001_obstable.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6f128611",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/giadarol/xsuite_packages/xtrack/xtrack/progress_indicator.py:95: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  from tqdm.autonotebook import tqdm\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "448c7c3e5be64d888db31617c4004d68",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done loading line from dict.           \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dcebf020507d419982ec0aa0e29c6f98",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done loading line from dict.           \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05ad715f45344b69a1a1685eb2d83519",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done loading line from dict.           \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3ca2ff0a7d4a4922b3b77db2359715b4",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done loading line from dict.           \n",
      "Found suitable prebuilt kernel `default_only_xtrack`.\n",
      "Found suitable prebuilt kernel `default_only_xtrack`.\n",
      "Found suitable prebuilt kernel `default_only_xtrack`.\n",
      "Found suitable prebuilt kernel `default_only_xtrack`.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import xpart as xp\n",
    "import xtrack as xt\n",
    "\n",
    "fname_model = './generate_models/lhc_at_injection_obstacle.json'\n",
    "\n",
    "collider = xt.Multiline.from_json(fname_model)\n",
    "collider.build_trackers()\n",
    "\n",
    "nemitt_x = 3.5e-6\n",
    "nemitt_y = 3.5e-6\n",
    "num_particles = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dca0356",
   "metadata": {},
   "source": [
    "## Investigating an obstacle\n",
    "\n",
    "We start by twissing. We need to build a bunch of particles. Since we will reuse the bunch later, we copy it as a template."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eb1c81e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Maximum RMS bunch length 0.11789516327996152m.\n",
      "... distance to target bunch length: -1.0000e-01\n",
      "... distance to target bunch length: 1.4638e-02\n",
      "... distance to target bunch length: 1.3573e-02\n",
      "... distance to target bunch length: -1.0721e-03\n",
      "... distance to target bunch length: 1.6142e-03\n",
      "... distance to target bunch length: 7.1106e-05\n",
      "... distance to target bunch length: 2.9063e-06\n",
      "... distance to target bunch length: -4.0653e-10\n",
      "... distance to target bunch length: 1.9477e-07\n",
      "--> Bunch length: 0.09999999959346687\n",
      "--> Emittance: 0.6422094474317409\n"
     ]
    }
   ],
   "source": [
    "bunch = xp.generate_matched_gaussian_bunch(\n",
    "    line=collider.lhcb1,\n",
    "    sigma_z=10e-2,\n",
    "    nemitt_x=nemitt_x,\n",
    "    nemitt_y=nemitt_y,\n",
    "    num_particles=num_particles,\n",
    ")\n",
    "bunch_template = bunch.copy()\n",
    "\n",
    "tw = collider.lhcb1.twiss()\n",
    "sigmas = tw.get_betatron_sigmas(nemitt_x=nemitt_x, nemitt_y=nemitt_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "de4bf1ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, \"$q_x$ = 62.31000 $q_y$ = 60.32000\\n$Q'_x$ = 1.95 $Q'_y$ = 2.03 $\\\\gamma_{tr}$ = 53.65\")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ryWV/9+3bFxqNxmpZq1at0KFDB5w4caLWx3///fdQKpVWVRIfHx9MnjwZKSkpuHTpkt3HKpVKxMXFIS8vr9bXeeihhxAeHo5vv/222n1//PEHOI7Db7/9VuvziMGV48uZffP1118jKysL//vf/6BQKFBcXAye590eE5E/SqBIg/TOO++goKAA77zzDtRqtc11EhMTAcDSv+Xo0aO499576/S6er0eOTk5Dv2r7aReV+Xl5fD19a223M/PDzqdDkePHq123wMPPICgoCD4+Pjglltuwb59+xx6LbH2N+D5fc4YQ1ZWFsLDw2td9+DBg2jdujWCgoKslvfq1QsAqvU/Ky4uRk5ODs6dO4fFixfjjz/+wIABA2p9nbFjxyIsLAyvvPJKtVjnzJmDfv36YejQobU+T008sV+dOb5c3Td///03goKCkJ6ejjZt2iAgIABBQUGYNm0aysrK6hQTaWDELoERUt94nmfh4eG1lvu/+OILBoB98sknbnttc7OAI/9SU1Odem5nm/ASExNZ69atLU1mjDFWXl7O4uPjGQD2/fffW5bv3LmTjR49mq1cuZL98ssvbMGCBaxRo0bMx8eHHThwoMbXEXN/M+bZfS6Me+XKlbWu26FDB3brrbdWW37s2DEGgC1fvtxq+dSpUy2xKRQKNmbMGJabm+tQXG+//TZTKpVMp9NVi/Xff/916Dlq4s796srx5eq+6dSpE/Pz82N+fn5s5syZ7IcffmAzZ85kANjdd99dp5hIw0KdyEmDc+LECeTk5GDixIk1rnf+/HkAQExMjNteu3PnztVGXNnjatOcox599FFMmzYNkydPxjPPPAOe5/Haa6/hypUrAIDS0lLLun379kXfvn0tf48YMQJjxoxBp06dMGfOHGzYsMHu64i5vwHP7vOTJ09i+vTpSEpKwqRJk2pdv7S0tFrzJWBqxjPfL/TEE09gzJgxyMjIwHfffQej0QidTudQbK1bt4bRaERqaipat24NnU6HuXPnYuTIkUhKSnLoOWrizv3qyvHl6r4pKipCSUkJHnnkEcuou1GjRkGn0+Gjjz7C/Pnz0apVqzod86SBEDuDI6S+/fnnnwwAW7JkSY3r9e/fnymVSqtO1t7M2QoUY4w9//zzTK1WW37J9+jRg73wwgsMAPvpp59qffzdd9/NNBqNVRWrKrnu7ytXrrCEhAQWFxfH0tPTHXqMsxWoqm677TbWs2dPxvN8ra91/PhxBoCtW7eOMcbYkiVLmFKpZMePH7esw/M88/f3Z1lZWQ7FX98cOb7MHN03HTp0YADYtm3brJZv27aNAWCfffaZ22Ii8kYVKNJglZSU2L3vxIkT+OeffzBs2DCrTtZ1pdPpkJub69C6jRs39vj8R//73//w1FNP4dixYwgODkZiYiKef/55AKYKRm3i4uKg0+lQXFxcrV9PVWLsb8Az+zw/Px+333478vLysH37doerZtHR0UhPT6+23Fz1q+15xowZg6lTp+L06dM2p5kQSkhIgEKhwJkzZ1BYWIj//e9/uP/++9GuXTvLOqmpqfDz80NERITd5zEajTb3SX0cy84cX47um5iYGBw7dgyRkZFWy8374Pr1626LicgbJVCkwTEnBkeOHLF5P2MM06dPh0KhqNYJt6oZM2ZAr9fjo48+As/zuPPOO5GQkIDFixfbXP/ff//FLbfc4lCcqampaNasmUPr1kVoaKhl/iHA1Mm2SZMmaNu2ba2PPX/+PHx8fBAQEGB3HXft79zcXERERKCgoAB+fn4AgG+//RYffvghtm3bZvdx7t7nZWVlGD58OE6fPo2///4b7du3d+i5AaBLly7YsmULCgoKrL58zSMPu3TpUuPjzU18+fn5tb6WVqtFbGwszpw5g7feeguFhYV4+eWXLfefOHECXbt2hdFoREBAANq1a4e9e/fi3XffxdatW6FSqfDHH39g7dq1uP3226s9f30cy44cX2aO7pvu3btj48aNlk7kZhkZGQBMyZ67YiIyJ3YJjBAx9OrVi6nVavbff/9ZLTcYDOzhhx9mANjrr79e6/NcvnyZBQUFscuXL7PZs2ez4cOH1zi3TG5uLtu4caND/0pLS53appqa8IqLi9mJEyfY1atXa3yONWvWMADs7bfftlqenZ1dbd1Dhw4xtVrNRowYUWts7trfzZo1YykpKYwxxvR6PWvdujXbsWNHjY9x5z43GAxsxIgRTKVSsd9++63GdW3t8127dlWbB6qsrIy1bNmS9e7d27LMVpOaTqdj3bp1Y76+vqywsLDG1za75ZZbWOfOnZm/vz97+umnq93/+uuvs5kzZ1ote/DBB1njxo3Ztm3bmNFotOqELuTO/erM8eXMvrH1Hhw4cIABYPfcc4/Vc4wfP56pVCpLc2xdj3kif1SBIg3SihUr0L9/f/Tt2xdTp05FmzZtkJGRgS+//BKpqal49dVXMWfOnFqfJzY2FhMnTsSwYcMAADt27IBCYX92kNDQUCQnJ7ttOwDggw8+QF5enuUX9Lp16ywzKs+cORPBwcHYs2cPbrnlFsybN89Shfjnn38wf/58DBw4EI0aNcKuXbuwatUqDB48GI8//rjVa4wbNw6+vr7o27cvIiIicPz4caxYsQJ+fn5YuHBhrTG6a3/36NEDBw8eRJ8+fbB69Wq0aNECN9xwQ42Pcec+f/LJJ/Hrr79i+PDhyM3NrTZLunDaBVv7vHfv3hg7dizmzJmD7OxstGzZEp999hkuXLiAlStXWh47depUFBQUoF+/foiNjUVmZia++uornDx5Eu+8847D1Y+WLVvi448/RkhIiM39e/jwYdx6663Vls2bNw/9+vUDALvHszv3qzPHlzP7xtZ70LVrVzz44IP49NNPYTAY0L9/f2zduhVr167FnDlzLM2odT3mSQMgdgZHiFhOnz7NJk6cyKKiophCoWAAWKtWrdi+ffucep4VK1a4bWi4K5o2bVrr8HHzkPN58+ZZHnf27Fk2cOBAFh4ezrRaLWvbti1bsGBBtZm2GWPs3XffZb169WJhYWFMpVKx6Ohodu+997IzZ844HKc79vfChQvZlClTWFlZGYuPj2f79+93+LHu0L9//xqH6wvZ2ueMmWYef+qpp1hUVBTTarWsZ8+ebMOGDVbrfPPNNyw5OZlFRkYylUrFQkNDWXJyMvvll1+cinfhwoUMAFu4cKHN+9u3b8927dpl+dtoNDI/Pz+HO8W7izPHlzP7xt57oNPp2Msvv8yaNm3K1Go1a9myJVu8eLHLMZGGiWOMsfpN2QjxTuPHj8cPP/yAXbt2oVu3bg49Zvfu3RgzZgx69+6N8PBwLF++3MNRyocr+3vTpk147rnnMGHCBOzYsQPff/+9h6OUtsWLF2P27NnIzc1FaGio1X06nQ4BAQG4fv06/P39AZgmMe3Xrx8yMzPFCJcQSaEEipAK169fR2JiIgIDA3HgwAGbs3QLpaWl4cYbb8QXX3yBli1bol27djh69Cji4+PrKWJpc3Z/A0BeXh5iYmIQHByMTZs2OdWBuyGaPHky/vrrL5uXiLl27Rqio6ORlZVlSa6+//57fPzxx/jzzz/rO1RCJIcu5UJIhdDQUFy+fBknTpyo9cu8sLAQw4YNw7x589C/f3/ExsZiwoQJeP311+spWulzZn+bhYSEICYmBrfddhslTw44cuSI5RI5VTVq1Ajjx49HfHw8+vTpY1m/U6dO9RkiIZJFFShCiGQUFRWhVatW2LlzJxISEsQOx6sxxhAYGIhHH30Ub775ptjhECI7lEARQiRj1qxZMBqNlktwEEKIWCiB8hCe55GRkYHAwEBwHCd2OIRI2uHDhzF06FB06dIF33zzDU1iSAjxGMYYCgsLERMTU+O0NJRAecjly5cRFxcndhiEEEIIccGlS5fQpEkTu/fTRJoeEhgYCMD0BtD1kgghhBBpKCgoQFxcnOV73B5KoDzE3GwXFBRECRQhhBAiMbV1v6FpDAghhBBCnEQJFCGEEEKIkyiBIoQQQghxEiVQhBDPunoKOL9N7CgIIcStqBM5IcSzlvYy/T99L9C4tbixEEKIm1AFihBSP66eFDsCQghxG0qgCCGEEEKcRAkUIaSe0EUPCCHyQQkUIaR+0FWjCCEyQgkUIYQQQoiTKIEihNQTqkARQuSDEihCCCGEECdRAkUIqR/UB4oQIiOUQBFCCCGEOIkSKEJIPaEKFCFEPiiBIoTUD2rCI4TICCVQhBBCCCFOogSKEFI/qAJFCJERSqAIIZ5jlTRRAkUIkQ9KoIjXKtUZ8ez3h7HlZLbYoRBXUdWJECJTlEAR7/DLdGD9bKtFy7edw7f7LuGB1XtFCorUGeMFtymZIoTIByVQRHwFGcDBL4F9K4HyIsviK/mlIgZFnFKSC3w5Bjj2U5U7KGkihMiTLBOo9PR03HvvvWjUqBF8fX2RmJiIffv2We5njGHu3LmIjo6Gr68vkpOTcebMGavnyM3NxYQJExAUFISQkBBMnjwZRUVFVV+KuINRL/ij8guXA1f/sRDXbH4NOLsRWHu/9XLqA0UIkSnZJVDXr1/HDTfcALVajT/++APHjx/HO++8g9DQUMs6b775Jt577z0sX74cu3fvhr+/PwYNGoSysjLLOhMmTMCxY8ewceNGrF+/Hv/88w8efvhhMTapAbD9xcpR/iQdpbm2l1s14fG21yGEEAlSiR2Au73xxhuIi4vDqlWrLMuaN29uuc0Yw5IlS/Diiy/ijjvuAAB8/vnniIyMxM8//4y7774bJ06cwIYNG7B371706NEDAPD+++9jyJAhePvttxETE1O/G9WQ8EaxIyCu4JR27qCqEyFEnmRXgfr111/Ro0cPjB07FhEREejatSs+/vhjy/2pqanIzMxEcnKyZVlwcDB69+6NlJQUAEBKSgpCQkIsyRMAJCcnQ6FQYPfu3TZft7y8HAUFBVb/iIOEzTxUpZAmzvapZOupzMo/qBM5IURGZJdAnT9/HsuWLUOrVq3w559/Ytq0aXjsscfw2WefAQAyM00n9MjISKvHRUZGWu7LzMxERESE1f0qlQphYWGWdapasGABgoODLf/i4uLcvWkyJkygBH2gqAlPOhS2i9mPfnmgngMhhJD6IbsEiud5dOvWDa+//jq6du2Khx9+GFOmTMHy5cs9+rpz5sxBfn6+5d+lS5c8+nqyQhUo6VPYPpVwoE7khBB5kl0CFR0djfbt21sta9euHdLS0gAAUVFRAICsrCyrdbKysiz3RUVFITvbevJGg8GA3NxcyzpVabVaBAUFWf0jDhL2e7JKoKgEJRl2KlAKO9VFQgiROtklUDfccANOnTpltez06dNo2rQpAFOH8qioKGzatMlyf0FBAXbv3o2kpCQAQFJSEvLy8rB//37LOps3bwbP8+jdu3c9bEUDw+wlUEQy7HQi56jqRAiRKdmNwps1axb69u2L119/HXfddRf27NmDFStWYMWKFQAAjuPwxBNP4LXXXkOrVq3QvHlzvPTSS4iJicHIkSMBmCpWgwcPtjT96fV6zJgxA3fffTeNwPME3lB5W5BAUR8oCVE4MgqPkilCiHzILoHq2bMnfvrpJ8yZMwfz589H8+bNsWTJEkyYMMGyzjPPPIPi4mI8/PDDyMvLw4033ogNGzbAx8fHss5XX32FGTNmYMCAAVAoFBg9ejTee+89MTZJ/uw24RHJcKgJj95bQoh8yC6BAoBhw4Zh2LBhdu/nOA7z58/H/Pnz7a4TFhaGr7/+2hPhkarsNOFRAUpC7ExjQE14hBC5kl0fKCJBPM1WLXU7U/NtLqdO5IQQuaIEiojPTh8oOTqVWYjnfjiM/M2LgX8/EDsctzl42fbEsdZVREqgiPgYY3jnr1PYsGUL8MsMIC9N7JCIRMmyCY9IjFUTnry/ZEcu3QmVvhALfV42Leh6L+AbImZIdcYYg1H4W4wxywgAjvpAES9z+HI+3t98Fke1DwJcGZBxCJi2Q+ywiARRBYqIrwF1Ii/VG6GCoOJmKLO/skQwBvBMcCoRVBQ5asIjXqawzHR8BnAVn72sIyJGQ6SMEigivgY2jQEvs48dX7UCZdRbblIFingbhUzPK6T+yetMTqSJNaxO5HIbmcazKkmhvQqUsNJIiEgUlEERN6EEiojPThMe1yAmMpD+NvKMwWAngaJ5oIi3Uci1tE3qHSVQRHwN+lIu0q9G8YyBCRNBasIjXozyJ+IulEAR8TWwPlBMBlUnIZ5VqTTxggSKowSKECJPlEAR8TWgUXhyxDNmXWmiChQhpAGgeaCI+Kw6kVd+4cqrTmPShktDJ8V5y99GnsHeZXilgvFVK1CmiiJjVWptlEARL8AZyjBK8Y/YYRAZoASKiK8BVaD+1D5n9fdPB9Mx5uZokaJxDyNjgI0KlM7IQwHbyTEhYmly4G0s0qwUOwwiA9SER8TXgC7lUtXu1Gtih1BnPGM2+0CVG3iqQBGvE3pxg9ghEJmgBIqIrwGPwpNDUaZ6HyhTQqwzVK1A0TxQxBvI4ENHvAIlUER89uaBkuswPAGel37CyOyMwis3VNm2BpYcE0LkjRIoIr4G3ITHy6AEZeSZ9XQF5j5QBp5G4RFCZIsSKCI+By7lwmSQaNgkg82q1oRnqUAZaSZyQohsUQJFxGd1jTTbGYVc8ydeBkkFY7DbB4oqUMTbcHI9mZB6RwkUEZ8Dnchle8qTwcm82ig8ow4AjcIj3oqOQ+IelEAR8TnQB0oOfYVskUPTpJGvOgqvHICtUXjS31YiA5TIEzehBIqIz2oUXuWXbELhXoxQ7DStItMvXyaDkznPqswabzBXoIxUgSJeh6PjkLgJzUROxGenE/l9Zx7HfRrgUHlLeRQvbExZwHjpbxir1oRXWYHiUPsAAUIIkaJ6S6B+/fVXpx9z2223wdfX1wPREK9iqwlPkGyEI18eFSgbE0kyGfTuMuWAgu0wmBKoan2geJpIk4iPKlDEXeotgRo5cqRT63MchzNnziAhIcEzARHvYWsiTX2JZVEZNJBBocZmAiGPJrwqFSirBIpG4REvQ8chcZN67QOVmZkJnucd+ufn51efoREx2RqFpyu2LCqHWsYVKOmz14m83MDTPFDEC9FxSNyj3hKoSZMmOdUcd++99yIoKMiDERGvYasCpSuyWkUW3702K1AixOFm1S7lUtGJnOaBIt6I5oEi7lJvTXirVq1yav1ly5Z5KBLidWwmUJUVKA5MthUoOSQV1S8mbK5AGSmBIt6HjkPiJjSNARFfLU14HOTR1GWr3CSXPlCcQxUoWbyLROKoEzlxF9GmMSgrK8Phw4eRnZ1d7Yr0I0aMECkqIgpb80DJsQJlqwlPBudy+xUoasIj3kgG5xLiFURJoDZs2ID77rsPOTk51e7jOA5GIw13blAE0xj8ffwKkhNh1QdKIZcEylYnchkkFXwNfaCoEznxOjYrwQwcx9lYmRD7RGnCmzlzJsaOHYsrV65UG31HyVMDJEgsfvsv3XSjSgVKDvmT7U7k0t8w3u4ovKozkdNnm3iD6p85WUyTQuqdKAlUVlYWZs+ejcjISDFenngbQROupWIhxyY8m53Ipb9d1StQgpnIOapAEe9iqw+UkTIo4gJREqgxY8Zg69atYrw08UaCxELBVZ/GwJRA1XdQHiDjiTStK1Dma+FRHyjihSiBIm4iSh+oDz74AGPHjsX27duRmJgItVptdf9jjz0mRlhELII+UJy9CpQcTnC2EghZVKCqjsITXguPEijiXTgbTXhGGXwOSf0TJYH65ptv8Ndff8HHxwdbt2616rzHcRwlUA2NoDJjuwlPFnmG7QqUDGZF5hlsJlBUgSJSQRUo4gpRmvBeeOEFvPLKK8jPz8eFCxeQmppq+Xf+/Hm3vtbChQvBcRyeeOIJy7KysjJMnz4djRo1QkBAAEaPHo2srCyrx6WlpWHo0KHw8/NDREQEnn76aRgMBhAPYLYSKOEoPF4WF921PQpPhDjcrNq18IyVFSgFzQNFJEAWFW5S70RJoHQ6HcaNGweFwrMvv3fvXnz00Ufo1KmT1fJZs2Zh3bp1WLt2LbZt24aMjAyMGjXKcr/RaMTQoUOh0+nw77//4rPPPsPq1asxd+5cj8bbYFlVoGxPpCmL85vNeaCkX5VhdibSpJnIiVRQEx5xhSgJ1KRJk/Dtt9969DWKioowYcIEfPzxxwgNDbUsz8/Px8qVK7Fo0SLceuut6N69O1atWoV///0Xu3btAgD89ddfOH78OL788kt06dIFt99+O1599VUsXboUOp3Oo3E75PgvwObX5POLXpBY2O0DJYdtlekoPCMPKDhHKlCUQBHvRE14xBWi9IEyGo1488038eeff6JTp07VOpEvWrSozq8xffp0DB06FMnJyXjttdcsy/fv3w+9Xo/k5GTLsrZt2yI+Ph4pKSno06cPUlJSkJiYaDXNwqBBgzBt2jQcO3YMXbt2rfZ65eXlKC8vt/xdUFBQ522w67v7TP837w80v8lzr1NfqjThMcbAVZsHSgYnOBsVKDnMimxKbm33gbJabnP7CREfJVDEFaIkUEeOHLEkIUePHrW6zx2zwa5ZswYHDhzA3r17q92XmZkJjUaDkJAQq+WRkZHIzMy0rFN1jirz3+Z1qlqwYAFeeeWVOsfulLy0+n09T6nShFdu4OEjx2kM7MyALHWsWh8o4bXwhCvKrAJlNABggFJd66rEu1ECRVwhSgK1ZcsWjz33pUuX8Pjjj2Pjxo3w8fHx2OtUNWfOHMyePdvyd0FBAeLi4jz7onL5QhJMY6AAQ6nOCB9BBUrWl3KRRQXK/ig8hXCUoVyOV8CUDK9MBoqygccOASqN2BGROpDF+YXUu3rrA3X48OFqFw2uybFjx1wa9bZ//35kZ2ejW7duUKlUUKlU2LZtG9577z2oVCpERkZCp9MhLy/P6nFZWVmIiooCAERFRVUblWf+27xOVVqtFkFBQVb/PG3zySzsSc31+Ot4nOCLlQNDid5oYx4oMQJzM1tNWDJIKoy87QpUuZwrULwRyDgIFKQDWUfEjobUEVWgiCvqLYHq2rUrrl275vD6SUlJSEtzvolqwIABOHLkCA4dOmT516NHD0yYMMFyW61WY9OmTZbHnDp1CmlpaUhKSrK89pEjR5CdnW1ZZ+PGjQgKCkL79u2djslT/jp2BXd9lCJ2GHVXpQmvVGeoMhO5TH4hyngaA9sVKBmPwuP1lbcFyT6RJlmcX0i9q7cmPMYYXnrpJfj5+Tm0vquj3QIDA9GxY0erZf7+/mjUqJFl+eTJkzF79myEhYUhKCgIM2fORFJSEvr06QMAGDhwINq3b4+JEyfizTffRGZmJl588UVMnz4dWq3WpbjcRvBBtzWjriRVacIrKdcD5YWVyzheFomGfKcxqHIs8nqA5+U9D5RRkEDJabsaKANVoIgL6i2B6tevH06dOuXw+klJSfD19fVILIsXL4ZCocDo0aNRXl6OQYMG4cMPP7Tcr1QqsX79ekybNg1JSUnw9/fHpEmTMH/+fI/E4xRbs3ZLXZVReGVF+ag6Ok0OfYXk2weKVUvmmbHcNApPIdcKFE2qKyfUhEdcUW8JlJgXD6762j4+Pli6dCmWLl1q9zFNmzbF77//7uHIXMBszJkkdVXmgdKX5FndLZtReDaqTZwMqhfV+kAB0JeXAYB854GyqkDJaLsaKBkUgokIRJlIk9SBVXOXTD71zLoPlKFKAiXrUXgy2C5TE541XXkpgCpJvq2JRKVK2AeK5reSPJqJnLiCEiipkWPTQZVmSWNJvtXdCvCySDRs9oGSwXaZroVnnczrLQmUgAwqNUaeYc6PR7D+UOUAl/WH0uhaahJHTXjEFZRASY0cf+0KEyiOgZXmWd2tkEsTnkwv5cLbrECZm/DkNQ/U3yey8M2eNLyz4bhl2bqDafjhwGURoyJ1JYsEas/HwIHPxY6iQRFlIk1SBzY6kRt5BqWi7jO4i8YqsWBAmXUFyjQPlAxOcDK+lAtXpQJl0JkSKLlVoHKKTFM0qFD5XiphxLmrNJWBlEk+gSrKBn5/ynS70900sWs9oQqU1Aia8JQVX1oGqfeArDKNAVdufR1BBXjZVqDk0oRXNX3XVyRQkFknckXFpabUqDxmVTDCYJT+tjVkku9jqS+tvC3sn0c8yisSqEceecRq0kpSA6sEyvSFLPX8SbgBCvBQVEugZHIxYZvXwpP6mwfwfPU+UEadrVF40n8PzYVeYQVKBSN+O3JFpIiIO0i+AqVQVt6WYz9ZL+UVCdTtt9+OIUOG4OWXX0ZxMZXCa8SEJ27Tl5bkfz1VmQdKoS+0uls2faBsNOHJYSoKW32gKpvw5FWB4iq21CqB4ozo1TxMrJCIG0h+FB4n+CqXYz9ZL+UVCdQdd9yB3bt3IzIyEn379sXy5cudum5eg8Jb970AZPDhrzIPlFpfUGUFJosJJ2014ckjgbJRgdLLdB6oikxRLUigblPsRyN/ka9QQOpE+n0sBT9hpP59ICFekUABptm/hw4dilmzZuHFF19E+/btsW7dOrHD8j6C8qyKM30hSf57qUofKI2+ehOe5M9vgGxL67YqoEadqbM1xwkv8SLxX8aMISz/BPxRChVX+V5ycknwa7DzbA7e2HBStn29pH8pl9rnW9uTmoufD6bXUzwNg1eMwhs8eDBOnDiBuLg49OrVC++//z5at26NDz/8EJs2bcKSJUvEDtF72OgDJfkKVJWJNP0NeVZ3K8BglENF0kYCJY8KVPVJXU0VqGCohKNDpZ7pn9mI5O1j8YsmBq8Z7rUs3sEnymEwZY0mfLIbABAb4ot7+zQVORr3k3wFSvjZsvNDxXzh+VaRAegQE1wfUcmeVyRQCxcuRGJiIpRKpdXylStXom3btiJF5aV4GfaBsupEziwJFA/O1CcKPGTxw1e2faCqj8Iz6k0VKK3wIy31BOrYjwCAloqMKtMY8JB4bc1hF6/Js4+q9H+EOt5Ufim3lBIoN/GKJrwuXbpUS57MvPJ6dGKyNQpP6h9+3ro5JIg3zQOVrwixLJNvBUr6TJdyqXIxYYOpD5RGKaMKlODdEiZQspkp3wGy+CFjg+RH4Qk/W7VcMqmhHKv1wSsSqJokJCSIHYJ34a2HTwMymMZA8IFXw4BAZhqFl68IBWCqSkm/jwJk24Rn5BkUnPV28HodAEBjVYGS/raaCeeBUsrhmpQ8D5Rer3U1ufb1kvyPUAea8Mw4Tg4/27yDVyZQV65cQXl5udhheCdm3XQAyODDL/jAh3GFlpFb+QpTmdnUhCfxbQRkm0CZmvBM22HgTDMgM4Pp86uRUx8owXtVrQIlRjju9M044I1mQObRGlfjpF4ztfNrU/KVNasKVM0bI4v8yUu+87wygZo4cSLatm2Lp556SuxQvA9vPQMyIIfyc+WXUThXcRkX31AYYCpfcAAMRmlv46nMQhxPr/4LvyWXARz8StJlRF7QhKdXVAznr2jCU8uqCa+SirP+ISP5HzFn/jL9v/cTcePwoHKDEd/tTbV5X/yF74Fr5+o5IjeqpQ+UsNlOIfUM6uVg4JUQLF2/CzvP5ogailcmUH///TdSU1Px0EMPiR2K97Ea8l8xjYGUz92MWX3gw2FKoJhfOPiKX7sKTvoVqEFL/sGGw9WHEM9VfwH88qilg7IUMUEFyqiouAaXwVYTnsQTKGa7AqXkJL5dQjKuXnyyPRUv/XTY5n29jswDlvaq54jcqebpQoSnTylfNtXKrqWW0aFi8YoE6t1337W5nEbg2SDsA1Vx4pb0CJIqH3ZzBYr5NQKrODw5mfSBUnI19E24cqje4nA300SaFRUozlSBYkZTE57aqglPPmPVqnciFzEYt6p5Q6T83bvr/DWr960aKc/TVksTnvB6qZKuQAk+aBdYlIiBmHhFAnXkyBFMnToVRqPp4D5+/DjGjx8vclReysZM5JJuPqjypRrEmS6KafCPqqxAyWQUXo0nb610hxUb+comPENFBYqr6AMlrya8ys+Zuso0BhL+BFqTcQUq2FdtOWfKTi2j8KxOnxJ+D70tyfWKeaA++eQTLF68GIMHD0ZwcDAuXLiA5557TuywvJONPlCSngTOzoiRcv9Y8MgCIJ9ReDWO1tIG1F8gbsaEFShzHyijDJvwBFRWo/Ckf2xayGhTqgryVVvmzpOdWkbh6QUZlFLKWXCVKW/E5hUJ1N69e7F9+3Zcv34d58+fx+bNm9G0qfxmu3ULvvrwaUnnFnZ+UZT5xYBnBwHIZxRejRUojXQTKFMF1HoUnqIigVILa9xST6Ds9IGSVRNerRUo6X75yrsCVfOlXIyCQThKKXeCMuotN71hK7yiCW/WrFl45JFHsG/fPqxZswYjR47Ezp07xQ7LOwk+HLIYhWenX0yxTxR4ufWBqunXL+cVH0WXmC7lYt0HiuPNfaAEK0o+y6ihCU/y21ahtgSqnsLwhEAfVcOoQNk4FuVw/gRAFShbduzYYbnds2dPrF+/HnfddRclUbbw1iduQOJ9oOz0bbqujbV8PEx9oCS8jRVqTKC8rG3fGUaeCaYxqKhA8eYKlIz6QAniF05jILsKVEku2Mn1+N3YG22axqBlRGDl/RLOoDhw8hoxKVS1Ca8wE8bTG/GrsS96tIyGStAXUdLHqo0uLGKqt5+9v//+O5o2bYqwsDAMGDAAGzZsAADMnz8fQ4cOxcKFC5GdnQ0AiI2NxaZNm+orNGmx1QdKyp8IG4mDjimRpY61dCLnwCQ/DxRQywdewgkUszEKjzP3gVLJKYGyX4GS9GdQiPHAmnvA/ToT+HUGkhf9I3ZEbsMzJuMmvCqdyD+5Dcp1M5Dz64u46c0tVudPaY/arjxPair6IYpZ/a23BOqpp57CqFGj8N1336Fr164YOXIkxo4dizfffBPx8fH49ddf0bVrV5w+fRoA4OPjU1+hSYvNUXhiBeMGNprwjrAE5JUDPDMdnqZO5BL/8kVtFSjpnthNE2ma6Cv6QJkTKB85jcITVqCqXMpFDhVSAKbPY1oKAGCock+1u6U8E7mRZ15RtfCIqhNp5qcBAAYq9gGwbsKTdLIv6AOlhem2mJ+9emvCu3jxIh5//HE0a9YMycnJaNu2LaZOnYpFixbh8ccfBwA88cQTeOGFF7B27dr6Ckt6hBUo8zxQUj5520gcFhnGoF+pHo0sFSheFm34qprmgZLwHEmmS7mYjkXzKDxzE56sKlBWfaCsJ7TVy+D4BGDzPRKO8pVwH3IYeFbzjxgJD+SwN5Gm0vIdUbndku6vJ/j+03Kmc4yRMdH6ItVbBapZs2bYs6fyF82ECRPAGMMNN9xgWfboo49a9YciNliVMPVYrX4DsXteEzGgOhIkDn8Ye+JL9Wjs5BORX6qvMg+UhD/0FWpsPpBwE54pgTLRc2oAgJI3/TrUChMoCVfZAFglF5pqFSipJ4cVbCRQwiYfCedP4GurQKl96y8Yd7MzD5T5ahVWFSgpH6rCBKohVaCefvppTJ48GSdPnsTtt9+Ozp07Y8eOHVazjZeUlKC4uLi+QpImwQelm+Ks6cax/4Cx74gUUB1VfKkWww/T9LPQNToEKMyrlkDpJd4HSsGh5hFAUk6g+MoTta6iD5SyogKlldM8UIJEQstVNiUoOV4WffQA2Px2lcOPF8CUCMq1Gd3eTOTmvonC41PSTXgNNYG6//77ERgYiMWLF2P+/PlQKpVo27YtunXrhm7duqFdu3aYP38+kpKS6iskaZLwF61NFSctvmIYf5ifqQ9N9QqUdL98GWPgWW0VKOmevK0qUDC9f2pmnkjTah4DUxIi1XYgYQIFneW2XOYpA2C7CY/Jowmv1j5QEm5Gtx6FZ+tSLsIEqj4C8hAbfaDE/Gqo16bD0aNHY/To0SgqKsJ///2HQ4cO4dChQ/j8889x7NgxlJWVISYmBqNHj0anTp3QqVMn3HnnnfUZovezl0DxRkChtH2fN6s4aRkrWpND/SsTKMbk0QfKHHrNFSjpnrxN80BZV6BUFSc3TdVDUqIJVEZeKQLLdDAP6DefvAFTE56Uj08rtprwZLJtRr6WUXgS/gzaa8KzXORbkGVIuwJVuW0acwVKxO0Rpe9VQEAAbrjhBqv+T0ajESdPnrQkVTt27MCHH35ICVRV9hIoow5QSLANv2J7zJNmhgkSKKOlE7m0T+Lm2BVybcITnMDMfaDMfYS0yirJEuPhJfP3OqXvws34WH0Nt1UkhJoqncilfHxaqeU6alIfhVfjZXck/BmsNgqvCr1smvAEFaiKZnQxR2h7xUSaAKBUKtGhQwd06NABEyZMEDsc72XvV5JRL81OkLy5AmX6ZgrxM30B55fqwSxNeNL+hW/+cpXrPFA8Y9UqUOZfh2qbCZQ0CWc+tuoDBR56o3S3y4qN90f4BSXB4qGFkWdQ1jQSVsKfQXvXwjO/XUa5NOHZ6AMlZhOe9H4KNnQ1JVBSVKUJL9zf9AWcW6SDkcljFJ65xFxj84GE+18I54HSCSpQfhqlpROrhYQTKGEHZOs+UNI+Pq3UMgpPytUL03D3htmEJ0zwJT2Nga15oBrCRJrETez2gZJoAlXx88HchBcVbJpAtbDcAF3FZ17BSftaeJYKVE2XkZDwyZsXjG7SobIC5adRVb9elWwSqMrPm9QrpFZszgMluC3hzeRrmwcKTLpj/O2MwoOlD5Q8kmDhebKyAkUJFHGUvUqFUWd7uberSAgNFYdikK8afhU9j8sqckVO4vPsmE9etipQnxhuN92QcPMBYwwcZ9rG8oomPC1ngL9GUf3CWxKstJl/sQurF1U7kcumAmVrGgNhBUrC22mwMwrvQ8OIyj+k+jkUvEdr916sdrcwwZdya3NxWanltnkiTTF/vFACJTV2O5FLtAJV8YVqrkApOQ6RQaYqFBNMYyDleXYq+0BZn7kO8C1RjIp+axKuQBn5yj5QZRWXcgGAYA0sM5RbSLACZT4/Cy9E68NV/mCR+yg8XibVC3sVqD18m8o/JJjgm1S+L/+eybbcNjety2EeKIORx+xv9lv+7qY4i/Wa5+F3Qrwrl8gugVqwYAF69uyJwMBAREREYOTIkTh16pTVOmVlZZg+fToaNWqEgIAAjB49GllZWVbrpKWlYejQofDz80NERASefvppGAxe8OvE3hetVH85mTuRV1z3juOAiEBTFUMuM5HzNioYgClBNFRst2TfP5inMTBto7kJDwBCtXz1LyQJJlDmTtTCy7dUa8KT8s96IZudyOVRvbDXB8ogHEsl1c+hjckzgco+UAYZXMqlWGes9v51VFwAV5orUkQyTKC2bduG6dOnY9euXdi4cSP0ej0GDhxoNcP5rFmzsG7dOqxduxbbtm1DRkYGRo0aZbnfaDRi6NCh0Ol0+Pfff/HZZ59h9erVmDt3rhibZK37/cDtb1ZfLvkmPFOznVJhpwIl4QTKYKcPFANn6Twv2RM3TCfkygSqsgIVqgW4ak140nsfzd899vpAyaoJz0YFRi79Zwx25oEyn3sASPdzKEygBOcZzmYfqPoLy514O+8fz4k3/6HXTGPgLhs2bLD6e/Xq1YiIiMD+/fvRr18/5OfnY+XKlfj6669x6623AgBWrVqFdu3aYdeuXejTpw/++usvHD9+HH///TciIyPRpUsXvPrqq3j22Wfx8ssvQ6PR2Hrp+hHeClCogD+esV4u2SY8607kSgWH5uH+FcvMBWiJV6AsfaCqJlCV0zdIuQlPOJGmEQoYORWUzIAwLQMnqwqUnT5QnMyb8GQyCs9eE56lCgxI93MoeN+E2yinJjw9z1t9Bs14iJdAya4CVVV+fj4AICwsDACwf/9+6PV6JCcnW9Zp27Yt4uPjkZKSAgBISUlBYmIiIiMjLesMGjQIBQUFOHbsWD1Gb4fCRt4r1QTKMg9URbWJA9pEmeZ7ZhWHp6kCJb0vXjN7faAYOEvneen2vbDuA2UAB4PC1IwXpjHKIoGqfP8qqxNqwXxC8ppI0/r9uUlxGIFHv8BE5V8ApPvlC5jngbKRIEJh+QEn3QSq8n0RNuGFckXowZ2UxaVcDEYGlY15vIyUQHkGz/N44okncMMNN6Bjx44AgMzMTGg0GoSEhFitGxkZiczMTMs6wuTJfL/5PlvKy8tRUFBg9c9jbCVQO98FrlcffeH1qswDpeA4dIgJAiDsAyW9Lygjz/DzwXRcyi2xnLyqTuJnasIzV6Ak2nQA80SaFfPN8BzKFH4AgDB1efWESYIJlKUJ1s4cQqZO5NLbLpuqJBBfaBYievscvKpejVAUSK4P1KnMQvxx5AqAmq+FJ/mmdKs+UNZv0vfa+VCXZiMIpm4sUh1JqTfyNt8/IydeGiPrBGr69Ok4evQo1qxZ4/HXWrBgAYKDgy3/4uLiPPdithKoU78BK/p77jU9xdwHipkSCQXHoWkjf/RuHmbVidzyC8qoB4quihKqM9buu4Qnvj2Em97cYrcTOSDdE3dxuQHXNy0GNjwPxlcmUGUGhlLONLIwVFkOTlds/UAJJlDmLxwNZ/s9qprg//pfBu5YuhPpeaU21/dqNVSY/LkyyXVAHrTkH0z76gBSzl2DkdluwmPgKpuBJPY5tLDThGc2dusAHPaZAkC6VUS90XYCzHPi9USSbQI1Y8YMrF+/Hlu2bEGTJk0sy6OioqDT6ZCXl2e1flZWFqKioizrVB2VZ/7bvE5Vc+bMQX5+vuXfpUuX3Lg1Vdi7aHDpdYz6cCfe3HDSc6/tDsXXKk/UvHUFSqkwJU2fPdgL43s3A2DqCGn5gvr4VuDtlkDOmXoN2Vm7zl+z3Dba6QMFVO8DdTa7CMPf34G/jtmudHqL/m9tQej2l4FdSxGnO2f51VusY5apGYIV5eB0RdYP5I3A3k+A/zz/o8ZdzMm7cBSekOlSLhXH59m/EfvDCBRdPoZ5v3hBc78jarmOmpmUO8sfy8i3W4Gy1ZQ+75ejmPL5Pu9PGDMOms6JF3ZYFtV4zU0waTXhZZ8ArppG0Rt4OxUoasJzH8YYZsyYgZ9++gmbN29G8+bNre7v3r071Go1Nm3aZFl26tQppKWlISkpCQCQlJSEI0eOIDu7cj6NjRs3IigoCO3bt7f5ulqtFkFBQVb/PMZWBarCgbQ8fLj1nOdeu66OfA+8lYAdy6bjUm6J5YRtvmyL+VpbPmolGgWYRuM14zJxe/GvgL4UyDxsWuHoj/UeuqPK9EarE6+9a+FZnbgLTQnTE98exPn0TKz/6n2gvLB+AnZBTlHlqE81X2qpQBWUGVHETO9boKIcnK7KNhSkA789Cfw0tbK5qCy/cn/xPFh5kWmfMVb55V7T/y6uwxt5oKyg4k8GpisGDOUAgNKsM8A39wDLbkT4l8nQQF9DAlWR4PM88OVodFecwQfq96EvyTO9Hs8DBVdM7yfPm5blnKkel6PbUdu65n+5qUB5UUUMRuDqKTDz65uV5Fr3n6yhL54SfOWXb2Gm18/aXaozwjw/EmPmiwnbSqAqB7HAaHqPP0u5iLwT23Duv+31FK1z8kv10BlMxxvS9wN7P7bcV1MCpUbFuencFhg/GYQzR704SdSXAh/2AZb2Agzlpj5QXtaJXHaj8KZPn46vv/4av/zyCwIDAy19loKDg+Hr64vg4GBMnjwZs2fPRlhYGIKCgjBz5kwkJSWhT58+AICBAweiffv2mDhxIt58801kZmbixRdfxPTp06HVamt6+fpRQwJlNm3RF3i19HXs4RLRSnUVF/XB6MidwzFle/hwesQZ05DJhyIB6TiJeLTFRZxCU7TiLuMyIhDMlUDBjLiGYMQjE6dZE7TDBZxAM7TmLiGNRaExlw89p0Yh80UMsnGWNUEbXMS/LBFttTnINgQgnqXjgrIpmEKN1oZTiC5PBQDcmP0Vbls9DhsHVJ/GoJLp9i3K/3BL6X/ANr/Ku7a+jsP7t0PF9LjCh6All45jfFN04lJxmDVHB8VFnGexiFDkwwgFrrMANMUVnODj0JFLxRGWgI7cBZxmcWiivIZi5oNSaBGDbJzmm6ADl4r/+BborDiPE6wpEhSZyEEIAA7huI5zfBTacxdxmCWgE3cOx1hztFakI8MYitwSHR7HOfRUdcCLhsl2K1CMcZUn7vR9KLl6Ec9cn48btHuh5Biw4AOk+XXAaRaPOMVVFMEXZdAiGldxmo9FR+4C/uMT0MkSYxauIRgA0Ah5OM9HoR13EYf5BHRWnMcx1gytuXSkozH8OB38UYrLfCO04i7hCN8cnRXncYRvjnaKNKQhGiFcEZTgkc2CkYAMHOPj0QHn8T/DvQhGY8F2VJ60r5YYkKcxfUZCVOWmL3CB4vwc+FfcLlvUBVeL9YhjV5CKWIQqSqHiyxGIYpxHPNQqJfxRhkw0QlOWgbOIQytcxFkuHs2QgWw0QgBXBgV4XGeBiGWZOI8maIk0nEU8Erh0pCMSoVwheChQyPwQhau4zBojjl3BdaMvmnFXkKZoAoBDPG+qGqcpYhHPp1ti1gC4VXHQ5gggwHwpFx5YeZtlWTtFGj7LGo1rr0dDYyhCIDMlkiXKIPgYC6EAgw5qlCkDkcFVxlgEX0SwHFxADFqwNJzlmqIFu4RLXDQiuOsohRZ6qBHK8nAZkWjOLuEs1xQt2UVc4JogBjko4AIQZsxBEG8aQFOsDIa/0XTb9JpB+KDR87inUxCa/z0FiO5sibugVAd7P/1UMJqaf07/BXw9FgX+zWBQByKlrCkSFRWfJcU1lMAHJdAipuI47cCl4jBrgU6c6ThtrshCLoLBAIRXOU47KSo/S1cQDh/oEYASpPHhaMNdwhHWHJ258zjCmqGt4jLSEIUQFEPFGZHFB6MFl44TxiZoariAU+Wh2KU9gbG6ueC4dhUVKNtNeIEV/YMK/5gH/3a34XP1p+inPAL8DJzb2BXnbHyWWinSkYlwaCtivMQ3RmsuDUf5ZuikOI+jLAFtOdNnKZgrhhpGZLEgtEAGjvJN0UmRisN8xbkKMYjkCqCHEvnMH/G4ghMsHolcKv7jE5CoSMUZFocmihxcN/rgdJEvlvlPQ0r5tWrbU9PlarTQ4dzVYmDTSCgBtPp+ANJ/iEK6pjn8uTIE8fnIYqGIUuTjHB+NzuwkTqEpWnKXcRmRCEURGMchn/kjBtk4z2LRChdxCs3QGmm4yMUgAtdRDg3KoEFj5CKVRaMV0nAKzdAGF3Gea4JYXEUBAmDgVPBVGHCZb4SO/Gn8h9ZorczAZb4RuhoOQ10R97y1u3DHDZ1sJsBGEacx4JjXpp+u4excLnzVqlW4//77AZgm0nzyySfxzTffoLy8HIMGDcKHH35o1Tx38eJFTJs2DVu3boW/vz8mTZqEhQsXQqVyLOcsKChAcHAw8vPz3V+NMuiA1xrbvKt52ZfooziBZeolCOGKba7jLdqXfYrjyUeBHYuw1dgZ9+ufxe7nB1jmgcLWN4Ctr1c+ILYHkL5PnGBd1Kzsa/z4aF+M+vBfHPZ5GEGoTCj+NbbHL/wNeENt+vV4WJWIToYjYoXqshdC38Hc3Geh5QzoU/Y+nlF/i1HKHci/aS6CTn4H7mplk/IU3Wx8rFkkYrSueVT3GBarl0HLVR/teoaPxRDj2zijHi9CZO51jo9GC8UVm/cNLX8dLTv3xduFz0KdvrueI3PdOmMfZA9ajg1Hr6DnpdV4Rv2t1f3Dy1/DOu2LIkXnugX68Zij/qba8oX6u/Gc2nYTefeyZbiGYFzwucfT4blVj7JlyFeG4FF8j1nqH6zuO9LvIyTeerdbX8/R72/ZVaAcyQd9fHywdOlSLF261O46TZs2xe+//+7O0NynhgpUqs+99RhI3Rz3eRCoaLo3CEbhWVQdXSGx5AkwXcok7MhKdOI01X49MXDwQWVTmEpXIM1GdVY5Q7ARChQxUx8of5SCq1KBEg6xlhIO9vtAaaHD/xTL6zcgD6mp+UcDPdJyS7A/swB9JHScNuWywBccx03X/gZvIwGW5hEJm8kTUPN7KJy/TErUMEBvZFCqbFWgxEtjZJdANQgKBYzgoJTsR786vkoncgCVHaIkbLhiF5rt/QC/aoFSpqmc2Q6mE7cvykWLzV1CjNeg5Bh0TIlcBKEYpgqiSl8MVOkDxUn0mFXCCAVnO/Z4xVXEY1s9R+QZNTX/xHI5+DMtBwqNd/d9qqqTIhWd9pgqLunKRtXuZ5D+eUaopvfQh9NJMmNUcQa0xwUkcNWro+YR3GKgBEqidEwNX06il2+xwdIHqqYKlAS9p/nActtWJ3Jfq4vSSnMSvyhjBgAgWxkFI5SWChTKC6t1hK/p5O7NbDXdyZG9JBEAPtC8X4+ReEYsV73PEOSWQNmYLNRMqhWoaOTiO+2rNu+jeaCI03SW7nXyYK5AqZTySqCE1DZm0fURVKDsTfLn7aIrEijfqJY4/drteGJoV9MdxTmWUZZlzHS8aiR6ArfXgVxuah4CL08SLMjUqKYqr1QTqOYK+9O6GEWsA8nrG6oBKYOI1+PzgKrzQAGQXQJVFQOH/Xxry99Src4kl5umBCkLaAaNSgG1r2kkIK6dBQDomRJFFXND2ZuM0ttJNfFzllSPwboQNuGVM+k3ytQ8Ck+ax3FNSaFBxDRG3t9QMlYuswqU+UOgVgoOSZknUADwF9/DctvWdZ6kpCyoqemGn+m6k7hmmuz0NOIsCbJUE5GX1Z+LHUK9kGon/7pg4PC+YSQAYCffUdxg3KDWPlASVFM/NSMlUMQZjDGUM3klUCF+Pnh/fFfZdSKviemkwOEEHw9A+r/+y4ISTDf8wq2WH2UtJJ9ANRRS7eRfFwzAOT4GgHSb0YVqbsKTZgJV0/tioCY84gy9kUEns/7/LaOCMbxzjPVCmVegzL+q9BUd6KV+8i6OvcF0w996pNNRtLD0cdNIfBvlTupJvCtMVwQwfQbl0NdNjk14PjUkfkYRR+HJ+xtKpvRGXnZNeDaTJdknUCaWEYgSPnm/pp8ApXmS2SoVqGNoAVZxqR6qQHk3KR+DdWH+EaORwWjLmhKoAK4UX6hft3u/t6qpckZ9oIhT9EYe5Uxencht9r2QfQJlrkCZEg9bl5mQCiMUUCoq3i9toNV957h48BXbWnUWYSmTQ4fjqqR8DLpKWIGqqdIhFTUlwUMVu3CT8mg9RuMeNfXdMr93YpD3N5RM6Y1MdhUoJbMxOkvmfaDMjKxiBKKEf/0boYDK3H9N+L4pVOChtCRQclIIv9pXkhiVndnW5YwBMMoogaopCQ7iSuoxEvd5TPWz3fsMjCpQxEH7L+Zi5Y5U2SVQCpsJlLwPT3MFymDpAyXdX/9GKKtcCLqCXzg4TnC1exmxTBgqI1I+Bl3FwFma8OQwYWpNP8Sk2geqqmKmtdzWUwWKOOqPI5lYvu2cDBMoGx96mSdQUVwugMomPClXoAxVE6ibnjT9f+cycBwnu8tlALDMbSUnNc1ELmeGiuZYqY5SE7I1Ya9ZG8XleozEc0oqLhcF0DQGxAkalektk9s0BjYrUDL80hXqoLgIQFCBquESDN7O1AdK8H7d+hLw9Dmgxa1QcJBlE54cE6iGSFiBaswViBxN3Un5h5ijygR9gJs0ChItDkqgJMY80aT8KlANrwnvHB8NQNwStLsYmLKyDxRg6gflH15xkxP1V6KnFMqwCa8h8kW5qB2R3a0hNMMKp/G5uX0T0eKQ31lN5swVKLldC0/BN7wE6gQzTaAph5N3tQqUgIIDmAxPNVSBkod8+MviM2gm9fnkHGE1D6JKa39FD5PfWU3mtCqqQMmFuWO1mDPpuotpFJ6994uTZxMeVaBkwcCUln6IctAQmvCs3i8FdSInDqImPGljnLLabb2IM+m6ixFK2MufqA8U8TbCUaFGKGVRgTL3i20IFShvSXjl9w0lc5WdyOU1kSbXQBIoXi2YO0hhOgnI4eRtgAJKO/N2KTh5VqAKmfzmgWoodFzl+VOlVsuiH6L5R3VDuByPt4zqld83lMxpZFqBsvlxkOFEmryqsmrBKeWTQBmhhFpl+3QS6KOSZR+oQqpASZZekED5aLUwyKAKbO4XJIfr+dUmlwXWvlI9kN9ZTebUMu0DVRbbt/pCGVagmKqyasFVVKC8pRxdFwYoLcl9VSF+allWoIqZT+0rEa+k5yrPn74+Gln0QzQnUMoa5oGSi6+Mycj2TQAGzBU1Dvl9Q8mc+UtKJ4MPvNkpvgnyezxe/Q45VqAETXiKigqUHIb4W13KpYpgX40sqmxVFcMHPJPfMdoQGAQ/QP20WlEvSOsuuoo+UL0Up0SOxPMuscZY3fmbygl7RSL9o6aBMY/CM4p4/R93W2QYC5WvjZKsHCtQNhIoOfS/MDL70xiE+Kmhk+GFd0uhlcV71xAJK1B+Pj6ySPDl1ipREx3Ulv7AYhI/AuIU8yg8pYRnra6qHCrbX75yTKBUwgTKdMIzyCC5MEAJzk7FMNhXLYtmSgC4zgIst0uYePPPkLrRobIPlJ9WI4vjU06tErUpYxrLd6GYxI+AOMWcdctppIUOaqiUNr58lfIaaQgATONvua1QyakTuf1TSYiMEqh8Vvn+lcKnQXTYlSPh8Rroq5bFZ1AO/bgcVQQfS2uMmMSPgDhFXZFo7ObboRxqHORbWu67wsLECqtOdExlexJGGSZQ0FY2VSqV5pK79C/gWtMXUIifWja/jgtQWUEshrbBXnxX6piguu2vVcmiH6JcPmO1eVl/H8qg9YoKVMPY4zJirkClsmjc5fsRTl3nEIZC6KDGMGUKXlZ/LnKEzrNbgRJxin6P0VZe+FJVUYGSw7WrjDUkUMF+Gtmc3IWzj5fQKDzJEs4j5KtRQg4XLpfbBeZt+c7QH6uNgwGA+kAR5wnLlqWacJRBiwyEIwfBKIY0T+g6qG2P4FLKL4Hi/BtZbiuUpqRDw+nFCsdtahrFFOKrhl4G/bwAIBeVFcRSWB+fnxtuq+9wiIuECZQ3NAW5w3Y+UewQPE5Y6fbXin9OkceR04AIy5a+autf/VId6WSwdx01lfya8IQJlPkU7gOdOMG4EV9TAuUn7T5Qwl/2J/l46JgSvMoXRfDBj8YbAQBLDSPwuuEe/GpMEitM4pTKBEpvlH4FeJrucfxm7CN2GB4nrGSH+Yn//UAJlMQIy5bekIG7gwLMdgVK0OFaLpTCC18y04lbC+lXoGoazh/iq5F0H5P1fG/L7SyEYrTuFWTc/TcYFJivn4jTvf+H9wyjUAYtHtPPFDFS4ijh9F1leuknUH/wvVEK8RMKTzNwKrw1phNm3NISfVs0qv0BHiaPb+AGRDjbc6gXZODucIk1hsJWAqUNttzcw7dBAfNDPJeN1or0eozOvZS+ldtkTqDkUIHS1dD/IthPun0zntY/jF+MN2C0cgcA4CoLxhGWgIhm7dE8PAdBPsFoNXgcJrGTWPHPeZGj9Zydxg64QXlM7DDcRnhpobhQeVySp6wBJFAT+yZA2yNO7DAspPuzsIESXm8szN/6A3MNwVVX93rdypaj1F7fLZ/KDtebjV3xkP5pDNS9VU+RuddR355Aj8lQdLxTsJTHqvt7oiS+v2hxuUtNncQDtSqvufins9Yab4YOavzZ7BmsMgzCNr4zAFMlePOT/fH9tL7gOA4jOseIHGnd2eqEXFax7HPjwPoOxyOK2owGHvzTKoG6q2ccRndrImJU7lFbAnWGj62nSDxHq/Ku6SYogZIYYQWqUYAG743vavk7hW8PY5uhYoTlklN8E+QiyP4KysoTutSbudoPnQ4MW2S5gDAAcIzhlrYRmPTQbLyqv1fE6OquvIaTt83qosQcixmDVwyTrL54OY6z9EkM9pVulc3AFHhMNx2T9M/iV5/hOD3qT8t9PzR+FP3KF+NPvofVYwqYNKs2AeM/BeL7WF0myk+jwjt3dcZvN/8mYmSu+cvYHQ/qngJgXVUzqCq7P/zYdC76lr2H23RvYXXywXqPUc4ogZIYYQKlViowonMM1s+8EU1CffHm2G5Qjv8aGPWJiBE6hmcclhrucHh9rWCk2hO6R/G9sZ8nwvIYha0O8RVNeFAo8IPxpvoNyM1qm6ZgaMcIq7+vsUCk8Y09GZJbaVQKPDO4DQDgieRW1e4P9JFeb4itrBvm6yeiXflq/MrfgF18e+T1fw2BjSqrafGRYUhjkag6zH9Hn49RogqV7LUAbVVESwKaihCJ61hIUzxieBKb+W7V7isJ72S5Hd4oDBkIBwCoVEpkstB6i9EdbixfIvjLu4436X3qGzjhr/mIQNMw6o6xwdjx7K2VK3UchXKosDk3HN39snBZ1RSRpWdxyhiNtkF68DyPC0VKNFHk4IQ+ComqdBwxxqGD8hJOG2PQVH0dhcwXOqZEYy4P5wwRaKe8jCOGOCSqLuEk3wQJyqu4xoKh4IwIRgku6MPQRpWBY4Y49PRNx2WflggpvYwrej/EBiqh4XgcL9Di6OH9+OxyFNrHR6Bc4Q9cyHVouzWCCtQtd81Au+ggnE4/iHJOg3BcxwU+Aq0U6TjFx6ENdxnnEIMmXI5p5miOQzCKcJmFowUycIo1QRvFJZzlY9FUkY0chEILPXxRhkwWgmbIxOmKdc7wTZCguIIraIxAlEAJHrl8AJpw2ThvaIyO6WtwNPYutFBdQxoiEaksBMs+gbidz1tvgK0pGVhl59V8+OMaC0QjrhAA8IHfo+jdpROMjVpXxsh08OXKq8V4mo9DC0UGMlhjBHHCGK/iLItGG+4STrE4tOIykIZIhHMFMECFQuaDaC4H543RaKW8jFN8HFpzl5HKohCjzEMx84EeKjRCPi7yjdGSS8dpPhY9NBeh+fEBq02pbSbnCL/KxP9V/b0Y8MAruJxbiJKindid649+qqM45tsD8cjGuQIObf3yUeofh2tGPzRV5uC4Phqd1Jfxnz4OndSXcdIQhabqPBTzPihhKkQr8nDaEIGOqvSKdS7huD4GLdXXcA0BAM/QSFGM87ogJF35AvvDhqKF8Tx2GDugZ2gJ8soBY8l1KP3DUAYfhJxei7cyu1hiVisVeOimBPRqFoau8dW/gGwN6MjTRuNi0v9wTRWFI+kFSPTJRl5AApoo87A+TYNRkVm4qIhDS/U15LAAcAAaKYpwVheGDppMHNLFoqvmMo7oY9FGnY10Yyj8FTr4czqk6YPQRp2NQ+Ux6Ka9jEPlseigycJFYyM0UpaCB4ed6Xq0D2X47ATDwy3ysLkwDsPjdcjRazA4niHEtzc+XbrTKuZ7esUj73rlZzLQR4NhnaLho1YCgi5QqT7tcOszZ7H95AV0MB6v+CxdxTUEQw0D/Gr6LLFwBHKlUMGIHBaEeGThDItBa8VlnObj0JLLwGXWGGGKIhihQAHzQwx3tdpxegFRMOZdwXWDGk0ahyIc13GRj0BLRToulmgRmbEZTY4tt3k82kqg9EaGV/UT8JL6K/AKDQ7434DdjUajT6e2FTHGonXF562VIgOXWATCuEKrGM+xGLTmLlnFGMXloRQ+0EGFRsizxFjtXIUAcGAIQrHlXHWSb4J4wwX8kh6I0c11uMaFQct08DfmI7ZpS/T4KQ97UnPRMiIAzwxqA6yt2BjBJMQhgf5YN+NGhPipsfVUNh7VPY5F6mVo0nMYynLTsS/yLsQHK1HG+SKnXIFYLgeXlXFoiUs4UB6LRGUaThpj0FyVgzwWAB4cGqEA540RaKdKx2F9HDqp0nDc2AStVFnI5MOg5fQI5Epx0RCK1qpMHNU3QSdVGo4a49BWeQWXWDhCFKVQcwwFylDEsXQcLItBN590nEFTNDNexB59Apb9fRSXmeCHlpddYJ5jjNFUuh5QUFCA4OBg5OfnIyiohmYqF/x08DL2XbiOl4a1N53YJKREZ8D6w1cwoG0EHltzEDvPXgMAXFhop+nxl+nQH/wWt5S/bfkg2V3Xm3xzD3BK0CRw7w9Ay2TT7ZdNfdVSIsYj6VHTCb7Zc78hACXgABxZOLaeg3XBDw8BR9Za/mxW9nXN78uvM4EDpkleb/T9yTrh90KMMZzOKsKLPx/B8YwC/DW7P2JDam62avac6f1eNzYEiZk/Abe94tUjSRlj+PW/DLy76QzOXy3GvheTER6gRX5hCYLfiQYAHO7+P3QaPsP0gJcr+1i+0v1fzBveQYywnfNylX6hL+cDAI4suAWJ5Qeslj37/WF8uy8NUcjFP69OgEYC59aMvFK8/ecpTO3fAm2iAi3bm9N0KMIvmo7H/274EJ1vmwAAWLUzFa+sOw4ASF0wxO71K73FmxtOwsgzzNlTMUXDDY8Dt833+Os6+v1NFagaLF26FG+99RYyMzPRuXNnvP/+++jVq5fYYeHOrk1wZ1dpdnr006hwV8UoiqrzWNk04gMU3rwAQ3am4+Pt5/HYrdWbT7xS9/utEygbv1O4KjOQF8EPkUESmTx01MdAWAKw7Q3H1o9ob7n5sgS+eDmOQ5uoQHw9pQ9KdEaH+ji9MKQdzucUo2O3jgB3Qz1EWTccx+GOLrEY0TkGRp5BVdE9QKMVHIO87SH+7aPd+6PQU/TaUKjLr1dbzmxcqLxtdCAADploZDVYx5vFhPhi0bgulQtGLgNSt6O841SgIoEy6sttPtbbkycAeGZwW9ONPRULjAbRYrFFGkeJCL799lvMnj0b8+bNw4EDB9C5c2cMGjQI2dnZYocmG4M6RAEAAmqaz4rjEBYchOeHtMOJ+YMx67bW9RRdHbW6DV90WFn5t63r+gma8MxfSH1bhHs6MvfgOMC/srS+94Xkmtfv/gBYr4eRP+ZbJLeP9HBw7qNWKhzuID6lXwIWjEqUxBeTEMdxluQJsJ5rzmDnC6ttlDQSqPwHd9i5p/p71KlJZbVKau+hRZd7gDuXISC88gd2QFQLy+0hidHQqBTo3Vya101FowSxI7BCFSg7Fi1ahClTpuCBB0x9PZYvX47ffvsNn376KZ577jmRo5OH4Z1jcLWoHMntHPtClVRzJcdh9B0jca3gW4Tps8DF9a6+iiCBem98F6z77wqm9veuE0SN2o8E/lsDtLkdjQNrqZypfcANeUuCE200PEpBP8vwdoLBDaM+Af/LdOzu/jaSmkjjnQyPbIKLQ75Gky0zoRy2yLKc2UiQusWH4sWh7dBOItW1mgQGVfbTC4tqZrkdGeSDf5+7VXqDHu7/DUj9B+h2v9iRWKE+UDbodDr4+fnh+++/x8iRIy3LJ02ahLy8PPzyyy+1Pocn+0ARCWGsWsfH9FdaI5Zl4dhtX6PDDRLoz0UanLTTh1B4Nb368ckbAYWEfsiYVfkcHt7yPTptm4yzyhZo+dIBEQPznLOH/4VRV4Y2Pby7v6E3oj5QdZCTkwOj0YjISOvKSGRkJE6ePGnzMeXl5Sgvr2xrzs83dUwsKCjwXKBEkoz3/YYDl86gZeKNdHwQrxQSlYCQqATZHp9NuyZjH/sMkS06y3YbI5p1BEDfQa4w77Pa6kuUQLnJggUL8Morr1RbHhfnPdPOE0IIIcQxhYWFCA6231xNCZQN4eHhUCqVyMrKslqelZWFqKgom4+ZM2cOZs+ebfmb53nk5uaiUaNGbu2QWFBQgLi4OFy6dImaBt2E9qn70T51L9qf7kf71L3ktD8ZYygsLERMTM2XaKIEygaNRoPu3btj06ZNlj5QPM9j06ZNmDFjhs3HaLVaaLXWHWlDQkI8FmNQUJDkD1JvQ/vU/WifuhftT/ejfepectmfNVWezCiBsmP27NmYNGkSevTogV69emHJkiUoLi62jMojhBBCSMNFCZQd48aNw9WrVzF37lxkZmaiS5cu2LBhQ7WO5YQQQghpeCiBqsGMGTPsNtmJRavVYt68edWaC4nraJ+6H+1T96L96X60T92rIe5PmgeKEEIIIcRJdCkXQgghhBAnUQJFCCGEEOIkSqAIIYQQQpxECRQhhBBCiJMogSKEEEIIcRIlUIQQQgghTqIEihBCCCHESZRAEUIIIYQ4iRIoQgghhBAnUQJFCCGEEOIkSqAIIYQQQpxECRQhhBBCiJMogSKEEEIIcRIlUIQQQgghTqIEihBCCCHESZRAEUIIIYQ4iRIoQgghhBAnUQJFCCGEEOIkSqAIIYQQQpxECRQhhBBCiJMogSKEEEIIcRIlUIQQQgghTqIEihBCCCHESZRAEUIIIYQ4iRIoQgghhBAnqcQOQK54nkdGRgYCAwPBcZzY4RBCCCHEAYwxFBYWIiYmBgqF/ToTJVAekpGRgbi4OLHDIIQQQogLLl26hCZNmti9nxIoDwkMDARgegOCgoJEjoYQQgghjigoKEBcXJzle9weSqA8xNxsFxQURAkUIYQQIjG1db+hTuSEEEIIIU6iBIoQQgghxEmUQBFCCCGEOIn6QBEiBkM5wCkApVrsSAghxCaj0Qi9Xi92GG6nVquhVCrr/DyUQBFS33gjsKwvYNQBMw8CSvoYEkK8B2MMmZmZyMvLEzsUjwkJCUFUVFSd5mmkMzch9a0kF7h21nS7LA/wDxc1HEIIETInTxEREfDz85PVZNCMMZSUlCA7OxsAEB0d7fJzUQJFSH1TCErHvEG8OAghpAqj0WhJnho1aiR2OB7h6+sLAMjOzkZERITLzXnUiZyQ+saY7duEECIyc58nPz8/kSPxLPP21aWPFyVQhNQ3xlfellFpnBAiH3JqtrPFHdtHCRQh9U2YQFEFihBCJIkSKELqGzMKbvP21yOEEOK1KIEipL5ZVaAogSKEECmiBIqQ+kYJFCFeIe1aCcoNxtpXJMQGh6cx+PXXX51+8ttuu80yXJAQUoGnJjxCxLbvQi7GLE9B++gg/P74TWKHQ+ro6tWrSExMxGOPPYbnn38eAPDvv//i5ptvxh9//IEBAwa4/TUdTqBGjhzp1BNzHIczZ84gISHB2ZgIkTeqQBEiup8OpgMAjl8pEDkS78YYQ6lenCqdr1rp8Gi5xo0b49NPP8XIkSMxcOBAtGnTBhMnTsSMGTM8kjwBTk6kmZmZiYiICIfWDQwMdCkgQmSPRt4RIjq1knqwOKJUb0T7uX+K8trH5w+Cn8bxNGXIkCGYMmUKJkyYgB49esDf3x8LFizwWHwOH0GTJk1yqjnu3nvvRVBQkEtBESJrNAqPENEpZD7PUUP19ttvw2AwYO3atfjqq6+g1Wo99loOp3arVq1y6omXLVvmdDCENAjUhEeI6KgA5RhftRLH5w8S7bWdde7cOWRkZIDneVy4cAGJiYkeiMyEroVHSH2jBIoQ0SkUVIFyBMdxTjWjiUmn0+Hee+/FuHHj0KZNGzz00EM4cuSIw12PnOXyXikrK8Phw4eRnZ0Nnrf+EhgxYkSdAyNEtmgUHiGiU1ITnuy88MILyM/Px3vvvYeAgAD8/vvvePDBB7F+/XqPvJ5LCdSGDRtw3333IScnp9p9HMfBaKR5NQixiypQhIhOSRUoWdm6dSuWLFmCLVu2WPpff/HFF+jcuTOWLVuGadOmuf01XUqgZs6cibFjx2Lu3LmIjIx0d0yEyBtdC48Q0VECJS8333wz9Hq91bJmzZohPz/fY6/pUje6rKwszJ49m5InQlxBFShCREdNeKSuXEqgxowZg61bt7o5FEIaCEqgCBGdsBO5kadKMHGeS014H3zwAcaOHYvt27cjMTERarXa6v7HHnvMLcERIkvUhEeI6IRNeHojD6XC+SHzpGFzKYH65ptv8Ndff8HHxwdbt261mmqd4zhKoAipCY3CI0R0wiY8A1WgiAtcSqBeeOEFvPLKK3juueegUNBsZIQ4hZrwCBGdsAlPb+ABz01YTWTKpexHp9Nh3LhxlDwR4gpKoAjxKnojfQ6J81zKgCZNmoRvv/3W3bEQ0jBYXQuP5kwjRAy8oNlOT014xAUuNeEZjUa8+eab+PPPP9GpU6dqncgXLVrkluAIkSVh1cmot78eIcRjeMEADr2BKlDEeS4lUEeOHEHXrl0BAEePHrW6j6O5NQipmfDSRzxVoAgRg1GQQBl4SqCI81xqwtuyZYvdf5s3b3Z3jLVaunQpmjVrBh8fH/Tu3Rt79uypcf21a9eibdu28PHxQWJiIn7//Xer+3/88UcMHDgQjRo1AsdxOHTokAejJw2OsNmON4gXByENmHAGEZ2BmvCI8xxOoA4fPlztosE1OXbsGAwGz385fPvtt5g9ezbmzZuHAwcOoHPnzhg0aBCys7Ntrv/vv/9i/PjxmDx5Mg4ePIiRI0di5MiRVpW04uJi3HjjjXjjjTc8Hj9pgHhKoAgRm3DyTKpAEVc4nEB17doV165dc/iJk5KSkJaW5lJQzli0aBGmTJmCBx54AO3bt8fy5cvh5+eHTz/91Ob67777LgYPHoynn34a7dq1w6uvvopu3brhgw8+sKwzceJEzJ07F8nJyR6PnzRAVIEiRHRWfaBoFB5xgcN9oBhjeOmll+Dn5+fQ+jqdzuWgHKXT6bB//37MmTPHskyhUCA5ORkpKSk2H5OSkoLZs2dbLRs0aBB+/vnnOsVSXl6O8vJyy98FBQV1ej4iY1SBIkR0VqPwjNSEJ3Wff/45Zs2ahYyMDGi1lZN6jRw5EoGBgfjiiy/c/poOJ1D9+vXDqVOnHH7ipKQk+Pr6uhSUo3JycmA0Gqtd1DgyMhInT560+ZjMzEyb62dmZtYplgULFuCVV16p03OQBkI4Co8SKEJEIZy5gCpQNWAM0JeI89pqP8DBgWljx47FY489hl9//RVjx44FAGRnZ+O3337DX3/95ZHwHE6g6OLBNZszZ45VZaugoABxcXEiRkS8FiVQhIjOahQeVaDs05cAr8eI89rPZwAaf4dW9fX1xT333INVq1ZZEqgvv/wS8fHxuPnmmz0SnkvTGHiL8PBwKJVKZGVlWS3PyspCVFSUzcdERUU5tb6jtFqtVdmQELuoCY8Q0Qn7QOmoAiULU6ZMQc+ePZGeno7Y2FisXr0a999/v8emV5J0AqXRaNC9e3ds2rQJI0eOBADwPI9NmzZhxowZNh+TlJSETZs24YknnrAs27hxI5KSkuohYkJAncgJ8QJGI1WgHKL2M1WCxHptJ3Tt2hWdO3fG559/joEDB+LYsWP47bffPBScxBMoAJg9ezYmTZqEHj16oFevXliyZAmKi4vxwAMPAADuu+8+xMbGYsGCBQCAxx9/HP3798c777yDoUOHYs2aNdi3bx9WrFhhec7c3FykpaUhI8N00Jj7fkVFRdW5UkWIVQXK4PnBFoSQ6gw8jcJzCMc53IzmDR566CEsWbIE6enpSE5O9mhXGslfDXjcuHF4++23MXfuXHTp0gWHDh3Chg0bLB3F09LScOXKFcv6ffv2xddff40VK1agc+fO+P777/Hzzz+jY8eOlnV+/fVXdO3aFUOHDgUA3H333ejatSuWL19evxtH5ElYgTKUiRcHIQ2YcB4oasKTj3vuuQeXL1/Gxx9/jAcffNCjr+WWCtQjjzyC+fPnIyIiwh1P57QZM2bYbbKz1fl97Nixlk5mttx///24//773RQdIVXwlEARIjZhBYqa8OQjODgYo0ePxm+//Wbp2uMpbqlA3X777RgyZAhefvllFBcXu+MpCZEv4Sg8SqAIEYVRMPs4NeHJS3p6OiZMmODxgV1uSaDuuOMO7N69G5GRkejbty+WL1/u1GVfCGlQhAmUnhIoQsRwKbfUcpsSKHm4fv06fvrpJ2zduhXTp0/3+Ou5rRO5UqnE0KFD4evri6eeegpLlizBW2+9heHDh7vrJQiRB6smvFL76xFCPCblfOWlyWgmcnno2rUrrl+/jjfeeANt2rTx+Ou5JYEaPHgwTpw4gbi4OPTq1Qvvv/8+WrdujQ8//BCbNm3CkiVL3PEyhMiDVSfycvvrESKC7/Zewm9HrmDphG4I0Ep+oLZDDHKsQDEG/PQIENwEGPCS2NHUiwsXLtTr67nl07Fw4UIkJiZCqVRaLV+5ciXatm3rjpcgRD6EFSg9VaCId3nmh8MAgE93pOKxAa1EjqZ+yLIJL/s4cHiN6fYtLwAKyQ+69zpu2aNdunSpljyZ/f777+54CULkgypQRAIKSvVih1Bv9LzMm/DEupadzHk8JU1ISPD0SxAiLcIBFtQHingpH7XtH8Vy0TiwcoSW3iDDCpRSMAJN5/zoeLkPBHPH9nmkgfvKlSsICwuja8MRYouwAkWj8IiX0qrk3eTDC+eBkmMFSnie0RUBiHToYRqNBgqFAhkZGWjcuDE0Go3HriUnBsYYdDodrl69CoVCAY1G4/JzeSSBmjhxIs6dO4fRo0fj7bff9sRLECJdNA8U8VLCztRatbwTKIPcZyIXXmezvNDhhykUCjRv3hxXrlyxXM5Mjvz8/BAfHw9FHfqGeSSB+vvvvwEAJ0+e9MTTEyJtNBM58VLFuspjU+5NeMIKlCyb8IQJlJNNeBqNBvHx8TAYDDAajbU/QGKUSiVUKlWdK2tuSaDeffddPP7449WW0wg8Qmyga+ERL1VcXvmlK6dmG1tkfzFhvmoTnnM4joNarYZarXZjUPLilhrtkSNHMHXqVEumevz4cYwfP94dT02I/PDUB4p4pxJdZQIly7mRBIxMmEDJsA+U8DzjRBMecZxbKlCffPIJFi9ejMGDByM4OBgXLlzAc889546nJkR+qA8U8VJF5ZVfukY5dqwW4GVfgRI24TlfgSK1c0sCtXfvXmzfvh3Xr1/H+fPnsXnzZjRt2tQdT02I/FAfKOKlSgRNeLIcmSYg/yY81/tAEce4pQlv1qxZeOSRR7Bv3z6sWbMGI0eOxM6dO93x1ITIj7APlFFnnVARIqIiQQIl5woUX2Xb5NmEJxyFRxUoT3BLBWrHjh2W2z179sT69etx1113URJFiC1VEyZDOaDxEycWQgSKrfpAyTCpqFC1uibPaQyEncipD5QneGSij9jYWGzatMkTT02I9LEqJ2tqxiNeoljQB8og45moeVa1AiXDbeUFl+KhJjyPcLkCpdfrkZmZiZKSEjRu3BhhYWFW9/v4+NQ5OEJkiVWpQNEFhYmXKG4gfaCqbpssq23UhOdxTlWgCgsLsWzZMvTv3x9BQUFo1qwZ2rVrh8aNG6Np06aYMmUK9u7d66lYCZGHqr/sqQJFvIRwIs2j6fkiRuJZVft3ybICde1c5W0ahecRDidQixYtQrNmzbBq1SokJyfj559/xqFDh3D69GmkpKRg3rx5MBgMGDhwIAYPHowzZ854Mm5CpKtqBYoSKOIlhBWo7WdyRIzEs6p2IpdlH6i/51XepgTKIxxuwtu7dy/++ecfdOjQweb9vXr1woMPPohly5Zh9erV2L59O1q1auW2QAmRjWqdyCmBIt5BmEDJWdUmPFlWoISoCc8jHE6gvvnmG8vttLQ0xMXF2ZzqPzs7G4888oh7oiNEjqr1gaIEingHYROenFXtRC7LPlCcsvJcQxUoj3BpFF7z5s1x9erVasuvXbuG5s2b1zkoQmStWgWKOpET7yCsQLWODBAxEs9qEBWozoLLqdEoPI9wKYFijNmsPhUVFdHoO0JqU20ag3Jx4iCkiobShFetD5RBhgmUcBoDasLzCKemMZg9ezYA01WaX3rpJfj5VU7+ZzQasXv3bnTp0sWtARIiO1UTKJrGgHiJhjKRZvVReDLcVqOu8rauEGAMsFH4IK5zKoE6ePAgAFMF6siRI9BoNJb7NBoNOnfujKeeesq9ERIiN7ZmIifEC5QIJtLUy3gizQbRhGcUVKAYb/qhRlc8cCunEqgtW7YAAB544AG8++67CAoK8khQhMhatWkMqAJFvIPwWnhyrkBV60TOM7tdUyRLmEABpn5QlEC5lUt9oFatWkXJEyGuqlqBolF4xEsI+0DJslmrgjk51KoqvwJlt73CJjyArofnAQ5XoGbPno1XX30V/v7+lr5Q9ixatKjOgREiW+YKlNoP0JfQPFDEK/A8Q4m+YV0Lz1ejRHlFB3K9kYdG5ZHLw4qjagWKOpK7ncMJ1MGDB6HX6y237ZFVCZQQTzB/MWn8KYEiXqNUb4SwZUvOTXjmPlA+KiUA0/ea7PpBVatA0VQG7uZwAmXu/1T1NiHEScIKFEAJFPEKucXWX7iySygEzKPw1CoOHGcaoCa7y7nwVftAUQXK3epcr2TM1PmOEOIgJqhAAdQHiniFnCLTaFCVwtSKUHWkmpyYm/BUCgXUStPXoOwqbtWa8KgPlLu5nECtXLkSHTt2hI+PD3x8fNCxY0d88skn7oyNEHniq1agaBQeEV9OkakCFRlkmgzZyMv3x7E5WVIqOGgqEijZVdzMTXiaihnlqQnP7ZyaxsBs7ty5WLRoEWbOnImkpCQAQEpKCmbNmoW0tDTMnz/frUESIivmJjxzBYrmgSJe4FpFBSo62Afpeaak3sAzqJXy69dqrkApOQ6qiu2TbQLlG2pqvqMmPLdzKYFatmwZPv74Y4wfX3mtnREjRqBTp06YOXMmJVCE1MRcgTL/MqSZyIkXMDfhRQVXXo7LYGRQK8WKyHPMzZMKBWdpwtMZZFZtMzfh+YYA+ZdoFJ4HuNSEp9fr0aNHj2rLu3fvDoOh/q+ltHTpUjRr1gw+Pj7o3bs39uzZU+P6a9euRdu2beHj44PExET8/vvvVvczxjB37lxER0fD19cXycnJOHPmjCc3gTQklj5Q5iY8qkAR8Zmb8KKCKhMouc5Gbr4WnkrQhCe7aRvMCZR/Y9P/pdfFi0WmXEqgJk6ciGXLllVbvmLFCkyYMKHOQTnj22+/xezZszFv3jwcOHAAnTt3xqBBg5CdnW1z/X///Rfjx4/H5MmTcfDgQYwcORIjR47E0aNHLeu8+eabeO+997B8+XLs3r0b/v7+GDRoEMrKqLMvcQO+ahMeHVdEfPYqUHJktKpAybwJL6Sp6f/CK+LFIlMOJ1CzZ8+2/OM4Dp988gk6duyIhx56CA899BASExPx8ccfQ6Go34nIFi1ahClTpuCBBx5A+/btsXz5cvj5+eHTTz+1uf67776LwYMH4+mnn0a7du3w6quvolu3bvjggw8AmKpPS5YswYsvvog77rgDnTp1wueff46MjAz8/PPP9bhlRLYs0xhQAkW8hzmBahyoRcVAPChP/Ax80BPIOCRaXJ5gEFSg5NSEl3atBLe/ux3vbzpTWYEKrUigCjLEC0ymnJpIU6h79+4AgHPnzgEAwsPDER4ejmPHjrkxvJrpdDrs378fc+bMsSxTKBRITk5GSkqKzcekpKRUm0l90KBBluQoNTUVmZmZSE5OttwfHByM3r17IyUlBXfffbfN5y0vL0d5eWVTTEFBgaubVaOU1c+h+YW1HnluKXBlnlbHHsLVdrNur8cB4FTwGfAMAiqa8L48cBX3AtBfPohrL7eo9XkdOb2zGiLmHFjHkeepKZ6qj2IOvGGuvlbtr+7Z13PkeRyJyV2vVdd15vE8LqkbI8jnM6iUCugMPIL+mAEYy4EV/ZGJxg5EWtvru4cjx5U9agWHvn6B6MHdAwXXF9FcDl5Wv4NmX2ShskbDVcRqeh1z3ML9x1y4T7j9lcs44cJaHs8JllWPlWcMj7JIPL1xKmb6mSpQfHBTKAAYLu1FjgPnGam52OFR9B77pCiv7dJEmt4iJycHRqMRkZGRVssjIyNx8uRJm4/JzMy0uX5mZqblfvMye+vYsmDBArzyyitOb4OzuLICRCHH46/jterjR6IHX+PyjpUI0JgqUHuKIjBOrYSaM8j3PZX+j/qGgwPaKdNQULgTakUIdADyfeMQUnQWABCFq+LGJ1SX48oIoDALo5Q7sE5xAx5nX6G7sv5++NeorgMeOaATUnGRRVom0jyNpohhvgjiSmV5nrkg4vxWTo/C0+v1GDx4MJYvX45WrVp5IiZJmjNnjlVlq6CgAHFxcW5/nWZDZ+NMju0qmBwxJvwVxqyWm+8RrgMGy9w1zPLP8rPO9vNaPZ5V+ZXILJe3YFaPZ1aPt8QC63VZxe28Uzsw4sq7KCguBlMbwQFIYxH49sYNSIqorRO5A98WDpUxHKt1uPo8wqUcY2COBeXy6zmv9ufhPLyPnH4et8Vcu9DT3yH03C8IytgBlXIEACOKy3QIAfB5wGQkDRhZx1dwQ5x13NY3NpxEt5KdeFT1K8K5fGg5PbqVmQYdZSTNR0lE18oqt+W1mGkZY+AqMhxhHcj6PmZJgjgbjwe4ysdaHmdeh7P627yKqe7ECZZVOZFx5mUc9JcPInr/2xiurGyB2XNViUXlSzCkiR4P3NjM5X3nrVpGNRXttZ1OoNRqNQ4fPuyJWJwWHh4OpVKJrKwsq+VZWVmIioqy+ZioqKga1zf/n5WVhejoaKt1unTpYjcWrVYLrVbrymY4JSquJaLiWnr8dYh7GWNVwFfvguMN0OuN0AAwQoGkLu3RonGA2OERAoRpgXO/AOe3Qa0YAQDwM5q6IgS0vw2tuvYTMzq3uLRNAWWRqSWhMZePDroj4HSFQEAkYm6bCdRzH15300fGA/vfRjjyLcv2pBUiD4GIT2yLVl3k14QnJpeOlnvvvRcrV650dyxO02g06N69OzZt2mRZxvM8Nm3aZJngs6qkpCSr9QFg48aNlvWbN2+OqKgoq3UKCgqwe/duu89JSG2Uag0AQAMDynWm0joPBcL8NGKGRUil+CRAoQLy09BUcRUAQyAzNY9oAsPFjc1NtGoFrrJgAEA48tG9rKJS03qw5JMnAFD7hQAA/LnKqvaeNNP8Tz2bhYkRkqy5NJGmwWDAp59+ir///hvdu3eHv7+/1f2LFi1yS3COmD17NiZNmoQePXqgV69eWLJkCYqLi/HAAw8AAO677z7ExsZiwYIFAIDHH38c/fv3xzvvvIOhQ4dizZo12LdvH1asWAEA4DgOTzzxBF577TW0atUKzZs3x0svvYSYmBiMHDmy3raLyIzSlCipYIROb0qgjFAg0MeljyAh7qcNAGK6AZf3oBNO4TQSoYKpv55PcN07kHsDjVKBLJgSqAguD/4lFQlU26EiRuVG2sBqi7JLjPBRK5EYGyxCQPLm0tn76NGj6NatGwDg9OnTVvdxdRgd4Ypx48bh6tWrmDt3LjIzM9GlSxds2LDB0gk8LS3NamqFvn374uuvv8aLL76I559/Hq1atcLPP/+Mjh07WtZ55plnUFxcjIcffhh5eXm48cYbsWHDBvj4+FR7fUIcojB91FSc0TSNAQdoNWqolNL/1UtkJNaUQHVg5/A31wwAUMo0CAyo/sUsRVq1AjkVFSgtp4fWeBVQ+QLNpd88CQDQBln9aeRUADh0iQuBRkXnGndzKYHythF5M2bMwIwZM2zet3Xr1mrLxo4di7Fjx9p9Po7jMH/+fLokDXEfpRoAoIUBSpimMfDTUvMd8TIxph/GbdlZhKI3AOA6AhDspxYzKrfRqpQohQ9K4As/VFxCKTASUPuKG5i7KJQo5XzhyyquZQjT+0bNd57hcvtBXl4eVq5ciRMnTgAAOnTogAcffBDBwVQmJKSaiiY8NWe0jM7x08rjS4nISExXAEBL43k04kwdyPNYIEJ95XGsmi/bUsAFwq8iybBMaCsT5Up/+BpM21bOTBcypATKM1yq6e3btw8tWrTA4sWLkZubi9zcXCxatAgtWrTAgQMH3B0jIdJnbsKDEYqKCpS/D1WgiJdp1BLQBMIHOvRSmObSu84CECyTBEqrNn3l5UOQNGnklUDpVZWjenXMtL0tI2ikrye4lEDNmjULI0aMwIULF/Djjz/ixx9/RGpqKoYNG4YnnnjCzSESIgMVTXgqQRNeTCid1IiXUSiAcNP8fonceQBAPhcAX7VSzKjcxlyBSmeNKheW5YkTjIcY1JX91QwwvW/+Ghqs4gkuV6CeffZZqFSVb4pKpcIzzzyDffv2uS04QmTDPAqPGSwVqBnJbcSMiBDbKvoDRXB5AIASZXC9Dw7yFHMF6hX9/ZULyzxz2S2x8JrqCZSvRh4JsLdxKYEKCgpCWlpateWXLl1CYKA8RmsQ4lYKUwVKyTGoK4aGB/h4fuJVQpx2/SIAoLUiHQBQppZPv1atypRIXDQK+gRVXJtSNnwqR+IZK5rwaASeZ7i0V8eNG4fJkyfj22+/xaVLl3Dp0iWsWbMGDz30EMaPH+/uGAmRPmVltVbBVVyKQUG/CokXKrhs9adOEyJOHB5gM5GQWQKlECZQrn3FEwe51DD69ttvg+M43HfffTAYDABMl3iZNm0aFi5c6NYACZEFpY0O4xwlUMT7GbShYofgNlpbCZRMmifNzLORA4ARdI7xJJcSKI1Gg3fffRcLFizAuXPnAAAtWrSAn5+fW4MjRDYUNkYxyeDSEUT+mK+cEigbCUVos3qPw5PU/pVNrgaqQHlUnbrm+/n5ITEx0V2xECJftprrODq5Ee/H+clnDiFhE969ujmYH74JCaNWiBiR+/kEVCa8PCVQHkV7l5D6wHHVm/GoCY94o8iOVn8q/RvZWVF6hE14O/hErG3/PhDWXMSI3E9jVYGic4wnUQJFSH2p2oxHnciJN7rrc6s/tQHyacKr2olcKbP+TwDACa6Hx0OBbx/uI2I08kYJFCH1RVklgaIKFPFGjVpY/akJihApEPer2olcoZBfAiWcxsAABXonyKeC6G0ogSKkvlRNoKgCRSQg2E8+lxyq2olcJccESiucxoDOMZ7kcCfy2bNnO/ykixYtcikYQmStahMedSInXirbpzkiylKRw4Jkcx08oHoFSin3BIrROcaTHE6gDh48aPX3gQMHYDAY0KaN6XIUp0+fhlKpRPfu3d0bISFyIaxAcQrZzT9D5OOH1m8g9uBifGi4A4t8KIGSFEETXsdY+cwi740cTqC2bNliub1o0SIEBgbis88+Q2ioqYPh9evX8cADD+Cmm25yf5SEyEHVBIoQL1Xo1xSP6WcCAPxkdB21htCJXFiBClYbRQxE/lw6i7/zzjtYsGCBJXkCgNDQULz22mt455133BYcIbIibMKjDuTEiwn7Bvmo5XOsVu0DJcsKlEpwjU1diXhxNAAuJVAFBQW4evVqteVXr15FYWFhnYMiRJaEFSjqQE68GSdMoORTLdWqG0ATnrCqpi8WL44GwKVPxp133okHHngAP/74Iy5fvozLly/jhx9+wOTJkzFq1Ch3x0iIPCipAkWkgeeZ5bbNy59IlEbZAKYxEKIKlEe5dCmX5cuX46mnnsI999wDvV5veiKVCpMnT8Zbb73l1gAJkQ1hEx5dB494MYNVAiWfY7VqBUqW0xgIMeoD5UkuJVB+fn748MMP8dZbb1ldTNjf39+twREiK1SBIhIhbAWSU5WmagVKlp3Irch9+8RVp4sJ+/v7o1OnTu6KhRB5oz5QRCLk+rWrrdIhXvb5E/Eol2uz27dvx7333oukpCSkp6cDAL744gvs2LHDbcERIisKmsaASINCpplFtUu5yHQ7Sf1w6Sz+ww8/YNCgQfD19cXBgwdRXl4OAMjPz8frr7/u1gAJkQ2rClSdir+EeJRc8wqVgqvSPCleLET6XDp8XnvtNSxfvhwff/wx1OrKL4UbbrgBBw4ccFtwhMgKJVCEiIrjOKsqFFWgSF24lECdOnUK/fr1q7Y8ODgYeXl5dY2JEHlSUAJFpEHOaYVwWgZO7gmU3LdPZC4lUFFRUTh79my15Tt27EBCQkKdgyJElpSCq9qX5YsXByG1kfEXr8aqAiViIPVC9hsoKpcSqClTpuDxxx/H7t27wXEcMjIy8NVXX+Gpp57CtGnT3B0jIfKgFFSdSnLEi4OQWsj5a5ea8Ii7uNSO8Nxzz4HneQwYMAAlJSXo168ftFotnnrqKcycOdPdMRIiDwr5XNWeEKlqWBUo4kkuJVAcx+GFF17A008/jbNnz6KoqAjt27dHQECAu+MjRD6ETXiEeDE5F2YaVB8o4lEuNeGVlpaipKQEGo0G7du3R2RkJD755BP89ddf7o6PEPkQNuE1biteHITUgpNxI16DasKjwSoe5VICdccdd+Dzzz8HAOTl5aF379545513cMcdd2DZsmVuDZAQ2RA24SXcIl4chDRgDaoJj6544FEuJVAHDhzATTfdBAD4/vvvERkZiYsXL+Lzzz/He++959YACZENYRMendiIF2sW7id2CB7TICpQSq3p/6Y3iBuHzLlU3yspKUFgYCAA4K+//sKoUaOgUCjQp08fXLx40a0BEiIbwiY8upQL8WLDO8XgSn4ZejQNFTsUtxMmUHLNnzBtJ3D0R6DPI2JHImsuncVbtmyJn3/+GZcuXcKff/6JgQMHAgCys7MRFBTk1gAJkQ0FXUyYSINCweGR/i3Qo1mY2KG4nbATuWwrUOGtgJufBXyCxY5E1lxKoObOnYunnnoKzZo1Q+/evZGUlATAVI3q2rWrWwOsSW5uLiZMmICgoCCEhIRg8uTJKCoqqvExZWVlmD59Oho1aoSAgACMHj0aWVlZVus89thj6N69O7RaLbp06eLBLSANirAJj6MEihAxNIgmPFIvXEqgxowZg7S0NOzbtw8bNmywLB8wYAAWL17stuBqM2HCBBw7dgwbN27E+vXr8c8//+Dhhx+u8TGzZs3CunXrsHbtWmzbtg0ZGRkYNWpUtfUefPBBjBs3zlOhk4ZI2IRHFShCRNGgOpETj3J5jGNUVBSioqKslvXq1avOATnqxIkT2LBhA/bu3YsePXoAAN5//30MGTIEb7/9NmJiYqo9Jj8/HytXrsTXX3+NW2+9FQCwatUqtGvXDrt27UKfPn0AwNIR/urVqzh8+HA9bRGRPWETHlWgCBGFsAIl49kaSD1wOYHKy8vDypUrceLECQBAhw4d8OCDDyI4uH7aXFNSUhASEmJJngAgOTkZCoUCu3fvxp133lntMfv374der0dycrJlWdu2bREfH4+UlBRLAuWK8vJylJeXW/4uKChw+bmITFmNwqNO5ISIQatuAH2gSL1w6Sy+b98+tGjRAosXL0Zubi5yc3OxaNEitGjRAgcOHHB3jDZlZmYiIiLCaplKpUJYWBgyMzPtPkaj0SAkJMRqeWRkpN3HOGrBggUIDg62/IuLi6vT8xEZUlIFihCxaZTUB4q4h0sJ1KxZszBixAhcuHABP/74I3788UekpqZi2LBheOKJJ+oU0HPPPQeO42r8d/LkyTq9hifMmTMH+fn5ln+XLl0SOyTibRTUB4oQsWmpDxRxE5ea8Pbt24ePP/4YKlXlw1UqFZ555hmrJjVXPPnkk7j//vtrXCchIQFRUVHIzs62Wm4wGJCbm1utb5ZZVFQUdDod8vLyrKpQWVlZdh/jKK1WC61WW6fnIDJHo/AIEZ3Gah4oyqCI61xKoIKCgpCWloa2ba2v53Xp0iXLBJuuaty4MRo3blzreklJScjLy8P+/fvRvXt3AMDmzZvB8zx69+5t8zHdu3eHWq3Gpk2bMHr0aADAqVOnkJaWZpmKgRCPUdI8UISIjSpQxF1casIbN24cJk+ejG+//RaXLl3CpUuXsGbNGjz00EMYP368u2O0qV27dhg8eDCmTJmCPXv2YOfOnZgxYwbuvvtuywi89PR0tG3bFnv27AEABAcHY/LkyZg9eza2bNmC/fv344EHHkBSUpJVB/KzZ8/i0KFDyMzMRGlpKQ4dOoRDhw5Bp9PVy7YRmRI24VEFihBRUCdy4i4uVaDefvttcByH++67DwaDAYwxaDQaTJs2DQsXLnR3jHZ99dVXmDFjBgYMGACFQoHRo0dbXYtPr9fj1KlTKCkpsSxbvHixZd3y8nIMGjQIH374odXzPvTQQ9i2bZvlb/PkoKmpqWjWrJlnN4rIF43CI0R01ImcuAvHGGOuPrikpATnzp0DALRo0QJ+fvK9AKWzCgoKEBwcjPz8fLq8DTG5tAdYeZvp9rAlQI8HRA2HkIbol0PpeHzNIQDA+pk3omMsXe6EWHP0+9vhCtTs2bMdfvFFixY5vC4hDQaNwiNEdEpBxyeqQJG6cDiBOnjwoEPr0agGQuygUXiEiE6YNFFLOqkLhxOoLVu2eDIOQuSPRuERIjrhyDuqQJG6oPybkPpCo/AIEZ1VBYryJ1IHTiVQmzdvRvv27W1e5y0/Px8dOnTAP//847bgCJEVqyY8OnMTIgZhAkVdTkhdOJVALVmyBFOmTLHZKz04OBhTp07F4sWL3RYcIbJCTXiEiE7Y74ma8EhdOJVA/ffffxg8eLDd+wcOHIj9+/fXOShCZEkhSKBAJ25CxCCsOtGnkNSFUwlUVlYW1Gq13ftVKhWuXr1a56AIkSVhBYoZxYuDkAZMadWEJ2IgRPKcSqBiY2Nx9OhRu/cfPnwY0dHRdQ6KEFmySqBcnr+WEFIH1p3IKYMirnMqgRoyZAheeukllJWVVbuvtLQU8+bNw7Bhw9wWHCGyImzC46kCRYgYrKYxoGF4pA6cuhbeiy++iB9//BGtW7fGjBkz0KZNGwDAyZMnsXTpUhiNRrzwwgseCZQQyRN2HKcmPEJEwdE0BsRNnEqgIiMj8e+//2LatGmYM2cOzJfR4zgOgwYNwtKlSxEZGemRQAmRPGFzAVWgCBEFXcqFuItTCRQANG3aFL///juuX7+Os2fPgjGGVq1aITQ01BPxESJPVIEiRBTCqhOlT6QuHE6g0tLSEB8fb/k7NDQUPXv2tLt+eno6YmNj6xYdIXJFFShCRMHRRJrETRzuRN6zZ09MnToVe/futbtOfn4+Pv74Y3Ts2BE//PCDWwIkRJYYL3YEhDRI1tfCEy8OIn0OV6COHz+O//3vf7jtttvg4+OD7t27IyYmBj4+Prh+/TqOHz+OY8eOoVu3bnjzzTcxZMgQT8ZNiLQFx4kdASENHvWBInXhcALVqFEjLFq0CP/73//w22+/YceOHbh48SJKS0sRHh6OCRMmYNCgQejYsaMn4yVE2u7+BrjyH9B6kNiRENIgcTQPFHETpzuR+/r6YsyYMRgzZown4iFE3toOMf0jhIiP8idSB05NpEkIIYRImTBnoj5QpC4ogSKEENIgURMeqQtKoAghhDRIlECRuqAEihBCSIMhvIw35U+kLiiBIoQQ0mDojZVzsGlV9BVIXEdHDyGEkAYjQFs5+JxmIid14fQ0BoQQQohUtYsOwpO3tUZsqK/YoRCJowSKEEJIgzJzQCuxQyAyQE14hBBCCCFOogSKEEIIIcRJlEARQgghhDiJEihCCCGEECdRJ3IPYcw0XVtBQYHIkRBCCCHEUebvbfP3uD2UQHlIYWEhACAuLk7kSAghhBDirMLCQgQHB9u9n2O1pVjEJTzPIyMjA4GBgW6drK2goABxcXG4dOkSgoKC3Pa8DRntU/ejfepetD/dj/ape8lpfzLGUFhYiJiYGCgU9ns6UQXKQxQKBZo0aeKx5w8KCpL8QeptaJ+6H+1T96L96X60T91LLvuzpsqTGXUiJ4QQQghxEiVQhBBCCCFOogRKYrRaLebNmwetVit2KLJB+9T9aJ+6F+1P96N96l4NcX9SJ3JCCCGEECdRBYoQQgghxEmUQBFCCCGEOIkSKEIIIYQQJ1ECRQghhBDiJEqgJGbp0qVo1qwZfHx80Lt3b+zZs0fskLzCyy+/DI7jrP61bdvWcn9ZWRmmT5+ORo0aISAgAKNHj0ZWVpbVc6SlpWHo0KHw8/NDREQEnn76aRgMBqt1tm7dim7dukGr1aJly5ZYvXp1fWyex/3zzz8YPnw4YmJiwHEcfv75Z6v7GWOYO3cuoqOj4evri+TkZJw5c8ZqndzcXEyYMAFBQUEICQnB5MmTUVRUZLXO4cOHcdNNN8HHxwdxcXF48803q8Wydu1atG3bFj4+PkhMTMTvv//u9u2tD7Xt0/vvv7/aMTt48GCrdWifVlqwYAF69uyJwMBAREREYOTIkTh16pTVOvX5OZf6udiR/XnzzTdXO0YfeeQRq3Ua9P5kRDLWrFnDNBoN+/TTT9mxY8fYlClTWEhICMvKyhI7NNHNmzePdejQgV25csXy7+rVq5b7H3nkERYXF8c2bdrE9u3bx/r06cP69u1rud9gMLCOHTuy5ORkdvDgQfb777+z8PBwNmfOHMs658+fZ35+fmz27Nns+PHj7P3332dKpZJt2LChXrfVE37//Xf2wgsvsB9//JEBYD/99JPV/QsXLmTBwcHs559/Zv/99x8bMWIEa968OSstLbWsM3jwYNa5c2e2a9cutn37dtayZUs2fvx4y/35+fksMjKSTZgwgR09epR98803zNfXl3300UeWdXbu3MmUSiV788032fHjx9mLL77I1Go1O3LkiMf3gbvVtk8nTZrEBg8ebHXM5ubmWq1D+7TSoEGD2KpVq9jRo0fZoUOH2JAhQ1h8fDwrKiqyrFNfn3M5nIsd2Z/9+/dnU6ZMsTpG8/PzLfc39P1JCZSE9OrVi02fPt3yt9FoZDExMWzBggUiRuUd5s2bxzp37mzzvry8PKZWq9natWsty06cOMEAsJSUFMaY6ctOoVCwzMxMyzrLli1jQUFBrLy8nDHG2DPPPMM6dOhg9dzjxo1jgwYNcvPWiKvqlz3P8ywqKoq99dZblmV5eXlMq9Wyb775hjHG2PHjxxkAtnfvXss6f/zxB+M4jqWnpzPGGPvwww9ZaGioZX8yxtizzz7L2rRpY/n7rrvuYkOHDrWKp3fv3mzq1Klu3cb6Zi+BuuOOO+w+hvZpzbKzsxkAtm3bNsZY/X7O5Xgurro/GTMlUI8//rjdxzT0/UlNeBKh0+mwf/9+JCcnW5YpFAokJycjJSVFxMi8x5kzZxATE4OEhARMmDABaWlpAID9+/dDr9db7bu2bdsiPj7esu9SUlKQmJiIyMhIyzqDBg1CQUEBjh07ZllH+BzmdeS+/1NTU5GZmWm17cHBwejdu7fV/gsJCUGPHj0s6yQnJ0OhUGD37t2Wdfr16weNRmNZZ9CgQTh16hSuX79uWach7eOtW7ciIiICbdq0wbRp03Dt2jXLfbRPa5afnw8ACAsLA1B/n3O5nour7k+zr776CuHh4ejYsSPmzJmDkpISy30NfX/SxYQlIicnB0aj0epABYDIyEicPHlSpKi8R+/evbF69Wq0adMGV65cwSuvvIKbbroJR48eRWZmJjQaDUJCQqweExkZiczMTABAZmamzX1rvq+mdQoKClBaWgpfX18PbZ24zNtva9uF+yYiIsLqfpVKhbCwMKt1mjdvXu05zPeFhoba3cfm55CTwYMHY9SoUWjevDnOnTuH559/HrfffjtSUlKgVCppn9aA53k88cQTuOGGG9CxY0cAqLfP+fXr12V3Lra1PwHgnnvuQdOmTRETE4PDhw/j2WefxalTp/Djjz8CoP1JCRSRhdtvv91yu1OnTujduzeaNm2K7777TraJDZG2u+++23I7MTERnTp1QosWLbB161YMGDBAxMi83/Tp03H06FHs2LFD7FBkwd7+fPjhhy23ExMTER0djQEDBuDcuXNo0aJFfYfpdagJTyLCw8OhVCqrjSjJyspCVFSUSFF5r5CQELRu3Rpnz55FVFQUdDod8vLyrNYR7ruoqCib+9Z8X03rBAUFyTpJM29/TcdeVFQUsrOzre43GAzIzc11yz5uCMd4QkICwsPDcfbsWQC0T+2ZMWMG1q9fjy1btqBJkyaW5fX1OZfbudje/rSld+/eAGB1jDbk/UkJlERoNBp0794dmzZtsizjeR6bNm1CUlKSiJF5p6KiIpw7dw7R0dHo3r071Gq11b47deoU0tLSLPsuKSkJR44csfrC2rhxI4KCgtC+fXvLOsLnMK8j9/3fvHlzREVFWW17QUEBdu/ebbX/8vLysH//fss6mzdvBs/zlpNuUlIS/vnnH+j1ess6GzduRJs2bRAaGmpZpyHuYwC4fPkyrl27hujoaAC0T6tijGHGjBn46aefsHnz5mpNl/X1OZfLubi2/WnLoUOHAMDqGG3Q+1PsXuzEcWvWrGFarZatXr2aHT9+nD388MMsJCTEagREQ/Xkk0+yrVu3stTUVLZz506WnJzMwsPDWXZ2NmPMNLw5Pj6ebd68me3bt48lJSWxpKQky+PNw3EHDhzIDh06xDZs2MAaN25sczju008/zU6cOMGWLl0qm2kMCgsL2cGDB9nBgwcZALZo0SJ28OBBdvHiRcaYaRqDkJAQ9ssvv7DDhw+zO+64w+Y0Bl27dmW7d+9mO3bsYK1atbIacp+Xl8ciIyPZxIkT2dGjR9maNWuYn59ftSH3KpWKvf322+zEiRNs3rx5khxyz1jN+7SwsJA99dRTLCUlhaWmprK///6bdevWjbVq1YqVlZVZnoP2aaVp06ax4OBgtnXrVqth9SUlJZZ16utzLodzcW378+zZs2z+/Pls3759LDU1lf3yyy8sISGB9evXz/IcDX1/UgIlMe+//z6Lj49nGo2G9erVi+3atUvskLzCuHHjWHR0NNNoNCw2NpaNGzeOnT171nJ/aWkpe/TRR1loaCjz8/Njd955J7ty5YrVc1y4cIHdfvvtzNfXl4WHh7Mnn3yS6fV6q3W2bNnCunTpwjQaDUtISGCrVq2qj83zuC1btjAA1f5NmjSJMWaayuCll15ikZGRTKvVsgEDBrBTp05ZPce1a9fY+PHjWUBAAAsKCmIPPPAAKywstFrnv//+YzfeeCPTarUsNjaWLVy4sFos3333HWvdujXTaDSsQ4cO7LfffvPYdntSTfu0pKSEDRw4kDVu3Jip1WrWtGlTNmXKlGpfGLRPK9nalwCsPoP1+TmX+rm4tv2ZlpbG+vXrx8LCwphWq2UtW7ZkTz/9tNU8UIw17P3JMcZY/dW7CCGEEEKkj/pAEUIIIYQ4iRIoQgghhBAnUQJFCCGEEOIkSqAIIYQQQpxECRQhhBBCiJMogSKEEEIIcRIlUIQQQgghTqIEihBCCCHESZRAEUIIIYQ4iRIoQgghhBAnUQJFCCGEEOIkSqAIIYQQQpz0f3PufU30F6UwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (spbet, spco) = plt.subplots(nrows=2, ncols=1, sharex=True)\n",
    "spbet.plot(tw['s'], tw['betx'])\n",
    "spbet.plot(tw['s'], tw['bety'])\n",
    "spbet.set_ylim(bottom=0)\n",
    "spco.plot(tw['s'], tw['x'], label='x')\n",
    "spco.plot(tw['s'], tw['y'], label='y')\n",
    "spco.legend()\n",
    "spbet.set_ylabel(r'$\\beta_{x,y}$ [m]')\n",
    "spco.set_ylabel(r'(Closed orbit)$_{x,y}$ [m]')\n",
    "\n",
    "fig.suptitle(\n",
    "    r'$q_x$ = ' f'{tw[\"qx\"]:.5f}' r' $q_y$ = ' f'{tw[\"qy\"]:.5f}' '\\n'\n",
    "    r\"$Q'_x$ = \" f'{tw[\"dqx\"]:.2f}' r\" $Q'_y$ = \" f'{tw[\"dqy\"]:.2f}'\n",
    "    r' $\\gamma_{tr}$ = '  f'{1/np.sqrt(tw[\"momentum_compaction_factor\"]):.2f}'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2b5b883",
   "metadata": {},
   "source": [
    "### Track the bunch\n",
    "\n",
    "We track the bunch and observe the losses:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "991ef5b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lost beam fraction: 24.30%\n"
     ]
    }
   ],
   "source": [
    "collider.lhcb1.track(bunch, num_turns=5)\n",
    "print(f'Lost beam fraction: {np.sum(bunch.state <= 0)/num_particles*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a19cf8fc",
   "metadata": {},
   "source": [
    "To find out where we lose the particles, we filter them, and inspect the element where the losses occur:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "21433e72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The element where we lose most particles is called `obstacle`.\n"
     ]
    }
   ],
   "source": [
    "lost_particles = bunch.filter(bunch.state <= 0)\n",
    "\n",
    "# Find the name of the element where we lose most particles\n",
    "from collections import Counter\n",
    "i_most_losses = Counter(lost_particles.at_element).most_common(1)[0][0]\n",
    "\n",
    "print(f'The element where we lose most particles is called `{collider.lhcb1.element_names[i_most_losses]}`.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e8125a6",
   "metadata": {},
   "source": [
    "### Plot the lost particles at the obstacle\n",
    "\n",
    "The element where we lose the particles is of an aperture type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fa76daf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LimitPolygon(x_vertices=[ 0.022       0.02182652  0.02130883  0.02045508  0.01927875  0.01779837\n",
       "  0.01603731  0.01402333  0.01178819  0.00936714  0.00679837  0.00412239\n",
       "  0.00138139 -0.00138139 -0.00412239 -0.00679837 -0.00936714 -0.01178819\n",
       " -0.01402333 -0.01603731 -0.01779837 -0.01927875 -0.02045508 -0.02130883\n",
       " -0.02182652 -0.022      -0.02182652 -0.02130883 -0.02045508 -0.01927875\n",
       " -0.01779837 -0.01603731 -0.01402333 -0.01178819 -0.00936714 -0.007\n",
       "  0.          0.01        0.02045508  0.02130883  0.02182652  0.022     ], y_vertices=[ 0.00000000e+00  2.75733114e-03  5.47117752e-03  8.09874016e-03\n",
       "  1.05985808e-02  1.29312756e-02  1.50600363e-02  1.69512913e-02\n",
       "  1.80000000e-02  1.80000000e-02  1.80000000e-02  1.80000000e-02\n",
       "  1.80000000e-02  1.80000000e-02  1.80000000e-02  1.80000000e-02\n",
       "  1.80000000e-02  1.80000000e-02  1.69512913e-02  1.50600363e-02\n",
       "  1.29312756e-02  1.05985808e-02  8.09874016e-03  5.47117752e-03\n",
       "  2.75733114e-03 -7.07582864e-18 -2.75733114e-03 -5.47117752e-03\n",
       " -8.09874016e-03 -1.05985808e-02 -1.29312756e-02 -1.50600363e-02\n",
       " -1.69512913e-02 -1.80000000e-02 -1.80000000e-02 -1.00000000e-02\n",
       " -1.30000000e-03  1.00000000e-03 -8.09874016e-03 -5.47117752e-03\n",
       " -2.75733114e-03 -1.00000000e-12], x_normal=[-0.99802673 -0.98228725 -0.95105652 -0.90482705 -0.84432793 -0.77051324\n",
       " -0.68454711 -0.4247618  -0.         -0.         -0.         -0.\n",
       " -0.         -0.         -0.         -0.         -0.          0.4247618\n",
       "  0.68454711  0.77051324  0.84432793  0.90482705  0.95105652  0.98228725\n",
       "  0.99802673  0.99802673  0.98228725  0.95105652  0.90482705  0.84432793\n",
       "  0.77051324  0.68454711  0.4247618  -0.         -0.95890337 -0.77911836\n",
       " -0.22414769  0.65648121 -0.95105652 -0.98228725 -0.99802673 -0.70710724], y_normal=[-0.06279052 -0.18738131 -0.30901699 -0.42577929 -0.53582679 -0.63742399\n",
       " -0.72896863 -0.90530515 -1.         -1.         -1.         -1.\n",
       " -1.         -1.         -1.         -1.         -1.         -0.90530515\n",
       " -0.72896863 -0.63742399 -0.53582679 -0.42577929 -0.30901699 -0.18738131\n",
       " -0.06279052  0.06279052  0.18738131  0.30901699  0.42577929  0.53582679\n",
       "  0.63742399  0.72896863  0.90530515  1.          0.28373284  0.62687684\n",
       "  0.97455519  0.75434238  0.30901699  0.18738131  0.06279052  0.70710632], resc_fac=1.0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collider.lhcb1.elements[i_most_losses]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d291e7ee",
   "metadata": {},
   "source": [
    "We can plot the lost particles and the obstacle cross-section:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d0fef11d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17ff27760>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the lost particles and the obstacle\n",
    "obstacle = collider.lhcb1['obstacle']\n",
    "fig2 = plt.figure(2)\n",
    "plt.plot(lost_particles.x, lost_particles.y, '.', markersize=1)\n",
    "plt.plot(obstacle.x_vertices, obstacle.y_vertices, 'k')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aa3881d",
   "metadata": {},
   "source": [
    "### Correct the orbit to avoid the obstacle\n",
    "\n",
    "Find an element close to the obstacle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8eed85b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TwissTable: 8 rows, 41 cols\n",
       "name                               s           x           px            y          py ...\n",
       "mb.c15r8.b1..1               17296.5 1.68121e-06 -2.16691e-08 -2.04115e-07 -3.1632e-09\n",
       "drift_7471                   17296.5 1.68121e-06 -1.82169e-08 -2.04115e-07 -3.1632e-09\n",
       "obstacle                     17301.2 1.59437e-06 -1.82169e-08 -2.19193e-07 -3.1632e-09\n",
       "chamber                      17301.2 1.59437e-06 -1.82169e-08 -2.19193e-07 -3.1632e-09\n",
       "mb.c15r8.b1                  17301.2 1.59437e-06 -1.82169e-08 -2.19193e-07 -3.1632e-09\n",
       "drift_7472                   17301.2 1.59437e-06 -1.82169e-08 -2.19193e-07 -3.1632e-09\n",
       "mb.c15r8.b1..2                 17306 1.50754e-06 -1.82169e-08  -2.3427e-07 -3.1632e-09\n",
       "drift_7473                     17306 1.50754e-06 -1.47647e-08  -2.3427e-07 -3.1632e-09"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_obstacle = tw['s', 'obstacle']\n",
    "tw.rows[s_obstacle-5.:s_obstacle+5.:'s']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "801707ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "element_close_to_obstacle = 'mb.c15r8.b1'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f033cab",
   "metadata": {},
   "source": [
    "Find the vertical correctors close to the obstacle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e99e89ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TwissTable: 6 rows, 41 cols\n",
       "name                               s           x           px            y           py ...\n",
       "mcbv.11r8.b1                   17101 1.63309e-06  1.15161e-08  2.39107e-07 -3.48957e-09\n",
       "mcbv.13r8.b1                 17207.9 1.32796e-06  1.71837e-08 -4.27231e-08 -8.33154e-10\n",
       "obstacle                     17301.2 1.59437e-06 -1.82169e-08 -2.19193e-07  -3.1632e-09\n",
       "mcbv.15r8.b1                 17314.8   1.474e-06  2.39467e-08 -2.45224e-07  3.60209e-09\n",
       "mcbv.17r8.b1                 17421.7 1.62308e-06  2.18696e-08   4.2666e-08  8.33992e-10\n",
       "mcbv.19r8.b1                 17528.6 1.47708e-06   1.5106e-08  2.45234e-07 -3.60189e-09"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw.rows[s_obstacle-300.:s_obstacle+300.:'s', 'obstacle|mcbv.*']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d8e967",
   "metadata": {},
   "source": [
    "Find out the knob that controls the corrector:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ae6228f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vars['acbv15.r8b1']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_corr = 'mcbv.15r8.b1'\n",
    "collider.lhcb1.element_refs[v_corr].ksl[0]._expr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d5b0532",
   "metadata": {},
   "source": [
    "Perform a match to move the beam vertically at the obstacle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f4d13e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found suitable prebuilt kernel `only_xtrack_frozen_energy`.\n",
      "Matching: model call n. 12       \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xdeps.optimize.optimize.Optimize at 0x17fe67c40>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collider.lhcb1_co_ref.match(\n",
    "    method='4d',\n",
    "    ele_start='bpm.10r8.b1',\n",
    "    ele_stop='bpm.18r8.b1',\n",
    "    twiss_init=tw.get_twiss_init(at_element='bpm.10r8.b1'),\n",
    "    vary=[\n",
    "        xt.Vary(name='acbv11.r8b1', step=1e-10),\n",
    "        xt.Vary(name='acbv13.r8b1', step=1e-10),\n",
    "        xt.Vary(name='acbv15.r8b1', step=1e-10),\n",
    "        xt.Vary(name='acbv17.r8b1', step=1e-10),\n",
    "    ],\n",
    "    targets=[\n",
    "        # I want the vertical orbit to be at 10 mm from obstacle with flat angle\n",
    "        xt.Target('y', at=element_close_to_obstacle, value=10e-3, tol=1e-4, scale=1),\n",
    "        xt.Target('py', at=element_close_to_obstacle, value=0, tol=1e-6, scale=1000),\n",
    "        # I want the bump to be closed\n",
    "        xt.Target('y', at='bpm.18r8.b1', value=tw['y', 'bpm.18r8.b1'],\n",
    "                  tol=1e-6, scale=1),\n",
    "        xt.Target('py', at='bpm.18r8.b1', value=tw['py', 'bpm.18r8.b1'],\n",
    "                   tol=1e-7, scale=1000),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5536e555",
   "metadata": {},
   "source": [
    "However, we observe that after matching, if we track, all particles are lost!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75c3b76b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lost beam fraction: 100.00%\n"
     ]
    }
   ],
   "source": [
    "bunch = bunch_template.copy()\n",
    "collider.lhcb1.track(bunch, num_turns=5)\n",
    "print(f'Lost beam fraction: {np.sum(bunch.state <= 0)/num_particles*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1690be0",
   "metadata": {},
   "source": [
    "Why is that? We need to match less aggressively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eec21f19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matching: model call n. 12       \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xdeps.optimize.optimize.Optimize at 0x2a4164580>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collider.lhcb1_co_ref.match(\n",
    "    method='4d',\n",
    "    ele_start='bpm.10r8.b1',\n",
    "    ele_stop='bpm.18r8.b1',\n",
    "    twiss_init=tw.get_twiss_init(at_element='bpm.10r8.b1'),\n",
    "    vary=[\n",
    "        xt.Vary(name='acbv11.r8b1', step=1e-10),\n",
    "        xt.Vary(name='acbv13.r8b1', step=1e-10),\n",
    "        xt.Vary(name='acbv15.r8b1', step=1e-10),\n",
    "        xt.Vary(name='acbv17.r8b1', step=1e-10),\n",
    "    ],\n",
    "    targets=[\n",
    "        # I want the vertical orbit to be at 1 mm from obstacle with flat angle\n",
    "        xt.Target('y', at=element_close_to_obstacle, value=1.5e-3, tol=1e-4, scale=1),\n",
    "        xt.Target('py', at=element_close_to_obstacle, value=0, tol=1e-6, scale=1000),\n",
    "        # I want the bump to be closed\n",
    "        xt.Target('y', at='bpm.18r8.b1', value=tw['y', 'bpm.18r8.b1'],\n",
    "                  tol=1e-6, scale=1),\n",
    "        xt.Target('py', at='bpm.18r8.b1', value=tw['py', 'bpm.18r8.b1'],\n",
    "                   tol=1e-7, scale=1000),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d2aa4b74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lost beam fraction: 0.90%\n"
     ]
    }
   ],
   "source": [
    "bunch = bunch_template.copy()\n",
    "collider.lhcb1.track(bunch, num_turns=5)\n",
    "print(f'Lost beam fraction: {np.sum(bunch.state <= 0)/num_particles*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77918526",
   "metadata": {},
   "source": [
    "There are still some losses, we can plot the beam at the obstacle, to see what happens. To do that, we need to insert a monitor at the obstacle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1ab6d5f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found suitable prebuilt kernel `default_only_xtrack`.\n"
     ]
    }
   ],
   "source": [
    "collider.lhcb1.discard_tracker()\n",
    "monitor_obstacle = xt.ParticlesMonitor(start_at_turn=0, stop_at_turn=4,\n",
    "                                       num_particles=num_particles)\n",
    "collider.lhcb1.insert_element(\n",
    "    index='obstacle',\n",
    "    element=monitor_obstacle,\n",
    "    name='monitor_obstacle',\n",
    ")\n",
    "collider.lhcb1.build_tracker()\n",
    "bunch = bunch_template.copy()\n",
    "collider.lhcb1.track(bunch, num_turns=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6865bd11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16e236980>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "obstacle = collider.lhcb1['obstacle']\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(monitor_obstacle.x, monitor_obstacle.y, '.', markersize=1)\n",
    "ax.plot(obstacle.x_vertices, obstacle.y_vertices, 'k')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a3c51fa",
   "metadata": {},
   "source": [
    "Some particles are still lost at the obstacle. We can shift the beam a little horizontaly to fix this issue completely. As before, we find the knob that controls the horizontal corrector close to the beam."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1b976526",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TwissTable: 6 rows, 41 cols\n",
       "name                               s           x           px            y           py ...\n",
       "mcbh.12r8.b1                 17154.4 2.68505e-06  -3.8315e-08  5.64296e-08 -1.90379e-09\n",
       "mcbh.14r8.b1                 17261.3 2.66895e-06 -3.54758e-08 -9.29943e-08  -3.1632e-09\n",
       "obstacle                     17301.2 1.59437e-06 -1.82169e-08 -2.19193e-07  -3.1632e-09\n",
       "mcbh.16r8.b1                 17368.2 3.14394e-06 -4.17711e-08  -5.6977e-08   1.9138e-09\n",
       "mcbh.18r8.b1                 17475.1 3.17905e-06 -4.49976e-08   9.2981e-08  3.16365e-09\n",
       "mcbh.20r8.b1                   17582 2.70406e-06 -3.87031e-08  5.69987e-08 -1.91306e-09"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw.rows[s_obstacle-300.:s_obstacle+300.:'s', 'mcbh.*|obstacle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2c741658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-vars['acbh14.r8b1'])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h_corr = 'mcbh.14r8.b1'\n",
    "collider.lhcb1.element_refs[h_corr].knl[0]._expr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4434ac14",
   "metadata": {},
   "source": [
    "Match again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "86aeac31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matching: model call n. 12       \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xdeps.optimize.optimize.Optimize at 0x107877ee0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collider.lhcb1_co_ref.match(\n",
    "    method='4d',\n",
    "    ele_start='bpm.10r8.b1',\n",
    "    ele_stop='bpm.20r8.b1',\n",
    "    twiss_init=tw.get_twiss_init(at_element='bpm.10r8.b1'),\n",
    "    vary=[\n",
    "        xt.Vary(name='acbh12.r8b1', step=1e-10),\n",
    "        xt.Vary(name='acbh14.r8b1', step=1e-10),\n",
    "        xt.Vary(name='acbh16.r8b1', step=1e-10),\n",
    "        xt.Vary(name='acbh18.r8b1', step=1e-10),\n",
    "    ],\n",
    "    targets=[\n",
    "        # I want the vertical orbit to be at 1 mm from obstacle with flat angle\n",
    "        xt.Target('x', at=element_close_to_obstacle, value=-3e-3, tol=1e-4, scale=1),\n",
    "        xt.Target('px', at=element_close_to_obstacle, value=0, tol=1e-6, scale=1000),\n",
    "        # I want the bump to be closed\n",
    "        xt.Target('x', at='bpm.20r8.b1', value=tw['x', 'bpm.20r8.b1'],\n",
    "                  tol=1e-6, scale=1),\n",
    "        xt.Target('px', at='bpm.20r8.b1', value=tw['px', 'bpm.20r8.b1'],\n",
    "                   tol=1e-7, scale=1000),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17170c68",
   "metadata": {},
   "source": [
    "Track and inspect the beam again, this time there should be no losses:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3c23d0b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lost beam fraction: 0.00%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bunch = bunch_template.copy()\n",
    "collider.lhcb1.track(bunch, num_turns=5)\n",
    "\n",
    "# See the beam at the obstacle:\n",
    "obstacle = collider.lhcb1['obstacle']\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(monitor_obstacle.x, monitor_obstacle.y, '.', markersize=1)\n",
    "ax.plot(obstacle.x_vertices, obstacle.y_vertices, 'k')\n",
    "\n",
    "# We don't lose so many particles anymore\n",
    "print(f'Lost beam fraction: {np.sum(bunch.state <= 0)/num_particles*100:.2f}%')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}