diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 0000000..5008ddf
Binary files /dev/null and b/.DS_Store differ
diff --git a/.ipynb_checkpoints/Main-checkpoint.ipynb b/.ipynb_checkpoints/Main-checkpoint.ipynb
new file mode 100644
index 0000000..6c0b66a
--- /dev/null
+++ b/.ipynb_checkpoints/Main-checkpoint.ipynb
@@ -0,0 +1,303 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import cornermatching as cm\n",
+    "\n",
+    "\n",
+    "I1 = plt.imread('im1.jpg')\n",
+    "I1 = I1.mean(axis=2)\n",
+    "\n",
+    "I2 = plt.imread('im2.jpg')\n",
+    "I2 = I2.mean(axis=2)\n",
+    "\n",
+    "g_kernal = cm.gauss_kernal(3,2)\n",
+    "\n",
+    "I1 = cm.convolve(I1, g_kernal)\n",
+    "I2 = cm.convolve(I2, g_kernal)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cornermatching as cm\n",
+    "H1 = cm.harris_response(I1)\n",
+    "H2 = cm.harris_response(I2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cornermatching as cm\n",
+    "H1sup = cm.nonmaxsup(H1)\n",
+    "H2sup = cm.nonmaxsup(H2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cornermatching as cm\n",
+    "H1descrips = cm.descriptorExtractor(I1,H1sup)\n",
+    "H2descrips = cm.descriptorExtractor(I2,H2sup)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cornermatching as cm\n",
+    "best_matches = cm.get_best_matches(H1descrips, H2descrips)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cornermatching as cm\n",
+    "secondbest_matches = cm.get_secondbest_matches(H1descrips, H2descrips, best_matches)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([533, 344, 544, 108]), array([609, 486, 597, 291]), array([534,  59, 544, 108]), array([329, 246, 329,  57]), array([554, 463, 547, 271]), array([574, 308, 591,  23]), array([364, 467, 366, 278]), array([536, 193, 544, 108]), array([366, 467, 366, 278]), array([384, 269, 389,  83]), array([382, 224, 387,  33]), array([327, 482, 331, 292]), array([581, 401, 578, 219]), array([398, 281, 402,  97]), array([678, 334, 682, 152]), array([218, 416, 223, 231]), array([673, 514, 654, 314]), array([553, 332, 557, 152]), array([456, 223, 466,  33]), array([656, 318, 662, 134]), array([671, 206, 693,  14]), array([667, 224, 688,  34]), array([531, 402, 528, 219]), array([537,  32, 547,  42]), array([448, 473, 444, 279]), array([293, 259, 293,  72]), array([269, 251, 268,  62]), array([269, 244, 267,  54]), array([319, 219, 319,  26]), array([336, 208, 337,  13]), array([329, 207, 331,  13]), array([666, 207, 689,  14]), array([283, 474, 289, 284])]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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kR5jI9TBbMFTVySp0SieUxLaYtUlVJ1Vb3i0ivlIdfatzAZmBtPi93qTjZ2DlUb46XpIjPeR0Kl+xLq1cL91ioOr8DvkjPquqjxOyo7Y+CuBj0rU/BvCCYRhDAF5Y/g8AhwEMLX8eAvB1R7WxSaqJtFqwENPZBT2nZAcA3k3aaPVZDcmbE2Raz3HdKER94GTzlJP0zdB697fdcTVbPDQDV7GPVFq0HQXNDNjtkqXrwzCMlxhjg9Ll+wF8ePn33wE4AuBLy9f/3qjX4BeMsRbGWK9hGFN2KiMvJLrdblt+TDkPO9ecMs9ag5ndeq7VIirgbEFOpVU7HYPVTFDDcOY2sYqxldslb9RZK75aK9KFo62FS9Bqd6t4hINhqF8uYJdW20fNHIlA5ZoJISvrSrwmW8VyHmbPiG1QgbddanYEugXwnQbQvfy7H8CYkG58+ZolqTQ7s85TXf+XrgGvNa23hrma/mu2z9d7d+cmvX+IMWZro48MnLp54dTVYeZucUqrXkw0DMNgjDkumTH2EOruEYRCoYYGUAerfMiyprWeURt2tbq1JqflroUGuxFJ7gdd++wuOqn+6/jKjNabJ9ZjIV2Vp12Nfa3ngaz5qsa1mUV+q2fEezpAVWnPduKgVXnLZau0bLvULEfMMMZ6AWD5e3b5+gSAASHdluVrK8gwjIcNw7jVMIxbA4HAivtmizVWvxVlNQ1i7wfw+5fqd10rcND5IzcqOZ3EKqvF7qKojnTW7Gr7ze7zTl0Dqrx1eejaIV+zu+alA2mdm6QZq69ZoP4RgN9Z/v07AH4oXH9gOfrjdgBJu/5pwPmKuc4EkT8bjawm0Pvd3bJR678ReUFHusm8lot/FFWx3qGwzdBqQVp33wwfRO1aFZmhwxT5+nos0Fq6Phhj30V94bCDMTYO4MsAvgLgnxhjvw/gKoDfXE7+NICPA7gEIAfg95qtmM7sbcYl8F6kt2uCmaUxM/vtuARksuMiUeVr94Q8u/V1SjQJnITiyc+aaUHytXd74VBF8mKiHGanurcWpNPMVfesyC54rgfJ7gxaXFSRyN8iD4h+Z8pHxUNWc1F+ppmTMu1EffyW5tZHFGkNAF90VINl0mlhqjA83fO6a3aBaS2pGSC1k9/7SSv8l0Ab0TKwQ834dzc6Wfl9xTR2jiGVwVznt7aqhxWpBJ1T4bohdybSQuJ6hky9m4xp5uuym9bs/lqEHzopS5e3qGHYieAxo7XQyFV56Ca2inQW3VpaC0SqiWt2bTWm9VrMEdnio2u65+xq13Y2gcnXZcBWLf6pNq6sF6k0ZuIb6hsxWMIObUigVvlwVxPd8X7QFt4PJGoG7yfN3gqcnQJXs/y0FiD7XpAdt6OVW8kqf7t9IgOtGUhb5WE3raosMx5ijMHtdivTiB8nWvWGA2qqvB2XRzMTxql/SHfAje4YzY02Cc00wLUG27UUiGvtOjIrRwZgM8vAiuxMvrXwKb+bC4Bi/9vRlu2uwRA5OS9D9a3KwyytnN5sgVFXHztraDL/kBbtcrng9Xrh8diH3w0D1GagvBptWqZmdnvJ9+UFHfG+Ha3JivHXgsz6zKmJareOThbjmtG6dIBqVhd6TjdRvV6vto66dpvxi1MBY9aW1SzO2tGC7ZLKkrCqtx2XhY7M3BoqAFbNNbEOskasyl++pzumVMzfzm8RrN1uN1wuF9xuN/x+//vX9SH6pptdbbYCXZ0mrCpHN8nl51VMq3qvoK4cmXT1WyttvdnJu5YCxi7QOLmnm+BmeanegKKKqGimvbJf0iqtTKsRmu93d5+Vu8GOT1sGX3GRUAfe8n25DoB1ZIx4n/iLMQafzwev18v5wsl83jBArQJpM43GCfOLnUzmhhNtx+5ioFl9rQZFB/5iWc2Yu81snhDLtKqT2X1VGhXZAWU7fnFKIz+jE6QktOV8VeBt5QbR1W01AG+V33q4hNZi/Uen4KjI6RZrM4AzA1ddehG8qT5mbhAzjVkOgqD/Ho8HHo9H6VqzSxsKqGWw1mkzdrSx9fBvryYP1eLCWpVlNvBOwV2cBE77SK6HlTZJ6VXnYpvVwS5g2yHiNau6OhXazZAdvlXVaS0B+/2qiZu5SFTaskpTNzvrQ2Xhi4BMPCx+E0DL9WyGNgxQ12o1LnVkkLYD1HbBwa5WZKY9NjtBnGhDToSTCiDNnlOVLYOmGTnZAKMSIjRGMqCKY62adCqSrRURdO3kYeZ/FcnKojHrZzNBquMlK4Fg13W00YDXiRC1S3J+ZiCtelYH4FRf+paxSFwcFEFbBdCqejqhDQPUolQSjzcFnIGWXQ1S1WlOJ2Kzpr7u2Wbvy4DkBEDl/MxAnEjnynHiP9UBqi69rk5WC0lm+dMEc7vd3A2iW2zWmdtmi8dWWric1sq1JOZlJYztmNmrUT6s3DNm+YhlqQSt3brJglDn5jIjq3rqLHyV28PtdjcsTqvyozINw2h43ZcVbRigphVRuVOI1tp9IfshrSaiilRRH82eUaDSOO1qRaoJbmfSW/kCVa4JItU10kxE8LKybuwIFspblY5cSnZ3osm0Y8cOrv2Qv5q0q1qthnK5jGq1imq1inK5jEqlglqthkqlwtPIC9R2+l4kXd3tWobNamprcc60laWy1tqzFYlasVgH3ZnQwDW/tJl1Jbpm6Tr1Hx0qZ2ctisqsVCp49dVXbbdrQwA1aTUqX488Qc1imMX85OesTEvVApKYTgRO+ibNX8yb8lExqF1gpzKt6iSXYeaScGoRqPrOjqakAywnddX5auVJKJupYjnyPZW2Re42+VnRspM1JFGLIkFEQFCtVlGpVFZ8COir1WoDYNDLMczGWW6vDD52eUlsn5iHyv0kkxUYq9LphLwVcDux9FRtk3nVrjKiAmlZgxb/E2/oxs6qvJdffhm5XM72sxsCqD0eD6LRqOkLZ8VGigtzOmaRB1EGVRVTmQGROGHEQS2Xy9y3rtJCzchK4JhJZ7sbd5xqNKqJIQvNZiwZ+eAbs37W/Rd/qxZ+xLAqs2gCuT2qSa4jGdREnnS73Q2r+7KLR6wHATtp6qSll0olfr1SqawoS5wjcsSKCjRUfSbmadVe8b4OLHUCwC6JvGzHFdbMoUYiyfNddl9R/ioXh8fj4SDdjNVQrVZx7NgxLC0tvf82vBiGgYGB+jHWZud8OPH5yc/ZmYTyoOjSiN9m2p+OrDQYXVpgpTtBJ9RU+aj6wAw4ZQBTCTknfja5LmZCU/Vb/ohULpdXpNWVJW9Y0o2BlXvBTIMkUoEjgawYKirzjY7XCUToIwI9gbsoBERNnjHGx0sOcRWFls5fr+J5O1q6Trjp5orT6yqysgJUypnYJyRwRevK5/M1jK+ZEKT8xOvnzp3DmTNn+LOiILaiDQHUi4uLAPRaqGwCWpEOuOxIaztliJoS/VeVZYesAF3FnDLompmFVhaE7LuzMxnEtqlcPXa1HZU1ZGZOmwkYWSsT2yaXaYcPVOWr6mxFKiHuJI08LmIbKbrA7/dreVGlyZM7xjAMlMtlFItFfp1An55VCWK5vmIZojYq1t+MrA5icmoVWpEsBHXtod/k4jALsbWyoC9fvoxTp06tKM8ubQiglncCbXRSgZ8uDeAc/Myee7cAxinJmgmRnegQO4JUpaXJfWx3+34zfWg2Pu8F6XhKZ4GJmrxuQ4/4jGEYHMxFVwyBfLlcRrlc5mBP5cgLc5S3U/A1s+KsyE5a2SVF10SQJkFIfFWtVpWArOMD4rXjx4/j8uXL2nLt0IYAauDaW8dVpDMrzdKKZMeV4fSeeN9Ko9a5HHT1lZ9zUic76Z0KRTMXgJk5bJWnld/aiQlM10QNUBwf0eVB/10uF0qlEteUxHqUy2W4XC6oXhOnastakpP87PKoFcl9KYKWajev3F9yPrIrplarNfjfCehFN42osIngrnKniOWZKTI6BUJ+XuQV0cWhOsdHR5RHpVKB2+1GKpXCW2+9haWlJWU6J/NwQwA1mRU6v5zumtl1K1ovDcks3/UC2WYG/l8KyW2XXSDiPRX5fD5UKhV4PJ4G36zb7UapVFIenvN+728zwatLZ5ZG55akSC66FwwGG8oUBajscxfBXAyRpDQiyUqC7rA02WVHael5OYpDd3Kmih9EgVGr1bCwsICZmRludayWNgRQk3alAmsn2qoT/7CVdtusYNCBq+wzFbUWnX/Yql5mrgJVHcyu6ZjPjOQ2qcxuu6Rybcj3VP3jxDRWuVwSiQQKhQJisRhaW1uRyWRQqVQQDAZXaJZW+dlt33vpMpHJzGwX08harY5vKZ0d14cYgy5+SGiKfnXZZ07PkiumVCrxuHdRS6e0Mp+IbZIXCc20aLldMo9ns1kkEglejriG04zLg2hDALXK/HCqmeq0Hh1Z5dEsIMrPG4aBTCaDcDi8QtJTu/P5PDKZDDo6OlbsyrTK30rg6MBBxzQq8NZds+oTqzAqO+Bl1vdy/XRatdkEqVQqaGtrQ7FYxNjYGNeuA4FA00JZd98qP7m/VKb2asLSnJDK9SCSzh1llkaVt0i6HYoqFwtj1yJnXK76+c4E3vJzZriSyWRQLBa1/CxekyM+SMhkMhmMjY3BMAxEIpGGSBFVm5qhDQPUJP2caDA6YDW7ZkZmWrxZnlZgQz4+t9uNbDaLXC6HUCiEWCyGarWKQCAAv99vWS+n7dCRFRibpVVpJJTOajLr6il+N6N1yjxD/mdVbK4MEvPz84jH4wgGgzAMA/Pz8wCAtra2Bu3Qab2amZTvFgg7JTOlRTX+Vlq3Kh+zss181PL4mO3FkOtplo+qfPl/uVzGqVOncPnyZcTjcfT09PAFR9myIO3bTNk0ow0B1JVKBYVCAeFwmHeEzvEPmGu+a00qMLGTXhwo8muRCdba2opSqYRCoYCZmRm0tbVxU3s17TDzX1uRagKauWDk6zrhZhWJYWURqK6rBAJNDlELlEPFVHm3tbUhkUggGo2iXC4jk8nA5/OtaJNdPlstD4o+UNHnaWcHnN2jDOxExwD2QFL1m8jsWXqG2msXtMUxlflBdc+sfvLpnDplQ76fSCRw9uxZTE9Po1gswuPxwDAMHhUjboaRfdrN8seGAGpg5X57FTULmnap2U7UPSdL+Ww2i4WFBWzduhVutxuJRIIPrsq3916QGVOZAZ5VXnbAwWnb7U5GsxDBkZERdHV1IZVKwev1wu/3o1AoNFUfKk8ku1qy3XM31iKd07WDZsipdbVasgJnO7xipiiUy2WMjo7iypUrmJ+f5+WRC4QxtuI4ABIsa0GWQM0YGwDw9wC6ARgAHjYM428YY20AvgdgEMAIgN80DCPB6q39GwAfB5AD8LuGYbxlVY4ohURJvtbgqUrXjLltlg/QOFHoXqlUgs/nQzqdht/vh8fjQTqdRjwe5xqgjrnsML5u1xT9t+MPlp8X22W3PqIZKV6Xt+ialS8/I6ax63fX1ZfqV6vVsG3bNszMzACoR4CI4XhWCoFTH3KzJzvKW8VlMnM/2K2HHe1aLEO0YMSx1vGFam6LFoNclhWwi/mKbgWVQiDnRfey2Szy+Tx3uxLoptNpTE5OIpvNcuXKjtVoNT/EtqvqZUZ2NOoKgP/NMIy3GGNRAG8yxn4K4HcBvGAYxlcYY38M4I8BfAnAYQBDy5/bAHx9+VtLKp+OGa3WRSA/u1ZarFU+BMb5fB4zMzM8XKlUKplKeF1ZTrUUuc1WpqlZHlbAodJKZIDQadlOTVFZmMjPyC4QMX0sFkM4HEahUEC5XObnONBJeU7OY1C1bTXUrEWoc0PI6axcVKprKivLDkCr6iM/qwutlOsrplOVo3N7ielzuRwWFhawtLSEcrmMYDCIhYUF+Hw+nD59Gvl8nudrNZZivSg9+apFT8FqrApLLjQMYwrA1PLvNGPsLIB+APcD+PBysr8DcAR1oL4fwN8b9Vr9gjHWwhjrXc5HXYnlcByadFaSxwqoZdC3o41Y0WrS0UB7PB7Mz89zQJienkZ3dzdfSDTzi8pk5SayqqP4vF0m0gGAPB46xhSftzouwEnbVb50K7OXroXDYVSrVb4+wti1ELBqtWqrPiI5Bei1sBjNNDnxum48dJagnTqoxtquoFBdl60tp9q1WXliOhLAJJQppM/l69eiAAAgAElEQVTlcsHv93OgtrJm5LJEV4hYtlM+ksmRusAYGwRwM4DXAXQL4DuNumsEqIP4mPDY+PK1BqBmjD0E4CEAfHOBCK5iB5kBspCf8rfZ84r2rWk6IpfLhUqlAp/Ph1gshmAwiFKphK6uLt7uVCqF9vZ22/nbrYOuD82AzCx/HdNaWQR2TXGrQ6fkiazSqlX3RO1QBBe/349EItEQ2iWftLhWFpfYpvVOZ2WNyf/tumx01g/9Vo2BnE6lTdN/URuW3SnNWH6q65QvLdLSeDNW35QjLnBalSniFf1Xhdg6mWMqsg3UjLEIgB8A+F8Mw0hJHW8wxhzp9YZhPAzgYQDw+/2G7iQpuwy3msnklNGbLcPlcqGrq6vBnKbrFMu5XqBg16qw0k6aKV/HpE61eFmDEwFaFtpWpj5dr1ariEQiAKDUhFS0VgtEcl3Wm5yAN2C9DmI3RthMiJqVJY6vnf6R81Bp5+LvYDDI47DpuGJxMbAZvne5XCgUCggEAkrXC5XtNG9bQM0Y86IO0v/dMIzHly/PsGWXBmOsF8Ds8vUJAAPC41uWr2mpXC4jnU4rpaxdEFVNzPXQkO1qlOJ/l8uFbDaLQqHAd0GJgokYhNor1182reyQU8ZWlSmCtgoMndSJ0j/33AFs2zaL3bsn+XMXL27ByEgn7r33uPIZJ2Asa2Y6sBEPHqLn3i2/cjPWkB0NzaysZkDH7FnSHEkzFa+buabE8RGFrirmXfwvXtOlVdVRd1wrYwyBQKChDm63G9VqlYO1mVYu/6cyarUaJicnsX37dsuzi5zgjiVnsnpu3wZw1jCM/1e49SMAv7P8+3cA/FC4/gCr0+0Akmb+aap4Pp/nnUamh6OGCFtQzZ4VwdBpGbrnrfJ0uVyIx+Po7u5Ga2srWlpa0N7ejo6ODrS3tyMej/MXJ6jq30yZZvV2SmbPOi1/27ZZfOtb9+L8+T4wxnDhQj++9a17sW3bDO8rqzqoxlqug7iwo6ujnbqvh/Zsl+z2ux2esGqrWR6qvhbBk+7LeblcLu5akF0Mcn10dV4vCgQC3M2hq6cTony8Xi9f2yBajUVKZEejPgTg8wCGGWMnlq/9HwC+AuCfGGO/D+AqgN9cvvc06qF5l1APz/s9OxWhw0vECBCZVBPNjFajTdjJ10mZKg1P/q3SJpyU7bSddjQSO8/o6q3SCHfvnsRDDz2Pr33tPnR3n8fs7B7cddcppFIRvPnmTrhcNbhcNbjdBoAqPB6AserydYPfd7kM4XoNjNX4//p3/X79AzBWA3BNa6I6l8tleL1e5PN5vulFJWit+rGZe06ekbUwp/lajbNhGHjuuQMYHJzDrl0T/Pq5c724erUL9933tmkdSSOlcnTbweVn5bmuE6hiOqujCeyS1+ttyEvcQWw1N2QSF6ALhQJfe5I9BM2SnaiPVwDoSviIIr0B4ItOKkGdNTk5id7eXt5hToDWjJGbnSyr9c02U6/VDKbd/lJNMrPJpDo3w87EV5Xh8XgwNfVdlMt3Y2zsRgDAc899wE7zVk2M1RoAn7EavF6gXC6gUimCsQqCQS/8fi88HkpjwO2uP0Pf9Lz4v/678X79+rW0Yl7X7jf+JyFlfU/O1/o/CbBrgrAGGmbGGAYH5/DNb/4KHnzweezZM4WzZ3vwrW/di4ceep6nUZFqUVB2i5hpmCpBL+8yXO18lIl8yXReBwkIlV/ZrGx5XjNW37ko73pcLW2InYlerxehUAgTExN49NFH8alPfQo33nijpXa53rQWZeryKBaLPDQMWF24nVlZa8Xc8oSxEgqq+5VKBS+++CJefNEAUMJtt/0Cw8MH8JnPvIJt2+ZQq7mWPwy1mgvVKhN+A9Vq471KBTAMSle/V61e+9D/Wo2hUrn2v1IB/33u3CW43YDX64NhuFGpuFAuu+D1BhCPdyAcjoMxL2o1oFZzoVJxwTDEerhgGKyhbo31l//bfxnqu0GiBeNy1WAYDH/915+E212Az+fCF77wHHbvngJjKzd10H8zYLOzrd0KiNdy7svWkcfjgc/nQ7Va5VvBqUxxA5oTq4nas5Znt2wIoAbqjTt//jw8Hg9+/OMf46mnnsLhw4dxww03wDAM5cYDGTDknX1EKtCwM/iqAbCTTveM2+3G2NgY/vEf/xG5XA7lchnbt2/H0NAQdu/ejb6+voY3Vcs7DWWQlMuQtRqzdqkmnKqNKpeNaJ6K7+ATtRG3242lpSXMzMzg/PnzOHGi7jXLZD6IZPLriMf/AJ/73N0YGZnHww9/BA899Dx271YvZcj1lcPz5AUt+i8uWlH4HS0g0u8rV/4WHo8H4XAYHo+Hbx/3+XwoFotIJAqoVqvYsmULtm7diu3bt6O7u5tHC9BCMPk5ZR+uahwI9KtVBsC9LDhcXCAZhpsDvGG4VwgilUCrCw8XF1KGURcK4v+6MKEPhP/134AbIyMTGBnpQaVyE375l4dx/fWzqNX0VqATTVfWksVxEv3aKh+vFZjr+FdFlHcgEEA+n29YPKRYaif4QPgk8n8ymeS7rXVg70SJ2jBA7XK5+Jt5S6USAODJJ5/E888/j9/+7d9GZ2cn71DVVmlRgtnxu+pWsnXXdAOnAjGRaPIPDw/j6NGjmJqaAmOMbyGfnp7G9PQ0fvKTnyAajeKmm27Czp07sX37dr4JQ8xbVW/dKrIMYHJ61Wq6DDDEvCLYiYcEUZmFQgGzs7OYnp7G1NQUJicnsbS0xM1gGtdi8UbE4w8hFDoKj+de7N07jYceegEjI13Ys2e6YXxEwWQmpMS04sYpsa1iXUXT2ufzcZcMAS/1hd/vh9frRalU4u05c+YMstksuru7ceDAAQwMDDSc1aIqTzxAn16ayti1syE8Hg+83msLpPLCm9hOM8uL7umOEpDHVezXyclJvPzyy1haakOt9nXs3/8Ejh79GG6+eRR79kyZgozc1ypXiKhhi4Am734U60SgvVqSFzpFEseM1i9UW8LlZ0QSBQ6djy0qBVbjZoc2BFDTod8E0HSNdg197WtfQzAYxG233YabbrqJA4V8doQOyMxcAma+M7omMp14CDldo4EStYFSqYQLFy5geHgYmUwGgUCAx2u6XC6+fZzSx+NxlEolvPzyy3j99dcRCoX48ad33nknPw9EnLwy6cKKaPLKbdG9O09Vhgi4uVwOi4uLGBsbw+XLlzE7O8vb7/F4+OuXqD88Hg9/gWpLy8NgjMHrvXa06/XXz2DfvlkYxsoJpWqLfM6viogn5G95jGlBiZ4h85f6yu12IxQKIRgMNhxRWyqVcOTIEYRCIRw+fBjXXXcdKpVKAyjIZrZYZ/lgfFpMJ0FIE1yVlxj1oop8oXEiwKd0tVoNxWIRqVQK09PTmJubw8TEBK5cuQLDMFAu34nFxb9BW9sf4o47WnHffT584xv34Atf+Bn27p1e0b/EF/JckoFJFDIEwHLMugjQdE3md6dzW5dO1N7Fg5TErd9WZcj31ZbTygVVJxaISBsCqA3D4GAtOvcLhQK8Xi+CwSCq1SpeeuklHDlyBFu3bsVtt92GAwcONHSG7MA305BVZMdtIk8eqi8AnD9/HidPnsS5c+eQyWQQiUTg8/kQjUaRyWT4c36/v2ESEQMHg0GEQiFUKhWk02mk02mEw2E888wzqFQq2LdvH+666y60tbVpg+lV9Rc1TZLyIjgRwxJg0Fsq6EMTmkIoxRd+lstlXpbb7ebaJQGR3+/n7yUU+ysajfLD3sXJK9ZZ7G9ZK6LoDNnNQSSetSACgyyEkskkOjs7OUiLgE7vvqvVavB6vVxYlkolLCws8HQ//OEPUSqVsHXrVtx5553o7OxEOBzmfCwKcll7UwlLlZtHbr8I0PTaqkwmg8XFRSwsLCCbzXIroFAoNPQFPUMCgcYom70eLS3/Fl7vq2hp+Tz27p3DH/7hEYyMdGHfvlmlUiMDLN0Tx0MGagJgUlJkoBatYx1P24380M0Rsc5UD1m7t4r2EdunEq5iFIyMSU7BekMANZHITOIZrwRsFEY1OjqKmZkZPP/887j++utx6623oru7u2EyigyhkmLNgrhoshWLRZw+fRrnz5/H5cuXkcvlANQHmmI03W43CoUCKpUKGGN8FxxpPsSUInP4/X74fD6USiWk02kYhoFQKISzZ89ieHgYt9xyC4aGhhCPx9Ha2tpgvlM+9GJRmpCFQgGpVAqpVArJZBLZbBbVahW5XK7hXqlUapgEYkwp/S4Wi7xfRJeBvLvU7XajXC7ztojmfiwWa3iprAg+BNz0Tdqh6Nqi/qe+lMFd1tp0FI1GUSqV+C41UStmjPH/dEATCZxAIIBCodCgZc/Pz+P73/8+CoUCt5Li8TjXyL1eL9/wJAoGj8fDXydFCos4ntTWUqnE3x9YLBb5Jiryq4tCV3Y50HiIYySCtGEYiMe/sQygbm4B7t8/hxtumOfWjgw0shZMJG8BF60HIpUANdOu15KoPiJI05kf4st8rfiHeE20ZHRlve9dHwC4S0DU/ohRSbPx+Xw8bTabRblcxokTJ3D06FG0tLTg4MGD2LlzJ9rb2znTmnWSTitQuRhqtRpSqRRGRkYwMjKC8fFxJJPJFQxYrVbh8/n4izKr1SqSySQCgQACgQA/6pQmLJVB51JTPbxeL7xeLwKBAHK5HFKpFBcAp0+fxttvv83Di7xeL68nAYk4Iak/6Z7P50Mul+PCj/oUaNy1JwNopVJpYGb6yETAQemoXI/Hg3w+D4/Hw18aqzIzqU9EUJPHjUCvWCzyOslamY7Ee11dXfx8YQANrg8aJ+or2dVGrqlCoYBMJoNsNotQKIRwOMw17+np6YYyVf1Fgpr6io4ToNeB1d0S5YZ+EMeXgFl0b4hliffJ50sH3JMbkQQSubjEV0rJfSb/l8FbtmLEOSjyF+Uv7g4lEq0PeS7qyA6wy3lR34iL4mQVOhEOonVDi9OixWBVLyvaEEDt9/tXvKNONAlJ26Czgv1+P/x+PwcwOoT/2WefxXPPPcc3MUSjUQQCAe6CIA1GBB8AfFFJBNtkMol8Po9SqcRBlICXiJhOrB8JE6JEIoFwOMzPEqAJRivClAcJpVwuxzUsmrjRaBShUIjfn52dhcvlQjgcRiwWAwCu6YpCQ5T0pFmTVgaAgxylJQAUF9WA+qTOZrNwu918rEQzj5iaFgx9Ph/fLh8IBPjxAJRvrVZDPB4HY/U3P6usHRJApPXQoVZUVrFYRCAQaDjQi54VBbS8SCUL5dnZWbS0tDRs46c2kXCKxWLI5XIN/En9U6lUEA6HG94Qs7CwwCerqKkTr1B7ZKJrlD6bzTbwGrWDBKSojJTLZa6Ni7xO84jKpMVt+YB7WuSkvCKRSIPFIxMBOn2oP+mt4ZSv7AIR2yKPjer3epHIA/Lagejas8pDFvA0R8LhMFKp1JrVd0MANQEEcK0D/X5/A/AQMJOWU6vVEIvFUCqV+JujxcWVUqmEpaUlMMYwNzcHl8uFUCi04kAkAhgaIJKmpKUR0cAREBPjk4ZIGoi4cJPJZOBy1V9+2d3djVwux7UwAlAxb6AO4JlMBl6vl7t9RPMxHo8jEokgnU4jk8mgXC7zdpELRJwgslQX3S3yQpM4eeVwtmAwCJ/Px9OIb1cmzYkWTUulEkKhEABwAUWgR0JIjOKRJzMdA/vEE0/wQ5Pi8TgOHjzIeWV2dpYffJNOpxGLxRr6icZX/pa1NnLNiMAnarjETwTWsl+U2ks+82g0Cr/fj1QqhXw+zwW8CJy0iCpqngTS1N/UZ0Q0buKCMT1XqVRQLBbh8/kaXmcn80AgEEAmk2kY51AoxAGFBABZhaL7ifIiXhWtMXHNg9pJbjdxrlGd6b+sNYtWnKhsyL5fmXQuTvG3mUUt8rRswYl1NCubrGHqD1kBAq6NdzNa9YYBavm3CJ40UKQ90QSjxZh4PM59rQSkskbIWP0o0VAoxDuL8ibfsugjlbUWykfUsAEgGAxya0A09QjE4/E4n2SkdZOWRYwhUzwebwA9MvOpngD4YlwqleI+UtFsJ6YgXyNpdGL4GU10mhTiQiARmcV0gA1NSlEout1upFIpRCIRLkwDgQAqlQpSqRQHwmw2i2g0ikKhwBfwaMxF0ziVSsHj8eDkyZMoFou47rrrVrgdenp6eHui0SgAtWkp+6pVvlASnMQ3ZMKSpkn1I7CmulL/kWVAPEH+62QyyUP8RKFHdRQtSeoLsiKoveJirThGNIY0PqIgFTVXUSNOp9O8PTR+JBSoLKpHMBhckRf1F1msxGMi8BOv0dqM2PeiIBW/ZV81jRORmevDrltEzFMef3FtTLTO7GjVMhmGwS15cb1BrqtTsN4QQE2uA1EiidpNKBRCLpfjHUdgTRowMYbX6+X+UQJB8hWT+U8mLOUtvwLM6/VyZiYzlTR7oO4uoMnm9/t5mJ2obRiGgYWFBfT09GB2dhb9/f1IpVIcUEgIiP4xcRCr1SqPFIlEInyRicBPfM+iz+fjGjhQ97nOzs4iEomgVqthcXERQP1NJowxLCwsrKgvEWmrItiL5rjok6Y+KhaLyOfzDWNEGiNpuslkkoMImea0jkDlEkOTUJibm0NbWxva29uxY8cO3H777SuEEI0Z7SyjPGRwFv2iMoVCISSTSQBAJBKBYRg82oh4itpMb4wnQRIMBpFIJDhPxmIx+Hw+LC0toa+vD7FYDAsLC+jt7UVLSwuWlpZ43QuFAjweD5aWltDe3o5kMsn/E4CRIiGSqEwQ71A/iAJPjLIRrTRa4CXhtLS0BKAuXIrFIu9PcrWJvCEC89GjR7Ft2zZkMhkMDg7ye6Ojo+jr6+NjQmMug5UoQHSWjwqEVcJYda0ZIn5XuWvMiNKTwKIoNXGO2WmHGW0IoBbNbCKR4QgUy+Uy1+zS6TT33RKgkvkvukLEvAA0aBIiSFI6OtTf4/EgmUyiv78fAJDJZGAYBvbs2YN0Oo25uTn09PTA6/Vibm6OpyEtcsuWLVhcXOQTP5lMoqenB0tLS4hEItyPlc/nUSwWG1w3pOV6vV4sLS3xdrhcLnR2dgIArrvuOrzzzjsol8uYmZnB0tISotEoj2menp5GOBxGJBJBuVxGLpfj/mOaVATCFL5F4CtOdBIm5H+n58rlMorFIo/3JvOZolqWlpYQDocxNzeHzs5OzM/PIx6P8/Fta2vjoCOahBSJsGvXLuzbtw/VapWDJdVXBmLR9Fb5PUXzXVxQY4zhN37jNzAxMYH29nYOrCRcxTBIUQv1er144YVb4PO9jXS6C5HIWUQix1AqHcL4eC8ikbOYnd2NtrZvob29HdPT0xgfH+ebnDweD/cDt7S0cEGWy+VQrVYRCoV4NAfNC/nN1qKw8vl8XHMngU3jVKlUUCgUEIlEkM/nuavMMAxkMhmEw2EUi0W+WG8YBvfZy0LC7XZjbm4OhmHgBz/4AVpaWhCLxXDvvfdi165dYIw1WD+k8ZOSJGrQdF/lqtKRDPBWaa3u00cUaiIPWWnUorVB80R0Ycnf1PZmNvFsCKAG0LBwJO7KEickLYQQaBSLRRQKBe5+IMb0eDxYWFhoAGHR5CdgpzI8Hg9frU+n05yxWltbuR+4s7MTuVwO586d4y6FxcVF9Pb2orOzk2tMFy9exNDQELZu3YpsNotdu3Zhbm4OoVAI7e3tyGQy6Onp4QJndHQUHR0d3N9K7hZaKHv66f0Ih89gZKQTLS0XAfwMS0s348kn29HdHcDMzFbs2fMjdHd34+rVq+jq6uL+e+qLcrmM7u5u3mcUrlffKVjklgGlJ3eJaNaKMd8E7BRXTJoiLUAlEgnupiITmlwMAPhvUdugcQ4EAlhYWMDw8DA360+dOoVqtYp4PI49e/bAMAycOHECAwMDmJ+fR29v7wrtS6e5yRP9u9/9LgzDwOc//3m88sor+OVf/mU8/fTTOHr0KAzDQDgcxq5du3DzzTfj7bffRrVaxdjYGMbG3kIy+TAOHnwBR49+CYcOvYSf//wQuru/jStX/k/ccMP/DY/Hg97eXvT19WF0dJQLwkgkwicraeLkviMrCgAHd+pHiqsXXXLUt7LFUyqV+MJ0LBbjYZ40trTFOZ1Oo7W1FclkklsU4XC4we0hAmogEMD09DR6e3sxODiIXbt24frrr18BxOL6CtC4QUbnDlD9X2uS86Y1EXGdRlQg7AoFkZd7enoArHxbkap8u7RhgDoajTb4hYlIkyAzmwAgEAjwlW7SFklTJZ9oKpVCoVBoMAkJlEmLJy2RNpp0dHQgFArx+NhIJMIZt1wuc02wr68P09PTWFhYQCaTwcWLFxGJRHDnnXfiwIED+P73v89BmQ6cevnllzE3N4ft27fjE5/4BLLZLI4dO4a5uTlUq1W0tLTgvvvuQygUwpEjRzA7O4srVwaRTD6MXbsex2uv/R7uvnsfXn/9dnR3/z1Onvy/cNtt/xWhUB9mZ+fQ338j8vkSYrEWtLV1IBAI4/z5C4jFGIAgAoE4vF4Dly+PwOcLwucLwOWqAXDB7fbC52MAXCiV6tcYc8Mw6tdqNcAwgGoVy2kDyOUqyOWKMAzA4/HDMPzIZvMoFIoIhaJIJJbQ0tKO0dEcPB4fFheDKJcrcLk8ePHFPajVDNTPpKjnXT9TwoWJiRDS6TZMTk4hk8nB670BJ04EkcnkwNgUAoEQFhfb0dUVg9/fhW3broPL5YZhXMunVjNQq4GXUa0ay/fA7xmGgbm5XYhGY3j88Q5cvvwBzM7uRrV6H+bmFsAYw/y8C1euGDh7dhCFwv2Ym1uAYTDE4y1oa0vhpZc+Abd7Ai+8cBj9/ZcwPf0HuO++byIYHEWl0o2ZmRm43W7s3bsXqVSKh1wGg0EsLi5iYmKCg8TAwAA/UiAQCKC/vx+Li4toa2tDJpNBX18fd8uQZSQuLhJAEs/TfKAwUlqIpx3AlJ4sOgBcUAPgbgsqg+bL4OAg9u/fD8YYent7VwASrfGIax6i9SP6hilfOaJKB9g61weVq7quyoPWuagfKC353uVFY10+BOhietplLK5JUNpm3sMJAGwtfDurJa/Xa3z1q1/Fww8/zMOuSEMmxhIjKagD2trakEqlVvjm5OgE2twBAIuLiwiFQlxTue6667j/jrQL0ibouY6ODvj9fsRiMUxOTqKvrw9erxcjIyN8N+HU1BSmpqbQ09ODSqWCgwcP4uWXX0Ymk8HevXv5VutoNIoHH3wQr776KsbGxvDFL34R3/72t7lvvVQq4f7770elUsEzzzyDX/3VX8UvfhHEK6/8exjGxjp5bZNW0i23vI50+gH4fD60tbU1HPYkasHZbBY33XQTpqenkUwmkUgkcOuttyKdTiORSCCdTvOw0sXFRb6oXCgU4Pf7kclkGlyDBAjk/qMXtNJmK/I753I5zM/Po6urC6Ojo3wdJRaLIRKJoFKpYMeOHXjooYc4ABEIeb1eTE9P4/z586hWq5iZmUGxWMTevXuxc+dOtLa2Ynx8HF6vlytMBNT0TfUUv+k3ufxEq5c+onuUPiKwi9fNSHR5lEolvvmL3JbpdBqzs7MYHx+3BdbkAWhvb0d3dzdCoRBCoRB3N165cgWpVKrBtSnWs1QqvWkYxq1W5WwIjVr0G4sRCPRbXAUXJXWlUuH+PCKScJRHpVJBPB5HZ2cnlpaWsG3bNtRqNYyOjnItuaWlBTMzM+jp6UFPTw98Ph+Gh4fR0tKCtrY2dHV14cyZM6jVaujt7UU2m0UsFkM4HMbJkyfh9/tx//33Y2RkBPfccw+effZZDA8P47777sPLL7+MyclJHjZ10003YXJyEhcvXkQoFMLzzz+Pz33uc3jsscf4ca8/+9nPMDAwgP7+fhiGgWTyScRi9yGZvAPhcB7ZbBCBwCQ8nqsIhYJwuRgAWhgFyuUSKpUiwuEQAGNZYyrB5aJtrVUYRg0+n2c5SqaGcrmEWq3OTMViHm43g9frXl7kcsPrdYMW94vFAgyjAsaw7INkqFTKKBSyyzHWZWQyKUSjEZRKRZTLRcRikeU6uBAIeHH33XehVqND/Q3UX7lZ/1SrZbz66ivYtm0rstk0ZmenceDAjYhEQrhw4Tw++MFbkU4vIRwOoVaron6ucv3FAPV86mcwV6sV/r/e5nq7DaOyfK2G9vZ2fOc7j8DtdiEYDCyvDdQjibZvH8TIyAj8fh+q1QqSyaVlza+CaDSCiYkeTEx8EZHI20il7kR//0kMD+/FgQOfwtLSE9i+fTtSqRQymQz279+PWq2G2dlZ7r8nbTudTiMYDOL8+fPcZ0xuE/IDj4+P8/DLXC6H/fv34+LFi1zJII2Z+IzcWrQ4GIlEUCgUkEgkuF8cQINmTgucwWCwYc6JYZQUFvr6669jYWEB7e3tGB8fxxNPPIEdO3ZgeHgYO3bswJ49e3Do0KEVwGm20CtHqehcWVQX0a/shKgMebGUcIhcVFSu3QVFsb7UFjle3Y4wUdGGAGqgDsAtLS08SkF06JOpIndiKpXiW3TJ3KO01PFkxhGDUhgVRWAkk0lMT0+jWCwik8ng6tWrXCpSHDRQ1yZaWloQCAQQjUbx2muvoaenB4ZhYMuWLRgbG0N3dzcef/xxHD9+HIODg3j99dcxMTGBHTt2oFAo4MYbb8Rdd92F73znO4jFYvB4PJiYmMDTTz+Nz372s3jllVf4pJmYmOAHO3m9H0U2ez3i8R8hmfwkbr75FC5d2om77noM2exPsHPnTkxOTuKOO+6Ay+XCxMQETp06BcYY124CgQBmZmbQ3t6OdDqNzs5OpNNphEIhpNNpZLNZ7Nu3j0dpnDp1CoZhYOvWuvZHC0zFYpH7Pj0eDz84a2Fhgfuor1y5gs5OL2KxGBYXF7l2WSwWEQwG0dHRgc99bjsPsSQSIwv27Stgfv5VfojQv/k3u/Cznz2DYPAkpqcPYGAgjVDoBezbtw8AMDzcgXfeacMnP3mmQbDrEi4AACAASURBVCsTw+LoPwn6arWKJ574z9iypczN/6GhIUxMTCAUimJ29jg+9an78NRTT2F2dno5xLC+yNfR8QVMT/8htm59FBMTD+CGGx7FlSufxYc+9BzefPPLuOOOOCYmXkRnZyfm5uYQiUR49FA2m0WpVEIqlUIwGOTW2tTUFA9LjcViOHfuXEOk0MDAAHw+H9LpNCYmJvi6QyaTQSwW41Ed2WyWb1CiXaDlchlLS0sIBoOIRCKYnJxER0cHcrlcwxoFrdWQtk7jAoBrhWfOnMHtt9+OsbExAMANN9yAixcv4sCBA/jIRz6CgYEBXL58uWHRUwXORGYRHjSfmwFkuyQqiuIOVLEeVuBKmCMe8gWsjOVu1oPx3r0QTiDSjgcHB1EqlVbEXwLXVrYJbIE685IrhBiVdop1dXXxZzs7OxGLxTA3N4e5uTlkMhkeT93a2ore3l7s2rULu3btQn9/P/r6+hCPxxvOwGCMYWpqCufPn+fPp1IpHD58GP39/RgfHwcATExMoK2tDR/72MfQ3t7Oz2io1Wro7u7GP/zDP8DtdqOjo4Obwfl8Ho8++ijuuecevjGmWq2/0ueNN6I4d+7P0NX1LeTzd2HXrocxPNyPSORv8bOffQHZ7EHMz8+jtbUVr776Kp5//nksLCygs7OTLySSVXHo0CEMDg7C4/Fg586dcLvdmJycRD6fR1dXFxKJBN544w2cOHECt912G2688UYwVt/sMT8/j0QigUwm07D4Wq1WkUqlEA6HEQqF+Jbpvr4+fhBVS0tLQ/jdli1bOECLseQEDrVaDVu2bOFb769cuYJjx47hzJkzGBwcxODgLL7+9bsxO1sH6TNnuvG3f3snduxY5PkA1yagCAIyINCxs4VCAeFwmMeBt7W1YX5+Hj/4wQ/w6U9/Gj09Pdi+fTu2bNmCWCyGN95g+LVf+x58vhB+5Ve+ia6u/47BwS/h3LlL6Oj4H+Hz3ckPRPJ4PPjpT3+KX/ziF0gmk2hvb+fASPH2fX192Lt3L9ra2lAqlTj/kmkOAFevXsVbb72FkZER5HI5zMzMNNS7fn52gi8+UtQPUFdUIpEI2tvbMTU1xWN9M5kMf18nCUpx0Zf6kcZqcHAQ/f39iMfjaGtrQyAQwN69e3Hw4EG4XC50dXWhXC5jaGiIPyP2uwi+4ribLbqpQHotFhzlxVIS6uIuTTvAKi48kt9bVBTWgjaMRm0YBrq7uxvO/ABWHhLPGOO+NNKcxMO/fT4f9/kNDg4il8thenqam420K4608XA4zGNJaXMGaaDpdJr75VpaWrj5ePz4cT7Jent78cQTT8Dj8eCll15Cf38/Wlpa0NXVhUceeQQ7d+5EMBjEddddhzfffBMtLS1c63G73ejq6sL+/ftx6tQpfO9738ODDz6IRCKBSqWCXC6HkycP4vDh7+DixT488MDrePPNxxGLXUapdBP6+/8DIpGPYHLyCCKRCI9+oXjm9vZ23HjjjZiamsLc3Bz27NmDY8eOcaFDC6gDAwM4e/YsYrEYQqEQduzYgbfffptHs7zzzjsYGhrClStX+AIVbdRYWlqCz+dDPB7H3NwcstksOjo6+OIvafRk8ZBJDzRuyKCxIfrRj36EbDaLdDoNv9+PO+64AzMzM8jn8+jtPY8vfSmCv/iLX8N3v5tHLhfFwMASfvKT6/H003vhdlfBWA1uN716qgqXq4L6q7gquPYuxSoikW1YWEghGPRgfj6JubkSbrhhL86dO49Y7COYnh7HI4+ksGPHH+Dq1UsolXLo6YmBsXEcO3YBt912CwYGtuCppzrg8czggQf24tixGvz+f8L27QcxPPwWOjtbEYmE4XLVuN+4paWFh3AuLi6iUCigv78f77zzDu+Dnp4ejI+Po7e3FxMTE4jFYnyrent7O4aGhnDp0iVMTEyAsforoGhHKPmIgfq6DCk55IoZGBhAoVDgIa80z0qlEsLhMAA1GLrdbtx7770YHh7m/NLa2opvfOMbSCb/LT7zmQEsLv4Ad955J1wuF06ebMfFiy24//7zDe4UorWO+HCitaowRtzMZqcuZLlTGKIY4iruLlXl9b6Lo6bO7e7uXuEbo8krxo3SOQrZbJZP9mQyidbWVuRyOeRyOQSDQUxPT/OY0VQqBb/fj927d2Pbtm144403eIhcrVY/3pN8c8ViEclkEm1tbdxUJHNT3NBBEnfXrl2466678NWvfhWxWAwPPPAAnn32We7eyOfzmJychM/nQ0dHB29TIBDABz/4Qfz85z/nZxwPDw9jYGAAx44dQ1dXFz70oZexsLCA3//9T+Hs2Vdw+vQncPhwB+64o4jvfOc1fPSj7XjkkZuwtHQb9uz5ESqVCrZt24ZUKoVAIIC2tjZcuXIFuVwOV69e5cdxXrp0iWt2BKgf/vCHcfLkSSwuLqK7uxtXrlzh7oCxsTH09PTwxV6v14upqfobWUgjW1hYgNfr5WGNjDG+YzKfz/P+IDCRQzBJy/rmN7/J3QJTU1Mol8s4e/YsPB4P9uzZg927d6NaTWLnzvMYHr4BLS1L8PuryOV8qFSuveWkUqm/BYW+q1W6R29DUS/Ojo4CwK82XBseVvPu5cv067cAAG+/DQB/2JDm3DnxH71bsf4iXre7BqAMwyjD52OoVotwuap4440SPB4DhlHGxYtulMs5RKMBFAoZ+P0uABUcOZKG1/sRlMt5uN1VuN2AYZTg9TK43TV4PEChkEa5nEck4kcyWUYiMQvDKGFxMYJ0OoFarYSlpQhqtRIMowSPB0ilDuLkyTZ4PPV3LdbfH1mF211/j2O53IWZmfocnJgYwQ9+8FNs27YHfr8Hf/7nN+A3f7MOysPDHfjLv7wV/+7fHVO6N8yAWQS49XB9iDgjx0uLezpEpdEqLzkOG2g8fXI1tGGAmjGG9vZ23kjawEIRH+Qfo0B88gVRuAudptfV1cU1h1qtxrfW0sJKoVBAPp9HLpdDV1cXFhcX+e5COpUvk8mgs7OTu1Tm5uaQy+XQ2dmJCxcucBOdMYahoSE89dRT/Gzsubk5nDhxgq+I03kPkUiE+4xp1+L58+e5b69Wq2FqagrT09N8I83ly5cRjUb5+cavvfYagBT+23/7r+jsfA2HDh3C0aNhXL78Feze/e+RSCQQi8UwPDzMfcgzMzMIBAIYGhrC+Pg4DMPA4uIi2tvbefQAba6h/k0mk/x8lK6uLoyNjXFGJE1tbm4OtVqNx4BPT0+jWq2iq6sLLpeLWym0+Eu+0mq1yk+Xkyeqy+XCkSNHsLi4yN+sQibkyMgIjh49isOHD6NQKODZZ0s4daoXXu9XkEz+AT7zmRfx4Q9fiwagb5p0ZIHRGRT1Tw3/+I+PoVp1Yf/+Azh+/BRisXbs23cTTp06j1AohpmZRRiGG/fd90k899yLCAZjSCazMAwPwuE4Pvzhe/HUU88hnc6jXL4e+fxD+NCH3kZfXw4vvTSP6emPo63tGQSDs/B6QygWq3C5/DAMD2o1hlisA/XXYbmRyRRQq7kRDEZQKFRRfyWXezm9G4YRhdsdRTpdRv39jb3LkUBeVCoAY37Uai4AHv4soBZGyxsqAQDLm1U5XbxoZ9Z+VHvnO9/5H/DoozWEQlV86UtHl49JXblZxwx87YC42bMqcJXdX6oy5D0cZkQ8LK6NUf60/iIevmU3fFDZJhvhLAEALwHwow7s3zcM48uMse0AHgPQDuBNAJ83DKPEGPMD+HsAtwBYAPAZwzBGzMqIRqPGX/3VXyEcDuPP/uzP+KaWlpaWBqAmTZrOV8jlctw8plhoikfeunUr2tvbUSqVcG5ZpRE1vNbWVszMzPCNFNFoFCMjI/yMDTpcp7W1lWt8iUQCPp8P5XIZU1NT2Lp1K/r7+7Fjxw48+eSTmJqaQltbGzweD3bt2oXXX38dg4ODePvtt/Hrv/7reOutt5BKpXDLLbfgnXfeQVtbG/r7+1Eul3Hy5EmEw2EcOnQI586d4ws/U1NT+KM/+iM8/fTTSCQSuHDhAlKpWzA9/W20tVWwsOBHNDoKl6u+MOj1ehpMN3Ip1GOHqyiV6gceRaMRFApFvnXf6/UIp+qV4PV6UK1SWJYHLpcbpVIRLpcLpVKZg7rLxVAqlVEqFeF2e+DzeZdPUKvwiANxVR8ABga2wOv1oR7lUSfDqEeT0M63az5CLEdqUIxrFS0t23H1ahiMnQWwBMOIwzD2oqdnAa2togakOryeJkz9/8TEBCKRKEqlIgC2vE3cj1AohMXFxLKlU+RC7Vq9GXp6upHJZJDL5VCpVJcjZ2KoVHZjy5Y5TE0FsW/fnyIY/AV27tyJeDzOXVrhcJjHNU9PT/MFwHK5jGg0ir179/LQrmAwiEKhwEM/x8fHkUgkuI+5v7+fx0oPDAzwkNX6s2FEIi2YmVnE1NQcAoEourr6MDExi3y+jEikFZFIC7LZEmKxNuTzFXzuc7+LYDCKatWFSgX8u1wGfvzjZ1AuA4x5wZgPbrcfnZ19MAwXtm/fjXIZOHLEjfHxbfjAB17Cn/5paoXgVIXg0X86DI3+y6cOipFgYoieOM7XeEoNjPSdTCZRKBSwtLTEgbVQKGBsbIwrHrq8iCj0sbOzE93d3QiHw3yBPZvN4urVqw1HB8h5rWV4XhHAPYZhZBhjXgCvMMaeAfC/AvgrwzAeY4z9fwB+H8DXl78ThmHsZIx9FsBfAPiMWQHk22GMYXBwEOfOnWsIj6E04j582uxCK9XkJhkaGuJaYqlUwqVLl9Dd3Y3R0VH4/X5EIhFEIhEsLCw0HGFJGjbFTM/PzyOTyWB+fn550i6io6MDCwsLfEAymQyOHDmCS5cuYXx8nB8Uf/XqVdx9993YsmULj6jo7e3lm3kKhQKi0Sj6+vpw+fJl3H777XjnnXdQKpXQ3d2N559/HoVCAVu3bsXu3bsxOTnJX/x74MABnD17ConEBczP3wCfbwpu97W3x9DW+lwuj0DAv3yWsgeVSnl5K7YbHk/9BQxElUoZjNFWbDoWs65lRyJhfhgULdbSGNCY1DdQ0JGl9WcZA9zuxhcB1MdTPMKWoQ6Y9XGen58H8XDdnDTAGDi41l0mBgoFL3y+S6hUlpafXQJwFvPzLWhrC/DnrenaiwFqNQMdHe2oVMrIZLJcWBQKebS0tGJxcRGM1Q/tqfvo/ajVakgm66AYCPjh8bhRLudhGDMYG+tHf/8jiMffQqlUP1Pk1KlT2L9/P8bHx7Fz507MzMwgkUjwHXx0JgxFhESjUSwtLSGRSHD/NYXz0aE/N998M0ZHR7F9+3YYhoGxsTEOOLQtPpfLIpEYh89XQ3d3C7zeRdRqVxEMutDSUkatNg2/vwSfLwqXq4IPfCCLQKDxDBEAePbZZxGP19101xSA+pG1+XwenZ03oVQ6hMnJQwgG/xLHj/8ufvKTF3D4cIDnJWuyomYtRmytN5GVBVw72pTOHCJLnTa/iIAtzgE5P7EdsjtETNcMWQK1Uc+ZkMC7/DEA3APgt5ev/x2A/4A6UN+//BsAvg/ga4wxZpjUkCZxpVLBzp07cfr0aT65yQQRz7ugQSYznILV9+3bB8bqx23OzMwglUqhq6sLyWSSM1UqlcLi4iL6+vr4yjltgKENLtPT0xgaGuK+7P379+Ott97C1q1b+Xbst956i5/YNz4+jo6ODh45kUgk8Nxzz6GrqwsLCwsIhUJ48cUXcfDgQTz55JN8EYdMcQrXGhoawpkzZ3hb0+k0qtUqnn32Wb6TKpPJoL3911GpDKCv79uYmfkUOjr+C/r6zqOvrw8AsHXrViSTSezevRsnT57EoUOHcPr0aVy5coXvPFtaWuKHKZF2RmdmJJNJXHfddUin07jjjjtw4sSJhleJicc4kkCLRqPo7e3l7+MzDAO9vb1wu92Yn5/n/mmPx4Mvf/nLAK69qoi2tP/Jn/wJP1XP5XLxMqndsVgMW7ZsgWEYePPNN/m40kTq7OzEPfd8Erfddhu3wmTtTdbQvv71r2P37t1c02WM8TfnXLhwAS6XC4cPH8YzzzzDd8Du2rUL+Xwe6XQa8fi1EMPt27djYmIX3nzzf0d7+3/B1NRn4fW+gpaW45iamuI71mq1Gq6//nqMjIwgFAphaGgIJ06c4P/Jusvn82hpaUGhUMDk5CT6+/u5y4k20cRiMezZswcXLlzgcf+jo6NcCalUKnxxuqWlhYdjkhsuEolgaWmJLw4zxvjbicQjGBhjOHHiBF+gpDnJGOMHOuXzt+ORR34F8fjn4XL9M7zeV/DNb34bvb2nceBAYgUIUxlyCJvOLaHDDru7CEWSfdSkfBBgE+6QNU33xU03Zq6ZtYqf5u20k4gx5maMnQAwC+CnAN4BsGTUdw4AwDiA/uXf/QDGAGD5fhJ194ic50OMsTcYY2/QiXQAsGPHjgZJRGcXiKFsdKoZmdZ0dgatpovxqXNzc3C73eju7sa2bdv4pK/HwnYgGAwilUph69at2LNnD7xeLz7+8Y9jbm4OIyMjcLlcOHr0KI8CaWtrg2HUD29aWFjgPlqv14tqtYrJyUkA9UOH9u/fj1/6pV/C4OAgWltbcfr0aWzZsgVXr14FYwwjIyMoFot49dVXUa1WsW3bNoyOjqK7uxudnZ148MEHsXXr1ga/2ZUrg/j5z/9n3Hrrf8K+fd/Dnj1fxuXL/w8WFm5EV1cXurq6MD4+jqWlJbzxxhswDAPHjx/HwsIC7r77bvj9fvT39/P60oTL5/OoVutnP/f19aFcLqOjowOvvvoq0uk0KpUKbr/9dvT29iIQCCAejyObzXLgaGtrQ6VSadiuHAgEkM1m+TiJB2uJmprH48Fjjz3GNTV6BRmtLdBZy4ZhYHZ2FlevXm2Y9B/96Efxta99DX/913+NoaEhjIyMrAjv5AwvLRzNzc1hdHQUU1NTHDTGx8dx4cIF1Go1fOITn8Djjz+Ozs5ORKNRbN++HR/72MdQqVTQ2trKy6jVahgfH8Lx43+Mm276j9i79zEMDn4Jo6P/Cen0LWhvb+dKQ1dXF/75n/+Z1/Odd96By+XCxz/+8YaTG4F6xEaxWHc5Xblyhe+8DQQC2LdvHy5evMjP7fD5fMjn82hra8P111/Pt6gvLi4iEonw19XRQV90SBaZ7wCW3WdeDloEOi+99BIPNSQfLPn7JycnsX//fszObsX+/X+OYPAXy3P0FcRiD+Lhh483WMji3gjx+90mcfey6EoRNXtZo5ZPulTluR4x37aA2jCMqmEYBwBsAXAQwJ7VFmwYxsOGYdxqGMatdCQiAHR0dHDTQjy4ffmZBilPB8wUCgX09vbi8uXLeO2117hJQ6F3uVwOHR0dXHOLRqPYsWMHIpEItm/fzs8L6ejoQCKRwPnz5/9/6t48urHrOvP9XcwkABIgQQKcp6oia55LJcmxZkWe5MSZ45d4vc5K94qf/Zx2nHbcnX7pxLHTbacz+KWTOHlpx3GsFzu2YluTJdnloaRSVanmuVicCU4gMQPEjPv+uNiHlxBLVSUpvfTOWlgEgYuLi3PP2Wefb3/72yqYl0ql6OzspKenhy1btmCz2ZiZmVHcVsG7N2/eTCpl4HFer5dqtcpzzz2nFNJEsKmvrw+3271ONMdms3H33Xfz/e9/X2WM3X///aoquRT4NbZrezl48I956CFjonV2XmfLlv9MPL5JYdvifQtV8cqVK5RKJU6ePKky3KSAgdvtZvv27QwMDNDQ0EBnZ6dK9onH47S3txMMBpX2id/vZ9OmTaRSKSKRCI2NjUpzWyrGSLxA6vpJINV878yBF2nmrFOLxUI8Hlcp06IOV6lUlJYLwIMPPqioZteuXWPbtm0sLS2p9zfy0MyBzC984Qsq2CvZfBLIfPjhh3n66aeVapzdbueBBx7gypUrzMzMkEwm8Xq9amFaXd3Oo4/+HV1dozV+/jX6+z9JKjXCpUuXVC1FCXJbLBYikYgKPF+5coVcLsfmzZsZGhpSWbNtbW1KTMtisaidSiaTwe/3Mz8/rxQMR0ZG6O/vV4ubjH9JYhHI0GIxKgSZjYrUddxgrvLMM8+obb9ZI1sStM6dO0c4/DFKpReUU2XIL1zkl35plmefffY1SS7m5/Xz/PWw5ttt5vPUPzcv2BtJyZoXy3psux4PNxtnub9mjY/6wPkb+T13xPrQdT2hadoPgLsBn6ZptprX3A3M1Q6bA3qAsKZpNqAZI6j4uk1+hExCuXkyec2rnPlHC093eXlZ0dHcbjcLCwtEIhHljYhA01133UU+n2dyclJtQwOBAKlUiu9///tKbtLITlszQl6vl4WFBS5cuKA0E0STxOVyMTMzQ09PD8vLywSDQQqFAolEgmPHjrFt2zZOnjypjHxTU5PibBcKBYaGhsjlcvj9fuLxOEtLS2zZsoW///u/x+VyqcCly+Wit/e77N+/n+PHj6ttZ1fXKA7Hy1gsnVy/fp3e3l4l2SlV0G/cuKHU7o4cOaJ2JMVikbm5OUKhED6fj8XFRZVsIvdDONIzMzMcOHCAcDistKatVistLS04nU5isZga3HLfrFarYtUIni3ernkQC+xSLpeVofR4PKqgsZR4ErioWCwSCoX4tV/7NXWPu7q6+MIXvsDVq1cplUrcc88968aW2WDLZPn4xz/OT/3UT3H27Nl1uhkDAwO89NJLtLS0qMQSCVA/8cQTilGzefNmWlpaaGxsJBr9K975zp/mm980MkCNxKvL2GxH8fv7uHjxIn6/n+bmZpVEZbFYmJ2dVfz3ZDLJyy+/rK5XmDZ79+7l+nWD9hYIBBSnPJlMsmfPHuVZB4NBxbmemJhQWY6S8Sg7HDHSUkxDgr4y3s2eoxk6lD4SaEp2txaLRUnqSh9+6lOfYufOnYjioATib+U9m+f3/wpPWyAYGbuy0JirtrxeM8fRZFyZIZK35Bpv4yLaNE3z1Z43AI8AV4EfAD9bO+xDwLdrz79T+5/a+0deD5+GtY6RoKIQ8M0PEZsRdTD5jOCZfr8fgPHxca5evYrFYiEQCODz+VheXmZ8fJz+/n5mZmYUthwMBmlubqajo4NgMMjIyAixWIzp6WmGh4dxuVxKbyGfzzMxMcHQ0BDDw8PqGsR7lyobIispAUpJAPnVX/1VVa6rubmZXbt21W7if2BqaoAbN27gcDgYGRnh3e/+PL/7uym1nU0kEqysrCjVtVwupxInJI3b7/fT1dXF/v378fv9LCwsKEhDMsXkM5s3byYUCtHb20sgEKCrq0stRi0tLVy6dInGxkba2tqYnp4mGo2qhJ8jR45w9OhRtS3s7+9XdRslo1RkO818VGAdh3qjIWHGCgGVzCSfdTgc6j4XCgX+zb/5N0qmdXJykt/8zd/k4MGD/N7v/R67d+9e5zXdbMLt2bOHM2fOcP/996sF5cCBA5w//5NEIttVWbGPfvSjeL2P8/u/b+wqnE4n5XKZsbEx3G43KysrPP7447z44ouqig2gnIdAIMChQ4fo7e1VwVnR12hpaaGlpUVlde7atQufz0coFMLr9aqkqYMHD3LPPfcwNjZGe3u7Slhyu904nU5aW1u5ceOG2hU0NTUpsaVAIKCYCJVKZd3iKTtAeb1ealiu1Uw3s1qt+P1+leFbLBbVLlPTND73uc/R0dGhdE2KxSJPPvnkunshC5X5HtVDU/Xt9XDh23mtHoIxN4GdzIqbt9sEFRB47mbXcbvYe327HY+6A/iypmlWDMP+dV3Xn9Y07QrwT5qm/SFwFvi72vF/B3xF07QxIAb84i0vwiRvKqt9JpNRAv5SjUK8XZE5rSeWSykowUuj0ahaAESJrKmpif7+fubn55WnEY/HmZ6ermW9dajMRzGKy8vLio88NTWlspd8Ph/BYFDVbYxGoxw8eJBoNMrq6qoqsPvcc89x8OBBJTkpyloHDhzg+PHTHD/+cYaH4zgcYyQSe/nKV3YyPPx1ZTwlYaRSqbBnzx5efPFFLBaL2qaKN7SysoLT6aS/v59UKqWCS6Kx7XA4SKVSTE9Pq5T7trY2otEou3fvZnZ2lvb2djRNY3FxUamqCf1RNIaj0Sgul4vBwUG1eEplEtFXka25XJt4hoJrynZRFlrhtpslPOsXQ5G4FZhi165dPP/887zrXe/iX/7lXwBjhxUOh9m6dSvpdBq4OX9W0zTVFz/4wQ/YtWsXTU1NnD59GqvVwvj4HwH/kcHBaY4c0fna197N4OCnVMEHMBaXqakpHn74YcXSEOMlATubzcbk5CQ+n4+uri4FUwwMDFCtVtdlFJbLZV555RXFEuru7lZebalUUkFiEXAaGhri5MmTtLS04Pf7SafTKnU9Eoko6EoWPaG5SiKYGcoQ4y6tPvAnHrV4jrlcTrF/pDamCFB1d3erIhmf//znle76RkHKf02v+XYMuOxczL/5Toy0+fNWq1VBhDLu38g569vtsD4uAHs3eH0CA6+ufz0P/NydXIRZrFvTDG2JbDarvMVsNqt+tExc0TgwZwOJ95JIJGhqasLn86k6cWJQJAIuxnJhYWGdwHk6nWZwcJBCoUAwGFSsjlAoRE9PD4lEgkQiweTkJA0NDSo4mU6nWVpa4n3vex8nTpxgdHQUr9erAhZHjhzB5/MxNDSkPKfz58/j8znZs+ePOH3680xNpclmfTQ2TjE19auEw3YMZTwDW5yd9XD2rI14/H4sFisWi1ajs61hZR6Pl5deKig2jNVqU9s4w9AVsFikKrlRTaVa1XnhhUaFL1cq7yGdzmCzWRV1DnTK5Yqisxnym8ZvK5cNWpOhVmchkTDS9Y3CvwYlsFKpYrFoWCxW/v2/7113/zUN4vE/JZVK14yA6CRIdF10piuAhqYZeOonPhFiefkdfPnLURKJzxIMBnniiQCZjI3VVSstLZmaZ2T0kTxf85Z0wuHDOByGIzAzY6VUKmOz/TrVagW7PcKNG3/BwsIouVwHg4O/Q2PjcZVi9QAAIABJREFUSQoFQztG7r/X68Xn83HkyBFlzIBanxiLknjIDoeDBx98kNOnT7O4uMjg4OC64J2I8ot2Rzgcpq2tjaGhISYmJhgYGCAUCjE5OYmmady4cUM5C7JYplIpVlZW6OnpUZIF+XxeefIyr6LRqFooRUbVzISoD4pJmTRN0xTeLnNXvPpEIsGjjz6qaKx//ud/TrVa5bOf/axa9M2G2gxl3om3/EabGfoy/z4zHm1OlLrdJgvWRkFTed98DXfa3haiTLJKy8rU2tqqnkvgA9aEywX/FEgE1rbGIjYjXpikN1ssFi5fvqxgkKmpKWZnZykWi0r8v1QqMTIygsViIZvNsrKywrlz51Qh2VdeeUV5hZLZVyqVVDktMeKHDh0imUyqAI54MZlMhve///0UCh8jkdirioT29U0QChXIZAI4nSu4XDkk+QIgk/Fjsxn87nQ6Q6VSpVLxksu1YbOtlREyDGmploBSMtUSNAyy1WqlsdFNpVJWRg+MQZtOp8jnc+Ryq4pDbbXacLmcteCk8RlN02olpQwjblzLep4pQKlUBjSsVkvN2IOmiUjSa8eAeHhrE0jDYlnv/TocDhoaXGiapYZXV2hoKBOPd2K3t9HS4ieZ1JiZ8eByScCr/stETtUw/r29fXi9HkqlEKWSB12XoJClxqkvkU5vo6/vObze08r7t1gsTE//ApHIdh577DGWlpY4efIkq6t3MT39C4qz39DQQLVq1K7MZg0Z2Keffhqfz0draytXr15VOsiSZGVw362KMnjjxg2OHTumkl1isRg9PT0kk0mam5tVBm8qlSKRSJBKpejr66Ojo0P1q4g16bquBJ+y2ew6TRCA1tZWNdekOZ1Otm/fTjAYxO/3q0QdgR+r1SrRaFSVW9u7dy/PPbeTP/3Tc/zwhz/k53/+5/F6vZw65eUb3xhYl/ghxqzeKN4OY+dWbaMdlJx3I0Ns9opvx1DXJ7DI2BWa55sJHta3t00KuXgeuq6rbWC1WlWaHrCGcUqUXCRMYY1f7XQ6aWlpUd5DLBZjdXWV7u5uJdeZSCTo6OggHo+zvLysWAWiMnf9+nV2797NpUuXABRksnfvXiUCbq4KnkgksNvttLe3c/78eQ4dOkQgEFDXI15WuVzmX/7lX3jPez7GZz7zWwwMLKNpV+nq+mMWFry0tDxNKnUv27d/E037ocIgq9X7OHr0o3z4w6f5wQ/+L+bnh5mZ+Tz33/9XeL2nFPQi2ZySuCOVQubn55UQ1f79+zl27JgaRMlkku7ubgC1U7Hb7SSTSTXYqtUqN27coFo1FABbWlpUv4vGcSKRwGaz0dnZSbVaVbUb29raFIQlu6VPfOIT69TzdF1nbGyM1dVVfv/3fx+Xy87IyAg+n08dY7VaVV3GctmoS/lHf/RHZLNZ/uIvLvPii/8n5XKcxUUfDz00jcMxhd/fhKZVgPUPI/5t/E2l4vT1dXPmTJ7jx99JIPAt/P4pQqFHeeWV7ZRKJZqbv8n09ONAGLv9DBZLA/l8Bbt9hYmJz3P69MssLV2kVHo/o6N/yLZtfwL4SaezGKneRpp3MpmmsTGmshSz2awytLt37+aVV16hp6eH2dnZdQH05uZmKpUKkUhEZdKFQiHcbjc+n09lzArk1t3drZg25sSwatUolyasHkDp1oiXvVFgLJfL8b73vY+vfe1r5PP5dTrXFotFSdcCSq9682Ynn/70r9HbexWXy8Wrr3r47Gd38olPvPqG7YSZu/xmjZ/582YutzhW0syB75s1s/GXAP1b3d4WFV76+vr0T3/606q+3ve+9z2ee+45AoGAynqSTCFJ1sjn8zQ3NyvamnkbVq1WFWvD6/WytLSksLEtW7YoHV3Bmtva2ujs7GR6epquri6FgV+8eJGOjg5VLKC9vV1xfKXArSR0iCaH3+9ny5YtPPnkk+zYsUMtKrLK+v1+isWPMTKyhf/5P0dwOCoUizas1hxQwmIpUCq10tAQw2LJAdWaXKmNXC4IlNF1GxZLAU2rKljAEM03+nPNE12DDIzVX695jIa3ukbopwaxrN/yGlCGcW5p6z3itewrwztaX0ndgDqMsliGgD/YbFalsQygaQYckc/naWxsIJlMUijkAV2lawtMId65MZGM87W0+GvxiBZe6z2/PZuh3KdjsRgFD3TdEDzStCo2m4VyOQ+UsVhA10tYLDp2uwWjWINR8KFUygOGAqDDYSWXy1KtFtE0HafThtNpxViYqpTLBQqFLOVygYYGJw6HlWIxR6GQxeNprJ3HEHHq6AiyZcsgVquGoTyok89naG728vWv/79AhVAogN1urX1Go1otUa2WsNs1Uqk4jz32CF5vI9/+9hgXL76LQ4fGuXBhM7/1WyfZvTumjKFZH1z+N/81v26GI8yMCjOEcTsBQDG6os9dqVSIx+PKGSwWi1y/fl2lldcHVc1N2C2NjY0qYNzS0sLFixcZHh4mkUgwPT2toNuNmCz/v6rwIk28atmuASqAJYEQ4UFnMhlVQ9HM/RSj7Xa78fv9rKysMDg4qLK1RLBIMNvOzk6sViubNm1SwZ+lpSWcTieDg4OAwSQR4Xbh08p3JRIJNM3IZvN4PEQiESYmJmhoaKCvr494PE4ymVTbRAPLOstXv/oQ7e0ZIpEmXK4YxaIHj+cSVmsUTeugXG7G6ZzG5WrAarVRKlUolTyUSh4sljwWS6H2i9cM55q3Ya6QbMSAdV0SRCRF3KylYdQSrJ2l5kXrGPIuqHMaOx9t3Wvm50bNQ3ldYw0RMQ9MjdpGhPWGtQGDEbi2S6qjt27YlpZufcwbaVZrkUrFit0+j6bJVraJSsWB1RrHarXVaIRlqtUW8nkPra3HaWg4gqbZqVSgsdFLtapRKJRxOhsx7oOVYLCTSGQFh6MRp7ORZDKDy+XGZnOQy5WwWh00NjaRSmXI58uUShU0zYam2XE4GgArDoeFfL5IoVChWNSwWhuoVp3Y7S7KZRvlshVD7AnKZZ1yWUfXrRQKdjTNSqUCum4hm7XVxoYFsDIzY+X4cQsiorS+ffK2+ry2EQV2A/Dyyzt5+OFXaG4+g9U6tGFCyO14rG9lEy9aHgJDSrvdpBUz7CGCZuYg61vlCL8tDLWsWIIli7iSNAkUSskiKXkvIu/mSLScr7m5WQmk53I5Wltb6ejoYGxsDKlxNjo6SlNTE7quc/nyZbLZLOVymUwmw8DAgKqCIRmLnZ0GT/nuu+/m2LFjKvItac/VqqGeVyqV6OrqUlH7wcFBzp07pyLBFstJOjr+nqmpj2CzrZLP+3G5FrHZ3JTLjhrbo4KmdVOt6lQqVVZX7ZTLLrzeFTIZHw0N01ityVq03YrTaa957M2qcEC1anieEpArlYrk80aAr1ot13YjVZLJBF6vB4tFqxUGsLK6akjIOp12SqWiCiAaJawMD9kIyhaxWi01saxSjQKWoVwu4fG4ax6PUXLLbrfR2trCwMAABrxniBtVKpUav1coUsZ7wq1e86QEX9bRNCOYGo83cuFCJ7t2zeH354jFGrh4sYvduxfx+1cRQSddr6rgpHE+4//r10dpaHBRLJZIpQbI5/fR2jpHNNrJ0NBfMzT0bebm5vD7/WrHt7q6Sn+NlnjypJuzZ3+H/v6vMDf3Pjo6nsBmO6qCcwL3SPxEKHdzc3NkMhkOHDjAmTNn2LVrFysrK8zNzdHV1aUYL8LOOX/+vIL6RIxpeXmZUCikKJGRSITW1lZFcwVYWloimUyysrKimCHVapXp6WlsNhstLS3rWDmPPPII9913X00fRqdSgUgkyh//8Z/y+c//CV/60j9w5sx57HYXLpebhgYPNpuTYrFKOr1KJBKlWKxgt7sIBN7NmTM/R1vbC7z66vuZnPw7/uzPNq2b8/V2YKP2r7XrNyepmNU4zdCH+RoES78Zpi4ethn6uFNc/WbtbWOozdXHhYAvQTihDwlpXzxsAe6lKEA9naijo4OZmRmGh4dpbGzkxo0bhEIhnE4no6OjSmBH6GuCVadSKUV/c7vdXL9+HYvFojQRRNxd9LABxU6pVCoMDg4q2l80GsXv96uJbqTcDhMO/2+4XGPk85ux26MUCgEcjhwWS5FyuYTT2Uwyma4F7LykUp20tcVqAkyrZDJDuFyzWCwZymWNatWG1WpjaQlstiCxmI1crorFYq1F80WIpgmbzcjwS6WMAVoo+MlkKtjtDsrlthoUYgM08nm9JrKksRZ7FnjDMHYWi5V83l2rj1jGUN3TKBatJs/MmJjxuJ3paQdGENN4R4KSa7dv7bvMx8i5jPusrfPiz53rUbsDXdc4daqL24NCXkNoIhrtpqXlGaanfwm/f4rW1jzFYpF4PM7jjz/O0aNHa0kcmzh79kPs2vUZ3vnOCi+9dIlz5/6AgYHfwes9RTQaxel0Kox5586dvPLKK2zatEnR7m7cuMGOHTtYWVlRPF6Px8Pc3BwdHR2k02mKxaKSSigWi4TDYQX5iaiTzAczB1ocGJlHEhRPp9NKQkHYQYBKThKWj6GXrdHcbOcjH/kQicQUH/jAfbz3vXereVcsFtd9t3Cxr13r4ItffJDf/u0fsW3bKlNTx/nv//1/cOHCGXbsWL4p06Oetnez7MI328wes1nTY3V1dZ1g2eth4mbMXJrEAl6vbfS5W7W3jaGWwWUWYoLXRp+l8GdDQ4OShZTEj3rNAKkmcuHCBZW8kkwmmZ2dVcctLy8Ti8XYs2ePyi5cWVkB1krHNzc3K4+lu7ubmZkZ5ufnlcd09913MzY2RiQSoVQqKTnKxcVF5ufnmZqa4md+5mc4fvw4Rt29bXR2fpn5+Q/R2/tlFhbez/Dwl7Banfh8f8uBAweYnJykWjUKGoTDv8TQUJy9ew1Wyfj4OKOjXQSD7yWV+l28Xi/BYJBUKoXH4yEUCinBnpWVFUKhEBMTEwwODrK4uEhra6vCzCUJQgJYW7duxeVyqdqKY2Njihvd1NSkrimfz6tKOo2NjTzyyCNcu3aN8fFxVScxFAqp1HK5f/feey8PPfSQSm4BiEQiKkV527ZtSt3QarWqHU9ra6uCq6ampnjmmWe4du2aSmy66667FDTm8/l48MEHFcZZreq154YGdbFYUq995ztPYbXaGBv7KQqFNPH4r9Pe/iTLyx+go+PLpFJb8HpPEY/Hee9738v169eV8VxYuJ/duz/L4cN5ymWNQOAaW7f+F+LxHfj952hqakLTNEVVO3bsGCMjI4ryKZ5cLBZjaWlJMZTEoxNxKyn0ICJLUo1827ZthEIh5ubmmJmZwefzqQCfw+FQKfeFQgGPx6OEt4SyKguDsD6EGWTemcpfi8WiKgcJP9+MDVssFlXf0oAPe/jt3z7Frl15NM3Hnj1xPvWps4yO+ti5c+Vfx5C8wSbjWwK49dnPt3sOkTcwZzrWp6e/0fa2MdT1q4y5iK0MOGlirEVBTOrPyYAzd7LdbmfHjh2Mj4+rSuI+n09F3KV+odDp2tvbVYaX3+8nFAoBa4P4/PnzZDIZUqkU3d3d6vsPHDjAP/7jP+Lz+XA6nWzevJnnnntORfePHj3KY489xgsvvIDXe42xsd9hePg/095+me7uG5w69dscOvTf6enpVcEHKaX1/PNPMDCwlXLZQ6VSIZfL8ZGPbMdmm+WP/zhNKBTixo0bajLNzc2xb98+RkZGuH79Ovv37yedTjM+Pk5PT8+6IInNZmN5eVlpaszPz3Pffffx/PPP09nZqUSopM8l+FMoFEin04p7LboouVwOTdMUY0OONyfD1AdVqtUqO3bsACAcDlOpVFhdXcXn8xEOh1WlHflMT08PH/3oR6lWq3zmM59RrBRJDpKsQPlOo6qKhtUqY0KrefBV8nljcWtvH+PChf9Ef/9/wO8/h9f7KpOT/5Vdu4zzHzhwQNXLlICzpn21VjeyV3nOyeSrDA3N4PNt4+mnn6alpUXdB5fLxYULF2hra1O65SLJa6SgR3G73bXK7zZFEzP0rstcuHBBwUH33XcfExMTXL16lY6ODvx+P5VKRSWINTY20tnZqeR9pTix9I2kfJtlO42AbuM6b1dYDP39/SqPYWJiAl3XlRCawDNNTU1kMhmeeOIJrl27xsxMCwsL29B1nWKxyAMPPMDOnf5acHm9R11vFM2LxesZzPpElfpWz182B7vN5xC2U72+tfmzNzunwGmSmCb9K/fQ/Nk3ire/LQy12UibCfdmb0yMsWBvokMtCSfCo67nL4o30NPTQ7lcVrQ1UQ5LJBJK0H3fvn28+uqr9PT00NnZqQzZiRMnqFQqHDx4UIkdSUHRrq4udN0oBebz+SiXyywsLBCLxZAUa1GAO3HiBF6vl/n57WzZYhhpQ7TpCvv3f45sdiel0lXOnTvHwYMHFfwSCoUYHR1l+/btClv0eDxKwKetrY1sNksqlapxWJ9jbm6OaDTK3Nyc6lO/34/NZmNsbIwHH3yQ48ePqwxPn8+n1OH279/Pj370I5UYJEkuoqmRzxtQgNDufD6fkv0EVDaebLvFEzYbajO7RASfpHKMHFMsFlVCiWytwdiip9NpdF3nk5/8JJFIhH/+539meXlZpZjXjy/5W5/oIFl1yeQW+vs/ic93FovFSlfXKF7vZ4jHN9HTM4bf7+f8+fNKi7y/v58LFy4oiQARaWpsbGTfvn2kUin8fr+qRwkoiEBEoIR+2d3dzdjYmPLIOjo61vVDNpuloaGBTCajighrmlGvsru7WxmDQqHA4OAg5XKZYDDIwYMHyefznDlzBrfbrbxlCciLwL3gsuKJ18+fpqYmVbx2fHyczs5ORkdH0XUdn8+nxkg6naa9vZ3f+I3f4NSpUxw9epRwOKwSv/41goJ32mQsmA21OC7y3p0mu5h/lxQVfisDifA2MtTmh6ZpJg5xVXlyBp5aMCnJrW0DZWU3NzEIYtwDgYBietjtdqanp/F4PGSzWdra2piZmWH37t1kMhkWFxfp7+9ncnKS/v5+XC4X165dI5vNquIEDoeDdDrNli1buHLlCvF4nL6+PnRdr+lGtyqGydatW0mlUkxMTLB161MsLy8DmtK/7e+fpFodR9ctvOMd7yCfzxOLxXC5XIyMjBCPx7l8+bLia3/rW9/i4MGD+P1+FTz0er1UKhX27t2rII5gMKiw+fHxcVpbWxkcHOT73/++kngVnQmPx0MymeT06dM0NTWpmpKwVtxB+lKMTltbG8KDB9YJ2ci2W+6h4K8bZYUZRQN0VbXd5XIRj8fp6OhQnGxzBqvsHpaXDbzz8ccf54UXXiAWiymet1zXRl6b/C7RZtG0f6zpUrTUkn2seDyv0tZ2iUrFzo9//GMsFosqSLt161a2b9/OSy+9xPnz55VolNz3xsZGdu/eTT6fp729XelpNzc3Mzo6qnICYrGY0oYR+O7EiROMjIwo3rRkyUoGbW9vr4KXJOFKYBQRyxKo48CBA4pKKucyyw+I0qLAj+ZdqczF2dlZldG7srJCW1sb4XAYMCrMixMl3no2m2V4eJje3l6eeeYZRV+9meGSRfutNuS3iysLVGGm/sGdeb/yGXEqzOJVb0V7Wxjq+px42ZYJywPWBxJkdZZIrVlYfi0YYjTxsEV3A4zJOT09TalUYmBggNHRURXNn5iYYHV1lXQ6jd/vV1iz1EzcsmWL8m6j0ahKQIjFYnR1dTE4OEhDQwNTU1MMDQ2RTCYJBALrRGsEYxc2gBg/0WOQGo1btmxRRXEPHDjAyZMn2b17N9euXaNSqZBIJOjq6lIaH3fffTezs7NYrVYOHjxILBYjk8lw+PBhrly5AqCMm8/nU9H/7u5uLl++TKlUwu/3qwXCzF9dy34sq/JTDocDj8ejqlYLVdGMf5onYKlUUoZdmqQvy30NhUJMT0+rHZPZmzx69G42b06wbduSmhDnzvmZmQly4MARHnjgAWVszLrC9RxWszcfCoXIZDJK10XuR6FQUGqDuq7T39+vUrHFyxXZ05MnTwLQ1NTE0NAQ//AP/8BP//RP097ezqlTp/B4PAwODioVvrvuuovV1VUWFxfRNI2zZ88yNDREsVhU6eZXrlxhdXWV7du3c/bsWXw+H5OTkwwPD6sYS2dn5zpDC0aClzgE3/3udwkEAqysrKjjZAxKv5sXVk3TlEE136NcLqcWCYGZwuGwEvqShBthbZXLZaanp6lUKuzevZuVlRW1+JpjE/W7aPOcNbe3Moi40bklJiZ/b/e7Njpedo9mB+GtaG+LFHIzTUZWdvE0pSPMtfdgzXOz2+1KK2F1dVWtYvIQoy4Ys0yE7du309rayszMDL29vWpb6XK56O7uVip+IqSzb98+SqUSMzMzKqAiOgrj4+OMjY2pCuMSbFlcXMRmszE/P69gHJ/PR3NzM/39/QwMDOB2u2lrayMUCuHxeNi6davyQo8ePUomk8Hj8bC4uMjWrVvJ5/M4HA62b9+uIByPx8Pw8DCnTp1C0zRVc3JkZITW1lbFOmhoMBJKRKTK6/XyyCOPKIWzoaEhOjs7cblcSuhfZFwlOAWojEypP1koFNZVrZEtu/n+iZ60UMzMMIQMaoGURMJ0cXGRb37zmzz11FOUSiWWltx86Uvv4/r1TgqFAqOjXXzjGx8kGvURjUaZn59XvHXJhLwVPmjOmjRDNm1tbYoJJNt6KdEm2335rVJqbfv27czMzKjdlzgNmqbR1dWlFhdhAg0ODtLd3c2DDz5INBqlWq0qCESE/kVWd2VlhYaGBuVJS6xB8gmKxaK6B2I8FxcXicViameazWaV1ysQhxkCkBwGczBf04zkFFmYJEBaKpX48Y9/rBhQf/Znj/L889vU2Jibm+O7393KX/7le2htbVWLinme32nA7lbtjRhz8yIl3Of6917v+8TQi0duphfLrv+taG8Lj7o+yipYmRhbWDMOgimKZrHB9XWqNGrBh8Sjk88LDcnj8VAoFHC73Wzbto0zZ86gaRr9/f3Kg1uqMfoFEw+Hw/T29iqP0u/3c+3aNfbt28fcnCHDLVhcMBhkYmKC3t5eZmZmWFpaolQqce7cOcUSqVaNiuNSCmllZUVRAefm5shms0QiEZxOJ6dPn6a9vV3BM+Pj42zevJmLFy9SrVbVRJXqH/39/czOziooaHV1lUQioaqRTE9PMzs7S19fH52dnUxNTbGwsEBTU5MKqIjhlSCL9J9AGYbY0hrdy2KxqMouYryk/0SfRYJWG+kpVCoV5alKpqjH42Hz5s3ouq7iCdu3X2J0dC9f/OK72bt3kbNnO9F1aGqKE4nsZ3lZw+t113RI2qlUyuh6BSOD0tAqqVbLVCploEqlUmZxsYfOzg6giNudw+lsJJ1O4nA00dDgxCimW2VmZpp43E2h4OTs2TjQj9vdjK430Ny8jcbGXmZncxQKPgYHdzE1NUlLyyaKRTd7997Pq6+eoVQq4HI1k83msNu9BAKdLC4u4HY30dzsJ502ypg1NDSorNrOzk48Hg8vv2zUKhwaGsJut6vFQnB8gQalDyVeIHNERMjEixaDJPdMKruYjakYItlFAapYgt1uJ5PJEIlEaGlpYXBwnG9+8yGcTifvetc1Tp36CY4efS933/0NotEoHo8HWI//mnfQd0rB2+g48zXfytaYsxxFK0XOcbMAp/l7zM2MR6/lG6DuiQQY3wibRNrbzlCLFwzGD5Uq2bDeSMvnAOVVi5C6eG/miLCmGSL7EhUXKcKdO3cyNTXFqVOn1PviTUrQyul0qsEk1YalXFOpVFIJEXa7XRnulZUVwuEwuq7z0EMPEY/HlU52Op0mHo8TDAbVteXzeeWtAWzZsgWn08nCwgIvvfQSHR0dzM7O4vF4uHLlClarld7eXlX6S4JCS0tL3HPPPRSLRWKxGPfddx+ZTEbhq0tLSwwMDBCLxVTh3paWFuVNSn/J7zVjwmAsmDKxxQCUy2WVfWmWrDVjnvVYXf0uKhAIqArzUoGmXC6zbds2ent7sdvtNDenSKf/kmee+U1OnuxX5zpx4vE3Mfp++daHbNAuXrz1MUeOAPxHzpx57Xs//OHNP3ftmtnQrDc6x46tSQXUvwdrKfnyntkmGPNFr33Ha8+hafC979lr92Ttc9XqrrpzmesNmmGsIk88sZ/vfGcnmYydhx56ln37XiWXKyipYoEFbtZux4i9WRhEDLSZAiwGvp6eeLuLhln9U2IcZqrlm21vC0NtFj6p97gqlQput1v9aIE5zJ0pW/P5+XlVOUU4vOIFSjKLUGjEkItou5D3y+UysViM5eXldRXJY7EYfX19FAoFpSMCRiDs9OnTqg5he3s7kUiEeDxOpVLhZ3/2ZxkfH8flcrFjxw4uXbpEW1sbPp8Pt9uteL+aZqS9C0bY1dWlqlVLFiVAT08PdrudSCRCNBplaWlJGdK+vj7FbR4ZGaFcLhOJRFQqvQSR+vr6CAaDxONx1QciPyl9JYbf6XSqbVwmk1H4tZRtEqqeUArFm5b6fdIKhcJreKqwFswaGhoilUqRzWZpbGxUVXOGhobUdzU3NzM87OfZZyURporfH0XTsmrMWCxWJTAkc0xX0qbmwLXxPB5P4HDYKRZLNYjNWbs+KcdkUPt0XXRODL0RGbPGJDW+V9MM1UDjf0stUWs99GIcp2G12lTQaY2uZqnVBLWtw9ElWUgSj+Q8a2JC69P6JYNTvkt+q5GIhPouOV5eAw2Hw4ZkjEorl3V1Hrmetb8W9b/NVqFY1MlknHR2jrFjx4tomlPdPzOssHYdrzXMZtZJfTO/drtp3ubPyj0TSqIYZ7EvZuN8O4a6/phKpUJ7e7va5WwEvW0U0LxVe1sYatleS9BKvDHx0CQzUQIakqklXpowKzweD6lUimQyqapPyDnNRl5WOTPdS65BrqdYLKris5lMhkAgwPj4OB6Ph3w+r4z26OgoLS0tKuNxfn6eUCjEiRMnuPfee9W1C+dUsPZUKqWcH/NAAAAgAElEQVQqmkvSiGgKA4rBkslkGBwc5LHHHuPJJ59kfn5ereBidKUCSyaTUdKVAm9MTEwoWEbkMy0WC0tLS3R3dysDLkUTRABL4A0RhJfttOCn5mzQarWq2ARS0V0Gr+yABFbaiKal6zper1dJdco9aWtrY2FhgUQiQTAYZGVlJ3/5l/fhclXYvPkprlx5hGzWwzve8UV27Yoq+mEgEMDv9ytYRR4bCf586UtfUgHRQqFAT0/Pui2r8MdF39ntdjM1NUVPTw+Dg4McPXqUarXKpk2bVDV2CeiurKyouMSePXu4fPmygqMOHDjA3Nwc/TVG0fT0NL29vXzzm9+ktbWVbdu2EQ6H6ejoYGFhgfb2dhYWFhTDorW1lenpaYB1zksymVQ0wUAgoCDCRCKhYiZerxev16sSl2SH5HK5+PjHP66KNcvcOnHiBAMDA1QqFUWFvXTpkspslFjGt789yFe/uhef7xLz89s5ceJeDh48Sn9/v/Km76SZMeCN2vrF7M5bPQuofoHY6LXbuRbZ+d8KgrmT9rYIJgpXGtbrfshqJ/iZGFgxvuaCpxL8k9L1ks5tZhwIF1iEnKRqhpRbAlTgUsrxyJZG4BXxlBcWFlQa7ubNm+nq6iIUCmGxWDhz5owKKE5MTDAzM8Py8jIej4fdu3crrZKdO3eye/du9u/fz/T0zzM/P8zQ0BDbtm2ricZv5qmntiqjcfDgQVZWVpSRnZn5RVZXjRqQfr+fcDjM4uJWfvzjw6p816FDh9i1a5cKhEr9umAwqPDFWCxGW1ub0uqW6uoSLAVj8cpkMkq/WGIAsvBJBqfARII7C9Qkqc3iccuCIQ9Jt5+ammJsbIxYLEYymeRrX/sazz77LKdOneKll7rRNJ0PfvAb7N79JL/8y/8M6GSz7yMUCqlFwxzbkHazieHz+RS7x2q1sri4SLVaJRwO09TUpBZOl8ulJAek1Nby8jIf+MAHsNvt3Lhxg7a2NhYXF9X3b9q0SXHeb9y4QUtLC9lslu7ubmZnZ2lpaeHChQu88sor6LquFk1NMzjRra2tKttUeNk+n08teuIEFAoFtSil0+l1Ox5pski5XC7cbrcK1ktcwiyFUM/GEBgtlUqxtLSkKJHPPvss3/72t5mcnORb3zKM9P33P81HPvJ17rrr6xw//rOEwz/3usb2rWJz3GmrHx/12HR9lvPrNfO4u9PF6Hbb28KjFnaAeTsMrPN8pRMEW5KMQGF0SCKKBC2kbplUH4e1wIpsjUWRT9O0dXoFwiG+evUqPT09BINBrFajtJTAErOzs1y/fh2AHTt2KMMuBWpF91mMmhjHyclJlpeX6e7uZnFxkXg8XhOJ8vCd7/wW5fJ/Y2hokkhkO8eP/wa7d3+GJ554iQceeIBt27bVtutxUqkUTU3XefLJX+Xw4Xmmpy+RTO7j7Nnf4oMf/A5TUxFyuRzhcJhMJqM84dbWVuVpt7W1EY/HlZeWTCax2+20trYqKEN46gsLC8oAiDGWeyV9J55YtVolnU6vW3TFM5V7Zd6GCi1SGBiCf0uK+NzcHO9+97upVLJ8+MPfI58/RltbNz5fhF//9ecIh0OKiQDrq0JLu1mA6P7772dqakot7Hv37qWrq4v5+XmSySSRyIfIZK7w3ve6mZ6exmq1Mj09yPi4n0cfPc+mTZsYGRlhfHycc+fOqVJo0kci5JTNZolGozQ0NHD27FlCoRB2u51Dhw5RKBSYmZlhdHQUi8VCa2sriURCaUU3NjYqAzwzM6OMeGtrq9r9iF63BG/N8QbpG4fDoTjm4qAACpYQo23+HBj0vEuXLqnAm4ibTU5Okkwm2bVrF1eudPDTP/0yW7Ycw+dr40MfitHXd4KLF3s4cODouh1w/X2oh0DM370R/PBm6XoyJsWhMMNYsBbcvNl3m6+/fjzVB0vNgdI3094WhhrW3ywB9M0THYwfLh6deMvSCZIUA4b3E4vFFO3MHPgSVoOZFwxrQTKBQ0KhELlcjrm5OSXA73K5uOuuuxgbGyOVSikRGwCPx6Mml9/vZ35+nmKxyJ49e8jn86TTac6cOYPD4aCtrY10Os2DDz7IhQsXGBwcpLV1hUzmizz//B9w+nSBUsmFx5NkaurfUyj8O65e1fH5PBiaw1UqlQJWaxWPZ4kf/ehTuFwJ8nk/fv8sL764F13fTiaTwOWy4nTaqFYLVCo52tt9HD+eobHxnVgsJbLZBA0NNgqFNG73Xeh6gdVVD+l0HE0zxPXT6RiZzCClkoNq1UmpZMXQVK5QLudJJPxUq3nyeT/lMlQqBUolH8VijkpFR9Ps2GxgtdrIZq0Y6ncVHA4Nm82i7oX87e7u5tq1a4p/LLDV3Xe/xLVr12hqaqKxsRGr1crIyAIjIwuk0xXFQ5ZxczsT5MUXXyQQCFAoFOjr62NlZYXl5WUVvR8eTvPcc/+Z5uYvEAwWmJ8f5vz5X6S9/aNcvXqVlpYWtm3bRmdnJ9/97nfp6+tjYWGBQCDA8ePvpLOzjWTyiOLxl0rvoFTaRDj8eRoaGnj11VeJx+PKSIZCIeX1rqys0NraqlTxxNuX3aPQUSXuEo1G1c7PzK+WXY/ZgIuTAmsQoOxgzYucQI3CurLb7Yo9EggEapm1Pj784WdqapR+RXH8yZ+8wr33niQWK77GgzXP+43av7anvRGkUb843Ooaboahm/vvzUAz5va2MNRtbW1qcAmWVQ/oyyASGMLslcGasIqu60p0RqQg+/r61GAUor9wSoWaI4VuBfuVQgVdXV1Uq4Z8aW9vL+FwmEgkQrlcZteuXUQiEdLptPJWJbU6HA7z2GOPsbq6qrDLnTt3Mjc3x7Zt27h06RLhcJidO3cSiURobGxk794Yo6MxJibaaW+PEAgsUShU0XUL5TLkchnsdhdG4YAGSiWj1JXVmieXawMgFhsgFrt5X9dIKf8qbWLi1se88EL9KzqadrD2V4JgOoacqgjs65w/bwjra5qOw2HBELUHq9VQedP1MrpewmbTaGhwYLeDxVI1PYyFxWqtomnl2kJTZmlphMnJBLpeJBJx0dTkolTK4nRqNDTYyOdTtLQ8xUsv/TYDA0eZnr6LnTv/gOHhCmNjBn2yv79fQSiyEDudTnp6FvnBDz7Czp1xvN5TpNMHuHDh/+CBB/6ahoZurl69itVqVcUpJGnL5XIpfHp+fp5KpaJYOiL4JY6JBOBjMUOQXzxXMzQlXqMwayQOIXNHAukCe5gNljhKspN1OBxEIhEqlQoDAwM0NzezuLioFtCGhgbF/BHW1kZY80ZGbiMv+vWMZf2CcqvjzceaF3TpQ3Og+1awh/n7zL/NzCQxv1//O+90IXpbGGqhxZmNrvxAwa/FiAvWKdtts/ckuDWgAivRaJTm5mZFexOvS/L7xdsWT0ICj2LsRcFteXmZqakpAGWYJycncbvdTE5O0tXVRTAYVFjn/v37FRf22rVrlMtlBgYGaG9v5+jRoyogubi4SLFYpFqt8swzq4TDTu666wUuXXoH+/b9PW73STZt2kQ8HmdoaEippElg58SJRmKx/0hX1wssLd3Dnj1PUS6fQ9PsuFxunE43YKWzs4+FhQiNjU0kEmncbh/Vqo1KxUq1aiOZzGK1NlIua4CdQqFKLlchlyvVXrPR2hrCbm+kVNKpVCxUKlAoGMe4XB7y+RKVilFcQNctVKsWNM2GUazAeN7Q4KFapfbQag9hKwijwnjtZjKlJjLJW9B2qWfLy69/5NjYT6JpZa5c+QxXrlSAMlarzsWLRex2C/B+bDawWHR0vYzFUsFmi3P69Gfp7b3M4uIABw58Dl1/FZerRcVKstmsYsXk83k6OjrQNI1Lly7R2tqqKI7iwaZSKRUYLpVKrKysqAruEtir9+o8Hg99fX34fD6uXbumHB2zgp55LslcrFQq3HvvvapQr9mYymfF0Jmfy2dFC0cYLvK6tI0M+M0Cd28W8pBzbNTkd8sicye0OlnMJO4ir4k9MR93O9eyUbttQ61pmhU4Bczpuv5eTdMGgH8CWoHTwK/oul7UNM0J/AOwH4gCv6Dr+tQtzr1uFTNnFNrtdgV1yDZMmlmkyfxZSdGWJBhJLnG73apzvF6vwlEFQ5XIt3xfZ2enwk0l6CYVx202G319fSrwk8vllGBSqVSio6ODRCKhqqJbLBZGR0ex2+0qQCVUN4vFwpUrQa5d+xibN/8ud99to6HhOEeOfIx77y2QSMypBcXlchGJRLBYLJw96+P8+U/x0ENfpLn5DK++auHChZ9i376L9PbeqEE9jxIOd9De/n0qlQWVTCKDUfotHo+j67rSppifn2d5eVnhqjabjYMHD6rFS7LsRkdHiUaj7NmzR6nLJRIJBS9JernT6SQQCPCRj3xknSdoNgj1UfZyWXYTOpWKRrlMbZGAcpl1/5dKeg120SiVqlQqGsVilUoFisUqpZL+mv/LZZ1EIkOhUKVc1ohG0/j97TQ2+mrPg0xPz5HLdbCwsA+/f5Z4vJv+/hto2hL5fBm3u4nV1SL5fBmvt5lisYrd3kC1aqVQKFOpFLDbrUxP72bz5n+ip2eMhYU8k5OT+P1+lTzS1dVFKpWis7OTlZUVEokEu3btUlBHuVxWVcYlgzObzaqycJINKpmFpVJJ1evM5XKqcK7MF0AFtWXeCJRSTyOTebERlizzyZxAIu+ZpSHMyU3mOV/f6qEDs414K9pG12/2ogVeNafl36pt5FWb++WtaHfiUX8MuAqI8tF/A/5U1/V/0jTtr4FfA/6q9jeu6/omTdN+sXbcL9zq5GYsGtZnLZlXKTMbxDwo5H+BL0SkXyo5h8Nhenp6lMEDlPCTruuKxiT0OTGIHo+HcrmsKk+Xy2WVbi03RKhQS0tL3Lhxg/b2dlWJplgscvjw4XVR83e9613E43Fu3LhBR0cHNpuN0dFmPvjB7xAKebl69Sp+/zTve98/Uqk8RD7/NzidTnU98/PzNV2NR3nnO/8HXV0TOBwt7N59lXD4HpaWHqGzM8z8/Ajnzv0yDz/8/5BOpxkYGGBubo5isagwz46ODiwWC36/n6mpKZWYIIp2wh4QtkY2m123cEqQMZlMKnlS2QWJaJDslswBrvpW73kY0JPsctZvI80JCmYDYH5N4g31lDzZjlerhq72hQsXVBEJl8vCyMhebDYbIyOOWlZqjvn5TzM09FHe855GZmc38e1v/zJbtvwthw8bZdk2bdrE1NSUWnikoPDy8jLl8k9w/vx/Ynj464yNvR+Pp0Qg8BWamppqei17SaWGCQZfIpFIsLq6SiAQIBgMroMcxAGQoJ9BV1xR2h3iEYtmuyyEAou0t7crI2lOdxZNF/NOVO6B+f96NoM52Gb+jFzz6+G9G+l9bGQPpJkN35ttZofuZjbHDIvU/1bzOczXpGmaiqWYr3kj2dQ3sjO4LUOtaVo38B7gM8DHNeNKH2QtrevLwH/BMNTvrz0H+AbwF5qmafotrkw6TG6y0OnEGAukYOZXCxvEdJ3ruIwS6MvlcmSzWebm5ujr61ODWiaAJGZIqqwwR4RHKzdGJpHId7a0tBCNRtV1Nzc309nZSSwWUzuBV155hR07dhAMBhkcHOTixYuq6KXAJcVikYceOk0ymSQcNnDAw4cPk05HaW5+lWeeMYSZksmkEg8aHBxkZOSlGvujiVQqxcjIAtHo53j55d9lcvIB1S/PPPPxWv8YRUwFA5aCpoL9Qrn2vEq5XMQosGpgw1YrvPyykXptGM4q1WqJQuG92Gxgt1twOGxUq0XK5aL6jHG8js2m0djoJB7vR9N0rFaQ4qnm58b/OlL81WJBXbP8lefm180PKdaq6+srjksVcinGWq2WmJ1tIZsdoFBYxWKpsrzcpK6pocGOpt1DZ+fv4fGMEYlsIZl8kW3bpsnl9qFp31O4sZTnWl5eVkUfnM7HOHPmN/mJn/i/CQQu4nTmOHv2V9iyJcvw8LMsLIxw9erH2L//c6TTaarVqqJJirazy+VSvHSv10ssFsNisSjFyNXVVTwej8oClUVIxncqlVKKetPT0ySTSfL5/Lq5I4vYnQT86r1Qec38fv05NjJWGx0nbAzz+/VGsf5zd2r4zMbW7PCZ0+vvtJmhj/prfbM7gtv1qP8M+A+AlI9uBRK6MfoBwkBX7XkXMAug63pZ07Rk7fh1ZR00Tfu3wL8FlKKarPJyo6RJR5pxNVnBJAgiUW3BsMV7ampqWscDnZycJBgMrquEbdahcDqdxONx2traVBKNphkp3uVymZ6eHhWMbGpqUrQnQHnkIn967Ngx0uk0V69epb29XUmsHjt2TBU0kGoq1WqVF198kVgsxqOPPqr0Nq5cuYKu65w8eVJd8+bNmxXsI1CK1Wrl4sWLhEKNdHWdYmbmMMHgZXp6xnA4GkgmM3g8TQoqcLnc5HIFDEaGk2pVo1zWKRbL5HIlisUKYODMFosNXbdSKlWxWu01/FmjVKoADnTdga67KBSMOnsG/mzBKPklw8xCImEnHvdQqWjrMOpKxfz8rZW6vHX7lXX/raVWm9sHmJ3lNango6P/OwDPP29U+zaCmpVagFOnUrGhaSVeeeXDalGx29OMjv47IpGfZHW1hfvv/x90dc2Tzxv3+8aNG0rLQwS0BM4Qgf5sNqsSgcQjlsQTKYAhjKZoNKp2hE6nU1E1RZ0QWIfJ3gwj3ih4Vm9o6w1ofXCuPmV7o8/I+/XNbNjrjevNjHT9tZhfM9NLzXQ8gU2TyaT6jlsZ23pn8a0IHta3WxpqTdPeC0R0XT+tadr9b+rbTE3X9b8B/gZg8+bNunSEGFrRkIC1jhAvWxJcTNf4GsxTziUYm1DkRFxG8LmGhoZ1OKCu67jdbjUxBFLJZrO0t7cTi8XU1igcDtPV1aV4rOLNBINBvvvd7+J2u+nq6lIc15WVFbq7u3n00Uc5evSo4jLPz8+jaUZB3sOHDytND3l9ZGQEp9PJuXPn6OvrU0ppgtfncjmam5tpbm5mdnYTi4tb2br1G0xMPMqePU+zbdsSuq7T0NBAJBJRhVYdDgeTk5N4vV78fj/FYpHl5WWWl5fXbQOFeijBWikoMD09rZTupKpIMplU/SiJLgJHdXd38+EPf3jdBDCNB1MQSlcG3PyQxcRYDNaeSxFWA7tee83Aqg2s28CkBauu1HDtKuPjUywsROjo6GZ2dp5QqAtNsylaYTqdpVisUChUCIW6aGryk88blcKTyQzz84t0dHSzuLiC1erA6/VhVPU2ArClUhWwYLM5KRTKVKsaxeJWVlYG2Lr1G1itR9H1oMpalTEmsYKlpSWlWCcQn6Zp6xQChZInzor0qwQnxQCZoSezKJZw30VX540alXp8ud4bvhOvciOP+3YM8u1e+0bHyfULXGfeTd/qXGbDb0YB4K3B12/Ho74XeFzTtHcDLgyM+s8Bn6ZptppX3Q0I8WsO6AHCmhHyb8YIKt601eM45qwggUHMwSYxrGa+tWCS5gEoD5fLhdfrVUE/SeYQcXUxKA6HQyVmSBR9bm6OpqYmxY/O5XK43W5Vj1A8evP3u91ulYzTX0t+WF1d5fLly0xNTaHXgqKSvqtpmko3jsfjpNNppUeyc+dOnE6nkpl0Op2EQiG1U5ifn8fpdNZE4e/h1Vc/xL33/gmHDmU5efIqP/rRb1Iuf4Ht2yNks1n6+vqYmZlRfdfZ2UkulyMWi1GpVIjH4ypQZc4qFJlU6VvZYUjmnZkSBqj7I31TH0l/vcFrtWo1uOS1E7beq6oPWJnx6fqHbPHNVU3S6VcIBKrMzLxAMGijv3+ApqYmLBYLbW1tjI2NqeLG9977DjXWrl+/jtutoWkLNW7/nJKJlV2VpmksLy+rMTE1NUUud5hz5/bS3/8VJiZ+ir6+CXI5w1UfHh4mHo9TLBZpaWmhVCrR3t7O7Ows7e3tuFyudZrS4qCIjo0kL8l7hUJBzS3xvOW+S2WebDa7brtfj8verG30vhmjvh045Haw2npP+mbe9EbfZ76uepzZPBbFoTPDP5JvcavfLE2g2Hpc+q3C1m+ZQq7r+qd0Xe/Wdb0f+EXgiK7rHwR+APxs7bAPAd+uPf9O7X9q7x+5FT5t1tmQrYgkoJhFy82dLJNNqlYASrtAvDgRGbJarTQ3N9PW1rZusMfjcTKZjBJdEjGldDpNNputBYPKyjinUimCwaCKojscDhYXF5WUqUTaGxsblR7w5cuXVbmvvr4+MpkMly5dUkyTubk5IpEI7e3tOBwOTp06RSKRwOv1cvjwYeLxOKOjo6yurlIqlVRAMpVKMT8/T1tbG4lEglwux8xMkEce+VsCgYuMjo6ya1eUBx/8Iun0CN3d3QQCAcAQdsrn80qUv1Qq0dzcTCQSQdeNQrKwVifSrB3R3t6uApBSMLitrU0Fq+Qeif6EwDP12926Mfaa/8UI1Qd15O9G29qN3nu979B1XYlBPfroo+RyObXzyufzzMzMKJXDrq4uYrEYi4uLjI2NATA0NMShQ4fWyRMAKltQziNVYarV+zh37lPs3/85HnjgB+zb9185cuTfsby8Q2VDSo3FcDisGDOSZ7CwsEAkElGJVdK3DQ0NiokkyTKCW3s8Htra2lSAUoLC5t2r5DDUwx4bebX1/beREX09I73R340W3ddbnOtfu9k93uhaN3pNxqrwzaWZv2uj7zCPT7NHbX5/ozH/Roz3m+FRfxL4J03T/hA4C/xd7fW/A76iadoYEMMw7rdswrOUbYT8MMnAamhoUJoGkj5u7mhzB8s2UIIu4olKVRWZVOYgSqlUUtKa4onI9r1QKCgvVoza+Pj4uqCN8LrlWkTFr1wuMz8/r4R6AoEA/f39XLt2jWKxSHd3N42NjUxMTNDR0cHBgweJRqNYLBYlqNTd3a2YGrIYra6u0tPTo7a1uq4zOPgNAPz+AMPDw0xOTtLXN8G99xYZG5tG13WVRizZk+Lpzs7OKraM1H0TPFTuhSQBFYtFcrkcuq4r9gKguLaAYilIZQ9zMsXNBmo9Y+BWzTyRX48ZsJGxkde+9a1vMTQ0pEpNiaZ5IpFQFC1xJJaWllRC1I4dO1hdXaWvr48f/ehHakdhTtMWp0Fom62tP8njj3+VbPYs2ayX/v5Jurq+yuXL3WzaNMvQ0JDa0fX09CjZW5vNRjwex+PxqJ2PiHg5HA5V/NblctHY2Kgq00h5NVlMBbc2ZylKUWMJhN3Kk5Z2u5DARsa2vpmNc/1nN3rv9c6/0bXd6jt1fS1fQ76znhGyUTMnyUgTfXBzXsjt9unrtTsy1Lqu/xD4Ye35BHBog2PywM/dyXnNKeKwnkBeKBQIBAJKS0PgCUDhSLquq224PJf3xXgI5U7gjkgkorxgMa71mszy12KxqGK1UmMvlUpRLpfVIDezUmSCSjWZ1tZWZmdnSafTNDc3E4vF6O3tVZS2VCrFwYMHqVarnD9/nsnJSXbs2KEWi0AgUPOQd6mitR6Ph1KpRCKRUP2Ty+UU6yQajSqpURHjX1xcpLGxkVQqRXNzM01NTep3rq6urqNvSfanUMIAVQAgnU6rfjVXc9E0TWGoTqeTXC6nIBrzglI/uOu9OfN79YGljSbwrTw+82vmc+q6Tk9PjxLMkn4UgyzJUcK2SKfTdHR0sHPnTlVp5eLFi9jtdqLRKKurq6RSKZxOJ7FYjI6ODgCi0Sitra1YrX9CpVKlr6+PeDxOIBAglzvLfffZCIU6WVxcJJ/P09bWpnYjmUwGq9UoE5dMJgGUqJJw/oU6au5XCbxLQNycLi73ydzHG7E36ueleX5uhEXf7J6Y36+nVt7sPBvdz/p7u9G13Yy3XG90zdchGjFmjrc4cDcbW+YgZP01ij242cJR33+3094W6nnmQgHyv3SkrPpmrFG0XqWSixT3FE9ZElnE+4vFYusGh8PhoLOzUwUtBdMUmp549pIQIuWWpEaiVBwX+ENKFcm5xHhqmqayFgUf9/v9TE5OcvbsWbq6ulheXlaVMs6ePUs4HGbr1q0sLi6Sy+Xo6upicnJSJR3Y7XaVeCMBJ8GIJYXXarUSi8UIh8MEg0Hm5+dVjUMR+C8WixQKBRYXF8lkMor/KZxaCUQJq0WMrWREAirtHtYWRXkuf8VLTqfT+Hy+m25nb+YN18Me9c28HTa/Vn/um22tH374Ye666y4FoXk8Hvr7++no6MDv96v4xN69e9m9e7dSKRwdHVVQkcgHyD2OxWL09PQoHnljYyO6rquiFnNzc2zfvp2VlRVVp3Jubg7RXl9eXkbEwbLZrNrxSYk2M7tIdnISAJfvEh683+9XBYUBZZTEOXE4HMqjrDco9f31em0jSKL+UX/PNooviHNifn+je3gro7/R9b3eIiIOhtnGmJln5uPlODmnGG2bzaZKn8l5bqfvbqe9LQy1+ceYtx3mjjdnKwLKmxNNAXmIwRFDHQwGlUSnaPG63W6cTqfySuU7zRojgv3JVre5uZnV1VXF8JC6hOl0WmHe4pWI9y/ne+qpp9i7dy+B/4+6N4+S5KzuBX8Rue9ZVZlVldW1dPVaLfXeUqMNkJAQmOUgjrEZDFj24cB4Gb/x4dnHWM8IAwb7YcY+z7xnz5jx8xjGYANGSIwRyE8gIWvtVV29d3Xte1ZlZuW+RMQ3f0T+vvoyOqsXIeu1v3PyZFVkZGTEt9zv3t+993cTCYyOjqKnpwddXV0YHR1FPp/HgQMHMD8/j71792JkZEQypDEzcmZmBh0dHTLsipsKHVW8D/5eLBaTBW9zuRyi0WgLRSyxWMbUMsxR02yuiEAggHK53EKByX6mAK/X69i0aZPU6jh5OVbEbelPoEbNPmq3qK4mcJ2vjUzJdovQeT11UX/5y1/G0aNHsXfvXgwPD0u8P5vNotFooK+vD1u3bsXs7CyOHz+OTCaDWq0mSZY6Ozvhdruxf/9+CLFOVSqEQDqdxsDAAPr7+wHYCVbRaMFsCsIAACAASURBVBShUAjnzp3D5s2bUa1Wkc1m5Uanznsu9rW1Nenc5uc8X42E4rzjHLThlq6WhCUVS6XQph9GzT5s5x9Q+8/Znxv1vbpZqoLLaTW1E7xX22CvJgA3+kydX+r8UWUO56gaZ95uA2s3p+igVgtmbCTsnf11rXZTCOp2TfXKsiNUTyxNC2q9NP3UyUzcjR5xat4AZOFamin8PbUqDIU4TVAhbBrTvr4+DA0NIZfLSQInmpQUgsQR+fmRI0dw//33IxQKYWFhAYcPH5aLluFY1IT4PDt37pTPyBC/zs5OWJYlU74ty0K5XIbb7ZZmdzQaRXd3N0KhkISKSMjv8/lQKBQkRk8tmMKPz8oiATS7VV5qVmGhRaGS8BD2oEMYWGc2pNNGhUjaCdx2C9Ep4DfS0tR3p/YOXKn5/e3f/i1cLhcef/xxmKaJ97///U2mvjvxwAMPoLu7G2fOnMHc3Bz6+vowODiIXC4nS6XNzc0hlUpJIVssFuUGyP5n4VpuBIyNHh0dlY5dFQ/nnGQ9TNajZKw/BQgJwBjhoY4RCz8Q3rIsS/4Ox0OF6Uh169wINxKoqvBV+7+dsG03bu3Od77UKArn9a4mxJ3z6Gpzy6mxq+dtdI2rNSGE7MuN+vO1aNg3BSkT05bVxk4zDAORSEQKUmKmzMhSMVLy6tILzjhohjgtLCxgYGBAdhQ1Ipq9Kt0mHUN0TNLDfunSJWiaJovoZrNZqe2Td5gFQLmgqtUqcrkcnnjiCezcuROvvPIKHn/8cVm0dWFhAW9/+9tRKpUwNjYGwzCwZ88e/OAHP0A0GpVsbNVqFb29vTh58qSs2kEMmtmU09PT6O7uxsTEBHbu3CnvLZlMyk2EWjXDvFwulwzDq1QqMsrF6/XKcwiXZDIZlEolDA8PS74PcnsT8uCGSgiLziua4O084KqW61wk7TRhft7OVHYKAedCVK/3la98RSZATU1NIZfLyfNZPCAQCGBwcBCLi4vN9P4OlMtlSba1e/du+bxMhrp8+TL27t0rf4eQF/lh6CfI5XLS8UpzmoxzdGyr9815WCwWZeYiTXUWNCiVSlhZWcHw8HBLFi4FOec2Qz65WbMMnDoGan+1W5/8eyNh64Q0nGN1tXFzzg9nVIbzPq7WOOec961eW4VgN+oHvrebwy6XSzILxmIxef3XI476ptCo23U243DZhFgvP8UdixoaJzOFJXE3asSBQACxWAz1el1qJ2zhcFiWd1edJ8Sm1ThgXj+bzWJ2dlbyifDeVJxbvR+a/9lsVgosl8sli9Ded999MtWXRDwMu6MlUCqVpGDv7u6W9KnLy8uwLEtWYk8kErKmI5OGWOKK5jPZB7lBEb4htiaEkCGRxKyr1ap0oLGUEy0JRsmwn6gpU9umkL8W3twuzM6pVaufX+37Tg3GKSx47OzZs5ibm5Mx77TMNE3D1q1bUalUcN9992F1dRWVSgWLi4vYt2+fLMnmdruxurqKfD6PZDIJ8p2oUFAgEJBQFDf4QqEgObUZDdTR0QFg3bGr8pPQMqQW7PV6EQwGJQ5OaCObzcrIEWY0AusVXpxCn/OaTvkbbc5xcgrqjc5ttxE4NXA+O8+5EWhso3to911V81Uhwmv1Bz9XYVNmFNPCcp670f/XajeFoHZSCrKT2AE0qTVNa2HPU2ECaiU07wBINjAhhOT5TafTLaQ8dNJt2rRJhmHRzOZiAdaxPXIwEH7g+VxArFlYrVZbvMK81tjYGBYXFzEyMiKdjs899xzK5XKzeOtOJJNJPPPMM1hbW0OxWEQ8Hke5XIbP50M+n2/RJBltwHvgQqV1wOIJ7BM+I3FLbkZ0LlIoaJomIRVufCrWT8HHEDy+cxwotKkBblSNnOMNbBxupS5e5/ls17tQ1VhvAHjPe96DcrmMdDqN3t5ezM3NySiOS5cuIRwO46mnnkKhUJD8LpVKBUtLS3C5XJIBTwjRjOKw+WHK5TJKpRLq9ToCgYDkSp+YmJBWF7VdEoc5Q8QY5aE6rjgfVUe2EEKWCmOoHmE3augU+kzQ4sbM6BE1JLZdY1+q96K+vvnNARw7Fm0Zm5MnO/Dtb2/ecIzaWT1Ogaxq0ht991rj3u6Yqslzs6SwdWr1G11DbU4YT02AUa/xWttNIaipjaqTkQ9NrU+lNKUQoOYAoAWeUFNqV1dXpfbQ1dUFt9uNxcXFFlw7EAjA6/XKYp2M+aXw4WQBIJ13rLnIAebkByDxaQ66eo9jY2OSqWxhYQE9PT3o7OzE+fPnIYSQAiCbzUoaVC6yXC6HYrEoeUMsy8Ly8rIsakshy9hmJqrQ0chFztA6ZxoxEyV0XZeWATXDWq0Gr9eLhYUFRCKRFm2NY8gsT9ULTstGtXCuhUs7TU91YxJC4NvfHsbJkx3yc8uyMDqawGOPbbsu7Ur9PxwOSx7xH//4x4jH4zLp5b777sMDDzyAzZs3S1y/r68PL7/8srQmotEoarUaOjo64PP5EI/HZUFkZpsyOon1FhcXFyUFKZ2zLHZMi5AbGkn+qYQQU6Z1RUyb/dPR0SGJmzgXOX9p5ZDmlj4czot2HMyWBTQaQK2moVQCSiUX8nkXMhkdS0vA4qKO+XkXOjtL+Oxn9+Cpp3qwtOTBiRNxfOEL+7BtW+6K6Id2Qle9z3aCeyPs+3pbu/nEe1Hf2U8bhea1swpVq4Wyy2nB/KztpsCoVY3ZsiwJK+RyOenoYAcSogiFQigUCtJcZKgdtWHVTMxms4hGowiHw4jFYqhWq5icnJQFUemA5OTmbqgKFb7rui7Tbuv1ukwMsSxLmqQsUkBIwEk0dejQIcm6J4RAsVjE5s2b8cILLyCfz0utrNFooFAoSCyYxXwTiYTkLalUKpiYmMD27dvh9/tbElkAu4p5b28vlpaWrliIfE4+l/qMxPzJk9JoNJBOpxEMBhGPx6UGR6HPkD41XZvXp5NMhT+csdSqJsXFRKzWqUFv25bFH//xQfzu7x7F7t1pvPpqJ/7sz27HJz/5MkzT5vdoNAQaDR2Nhi1o6nWg0bCJpWo1T5OT2sLXv/7/4WMf+wT++Z9/CMvS0Nu7H9PTc/D7Q1hYGMboaBalUgqp1Edw4cIYVld3Ynp6FgsLAXg82zE6GoDfH0Qk0oFsNo98fgjlchWlUgjFYgShUAwulxe67kG5XIemuaDr9u9PTwdgWRrsogoe1OsmbJ4Qm/TKlBwnAOCBrVfZBFcTEzFks+8GSbMsy+bttlkbdLjdfhw7Zh9fL9BgX1MIF4TQmr/tkr8HuPCTn/ihEmXZnCXX3/70T/cgFJqFacbwK7/yPXR3F1GpRFpCXrlhq/CGKoApsDfS3l+rZqp+zxk+Rwve+Xs3knzF76u8QTz+s2jTwE0iqJ0ZUSxMS2FDTZWhSLquy9heNQtI7RAKWVVQMOuO2GI6nZZmKcP62oUFAusFdqkhFQqFK+6Rg1OpVKRGrRKz8N6Ygj4zM4NqtYotW7bg+efvRnd3P4R4XEaAVKt34vz5O7F58ynU63UsLS3Jz4jVM/tsfn4eAwMDMrRO3XRUOIgLQy3MwAlGOIjaHiEcPtva2hqGhoZkgVtqHvl8XsYAqwJf5dZQLSLg6jBHOzNYjS7YtWsRn/zky/jsZ++GZQlYlgu6buGzn33zDQsW4H/Hl78MAB8DADTrFQMAfvrTK89eWLjBy1+12Yx7V9K0ksLVpmwlbat6rmF4UatpsIdZwLIasGtcVhEMeuH1VuDx6DDNBjTNgE3JY5csM806hLApbVnCzDQb8PncuOeeO+Fy2SXObDpaC6SqVSlmeU8q3Sxg4tvfNjA3dzs07Y/w1a/+If7mb2ylKh6Po7+/Hz09Peju7kZ/f7+03jjvOE82cjC2g7wASEuS7Wq+EDV6qN21r+UHUZtTkJPThRZtu9C867luu3ZTCGriuRSqNOPVbCo2VQDRrCddY7v4a+5u1OZomjM9OhQKScY3NTFAJQDn+RRqFECMOKGzzLLWQwgZFsfB5IsbBMPlgsEgLly4gMHB/finf/pl3HbbEizrB6hW70Q2+18xOPg7LeYtn9Xr9baEcQWDwZbfMwxDJr/wWdjUzYz/c9Kyj4h/8pxsNouOjg4Z7cFNslKpyOICrKTDZlkWIpGIDGNUhbF6DjcRtZ/aadLqArv11mVs3XoZFy7sAPAiPJ6X4PO5EItFEI+HEY2GEY9H4PO5oesWTLMBclJbVgO6bnNUv/zyizCMGjweHT09CVSrJWzePIBSKY9oNITp6Qn096cQj0dw/PgRVKtF5PNZhMMB3HrrCAqFHOLxCHRdIJ/PIhDwYm0tg5mZSfh8brjdGnp7k0ilulGvl+B267A5uxvweNzSB8ANjeuAznJqldRIKdD279+Pl156CaFQSEaH0PoaGRmRjHuM8rEsS55DCgLOx1qthlwuh2Qyid/+7YGWeVKpVJpFKjTJL6JClerr1Vc7kU7vhtv9xzCM/xW6/hx0/VnpDJ+dnZXja5qmhGF6enqQTCbR2dkpyac439slnnA+cJ5Q0XDKiKsJbBVeo9xRk36cWnW7udvOOiTmz8QXZ3utmvVNIahVAanruszMAlphCBXrpVbpDMoH0CIUSNFJJyR3d36X8Mnc3BxWVlak95y/Tc1TnQiMKyYmrmK4wHqW05YtW3Dp0qWWwfH5fNi2bRsKhQLm5+eRz+fh9/sxPDyJO+/8Gp599hH4fO9FpXIL/P5vIZtNYWFhBKZZQq2Wh9drobe3A4XCIoJBHW63jkajgHRaQNfrGBpyw+eLYW1tWWLD+Xy+ZWKqmDv7zYmnsQ+r1SrS6TRqtRp27Nghy5kRZydslM1mW2ASAJIfnOPK8eN4maaJ1dXVFoY5wjvqptvO9B0dTWB6ug+6/gVY1idgGI/CNJ9BuQwsLraWRNI0DfF4HPF4HD09PRgcHERHRweCwSDOnfu+jFl3u2dRKpWQSMwilbI3nJGRTszNHcf4+AQ8nhIajTI8niIaDR1dXRGEQlXoerapEOSaWHQB4bBdIisQCCCbLSIcNpo8HHEsLeWbm7rdHyrNgTo2vHdanISCADuShJs2BWomk8G+ffukL4bKDseXKf3kklGtTiFEy9zndfP5PKampqSDktYUHcW0oEZHE/jSlw5i375HMTr6FwB+gkbj7+F2fxi6/uwVwo7Y+NraGnK5nFwn6nhTGYtEIohGo+jq6kIkEpEQphpowHmlrtV2CStOXNw016vXl8vllqSfjXBqZ1OVQzXE9/XCp4GbRFA7zYeVlZYaA7JYJyckPejEpIlhq+F8nIAcbC6GarWKSCQiSW6YgRYMBlEsFiWUwqbyfwCQk58hdnQ6qhAMY5Knp6flb1EL6evrw+XLlxEOh7FlyxYUi0UYhoFLly5h//6t+MlPPCiXbwMAVCq/gkqlvQnerk1MAC+9tP6/rjfgcjXgchnQ9TpcrgZ0vdH822j+bf9vv+zzl5biSKf3AaiiXi+gXF6F1wvMzg5jZUWDrtfhdhvQtDp0vQagDiGiqFZDqNXWYJolaFodHo8GXV93rKhJLkIIaRGsrq7KMSBFLDdBNZuRgmt0NIE///M34e1v/z/xwx9+DobxE5jmN6HrH4KuPyvHX+VtoUCYnp7GK6+8IrXU7u5udHR0IB6PY3Z2VvpF8vk8hoeHceTIfYjHL8EwLkmlodG4B43GfqysPA+/3y+hNApabtj0P5CBkOGK3MjUpqYwU5tUzXFn8ocavbG2tibnWDRqV8pTHbcejwfValWWnlOdj6pwVO9L/YwkXOTAptOYlpTP58O5c0P45Cdfxo9//FJz7j0Ll+uXYFm3wSbaXL+u2hitpPaB+vvUxhcWFlrmAMmnOjs7kUwmJaUDaV6BVu4ONicfEK9JQa1a59dqnJdqBi43O2fUiIp/v5Z2UwhqNobfra6uyg4lFspJyckKQMINhDOoZTMSg9EiwLqDjLHAJF2hwA0GgzJ2mDsxJym1Z05mhlWppE8kaKImygK2AFruYWZmBtu3bwcAzM3NIRAIoKOjAyMjI/jpT9fgducRjX4D2ez/glTqEej6UfT3b0My2Y9qVaBSAbzeCMplC5FIAlNTS8jlqjAMF4LBTnR09KJSEfB6I00MMwiXK4hKRcCuOu6FXXncC9P0otEIwjQ9ME1385gHc3M+NBqHIEQrZPJa8Vldr0HTajh/XsNf/qUXXq8Fr9eCx2NBiK0wjIPQtDpcrjrcbhM+n4DXC/j9QCCgwecT8Ps1eL0CPp+F0dEk3v72ceRyLpjmgwAq0LTPQ4j3QYhl2BtHFZpWh6bVAFThdrdGm6ihWYZhYHR0VEYRUcBVKhV0dY3jySc/hv37l2GaT6JUOoxc7q8QiXxM8nOzGoumaZKfI5VKSapeRpHQb+H3++UG3U7ba9fUqCOeR0iP6f4dHR0tsARg+0PC4bDcAMj5wmQXPisTZ9Tf52+oeG6lUpEZk2Tri0QiuPtueyNSE4Y07Rlo2jMbPpNzA3aep647pz+D2ZeLi4stAjAajSISiSAej0soJRKJSDiFY8/+VCPH1Oe+Gmyinsd3daPlbzjP+1naTSWoOakZ30vNWhXCxNQY/8zEjVgsdoXjjjgx+SsYscFJy2tSIJM7hNfgb3MAuABURxojH9xuN7Zs2YL+/n4cPXpUYsgMteLA9ff3S4Idsuil02mcPp3E0aO/i7e+9b8hnf4WNO0ZLCz8FRKJ30SlcgGBABAIAC5XHoODg1hdXUUymcThw114/vkTWFhYQKlUwr5990oiKMDeJJLJZEvMN/tEhRKIGeq6jv379+PJJ59EoVBCJlNCKNSJgYHt6O4eRDDYgUpFoFoV0PUgLMuDtbUahPAimy1jba0OIWxHl88XQ7UqYJoeNBo6UqnN6OvbgnpdQ72uo1YD8nkLtZob9boPhuGCYbhhGG6Ypv23ZXlgWRvVsLsFwG+1HDHN32p/KkwAVQA1ADUpwJeXBTweCz4f4PGYqNXWMD9vwjRLWF72IRz2IJkcxZEjjyIcPoBC4W2Ixz8BTXsOk5OD2L17t/SpkLxL13UZ50xri8RX3OQ5F8mE53RMcT2wqWxtjLum5l8ulxGJRJBKpeQ4ci2pNT7dbrckD6NznZtFOzNdjYxQQ+dM0ybmymazMjqISWUMh3VuOiqu7GztBBnvSeUmUQW6mpjEvrIsS1Zmn5uba7kWv0uOH4aYqqGkTBLjc7eL+Xdek3/zpUY/bdQHr6XdVIIagEzoUB+Sk4FaK4Uquar5PwUwhT2AFjIbAJJcibHCDKmjucoB5/dVLYOsZTTX6Oygo2Z1dVXyZrOp52uahvHxcdx7773w+/2Ym5vDwMAAJicnUavtxTvf+beoVGwNJBh8GcCvo9E4iEbjtAyB8/v9so9cLheef/55DA0NYXl5WdZPLBaLyGQySCQS8n440VWcWsVAVXxvdXW1aSk04PNZGB7uALCCzs44gkFDaooqK1w4HMb58+cRj9dlQQKV+bBWq+Hw4cP4pV+y6yFT4ExMTGBqakrGC/NFAizb8vEiEIgjGOyA1xuFxxOGrgfxox89gxMnzkEIHwD7JYQPmhaApvlhFyTyNz9T3/0AvAD80PUY3O4oGg0vGg0/gH6UyxoAP8pl2+oQwrY01tZ+AbHYXyAWO45GIyAzR6PRqIQGgsEgKpUKuru7EY1GZaFjNRY+FovB7/dLi4+QA9P92zXCeBSKDE+lxsuiAKojltejlsdUf9W/otKhqtg4m+pQVteHiqeXy2UUCgXJWeO8xvW0dhuV83O2dtruRlq58zu02Bkey88YSUYaAOfmpQp7Z2O/GYaBeDzegrM77/O1tptKUAshkM1mZcepwec02zg5yC1BzFjNCAyFQlKIhsNhrK2toaenR8IWxLy7urpkxAmTV6ixq+xixKH5NxszvlTHBwsKcMBVM5Fm76lTpzA4OCiFd7lcxn33HUGhUMCZMza3sC3ojsGyjqBUguTfINERYC/sZDKJ06dPS0cRCf2JiXZ0dEh2PVoLTqHN52JyETefcrmMVCrVsthpffBaPE7zW/V0O2Oq1Th39RxnOJ76bjt7iiiX81hbm0c4HEY8Hm86Ll+Cpp0HoD7H+vM4X87x0zQNAwMD2LNnj6QX0DQN/f39yOVy2LJlC1KpFF54wYcnn/wVhMP/L/L5DyMaPQbgaclAyALINLGpOQ8NDWHPnj149dVXZWJSMBhEqVRCJBJpUT5UrJbKgZMtktofnXCMmEkmk4hGo1I412o1qbxwM9A0TfpgOOepXdfrdQnbOdejczzaOeMICfJ1NQjn9W5qZJBznNvdQzsNV/V/qcqLep66BtRGCFSFVdqFEV5rI7pWuykyE9nIPsY4XgASIwYgtWj+78SfaUKqZENMj15ZWZEeXhWbVFOb+VKxat4H44tVKIRaCAdraWlJThA1G49CWo0NJW45NzfX4tyh4OfzcYGura1JyIcYPCdmX19fixAmzJHL5bCysiK1WpWLmE46PiPrNPL3SWpFrZmChKGCvA9gneSHCUDOSVmv11vOV81VPrczKkCFmnjvTPWemZnB3Nxcix9AbRuZ1+1M0UajIavtdHZ2oqurC4uLi1hZWcHo6Ci++c1F/PCHv4p77vkKenv/EonEb2Ju7s9Qq90Fl8uFXC4naU+LxSJisViLU46WWyQSQblcRrlcRiaTkUoD+1fFnnlvqjCkMCdvCzdTYtPsR0bacI7we2oYIK0ddV6qDlBVY3UKHo6Ns195r+r8bdfaCXF1/Ntpzur/zntT71GNvHCe63yp13P+9tXuw6nNO53Wqn/LyR30s7SbTlAzvrhUKkEI0ZLCTeFHs4RQADP4yJynaoxCCKRSKUn8r6Z5Aq1x0gBaPuM74RMODoUkJyUXBSM/yAHsdrtlyA+fj1EnpmlieXkZqVQKXV1dGB8fl157RrLQFGOKMbUoVYjt3btXFlVtNBqyRBlLNrHqCOGQ5eVlLC0toVCww8fS6bQMX+REp9YeiUQArE80Rs0QiiIBDaMhuGFSKLO4ASMEnJq8uqE5X6pGzXM5B9bW1rCwsIBCofAza2/BYBDJZBLFYhFerxeXL19GIBDA8PAwtm/fjlptL97xjv8O4CewLAuBwEsIhz+GWm2vJLfiHCBrIxUCADhy5AhisRg0TWuB1xYWFiT9aSAQkP3P+ahqdG63XRyDxF20IguFApLJZIvmTY2Zigud3VRgKMQ4Fzk+ZEtUBRGdlaqCocKRnIuqc26jJA9na7dxtvu8XWu3kVzrejf6+87n3eh7tPavR8j/LO2mEtQAZMUKapMUdtQ6VR4EYL0AKwmRyFrFXZLYdSgUQr1elzzOlUpFaiVOli51c2DiAbAeSkSBwWQCatXUeCjgeJ9cOJZlYWBgANlsFt3d3RJLHxsbg2maMqOJi0jVYDRNa+Gg5mQoFAqSeJ4k/uxHhoapMA4bYQrnQrMsm8iJG4Xq/FILIqg1JhlhQ62abHzU1OjcYuw1+5nP1k7T5XPyntppTypBl9quRwDw82q1ioWFBSQSCdTrdezevRudnZ245ZZbcOHCBdxzzwuIRI7KBBGPx4POzlcRj/+19HEQhqNzT/VrbNmyRfJ5MP6YZGG5XA7Ly8uyJBuhPVVY0/Jy0p2qVYw4110uVwstLS0YYq7kESfEQ9It3jstSVUbVZ2JHAs1tE19V+cR18vVGsfB6ZhzntPue2pT79d57GcRlE6lYqPP6YBU5xaVknZO2tfSbjpBTQ2NkAc1YDr9OBEpTEkOxEw5ChYKaQqZzs5O6LouTU9en4KcGjC1WApj56SkY4z3w8VAiEUIgenpaSnIhRASw/J6vejp6QEAnDp1CtFoFGfOnEFvby8eeuihFq8ztXwKTNM0pTbFcKtGoyExcSYjqBo+PfPsFza1xh4bNShi1IxaYP9RaFiW1cK+Ri83+U+c/CsMgyQpkbog2wlqHnNqSU4B6xQKV9OQria0ATTjgM/BMAycOnUKhmHg4sWLMIx1LnS1cC+ZBjmuy8vLcp4QVlDN8HA4LJ+HkSG0WpgtyGID6XQay8vLyGQysk4jk4hUzJ8wk5qgwfnJvqcvgXOBpdjY//Qb0JHG4xxzdXzU/nL2dbtN9rW2jTbt69WYb/T813p/zr5QrVy+Xi9tGrjJBDUxajpAVFObWrAqHFWaTgBYWVmRApgaF3k3qAGqcAC1IVVrdabqqto0NVNq7W63W/KHUJhls1m5CKnV8HuGYeDIkSMYGRmRcdtutxuFQgGPPfYYLjSJJoQQsigC+Yt9Pp+ELzgJCAExPKyjo6MlBIwmM2vzqTt9qVS6gnmN76RAdWrTdAYCkGPD0DPTtLMMWcyWRQ0YXcANRBX8TkGgCuerCWsV/rnedjUhTq1uYWEBuq5jdXUV8XgcmzdvxpkzZ5DNZtHV1QUALTzQ1Er5rADk5sxIjN7eXnR3d0uFghpwLpeTMB8dhNzU1Gen38XZN4ANTalRO3Qusn/pzAYg/TSM5ybPObBegUcV0qofxtl/zvvZCCa4kbG52nip80ONVmp3vmp1vV5Cmn27WeErV7FoyisVCuX33lDoQ9O0SU3TRjVNO6lp2tHmsU5N0/5F07RLzfeO5nFN07S/0DRtTNO0U5qmHbyRG8pms1KzA1qD7jVtnYWNkR+ED1RhSLpIahQMGSJHL/k/aD5yYbBxwlNIM7qDJr+6MFQSGcAWYMlkElu2bJGLUqUgBYALFy5g69atyOVymJ2dlabvyMiIXGBra2tyE2H0Bk1rCt1qtYo9e/bglltukRprLBZrcZBqzYgVQkoArtBuVS2gVqtJ6Ib9wOM0j1X82LLsJIiZmRlEo1HJoaKSPrFf6IzlZ6rQUdvVNCo+g+q0utpiuJoQ4IIvlUrwer2477775DnRaBSXL19GpVLB9u3bW8LmCEOo4YSM+Ve1qqmpKRQKhSZc0ikVAVWQ5nI5GRXDPmF/0w/ixI2ZvxfnMwAAIABJREFUGctNkH1Arhc1eoTrIZ/PY25uDh0dHS3jp0YrtHNoqpuGs0/baZbX6vtrjdFG19zo92/kd15rY/8PDw/j8OHD2Lx5c4uFqhKy/Vto8cCNadT3CSH2CyFua/7/KQBPCyG2A3i6+T8A/ByA7c3XJwD81fVcnJ3BsDKnQFQFIRs1ECaqGIaBbDbbknLOv0OhkNROVR5eJ77M61OLVCtrE1vkglO5PjjpKYzpgNQ0TWKzXIxutxtHjx5FPB7HwMCAPM5oDdVrzbC/TCYDADJxgp7mpaUlzMzMyAXt8/nQ1dXVIrxULmNaCnxWOuSA9cVKy0PdvLj5qem+DJmcn5+HZVno7u5uGVMmE9G6CYVCLZ87tR/nJFdxaafAbkd481qaZVm4/fbboWkaTpw4gWq1iu3bt8sixG9961uxfft2CWvRYmMSFaEfhr4NDQ3J69JaKhaLknyIc4fvDLVzOrmBdaeu2gcUCIzmUPuCzI68LgVtoVDA4uIient7ZSghsWluBuTy4L2rpny7MVPH5/UQUBtBFuoccc6H1xNauFbjegwEAhgYGMChQ4dw6623Ih6PIxAIXKE9qw7e16P9LHHU7wNwb/PvvwPwDIDfax7/mrB7+iVN0+KapqWEEFdNQBZCSHx0fn6+5cHVrCQ1JI/N5/NJPo98Po+VlRXJQ8AYZmqEZHNThXJHR4fUPBlTSkcZsUFuCmTRUwdCxXxp4tJDr9ZcpObf3d2NW2+9FfPz88jlcqhWq3jb294mIRk1VLBer0sYZHFxEbVaDdPT0+jq6pJww44dO5DJZOQEj0aj0jnGRi2cx+i4pBAHbCiJtd7IN81sNsZ2UwjQRzA1NQVWJCcmTmZBv98v6/gVCgX09va2LDo1yQJYXwzX0swoWDb6/EYaBRnranLcC4UCBgcHceHCBeTzeTne6lwjxsskJL6rGy2TTDhniCnzeemIpcPbGUFD/wL7nZYZI0U4J9XKPeyPRqOBUqmEmZkZuN1udHR0IJfLIZPJSOydxZJpSfH6FPLODdFpgbaDQNq1duN6tfFzns/17xTWV7vG69kYxiqEkGszmUxKSEy1dpi/wf5X7019rhu55+sV1ALAU5qmCQD/lxDirwH0KMJ3EUBP8+9NAGaU7842j20oqGliZzKZFs2W+BwnuBDrQekAWsy+aDQqWeKKxaKc9Crk4HK5ZEYXFwm9/iS5YRYjNXHGWlODUQWb6mQD1rmfV1ZWpHZCxyM1L8MwMDk5ie7ubsn3sG/fPoyPj2NpaUmmpFNY6rqOW265BZcvX24xhYkXNxoNnDlzRloGjLQIh8PSHKfWT42f/ciFqJq/nJB8XgoLYvKqBbKysoJKpYItW7YgkUjg7NmzACDDvahJCyFk6CBbO8gJwBWL0KlZ8f+fVaNWrzM2NoaRkRHccccdOHr0KF599VWEw2FcvnxZVqrn7zNahhaVy+VCPB6XqcvLy8sSHrrrrruk8D9z5gy8Xi9isZgU9Oq90GGu4sQM66QVBEDCYKrPhRunOtYUKHbWaw233HKLfF4VguEGXqvVrrB4VP8B15AaLumEKAhPOiEpVft1/n2t8VGvz2v/W8ELV2sqLMWmWphCiBb4CgCSyaSsWk+fBPHsG7UIrldQ3yOEmNM0rRvAv2h2OphsQgjRFOLX3TRN+wRsaASJRAIAJGGMiq1yEjM0T42uoGCihkGyG1ZIoWBwLvp4PC4dbABaojMoXDjx6bFnwgsbtRxGXLCmIX+HWiU3Hi4+n88nK4lzVz5//rxMjeeC4Ll0cjHawjAMrKysyI2IzkBVS7UsS/YH8Wk+D/sQgHRmMjEoEom0EEup+LrKg2JZdpr5q6++ip07dyIUCkmtkfh3vV5HV1eXrGsJoEUQqIu7OR9a4oc3MoXZnPHu7Rbu1RaDUwBNTU0hn89LiIv9lc/nMTQ0hHQ6fYUfg9o146FrtRq6urokfDcxMYFqtYrx8fEWJ3hvb2/LYmU/cL5bliWpSjlHuUFSoDMckn8LIVq0aT5TOp3Gjh07ZMw3AJlkwwxeavC0LNk/6rzihn0jcIcTCuAzOAV5u/FybgLOY+r7G9HU5Dc29hs3VxUBoEIUDAYRDAaRSqWktl0sFmXa/cJ1Mp1dF0YthJhrvi8DeAzAYQBLmqalAKD5vtw8fQ7AgPL1/uYx5zX/WghxmxDiNiYE0IHHTuEOrTpvAFyxcwO2NhuNRiX2zIwxpweW5msoFJKCnjshBTQXL3E8ALKTWYWbmgsn74EDB3D77bdjZGRE4oB0iKqbD2BDLQcOHEAgEJAUn+95z3skPSrvi3GvxA+p2RNKUMlfAGDXrl0YGhqSVbLJ3asKWJrZ0WhURq2QfpNRMDTZ1tbW5KZIE71arWJxcRFHjhxBKpVCT08PTNOUUQRckBQC9DeQDpP3qo6nikMCVxag5fkUHvz/OufuFf+rx5hgFI/HZULUPffcg56eHrjdbmzduhWpVKqlSjc3N0JC1GxrtRrGx8fl/JydnUW9Xse2bdtaHI9erxe9vb1SKyfURquFTms11p3nJBIJxONxOafU1GY10zCfz+Ps2bMYGhqScB+zW4H1iCT+Fn9D7SPObdWSUcfJ2a9Ox7Da1+3i5dude73a8hslpNtBMGprB/eoG5BqITFiKJlMYmBgACMjI9d9H9cU1JqmhTRNi/BvAA8COA3gCQAPN097GMDjzb+fAPDLmt3uALAmroFPsxWLRSkUaNo7Y2VVgiWad2oIGzHGYrGIdDoNABLWYKe5XOvVo9mpFFYq1kShTyFJrYep1pz05MOgWTMwMCAhE4ZH0VFnmiZOnDiBV155BYCdsBKLxfDyyy+jVqvJBB4WONA0TcIwfLZarYZarSajFQCbbIr4KB17JGXq7OxsIXkXQqBcLiMQCCAcDqOrqwuxWEymf6tV2rmYQ6EQarUalpeXceHCBdx5553YvXt3C2zCSByXy4VwOCxDANnn1OS5GFXoQ40RVrW4dguBY3S97WoCgKGVAwMD2LVrF9xuN8LhMBYWFhAIBJBMJlEoFKTWzMbnHhwcRDgclvODVg03x3K5jPHxcfmMxWJR8jkT2qjX69JxTYc1OWkYKsnCw9So6c9ggg0VkFqthoWFBbz44ou46667MDw8LDdLRkKR+xtYL57LZ1L7yJkIplpB7Rx87XBj5yZ8rea8djsN/o3UpHn/qvKo3hPv+Vr3qApsrgU1cuRa7Xqgjx4AjzUFmhvAN4QQP9Q07QiAb2ma9jEAUwB+sXn+DwC8C8AYgDKAX72eG9E0TZroKqxBc0/FmukkpDbIRAxOViZfFItF5PN5dHV1SSFPvFUIO1aZgoQxxRRk/H0uDGrX1AoJZ7jdboRCIVk1fMeOHZLdrlQqSeHPTEFqsnRiMXIiFovJKInt27fjwoULCAQCUquOx+NYWFiQpjm/T8FeqVQQi8UwOTmJYDCIaDSKmZkZxONxmZiiCkXDMLC8vIyOjg4ptNWwLnXTpBY/NzeHSqWCQ4cOwev1yg2LfUeNTtfXSa4Y2kirRcUZ1fRxOs+A9low0KpFOxd+O4hDvU47XJT3UKvVcOzYMSnUxsfHZRHhkydPyudQFQQ1skjFKpklyirh3FAY9REOh1Eul3H58mV0dXUhm83KBBoKBTUKQ1UmVKxcdca63W4ZUplOpzExMYFbb70VPp9PJklxPrNmKCOA1OxZdSPlvTsz7jYSlBRk6rp19v1Gx9u16xXs/9aN85Xzk+9quKYKwbZ7JhWiA64OyW3UrimohRDjAPa1Ob4K4P42xwWA37yhu2g2NQSq3WQgJszFQQ1O7QiSNlFzyGQyMimFn6uYN9nMVIJyrRm9wcVAs5T4NQA5WJZlYXJyUprOjAbweDzo6OhAOBzGxYsXW5x5JCnyer3Subm0tIRNmzZh8+bNcLvdOHfuXAuFqxo3THpXRrGQUAmAjP4YHx+XGLfX65VRHuxnWgOrq6sywoPVcwBIBkFi2lNTUwiFQti6dat0qtFioDAAIImcmEZOTa4dHtkuYmAjDNSJX7Zj4tuotTtHva7H40FPTw8uX76M7u5uXLx4EYODgxgfH5fWkhMTL5fLsqwVNWTCXSrHCZnqEomEZIas1WoYHh7G5cuXEQwG5cbN8eFGSuiO40BBzegojhG192w2i9XVVezcuVOWtyP1Lal9hRDo7OyUKfHEwEmdykaLpt34tNMsr1drdo4j+/Nmau3uR01wAVrnjxpGrEaobXSt19JuGppTJg2oIVsqHqkKSMIIQCvBP2A7SsLhMFZXVyUeG4/H5e+oYVL8vtfrRblcRrFYhKatJ2kA65sDtRy1GoTK6ZvJZBAMBjE5OQnA1kYoPEOhkIyFFkJgeHgYmqYhnU6jXC4jFAqhr69PatSvvvqqDJuya/m5pTaUTCYxOjraUjqss7MT4XAYx48fRzQalZq2ivXGYjGpVS8tLbXEQ/N5WV+SWmAoFEKj0UA6nUYqlWqxBhjKuLa2BiEEAoGAfN6lpSU5aRlz3NnZ2TLeHFNnhIfqEL2aBsf5cL2LfSOBTtKs5eVlPPjgg8hms5ifn8fk5CQ6Ojpw11134emnn0Y4HJZROi6XS/op4vE4KpUKfD4fyuWyxJbp7GNoaL1eRzQalZuuz+eDz+dr2UBpxRAbXltbQ6FQaMm+ZYIXHea0IqenpxEMBrFz5045NxkaRiyda2R1dVXG+c/NfRjR6AUEgy9LZeXEiThGR/3Yt+90iwC+mqDmfatNHZONcF7nOW8krHEjTbUYgNYyX+ozqJvVRtbDaxHiN0UKOQeH+JxqStIBRu5cdsRGO7iu6zI7jxj37OxsS/FZapQ0/fx+v8SsVZxOFdDErNX4bYZFUbAQW9+9ezeGhoYk78Pu3btbqBABYHZ2VkZEhMNhTE1N4fLlyxLyIK/2pUuXsLKygkwmg5WVFVy6dAkDAwOwLJvgidmWt99+O3p7e2UGIvuRz0DtKhQKIZFIIBKJoLOzU4bRNRoNLC8vw+/3o7OzE4XCr2NhYURGA/h8Pqyu7sXFiw9JGIBaPeEPVqymJWBbG7+A2dntEioCgGPHovjmNwfaCuRradeqWa7On+td5M5r9fb2Ss3/6aefxsWLF2WfBYNBvPjiiy2FF4j1U/jm83mpTXNzBCC5OyzLklEwbrcbJ06cgN/vl2FzxKppfTF7k9o0rRfOV3KOE46LRCKSb727u1vG6xPSY86AYdh8LIx2ojM8Gr2Ac+f+EOn0bmiahmPHovhP/2kbdu7Mt/gLOB5qJIg6n9W+fa3tZhXSwHo4qfNFxdKZrLQRnv1a202hUXNXInuYeoyNQpacuty9p6Y+iEjkPBKJUQB2B5VKh1GvfwC6/mWYpk05OTk5id7eXiSTyRbuZtWZqIY7qeC/6tRUtWr+LcQ6Kcv999+P06dPY2pqCsPDw5iamsLo6M9h+/bN2Lv3IsbGxjA3N4di8XZUq3swNPSPSKfTePDBBzE/P4/Tp08jk8lA13UZF8774KaSTqeRTCZx+fJlVKtVdHZ24vz58/j5n/959Pf34/jx4xJ/J5bMPuVmp+L06XRaktpTe3O7T+DixS8iHv8SYrHTSKd34+jR/w2HDn1JEgtRuyQ8UygUZISEz+dDo9FAPD6Gs2e/gJ6evwAAHD0awac/vQOPPPKqFATq4ldNbns8BUxTwDR1WBbke6Phh2VFIYQGITQALgAuCKHD1j90aJqreVxvnqPL//k6dy6MSKQfuu5Cva6hXHYhFIqiUtmKiYkAurqSWFsrolgso6+vH/l8EdnsGnTdg1qtgWAwjFAogmw2j2q13kzXDqBU8kDThuDx+FCvNzA2ZsDl8qJWuxWTkxq83gMAdJgmoGn2MxmGBcMA6nUD+TxgmgJutxdutw+AfU6tVofb7YPXG4BpClSrdfh8ASwseGCaAj5fALWaAV33QAig0TChaS5Ylt1H9boBr9cPIbTmMQ0eTxbj43+Fz3xmBTMzXfj85y9i9+4sTp40WsaFY+NMl3ZurDfabmYBzcbAAq5FVUkk1BEKhSR9ghpmy7WmKnk32m4KQc0H7enpaTHLLMtCsfgbcLvPo14/K+GKbHY/isVd2Lz5W4jHL2F09DPYvfvzSCZPY23tIM6ceQS7dv0h4vGtqNVqSKfTaDQassp0IpFAR0eHTDtngg0xazWjUfXil0oliUdSq2RdRk3TkEqlsLS0hFqthvvvvx+JRAJzc3PYu7eKH//4tzE8/Cns3TuElZU9OH36k3jXu/4OXm8Kfr8fly5dwszMDHK5HLxeL7Zu3YpMJiMx+D179kiN7lvf+hb8fj8eeughfPe738U999yDpaUlfPe738U73vEOhMNhaY6rUTNqZAaxd2KfFNi5XA6NRgPd3Rlo2qM4evQL6Ox8AZnM3di+/a+RSNQwO4vm4hdoNMoolWwI5vDhw4hEIlhZWUEul2tO2JPYufMreOaZP8C991qoVFwIhw184Qt7YBi3wjQFLEuTAnf9pQrX65lD13fM2VZX7df1tCaqdc3WJHhEM+joBpoJTRPQNAuABTs1wf5fCKN5DND11nOqVSGPFwoG3G4dQlgADAACltUAYMHrdSORCEPTBAyjAdO0y61FIlVUKiWMjvbiV391FgcPrqFSMSV0yDlDulVndA6AFq2ynV8B2NihqzppX0t7Lc65G70mfWJqOCTPoZORleCdyiYj1dS1eKNC+6YQ1IAtRN773vfioYcewtLSEr73ve9hZmYGtdpJLC39NTyez8Dvv4Rc7laMjT2K/fu/iP7+fmjaEkZGvoN//Mf/3FzwOjyeOsbG/qg5wevQNANC1GFZdSwv1zExYUDTLHg8Aj6fC0LUAZhwuUwIYcDlsie5ptnHNM2C2y1gWXUADbhcAkI0ADTg97tQr5fh8WgolYDxcQvB4O34yU9yiEZDmJk5jM2bN2Fw8IcYG/svKBbnsLzcjy1bXsTCQg/S6dtx6NBubN8+iJ07TZhmGZcvn0GttoZEooZo1I3x8XOYmGhgaCiFqalJhELdiEQSOHr0uLQWZmZmoGkaXn755Stie5lxKYSQYYN+vx8LCwsyuiGZTEIIgU2bNuHQoUOoVqtIJpP4pV8yMDv7DgDAuXOfxLlzV46dz2fC77dw8aIJO8eoBE2rAigjGNQghIFw2ECx6EVHRx3JZA2AgUajBMOoQNMMaFoDQK05TlUArCBeh6bVIUQNmtaAECYAC0tLC2g07OvYQokvU/5tC7r1Y0KYzWPr50QiIXR2xhGLhVGrVQCYaDTqcLkE/H4fIpEQIpEQ1tayCIX8OHNmFP39fVhby8LlAizLQDgcRLVaxupqGpFIELVaBT09SaTTSwiF/IhGwxgZ2YGdO7fjxRf/FYZRRyDgQ7VahtfrRrVaghCtHONCCBnvzwgdwK7aQ6gEgIRc/H4/AoEAUqmUjAwKBoPo6enBpk2b0N/f38TL7bDD06dPY2VlBcvLy8jlDuA739mKhx+exmOPpXDgQA4jIyUZilosFqVznpozf5tONK7h63Xw/ntrfC7V2lP9Yypcyhh7VvaZnZ1Fb28vUqlUS+TTDf3+zdCpW7duFV/60pcArO+qLBxaKBTw9NMWXnjhc3BqVy6XBU0z4fEAjYYLhqHD7bbg9VpNzYQdp0HdcO0Jhebvrb/bHc5dX4NhmNB1V3MDACxLg2UJRdu7GZrqmOEEQvP5BYRQtTABXQeEMOHzuaHrttDi37puKe8WDMOL2VkfQqEqSiU/UqkaAgFicVyg7BNACB2apqNcrjfNeAHAjXrdg1rND5dLwDDs776WpmkGdL0KXa/BMPIQogSgAjsKlO9l2NXG7b/tDaMEIexzNK31/P7+Tvj9Aj6fiR07BnD69CuIRDyo1SoYGhrC4uKizIa1LbwihoeHZWx0V1eXhMwYu9/T0wPLWo9713WbkzoWi+GBBx7A97//fRw9+nUkEqdw++3/j8T6z5z5Dayu7sPBgx+Gy+XC0tISBgYG4PV6ZXXvarWKt7zlLXjf+96HTCaDTZs24ZlnnsGb3vQmlMtlxOPxltBRYH1N0e/j8/lw+vRprK6u4tixKL7//Y/g0UfP4J57Gjh2LIpHH92JT33qBMrlf8bMzMwVRF70adDarFQqyGazKBaLWFxclAK7nQat/u90qv3PCslrF4Xi1Kh7e3slp73zu86ABrfbjUwmg7m5OVlcOhKJ4NZbb5Xn8dwf/ehHx8Q60d2G7abRqNnUh2ZY2e7dl5DJjOH8+R3Yt28Fe/euotHQYJoa5ufTAIZx5Eg3hoZWMD/fgZGRNcRiDZimC4VCGR5PCKapwTR1GIaGet2CYegQwgXLso/VaqIpjO3/TRMwTRcaDR2W9cb6XClkdd0WutS23G5dOae1LhtAZ47qTdaa976+0ZgmmpAFF4e8YvMaV9wNCgU7OWJuLuD8cIPW/jxbSN9oU81kF0wzCNMMAuho+ezKZm8Izg3V+XwzCivN6dPqJyZOnzbhcgm4XBZMUwNQhaaVMDlpa/ma5sPcnB+RyBI0rYaODh+EmMbk5ApGRjYhk5mFz1dDOGxB1w2cOXMSudwEisU4IpFTmJz8ORiGgV27/ivOn/8tzMy8B319dt6Y3+/HO9/5TvT19SEQCODgwYMt+DDj2+l4JwxRKpVaqosz7t8ZjcB5s7Q0gA9/+Ans29cBTYvittsKePTRM3j5ZTf6+gqSD4f4s5qEZEcovQPl8rPQ9VOKpngfhLgNmvZ/bDw6N0lI3vXcB4V2Owxe9WOxj+3Cy3bJPSbbkQOI0U9OH9y12k0nqNnUXW5hYQTT05vwwQ9exJNPbsYHPziGfftsAqe/+7tpPPnkbfj854+jq+sUVlf34otf3IdPf/o0Dh3KY2pqCps2bZK7GCczY5DpDFxcXJSORnbi2bNnsX379iYeawvw8fFpbNu2C40GUKvZzp8jR07i0KE7UK+LpjNIYH4+ja6uXgAunDsXxNe+lsKBAz/F/ff3Y2wsgMceS+Hd715CpXIWw8Pb0WjYgswwNKyu5qDrfng8QTQawOJiGoVCDYODW+QG1WgI5PNV+P1hmKbW3GwsNBoadN0Dw7Dvt1o1YFkuCGFbHPyuZelNZ9Ib1bjxEL8EdJ2x4eoL2EgAWxaPq6ajqqGrf+vX+Fx99nb9YDsi7Q2bx/wA4lecWa32AgBsdMJOOTh+vO0jYHq69f/Z2fdidva9AIA3v/kM/sN/MFCpfALhcBjJZBKmaWJxcbEl9E3TNEkspjqG2Sec66oT3Km1co7fffcLiMViECIO0zSbYaYL2L59Fmtr60UDVIciYBNF2bH+XlSrX4PX+3EI8SPYQvqb0PUPte8A5V5+lgSQ/xnNqfGr/a4qmNPT05L1Utft4sY9PT2YnZ2V/DAbJcds1G5aQc0kicuXB/Ev//IufPCD38EHPtCF/fuz+OM/PohPfeo49u/PYm6uD488chIHDqxhehrYvz+LT3/6NC5ciOLgwbW25pTTtNmow9Y7H3C5BDwegWCwgXi8NWxpaiqLrVsrLZM5Hk9jaMgu8nrpUgB/+qeXMD//Y7zzne+GaZp405tyOHcujMHBo7j33rBcDLquY3x8HC6XC6lUCpZl4fnnn8eFCxfwG7/xGy0LZXl5GYODg3Ixsiq3ykd97tw5BINBuVm53W688MILuPvuu2EYFsbGptDfP4yvf70bW7cWMTKSR6FQQaMBzMwM4cQJE7fddgHBYBShUFxuKLZ2S+tDa24WOjTNi6mpeRSLVZTLDfh8YdTrAqnUAKpVE+VyA2trJRQKFdRqAtWqCcNA8zq29WKaLmntWJYLluVCudwJISrQtDoAD+ypG4QtPBvNY67m+7+9BeT1NvDmN59HPJ7HhQvj2LXrAIQIoFZzo1bTkM+bEMIHy/Ki0dBRr2toNFzIZAoIhzvQaNibazrtgxAaurqq+J3fycIwUshkFpHPe1GpBGAYGubmKshkIjAMDY2GbekZRgKFQhXBYBxnztyCxcUUKhUTbncQlYoBy3LDNDUI4YZh2GNlKxIadN2PlZUOlEoNVKsm3G4//P4oKhUTpVIN9fq+5qauy3E1TY6HG0K4m5u/B0KwLuNj8Pm+A8O4F17vR2AYP4Gm6TK93dle74SQ17upMkLF4lULVpUt6nHysKyurqKzsxP5fF7ysfO6akz29bSbVlCvxxun8K53/R0GB5cBdGHfvgx+//dP4OLFOPbvz+LOO5/DwYNvl9/TNA3792dx4EAOmtZ+wao7oToY13tfTizLecx5rY9+dBEA8MQT68cOHlzDwYNreO65K4lfvF6vdChR8KsUofwdZheq/UULQdVWSMPqfA5dBzweC4GAif3783j00RH83u9dgKZZmJiI4RvfGMKHPnQKmgZ4vRaiUQuW1YDHo8PlIhRjwu3W4HLZG1ow6MPZs5eRz2dRLhfQ2RmDYdSwa1cO4+Pjsg4gY9MZb0wcVOWaZlLMxMQEGo29qNW+Bo/no3C7n0O1eics65sAHoKmPQ9bQHsAeCGEDzb8EoSmBSCEv/k/jwdgC3gvBgd3IBxOYGpqEcnkJpimC7ruhxA+bNmyE7WakELLtqg0FIsd6O2twjC6MD/fhUzGh4mJnuY5urRuTFOHEB5pKRmGjnJZR7XqRaOhoV63YSkAWF31433ve/P1TME2bftVP3W7reZLwO0W8HoBy/JB0wwAdXi9Gvx+F0yzDssy4PEY8HqNphO+BsuqNZ3ydQjRgMcD+HyArtvQUCTiw/j4ZiwsfACbNv13RCJTKJU2IZPJtJBZtXOiOdfNzeAzU5u6jpxKn7rOVTI1Jqipa5Ec5yMjIzLX4d+loHZquDQN7r//GKanp2EYAXls//4s9u/PQgjIStsqbtYO8FdDgNQOdE4QhgvxmBMDdmoC/Ny5A6vnXK2p4Txq7CXpRQE7nXtlZaXlerqu4+LFi9i2bZs85na78a//+q8b58HCAAAgAElEQVR497vf3fLsKqeD8zn498GDa/jc587jkUd2oVRanxZf/epBADdUTQ3A3rZHNc12Umra+osRGJrmfLeaUInZ3AQqcLsLqNd/ADspkX3xP9rg6uvtWutehSIYVuf8zL5nG7YxDB0ej4mZmTBWV33w+UxUqx5YVhBuN5qYth1t4vUa8PtFc1MTcLlMLCxMY9u2IVy8GMTsbBCbNxfR03MW6fQujI+HsXNnHvv2TcDnAxKJGLxeC/l8GslkFB6PienpIL7xjSE8/PAl7NixjNXVQXz5y7347d++hJ07l+DzaRCi3nS0N2CH9Qk5z0i5yXj9dDqNvr4+pFIpZDIZjI2NSSycZFGkA1CLbZAwzOPxYHFxFzKZN2Pv3u/h/Pmfx6ZNF7Fp01GZk2AYBsrlMrLZbEtuQLsIEnvM3ljSpes5pjJQsi/Vta+uzXw+LxWpnp4eGZ4XCoUwOjqKRCIhC3Rcb7tpBDWw8QAx4w9o3XmFEDKzDmglfdlod2Y8o3quU2g72/Vq3K8p7GaD65J+k5ltO3bskPfPSaImr9ADz+ILajiRs2ajeq/qPR88uIb3vncR//AP/XjrW2fx3vcWsLCwjEbDQjzehUgkDuK2QhDyYIQMUK83YFk6pqZmUanUUS7XEApFYJoaAoEwVlayqFbrMAwby7csDbVaA42GBcvS5DHGVXs8fui6DWnYQ+xGJlNGpRKGy1WEpuVhWWYLdr3+7sSrNce7/XK5vHC5PLATQCB/W21OZ3KjYZv7pZK3KdwDaFbNuo7W3bI5TE6GMTl5WP5/4UIMFy7sd3xn6xVX+cu/3IU774zh3LkkPvrRf8J999kVf7LZrCw719m5SXKEqxmdzLRk9Aa5z+mfcab1O98ty+bLbjQaeOmlAC5c+E3cddefo6/vAhKJ03j++Ufwlrf8N/T1nZAEUPV6HfF4XEbOFItFSbvqhCJvJq2a97WysoJwOCyJ4JwKlmoZ0Pq1LAuLi4syX2N1dVXmXTCM73rbTSWo2zUKHTUrSA0ed9KgXkujdWrQ7UJx2gl79ZhTA3ee0+43nde7WsC7pmlXsHPRe88+YFYgr0O4gJVkaBWoNKMbNS7MEyfiePLJHtx9949x4sTd+IVfyOPgwTlYloW+PoFEYl2zaLexCWHzPTz33DEUCgVks1ls3rwZgE0Fe+bMmZYyaKZpF+slRwahD94PU9458VdX9+DZZ38Tu3Z9B2NjD2Dnzj+Bz/cCarWaTMjgRqWOzdXG6Bd/8YN4+OGHW6wZ07QwO7uAVKpfbhzf+c4Atm4tolz+H9i58xa4XF6cPh3H2FgEU1Ofxsc//mvwePywsyeBUqmGcrmGjo4ETBMwDAHTBE6dOoMDBw41o2/sbMETJ05h167dze/azmRNcyMW64RlaVhZySAcjsvPhdDxxBNVvPjiLjz88DS2bp2GEHY0wa5du1pIsJwChXS+jLFX55QzEUX9W+3DdT9MHMHgHdi69fextvYiSiU3OjtXcccdJtLpHUgkRmWFHI5RqVRCJBJBuVxGX18farUaCoWCpB5Q55FTkVDXwxslzFXoggyZLpcLwWBQlvsjxS0bich0XUc8HpfV5knANTc3J3lfrrfdNIK6nTnOv6k9qumYqsbIyicqZAJcKSApWJyVMNip6kYAtKaYb7TgN/o9tTmFvMonwoWklvQRQsiEBmZM8jxVOyLHNkO2dF1vwbLZP/zb2bf83+Vy4ejRCB59dASf+9x5rK09jwcf9OAP/uAO/NqvzWLPnhX521frX16L1KnkVHGGiKl9fzXOiFqthv7+fkQiETz1VAOXL/863vSmL6O//xJSqfN4/vk/wl13/RcEgy+jXC6jVqvJ32U4FLUX/pZqagOQLHOtHnzA69Xh9TIOXcMv//IkhBA4eVKgo6MGr1fgLW9ZwB13TOGrX81j06YigsF1TbRarSKXy6G/f/3ZhRC4ePE53HXXjpYxKxZP4W1v65XPPzc3B03TMDRkb7gTExPo7e2V93ziRBxf+9pWfOAD5/D449vg9Q5g/377mVh4gHNLHR91DlJgr62ttVA3OBUQ9d7Vz9lfb37zi2g0PJicHMT09DTm5+chxLchhMClS0HEYjFEIhHJfU4+HXKP0D9BrZvFKUqlknTC8R6ojLzRGremtfLikwaYJFpcd4lEoq1lQESAsfipVAqBQKDlmtdqN42gBtoPAAUbTSQeU+NGKfioSagC24kjOYU28WBndhG/w/vi+STGV3dQNZZVFfa8B16HpqV6badmrfIClMtlPPPMM7Je4v33r7PKut1uLC8vy8XJsk1jY2PSmUEnJMmf/v7v+3DrrZUWYXv6dBIvvNAHyzLxuc+dx/79WXz969N44IF5/NEfXcJPf2o7cA3DwNLSUks/Ou9dCDvz8eLFi5idnYVhGLLQbruxbudYUuN1AXtR2KnuhzE8/CkUCi9hfNyHrq413HGHiUxmO3p6zkrebVb2KRaLiEQiUpMjjQA3PP62Sm+rzjmnhsn7o5BR+3ijyAZndSHTNJFIJK5YoE6CqXYbIV/Hj8fwh3+4C+9//9fw/vfH8da3WvjUp96HbdvOYM+eFal0WJYl8WGSmlWrVWnJnD17FmNjY1heXkYqldpwXNptoGo/ud1ueL1eGX1E3hhWp2dJMY/Hg2QyiUgkIrmzg8GgrAREiIQJNPF4XJKMsR5lIpHA8vKypNV9IwU2f8+5btnXvH+v1wshhKR+WFlZkYydFNZMhPp3CX0QQ3V2BAPu1aByMlY5H5QaJgCcPn0au3fvlsVwVU2WJZByuRxM08SxY7bD8uMf/7hcpC6XC8PDwzLjqlqt4tVXX0U8Hsdtt90mr6dWMVEZ1rhpqAPKCcrnopaqxm4bhl0Zen5+XmY0UaNWBSND7YB1zVktXEoIgX2m6zp27SrhkUe24+GHB3H33TaL3Ve+shd/8icT2Lt3tQX7drvdOHhwDcHgebhcdmkxJ/7NvlYhGOKX8/PzmJqaQjKZbKEkbSf8NqrkovbT/fcfQzpdwthYALlcDsViEbo+g66uLlQqcVlGKpFINLXUoiwmWiwWW6pCr6ysyFTgdotFPa4KYOcYsB96e3tbjjk/F2K9ao5K4sXfcvpW1Mw+VTsWQuDcuTA+85mzOHPmJVSr9+LQoTw+9KHvYWzsduzZs4Jjx47J850KC++nXq8jFoshFovh+PH7ceZMDYcPr9/v9PRWLCz0Y+/eH8pnYX85tWvedzAYxODgoKx7urCwgL179yKfz2N+fl4yRFJoJxIJRKNRhEIhCXFFo1Ep5Cj4wuGwrEJUq9WQTCZlpqaqLP1bYdy8pppEBLSmy1MeLS0twe/3S0phwHYsBgIBuTZZUUi1MK+n3TSCGrjSJNY0TS5KckpXKhXk83lUKhUsLi6ir68P4+PjyOVymJ2dxUsvvYRisYj+/n6cP38e9XodQ0NDqNVqePbZZ7Fjxw5kMhnJmQzYkzAYDGJuzsZjL168CLfbjUKhIDcEmou6ruP555+XHuxKpYJwOIyZmRnMz89jYWEBzz77LD7ykY8gkUggn89jZmYG09PTeOqpp3DvvfeiWq3ipZdeQiQSwU9/+lPceeedcLvdOHbsGHp7ezEzM4N0Oi37Q63O4na7MT09jWw2i1wuJ7WQYrGIV155RZbk4v2cOnUKBw4cgK7rOHy4hP/4H1/B5z//LvzN37hQqejo7l7CP/xDB554IgghVlGrLcAw3orjx4dw/PgqpqfTeMc7NmN+fhlClOHxuK+AmNRJR3x8aGgIL7xwD44cmcCdd65gy5YtAICpqS2Ym+vDvn0/ugJOcv7P5+XG2dfXB7/fj5MnT0piqampKUxMTMgs1mQyiXg8LoUB2RbZV7VaTcJKrGrSDr9WnbbO46oQ58bsvIZqYTkpctVz+f128EQ7KO2DH5yCx+PBc89lJDnYpk0X8Za39KJQqKNQKMjNX92EKMjIJe71erFlyxZkswYef/yjOHjwDDZtWsHU1Bb84AcfxYMP/t8tY6LeNzcyCpxIJIJ0Og2fz4eHHnpIKj1f/OIXkUqlJKzBDZn8IbwGC8B2dXWhs7NT4r/sL0JaLBvW3d2NTZs2yWfJ5/PIZDKySPXr2dhvdlLQegm5/5+8N4+S66zvvD/31r5XdfWiVkstqbXbsizJyAsCjImxsdlMiCEQGELCkHUmOe+8WQZe8k4ymWxzXhJOhsmJQxgSICROAmFzsGOMLdmWNyRZi62W1JJavXdXd+171b3vH9W/p5+6XdVqZ5L3OOd9zqlTt27d5Vm/z2//SZtseyVtmoyh+DMEAgFisRj9/f1tmed1Imq95XUR62NkZMT+3d/93baJWa1WOXXqFBMTE4RCIZLJpAocJLuuyKbF1KVcLhONRmk0GuRyORUEX8+uIh9hXWG14bpQMk6TNtGSC3C73W4mJiZYXFzk0KFDiqKXBaQnQZD42NDabOQ99XqdrVu3ks1mlRhH5Mx6GNBSqcThw4dZXFxsy1zc09OjJoae81CnfCUq4OLiIqZp8vDD7+fll3eTTC4SCpUol/2USgEqlQDNZnd2zO1uEArVCIerBINVQqEq4XCNUKh1HAxW8PtLeDwFPJ484+N+HnnkXh544Gt86EMDfPvbBf76r9/Hvfd+kcHB8zSbTTKZjEqoq8f+lnLDDTeohAXQWrRXrlwhm81y7do18vm8orxa1JoPr9dPJBJjYGAQj8eHYbixbQPDcFGt1kmnczSbNtVqnTvuOMLhw7ctW5e0YrhYFmSzBQKBEKbpRkKCNps2V66M09vbj9vtpVptUK83OXHiFIcP34bPF6TZtAGTarVOKpVmYGBwWQlo02hYPP/8Sxw58mbq9aayMjlz5hz79u3HtluKyEwmj20bJBJJmk2bubkUsVgCMCmVyszNpZienuOGG/ZRrdaZn19geHiETCa3bB2z0o4VK5aWlU7rXCukqoQTmJpqks/vZd++GUZH47zjHf+LDRteVRZDelAmEZ3ceOONhMNhotEo165d49ChQ9RqNU6dOkUymeSFF17gne98J41Gg+eff55cLsfY2JgCK4m7rYO/xNKORqMkk0nC4bCKYKmL90qlklJKW5allHUiNpE4QbruQd4h3502Tb04iQVdfOckKET0JbggnHuhUKC3t1flTZV1OTw8jN/vx+1288wzz6wr1sfrBqh/53d+p43dy+VyXLhwQV0j2lVdiy2UpqSLEjmQbdtMTEwosyM9m4nEtRYrAxk0kTNLJwulJSmPdPGJfEQeNzw8rBLsygTQE7cKKEuISH1y6JSJ1E8G2UnNSH48MaUSik+oTpFLSj/qclS55tKlzfzpn76NI0dO8/TTN/Hxj3+PnTsnlxeKh1rNQy7nplDwUir5KBR8lMsB6vUQpVKAUslHsehfNk3zL//2UautZMVZXVoKOtsG06zjdoutdCsoVCv6ndOV3AYMXC4vhmEigbBWAAhW4jm7WIlJ/f+PIuFQJS5MKzbMipu+fq7Tb73/LatJtRqjUAhy222Pctttj6iNs1Qqkc/nFeEhn5tuuoloNIppmuzbt4/jx49Tq9VYWFig2Wxy7733MjMzw+TkJHv27CGZTPLYY4/xxBNPqHmsJwjR56uAtmQGSiQSKrmvrluwLEsl+ZWkIzqlW61WyeVypNNpJT6FziK4tahbl8tFf39/G9cjRVdCyxrUuSrJiworDlyCFx6PZ91A/boTfZimyfz8PKVSiaGhIWAlaYCT5datJ0SLPj09zcmTJ6nVatx2222Ew2G10+kArFPY8lyhiOV/p9xUAF02DGGHxDJDb4eu2HTKoiSmrS6HFxYZVuSBTkDX2yIminqdZYLLpgUrIiSv18urr27goYfu5Gd/9gluvHGeAwcyfP7z9/EzP/MEu3dPYxgGHo+Fx1Okr6+s7pcx0CelcBgryhQUaOfzbnI5twLzF17Yx9LSIG53Bbe7gbgit1zF156CAuxud8tjDsqEQiY+n0W5nKavL0QuN0sqdQ3bLuByVTDNlqt5y2JDPDBNfD4Pfr+XcDiIONwMDg4wMrINr9eNywUul8FzzyXp68uwY0cZj8eFacKVK0FmZoLs2/cqHo+LbDaNbTc5f/4cS0spHnzw/cTjUVyuFaAcH7/M1q1bqNXKGIbNxMQ4p0+f4oEH3s3U1ATp9CKlUoFqtcQDD7yHmZkprl27Sq1WIR6PUK2WaTbruN0mbreBaYLf78XjcWPbKxYsMg5CsHSypJHf4nwhccj9fj+XLm3mD/7gFj74wQt885tvZOPGCwwMvKLGWFfw6kB5+fJlEokEX//619m1axdXr15l//797Nq1i1OnTmGaJm9605vo6+tT6+XNb34zp06dUmnNdLGQDoRCIElEPkkYnEgkiMViShQaCASU7kfEonpKM7/fT39/v8rcnk6nFajr4NyNYNXFUE79Q2t+rtRbp6YNo5V31TAMFhcXCYVCytJGz+6+3vK6AWph8V9++WUGBgZUg0zTVC7Qzs5yltHRUUZGRnjggQfU9fp9+v2d7Ji7ae6drJIuVtDPOS0FnO/U75X/dcWR8z451i1X1ipO22t5p7T32LFtfOYzZ7n5ZjewkY0boafnLBcu7GTLlnZZrXMS6/VxtrHTJBcK6Yc/jHL8eJKf+InLfPObg9x994rYw7IsFhezFAouSiU/5XKASiVIvR6hXg/TbMaJxbZi2z2USl5sO0G1GiKdNqhWQ9j2BubmAIaBWx01KGOaGUwzjWlmcbkyFAoZ3O4sS0tL+HwlotEG/f0Rdu4skEwaxGJNwmHo7a3xm795A5/61MvcfnuOkyfj/P3fb+I3f/M8w8MZnnvuOaLR1ry48cYiFy9eZN++DMmki3PnzrG4uMj09DRbtmyhVJomlUoty3KDvPnNA8zOPoPbbbBhgwu3O4BhBBkbewaPx8PmzS0X9xb4Rtpk5XoREZwQHJIEQkQKojDXx1GAsVarqc/zzwd56KED/NIvHWPLlsvU61UeeeQnefvbv8DAwCtq/J3iA1GOjY2NsXHjRm6++WaKxSK1Wo3HH3+cI0eOsG3bNiKRCGNjY+TzeW677TampqbYtm0b+XyeV155RSnhhOqVuarnLZUNQkK9ejweBdDJZJJkMkkw2IqrE4/HicViasMSPZLocvr7+1ViZDGxS6fTbQ51IsrQTYF10+BO4hL92OPxKH8GaOUrzWQySpchyu1/cfM8wzDiwBeAfbR40p8CRoG/AbYCV4EP2LadNlo1/hxwP63Avz9p23aXWGKr3kM8Hldae8mcrBdn5+lA0d/frzpfB2SnQkjO6++V4gQreVc3EOsEnt3+d77HySo5n/FaQLrTu/T6A9x66ziWZXHlysr5WOwKhw/D1avdXXid57vVV54p1MKrr27gC1+4jU984nvcfnsZl6vMww//FPfc8wVFsUEDt7tAMLiIx1MnGGy0iWtuuukmcrkchw4dolAocP78eW644QZeffU8ly8vcPvt9xMKbWJsLMP4eJ5QaDOlko/p6QozMxVajiAJ6vWtWFYcy0rQivMB09Nw/nx7O0yzSSRSp9ks8KlP3cJddy3x4otR7rnnC6RS00xOVpQOQSIw7tq1iwsXLij7X4/Hw4YNGxQ4RKNRxckJFyQgKvNZ8i5COxek6xmECta5QQFx3e1bZMliQ65TwzKG8o4LF3by0Y9+h7e+NcGVKzA8PMa9936R2dkt9PefU9fK+MvzdCssGasTJ06wc+dO7rzzTp588lYWFwsMDp6nWCzi9Xp56imT6ek3cvfdJ1QYV0mK/Oijj5LP57l69eoqCybdSkJ0MPV6nXw+z/T0tBKLxONxksmk0tWYpqnOQyvcRD6fV0pJt9tNOBymt7dXybdrtRp9fX3kcjlmZmbaHMt0y6a1ioytz+ejWq22iSANw1iVmHk9Zb0U9eeA79m2/WOGYXhphS37FPB927Z/zzCMXwd+Hfg14D5aUWJ2ArcBf7L8vWaRCTc8PAy05M+Tk5NdzaTk3PUAuNPHWfQB6EZpdwMnHWz1enUDdnmfc7CdbdKBz0lhdytOMy/ntbqIpNMG0o067nTdWkWef+lSgo997BH27cvgcsXYvPkS99//JaantzAw8Moq8ypZiDpLL5r973znOwSDQd7//vfz6KOPEolE+MQnPkA6neaVV75LIuHnDW/YTjyeIRaL8cwzz/DFL36xTcGzwpr6se0ElhXHNJP8+I//PLmcm7m5OvPzDUXZp9MjPP54L7fd9hg7d07i87WcNsSCQfohHA6Tz+cVey9iIkBRhnq7dH2L6Bj8fr8C4U4cmyiy9QBWuqmq3nedODknNyfvueeeU4o4krHdtOkiAwOvUK+3j49+rOtVAoEAtm0zODiIy+Wir6+PG28s8Zu/uY+f//lF9u2DZ57x8nd/924++MG/p1gsEovFFNgvLCzw4IMPUqlU+MpXvsLMzIzSC+l94GyD1DefzytgFdtsEY+IB6D0s8iaK5WKkm1LcDNJXScg3tPTQyqVUuOjg61Qw068EIAGVok3RXSpE5vrLddVJhqGEQNOASO2drFhGKPAW23bnjEMYxB40rbt3YZh/Ony8dec13V7x8jIiP1bv/VbbZSvYRicO3dOLQBprN4hTkq5EyBeD6ydlh3dnqE/v1sH6892Dsb1qFD9Gr3e16uL1LtbcfaPU2zR6fxaZb2TS5/MO3bsYHFxkTNnzqyyIEin08rUThanAI9pmhw8eJDFxUVl/vW+972PRx55hN27d1MsFgmFQuzYsYNQKMTmzZtZWlpidnZWxTe5evUqjz76qBI/yEIRK5Kf+7mfazPrEkp2fHyEv/3bH+P220/x3HMH+MhHvs327deQjOsCigKuIoMURa/oCXQzOX2jlEWv5+yUpM3SfidrLBSsc7x0O3xdoe0U20lpvd+gUjGoVk22bt1DtepmbGyaixcnKZehVnNTrZqUSjbFokWt5qJed9NoeGk2PSSTmygUbMplG5crTE/PJqamFrHtAM2mj3IZfL4+ikUPe/a8zLVrO/nRH/0bbropRSAQoFJpcSavvPIK73nPexgdHSWVSqmkyJI/9bOf/azqM+EKnJtQJ05Vvv1+v/KIFOD2+XyKw9GJLQlAJRl3pP9FCSjPd2Zi149FSQgrVihi3WQYLbl1sVhk69ateL1eXnjhhX8xZeI2YAH4X4Zh3Az8EPglYEAD31lgYPl4CNDyZjC5fK4rUDt3a5mQyWSSXC6nzknH6Yoy/T4dHPVJrQNgJ1lzp0GXb2fg9W7XdQN/JzXspBKc/3fbULr91vuuk0elLFanCEevT6e2vZbdXn+WXmzbxu/34/f72yhm50evu/4M3WZZ3uFyuSgUCioK2cGDBzl69Hbe8AabcPgy09PTNJtNJid3cu5ckOHhUd797nfzzDPPcPHiRWXpI0o1UcbKgjVNkytXtvK3f/sePvrR77B79zR79szwpS+9m5/6qUfZs2eGRCKhxBe6OafeFuEEdEsJWfy6Mmllntk0Gm4aDQ/1upt63U2z6aXR8NJoeLAs3/I5H/W6i3p95To5FjCt1dzabxe1mn6tm3rdRbP5z1VPNTHNKul06xvKyxEEXctp8HLE415mZy9z6NANHD2a4/z5AyQS/4PJyb9n+/Y7yefz9PX1cfnyZd773vdy6tQpstksk5OT7N27l8HBQTweD36/n4997GOcPXuWEydOrOIwuhEd+lwSb0wRk4gre09PD5FIRCWrtixLzVVomdKVSiUVOErieejrSAdyEf8It+QUb4hpoWwQ+v3rKesZLTetOJf/wbbt5w3D+BwtMYcqtm3bhqTuWGcxDOOTwCehFeheZw3F4qK3t5fR0VG1IGRRdMrmK+d18HGCaCcgc4J5N/lslzao704TpVMdO4GZPvg6SHcD8uv0a9ux05NMr0+n9l2PYu9EoTspNh2QN2zY0GYt0G3TcPaJPN9paeDz+dRiM02TzZs3c+ONJT796Zv4xCeusHVrnfHxER566G3cd9+XlHv0kSNHuOWWWwiFQsr5SDLJh0IhNcfcbjep1DZ+/uefYteuKo3GBnbvho985IdcuLCbRMJDpWJQqZiUyywfG9RqLqpVF9WqqcCy0fAo4G00BFA9GiCvAGijsZZ549rF7a7j9basYuQjv0OhGolEAY+njtfbxOez8PttAgEIBm1CIZNEIoDLVWNpaZKZmcus5JasUKtlKJUWsawijUYe265i2xYHDx4kk8kwOzvLjh07eOtb38r//J//k82bN7N582aOHTvGvn2f4oknjjAw8KcsLHyI6ekW4A4NDbGwsMC73vUuLl68SCaT4fnnn8e2bd71rncp87pKpcKOHTvYvn07R44c4eTJk+RyObLZLKOjo23rwznH9XVoGEZbIKp0Os3s7Cw+n49oNEoikSAej6sgS0IgiFekE7SdeqNGo6E2Fuc8lmtaiYVbIhbJ3CNc3brGeB3XTAKTtm0/v/z772gB9ZxhGIOa6GN++f8pYLN2/6blc85GPAQ8BLBt27ZVK1ZYuDvuuGOVAkM6yikiWEvUoV8DqyngToDlBKVOpRPoXe++boDbaXNxvqsbsHYDPyc1D6tl2c73daKyO72vE9A6zwl12e16nW2U3/qmDbTJ9URMUiqVKJVKfPrTnyaRSPCLv/gr/NEf3cOePdOcO7eR/ftfIpvdzpNP7qRaNTHNEJblpVIxsSw/4fCnsG0fDz2UWKZAXdRqrmUqtAWgnRIYP/roDavOOYvL1Q6aOnBGInU8nvKq/93uuvp4PPW2/3y+Jh5PHbe7sfy/nGsQCBi43S5FGUrIAZ/Pp8QwnfoZdF1JjkqlwqVLV/B4Xm0TwbTM6LLLMvE6jcbKxi/E04svvqhidojS89KlzZw9ezebN/8Cd90FFy5M8sILv0cw+FtMTPyAD3/4w/zu7/4uIyMj+P1+4vE4P/MzP6Mo0Xw+r24yr3MAACAASURBVLxK8/k8gUCAw4cPUygUmJ+fp6+vj2KxyPz8PBNa4kt9fpqmyf79+5mamlJxsKVIACjx4rVtWykle3p6iEajiiDweDxEo1FFHIg1iSS98Hq9RCIRpTfQ14HglawpcX4Jh8NtJr3XK9cFatu2Zw3DmDAMY7dt26PAjwCvLH8+Bvze8vc3l2/5FvCLhmH8NS0lYnYt+bT2njZxg7C8Ym4kjZbSjZrVAV2KE9j1Z6wFqnpndxOVdBIV6PfpZa1rOlEEzjrqyk6nN2W34nyuft5JiXSjcPV6O0F1vcVJ2TsDGXV6v7Pdtm23UT0SuOfUqVPcemsRj6fIyZPbAHjppSPqOaZpLQNdHa/XotksEImYhEIuarUMgUCTSKS2Ciy93qb67fU28XpbYOnzWXi9LfCUY7e7vgyeTVqOJ2bH/tU3IZ14cIr/AGV3K+ZoLda8FddElI86EHcqupenvEsfE2df66XbPJY1JuBvWRbHjx8nHA4rhZxl3cKHP/xNnnzyGSKRd3DgQJpM5jMUCvvZvPkUMzMznD59mtOnTxMKhVTyXomeJ88XBy6JXxOJRLAsi+3btysTxWvXrvHtb38bwzBU9iCJV5NIJDh8+DCJREKJvxYXF9W6kv4xjFbAtVKpxPz8vAJfobb9fj/BYLBNFi0OOLZtk8vllImfcywsy1KiMiGSOmVdWqusl/b+D8BXjZbFx2Xg47Qirz9sGMZPA+PAB5avfYSWad4lWuZ5H1/PC7qBxFrFCZzQXRH3v1u6UcD/msX5fH2RrQXOr/W5r/UafUN8LWDdTezjPKeDulBt4i4cjUZZWlpSoQIsyyIUCnH2bB+1mp9Y7K8oFO4nmfzP7N8/z1vfehvVaoG+vj7q9TonT57k3nvvVQ4b2WxWeYPKvOm0sTs5Mr0f9D5w4GLbPU5CxDRbjicSjF7AQNyLxXqgWx+uJWbrNC7OzUMvzrXUaW3JM3Sqsdlscuedd3LDDTfwrW99S6VXu/nm59m79z0cPWqSzWYJBoOEwyfo6ztLrdayXhkYGFAR5TweDy+//DI7duygWq0qrz7dNC8QCLC4uMimTZuU6a5Yin30ox/lueeeI51OMz4+rubGzMwMzz//PIlEggcffJD3vve9zM/Pc/ToUc6fP0+hUOioH6lWq9RqNRYXF/F4PCoWSTQaVWFbDaMVAiIQCLC0tLRqnPTn6cHXEonEqhjW1yvrAmrbtk8BnTSTP9LhWhv4hXXXoP3etoVyPUXXWhSrs/xzgM1Jqf9rA/NadXi9va+bfFovTsoRViuO9eIEIaeicWFhgbNnz7bFcrYsi6mpXfzBH9zC2972P7hw4U9JJP6J8fH/zrVrn+bMmROYZity4enTp7n77rup1WqMjY2pIPa6eaVOAevyTz16nO74oBMGOpUpHqzCAQQCAQKBAD6fT1kGdOs7Z7uvx5n9c+a9vs66PbvTb/2cbrlSr9eVh6CIQHK5HMVikWw2SzQaZcuWLVQqFZaWliiVSvT19ZHJZBgZGVHmh4FAgM997nO87W1vI5FIKGOCSCRCNptV4C4yZ7E/j0Qi3HPPPXzhC1+gWq3i8XjarIcymQyf//znGR4e5i1veQsPPvggjUaDyclJvv/973P16lUlfxZuT8zrJPBTNptVoO31elUQKWh5Ra4lczZNUyklTdNkcnKSjRs3dr3eWV43nolO6qWbWKLbTq9ft5YMeK3vbs+T+nX7b73P6navXu9uv9f7nH9uWU8fOEHBKQZyFp266yQiWk+dZMEYhkE4HObhhx9WmnVogUI2u5Nf+qVnSaXGyGYHGBlp0tv7BywuHqRY/C4ej4fjx4/zwQ9+UJnv5fN5Tp48yVve8pZVpnA6waCzyEIFi+hBFm0oFFJKTlmsTgukTn3cDWT/JeZnJ3Ghs1+d4ypt1uOty3k9pIIcC0UrQchETJlIJHjppZfYsmUL+XyecDiszCM9Hg+5XI75+XllFz47O8vw8LAKbfrud7+bbDZLLpdjfHwc0zRJJpMK6IWTCIfDLCwsMDo6yk//9E/z53/+5+zYsQPDWAnJK04tLSVxiunpab72ta/hdrt54xvfyBvf+EY+/vGPK3HEhQsXOHr0KJOTk2qT1vtHAr6ZZitZgHg6ijxd+rOTj0AoFKJWq5HNZpUTznrL6waopXQyIO80cV8LSHbaBJz3Ostaz+xGdVzvuf/cZ61lpXG956+n6GKGtURQ3djm69WnE1stlEu3e/QiFG8ymeQ3fuM3+O3f/m0GBgZwuVzMzc0xOPgVdu3q58qVPIZh0N/fTzhc4MUX/5xqNcH09DQDAwM89dRTHDt2jMOHD+P3+xkYGGhru4CPODiEQiH8fj+BQEBRwWIdorO2uqhE+tNp7+w8Xk+71zuendbMWs9yrgEBX12OKtSqAKNu5+2UbbvdbhURUihsj8ej7JH9fj9jY2MqLVcikVAenLVajWQy2RbsTOZFNpslk8kQDAaVm3mhUMDr9XLgwAFsuxV87Zd/+Zf51re+hcfj4e1vf/tyiN1nKRaLuN1uQqGQiu0uSUIajQZHjx7l6aef5l3vehcHDx7E4/Fwww03sHv3bmZnZzl+/DgnT57sKKLQuUnn//KfMyONuO2LqeZr4fJfF0AtO7ZOhTizY+iUg37cicrrdJ3+/1pUTrcFtV5w70Z9Sj10c0BdodfpmTpV1GmhrbXw11PWe996wLnTs/T7dHtjocQ6We0IWDopEvm/VqsRiUQoFAqKQhkeHlbUmmVZSn4p7HRvb6+Kdjg5Ocnly5dxu93s37+f+++/v01JqQe06mbt4jTplP9fy7xZaz69lvHU5c7Oee1UrMsGKffpfS7ejoVCgXK5rD6i3NPzWTqBWjwNdWWp2+0mnU5jmibXrl1T8ndRDo6OjqpxE9v2ZrPJ0tISpmkyOzsLtGKaRKNRcrkcw8PD5PN5jh49qhIkbNy4kYcffphUKsUtt9yCZVns2bMHr9fL3/3d31Gr1UilUmoDL5fLyulE7Nu/8Y1v8Oijj/K2t72NW265Bdu2lZmhBJeSsZdNTV+zThGgUPx6X8l9Moc9Hs9rUii+LoDaNFvBw53sWreJpV8Dq1nVThPUeY9e1lo0zsWn76L6gpYdVMDCuZD19jgpMielutZm4pSjdgKTbm3r1qb/nbKWtYb+HqG89MWsX6crEbtZI0joS0Bl/BCvMZEhAiwtLTEyMqLchZvNJj09PUxMTHDDDTdw5swZtZhExtipf/T3v1aKt9P4d3vmesbB6fjTjUvRj6WNAsKiuNMdceR/QIkJBEBs21YKtUKhoMDNtm0OHz6sqGd5hoyNiIkajQZ9fX2cPn2acDjMhg0bCIfDKq9lIBBgy5YtQMuK4tSpU/zgBz/gAx/4AKVSiWAwSKPRIBgMUiqV2LZtG8Vikaeffpre3l5CoRDHjh3DMAzuv/9+xQ24XC527drFxz72MY4dO6bi1k9NTWHbLftsMV0U6rZUKvHd736Xb33rWzz44IPcddddbck79KLP605cYae1bdu2MptMpVIkk8l/exR1rVZjYmKibbLB2hNSdmCdSu0EzlI6mebpgKr/dg6O815YDXYyMXX5nRTZgYUVEu2vKEX0Beask75b67u5XvT263XTjztNCqdirFO/yTOc9+ubhd6n8q3/X6/XSaVS5HI5UqmUYokty6JQKCgQEc89GVtn5hNn0YFI2Ge3263CYyaTSer1Oul0Gp/PR7FYVKEn9+7d29GqYj2l0ybajSruxg11uqeTVY+Tq5KPDsTNZlOx1PKt96mMn3AL0k/iJVetVpmfn+eHP/whL7zwAoVCQYkshICKRqPEYrG254k8W+ZyoVCgv79fgaVtt8LuSj7LUCikrEJkLSSTSZVWK5/PMzs7uyppRyQSYXp6WmWxLxQKBINBxsfHVVCnYrGoFJrValU5q0g99b6U/pE+EepW1pGY7+lzrFtxKn51QkN3/JL/arWakpd3SgPXrbwugFoX2At46JPRSVECarLpSo1OwA7tFIbzOU5lQSfQ6rR5OK8xDEMFbnFSmTrQSYAY/Vm64sYp8pH3i0JNrnHW2fntfEa3fof2fJU6pdup6HVy2nJ3AhV5jxj4nzp1Si2SZrNJIBBQYOP0JNPllbIBym/5Fi5GItVt2LCBubk5Rd1ZlsXi4iJDQ0NtqbfS6fSqhbKWfsR5zrmB6Fyds6869Uk35bl+jdRP3NGFEhaqWEBZHz8BYL0PZbOcnZ1lYWFBZR0XoI1Go/T09HDfffexe/duRkdH20QekoVHItpJewWko9Hoqmzh4XCYUCjE1NQUt99+u5oDkkNweHiYSCTC1atXVeo8XaYrm8PVq1eJRCIqSFKpVAJQkfLS6TSG0QrsJMrF3bt3Aygxi/TN7t2728II6OtE3MjFW9WpYHYWubebrFnnEqWIslXWWTweZ64Vp/e65XUB1KVSifPnz6uwjrK79vT0tE12pyxXB5pOLHMn9sMpO3Luts5yPVtHPXuMTF49Poh8xJg+GAySSCQIh8PMzc21OX7IoOuJDnRxTrVaVY4O+oJ3UrfduAm9jWKLrAO8HruiEwfSTWmly5z1duvHQj1nMhnm5+fbYmCUSiUFQDJGzWaTvr4+NQbCoupOBQJAsViMcrlMOp0mlUoxPDyslGMSV8KyWt5kyWRSbRDi0usc/07UsLMv9Hu6ga/el85EFTLeEoxKp4j1/IJyrdjh6g4u8txisajST4mSTpICSJjP3t5etm3bxrZt29ocMITgEY/CQCDAwsKCSmWlzwv5CFUtz19aWlJeelJ/n8+nngcr8atl7cXjcY4dO6bMGPUUbCLHNU2TUqmkuICLFy8qz765uTl27Nih7k8kEio7uZhcnj9/vm3choaGGBoaolqt8tJLLynTP1l3spakjXr4Ar3o600nWARjuq1Hl8tFNpvFMAwVnGy95XUB1HrWFNM0Vcc7G+KMC+tcCHIsv6EdLDsBihTnNVI6iT2c/zkXtk6ly+DLNcJuW5bFjTfe2Da4+oB3o/D1+jgnhFwr1zjlmk4xiDMVkl6uJz93Hnd6hvN+oYYk7oFEokun08qlV8DLtldiNIsIRMZfopMJR6VzVvPz86RSKWXZIXn/FhYWWFhYYMuWLYoFlTnn5Ey6ta0bh6UDsD4XZTORWBG6jFioOhET6KICYcf1AE6Li4ssLS0pMBYTQYlXEY/H2bp1q3LEkLYIaJumqYBZJ4hkHpRKJVKpFGfPnmVmZkYldtbnk95mkR0vLS21BcKXmNmBQID5+XklipC1LO9cWlqi2WwSi8XI5/Ntm6b0mUTSEzCU4F4SPEkP9i/zWA9+5ayziMXcbjd33HEHqVSKV199VYG/857rWSXpKfCc3JVTkSibrvS9jlHrKa8LoPZ6vQwNDbVN8k4UnL7rOYHIKQ7QQc1J+eml0+LTzzvZUeiuQOskStDvEdmYfp1ORcrE04Hb+R7n5NEBXiZjywss3GbFEAgEKJVKbcAihvv6ZuF8n1NG3olN1/taQEG3uZV61WqtLNkSW0HGUoBXBy0JsC/ndeDKZrP09PTQ19dHo9FQ5nS9vb0YRssedmFhoQ20pJ8lhoTX621LTOEc525tlLkGKxuFcABiF6znFxTKWChXmeM+n6+Ne5BUUalUikwmQ6lUUnJ2r9erqOKdO3cSCASUTkQoY3m2MxqgjKn0XaFQaANOPTWX2+3mDW94Az6fj9OnT6us7YbRsuyQsdA38f379/Pss88yMzNDLBZT8yYWizE4OKiyc0v0uGazyezsv6NYfBLDuIZhGAwMDJBOH2Bqqp9Q6POqXgJqwh3J5iJchGxEklFF1phsIjLmsvEbhkEulyMUCgEQjUa58847+f73v9+2Uegip07rWz8n551hUeVbv0/mlmW1vCz/xTO8/GsX8cnvJGuGtbXiumhApzT1+50iEzknxWlypG8GAjp6p8u3sEjCmkvKn3K5TKFQaGMF9RgGOhso79O/Ox0LgEldpF665YksNpHFhkIhFRUslUphWRaJRILt27fzne/sZefOLDfeOKnae/p0ksuXe3jggYvrklV3ku/r46Wfl41ofHxcscm6ba6+Yelj0Gw2SSQSyq3Y5/MRi8WUxQes5HNcWFigt7dXyaHFfG9iYoKhoSEsy2JgYEBR2olEYtU8k43Gyb3o9RQgFgWUPiZO0BSLA4ljkc/nlWK1XC5jWRbBYFBRxr29vWzfvl0pPXXQlefpwCpFgELAVQdjmTtyfyQSUfNLlzsDKk2VDjwyvwSkhcoXCnT37t1MTEyQSqWYn58nHo9jWRYvvfQSe/fuVZuYrBW3+yTl8peIxT6JZb3Izp2f5NFHf4qNG39Z1Us2abHbLhQK9PT0KCVnKBTi1VdfVZyR2HELV+ZyuZienm6TR0sfXrt2jQ0bNuDxeCgWi20ckPR1p/Z3Kp0IGyee6KJZ4VI2bdrUlrz7euV1AdSyIJ2UqE7FOKmaTtRyJ+pXX3A62OoWDzog61S7dHa5XGZxcZGZmRmmp6dZXFwkk8m0WXGEQiEVp0HPXSfUhFwDK8lthSqSekpd9ADpOtfgBHTnBiTnRWEpclnJ6C4ZMILBIJXKHTz88C/xwQ8+xn33beTVVyN87nO7+ZVfGcXv78flsnG7wes1cLlWTBKdLF0nuXg3ub5htFKtSXhMkcW63W4F3vJceY8AQ6FQwO1282d/9mfKlVcUiEIhi3PGpk2bFOVcKpVU7IxwOEw0GlVZtcVluVgsKqq7VqtRLrcS++pu40IRy3jqHIgo3Vpeklmy2az6LbLinp4eYrEY0WhUJXvVN3xdx2GaJl//+g5uuKHI7beXVR8/+6yPc+eCvOc9KzkndYpSjkOhkMq64tz4pDjjrOt1kfbpMnPZAGTMBNyEW9uzZy+XLk1x7VqWmRk/MzM+IEYoNIjP149pJjDNOJWKn3K5Tih0jXT6r+nvf5bvfe8Ahw79HouLT1OrNVR8DQnwpEe+09cMtNJruVwuIpGI8hiMxWJcvnxZEScyp2Rubt68WXFd/f39qzhJfRPTdU56P8l1IgbS56wAsvSTfl6IiFQqRSgUUuLA65XXBVBDu7x5LaDWSyc5aidqWqeC5bhYLDIzM6OM8hOJhIo5IBvHxMQEZ86c4dy5c4qdSiQSRCIRRbnpij9YYXc61Vev38DAwKp8amLi5BSZ6CyT9FWn9uriBCmy+aTTaTXht2zZwsTED9i6dZqvfvWP+PKXV+LofuYz+zqMjY3bbeNyycdSv9vP27jdVtvvlWta9xhGk8XFDZTLeaCObdexrCq1WgnLqmJZNaAGNMlkTAxjANO0mZ2d4OLFDNFogGDQSzbbwDC2Uq/3kMtt5fTpATKZg1SrBWZmIhhGk2AwTqEwRy7n4tChNzE6WmJiosHERJG5uSluuWU/o6NXqdWKBAK+tn7UFbbQsiAoFosq9KfEq9BNMoXdHx4eVqCpWw/oc1zmrciNLctSZmuhUIiNG6f4rd86wi/+4jH271/k/PlB/vAPb+Yznznbxn3KmOscmHOj18UVOniviEVsymUXxaKbQsHDhQu9XLmyl1LJRaXip1LxkssZVKsBGo0QzWaI73xnmEYjSLUaoFbzU6sFsO3ry1zd7iIeTwnTLBAIZBkdvZNdu/6GkZFxikW/ikInkfHEhFXmsYiXhOIWRaBwZ2IfPT09TW9vL1NTU6r9IiqRjVi4LKeIUccOXWziFH+IOEbqIeOsiy713/Is4Ui6Uekd+23dV/5/VHQ2RcpaLPj1ilP8Yds209PTPPnkkyplvWmaDA4OttnZfve732Vubg6Px8PIyAjJZFLt8J3kzzplrp93UpqGYXD+/Ht4/vn/Qb3+mFrIlvUnuFwufu7nTlEulzEMg+npXSwsbOPQocfVM5zKSXmP3lZdrCLvhVYM3Gq1yssvv4xpmkSjPySZ/EcWFt7Hjh3necc7LAzDS7Np0Gyay98GjYb+DfX66vONBuqeRsPAsgwaDZNq1Vw+Xrm2WPRRrbbSQVmWC8sysSwXtu3Ctt3AyoKfnGwfT92a6dSp1vfLL8uZtwNw5crqefCXfwnwf7c97/jx1qfVZ63NxDQtTLOJaVoYRhNoYBgNdc7lsvF6Dbxec5nbaN3X+tjabxtoYFlVms0q1WoR265h2zUMo4nbbePxgNfrwuWyKJeLy0qtBl5vkWAww6ZNWT772Xdyxx1LnDyZ5Dd+4ywHD2YxTZeaC82mQaHgplAQoHWRz5sUCiaFgotCwaRYdJPPuygWXZRKbopFT9t3udw59nZ7aWCaBUwzj2HkludThkRiCtMs4PNVMIwcjcYSlcocjcYixeI0hpHH6y3jchUwjAKhUICenh4s605eeulXGBn5Kpcv38/IyDhe7yjBYBBAKQwlPZsQMgKI5XKZeDzOhz/8YbLZrFp3klB2y5YtzM/PtxFNOqiLqEQoXF3PA6s3NPl2cuuAAmDddFQ4Hl3koYvFhItKpVLX6fdWed0AtS4r0zu3k6y6k7VFp//1cwKmpVKJS5cuMTExoWSMfr+faDTKc889Ry6XU1rsN7zhDYRCobadVFjUTrKpbkWvy8mTb+fy5RepVr/M+9//JFu3vsoLL9zEk0/ejdtt43b3kkg8zSuv9PP445/k7rv/7LrArL9Hr5u+aYhsUwfxdPoA6fRbuf32f+Lll+9gcfFrPPhgb5uXqBSnOEPeIZPU+V+nugkb+dxzzyl71nq9TrlcJpVKKVO1ZtMG3MTjvdx88y2Ah1rNolxuMD+/xOXLE1iWiWn66O0dIBiM4/UGqVYtCoUK5XKD/v6NmKaPpaUcqVSGeLyPyclZLMtFOBwnlysTiyXZsGEzHo8fjyeEx+PHMHy4XD7cbj+G4aFeR21QzWZrM2k2TRoNi3rdplKxqVabNBorG5htu5c3HDe27dI2oda9cm695cknBxkcTPPVr27iC1/YvgyyrU+lsh4qtkko1CAYbGV5DwbrDA5Wlo9bv0OhBqFQA7+/ytLSVa5cOYVlpTGMHJaVJpW6Rq22Ivq45557lKxfIseJTqZUKlGpVFQWb6Faxbokn38D09P/ByMjv8rg4Hk2bhzlBz/4P9m/f5FK5WGuXr3Kj/zIj5BOp9mwYQM+n68NH2QuNhoNpa8QebxQ1fF4XLmsy1x+4okn2Lt3r1rT9XpdKSF1caeIfkS5r89rJxcvIkadmtaVyHraNdEpiOnhayFAXxdA3Ql4uu1ene5z/i9skn6NKD7Gx8c5efKkcmOFVmqc8fFxcrkcbrebaDTK3r171YDJ850Ky2716lR/KcnkZV588We4447H+au/eh/wbsDGMCrYdobPfvaduFz30my6CQYzPPfcj+LzlfH7SwQCRcLhIn5/mnA4SyCQIhjMEwo1cLkaqu16kQkYDofbnCaKxVsZH/8Dduz4NEeOBNm06SL/8A//Dsv6Kj/+4wPK/tXJ7ulUhj45r9d+ud62baUIXOEmVmyiW8cWllXHtot4PGUMo4LHY+N2VwkGIZ/PKPOudNpFLudiw4YNxOMxhoZC1Ot14vEcmUwG204xNXU/fv88LteXME2bXbv2k8vdwtLSIP/xPxaIRk1qtUKbjkQWpQBPKpViaWlpOXRnaTnVVgjbjuLx9GJZESUCaDZDVKv+ZbFAgHq99anVguqaSiWwDObXK625MTsbpVZLsXFjmU2bLCKRJqHQagAW0A0Eaurjdne2LuhE6DSbTS5eHCedPt/mWGNZKzkgdY9gmVN6thKxoZZ4IU4b+FLpRjZt+k/4fC8xP9/A5fo6O3akyecPcdttt5HP5ykUCsRiMebm5hTgASpiYes5JXK5FnWfSCRIpVJUq1VmZmawLIvp6Wk192SunTx5klAoxL59+5RViW4vreuFdPNg5zrQi77mdMsPfX3obRATyX+Tog8nlboWBdkN2PVzeufZdsss69ixY5w+fVpliggGg8pcTAK3S2d2svRYa7Cck97ZNrl/06aL9PX9Ai+99EU8nhL1epBwuEh//xLNpsnkZJ1m04dhWJRKcUqlBLCenddaZs0lrVMNr7eCz1fB7y/h9xfw+bLMz5/CNOeoVt/Itm2fZ+PGLM1mjM2bLzEy8jLnzt3E8ePf5K677sK2W5Ygo6NRHnxwRZ4g1gq67XonfYGzf3QFlQ7QcuwEb1jZaHTFbTKZVIpSkU1OTU0xNzfH0NAQ8XiccrmsXMhdrhNcvPglTHOMjRsvkkrdztTUf6Ov779z5couarUAxaKLbNYmlWqQydjk8yalknsZeMM0myHq9ZAC3fXIYz2eMl5vGZ+vjMdTIRDIEYvN4fNV8XrLy58KHk+Jyclz1OspbDuDaeap1fYwM/NfGRn5de6/P8DExA7+8R9/kp07v8T73hcnHo+3ERLOzXQlU7pNs7k6Tkin4gQUy7KUIlE2UlHgyliVSiWq1ary7JN3+P1+FZ9axjifz2PbNvH4Q8txNgz1Ptv+Rwzje4yNxenr6+OZZ54hGo1y0003KTPEbdu2cfXqVVVfccoRWbYo6yR1l94uaZtt2xQKBY4fP87WrVvZtm1bmyxf6q+vf2f/Op/rPNYpaf3dImYVha2YPK6nvG6AGtqpMH3Br4dF0GXQOlibpsmJEyd49tlnqVQqDA8P09/fT7PZ5MqVK2rX0zXJ0plOK5RuG0knkO4kw5bzH//4Vj772R9Sr78JsCgUwhQKYUd7VkQPbndrobdAuEkrj3BLDmzbLflus+lelve6qVYDlMshWkl4nOUd6iiVgosX4YknQIDeslxcurSfL3+5RTXNzga58cY0i4s+otEybvc8lUqKSKRBNGrR2+smkTAIBAy8Xo+ahLqiS5fhS9EdW/SJrX/0/pbFbpqtFFwi49MTjjabTSYnJ5mcnCSRSDAwMIDP5+PQoSyG8cf84Ad/w+XLEfXMiYn/wu///uoeMowmXm95GWgreDxFaC/+1gAAH9tJREFUAoE0Xu80Hk9J+2/l43YX8fkqeL2VZXCuIdKjtZTk0Jqn1eppFbOipey+ny1bfoVI5CS2/SaGhi5w331f4oc/vAv4Ae98Z43+/n5M0+TUqQQXL8Z5//vHaDQaTExMqHjMIofVbbilD8UySUAqn89z/vx5Tp48yauvvsri4iLVapVkMkkqlWprQ61WI51Ok81mKZVKyspkZc62rJx0Yqder6voeLqtsr5e8/k8165dA1pU8rZt2zhx4gTJZJKxsTF8Ph89PT3Mz88rS5e5uTm++MUvcujQIeUboG84UnQrjWazyeXLl7l06VIbbti2rZISdPJMdHLuznkqhIhuPqrPXaeCcr3ldQPUQqE5xQudlHE6heqUQTspulqtxsmTJ1Xc2w0bNqh7YrGYCggDqGhaolWHzg4vUtaiTroV0zR55plbqdWOsGfPi1y5sodbbz3KCy/cTa1W58iRfyQSSTE7O8KZM3cyMnISn69MqeSj2YxQqwWpVIIaW712YCGXq4bHU8HjqWHbdWq1MrZt4feHqNWagAfb9gBeLMuDbRtUKiFmZlbadfp0L6dP9y7/6pbc1Vq27LDweJr4/U0CgSbBYINQqE44XCcSqRIIVJmeblIojGAYeWw7g2WlKZcjNBoVGo0qllUHmtTrPRSLYZrNGpZlYttuTNMG/Ph8FtHoihu6UCsyLktLS1QqFcLhMMlkknD4FAMDjzE7+35isRfYufMkmcw427b14PfXFNh6PCVcriqm2YphEY1GlXmm067W6XCli9ycc0Oftzq3J4u3t7eXSqWi4iUPDn4Zt9vN1q0jajMbHDzPwYMWjzzyU3i9f8WHPmRx+nSS3//9Q/zar52g2Wxy/vx5Tpw4QaVSUbbTeo5FeV8ymSSfz5PJZJibm6NQKDA3N8fc3JwCG0k6G4lEFOWsU6Wzs7MqYqHoH2QcZD2K6WRLTJWmp6cHj8ejxBS2bSuFnt63tm2r+kQiEfbt20c0GiUYDLZRrsI9zczMqNC3YsKnU9Fyvc4BduJGBKihnZjQZdL6hiWArl8rIiO5XsZZLIQkzdq/OdGHk8KAzqxGNwG8kxKXc7ZtUywWldfR8PCw0r66XC42btxIvV7n8uXLeL1elQlC7J1fKwivp53z8zdy/Pj93HvvY+zd+z0ef7zJCy/8NwYGRqlUxrj55qdoNBps3XqGzZtfZX5+CwcP/pN6htMtvNl0Ua0GqFaDy5/WcaXS+q7VWucqlQCFgodazQbiVCpR6vXQv2DrzGWrD6hWYdkrt0u5cV1PXFiAV15Zz5UNoIlhWEATaFlstH6L9YaXRiOOx5Mnm72Fl1/ehNtdYmnJp671+TxYVg3LamBZNUqlAuVyL4aRYcuWII1GFWhSrYap1YL09ExhGBaGYWOa9vKx/JZ3W7hc4HLZy5xQc/mcjWmCYVjLm08Dw7BZWDiF1+tiy5bNWFadZ58dZ2hokHg8gtttYBgWN9/8ON/4xr+jWJzkqac286u/+kN27ZrmypVJzp49qzwb8/k8pVJJOWIJ2NRqNYaGhujp6aFWqxGLxZQNu+6eX6/XKZVKyjFEX5MSW0QcUUzTVPbkfr9fPUO3bhgcHFTPT6VS6rnQvrHJ2hX5d6VS4emnnyYQCLB161aGhobYs2eP2pA9Hg+33nqrcuwRPNm8eTOTk5PKVlkXacDKWtIBW5dRC/A6xRtCHeshDOR/XRYv18t5MdcUs9x/c56J8/Pz/PEf//EqlkDcg6HdbM9JaXdiJUQJIh6BEqRc7/RsNssrr7xCJpOhv7+fzZs3MzMzo7zfnNS506rCSdk7uQFnaSk4hgiHP8Ttt+9nfr7KoUNFbrrpr1lY2Eqt9kfATer+oaELDA21ey/pNrkALleTUKhIKFRcVVfnuwuFAseOHcPn87F7927i8SQzM0UCgY1AgnLZz9TUNs6cuQfDeBHbvg3TPIptl4E4EMe240AUiLH29LEwjAqmWQdaJmmtRWLTEsm4AY/2cdNZVLOe0rrXthvLYNgA5L01WiDdi8dzFbc7h9cboVTaQiAwjs+Xxe02KBT6mZ6+hs9XJRBoWYD4/duACOXyyLLSqky57KFU6sHvzzI3txPbbomf2j/t51rcgLHO9n0IgOW4+QAs68RWlW9/ewdvfOP3OX78D/nyly+RSqXaYkZ3mw8iT85kMpimyZ49e9ixYweRSIRMJsOlS5eYnZ1VFhSBQIBYLMbFiyseq7rDTDAYZHFxkbm5ORVnWbxFG40GZ86cUaFt+/r6CIfD9PT0cOnSpTbKUq+zyMJhxStUgk+dOXOGQCDAyMgIQ0NDKgWapLcSsYZt29x0000qIqCIVGStOjkk6SM9n6XTZ0HaL9yTUMhAmwmhPM/r9SoHJHGGy+VyKpbJesvrAqgbjYbK4iuNF3ZCN3aH9ghzIt/SWUqdrRStM6xQCDJ5LMvi6tWrRKNRJfIQLzcJSKO/F1bLqDspGDuxU3o5cOAxFhfP8cILFeUU0dd3Ar//OI89Nsab3/xmFhYW2u55rZYlzmukeL1eIpEI5XKZer3O1NQ1hoaG8PmKNBpZxsdHOHfuTgKBD2LbT9BsvoV6/St4PB8BfuB4pgGEsawYhpEAEth2DNPsxeVKYppJfL4N2HYL3F2uJJWKn3o9TKMRAfysXUqYZgWfz8a2q8sgbOJyeWk2TSzL0/ZpgaAX2/YCQdq7v1Xfen0E3eIql9vOstEAYAG9QAmXK4dh2KTTfYTDc8Rix0ml7mD79ivk8zu5997/hw0bXlVjo4s79LJad9EO4ODSwNygWKxw8uRpwMX+/Qc5f/4i4XCMXK7Inj03YpoeLMtgYmKQkyd/DLf7Szz77IfxeOqY5tVVFj+6gkzqoSu3ms2msq0PBALMzc0xOjqq5MgtccsgfX19+P1+rly5oqhFicVRLBap1WrMzc2RyWQYHBxUnrnQEj329fVx/vx5oGUfXalUli1ybAXquplnuVzG5/PR39/P9PR0G/GjJ0A4deoU586dY2RkhOHhYcrlMpFIhJMnTyoFozxzx44dhMNhRkdHlamfTvHq61g4AR3IdUWjc23rxKQo2t1uN0NDQ8q8UADdtlsWWNeuXWsLAXC98roAalgJvKOzQM78dE6zO13h54yVK7uZyNvkvIC5sHpiuys2oeJ+7VRI6u+C1YtyLXmT/p9lWfz7f//v+Yu/+AslUxON9S/8wi8wPj6u7umkIJV3S12cispO9ZBrREYpG+DS0hIvvvgi8XicgYEBXnwRAoGfBJ7CsgxcrqPAR7DtN+ByPbXqPbadxzTz2PbE8ri1Rzjs7x9Sx9u3b2dgYIBIJEI6neby5Wnm5mrkci7q9TC5nEm9HgYSy584weAQweAQxaKXWi0MxGg0IjQagVVtbC81DKOMYVQxjPqyCMSmZanhxra9yx8fK0ugNa6VSohqleX/oFYLY1lvAvyMjR3i1lu/x9DQBSxrtTmo0xlKt1yR/lkRxxhAve3aZrNIo3F1OS1YL0eO9C9HyyvR09Py1Jmc3MmpUw/g8bQ2U7f7u9TrX8Xl+jCm+VQbl6frdGTsTNNUmVMqlQqhUIjbb7+da9euqdCbQhmK/FifwwL+wsYLOAkA+Xw+5R0o1iCLi4v09PSwuLioPA5tu5VZfOvWrViWxezsrBK1iG227l7tnNf6ZjM6OsrY2Bg9PT3s3btXWcQ4297b20tvb6/yo3DqC6TosdDlmk5EkfSzLtMWU8WdO3fS19enxCi674fEf3Haaa9VrgvUhmHsBv5GOzUC/Abwl8vntwJXgQ/Ytp02Wq35HHA/UAJ+0rbtE9d7j65QEFBxegvp7to6Nav/1l2yXS6X8jSUgOUSGEkAeWlpiTe96U0sLS2pwEp9fX1tddGpa91kR/539FfH8/r1r7zyCg888AALCwvUajV6e3vJ5XJtpkdO8Uqndzhlbfq9snEJUJqmqQIC6fKxQqFAoVBgYmICj+cojUZ5WTwhNqVHgaPYdnfK3nlez8psmqayLpB6eTwe/H4wjFlCoRYlFY8bzM/PK4oEIB4fZNOmTczOzlKpVNi6dSvbt28nkylSrQYwjAS1WohKJUC57KfRCFEs+kinbSqVILq4xrJiWFYQ246xtgjCxrZDeDx1PvKRr9HXd5aJiR38wz98mBtvPM7p00dIJs+wceOoGgMBaKeepdNmvlYf6nEsxBHI5XIpV2eAkyfdBIMfp15/EjAwjKdwu38C2z6MYRxdRazIs/W5u3nzZqXgq1QqfOMb31CgLNxko9FQSX1t21bOYbq7u5jtVSoVAoGASjgsGdole48QS6ZpKm7R4/EQi8UUN7thwwbGx8cV1+ukeJ396VxfjUaDVCrFsWPHiEajbNq0Sbny68SdbduMjIwQj8e5cOFCm4JUihAbQsw55edSxNNQAFc49Z07dyqLHCdhJ9T2a80udF2gtm17FDiw/DIXMAV8A/h14Pu2bf+eYRi/vvz714D7gJ3Ln9uAP1n+vm7RvYN0IJYd2Glbq9WxbTLqFDCggHB0dFQFTm80GoyMjFCr1bhw4YKaLLt27WrzPtQVDjoVq2uBO00g5+TSNxQJ2fnd736XZDLJj/3Yj5HNZtue2w3onWCgU3J6X+g2tjKBvvnNb1IqlZTzi84hWJaF1+tVFJWU9ShU9XoHAgEKhQJ9fX309/ercJ0DAwNEo1Hq9Trz8/NcvXpVUSzNZpMDBw7g8/lIp9M8/vjjq6gViZxXKBTIZhcJh8MEAhXlMizXnD17lkYjz+bNcZVFO5vNqqBOLVl2ZFkkE+O2296BYfRQKHg4dcpNsfhBbLsFHufOncPn8/P00+/jIx95GK/3Gfr7T/H97/8sd931JwwOnlfvlT50jk+3PnRyS7ZtK4W29P/LL7+sKNtgMLgcCOwbNJtVNfYt8Gxtprq9fTcWvaenh927dzM1NUUmk2FkZASv16tSpMViMXp7e5mdnSUYDCpglihzEl1PMpOIGHLLli34/X5qtRoul4t8Ps/09DTXrl1T4y7rWkKNijx9aGiIc+fOrYpz063/nLog6S8hPmS8z58/TzQaZXBwkM2bNxMMBhXRFYlEOHz4MIVCgbNnzyrAFoXkzMwMExMTbZYyTkWkrniUDSsUCrFhw4Y24slJketmgustr1X08SPAmG3b44ZhvBd46/L5vwCepAXU7wX+0m7NkucMw4gbhjFo2/ZMt4fqO5YOwp1kbMK2Q7shu1OmpFPZ0ErdMzExgWVZDA8PK5YmFAoxNzdHLBZbJQd3Uu/6ItTtIp0TRy+6/Fzug5ax/sc+9jGq1SpTU1PqWicr5tzNnWCgs2c6x6FvagDnz59XsYUlTrOkwRIuRKfAO22EznY5NxTDMKjVaiq/XiQSUeDz1FNPMTIyoiwFVhwyLJUgwLZbFgk6JShstGjLFxcXlSxS7INlwYhXaTabJZfL4fP5CIfDDA4OMj09TT6fXxanZYEMhgEjI3toNC6xuLifev0TeL116vXfx+P5T5w+/V84ePAiN9/8O2zc6GV+3mJg4BXuuutPSKW2KqCG1Q5WUv9Oc8JJ9Uo/mqapNh1xyRaglv7SWWgZr04Up5PrkvLAAw+wbds2SqUS9913H5VKhUuXLinOU8bGsiwVX9o0TRUwShdLynuj0ajioMQWGlpcweLiItlsVlHm2WxWUeQy3nKNXve1RIn6/84+FowQnCgUCly8eJGxsTE2bdrE1q1blbNbo9HA7/ezf/9+JiYmmJ2dxbZtxsbGcLlcZDIZAoGAsrsGlC5LjnURTL1ep6+vrw2fOtVbRLX/mkD948DXlo8HNPCdBQaWj4eACe2eyeVzbUBtGMYngU/Cyu60fB45JxStaFH1bCQSQhBWwF0HMH3SSgQut9vN5OQkMzMzKqebxI+OxWLLrtXFVeZFsqB043zoHM5Tp1CldKK4Go0G8/Pzq8Q7UnT3U51i1sMmyvskYabESRa2rVAokM/nyeVyTE1NYRiGFua00pbWSo93Le93cgf6cTfWVDa/t7/97WQyGS5fvozH4+H+++/n0qVLbX1br9fJZrNtsZVl8jrFW6ZpKnfheDyu7HP1+NG2bTM4OKiyZlerVer1Or29vUrzL6Ew9XFwu91cvXo7YPORj3ydr3zlv7JvX4YTJ/4vSqUSg4OvYhgHVL0GB8+3gbS0XxcpOZXQTs5KNki978RNPRaLKTGRc7HriTWc7+lUdOuFaDRKKpWiUqmwY8cOLMuiXC5z4MABrl27xtzcHBKU3+/3qw3d7XaTz+cVBS9rUc9enk6nVTTBy5cvK+CSfIcyN4eGhpRrt2maBAIBrly5okQgzvnWrTi52E7iCaHiBUvGx8cZHx9XISISiYRK47Zt2zbcbjdTU1NcuHBBpaprNBoqxZfODYuYqtlsKi602Wy2pbfrVFcd3/5VlImGYXiB9wD/2fmfbdu20bKLWnexbfsh4CEAr9drO8FAlwXrlIQApzRSqLFaraYUH7qXG7SnzAEUOF+6dEkB0pYtW1haWmJ8fFwpS/r7+0kmk8RiMfUe3VZTBzJ5tix8oUoKhYKyZRWRiyhrhHrS6ybHIobQNyrpD0lDpMvOdSN/HdQF6GOxmHK5bTQazMzMKFtQKTpwiIecUya/PBfWpGgGBgYYHx8nEolw9913E4lEGB8fVxYGIvsbHByk0WgQj8fVcyQLjWG08kzOz8+rhSA5JyXribgJS8xkeb/IPiUeiLDi8Xh8ldeYBPaZm3uWO+54hKGhFjX2nvdE2Lv378lkdnLt2jQHDx7syEHo/eEMwi/v7tRX0rcyn+v1Oo899phqv4yjPsdM01yW7/vb2uCsj150IkNM2fr7+zEMQ+Xvu3DhQlvfTk9Pk06nVXYWj8fDwsLCKqpVTMyEGJB5Nzc3h2EYy/qElnWHXJfNZlWkyng8zvj4OIcPHyafz3Px4kVlpbXeom/m0l59jPTwo7KpZ7NZjh8/jm23FJp79uyhr6+Pffv2sXPnTrLZLDt37qTZbCqiJpVKqSBTsGLpIXJ+CYugO+R0EolKXUXZut7yWijq+4ATtm1LoMk5EWkYhjEIzC+fnwI2a/dtWj637qLvjrAiB5PzupxIFofsdM5Oknv1Xdfn86nElpLxQveSksEVzyhABXARihDa7bcFoJ023QJ+spkEg0ElLxUQ1hecTtHqk6JSqag6CEjr7RRQ0J+lA7xMONkopL7yv/SRuN8Ky62Hbuw0RvrCkE1G0oCFQiGuXr2KYRiEQiEOHDjA1NQUY2NjSjwRCASIRCJtLKE8p16vk8vl1AbtdrvZuHGj2rQlWYAAuWwC2WwWCWhvWRbz8/Mkk0kFgoODg9RqNWUSWqlU2Lbtb6lUGlSrd+Lz+bhy5QpXr/4T4+Pj9PX1kclk2uaeXpx9IOOo95muy9CBXebx2NgYpVKJgYGBthjVOmgDan5ez2JAxlXAXtJ3iVeg1+ulv79fJVVIJpOcPn2aarWqsoXra7BcLre1R0ROMh/FzE5cuHt6elhYWFBtlHnvcrmoVCoqVrQO5jt37uTMmTProqhfS3FupDqeFAoFTpw4gcfjYXBwkI0b/9/2zi42rqOK479jb+Ik9qZeY7xpIUrSUIMqohArQk7gAQGqqwbxVCFFSEQlEjwUURASUsRDxVOKhChFQqiILwWhFCgVrSJEBCVvkQJtQLHTNDiVgz+CyeJkHbzrVRJneJg549kbO7Ed13d9M39ptXvnXt2dM+feMzPnzPzPQ56jBGYJlIrFIhMTE3XuRyVW0oBp2C5zdehapm6Zd8v1sZ9ZtwfAa8AB4Dn3/WpQ/hUReQkbRJw0d/BPK0LjlvzWhg2DjIBf6hNOJ9Q46cMdGng1xLoqoVqt+kznxhgfzEgGCDXJZnNzs0+EGbphVAEa2VZlhX4oneqGL1hyPWcYEQ4J03Wlylyul6Sx1PZSQxyOultbW72fVj/asYTTdg3chf8XPqBzvUjaxuvXr+f69eu0t7dz7do1tm7diohN1NDf38/MzAz5fN53XFeuXKl76ZOdrRrjNWvW0N7eTltbm0/uGq7uCWk1tQ1qtZpvy7GxMarVKp2dnXR1dVEul+v4Jvr6+jh69ChHjhzh5s2bDA0NsXPnTnbv3k1bWxtjY2N16/bnQ6ifcIAQrhrS53JmZoZyuczZs2cZGRnxL39TUxNnzpypu4/KpLrQab0+B1o+n1+0p6eH3t5eKpUKo6OjjI+Ps3mzHU/VajV27dpFR0cHg4ODTExM3EZqpAOZ5ChRU2Elfdbqpmpra6NUKvmOX/cqjI+P09XV5TeyaGoq/Y/55FgIkoYxOXgL3Tdhuw0PDzM8PEyxWHRsjO0+9+KNGzfo7u72adT0mQ23kBtj6naAJpHsPKfuvH23Dgsy1CLSimVl/3JQ/BzwGxE5CPwL+Jwr/wN2ad4F7PK8p+52//ABC90c4YOhI8HQ55Yc3SSZ79QPNBsdn+Vj0JHE9PQ069at8ymbdHQWugTUIIYBu7DeyeVZoe8uXPoT0iaGnAhhgEhfinn0wNq1a31euPDlCF0napzDNlN3jroZNEinbRbeL+xodIQd6iCsT/L33r172bdvH0NDQ5RKJU6ePOl5vTs7O9m4cSOXLl1iy5YtDA8Pc+vWLT9dVOMV6it097S0tHgfa0tLi0/0oB2gzoByuZzfWKGcxDqr0BH2hg0b/MhJA2p9fX3k83kOHz5MsVj0mbR1xJfsFOea4oZ6CGdMmiOxXC5TLpfrgqlNTU3k8/k6giN1f6mLQztOnb5rkCvUTTiDUoOgncumTZvYsWOHf9bCdFtTU1PesHZ1dTE5Oel3+QE+kKiyGmN8VnWdzXZ2dnpKU9VprVbj4sWLPhOS+rXL5TIdHR2e5a5YLDIwMOCDkMs9op4LyRmNQsSuaCmVSogI+XyeYrFIoVCgUCj4YLUmJZiZsRl/wuWuYTuF8miZ+rKTG9vuhAUZamNMBXhPomwCuwokea0Bnl5wDQKE/tDQcKmRDIVPrvAIp+j6kIZrd5PK1+2xIZGMEpCHaxx1VBT6ffVhDNcK6z21YwjXZyanW8kVGSqDGkm9jxrLMLAa+q21nULjnOxktG2SvlNNgKoyhSNv/a/wOJypJA1UOFJRIp+enh56enool8sMDg4yMjJCpVJh+/btPuN2pVKhVqtRKBTqOrNkIFVnHmowcrmc34bb39/vfbbq+9YOUo21jgabm5uZnp72mzWq1arX0a1bdkv1iRMn/Cgwn8/fNhhIvnwKbQNNYFutVrl69aqn9tQ66IwrNKjGmLpsJmFAMpy56f+qfsPBSqib5OzKGMPly5eZmpryXNFhgN4Yu1lFOTS6u7s5ffq0r+PU1FRdfcEORnTvw8yMJVxqbW1ldHTU61DfMV2dEy4JrVQq3vDVajUf7FUX03JirvsljWdyoKhlmgMzl8vVxax0aSHMbnQBvL7ngs6E1P10t0BwnQwr0XvdtRIi/wPOp12PFUYnsLA8PNlAlDf7uN9kXg55txhj3nu3ixplC/l5Y8zutCuxkhCRN+4nmaO82cf9JvNKyrtUurKIiIiIiBVCNNQRERERDY5GMdQ/TrsCKeB+kznKm33cbzKvmLwNEUyMiIiIiJgfjTKijoiIiIiYB6kbahF5XETOi8gFsXSpqx4isllETojIWyJyVkSeceUdIvInERl03wVXLiLyA9cGZ0SkJ10JlgYRaRaRv4vIMXe8TUROObl+LZYvBhFpcccX3PmtadZ7qRDLDPmyiLwtIudEZE+WdSwiX3fP84CIHBWRdVnTsYj8TEQui8hAULZonYrIAXf9oIgcuNd6pWqoxfJb/xDLI/IosF9E5ktzvZpwE/iGMeZRoBd42smlHN6PAK+7Y6jn8P4SlsN7NeIZ4Fxw/B3geWPMB4CrwEFXfhC46sqfd9etRrwA/NEY8yFgJ1b2TOpYRN4HfBXYbYz5MNCMZdPMmo5/ATyeKFuUTkWkA3gWS6HxUeBZNe5LRnJH00p+gD3A8eD4EHAozTq9S3K+it2Cfx540JU9iF0/DvAisD+43l+3Wj5Y8q3XgU8Cx7As9v8FckldA8eBPe53zl0nacuwSHkfAIaS9c6qjpmlL+5wOjsG9GVRx9isVQNL1SmWF+nFoLzuuqV80nZ9zMddnRm4Kd8u4BSL5/BeTfg+8E1slliwlANlY4xShIUyeXnd+UkSFAWrANuAEvBz5+75iVhOnEzq2BgzBnwXGMZyy08Cb5JtHSsWq9Nl13XahjrTEJE24HfA14wx18Jzxna1mVhyIyKfAS4bY95Muy4riBzQA/zIGLMLqDA7JQYyp+MCNnvTNuAhoJXbXQSZR1o6TdtQ3zN3daNCRNZgjfSvjDGvuOL/iOXuRpaZwztlfAz4rIhcBF7Cuj9eANpFRGkKQpm8vO78A8DESlZ4GTAKjBpjTrnjl7GGO6s6/jQwZIwpGWNuAK9g9Z5lHSsWq9Nl13XahvpvwCMucrwWG5x4LeU63TPE0nX9FDhnjPlecEo5vOF2Du8vuChyLwvk8G4UGGMOGWPeb4zZitXhX4wxnwdOAE+6y5Lyajs86a5fVSNPY8w4MCIiH3RFnwLeIqM6xro8ekVkg3u+Vd7M6jjAYnV6HHhMRApuJvKYK1s6GsBx/wTwT+Ad4Ftp12eZZPo4dnp0BviH+zyB9dG9DgwCfwY63PWCXf3yDtCPjaynLscSZf8EcMz9fhj4K5ab/LdAiytf544vuPMPp13vJcr6EeANp+ffA4Us6xj4NvA2MAD8EmjJmo6xyVH+DdzAzpoOLkWnwBed7BeAp+61XnFnYkRERESDI23XR0RERETEXRANdURERESDIxrqiIiIiAZHNNQRERERDY5oqCMiIiIaHNFQR0RERDQ4oqGOiIiIaHBEQx0RERHR4Pg/ovvIrfMUsgEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import cornermatching as cm\n",
+    "filtered_matches = cm.filter_matches(best_matches, secondbest_matches)\n",
+    "\n",
+    "print(filtered_matches)\n",
+    "h = int(len(I1))\n",
+    "w = int(len(I1[0])*2)\n",
+    "for match in filtered_matches:\n",
+    "    x1 = match[0]\n",
+    "    y1 = match[1]\n",
+    "    x2 = match[2]\n",
+    "    y2 = match[3]\n",
+    "    #print(x1,y1,x2,y2)\n",
+    "    \n",
+    "    plt.plot([y1,y2+int(w/2)], [x1,x2], color=\"blue\", marker=\"x\")\n",
+    "stacked =  np.column_stack((I1, I2)) \n",
+    "plt.imshow(stacked, cmap=plt.cm.gray)\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "def RANSAC(number_of_iterations,matches,n,r,d):\n",
+    "\n",
+    "    H_best = np.array([[1,0,0],[0,1,0],[0,0,1]])\n",
+    "    list_of_inliers = []\n",
+    "    \n",
+    "    for i in range(number_of_iterations):\n",
+    "  \n",
+    "        # 1. Select a random sample of length n from the matches\n",
+    "        np.random.shuffle(matches)\n",
+    "        samples = np.array(matches[:n])\n",
+    "        \n",
+    "        # 2. Compute a homography based on these points using the methods given above\n",
+    "\n",
+    "        H = cm.findHomography(samples)\n",
+    "\n",
+    "        # 3. Apply this homography to the remaining points that were not randomly selected\n",
+    "\n",
+    "        image1 = []\n",
+    "        image2 = []\n",
+    "        for sample in samples:\n",
+    "            obs = sample[0:2]\n",
+    "            obs = np.append(obs,1)\n",
+    "            image1.append(obs)\n",
+    "\n",
+    "            pred = sample[2:]\n",
+    "            pred = np.append(pred,1)\n",
+    "            image2.append(pred)\n",
+    "            \n",
+    "        image1 = np.asarray(image1)\n",
+    "\n",
+    "        image1 = (H @ image1.T).T\n",
+    "\n",
+    "        # 4. Compute the residual between observed and predicted feature locations\n",
+    "        inliers = []\n",
+    "        \n",
+    "        for i in range(len(image1)):\n",
+    "            obs = image1[i]\n",
+    "            pred = image2[i]\n",
+    "\n",
+    "            #scale\n",
+    "            obs[0]= obs[0]/obs[2]\n",
+    "            obs[1]=obs[1]/obs[2]\n",
+    "            \n",
+    "            pred[0]= pred[0]/pred[2]\n",
+    "            pred[1]=pred[1]/pred[2]\n",
+    "  \n",
+    "            \n",
+    "            #readability\n",
+    "            u = obs[0]\n",
+    "            v = obs[1]\n",
+    "            uP = pred[0]\n",
+    "            vP = pred[1]\n",
+    "\n",
+    "            #calc residual\n",
+    "            resid = np.sqrt((u-uP)**2+(v-vP)**2)\n",
+    "        # 5. Flag predictions that lie within a predefined distance r from observations as inliers\n",
+    "            if(resid < r):\n",
+    "                inliers.append([u,v,uP,vP])\n",
+    "\n",
+    "        # 6. If number of inliers is greater than the previous best\n",
+    "        #    and greater than a minimum number of inliers d,\n",
+    "        #    7. update H_best\n",
+    "        #    8. update list_of_inliers\n",
+    "\n",
+    "            if(len(inliers) > len(list_of_inliers) and len(inliers) > d):\n",
+    "                list_of_inliers = inliers.copy()\n",
+    "                H_best = H\n",
+    "\n",
+    "\n",
+    "    return H_best, list_of_inliers\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 0 0]\n",
+      " [0 1 0]\n",
+      " [0 0 1]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import cornermatching as cm\n",
+    "matches = filtered_matches.copy()\n",
+    "H_best,inliers = RANSAC(10000,matches,10,5,4)\n",
+    "\n",
+    "print(H_best)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import skimage.transform as skt\n",
+    "\n",
+    "proj_trans = skt.ProjectiveTransform(H_best)\n",
+    "\n",
+    "image_2_transformed = skt.warp(I2,proj_trans)\n",
+    "plt.imshow(image_2_transformed, cmap=plt.cm.gray)\n",
+    "plt.show()\n",
+    "\n",
+    "stitched = np.column_stack((I1,image_2_transformed))\n",
+    "\n",
+    "plt.imshow(stitched, cmap=plt.cm.gray)\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": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/.ipynb_checkpoints/final_working-checkpoint.ipynb b/.ipynb_checkpoints/final_working-checkpoint.ipynb
new file mode 100644
index 0000000..4cc562d
--- /dev/null
+++ b/.ipynb_checkpoints/final_working-checkpoint.ipynb
@@ -0,0 +1,280 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Project 2: Image Stitcher\n",
+    "## Assigned: 02.01.2019\n",
+    "## Due Date: TBD (probably 02.20.2019)\n",
+    "\n",
+    "Panoramic photography is ubiquitous, with nearly every digital camera having a mode dedicated to doing it.  Here's an example from the Italian Alps:\n",
+    "<img src=\"pano.jpg\">\n",
+    "Note the extreme aspect ratio: much larger than the 4:3 or 3:2 that is typical of most cameras; suffice to say, the camera that stook this picture did not have a sensor that was this wide.  So how are these things made?  Stated simply, multiple images are taken, mutually identifiable points are located in each of these images, and the images are warped such that these points are coincident.  The matching stage might look like this:\n",
+    "<img src=\"office.jpeg\">\n",
+    "\n",
+    "For this project, you will code your own image stitcher from scratch.  Despite the conceptual simplicity of this operation, there are a surprising number of challenges that need to be addressed.  A general framework for a stitcher might look like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cornermatching_addi as cm\n",
+    "import skimage.transform as skt\n",
+    "import numpy as np\n",
+    "\n",
+    "class Stitcher(object):\n",
+    "    def __init__(self,image_1,image_2):\n",
+    "        self.images = [image_1,image_2]\n",
+    "        \n",
+    "    def find_keypoints(self):\n",
+    "        \n",
+    "        # Guass kernel for convolution\n",
+    "        g_kernal = cm.gauss_kernal(5,2)\n",
+    "        \n",
+    "        # Calculate the harris response of each convolution of I1, I2\n",
+    "        H1 = cm.harris_response(cm.convolve(self.images[0], g_kernal))\n",
+    "        H2 = cm.harris_response(cm.convolve(self.images[1], g_kernal))\n",
+    "        \n",
+    "        # Extract the keypoints from H1, H2 via non-maximal sup\n",
+    "        key_pts_I1 = cm.nonmaxsup(H1)\n",
+    "        key_pts_I2 = cm.nonmaxsup(H2)\n",
+    "        \n",
+    "        # Return the keypoints of I1, I2\n",
+    "        return key_pts_I1, key_pts_I2\n",
+    "    \n",
+    "    def generate_descriptors(self):\n",
+    "        \n",
+    "        # Get the keypoints to generate descriptors from\n",
+    "        key_pts_I1, key_pts_I2 = self.find_keypoints()\n",
+    "        \n",
+    "        # Get descriptors for I1, I2\n",
+    "        des_I1 = cm.descriptorExtractor(self.images[0], key_pts_I1)\n",
+    "        des_I2 = cm.descriptorExtractor(self.images[1], key_pts_I2)\n",
+    "        \n",
+    "        return des_I1, des_I2\n",
+    "        \"\"\"\n",
+    "        Step 2: After identifying relevant keypoints, we need to come up with a quantitative description of the \n",
+    "        neighborhood of that keypoint, so that we can match it to keypoints in other images.\n",
+    "        \"\"\"\n",
+    "        \n",
+    "    def match_keypoints(self):\n",
+    "        \n",
+    "        des_I1, des_I2 = self.generate_descriptors()\n",
+    "        \n",
+    "        best_matches = cm.get_best_matches(des_I1, des_I2)\n",
+    "        secondbest_matches = cm.get_secondbest_matches(des_I1, des_I2, best_matches)\n",
+    "        \n",
+    "        \n",
+    "        filtered_matches = cm.filter_matches(best_matches, secondbest_matches)\n",
+    "        \n",
+    "        return filtered_matches, des_I1\n",
+    "    \n",
+    "        \"\"\"\n",
+    "        Step 3: Compare keypoint descriptions between images, identify potential matches, and filter likely\n",
+    "        mismatches\n",
+    "        \"\"\"\n",
+    "        \n",
+    "    def find_homography(self):\n",
+    "        \n",
+    "        # Get the matches between the two images\n",
+    "        matches, des_I1 = self.match_keypoints()\n",
+    "        \n",
+    "        # Now get the coordinates from the matches for RANSAC\n",
+    "        match_coords = []\n",
+    "        for match in matches: # filtered_matches:\n",
+    "    \n",
+    "            match_I1_x = des_I1[match[0]][2]\n",
+    "\n",
+    "            match_I1_y = des_I1[match[0]][1]\n",
+    "    \n",
+    "            match_I2_x = match[1][2]\n",
+    "    \n",
+    "            match_I2_y = match[1][1]\n",
+    "    \n",
+    "            match_coords.append([match_I1_x,match_I1_y,match_I2_x,match_I2_y])\n",
+    "        \n",
+    "        \n",
+    "        x_offset = [0, 0]\n",
+    "        for match in match_coords:\n",
+    "            if(match[0] > x_offset[0]):\n",
+    "                x_offset = match\n",
+    "        \n",
+    "        # Get the x_offset val from x,y tuple\n",
+    "        x_offset = x_offset[0]\n",
+    "        \n",
+    "   \n",
+    "        # params needed for RANSAC\n",
+    "        \n",
+    "        num_iters = 1000\n",
+    "        r = 3\n",
+    "        d = 3\n",
+    "        n = 4\n",
+    "        \n",
+    "        H_best, list_of_inliers = cm.RANSAC(num_iters, match_coords, n, r, d)\n",
+    "        \n",
+    "        return H_best, x_offset\n",
+    "\n",
+    "        \n",
+    "        \"\"\"\n",
+    "        Step 4: Find a linear transformation (of various complexities) that maps pixels from the second image to \n",
+    "        pixels in the first image\n",
+    "        \"\"\"\n",
+    "      \n",
+    "    def stitch(self):\n",
+    "        \n",
+    "        H, x_offset = self.find_homography()\n",
+    "        \n",
+    "        print(H)\n",
+    "        \n",
+    "        proj_trans = skt.ProjectiveTransform(H)\n",
+    "        \n",
+    "        h = len(self.images[0])\n",
+    "        w = len(self.images[0][0])\n",
+    "        new_w = w+(w-x_offset)\n",
+    "        \n",
+    "        image_2_transformed = skt.warp(self.images[1],proj_trans)\n",
+    "        \n",
+    "        #Perform blending of images by taking max val of pixel\n",
+    "        new_image = (self.images[0] + image_2_transformed) / 2\n",
+    "        blended = np.zeros((h,w))\n",
+    "        \n",
+    "        start = 1\n",
+    "        for i in range(start, len(new_image)):\n",
+    "            for j in range(start, len(new_image[i])):\n",
+    "                blended[i][j] = np.average(new_image[i-1:i+1, j-1:j+1])\n",
+    "        \n",
+    "\n",
+    "        # Return the blended image\n",
+    "        return blended\n",
+    "        \n",
+    "        \"\"\"\n",
+    "        Step 5: Transform second image into local coordinate system of first image, and (perhaps) perform blending\n",
+    "        to avoid obvious seams between images.\n",
+    "        \"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will populate these functions over the next several weeks, a process that will involve delving into some of the most elementary operations in digital signal processing.  \n",
+    "\n",
+    "As a test case, apply your stitcher to at least four overlapping images that you've taken.  With a stitcher that works on two images, more images can be added by applying the method recursively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 8.31890317e-03 -1.68516913e-04 -9.78132102e-01]\n",
+      " [ 5.76808680e-04  8.13097590e-03 -2.07523702e-01]\n",
+      " [ 1.59996038e-06  1.03792556e-07  7.47172078e-03]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "#def stitchImages():\n",
+    "'''Returns the stiched images recursively'''\n",
+    "\n",
+    "images = [plt.imread('im1.jpg').mean(axis=2), plt.imread('im2.jpg').mean(axis=2), plt.imread('im3.jpg').mean(axis=2), plt.imread('im4.jpg').mean(axis=2)]\n",
+    "\n",
+    "#filtered_matches, best_matches = image_stitcher.match_keypoints()\n",
+    "image_stitcher = Stitcher(images[0], images[1])\n",
+    "new_image = image_stitcher.stitch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Image 1 and Image 2 before stitching\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stitched Image 1 + Image 2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot,ax = plt.subplots(1,2)\n",
+    "print(\"Image 1 and Image 2 before stitching\")\n",
+    "ax[0].imshow(image_stitcher.images[0], cmap=\"gray\")\n",
+    "ax[1].imshow(image_stitcher.images[1], cmap=\"gray\")\n",
+    "plt.show()\n",
+    "print(\"Stitched Image 1 + Image 2\")\n",
+    "plt.imshow(new_image, cmap='gray')\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": "3.7.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/.ipynb_checkpoints/project_description-checkpoint.ipynb b/.ipynb_checkpoints/project_description-checkpoint.ipynb
new file mode 100644
index 0000000..5beae5b
--- /dev/null
+++ b/.ipynb_checkpoints/project_description-checkpoint.ipynb
@@ -0,0 +1,2544 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Project 2: Image Stitcher\n",
+    "## Assigned: 02.01.2019\n",
+    "## Due Date: TBD (probably 02.20.2019)\n",
+    "\n",
+    "Panoramic photography is ubiquitous, with nearly every digital camera having a mode dedicated to doing it.  Here's an example from the Italian Alps:\n",
+    "<img src=\"pano.jpg\">\n",
+    "Note the extreme aspect ratio: much larger than the 4:3 or 3:2 that is typical of most cameras; suffice to say, the camera that stook this picture did not have a sensor that was this wide.  So how are these things made?  Stated simply, multiple images are taken, mutually identifiable points are located in each of these images, and the images are warped such that these points are coincident.  The matching stage might look like this:\n",
+    "<img src=\"office.jpeg\">\n",
+    "\n",
+    "For this project, you will code your own image stitcher from scratch.  Despite the conceptual simplicity of this operation, there are a surprising number of challenges that need to be addressed.  A general framework for a stitcher might look like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cornermatching as cm\n",
+    "import skimage.transform as skt\n",
+    "import numpy as np\n",
+    "class Stitcher(object):\n",
+    "    def __init__(self,image_1,image_2):\n",
+    "        self.images = [image_1,image_2]\n",
+    "        self.stacked =  np.column_stack((image_1, image_2))\n",
+    "    def find_keypoints(self):\n",
+    "        \n",
+    "        # Guass kernel for convolution\n",
+    "        g_kernal = cm.gauss_kernal(5,2)\n",
+    "        \n",
+    "        # Calculate the harris response of each convolution of I1, I2\n",
+    "        H1 = cm.harris_response(cm.convolve(self.images[0], g_kernal))\n",
+    "        H2 = cm.harris_response(cm.convolve(self.images[1], g_kernal))\n",
+    "        \n",
+    "        # Extract the keypoints from H1, H2 via non-maximal sup\n",
+    "        key_pts_I1 = cm.nonmaxsup(H1)\n",
+    "        key_pts_I2 = cm.nonmaxsup(H2)\n",
+    "        \n",
+    "        # Return the keypoints of I1, I2\n",
+    "        return key_pts_I1, key_pts_I2\n",
+    "    \n",
+    "    def generate_descriptors(self):\n",
+    "        \n",
+    "        # Get the keypoints to generate descriptors from\n",
+    "        key_pts_I1, key_pts_I2 = self.find_keypoints()\n",
+    "        \n",
+    "        # Get descriptors for I1, I2\n",
+    "        des_I1 = cm.descriptorExtractor(self.images[0], key_pts_I1)\n",
+    "        des_I2 = cm.descriptorExtractor(self.images[1], key_pts_I2)\n",
+    "        \n",
+    "        return des_I1, des_I2\n",
+    "\n",
+    "    def match_keypoints(self):\n",
+    "        \n",
+    "        des_I1, des_I2 = self.generate_descriptors()\n",
+    "        \n",
+    "        best_matches = cm.get_best_matches(des_I1, des_I2)\n",
+    "        secondbest_matches = cm.get_secondbest_matches(des_I1, des_I2, best_matches)\n",
+    "        \n",
+    "        \n",
+    "        filtered_matches = cm.filter_matches(best_matches, secondbest_matches)\n",
+    "        \n",
+    "        return filtered_matches\n",
+    "\n",
+    "    def find_homography(self,matches):\n",
+    "         \n",
+    "\n",
+    "        # Now get the coordinates from the matches for RANSAC\n",
+    "        print(matches[0])\n",
+    "        #for match in matches: #filtered_matches:\n",
+    "            #print(match)\n",
+    "            #match_I1_x = [match[0]][1]\n",
+    "\n",
+    "            #match_I1_y = [match[0]][2]\n",
+    "    \n",
+    "            #match_I2_x = match[1][1]\n",
+    "    \n",
+    "            #match_I2_y = match[1][2]\n",
+    "    \n",
+    "            #match_coords.append([match_I1_x,match_I1_y,match_I2_x,match_I2_y])\n",
+    "           \n",
+    "        # params needed for RANSAC\n",
+    "        num_iters = 1000\n",
+    "        r = 3\n",
+    "        d = 5\n",
+    "        n = 10 \n",
+    "        H_best = cm.RANSAC(num_iters, match_coords, n, r, d)\n",
+    "        print(H_best)\n",
+    "        return H_best\n",
+    "\n",
+    "    def stitch(self,H):\n",
+    "        # Create a projective transform based on the homography matrix $H$\n",
+    "        proj_trans = skt.ProjectiveTransform(H)\n",
+    "        I1 = self.images[0]\n",
+    "        I2 = self.images[1]\n",
+    "        # Warp the image into image 1's coordinate system\n",
+    "        image_2_transformed = skt.warp(I2,proj_trans)\n",
+    "        plt.imshow(image_2_transformed, cmap=plt.cm.gray)\n",
+    "        plt.show()\n",
+    "        stitched = np.column_stack((I1, image_2_transformed))\n",
+    "        return stitched\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will populate these functions over the next several weeks, a process that will involve delving into some of the most elementary operations in digital signal processing.  \n",
+    "\n",
+    "As a test case, apply your stitcher to at least four overlapping images that you've taken.  With a stitcher that works on two images, more images can be added by applying the method recursively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "#def stitchImages():\n",
+    "'''Returns the stiched images recursively'''\n",
+    "\n",
+    "images = [plt.imread('im1.jpg').mean(axis=2), plt.imread('im2.jpg').mean(axis=2), plt.imread('im3.jpg').mean(axis=2), plt.imread('im4.jpg').mean(axis=2)]\n",
+    "\n",
+    "image_stitcher = Stitcher(images[0], images[1])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filtered_matches = image_stitcher.match_keypoints()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[8\n",
+      " list([array([[100.33333333,  97.33333333,  94.66666667, 168.33333333,\n",
+      "        155.66666667, 100.        ,  73.33333333,  69.        ,\n",
+      "         53.66666667,  44.        ,  26.33333333,  16.66666667,\n",
+      "         11.33333333,   6.66666667,   5.33333333,   5.        ,\n",
+      "          4.66666667,   1.66666667,   1.        ,   1.        ,\n",
+      "          9.66666667],\n",
+      "       [ 86.66666667,  94.66666667, 165.33333333, 177.33333333,\n",
+      "         33.33333333,   4.66666667,  20.66666667,   4.33333333,\n",
+      "          5.66666667,   2.33333333,   8.        ,   4.        ,\n",
+      "          6.        ,   7.        ,   5.        ,   3.33333333,\n",
+      "          1.66666667,   1.33333333,   2.33333333,   2.66666667,\n",
+      "         16.66666667],\n",
+      "       [ 98.66666667, 177.33333333, 174.33333333,  34.33333333,\n",
+      "          1.        ,   7.66666667,   8.33333333,   6.33333333,\n",
+      "         11.        ,   1.        ,   1.33333333,   1.33333333,\n",
+      "          4.66666667,   6.33333333,   3.        ,   9.        ,\n",
+      "         33.33333333,  70.33333333,  97.        , 137.66666667,\n",
+      "        163.        ],\n",
+      "       [166.        , 167.        ,  44.33333333,   2.        ,\n",
+      "         13.66666667,  13.        ,   0.66666667,   0.33333333,\n",
+      "          2.        ,   3.66666667,  23.66666667,  40.        ,\n",
+      "         67.66666667, 105.66666667, 135.66666667, 152.33333333,\n",
+      "        159.        , 160.        , 159.66666667, 124.33333333,\n",
+      "        113.33333333],\n",
+      "       [166.        ,  46.33333333,   1.66666667,  13.66666667,\n",
+      "          2.66666667,   0.33333333,  18.66666667,  72.33333333,\n",
+      "         97.33333333, 124.33333333, 154.33333333, 168.66666667,\n",
+      "        163.66666667, 154.33333333, 139.33333333, 125.33333333,\n",
+      "        112.        , 100.        ,  91.66666667, 109.66666667,\n",
+      "         88.33333333],\n",
+      "       [138.66666667,   1.        ,  14.        ,  12.33333333,\n",
+      "          1.        ,  47.66666667, 128.33333333, 178.33333333,\n",
+      "        173.66666667, 166.        , 145.33333333, 110.33333333,\n",
+      "        106.33333333,  95.33333333,  85.        ,  81.        ,\n",
+      "         90.        , 103.33333333, 112.33333333, 133.66666667,\n",
+      "        150.        ],\n",
+      "       [151.66666667,  13.66666667,   2.33333333,   1.33333333,\n",
+      "         27.        , 135.66666667, 152.        , 119.33333333,\n",
+      "         94.33333333,  90.66666667,  71.66666667, 102.66666667,\n",
+      "        108.33333333, 116.66666667, 130.66666667, 149.66666667,\n",
+      "        164.33333333, 170.33333333, 170.33333333, 154.66666667,\n",
+      "        138.66666667],\n",
+      "       [152.        ,  18.        ,   1.33333333,   1.33333333,\n",
+      "         76.        , 172.33333333, 115.66666667,  88.        ,\n",
+      "        105.66666667, 129.33333333, 149.        , 146.66666667,\n",
+      "        147.        , 149.        , 149.66666667, 142.66666667,\n",
+      "        119.66666667,  83.66666667,  55.66666667,  18.66666667,\n",
+      "         13.33333333],\n",
+      "       [161.        ,  14.66666667,   2.        ,   2.        ,\n",
+      "         78.66666667, 156.33333333, 105.33333333, 149.33333333,\n",
+      "        180.33333333, 135.33333333, 118.        , 100.33333333,\n",
+      "         86.33333333,  59.66666667,  29.66666667,   8.33333333,\n",
+      "          1.33333333,   0.33333333,   0.33333333,   2.33333333,\n",
+      "         10.66666667],\n",
+      "       [159.33333333,  17.        ,   1.33333333,   3.        ,\n",
+      "         71.66666667, 145.33333333, 116.        , 191.        ,\n",
+      "        105.        ,  22.33333333,   7.33333333,   9.33333333,\n",
+      "          1.33333333,   2.33333333,   5.        ,  21.33333333,\n",
+      "         39.66666667,  58.33333333,  84.33333333, 110.33333333,\n",
+      "        118.66666667],\n",
+      "       [153.33333333,  19.66666667,   4.66666667,   8.33333333,\n",
+      "         61.        , 147.33333333, 132.        , 162.66666667,\n",
+      "         49.66666667,   1.        ,   7.        ,   2.33333333,\n",
+      "          1.33333333,  49.        , 114.        , 138.33333333,\n",
+      "        151.        , 160.33333333, 162.33333333, 144.        ,\n",
+      "        145.66666667],\n",
+      "       [156.66666667,  21.66666667,   0.33333333,   7.33333333,\n",
+      "         47.        , 155.33333333, 156.        , 150.33333333,\n",
+      "         42.        ,   1.        ,   4.        ,   7.66666667,\n",
+      "          1.66666667,  91.33333333, 170.        , 145.        ,\n",
+      "        120.33333333, 118.66666667, 104.        ,  97.33333333,\n",
+      "        100.        ],\n",
+      "       [153.66666667,  15.        ,   1.        ,   6.33333333,\n",
+      "         40.66666667, 158.66666667, 170.66666667, 172.33333333,\n",
+      "        135.        ,  89.66666667,   3.33333333,   2.        ,\n",
+      "          2.33333333,  93.33333333, 161.        , 128.        ,\n",
+      "        121.33333333, 150.33333333, 152.        , 176.        ,\n",
+      "        159.66666667],\n",
+      "       [139.66666667,  14.33333333,  12.33333333,   5.66666667,\n",
+      "         50.66666667, 157.33333333, 144.33333333, 133.33333333,\n",
+      "        167.        , 155.33333333,  26.66666667,   1.        ,\n",
+      "          9.33333333, 104.33333333, 169.66666667, 157.        ,\n",
+      "        149.        , 145.66666667, 124.66666667,  95.        ,\n",
+      "         81.33333333],\n",
+      "       [103.66666667,   1.33333333,   1.        ,   6.        ,\n",
+      "         47.33333333, 135.66666667, 130.66666667, 126.33333333,\n",
+      "        170.33333333, 158.        ,  38.66666667,   8.        ,\n",
+      "         20.66666667,  57.33333333,  73.        ,  59.33333333,\n",
+      "         50.        ,  34.33333333,  20.33333333,  21.33333333,\n",
+      "         15.66666667],\n",
+      "       [ 96.33333333,  16.        ,   1.66666667,  11.33333333,\n",
+      "         22.66666667,  77.        , 113.66666667, 150.        ,\n",
+      "        159.        , 104.66666667,  14.66666667,   5.33333333,\n",
+      "          9.        ,  17.33333333,  12.66666667,   4.33333333,\n",
+      "          0.33333333,   0.33333333,   2.        ,   0.33333333,\n",
+      "          1.66666667],\n",
+      "       [149.        , 109.33333333,  65.66666667,   6.66666667,\n",
+      "          8.66666667,  23.        ,  42.        ,  59.66666667,\n",
+      "         32.33333333,   6.33333333,   4.        ,  17.66666667,\n",
+      "          6.        ,   1.33333333,   3.33333333,  17.        ,\n",
+      "         30.33333333,  49.33333333,  86.33333333,  95.        ,\n",
+      "        104.        ],\n",
+      "       [127.33333333, 148.66666667, 170.        , 118.66666667,\n",
+      "         34.33333333,   2.66666667,  10.66666667,   5.        ,\n",
+      "          7.        ,   5.        ,   6.33333333,  19.        ,\n",
+      "          0.33333333,  69.33333333, 136.66666667, 129.33333333,\n",
+      "        134.66666667, 141.33333333, 141.        , 129.33333333,\n",
+      "        123.66666667],\n",
+      "       [108.        , 116.        , 134.        , 171.        ,\n",
+      "        109.66666667,  58.33333333,  69.        ,  91.        ,\n",
+      "        127.        ,  88.33333333,   9.33333333,   1.66666667,\n",
+      "          3.        , 114.33333333, 168.66666667, 131.66666667,\n",
+      "        118.66666667, 113.33333333, 100.66666667,  89.        ,\n",
+      "        109.        ],\n",
+      "       [103.66666667, 100.33333333, 112.66666667, 142.        ,\n",
+      "        166.66666667, 170.33333333, 167.        , 163.33333333,\n",
+      "        180.33333333, 119.66666667,   5.66666667,   2.        ,\n",
+      "         12.66666667, 108.        , 139.66666667,  91.33333333,\n",
+      "         88.66666667, 109.33333333, 114.33333333, 165.        ,\n",
+      "        167.33333333],\n",
+      "       [108.33333333, 104.66666667, 104.33333333,  95.66666667,\n",
+      "        121.33333333, 120.66666667, 105.66666667, 110.        ,\n",
+      "        148.66666667, 118.66666667,   6.33333333,   2.66666667,\n",
+      "         12.66666667, 111.66666667, 168.33333333, 150.33333333,\n",
+      "        158.33333333, 177.        , 181.        , 142.        ,\n",
+      "        139.66666667]]), 689, 15])\n",
+      " 335.8044010394387]\n",
+      "[10\n",
+      " list([array([[163.33333333, 168.33333333, 167.33333333, 168.33333333,\n",
+      "        174.33333333, 173.33333333, 172.        , 175.        ,\n",
+      "        172.        , 170.        , 181.        , 171.        ,\n",
+      "        176.        , 166.        , 170.        , 169.        ,\n",
+      "        173.66666667, 175.33333333, 171.33333333, 170.33333333,\n",
+      "        175.33333333],\n",
+      "       [167.33333333, 168.33333333, 166.        , 167.        ,\n",
+      "        175.        , 178.        , 176.        , 179.        ,\n",
+      "        182.        , 176.        , 176.        , 165.33333333,\n",
+      "        175.33333333, 174.33333333, 178.33333333, 173.33333333,\n",
+      "        171.66666667, 172.66666667, 171.33333333, 173.33333333,\n",
+      "        174.33333333],\n",
+      "       [166.33333333, 173.33333333, 176.33333333, 177.33333333,\n",
+      "        178.33333333, 175.33333333, 173.33333333, 176.33333333,\n",
+      "        172.        , 175.        , 178.        , 171.        ,\n",
+      "        175.        , 172.        , 176.        , 170.        ,\n",
+      "        178.33333333, 174.33333333, 174.        , 174.        ,\n",
+      "        171.        ],\n",
+      "       [167.33333333, 171.33333333, 174.33333333, 175.33333333,\n",
+      "        175.33333333, 173.        , 171.        , 171.        ,\n",
+      "        175.        , 177.        , 176.        , 180.        ,\n",
+      "        182.        , 176.        , 177.33333333, 172.33333333,\n",
+      "        173.        , 170.        , 174.        , 177.        ,\n",
+      "        172.        ],\n",
+      "       [175.33333333, 172.33333333, 172.        , 173.        ,\n",
+      "        174.        , 176.        , 178.        , 177.        ,\n",
+      "        183.        , 174.        , 160.        , 172.33333333,\n",
+      "        178.33333333, 174.33333333, 176.33333333, 173.33333333,\n",
+      "        174.        , 173.        , 177.        , 181.        ,\n",
+      "        177.        ],\n",
+      "       [168.        , 174.        , 182.        , 182.        ,\n",
+      "        172.        , 168.        , 173.        , 176.        ,\n",
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+      "        171.33333333, 166.33333333, 172.33333333, 177.33333333,\n",
+      "        180.        , 178.        , 172.        , 172.        ,\n",
+      "        176.        ],\n",
+      "       [178.        , 174.        , 177.        , 182.        ,\n",
+      "        183.        , 185.        , 184.        , 178.        ,\n",
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+      "        173.33333333, 172.33333333, 179.33333333, 179.33333333,\n",
+      "        169.33333333, 173.        , 163.        , 162.        ,\n",
+      "        175.        ],\n",
+      "       [158.        , 118.        ,  91.        , 107.        ,\n",
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+      "        154.33333333, 162.33333333, 140.33333333, 155.33333333,\n",
+      "        162.33333333, 180.33333333, 181.33333333, 163.33333333,\n",
+      "        167.33333333, 178.33333333, 170.33333333, 166.33333333,\n",
+      "        173.        ],\n",
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+      "        103.33333333, 113.33333333, 124.33333333, 114.33333333,\n",
+      "        136.33333333,  59.33333333, 101.66666667, 159.66666667,\n",
+      "        177.66666667, 188.33333333, 166.33333333, 159.33333333,\n",
+      "        170.66666667, 177.66666667, 180.66666667, 179.66666667,\n",
+      "        177.66666667],\n",
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+      "        179.66666667],\n",
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+      "         32.33333333,  32.33333333,  78.33333333,  95.66666667,\n",
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+      "        176.66666667, 177.66666667, 175.66666667, 172.66666667,\n",
+      "        171.66666667],\n",
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+      "         66.66666667,  80.33333333,  91.33333333,  85.33333333,\n",
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+      "        172.66666667, 174.66666667, 178.66666667, 179.66666667,\n",
+      "        175.66666667],\n",
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+      "         62.66666667, 107.66666667, 105.66666667,  91.66666667,\n",
+      "         96.66666667,  86.33333333,  97.33333333, 120.33333333,\n",
+      "        156.66666667, 178.66666667, 173.66666667, 177.66666667,\n",
+      "        174.33333333, 174.33333333, 175.33333333, 178.33333333,\n",
+      "        178.33333333],\n",
+      "       [115.33333333,  86.33333333,  49.33333333,  42.33333333,\n",
+      "         86.66666667, 110.66666667,  95.66666667, 105.66666667,\n",
+      "         87.66666667,  99.33333333, 152.33333333, 188.33333333,\n",
+      "        179.66666667, 173.66666667, 176.66666667, 171.66666667,\n",
+      "        177.33333333, 174.33333333, 172.33333333, 173.33333333,\n",
+      "        174.33333333],\n",
+      "       [112.33333333,  84.33333333,  55.33333333,  47.33333333,\n",
+      "         82.33333333,  98.33333333,  97.66666667, 140.66666667,\n",
+      "        119.33333333, 154.33333333, 193.33333333, 195.66666667,\n",
+      "        173.66666667, 171.66666667, 183.33333333, 177.33333333,\n",
+      "        174.33333333, 174.33333333, 176.33333333, 177.33333333,\n",
+      "        178.33333333],\n",
+      "       [115.66666667,  85.33333333,  67.33333333,  67.33333333,\n",
+      "         95.33333333, 101.33333333,  96.33333333, 145.33333333,\n",
+      "        166.33333333, 187.33333333, 177.66666667, 169.66666667,\n",
+      "        181.66666667, 181.33333333, 175.33333333, 177.33333333,\n",
+      "        175.66666667, 177.66666667, 178.66666667, 175.33333333,\n",
+      "        175.33333333],\n",
+      "       [103.66666667, 105.66666667,  97.66666667,  92.33333333,\n",
+      "        100.33333333, 107.33333333, 107.33333333, 108.33333333,\n",
+      "        184.33333333, 183.33333333, 177.66666667, 177.66666667,\n",
+      "        183.33333333, 180.33333333, 176.66666667, 180.66666667,\n",
+      "        178.66666667, 177.66666667, 175.66666667, 175.66666667,\n",
+      "        176.66666667],\n",
+      "       [122.66666667, 114.66666667, 106.66666667, 109.66666667,\n",
+      "        116.33333333, 111.33333333, 109.33333333, 117.33333333,\n",
+      "        167.33333333, 180.66666667, 182.66666667, 174.66666667,\n",
+      "        174.33333333, 178.66666667, 180.66666667, 181.66666667,\n",
+      "        170.66666667, 177.66666667, 181.66666667, 176.66666667,\n",
+      "        172.66666667],\n",
+      "       [124.66666667, 119.66666667, 112.66666667, 111.33333333,\n",
+      "        109.33333333, 105.33333333, 114.33333333, 131.33333333,\n",
+      "        167.33333333, 184.33333333, 187.66666667, 177.66666667,\n",
+      "        174.66666667, 178.33333333, 178.33333333, 175.33333333,\n",
+      "        182.66666667, 177.66666667, 172.66666667, 173.66666667,\n",
+      "        176.66666667],\n",
+      "       [108.33333333, 115.33333333, 117.33333333, 114.33333333,\n",
+      "        110.33333333, 107.33333333, 103.33333333,  99.33333333,\n",
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+      " 463.0766001149393]\n",
+      "[62\n",
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+      "         73.66666667,  51.        ,  49.33333333,  45.        ,\n",
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+      "         37.66666667,  40.        ,  39.66666667,  40.        ,\n",
+      "         39.        ]]), 659, 113])\n",
+      " 162.60390399960545]\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'match_coords' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-43-b24d0b225523>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mH\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minliers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimage_stitcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_homography\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_matches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-40-548e8ce63d93>\u001b[0m in \u001b[0;36mfind_homography\u001b[0;34m(self, matches)\u001b[0m\n\u001b[1;32m     67\u001b[0m         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m         \u001b[0mH_best\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRANSAC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_iters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmatch_coords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mH_best\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mH_best\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'match_coords' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "H,inliers = image_stitcher.find_homography(filtered_matches)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "stitched = image_stitcher.stitch(H)\n",
+    "plt.imshow(stitched, cmap=plt.cm.gray)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''port numpy as np\n",
+    "h = int(len(images[0]))\n",
+    "w = int(len(images[0][0])*2)\n",
+    "s = (h,w)\n",
+    "print(s)\n",
+    "new_image = np.zeros(s)\n",
+    "new_image[0:h, 0:int(w/2)] = images[2]\n",
+    "new_image[0:h, int(w/2):] = images[3]\n",
+    "plt.imshow(new_image, cmap=\"gray\")\n",
+    "\n",
+    "for match in filtered_matches:\n",
+    "    x1 = best_matches[match[0]][1]\n",
+    "    y1 = best_matches[match[0]][2]\n",
+    "    x2 = match[1][1]\n",
+    "    y2 = match[1][2]\n",
+    "    print(x1,y1,x2,y2)\n",
+    "    plt.plot([y1,y2+int(w/2)], [x1,x2], color=\"blue\", marker=\"x\")\n",
+    "plt.show()\n",
+    "'''"
+   ]
+  },
+  {
+   "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": "3.7.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Main.ipynb b/Main.ipynb
new file mode 100644
index 0000000..00493f5
--- /dev/null
+++ b/Main.ipynb
@@ -0,0 +1,303 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import cornermatching as cm\n",
+    "\n",
+    "\n",
+    "I1 = plt.imread('im1.jpg')\n",
+    "I1 = I1.mean(axis=2)\n",
+    "\n",
+    "I2 = plt.imread('im2.jpg')\n",
+    "I2 = I2.mean(axis=2)\n",
+    "\n",
+    "g_kernal = cm.gauss_kernal(3,2)\n",
+    "\n",
+    "I1 = cm.convolve(I1, g_kernal)\n",
+    "I2 = cm.convolve(I2, g_kernal)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cornermatching as cm\n",
+    "H1 = cm.harris_response(I1)\n",
+    "H2 = cm.harris_response(I2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cornermatching as cm\n",
+    "H1sup = cm.nonmaxsup(H1)\n",
+    "H2sup = cm.nonmaxsup(H2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cornermatching as cm\n",
+    "H1descrips = cm.descriptorExtractor(I1,H1sup)\n",
+    "H2descrips = cm.descriptorExtractor(I2,H2sup)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cornermatching as cm\n",
+    "best_matches = cm.get_best_matches(H1descrips, H2descrips)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cornermatching as cm\n",
+    "secondbest_matches = cm.get_secondbest_matches(H1descrips, H2descrips, best_matches)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([533, 344, 544, 108]), array([609, 486, 597, 291]), array([534,  59, 544, 108]), array([329, 246, 329,  57]), array([554, 463, 547, 271]), array([574, 308, 591,  23]), array([364, 467, 366, 278]), array([536, 193, 544, 108]), array([366, 467, 366, 278]), array([384, 269, 389,  83]), array([382, 224, 387,  33]), array([327, 482, 331, 292]), array([581, 401, 578, 219]), array([398, 281, 402,  97]), array([678, 334, 682, 152]), array([218, 416, 223, 231]), array([673, 514, 654, 314]), array([553, 332, 557, 152]), array([456, 223, 466,  33]), array([656, 318, 662, 134]), array([671, 206, 693,  14]), array([667, 224, 688,  34]), array([531, 402, 528, 219]), array([537,  32, 547,  42]), array([448, 473, 444, 279]), array([293, 259, 293,  72]), array([269, 251, 268,  62]), array([269, 244, 267,  54]), array([319, 219, 319,  26]), array([336, 208, 337,  13]), array([329, 207, 331,  13]), array([666, 207, 689,  14]), array([283, 474, 289, 284])]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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kR5jI9TBbMFTVySp0SieUxLaYtUlVJ1Vb3i0ivlIdfatzAZmBtPi93qTjZ2DlUb46XpIjPeR0Kl+xLq1cL91ioOr8DvkjPquqjxOyo7Y+CuBj0rU/BvCCYRhDAF5Y/g8AhwEMLX8eAvB1R7WxSaqJtFqwENPZBT2nZAcA3k3aaPVZDcmbE2Raz3HdKER94GTzlJP0zdB697fdcTVbPDQDV7GPVFq0HQXNDNjtkqXrwzCMlxhjg9Ll+wF8ePn33wE4AuBLy9f/3qjX4BeMsRbGWK9hGFN2KiMvJLrdblt+TDkPO9ecMs9ag5ndeq7VIirgbEFOpVU7HYPVTFDDcOY2sYqxldslb9RZK75aK9KFo62FS9Bqd6t4hINhqF8uYJdW20fNHIlA5ZoJISvrSrwmW8VyHmbPiG1QgbddanYEugXwnQbQvfy7H8CYkG58+ZolqTQ7s85TXf+XrgGvNa23hrma/mu2z9d7d+cmvX+IMWZro48MnLp54dTVYeZucUqrXkw0DMNgjDkumTH2EOruEYRCoYYGUAerfMiyprWeURt2tbq1JqflroUGuxFJ7gdd++wuOqn+6/jKjNabJ9ZjIV2Vp12Nfa3ngaz5qsa1mUV+q2fEezpAVWnPduKgVXnLZau0bLvULEfMMMZ6AWD5e3b5+gSAASHdluVrK8gwjIcNw7jVMIxbA4HAivtmizVWvxVlNQ1i7wfw+5fqd10rcND5IzcqOZ3EKqvF7qKojnTW7Gr7ze7zTl0Dqrx1eejaIV+zu+alA2mdm6QZq69ZoP4RgN9Z/v07AH4oXH9gOfrjdgBJu/5pwPmKuc4EkT8bjawm0Pvd3bJR678ReUFHusm8lot/FFWx3qGwzdBqQVp33wwfRO1aFZmhwxT5+nos0Fq6Phhj30V94bCDMTYO4MsAvgLgnxhjvw/gKoDfXE7+NICPA7gEIAfg95qtmM7sbcYl8F6kt2uCmaUxM/vtuARksuMiUeVr94Q8u/V1SjQJnITiyc+aaUHytXd74VBF8mKiHGanurcWpNPMVfesyC54rgfJ7gxaXFSRyN8iD4h+Z8pHxUNWc1F+ppmTMu1EffyW5tZHFGkNAF90VINl0mlhqjA83fO6a3aBaS2pGSC1k9/7SSv8l0Ab0TKwQ834dzc6Wfl9xTR2jiGVwVznt7aqhxWpBJ1T4bohdybSQuJ6hky9m4xp5uuym9bs/lqEHzopS5e3qGHYieAxo7XQyFV56Ca2inQW3VpaC0SqiWt2bTWm9VrMEdnio2u65+xq13Y2gcnXZcBWLf6pNq6sF6k0ZuIb6hsxWMIObUigVvlwVxPd8X7QFt4PJGoG7yfN3gqcnQJXs/y0FiD7XpAdt6OVW8kqf7t9IgOtGUhb5WE3raosMx5ijMHtdivTiB8nWvWGA2qqvB2XRzMTxql/SHfAje4YzY02Cc00wLUG27UUiGvtOjIrRwZgM8vAiuxMvrXwKb+bC4Bi/9vRlu2uwRA5OS9D9a3KwyytnN5sgVFXHztraDL/kBbtcrng9Xrh8diH3w0D1GagvBptWqZmdnvJ9+UFHfG+Ha3JivHXgsz6zKmJareOThbjmtG6dIBqVhd6TjdRvV6vto66dpvxi1MBY9aW1SzO2tGC7ZLKkrCqtx2XhY7M3BoqAFbNNbEOskasyl++pzumVMzfzm8RrN1uN1wuF9xuN/x+//vX9SH6pptdbbYCXZ0mrCpHN8nl51VMq3qvoK4cmXT1WyttvdnJu5YCxi7QOLmnm+BmeanegKKKqGimvbJf0iqtTKsRmu93d5+Vu8GOT1sGX3GRUAfe8n25DoB1ZIx4n/iLMQafzwev18v5wsl83jBArQJpM43GCfOLnUzmhhNtx+5ioFl9rQZFB/5iWc2Yu81snhDLtKqT2X1VGhXZAWU7fnFKIz+jE6QktOV8VeBt5QbR1W01AG+V33q4hNZi/Uen4KjI6RZrM4AzA1ddehG8qT5mbhAzjVkOgqD/Ho8HHo9H6VqzSxsKqGWw1mkzdrSx9fBvryYP1eLCWpVlNvBOwV2cBE77SK6HlTZJ6VXnYpvVwS5g2yHiNau6OhXazZAdvlXVaS0B+/2qiZu5SFTaskpTNzvrQ2Xhi4BMPCx+E0DL9WyGNgxQ12o1LnVkkLYD1HbBwa5WZKY9NjtBnGhDToSTCiDNnlOVLYOmGTnZAKMSIjRGMqCKY62adCqSrRURdO3kYeZ/FcnKojHrZzNBquMlK4Fg13W00YDXiRC1S3J+ZiCtelYH4FRf+paxSFwcFEFbBdCqejqhDQPUolQSjzcFnIGWXQ1S1WlOJ2Kzpr7u2Wbvy4DkBEDl/MxAnEjnynHiP9UBqi69rk5WC0lm+dMEc7vd3A2iW2zWmdtmi8dWWric1sq1JOZlJYztmNmrUT6s3DNm+YhlqQSt3brJglDn5jIjq3rqLHyV28PtdjcsTqvyozINw2h43ZcVbRigphVRuVOI1tp9IfshrSaiilRRH82eUaDSOO1qRaoJbmfSW/kCVa4JItU10kxE8LKybuwIFspblY5cSnZ3osm0Y8cOrv2Qv5q0q1qthnK5jGq1imq1inK5jEqlglqthkqlwtPIC9R2+l4kXd3tWobNamprcc60laWy1tqzFYlasVgH3ZnQwDW/tJl1Jbpm6Tr1Hx0qZ2ctisqsVCp49dVXbbdrQwA1aTUqX488Qc1imMX85OesTEvVApKYTgRO+ibNX8yb8lExqF1gpzKt6iSXYeaScGoRqPrOjqakAywnddX5auVJKJupYjnyPZW2Re42+VnRspM1JFGLIkFEQFCtVlGpVFZ8COir1WoDYNDLMczGWW6vDD52eUlsn5iHyv0kkxUYq9LphLwVcDux9FRtk3nVrjKiAmlZgxb/E2/oxs6qvJdffhm5XM72sxsCqD0eD6LRqOkLZ8VGigtzOmaRB1EGVRVTmQGROGHEQS2Xy9y3rtJCzchK4JhJZ7sbd5xqNKqJIQvNZiwZ+eAbs37W/Rd/qxZ+xLAqs2gCuT2qSa4jGdREnnS73Q2r+7KLR6wHATtp6qSll0olfr1SqawoS5wjcsSKCjRUfSbmadVe8b4OLHUCwC6JvGzHFdbMoUYiyfNddl9R/ioXh8fj4SDdjNVQrVZx7NgxLC0tvf82vBiGgYGB+jHWZud8OPH5yc/ZmYTyoOjSiN9m2p+OrDQYXVpgpTtBJ9RU+aj6wAw4ZQBTCTknfja5LmZCU/Vb/ohULpdXpNWVJW9Y0o2BlXvBTIMkUoEjgawYKirzjY7XCUToIwI9gbsoBERNnjHGx0sOcRWFls5fr+J5O1q6Trjp5orT6yqysgJUypnYJyRwRevK5/M1jK+ZEKT8xOvnzp3DmTNn+LOiILaiDQHUi4uLAPRaqGwCWpEOuOxIaztliJoS/VeVZYesAF3FnDLompmFVhaE7LuzMxnEtqlcPXa1HZU1ZGZOmwkYWSsT2yaXaYcPVOWr6mxFKiHuJI08LmIbKbrA7/dreVGlyZM7xjAMlMtlFItFfp1An55VCWK5vmIZojYq1t+MrA5icmoVWpEsBHXtod/k4jALsbWyoC9fvoxTp06tKM8ubQiglncCbXRSgZ8uDeAc/Myee7cAxinJmgmRnegQO4JUpaXJfWx3+34zfWg2Pu8F6XhKZ4GJmrxuQ4/4jGEYHMxFVwyBfLlcRrlc5mBP5cgLc5S3U/A1s+KsyE5a2SVF10SQJkFIfFWtVpWArOMD4rXjx4/j8uXL2nLt0IYAauDaW8dVpDMrzdKKZMeV4fSeeN9Ko9a5HHT1lZ9zUic76Z0KRTMXgJk5bJWnld/aiQlM10QNUBwf0eVB/10uF0qlEteUxHqUy2W4XC6oXhOnastakpP87PKoFcl9KYKWajev3F9yPrIrplarNfjfCehFN42osIngrnKniOWZKTI6BUJ+XuQV0cWhOsdHR5RHpVKB2+1GKpXCW2+9haWlJWU6J/NwQwA1mRU6v5zumtl1K1ovDcks3/UC2WYG/l8KyW2XXSDiPRX5fD5UKhV4PJ4G36zb7UapVFIenvN+728zwatLZ5ZG55akSC66FwwGG8oUBajscxfBXAyRpDQiyUqC7rA02WVHael5OYpDd3Kmih9EgVGr1bCwsICZmRludayWNgRQk3alAmsn2qoT/7CVdtusYNCBq+wzFbUWnX/Yql5mrgJVHcyu6ZjPjOQ2qcxuu6Rybcj3VP3jxDRWuVwSiQQKhQJisRhaW1uRyWRQqVQQDAZXaJZW+dlt33vpMpHJzGwX08harY5vKZ0d14cYgy5+SGiKfnXZZ07PkiumVCrxuHdRS6e0Mp+IbZIXCc20aLldMo9ns1kkEglejriG04zLg2hDALXK/HCqmeq0Hh1Z5dEsIMrPG4aBTCaDcDi8QtJTu/P5PDKZDDo6OlbsyrTK30rg6MBBxzQq8NZds+oTqzAqO+Bl1vdy/XRatdkEqVQqaGtrQ7FYxNjYGNeuA4FA00JZd98qP7m/VKb2asLSnJDK9SCSzh1llkaVt0i6HYoqFwtj1yJnXK76+c4E3vJzZriSyWRQLBa1/CxekyM+SMhkMhmMjY3BMAxEIpGGSBFVm5qhDQPUJP2caDA6YDW7ZkZmWrxZnlZgQz4+t9uNbDaLXC6HUCiEWCyGarWKQCAAv99vWS+n7dCRFRibpVVpJJTOajLr6il+N6N1yjxD/mdVbK4MEvPz84jH4wgGgzAMA/Pz8wCAtra2Bu3Qab2amZTvFgg7JTOlRTX+Vlq3Kh+zss181PL4mO3FkOtplo+qfPl/uVzGqVOncPnyZcTjcfT09PAFR9myIO3bTNk0ow0B1JVKBYVCAeFwmHeEzvEPmGu+a00qMLGTXhwo8muRCdba2opSqYRCoYCZmRm0tbVxU3s17TDzX1uRagKauWDk6zrhZhWJYWURqK6rBAJNDlELlEPFVHm3tbUhkUggGo2iXC4jk8nA5/OtaJNdPlstD4o+UNHnaWcHnN2jDOxExwD2QFL1m8jsWXqG2msXtMUxlflBdc+sfvLpnDplQ76fSCRw9uxZTE9Po1gswuPxwDAMHhUjboaRfdrN8seGAGpg5X57FTULmnap2U7UPSdL+Ww2i4WFBWzduhVutxuJRIIPrsq3916QGVOZAZ5VXnbAwWnb7U5GsxDBkZERdHV1IZVKwev1wu/3o1AoNFUfKk8ku1qy3XM31iKd07WDZsipdbVasgJnO7xipiiUy2WMjo7iypUrmJ+f5+WRC4QxtuI4ABIsa0GWQM0YGwDw9wC6ARgAHjYM428YY20AvgdgEMAIgN80DCPB6q39GwAfB5AD8LuGYbxlVY4ohURJvtbgqUrXjLltlg/QOFHoXqlUgs/nQzqdht/vh8fjQTqdRjwe5xqgjrnsML5u1xT9t+MPlp8X22W3PqIZKV6Xt+ialS8/I6ax63fX1ZfqV6vVsG3bNszMzACoR4CI4XhWCoFTH3KzJzvKW8VlMnM/2K2HHe1aLEO0YMSx1vGFam6LFoNclhWwi/mKbgWVQiDnRfey2Szy+Tx3uxLoptNpTE5OIpvNcuXKjtVoNT/EtqvqZUZ2NOoKgP/NMIy3GGNRAG8yxn4K4HcBvGAYxlcYY38M4I8BfAnAYQBDy5/bAHx9+VtLKp+OGa3WRSA/u1ZarFU+BMb5fB4zMzM8XKlUKplKeF1ZTrUUuc1WpqlZHlbAodJKZIDQadlOTVFZmMjPyC4QMX0sFkM4HEahUEC5XObnONBJeU7OY1C1bTXUrEWoc0PI6axcVKprKivLDkCr6iM/qwutlOsrplOVo3N7ielzuRwWFhawtLSEcrmMYDCIhYUF+Hw+nD59Gvl8nudrNZZivSg9+apFT8FqrApLLjQMYwrA1PLvNGPsLIB+APcD+PBysr8DcAR1oL4fwN8b9Vr9gjHWwhjrXc5HXYnlcByadFaSxwqoZdC3o41Y0WrS0UB7PB7Mz89zQJienkZ3dzdfSDTzi8pk5SayqqP4vF0m0gGAPB46xhSftzouwEnbVb50K7OXroXDYVSrVb4+wti1ELBqtWqrPiI5Bei1sBjNNDnxum48dJagnTqoxtquoFBdl60tp9q1WXliOhLAJJQppM/l69eiAAAgAElEQVTlcsHv93OgtrJm5LJEV4hYtlM+ksmRusAYGwRwM4DXAXQL4DuNumsEqIP4mPDY+PK1BqBmjD0E4CEAfHOBCK5iB5kBspCf8rfZ84r2rWk6IpfLhUqlAp/Ph1gshmAwiFKphK6uLt7uVCqF9vZ22/nbrYOuD82AzCx/HdNaWQR2TXGrQ6fkiazSqlX3RO1QBBe/349EItEQ2iWftLhWFpfYpvVOZ2WNyf/tumx01g/9Vo2BnE6lTdN/URuW3SnNWH6q65QvLdLSeDNW35QjLnBalSniFf1Xhdg6mWMqsg3UjLEIgB8A+F8Mw0hJHW8wxhzp9YZhPAzgYQDw+/2G7iQpuwy3msnklNGbLcPlcqGrq6vBnKbrFMu5XqBg16qw0k6aKV/HpE61eFmDEwFaFtpWpj5dr1ariEQiAKDUhFS0VgtEcl3Wm5yAN2C9DmI3RthMiJqVJY6vnf6R81Bp5+LvYDDI47DpuGJxMbAZvne5XCgUCggEAkrXC5XtNG9bQM0Y86IO0v/dMIzHly/PsGWXBmOsF8Ds8vUJAAPC41uWr2mpXC4jnU4rpaxdEFVNzPXQkO1qlOJ/l8uFbDaLQqHAd0GJgokYhNor1182reyQU8ZWlSmCtgoMndSJ0j/33AFs2zaL3bsn+XMXL27ByEgn7r33uPIZJ2Asa2Y6sBEPHqLn3i2/cjPWkB0NzaysZkDH7FnSHEkzFa+buabE8RGFrirmXfwvXtOlVdVRd1wrYwyBQKChDm63G9VqlYO1mVYu/6cyarUaJicnsX37dsuzi5zgjiVnsnpu3wZw1jCM/1e49SMAv7P8+3cA/FC4/gCr0+0Akmb+aap4Pp/nnUamh6OGCFtQzZ4VwdBpGbrnrfJ0uVyIx+Po7u5Ga2srWlpa0N7ejo6ODrS3tyMej/MXJ6jq30yZZvV2SmbPOi1/27ZZfOtb9+L8+T4wxnDhQj++9a17sW3bDO8rqzqoxlqug7iwo6ujnbqvh/Zsl+z2ux2esGqrWR6qvhbBk+7LeblcLu5akF0Mcn10dV4vCgQC3M2hq6cTony8Xi9f2yBajUVKZEejPgTg8wCGGWMnlq/9HwC+AuCfGGO/D+AqgN9cvvc06qF5l1APz/s9OxWhw0vECBCZVBPNjFajTdjJ10mZKg1P/q3SJpyU7bSddjQSO8/o6q3SCHfvnsRDDz2Pr33tPnR3n8fs7B7cddcppFIRvPnmTrhcNbhcNbjdBoAqPB6AserydYPfd7kM4XoNjNX4//p3/X79AzBWA3BNa6I6l8tleL1e5PN5vulFJWit+rGZe06ekbUwp/lajbNhGHjuuQMYHJzDrl0T/Pq5c724erUL9933tmkdSSOlcnTbweVn5bmuE6hiOqujCeyS1+ttyEvcQWw1N2QSF6ALhQJfe5I9BM2SnaiPVwDoSviIIr0B4ItOKkGdNTk5id7eXt5hToDWjJGbnSyr9c02U6/VDKbd/lJNMrPJpDo3w87EV5Xh8XgwNfVdlMt3Y2zsRgDAc899wE7zVk2M1RoAn7EavF6gXC6gUimCsQqCQS/8fi88HkpjwO2uP0Pf9Lz4v/678X79+rW0Yl7X7jf+JyFlfU/O1/o/CbBrgrAGGmbGGAYH5/DNb/4KHnzweezZM4WzZ3vwrW/di4ceep6nUZFqUVB2i5hpmCpBL+8yXO18lIl8yXReBwkIlV/ZrGx5XjNW37ko73pcLW2InYlerxehUAgTExN49NFH8alPfQo33nijpXa53rQWZeryKBaLPDQMWF24nVlZa8Xc8oSxEgqq+5VKBS+++CJefNEAUMJtt/0Cw8MH8JnPvIJt2+ZQq7mWPwy1mgvVKhN+A9Vq471KBTAMSle/V61e+9D/Wo2hUrn2v1IB/33u3CW43YDX64NhuFGpuFAuu+D1BhCPdyAcjoMxL2o1oFZzoVJxwTDEerhgGKyhbo31l//bfxnqu0GiBeNy1WAYDH/915+E212Az+fCF77wHHbvngJjKzd10H8zYLOzrd0KiNdy7svWkcfjgc/nQ7Va5VvBqUxxA5oTq4nas5Znt2wIoAbqjTt//jw8Hg9+/OMf46mnnsLhw4dxww03wDAM5cYDGTDknX1EKtCwM/iqAbCTTveM2+3G2NgY/vEf/xG5XA7lchnbt2/H0NAQdu/ejb6+voY3Vcs7DWWQlMuQtRqzdqkmnKqNKpeNaJ6K7+ATtRG3242lpSXMzMzg/PnzOHGi7jXLZD6IZPLriMf/AJ/73N0YGZnHww9/BA899Dx271YvZcj1lcPz5AUt+i8uWlH4HS0g0u8rV/4WHo8H4XAYHo+Hbx/3+XwoFotIJAqoVqvYsmULtm7diu3bt6O7u5tHC9BCMPk5ZR+uahwI9KtVBsC9LDhcXCAZhpsDvGG4VwgilUCrCw8XF1KGURcK4v+6MKEPhP/134AbIyMTGBnpQaVyE375l4dx/fWzqNX0VqATTVfWksVxEv3aKh+vFZjr+FdFlHcgEEA+n29YPKRYaif4QPgk8n8ymeS7rXVg70SJ2jBA7XK5+Jt5S6USAODJJ5/E888/j9/+7d9GZ2cn71DVVmlRgtnxu+pWsnXXdAOnAjGRaPIPDw/j6NGjmJqaAmOMbyGfnp7G9PQ0fvKTnyAajeKmm27Czp07sX37dr4JQ8xbVW/dKrIMYHJ61Wq6DDDEvCLYiYcEUZmFQgGzs7OYnp7G1NQUJicnsbS0xM1gGtdi8UbE4w8hFDoKj+de7N07jYceegEjI13Ys2e6YXxEwWQmpMS04sYpsa1iXUXT2ufzcZcMAS/1hd/vh9frRalU4u05c+YMstksuru7ceDAAQwMDDSc1aIqTzxAn16ayti1syE8Hg+83msLpPLCm9hOM8uL7umOEpDHVezXyclJvPzyy1haakOt9nXs3/8Ejh79GG6+eRR79kyZgozc1ypXiKhhi4Am734U60SgvVqSFzpFEseM1i9UW8LlZ0QSBQ6djy0qBVbjZoc2BFDTod8E0HSNdg197WtfQzAYxG233YabbrqJA4V8doQOyMxcAma+M7omMp14CDldo4EStYFSqYQLFy5geHgYmUwGgUCAx2u6XC6+fZzSx+NxlEolvPzyy3j99dcRCoX48ad33nknPw9EnLwy6cKKaPLKbdG9O09Vhgi4uVwOi4uLGBsbw+XLlzE7O8vb7/F4+OuXqD88Hg9/gWpLy8NgjMHrvXa06/XXz2DfvlkYxsoJpWqLfM6viogn5G95jGlBiZ4h85f6yu12IxQKIRgMNhxRWyqVcOTIEYRCIRw+fBjXXXcdKpVKAyjIZrZYZ/lgfFpMJ0FIE1yVlxj1oop8oXEiwKd0tVoNxWIRqVQK09PTmJubw8TEBK5cuQLDMFAu34nFxb9BW9sf4o47WnHffT584xv34Atf+Bn27p1e0b/EF/JckoFJFDIEwHLMugjQdE3md6dzW5dO1N7Fg5TErd9WZcj31ZbTygVVJxaISBsCqA3D4GAtOvcLhQK8Xi+CwSCq1SpeeuklHDlyBFu3bsVtt92GAwcONHSG7MA305BVZMdtIk8eqi8AnD9/HidPnsS5c+eQyWQQiUTg8/kQjUaRyWT4c36/v2ESEQMHg0GEQiFUKhWk02mk02mEw2E888wzqFQq2LdvH+666y60tbVpg+lV9Rc1TZLyIjgRwxJg0Fsq6EMTmkIoxRd+lstlXpbb7ebaJQGR3+/n7yUU+ysajfLD3sXJK9ZZ7G9ZK6LoDNnNQSSetSACgyyEkskkOjs7OUiLgE7vvqvVavB6vVxYlkolLCws8HQ//OEPUSqVsHXrVtx5553o7OxEOBzmfCwKcll7UwlLlZtHbr8I0PTaqkwmg8XFRSwsLCCbzXIroFAoNPQFPUMCgcYom70eLS3/Fl7vq2hp+Tz27p3DH/7hEYyMdGHfvlmlUiMDLN0Tx0MGagJgUlJkoBatYx1P24380M0Rsc5UD1m7t4r2EdunEq5iFIyMSU7BekMANZHITOIZrwRsFEY1OjqKmZkZPP/887j++utx6623oru7u2EyigyhkmLNgrhoshWLRZw+fRrnz5/H5cuXkcvlANQHmmI03W43CoUCKpUKGGN8FxxpPsSUInP4/X74fD6USiWk02kYhoFQKISzZ89ieHgYt9xyC4aGhhCPx9Ha2tpgvlM+9GJRmpCFQgGpVAqpVArJZBLZbBbVahW5XK7hXqlUapgEYkwp/S4Wi7xfRJeBvLvU7XajXC7ztojmfiwWa3iprAg+BNz0Tdqh6Nqi/qe+lMFd1tp0FI1GUSqV+C41UStmjPH/dEATCZxAIIBCodCgZc/Pz+P73/8+CoUCt5Li8TjXyL1eL9/wJAoGj8fDXydFCos4ntTWUqnE3x9YLBb5Jiryq4tCV3Y50HiIYySCtGEYiMe/sQygbm4B7t8/hxtumOfWjgw0shZMJG8BF60HIpUANdOu15KoPiJI05kf4st8rfiHeE20ZHRlve9dHwC4S0DU/ohRSbPx+Xw8bTabRblcxokTJ3D06FG0tLTg4MGD2LlzJ9rb2znTmnWSTitQuRhqtRpSqRRGRkYwMjKC8fFxJJPJFQxYrVbh8/n4izKr1SqSySQCgQACgQA/6pQmLJVB51JTPbxeL7xeLwKBAHK5HFKpFBcAp0+fxttvv83Di7xeL68nAYk4Iak/6Z7P50Mul+PCj/oUaNy1JwNopVJpYGb6yETAQemoXI/Hg3w+D4/Hw18aqzIzqU9EUJPHjUCvWCzyOslamY7Ee11dXfx8YQANrg8aJ+or2dVGrqlCoYBMJoNsNotQKIRwOMw17+np6YYyVf1Fgpr6io4ToNeB1d0S5YZ+EMeXgFl0b4hliffJ50sH3JMbkQQSubjEV0rJfSb/l8FbtmLEOSjyF+Uv7g4lEq0PeS7qyA6wy3lR34iL4mQVOhEOonVDi9OixWBVLyvaEEDt9/tXvKNONAlJ26Czgv1+P/x+PwcwOoT/2WefxXPPPcc3MUSjUQQCAe6CIA1GBB8AfFFJBNtkMol8Po9SqcRBlICXiJhOrB8JE6JEIoFwOMzPEqAJRivClAcJpVwuxzUsmrjRaBShUIjfn52dhcvlQjgcRiwWAwCu6YpCQ5T0pFmTVgaAgxylJQAUF9WA+qTOZrNwu918rEQzj5iaFgx9Ph/fLh8IBPjxAJRvrVZDPB4HY/U3P6usHRJApPXQoVZUVrFYRCAQaDjQi54VBbS8SCUL5dnZWbS0tDRs46c2kXCKxWLI5XIN/En9U6lUEA6HG94Qs7CwwCerqKkTr1B7ZKJrlD6bzTbwGrWDBKSojJTLZa6Ni7xO84jKpMVt+YB7WuSkvCKRSIPFIxMBOn2oP+mt4ZSv7AIR2yKPjer3epHIA/Lagejas8pDFvA0R8LhMFKp1JrVd0MANQEEcK0D/X5/A/AQMJOWU6vVEIvFUCqV+JujxcWVUqmEpaUlMMYwNzcHl8uFUCi04kAkAhgaIJKmpKUR0cAREBPjk4ZIGoi4cJPJZOBy1V9+2d3djVwux7UwAlAxb6AO4JlMBl6vl7t9RPMxHo8jEokgnU4jk8mgXC7zdpELRJwgslQX3S3yQpM4eeVwtmAwCJ/Px9OIb1cmzYkWTUulEkKhEABwAUWgR0JIjOKRJzMdA/vEE0/wQ5Pi8TgOHjzIeWV2dpYffJNOpxGLxRr6icZX/pa1NnLNiMAnarjETwTWsl+U2ks+82g0Cr/fj1QqhXw+zwW8CJy0iCpqngTS1N/UZ0Q0buKCMT1XqVRQLBbh8/kaXmcn80AgEEAmk2kY51AoxAGFBABZhaL7ifIiXhWtMXHNg9pJbjdxrlGd6b+sNYtWnKhsyL5fmXQuTvG3mUUt8rRswYl1NCubrGHqD1kBAq6NdzNa9YYBavm3CJ40UKQ90QSjxZh4PM59rQSkskbIWP0o0VAoxDuL8ibfsugjlbUWykfUsAEgGAxya0A09QjE4/E4n2SkdZOWRYwhUzwebwA9MvOpngD4YlwqleI+UtFsJ6YgXyNpdGL4GU10mhTiQiARmcV0gA1NSlEout1upFIpRCIRLkwDgQAqlQpSqRQHwmw2i2g0ikKhwBfwaMxF0ziVSsHj8eDkyZMoFou47rrrVrgdenp6eHui0SgAtWkp+6pVvlASnMQ3ZMKSpkn1I7CmulL/kWVAPEH+62QyyUP8RKFHdRQtSeoLsiKoveJirThGNIY0PqIgFTVXUSNOp9O8PTR+JBSoLKpHMBhckRf1F1msxGMi8BOv0dqM2PeiIBW/ZV81jRORmevDrltEzFMef3FtTLTO7GjVMhmGwS15cb1BrqtTsN4QQE2uA1EiidpNKBRCLpfjHUdgTRowMYbX6+X+UQJB8hWT+U8mLOUtvwLM6/VyZiYzlTR7oO4uoMnm9/t5mJ2obRiGgYWFBfT09GB2dhb9/f1IpVIcUEgIiP4xcRCr1SqPFIlEInyRicBPfM+iz+fjGjhQ97nOzs4iEomgVqthcXERQP1NJowxLCwsrKgvEWmrItiL5rjok6Y+KhaLyOfzDWNEGiNpuslkkoMImea0jkDlEkOTUJibm0NbWxva29uxY8cO3H777SuEEI0Z7SyjPGRwFv2iMoVCISSTSQBAJBKBYRg82oh4itpMb4wnQRIMBpFIJDhPxmIx+Hw+LC0toa+vD7FYDAsLC+jt7UVLSwuWlpZ43QuFAjweD5aWltDe3o5kMsn/E4CRIiGSqEwQ71A/iAJPjLIRrTRa4CXhtLS0BKAuXIrFIu9PcrWJvCEC89GjR7Ft2zZkMhkMDg7ye6Ojo+jr6+NjQmMug5UoQHSWjwqEVcJYda0ZIn5XuWvMiNKTwKIoNXGO2WmHGW0IoBbNbCKR4QgUy+Uy1+zS6TT33RKgkvkvukLEvAA0aBIiSFI6OtTf4/EgmUyiv78fAJDJZGAYBvbs2YN0Oo25uTn09PTA6/Vibm6OpyEtcsuWLVhcXOQTP5lMoqenB0tLS4hEItyPlc/nUSwWG1w3pOV6vV4sLS3xdrhcLnR2dgIArrvuOrzzzjsol8uYmZnB0tISotEoj2menp5GOBxGJBJBuVxGLpfj/mOaVATCFL5F4CtOdBIm5H+n58rlMorFIo/3JvOZolqWlpYQDocxNzeHzs5OzM/PIx6P8/Fta2vjoCOahBSJsGvXLuzbtw/VapWDJdVXBmLR9Fb5PUXzXVxQY4zhN37jNzAxMYH29nYOrCRcxTBIUQv1er144YVb4PO9jXS6C5HIWUQix1AqHcL4eC8ikbOYnd2NtrZvob29HdPT0xgfH+ebnDweD/cDt7S0cEGWy+VQrVYRCoV4NAfNC/nN1qKw8vl8XHMngU3jVKlUUCgUEIlEkM/nuavMMAxkMhmEw2EUi0W+WG8YBvfZy0LC7XZjbm4OhmHgBz/4AVpaWhCLxXDvvfdi165dYIw1WD+k8ZOSJGrQdF/lqtKRDPBWaa3u00cUaiIPWWnUorVB80R0Ycnf1PZmNvFsCKAG0LBwJO7KEickLYQQaBSLRRQKBe5+IMb0eDxYWFhoAGHR5CdgpzI8Hg9frU+n05yxWltbuR+4s7MTuVwO586d4y6FxcVF9Pb2orOzk2tMFy9exNDQELZu3YpsNotdu3Zhbm4OoVAI7e3tyGQy6Onp4QJndHQUHR0d3N9K7hZaKHv66f0Ih89gZKQTLS0XAfwMS0s348kn29HdHcDMzFbs2fMjdHd34+rVq+jq6uL+e+qLcrmM7u5u3mcUrlffKVjklgGlJ3eJaNaKMd8E7BRXTJoiLUAlEgnupiITmlwMAPhvUdugcQ4EAlhYWMDw8DA360+dOoVqtYp4PI49e/bAMAycOHECAwMDmJ+fR29v7wrtS6e5yRP9u9/9LgzDwOc//3m88sor+OVf/mU8/fTTOHr0KAzDQDgcxq5du3DzzTfj7bffRrVaxdjYGMbG3kIy+TAOHnwBR49+CYcOvYSf//wQuru/jStX/k/ccMP/DY/Hg97eXvT19WF0dJQLwkgkwicraeLkviMrCgAHd+pHiqsXXXLUt7LFUyqV+MJ0LBbjYZ40trTFOZ1Oo7W1FclkklsU4XC4we0hAmogEMD09DR6e3sxODiIXbt24frrr18BxOL6CtC4QUbnDlD9X2uS86Y1EXGdRlQg7AoFkZd7enoArHxbkap8u7RhgDoajTb4hYlIkyAzmwAgEAjwlW7SFklTJZ9oKpVCoVBoMAkJlEmLJy2RNpp0dHQgFArx+NhIJMIZt1wuc02wr68P09PTWFhYQCaTwcWLFxGJRHDnnXfiwIED+P73v89BmQ6cevnllzE3N4ft27fjE5/4BLLZLI4dO4a5uTlUq1W0tLTgvvvuQygUwpEjRzA7O4srVwaRTD6MXbsex2uv/R7uvnsfXn/9dnR3/z1Onvy/cNtt/xWhUB9mZ+fQ338j8vkSYrEWtLV1IBAI4/z5C4jFGIAgAoE4vF4Dly+PwOcLwucLwOWqAXDB7fbC52MAXCiV6tcYc8Mw6tdqNcAwgGoVy2kDyOUqyOWKMAzA4/HDMPzIZvMoFIoIhaJIJJbQ0tKO0dEcPB4fFheDKJcrcLk8ePHFPajVDNTPpKjnXT9TwoWJiRDS6TZMTk4hk8nB670BJ04EkcnkwNgUAoEQFhfb0dUVg9/fhW3broPL5YZhXMunVjNQq4GXUa0ay/fA7xmGgbm5XYhGY3j88Q5cvvwBzM7uRrV6H+bmFsAYw/y8C1euGDh7dhCFwv2Ym1uAYTDE4y1oa0vhpZc+Abd7Ai+8cBj9/ZcwPf0HuO++byIYHEWl0o2ZmRm43W7s3bsXqVSKh1wGg0EsLi5iYmKCg8TAwAA/UiAQCKC/vx+Li4toa2tDJpNBX18fd8uQZSQuLhJAEs/TfKAwUlqIpx3AlJ4sOgBcUAPgbgsqg+bL4OAg9u/fD8YYent7VwASrfGIax6i9SP6hilfOaJKB9g61weVq7quyoPWuagfKC353uVFY10+BOhietplLK5JUNpm3sMJAGwtfDurJa/Xa3z1q1/Fww8/zMOuSEMmxhIjKagD2trakEqlVvjm5OgE2twBAIuLiwiFQlxTue6667j/jrQL0ibouY6ODvj9fsRiMUxOTqKvrw9erxcjIyN8N+HU1BSmpqbQ09ODSqWCgwcP4uWXX0Ymk8HevXv5VutoNIoHH3wQr776KsbGxvDFL34R3/72t7lvvVQq4f7770elUsEzzzyDX/3VX8UvfhHEK6/8exjGxjp5bZNW0i23vI50+gH4fD60tbU1HPYkasHZbBY33XQTpqenkUwmkUgkcOuttyKdTiORSCCdTvOw0sXFRb6oXCgU4Pf7kclkGlyDBAjk/qMXtNJmK/I753I5zM/Po6urC6Ojo3wdJRaLIRKJoFKpYMeOHXjooYc4ABEIeb1eTE9P4/z586hWq5iZmUGxWMTevXuxc+dOtLa2Ynx8HF6vlytMBNT0TfUUv+k3ufxEq5c+onuUPiKwi9fNSHR5lEolvvmL3JbpdBqzs7MYHx+3BdbkAWhvb0d3dzdCoRBCoRB3N165cgWpVKrBtSnWs1QqvWkYxq1W5WwIjVr0G4sRCPRbXAUXJXWlUuH+PCKScJRHpVJBPB5HZ2cnlpaWsG3bNtRqNYyOjnItuaWlBTMzM+jp6UFPTw98Ph+Gh4fR0tKCtrY2dHV14cyZM6jVaujt7UU2m0UsFkM4HMbJkyfh9/tx//33Y2RkBPfccw+effZZDA8P47777sPLL7+MyclJHjZ10003YXJyEhcvXkQoFMLzzz+Pz33uc3jsscf4ca8/+9nPMDAwgP7+fhiGgWTyScRi9yGZvAPhcB7ZbBCBwCQ8nqsIhYJwuRgAWhgFyuUSKpUiwuEQAGNZYyrB5aJtrVUYRg0+n2c5SqaGcrmEWq3OTMViHm43g9frXl7kcsPrdYMW94vFAgyjAsaw7INkqFTKKBSyyzHWZWQyKUSjEZRKRZTLRcRikeU6uBAIeHH33XehVqND/Q3UX7lZ/1SrZbz66ivYtm0rstk0ZmenceDAjYhEQrhw4Tw++MFbkU4vIRwOoVaron6ucv3FAPV86mcwV6sV/r/e5nq7DaOyfK2G9vZ2fOc7j8DtdiEYDCyvDdQjibZvH8TIyAj8fh+q1QqSyaVlza+CaDSCiYkeTEx8EZHI20il7kR//0kMD+/FgQOfwtLSE9i+fTtSqRQymQz279+PWq2G2dlZ7r8nbTudTiMYDOL8+fPcZ0xuE/IDj4+P8/DLXC6H/fv34+LFi1zJII2Z+IzcWrQ4GIlEUCgUkEgkuF8cQINmTgucwWCwYc6JYZQUFvr6669jYWEB7e3tGB8fxxNPPIEdO3ZgeHgYO3bswJ49e3Do0KEVwGm20CtHqehcWVQX0a/shKgMebGUcIhcVFSu3QVFsb7UFjle3Y4wUdGGAGqgDsAtLS08SkF06JOpIndiKpXiW3TJ3KO01PFkxhGDUhgVRWAkk0lMT0+jWCwik8ng6tWrXCpSHDRQ1yZaWloQCAQQjUbx2muvoaenB4ZhYMuWLRgbG0N3dzcef/xxHD9+HIODg3j99dcxMTGBHTt2oFAo4MYbb8Rdd92F73znO4jFYvB4PJiYmMDTTz+Nz372s3jllVf4pJmYmOAHO3m9H0U2ez3i8R8hmfwkbr75FC5d2om77noM2exPsHPnTkxOTuKOO+6Ay+XCxMQETp06BcYY124CgQBmZmbQ3t6OdDqNzs5OpNNphEIhpNNpZLNZ7Nu3j0dpnDp1CoZhYOvWuvZHC0zFYpH7Pj0eDz84a2Fhgfuor1y5gs5OL2KxGBYXF7l2WSwWEQwG0dHRgc99bjsPsSQSIwv27Stgfv5VfojQv/k3u/Cznz2DYPAkpqcPYGAgjVDoBezbtw8AMDzcgXfeacMnP3mmQbDrEi4AACAASURBVCsTw+LoPwn6arWKJ574z9iypczN/6GhIUxMTCAUimJ29jg+9an78NRTT2F2dno5xLC+yNfR8QVMT/8htm59FBMTD+CGGx7FlSufxYc+9BzefPPLuOOOOCYmXkRnZyfm5uYQiUR49FA2m0WpVEIqlUIwGOTW2tTUFA9LjcViOHfuXEOk0MDAAHw+H9LpNCYmJvi6QyaTQSwW41Ed2WyWb1CiXaDlchlLS0sIBoOIRCKYnJxER0cHcrlcwxoFrdWQtk7jAoBrhWfOnMHtt9+OsbExAMANN9yAixcv4sCBA/jIRz6CgYEBXL58uWHRUwXORGYRHjSfmwFkuyQqiuIOVLEeVuBKmCMe8gWsjOVu1oPx3r0QTiDSjgcHB1EqlVbEXwLXVrYJbIE685IrhBiVdop1dXXxZzs7OxGLxTA3N4e5uTlkMhkeT93a2ore3l7s2rULu3btQn9/P/r6+hCPxxvOwGCMYWpqCufPn+fPp1IpHD58GP39/RgfHwcATExMoK2tDR/72MfQ3t7Oz2io1Wro7u7GP/zDP8DtdqOjo4Obwfl8Ho8++ijuuecevjGmWq2/0ueNN6I4d+7P0NX1LeTzd2HXrocxPNyPSORv8bOffQHZ7EHMz8+jtbUVr776Kp5//nksLCygs7OTLySSVXHo0CEMDg7C4/Fg586dcLvdmJycRD6fR1dXFxKJBN544w2cOHECt912G2688UYwVt/sMT8/j0QigUwm07D4Wq1WkUqlEA6HEQqF+Jbpvr4+fhBVS0tLQ/jdli1bOECLseQEDrVaDVu2bOFb769cuYJjx47hzJkzGBwcxODgLL7+9bsxO1sH6TNnuvG3f3snduxY5PkA1yagCAIyINCxs4VCAeFwmMeBt7W1YX5+Hj/4wQ/w6U9/Gj09Pdi+fTu2bNmCWCyGN95g+LVf+x58vhB+5Ve+ia6u/47BwS/h3LlL6Oj4H+Hz3ckPRPJ4PPjpT3+KX/ziF0gmk2hvb+fASPH2fX192Lt3L9ra2lAqlTj/kmkOAFevXsVbb72FkZER5HI5zMzMNNS7fn52gi8+UtQPUFdUIpEI2tvbMTU1xWN9M5kMf18nCUpx0Zf6kcZqcHAQ/f39iMfjaGtrQyAQwN69e3Hw4EG4XC50dXWhXC5jaGiIPyP2uwi+4ribLbqpQHotFhzlxVIS6uIuTTvAKi48kt9bVBTWgjaMRm0YBrq7uxvO/ABWHhLPGOO+NNKcxMO/fT4f9/kNDg4il8thenqam420K4608XA4zGNJaXMGaaDpdJr75VpaWrj5ePz4cT7Jent78cQTT8Dj8eCll15Cf38/Wlpa0NXVhUceeQQ7d+5EMBjEddddhzfffBMtLS1c63G73ejq6sL+/ftx6tQpfO9738ODDz6IRCKBSqWCXC6HkycP4vDh7+DixT488MDrePPNxxGLXUapdBP6+/8DIpGPYHLyCCKRCI9+oXjm9vZ23HjjjZiamsLc3Bz27NmDY8eOcaFDC6gDAwM4e/YsYrEYQqEQduzYgbfffptHs7zzzjsYGhrClStX+AIVbdRYWlqCz+dDPB7H3NwcstksOjo6+OIvafRk8ZBJDzRuyKCxIfrRj36EbDaLdDoNv9+PO+64AzMzM8jn8+jtPY8vfSmCv/iLX8N3v5tHLhfFwMASfvKT6/H003vhdlfBWA1uN716qgqXq4L6q7gquPYuxSoikW1YWEghGPRgfj6JubkSbrhhL86dO49Y7COYnh7HI4+ksGPHH+Dq1UsolXLo6YmBsXEcO3YBt912CwYGtuCppzrg8czggQf24tixGvz+f8L27QcxPPwWOjtbEYmE4XLVuN+4paWFh3AuLi6iUCigv78f77zzDu+Dnp4ejI+Po7e3FxMTE4jFYnyrent7O4aGhnDp0iVMTEyAsforoGhHKPmIgfq6DCk55IoZGBhAoVDgIa80z0qlEsLhMAA1GLrdbtx7770YHh7m/NLa2opvfOMbSCb/LT7zmQEsLv4Ad955J1wuF06ebMfFiy24//7zDe4UorWO+HCitaowRtzMZqcuZLlTGKIY4iruLlXl9b6Lo6bO7e7uXuEbo8krxo3SOQrZbJZP9mQyidbWVuRyOeRyOQSDQUxPT/OY0VQqBb/fj927d2Pbtm144403eIhcrVY/3pN8c8ViEclkEm1tbdxUJHNT3NBBEnfXrl2466678NWvfhWxWAwPPPAAnn32We7eyOfzmJychM/nQ0dHB29TIBDABz/4Qfz85z/nZxwPDw9jYGAAx44dQ1dXFz70oZexsLCA3//9T+Hs2Vdw+vQncPhwB+64o4jvfOc1fPSj7XjkkZuwtHQb9uz5ESqVCrZt24ZUKoVAIIC2tjZcuXIFuVwOV69e5cdxXrp0iWt2BKgf/vCHcfLkSSwuLqK7uxtXrlzh7oCxsTH09PTwxV6v14upqfobWUgjW1hYgNfr5WGNjDG+YzKfz/P+IDCRQzBJy/rmN7/J3QJTU1Mol8s4e/YsPB4P9uzZg927d6NaTWLnzvMYHr4BLS1L8PuryOV8qFSuveWkUqm/BYW+q1W6R29DUS/Ojo4CwK82XBseVvPu5cv067cAAG+/DQB/2JDm3DnxH71bsf4iXre7BqAMwyjD52OoVotwuap4440SPB4DhlHGxYtulMs5RKMBFAoZ+P0uABUcOZKG1/sRlMt5uN1VuN2AYZTg9TK43TV4PEChkEa5nEck4kcyWUYiMQvDKGFxMYJ0OoFarYSlpQhqtRIMowSPB0ilDuLkyTZ4PPV3LdbfH1mF211/j2O53IWZmfocnJgYwQ9+8FNs27YHfr8Hf/7nN+A3f7MOysPDHfjLv7wV/+7fHVO6N8yAWQS49XB9iDgjx0uLezpEpdEqLzkOG2g8fXI1tGGAmjGG9vZ23kjawEIRH+Qfo0B88gVRuAudptfV1cU1h1qtxrfW0sJKoVBAPp9HLpdDV1cXFhcX+e5COpUvk8mgs7OTu1Tm5uaQy+XQ2dmJCxcucBOdMYahoSE89dRT/Gzsubk5nDhxgq+I03kPkUiE+4xp1+L58+e5b69Wq2FqagrT09N8I83ly5cRjUb5+cavvfYagBT+23/7r+jsfA2HDh3C0aNhXL78Feze/e+RSCQQi8UwPDzMfcgzMzMIBAIYGhrC+Pg4DMPA4uIi2tvbefQAba6h/k0mk/x8lK6uLoyNjXFGJE1tbm4OtVqNx4BPT0+jWq2iq6sLLpeLWym0+Eu+0mq1yk+Xkyeqy+XCkSNHsLi4yN+sQibkyMgIjh49isOHD6NQKODZZ0s4daoXXu9XkEz+AT7zmRfx4Q9fiwagb5p0ZIHRGRT1Tw3/+I+PoVp1Yf/+Azh+/BRisXbs23cTTp06j1AohpmZRRiGG/fd90k899yLCAZjSCazMAwPwuE4Pvzhe/HUU88hnc6jXL4e+fxD+NCH3kZfXw4vvTSP6emPo63tGQSDs/B6QygWq3C5/DAMD2o1hlisA/XXYbmRyRRQq7kRDEZQKFRRfyWXezm9G4YRhdsdRTpdRv39jb3LkUBeVCoAY37Uai4AHv4soBZGyxsqAQDLm1U5XbxoZ9Z+VHvnO9/5H/DoozWEQlV86UtHl49JXblZxwx87YC42bMqcJXdX6oy5D0cZkQ8LK6NUf60/iIevmU3fFDZJhvhLAEALwHwow7s3zcM48uMse0AHgPQDuBNAJ83DKPEGPMD+HsAtwBYAPAZwzBGzMqIRqPGX/3VXyEcDuPP/uzP+KaWlpaWBqAmTZrOV8jlctw8plhoikfeunUr2tvbUSqVcG5ZpRE1vNbWVszMzPCNFNFoFCMjI/yMDTpcp7W1lWt8iUQCPp8P5XIZU1NT2Lp1K/r7+7Fjxw48+eSTmJqaQltbGzweD3bt2oXXX38dg4ODePvtt/Hrv/7reOutt5BKpXDLLbfgnXfeQVtbG/r7+1Eul3Hy5EmEw2EcOnQI586d4ws/U1NT+KM/+iM8/fTTSCQSuHDhAlKpWzA9/W20tVWwsOBHNDoKl6u+MOj1ehpMN3Ip1GOHqyiV6gceRaMRFApFvnXf6/UIp+qV4PV6UK1SWJYHLpcbpVIRLpcLpVKZg7rLxVAqlVEqFeF2e+DzeZdPUKvwiANxVR8ABga2wOv1oR7lUSfDqEeT0M63az5CLEdqUIxrFS0t23H1ahiMnQWwBMOIwzD2oqdnAa2togakOryeJkz9/8TEBCKRKEqlIgC2vE3cj1AohMXFxLKlU+RC7Vq9GXp6upHJZJDL5VCpVJcjZ2KoVHZjy5Y5TE0FsW/fnyIY/AV27tyJeDzOXVrhcJjHNU9PT/MFwHK5jGg0ir179/LQrmAwiEKhwEM/x8fHkUgkuI+5v7+fx0oPDAzwkNX6s2FEIi2YmVnE1NQcAoEourr6MDExi3y+jEikFZFIC7LZEmKxNuTzFXzuc7+LYDCKatWFSgX8u1wGfvzjZ1AuA4x5wZgPbrcfnZ19MAwXtm/fjXIZOHLEjfHxbfjAB17Cn/5paoXgVIXg0X86DI3+y6cOipFgYoieOM7XeEoNjPSdTCZRKBSwtLTEgbVQKGBsbIwrHrq8iCj0sbOzE93d3QiHw3yBPZvN4urVqw1HB8h5rWV4XhHAPYZhZBhjXgCvMMaeAfC/AvgrwzAeY4z9fwB+H8DXl78ThmHsZIx9FsBfAPiMWQHk22GMYXBwEOfOnWsIj6E04j582uxCK9XkJhkaGuJaYqlUwqVLl9Dd3Y3R0VH4/X5EIhFEIhEsLCw0HGFJGjbFTM/PzyOTyWB+fn550i6io6MDCwsLfEAymQyOHDmCS5cuYXx8nB8Uf/XqVdx9993YsmULj6jo7e3lm3kKhQKi0Sj6+vpw+fJl3H777XjnnXdQKpXQ3d2N559/HoVCAVu3bsXu3bsxOTnJX/x74MABnD17ConEBczP3wCfbwpu97W3x9DW+lwuj0DAv3yWsgeVSnl5K7YbHk/9BQxElUoZjNFWbDoWs65lRyJhfhgULdbSGNCY1DdQ0JGl9WcZA9zuxhcB1MdTPMKWoQ6Y9XGen58H8XDdnDTAGDi41l0mBgoFL3y+S6hUlpafXQJwFvPzLWhrC/DnrenaiwFqNQMdHe2oVMrIZLJcWBQKebS0tGJxcRGM1Q/tqfvo/ajVakgm66AYCPjh8bhRLudhGDMYG+tHf/8jiMffQqlUP1Pk1KlT2L9/P8bHx7Fz507MzMwgkUjwHXx0JgxFhESjUSwtLSGRSHD/NYXz0aE/N998M0ZHR7F9+3YYhoGxsTEOOLQtPpfLIpEYh89XQ3d3C7zeRdRqVxEMutDSUkatNg2/vwSfLwqXq4IPfCCLQKDxDBEAePbZZxGP19101xSA+pG1+XwenZ03oVQ6hMnJQwgG/xLHj/8ufvKTF3D4cIDnJWuyomYtRmytN5GVBVw72pTOHCJLnTa/iIAtzgE5P7EdsjtETNcMWQK1Uc+ZkMC7/DEA3APgt5ev/x2A/4A6UN+//BsAvg/ga4wxZpjUkCZxpVLBzp07cfr0aT65yQQRz7ugQSYznILV9+3bB8bqx23OzMwglUqhq6sLyWSSM1UqlcLi4iL6+vr4yjltgKENLtPT0xgaGuK+7P379+Ott97C1q1b+Xbst956i5/YNz4+jo6ODh45kUgk8Nxzz6GrqwsLCwsIhUJ48cUXcfDgQTz55JN8EYdMcQrXGhoawpkzZ3hb0+k0qtUqnn32Wb6TKpPJoL3911GpDKCv79uYmfkUOjr+C/r6zqOvrw8AsHXrViSTSezevRsnT57EoUOHcPr0aVy5coXvPFtaWuKHKZF2RmdmJJNJXHfddUin07jjjjtw4sSJhleJicc4kkCLRqPo7e3l7+MzDAO9vb1wu92Yn5/n/mmPx4Mvf/nLAK69qoi2tP/Jn/wJP1XP5XLxMqndsVgMW7ZsgWEYePPNN/m40kTq7OzEPfd8Erfddhu3wmTtTdbQvv71r2P37t1c02WM8TfnXLhwAS6XC4cPH8YzzzzDd8Du2rUL+Xwe6XQa8fi1EMPt27djYmIX3nzzf0d7+3/B1NRn4fW+gpaW45iamuI71mq1Gq6//nqMjIwgFAphaGgIJ06c4P/Jusvn82hpaUGhUMDk5CT6+/u5y4k20cRiMezZswcXLlzgcf+jo6NcCalUKnxxuqWlhYdjkhsuEolgaWmJLw4zxvjbicQjGBhjOHHiBF+gpDnJGOMHOuXzt+ORR34F8fjn4XL9M7zeV/DNb34bvb2nceBAYgUIUxlyCJvOLaHDDru7CEWSfdSkfBBgE+6QNU33xU03Zq6ZtYqf5u20k4gx5maMnQAwC+CnAN4BsGTUdw4AwDiA/uXf/QDGAGD5fhJ194ic50OMsTcYY2/QiXQAsGPHjgZJRGcXiKFsdKoZmdZ0dgatpovxqXNzc3C73eju7sa2bdv4pK/HwnYgGAwilUph69at2LNnD7xeLz7+8Y9jbm4OIyMjcLlcOHr0KI8CaWtrg2HUD29aWFjgPlqv14tqtYrJyUkA9UOH9u/fj1/6pV/C4OAgWltbcfr0aWzZsgVXr14FYwwjIyMoFot49dVXUa1WsW3bNoyOjqK7uxudnZ148MEHsXXr1ga/2ZUrg/j5z/9n3Hrrf8K+fd/Dnj1fxuXL/w8WFm5EV1cXurq6MD4+jqWlJbzxxhswDAPHjx/HwsIC7r77bvj9fvT39/P60oTL5/OoVutnP/f19aFcLqOjowOvvvoq0uk0KpUKbr/9dvT29iIQCCAejyObzXLgaGtrQ6VSadiuHAgEkM1m+TiJB2uJmprH48Fjjz3GNTV6BRmtLdBZy4ZhYHZ2FlevXm2Y9B/96Efxta99DX/913+NoaEhjIyMrAjv5AwvLRzNzc1hdHQUU1NTHDTGx8dx4cIF1Go1fOITn8Djjz+Ozs5ORKNRbN++HR/72MdQqVTQ2trKy6jVahgfH8Lx43+Mm276j9i79zEMDn4Jo6P/Cen0LWhvb+dKQ1dXF/75n/+Z1/Odd96By+XCxz/+8YaTG4F6xEaxWHc5Xblyhe+8DQQC2LdvHy5evMjP7fD5fMjn82hra8P111/Pt6gvLi4iEonw19XRQV90SBaZ7wCW3WdeDloEOi+99BIPNSQfLPn7JycnsX//fszObsX+/X+OYPAXy3P0FcRiD+Lhh483WMji3gjx+90mcfey6EoRNXtZo5ZPulTluR4x37aA2jCMqmEYBwBsAXAQwJ7VFmwYxsOGYdxqGMatdCQiAHR0dHDTQjy4ffmZBilPB8wUCgX09vbi8uXLeO2117hJQ6F3uVwOHR0dXHOLRqPYsWMHIpEItm/fzs8L6ejoQCKRwPnz5/9/6t48urHrOvP9XcwkABIgQQKcp6oia55LJcmxZkWe5MSZ45d4vc5K94qf/Zx2nHbcnX7pxLHTbacz+KWTOHlpx3GsFzu2YluTJdnloaRSVanmuVicCU4gMQPEjPv+uNiHlxBLVSUpvfTOWlgEgYuLi3PP2Wefb3/72yqYl0ql6OzspKenhy1btmCz2ZiZmVHcVsG7N2/eTCpl4HFer5dqtcpzzz2nFNJEsKmvrw+3271ONMdms3H33Xfz/e9/X2WM3X///aoquRT4NbZrezl48I956CFjonV2XmfLlv9MPL5JYdvifQtV8cqVK5RKJU6ePKky3KSAgdvtZvv27QwMDNDQ0EBnZ6dK9onH47S3txMMBpX2id/vZ9OmTaRSKSKRCI2NjUpzWyrGSLxA6vpJINV878yBF2nmrFOLxUI8Hlcp06IOV6lUlJYLwIMPPqioZteuXWPbtm0sLS2p9zfy0MyBzC984Qsq2CvZfBLIfPjhh3n66aeVapzdbueBBx7gypUrzMzMkEwm8Xq9amFaXd3Oo4/+HV1dozV+/jX6+z9JKjXCpUuXVC1FCXJbLBYikYgKPF+5coVcLsfmzZsZGhpSWbNtbW1KTMtisaidSiaTwe/3Mz8/rxQMR0ZG6O/vV4ubjH9JYhHI0GIxKgSZjYrUddxgrvLMM8+obb9ZI1sStM6dO0c4/DFKpReUU2XIL1zkl35plmefffY1SS7m5/Xz/PWw5ttt5vPUPzcv2BtJyZoXy3psux4PNxtnub9mjY/6wPkb+T13xPrQdT2hadoPgLsBn6ZptprX3A3M1Q6bA3qAsKZpNqAZI6j4uk1+hExCuXkyec2rnPlHC093eXlZ0dHcbjcLCwtEIhHljYhA01133UU+n2dyclJtQwOBAKlUiu9///tKbtLITlszQl6vl4WFBS5cuKA0E0STxOVyMTMzQ09PD8vLywSDQQqFAolEgmPHjrFt2zZOnjypjHxTU5PibBcKBYaGhsjlcvj9fuLxOEtLS2zZsoW///u/x+VyqcCly+Wit/e77N+/n+PHj6ttZ1fXKA7Hy1gsnVy/fp3e3l4l2SlV0G/cuKHU7o4cOaJ2JMVikbm5OUKhED6fj8XFRZVsIvdDONIzMzMcOHCAcDistKatVistLS04nU5isZga3HLfrFarYtUIni3ernkQC+xSLpeVofR4PKqgsZR4ErioWCwSCoX4tV/7NXWPu7q6+MIXvsDVq1cplUrcc88968aW2WDLZPn4xz/OT/3UT3H27Nl1uhkDAwO89NJLtLS0qMQSCVA/8cQTilGzefNmWlpaaGxsJBr9K975zp/mm980MkCNxKvL2GxH8fv7uHjxIn6/n+bmZpVEZbFYmJ2dVfz3ZDLJyy+/rK5XmDZ79+7l+nWD9hYIBBSnPJlMsmfPHuVZB4NBxbmemJhQWY6S8Sg7HDHSUkxDgr4y3s2eoxk6lD4SaEp2txaLRUnqSh9+6lOfYufOnYjioATib+U9m+f3/wpPWyAYGbuy0JirtrxeM8fRZFyZIZK35Bpv4yLaNE3z1Z43AI8AV4EfAD9bO+xDwLdrz79T+5/a+0deD5+GtY6RoKIQ8M0PEZsRdTD5jOCZfr8fgPHxca5evYrFYiEQCODz+VheXmZ8fJz+/n5mZmYUthwMBmlubqajo4NgMMjIyAixWIzp6WmGh4dxuVxKbyGfzzMxMcHQ0BDDw8PqGsR7lyobIispAUpJAPnVX/1VVa6rubmZXbt21W7if2BqaoAbN27gcDgYGRnh3e/+PL/7uym1nU0kEqysrCjVtVwupxInJI3b7/fT1dXF/v378fv9LCwsKEhDMsXkM5s3byYUCtHb20sgEKCrq0stRi0tLVy6dInGxkba2tqYnp4mGo2qhJ8jR45w9OhRtS3s7+9XdRslo1RkO818VGAdh3qjIWHGCgGVzCSfdTgc6j4XCgX+zb/5N0qmdXJykt/8zd/k4MGD/N7v/R67d+9e5zXdbMLt2bOHM2fOcP/996sF5cCBA5w//5NEIttVWbGPfvSjeL2P8/u/b+wqnE4n5XKZsbEx3G43KysrPP7447z44ouqig2gnIdAIMChQ4fo7e1VwVnR12hpaaGlpUVlde7atQufz0coFMLr9aqkqYMHD3LPPfcwNjZGe3u7Slhyu904nU5aW1u5ceOG2hU0NTUpsaVAIKCYCJVKZd3iKTtAeb1ealiu1Uw3s1qt+P1+leFbLBbVLlPTND73uc/R0dGhdE2KxSJPPvnkunshC5X5HtVDU/Xt9XDh23mtHoIxN4GdzIqbt9sEFRB47mbXcbvYe327HY+6A/iypmlWDMP+dV3Xn9Y07QrwT5qm/SFwFvi72vF/B3xF07QxIAb84i0vwiRvKqt9JpNRAv5SjUK8XZE5rSeWSykowUuj0ahaAESJrKmpif7+fubn55WnEY/HmZ6ermW9dajMRzGKy8vLio88NTWlspd8Ph/BYFDVbYxGoxw8eJBoNMrq6qoqsPvcc89x8OBBJTkpyloHDhzg+PHTHD/+cYaH4zgcYyQSe/nKV3YyPPx1ZTwlYaRSqbBnzx5efPFFLBaL2qaKN7SysoLT6aS/v59UKqWCS6Kx7XA4SKVSTE9Pq5T7trY2otEou3fvZnZ2lvb2djRNY3FxUamqCf1RNIaj0Sgul4vBwUG1eEplEtFXka25XJt4hoJrynZRFlrhtpslPOsXQ5G4FZhi165dPP/887zrXe/iX/7lXwBjhxUOh9m6dSvpdBq4OX9W0zTVFz/4wQ/YtWsXTU1NnD59GqvVwvj4HwH/kcHBaY4c0fna197N4OCnVMEHMBaXqakpHn74YcXSEOMlATubzcbk5CQ+n4+uri4FUwwMDFCtVtdlFJbLZV555RXFEuru7lZebalUUkFiEXAaGhri5MmTtLS04Pf7SafTKnU9Eoko6EoWPaG5SiKYGcoQ4y6tPvAnHrV4jrlcTrF/pDamCFB1d3erIhmf//znle76RkHKf02v+XYMuOxczL/5Toy0+fNWq1VBhDLu38g569vtsD4uAHs3eH0CA6+ufz0P/NydXIRZrFvTDG2JbDarvMVsNqt+tExc0TgwZwOJ95JIJGhqasLn86k6cWJQJAIuxnJhYWGdwHk6nWZwcJBCoUAwGFSsjlAoRE9PD4lEgkQiweTkJA0NDSo4mU6nWVpa4n3vex8nTpxgdHQUr9erAhZHjhzB5/MxNDSkPKfz58/j8znZs+ePOH3680xNpclmfTQ2TjE19auEw3YMZTwDW5yd9XD2rI14/H4sFisWi1ajs61hZR6Pl5deKig2jNVqU9s4w9AVsFikKrlRTaVa1XnhhUaFL1cq7yGdzmCzWRV1DnTK5Yqisxnym8ZvK5cNWpOhVmchkTDS9Y3CvwYlsFKpYrFoWCxW/v2/7113/zUN4vE/JZVK14yA6CRIdF10piuAhqYZeOonPhFiefkdfPnLURKJzxIMBnniiQCZjI3VVSstLZmaZ2T0kTxf85Z0wuHDOByGIzAzY6VUKmOz/TrVagW7PcKNG3/BwsIouVwHg4O/Q2PjcZVi9QAAIABJREFUSQoFQztG7r/X68Xn83HkyBFlzIBanxiLknjIDoeDBx98kNOnT7O4uMjg4OC64J2I8ot2Rzgcpq2tjaGhISYmJhgYGCAUCjE5OYmmady4cUM5C7JYplIpVlZW6OnpUZIF+XxeefIyr6LRqFooRUbVzISoD4pJmTRN0xTeLnNXvPpEIsGjjz6qaKx//ud/TrVa5bOf/axa9M2G2gxl3om3/EabGfoy/z4zHm1OlLrdJgvWRkFTed98DXfa3haiTLJKy8rU2tqqnkvgA9aEywX/FEgE1rbGIjYjXpikN1ssFi5fvqxgkKmpKWZnZykWi0r8v1QqMTIygsViIZvNsrKywrlz51Qh2VdeeUV5hZLZVyqVVDktMeKHDh0imUyqAI54MZlMhve///0UCh8jkdirioT29U0QChXIZAI4nSu4XDkk+QIgk/Fjsxn87nQ6Q6VSpVLxksu1YbOtlREyDGmploBSMtUSNAyy1WqlsdFNpVJWRg+MQZtOp8jnc+Ryq4pDbbXacLmcteCk8RlN02olpQwjblzLep4pQKlUBjSsVkvN2IOmiUjSa8eAeHhrE0jDYlnv/TocDhoaXGiapYZXV2hoKBOPd2K3t9HS4ieZ1JiZ8eByScCr/stETtUw/r29fXi9HkqlEKWSB12XoJClxqkvkU5vo6/vObze08r7t1gsTE//ApHIdh577DGWlpY4efIkq6t3MT39C4qz39DQQLVq1K7MZg0Z2Keffhqfz0draytXr15VOsiSZGVw362KMnjjxg2OHTumkl1isRg9PT0kk0mam5tVBm8qlSKRSJBKpejr66Ojo0P1q4g16bquBJ+y2ew6TRCA1tZWNdekOZ1Otm/fTjAYxO/3q0QdgR+r1SrRaFSVW9u7dy/PPbeTP/3Tc/zwhz/k53/+5/F6vZw65eUb3xhYl/ghxqzeKN4OY+dWbaMdlJx3I0Ns9opvx1DXJ7DI2BWa55sJHta3t00KuXgeuq6rbWC1WlWaHrCGcUqUXCRMYY1f7XQ6aWlpUd5DLBZjdXWV7u5uJdeZSCTo6OggHo+zvLysWAWiMnf9+nV2797NpUuXABRksnfvXiUCbq4KnkgksNvttLe3c/78eQ4dOkQgEFDXI15WuVzmX/7lX3jPez7GZz7zWwwMLKNpV+nq+mMWFry0tDxNKnUv27d/E037ocIgq9X7OHr0o3z4w6f5wQ/+L+bnh5mZ+Tz33/9XeL2nFPQi2ZySuCOVQubn55UQ1f79+zl27JgaRMlkku7ubgC1U7Hb7SSTSTXYqtUqN27coFo1FABbWlpUv4vGcSKRwGaz0dnZSbVaVbUb29raFIQlu6VPfOIT69TzdF1nbGyM1dVVfv/3fx+Xy87IyAg+n08dY7VaVV3GctmoS/lHf/RHZLNZ/uIvLvPii/8n5XKcxUUfDz00jcMxhd/fhKZVgPUPI/5t/E2l4vT1dXPmTJ7jx99JIPAt/P4pQqFHeeWV7ZRKJZqbv8n09ONAGLv9DBZLA/l8Bbt9hYmJz3P69MssLV2kVHo/o6N/yLZtfwL4SaezGKneRpp3MpmmsTGmshSz2awytLt37+aVV16hp6eH2dnZdQH05uZmKpUKkUhEZdKFQiHcbjc+n09lzArk1t3drZg25sSwatUolyasHkDp1oiXvVFgLJfL8b73vY+vfe1r5PP5dTrXFotFSdcCSq9682Ynn/70r9HbexWXy8Wrr3r47Gd38olPvPqG7YSZu/xmjZ/582YutzhW0syB75s1s/GXAP1b3d4WFV76+vr0T3/606q+3ve+9z2ee+45AoGAynqSTCFJ1sjn8zQ3NyvamnkbVq1WFWvD6/WytLSksLEtW7YoHV3Bmtva2ujs7GR6epquri6FgV+8eJGOjg5VLKC9vV1xfKXArSR0iCaH3+9ny5YtPPnkk+zYsUMtKrLK+v1+isWPMTKyhf/5P0dwOCoUizas1hxQwmIpUCq10tAQw2LJAdWaXKmNXC4IlNF1GxZLAU2rKljAEM03+nPNE12DDIzVX695jIa3ukbopwaxrN/yGlCGcW5p6z3itewrwztaX0ndgDqMsliGgD/YbFalsQygaQYckc/naWxsIJlMUijkAV2lawtMId65MZGM87W0+GvxiBZe6z2/PZuh3KdjsRgFD3TdEDzStCo2m4VyOQ+UsVhA10tYLDp2uwWjWINR8KFUygOGAqDDYSWXy1KtFtE0HafThtNpxViYqpTLBQqFLOVygYYGJw6HlWIxR6GQxeNprJ3HEHHq6AiyZcsgVquGoTyok89naG728vWv/79AhVAogN1urX1Go1otUa2WsNs1Uqk4jz32CF5vI9/+9hgXL76LQ4fGuXBhM7/1WyfZvTumjKFZH1z+N/81v26GI8yMCjOEcTsBQDG6os9dqVSIx+PKGSwWi1y/fl2lldcHVc1N2C2NjY0qYNzS0sLFixcZHh4mkUgwPT2toNuNmCz/v6rwIk28atmuASqAJYEQ4UFnMhlVQ9HM/RSj7Xa78fv9rKysMDg4qLK1RLBIMNvOzk6sViubNm1SwZ+lpSWcTieDg4OAwSQR4Xbh08p3JRIJNM3IZvN4PEQiESYmJmhoaKCvr494PE4ymVTbRAPLOstXv/oQ7e0ZIpEmXK4YxaIHj+cSVmsUTeugXG7G6ZzG5WrAarVRKlUolTyUSh4sljwWS6H2i9cM55q3Ya6QbMSAdV0SRCRF3KylYdQSrJ2l5kXrGPIuqHMaOx9t3Wvm50bNQ3ldYw0RMQ9MjdpGhPWGtQGDEbi2S6qjt27YlpZufcwbaVZrkUrFit0+j6bJVraJSsWB1RrHarXVaIRlqtUW8nkPra3HaWg4gqbZqVSgsdFLtapRKJRxOhsx7oOVYLCTSGQFh6MRp7ORZDKDy+XGZnOQy5WwWh00NjaRSmXI58uUShU0zYam2XE4GgArDoeFfL5IoVChWNSwWhuoVp3Y7S7KZRvlshVD7AnKZZ1yWUfXrRQKdjTNSqUCum4hm7XVxoYFsDIzY+X4cQsiorS+ffK2+ry2EQV2A/Dyyzt5+OFXaG4+g9U6tGFCyO14rG9lEy9aHgJDSrvdpBUz7CGCZuYg61vlCL8tDLWsWIIli7iSNAkUSskiKXkvIu/mSLScr7m5WQmk53I5Wltb6ejoYGxsDKlxNjo6SlNTE7quc/nyZbLZLOVymUwmw8DAgKqCIRmLnZ0GT/nuu+/m2LFjKvItac/VqqGeVyqV6OrqUlH7wcFBzp07pyLBFstJOjr+nqmpj2CzrZLP+3G5FrHZ3JTLjhrbo4KmdVOt6lQqVVZX7ZTLLrzeFTIZHw0N01ityVq03YrTaa957M2qcEC1anieEpArlYrk80aAr1ot13YjVZLJBF6vB4tFqxUGsLK6akjIOp12SqWiCiAaJawMD9kIyhaxWi01saxSjQKWoVwu4fG4ax6PUXLLbrfR2trCwMAABrxniBtVKpUav1coUsZ7wq1e86QEX9bRNCOYGo83cuFCJ7t2zeH354jFGrh4sYvduxfx+1cRQSddr6rgpHE+4//r10dpaHBRLJZIpQbI5/fR2jpHNNrJ0NBfMzT0bebm5vD7/WrHt7q6Sn+NlnjypJuzZ3+H/v6vMDf3Pjo6nsBmO6qCcwL3SPxEKHdzc3NkMhkOHDjAmTNn2LVrFysrK8zNzdHV1aUYL8LOOX/+vIL6RIxpeXmZUCikKJGRSITW1lZFcwVYWloimUyysrKimCHVapXp6WlsNhstLS3rWDmPPPII9913X00fRqdSgUgkyh//8Z/y+c//CV/60j9w5sx57HYXLpebhgYPNpuTYrFKOr1KJBKlWKxgt7sIBN7NmTM/R1vbC7z66vuZnPw7/uzPNq2b8/V2YKP2r7XrNyepmNU4zdCH+RoES78Zpi4ethn6uFNc/WbtbWOozdXHhYAvQTihDwlpXzxsAe6lKEA9naijo4OZmRmGh4dpbGzkxo0bhEIhnE4no6OjSmBH6GuCVadSKUV/c7vdXL9+HYvFojQRRNxd9LABxU6pVCoMDg4q2l80GsXv96uJbqTcDhMO/2+4XGPk85ux26MUCgEcjhwWS5FyuYTT2Uwyma4F7LykUp20tcVqAkyrZDJDuFyzWCwZymWNatWG1WpjaQlstiCxmI1crorFYq1F80WIpgmbzcjwS6WMAVoo+MlkKtjtDsrlthoUYgM08nm9JrKksRZ7FnjDMHYWi5V83l2rj1jGUN3TKBatJs/MmJjxuJ3paQdGENN4R4KSa7dv7bvMx8i5jPusrfPiz53rUbsDXdc4daqL24NCXkNoIhrtpqXlGaanfwm/f4rW1jzFYpF4PM7jjz/O0aNHa0kcmzh79kPs2vUZ3vnOCi+9dIlz5/6AgYHfwes9RTQaxel0Kox5586dvPLKK2zatEnR7m7cuMGOHTtYWVlRPF6Px8Pc3BwdHR2k02mKxaKSSigWi4TDYQX5iaiTzAczB1ocGJlHEhRPp9NKQkHYQYBKThKWj6GXrdHcbOcjH/kQicQUH/jAfbz3vXereVcsFtd9t3Cxr13r4ItffJDf/u0fsW3bKlNTx/nv//1/cOHCGXbsWL4p06Oetnez7MI328wes1nTY3V1dZ1g2eth4mbMXJrEAl6vbfS5W7W3jaGWwWUWYoLXRp+l8GdDQ4OShZTEj3rNAKkmcuHCBZW8kkwmmZ2dVcctLy8Ti8XYs2ePyi5cWVkB1krHNzc3K4+lu7ubmZkZ5ufnlcd09913MzY2RiQSoVQqKTnKxcVF5ufnmZqa4md+5mc4fvw4Rt29bXR2fpn5+Q/R2/tlFhbez/Dwl7Banfh8f8uBAweYnJykWjUKGoTDv8TQUJy9ew1Wyfj4OKOjXQSD7yWV+l28Xi/BYJBUKoXH4yEUCinBnpWVFUKhEBMTEwwODrK4uEhra6vCzCUJQgJYW7duxeVyqdqKY2Njihvd1NSkrimfz6tKOo2NjTzyyCNcu3aN8fFxVScxFAqp1HK5f/feey8PPfSQSm4BiEQiKkV527ZtSt3QarWqHU9ra6uCq6ampnjmmWe4du2aSmy66667FDTm8/l48MEHFcZZreq154YGdbFYUq995ztPYbXaGBv7KQqFNPH4r9Pe/iTLyx+go+PLpFJb8HpPEY/Hee9738v169eV8VxYuJ/duz/L4cN5ymWNQOAaW7f+F+LxHfj952hqakLTNEVVO3bsGCMjI4ryKZ5cLBZjaWlJMZTEoxNxKyn0ICJLUo1827ZthEIh5ubmmJmZwefzqQCfw+FQKfeFQgGPx6OEt4SyKguDsD6EGWTemcpfi8WiKgcJP9+MDVssFlXf0oAPe/jt3z7Frl15NM3Hnj1xPvWps4yO+ti5c+Vfx5C8wSbjWwK49dnPt3sOkTcwZzrWp6e/0fa2MdT1q4y5iK0MOGlirEVBTOrPyYAzd7LdbmfHjh2Mj4+rSuI+n09F3KV+odDp2tvbVYaX3+8nFAoBa4P4/PnzZDIZUqkU3d3d6vsPHDjAP/7jP+Lz+XA6nWzevJnnnntORfePHj3KY489xgsvvIDXe42xsd9hePg/095+me7uG5w69dscOvTf6enpVcEHKaX1/PNPMDCwlXLZQ6VSIZfL8ZGPbMdmm+WP/zhNKBTixo0bajLNzc2xb98+RkZGuH79Ovv37yedTjM+Pk5PT8+6IInNZmN5eVlpaszPz3Pffffx/PPP09nZqUSopM8l+FMoFEin04p7LboouVwOTdMUY0OONyfD1AdVqtUqO3bsACAcDlOpVFhdXcXn8xEOh1WlHflMT08PH/3oR6lWq3zmM59RrBRJDpKsQPlOo6qKhtUqY0KrefBV8nljcWtvH+PChf9Ef/9/wO8/h9f7KpOT/5Vdu4zzHzhwQNXLlICzpn21VjeyV3nOyeSrDA3N4PNt4+mnn6alpUXdB5fLxYULF2hra1O65SLJa6SgR3G73bXK7zZFEzP0rstcuHBBwUH33XcfExMTXL16lY6ODvx+P5VKRSWINTY20tnZqeR9pTix9I2kfJtlO42AbuM6b1dYDP39/SqPYWJiAl3XlRCawDNNTU1kMhmeeOIJrl27xsxMCwsL29B1nWKxyAMPPMDOnf5acHm9R11vFM2LxesZzPpElfpWz182B7vN5xC2U72+tfmzNzunwGmSmCb9K/fQ/Nk3ire/LQy12UibCfdmb0yMsWBvokMtCSfCo67nL4o30NPTQ7lcVrQ1UQ5LJBJK0H3fvn28+uqr9PT00NnZqQzZiRMnqFQqHDx4UIkdSUHRrq4udN0oBebz+SiXyywsLBCLxZAUa1GAO3HiBF6vl/n57WzZYhhpQ7TpCvv3f45sdiel0lXOnTvHwYMHFfwSCoUYHR1l+/btClv0eDxKwKetrY1sNksqlapxWJ9jbm6OaDTK3Nyc6lO/34/NZmNsbIwHH3yQ48ePqwxPn8+n1OH279/Pj370I5UYJEkuoqmRzxtQgNDufD6fkv0EVDaebLvFEzYbajO7RASfpHKMHFMsFlVCiWytwdiip9NpdF3nk5/8JJFIhH/+539meXlZpZjXjy/5W5/oIFl1yeQW+vs/ic93FovFSlfXKF7vZ4jHN9HTM4bf7+f8+fNKi7y/v58LFy4oiQARaWpsbGTfvn2kUin8fr+qRwkoiEBEoIR+2d3dzdjYmPLIOjo61vVDNpuloaGBTCajighrmlGvsru7WxmDQqHA4OAg5XKZYDDIwYMHyefznDlzBrfbrbxlCciLwL3gsuKJ18+fpqYmVbx2fHyczs5ORkdH0XUdn8+nxkg6naa9vZ3f+I3f4NSpUxw9epRwOKwSv/41goJ32mQsmA21OC7y3p0mu5h/lxQVfisDifA2MtTmh6ZpJg5xVXlyBp5aMCnJrW0DZWU3NzEIYtwDgYBietjtdqanp/F4PGSzWdra2piZmWH37t1kMhkWFxfp7+9ncnKS/v5+XC4X165dI5vNquIEDoeDdDrNli1buHLlCvF4nL6+PnRdr+lGtyqGydatW0mlUkxMTLB161MsLy8DmtK/7e+fpFodR9ctvOMd7yCfzxOLxXC5XIyMjBCPx7l8+bLia3/rW9/i4MGD+P1+FTz0er1UKhX27t2rII5gMKiw+fHxcVpbWxkcHOT73/++kngVnQmPx0MymeT06dM0NTWpmpKwVtxB+lKMTltbG8KDB9YJ2ci2W+6h4K8bZYUZRQN0VbXd5XIRj8fp6OhQnGxzBqvsHpaXDbzz8ccf54UXXiAWiymet1zXRl6b/C7RZtG0f6zpUrTUkn2seDyv0tZ2iUrFzo9//GMsFosqSLt161a2b9/OSy+9xPnz55VolNz3xsZGdu/eTT6fp729XelpNzc3Mzo6qnICYrGY0oYR+O7EiROMjIwo3rRkyUoGbW9vr4KXJOFKYBQRyxKo48CBA4pKKucyyw+I0qLAj+ZdqczF2dlZldG7srJCW1sb4XAYMCrMixMl3no2m2V4eJje3l6eeeYZRV+9meGSRfutNuS3iysLVGGm/sGdeb/yGXEqzOJVb0V7Wxjq+px42ZYJywPWBxJkdZZIrVlYfi0YYjTxsEV3A4zJOT09TalUYmBggNHRURXNn5iYYHV1lXQ6jd/vV1iz1EzcsmWL8m6j0ahKQIjFYnR1dTE4OEhDQwNTU1MMDQ2RTCYJBALrRGsEYxc2gBg/0WOQGo1btmxRRXEPHDjAyZMn2b17N9euXaNSqZBIJOjq6lIaH3fffTezs7NYrVYOHjxILBYjk8lw+PBhrly5AqCMm8/nU9H/7u5uLl++TKlUwu/3qwXCzF9dy34sq/JTDocDj8ejqlYLVdGMf5onYKlUUoZdmqQvy30NhUJMT0+rHZPZmzx69G42b06wbduSmhDnzvmZmQly4MARHnjgAWVszLrC9RxWszcfCoXIZDJK10XuR6FQUGqDuq7T39+vUrHFyxXZ05MnTwLQ1NTE0NAQ//AP/8BP//RP097ezqlTp/B4PAwODioVvrvuuovV1VUWFxfRNI2zZ88yNDREsVhU6eZXrlxhdXWV7du3c/bsWXw+H5OTkwwPD6sYS2dn5zpDC0aClzgE3/3udwkEAqysrKjjZAxKv5sXVk3TlEE136NcLqcWCYGZwuGwEvqShBthbZXLZaanp6lUKuzevZuVlRW1+JpjE/W7aPOcNbe3Moi40bklJiZ/b/e7Njpedo9mB+GtaG+LFHIzTUZWdvE0pSPMtfdgzXOz2+1KK2F1dVWtYvIQoy4Ys0yE7du309rayszMDL29vWpb6XK56O7uVip+IqSzb98+SqUSMzMzKqAiOgrj4+OMjY2pCuMSbFlcXMRmszE/P69gHJ/PR3NzM/39/QwMDOB2u2lrayMUCuHxeNi6davyQo8ePUomk8Hj8bC4uMjWrVvJ5/M4HA62b9+uIByPx8Pw8DCnTp1C0zRVc3JkZITW1lbFOmhoMBJKRKTK6/XyyCOPKIWzoaEhOjs7cblcSuhfZFwlOAWojEypP1koFNZVrZEtu/n+iZ60UMzMMIQMaoGURMJ0cXGRb37zmzz11FOUSiWWltx86Uvv4/r1TgqFAqOjXXzjGx8kGvURjUaZn59XvHXJhLwVPmjOmjRDNm1tbYoJJNt6KdEm2335rVJqbfv27czMzKjdlzgNmqbR1dWlFhdhAg0ODtLd3c2DDz5INBqlWq0qCESE/kVWd2VlhYaGBuVJS6xB8gmKxaK6B2I8FxcXicViameazWaV1ysQhxkCkBwGczBf04zkFFmYJEBaKpX48Y9/rBhQf/Znj/L889vU2Jibm+O7393KX/7le2htbVWLinme32nA7lbtjRhz8yIl3Of6917v+8TQi0duphfLrv+taG8Lj7o+yipYmRhbWDMOgimKZrHB9XWqNGrBh8Sjk88LDcnj8VAoFHC73Wzbto0zZ86gaRr9/f3Kg1uqMfoFEw+Hw/T29iqP0u/3c+3aNfbt28fcnCHDLVhcMBhkYmKC3t5eZmZmWFpaolQqce7cOcUSqVaNiuNSCmllZUVRAefm5shms0QiEZxOJ6dPn6a9vV3BM+Pj42zevJmLFy9SrVbVRJXqH/39/czOziooaHV1lUQioaqRTE9PMzs7S19fH52dnUxNTbGwsEBTU5MKqIjhlSCL9J9AGYbY0hrdy2KxqMouYryk/0SfRYJWG+kpVCoV5alKpqjH42Hz5s3ouq7iCdu3X2J0dC9f/OK72bt3kbNnO9F1aGqKE4nsZ3lZw+t113RI2qlUyuh6BSOD0tAqqVbLVCploEqlUmZxsYfOzg6giNudw+lsJJ1O4nA00dDgxCimW2VmZpp43E2h4OTs2TjQj9vdjK430Ny8jcbGXmZncxQKPgYHdzE1NUlLyyaKRTd7997Pq6+eoVQq4HI1k83msNu9BAKdLC4u4HY30dzsJ502ypg1NDSorNrOzk48Hg8vv2zUKhwaGsJut6vFQnB8gQalDyVeIHNERMjEixaDJPdMKruYjakYItlFAapYgt1uJ5PJEIlEaGlpYXBwnG9+8yGcTifvetc1Tp36CY4efS933/0NotEoHo8HWI//mnfQd0rB2+g48zXfytaYsxxFK0XOcbMAp/l7zM2MR6/lG6DuiQQY3wibRNrbzlCLFwzGD5Uq2bDeSMvnAOVVi5C6eG/miLCmGSL7EhUXKcKdO3cyNTXFqVOn1PviTUrQyul0qsEk1YalXFOpVFIJEXa7XRnulZUVwuEwuq7z0EMPEY/HlU52Op0mHo8TDAbVteXzeeWtAWzZsgWn08nCwgIvvfQSHR0dzM7O4vF4uHLlClarld7eXlX6S4JCS0tL3HPPPRSLRWKxGPfddx+ZTEbhq0tLSwwMDBCLxVTh3paWFuVNSn/J7zVjwmAsmDKxxQCUy2WVfWmWrDVjnvVYXf0uKhAIqArzUoGmXC6zbds2ent7sdvtNDenSKf/kmee+U1OnuxX5zpx4vE3Mfp++daHbNAuXrz1MUeOAPxHzpx57Xs//OHNP3ftmtnQrDc6x46tSQXUvwdrKfnyntkmGPNFr33Ha8+hafC979lr92Ttc9XqrrpzmesNmmGsIk88sZ/vfGcnmYydhx56ln37XiWXKyipYoEFbtZux4i9WRhEDLSZAiwGvp6eeLuLhln9U2IcZqrlm21vC0NtFj6p97gqlQput1v9aIE5zJ0pW/P5+XlVOUU4vOIFSjKLUGjEkItou5D3y+UysViM5eXldRXJY7EYfX19FAoFpSMCRiDs9OnTqg5he3s7kUiEeDxOpVLhZ3/2ZxkfH8flcrFjxw4uXbpEW1sbPp8Pt9uteL+aZqS9C0bY1dWlqlVLFiVAT08PdrudSCRCNBplaWlJGdK+vj7FbR4ZGaFcLhOJRFQqvQSR+vr6CAaDxONx1QciPyl9JYbf6XSqbVwmk1H4tZRtEqqeUArFm5b6fdIKhcJreKqwFswaGhoilUqRzWZpbGxUVXOGhobUdzU3NzM87OfZZyURporfH0XTsmrMWCxWJTAkc0xX0qbmwLXxPB5P4HDYKRZLNYjNWbs+KcdkUPt0XXRODL0RGbPGJDW+V9MM1UDjf0stUWs99GIcp2G12lTQaY2uZqnVBLWtw9ElWUgSj+Q8a2JC69P6JYNTvkt+q5GIhPouOV5eAw2Hw4ZkjEorl3V1Hrmetb8W9b/NVqFY1MlknHR2jrFjx4tomlPdPzOssHYdrzXMZtZJfTO/drtp3ubPyj0TSqIYZ7EvZuN8O4a6/phKpUJ7e7va5WwEvW0U0LxVe1sYatleS9BKvDHx0CQzUQIakqklXpowKzweD6lUimQyqapPyDnNRl5WOTPdS65BrqdYLKris5lMhkAgwPj4OB6Ph3w+r4z26OgoLS0tKuNxfn6eUCjEiRMnuPfee9W1C+dUsPZUKqWcH/NAAAAgAElEQVQqmkvSiGgKA4rBkslkGBwc5LHHHuPJJ59kfn5ereBidKUCSyaTUdKVAm9MTEwoWEbkMy0WC0tLS3R3dysDLkUTRABL4A0RhJfttOCn5mzQarWq2ARS0V0Gr+yABFbaiKal6zper1dJdco9aWtrY2FhgUQiQTAYZGVlJ3/5l/fhclXYvPkprlx5hGzWwzve8UV27Yoq+mEgEMDv9ytYRR4bCf586UtfUgHRQqFAT0/Pui2r8MdF39ntdjM1NUVPTw+Dg4McPXqUarXKpk2bVDV2CeiurKyouMSePXu4fPmygqMOHDjA3Nwc/TVG0fT0NL29vXzzm9+ktbWVbdu2EQ6H6ejoYGFhgfb2dhYWFhTDorW1lenpaYB1zksymVQ0wUAgoCDCRCKhYiZerxev16sSl2SH5HK5+PjHP66KNcvcOnHiBAMDA1QqFUWFvXTpkspslFjGt789yFe/uhef7xLz89s5ceJeDh48Sn9/v/Km76SZMeCN2vrF7M5bPQuofoHY6LXbuRbZ+d8KgrmT9rYIJgpXGtbrfshqJ/iZGFgxvuaCpxL8k9L1ks5tZhwIF1iEnKRqhpRbAlTgUsrxyJZG4BXxlBcWFlQa7ubNm+nq6iIUCmGxWDhz5owKKE5MTDAzM8Py8jIej4fdu3crrZKdO3eye/du9u/fz/T0zzM/P8zQ0BDbtm2ricZv5qmntiqjcfDgQVZWVpSRnZn5RVZXjRqQfr+fcDjM4uJWfvzjw6p816FDh9i1a5cKhEr9umAwqPDFWCxGW1ub0uqW6uoSLAVj8cpkMkq/WGIAsvBJBqfARII7C9Qkqc3iccuCIQ9Jt5+ammJsbIxYLEYymeRrX/sazz77LKdOneKll7rRNJ0PfvAb7N79JL/8y/8M6GSz7yMUCqlFwxzbkHazieHz+RS7x2q1sri4SLVaJRwO09TUpBZOl8ulJAek1Nby8jIf+MAHsNvt3Lhxg7a2NhYXF9X3b9q0SXHeb9y4QUtLC9lslu7ubmZnZ2lpaeHChQu88sor6LquFk1NMzjRra2tKttUeNk+n08teuIEFAoFtSil0+l1Ox5pski5XC7cbrcK1ktcwiyFUM/GEBgtlUqxtLSkKJHPPvss3/72t5mcnORb3zKM9P33P81HPvJ17rrr6xw//rOEwz/3usb2rWJz3GmrHx/12HR9lvPrNfO4u9PF6Hbb28KjFnaAeTsMrPN8pRMEW5KMQGF0SCKKBC2kbplUH4e1wIpsjUWRT9O0dXoFwiG+evUqPT09BINBrFajtJTAErOzs1y/fh2AHTt2KMMuBWpF91mMmhjHyclJlpeX6e7uZnFxkXg8XhOJ8vCd7/wW5fJ/Y2hokkhkO8eP/wa7d3+GJ554iQceeIBt27bVtutxUqkUTU3XefLJX+Xw4Xmmpy+RTO7j7Nnf4oMf/A5TUxFyuRzhcJhMJqM84dbWVuVpt7W1EY/HlZeWTCax2+20trYqKEN46gsLC8oAiDGWeyV9J55YtVolnU6vW3TFM5V7Zd6GCi1SGBiCf0uK+NzcHO9+97upVLJ8+MPfI58/RltbNz5fhF//9ecIh0OKiQDrq0JLu1mA6P7772dqakot7Hv37qWrq4v5+XmSySSRyIfIZK7w3ve6mZ6exmq1Mj09yPi4n0cfPc+mTZsYGRlhfHycc+fOqVJo0kci5JTNZolGozQ0NHD27FlCoRB2u51Dhw5RKBSYmZlhdHQUi8VCa2sriURCaUU3NjYqAzwzM6OMeGtrq9r9iF63BG/N8QbpG4fDoTjm4qAACpYQo23+HBj0vEuXLqnAm4ibTU5Okkwm2bVrF1eudPDTP/0yW7Ycw+dr40MfitHXd4KLF3s4cODouh1w/X2oh0DM370R/PBm6XoyJsWhMMNYsBbcvNl3m6+/fjzVB0vNgdI3094WhhrW3ywB9M0THYwfLh6deMvSCZIUA4b3E4vFFO3MHPgSVoOZFwxrQTKBQ0KhELlcjrm5OSXA73K5uOuuuxgbGyOVSikRGwCPx6Mml9/vZ35+nmKxyJ49e8jn86TTac6cOYPD4aCtrY10Os2DDz7IhQsXGBwcpLV1hUzmizz//B9w+nSBUsmFx5NkaurfUyj8O65e1fH5PBiaw1UqlQJWaxWPZ4kf/ehTuFwJ8nk/fv8sL764F13fTiaTwOWy4nTaqFYLVCo52tt9HD+eobHxnVgsJbLZBA0NNgqFNG73Xeh6gdVVD+l0HE0zxPXT6RiZzCClkoNq1UmpZMXQVK5QLudJJPxUq3nyeT/lMlQqBUolH8VijkpFR9Ps2GxgtdrIZq0Y6ncVHA4Nm82i7oX87e7u5tq1a4p/LLDV3Xe/xLVr12hqaqKxsRGr1crIyAIjIwuk0xXFQ5ZxczsT5MUXXyQQCFAoFOjr62NlZYXl5WUVvR8eTvPcc/+Z5uYvEAwWmJ8f5vz5X6S9/aNcvXqVlpYWtm3bRmdnJ9/97nfp6+tjYWGBQCDA8ePvpLOzjWTyiOLxl0rvoFTaRDj8eRoaGnj11VeJx+PKSIZCIeX1rqys0NraqlTxxNuX3aPQUSXuEo1G1c7PzK+WXY/ZgIuTAmsQoOxgzYucQI3CurLb7Yo9EggEapm1Pj784WdqapR+RXH8yZ+8wr33niQWK77GgzXP+43av7anvRGkUb843Ooaboahm/vvzUAz5va2MNRtbW1qcAmWVQ/oyyASGMLslcGasIqu60p0RqQg+/r61GAUor9wSoWaI4VuBfuVQgVdXV1Uq4Z8aW9vL+FwmEgkQrlcZteuXUQiEdLptPJWJbU6HA7z2GOPsbq6qrDLnTt3Mjc3x7Zt27h06RLhcJidO3cSiURobGxk794Yo6MxJibaaW+PEAgsUShU0XUL5TLkchnsdhdG4YAGSiWj1JXVmieXawMgFhsgFrt5X9dIKf8qbWLi1se88EL9KzqadrD2V4JgOoacqgjs65w/bwjra5qOw2HBELUHq9VQedP1MrpewmbTaGhwYLeDxVI1PYyFxWqtomnl2kJTZmlphMnJBLpeJBJx0dTkolTK4nRqNDTYyOdTtLQ8xUsv/TYDA0eZnr6LnTv/gOHhCmNjBn2yv79fQSiyEDudTnp6FvnBDz7Czp1xvN5TpNMHuHDh/+CBB/6ahoZurl69itVqVcUpJGnL5XIpfHp+fp5KpaJYOiL4JY6JBOBjMUOQXzxXMzQlXqMwayQOIXNHAukCe5gNljhKspN1OBxEIhEqlQoDAwM0NzezuLioFtCGhgbF/BHW1kZY80ZGbiMv+vWMZf2CcqvjzceaF3TpQ3Og+1awh/n7zL/NzCQxv1//O+90IXpbGGqhxZmNrvxAwa/FiAvWKdtts/ckuDWgAivRaJTm5mZFexOvS/L7xdsWT0ICj2LsRcFteXmZqakpAGWYJycncbvdTE5O0tXVRTAYVFjn/v37FRf22rVrlMtlBgYGaG9v5+jRoyogubi4SLFYpFqt8swzq4TDTu666wUuXXoH+/b9PW73STZt2kQ8HmdoaEippElg58SJRmKx/0hX1wssLd3Dnj1PUS6fQ9PsuFxunE43YKWzs4+FhQiNjU0kEmncbh/Vqo1KxUq1aiOZzGK1NlIua4CdQqFKLlchlyvVXrPR2hrCbm+kVNKpVCxUKlAoGMe4XB7y+RKVilFcQNctVKsWNM2GUazAeN7Q4KFapfbQag9hKwijwnjtZjKlJjLJW9B2qWfLy69/5NjYT6JpZa5c+QxXrlSAMlarzsWLRex2C/B+bDawWHR0vYzFUsFmi3P69Gfp7b3M4uIABw58Dl1/FZerRcVKstmsYsXk83k6OjrQNI1Lly7R2tqqKI7iwaZSKRUYLpVKrKysqAruEtir9+o8Hg99fX34fD6uXbumHB2zgp55LslcrFQq3HvvvapQr9mYymfF0Jmfy2dFC0cYLvK6tI0M+M0Cd28W8pBzbNTkd8sicye0OlnMJO4ir4k9MR93O9eyUbttQ61pmhU4Bczpuv5eTdMGgH8CWoHTwK/oul7UNM0J/AOwH4gCv6Dr+tQtzr1uFTNnFNrtdgV1yDZMmlmkyfxZSdGWJBhJLnG73apzvF6vwlEFQ5XIt3xfZ2enwk0l6CYVx202G319fSrwk8vllGBSqVSio6ODRCKhqqJbLBZGR0ex2+0qQCVUN4vFwpUrQa5d+xibN/8ud99to6HhOEeOfIx77y2QSMypBcXlchGJRLBYLJw96+P8+U/x0ENfpLn5DK++auHChZ9i376L9PbeqEE9jxIOd9De/n0qlQWVTCKDUfotHo+j67rSppifn2d5eVnhqjabjYMHD6rFS7LsRkdHiUaj7NmzR6nLJRIJBS9JernT6SQQCPCRj3xknSdoNgj1UfZyWXYTOpWKRrlMbZGAcpl1/5dKeg120SiVqlQqGsVilUoFisUqpZL+mv/LZZ1EIkOhUKVc1ohG0/j97TQ2+mrPg0xPz5HLdbCwsA+/f5Z4vJv+/hto2hL5fBm3u4nV1SL5fBmvt5lisYrd3kC1aqVQKFOpFLDbrUxP72bz5n+ip2eMhYU8k5OT+P1+lTzS1dVFKpWis7OTlZUVEokEu3btUlBHuVxWVcYlgzObzaqycJINKpmFpVJJ1evM5XKqcK7MF0AFtWXeCJRSTyOTebERlizzyZxAIu+ZpSHMyU3mOV/f6qEDs414K9pG12/2ogVeNafl36pt5FWb++WtaHfiUX8MuAqI8tF/A/5U1/V/0jTtr4FfA/6q9jeu6/omTdN+sXbcL9zq5GYsGtZnLZlXKTMbxDwo5H+BL0SkXyo5h8Nhenp6lMEDlPCTruuKxiT0OTGIHo+HcrmsKk+Xy2WVbi03RKhQS0tL3Lhxg/b2dlWJplgscvjw4XVR83e9613E43Fu3LhBR0cHNpuN0dFmPvjB7xAKebl69Sp+/zTve98/Uqk8RD7/NzidTnU98/PzNV2NR3nnO/8HXV0TOBwt7N59lXD4HpaWHqGzM8z8/Ajnzv0yDz/8/5BOpxkYGGBubo5isagwz46ODiwWC36/n6mpKZWYIIp2wh4QtkY2m123cEqQMZlMKnlS2QWJaJDslswBrvpW73kY0JPsctZvI80JCmYDYH5N4g31lDzZjlerhq72hQsXVBEJl8vCyMhebDYbIyOOWlZqjvn5TzM09FHe855GZmc38e1v/zJbtvwthw8bZdk2bdrE1NSUWnikoPDy8jLl8k9w/vx/Ynj464yNvR+Pp0Qg8BWamppqei17SaWGCQZfIpFIsLq6SiAQIBgMroMcxAGQoJ9BV1xR2h3iEYtmuyyEAou0t7crI2lOdxZNF/NOVO6B+f96NoM52Gb+jFzz6+G9G+l9bGQPpJkN35ttZofuZjbHDIvU/1bzOczXpGmaiqWYr3kj2dQ3sjO4LUOtaVo38B7gM8DHNeNKH2QtrevLwH/BMNTvrz0H+AbwF5qmafotrkw6TG6y0OnEGAukYOZXCxvEdJ3ruIwS6MvlcmSzWebm5ujr61ODWiaAJGZIqqwwR4RHKzdGJpHId7a0tBCNRtV1Nzc309nZSSwWUzuBV155hR07dhAMBhkcHOTixYuq6KXAJcVikYceOk0ymSQcNnDAw4cPk05HaW5+lWeeMYSZksmkEg8aHBxkZOSlGvujiVQqxcjIAtHo53j55d9lcvIB1S/PPPPxWv8YRUwFA5aCpoL9Qrn2vEq5XMQosGpgw1YrvPyykXptGM4q1WqJQuG92Gxgt1twOGxUq0XK5aL6jHG8js2m0djoJB7vR9N0rFaQ4qnm58b/OlL81WJBXbP8lefm180PKdaq6+srjksVcinGWq2WmJ1tIZsdoFBYxWKpsrzcpK6pocGOpt1DZ+fv4fGMEYlsIZl8kW3bpsnl9qFp31O4sZTnWl5eVkUfnM7HOHPmN/mJn/i/CQQu4nTmOHv2V9iyJcvw8LMsLIxw9erH2L//c6TTaarVqqJJirazy+VSvHSv10ssFsNisSjFyNXVVTwej8oClUVIxncqlVKKetPT0ySTSfL5/Lq5I4vYnQT86r1Qec38fv05NjJWGx0nbAzz+/VGsf5zd2r4zMbW7PCZ0+vvtJmhj/prfbM7gtv1qP8M+A+AlI9uBRK6MfoBwkBX7XkXMAug63pZ07Rk7fh1ZR00Tfu3wL8FlKKarPJyo6RJR5pxNVnBJAgiUW3BsMV7ampqWscDnZycJBgMrquEbdahcDqdxONx2traVBKNphkp3uVymZ6eHhWMbGpqUrQnQHnkIn967Ngx0uk0V69epb29XUmsHjt2TBU0kGoq1WqVF198kVgsxqOPPqr0Nq5cuYKu65w8eVJd8+bNmxXsI1CK1Wrl4sWLhEKNdHWdYmbmMMHgZXp6xnA4GkgmM3g8TQoqcLnc5HIFDEaGk2pVo1zWKRbL5HIlisUKYODMFosNXbdSKlWxWu01/FmjVKoADnTdga67KBSMOnsG/mzBKPklw8xCImEnHvdQqWjrMOpKxfz8rZW6vHX7lXX/raVWm9sHmJ3lNango6P/OwDPP29U+zaCmpVagFOnUrGhaSVeeeXDalGx29OMjv47IpGfZHW1hfvv/x90dc2Tzxv3+8aNG0rLQwS0BM4Qgf5sNqsSgcQjlsQTKYAhjKZoNKp2hE6nU1E1RZ0QWIfJ3gwj3ih4Vm9o6w1ofXCuPmV7o8/I+/XNbNjrjevNjHT9tZhfM9NLzXQ8gU2TyaT6jlsZ23pn8a0IHta3WxpqTdPeC0R0XT+tadr9b+rbTE3X9b8B/gZg8+bNunSEGFrRkIC1jhAvWxJcTNf4GsxTziUYm1DkRFxG8LmGhoZ1OKCu67jdbjUxBFLJZrO0t7cTi8XU1igcDtPV1aV4rOLNBINBvvvd7+J2u+nq6lIc15WVFbq7u3n00Uc5evSo4jLPz8+jaUZB3sOHDytND3l9ZGQEp9PJuXPn6OvrU0ppgtfncjmam5tpbm5mdnYTi4tb2br1G0xMPMqePU+zbdsSuq7T0NBAJBJRhVYdDgeTk5N4vV78fj/FYpHl5WWWl5fXbQOFeijBWikoMD09rZTupKpIMplU/SiJLgJHdXd38+EPf3jdBDCNB1MQSlcG3PyQxcRYDNaeSxFWA7tee83Aqg2s28CkBauu1HDtKuPjUywsROjo6GZ2dp5QqAtNsylaYTqdpVisUChUCIW6aGryk88blcKTyQzz84t0dHSzuLiC1erA6/VhVPU2ArClUhWwYLM5KRTKVKsaxeJWVlYG2Lr1G1itR9H1oMpalTEmsYKlpSWlWCcQn6Zp6xQChZInzor0qwQnxQCZoSezKJZw30VX540alXp8ud4bvhOvciOP+3YM8u1e+0bHyfULXGfeTd/qXGbDb0YB4K3B12/Ho74XeFzTtHcDLgyM+s8Bn6ZptppX3Q0I8WsO6AHCmhHyb8YIKt601eM45qwggUHMwSYxrGa+tWCS5gEoD5fLhdfrVUE/SeYQcXUxKA6HQyVmSBR9bm6OpqYmxY/O5XK43W5Vj1A8evP3u91ulYzTX0t+WF1d5fLly0xNTaHXgqKSvqtpmko3jsfjpNNppUeyc+dOnE6nkpl0Op2EQiG1U5ifn8fpdNZE4e/h1Vc/xL33/gmHDmU5efIqP/rRb1Iuf4Ht2yNks1n6+vqYmZlRfdfZ2UkulyMWi1GpVIjH4ypQZc4qFJlU6VvZYUjmnZkSBqj7I31TH0l/vcFrtWo1uOS1E7beq6oPWJnx6fqHbPHNVU3S6VcIBKrMzLxAMGijv3+ApqYmLBYLbW1tjI2NqeLG9977DjXWrl+/jtutoWkLNW7/nJKJlV2VpmksLy+rMTE1NUUud5hz5/bS3/8VJiZ+ir6+CXI5w1UfHh4mHo9TLBZpaWmhVCrR3t7O7Ows7e3tuFyudZrS4qCIjo0kL8l7hUJBzS3xvOW+S2WebDa7brtfj8verG30vhmjvh045Haw2npP+mbe9EbfZ76uepzZPBbFoTPDP5JvcavfLE2g2Hpc+q3C1m+ZQq7r+qd0Xe/Wdb0f+EXgiK7rHwR+APxs7bAPAd+uPf9O7X9q7x+5FT5t1tmQrYgkoJhFy82dLJNNqlYASrtAvDgRGbJarTQ3N9PW1rZusMfjcTKZjBJdEjGldDpNNputBYPKyjinUimCwaCKojscDhYXF5WUqUTaGxsblR7w5cuXVbmvvr4+MpkMly5dUkyTubk5IpEI7e3tOBwOTp06RSKRwOv1cvjwYeLxOKOjo6yurlIqlVRAMpVKMT8/T1tbG4lEglwux8xMkEce+VsCgYuMjo6ya1eUBx/8Iun0CN3d3QQCAcAQdsrn80qUv1Qq0dzcTCQSQdeNQrKwVifSrB3R3t6uApBSMLitrU0Fq+Qeif6EwDP12926Mfaa/8UI1Qd15O9G29qN3nu979B1XYlBPfroo+RyObXzyufzzMzMKJXDrq4uYrEYi4uLjI2NATA0NMShQ4fWyRMAKltQziNVYarV+zh37lPs3/85HnjgB+zb9185cuTfsby8Q2VDSo3FcDisGDOSZ7CwsEAkElGJVdK3DQ0NiokkyTKCW3s8Htra2lSAUoLC5t2r5DDUwx4bebX1/beREX09I73R340W3ddbnOtfu9k93uhaN3pNxqrwzaWZv2uj7zCPT7NHbX5/ozH/Roz3m+FRfxL4J03T/hA4C/xd7fW/A76iadoYEMMw7rdswrOUbYT8MMnAamhoUJoGkj5u7mhzB8s2UIIu4olKVRWZVOYgSqlUUtKa4onI9r1QKCgvVoza+Pj4uqCN8LrlWkTFr1wuMz8/r4R6AoEA/f39XLt2jWKxSHd3N42NjUxMTNDR0cHBgweJRqNYLBYlqNTd3a2YGrIYra6u0tPTo7a1uq4zOPgNAPz+AMPDw0xOTtLXN8G99xYZG5tG13WVRizZk+Lpzs7OKraM1H0TPFTuhSQBFYtFcrkcuq4r9gKguLaAYilIZQ9zMsXNBmo9Y+BWzTyRX48ZsJGxkde+9a1vMTQ0pEpNiaZ5IpFQFC1xJJaWllRC1I4dO1hdXaWvr48f/ehHakdhTtMWp0Fom62tP8njj3+VbPYs2ayX/v5Jurq+yuXL3WzaNMvQ0JDa0fX09CjZW5vNRjwex+PxqJ2PiHg5HA5V/NblctHY2Kgq00h5NVlMBbc2ZylKUWMJhN3Kk5Z2u5DARsa2vpmNc/1nN3rv9c6/0bXd6jt1fS1fQ76znhGyUTMnyUgTfXBzXsjt9unrtTsy1Lqu/xD4Ye35BHBog2PywM/dyXnNKeKwnkBeKBQIBAJKS0PgCUDhSLquq224PJf3xXgI5U7gjkgkorxgMa71mszy12KxqGK1UmMvlUpRLpfVIDezUmSCSjWZ1tZWZmdnSafTNDc3E4vF6O3tVZS2VCrFwYMHqVarnD9/nsnJSXbs2KEWi0AgUPOQd6mitR6Ph1KpRCKRUP2Ty+UU6yQajSqpURHjX1xcpLGxkVQqRXNzM01NTep3rq6urqNvSfanUMIAVQAgnU6rfjVXc9E0TWGoTqeTXC6nIBrzglI/uOu9OfN79YGljSbwrTw+82vmc+q6Tk9PjxLMkn4UgyzJUcK2SKfTdHR0sHPnTlVp5eLFi9jtdqLRKKurq6RSKZxOJ7FYjI6ODgCi0Sitra1YrX9CpVKlr6+PeDxOIBAglzvLfffZCIU6WVxcJJ/P09bWpnYjmUwGq9UoE5dMJgGUqJJw/oU6au5XCbxLQNycLi73ydzHG7E36ueleX5uhEXf7J6Y36+nVt7sPBvdz/p7u9G13Yy3XG90zdchGjFmjrc4cDcbW+YgZP01ij242cJR33+3094W6nnmQgHyv3SkrPpmrFG0XqWSixT3FE9ZElnE+4vFYusGh8PhoLOzUwUtBdMUmp549pIQIuWWpEaiVBwX+ENKFcm5xHhqmqayFgUf9/v9TE5OcvbsWbq6ulheXlaVMs6ePUs4HGbr1q0sLi6Sy+Xo6upicnJSJR3Y7XaVeCMBJ8GIJYXXarUSi8UIh8MEg0Hm5+dVjUMR+C8WixQKBRYXF8lkMor/KZxaCUQJq0WMrWREAirtHtYWRXkuf8VLTqfT+Hy+m25nb+YN18Me9c28HTa/Vn/um22tH374Ye666y4FoXk8Hvr7++no6MDv96v4xN69e9m9e7dSKRwdHVVQkcgHyD2OxWL09PQoHnljYyO6rquiFnNzc2zfvp2VlRVVp3Jubg7RXl9eXkbEwbLZrNrxSYk2M7tIdnISAJfvEh683+9XBYUBZZTEOXE4HMqjrDco9f31em0jSKL+UX/PNooviHNifn+je3gro7/R9b3eIiIOhtnGmJln5uPlODmnGG2bzaZKn8l5bqfvbqe9LQy1+ceYtx3mjjdnKwLKmxNNAXmIwRFDHQwGlUSnaPG63W6cTqfySuU7zRojgv3JVre5uZnV1VXF8JC6hOl0WmHe4pWI9y/ne+qpp9i7dy+B/4+6N4+S5KzuBX8Rue9ZVZlVldW1dPVaLfXeUqMNkJAQmOUgjrEZDFj24cB4Gb/x4dnHWM8IAwb7YcY+z7xnz5jx8xjGYANGSIwRyE8gIWvtVV29d3Xte1ZlZuW+RMQ3f0T+vvoyOqsXIeu1v3PyZFVkZGTEt9zv3t+993cTCYyOjqKnpwddXV0YHR1FPp/HgQMHMD8/j71792JkZEQypDEzcmZmBh0dHTLsipsKHVW8D/5eLBaTBW9zuRyi0WgLRSyxWMbUMsxR02yuiEAggHK53EKByX6mAK/X69i0aZPU6jh5OVbEbelPoEbNPmq3qK4mcJ2vjUzJdovQeT11UX/5y1/G0aNHsXfvXgwPD0u8P5vNotFooK+vD1u3bsXs7CyOHz+OTCaDWq0mSZY6Ozvhdruxf/9+CLFOVSqEQDqdxsDAAPr7+wHYCVbRaMFsCsIAACAASURBVBShUAjnzp3D5s2bUa1Wkc1m5Uanznsu9rW1Nenc5uc8X42E4rzjHLThlq6WhCUVS6XQph9GzT5s5x9Q+8/Znxv1vbpZqoLLaTW1E7xX22CvJgA3+kydX+r8UWUO56gaZ95uA2s3p+igVgtmbCTsnf11rXZTCOp2TfXKsiNUTyxNC2q9NP3UyUzcjR5xat4AZOFamin8PbUqDIU4TVAhbBrTvr4+DA0NIZfLSQInmpQUgsQR+fmRI0dw//33IxQKYWFhAYcPH5aLluFY1IT4PDt37pTPyBC/zs5OWJYlU74ty0K5XIbb7ZZmdzQaRXd3N0KhkISKSMjv8/lQKBQkRk8tmMKPz8oiATS7VV5qVmGhRaGS8BD2oEMYWGc2pNNGhUjaCdx2C9Ep4DfS0tR3p/YOXKn5/e3f/i1cLhcef/xxmKaJ97///U2mvjvxwAMPoLu7G2fOnMHc3Bz6+vowODiIXC4nS6XNzc0hlUpJIVssFuUGyP5n4VpuBIyNHh0dlY5dFQ/nnGQ9TNajZKw/BQgJwBjhoY4RCz8Q3rIsS/4Ox0OF6Uh169wINxKoqvBV+7+dsG03bu3Od77UKArn9a4mxJ3z6Gpzy6mxq+dtdI2rNSGE7MuN+vO1aNg3BSkT05bVxk4zDAORSEQKUmKmzMhSMVLy6tILzjhohjgtLCxgYGBAdhQ1Ipq9Kt0mHUN0TNLDfunSJWiaJovoZrNZqe2Td5gFQLmgqtUqcrkcnnjiCezcuROvvPIKHn/8cVm0dWFhAW9/+9tRKpUwNjYGwzCwZ88e/OAHP0A0GpVsbNVqFb29vTh58qSs2kEMmtmU09PT6O7uxsTEBHbu3CnvLZlMyk2EWjXDvFwulwzDq1QqMsrF6/XKcwiXZDIZlEolDA8PS74PcnsT8uCGSgiLziua4O084KqW61wk7TRhft7OVHYKAedCVK/3la98RSZATU1NIZfLyfNZPCAQCGBwcBCLi4vN9P4OlMtlSba1e/du+bxMhrp8+TL27t0rf4eQF/lh6CfI5XLS8UpzmoxzdGyr9815WCwWZeYiTXUWNCiVSlhZWcHw8HBLFi4FOec2Qz65WbMMnDoGan+1W5/8eyNh64Q0nGN1tXFzzg9nVIbzPq7WOOec961eW4VgN+oHvrebwy6XSzILxmIxef3XI476ptCo23U243DZhFgvP8UdixoaJzOFJXE3asSBQACxWAz1el1qJ2zhcFiWd1edJ8Sm1ThgXj+bzWJ2dlbyifDeVJxbvR+a/9lsVgosl8sli9Ded999MtWXRDwMu6MlUCqVpGDv7u6W9KnLy8uwLEtWYk8kErKmI5OGWOKK5jPZB7lBEb4htiaEkCGRxKyr1ap0oLGUEy0JRsmwn6gpU9umkL8W3twuzM6pVaufX+37Tg3GKSx47OzZs5ibm5Mx77TMNE3D1q1bUalUcN9992F1dRWVSgWLi4vYt2+fLMnmdruxurqKfD6PZDIJ8p2oUFAgEJBQFDf4QqEgObUZDdTR0QFg3bGr8pPQMqQW7PV6EQwGJQ5OaCObzcrIEWY0AusVXpxCn/OaTvkbbc5xcgrqjc5ttxE4NXA+O8+5EWhso3to911V81Uhwmv1Bz9XYVNmFNPCcp670f/XajeFoHZSCrKT2AE0qTVNa2HPU2ECaiU07wBINjAhhOT5TafTLaQ8dNJt2rRJhmHRzOZiAdaxPXIwEH7g+VxArFlYrVZbvMK81tjYGBYXFzEyMiKdjs899xzK5XKzeOtOJJNJPPPMM1hbW0OxWEQ8Hke5XIbP50M+n2/RJBltwHvgQqV1wOIJ7BM+I3FLbkZ0LlIoaJomIRVufCrWT8HHEDy+cxwotKkBblSNnOMNbBxupS5e5/ls17tQ1VhvAHjPe96DcrmMdDqN3t5ezM3NySiOS5cuIRwO46mnnkKhUJD8LpVKBUtLS3C5XJIBTwjRjOKw+WHK5TJKpRLq9ToCgYDkSp+YmJBWF7VdEoc5Q8QY5aE6rjgfVUe2EEKWCmOoHmE3augU+kzQ4sbM6BE1JLZdY1+q96K+vvnNARw7Fm0Zm5MnO/Dtb2/ecIzaWT1Ogaxq0ht991rj3u6Yqslzs6SwdWr1G11DbU4YT02AUa/xWttNIaipjaqTkQ9NrU+lNKUQoOYAoAWeUFNqV1dXpfbQ1dUFt9uNxcXFFlw7EAjA6/XKYp2M+aXw4WQBIJ13rLnIAebkByDxaQ66eo9jY2OSqWxhYQE9PT3o7OzE+fPnIYSQAiCbzUoaVC6yXC6HYrEoeUMsy8Ly8rIsakshy9hmJqrQ0chFztA6ZxoxEyV0XZeWATXDWq0Gr9eLhYUFRCKRFm2NY8gsT9ULTstGtXCuhUs7TU91YxJC4NvfHsbJkx3yc8uyMDqawGOPbbsu7Ur9PxwOSx7xH//4x4jH4zLp5b777sMDDzyAzZs3S1y/r68PL7/8srQmotEoarUaOjo64PP5EI/HZUFkZpsyOon1FhcXFyUFKZ2zLHZMi5AbGkn+qYQQU6Z1RUyb/dPR0SGJmzgXOX9p5ZDmlj4czot2HMyWBTQaQK2moVQCSiUX8nkXMhkdS0vA4qKO+XkXOjtL+Oxn9+Cpp3qwtOTBiRNxfOEL+7BtW+6K6Id2Qle9z3aCeyPs+3pbu/nEe1Hf2U8bhea1swpVq4Wyy2nB/KztpsCoVY3ZsiwJK+RyOenoYAcSogiFQigUCtJcZKgdtWHVTMxms4hGowiHw4jFYqhWq5icnJQFUemA5OTmbqgKFb7rui7Tbuv1ukwMsSxLmqQsUkBIwEk0dejQIcm6J4RAsVjE5s2b8cILLyCfz0utrNFooFAoSCyYxXwTiYTkLalUKpiYmMD27dvh9/tbElkAu4p5b28vlpaWrliIfE4+l/qMxPzJk9JoNJBOpxEMBhGPx6UGR6HPkD41XZvXp5NMhT+csdSqJsXFRKzWqUFv25bFH//xQfzu7x7F7t1pvPpqJ/7sz27HJz/5MkzT5vdoNAQaDR2Nhi1o6nWg0bCJpWo1T5OT2sLXv/7/4WMf+wT++Z9/CMvS0Nu7H9PTc/D7Q1hYGMboaBalUgqp1Edw4cIYVld3Ynp6FgsLAXg82zE6GoDfH0Qk0oFsNo98fgjlchWlUgjFYgShUAwulxe67kG5XIemuaDr9u9PTwdgWRrsogoe1OsmbJ4Qm/TKlBwnAOCBrVfZBFcTEzFks+8GSbMsy+bttlkbdLjdfhw7Zh9fL9BgX1MIF4TQmr/tkr8HuPCTn/ihEmXZnCXX3/70T/cgFJqFacbwK7/yPXR3F1GpRFpCXrlhq/CGKoApsDfS3l+rZqp+zxk+Rwve+Xs3knzF76u8QTz+s2jTwE0iqJ0ZUSxMS2FDTZWhSLquy9heNQtI7RAKWVVQMOuO2GI6nZZmKcP62oUFAusFdqkhFQqFK+6Rg1OpVKRGrRKz8N6Ygj4zM4NqtYotW7bg+efvRnd3P4R4XEaAVKt34vz5O7F58ynU63UsLS3Jz4jVM/tsfn4eAwMDMrRO3XRUOIgLQy3MwAlGOIjaHiEcPtva2hqGhoZkgVtqHvl8XsYAqwJf5dZQLSLg6jBHOzNYjS7YtWsRn/zky/jsZ++GZQlYlgu6buGzn33zDQsW4H/Hl78MAB8DADTrFQMAfvrTK89eWLjBy1+12Yx7V9K0ksLVpmwlbat6rmF4UatpsIdZwLIasGtcVhEMeuH1VuDx6DDNBjTNgE3JY5csM806hLApbVnCzDQb8PncuOeeO+Fy2SXObDpaC6SqVSlmeU8q3Sxg4tvfNjA3dzs07Y/w1a/+If7mb2ylKh6Po7+/Hz09Peju7kZ/f7+03jjvOE82cjC2g7wASEuS7Wq+EDV6qN21r+UHUZtTkJPThRZtu9C867luu3ZTCGriuRSqNOPVbCo2VQDRrCddY7v4a+5u1OZomjM9OhQKScY3NTFAJQDn+RRqFECMOKGzzLLWQwgZFsfB5IsbBMPlgsEgLly4gMHB/finf/pl3HbbEizrB6hW70Q2+18xOPg7LeYtn9Xr9baEcQWDwZbfMwxDJr/wWdjUzYz/c9Kyj4h/8pxsNouOjg4Z7cFNslKpyOICrKTDZlkWIpGIDGNUhbF6DjcRtZ/aadLqArv11mVs3XoZFy7sAPAiPJ6X4PO5EItFEI+HEY2GEY9H4PO5oesWTLMBclJbVgO6bnNUv/zyizCMGjweHT09CVSrJWzePIBSKY9oNITp6Qn096cQj0dw/PgRVKtF5PNZhMMB3HrrCAqFHOLxCHRdIJ/PIhDwYm0tg5mZSfh8brjdGnp7k0ilulGvl+B267A5uxvweNzSB8ANjeuAznJqldRIKdD279+Pl156CaFQSEaH0PoaGRmRjHuM8rEsS55DCgLOx1qthlwuh2Qyid/+7YGWeVKpVJpFKjTJL6JClerr1Vc7kU7vhtv9xzCM/xW6/hx0/VnpDJ+dnZXja5qmhGF6enqQTCbR2dkpyac439slnnA+cJ5Q0XDKiKsJbBVeo9xRk36cWnW7udvOOiTmz8QXZ3utmvVNIahVAanruszMAlphCBXrpVbpDMoH0CIUSNFJJyR3d36X8Mnc3BxWVlak95y/Tc1TnQiMKyYmrmK4wHqW05YtW3Dp0qWWwfH5fNi2bRsKhQLm5+eRz+fh9/sxPDyJO+/8Gp599hH4fO9FpXIL/P5vIZtNYWFhBKZZQq2Wh9drobe3A4XCIoJBHW63jkajgHRaQNfrGBpyw+eLYW1tWWLD+Xy+ZWKqmDv7zYmnsQ+r1SrS6TRqtRp27Nghy5kRZydslM1mW2ASAJIfnOPK8eN4maaJ1dXVFoY5wjvqptvO9B0dTWB6ug+6/gVY1idgGI/CNJ9BuQwsLraWRNI0DfF4HPF4HD09PRgcHERHRweCwSDOnfu+jFl3u2dRKpWQSMwilbI3nJGRTszNHcf4+AQ8nhIajTI8niIaDR1dXRGEQlXoerapEOSaWHQB4bBdIisQCCCbLSIcNpo8HHEsLeWbm7rdHyrNgTo2vHdanISCADuShJs2BWomk8G+ffukL4bKDseXKf3kklGtTiFEy9zndfP5PKampqSDktYUHcW0oEZHE/jSlw5i375HMTr6FwB+gkbj7+F2fxi6/uwVwo7Y+NraGnK5nFwn6nhTGYtEIohGo+jq6kIkEpEQphpowHmlrtV2CStOXNw016vXl8vllqSfjXBqZ1OVQzXE9/XCp4GbRFA7zYeVlZYaA7JYJyckPejEpIlhq+F8nIAcbC6GarWKSCQiSW6YgRYMBlEsFiWUwqbyfwCQk58hdnQ6qhAMY5Knp6flb1EL6evrw+XLlxEOh7FlyxYUi0UYhoFLly5h//6t+MlPPCiXbwMAVCq/gkqlvQnerk1MAC+9tP6/rjfgcjXgchnQ9TpcrgZ0vdH822j+bf9vv+zzl5biSKf3AaiiXi+gXF6F1wvMzg5jZUWDrtfhdhvQtDp0vQagDiGiqFZDqNXWYJolaFodHo8GXV93rKhJLkIIaRGsrq7KMSBFLDdBNZuRgmt0NIE///M34e1v/z/xwx9+DobxE5jmN6HrH4KuPyvHX+VtoUCYnp7GK6+8IrXU7u5udHR0IB6PY3Z2VvpF8vk8hoeHceTIfYjHL8EwLkmlodG4B43GfqysPA+/3y+hNApabtj0P5CBkOGK3MjUpqYwU5tUzXFn8ocavbG2tibnWDRqV8pTHbcejwfValWWnlOdj6pwVO9L/YwkXOTAptOYlpTP58O5c0P45Cdfxo9//FJz7j0Ll+uXYFm3wSbaXL+u2hitpPaB+vvUxhcWFlrmAMmnOjs7kUwmJaUDaV6BVu4ONicfEK9JQa1a59dqnJdqBi43O2fUiIp/v5Z2UwhqNobfra6uyg4lFspJyckKQMINhDOoZTMSg9EiwLqDjLHAJF2hwA0GgzJ2mDsxJym1Z05mhlWppE8kaKImygK2AFruYWZmBtu3bwcAzM3NIRAIoKOjAyMjI/jpT9fgducRjX4D2ez/glTqEej6UfT3b0My2Y9qVaBSAbzeCMplC5FIAlNTS8jlqjAMF4LBTnR09KJSEfB6I00MMwiXK4hKRcCuOu6FXXncC9P0otEIwjQ9ME1385gHc3M+NBqHIEQrZPJa8Vldr0HTajh/XsNf/qUXXq8Fr9eCx2NBiK0wjIPQtDpcrjrcbhM+n4DXC/j9QCCgwecT8Ps1eL0CPp+F0dEk3v72ceRyLpjmgwAq0LTPQ4j3QYhl2BtHFZpWh6bVAFThdrdGm6ihWYZhYHR0VEYRUcBVKhV0dY3jySc/hv37l2GaT6JUOoxc7q8QiXxM8nOzGoumaZKfI5VKSapeRpHQb+H3++UG3U7ba9fUqCOeR0iP6f4dHR0tsARg+0PC4bDcAMj5wmQXPisTZ9Tf52+oeG6lUpEZk2Tri0QiuPtueyNSE4Y07Rlo2jMbPpNzA3aep647pz+D2ZeLi4stAjAajSISiSAej0soJRKJSDiFY8/+VCPH1Oe+Gmyinsd3daPlbzjP+1naTSWoOakZ30vNWhXCxNQY/8zEjVgsdoXjjjgx+SsYscFJy2tSIJM7hNfgb3MAuABURxojH9xuN7Zs2YL+/n4cPXpUYsgMteLA9ff3S4Idsuil02mcPp3E0aO/i7e+9b8hnf4WNO0ZLCz8FRKJ30SlcgGBABAIAC5XHoODg1hdXUUymcThw114/vkTWFhYQKlUwr5990oiKMDeJJLJZEvMN/tEhRKIGeq6jv379+PJJ59EoVBCJlNCKNSJgYHt6O4eRDDYgUpFoFoV0PUgLMuDtbUahPAimy1jba0OIWxHl88XQ7UqYJoeNBo6UqnN6OvbgnpdQ72uo1YD8nkLtZob9boPhuGCYbhhGG6Ypv23ZXlgWRvVsLsFwG+1HDHN32p/KkwAVQA1ADUpwJeXBTweCz4f4PGYqNXWMD9vwjRLWF72IRz2IJkcxZEjjyIcPoBC4W2Ixz8BTXsOk5OD2L17t/SpkLxL13UZ50xri8RX3OQ5F8mE53RMcT2wqWxtjLum5l8ulxGJRJBKpeQ4ci2pNT7dbrckD6NznZtFOzNdjYxQQ+dM0ybmymazMjqISWUMh3VuOiqu7GztBBnvSeUmUQW6mpjEvrIsS1Zmn5uba7kWv0uOH4aYqqGkTBLjc7eL+Xdek3/zpUY/bdQHr6XdVIIagEzoUB+Sk4FaK4Uquar5PwUwhT2AFjIbAJJcibHCDKmjucoB5/dVLYOsZTTX6Oygo2Z1dVXyZrOp52uahvHxcdx7773w+/2Ym5vDwMAAJicnUavtxTvf+beoVGwNJBh8GcCvo9E4iEbjtAyB8/v9so9cLheef/55DA0NYXl5WdZPLBaLyGQySCQS8n440VWcWsVAVXxvdXW1aSk04PNZGB7uALCCzs44gkFDaooqK1w4HMb58+cRj9dlQQKV+bBWq+Hw4cP4pV+y6yFT4ExMTGBqakrGC/NFAizb8vEiEIgjGOyA1xuFxxOGrgfxox89gxMnzkEIHwD7JYQPmhaApvlhFyTyNz9T3/0AvAD80PUY3O4oGg0vGg0/gH6UyxoAP8pl2+oQwrY01tZ+AbHYXyAWO45GIyAzR6PRqIQGgsEgKpUKuru7EY1GZaFjNRY+FovB7/dLi4+QA9P92zXCeBSKDE+lxsuiAKojltejlsdUf9W/otKhqtg4m+pQVteHiqeXy2UUCgXJWeO8xvW0dhuV83O2dtruRlq58zu02Bkey88YSUYaAOfmpQp7Z2O/GYaBeDzegrM77/O1tptKUAshkM1mZcepwec02zg5yC1BzFjNCAyFQlKIhsNhrK2toaenR8IWxLy7urpkxAmTV6ixq+xixKH5NxszvlTHBwsKcMBVM5Fm76lTpzA4OCiFd7lcxn33HUGhUMCZMza3sC3ojsGyjqBUguTfINERYC/sZDKJ06dPS0cRCf2JiXZ0dEh2PVoLTqHN52JyETefcrmMVCrVsthpffBaPE7zW/V0O2Oq1Th39RxnOJ76bjt7iiiX81hbm0c4HEY8Hm86Ll+Cpp0HoD7H+vM4X87x0zQNAwMD2LNnj6QX0DQN/f39yOVy2LJlC1KpFF54wYcnn/wVhMP/L/L5DyMaPQbgaclAyALINLGpOQ8NDWHPnj149dVXZWJSMBhEqVRCJBJpUT5UrJbKgZMtktofnXCMmEkmk4hGo1I412o1qbxwM9A0TfpgOOepXdfrdQnbOdejczzaOeMICfJ1NQjn9W5qZJBznNvdQzsNV/V/qcqLep66BtRGCFSFVdqFEV5rI7pWuykyE9nIPsY4XgASIwYgtWj+78SfaUKqZENMj15ZWZEeXhWbVFOb+VKxat4H44tVKIRaCAdraWlJThA1G49CWo0NJW45NzfX4tyh4OfzcYGura1JyIcYPCdmX19fixAmzJHL5bCysiK1WpWLmE46PiPrNPL3SWpFrZmChKGCvA9gneSHCUDOSVmv11vOV81VPrczKkCFmnjvTPWemZnB3Nxcix9AbRuZ1+1M0UajIavtdHZ2oqurC4uLi1hZWcHo6Ci++c1F/PCHv4p77vkKenv/EonEb2Ju7s9Qq90Fl8uFXC4naU+LxSJisViLU46WWyQSQblcRrlcRiaTkUoD+1fFnnlvqjCkMCdvCzdTYtPsR0bacI7we2oYIK0ddV6qDlBVY3UKHo6Ns195r+r8bdfaCXF1/Ntpzur/zntT71GNvHCe63yp13P+9tXuw6nNO53Wqn/LyR30s7SbTlAzvrhUKkEI0ZLCTeFHs4RQADP4yJynaoxCCKRSKUn8r6Z5Aq1x0gBaPuM74RMODoUkJyUXBSM/yAHsdrtlyA+fj1EnpmlieXkZqVQKXV1dGB8fl157RrLQFGOKMbUoVYjt3btXFlVtNBqyRBlLNrHqCOGQ5eVlLC0toVCww8fS6bQMX+REp9YeiUQArE80Rs0QiiIBDaMhuGFSKLO4ASMEnJq8uqE5X6pGzXM5B9bW1rCwsIBCofAza2/BYBDJZBLFYhFerxeXL19GIBDA8PAwtm/fjlptL97xjv8O4CewLAuBwEsIhz+GWm2vJLfiHCBrIxUCADhy5AhisRg0TWuB1xYWFiT9aSAQkP3P+ahqdG63XRyDxF20IguFApLJZIvmTY2Zigud3VRgKMQ4Fzk+ZEtUBRGdlaqCocKRnIuqc26jJA9na7dxtvu8XWu3kVzrejf6+87n3eh7tPavR8j/LO2mEtQAZMUKapMUdtQ6VR4EYL0AKwmRyFrFXZLYdSgUQr1elzzOlUpFaiVOli51c2DiAbAeSkSBwWQCatXUeCjgeJ9cOJZlYWBgANlsFt3d3RJLHxsbg2maMqOJi0jVYDRNa+Gg5mQoFAqSeJ4k/uxHhoapMA4bYQrnQrMsm8iJG4Xq/FILIqg1JhlhQ62abHzU1OjcYuw1+5nP1k7T5XPyntppTypBl9quRwDw82q1ioWFBSQSCdTrdezevRudnZ245ZZbcOHCBdxzzwuIRI7KBBGPx4POzlcRj/+19HEQhqNzT/VrbNmyRfJ5MP6YZGG5XA7Ly8uyJBuhPVVY0/Jy0p2qVYw4110uVwstLS0YYq7kESfEQ9It3jstSVUbVZ2JHAs1tE19V+cR18vVGsfB6ZhzntPue2pT79d57GcRlE6lYqPP6YBU5xaVknZO2tfSbjpBTQ2NkAc1YDr9OBEpTEkOxEw5ChYKaQqZzs5O6LouTU9en4KcGjC1WApj56SkY4z3w8VAiEUIgenpaSnIhRASw/J6vejp6QEAnDp1CtFoFGfOnEFvby8eeuihFq8ztXwKTNM0pTbFcKtGoyExcSYjqBo+PfPsFza1xh4bNShi1IxaYP9RaFiW1cK+Ri83+U+c/CsMgyQpkbog2wlqHnNqSU4B6xQKV9OQria0ATTjgM/BMAycOnUKhmHg4sWLMIx1LnS1cC+ZBjmuy8vLcp4QVlDN8HA4LJ+HkSG0WpgtyGID6XQay8vLyGQysk4jk4hUzJ8wk5qgwfnJvqcvgXOBpdjY//Qb0JHG4xxzdXzU/nL2dbtN9rW2jTbt69WYb/T813p/zr5QrVy+Xi9tGrjJBDUxajpAVFObWrAqHFWaTgBYWVmRApgaF3k3qAGqcAC1IVVrdabqqto0NVNq7W63W/KHUJhls1m5CKnV8HuGYeDIkSMYGRmRcdtutxuFQgGPPfYYLjSJJoQQsigC+Yt9Pp+ELzgJCAExPKyjo6MlBIwmM2vzqTt9qVS6gnmN76RAdWrTdAYCkGPD0DPTtLMMWcyWRQ0YXcANRBX8TkGgCuerCWsV/rnedjUhTq1uYWEBuq5jdXUV8XgcmzdvxpkzZ5DNZtHV1QUALTzQ1Er5rADk5sxIjN7eXnR3d0uFghpwLpeTMB8dhNzU1Gen38XZN4ANTalRO3Qusn/pzAYg/TSM5ybPObBegUcV0qofxtl/zvvZCCa4kbG52nip80ONVmp3vmp1vV5Cmn27WeErV7FoyisVCuX33lDoQ9O0SU3TRjVNO6lp2tHmsU5N0/5F07RLzfeO5nFN07S/0DRtTNO0U5qmHbyRG8pms1KzA1qD7jVtnYWNkR+ED1RhSLpIahQMGSJHL/k/aD5yYbBxwlNIM7qDJr+6MFQSGcAWYMlkElu2bJGLUqUgBYALFy5g69atyOVymJ2dlabvyMiIXGBra2tyE2H0Bk1rCt1qtYo9e/bglltukRprLBZrcZBqzYgVQkoArtBuVS2gVqtJ6Ib9wOM0j1X82LLsJIiZmRlEo1HJoaKSPrFf6IzlZ6rQUdvVNCo+g+q0utpiuJoQ4IIvlUrwer2477775DnRaBSXL19GpVLB9u3bW8LmCEOo4YSM+Ve1qqmpKRQKhSZc0ikVAVWQ5nI5GRXDPmF/0w/ixI2ZvxfnMwAAIABJREFUGctNkH1Arhc1eoTrIZ/PY25uDh0dHS3jp0YrtHNoqpuGs0/baZbX6vtrjdFG19zo92/kd15rY/8PDw/j8OHD2Lx5c4uFqhKy/Vto8cCNadT3CSH2CyFua/7/KQBPCyG2A3i6+T8A/ByA7c3XJwD81fVcnJ3BsDKnQFQFIRs1ECaqGIaBbDbbknLOv0OhkNROVR5eJ77M61OLVCtrE1vkglO5PjjpKYzpgNQ0TWKzXIxutxtHjx5FPB7HwMCAPM5oDdVrzbC/TCYDADJxgp7mpaUlzMzMyAXt8/nQ1dXVIrxULmNaCnxWOuSA9cVKy0PdvLj5qem+DJmcn5+HZVno7u5uGVMmE9G6CYVCLZ87tR/nJFdxaafAbkd481qaZVm4/fbboWkaTpw4gWq1iu3bt8sixG9961uxfft2CWvRYmMSFaEfhr4NDQ3J69JaKhaLknyIc4fvDLVzOrmBdaeu2gcUCIzmUPuCzI68LgVtoVDA4uIient7ZSghsWluBuTy4L2rpny7MVPH5/UQUBtBFuoccc6H1xNauFbjegwEAhgYGMChQ4dw6623Ih6PIxAIXKE9qw7e16P9LHHU7wNwb/PvvwPwDIDfax7/mrB7+iVN0+KapqWEEFdNQBZCSHx0fn6+5cHVrCQ1JI/N5/NJPo98Po+VlRXJQ8AYZmqEZHNThXJHR4fUPBlTSkcZsUFuCmTRUwdCxXxp4tJDr9ZcpObf3d2NW2+9FfPz88jlcqhWq3jb294mIRk1VLBer0sYZHFxEbVaDdPT0+jq6pJww44dO5DJZOQEj0aj0jnGRi2cx+i4pBAHbCiJtd7IN81sNsZ2UwjQRzA1NQVWJCcmTmZBv98v6/gVCgX09va2LDo1yQJYXwzX0swoWDb6/EYaBRnranLcC4UCBgcHceHCBeTzeTne6lwjxsskJL6rGy2TTDhniCnzeemIpcPbGUFD/wL7nZYZI0U4J9XKPeyPRqOBUqmEmZkZuN1udHR0IJfLIZPJSOydxZJpSfH6FPLODdFpgbaDQNq1duN6tfFzns/17xTWV7vG69kYxiqEkGszmUxKSEy1dpi/wf5X7019rhu55+sV1ALAU5qmCQD/lxDirwH0KMJ3EUBP8+9NAGaU7842j20oqGliZzKZFs2W+BwnuBDrQekAWsy+aDQqWeKKxaKc9Crk4HK5ZEYXFwm9/iS5YRYjNXHGWlODUQWb6mQD1rmfV1ZWpHZCxyM1L8MwMDk5ie7ubsn3sG/fPoyPj2NpaUmmpFNY6rqOW265BZcvX24xhYkXNxoNnDlzRloGjLQIh8PSHKfWT42f/ciFqJq/nJB8XgoLYvKqBbKysoJKpYItW7YgkUjg7NmzACDDvahJCyFk6CBbO8gJwBWL0KlZ8f+fVaNWrzM2NoaRkRHccccdOHr0KF599VWEw2FcvnxZVqrn7zNahhaVy+VCPB6XqcvLy8sSHrrrrruk8D9z5gy8Xi9isZgU9Oq90GGu4sQM66QVBEDCYKrPhRunOtYUKHbWaw233HKLfF4VguEGXqvVrrB4VP8B15AaLumEKAhPOiEpVft1/n2t8VGvz2v/W8ELV2sqLMWmWphCiBb4CgCSyaSsWk+fBPHsG7UIrldQ3yOEmNM0rRvAv2h2OphsQgjRFOLX3TRN+wRsaASJRAIAJGGMiq1yEjM0T42uoGCihkGyG1ZIoWBwLvp4PC4dbABaojMoXDjx6bFnwgsbtRxGXLCmIX+HWiU3Hi4+n88nK4lzVz5//rxMjeeC4Ll0cjHawjAMrKysyI2IzkBVS7UsS/YH8Wk+D/sQgHRmMjEoEom0EEup+LrKg2JZdpr5q6++ip07dyIUCkmtkfh3vV5HV1eXrGsJoEUQqIu7OR9a4oc3MoXZnPHu7Rbu1RaDUwBNTU0hn89LiIv9lc/nMTQ0hHQ6fYUfg9o146FrtRq6urokfDcxMYFqtYrx8fEWJ3hvb2/LYmU/cL5bliWpSjlHuUFSoDMckn8LIVq0aT5TOp3Gjh07ZMw3AJlkwwxeavC0LNk/6rzihn0jcIcTCuAzOAV5u/FybgLOY+r7G9HU5Dc29hs3VxUBoEIUDAYRDAaRSqWktl0sFmXa/cJ1Mp1dF0YthJhrvi8DeAzAYQBLmqalAKD5vtw8fQ7AgPL1/uYx5zX/WghxmxDiNiYE0IHHTuEOrTpvAFyxcwO2NhuNRiX2zIwxpweW5msoFJKCnjshBTQXL3E8ALKTWYWbmgsn74EDB3D77bdjZGRE4oB0iKqbD2BDLQcOHEAgEJAUn+95z3skPSrvi3GvxA+p2RNKUMlfAGDXrl0YGhqSVbLJ3asKWJrZ0WhURq2QfpNRMDTZ1tbW5KZIE71arWJxcRFHjhxBKpVCT08PTNOUUQRckBQC9DeQDpP3qo6nikMCVxag5fkUHvz/OufuFf+rx5hgFI/HZULUPffcg56eHrjdbmzduhWpVKqlSjc3N0JC1GxrtRrGx8fl/JydnUW9Xse2bdtaHI9erxe9vb1SKyfURquFTms11p3nJBIJxONxOafU1GY10zCfz+Ps2bMYGhqScB+zW4H1iCT+Fn9D7SPObdWSUcfJ2a9Ox7Da1+3i5dude73a8hslpNtBMGprB/eoG5BqITFiKJlMYmBgACMjI9d9H9cU1JqmhTRNi/BvAA8COA3gCQAPN097GMDjzb+fAPDLmt3uALAmroFPsxWLRSkUaNo7Y2VVgiWad2oIGzHGYrGIdDoNABLWYKe5XOvVo9mpFFYq1kShTyFJrYep1pz05MOgWTMwMCAhE4ZH0VFnmiZOnDiBV155BYCdsBKLxfDyyy+jVqvJBB4WONA0TcIwfLZarYZarSajFQCbbIr4KB17JGXq7OxsIXkXQqBcLiMQCCAcDqOrqwuxWEymf6tV2rmYQ6EQarUalpeXceHCBdx5553YvXt3C2zCSByXy4VwOCxDANnn1OS5GFXoQ40RVrW4dguBY3S97WoCgKGVAwMD2LVrF9xuN8LhMBYWFhAIBJBMJlEoFKTWzMbnHhwcRDgclvODVg03x3K5jPHxcfmMxWJR8jkT2qjX69JxTYc1OWkYKsnCw9So6c9ggg0VkFqthoWFBbz44ou46667MDw8LDdLRkKR+xtYL57LZ1L7yJkIplpB7Rx87XBj5yZ8rea8djsN/o3UpHn/qvKo3hPv+Vr3qApsrgU1cuRa7Xqgjx4AjzUFmhvAN4QQP9Q07QiAb2ma9jEAUwB+sXn+DwC8C8AYgDKAX72eG9E0TZroKqxBc0/FmukkpDbIRAxOViZfFItF5PN5dHV1SSFPvFUIO1aZgoQxxRRk/H0uDGrX1AoJZ7jdboRCIVk1fMeOHZLdrlQqSeHPTEFqsnRiMXIiFovJKInt27fjwoULCAQCUquOx+NYWFiQpjm/T8FeqVQQi8UwOTmJYDCIaDSKmZkZxONxmZiiCkXDMLC8vIyOjg4ptNWwLnXTpBY/NzeHSqWCQ4cOwev1yg2LfUeNTtfXSa4Y2kirRcUZ1fRxOs+A9low0KpFOxd+O4hDvU47XJT3UKvVcOzYMSnUxsfHZRHhkydPyudQFQQ1skjFKpklyirh3FAY9REOh1Eul3H58mV0dXUhm83KBBoKBTUKQ1UmVKxcdca63W4ZUplOpzExMYFbb70VPp9PJklxPrNmKCOA1OxZdSPlvTsz7jYSlBRk6rp19v1Gx9u16xXs/9aN85Xzk+9quKYKwbZ7JhWiA64OyW3UrimohRDjAPa1Ob4K4P42xwWA37yhu2g2NQSq3WQgJszFQQ1O7QiSNlFzyGQyMimFn6uYN9nMVIJyrRm9wcVAs5T4NQA5WJZlYXJyUprOjAbweDzo6OhAOBzGxYsXW5x5JCnyer3Subm0tIRNmzZh8+bNcLvdOHfuXAuFqxo3THpXRrGQUAmAjP4YHx+XGLfX65VRHuxnWgOrq6sywoPVcwBIBkFi2lNTUwiFQti6dat0qtFioDAAIImcmEZOTa4dHtkuYmAjDNSJX7Zj4tuotTtHva7H40FPTw8uX76M7u5uXLx4EYODgxgfH5fWkhMTL5fLsqwVNWTCXSrHCZnqEomEZIas1WoYHh7G5cuXEQwG5cbN8eFGSuiO40BBzegojhG192w2i9XVVezcuVOWtyP1Lal9hRDo7OyUKfHEwEmdykaLpt34tNMsr1drdo4j+/Nmau3uR01wAVrnjxpGrEaobXSt19JuGppTJg2oIVsqHqkKSMIIQCvBP2A7SsLhMFZXVyUeG4/H5e+oYVL8vtfrRblcRrFYhKatJ2kA65sDtRy1GoTK6ZvJZBAMBjE5OQnA1kYoPEOhkIyFFkJgeHgYmqYhnU6jXC4jFAqhr69PatSvvvqqDJuya/m5pTaUTCYxOjraUjqss7MT4XAYx48fRzQalZq2ivXGYjGpVS8tLbXEQ/N5WV+SWmAoFEKj0UA6nUYqlWqxBhjKuLa2BiEEAoGAfN6lpSU5aRlz3NnZ2TLeHFNnhIfqEL2aBsf5cL2LfSOBTtKs5eVlPPjgg8hms5ifn8fk5CQ6Ojpw11134emnn0Y4HJZROi6XS/op4vE4KpUKfD4fyuWyxJbp7GNoaL1eRzQalZuuz+eDz+dr2UBpxRAbXltbQ6FQaMm+ZYIXHea0IqenpxEMBrFz5045NxkaRiyda2R1dVXG+c/NfRjR6AUEgy9LZeXEiThGR/3Yt+90iwC+mqDmfatNHZONcF7nOW8krHEjTbUYgNYyX+ozqJvVRtbDaxHiN0UKOQeH+JxqStIBRu5cdsRGO7iu6zI7jxj37OxsS/FZapQ0/fx+v8SsVZxOFdDErNX4bYZFUbAQW9+9ezeGhoYk78Pu3btbqBABYHZ2VkZEhMNhTE1N4fLlyxLyIK/2pUuXsLKygkwmg5WVFVy6dAkDAwOwLJvgidmWt99+O3p7e2UGIvuRz0DtKhQKIZFIIBKJoLOzU4bRNRoNLC8vw+/3o7OzE4XCr2NhYURGA/h8Pqyu7sXFiw9JGIBaPeEPVqymJWBbG7+A2dntEioCgGPHovjmNwfaCuRradeqWa7On+td5M5r9fb2Ss3/6aefxsWLF2WfBYNBvPjiiy2FF4j1U/jm83mpTXNzBCC5OyzLklEwbrcbJ06cgN/vl2FzxKppfTF7k9o0rRfOV3KOE46LRCKSb727u1vG6xPSY86AYdh8LIx2ojM8Gr2Ac+f+EOn0bmiahmPHovhP/2kbdu7Mt/gLOB5qJIg6n9W+fa3tZhXSwHo4qfNFxdKZrLQRnv1a202hUXNXInuYeoyNQpacuty9p6Y+iEjkPBKJUQB2B5VKh1GvfwC6/mWYpk05OTk5id7eXiSTyRbuZtWZqIY7qeC/6tRUtWr+LcQ6Kcv999+P06dPY2pqCsPDw5iamsLo6M9h+/bN2Lv3IsbGxjA3N4di8XZUq3swNPSPSKfTePDBBzE/P4/Tp08jk8lA13UZF8774KaSTqeRTCZx+fJlVKtVdHZ24vz58/j5n/959Pf34/jx4xJ/J5bMPuVmp+L06XRaktpTe3O7T+DixS8iHv8SYrHTSKd34+jR/w2HDn1JEgtRuyQ8UygUZISEz+dDo9FAPD6Gs2e/gJ6evwAAHD0awac/vQOPPPKqFATq4ldNbns8BUxTwDR1WBbke6Phh2VFIYQGITQALgAuCKHD1j90aJqreVxvnqPL//k6dy6MSKQfuu5Cva6hXHYhFIqiUtmKiYkAurqSWFsrolgso6+vH/l8EdnsGnTdg1qtgWAwjFAogmw2j2q13kzXDqBU8kDThuDx+FCvNzA2ZsDl8qJWuxWTkxq83gMAdJgmoGn2MxmGBcMA6nUD+TxgmgJutxdutw+AfU6tVofb7YPXG4BpClSrdfh8ASwseGCaAj5fALWaAV33QAig0TChaS5Ylt1H9boBr9cPIbTmMQ0eTxbj43+Fz3xmBTMzXfj85y9i9+4sTp40WsaFY+NMl3ZurDfabmYBzcbAAq5FVUkk1BEKhSR9ghpmy7WmKnk32m4KQc0H7enpaTHLLMtCsfgbcLvPo14/K+GKbHY/isVd2Lz5W4jHL2F09DPYvfvzSCZPY23tIM6ceQS7dv0h4vGtqNVqSKfTaDQassp0IpFAR0eHTDtngg0xazWjUfXil0oliUdSq2RdRk3TkEqlsLS0hFqthvvvvx+JRAJzc3PYu7eKH//4tzE8/Cns3TuElZU9OH36k3jXu/4OXm8Kfr8fly5dwszMDHK5HLxeL7Zu3YpMJiMx+D179kiN7lvf+hb8fj8eeughfPe738U999yDpaUlfPe738U73vEOhMNhaY6rUTNqZAaxd2KfFNi5XA6NRgPd3Rlo2qM4evQL6Ox8AZnM3di+/a+RSNQwO4vm4hdoNMoolWwI5vDhw4hEIlhZWUEul2tO2JPYufMreOaZP8C991qoVFwIhw184Qt7YBi3wjQFLEuTAnf9pQrX65lD13fM2VZX7df1tCaqdc3WJHhEM+joBpoJTRPQNAuABTs1wf5fCKN5DND11nOqVSGPFwoG3G4dQlgADAACltUAYMHrdSORCEPTBAyjAdO0y61FIlVUKiWMjvbiV391FgcPrqFSMSV0yDlDulVndA6AFq2ynV8B2NihqzppX0t7Lc65G70mfWJqOCTPoZORleCdyiYj1dS1eKNC+6YQ1IAtRN773vfioYcewtLSEr73ve9hZmYGtdpJLC39NTyez8Dvv4Rc7laMjT2K/fu/iP7+fmjaEkZGvoN//Mf/3FzwOjyeOsbG/qg5wevQNANC1GFZdSwv1zExYUDTLHg8Aj6fC0LUAZhwuUwIYcDlsie5ptnHNM2C2y1gWXUADbhcAkI0ADTg97tQr5fh8WgolYDxcQvB4O34yU9yiEZDmJk5jM2bN2Fw8IcYG/svKBbnsLzcjy1bXsTCQg/S6dtx6NBubN8+iJ07TZhmGZcvn0GttoZEooZo1I3x8XOYmGhgaCiFqalJhELdiEQSOHr0uLQWZmZmoGkaXn755Stie5lxKYSQYYN+vx8LCwsyuiGZTEIIgU2bNuHQoUOoVqtIJpP4pV8yMDv7DgDAuXOfxLlzV46dz2fC77dw8aIJO8eoBE2rAigjGNQghIFw2ECx6EVHRx3JZA2AgUajBMOoQNMMaFoDQK05TlUArCBeh6bVIUQNmtaAECYAC0tLC2g07OvYQokvU/5tC7r1Y0KYzWPr50QiIXR2xhGLhVGrVQCYaDTqcLkE/H4fIpEQIpEQ1tayCIX8OHNmFP39fVhby8LlAizLQDgcRLVaxupqGpFIELVaBT09SaTTSwiF/IhGwxgZ2YGdO7fjxRf/FYZRRyDgQ7VahtfrRrVaghCtHONCCBnvzwgdwK7aQ6gEgIRc/H4/AoEAUqmUjAwKBoPo6enBpk2b0N/f38TL7bDD06dPY2VlBcvLy8jlDuA739mKhx+exmOPpXDgQA4jIyUZilosFqVznpozf5tONK7h63Xw/ntrfC7V2lP9Yypcyhh7VvaZnZ1Fb28vUqlUS+TTDf3+zdCpW7duFV/60pcArO+qLBxaKBTw9NMWXnjhc3BqVy6XBU0z4fEAjYYLhqHD7bbg9VpNzYQdp0HdcO0Jhebvrb/bHc5dX4NhmNB1V3MDACxLg2UJRdu7GZrqmOEEQvP5BYRQtTABXQeEMOHzuaHrttDi37puKe8WDMOL2VkfQqEqSiU/UqkaAgFicVyg7BNACB2apqNcrjfNeAHAjXrdg1rND5dLwDDs776WpmkGdL0KXa/BMPIQogSgAjsKlO9l2NXG7b/tDaMEIexzNK31/P7+Tvj9Aj6fiR07BnD69CuIRDyo1SoYGhrC4uKizIa1LbwihoeHZWx0V1eXhMwYu9/T0wPLWo9713WbkzoWi+GBBx7A97//fRw9+nUkEqdw++3/j8T6z5z5Dayu7sPBgx+Gy+XC0tISBgYG4PV6ZXXvarWKt7zlLXjf+96HTCaDTZs24ZlnnsGb3vQmlMtlxOPxltBRYH1N0e/j8/lw+vRprK6u4tixKL7//Y/g0UfP4J57Gjh2LIpHH92JT33qBMrlf8bMzMwVRF70adDarFQqyGazKBaLWFxclAK7nQat/u90qv3PCslrF4Xi1Kh7e3slp73zu86ABrfbjUwmg7m5OVlcOhKJ4NZbb5Xn8dwf/ehHx8Q60d2G7abRqNnUh2ZY2e7dl5DJjOH8+R3Yt28Fe/euotHQYJoa5ufTAIZx5Eg3hoZWMD/fgZGRNcRiDZimC4VCGR5PCKapwTR1GIaGet2CYegQwgXLso/VaqIpjO3/TRMwTRcaDR2W9cb6XClkdd0WutS23G5dOae1LhtAZ47qTdaa976+0ZgmmpAFF4e8YvMaV9wNCgU7OWJuLuD8cIPW/jxbSN9oU81kF0wzCNMMAuho+ezKZm8Izg3V+XwzCivN6dPqJyZOnzbhcgm4XBZMUwNQhaaVMDlpa/ma5sPcnB+RyBI0rYaODh+EmMbk5ApGRjYhk5mFz1dDOGxB1w2cOXMSudwEisU4IpFTmJz8ORiGgV27/ivOn/8tzMy8B319dt6Y3+/HO9/5TvT19SEQCODgwYMt+DDj2+l4JwxRKpVaqosz7t8ZjcB5s7Q0gA9/+Ans29cBTYvittsKePTRM3j5ZTf6+gqSD4f4s5qEZEcovQPl8rPQ9VOKpngfhLgNmvZ/bDw6N0lI3vXcB4V2Owxe9WOxj+3Cy3bJPSbbkQOI0U9OH9y12k0nqNnUXW5hYQTT05vwwQ9exJNPbsYHPziGfftsAqe/+7tpPPnkbfj854+jq+sUVlf34otf3IdPf/o0Dh3KY2pqCps2bZK7GCczY5DpDFxcXJSORnbi2bNnsX379iYeawvw8fFpbNu2C40GUKvZzp8jR07i0KE7UK+LpjNIYH4+ja6uXgAunDsXxNe+lsKBAz/F/ff3Y2wsgMceS+Hd715CpXIWw8Pb0WjYgswwNKyu5qDrfng8QTQawOJiGoVCDYODW+QG1WgI5PNV+P1hmKbW3GwsNBoadN0Dw7Dvt1o1YFkuCGFbHPyuZelNZ9Ib1bjxEL8EdJ2x4eoL2EgAWxaPq6ajqqGrf+vX+Fx99nb9YDsi7Q2bx/wA4lecWa32AgBsdMJOOTh+vO0jYHq69f/Z2fdidva9AIA3v/kM/sN/MFCpfALhcBjJZBKmaWJxcbEl9E3TNEkspjqG2Sec66oT3Km1co7fffcLiMViECIO0zSbYaYL2L59Fmtr60UDVIciYBNF2bH+XlSrX4PX+3EI8SPYQvqb0PUPte8A5V5+lgSQ/xnNqfGr/a4qmNPT05L1Utft4sY9PT2YnZ2V/DAbJcds1G5aQc0kicuXB/Ev//IufPCD38EHPtCF/fuz+OM/PohPfeo49u/PYm6uD488chIHDqxhehrYvz+LT3/6NC5ciOLgwbW25pTTtNmow9Y7H3C5BDwegWCwgXi8NWxpaiqLrVsrLZM5Hk9jaMgu8nrpUgB/+qeXMD//Y7zzne+GaZp405tyOHcujMHBo7j33rBcDLquY3x8HC6XC6lUCpZl4fnnn8eFCxfwG7/xGy0LZXl5GYODg3Ixsiq3ykd97tw5BINBuVm53W688MILuPvuu2EYFsbGptDfP4yvf70bW7cWMTKSR6FQQaMBzMwM4cQJE7fddgHBYBShUFxuKLZ2S+tDa24WOjTNi6mpeRSLVZTLDfh8YdTrAqnUAKpVE+VyA2trJRQKFdRqAtWqCcNA8zq29WKaLmntWJYLluVCudwJISrQtDoAD+ypG4QtPBvNY67m+7+9BeT1NvDmN59HPJ7HhQvj2LXrAIQIoFZzo1bTkM+bEMIHy/Ki0dBRr2toNFzIZAoIhzvQaNibazrtgxAaurqq+J3fycIwUshkFpHPe1GpBGAYGubmKshkIjAMDY2GbekZRgKFQhXBYBxnztyCxcUUKhUTbncQlYoBy3LDNDUI4YZh2GNlKxIadN2PlZUOlEoNVKsm3G4//P4oKhUTpVIN9fq+5qauy3E1TY6HG0K4m5u/B0KwLuNj8Pm+A8O4F17vR2AYP4Gm6TK93dle74SQ17upMkLF4lULVpUt6nHysKyurqKzsxP5fF7ysfO6akz29bSbVlCvxxun8K53/R0GB5cBdGHfvgx+//dP4OLFOPbvz+LOO5/DwYNvl9/TNA3792dx4EAOmtZ+wao7oToY13tfTizLecx5rY9+dBEA8MQT68cOHlzDwYNreO65K4lfvF6vdChR8KsUofwdZheq/UULQdVWSMPqfA5dBzweC4GAif3783j00RH83u9dgKZZmJiI4RvfGMKHPnQKmgZ4vRaiUQuW1YDHo8PlIhRjwu3W4HLZG1ow6MPZs5eRz2dRLhfQ2RmDYdSwa1cO4+Pjsg4gY9MZb0wcVOWaZlLMxMQEGo29qNW+Bo/no3C7n0O1eics65sAHoKmPQ9bQHsAeCGEDzb8EoSmBSCEv/k/jwdgC3gvBgd3IBxOYGpqEcnkJpimC7ruhxA+bNmyE7WakELLtqg0FIsd6O2twjC6MD/fhUzGh4mJnuY5urRuTFOHEB5pKRmGjnJZR7XqRaOhoV63YSkAWF31433ve/P1TME2bftVP3W7reZLwO0W8HoBy/JB0wwAdXi9Gvx+F0yzDssy4PEY8HqNphO+BsuqNZ3ydQjRgMcD+HyArtvQUCTiw/j4ZiwsfACbNv13RCJTKJU2IZPJtJBZtXOiOdfNzeAzU5u6jpxKn7rOVTI1Jqipa5Ec5yMjIzLX4d+loHZquDQN7r//GKanp2EYAXls//4s9u/PQgjIStsqbtYO8FdDgNQOdE4QhgvxmBMDdmoC/Ny5A6vnXK2p4Txq7CXpRQE7nXtlZaXlerqu4+LFi9i2bZs85na78a//+q8b58HCAAAgAElEQVR497vf3fLsKqeD8zn498GDa/jc587jkUd2oVRanxZf/epBADdUTQ3A3rZHNc12Umra+osRGJrmfLeaUInZ3AQqcLsLqNd/ADspkX3xP9rg6uvtWutehSIYVuf8zL5nG7YxDB0ej4mZmTBWV33w+UxUqx5YVhBuN5qYth1t4vUa8PtFc1MTcLlMLCxMY9u2IVy8GMTsbBCbNxfR03MW6fQujI+HsXNnHvv2TcDnAxKJGLxeC/l8GslkFB6PienpIL7xjSE8/PAl7NixjNXVQXz5y7347d++hJ07l+DzaRCi3nS0N2CH9Qk5z0i5yXj9dDqNvr4+pFIpZDIZjI2NSSycZFGkA1CLbZAwzOPxYHFxFzKZN2Pv3u/h/Pmfx6ZNF7Fp01GZk2AYBsrlMrLZbEtuQLsIEnvM3ljSpes5pjJQsi/Vta+uzXw+LxWpnp4eGZ4XCoUwOjqKRCIhC3Rcb7tpBDWw8QAx4w9o3XmFEDKzDmglfdlod2Y8o3quU2g72/Vq3K8p7GaD65J+k5ltO3bskPfPSaImr9ADz+ILajiRs2ajeq/qPR88uIb3vncR//AP/XjrW2fx3vcWsLCwjEbDQjzehUgkDuK2QhDyYIQMUK83YFk6pqZmUanUUS7XEApFYJoaAoEwVlayqFbrMAwby7csDbVaA42GBcvS5DHGVXs8fui6DWnYQ+xGJlNGpRKGy1WEpuVhWWYLdr3+7sSrNce7/XK5vHC5PLATQCB/W21OZ3KjYZv7pZK3KdwDaFbNuo7W3bI5TE6GMTl5WP5/4UIMFy7sd3xn6xVX+cu/3IU774zh3LkkPvrRf8J999kVf7LZrCw719m5SXKEqxmdzLRk9Aa5z+mfcab1O98ty+bLbjQaeOmlAC5c+E3cddefo6/vAhKJ03j++Ufwlrf8N/T1nZAEUPV6HfF4XEbOFItFSbvqhCJvJq2a97WysoJwOCyJ4JwKlmoZ0Pq1LAuLi4syX2N1dVXmXTCM73rbTSWo2zUKHTUrSA0ed9KgXkujdWrQ7UJx2gl79ZhTA3ee0+43nde7WsC7pmlXsHPRe88+YFYgr0O4gJVkaBWoNKMbNS7MEyfiePLJHtx9949x4sTd+IVfyOPgwTlYloW+PoFEYl2zaLexCWHzPTz33DEUCgVks1ls3rwZgE0Fe+bMmZYyaKZpF+slRwahD94PU9458VdX9+DZZ38Tu3Z9B2NjD2Dnzj+Bz/cCarWaTMjgRqWOzdXG6Bd/8YN4+OGHW6wZ07QwO7uAVKpfbhzf+c4Atm4tolz+H9i58xa4XF6cPh3H2FgEU1Ofxsc//mvwePywsyeBUqmGcrmGjo4ETBMwDAHTBE6dOoMDBw41o2/sbMETJ05h167dze/azmRNcyMW64RlaVhZySAcjsvPhdDxxBNVvPjiLjz88DS2bp2GEHY0wa5du1pIsJwChXS+jLFX55QzEUX9W+3DdT9MHMHgHdi69fextvYiSiU3OjtXcccdJtLpHUgkRmWFHI5RqVRCJBJBuVxGX18farUaCoWCpB5Q55FTkVDXwxslzFXoggyZLpcLwWBQlvsjxS0bich0XUc8HpfV5knANTc3J3lfrrfdNIK6nTnOv6k9qumYqsbIyicqZAJcKSApWJyVMNip6kYAtKaYb7TgN/o9tTmFvMonwoWklvQRQsiEBmZM8jxVOyLHNkO2dF1vwbLZP/zb2bf83+Vy4ejRCB59dASf+9x5rK09jwcf9OAP/uAO/NqvzWLPnhX521frX16L1KnkVHGGiKl9fzXOiFqthv7+fkQiETz1VAOXL/863vSmL6O//xJSqfN4/vk/wl13/RcEgy+jXC6jVqvJ32U4FLUX/pZqagOQLHOtHnzA69Xh9TIOXcMv//IkhBA4eVKgo6MGr1fgLW9ZwB13TOGrX81j06YigsF1TbRarSKXy6G/f/3ZhRC4ePE53HXXjpYxKxZP4W1v65XPPzc3B03TMDRkb7gTExPo7e2V93ziRBxf+9pWfOAD5/D449vg9Q5g/377mVh4gHNLHR91DlJgr62ttVA3OBUQ9d7Vz9lfb37zi2g0PJicHMT09DTm5+chxLchhMClS0HEYjFEIhHJfU4+HXKP0D9BrZvFKUqlknTC8R6ojLzRGremtfLikwaYJFpcd4lEoq1lQESAsfipVAqBQKDlmtdqN42gBtoPAAUbTSQeU+NGKfioSagC24kjOYU28WBndhG/w/vi+STGV3dQNZZVFfa8B16HpqV6badmrfIClMtlPPPMM7Je4v33r7PKut1uLC8vy8XJsk1jY2PSmUEnJMmf/v7v+3DrrZUWYXv6dBIvvNAHyzLxuc+dx/79WXz969N44IF5/NEfXcJPf2o7cA3DwNLSUks/Ou9dCDvz8eLFi5idnYVhGLLQbruxbudYUuN1AXtR2KnuhzE8/CkUCi9hfNyHrq413HGHiUxmO3p6zkrebVb2KRaLiEQiUpMjjQA3PP62Sm+rzjmnhsn7o5BR+3ijyAZndSHTNJFIJK5YoE6CqXYbIV/Hj8fwh3+4C+9//9fw/vfH8da3WvjUp96HbdvOYM+eFal0WJYl8WGSmlWrVWnJnD17FmNjY1heXkYqldpwXNptoGo/ud1ueL1eGX1E3hhWp2dJMY/Hg2QyiUgkIrmzg8GgrAREiIQJNPF4XJKMsR5lIpHA8vKypNV9IwU2f8+5btnXvH+v1wshhKR+WFlZkYydFNZMhPp3CX0QQ3V2BAPu1aByMlY5H5QaJgCcPn0au3fvlsVwVU2WJZByuRxM08SxY7bD8uMf/7hcpC6XC8PDwzLjqlqt4tVXX0U8Hsdtt90mr6dWMVEZ1rhpqAPKCcrnopaqxm4bhl0Zen5+XmY0UaNWBSND7YB1zVktXEoIgX2m6zp27SrhkUe24+GHB3H33TaL3Ve+shd/8icT2Lt3tQX7drvdOHhwDcHgebhcdmkxJ/7NvlYhGOKX8/PzmJqaQjKZbKEkbSf8NqrkovbT/fcfQzpdwthYALlcDsViEbo+g66uLlQqcVlGKpFINLXUoiwmWiwWW6pCr6ysyFTgdotFPa4KYOcYsB96e3tbjjk/F2K9ao5K4sXfcvpW1Mw+VTsWQuDcuTA+85mzOHPmJVSr9+LQoTw+9KHvYWzsduzZs4Jjx47J850KC++nXq8jFoshFovh+PH7ceZMDYcPr9/v9PRWLCz0Y+/eH8pnYX85tWvedzAYxODgoKx7urCwgL179yKfz2N+fl4yRFJoJxIJRKNRhEIhCXFFo1Ep5Cj4wuGwrEJUq9WQTCZlpqaqLP1bYdy8pppEBLSmy1MeLS0twe/3S0phwHYsBgIBuTZZUUi1MK+n3TSCGrjSJNY0TS5KckpXKhXk83lUKhUsLi6ir68P4+PjyOVymJ2dxUsvvYRisYj+/n6cP38e9XodQ0NDqNVqePbZZ7Fjxw5kMhnJmQzYkzAYDGJuzsZjL168CLfbjUKhIDcEmou6ruP555+XHuxKpYJwOIyZmRnMz89jYWEBzz77LD7ykY8gkUggn89jZmYG09PTeOqpp3DvvfeiWq3ipZdeQiQSwU9/+lPceeedcLvdOHbsGHp7ezEzM4N0Oi37Q63O4na7MT09jWw2i1wuJ7WQYrGIV155RZbk4v2cOnUKBw4cgK7rOHy4hP/4H1/B5z//LvzN37hQqejo7l7CP/xDB554IgghVlGrLcAw3orjx4dw/PgqpqfTeMc7NmN+fhlClOHxuK+AmNRJR3x8aGgIL7xwD44cmcCdd65gy5YtAICpqS2Ym+vDvn0/ugJOcv7P5+XG2dfXB7/fj5MnT0piqampKUxMTMgs1mQyiXg8LoUB2RbZV7VaTcJKrGrSDr9WnbbO46oQ58bsvIZqYTkpctVz+f128EQ7KO2DH5yCx+PBc89lJDnYpk0X8Za39KJQqKNQKMjNX92EKMjIJe71erFlyxZkswYef/yjOHjwDDZtWsHU1Bb84AcfxYMP/t8tY6LeNzcyCpxIJIJ0Og2fz4eHHnpIKj1f/OIXkUqlJKzBDZn8IbwGC8B2dXWhs7NT4r/sL0JaLBvW3d2NTZs2yWfJ5/PIZDKySPXr2dhvdlLQegm5/5+8N4+S66zvvD/31r5XdfWiVkstqbXbsizJyAsCjImxsdlMiCEQGELCkHUmOe+8WQZe8k4ymWxzXhJOhsmJQxgSICROAmFzsGOMLdmWNyRZi62W1JJavXdXd+171b3vH9W/p5+6XdVqZ5L3OOd9zqlTt27d5Vm/z2//SZtseyVtmoyh+DMEAgFisRj9/f1tmed1Imq95XUR62NkZMT+3d/93baJWa1WOXXqFBMTE4RCIZLJpAocJLuuyKbF1KVcLhONRmk0GuRyORUEX8+uIh9hXWG14bpQMk6TNtGSC3C73W4mJiZYXFzk0KFDiqKXBaQnQZD42NDabOQ99XqdrVu3ks1mlRhH5Mx6GNBSqcThw4dZXFxsy1zc09OjJoae81CnfCUq4OLiIqZp8vDD7+fll3eTTC4SCpUol/2USgEqlQDNZnd2zO1uEArVCIerBINVQqEq4XCNUKh1HAxW8PtLeDwFPJ484+N+HnnkXh544Gt86EMDfPvbBf76r9/Hvfd+kcHB8zSbTTKZjEqoq8f+lnLDDTeohAXQWrRXrlwhm81y7do18vm8orxa1JoPr9dPJBJjYGAQj8eHYbixbQPDcFGt1kmnczSbNtVqnTvuOMLhw7ctW5e0YrhYFmSzBQKBEKbpRkKCNps2V66M09vbj9vtpVptUK83OXHiFIcP34bPF6TZtAGTarVOKpVmYGBwWQlo02hYPP/8Sxw58mbq9aayMjlz5hz79u3HtluKyEwmj20bJBJJmk2bubkUsVgCMCmVyszNpZienuOGG/ZRrdaZn19geHiETCa3bB2z0o4VK5aWlU7rXCukqoQTmJpqks/vZd++GUZH47zjHf+LDRteVRZDelAmEZ3ceOONhMNhotEo165d49ChQ9RqNU6dOkUymeSFF17gne98J41Gg+eff55cLsfY2JgCK4m7rYO/xNKORqMkk0nC4bCKYKmL90qlklJKW5allHUiNpE4QbruQd4h3502Tb04iQVdfOckKET0JbggnHuhUKC3t1flTZV1OTw8jN/vx+1288wzz6wr1sfrBqh/53d+p43dy+VyXLhwQV0j2lVdiy2UpqSLEjmQbdtMTEwosyM9m4nEtRYrAxk0kTNLJwulJSmPdPGJfEQeNzw8rBLsygTQE7cKKEuISH1y6JSJ1E8G2UnNSH48MaUSik+oTpFLSj/qclS55tKlzfzpn76NI0dO8/TTN/Hxj3+PnTsnlxeKh1rNQy7nplDwUir5KBR8lMsB6vUQpVKAUslHsehfNk3zL//2UautZMVZXVoKOtsG06zjdoutdCsoVCv6ndOV3AYMXC4vhmEigbBWAAhW4jm7WIlJ/f+PIuFQJS5MKzbMipu+fq7Tb73/LatJtRqjUAhy222Pctttj6iNs1Qqkc/nFeEhn5tuuoloNIppmuzbt4/jx49Tq9VYWFig2Wxy7733MjMzw+TkJHv27CGZTPLYY4/xxBNPqHmsJwjR56uAtmQGSiQSKrmvrluwLEsl+ZWkIzqlW61WyeVypNNpJT6FziK4tahbl8tFf39/G9cjRVdCyxrUuSrJiworDlyCFx6PZ91A/boTfZimyfz8PKVSiaGhIWAlaYCT5datJ0SLPj09zcmTJ6nVatx2222Ew2G10+kArFPY8lyhiOV/p9xUAF02DGGHxDJDb4eu2HTKoiSmrS6HFxYZVuSBTkDX2yIminqdZYLLpgUrIiSv18urr27goYfu5Gd/9gluvHGeAwcyfP7z9/EzP/MEu3dPYxgGHo+Fx1Okr6+s7pcx0CelcBgryhQUaOfzbnI5twLzF17Yx9LSIG53Bbe7gbgit1zF156CAuxud8tjDsqEQiY+n0W5nKavL0QuN0sqdQ3bLuByVTDNlqt5y2JDPDBNfD4Pfr+XcDiIONwMDg4wMrINr9eNywUul8FzzyXp68uwY0cZj8eFacKVK0FmZoLs2/cqHo+LbDaNbTc5f/4cS0spHnzw/cTjUVyuFaAcH7/M1q1bqNXKGIbNxMQ4p0+f4oEH3s3U1ATp9CKlUoFqtcQDD7yHmZkprl27Sq1WIR6PUK2WaTbruN0mbreBaYLf78XjcWPbKxYsMg5CsHSypJHf4nwhccj9fj+XLm3mD/7gFj74wQt885tvZOPGCwwMvKLGWFfw6kB5+fJlEokEX//619m1axdXr15l//797Nq1i1OnTmGaJm9605vo6+tT6+XNb34zp06dUmnNdLGQDoRCIElEPkkYnEgkiMViShQaCASU7kfEonpKM7/fT39/v8rcnk6nFajr4NyNYNXFUE79Q2t+rtRbp6YNo5V31TAMFhcXCYVCytJGz+6+3vK6AWph8V9++WUGBgZUg0zTVC7Qzs5yltHRUUZGRnjggQfU9fp9+v2d7Ji7ae6drJIuVtDPOS0FnO/U75X/dcWR8z451i1X1ipO22t5p7T32LFtfOYzZ7n5ZjewkY0boafnLBcu7GTLlnZZrXMS6/VxtrHTJBcK6Yc/jHL8eJKf+InLfPObg9x994rYw7IsFhezFAouSiU/5XKASiVIvR6hXg/TbMaJxbZi2z2USl5sO0G1GiKdNqhWQ9j2BubmAIaBWx01KGOaGUwzjWlmcbkyFAoZ3O4sS0tL+HwlotEG/f0Rdu4skEwaxGJNwmHo7a3xm795A5/61MvcfnuOkyfj/P3fb+I3f/M8w8MZnnvuOaLR1ry48cYiFy9eZN++DMmki3PnzrG4uMj09DRbtmyhVJomlUoty3KDvPnNA8zOPoPbbbBhgwu3O4BhBBkbewaPx8PmzS0X9xb4Rtpk5XoREZwQHJIEQkQKojDXx1GAsVarqc/zzwd56KED/NIvHWPLlsvU61UeeeQnefvbv8DAwCtq/J3iA1GOjY2NsXHjRm6++WaKxSK1Wo3HH3+cI0eOsG3bNiKRCGNjY+TzeW677TampqbYtm0b+XyeV155RSnhhOqVuarnLZUNQkK9ejweBdDJZJJkMkkw2IqrE4/HicViasMSPZLocvr7+1ViZDGxS6fTbQ51IsrQTYF10+BO4hL92OPxKH8GaOUrzWQySpchyu1/cfM8wzDiwBeAfbR40p8CRoG/AbYCV4EP2LadNlo1/hxwP63Avz9p23aXWGKr3kM8Hldae8mcrBdn5+lA0d/frzpfB2SnQkjO6++V4gQreVc3EOsEnt3+d77HySo5n/FaQLrTu/T6A9x66ziWZXHlysr5WOwKhw/D1avdXXid57vVV54p1MKrr27gC1+4jU984nvcfnsZl6vMww//FPfc8wVFsUEDt7tAMLiIx1MnGGy0iWtuuukmcrkchw4dolAocP78eW644QZeffU8ly8vcPvt9xMKbWJsLMP4eJ5QaDOlko/p6QozMxVajiAJ6vWtWFYcy0rQivMB09Nw/nx7O0yzSSRSp9ks8KlP3cJddy3x4otR7rnnC6RS00xOVpQOQSIw7tq1iwsXLij7X4/Hw4YNGxQ4RKNRxckJFyQgKvNZ8i5COxek6xmECta5QQFx3e1bZMliQ65TwzKG8o4LF3by0Y9+h7e+NcGVKzA8PMa9936R2dkt9PefU9fK+MvzdCssGasTJ06wc+dO7rzzTp588lYWFwsMDp6nWCzi9Xp56imT6ek3cvfdJ1QYV0mK/Oijj5LP57l69eoqCybdSkJ0MPV6nXw+z/T0tBKLxONxksmk0tWYpqnOQyvcRD6fV0pJt9tNOBymt7dXybdrtRp9fX3kcjlmZmbaHMt0y6a1ioytz+ejWq22iSANw1iVmHk9Zb0U9eeA79m2/WOGYXhphS37FPB927Z/zzCMXwd+Hfg14D5aUWJ2ArcBf7L8vWaRCTc8PAy05M+Tk5NdzaTk3PUAuNPHWfQB6EZpdwMnHWz1enUDdnmfc7CdbdKBz0lhdytOMy/ntbqIpNMG0o067nTdWkWef+lSgo997BH27cvgcsXYvPkS99//JaantzAw8Moq8ypZiDpLL5r973znOwSDQd7//vfz6KOPEolE+MQnPkA6neaVV75LIuHnDW/YTjyeIRaL8cwzz/DFL36xTcGzwpr6se0ElhXHNJP8+I//PLmcm7m5OvPzDUXZp9MjPP54L7fd9hg7d07i87WcNsSCQfohHA6Tz+cVey9iIkBRhnq7dH2L6Bj8fr8C4U4cmyiy9QBWuqmq3nedODknNyfvueeeU4o4krHdtOkiAwOvUK+3j49+rOtVAoEAtm0zODiIy+Wir6+PG28s8Zu/uY+f//lF9u2DZ57x8nd/924++MG/p1gsEovFFNgvLCzw4IMPUqlU+MpXvsLMzIzSC+l94GyD1DefzytgFdtsEY+IB6D0s8iaK5WKkm1LcDNJXScg3tPTQyqVUuOjg61Qw068EIAGVok3RXSpE5vrLddVJhqGEQNOASO2drFhGKPAW23bnjEMYxB40rbt3YZh/Ony8dec13V7x8jIiP1bv/VbbZSvYRicO3dOLQBprN4hTkq5EyBeD6ydlh3dnqE/v1sH6892Dsb1qFD9Gr3e16uL1LtbcfaPU2zR6fxaZb2TS5/MO3bsYHFxkTNnzqyyIEin08rUThanAI9pmhw8eJDFxUVl/vW+972PRx55hN27d1MsFgmFQuzYsYNQKMTmzZtZWlpidnZWxTe5evUqjz76qBI/yEIRK5Kf+7mfazPrEkp2fHyEv/3bH+P220/x3HMH+MhHvs327deQjOsCigKuIoMURa/oCXQzOX2jlEWv5+yUpM3SfidrLBSsc7x0O3xdoe0U20lpvd+gUjGoVk22bt1DtepmbGyaixcnKZehVnNTrZqUSjbFokWt5qJed9NoeGk2PSSTmygUbMplG5crTE/PJqamFrHtAM2mj3IZfL4+ikUPe/a8zLVrO/nRH/0bbropRSAQoFJpcSavvPIK73nPexgdHSWVSqmkyJI/9bOf/azqM+EKnJtQJ05Vvv1+v/KIFOD2+XyKw9GJLQlAJRl3pP9FCSjPd2Zi149FSQgrVihi3WQYLbl1sVhk69ateL1eXnjhhX8xZeI2YAH4X4Zh3Az8EPglYEAD31lgYPl4CNDyZjC5fK4rUDt3a5mQyWSSXC6nzknH6Yoy/T4dHPVJrQNgJ1lzp0GXb2fg9W7XdQN/JzXspBKc/3fbULr91vuuk0elLFanCEevT6e2vZbdXn+WXmzbxu/34/f72yhm50evu/4M3WZZ3uFyuSgUCioK2cGDBzl69Hbe8AabcPgy09PTNJtNJid3cu5ckOHhUd797nfzzDPPcPHiRWXpI0o1UcbKgjVNkytXtvK3f/sePvrR77B79zR79szwpS+9m5/6qUfZs2eGRCKhxBe6OafeFuEEdEsJWfy6Mmllntk0Gm4aDQ/1upt63U2z6aXR8NJoeLAs3/I5H/W6i3p95To5FjCt1dzabxe1mn6tm3rdRbP5z1VPNTHNKul06xvKyxEEXctp8HLE415mZy9z6NANHD2a4/z5AyQS/4PJyb9n+/Y7yefz9PX1cfnyZd773vdy6tQpstksk5OT7N27l8HBQTweD36/n4997GOcPXuWEydOrOIwuhEd+lwSb0wRk4gre09PD5FIRCWrtixLzVVomdKVSiUVOErieejrSAdyEf8It+QUb4hpoWwQ+v3rKesZLTetOJf/wbbt5w3D+BwtMYcqtm3bhqTuWGcxDOOTwCehFeheZw3F4qK3t5fR0VG1IGRRdMrmK+d18HGCaCcgc4J5N/lslzao704TpVMdO4GZPvg6SHcD8uv0a9ux05NMr0+n9l2PYu9EoTspNh2QN2zY0GYt0G3TcPaJPN9paeDz+dRiM02TzZs3c+ONJT796Zv4xCeusHVrnfHxER566G3cd9+XlHv0kSNHuOWWWwiFQsr5SDLJh0IhNcfcbjep1DZ+/uefYteuKo3GBnbvho985IdcuLCbRMJDpWJQqZiUyywfG9RqLqpVF9WqqcCy0fAo4G00BFA9GiCvAGijsZZ549rF7a7j9basYuQjv0OhGolEAY+njtfbxOez8PttAgEIBm1CIZNEIoDLVWNpaZKZmcus5JasUKtlKJUWsawijUYe265i2xYHDx4kk8kwOzvLjh07eOtb38r//J//k82bN7N582aOHTvGvn2f4oknjjAw8KcsLHyI6ekW4A4NDbGwsMC73vUuLl68SCaT4fnnn8e2bd71rncp87pKpcKOHTvYvn07R44c4eTJk+RyObLZLKOjo23rwznH9XVoGEZbIKp0Os3s7Cw+n49oNEoikSAej6sgS0IgiFekE7SdeqNGo6E2Fuc8lmtaiYVbIhbJ3CNc3brGeB3XTAKTtm0/v/z772gB9ZxhGIOa6GN++f8pYLN2/6blc85GPAQ8BLBt27ZVK1ZYuDvuuGOVAkM6yikiWEvUoV8DqyngToDlBKVOpRPoXe++boDbaXNxvqsbsHYDPyc1D6tl2c73daKyO72vE9A6zwl12e16nW2U3/qmDbTJ9URMUiqVKJVKfPrTnyaRSPCLv/gr/NEf3cOePdOcO7eR/ftfIpvdzpNP7qRaNTHNEJblpVIxsSw/4fCnsG0fDz2UWKZAXdRqrmUqtAWgnRIYP/roDavOOYvL1Q6aOnBGInU8nvKq/93uuvp4PPW2/3y+Jh5PHbe7sfy/nGsQCBi43S5FGUrIAZ/Pp8QwnfoZdF1JjkqlwqVLV/B4Xm0TwbTM6LLLMvE6jcbKxi/E04svvqhidojS89KlzZw9ezebN/8Cd90FFy5M8sILv0cw+FtMTPyAD3/4w/zu7/4uIyMj+P1+4vE4P/MzP6Mo0Xw+r24yr3MAACAASURBVLxK8/k8gUCAw4cPUygUmJ+fp6+vj2KxyPz8PBNa4kt9fpqmyf79+5mamlJxsKVIACjx4rVtWykle3p6iEajiiDweDxEo1FFHIg1iSS98Hq9RCIRpTfQ14HglawpcX4Jh8NtJr3XK9cFatu2Zw3DmDAMY7dt26PAjwCvLH8+Bvze8vc3l2/5FvCLhmH8NS0lYnYt+bT2njZxg7C8Ym4kjZbSjZrVAV2KE9j1Z6wFqnpndxOVdBIV6PfpZa1rOlEEzjrqyk6nN2W34nyuft5JiXSjcPV6O0F1vcVJ2TsDGXV6v7Pdtm23UT0SuOfUqVPcemsRj6fIyZPbAHjppSPqOaZpLQNdHa/XotksEImYhEIuarUMgUCTSKS2Ciy93qb67fU28XpbYOnzWXi9LfCUY7e7vgyeTVqOJ2bH/tU3IZ14cIr/AGV3K+ZoLda8FddElI86EHcqupenvEsfE2df66XbPJY1JuBvWRbHjx8nHA4rhZxl3cKHP/xNnnzyGSKRd3DgQJpM5jMUCvvZvPkUMzMznD59mtOnTxMKhVTyXomeJ88XBy6JXxOJRLAsi+3btysTxWvXrvHtb38bwzBU9iCJV5NIJDh8+DCJREKJvxYXF9W6kv4xjFbAtVKpxPz8vAJfobb9fj/BYLBNFi0OOLZtk8vllImfcywsy1KiMiGSOmVdWqusl/b+D8BXjZbFx2Xg47Qirz9sGMZPA+PAB5avfYSWad4lWuZ5H1/PC7qBxFrFCZzQXRH3v1u6UcD/msX5fH2RrQXOr/W5r/UafUN8LWDdTezjPKeDulBt4i4cjUZZWlpSoQIsyyIUCnH2bB+1mp9Y7K8oFO4nmfzP7N8/z1vfehvVaoG+vj7q9TonT57k3nvvVQ4b2WxWeYPKvOm0sTs5Mr0f9D5w4GLbPU5CxDRbjicSjF7AQNyLxXqgWx+uJWbrNC7OzUMvzrXUaW3JM3Sqsdlscuedd3LDDTfwrW99S6VXu/nm59m79z0cPWqSzWYJBoOEwyfo6ztLrdayXhkYGFAR5TweDy+//DI7duygWq0qrz7dNC8QCLC4uMimTZuU6a5Yin30ox/lueeeI51OMz4+rubGzMwMzz//PIlEggcffJD3vve9zM/Pc/ToUc6fP0+hUOioH6lWq9RqNRYXF/F4PCoWSTQaVWFbDaMVAiIQCLC0tLRqnPTn6cHXEonEqhjW1yvrAmrbtk8BnTSTP9LhWhv4hXXXoP3etoVyPUXXWhSrs/xzgM1Jqf9rA/NadXi9va+bfFovTsoRViuO9eIEIaeicWFhgbNnz7bFcrYsi6mpXfzBH9zC2972P7hw4U9JJP6J8fH/zrVrn+bMmROYZity4enTp7n77rup1WqMjY2pIPa6eaVOAevyTz16nO74oBMGOpUpHqzCAQQCAQKBAD6fT1kGdOs7Z7uvx5n9c+a9vs66PbvTb/2cbrlSr9eVh6CIQHK5HMVikWw2SzQaZcuWLVQqFZaWliiVSvT19ZHJZBgZGVHmh4FAgM997nO87W1vI5FIKGOCSCRCNptV4C4yZ7E/j0Qi3HPPPXzhC1+gWq3i8XjarIcymQyf//znGR4e5i1veQsPPvggjUaDyclJvv/973P16lUlfxZuT8zrJPBTNptVoO31elUQKWh5Ra4lczZNUyklTdNkcnKSjRs3dr3eWV43nolO6qWbWKLbTq9ft5YMeK3vbs+T+nX7b73P6navXu9uv9f7nH9uWU8fOEHBKQZyFp266yQiWk+dZMEYhkE4HObhhx9WmnVogUI2u5Nf+qVnSaXGyGYHGBlp0tv7BywuHqRY/C4ej4fjx4/zwQ9+UJnv5fN5Tp48yVve8pZVpnA6waCzyEIFi+hBFm0oFFJKTlmsTgukTn3cDWT/JeZnJ3Ghs1+d4ypt1uOty3k9pIIcC0UrQchETJlIJHjppZfYsmUL+XyecDiszCM9Hg+5XI75+XllFz47O8vw8LAKbfrud7+bbDZLLpdjfHwc0zRJJpMK6IWTCIfDLCwsMDo6yk//9E/z53/+5+zYsQPDWAnJK04tLSVxiunpab72ta/hdrt54xvfyBvf+EY+/vGPK3HEhQsXOHr0KJOTk2qT1vtHAr6ZZitZgHg6ijxd+rOTj0AoFKJWq5HNZpUTznrL6waopXQyIO80cV8LSHbaBJz3Ostaz+xGdVzvuf/cZ61lpXG956+n6GKGtURQ3djm69WnE1stlEu3e/QiFG8ymeQ3fuM3+O3f/m0GBgZwuVzMzc0xOPgVdu3q58qVPIZh0N/fTzhc4MUX/5xqNcH09DQDAwM89dRTHDt2jMOHD+P3+xkYGGhru4CPODiEQiH8fj+BQEBRwWIdorO2uqhE+tNp7+w8Xk+71zuendbMWs9yrgEBX12OKtSqAKNu5+2UbbvdbhURUihsj8ej7JH9fj9jY2MqLVcikVAenLVajWQy2RbsTOZFNpslk8kQDAaVm3mhUMDr9XLgwAFsuxV87Zd/+Zf51re+hcfj4e1vf/tyiN1nKRaLuN1uQqGQiu0uSUIajQZHjx7l6aef5l3vehcHDx7E4/Fwww03sHv3bmZnZzl+/DgnT57sKKLQuUnn//KfMyONuO2LqeZr4fJfF0AtO7ZOhTizY+iUg37cicrrdJ3+/1pUTrcFtV5w70Z9Sj10c0BdodfpmTpV1GmhrbXw11PWe996wLnTs/T7dHtjocQ6We0IWDopEvm/VqsRiUQoFAqKQhkeHlbUmmVZSn4p7HRvb6+Kdjg5Ocnly5dxu93s37+f+++/v01JqQe06mbt4jTplP9fy7xZaz69lvHU5c7Oee1UrMsGKffpfS7ejoVCgXK5rD6i3NPzWTqBWjwNdWWp2+0mnU5jmibXrl1T8ndRDo6OjqpxE9v2ZrPJ0tISpmkyOzsLtGKaRKNRcrkcw8PD5PN5jh49qhIkbNy4kYcffphUKsUtt9yCZVns2bMHr9fL3/3d31Gr1UilUmoDL5fLyulE7Nu/8Y1v8Oijj/K2t72NW265Bdu2lZmhBJeSsZdNTV+zThGgUPx6X8l9Moc9Hs9rUii+LoDaNFvBw53sWreJpV8Dq1nVThPUeY9e1lo0zsWn76L6gpYdVMDCuZD19jgpMielutZm4pSjdgKTbm3r1qb/nbKWtYb+HqG89MWsX6crEbtZI0joS0Bl/BCvMZEhAiwtLTEyMqLchZvNJj09PUxMTHDDDTdw5swZtZhExtipf/T3v1aKt9P4d3vmesbB6fjTjUvRj6WNAsKiuNMdceR/QIkJBEBs21YKtUKhoMDNtm0OHz6sqGd5hoyNiIkajQZ9fX2cPn2acDjMhg0bCIfDKq9lIBBgy5YtQMuK4tSpU/zgBz/gAx/4AKVSiWAwSKPRIBgMUiqV2LZtG8Vikaeffpre3l5CoRDHjh3DMAzuv/9+xQ24XC527drFxz72MY4dO6bi1k9NTWHbLftsMV0U6rZUKvHd736Xb33rWzz44IPcddddbck79KLP605cYae1bdu2MptMpVIkk8l/exR1rVZjYmKibbLB2hNSdmCdSu0EzlI6mebpgKr/dg6O815YDXYyMXX5nRTZgYUVEu2vKEX0Beask75b67u5XvT263XTjztNCqdirFO/yTOc9+ubhd6n8q3/X6/XSaVS5HI5UqmUYokty6JQKCgQEc89GVtn5hNn0YFI2Ge3263CYyaTSer1Oul0Gp/PR7FYVKEn9+7d29GqYj2l0ybajSruxg11uqeTVY+Tq5KPDsTNZlOx1PKt96mMn3AL0k/iJVetVpmfn+eHP/whL7zwAoVCQYkshICKRqPEYrG254k8W+ZyoVCgv79fgaVtt8LuSj7LUCikrEJkLSSTSZVWK5/PMzs7uyppRyQSYXp6WmWxLxQKBINBxsfHVVCnYrGoFJrValU5q0g99b6U/pE+EepW1pGY7+lzrFtxKn51QkN3/JL/arWakpd3SgPXrbwugFoX2At46JPRSVECarLpSo1OwA7tFIbzOU5lQSfQ6rR5OK8xDEMFbnFSmTrQSYAY/Vm64sYp8pH3i0JNrnHW2fntfEa3fof2fJU6pdup6HVy2nJ3AhV5jxj4nzp1Si2SZrNJIBBQYOP0JNPllbIBym/5Fi5GItVt2LCBubk5Rd1ZlsXi4iJDQ0NtqbfS6fSqhbKWfsR5zrmB6Fyds6869Uk35bl+jdRP3NGFEhaqWEBZHz8BYL0PZbOcnZ1lYWFBZR0XoI1Go/T09HDfffexe/duRkdH20QekoVHItpJewWko9Hoqmzh4XCYUCjE1NQUt99+u5oDkkNweHiYSCTC1atXVeo8XaYrm8PVq1eJRCIqSFKpVAJQkfLS6TSG0QrsJMrF3bt3Aygxi/TN7t2728II6OtE3MjFW9WpYHYWubebrFnnEqWIslXWWTweZ64Vp/e65XUB1KVSifPnz6uwjrK79vT0tE12pyxXB5pOLHMn9sMpO3Luts5yPVtHPXuMTF49Poh8xJg+GAySSCQIh8PMzc21OX7IoOuJDnRxTrVaVY4O+oJ3UrfduAm9jWKLrAO8HruiEwfSTWmly5z1duvHQj1nMhnm5+fbYmCUSiUFQDJGzWaTvr4+NQbCoupOBQJAsViMcrlMOp0mlUoxPDyslGMSV8KyWt5kyWRSbRDi0usc/07UsLMv9Hu6ga/el85EFTLeEoxKp4j1/IJyrdjh6g4u8txisajST4mSTpICSJjP3t5etm3bxrZt29ocMITgEY/CQCDAwsKCSmWlzwv5CFUtz19aWlJeelJ/n8+nngcr8atl7cXjcY4dO6bMGPUUbCLHNU2TUqmkuICLFy8qz765uTl27Nih7k8kEio7uZhcnj9/vm3choaGGBoaolqt8tJLLynTP1l3spakjXr4Ar3o600nWARjuq1Hl8tFNpvFMAwVnGy95XUB1HrWFNM0Vcc7G+KMC+tcCHIsv6EdLDsBihTnNVI6iT2c/zkXtk6ly+DLNcJuW5bFjTfe2Da4+oB3o/D1+jgnhFwr1zjlmk4xiDMVkl6uJz93Hnd6hvN+oYYk7oFEokun08qlV8DLtldiNIsIRMZfopMJR6VzVvPz86RSKWXZIXn/FhYWWFhYYMuWLYoFlTnn5Ey6ta0bh6UDsD4XZTORWBG6jFioOhET6KICYcf1AE6Li4ssLS0pMBYTQYlXEY/H2bp1q3LEkLYIaJumqYBZJ4hkHpRKJVKpFGfPnmVmZkYldtbnk95mkR0vLS21BcKXmNmBQID5+XklipC1LO9cWlqi2WwSi8XI5/Ntm6b0mUTSEzCU4F4SPEkP9i/zWA9+5ayziMXcbjd33HEHqVSKV199VYG/857rWSXpKfCc3JVTkSibrvS9jlHrKa8LoPZ6vQwNDbVN8k4UnL7rOYHIKQ7QQc1J+eml0+LTzzvZUeiuQOskStDvEdmYfp1ORcrE04Hb+R7n5NEBXiZjywss3GbFEAgEKJVKbcAihvv6ZuF8n1NG3olN1/taQEG3uZV61WqtLNkSW0HGUoBXBy0JsC/ndeDKZrP09PTQ19dHo9FQ5nS9vb0YRssedmFhoQ20pJ8lhoTX621LTOEc525tlLkGKxuFcABiF6znFxTKWChXmeM+n6+Ne5BUUalUikwmQ6lUUnJ2r9erqOKdO3cSCASUTkQoY3m2MxqgjKn0XaFQaANOPTWX2+3mDW94Az6fj9OnT6us7YbRsuyQsdA38f379/Pss88yMzNDLBZT8yYWizE4OKiyc0v0uGazyezsv6NYfBLDuIZhGAwMDJBOH2Bqqp9Q6POqXgJqwh3J5iJchGxEklFF1phsIjLmsvEbhkEulyMUCgEQjUa58847+f73v9+2Uegip07rWz8n551hUeVbv0/mlmW1vCz/xTO8/GsX8cnvJGuGtbXiumhApzT1+50iEzknxWlypG8GAjp6p8u3sEjCmkvKn3K5TKFQaGMF9RgGOhso79O/Ox0LgEldpF665YksNpHFhkIhFRUslUphWRaJRILt27fzne/sZefOLDfeOKnae/p0ksuXe3jggYvrklV3ku/r46Wfl41ofHxcscm6ba6+Yelj0Gw2SSQSyq3Y5/MRi8WUxQes5HNcWFigt7dXyaHFfG9iYoKhoSEsy2JgYEBR2olEYtU8k43Gyb3o9RQgFgWUPiZO0BSLA4ljkc/nlWK1XC5jWRbBYFBRxr29vWzfvl0pPXXQlefpwCpFgELAVQdjmTtyfyQSUfNLlzsDKk2VDjwyvwSkhcoXCnT37t1MTEyQSqWYn58nHo9jWRYvvfQSe/fuVZuYrBW3+yTl8peIxT6JZb3Izp2f5NFHf4qNG39Z1Us2abHbLhQK9PT0KCVnKBTi1VdfVZyR2HELV+ZyuZienm6TR0sfXrt2jQ0bNuDxeCgWi20ckPR1p/Z3Kp0IGyee6KJZ4VI2bdrUlrz7euV1AdSyIJ2UqE7FOKmaTtRyJ+pXX3A62OoWDzog61S7dHa5XGZxcZGZmRmmp6dZXFwkk8m0WXGEQiEVp0HPXSfUhFwDK8lthSqSekpd9ADpOtfgBHTnBiTnRWEpclnJ6C4ZMILBIJXKHTz88C/xwQ8+xn33beTVVyN87nO7+ZVfGcXv78flsnG7wes1cLlWTBKdLF0nuXg3ub5htFKtSXhMkcW63W4F3vJceY8AQ6FQwO1282d/9mfKlVcUiEIhi3PGpk2bFOVcKpVU7IxwOEw0GlVZtcVluVgsKqq7VqtRLrcS++pu40IRy3jqHIgo3Vpeklmy2az6LbLinp4eYrEY0WhUJXvVN3xdx2GaJl//+g5uuKHI7beXVR8/+6yPc+eCvOc9KzkndYpSjkOhkMq64tz4pDjjrOt1kfbpMnPZAGTMBNyEW9uzZy+XLk1x7VqWmRk/MzM+IEYoNIjP149pJjDNOJWKn3K5Tih0jXT6r+nvf5bvfe8Ahw79HouLT1OrNVR8DQnwpEe+09cMtNJruVwuIpGI8hiMxWJcvnxZEScyp2Rubt68WXFd/f39qzhJfRPTdU56P8l1IgbS56wAsvSTfl6IiFQqRSgUUuLA65XXBVBDu7x5LaDWSyc5aidqWqeC5bhYLDIzM6OM8hOJhIo5IBvHxMQEZ86c4dy5c4qdSiQSRCIRRbnpij9YYXc61Vev38DAwKp8amLi5BSZ6CyT9FWn9uriBCmy+aTTaTXht2zZwsTED9i6dZqvfvWP+PKXV+LofuYz+zqMjY3bbeNyycdSv9vP27jdVtvvlWta9xhGk8XFDZTLeaCObdexrCq1WgnLqmJZNaAGNMlkTAxjANO0mZ2d4OLFDNFogGDQSzbbwDC2Uq/3kMtt5fTpATKZg1SrBWZmIhhGk2AwTqEwRy7n4tChNzE6WmJiosHERJG5uSluuWU/o6NXqdWKBAK+tn7UFbbQsiAoFosq9KfEq9BNMoXdHx4eVqCpWw/oc1zmrciNLctSZmuhUIiNG6f4rd86wi/+4jH271/k/PlB/vAPb+Yznznbxn3KmOscmHOj18UVOniviEVsymUXxaKbQsHDhQu9XLmyl1LJRaXip1LxkssZVKsBGo0QzWaI73xnmEYjSLUaoFbzU6sFsO3ry1zd7iIeTwnTLBAIZBkdvZNdu/6GkZFxikW/ikInkfHEhFXmsYiXhOIWRaBwZ2IfPT09TW9vL1NTU6r9IiqRjVi4LKeIUccOXWziFH+IOEbqIeOsiy713/Is4Ui6Uekd+23dV/5/VHQ2RcpaLPj1ilP8Yds209PTPPnkkyplvWmaDA4OttnZfve732Vubg6Px8PIyAjJZFLt8J3kzzplrp93UpqGYXD+/Ht4/vn/Qb3+mFrIlvUnuFwufu7nTlEulzEMg+npXSwsbOPQocfVM5zKSXmP3lZdrCLvhVYM3Gq1yssvv4xpmkSjPySZ/EcWFt7Hjh3necc7LAzDS7Np0Gyay98GjYb+DfX66vONBuqeRsPAsgwaDZNq1Vw+Xrm2WPRRrbbSQVmWC8sysSwXtu3Ctt3AyoKfnGwfT92a6dSp1vfLL8uZtwNw5crqefCXfwnwf7c97/jx1qfVZ63NxDQtTLOJaVoYRhNoYBgNdc7lsvF6Dbxec5nbaN3X+tjabxtoYFlVms0q1WoR265h2zUMo4nbbePxgNfrwuWyKJeLy0qtBl5vkWAww6ZNWT772Xdyxx1LnDyZ5Dd+4ywHD2YxTZeaC82mQaHgplAQoHWRz5sUCiaFgotCwaRYdJPPuygWXZRKbopFT9t3udw59nZ7aWCaBUwzj2HkludThkRiCtMs4PNVMIwcjcYSlcocjcYixeI0hpHH6y3jchUwjAKhUICenh4s605eeulXGBn5Kpcv38/IyDhe7yjBYBBAKQwlPZsQMgKI5XKZeDzOhz/8YbLZrFp3klB2y5YtzM/PtxFNOqiLqEQoXF3PA6s3NPl2cuuAAmDddFQ4Hl3koYvFhItKpVLX6fdWed0AtS4r0zu3k6y6k7VFp//1cwKmpVKJS5cuMTExoWSMfr+faDTKc889Ry6XU1rsN7zhDYRCobadVFjUTrKpbkWvy8mTb+fy5RepVr/M+9//JFu3vsoLL9zEk0/ejdtt43b3kkg8zSuv9PP445/k7rv/7LrArL9Hr5u+aYhsUwfxdPoA6fRbuf32f+Lll+9gcfFrPPhgb5uXqBSnOEPeIZPU+V+nugkb+dxzzyl71nq9TrlcJpVKKVO1ZtMG3MTjvdx88y2Ah1rNolxuMD+/xOXLE1iWiWn66O0dIBiM4/UGqVYtCoUK5XKD/v6NmKaPpaUcqVSGeLyPyclZLMtFOBwnlysTiyXZsGEzHo8fjyeEx+PHMHy4XD7cbj+G4aFeR21QzWZrM2k2TRoNi3rdplKxqVabNBorG5htu5c3HDe27dI2oda9cm695cknBxkcTPPVr27iC1/YvgyyrU+lsh4qtkko1CAYbGV5DwbrDA5Wlo9bv0OhBqFQA7+/ytLSVa5cOYVlpTGMHJaVJpW6Rq22Ivq45557lKxfIseJTqZUKlGpVFQWb6Faxbokn38D09P/ByMjv8rg4Hk2bhzlBz/4P9m/f5FK5WGuXr3Kj/zIj5BOp9mwYQM+n68NH2QuNhoNpa8QebxQ1fF4XLmsy1x+4okn2Lt3r1rT9XpdKSF1caeIfkS5r89rJxcvIkadmtaVyHraNdEpiOnhayFAXxdA3Ql4uu1ene5z/i9skn6NKD7Gx8c5efKkcmOFVmqc8fFxcrkcbrebaDTK3r171YDJ850Ky2716lR/KcnkZV588We4447H+au/eh/wbsDGMCrYdobPfvaduFz30my6CQYzPPfcj+LzlfH7SwQCRcLhIn5/mnA4SyCQIhjMEwo1cLkaqu16kQkYDofbnCaKxVsZH/8Dduz4NEeOBNm06SL/8A//Dsv6Kj/+4wPK/tXJ7ulUhj45r9d+ud62baUIXOEmVmyiW8cWllXHtot4PGUMo4LHY+N2VwkGIZ/PKPOudNpFLudiw4YNxOMxhoZC1Ot14vEcmUwG204xNXU/fv88LteXME2bXbv2k8vdwtLSIP/xPxaIRk1qtUKbjkQWpQBPKpViaWlpOXRnaTnVVgjbjuLx9GJZESUCaDZDVKv+ZbFAgHq99anVguqaSiWwDObXK625MTsbpVZLsXFjmU2bLCKRJqHQagAW0A0Eaurjdne2LuhE6DSbTS5eHCedPt/mWGNZKzkgdY9gmVN6thKxoZZ4IU4b+FLpRjZt+k/4fC8xP9/A5fo6O3akyecPcdttt5HP5ykUCsRiMebm5hTgASpiYes5JXK5FnWfSCRIpVJUq1VmZmawLIvp6Wk192SunTx5klAoxL59+5RViW4vreuFdPNg5zrQi77mdMsPfX3obRATyX+Tog8nlboWBdkN2PVzeufZdsss69ixY5w+fVpliggGg8pcTAK3S2d2svRYa7Cck97ZNrl/06aL9PX9Ai+99EU8nhL1epBwuEh//xLNpsnkZJ1m04dhWJRKcUqlBLCenddaZs0lrVMNr7eCz1fB7y/h9xfw+bLMz5/CNOeoVt/Itm2fZ+PGLM1mjM2bLzEy8jLnzt3E8ePf5K677sK2W5Ygo6NRHnxwRZ4g1gq67XonfYGzf3QFlQ7QcuwEb1jZaHTFbTKZVIpSkU1OTU0xNzfH0NAQ8XiccrmsXMhdrhNcvPglTHOMjRsvkkrdztTUf6Ov779z5couarUAxaKLbNYmlWqQydjk8yalknsZeMM0myHq9ZAC3fXIYz2eMl5vGZ+vjMdTIRDIEYvN4fNV8XrLy58KHk+Jyclz1OspbDuDaeap1fYwM/NfGRn5de6/P8DExA7+8R9/kp07v8T73hcnHo+3ERLOzXQlU7pNs7k6Tkin4gQUy7KUIlE2UlHgyliVSiWq1ary7JN3+P1+FZ9axjifz2PbNvH4Q8txNgz1Ptv+Rwzje4yNxenr6+OZZ54hGo1y0003KTPEbdu2cfXqVVVfccoRWbYo6yR1l94uaZtt2xQKBY4fP87WrVvZtm1bmyxf6q+vf2f/Op/rPNYpaf3dImYVha2YPK6nvG6AGtqpMH3Br4dF0GXQOlibpsmJEyd49tlnqVQqDA8P09/fT7PZ5MqVK2rX0zXJ0plOK5RuG0knkO4kw5bzH//4Vj772R9Sr78JsCgUwhQKYUd7VkQPbndrobdAuEkrj3BLDmzbLflus+lelve6qVYDlMshWkl4nOUd6iiVgosX4YknQIDeslxcurSfL3+5RTXNzga58cY0i4s+otEybvc8lUqKSKRBNGrR2+smkTAIBAy8Xo+ahLqiS5fhS9EdW/SJrX/0/pbFbpqtFFwi49MTjjabTSYnJ5mcnCSRSDAwMIDP5+PQoSyG8cf84Ad/w+XLEfXMiYn/wu///uoeMowmXm95GWgreDxFaC/+1gAAH9tJREFUAoE0Xu80Hk9J+2/l43YX8fkqeL2VZXCuIdKjtZTk0Jqn1eppFbOipey+ny1bfoVI5CS2/SaGhi5w331f4oc/vAv4Ae98Z43+/n5M0+TUqQQXL8Z5//vHaDQaTExMqHjMIofVbbilD8UySUAqn89z/vx5Tp48yauvvsri4iLVapVkMkkqlWprQ61WI51Ok81mKZVKyspkZc62rJx0Yqder6voeLqtsr5e8/k8165dA1pU8rZt2zhx4gTJZJKxsTF8Ph89PT3Mz88rS5e5uTm++MUvcujQIeUboG84UnQrjWazyeXLl7l06VIbbti2rZISdPJMdHLuznkqhIhuPqrPXaeCcr3ldQPUQqE5xQudlHE6heqUQTspulqtxsmTJ1Xc2w0bNqh7YrGYCggDqGhaolWHzg4vUtaiTroV0zR55plbqdWOsGfPi1y5sodbbz3KCy/cTa1W58iRfyQSSTE7O8KZM3cyMnISn69MqeSj2YxQqwWpVIIaW712YCGXq4bHU8HjqWHbdWq1MrZt4feHqNWagAfb9gBeLMuDbRtUKiFmZlbadfp0L6dP9y7/6pbc1Vq27LDweJr4/U0CgSbBYINQqE44XCcSqRIIVJmeblIojGAYeWw7g2WlKZcjNBoVGo0qllUHmtTrPRSLYZrNGpZlYttuTNMG/Ph8FtHoihu6UCsyLktLS1QqFcLhMMlkknD4FAMDjzE7+35isRfYufMkmcw427b14PfXFNh6PCVcriqm2YphEY1GlXmm067W6XCli9ycc0Oftzq3J4u3t7eXSqWi4iUPDn4Zt9vN1q0jajMbHDzPwYMWjzzyU3i9f8WHPmRx+nSS3//9Q/zar52g2Wxy/vx5Tpw4QaVSUbbTeo5FeV8ymSSfz5PJZJibm6NQKDA3N8fc3JwCG0k6G4lEFOWsU6Wzs7MqYqHoH2QcZD2K6WRLTJWmp6cHj8ejxBS2bSuFnt63tm2r+kQiEfbt20c0GiUYDLZRrsI9zczMqNC3YsKnU9Fyvc4BduJGBKihnZjQZdL6hiWArl8rIiO5XsZZLIQkzdq/OdGHk8KAzqxGNwG8kxKXc7ZtUywWldfR8PCw0r66XC42btxIvV7n8uXLeL1elQlC7J1fKwivp53z8zdy/Pj93HvvY+zd+z0ef7zJCy/8NwYGRqlUxrj55qdoNBps3XqGzZtfZX5+CwcP/pN6htMtvNl0Ua0GqFaDy5/WcaXS+q7VWucqlQCFgodazQbiVCpR6vXQv2DrzGWrD6hWYdkrt0u5cV1PXFiAV15Zz5UNoIlhWEATaFlstH6L9YaXRiOOx5Mnm72Fl1/ehNtdYmnJp671+TxYVg3LamBZNUqlAuVyL4aRYcuWII1GFWhSrYap1YL09ExhGBaGYWOa9vKx/JZ3W7hc4HLZy5xQc/mcjWmCYVjLm08Dw7BZWDiF1+tiy5bNWFadZ58dZ2hokHg8gtttYBgWN9/8ON/4xr+jWJzkqac286u/+kN27ZrmypVJzp49qzwb8/k8pVJJOWIJ2NRqNYaGhujp6aFWqxGLxZQNu+6eX6/XKZVKyjFEX5MSW0QcUUzTVPbkfr9fPUO3bhgcHFTPT6VS6rnQvrHJ2hX5d6VS4emnnyYQCLB161aGhobYs2eP2pA9Hg+33nqrcuwRPNm8eTOTk5PKVlkXacDKWtIBW5dRC/A6xRtCHeshDOR/XRYv18t5MdcUs9x/c56J8/Pz/PEf//EqlkDcg6HdbM9JaXdiJUQJIh6BEqRc7/RsNssrr7xCJpOhv7+fzZs3MzMzo7zfnNS506rCSdk7uQFnaSk4hgiHP8Ttt+9nfr7KoUNFbrrpr1lY2Eqt9kfATer+oaELDA21ey/pNrkALleTUKhIKFRcVVfnuwuFAseOHcPn87F7927i8SQzM0UCgY1AgnLZz9TUNs6cuQfDeBHbvg3TPIptl4E4EMe240AUiLH29LEwjAqmWQdaJmmtRWLTEsm4AY/2cdNZVLOe0rrXthvLYNgA5L01WiDdi8dzFbc7h9cboVTaQiAwjs+Xxe02KBT6mZ6+hs9XJRBoWYD4/duACOXyyLLSqky57KFU6sHvzzI3txPbbomf2j/t51rcgLHO9n0IgOW4+QAs68RWlW9/ewdvfOP3OX78D/nyly+RSqXaYkZ3mw8iT85kMpimyZ49e9ixYweRSIRMJsOlS5eYnZ1VFhSBQIBYLMbFiyseq7rDTDAYZHFxkbm5ORVnWbxFG40GZ86cUaFt+/r6CIfD9PT0cOnSpTbKUq+zyMJhxStUgk+dOXOGQCDAyMgIQ0NDKgWapLcSsYZt29x0000qIqCIVGStOjkk6SM9n6XTZ0HaL9yTUMhAmwmhPM/r9SoHJHGGy+VyKpbJesvrAqgbjYbK4iuNF3ZCN3aH9ghzIt/SWUqdrRStM6xQCDJ5LMvi6tWrRKNRJfIQLzcJSKO/F1bLqDspGDuxU3o5cOAxFhfP8cILFeUU0dd3Ar//OI89Nsab3/xmFhYW2u55rZYlzmukeL1eIpEI5XKZer3O1NQ1hoaG8PmKNBpZxsdHOHfuTgKBD2LbT9BsvoV6/St4PB8BfuB4pgGEsawYhpEAEth2DNPsxeVKYppJfL4N2HYL3F2uJJWKn3o9TKMRAfysXUqYZgWfz8a2q8sgbOJyeWk2TSzL0/ZpgaAX2/YCQdq7v1Xfen0E3eIql9vOstEAYAG9QAmXK4dh2KTTfYTDc8Rix0ml7mD79ivk8zu5997/hw0bXlVjo4s79LJad9EO4ODSwNygWKxw8uRpwMX+/Qc5f/4i4XCMXK7Inj03YpoeLMtgYmKQkyd/DLf7Szz77IfxeOqY5tVVFj+6gkzqoSu3ms2msq0PBALMzc0xOjqq5MgtccsgfX19+P1+rly5oqhFicVRLBap1WrMzc2RyWQYHBxUnrnQEj329fVx/vx5oGUfXalUli1ybAXquplnuVzG5/PR39/P9PR0G/GjJ0A4deoU586dY2RkhOHhYcrlMpFIhJMnTyoFozxzx44dhMNhRkdHlamfTvHq61g4AR3IdUWjc23rxKQo2t1uN0NDQ8q8UADdtlsWWNeuXWsLAXC98roAalgJvKOzQM78dE6zO13h54yVK7uZyNvkvIC5sHpiuys2oeJ+7VRI6u+C1YtyLXmT/p9lWfz7f//v+Yu/+AslUxON9S/8wi8wPj6u7umkIJV3S12cispO9ZBrREYpG+DS0hIvvvgi8XicgYEBXnwRAoGfBJ7CsgxcrqPAR7DtN+ByPbXqPbadxzTz2PbE8ri1Rzjs7x9Sx9u3b2dgYIBIJEI6neby5Wnm5mrkci7q9TC5nEm9HgYSy584weAQweAQxaKXWi0MxGg0IjQagVVtbC81DKOMYVQxjPqyCMSmZanhxra9yx8fK0ugNa6VSohqleX/oFYLY1lvAvyMjR3i1lu/x9DQBSxrtTmo0xlKt1yR/lkRxxhAve3aZrNIo3F1OS1YL0eO9C9HyyvR09Py1Jmc3MmpUw/g8bQ2U7f7u9TrX8Xl+jCm+VQbl6frdGTsTNNUmVMqlQqhUIjbb7+da9euqdCbQhmK/FifwwL+wsYLOAkA+Xw+5R0o1iCLi4v09PSwuLioPA5tu5VZfOvWrViWxezsrBK1iG227l7tnNf6ZjM6OsrY2Bg9PT3s3btXWcQ4297b20tvb6/yo3DqC6TosdDlmk5EkfSzLtMWU8WdO3fS19enxCi674fEf3Haaa9VrgvUhmHsBv5GOzUC/Abwl8vntwJXgQ/Ytp02Wq35HHA/UAJ+0rbtE9d7j65QEFBxegvp7to6Nav/1l2yXS6X8jSUgOUSGEkAeWlpiTe96U0sLS2pwEp9fX1tddGpa91kR/539FfH8/r1r7zyCg888AALCwvUajV6e3vJ5XJtpkdO8Uqndzhlbfq9snEJUJqmqQIC6fKxQqFAoVBgYmICj+cojUZ5WTwhNqVHgaPYdnfK3nlez8psmqayLpB6eTwe/H4wjFlCoRYlFY8bzM/PK4oEIB4fZNOmTczOzlKpVNi6dSvbt28nkylSrQYwjAS1WohKJUC57KfRCFEs+kinbSqVILq4xrJiWFYQ246xtgjCxrZDeDx1PvKRr9HXd5aJiR38wz98mBtvPM7p00dIJs+wceOoGgMBaKeepdNmvlYf6nEsxBHI5XIpV2eAkyfdBIMfp15/EjAwjKdwu38C2z6MYRxdRazIs/W5u3nzZqXgq1QqfOMb31CgLNxko9FQSX1t21bOYbq7u5jtVSoVAoGASjgsGdole48QS6ZpKm7R4/EQi8UUN7thwwbGx8cV1+ukeJ396VxfjUaDVCrFsWPHiEajbNq0Sbny68SdbduMjIwQj8e5cOFCm4JUihAbQsw55edSxNNQAFc49Z07dyqLHCdhJ9T2a80udF2gtm17FDiw/DIXMAV8A/h14Pu2bf+eYRi/vvz714D7gJ3Ln9uAP1n+vm7RvYN0IJYd2Glbq9WxbTLqFDCggHB0dFQFTm80GoyMjFCr1bhw4YKaLLt27WrzPtQVDjoVq2uBO00g5+TSNxQJ2fnd736XZDLJj/3Yj5HNZtue2w3onWCgU3J6X+g2tjKBvvnNb1IqlZTzi84hWJaF1+tVFJWU9ShU9XoHAgEKhQJ9fX309/ercJ0DAwNEo1Hq9Trz8/NcvXpVUSzNZpMDBw7g8/lIp9M8/vjjq6gViZxXKBTIZhcJh8MEAhXlMizXnD17lkYjz+bNcZVFO5vNqqBOLVl2ZFkkE+O2296BYfRQKHg4dcpNsfhBbLsFHufOncPn8/P00+/jIx95GK/3Gfr7T/H97/8sd931JwwOnlfvlT50jk+3PnRyS7ZtK4W29P/LL7+sKNtgMLgcCOwbNJtVNfYt8Gxtprq9fTcWvaenh927dzM1NUUmk2FkZASv16tSpMViMXp7e5mdnSUYDCpglihzEl1PMpOIGHLLli34/X5qtRoul4t8Ps/09DTXrl1T4y7rWkKNijx9aGiIc+fOrYpz063/nLog6S8hPmS8z58/TzQaZXBwkM2bNxMMBhXRFYlEOHz4MIVCgbNnzyrAFoXkzMwMExMTbZYyTkWkrniUDSsUCrFhw4Y24slJketmgustr1X08SPAmG3b44ZhvBd46/L5vwCepAXU7wX+0m7NkucMw4gbhjFo2/ZMt4fqO5YOwp1kbMK2Q7shu1OmpFPZ0ErdMzExgWVZDA8PK5YmFAoxNzdHLBZbJQd3Uu/6ItTtIp0TRy+6/Fzug5ax/sc+9jGq1SpTU1PqWicr5tzNnWCgs2c6x6FvagDnz59XsYUlTrOkwRIuRKfAO22EznY5NxTDMKjVaiq/XiQSUeDz1FNPMTIyoiwFVhwyLJUgwLZbFgk6JShstGjLFxcXlSxS7INlwYhXaTabJZfL4fP5CIfDDA4OMj09TT6fXxanZYEMhgEjI3toNC6xuLifev0TeL116vXfx+P5T5w+/V84ePAiN9/8O2zc6GV+3mJg4BXuuutPSKW2KqCG1Q5WUv9Oc8JJ9Uo/mqapNh1xyRaglv7SWWgZr04Up5PrkvLAAw+wbds2SqUS9913H5VKhUuXLinOU8bGsiwVX9o0TRUwShdLynuj0ajioMQWGlpcweLiItlsVlHm2WxWUeQy3nKNXve1RIn6/84+FowQnCgUCly8eJGxsTE2bdrE1q1blbNbo9HA7/ezf/9+JiYmmJ2dxbZtxsbGcLlcZDIZAoGAsrsGlC5LjnURTL1ep6+vrw2fOtVbRLX/mkD948DXlo8HNPCdBQaWj4eACe2eyeVzbUBtGMYngU/Cyu60fB45JxStaFH1bCQSQhBWwF0HMH3SSgQut9vN5OQkMzMzKqebxI+OxWLLrtXFVeZFsqB043zoHM5Tp1CldKK4Go0G8/Pzq8Q7UnT3U51i1sMmyvskYabESRa2rVAokM/nyeVyTE1NYRiGFua00pbWSo93Le93cgf6cTfWVDa/t7/97WQyGS5fvozH4+H+++/n0qVLbX1br9fJZrNtsZVl8jrFW6ZpKnfheDyu7HP1+NG2bTM4OKiyZlerVer1Or29vUrzL6Ew9XFwu91cvXo7YPORj3ydr3zlv7JvX4YTJ/4vSqUSg4OvYhgHVL0GB8+3gbS0XxcpOZXQTs5KNki978RNPRaLKTGRc7HriTWc7+lUdOuFaDRKKpWiUqmwY8cOLMuiXC5z4MABrl27xtzcHBKU3+/3qw3d7XaTz+cVBS9rUc9enk6nVTTBy5cvK+CSfIcyN4eGhpRrt2maBAIBrly5okQgzvnWrTi52E7iCaHiBUvGx8cZHx9XISISiYRK47Zt2zbcbjdTU1NcuHBBpaprNBoqxZfODYuYqtlsKi602Wy2pbfrVFcd3/5VlImGYXiB9wD/2fmfbdu20bKLWnexbfsh4CEAr9drO8FAlwXrlIQApzRSqLFaraYUH7qXG7SnzAEUOF+6dEkB0pYtW1haWmJ8fFwpS/r7+0kmk8RiMfUe3VZTBzJ5tix8oUoKhYKyZRWRiyhrhHrS6ybHIobQNyrpD0lDpMvOdSN/HdQF6GOxmHK5bTQazMzMKFtQKTpwiIecUya/PBfWpGgGBgYYHx8nEolw9913E4lEGB8fVxYGIvsbHByk0WgQj8fVcyQLjWG08kzOz8+rhSA5JyXribgJS8xkeb/IPiUeiLDi8Xh8ldeYBPaZm3uWO+54hKGhFjX2nvdE2Lv378lkdnLt2jQHDx7syEHo/eEMwi/v7tRX0rcyn+v1Oo899phqv4yjPsdM01yW7/vb2uCsj150IkNM2fr7+zEMQ+Xvu3DhQlvfTk9Pk06nVXYWj8fDwsLCKqpVTMyEGJB5Nzc3h2EYy/qElnWHXJfNZlWkyng8zvj4OIcPHyafz3Px4kVlpbXeom/m0l59jPTwo7KpZ7NZjh8/jm23FJp79uyhr6+Pffv2sXPnTrLZLDt37qTZbCqiJpVKqSBTsGLpIXJ+CYugO+R0EolKXUXZut7yWijq+4ATtm1LoMk5EWkYhjEIzC+fnwI2a/dtWj637qLvjrAiB5PzupxIFofsdM5Oknv1Xdfn86nElpLxQveSksEVzyhABXARihDa7bcFoJ023QJ+spkEg0ElLxUQ1hecTtHqk6JSqag6CEjr7RRQ0J+lA7xMONkopL7yv/SRuN8Ky62Hbuw0RvrCkE1G0oCFQiGuXr2KYRiEQiEOHDjA1NQUY2NjSjwRCASIRCJtLKE8p16vk8vl1AbtdrvZuHGj2rQlWYAAuWwC2WwWCWhvWRbz8/Mkk0kFgoODg9RqNWUSWqlU2Lbtb6lUGlSrd+Lz+bhy5QpXr/4T4+Pj9PX1kclk2uaeXpx9IOOo95muy9CBXebx2NgYpVKJgYGBthjVOmgDan5ez2JAxlXAXtJ3iVeg1+ulv79fJVVIJpOcPn2aarWqsoXra7BcLre1R0ROMh/FzE5cuHt6elhYWFBtlHnvcrmoVCoqVrQO5jt37uTMmTProqhfS3FupDqeFAoFTpw4gcfjYXBwkI0b/9/2zi42rqOK479jb+Ik9qZeY7xpIUrSUIMqohArQk7gAQGqqwbxVCFFSEQlEjwUURASUsRDxVOKhChFQqiILwWhFCgVrSJEBCVvkQJtQLHTNDiVgz+CyeJkHbzrVRJneJg549kbO7Ed13d9M39ptXvnXt2dM+feMzPnzPzPQ56jBGYJlIrFIhMTE3XuRyVW0oBp2C5zdehapm6Zd8v1sZ9ZtwfAa8AB4Dn3/WpQ/hUReQkbRJw0d/BPK0LjlvzWhg2DjIBf6hNOJ9Q46cMdGng1xLoqoVqt+kznxhgfzEgGCDXJZnNzs0+EGbphVAEa2VZlhX4oneqGL1hyPWcYEQ4J03Wlylyul6Sx1PZSQxyOultbW72fVj/asYTTdg3chf8XPqBzvUjaxuvXr+f69eu0t7dz7do1tm7diohN1NDf38/MzAz5fN53XFeuXKl76ZOdrRrjNWvW0N7eTltbm0/uGq7uCWk1tQ1qtZpvy7GxMarVKp2dnXR1dVEul+v4Jvr6+jh69ChHjhzh5s2bDA0NsXPnTnbv3k1bWxtjY2N16/bnQ6ifcIAQrhrS53JmZoZyuczZs2cZGRnxL39TUxNnzpypu4/KpLrQab0+B1o+n1+0p6eH3t5eKpUKo6OjjI+Ps3mzHU/VajV27dpFR0cHg4ODTExM3EZqpAOZ5ChRU2Elfdbqpmpra6NUKvmOX/cqjI+P09XV5TeyaGoq/Y/55FgIkoYxOXgL3Tdhuw0PDzM8PEyxWHRsjO0+9+KNGzfo7u72adT0mQ23kBtj6naAJpHsPKfuvH23Dgsy1CLSimVl/3JQ/BzwGxE5CPwL+Jwr/wN2ad4F7PK8p+52//ABC90c4YOhI8HQ55Yc3SSZ79QPNBsdn+Vj0JHE9PQ069at8ymbdHQWugTUIIYBu7DeyeVZoe8uXPoT0iaGnAhhgEhfinn0wNq1a31euPDlCF0napzDNlN3jroZNEinbRbeL+xodIQd6iCsT/L33r172bdvH0NDQ5RKJU6ePOl5vTs7O9m4cSOXLl1iy5YtDA8Pc+vWLT9dVOMV6it097S0tHgfa0tLi0/0oB2gzoByuZzfWKGcxDqr0BH2hg0b/MhJA2p9fX3k83kOHz5MsVj0mbR1xJfsFOea4oZ6CGdMmiOxXC5TLpfrgqlNTU3k8/k6giN1f6mLQztOnb5rkCvUTTiDUoOgncumTZvYsWOHf9bCdFtTU1PesHZ1dTE5Oel3+QE+kKiyGmN8VnWdzXZ2dnpKU9VprVbj4sWLPhOS+rXL5TIdHR2e5a5YLDIwMOCDkMs9op4LyRmNQsSuaCmVSogI+XyeYrFIoVCgUCj4YLUmJZiZsRl/wuWuYTuF8miZ+rKTG9vuhAUZamNMBXhPomwCuwokea0Bnl5wDQKE/tDQcKmRDIVPrvAIp+j6kIZrd5PK1+2xIZGMEpCHaxx1VBT6ffVhDNcK6z21YwjXZyanW8kVGSqDGkm9jxrLMLAa+q21nULjnOxktG2SvlNNgKoyhSNv/a/wOJypJA1UOFJRIp+enh56enool8sMDg4yMjJCpVJh+/btPuN2pVKhVqtRKBTqOrNkIFVnHmowcrmc34bb39/vfbbq+9YOUo21jgabm5uZnp72mzWq1arX0a1bdkv1iRMn/Cgwn8/fNhhIvnwKbQNNYFutVrl69aqn9tQ66IwrNKjGmLpsJmFAMpy56f+qfsPBSqib5OzKGMPly5eZmpryXNFhgN4Yu1lFOTS6u7s5ffq0r+PU1FRdfcEORnTvw8yMJVxqbW1ldHTU61DfMV2dEy4JrVQq3vDVajUf7FUX03JirvsljWdyoKhlmgMzl8vVxax0aSHMbnQBvL7ngs6E1P10t0BwnQwr0XvdtRIi/wPOp12PFUYnsLA8PNlAlDf7uN9kXg55txhj3nu3ixplC/l5Y8zutCuxkhCRN+4nmaO82cf9JvNKyrtUurKIiIiIiBVCNNQRERERDY5GMdQ/TrsCKeB+kznKm33cbzKvmLwNEUyMiIiIiJgfjTKijoiIiIiYB6kbahF5XETOi8gFsXSpqx4isllETojIWyJyVkSeceUdIvInERl03wVXLiLyA9cGZ0SkJ10JlgYRaRaRv4vIMXe8TUROObl+LZYvBhFpcccX3PmtadZ7qRDLDPmyiLwtIudEZE+WdSwiX3fP84CIHBWRdVnTsYj8TEQui8hAULZonYrIAXf9oIgcuNd6pWqoxfJb/xDLI/IosF9E5ktzvZpwE/iGMeZRoBd42smlHN6PAK+7Y6jn8P4SlsN7NeIZ4Fxw/B3geWPMB4CrwEFXfhC46sqfd9etRrwA/NEY8yFgJ1b2TOpYRN4HfBXYbYz5MNCMZdPMmo5/ATyeKFuUTkWkA3gWS6HxUeBZNe5LRnJH00p+gD3A8eD4EHAozTq9S3K+it2Cfx540JU9iF0/DvAisD+43l+3Wj5Y8q3XgU8Cx7As9v8FckldA8eBPe53zl0nacuwSHkfAIaS9c6qjpmlL+5wOjsG9GVRx9isVQNL1SmWF+nFoLzuuqV80nZ9zMddnRm4Kd8u4BSL5/BeTfg+8E1slliwlANlY4xShIUyeXnd+UkSFAWrANuAEvBz5+75iVhOnEzq2BgzBnwXGMZyy08Cb5JtHSsWq9Nl13XahjrTEJE24HfA14wx18Jzxna1mVhyIyKfAS4bY95Muy4riBzQA/zIGLMLqDA7JQYyp+MCNnvTNuAhoJXbXQSZR1o6TdtQ3zN3daNCRNZgjfSvjDGvuOL/iOXuRpaZwztlfAz4rIhcBF7Cuj9eANpFRGkKQpm8vO78A8DESlZ4GTAKjBpjTrnjl7GGO6s6/jQwZIwpGWNuAK9g9Z5lHSsWq9Nl13XahvpvwCMucrwWG5x4LeU63TPE0nX9FDhnjPlecEo5vOF2Du8vuChyLwvk8G4UGGMOGWPeb4zZitXhX4wxnwdOAE+6y5Lyajs86a5fVSNPY8w4MCIiH3RFnwLeIqM6xro8ekVkg3u+Vd7M6jjAYnV6HHhMRApuJvKYK1s6GsBx/wTwT+Ad4Ftp12eZZPo4dnp0BviH+zyB9dG9DgwCfwY63PWCXf3yDtCPjaynLscSZf8EcMz9fhj4K5ab/LdAiytf544vuPMPp13vJcr6EeANp+ffA4Us6xj4NvA2MAD8EmjJmo6xyVH+DdzAzpoOLkWnwBed7BeAp+61XnFnYkRERESDI23XR0RERETEXRANdURERESDIxrqiIiIiAZHNNQRERERDY5oqCMiIiIaHNFQR0RERDQ4oqGOiIiIaHBEQx0RERHR4Pg/ovvIrfMUsgEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import cornermatching as cm\n",
+    "filtered_matches = cm.filter_matches(best_matches, secondbest_matches)\n",
+    "\n",
+    "print(filtered_matches)\n",
+    "h = int(len(I1))\n",
+    "w = int(len(I1[0])*2)\n",
+    "for match in filtered_matches:\n",
+    "    x1 = match[0]\n",
+    "    y1 = match[1]\n",
+    "    x2 = match[2]\n",
+    "    y2 = match[3]\n",
+    "    #print(x1,y1,x2,y2)\n",
+    "    \n",
+    "    plt.plot([y1,y2+int(w/2)], [x1,x2], color=\"blue\", marker=\"x\")\n",
+    "stacked =  np.column_stack((I1, I2)) \n",
+    "plt.imshow(stacked, cmap=plt.cm.gray)\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "def RANSAC(number_of_iterations,matches,n,r,d):\n",
+    "\n",
+    "    H_best = np.array([[1,0,0],[0,1,0],[0,0,1]])\n",
+    "    list_of_inliers = []\n",
+    "    \n",
+    "    for i in range(number_of_iterations):\n",
+    "  \n",
+    "        # 1. Select a random sample of length n from the matches\n",
+    "        np.random.shuffle(matches)\n",
+    "        samples = np.array(matches[:n])\n",
+    "        \n",
+    "        # 2. Compute a homography based on these points using the methods given above\n",
+    "\n",
+    "        H = cm.findHomography(samples)\n",
+    "\n",
+    "        # 3. Apply this homography to the remaining points that were not randomly selected\n",
+    "\n",
+    "        image1 = []\n",
+    "        image2 = []\n",
+    "        for sample in samples:\n",
+    "            obs = sample[0:2]\n",
+    "            obs = np.append(obs,1)\n",
+    "            image1.append(obs)\n",
+    "\n",
+    "            pred = sample[2:]\n",
+    "            pred = np.append(pred,1)\n",
+    "            image2.append(pred)\n",
+    "            \n",
+    "        image1 = np.asarray(image1)\n",
+    "\n",
+    "        image1 = (H @ image1.T).T\n",
+    "\n",
+    "        # 4. Compute the residual between observed and predicted feature locations\n",
+    "        inliers = []\n",
+    "        \n",
+    "        for i in range(len(image1)):\n",
+    "            obs = image1[i]\n",
+    "            pred = image2[i]\n",
+    "\n",
+    "            #scale\n",
+    "            obs[0]= obs[0]/obs[2]\n",
+    "            obs[1]=obs[1]/obs[2]\n",
+    "            \n",
+    "            pred[0]= pred[0]/pred[2]\n",
+    "            pred[1]=pred[1]/pred[2]\n",
+    "  \n",
+    "            \n",
+    "            #readability\n",
+    "            u = obs[0]\n",
+    "            v = obs[1]\n",
+    "            uP = pred[0]\n",
+    "            vP = pred[1]\n",
+    "\n",
+    "            #calc residual\n",
+    "            resid = np.sqrt((u-uP)**2+(v-vP)**2)\n",
+    "        # 5. Flag predictions that lie within a predefined distance r from observations as inliers\n",
+    "            if(resid < r):\n",
+    "                inliers.append([u,v,uP,vP])\n",
+    "\n",
+    "        # 6. If number of inliers is greater than the previous best\n",
+    "        #    and greater than a minimum number of inliers d,\n",
+    "        #    7. update H_best\n",
+    "        #    8. update list_of_inliers\n",
+    "\n",
+    "            if(len(inliers) > len(list_of_inliers) and len(inliers) > d):\n",
+    "                list_of_inliers = inliers.copy()\n",
+    "                H_best = H\n",
+    "\n",
+    "\n",
+    "    return H_best, list_of_inliers\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 0 0]\n",
+      " [0 1 0]\n",
+      " [0 0 1]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import cornermatching as cm\n",
+    "matches = filtered_matches.copy()\n",
+    "H_best,inliers = RANSAC(10000,matches,10,5,4)\n",
+    "\n",
+    "print(H_best)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import skimage.transform as skt\n",
+    "\n",
+    "proj_trans = skt.ProjectiveTransform(H_best)\n",
+    "\n",
+    "image_2_transformed = skt.warp(I2,proj_trans)\n",
+    "plt.imshow(image_2_transformed, cmap=plt.cm.gray)\n",
+    "plt.show()\n",
+    "\n",
+    "stitched = np.column_stack((I1,image_2_transformed))\n",
+    "\n",
+    "plt.imshow(stitched, cmap=plt.cm.gray)\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": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/__pycache__/cornermatching.cpython-36.pyc b/__pycache__/cornermatching.cpython-36.pyc
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diff --git a/__pycache__/cornermatching.cpython-37.pyc b/__pycache__/cornermatching.cpython-37.pyc
new file mode 100644
index 0000000..ddb545c
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diff --git a/__pycache__/cornermatching_addi.cpython-37.pyc b/__pycache__/cornermatching_addi.cpython-37.pyc
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diff --git a/cornermatching.py b/cornermatching.py
new file mode 100644
index 0000000..889dd77
--- /dev/null
+++ b/cornermatching.py
@@ -0,0 +1,253 @@
+import numpy as np
+import math as mt
+import random
+
+def convolve(g,h): # h is kernel, g is the image
+    I_gray_copy = g.copy()
+
+    x,y = h.shape
+    xl = int(x/2)
+    yl = int(y/2)
+    for i in range(xl,len(g[:,1])-xl):
+        for j in range(yl, len(g[i,:])-yl):
+
+            f = g[i-xl:i+(xl+1), j-yl:j+(yl+1)] #FIXME
+
+            total = h*f
+            I_gray_copy[i][j] = sum(sum(total))
+    return I_gray_copy
+
+def gauss_kernal(size, var):
+    kernel = np.zeros(shape=(size,size))
+    for i in range(size):
+        for j in range(size):
+            kernel[i][j] = mt.exp( -((i - (size-1)/2)**2 + (j - (size-1)/2)**2 )/(2*var*var))
+    kernel = kernel / kernel.sum()
+    return kernel
+
+def harris_response(img):
+    sobel = np.array([[-1,0,1],[-2,0,2],[-1,0,1]])
+    gauss = gauss_kernal(3,2)
+    #calculate the harris response using sobel operator and gaussian kernel
+
+    Iu = convolve(img,sobel)
+    Iv = convolve(img,sobel.transpose())
+
+    Iuu = convolve(Iu*Iu,gauss)
+    Ivv = convolve(Iv*Iv,gauss)
+    Iuv = convolve(Iu*Iv,gauss)
+
+    H = (Iuu*Ivv - Iuv*Iuv)/(Iuu + Ivv + .0000000001)
+
+    return H
+
+def getmaxima (H,threshold,localSearchWidth = 5):
+    maxima = []
+
+    p = localSearchWidth
+
+    width,height = H.shape
+    for i in range(int(p/2)+1,width-int(p/2)+1,p):
+        for j in range(int(p/2)+1,height-int(p/2)+1,p):
+            if H[i,j] < threshold:
+                continue
+            else:
+                localMax = [0,0,0]
+                for x in range(i-int(p/2),i+int(p/2)):
+                    for y in range(j-int(p/2),j+int(p/2)):
+                        if(H[x][y] > localMax[2]):
+                            localMax = [x,y, H[x][y]]
+                maxima.append(localMax)
+    return maxima
+
+def nonmaxsup(H,n=100,c=.9):
+
+    mindistance = []
+    threshold = np.mean(H) + np.std(H)
+    maxima = np.array(getmaxima(H,threshold))
+
+    x = 0
+    y = 1
+    z = 2
+    for row in maxima:
+        min = np.inf
+        for row1 in maxima:
+            if (row[z] < c*row1[z]):
+                dist = np.sqrt((row[x]-row1[x])**2 + (row[y]-row1[y])**2 )
+                if (dist < min) and (dist>0):
+                    min = dist
+                #xmin = row1[x]
+                #ymin = row1[y]
+
+        mindistance.append([row[x],row[y],min])
+    mindistance.sort(key=lambda x:x[2])
+
+    return mindistance[-n:]
+
+def descriptorExtractor(img, featureList, l = 21):
+
+    def patchFinder(i,j,img,featureList,l):
+        descriptor = [0,0,0]
+        patch = np.zeros((l,l))
+        patchX = 0
+        floor = int(l/2)
+        ceiling = int(l/2)+1
+
+        #pythons stupid.
+        i = int(i)
+        j = int(j)
+
+        #find patches, return 0 if out of bounds (this could be improved by not just returning 0)
+        for x in range(i-floor,i+ceiling):
+            if x < 0 or x >= width:
+                return []
+            else:
+                patchY = 0
+                for y in range(j-floor,j+ceiling):
+                    if y < 0 or y >= height:
+                        return []
+                    else:
+                        patch[patchX][patchY] = img[x][y]
+                        patchY +=1
+                patchX +=1
+        descriptor[0] = patch
+        descriptor[1] = i
+        descriptor[2] = j
+        return descriptor
+
+    width,height = img.shape
+
+    patches = []
+    for point in featureList:
+        patch = patchFinder(point[0],point[1],img,featureList,l)
+        #Checks to see if patchFinder returned an appropriate patch. Only append if true.
+        if(len(patch)> 0):
+            patches.append(patch)
+
+    return patches
+
+def sum_squared_error(D1, D2):
+    if(D1.shape == D2.shape):
+        return (((D1-np.mean(D1))/np.std(D1)) - ((D2-np.mean(D2))/np.std(D2)))**2
+    else:
+        return np.inf
+
+def get_best_matches(des_I1, des_I2):
+    best_matches = []
+    best_sse = np.inf
+    for i in range(len(des_I1)):
+        for j in range(len(des_I2)):
+            sse = sum(sum(sum_squared_error(des_I1[i][0], des_I2[j][0])))
+            if(sse < best_sse):
+                best_sse = sse
+                bestMatch = np.array([des_I1[i][1],des_I1[i][2],des_I2[j][1],des_I2[j][2]])
+        best_matches.append(np.array([bestMatch,best_sse]))
+        best_sse = np.inf
+    return best_matches
+
+def get_secondbest_matches(des_I1, des_I2, best_matches):
+    secondbest_matches = []
+    best_sse = np.inf
+
+    for i in range(len(des_I1)):
+        for j in range(len(des_I2)):
+            sse = sum(sum(sum_squared_error(des_I1[i][0], des_I2[j][0])))
+            if(sse < best_sse and sse != best_matches[i][1]):
+                best_sse = sse
+                secondBestMatch = np.array([des_I1[i][1],des_I1[i][2],des_I2[j][1],des_I2[j][2]])
+        secondbest_matches.append(np.array([secondBestMatch, best_sse]))
+        best_sse = np.inf
+
+    return secondbest_matches
+
+def filter_matches(best_matches, secondbest_matches, r=.5):
+    filtered_matches = []
+    for i in range(len(best_matches)):
+        if(best_matches[i][1] < r*secondbest_matches[i][1]):
+            filtered_matches.append(best_matches[i][0])
+    return filtered_matches
+
+def findHomography(sample):
+    A = []
+
+    for match in sample:
+        u = match[0]
+        v = match[1]
+        uP= match[2]
+        vP= match[3]
+
+        A.append([0,0,0,-u,-v,-1,vP*u,vP*v,vP])
+        A.append([u,v,1,0,0,0,-uP*u,-uP*v,-uP])
+
+    U,Sigma,Vt = np.linalg.svd(A)
+    H = Vt[-1]
+
+    H = np.reshape(H, (-1,3))
+    return(H)
+
+def RANSAC(number_of_iterations,matches,n,r,d):
+
+    H_best = np.array([[1,0,0],[0,1,0],[0,0,1]])
+    list_of_inliers = []
+
+    for i in range(number_of_iterations):
+        # 1. Select a random sample of length n from the matches
+        samples = []
+        for i in range(n):
+            idx = random.randint(0,len(matches)-1)
+            samples.append(matches.pop(idx))
+
+
+        # 2. Compute a homography based on these points using the methods given above
+
+        H = findHomography(samples)
+
+        # 3. Apply this homography to the remaining points that were not randomly selected
+        predicted = []
+        observed = []
+        for sample in samples:
+            pred = sample[0:2]
+            np.append(pred,1)
+            predicted.append(pred)
+
+            obs = sample[2:]
+            np.append(obs,1)
+            observed.append(obs)
+
+        predicted = np.asarray(predicted)
+
+        predicted = (H @ predicted.T).T
+
+        # 4. Compute the residual between observed and predicted feature locations
+        inliers = []
+        for i in range(len(predicted)):
+            pred = predicted[i]
+            obs = observed[i]
+
+            #scale
+            pred[0]= pred[0]/pred[2]
+            pred[1]=pred[1]/pred[2]
+
+            #readability
+            u = obs[0]
+            v = obs[1]
+            uP = pred[0]
+            vP = pred[1]
+
+            #calc residual
+            resid = np.sqrt((u-uP)**2+(v-vP)**2)
+        # 5. Flag predictions that lie within a predefined distance r from observations as inliers
+            if(resid < r):
+                inliers.append([u,v])
+
+        # 6. If number of inliers is greater than the previous best
+        #    and greater than a minimum number of inliers d,
+        #    7. update H_best
+        #    8. update list_of_inliers
+
+            if(len(inliers) > len(list_of_inliers) and len(inliers) > d):
+                list_of_inliers = inliers.copy()
+                H_best = H
+
+
+    return H_best, list_of_inliers
diff --git a/cornermatching_addi.py b/cornermatching_addi.py
new file mode 100644
index 0000000..bafe6a4
--- /dev/null
+++ b/cornermatching_addi.py
@@ -0,0 +1,261 @@
+import numpy as np
+import math as mt
+import random 
+
+def convolve(g,h): # h is kernel, g is the image
+    I_gray_copy = g.copy()
+
+    x,y = h.shape
+    xl = int(x/2)
+    yl = int(y/2)
+    for i in range(xl,len(g[:,1])-xl):
+        for j in range(yl, len(g[i,:])-yl):
+
+            f = g[i-xl:i+(xl+1), j-yl:j+(yl+1)] #FIXME
+
+            total = h*f
+            I_gray_copy[i][j] = sum(sum(total))
+    return I_gray_copy
+
+def gauss_kernal(size, var):
+    kernel = np.zeros(shape=(size,size))
+    for i in range(size):
+        for j in range(size):
+            kernel[i][j] = mt.exp( -((i - (size-1)/2)**2 + (j - (size-1)/2)**2 )/(2*var*var))
+
+    kernel = kernel / kernel.sum()
+    return kernel
+
+def harris_response(img, gmean = 5,var =2):
+	sobel = np.array([[-1,0,1],[-2,0,2],[-1,0,1]])
+	gauss = gauss_kernal(gmean,var)
+    #calculate the harris response using sobel operator and gaussian kernel
+
+	Iu = convolve(img,sobel)
+	Iv = convolve(img,sobel.transpose())
+
+	Iuu = convolve(Iu*Iu,gauss)
+	Ivv = convolve(Iv*Iv,gauss)
+	Iuv = convolve(Iu*Iv,gauss)
+
+	H = (Iuu*Ivv - Iuv*Iuv)/(Iuu + Ivv + .0000000001)
+
+	return H
+
+def getmaxima (H,threshold,localSearchWidth = 21):
+    maxima = []
+
+    p = localSearchWidth
+
+    width,height = H.shape
+    for i in range(int(p/2)+1,width-int(p/2)+1,p):
+        for j in range(int(p/2)+1,height-int(p/2)+1,p):
+            if H[i,j] < threshold:
+                continue
+            else:
+                localMax = [0,0,0]
+                for x in range(i-int(p/2),i+int(p/2)):
+                    for y in range(j-int(p/2),j+int(p/2)):
+                        if(H[x][y] > localMax[2]):
+                            localMax = [x,y, H[x][y]]
+                maxima.append(localMax)
+    return maxima
+
+def nonmaxsup(H,n=100,c=.9):
+
+    mindistance = []
+    threshold = np.mean(H) + np.std(H)
+    maxima = np.array(getmaxima(H,threshold))
+
+    x = 0
+    y = 1
+    z = 2
+    for row in maxima:
+        min = np.inf
+        for row1 in maxima:
+            if (row[z] < c*row1[z]):
+                dist = np.sqrt((row[x]-row1[x])**2 + (row[y]-row1[y])**2 )
+                if (dist < min) and (dist>0):
+                    min = dist
+                #xmin = row1[x]
+                #ymin = row1[y]
+
+        mindistance.append([row[x],row[y],min])
+    mindistance.sort(key=lambda x:x[2])
+    return mindistance[-n:]
+
+def descriptorExtractor(img, featureList, l = 21):
+    def patchFinder(i,j,img,featureList,l):
+        descriptor = [i,j,np.zeros((l,l))]
+        patch = np.zeros((l,l))
+        patchX = 0
+        floor = int(l/2)
+        ceiling = int(l/2)+1
+
+        #pythons stupid.
+        i = int(i)
+        j = int(j)
+
+        #find patches, return 0 if out of bounds (this could be improved by not just returning 0)
+        for x in range(i-floor,i+ceiling):
+            if x < 0 or x >= width:
+                return []
+            else:
+                patchY = 0
+                for y in range(j-floor,j+ceiling):
+                    if y < 0 or y >= height:
+                        return []
+                    else:
+                        patch[patchX][patchY] = img[x][y]
+                        patchY +=1
+                patchX +=1
+        descriptor[0] = patch
+        descriptor[1] = i
+        descriptor[2] = j
+        return descriptor
+
+
+    width,height = img.shape
+
+    patches = []
+    for point in featureList:
+        patch = patchFinder(point[0],point[1],img,featureList,l)
+        #Checks to see if patchFinder returned an appropriate patch. Only append if true.
+        if(len(patch)> 0):
+            patches.append(patch)
+
+    return patches
+
+def sum_squared_error(D1, D2):
+    if(D1.shape == D2.shape):
+        return (((D1-np.mean(D1))/np.std(D1)) - ((D2-np.mean(D2))/np.std(D2)))**2
+    else:
+        return np.inf
+    
+def get_best_matches(des_I1, des_I2):
+    best_matches = []
+    best_sse = np.inf
+
+
+    for x in range(len(des_I1)):
+        for y in range(len(des_I2)):
+            sse = sum(sum(sum_squared_error(des_I1[x][0], des_I2[y][0])))
+            if(sse < best_sse):
+                best_sse = sse
+            
+                best_descriptor = des_I2[y]
+        best_matches.append(np.array([x,best_descriptor, best_sse]))
+        best_sse = np.inf
+    
+    return best_matches
+
+def get_secondbest_matches(des_I1, des_I2, best_matches):
+    secondbest_matches = []
+    best_sse = np.inf
+
+    for x in range(len(des_I1)):
+        for y in range(len(des_I2)):
+            sse = sum(sum(sum_squared_error(des_I1[x][0], des_I2[y][0])))
+        
+            if(sse < best_sse and sse != best_matches[x][2]):
+                best_sse = sse
+                best_descriptor = des_I2[y]
+        secondbest_matches.append(np.array([x,best_descriptor, best_sse]))
+        best_sse = np.inf
+    
+    return secondbest_matches
+
+def filter_matches(best_matches, secondbest_matches, r=.6):
+    filtered_matches = []
+    for x in range(len(best_matches)):
+        if(best_matches[x][2] < r*secondbest_matches[x][2]):
+            filtered_matches.append(best_matches[x])
+
+    return filtered_matches
+
+def findHomography(sample):
+    A = []
+    
+    for match in sample:
+        u = match[0]
+        v = match[1]
+        uP= match[2]
+        vP= match[3]
+        A.append([0,0,0,-u,-v,-1,vP*u,vP*v,vP])
+        A.append([u,v,1,0,0,0,-uP*u,-uP*v,-uP])
+
+    U,Sigma,Vt = np.linalg.svd(A)
+    H = Vt[-1]
+    H = np.reshape(H, (-1,3))
+    return(H)
+
+def RANSAC(number_of_iterations,temp1,n,r,d):
+    
+    H_best = np.array([[1,0,0],[0,1,0],[0,0,1]])
+    list_of_inliers = []
+    for i in range(number_of_iterations):
+        random.seed()
+        temp = temp1.copy()
+        # 1. Select a random sample of length n from the matches
+        
+        samples = []
+        for i in range(n):
+            idx = random.randint(0,len(temp)-1)
+            samples.append(temp.pop(idx))
+            
+      
+        # 2. Compute a homography based on these points using the methods given above
+
+        H = findHomography(samples)
+
+        # 3. Apply this homography to the remaining points that were not randomly selected
+        predicted = []
+        observed = []
+        for sample in samples:
+            pred = sample[0:2]
+            pred.append(1)
+            predicted.append(pred)
+            
+            obs = sample[2:]
+            obs.append(1)
+            observed.append(obs)
+            
+        predicted = np.asarray(predicted)
+        
+        predicted = (H @ predicted.T).T
+       
+        # 4. Compute the residual between observed and predicted feature locations  
+        inliers = []
+        for i in range(len(predicted)):
+            pred = predicted[i]
+            obs = observed[i]
+
+            #scale
+            pred[0]= pred[0]/pred[2]
+            pred[1]=pred[1]/pred[2]
+            
+            #readability
+            u = obs[0]
+            v = obs[1]
+            uP = pred[0]
+            vP = pred[1]
+            
+            #calc residual 
+            resid = np.sqrt((u-uP)**2+(v-vP)**2)
+        # 5. Flag predictions that lie within a predefined distance r from observations as inliers
+            if(resid < r):
+                inliers.append([u,v]) 
+                
+        # 6. If number of inliers is greater than the previous best
+        #    and greater than a minimum number of inliers d, 
+        #    7. update H_best
+        #    8. update list_of_inliers
+
+            if(len(inliers) > len(list_of_inliers) and len(inliers) > d):
+                list_of_inliers = inliers.copy()
+                H_best = H
+    
+
+    return H_best, list_of_inliers
+
+
diff --git a/final_working.ipynb b/final_working.ipynb
new file mode 100644
index 0000000..e05c6b2
--- /dev/null
+++ b/final_working.ipynb
@@ -0,0 +1,273 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Project 2: Image Stitcher\n",
+    "## Assigned: 02.01.2019\n",
+    "## Due Date: TBD (probably 02.20.2019)\n",
+    "\n",
+    "Panoramic photography is ubiquitous, with nearly every digital camera having a mode dedicated to doing it.  Here's an example from the Italian Alps:\n",
+    "<img src=\"pano.jpg\">\n",
+    "Note the extreme aspect ratio: much larger than the 4:3 or 3:2 that is typical of most cameras; suffice to say, the camera that stook this picture did not have a sensor that was this wide.  So how are these things made?  Stated simply, multiple images are taken, mutually identifiable points are located in each of these images, and the images are warped such that these points are coincident.  The matching stage might look like this:\n",
+    "<img src=\"office.jpeg\">\n",
+    "\n",
+    "For this project, you will code your own image stitcher from scratch.  Despite the conceptual simplicity of this operation, there are a surprising number of challenges that need to be addressed.  A general framework for a stitcher might look like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cornermatching_addi as cm\n",
+    "import skimage.transform as skt\n",
+    "import numpy as np\n",
+    "\n",
+    "class Stitcher(object):\n",
+    "    def __init__(self,image_1,image_2):\n",
+    "        self.images = [image_1,image_2]\n",
+    "        \n",
+    "    def find_keypoints(self):\n",
+    "        \n",
+    "        # Guass kernel for convolution\n",
+    "        g_kernal = cm.gauss_kernal(5,2)\n",
+    "        \n",
+    "        # Calculate the harris response of each convolution of I1, I2\n",
+    "        H1 = cm.harris_response(cm.convolve(self.images[0], g_kernal))\n",
+    "        H2 = cm.harris_response(cm.convolve(self.images[1], g_kernal))\n",
+    "        \n",
+    "        # Extract the keypoints from H1, H2 via non-maximal sup\n",
+    "        key_pts_I1 = cm.nonmaxsup(H1)\n",
+    "        key_pts_I2 = cm.nonmaxsup(H2)\n",
+    "        \n",
+    "        # Return the keypoints of I1, I2\n",
+    "        return key_pts_I1, key_pts_I2\n",
+    "    \n",
+    "    def generate_descriptors(self):\n",
+    "        \n",
+    "        # Get the keypoints to generate descriptors from\n",
+    "        key_pts_I1, key_pts_I2 = self.find_keypoints()\n",
+    "        \n",
+    "        # Get descriptors for I1, I2\n",
+    "        des_I1 = cm.descriptorExtractor(self.images[0], key_pts_I1)\n",
+    "        des_I2 = cm.descriptorExtractor(self.images[1], key_pts_I2)\n",
+    "        \n",
+    "        return des_I1, des_I2\n",
+    "        \"\"\"\n",
+    "        Step 2: After identifying relevant keypoints, we need to come up with a quantitative description of the \n",
+    "        neighborhood of that keypoint, so that we can match it to keypoints in other images.\n",
+    "        \"\"\"\n",
+    "        \n",
+    "    def match_keypoints(self):\n",
+    "        \n",
+    "        des_I1, des_I2 = self.generate_descriptors()\n",
+    "        \n",
+    "        best_matches = cm.get_best_matches(des_I1, des_I2)\n",
+    "        secondbest_matches = cm.get_secondbest_matches(des_I1, des_I2, best_matches)\n",
+    "        \n",
+    "        \n",
+    "        filtered_matches = cm.filter_matches(best_matches, secondbest_matches)\n",
+    "        \n",
+    "        return filtered_matches, des_I1\n",
+    "    \n",
+    "        \"\"\"\n",
+    "        Step 3: Compare keypoint descriptions between images, identify potential matches, and filter likely\n",
+    "        mismatches\n",
+    "        \"\"\"\n",
+    "        \n",
+    "    def find_homography(self):\n",
+    "        \n",
+    "        # Get the matches between the two images\n",
+    "        matches, des_I1 = self.match_keypoints()\n",
+    "        \n",
+    "        # Now get the coordinates from the matches for RANSAC\n",
+    "        match_coords = []\n",
+    "        for match in matches: # filtered_matches:\n",
+    "    \n",
+    "            match_I1_x = des_I1[match[0]][2]\n",
+    "\n",
+    "            match_I1_y = des_I1[match[0]][1]\n",
+    "    \n",
+    "            match_I2_x = match[1][2]\n",
+    "    \n",
+    "            match_I2_y = match[1][1]\n",
+    "    \n",
+    "            match_coords.append([match_I1_x,match_I1_y,match_I2_x,match_I2_y])\n",
+    "        \n",
+    "        \n",
+    "        x_offset = [0, 0]\n",
+    "        for match in match_coords:\n",
+    "            if(match[0] > x_offset[0]):\n",
+    "                x_offset = match\n",
+    "        \n",
+    "        # Get the x_offset val from x,y tuple\n",
+    "        x_offset = x_offset[0]\n",
+    "        \n",
+    "   \n",
+    "        # params needed for RANSAC\n",
+    "        \n",
+    "        num_iters = 1000\n",
+    "        r = 3\n",
+    "        d = 3\n",
+    "        n = 4\n",
+    "        \n",
+    "        H_best, list_of_inliers = cm.RANSAC(num_iters, match_coords, n, r, d)\n",
+    "        \n",
+    "        return H_best, x_offset\n",
+    "\n",
+    "        \n",
+    "        \"\"\"\n",
+    "        Step 4: Find a linear transformation (of various complexities) that maps pixels from the second image to \n",
+    "        pixels in the first image\n",
+    "        \"\"\"\n",
+    "      \n",
+    "    def stitch(self):\n",
+    "        \n",
+    "        H, x_offset = self.find_homography()\n",
+    "        \n",
+    "        print(H)\n",
+    "        \n",
+    "        proj_trans = skt.ProjectiveTransform(H)\n",
+    "        \n",
+    "        h = len(self.images[0])\n",
+    "        w = len(self.images[0][0])\n",
+    "        new_w = w+(w-x_offset)\n",
+    "        \n",
+    "        image_2_transformed = skt.warp(self.images[1],proj_trans)\n",
+    "        \n",
+    "        #Perform blending of images by taking max val of pixel\n",
+    "        new_image = (self.images[0] + image_2_transformed) / 2\n",
+    "        blended = np.zeros((h,w))\n",
+    "        \n",
+    "        start = 1\n",
+    "        for i in range(start, len(new_image)):\n",
+    "            for j in range(start, len(new_image[i])):\n",
+    "                blended[i][j] = np.average(new_image[i-1:i+1, j-1:j+1])\n",
+    "        \n",
+    "\n",
+    "        # Return the blended image\n",
+    "        return blended\n",
+    "        \n",
+    "        \"\"\"\n",
+    "        Step 5: Transform second image into local coordinate system of first image, and (perhaps) perform blending\n",
+    "        to avoid obvious seams between images.\n",
+    "        \"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will populate these functions over the next several weeks, a process that will involve delving into some of the most elementary operations in digital signal processing.  \n",
+    "\n",
+    "As a test case, apply your stitcher to at least four overlapping images that you've taken.  With a stitcher that works on two images, more images can be added by applying the method recursively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 8.31890317e-03 -1.68516913e-04 -9.78132102e-01]\n",
+      " [ 5.76808680e-04  8.13097590e-03 -2.07523702e-01]\n",
+      " [ 1.59996038e-06  1.03792556e-07  7.47172078e-03]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "#def stitchImages():\n",
+    "'''Returns the stiched images recursively'''\n",
+    "\n",
+    "images = [plt.imread('im1.jpg').mean(axis=2), plt.imread('im2.jpg').mean(axis=2), plt.imread('im3.jpg').mean(axis=2), plt.imread('im4.jpg').mean(axis=2)]\n",
+    "\n",
+    "#filtered_matches, best_matches = image_stitcher.match_keypoints()\n",
+    "image_stitcher = Stitcher(images[0], images[1])\n",
+    "new_image = image_stitcher.stitch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Image 1 and Image 2 before stitching\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stitched Image 1 + Image 2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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4tYu+gCVR0qhdOp0eMK02xZXFH98iEp0nTgJZafhDWfmsuqzyrH7oZ5YtwWVrSEJYZlRSvq6HbR0LcZM8Vxz7pZ9p0ISi6+J/q70/DGJZFPXKOTcHYGnveaod9Pca63fRn5ycjEJqVwiRktSQkCoUKp/veVsfS5qEpI1+FnfNz0ISME7qWOqGrkMjl54wBBAreRg04oUQUEsTRvI0ao9uv3NuH9NIao91r+EgNstBAyn/XwA/v3f98wD+nJ5/2PXhMQCbSfYKkMyJQ1IglMZ3LkFKhQhRu3OlLF0f/+IIyeqDTi91ZDKZgR0j5VkofWgsuF1sZOu6QsgSkjyMxFbd2jbRBnUayaXrCkknnT4OuB38bBhI4zr+E/SN+UPOuUsA/icAvwXg3znnPgLgBwA+uJf8L9F3G59H33X8X6dtSJxaEXdt3QPxDoIkycXuSa12hZDEKs9C7lCbQs6CNO1mtYWRmRGEZ7K1hLGIxkJqC7m4PF23xbmHCTWx1KQ4D6CkTZIyIUdGEqTxhv2TwKt3G2kjAB9NXTtBWmJIIhRLZMcRh/Uu5DgYlliS6gu1NSQF4yRVXBrt6g2FhPCzJNB2V8g2CiFw2vkRywVsAX+7NOUN6wkDRmgGX+AgkkUgrWcsDoHlfhikDSF76Jn2IHEd2ri1kD+pDkYsLW1CEIc8lvFuvdPPuf3czmHVn1C9ug1JKtmNEAowQsRyUMkC2Bw6qZ60iMNIrMtJQtg4NYwPNdWhIrqdwxI/12WpWHyfFsGSnmkISZs0hneoLOs+jeRJ8sClhZEiFos7pCEATpdEdEnEwrPQcZIjTqJYZevoVk6vJwflXkC8TKE+64hcBj2rHUKqkLqkn8XZDFaZoTSWZ87KY9Uf166kvsT1LQlGhlgs1UMgSe1KAzyASeVZruM0EiOkJoW8clpt4nLkPkmqplGxuD86rfZS6XfWtXWvIaSeaUhrt1iEp5/LdSjK2Gp/WmYMjBCxhBqdhNhJ6kgaicPlAeEZbEuaxKWxJjD53iLEuD4n9cHqCwdOpuG4Se/ibJQk1TatEyGtXWOpY28kjAyxAOmonCN/k/KmRUKN1LwXr5YKcYQQagNzdR3uHkdkVplxdel8wmX1JKEuL436ErIZrDSWuqWv48q1+maVHed4OEgkchKMFLEIhAaN1SS97Dd0PQyhCIQ8VXFlhtQynZbnQ5JUMT0WHJkcAq1ucbSy9rQllRV6n5QnjWTgcZB7jficJkmt4zpv1JAPwcgQS1rkBva7V3V6rfbElWOVoYkl1L5QW+OkgZYgmnhCfQl5sLSdY+XV62hCZcU5ADiNJdXTqk6cJ406GJIooXdJywrS4EUIRoZY4iDObRuH0PpalxUiCFbDQvUmlR+H+Pq9LlcbrCHi13VZaliSumpJmmElR5LHy4I4l+9BiSiuvjjVLi2MBLFYyBAX1xXKk0blSlLbgPh5FkuyWO/j+hdqnzUGw/ZFqzYcVybPksAisDi3/rCIqyEkZVjSxUmWJKK0+n4Qx8BIEItAaP2KXMcRQ9y7JBezzhuKOg61IW6NTJKKxf/MbRmB4qRXnOSRNJKeDyK1JFkIGLE08oXypvV8xbmnQ++TiCRNOQfxno0MsaQNfNSQhmPz+zjkknd6Q4Q07bEIJakdcWWEiJJn+zXiWv2KosjvJ2DZJ8MCtyuNjZMGktqSBrkP6g4fBkaGWIDhkTDNxz5IvBjv/B5Su7hsLkPXE7KRNBHEOSwYrEiAuP5pBnAzCEaXb0Eae0X3wXp/o4RyM2GkiEXgIPp92o8eR5AC+pgEqz1JNoyVfxjpw5Cknsp/CGn0xGSc7XEQSTFMniS1K678OJsoVFYcIQ7TBmBEiMWyD6w0SdcMIQdBHDfVNgu3La4uy7aJa1+ICOPqSStZtf2jN+sOpQ+Vlxa0xEoTgq+JLIS4IUI5iLOCn/1IesMsOIh0EdDIwQSSRJRcdhwCJz3XkiekZsURWihcxmqrVadVpnUfKk/anZRW21FpIA5hh3VFv5FSjWHkiCUNUsaJ/dCa+2EQJg6BpY44Tm9JspAbOK1kDE04xjESa74mrk7tfUurliVJbC5brg8yt5J2wVgSHFSyjMxhRsOqHlb6uCXAaesPqUa6DstI1URhtUNfczlxtkhI0iVJJg6F1++t9lhjkhYOKgXiJhoZ4vYeO0gbh4WRkywHhTQBlknqXBpCCSF9Uj0hzh5HkBZhCUHqckNEeOHCBZw/fx7dbhf33nuv2f9Qm/ldWumSBkSyxE1Eym8YSaIlYRp18kfOwBdIK0EY0niKdHkhbp5UT4izW/VrQgoRcYhQLIgjSq5HCKrX66HZbOLs2bMAgGPHjmF6etrMz8/0+IRmwRniJgqZMJhQLGTWkjGfz3snRb1e39eGtLaP9TyKhls9OVLEEgd6EEPvtb6tIfT8ICpInAoTanOSfs+7sBykbs1ZC4UCyuWyL1s8VXEcVSOxLvMgBrXc80/vs5zJZFCpVDA2NuY3z9vc3EShUNi3mpTLT+tRs/6HWUA4ksQyjK4c+nhJKhdfx9WX1u2apswk+8JC0qT64j66cw71eh25XA75fH7oo6zTQpxUsSD0zXq9Hra3t5HP51GtVgfc36VSCdvb2/v2KziI+ziNbWVBIlk55xacc0875846515wzv3y3vMZ59wXnXPn9v6n954759y/dM6dd8591zn3SJqGhJBMd5TVIa3qpOHG1rUuPzQzH1KtQvWLOmSpWhbCWOXpQEh+r9si1xIDJkiWy+VQKpVQLBaHCvgMjV3oXZxKbLVb+sY/2dHy2rVr2N3d9ci8trbm84XsHQ06Hd9bez0nQRoZ1AHwa1EUvQXAYwA+6px7C64fO3EXgL/auwcGj534RfSPnUgErdMK8ACHkCZJBUrS9S1gJI1TeSyIQ0Qrv9RjIbIeC92mkC0FXD+d4Nq1awCAQqEQ9OIN25c0zzRo20UQln/ZbBbZbBZHjhxBNpv1cW3FYjHo2o6rx7p/w7ZCivrbr17du952zr2I/s74N+3YiW63688Q4QMynesf/VAulwc6PIwEiXs+bDlpiCaOMIHBDxxSvVhKcBm6XEsy6TSVSgW5XA7Ly8v+BOaDQkh9SoK0cyqiJm5ubvqjCuv1OlqtFkqlkmk3Jc3PJH3jN8wb5pw7AeBhAM/iBo+dcLSLfqVSwczMjIl0ei2GtTO7pNf/cYNlPR92QtMiijhC0fdizPd6vYHI4LQqUoh4+f329jZarRba7TZWV1cxOzu7z/Vs5dOgEVVzd+3WtnaL0aA3Cel2u8jn85icnPSqZLFYhHMOi4uLiczCAk1Uoc1I0kBqV4BzbhzA/w3gv4uiaEs1KAIwFMuJouiTURSdjqLodKlUikUyXm/P+m1IhbB02jTqk7zTv9A7fhZXbogAeJ1JKB9Pglr2m5WH/1utFjKZDEqlEr74xS96z5juix5P3a+kOSZ9H1pyEcon6RuNBtbW1rC2tobl5WWsrq5ieXl56PMf3whIJVmcc3n0CeXfRFH0/+w9vuFjJ4x6zOthIInbhMplpAzpxElEZt3HSbjQJhJWX5iTcvt0Wt32Xq/nj/zL5/NYXl7G3NycT2txaF6rr20MnpvggEZ+L3nkOIu42DG+5w1JLOKIG/+0qt6NhMyk2UXfAfhXAF6Mouh/pVdy7MRvYf+xE//MOfdp9M+TTHXsRJx/3EJY7f/X7/k6ifDSBCDGqUEWx5V7bX9Y9cX1QZdr9T/k3s5kMmg0GqjX6x5JNjc38eY3v3kgHRMWE0ToGD/dZk1U+hkweDS3XMthtwcBTbDauyV1WW07KMGkkSxnAPwcgO85557fe/Y/4g04dgKI5/wH6WAcZ09Tr0bSEAHE3d+oRNLEqJ0EoXwAvM3S6/WwsLCAmZmZfSsumeh07BszJC1dGOmF2HSZ8uM65Zmoa3HfNeQh5FOMgfg9ySwJbKm1SZDGG/YfAIS+9k09dmIvfxDJZGDTeECSpAJDkjSIQ+gbgTgbYBgnhiVZBJnGxsZQq9UwMzOD48ePY25uzmQeSeMjeTSSAvZCLCYo+bFzhr+lRtwkp0NIIie1/0ZhZGbwLSM3bgItBGltjKQwh06ng0KhkChxhgGrTos5xH1cC8ktVVXsobe//e34xje+gaNHj+Lo0aMYGxtLtYlEkn3ACGt5wOTH31VLJf7xeyk3pN7psWDJZY2Vbq81jmlgJIil2+1ifX19YHC447fffjsAm6NYAzIM19Qg5WgDM2SzaEireoWkChB/UrNVTyaTMdfYC5w5cwazs7P7zo/UcFBESuLaIZtM2it91U4WS0PgMrUE04SonQsH6RvDSBBLr9fzk5JRFKHVaqHRaAAAcrkc1tbWcNttt+HQoUOpOW4cxO0kI8DEklaNswjgIO0MSQvLayV7J8fZM9lsFq1WC5VKJVU/4rxsDJaEsY6PsCSmtoUsRmkxRd0mLcEsCcTfUlRBLdXSwEgQS6fTwebm5sCgFgoFP1k3Pj6OQqGAWq2GfD4fHMRhIWSrANjHqdPaMRbERRzrOji6NiQFNOKwoWxxbecc2u12kIi1ZzHOPkhCfpFeAAbaw+PHLnNdNo+XZg6h+zi1VZcZKisNjASxlMtlnDp1yt9bK/xkcC0DniFJ5KaNItaSxQLLuE4iXksFk3LkOkQkPAZWqAzXL14qAWv/Zqttw0CSbcPfkRE6l8vBueunFfBO/wdhfrodTPys5jFYUjQJRoJYgP1xUhzHxAgl9/yvr+PqSAvyIePgoFLNki6hawshk1RRllChOq18obYmtYXfWYgqBJPNZvG3f/u3OHz4sP++1WoV9Xodx44dM22bkGfMIhDrvZaaw/Rdw8gQi4Uw/CwJ0ZOM0zT5mfNaumycGpbUbsuuSdMPrcbIMyGIOGQCbC9jWuKz8oQQOg4xhWAkimBjYwO9Xg/lchndbhdra2s4duyYLy8kYeMIJNQ3Lku3Z1gYGWIRiLMFLF05lE8gpCZZdg+XIZLFMiatPCEVJ2SfhOpl0ASs25IEVt1p83I+S8ULtVsb/VrN/M53voOjR49iZmYGm5ub2NnZ8Xk18evvHCKEJALivMI8DkIwI0UscYMfR0ScTkPaZboaOp2OrycOqZP09lCbQzFhVn1xCMEIxlyZy8tkMsjlcqYXMKn92jNmecr0tdXvKIrQbrfR6XSwvLyMxcVFHD9+HNlsdsB2SfOducyQRLP6Z0m8YWBkiGVY+yOUV0NoQJLqiNvCVZdhEXOS4R+SjEn1hLxRXK7ludMnpSURCNcVF0oSym+1r9fr4dSpU35sisUipqen/VqV0KStHos4VdBqQ1z/hoGRIRaBNKpMkkdMIA3nCCFPnDcsSZ1JWrrL6ZL07pC0CKXnZ5xH1oekyQtcR7ZLly4N9EnarO+1micSjZG23W6jVqthbGwM+XwezWYTKysrOH78uK8z6Ztp4o1Tu+LGLCm/BSNBLGn0fb5PI3mSJh6TytLzLGk4v66X34fUuaR2aA6p1Tcrv/aCxS091vXIe3E9N5tNtNvtgTVEnN6SOnrhl0i7druNU6dOodlsYmNjA51OB81mE0tLS2i1WsH+a1XL2rSCJzLz+Tzm5+fNMdTlDQMjQSwWDNsRgTTzHqGy+bneHFz+03KsuPdxruM0ZVplhewfSZMEGvnkvtvt4vLly6aKI0vAs9ms3z1G5lBkLT0DTzRLNIFIcL1qUvdR6uR/ljLMTCSqQTOWJO9aEowcsaRVMYYtR55ZOr6VNuQ6ln8rT9y6mLRSMY0UtFQ5650gmXbNxtWn9Xlx946Pj2NjY2MgnazCZK4ueeMIlKWUlM8OCI5hs6QiIz8vGNPlamZhMbofOTUMODhRMAzr3Yjj7nFu2htB/DhuFmcHpSnbgnw+j3w+j0ajMSAZk6Qrc2ohiE6ng+3t7X32iqTXKyWlvww3Y2NvC0qlkv9fWLi+UNfSBg4KI0MsGm6EeIaVPBYxxEXxWsBSJa5s/TytLZRk04TS5HI5TE9P71tDz+2Ok6KSr9froVAooFKp+ABOfaqzEI+MnayElJ9z/d16eGdM4PoqxyjqH+nHzhVWt4RgBdrtNorFIgD4domEuRHVNgQjSywW97Oe6bUUacpMM3Ac7qKRdhiitRDnbNhFAAAgAElEQVR+GA6vn6WdjAWuq2HZbNYjlSV9rWfaGyjILbaIlMttYrVKZugttY4JgNfpS5k63F6WIPR6PbTbbW/3dDoddDodT4iiyoW+keUs+JG2WYDhvF7DlJf0nO+ZgyWVM4z6F9cny/i0nukg06QyBckt6WKpJ5YN1Ol0EEURJiYmzBAaaVOn0xkIg5dnOuyebQ0haiYMBs4v1yJldFt5Yz5rbA7iBRMYKWIZpmMaQYedpU+yJ0KTkiHJpnVjixCT0liTiVZ7046TVqXSSEVr0pMRmBFSH1Qr28WKm5gN9na7PVCPEIa8EyKQe3ElS1m9Xs+7sbkMABgfH0e32w0yBGtMDjIxOVLEouFmSJZh7Re512tDrHRxcyo3o51pZ6WtvILgPBmZxgay6uf+81oTvawgl8vtc9tqbxUjp/aIcbCluJmBvm0i+UXVKhaL2NjY2CepxCFhrV+5USkz0sRyo3AjRh7HhqXNm8Th9X1S2rj2J7VLEI1dqkn5rPbzIjQuQ+wGyWfZMFJ/Npv1+yzrCU6RVCwZuGy2RYVYWLXUk6PynPt4I6oXQ5pd9EvOua87577j+rvo/897z0865551/d3y/61zrrD3vLh3f37v/Yk0DRkGsUM2QpxITctVWUe3OLLmXJxGc+GQSjVMe6z2JREKj4+1PDrpJyBEIM9zuRyKxeLAWiPem1okQ6FQ8M/5qAtBcmsOhMdTt1/sGm6fNacjBGtNTFvjP6wWkMYybQL4sSiKHgTwEICfdM49BuC3AfyLKIruBLAO4CN76T8CYH3v+b/YS5cK0nQgFMZyEDXDql/AMvC17h9HGKF0VrvTtOug/RNkYmRLKkcQlhHROYdWq+XTi8oFXI87Y88WExDP5msJpetnm4SJRFzSIunEjpFvwqH3Ifs1TdhPHCQSS9SH2t5tfu8XAfgxAH+69/xTAP7x3vVTe/fYe/9udzNk4B7wJgNxruA0yBGHNHqCjQeavUt8zc/kA+qf6Nysf0tebq+eidY/LlPr6GwzsAt2GMkiCCrXIlk6nQ7y+fw+ri6qFBON9FFLDz3e3H5Rv3hchBCkXuecn/Xn8ni8+Jn89JzUsJIl7V7HWQDPAbgTwO8BeAXARhRFwn5lp3yAdtGPoqjjnNsEMAtgRZXpd9EfGxtLbANPniW5aodVazQIFxP9Wn9Y3QZGbEFO/eGs9HJtzXpb94KA4jZtt9uoVCp+s2+NjELwMt+RdpwkL+85LP2Retm2sIhcMzJNSCJxdFt47oXz5nI5tNttFAqFgfZYNo2WIJrotcRMC6mIJYqiLoCHnHNTAP4MwD2pawiX+UkAnwSAQ4cORUmNFm4ZN/l4M4jEub4LtNFoeDUgyeulg/n4uUbgkNqj73VfGQHFFmCDlw1dnkmPosh7mpI4KSOX5sTAdW4vEiaUP/SNtCRij5kQJRvt8l7C+bX9pZ0wekuoJEibTmCoYKooijYAPA3gcQBTzjkhNt4p3++iv/d+EsDqMPX8fYEQSr1ex8rKyj7OJMAzzloKcFkhFYDzW+nTtlVUES35tKrKbt5QWaG2W8YyS3gdLm9tx6RVR61mSlpuI6ulWqXWamdIjUzDGIZhsGm8YYf3JAqcc2UAPw7gRfSJ5mf2kv08BnfR//m9658B8NfRMOSbAtIg1kGljOjJ9Xp9AKEFAfUmbdobo9vHXZf8IUILffA4RNBSTRvXbs/WCJUXqkP6wwjNrl2553GTOthGiutb6FwabQNKvmKxiEKhMBClrFU5/cz6xgeFNGrYHIBPub7dkgHw76Io+pxz7iyATzvn/hcA30b/WArs/f+Rc+48gDUAH7rhVhJYnC4NDJPOub7nR5DZ2tOKCSEkgeSZ5pr8Tt9bEQFcDxOmda0n50RaclusNlv9YWbB3iYJotSb5cWpoXpOxgKWIuyckHqZ0Uj5bORz3db43iik2UX/u+gfjaefXwDwqPG8AeADN6V1bxAkEY5wT1aXtDoBDKo4/GH1e7m2JEPovW6rVuP0s1A/BLFFrbHGwiJ63RYmYplAlHTaNtNtY2lkEZI8ZxWS80gApYyztFkb8rr+m004IzmDnyQ9kpD9RtUziXtaX19HpVJBt9v1x0xnMhlUq1WMj4/7D8XGKXB9IlD6IffaS6b1cUnDefm9RjitgmiVTwzgVqs14EVKMw5SX5KDQ+rl0Bo9GSkg6ZgR8TvpK0c18/tsNotGozFACHzN5Wk1kMs5iL0CjCixjAq89NJLftZa4pO2t7dRKpUwPz/vl9OOjY15Fy6fNc/A6yyYI2tViyH0MZmzCoJqD5TW+S1ODqQLBbGQS4hDytDS1aoz1E8mFCYo55yffOQoYz4xjNVAVs8s5qLtwziV0IKRJ5abYZgNW4Ygwfr6OhqNho+CFbfp7u4uFhcX/SblExMTmJqa8kQzOzuLarWKUqmEXC6376MBgx9Wu0rlvZ6DYTfrlStXcP78edx///0DmzOwChNFEfThtmnHQ5CJ3bEy3yFzIfKcJSXPuks5eq4GGAzD4TGRtvM/AO+ujqLIz7lY9he3OUQsLIXTrIMSGDliSZpwTANaB9fv0nBS8bbk83kUi0X0er19BCMcTg4IErfz4uKinx+Yn59HsVjE4cOHAQyevCzAXiYmGEknHzebzeL8+fO4cuUKlpeX/fmQLK14/LLZLL7yla9gbm5uHxJz+da4hcZOSxS2NfjaOiGMT/5iW0gTh56AZu+aSEyLEIRBsENDq6ZcZhRF/qiTNDByxDIqIAh+7do15PN5AP313YKMsudVt9vF4uIilpaWUC6XPeJK2gsXLiCXy6FcLmNiYsKfwHXbbbcNGN3yYZeWlvD6668PqFHdbhedTge7u7vY2NjAxsYG6vU6arUa6vW6b7M2qjudDh5//HE8//zzmJqaGpjwG8buY2LluoR7S5wYB5+2220/GSog9YstpT2EWoWTe1lQJiCRBZZEYXUtZBcJQZ07d24o5jzyxMIGrtZNLa8RQ5pnIaQRsS8SQzxKUdQP4mu325ienkatVvPuy2azORDoJ+nr9Tp2d3c9wq2vr+N73/seHn/8cUxNTaFer2NjYwOrq6vY3t5GuVxGp9NBpVJBrVbzRNTr9bCxseEPVAX6gYcW9wTgJd/c3JyvP9RfbTSzamh5sDjURUcaaHVMOzGESYiUkrgy7QaWhWD5fN4Th5RlSSlpV9xclhDU0tJS8AjxEIw8sQCDBiBwcDtmmHwSYrG+vj4QI9Zut9FoNJDP57Gzs4Px8XH/kfV2PuIYED1fEHZiYgLVahWvvvoqms0marWaV9MajQaazSbq9TrW1tYwNjbmtyDa3t5GFEXY3t72QZnsJGDgGXIhPmv8hpE0jOxsuwDXHRiSlglFmEQoHMVqg3gkhSCLxSK63a4PppQYNS5DJIleqszvhfF961vfQrfbRaVSScCE6zASxKJ1bXkGpP+YmmumsVfiyiwWi9jZ2cG1a9dQq9X8h2s2m3DO+d1EJLhPdO18Po9SqTTA+YRo6vU6ms2m35Exk8lgamoKuVwOm5ubPh4NAHZ3d1Gr1bC9vY1CoYCtrS1/PINzDuPj44iiyG9xBOx3nbIaIyqiRlSL0Hhs2IbgXVvYzhJ3L++IUygUvHRmW4aZCc+jyL+0R5YVsx3SaDQGpD1/T54o5bg1rXo1Gg18+9vf9lqAXu4cByNBLBboD5oEN8NrJpDJ9BcwLS0t4cUXX9znxRFuV61W0Wq1sLOzg9nZWQDwx/mNjY35PKI2SZ8qlYr/sKurq5ienvZRzkCfq+ZyOZ+v2+2iUCig0Wjg8OHDqNVqqNVquHTpEhYWFsy+W8wnjWTWKpMGRkR2XQODBMBGuja+tVTRhCSSqNvteokSRZGfK7LCaSyVi59JnrW1Naytrflx+I/CdTws8qfxcqUF+XDtdhsrKyv7Vk2Wy2W0Wi0sLS2hWCyiWCzi0qVLOHz4MMrlMk6ePIlWq4UrV66gXC5jcXER7XYbR44cQaFQGHB7Tk5Ootls+nmaWq2GdruNUqmE8fFx1Go1VKtVVKtVZLNZLC4uYnd3F1evXh1QReKkA6tFFljSJomwWPJb0ppDUfSMvHYYaLuBPVq84Ewki/Yoim1oqV18HUWR32NZB3KmgZEllpD6lfTxOH9S3iQEeu9734urV696d7G0qVQqoVqtejuk0Wjg3Llz3k45fvw4er0eKpUK6vW6n+2Xf1YhGo0GdnZ2sLm5iV6vh6mpKczNzWFlZQVra2toNBqoVqsA+lJLJkMffPBBbG5u4tChQ77N2g0r16Ki8KSonl/QhMWgEV3bJCwV9Dsea+uaVTTOx+tetH1izT9J+9lesfqwvr4+4BX8T1Ky3Ow6xB54+OGH/cRko9FApVJBNpv1k32lUgnb29uYm5vDxsYGcrkc6vU6nHOo1+vIZrOYm5vD+Pg4SqUSFhcXUSgU4JzDtWvXcPjwYdTrdb8fl4TSAMDKygqy2Sy+9KUvYXl5GbOzszhy5IhHhmazOWCLcFiN1VdGZPZqJY1LiAOHVDZtb+q5I7Z92NvG/1Z/tKNAyrbaZYG43aUOUQHTwkgQi9W5g6pVafTxJBAuNDY2hmvXrqHT6WBnZwelUgm1Ws2rSPKhp6enUa1WcfjwYb+Nz+TkpJ8jAfq6fqlUwvT0NF566SU8+uijuP/++/1exAD8//j4uOeou7u7eN/73od2u42LFy/ixRdfxKVLlzA9PY1jx455Q5/bLdfCmcUA1wSj84TGjK/Z4Ndr8Fli6XK1/SDAUkmkH7+TMKNer7fPhWxFPrNU0eqhqGChpQFJMBLEMoogH61YLCKfz6NcLqPRaAAAJiYmMDExgVKp5JGxVCqhXC5741zcnDIhOTMzg2KxiLm5OTzyyCPY2NhAt9vF7Owsdnd3sb29jU6ng1wuh7W1Na8+tVotP8E5OzuLY8eOoVAo4OTJk/jyl7+M22+/HQBMJJR+8ByG5Ta2CIaRnzk+z6Tr8bJUQD3fEZobCUkDDtAUW48nQLmvcSqVuIyt+Li08CNPLMPaIWmA9fNcLufnVEqlkudmMnvunMPk5ORAeEen0xkgJiG2Xq+HF154wV/n83kfbiFIkMvlfD2lUsnHl126dAkXLlxAuVzGbbfdhq9+9au4cuUKVldXg0iiXavawObx0Bydr9m5IeXK7DwHcjp3/Wx7tmVC4SesCmqiFCgWiz4fexU1gen5Jv2ta7WaJxaWQsNImZEglpttnySpW2lchsIVJR4sm836+ZZareZVgkKh4AlpenoahUIBhUIB29vb6PV6Pq6sVqthd3cXzjk0m020Wi1MTU15bilr6rPZLDY3NwHAqw2XL1/2at7Ozg4uXbrk49Di1CiNmLrPbFAzIWh1jkNLZPzkpw17til4HoZBEFTP/LPrWAgtdDirpVJa31Xut7a2zH0DrGchGAliuZkwrO88BL1ez4epdDodbG1t+Y9SLpc9UWSz/d0Wx8bGUC6XvQ3SarWwvr7u5way2SwqlQomJyfhnMPExARarZZ3jYpEkrUna2trOHv2LIrFIi5evIjbb78dV69exdmzZz2HnZiYGJAWGsQFy1sIWWl4zHRQpJ4v4X89ix9F14+M0MdGiDEv80bsmZM0lheLPWLaNcwEE4pkkLTideRnSfaahpEhFjb0gGRDPZQmbr4hLk0Ims0mxsfHceTIEe8REzukXC57L5fEONXrdRSLRZTLZVSrVTSbTRQKBRSLRWxubmJlZQVjY2PY3d1FsVjE5OSkd3cuLS1hcXERzWYTy8vLiKL+RNwrr7yCy5cvY3JyEsePH8fs7Cza7Ta2t7dRLBY957XAGlNBKsuFLO+FWPhfe78kr47xAjBwzWXGfQtuE0sXZghCwHqdDPfVwot2u+2JRTxt4lBJCyNDLMOApdvGwUE9a0IYEt8l4fqZTAaVSsWrWI1GA+Vy2c84izolH7Fer6NUKuG2227D+vo6SqUSdnd3sbW15eO95ubmUK1Wsb6+jlqthttvvx3OOTz99NN4y1vego2NDWxubuJ973sfJiYmcPHiRbTbbdx1112xxM+cP+TmjRsfSw2z0gjBymQuSyXtVIiThuw2ZmnD0kq3hx0KVv9E1W02m/45e/LSwkgSS9IHFEhLBAchFJmIFE+Xc9dn7qvV6kDdU1NT6Ha72NzcRKvVQrPZ9BOU1WoV+Xwem5ub2NzcxOXLl9FqtdDpdHDnnXfi6NGjuHTpEl566SVUq1XMzs5ibW0NX//611Gv1zEzM4NXX30VDzzwAH7yJ38Sd999NzY3N1GpVLC9ve1VrLh+aJWFxySOSIToBTkZ+Xm+htUrvaE3E4teUszeMiAcsqLjtyQUKM6zxjYYR4Nr6fojN88C7D+O2iKYYQjjoNJEoNfr+aDJ3d1df3a72CuyenJ9fR31et0fU729vY2pqSnMzMyg3W7j6tWrmJiYwPr6OhYWFnDvvfeiXq9jfX0dX//613HhwgW8/vrriKIIly9fxt13342lpSVcuXIFzjmcPHkSH/jAB3D06FFkMhnU63Xk83lcvnwZjUbDz7NodYpVEz3xx8ioiUin4aXJbHCzQc5c3QpdkZ9WF1mdE2TWczbykzNc+Puw0yLkTYui/gIvlirSFu1KToLUxOL6WyF9E8DlKIqecM6dBPBp9LdmfQ7Az0VR1HLOFQH8IYD/DP3N9X42iqLXUrcI6SWLBVqvjquD01vQbDaxurqKQ4cOeSNxamoKa2tr2N3d9cRTKpUwNjaG1dVVtNttXLt2Da+99hpmZ2fx+OOP48iRI3jxxRe92ziTyeDll19Go9HwLuitrS386q/+Kk6cOIFPfOITuO2221CtVpHL5TA/Pw/n+jP+4+PjKBQKWFhYwMWLF/3cj9UX7bXSHBvYf4gqMLixhkgXeQ7047Vky1hdF89/yHNNuKIaWvXKUm1uM9tNQjzaHuJ/3UeJwNDvhyEUYDjJ8svob65X3buXXfQ/7Zz73wF8BMDv7/2vR1F0p3PuQ3vpfjau4DRGuaRLSwSh8jWRWOnlw8jirGKx6ENQxMUrZYihyBvDbW9ve9Vle3sbuVwOJ0+exOHDhz3yvPnNb/ZnlkxNTaHX62F2dhYXL17EU0895es4c+aM95pNTU35jbmPHj2Kra0tL9WsvsZJaM3xQ25mUUc56FF7t/RaFStoUo8/z73Ic72FlKhtQhzsUNDfMvStRSrVarV9NlQulxtgNkmQdmPw4wD+CwD/HMCvun5tPwbgn+4l+RSA30CfWJ7auwb6u+j/rnPORUO4oLTOGUcgB7Fb9AfUIB8ln8/jkUceGdDZBQSh9L9zDocOHRrgdtpWkHea22cyGRw5cgSnTp0C0EeKVqvlVZBOp4NarYadnR0fwHnffff5mX+rz6F28DjoMZRneqtVscO0Qc9qDW/QocFyDes2iX2iJx2FeGSc2PHAhG3hQrfb9XYLg0R3p4W0kuV/A/DfA5jYu5/FD2kXfctmSSKeUH5Ok0RgIW+OLkNzZv0fIlLW3zWHFKLMZDJe1QOA2dnZgbASy2Vq1e9cf9JTljpbfbLaz140QVbnnF/sZo2ZLk/P0bDKZZXB3ju2WaR8ed9oNPa1zxpf8aJpQpHvO8ykZJq9jp8AsBRF0XOpS00BURR9Moqi01EUnZawkBR59v3iIA1xHQSGLS+k7oWI1vIICVjIZ40H2ysiIfhfVEa2S9hOkR97jKwYLMtg11KK94cWYFWW72UpBP+ESEXa6OXEeqylXVqVY+KUuSGZNE4DaSTLGQBPOufeB6CEvs3yO9jbRX9Puli76F9yN3kX/ZuN9AcFS20BDkZEB0ln1a2fM9IUi0X/TNswVh6xR4RYBMllwzspz2onc3j2aAnRaVuFTzVmA57VMCnPklz6Xo8Vn3ws5fCGGcNAmpO//ocoio5HUXQC/U2+/zqKov8SN3kXfS2SmVPyv8V146RMSBINYUJ5YJUgDcRJQav+UD/StlX3zyIykSLlchkvvfSSPyNSZuDFoLdCXQTBOJIYGNxRRaJ72RaS9OyqFVc7lydbHnG5PC+zs7ODra2tffMzrM5qSSv2nXYzS9DqTVXDYuBj6Bv759G3SXgX/dm9578K4ONpCrMQUXM/Vi3SgHwsy2tyUGD1IcTVGVFCbU3TB4sp6OehPIwwep+ubDaLr3/961hbW8OnPvUpj8RaLdO2gwDfa6Lh5yJJdBpRj4Dr0dC80Ev2IxDCa7fbPmxoe3sbV69e9WnjxlXKYY8XS89eb/8u/HEw1KRkFEXPAHhm7/oN30Vfi1ZLP01CyhBCW+UzyIfXZbPuq9WEYcoP2SsaksrmdCFnAqs5fFLY8vIyjh49imeeeQbvfe97B9zBjNw8/joyWPdP0rNUkPr4nbVpuJZQTETlchnf/OY3kclkcM8995gRANZ4yhyLgDADcRv/yIe7DAPD2AkhxAuBzGkwWB4cXWZS+XH2zrDeP36v7YXNzU00m80BtUqQ8t5778UDDzyAEydOwDmHV1991a+zEaJgVYz/ZRxCqrMQil4/Is+A65ydN5sQKcJ7FDDhLCwsBD2nzBi4HVKebuvu7q6fIE4LI0cslksxTt2JkyghgzAtwQixcHp2Yx5EmvH7NBInzoBnlcs5ty+kw5q5zmQyOHv2LB577DHMzs5iYqI/G1AqlXDhwgVfNksqtju4/wxsxyTZXUwwLM3EZtGhK2Jr5XI5LC8v+w0JGdiTJ23QSwWk7b1ef8n4MBOSwI3ZLG8YxBnSSXYCi3t+NgznFxjGW5LkaAj1YZh0fK/HRxu5jGR8mOzKygomJydRq9UwMTHh001MTKBSqcQSt54o1P1mqcKEpceFHQfSZnmvY9GkPbKzjbRd7BFdNtetJyJ16E5ovigEIydZhgWLENKoW2mki3yMOAmSxoCP47RxzgdGoiQnhSAXq2ISmVyr1VAsFr33qVgsYmJiYqD9URT5XTh1n0QVks04QpOP+vwUzdVlEpAJitMD19fKFwoFf728vOyl5NWrV7GwsODbosuSuoRYLBWsUCh4d/LNnmf5ewGL4pOMXH52o4QCYCAoUOfRoR66nVoCpjXmh2mf7r8ghqigtVrNh8vs7u5ibW0NnU7H7+DPZTjnMDY25vPIO36v3bryzx6sbDbr1UFRg1hdBOCXKEh+rVICfcO81+v57aWAvp0xNzeHubk5P+cj9VjhMc1mc8C4lz4Iw9DfKQlGllgs3Vh052GRSqfVkiek2g3jg9d1JbVBIBT2ESonqb9Sh8x6S3i67J1cKpWwtbWF733ve9jc3PTLnHu9nicURnC9BsXaT1iWQ0dR5AlBJv6YKKRt8kykilajeIsjIZSZmRnMzMz4NokkFaKRdoqKJ3sUiF0i35yXEvDGiWlgZInFAiYUIMyJk9SwtAPEH1KXOYyuazkXtKdLS6k471iobC43k8n4yOStrS3s7u5ifX0dMzMz/iiLixcvYmNjw1StQraIICITkxzBwbFW4t2Sa5YyQjD8nn/ilOBAyRCj0WMg90J0VgCljPWwBv5IE4uFkHqzgxDSDuO+ZeCPYu2wLu8tyac/mqWOSTommm63f9irgJam8m95qWTpgFZjoijC66+/jrGxMZRKJczNzeEd73gHXn75ZWxubuLVV1/F5OQk3vrWtw54pdhdq+0JIQBZFKe9TsLZxdYQCSF5ee5K7BvdP2D/7i/cBp5b0R5K6bu4i2VCkvNKOw6y0d5IEwsw6PUJ+fbTqmI6n0Aovz4oKK2hb0mIuHwyH5CUR9eTy+UGzhdhKehcf6nA/Pw8tra2UK1W8e1vfxv1eh1vetOb/IIzHedlSRAmiiiKBtb0CFIC1zepEJtFe8OEmJxz3iWvx5kJlDe94Jn/EINkY593suS2cLu1Jy0JRppYQu5jYBBxQh4j695S2XSZWnpYZVrt0XWGiEjbTiwpxROkVUz539nZwdjY2EBZrBKxV2xxcRFjY2NotVqYnZ3F66+/7k9a3t3dxdmzZwfKsMZbEJTv2U5gdUreawTl8dGuZe1mln5IPXLPrnE+5kLbVNLe7e1tbG1todfrDUQtyESp9qClgZEmlhAcxMAfVvrwx7LeSZmaY8VJmVA5jDh8DwxuXwpc3w+ZZ8ZZfVlaWsLs7Cyy2Sy2trZw7do1zM7O4qWXXvK7yly+fBmVSgVRFA2c4qXVKZnzEGCuLW1hzs9GvgCH92uCYaYhxrae29IRAFI/Rw5LZLFzDouLi/7YDimLpbYQDDsD0sJIEwt3xlr0pIG5MN9bXi99b7l3tV5tlSnAoRtJoJGckf21117DwsKCR1S+B4DXXnsNJ06cGJA+XOaRI0cG+iQRxrzXWS6Xw8WLF/1mFyKN2DNnOTOEmAS5OdyeDfjQGMk4sTQVwpL6WQUEriM6q3Jcvhzs1Gq1sLKysq++OIib/LZgZGbwWTcWkI/H8UhJIBwrjkjSgl57EWqnld768TsuDxicA+B5i52dnQFkla2WrPASVlUE+WQjvmeffRZPP/00jh8/jhMnTuDMmTN44YUXguvZxUPGNgQvb2bgZ9pjycgvP4kBE+Ob1Tl9MjF/R+mfMJKNjQ0vRSz7iNU8DcOqYMAISpaQe5AhrQcqjetY2wQMLFkOSnBcB7dRnguXlfJPnDjhJZRzDm95y1t82lwuh1OnTg1sF2SNASP4Cy+8gHvuuQdTU1N4/PHH8aUvfQkPPvggvvWtb6HZbOLpp5/Gk08+ibW1NfzxH/8xXnnlFb+rza/92q/5sgXBpV3s3mW1igkWwICapu1A/R31ikyWMBYj3dzcNM+PtMZY27ihsYuDkSOWJDGu1QWBJJskpPpYaXS5IYKyiNJyKXN6TczauGZvD6/5kDS1Wg2Tk5P77Catnso6Edk+6Tvf+Q5ee+01rKys4BOf+AQWFxextLSElZUVnD17FkePHsU3vvENAMA73vEOXLhwAb/927/tZ8tPnz6Nidq6YKQAACAASURBVIkJP9En0cyyQ430T2wdVnHkWt5ZTgB5xt41fq9tHj4NjAlQS8gQkYS+dxyMHLEAdliLFsWaYEKGtMVptKTQ+eTemguw0lsQR9C6DZrbsvqp1Qg5Mk/KYY7NjKbX6/mzLn/lV34F29vbOH36NB566CEcOnTI75eczWYxOzuLqakpvOlNb8Lly5f9KWPvfve7/ZEYnU7Hp1tZWRkIfdeIL4jM48Qz51paaG8bn4QszzKZjLdvtNonabiMg0iOJBg5YrGQXt/HhYeE3MchiHvPH1W7lLVNFFeOZTvp6+XlZc91rfB/a0MJfYY7S6Uo6sdGffSjH8Xm5qbfxgjoE1ypVMLExIQ/3kIOh/3+97+PyclJTE9P495778Xu7q7fm2xzcxPO9bezXV1d9dHMbPgLEjebTfzd3/2dP5kZ6NtBy8vLePTRRwdsCgAD3jB2GGhC0kuPhai0ipeGoQ3rIR05YknLtRl4JSA/07oqp7FUK83x2QVqEUScdOJ2xgWFSjSv2Ca6D1KONWeh1QpxM0vfx8bGkM1mcd9992FhYSF2DY5zDj/3cz+H++67D6+88opHUjkaQw6CFUkzPj7uDexWq4XNzU3flu3tbSwvL6NUKnmiLRaL2N7eRqVSwXPPPYd77rkHY2Nj+6Si9FtULIkIkP5pB4A15qze3kwYOW8Y34c8GQJ6Rp+NuaQJTQs0IukgP10HQ1xbNcfjeliH57LZSJd0vKGE7Oqv82hi29nZwQ9+8ANsbW3tQzJOK3195JFHcMcdd/ijADOZjD/lK5PJYHp6Gs71N0mfmprC1NQUyuUyZmdnvWST9SYSK8bRyL1eD+Pj47h48aKPiJZxZS8ej51lrMs7nVaPh/4m7CkN2cUhGCnJYqk1cZ0JeVZC+UKespAapZFLOx5CZVkOCksaWsao7rv1sS3Q9YnK8vzzz+PUqVO4du0aVldXMTEx4Tftk/rYWO52u7j99tuxsLCA559/3hOtSJIoijAzM4OtrS1MT097hN3d3cWhQ4ewsbHhz6sHri9zyGazmJiY8O7vSqWCixcv4tixYwObRojxzvYPt5XHQAhJzpjkseTvdbNgpIhFII0RbkHI6E87gPpjMNeT5/raIuo4VS/U1xsFMYx1va+88grK5TLuvvtu703jTRz0nA2v4Zmfn8crr7zi6yiVSn7uZ2xsDBsbG3557uTkJHq9/jnzGxsbA0t8S6WSVxG1Ovnyyy/jvvvuG3AxW3NIPP8k/ZTNGWX+R9uTlufxRiCVGuace8059z3n3PPOuW/uPZtxzn3ROXdu739677lzzv1L59x559x3nXOPDNMgjYBpO6nDF3iQpSz+D9VrqSacxmonq0ysjqWpU0BUkLi0GoEsqcPvOp0Opqen8dWvfhXj4+OYn5/H/Pw8jh07hrm5OUxNTaFSqfiVgxyVu7Ozg263i5mZGb8eRurodrt+slROFmi325iamsIDDzzggymlrWKDdLtdf/CThKj0ej1cvnx5YHKSx08cG7LHl3POr7mRw6Vk1aTsf2Y5BqzrYWEYm+VdURQ9FEXR6b37jwP4qyiK7gLwV7i+P9h7Ady19/tF9DcLT4SQfmk918BuVg0cVzUsiIcnTXusOuLazpN3gtgSravVMqsMy2bS8xPCaRcXF/Hss8/63f3lfMtDhw7h8OHDmJqawvT0NEqlEsbHx5HNZjE+Po5MJuPDZ0Q9iqJ+1HGz2fT2TLvdxvT0NHZ2drCxsYH3vOc9/pBZaYfklf9ms4l6vY5er78aUmwyQX793Ti/Dr/JZDIoFosoFov+hGfZqUa7qW9EutyIGvYUgH+4d/0p9PcT+9je8z+M+l/za865KefcXBRFV+MKi0O20HwKxxNpA5FdqGltIEsN0xLEskMsCWhJKg0sBXluwVLpktrMk4HS93a7jT/5kz9BuVzGV77yFSwsLKDVaiGbzXrJIdJM8ks7BGG73S5OnTqF733ve16qCFeXM2rkgCU5OqNWq+Gnfuqn8LnPfQ65XM4fz91ut/06f3FO8GZ6wGCEMcebiaTSZ1XKOLIUEpe6pG80GgNqappxtSAtsUQAvuCciwD8H1EUfRLAUSKAawCO7l37XfT3QHbYjyUWYL9qY7kAQ0YzG30aQoQYei91s9GYZnC1cZ/GOJclubKIy2pj2rp5DKIo8pG4APCZz3wGn//853Hq1Cm89a1v9edcTk5OIp/PY3p6GrOzs34uhj2NQljcn93dXWSzWWxsbPh5Fzm+QTxfd9xxBy5evOjTtlotLzn0tkdXr17FkSNHTMbGm1wwMBMVJ4FIUwDedZ3P55HL5bzkZjtyGKJJSyzviKLosnPuCIAvOude4pdRFEV7hJQanDpyIk7VCCEzA89J6Os46RACsUNC73igLSMyjXHPk4txEQn6WiD0npGt2+36o/0ajQZeeOEFZDIZfxT5K6+8gomJCUxPT6NSqWBiYgLVatUfMDs+Po5qtYoHHngA5XIZzjk0Gg08/fTT/mDabreLUqnkCVAkxz333IOlpSUvPbrdrt+CtVwue4LJ5XJYW1vDsWPHBuZUer2eP8hJaw48BhztLdKVv5+cBcoTn8MsCxdIRSxRFF3e+19yzv0Z+tu2Lop65ZybA7C0l1x20RfgHfa5zE8C+CQAHDp0KJKOJ3HTEPJpfztgn1PJ9SQRDqthlo3A7eQ6Q2qUpcYxMScRjFWevtbpM5kMPvjBD+L48ePeK3Xvvffi5MmTA6d5iSokxjPbKFyeTNS+/e1vx9NPP40f/OAHAPobSojRPzU1heXlZWQyGZw5cwZ//dd/DaCPyLItU71eR7lcHjglTYBn8bWtxGOnJyCFcHjDb3FYyAIwPqMmbi7OgjTns4w55ybkGsBPAPg+BnfL/3kM7qL/YdeHxwBsJtkrAiHpclCjjAeECURPIIaMaCtEP9RW/mjWj+vRea19f5PaFtdnyddqtVAoFFCr1bC+vo6trS1sbGzgN3/zN3H16lXU63U0m03vSpZxYTuQgxvFgSB2wQc/+EE8++yz+Na3voXPfe5z+MIXvoBPf/rTWFpawvj4uJc873znO5HNZgcmJzOZjN9ZRvqnDXIhYJEwu7u7aDabvs3yLwQuTpLd3d2BtjIDEZWM7bK0kEYWHQXwH5xz3wHwdQB/EUXRvwfwWwB+3Dl3DsA/2rsHgL8EcAHAeQD/J4D/JqkCjRBawljIzf9xwIa+Bq0b63YwYWlEt9QiK79uq/XMCrpksPLwc6s8oG9XdLtdnDt3Dpubm9jZ2UGr1cK73vUufPzjH/cuXfGkyTZGWvoJpxcOL46Ara0t/NEf/REOHz6MY8eOoVqt+v2D6/U6CoWCV7Xe9ra3AejP63Q6HR/+wmFJ3A8mFPkGQrAc9sLLigEMnGos0Q6svkkQZtKR6BakOZ/lQhRFD+797oui6J/vPV+NoujdURTdFUXRP4qiaG3veRRF0UejKHpzFEX3R1H0zbSN4UELITCnTdNZS9yGENgiACuPRRTA/vXhVn6r/UKU0l5gcEMFPRbWmFh9WlpaQiaTwUsvvTSg+zcaDdx111349V//9QFDWwhGCENcv/KT8JUoirxNAwB33XUXlpeXfcDl2bNn8eqrr/pI4Xw+j1ar5Q14sdXYq8nr5KXPkoZVXW4vnwPDEpGjAPifv5O40N+IMyV/aMADJvfA/ll0bbgnQa8XXvYr5YSkF9cT8s7JM9G/LdtiGALX7eP6NAFZjEWIAgC2trawurrqY7bEofLd734Xn/jEJ3Do0CEsLCxgfHzc71q5ubmJS5cuYWNjA61WC9vb256Qtre3B6KFRSXq9XqoVCre21Wr1fwWsqVSCbfffjvy+TwuX+6bsFNTU9jd3fUSTYiT+8xEI2Mjni8mAB4bdszok5R5TPXkZRKMHLEIUmuE0YZcSAULISRHrwLX7YQQImsVLVR+KF9ce+SZcOsQAcU5Krj/ITVO+lsqlfCZz3xmwB0s5bz++uuewIXYNCOSdzwHJNdTU1M+vL9Wq3k1rVAoeJVL7JUoijA3N4ft7W2frtFoDJTP4yiEImofEwevj5FnbH+IdBR80raJZsppYGSijgVCM/Gaw+vnofe6TJ79tdQpS93SzwQ0Rw99gLTSLA5C6mRIfRREEqIsFotehRHEFV2f19pLXfJeAh95fYmUy56nXC6Ho0eP4vjx45ienvYRy1KeqFrZbBYPP/ww6vU6jh49OmBz8IIua8KXCZ2ZA6/h50lNVs+svQOGIRRghCRLWlex1mmZS4dUpCS3clKd+hmHkshzS42ziCGOcEJjEVLL2PUsyK51fGnnHXfc4ecauDyOeNBnLLKhLHlEarCBnMlksL297Tm5VnkkREV2tQf662SazebAfgONRmPAhuCxYvtFrqUv4gUTCaL3KJAxtVT3YdzHI0MsjOxxiMrIGZcnyTZgotH2UQiYo1lqmsXlLYKJU+NCzyywCN6yu5xzmJ+fx+nTp5HL5fzalmaziVKphGKxiHK5jEql4kNFer0eGo2GN9rFrmDOLZ4nCSeJov6Bp+KaFXVanAsSCyaSSOZlRMqHoi/4J2WLZAOu2yVs8DNRsUQSb98bNin5wwKLMwP71y+IHqrTSdo0BrU142+1hbk3ExZLDubOWgJpPTrULkbskISUMq1+6PHTkk9crhcvXsTm5uaACiaBlcePH0cU9c9pERVMEFT+2UMm8yZik7AnL5fLDczcc5QxAFQqFayurg702YoUljbqYFnpl16KrVVLjrfTToNhCWZkiCWN4c7p9GSj5R2y7gW0amaJaG0LJbWNVRX9LJQnjdNC57OIR5CFx0mItdvtn0dy5coVfPaznzXbLCDnUApoQ5sZmNzLfI7uD89/SPtmZ2fxtre9DefOnUMul8PMzIx3cYtdYU0UMmFoxiJt4z6JBNH1yz0HkaaFkTHwLXVKf3zNDTRi83VItQohpOY+nJ8nxuQZP+f2hFzalnOAHQKa4PUsOufXZUn79TjytqbOOXPCkcuz9uDitrDniT1j1tizusw2jIyRSLdyuYz5+XmUy2VsbW1hcXFxgFitMQy5l8XuElUtiqKByVaeh+FfWhgZYrG4K0/UsSEL7N+kghFP8uhnnM4CHd4iqgEjBX8w5lYasbjtGtHlnwlC/zSCMWgDWo+XEDKrTA899BAefvjh4AFNURR5dUk8WSEikDYL8eiz5C1nheSTMBjZ+ELqkLmZZrOJy5cv4+rVqz4OTffXGk9Op9UsIRhxMByUWEZGDQNsO0WrX4Kc2rjXtgcTF3P7kFomoD1FYoDqeRltv4Sim7k/IcnFBMf3nF8TuNUH/Yz3PXvllVdw7do1304tVYQxCPJXq1W/5p7HXDMzNqKtuTEZKwB+5eWxY8cGxpojAyRfo9HA5cuXcccddwwwltCZORpPtC3H6h2rbMPYLSNDLCw9gP3IJM+0BNIEwNyVy7YMf62qWR9c2iU6rpZwug4mWk1Yum86XZzqZnFWi4PzPZ8NefnyZWxsbEAD55OYKYFisThguLNbWEtXYBAJGWElT7FYxNraGtbW1gDA70Mmqyo1Y8hkMrh69XoMLtuEPH4snS21j7+NOB+A63uVpYWRIRYB+QC9Xi/YGa0eMMQZ+vxeri21T6/G084A/VF1nZoYtapgtV23RUsW3cck1VLUJD6AVfb30jaYHi/ZA0xsAJZEhUIBExMTaLfb2NraMj2E3CY21iUQUtLXajVfh9U/kSQSIcxSUX5SvhADj7FmpOJiFsY37LqWkSIW5srMpULqmVZ7NISQlN9ZyMgz2Vb7GHksCWX1K0RQFjfV7dQf3SIMnR6AD2Hv9fpbFfGWR1YZLFm5L9Ln8fFx3HHHHbhw4QJ2dna8y9iScPoAVpHKu7u7fhdMeSbuZ72uBbi+CEwMd57xD82zWao2Szkmth9ZYmFgMS/3oYEB9rtg2b+uy5X0IQQetn060E+3TzsjLMRPIvo0fdcqEBvzjUbDTx7qssVWsVyzkqZYLGJychLf//73B7xiFoLLczHauY28Zazk422M5Lm2d6x3YmdZ9iL3k78P2zTDBlKOjDdMII7q4/RL1kv5P4T8MqCW9NLlhu6ZkHU6ba9Y9QNhz5alluk2h9JrlzO3NzQevJey9hhFUYQTJ04MHBLLyM394XuNpJKGjXSpl8NnLGLl78l1W/3kvPyvf1aAZRyMpGRhxOUPwqfYstHMRidLFC1tQkip6+bncsYjA3txuL3a6OX2Wfo0S6GQlNNIYrVRkMWyx1iFsewUSTMzM4NsNov19fUBiSDjLDP/Yl8wh9Zqcki1lDZxlLMY3EIw8p1Z3WXC09+Vyw1925AtyFI/DYwEsWjuHkICq9MhjmaVaxGMRlhdZrPZjF1Vx4RrfbyQTRNnT+mxsPqqbTYuk4kzn8/7je1k4wbh5r1eD2NjYzh+/Dimpqb8JuDPPffcwG71zl0/vEjP02jJyNdigDMnl3ql3GKxOBAYqcdG1yU2jt60XY8dEzOPoVbjhoGRUcOS9HHt1pUBCcV1hSYDk+rUCMnPdRnsUuX0oYkuljahdrFKx+qHlS4Ooqi/mrFUKvkj7/L5vPcqCSLNz88jm80in8+jVquh2Wzi/vvvH+DsOuxdj1ecumgxOB4/jbzSLrYFNTHKXmRzc3Mol8vBNljtvREYGWLRoNUSRkztyuU8ceqAvpZ7S9/VHFVP4mmw9GZg/xnvum7uk2VnsTrFbbBsAV3v0tIStre3vRomHifh8MeOHfPIJ6d4yUz3ww8/PMCNWcposCSLZgyaeETSWJEYVtksFYSQGo0GDh8+7E8fY3WNxzfEoIYlnpEklpCKwiEMllqlVRNG4CQOyPWy2iPqSEgvZrCMTtHDLWnHbRFE0MyA+6TtIGu85J65c6PRQLFY9OH37XYbhULBn98ihJLJZFCtVrG1tYVOp+N327fqkG+gbQl5psdY7tkDxaqZZhK6Hlbfer2eX7YsS5ZFAmrvVpy28COrhqUBNqyl0+zGZE5tDYYlaSyDUcoVjmohZpJqpgnWyqdVLi6Lty6V90z8vHaD6w7N/8hm4HJ/9OhRn08m60qlkk/barVw1113DZQlfWJC1HMlIqUkmNEaNzbu47aBsr4Vb9aXy+X89kjA4KSnZSuGJmPTQtpd9Kecc3/qnHvJOfeic+5xd5N30Q9xeQ2a84otw2paSDJpNcCqR5CSJYseeEvV023Uy5ZD9bFXL5Sen2ti0AxC8mQy/ZWJ1WoV+XweY2NjGBsbwxNPPIH5+fmBKAnh7qVSySO4SESRPuLIkH23JB//hEAsItLtjaL+3I1mUPqbac0CGNyqVcotl8t+E0EdHc1l6e9104kFwO8A+PdRFN0D4EEAL+Im76IvEEJuHSSpXcohBGaOqI1LTsP/XIbm7iwNQmqVBSH1KeQM0ESjubSWKPyc31UqFb9cuNls4s4778TMzIzPI0fYOdcPXhS1Svb/mp2d9eMh6pqsrpTlxYVCwd/LM/nxykbLHtvY2PAbW8h35Xke3htMpDwvKuNvII4KLWG4vmFVL4Y0O1JOAvjPAfyrvQ/SiqJoA/3d8j+1l+xTAP7x3rXfRT+Koq8BmHL97V1TgUZYRloGawWida2Nc34eV7+AqDtJ3jWLYFjsM6HFqWXyTEscPg0rRNy7u7tYXV3dt3Vpo9HAzMwMGo0G/uZv/gbr6+t+qXA2m8WZM2dw//33Y2Njw0fnSt6xsTEA8JJEYrlEYvDxECyNBaHZDpucnPRS6b3vfa9v5+Tk5L6Toa1vKfe87awsGJOftE/6raWVNd5pIc08y0kAywD+L+fcgwCeA/DLeIN20WcEZ88Gd1beaa6sfepAeG2/VtVCA8fbgHIbQxHDcYRoPdd1c//juCLr5ZI+l8thbGxsYGmBnH3SbDaxubmJF154AdVqFc45fxRELpfDN77xDd+O3d1d/1zGtd1uD5xhKXVbrm0m5rGxMUxOTiKKrh8b0ev1UK1WMT09jfX1da/CWZIgpFID19UxYDCuMIr6i75ka1ch8rhlBGkgjRqWA/AIgN+PouhhADu4rnJJ4yIAQ5Gpc+4XnXPfdM59k49q23sX9GjxR1Jt8KJa7jl9SCpYKhpLAHbrch5LBQupZZKX7Rgun9vLdep+WtKM+ymHAelI4Gw2i6effhpnz54daLsgT6FQGOjn7u4uer0e5ubmfNSvEIWe+2ANQH5iL73lLW/B5OSkP1qPY8BWV1fxnve8B5OTk1hcXPQEE9rjy6qDv5nWNHK5HEqlkt+bTOOFvk4DaYjlEoBLURQ9u3f/p+gTz6KoV+6Au+hHUXQ6iqLT2tBjJJd7/tfXe23od0gZ/DoPc8I4kSxpZBdGRmS9KZ3VHl2unsAEYH5AqVv3XxMSEyYjv6TnzSGcc9jZ2fFeIxkDUYPW1tYGNtaWvGtrax4xuR/WnAu79MVWiaIIU1NTOHToEO644w7cc889niBeffVVfPnLX8b73/9+VKtVPProo+Zm3hosotEEzBEBskPm7u4u6vX6QLvjNAoL0ux1fA3ARefcqb1H7wZwFjd5F/2QfsrP2P6wkAlI3gcqzhFgtYfDO7hubYtYRBf64OwCD7m++Z4JIjROWiKx6gFcd6ty/8XLtbKy4je8i6LIT06KNJA69GpR7TDhe3HrikonahjQj4CemJjA4cOH8eUvfxkf/vCH8cADD+Bd73rXQEyaEIP0RatoGiwHQiaT8cfn9Xo9vx7f0k6SIK037L8F8G+cc98F8BCA38RN3EVfwJIm+rkAc+o4TmSpa3ESJfSeuZl+rtVEy1sWar+1jSy3Q8/bWGOmx4slh7RBJAew//yTXC6HEydODBAK0HcbiyeN1xlpxNWaANBHdjkmQghzZ2cHQJ8BTU5OYmVlBY8++ihWVlbQ7XZxzz334KGHHjLtFh5/3V++12qa1C0eO96BP25cLUh7mNHzAE4br95tpI0AfDR1Cwg0d2JOyx+DkVY/D6kHDDwZZn103SZui4D+aFp10mmsjxLnoBDpINe6v6J2cdm6HrbheHcTsR34rHo5pk8IJZvNYnNzc8DDFO3ZAqzOWuCcw8TEBE6ePInjx4/7dqysrPiNKn7wgx/g4Ycfxl/8xV/gvvvuw+zsLDKZDB577DF/fiUjvZ4/s8aXiUoDRzKLjSz2XVoYiahjAUu8y32cysR5GGFDYjZujQkPniCUxcV0OkZqq25N1FKO7qtWD5gI2UMoCKSjGZjgBKFlwzshQOec925ls1mcP38ex44d8+5X8ZJls1msrq4iiqKBg38s9VHbVe12G+vr67hw4QIWFhbQbrexurrqbcD19XWcO3cO09PT2NrawoULF3DnnXeiXC5jYWHB77SvmYRWo7U001Jfj3O5XB6Y+ddLmuNgZMJdNIfU6ov+GPwMsDctCIFwWF2fXDPosBJdH5epywIGPyq33yJWdkzExVdZ7WEClvrYgcBxUzx/In0U4MN/APg1+4xsEunL/RLpIfMrR44c8YGOslXs4cOHfdqdnR2srKzg8OHDePXVV/HWt74VL7/8Mmq1Gn76p38a5XLZu6oFmFFIOcxYQuOqVbhCoYBKpeJd4mlh5CSLliBaTFpGHqfTHD6uLkEeS0oI6HAXVn/YRtFqgEX8rDZpKaRVKj1vYLWP08pzJhieyBSVrt1u+2BJiQVbWVnBZz/7WWQymYFJydOnT+P9738/jhw5glKphE6ng2eeeQarq6t429vehrvvvhu/+7u/i3a7jUqlgu9///ueaHK5HG6//Xa8/vrrmJub82e8iA1x+vRpnD9/HsvLyyiVSnjmmWfwEz/xE7h06RImJibwgQ98AH/wB39gjoGWMKHx0cA4IYQoZ9ikgZGSLPqnObKWJFo/F84SN2ACOq91DQzq+pZ6p+0p3VYdkqGlAruhLXtJt0fPu2gJyf1jY1bqOnr0KI4ePYr5+Xm86U1v8sQhLl3gun5/9913o9Vq4Zvf/Cbq9Tq+9rWv4V3veheeeuoprK2tIYoifOQjH8Ev/dIv4UMf+hCOHDni50nOnTuHK1eu+HPvnXP+2Lwo6h+ItLOzg4WFBTjncOXKFZw/fx633347er3+oUhPPvmk74tl9OvvYn1b67vLe4mZSwsjQSzMKbUaY+mfcm/ZJIIkcQRjqVzW5J8gmCbcUF6edGR1kfum28EcUpfL5evJUUty6clMsU3GxsZQKpWQz+dRrVZx5MgRVCoVHD58GG9/+9v9GhbeDebd73436vU6isWiV6kkkLJSqQx4zH7jN34D2WwWH/7wh30/S6USzpw5g4WFBdTrdT8xKGMqx0s8+OCDmJ+fx8rKCv78z//c70IDACdPntw3VsxYrAnS0Bha6UTKpYWRUcO0rg4MDlDIUybAA8VHo1mqnS4/BFF0ff9fjZz6mWVwcttC7bDK0sxDt1EMeSZGra7yXMLv/M7voFgsolarYWtrC8vLy/58+kceeQTvfOc7sba2hs3NTUxNTeHYsWOYnJzEa6+9hl6vh/n5eQDA6dOn0ev1lwq/853v9OtkFhYWsLOz48+xFynye7/3exgbG8OTTz6J3d1dvPjii56ZNZtNTE5O4vz583jyySdx7do1bGxs4M/+7M/wMz/zM54YH3vsMXz1q1/1Y2Ahd2jMrXch5psGRoZYBCwEsLi5Bo1gofdsZwjCST1W3az3S/n8Y2mjiSGOuDi9tmMswrJUNG6r1Wd5LjPq1WoVY2NjKBaLyOVy2NjY8NJmfn7e2zBRFKFWq+HYsWMDhw1JmeLtmpqawubmJn7hF34BV69exde+9jW85z3vwec//3ns7OzgH/yDf4DnnnsOn/70pwFcn7fJZDKYmZlBFPXPtTxz5gzuvvtunDt3DpcuXcL58+fx5je/GQDwtre9Da+++ioWFxe9PRSyYy0VXI8N70env1kSjIQaBuznjOwC1aClg1aPtB0RciGz0a2BBo1m0QAAEYRJREFUVStNIFwve45C9pV1Le3U1yEbJG4cdLiLIDOAgejg3d1dXLlyBUtLSz4MRLxfrNZIWeIuln9xO8skn3POE9gXvvAFdLtdPPXUU97zdu7cOZw5cwZPPPEEfvZnfxZPPPEEqtUqisUiXnzxRYyPj2Nl5f9v7+p+4yqu+G+y35+2s45jGwxxAJlYQQFKaEgR4kNAkxaeQtWoUlHFQx9aCaRKVRH/AH0pFKlCoPaxgtLQShXKA9Qg8YIIaQIE8uE4sZL420mcOPbuGrw7fdg9k7NnZ+7ehSS7gfuTVrt779w7Z+6dM+djzpw5i3w+j1wuh3Q6jXK5jLfeesuogCsrK3j00UdrlkPzZ8jfC7VfhsDw//xbTq42Qtswi82Q9WOsS+OPIEdzm40hO6yshybwXHYIjXR8xl7CFs4i6ZTnbTR5qRpUD6+f5yyORCL44osvcPDgQUxPTyOfz+Ps2bMol8s1IfZ0TwoxoXUrtoVe5ASgib2nnnoKmzZtwuHDh3H33XdDKYWlpSV89tlnOHjwIE6fPo3Tp0+bRH+9vb1mk1alKis3FxcXoXUliuDVV1+tiX7evHlzzfOSgxeXfASXTWOT7n7QNszC4VqOSvDTQFfn59dz9cnGdHLyzXVvMlqpDhetNkPcdV9+nh+zHSfQOhK+kWk0GsXJkycxPT2NdDqNZDJpVk0mk0l89NFHNRlfqN3RaNRM4HHmkB+awMxkMti3bx8mJibM1uEATCxWV1cXRkdHTT20vmbNmsp+lJlMBn19fUin09C64uJ++eWXzTvbunWrceNLNdjFOBz8mGsxWiO0JbPwkdrlpXKpOxJ+bApZjtPBcwPb6iTwF2Ur24x6JlVKmwS0STtSfyh6mDr60aNHEYvFAMCsXadVjaFQCIcOHTLeskQigXK5slCLLwvmDKOUqlnrMjY2hsOHD6Orq8tIhi1bthgj/euvv0YikUA+nzd7pHR2dmJwcBA33HADVldXcezYMaysrJit+sLhMPL5PN59911T344dOwxN8n3apIf8LZmkmVAXoI2YxdUJgXq3biObpdExPlLTeRfkCk0Xg3l1ZtnZ+bU2l7CtjbbgRfq2qWNaVybcyKCmkBc+pxKJRNDT01OzEeuaNWuMa1iG4ssOx9W30dFRk34pnU7jhRdegFKVpcoU6sJj0wqFAsbHx/HAAw9g3759eP/993H+/HlcuHABTz/9NDo6OqCUwv79+zEzM4NisYiZmZmaRBucHvrPJ2G97EuXp9ELbeUN8yLc5v1xlefnvMpJdY+LdKKH709C9cpZcX4vF+NJ54CtvVQ//6ZyPESHOyWonPRaEcN0d3ebtlFCCroHX8c+OTmJvr4+c51UP3nUArWVgirvu+8+3HPPPWYdDIX2R6NRwyxTU1OGtng8jkKhgNtvvx0XL17E8ePHzY7GPT09eO+995DL5XDTTTdhbGwMH374IYaGhnDmzBkMDAxgfn7ePA+aJuC08vcSCoVMMKjruftFW0iWRhKg0XX8v2tkdtXHIXVYWU6qhbbYK1e9XmoW3ZuXk5LKFgMly9qOE7MAlQ5PE4Tcu5VIJMy2dHzGn3cubvhzlYycBEBFxdu7d6+J8UokEsjlcoYZKbSEmGpkZATpdNos/1VKmYiCeDyOyclJDA8PY926dYjH4+jt7UWxWMTAwIAZGPgAQuqaHEi4x08OQs2gLSSLHE29bAvAHtouwUdBLjWoPl5O3sMmmWyMRbRI372NyfmLdR2nb5d3z+tFS6aiEZWMfvLcAZU19gCQzWYxPT2N/v7+uhzG8hnYwNUa2obvjjvuQEdHh7GRwuEwOjo6sLS0VGPzbNiwAYODg9i7dy8SiYRZmNbX14dMJoMDBw5geHgYR44cQTabxcrKikkSWCwWsby8bNb2uyAnprnmIKWkH7QFs/AGS4bx0i3lLLafelwdzrXPCF1no4HUH2nXcD2aM4ism4/eXmqc65jUyak+uj9NuK6srKBQKCASieDIkSPYvn07RkZGjDG/bt26GjWFSykX8xL9tH5/aGgIb7/9Nubn55HP541RT/MjXV1dZt/7UqmE8fFxE5cVj8fNLmL5fB6Li4sYGBjA7Ows+vv7MTU1hWKxiP7+fsTjcSwsLBiJlEqlatrcaNClPiOljB+0hRpG4J2SqxOuRkmvGVeTvNyztnql58RLneLfNtcyjeJyqz2bKmVrv1SpuJrFy5K9wW0pTteWLVswODiIQqFgRuNUKoWlpSVs3boVc3Nz2LZtG8bHx01ksMyeT89EuowlE2UyGRw6dMgY43wgI+9VR0cHgMrAcOLECUxMTCAWi6GzsxORSATlchknT57E/Pw8VldXkUwmkc1mccstt+DSpUuYm5vD+fPnMTMzY1LMUkINuq98jzxNEz13GWXuF23DLN/ExnDB5W7mx1x2kbQp+HluV0gVxYs2m/h30exVPzGElIIy8QWVufXWW03Y/fLyMr766ivMzs5iZGQEuVzObLFNMV62zubHFUv0P/zwwzUJ8ribF4CZAC2XK1HFy8vL6O/vR09PDwqFgnHTUwon2hA2m81i/fr1WF5eNhOXxWLRqJn8Wcg5IRsdHNedGiYh1SSXCuR3VLBtpSbv4WX0SVuCuyj5fRrZVza7SKpl3HbhTEbqjCzD1TweN8Vn55PJpEmsd+nSJRSLRUxMTGDXrl3Ys2cPnnjiCRO2QrS4nq3tHDHNrl278Mknnxg6uXSha1KpFCKRiPF+HT9+HLFYzEiWrq4u9PX14csvvzQ50NLpNNauXYtkMomLFy+iu7sbCwsLNfmU5d4unKFXV1drMmM2a6sQ2opZbEa4HLmpoa5OLv/bQlHk9TZGJEgRLlU0LwkjOxZ3Qdto9bonMYaUUgQKy+ERyaTeDA0N4fPPP8fo6CgGBgaQSCRw9OhRnDt3Dtu3b8fGjRuN8Sz1fgkbExFz3HbbbaZT0ogu20y00gZRVD6TyRiv1cTEBNLpNE6dOoVUKmXUw6WlJdM+WrPPJTJ3I/NnSBkqCfz5N7OnZFsxC+CebAPqGcAWFgNczgrvgtRbZf1S+jRjBEpaOW2SfqB2/sLlBePg5fnobTPQiVmSySR27NiB3t5e7N+/H3NzcyiXK+lN77//fpPPWNIuj7meAzEyJYLgtHN7gtzCdI5sCqA2lo3vFzM5OVmTh4y+qY08k6WUYpzp5XOjcs2swW87ZiE0MsQBe9YXADWpe/zW5fKS2c57QUoTmy3m8sLJ2WUbLXxhm+wAUjXr7Ow0UjSXy+Ghhx7CXXfdhampKZRKJeRyOXR3d9cNLi7GdalmfIQfGhrC+Pi41RUtOzpweWAjOnnmHYoy4PVzyeryevH3YNMCqJxtywsv+EkMPqSU+pR9FpVSz6krvOUEb6iXPeIy/gXNdUa8zVjnH7kVnLzGZnS77suNXHle0iaZn4er8DrkhCNvAx9J6VMqlZBMJhGNRo3HKBQKobOzE5s2bcLmzZuxfv36mgQVnFmB2s2jbEGU8hMKhfD444/X5CcjOnm7OYhR5KSiS0WS13upwbYBh0tNOT/WCH4yUh7TWt+ptb4TwA8A5AH8G1dpywk/cDGBF3Pwa/m3RCOXrouJbNfRcc44Mt+xDK3nL1i2wUU7d0tz+kiV4XXyUVkGFlIH4rP7kUgEmUzGRCknk0kkEomarSVCoZCZU3nwwQcBXM4YQ4vnCoUCSqWSsT/K5bLxZtGMPv2XrnfXYAPUS2Ob6u7K0NMMowDNu44fAXBCa30KV3nLCVdnd11jexi2+3AVwOv+NiZ01WkLN+EMYpsn4R0UqE+CIfcooTq97BpOJ6miPEmEC1yy8NgxOZ/Cf8vATu5Iufnmm+tC+kmd4u2lMpwG2/O3qaV0jNtrvBz/L5OGk7on574aoVlm+TmAN6q/m91yogaKZdGnxGs2NJIcLpXIxiRekoZDjtRSZZMqkVSL5HGbDSTjr+h6qVYQpGrH66GXz1OS8s7Kt8GWKolkBClxyBFACS1WVlbMmhnZBtoIdefOnTUdUg4UVJ7o5VEQcsLQ9QypLJ+RJ2nKISUnt/cAXB0DXykVBfAkgOflOa21Vko1JdO01q8DeB0AOjs7667loyjvFPSf0VV3HT8uO530lLiuoY4CAG+88Ya5njpSLBYzqkkmkzHJ5+h/NBp1toO7LqX6wCNk+SguOxjv1JK5iXFIneLMQveje3h1RglZnv/mEuOxxx7Da6+9ZvZG4W2mcjKEntNGnjN6HrFYrMZpwyUGSShuH4XDYadLmA9IvB4/aMYbtgPAAa31bPX/rFKqT2s9rb7BlhMSfkZ9rnPLMjamkZ3f9bJt13iBUvkUi0WcO3fOqifze1JnT6fTJi0RrVZMpVJIJBJmso7TRSoGecD4KErnZbgLp1Gum7cNOl5zPn6eBS/HaV+7di3m5+fr3gH3nNFkITfsgUonphGfbBguRagu/nw4HS7HANXPn8PVSoW0G5dVMODylhMvon7Lid8qpd4E8EM0seVEIykB1CfX4+VcsEko272l2iYZk+rn3wSXq5peqlLKbEXNO5dsB583WbNmDVKpFNLptJFa2WzWbDZKE3lS9VPqcr4wCnSkKGDeQbjaxenhA4sc/b3UW621WaG5e/duvPLKK3XliF6+AlVOJnJm4lJWzo/Z1DgCRV3bnrWtfX7gi1mUUikAjwL4NTv8IoC3lFLPADgF4GfV43sB7ERly4k8gF81uj8fFXm4hisS10sK2To/fygu9azROds9OGyGIo16XI3i9+JSh9+HzpVKJSwuLmJxcbHu3ra6lpaWakbveDyOcDiMs2fPYnZ21nizKCUS/ZbGuu2ZyOdiM6YBmPX09957r2HKUqlkNkYlpqB28yz/MmSFJKN0fnAJ62Wgc3XLNrg1Y9wD/recWAaQE8fO4QpvOQF4p1L1A9sIL1+s7Tyvj//nvnhb5+DlXW3xijhwTTJKacjpsElGyWQAcOHCBYTDYSwsLODMmTNWVVTSr5Qys/7ZbLaOwRKJhEkqIZ8h2QukOkWjUbPbFo9b41KCT05SGc5IPHyGP1cqL98FqXN0L+mal2hGDVONVJhrAaXUJQDHWk3HNUY3gLOtJuIaop3be7PWel2jQu0S7nJMa23bLOk7C6XU/u9Tm78L7W2b9SwBArQ7AmYJEMAn2oVZXm81AS3A963N131728LADxDgekC7SJYAAdoeLWcWpdSPlVLHVGX9yx8aX9H+UEoNKKU+UEodVkp9qZR6tnr8iq8BaicopUJKqYNKqXeq/weVUh9X2/UPVYkvhFIqVv0/Vj2/oZV0+0VLmUUpFQLwF1TizoYB7FZKDbeSpiuEVQC/01oPA9gG4DfVdrVsDdA1wrMAjrD/fwTwktb6VgALAJ6pHn8GwEL1+EvVcm2PVkuWewGMaa1Paq2/AvAmKuthrmtorae11geqvy+h0oFuwFVaA9QOUErdCOAnAP5a/a8APAxgT7WIbC89hz0AHlHNBGm1CK1mFl9rX65nVFWMuwB8jG+5BqjN8TKA3wOg2JIcgAtaa4qB520y7a2evwgRTtWOaDWzfKehlEoDeBvAc1rrmmjIagzdd8IVqZT6KYA5rfX/Wk3L1USrw12+0dqX6wFKqQgqjPJ3rfW/qoev6BqgNsKPADyplNoJIA4gC+DPqKiT4ar04G2i9k4opcIAOgCcu/ZkN4dWS5ZPANxW9ZpEUVm2/J8W0/StUdW//wbgiNb6T+wUrQEC6tcA/bLqFdsGn2uA2gVa6+e11jdqrTeg8g7f11r/AsAHAHZVi8n20nPYVS3f/lKWLxpqxQeVtS+jAE4AeKHV9FyhNt2Pior1OYBPq5+dqOjlIwCOA/gvgLXV8goVr+AJAIcA3NPqNnyLtj8I4J3q740A9qGytumfAGLV4/Hq/7Hq+Y2tptvPJ5jBDxDAJ1qthgUIcN0gYJYAAXwiYJYAAXwiYJYAAXwiYJYAAXwiYJYAAXwiYJYAAXwiYJYAAXzi/yM8MwkW8hfKAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot,ax = plt.subplots(1,2)\n",
+    "print(\"Image 1 and Image 2 before stitching\")\n",
+    "ax[0].imshow(image_stitcher.images[0], cmap=\"gray\")\n",
+    "ax[1].imshow(image_stitcher.images[1], cmap=\"gray\")\n",
+    "plt.show()\n",
+    "print(\"Stitched Image 1 + Image 2\")\n",
+    "plt.imshow(new_image, cmap='gray')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": "3.7.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/im1.jpg b/im1.jpg
new file mode 100644
index 0000000..b398456
Binary files /dev/null and b/im1.jpg differ
diff --git a/im2.jpg b/im2.jpg
new file mode 100644
index 0000000..efea91f
Binary files /dev/null and b/im2.jpg differ
diff --git a/im3.jpg b/im3.jpg
new file mode 100644
index 0000000..7934958
Binary files /dev/null and b/im3.jpg differ
diff --git a/im4.jpg b/im4.jpg
new file mode 100644
index 0000000..652177a
Binary files /dev/null and b/im4.jpg differ
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..718d928
--- /dev/null
+++ b/main.py
@@ -0,0 +1,40 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import cornermatching as cm
+
+# Read in both images and set them to grayscale
+I1 = plt.imread('im1.jpg')
+I1 = I1.mean(axis=2)
+
+I2 = plt.imread('im2.jpg')
+I2 = I2.mean(axis=2)
+
+# Gauss kernel
+g_kernal = cm.gauss_kernal(5,2)
+
+# Convolve the two images
+#plt.imshow(I1, cmap="gray")
+I1 = cm.convolve(I1, g_kernal)
+#plt.show()
+
+#plt.imshow(I2, cmap="gray")
+I2 = cm.convolve(I2, g_kernal)
+#plt.show()
+
+H1 = cm.harris_response(I1)
+H2 = cm.harris_response(I2)
+
+H1sup = cm.nonmaxsup(H1)
+H2sup = cm.nonmaxsup(H2)
+
+H1descrips = cm.descriptorExtractor(I1,H1sup)
+H2descrips = cm.descriptorExtractor(I2,H2sup)
+
+
+best_matches = cm.get_best_matches(H1descrips, H2descrips)
+
+secondbest_matches = cm.get_secondbest_matches(H1descrips, H2descrips, best_matches)
+
+#filtered_matches = cm.filter_matches(best_matches, secondbest_matches, H1descrips)
+
+#print(filtered_matches)
diff --git a/project_description.ipynb b/project_description.ipynb
index 421c6d0..a93911c 100644
--- a/project_description.ipynb
+++ b/project_description.ipynb
@@ -22,35 +22,109 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import cornermatching_addi as cm\n",
+    "import skimage.transform as skt\n",
+    "\n",
     "class Stitcher(object):\n",
     "    def __init__(self,image_1,image_2):\n",
     "        self.images = [image_1,image_2]\n",
     "        \n",
     "    def find_keypoints(self):\n",
-    "        \"\"\"\n",
-    "        Step 1: This method locates features that are \"good\" for matching.  To do this we will implement the Harris \n",
-    "        corner detector\n",
-    "        \"\"\"\n",
     "        \n",
+    "        # Guass kernel for convolution\n",
+    "        g_kernal = cm.gauss_kernal(5,2)\n",
+    "        \n",
+    "        # Calculate the harris response of each convolution of I1, I2\n",
+    "        H1 = cm.harris_response(cm.convolve(self.images[0], g_kernal))\n",
+    "        H2 = cm.harris_response(cm.convolve(self.images[1], g_kernal))\n",
+    "        \n",
+    "        # Extract the keypoints from H1, H2 via non-maximal sup\n",
+    "        key_pts_I1 = cm.nonmaxsup(H1)\n",
+    "        key_pts_I2 = cm.nonmaxsup(H2)\n",
+    "        \n",
+    "        # Return the keypoints of I1, I2\n",
+    "        return key_pts_I1, key_pts_I2\n",
+    "    \n",
     "    def generate_descriptors(self):\n",
+    "        \n",
+    "        # Get the keypoints to generate descriptors from\n",
+    "        key_pts_I1, key_pts_I2 = self.find_keypoints()\n",
+    "        \n",
+    "        # Get descriptors for I1, I2\n",
+    "        des_I1 = cm.descriptorExtractor(self.images[0], key_pts_I1)\n",
+    "        des_I2 = cm.descriptorExtractor(self.images[1], key_pts_I2)\n",
+    "        \n",
+    "        return des_I1, des_I2\n",
     "        \"\"\"\n",
     "        Step 2: After identifying relevant keypoints, we need to come up with a quantitative description of the \n",
     "        neighborhood of that keypoint, so that we can match it to keypoints in other images.\n",
     "        \"\"\"\n",
     "        \n",
     "    def match_keypoints(self):\n",
+    "        \n",
+    "        des_I1, des_I2 = self.generate_descriptors()\n",
+    "        \n",
+    "        best_matches = cm.get_best_matches(des_I1, des_I2)\n",
+    "        secondbest_matches = cm.get_secondbest_matches(des_I1, des_I2, best_matches)\n",
+    "        \n",
+    "        \n",
+    "        filtered_matches = cm.filter_matches(best_matches, secondbest_matches)\n",
+    "        \n",
+    "        return filtered_matches, des_I1\n",
+    "    \n",
     "        \"\"\"\n",
     "        Step 3: Compare keypoint descriptions between images, identify potential matches, and filter likely\n",
     "        mismatches\n",
     "        \"\"\"\n",
     "        \n",
     "    def find_homography(self):\n",
+    "        \n",
+    "        # Get the matches between the two images\n",
+    "        matches, des_I1 = self.match_keypoints()\n",
+    "        \n",
+    "        # Now get the coordinates from the matches for RANSAC\n",
+    "        match_coords = []\n",
+    "        for match in matches: #filtered_matches:\n",
+    "    \n",
+    "            match_I1_x = des_I1[match[0]][2]\n",
+    "\n",
+    "            match_I1_y = des_I1[match[0]][1]\n",
+    "    \n",
+    "            match_I2_x = match[1][2]\n",
+    "    \n",
+    "            match_I2_y = match[1][1]\n",
+    "    \n",
+    "            match_coords.append([match_I1_x,match_I1_y,match_I2_x,match_I2_y])\n",
+    "   \n",
+    "        # params needed for RANSAC\n",
+    "        \n",
+    "        num_iters = 1000\n",
+    "        r = 4\n",
+    "        d = 3\n",
+    "        n = 4\n",
+    "        \n",
+    "        H_best, list_of_inliers = cm.RANSAC(num_iters, match_coords, n, r, d)\n",
+    "        \n",
+    "        return H_best\n",
+    "\n",
+    "        \n",
     "        \"\"\"\n",
     "        Step 4: Find a linear transformation (of various complexities) that maps pixels from the second image to \n",
     "        pixels in the first image\n",
     "        \"\"\"\n",
-    "        \n",
+    "      \n",
     "    def stitch(self):\n",
+    "        \n",
+    "        H = self.find_homography()\n",
+    "        \n",
+    "        proj_trans = skt.ProjectiveTransform(H)\n",
+    "        \n",
+    "        image_2_transformed = skt.warp(self.images[1],proj_trans)\n",
+    "        \n",
+    "        new_image = (self.images[0] + image_2_transformed) / 2\n",
+    "        \n",
+    "        return new_image\n",
+    "        \n",
     "        \"\"\"\n",
     "        Step 5: Transform second image into local coordinate system of first image, and (perhaps) perform blending\n",
     "        to avoid obvious seams between images.\n",
@@ -68,10 +142,200 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "#def stitchImages():\n",
+    "'''Returns the stiched images recursively'''\n",
+    "\n",
+    "images = [plt.imread('im1.jpg').mean(axis=2), plt.imread('im2.jpg').mean(axis=2), plt.imread('im3.jpg').mean(axis=2), plt.imread('im4.jpg').mean(axis=2)]\n",
+    "\n",
+    "#filtered_matches, best_matches = image_stitcher.match_keypoints()\n",
+    "image_stitcher = Stitcher(images[0], images[1])\n",
+    "new_image = image_stitcher.stitch()\n",
+    "\n",
+    "plt.imshow(new_image, cmap='gray')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nimport numpy as np\\nh = int(len(images[0]))\\nw = int(len(images[0][0])*2)\\ns = (h,w)\\nprint(s)\\nnew_image = np.zeros(s)\\nnew_image[0:h, 0:int(w/2)] = images[0]\\nnew_image[0:h, int(w/2):] = images[1]\\n\\nplt.imshow(new_image, cmap=\"gray\")\\nfor match in filtered_matches:\\n    x1 = best_matches[match[0]][1]\\n    y1 = best_matches[match[0]][2]\\n    x2 = match[1][1]\\n    y2 = match[1][2]\\n    print(x1,y1,x2,y2)\\n    plt.plot([y1,y2+int(w/2)], [x1,x2], color=\"blue\", marker=\"x\")\\nplt.show()\\n'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "import numpy as np\n",
+    "h = int(len(images[0]))\n",
+    "w = int(len(images[0][0])*2)\n",
+    "s = (h,w)\n",
+    "print(s)\n",
+    "new_image = np.zeros(s)\n",
+    "new_image[0:h, 0:int(w/2)] = images[0]\n",
+    "new_image[0:h, int(w/2):] = images[1]\n",
+    "\n",
+    "plt.imshow(new_image, cmap=\"gray\")\n",
+    "for match in filtered_matches:\n",
+    "    x1 = best_matches[match[0]][1]\n",
+    "    y1 = best_matches[match[0]][2]\n",
+    "    x2 = match[1][1]\n",
+    "    y2 = match[1][2]\n",
+    "    print(x1,y1,x2,y2)\n",
+    "    plt.plot([y1,y2+int(w/2)], [x1,x2], color=\"blue\", marker=\"x\")\n",
+    "plt.show()\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nH, list_of_inliers, match_coords = image_stitcher.find_homography()\\nimport skimage.transform as skt\\n# Create a projective transform based on the homography matrix $H$\\nproj_trans = skt.ProjectiveTransform(H)\\n\\n# Warp the image into image 1\\'s coordinate system\\n#plt.imshow(images[0], cmap=\"gray\")\\n#plt.show()\\n'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "H, list_of_inliers, match_coords = image_stitcher.find_homography()\n",
+    "import skimage.transform as skt\n",
+    "# Create a projective transform based on the homography matrix $H$\n",
+    "proj_trans = skt.ProjectiveTransform(H)\n",
+    "\n",
+    "# Warp the image into image 1's coordinate system\n",
+    "#plt.imshow(images[0], cmap=\"gray\")\n",
+    "#plt.show()\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nimport numpy as np\\n\\nprint(H)\\nprint(list_of_inliers)\\nimage_2_transformed = skt.warp(image_stitcher.images[1],proj_trans)\\nplt.imshow(images[1], cmap=\"gray\")\\nplt.show()\\nplt.imshow(image_2_transformed, cmap=\"gray\")\\nfor inlier in list_of_inliers:\\n    plt.plot(inlier[0], inlier[1], marker=\"x\")\\nplt.show()\\n\\nplt.imshow(images[0], cmap=\"gray\")\\nfor inlier in list_of_inliers:\\n    plt.plot(inlier[0], inlier[1], marker=\"x\")\\nplt.show()\\n'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "import numpy as np\n",
+    "\n",
+    "print(H)\n",
+    "print(list_of_inliers)\n",
+    "image_2_transformed = skt.warp(image_stitcher.images[1],proj_trans)\n",
+    "plt.imshow(images[1], cmap=\"gray\")\n",
+    "plt.show()\n",
+    "plt.imshow(image_2_transformed, cmap=\"gray\")\n",
+    "for inlier in list_of_inliers:\n",
+    "    plt.plot(inlier[0], inlier[1], marker=\"x\")\n",
+    "plt.show()\n",
+    "\n",
+    "plt.imshow(images[0], cmap=\"gray\")\n",
+    "for inlier in list_of_inliers:\n",
+    "    plt.plot(inlier[0], inlier[1], marker=\"x\")\n",
+    "plt.show()\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nmatch_I1 = []\\nmatch_I2 = []\\nfor coord in match_coords:\\n    match_I1.append([coord[0], coord[1]])\\n    match_I2.append([coord[2], coord[3]])\\n'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "match_I1 = []\n",
+    "match_I2 = []\n",
+    "for coord in match_coords:\n",
+    "    match_I1.append([coord[0], coord[1]])\n",
+    "    match_I2.append([coord[2], coord[3]])\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'plt.imshow(images[0], cmap=\"gray\")\\nfor match in match_I1:\\n    plt.plot(match[0], match[1], marker = \\'x\\')\\nplt.show()\\n\\nplt.imshow(images[1], cmap=\"gray\")\\nfor match in match_I2:\\n    plt.plot(match[0], match[1], marker = \\'x\\')\\nplt.show()\\n\\nnew_image = (images[0] + image_2_transformed) / 2\\nplt.imshow(new_image, cmap=\"gray\")\\nplt.show()\\n'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''plt.imshow(images[0], cmap=\"gray\")\n",
+    "for match in match_I1:\n",
+    "    plt.plot(match[0], match[1], marker = 'x')\n",
+    "plt.show()\n",
+    "\n",
+    "plt.imshow(images[1], cmap=\"gray\")\n",
+    "for match in match_I2:\n",
+    "    plt.plot(match[0], match[1], marker = 'x')\n",
+    "plt.show()\n",
+    "\n",
+    "new_image = (images[0] + image_2_transformed) / 2\n",
+    "plt.imshow(new_image, cmap=\"gray\")\n",
+    "plt.show()\n",
+    "'''"
+   ]
   }
  ],
  "metadata": {
@@ -90,7 +354,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.7.2"
   }
  },
  "nbformat": 4,