{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# The Okada model for computing seafloor deformation\n", "\n", "This notebook is part of the GeoClaw documentation, see \n", " - For general Clawpack documentation,\n", " - for more documentation on the use of the Okada model in GeoClaw. \n", " - for information on obtaining the *apps* repository. The source for this notebook is maintained in `$CLAW/apps/notebooks/geoclaw/Okada.ipynb`.\n", " - : In GeoClaw the Okada model is implemented in `dtopotools.SubFault.okada`, \n", " \n", "## Contents\n", "\n", "- Examples\n", "- Typical subduction zone thrust event\n", "- Varying the strike\n", "- Varying the dip\n", "- Varying the rake" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Introduction\n", "\n", "The \"Okada model\" takes as input the slip on a rectangular patch of a fault deep in the earth and produces the resulting deformation of the earth's surface. For tsunami modeling, we are particularly interested in the vertical motion of the seafloor above the fault. \n", "\n", "The Okada model makes many assumptions that only approximate reality, in particular:\n", " - The earth is modeled as a perfectly elastic half space. The slip is uniform on a rectangular patch and a Greens' function solution to the half space problem is integrated to obtain the vertical deformation at the surface of the half plane. The seafloor topography is ignored in computing the Okada deformation (which is then generally added to the real topography to get the modified topography during the earthquake).\n", " - The half space is assumed to be isotropic with uniform elastic moduli. The Poisson ratio is generally taken to be 0.25 and the shear modulus (or rigidity) is constant, often taken to be $4\\times 10^{11}$ dyne/cm$^2 = 40$ GPa.\n", " \n", "Complex earthquake fault surfaces are generally approximated by a subdividing into a set of rectangular subfaults, each of which might have different orientation and slip properties. The Okada model is applied to each and then the resulting surface deformations are summed. The Okada model is linear, so that if a single planar fault is split into subfaults the resulting surface deformation should be independent of the number of pieces its split into.\n", "\n", "The GeoClaw *dtopotools.Fault* class provides tools for specifying a fault as a collection of *dtopotools.SubFault* objects, and an object of this class has a method *create_dtopography* that applies the Okada model and returns a *dtopotools.DTopography* object expressing the surface deformation. In this notebook we illustrate the Okada model by working with a fault that consists of a single subfault.\n", "\n", "A subfault is specified via the following standard parameters:\n", " - *length* and *width* of the fault plane (specified in meters below),\n", " - *latitude* and *longitude* of some point on the fault plane, typically\n", " either the centroid or the center of the top (shallowest edge),\n", " - *depth* of the specified point below the sea floor (in meters below),\n", " - *strike*, the orientation of the top edge, measured in degrees\n", " clockwise from North, between 0 and 360. The fault plane dips downward\n", " to the right when moving along the top edge in the strike direction.\n", " - *dip*, angle at which the plane dips downward from the top edge, a\n", " positive angle between 0 and 90 degrees.\n", " - *rake*, the angle in the fault plane in which the slip occurs,\n", " measured in degrees counterclockwise from the strike direction.\n", " Between -180 and 180.\n", " - *slip > 0*, the distance (measured in meters below) the hanging-wall block moves\n", " relative to the footwall block, in the direction specified by the rake.\n", " The \"hanging-wall block\" is the one above the dipping fault plane (or to the\n", " right if you move along the fault in the strike direction).\n", " \n", "\n", "For other descriptions and illustrations, see e.g.\n", " - \n", " - \n", " " ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Import modules and define some utility functions used below..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "from clawpack.geoclaw import dtopotools\n", "from clawpack.visclaw.JSAnimation import IPython_display\n", "import clawpack.visclaw.JSAnimation.JSAnimation_frametools as J" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This sample fault has 1.0 meter of slip over a 100.0 by 50.0 km patch\n", "With shear modulus 4.0e+10 Pa the seismic moment is 2.0e+20\n", " corresponding to an earthquake with moment magnitude 7.50068666378\n", "The depth at the top edge of the fault plane is 5.0 km\n" ] } ], "source": [ "def set_fault(strike, dip, rake, depth):\n", " \"\"\"\n", " Set the subfault parameters.\n", " Most are fixed for the examples below, \n", " and only the strike, dip, and rake will be varied.\n", " \"\"\"\n", " subfault = dtopotools.SubFault()\n", " subfault.strike = strike\n", " subfault.dip = dip\n", " subfault.rake = rake\n", " subfault.length = 100.e3\n", " subfault.width = 50.e3\n", " subfault.depth = depth\n", " subfault.slip = 1.\n", " subfault.longitude = 0.\n", " subfault.latitude = 0.\n", " subfault.coordinate_specification = \"top center\"\n", "\n", " fault = dtopotools.Fault()\n", " fault.subfaults = [subfault]\n", " return fault, subfault\n", "\n", "# Create a sample fault and print out some information about it...\n", "fault, subfault = set_fault(0,0,0,5e3)\n", "print \"This sample fault has %s meter of slip over a %s by %s km patch\" \\\n", " % (subfault.slip,subfault.length/1e3,subfault.width/1e3)\n", "print \"With shear modulus %4.1e Pa the seismic moment is %4.1e\" % (subfault.mu, subfault.Mo())\n", "print \" corresponding to an earthquake with moment magnitude %s\" % fault.Mw()\n", "print \"The depth at the top edge of the fault plane is %s km\" % (subfault.depth/1e3)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def plot_okada(strike, dip, rake, depth, verbose=False):\n", " \"\"\"\n", " Make 3 plots to illustrate the Okada solution.\n", " \"\"\"\n", "\n", " fault,subfault = set_fault(strike, dip, rake, depth)\n", " ax1 = subplot(2,2,1)\n", " ax2 = subplot(2,2,2)\n", " ax3 = subplot(2,2,3)\n", " ax4 = subplot(2,2,4)\n", "\n", " # Subfault projection on surface on ax1:\n", " ax = fault.plot_subfaults(axes=ax1, plot_rake=True, xylim=[-.5,1.5, -1,1])\n", " text(0.6,0.8,\"Strike = %5.1f\" % strike, fontsize=12)\n", " text(0.6,0.6,\"Dip = %5.1f\" % dip, fontsize=12)\n", " text(0.6,0.4,\"Rake = %5.1f\" % rake, fontsize=12)\n", " text(0.6,0.2,\"Depth = %5.1f km\" % (depth/1e3), fontsize=12)\n", " ax1.set_ylabel('latitude (degrees)')\n", "\n", " # Depth profile on ax3:\n", " z_top = -subfault.centers[0][2] / 1e3 # convert to km\n", " z_bottom = -subfault.centers[2][2] / 1e3 # convert to km\n", " ax3.plot([0,cos(subfault.dip*pi/180.)*subfault.width/1.e3], [z_top, z_bottom])\n", " ax3.set_xlim(-50,150)\n", " ax3.set_ylim(-55,0)\n", " ax3.set_xlabel('distance orthogonal to strike')\n", " ax3.set_ylabel('depth (km)')\n", " ax3.set_title('Depth profile')\n", " \n", " \n", " # Grid to use for evaluating and plotting dz\n", " x = numpy.linspace(-0.5, 1., 101)\n", " y = numpy.linspace(-1., 1., 101)\n", " times = [1.]\n", "\n", " # color map of deformation dz on ax2:\n", " fault.create_dtopography(x,y,times,verbose=verbose)\n", " dtopo = fault.dtopo\n", " dtopo.plot_dZ_colors(t=1., axes=ax2)\n", " \n", " # transect of dz on ax4:\n", " dZ = dtopo.dZ[-1,50,:]\n", " ax4.plot(x,dZ)\n", " ax4.set_ylim(-0.5,0.5)\n", " ax4.set_title('Transect of dz along y=0')\n", " ax4.set_xlabel('Longitude (degrees)')\n", " ax4.set_ylabel('Seafloor deformation (m)')\n", " " ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "
\n", "## Examples\n", "\n", "
\n", "### Typical subduction zone thrust event\n", "\n", "For a subduction zone earthquake, the rake is generally close to 90 degrees. In the first example below we take it to be 80 degrees -- note that the green line on the fault plane extending from the centroid in the rake direction is 80 degrees counterclockwise from north (the strike direction, since *strike = 0*). \n", "\n", "Note that the fault plane plot shows the projection of the fault plane on the flat surface. It dips down to the right as seen in the depth profile. The *dip* is 10 degrees and the fault extends from a depth of 5 km at the up-dip edge to about 14 km at the downdip edge. Note that above the width of the subfault is set to 50 km and that the \"Fault planes\" plot axes are in degrees. This fault is located at the equator where 1 degree is approximate 111 km in both directions.\n", "\n", "The rock above the fault plane (the hanging block) is moving to the left relative to the lower block (as shown by the green line) and hence the rock above the fault will be compressed to the left and bulge upwards. The rock to the right is under tension and the surface dips downwards as a result. This can be seen in the \"seafloor deformation\" plot, where the colors indicate meters of vertical motion. \n", "\n", "The *slip* on this subfault was set to 1 meter above. Since the Okada model is linear, changing the slip to some other value would simply multiply the deformation by the same factor everywhere." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Making Okada dz for each of 1 subfaults\n", "0..\n", "Done\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/rjl/anaconda/envs/jupyter/lib/python2.7/site-packages/matplotlib/axes/_base.py:1057: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal\n", " if aspect == 'normal':\n", "/Users/rjl/anaconda/envs/jupyter/lib/python2.7/site-packages/matplotlib/axes/_base.py:1062: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal\n", " elif aspect in ('equal', 'auto'):\n" ] }, { "data": { "image/png": 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LPo1Go9E4U9nKgdrW6a0NTxPYlLJ5zSqb6OamiKxWK6WlpZSVlWExmx0blYNN\ndJlMJvz9/Qnw9ycgIAB/f3/f9iyxv3YXwd7EnrO3z2RystG1rap0rb37VWFvojpbp7hrevfuVna+\noaiz9rXgAxpZ8InIeOAlbEudlyqlnnXLHw18iu3BxACrlFJPNKiRGo1GU0c0qTGvPue+VRW2df/f\nm5BzF3xWK8VFRaz/8Ue2bNvGjj17SE5J4VhGBrl5eRSXlBAUFESAvz9+fn74mUzG1ii2J+dYlcJi\nsWCxWCgzmxERgoOCCA8LIyI8nFaRkcRGRxPXti0d4+LoEh9Pj27d6N+7N61jYysJ2bqJO/trF8Fo\nOuXp86Jf7d117nZeXh5JSfvJyDhOYGAQMTGt6d27H8HBQY6q7fqyMjyJO19Dtt7KeSrrTp166mqK\nFnxAIwo+EfEDXgUuwraT9RYR+cx5M0OD75VSExvcQI1Go6lDmuSYVx+iz5c4pLd8u0fOWflYrWRl\nZbF6zRo+/eorNv7yC2f078/wIUO4+pJL6BofT4e2bYmOiiIsNLRaWyhZLBZKSkspKi4mv7CQnLw8\nTuTmkp6ZScqxY/y8ZQvvfvQRO/ftIzI8nCEDBjB86FBGn3suZ55xBn52D6FzTNUu9JQCPz/H/RBR\nLtuzVObVy88v4L333uDrrz9jx47f6NKlO+3adcBsLiMj4zjJyUkMGDCEsWMvZ9Kkv9C5cxcXM5xv\nc2W3w9PKXXdtVFlo1xex56mtBkcLPqBxPXxnAQeUUocBROQDYBLgPvg19m8DjUajqQsafsyrTHjV\nl9ir6ry3eXju7i2rlYL8fJ556SVef/ddxo4axQ1XXcW7zz9P6+hoVzeYvZ6yslP1+qAw/EWICAwk\nIiiIuJiYigUMBaWU4kByMtt27eKn337j1vvuIzM7m2svv5zbbryRnj16VJxE5z7PTyk4tTNfha5b\nrVBervjXv97m2WfnMWLEBcya9SAjRlxIUFCwi1knTxbzyy8/8O23n3HxxUMZM2Y89967gB49qn7c\nY1UePV/POec1ugevKrTgAxpX8HUEnJ9VlQKc7VZGASNEZDu2X8T3K6V2N5B9Go1GU5c07JjX0GLP\nW92+iD135WO1sumXX/jLrbcyevhw/vj6azq1bWtXRVBaWlHwueO+itYb7osvPFwrIvTo1IkenTtz\n7YQJIML+Q4f41yefMHLSJC4aNYpn580jPj7e1U3m7PnDJvXcrbF3obxc8eSTj/D115+xbNkX9O8/\nxOW2OBPVcRHYAAAgAElEQVQUFMp5543n/PPHM3fu07z33utMnDiCWbPuY86cBwgI8K/g7XPvsvtt\n82URh6dz3lb/Nim04AMaV/D5MuL8BsQrpYpF5BLgE8DjT5iFCxc6Xo8ePZrRo0fXgYkajaYpsH79\netavX9/YZtSWhh/z6lPYeWrLVw+f+zVOQg+rldffeosFzz3H0uefZ+IFF4DFgiot5eetW/nvTz+x\n+Y8/SElPJzMnB7PFglKK4KAgQoODiQwLIyo8nFYREURHRp5KERHEREURHRlJbKtWtG7VijYxMYSG\nhNhs8bTy1o6H+Xo9O3fm8bvv5oEZM/jHm28yZNw4nvn737l56lRXdWSf1+dyf6RC9+fOvY3ffvuF\njz5aT1RUa8rLKzowPS0MDg+PYs6ch5g48Trmzr2VdevWsmTJh7RrF+f1rarKU1cdAVefYm/9+vX8\n8MP62lekBR8AohpyQHBuWOQcYKFSarxx/DBgdZ/E7HbNIWCoUirb7bxqrH5oNJqGx5iI35R9ChWo\n8zHP2e3jafxriDGxMm+e/biyRRru3r3ycpa88w7PvPIK/1u5km4dO4LZzI+//MLDL7xAelYWk0aN\nYkS/fnRp25Y2UVEEBgQAUGo2U1RaSkFJCbmFheQUFpJTUHDqfyNl5+dzIi+PE3l5ZObm4mcyERcb\nS4c2bUho356uHTvSq0sX+iYm0jcxkaDAQM+LM0Rsc/RMJjCZ2Ll/P9fedhvXTpzIwgcftOX5+9v+\nd3pdrkyUWwWLBUf68MP3eeGFRaxZs4Xg4HDKy11X6DpHir2ZYhNdVl566XFWrlzKsmWfMWjQEEe+\nczlPjszKNK77OXcq8ya6t1NdnK81mar/uRcRpZx+HFVZfuHCZje2+Epjevi2Aj1EpAuQBvwFuN65\ngIi0AzKUUkpEzsImULPdK9JoNJpmQP2Meb4skqhPKvPqeROAXkK5qz/7jEUvvMD3H31Et44dsZaW\n8tRrr/HPlSt5es4czkxMZMO2bXzxww/kFBRQWlZGRGgorSMj6RoXR49OnejbpQtD+vbF5OfnWSE5\nqROlFAUlJRzPziYlM5MjGRkcTEvj83XrePqttziYmkr/7t0ZPXQol4wcyXlDh55aqGH33BmCr3/3\n7qx7/30umDKF0JAQHrzzzlPLZ+1btRiLOJQSR9ePHj3C/Pl38957awkKCsdiOeXsdBZ8nvZ1dl8U\nbDKZuOeehfTpM5ApU8bz7rtrGDr0LNedYQyqWnVblwsuauMFrJM/be3hAxrRwwdghCzsWxS8pZR6\nWkRmAiillojIbcBswAIUA/cqpTZ5qEd7+DQNwrJly3jrrbfYsGFDY5tyWtMcPXxQx2NeefmpE40x\n/vkSwq3Mu+e2Ejc5OZlhY8fy5fLlnNmvH+UnTzLl7rs5kpbGa3ffzYsrV/Lt1q2MHTiQuMhIIoOC\niAwJISo0lOyiIg5lZbH/+HF2p6SQXVhI3/h4BnTpQv+EBPp37Ur/Ll2Ii41FXF1inl1lxnFxaSlb\n9+5l3W+/8dnGjaRkZDD9ssu4869/pWNcnO0au5fP8OSlpKczaMIEfvvmGxK6drV59pySRZmwlJuw\nWMBshmuvvZQzzxzJrFkPY7HYpijaPXzuTlxPgs+e7A5He953363hgQdu5t//Xkfv3n29dtObt8+O\np3Lu4s0XD15VXsCqsPWvhh6+J3zf2UjmzWuWY4svNKrgqyu04NNUhy5dupCRkYGfnx9hYWGMHTuW\n1157jcjIyCqvbWqCLz8/nzlz5vD1118DcPHFF/P6668TEREBwO+//87NN9/M3r176dOnD2+99RaD\nBg3yWFdpaSmzZ89m1apVhIaG8uCDD3LPPfc0WF+qQ3MVfHWFi+BrTmLP/r+b2Cs3m7lg8mQmjBnD\n3JkzwWzm/iee4NedO3l25kwmPvggV559NpnZ2XyzYwfdYmMJ9vcnt6SEIzk5nBEfz6UDBzL5zDPp\nEx9PXmkpO1NT2ZmSwo6jR9mVmsrOI0cot1rp17kz/RMSGNClC4O7dWNIjx4EBwdXVEPusVCTiT9T\nUnht1SqWr13LtAkTeHzOHCKjok6JPiNsO+/FFzmelcXSl1+GgIBTgi8gAIvVhMVqwmyGTZt+Yfr0\nq/n++wOYTIGOEG95ORw7lsLvv/9MUtIuQkLCaNu2I8OHjyMmprVHweeeRGD16uW8+OJC1qzZRNu2\nbX0SfZ4EILg6yWoi+hpV8D31lO/l//73Fju2aD+n5rRDRFizZg0FBQVs376dHTt28EQ1fgE2JRYu\nXEhWVhaHDh0iKSmJ9PR0x2T+srIyJk2axLRp08jNzeWGG25g0qRJmM1mr3UlJSVx5MgR1q1bx3PP\nPecQkpomSGOFcn1ps6q5ffZzRlrx0UeYzWbunzEDystZ/+OPfPz117zx4INc+fDD3HHJJXyyaROB\nSjG+Rw/yioo4efIk/WJjeWPiRB4aOZJjmZlc9NxzDF+wgH9v2MDgtm2ZOXIkr06dyrqHHybzjTfY\n++KLPH7NNfRt355tf/7JnYsX0+a66xg7dy7Pf/ABe5OSUKWlNtebPZWVOV73iIvjpdtvZ9/775Od\nm8u5N95IZlYWjjis4Zq77+abWfnpp5wsKXF9n0QAcWjdF154ilmzHsDPL5Dycls1+/fv4b77ruXa\nawfzxRf/wmwuJysrg2+++ZjLL09k9uzx7Nq1zeEJdPcIOoeDr7pqGpdf/hfmzPkrFou1yoXN3qLw\nvrztvuQ3ml+mMmXsnlowLbt3Gk0VtGvXjnHjxrFr1y7HuWeeeYbu3bsTGRlJv379+OSTT7xe/8AD\nDzBq1CgKCgrIy8vj5ptvpkOHDnTq1In58+e7PNqpPti1axeTJ08mPDycyMhIJk+e7OjL+vXrKS8v\n56677iIgIIA77rgDpRTfffedx7qWL1/O/PnziYqKonfv3syYMYNly5bVq/2aZkZV3/5VLdrwkEpL\nSljw3HM88/DD+IlQXlbGPU8/zXO3385Dr77KuMGDefGzzxjTvTs/HzhAoMXCoJgYRrVpQ+/QUN7c\ntInZq1czrE0bDt17L4+MGcOnv/1G/P33M+ftt9m2f79tG5fSUtqGhDCmZ0/uGDuWN2+5ha1PPUXK\n4sXcccklHDp2jLGPPELfmTN58l//4khqKi4rK5zirW0iI3n3kUe4dMQIrrr/fspKS0+prvJyoiMj\n6dG1K3/s3l3hnihsxfbu3ceWLT9x7bU3O8Tet99+yo03nkevXmfyn/8k8/TTn3LzzYuYPft5nnrq\nP3zxRTqjRk3mjjsm8OSTcygpKXXaw8/jg0m4775FlJaeZMmSFyu8hRUWD1fyVnp6W2uyVqhRRJ8W\nfIAWfM2emJgYRKTFphhPm6HWAfYpACkpKaxdu5azzz61HVr37t3ZuHEj+fn5LFiwgKlTp5Kenl7h\n+ltvvZWdO3fy7bffEhERwfTp0wkMDCQpKYlt27bxzTffsHTpUo/tv//++0RHR3tMMTExpKSk+NSP\niy++mFWrVpGbm0tOTg6rVq1iwoQJgE0MDhw40KX8oEGDXMStnZycHI4dO+YS7h04cKDHsprTnKrc\nQ+6KwlMZp9dvvvce/Xv35ryzzwarlfdWryY8JISwwEB2JiXxx8GDjExMZPPBg+QWF5NbUECCvz+U\nlPD+nj2EAe+MHs1rmzYx4vXXGRwdzWdTp7L93nuJCwtj8quvMvjRR/liyxab8CsrO5XMZqICA5k4\neDD/vOkmjrz+Ou/MmUNqZiZn3Hknf336abKzs10Fn6GuxGrlqVtvJSYiggdfeqmC6jpzwAB+3b79\nVN+NmKZdiC1Z8jJTp84iICCU8nJYs+ZDFi2ayUsvfcX11z+Iv3+Yw8lob95kCmbixFl88ME+srLS\nmTFjDNnZJyp495y9fH5+/rz00nu89toz7Nu3u8JbVRvRV9WfQHXqq1e04AO04Gv25OTkYH9OZEtM\nOTk5dX7PlFJMnjyZyMhIOnfuTGJiIvPmzXPkX3311cTF2fawuvbaa+nRowebN2925JvNZq677jpy\nc3P5/PPPCQ4OJj09na+++ooXX3yRkJAQ2rRpw913380HH3zg0Ya//vWv5OTkeEzZ2dl06tTJp77c\ndtttAMTGxtK6dWsCAgKYPXs2AIWFhURFRbmUj4yMpKCgoEI9hYWFAC7lvZXVnKbU5tvam7pQindW\nruSeW25xCKZ3Vq/mgalTWbJ6Ndecey75RUWs37ePVoGBXJGQwB9ZWXx59ChrUlOZ3707CYGBPL55\nM+svu4wrExMZt2wZWdnZxIeG8ugFF3Bo3jyevuwy7li5kmlvvklWTk5Fz52RxGrlnMRE/nnLLSS/\n/jptIiIY+eCDpGZknBJ0TvFTE7DkwQdZ9vnnlDiHb5UiOiqKwqIi19uAoJRQVFTCJ598yHXX3Up5\nOezbt4ennrqdV175lh49znQs6LCLPXfhFxQUyaJFH9O//whuu+0SCguLPYo9e+rUqSt33/0o8+bd\nidWqqhR8TU6w1ZZaCj4RGS8ie0XkTxGZ6yG/t4j8LCInReQ+t7xWIvJvEdkjIruN7ZkaBS34NKcd\nIsKnn35Kfn4+69ev57vvvmPr1q2O/OXLl3PGGWc4PG47d+7kxIkTjvwDBw7w+eef8+ijj+Lvb9vZ\nKDk5GbPZTPv27R3XzZo1i8zMzHrty5QpU+jVqxeFhYXk5+fTrVs3pk6dCkB4eDj5+fku5fPy8jwu\nTgkPDwdwKZ+Xl+dY/KE5zfHFdeOL588tHUhKIvXYMc4/5xwoLyctLY0d+/cztEcPfvjjD1IyMkiI\njWVw+/YUnDzJtykpPNOtG9Nbt+blzp15ct8++gcH0zoggDnr1/PwwIFMTkzkomXLyMrNBbMZU3k5\nl/TsyR8PPUTr0FD6L1zI8g0bUM5LYj1MhgsPCuLlm25i2ujRnPfQQxxOS/PoQmvXqhUDEhPZ8Ntv\nLkLWYrE4xgdEDLEHVgVr1nzKgAFDiYuLp7TUwt//fgMzZy4iIWGAy/RBd6Hn7GhUysScOc+TkNCb\nRx6Zitls9TiXzy7+pk6dTUbGcb766tNKPXrVOd9sqIXgc3oG9nigL3C9iPRxK3YCuAP4Pw+tvwx8\nqZTqAwyk4qMUGwwt+DSnNeeddx533HEHc+fafrQlJyczY8YMXnvtNbKzs8nJyaF///44rwLv06cP\nb7/9Npdccgn79+8HID4+nqCgIE6cOOHw1OXl5bFjxw6P7a5YsYKIiAiPKTIy0ueQ7tq1a5k5cyYh\nISGEhYUxc+ZMvvzySwD69evHH3/84VL+jz/+oF+/fhXqiY6Opn379vz++++Oc9u3b6d///4+2aE5\njamJGjCu+fCTT7j6ssvwMzaKW/vDD1w8fDjrtm7lgkGD+HTLFv48fpwjOTmEmExM79iRu/bt44/8\nfO49dIh/d+vG/P37eapHD348fpzvDh/mybPOYkx8PLetWXNKLZWXE+7nxwuTJvH5zJk8s3YtL3/7\nrVex55weuuIKbr7wQm5fvLiiijL+HzFgAFvs0x+Me1FcUkKIffWvs+CzwocfvsfVV99AeTm8996r\nhIREcPnlMx0Cb+fOLTz22JXMmtWfadM68/zzN7B581r3qYRYrcLcuW+Sk5PJ8uX/cDgh7e04e/r8\n/Px59NEXePLJuVgs5S5vnS8h3qo0f5Omdh4+xzOwlVJmwP4MbAdKqUyl1FbAZUWciEQBo5RSbxvl\nLEqpvHrpow9owac57bn77rv55Zdf2Lx5M0VFRYgIrVu3xmq18s4777Bz584K11x33XU89dRTXHTR\nRRw8eJD27dszbtw47r33XgoKCrBarSQlJfHDDz94bHPKlCkUFBR4TPn5+T6HdAcOHMibb77JyZMn\nKSkp4Y033nDMwxs9ejR+fn688sorlJaW8sorr2Aymbjgggs81jVt2jSeeOIJcnNz2bNnD0uXLmX6\n9Om+3URNy6Sm3/KVlXeqc9WaNVx96aUOdbJ5+3bO6d+fbXv3Eh8bS2xYGNnFxZwoLuZAXh6vHTrE\nxwkJfNCuHeNCQngqNZXrY2P58OhR5nTrxtv79iHl5Tw+fDj/O3SIPzMzKwi4YfHxfDFrFk999RW/\nJCVVXOXgId05YQIbdu/mRF6ex2f4hoeEUFJaajsw9h45nJpKQqdOTvuRCOVWOHEil82bN3LBBRMp\nKChh6dKnueeeVygvF0pLLbz44mwef3wSAweO46673mfBgv/Srdswliy5k+efv4GCgnwX0efnF8S8\nee/y7rvPkpycVEHsOZs7atRYoqKi+eqr/1Qq7tzfKm9/DrXQ+g0rHGsn+Dw9A7ujjy13BTJF5B0R\n+U1E3hSR0Fr2psZowac57WndujU33HADzz77LH379uW+++5j+PDhxMXFsXPnTkaOHOkoa19MAjaB\n9Oijj3LBBRdw5MgRli9fTllZGX379iUmJoZrrrmG48eP16vty5YtY//+/XTs2JFOnTpx+PBh3n33\nXQACAwP55JNPWL58OdHR0SxfvpxPPvnEEWZasWKFiwfvscceIzExkYSEBMaMGcPcuXMZN25cvdqv\naQbU0zf64eRkUtLSGDVsmEOZfPPTT1w0bBhb9u7FarWS2LYtCa1akdiqFb0iIhgcHs4ZJhMPHTvG\n82FhbCgs5MKwMN5MSWFqhw58lZJCWn4+EX5+3H7GGTzxww8e5951jY3l9euu4y9Ll1JUUuI5BuqU\nwoODGX/GGaz68UePfQsKDKS0rOzUCREOHT1K14QEx8Z3CsFqFb788nNGjBhDSEgEq1cvp2/fs+jc\nuR8WC7z66l2kph7g5Zf3cOGFs+jUaSBt2/Zk3Ljbee65bfj5BXLvvcPJzc1xcUi2b9+NqVPn8vTT\ns72KPaVs49fttz/Myy8/5XEun7e33ZPo83bs659Ig1KJwFt/4AALv/zSkTyZW4uW/YEhwD+VUkOA\nIuChWtRXK/TGy80cYwPaxjaj3mjp/dPUDL3xsihlsdRvI85unKrKeVMAnuKEhiJZvnIlX3zzDR++\n9hpYLGQdP073cePI/uYbWl14IdeOGEFuTg5HMzLwM5sJtFgYFRREQHExC3JyWBcdzUfl5fQMC+O1\nnBz+c9ZZLDp8mAldunDDoEHkKEXCG29w/P77CQ0NPfVcW/v/JhMXv/YaN44YwXVnn+0qBJyekWt/\n/Y81a0g5cYIXZ8yo8PSMhcuWoUR47PbbISCAUqDVkCHk/vknQZGREByMRflTVu7HlKl/YeTIi5k4\n8SYmTx7GjBlPMHjwxXz55b9YsWIRzz67hYCASEdoFk5tluznB++8cydZWck8+ugnBAaKwwylLEyZ\n0of589/k7LNHOx7ha0/268HKBRf05f/+703OOWeUy/7S9tf2Nu3/e3r6RmUbMdfHkzdqtfHy4sW+\nl581y6WN6jwDW0QWAIVKqX8Yx3HAz0qprsbxSOAhpdRl1elDXaE9fBqNRqNxpaZiz1Md7mWNtGnr\nVs4ZMsSRt2P/fgZ0786xjAxCAgM5npODslqxWq2UlZdTWl5OvL8/r+TlMSckhHeKiznH359NRUUM\nj4hgU3Y2o2Jj2XDsGFitRAcGclaHDnxz4IBXEXr90KF8uHWrZ9HqRmx4OCe8rFrPyMmhTXS07UCE\n/YcP0yU+nqDgYIeKUghlZRbWr/+W8867mD17dpKZmcaQIReRl5fH4sX3cN99HxIQEOltETEWC0yd\n+n/k5KSzatULLl4+Pz9/brjh77z55iKPGvuUl8/EtGlzePvtVyu81XUVaq2P3+i1qrN2IV3HM7BF\nJBDbM7A/89KSixhVSh0HjopIT+PURUCj7XWlBZ9Go9FoKlIbsedexoOq+HX7doYNGuQ43n3gAP0S\nE/nz6FF6duzIkcxMSs1mSi0W8svKyDWb2VVczKVBQSwIDmZ1aSlDTCa2lpQwLDSUrbm5jIiOZpPT\nvL3LExP56sABj2FalOKKQYP4Zs8eyuze0kr6FBUaSl5xsce89Oxs2sbEOFxWu/78k749euDsPlPA\ntm2/0a5dB9q06cgXX6zkkkumAn58+ulihgwZT3z8YMcmzF72fEYkkLvuWsnHHz9FVtZxl0j0uHFT\nSUk5wK5dv3p0rtrT1VffwPfff01WVpbXt6u2oq3B5+lVRi0En1LKAtwOfA3sBj5USu0RkZn252CL\nSJyIHAXuAeaJyBERCTequANYISLbsa3S9f05b3WMFnwajUajOYUv39K+evW85Cml2HvgAH26d3co\ngyNpaSTExXEsK4uOrVuTVVBAqcVCqcVCodlMntnMr0VFXBkcTFugg8nESauVoxYL3QICOFRSQq/w\ncA4ai6ZQimFxcWyzz6P1oHyigoNJiIlhz7FjFW11Uz7m8nIC7dusuHE0I4PO7ds74pR7kpLo27On\ny4INhbBp80+cffYorFbYsGEtI0Zchtls5YsvFjNhwp0uC4YPHvyV3bvXk5y8wyEC7Xlt2nTlvPOm\nsHr1P1ymJ5pMAVx22U189tmyyqYkEhERxfnnX8zatf+p0N3qvuXNglruw6eU+kop1Usp1V0p9bRx\nbolSaonx+rhSKl4pFaWUilZKdVZKFRp525VSw5RSg5RSV+pVuhqNRqNpfKrj3qksZFvFNRmZmQQE\nBBAbE+Mi+OLbteP4iRO0a9WKEwUFFJWWUlRWRn5ZGdllZfxaUsJof3+wWhkqwk6zmWiTiTCTiUPF\nxYT5+dEqMJDUggJQigGtW7MrMxOLxeLVfTWoUye2e9oGya0fpWYzQQEBtgO3CWhHjh8nPi7O4c3b\nfeCATfAZZZUR0t28+SeGDBlBZmYmR48eoG/fc9i8+RvCw2Po2vVMysvBbLby3nu389prV/DZZwt5\n+eXxrFhxB2VlFucHfXDZZQ/w7bdvkZ2d5fKQj/Hjb+Drr1dSWlrq0btn57LLruXTTz+s1VvebNBP\n2gC04NNoNBoN1O23eRWi71ByMt06d3ZRIuknThAXG0tWbi5RYWEA5J88SYnZTJHFQrlSCBAFYLXS\n12Riv8VCez8/RCmOl5WBUsSHhpJSWAhKEREQQExwMCn5+V7n6fVs25YkTxuku/Wh8ORJwu376tkR\nobi0lNzCQtq3bu0Qgn8ePkzPxETH6lz7PLrt239l4MBh/Pbbz/Tvfw4iAaxf/xGjR0/DahXKy2HZ\nslmkpOzkkUd2cNdd63nkkV2kp+/n7bdvcJmzFxsbz9Chl7Ju3QoXL15cXBcSEnrxyy/feRR79tdj\nxkxg+/YtZGefcOmyr38GPkx7bDpowQdowafRaDQaO3U5Y7+SupIOH6arXfAZZOXk0KZVK3ILCwkN\nDCQ8OJiCkyc5abEQbDIR5udHuLFBM1YrnYEj5eXEmkyUWq2cNBZ3tAsOJr242KFIEqKiSM7Jqah4\nDDpGRZGam1tl33OLimxC1M27d/jYMRLi4jDZlsCigAPJyXRPTHR6fq5QUFBIRsZxOnfuzu+//0K/\nfmdTXq747bdvOeOMCVitcODAFnbsWMvs2V8QFBSF1QrBwa249dbPOHLkV7Zt+9xF9I0aNZXvv1/h\nEJT2Lpx//pWsW/cfr2JPKQgODmHEiAv473+/8PgWVjafz5vYq6pco6EFH1CF4BORtiJym4h8KCKb\nRWST8fo2EWnbUEZqNBqNph6p62/mKuran5REr27dXBTFibw8YiIjySssJNDfn7CgIApLSymxWAj2\n9yfULvgMZRMPHLVaiTGZyDGbifH350RpKW2CgsiwP9cWSIiMJDkvz7WfTvZ1iIoiLTe3yi7lFRcT\nFRpaYc+Rw8eP08Vp/l5mdjbBQUG251Lb999TsGvXLhITe2Ey+bNr11b69BlGauohlLLSrl13rFb4\n6qv/46KL7sPfP8xlPp+fXxBXXvkK//73fVgsVofg69//QjIykklLO+jStfPPv4Lvv//Msdeee9ft\nady4SXz77ZoKb11lYs+9XFVveZMQfVrwAZUIPhF5C/gICAcWAzcANwJLgAjgIxFZ2hBGajQajaae\nqM63cWUun6rKO5VJTkmhS3y8S9H8wkKiwsIoLCkhwGQiJDCQImMz40CTiUCTiRARR12tlSLLaiVc\nhEKrlQg/PwrMZqIDA8lz2gQ5PjLSFtL1QkxYGLklJRUz3DaJyy0qItp45rSz6DuamUln+/w9EQ4e\nPWoLVzuut4V0k5L+JDGxl+HJ20XXrv3Zt+9XevQYZngAc9i582vOOmuaw2PnnHr1GktwcBTbt69x\n3E4RfwYPvpjffvvGcc5qhQ4duhEcHMrBg7bHtnp4OAgAI0ZcwM8/r8dq9f1voK7EW3XmDtYaLfiA\nyj18LyulRiulnlVKrVNK7VVK7VFKfaeUekYpNRp4pYHs1Gg0Gk1d40P41es1VZ2vxE10JCWFzh1P\nPZ3KarVSfPIk4SEhFJ88ib+fH0H+/pRaLAT4+REgQoAIwU6CLwIohAqCL8rf30XwdQwPJ8XL/nkA\nrUJCyPW03YqbzbnFxURHRJw6YQi8oxkZpxZsiHAwJcUWrnY8QxdD5P1Jly49KCgoIC8vm3btEti/\n/ze6dj0DpeDnnz+ib99xBAdHe3zCGwhjxtzDunWvuqy+HTRonEPwOT9hY+jQMWzZss6jx86eOnbs\nTHh4BPv27a6TP4Hq0mCePy34gEoEn1LqD/dzIhIjIgMrK6PRaDSaZkRtv3VrMNM/LT2dDnFxjtPF\nJSUEBwZiMpkoKS3FJEKAvz9+IgSYTPibTPiJEOgk+MKUokgpQk0miq1Wwvz8KLJYiAwIOCX4lCIu\nPJyMoiKvJoUboeMKuC3OyC0qsoV0nfNFSMvMpEObNo5zR9LSKjxDVyk4fPgQnTp1JTk5iU6dugEm\nDh/eRefOA7Ba4ddf/8OQIX9x8dS5pwEDJnPw4M8UFeU7yvTtO5o9ezZWeFTagAHnsmPHJo8hXWeG\nDRvJ1q0/+/b+NVfcnoxSaWoGiEiwiARV97oq5ayIfC8ikSISA/wKLBWRF2tipEaj0WiaCA09ucpJ\ncbSAUs4AACAASURBVKRnZtIuNtaRVXzyJGEhIYBt+xMMoRfo7+8Qe35gE3wGwcBJpQgGSq1Wgo19\n+SL9/Skwmx1txQQFke0pZGsQEhBAiVN5F3udyC0qopU9pOvE8exs2wpdg9T0dDq2b287MDx8SkFK\nyhE6dOhMamoy7dt3Mc7tp337nlitioMHN9O9+6gKz8J1Tv7+oXTpcjZ79653nIuO7ojJ5Ed6+hEX\nUder11D27v3Vq9CzM2jQMLZt2+zTvL1mSzP38ImISUSuFJGPRSQVOAQki0iqiPxbRK4QEamqHl96\nF6WUygeuBJYrpc7C9ngQjUaj0TRXGulb3Ww2U1hURHSrVo5zxSUlhAQHAzbBZxLBz/DsmUQwASYR\nnP0vwUAJEASUGIKvpLycCH9/8o0tWlCKmJAQTnh5QgZKEWwXfMaxA7fvT8eiDTeOnzhxSryKkJaR\nYfNeumy6DGlpKbRvH09qajJxcZ0xm8tJTz9M27aJZGQkExAQQlhYWxeh5ykM27PnRezZ8z+nc0Ji\n4jD279/qUq5Ll76kpR2mpOSUd9OToBsy5Bx+/fVnl3wPt6l5i8BmLviA9cBQ4P+Abkqp9kqpOKCb\ncW4Y8H1VlfjSOz8RaQ9cC9jXbzfnt16j0Wg01XlyfR26fnJyc2kVGYnJvsWKUpwsLSU4MBCUosxs\nRpTC38/P4d0zmUyYgAAnm/0BK+AHWLB5/8xWK2H+/hSXlzvKRQQEUFiJBy/AZMLiVN49305+cbHH\nbVmy8vJsz9E1zqVnZRHXtq3Twg5AQUbGcVq3jiMjI402bTqSnX2c8PBWBAaGkJq6j/bt+3gVes5e\nvi5dziE5eYtLfufOAzh8eIfLOT+/AOLjezgWbnjqnlLQu/dAkpMPUlRUXCHP07RMb/VUVqbRaf6C\nb6xS6hGl1GallGP+gVKqVCm1SSn1d2BsVZX40rvHsT1DLkkp9YuIJAJ/1thsjUaj0TQ/6uibPCcv\nz+bdc5pcVlpaSpAh+MwWCxhePbuHz+YnAz+7iFK2TZgDsAk+s9VqE3xKEeLnR4n92bhAeEAAhXaP\nn/NmdUY9fiYTFtuqiErJLy4m0nlbFgARsnJzaW0XfMa2LG1iY108fEVFRVit5YSFRZCZeZzY2Pak\npx+ldet4lILjx/+kbdueLgLPm6evU6chpKRsx2KxOLrSuXN/jhzZZe+Sg+7dB/Lnnzs8Cjd7CggI\npEePPuzevd1jvyvT+nYbfSnbqDRzwecs8kQkWkQGicgQe3Iv440qZygqpT4GPnY6TgKuqpnZGo1G\no2l0GvFbOS8/nyj7alfDhjKzmUDjsWV2b5sYnj2sVgSbd8LPra4Ao5wZm/evzGq1CT4nj11oQADF\ndg+fM8axSQSrp3vhJOysVivFZWWEGWFne15JaSlWpQhzCvVmZWfT2inEqxRkncgiNrYNIkJ2dgat\nWrUhKyuNmJgOKAWZmQeJje1WwaunlK0p5/PBwZFERXXg+PH9JCT0RSno0KEPKSl7XRZoAHTr1o+D\nB12FoKeu9u07mN27/+Css4a73x73W9E8aaJCrrqIyCJgOnAQm4Pbzhhfrvdl0UYvEfmfiOwyjv8/\ne2ceLkdV5/3Pr/e+ffcle0iIIQkEgiCrAQw4IDvDCCoyzsAgjDqIM+qMyCuCMI7i6zDKOKOADKDC\nC6iooCAgQ5AAImsIEBJCErLf5OZufXvv6vP+UVXdVd3VffsuIbmX83meerrr1DlVp3q5/b2/7SwR\nka+NYq4azXuWuXPn8vjjj+/taewxJvv9TSrGU+x5+fOGOXd8aIimsuSHXD5P0MqQzBuGKfDs+D3L\n2idW4oYTP+aPmKEUfhEMpQj5fOQcZqeIVd6l2hw9tYxtnbOUzlA6TYOVRezs0xePu0q1FAoFBuJx\nWltaXOfp7e2jtbUdpaC/fzctLZ309e2ktXUqSsHu3Ztpa5vtGbNXLtSUgmnTDmTHjjVFYTh16vvo\n7l7vKrQMMGvWAWza9FZNsQcwf/6BvPXW6n3PMjdeTHALn4OPA+9TSn1IKXWivdU7uJ67uxW4CrAL\nG60CLhj5PDWafYO5c+fS0NBAc3MzbW1tLF26lJtvvhk1Tn/tLrroIq6++mpXm4hQRxLVHuWiiy4i\nHA7T1NREU1MTzc3NNe/57rvvZs6cOTQ2NnLuuefS19dXte++cH+avUydQjI+NESjtVauTS6XKwo+\nw7LoAUVXri28yj9hAUAKBfJKERAhrxRBn4+sQ/AFLZdtoVyYuqZew8InQjyVotHKInYe74vHaXUI\nvoGhIWINDQQCAZdZrK+3l5aWNpSCgYFempvb6evbSUuLmaTR17elpuAr36ZMWciOHW86rH5NhMMx\nent3uKY4e/YBbN78VvGWvVyuSsEBBxzIunWr90137HgweQTf60DbaAfXc3cNSqnn7B1lfjNyo72g\nRrO3ERF++9vfMjg4yKZNm7jyyiu54YYbuOSSS/b21PYoIsJXvvIV4vE48XicwcHBqiLt9ddf5zOf\n+Qx33XUX3d3dNDQ08LnPfe5dnrFmMpJMpSoEX94wCFhr0RqFghnDZ7l0bdHnw4rhcygSn6O4sQ8o\nKEUAzJg8q5+IEKgRp6fs89RgKJ2mqVzw4UjkMC9krhbS1OSyECqEwfggzc1mVnI83k8s1ko83ktT\nU6clAnfQ1DStpihzbp2d72PXrg2uuXR2zqanZ4ur3/Tpc9mxY5OnoHVeY+7c+Wzc+HbN16AW9YjE\nvSokJ4/g+zfgZRF5VEQetLYH6h1cz93tEpH59o6InAdsH8VENZp9jqamJs466yzuvfde7rzzTl5/\n3Yx3yWQyfPnLX2bOnDlMmzaNz372s6TTaQCWL1/OrFmz+Na3vkVXVxf7778/d999NwC33HILd999\nN9/5zndoamrinHPOKV7r5Zdf5tBDD6W1tZVPfOITZLyKve5h6rVi3nXXXZx99tkcd9xxxGIxrr/+\neu6//34SNQrY2qxevZp58+Zx7733AqZF9bvf/S5LliyhqamJSy65hO7ubk477TRaWlo4+eST6a9j\nLVPN5CCZStFQJp6MQsEt+LBi+KxHGwGXcvBbbQXLpVuwLH1GmWoK+HzkvDJxrbH1CL7GSKQimG0w\nkaDZIV4HE4lSfKJ5E2b74CBNTaZFPR4fIBZrIR7vIxZrtY7vpKlpanHKhmHw619fzOrVv64oqKwU\ntLXtR2/vJpeI6uiYRU/PluK+UtDQ0IzfH2BgwG2dLz/fzJlz2b59M3mn63sc2etWw8kj+H4CfNva\n/t2x1UU9d3c55vq5i0RkG/BPwGdHPk+NZt/lyCOPZNasWaxYsQKAK6+8knXr1rFy5UrWrVvH1q1b\nue6664r9u7u72b17N9u2bePOO+/ksssu46233uKyyy7jwgsvLFrSfvOb3wCm0Pr5z3/OI488woYN\nG3j11Ve54447POeyYsUK2traqm7PPPPMqO/zv//7v+no6OCII47g/vvvr9rvjTfe4NBDDy3uz5s3\nj3A4zNq1a2ue/6WXXuLUU0/lBz/4AR//+McB8wf7/vvv5/HHH2fNmjX89re/5bTTTuPb3/42O3fu\npFAocNNNepXGd4XR+OzG49facY5UOk0k7F4kIJ/P47cEX8GyzolSRbHndPE6z2db/wzcsXzl1rxa\nFj6jUCjF5pULP2s/kcmUEjYcxJNJmpyCz45PLCvdEo8PEos1kc2a/+QFg2ESiQGi0WbS6SSFgkEo\nFCve2o4dr/DKK3fwxBNf5957SzmS9svY0jKTvr4trrb29hn09m5ztQFMmTKL7m635a+ccDhMe3sX\nO3Zs9XyNJjxjFHwicqqIvCkib4nIV6r0uck6vlJEDnO0f1VEXheRVSJy92hWyHAwpJS6yVridrm1\nDVt/z2ZYwaeUelsp9WGgE1iklFqqlNo4hglrNPskM2bMoLe3F6UUt956KzfeeCOtra00Njby1a9+\nlXvuucfV//rrrycYDHLCCSdwxhlnFC1aSqkKS5qIcMUVVzBt2jTa2to466yzeOWVVzzncdxxx9HX\n11d1++AHPziq+7viiitYt24du3bt4vrrr+eiiy6qKh6HhoZosQPPLZqbm4nXWJP0ySef5JxzzuGn\nP/0pp59+uuvY5z//ebq6upgxYwbHH388xx57LIceeijhcJhzzz2Xl19+eVT3pBkBoxFuw0X7j+Ic\naQ/BZxQK+K0f24Il9MTnc1n5xCrP4sT+AVNWH6VU0bXr6me5fb0wCgUCPl/1VFQRkpkMsXDl73Qi\nnabRUaolkUya+2UkkwkaGmIkEglisSaUgmQyTjTaRDzeSyzWjjNC8Z13nuHwwy/l0ktfZP36P5BI\n9LpexsbGacTj3a62lpap9PfvrHirOjqm0dOzo6q72GbGjNls3bq5eL7h2OtWu5EwBsEnIn7gB8Cp\nwEHABSJyYFmf04H5SqkDgMuAH1rtc4FLgcOVUodgGqU/MYY7eUpEviUix5aXZamHYcuyiMg04JvA\nTKXUqSJyEHCsUuq2MUxao9nn2LJlC+3t7fT09JBMJvnABz5QPKaUMi0PFm1tbUQdbqk5c+awfbsZ\n6VAtLm6aY+3QaDTKtm3bxvsWanLYYcV/OjnttNO48MILuf/++z0FZGNjIwMDA662gYEBmpzuKgdK\nKW6++WaWLVvGCSecUHF86tSpxefRaNS1H4lEGBoaGvH9aEbBWETfOF03k82aNfccFBxWtkKhYP7D\npJRb4JXvU5JIBVvoQTFb14lPpOgqLifvEJvVRF8im6XBQ/ANpVIuy99QMukq0VIcnxiyBN8Q0WgM\npSCVGiISaSKR6KOhwR2Hv2XLs+y//8n4/UGmTz+cbdueZ8GCjxTFWizWSTLZRz6fIxQyy9m0tExh\n8+ZVFdfu7JxOT4/5t6nWWzljxmy2bduMUvWXYRlJ373K2Fy1RwHrbEOXiNwDnAM4K1qfDdwJoJR6\nTkRaRWQqMIiZ89AgIgbQAIzFjHo45v83x5S1j09ZFuAO4FFghrX/FqZbV6OZNDz//PNs27aN4447\njo6ODqLRKG+88UbRqtbf38/g4GCxf19fH0nHck3vvPMOM2aYX5F6slVr9XnqqaeKmbRe29NPPz2G\nO62PxYsXs3JlqRDr22+/TTabZcGCBZ79RYSbb76Zd955hy9+8YvDnn+8MqI1+yg13t9sNku4TDwV\nyix8YH1HHJa9YsauA7tNOR89rHlebTY5wyDoLy/44iaVyXgKvkQqVVwDGKw1gV3Fmc2kjVQqRTTa\nQDKZJBIxBWEmkyQSiZFMDtDQYMby2YJu+/ZXmD79AygFU6ceys6drxevoRT4fH6i0VaSyb5iW2Nj\nB0NDvRWWvNbWLvr7e2reH8DUqTPYubMkDEfyFR2L6HtXMoPH5tKdCWx27G+x2obto5TqxYyx2wRs\nA/qVUn8Y7W0opZY5y7HsibIsnUqpezHDJFBK5TBXstFoJiy24BgcHOS3v/0tF1xwAZ/61KdYvHgx\nPp+PSy+9lH/8x39k165dAGzdupVHH33UdY5rrrmGXC7HU089xe9+9zvOP/98wLRmrV+/vq7re3H8\n8ccXM2m9tqVLl47qnn/xi18wNDREoVDg0UcfLSZmeHHhhRfy4IMPsmLFChKJBFdffTUf/ehHiZVl\nVzppamri97//PX/84x/56le/Oqo5aiYBtfyGmEWW7RIsNgWlihY+Zbt0PVy4nvsecX4Vgq/GdPOF\nwrCCL1nFwpdMp2lwFGNOplI02BY+h8hMpVKEw1EyGfMRTMEXCjWQSg0SiTQXX7JsNk1//wY6Ohah\nFHR0HMSuXW9UvKSxmFvgxWJtxOO9xeN2e0tLZ12Cr6trGt3d7nzMd+v/sj0u+sYm+OqdWcXHzFqZ\n7B+BuZhGs0YRuXCk0xeRi0SkqkdWREIicvFw5xnWpQsMiUiH48THAAM1+ms0+zxnnXUWgUAAn8/H\n4sWL+dKXvsRnPvOZ4vEbbriB6667jmOOOYaenh5mzpzJ5z73OU455RSAYizejBkziMVi3HzzzUXr\n1yWXXML5559PW1sbJ554omdyxN6oW3fTTTfx6U9/GqUU8+bN48c//rHL/WoLtqVLl3LQQQfxox/9\niAsvvJDdu3dz8sknc/vttw97jZaWFh577DFOPPFEQqEQ3/jGNzz7uTIvdQ2/Pc8+ZFHN5XJmnToH\nhUKh+BlQSqGsR3vedls5UnZMWW5f7zIk3q9BzjDMGL4aJDMZorbgcyRkJDMZOtpK7tikHZ/oXEcX\nW/BFSKVShEKmQDQFX5R0Ok443Fh8i3bvXktr6/74/SGrBMsiXnvtp477sAVeuyu2LxZrY2ioZPGz\naWnpoLt747AfgWnTZrB27Wuu6zhWsvN87uxrvzT7JDXe3+UvvMDyF1+sNXorMNuxPxvTglerzyyr\nbRnwjFJqN4CI3A98ELirzpnbNALPi8ibwAuYlVIEmAYcASzCrJlck3oE35eAB4F5IvIM0AWcN8LJ\najT7DBs2bBi2Tzgc5pvf/Cbf/OY3q/a56qqruOqqqyra58+fX5GEUH7Na665ps7Zjh9//OMfax4v\nT8i44IILuOCC+mqsO++vra3NlZBSfu8//elPXfuXXHLJpK+BuFcZj8SLcbyuUShUWPiUKpVGccbv\nFbN0bYtfeTIUDsueo2/FVKq0Q30u3XQ2SzQUchVjRoS0LQSt/Uw2S9QjmzebzRAOR4qPZlvaEnwJ\nwuFG696hp2cdHR2l0InW1v3p76/8mxWJtJBMlmwvDQ0tpFKDFW9zY2Mr8Xip7FE1A2xHx1R27equ\nuI4t8JyibsKJvhqCb9lRR7HsqKOK+9+4tUI3vQAcYCVgbMNc7aL8D+MDmBVN7rGMYv1KqW4RWQNc\nLSJRIA38BfDnkU5fKfUDEfkvYClwnLUBvIOZUPKMqiNOpqbgs7JTTrC2RZjfrTVKqWytcRqNRqPZ\nh9hbFj6P6zpLsNi4LHyUXLX1aAdFyedW/liaRlnCh0OV1CX4cjki1lq/TtGXzuXMBBSHACxPSAHI\nZNKEQmGy2TTBoGkpzGbTBAJh0ukEoVBDUYT19q6jre19xbFNTTNJJneTy6UJBCLW/ZiCL5UqCb5o\ntJlEYqBCzDU1tTI42DfsR6CryxR85dY8+3GyWviGQymVF5HLgUcws2xvU0qtFpG/t47frJR6SERO\nF5F1QAK42Dr2ioj8BFM0FoCXgFtGOQ8FrLC2UVFT8CmlDBH5pFLqP4DXavXVaN5LaBekRjM68vl8\nhUsXyr5TZS5/zz6UWfjKHp3UsvBl83lCwyVtZLOuNXNt0pmMq8RMOpulqbnZfWHMRJVIJEIikSUc\njqCUIpfLEAiEyWbNWD6b/v53aG9fVBRuIn6amqYzOLiNaHResV8k0kw6bVrllYJotIl0upTtXnL1\nNpNIlBLOnMedIrC9vYve3uqxfiMVdftUBu8YCyorpR4GHi5ru7ls//IqY78DfGdMExgn6nkVVojI\nD0TkeKvmywdGUvdFo5lsLFu2jE2bNu3taWg0ExJnzT2bPW1/dFn4yooi5woFQh4C1EnKdumWkc7l\nXIIv61Fyxm4PBIJksxmCwRD5fB4RH36/n2w2RTBYyvQdGNhMS4u5rm6hYG6NjTMYHNzqEmjhcIxM\nZqjYFgpFyWZTGEbBJeZisWaSyZIwdB5zPm9r66Cvr2dcMuj3VhRBVSbPShtjop4YvsMwv4/XlbXX\nnQqs0Wg0Gg24a+7Z2Jm5KLNo+WhERzV3rt1WYeGz9rP5fF0xfBEvV202S9h29Vr7Qce+TS5nCz7z\n0d43j6UJhUpFzgcHt9DUNMs1vrFxBvG4u25nONxIJlOy6Pl8PsLhBjKZpCsmMBptIpkcvs5lOBwh\nEAiSSAzR3Oxdb3MkTCYL32RhWMGnlFr2LsxDo9FoNO8BvAQfyh1jV3TVjlAx2Bm65aOqrpcrQs4w\nhnXppnM5Twtf1o7hq7IPpvDJ5/OWZS9HIFB6NI9liEZLVsJEYicNDVNd1rGGhi6SyV0uC10w2EA2\n6xZytpXPTOo0+4XDDaRS1dfALrmOobm5lcHBgXERfPsUk0TwiUgE+ChmmRdbvymlVLlBzpN6Vtr4\nEpX/NA0ALyqlvNeG0mg0Gs3kZzSWOKUqrG1qHH2AXtY8LxFou3azdWbphj0sd9l83pVxnMvnq1j4\ncvj9AaskTRDDMB+VohjLZ5PJxAmH3YIrFptCIrHLuhezLRhsIJnc6epnW/hK9w2RSIx0urrgs/uJ\nQEtLK4OD/ZhVRSYRk0TwAb8B+oEXMbN+R0Q9Lt0PYNZ5eRDzH6gzgFXAZ0TkF0qpG0Z6UY1Go9FM\nYMYgzJSjyLKT8UyE8rLwlccN2uQMwx3D5zGPdC7nLficWbqYsXohq59CrHqCdqJK0PXo95v9DCPr\nEny5XJJg0L08WzTaTl/fWldbMBghl0uXWf0iZLNuHRAKRcjlshiGUcyOrvZSNzY2E49XJnhMeCaP\n4JuplPrIaAfX8yrMxlz490tKqS9iCsApwIeAi0Z7YY1Go9FMYEYp+vb0snpeZ6/q0gVvC19Z34yz\nLItzbNmqIUULn50YoszzmC7cAIaRx+8PYBimxc88liUQMN3AhpFHKQOfz32tSKSNdLrP1RYIhMnl\nSuLOFHzhCsEnIoTD0Yp2L2KxJhKJ+LD9JhyTJ2njGRFZMtrB9Vj4ugBn3b0cMFUplRSREZsUNRqN\nRvPexdO9OtpzgWuVjWrnL9S4ZkUdPg9hmM3nPTN5c2Uu3bxheJacMS16tuDzF4WfeSyL32+6dw0j\nQyAQqbB2RiJtpFJuwRcMRsjn0xVtThFoEw5HSaeTNDZWXxoRoLGxiaGhSSr4JgfHAxeLyAYgY7Up\npVRdIrAewXcX8JyI/BrTUn4WcLeIxIA3RjFhjUaj0byHGW/3rXO1Da8Yvlou3WwdSRvZfN7TpZsz\nDLfgc1r4HBiWEDTdqgGX4DOMXNGiZxZjrlypIxxuIZOJu4yqfn+IfN69BoKdEFI5PkImM7x9pqEh\nRjJZO95vQjJ5BN9p1qP9SRjRF6meLN3rReT3mOu/Afy9UuoF6/mIFwHWaDQajWY0qLJaH9VW2RiT\nhc+DbD5fjM1zjfWw8JWvIgK4LHs+nx/DMPD5zH75vCn47Ixdvz9s3WspmSIUaiSTccfWBQJh8vlM\nWVuIXM7dBqaFL5NJ1bxHgGi0QQu+fRil1EYReT+mpU8BTymlVtY7vt5XIQLElVLfB94Rkf1HPlWN\nRqPRaEZHNcEmjiXYvMqyKKVQUDWGbyyCL5vLma5ey/SWNwwCHucyS9H4i4+2pc88VhJ/hpHF768s\n/xIIRDAMt5Dz+81sX3e/yjawY/tK46uFUUYiDaTTwwvDCcckieETkS8AP8MMtZsK/ExErqh3fD1l\nWa7FTNRYCPwPELIuuHQU89VoNBqNZtxxWfjKrIBCdTdyXYLPFnZllMfsGVVi+EyLns8Sdz4Mw0DE\nFBeFQh6fr+Te9RJ8fn+4LsHn9wc9XbqhUJhcLlvRXk4kEiWVSu1bRZPHg31cyI2ATwNHK6USACLy\nbeBPwE31DK4nhu9czNU2XgRQSm0VkUlWlVGj0Wg07xZembrlbcpsdO+Xj3GMs4VduUvXKBSqWveg\nhuCzM21Fqq7GUXTpWv3M5Awvl65huXQNy9JXsupVWvgqLYl+f6X7tprg87LwVXP1llNvrN+EY/II\nPoBClefDUo/gyyilCvZ/R1ayhkaj0Wg0I8ZOrHC12T/Ipd8Z9/Fa53McL7p0HeNrJWyAQ/A5r1n2\nvDw5w8blwhXxXCcYyl26oeI+gFIFh/jLV5RkUQp8viCFglvI+XwBCoW8az1c06Wbr7i+uZxbabxz\njJNwOFxcd3dSMXkE3+2YSbT3Y37c/xLT81oX9Qi+n4vIzUCriFwG/B3w49HMVKPRaDTvbWpm6I6D\nH7HcwlerBh9AvlDwFHNFCx+msPOy8BUFn9WvUCh4WvhMUedDqQJ+v6+4b4/xcu868fkqLXc+X6BC\n3Jnxgd6Cz8vVW04wGHLF+k0aJongU0rdKCJPAsdhftQvUkq9XO/4erJ0/6+InALEgQXA1Uqpx0Y7\nYY1Go9FMAkRGVXxZRCgUKj1RqtpzpVCWoCq/Ws0sXUuwGcMIvpxhEBhGEOTKkzGs8xmG4bLo2bF6\n5ZiiTlyPtixVqvTcFHyVgtHvL1n4bOuc7Rp296sUgTBSwTd8rN+EY4ILPhFpVkoNikg7sAHYaB1S\nItKulOqt5zz1WPhQSj0KPDqqmWo0Go1mcjFKsWcOlYp4PafVbzQ1+uxziggF3Bm5BaXwjyaGz9mn\nrPyKTb7M1WtUsfDZlj/blWta+upz6YLt0s27XnLzPEZZv0CFCARbCFa2lxMM1icMJxwTXPAB/w9z\nWduX8A5nratySlXBJyJDVU4MZmXn5nouoNlz2H80L33gUg6ZeghLpi7hkCmH0NHQsZdnptFoJj22\niBqh8PMSfOZplHMHrHIqrrbyMVRm5xazdG0rXB0Wvkg4XPU4mG7fgM9XEdtXXnevUCVBxLbsKeVt\n4ZOixTBvCTn3eNN967bwiVRa+Kq5dKu1l1Me6zdpmOCCTyl1hvU4dyznqfoqKKUalVJNwPeBrwAz\nre1frLYxIyKnisibIvKWiHylSp+brOMrReSw8bjuZOOw6Yexetdqvva/X2PeTfOY8e8z+MjPPsKX\nH/0yd75yJy9tf4l0fhJmXmk0Ewz9Nw8rlq3MwkdJ8InZaUTxfK6kDcc+DJ+0kS8UhrXwuSx5jnmV\nJ2lUi+FzJ21UxvCVEjgMRCrHmzF+yvW62ZZCJ+Y5K4WxaV0c3sJXb78Jxxjr8I3le1vP2HoRkcfr\naatGPS7ds8vWafuhiLwKXF3vRbwQ81P9A+AvgK3A8yLygFJqtaPP6cB8pdQBInI08EPgmLFcdzJh\n/1f4uSM/V2xTSrFpYBOrdq5iVfcqHnn7Eb777HdZ17uOua1zOWTKIeY21Xzcv21/fDKx//vRJwnv\nYwAAIABJREFUaCYC+m+eiVmPzi1UxrrUmqvQctn56kraqEPwObNxqyVpFJSqGcNnCrbSI7hj+Exr\nX6WFT0QQ8VliLGC1eb2OlW1AUWwOh10UetIxBgvfWL639Yytcw5RoAHosuL4bJoxDXF1UY/gS4jI\nX2P6kAE+AQzVe4EaHAWsU0ptBBCRe4BzAOcLcTZwJ4BS6jkRaRWRqUqp7nG4/qRERJjTOoc5rXM4\nc8GZxfaskeXNnjdZ1b2KVTtXcetLt7KqexW9qV4WT1nMIVNKLuFDph5CZ0On67w//83PufVXtwJw\n6bmXcv4557+r96XRTAL03zzM+DrDK2mjzKWrytuoM2mjbKWNYh0+h1BzkjOMmjF+SinPciu2gHKJ\ny0KhinhVrjjD0mPpmHmtauOxhKBb8JXH8JVEoRu76PNw2LUCJx1jc+mO9ns7DTO2brix9fD3wBeA\nGVg1kS3imIKyLuoRfJ/EdOF+z9p/2mobKzOBzY79LcDRdfSZBUyaP37vFiF/iCVTl7Bk6hJXe1+q\nj9d2vla0CN73+n2s2rmKhmBD0RqY3prmZ3/8GYNzzbUcX7znRUSE884+b2/cikYzUdF/86CYvODE\nGddnx98VM21rUFxFg5Jlr2rSRhXRV63kio0t9sqFmFeCRqFmHb6SK9cUdj6UcpdlsS18Xpj9lWO/\nMhbSy13uNbYa1cZPeMYm+Eb7vZ2JKdCGGzssSqnvAd8TkSuUUnWtquFFPWVZNmCq1/Gm3k9V+Td+\nEn4a9x5t0TaOn3M8x885vtimlGLz4GZe7X6VVd2ruGn1TabYs96J3oW93HL/LVrwaTQjQ//Ng+LS\nYk6cxZjFkQFsW/lsS56Xha/43LLsVVj4nC5dDwGZLxQ8178tHi8rvWLjlaBRKCv67Jyb05XrLg5d\nr4Wv3KLnlfwixbg+56FqpXDKqdcSOOEYm+Ab7fd23FFK3SQiBwMHARFH+0/qGV8rS/da4IfVXAki\nMh34jFLqmhHNuMRWYLZjfzam+q3VZ5bVVsG1115bfL5s2TKWLVs2ymlpRIT9WvZjv5b9OHPBmTxx\n2xPsiO3Y29PSvIdZvnw5y5cv39vTGCvj+zfvG98oPl/2oQ/te3/znMLFlWzgHcPnsvBRKdycQtBJ\n+ZJqivEty5Ivr8Fn4WXhUx4xfGZWrVvouWP43PF8UiWmutyiZ75OleVtlFKeMYD16JaxxlKON8uX\nL+fJJ5eP+Tyqhhar42/LaL+3W4BgHWPrxtJlHwIWA78DTgNWAGMTfMALwD0iEsKs/bId81M5DTgc\nyADfHe3ErfMfICJzgW3Ax4ELyvo8AFxuzeMYoL+aAHUKPs34cum5l/LiPS/Su9Cs7di+pp3LLrhs\nL89K816i/J+4bzjEzgRifP/mXTPa/7XfRTxEmr+epI0RCo9ilq6IaWVzHBuuLEux5EoVai2X5hXX\nV1002TF7lcvHOUVudUORW7R5Xad0nkoL30Sk/Ht//fWj+97XMm6ecMIyTjihdI3rrqu4xqi/tyKy\nu46xI+E84FDgJaXUxSIyFbir3sFVBZ9S6rfAb0VkNrAU2M86tAK4QSk1apVqnT8vIpcDjwB+4Dal\n1GoR+Xvr+M1KqYdE5HQRWQckgIvHck3N6Dj/nPMREW65/xYALrvgMu3O1WhGyF79mzeGQsnjjc/n\n80zacIlApSxLlcO1yzAuXcejU+BUE2w2w7l0qwo+2y3ruFb5Or7Oduejc+Zut6z3ePCO2Ru2nmEd\n7aPtN5Gow5tdlbF8b6uNHcOtpJRShojkRaQF2InbgliTemL4NgP3jGGCtc79MPBwWdvNZfuX74lr\na0bGeWefp0WeRjNG9srfPFvs7SOizysT1Od06VptLgtYlXPZItCOARSs0iiOPsZYXbpVBGHRwleW\npetVlsUrds8t7Oq18NXax+O8w7c7qTb/ic5YBB+M7XvrNXYMPC8ibcCtmJbHBPBMvYPrWlpNo9Fo\nNBOYfUTsAQQCAfJ596oP5Ukb5RY9p6XPSXEMjqQNRliHz7mWrke/YS18jnG1LHzlZVkq78I+VS1h\nVn2cfR2v2xiZdW9iun9rMVbBt6+glLKL7v5IRB4BmpRSr9Y7Xgs+jUaj0YwfVZI1bAJ+f4VLtzxp\no5i/6lhezdN9aV3DVZalzKJnKFUzRq9o4asitIzhLHzO+VQRfPZ9Wc/K9kcSY+fuVz7OKdjcb4Oq\nmgziRFv49n1E5FBgLqaLWERkvlLq/nrGasGn0Wg0mvGlhkUxEAiQzWTKuldm6bqOD3c5Slm65XX4\n8nXE8AWdq2iUH69WlsWZkeuwQO7tBAmfTypuo3YySQmjbG3gycJkEXwicjtwCPA65kfdZnwEn4gs\nBP4bmKaUWiwiSzCXW/vXUcxXo9FoNBOJcXYHBwIBEomEq81XVidOOdJMK1bTcCRKuCyAlGL4XFm6\nhcKwMXyjzdKtKJiovAuAjE8ixPAJG+4sXnd7fTF8WvDt4xwNLFaj/EDVY7u9FbgKyFr7qxhbWrFG\no9FoJgI1ChaPloBH0obTwmcvg1Zbzrj3XRa+ai7dKveQNQxCgeq2j2p1+ApKedbhG0vShH0O73Z3\njT4vN61z1Q4nhmHg8w0v5PL5PH7/5HP8FQr1b/s4z2MWXR4V9byzDdbacAAopZSI5EZ7QY1Go9FM\nIPaAhS+Xc/+E+Hw+Ck6XbtlauhUWPuc+jqQNa2k1p7jKK1XTpVtP4eWqFj6PGLpaZVmq4XZnVy+r\n4jy396oc1a5fX2yeYeQJ1BC/E5UJIOTq5XbgWRHZgVkLGUxZtqTGmCL1vLO7RGS+vSMi52EWYdZo\nNBrNRGJvZOvaLljrusFgkHwNC195bTsYpiyLSEVZlpEmbdSy8BnOGD8Ho0/aqDjiel7bwuech5eF\nz9uSZ8bmDf9zn8vlCASCw/abaEwiwXcb8NfAa7hj+OqiHsF3OXALsEhEtgEbgAtHeiGNRqPR7EWG\nyZ7d49e2RF/A7/e08Lnq8FkxfE7LXi0Xr9Ola5TV4RtuJY1MPk9oFHX4DGdG6zBlWeqltoXPcAm8\nQsEYgeDL1+nSzREKhUY26QnAJBJ8O5VSD4x2cD2Fl98GPiwiMcCnlIqP9mIajUajeW8TCoXIldXh\n84kUS7UUw4fszSN5w6ZC8FllWZxZusMlZWTy+ZoWvlw+XzWGr7y+X4XbtepZy7Gtmz6U8lYn5WLO\na93dWoKvHldtLpfVFr59m1dE5G7gQUp5FWrMZVlE5EuOXeVot69w44inqtFoNJr3NMEqMXzFpA1n\ngkX5UmJl57Jj+ny20KNyZY1coUCwluDL5YgEq4ucajF+hkdsn0vwqaKhsiblK25UXy5NVVj4ysWd\nmWVb+bOez7tdtR5ecwDS6TSRSGQ8c3T2CSaR4Itgxu6dUtY+5rIsTZjfp4XAkZiLAwtwJvDnEU9T\no9FoNJObOmIEQ6EQmWzW1eZcX9d26Sqliokc1YovFyi5cp0uXafgy3sJPsfxTD5PeBgLn2cMn7MO\nnz0/RpeNW4pf9Lbw2e5cESmKNS/BV811W27hqzbFbDZDJBKua/4Tickg+MQM4OxVSn1p2M5VqPop\nV0pda13kKeBw25UrItcAD432ghqNRqPZy4xn8kb5Wr3DnDsUDFZa+IYpvGzjZeFzFlz2i1QkaWQN\nwx2jV2beSuVyRJ0WvrLrZ6u4fIe18NWgcpUNt+CrLJycx+cLlIVhGhVt1S18WQKB4WPz0ukUra2t\nw/abaEwGwaeUMkRkqYjIaOvw1ZO0MQVwfjtzVptGo9FoJhp7QuyN4NzhcNjbwmdl7touXa9l1Txd\nulYsnb3CRr7MwpctFLyTMizhl8rliNZIVMgZBiEPl69RKJTq8NnzG2bdXtfci6+Towahzw9UE3zB\n4rTBtNrZ4s5uy+dznoIvl8sSCpUsd9XeolQqSTTaUNf8JxKTQfBZvAL8RkR+DiSttrHH8Dn4CfBn\nEbkf8x+pvwTuHM1MNRqNRrMPMBrRN05CMRQMki5bWs3vcOn6nGLP6fakUvA5Xbr2Chsul65IzaSM\nrJU8UuGydbp8czlzfJll0GsFjvqWMCs5oc1TOq2bfgqFfMUIw8jh87ldsoWCu83u5/dXitNsNkMw\nWI+FL0kkEh2230RjEgm+CNALnFTWPj6CTyn1TRH5PXA85if1IqXUyyOdpUaj0WgmKHW6a+shUsXC\nZy+t5oyLs0Wf69GB/Tvup+TSTdkxe5ZAyxQKRKoIvkQ2S2O4dsxa2rYAlgm5vGEQLDuv8ojrc2KL\nQVvk2YLPlrI+n59CobxGISiVw+93CzYvcWcY3nX0stk04fDwQi6ZTBCLNQ7bb6IxWQSfUuqisYwf\ntvS2iOwH7AJ+Bfwa2G21aTQajWaiMtJUzHFK3YxEIqTTaVeb3+93WfjAFE+GlbwBpiQq/90uWJtY\nsXu2S9cZw5fO5wlXqbM3lMkQG6buXDqXIxwMVlj48kblurMFZwyfK97OnaDhFbcH4PMFqlj4sp7i\nrrwtn6/sB5DLZVwu3WokkwkaGmLD9ptoTJal1URktoj8SkR2WdsvRWRWvePrcek+RMmSHgH2B9YA\ni0c+XY1Go9HsM+yFlTci4bC3S9cZw4fbumeXX1H2L7IlvpwWPtuVm1OqZOEDUvk80SoWvng6TVMk\n4l2nxGpLZjI0eFgBvbJ3C4UCftv9W7Y+iFgrgtjPS+3OkjQBlHJb+AAMI0Mg4C6XYlrz3GI1l8sS\nDFbO1XTVDh+bl0jEaWxsHrbfeFOtTMx4sa8LuRFwO3AX8DFr/0Kr7eR6Bg9r4VNKHayUOsTaDgCO\nAv40yslqNBqNZl9gLxVbi0YiJFMpV1uFhU/ELfYsS59Tmjotf86kDVeShgipfJ6GKnX2BtNpWqJR\n79fCmkcikyEWiVQczuZypWQOa3zBtfpG7dfBqxSLzxfAMCqXqs/n0/j94bK2TIWbN5/PVBV8Tpdu\ntbd+cHCA5uaW2hMfZ/a02IM9Z+ETkXYReUxE1orIoyLimeIsIqeKyJsi8paIfMXRfpSI/FlEXhaR\n50XkyGEu2aWUul0plbO2OxhBEu3wqymXoZR6CTh6pOM0Go1Gs4/xbvzaltHQ0EDKdula1w/4/eb6\nuiKmYFKKAhRFICIVLl0D8wes+Fhu4bNI2mVX7Ht13G9/KmUKPhuP16JC8FnnyJXH8IlgGIZnlm5l\nzT1nZq6vGLfn9wcpFCoFXy6XIhh0x+AZhrvUiojpunUKPrteXy6XJRyuFK3ltx2PD9DUNHILXw0D\n6T7BHnTpXgk8ppRaADxu7buw6uf9ADgVOAi4QEQOtA5/B7haKXUY8HVrvxa7ReRTIuIXkYCI/DXQ\nU+9kh3Xplq244QMOB7bWewGNRqPR7MMUV4bYw65dSwE0RKOmhc8hwAKBgCnuRIoWPtui5yrP4jid\ngeXKpeTSDYiQMQzCDsEXz2Zpsl2yZQpkdyJBZ2Oj5zGbwWSS/adPd90DIqRzOSLO+D8xl4cL2OKy\n4vbdLl23hc987veHPC18mcwAkYjbeJTNlkSgfblsNkUo5LbkZbMpwuFo1exhpzAbGOijpaXNs99w\nOM+zL4k92KMu3bOBD1nP7wSWUyn6jgLWKaU2AojIPcA5wGpgO2CbVFsZXlv9HfCfgL3S2TPAxfVO\ntp4YPnvFDYA88Fvgl/VeQKPRaDQam1hDA4lk0kxesNoCjmLMfr+/uMpGwbL02Y+GQ5S6BJ+j/l75\nUmpD2SyxchFmPd+dSNARi7nby5TKYCpFS6wykSGTyxG2BZ81Jp/PVyRylGNn8tpuXFPw2Ra+EIaR\nKZ7Svt1Mpp9w2O1qzefTBIMRl8iyxZ3zdtLpBNHo8IkY+XyOdDpFY2OTq70e4bavCbxy9qDgm6qU\n6raedwNTPfrMBDY79rdQ8pJeCawQke9iGtSO9bqIiNyglPoKcJRS6qzRTrYewfeGUuq+soufD/x8\ntBfVaDQazQRlLIkeIgSCQYJWLT57hYuAI4bPWdtOKYVyXE85s2Qxf8DyShEVIQ8ELZeuc6m0oVyO\nxjJLnP3YHY8zpcktcMrVS288TquH4Eum0zQ4XL12WZlqgs9p4XO6dCstfFnHGPMxleolEnFb3rzc\nvJmMmZzhvIVUaohotNHr1lz7fX27aW1tx+cT13Fnv31d2FWjluB74YXlvPji8qrHReQxYJrHof/j\n3FFKKRHx+mLU+rLcBlyhlPqVpav+B+8EjDNE5Ergq8B9Hsfroh7B53WBq9CCT6PRaN5bOH/tx+AC\nbmpsJJ5IELWW8Qr4/eSsIsgBv9+M1ysUKNhuXcsFWrBrAWJa9gKW0AtYQi/g85ExDFdtvSGnS9d5\nHyJsGxjg+AULat5nTzxOV3NlXFsinSbmiP/L5XKu9WptvFbBMhM7KmP4gsEI+Xy6on8qtZuGhk5X\nWy6XJBx2C9FMJkEkEnPdQiIxSCxWOf9y8dbbu4uOji7PftXGlO5x3xaCtQTf4Ycv4/DDlxX3b731\nG67jSqmqGbAi0i0i05RSO0RkOrDTo9tWYLZjfzamlQ9Mi91fWM9/Afy4yqUeBvqARhGJlx1TSqm6\nAi+rJm2IyGki8p/ATBG5SUT+09ruwL3Umkaj0WgmMmMx3dQaWyX7tSkWYzBe+t0KBoNFwef3+Uru\nXMuVq6ws3YLjHHkR08JHSfAFfT4ydpaude2KGD6H2WpLfz8zWlo83b12312Dg3R6Cb5UyhR8Vn9X\n1q6DSsFn1uGzLYIi/qJL10vwiUAyuaso+OzpZTIJl+DL53MUCgUCgZDrdpLJOLFY07Bvb7ngq0fo\nTRT2YNLGA8DfWs//FrNWcTkvAAeIyFwRCQEft8YBrBMROwbwJGCt10WUUv+slGoFHlJKNZVtdWfZ\n1LLwbQNexAwufBGK4RaDwD/VewGNRqPRTADGkrzhNdZ2xXq4gFuam12CL+QQfAG/31wqrVAwkyBE\nSjF8jnPnRAiCKfTEKsfi85EqFMy6e5ZK6Uunaa1Sa299Tw/7d3W578F+LmZm8Nbdu5nZ2VkxfmBo\niJampmJbOpMh6lG+xb36hp204XTjBjCMPCKm4DOMrBXf5ytecmhoO9Onv9/lVs1k4nR2zna4bgdp\naGiuSM6Ix/toaiq5g6uJt507d9DVNc31UkxkkedkD8bwfRu4T0QuATZi1ccTkRnArUqpM5RSeRG5\nHHgEM+z0NqXUamv8ZcB/iUgYSFn7VVFKnS0ic4ADlFJ/EJEGwK+UKrf6eVJV8CmlVgIrReQupZS2\n6Gk0Go1mZFSJ92tpbqZ/cLC4HwoGyVpJGwE7aQOzLEvB5yvW43MmbeQsC18OM3YvqxRhv5+0YRBx\n1OHrS6dpKxdiIuQNgy39/czt6KiqcAaSSfw+H02xWIUgHEgkmGVn72IKvohHgWav9XXNLF075q9U\nbFlECASi5HIp/P6S9S6R2EFT03TXOdLpQaLRpuK0U6k40WilsWdwsI+mptYK8Va+v3PndqZOnV71\nuBcTRRDuKcGnlOoF/sKjfRtwhmP/YUy3bHm/FxhBmTsRuQy4FGgH3gfMAn4IfLie8VUFn4j8XCl1\nPvCSRzq3UkotqXeSGo1Go5kAjCRGr1ryRvk5bJOSo29bSwt9AwPF/VAwSCabRSlFMBCgoBRGoYBh\nLa9mWO7dvH1+S+CF7EfblevzkcznabAsfHmlGMxkaC2vtSfCW7t2Mau11Vw2zdHuNIFt6O5mzpSy\nurbWsd7BQdodrt5kOk1DtHK9Wlcx5irLqTlLsYRCMXK5OIFArPiSDQ5uobl5pkuXplIDRKOlzN1k\nsp9YrKXCUDk4uJuWlvaKeZXjtPCNN3tbGE6ilTb+AcfiF0qptSJSd+HlWi7dL1iPZ0JFzfB3dy0e\njUaj0bw7VBNyI+lTfrxM9HW0tdHb31887Pf78VvFl23BV7D62skbBUv4FV2oQESEDBAWMV25fj9D\n+TyNlojrSSZpj0YJOGL6bFZu2cKhs2bVVCNrt21j4YwZlfchws6+PqZ0dBTb44mEaQksw6hYc1es\nsiy2S9cstmxPo7FxGvH4dqJRU3wpVWBgYBNtbXNd502leonF2otTSiR6aWpqr7idgYEe2toqkzHK\n2bFjKwcffOiw/Yp34RH2WG//d5tJJPgySqmMo55jgBHosapJG5ZJEuBzSqmNzg343BgmrNFoNJp9\nneF+occQyd/R3k5Pb6/rHJFQiHQ2S8gSfLl8noDPZ1r3MJMznBa+JNAAZCxLX7pQIFIm+HYkEkx1\numMdgu1lW/CVtTu3N7ZsYdHs2e7JW8e6e3uZ0tZWSg5JJGh0uX6tbGLDwOczBZ+zFIudmRsIhMjn\nS6VYWlvnMDCwsXipoaHtRCKthEJR1/SSyT5isVJs3tBQL42N7RW309e3qyj4yj3Xzrdvx46tTJky\nfa9b4/YEezBp493mSRH5P0CDiJyMWS3lwXoH17O02ikebafXewGNRqPRTDD2cNT+lM5Odvb0uJRJ\nJBwmlc0SCgZNwWcYBP1+06VrWflyDqthHGgSIWXV4UsaBrFAgIFcjhYrlm5zPM7slpbSvTju56m3\n3zZLsngFt1nbs2vWcLTdxzFeAZu6u9lv+vRScsjgIG12xq9I0S9mGEaxXItSZoau08JnCr5M8fKd\nnYvo6XmzeJre3jfp7Fzomh5APL6T5uYpxf2BgZ20tFR693bv3k5X14zyW6i43W3bNjFr1hzXNfYl\nxjKnSST4rgR2AauAvwceAr5W7+BaMXyfxbTkvU9EVjkONQFPj2qqGo1Go5kY1OPaHSVTu7p4ceVK\n17Wi4TCpTIZwKIRRKJAzDEKBAKlcDsOK3ysKPhEGRWgC4krh9/kI+Xz4fT76s1laQyEQ4Z3BQeY4\nRZg1NpXLsXLLFo6ZN8/buoeZMPLcW29x96JFpdfDFnfxOH6fjxY7hk+E3oEB2tvaivv2OiK5XN5V\nn88swFyK4QsGw0XBJwJTphzE228/UZxuT88bdHUd5JpCoZAnmeynsbGj2Nbf301r65QKYbdr1zZP\nwefcz+fzdHdvY+bMUtbvZGICCLm6UEoZIvJr4NdKKa+afzWpZeG7GzgLs17Mmdbzs4APKKUuHM1k\nNRqNRjPJ8FIQtfqKMLWrix073b9XsWiURCpFOBjEKBTI5vOE/H6zuLJS5JUi64jhGwBafD4Slghs\ntOL0erNZMytXhLf7+5lnu10d2/J16zhs9mxzpQwR8Pkq+vxp7VrmdHXR2dpacWzt5s3Mt1291nx2\n7t5NlzPjF1Mv5/M5/P6ShQ/Kiy1HyeVKtfemTz+UbdteKJ6mu3slU6Yc7Hqthoa6aWzsLMYGmoJv\nO21t0yq0686dW5g6dabnW2azffsW2tu7CHtkGU8GJrqFT0yuFZEeYA2wRkR6ROQaqbZIsge1YvgG\nrJi9Tyil3gGSQAGIich+Y74DjUaj0UxsvPyDdfTt6uxkd1+f67At+KLhMDnDIGcYhAMBgn4/+UKB\nnFPwidALdPh8DBUKKBFaAwEyhQIpwzDr7vl8vLF7NwdNmVIh2H796qv85fvf7xZ79qM111/+6U/8\n1bHHeorYV99+m0Pmz3f139rdzcxp09yCD0inM4RCYZSyM3b9+Hx+DMMUfKGQWYbFvsy0aUuIx7eS\nSvVYrtbnmTnzKNfL2Ne3mbY2d2zh7t1b6OqaXewjYi6rlkolaG+vdPU6b2n9+rXMn79w1Na9PWQI\nHjcmuuDDrH28FDhSKdWmlGrDzNZdygjqIg8bwyciZ4vIW8AG4EnM4oIV9WQ0Go1GMwkZi4+vih+x\ns6ODXbt3u441xmLEk0kaLMGXyecJBwLF5dKyhQIZh0t3t1K0+3zElcIAWgIBdmazdIbDiCXeXtu1\ni8VTprgseDml+M2rr3LOYYdVunJ9PvD5yBcK/OLZZ/no0qXFNuc5XnnrLZYccIBr7NbubmZMm+Z+\nzRRkMpmi5axQMDBj+PzWcwiFGshmk8VT+f0BZs8+lnfeeZL+/vXs3v0W06YtcU2hv38T7e2zXVPv\n6dlEV5fbJbt9+0ZmzJhruZGrG2HXr1/DvHnuJebqMdg6+zn36+Xdch9PAsH3N8AnlVIb7Aal1Hrg\nQutYXdSTtPGvwLHAWqXU/pgF/p4b2Vw1Go1GM668G7+WTqE22rEeTOnspKe3F6NQKCqGlsZGBhMJ\nYtEo2VyOdDZLNBQi5PdjAKlCgZTDpbtDKab7/fQVCmSBrlCILZkMs2Mx8PnYHI+TKxSYY7t0LcV0\n/8qVLJo2jQOc1rgyNfTL555jdmcnh5TH+FmK6/Hnn+dDH/iA2827fj0HvO99DgVkxvGlUimi0QbA\nLtESwO/3F1260Wgj2eyQaxoHHngu//u/X+P220/gIx/5d0KhsMvrvGvXWqZOXVCcklIFduxYz/Tp\n81wv+zvvvMl++y0Y9m18881VLFx4cEX7cKLPS88P89a7xtbbd6xMAsEXUErtKm+02mqV13NRj+DL\nKaV6AJ+I+JVSTwBH1D9PjUaj0ewR3k3RV+t4PYrA0ScUDtPS3GyWZrH6tTY30z80RGNDA6lslnyh\nQCQYJOT3E7aWW8s7ii9vU4opfj/JQoGEUkwJh3knmWSOtdzZM9u388FZsxC/3+W2/c8nn+TzJ51U\nabmznheAf/vFL7jqYx9ztdv91m7ZwlAqxWEHHlg8njcMNmzezAKH4FNiujoTiURR8BUKZk0+p4Uv\nEmkinY67BNDBB59PT8+bnHrqjRx99GcrXsbu7jeZNm1Rcb+vbyuxWCuxWGnlDYCNG1ez//4Heooy\nJ2++uYqDDiqtpVCPZc/Zt9b+SMbuKSaB4Ku12lndK6HVowz7RKQJeAq4S0R2AkP1XkCj0Wg0ewCR\n0koWNnsymMq+3ljm41ASs6ZPZ/O2bUy1MlvbmpvpHRykqaGBeCpFLBwuxvCFfT5CQEyEbcB+Ph+b\nlCLm89Hh97M9n2daOMzbiQT7NzWBz8eTW7Zw/Jw5LtH27MaNbOrtdbtzfT6wCzOL8KO3aaF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K3XzvXXj+aSS87i2mvvZvPNt1wq4UrX4p16noTUGr5M+xeqZi+X17S0hu/kk3PPD849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\nN4dZs2DkyCWfnz493X931ixYY42Bj8+W5HH4zMxq64Lc3u5k4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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig=figure(figsize=(10,10))\n", "plot_okada(strike=0, dip=10, rake=80, depth=5e3, verbose=True)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "
\n", "## Varying the strike\n", "\n", "Changing the strike simply rotates the fault and the resulting deformation. The animation below illustrates this...\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Figure files for each frame will be stored in okada_strike_plots\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "
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\n", "## Varying the dip\n", "\n", "When *dip=0* the fault plane is horizontal. If the *rake* is near 90 degrees we expect compression and uplift to the left and subsidence to the right, as illustrated above. \n", "\n", "When the *dip=90* the fault plane is vertical with the hanging-wall block to the right moving upward and the footwall block to the left moving downward. We then expect to see uplift on the right and subsidence on the left, opposite to what is seen when *dip=0*. \n", "\n", "Note how this transition takes place as the dip is changed..." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Figure files for each frame will be stored in okada_dip_plots\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "
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\n", "## Varying the rake\n", "\n", "If we fix the *dip* at 10 degrees and vary the direction of slip on the fault plane (the *rake*), we get the following patterns:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Figure files for each frame will be stored in okada_rake_plots\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "
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\n", "## Varying the depth\n", "\n", "If the fault surface is near the surface the surface deformation will be more concentrated near the fault plane than if the fault is deeper, as the next animation illustrates.\n", "\n", "Note that the grid used in this example for evaluting the seafloor deformation *dz* does not extend out far enough to capture all of the deformation, particularly for deeper faults!" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Figure files for each frame will be stored in okada_depth_plots\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "
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