqt2uptyA\nPQYPJnEvLDRvS01Vt66rGnFPTaXRWMOG9P+QIfSw1/shphZXuWZUi3t4eDji4+Px6KOPOt3X15f8\nknrRtSvd0ErqvTCmTZzatlXm55czJdwdSIl7RgallFr6hlu3JkG9f1/6OMXFZCWqGfk4Q4tbpmlT\n+m3FUdWuXeT+s1y6sV49GjnGxlKp4L59yf9qixpxdxXt2slbxk2N5X74sP2Znvv3U7/7+ck/pjP8\n/MivLdb5z8+nGlNq4lExMZSuqiQNXExvFZk5k1wzS5dSfRhPI6d0tBpUi3twcDCC5CwVDmDdOv1c\nMgDlEUdGKithmp9PqYS1a6s7p9I1E71Z3KX8ndWrU5qqvVIAeXkUHHPFENbWLVNSQqIlZ5RlMFB2\ni+iasVfB8OOPyYJbuBB45hlg3ryycRut4q7VWrekSRPyxTpbL1apuNevT/eAvd9561bXWLPPP091\npQDg888F9OhBWVpKEddGsFePSIrMTOustfBwKvj144/6BI21EhSkLkHEGTrE9Z3z9dem0r+NRqMu\nN8GQIRQBl5vzffIkWadqCQ6m4apcjhzxbnGX8neKF5nUBe8qlwxgdsswRmKdm0siVL26vM936wbs\n2EHZWAkJVL/Ilvr1aR+R/Hyq4bN0Kb2+dIlyxrV8Rz3F3WAwW3SOqlMqdcsAZr+71HfdupXmV+jN\nk08C//wnsHYt8NNPAqZNM6o+luiasXQrOiIzs2x9n759VZ9ed8T7ThAEzbEaSxyKe79+/XBOIho3\nZ84cDBs2TPZJTCaT4oY549lnyfqcN0/eRAit4q7Ecr92jYTC05kygLS4p6fTzWaLONtyyJCy77lS\n3MUlD69do8k4J09ar5vqjL59KeXxlVdI7Lp0cf6ZqVPpIXbrFuWVp6Q4XszbE4j14u2J+82b5MeW\nM4HJErEIl20e++XL1A9KC53JoVo1Wn1oyBDq42eeUX8s0TUzdqy8/TMzHde69zStWtF1262bteEb\nFxen6bgOxX2LbYjbi2jcmHJW333XPNxzxMmT8p/0UrRsSSJ5+7a1P1cKOfW13UXDhpSSWVRk9rE7\nstzt5UC7KlMGMNd1z8oioTpxQpm4x8SQtR8bS9a7nDzzhg3pIbBhAz3ovMnfLuIsqCqmQSpNWezQ\ngVwSAFmKF9i0AAAgAElEQVSLoqCsWkVrxupZXkJEtEqfew744IM4fPghbVczku/cmUYBcrh/n+I3\nWgw7V1OtGv2Op06pm/RoD13kR48iN2qYPZsCZJZ1URITqYNsZyVqtdyrVqXPy/GNeYtLBiC/Zv36\nVF4AoIs9K0vaknFUm9yVljtAfSvWDDpxQtlv5etrDpZK1dW2h2VN+Ph4wSro5g04W8ZPqb9dpH9/\nuk+uXyfRzcujB/5HH1G5BldgNBphMpkwf772rKJOnehhXFTkfN/sbAq6ezLdUQ6u8LurFvf4+Hg0\na9YMe/fuxZAhQzBI6YKoOlCvHvlXJ0ygIWpeHq2v+frrwFtvkcUqolXcAUptdDa7D/B8vQpbLF0z\nJ06QIEhd7I7SIV0t7uHh5vzr48eVWe4A1UG5dk1Z7vSoUTSz9OJFIClJ8Co/LCDPclcj7vXrU9D0\n1Vcp57tDB3KXDB0qP4blSerWpYCzHDept7tkRLxK3EeNGoWcnBzcuXMH586dw6ZNm/Rsl2xGj6Za\nHkOG0JDylVdo0srw4eYVxouL9cleiYqSNzPWm8XdUS2NgADKzpCqPeJKtwxgnX998KB7XCR169Ki\nH+PG0SxkuYtYu4u2bemhai89VU0wFSBrvUULE5KTTThxIg5//7sJEyeaMHSooNusVEfoEXiWm+9e\nmcXdLdkyruazz4D//pdWmReHlZMnk2Xy+utkCTZqZA7cqSUy0jx12R6MkYCWR3H38THn81sWzyos\npM+rERK5iAWm4uMF3LhhVGy5q0EQBPj7C1i/HsjLi4MY99cro0srYnqqPTdabq6yImcilt/PZHJN\nwoOz82ulUycyAqZMcbxfRoZna8fIJSjIHAfRiwoh7lWqAC+/bL2tZ08SpIwMYPlyAR07GjWfR6xp\nI6bsSXHhAr2vtD6JKwkIMK/JmZoKTJxof1/RgrAUjTNnSGT0KIhlj5Yt6SHy1VcCoqOVF0hTg6dF\nTg6ia8aeuOtRubE8Ehwsr1Z/Ziat2ezt2FruR45oP6amgOqMGTMQEhKCiIgIjB49GtcdLQPvZqpU\nMa9OvnmzgM6dtR/Tz4+sKUdlCESXjDetLihaxWLVQ9uV2C2RCuK5otSvLT4+5ErbtEn7gi4VCTEd\nUgolFSHt4Q0jFDW0bUsjcmeUF7dM06YUM7p5k+7T/v21H1OTuPfv3x9paWk4dOgQgoKCMHfuXO0t\n0pHx44HPPydXhIK0fIc4q0jpbf52wFx/ZcUKWtXJUWBZyvfn6mCqIAgwmUx46CETgDhcvqxP8S0l\neKvIhYTYLyqlNqBqibd+b2e0aEErdTla16GggHL3XelO1AsfH/qtDx+mUbachVqcoWmg3c9innKX\nLl2watUqzQ3SCzGvtmVLYPfuOKxcSdu1+lNFcbc3HD5yRJ0f1JW0akWTqr7+WsBjjzl2ebRrV7bu\niriwgquw/E1q1/aMe8RbRa5jR+l5HDdvUglqJSsZVSSqVqUA/8mT9hMlxPkS3jDfRA7du1NtpIcf\nphIaYpquWnTzon7zzTcYP3685HuWN6u7glXieciXqp9gREVR+pg9MjJo7U5vwseHsom+/x745RfH\n+4qWu2Vc4cSJ8uG3rIiEh1P/206eO32axM2b3H/uRnTN2BP3zEzP1mpXysMPC1i8WECVKvrUvHEq\n7nJKEMyePRvVqlXDU3ZUzRsDVWqJjARmzLD//qlT3jUbThzB0Nq1cTh0iFIO7T1kH36Y4grnz1OW\nDUA3kLsKLHmrBe0pHniAhuuHDllXNlS6uHRFxJnfvbz420WmTTNi3jwaWW/bBqxZ48LyA4DzEgRL\nly7Fxo0b8dtvv2lqiCvRUzBatyY/3tWrZWt63L1LBaiaNtXtdJqxFPGaNeU9aMWZqo0bkwWvtByA\nFri4l6VTJ+DAAWtxz87m4t62reNFezIz9a1L72oaNgSWLaO/9Si5rMkblZCQgPnz52PNmjWoLreE\nnwfQUzB8fGhGn9SCB1lZVCxMTSlTb8IyqHruHLkD6tTxbJsqM507W5fYAMxumcqMM8v96NHy5ZYB\nKMNPrxrzmsT9pZdeQkFBAfr164eoqCj8/e9/16dVXk5QkPRFdeqUtuJkrkbuQ659e3MpgJQUephx\nPEefPlQmwXKRb265Oxb3khISd2+p8eQJNAVUj8tJNK2AtG5NUXpbKoq4d+9OSyMC5A7o1Ml1beI4\np2VLWqDm8GHzHIX0dNfUXS9PNGtG9aMKCmh2uiVZWbS4zEMPeaZt3kA5SRLyLtq0KZ/iLpeOHen7\nXbsGJCQIXNy9gP79gV9/pb/v3aPfR80ydRUJHx+6F6VqsnjjfBN3w8VdBY4sd2/KlFFLtWpUvmHN\nGiA5WUC3bp5uEWfIEGD1avr72DGy5r04zOU27JVF5uKuQdzfeecdREREIDIyEn369EFOTo6e7fJq\nRHG3LWNfUSx3AHjuOVrtyt8ff6ZRcjxJ376UtXTyJPDDDwKPg/yJvTLVhw9Xbn87oEHc33jjDRw6\ndAgpKSkYOXKk5iWhyhP16tGQ8NIl8zbGXD+T010IgoCUFBPGjzfh1Km40oUV3FkOgGONry9VOv34\nY2DdOgG9e3u6Rd6BPct93z4gOtr97fEmVAdUa9euXfp3QUEBGjRooEuDygui9d6wIb2+cIFWZa8I\nARzL3PigoIo1Ca0889pr5Ge/fVt6DdzKSFAQLXRuSX4+xYv0XLKuPKIpW+btt9/Gd999hxo1amCv\nbSKuBSbBBJPRVPo3gHL/unVrE06dAhLu0usBD5jQqpX3tE+v1wKECvn7lbfXRhghCAKCnhOwd+92\nLFgAAEB2YDYCAwM93j5PvV59zYTD9YHiYhOqVKH309OBbt1M8PHxfPu0vtaCgTlYAFVO6QEAmDdv\nHjIyMrBkyZKyJzAYEBsbW/raWxZC0Mpbb5Gl/s479Pr77ykAqXfBfU9juYAyxzsQ3WQcom1bID7e\nHECdOpXmZrzyimfbpRSxVIhIXFycpvWpHYq7XM6cOYPBgwfjiESFeYPB4LEFtF3J4sXAzp3A0qX0\nevZsqtQ3b55Hm8WpBHBxt+bppyngPHkylcpt0gTYs6f8Jzdo1U7VAVXLCUxr1qxBlDsWvfQiWrWi\nAKqIq2ueczgifCRlTUwMsHs3Wb6bN9PkpvIu7HpQVe0HZ82ahYyMDFSpUgWtW7fGokWL9GyX12Ob\n637qFDB2rOfaw6k8cHG3ZtgwGjn7+wvYv9+I55/3dIu8A13cMg5PUEHdMsXFVGXx6lXyvbdsCWzZ\n4r7qiRwOx0z37sC1aybcv2/C4cNUKrm8o1U7ubhroF07CuS0bUu1LW7epNmdHA7HPYhByIsXgc8+\ni8MLL8TCz69iJG5o1U7VbhkOiXpGBk1gatGCCzuH424sRbxhQ/BAswWaa8t8+OGH8PHxwZUrV/Ro\nT7kiKooWCzh0yFytj8PhcLwBTeKek5ODLVu2oEUlLSzdsSOQlETTwbm4cziepby7YfRGk7i/+uqr\n+Pe//61XW8od0dHA/v3Anj0CKlkmKIfjdXBxt0a1uK9ZswYBAQHoUInL0zVrRsvPnT4NXsiJw+F4\nFQ4DqvbKD8yePRtz587F5s2bS7c5iupaBjkqQhQbMEfpBw0Cjh+PgziAqSjfj8PhuBfb8gNaUZUK\neeTIEfTp0wc1atQAAOTm5qJp06bYt28fGjVqZH2CCpwKKcKng3M4HL3xSCpkWFgYzp8/X/q6ZcuW\nOHjwIOrVq6e6IRwOh8PRD12W2TMYDHocptzC3TAcDsfb4DNUORwOxwvxWFVIDofD4XgvXNw5HA6n\nAsLF3Y3wBabN8L4ww/vCDO8L/VAt7iaTCQEBAYiKikJUVBQSEhL0bFeFhF+4ZnhfmOF9YYb3hX6o\nrgppMBjw6quv4tVXX9WzPRwOh8PRAU1uGZ4Fw+FwON6J6lTIuLg4LFmyBHXq1EGnTp3w4Ycfom7d\numVPUMlz4DkcDkctLluJyVFtma5du6Jhw4YAgHfeeQf5+flYvHix6oZwOBwORz90mcSUnZ2NYcOG\n4fDhw3q0icPhcDgaUe1zz8/PL/07Pj4e4eHhujSIw+FwONpRbblPnDgRKSkpMBgMaNmyJb744gv4\n+fnp3T4Oh8PhqEC15f6///0PqampiI6Oxp49e9C3b9/S965cuYJ+/fohKCgI/fv3x7Vr10rfmzt3\nLtq2bYvg4GCrevAVgSlTpsDPz89qFDNjxgyEhIQgIiICo0ePxvXr10vfq6h9IdUPIlJr7lbUfgDs\n98XChQsREhKCsLAwzJw5s3R7ZeuLffv2ISYmBlFRUejcuTP2799f+l5F7oucnBz07t0b7du3R1hY\nGD755BMAOmsn08iOHTtYUlISCwsLK902Y8YM9v777zPGGJs3bx6bOXMmY4yxtLQ0FhERwQoLC1lW\nVhZr3bo1Ky4u1toEr0GqLzZv3lz6HWfOnFkp+kKqHxhj7MyZM2zAgAEsMDCQXb58mTFWsfuBMem+\n2LZtG+vbty8rLCxkjDF24cIFxljl7ItevXqxhIQExhhjGzduZEajkTFW8fsiPz+fJScnM8YYu3nz\nJgsKCmLp6em6aqfm8gM9e/bEww8/bLVt7dq1mDRpEgBg0qRJWL16NQBamm/8+PHw9fVFYGAg2rRp\ng3379mltgtcg1Rf9+vWDjw91c5cuXZCbmwugYveFVD8A0mvuVuR+AKT7YtGiRZg1axZ8fX0BoDTr\nrDL2hb+/f+lo9tq1a2jatCmAit8XjRs3RmRkJACgVq1aCAkJQV5enq7a6ZLaMufPny/1v/v5+ZUu\n7HH27FkEBASU7hcQEIC8vDxXNMEr+eabbzB48GAAla8v7K25W9n6AQCOHz+OHTt2oGvXrjAajThw\n4ACAytkX8+bNw2uvvYbmzZtjxowZmDt3LoDK1RfZ2dlITk5Gly5ddNVOlxcOMxgMDicyVZZJTrNn\nz0a1atXw1FNP2d2novbF7du3MWfOHMTFxZVuYw7i+BW1H0SKiopw9epV7N27F/Pnz8eTTz5pd9+K\n3hdTp07FJ598gjNnzuDjjz/GlClT7O5bEfuioKAAY8aMwYIFC1C7dm2r97Rqp0vE3c/Pr3TyU35+\nfum6qk2bNkVOTk7pfuLaqxWdpUuXYuPGjVi+fHnptsrUFydPnkR2djYiIiLQsmVL5ObmIjo6GufP\nn69U/SASEBCA0aNHAwA6d+4MHx8fXLp0qVL2xb59+zBq1CgAwOOPP17qaqgMfXH//n2MGTMGzzzz\nDEaOHAlAX+10ibgPHz4c3377LQDg22+/LW348OHD8cMPP6CwsBBZWVk4fvw4YmJiXNEEryEhIQHz\n58/HmjVrUL169dLtlakvwsPDcf78eWRlZSErKwsBAQFISkqCn59fpeoHkZEjR2Lbtm0AgMzMTBQW\nFqJBgwaVsi/atGmD7du3AwC2bduGoKAgABX//mCMYerUqQgNDcU//vGP0u26aqfWqO+4ceOYv78/\n8/X1ZQEBAeybb75hly9fZn369GFt27Zl/fr1Y1evXi3df/bs2ax169asXbt2pVHyioJtXyxevJi1\nadOGNW/enEVGRrLIyEj2t7/9rXT/itoXYj9Uq1at9JqwpGXLlqXZMoxV3H5gTLovCgsL2YQJE1hY\nWBjr2LEjS0xMLN2/MvSFpVbs37+fxcTEsIiICNa1a1eWlJRUun9F7oudO3cyg8HAIiIiSrVh06ZN\numqny9dQ5XA4HI774SsxcTgcTgWEizuHw+FUQLi4czgcTgVE9TJ7lgQGBuKhhx5ClSpV4OvrW6Fm\nknE4HE55RBdxNxgMEAQB9erV0+NwHA6Hw9GIbm4ZnnTD0ZPLly8jKioKUVFR8Pf3R0BAAKKiolC7\ndm1MmzZN9/M9++yzaNWqFb788kvdjjljxgz4+/vjww8/1O2YHI5cdEmFbNWqFerUqYMqVarg+eef\nx1//+lfzCSrglGEOh8NxB5rkWY+E/LNnzzLGqHRpREQE27FjR+l7Op2iQhAbG+vpJngNSvrCZDKx\nDz74gDHGWGJiIhs6dGjpMSZOnMh69uzJWrRowVatWsVee+01Fh4ezgYOHMju37/PGGPswIEDrFev\nXiw6OpoNGDCA5efnlznHs88+y37++efS1ytXrmRhYWEsIiKCPfroo4wxxoqKitjrr7/OOnfuzDp0\n6MC++OKL0v3nzZvHwsPDWUREBHvzzTcl265HX1R0eF+Y0aqduvjc/f39AVDp0lGjRmHfvn3o2bOn\nHofmcBySlZWFxMREpKWloWvXroiPj8cHH3yA0aNHY8OGDRg8eDBeeuklrFu3DvXr18ePP/6It99+\n2+li7u+99x42b94Mf39/3LhxAwCwePFi1K1bF/v27cO9e/fQo0cP9O/fH0ePHsXatWuxb98+VK9e\nHVevXnXHV+dwHKJZ3G/fvo3i4mLUrl0bt27dwubNmxEbG6tH2zgchxgMBgwaNAhVqlRBWFgYSkpK\nMGDAAABUzyY7OxuZmZlIS0srXSmsuLgYTZo0cXrs7t27Y9KkSXjyySdLi3xt3rwZhw8fxs8//wwA\nuHHjBo4fP47ffvsNU6ZMKa0dJFXLnsNxN5rF/fz586VV3YqKivD000+jf//+mhtWETEajZ5ugteg\nV19Uq1YNAODj41O6+IX4uqioCIwxtG/fHrt371Z03EWLFmHfvn3YsGEDoqOjcfDgQQDAp59+in79\n+lnt++uvv2ryjfLrwgzvC/3QnC3TsmVLpKSkICUlBUeOHMGsWbP0aFeFhF+4ZvToCzmC2q5dO1y8\neBF79+4FQGVW09PTnX7u5MmTiImJQVxcHBo2bIicnBwMGDAAn332GYqKigBQRcfbt2+jX79+WLJk\nCe7cuQMAit0y/Loww/tCP3TxuXM4rkbMurJcwMB2MQPbzCyDwQBfX1/8/PPPePnll3H9+nUUFRVh\n+vTpCA0NtXsOAHjjjTdw/PhxMMbQt29fREREoEOHDsjOzkbHjh3BGEOjRo2wevVqDBgwACkpKejU\nqROqVauGIUOG4F//+pcruoHDkY3Lq0IaDAaeA8/xeiZPnoyhQ4dizJgxku8LgqDKqjSZTKhduzZe\ne+01jS3kVDa0aqcuk5iKi4sRFRWFYcOG6XE4Dsft1KlTB++8847dSUyCICg+5owZM7B8+XLUqlVL\nY+s4HOXoYrl/9NFHOHjwIG7evIm1a9dan4Bb7pxyjiAAL79swm+/mdCwoadbw6ksaNVOzT733Nxc\nbNy4EW+//TY++ugjrYfjcLwGQRCwebOA//4XuHEjDsOGAQMHUtCPB/443o5mcZ8+fTrmz59fOtGD\nw6koGI1GbN5sxLBhwMMPA/HxJphMnm4VhyMPTeK+fv16NGrUCFFRUQ59kiaLO4JbPZzyQnIy8PXX\nQEoK8OWXwP37wJkzQPPmnm4ZpyIiCIKq2I49NPnc33rrLXz33XeoWrUq7t69ixs3bmDMmDH43//+\nZz4B97lzyiG7dwOjRwMLFwJPPEE33oIFRowdC4wb5+nWcSoDWrVTt1TI7du344MPPsC6deusT8DF\nnVPOyM4GYmKAb78FBg0yb58zB7h6FZg/32NN41QivCIVUoSX9+VUBD75BJg82VrYASA4GMjI8Eyb\nOByl8ElMHI4FjAF+fsCePUDr1tbvpacDI0cCmZmeaRuncuFVljuHU97JyABq1Cgr7ABtO3MGKCx0\nf7s4HKVwcedwLNi1C+jRQ/q9Bx4AAgKAU6fc2yYORw2axP3u3bvo0qULIiMjERoayitCcso9SUlA\n58723w8MJOudw/F2NIl79erVkZiYiJSUFKSmpiIxMRG///67Xm3jcNxOVpa0S0akeXMu7pzygWa3\nTI0aNQAAhYWFKC4uRr169TQ3isPxFFlZZJ3bg4s7p7ygWdxLSkoQGRkJPz8/9O7dW7JONodTHmCM\nctydifvp0+5qEYejHs21ZXx8fJCSkoLr169jwIABknWvefkBTnng3Dmgdm3AUYVebrlzXIXe5Qd0\ny5apU6cOhgwZggMHDpR5zwQSeJPJBKMgwKr6ksnEX/PXXvE6OxuYXc3x/lFrTRh5yDvay19XrNdG\noxEmgP5Z7qMSTZOYLl26hKpVq6Ju3bq4c+cOBgwYgNjYWPTp08d8Aj6JiVNO+P57YO1a4Icf7O9z\n5w5Qty7978MTiTkuxKP13PPz8zFp0iSUlJSgpKQEzzzzjJWwczjlCWfBVAB48EES9/PnAX9/tzSL\nw1GFJnEPDw9HUlKSXm3hcDxKVpbjHHeRgAAgN5eLO8e74QNLDudPsrOBli2d79e4MVnuHHXoGTTU\nG29um1LKvbgnJgLPPgvcuuXplnDKO1lZ8sX93DnXt6ei4s0C6s1tU0q5F/dXXwXWrweWLPF0Szjl\nmeJicrXIWWWpvIq7NwhXWhpQUuLpVlhTVAT89a9UDTQtzdOt0Q9NPncAyMnJwcSJE3HhwgUYDAY8\n99xzePnll/Vom1Nu3qTyq0uWAN98A0yb5pbTciogublAw4ZUHMwZjRuXz7ruUnNQ3HnuhQsF/PIL\nAMSVZhp5w7yXF18UsGWLgCFDgCVL4vDmm0D16t7RNk0wjeTn57Pk5GTGGGM3b95kQUFBLD09vfR9\nHU5hlx07GIuJYezqVcZq1WLszh2XnYpTwREExnr0kLfvypWMjRnj2vbozYIFjD35ZKxH29C9O2Pf\nf8/YAw/EssuXPdqUUgoKGKtfn7Fjx+h1aGgs++wzz7ZJRKt2arbcGzdujMaNGwMAatWqhZCQEJw9\nexYhISFaD+2UpCQgOppS04KDgf37gZ49XX5aTgVErr8dKF9uGUEQsGaNgP/8BwDi0K4d5ee72yrN\nywOOHgXGjAH+9S/gl1+Av/zFbae3yy+/AF26AO3a0esOHYCffwb+9jfPtksPNIu7JdnZ2UhOTkaX\nLl2stlvOttLzokpKMov5I48Ae/dWDHH35PC5slJRxd1oNCItzYjJk4ENG4Bhw0yy0j31Zu9eoFs3\noFo1YPhwI377zTvEfcUKYOJE8+unnjLiqaeA+/cBX1/3tkXv8gO6+Uxu3rzJoqOjWXx8vNV2HU9R\nhrAwxg4epL+//56xUaNcdiq3Ehsb6+kmVDqeeYaxb76Rt++NG4zVqMFYSYlr26QXY8cytmQJY1FR\nnnM5vP02Y+++S39nZjLWvLln2mHJnTvkzr1yxXp7+/aM7d+v7diJiYnaDsC0a6cu2TL379/HmDFj\nMGHCBIwcOVKPQzrl9m3g5EkgLIxed+1K616W90oHmzcDGzfS9HZ34A0ZFN6A3Bx3wFxYrKDAZc3R\nDcaAHTtoRBsTY/TY+q9JSUDHjvR3mzZ0/+bleaYtIr//DoSHAw8/bL09JgaQKJGlCG+4rzSLO2MM\nU6dORWhoKP7xj3/o0SZZpKYCISE0zANo2jhjQE6O25qgK4Ig4O23TRg1yoT9++MwYoQJJpPJ5ReJ\nN1yE3oASt4zB4B2umS++cJ4CfOoUtbdVK2DwYM+J+5EjJKQAtadjRyA52TNtEfnjD+klFUNDgWPH\n1B2zoAAYPZrqE12/rq19WtHsc9+1axeWLVuGDh06ICoqCgAwd+5cDBw4UHPjHGFpCQB0wYjWu5xc\nZW/DaDTi9GkjevcG6tcHjh41QYfCcA65ehW4csW15ygP3LsHXLgANG0q/zMNGwKXLgFt27quXY64\neJFSf6tUAYYPp2tGiu3byWo3GICgIHhE3MX+tbwvw8OBw4eBoUPd3x6RgweBsWPLbg8OBrZuVX48\nQRAQGyvg0iUgIyMO/fsDgwZ5LqVSs7j36NEDJR6YlXDwYNk6IGJQVeoHKw+sWAFMnQocOgSsWgXc\nuAE89JD+5xEEAdu2CVi0CLh0KQ6FhWSJelter7sCy2fOkLBXVXA3NGhA4u4pdu0C+vQBatYE1q2j\nWdpSbN0K9O1Lf7dqRSNbdwcLs7KAZs2s+zc8nFyQniQpCXj//bLbg4PVWe6PPmrEqVNGJCQAS5cC\nixebsGEDXSueoNzOULW13AGz5e5u9HBt3LkD7N4N9O8P9O1rRKdOdAO7AqPRiLAwE9q1M2HgwFic\nP08uIG8SdsB9LiMl/naR+vWBy5dd0hxZ7NxJFvnIkVSmWIriYuC33+ghAJAL08+PJmy5k1On6MFi\niWi5e4rLl2nkKrVebsuW5HJTGvfas4f89+3b00O3Tx/7v407KJfifu8ezRDs0MF6e6dOdMHcu+fe\n9ughQjt2AJGRQJ06JL7du9MoxFUsXw688AI9IAUBOHvWdedSSkkJMHkysG2be86nxN8u4mnL/fff\nyV/cpw+5XsTBc3ExMGEC5ZN/+CG5Qiy/W4sW7l8m8OTJsiIaEgIcP06jCE+QlARERUnX5K9ShSp/\nKo3f7dwJPPYY/W00GjFqFLB6tfa2qkWzuE+ZMgV+fn4IF6MlbiA1lXyd1atbb69Zk56au3e7rSmY\nPZvcKbdvazvO3r3WwZ2YGGDfPm3HtMfduyTogwYB/foZMXo0ib03IAgCnnrKhA0bTNi5Mw7Tprk+\nsKxG3D1pud+6RQHKmBigSRN60KSm0nvx8eRS6NQJWLkSWLjQ+rOeEPfTp+m8ljz4IG3zhuwdKdSs\nlbtnD7mGARL3Pn3IaPNULR3N4j558mQkJCTo0RbZWHaiLYMH02QNVyMIAl580YS5c03IzIzDmDHa\nRGjfPrpZRURxt0zt/PprKpSmNd1TEGjUU78+XYSTJgHffqtfGqkWITYajWDMhDlzTIiJiUWzZq53\nGZ04IT08d4QnLfc//gAiIkggAaB3b/pNGQPmzgXefReYNYvS+bp2tf6sJ9aAzc+nh5Atan3benDw\nIM1ut4eah+Aff1j3t58fBd49VYxMs7j37NkTD9smirqYXbtotpsUQ4e6R9yNRiNKSkyYMcOEp56K\nxd276kWIsbLi7u9PI5GTJ+n1jRvA9OnAd9/R8E8L69cDQ4aYX/foQSMPvdZd0SLu9+4BCQnAiBFk\nTYeuKrIAACAASURBVLvD7X7ihPKsF09a7qK/XaRPH+qzLVtoVOYoA8UTlrs9cW/XznMF2JxZ7i1a\nKHsIXr5MPnrbTL3u3d3rSbBE1/ID9tCz/EBxMQ115syRfr9jR+DaNbph27RRfRqnXL1KuaxHjwKf\nfAKsWUM3lq2rSA7Z2VSN0PYGEK33Nm3ogfXoozTcTkigv9XAGIm75QPQx4emYC9d6tiakXv84mL1\nn09OJlFv2JCmgk+YQCVZlWSyKIExddeKJy33338HXnnF/HrwYKqF8te/0n3haG3XFi2An35yfRst\nOXtWetWqoCC6l93NtWu02EpQkP19mjenWIZcjh6lOILBYL09KsrsMnOG3uUH3C7uSjlyBPjoI+Cp\npyila8cOulDsDaN9fMgq3bDB+gbQm88/p/M0bgz072/Etm3kN1fz3BILoNkiivtTT1Exo8cfp+8u\nlb4ll7Q0ugBDQ623P/ssPThef72sf1QOgiAgMVHAd98BWVlxuHbN7PZR8jDfu9fschs+3Ah/f7px\nXBXSOX+e3Bt16ij7nKcs9/v3afhvOXKtWZPcaklJwPjxjj/vTW6Zdu3I1ehukpPJrVWliv19xKUU\n5SKKuy1hYfizzLFzbO+VuLg4+Q2QwC3irpbCQuCJJ0hEJ02i7I7du60L/UgxZAjw2WeuE/fcXHrg\niO4Ro9GImBi6udSI+6lT0pZjTAzw1ls0623rVuDLL+nhdeCAemt29WoatttaGIGBwJtvAqNG0epW\nSsXOaDTi0iUjGjYky/vmTVOZYJ4c9uyxdhl16kTf11Xifvy4uhGeqyz3s2eB994Dnn5aevbkjh0k\nIvXqWW8fMsS63+whijtjZa8BV3DrFt3HUteT6JZxV1tEDh507JIByIjKz5d/zGPH7Iv7kSPu/46A\nl6dCrl9Pw/P588mC3buX8kidleN89FGybuRGqZUMha5fp2HwG29QQEgkMhJISZF9GCtOnZLO1oiO\npglNP/9Mvrv69en7N2pk9sUroagI+Oor+xNeXnuNzjN8uLraNp9+CsyYQX2xdi3d1EqxDZaL4u4q\n1PjbARLXK1f0r2X04ouUYz1mjLSF/csvlNuulpo1qTbOhQvqjyEi577JzyehlBI2cXKPu91brhB3\ne3MlGjUig8wTpSo0i/v48ePRrVs3ZGZmolmzZlii43p3GzfSRW4w0AzCDRuA7793vlpO/fpkKWRl\nyTuPXHFnjPKvu3cn94UlWsQ9K6vsJA8AqF2bzjV5MvDMM+btISHqsgzWrKGZgvb86gYDsGABXdiz\nZik79oUL9P2HDQOGDTMiOFi5PzUvjwK7lpa0q8VdreVerRpQo4a+9UOOHSOjZMUK4KWXyIgRYxiC\nACxeTHGep5/Wdh69gqpy7puzZ6VdMgBdb54IqtomL0hRvz5di3fvyjtmTg7dW7YYDGbr3d1oFvcV\nK1bg7NmzuHfvHnJycjB58mQ92gXGgE2bKBdbDZGRZPU6oriY3DfXrsk75s6d9CP95z9lLZHQUBIK\nuReDJfYsd4B8kl99ZV1SQW0K2YIFgLMVEH18yOX07bfK6s6sXQsMGEAPXqPRiEcfVZ4lsGcPpZJZ\n9m1UFPW5mlGAHI4fV18fRm+/+88/kxuyenUaGebm0kM9MhL4xz8oG2b5cu21k/Twu2/fTu47Z8Fz\n0XK3hxJx1yPYePEi/WaWo24pDAZKZZRrcdsTd4Dm3nhC3L3W556aSoEutTdeRAQdY/Ro6fcFQcAn\nnwiIjweAONStS9sdBQAXLaJhs9TIoXp1sgDT0pRlnJSU0I0WGCj9fvPmZRc1CA5WXpogOZlGCKNG\nOd+3SROgXz9lq+WsXm1tUT7yCPWXEiyDqSK1atGDLy2NhF5vtGRVieKuNEfeHj//TJlXAI0Mtm2j\n108/DQwcqJ/PVovlLmZ0fPIJcPVqHCZMIIG2d984stwB5eKuJDjPGPD3v9N1v2oVuaT++INqUjnK\nKBIRXTP27k2RwkIyhP5ckK4MwcHl1HJ3FRs3km9b7QXdti3duPYwGo3IyTFh9WoTHnggFi++6DhP\n/fx5Gkk4CuZGRCivl3H2LPlvxQkpclBjuX/+OQ3z5RaMeuIJ+SlzN2+SC2bwYPO2Rx5RFvcA7E9O\nc5VrRkyDVGtAiH53PThzhtxS3bubt9WvD8TF0ehVz2CcFnE3Go0YMcKEBg2oLtFDDzm+b5xZ7kFB\n8sT9xAnlcYK9e2m0U7s2MHMmbduwgeo3yUGu3z0vj4TdXvaNpyZraRb3hIQEBAcHo23btnhfS46e\nDaK4q6VVK8dBxwsXaEg+eDANp5y5EL75hkYBjuZrhYUpF3dHLhl7iBeL3GBeYSFZLhMmyD/HwIHU\nJ3LKKiQkUGqeZUZEo0YkfnIv6sJC8tlL+UJdJe75+eQ3V5oZJKKnuO/YQYkAjtLz9ELN1HpLtm+n\nGiotWjiPq2h1ywiCgLfeMiEiwoRFi+Lw0kvyZ4J/8w2NPL/8kgyVpCQaYcoNSDduLE/cHblkAM9N\n1tLklikuLsa0adOwdetWNG3aFJ07d8bw4cM1L4599Sr5y3v1Un+M1q0di/tvv9HxfX2Brl2NSEqi\nWZFSFBfTBbJypeNzhoebh9VysRdMdUSDBiQC58/bHwpasn07WUhKfLW1a5Ovd9cuctE4YvVqaXdP\n165kPdnm1EuRnEwWtLjKkSWdOjlflEINWvztAFnWeom77axTV6J09qUtu3ZRRlXTpkb88ANlu9gr\na+vMLdOqFT1oSkqkXSVGoxH5+bTOQUEBcP++vHUObt0iN1daGhlk771Ho6KePeX/5nIt99xcyou3\nR9OmNLp1VQlve2iy3Pft24c2bdogMDAQvr6+GDduHNasWaO5UZs304+gxFVhi78/XQw3b0q/v2WL\neXg2apTR4dT7X36h4IqzhYXdZbkDyoZ627Y5F2gp+vRxvmjB3bs0ypJ6MHbpQq4ZOYjBVCkiIui7\n6r2sXWam41mKzqhXT7+A6s6d6mcdK0WLW4YxEvcePYDHHjOWxrbs4cxyr1EDqFvXsYiKtes7daJs\nITlpuqtWkZiLD5bnnqMAsNwJRQC1W05A1Znl7uMj3/2kJ5rEPS8vD80svlVAQADydFgY0bb2iRrE\npcXsWe87dlDBJcDxkl937lAhprffdn7O5s3JYlByw6upSAhQOuTRo/L23bbNXNNbCX37Ohf3X38l\nC19qBKFU3O0Vg3vwQXrYb9wo71hyOX5cu7jrYblfuULWn20Ja1dRvz7NdL16Vflns7PpfzHI6CzN\nz5nlDtB9euqU/ffFOjBDhxoRFUWGmS1FRTRCvXuXHkCffw5MmWK9T9eu0iNDe8i13J2JO0CuGXf7\n3Q1/rrKtilWrViEhIQFfffUVAGDZsmX4448/sNBiaqLB3dOyOBwOp4KgQZ61We5NmzZFjkVF+5yc\nHARIOJ8YY7L/LV/OYDTK39/RvxdfZFiwoOz2rVsZeva03hYTw7BrV9m2hIYy3Lgh/5zPPcewcKH8\n/Zs0YTh9Wvl327CBoV8/5/slJTGEhanvwyFDGFaulH7v9m2GOnUYzp+3//mYGIYdOxyfIy+PoX59\nhpIS+/sUFDA0asSQlua4T+rVY5g2Td53Cw1lSE1V3zfr1zMMHqz+8+K/BQsYXnhB+3GU/HvpJYYP\nP1T+uQkTGL74wvx6xw6GRx6R3vfECYYWLZwf89136Z/UeydPMjRvbn59+jRdK/fvW2+rV4/hwgX6\nTiEhDFu2aO+jnBy6P53tFxXFsH+/432+/57h8ceVnV8rmsS9U6dOOH78OLKzs1FYWIgff/wRw4cP\nV328oiJK/ZLjApFDixbmYaQlUrWcbScaFBcDJhPw3/9ScFEu4eHyc1rv3iUXjpKFmUXk+tzFFWfU\n0r+//bUuN20iP2ijRvY/L8c1IzV5yZaaNWlW8DvvSL9//z7lNC9ZQsFyZyvgFBeTK0BLjrpePvfD\nh93nkhGJiqL7wBnFxZQW++yzlDm1dau1i0+8b6S0SI5LBnDslsnNtU4EaN6cXEKWZa+//56K6jVs\nSOsdpKeb143VQqNGlFXnbKKWHLeMJ9IhNYl71apV8emnn2LAgAEIDQ3F2LFjNWXKLFtGfi41/mEp\nAgOlA0f2xN2yqP6ePTQxSWnGjpKganY2XRRq0t9atKAsBWdBRmd1q50hirvUzbtyJfDkk44/L1fc\n7fnbLZk2jaaOS61QtW4d3fjDhwP//CfNsnXEmTMkBjVqOD+vPfTyuael0fXnTh57jOIlzpa527iR\nYhM3blBsqE0b6wdivXrkx5Zaki4vz3EWiUjLlvbFPSen7DFGj7YOjC5frr0kgxTVqlGarKMH+J07\n1DcNGzo+VlAQ5eprKYetFM157oMGDUJGRgZOnDiBWUoLklhQWEhW+3vv6TsTT4nlbinuGzZQnRSl\nbREtdzmjKrXBVIAeCG3bOo/AJydrs9zbtaM0Ndvl0K5do/z2MWMcf75LF0qHdNQfjjJlLHnwQZqG\n/9//ln1vwway3gDK3MnIcByk05oGCeiXCnnypGvXHpCiRQsSHGdB6o0baeLeypU0ov7227L72Auq\n5uXJG5U6s9ylxD0+nq7LxYsFXL8uXUFTD5xlzIjf0dmM15o1KV3UneWWvWaG6ooVdIHrmesrZblf\nvSpdqF9K3NVk7NSvTz+knMV1pVaFV4KzjJniYkpTi4xUfw6Dgaz3X3+13v7DD5ReWb++48+3bk0X\nvr12Opq8JMXEiVQAzTLFlf1Zh0ic9ObrS7M6N22yfxytaZAApfBdu6ZtjcybN+mfo3RBV/F//0e1\nhs6ft7/PkSNkHFStSvtKXa/2Rqtyxd3fnx6SUnWZpMQ9OJjyxXfsAD7/XMDEifLKCajB2UQmOS4Z\nkeBg96ZDeo24f/opLSOnJw0bkp/QUgiSkkjsbF0hAQG075Ur9HQ9e5asTjXIdc1osdwByv+2l8IJ\nkIA1bqx+BqbIsGGUN2zJkiVlU82kMBiofvy6ddLvp6TQQ11uXKNhQ7LS1q41bzt0iFwDltbv4ME0\nsrCHHpZ71ap0Xi2VIcV5Dp5IKhs8mCqOjh4tPbJiTJ7LKCJCukhfbq48ca9She4/KYPI3gShmTOB\n55+nh8+0ac7PoZbGjR1b7krE3d3pkF4h7idOUCcNHKjvcQ2GshM27C2MazCQSyUpCfj4YwEDB6qf\nCi43qKp2ApOIuFKTPbQGU0UGDaKH1ZkzNB08OZkefnJrdDzxBMVTpARErr/dkiefBH780fx648ay\n1UO7d3fsDtLDcge0+921BnW18u67tG6t1OSe8+fJInbmT7Yn7nItd8C+CzU3t6x4CoKAU6dMaNbM\nhLt34/D559oWp3eE3uLuzHLfsqWsIaUW1eUHfvrpJ5hMJhw7dgz79+9HRw1RuzVrKBDmiqGV6JoJ\nC6PXBw+SJSpFv37kfli9WsDHHxtVnzMsjDI2nKGm9IAlnTqR5W5vVSa9xP2BByhb4qOPgLp1BaSk\nGPHqq/Iffr16UeAuMZECeZbs2aO8rPOIEWStXbtGrpGNG8tm0TRtSgExe4so6CXuot9drUBnZTmv\nOuhKfHyoL5cvLxs/SU+n0hHORhUhIfSQsl1DWKm4SyU/SFnulhUoTSZty3g6w9/fsYs1J0d+plO7\ndvizCq00+/dTiedateSvReEI1XIaHh6O+Ph4PKrDnOktW7QVCXOErUVgz3IHqA2LFpGPTcsoQo7l\nzph2y71OHcoQsYwVWOLouyplxgzys8fHkzA+/7z8zxoMdBO+/rp1tgBjtAiF0kuoTh16SKxZQ9bl\nkSPSWU2dO0uPbAoLSXi09L2I1nTIs2fVpcLqyfDhZIzYFolLT5eXxfPAA+TisrwOS0roPpL73aTi\nY4WF1Ld+fvKO4Qr09rk7csvMnk1GiiAAH3+sqJmSqBb34OBgBOlg+hQVUfVBVxVNsrxo7AVTARrq\nbdhgQseOJty7F4d589QP9UJDafjlKM3s0iUSPdu1MJUSE0PuB1tKSsiq10PcBUHAF1+YMHKkCamp\ncejf34R//1tZ34wdS9kuX3xh3nbsGFl6akR27FhyzaxcSeJkaTGKxMSQNWTLqVN0Q8otf+wIrW4Z\nbxD3evXI+ratvCla7nKwdc1cukRxFKnfRQopt0x+vuNSugAU1XdXgxy3jJx0T4D2u36dUidtuXCB\nRraTJ9N+SmtUSeGWxTosh022Rf1TUsj6dJZ1oZYWLVBaFMxeMNWyXTTM0zbUq1GDfqDjx+3fHJmZ\nNEzTGkh77DEKLtpa0idOkMvCXrU+JVj+Zo0bq+sbg4FWlerZk9wwp08LOHDAqKqgGUCutRdfJMvc\nXjGozp0ptdYWPYKpInqIu5yJPq6mWzcq0GU5ikpLc57qKiKOksQguxLRA6Qtd2fVFgHvEHe5lruP\nD113mZnkUrVk1SqgUycB//63oLqttjgU9379+uGcxDebM2cOhtlzXEvw5JMmGAzSq4OLdaxdRWCg\n2SI4cEA/N4UzRNeMM3HXSv/+wCuv0CjB0hLV0yWjFyEhtHzc1KlAz54CVq0y4rPP1B2rVi1KV83L\ns3/9iEst2paT1cvfDmjPdfcmcbfNYZfrlgEogP3ll+bXSvtYynKXI+6uxlGee0EBBaOVGKZiOqSt\nuP/4IzB9uhEjRhhLt8XFxSlvsAUOxX2LVPk1FXTpQgG/zMyykfedO63XB9Wb4GDKsS4uJvePnJls\nelgDYjqkvRmceglM48bk1vjjD+uJHAcOlL2A9EBr37z2Go00vvyS2q7FHecsy6ZhQ3INZGdbB64z\nM/Wb7l+vnvrgF2PeI+5dutBIiDEaZV28SPeMXH93RARlU125Yl6kRYnxEhBALlNLI8UbxL1uXZqF\neudO2RLkYvuUjL4jIujetNShM2dIK/TOFtQlP8VZkZvhw2l4Z+lvBciicvUiBQ8/TDdPWhr5ji2X\nMbOHHuLuLKialuZ8kV65DB1a1jUhCNoWO7GHlr4RBAHvvWdCr14mnDsXhz59TIiLc00Km0hUFLn+\nLDl2THoUqQYtbhlx/oWS2kWuIiCARjfiDMq0NHmZMiJVq1KMQ1zRLCNDmbj7+tKDJDfXvM0bxN1g\nsO+aUeKSEXnssbKZdMuX0+xqqbWZtaBa3OPj49GsWTPs3bsXQ4YMwSAH+WwTJ1JRp2++sc47PnyY\nhjSuDijFxJC/18dHcFvwytFEJsYoCCpnyr0cnn6aZvgWFdHrK1fIr+xscRF3QzENE2bPNiE2NhYf\nfOB4/U09iIwsO9Hr6FH9HqxaxF202r2hKrbBYD1vQolLRsRoNAvX0aPK3Y62fndvEHfAvrirmYQY\nHU0PhdxcMnaKi4GlSykFUm9Ui/uoUaOQk5ODO3fu4Ny5c9jkYK53z55kQfn6WmcvbNtWNu/ZFQwc\nCCxcSNa0u2jblvzBt26Vfe/kScoi0OvCbdeObgyxRMC//y2gd2/K867sREZaW+6XL5OfVK/p/lp8\n7t7ikhHp3Nl8fzqKF9ljxAhKlS0oADIyBMX3m63fXWlQ1lXYW7RDTSpz1apkjH3xBYn78uV0Dcnx\nKCjFLTNUa9Qgy2DcOMqVFtm2zbwakqsQBAGZmSaMHWvCzp1xMJlcN5vNkqpVSXTT08u+t2WL/q6o\nadMoT5YxYMUKAZMm6Xt8vXF1loOIrVsmI4Osdr2sZS157t4m7paWu5qYTVgY+aVnzQKaNBFkp0GK\n2E5k8hbLvUkTa3eRiNryIa+8QitF7dhBMagFC1wzenNLKqTI2LE0C/SDD2g2286dwOLFrj2nZRpf\ncLBrZ7PZEh5OrhlL90heHk1QUJslYo9x46g+T69eNFoYOlTf4+uNu8Q9MJDyisVFnPV0yQD6uGW8\nhU6dKMvq9m0gNVVAVJRR0ecNBuBf/yL/8VNPKT9/YKDZZ19URLnfniioZou90uFqxF0QBAiCgCFD\ngG+/jcOUKZT1deuWUfd7QpO4z5gxA+vXr0e1atXQunVrLFmyBHUcVKkKDaUbbPt2GuZ07ep4oYfy\njijuIl9/TQWPXnlFn8UELNm5U0CvXgJSUoDLl+MwZw5tt51XUNnw8TG7Zvr21V/cH37YXBlSafmM\ns2etF6LwNPXqkZB9/jmVmahRw6jo84Ig4PBhAe++C/zzn3GlcwnkXoMt/r+9s41pq/7i+LeTGYa6\nKchAV8COAbU8CARdNqebkrpk2ZhzM8p0GjQagzrnQzKNIZQXPEzUDZ8TFbdsyUBnlpE5a+E/OzWM\nGdbpNkFAbWMFXDYYBpZFNjj/Fz/L01oo7b3tpfd8XvXetvece7ic++u55yFBDN4AhH+48UZpCs38\nJSHBfaWzL859rC1uuUXmxSb5gcVioaGhISIi2rp1K23duvWKz0wU8emnRJmZROnpRF9+6Y/06fPt\nt98GVN6RI0TZ2ULur78SRUURtbfLL7e4uFh+ITOIzZuJKivF67vvJvrmG2mPP28eUW/v9L/34INE\ne/dKq4u/lJYSAUTLlxf7dRxfrsH2dqKFC8Xr774jWrLELxUko6mJKCdn/L7+fqI5c4iGh30/7lQ2\n8tM9k18rd+OY8sLFixfjSy/amRUUiNVTeDiwbp0/0qdPoFewS5eKu3tdnRUnTqxAUZF0lZGM92Rm\nivFwDQ1WHD++wudWzp5whWZuuGF631NaWMZqteLCBet/vfBL4FpUBurXX1yciG0PDfnfDltK3BVY\nuRq++RMrl9umksXcq6urkZ+f7/a9ie0HKitXSCVW0YSFiV7Ze/aIC/fZZwMjV81hGHcsXw68+ioQ\nHm6FTrfC7/72E4mJEaly0+0MOZ2uiYFAym6LvlyD4eEic6S723M3z2AQEyOeY/X3j9YkdHT4Pz1r\noo1c8XipmNK5e9OCoLS0FFdffTU2eniKEsiHmErB9YeKjATOni3BI4+Ih02BWAWxcx/PwoXCUezf\nP71ult4SHy+Kf+68UziA6mohZ7JsESLhxJS0cpcSX69B1yrZbpcnPdAXNJrRSnfXxLBTp6RPrZ7o\nG2RtPwBM3YJg586dOHToEP7nTQNzFTH2DxURoc4bnBJw3WSTk4Fjx0pw+bJYlUp5k3U5d0CktRUV\niWrDZ57x/J2eHnFdTCxpVwrBWiC4MlPsduDRR4OigltcYzjHOnfXzF6l4ldYxmw2o7KyEkeOHEH4\ndJNaGSYAjHXiCxfKc5ONjx/t033wIPD002J+62TOXWnx9okEy7knJAjHbrNZkZoaHB3cMXHG8smT\ngJ8La9nxq4jp+eefx8DAAIxGI7KyslBYWCiVXiEFh0lCG9fKvadH/HR/5ZXJxx8Cyou3K4WMDNEh\nkciK2NhgazNKevpoMdzZs6LJmRRdXeXEr5V7R0eHVHqENOzclYFcf4f4eBEnrq8XD28XLRIl+K4x\ngO5Q+so9WBiNojxfae2qly0D8vNF8eV771lx110r3I62VBIKV49hpEMu567Xi35B+/eLPkYazegw\nZE9pl0qYwKQ0XM9HCguBDz4IfCrmZMybJ9or/PCDSG1+/PHg6uMN7NwZxk/mzBG59J9/DlRWin2u\neZmenHtnp3Q95UOFsU48Olp5SQgPPST65rS1iXYfSsdn515UVIS6ujpoNBpERUVh586diJtuc2OG\nCRGqqsRoQ1c7gamGIXd1iXGDzMzAarXi3DkrwsKAixdL8NFHYr8SflV4QvNfmeu06e/vx3X/ZfS/\n++67+Pnnn/HJJ59cKUCjmXKYB8OEGrW1wBdfAPv2uX8/I0P08c7ODqhaMwar1apYp+nqLCs3/vpO\nn7NlrhszPmZgYAA3SjGJmWFChEWLxEreHcPD4j2p5riGIkp17DMJv2Lur7/+Onbv3o2IiAg0NTV5\n/JzJaoJphWnkNQDe5u2Q3t6SacJvv7l//59/gMhIE669Vjn68rb3245bHHAhtzx/mDQs403rAQCo\nqKhAW1sbPvvssysFaDQoLi4e2VZyjIphpCQ6WlQyTszXtliAigoxrIZhXEzsLVNSUuJXWMbnmPtY\n/vzzT6xatQqn3UyE5pg7o1aWLBHZM8uWjd+/YwfQ3i79wBYmtAhazH1sAdOBAweQlZXlsxIME4ok\nJbmPuzc2ek6RZBipCPP1i6+99hra2tpw1VVXITExER9++KGUejHMjGfRIpET7cLpFJWr9fUidZJh\n5MRn577PU44XwzAARAn99u3itdksCl/Cw0VffyXMBmVCG0li7pMK4Jg7o1LOnRMDPPbts2Lz5hV4\n+20uXGK8x1/fyc6dYWQkJQWYO9eEiAgTrFb/xrIx6iJoD1RdvPXWW5g1axZ6e3v9PVTII+UIrZmO\nWmxRWAg0NwNvvunZsavFFt7AtpAOv5y70+lEfX09EhISpNInpOELd5RQt4XVaoXJZML58yYAJfjq\nKxNMJpPb8w51W0wHtoV0+PxAFQBeeuklvPHGG1i7dq1U+jBMSDCxWE9pHQ6Z0MfnlfuBAweg1WqR\nwX1LGYZhFIdP7QdKS0tRVlYGi8WCuXPnQqfTobm5GVFRUVcK4CdIDMMwPhHwbJnTp08jNzcXERER\nAIC//voLCxYswI8//oj58+f7rAzDMAwjDZKkQup0Ohw/fhyRkZFS6MQwDMP4id+pkACHXhiGYZSG\nJM79jz/+cLtqN5vN0Ov1SEpKwrZt26QQNWNwOp245557kJqairS0NLzzzjsAgN7eXhiNRiQnJ+O+\n++5DX19fkDUNDENDQ8jKyhppFa1WO/T19WHDhg249dZbYTAYcOzYMdXaory8HKmpqUhPT8fGjRvx\n77//qsYWTzzxBGJiYpCenj6yb7JzLy8vR1JSEvR6PSwWi1cyJHHu7hgaGsJzzz0Hs9mMlpYW7N27\nF62trXKJUxyzZ8/G9u3b8csvv6CpqQnvv/8+WltbUVFRAaPRiPb2duTm5qKioiLYqgaEqqoqGAyG\nkV95arXDCy+8gFWrVqG1tRUnT56EXq9XpS0cDgc+/vhj2Gw2nDp1CkNDQ6ipqVGNLQoKCmA20gFR\nPwAAA3ZJREFUm8ft83TuLS0tqK2tRUtLC8xmMwoLCzE8PDy1EJKJxsZGWrly5ch2eXk5lZeXyyVO\n8axdu5bq6+spJSWF/v77byIi6u7uppSUlCBrJj9Op5Nyc3Pp8OHDtHr1aiIiVdqhr6+PdDrdFfvV\naIuenh5KTk6m3t5eunTpEq1evZosFouqbGG32yktLW1k29O5l5WVUUVFxcjnVq5cSUePHp3y+LKt\n3Ds7OxEXFzeyrdVq0dnZKZc4ReNwOHDixAksXrwYZ86cQUxMDAAgJiYGZ86cCbJ28vPiiy+isrIS\ns2aNXm5qtIPdbkd0dDQKCgqQnZ2Np556ChcuXFClLSIjI/Hyyy8jPj4eN998M66//noYjUZV2sKF\np3Pv6uqCVqsd+Zy3vlQ2584PWQUDAwNYv349qqqqxg0VB4SNQt1OBw8exPz585GVleUxZ1cNdgCA\ny5cvw2azobCwEDabDddcc80VYQe12OL333/Hjh074HA40NXVhYGBAezZs2fcZ9RiC3dMde7e2EU2\n575gwQI4nc6RbafTOe7uowYuXbqE9evXY9OmTbj//vsBiDuyqzCsu7s75OsCGhsbUVdXB51Oh/z8\nfBw+fBibNm1SnR0AseLSarW4/fbbAQAbNmyAzWZDbGys6mzR3NyMpUuXIioqCmFhYXjggQdw9OhR\nVdrChaf/iYm+1FVXNBWyOfecnBx0dHTA4XBgcHAQtbW1yMvLk0uc4iAiPPnkkzAYDNiyZcvI/ry8\nPOzatQsAsGvXrhGnH6qUlZXB6XTCbrejpqYG9957L3bv3q06OwBAbGws4uLi0N7eDgBoaGhAamoq\n1qxZozpb6PV6NDU14eLFiyAiNDQ0wGAwqNIWLjz9T+Tl5aGmpgaDg4Ow2+3o6OjAHXfcMfUBpXxA\nMJFDhw5RcnIyJSYmUllZmZyiFMf3339PGo2GbrvtNsrMzKTMzEz6+uuvqaenh3JzcykpKYmMRiOd\nP38+2KoGDKvVSmvWrCEiUq0dfvrpJ8rJyaGMjAxat24d9fX1qdYW27ZtI4PBQGlpafTYY4/R4OCg\namzx8MMP00033USzZ88mrVZL1dXVk557aWkpJSYmUkpKCpnNZq9kyD6sg2EYhgk8soVlGIZhmODB\nzp1hGCYEYefOMAwTgrBzZxiGCUHYuTMMw4Qg7NwZhmFCkP8D0TTAdPkdbBwAAAAASUVORK5CYII=\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": [ + "" + ] + }, + { + "output_type": "display_data", + "png": 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7IKUT6WZqTj8pruJiSxlwZua+9a4u72eqBkMyQZeuBVepQI4/DMUVk6l4gSrmIgfI5ff5\nUUb8HnkVtFdd/Lo0dXGhobarbhyUlNgJAFwxZTqwm6lEgXxVLGlTAJs3bxYffPCB9ofnz58Xmzdv\nDvQy30QBIhZz+hdVFj1gWV4qpiXG6uiw/dbp9oly62/2bNsfnQ6m5s+gAcMHFA1g7ltvbvZ+Zir1\n91LEqnbxYwGqfqu6xp8lz3yCwkuwBRnMuoVyfNVyMl7zwx/8HtmN51UXP2m8RUXJ+YDzdmOjO3tL\nl+68dq1tZBHdPA41VEUeFJlKysj2uhe/yKsYwPr160VDQ4NjO4i//Mu/FJMnTxbTp08XK1euFOfO\nnXMTBQw2bne3OhMFEKKsTO9S8FIKyZjBT0fraPLK5ggCL/r5gIpGk7dF0Pr7+Y0s6CklsLbWuuYV\nt1AJR9U1EliRiP+4QSqZW8mUFk95JMOE6CSBJ6dC6lI9hXAHs1V08Xt0/BR0Rsd5mfpUt7pbNbOh\n2aiXguPXu7udcShAiPJy/zPwVDL0ZFB7eK1i9wvVrDzfUlLTrgDeeustcffdd4uenh5x0003iZtu\nukncfPPNvh6+b98+8eqrrzoUwO7du8Xly5eFEEJs3LhRbNy40U3U7xSAypqqqhJi6VLL2uUCzw9D\n+B1Ifjqa30NWuB9mGApTE/0jR9ptceCA27cuP2coi9N0baab6tOsjF+TrXdVUJMEa22tLTBnz05u\nZU2aZLVDXZ3VBrp4kM6VJIRzGwseb1EpJyok2PmMVGXZqtwgI0fadfSyJINkaCWzQnkbt7cnnxXL\nMxvZyAmSIDEwYCmDkhL7GT093vR61U/Vn+lsKx1Us/J8Ev5CZEABtLe3i82bN4sXXnhB7NmzR+zZ\ns0fs3bvX9wu8NoT70Y9+JP7wD//QTRTgsJCIcaur1VZusgGeDLIFyN1PvKO5JUiWbixmWacqi05l\ndZBCAYRoaNArFTmYzZ+RbPrpFZhLNuVP5pYhcMWgSgn0mz0k9x0XEg0NesVKtHEXWDyutrxlZUT9\nQoqquNj+PhSy/lZU2HTz+pHwVFnTXi4Z2Q2iMjBkfvGboeWVjaZynQ4bZrcNWefyTGVgwBoLs2db\n/UBxiIoKtx+f90ljo9Weixa524PPnrq7VVyYvH5CuI0vneL386wgyLS7Kh1IuwLo7OxMmRghvBXA\nTTfdJP7t3/7NTRQgNm3aJDZt2iRaWzcJYI+LabiPkrsa+AD3Sk3j17iPlQ9smUm58CaBw5lh0iSn\nf5ULM3qWHMNQWV2RiFWPxYu9U1dpMHpZ58kCbSoBQe8ZMcJpsanacdIkq/2jUWtm5scVwcEHbnGx\nU6AvX67PYpEFcGmpJQhUljf1GykjnXtDJZwHBiw3T22tJQhlpcBjTCoQPdwNwnmVC1LOX93dyYUW\n97er4iay8lcVctPw9iotdc9uRo2y3Y2A9V6e+bRunXu2oLPa29v9CdFks0/+nnRls+VrcFeHPXv2\nDMrKTZs2pV8B7NixQ2zatEkcPHhQ/O///u9g8QudAvibv/kbccstt6iJYpXQ5UCHw25mjkbdA7y+\n3snIoZA1mPlUv7vbXiRWV6e3yPg7KyvdDMIHQDTqtHhoyssFgbyAZmDAss50FiIXrqr98WmGUl1t\nCatx4yyaamosYepHkNbV2UE/Loxra9VKgtc5HvfNFoM0UHvHYs4+IcuOQ+Wa45/5s1WzuAkTLHpJ\nkBHtpLTLy9081dOjzp6RZ4Z+3I/d3VbbcuHPwduXlIqX0FLFLmT+5TxIhQesice5sSIX2TDifMHb\nhX+WeVuODQSFnHBBszY/gfFUU8rJAPBLWxClkSlFk3YFsHHjRtHc3CwWLFggFi1aNFj8QqUAtm3b\nJv7gD/5A/OY3v1ETxSohdyy3SlXWlBDqbAe5EMNXVFg+WT6LCIfd1mhvr/N5xcXujqPBV1RkuYVI\n2PPBQIKgp0c9U+HvJJeXygrmA11l/ZGyUwlnfh8Jw1jMeqZKYMiCkb+T2tHvAi/VjINmUirLjkP2\nwZJClt/NU2OXLlW7i0pLhRg71tlGXV1W3/BrZGETr6hoS+Z+0OXmy+5C2mtp+nS3ayVIPj21p8wP\nlZWWIZBIWH1aWemsqyzgy8stg2TOHJvfQiG3AuWZSlVVtrGhcpmS0lHtC+UlFGWFz+uZDPLMygvJ\neFDVt6m4nzOVnpp2BTB27Fjx6aefpkyQrAB27twp2traxJkzZ/REAVpG4P5aPkj5gOKblgEWo/Hp\nazgsxLx5aqtHNSAoiCm7b4qLnQwsL3YiP6q8UlKXUcKZIhRSC1TOzM3NaoEgF8oQovaheuvcVbw0\nNzsVmezu4FY7zwbSZXrIewKRG2vdOqc/XjWVl40BHiTm2Ttc0IfDTuVO5cAB58ANhaz3x+Pu2WUk\nYtE1b56aNi6IuVKn/3WBYt6XJCAB79gQr2eyfHq6xhU49ZGKz6lwga8bG6R05UylCROssRaNusch\nCWxZmKuC7XI2ljz2dO5PIdyKlfMDDz7LCQTEY0SfKoNLhpyq7DfbKJ3nZ3CkXQF0d3eLvr6+lIih\nQ2Gi0aiIx+Pi8ccfF+PHjxejRo0SM2fOFDNnzhRf/vKX3UQBLuYgqBQA5b/LwoUsDy5oVa4jPtDl\na0VFVocODFgdpRKU5DOVO12OCeh8ssRo8rNVefdckcmZFOSrpnva253ZUjp/cGenc7BSG9PMZd06\nS8mOHOnOCedbRHNloKJfZcVR4S6E4cP12wtUVlptGo3a7hT53byNdCUedwo5VbaLzC/Nzd4rw2U/\nuOwWkRUHF2pUF1Xb8dgQ51ESksOHWwIxHLbqFA5bwrqoyOIp6tvaWrUyVBW5DeUZsY4/VWOIBDYZ\nIfyeUMiOd/GANKdzzBi7DlOnOvtNNsJkPud9wN81MKB3XxK/c1mjy1qSkx28Mpe4YlCt6UmHKyjt\nCmDBggWiurpaLFmyJHAaaKogBaDS8rKLgue/y5afLJD4VDWZEJILMVoi4S3IeKfLMQHZzdHebu2l\nohqUcuqkLLzLy+10QgrGqdwqHDyvHrAtei642tvdbied4qivdyoO3i5yvrdXMFJe7MSFoWypylYo\n7x/iGa5M+f0k1IqKLN5YuNBtUMiKQF5nocuyufFGJ93V1eqZE79/0SL7e85XRUX2KnchrN/JxkE0\n6ubvZIXPMoIW+f3ybEeOJQGWsOa/Ky31VkC1te6tT0IhdyxKNTtRKUyv8d7b66Rt1ix19hUV2XXE\nU6v5fki6vbfk2EIyt2KqSJsCuHLlihBCDKZ+8kJpoHRPugFAewg4NVxbm3stgDxNpMHLB6BqCsoF\nGh/EqhgC+YSXL3daoKpO535TYjDyv0aj7mA0FVr4xIUF5f5XVrrrKReaUZBioL9cWIfDdqoeZ87a\nWrdwUw04OS5QVWXR3N3tvJ98xiqaeT46d++o4hCq4nXfkiXW83idR460Bj0X+pRqSjzCBXFdnS00\nKyutfuf80d2tjqcAllUeiVj1nzdPH2yNRu0Z5sCA8xnxuHrmR4XqobO6iQ5ZGJKf3m87V1e7Z4hy\nhhrNvisqrLgBrXfQxeFkxS7vFuuHLirTpztdT/PmOflKfh6tNdGtcSD3DE8O8JplcH6irKpkmUut\nrRYvcGMsr2YACxYsEA8//LD45S9/6fruF7/4hXjwwQfF/Pnzg1PohyhgUIjIswCvaH8i4WYs2RKm\nTdaIKWSXUEODNThUQk925xAtiYTVkeSaIN99ImENQHlFrCrvXR7E5eXO93HrjRiOnkN/SYjJ7gSv\nwlPouDVSXKx2txHNJBBIwNCzhLCfJw/+cNhtzaksaa/Zwrx5zj7T+bLJLUhCT+f+qKmxBP6aNc4D\nTnQzM94mra3ObCOiu7bWXXcvnztguwGon0MhdZBW5pN43OK3eFwtOLu6nH56HpOiGYiqbvSe4mJr\nTNBn3t+kKIn3eV286koxFb886jUOAUvoysK8ocFWurxdYjH9rI8MOK7YePvzuAQZZLyodmslGrjx\noFo8mUpmlE52Brpf98Vvf/tb8fjjj4vFixeLpqYmMWHCBDF+/HjR1NQkFi9eLLZt2zak4LAnUcCg\noJEHiJeWvOMOp6VCPtCGBttvLa/MVDHY8uXOPO/ly533Vle7Mxc4o5SW2t9xxiwudisfslbkqSp/\nXijkDqiFw06Lh6ahfIAmK2TZ0CZ5XvERXaG60NqFkSPtfZGSzVSoNDY6lZacukuF/Mh8sRe1rywY\naXajWhdAhbeVLBRUQmz6dKfbRl4z4vUuv6WmxlJeKuVTVuZMgaQMNprlyfd3dNgCaPhwq684X3V3\nW6WlxWpL3obFxeoFb3xxXiKhbqegFjwvXrMZOcYnjxO58JTqcNiahcv3lJRYCnTMGO9nyWOQ17Go\nyF4jIo953j6Ubt7Y6HxXTY3/o039yM5A9/u56dKlS6Kvr0/09fWJS5cupURYENAMQBZ6NOhVwVae\n3+1VeF4999HyDqHsGp6ux7/nqYUqS4bcPbIloWJAwH4PDS55EMjCQJWv7pXHLRdaG6BiUtV7dKtY\n+eBSXY/FnNNcryL3HW/vsjKnEKAAnYo/5HYl8EBqMuFRXq72l3MhzwPg5I8ni4+7VnTv8BI2sn+a\n+syP8cJLa6s+uE3GiLyQUqaDK8lw2KpbS4vb560roZAQM2fa4yTZGD1wQO02ikaddQ6FvNswEnHz\nB419ub6yKy/VIq/9oTEA2DETXX9QSbajrx/ZGej+ob0uMwCQNFuBL36SGUYnsOStHfhgpgFPwT6e\nIib76eXFQXyAcCHvFSymwlPNyBJPJjBJUfB6eflzueVEg5jnZasGMbmyaDuAYcPUAre6Wq0EeZ/I\n7gO5v8rLnfQHdZ+o+ruoyFozQm1Lh8hTHWQlShYeF1Bc4ESjtpuI3GMquvy4Njo6bLeNPNMhX7DM\nc1VVdp9Rxg//3o+S1fUPFT7mKA6lGofyLEKlLKuqLJ7h46G4OLkA9Jua7afwcVJZaaet8jUkgHPG\n71UopkOfg8yY5dmXrm7JdvT1IzsD3T+012UGPA1UV2pqrI6UF4Zx/zQvulRNsuQXLrQan1bPyi4Y\n+p8W6ciuFppu803N+HRdLpRWxweul++UBgZNvbu7LcalYBcPjstCjNwWsqCk+MjateqBp3PF8JJs\ndsBLXZ23VUoDxe/zVL5kL4s7SKBRNwsYauE550JY/zc3W7NKyshSuTqopOKmU7Wb6ros8JMpFUqr\n9GonamfVrNWPuzKI8Of3FhVZrlHOv2PG2GeJ8N/V1qp9+vK7ly4NNtNOVsrK3O8guTYU2Rno/tRf\nlTmQAkjGgMOH+2OQ6mp33i3tc87f0dTktnjkPHDyqfJ7iorcefyc8eRUuOXL3e/xSlmjtQh+9pxR\nLf/v6nJav4Az68BL4MjCoLPTuQAraPEbF+AuvXA4ubsnlZKqZakayDrBXFQkxM6dTh6g2SufZa5Z\n4140pmqTZO2QTEFEIvp7uHCUA/Z++MPrN2SN85nHUGIFfgsZCCUl6etvr1Ja6o/HQyG98RRkSxWV\n7Ax0f+qvyhxoN1BZIHJrjxa9eDUwMebRo2ofsBfD0DNowFGQU+VqUVn5crCsq8tSIDTQiflLS22f\nKs8UUAU3k+1VLwe5qchB1hEjnDtiBmFw3VRW1/6667pZQFmZ1XZr1liKRlbAcmAv0wM6WfEjJEMh\nWyjwrLZk7hAVn6nep1rrMFSa01n4pnfpsqCD9H1Rkfe2MOksqWQ3yWNlKGdm540CUB0G09/fLxYv\nXiwmTJgglixZIgY05ixVYu1aZ5ZJVZV7Px7O1DqhQxkytFGariNKStRT1WQCT7X+gAKV3A8t59zT\nDpZyLjn5TktLbXrlrJNw2JmeR4XiDrJ1IWcdDCUNb6iFFGGQQUFBNL8rWdNZ5A36vIqXcJVTCrm1\nrbqX3x8ku2vYMOcz+f9eLpuhKgail4/RUMji72SuLT+FPzdIn+RziUTc7TKUBWF5owBUh8F89atf\nFQ899JAQQogHH3xQeRiMEHYlvCLzPAVQtQ0BL9wqkgOoyQaAfK2iQu2rJ8umuFhvhZWUOBenyXu2\neBWec+41AIuKLBoOHNArg3QMQL8lHLZcIFyY6YLoqudHIvbmYjp+4GmRumcRfwStgxyg1vFVkCKv\n4vXKgApee5T8AAAbIElEQVTaD5TEoBOQlZWWUaGzoIdiod9wg/PAIrkfg9RRLrW1tuLK9gwmm4V2\nDEgVeaMAhHBvBDdp0qTBfYVOnTolJk2apCbqd5VQnUpEq0e5S6OrSy9EZesnkbA1bmWl86ALVURe\nNfhpdStnxEjEUi46RSQz7fLldgB65Eh90BmwBnxQpqcAr04QBBWE0ajlkikt9RZ8cpoluZ/oOuU8\nc0EzebK3O6+ry7nFs9w+FOOor7fTBWmmQC5AipFQ9k0yFwKts+DXaOZE/R/EhSMrYT8ZYqkUry0i\nkgndUMg7nTpZnjzts5NsIVhRkT1+afGk1/18Vp7OIGy6SjpjC0Nx/3DZ6fv+ob3OG7ICqK6uHvz/\nypUrjs8OogBRW7tJhMObBLBJtLXtGVysQv5T7nah9Eta8OU1COvqnMKmpUXNgLQfPcUh/ApMHTPI\nLpeaGr0QogGsmvb7VQS0FkF+L+0/xPfF4d8vWeJUkPKA44NbpoUWgXnRpaozbZvg9Rvd90VF7u28\neenuVgdb+fOS9S1to6xbYd3W5q1QwmGnYK2rc2aIZcs/nQrf8qJTwH5LOGzH7nidu7rcfFZZ6U4j\n5VtRZ7ststFH8h5QfpDxA2GGAi8FIIQQsVhMTRTgGvBckMkZLbxwf5rKnx+E8WnZfLaDjbTCVzWA\nhsKII0ZYylPniyW3lG4pfHW18zxiWSCmmsOd7uwMsrZpaT5vS3mmVV9vucv4+RB8QNL/vb1642LJ\nEr1lKveXfF5yJoufOA8pRn5t6lRrlq2qUyqzUa8SiTh3AuWFb1Mi91tdnfesOV2lrc1qC5VbK1Ml\nm1lAYWQRjY2N6OvrAwCcOnUKDQ0N2nsvXXJ+vnDB+tvZCbzzDnDunPs3kQgwfLj9ee5coKnJmyYh\n1Nc7OoDmZuAnPwGuXPF+RlCUl3t/f/GiVeR7r1wBLl9O/b0dHUB1tf1sGaNGWd+/8459jdonEgGO\nHAFGj7Y+nz8P/PrX9n1FRVZ7y2hpSU6Xrg+8UFxs0SQjFAJ+7/eAnh5g926rPrp3CQGcOQNs3gz0\n9QG9vcDPfgaUlFjfU1t3dgJbtljPBYCqKus9hJ//HCgtVdPJ+2v4cGDyZP29AFBW5nz2UFBXB3R1\nOa+FQkA0an8Oh4Fp05z3vPEGsG+f3Q4cly+7x6YfFBWpr1+6BDz/PHD8uJOujg6L355/3qKZ/14I\n4OxZJ2+mwkMq/pHxxhvAyy8Dp08Hf36qePbZ7L0rqwpgxYoV2L59OwBg+/bt6OnpCfT7ESOsQa0T\nyJcuAe+/b38+cgR46SVg2DDv53JGaGqyhMf06cCxY4HIA2AJh/p66/+ODvv/WMwqDQ3AwYPOwaUa\n8O3tFh2vv24rsaoq629lpSUAg6K4GLjzTvV3kYglCAFb2RKiUUs4rlsH/PKX9nUadNEocPgw8NRT\ntnALhYADByxFoUJJid02yVBU5BQOsZglTGVBFA5bNL34oqW4b7vNMhTq6vTPjkSs0tMDfPQRsGmT\nU2g3N9uK5Ac/sNohkQBqaux3TpwIzJiRvB4ff2wJtOeft6/xvo9GLeWcTJjt3GnRsXix8/oNN9h8\nUVYGLFwIvPqqk7+FcBoAAwPA3r3O9r1wwRpHH3xgfe7oABobne8Khax3qXi3qsp+Z1sb0NrqbfTE\nYpahQHQVF1vGxdtv2+2hUzrDhiU3qFSIRoGZM236QyHrc0ODky8vXgTee8+/0gt7SFRZ4egU/de+\n5u9daUHqkw1vyIfBbN26VfT394vPfvazvtJAVVMjOinJbxASsIJtXot06ursqS6dziMf5KIrMh0U\n3KqutvyXDQ32NJxPUXXbCFCpr3f6Aenwb74YTA56y2mD8jm35N5JdpaB6sCOo0fVR3HSoja+pkDe\n31wV3KXV1AMD/rc0aGlRbyVAwdiSEvvELjngmqwv5dPi+O/otDKqH6X0rlrlbO9Ro4JP9elUrWQr\njqurnXUiF8HAgPO+JUvsHWhVKcIyH8q0qNqKzsX1ch3yZ9TXO4PQDQ3eLq9QyHkCWVAf//Ll3hsp\nqvpY5jM54B+L+dt+RHaxlZVZ/akbY/L9y5ap7xvKdhBBRXqwu7MEnQIALAa7/np3gIiY1++KUd3u\ngMXF+oAiHSQCOLf/BazBEfSADq/Cdz9VnTLkNSDpnITeXivoWVJiZ2jolGEo5F6TQIX2v5Gv19Xp\n0zNps7GmJvvsA9rugC9ik4XUsGHuwSevgaC+kGMltPVxsg3KghaVMOD+cS+e8Spz5ljCXOerp6Dn\nokXOvaookC0v4iPBEXRjs7Iyd7CV/ucnYemC4HyzPsAtbHVKntb2JKNPpxTa291nfMRibuXM3ykX\nP/GMsjJ3HIkvLpVP3hsYsJIPkm3Lrjtd0GwFAQwOcl1n0baqKquwqclu/IoKfScT0wRJ52tqcm4m\n19KiPkR+KIUHr+vrnQfA8wO1VXTLB0sEEQZjxugzPXQWEQ/UET20HkF1L6W+6nah5IHWSMReJU2D\npbJSLzBpp1jVuRBUVHS1t/tbWET9q9o6md/nxxAIujYjFrNo1FmYZEnLO3QSnap6L1pk9cOaNc49\nrKgf5cPp/ayH8DNz7upKnioKWMYcrQjn51PQqWC6c0JUs1Ud3fKZCJwndOeCAM7FpaSQ6ewP2u+J\nFIFKgZWVOd89fLhF91DPB76mFAAvVVV2x0aj3hYX7c3NmYyvqgXs1EIaOH4zfegAeoJKwHLhQ0rK\n65mhkJWJ0t3tzDjgg4ny+pMJdHk7WT+LzAC3Feu16IyECm1rQLuYqk684s9Lth0uzzCJRKwBoprl\nqA4ukZWmiv6yMqfgpvROOfNJJ4xoN0l+b3W1812ksEpL/WWhycJBpRj4c3SGBr+H9vmnE850yq2x\n0d0nnHdp/YWfg3rkftEpAjqNy2vdB2ALUf5OOm1LPomM3HKqA2CoPh0dTl4idy/fbYAXOqCFnwvC\n7+NuO93CUDqlTP6utNSSUfLus3l1JGQuQQrA7/YF7e3uTpAtZ75jpsoHH2SzMdU+8/w5pKgiEUu5\nqIQRDWJanMYhCyN+/iofOKp1AnzKLp9+5lW45U3uIt29S5c625v/L++lxOuoEiCRiPU8nUBQbQlB\nllMk4j5QRyd4olFnu5JvW9ePpMiKiqz60S6xdXXO95OLi+5V8RHFmng7hcNWvfm6ieJi9+FCjY36\nFETZ9ULPkHe95fwnjyGVMuW8xK3plhZL8HF+4sYACcVYzNlG1AZ8gR7vW116s2oMq/pKxcM0Fklp\ny0qeBLxqNsUPaOEuVD5L4lu06MaKatw1NanHgXyc7FBkZ6D7h/7K9AOAiMWSC+WiImsQyX5AzkzD\nh7v3hFd1DF2rrLQG3dKl6t1GyXIgrF1rM3llpfWdlzUZiThPzaLpIx+wxOC64GBJiTUY+UZ1xIyc\niZIF7XjuOw1YEtZeMQb5KER+EDY9Rx58QtgHqPC2oJWPukFN/RIK2cG6SMRtOQ0MOAVsLOYUaEuW\nOI++lM945fzT2Wn1i86N1NCQ3IWh2syvu9v5OR5XH59J90yd6j074ftkye8F7M0DSTCWl1ufVb5p\nebtscv9w5SHzY3GxFTvgSoRmquSSGTdOvw9+NKo/2IcbN8OHWzTX1FhKT7Vqn+ghnlTtnMs3hCQj\nJ9nmjrKskNdx1NZaz5ENmI4Od3tR0J/zOj8XO12yM9D96XltegHAYbF7Fb61rnyGquzWqanxXinM\nlYW8W6O8kRcJbdmqmDRJ7ZbQMRgf+DU1zsPEvdwlvG5LlzpPLyPavGISHR3u/WjIdSaE9RyVm00+\nmYp+ozt3ltOkGugU7E4k7H2aeObS0aPey//5Oaw0mKurnTOO4cMtXpLdgvKUm/uRvdwdTU16hUWb\nrVHGFvEwKR0uFKqr3W4zWWEIod7JlvOWqp/lBVv8Hd3dtrLr6HALLzoRTwibfn4+AgXgZf5UWbGq\nMeQnPsN309UpQHlvfxoHNFuWz/6QjQT+Hno3983L7jg6QpX3KecJGh/kVqSzHuRjYRcudJ7lkU5c\nMwpACPduoKrBRg0oT21jMX+ZGfQ7mdF6e52+UxKW8n3U8XSyl3zoiCq24HVIRiqltdUOQvldEak7\nBUm2Gnnb0+xA/l1jo5OR+bSaCxu5/rzU11szo5ISa8DwM1ZVrj9arUrCnw8yEpzr1jnfpXMpELjy\n5MYHt0YpA2RgwE1Xc7Obh2Sho9r2QKZRpo+nAZPgUAkhwLIo5fblZ03Qc7my4/1ZVGSfpyyEm36V\nMi4vt8YKZTVx5c+VS0OD9Vmmj7LE+Cl13KhQ8SltekcuqvJy23jhylveQp0/i58QSHtEUVygvt6m\nRz5ClWIRvE05vTJU9POEiHScBUy4ZhRAaalTmMm7eMquGDkrI5FQ+5XlU7hoI7HGRvdOndw/O2qU\nxRSLFzu3Q1AFBgF9UFnlt9WVyZOtHRZLSvTuINki190jH4hBVgoXzqp0S0qr46mkiYR7EHNrmg+W\nxkbnNJum8HKfqOju7VX7aGX/PR/w3AfO6yK7XlQDjr+rq8sa6CoeIqHC+4R4hgY7F+Dk+qqqcmbc\nkMDlLjyVINGlAXMhxC1PopnWacgCn0MXJyopsc+Y4IKOK8b2du9gbm+vEKtXq40SGh+xmHMmOmyY\nOybGz8vm/SOEW+EK4ewDlUKmADlPRyZBLAfFiVdU/aprUxmqcaZL7x4qrhkFwDs7FHJbHrW1TheD\nbO3T2Z+yIgFsZlJ1BOCeend2Ov2mXMDJTCczOA3uqiqn356EoCwM5bNKCQMD+mMbVa4yslpVATv6\nv6jIdjWRxc2zHmiQyO0jb/nLrS8h3PQQDdOnuxVMdbU95ea0kg9adtmVl1vv5lYm0SwLExp03M+s\n+j0JAP5+CqbLKa6yAdHc7JyJUAYQpWTKPnxuTcvuSlU8SOZDngZMylgWQqtX264GL+Ekz5x4G/PP\ny5c7XXxcgMrtQ39Vh6DzU+W8LHyVMOQxKW78eQlm+YhW1ZGwQjj5W+4fMnoo002OHfmFSvGoaB8q\nrgoFcP/994u2tjYxbdo0sWbNGvHb3/7WSZSkAORADR84vb1uAcUbVPWdPGjkaaFKw3MaVHv6c5eA\nvLBGlcJJi5YSCfWzuSKiU7/kgUqlocFWUJRVozpgnqy1jg59nr7KqlG1jyzI+ZSWn2zGFVx3t1Ng\nyNYpn3FR3bmFq4rfyDTT82nHzaYm77x81bnHdL4z1YXSKVWuG54dpHoPXyfBLeiKCrfVzWcUXAh6\nxSZUwtLLsuTWLu9D6g/VxmfcEJFBtPGFhyQo+fOjUXdSAH8GD8DrhKHK2lfFnFTtoGtbzt+8f+RZ\nqVfsKBn42hdOp98ZRBDkvQI4ceKEGDNmzKDQ/9znPif+5V/+xUkUUwBVVW6LkvsK+aBXRf9p+s1z\nvjnIamtosHO4eU4xdRYPhnHBy6eRs2dbgnXWLPfiLZ7CKTM5z07hFgtlPqh84LIwIr+kTvHJ7gWV\nn1g1JVZZfZxmrhTkAdfUZFv3Kt+z3A88s0ilhGVrUyUs6H5VnEa2UuXZl27WQy4nlQJS7UhJbUuW\nKq+DKhBJhZSmlxDkq3/lhVoyP6meI9cLcKciy8qdlLEf+Hm+Cn6Eoa5eOoWnEuxePMP5k8scOe0z\nqMBWJRQUbAygv79fTJw4Ubz//vvi4sWL4qabbhLPPfeckyjYh8JTwI0EXnm505Kg7B/a2kFuTJXV\nwME7hwd9uW+zqcli4PJyt+VMz1R1ssryp2u88+XslGRbBk+fbv1GDkBzxcWtfVW2wdGj1pT6wAF3\nBhFXLnxdAYeXUuCCtbXV39RZXvCjAgXnmprcK0EJVAeubEnxyX9pkKvaSOWWULnaZPdiPO6cCajO\ncVYFBXX+fxmqXHZV3yQLSHoFLuU0xSDCSfV82ZhKVfDp6qVTDCrB7vd9nL+92txPXXSGS8HGAP7p\nn/5JlJeXi/r6evGFL3zB9b2lADYJ6zCYTWLPnj1aQa7yYeqsAK8BIW8ZIQedenvd7+KCV3WAvfyd\nzjXV1OS+h+fM88U03H9NA4zHJ3TBTjnQpWJalRILYv3J1lNJiXtPfR28+olo17mAOFTKNhm9qnu4\nW4L6mdxBnA+SBYJ1bguehhkkHXCofmM/gnBgwFLcOiUb9PmyoEu34MuEK4XD74xKVxfZTZbOGEBe\nHwijwptvvimmTJkizp49Ky5evCh6enrE9773PSdRgKthdI0lL3PneeHch8t/42V906DmJzbRM8mq\nLCqyhBH33fN0x1jMqUB4EI0gW4GypcwFB3+PyhepchHJK0I5o8o54TJNFFBPZborhO2X5cJfd9ap\nVz8RZMXk5QLyWuyVSj1kdxJXcMQnw4bZe9XEYupdW72sUy+o3HGZFHYEv0I6iAUsu2zTGfzMJLzi\nDKnUxQ/Pp4q8VwD/8R//IW6//fbBzzt27BB/8id/4iQKcDG5jvF54JAv5PJy/ch5wiRgVecN80Ux\niYR7is87n9MoKwcC73wSkJGIM2isCxzqfJlykFzOhpGfx+/nLh5uqagYPsjUXRba8+YlF+46QSPH\neLxcJbKPPZmFmaxOqtiESjHoZiZBBbZMj5/20QUZhwK/gi2IBUzPyZYSSyd09UylLnLWUUHFAI4c\nOSKmTp0qPvnkE3HlyhWxdu1a8Q//8A9OogJUQjfdlq13P78hQc0FJA/0CuEUCF7LuLl/WTcdVq3U\nVCmTZL5MehenRx7A/Hc62jhUDJ9ssKsWUskZW179p6MlyCDTBZN1SFYnr9iE7NulkurMSUWPn/bR\nBRmHAr9tfrVZ86kinfVUxcoKKgbw0EMPDaaBrl27Vly4cMFJVIBKcEaV3So6H7DqN7xjZX8+7xw/\nwUrKLKLgMQd/H9/jXZUi59fiVg3WZP7tVDIukvnp5dW43F2iGzjpsAZ1qY1e/SMrKl2dvJQJnzF1\nd6dneb+X4k72Gy/XWKZwNVrzqSCd9aRnJRsbqeCqUADJkOpBZQMD/vKJ5d+ohKfuOUEtMlmz8/eR\nS0mXIue1pD3TCKpU+K6R1dXZne6rUg/99o+cPut1XzaQSnuRj5rWPQx1T3mD7CATY6OgFYAQ6WtU\n3XNStZ5TQbJsknwCd5vJZxJkGnLqoS5gp7o/G/0oRGZyvjkykVJocPWh4BVAPiCoz1oWDKosgXzx\nteoEmZ+4QqYgt3cyYei3f9JpoaVLQOvaP1/4wyC3MArgKkOyYCu5fjKRMpYKdBkM69YNba+UdCIf\nhWG6aEpnNorBtYegsjMMg5yirMz629kJbNnivtbcDLz4IvD880A0ClRXq59z553AokVAVxdw7lzm\n6a2oAM6cAXbutN6dSFifn3/e+pxLPPEE0NsL7N6tb69sI100qfgFsJ755JP5U1+DqwQZUkRDQp6S\nlREkC7amMx9biKH7onUZDPlodV+LMJa+gReCys7Q736UVwiFQshDstKGO+8Ejh+3rLknnvC22s6d\ns+7fssX7vq4uyxrv7PS2MhctsmYUgGWRPvlkanWQ6fJLp4GBQeYQVHYaBZADpEsIc6RbURgYGFx9\nCCo7TQwgB9D5cVXYu3evr2f69QHno3/cL/y2RSHAtIUN0xapIycK4Ny5c7j11lsxZcoUtLW14aWX\nXsoFGTlDfb1VqqqS35tu5r6ag4VmoNswbWHDtEXqyIkCuOuuu9DV1YX/+7//w9GjRzFlypRckJEz\n/OpX+ZMxY2BgULiIZPuF58+fx/79+7F9+3aLgEgEVX5M4WsIQVxABgYGBplC1oPAR44cwR/90R+h\nra0Nr732Gn7/938fmzdvRhlJRViBDAMDAwOD4MjrLKBXXnkFc+fOxcGDB9HZ2Ym7774blZWVuO++\n+7JJhoGBgUHBI+sxgHg8jng8js7OTgDArbfeildffTXbZBgYGBgUPLKuAJqamtDS0oLjx48DAJ5/\n/nlMnTo122QYGBgYFDxyshDstddew5e+9CVcuHAB48aNw7Zt2wouEGxgYGCQa+QkDXTGjBl4+eWX\n8dprr+FHP/qRQ/jv2rULkydPxoQJE/DQQw/lgry8wejRozF9+nR0dHTguuuuyzU5WcWGDRvQ2NiI\n9vb2wWvvv/8+lixZgokTJ2Lp0qU4l8ld7/IIqra49957EY/H0dHRgY6ODuzatSuHFGYH7777Lm64\n4QZMnToV06ZNwyOPPAKgMPlC1xaB+SJdmxClA5cuXRLjxo0TJ06cEBcuXBAzZswQx44dyzVZOcPo\n0aNFf39/rsnICfbt2ydeffVVMW3atMFrX/3qV8VDDz0khBDiwQcfFBs3bswVeVmFqi3uvfde8c1v\nfjOHVGUfp06dEocPHxZCCPHhhx+KiRMnimPHjhUkX+jaIihf5NVWEIcOHcL48eMxevRoRKNRrF69\nGk8//XSuycopxDW8J5IX5s+fj1gs5rj2zDPPYN26dQCAdevW4T//8z9zQVrWoWoLoPB4o6mpCTNn\nzgQAlJeXY8qUKXjvvfcKki90bQEE44u8UgDvvfceWlpaBj/H4/HBShUiQqEQFi9ejFmzZuG73/1u\nrsnJOU6fPo3GxkYAQGNjI06fPp1jinKLRx99FDNmzMDtt99eEG4PjkQigcOHD2P27NkFzxfUFnPm\nzAEQjC/ySgGYBWBO/OQnP8Hhw4exc+dOfPvb38b+/ftzTVLeIBQKFTS/fPnLX8aJEydw5MgRjBgx\nAn/xF3+Ra5Kyho8++girVq3C5s2bUVFR4fiu0Pjio48+wq233orNmzejvLw8MF/klQIYOXIk3n33\n3cHP7777LuLxeA4pyi1GjBgBAKivr8fKlStx6NChHFOUWzQ2NqKvrw8AcOrUKTQ0NOSYotyhoaFh\nUNh96UtfKhjeuHjxIlatWoUvfvGL6OnpAVC4fEFt8YUvfGGwLYLyRV4pgFmzZuGNN95AIpHAhQsX\n8P3vfx8rVqzINVk5wSeffIIPP/wQAPDxxx9j9+7djiyQQsSKFSsG95Davn37INMXIk6dOjX4/1NP\nPVUQvCGEwO233462tjbcfffdg9cLkS90bRGYLzIQoB4Snn32WTFx4kQxbtw4cf/99+eanJzh7bff\nFjNmzBAzZswQU6dOLbi2WL16tRgxYoSIRqMiHo+LrVu3iv7+fvHZz35WTJgwQSxZskQMFMi5iHJb\nPP744+KLX/yiaG9vF9OnTxfd3d2ir68v12RmHPv37xehUEjMmDFDzJw5U8ycOVPs3LmzIPlC1RbP\nPvtsYL7IyxPBDAwMDAwyj7xyARkYGBgYZA9GARgYGBgUKIwCMDAwMChQGAVgYGBgUKAwCsDAwMCg\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": {} + } + ] } \ No newline at end of file diff --git a/pywafo/src/wafo/doc/tutorial_scripts/WAFO Chapter 2.ipynb b/pywafo/src/wafo/doc/tutorial_scripts/WAFO Chapter 2.ipynb index 1710194..806de65 100644 --- a/pywafo/src/wafo/doc/tutorial_scripts/WAFO Chapter 2.ipynb +++ b/pywafo/src/wafo/doc/tutorial_scripts/WAFO Chapter 2.ipynb @@ -1,381 +1,706 @@ -{ - "metadata": { - "name": "WAFO Chapter 2" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": "CHAPTER2 Modelling random loads and stochastic waves\n====================================================\n\nChapter2 contains the commands used in Chapter 2 of the tutorial and present some tools for analysis of random functions with respect to their correlation, spectral and distributional properties. The presentation is divided into three examples: \n\nExample1 is devoted to estimation of different parameters in the model.\nExample2 deals with spectral densities and\nExample3 presents the use of WAFO to simulate samples of a Gaussian process.\n\nSome of the commands are edited for fast computation. \n\nSection 2.1 Introduction and preliminary analysis\n=================================================\n\nExample 1: Sea data\n-------------------\nObserved crossings compared to the expected for Gaussian signals\n" - }, - { - "cell_type": "code", - "collapsed": false, - "input": "import wafo\nimport wafo.objects as wo\nxx = wafo.data.sea()\nme = xx[:, 1].mean()\nsa = xx[:, 1].std()\nxx[:, 1] -= me\nts = wo.mat2timeseries(xx)\ntp = ts.turning_points()\n\ncc = tp.cycle_pairs()\nlc = cc.level_crossings()\nlc.plot()\nshow()", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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iooD1P2Xi6O8CHcoex9tZG1Bj2ki4ve4Pf3+lIyRrwkRBlE1yMrB/P3DlCrBl1VPcS9Jg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\ngg==\n" - } - ], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "Average number of upcrossings per time unit\n----------------------------------------------\nNext we compute the mean frequency as the average number of upcrossings per time unit of the mean level (= 0); this may require interpolation in the crossing intensity curve, as follows. \n" - }, - { - "cell_type": "code", - "collapsed": false, - "input": "T = xx[:, 0].max() - xx[:, 0].min()\nf0 = np.interp(0, lc.args, lc.data, 0) / T #! zero up-crossing frequency \nprint('f0 = %g' % f0)", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": "f0 = 0.224071\n" - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "Turningpoints and irregularity factor\n----------------------------------------" - }, - { - "cell_type": "code", - "collapsed": false, - "input": "fm = len(tp.data) / (2 * T) # frequency of maxima\nalfa = f0 / fm # approx Tm24/Tm02\n\nprint('fm = %g, alpha = %g, ' % (fm, alfa))", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": "fm = 0.456159, alpha = 0.491212, \n" - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "Visually examine data\n------------------------\nWe finish this section with some remarks about the quality of the measured data. Especially sea surface measurements can be of poor quality. We shall now check the quality of the dataset {\\tt xx}. It is always good practice to visually examine the data before the analysis to get an impression of the quality, \nnon-linearities and narrow-bandedness of the data.First we shall plot the data and zoom in on a specific region. A part of sea data is visualized with the following commands" - }, - { - "cell_type": "code", - "collapsed": false, - "input": "clf()\nts.plot_wave('k-', tp, '*', nfig=1, nsub=1)\n\naxis([0, 2, -2, 2])\nshow()", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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I5XKoVCrs3LkTGRkZANrDGltbW6FUKiGRSLB3717s37+fuUZYWBhqa2stnq0j76I93Hbb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\nHCkpKfDw8EBlZSXvuIMHD0Kj0WDatGlYv369zWMeMtzalFpTsgZrStb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ncOswAAAAAElF\nTkSuQmCC\n" - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "Finding possible spurious points\n------------------------------------\nHowever, if the amount of data is too large for visual examinations one could use the following criteria to find possible spurious points. One must be careful using the criteria for extremevalue analysis, because\nit might remove extreme waves that are OK and not spurious." - }, - { - "cell_type": "code", - "collapsed": false, - "input": "import wafo.misc as wm\ndt = ts.sampling_period()\n# dt = np.diff(xx[:2,0])\ndcrit = 5 * dt\nddcrit = 9.81 / 2 * dt * dt\nzcrit = 0\ninds, indg = wm.findoutliers(ts.data, zcrit, dcrit, ddcrit, verbose=True)", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": "Found 0 spurious positive jumps of Dx\nFound 0 spurious negative jumps of Dx\nFound 37 spurious positive jumps of D^2x\nFound 200 spurious negative jumps of D^2x\nFound 244 consecutive equal values\nFound the total of 1152 spurious points\n" - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "Section 2.2 Frequency Modeling of Load Histories\n---------------------------------------------------\nPeriodogram: Raw spectrum" - }, - { - "cell_type": "code", - "collapsed": false, - "input": "clf()\nLmax = 9500\nS = ts.tospecdata(L=Lmax)\nS.plot()\naxis([0, 5, 0, 0.7])\nshow()", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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dO9q2YJCcRurdu7lPJaOuCYJwX2S5uMzMTFz763XZ29sbaWlpmDlzJubOnYub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\nsvi81PM/e9bQM0oYncTHA99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2bVuN/V0UFxfDz88PlZWViI2NxbJlyxAbG6u2WaqxfPly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\nRU5ErkJggg==\n" - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "Calculate moments \n-------------------" - }, - { - "cell_type": "code", - "collapsed": false, - "input": "mom, text = S.moment(nr=4)\nprint('sigma = %g, m0 = %g' % (sa, sqrt(mom[0])))", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": "sigma = 0.472955, m0 = 0.472955\n" - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "Section 2.2.1 Random functions in Spectral Domain - Gaussian processes\n--------------------------------------------------------------------------\nSmoothing of spectral estimate \n----------------------------------\nBy decreasing Lmax the spectrum estimate becomes smoother." - }, - { - "cell_type": "code", - "collapsed": false, - "input": "clf()\nLmax0 = 200; Lmax1 = 50\nS1 = ts.tospecdata(L=Lmax0)\nS2 = ts.tospecdata(L=Lmax1)\nS1.plot('-.')\nS2.plot()\nshow()", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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TyKRJk1AUhaFDh1JcXMyxY8eaI64WlZ6f3uylkEoBHQM4lHmoRZ4lhBDNrd42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\nWcjL0zqKptPbuzefT/6c+76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- } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": " Estimated autocovariance\n----------------------------\nObviously knowing the spectrum one can compute the covariance\nfunction. The following code will compute the covariance for the \nunimodal spectral density S1 and compare it with estimated \ncovariance of the signal xx." - }, - { - "cell_type": "code", - "collapsed": false, - "input": "clf()\nLmax = 85\nR1 = S1.tocovdata(nr=1) \nRest = ts.tocovdata(lag=Lmax)\nR1.plot('.')\nRest.plot()\naxis([0, 25, -0.1, 0.25])\nshow()", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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So74nQghRRQpPILGxsbCzs4O1tTU0NTXh7++P8PBwiWV69eoFfX19AEDPnj3x9OnTeq9b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\nUAIhhBAiE0oghBBCZEIJhBBCiEz+H72XH4/yYkS0AAAAAElFTkSuQmCC\n" - } - ], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "We can see in Figure below that the covariance function corresponding to the spectral density S2 significantly differs from the one estimated directly from data. It can be seen in Figure above that the covariance corresponding to S1 agrees much better with the estimated covariance function." - }, - { - "cell_type": "code", - "collapsed": false, - "input": "clf()\nR2 = S2.tocovdata(nr=1)\nR2.plot('.')\nRest.plot()\nshow()", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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uLi5wdnaGvr4+QkJCEB0dXa1O//79YWJiAgDo168fMjIyGty2KtsOtriVf6vpDoaISIvp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\nfrbG5h13MWwYkJe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sAmuH/1dlfbtauOKX8b/gpciXkJKTgneffrfOGzbv/30f03ZPg6G+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\nixEVFYXg4JbzqNmCggLk5+cDAB49eoT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- } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "Section 2.2.2 Transformed Gaussian models\n-------------------------------------------\nWe begin with computing skewness and kurtosis for the data set xx and compare it with the second order wave approximation proposed by Winterstein:" - }, - { - "cell_type": "code", - "collapsed": false, - "input": "import wafo.stats as ws\nrho3 = ws.skew(xx[:, 1])\nrho4 = ws.kurtosis(xx[:, 1])\n\nsk, ku = S1.stats_nl(moments='sk')", - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 13 - }, - { - "cell_type": "raw", - "metadata": {}, - "source": "Comparisons of 3 transformations" - }, - { - "cell_type": "code", - "collapsed": false, - "input": "clf()\nimport wafo.transform.models as wtm\ngh = wtm.TrHermite(mean=me, sigma=sa, skew=sk, kurt=ku).trdata()\ng = wtm.TrLinear(mean=me, sigma=sa).trdata() # Linear transformation \nglc, gemp = lc.trdata(mean=me, sigma=sa)\n\nglc.plot('b-') #! Transf. estimated from level-crossings\ngh.plot('b-.') #! Hermite Transf. estimated from moments\ng.plot('r')\ngrid('on')\nshow()", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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- } - ], - "prompt_number": 14 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "Test Gaussianity of a stochastic process\n------------------------------------------\nTESTGAUSSIAN simulates e(g(u)-u) = int (g(u)-u)^2 du for Gaussian processes given the spectral density, S. The result is plotted if test0 is given. This is useful for testing if the process X(t) is Gaussian.\nIf 95% of TEST1 is less than TEST0 then X(t) is not Gaussian at a 5% level.\n\nAs we see from the figure below: none of the simulated values of test1 is above 1.00. Thus the data significantly departs from a Gaussian distribution. " - }, - { - "cell_type": "code", - "collapsed": false, - "input": "clf()\ntest0 = glc.dist2gauss()\n# the following test takes time\nN = len(xx)\ntest1 = S1.testgaussian(ns=N, cases=50, test0=test0)\nis_gaussian = sum(test1 > test0) > 5 \nprint(is_gaussian)\nshow()", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": "False\n" - }, - { - "output_type": "display_data", - "png": 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fP6SkpLi8XIVW2gIA9u3bh/79++PEiRPIzMxEXFycw/OdaQvFniF0Zh2D1oSG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\nY0it2QAAAABJRU5ErkJggg==\n" - } - ], - "prompt_number": 15 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "Normalplot of data xx\n------------------------\nindicates that the underlying distribution has a \"heavy\" upper tail and a \"light\" lower tail." - }, - { - "cell_type": "code", - "collapsed": false, - "input": "clf()\nimport pylab\nws.probplot(ts.data.ravel(), dist='norm', plot=pylab)\nshow()", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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+fJmbN29qH7dp0+beqqsBEg6isdBpLTjlwMgT0CQftrrCRavb7pZQEHdWI+c5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\nc/OmOdbWRcycOVS6k4SoBju7u1v/8/P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- } - ], - "prompt_number": 16 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "Section 2.2.3 Spectral densities of sea data\n-----------------------------------------------\nExample 2: Different forms of spectra" - }, - { - "cell_type": "code", - "collapsed": false, - "input": "import wafo.spectrum.models as wsm\nclf()\nHm0 = 7; Tp = 11;\nspec = wsm.Jonswap(Hm0=Hm0, Tp=Tp).tospecdata()\nspec.plot()\nshow()", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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RtGPHDri5ueG5555DixYt0KFDB7Rv3x4tWrTA7Nmz4enpiV27dskda4NmrE7r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\n" - } - ], - "prompt_number": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "Directional spectrum and Encountered directional spectrum\n=========================================================\nDirectional spectrum\n---------------------" - }, - { - "cell_type": "code", - "collapsed": false, - "input": "clf()\nD = wsm.Spreading('cos2s')\nSd = D.tospecdata2d(spec)\nSd.plot()\nshow()", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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JrbQYPJgNr7zCrVOnsvKJJ4iaOBF++R+ERpHDAdxozkFyaIKrdZ0/HYQOTWyr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\nRU5ErkJggg==\n" - } - ], - "prompt_number": 18 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "Encountered directional spectrum\n--------------------------------- " - }, - { - "cell_type": "code", - "collapsed": false, - "input": "#clf()\n#Se = spec2spec(Sd,'encdir',0,10);\n#plotspec(Se), hold on\n#plotspec(Sd,1,'--'), hold off\n##!wafostamp('','(ER)')\n#disp('Block = 17'),pause(pstate)\n#\n##!#! Frequency spectra\n#clf\n#Sd1 =spec2spec(Sd,'freq');\n#Sd2 = spec2spec(Se,'enc');\n#plotspec(spec), hold on\n#plotspec(Sd1,1,'.'),\n#plotspec(Sd2),\n##!wafostamp('','(ER)')\n#hold off\n#disp('Block = 18'),pause(pstate)\n#\n##!#! Wave number spectrum\n#clf\n#Sk = spec2spec(spec,'k1d')\n#Skd = spec2spec(Sd,'k1d')\n#plotspec(Sk), hold on\n#plotspec(Skd,1,'--'), hold off\n##!wafostamp('','(ER)')\n#disp('Block = 19'),pause(pstate)\n#\n##!#! Effect of waterdepth on spectrum\n#clf\n#plotspec(spec,1,'--'), hold on\n#S20 = spec;\n#S20.S = S20.S.*phi1(S20.w,20);\n#S20.h = 20;\n#plotspec(S20), hold off\n##!wafostamp('','(ER)')\n#disp('Block = 20'),pause(pstate)\n#\n##!#! Section 2.3 Simulation of transformed Gaussian process\n##!#! Example 3: Simulation of random sea \n##! The reconstruct function replaces the spurious points of seasurface by\n##! simulated data on the basis of the remaining data and a transformed Gaussian\n##! process. As noted previously one must be careful using the criteria \n##! for finding spurious points when reconstructing a dataset, because\n##! these criteria might remove the highest and steepest waves as we can see\n##! in this plot where the spurious points is indicated with a '+' sign:\n##!\n#clf\n#[y, grec] = reconstruct(xx,inds);\n#waveplot(y,'-',xx(inds,:),'+',1,1)\n#axis([0 inf -inf inf])\n##!wafostamp('','(ER)')\n#disp('Block = 21'),pause(pstate)\n#\n##! Compare transformation (grec) from reconstructed (y) \n##! with original (glc) from (xx)\n#clf\n#trplot(g), hold on\n#plot(gemp(:,1),gemp(:,2))\n#plot(glc(:,1),glc(:,2),'-.')\n#plot(grec(:,1),grec(:,2)), hold off \n#disp('Block = 22'),pause(pstate)\n#\n##!#!\n#clf\n#L = 200;\n#x = dat2gaus(y,grec);\n#Sx = dat2spec(x,L);\n#disp('Block = 23'),pause(pstate)\n# \n##!#!\n#clf\n#dt = spec2dt(Sx)\n#Ny = fix(2*60/dt) #! = 2 minutes\n#Sx.tr = grec;\n#ysim = spec2sdat(Sx,Ny);\n#waveplot(ysim,'-')\n##!wafostamp('','(CR)')\n#disp('Block = 24'),pause(pstate)\n\n", - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "Estimated spectrum compared to Torsethaugen spectrum\n-------------------------------------------------------" - }, - { - "cell_type": "code", - "collapsed": false, - "input": "clf()\nfp = 1.1;dw = 0.01\nH0 = S1.characteristic('Hm0')[0]\nSt = wsm.Torsethaugen(Hm0=H0,Tp=2*pi/fp).tospecdata(np.arange(0,5+dw/2,dw)) \nS1.plot()\nSt.plot('-.')\naxis([0, 6, 0, 0.4])\nshow()\n", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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WKHPmzBlGjhzJF198QVBQkEV1bVWhUsjCHQtLdFz9+ivccQc0bQpeXlCFPC6E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\ncQghhLCIJA4hhBAW+X/+nuw44UNfwAAAAABJRU5ErkJggg==\n" - } - ], - "prompt_number": 19 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": "Transformed Gaussian model compared to Gaussian model\n-------------------------------------------------------\n" - }, - { - "cell_type": "code", - "collapsed": false, - "input": "dt = St.sampling_period()\nva, sk, ku = St.stats_nl(moments='vsk' )\n#sa = sqrt(va)\ngh = wtm.TrHermite(mean=me, sigma=sa, skew=sk, kurt=ku, ysigma=sa)\n \nysim_t = St.sim(ns=240, dt=0.5)\nxsim_t = ysim_t.copy()\nxsim_t[:,1] = gh.gauss2dat(ysim_t[:,1])\n\nts_y = wo.mat2timeseries(ysim_t)\nts_x = wo.mat2timeseries(xsim_t)\nts_y.plot_wave(sym1='r.', ts=ts_x, sym2='b', sigma=sa, nsub=5, nfig=1)\nshow()", - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "display_data", - "png": 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7h8Eh9TSCkn+G+/m1MDQUenAX4wzx9bMP8FvyGzCZNU6u+HvaXMU+BGKK/lIs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\nH1OnTpXHbnneHhqqwLpDLvZgBLbbhuHla0swMgRYtgyoh0crzFdfAYs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The presentation is divided into three examples: \n", + "\n", + "Example1 is devoted to estimation of different parameters in the model.\n", + "Example2 deals with spectral densities and\n", + "Example3 presents the use of WAFO to simulate samples of a Gaussian process.\n", + "\n", + "Some of the commands are edited for fast computation. \n", + "\n", + "Section 2.1 Introduction and preliminary analysis\n", + "=================================================\n", + "\n", + "Example 1: Sea data\n", + "-------------------\n", + "Observed crossings compared to the expected for Gaussian signals\n" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import wafo\n", + "import wafo.objects as wo\n", + "xx = wafo.data.sea()\n", + "me = xx[:, 1].mean()\n", + "sa = xx[:, 1].std()\n", + "xx[:, 1] -= me\n", + "ts = wo.mat2timeseries(xx)\n", + "tp = ts.turning_points()\n", + "\n", + "cc = tp.cycle_pairs()\n", + "lc = cc.level_crossings()\n", + "lc.plot()\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "png": 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FtVOrZelwrTbfQ5KSZEmDOXNYzoDshJ+fTBTnzhV6aJ06wK+/AoMHA0eOmCE2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\ngg==\n" + } + ], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Average number of upcrossings per time unit\n", + "----------------------------------------------\n", + "Next we compute the mean frequency as the average number of upcrossings per time unit of the mean level (= 0); this may require interpolation in the crossing intensity curve, as follows. \n" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "T = xx[:, 0].max() - xx[:, 0].min()\n", + "f0 = np.interp(0, lc.args, lc.data, 0) / T #! zero up-crossing frequency \n", + "print('f0 = %g' % f0)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "f0 = 0.224071\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Turningpoints and irregularity factor\n", + "----------------------------------------" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "fm = len(tp.data) / (2 * T) # frequency of maxima\n", + "alfa = f0 / fm # approx Tm24/Tm02\n", + "\n", + "print('fm = %g, alpha = %g, ' % (fm, alfa))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "fm = 0.456159, alpha = 0.491212, \n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visually examine data\n", + "------------------------\n", + "We finish this section with some remarks about the quality of the measured data. Especially sea surface measurements can be of poor quality. We shall now check the quality of the dataset {\\tt xx}. It is always good practice to visually examine the data before the analysis to get an impression of the quality, \n", + "non-linearities and narrow-bandedness of the data.First we shall plot the data and zoom in on a specific region. A part of sea data is visualized with the following commands" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "clf()\n", + "ts.plot_wave('k-', tp, '*', nfig=1, nsub=1)\n", + "\n", + "axis([0, 2, -2, 2])\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "png": 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I5XKoVCrs3LkTGRkZANrDGltbW6FUKiGRSLB3717s37+fuUZYWBhqa2stnq0j76I93Hbb\nbZDL5fjHP/4BnU4Ho9GIc+fOWYRQz549G59//jl27tyJ2bNnW7Rj7dq1OH/+PABArVbj66+/FlTv\nxIkTcfDgQdZzMpkMv/76Kz7++GPW835+fhgyZAjWrVtnYdmPGjUK69atY+6tOVpaWqDX65k/ejA/\nePCgTShod8NNT/xyuRzHjx/H0KFD4e/vj+HDh6N///54++23AQBjx47FjBkz0L9/f9x6662YPHmy\nTadk+2x+7J///Ce2bt2KgIAALFq0CDNnzmTOy+Vy/Oc//8Hu3bsRERGB5ORk5OTkAABWrFiBKVOm\n4O6770ZAQACGDx/ODArWuOWWW/DRRx9h2bJlCA4ORlJSEjZv3sxKIPau6+3tjWnTpuHAgQMWnW78\n+PEYP348kpOTER8fDz8/P8TGxjLnH3jgAQBASEgIhgwZYlPvI488gnnz5mHMmDFISEiAVCrFe++9\nx3kfuY6x3WO2dQrvvfceZDIZEhISMHr0aMyZMwcLFizgLO/oOgd79Zs/4/3792P79u2IiopCREQE\nVq1ahdbWVgDAv//9b7z88ssICAjAq6++ajNLc+S+AEBmZiaUSiWioqKYzwCYeHW5XI5//etfePDB\nBxEcHIxt27bh3nvvZb6fmpqKWbNmISEhAcHBwaioqLD7zgi5d3QZDw8P/Pjjjzh16hQSEhIQGhqK\nRYsWWQw0U6ZMQV5eHiIiItCvXz/m+NSpU/GXv/wFM2fORGBgIPr164eff/5ZUDsmTZqEixcvory8\nnLX84MGDmdh9tmtlZGSgurqakZ0AYPTo0aipqWGVefr06QOpVMr8ffbZZwDa5dPFixcz5ZYuXYql\nS5dyttsdoIg900qECBEibhB89NFHOH/+PNavX++W+nfv3o0vv/wS27dvd0v9QuES8RcXF2P+/Pmo\nqqoCRVFYtGgRli9fblNu+fLl2Lt3LzMqDho0yKVGixAhQoQI5+Hpype9vLywfv16DBw4EFqtFrfc\ncgvGjh1rsap1z549yMvLw5UrV3D8+HEsXboUx44dc7nhIkSIECHCObik8YeHh2PgwIEAAH9/f6Sl\npdmEF/7www946KGHAABDhw5FQ0MDa7iiCBEiRIjoGnSYc7egoAAnT57E0KFDLY6XlpZaxP9GR0ej\npKSko6oVIUKECBEOwiWph4ZWq8X999+Pd999lzV21dqN4GgUgwgRIkSI4IajrlqXLf62tjZMnz4d\nc+fOxdSpU23OR0VFobi4mPlcUlLChKFZg7SnkBD/XPxbvXq129twM/2J91O8n935zxm4RPyEECxc\nuBDp6emcKRCmTJmCzZs3AwCOHTuGoKAgp1ZyihAhQoSIjoFLUs+RI0ewZcsW9O/fnwnRXLt2LbP6\nb/HixZg4cSL27NmDxMREyGQybNq0yfVWixAhQoQIp+ES8Y8aNUpQzpENGza4Uo0IB0Gv5BTRMRDv\nZ8dCvJ/uR7dZuUtRlNN6lQgRIkT0VDjDnTd9rh4RIkSIEGEJkfhFiBAhoodBJH4RIkSI6GEQiV+E\nCBEiehhE4hchQoSIHgaR+EWIECGih0EkfhEiRIjoYRCJX4QIESJ6GETiFyFChIgeBpH4RYgQIaKH\nQSR+ESJEiOhhEIlfhAgRInoYROIXIUKEiB4GkfhFiBAhoodBJH4RLoEQglWr/iGm1BYh4gaCSPwi\nXMLOnT/j/ffL8e23+93dFBEiRAiESPwinEJ29hb06TMJTz+9H42N67Bq1W/o02cSsrO3uLtpIkSI\nsAOXtl4U0XORlTUHCkUIHnxwCwAKer0Ja9cuw/Tp49zdNBEiRNiByxb/I488grCwMPTr14/1fE5O\nDgIDAzFo0CAMGjQIr732mqtViugGoCgKFEUB8AMwDvX1TWbHRIgQ0Z3hssW/YMECPPHEE5g/fz5n\nmYyMDPzwww+uViWim+Hcucvw8fkOQ4akYezYOFy5UuzuJokQIUIAXLb4R48eDYVCwVtGjPi4OTFt\nWiYSEyOQnp4OlUqKlSsfdXeTRIgQIQCd7tylKApHjx7FgAEDMHHiRJw/f76zqxTRRSgoKEB8fDxS\nUlJw8eJFdzdHhAgRAtHpzt3BgwejuLgYUqkUe/fuxdSpU3H58mXWsmvWrGH+z8zMRGZmZmc3T4QL\nKCoqQmxsLBITE/Hrr7+6uzkiRPQI5OTkICcnx6VrUKQDdJiCggJMnjwZZ8+etVu2V69e+OOPPxAc\nHGzZEIoSJaEbDG+++Sbq6+sxY8YMLFiwAKdPn3Z3k0SI6HFwhjs7XeqprKxkGnXixAkQQmxIX8SN\niZaWFvj4+CAmJgbFxaJjV4SIGwUuSz2zZs3CwYMHUVNTg5iYGLzyyitoa2sDACxevBjffPMNPvjg\nA3h6ekIqlWL79u0uN1pE94Ber4dcLodSqYROp0NTUxNkMpm7myVChAg76BCppyMgSj03Hp5++mlE\nRUXhmWeeQVJSEnbv3o3U1FR3N0uEiB6Fbin1iLh5QUs9AES5R4SIGwgi8YtwGnq9niH+tLQ0nDx5\n0s0tEiFChBCIxC/CabS0tMDX1xcAMGXKFOzatcvNLRIhQoQQiMQvwmmYSz233347Tp8+jcbGRje3\nSoQIEfYgEr8Ip2FO/N7e3oiMjERJSYmbWyVChAh7EIlfhNPQ6/WM1AMAUVFRKC0tdWOLRIgQIQQi\n8YtwGuYWP9BO/GVlZW5skQgRIoRAJH4RTsOa+CMjI0WLX4SIGwAi8YtwGmwWv0j8IkR0f4jEL8Jp\nsGn8otQjQkT3h0j8IpyGtcWvVCpRW1vrxhaJECFCCETiF+E0rInf29sbra2tbmyRCBEihEAkfhFO\nwzxlAwD4+PiIxC9CxA0AkfhFOA3zlA2AaPHfKCCEYNWqf4jZcHswROK/ydCVnVqUem5M7Nz5M95/\nvxzffrvf3U0R4SaIxH+Toas6tdFohNFohJeXF3NMJP7ujezsLejTZxLmzt2CxsZ1WLXqN/TpMwnZ\n2Vvc3TQRXQyR+G8S0J16+fI9XdKpaWufoijmmEj83RtZWXOwZMk0tLQQABT0ehNeeWUZsrLmuLtp\nIroYIvHfJMjKmoM1ax5HfX0TuqJTW8s8QNcSv6hTOw6KonD+/HkAvujd+3E0NOhAUZTF4C2iZ8Dl\nPXdFdA/QHbi11QMhIXPQ0BDaqZ2ai/hbWlo6pT5r0JLWkCH7MX36uC6p82bAiRNnARzGF1/8grIy\nDa5cEXdN64lw2eJ/5JFHEBYWhn79+nGWWb58OZKSkjBgwABxl6ZORF5eMeTyPRg71oRNmyZ0aqd2\nl8VPS1pZWTtFnVoArGdGwcEmAM3QarWYPn0cVq581L0NFOEWuEz8CxYswL59+zjP79mzB3l5ebhy\n5Qqys7OxdOlSV6sUwYHnnnsEjY3lKC0t7fRObZ2uAbhO/J0pv9CSVmNjC0Sd2j6snf0VFRVITEyE\nRqNxc8tEuBMuE//o0aOhUCg4z//www946KGHAABDhw5FQ0MDKisrXa1WBAuqqqpgMpm6JFEam8Xv\n4eEBiqJgNBo7rV6KomAwGGA0eiEy8hFRp+YAPTNateo3i5lRfn4lkpOTReLv4eh0jb+0tBQxMTHM\n5+joaJSUlCAsLMym7Jo1a5j/MzMzkZmZ2dnNu6lQUVGBpKQkFBUVgRDSqWTIRvzA9dW7np6d92r9\n739nAWzHX/7yBqKiUkSdmgVZWXOgUIRgxYoDACg0Nuqxfv1izJv3MxISEkTiv4GRk5ODnJwcl67R\nJc5d66k/FyGZE78Ix1FeXo6EhATU1taipqYGoaGhnVYXF/HTco9UKu20uu+8cwDWr2+GRqPB8uWi\nY5cN9CxIo2kFMA51db2gVmugUqkQFBQkEn83ASEEL7zwFtaufU6woWZtFL/yyisO19vp4ZxRUVEo\nLr5ukZWUlCAqKqqzq+2RqKurQ3BwMDOr6kywafxA1zh48/PzAQANDQ2dWs+Njry8YqxenQpgP267\nrRBnz15GREQEAgICROLvJnDXKupOJ/4pU6Zg8+bNAIBjx44hKCiIVeYBAJhb/GvWiJ8d/Jy2Ywdk\nMhmio6MhfeutTq0vdtMmPFJUZHOeIf5O/L21tbX4p78/xvy//9dpv+9m+LxSX4oRIwYAABZX5OGJ\n2ssIDw9HQEAAxvz6q9vb15M//zF5Gt5XJeOFFw6hsXEdyha9jvdVydej0xy9voOgiIshGLNmzcLB\ngwdRU1ODsLAwvPLKK2hrawMALF68GACwbNky7Nu3DzKZDJs2bcLgwYNtG0JR4mIcF/Huu+/i6tWr\naGlpwaBBg7BkyZJOq+vbb7/FF198ge+++87ieEJCAn755RckJCR0Wt1PPvkkjh07hpiYGHz99ded\nVs/NgAMHDmDy5Mno168fFi9ejEOHDmHs2LHYvXs3tm3b5u7m9VgQQvDNN/uwfPkvqKh4GzExq7Bu\nXQamTx/nsG/OGe70dKg0C4S8PBs2bHC1mm4PZ7S6jkZzczOkUimUSuVNLfWo1WrEx8eLm74IgF6v\nR2RkJOrq6lBbW4uQkBBR6ukGMPfBUNQENDSkOBWd5qyxfEOkbLgRlud3h4yHOp0OUqm0SzR+Pudu\nZ6/eVavViIuLEzV+AWhpaUFERATq6+sZ4pfL5SLxdwPk5RXjiSdUIGQfNmwY41R02s6d3Guo+HBD\nEH93IFUuZGdvQWrqBDz11M9uX0lKW/xRUVFuJf6usPhjY2OhVqs7tZ6bAXq9HhEREWhoaEBNTQ1C\nQkIglUqh1+vd3bQej5Urs9C3by8AwKBBSQ4tuMzO3oLk5HGYN2+rU3V3a+K/vgjloNtJlQtZWXPQ\np08oSkqq4O6VpM3NzfDz84NSqURdXV2n1uVO4m9oaBAtfoHQ6/Xw9/eHTCZDfn4+goOD4ePjIxJ/\nNwE98zKPfBSCrKw5SE4OhKenc2HTLmv8nYmsrDkIClJgxoytoEl17dpl3SopV3titFb4+ASjrW0i\n6uuT3LaSlJZ6goKCOp0U3a3xx8XFQa1Wd/pCtRsd9PaYwcHByMvLQ0hICHx9fUXi7yZobGwE4Djx\nUxSF+no1DAaVU/V2a4ufoijodDoAvggPX9Btl+fX1GgxZ44nxo41YenSELetJKWlHoVCgfr6+k6t\ny91ST1hYGCiKEgnMDugBOigoCMXFjQgODoavr2+XZVEVwQ+NRgMvLy+UlZU5/N3KygY891ysU/V2\na+IHgLNnrwDYjltvre30jJPOom/fCAwd2hdjx47Fhx/+E2PHDnJLO2ipJzAwEBqNBiaTqdPqspey\nobNACIFarUZgYGCXzGxudND7IhMiBTANx4/n3vQW/40QDEJDo9EgLCwMTU1NDn9XJtNj2rS7naq3\n2xP/9OmZAJpRWVnZbdPI0iT41FNPYezYsbh48aJb2kFLPR4eHvD392emkZ0Bd1n8Op0OEokEPj4+\nCAwMFB28dnDkyEl88sn/Q0XFIAAf4623ziEj42FoNAZ3N63T0J2DQazR2NiIsLCw/1M2HENVVRVU\nqptQ6gGAmpoaJCYmdrp04QraNX4fSCQSxMTEuC37KC31AEBQUFCn3jN3afy0tQ9AtPgFID09HmPH\nDoSXlz8ACi0tBC+/vBRGY+cZBe4CHQzy9NP7u20wiDVoi7+5udmh7xFCUFtbC6VS6VS9IvF3AFpa\nWuDt7Q0ACAsL6zbE35mk6C6LX6PRWBB/d7D4u7O00P5uekGtbkF6+tNoaNDBy8sLBoOhU6VAd4De\nq0Gt1sHdEXZCQVv8jhJ/Q0MDZDIZwzuO4oYg/qSkJDQ0NHTLjgVYkqA7iV+n08HPzw8AOt3B6y7i\nb2xshFwuBwAEBga63eInhOC++5Zg3boC7Nz5s1vbwga9Xo/6ej02bRqPc+fexqZNE5CXV3JTOnjp\nwI/mZgKJZGK3DQYxh0ajQXh4uMNST1VVlUvZd28I4o+MjIS3t7dTDpCugDXxV1VVuaUdXWnxu0vq\n0Wq18Pf3B+B+qSc7ewtiY2/Hrl1taG19H8uX7+t20kJLSwumTh3F5ICh/WQ3q4M3L68YycknYTLt\nxZtvDu6WwSDmcFbqqampcVrmAW4Q4lcqlV0SougsaI0f6D5Sj0KhcJvU05mWpDXxu0vqyc7egpdf\nfg+lpbX8HGzeAAAgAElEQVQAfABQKC9vRllZRbeamXIN0Dcr8a9cmQW9vgZxcXHo1Su0WwaDmMNZ\n565er3dpz4tuT/zl5eVQqVTdmvi7i8ZPR/UAne/c7elST1bWHKxatQiEBAKgADwNQIKFC6dj0aK5\nbmkTG7iI/2ZdvUtvPdq3b99u4f+xB2ct/tbWVqf1faCbr9wFgKtXryIxMbHTicwVmJNgaGgoqqur\nu7wNJpPJopPfrM5da4vfmYUvHQGKovDHH7+j3XaqAOAFLy8jKioCupWmTK/ctcbNavFXV1fD398f\nkZGR3Z74TSYTmpqaoFKpHLb4XSX+bm3xG41GFBYWolevXt3e4qc7V/tiGdLljjOdTgdfX1+GdDr7\nfnUXjb+zcxLx4cSJMxg1KgJff50FitqD6dPj0LdvstvawwY+qedmc+4C7bvQKZXKG2KNR1NTE6RS\nKfz9/bvc4u/WxF9cXIzQ0FD4+flBoVC4tZPzwVzjB4CAgIBOXTzFBvMwR8A5i9+RsER3rdzVarWM\n1BMeHo6KiopOq4sPBoMBFRW52LnzPdx//wQoFN54771V3U5TplfuWuNmtfhpP1d3In6ufqXRaCCX\ny+Hn5yda/DTKy8vxn//8B7179wYA3HnnnVi9enW3eZjmMNf4Abgl33lDQ4MF8Tvj3HVkxaM7NX7a\n4o+MjHSb1HPq1ClER0czKyeFDrRdHfPf05y75sTv7lBfGlz9qrGxEQEBAZBKpaLFT+OBBx7A22+/\njUmTJgEA5s+fj9jYWJw+fdrNLbOFNQnK5fIut/jNV7QCjjl3nUl/7U6ph7b4IyMjUV5e3ml18SEn\nJweZmZnMZ6EWZlenE+hpzt2mpibIZLJusbiP7lf0vrrW/Uqj0SAgIICx+B0xBm5a4q+ursauXbvw\n7LPPMsdSU1PdlgeHD91B6rEmfkcs/qysOVi9einy8oogdMWjXq9nFouZoys1/oCAABgMBmi12k6r\njws5OTnIyMhgPtsjfpoEnnjiJ5fSCTg6Y+hpzt3uJPXQK4m12haw9Sta6vHw8ICXl5dDPhe3E/++\nffuQmpqKpKQk/P3vf7c5n5OTg8DAQAwaNAiDBg3Ca6+9Jui6dXV1UCgUFse6K/GzWfxdLfW4YvHT\newoAvvDxuVfQisfu4NylKMotVr/BYMDhw4dtiJ9voM3KmoPbb++L6moN2knA6FQ6AUdnDD3NudsZ\nUo+z8hzdh9TqFtaVxLTUAwB+fn4OyT1uJX6j0Yhly5Zh3759OH/+PLZt24YLFy7YlMvIyMDJkydx\n8uRJvPTSS3avazKZUF9ff0MQPyEEra2t8PLyYo65Q+ppaGhAUFAQ89lR5+6FC9cAbIdMdlhQ+mt3\nEb95HD8AREREdLnOf+rUKURFRVlkRrRnYVIUhfz8q/D0lMHXdypqa5scSidAzxgWLfrOoRlDT9P4\n6UiZjrT4XZHn8vKKsWCBDCbTXnz44R0W/YqWegBAKpU65OC19is6CpeI/8SJE0hMTER8fDy8vLww\nc+ZM7Nq1y6acoyNlY2MjpFKpBZkCQEJCAq5du+ZKkzscNOlLJNdvZXfQ+GUyGVpbWwWT8MKFUxEb\nq4RWq8WkSZm80SmEEOh0OrfH8QMd5+B1xKo7e/YsBg8ebHFMiKZ85UoxnnkmBo8/nogpU3QOpRPI\nypqDJ5+cjYaGJjiSgMzdUT1d7cxubm7uMI2/I7Z+XbkyC1FR7f1y2LC+Fv3K3Ihx1MHrVou/tLQU\nMTExzOfo6GiUlpZalKEoCkePHsWAAQMwceJEnD9/nvN6a9aswZo1a7B69WrW5cghISGora11pckd\nDmt9H2jXnt0t9VAU5ZDV39TUBH9/f4SHh9sl0ra2Nnh4eMDT03b9X1embADaLf6OkHocseq0Wi1j\nqdEQIi20tJTh0UdnYuTIkWhsrHAo9LN9wdif8PDwh4fHRNTXN9udMRBCODX+rnLu8t1XVwYFru92\npNRDa/R8vi8hv4HuT9bvqbnF70hIZ05ODg4cOIBDhw5hzZo1jv2o/4NLxC9kmjp48GAUFxfj9OnT\neOKJJzB16lTOsjTxz58/HxERETbng4ODUV9f361yobCFNbpL6jEnfqD9fgld+0BHQ0RFRdkM3tbg\ncuwCzln8jhCAtQToqsVPW3XPPvuLYKvOevAB2mdYfBabVqtFTU0NYmNj0bt3bxQUFDjc1gsX8vHA\nAyb07n0Vr73W3+6MgR6gPTw8bM51tsVP39fly/dw3ldXJBSu79JSj1wuh1ardYkrKIr6v+/7wtf3\nPlbfl5DfUF5eDg8PD5s1J7RzF3DM4s/MzMTQoUMxYcIE9xB/VFSUxSbBxcXFiI6Otigjl8sZ633C\nhAloa2uzS0Z1dXUIDg62Oe7t7Q0/Pz+3e+vNwaa1dQfnLgCoVCrBeYMcJX42+QBwjviFEgAhBDU1\nNQgJCWGO0c5dZ61H2qorLCyHUAmFnh2Zw57FVlRUhNjYWHh4eCAuLg4FBQUOt1Wl8sCUKXcgNDQU\ngwcn250x8D2nznbuLlgwA6GhQHl5Lazv68aNX0CpHIaFC3c6LKFkZ29BauoEzJjxGet3aanH09MT\nPj4+NmTq6HtCb/0aHX3OwvflyKYv5eXlSEtL6zCLH3Cz1DNkyBBcuXIFBQUFaG1txY4dOzBlyhSL\nMpWVlcxNPnHiBAghrKRuDi7iB7qf3MNm8XeHcE6gfWWro8QvJMlcRxH/dQ31N0EE0NTUBE9PT4vZ\nRkREBEpLSzFt2lKnrMfrFpwvpNJpgiKatFotZDKZxTF7FnRFRQUziw0MDIS3t7fD73F+fj569+4N\npVKJmpoau+XtEX9nWvy7du1CUVER/PwU8POztJaDg5XQaOTQaCRwdMOUrKw5yMqaCpPJg/W75hlq\n2Wbejs4y5s2bAH9/CTQajcXWr7TBUFxcafc3lJeXY9CgQTbEX1tbyxgxN5TG7+npiQ0bNmDcuHFI\nT0/HjBkzkJaWho0bN2Ljxo0AgG+++Qb9+vXDwIED8eSTT2L79u12r8tH/I7IF10BNo2/o6QeRyUQ\n86gewLGUBjTx03IaH/gIxZGUDXTnqa8X5rBky0F++PCfOHKkHLt2GZ12wF26VABgO+6/Xy4ooolN\n6vHz8+Ml0vLycoSHhzOf4+PjHZJ7CCG4evUqEhISBBM/l2MX6FziJ4TgnXfewdChY/DJJ+PQ1vYj\nPvlkHLZs+Q4REcMwZ87LaGuLACAF8CBKSmrwyy+/CZKO2zdaaQLgC5lsus1ATUs9gGU/dNTIoFFb\nW4uEhASbGXx7nQDgi8jIRzgNBkIIysvL0b9/f5tnZk78N5TFD7TLN5cuXUJeXh5WrVoFAFi8eDEW\nL14MAHj88cdx7tw5nDp1CkePHsWwYcPsXpMtlJPGjWDxy2SyDtk0xhHrpLa21mZHHkeJ39/fX9DA\nSieEY4MjFj/dUbRaA7y9p9i1tq2JPzt7C7Zu/X8wGGJAiBIAhWvXKjFqVD+H4uOnT88E0AyJRGJh\n1XGBTerx9fXl7bjmFj/QTvxvvPEG2traBLWR9m0FBwd3iMXfmc7d7du3o6mpCV988Q5mzZoMhUKB\nMWMG49tvP0BW1n0wGsMANAEYD8ALK1bEIT6+l+DrX7lSDLl8D9LTi20GalrqASwlV9rIqK5ugCOz\njJqaGkRHR8NgMNi81+fP5wPYjqlT/TgNBrVaDU9PT8TFxdnwlvn7fENZ/J2F7iT12LO62TR+e44+\ne3DGOmGzhsPCwpyy+O0Rf0dq/Hl5xbjnnkZ4ePyCTz8dz2ttV1dXW/zGrKw5eO21J9CeXVwPYAa8\nvHwxdmyGQ6mR6XskdLB2RuqxtvhffvllHD16FPn5+YLqzM/PR0JCAiiKcoj42SJ6hLTXWRBC8Pe/\n/x1vvvkmE/UVHh6O8vJyUBSFI0eOAvCCRKICRX0OwBf9+6dg5coswXXceedApKXFoqGhwWag5pJ6\nrhsZRvj6ThW8LSO9oXlgYKCN1b9kyQMAmnH69GlOg6G8vBwRERGsvHVDW/ydge5E/PasbjaLXyqV\nCiIRrkHFUQnEZDKx3jNnNH4h6Zw7Mqpn5cosBAV5QafT4Y47buO1tq0Ht/aOK4G3tw+iosoA+CMs\n7Djy8koE1w+ASfkglPidce7SBEBj4MCB6Nu3L65duyZIzqP1fQDdWuM/e/YsNBoNxo4dyxyLiIhg\nBtfS0hqMGuWDHTvuxVdfzYdUehAXLggb/GjU19ejV69erOGaXFIPAFy5UgQ/v10wmfYiO/suQeso\n6GCCgIAAm6AS+n3hCyemnzvbM7thNf7OAh/xK5XKLtnTlra6s7L4Iw/YNH6hD5FrULm+abQJwDi7\nMdsNDQ2Qy+U2C96c1fi70uIHrlvc5hFibGCb1eTlFWPr1sdQXLwdX355L2pqqjF9egbHFdih1WoR\nGhoquOOxafyOOHdpxMXFYeXK9YLkPFrfB9qNHyGb/bgjqqeoqAhpaWkWCxppix8AZDI9/vnPZ3D/\n/eNx//3jkZwsx4wZdzlUhznxs8Xxm0s95sQ/e/Y4yOUSpKSkIDExXNA6Cj6LX6vVIiYmhte4Ki8v\nR2RkpI3B2tzcDEIIM0iJxA9+jf+WW27B8ePHO70NWVlz8MILj6KhoRl8VjeXxs/3EIVIOXl5xVi6\nNBjAfjz3XCyvdcK18fKNQvyVlZVQKpVOEf/KlVnMRuKzZ0/B7NnjWFeP80Gr1UKlUrkk9diz+Kur\nqy18MNnZW/D113/i9OkYQXIeLfUAwvcadodzt6qqysbXZL7IznoAdCa1Qn19PcLCwuDl5QWtVmsx\nY+KL6ikrK0NMTAxSUlJw9epVQXXRVjmXxR8eHg6j0cj57lhLPXQ76evSxpwo9YDf4h89ejSOHTvW\nqWkBgHar+8KFiwB8ERQ0k1MTZNP47Uk9tJRTUWEb40xj5cos9OkTBwAIDPR0SAKhoVKpUF1dDZPJ\nZPf3OkL8HeXcpVFRUcEa7mYNrt9pjqlTp+L77793qP6mpiaEhYW5JPXYI1LzjXKys7fgnXe2gaIS\nAIRCiFPanPjZrE82uMO5W11dbZHDCLhugLS1taGsjFg8Q2eIv6GhAQqFAkFBQXjggWUWMyY+4qfJ\n1pGIKvqdY7vn9HugUqk4VQia+H19fZmBir6u+XoU0eIHP/EHBQUhISEBZ86c4b1GR+QIyc8vgbf3\n9xg2TM3ptefS+PkeormUExw8m3NQoV80e9YJFyF6e3tDLpcLCn+11vj57ps9i98RCcFkMqGqqgq9\ne/e2S7xCiP+OO+7AuXPnHNrwnrb4XZF67IVzmi/Wycqag1deWQbaKU1RM+06pc01fqEpQTrCueto\nP7Ke2QDXF3ouX/43AFPw17++w5xzJqdOfX09jh8/i6oqOfbv97aYMdXW6jilnrq6OoeJnx4s2AYo\n+j3gW/ti7tsxl3toCYmGaPGDn/iB9hfJns7fERte3HHHQPTpE4fy8nJOrz2bxi8kquf8+asgZAcy\nMvScg4pGo0FaWppTEggNoXIPTfze3t7w8fHhzXEvROoRShT19fXw9/eHQqHoEOL38fHBoEGDcPbs\nWUH1A+0d2BGLnyuqh6vjEkIslufTg7xOp0NoaAEoyh+PPx7N6ZRubW1FWVkZYmNjAQi3kjvCueto\nP2KTejZv/gHff38BH35YCuADvPvuNfj49Mfcucucyqlz4UIh9uz5AxSVYBbGW4FRo/rBaGzktfiD\ng4MdtvhpqYfN4re36LGsrMyC+GkHr2jxW0Gv18NgMLAmaaPBF9Vwfdebdv18xYq9SEu7x+ENL4D2\nDp6amsqbwoDN4vfy8oLJZOKN0R46NBG+vgYUFxdzDiqNjY1IT0+3S/xnzpxBUlIS6zmhxG++paE9\nYuGL6vHw8ABFUTAajXbrBNplnvDwcEGDpRDiB65LXELhCPG3tbXBYDDYECqfxa/T6eDt7W3hfKed\n0n/++R7k8r0ICVFyynlFRUWIjIxkvk8Tmr3B1RXnrr3do7jAZvHv3PkJAA0ALwAUTCYJnnhiFjZv\n/pdTUo+fH8Hjjz8ILy8Z6BkTRXnhrrvGoLW1lfnNHSH1mDt3XbX4lUqlaPFzob6+HsHBwbzxtXzE\nf33XmzYAFMrL65CRkebwhhdAOxn26tULarWas5OwafwURdkdwZ9//nmsXbuWl9Q1Gg369OnDW4YQ\ngu+//94mVQYNISkYAMsUsfZeQj5CARxbvVtZWYmwsDBBIbBCiT80NNRh4lepVIK2v6N1Xev3k8/i\nN5d5aNBO6cjISLS21uPxx2dw1mmu7wPtK+Z9fX3t7jzminOX7kcajQ4ABZ3OIGjBU1VVlYXGn529\nBYMGTYNEkoB2unkQRqMJZ85cgEQicYr46ZTgBkMroqPL4ekZiOHDq3DhQj78/PyYiCIu4o+NjUVh\nYaHdZ00IYeQhtkWZtMUfGhpqV+MHbKUe0eI3A1vqAWvwEf/1XW/0kMmmwctLhpqaaocW9NCg0+9G\nRkaipIR7Gs6mo/JZsDU1NSgoKMATTzyB+vp6zkFFo9EgKSmJt0xBQQEMBgP69OnDel6oxW++l609\n4udz7gKOOXiFWvz0WgXzzsIFPmcbG+jf7uPjY9fqYpN5gHYi5ZK42IifhkQiQVxcHAoLCznrNNf3\naQhx8Lri3KX7UV1dM4DxqK3VClrwZG3x0wMIRZkAxCEqqhcmTwYkkvZ2ObphEND+/pWV1WHr1sdQ\nVLQN999PQFESLFnygIVSwEX8crkcEonEbloVjUYDHx8feHt7sxIz/S6EhISwrn1pampCa2srw2fm\nvGUt9fR4i58tYsIa9haw5OUVY9YsD8yZE4qFC+XIz3cuXzttBffu3ZtzhSWb1APwj+CXLl1CWloa\nPDw8EBYWxhnN0tjYiKCgIN6Bp7q6GhEREZwdkk5iZg/mFr+99AOdQfz2LH61Wg2ZTGazVoENzlj8\n/v7+glJtcL2fFEXB29ublUz5iB9ozz/FR34FBQWIi4uzOCbEweuqczcvrxhhYQeRlRWLYcOKBC14\nUqvVFqHY9GAhlQ5FenoVNJoWPPTQNOzblw3AudxbOp0Ozz23gAnjHTVqINLTVRYRPYBtskRzw0HI\nPg7mcgxbf6bfBYVCwfob6Bh+um/yOXd7vMVvvvKOC/aIf+XKLAQESJCYmIipU8ciKMjgVFto3bt3\n797Iy8tjLcNF/HwkcvHiRaSmpgJot0q4ytEOwbCwME4is+cIT05OxuXLlznP03BE6rE3ODtC/LTU\nY8/iFyrzAK4RP1cb6OiWxsZGVosf4CZTc8cuG+xZvYWFhazEb08icdW5u3JlFpqbqzBx4kQ0N1fb\nXfBECGGdEeXlFWPTpvE4d+5tm0AGPpmECzqdzsLHRN8/WnqhwWXxA5aLyrhgbpVzET9fYkPr1drm\nxM/m3O1Ki992CyU3w3zlHReELFm/evUqRo0ahZiYGLvOUS7QEkBiYiJnSGVLSwtrp+Ybwc2Jn8/S\npTdj9vf35yxD+0S4kJqaikuXLnGeB653WJrM7RG/uSzEBkdCOisqKpCWlmbX4j9y5IhNfDgXHJV6\n6IGMrw10dItU+hvnoEffN+vFh/YsfoVCwUv8RUVFNsQvROppampCVFQU6zkhK3dbWlrQ2NiIkSNH\n4uLFiyCE8Eo9er0eXl5eNjuzmefhmT59nMU5Rx3xJpPJZkCjid/a4ucjfvM0Elywl1KB7jNcsxZr\n4lcqlThy5AhzbWvnriMWv1v33O0MWI/abBBC/HS2ypiYGJSUlDgVzy/E4ufS+PmI/+rVq0hMTATA\n7wugCcPf35/TkVdXV8e5yhlo36e4pKSEt5M3NzfD29ub6bD2YtLNI4DY4MiqUDp5Gd8MSafTYfny\n5Vi/fr2ga3ak1JOdvQVpaROxeHH7JucffliIkyfVrNEtfBY/H/ELsfjpUE4aQqQevr7k6ekJk8kE\ng4F7NkzPxkJDQ+Hl5WU3SIBtfYM9OPqsaNI3TwnhCPHTRpIQi7+0tJQZOO1Z/EKI357F7wjxW896\nHEW3JH57Uk9oaKjdl5CWLmQyGXx9fZ3K4U9btikpKZx7BTsj9dC6NsD/wGmJwB7x81n8Xl5eiI+P\n5xy4AEuZBxBm8fN1cEccVcXFxYiJieEdAI8dO4Y+ffpg6NChgq6pVCodJn6ZTMbahqysOYiPl6Gu\nTguAQksL0LdvKGt0C9eA6Qrxt7W1oaqqysZyFxINw/ecKIqCj48Pr0FgnlE0NTUVFy5ccLo+LgQF\nBaGpqUnwDJGN8IRIPXq9Hm1tbUz7hFj8RUVFzJ7ifM5dRyx+Om2DdVSPI7v20YO1EH8XF7od8QuV\nenQ6Ha80YE5mQjYX4bqGv78/0tPTUVNTw2ohOOPcpS0pupw9qUcmkzlN/EB7p7148SLneWvpxh5x\nWw8U1rDnHKZBCGE6F999yMnJQWZmpt3r0aBlEKGzPNpSZGsDRVE4e/YMAF/Exy9BU1Mb/Pz8WCUP\nrt/tCvGXlJQgPDzcRj5x1eKn2ys0sVxMTIzdvY2dIX6JRCI42yjAT/zWFr9UKkVLSwsMBoNNbhwh\n0W60UUJfi8u564jGT0f0+fv7W7wTAQEBaG5u5p2Bmd8DvuAKIeh2xC9E6qEoCjExMSgqKuIsY05O\nQlINs4EmRIlEgoyMDBw8eNCmDJfWFhAQwNmZq6qqGOLnsnRbWlpgNBrh4+PDa/HzJbSjYY/4O8Pi\nFyL11NfXw9PTkxnc+KKg+vXrZ/d6NOjFUnQGRHspB2hHGdssrbKyErW1Tejf/wLef38KZs/2hF7P\nrnNzWdD2pDE+4mfT9wFhzl17z8mRPQSE9CFniB9wTO5pbm4WTPwURTF9xzoU2J5fBbBP/LTF7+fn\nB0KITZ+xJv6IiAjU1NRg8eKVyMiwTM8hkUh4OcMcfAsohaJbEr89qQdoT2nLR/zmVlZQUJDTFj9N\niCNHjsTRo0dtynBp/FxOK3pUp6/LNTOgrX365eWyhjvC4rcmpq6Seqw7FtdvFBLiaw3a6heScoCe\ntbENPvv378e4cUOQkhKNxsZGJCZGYMyYdNbreHp6sq5Yttd+PuJn0/fNfx8fhFj8fBJLSUkJoqOj\nAQgjfmeeE+BYZI9Op7PhBz8/PxgMBtTX19v8XlrusZZWhKSKKC4uZu49n8UPsIelWhO/t7c3IiKS\n8MsvAQgIiLSpT6iBam8BpRB0O+IXIvUAYFbfsaG1tRUmk4khZFcsfvrBDhgwgDX/C5fUw2XFVFZW\nQqVSMaO9PeIH4JLGD3S8xW9P6nGG+PksfiGzQGuYTH4YPXq+3ZQD9NTa09OTdfD5/vvvMXXqVCYm\nnI/cPD09Wafq9gZKZy1+IcRvz+Lne055eXnMwjEhxpOzFn9ERIRdGYkGm9TTvoF7MIqKimwGBS7i\nt5ccjhAiSOqh30s2jjEnfjoFRm3trSAkGwcOeNu8j0JmIfQ9cDvx79u3D6mpqUhKSsLf//531jLL\nly9HUlISBgwYgJMnT/JeT2gn57P4aWKiyVXoDTVHS0sLsygHAPr374+zZ8/aSAZCiZ+WHCoqKhiZ\nB+B2ApvHfvMRf3V1td3VrElJSbyx/NZEbo8QOsriN7covb29YTAYWInTGeKPjQ3BggWTcPVqMYTu\np2D9LAgh+M9//oN77rmHIRC+3+7p6cman8le++0RP5vFL1Tq4auXz7lrMpmwe/fvTKoIIX3IWeJP\nSUmxG3JMgyuaJTo6GpcuXeIlfnMDyZ7FT7+/9O/hC+ekr2f+PIxGIxoaGpi+Sa9g9vb2B0ChrY2y\neR8dsfjdKvUYjUYsW7YM+/btw/nz57Ft2zYbz/+ePXuQl5eHK1euIDs7G0uXLuW9plCpxzzfhrWG\na+1Mc8bit36Jw8LCIJFIbBy8XBq/9fSVlhx27frFgviFWPxczl2j0YjCwkL06sW/UbVCoYBOp+NN\nDSHU4udKUmYOocRfWVnJaMgURXFa/c4Qf2BgIKqrq2AyeSE5eTnvfgrmxG9ef1lZGaRSKUJDQ5mo\nCz4y5bL4hRA/V9QZ2+It+vd1hMXP9U48//ybaG6+C1u27AMgXON39DkB9mek5rDW8WnExMTg3Llz\nNjNR+rlt3brXgvjtWfwNDQ0WqWPoOHtznjF/rtaDt1qthlwuh4eHB4DrK5j1egrp6U+zvo9Cecrt\nFv+JEyeQmJiI+Ph4eHl5YebMmTa7H/3www946KGHAABDhw5FQ0MDbyimI1JPUVGRhYZLDwLWROYM\n8bPJGSkpKbhy5YrFMXsaPz3FW7p0Fxob1+Gzzypx+HApM8XjIn4hFn9JSQmUSqXd0Z+iKF4HmnVi\nLT7ipgdEvoU8jhC/9SDINvtxlvhzc68C2I7du5dxpr42XwFpXf/ly5eRnJwM4LrlyEemXl5eTkk9\nXAvOCCG4fPmy01KPEIvf2rk7d+4y+Pj0x9tvXwXwAT76qAI+Pv3x739v6zSpJy0tTTDx81n8hYVq\npKSkWByXy+XYs+cgDh9Wobr6ej+j7x/XJkXWxO/p6QlPT09mRTotJ9PvjrXFzybB8q1gBoT7IjvC\n4gdxAV9//TV59NFHmc9ffPEFWbZsmUWZSZMmkSNHjjCf77zzTvL777/bXAuA+Cf+iX/in/jnxJ+j\ncMniF5rxkljp4lzfI4QgMzMTBw4cACGE90+v10MiCYBSuRzAF/D0vBXh4csBmBAVtQIyWV9s3PgF\nCCHYsWMH7r//frvXNP87evQohg0bZnHs6aefxj/+8Q+LY/3798epU6dY20evjoyISIKn5yLI5ffD\n23sJZs3KYspt3boVM2fOtPl+dnY2Hn30URBCcPjwYQwfPtymzMaNG/HII48I+j133XUX9u3bx3ru\ngQcewLZt25jP27dvxwMPPMBa9vz580hJSeGt629/+xtefPFFu20aOXIkDh48yHzu27cvTp8+bVNO\nKpUy+eeF/i1fvhwjR44E0B6Zw1Xu0qVLSExMBCEEn376KR5++GHm3DPPPIM333wThBB8+eWXmDlz\nJh1tROcAACAASURBVG6//Xb88ssvnPdxx44dNsdTU1ORm5vL297Y2Fjk5+dbHHvsscds3jf6r6io\nCFFRUZzX02q1kEqlvHXee++9+O6772yOP/zwMwAWw9t7FoDFePbZN1BQUICYmBje6z377LOc7bX3\nt3DhQjz99NN2y3344YdMvyCEYOPGL5Cefg/Cw58AYEJS0gtIT78HGzd+AZPJhM8++wa+vgsBEMTE\nrMTXX++FyWQCIQRRUVGMXGz999NPP2HChAkWx+hkiYS0O34jIyOZc3Sadfrz3r17cffddzt0D954\n4w0899xzdst99913uPfee5nPzsAl4qe3VKNRXFzMOOu4ypSUlHDmDwGET+t9fHwQGBiAhoYWAF+C\nkChUVNSh3ZFH0K9fGOM4USgUFjvc0yDkun/A/H+APfY6MjLSJvqAK1kSHR7Y0NCA2tomPPNMNAYM\nqEBGRgV0uuvTS1ekHus87Xzgy4liLbnwSTX2Inro7wuJ47eul83RbTKZWEP47CEwMJBZrcznxONz\n7rJJPZWVlTYbjdBwVuMH2PdNOH/+PAYOHMha3p5zV4jezpWa+dSpK0hIOAmdbguefTYeZ8/md2pU\nDwA8+uijOHTokN1y1u/C9bTP3rB24tN+Iy8vOauuzqfzW0s9gGVftf6tbFKPvfU11jBP6cAHt4dz\nDhkyBFeuXEFBQQFaW1uxY8cOmw1BpkyZgs2bNwNoX3ofFBRk0dmt4Yie6++vhESyGxQVDaPxdwAy\nSKX326yuHDRoEM6dO2ezT6+5f8A63pstEVlUVJRNimOuqB6g3cFbXFwMQqoxZ85U1NXVISjICzNn\njmXKcEX1WIdzspXhCvXjagtXrLQjxC+EUISu3DVfyAawD4K0I8s8N4sQBAQE/B+R8hOWuY+GT+On\nNWHzZfzWcDacE2AnfvNkftaQy+Vobm7m3OlMSEw9l3N3+vRbMX16BiQSCd56ayX27cuGXC5n0h5w\nwRXiF5JCAbDV+Gki12qNrOTOp6vzpb1gI36ZTAa1Wo1Vq/5h0w+sBxF7yRPZEBUVxZl+3Rwd4dx1\nKTunp6cnNmzYgHHjxsFoNGLhwoVIS0vDxo0bAQCLFy/GxIkTsWfPHiQmJkImk2HTpk281+Ty2lsj\nO3sLmps90dqaAKAcQBqAqWht/QJ+fv9FScl10lYqlVi0aBG2b9+O/v37Izt7C959dzuqqmLR2Dgc\ns2b9FQZDMgj5AqtWvYSXX34Pt90Wy2rxO0r858+fR2hoKJOnw9rpw2fx0zMjrhV95nHG9sDn3HWE\n+IUQihDnrl6vh16vR2BgIHOMb5cjR9F+j6UApuHw4ZNYtIi9HFdUj8FgQEFBARPHLpfLmWk+10ZB\nHWnxNzQ0oLGx0WYGTUMikTDRXub3kIbQAdra4s/NzcW7775rE6QhkUiYHFlcbXKF+OnfTwh/BlA2\n5y5N7tOm3Y1vv91vQe58mUH5wmjZiD8uLg47dvyIjRvVkEoP2lj85tcSsr7GGkIzCXeEc9fltMwT\nJkzAhAkTLI4tXrzY4vOGDRsEX09oR8/KmgNfXz8sWbIXOt10UNT3IGQ8CNmGIUOiMWPGJIvyaWlp\n+OGHH5jvKhQhmDv3KwBzIZfvQ3s0XftUce3aZaioyLMJTWWz+PmmXSqVCrm5uQgNDWVW9gkl/pqa\nGiZNgUKhgFqthtFoZMLDAMeIX6VS4dq1azbH9Xo9mpqaLKalQqJ6+CCE+K0XsgHsi7icIf7s7C1Y\nt+5rABMBfIyfflqCPn0mYcWKmVi0aK5FWfNwXPOBp7CwEOHh4cyzjYiIwLVr1xAcnMhZLxvxt7W1\nMak3+KBSqSyI/9KlS0hJSeElQVruYSN+tVrNmx8IYJd6cnJycN9992HEiBE25WmrvDOI39fXF1Kp\n1K6l3NzcbKMY8JE7H+xZ/ObrY7Kzt+Dw4VIcORKBxsYP8OGHT0KtbkB29hYsWjSXVerhuk9coInf\n3uDndqmnMyC0o7fv6iODp6cc0dGfgRAJoqOzYDRKcPp0HpP2mIZ5Tv32qSDQ2iqBUjkXWm0rKMoP\nFDUBDQ3N/zd1tJV6lEqlTbw138seGhqK3NxcqFQq+Pj4wMvLCxcuVFiQLJfUc+3aNUa/9/T0RGBg\noIVkYTQaUVZWxusvsW4Lm9RDh3KaSyl8Uk1HWfzWswyAPZzTGeLPypqDV19djsjIeAAUWluBV15Z\nhkcfnW2z5sPc4jev//jx4xYb2EdERCAsLAFq9e2c6R/YiJ9uv71AiLCwMIvnU1JSwrpwyxx8sfzm\nayS4wCb1nD17ljMvkr1Uxs7G8Ztf357c42o6YnM4YvFnZc3B1KkjUVvbBDpLa79+KsaP2BEaPz2A\n21uY1xH3oFsRPyHEoR9FT/Eee+xOPPtsHB577DbIZL+irm4krlyxfEHpnPp0p//zz/MAtmPSJG/0\n79+KhIQL6NOnBC+/nIYrV4pZCZ3eLYvu3PQeq0KkHgCQSkOh10/A4cOnmDJcFr+149Y6g2FFRQWC\ng4PtWpLmbeFKIWFNwK5q/M4Sf0dZ/LTG29hoQFTUI9Dr2499++1+m7w95sRPO3B1Oh2WLl2K559/\nHsD15fY6XSaMxo2c6R+4iF+IFWzt2BNC3Hyx/Oapv7nAJvWcOXMG/fv3Zy1vT4d3xeIHup74HdH4\n6X28ARko6h40NbVBJru+nsVa6rHee1gIKIpCbGysXbnnprP4dTodfHx8LOQMPqxcmYXp08dh1apF\nSEqKxpYtuyCR3AHgI6xbd8Gic9LTNtpiv+uuQZBI9KioqMDAgSo8/fRspKWlISoqECtXPsoavUJn\n0KM7m70XPTQ0FJcuVaK8XIM+fSahsXEUgI/x2mt/MG3jcmjW1tYiMvJ6Iidr4udays8FrqgeZ4i/\nu1v8wHWj4IMP7kN4+DE89tjfsGzZTzZ5e8ydu3Qe/8rKSgQGBmLs2HYnPB05IpUGgS/9A1vKBqFW\nsDXxCyFuPuIS8n3rlA2EEOTm5qJv376s5e1Z/M4maTO/fnex+M2lMnrgP3CgGsBUeHkFwdv7vygq\nuj77p2Vg2rBke7+FQIjOf9NZ/M52cuB65wwIUIKtc1IUhfj4eBQUFABol1L69OmDsrIy7N59GAMH\nDkRKSgqzgpCL4MxfFnskeO1aNYBpCA8P5yQONqmHzshoPgBaE795xIkQcEk9XMTPFY4p5BkJCee0\njugBOs7iB64bBQqFAjExMmzY8BKamtpgff+tnbseHh7Iz8+3WMlMzyCamkycy+0BfqnHHoKDg20s\nfnvEwWfxC/m+tcWv0WhgNBo5NXZ7G5R3hcUvNPhDCPgGTnOpl+YWuTwFwHgYjZ64/fZ0PPzweKZ8\nSEiIxS5lzhK/kHtw01n8rjxUuiNqNG2cnTM8PNziwQwcOBB5eRWorByBa9eqERkZyZznyqFuTvxc\nZWgLYefOJgAf49ixYDz99BvQ6WDTNi8vL5hMJgtLkS0+35r4+UL92BAYGAi9Xm+j6VonjQM6xuK3\nt41cZ1v8NOgwu/Y8KYCf330W9986KissLAxnz561mabbW24PsKdsEEqGISEhFv4jIRY7Xyy/UIvf\nnPhpnxGXP6I7EH9XWfzmv4V+Vxoa9EhNXQGj0RNabZPNAEnnHCKEsBo2QiBkv+huEdXTkXC1k/OF\ndQGWL9Yvv/wXubk1aG4eA+BjvPLKS9DpDiE0tH1xFVe0hLXFz7aYiY4aeuaZ3wBQMBgkGDNmKO67\n705Mnz7Oom3tTmopdDods5Vafn6+TeI1NuKfM8d2+z8uUBTFSBnm0QaVlZU2ddGZMq2jiOjfbK9z\nC9kas7Ky0mYrxY4M56RBP6+8vGKkp59BRcVV/PvfS5j7b51kLywsDOfOnbMhfiGRI65Y/M5KPa5q\n/OaGgPkes2xISEhgAiSsYTQa0dLS4hIhhYeHIzc3l7dMV2n81lIvzS333TcWvr4hKC5Os3He0sQ/\nYMAA+Pn5OWWVh4WF2Y3lp1dlu4KbivjtdU7zWGmVSoa+ffviyy+bUFfXPv1fuHAKfv11NwDhxM9G\nguYWQnr60yguNmHatAlMm6zbRhMerSmaR/TQUCqVFmTqqMXf/pvbrQlr4h8+fLhN+2mr3/r3CXlG\nKpWKcZJydVIui7+jpB4a9PNauTILX331AaqqqnD33SMwfXp7p2az+M+dO8eke3AE5km8zNsvxAoO\nDAxEU1MT2traGMmgq6We0tJSC7+SNZKTk5GXl8dqEAiNXuKDUKmnqy1+wJJbIiMDYTBU2hB/Wloa\ncnNznZZ5gPb3748//uAt44zj2Bo3jdQjBOYvVlVVFYKCFGhr82Dkl4CAAMbqEkL8fFvqCZEGaFgT\nHpvU05598PrGM46s2qXBFtnD9ZJyyT1CLH6JRGJ3FWJNTY3Ny9sZFr9MJkNLSwtaW1tRU1OD4OBg\nnDhxgjlvnV1VpVLhzJkz+OOPqw7nQWGz+IU6dyUSCRQKBerq6kAIYZXgrMEl9RBCBJGPtXPXXniw\nVCpFWFgY4yczh6syDyBc6ukqjZ/r94SFhSE/P9+G+AcPHozff//dZeK3N1u2zqbrDLoV8bvaye3B\n/MWqrKyEWt1iQc61tVpGTnFF6gGuOxcpisL06eOwcuWjnO0SQvypqanMZhX0Un1HOxpbLpDOIH7A\nfnQC217BnWHxUxTF6Pw1NTV46623sHLlSuY8m8Xf3AwcOxbJu10jG1wJ5wSuPx+1Wg1vb2+7BMcl\n9dTX10MqldqVGtgsfnvrQrhy57sa0QN0H43fYDCgpaWF8/63S6NBNit7b7nlFpw+fRpvvfWx08Rs\nvZ6DDSLxOwjz0bSyshIvvrjYgpxXr16G2tpaEEJYl2wDwi1+R2Du1DSZTLh27ZqN7p6cnIwrV67A\naDSitrYWSqXS4Wk1HaduDi7i51rEJfQZOUP8nWHxA+3PrLy8HIQQzJ8/H7m5ucx9MCf+7Owt+Oyz\ngwAmQq/fwBmvzwVXLH6gnfhramoExfAD3Ba/UIvTmvhPnDhhVz6k30NruLp4C2iXM+vr63nzAXWF\nxm9PtgoIiAAwDUeOnLY4LpfLERISi717/RAYyC2Z8cF6Bbc1TCYT0/9dQbci/q6SegwGA9Rqtc2W\nhbSVVVNTg7a2Nta2mGcp7IjpLWAZxnjixAnExMSwJohSqVQoLCxETU2NUw+e3jeWRktLCxobG1nD\n9zrC4ufaGrP1/7d39lFR1fkff8/wjCDPAgI/KBUGeRxWdDe1cBMLCUtrq13d06/j8mO3NDe3tvbs\n1moPlttx+1n9auVY2WY/s8ync3xIbYVWjFCBTNHEBxQZBOKZERiYub8/+N1xGO4d7r1z7zDDfF7n\neI7DfOfeL5fvvO/nfr6fB4MBBoNhhFAoYfEDQ3+zixcvIjw8HJ6enmZfLDBc+AsLl2LDhucRFfUf\nsBWvz4e9Fj+bZChkYxbgt/iFft7S1VNZWYnm5mbcfffdNj8THx/P2etaju+Ch4eHOQCBDzk1wt/f\nHwMDAyP2Zfh+FzZa79AhTwCbsW5dpdkwYN/r7/85TKZNKCnxE2U0sERERKCtrY23+F5bWxuCgoLM\ngSBScSrhd5Srp76+HtHR0ZyJYuHh4bh06RImTpzIece3DLuTS/gtBY9t7s0F+5gtVfjZNnQsXOUa\nWPhi8YX+jTQaDc6cOcP5Xnt7O4KDg0dcXyUt/u+++w6Dg4FgmKEeCmylVsuoHqHx+nzYa/GnpaXh\n9OnTgvz7AP/mrlDht7T4q6urMW/evFGTJ9nOd9bI9V1gk8SsS2uwyGnxq1QqTqufr/Q4G89vMnmC\nLf/OGgbse76+QRiK5PMQZTSweHh4YMKECSOezFnkcPMAbib8bO/Zs2fPjqjlwxIREYHa2lpO/z77\nPmuRCKlNLwRLV8+pU6cwd+5cznG33XYb6urq7BJ+ywVlyyVgr8WfnZ2NEydOcL7H5eYBuBO45HAh\nBAcHY9OmT9DWloOdOw8hLS3NfFOy3twVsylvjb0WP3tDstfVI/TGYRnO2dXVxbvmLeGz+IUUhRNC\nVFQUvvji4IjSGsCQ791kMtlt7VrCVZNfaLSepWFg6z0pc+KLNpJL+J0qnFNpV49KpcKkSZNQVlZm\nLrdrTUJCAqqrq3m/BJYJFt3d3bLcqCwFr7GxkTekjs08NhqNsrh6xAo/29lJyO+clJSE1tZWzpsU\nn/ArkcBVXLwVX311GR0dWgD/gz/9aShfIzzcaD6+5ZqTWukR4C7ZIGb+bDe33l5f5OePrI5pDZ+r\nR+iNw8fHx/w37urqEiTcfBa/lPrz1hQXb0V5+Q0cO3YRPT0fmEuks1VVWWvfnpBRa6xr7AC2jRtb\nuUKj5REJxe2EX6/Xj/C7y01UVBTKysqQn5/P+f6UKVNw/Phx3prrlha/VMvbGktXj63H9ISEBFRW\nVsLPz08WV49Y4TcYDFCr1Zwdx6xRq9VmyzonJ2fYex0dHbwWv9zCP9SJSY0XXjiFpqYhv/1//ddi\n7N//GQB5BIuFz9Uj1OKPjo6Gv38EKipikJlpu0IjwO/qaW5uHlZZlA/LBj9dXV2CbhaTJk1CV1fX\nCCNNSv15awoLl+LAgYPYvdsAQAW9fgD//d9P4tSp02AYRhHDkMvit/Ukb8swsMdosJ6T0sLvVK4e\nOf13fERGRuLf/z7D27JwypQpKCs7x2v9sMJvT1q2NazwGwwGdHV18Yo6a/FzxcALwV5Xj1g/bnR0\nNGdoGp/Fz7oeTKZbrSnlCOcMCQnFzZu3/PahoaHmsEE5BIuFq2SD0Pmzm4M3b+bAZCrGoUPqUTcH\nJ0yYwNkViy8izRrL9SDU4ler1ZwRW1LKEFvDht4CfvD0zEdrazfeeKMY7757Azt3HlJEH8Ra/I7A\n7YS/v7/f7uJDozFk4CxGayt3EbGGhg4MDORDr+d+nPTx8YGfnx86OzvtStSwhLV02eYkfG0GWeGv\nq6sT3eQBGOnqsfV0IYfw89Ud4RN+tVo9os6PHPs+1n771la9ObxTDsFisWdzl90c9PMb2hwcGFCP\nujmoUqlG/E2BIX+7EOFnLX6TySRY+AFuP79cN9Da2iYAzcjIMIBhvsaJE6Hmiqrz5xeiv19Y5V6h\nsBY/23PbaDRi9+7dsnyvpRISEuJewt/X1ye4vrxYWIvq229DAWzG3/9+fphFxb6/ZUsTgM24dGka\nr8XFWv1yCX9wcDDa2trQ2Nho83E7MjIS/f39+Ne/qpCUlCT6PPZa/GJFmE/4dTodr0vPOqRTDuG3\nTqZ78cUn4O3tjc7OTlktfns2d2/1EOAvMsgF1wZvR0eHoI1atVpt3lfp6uoSHKjA5ee312XGfv+u\nXo0F8At89107DIZYAJMAqHDlShPS0+MRFWX/BrIlrMXP9tx+881ifPnlt3jllVdkPY8Y3M7il6Pc\nKB+sRRUQEAquGG32/YEBNQAVjEZPXosrIiICV69excDAgCyRDJMnT4ZOp0NjYyOio6N5x6lUKsTF\nJaOvLw+nT3MXy7KFGB8/VwKXHBa/0WjEJ598gvvvv5/zM9Z+fqUivaKjo6HT6XifPqRgbzinlIgi\nrg1eoa4e4NaakMPit+c6st8/hvEGsAxBQRr4+08E0Ae1+pfw8vKFVpssSxSdJRcv6vDaa9vx3HNH\n0d39d7zyylfo7FyAZ599TdbziMGphb+trQ25ublITEzEggULeCeakJCA9PR0aLVazJw50+YxlXT1\nsNZTX5+K06ISE44VExODEydOIDIyUpYIA7aJg06n443oYS0inS4dwGb85S/HRSeIiInq4UqmkkP4\nP/roI8TExGDGjBmjnpfd0FNK+C9cuAA/Pz/ZwgPtDecUU+aDhWuDV6irB7j1FNjd3S1Y+Lksfnuf\nnG6VVTdg+vQ/QK8fgMHQh6CgWqjVgXjyyVhcvtwou/D//Oc/RXZ2PJqaagAUoLMzCMAm/OtfKkkJ\nWHJgS/hZd7C9SBb+119/Hbm5ubhw4QLuvvtuvP7665zjVCoVSkpKUFVVNaw4FhdKunqA0S0qoRaX\nVqvFu+9uk+UPAAwJv06nw9WrV5GQkMA5hrWIvLwmQEpWKSDO1WMZ8cEih6vnpZdewptvvsn7GUuL\nv6+vD56envD0lD/4bOrUqSgvL5fNzQNwC78YF4oUrF09bLkRIa4e4NaacAYfv+X3b8mS/8Ajj2jw\n4YfLkZlZi7CwcNx7b7bsm64hISG4efMmBgbiALQB8IDU75dcOHU45969e1FaWgoAeOyxx5CTk8Mr\n/kKrHCq9uTtauJXQcKz+fi9cv56NyZOvyDIvtpfp5cuXsXjxYs4xrEXENnOprzeJThDx8fGByWRC\nf3///1tXXby+9sDAwBG11+21+PV6PZqbm3mtfWC4xS80qUgKqamp+NvfNmPSJHncPMBI4R8cHMTA\nwICikWrWrh69Xg8fHx/BTzHsE4MY4ecqxyGHy8zy+/fJJ28AGGp6r1Lp8fzzv8H7778vu/AHBQWh\nubkLMTHVmDMnAzt2eCIw8HF0dARJTsCyF3bPzxq2lIwc3wnJwm9pLdoqJapSqTB//nx4eHigqKgI\nhYWFnOMA4OLFi9i6dStKSkqQk5MzIv57rCku3oqNGz9Ff38agM1obHwGKSn3mRNMpOLj44PAwEAc\nOlSBVatW8Y6zN0HEMgqkr68PERERvBFEAQEBI6JFxFr81pUG2QYzfOcEhlv8fBVS5aCjYxCNjbMQ\nFma78YcYrIWfjQdXUjysXT1ir5mlxS/0ySQ2NhYNDQ0wmUxQq9Xo7OyEh4eHIjc4yz6/tqrhSmXK\nlCn48cfraGvLxdWr7fj448ftTsCyFz49/eCDD/Dwww+jtLQUJSUldp3DpvDn5uZylkl99dVXh722\ndWcsKytDdHQ0WlpakJubC41Gw1uSIDw8HE8++SS0Wq3Q+TsU685agDfWrl1hV7IGS2BgFOrqZqGm\n5hpvExA5EkTYL3pbW5vNiKSAgAD09PQM+5lYiz8oKAi9vb3mQmiXLl3izZhmsczeFWOFCuXWzTsV\nwGbodL+V5eYNjBR+pd08wMgKk2I2doGh9dDU1AQPDw9BiXnAUMTXxIkT0dzcjKioKNTV1eG2225T\n5AbHtks1mUyyVcNlYddCe/ssGI3/wNWrz+LFF99Ga2uL3WvBHlgPgDU7d+7Ea6+9NsIoXrt2rehz\n2PTxHz58GN9///2If4sWLUJkZKR5co2Njbx+JzZKJSIiAosXL7bp53dEHL89yFmPg4XdtO3pmQtg\nM95444yim0psFMdooaiBgYF2C79KpRqW6Xzx4kXeGkksluUrlLD42b0Sg0EFQIUJE0Jl8+Val2yQ\nq5aTLax9/GKFf+LEiTh79izi4uJEndcyiauuro53b8pefHx84O/vj66uLtkTq6wj/UwmrzHz61vC\nFpO0dJGbTCacOXMGWVlZspxD8ubuokWL8NFHHwEYitTgqih58+ZNs7tAr9fj0KGhAll8KL25Kwf2\nFPHi4lbiTjAcsanEunpGE345XD3AcD8/V58BayxdPUpY/ErcvFm4LH65529NSEiIuUw4IM3VU1VV\nNeqTmDWOEn7gVr9puV097N/daPSWfS3Yg6+vL/z9/Yf9XS9fvozw8HDZDCHJPv7nn38eDz/8MN5/\n/30kJCTgs8+Gap/odDoUFhZi3759uHHjBpYsWQJgaKNr6dKlWLBgAe8xlYzjlwu56nGwcPXnVXLx\nsa4eIcLPZfHbyjPgwrKxhJBSE5abu0r5+OUqpmUNn49fSayzPKW4eiorK6HR/AwMwwhed5YbvI4S\nfrldPYBya8FeoqOj0djYaI6UOn36tE2jWSyShT80NBRHjhwZ8fPJkydj3759AIDbb78d1dXVgo/p\n7K4epXDk4rN09dh6vOdz9dhj8be2to5ahE9K/RixyH3zZrGu1eMo4be0DMWEcgJDfRNu3gTOnk3C\nzp2HBF8PjUZj/m7/8MMPmDNnjriJi8DS4pdb+JVaC/bCbmqnpKQAAM6dO4fp06fLdnyny9x1dleP\nEkhJ3JEK6+oZrWY7l8UvJWRPrPBbVktUMqpHCcbC1WPZEQ4Ql7xVXLwVL774IYCF6O8X12ryrrvu\nQmlpKXQ6Hb755hvMnz9f6q8wKkq5epwZ6w3eixcvCqq4KhQSfjdDjKunu7t72AaTEOG2Rorws64L\nJeP4lWCsXD3WFr9Q4S8sXIq1a1cgOjoeYveXUlJS0N7ejvvvfxxLlixR9O+kpMXvrFiGsQIQFBEn\nBqcSfg8PD0WyNIlbCI3q8fb2hlqtHtaPVIrwWy5gscIvV1cnR+Eswi9UhNm9pJ4eo+jNTbVaDY1m\nBk6ejEVwsPhKsWJQ0sfvrHBZ/KNFxInBqYSfrH3lYeOvr169OmqEjbW7R4rwT5s2DRcuXDDXjR9t\nj8DVLX7LcE5HRfV0dHSYn8zEuHoAaVFqbAjypUuJADZj9+5+RUOQ3dXVwxpMer0e7e3tiImJke34\nTmVeu+PGrqMJCAjAtm0HkZqaOqr1xAp/WFiYuXa9WOFPSkrC+fPnzTeN0axJV7b4fXx8zD1sgSGL\nX8loF2Doyczb29tcDE7s5q6UzU02kXH16lIM9Q5QYf16eRIZuQgPD0dzczNaWloU79DnLFg+KV+7\ndg1xcXE2M97FQha/m3HuXD3a23MQHT36RpFlhE13dzd8fX0FZ3eyhIaGws/PD99//72gL62l8Ntq\nFOOMWDYvB5TvIc1i6e4Ra/FLgXUHdXb2OyT+/fbbb0d5eTl8fHxc6gnQHixdPXL1/bDEqYSfLH7l\nYB/PP/+8B8BmVFdHjfp4btmWrrW1VXL1xaSkJJSVlYkW/oaGBt4y1c6It7c3+vv7zW4XR7QSBYYL\nv9g4fqnInchoi2nTpqGlpQXe3lGCCz66OpauHrlavFpCrh43wfrxnG00Y+vx3FKEpfj3WdLT07F9\n+3b4+kaNmiQUGBgIvV6P3t5edHR0yFb62hGwjegNBgN8fHwcJvyxsbG4du0a0tPTRbt6pOLIE5rJ\nvgAAEb1JREFU+Hc/Pz9ERNyGlpY5onINXJng4GD09fWht7d3/Fv85OpRDimP55aWpD3Cn52djdra\nBly4kIadOw/ZHKtWqxEYGIjz588jKipKVr+mI/D19UVf31A/597eXocYMxqNBufPnwfgGFePI2Gf\nVPX6OzE4+J6oXANXRqVSmWv2jHvhJ4tfWcQ+nlsKf0tLy6jlFrgoLt6Kl1/eCjFJQsHBwaipqXEp\nNw+Lj4+PWfj7+vocYvGzwt/b2wuj0eiQfQVHYd2EfiwbpDgadoNXrq5blpCrx40Q+3huKfwNDQ2S\nwskKC5ciODgE//mfu9HbO/TFXbfOtospLCwM1dXVsoavOQrLDV5HuXo0Gg0+/vhjNDY2IioqasyL\njMkJ+1RqMKgdUsvKmWA3eMe9xa9Eb1VCOpbCr9PpJAmxSqWCWu0BT88AwS6m+Ph4HDt2DLGxyiYG\nKYGlxe8o4U9JScGZM2dw6dIl0UX0XAFHbiQ7E+wGb2Nj4/je3B1Pj6jjgZCQEFRXV4NhGBw8WIGX\nXrpD0nHEFqFLSEjA7t278etf/1rS+cYSax+/I4Q/PDwcd911FzZs2DAuhd9ZC6kpTWRkJNavL4bR\n+CPS09NlPbZTCT9Z/M4Fa/Hv2HEQtbW+qK3VSTqO2C9ufHw8TKaJSEpKknS+sWQsXD0AsGzZMjz8\n8MPQaueJKq9MOC+Njd2or5+BoqJg2UtVkKuH4OXf/z6FI0cuY+XK/wXDZOKtt046JKJCp+sEsASX\nLzePOtbZGAtXDwDMmjULgD9qajSjRk4Rzg0bybR3rwHAZhw54iX7986phJ9cPc6Fn58fBgYG0dTk\nDeDvaG+fgK6uHsWSaNgF/+mnnXBEG0olGAtXT3HxVuTlPQG1ugD9/f/jNiGP4xU2kgnwBqCCwaCS\nPZLJqYSfLH7noqjo1/D2vokhj6AKRqMajzxyr2KNqNkFbzJ5wVVD91hXj9FoxODgoOgSF1Jw55DH\n8Yg9VVOFQsJP8BIZGYmbNwcBGJGY+BR8fT1x40arYv5jJfvhOgrW1cPG8Dti7uw1Uqv9Xfa6EcNR\nOpLJqTZ3ydXjXHh5eSEgwBs9Pdtx9mw79uz5SvFQOmftgSoU1tXjSP8+4PrXjRiO0pFMki3+zz//\nHCkpKfDw8EBlZSXvuIMHD0Kj0WDatGlYv369zWMeMtzalFpTsgZrStb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ncOswAAAAAElF\nTkSuQmCC\n" + } + ], + "prompt_number": 5 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finding possible spurious points\n", + "------------------------------------\n", + "However, if the amount of data is too large for visual examinations one could use the following criteria to find possible spurious points. One must be careful using the criteria for extremevalue analysis, because\n", + "it might remove extreme waves that are OK and not spurious." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import wafo.misc as wm\n", + "dt = ts.sampling_period()\n", + "# dt = np.diff(xx[:2,0])\n", + "dcrit = 5 * dt\n", + "ddcrit = 9.81 / 2 * dt * dt\n", + "zcrit = 0\n", + "inds, indg = wm.findoutliers(ts.data, zcrit, dcrit, ddcrit, verbose=True)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Found 0 spurious positive jumps of Dx\n", + "Found 0 spurious negative jumps of Dx\n", + "Found 37 spurious positive jumps of D^2x\n", + "Found 200 spurious negative jumps of D^2x\n", + "Found 244 consecutive equal values\n", + "Found the total of 1152 spurious points\n" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Section 2.2 Frequency Modeling of Load Histories\n", + "---------------------------------------------------\n", + "Periodogram: Raw spectrum" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "clf()\n", + "Lmax = 9500\n", + "S = ts.tospecdata(L=Lmax)\n", + "S.plot()\n", + "axis([0, 5, 0, 0.7])\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "png": 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dO9q2YJCcRurdu7lPJaOuCYJwX2S5uMzMTFz763XZ29sbaWlpmDlzJubOnYub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\nRU5ErkJggg==\n" + } + ], + "prompt_number": 7 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate moments \n", + "-------------------" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mom, text = S.moment(nr=4)\n", + "print('sigma = %g, m0 = %g' % (sa, sqrt(mom[0])))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "sigma = 0.472955, m0 = 0.472955\n" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Section 2.2.1 Random functions in Spectral Domain - Gaussian processes\n", + "--------------------------------------------------------------------------\n", + "Smoothing of spectral estimate \n", + "----------------------------------\n", + "By decreasing Lmax the spectrum estimate becomes smoother." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "clf()\n", + "Lmax0 = 200; Lmax1 = 50\n", + "S1 = ts.tospecdata(L=Lmax0)\n", + "S2 = ts.tospecdata(L=Lmax1)\n", + "S1.plot('-.')\n", + "S2.plot()\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "png": 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TyKRJk1AUhaFDh1JcXMyxY8eaI64WlZ6f3uylkEoBHQM4lHmoRZ4lhBDNrd42\nkf3791fZHjZsGEOHDgUMqxzeOBDx5nMtRUu0h1QK6BggJREhRJtRb0lk69at/P777ybdLDU1lQED\nBjQ6qJa0cSMcVFouiciAQyFEW1JvEvnHP/7REnFopqICLpal4e/dMklkX4IP2YXZlOnLsLO2a5Fn\nCiFEc2n3i1JNnAg6p7RmnQL+RslnbOjk4ENaXlqLPE8IIZpTu08iYGhYb+55syo98YR08xVCtB2S\nRID0vJbrnQWywqEQou1oUBIpKSmhtLS0qWPRTEZ+Br4uvi32vMCOMlZECNE2mJREKioq+OKLL7j3\n3nvx8/MjODiYwMBA/Pz8uOeee/jyyy8tdmba/6wrJa80j05OnVrsmUFuQSTnJrfY84QQormYlERi\nY2PZv38/Cxcu5OzZs1y4cIHMzEzOnj3LwoUL2bt3LyNGjGjuWJvF6g8z8LD1wUppmZq9s2fh0qkQ\nzuacbZHnCSFEczJpxPp3332Hvb09AHq9HlVVURQFe3t7Bg0axKBBgyy2eqvIOoPODi1XlXXqFGz5\nKJhzt0lJRAhh+UxKIpUJBGDkyJHccccdBAcH4+rqyl133VXtHEsydGwGqW4tl0Q8PKA4sysXCy/K\nWBEhhMUze+6sHTt2GF//9NNPLFiwgBUrVjRpUC0pIiYdu5yWSyIBATD+Lhsuu/hy/up5Qj1CW+zZ\nQgjR1BrUEHD27Fl+/vlnbrnlFj7//POmjqlFZeRntNhAQ4AuXQwLYQW7BUu7iBDC4jUoiXTp0oXs\n7Gwef/xxFi1a1NQxtaiW7t5bKcQ9hOQcaRcRQlg2s5PIvn37cHJy4u677+b9999n1apVzRFXi0nP\nT9ckiQS7BUs3XyGExTO7TcTPz4+NGzdSVlbGyZMn+cMf/tAccbWYY+cycBvRctVZlYLdg9mUuKnF\nnyuEEE3JpCRS2aUXwMfHh7vvvrvOcyzJVTUDnxYuiWzcCHY+wVKdJYSweCYPNly+fDmnTp2qdiwx\nMZHnn3/eIgcb5pXmYWNbga+Ha4s+NzsbOllLw7oQwvKZlES2bduGp6cnf/7zn/Hx8aFHjx50794d\nHx8f5s+fj7e3N9u3b2/uWJtcZc+sli5BzZ4NIwZ4U6wrJr80v0WfLYQQTcnkwYYPPfQQDz30EHq9\nnuzsbBRFoVOnTlhZWe5EwFr1zAJQFMU4h1akd6QmMQghRGOZlAH27NnDhQsXALC2tiY+Pp7Zs2ez\nYMECrlxTVQ6JAAAcaklEQVS50qwBNqf0PG16ZlUKdpN2ESGEZTMpicydO9c4rclPP/3Ek08+yYwZ\nM3B1deXhhx9u1gCbU1peBud+b/meWZWC3aVdRAhh2UyeCt7DwwOADRs2MHfuXCZNmsS//vUvkpKS\nmjXA5pR2NYPd21u+JJKWBh99JGNFhBCWz6QkotfrKS8vB2D79u3cdtttxmM6na55ImsB6Xnp2BS3\nfBLJzIQXX7w2al2SiBDCgpnUsD5t2jRGjBhBp06dcHJyYtiwYQAkJSXh5ubWrAE2p8zCDGZObPnq\nrE6dDN18Q9xDOHPlTIs/XwghmopJSWTRokXcfvvtZGZmMnr0aGOPLFVVee2115o1wOaUWZjB/5vX\n8iURLy948EEI9QjlbM5ZdBU6bKzMnjxACCE0p6iWuq5tPRRFqXPJ3gq1AsdnHcn9ey6Oto4tGFlV\nQSuC2P7AdpkSXgjRKtT32Xkzyx3k0UiXiy7jbOusaQIBCO8UTmJ2oqYxCCFEQ7XbJJKWl0bXjl21\nDoOwTmGczD6pdRhCCNEg7TqJdLb34403tI0j3DOcxMtSEhFCWKZ2m0TS89PxcfbX7PlffQWHDklJ\nRAhh2dptl6C0vDS6e/vzp8naPF+nM/yEd5KSiBDCcrXrJDI8cLhmz58wwfBfVfWhuLyYnOIc3B3d\nNYtHCCEaol1XZ/m5aDdvViVFUQjrFCalESGERWq3SSQtLw1/V+3aRG4U5intIkIIy9Quk4iqqqRe\nTaUky58PP9Q6GmkXEUJYrnaZRPJK81AUhbQzrnzyiTYxZGdD5YwxUhIRQliqdplEKttDdDoFW1tt\nYigpgWXLDK9l1LoQwlK1yyRS2R4SHg5Tp2oTg6enoTSiqoaJGJNzk9FVWO60+kKI9qldJ5FbboHJ\nGo0TcXSEf/4T9HpwtHXEp4OPrHIohLA47TKJpOelt4qeWYsWgc21kTp9u/TlUOYhbQMSQggztcsk\nkpaf1irGiNyov09/Dlw4oHUYQghhlvaZRFrRGJFKkkSEEJZIsyQSHx9PeHg43bt35/nnn6/xnMce\ne4zu3bvTp08fDh48aNwfFBREZGQk/fr1Y+DAgWY/u7I669df4dtvG/wWmlRlEmmja4QJIdooTebO\n0uv1zJ8/n+3bt+Pn50d0dDRxcXFEREQYz9myZQunT58mKSmJ3bt3M2/ePHbt2gUYpgpJSEjAw8Oj\nQc9Py0vDz9WP003ybhpu5064ehXuvBO6dOiCnbUdqXmpBHQM0DgyIYQwjSYlkT179hAaGkpQUBC2\ntrZMnTqVTZs2VTln8+bNzJgxA4CYmBhyc3O5ePGi8XhDv7EXlxeTX5ZPJ6dODBkCY8Y0/H00lr29\noZdWJanSEkJYGk1KIunp6XTten1VQX9/f3bv3l3vOenp6Xh7e6MoCiNHjsTa2pq5c+cyZ86cGp+z\nZMkS4+vY2FhiY2ONAw2tFO2bg26uievv05/9F/Zzd/jd2gQkhGh3EhISSEhIaPD1miQRRVFMOq+2\n0sbPP/+Mr68vly5dYtSoUYSHhzNs2LBq592YRCq1lu69Nenv0593DryjdRhCiHak8gt2paVLl5p1\nvSZfx/38/EhNTTVup6am4u/vX+c5aWlp+PkZuuX6+voC0LlzZyZMmMCePXtMfnZle0hrJNVZQghL\no0kSiYqKIikpiZSUFMrKytiwYQNxcXFVzomLi2P9+vUA7Nq1Czc3N7y9vSkqKiI/Px+AwsJCtm3b\nRu/evU1+9rmr54wN15s3w759TfSmmkBX166U68u5kH9B61CEEMIkmiQRGxsbVq1axZgxY+jZsydT\npkwhIiKC1atXs3r1agDGjRtHSEgIoaGhzJ07lzfeeAOAzMxMhg0bRt++fYmJieHOO+9k9OjRJj87\nOTeZELcQADZuhMOHm/79mePVV+HUKcNrRVGkNCKEsCiaLY87duxYxo4dW2Xf3Llzq2yvWrWq2nUh\nISEcOtTw6UHO5pzl3p73AlBWBnZ2Db5Vk/jpJ+jSBXr0MGz39+nPvox9/LHHH7UNTAghTKB9F6UW\nlpyTTIi7oSQyYQL07attPD16QOINs8APDxzOj+d+1C4gIYQwg6K20SHSiqJU692lq9Dh/Jwz+U/l\nY2etcRHkmiNHoLAQBg82bOeX5uPzkg+X/nYJR1vHui8WQogmVtNnZ13aVUkk9WqqcWR4axEZeT2B\nALjYuxDpHclvab9pF5QQQpioXSWRszlnCXYL1jqMet0WfBs/JP+gdRhCCFGvdpVEknOvt4e0ZrcH\n3S5JRAhhEdpVErm5JPLGG3ChFQ7JGNJ1CEcuHiG/NF/rUIQQok7tLoncWBKxtgYTZ2BpVmVlMHw4\nlJQYth1tHYn2i2bn+Z3aBiaEEPVoV0kkOTeZYPfrJZG5cw1jNLRmZwcrVoCDw/V9UqUlhLAE7SqJ\n3FwSaU3696+6fXvw7Ww/u12bYIQQwkTtJonkl+ZTWFaIt7O31qGYJMY/hgsFFzh9Reuls4QQonbt\nJolUVmWZOg29lnQ6eO1VGyaG3cOG3zdoHY4QQtSq/SSRnORqY0SefhrKyzUKqA5WVrBjB1xKmMqG\nY5JEhBCtV7tJIjW1hyxd2jp6Z93Mygreew8ObLyVzKtXOH7puNYhCSFEjdpNEknOrVoS0esN/7XR\nbB7jurm7wy8/W3F/v8lSGhFCtFrtJoncXBJRVXjmGQ0DMoGPD0ztNZUNv28wa0I0IYRoKe0mifye\n9TsRnSOM2zY28L//q2FAJor2jaZMX8b+C/u1DkUIIappF0nkctFlckpyCPUI1ToUsymKwiNRj7By\n90qtQxFCiGraRRLZf2E//br0w0qxzLc7PXwO/z38NRl5rXCiLyFEu2aZn6pm2p+xnwG+A7QOo8H8\nPNyJcZrOa7tf1zoUIYSoon0kkQv7GeBTNYmkpMB//qNNPOZSFHh71mO8e+htisuLtQ5HCCGM2m0S\nURTDxIeWoodnD2L8Y1h/eL3WoQghhFGbX2P9ctFlgl8NJvfJXIttE6n0a+qvTP1sKqcePYWDjUP9\nFwghhJlkjfWbHMw8SD8fy21Uv9GQrkPo5z2A575/TetQhBACaAdJZH/Gfvr79K//RAvRO+vfvPDz\nC+QU52gdihBCtIMkUkN7iCX764xwKo5PZMn3/9Y6FCGEaL9JJCEBvvuu5eNpLHd3mBm4hPVH13Li\n0gmtwxFCtHNtOolcyL/ApcJL9PDsUe3Yjh3w668aBNUE3n7Zh2dHLmX2V7OpUCu0DkcI0Y616STy\nfwn/x5wBc7C2sq52rLAQnJw0CKqJPBL1CFaKFW/sfUPrUIQQ7Vib7uLrtdyLxPmJuDm4VTu+bRu4\nucHAgRoE10ROZp9k6Jqh7J2zl2D34PovEEKIepjbxbdNJ5E39rzBvOh5WofSrF757RU++v0jfn7w\nZ+xt7LUORwhh4SSJXKMoCuX6cmysWumqU03g+efhzjtVFv8+CR8XH14fJ3NrCSEaRwYb3qAtJxCA\nfv3AzU1h7fi1fHv6Wz448oHWIQkh2pk2XRJpo2+tRkcvHmXk+yP5YMIHjOo2SutwhBAWSkoiJnr2\nWcjL0zqKptPbuzefT/6c+764j91pu7UORwjRTrTbJNKhg2GJ3LZkaMBQ1o5fS9wncfxy/hetwxFC\ntAPtNok8/rhljxO52Y8/wr33wiDPP7L+7vXcveFuvjjxhdZhCSHauHabRNqaQYMgIAC6dYPLe8bw\n7f3f8ujWR3n6x6fRV+i1Dk8I0UZJw3obk51tWHDL0xMy8jO4/4v7qVAr+M+4Dwj18tc6PCFEKycN\n6+1cp06GBALg6+LLd//zHcN8RxH+Sj9W/PK6lEqEEE2qXZZEdu82rLE+ZUrLxqSlvSnH+euORyjR\nlfDi6BcZHjhc65CEEK2QlERMkJAAe/dqHUXLig7qScLMBB6LeYwZG2cw7sNxHMo8pHVYQggL1y6T\nyNmzEBKidRQtz0qx4v7I+0mcn8jtXccx4p2xTPtsOkmXk7QOTQhhodp0EqmtRDZhAtxxR8vG0prY\nWdsxrdt8+v6URMJnEUS9NYR7/3sve9L3aB2aEMLCtOk2kbfeUpk7V+tIWi9VhU2bQG9dQFrnd1mx\newUejh483P9hpvSaUuMU+kKItk1m8b1GURS8vFSSksDVVetoLEOFWsF3Z77j7QNvszXxO4Z1jWVa\n34mM6TYGHxcfrcMTQrQASSLXKIpCfLxKbCzYyzIbZntyyVWCRn/F9rSNfJ/8Pf6u/gzyH0SUTxRR\nvlH09u6NnbWd1mEKIZqYxSSR+Ph4FixYgF6vZ/bs2fz973+vds5jjz3G1q1bcXJyYt26dfTr18/k\na2v6RWRnG8ZQKErzvKemlJCQQGxsrNZhAKCr0LE/Yz87z+5l2Xv7KffaR5H9GW7p0oNuHt0Idgsm\nxD2EwI7BdHH0x9/di2N7jnH77bdrHXqDtabfv7ksOXaQ+LVmbhLRZApCvV7P/Pnz2b59O35+fkRH\nRxMXF0dERITxnC1btnD69GmSkpLYvXs38+bNY9euXSZdW5uZM2HxYoiJacY310Ra0/+INlY2xPjH\n0N87hqF2hjE2OQWFRI09TnJuMsk5yRy+eJgNhzfy2+/pdPTL4srWK3ju98TL2Qtnm45cSnPBydoF\nNycXBvR2wcXO8ONk64S+1IHD+x354xgHHG0ccbBxwNHWETsrRyh3wK2DIx0crh+zs7ZDaeZvAq3p\n928uS44dJH5Lo0kS2bNnD6GhoQQFBQEwdepUNm3aVCURbN68mRkzZgAQExNDbm4umZmZJCcn13tt\nbTZtAmvrJn877YatrWGOrkGDAJyBaKL9oquco6qGkt4/C/7Jn+f9mazCLNKz89iwKY+rxfmoJfkE\nu+WTX5pPdnE2xXnFXC0s4VhhMXm/l1BcXkyJroRiXTFXC0o4eaYY1boEa4dinDsajusqdDjYOGBv\n5UhpoQO+XtcTj4ONA4rOkTOJDthZOdLR2YFBA244bu0AOkcupDoyINIBBxsHbKxssLGyobTEmsMH\nrXGws2FP4mm2n92OtWKNtZU1NlY2lJdak3nBmrDuNlgrhn3WVtboy625mGmDva01Tg7W+HYx7K88\nx0qxxlqxwdbautmTnxAtTZMkkp6eTteuXY3b/v7+7N69u95z0tPTycjIqPfa2kgCaX6Vn5FWihXe\nHbzx7uBNb2/4wy0Nv6eqgk5nSGIA+go9JboSLucVcyKphJAe1xNPia6Ey1eL+YUSCkqLUWxLiPSu\nejzr6iX2nClB52nY1lXo0Kt68gt0HD2pR1ehpyQtkWU/ZxqP6Sv0FBbrSE3TExCkr7K/uFRH5kU9\nqqLD2laPq9u14xV69Kqecr2Ocp0erPQoKFgphp71Cgo6nWJ8bWd37bWioKCgqlBWquDoqBiTj4KC\nWqFQWASoCtZWCh1cDPsrzyn6uZjn/vE6HTtW3V+hV8i5YnhtY6Pg6Wl4TuUz9TqF3Fzo3Lnqfl25\nQlaW4Xm2tgpdulyPEUBXrpCdreDre32/oiiUlUF6mgKqgp09BHSt+j7KyhQyL0BQUNX9qXsyWP6X\nrwFwcICQkKqJt7QE0jOg2837S+HMGcM97B0Mk5FWvU4hPb2G/aUKp09jfF5oKMZYAEpKIC3NsP/G\nLwElxZB07TpHB4Xu3Q2vzx88z/Y12ykpgdRUhR49qj6vpBhOnTLcx9ER4/Fvpn+Di70LlkaTJGLq\nt7HGNtdY+re+pUuXah1Co7T2+A/Wc/z7n8/UuD+njmv0QHYdx1VU9FSfv0wFSmu5prCO++mA3JoO\nfF9cZxx6IKOWY+fruK4MqPm3Aol1XFcMHK3lWI2/z60XACgCrtRyXW37AQqAy7Ucq+v3UlDH8Ut1\nXJcPZN2wnbo51fg6q9rZ1+UBF6+9dn3IMruRapJE/Pz8SE29/ktOTU3F39+/znPS0tLw9/envLy8\n3muh8QlICCFE/TQZsR4VFUVSUhIpKSmUlZWxYcMG4uLiqpwTFxfH+vXrAdi1axdubm54e3ubdK0Q\nQoiWoUlJxMbGhlWrVjFmzBj0ej2zZs0iIiKC1atXAzB37lzGjRvHli1bCA0NxdnZmbVr19Z5rRBC\nCA2obdDWrVvVsLAwNTQ0VF22bJnW4Zjl/PnzamxsrNqzZ0/1lltuUV999VWtQzKbTqdT+/btq955\n551ah2K2nJwcddKkSWp4eLgaERGh/vbbb1qHZJbnnntO7dmzp9qrVy912rRpaklJidYh1enBBx9U\nvby81F69ehn3Xb58WR05cqTavXt3ddSoUWpOTo6GEdatpvgXLlyohoeHq5GRkeqECRPU3NxcDSOs\nXU2xV3rxxRdVRVHUy5cv13ufNjcBY+U4kvj4eI4fP87HH3/MiRMntA7LZLa2trzyyiscO3aMXbt2\n8frrr1tU/ACvvvoqPXv2tMiODY8//jjjxo3jxIkTHDlyxKJKuSkpKbzzzjscOHCAo0ePotfr+eST\nT7QOq04PPvgg8fHxVfYtW7aMUaNGcerUKe644w6WLVumUXT1qyn+0aNHc+zYMQ4fPkyPHj3497//\nrVF0daspdjC0M3/33XcEBgaadJ82l0RuHINia2trHEdiKbp06ULfvn0B6NChAxEREWRk1NaPpvVJ\nS0tjy5YtzJ492+I6N1y9epWdO3fy0EMPAYaq044dO2oclelcXV2xtbWlqKgInU5HUVERfn5+WodV\np2HDhuHu7l5l341jxGbMmMHGjRu1CM0kNcU/atQorKwMH60xMTGkpaVpEVq9aood4IknnuCFF14w\n+T5tLonUNr7EEqWkpHDw4EFiLGGI/TV/+ctfWL58ufGPyJIkJyfTuXNnHnzwQfr378+cOXMoKirS\nOiyTeXh48Ne//pWAgAB8fX1xc3Nj5MiRWodltosXL+Lt7Q2At7c3Fy9erOeK1mvNmjWMGzdO6zBM\ntmnTJvz9/YmMjDT5Gsv7S6+HJVah1KSgoIB77rmHV199lQ4dOmgdjkm+/vprvLy86Nevn8WVQgB0\nOh0HDhzgT3/6EwcOHMDZ2blVV6Xc7MyZM6xYsYKUlBQyMjIoKCjgww8/1DqsRlEUxWL/pp999lns\n7OyYPn261qGYpKioiOeee67K+C5T/o7bXBIxZQxKa1deXs6kSZO4//77ufvuu7UOx2S//vormzdv\nJjg4mGnTpvHDDz/wwAMPaB2Wyfz9/fH39yc62jCVyz333MOBAwc0jsp0+/btY8iQIXh6emJjY8PE\niRP59ddftQ7LbN7e3mRmZgJw4cIFvLy8NI7IfOvWrWPLli0WlcTPnDlDSkoKffr0ITg4mLS0NAYM\nGEBWVl3DJdtgErH0cSSqqjJr1ix69uzJggULtA7HLM899xypqakkJyfzySefcPvttxvH+liCLl26\n0LVrV06dOgXA9u3bueWWRszX0sLCw8PZtWsXxcXFqKrK9u3b6dmzp9ZhmS0uLo733nsPgPfee8+i\nvkiBYZbx5cuXs2nTJhwcHLQOx2S9e/fm4sWLJCcnk5ycjL+/PwcOHKg/iTdxr7FWYcuWLWqPHj3U\nbt26qc8995zW4Zhl586dqqIoap8+fdS+ffuqffv2Vbdu3ap1WGZLSEhQ77rrLq3DMNuhQ4fUqKio\nVt89szbPP/+8sYvvAw88oJaVlWkdUp2mTp2q+vj4qLa2tqq/v7+6Zs0a9fLly+odd9xhEV18b47/\n3XffVUNDQ9WAgADj3++8efO0DrNGlbHb2dkZf/c3Cg4ONqmLb5tdlEoIIUTza3PVWUIIIVqOJBEh\nhBANJklECCFEg0kSEUII0WCSRESbYW1tTb9+/Yw/58/XtbyS5Vi3bh2dO3fm4YcfbtR9lixZwksv\nvWTc3rVrV633LCkpoW/fvtjb23PlSl3LP4n2TpOp4IVoDk5OThw8WPN6hZWdEC1x9LOiKEybNo2V\nK1dWO6bT6bCxMe3P+Ob3vnXrVsaOHVvjuQ4ODhw6dIjg4GDzAxbtipRERJuVkpJCWFgYM2bMoHfv\n3qSmprJ8+XIGDhxInz59WLJkifHcZ599lrCwMIYNG8b06dON39hjY2PZv38/ANnZ2cYPVb1ez9/+\n9jfjvd5++20AEhISiI2N5d577yUiIoL777/f+Iy9e/dy66230rdvXwYNGkRBQQEjRozg8OHDxnOG\nDh3K0aPVF5K9sSf+unXriIuL44477mDUqFEUFhYycuRIBgwYQGRkJJs3b67xfSUmVl3A9ocffmDk\nyJEcO3aMmJgY+vXrR58+fThdueC4ECaQkohoM4qLi+nXrx8AISEhvPzyy5w+fZr333+fgQMHsm3b\nNk6fPs2ePXuoqKhg/Pjx7Ny5EycnJzZs2MDhw4cpLy+nf//+REVFAbXP3fTuu+/i5ubGnj17KC0t\nZejQoYwePRqAQ4cOcfz4cXx8fLj11lv59ddfiYqKYurUqXz66acMGDCAgoICHB0dmTVrFuvWreOV\nV17h1KlTlJaW0rt373rf68GDBzl69Chubm7o9Xq+/PJLXFxcyM7OZvDgwcTFxbF///5a31d2dja2\ntra4uLjw1ltv8fjjjzN9+nR0Oh06na6p/klEOyBJRLQZjo6OVaqzUlJSCAwMZODAgQBs27aNbdu2\nGRNNYWEhSUlJ5OfnM3HiRBwcHHBwcDBpmpxt27Zx9OhRPvvsMwDy8vI4ffo0tra2DBw4EF9fXwD6\n9u1LcnIyLi4u+Pj4MGDAAADjpJr33HMPzzzzDMuXL2fNmjU8+OCD9T5bURRGjx6Nm5sbABUVFTz1\n1FPs3LkTKysrMjIyuHjxIjt37qz2vipLNNu2bWPMmDEADBkyhGeffZa0tDQmTpxIaGho/b9sIa6R\n6izRpjk7O1fZfuqppzh48CAHDx7k1KlTxrVDbqwuuvG1jY0NFRUVgKGx+UarVq0y3uvMmTOMHDkS\nVVWxt7c3nmNtbY1Op6u1LcbJyYlRo0axceNG/vvf/3LfffeZ9L6cnJyMrz/88EOys7M5cOAABw8e\nxMvLi5KSEhRFqfa+KuOIj4/nD3/4AwDTpk3jq6++wtHRkXHjxrFjxw6TYhACJImIdmTMmDGsWbOG\nwsJCwLD2zKVLlxg+fDgbN26kpKSE/Px8vv76a+M1QUFB7Nu3D8BY6qi81xtvvGGs+jl16lSta48o\nikJYWBgXLlww3is/Px+9Xg/A7Nmzeeyxxxg4cKBJi2DdPFNRXl4eXl5eWFtbs2PHDs6dO4eiKLW+\nL1VVOXLkCH369AEM66gEBwfz6KOPMn78+BrbZISojVRniTajpm/7N+4bNWoUJ06cYPDgwQC4uLjw\nwQcf0K9fP6ZMmUKfPn3w8vIiOjra+EG9cOFCJk+ezNtvv80f//hH4/1mz55NSkoK/fv3R1VVvLy8\n+PLLL2ttQ7G1tWXDhg08+uijFBcX4+TkxHfffYezszP9+/enY8eOJlVlVb6nG59x3333cddddxEZ\nGUlUVJRxSd+b31dltd7+/fuNVXoAn376Ke+//z62trb4+PiwaNEik+IQAkAmYBTiJkuXLqVDhw78\n9a9/bZHnZWRkcNttt1XrPVXpvffeY9++fbz22mtN8rxnn32W7t27M3ny5HrPDQ4OZv/+/Xh4eDTJ\ns0XbI9VZQtSgpcaTrF+/nkGDBvHcc8/Veo6joyNbt25t9GDDSosWLao3gVQONtTpdBa51LFoOVIS\nEUII0WDyFUMIIUSDSRIRQgjRYJJEhBBCNJgkESGEEA0mSUQIIUSDSRIRQgjRYP8fk3L47sAbKMwA\nAAAASUVORK5CYII=\n" + } + ], + "prompt_number": 9 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " Estimated autocovariance\n", + "----------------------------\n", + "Obviously knowing the spectrum one can compute the covariance\n", + "function. The following code will compute the covariance for the \n", + "unimodal spectral density S1 and compare it with estimated \n", + "covariance of the signal xx." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "clf()\n", + "Lmax = 85\n", + "R1 = S1.tocovdata(nr=1) \n", + "Rest = ts.tocovdata(lag=Lmax)\n", + "R1.plot('.')\n", + "Rest.plot()\n", + "axis([0, 25, -0.1, 0.25])\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "png": 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So74nQghRRQpPILGxsbCzs4O1tTU0NTXh7++P8PBwiWV69eoFfX19AEDPnj3x9OnTeq9b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\nUAIhhBAiE0oghBBCZEIJhBBCiEz+H72XH4/yYkS0AAAAAElFTkSuQmCC\n" + } + ], + "prompt_number": 10 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see in Figure below that the covariance function corresponding to the spectral density S2 significantly differs from the one estimated directly from data. It can be seen in Figure above that the covariance corresponding to S1 agrees much better with the estimated covariance function." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "clf()\n", + "R2 = S2.tocovdata(nr=1)\n", + "R2.plot('.')\n", + "Rest.plot()\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "png": 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sAmuH/1dlfbtauOKX8b/gpciXkJKTgneffrfOGzbv/30f03ZPg6G+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\nixEVFYXg4JbzqNmCggLk5+cDAB49eoT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+ } + ], + "prompt_number": 11 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Section 2.2.2 Transformed Gaussian models\n", + "-------------------------------------------\n", + "We begin with computing skewness and kurtosis for the data set xx and compare it with the second order wave approximation proposed by Winterstein:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import wafo.stats as ws\n", + "rho3 = ws.skew(xx[:, 1])\n", + "rho4 = ws.kurtosis(xx[:, 1])\n", + "\n", + "sk, ku = S1.stats_nl(moments='sk')" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 13 + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Comparisons of 3 transformations" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "clf()\n", + "import wafo.transform.models as wtm\n", + "gh = wtm.TrHermite(mean=me, sigma=sa, skew=sk, kurt=ku).trdata()\n", + "g = wtm.TrLinear(mean=me, sigma=sa).trdata() # Linear transformation \n", + "glc, gemp = lc.trdata(mean=me, sigma=sa)\n", + "\n", + "glc.plot('b-') #! Transf. estimated from level-crossings\n", + "gh.plot('b-.') #! Hermite Transf. estimated from moments\n", + "g.plot('r')\n", + "grid('on')\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "png": 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+ } + ], + "prompt_number": 14 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test Gaussianity of a stochastic process\n", + "------------------------------------------\n", + "TESTGAUSSIAN simulates e(g(u)-u) = int (g(u)-u)^2 du for Gaussian processes given the spectral density, S. The result is plotted if test0 is given. This is useful for testing if the process X(t) is Gaussian.\n", + "If 95% of TEST1 is less than TEST0 then X(t) is not Gaussian at a 5% level.\n", + "\n", + "As we see from the figure below: none of the simulated values of test1 is above 1.00. Thus the data significantly departs from a Gaussian distribution. " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "clf()\n", + "test0 = glc.dist2gauss()\n", + "# the following test takes time\n", + "N = len(xx)\n", + "test1 = S1.testgaussian(ns=N, cases=50, test0=test0)\n", + "is_gaussian = sum(test1 > test0) > 5 \n", + "print(is_gaussian)\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "False\n" + ] + }, + { + "output_type": "display_data", + "png": 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fP6SkpLi8XIVW2gIA9u3bh/79++PEiRPIzMxEXFycw/OdaQvFniF0Zh2D1oSG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\nY0it2QAAAABJRU5ErkJggg==\n" + } + ], + "prompt_number": 15 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Normalplot of data xx\n", + "------------------------\n", + "indicates that the underlying distribution has a \"heavy\" upper tail and a \"light\" lower tail." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "clf()\n", + "import pylab\n", + "ws.probplot(ts.data.ravel(), dist='norm', plot=pylab)\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "png": 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+fJmbN29qH7dp0+beqqsBEg6isdBpLTjlwMgT0CQftrrCRavb7pZQEHdWI+c5\nbN26FR8fH9q1a0f//v3x8vJi2LBhNVakEOLOtMFg7g79z8PU3+GEE6zyKAkGm3J3SzCImlFpOMyb\nN4/ffvuNjh07kpiYyC+//EJQUJAxahOiUYuMjC4bdG5jB8/9Bm7psPJ++N285AAeKFu34FvyWN1a\nW4JB3ItK1zlYWlrSsmVLiouLKSoqYsCAAfzjH/8wRm1CNFra1oJ1Kwg5CT4XYWcniL8FXLntbmkt\niJpXaTg4OjqSlZVF3759mThxIi4uLtjb2xujNiEanbKxhZbgnwGhv0K8Cyx7APIvou6VVJ4Eg6gd\nFQ5If/PNN4SFhVFUVIS1tTXFxcV89dVXZGZmMnHiRJycnIxdqx4ZkBYNRVkoAM2dYMRJaJoH23wh\nJbvkCQcgq+RrCQVRffc0W2n06NH8+uuvDB06lPDwcEJDQ7VHhdYVEg6iIdAGg5k79DkHDyXCgfZw\noA0Up952txXgSmkw9OvXin37vjBuwaLeu+eprDdu3GDz5s1s3LiRP//8k9GjRxMeHk7//v1rvNjq\nkHAQ9ZnO9FS3GxAWD7lWsL0zXNcgYwuittToOocrV67w3XffsWzZMq5du0ZKSkqNFHkvJBxEfaTT\nhWTVGgYmgX8y7O4ER1oDpf9vdQZOlHwtwSBqTo1t2X39+nW+//57Nm3axLVr13jsscdqpEAhGpOy\nsxYAPKHTZRj+K5xtAcsehLxLlAUDqMEgB/EI06iw5ZCVlaXtUjp8+DCjRo0iPDyc4ODgCvdcMjZp\nOYj6QqcLyeEmDEsA1xvqgHNSbgWvKgsGG5vL5ObGGqdY0eDdU7dSy5YtCQ0NJTw8nCFDhmBldfsS\nfdOTcBD1gXYhm0aBXpcg+Dgc8oT97aHwgoFXSBeSqF33FA65ubnY2trWSmE1RcJB1GU6rQXXQgg7\nDMXFsM0f0q8beIWEgjCOe9pbqTaDYdeuXXTu3BkfHx8WL15s8J5Zs2bh4+NDt27diIuLq7VahKgN\n2mCwcINBifDkPohrDV+4STCIeuGuz5C+V0VFRcyYMYOff/4Zd3d3evfuzahRo/D19dXes2PHDk6f\nPk1CQgK///47zz//PAcPHjR2qULcNZ3WQvsrMPIAXHCAFe3Uz/07rnCWAWdRdxg9HGJiYvD29sbL\nywuA8ePHs2XLFp1w2Lp1K5MnTwYgKCiIjIwMLl26hKurq7HLFaJKdKan2rpC6HFoewUifSHhpoFX\nSCiIuq33235tAAAgAElEQVTCcAgLC9N+fXv/lEajYevWrdX6hqmpqTpnUHt4ePD7779Xek9KSorB\ncIiIiNB+HRwcTHBwcLXqEqK6dA/gSYXBv6rrFZY9AAVpt90toSCMLyoqiqioqLt6TYXh8OqrrwKw\nefNmLl68yKRJk1AUhQ0bNtzTb/BVnQZ7+2BJRa8rHw5CGFPZuoXSA3gOQ5NCWNcTLt4A7hQMMq4g\njOf2X5wXLFhQ6WsqDIfSN3r11VeJjS2bXz1q1Ch69uxZ7SLd3d1JTk7WPk5OTsbDw+OO96SkpODu\n7l7t7ylETWrbNoTz50s++M3d4aHzEHQa9rWHmLagGNo9QIJB1C+VHvaTm5vLmTNntI/Pnj1Lbm5F\ni3Yq16tXLxISEkhKSuLWrVts2rSJUaNG6dwzatQovvzySwAOHjxI8+bNZbxBmFz//lPQaPxLgqGi\nA3huDwY5hEfUT5UOSH/00UcMGDCAdu3aAZCUlMSnn35a/W9oYcEnn3xCaGgoRUVFTJ06FV9fX1au\nXAnA9OnTGT58ODt27MDb2xs7Ozu++EJ2nRSmpTMLybqgCgfwlNxb0lowN0+lsFBCQdQfVdp47+bN\nm5w8eRKAzp0706RJk1ovrCpkEZwwBu0KZ5SSA3j+Ug/g+cWn5ACe20kXkqjbamRX1pycHP79739z\n/vx5Vq1aRUJCAidPnmTkyJE1Wmx1SDiI2qTTWmiugRFx0DQbtnUpdwBPeRIKon64pxXSpaZMmYKV\nlRUHDhwAwM3Njblz59ZMhULUQRqNv+4BPA+kwrM/wzkHWNlWgkE0CpWOOZw5c4avv/6ajRs3AmBn\nZ1frRQlhCjoL2bQH8MRArjn8xweulU6ntgJuld0n6xZEA1RpODRp0oS8vDzt4zNnztSZMQchaopO\nF5JVIQw8Xe4AnkLUbS9Kz3C+hYSCaOgqDYeIiAiGDh1KSkoKEyZM4Ndff2X16tVGKE2I2qcTCgCd\nMmF4nHoAz/IHIfcSZfshZSGhIBqLOw5IFxcX88033zBo0CDtxndBQUE4OzsbrcA7kQFpUV16XUgO\n5jDsT3C9rg44GzyAR8YVRMNQI7OVevbsqbNCui6RcBDVodNa0B7AcwwOtYH9VlBoaJ7G7WsWjhiv\nYCFqWI2Ew+uvv07Lli0ZN26czmB0ixYtaqbKeyDhIO6GXheSSxGExYJSDNucIN3awKuktSAanhoJ\nBy8vL4Ob3iUmJt5bdTVAwkFUlU4wWNhA/3jocRb2+MBhBZQ7nbMgoSAalhoJh7pMwkFURq+10D4H\nRh6CC81gV1PItrztFRIKouGrymdnpbOV6vIKaSEqojfgbGupbnvRNh0inSHBwcCrJBiEKCUrpEWD\no9ta8IDuV+CFHyHHDJZ5GQgG2TlViNvJCmnRYOh1ITkpJQfw5MM6D7hoc9srZM2CEBWRFdKiQdAJ\nBnMbeOgUBJ2CfR0gxuyOA86ynbYQ+mSFtKjX9FoLbW5C2H64ZgsrveCG+W2vkNaCEFVRpdlKV65c\n0a6Q7tOnDy1btqz1wqpCZis1ThMmzGbDhshyVzzBugmEHAGfC7DTBeIdKNv2otx9MuAsxL1NZY2N\njdVb36AoivZajx49aqjM6pNwaHz0ZiFpD+D5E064wM92kH+n1oKEghD3FA7BwcFoNBry8vKIjY2l\na9euABw5coRevXrx22+/1XzFd0nCofEoCwUPtC0CnQN4nCHFloq305ZQEKLUPR32ExUVxd69e3Fz\nc+Pw4cPExsYSGxtLXFwcbm5uNV6sEIb4+4+5bVzBAczs4IEL5Q7gaQMpg0ruuYUaHmXB0KZNoQSD\nEHep0jEHPz8/jh8/Xum1qrh27Rrjxo3j3LlzeHl58fXXX9O8eXO9+7y8vGjatCnm5uZYWloSExNj\nuHhpOTRoeoPN2INbKoQdhVwriHSEa+VnzpWEB1lAMlZWFuTn/2nMkoWoF2pk+4zx48djb2/PpEmT\nUBSF9evXk52dzYYNG+66oNdee42WLVvy2muvsXjxYq5fv86//vUvvfvatWtHbGxspZv7STg0TPqh\n4ABWBTAwDvxTYbcrHGmGOtmuqNwry1oLVlZpEgxCVKBGwuHmzZssX76c/fv3A9CvXz+ef/55rK0N\n7WB5Z507d2bfvn24urpy8eJFgoODOXHihN597dq149ChQzg5Od25eAmHBsPgDCTsAQ10OgPDj8NZ\na/jJFXJdgasG7s3G2Tmby5f3G7FyIeqfew6HwsJCQkJC2Lt3b40U5OjoyPXr1wF15lOLFi20j8tr\n3749zZo1w9zcnOnTpzNt2jTDxWs0zJ8/X/s4ODiY4ODgGqlVGI/+DCQAe3C4AsPi1QN4treGxBbI\nYLMQdy8qKoqoqCjt4wULFtx7y2HQoEF89913BscGDAkJCeHixYt61xcuXMjkyZN1wqBFixZcu3ZN\n7960tDRat25Neno6ISEhfPzxx/Tt21e/eGk51GsuLn1JT7+Ozgwk7NUvex2D4FNwyBH2PwSFCSXP\na0ruLxtsPnfuJ+MXL0Q9ViO7strZ2REQEEBISIh2XyWNRsPSpUsN3v/TTxX/j1randSqVSvS0tJw\ncXExeF/r1q0BcHZ2ZsyYMcTExBgMB1E/2dr2JC8vv+RRue4jAJcLEHYMlFuw2gvSHYEEoCXqYLMl\ncJ3t299gxIh+Rq9diMai0nAYO3YsY8eO1UkaQ4f/VMWoUaNYs2YNs2fPZs2aNYwePVrvntzcXIqK\ninBwcCAnJ4fdu3frdB2J+stw91HJ7CKLIuh/Bnqcgz0ucNgLlAIgD90upETpQhLCCCrtVsrLy+P0\n6dNoNBq8vb2rNRBd6tq1azz++OOcP39eZyrrhQsXmDZtGpGRkZw9e5axY8cC6pjHxIkTeeONNwwX\nL91K9YK//xiOHSvtFio3poAGyIL2Z2BkElywgV2+kJ1dco90IQlRG+5pQLqgoIC5c+fy+eef06ZN\nGwDOnz/PlClTWLRoEZaWt5+gZXwSDnVbhQPNpaFgewtCY6FtLkR2goTSf5c2qN1IaiiEh3dl/frF\nxitciAbunsLhpZdeIjs7m48++ggHB/VwlMzMTF599VVsbW1ZsmRJzVd8lyQc6i6DC9hKQwEFuv8N\ngy/BET/YWwgFZsgMJCGM457Cwdvbm1OnTmFmprvDRlFREZ06deL06dM1V2k1STjUPU2adOfWrUIM\nhwLgdBpGXoAmxbDNE9KskFAQwrjuaW8lMzMzvWAAMDc3N3hdNG4TJsxGo/EvFwz2qIPN2UAWmJ+D\n/rEwNRFONoX/tJdgEKIOq3C2kq+vL2vWrGHy5Mk619euXUvnzp1rvTBRfxjuQiodVL4AbTIhLA2u\nWcHK9nDj9lCQQ3eEqGsq7FZKSUlh7Nix2NjY0LNnT0A94yE3N5fNmzfj4eFh1EINkW4l04qIWM6C\nBcspW8RWvgspGayLIOQS+GTDzlYlB/C0QVoKQpjWPW+foSgKe/bs4dixY2g0Gvz8/Bg0aFBFtxud\nhIPp6LYWbhtX4Dx0yYShF+GEA/zsCvleSCgIUTfUyMZ7dZmEg/GVhYI76pBV+S6kZGh+C0akQdMC\n2OYGKZ2QUBCibqmR7TOEAENrFsq3Fq6AWS70uQoPXYEDTvCbExS1RYJBiPpJWg7ijszM/Cn7Ky6/\n5UU2oADJ4JYHYZfUnS62t4Zr3kgoCFF3SctB3BODh+4AamshA6wyYOBl8M8sOYDHn7LgkBlIQtRn\n0nIQetq2DeH8+TQMhwJAMnTKhOHp5Q7gaUdpa8HG5jK5ubHGLlsIUUXSchB3pWxqKhiehZQMDgUw\n7Ba4XoIfWkOiL7JeQYiGR1oO4g67ppabhaRRoJdS7gCeQChshowrCFH/3NP2GaLh8/cfg0bjXxIM\n7uhve5Gs/nFpBk8nQsA5WN0F9vYuCYZM5s8Pk2AQogGSbqVGquw0tgo2yCMZLJpA//PQ4wTsaQOH\nO4FijxoKA4iIeMEUpQshjEC6lRohdRbS7aFQOjU1Rf1n+1slB/DYwq5AyG4JZGNllUZ+/p8mqVsI\nUTNkQFro0N1O+/aWQob6tW0hhF6GttkQ6Q4JnSkdf+jSxZqjRyUYhGgMpOXQwPXvP4Xo6D/KXSm/\nGyrABaAI9QAeMxgcD0eaw95AKGiOTE0VouGRvZUasbK1ChrU7qLyaxayKAsFc3AqhpFnSw7gCYQ0\ndyAbjSaZ4mIZbBaioalzs5W++eYbunTpgrm5OYcPH67wvl27dtG5c2d8fHxYvFjODr5bGo0/589f\nRA0Ej5J/OlAWDJcAczBvAv0uwtR4ONkG/jMc0prh7HwRRdkpwSBEI2bUcAgICGDz5s3069evwnuK\nioqYMWMGu3bt4vjx42zYsIH4+HgjVlk/qSexdS032OxB2bTU0lDIAtKAImiTD88dA3cFVobCQS8o\nPomi7OTy5f2m+jGEEHWEUQekq3KCXExMDN7e3nh5eQEwfvx4tmzZgq+vby1XV/9MmDCbDRt+BAoB\nK9R/na7ob3cBaigUgnUPCIkEn0zY5Q/HOwA5ODtnc/mytBSEEKo6N1spNTUVT09P7WMPDw9+//33\nCu+PiIjQfh0cHExwcHAtVlc3lG1zYYPa+PMs92xpKwHgMnALdcyhD3T5GYZugBOtYVlfyM/Hyuq0\nTE0VooGLiooiKirqrl5T4+EQEhLCxYsX9a4vWrSIsLCwSl+v0Wju6vuVD4eGLjIymrFjX+XWLdAN\nhNJpqaA9X4GbJdetoPlVGLEJmgKb+kJKE2xskslVZAaSEI3B7b84L1iwoNLX1Hg4/PTTT/f0end3\nd5KTk7WPk5OT68R51aYUEbGct976DEWxAFqWXHUod0f57qNsoC2QBGaZ0CcDHkqHA53gNzf6PejG\nvuQvjFS5EKK+Mlm3UkXTqHr16kVCQgJJSUm4ubmxadMmNmzYYOTq6obIyGgef3w2ubkATqj/uuxL\nni1d0QxwFXXcAaAncALcFAhLgbwm8J8ebP/yPUaMqHgigBBClGfUcNi8eTOzZs3iypUrjBgxgsDA\nQHbu3MmFCxeYNm0akZGRWFhY8MknnxAaGkpRURFTp05tlIPR6uK1BMCx5IoNYA7kAMXADaAAda1C\naSviOlj9CgNzwf8i7O6IZ4YL56/+bOzyhRD1nCyCq4PUBWwWqNlthxoGeahBkIfaanBCHVfIKLlu\nDZ2KYXg8nG3BWIcxfLf2Y9P8AEKIOk32VqpnysYWXFG7j8xQWwpFqEFQCNgC1qgzkawAN3Awg2GH\naOGr8M3k7QxsN9BEP4EQoqGQlkMdoa5Z+As1EJqithCKUQPgQsk/nYF01O6k5qCxhV7HsR2ezCv9\nX2Ju37lYW1ib6CcQQtQXsrdSPRAZGc3UqQu5dMkcdexAQ1lroRA1KIrRbqVNS8AeXNJwmpJI547t\n+DTsU/yc/Uz0Ewgh6hvpVqrDykKhGLWbyK7kn1cBP+AvoDmQibqQzRWwxLZZLiMWN2Fv5kneGfAO\n03pOw0wjB/oJIWqWhIMJREZGM2nS/5GRYUXZGMI11JaBX8nX3YD/As0AJzSabCb9bzsOtNiBxqkZ\nRyYcobVDa1P9CEKIBk66lYwsMjKaxx57j7y80paCBnWwuQDIRV3A1gw4jtqayKRTIPR+04n95/az\nfMRyhvsMN1X5QogGQLqV6hh10DkOddzACjUUFNSxBqXkzynUNQ12QAZB081I9I7F1e4Jjr1wDDsr\nOxNVL4RoTCQcjEQNhpOoi9raAwmooXANdVzBAXXtQhPACk3L87R9MZVCt6bsDNtJj9Y9TFW6EKIR\nknCoZZGR0cyatYSzZ28CvYEkYAhqt1EmaljcKLluC+YW2If+jdkDKfxjUAQz7puBhZn8axJCGJd8\n6tSwyMho3nzzS06dSiMn5wbgAliirlEoRF2/ULrH0VK0oUAz3PrkYj76b7q1CWDZ8B20adbGBD+B\nEELIgHSNiohYzrvvRnHrVuleR9mAD2oGxwMvAv+HGhj/T/s6M7vJBP7PGdKaJrJk6BIe8X3krrcu\nF0KIqpJFcEakzkJaRl6eT7mrpQ2zQsANOAJMQG0x3AKsMO+WiM3oMzzRewLvDnqXZtbNjFu4EKLR\nkXAwoh49XiQuzvm2q6XbaA8BfgTcgWjAGpqfp/Uz53Bqb8unIz/lfs/7jVmuEKIRk3CoRZGR0Sxd\nupvU1HTOnUsmJ6c5itL+truGAGuAVkAo8BOYaTB/8AesBp7lzUFzefWBV7EytzJ6/UKIxkvCoZZE\nRCznvfeOkJc3AbVFUDo+UBoGpUpDYS2QDW6XsXrkT3y92vHtUxvxbuFt3MKFEAIJh1pRNrawCZgH\nvANEAANRg6I0DNKAXDQaO2yb22A78iQFnVJYOvL/mNR1kgw4CyFMRlZI16DSbqQ//jhNXl7pyXTl\nB5xLp6f+hDq20IoePS4yf30YM3bMYFD7Qbwf8gstbVve/tZCCFHnSDhUQWRkNP/4x4+cObMQtZVQ\nOtBcfsB5LrCQ0pBo6z8Tm8kJ/M/u/2H16NVyAI8Qol6RcKiCpUt3lwQDqIFQGgahlIUCwJs0sT6H\ny/BzXO/xF08EzJQDeIQQ9ZJRw+Gbb74hIiKCEydO8Mcff9Cjh+H9gry8vGjatCnm5uZYWloSExNj\nzDL15OeX/2sqnZZaMvuIK5iZheHp2Rr3HhquPRCLUwtHVo78lS4uXUxSrxBC3CujhkNAQACbN29m\n+vTpd7xPo9EQFRVFixYtjFTZnTVpUljuUdnYgqPjee67rw3Pvvgoh2z3surwKt4JlgN4hBD1n1HD\noXPnzlW+ty5Moiq/lsHG5jny8kq3vOhHhw67WLJkKk18b/Hc9ufo6daTI8/JATxCiIahTo45aDQa\nBg8ejLm5OdOnT2fatGkV3hsREaH9Ojg4mODg4BqpQXcQGiAaG5txeHu74eZmz5PP9WHjrVXs3yoH\n8Agh6raoqCiioqLu6jU1vs4hJCSEixcv6l1ftGgRYWFhAAwYMIAPP/ywwjGHtLQ0WrduTXp6OiEh\nIXz88cf07dtXv/haXOcQGjqP3bvf0bs+JHQe49/twOu/vM4TXZ9gQfACOYBHCFGvmGSdw08//XTP\n79G6tdo14+zszJgxY4iJiTEYDrVJdxC6hNMpYjp/xdU/nNg5UQ7gEUI0XCYbNa0otXJzc8nKygIg\nJyeH3bt3ExAQYLS6IiOjCQ2dx5EjJ8oumt+Cfm/D1AdwzfDh4DMHJRiEEA2aUcNh8+bNeHp6cvDg\nQUaMGMGwYcMAuHDhAiNGjADg4sWL9O3bl+7duxMUFMTIkSMZMmSIUeorHWfYvfsdrl9/AZgLbf4L\nz3UH9xja7BzHh4/Nk5PZhKim6dOnY29vz969e3Wu//vf/6ZLly5069aNwYMHc/78+Sq/Z2JiIkFB\nQfj4+DB+/HgKCgoM3jd79mwCAgIICAjg66+/1l7fs2cPPXv2JCAggKeeeoqioiIArly5wtChQ+ne\nvTv+/v6sXr1a+5qnn34aV1dXo/7ianRKPVbT5Q8ZMlcBRf1jfU1hZJjCKw6KXe/+ypDQucr27ftq\n9PsJ0RgUFxcrRUVFyttvv62MHz9eOXr0qOLr66scOXJEe8/evXuVvLw8RVEUZcWKFcq4ceOq/P6P\nPfaYsmnTJkVRFOW5555TVqxYoXfP9u3blZCQEKWoqEjJyclRevfurWRlZSlFRUWKp6enkpCQoCiK\novzv//6v8tlnnymKoijz589XXn/9dUVRFCU9PV1p0aKFUlBQoCiKokRHRyuHDx9W/P39q/E3YnpV\n+eyUyfjlqOMMCnTZBC92AcUDliXTyzaYH3e9w4gR/Sp9DyEEJCUl0alTJyZPnkxAQADr1q0jPj6e\n9evX06VLF7Zu3cq0adNITU0F1JmG1tbqTgJBQUGkpKRU6fsoisLevXt59NFHAZg8eTI//PCD3n3x\n8fH069cPMzMzbG1t6dq1Kzt37uTq1atYWVnh7a3ukDx48GC+++47QB37zMzMBCAzMxMnJycsLNRe\ng759++Lo6HgPf0N1n/SPlFPc9ApMHAFNk2HTd5CiHsBjbV1k4sqEqH9Onz7N2rVrue+++wB48skn\ntc95e3tz8OBBg6/77LPPGD5cnRqelZVFv376v5RpNBrWr19Py5Ytad68OWZm6u+57u7u2sApr1u3\nbixYsIBXX32VnJwc9u7dS5cuXXB2dqawsJDY2Fh69uzJt99+S3JyMgDPPPMMgwYNws3NjaysLJ2u\nqMagUYdD6SK3m7fMuOh1gOQeB2kR15trG2OhSD2Ap0OHOcycOdTElQpR/7Rt21YbDFW1bt06Dh8+\nzEcffQSAg4MDcXFxFd5/5cqVKr1vSEgIf/zxBw888ADOzs7cf//92kDZuHEjL7/8Mvn5+QwZMgRz\nc3MA3n33Xbp3705UVBRnzpwhJCSEv/76CwcHhzt9qwaj0YaDdpFb3hgIexbyWtBmyxNMeTiAg/lv\nc/OmOdbWRcycOVS6k4SoBju7u1v/8/PPP7No0SKio6OxtLQE1JZD3759DZ5/smHDBjp16kRGRgbF\nxcWYmZmRkpKCu7u7wfefM2cOc+bMAWDixIl06tQJgD59+hAdHQ3A7t27SUhIAODAgQPMnTsXgA4d\nOtCuXTtOnjxJr1697urnqq8abTj8+5NtnPEpAP+RsPt9ODKJ82g46Pomu3a9beryhGhU4uLieO65\n5/jxxx9p2bLszBMHBwf+/PPPO752wIABfPPNN4wbN441a9YwevRovXuKi4u5fv06Tk5OHDlyhCNH\njmhnQaanp+Ps7Ex+fj7vvfce8+bNA9Ttfn7++WcefPBBLl26xMmTJ2nf/vajgBuw2h8Xrz0Vlb99\n+z5lyJC5Sv/+85UhQ/RnGW05sUVp8npThdGTFWzTy2YooSj9+883QuVCNGyJiYlKQEBAle8fPHiw\n0qpVK6V79+5K9+7dlYcffrjKrz179qxy3333Kd7e3srjjz+u3Lp1S1EURTl06JDyzDPPKIqiKHl5\neYqfn5/i5+en3H///cpff/2lff0///lPxdfXV+nUqZOyZMkS7fX09HRl5MiRSteuXRV/f3/lq6++\n0j43fvx4pXXr1oqVlZXi4eGhfP7551Wuty6oykd/gzsmVH9PJOjQYS5LloQS2M+bWTtnceTSEZpG\n30fst+v03jM0VFoOQoiGrSrbZzS4qay6B/Oozpx9m39+/S7d/l83fJ19OfL8ERY89SwdOszVuU8d\nfA4xZrlCCFEnNbgxB709kVz+hrBnSXVI5cDkKO0BPKWDzB9//KYMPgshxG0aXDhoD+axyIP+b0OP\nVbDnHfq0TNY7mW3EiH4SBkIIYUCD61aaNWuI2l3U/hdwPAMrjtAh4xyzZhpnfyYhhGgIGtyANKiD\n0h9//FO57qIQaSEIIUSJqgxIN8hwEEIIUbFGOVtJCCHEvZNwEEIIoUfCQQghhB4JByGEEHokHIQQ\nQugxajj885//xNfXl27dujF27Fhu3Lhh8L5du3bRuXNnfHx8WLx4sTFLrBVRUVGmLqFS9aFGkDpr\nmtRZs+pLnVVh1HAYMmQIx44d46+//qJjx468++67evcUFRUxY8YMdu3axfHjx9mwYQPx8fHGLLPG\n1Yf/YOpDjSB11jSps2bVlzqrwqjhEBISoj19qaJzYmNiYvD29sbLywtLS0vGjx/Pli1bjFmmEEI0\neiYbc/j888+158SWl5qaiqenp/axh4eHwTNhhRBC1J4aXyEdEhLCxYsX9a4vWrSIsLAwABYuXMjh\nw4f57rvv9O777rvv2LVrF6tWrQLUM2V///13Pv74Y/3iDRwdKIQQonKVffTX+K6sP/300x2fX716\nNTt27OCXX34x+Ly7uzvJycnax8nJyXh4eBi8V7bOEEKI2mHUbqVdu3bx/vvvs2XLFqytrQ3e06tX\nLxISEkhKSuLWrVts2rSJUaNGGbNMIYRo9IwaDjNnziQ7O5uQkBACAwN54YUXALhw4QIjRowAwMLC\ngk8++YTQ0FD8/PwYN24cvr6+xixTCCFELZxdbXQffPCBotFolKtXr5q6FIPmzZundO3aVenWrZsy\ncOBA5fz586YuyaD/+Z//UTp37qx07dpVGTNmjJKRkWHqkgz6+uuvFT8/P8XMzEyJjY01dTl6du7c\nqXTq1Enx9vZW/vWvf5m6HIOmTJmiuLi4KP7+/qYu5Y7Onz+vBAcHK35+fkqXLl2UJUuWmLokPXl5\necp9992ndOvWTfH19VVef/11U5d0R4WFhUr37t2VkSNH3vG+eh8O58+fV0JDQxUvL686Gw6ZmZna\nr5cuXapMnTrVhNVUbPfu3UpRUZGiKIoye/ZsZfbs2SauyLD4+Hjl5MmTSnBwcJ0Lh8LCQqVDhw5K\nYmKicuvWLaVbt27K8ePHTV2WnujoaOXw4cN1PhzS0tKUuLg4RVEUJSsrS+nYsWOd/PvMyclRFEVR\nCgoKlKCgIGX//v0mrqhiH374oTJhwgQlLCzsjvfV++0zXnnlFd577z1Tl3FHDg4O2q+zs7Np2bKl\nCaupWFXWodQFnTt3pmPHjqYuw6D6sk6nb9++ODo6mrqMSrVq1Yru3bsDYG9vj6+vLxcuXDBxVfps\nbW0BuHXrFkVFRbRo0cLEFRmWkpLCjh07eOaZZxr2eQ5btmzBw8ODrl27mrqUSs2dO5c2bdqwZs0a\nXn/9dVOXU6mK1qGIO5N1OrUnKSmJuLg4goKCTF2KnuLiYrp3746rqysDBgzAz8/P1CUZ9PLLL/P+\n++9rfwm8kxqfylrTKlo3sXDhQt599112796tvVZZEtamytZ3LFy4kIULF/Kvf/2Ll19+mS+++MIE\nVVZ9HYqVlRUTJkwwdnlaVamzLpK1N7UjOzubRx99lCVLlmBvb2/qcvSYmZnx559/cuPGDUJDQ4mK\niiI4ONjUZenYvn07Li4uBAYGVmmbjzofDhWtmzh69CiJiYl069YNUJtLPXv2JCYmBhcXF2OWCFS+\nvsaQ+t8AAAUeSURBVKPUhAkTTPob+b2uQzGWqv591jV3s05HVE1BQQGPPPIIkyZNYvTo0aYu546a\nNWvGiBEjOHToUJ0LhwMHDrB161Z27NjBzZs3yczM5Mknn+TLL780/AKjjIAYQV0ekD516pT266VL\nlyqTJk0yYTUV27lzp+Ln56ekp6ebupQqCQ4OVg4dOmTqMnQUFBQo7du3VxITE5X8/Pw6OyCtKIqS\nmJhY5weki4uLlSeeeEJ56aWXTF1KhdLT05Xr168riqIoubm5St++fZWff/7ZxFXdWVRUVKWzler1\nmEN5dbk5/8YbbxAQEED37t2Jioriww8/NHVJBlW0DqWu2bx5M56enhw8eJARI0YwbNgwU5ekVV/W\n6YSHh/PAAw9w6tQpPD09TdbNWZlff/2VdevWsXfvXgIDAwkMDGTXrl2mLktHWloaAwcOpHv37gQF\nBREWFsagQYNMXValKvvMrPG9lYQQQtR/DablIIQQouZIOAghhNAj4SCEEEKPhIMQQgg9Eg6iUUtJ\nSeHhhx+mY8eOeHt789JLL1FQUFCj32Pfvn389ttv2scrV65k3bp1ADz11FMGD70SwtQkHESjpSgK\nY8eOZezYsZw6dYpTp06RnZ3N3Llza/T77N27lwMHDmgfT58+nUmTJgHqdMK6PA1bNF4SDqLR2rNn\nDzY2NkyePBlQt0D46KOP+Pzzz1mxYgUzZ87U3jty5Ej27dsHwAsvvEDv3r3x9/cnIiJCe4+XlxcR\nERH07NmTrl27cvLkSZKSkli5ciUfffQRgYGB/Pe//yUiIkJnrUvpbPLY2FiCg4Pp1asXQ4cO1W4f\nsnTpUrp06UK3bt0IDw+v7b8WIYB6sH2GELXl2LFj9OzZU+eag4MDbdq0oaioSOd6+d/wFy5ciKOj\nI0VFRQwePJijR4/i7++PRqPB2dmZ2NhYVqxYwQcffMCqVat47rnncHBw4JVXXgHgl19+0WktaDQa\nCgoKmDlzJtu2bcPJyYlNmzYxd+5cPvvsMxYvXkxSUhKWlpZkZmbW8t+KECoJB9Fo3ak7507jDps2\nbWLVqlUUFhaSlpbG8ePH8ff3B2Ds2LEA9OjRg++//177mtvXmpZ/rCgKJ0+e5NixYwwePBiAoqIi\n3NzcAOjatSsTJkxg9OjRdX5vIdFwSDiIRsvPz49vv/1W51pmZibJyck4Oztz+vRp7fWbN28CkJiY\nyIcffsihQ4do9v/buX8VxaE4iuNfCcFCzDyBRRr/gmBvIQh2dlaChYWVoIiPIIK1hS9gqSAoYm+j\nWAmCD6CVpApBKyFbuCsOmWEZ2F0W5nzKXMgltzn5ceC+vVGv159rAOFwGADDMLjf75/u/VEwZTKZ\nd93EL8vlkvV6zWKxoN/vczgcMAzjax8r8kXqHOTbKhaL3G43xuMx8Phb73a7VKtVbNtmv9/j+z7n\n85ndbgeA53lEIhEsy+JyubBarX67TzQaxfO8d89eJ4dQKEQikcBxHLbbLfCYXI7HI77vczqdKBQK\nDAYDXNfler3+qSMQ+ZQmB/nWZrMZzWaTXq+H4ziUSiVGoxGmaWLbNul0mlQq9ewmstksuVyOZDJJ\nLBYjn89/+N7XjqJcLlOpVJjP5wyHw+f6K9M0mU6ntFotXNflfr/T6XSIx+PUajVc18X3fdrtNpZl\n/cUTEXnQxXsiP202GxqNBpPJ5L+8SVXkX1I4iIhIgDoHEREJUDiIiEiAwkFERAIUDiIiEqBwEBGR\nAIWDiIgE/ABEguqfnObDqQAAAABJRU5ErkJggg==\n" + } + ], + "prompt_number": 16 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Section 2.2.3 Spectral densities of sea data\n", + "-----------------------------------------------\n", + "Example 2: Different forms of spectra" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import wafo.spectrum.models as wsm\n", + "clf()\n", + "Hm0 = 7; Tp = 11;\n", + "spec = wsm.Jonswap(Hm0=Hm0, Tp=Tp).tospecdata()\n", + "spec.plot()\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "png": 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RtGPHDri5ueG5555DixYt0KFDB7Rv3x4tWrTA7Nmz4enpiV27dskda4NmrE7r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\n" + } + ], + "prompt_number": 17 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Directional spectrum and Encountered directional spectrum\n", + "=========================================================\n", + "Directional spectrum\n", + "---------------------" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "clf()\n", + "D = wsm.Spreading('cos2s')\n", + "Sd = D.tospecdata2d(spec)\n", + "Sd.plot()\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "png": 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JrbQYPJgNr7zCrVOnsvKJJ4iaOBF++R+ERpHDAdxozkFyaIKrdZ0/HYQOTWyr\nZ/36JDp2DMLRUVOYi0hxNu0Vli1bRmRkpHWYKCEhoUYO79hDUJAXx4//fpNARKPC2U5r4UBTXEmi\nNefZS0HEANj2BbfNnMna6dP5sFs3anl5ETFmDBxYDS1u4zy7cCeMvZynNb//hb92D9zSxrZ64uIO\n06+fnq0sIiXZFAhTp07lp59+sl52GhkZya+//mrXwmqKoCCvYtNNtwmBwycgNw/CcWcnBbjRiqyW\nPpB6AB8Pg8ePH+eWyZMZ+sUXmPJz4fA6jBa3cp69uNOGPeTQtigQLBb43z7bA2HFisP0769AEJGS\nbAoEZ2dnfH2LP6NRT02zjbe3C4ZhkJlZ+Bxk11rQtB7sSYIwPNjJebzpQYbTRoj4E2xdjFdQEM0H\nDsStdm3YsRRCo8j1ysKZOjjhwx7O0wY3APYehQAfCLjKIzQBkpLSOXkyu9jT20RELrJpr96mTRsW\nLlyI2Wzm0KFDPPLII3Tt2tXetdUIJpOJRo38OHTo9+dHdGsN32+Hm/HkJzJxpRdn+Y6CbvfD6lch\nI7VwQXMefPsC3PoQGfyIJ504xgUysNCo6BzC8p/htnDbann//W3ExLQqdqOciMhFNgXCW2+9xZ49\ne3BxcWH48OF4e3vz+uuv27u2GqNdu0C2b0+xvh7eAz5bC/WoRTs8+AFf3GjF2UYnC5+Q9m4M7F0J\nb/aDoLYY4f1J4zP8uZs4ztEbHxwxYRjw0Q8Qe9vVazh9+jxvv/0zTz3VzY49FZHq7KpXGZnNZu64\n4w7WrFnDzJkzK6KmGufmm4PYvPk4Y8dGAtC9NZzJKhw2Gh5yE2+QTG8eIIm/43fHf3EwCmDFdGge\nDXdM4awpjloE4UE4K9jPkwQDhVcXGUDnFlevYdasDQwZ0prGjf3s11ERqdauGghOTk44ODhw7ty5\nEucRxDZRUfWZP3+H9bWDAwzrAQvjYfr9XsykgIO0xJfmpDksImDwdOuyBmZO8gH1eJTfyOUU+dxM\n4V3GH/8A9/eEq80Ccvx4Bu+9t43t2yfYo3siUkPYdB+Ch4cHYWFh9O7dGw+PwjtcTSYTc+bMsWtx\nNUVkZF0OHTrNuXO5+PoWjv2P6QW3TYZ/3GNijFsAc0nhdR7nMKPxpCPutAUgmddwxBdvbuE9TtAf\nPxwxkZUDi9fDllev3LZhGEycGMeECR1o0MDH3l0VkWrMpkCIiYkhJiam2Hu2Tk4nhfMZ3X57Y77+\nej+xsYXbt9tjAAAVDUlEQVRPQ2sdAre3g5e/hGdH1uZDUtlBCC2ZxkHu5yaGkcMB8jlFMz4mDQtf\ncpqvKLwx8O1voVc7CA28ctuvv76JX389yyefxFx5QRG54ZmMKvxwXZPJVGOe/btkyR7efHMza9eO\nsb53LA0iJsKGVyAp+ByvkcyXtASSOMdKnAnAjwE4UItn+A1vHHmK+mSch2YT4IcZhfc1XM7Klb8Q\nG7uUTZvG0bChhvtEbhTl3XdeMRDuuecelixZQlhYWKkN7ty5s8wNlqm4GhQIZnMBrVr9i/feG0R0\ndKj1/deXwdJN8MN0+D+HRPxw4hmKz/r6DWd4kxS+oAWeODLlU0g8CR9Nunx7W7cm07//Qv7zn3vp\n3v0KqSEiNY5dAiE5OZmgoCASExNL/fzi087spSYFAsDHH+/g/fe38eOPo61DbhYLdPk/+HNfGNLH\nzCgO0QNvHicIR2AZZ3mF47xPE1rhTspZaPNw4bmDRpcZLjpwII2ePT/i7bfv4K67rt/cUyJSPdgl\nECpbTQsEs7mAqKj3GT26HY88EmV9f/uv0GcK7P0XOHqb+T8S2U8OTpjwwpHZhNKs6M7koS9Dk3rw\n4v2lt7F9ewoDBizkxRd7Wc9XiMiNxS6B4OnpedmTxyaTiYyMjDI3WBY1LRAAfvnlDF26fMCiRUPo\n2bOR9f3H3ocTZ+GzvxVeRnqUCxRgEIILpqJprhfGw/OLYPvr4FbKozI3bDjKXXd9zttv38GQIa0r\nqEciUtXY9Qhh8uTJBAUFMXLkSAAWLlxIcnIyL7zwQtkrLUtxNTAQANasOcK9937BG2/0Y/jwwvMz\nORcKh44e6AMP3VHyOz8dgIEvwOrpEB5a8vPvvjvMqFFfsWDBn+jbV5PXidzI7BoI4eHhJU4gl/be\n9VZTAwEgIeEE9933H9q2DeDjj+/Czc2ZQ8kQ/Q94/C54bHDhDWyGUTjNxcT3YP5EuOPmkuv6/PPd\nTJwYx1df3UvXrg0qvjMiUqWUd99p01xGHh4efPLJJ1gsFiwWCwsXLsTTU8/kvRaRkfVISJiAk5MD\n9933HwoKDJoFwcZZ8MX/oOF4GPIStH248F6FldNKD4OPPtrOE0+sZNWqUQoDEbkmNh0hHDlyhIkT\nJ7JhwwYAunXrxhtvvKGrjK6DvDwLPXt+RExMS5544vcZZPckwdbDEBoAPdqUPj3FRx9t5x//+IHV\nq++nZcs6FVi1iFRlusqoGvv117NERb1PfHwsbdoE2PSd997byrRpP7JqlcJARIqz65CR2Ffjxn78\n85+9ufPOz0lJybrisgUFBtOmxfPii+tZsyZWYSAi141NcxmJ/cXGRpCUlM7tt39MXNxI6tf3LrHM\n2bM53H//UtLSzrNhwzjq1tV5HBG5fnSEUIVMnnwLI0aE0a3bh+zde8r6vmEYrFz5CxER79K0aW1+\n/HG0wkBErjubziGkpKTwzDPPcPz4ceLi4ti7dy8bN25k3Lhx9i3uBjmH8Efz5iXwf/+3inbtAgkM\n9GT79hQKCgxmzerNwIHNK7s8Eani7HpSuV+/fowZM4YZM2awc+dO8vPziYyMZPfu3eUq1ubibtBA\nADh3LpeNG49y6tR5WrTw5+abg/UsZBGxiV0DoWPHjmzZsoXIyEgSEhIAiIiIYPv27WWvtCzF3cCB\nICJSXna9ysjT05PTp09bX2/atAkfHz19S0SkJrEpEGbPns2gQYP49ddf6dq1K6NGjbqmx2cuWbKE\nNm3a4OjoyLZt28q9HhERuX5svjHNbDazf/9+DMOgRYsW1KpVq9yN7t+/HwcHByZMmMDs2bNp3759\n6cVpyEhEpMzKu++06T6E8PBwhg0bxr333kuTJk3K3MgftWyph7aIiFQ1Ng0ZLVu2DEdHR4YOHUrH\njh355z//SVJSkr1rExGRCmTTEUJoaChPPfUUTz31FIcOHeKFF17gqaeewmKxXPY7vXv3JiUlpcT7\nM2fOZNCgQTYXOHXqVOvP0dHRREdH2/xdEZEbQXx8PPHx8de8HpvPISQmJrJo0SIWL16Mo6Mj9957\nL0888cQ1Nd6zZ0+dQxARuc7seg4hKiqKvLw8hg4dypIlS2jcuHGZG7oc7fBFRKoGm44Q9u/ff11P\nBH/11Vc8+uijpKWl4ePjQ2RkJCtWrChZnI4QRETKzO7PQ1i+fDl79+4lJycHU9HTWp577rkyN1im\n4hQIIiJlZtc7lSdMmMDixYutN6MtXryY3377rcyNiYhI1WXTEUJYWBi7du0iPDycnTt3kpWVRb9+\n/Vi/fr19i9MRgohImdn1CMHNzQ0Ad3d3jh8/jpOTU6mXlIqISPVl01VGAwcO5OzZszz55JN06NAB\ngAceeMCuhYmISMW64pDRa6+9Rrdu3Wjfvj1OToXZkZubS25uLr6+vvYvTkNGIiJlZpf7EI4dO8ak\nSZPYt28fYWFhdO/ena5du9K1a9dyFyoiIlWTTSeVL1y4wJYtW9i4cSMbNmxg48aN+Pr6sm/fPvsW\npyMEEZEys+udyjk5OWRkZJCenk56ejpBQUGEh4eXuTEREam6rniE8MADD7B37168vLzo1KkTXbp0\noXPnzvj5+VVMcTpCEBEpM7tcdpqUlMSFCxeoW7cuwcHBBAcHV8jJZBERqXhXPYdQUFDAnj17rOcP\ndu3ahb+/P507d+b555+3b3E6QhARKTO7z2V09OhRNmzYwP/+9z+WL1/O6dOnSU9PL3ODZSpOgSAi\nUmZ2CYQ33njDelWRk5MTXbt2pVu3bnTt2pW2bdvi6Oh4TUVftTgFgohImdnlKqPExESGDh3Ka6+9\nRlBQULmLExGRqs/mIaPKoCMEEZGys+vkdiIiUvMpEEREBFAgiIhIEQWCiIgACgQRESmiQBAREUCB\nICIiRRQIIiICKBBERKSIAkFERAAFgoiIFFEgiIgIUEmB8OSTT9KqVSvatWtHTEyM3Z+rICIiV1cp\ngdCnTx/27NnDjh07aN68OS+++GJllCEiIpeolEDo3bs3Dg6FTUdFRXHs2LHKKENERC5xxQfkVIQP\nP/yQ4cOHX/bzqVOnWn+Ojo4mOjra/kWJiFQj8fHxxMfHX/N67PaAnN69e5OSklLi/ZkzZzJo0CAA\nZsyYwbZt2/jyyy9LL04PyBERKTO7PFPZnubPn897773H6tWrcXV1LXUZBYKISNnZ5ZnK9hIXF8es\nWbP48ccfLxsGIiJSsSrlCKFZs2bk5eVRu3ZtALp06cLbb79dsjgdIYiIlFm1GzKyhQJBRKTsyrvv\n1J3KIiICKBBERKSIAkFERAAFgoiIFFEgiIgIoEAQEZEiCgQREQEUCCIiUkSBICIigAJBRESKKBBE\nRARQIIiISBEFgoiIAAoEEREpokAQERFAgSAiIkUUCCIiAigQRESkiAJBREQABYKIiBRRIIiICKBA\nEBGRIgoEEREBFAgiIlJEgSAiIoACQUREilRKIDz77LO0a9eOiIgIevXqxdGjRyujDBERuYTJMAyj\nohvNzMzEy8sLgDfffJMdO3bw/vvvlyzOZKISyhMRqdbKu++slCOEi2EAkJWVRZ06dSqjDBERuYRT\nZTX8zDPPsGDBAtzd3dm0aVNllSEiIkXsNmTUu3dvUlJSSrw/c+ZMBg0aZH390ksvceDAAebNm1ey\nOJOJKVOmWF9HR0cTHR1tj3JFRKqt+Ph44uPjra+nTZtWriGjSjmHcKmkpCQGDBjA7t27S3xW088h\nxMfH19iAq8l9A/Wvuqvp/atW5xAOHTpk/fnrr78mMjKyMsqodJcmek1Tk/sG6l91V9P7V16Vcg7h\n6aef5sCBAzg6OtKkSRPeeeedyihDREQuUSmB8MUXX1RGsyIicgWVfg7hSkwmU2WXICJSLZVn115p\nl53aogpnlYhIjaO5jEREBFAgiIhIkSoRCHFxcbRs2ZJmzZrx8ssvl7rMo48+SrNmzWjXrh0JCQkV\nXGH5Xa1v8fHx+Pj4EBkZSWRkJNOnT6+EKstn7NixBAYGEhYWdtllqut2g6v3rzpvO4CjR4/Ss2dP\n2rRpQ9u2bZkzZ06py1XXbWhL/6rrNszNzSUqKoqIiAhat27N008/XepyZd52RiUzm81GkyZNjCNH\njhh5eXlGu3btjL179xZb5ptvvjH69+9vGIZhbNq0yYiKiqqMUsvMlr6tWbPGGDRoUCVVeG3Wrl1r\nbNu2zWjbtm2pn1fX7XbR1fpXnbedYRjGiRMnjISEBMMwDCMzM9No3rx5jfm/Zxi29a86b8Ps7GzD\nMAwjPz/fiIqKMtatW1fs8/Jsu0o/Qti8eTNNmzYlNDQUZ2dnhg0bxtdff11smWXLlhEbGwtAVFQU\n586dIzU1tTLKLRNb+gbV9+R5jx498PPzu+zn1XW7XXS1/kH13XYAdevWJSIiAgBPT09atWpFcnJy\nsWWq8za0pX9Qfbehu7s7AHl5eVgsFmrXrl3s8/Jsu0oPhOPHj9OgQQPr6/r163P8+PGrLnPs2LEK\nq7G8bOmbyWRiw4YNtGvXjgEDBrB3796KLtNuqut2s1VN2naJiYkkJCQQFRVV7P2asg0v17/qvA0L\nCgqIiIggMDCQnj170rp162Kfl2fbVfplp7bea/DHFK8O9yjYUmP79u05evQo7u7urFixgrvuuouD\nBw9WQHUVozpuN1vVlG2XlZXFkCFDeOONN/D09CzxeXXfhlfqX3Xehg4ODmzfvp309HT69u1b6vxM\nZd12lX6EEBwcXOyJaUePHqV+/fpXXObYsWMEBwdXWI3lZUvfvLy8rId+/fv3Jz8/nzNnzlRonfZS\nXbebrWrCtsvPz+fuu+9m5MiR3HXXXSU+r+7b8Gr9qwnb0MfHhzvuuIMtW7YUe788267SA6Fjx44c\nOnSIxMRE8vLyWLRoEYMHDy62zODBg/n4448B2LRpE76+vgQGBlZGuWViS99SU1OtKb5582YMwygx\nFlhdVdftZqvqvu0Mw2DcuHG0bt2aSZMmlbpMdd6GtvSvum7DtLQ0zp07B0BOTg7ff/99iUlCy7Pt\nKn3IyMnJibfeeou+fftisVgYN24crVq14t133wVgwoQJDBgwgG+//ZamTZvi4eFR6rMTqiJb+vbF\nF1/wzjvv4OTkhLu7O59//nklV2274cOH8+OPP5KWlkaDBg2YNm0a+fn5QPXebhddrX/VedsB/O9/\n/+OTTz4hPDzcujOZOXMmSUlJQPXfhrb0r7puwxMnThAbG0tBQQEFBQWMGjWKXr16XfN+s0rPZSQi\nIhWn0oeMRESkalAgiIgIoEAQEZEiCgQREQEUCFLFODo6Wicai4yMtF4RUt3Nnz+fm266iT//+c/X\ntJ6pU6cye/Zs6+tNmzZddp25ublERETg4uJS7a6tl8pR6ZedilzK3d39srMyXrwgrrrdKQuFNQ8f\nPrzUGTfNZjNOTrb9V/xj31esWEH//v1LXdbV1ZXt27fTqFGjshcsNyQdIUiVlpiYSIsWLYiNjSUs\nLIyjR48ya9YsOnXqRLt27Zg6dap12RkzZtCiRQt69OjBfffdZ/1LOjo6mq1btwKFN/Rc3EFaLBae\nfPJJ67rmzp0LYJ0C4J577qFVq1aMHDnS2sbPP/9Mt27diIiIoHPnzmRlZXHrrbeyY8cO6zLdu3dn\n165dJfpy6RXe8+fPZ/DgwfTq1YvevXuTnZ3N7bffTocOHQgPD2fZsmWl9uvAgQPF1vnDDz9w++23\ns2fPHqKiooiMjKRdu3YcPny4vL9yuYHpCEGqlJycHOtNRI0bN+bVV1/l8OHDLFiwgE6dOrFy5UoO\nHz7M5s2bKSgo4M4772TdunW4u7uzaNEiduzYQX5+Pu3bt6djx45A4V/VpR1VfPDBB/j6+rJ582Yu\nXLhA9+7d6dOnDwDbt29n79691KtXj27durFhwwY6duzIsGHDWLx4MR06dCArKws3NzfGjRvH/Pnz\nee211zh48CAXLly44jMiLkpISGDXrl34+vpisVj46quv8PLyIi0tjS5dujB48GC2bt162X6lpaXh\n7OyMl5cX//73v5k4cSL33XcfZrMZs9l8vTaJ3EAUCFKluLm5FRsySkxMpGHDhnTq1AmAlStXsnLl\nSmtoZGdnc+jQITIzM4mJicHV1RVXV9cSU4SUZuXKlezatYsvvvgCgIyMDA4fPoyzszOdOnUiKCgI\ngIiICI4cOYKXlxf16tWjQ4cOANaJ0oYMGcILL7zArFmz+PDDDxkzZsxV2zaZTPTp0wdfX1+gcObK\np59+mnXr1uHg4EBycjKpqamsW7euRL8uHmmsXLmSvn37AtC1a1dmzJjBsWPHiImJoWnTplf/ZYv8\ngYaMpMrz8PAo9vrpp58mISGBhIQEDh48yNixY4HiQzKX/uzk5ERBQQFQeKL1Um+99ZZ1Xb/88gu3\n3347hmHg4uJiXcbR0RGz2XzZcxfu7u707t2bpUuXsmTJEkaMGGFTvy5OqgawcOFC0tLS2LZtGwkJ\nCQQEBJCbm4vJZCrRr4t1xMXF0a9fP6Bwmo3//ve/uLm5MWDAANasWWNTDSKXUiBItdK3b18+/PBD\nsrOzgcI530+dOsUtt9zC0qVLyc3NJTMzk+XLl1u/Exoaap0J8uLRwMV1vf3229bhlYMHD3L+/PlS\n2zWZTLRo0YITJ05Y15WZmYnFYgFg/PjxPProo3Tq1AkfH5+r9uOPM8ZkZGQQEBCAo6Mja9as4bff\nfsNkMl22X4ZhsHPnTtq1awfAkSNHaNSoEY888gh33nlnqecwRK5GQ0ZSpZT2V/il7/Xu3Zt9+/bR\npUsXoHD64k8++YTIyEjuvfde2rVrR0BAADfffLN1p/u3v/2NoUOHMnfuXO644w7r+saPH09iYiLt\n27fHMAwCAgL46quvLnvOwdnZmUWLFvHII4+Qk5ODu7s733//PR4eHrRv3x4fHx+bhosu9unSNkaM\nGMGgQYMIDw+nY8eOtGrVCqBEvy4OnW3durXY7JaLFy9mwYIFODs7U69ePZ555hmb6hC5lCa3kxpp\n2rRpeHp68sQTT1RIe8nJyfTs2bPEVUAXffTRR2zZsoU333zzurQ3Y8YMmjVrxtChQ6+6bKNGjdi6\ndWu1mNZZKpeGjKTGqqj7FT7++GM6d+7MzJkzL7uMm5sbK1asuOYb0y565plnrhoGF29MM5vNODjo\nv7pcnY4QREQE0BGCiIgUUSCIiAigQBARkSIKBBERARQIIiJSRIEgIiIA/D/vK76bjLbnAwAAAABJ\nRU5ErkJggg==\n" + } + ], + "prompt_number": 18 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Encountered directional spectrum\n", + "--------------------------------- " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#clf()\n", + "#Se = spec2spec(Sd,'encdir',0,10);\n", + "#plotspec(Se), hold on\n", + "#plotspec(Sd,1,'--'), hold off\n", + "##!wafostamp('','(ER)')\n", + "#disp('Block = 17'),pause(pstate)\n", + "#\n", + "##!#! Frequency spectra\n", + "#clf\n", + "#Sd1 =spec2spec(Sd,'freq');\n", + "#Sd2 = spec2spec(Se,'enc');\n", + "#plotspec(spec), hold on\n", + "#plotspec(Sd1,1,'.'),\n", + "#plotspec(Sd2),\n", + "##!wafostamp('','(ER)')\n", + "#hold off\n", + "#disp('Block = 18'),pause(pstate)\n", + "#\n", + "##!#! Wave number spectrum\n", + "#clf\n", + "#Sk = spec2spec(spec,'k1d')\n", + "#Skd = spec2spec(Sd,'k1d')\n", + "#plotspec(Sk), hold on\n", + "#plotspec(Skd,1,'--'), hold off\n", + "##!wafostamp('','(ER)')\n", + "#disp('Block = 19'),pause(pstate)\n", + "#\n", + "##!#! Effect of waterdepth on spectrum\n", + "#clf\n", + "#plotspec(spec,1,'--'), hold on\n", + "#S20 = spec;\n", + "#S20.S = S20.S.*phi1(S20.w,20);\n", + "#S20.h = 20;\n", + "#plotspec(S20), hold off\n", + "##!wafostamp('','(ER)')\n", + "#disp('Block = 20'),pause(pstate)\n", + "#\n", + "##!#! Section 2.3 Simulation of transformed Gaussian process\n", + "##!#! Example 3: Simulation of random sea \n", + "##! The reconstruct function replaces the spurious points of seasurface by\n", + "##! simulated data on the basis of the remaining data and a transformed Gaussian\n", + "##! process. As noted previously one must be careful using the criteria \n", + "##! for finding spurious points when reconstructing a dataset, because\n", + "##! these criteria might remove the highest and steepest waves as we can see\n", + "##! in this plot where the spurious points is indicated with a '+' sign:\n", + "##!\n", + "#clf\n", + "#[y, grec] = reconstruct(xx,inds);\n", + "#waveplot(y,'-',xx(inds,:),'+',1,1)\n", + "#axis([0 inf -inf inf])\n", + "##!wafostamp('','(ER)')\n", + "#disp('Block = 21'),pause(pstate)\n", + "#\n", + "##! Compare transformation (grec) from reconstructed (y) \n", + "##! with original (glc) from (xx)\n", + "#clf\n", + "#trplot(g), hold on\n", + "#plot(gemp(:,1),gemp(:,2))\n", + "#plot(glc(:,1),glc(:,2),'-.')\n", + "#plot(grec(:,1),grec(:,2)), hold off \n", + "#disp('Block = 22'),pause(pstate)\n", + "#\n", + "##!#!\n", + "#clf\n", + "#L = 200;\n", + "#x = dat2gaus(y,grec);\n", + "#Sx = dat2spec(x,L);\n", + "#disp('Block = 23'),pause(pstate)\n", + "# \n", + "##!#!\n", + "#clf\n", + "#dt = spec2dt(Sx)\n", + "#Ny = fix(2*60/dt) #! = 2 minutes\n", + "#Sx.tr = grec;\n", + "#ysim = spec2sdat(Sx,Ny);\n", + "#waveplot(ysim,'-')\n", + "##!wafostamp('','(CR)')\n", + "#disp('Block = 24'),pause(pstate)\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Estimated spectrum compared to Torsethaugen spectrum\n", + "-------------------------------------------------------" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "clf()\n", + "fp = 1.1;dw = 0.01\n", + "H0 = S1.characteristic('Hm0')[0]\n", + "St = wsm.Torsethaugen(Hm0=H0,Tp=2*pi/fp).tospecdata(np.arange(0,5+dw/2,dw)) \n", + "S1.plot()\n", + "St.plot('-.')\n", + "axis([0, 6, 0, 0.4])\n", + "show()\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "png": 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WKHPmzBlGjhzJF198QVBQkEV1bVWhUsjCHQtLdFz9+ivccQc0bQpeXlCFPC6E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\ncQghhLCIJA4hhBAW+X/+nuw44UNfwAAAAABJRU5ErkJggg==\n" + } + ], + "prompt_number": 19 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Transformed Gaussian model compared to Gaussian model\n", + "-------------------------------------------------------\n" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "dt = St.sampling_period()\n", + "va, sk, ku = St.stats_nl(moments='vsk' )\n", + "#sa = sqrt(va)\n", + "gh = wtm.TrHermite(mean=me, sigma=sa, skew=sk, kurt=ku, ysigma=sa)\n", + " \n", + "ysim_t = St.sim(ns=240, dt=0.5)\n", + "xsim_t = ysim_t.copy()\n", + "xsim_t[:,1] = gh.gauss2dat(ysim_t[:,1])\n", + "\n", + "ts_y = wo.mat2timeseries(ysim_t)\n", + "ts_x = wo.mat2timeseries(xsim_t)\n", + "ts_y.plot_wave(sym1='r.', ts=ts_x, sym2='b', sigma=sa, nsub=5, nfig=1)\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "png": 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7h8Eh9TSCkn+G+/m1MDQUenAX4wzx9bMP8FvyGzCZNU6u+HvaXMU+BGKK/lIs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\nH1OnTpXHbnneHhqqwLpDLvZgBLbbhuHla0swMgRYtgyoh0crzFdfAYs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+ } + ], + "prompt_number": 20 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] } \ No newline at end of file diff --git a/pywafo/src/wafo/doc/tutorial_scripts/WAFO Chapter 3.ipynb b/pywafo/src/wafo/doc/tutorial_scripts/WAFO Chapter 3.ipynb new file mode 100644 index 0000000..8787457 --- /dev/null +++ b/pywafo/src/wafo/doc/tutorial_scripts/WAFO Chapter 3.ipynb @@ -0,0 +1,73 @@ +{ + "metadata": { + "name": "WAFO Chapter 3" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "CHAPTER3 Demonstrates distributions of wave characteristics\n", + "=============================================================\n", + "\n", + "Chapter3 contains the commands used in Chapter3 in the tutorial.\n", + " \n", + "Some of the commands are edited for fast computation. \n", + "\n", + "Section 3.2 Estimation of wave characteristics from data\n", + "----------------------------------------------------------\n", + "Example 1\n", + "~~~~~~~~~~ " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "speed = 'fast'\n", + "#speed = 'slow'\n", + "\n", + "import wafo.data as wd\n", + "import wafo.misc as wm\n", + "import wafo.objects as wo\n", + "xx = wd.sea() \n", + "xx[:,1] = wm.detrendma(xx[:,1],len(xx))\n", + "ts = wo.mat2timeseries(xx)\n", + "Tcrcr, ix = ts.wave_periods(vh=0, pdef='c2c', wdef='tw', rate=8)\n", + "Tc, ixc = ts.wave_periods(vh=0, pdef='u2d', wdef='tw', rate=8)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "AssertionError", + "evalue": "", + "output_type": "pyerr", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in 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\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2936\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0myp\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mAssertionError\u001b[0m: " + ] + } + ], + "prompt_number": 12 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/pywafo/src/wafo/doc/tutorial_scripts/WAFO Chapter 4.ipynb b/pywafo/src/wafo/doc/tutorial_scripts/WAFO Chapter 4.ipynb new file mode 100644 index 0000000..7ab36e8 --- /dev/null +++ b/pywafo/src/wafo/doc/tutorial_scripts/WAFO Chapter 4.ipynb @@ -0,0 +1,468 @@ +{ + "metadata": { + "name": "WAFO Chapter 4" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Chapter 4 Fatigue load analysis and rain-flow cycles\n", + "=====================================================\n", + "\n", + "Section 4.3.1 Crossing intensity\n", + "--------------------------------\n" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import wafo.data as wd\n", + "import wafo.objects as wo\n", + "\n", + "printing=0\n", + "xx_sea = wd.sea() \n", + "ts = wo.mat2timeseries(xx_sea)\n", + "tp = ts.turning_points()\n", + "mM = tp.cycle_pairs(kind='min2max')\n", + "lc = mM.level_crossings(intensity=True)\n", + "T_sea = ts.args[-1]-ts.args[0]\n", + "\n", + "subplot(1,2,1)\n", + "lc.plot()\n", + "subplot(1,2,2)\n", + "lc.setplotter(plotmethod='step')\n", + "lc.plot()\n", + "show() \n", + " \n", + " \n", + "m_sea = ts.data.mean() \n", + "f0_sea = interp(m_sea, lc.args,lc.data)\n", + "extr_sea = len(tp.data)/(2*T_sea)\n", + "alfa_sea = f0_sea/extr_sea\n", + "print('alfa = %g ' % alfa_sea )" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "png": 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dywboLDA8PDxw6dIl1ePt27fDy5Kd5a0QEbB/P9C/v9SRWIlOncQFMg4dwogR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\nkT+tW9R/eaN3FgAAAABJRU5ErkJggg==\n" + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "alfa = 0.491212 \n" + ] + } + ], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Section 4.3.2 Extraction of rainflow cycles\n", + "-------------------------------------------\n", + "Min-max and rainflow cycle plots\n", + "---------------------------------" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mM_rfc = tp.cycle_pairs(h=0.3)\n", + " \n", + "clf()\n", + "subplot(122), \n", + "mM.plot() \n", + "title('min-max cycle pairs')\n", + "subplot(121), \n", + "mM_rfc.plot()\n", + "title('Rainflow filtered cycles')\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "png": 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nmzs3gFWhjY1llWgB5izXOl9X33NPqQ4rlUQI46mQtrSwC+Pdd927ufSUi8DX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\nAAAASUVORK5CYII=\n" + } + ], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Min-max and rainflow cycle distributions\n", + "-------------------------------------------" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import wafo.misc as wm\n", + "ampmM_sea = mM.amplitudes()\n", + "ampRFC_sea = mM_rfc.amplitudes()\n", + "clf()\n", + "subplot(121) \n", + "wm.plot_histgrm(ampmM_sea,25)\n", + "ylim = gca().get_ylim()\n", + "title('min-max amplitude distribution')\n", + "subplot(122)\n", + "wm.plot_histgrm(ampRFC_sea,25)\n", + "gca().set_ylim(ylim)\n", + "title('Rainflow amplitude distribution')\n", + "show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "png": 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IAAsCERHpsCAQEREAFgQiItJhQSAiIgAsCEREpMOCQEREAFgQiIhIhwWBiIgA\nsCAQEZEOCwIREQFgQSAiIh0WBCIiAsCCQEREOiwIREQEgAWBiIh02iwIS5YsgaenJ8aMGSM/l5CQ\nAKVSidDQUISGhuLQoUPytMTERPj7+yMwMBCHDx+2Tq+JzMS8JmpJEkKI1mY4fvw4HBwcsHDhQvz0\n008AgHXr1kGhUODZZ5/VmzczMxOPPfYYvvvuOxQWFmLq1KnIysqCjY1+3ZEkCYaalSSg9d4Qtc1Y\nfjXVmXlNZCnWzrE2jxAiIiLg4uLS4nlDnUpNTUVsbCzs7e2hUqng5+eH9PR0y/SUyIKY10Qt2XX0\nhW+++SY++OADhIWFYfPmzXB2dkZRURHCw8PleZRKJQoLCw2+PiEhQb6v0Wig0Wg62hXq47RaLbRa\nrUViMa+pO7FkbptEmCAnJ0eMHj1aflxaWioaGxtFY2OjWLVqlViyZIkQQoinn35a7NixQ55v6dKl\nYs+ePS3iGWvWtN4Qtc7EtO60vCayFGvnWIdGGXl4eECSJEiShGXLlsmHz97e3igoKJDnu3TpEry9\nvc2vWkSdgHlNfV2HCkJxcbF8//PPP5dHakRHR2PXrl2ora1FTk4OsrOzMW7cOMv0lMjKmNfU17V5\nDSE2NhbHjh3D1atX4ePjg3Xr1kGr1eLMmTOQJAm+vr549913AQBqtRoxMTFQq9Wws7PD22+/DUmS\nrP4miNqLeU3UUpvDTq3SKIedkhV11fBPDjsla+vyYadERNQ3sCAQERGAblgQJKnlzdW1q3tFRNT7\ndfiLadZi6PQYr98REVlftztCICKirsGCQEREAFgQiIhIhwWBiIgAsCAQEZEOCwIREQFgQSAiIh0W\nBCIiAsCCQEREOiwIREQEgAWBiIh0WBCIiAgACwIREemwIBAREQAWBCIi0mFBICIiACwIRESkw4JA\nREQAWBCIiEiHBYGIiACwIBARkQ4LAhERAWBBICIiHRYEIiICYEJBWLJkCTw9PTFmzBj5ubKyMkRF\nRSEgIADTpk1DRUWFPC0xMRH+/v4IDAzE4cOHrdNrIjMxr4laarMgLF68GGlpaXrPJSUlISoqCllZ\nWZgyZQqSkpIAAJmZmUhJSUFmZibS0tLw61//Go2NjdbpOZEZmNdELbVZECIiIuDi4qL33L59+xAf\nHw8AiI+Px969ewEAqampiI2Nhb29PVQqFfz8/JCenm6FbhOZh3lN1JJdR15UWloKT09PAICnpydK\nS0sBAEXKToxdAAALYklEQVRFRQgPD5fnUyqVKCwsNBgjISFBvq/RaKDRaDrSFSJotVpotVqz4zCv\nqbuxVG6bqkMFoSlJkiBJUqvTDWm64RCZo/kH77p168yOybym7sAaud2aDo0y8vT0RElJCQCguLgY\nHh4eAABvb28UFBTI8126dAne3t4W6CYgSS1vrq4WCU0EoGvymqg76VBBiI6ORnJyMgAgOTkZs2fP\nlp/ftWsXamtrkZOTg+zsbIwbN84iHRWi5a283CKhiQB0TV4TdSuiDfPnzxeDBw8W9vb2QqlUivff\nf19cu3ZNTJkyRfj7+4uoqChRXl4uz79hwwYxYsQIMXLkSJGWlmYwprFmjfWmvc9T32ZCWndqXhNZ\nirVzTNI10qkkSYKhZiXp9p6/uc9T32Ysv3pru9R3WDvH+E1lIiICwIJAREQ6LAhERASABYGIiHRY\nEIiICAALAhER6bAgEBERABYEIiLSYUEgIiIALAhERKTDgkBERABYEIiISIcFgYiIALAgEBGRDgsC\nEREBYEEgIiIdFgTc/m9m/mczEfV1dl3dge6gvNz4P7IREfUVPEIgIiIAvaAg8FQPEZFl9PhTRjzV\nQ0RkGT3+CIGIiCyDBYGIiACwIBARkQ4LAhERAWBBICIiHRYEIiICwIJAREQ6Zn0PQaVSwdHREba2\ntrC3t0d6ejrKysowb9485OXlQaVS4ZNPPoGzs7Ol+msWV9fbP1NB1JqeltdElmLWEYIkSdBqtTh9\n+jTS09MBAElJSYiKikJWVhamTJmCpKQki3TUEu78ZlHzG1FTPS2viSzF7FNGotkn6r59+xAfHw8A\niI+Px969e81tgqjTMa+pLzL7CGHq1KkICwvDe++9BwAoLS2Fp6cnAMDT0xOlpaXm97IL8beS+p6+\nkNdEhph1DeHrr7/G4MGDceXKFURFRSEwMFBvuiRJkIz8sFBCQoJ8X6PRQKPRmNMVq+FvJXV/Wq0W\nWq3WYvH6Ql5Tz2Dp3G6LJJofG3fQunXr4ODggPfeew9arRZeXl4oLi7G5MmTcf78ef1GJanFIfnt\n541/APeE56l7MJZfHWGJvCayFGvnWIdPGd28eROVlZUAgJ9//hmHDx/GmDFjEB0djeTkZABAcnIy\nZs+ebZmeEnUC5jX1ZR0+QsjJycEvf/lLAEB9fT0WLFiAlStXoqysDDExMcjPzzc6PK83HCEY4uIC\nlJUZnkadx5y9KGvkNZGlWDvHLHbKqF2NdkJBMIanknq/rvpgZkEga7N2jvX4P8gxhheDiYjahz9d\nQUREAFgQiIhIhwWBiIgAsCAQ9XqurvzGPZmm115UJqLb7vyoY3McZEHN8QiBqBcxdDRg7ut5JNF3\n8AiBqBcxdDTQnqJg7PWGYvCLmL0PjxCIqE2G/kfEGn82xesdXYsFoQfhxkLdXXtOORma19ifWPGf\nDjsHTxn1ILw4SN2Nodwz9ZSTsXmp6/AIoRO0d8/e2PxElmZujpn6d7T869qegUcInaC1PXtT95zu\nzE9kSdxDp6Z4hNDFrLnnxGsOPQ+HfVJX4hFCL8ZrDj2PucNGiczBgmBh3HjJGphXpi0DfjfCPCwI\nFsY9crIGU/+QqTfnmqH32pfef2fgNQQiIgLAI4Reg3tGRGQuFoRegqeqiMhcPGVERL0Kh1p3HI8Q\niKhX4dFyx/EIgWT8IhtR38YjhD6KP5lBfQ2/x9A2FoQ+qr0f/PyDFOrp+D2GtrEgkEl45EDU+/Ea\nAhERAWBBICLqkN44CMMqBSEtLQ2BgYHw9/fHxo0brdGEQVqttkfE7E1xzfvjH22P2oC6Kq8BbY+K\na50ctEZMw301Nadb+7tPa22H1mbxgtDQ0ICnn34aaWlpyMzMxM6dO/GPf/zD0s0YxILQ+XHb8/+3\nzTegtWu1Peb/crsyr1kQgM7sq7Gcbs+/y02erO2RRw0WLwjp6enw8/ODSqWCvb095s+fj9TUVEs3\nQ91ce/8C1BJ/MWrNjY55Te35I6u1a9suKO25dVZBsfgoo8LCQvj4+MiPlUolTp48aelmqJtr76ik\n7v4Xo8xrMpcpw16NPd9pI/qEhX366adi2bJl8uMPP/xQPP3003rzAOCNN6vemNe89dabNVn8CMHb\n2xsFBQXy44KCAiiVSr15hCX/OJioEzCvqS+w+DWEsLAwZGdnIzc3F7W1tUhJSUF0dLSlmyHqVMxr\n6gssfoRgZ2eHP/3pT5g+fToaGhqwdOlSjBo1ytLNEHUq5jX1CdY8H3Xo0CExcuRI4efnJ5KSkgzO\ns3z5cuHn5yfGjh0rTp06ZXbMHTt2iLFjx4oxY8aIiRMnirNnz1qsr0IIkZ6eLmxtbcWePXssFvfo\n0aMiJCREBAUFicjISLNjXrlyRUyfPl0EBweLoKAgsW3btjZjLl68WHh4eIjRo0cbnae968qUuB1d\nX6b0V4j2ry9TWCOvTYnbnXLbGnltSlzm9r9ZI7etVhDq6+vFiBEjRE5OjqitrRXBwcEiMzNTb54D\nBw6ImTNnCiGEOHHihBg/frzZMb/55htRUVEhhLidXG3FNDXunfkmT54sHnroIfHpp59aJG55eblQ\nq9WioKBACHE74c2NuXbtWvHiiy/K8VxdXUVdXV2rcb/88ktx6tQpo0nY3nVlatyOrC9T4grR/vVl\nCmvktalxu0tuWyOvTY3L3L7NGrkthBBW++kKU8Zt79u3D/Hx8QCA8ePHo6KiAqWlpWbFnDBhApyc\nnOSYly5dskhfAeDNN9/E3Llz4e7u3mZMU+N+/PHHmDNnjnyBctCgQWbHHDx4MG7cuAEAuHHjBtzc\n3GBn1/rZwYiICLi4uBid3t51ZWrcjqwvU+IC7V9fprBGXpsat7vktjXy2tS4zO3brJHbgBV/y8jQ\nuO3CwsI252ltoZkSs6mtW7di1qxZFutramoqnnrqKQCAZMLAYFPiZmdno6ysDJMnT0ZYWBg+/PBD\ns2M+8cQTyMjIwJAhQxAcHIzXX3+9zb525L2YmuCmMnV9maIj68vUuJbOa1PjNtWVuW2NvDY1LnPb\nerkNWPHnr03tpGg2VK+117XnjR89ehTvv/8+vv766zbnNSXuihUrkJSUBEmSIG6farNI3Lq6Opw6\ndQpffPEFbt68iQkTJiA8PBz+/v4djvnqq68iJCQEWq0WFy5cQFRUFM6ePQuFQtHma1vTnnXVXu1Z\nX6boyPoyhTXyuj1xga7PbWvktalxmdvWy23AigXBlHHbzee5dOkSvL29zYoJAD/++COeeOIJpKWl\ntXnoZWrcH374AfPnzwcAXL16FYcOHYK9vX2rQw9Nievj44NBgwahf//+6N+/PyZNmoSzZ88a3XBM\nifnNN99g1apVAIARI0bA19cX//znPxEWFtbaYmhVe9dVe7R3fZmiI+vLFNbIa1PjAt0jt62R16bG\nZW5bL7cBWG+UUV1dnRg+fLjIyckRNTU1bV58+/bbb9u86GJKzLy8PDFixAjx7bffWrSvTS1atMik\nK/umxP3HP/4hpkyZIurr68XPP/8sRo8eLTIyMsyK+dvf/lYkJCQIIYQoKSkR3t7e4tq1a232Nycn\nx6QLb6asK1PjdmR9mRK3KVPXlymskdemxu0uuW2NvDY1LnNbnyVzWwgrfFP5DmPjtt99910AwH/8\nx39g1qxZOHjwIPz8/DBw4EBs27bN7Jjr169HeXm5fH7N3t4e6enpZse11jIIDAzEjBkzMHbsWNjY\n2OCJJ56AWq02K+bvf/97LF68GMHBwWhsbMQf/vAHuLbx61ixsbE4duwYrl69Ch8fH6xbtw51dXVy\nzPauK1PjdmR9mRLXWqyR16bG7S65bY28NjUuc9t6uQ0AkhD8vj0REfEf04iISIcFgYiIALAgEBGR\nDgsCEREBYEEgIiIdFgQiIgIA/D/Pv8fbekijgAAAAABJRU5ErkJggg==\n" + } + ], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#!#! Section 4.3.3 Simulation of rainflow cycles\n", + "#!#! Simulation of cycles in a Markov model\n", + "n=41; param_m=[-1, 1, n]; param_D=[1, n, n];\n", + "u_markov=levels(param_m);\n", + "G_markov=mktestmat(param_m,[-0.2, 0.2],0.15,1);\n", + "T_markov=5000;\n", + "#xxD_markov=mctpsim({G_markov [,]},T_markov);\n", + "#xx_markov=[(1:T_markov)' u_markov(xxD_markov)'];\n", + "#clf\n", + "#plot(xx_markov(1:50,1),xx_markov(1:50,2))\n", + "#title('Markov chain of turning points')\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 5'),pause(pstate)\n", + "#\n", + "#\n", + "##!#! Rainflow cycles in a transformed Gaussian model\n", + "##!#! Hermite transformed wave data and rainflow filtered turning points, h = 0.2.\n", + "#me = mean(xx_sea(:,2));\n", + "#sa = std(xx_sea(:,2));\n", + "#Hm0_sea = 4*sa;\n", + "#Tp_sea = 1/max(lc_sea(:,2));\n", + "#spec = jonswap([],[Hm0_sea Tp_sea]);\n", + "#\n", + "#[sk, ku] = spec2skew(spec);\n", + "#spec.tr = hermitetr([],[sa sk ku me]);\n", + "#param_h = [-1.5 2 51];\n", + "#spec_norm = spec;\n", + "#spec_norm.S = spec_norm.S/sa^2;\n", + "#xx_herm = spec2sdat(spec_norm,[2^15 1],0.1);\n", + "##! ????? PJ, JR 11-Apr-2001\n", + "##! NOTE, in the simulation program spec2sdat\n", + "##!the spectrum must be normalized to variance 1 \n", + "##! ?????\n", + "#h = 0.2;\n", + "#[dtp,u_herm,xx_herm_1]=dat2dtp(param_h,xx_herm,h);\n", + "#clf\n", + "#plot(xx_herm(:,1),xx_herm(:,2),'k','LineWidth',2); hold on;\n", + "#plot(xx_herm_1(:,1),xx_herm_1(:,2),'k--','Linewidth',2);\n", + "#axis([0 50 -1 1]), hold off;\n", + "#title('Rainflow filtered wave data')\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 6'),pause(pstate)\n", + "#\n", + "##!#! Rainflow cycles and rainflow filtered rainflow cycles in the transformed Gaussian process.\n", + "#tp_herm=dat2tp(xx_herm);\n", + "#RFC_herm=tp2rfc(tp_herm);\n", + "#mM_herm=tp2mm(tp_herm);\n", + "#h=0.2;\n", + "#[dtp,u,tp_herm_1]=dat2dtp(param_h,xx_herm,h);\n", + "#RFC_herm_1 = tp2rfc(tp_herm_1);\n", + "#clf\n", + "#subplot(121), ccplot(RFC_herm)\n", + "#title('h=0')\n", + "#subplot(122), ccplot(RFC_herm_1)\n", + "#title('h=0.2')\n", + "#if (printing==1), print -deps ../bilder/fatigue_8.eps \n", + "#end\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 7'),pause(pstate)\n", + "#\n", + "##!#! Section 4.3.4 Calculating the rainflow matrix\n", + "#\n", + "#\n", + "#Grfc_markov=mctp2rfm({G_markov []});\n", + "#clf\n", + "#subplot(121), cmatplot(u_markov,u_markov,G_markov), axis('square')\n", + "#subplot(122), cmatplot(u_markov,u_markov,Grfc_markov), axis('square')\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 8'),pause(pstate)\n", + "#\n", + "##!#! \n", + "#clf\n", + "#cmatplot(u_markov,u_markov,{G_markov Grfc_markov},3) \n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 9'),pause(pstate)\t\n", + "#\n", + "##!#! Min-max-matrix and theoretical rainflow matrix for test Markov sequence.\n", + "#cmatplot(u_markov,u_markov,{G_markov Grfc_markov},4)\n", + "#subplot(121), axis('square'), title('min2max transition matrix')\n", + "#subplot(122), axis('square'), title('Rainflow matrix')\n", + "#if (printing==1), print -deps ../bilder/fatigue_9.eps \n", + "#end\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 10'),pause(pstate)\n", + "#\n", + "##!#! Observed and theoretical rainflow matrix for test Markov sequence.\n", + "#n=length(u_markov);\n", + "#Frfc_markov=dtp2rfm(xxD_markov,n);\n", + "#clf\n", + "#cmatplot(u_markov,u_markov,{Frfc_markov Grfc_markov*T_markov/2},3) \n", + "#subplot(121), axis('square'), title('Observed rainflow matrix')\n", + "#subplot(122), axis('square'), title('Theoretical rainflow matrix')\n", + "#if (printing==1), print -deps ../bilder/fatigue_10.eps \n", + "#end\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 11'),pause(pstate)\n", + "#\n", + "##!#! Smoothed observed and calculated rainflow matrix for test Markov sequence.\n", + "#tp_markov=dat2tp(xx_markov);\n", + "#RFC_markov=tp2rfc(tp_markov);\n", + "#h=1;\n", + "#Frfc_markov_smooth=cc2cmat(param_m,RFC_markov,[],1,h);\n", + "#clf\n", + "#cmatplot(u_markov,u_markov,{Frfc_markov_smooth Grfc_markov*T_markov/2},4)\n", + "#subplot(121), axis('square'), title('Smoothed observed rainflow matrix')\n", + "#subplot(122), axis('square'), title('Theoretical rainflow matrix')\n", + "#if (printing==1), print -deps ../bilder/fatigue_11.eps \n", + "#end\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 12'),pause(pstate)\n", + "#\n", + "##!#! Rainflow matrix from spectrum\n", + "#clf\n", + "##!GmM3_herm=spec2mmtpdf(spec,[],'Mm',[],[],2);\n", + "#GmM3_herm=spec2cmat(spec,[],'Mm',[],param_h,2);\n", + "#pdfplot(GmM3_herm)\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 13'),pause(pstate)\n", + "#\n", + "#\n", + "##!#! Min-max matrix and theoretical rainflow matrix for Hermite-transformed Gaussian waves.\n", + "#Grfc_herm=mctp2rfm({GmM3_herm.f []});\n", + "#u_herm=levels(param_h);\n", + "#clf\n", + "#cmatplot(u_herm,u_herm,{GmM3_herm.f Grfc_herm},4)\n", + "#subplot(121), axis('square'), title('min-max matrix')\n", + "#subplot(122), axis('square'), title('Theoretical rainflow matrix')\n", + "#if (printing==1), print -deps ../bilder/fatigue_12.eps \n", + "#end\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 14'),pause(pstate)\n", + "#\n", + "##!#!\n", + "#clf\n", + "#Grfc_direct_herm=spec2cmat(spec,[],'rfc',[],[],2);\n", + "#subplot(121), pdfplot(GmM3_herm), axis('square'), hold on\n", + "#subplot(122), pdfplot(Grfc_direct_herm), axis('square'), hold off\n", + "#if (printing==1), print -deps ../bilder/fig_mmrfcjfr.eps\n", + "#end\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 15'),pause(pstate)\n", + "#\n", + "#\n", + "##!#! Observed smoothed and theoretical min-max matrix, \n", + "##!#! (and observed smoothed and theoretical rainflow matrix for Hermite-transformed Gaussian waves).\n", + "#tp_herm=dat2tp(xx_herm);\n", + "#RFC_herm=tp2rfc(tp_herm);\n", + "#mM_herm=tp2mm(tp_herm);\n", + "#h=0.2;\n", + "#FmM_herm_smooth=cc2cmat(param_h,mM_herm,[],1,h);\n", + "#Frfc_herm_smooth=cc2cmat(param_h,RFC_herm,[],1,h);\n", + "#T_herm=xx_herm(end,1)-xx_herm(1,1);\n", + "#clf\n", + "#cmatplot(u_herm,u_herm,{FmM_herm_smooth GmM3_herm.f*length(mM_herm) ; ...\n", + "# Frfc_herm_smooth Grfc_herm*length(RFC_herm)},4)\n", + "#subplot(221), axis('square'), title('Observed smoothed min-max matrix')\n", + "#subplot(222), axis('square'), title('Theoretical min-max matrix')\n", + "#subplot(223), axis('square'), title('Observed smoothed rainflow matrix')\n", + "#subplot(224), axis('square'), title('Theoretical rainflow matrix')\n", + "#if (printing==1), print -deps ../bilder/fatigue_13.eps \n", + "#end\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 16'),pause(pstate)\n", + "# \n", + "##!#! Section 4.3.5 Simulation from crossings and rainflow structure\n", + "#\n", + "##!#! Crossing spectrum (smooth curve) and obtained spectrum (wiggled curve)\n", + "##!#! for simulated process with irregularity factor 0.25.\n", + "#clf\n", + "#cross_herm=dat2lc(xx_herm);\n", + "#alpha1=0.25;\n", + "#alpha2=0.75;\n", + "#xx_herm_sim1=lc2sdat(cross_herm,500,alpha1);\n", + "#cross_herm_sim1=dat2lc(xx_herm_sim1);\n", + "#subplot(211)\n", + "#plot(cross_herm(:,1),cross_herm(:,2)/max(cross_herm(:,2)))\n", + "#hold on\n", + "#stairs(cross_herm_sim1(:,1),...\n", + "# cross_herm_sim1(:,2)/max(cross_herm_sim1(:,2)))\n", + "#hold off\n", + "#title('Crossing intensity, \\alpha = 0.25')\n", + "#subplot(212)\n", + "#plot(xx_herm_sim1(:,1),xx_herm_sim1(:,2))\n", + "#title('Simulated load, \\alpha = 0.25')\n", + "#if (printing==1), print -deps ../bilder/fatigue_14_25.eps \n", + "#end\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 16'),pause(pstate)\n", + "#\n", + "##!#! Crossing spectrum (smooth curve) and obtained spectrum (wiggled curve)\n", + "##!#! for simulated process with irregularity factor 0.75.\n", + "#xx_herm_sim2=lc2sdat(cross_herm,500,alpha2);\n", + "#cross_herm_sim2=dat2lc(xx_herm_sim2);\n", + "#subplot(211)\n", + "#plot(cross_herm(:,1),cross_herm(:,2)/max(cross_herm(:,2)))\n", + "#hold on\n", + "#stairs(cross_herm_sim2(:,1),...\n", + "# cross_herm_sim2(:,2)/max(cross_herm_sim2(:,2)))\n", + "#hold off\n", + "#title('Crossing intensity, \\alpha = 0.75')\n", + "#subplot(212)\n", + "#plot(xx_herm_sim2(:,1),xx_herm_sim2(:,2))\n", + "#title('Simulated load, \\alpha = 0.75')\n", + "#if (printing==1), print -deps ../bilder/fatigue_14_75.eps \n", + "#end\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 17'),pause(pstate)\n", + "#\n", + "##!#! Section 4.4 Fatigue damage and fatigue life distribution\n", + "##!#! Section 4.4.1 Introduction\n", + "#beta=3.2; gam=5.5E-10; T_sea=xx_sea(end,1)-xx_sea(1,1);\n", + "#d_beta=cc2dam(RFC_sea,beta)/T_sea;\n", + "#time_fail=1/gam/d_beta/3600 #!in hours of the specific storm\n", + "#disp('Block 18'),pause(pstate)\n", + "#\n", + "##!#! Section 4.4.2 Level crossings\n", + "##!#! Crossing intensity as calculated from the Markov matrix (solid curve) and from the observed rainflow matrix (dashed curve).\n", + "#clf\n", + "#mu_markov=cmat2lc(param_m,Grfc_markov);\n", + "#muObs_markov=cmat2lc(param_m,Frfc_markov/(T_markov/2));\n", + "#clf\n", + "#plot(mu_markov(:,1),mu_markov(:,2),muObs_markov(:,1),muObs_markov(:,2),'--')\n", + "#title('Theoretical and observed crossing intensity ')\n", + "#if (printing==1), print -deps ../bilder/fatigue_15.eps \n", + "#end\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 19'),pause(pstate)\n", + "#\n", + "##!#! Section 4.4.3 Damage\n", + "##!#! Distribution of damage from different RFC cycles, from calculated theoretical and from observed rainflow matrix.\n", + "#beta = 4;\n", + "#Dam_markov = cmat2dam(param_m,Grfc_markov,beta)\n", + "#DamObs1_markov = cc2dam(RFC_markov,beta)/(T_markov/2)\n", + "#DamObs2_markov = cmat2dam(param_m,Frfc_markov,beta)/(T_markov/2)\n", + "#disp('Block 20'),pause(pstate)\n", + "#\n", + "#Dmat_markov = cmat2dmat(param_m,Grfc_markov,beta);\n", + "#DmatObs_markov = cmat2dmat(param_m,Frfc_markov,beta)/(T_markov/2); \n", + "#clf\n", + "#subplot(121), cmatplot(u_markov,u_markov,Dmat_markov,4)\n", + "#title('Theoretical damage matrix') \n", + "#subplot(122), cmatplot(u_markov,u_markov,DmatObs_markov,4)\n", + "#title('Observed damage matrix') \n", + "#if (printing==1), print -deps ../bilder/fatigue_16.eps \n", + "#end\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 21'),pause(pstate)\n", + "#\n", + "#\n", + "##!#!\n", + "##!Damplus_markov = lc2dplus(mu_markov,beta)\n", + "#pause(pstate)\n", + "#\n", + "##!#! Section 4.4.4 Estimation of S-N curve\n", + "#\n", + "##!#! Load SN-data and plot in log-log scale.\n", + "#SN = load('sn.dat');\n", + "#s = SN(:,1);\n", + "#N = SN(:,2);\n", + "#clf\n", + "#loglog(N,s,'o'), axis([0 14e5 10 30])\n", + "##!if (printing==1), print -deps ../bilder/fatigue_?.eps end\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 22'),pause(pstate)\n", + "#\n", + "#\n", + "##!#! Check of S-N-model on normal probability paper.\n", + "#\n", + "#normplot(reshape(log(N),8,5))\n", + "#if (printing==1), print -deps ../bilder/fatigue_17.eps \n", + "#end\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 23'),pause(pstate)\n", + "#\n", + "##!#! Estimation of S-N-model on linear scale.\n", + "#clf\n", + "#[e0,beta0,s20] = snplot(s,N,12);\n", + "#title('S-N-data with estimated N(s)','FontSize',20)\n", + "#set(gca,'FontSize',20)\n", + "#if (printing==1), print -deps ../bilder/fatigue_18a.eps \n", + "#end\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 24'),pause(pstate)\n", + "#\n", + "##!#! Estimation of S-N-model on log-log scale.\n", + "#clf\n", + "#[e0,beta0,s20] = snplot(s,N,14);\n", + "#title('S-N-data with estimated N(s)','FontSize',20)\n", + "#set(gca,'FontSize',20)\n", + "#if (printing==1), print -deps ../bilder/fatigue_18b.eps \n", + "#end\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 25'),pause(pstate)\n", + "#\n", + "##!#! Section 4.4.5 From S-N curve to fatigue life distribution\n", + "##!#! Damage intensity as function of $\\beta$\n", + "#beta = 3:0.1:8;\n", + "#DRFC = cc2dam(RFC_sea,beta);\n", + "#dRFC = DRFC/T_sea;\n", + "#plot(beta,dRFC), axis([3 8 0 0.25])\n", + "#title('Damage intensity as function of \\beta')\n", + "#if (printing==1), print -deps ../bilder/fatigue_19.eps \n", + "#end\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 26'),pause(pstate)\n", + "#\n", + "##!#! Fatigue life distribution with sea load.\n", + "#dam0 = cc2dam(RFC_sea,beta0)/T_sea;\n", + "#[t0,F0] = ftf(e0,dam0,s20,0.5,1);\n", + "#[t1,F1] = ftf(e0,dam0,s20,0,1);\n", + "#[t2,F2] = ftf(e0,dam0,s20,5,1);\n", + "#plot(t0,F0,t1,F1,t2,F2)\n", + "#title('Fatigue life distribution function')\n", + "#if (printing==1), print -deps ../bilder/fatigue_20.eps \n", + "#end\n", + "#wafostamp([],'(ER)')\n", + "#disp('Block 27, last block')" + ], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/pywafo/src/wafo/doc/tutorial_scripts/WAFO Chapter 5.ipynb b/pywafo/src/wafo/doc/tutorial_scripts/WAFO Chapter 5.ipynb new file mode 100644 index 0000000..f598c9d --- /dev/null +++ b/pywafo/src/wafo/doc/tutorial_scripts/WAFO Chapter 5.ipynb @@ -0,0 +1,485 @@ +{ + "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": 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LMt+8eZOff/6Z5cuXk5yczKBBgx64QCGqE/UmKnHgbAtP74csG/i6H6Q5AvHA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\nSUVORK5CYII=\n" + } + ], + "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": 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BwaW+FxISovs6ICCAgIAAwxQlhHhgV9OvMn7LeC6mXSRiTAQezh7GLqlGCg8PJzw8/L6P\nr7RZPQkJCQQGBhITE1Ns+8yZM4mKiuKnn34qWYzM6hGi2gqNDeWFrS8wpv0YQgJCsDK3MnZJJqOi\n2WnQEf93333Htm3b2L17tyFPK4SoROk56UzeOZmf435m3dB1pB5XCHxqRo16cElNY7Dg37FjB3Pn\nzmXfvn1YW1sb6rRCiEp0+MJhRm4cSVfXrhybdIxfd0fXyAeX1DSV0uoJDg5m3759XL9+HRcXFz74\n4ANmz55NTk4O9erVA+CRRx5h8eLFxYuRVo8Q1UJefh5zfp3DokOLWPTkIoZ5DgOgb9932bnzoxL7\n9+37Hjt2fGjoMk1GlWj1rF27tsS2cePGVcaphBAGFnczjuc3Po+dlR1RL0TRpE4T3Xs19cElNY3c\nuSuEuCeKorDs6DK6Lu3KMM9h/Dzy52KhDzX3wSU1jazOKYQo1/WM60wKncTZm2fZO3ovXg28St1P\nVs6sHiT4hRD/6OezPzNuyziGew9nTdAaalnUKvOZtzX1wSU1jazOKYQoVWZuJlN3TWXTX5v4btB3\n9GjRAyj9mbfu7tNZsKCvBLyRyOqcQogHdvTSUfy+9uNaxjWiX4zWhT4Y9pm3onJIq0cIoaPN1/Lp\n758y78A8Puv3GcO9h5fYR2buVH8S/EIIAP5O/ptRm0ahQcPhiYdxq+tW6n4yc6f6k1aPECZOURRW\n/bmKzks683Srp9k9aneZoQ/qzB139+nFtqkzd3pXdqlCT+TirhAm7FbmLV4Ke4mYqzG86PwWod/E\nF5upA5Q6e0eeeVu1VIk7d4UQVd+e+D2M2TSGwR6DGVZrPFPeDC920fbPP8cDDly+/H+6bUXX3ZGg\nr75kxC+EicnKyyL4m9FsP7+d1qcG0ijDjWvXbnH06Od37fkuIOvuVAcy4hdClCosLIKZ36wiqvlP\n5F5xIH9TPDGZTsQA1tajSzlCZu/UVBL8QtRQRe+uTUlNJM75LGm+J+GXR+HYFkCj2zcrq2kpnyCz\nd2qqCgW/VqslPT2dOnXqVFY9QogHUBD2Fy5c49w5DZmZX0KdJBjUDSwawzd/wK0VFA19VR+srV8i\nK+sL3ZaGDS8CbxXr8cu6OzVDucEfHBzMV199hbm5OZ07dyYlJYXXX3+dKVOmGKI+IUQZiob85cvJ\n1K5tydWrtdWwL+jPe/4AT/0LDnrBbzsh34LSR/L+tGu3EmfnomvsjAFk3Z2aqNyLu76+vkRHR7N6\n9WqioqKYM2cOHTt2LPEsXb0UIxd3hbgnhevl9AV+BmZS7GJsrf/AUxegySHYsBoubip8j4gix6jc\n3aexYIGEenWl94u7eXl55ObmsmnTJl555RUsLS3RaO7+NVEIUZnuHt1nZ2u5fXs9atgXBPidf85u\nETD4SzgzHL6Kglw7IAOYfmdfNdxtbJ6lZcvGNG5cW0byJqbc4J80aRLNmzfHx8cHf39/EhIScHBw\nMERtQpi8sLAI3ntvBadOWZKVFYw6Uv8KCLmzR5F/wubZEPAOtF8OW96BM6mA3Z03/WnY8DsaN34F\ne3vnO22bVyTsTVSF5/ErioJWq8XCQv8TgqTVI0ShwnaOBrVNU3Re/bvFtzmfhKBBkAJs/RXSGwAR\n2Nh8XmRUL3fX1lR6X5b58uXLjB8/nn791Cv5p06dYvny5WXuP27cOFxcXPD29tZtu3nzJr1796Z1\n69b06dOH5OTkey5QCFNVuPxxwSCr6GCrD2rrpjc83BfGdIfDb8P3S7DJfxVv7zfp2/cXfvzxFf78\ncz47dnwooS90yg3+MWPG0KdPHy5evAhAq1atmD9/fpn7jx07lh07dhTbNmfOHHr37k1sbCw9e/Zk\nzpw5D1i2EDVf4fLHeXf9CeAPtTvBiPGYtT+E44aH8c49Sd++uyTsRbnKDf7r16/z7LPPYm6u3q1n\naWn5j22ebt264ejoWGzbli1bGD1avTNw9OjRbNq06UFqFqLGCwuL4PjxU3deFYzuC/4EPDbAiy/h\nmNGIDf1/4ubZMAl7cc/KbdTXrl2bGzdu6F4fPHiwwhd3r1y5gouLCwAuLi5cuXKlzH1DQkJ0XwcE\nBBAQEFChcwlR3RRcwE1IuE1OTib5+ZlotS3IyXmFwpk4AL9gYXcMTb/maFok0yF+MO+NHytBb4LC\nw8MJDw+/7+PLvbgbGRnJq6++yokTJ/D09OTatWusX78eX1/fMo9JSEggMDBQN9ff0dGRW7du6d6v\nV68eN2/eLFmMXNwVJqIg7GNjL5Gebg20BQrm5BdczAV1zv0vgDkOXr9iM+IU/T2fYn7f+dS2qm2c\n4kWVo/d5/H5+fuzbt4/Tp08D0KZNGywtLStUlIuLC5cvX6Zhw4ZcunSJBg0aVOh4IWqKgsA/fjyN\n3NyWQMM77xTM0JlJ4VRNAH8wewS6f0hG14N8N2gVg9oOMnDVoqYpN/iXL19e7KdJVFQUAKNGjbrn\nkwwYMIDly5czdepUli9fzqBB8j+uMD0hIYv55JM/ycxsiBr4H1E85O++mAs4xULQSMhw4rGYCRL6\nQi/KDf7Dhw/r7tTNyspi9+7ddOzYsczgDw4OZt++fVy/fp2mTZsyY8YM/vOf/zBs2DCWLl1K8+bN\n+eGHH/T7XQhRxYWFRfDJJ/vIzFxH8bDPK+XrPsA08HODHu9C+Ac8dDORfy940lDlihquwjdwJScn\n8+yzz/Lzzz/rvxjp8YsaqmPHVzh61Bk19N+9s/Uj1B7+ctTfAO70+O1ehwGDwf4cVmEP4+XiyowZ\nz8pFXFEmvd/AdTdbW1vi4+MrepgQJiksLIKOHScQHX2L4iP6S6gzdvyB0cBVNJp5WHruRPOSG85K\nLr3Pj2XDV5OJjPxcQl/oVbmtnsDAQN3X+fn5nDx5kmHDhlVqUULUBMV7+lA4D79geuZKYAC1a9vj\n7mFH4zFwKvc6KwbtpJtbN2OULExEua2eonNFLSwscHNzo2nT0p7Wo4dipNUjaoCwsAhee20B585p\ngU2o7Z0eqFM1+1IwPVOj+ZP//rcXT0/szMiNI+nq2pWF/RbiYC2LIIqK0ft0TrmBSoh7Uxj4CmAJ\nFKxXlUfBUsgFoQ9a2nd0xPyJG/Rf259FTy5imKf8Ji0Mo8zgr127dpnr7ms0GlJTUyutKCGqm5CQ\nxcyeHU5OjgXQCvWfVtGefvG18Jv5vkTmc38Qcf48US9E0aROE2OULUxUhWf1VCZp9Yjqpnhbx4vi\nc/H7UPikK/UOXI3ZWZoGXiG5cxQf9AzhtS6vYaap8BwLIYrRe6unwNWrV8nKytK9btasWcUqE6KG\nCQuLYMKE5Vy+bEHxtg4Uhn5f4D3AHI1dFG3+fQGrhgphQb/i1cDLCFULcQ/TObds2UKrVq1o0aIF\n3bt3p3nz5jz5pNxIIsTChTu5fLkR4IEa+AWj/EvcfSHXos0uav/7N/o/1ptDEw5J6AujKjf43333\nXQ4cOEDr1q2Jj49n9+7ddOnSxRC1CVGlqevlF/Tyiwa+Oi8fvgDLE9R5bh2Oo8+wafQG5vaeSy2L\nWsYrWgjuIfgtLS2pX78++fn5aLVannjiCY4cOWKI2oSokgpuytq//xDFe/lFAp9kXDvdpumHUTw1\ntD2n3/iLHi16GLFqIQqV2+N3dHQkLS2Nbt26MWLECBo0aEDt2rIcrDAdBRdwExJukZ+fDbgAjkAj\nSrZ1mmBZKwb//2j50+4PPuv3GcO9hxuveCFKUeasnh9//JHAwEC0Wi3W1tbk5+ezevVqUlNTGTFi\nBE5OTvovRmb1iComJGQxH320Fa3WHrAHbqNO14TCtXZW3tluhV2TyzR/8zL1nRxZPmg5bnXdjFO4\nMCkVzc4yg3/QoEH89ttv9OvXj+DgYPr27at7/GJlkeAXVcnw4VNZu/Y0UIvCsC/6S3JIka8V8FmN\nZf9JfPTU+0x+ZDLmZpX770WIAnoLfoCUlBQ2btzI999/z7Fjxxg0aBDBwcF0795dL8WWKEaCX1QB\nYWERjB8/kytXbID2QALQ/M67RZdRvvOULOtb0P8laBDDI5ce5fcNSwxYrRB6Dv6irl+/zk8//cTn\nn3/OzZs3SUpKuu8iyyxGgl8YmTrKPwPkAn6oQX+GwhF/H9RllAEaQoseMGgMnBqCS4yWpV89Iytp\nCoOrlGWZb926xYYNG1i3bh03b97kmWeeue8ChaiqQkIWs3ZtLNAOcKJwxk4uEEux6ZoW+dBnLQzu\nj+V2dzpezZXQF9VGmSP+tLQ0XZsnKiqKAQMGEBwcTEBAQJlr+DxwMTLiF0ZUp04QaWk+d16dAl6h\ncMbOQuAWoECDPDRD/8Qhz4nFT31G8MDAMj5RCMPQW6unfv369O3bl+DgYPr06YOVlZXeiiyzGAl+\nYQQhIYuZNWsVubmuQOs7WxsDfwLD0a2oqTlGh5dSSWwew9zecxntO7rSBkFCVITe1uo5f/48tra2\neilKiKoqJGQxH3ywFXVuvobCHr4C+ACLAWuok4jLpLPYtm3KH4P/4CHHh4xXtBAPyKCrc86ePZtV\nq1ZhZmaGt7c33377LbVqFd6+LiN+YUhhYREMHPgxWm1t1PV2GlA4yi+Ym5+Lw2MX4Mm/eLvbv5n6\n+FQszO55bUMhDKLSn7l7vxISEliyZAlRUVHExMSg1Wr5/vvvDXV6IYopWFlTvTHLBvVC7ssUjvJz\noVY2tZ8/jMtzN9k19hem+0+X0Bc1gsH+L65Tpw6WlpZkZGRgbm5ORkYGTZrIwyeEcRSurHkGyKT4\nw1JeBrcIGPw0/q168sOE1dhZ2RmzXCH0qszgL/qQ9bt/jdBoNGzZsqVCJ6pXrx6TJ0+mWbNm2NjY\n0LdvX3r16lViv5CQEN3XAQEB8uhHoXdhYRHs3XsSdXTfHdgKrAGGg/k0eCICfCPxT3masJfXG7VW\nIUoTHh5e7HnoFVVmj7/gQzdu3Mjly5cZOXIkiqKwdu1aXFxc+Oyzzyp0ori4OAIDA9m/fz8ODg48\n88wzDB06lBEjRhQWIz1+UcnCwiIYNuxTMjK0QAfUu28XA+vBOQ+CYiDVin+3fou5IVONW6wQ90jv\nd+76+fkRGRlZ7rbyrFu3jl9++YVvvvkGgJUrV3Lw4EE+//zzwmIk+EUlCAuL4L33VnDy5Dmys81Q\nb86qi9rXbwh8BA//D7rPwDy8LZv++xH9+1fOsiRCVAa9X9zNyMggLi5O9/rcuXNkZGRUuLC2bdty\n8OBBMjMzURSFXbt20a5duwp/jhAVERYWwbPPLubo0ZtkZ9dCDXwbwBkYDbUTYGRj8JkBS/vgq20r\noS9qvHIv7s6fP58nnniCFi1aAOrsnK+//rrCJ/L19WXUqFF06tQJMzMzOnbsyAsvvFDxioWogNde\n+4b09JaoF3EtUefq37mY67EAnv4NjrwIEdMxYxIzZjxv1HqFMIR7mseflZXF6dOnAXXkXnTuvV6L\nkVaP0DNz86Hk53uhrrAJYAFWV6DfGWh+Gzb0hiQ3IJrg4NasWfOx8YoV4j7pvdWTnp7O3Llz+d//\n/oevry/nz58nNDT0gYoUorKFhCzGwuLhO0/MykMd5WdC047w4j5QsuFLH0jKBA5J6AuTUu6If9iw\nYfj5+bFixQpOnDhBeno6jz76KNHR0fovRkb8Qg/UpZWjAXPUpRcagdkN6H4c/C5C6HT4K+vO+zLS\nF9Wf3tbqKRAXF8cPP/ygu8vWzk5uZBFVV/fuY4mIuAo4oF7EfRictkJQJGRo4UtPuP0bYIeNTQ5T\npvQlJORl4xYthIGVG/y1atUiMzNT9zouLq7SevxC3K+QkMXMmLEURWmEOmMHIAP8zKHHrxDeGg57\nAdnY2FwlI2ObEasVwrjKDf6QkBD69etHUlISw4cP57fffuO7774zQGlC3Bt1lH8RdYVNJyAT7LJg\nwDmw3wvfHoLrHnf2nsCUKfKwFGHa/rHHn5+fz48//kjPnj05ePAgAF26dMHZ2bmsQx6sGOnxiwpQ\n78KdSkaGI2CPOlUTaO0Igd/CsXYQPga0vwPWwDX8/Ruwb9+3RqtZiMpgkDt3K4sEv7gXISGL+eij\n79BqawF1UEPfGiyvQN8EcL8EG8fB+WOofX5bNJrb/Pe/gdLPFzWS3oP/P//5D/Xr1+fZZ58tdmG3\nXr1691+xxAqNAAAgAElEQVRlWcVI8ItyqG2dcxSO8J2AG9A4C4ZEQqI9bPeH7Oaos3a0WFv/xfr1\nr8rzcEWNpffgb968eamPl4uPj694deUVI8Ev/oEa+ldQA70ekA1mWnj8BDwcB9v84WRH4DBgB1hh\nZ5fFunVTJfRFjab34DckCX5RlsLQr1O40fEqBCVATjps6ghp2aitHTsgjeBgH5mfL0xCpdy5++GH\nHzJx4kQAzpw5I3fuCoMKCVlcJPTTgdvQ4SJM+BVOuMCq9yDN7M771tjZpRMa+o6EvhBlkDt3RZVn\nazuAzExbIB1sFQj8AxxzYUMPuJqB2vpRR/n+/g1l1o4wOXof8cfFxTF16lSsrKwAuXNXGFZIyGIy\nM2sB6eB+C17cDTcdYYkHXL2FuuJmLRwdcwgNfUdCX4h7IHfuiipL7etfAgsz6H0a2l6CDZ0goQ5q\n4FsBqbz//pMyTVOICpA7d0WVExYWQVDQZHJy6kMjLQT9Dpfrwhc9IEuheOgPkNAXooLuaVbP9evX\ndXfudu3alfr161dOMdLjN3khIYv54IO1oLGDRy/Co6dhx1SIOQ5kI6EvREl6m84ZGRlZYv6+oii6\nbR07dnyAMssoRoLfpKmtnSSomw+D/wRFgY3ekPIo0Bf4BfVC7ilsbK6RkbHHuAULUUXobVnmyZMn\no9FoyMzMJDIyEh8fHwD+/PNPOnXqxIEDBx68WiHucHPrzfnz5uCTDH1j4Pfm8HsLUGxQH5toRsGd\nuJDHlClDjVmuENVambN6wsPD2bt3L40bNyYqKorIyEgiIyM5evQojRs3NmSNogYbPnwqGo0P56/l\nw9AT8PhfsPJh+M1NHfFjAeQD0cBfQCT+/nWkxSPEAyh3Oudff/2Ft7e37rWXlxenTp26r5MlJycz\ndOhQPDw8aNeune66gTBNDRp0Y+3ag9DCGl48ALet4OuOcNkadS2efOACkAHUApIJDvaWKZtCPKBy\nZ/X4+PgwYcIERo4ciaIorFmzBl9f3/s62euvv85TTz3F+vXrycvLIz09/b4+R1Rvai//MFg0hj5x\n4HUJNntBXH3U5+PWAW7f+boeYIm1dRrr10+TNXeE0INyZ/VkZWWxePFi9u/fD4C/vz8vvfQS1tbW\nFTpRSkoKHTp04Ny5c2UXIxd3azwvr8GcOHEZGpjDkONwwx62toFMc9TVNusAqRS9G9fT05rjxzca\ns2whqjS9PnM3Ly+PJ598kr179/LWW289UGHx8fE4OzszduxYoqOj8fPzY8GCBdja2hbbLyQkRPd1\nQEAAAQEBD3ReUXV4eQ3mxMlMeCQZHj8Hv7SBY82AXNTWTipqa8cBsAVSCQ72lTV3hLhLeHg44eHh\n9318uSP+nj178tNPP1G3bt37PgnAkSNHeOSRR/j999/p3Lkzb7zxBnXq1GHGjBmFxciIv8YJC4tg\nxIj3SEm5AXXqwaDTYKGFjV5wqxaQhfqM3FTU1o66nDKk8f77g+QirhD3QK8jflDX5vH29qZ37966\ndXo0Gg0LFy6sUGGurq64urrSuXNnAIYOHcqcOXMq9BmielHbOheBfPC0gKcOwcFm8NtDkN8ISAMU\n4DzqCN8esMTRUcvKle9KP1+ISlJu8AcFBREUFFTsJ0ppD2YpT8OGDWnatCmxsbG0bt2aXbt24enp\nWfGKRbVga+tHZqYj1KoDT52EJqmwujNcNANSgKaoQX8D9SHplpibp7J589sS+EJUsnJbPZmZmZw9\nexaNRkPLli0rfFG3qOjoaCZMmEBOTg7u7u58++23ODg4FBYjrZ5qLywsgv79XwaaglsGDD4CZxrB\nzocgNw9ogXpDVjrQAKgN3JYLuEI8AL0t2ZCbm8v06dNZtmwZzZo1A+D8+fOMHTuWWbNmYWlpqZ+K\nixYjwV+tqaE/GczrwRN/g28CbPGCM85AQ+Acaj/fEXW6phVwA39/Z5mbL8QD0Fvwv/HGG9y+fZv5\n8+djb28PQGpqKpMnT8bW1pYFCxbop+KixUjwV2sWFr3Q1suGoOOQYgVbW0K6OZAJtEe9eHsC9QHp\n6khfHo8oxIPTW/C3bNmS2NhYzMyK39yr1Wpp06YNZ8+efbBKSytGgr9aUlfU/BwetoDuf8HuVhBV\nD3WaZhPgGmpfvxHS2hFC//Q2q8fMzKxE6AOYm5uXul2YJi+vwZw4fw5GXgXrPFjaBW46od6AdRWI\nR+3lN0AN/DyOH99uzJKFMHllJriHhwfLly8vsX3lypW0bdu2UosS1YOtrR8n8hNg0l+Q1ACW+cDN\nHKA+6iqaZkBjwAaN5gahoe/IKF+IKqDMVk9SUhJBQUHY2Njg5+cHqGv0Z2RksHHjRlxdXfVfjLR6\nqgUvr8GcOPMXPJkGbsmwoQskWaEuqmaPevdt3Ttfp6HRXCQ/P9qYJQtRo+mtxw/qg1f27NnDiRMn\n0Gg0tGvXjp49e+ql0FKLkeCv8iwsfNA2zoXBf0N8Q/i5JeSYA9ao7Z1U1JU07VCnbCYTGjpT5uYL\nUYn0GvyGJsFfdXXvPpaIXw9B92zwS4TQTvCXA5CMehG3PXAduEXBHbgaTTJbt4ZI6AtRyST4hV7p\nllB2yoegi5BhA5s7we081Jk6CuqiatdQL+Cq7R0bm6tkZEQasXIhTIfe1+oRpktddiEf/IAeZyHc\nCw43QJ2PX7BufhaFoV8bSKNZszz+/ltCX4iqSuZlihLCwiLQaLzINHOA4AvgdwG+7QGHXVBH+deB\n5qgzd7JR19qpDaTy/vuB/P33L0arXQhRPmn1CB31RqwvATNobQ6BJ+FYcwhvBloz1FG+A5CEejdu\nYwpuyFJH+RL4QhiD9PhFhYWFRRAY+CqKYg6W9tA3HtxvqGvmn6+HuoJmHmrPJ4HCB6dYArcIDf1A\nLuAKYUQS/KJC1DXz4wEXdenkoOOQ6ATbW0J2wcNRGqC2eNJRl2BQR/l16twkJeUP4xUvhAAqnp3S\n4zdRISGL0Wi8OHEiBcxcwD8Jgo/C7tawyQOy01EfdF4LuHjnKFcKLuC+/36ghL4Q1ZSM+E2QOsr/\nG3ABRy0ERUEOsMkb0hTUJZObo/byNajLKNuhPgO3vaymKUQVIyN+UabCUX4G0AA6XIMJ++CEM6xq\nCWkpqHfeNgf+Rr0bt/6dP68TGjpdQl+IGkBG/CZAfUDKK+gWTbO1hMAocEyFDW3gqoI6sncCLqNO\n0yxo68iMHSGqOrm4K4pRQ/9V1CdgmUHL2zDgMMQ0gj11QasBHkId4edQdIqmrJkvRPUgwS901Hn5\nPwLWYGEDvWOgbRJsbAEJ1qhTMnO4e7aOLLcgRPVS5Xv8Wq2WDh06EBgYaOhTm5Tu3cfywQdfAY7Q\nKA8m7QTbdPjCAxLsKJyimUfR0A8O9pHQF6KGM/haPQsWLKBdu3akpaUZ+tQmQ11jpz5oGsFjp+CR\nc7CjGcTYok7PtELt5Rc+CtHG5pwEvhAmwqAj/qSkJLZt28aECROkpaNnw4dPRaPxUtfYyawPdc1h\nTCS0vARft4SYuqhBnw6kUTT0/f0bSugLYUIMOuJ/8803mTt3LqmpqWXuExISovs6ICCAgICAyi+s\nmlNH+HmoD0JpBD43oe8x+L0F/O4BigVwDrhE0baOPP9WiOopPDyc8PDw+z7eYBd3Q0ND2b59O59/\n/jnh4eHMmzePrVu3Fi9GLu5WiHrxdjHQVN1gkwNPx0KDZNjQAi5rgIdRV9PMovBRiInk5x83VtlC\nCD2rsrN6pk2bxsqVK7GwsCArK4vU1FSGDBnCihUrCouR4L9nDg5dSE3NQjf9ssVVGHQYTjWAXS6Q\nl486L/8i6ihfQl+ImqrKBn9R+/bt49NPP5UR/31wc+vN+fOXUG+w0oCFLfSMAs9LsNkb4qxRR/dO\n3P1ULCurS2RnHzNe8UKISlHlp3MW0Gg0xjp1tWVm5sX58zdQWzv24KKFiTvBIQW+cIe4HKAt6hIL\nlyj6VCxPT2sJfSEEIDdwVQvqompn0PXyNbWh63F4PA52NoHopqhLLty4c0RTCi7gOjvf5urV/cYo\nWwhhINVmxC/KV7io2t/oRvl1zOH5PeBxAb55CKL9gIw7/zUFPADw96+NomyX0BdClCAj/ipo+PCp\nrF0bhnqjlcudrfbgGQtPnYKDzvCbK+TXRb14Ww95IpYQpqtaXNwtiwQ/NGjQjWvXCi7OagB7qJUL\nTx2CJjdgQ3O46IX6CMR8Ch90LouqCWGqJPirMfVGrAZ3XtUGNOD2NwyOgTNOsNMPcpOAbNQfDOoo\n39LyNhs3viujfCFMlAR/NdS9+1giIg5TeFFWA+ap8EQs+CbBlqZwpgfyoHMhRGkk+KuRwjtvbVCf\ndKXOt8f5tvo4xBRL2Po4pF9HXWOn8OEoMltHCFFAgr+aUEf5x1EDH9TQT4WHT0HARdjVFKJaAVeA\nuhSM8jWaZLZuDZFRvhBCR4K/GlDn5WfdeWV/549rMDAarPNgQ1u4mYM627bw4q2/f0P27fvWKDUL\nIaquimanwdfjN2WF0zTvzMkHIA08LsPTp+DIQxBRD/JzKL5scm327fvRWGULIWoYGfEbiLrGzm0K\n2zbpYJUDT8aAWzJs6ANJ5sAtpK0jhKgIuXO3ilHvvm3P+fMWgCO60G96CV7cD/k28KUfJJ1HXVzN\nGcijWbM08vP3SOgLIfRORvyVSO3l3wRs0bV2zFKh+1/gdwlCPeGvOzdpyZx8IcR9khF/FVAwyj9x\nIhuogxrqt8HpCow/AI3T4ctH4a+6QEPUVTTz8Pd3ICdnp4S+EKJSycVdPVIv3u5CvSjbGDX0AVKg\n09/wxBkI94LD9VEXVXNA/W0gleBgX9as+dg4hQshTIq0evQgLCyCYcOmkpHhgPpLlDlgB6SDXTYM\nPAq182CDB1zPpmhrx84um3XrpsooXwhx36TVY0BhYRG4uw+hf/8PychwRB3B10EX+q1vwIv74IoT\nLPWD69aovwk4Adm8/34vbt8Ok9AXRjVp0iRq167N3r17i23/v//7Pzw9PfH19aVXr16cP3/+nj8z\nPj6eLl260KpVK5577jlyc3NL3W/q1Kl4e3vj7e3NDz/8oNu+Z88e/Pz88Pb2ZsyYMWi1Wt174eHh\ndOjQAS8vLwICAop9nlarpUOHDgQGBt5zraZIgv8+hYQsZuDAhZw7p0VdFrkO6tIL6WCZAv0T4clo\n+LEL7HYCrRb1B4Iljo5aQkPfJSTkZWN+C8KEKYpCfn4+H330Eampqfzxxx+88sorxMTE6Pbp2LEj\nkZGRREdHM3ToUKZMmXLPnz916lQmT57MmTNncHR0ZOnSpSX2CQsL4+jRo0RHR/PHH3/w6aefcvv2\nbfLz8xkzZgzr1q0jJiYGNzc3li9fDkBycjKvvPIKW7du5fjx46xfv77YZy5YsIB27drJE/7KIcF/\nD8LCIujYcQK1az+NuXkvNJrH+eCDMLTatqij/DuBTyY0ua2O8i0y4MuJcN4C9QdDPaytcwkNfYOb\nNzfJKF8YXEJCAm3atGH06NF4e3uzatUqTp06xZo1a/D09GTLli1MnDiRCxcuABAQEIC1tTUAXbp0\nISkp6Z7OoygKe/fuZejQoQCMHj2aTZs2ldjv1KlT+Pv7Y2Zmhq2tLT4+Pmzfvp0bN25gZWVFy5Yt\nAejVqxc//fQTAGvWrGHIkCG4uroCUL9+fd3nJSUlsW3bNiZMmFAtW8aGJBd3yxESspjZs8PJybFH\nnYED6iMOHVD/+rIBBczaQrdN0DkRtvnCSQsgFnUtnjT8/euzb99PRvkehChw9uxZVq5cycMPPwzA\nqFGjdO+1bNmSgwcPlnrc0qVLeeqppwBIS0vD37/kwEWj0bBmzRrq169P3bp1MTNTx5VNmjTR/TAp\nytfXlw8++IDJkyeTnp7O3r178fT0xNnZmby8PCIjI/Hz82P9+vW6HzpnzpwhNzeXJ554grS0NF5/\n/XWef/55AN58803mzp1LamrqA/wNmQaDBn9iYiKjRo3i6tWraDQaXnjhBV577TVDllCqsLAIFi7c\nSXa2BampSUAtcnLyiIuLJSvLAfC664g81MDPA2qD43kICoccG/jKC9Js1e1Y4e5uw4IF/5IRvqgS\n3NzcdKF/r1atWkVUVBTz588HwN7enqNHj5a5//Xr1+/pc3v37s3hw4d59NFHcXZ25pFHHtH9sPj+\n++958803yc7Opk+fPrrtubm5REVFsXv3bjIyMnjkkUfo2rUrp0+fpkGDBnTo0IHw8PAKfX+myKDB\nb2lpyfz582nfvj23b9/Gz8+P3r174+HhYcgyigkLi+D1138mLm4mEAH8DPQFlqPOry+ttmzUYL8I\nHc5AryOwvzX80QwUOyCN4OB2Mj1TVDl2dnYV2n/Xrl3MmjWLiIgILC0tAXXE361bt1L76GvXrqVN\nmzYkJyeTn5+PmZkZSUlJNGnSpNTPnzZtGtOmTQNgxIgRtGnTBoCuXbsSEREBwM6dOzlz5gwATZs2\npX79+tjY2GBjY4O/vz/R0dFERUWxZcsWtm3bRlZWFqmpqYwaNYoVK1ZU6Ps1GYoRDRw4UNm1a5fu\ntaHKCQ3dp/TpM13p3v19xclpmALKnf+mF/lzugLvF/m66H/jFWyHKzzrpvBiPYUG/goMUCBIcXcf\npYSG7jPI9yFERcTHxyteXl73vH9UVJTi7u6unD17tsLneuaZZ5Tvv/9eURRFmTRpkvLFF1+U2Eer\n1SrXr19XFEVRoqOjFS8vL0Wr1SqKoihXr15VFEVRsrKylJ49eyp79+5VFEVRTp06pfTs2VPJy8tT\n0tPTFS8vL+XEiRPFPjc8PFzp379/hWuuziqanUbr8SckJHD06FG6dOlSbHtISIju64CAgBLTtR5U\n8RE+QEiRdy3u+jMP6IM6+i+iZRIM+BVi3GH9w6C1wcIimenTe8pMHVGlVWS2y5QpU0hPT9ddpHVz\ncyv1Im1pPv74Y5577jneffddOnbsyPjx4wGIjIzkyy+/ZMmSJeTk5OiuFTg4OLB69WpdS2fu3LmE\nhoaSn5/Pyy+/rMuBtm3b0q9fP3x8fDAzM2PixIm0a9fugb7P6ig8PPyBWlpGuYHr9u3bBAQE8O67\n7zJo0KDCYgxwA1ffvu+yc+dHRba8C3x019fv3nndh8LWz0qwTIJeMdD2Omxsh9l5V2xt7Wndui4z\nZjwrfXwhhFFU+fX4c3NzGTJkCCNHjiwW+oaSnX33t9wHmA7MLPJ1QY+/IPR/gUZmEPQbXvVbEzE1\nBsf/czRk2UIIoTcGDX5FURg/fjzt2rXjjTfeMOSpdWrVyrtrizpKr1//OTw925KWdhVYR3a2BefP\nHyOfv8jvGk9up794o+2/mTtqhsFrFkIIfTJoq+fXX3/F398fHx8fXQ9u9uzZ9OvXTy3GAK2ekj1+\ncHefxoIF/Uq0ahKSExi1cRRmGjNWDF5BM4dmlVqbEELcD3nm7j0IC4tg0aJfyMoyx9pay6uv9i4W\n+oqisOrPVby18y2mPDqFtx55C3Mz80qvSwgh7ocE/wO6mXmTl8Je4vjV46wOWk37hu2NWo8QQpRH\nVud8ALvP7cb3S18a1W7EkYlHJPSFEDWSrNUDZOVlMW33NH448QPLBi6jj3sfY5ckhBCVxuSD/88r\nfzJiwwjaOLUh+sVonGydjF2SEEJUKpNt9eQr+fzfgf+j54qe/PuRf/PjMz9K6AshTILJjviH/DCE\na+nX+GPCHzzk+JCxyxFCCIMx2Vk90Zej8WzgiYWZyf7sE0LUEDKdUwghTIxM5xRCCPGPJPiFEMLE\nSPALIYSJkeAXQggTI8EvhBAmRoJfCCFMjAS/EEKYGAl+IYQwMRL8QghhYiT4hRDCxBg0+Hfs2EHb\ntm1p1aoVH3/8sSFPrVfh4eHGLuGeSJ36VR3qrA41gtRpbAYLfq1Wy7/+9S927NjByZMnWbt2LadO\nnTLU6fWquvzPIHXqV3WoszrUCFKnsRks+A8dOkTLli1p3rw5lpaWPPfcc2zevNlQpxdCCHGHwYL/\nwoULNG3aVPfa1dWVCxcuGOr0Qggh7jDYssw//fQTO3bsYMmSJQCsWrWKP/74g0WLFhUWo9EYohQh\nhKhxKhLlBnsKSZMmTUhMTNS9TkxMxNXVtdg+sha/EEJUPoO1ejp16sSZM2dISEggJyeHdevWMWDA\nAEOdXgghxB0GG/FbWFjwv//9j759+6LVahk/fjweHh6GOr0QQog7DDqP/8knn+T06dOcPXuWd955\np9h71WGOf2JiIk888QSenp54eXmxcOFCY5dUJq1WS4cOHQgMDDR2KWVKTk5m6NCheHh40K5dOw4e\nPGjskko1e/ZsPD098fb2Zvjw4WRnZxu7JADGjRuHi4sL3t7eum03b96kd+/etG7dmj59+pCcnGzE\nClWl1fn222/j4eGBr68vQUFBpKSkGLFCVWl1Fpg3bx5mZmbcvHnTCJUVKqvGRYsW4eHhgZeXF1On\nTi3/g5QqIC8vT3F3d1fi4+OVnJwcxdfXVzl58qSxyyrh0qVLytGjRxVFUZS0tDSldevWVbJORVGU\nefPmKcOHD1cCAwONXUqZRo0apSxdulRRFEXJzc1VkpOTjVxRSfHx8UqLFi2UrKwsRVEUZdiwYcp3\n331n5KpUERERSlRUlOLl5aXb9vbbbysff/yxoiiKMmfOHGXq1KnGKk+ntDp37typaLVaRVEUZerU\nqVW2TkVRlPPnzyt9+/ZVmjdvrty4ccNI1alKq3HPnj1Kr169lJycHEVRFOXq1avlfk6VWLKhuszx\nb9iwIe3btwegdu3aeHh4cPHiRSNXVVJSUhLbtm1jwoQJVfaCeUpKCvv372fcuHGA2gp0cHAwclUl\n1alTB0tLSzIyMsjLyyMjI4MmTZoYuywAunXrhqOjY7FtW7ZsYfTo0QCMHj2aTZs2GaO0Ykqrs3fv\n3piZqfHTpUsXkpKSjFFaMaXVCfDWW2/xySefGKGikkqr8YsvvuCdd97B0tISAGdn53I/p0oEf3Wc\n45+QkMDRo0fp0qWLsUsp4c0332Tu3Lm6f1hVUXx8PM7OzowdO5aOHTsyceJEMjIyjF1WCfXq1WPy\n5Mk0a9aMxo0bU7duXXr16mXsssp05coVXFxcAHBxceHKlStGrqh8y5Yt46mnnjJ2GaXavHkzrq6u\n+Pj4GLuUMp05c4aIiAi6du1KQEAAR44cKfeYKpEM1W3+/u3btxk6dCgLFiygdu3axi6nmNDQUBo0\naECHDh2q7GgfIC8vj6ioKF5++WWioqKws7Njzpw5xi6rhLi4OD777DMSEhK4ePEit2/fZvXq1cYu\n655oNJoq/29r5syZWFlZMXz4cGOXUkJGRgazZs3igw8+0G2riv+m8vLyuHXrFgcPHmTu3LkMGzas\n3GOqRPDfyxz/qiI3N5chQ4YwcuRIBg0aZOxySvj999/ZsmULLVq0IDg4mD179jBq1Chjl1WCq6sr\nrq6udO7cGYChQ4cSFRVl5KpKOnLkCI8++ihOTk5YWFgQFBTE77//buyyyuTi4sLly5cBuHTpEg0a\nNDByRWX77rvv2LZtW5X9QRoXF0dCQgK+vr60aNGCpKQk/Pz8uHr1qrFLK8bV1ZWgoCAAOnfujJmZ\nGTdu3PjHY6pE8FeXOf6KojB+/HjatWvHG2+8YexySjVr1iwSExOJj4/n+++/p0ePHqxYscLYZZXQ\nsGFDmjZtSmxsLAC7du3C09PTyFWV1LZtWw4ePEhmZiaKorBr1y7atWtn7LLKNGDAAJYvXw7A8uXL\nq+TgBNRZfHPnzmXz5s1YW1sbu5xSeXt7c+XKFeLj44mPj8fV1ZWoqKgq98N00KBB7NmzB4DY2Fhy\ncnJwcnL654Mq48rz/di2bZvSunVrxd3dXZk1a5axyynV/v37FY1Go/j6+irt27dX2rdvr2zfvt3Y\nZZUpPDy8Ss/qOXbsmNKpUyfFx8dHGTx4cJWc1aMoivLxxx8r7dq1U7y8vJRRo0bpZk8Y23PPPac0\natRIsbS0VFxdXZVly5YpN27cUHr27Km0atVK6d27t3Lr1i1jl1mizqVLlyotW7ZUmjVrpvt39NJL\nLxm7TF2dVlZWur/Polq0aGH0WT2l1ZiTk6OMHDlS8fLyUjp27Kjs3bu33M8x2Fo9QgghqoYq0eoR\nQghhOBL8QghhYiT4hRDCxEjwCyGEiZHgFzVKUlISAwcOpHXr1rRs2ZI33niD3NxcvZ5j3759HDhw\nQPf6q6++YtWqVQCMGTOGn376Sa/nE0LfJPhFjaEoCkFBQQQFBREbG0tsbCy3b99m+vTpej3P3r17\ni93ENWnSJEaOHAlUj7tlhZDgFzXGnj17sLGx0S1SZmZmxvz581m2bBlffPEFr776qm7f/v37s2/f\nPgBefvllOnfujJeXFyEhIbp9mjdvTkhICH5+fvj4+HD69GkSEhL46quvmD9/Ph06dODXX38lJCSE\nefPm6Y4rmCEdGRlJQEAAnTp1ol+/fro7ahcuXIinpye+vr4EBwdX9l+LECUY7EEsQlS2EydO4Ofn\nV2ybvb09zZo1Q6vVFttedGQ+c+ZMHB0d0Wq19OrVi+PHj+Pl5YVGo8HZ2ZnIyEi++OILPv30U5Ys\nWcKLL76Ivb09b731FgC7d+8uNsrXaDTk5uby6quvsnXrVpycnFi3bh3Tp09n6dKlfPzxxyQkJGBp\naUlqamol/60IUZIEv6gx/qnF8k99/nXr1rFkyRLy8vK4dOkSJ0+exMvLC0C3BkrHjh3ZsGGD7pi7\n73ss+lpRFE6fPs2JEyd0K3lqtVoaN24MgI+PD8OHD2fQoEFVdkkFUbNJ8Isao127dqxfv77YttTU\nVBITE3F2dubs2bO67VlZWYC6PPS8efM4cuQIDg4OjB07VvceQK1atQAwNzcnLy+vzHOX9kPH09Oz\n1AXdwsLCiIiIYOvWrcycOZOYmBjMzc0r9s0K8QCkxy9qjJ49e5KRkcHKlSsBdZQ9efJkhg8fTosW\nLTP62XgAAAEqSURBVDh27BiKopCYmMihQ4cASEtLw87Ojjp16nDlyhW2b99e7nns7e1JS0srtq3o\niF+j0dCmTRuuXbume5xkbm4uJ0+eRFEUzp8/T0BAAHPmzCElJYX09HR9/RUIcU9kxC9qlI0bN/LK\nK6/w4Ycfcu3aNfr06cPixYuxtLSkRYsWtGvXDg8PD921AB8fHzp06EDbtm1p2rQpjz/+eKmfW/Sa\nQGBgIEOHDmXLli265y7fPeK3tLRk/fr1vPbaa6SkpJCXl8ebb75J69atef7550lJSUFRFF5//XXq\n1KlTiX8jQpQki7SJGuvAgQNMnDiRH3/8EQ8PD2OXI0SVIcEvhBAmRnr8QghhYiT4hRDCxEjwCyGE\niZHgF0IIEyPBL4QQJkaCXwghTMz/A2nM+6pk09GHAAAAAElFTkSuQmCC\n" + } + ], + "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": 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B3MAjSx3tn3aCTV5QYIJ6UTcISAUuod5hS3r7wjDuqOd/J8E/adIkXF1d8ff3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\n" + } + ], + "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": [ + "" + ] + }, + { + "output_type": "display_data", + "png": 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rDqA9N/lCNWherSU/9JtE+bKujk6keNAQE1IsJSVZnb5vvw116liTwNwYmcCj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\nEQ+nRCAi4uGUCEREPJwSgYi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+ } + ], + "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": 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GYaxcaRiGYZw9axgREebPnj0Nw+wNNx8REU6OV/Ip6ndngaWnc+fOUbly5XzP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\nxmh4rIiI2JUShYiI2KREISI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+ } + ], + "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": 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J+++/n8GDB1fl96y2zu3ZSl5gO71j2N2A1gMoUkXsdDvNLV26wLp1cM89escS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\nrkJggg==\n" + } + ], + "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": 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fubO0sOvig2Du6f3uu+9y2223kZ2dzZtvvsmsWbNITk4u9E2Lizq9RVxHePhV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\nNlHCEBERmyhhiIiITZQwRETEJv8HnYF4liaW+BgAAAAASUVORK5CYII=\n" + } + ], + "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": 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w4ME8ePCgOGLLF9lHIUQRtWmjVrO/+grGj4f0dH0H2if3XIwZo22YouiMto9i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\nQgghRK7+P5JGatzzLvuCAAAAAElFTkSuQmCC\n" + } + ], + "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": 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axsmTJ+0dis0cP37caNCggXH9+nXDMAyjb9++xrx58+wclW0cOHDA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\nSUVORK5CYII=\n" + } + ], + "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": 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H0/6+uhw/XvDTfH3V1SQlp+Qg4gwyMmD9erN07ZIlUL8+K36ty3+yJrCKXmRdtWFj7drm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\n" + } + ], + "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": 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PVY2OjsbOnTtRWFiILl264I033kBMTAySk5Mxd+5cVFZWYubMmXjllVca/N6NWVjyRw4O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\nQmCC\n" + } + ], + "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": 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jR+YsXzp4eXnh5OQE/GOn27ZtA1DLYQwbNozt27dTq1atAgP3xo0bx86dOzEy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\nYTAYDAaDF/8PDafP7yMxRx8AAAAASUVORK5CYII=\n" + } + ], + "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": 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bNmzYQKVKlQgJCSnyE5dE48bBG29IURHCXvn7Q//+euKkKLw8C8utg/UZGRmA\nrmaDBg1irSwNWmBRURAfL9sNC2HvJk/W/19//tnoJI4rz9WpPvnkE0aNGkXp0qX57LPPst3nCOuF\n2ZMbN3RvJTwcSpc2Oo0QIj8VKsAHH8Dw4fr0tfyfLTiZeW8DixbpCZHbtsl+20I4AqXgscf03JaS\ndFrMJnve33Tu3DkGDx6Mj48P/fr148SJE0V+4pLi6lU9rvLee1JUhHAUJhPMmaP/3x47ZnQax5Nn\nYZkwYYL572PHjqVmzZqsWbOGwMBAnn/+eZuEcwaffKKX5374YaOTCCEKokEDGD1atjIujDxPhQUE\nBLB7924A/Pz82LNnD6Z/PnL7+fmxd+9e26UsIHs5FXbunF5ocvNmaNLE6DRCiIK6ehV8fGDmzJKx\nCrm13jvzHLxPS0vj/fffRynF+fPns91nD2/ajmDGDOjSRYqKEI6qbFl91mHIED3eUr680YkcQ56n\nwoYMGcLFixe5dOkSzzzzDGlpaQCkpqbi7+9vs4CO6tQp+PRTmDjR6CRCiKIIDtarkMuO7JaTq8KK\nyfjxcP68Li5CCMeWmqr3bdmyBby8jE5TfGyyVtiRI0dYsWIFf//9Ny4uLnh4eNCvXz8qVqxY5Ccu\nTkYXlpMnoXFjvTxEnTqGxRBCWNHs2fD99/Djj857hWexX2780UcfMWzYMK5evUpMTAxXr14lMTGR\nBx98kB9//LHIT+zM3nsP+vWToiKEMxkxAi5ehG+/NTqJ/cuzx9KkSRP27t2Lq6srGRkZPPbYY2zZ\nsoXExERCQ0PZs2ePrbNazMgeS2qqvrx4/36oVcuQCEKIYrJjh746LDYW7r7b6DTWV+w9FpPJxLV/\n9uq8cuUK6enpANStW9d8u8jp3Xdh4EApKkI4o8BA6NEDXn/d6CT2Lc/LjYcMGUJgYCAPPvggP//8\nM+PGjQPg5MmTVK1a1WYBHUlyMixYoD/NCCGcU3i4HkN95hl48EGj09infAfv9+/fz8GDB2nSpAme\nnp62zFUkRp0KGzVKX/c+Y4bNn1oIYUMLF8L770NMDJTK8+O54yn2U2Ggx1l69epltaKybNkyvL29\ncXV1ZdeuXdnue+edd2jYsCGenp5s3Lgx158/c+YMISEhNGrUiA4dOnDu3Dmr5LKGpCT47ruStWCd\nECVV//5QqZKePClysunOzj4+PqxcuZK2bdtmuz02NpYlS5YQGxvLhg0bGDFiBDdu3Mjx89OmTSMk\nJIS4uDgjpQnBAAAYfklEQVTat2/PtGnTbBX9tsLD9Xam1aoZnUQIUdxMJvj4Y5gyBf6ZOy5uYdPC\n4unpSaNGjXLcHhkZSd++fSldujTu7u40aNCAmJiYHI9bvXo1gwYNAmDQoEGsWrWq2DNbIiEBli6F\nV14xOokQwla8vOCpp2QgPzc2LSx5SUlJoXbt2ubva9euTXJyco7HnThxgurVqwNQvXp1u1m+/+23\n9aZA99xjdBIhhC299RasWQM7dxqdxL5YfdgpJCSE48eP57g9PDycrgVYHtR0m6mtJpMp38eEhYWZ\n/x4UFERQUJDFz10QR47AypUQF1cshxdC2LHKlfUHy9Gj4ZdfwMUuPqpbLjo6mujoaKsf1+qFJSoq\nqsA/4+bmRlJSkvn7v//+Gzc3txyPq169OsePH6dGjRqkpqZSLZ8BjVsLS3GaOhVeeME5J0sJIW7v\nmWfgs8/0TrEDBhidpmD+/aF70qRJVjmuYfX11kvaQkNDiYiIIDMzk/j4eA4dOkTz5s1z/ExoaCjf\nfPMNAN988w3du3e3Wd7cHDoEa9fCSy8ZGkMIYSAXF72O2PjxeskXYePCsnLlSurUqcO2bdvo3Lkz\njz32GABeXl488cQTeHl58dhjj/HJJ5+YT3MNHTqU33//HYDx48cTFRVFo0aN2Lx5M+PHj7dl/Bym\nTNG7y1WubGgMIYTBWrSAkBBZWv8mWTa/kA4ehLZt4fBhsPPFnoUQNpCaqneb/PVXvXOsI7LJBEmR\nt8mT4eWXpagIIbSaNfXpsJdfNjqJ8aTHUggHDuhtSg8fhgoViu1phBAOJjNT91pmztTbkjsa6bEY\naNIkPRlSiooQ4lZlysBHH+kLeq5eNTqNcaTHUkD79kHHjrq3Ur58sTyFEMLBdesGLVvqU2OOxCZb\nEzuq4iwsPXtC69YwZkyxHF4I4QSOHNFL6u/dC7lMybNbUljyUVyFJTZWj60cPQrlyln98EIIJ/J/\n/wfHjukl9h2FFJZ8FFdhGTBALzw3YYLVDy2EcDKXLukNwSIi9FkORyCFJR/FUViOHoXmzXUXt1Il\nqx5aCOGkFi+G6dNhxw5wdTU6ze3JVWE29t57MGyYFBUhhOX69NEX+Xz5pdFJbEt6LBZITtbXpsfF\nydL4QoiC2bNHX0n655/2v1itnArLh7ULy9ixcOMGfPCB1Q4phChBRozQi1XOmWN0kvxJYcmHNQvL\nqVN63Z8//nCsywaFEPbj9Gnw9IQtW/QFQPZKxlhsZNYs6NVLiooQovCqVtVbGJeU7culx5KPCxfg\nvvtg+3a4/34rBBNClFiZmeDtrU+HdexodJrcSY/FBj79VP8DkKIihCiqMmX0pcdjx0JWltFpipf0\nWPJw+TLUrw+bNkGTJlYKJoQo0ZTSq3f06QPPP290mpxk8D4f1nhx5szRRWXVKiuFEkIIYPdueOwx\n+Osv+5sXJ4UlH0V9cTIzoWFDWL4cAgOtGEwIIYBnn4V774V33zU6SXZSWPJR1Bdn/ny9FMPGjVYM\nJYQQ/0hJ0ZOud+7Up9zthRSWfBTlxbl+XS8c98UXEBRk3VxCCHHTlCl6ftzSpUYn+R+5KqyYLF+u\nu6gPP2x0EiGEMxs7Fn77DbZuNTqJ9UlhuYVSEB6uJzKZTEanEUI4s3Ll4J134OWX9ZJRzkQKyy3W\nrdMFpVMno5MIIUqCfv30B9rFi41OYl0yxvIPpaBVK/3p4YkniimYEEL8yy+/6AJz8KDxO9PKGIuV\nRUfDmTN6T3shhLCVhx6CFi1g5kyjk1iP9Fj+ERKiPzU880wxhRJCiDwcParnzP3xB9SqZVwOudw4\nHwV9cWJioHdvOHwYSpcuxmBCCJGHceMgLQ3mzTMugxSWfBT0xeneHYKD4YUXijGUEELk4/x58PCA\n9eshIMCYDFJY8lGQFyc2Vi8KFx8Pd95ZzMGEECIfn30GS5bA5s3GTHmQwXsrmTkTRo6UoiKEMN6Q\nIfp0WGSk0UmKpkT3WFJT9cY7hw7pHd6EEMJoP/ygT8sfOKD3cLEl6bFYwaxZ0L+/FBUhhP24ubng\n558bnaTwSmyP5eJFvapoTIzeflgIIezFvn16CkRcnG33bJEeSxF99ZUetJeiIoSwN76+ejOw994z\nOknhOG+PxegQQghhS9a4TNhKPZZSRT6CvcrnxVm8WF/Wt2WLDfMIIUQBjR8Pp07Bl18anaRgbHoq\nbNmyZXh7e+Pq6squXbuy3ffOO+/QsGFDPD092ZjH1o1hYWHUrl2bgIAAAgIC2LBhQ4EzKAXTp8Or\nrxaqCUIIYTPjx8Pq1bB/v9FJCsamPRYfHx9WrlzJ888/n+322NhYlixZQmxsLMnJyQQHBxMXF4eL\nS/a6ZzKZGDNmDGPGjCl0hs2b4coVWRpfCGH/KlfW+0ONHw9r1xqdxnI27bF4enrSqFGjHLdHRkbS\nt29fSpcujbu7Ow0aNCAmJibXYxT1/N+MGXrnNpcSe9mCEMKRDB+uVwj58Uejk1jOLsZYUlJSaNGi\nhfn72rVrk5ycnOtjZ8+ezYIFC2jWrBkzZ86kcuXKuT4uLCzM/PegoCCCgoL44w/YuxdWrbJqfCGE\nKDZly+qdbV97DbZvt+6H4ujoaKKjo613wH9YvbCEhIRw/PjxHLeHh4fTtWtXi49jymWhnOHDhzNx\n4kQA3nzzTcaOHctXX32V68/fWlhumjEDRo3SvyghhHAUTzyhl59auhT69LHecW9+6L5p0qRJVjmu\n1QtLVFRUgX/Gzc2NpKQk8/d///03bm5uOR5XrVo189+HDBlSoEL199+wZg18+GGB4wkhhKFcXPSc\nlsGD4fHH7f/DsWEjDbeOlYSGhhIREUFmZibx8fEcOnSI5s2b5/iZ1NRU899XrlyJj4+Pxc83axYM\nHAhVqhQttxBCGKFdO/Dygk8/NTrJ7dl0guTKlSsZPXo0p06dolKlSgQEBLB+/XpAnyqbN28epUqV\n4qOPPqJjx44ADB06lOHDh9O0aVMGDhzInj17MJlM1K9fn88//5zq1avnbNS/JvlcuKCXb/n9d3B3\nt0lThRDC6vbv1yuGxMXpK8asTfZjyce/X5wZM3RRWbzYwFBCCGEFQ4bAPffAtGnWP7YUlnzc+uJk\nZuqVQiMjoWlTg4MJIUQRJSfrtcT27IE6dax7bFmE0kJLlkCjRlJUhBDOwc1Nz2355wJZu+TUPRal\nwM8P3n1XrxQqhBDO4MIF/YF540bde7EW6bFYICpKrw326KNGJxFCCOupWBHeeAPGjTM6Se6curBM\nnw6vvAK5zLUUQgiH9txzcPgwbNpkdJKcnLaw7N4Nf/4JffsanUQIIayvTJn/LfVy44bRabJz2sIy\ncyaMHq1ffCGEcEa9ekHp0va3/qHTDt5XqaKIj7ftftFCCGFrqalQrRq4uhb9WLKD5G3MnClFRQjh\n/GrWNDpBTk7bY3HCZgkhRLGSy42FEELYJSksQgghrEoKixBCCKuSwiKEEMKqpLAIIYSwKiksQggh\nrEoKixBCCKuSwiKEEMKqpLAIIYSwKiksQgghrEoKixBCCKuSwiKEEMKqpLAIIYSwKiksQgghrEoK\nixBCCKuSwiKEEMKqpLAIIYS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+ } + ], + "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": 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Ss7ikeGIduY6R7Nixg5kzZ1KqVCnGjRuHv7+/tWMrMKPGSCZOhD//hNmzrf7S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\nAAAASUVORK5CYII=\n" + } + ], + "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": 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