hypopy-tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# hypopy tutorial, P-wave arrivals only\n",
    "\n",
    "## Outline\n",
    "\n",
    "- [Create synthetic data](#Create-synthetic-data)\n",
    "- [Initial hypocenter locations](#Initial-hypocenter-locations)\n",
    "- [Velocity data points](#Velocity-data-points)\n",
    "- [Calibration shots](#Calibration-shots)\n",
    "- [Constant velocity inversion](#Constant-velocity-inversion)\n",
    "- [Joint hypocenter-velocity inversion parameters](#Joint-hypocenter-velocity-inversion-parameters)\n",
    "- [Joint hypocenter-velocity inversion](#Joint-hypocenter-velocity-inversion)\n",
    "- [Results](#Results)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "import hypo\n",
    "from ttcrpy.rgrid import Grid3d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create first synthetic data\n",
    "\n",
    "Define limits and build grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin = 0.090\n",
    "xmax = 0.211\n",
    "ymin = 0.080\n",
    "ymax = 0.211\n",
    "zmin = 0.0\n",
    "zmax = 0.101\n",
    "\n",
    "dx = 0.005   # grid cell size, we use cubic cells here\n",
    "\n",
    "# coordinates of the nodes\n",
    "x = np.arange(xmin, xmax, dx)\n",
    "y = np.arange(ymin, ymax, dx)\n",
    "z = np.arange(zmin, zmax, dx)\n",
    "\n",
    "# coordinates of the center of the voxels\n",
    "xx = np.arange(xmin+dx/2, xmax, dx)\n",
    "yy = np.arange(ymin+dx/2, ymax, dx)\n",
    "zz = np.arange(zmin+dx/2, zmax, dx)\n",
    "\n",
    "nthreads = 4   # do calculations in parallel with 4 processes\n",
    "nthreads = 1   # there is currently an issue with relocating events in parallel\n",
    "\n",
    "g = Grid3d(x, y, z, nthreads, cell_slowness=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need some receivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcv = np.array([[0.112, 0.115, 0.013],\n",
    "                [0.111, 0.116, 0.040],\n",
    "                [0.111, 0.113, 0.090],\n",
    "                [0.151, 0.117, 0.017],\n",
    "                [0.180, 0.115, 0.016],\n",
    "                [0.113, 0.145, 0.011],\n",
    "                [0.160, 0.150, 0.017],\n",
    "                [0.185, 0.149, 0.015],\n",
    "                [0.117, 0.184, 0.011],\n",
    "                [0.155, 0.192, 0.009],\n",
    "                [0.198, 0.198, 0.010],\n",
    "                [0.198, 0.196, 0.040],\n",
    "                [0.198, 0.193, 0.090]])\n",
    "ircv = np.arange(rcv.shape[0]).reshape(-1, 1)   # vector of rcv indices\n",
    "nsta = rcv.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create 15 events randomly located in the grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nev = 15\n",
    "src = np.vstack((np.arange(nev),                                            # event ID\n",
    "                 np.linspace(0., 50., nev) + np.random.randn(nev),          # origin time\n",
    "                 0.160 + 0.005*np.random.randn(nev),                        # x\n",
    "                 0.140 + 0.005*np.random.randn(nev),                        # y\n",
    "                 0.060 + 0.010*np.random.randn(nev))).T                     # z\n",
    "\n",
    "h_true = src.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QZzKZgg5iZYB1SsnTCiRJgtvt1tRstoOKkx6KNVaVCrAEC7C1x153D8MQWrOl1ZReI5EI1tbWMDIyInek6sXIcSVJwtLSkuESb7HXKvbvzfAAUcr+KVWJSORfkqSqOoidfg3V569WcbK7KYLe+5gFWHvAAqpDKTZbWsn4iiiKWFhYgCRJFZuA6w2o1ZZ41ces5Ge1pNbnoUc0QW8HsdMXJXrOX8sUwU4jTfT9NkqxAJtKpQo6jFmANRcWUB1GudlSoxkqmYBv2bIFXV1dFX+p9PxdtSVeLdTvlUZ8otHoJkuxZnxgqEUTKp3pdCKVVGq0FiTkOmSmipNezFrU0HNC6QWrDrBKoX8WYCuDBVQHoS7xat3w1A2o57XC4TDC4TBGRkaqNgEvFcjNLPGqj6kkl8thbm4Ora2tmJiYkLtjrW7ecRJaM53JZBLhcHhTB7FZD/P1deAf/sGDpSUXJibyeNOb8vjV4S3FjPMv5zpk9cywGX0RWmgFWOWEAABZZII56einOZ8qDkTvbKmeDDWfz2N+fh5utxtbt2415Qtb7LhmlnhLHZOE+gcHBxEMBmXTay0D8cXFRYiiKD8IrS7l2blbVt1BTAF2bW0NqVRK7oKttCM2kwEeecSLRIJDd7eE5593IxZz4fd+L1eTkSazg4BZKk56qVXZvViAJdMLl8tVUCJmAVYbFlBtjtHZ0nIBNZlMYmFhAX19fejs7DT9XJVYUeJVI4oiVldXwfO8rOKk9f619srUpTw7qBPVE+VMZ1dXF9bX1wFsBA11R6zeDuL1dQ7RKIeRkY3PZGREwtwch3gcMPn220QtglGlKk56qdc+ttYeLAVY+jnzgt0MC6g2phLf0mJNSZIkYX19HbFYTHaIMRNlqdmqEq8aURSRyWSQz+cxOTlp6AutLuUpM41G9jc1Asdt+Hu2t7dX3EHs823IQYrihgzkr57HMPn206QelQG9Kk569/WtKvkaRSvACoLAzNZVsIBqU5SzpUbkA7UyVDIB9/l8mJyctOwLSgsAq0q8SijT9ng8GBwcrPr11JkGiSesr6/L5WOnqe2YjZGGnUAgAI7j0Nsr4bWvzeP4cTfcbkAUOVx3XQ4Gp7Iqot5dyloqTsqSuvK+opK6+nxFUbTlfr9WgFWbrTdjgLXfJ9XkVOtbqm5KIoeYgYEBOVhYAcdxyOVyOHfunKUlXkmSEAqFEIlEMDY2hrm5OUuOo/Y3JfGESkufTqRchqfVsJNMJhGPx7G6uirvJ155ZSt27gwgFuPQ0yNhcrJ2maOdHuLqkrpa9UoQhE0Btt6LAr2QUhOhDrAEiWc0aoBlAdVGVFLiVUMZqiRJWFlZQTKZLOoQYxY0qiIIAnbu3GnZipqaqTwej+zFWouyXinxhHA4DAAF+692KNHVA7fbrbmfGI1G4PUuYWhoo9yZzdZmjMnuwaiU6hUt3Oj+bmlpsWWmWgx1gM1ms1hdXcXAwID8bKMGJ9IhtvNnpRfnfEINjno/opp5UEEQcPbsWbS2threWzQKlXjb29vh8/ks+9Kn02nMzc2ht7cXXV1dlhxDL8Xs12iUQtnp6fTstZp7R2s/UavcaVUZ3c7d1VqoF26SJGFxcRGCIMid6XZScTIC7QXT84GSh0wmUxBgSezfqQGWBdQ6o2e21AiZTAbRaBSjo6ObTMDNRt3FG41GLTlOOBxGKBTC6Oho1fOyVqAepVB3eubzeUQiEccJTJgZkLTKncXK6GZp6to9Qy0HZXmdnZ1oaWnZVBmpVlaylqibq5QZrNpsXRRFxONxTExM1OVcq4EF1DpSTD6w0tdaXl5GIpFAd3e3pcGUSryZTMbyLl5amZs1L1sLlJlZLpfD0tISJEliAhMKtLKxYpq6ra2tFX/2Tg6oQKH0YLmmMMC+Ww+iKBY9H6UUIrBRrVNqEDuJ5vw21xm1fGC1N76ys7avr0/2S7QC5bFoP8QKyPWmu7sb3d3djn0w0kKJ3oeWwIQZgcMqanXdS80Jh0KhiiT/nJ6hAqXfQ71VnIxgRJO4VPC1Oyyg1hjaS8rn86aUeKPRKFZXV+Wyazwet2zvqBZCDcD591SJ643d0RM47PIQrCd6O4hLKRI5bQ9VCyNzqHpVnAKBQM3vLSPvwy6zt5XAAmoN0SsfqPe1FhcXkc/nCxxirOh8rWWJd3l5GblcrmLXG6dRLHDQQ9Dj8dRNYMJOAUndQSwIglweLqZI1OgZajmKqThFo9Ga31tGMtR8Pu/Y7z4LqDWg2tlSNeQQo1UONTug1qrEWytBCLujNXpC2Wsmk6m5wIRdPwePx4OOjo5NHcRKIQ76zpmtClZLzFwUKPf2gc33Fqk4BQIB0wOskTIuy1AZRTFjtlT5WpFIBOvr60U7XotJD1ZCrUq88Xgcy8vLGBoakjO1arFLIKj2PNTi9c0oMFGOYoIJc3NzWFlZQT6fL1iIOKkRrFI/VD0UM0ZQq4MVU3EygpEgyfZQGZrQCtmMEm8+n8fCwgI4jsO2bduK3nB67dtKUasSL4lPpFIpS4/TKGh1xtL+q1JbNxgMmtLlaaeSrxFIMMHj8WB0dLRkI5gT5jlrsTgsp+Kk7E6vxHnIaFOSU+U92RPMAswu8aZSKczPz+sSNai25KsUahgfH7fsy0zepcFgEBMTE7bJKJ2EsoEJKC0w0Wjm4UYo1wgG2HfcpF7oUXEyMjdstCnJqYtrZ561jRFFEdFoVO6iq7ZMotSt9fv9Zf+mmoBaqxKv0rvUqnlZJ5eNKkWry1PZuGPU6QSwT+ncTMqNm7jd7oIRnUa8BkYxMjeslfUb+T46+bvLAqpJKGdLFxYWMDU1VdUXkRxivF6vIVGDSgKqWSXecp2VkiRhbW1NVkGxSm4uFAphbW2tIEMj95N6UK/SabHGHb0CE04t+Rql2EKkHt2wTkEr69dScaIga6Tky5qSmhz1bClQ3cqerMn6+/vljjy9GG1KMrvEWyyg0gLB7/fLwvZmI4oiFhYWAADj4+MAUDC72MwPRq09MicJTNQS5UIE2PiOKD1N/X6/fK2cJCVpJaVUnEKhELLZLFwuF9rb28uW1UVRtP2+djFYQK2SYrOllbS7KzO4Sk3AjTQlmV3iLZYdV7NA0ItaWSmbzYLjuE0jKIlEQu5ibGYJQD0CE6Iowu/3N33ZU+1pWqxZpxnvo2Koy+qzs7MIBAKbVJzoHlTeX3oDqiiKOHjwIE6dOgWfz4dDhw4V6P8ePXoU999/PzweD/bt24cbbrhB/tmzzz6LP/uzP8Ojjz5q4rtmAbVilCVedeMRBTUjDyFBEDA3N4eWlpaqMjg9JV+runjVx1buAVe6QNADLQxIWYnOQf0ZeL1edHV1bRJnV2ZowWDQ9kLjVqC1r7iwsCCXPps5u1ei1axDmf7S0hLy+bxpHcSNVHKXJAltbW3ygppUnJTKVy+99BKCwSB6e3t1ff+efPJJZLNZPP7445iensZ9992Hw4cPA9hYPN9777144oknEAgEcNNNN+GKK65AX18fHnroIXz3u9+1RIWNBdQKKDdbSqt7vQ9lMgE3o0mnXEC1sotX+Vr0QHa5XJYJ21OpPRQKFSwM6DMpdR3UTRbKDG1tbc1WOqj1wO12ywuQlpaWugtM2BVlpg+gYC+ROogrdYSx416iIADr6xwCAQlGik3qxa1axUkQBJw+fRpPPPEEXnjhBUxMTOB1r3sdXvva1+JlL3uZ5nV4+umnsXfvXgDArl27cPLkSflnp0+fxvj4ODo7OwEAe/bswYkTJ3DttddifHwcX/ziF3HbbbdVcglKwgKqQSgrLTVb6nK5dO1jUqaYTqctNwEHrO/ipSBWC+9SQRAwOzsLjuMwPj5e9YNHnaEpG1OWl5cLOmSNZNpOD8R0/moRACp7MoGJQop55SYSCcMLNbtJJ66tcfj8531YXgYkCXjHOwRce20eek+x1HvxeDy4/PLLcfnll+PcuXMIBoM4ceIE/vIv/xJzc3N45JFHNn3veJ4vSEDcbjcEQYDH4wHP83KwBoBgMAie5wEAb3zjGzE3N2fgneuHBVSdGJkt1bOPSZliW1ub5XOYtRJq4DgOkUgEsVjMUu9S2pMdHBzEysqKJcdQd8gq980EQWjqAKJV9tQSmKinT6ddyqXlBOtLldLtFlAfftiD9XVgdFRCLgc88YQHO3aI2LHDfO3wsbExTE5O4vrrry/6e21tbUgkEvL/FkVRfrapf5ZIJAoCrFWwgKoDo76l5Tpt6ctkptReMWol1EAlU0mSMDk5aUmQoT3ZaDQq78murKxY/vAstm+WSCTkANKIwgB6r2sxgQllVhYMBpteYALQFqzX0tOla2mna3XmjBu9vRvPtY1iGoeVFRd27Mibehy9pe7du3fj2LFjuO666zA9PY2dO3fKP5uamsLMzAwikQhaW1tx4sQJHDhwwNTz1IIF1BKofUv1CjUUy1BFUcTS0hJyuVxNpPZqJdRAHbZutxv9/f2WBFPak3W73ZicnJS/cPV44Kj3zSiAqK3FgsGg47s+K7m+VghMGMVu2V0xipXSV1dX5emBWCxmiw7isTER8/Mc+vslCMJG2benp/yiy8iC18jvXn311Th+/DhuvPFGSJKEe+65B0eOHEEymcT+/ftx++2348CBA5AkCfv27cPAwIDu164UTrJLbcRmqEu8Rr6ci4uLaG9vL6jvU9Dp6urCli1bLP2y//d//zfa29uRyWQwMjJi6RdR6V26vr6Onp4e07vnyF2np6dn057smTNnMDY2tuk9KkeZag1lHclkEplMBoIgoK+vz3ENPIuLi+jp6TG1O1spMJFMJi0bX6IF2NjYmCmvVw9o9jUQCCCZTNZ9r3ppicOf/7kP4TAgihze9KYc3v728nuooihibm5Ong0vhSRJmJmZwSte8QqTzrq2OHv5bBHV+paqM9RIJIK1tbWaGGZns1lks1m43W7LS7zLy8vIZrOyd6nZ1nHA+YBdyl3Hbiizjnw+j9nZWeTzebmBxykCClastbUEJmh8ST12Uu31seO9YRSv14stW7bInehKub9a71UPDkr45CczWFnh0NqqLzsFjOv4OvlzYwFVQanZUiPQHiop90iSVBPDbCrxer1e9Pb21ty71Gz/RL1m43YustB9pH4oJhIJWUChmcdzSo0v0fVRBg0j8nVORx1ctNSIKNOv1aiX3w+MjRm7tkZlB538HWAB9VeY6VvqcrmQzWbx0ksvyWVKq26SeByYnQUSiVX09SUxOTmJmZkZy27MUt6lRmUPi5HL5TA7O6vLbNxpXz6tsQrleI7X65UbeOwga1fr42sJTCjlI91ut3x9SglMOP3BDJR/Dy6Xq2Cvmq4VLazt4jZkJEPN5/O2rtqUgwVU6Jst1YskSUgmk0ilUpiYmLBsdAQAZmaAu+7isLaWgsu1Bddc04/3vU8yxRNVjR7vUjO+sCRyobcD2ooys5mUuyZut1vu+lTuL66trdVdHtEO11V5fYDNXbHFBCYaIaAaNRcvdq3C4fCmDuJaLtaMiNzYUczCCE0dUM32Lc3n85ifn4cgCOjp6bE0mALAX/xFHtFoBtu2BeB2e/D//h+H1742j85Oc4MMiSi0traWnJmtJriRjjHP84ZFLuzw4DeDcgL2dpjvrDfFumJXVlYK5oM9Ho/jA2q1iwL1tVIv1mqldmXUXNzJ93XTBlQzS7zAebGBvr4++ea1ChJqmJnpxuhoG7zejXN3uYD1daCry7yASt6lAwMDZQejKw2o+Xwec3Nz8Pv9mJycNPRZWBHc7YKWgH0ymZTnO2tR0rNzUCo1HxwKhZDL5bC6uurYBYiZ2VqpZjCr1a6MvA8WUB2IIAiGZ0uLIUkS1tfXEYvFZLGBWCxm2cNcKdSwZ08Av/iFCyMjErJZQBSBsTFz9jIr9S41+r5TqRTm5+fR19cn624axemBUy/qPTOa71SW9JT7r82GcgHS1taGcDiMQCBQkeyfHbCybK1uBrNS7cqoubiTlceaKqBWM1uqBXl8+ny+TWIDZjTnqFErLP3P/yniz/6Mw0svcXC5gN/7vTx27JAwO1tddkYZo8/nM+R8YzQrDIfDCIVCGBsbg9/vr+hc7f5QBKwL+MXkEc3KOJy8UKGsqJhxuB0bwNTUcj9RrXal7LZeX18v6LY2qgbGSr4NiFH5wHKUKoWa3RRUTIu3uxs4dCiPaBQIBDZa2qs9PmWMlZqb6zmuKIqyZZoZTjRaxyQ/1Hqry9QKdflTObMYDocBQH5gGhk/sVuQMYL63NULEDs1gGlRz8YqPd3WercbjM6hsgzVxqjlA814eK+uriKRSBQthZqZoZbT4uU4QG3oUsn+odK7tNKMUc9x6f10dnaaohil/nsqwUciEfnfKgkkTqfYeA49EBvd37TcfWhEYKJaX9NKMdrlayVaHcSpVEqWk/R6vUXvJ6VofTmM/K4dce6Z64BWofl83pSsNJfLYW5uDq2trSWbZ8zKUCsV0TcaUM3yLi13fWmG1UxtYeV7pS5rj8eD8fFxSJIkN/KoA0kwGLRlmc8qtB6IiUQC6+vrRbMzp5d8jTa3qQUmlA1OAAr2X2tRlrTzCInX64XX692U7dP9pOwgNjo2wzJUG1KtfKAaCgZ6TMCrzVCrtVszcnwzvUuLHVfPDGu1kFYyCWnQXLE6kNA+I5X5lDJ3VnyR7RqwyTxcnZ1RKT4QCCCfzzu2SaTacmm5DF9pgGBVhu+UWdpyHcSZTAaZTEZudCr1/Wd7qDbD7NlSksAzEtyqyVDNsFvTm6GGw2Gsr6+b5l2qdVxBEDA3N4dAIGCJ7yvHceB5HrFYTJdWstacp5YNWzOVh4vJ//E8L1cunNQdS5h5npUKTFSDUwKqGvX9tLS0hJaWFmSzWUQiEXnBplVO17uAE0URBw8exKlTp+Dz+XDo0CFMTEzIPz969Cjuv/9+eDwe7Nu3DzfccANyuRzuuOMOzM/PI5vN4v3vfz/e8IY3mPreGyqgSpKEeDwOnudN2Z8rpllbDpfLVVGGapZParmAqm4KMisDUR+XZnP1zLBWAqlSAajofSjHLIDi+4zNVh6mhhSv14uRkRG5bE7dsUrFHTOdaMzE6nK1XoGJaiofTg2oaigz9fv98jy1Uq8ZAM6dOwcAGBsb03W9nnzySWSzWTz++OOYnp7Gfffdh8OHDwPYWOzce++9eOKJJxAIBHDTTTfhiiuuwA9/+EN0dXXhM5/5DMLhMN7+9rezgFoMajyih2JPT09Vr0cuJ5Xs9xkt+VZb4lVTKkOm0mh3dze6u7tN/cJSQJUkCeFwGOFwWJ7NNRvKfDmOQ19fnymLgnLl4ZaWFnnMwollUKPQPaTVHZtIJLC6umpa8DCbWgajYgIT1c502qkpqRrUe8FaHcQrKyv4x3/8R5w8eRL9/f249NJLcckll+DCCy/UvKeefvpp7N27FwCwa9cunDx5Uv7Z6dOnMT4+Ls+179mzBydOnMA111yDN77xjfLvWXGvOj6gqku8Ho+nqtUpmYALglBx9makKciMEq/e4yu9S62ykRNFEfPz8+A4zpSRGC1otGdgYACJRML01yeKyQA2U3l4c1f5+f0ytTqR+rrUqnmnGPX6TMopXOktoTdKhlpuYeB2u3HxxRfj4osvxvz8PAKBAJ599ll885vfxOnTp/HVr3510wgfz/MFvSxutxuCIMDj8YDn+YKKWDAYBM/zcgDneR4f+MAH8MEPftDcNwqHB1St2dJKy63AeSPrarM3vX9XyrmlGtQBVZIkLC0tFXiXWoEgCIhEIhgYGEB3d7clx1CLQVDJV40Ve7XKh2QzlIf1LAq1yua0/1qr5h0t7BSM9AhMaI2c2LnL1whGpQcHBwcxMTGBt7zlLUV/r62trWAxrRy3Uf8skUjIAXZxcRG33norbr75Zrz5zW+u5O2UxJEBVT1bquziraTDVpIkRCIRUxt0yh1veXkZ6XTakq5XjuOQz+cBFGbARvaBjRKLxbC2toZgMGhJMC0lBlGP8Q4j5eFmwu12FwSPWjTvaGHnkZ9iAhPqEaZGKfkaVUrSs+DfvXs3jh07huuuuw7T09PYuXOn/LOpqSnMzMwgEomgtbUVJ06cwIEDB7C2toZbbrkFd955Jy655JKK308pHBdQy82WGs1QaQaT4zhs27bN8hWhMsBZ0fUKbFwDQRAsy4CVKPd/BwcHLSnBkj+qlhiEXR44pcrD2WwWa2trDV8e1kLdvKMWZFeOLZn53XNKdldq5CSbzWJ2drbuAhNmoPee1/u5XX311Th+/DhuvPFGSJKEe+65B0eOHEEymcT+/ftx++2348CBA5AkCfv27cPAwAAOHTqEWCyGL3/5y/jyl78MAHjooYdMTaA4yc5LORV6Z0tffPFFbN++vezr0V6cGTOYes6hFgEO2NgrJf3NsbExy5RHSOgiGAyir69PVk4ZHh427Rjl/FFXV1fh8/k2CesLgiAvuurN2bNn0dPTg2QyiXQ67Zjy8MzMTMEogtkoxRNSqVSBnmy14znhcBhut9uwfKadmJmZwdjYmLw4o+0Nu+xR68XIfXT27FlcdNFFtv1OlMMRGaqyxGvGbKkZMntGj2dliVeJIAhYXV2Fy+UybIVmBNIyVgpdVCJ5WAyli085t5tix7TLPhrHcax7WINi4gnqvcVKFh52+eyrRa/AhJUWfrXCQbldUWwfUGnGyyxRe3KI8Xq9lnWhAueDC2VxVpZ4CQpynZ2dlmVnpQKdWQFVKSGodPHRwokPENY9rI1yX7pa8fpGeDhrUUxgQmnhZ/cZ4VKY8YyvJ7YOqJSVmiUfSEIDlTipGIXjOESjUaytrVle4lV6l46PjyOXyyEajZp+HD2BrtoHmVpCUA9Ofng2Y/ewHrT2Fmnhsbi4WHa2s1Ey1HIUE5hQzwiXk/yzCiOfg5O/x4QtA2q18oGUKSnbz5UBx+qVG62uw+Gw5SVeLe/SfD5v+s2pR/O32gyVlKKMzMk22kOTiUtoU262U136pL9pJqwSmKgGIx2+TmkkK4XtAqoZvqXU6UvDvnNzc2hpaTFkll0p1MXLcRyGh4ctDabUVNXX11fQlGPmXiYARCIRrK2tlR0pqvS4yk7hSuZkG2FlWwxWHtam2GwnlT7Jacjn81k6nmNntBYhJPlnRGCiGoyI3efzeRZQzaLUbKlRKKCmUiksLS1ZpiWrRtnFSxqVVlCuqcqsgGpUNaqSz4sWPK2trRUpRTVTENEqD6dSqaYvDwObZzuXlpbkhVo+n7elPGIprFgkqiX/9ApMVINRUQcWUE1AXeI144NcWVlBNpst2yFqBlpdvOFw2DSTcSV6vEvNCKg0+9nR0YGhoSFdn4nR4yolBCtd8FQi5NEoqEUUWHl4A9om6ujoQCAQkMdzKIMF7G86X4s94HICE2aIcLCSb40x27eUHiqdnZ2Wjo0oj6fVxWuWybgSkkbcsmVLSTWiaoNMudnPUsfV+57VEoLVoD4mifNns1m0tbU5ynKsGlh5uBDld7HU6All9mZmZtVS6+BSSmBCLcJhRGDCyPtgJd8qMHu2FDjf1NLa2orOzk7LvxilhBrMzpz07mPSsSvdy1xdXUUikagos9dz3FISgpWg/oxFUcTc3Bzcbjf8fj9isViB5RiVQxudZi8Pl7oPtUZPEonEJuk/PeM5VlHvLmWO2+yRSws02s7SIzBhpIzLMtQqMavES3t9uVwOk5OTWFtbs7QMqEeowawMtRLv0kqOTd3Cfr+/4sy+XEAtJSFYDXRMGrnZsmULOjo6kMvlNlmOkV+lVZJ3dqXZysNGApLX60VXV9emzGxpaUnOzILBYM06Y4H6B1Q1Wlm+lgmCWmDCCh1fO1O3gEpZqVlzi52dnfJenxXlVkKvFq8ZGSq9t66uLkMBqNK9TDPmc4sdt9IycjnomlC1gEZuyByAfkdpOaaUvAuFQuA4Tg4mTleb0Yue8rAoirZ7sBuh0kWhOjOrdWcsYL+AqqaYCYJaYIIF1BpSbfMMlUHVc4vVWLiVwogWb7XnUMlMJlGvvUytLw4pK8XjccsaxHieB8/zumd+1att9ciF3++XA2y9Sn61pFh5OBKJ4Ny5c44sD5sVkMp1xlqlTOQ0p5liAhOxWEzukylXRqcxJyfjyKeFKIpYWFiAJEmaZVCzA2olWryVZsm18i6lUjJdQytKWaSs5PV6LWkQy+fzWFtbAwBs27at4tdXdztSyU+pyEMlPyc95CqFsg+v14uJiQlHloetqlBpdcYmEolNykTVXhu7Z6ilUApMSJIEt9sNn88nf6dEUSxokKNnj949VFEUcfDgQZw6dQo+nw+HDh0qEN8/evQo7r//fng8Huzbtw833HAD8vk8/uRP/gRnzpyB2+3Gvffei/HxcdPfu+MCqrLTtaurS/OmI7UgM6jUbq2Sc6A9Rqu9S8kWqloj9VJUIiFYyesHg0FT9T+1Sn7NLgXoxO7hWgQk9VYCXZtEIlGgTBQMBg07wzRCgw4AOaAWE5hYX1/Hs88+izNnzuCiiy7Ca1/72rKv+eSTTyKbzeLxxx/H9PQ07rvvPhw+fBjAxjP03nvvxRNPPIFAIICbbroJV1xxBZ599lkAwGOPPYYf//jHuPfee+W/MZO6l3z1QqMQ4XAYIyMjJTtdXS6XLBBRDdXYrZEnaS2OZQQ6zvDwsGXm19WUq/Wg3C8VBMESD1ZCrchTTCvVztmamTipe7jWx1ZeG6B4404wGCw7nuPkDFWJVulaXUbv7OyEKIr4l3/5F3zlK1/B2NgYLrnkErzuda/D9u3bN/39008/jb179wIAdu3ahZMnT8o/O336NMbHx2XluD179uDEiRO49tpr8frXvx4AsLCwgN7eXkveryMyVCodut1uXeXJaku+Ztit6W1KkiQJKysrSCaTlur+0nFSqZRlx1FqGFtRrqaxHuW14nne1GOUQ52t0Uo7HA6D48zz83QKdu0etkNAKta4EwqF5L36YsIJdjh/M9AzNtPZ2Ym3vvWteM1rXoOBgQFEo1H8+7//Ox544AF89KMfxeDgYMHv8zwvX1MAssQsPQ+UIjHBYFB+Rng8HnzsYx/Dv/7rv+Iv/uIvTHyX57F9QCWHGLVebSmqCaiVlni1zqHcPg7J7gUCAUtFKJTHscpCjo7BcVxFEoLloEWVz+fb9B7qpeWrDKB0jolEQm5YoQdmI2sNq7FLediO11zduFNMOMFod6ydqUR6cGxsDPv378f+/fs1f6+tra2gKiWKopwgqH+WSCQKAuynPvUp/NEf/RFuuOEG/OM//qPpVTrblnyVvptGHWIqDahmll3LZajkXWq1zjAtSKw8jlJCcHl52fQHAe2X9vb2blpUlRpbqvVD1e12FzSsZLNZJBIJ5HI5zMzMaDZiNDL1LA/bPcMrJpxAo1z5fB5erxeBQMDR1Q4rxmZ2796NY8eO4brrrsP09DR27twp/2xqagozMzOIRCJobW3FiRMncODAAfz93/89lpeX8fu///vyYs6KioktM1QyAff5fGUNprUw2mFrRolX7zlUs1Awyvr6OiKRiKXHUY/dLC8vm/r6tB9bTCGqVOCs9/4ddTrG43GMjY0hlUoV2I1RKdQucndWU+vysJOuqXqUKxQKyb7GSuF6O+xNG8EKpaSrr74ax48fx4033ghJknDPPffgyJEjSCaT2L9/P26//XYcOHAAkiRh3759GBgYwG/91m/h4x//OH7nd34HgiDgjjvuqHpMUAvbBVQzMjcjGapZJV41Whmq0ru0koWCXsgCL5VKWTYSY7aEoJpa7PnWEnUjhlqI3AmjKGZjl/KwHaHsXi1cr1x81FseUQ9GS756RxLvuuuugn+bmpqS//vKK6/ElVdeWfDz1tZWfOELX9B1HtVgm5JvtTqySvQGVCs7a9UZajHvUrOh8qjb7cbQ0JAlwZQWIcUkBKsttyllEMstcupR2jUD9X6aVjChcQuO45BIAD/5iRvZLLBrl4iBAee951I4qXu4Fii/Q1rC9XS/KGel7bidwNxm6kAul5M9Mc1ozikXUK0o8aqhDFU57mOGGlEpotEoVldXMTIyguXlZUsCTTkJQQpwlX6G6XQac3NzhhYeTgyoSrSCCanMLC8vI5v14667RrCw4IXL5UIgIOFzn0tj+3Znv+9S2LV7uFaUCkTq+4VmpZXbCVq6uvWi3sevJXUPqJQlDg4OFrRCV0OprCWbzeLkyUX85V8O4fTpVmzbJuGP/ziPkRFTDi1DQX1ubq6kd6kZaKkrme12o1dCsJovT7n9UrOPZ1eUbiiSJOGb3+Rw7pwH/f0pSJKESMSLL32Jw5//ufPtrvTSbOVhI4tS9ay0WkrT5/PJiw+7Oi05fVFM1DWgptNphEKhmu2RxeNxLCws4957d+Cllzzo6AB+8hMO73sfhyeeEGCmBkE2m0UqlZLViKyCsvu2trYCdSUzS6FGJAQrOS7tl6bT6YrmVxvly6gFx3GIxbxoadlQm5EkQBBErK5uqGrR3qzZ2Yidr6me8rAgCMhms44tD1dT5VHLI1J2T05LdhUjMVPxrF7UNaAGAgFLZhbVKEu8gcBWnD3rQW8vwHGA3w+srXF48UUOr3iFOQ8REu33er2WBlNq4NLK7s0KqEYlBI0el/ZLW1paKroXnP4F1MOePXn87d96kUpJ8HiAeNyNt75VxMTERNML+wPaAgqzs7OOLg+btZ+o7DYneUSlGAkAW2T3dl7AGaHu3zirP0B1F28oxEGSAFEE3O6N/5/Pw5TsVN35eubMmepfVAPl6E2x8qsZFnaVSAgaCai0X1qtbZzW8ajT2co961rxG78h4iMfyeChh3xIJIC3v13Au961IWtppbC/UxcrXq8Xbrcbw8PDji0PWzVHqyVGoqVVbcY4l5Hnj5HxGjtT94BqJRQQlA00PT3ADTeIeOwxF+jzvvpqEVNT1QUfEpw36l1qFGX5devWrZYIG1BGn8lkLCvBUgOVkf1SLbTeP30WZFBQD4Nos3nTm/J405tSJX/HTGH/RskYnNo9XCthCuV+PQDZPYfGuaqpeBgJkiygmoBVN4wkSRAEoej+7Ec/mserXiXihRc4TE4C11wjoppTocBtpeA8YKwDttKmJJIQbG1trbgEW+phXG2wLvaaBHUhDw4Owuv1FpS4mk1QgQn7b0arPEzdsXYqD9dLetDr9aKrq0tu/qKKx9LS0iZ5xHIBsBLZQadT9wzV7DlCatJxuVwYGRnRXFVxHHD11RKuvrq64yq7a61urIpEIlhfX9ed0VVyXZUSgpWKapQ6brXButjxgI3PIhQKIRqNYmJiAm63G7lcrqyggvIB0ehBRY+wfzAYlMvkTl1sGLnv1QHELuVhO0gnalU86J4JhUJlzSCMyg6ygGozlEIN6+vrlpat9HqXVvvFEEVRXh1OTk7qfugbDahqCcFKKfZezdov1TqeKIqYn58HAFmBqlh2riWokEgkNINKvR9oVqLeS6PmpkgkgnQ6DZ/PB0EQkMvlbDtqUYpKPju95eFaVDfsKHKgXpzSPUPyiD6fr8A9h2WoDkVLqCEUCpk6h6mknLgBUa3IATVUdXR0oKenx9Dr6G1KMltCUCuQR6NRrK2tWSJsQRKLHR0dm/auy1175QMU2Dy/10wds+rmplQqhaWlJdkJxUnC/mZld8XKw7WQi7RDhloO9T1D14e2FDweDyRJQj6fL3t9WEC1CcpMUSlTV60nqhZG5RGr6bTVG7SLoSdDLSchWAnK49JCh0riZj90ksmkbKLQ09OjeS5G0OqYpdEkLTnARoVGLXw+H0ZHR+VSn1OE/a2qTNWyPOy0pjClPCKN54TDYfA8j4WFhYKO85aWlk3BU5KkhthyqXtArWYPtZQWr9kBtRLv0kpFDihoV7MvW+7Y1QbscsdV7peOjY2Z/tBVlqgXFhY0z6MalPtHWnKATlCfMQunCfvXIrurRXnYbgsVI1CADQaDBdeH53msrq4WyCP6fD7d1m2iKOLgwYM4deoUfD4fDh06hImJCfnnR48exf333w+Px4N9+/bhhhtukH+2vr6Od7zjHXj44YcLxPTNpO4BtRL0aPGaGVArdcAxeg5KUfhqNY2LdfnqlRCs5rjpdNoyr1dJkrC4uIh8Po+tW7ea+tqlUMsBUsesE0uieii1GDMq7F8Pan1cPeVhui52WHTUAmUZV+v6JJNJPPXUU/jyl7+M8fFxXHrppbjuuuswODhY9DWffPJJZLNZPP7445iensZ9992Hw4cPy69577334oknnkAgEMBNN92EK664An19fcjlcrjzzjurGtHTg+MCarESrxozhA2q9S41MrpCHbZmNe1oHduIhGClpNNpWXDC7P1SQRAwOzuLtrY2DA0NydlwrctjavUZdUlUPdvYyBQT9lc3qtTyWtihXFqsPEzlT2V3bCMswLQo1eVLi7KrrroKV1xxBX7yk5/gl7/8Jf74j/8Y4XAYu3fvxvve9z709/cX/N3TTz+NvXv3AgB27dqFkydPyj87ffo0xsfH5ZHCPXv24MSJE7j22mvxqU99CjfeeCO+8pWvWPRuN6h7QDXyUDdit1ZthmqGd6neoB4KhUx3o3G5XBAEQf7fRiUEjaIcIerv7zc9mJYa6an3A1SrJKpszsjlcuB5Xtfsnp2oZMGlzuTpWlAmb2yOEfjWtzx4/HEP/H7gf/2vHC67LF/2HOzW0GOn7uFaondf1O12Y2pqCnv27EFPTw8ymQymp6c1Exie5wtkVt1ut9wAxfN8wbMhGAyC53l85zvfwZYtW7B3797GD6h6qMRurZqAapZ3abkMVRRFef/PbDca5R5qJS4uRlBmjp2dnaY/EEhVSWvBUU7txw7D8WfPnkUqlZJn9yj42vnhacYiRd2oIoqiPKa0vr5e1mbsiSc8uOceH1paNuRB//f/9uMrX0ljz57S32u7BVQ1esrD+XxeV3eslaTTwNISB58PGBqSDIvf6DUMBwqDr9/vx2te8xrN32tra0MikdA8hvpniUQC7e3tePTRR8FxHP7jP/4Dv/zlL/Gxj30Mhw8fRl9fn7E3pAPbB1Slm0o5s2kl6gxND2Z7l5bKUEkej9xozH4AUDBfWloyVZVIjTpzXF1dNe21q1VVssNDleM4uFwu+ctLozmhUAiZTMZ2DT1W4nK5Ns2+0gyw1pjSt7+9kZmS+FgoxOF73/Ngz55s2WPZ4bPXi1Z5OJFI1LU8vL7O4aGHvIhGAVHksHt3Hu98pwAjh7dCenD37t04duwYrrvuOkxPT2Pnzp3yz6ampjAzM4NIJILW1lacOHECBw4cwDXXXCP/zrve9S4cPHjQkmAK2CCglrrxjZR4tV7XSIZK4gBmepcWO4dKROeNIooiotEouru7LXP00RKDMEv5qloXGrtSbDTHTg09hNXH93g8Bc1N6jEljpuAIPgBcAA4iCIQCJS/t+q9BVANtD/v9XoxNjZWt/Lwd7/rQTIJjI5KEEUJP/2pCxde6MJFF+l/phoRdtBbHr766qtx/Phx3HjjjZAkCffccw+OHDmCZDKJ/fv34/bbb8eBAwcgSRL27duHgYEB3edrBnUPqFpUUuJVY6Tkm06nMT8/jy1btphqt6bOUMn3M5VKWSpVmEqlsLy8DL/fv2lT3wxov1QQhE2LDzMCqhHNYidTbDSHGnr8fr8cYBtdWELrWvz+76fxwQ/6sLwsQpI4dHQAb3tbCpLktmWp3yyU51+v7uHlZQ6dnRvfY5cL8HiAaNTYNTUqPajn/F0uF+66666Cf1OOwFx55ZW48sori/79o48+qut8KsV239JKS7xq9AZU8i61Yn9RmaHSPmMwGKzqfZWDssbBwUHE43HTX5/eRzHJxWoDqtX7vXZGazQnkUjI0pP04KyFrmy9szy3243Xv96NRx7J4fvfd8PjEfDGN0bR0hLDuXOCXCoPBAKbHsSNEFCLZXa16h6emsrjxAk3xscl5HJAPs9hcNBYT4rRkm8jbHnUPaAqb/xqSrxqygVU2l+kLMuKD5MyVLrhrZjLJNQSgtls1vSHIr0PLUNzolKXGxK0SCaTlhsNOAHlaE4xKzalWpFV51Bvfv3XRfz6r9P91A6gvUDYXynS3gjC/oD+BYGV3cNvelMe8bgLL7zgAsdJeMtbBMMWl0ZLvk7qgC+GLZ5aZpR41ehpCOrq6pLnGa0iHo8jEolUNMeqFy0JQbNdfCjz1fM+jB5XOR9rZfbuZMpZsRkZR3E65YT93e6NkjCNUziNSjNsM8vDra3ALbfkkEgAXi9QyaPLSMmXBVSTyOVyOHv2rOml0GIZaq28S/P5vNxoYvZIjJJiEoKVZopqlG43et6H0UBOixuz968bHaUVW7FxlGpGc+pd8jWCutErEonI3wsnqliZVbIuVx4upa1LVFMo1FvytfO99pOf/ARf/OIX5f+9tLSErq4ufOtb39L8/boHVJfLhf7+flP1ZOl1lQFFOYJhdUmRRBQoW7DiS1xOQtCMDJVUqYy43Rg5Lj30rOh2DoVCWFtbkzO3RpZ8U4+j2F1r10o4jpNLnT09PY4T9gesMRevl7iE3tehqprdePWrXy03Mq2treHmm2/G7bffXvT36x5QaS/IbJQZmlKu0OoRDBIhGB0dhSAI4Hne9GPokRCsVnpRz36pFnqurVLS0Ww9YaXW7/j4uFz2mp+f37TXZscvsBmY5fnq5OujdJ3SUrGy82KjFuVPveXhZrAu1EMul8MHPvABHDhwAHv27Cn6ew17pegLZZWrihoaJcnlcnKTUzKZNN1CTq+EYDUZKkkhVqpfXOq4pA7FcVzFko7FUCo2DQ4OIpfLbRrFoJnPdDotCwk0cvaqzEwAFL0GjfTgLCfsryWiEA6HAcAWc8D16FIuVh6mmWA95eFKcUJX9t13343t27dj//79JX+vMb5BGpCF2OrqqiWuKkqUoz7KURKz9jEJIyMllQRU6hSuZt+31HGpUtDV1YUtW7YYfu1S0OwqmQtonYPb7dYUVaA9JQosdhBVsAqta6B8cFJW4uT3X0mXLABbCPsD9Q8w5crDZpfNrShxm8m3v/1tnDp1Co888kjZ3617QLXiQpIXpyRJlrmqEKTsolUaNcPxBqhMgs9oQKVgV63ZeLHjUgnZikoBqSoZmV3VI6rQaJmbGuU12LJlS8GDM5lMyg0+TnPNqaZLtpRFX606qesdUNVUUh428uwxMq9aa37+85/jwQcfxN/8zd/o+g7Y4klh5oiHcuZzeXnZshtTkiSsra2B5/miGbAZ70tp1G3V/q+ZwU7rPVdTQi4FfQb5fB5TU1NVBT71w1Qtg2eHUqDVKB+cSn9TZUAhMQW7PgCJaj+jYhZ91OxlRid1MeyesekpDxsRH7FzQP3c5z4HSZLwoQ99SP631tZWPPjgg5q/b4uAagZa3qUrKyuWrPb0+opWayFXyrLMDMgMwOw5WQqoRkdujEDayx6PB16v19QskmWvG9fA7XbL5g3FumUpe7VTALDiO69ubiJhf6XJgVlNPHbLUEtRrDzM87zc71GuPGxnlaS/+qu/MvT7DfE0oADn8XgKGl0ooJn5YRnRma1mD1VLeN5MlPulZjYHUYaqlCjUO3Kjl1wuh3PnzqG7uxtbtmzBiy++aOlDqJgkYDNlr8W6ZdfW1uTmr2JSgLWmFgGpnLC/cvbV6LnoFYq3I1Tl8Pl8EEURfX19ZcvDejNUURRx8OBBnDp1Cj6fD4cOHcLExIT886NHj+L++++Hx+PBvn37cMMNNwAA3va2t8kJyejoKO69914L3vkGtgio1ZRGS3mXmh1QjQa5SvZQ1RKCVpRCzNov1YIUas6ePWtJZq1Vnq5lEFNLAmo1slDgcXr2Wuq6qst+ailAykrqNZ5U63tCXdFQz3gakYl0UoZaDBr9KVUeXlpawr/9279h165duOiii8q+5pNPPolsNovHH38c09PTuO+++3D48GEAG8+0e++9F0888QQCgQBuuukmXHHFFejo6ABgvSg+4dhvvB7v0mpLroSydDk5Oak7QFeiGqSWEDQbWkVbNUYUj8eRSqWwbds20zPrUvKHWg+hWjyU9GSvoig67iFp5L4tJgVIPqdWOaIUo97XWt3Eo5aJLJfN1/v8zUBrH1hdHu7v70c4HMaPfvQj/OVf/iWGh4dx6aWX4rLLLsPU1NSmv3/66aexd+9eAMCuXbtw8uRJ+WenT5/G+Pi4nFTt2bMHJ06cwPDwMFKpFG655RYIgoAPf/jD2LVrl2Xv25EBlWYZAZTM4swYW6lFkAOsn5eVJAmhUAjRaNSSMSKlHjNlJma+tnLGV/152+XhU0zQPhKJYGZmRs5eW1tbHdU1a5Rinq9KRxQrS+R2k7JTykSWE/anZ5Zd7ulK0SNO4ff78Vu/9Vv4jd/4DXg8HkiShH//93/HF7/4RbzrXe/CxRdfXPD7PM8XTFK43W5Zr5nn+YJqWDAYBM/zaGlpwYEDB/DOd74TZ8+exf/4H/8D3//+9y2rHjkuoBrxLq02QyX3Gyt1f8tJCJrx+pIkWSamAGzsYc/OzqK1tRXDw8NYXFw0eI7AT3/KYWGBw6/9moSXvez8A1H52lp2cedfw14PUeC8oD2ZRdO+I3XN1tKOrRLMOKdyDV5WLDLsnOGVE/b3+/1yFutkKrFuGxgYwDvf+U68853v1Py9trY2JBKJgr+jwKj+WSKRQHt7O7Zu3SprxG/duhVdXV1YXV3F0NBQFe+uOLYIqHpvfqPepZUGVLISSyQSlur+6u0WrhSXy4VsNov5+XlLxBSAzYIKgiAYDm5/+qdufOMbbnDcRnD91KcE3HCDiEwmg9nZWfm1i1EqyNrlwVose43H41hZWambiEAxrFqgFCuRqxcZ1Sry2OVzL4c6m89ms1heXkYoFEIoFHKcsD9hhdPM7t27cezYMVx33XWYnp7Gzp075Z9NTU1hZmYGkUgEra2tOHHiBA4cOIAnnngCL7zwAg4ePIjl5WXwPI++vr6K31c5bBFQy1Gpd2klAZXmPgOBgKWiEHolBKtBFEWcO3cOIyMjlmTYWspNRveNT57k8I1vuOHzAS4XkMsBt9/uwRVXrCMaXda9eLJjhloKpR2bJEmOy17NoNgig+f5qjxfnXYvEHQ9fD4furu74fV6kUwmHSXsTxjRI9bbOHr11Vfj+PHjuPHGGyFJEu655x4cOXIEyWQS+/fvx+23344DBw5AkiTs27cPAwMDuP766/Hxj38cN910EziOwz333GNps6DtA2o13qVGAyp1DJfLiIyizpSMSAhWejxqU9+6davpTi6UwSeTSc0FjpEH2vIy4PFIcLk2ro/XKyGZlHD2bBivepW+6oCdHyx64DhO3mejmU8tM/FaZ6+1vq7lPF+VQvalHtZ2qkxUAp2/+no4QdifMDJdoTf4ulwu3HXXXQX/NjU1Jf/3lVdeiSuvvLLg5z6fD5/97Gd1nYcZ2CKgFrv5q/Uu1Tu2oqdjuFIoY6P/b1RC0CjUsEWWXmYfg8rUPp9P07/WaIb6a7+28buZDODzSeB5EX19ebzqVWPwePRbP9k5KzH6cNeTvVpdBrTD9azU89XpAbVYudQJwv6Ecn9Tz+/abUFQKbYIqGrM8i51uVwQBKHk7+jtGK4UCuq1kBCkbJ4ED86dO2fqg1FPmdpocBseBr76VQH/8396EI2KGBsT8Y1vcDD6kRc7ptMfrsWyVyoD1it7rTVqz1ctpSLK1gBnVy303LN2FfYnjJZ8nbQ/XArbBVRybgkGg1UHnnIlXwoQFICsgOM4WWDbKglB4PzYjTKbN0ucHzjf8VzODLySz+s3fiOB7353AZ2dg+jv1++9WuqYHMfZPnOtBK2yqDJ7NVNv185BSa1UpMzWcrkcOI5DR0eHLbI1o1SyCLSLsD9htCmJZagmQheegoJRU+tilAqoVE4uFyCqRRAELC8vWyYhWGrsxoyAonx9KzqeI5EI1tfXMTFRnZaw+n2m02ksLCzIYxlO65LUizp7VertVtrU4zTU2RptSSi1lylbc4J6lZHsTot6CvsTlYzNNAK2uLskScLKygoSiYSps5hawg5mlZPLQRKC+XzeUj3e+fl5uN1uTExMaAoeVBNQla9vdsez+nOo5gulPi/Kpvv7+2VD7XqXR2tRelbr7aqbeoxkr07P7Nva2tDd3a2pXqW8DnbMXs2+9rUU9ieMLAqqXUDYCVsE1HQ6bYl3qTpDJcF2M8rJpVCqK1n1IKX90lICF9UoRan3Y82E/EtbWlpM+Rxo4aDOpknyTx1gVlZWIAhCw4+mqJt6irnFFMtenXxN6NxLzf/Ws4O6HFZeeyuF/Qkr5lCdgC0CqlU2WMqASqLqZpWTi6GWECSheyuOoWc/s5LVLn25rFCIon3r3t7esm49RlA2l1G2ns1mC35HK8DQzKPX67WVsILZFHOLUWavtdxjs5JS93yxDmpaZDnJ89UMzBb2J/SWfJ1eCVFji4BqFRRQ19fXEY1GTTe4VlJsL9PMxhgy1OZ5Xle5upJjh0IhRCIRS2QQ9S4EjELSil1dXbqt4pQBxmphBTtmesoRDK2RFPo/J3ZJ6z1nrQ5q2mt0mpCCWegV9tczC6x3QUINhI1AQwdUYEOswefzWaJhS5SSEDRDoB/YWPHNzc0Zkik00uVrlT8qUCjMb/a+dTqdluXEent7K3qNUsIKJAvYKJZsWqhHUnK5HNbX15FMJnHu3DndD1E7UckDWiuLp0aebDZbkMU3ShNNOYwK+xN6FzUsQ3UIpDHrcrkwPDxs2XHKzWaaMbpCx9BjCKBEb4ZK/qgdHR2mm4FToAZgeqCm5qO2tjZTS9PqsmCzGYpT+dvv96Orq0vee6Xs1e5Zm1lZtdfrLTqaUyqYNCrlbPqU3dR6cWIFpBS2CKhmX9BoNIrV1VWMjo5ibm7O1NdWokdC0CzHm0rKpHqyYyv3lqkJrKOjw1TrO3Xz0crKimUrXXVTi3qA3u/3y1lNI2Yt6oeoVtZmt+zVintBPZqjFUzoOlRTxXBSxqYl7J9IJLC0tIRsNovV1dWyil6NJOoA2CSgAubsNYqiiOXlZdk306oHnBEJwUpLvkb3S4sdu9Q1LWXYXS1WBWqltCKVvmu5wlUP0Ku9Pim4Oj1rKXbfqLM2yl5DoZBcOrZyvlEvVh+7mOerGVUMJ9436oXn2bNnEQgEygr76+0GFkURBw8exKlTp+Dz+XDo0CFMTEzIPz969Cjuv/9+eDwe7Nu3DzfccAMA4MEHH8TRo0eRy+Vw0003FbWGMwvbBNRqUZYtS/lmVotRCUE98odqzLJ1KxbMlYbd1c6AakFBz+z522IZb70UkbQ6JJXelrT3SCM9TkOP/J2WHGCz7TkWuw9isZhhz9dGMRfnOK6ksL/b7cZzzz2HXbt26bo3nnzySWSzWTz++OOYnp7Gfffdh8OHD8uvfe+99+KJJ55AIBDATTfdhCuuuAIvvfQSnnnmGXzzm99EKpXCww8/bOn7BhokoKpHVayC3GiMSAgazVDNtHXTCubKBYHZCw8S6BAEATt27DC1S5j2xItlvFoBS5KkmqqwFMteyZO2ra3NFtmbHipZAJSSA+Q4rmDu1e7vvxrKyQCWMjdohD1FrfegFvaPRqN47rnn8Oijj4LjOLzhDW/AZZddhle84hWa1binn34ae/fuBQDs2rULJ0+elH92+vRpjI+Py2N4e/bswYkTJ/Dcc89h586duPXWW8HzPG677TYL3/UGtgmolWQZyrJoqTEPM25SKo8azbqMNCVVs1+qhfqaqs3AzUTpQuP3+03dF6G96mLXXv3ZUiCl/0//7Xa7ZVssq1FmLclkEgMDA0in05pi7o2YvWntOTrFesxMiskAKtW7lPPPjSByUG5flOM4dHV14UMf+pA8Bz4/P48nnngCn/jEJ/CHf/iHuPrqqwv+huf5goW02+2GIAjweDzgeb4gwQkGg+B5HuFwGAsLC3jggQcwNzeH97///fj+979vrWiGZa9sMaS24/f7S5ZFKaBp/TyfB+bnAY4DRkY2DK7VUJeqKIoVudHoyVCV/qJmjpUoj02NWlbIIJKqEmXVZ86cMaXEaWQfmY6nDKIej0eepczn8/K1EAQBLpdL/r9a4Ha7C/bcmi1705O9UmdxI75/opTARi6Xg9/vl+9VpwZWo04znZ2d+PVf/3Vce+21RZ8bbW1tSCQSBX9HzwP1zxKJBNrb29HV1YVt27bB5/Nh27Zt8Pv9CIVC6OnpqeLdlcaRAZVKr319fWXVdqjLVv0BJ5PAJz7hxs9/7gIgYfduCZ/4RB7KZl2lhGClXarlMtRy/qLVQAF1eXkZ6XTakkYtLVUlsxrM1M1HxaCfUeCkBZSyYYnetyiKEARB/t18Pg9g43OyKntVn3up7C2Xy9lm7tOqwFauY7ZZsldgcyk0FoshHA5jbm7OsQstI/vA6i2ZYn+3e/duHDt2DNdddx2mp6exc+dO+WdTU1OYmZlBJBJBa2srTpw4gQMHDsDv9+ORRx7Be9/7XqysrCCVSlW9jVYO2wRUvR+A0dJrsbGVxx5zYXrahZGRjQf/iRMuPPGEhP/v/9v4XbP2ZUtlqFbJ8CmJx+Po7Oy0RLu4mKpStQFVEAScO3cOnZ2duleTWsFUC5fLJXc0UyarLAtTUK1VaRjYnL1pzX2W0ty1glru5ak7Zpt13pMERgKBAAYGBkp6vtp5oWHUaUZPr8XVV1+N48eP48Ybb4QkSbjnnntw5MgRJJNJ7N+/H7fffjsOHDgASZKwb98+DAwMYGBgAD/96U9x/fXXQ5Ik3HnnnZZfN9sE1HJUWnotFlBffJFDMCiBvp+BgIQXX+RK2qFVQrEMVc8MazVkMhksLi7C5/NhcHDQ1NeWJEn+LLTEGqp56FH1wei4TTKZRCAQMPSFUZZ81XuulMlyHAe3212z4Ko19/mzn2XwzDMZDA5G8brXSQ2tNdvs2atyIaNVJqcucjsLjBh1mtHzObpcLtx1110F/zY1NSX/95VXXokrr7xy09/VohFJiSMCajWl12IBdedOCT/9qQtdXRvBLpXiMDW1MZZR7biKEnWGSp2wqVTKMvs4pX1ZPB439bVpdKW9vb2oqlKlGWq55iM1FADb2toQDocxMzMDr9crt+sbWQzRA0BZGhZFUS4L16I0rMU3vxnARz/aDbcbEEXgne9M4c47l2piR2eHh7RW9koBthGz12KVAeVCQzmaY0fP12Z1mgEcEFApOFTqfFKs5Lp/v4jnn+cwPc1BkoCLL87h4otfQkfHFlPr7MoMVdlIZfZ+KbC5iSefzyMWi5n2+tQlXG5syGhApfNOJBK693mV2aQyC89ms+B5XvaiDQaDaGtrMyxyT9mrx+MpyF4psObzeTmwWvVASKWAj3ykBZnM+fP+1rcCOHBgALt2iZucUpQZS7XnZMe5Wa2gQqVhpVqRHc9dL3oDjNZoDqkUmW3uYBSjTUmNVGmwTUDVGn0wI5MrlqEGAsA99+SxsAAkEjxcriWMjZlffqWATsFITyNVJZAZuMfjkbNrCjhmYCR7NBJQ1SbpegW16b2p90t9Ph+2bNkie18mEglEo1EsLS3B7/fLs6BG7idl9ur1euXAqmxsKjeWU8lDPhTiNnWeezzA4iKHXbs2O8Yo7ejs6vNpJurOaZr7zeVyOHfunFwatltJtBSV7F2rVYqKeb4atWCrFKN7qCxDtRgSHwgEAlVncqW0dDlOgsezjJaWDEZHrZEqJHGFubk5y/ZLi5mNm9Ftqxzp0Zs96j0uqVt1dXXpNjHX23wEbFx7tdACz/OyvjOVho2WC7X2XpVjOWZlr4ODEtraJKRSALBxfoIAXHTR5vtZPY5BGUs1dnROCULA+blfn8+HRCKBkZERTc1lq7yXzcKMZjCluQNw/l6olfetkSCpdw/VKdjuziINWCNqRKUoFlCNSghWglI56GUve5klNw51I2uVxKt1ulFaxhld2JQ7biXNR5IkycpPlcwDk9BCb29vwagKNbtQ9mq0sQko3Hul4KrMXvP5vOEA63YD3/1uCm9/ewBra4DfDzz8cApjY+U/02J2dGSm3sh2dNRIpqVaZXfHICukB4t5vlIXudn6y6zkaxNCoRDC4bCpYu1aAbUSCUGj0H5pS0sLvF6v6TeN0mO0WDdyNRlqsaxXD+WOa1RkolSJt1K0RlV4ni8YVaHs1Qjq7DUcDstZRz6fhyAIhhSbLrxQxKlTCcTjQFubtviInnMyakfn1H1IrQyvmNauHbNXq5t0ynm+mtFFbXQOlZV8LSCZTCKVSlWkRlQKtZ5tpRKCRlBL/JndaUuiB0Bpj9FKAypVCappBCumrWu0fGxFMNU6X/WoCs/zWFlZQS6XQ2trq+y5aqSURSW2iYkJeWFH2auywYmuQ/HPETBLKVK931YsuDhVpF3P/V6soccO2Wutu16t8Hw1WvKt9yLGTGzzTqwaXKcHWbUSgnpRerFasV9K+456RogqCahmWLppnZOyacqM5iMr8Xq96O7uLiiXUoDVM5ZDCx6/34/h4WH5vNXZqzKw1ktUolhwicfj4HkemUzGUWMpRvcgyy0wyCmmVuXxeorjl5sB1pvJG51DdcJ9pRfbBFSrLqrL5UIul8PZs2erkhAshyTp90itFCvNwCVpw9JNEISqFxzqQG5185GVqBs8qLGp2FgO7c1T922p1y0lKlGLsRw1yuBC40gcx22yo7O7qEI194qe7JWcYqx6jtglwGh5viaTybLXgs2hNjCZTAbxeBzj4+OWWbvpaXCq9otipRm42ZZuyoBK+9VGJByraT6yGgo4yjnISCSCpaUleL1epNNpDAwMGHLz0dPYRL9Xy+yVFhPqxh47iyqYGZC0stdUKoV4PI6VlRVLslc7BVQlyn1oZSavvBZ0P+gt+dr1vVZDwwZUkhCMRqPyB20FeizRSjnelINk/vL5vCWlaiss3SigRqNRrK2tGVoEUCCpZ1aqF+UcJHVbt7e3IxQKIRQK2X4spxzKc9Zq7FGKKtgle7Wymcrtdms2dy0tLUEURQQCgarFFJyyd63O5ElkZHl5GalUSr7/y0lkNlpQtU1ANfOikoOL1+vF6OgolpaWTHttJRQwyjU4VdocRDJ/bW1tGBoaMv3GIxUqs/d7KZgCG01Tdmk+sopIJIJIJFIgQKIeywkEAvJYjpEgWCx7pSBLmbzZXq/l7le72tHV6gFdSkxBmb22trYaEtZwYgmU47iC0ZyZmRm0trbKnq9Kgwev11vQSa7nsxJFEQcPHsSpU6fg8/lw6NAhTExMyD8/evQo7r//fng8Huzbtw833HADvvOd7+Dv/u7vAGxUKX/5y1/i+PHjpvtAq7FNQDULcnAhb05BEExTCyJovzSbzeoKGNQYZWTlbuVoj1qi0MxmC1EUEY1G4Xa7deshK2X9qKzpBKiTN5vNYnx8vOBBWGwshx4wlOkYLd+Xy17r4fWq1cyiNBOnzK1WdnT1uH+0RpOSyaRhYY1GydhoqwDY7PkaCATw4osv4sILL9T1Xp988klks1k8/vjjmJ6exn333YfDhw/Lr33vvffiiSeeQCAQwE033YQrrrgC73jHO/COd7wDAPB//s//wb59+ywPpoDNAmq1yj5aDi7VihuoUe43jo2N6bohSAZQL1aagSul/swyACCo+ailpUV32UvZfOSkYEpd4x6PByMjI2W7rbXGcpaXl5HL5eTGptbWVsOlYcC6vddKPwu9dnTKbMUs7DA/q8xetYQ1SslCNkpAVb4HtedrMpnET37yE3zpS1+Cz+fDNddcg7179+LlL3+55nt/+umnsXfvXgDArl27cPLkSflnp0+fxvj4uCznumfPHpw4cQLXXnstAOAXv/gFXnzxRXziE5+w8u3K2CqgVkqpDlujwawUlWaNeoO6FZ3CtEjhOK6iblu9KJuPstmsrmtu5+ajUgiCgPn5eXR0dBgWvQC0x3Ko/O7z+eTSsFENXj17r7VubNJaTKhl8My0o7NjQFJnr8r9RmX22tLSYsvzNxPaDvjQhz6ETCaDF198EUtLS/jqV7+KF154Ae9973vlzJLgeb5gqsHtdkMQBHg8HvA8X/AsDgaD4Hle/t8PPvggbr31Vuvf2K9wfEAt12Fr1s0ZiUSwvr5eUdaoJ6grlZXMlEKkgJpKpbCwsFC1YboW6uajXC5XdgGhfNA7KZhms1nMz8+jr6/PlNElrVIhz/NYWFiAKIoIBoNob283LDJQKnvVIyphVaanzlYoeyU7OqUMXiXYPSCp9xuVc85UEo3FYmhvb29YUwNCFEX09PTgta99Ld72trdBFDcclNS0tbUhkUgU/B1tU6l/lkgk5AAbi8Xw0ksv4bWvfa3F7+Q8tgqoRku+tZAQpPnMXC6nu8FGTbkM1UonGpojNFvSETivVZxOpwuuTanP0cnNR8lkUtZNtkK0Q1kqVHfSKudA29raDN+HpUQlAO3s1erPxqrs1Un3lHrO+ezZswAgZ69mZ/BWY+T5rW7AcrlcmsnK7t27cezYMVx33XWYnp7Gzp075Z9NTU1hZmYGkUgEra2tOHHiBA4cOAAA+OlPf4rXve51Vbwb49gqoBqhFhKCyi7bauYzS2WoWvu+ZkEZTzwer3gxUAwSzvf5fJsy6lLSg05sPgI2svBwOIyxsbGaZQ5anbQ8z2N2dlYunZk1lqMOrmZa/+lFy45Omb3qsaOzwx5qNXAch+7ubrlzWOsa1MqGrRKMVAjoOVCOq6++GsePH8eNN94ISZJwzz334MiRI0gmk9i/fz9uv/12HDhwAJIkYd++fRgYGAAAnDlzBqOjo1W9H6Nwko3uwFwuV/ZLrJQQHBkZ0fWBvPjii5iamjL00DEz+11ZWUFLS0tBl5lS13Z0dNR0WbN8Po/Z2Vlks1lMTEyYuuggv8liwvnxeBzJZFK+sQH7KB8ZheaZU6kUhoeHLZ2xdD31FKShIUhTU2V/VxAE8DwPnuflTtpKxnLUkIfsysoKBgYG4PF4ZIutWu69qqGZz0QiUbJrNh6PI5vNoqenpy7nWS0zMzMFIyFKqHM4kUhYsv9sBvl8HouLi7oCWSwWQz6fx/j4eA3OrDbYKkMt95DNZrOYm5szLCGobMzRg9nZrzpDVc7JVuv3qkUmk8Hs7Cz6+vrkeVCzIPnDcnuxynWak4Pp4uIiXC4XRkdHrT1vnofns5+FtHUrcp/97IYifgk8Hs+mzkkzxnKSyaQ8W03BtB6iEmqK2dGpFYvsvodaDXQNtDJ4Zfd0PbPXZnaaAWwWUEtBSjSVNNXQHGi5D89MPVv18SnAVGOLpgfqFh0ZGUEgEEA8HjetDEaNWeX2YpUlX6c2H9GiJxgMWqb/rMT9ve8B2Sxczz8P7uc/h/TKV+r+W6WQAgC5sWlpaUnO5vSM5SjL2sqKSTFRCfrvWmevxezoqNfB6/UiEAjYzuvUTLRs2GppIl4MI0FSFMWGa7yyfUClkls8Hi/q+1kOPWMrZu2XakEZaikz8GpRXielWEO1s7302lrNR8WgYzpJRlBJLpeTxUFqMQwOnof7sceA3l5IPA/P17+uK0sths/nw5YtWzYp+CjHctra2uR7RJI2vHWTyeQmgQoldhWVUCoWkRSiHb1OrUS9/0zKVbWY/VVi1GnGziYLlWCrO0z9QStLo9WIEGiZjCux0sUF2Hhf8Xi8pBl4NZBdGMdxsvem8tjVBNRSzUfl/s6JwTSVSmFxcRFDQ0Oy8o/p0Ofxq+vi/t73wIXDkHw+wOuF69lnDWepxSg2ljM/Py87hmSzWXAcZ6isbVdBf9IcplK4nbxOa4XL5SrZPa3UXTb7czFa8mUBtUaoJQSroVRAtdLFBdi4acLhMERRxLZt20y/gcv5o1YjbFFJeVqSNgyDs9ksZmZm5Ie5Ex5g8Xgca2trGB0dtXQfyvvBDwJ+P3Kf/vTGP6RSEHftkn8ujYyAi8VgdregeiyHMnFacC0uLsqNTWaN5VAmS0HV6uCqzJDU2WsxM/VK3q8V1Hr214rs1ai5uJO2gfRgqy5fEvs2e5RkcXERHR0dBXuvoijKe0x6u4WNopTic7vdBV2vZkCdyKUy66WlJVkcwAiUtRspT6ubj6hjlOd5pNNp0zpRzUaSJITDYfA8j5GREUsfrtzcHFp+lXmmTp4EhoZK/4EgABaUKklIhNSelGM5iUQCHMcVNDZV+qBVNjNRcKX7w+12m34fhMNhedyoFEo7Onq/9bajkyQJs7OzNe16pew1mUyaorscj8eRy+V0KbEtLS2hv7/f9Nn7emK7DHVpacl0k251lkaBrqOjAz09PZZ8eZTdsJIkIR6Pm/r6ep1uKtEy1tt8pEQ5x6gsByrnKLUE4uutCENyj6Io6tZmrgbPoUNAPg9wHLz33ovcX/xF8V/O5+E9eBDCvn2QXvUq085Ba49YKXDf19cnj+WQ+L+ysakStxx19kplYbNLw3rvdSoN28mOrh4dyuWyV6VylV5nGNblaxN4nofL5TJVeg8oLPlavV8KAKFQqECZKJlMmlbOqbRBSO9rG3HRob/Rs1+qVsWhvTzyeq1UYq8ayCggEAhYtrBSws3NwfOtb4H7lbya5xvfQO7jHy+apbpOnAD385/Dk8kg98pXAiY8fDKZjFzVKFV5KDaWoxR3r2QxRKVhj8dTVFSimrGcSoOSHezo6u2FqqVclUwmDbkGGS352qHUbia2Cqj0QDUbCqjqQGc2yjKycuymXFOUXirR+9W7h0oNYH6/X3emVo2MoLITVUtir9K9PL3kcjnMz8+ju7u7ZiUnz6c/DeRyG81HGycB75/92UZHr5p8Hu7HHtvYT52bg2t6GuLu3VUdn6QTR0ZGDM1XFxvLUS6G6EFbqd6w1+s1ZSzHjCxPma0DtbOjs9sMrdfr3eQalEwmEQqFii4yjOyLsqYkh8JxHMLhMHw+n6nzpUpo7Ka9vX1TtmPG6Ao1CBlt0tJz7Epe20xNXq3sIB6Py2WnSkUKipFOp+UqhdnjS6XIX389pB07Cv5NLFLKdZ04AW5pCdL4OCRRhPub39xoXKrw3qWGKzOkE9VjOYlEArFYrOhYjl70uOXoyV7NDkrF7OhCoVDBPGi1TT12btJRZ6/qRQaVyPP5vO7vKctQLcaK1Vkul8P6+jr8fn9Z38pKKSdTWG2GSvOrJNZghHIB1YzmI7NndpXZAXmHUuZP+rV6/VbV8DwvN7zVWlFG/M3fhPibv6nrd93/9/8CmQy4+fmNf1hdBfdf/wXpFa8wfNxwOIxYLIbx8XHTH2Aulwvt7e1ob2+XG32UYzn0eZnpllNq79XqHstiZdG1tTXkcrmqRlLslqGWQr3IoBJ5LBaTG5zKlcjrXeK2AlsFVLOhYEFZlxUfnh5bt0pHV2jgvpr5VZfLJXuOqqlkZKjWHqZq79BEIoFoNIqlpSXDpWFlYLHTkL/7u9+F+Bu/AUmxlyrccgugsKUCAGnrVkOvK0kS1tbWkMlkMDY2ZvnnpWz06e3tRT6fB8/zCIVCcqOPWWM5WqISbre75g9prbKo1kiKnu+XkwKqEuUimBa9tMVWrsGr3D0piiIOHjyIU6dOwefz4dChQwVax0ePHsX9998Pj8eDffv24YYbbkAul8Ptt9+O+fl5uFwufPKTn8SUDn1sM7DPU8VEaAwiEolgfHxc3vMx+xh6G3gq6bQlEwBJkjA5OVnxw1ArQ1Weu94SuB1s19TZEI15UOmtWGmYGrkEQahJYDECNzsLz0MPIf/SSxA++EH536Xt26t6XZLR5DjOsspMOdxu96Yshkr56s/LjOw1n88jk8kgGAwil8s5zky9ETI2khP0+/1FG7yeeeYZTE5O6hrle/LJJ5HNZvH4449jenoa9913Hw4fPgxg4/ree++9eOKJJxAIBHDTTTfhiiuuwLPPPgtBEPDYY4/h+PHj+PznP48vfvGLVr91ADYLqGbcTFqBSBAEU62oyMmltbVVVwOP0T3UUvuxRlEfW9nYVIvmI6tQj3lQaXh5eRmCIKC1tRXt7e3w+/1YXFyEz+fD8PBw/c9dFAv2Qd3f/jakYBCup54Ct28fpLExEw5R2+5lPWiV8inYVDOWA5wPsMp583qISqgxakfn1AxViXofWKvB6+TJk/j617+OM2fO4OKLL8bevXtx6aWXajYHPv3009i7dy8AYNeuXTh58qT8s9OnT2N8fFz+uz179uDEiRPYuXOnXL3geb6m1ShbBdRqKaYaZFaXLXDeDLy/v1+3zquRgEqvb5ZpurLcXO/mIyvRKg2Hw2HE43H4/X60t7fXv6swlYLvD/4AuTvugDQ1BW52dsOybWQE3Ooq3N/+dkGWWgmCIGB+fh6dnZ1VK4xZiVawof1tr9crZ696tjlokah+z8qxHOX8azVjOZWiFrMnSUQyEq+1iL1VlMuyPR4Prr32WrzxjW+UzdR/+MMf4pFHHsHrXvc6fOADHyj4fZ7nC8Yb3W43BEGAx+MBz/MFz8hgMAie59Ha2or5+Xlce+21CIfDeOCBB8x9kyWwXUCttCOW9Dq13Giqkd9TYqUZOLAh1rC6umqqaTpdT7o+dmk+shKXywWfz4dMJiN7zcbjcdmYmx7WVhnTF8P9j/8I1zPPbIjf33UX3N/+NpBOA5EIJI8HrqNHq8pSSbChr6/PshlrK9AKNuqxnGKNaIIgyCIV6gWoVmnYSlEJI2jZ0UUiEWQyGeRyOfl62GmvXw9651BJovTCCy/EK1/5SvzhH/6h5u+1tbUhoeglEEVRvibqnyUSCbS3t+NrX/saLrvsMnzkIx/B4uIi3vOe9+DIkSM1+b4769PSQE/jTrUZqtIM3EwFp1q8vsvlQjqdRjKZNNTYVOvmIzOhlb9y3rKlpaVAAWhlZUV+cOmxNquaZBLur38d4uTkRnn39GlIPT0QL79c/hWJ44BstqKXp1EgS0X9a4TWWA41ovn9frmxSZIkuVqkx9LRalGJSqH9ZFEUIQgCgsGgPDMsiqKjBP31lq3z+byua7x7924cO3YM1113Haanp7Fz5075Z1NTU5iZmUEkEkFraytOnDiBAwcO4PTp0/JzrrOzE4IgyJ+x1Tg6oCpdVko17lQTUEnwwOfzWWIGrnRzMfv1ydItm81ix44djmk+qoZIJCI3o2mt7pUKQGprM+XD2uzMwP1P/wQukYA0PLxhJv6rLNUMtBYQjUKxsZzZ2VlkMhl0dnbC7XYb3n8sJypBi8laer1SdkeC/srsleZ8nWBHp7cvQ881vfrqq3H8+HHceOONkCQJ99xzD44cOYJkMon9+/fj9ttvx4EDByBJEvbt24eBgQH87u/+Lu644w7cfPPNyOVy+NCHPlSzeXNbieMDG2UrPcEvm81ibm4OXV1dZYWYJUnCSy+9ZLh12kzHmxdffBHbVZ2bVpqN076S1+tFPp/HmI4yopODKY2IpNPpiswOlA9rnuerF4eXpI0GJLcbyOXgf+c7gfV1wO/f+PdcDtm//mtIihGASojFYgiFQnJpuxkgi72BgQG54lDtWI4SrbEcCgBWZq/hcBgul6uocpfSji6RSNjSjm5mZqZgrKUYqVQK0WgUO1RCJ07Hkd9Ao0bdlezLUtZSiZhCMZSr6Er2NPVCgbq3txctLS1YXl7WdW5ODabU2e3xeAx5eipRz1BqicO3t7frLg27jxwB97OfQTh4EHC7kfujP9pUzpV6ew2fp5JQKASe5zE2NtZwijPFoFKoUphDOQPK83zBDGgle+X18notl2VznLPs6ErRiML4gMMCKpUwY7GYJUbdymPE43FMTk6atuqnWVSO4xAKhRCJRCx5DxSoaSGQzWbLLiac2nwEnM/E29vbdVlG6aVUabisvF4yCfcjj4CLRpE/dQrSy14G8bLLTDs32nPP5XI1ccixC7TA0ZJP1JoBVe6VVzuWU04S0YzgalR60O12byqHJxIJLCwsyCpV9bSjK0XdO+4twnYBtZRM1cLCAlwuV1VCB6Wg+T23242JiQlTj8FxHPL5vCyhZ8V70ArU5UQlnNx8lM1mMT8/j97eXlNGjIqhFCGgsls8Hpfl9ZRdwxzHwf3P/7yxXxoIwPPoo8gdOlT8xVMpwOfbKA3rQJIkLC4uwu1222OutkZEo1HZ2ELPg1g9RkVuOZWM5Sgplb0qG5zo50a+U9XMoSorLPWyozNy/ixDrSNUwuzu7jY1C6nlMTiOk8UahoaGTG8+WlpagiAImwJ1qXI3PQiclpUC50t/te5qVZbdqDRMw/rZbBZBjsPw178OsbsbnN+/IXL/qyxVC/df/RWknTshXnVV2WNTA1swGERPT4/Zb8220Dzx+Ph4RQ9hrQWR3rEcPa+t9npVdw7rzV7NFHaohx2dEaWnRhTGBxwQUI3ulxaDAovWB15qhtUMaGxlYGDA9AehUrVpcHBw0/srJj3o1P1SYKMRh/ST62lODmwWCRf+/u+B5WVkAgFwLhe8qRS4v/1bSH/6p5v+ljt9Gq7nnwfOnYN4ySVAiXuP5i1raTdnB6jRbHR01JSMRrkgUmZykUikYCynra3NFL1hI6ISVkkPaqkVkQ1bJpMxzY7OSNapnCdtJGz3juiGUu5lmrHXSOIO6i+JlfuZwHkxCOXgullQF3Jvb2/Rh6xa1EI5e0crZ6dA90QymbTEOaVaOI6D95JLIP35n8MtSRv7eKkU+JYWZM6e3Vwa/t73IHV1gUsm4frP/4T4hjdovi51tA8MDFiy4LMjtE8sCIKlWsRamRyN5QDY9JnpRU9jk9rrtVbSgx6PRzN7VdrRtba2Gs5ejewBswy1hlixl0mzqMob3Azx+WLQGEcikcDk5CRWVlZM1RPWa+mm/EIom4+cGEyXlpYAwN6NOIODEAcHAWx8udp/9X/kvLK+vo5MJoP25WX0Pf883Nu3A+3tcP/rv0J87Ws3Zak0IjI8PGyJOpcdUQr7m709Ugq1PrTS85MyORrLsaKxyczng160NJaTyWRFZupGMmy2h1ojMpkMzp49a/psplLcgTR/Ozo6LBEPpwWBx+ORxRoqlVTUopKs2umdvPPz8wgGgwUazU5C7bySP3oUQiqF5HPPbQzziyKkZ56BS9ENTF2t9fBurReSJGF+fl7eo67nZ61lLM7zPNbW1qoyvtfKXhOJBLLZLDiOQy6X25S91opq7OiMlnxZhloDRFHE0NCQ6bOZVFIhj9TBwUFL9E5zuRzOnTu3qbnJDIH+Us1H5f7OqZ28pE/b09Oj24zA7nAcB88NN4B705vgxcYiMpJIIO71Qjx7Vi7r0oxpI+41aUFNV21tbZY1H1ZKsbGc5eXlqiUs0+k0VldX5W0MLa9Xq0UltDBqR8dKvjZUShJFEblczvTXXVhYgNvtlh9SVqz4KVhrNTetrKygpaWl4qCgbD7q6+szZLu2sLAg+0S2t7fbRlWlHFTubAR9Wr0IgoClpSWkUim43e6qyoxOophjjBNQjuUkk0l5TllpzVaMZDKJ5eVljI6Oav6ucu9V+aiuh6C/+rwoe02lUvB4PHC73XC5XOjv7y/79wsLCxgZGXGUiYMemiKgSpKE06dPw+VyYWJiwpKVUTgcRigUKhqsyQOxkodFJpPB7OysIcs4dSevJEmyrF46nbb9gzoej2NtbQ0jIyNNVe5cXl6WqzQA5DJjIpGAx+OpeH7SzpRyjHEayrEcnuchimLRhWwikZDdq/R8nlp7r0D9gyuw0Ti3vr6OdDoNl8slSyIWG0Wan5/HxMREwy2UbRdQ6YY0C/qySpKELVu2mD5yQGXYXC5XsrV/fX0dHMcZLmXpbT5Sn1Op/VLaG4nH40gkEvB6vWhvby+u/FNjQqEQ4vE4RkdHG7IspAVVEnw+X9EKhPJBnc/n5eDqlIqDFlTS1+sY4zRoLIcWsiSwwHGcPPpVyXdO3cykfIxTplhrIpEIOI5De3s7ksmknL36fD65sYkWDrOzs5iamiopCymKIg4ePIhTp07B5/Ph0KFDBTrBR48exf333w+Px4N9+/bhhhtuQDabxcc//nHMzs6ira0Nd955JyYnJ61+6zL1f3paiNIMXI8En1HKzYAqqWQPdX19HdFo1JAEop7mI/XeCInCz8/PAygcFaglygxtfHzcsUHCKFTu7OjoKNmIp7Q1k37yE+T+7d+w9sY3yg/q9vZ221YctMhkMpifn2/okr7WWA5pMPv9fkQikarGctSiEvX0eqXZUrWQBu29Li8v4+/+7u+QSqVw0UUXYdu2bSVf78knn0Q2m8Xjjz+O6elp3HfffTh8+DCAjYXYvffeiyeeeAKBQAA33XQTrrjiCvzzP/8zWltb8bd/+7d46aWX8MlPfhJ/+Zd/afl7Jxo2oKrNutfX101tSzdahjVick7ycqIoGmo+UpaCjHyBlIPuWn6h7e3tFanIGIE6o1taWjAwMNA0wbSipqt8Hr6vfQ3+lRX4rrsO0tatmh2o7e3tti0NN+M4EHXw5nI5bN++Xd6GIZUtM8Zy6un1qtXly3FcgZn6e9/7Xhw/fhzHjh3DN77xDWzduhWXX345fvM3fxO9KrOIp59+Gnv37gUA7Nq1CydPnpR/dvr0aYyPj8sVxz179uDEiRN48cUX8Zu/+ZsAgG3btuH06dOWvNdiNFxAlSQJKysrSKfTBWbdLpfLNJNZEkkfHR3V/TAop6lLUNYbDAZ1jw2YqXykFoVXmjuTPVZbW5upX8pcLof5+fmmUwGiDG1wcNBQV7vrxz8Gt7ICuN1w/9//i/z73ldQcdCS1rNTM5qWY0wzEI1GEYlECtyB6LtGEwhmj+Voeb1qiUqYgZ4u37a2NrzxjW/Ey1/+clxwwQV48cUX8YMf/ACPPPIIPvzhDxf8Ls/zBU1LbrcbgiDA4/GA5/mC/fZgMAie5/Frv/ZrOHbsGK666io8++yzWF5eRj6fr9nWke0CajVfeCqd+f3+TSVDl8tVdbNTNU40ejJUM5qPzHxgqs2d0+k04vE41tfXTWuQSafTWFhYaCoVIOBX3Z0vvYSR7dvhN5Kh5fNw//VfQ+rqAlpb4X7ySeTf+lZgYED+FWVpWCmSTqVhMzxDK4WazewgG1lLIpEIotEoxsbGNIOOUl8XOL8oojG5asZy9IhKmJG96hV2kCRJ3pLauXMndu7cqfl7bW1tSCQSBa9Pz1z1zxKJBNrb23HVVVfh9OnTePe7343du3fjwgsvrOl9bsvNlkqCQiaTwZkzZ9DV1aW5n6k3QywGlSSz2WxFtm7ljk9yZ6Ojo4aCaa0E7klRpb+/H1u3bsXAwAAkScLCwgLOnj0ra64aucY8z8vt880UTOPxOFZmZrD9q19Fy3//NwCA+6//gufLXy77t66f/ASuF18EF4+DW14GFw7D/d3vFv192sMbHh7G1q1b0d3djXQ6jXPnzuHcuXMIhUKmNgGWIhqNYn19HePj400VTMPhMGKxWNFgqgUtisbHxzE5OYnW1lbEYjGcOXMGc3NziEQi8my5EVwuF9xuN3w+H1paWuDz+eRMNZ/PI5fLIZvNVqTcZGQOVc/zavfu3fjhD38IAJieni4IvFNTU5iZmUEkEkE2m8WJEyfwqle9Cr/4xS+wZ88ePProo7jqqqswNjZm6D1Ui+0y1ErQYwZejbACKSt1dnZWLG5fKkO1qvnIStRZkHIvSI8ZdyQSQSQSwfj4uC06i2sFPVwnz56F+8wZuP72b5G78EJ4P/tZuE6cQP6aayCVaNaQenog/I//Ufhvo6O6jq0lM0dZULWuK+Wo1jHGqYRCISQSiarE/dWVIspeyT6QPrdKSvrlslcjohJGMlQ9XH311Th+/DhuvPFGSJKEe+65B0eOHEEymcT+/ftx++2348CBA5AkCfv27cPAwAC8Xi++8IUv4OGHH0Z7ezvuvvtuXccyC9uNzQDQ3ZFLerl6FGVSqRRCoRBGRkYMnYtZykrpdBpra2sYVTz8lM1Hw8PDhpWP7CghqB5y9/v9cvcpqcCsrq4im80aes9Oh+7VTCaD4e5utHz4w5CCQXArKxDe+lb4Pv1pAED+yiuR+9Snan5+tF/O8zxSqZRppWHaJkmn0031eQOQjRxGRkYse99aYzlmlfRFUYQgCHKJlijWOTw3N4fBwcGyC+R8Po+FhQVccMEFVZ2fHbFlaqBH91YpoD85OVk2sFSSoUYiEblEVW3zhDpDFQRB9kfVqyfsBNs1dct8JpNBPB5HKBSSr0FLS4ulDiJ2Qyn2PjIyAve//AukZBLo7YXU1gbvffdB8nqBzk64jx6F8NJLJbNUK9DaL+d5Xv7caE7ZyPeAGgTz+XxTfd5Aoe2cle+7mFsOfW7KxqZKslf6vJU2dJTJUlBVuuXoWTiQ01UjYsuAWg4yAzcioG90bGV5eRmZTAaTk5OmbGor91Dt1nxkFRzHoaWlBS0tLeju7sbs7Cx8Ph9yuRzOKuzM7NJ9agW08AsEAhsLJwDu730PnCgCCwtAKATXuXMQR0eBaBRIJOB58MG6ZKmE2nVFqVsrCIJc0i9VGqZFhMvlqqljTL2hSkQ2m635IkLrcyPtXdqKocYml8uF5WXgqadc4HkO27ZJeM1rRBRbL5XzehUEQd6CKodVvq92wHEBtVLDcb0ZKnUKt7S0mCouQAG9kpEbJwZTJTQe0t/fL5fNqVSlNDmmfddGWb2SSheNRsj//tGPApkMAIB7/nlIY2NAeztA7iMKNRg74PV60d3dje7u7k2jVFqG3HZyjKklSg/X4eHhur9vr9erOZazurqKdNqL//f/etHd7Ud3twe//CWHfN6F17++/DNSyy0nEonA7XbLzyn6Pa3ScKMK4wM23UPN5XKbgp8kSQiFQnLrudEuQVEUcebMGUxNTRX9HT2G3ZWSz+fx4osvwufzGZIbq3fzUbXQzGGpAX7llz2RSMDn88klRqd+8Uiwoa+vr+EEwAllST+RSMijHzzPo6Ojw3aOMVZC5W1RFMuqptmBF18U8A//IKC7OwlRzMPrbUE83orf/30XXC5j5x6NRhGNRuX+ELWoBICCsRzapy+nlOREbJmhqm9G0jkFULEZeLl92Uo0c/VC5S9RFA0ZpjvZdg3Y+KKFw+GyCyDlDB51McbjcczOzsr7QO3t7Y4RAaDZWiOSetz8PBCNQnJQo4aypN/X1ycvSDmOQyQSQS6Xq3h20knQFhEARwRTAAgGvQgE/OjtDUKSREQiaQBJnD27Krvl6NH2VopVKJ9RpUQlrHATswu2DKhKlCMr1ZhLl9rrocy3kvnSciibj0jnshxOL/HSPlI6nTY8JsFxnCyF2NvbC0EQ5DI5DbjbSfVHDWmWGlIBkiS4v/MdYHUVwh//MVBjDWUzEAQBCwsL6O/vR3t7u9ztTZ8dlYaDwWBDjUkp94r7+/tteU9qMTAgYccOCf/939RY1Iq3vKUFw8NtBdrepcZyYrGYZjBVot57FQQBsVjMcRZ9erH1nW21GbgoilhcXIQkSRVnvqUgcf6BgQG0t7cjEomU/ZtGCKaLi4twuVymdDh6PJ5N+3dK1R87CcLHYjGEQiHDs7Xc6dPgzp6F5HbD9bOfQbzkEgvP0ny0HGO0ur15npcz2Gq6T+0C3esej0e3R7FdcLmA179exMtexiGTAbZskbAR485XHXp7e+UZc+p1oLEc2jc1IlZB16uvrw8DCmWvRsKWAZXjONlf1IyRFS0EQcC5c+eqznyL0YzNR/l8HvPz82hra7Nk/0w92qEUhPd4POZZ0OXzgCgCBvbpyUFEqdOqSSp1Pmi6XBvZ6fe+B6mzEwgE4Pr+9yHu3u2YLFWPY4yyNExVB2qO0SsEYjdIJYzs9pyIywWMjJRuoXG73ejs7ERnZ6f8naN7vaWlRXbLKbcwomdDd3e3LgNyp2LLgBoOh8HzPLZu3Wp65kGzWiRKbnbmW6ner9Obj7LZLObn59Hb21sTk+hSFnSSJBXsuxqevzt+HFhdhbhvX9nfpc7OXC6HsbGxssfifvlLuP7zPyGNjECanNzITk+dgjQ4CGSz4EIhx2Sp5BgzMjJiyOpPbcCgLA0b2b+rF9TFTKNQzQLJEwqCgB07dkAUxQJnKvVYDkGjYx0dHQ3vJGXLO7azsxNtbW2mX3iO4xCNRmVxbrP9PqmEDKCpmo/owVpPX0stCzrKgAxZ0PE8XP/+70AmA/F1rwOGhor+KmUpHo9HHpPgXnwRiMchvepVm/8glYLrmWcgDQ/D9ZOfID8+DqTTEF/5yvOv2d8P1EhbtxrMcoxRl4apIU25MKrEL9QqKDgEg8Gm6mIGNho3yRTd7XbD7XYXbMdQxWhlZQWrq6t44YUXcPnll8vCIM0wj2zLgErzTGZCQSscDhfYupmFHuUj6jRW/qxW4vZWEYvF5C+ZXQTPtTIgtQVdMWk2189+tlHubW2F6/hxiNdfr3kMKmEFg8HzWYoowvPgg+BCIWS//OVNZVvul78EJAno7ATm5sCdOwfpoouQv+gi06+BlVjlGKPVkJZIJAo0orUyoFohiiLm5ubQ3t6uW1CmUSCBiGJbGi6Xq8Atp6urCz//+c9x5513IpVK4fLLL8frX/967Nq1y7aVBzOw5RyqKIqmtlbTwy+dTmNiYsL0zFTdfFSMl156CRMTE7KmrZP3S6k7OpFIbMjpOWBeVGlBl0gk4Ha75X1Xr9cL8Dw8X/oSpN5ewO0GNzu7IUSvylJJsEHt38o98wy899678Tvvex/Eq646/0epFNx//dcbiyq3G1wqBam3F/nrr9/YzHIIylGoWn7mWrPKtSwNUzDt6Oho2A7VYiQSCaysrBian6dqHamk/ed//id+8IMf4L/+67/wwAMPNOw+auMuFX4FyRRS1mj2+sFI8xHJDyoHn0lNxEnQqIAkSbr2De2C2m1FacQtiiK6//u/0RmNwkPVkWwWrulpiIqAms1m5cVTgeWcKMLzN38Dqb0d8PvheewxZPfuPZ+lchzEV796I/slbJLR66WejjFas8rqPXOrSsOkntbV1WW64IvdqTSYkooWdfpfddVVuEq5wGxQbBlQzfpCJBIJLC4uyjKFiUTCtIBaSfORUk+YhKSdEowIcoqQtWkddv5KNlnQ+f1Y6u1FNptFIBDYmL37lf4ucH6vWEv1iXv22fPNRYIAbmUFrh/96HyW2tICSbFX6iSUjjHV2JCZhbI03NPTI492rK+vmy5jScG0u7tbt+52o5BMJisKpsvLy/B4PJYbA9gRWwZUMwiFQohEIpiYmJD3earxRFVCyk0ul0uX0w3BcRyy2Sz8fn/dH0qVQPOGPT09Dfdwcbvd6BwaQufQkFxejMbjWFxfh5/n4fF4ZF9LrSYcLh4vaC7KDwwAPF/Lt2AJTnCMUY92KDVrPR6PnL0a3e/N5/OyCUej3e/lSCaTcuXNSDBdWVmBy+VqOt9bwpZ7qFTSqfRvi3mMLi8vyzNvlULNR6RVqvcBk8/nEYlEEAqFHKlVS3J6g4ODhkwJnA6pPkUiEXg8Hrjd7gJRgkaGvktut9tRKkBKqGuY53lDZty0T97T01OTMTA7QZUYI01nFEyByuVhG4GGCqjlOm1XVzd0KivdB6HmIyPzq+rmI6DwS05jA+3t7bbpklVDq/2RkZGGDyJKqNSZSqVkk2iyMovH48jn87aXQqwUqsI0kmMMORzF43G5NEwd38oAQM+RRjY2KEYlwVRpDGCFdoCTsGVABTYG9Y2gp9N2fX0dLperopb3WCwm7yfo7RLW03yUy+Xk4CqKYlWCBFYQCoUQj8cxMjLS0O3uakjwXBTFovNz9IDmeR7pdNr43p0kATb4jNVQR6tVild2QKm0lUgk5NJwS0sLlpaWCmQUmwUKpkZmi5WWddu2bWvqYArYOKBms1ndDUR6g10oFIIkSYbUTajkR7JyViofUXNFPB43LkhgMrTqFAQBQ0NDTfVFqSQ7owc0jeSUHetYWoL7b/8W+d//fVvJDDZrR2s2m0U0GsX6+jq8Xi86Ojp0lYYbBdrSMRpMqRFsamqqqZ4RxXB0ykGro2QyqUusgUp2eqm0+ahSGUFlcwUJwUciESwtLdXUgFsZUOxglFxLKKB0dHQYqmQopRCVij9qMXha8LmPHoXr2Wch2UhmkEqdtZKPtBvxeBwTExPw+Xy2NmEwm0qDaSgUQjqdxvbt2xvyulSCYzNUKkt5vV7dHoSxWAypVEqX04FSPN9IRkvKR4B5MoJa2Y9VTU3UjEFKQ80EdTGbHVBICjEej0MQBHQkk+j5ylfgHhwEl0rZwrJNyzGmWaDZYi3pTHVpmJrS7NzzYAQyNzCixyxJEsLhMBKJBLZv3+6YxspaYNuAmsvlio64kFjDli1bDGURPM+D53kMDg6W/L1UKmVYPL9WykdkhUX7rpvUfqqAvlybRAuaAHrvVncxi6II4Wtfgzg9jUx3NwLLyxCvvx6+K6+s2ypfj2NMo0LvXWu2WAsSlOB5Xm5Ka2trq8u2TLVUE0x5nseOHTtYMFXhuIBKHqkk1mCEZDKJSCSC4eHhor9TTfNRPWQEqakpHo8XuKwYlVckY2yjziGNAAm91+S9r63Be+gQ4HZD4jjkk0lk2tsx/3u/B8+vdIbNWBzppVLHmEaASp2VvndyW6GmtHI60XaCsnKj753Uslgw1cZRAZU8UsfGxioa30in01hbW8Po6Oimn1HzUSKRMKRTaifbNXVpUe9IRyQSQSQSMTTE3SiQ0Pvo6Ghtglgms+FIo/zaeTyQXvYyZBUd37VwWqFFVLWOMU6k2mCqRksn2q7zyhRM9WblRCQSQTQaxc6dOysKpqIo4uDBgzh16hR8Ph8OHTqEiYkJ+ef/8A//gK9//etwu93YuXMnDh48CAAl/8Zu2PbpqXyAkHasIAhVzTkVU0oiSya3242JiQnLm4+sQu2yomysoKamYDAonyc1dWWz2aZUNgmHw4jFYhgfH6/datvvh3ThhZo/8vl86OnpkS3olE4rVFo0y4TbKscYJ1DJeEg51DrRNK+8tLRkq9JwtcG0mj3TJ598EtlsFo8//jimp6dx33334fDhwwA2Fjif//znceTIEQQCAXz4wx/GsWPHkM/ni/6NHbFtQCWoSaa1tVV381ExtAJqLpfD7Owsurq6DM3cWdF8ZCYulwvt7e1ob2+X5dji8ThWVlbg9/vR1taGeDwOr9drW0k5q6BqRCaTwdjYmC0/P4/HU9DxnUwmEYvFsLy8DL/fLy+OKnm4kWNMTRcSNoHK+1YvJLxeb4FXaCKRkC0Eq/38KoUaz4aGhgwF01gshkgkgh07dlR1zZ5++mns3bsXALBr1y6cPHlS/pnP58Njjz0mL0gEQYDf78ePfvSjon9jR2wdUEmsob+/3xQtTaU4PWDv5iMzUTt1kGkAue9EIhG0t7c3RbmXqh0cxzlmIaE24U6n07IYvNGu01AoBJ7nm7IiQfq0tc7K1Ytb+vxCoZA8UkViLlZBiYPRxrN4PI5QKITt27dXfc14ni94zrrdbgiCAI/HA5fLhd7eXgDAo48+imQyiUsvvRTf+973iv6NHbHnWWHjg6TZKCOrqVIoM9RoNCob5jqh+cgscrkcVldXMTQ0hLa2Nnlecn5+HgBq8uWuF1Tad7JTjrK02NfXJzelLS4uIp/Py4FXvW9uN8eYWkM2ZDXbKy+C1ufH8zyWl5eRy+VML+0D1QXTtbU17Nixw5TnQVtbGxKJhPy/RVEsCIyiKOIzn/kMzpw5gy9+8YvyYqPU39gN256Z1+vVbYumF8rIVlZWdItBEI0QTKncpdw/Ue/b0ZfbSFOTE2jU+Vqv11tgQZdIJBAKhZDJZNDa2irv262trdnaMcZKSIvaiNJZrdAqDStL+7RAqrQ0TGXewcFBQ8GU53msra1h+/btpi2ud+/ejWPHjuG6667D9PQ0du7cWfDzO++8Ez6fD1/+8pflBV+5v7Ebtu3yzefzEATB1NcURRHPP/88uru7De3H2q35qBJisRhCoRBGRkZ0rdBpJICExMmlx8yVc63IZrOYn59vKrFz5b55NBqFx+ORnVOaad80Ho9jfX3dcR3synnzRCJRoLalV+eblK8GBgYMjRhSNr99+3ZTR6moy/eFF16AJEm455578NxzzyGZTOKiiy7Cvn37cPHFF8vv7d3vfjfe8IY3bPqbqakp087JbJomoFLZI5vN4uUvf7nuv5MkST4PJ5bJlI4pw8PDFT1MlQ/nZDIpz9u1tbXZ/prQeEQzihYoJSSpdMbzfM327eoNLSKNjMHZFeVInJ7SMAVTo8pXVMXavn27aVttzYRtA6ooioZ0d0tBzUdDQ0NYXFzE9u3by/5NI5R4yc+S47iqO6SVr6mct/N4PLJSk90ygGaesyzlGEP7djzPN1xpn4hGo/JstdODqRrq+uZ5HslkUpYiDQaD8Hg8LJjWkYYPqNFoVJ638/l8OH36NLZt21bywdEIwTSfz2N+fh7BYNCQEbpRMpmMvHLmOE4OrvUOYJSdOK3UZwZGHGPUaj/FPEKdBM1M2nUkykyoNEyfIbCxYOrt7UVXV5fu7z3N5m7bts1S6c1Gp2EDKokWpFKpglXqmTNnSs7fNUIwpUaEnp4eU8aNjBxXab6tlEGs5XWk0ZCRkZGGy07KUY1jjNqEwev12rb6UAySxmvGTuZ8Po9z586htbUVuVwO2WxWbkxTCrqooWC6devWptPwNhvbBlSywKoEGo/weDybSp0zMzMYHh7WbMxphOYj+nLUe8+QOk6pqakW3q5KD9dms50DzqvgmGVuoMx8JEkqqD7Y8dqGQiEkEgmMjIw0ZTCdnZ2VG88A7dKw2qOXegxYMDWHhguo1HxErehqzp07h4GBgU3da05vPgLOz42NjIzUveSqhL7Y8XgcqVTKEn9JSZKwsLAAj8eD/v5+Wz7wrcRqxxhqiuF5XpZCtHqBZIT19XUkk8mmDqZbtmwpWpFSevROT0/j4Ycfxqtf/Wq84hWvwOtf//qm9L+1goYKqMrmo2KrLSqF0kOnEUq8Skslu5c5lf6SPM+b4u1K+8VaDTjNQK0dY2hekud5eYFUz67vtbU1pNPpppyxpf3y7u5uQ9s7i4uL+Kd/+iecOnUK586dw8UXX4zXv/71uPTSS239/LA7DRNQ1c1HxVhYWEBXVxdaW1sbJpguLy9DFEUMDQ056j0oV808z8sSe0bMm0mwobu7u2wDTiNS705mLZcVs/x59RybzAOascQviqJcjTMSTGlrYHx8HB0dHchms3j66afxwx/+EDfffDPGxsYsPOvGxrYBFdgoY5WD9s1IUq3c6mppaUnepHd6MG0EKT0lOYV9mSiKBbOSWu/N7D1Dp1Fz6zkdkAG30p/XCgs6ajoUBMFxC0kzoGCqp5NbCW2JjY6ONpRimF2wdUDNZrModXo0a+fz+TAwMKDrS0VuKyQU79RgmsvlMD8/37CZWT6flx/MWnt2VOY0akPVKJBjjJ1FC+gz5HneVLUtWkSLomjafLWToOdeR0eHoaBIwXRkZESzv4RRPY4NqLlcDufOncOWLVsM3Ryrq6vI5/PYsmWLbR9E5aDOvMHBwaaYGaM9u3g8jnQ6DY/Hg2w2a6pxgpOgsSAnjYaQyxF1nFaqU0tbHAB0L6IbiUqDKY1TDQ0NNWWfQa1wZEBNJpOynJyRUl8+n0cmk8H6+jr++79FHD3aA0Fowete58Y114hwQnzleR4rKys1a0CxG+FwGKFQCK2trUilUqY0NTkFpWPM8PCwY4KpGrVOrd69c7Lec7lcTdnJTVs8bW1thpIIFkxrh60Dai6X22QIHolEsL6+Xrb5SIm6+Wh5mcPdd3vhdufgcmWwuAi88Y1p7NvnsrVCTDgcRiwWw8jIiGMG7c1CqUlMoxFaD+ZaNcTUGipz5vP5htsz1BIEUVvQkYymx+NBX19fQ71/PUiSJEtJGg2m1GfQ09Nj4RkyAAcFVKPNRwQF03w+D5fLBY7jcPy4C3/9125MTGy89VQKSCTyuO22ZXmUo6OjA8Fg0BZZDzVg5HI5DA0N2TbgW4XeTmZqalI2xJBSk5OhYOJ2uxs+M1MLgpAUYjQahd/vR19fX71PseZQMCUZUb3QfGpfX19TXrd64Ig0R9l8ND4+XpHtGgVTAPD5AGXim8sBHR0bD6u+vj4561lfX6+7+Ds5hvh8vqYdDSDHlHJ7ZkpvUBIiWFlZQS6Xk4Or0wTg6f23tLQ0RCd3OdxuNzo6OtDR0SG7HC0uLspbP5FIxFFSiNUiSRLm5+fR2tpqOJjOzc2ht7eXBdMaYusMVRAEpFIpWQXESKmjlIxgKgV89rMenDvHYeN7yeHWW3O48MLNl4LmJOPxeEVzktVA5ZrOzs6m7Mqjh0JHR0dV71/d1BQIBGSlJjsHqFKOMc0A7RkGg0F0d3cXzCw3gwUdBVMai9MLfW+2bNmCgYEBC8+QocbWATUajWJubg7Dw8OGulnz+bxcKi5WHk0mgWeecSGZBHbulOTybynUJcX29nbLvtAkJdff3980pthKSOC/EpH3Uqi9Xf1+vxxc7VDeJ4w4xjQitJhob2/XXEwJgiAHV7Kga2trs40UYrWQlKbf70dvb6/uvyPVsK6urqbsgq43tg6o6+vr8Pl8FTcfWXkz0ReaGimUwbXa45L6TbPOWNJiwuqxIGVTE8/zsspPe3t7XUuK1TjGNAJGR0PUFQgrtKJrCe2Z+3w+Q8FUed0arXHNKdg6oObzeVmwvhz1lBFUixBUs19HA/sjIyMN16mqBzI5rsdYkLKkCKAuJcVmV3+qNjNXakUnEgl4PB75c3TCvisFU6/Xa2jvUzlS04y9FnahIQKqnWzXyLDZqG0Z6ZKSyLcTV9bVYicpPWpqisfjcknR6qYmqx1j7I4e1xSjKBdJys5vO1rQ0Zyt2+02NBpEjWutra1VGQSIooiDBw/i1KlT8Pl8OHToECYmJgp+J5VK4b3vfS/uvvtuTE1NIZfL4fbbb8f8/DxcLhc++clPYmpqqqLjNwL2X7KVwU7BFNjYs1V2KSYSCUSjUSwtLRVthhFFUR5YHx0drft7qAc0Y1vK/L2WeDwedHV1oaurS14khUIhUyX0lNTaMcZuWBFMAcDn86Gnpwc9PT3yIml1dVWWs2xrazP1c6yUaoLp4uIiAoFA1W47Tz75JLLZLB5//HFMT0/jvvvuw+HDh+Wf/+IXv8AnPvEJWakKAH7wgx9AEAQ89thjOH78OD7/+c/ji1/8YsXn4HQcHVD1NB/VE+pEbGtrK2iGWVlZkfd5WlpasLCwgPb29qbs5KTMPJPJYGxszJafo3qRRJ/j8vKyKdZl9XaMqTe12jNWL5KSySRisRiWl5fr2pxGc9Yul8twMF1aWoLf7zdlIf70009j7969AIBdu3bh5MmTBT/PZrO4//77cdttt8n/tnXrVvk5zPO8I8rqVmLrd1/sBnGi7RrHcQgGg7IofzqdRiQSwfz8PFpaWuB2u5HP522RndUK2i9yuVyO8bLU+hyrmVmmMvf4+HhTPowomPb19dW0m51G4Gixm06nwfM81tfX4Xa7azYep9QmNiLaQcHU4/GYVtXieb7gM3C73RAEQb4v9+zZs+lvWltbMT8/j2uvvRbhcBgPPPBA1efhZBz3DXZiMFVD55xKpTA+Pg6Xy4V4PI5z587VXUiiVjSC9RzHcQgEAvJ+ZyaTAc/zmJub0zUnGY1GEYlEbFPmrjU0GtXf31/XBizl59jX1ydb0C0uLspSiKS4ZbYF3crKCgBjQv+iKGJ5eRlut1t+fphBW1sbEolEwXHKPYO+9rWv4bLLLsNHPvIRLC4u4j3veQ+OHDnSlNsWgMMCqt32SyslFovJesS0Am5paZG/zPF4XH4o0xhHvZt0zIQEK6j81ij4/X74/X55vy4ej2NpaUnzoUyOMXYtc1sNBdOBgQHbOSb5fD5ZcYs6+NfX1+X9c/JTrtaCbnV11bAFnSiKWFlZAcdxmJiYMPXe2b17N44dO4brrrsO09PT2LlzZ9m/6ejokJ9NnZ2dEAQB+XzetHNyGrbu8pUkCdlsVv5vpwdTSZIQCoWQSCQwMjJSNiuppZBErchms5ifn695ia+eqPVp6SFoZy9TK6HRIKd1M6tFQSp1OqrUHF3pA7t161bTF2LU5fvCCy9AkiTcc889eO6555BMJrF//3759971rnfh4MGDmJqaQiKRwB133CFrjb/73e/Gm9/8ZlPPy0k4IqBKkgRBEBwbSIHzXXwAKjJFpg7FWCxmaRnKSsjH1WkPUrOg/bJMJgOfz4dUKuV4EQKjODWYqiFREDJQp2pSW1tb2QWv0ujCSDClILxt27amuFeciK0DqiiKSKfTjt4vBc7LgZFbRLXvw0whiVrR7J2sWo4xyqYmcjmiz7IRM1eas21EBTC1BV2xuWXqaDcivkDWhZlMBlNTUyyY2hhbB1Rq8Ghra0NHR4cjm3Ror6inp8fU+TpCLbvW2tqKjo4OW2makvrT6OioIz/DatHrGKOUQWy0/XOqTjTDnC2NkPA8L5sxtLW1IZ1OI5vNGg6moVAIqVQK27dvZ8HU5tg6oNKeRTgcRjQalVvdKbjaJWAUg4b1rdakJUhIIh6PI5VK2cJVhZpv9OwZNyKVOsYoMx5RFOURDyeV+IlmCqZqSApxdXVVXvDq7eKXJAnhcBiJRALbt29vyu+P07B1QFVCNyYFVxpLsGtwpfnCkZGRupQ46XrFYrECV5VqBAiMHn9lZQWCIDSttiip/3R3d1flGKMu8euVs7QDtKhs1lI/sLGoTCaTGB4eLlgoKYVf1AsNCqY8z2PHjh0smDoExwRUJbT3FA6HEYlEAEAOrl6vt+4PmVAohHg8jtHRUVt8EbT26irpTjRyvIWFBXg8HkPD6o2EVeo/VOLneV6uQtAYh93KgWR0oBwPazaoq19LfEGpF53L5QBsNCzt2bNH7u5nwdRZODKgKqFO4FAohEgkAlEU5WBR6/IYZWX5fB6Dg4O2e8AB569XLBazxLKMGrCa1RQbqJ1jDFUh4vE4EomE5QslIySTSbkJrVmDKWWYeswuRFHEuXPn8MADD+C5557Djh078La3vQ179+613ZwuoziOD6hKKFhQ5lrL8RJS/mlpaUFvb69jsjISkqASVDWNMCTYYLbAuZOol2OMeozD5XLJwbXWAS2RSGBlZaXpgylVqYwsrCORCEKhELLZLP7t3/4NTz31FPr6+vChD30IL3/5yy08Y4YZNFRAVaMMrkoLLr/fb2r22CjKPyQkwfO8nOnrFZJodh9PwF6OMWpREOXC0krIzWVsbKwpO7qBjaAYi8UMB9NYLIZQKIQdO3YULETOnTvX1BUfJ9HQAVVJLpeTg6uysaOlpaWq4EodjI0WSNR+oKUyfQokjThfqBfKyurVhFaKfD4vL5SsnFsmk4BmHY8CNkbEotGo4WBK12779u22u38Y+mmagKokl8shEokgEokU+FsGAgFDXwKe5+WHaL0zEispJSSRSCSwurpqy0BSK6ij2wlZmXpu2azRKsqumlVOEThvdmBUn5nunx07djTtd6hRaMqAqiSfz8uZayqVKhBGKPWloIDcbKtx5QM5kUhAkiQMDg6ivb3dMfvGZkIPUbt0dBuB5rx5nkcikajYE9TJ18AsYrEYwuGw4WBKJfLt27c39KK8WWj6gKokn8/Lqj7JZFLOXFtbW+UvCXWxulwuDA8P27KT12pICi2ZTKK7u7tghEN9vRoZEq0wWt6zI9TURKVhvd3fkUgE0Wi0aV1zgPPB1OiCgrYJWDBtHFhALYIoinJwTSQSCAQC8Hq9+OQnP4ldu3bhve99b1NmZCTwTpkpXQP1CEethSRqiSRJBZqsjfb+gPPd3zzPF3U6qrSTtZGgvU+jpW7Stt6+fXvT9h00Iiyg6oBmxP7oj/4Ir3nNa/D2t79dVjhppgcJadL6/f6So0FKIYlEIgGv12ub+chqUc4aG3ELcTLqBrVgMChntCyYGg+mJHjBgmnjwQKqDs6cOYMPf/jD+NCHPoTLLrsM8XhcHtq20zC9leTzeczNzaGjowPd3d2G/pZKifF43HQhiVqi5RjTbIiiiMXFRSSTSbjd7oJtkWa6HjzPy41oRr731BG/bds2JtjQgLCAqoO77roLN9xww6bBamrQWV9fb+jgSo45ZsjomSkkUUv0OsY0Omtra0in0xgZGQGAAsPtRi7zK6l01pYF08aHBVSToOAaDocRi8Xg9Xrl8RKnZWJKSPnHCsccLUeVjo4O240OVOoY00jQvnEx+zF1md/j8eh2VXES1EhkNJjSvPrWrVsbal6dUQgLqBYgiqJsOxeLxRxb5qS9nlrM2RoRkqglZjnGOBlJkrC6ugpBEHTvGyubmgDIn6fdFktGqDSY0qJ0cnISbW1tFp4ho96wgGoxak9Xt9tdkLnatXRIw+b10GPN5/NIJBKIxWKyqlVHR4fpyj7lMLPU7VSoCUsUxYKubiMIgiCX+Wupr20mJPZvNJiSJOfExETT3kPNBAuoNcQpnq6UWdthUF+t7FOrJhimTXx+RAoABgYGTLnetFiKx+MFKmV2bmqq1DnHrGAqiiIOHjyIU6dOwefz4dChQ5iYmCj4nVQqhfe+9724++67MTU1BQB48MEHcfToUeRyOdx000145zvfWfE5MPThnPpjA8BxHFpbW9Ha2orh4WHZ03V+fh5A/T1dqbSXzWZtM6hPrint7e1yth+LxbC8vIyWlhZ0dHSYLiRRL8cYOyFJEpaWluByuUztaHa73ejo6EBHRwckSZIrEfR5klKTHe49YCNQVRJMqboxOjpadWb65JNPIpvN4vHHH8f09DTuu+8+HD58WP75L37xC3ziE5+QFz8A8OMf/xjPPPMMvvnNbyKVSuHhhx+u6hwY+mABtU5wHIdAIIBAIIChoSHZ03VxcbHAHcTn89UkuNJIiMvlwsjIiC2zBY7jEAwG5TlIEpJYWVkxrcPUTo4x9YLuBY/Hg76+PsvuBarQtLW1FTQ1ra2t2aKpie4FowbpuVwOs7OzGBkZMcV96umnn8bevXsBALt27cLJkycLfp7NZnH//ffjtttuk//tqaeews6dO3HrrbeC5/mCnzGsgwVUG8BxHPx+P4aGhjA4OCjbzi0vL9dkz4m8XAOBgGNGQpTZPokMxGIxrK+vw+PxoKOjw/D4krLpxO5jPFYhSRIWFhbg8/nQ19dXs+MqF5jA+dnlubm5uoxXVRpMycpxeHjY8Lx2MXieL2hmcrvdEARBXmjs2bNn09+Ew2EsLCzggQcewNzcHN7//vfj+9//viO+206GBVSbQcF1cHCwILhSl6XSds6ML0cjeLlyHIeWlhZZdYYexrOzswUl41KZjpMcY6xCkqSChVU98fv9siIXjVctLi7WZIGZTqexuLhouMwrCAJmZ2cxNDRk6nhVW1sbEomE/L9FUSx7j3Z1dWHbtm3w+XzYtm0b/H4/QqFQ3T/XRsceGxWMovh8PgwMDOBlL3sZdu7cidbWVqyvr+PMmTNYXl5GMpmEKIoVvXY2m8Xs7Cx6e3sdG0y1oAfx5OQkBgcH5Qx8ZmYGoVAIuVyu4Pej0ShCoRDGx8ebNpjSrG1ra6vtHrperxfd3d0YHx/H+Pg4fD4f1tbWCr4DZvVW0rzo6OiooREfWpgODg6aPqu8e/du/PCHPwQATE9PY+fOnWX/Zs+ePfjRj34kN5alUqmG+o7bleZ8ejgUr9eL/v5+9Pf3y56uoVAI2WxWtp3Ta5hOD45Gb7zx+Xzo6elBT0+PPL6xuLgoC0mIooh0Om2bJqx6QMG0vb3dtDKlVbjdbnR2dqKzs1Oe945Go1haWqq6qSmTyWBhYcGwty/Jcvb19VmyGLn66qtx/Phx3HjjjZAkCffccw+OHDmCZDKJ/fv3a/7NFVdcgZ/+9Ke4/vrrIUkS7rzzzrp37DcDbGymAVB7ulJZuJinKzldGF2FNxKCIGBxcRHpdBoej8eRs5FmQMG0o6PD0RmM2u3IqAwodXYbbUajYNrT04P+/v5q3gKjAWABtcEo5+n6ve99D93d3bj44oubtrypFiuQJElWacpkMgULkkYOrhQMurq6GkoFSpKkAqUml8slL5i09kSrDaZbtmzBwMCAmW+B4VBYQG1g1J6u//RP/4RnnnkGX/jCF5pWtaWcY0y9hCRqDUkqbtmyBR0dHfU+HUvJ5XJycKVSP42k0bzo8PCwISu1fD6P+fl5dHV1mSZ6wXA+TRVQK1EcyeVyuOOOOzA/P49sNov3v//9eMMb3lCnd1AZoijiM5/5DBYXF/G//tf/QiaTQSAQaDpPV6OOMSQkQW4qdhQeqIRmCqZq8vl8QTUin8+jv78fnZ2duoMilck7OzsrlmNkNCZNVfOrRHHku9/9Lrq6uvCZz3wG4XAYb3/72x0VUCVJwm233YbBwUF87nOfA8dxEEUR8XgcoVAIa2tr8Pl86OjoQDAYbNjGhUoab4oJSayurjrWqoxGO5pVn5iamgKBAGZnZ9HT04NkMolQKIRAIFCwPaIFdYx3dHSwYMrYRFMF1EoUR6655hq88Y1vlP+30wKOIAi45pprcNVVV8n/5nK5CjoleZ4vCK6N5ulqhmOMlpBEPB6XhSRo1tXO14yCaV9fX1O7nijLvNThXkx5S7nIpApHMBjU7brDaC6aKqBWojhCwug8z+MDH/gAPvjBD9bkXM3C6/UWBFM1LpdL1lZVerrOzMw0hKerFY4xSiGJvr6+ioQkag1dh/7+/qYV+wfOywKqx8WKLZjOnDmDw4cP45JLLsGrXvUqjIyM2Faak1F/7PONrwGVKI4AwOLiIm699VbcfPPNePOb32zlKdYVZTBQerrOzs4WjJbYKVCUolaOMWpVn3g8XmB4UG8fUAoiVpjEOwlaVAwODpacvVYvmD72sY/hyJEj+PM//3N4PB5cccUVuOqqq7B169Yanj3DCTjjyWgSu3fvxrFjx3DdddfpVhxZW1vDLbfcgjvvvBOXXHJJDc7SHtCoAQmX0z7T7OxsgaerXTVv6+UY4/V6sWXLFmzZskUWklhaWkI+n5cXK7UU3adFRaMLeJSDlIwGBgYMLSpEUYTf78dNN92E2267DZFIBMeOHcOnP/1p7N27FzfffLOFZ81wGk3Z5fvCCy/IiiPPPffcJsWRd73rXTh48CCmpqZw6NAhfO9738O2bdvknz/00EOGWuwbCdprCoVCiMVitvR0taNjDHWXxmIxWZO5o6PDUiEJFkw3oL1jo+VuURSxvLwMl8uFiYkJRzWfMepDUwVUhrmQ5RapNAH193Qlxxijwua1hBrBrBSSoAzd6Hxlo1FNMF1ZWQEATE5OOjqYfuADH8BFF12E973vfQA2viPveMc78IUvfAEvf/nL63x2jQULqAxTIHWaUCiESCRSF09XJzrG0F51LBZDOp1GIBCQTdMrvWak02ynDL0eVNrVrFTS2rp1q6ODKQCEQiHs27cPDz30ELZv344777wTk5OTuOWWW+p9ag0HC6gM06HgSplrLSy3IpEIotEoRkdHbT26UgozhCRYMN2ARqV6e3sNB1OySty2bZvjgylx9OhRfOUrX8GHP/xhHD58GA8//LAttmcaDRZQGZajDK5WeLqGQiHwPI/R0dGGeQBSOT0Wi8li7+XEN2jvuJlND4DzwbSnp8fQqJQkSVhfX0cmk8HU1FTD3EvExz/+cfz4xz/GN7/5TaY9bBHOqIsxHA15ug4MDCCXy8kiErlcriC4Gn2ASZKEtbU1ZDIZjI2NNdSKm+M4BAIBBAKBokISbW1tcmk7mUxiaWkJY2Njtt07rgXVBNNQKIR0Oo3t27c3XDAFgLe97W1Ip9MsmFoIC6iMmuL1eguCazgcrsjTlYyTJUlq+EH7YkISc3NzcLlc8Pl8SCaTLJgq3F+MBlNyZ9qxY0dDBlNGbWABlVE3lIbpgiAgEokgHA4jnU6X9HRVOsY0o9OHUkgiGo1iZWUFXq8X8/Pz8qxrs5V8Sau5u7vbkOA/BVOe51kwZVQN20Nl2A7ydA2FQkilUgUWatlsFp/73Odw4MCBpjd05nkeq6urclczCUnE4/G6CUnUA1EUMTs7W5GvazgcRjwex44dOxzbzMawDyygMmwNebqGQiGsr6/jz/7sz3DZZZfhlltuaepsgvZTR0dHNUeElDZl6r3qRsrolVZqRoMpdYbv3LmTBVOGKTTvE8mBiKKIO++8E/v378e73vUuzMzMbPqdVCqFG2+8EadPny749/X1dVx++eWb/t3uuFwudHd3o6enB5///Oexb98+/PZv/zbOnDmDhYUFxONxiKJY79OsKbFYDOvr6yXnbcmmbHR0FBMTE2hpaUEoFMKZM2ewvLyMZDIJp6+lKZh2dHRUHExZZsowE7aH6iAq8XMFNkTB77zzTscq5oTDYbzvfe/Drbfeite//vUANh6msVgM4XC4wJ+0kT1dASAajSISiWBsbEz3+1Q6CkmShEQigWg0iqWlJdkDNBgMOipzVfrbdnV1GfrbWCyGSCSCHTt2OEYAhOEMWIbqIPT6uSp1hwHgU5/6FG688UbH7jkKgoBPfOITcjAFNoJEV1cXtm7digsuuAD9/f1Ip9OYmZnB3NwcotEo8vl8/U7aAiKRiOFgqoa0l4eGhrB161Z0dHSA53lHZfxk8m3ELJ6Ix+MIhULYvn17VR3RzVgtYpSHLc8cRCV+rt/5znewZcsW7N27F1/5yldqdq5m0tfXh76+vqI/VxumJxIJhEKhhvF0Bc43z4yNjZm2d8xxHILBIILBoCwkEY/Hsbq6alujeUmSMD8/j2AwWFEwXVtbw44dO6rugm7WahGjNCxDdRCV+Ll++9vfxr//+7/jXe96F375y1/iYx/7GFZXV60+1bpBnq4TExO44IILMDIyIg/7z87OIhwOQxCEep+mIWqhBEVCEv39/di6dSt6e3uRzWZx7tw5zM7OyipX9UQZTLds2WLob3mex9raGrZv327KSFGzVosYpXHukr0JqcTP9Rvf+Ib832RLVyrbayT0eLqS7ZxdWV9fRyqVwujoaM32ONVCEtlsVhaS4DhOHseppYgEBdNAIGA4mCYSCayurmL79u2mjRA1a7WIURr7PkkYm7j66qtx/Phx3HjjjbKf65EjRzb5uTI2oy5vkqcrBQm7eboCG+b26XS67kpQPp8PPT096OnpQS6XQzwex8LCAiRJqomQhCRJWFhYQEtLC3p6egz9bSKRwPLysqnBFKi8WsRxHP7jP/5DrhYdPny4aRa4zQCbQ2U0NXb0dCWN4mw2i+HhYdsEeDWCIMim6eQo1NHRYapdHwVTUoYyAukbb9++3fQ9y3/+53/GsWPHcN9992F6ehpf+tKX8NWvfnXT71FVaGpqSte/M5wNy1AZTY1ShH5oaEj2dF1cXKyLp6vSPszOwRQAPB4Purq60NXVJQtJrK6uIpvNytetGiEJkpj0+XyGg2kqlcLS0hK2bdtmSQMQqxYxtGAZKoOhQT08XZXG1oODg7YOpqWgTut4PI50Oi2bHgQCAd3viYKpx+NBX1+foWtBNnbbtm1Da2trpW+DwTAMC6gMhg4oc1UG17a2NtOk/Mg9B0BDCf6TkEQ8HkcqldIlJCFJEpaWluB2uw0HUzJY37p1K4LBoFlvg8HQBQuoDIZBlJkr6eR2dHTA7/dXNNZCAcTlcqG/v79hgqkaagaLxWJIJpOyulVbW5t83WhhwXGc4WuRyWQwPz+PycnJgg5cBqNWsIDKYFQBebpGIhFks1nDhunVlDadjFJIgud5+Hw+tLW1IZVKVbSwyGazmJubw8TEhCEvVAbDTFhAZTBMQunpmslkZNs5LU9XoLoO1kZCkiRkMhksLS0hm82ipaUFHR0daGtr0zUjzIIpwy6wgMpgWEApT1eXy4VsNot/+Zd/wSWXXGJ4trLRoM7mfD6PwcFBedY1Ho+XFZLI5XKYnZ3F2NiYYccZBsNsWEBlMCyGgms4HEYymYTH48Hdd9+N1772tbjlllvqfXp1RR1M1WVeZXClMSaaz6VgOjIyYljXl8GwAhZQGTVFFEUcPHgQp06dgs/nw6FDhzAxMVHwO6lUCu9973tx9913y4PvDz74II4ePYpcLoebbroJ73znO+tx+lWTTCZx6623YteuXbjyyisLul6b0TB9dXUVuVwOQ0NDZfdMBUFAKBTCn/7pnyIUCmH37t14y1vegosvvrhp9p4Z9oYJOzBqSiUuHT/+8Y/xzDPP4Jvf/CZSqRQefvjhepx61SSTSfzBH/wB3vSmN+H6669vWk9XwkgwBTaEJPr7+3H//ffj+eefx0svvYS//uu/xt13341LL70Ub33rW3XpWzMYVsECKqOm6HXpuO222+R/e+qpp7Bz507ceuut4Hm+4GdO4mc/+xne8Y534Ld/+7cBnPd07erqgiiKiMfjCIfDWFtbs619mllUKq0oCALm5uYwNTWFV7/61bjxxhuRTqfx1FNP4Re/+AULqIy6wgIqo6ZU4tIRDoexsLCABx54AHNzc3j/+9+P73//+44r81122WVFf1bO05UacxohuK6vr1ck+p/P5zE3N4e+vr6CRq6WlhZcddVVVpwqg2EIFlAZNaUSl46uri5s27YNPp8P27Ztg9/vRygUatjuWPJ0bW9vhyiKsu3cuXPn4PF4HG2YTl3PlQTT2dlZ9Pb2MncWhm1pvi4IRl3ZvXs3fvjDHwKAbk/XPXv24Ec/+pGsopNKpdDV1WXxmdoD8nQdHx/HBRdcgNHRUYiiiNnZWZw7dw6hUKjuxt96CYVCSCQSFWemPT09zJibYWuct8RlOJpKXDquuOIK/PSnP8X1118PSZJw5513NkTp0yhO9HQlwuEwEomEYaP0fD6P+fl5dHd3s2DKsD1sbIbBcDgk4xcKhRCNRgGgQAyh3sE1HA4jHo9jdHTU0GiQKIqYm5tDZ2eno913GM0DC6gMRgNBMn4UXEkMoV6G6ZFIBLFYrKJgOj8/j/b2dt1jNQxGvWEBlcFoUMjTlYJrLTxdlVQTTBcWFhAMBm1vss5gKGFNSQzdHD9+HG95y1uQTqcBAMvLy3jzm99cIMLAsA8cx8Hv92NoaAgvf/nLsWPHDvj9fqysrODs2bNYXV1FOp2GFWvqaDSKaDRaUTBdXFxEIBBgwZThOFiGyjDEpz/9aSSTSfzxH/8x3vOe9+DWW2/FpZdeWu/TYhhE6ekqCAJaW1ur8nRVQupPY2NjFQVTv9+PsbExFkwZjoMFVIYhcrkcbr75ZnR1deGVr3wl/uAP/qDep8Sokmo9XZVQMB0dHTXUiS2KIpaWluD1eg0HYgbDLrCAyjDMt771LRw8eBD/+q//iuHh4XqfDsNEjHq6KonH41hfX8fY2JjhYLq8vAy3243x8XEWTBmOhQVUhiHm5+dxyy234KabbsK//uu/4pFHHmnKmdBmIJ/Py8FVy9NVSTXBdGVlBRzHYWJioqpgWomTUS6Xwx133IH5+Xlks1m8//3vxxve8IaKz4HR3LClIEM32WwWH/zgB3HHHXfgd3/3dzE0NIQvfelL9T4thkW43W709PRg+/btuOCCC9Dd3Y14PI4zZ85gYWEBPM9DFEU8+eST+Ku/+ivDwZS8UCVJqjqYAoVORh/5yEdw3333Ffz8F7/4BX7nd34Hs7Oz8r9997vfRVdXF/7mb/4GDz30ED75yU9WdQ6M5oYFVIZuPvWpT2HPnj24/PLLAQAHDx7EP/7jP+LHP/5xnc+MYTVutxtbtmzB1NQULrzwQvT09IDneXznO9/BV77yFbz97W831ESkNBbfunWrKWVevU5G27Ztk//tmmuuwf/+3/9b/t+s2sKoBiY9yNDNn/7pnxb877a2NvzLv/xLnc6mflRaWrz99tsxPz8Pl8uFT37yk7J5utNwuVzo7u7G888/jyNHjuBLX/oSJEnC2bNndXm6SpKE9fV15HI5TE1NmbZnWomTUTAYlP/2Ax/4AD74wQ+aci6M5oRlqAyGQSopLf7gBz+AIAh47LHHcOutt+Lzn/98jc/aXH72s5/hs5/9LB544AFMTk5i69atuOCCC9Df3490Oo2ZmRnMzc3JghKEJEkIhUJIp9OmBlOgMicjAFhcXMS73/1uvPWtb8Wb3/xm086H0XywgMpgGKSS0uLWrVuRz+chiiJ4nnek9ZqSYDCIBx98sMBCjzxdJycnccEFF2BoaAjZbBYzMzOYnZ1FJBLB+vo6kskktm/fbno3byVORmtra7jlllvw0Y9+FNdff72p58NoPpz9rWYw6kAlpcXW1lbMz8/j2muvRTgcxgMPPFCz87WCl73sZSV/ruXpSsF0586dlozGVOJk9MADDyAWi+HLX/4yvvzlLwMAHnroIbS0tJh+fozGhwVUBsMglZQWv/a1r+Gyyy7DRz7yESwuLuI973kPjhw5Ar/fb/Xp1h3ydFUuQqw6zl133VXwb1r71I8++qj833/yJ3+CP/mTP7H0vBjNAyv5MhgGqaS02NHRgfb2dgBAZ2cnBEEo2FtkMBjOh2WoDIZBKikt/u7v/i7uuOMO3HzzzcjlcvjQhz6E1tbWGp85g8GwEqaUxGAwGAyGCbCSL4PBYDAYJsACKoPBYDAYJsACKoPBYDAYJsACKoPBYDAYJsACKoPBYDAYJsACKoPBYDAYJsACKoPBYDAYJsACKoPBYDAYJsACKoPBYDAYJsCkBxmMJqacWfo//MM/4Otf/zrcbjd27tyJgwcPAkBZg3UGoxlhGSqD0cSUMktPp9P4/Oc/j0ceeQSPPfYYeJ7HsWPHyhqsMxjNCguoDEYTU8os3efz4bHHHkMgEAAACML/394dozgIRWEYvVZuwB3YWtqldAn2LsUNWAayNIs07kIQBpkiTJphJhAupHjn9K+w+vAp/F9R1/XLgXUolaBCwf4aS4947Is2TRMRjw3Rfd/jcrn8ewZK5hsqFOzVWPp5nrEsS2zbFtfrNaqqemtgHUrgDRUK9mosfZ7nOI4jbrfb8+r3nYF1KIE9VCjYz1++9/v9OZa+rmvs+x5d18U4jtH3fVRVFRER0zTFMAy/zrRt++Engc8TVABI4MoXABIIKgAkEFQASCCoAJBAUAEggaACQAJBBYAEggoACQQVABIIKgAkEFQASCCoAJBAUAEgwTfA7oX2PYLocAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,8))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "ax.scatter(rcv[:,0], rcv[:,1], rcv[:,2], c='b', marker='o')\n",
    "ax.scatter(src[:,2], src[:,3], src[:,4], c='r', marker='^')\n",
    "ax.invert_zaxis()\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_zlabel('Z')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For simplicity, we will use a velocity model with a small vertical gradient and mean Vp = 4 km/s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Vz(z):\n",
    "    return 4.0 + 10. * (z - 0.050)\n",
    "\n",
    "Vp = np.kron(Vz(zz), np.ones((g.shape[0], g.shape[1], 1)))\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.subplot(221)\n",
    "plt.pcolor(x, z, np.squeeze(Vp[:,0,:].T), cmap='CMRmap',), plt.gca().invert_yaxis()\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Z')\n",
    "plt.colorbar()\n",
    "plt.subplot(222)\n",
    "plt.pcolor(y, z, np.squeeze(Vp[0,:,:].T), cmap='CMRmap'), plt.gca().invert_yaxis()\n",
    "plt.xlabel('Y')\n",
    "plt.ylabel('Z')\n",
    "plt.colorbar()\n",
    "plt.subplot(223)\n",
    "plt.pcolor(x, y, np.squeeze(Vp[:,:,4].T), cmap='CMRmap')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now compute the travel times\n",
    "\n",
    "The raytracing routine takes 3 input variables:\n",
    "- the matrix of hypocenter data (hypo)\n",
    "- the matrix of receiver coordonates (rcv)\n",
    "- the slowness vector (ordered in 'C' order)\n",
    "\n",
    "hypo and rcv must have the same number of rows, with each row corresponding to a source-receiver pair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195, 5)\n",
      "(195, 3)\n"
     ]
    }
   ],
   "source": [
    "slowness = 1./Vp.flatten()\n",
    "\n",
    "src = np.kron(src, np.ones((nsta, 1)))   # use kron to replicate src-rcv pairs correctly\n",
    "rcv_data = np.kron(np.ones((nev, 1)), rcv)\n",
    "ircv_data = np.kron(np.ones((nev, 1)), ircv)\n",
    "\n",
    "print(src.shape)\n",
    "print(rcv_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# call the actual raytracing method\n",
    "\n",
    "tt = g.raytrace(src, rcv_data, slowness)\n",
    "\n",
    "plt.plot(tt,'o')\n",
    "plt.xlabel('Data number')\n",
    "plt.ylabel('Traveltime')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Add some noise to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_variance = 1.e-3;  # 1 ms\n",
    "tt += noise_variance*np.random.randn(tt.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine the event IDs, travel times, and receiver locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.hstack((src[:,0].reshape((-1,1)), tt.reshape((-1,1)), ircv_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial hypocenter locations\n",
    "\n",
    "Note that for efficiency reason when computing matrix M, initial hypocenters\n",
    "should _not_ be equal for any two event, e.g. they shoud all be different.\n",
    "\n",
    "Here we place the initial coordinates more or less in the center of the grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "hinit = np.vstack((np.arange(nev),\n",
    "                   np.linspace(0., 50., nev),\n",
    "                   0.150 + 0.0001*np.random.randn(nev),\n",
    "                   0.150 + 0.0001*np.random.randn(nev),\n",
    "                   0.050 + 0.0001*np.random.randn(nev))).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Velocity data points\n",
    "\n",
    "For illustrative purposes, let's add a couple of velocity data points to constrain the inversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vpts = np.array([[Vz(0.001), 0.100, 0.100, 0.001],\n",
    "                 [Vz(0.001), 0.100, 0.200, 0.001],\n",
    "                 [Vz(0.001), 0.200, 0.100, 0.001],\n",
    "                 [Vz(0.001), 0.200, 0.200, 0.001],\n",
    "                 [Vz(0.011), 0.112, 0.148, 0.011],\n",
    "                 [Vz(0.005), 0.152, 0.108, 0.005],\n",
    "                 [Vz(0.075), 0.152, 0.108, 0.075],\n",
    "                 [Vz(0.011), 0.192, 0.148, 0.011]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calibration shots\n",
    "\n",
    "Let's use 5 shots recorded at most receivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ncal = 5\n",
    "src_cal = np.vstack((5+np.arange(ncal),\n",
    "                 np.zeros(ncal),\n",
    "                 0.160 +  0.005*np.random.randn(ncal),\n",
    "                 0.130 +  0.005*np.random.randn(ncal),\n",
    "                 0.045 +  0.005*np.random.randn(ncal))).T\n",
    "\n",
    "src_cal = np.kron(src_cal,np.ones((nsta,1)))\n",
    "rcv_cal = np.kron(np.ones((ncal,1)), rcv)\n",
    "ircv_cal = np.kron(np.ones((ncal,1)), ircv)\n",
    "\n",
    "ind = np.ones(rcv_cal.shape[0], dtype=bool)\n",
    "ind[3] = 0\n",
    "ind[13] = 0\n",
    "ind[15] = 0\n",
    "src_cal = src_cal[ind,:]\n",
    "rcv_cal = rcv_cal[ind,:]\n",
    "ircv_cal = ircv_cal[ind,:]\n",
    "\n",
    "tcal = g.raytrace(src_cal, rcv_cal, slowness)\n",
    "caldata = np.column_stack((src_cal[:,0], tcal, ircv_cal, src_cal[:,2:], np.zeros(tcal.shape)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constant velocity inversion\n",
    "\n",
    "Before running the joint hypocenter-velocity inversion (JHVI), we use the constant velocity location algorithm.  This will improve convergence of the JHVI.\n",
    "\n",
    "Initial velocity model is of course homogeneous, we take the mean value of the data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " *** Hypocenter inversion ***\n",
      "\n",
      "Locating hypocenters no 0\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 1\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 2\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 3\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 4\n",
      "     Converged at iteration 2\n",
      "Locating hypocenters no 5\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 6\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 7\n",
      "     Converged at iteration 2\n",
      "Locating hypocenters no 8\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 9\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 10\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 11\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 12\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 13\n",
      "     Converged at iteration 3\n",
      "Locating hypocenters no 14\n",
      "     Converged at iteration 3\n",
      "\n",
      " ** Inversion complete **\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Vinit = np.mean(Vpts[:,0])\n",
    "\n",
    "hinit2, res = hypo.hypoloc(data, rcv, V=Vinit, hinit=hinit, maxit=15, convh=0.001, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for n in range(res.shape[0]):\n",
    "    ind = res[n, :] > 0\n",
    "    plt.semilogy(res[n, ind], 'o', label='event no {}'.format(n))\n",
    "plt.legend(loc='lower left', bbox_to_anchor=(1.03, 0.0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look now at error on hypocenter location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "err_x = hinit2[:,2:5] - h_true[:,2:5]\n",
    "err_x = np.sqrt(np.sum(err_x**2, axis=1))\n",
    "err_t = hinit2[:,1] - h_true[:,1]\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.subplot(121)\n",
    "plt.plot(err_x,'r*')\n",
    "plt.ylabel(r'$\\Delta x$')\n",
    "plt.xlabel('Event ID')\n",
    "plt.title(r'$\\|\\|\\Delta x\\|\\|$ = {0:3.2f}'.format(np.linalg.norm(err_x)))\n",
    "plt.subplot(122)\n",
    "plt.plot(err_t,'r*')\n",
    "plt.ylabel(r'$\\Delta t$')\n",
    "plt.xlabel('Event ID')\n",
    "plt.title(r'$\\|\\|\\Delta t\\|\\|$ = {0:6.5f}'.format(np.linalg.norm(err_t)))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joint hypocenter-velocity inversion parameters\n",
    "\n",
    "Define inversion parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vpmin = 3.5\n",
    "Vpmax = 4.5\n",
    "PAp = 1.0\n",
    "Vsmin = 1.9\n",
    "Vsmax = 2.3\n",
    "PAs = 1.0\n",
    "Vlim = (Vpmin, Vpmax, PAp, Vsmin, Vsmax, PAs)\n",
    "        \n",
    "dVp_max = 0.1\n",
    "dx_max = 0.01\n",
    "dt_max = 0.01\n",
    "dVs_max = 0.1\n",
    "dmax = (dVp_max, dx_max, dt_max, dVs_max)\n",
    "        \n",
    "λ = 2.     # weight of smoothing constraint\n",
    "γ = 1.     # weight of penalty constraint\n",
    "α = 1.     # weight of velocity data point constraint\n",
    "wzK = 0.1  # weight for vertical smoothing (w.r. to horizontal smoothing)\n",
    "lagran = (λ, γ, α, wzK)\n",
    "\n",
    "par = hypo.InvParams(maxit=3, maxit_hypo=10, conv_hypo=0.001, Vlim=Vlim, dmax=dmax,\n",
    "                     lagrangians=lagran, invert_vel=True, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's build an initial velocity model slightly off the true one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Vz2(z):\n",
    "    return 4. + 7.5 * (z - 0.050)\n",
    "\n",
    "Vpinit = np.kron(Vz2(zz), np.ones((g.shape[0], g.shape[1], 1)))\n",
    "Vpinit = Vpinit.flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joint hypocenter-velocity inversion\n",
    "\n",
    "Run the inversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " *** Joint hypocenter-velocity inversion ***\n",
      "\n",
      "Building velocity data point matrix D\n",
      "Building regularization matrix K\n",
      "\n",
      "Starting iterations\n",
      "\n",
      "Iteration 1 - Updating velocity model\n",
      "  Updating penalty vector\n",
      "  Raytracing\n",
      "  Building matrix L\n",
      "    Event ID 0\n",
      "    Event ID 1\n",
      "    Event ID 2\n",
      "    Event ID 3\n",
      "    Event ID 4\n",
      "    Event ID 5\n",
      "    Event ID 6\n",
      "    Event ID 7\n",
      "    Event ID 8\n",
      "    Event ID 9\n",
      "    Event ID 10\n",
      "    Event ID 11\n",
      "    Event ID 12\n",
      "    Event ID 13\n",
      "    Event ID 14\n",
      "  Assembling matrices and solving system\n",
      "    calling minres with system of size 12493 x 12493\n",
      "Iteration 1 - Relocating events\n",
      "  Updating event ID 0 (1/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 1 (2/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 2 (3/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 3 (4/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 4 (5/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 5 (6/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 6 (7/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 7 (8/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 8 (9/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 9 (10/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 10 (11/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 11 (12/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 12 (13/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 13 (14/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "  Updating event ID 14 (15/15)\n",
      "    Updating all hypocenter params - converged at iteration 2\n",
      "\n",
      "Iteration 2 - Updating velocity model\n",
      "  Updating penalty vector\n",
      "    Penalties applied at 12 nodes\n",
      "  Raytracing\n",
      "  Building matrix L\n",
      "    Event ID 0\n",
      "    Event ID 1\n",
      "    Event ID 2\n",
      "    Event ID 3\n",
      "    Event ID 4\n",
      "    Event ID 5\n",
      "    Event ID 6\n",
      "    Event ID 7\n",
      "    Event ID 8\n",
      "    Event ID 9\n",
      "    Event ID 10\n",
      "    Event ID 11\n",
      "    Event ID 12\n",
      "    Event ID 13\n",
      "    Event ID 14\n",
      "  Assembling matrices and solving system\n",
      "    calling minres with system of size 12493 x 12493\n",
      "Iteration 2 - Relocating events\n",
      "  Updating event ID 0 (1/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 1 (2/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 2 (3/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 3 (4/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 4 (5/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 5 (6/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 6 (7/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 7 (8/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 8 (9/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 9 (10/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 10 (11/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 11 (12/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 12 (13/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 13 (14/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 14 (15/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "\n",
      "Iteration 3 - Updating velocity model\n",
      "  Updating penalty vector\n",
      "    Penalties applied at 21 nodes\n",
      "  Raytracing\n",
      "  Building matrix L\n",
      "    Event ID 0\n",
      "    Event ID 1\n",
      "    Event ID 2\n",
      "    Event ID 3\n",
      "    Event ID 4\n",
      "    Event ID 5\n",
      "    Event ID 6\n",
      "    Event ID 7\n",
      "    Event ID 8\n",
      "    Event ID 9\n",
      "    Event ID 10\n",
      "    Event ID 11\n",
      "    Event ID 12\n",
      "    Event ID 13\n",
      "    Event ID 14\n",
      "  Assembling matrices and solving system\n",
      "    calling minres with system of size 12493 x 12493\n",
      "Iteration 3 - Relocating events\n",
      "  Updating event ID 0 (1/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 1 (2/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 2 (3/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 3 (4/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 4 (5/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 5 (6/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 6 (7/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 7 (8/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 8 (9/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 9 (10/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 10 (11/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 11 (12/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 12 (13/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 13 (14/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "  Updating event ID 14 (15/15)\n",
      "    Updating all hypocenter params - converged at iteration 1\n",
      "\n",
      " ** Inversion complete **\n",
      "\n"
     ]
    }
   ],
   "source": [
    "h, V, sc, res = hypo.jointHypoVel(par, g, data, rcv, Vpinit, hinit2, caldata=caldata, Vpts=Vpts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for n in range(len(res)):\n",
    "    ind = res[n] > 0\n",
    "    plt.semilogy(res[n][ind], 'o', label='event no {}'.format(n))\n",
    "plt.legend(loc='lower left', bbox_to_anchor=(1.03, 0.0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "err_xc = hinit2[:,2:5] - h_true[:,2:5]\n",
    "err_xc = np.sqrt(np.sum(err_xc**2, axis=1))\n",
    "err_tc = hinit2[:,1] - h_true[:,1]\n",
    "\n",
    "err_x = h[:,2:5] - h_true[:,2:5]\n",
    "err_x = np.sqrt(np.sum(err_x**2, axis=1))\n",
    "err_t = h[:,1] - h_true[:,1]\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.subplot(121)\n",
    "plt.plot(err_x,'o',label=r'$\\|\\|\\Delta x\\|\\|$ = {0:6.5f}'.format(np.linalg.norm(err_x)))\n",
    "plt.plot(err_xc,'r*',label=r'$\\|\\|\\Delta x\\|\\|$ = {0:6.5f}'.format(np.linalg.norm(err_xc)))\n",
    "plt.ylabel(r'$\\Delta x$')\n",
    "plt.xlabel('Event ID')\n",
    "plt.legend()\n",
    "plt.subplot(122)\n",
    "plt.plot(np.abs(err_t),'o',label=r'$\\|\\|\\Delta t\\|\\|$ = {0:6.5f}'.format(np.linalg.norm(err_t)))\n",
    "plt.plot(np.abs(err_tc),'r*',label=r'$\\|\\|\\Delta t\\|\\|$ = {0:6.5f}'.format(np.linalg.norm(err_tc)))\n",
    "plt.ylabel(r'$\\Delta t$')\n",
    "plt.xlabel('Event ID')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see a good improvement on relocation over constant velocity inversion (blue points are error after joint inversion, red are results for constant velocity).  What about the velocity model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "V3d = V.reshape(g.shape)\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.subplot(221)\n",
    "plt.pcolor(x,z,np.squeeze(V3d[:,9,:].T), cmap='CMRmap',), plt.gca().invert_yaxis()\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Z')\n",
    "plt.colorbar()\n",
    "plt.subplot(222)\n",
    "plt.pcolor(y,z,np.squeeze(V3d[8,:,:].T), cmap='CMRmap'), plt.gca().invert_yaxis()\n",
    "plt.xlabel('Y')\n",
    "plt.ylabel('Z')\n",
    "plt.colorbar()\n",
    "plt.subplot(223)\n",
    "plt.pcolor(x,y,np.squeeze(V3d[:,:,4].T), cmap='CMRmap')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Let's look at station corrections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sc,'o')\n",
    "plt.xlabel('Station no')\n",
    "plt.ylabel('Correction')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}