{ "cells": [ { "cell_type": "markdown", "id": "ff595b47-d637-4cd4-a75e-ead6f4b84dce", "metadata": {}, "source": [ "To start, we import the `matplotlib` library, which is essential for visualizing different aspects of our calculations, such as molecular geometry, as well as the resulting IR and Raman spectra. By setting `plt.rcParams['figure.dpi'] = 120`, we adjust the resolution of the figures to ensure they are displayed with sufficient clarity and detail." ] }, { "cell_type": "code", "execution_count": 1, "id": "63c23cca-ad08-4546-8a83-c4b0465077c7", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.rcParams[\"figure.dpi\"] = 120" ] }, { "cell_type": "markdown", "id": "fe5ed9e4-b774-4d12-996a-28d5418085bc", "metadata": {}, "source": [ "As previously mentioned, we use the PLAMS Python library to interact with AMS. The code below constructs the beryllium oxide (BeO) crystal structure by defining a `Molecule` object. Individual atoms, beryllium (Be) and oxygen (O), are then added to the molecule using their respective symbols and Cartesian coordinates, which are specified in Angstroms. Next, the crystal lattice is defined as a 3x3 matrix, with dimensions also in Angstroms." ] }, { "cell_type": "code", "execution_count": 2, "id": "4da77384-f36b-445a-9785-a863e2ff292c", "metadata": {}, "outputs": [], "source": [ "from scm.plams import Molecule, Atom, view, plot_image_grid\n", "\n", "mol = Molecule()\n", "mol.add_atom(Atom(symbol=\"Be\", coords=(0.00000000, 1.55554138, 0.00070090)))\n", "mol.add_atom(Atom(symbol=\"Be\", coords=(1.34713836, 0.77777069, 2.18871035)))\n", "mol.add_atom(Atom(symbol=\"O\", coords=(0.00000000, 1.55554138, 1.65124622)))\n", "mol.add_atom(Atom(symbol=\"O\", coords=(1.34713836, 0.77777069, 3.83925567)))\n", "mol.lattice = [\n", " [2.69427671, 0.00000000, 0.00000000],\n", " [-1.34713836, 2.33331208, 0.00000000],\n", " [0.00000000, 0.00000000, 4.37601889],\n", "]" ] }, { "cell_type": "markdown", "id": "e2ffbbfa-aa00-40d6-9770-258ea4964969", "metadata": {}, "source": [ "To visualize the BeO crystal structure we've defined, we employ the `plot_molecule` function. This function serves as an interface to the `plot_atoms` function from the Atomic Simulation Environment (ASE), enabling us to generate graphical representations of the atomic arrangement. For a more detailed control over the generated figures, you can consult the original ASE documentation. In this particular instance, we generate three distinct views of the BeO structure by applying different rotation angles. Specifically, we create a figure with three subplots, each displaying the molecule rotated along different axes: `('15x,15y,0z')`, `('0x,90y,0z')`, and `('0x,0y,0z')`." ] }, { "cell_type": "code", "execution_count": 3, "id": "736864d2-0a99-4631-9707-7f9b8d91fa7f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10.04|12:27:30] Starting Xvfb...\n", "[10.04|12:27:30] Xvfb started\n" ] }, { "data": { "image/png": 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keP3110mlUly9epWtW7eWFFBLPY/YfKhk9RZF/StRac6C3Fhfac4B5iyOPpebz+l03vRruN3uivNeMpm0atqoqnrTBZDnoqamhkAgQCwWs4ojyzbmEsnqwOVycd9999HX10cymeTw4cO8733vm3OcWymUKkEh1jarJW5vbm7mrrvu4pVXXqG3t5euri62b9++3KclWSHcHiPBMiNqJAwMDLBnzx4cDgfJZLLgPnbRxTRNhoeHCYfDuFyuigPyYjsX5jNQzlcsqcRc72MuQUe4miq9fqWFhmma8xLHKi1mHA7HnOLXXIuZucQxsQtf6TnmupZCJKl0njfjBBDPUW5RODU1ZTlzXC4Xa9asKduZxeFwoKoqhmHInGHJbYNpmly+fJne3l7Wr19PKBQq+LyU+gzP9bku/n2p+6uqSnNzMxcvXqSnp4e6ujpqa2vf5LuYmzc77t/sfDEXc7n+5jPvVRojdV1nfHyczs5ODMOgvb19UcUcyI2V7e3tXL161Uqr27hxo2xjLpGsElpaWtixYwfHjx/n6tWrXL16lTvuuGO5T+uGERt4kUiEsbExJiYmGB8fJxKJYBiGNY76/X6qq6tpbGykrq6O+vp6PB7PbTvmqarKvn37OH/+PJOTkxw5coS1a9dK4V4yL6SgswBks1k0TaOjo4N169ZZg409oCxerDY0NOB0OgkEAvh8vjm7/JRyMNgLK88ndWU+C4Zy96n0uxt5jXKI6zPfgLj4debzuFshrWc+C5mbFc9udqd/Po6rcqJTPB4nmUxSXV09p5gjnkekHWYymYqvKZGsFNxuN52dnbznPe9BVVXq6upoaGiY9xik63rF+2qaVtal0tTURFVVFYlEAofDQX19fcHnWYwx2Wy24pwj3JnlmI8IO5eDdK73KeadUvcR891cY5V9gVDpNUrdbv9qR9M0rly5QjgcZtu2bVaXscUmGAwSCoWYnJwkFosRiUQWvMC2RCK5NVFVlQMHDtDV1cX09DSHDx+ms7MTv9+/3Kc2LzKZDIODg5w9e5bLly8zPT1tzUNutxun04nD4bDiQvvY7vV68Xq91NTUsG7dOjZt2kR1dfWcm48rjaqqKg4cOMAzzzzD6Ogor7/+Ovfff/+qcSlJ3jxS0FkATNOks7MT0zTx+/0kk0lisRjxeNxaPLtcLoLBIIFAgGAwiK7rpFIpampq2Lhxo/U8lZgreJ7r8XOltBiGQTabrXifuX6vaVrF+3g8nornIRYJcy1m5vMc5ahUgFeIGS6Xq+xrVFpk2O8zn+tdibkWGmJBczM70QstcnV1daEoCl6vlw0bNlQUc6BQGJrrf0siWSkoikJbWxuxWIxUKkUikcDn8y1JPrxpmrjdbquoYlVVFU1NTQseEM5H5J/PODnX4+czTlZirsfrum41Mij3/MXzhRDFhLAWDoepq6ur+DoLgWi2EIvFyGQyDAwMUFVVddukXUgkksqEQiH279/P888/z/DwMGfOnOHAgQO39II/kUjw2muvceTIEfr7+9F1HbfbjcfjsYQcka1QLOjYN6s1TbNSl0+dOkVrays7d+6ks7PzthJ2tm/fzvnz5+nt7eX06dNs3759SeYXycpGRgE3QTqdZnBwkNOnT3PixAmGh4etQsiido09fUVRFDwejxXYh0IhMpkMnZ2d+Hy+OQfkuXL+VwtL4bRZCDFmrjoPcwlXQmAr93/hcDjmdHXNJWzNtUs/H3FMOGtUVaWzs5Ph4WFcLte8do7tNZykoCO5nfB6vbS1tdHT00MqlWJ0dJSOjo5Ff11FUWhsbCQcDhOLxRgaGqKqqopAILDgr3MrLyIWm+rqai5dukQmk2FkZIRQKLQkwkogEKCuro7h4WESiQRTU1M0NjYu+utKJJLlR1EUdu3axaVLl+jr6+PEiRPccccdi5pa+2ZJpVIcPXqUp556ipGREVwuFz6fj0AggNvtxu1243K5LDHHLuiIzb7icgxC5Nd1ncHBQYaGhqivr2fv3r2sWbPmthB2PB4Phw4dYmhoiHA4zOHDh3n3u999W7w3yeIhBZ0bxDRNpqenOXr0KCdPnmRsbIypqSn6+vrweDw0Nzfj9/sLBqdipVnXdRKJBLFYjOHhYbq6uujo6GDnzp00NTXJD+0cLMUiYq6/wXz+RquxOr3T6cTr9c77vSuKYglK8v9ecrthL2Q7MTFBfX09Pp9v0V/X5XLR2dnJ5cuXLZv7pk2bVrUAs9D4fD7q6+sZHh4mFosxOjq6ZF1nmpubmZqaIp1OMzo6SjAYxOv1LslrSySS5cXn83Ho0CG+//3vEw6HOXbsGO985ztvmdoypmnS09PDN7/5TS5evIjL5cLv9+PxeKzDLuiI9VKpdZN97SSeW4g6hmGgaRpTU1M8//zztLe3c999983q7rgSWbt2LVu2bOGNN97gwoUL7Ny5U3Y2lFRECjo3wNTUFM8++yxHjhwhEongdrvxer1UVVVZtkG3220JOnalWQxKxbUMxIDU3d1NX18fDQ0N7N+/n9bW1ltmcJZI5ovYodZ1fV5dq+yCjvx/l9xuOJ1OmpubSSQSpNNpBgYG2Lhx45IIK36/n0AgQCQSIRKJEIvFbotA91ZCpD9Fo1HGxsasop2Ljdvtprm5mb6+PhKJBENDQ6xbt04KdhLJKmHdunVs3LiRixcvcu7cObZt20ZnZ+dynxbZbJZnnnmGb3/72ySTSQKBQEUxp5JDx+7SsWMXdXRdt0o99Pf389hjj3H//fezdu3aFR1TOhwODh06xLVr14jFYrz00ku0tLQsyfwiWZms3P/2JSSTyfDjH/+YT3/60zz22GNEIhHLNuj3+/H7/daA5HK58Hq9eDweq4iXOHw+n/XV5/NZAbc4VFVldHSUZ555hh/96EdEo9HlfusSyQ0hBB3DMOadGicm7bnSvySSlUhdXZ2VfhgOh4lEIkvyuqqq0t7ebtUD6+vrmzMNVHJjuFwu6urqUFWVTCbD8PDwkhXfb2hoIBAIWJ0FiztrSiSS2xen08kDDzxAVVUVyWSSI0eOLHtjiWg0yl//9V/zpS99iUQigd/vtxzbdiHHLuaI7+2/sws/4vvi9VTx2kqsxTRN44UXXuDkyZMrvnNqfX09e/bsQVEU+vv7uXDhwnKfkuQWRgo6czA2Nsaf//mf89d//deMj49bXamEaCMGGpfLZSnK9kGo+Cgl9tgFHr/fj6Io9Pb28oMf/IDu7m65yJWsGOyCznwXj06n06r3cyt0IpNIFhJFUWhtbcXlcqHrOkNDQ0smrAQCAau2gkjzlSws9fX1BINBTNO0uk8tBQ6Hg9bWVpxOp1VPQsYKEsnqoaGhgR07dgDQ09NDV1fXsp3LxMQE//N//k9++MMf4nA4LCHHnlZld+LY3TnFAk+lNZR9HVVqLeX3+3E4HJw5c4YjR46s6E0M0ca8oaEBXdc5duwY8Xh8uU9LcosiU67KYJom586d4y//8i8ZHBykqqrKsg3aBxO3220VQIbcB1CkX4lDFEa254MWv5Y9H9TtdpPJZCxn0K5du7jrrrtkJwvJLY/b7S7oSDAXogOcSL0yDEPW0pHcdvj9furr6xkZGSEWizE+Pk5LS8uiv66iKDQ3NxMOh0mlUvT39+P3+2+qvpdhGKTTaWKxGNPT00QiEQzDsBylwWAQVVWpqamhtrbWer2VbH+vhNPppLOzk3g8TjabZXBwkM2bNy/J+62urqampobx8XHC4TDT09OyG4pEskpQVZX9+/fT1dXFxMQER48eZf369UvexnxycpLPfe5znDx5kmAwWDataq6jVLerUmlXIuVKpPbruj6rO1YqlbK6rh48eHDFxpVVVVUcPHiQH/7wh4yPj3P8+HEefPDB23Y+lbx5pEJQAtM0OXLkCF/4whdIJpNUVVVVHKDEICTsfcWCTqk2fMWvJxazTqcTTdNwOBw4nU7S6TTnzp0jFotx//33y05XklsaMfnO1WHLjhAq52pHL5GsVES76enpaavjVX19/ZKM516vl6amJvr7+616K2vWrLmheiuGYTA+Ps7ly5e5cuUKQ0NDRKPRWYUrVVUtmPcURaGqqor6+nrWrVvHunXrqKmpue2CUZ/PRygUYmpqilgsxtTUFPX19Yv+uuL/KhwOk81mGR4eprq6esUuXiQSyY1RXV3NPffcw9NPP83IyAinT5/m4MGDS1ZPK5FI8IUvfIGjR48SCoXmLHYs1jbiaynHTqm1k719eakaOsX3UxSFZDLJxYsXUVWVAwcOrNh554477uCNN96gp6eH06dPs23bNpqampb7tCS3GFLQKcIwDA4fPswf/dEfkclkCAaDFYUcp9NpuQwExb+3D0zllGaxANZ1vUDUUVWVdDrNtWvXAKSoI7mlsXcimG8+t6qqBaKmRHI74na7aWxspL+/n3Q6zdDQEJ2dnUsSeDc2NjI+Pk4ikSAcDpNMJue1i5tMJnnjjTc4fPgwvb29ZDKZAlt8qR3V4q4kqVSKgYEBBgYGOHHiBDU1NWzfvp1NmzbdNgUeFUWho6ODeDxOJpNhaGiI6urqJXHVim5bIyMjxONxRkZGlqzblkQiWX62b9/OuXPnuH79OidOnGDz5s00NDQs+utqmsbXvvY1nn32WQKBwLyKHBeLOS6bmGOts5xOnPnnEBkOxYKOiBeFO0fTtAIxB2bWVxcvXqS+vp4tW7Ys+jVZDDweD/fddx8DAwPEYjGOHDnCI488IrM2JAXI/4YiTpw4wWc/+1kymYylNpcbpOzf2x06pe5vb8VXrgWfEHQ0TZulNANcu3YNRVF44IEH5A6c5JZE/L8ahjHv3RDZtlyyWmhsbLQcHKKNeSAQWPTXFfVWrl27RjqdZnBwkA0bNpT9jKbTaU6ePMmzzz5LX18fiqJY9QlK1UUo7ug4a54zDEzDwDBNJiYnefnllzl16hQ7duxg69atN5UCdqvg9Xqpq6tjeHiYZDJJNBq16hctJoqi0NbWRjQaJR6PMz4+TkNDw21xTSUSydx4PB4OHTrE8PAwkUiEV199lXe/+92L7kh58cUX+eY3v2mtgUqlS9nnhWJhx+l0gq6TGRwkNTCAOj6OIxzGpWl4QyE8jY34N2zAuXkzntZWFLcbSqyZSrlzYGZ9lUgkOH78OI2NjUsyJi8GHR0dbN26ldOnT3P58mVrU0QiEUhBx0Z3dzef+9zniMVihEKhWcJMKZXZPpBlMhlM06woAhWnXRUrzfbBqVSKVnd3N1VVVezbt0+2KJXccgjHmqIo8065Kq6hI5Hcrog25pFIhGw2y9DQ0JK1Ma+trWV6epqJiQkikQjRaJTq6uqC+xiGQU9PD//0T//ExYsXcTgc+P1+fD5fQReSUpZ6e+BuJJNoQ0MYAwM4Rkdxx2K4MhkcXi+EQmQbG0k1N3M8HOb8+fPce++9rFmzZsVa4gXNzc1MT0+TTCbp7++3GigsNuL/6tq1a1ZKX3t7u4wRJJJVwrp169i8eTPnzp3j/Pnz7Nixg7Vr1y7a642OjvJnf/ZnpNNpqqurC9ZJxXVD7WKO+D49OcnEqVMYZ84QGBujOpvFb5r4FQUv4AJU0wRVJREIkLzjDvzveQ+Bt74VNRgsSLcq3kCAwrWVx+MhkUhw4sQJ3va2t61IZ4vD4eDee+/l2rVrhMNhjhw5Qmdn523jcpXcPCvvv3qRiEQifP7zn2dgYGDW4DTX4XQ68Xg8pFIpDMMoWbm9lCUdZhdEFoOTOMR97GlZZ8+epbW1lfb29uW8ZDeN6GikaxqmaeLI2yslKxfxfyvEyfkgJlfxWZBIbmeqqqoIhUJWQeHp6ekl2TVUVZXW1lZLTBobGyMQCFifv1QqxQ9/+EMee+wxUqlUyY6O9hazs9KtVJXE8DCRY8dwXLhAKBymVtcJAn7Aoyi4yQfpQNbtJtbQwNi2bbw8NsbG/fvZt3//inaWuN1uWlpa6O3tJZVKMTg4yPr165dMsBsfHycSiTA2NkYoFCIUCi3660okkuVHLPivX79ONBrl8OHDtLS0LMqCX9d1/v7v/56rV68SCoVmrW/s5SUKDkVBSyYZfuEFtBMnaIjFaFIUaoAgEFAUfIAXcCsKDkVBBcxYDP2110iePEn0m9/E94u/iP8d78DweArKWQCzUrKE8ONyubh+/To9PT1s3rx5wa/JUlBXV8fevXt54YUXrDbmu3fvlsK9BJCCDpDblfzWt77F8ePHqaqqKpkmVeqwCzqii4dpmtbPpZw6lRw65QQf8VUIOslkkpdffpn3vve9+Hy+ZbtubwZd1wmPjtJ3+TKDp0+jDQ7impzEYRi4g0HMpibq7riDlp07qV2zBpdUn1cU4v/XNE2y2ey8HiNcPYZhzLvujkSyUlFVlfb2dpLJpFVLJxQKLUnKoc/no66ujtHRUaanp61uW5OTk/zt3/4thw8fxu12U1VVNUvIsYs5xfNaJhrl2vPPY7z2Go3pNA1ANRBUFALkBR1yQbpTUVBNEzObRR8aonN4mMkTJ+i97z6eGR7mgXe8Y5ZzaCVRV1fH2NgYsVjMSoOqqqpa9Nd1OBx0dHRw+fJlq9tWVVWV3CSRSFYJTU1N7Nixg6NHj9Lb28vFixfZvXv3gr9OV1cX3/ve90qm2toL5NtToBRFYWpggKFnnqF2aIhmoCEv5oSYmSt8ioJXUXJzBaCClWalmSapnh4in/40kSNHCP3Gb+CuqZnlzCnOfBBrsmw2y6uvvkpHR8eKWztBLr12165dXLhwgeHhYY4ePcqGDRukcC8BpKADwOXLl/n7v//7WR06im2ClUQdu6BjD3aL7el29RooyAW1K83l1GaXy4WmaUSjUU6dOrViKren02kuv/Yap773PVJHj+IZGqJa0/AbBj5ywb4LcCoKhqoyXFPDlW3bqHn/+1n79rfjr6+XKvQKQPx/A/MWdFwul+Xqme9jJJKVjN/vp6amhpGREZLJJNPT00vaFSkSiZBMJhkdHcUwDP74j/+YCxcuWEJOcWpVufazTqeT0e5uur//ferGxmgB6qEwSFcUK0j3AA5mB+m14TCNTz3Fta4unpmY4K3vex+NjY2Lfj0WA4fDQVtbG93d3WQyGQYGBpasjXkgEKC6upqJiQni8bhsYy6RrCIUReHuu+/mypUrjI2NcezYMTZu3LiggrKu63zrW99iYmLC2oiYS8hRgMGuLoafeYa2RIIGoJb8HEFunqhSVapUFb+i4FFVHPZ43zTBNHEZBl7DwKdpRJ54gmhvL6E//mPczc0As1w5Yl1l76QVj8e5cOECe/fuXbBrspRUVVVx//3389hjjzE5OcnJkyd5y1veItdHEm59JWCRyWQyfO1rX2NqamqWi8Y+MJUTdMQC1uPxWItSMYDYBR1xiB3P4kP8rpyzxz4gud1uHA4HFy5cYGpqarkvYUVM06Tr/Hn+9jd/k//zH/4DsW9/m+reXpozGRp1nUbTpEkcQJNp0qrrtExMsO6ll/D9zu9w9d/9Oy4+8QSadG/c8tgFHU3T5vWYN9PqXCJZ6TQ1NeH1ejEMg8HBQdLp9JK8rsfjoampCUVRmJiY4FOf+hRnz57F5/PNSq0qbjtrL4TsdDrpOX2aM1//OsGxMerJu3IoDNCrHA6CTidVTidelwuX241THC4XXqeTkMNBs2my7coVNnzxi7zw7W8zNja2JNdjMaiurqa6uhrTNIlGo4TD4SV5XUVRaG1txePxYBgGw8PDUiSXSFYRwWDQ2ugdGxvj5MmTltN/Iejt7eXJJ5+ctQ6yb0QXCzqD165x5cknqU0kqAGqyLk2fYqCT1XxORwEHA4CTic+txuH243i8aB4vTOHx4PicqE4nXgdDupUldozZ4j+3/835thY2a5a9s154Qa/cOHCih4XN2zYwNq1azFNk1OnTjE8PLzcpyS5BVj1gs7p06d56qmnrA97qWJes9TmEiKPx+Mp6O4jgt5it45dtCkWesrtgJYqyCycOsePH1/QwXohyWaz/PC73+XLv/ALTD3+OHWRSE6ZN01qTJNqRaFGVal2OKhxOKi1fa1TVRpUleZslvbTp/H/1//KG5/+NOHR0eV+W5IKKIpiCTrz/b+07yzIlCvJasHr9dLU1ATk6tcspYBRX1+P2+3m7/7u73j55ZdLuklLCTr226+88Qanv/c9apLJmRoIgD/vyPGrKn6nMxeku1w4bAE6+UPxelHcbhSXC7fDQY2isGF0lE1/93e88L3vMX2Lb1iUQ3SecrlcmKbJ+Pj4vAXum8Xv99PS0gJAPB5f0cKYRCK5cbZu3cq6deusBf/4+PiCPK9pmjz77LMMDw+X7GhYXJgYYGpignM/+hGhdNqqpybq5HgVBW9e0PG5XHiEkOPzgd9fePh8uTkjL+w4VZVqVSV0+jSRz34WNZOZtVayb7qL751OJ9FodEWPiy6Xi0OHDuHz+YjH4xw+fHhFC1SShWFVCzq6rvP973+faDQ6q75NsdosKCfqCIeOaZoFqVv2IHiuo1J3rFKFmB0OB/39/UxPTy/TFSxPKpXim1/8Io///u/jHxigzjSpNk1Cpmnt3gZVlaDDQcjppMrlmtm9dblwuly4nE58qkqNotCYTNLyj/9I92/9FuGBgeV+e5IKpFIpIFcbZz6ijnDnACsifVAiWSgaGhrw+/0ATE1NkUgkluR1s9ks3/3udzly5AgAiUSi5LxTav5xOp2MDA1x9F/+hVA6be22esmlznryQbrX4cDndOK17bZSHKiLID0v6jgcDoKKwpqxMdq/8Q1eeuaZJXMuLTRer5eGhgYApqenGRkZWbLXrqurs7prTUxMWGOyRCK5/XG73Rw4cACPx0M0GuXIkSML4n6Ox+M899xzALPEm1IpP9lsllOHD+OLx6liZo5wAS5FwaWquB0OPE4nHpcLNT9X4PPljkBg5hBzhscDYr5QVUKKgu+ll4h+97uo+Q3FUulfdnHHMAxOnz59y26Gz4f29na2bduGoihcvXqVnp6e5T4lyTKzqldPfX19PPvsswUf/rmsg6VuUxSloPUyzKSe2PM3RXBc/Nji+5QKootVZvGYTCbDpUuXlu0alkLTNP7hK1/hub/4C0LxOEHTpMo0ZyyW9t1blwuv241q3721K/FuN6rLhU9VqTMMOn/yE67+zu8QXsLgWHJjuFwuICeYzqdrlf1zt5InWInkRnE4HLS2tuJwOEilUgwMDCx6pzfDMPjOd77D008/bRXNjcfjaJpWdt6xz2VaNsvzTzyBOxYjQL4jCTNBultVcdmC9FliTqkg3esFjwfF6cShqgSBtb29OB5/nFdffXVFdr8T9Yq8Xi+maTI2NrZku6gul4uWlhYURbG6bcmxVSJZPaxZs4Y777wTgEuXLtHX13fTz9nb28uFCxdKrovsDVzE157ubmIDAwTIF8UnV7jVqSg4VdUSdVxOJ06XC8Xtttyb1txgny+E0OPxgMsFTicuVSVkmmhf/zrawECBgFNuA15RFIaGhla00O1wOLjnnnuorq4mm81y5MiRFf1+JDfPqhV0TNPkhRdeYGxsbFZnqUrFpUr9Tgg6MLOILVdvp/g57ANNqR1Ru4BTnOYlft/b23vLpKqYpslzzzzDD/76r6lKJq0OJ5a9UuzeOp348pXnmcti6XYXDNztL75Iz//6X2SSyWV+t5JSiDbIhmHM26Ej0rR0XZcLD8mqoqamhqqqKkzTJBKJEIvFFvX13njjDf72b/8WRVGora212tpOTk6W3DgoFnZef/11xru7CTAj5NiDdIei4HI4rCAdEaTbx/hiUUcE6W43ihjrgTWvvkrfq6/S19+/qNdksXC5XDQ3N6OqKplMhqGhoSUb3+rr6606PtPT04v+fyWRSG4dnE4n9957L8FgkEwmw8svv3zTC/6TJ08Si8VKplZBoZiTyWS4fP48PtO0hBxREF8FVPu6x+FAEXOFx1M4X9jnCJv4L+YKRVXxqCr+8XFi3/seCrPXWHbEOiqRSDA5OXlT12O5qa2tZd++fSiKwsDAAGfPnpXx8ypm1Qo66XSaF154AcMwyg5OMNNlqvgoxu12Ww4d+3OWc/6UolSRsbmqxzscDiYnJ5es6OJcXL9+nb/5kz/BE4ngM028+cG8pMXS7c5Z7cXgXcpiabfki0DfMKj/wQ/o/dd/lYPXLYhwq83XoWMXdLLZrPybSlYVoo252+3GMAyGhoYWrTh4MpnkL//yLwmHw7jdbrxeL3V1dSiKQjweJx6PV+zmmMlkOHb4cMUg3aGqOFQ1F6Q7nTlBvjhIF2O7OESQnt91xeHAoyjUJpPUvPIKx44eXbG7j/X19VZbWdF9ailQVZWGhgYcDge6rjMyMrIinU4SieTNUVdXx549e1AUhf7+fi5cuPCmn0vXdU6fPj1rbiq3RhoZGSE+PY2bmTlCEYdYw6gqiqqiOhzgcOTGfzFfiDlDfBXf58UcMU8oDgdORcELZJ97Dm0eJShEfLrSBR1FUdizZw8tLS2YpsmJEyduyRIckqVh1Qo6o6OjvPHGGyULeZWyDpb6nf17UT3dMIyC4ofzbSVXrvCy/Xf257OLRdlslq6urjd1HRYSTdP42t/9HVNXr+LH1oqc/O6tsFk6HLidThx5O74V6AsRp4wdH5fLCvSrkkm0v/kbErK6+y2H3aEzn4WpoijWY5aqcKhEcisRCASoq6uzXDqLUbDRNE1+9KMfWUWQhUhTU1Nj1fERbczLNQHo7u5mcmSkZJCOokBxkO505o5SgbpdyBFBuhB08k4fL9B08SKR69e5dOnSihR7RRtzh8NBNptdkrQ6QU1NjVXHJxwOr/gFjEQimT+KorBv3z6am5vRdZ3jx48TjUbf1HNpmsbly5fLCjjiEJvafX19qIZhzREAZu6kMG0Hqpo7hKgj5g2Xa2beEF/tc0Re0CG/0e1SFJwjIyTytXGKN+CL5w5d1+nt7V2Rc4odr9fLwYMHcTqdTE1Ncfz4cSncr1JWraBz+fLlAiWz+ENdLNyI9JFyh0inMk3TchkUP24ubnRgsYtFSxkklqO7u5tn//Vf8eg6TtMs3L1VlJnd27zFkmKLpT1vtijtSgg6wmLpVRT8V65w7cknV/yAfLvhcDgKJva5EEXEZdtyyWqmpaUFj8eDaZqMjo4ueL2VaDTKN77xDXRdt1J7xWevoaEBVVVJJpNMTk6WdJgCnDt3DtU0ZwUOpqJg2r4vG6TbA3V7sG4P0vOCkKIoOBSFQCyG7/p1zp07t2ILJAcCAWpqagCIxWJLJqwoSq6NucvlQtd1pqampGgukawiAoEABw4cwOFwMD4+zokTJ95UzBwOh5mYmABKu3JEvCfWQBPj46iKgkJOyDEBA9DzX61DUTDymwHY5w77IeaQ4jlFVWc2EgCnrpM4caLgXEqt3yA3No6Ojt4W64fNmzezfv16TNPkjTfeYGhoaLlPSbIMrEpBxzRNurq6ZgWHxR/+4kGg1O1i4BDpUIZhkMlkrNvtRyVhp9LAU05tNk3TEnWmp6eXVdAxTZOnnnqKyNjY7N3b/GBrWSzzVvxZKnyxvdK+eyvulx/EnYqCR9OI/uAHpJfIwi6ZH+L/8s0WOb4dJliJ5EZxu900NTWhqirpdHrB660cPnyYU6dOFXR0FPNWMBgkGAwCMDIyQjabneXQyWaz9Pb2FgQNRtGhKwo6uaBdtwfp5QJ1EaCXul0E6aaJv7eXSCTCtWvXFux6LCWKkmtj7vf70XWd4eHhJRNW3G439fX1KIrC9PQ0o6OjS/K6Eonk1mDLli1WG/PTp0+/KQfo1NSUVYereP1jXxsZhkEymSSZT5G15gbbodkP00TLPydzHcXY5heT3DibvnoVLb8G03XdSv0vda7pdPq2iDddLhcPPPAAPp+PZDIp25ivUlaloGMYBleuXAFmiyXFok0ppbfUIYJewzBIpVIFA4n4XhR8LecGKr5vOUGolI0wlUotWW5+KRKJBD9+/nlUw0AxzQJV3oRCi2VxcC/EHaezcMe21A6uw5Gz85NL5Qp2dTF88eKyvW/JbEQ9nPk6dGAm1Uq0lJRIViNNTU0Eg0FM02RycnLB6sZks1kee+wxMplMgTvH7sJpbm626uQM51NZ7YJOPB4nEokU7LSWDNBthzVXmSYYRu6YK1AXzlMxXwDu0VFU0+TUqVO3TAOAG8Xn81FbW4uiKCSTScbHx5fstZubm/H5fACMjY2tWKeTRCK5cdxuN/feey9er5d4PM5LL710w4JyIpGwxo1KayEh6Oi6XnJ+yIrDNMkCGdMkbRjohoFpGKDroGmlD13PHbZ5xASMvCikA5mxMbKJBLqmld1YF9/run7bCB/Nzc3s3LkTRVHo7u7m6tWry31KkiVmVQo6pmkyODhofV8s3JQbAOwii114sXfmEaqvrutomkY2myWbzVrfa/lBplic0TQNTdNmPa94zXJquHh8NpslkUgs2zUdHh7m6tWrBbmyIvAvsFbafraEnVK7t3ZLZYmdW/IpXEo0ysSZM7eFyn67IOpJAfMWZ0SXuPl2xpJIbkfsbcyFsLIQAuf169c5duxYyRptkBNufD4fdXV1QG7RX7xBEIvFyGazGMwO0K1A3TTJmiYZ0yRjGGhCxBGBuKZBNps75gjSxWLAAJRoFNUwVrzDpLGx0RJWRkZGSC5Rp0a3201zc3NuBzudZnJyUo6zEskqoqOjg61btwK58gjd3d039HixHoHSgo59A1s4X3QgQ25uyNi/N00yQNowSOcFnZSuY2oappgfMhlIp3NHJjNzlJgzhEikAVo2SzaTQcuvwYo3yO3fZzKZ2yYFVVVV7rnnHmpra9E0jaNHjy7Z/CK5NViVgo6u64TD4QIRppyYU0rIKSXqiOfSdZ1kMmkJOeLIZDKzbrMLPsW3F4s7Wgm12S7qiDSv5cA0TXp6eojH4yV3b0sp9NrMgwsPEdTbd3KLXy9vrzTyTqDM2bOL/h4l80cIOuL/cj7YBR3p0JGsZqqqqqyuSJOTkwvSteLo0aOMj49brpxixPzV2NiI2+1G0zT6+/sLBNZkMolhGNYYnrEf+QA9A1aAntT1mSBd0wqDchGsi++FwGMP0vPCUBbQNQ01v/HR1dW1YsUIt9tNa2vroqXVVaKuro6qqiogtwEj25hLJKsHh8PBgQMHCIVCZDIZDh8+/KadeqLeYfHGs319BLk4PwOkbUfKNK2vKdMkqeskdJ2EppGxCzmpVOFRLO5oGqauoxmG9ZxpcnORpmloRWsp8dV+nit1HilHKBRi//79KIrC4OAgp06duu3eo6Q8q1LQMYyZTlSVrIPzEXLsh2iFl0gkCkScTCZDJpMhnU6TTqcLfhbfi0Pc3+7oKR6QSg2emqYtmxqrKAoDAwPohjFr99ZuscyIAD2/e6sbBqZ957bYWmkP8Ius+pZYZJpkR0YwZDHdWwa3220VRp7v7ododW7/bEokqxFVVeno6MDtdqPr+k23Mdc0jZdffrlAKC0n6og6PpATk0TxXnu9NhGk2480OSHHCtINg6RhENc00vkg3RQBeSoFyWRhoG4TdUSQns4H6RkgI+qvkWsAsJKDVNFVTNS0WapUadFty+l0ks1mGRoakuK5RLKKqKurK1jwnz17dt5jqb04PlBxPSTupwGp/FEs6oh5ImEYxHWdmKYRy69/zFQKU8wTySQkEjNzRjqNmc1i5tdDKcMgKZ4PSDocZHWdbH7tVCn7wTRNq8Pq7YCiKOzYsYP29nZM0+S1114jHA4v92lJlohVKegIJ4ygeGAqFnWKD7vAYh8sxII0mUzOEm2EmJNOp0mlUqRSqYKf7fcrdvSUG5CK80AXqt7CmyEajWIYxkx+LCV2cE0zF/QbBinDIJMXb0paLO1BfjZrWSytFDPb66RjMbTbJA/2dsCezjHf/GR7q3Mp6EhWO16vl7q6OhRFIZFI3FS9lXA4zOXLlwtuq1R0v76+Hr/fj2EYXL9+3era6PV6UVUVHUgyE6jbvy8I0jWNuKYRzWRIpdO5IN0enItDBOmZDGY2m3O5GgYJ0yRpmiSAlMuVK6avqsRisTfdevdWwC6s6Lqe2wxZog2JYDBIdXU1kEuhm5qaWpLXlUgky4+iKOzevZuWlhZM0+T48eNEIpF5Pdbv9+P1eq2fizeZ7Yeo02aSmx8S+SMpDtMkkT/ihkFM04hqGpFMhnA6TTKdxsjPF2YikZs3kklL6DHz66OErhPLzxVxIA5kqqtzG8lF66dSB3BbCTqQq9V233334XK5CIfDvPrqq7J77CphVQo6brd71sA036PYLWN30giHTrGgIwQbIeQUH3axxy4ClUrDKufWUVXVCtSWkyyFSry1c0uRKq/rxOeyWBbv3uZFnWxeGBI7uFLKubWw7/7PN+VK7OjciKtHIrldURSF5uZma54aHR190/b48fFxq8ixnVKtZ03TRFVVWltbURSFaDRqpQUFAgHcbjcmOfGmYpCu68R0nWg2SySbJZJOk0in0W1BuhB2zGQSMy/oZDSNuK4TzwfpCSBumiSDwVxhfHLFOVeyoAM5a7xYVEWj0QVJq5sPoo25x+NB0zSGhobkeCuRrCL8fj/33nsvTqeTqakpjh8/Pi+nnnAWCsRmcqk1kcPhwO12A4VzRcGRF3OK54pwJsN0KkUkmSSRTJJNJtGTSbRkEi2VIpNOk8hkiGoa0bygEzNNYkDENNHa2sjm6+PMJeqITYrbjXXr1rFp0yYAzp07x8DAwDKfkWQpuP3+k+eBw+HA4/FYP1dKo5rLmWOvgyPySpPJ5CyhppSQk0wmrftWEngqiTrisFvil4NgMIiqqgUWy4KjhJgT0zRimQxZ++6t3WJpE3XE7q2W371N5u2VKSBbVYUzH+zPl2JH1kq28N9qqKpqBQii49Vc2F09t+MEK5HcKB6Ph7a2NhRFIZVKMTw8fMPjlGmaXLt2rcC9WdzVsVQNuVAoRHV1NYZh0NfXRyKRwO/3W5sGcwbphpEL0jWNSDbLdCbDdDpNOJUinkqRTqXI5oP1bH6ui2WzVpAetQfpikK2tTVXEB8sIWIlj9mKolBdXZ0TyEzTahW/FPj9fhoaGqxuW2+mhbFEIlm5bNq0ifXr12OaJmfPnmVoaGjOx9TU1NDQ0GD9LDbfSq2JTNOkqqoqdx8gRs49I77GyQn1sfwR1XUiuk5Y05jOZpnKZJhMp5lKp5lKpZhOpQin00yn07m5JJslrGlEDIOoYRA1TSLAtKLg2Ly5bM1S+wZ8JpOxxsHbDafTyaFDhwgEAiSTSV599VUp3K8Cbi+v2TxRVdX6IJfqMiW+F5Zou+ps/ypySkXbV8i5fYQoI1KwhHggbIj2lrFQWMfH/vqlCiaXUpuz2ewskWqpaW9vR1VVMth2bPOHF/AAbtPEZRg4FQWHrqPmA1gTCBgGbqczdx0VBSVfGNnUcl1RzEyGrKaRzO/exsXuraKQbW5GLSMcmKZJOp0mEokwOjrKyMgI4XDYqlNkmiYejwev14vX66WlpYWmpiZCoRA+n++2HOyXghsVZRwOh/V5kfZQiSSH2BWNx+NMTU1RX19vFbadD4qiMDw8bH2migWcUsKO+F1rayvxeJxEIkFvby/bt29n7dq1uecjF5z7yI3vbnLBhMM0UWFm/CZX50wzDDK6TlJV8agqLsCRv58p5j1dJ6PrpPOifcw0CQNTioKyaZN1roqiWLV9VjJ+v5/W1lauX79OPB5ndHSU9vb2JXntpqYmJicnLUGntra2wLUskUhuX1wuF/fddx/9/f0kEgkOHz7M+973Pqs5RbnHbNq0qaDQrlh/OJ1OnE6ntT5yOBzU1tZaYnECiJJbB7jIzRVinlAMA1NVMXQ9VxPTMMioKh5Fwa0ouBQFh2nm5or8XKXpOtl8nbVUXvifAqZralizdi2ZdHrOdZRhGLS0tNy2MX5zczO7du3iyJEjXL16lYsXL7Jjx47lPi3JIrIqBR1FUVi7dq0VHApnTXGtGvvgVCzi2A+7oKNpmuW0gZkAWghE5QQde2v0UqJOcZHkYreO0+kkEAgs2zXdsGEDVVVVuSCRnALvJxfwW2IO4DRNVMNAzS8wTEDPB/u+bBaPquLMCzpKvmiypmlkNY20ppHSdRJi91ZRiABNRYOUaZqkUil6eno4deoUvb29Vlczce2defFI/Cy+P3fuHIqiUFVVRUNDA5s2bWLdunUEAoHbduBfaBRFsQQaUXhurmsn7q8oitxJkEjyiDbmPT09ZLNZBgcH2bx58w2NRdPT0wVNAMoJOMW3eb1e6uvrGR8fZ3Bw0Gp7Kyz6SWYH6Q5AzY/dmCaGaeaCdNMkrSh48oG6/b6mmCMNIxek5x2YVpBeXU3zunW5Ivr580wkErf0eGwXy+zzvD1WgFyR0pGREVKpFJOTk1aXscXG5XLR3t5Od3c3qVSKwcFB1q9ff0tfU4lEsnC0trayc+dOjh07Rk9PD9evX2fjxo1l7+9wONizZw/f//73C+aR4vWIEHaqqqrw+Xwkk0l0IIJN+AeU/IFY+ygKWr7WZsowcoIO5DaA8xsFZn4O0PKbBOm8Uz8KjAPs2IHp9VobtuVEHVEKYM2aNYt3gZcZRVHYv38/ly9fZmJigldffZV169bd0IaQZGWxKgUdVVW54447cLlcls25WESxCzl2EaZYyLEf4nmEoGPv2uNyuWY9VylBp5SoYxdzSg1MmUyGurq6ZXXotLS0sGXLFo4ePUqW2bu3LvK7t4aBCBnNfMCfFbuy+d1bsdOr2EQuTdfJaJpV0T4GhIF4MEjH7t2WMDc5OcmRI0c4fvw44+PjKIqCx+PB4/HgcrlwuVzWbkLx38IeaGcyGQYGBujv7ycQCNDW1sauXbtoamqadxrRakUIOoC1EzLXNRN/B/E/LpFIcgir+/DwMNFolHA4TE1NzbweKzYrBJUK/pdqDtDS0mI5Gq9evcqOHTtobW3NFfIlF0jbg3TV9roGoBlGLkg3jJygk991FTu0qmnOCD+GYdVHE0H6mGmi7N6Nw+8nm04XtMRd7jRjO5lMhng8ztDQEP39/YTDYaLRqJU2LeafUChEMBikoaGB1tZWamtraW5upq+vzxJW1q5duyTvq6amhmAwSDgcZnp6mmQyWVAjQyKR3L6oqsrdd99NV1cXU1NTvPjii7S2tlYcA/bu3UtVVVVB5yS7S6d4rdTU1ERvby+QS9OdJj/ukxNzTMAg7+IEa/z3Kkpuo0CI/0LQMU3L9Zk1czU04+TWAmOBAOvuuYdMJlNy/VTccTgUCtHc3Lzg13U+FJsJxM9iY3Oh5rZgMMjdd9/NM888w8jICCdPnuSBBx64ZeZNycKyKgUdgC1btuDz+azFo0i7EgOTGAjKiTmlBB0h3oiaOaqqWu4ZXddnPZf9Q1Us6IjnKhaaikUdkQu6Zs2aZa094vP5eMtb3sKxY8cw8ilRItXKbrEUNku70p5RVdKGgbco2Bf3K969Tdh2b7XNm+m84w7C4TDPPfccL774IpFIpCCNyuVy4Xa7Kwo69r+nwL4Dce3aNfr6+mhtbeWee+65bXNvFwJFUSzrrpis5kL8LWD+nbEkktWAoijU1dUxMTGBpmkMDw9TVVU1r+4cwq0oKNfNsdTtuq7j8XhoaWmhv7+fsbExpqenOXToEN/73vcwDIM0uWC6QMyhRJCuKLnxHawx3mGa1g6tCNIz+SA9QS74Hw8G2XzwILrtPMUcu9zous7IyAinT5/m4sWLjIyMWGOdw+Gw5hqHw2HFA1NTU5imicvlQlEU/H4/TU1N1NbW4na7mZqaoq6ujlAotOjnr6oqLS0txONxNE1jcHCQDRs2yBpmEskqobq6mv379/Pcc88xNDTEuXPnuPvuu8veXxTbfe2116zbRJfdUjF1bW0tExMTxGIxICe+FIs5BljzRAbwmSYpMVeQm1uEowew6vJkyYlEMXLuHN+BAzhrakin0wUbFfZ1kl3Q2bhxIz6fbwGvZmV0XSccDjM2NsbIyAijo6NWeQ6Yqe3qcrmor6+nqamJxsZGampqrPniRlEUhe3bt3PhwgV6e3s5ffo027dvp76+fqHfnuQWYNUKOmvXrqWjo4Pz589btxXngwqnTjkRB5glAIiBI5lMWqk9xbml5QSdYtt7cfpXOaXZMAw2bty4rAKDoii8+93v5utf/zpDQ0NkKbRYikGc/PvUFQVdUXKDuGmSzlss7dZ9K2e2aPc2Qa5Q5qTLxZ0/8zO8cfYs3/rnf2ZoaAiv12u1V3S73Xg8npJijn3ysbt07H/b4lQ4EfT+8Ic/ZNOmTezbt0/WHSiBoig4nU7r2s33MULQkSlXEkkhgUCAxsZGhoaGiMVijI2N0draOufjFEUpCFrFjmCxQ6dczThd16mrq7PqrVy5coW9e/eyefNmLl26BOTEFyvYJi/m5A+xi5o2TTxi15VCgR/IjfFAhlyQHgUmFIW6t74Vd21twa6rSAdbrvkunU5z7tw5fvzjH9Pb20s2m7VcoG63G6fTOWvjQKT0Fm8aZLNZ+vv7uX79Oh6Ph6amJrxeL4FAYEmcoKFQyHJ/TU9PMzk5WVD4VCKR3L4oisKuXbs4d+4cg4ODHD9+nI0bN1JXV1fy/lVVVTz00EO8/vrrBc1ExLqn1Bqno6ODK1eu5GpWkhvb7fOERm7cF2N/CvCYpuXsL1g/MCMAZcjNPVNAZt061t59N5lMpmLpCrHZrus6+/fvX3TxOpvNMjw8zLlz57hw4QITExOkUilL8LdfL/tX4WoS65iOjg62bNlCe3s7Ho/nhuY+r9fLoUOHGBwcJBKJcOTIEd797nfLTIPbkFUr6IRCIfbv38+FCxesQck0zQJBx+FwkM1myzpzoFDQEQNJJpMhlUqVDJDnEg/sAkK59Ktipdnn87Fu3bolv4bFrF+/nve97318+ctfznX7Mk2mKVLX80cpi6WbQoulwsx1se5LTpGfBuKdnVwaGeEbTz0FikIgELBcOSK4FocQdOy7pnO5dOx/E/vfIZvNcvHiRYaGhnjwwQdpampaysu8IrhRh45d4JSCjkRSiKIotLS0MDU1RTKZZGpqioaGhopFLMXjfD6f1XnOXi+uON2qlJgjvm9tbeXq1auEw2GGh4f5qZ/6KUZGRpiensYkt/NaHKRnISfmMBOke8gFHXZBRzyuOEh3bN1Kx8GDs7pMGoaxLGKOpmmcO3eOJ554gu7ubsthU1VVZblAxVxTvHGgqioOVUVxOHJzoa5jptOgaZi6jqmq6NksQ4ODTExMMDAwwN133211FVssFEWxCiRnMhmGh4eprq6e8/9KIpHcHogF/w9+8AOrjflDDz1UcsGvKArvete7+NrXvsbo6Kh1u2EYBRvg9vWNx+Ohs7OT69ev5+JBcjG8XcwR84QPW91NsIrn2109OjPunAiQbGhg58MP5+aQ/Aa3PctBxOxiXZZKpWhra6tYL+hmSafTnD17lh//+Mf09fWhaZq1wezz+cqK/uIQ11qsSa9evcq1a9cIhUJs3LiRbdu23ZCLc+3atdxxxx288cYbXLp0iZ07d7J27drFevuSZWLVCjoOh4N3vetdfOc73yGVSlmLTmHRK+XMsS86y7lrxOOTySQul6tkcWX7c5UTdErVOSgWdITavH///iWxaM+Fw+HgYx/7GM899xxdXV3WwG3XwEXAn2XGYunFpsgXCzoU7t4mgahpMqIo9BkG2uHD+Hw+An6/lWZlF3JEgG0PtItFHbs6Xi7tyr4QcrlcZDIZIpEIzz77LHffffcNFyq93Ukmk0Duf2I+go6iKFaqlbT8SySzcTqdNDU10dfXRzweZ3BwkDVr1sw57tTX1+N0Oq1CkKU2CYrnKPF7MTZWVVVRXV1NOBymp6eHAwcO8DM/8zN897vfJZFIWKJOcZAudlytTofML0g3Ojq4//3vR3E4yBYVuNQ0jWAwuKQ1dMbHx/nud7/LK6+8AuQ6VPl8PmvjQFjlix061jVVVbRYjNTVq5iXL+MeHsYXieDTNDxmrlg/fj/Zxkbi69bRNzLCQH8/e/ftY9u2bYu6m+r1emloaGBoaIhUKrWk3bYkEsnys2HDBjZt2sSFCxc4e/Ys27dvp6Ojo+R9169fz9vf/na+/e1vA8xy6ZTa/A6FQnR2dtLX12eJOglyc4WYI5LMFnSE+G/fEBYbBTEgU1/PXe95D65AgHQ6XbG5jFgzZbNZHn744UWpOappGmfPnuXxxx+nt7cXVVVz65NAYNbmcinRv9LGsq7rxONxTp06xYULF9i2bRu7d++eV5aAw+Hg4MGDXLt2jVgsxiuvvEJra+uSFOCXLB2rVtABuOuuu9i2bRsnT560bhOK6Fz1VYoR6VaiAGUqlcLj8RR0tyonHAglVjyPXUAoHpyKrYOGYbBv375bZhHc2dnJf/2v/5Xf/u3fJhKJYJgmEQp3YYstlt78zq0bcJmmlTMrEIsEUQBtUlHoDQbR/X6CeRGnuPBxpaNkwF1iZ6GUa8qePud0OkmlUrz88suk02m2b99+y/wdlhsxWc437Up0IIOZz4MUyCSSQhoaGpiamiIajVrpMXN1N1yzZk2BIGB3u9i/CkepGN+KA8yWlhYikQiJRILu7m62bdvGBz7wAR577DGi0SiQG8/tQXqCom6HzC6gbNgeEwPca9fytv/r/8JTVTWrw6OY/5qampZkfDAMgzNnzvDlL3+Z4eFhAoEA/vzmgT3NqtidU1A/Z2KC4VdegTNnqI1GqTVNQopCle26uAA1HofRUfTz50l4vYxv2MCZnh4GDhzg/gcfXNTuJM3NzcTjccLhMOPj49TX18t0YolkleByubj33nvp7e212pi///3vL+nUczqdfPSjH+VHP/qR1ZZcIDIaSmUxCOefKAAPuTVAlpyYI+aJUnOFXfhPA3FFoWrtWu568EG8waCValW8bireBI/H46xdu5a77rprwa/h8PAw3/nOd3j11VdRVdUS/YtLP5QV/UusN4vXIfa14OnTp+nu7ubAgQOsW7duzrVHY2Mj+/bt48UXX+T69euWU0dy+7CqBZ2qqip+9md/lrNnz5LNZgtcOuWU5lIOjuIPm2EYJJNJvF6vtds5Vy0e8VxAQRvZYoeOvX5OMpm0ipTdKiiKwk/91E/R39/Pn/7pn5JIJDBMkyg2Zw6FFkv77q3djg8zg3gGrBSuYb8fvbmZYD6wLg6ki9Xv4t+VE3XmU6zaXtxauK1SqRTHjx9H0zT27NkjhQhmUq5upI6OWHTOt9W5RLLacDgcViHbbDbL5OSklVJVjra2NkKhkOWaK9UAwC5U2zce7IfL5aKuro6xsTEGBgZoaWlhw4YN/NzP/RxPPPEEAwMDQE6ciTD/IF2I9QlVZc1dd/HWRx7Bm29YYC/+L74XTqXFRtd1nnrqKb761a+i67qV0msP0Eul9Ip5BcOg98UXifzkJzTGYjQCtUBIUQgCAcCrKHiZcaYqokB0KkXLhQuEr1yh99Qpnpuc5G0//dNUz7O72Y0iCnGKzlwjIyPzcn9JJJLbg+bmZnbt2sXRo0fp6emhq6uLbdu2lbzv9u3b+dCHPsTf/M3fFNTSMU2TTCZTUtAxTRO/38/mzZsZGRmxivyjKKTIzQGia6K9ILKY2cQ6wFFVxZadO9l8xx04XS6rqHCpdVNxupWu6zz66KML2s3PMAxee+01vvKVrzA2NmaJ/va5oriWZ7l6nsVrREFx9oZ4X8lkkhdeeIHNmzdzzz33VHQdqarKnj17OHfuHOPj4xw9epR169YRDAYX7FpIlpdVLeiIfNB/+qd/4ty5cwW/K1aaiwMbu5BT7OQwTdNqASoEnWJ3Trn0Hpg9MBXnggp3jqZpPPLII7ecbc7pdPILv/ALAPzZn/0Z0Wg0l36VT7FKAVXM7N7au2EJO769AJoQgMKqyrjbjaO5mVDewlhOoLEPluVEnVIdr+ZSxu3inP2+yWSS119/Hb/fzx133LGUl/uWxOl0Wp3f5tO1SnTGUhTFus7S7SSRzCYUClFdXc3U1JQVQJYrYglQV1fHhg0bGBkZsW4rbulaTsgp3shoaGhgcnKSVCrFlStX2L17N01NTXz84x/n2LFjHDt2zBrvhQvTHqTb060g335WValpaeH+t7+dbTt2WC5Zu5BjrxtXXV296CnG2WyW73znO3zzm9/E6XRSVVVVIOLM5QJNx+Oc+f73cZw/T5tpWmJODRBUFKoUhYCi4FNy3b9cilLYOMAw0E2TYDZLzWuv0TcwwI/Hx7n7gx+kvUwqxM0iOtKEw2EmJiaoqalZ9Bo+Eonk1kC0Mb906RJTU1McOXKEtWvXlnSAulwuPv7xj/P8889z8eLFgt8ZhjGrC6E9lna5XHR0dNDU1MT4+DhTU1NkMpncuG+aZMk58RVycaECqA4HwVCIdevXs379egKBALphoOfTrIoFD7ugI1KtkskkBw8eZO/evQt2zbLZLP/yL//Ct771LUzTtET/YgenmBdKuTgruXSKr6H9/blcLmtuvHz5MuPj4zz00EMVBZqqqioOHjzIk08+ydjYGK+99hpvectbpHB/m7CqBR3IqdIf+9jH+PSnP23lYAqE0lzqwwUUqNJ2d49oFSuqvovFaXGni0qFkUs5dOzpVvF4nO3bt9+yljm3280nPvEJamtr+bM/+zP6+vqAnDgzne9UFaC0Q8cKbBXFsuLHnU6mFQV/fT0Bv39W8Un7gGg/ikWd4hbm9ucoXsSU+juIuhKl/nbJZJITJ07Q2NhYcYG1GhDijHADzPcxgDVpSSSS2aiqSltbG9FolGw2y9DQENXV1WXrrPj9fu666y6OHj0KzASG9gYAwp1T3ASgePPB5XLR2NjIwMAAY2NjDA0N0dLSgsvl4oEHHmDPnj2cPn2a8+fPMzo6mhNlyLlwkvnzEYG61+ejo6ODu/buZdu2bbjd7lm1fUTAap/7WltbF6X+gUDTNL797W/zta99bZYbp9wOqz1IT0SjHP3Hf8TX00MzORGnitx851cUAqpKQFWpUlV8qopTVUHMJaYJpoliGCi6jtMw8Og6nuFhvH/7t7yqaRz6uZ+jZR4dzm4Uh8NBZ2cniUSCbDbL4OAgwWBQCusSySohGAxy991389xzzzEyMsKpU6c4dOhQyfVPe3s7v/Ebv2GVV7CvnYpFnVKb306nk46ODjo6OkilUiQSCZLJJJqmWfOOz+cjFApRX19PKBSy4spyz11qzSSyGerr6/nYxz62YBvgmUyGv//7v+d73/sebrfbEv1LuXGK54xKm8rzbdBSvO6ZmpriySef5OGHH6amgpNz69atnDt3jp6eHs6cOcOOHTtkZ8PbhFUv6CiKwvve9z6eeuopfvKTnwCFQo0oJGm/XXxvd9IYhmF1EtF1nVQqRTqdLmmlK65kbqf4g2vPBbW7c1wuFx/5yEcWNbC9WZxOJx/84AfZvXs3X/ziF3n22WdzAz85x00GiOSLIVstCvM7lcKKb7rdbNy+nZaaGk6fPYsvL+YU2xXtgkwlYaeSqFMsuM3HnQOFf7NEIsHzzz/Po48+ekv/beZC0zQMTUPXNMh3lXG4XCgOB468+6YS9mszX0HH6cwNR1LQkUgq4/f7qaurY3R0lGQyycTERNk0JEVRePDBB/na175mpV0BBWlXlXL4S9VCmJiYIJlM0t3dTSgUwuv1ous6fr+fBx54gPvuu4/JyUlGRkYYGxuzXlcUyGxubqa1tdVygIg051KCjr2gpa7ri5rWahgGjz/+OF/5yldwOp0FtdmKA+/i751OJ5l0mue/9S08PT3UAkFyLlQ/4FMUfKqKz+Eg4HDgzz8WhwNFVUFVc4KOYYCuo+g6aBpORSGk66wJhzH//u85Egzy9n/zbxbFPSMWUBMTE8TjcdnGXCJZRSiKwo4dOzh//jz9/f2cPHmSrVu3ltygVBSFhx9+mHPnzvHFL36xoGwFzIg6pTaq7dkHTqcTv99PMBi04vDieFxV1VxMmo8Ly5W9sAs69nnD6XTyiU98gpaWlgW5TplMhq9+9at897vfnVVLrVjsL7WpXKqWTnGt1eI1hr1GUHHTAjFnx+NxfvKTn/DQQw+Vra3ndrutNubRaJTDhw/zyCOPyDbmtwGrXtCBnA3tt37rt7h06RJDQ0MFvzMMwxJ1ih05xQOJvTtIKUGnVIercoJOsXWwuBjyBz/4QTZv3rw0F+gmUFWVO+64gz/+4z/m1KlTfO973+OVV15hZGQkl9OqKCTJ7d4q+cPhcFBbW8uOHTt478/8DHV1dfz33/u9kkJOsUhmv62US6dY1LEPwPbHCux/h2J3DpSeTMLhMKdPn+buu+9eUVbGbDbL6PXrXDp6lOlTp8h2d+OamsKXzeJUVYxQiMDatbi3bqX+4EHa77wTb5lJQ1yn+XS4EoiOWGLSkkgk5WlpaSEajZJMJhkZGaG6urqsiLxjxw42btzIG2+8Yd1mbzVbLh241BzldDppbGzk2rVrhMNh+vv76ezsnNX2vLa2loaGhordA7PZbMWOjvagPJVK4ff7F7Vm3PHjx/nzP/9zS5wqF5wXbxaIuf2lp58mcfUqjRSmE7sVBY+q4nE48DoceF2unDDucoHTCQ5HTtDJ/WFA0yCbBeESBqp0nfapKeJf/zrPV1fznve9D88CFy5WFIW2tjbi8TipVMr6v5JtzCWS1YHP5+OBBx7gn//5n4lEIrz66qs8/PDDJRf8LpeLT37yk1y/fp0nnnjCylAQiPVTqTWNy+UqiK1FUX5VVQuyGirVHYXSayZ7IWRd1/m5n/s5Dhw4sCDxuK7rfOtb3+If/uEfZjVkKSfklBN1KnW8KrXOKFXLs/jaTE5O8q//+q888sgjZUWdzs5Otm7dyqlTp7h06RK7du1i3bp1N31tJMuLFHTy7N69m1/7tV/jD/7gD3JtWEsMSoLiAUR8yBwOh1XvRqRc2UWGuQSdcip2cWGvu+66i/e85z0rygrtdru555572Lt3LxMTE1y8eJELFy5w7do1pqenMU0Tr9dLR0cHd955J3feeSednZ1omsav/dqvEY3FqK6urlhAzP61WOQp59IpVtTtz1dchGw+Yo7Ia71w4QLr16+nsbFxma/83GSzWc6eOMHxb3wD7ehRgpOTBDWNemYWJSIdTnn9dXjiCRI+H+d27sT9oQ+x8ad+Cn/RbrH437SnI86FrusF11YikZTH4/HQ1NREb2+vtfhes2ZNyfuGQiEefvhhLly4UBB0F9fQmasJAGCN1YFAgEgkwvXr16mpqcHv988KNIvrAZQbO0vVPrC7UsVxzz33LFoRx+HhYf7kT/6EeDxuiRjl7PClXKBXr1yh6/hx1lLYvcqVr5HjUlXcDgee/BykuN3gdoNd1IGcoJMXcxDpWORSkas0jc7eXsaefJKTa9dy4MCBBY8DfD4f7e3tdHd3k0gkiEQi1NfXL+hrSCSSW5e1a9eyefNmzp8/z7lz59i+fXvFueXTn/408Xic559/vqyoU+zMsXeOLe6wKOLtG2kkU7wBLtxB//bf/ls+8IEPLIgDxTRNfvKTn/CVr3wFVVVLOnLebPmHUhvVxfOlfdOjVGqWuK9oTf6Od7yj5PsWbcyvXr1KNBrllVdeobm5GZ/Pd9PXSLJ8SEEnj6qqfPjDH6anp4evfvWraJpW1j5oD0DtC3n7oCR2FEsVmywXLJcSCOxt91KpFOvWreM//+f/PGer2lsVp9NJc3Mzzc3NPPjgg8Bs+6T958cff5yjR49aHcMqiTmlDvv1r2R9tKdd2QUdMYjalXBxnuV2HdxuN/F4nMOHD/PTP/3Tt6yV0TRNeq9d4/H/7/8j/Mwz1EYi1JMv3AlUmSZ+RbEEHRegKgqKYWDEYmSOHCF54gSXv/tdQr/2a6w9dMh6r+I6zrcoMmDlNovrKpFIKlNfX8/4+DixWIzJyUnq6+tLzg2qqvLe976Xf/zHf2R4eNi6XaQV38jcJOof1NfXW23Me3t7Wb9+fUXBfa6d1UqCTjKZRFVVHnjggUXZyNA0ja985StcvnzZsv6XCrCL35d4r4Zh8NLzz+PNZnNdq5jp2OgAHKqKQ1Vx5uchxeUCjyd3CFHH4QBFgXyqFU7njGsnX1fHZRgEdZ01R49yds8eNmzYQHNz84Jfj1AohM/nI5lMMjAwYLXglUgktz8Oh4P777+fvr4+Ky2npaWlbP2ZxsZGPv/5z/M//sf/4Kmnnpq1fhKF7ovH+GIho9z8Md85xL5eAvj4xz/Oxz/+8QVzGPb29vInf/InpNNpq6ZP8TkXr1Ps78vpcKDoOunBQZJ9fSjDwzinp3Piv6LgrqnBt2YN/q1bcW/ejKu6GkpsLtsFr1Jzta7rDAwMcPbsWXbv3l3SmVRXV8fevXt58cUX6evr4/z58+zbt29BrpNkeZCCjg2Px8Ov//qvMzo6WtI+KILfUmKOUJrFh82eclXszim1Yymev9QHV7Sna2pq4rd+67cWJYBbDkoNMvbbYrEY3/jGNyzVvpS9sNJRaje1kv2x2O5oF3Tsr1vJ/mh/jZGREUZGRmhra1vKyzovDMPg1Rdf5Puf/SyeS5doNs2cmGOaVJPrxBJQVfxKrhOLR1EK28kbBoZpktU0QkeOMHXxIm/8+q+z9aMfxe31FqSuzbeGjviMiPOTSCSVcTgctLa20tPTQyaTYWBggE2bNpUUPdavX8+jjz7KV77yFaBwd1OIOqXEnHJBtMPhsOqtDA8PU1NTQygUKhDGy4lE9vTlSoKOSLdKJBIcPHiQtWvXLsp1PH36NP/yL/8yZ0pvuc2DK1euMNLby1oK27Ir5ERwNf84h5qrD4HLlTvcbvB6c1+FoCMcOkq+SLKuW4ei63gMg9p4nKoTJ3h5zRoeffTRBe906XK5aGlpsdxfg4ODbNiwYUWlEEskkjdPfX09u3bt4vDhw/T29nLx4kV27dpV9v4tLS187nOfIxQK8b3vfc8qXGxfQxV37RWpVuW6PZXKarBTqq5MJpPB7/fzK7/yK3zgAx9YMDEnnU7zV3/1VwwMDFjZAqVq35QrAaHF40wdOYJ28iSBkRGqMxmC+U1THzObAAC600m0uRnXffcRfP/78WzciGEr7VG8wSyus5hPPR4Puq5z5swZ1qxZU7YG0p49e7h48SIjIyMcO3aMzZs3L3oHScniIQWdIqqrq/nMZz6Doig88cQT81KahZjjcrlmFS+upDSXCpRLpVqJzh6f+tSn2LJly1JfkmXj6NGjnDp1CpfLVdHhVLz4KJXWVjzIlhJ27LUQBKZpznLmlFp8FNsrXS4X6XSakydP0tLSckulxxmGwXNPPMF3/sf/oG5sjDqg2jRzrhygSlUJKEruq6ritYkzkFukmIaBahg4dR2PaeKZmiL8P/8nXRMTbP61X8ORv56ZTMb6DM21GHC5XLmJz7bDIpFIKlNTU0NNTQ3j4+NEIhFisVjJoMzhcPDRj36Up59+muvXrxf8TrhK5+POEeOf0+mkurqa6elpMpkM169fZ9OmTSVt4/bntNfWEgG5vQlAKXeO1+vl0UcfXRS3YyqV4ktf+hLxeJyampqK6byl5hRFUTj1+us4dN0q6i/EHBTFOhRFyRU/duQKIeN05oQccTidM4KOqs6IOaKeTr7OjkNR8BgGDefPc+nee+nr62Pjxo0Lfl3q6uoYGxsjGo0SjUZJpVLSpSORrBJUVWXfvn1cvnyZsbExjh49ysaNGytmB9TV1fGZz3yGbdu28Rd/8ReMjo4CsxvKaJpmpUbZY+9y4ki5DQH7ZkA2m0XTNDZu3Mhv//Zvc/DgwQWNu19++WWefvpp3G73rHOtdJi6zuDRoyReeYW6qSkawSqYX6UoubIGijLjggfQdYyBATLf/jaJH/6QzCOPEPz3/x5XY2PJGjv2a2HPFEgkEhw/fpx3vOMdVtMRO1VVVdx777088cQTTE5OcuLECd761rfeUusVyfyRgk4J6uvr+exnP0ttbS3/9E//ZHXoKKc0C4eHCELFgjSZTM6yD9qDQzuVUq22bdvG7//+77Nx48ZVs0OWzWZ5/PHHyWazeDyesgsDQSmBrFjcKZWOZRd3iivLC8Qiwv73KWWxLPX8IyMjxOPxRav7cKOYpskrP/kJX/vMZ6gbG6OafGoV+S4sSq4Ti19VCTid+BwOFIetC0vuSVDynVjQNFRNIwA40mmUL3+ZLp+PLZ/8JA5VtQTQ+WAX0+br6pFIVjuKotDS0kIkErFcOn6/v2QAt379en7hF36Bz3/+81axSkE2my05v5Srd2MXdUZGRpiammJ8fJza2tp5CTqVasbZ3TmpVIpHHnmkbA2Hm+X48eO8/PLLlqA81+ZBscCTTCbp6enBpeRq3UCuS6M4DHEoCqYYR4WwUyzwCEHHNHM/i9o6+fsq+cNpmgQmJ3GOjXHy5Ek6OzsX3KWjqiptbW1cvXqVbDZrCUe3agqxRCJZWILBIAcOHODJJ59kfHyc119/nUOHDlVc8Pt8Pj7+8Y+ze/duvvCFL/DKK6/MajMuvgphpzget4/DYrwtRmwGiPkiEAjwwQ9+kF/8xV+ktbV1Qa9DNBrlq1/9KtlslkAgUFbIKZ4nkuEw1597Dl9XF62mSQNQB4TIu+CBgKLgtbngxehqmia6YZCJx4l/+9tEjx3D/9/+G558s5XiTWZ7V2Qh6rhcLvr6+hgYGCjrbt28eTPr1q3j6tWrnDlzhm3bti1YNzDJ0iIFnTJUV1fzu7/7u2zcuJE///M/n1NpLhZzABKJRMn2e+V2QO1BbSbf3eJd73oXv/Ebv7EiiusuJAMDAxw5cqSi3bISxaJPKadOueC9+HmES6fYhVNuF9d+30QiQVdXF3v37l24i3MTdHd38xd/8Ae4R0cJAj7TxEu+G4uS68TidTjwOZ1487UeFPuiAmZa62parguLqmJms3g1jZp0muzf/A3X7rwTZ75qvr3YcSXsaVrzFYEkEkkuiG5qaqK/v59YLMbY2FjJoFZVVT7ykY9w+PBhnnvuOYBZoo6dcsKLfa4S3aDS6TT9/f1Wx4/iOQ/m127W3gQgmUyyZcsWfvqnf3pRdg2z2Sz//M//TCqVIhQKzTutV7wXRVGYmJggGo1SR6GAo9sOQ1HQFcX62SWug/1r8WEXf/KHabt+Lk3DOzjIZFsbw8PDiyJ4hUIhy6kTiUSYmpqSbcwlklXE1q1beeONN7h27RonT55ky5YtNDU1VXyMqqrcddddfPGLX+TJJ5/kq1/9KhcvXrTWNVAo7Iixv1ScXs41KkpiBAIB3va2t/GJT3yCu+66q+RGxs3y0ksvceLECWtzudy6wX6u4bExun/4QxrGxy1XTjV5F3zeAV+lKJYL3inG/dwbBNPEaRh4dB2fYeDr6WHqN38T8/d/H9/DD1vXoThbpDhTIJPJcPz4cTo6OkqK8W63m/vuu4/+/n7i8ThHjhzh0UcfXZTrKFlc5F+sAl6vl4997GPs2LGDP/3TP+Xo0aOzWpiL74WYI2yEpmlWFHTslKqb09bWxq/8yq/w6KOP4l3g1qQrgddee42JiYmCRX4xxX+D4p8rUSlQL3VfoKSAM9curqqqXL16lT179iy7jTGdTvNXf/7nRK5cYR3kUqXI5+4quU4sbnWmE4vqds90YrG31jXNWWkA4sp5NY3qSISx//f/Jfv//D/AjCg2l6hjT8VY7mslkawkFEWhqamJsbEx0uk0o6Oj1NXVlWxjHgqF+G//7b9x9epVuru7C35nd9RV6kRldyo6nU5CoRDDw8PEYjFGRkZobGwsGC/FOYqvxSlXpdw5yWSSYDDIL/3SLy1aXv+1a9d46aWX5kyHtlMcwI+NjeUWFxSKOJrtyJommmmSzR/efMCOYcwcup4bX4VDR9wu7ptHiEaYJq6xMUzT5NSpU7S3ty+4e0a4v0Ra3cTEBDU1NTLYl0hWCW63m0OHDjE0NEQ0GuXo0aM88sgj8xprhGvm7W9/O88//zzf//73OX36NNFotKBOol3cKdUUQ4y54n5Op5Ompibuu+8+PvzhD7N3796Sc91CkEql+Id/+Aey2Sx+v79AzLGfm/2ITE9z4V//labJSSu9yk+u+6E/74D3q7mSBv78GlERNdRyF2JmTtA0nLpOSNdxxuOM/8EfkHQ48LzjHXOWfhDHxMQEExMTZYW4trY2tm/fzsmTJ7ly5Qrd3d2rqrzH7YKcledA5JH+zd/8DY899hj/+3//b65evYqu69Z9igs8apqGYRjE4/GyBSLt2IPkmpoaPvzhD/Pv/t2/Y82aNasmxcqOYRgcPXrUSrcqR/Husf02+30Wg1JOnmLEgD89PU0qlcLv9y/KucyXY8eOceTpp2k1DFymiYucvdNJTtBxqjNdWJyiYKe9E4tICRC1HTKZmUKe+UWHYhj4TBP/+fNcevJJqt797jcl6EgkkhvD6XTS0tJCX18fmUyGoaEh1q5dW/Jzt3nzZj796U/z27/927Pcp/bi/8V27lKCjqgh5/F4iMfjjIyM4PP5yqbK2l+rWCiyp1o5HA5+6Zd+aVHqw4hzeOWVVxgfH8fr9c5L3LefuyAajWJSJODYvmaBDJAxTdKmSdow8Ok6Tl1H0bScOC6EciHoiNt1vVDgyT+vEI2IxVBVlZGREWKxGNXV1Qt9mfB6vTQ0NDA0NEQkEmFkZIT29vYFfx2JRHJrsmbNGu644w7OnDnDpUuX2LlzJ+vXr5/34+vq6vjABz7AI488wqVLl3jppZd4+eWX6enpYWJiwqqzWKoZhlg7eTweWlpa2L59Ow8++CAHDx6kvb19wYoel+P8+fOcPHmyZO2aUvNEJp3m9eefp3pykhA5IUe44L3CBa+qlgtedTpnXPCqWhhj5zdOlfxXv65TH48z+rnPoXZ04Ni0ac7SD06nk3Q6zalTp3jnO99Z8pwdDgcHDhygu7ub6elpjhw5wpo1a1almWAlIwWdeRIKhfjYxz7Gww8/zP/5P/+Hf/mXf+Hy5cukUqmS97cHvoLiYFEEhg6Hg+bmZh599FF+9md/ljvuuGNV56mnUinOnz9fUpypdNgLbIpFwlz3Lb7PQglo9gE/Ho8TDoeXVdBJp9P84z/8A2YshpuZTiwqhf+Xal7UUUVrXbuo43IVFussFnjyh8sw8BkG0z/4Ac7778fv989LqFEUBYfDYaVcLOTfQyJZDdTX1xOLxZiYmGBqaorGxsaSRSwVReFtb3sb//2//3c+9alPMT09Pes+9iDbLuLYuzqKzo5Op5NAIEA0GiWZTDI8PExzc3NFQadcEwBhy//kJz/Jgw8+uGhjgKZpPP/889Y4M5eIU26jQNTYs4Sb/JEWX/NCTso0SRkGSV3Hq2kE8g5HxT6mivE0n85KNmuJO2b+WonnzSoKWjKJqigk8nV89uzZs+DXCaC5uZnp6WkSiQTj4+PU19fLYF8iWSU4HA4OHjxIT0+P1ca8tbX1hsYARVHw+Xzs2bOH3bt388u//MuMjo7S19dHd3c3AwMDhMNhwuEwhmHgcrmorq6mvr6edevWsX79elpbW6mrq1uy9ZFhGDz77LMkEgl8Pt+cLk7TNDl/5gyZgQGqyDlyPJAreKwouFQ154J3OvG4XDhdrpk4W4g69pg6m83NA5mM5YL3myY1k5OM/6//RejP/gy1qDtvuZqhAwMDJJPJsuuQuro69u/fz49+9CMGBgZ444032Ldvn4zBVxBS0LkBhP34E5/4BB/4wAd4/fXXee655zh27JiVf1jcJalUehXkbIxNTU3s3LmTt7/97TQ1NVFfX8/mzZtXtZgDMDo6yvDwsPXzjQgypX4uVfeheNfZMIxcRfoSIkLxbnKp1ynnEhKuk4GBgQUv1HYjdHd3c+zwYQKAapoFnViU/HmKgpuKvUinmGzc7tz34n9T02YKeIqJR+wk6Dpu08Q9NMTI+fNUNzRYu/mVEIJO7unn1xlLIpHM4HQ6aWhoYHp6mmw2y9DQEBs2bCiZwqiqKu9973vJZrP84R/+IZOTk8Ds4v+pVKpgzBSNAOzF5IUT1ev1Eo1GmZycxOv14vP5Sga/xbVzRLqycOb8h//wH+Zt63+zjI2N0dXVBZSu7WP/vtI4L4oRZ8mJOPYjZZqkFKVAzHHrOi5Nw5HJ4BPuRV2f2aG1j6n5YN7MCzp2cShtmmg2F1RPTw+7d+9elDFTtDG/du0a6XSasbExOjo65PgskawSGhoarDbm169f58KFC9x1111v6rkURcHtdtPR0UFHRwcHDx60YmVRG0fEg/N1eC8G8XicV155ZdbGfDmmpqboPX+eNeQcOS5m2pFbLnhVxeVw4BJdDoUD3u0uLGtQwgWvmCaqYRAwTWKvv07sySfxvfe9Zcs+2IWdeDzO2NhY2eLIADt27ODcuXMMDQ1x/PhxNm/evCiuT8niIAWdN4GiKNTW1vL2t7+dt771rUQiEa5du8b58+e5ePEiXV1dTE5OoigKmqahqipVVVUEg0Gam5vZtGkT27ZtY+PGjTQ2NjI8PMzg4KAlGKx2xsfHmZqaAiqLOZVEm1JV30sdIu9UBMUOh6Ng8hDBe/Hj7K9TSeAxzVzhbPF+lgPTNDly5AixSIQQzOrEYihKrgMLYCpKrvCmEHXs4o7YQYAZMUfcXmQZVckVXO46fJhN991XkKJYDkVRrNoMQtCRSCQ3RigUoqamhomJCaanp5mcnCxbyNbpdPKhD32IUCjEZz7zGfr7+2elPooULLuYI1w5dlHH4XDgdrsxTdOq4yNq6VRy59jr5gSDQf7Lf/kvvOc971n0jY2enh4r3czOXE7Q4vFeOKCyQApI5o+UOEyTpGniNgxchoFD03AoCmr+mnhFipXYDDJtdvtMBjObxdQ00ppG3DBImCZJIA5kAgGr+2AkEiGbzS54tytBbW0tExMThMNhxsbGCIVCMtiXSFYJiqKwf/9+urq6GB0d5dixY2zcuHFB6pvZHe2l6nMtl3Dc19dHT0/PrMyKcselCxdwplJWxyonORe8mh/vVUXBkXfBO+wueK93ZtO02AUvfhZCv67jMk0Cuk70W9/C/fa3g8tVcuPEXv/TMAy6u7srCjqBQIAHHniA73//+0xOTlotz6VwvzKQgs5NoqoqNTU1lo1wcnKS7u5uDMOgpaWFYDCIoihWCzkRbNk/IFLEKWRsbMxqWz0fp82NiDn2rmT2ImJiB9s0zQJRR+wY2B9b6igWeIqD/0gkYrmAlhpd1zl9+vTMNaWwE4v1vaJYP5tQvusK5CYX8XPx/fL/225g6uJF0snkvP7H7ZO52KWRSCQ3hqIotLe3k0gkrPSnSoVsVVXlXe96F01NTfzhH/4hJ06csMYxO2Ksy2azBQ4du6Cjqioul4tUKkUkEsHpdM6yeJcSc7LZLFu2bOF3fud32Lt376KPk6ZpcunSJUs4nq8LtHiMNwyDuro6K2BOMCPoJMjb7U0zV7PMNHHquhXYQ27u13Qdj9OJy/6e84G7mc2iZ7OkNI2kYZAwDOKmSQyIAVpjoxXERaNREonEogk6DoeD9vZ2YrEYmqYxPDxMMBiUBewlklVCVVUVBw4c4Ic//CHj4+OcPHmSt7zlLbftgv/SpUvE4/GSvyt2aiYSCQb7+2lQlJlyBtgc8OJwOFDtm6T20gbCBW8WpeAW1dNRdR23ouDq7SXx2mu4Dh4sGy/b/zaDg4NzuuVFeltXVxdnzpxh69atsmbaCkHOxAuIEG7EjmQgEKCuro7a2lqqqqrweDxl7efi8atd3DFNk+npaUtMKSfYVCrYWc6JY1882I9MJkMmk6n4ffEhnms+4o6iKCQSiWUTKLLZLFeuXAFAN82CLizF3VhEIU/DNDENo7ALi/0wzVndVwBL0DEUBaeikJ6cJB4Ok813h6uEEHSEiCaRSN4cHo+HUCiEoigkk0nGx8cr3l9RFPbt28eXvvQlfvEXf5Hq6uqytQJEGlYikSCRSBCPx63vxTgnXDrj4+PEYjHi8TjxeJxYLEY0GiUWixGJRAiHw5imyYc+9CH+6q/+iv379y+ZQNDX12fNt5Xcn8XzUPG8U19fj8/nA3KOnBg590zCfpgmMcPIHbpORNMIZ7NMZzJMpdNMp1JEkkniqRTJZDJ3fVMpopkM4WyWiKYR1XWipknMNIkCYYcDpbMzN06Tq+WTSCQW9ZoFAgFqa2uBnIBUqvaSRCK5fdm6datVEPn06dOMjY0t8xktDoZh0NXVZXU2FpQS+w3DYHR0lHQyiYMZIcdywYvv8/GxKTZD7cKOEHeEwGP/WuyCz7c5d2saiRdemNNRKohEInPG1i6Xi/vuuw+fz0cymeTw4cNW50vJrY106CwwTqfTCoKFI2Iu3G63JeasdkEHIBaLFajflVw4xU4c+/d2EUcIOfZWfsWpAIZhWL+zt0ksLtpZLOrYhZ3icxFHKpVaNkEnkUgwPT2NQaFoYz8ypjnThcXMdWJxikLHouOKSLOCmdvFffIij2maGGauRa9DUdBSKVKx2LzP1T55SoeORPLmUBSF1tZWy7UxOjpKTU3NnEUsGxsb+d3f/V0eeughvvSlL3HkyBHi8fisFFTIjZeigLFIV7W3cxWCuGmaeL3eggBY13U8Hg8HDx7kl3/5l7n77ruXtBW2pmmMjo6WdeYUizeVnKGBQIC2tja6urrIkhN0RDFMUT/BYZq5IN8wcgG+aaKbJlnDIK3reFUVd96mr4r75V8raxhkdJ20YZA0jJyYA4RDIWpbW9Hy563rOuFwmLa2tkW7buL/KhaLkUqlGBgYIBgMLnqnGYlEcmvgcrk4ePAg/f39xGIxDh8+zE//9E8v6fi9VHR3d1trs3KlFcRtIyMjKOZMuQaj6NDzG53ia1kHvNjQsP9cfORfw6EoZM6dIxuLYeZdouUEHdM0yWazxOPxOV2cra2t7NixgxMnTtDd3c3Vq1e58847F+ciSxaM2+8TuMyIoFbUBJgPIhgS9VZWO8WLhvnUwilVF0d0YCkWc+wt5MXriYC4eFFi/10pd0+xW0d8LT7f5RTqxHnq5LujFH1Nm+ZMNxbDIKXrJHUdj67jzGZzRZKFmCOsmjYLaEF7XcNAyz+fST6lIJ3GmKc4I4ICkcIgkUjeHG63m87OTrq6ukilUkxPT9Pc3DynPd7hcHDvvfeye/dujhw5wre+9S2OHj1qdR+xUxzYChRFscY9UdtFFGgMhUIcOHCAj3zkI9x3333L0v1P0zSi0aj181ypu+U2E0TXr507d1oFlhNAlHxnE2bqKCgisDaMnLhummRUlbSi4FEUXHlBx5G/r2kYOXHcMMgYuYLI4rknAH3rVlS/HzOdtubMZDJpuUIXC5/PR1NTE319faRSKUZHR6UlXyJZRXR0dLB9+3Zee+01urq6uH79Ohs2bFju01pQDMNgbGzMGlvLCfxi3RYOh3PudLBc8Dqg5UsZWG74/IanLkR+4XQX7ncxdpdzwOcxxfplfJzMxARmfX3FTAbIzXuxWMxyWZZDVVXuuecerly5wtTUFEeOHGHt2rWWE1VyayIFnQVGiAbAvG1qrnxBKyno5BB1h4CCGjbzFXIcDkfBz9lsFlVVC1w59ucXAbrL5aoo6Ij6Efb2uuVSsIq/Fy6s5cB6r8y00y3ZjcU0SRoGHl3HrWm4slmqFAWHouQmHV0vrKEjWuvm2+uamoZuGFYHFp18kWWYdwqVEDdL1fCQSCQ3RiAQwO/3E4/HGRoaIhAIEAwG5/VYv9/PO97xDh544AGuXLnCj370I15++WUuXrxILBaz3DnlPqcej4dEIoHT6aSqqopdu3Zx//3389BDD7Fp06ZFq/XyZiiVujtXGq/dBXrnnXfS2NjI2NgYOhCBgqKYYuQ3TDMn5hgGWUXJdaxSlJybRzh08oE+Nrdj1si1K4+Tc+eMut203X235eIR5xm7ATfkzdDQ0MD4+LjVxry2tnZZhDmJRLL0OBwODhw4QFdXF5FIhJdffpm2trYbamN+q5PJZKzxtFItT8MwyGazpNNpS8wpKGNgmmQVhWzeBS+OrGkWuuCdztxXMZ8axiwHvPidyUzJBD2dJjU+jrO6umBDuZSok8lk5r1RWl1dzd13382zzz7L0NAQZ86c4Z577rlt6yXdDkhBZ4ERu5Aw/0WsEBlEkcjVTnV19ayixJUKElfqXFV8FNeEsAftdkHInnJVXBi5VOpVqRQs+8+iftJy4Pf7qaqqwmSmC0vK9r0XSJomHsPApSi4dB1nvhOLAgQAh56ruK/YBR0911bRzGQg/55F4c4EkFEUDJcLdyAwb7eacOhIcVMiuXmcTqfl0tE0jcHBQTZv3nxDdWrcbjfbtm1j69atfOITn2BoaMjq5tjT08Pw8DDxeNwKFEX9uJaWFtavX09tbS3t7e1s376d1tbWWzIgnCuNt5SwIzp9ifH93nvv5YknnsjVDyInvIh6CmCz3ueD+Yyi4ANSipIrcAmFDp28+KPnHY+iPs+EaaLs2YO/rc0K0MV5LtW1dTqdtLa20tvbSzqdZmhoiA0bNtySf1uJRLLw1NTUcNddd/Hiiy8yMDDAuXPn2Lt3720zBohNW4F9XiieDyyxB1sZA/uRnxNESYNU3gnv1jQcwgUvulnZXfDZ7CwXvJl3wWdNkyy5zQEjHIYSm8mlnKXzRVEUdu7cyRtvvMHg4KDVxryurm6hLrFkgZGCzgJj37Gcr0NHCA2mac574Xu7oigKjY2NOJ1O61oUiyn2YLpYwJmvkFMs1IiuLcUuHrsyX0nUqSTw6LpudUJZDtxuN83NzXR1dVm2fdGFxQt4TBMXuW4sznztHDUv5hiAbhj4nE7cDkeuO4tIv8pPOGb+/aY0jYSuEzdNEqZJCiAQwFdVdUP1pES6lRR0JJKbp6qqilAoxOTkJLFYjEQiQVVV1Q0/j6Io+P1+Nm7cyMaNGwus3CKoFfcTLtVMJsPly5dJp9NMTU3R1NR0S9RaKCXsz2eToFj4t3+/a9cuLly4YKVeJYEpZopiip1be6pritz4axd0RHqWeJyWv3+CnEgUaWhgz0MPFWxEiK/zdV8tBKKN+fT0NNPT08RisSV9fYlEsnwoisLevXvp6uoqWPAvRBvzW5FiMaeUSGKQc7wLIafACV/KBe9wEMhmoZwLPpu1HPAi1jaEKGR7boeuo+bXHOU2wG9U0AHwer0cOnSIH/zgB4TDYY4dO8Y73/nOil2yJMuH7HK1wIjaAcC8LeV2EehWsqEvFw0NDQU5nmJxX5zOVM4NUyy4FHetSqfTpNNpUqmU9TWZTJY9UqlUwSEeX6oLVrGYk81mMQyDmpqaZbueLpeL7du3A7nCbHFKd2GJm6bVhSWq5zuxZDJMZzK5LiypFPFUilQySSaVIp1KkcxkiGYyRLJZIrqee7yZ68ISA4IdHXiDwXmLM3bR60YnH4lEMhtRyNbtdqPrOn19fQvStUIINw6HA7fbjcfjwePx4Ha7rdt9Ph/19fVWp7+JiYkFeEc3j9PppLq62vq5eI4pFnPKpVrZx35VVXnPe95jzV0mubF10nZMlTtMk0n7kb//BDAOjALDwKDHw9qf+Rk8odCsOTGTyTAxMbFkaVeqqtLS0oLL5ULXdaslrkQiWR34/X4OHDiA0+lkcnKSY8eO3Ta1D8WcJqgk+gt3ZCkXvPg5mRdzkoaR2/jUNKLZLIl0GiOdxkylIJksPFIpSKchncbMZjF1PVfjUohDQFJVMbzegjVIudIPb6bxzoYNG6z6SOfOnWNwcHBBrq9k4ZGCziIgdiDFB30uhCsE5u/quZ1pamqiubm54DZ78FxKvJnLNVMs5ojDLtiUE3Ps3xc/vlJrc/vg2tLSskxXc6bAmZickuTEFnEIgSdm5lviGgZRTSNsb62byTCZSjGZSjElvqbTTGUyTGezhDWNiK4TMQwipkkEiCgKbfv335CaL7rEibbHEonk5gkEAjQ0NKAoCrFYbM425gtJU1OT1eVqZGSEVCq1ZK9dDofDQUtLS4FLp9xmQbGIU3wf+3hfU1PDBz/4QUssMsmNt0KYsX8tdYznj7H8IYScAaDP7Wb9+95H65YtJec8yI31Q0NDS7aoCgaD1lwdjUaZnJxckteVSCS3Bps3b2b9+vWYpsnZs2cZHR1d7lNaEFwuV4HbqJKgY5qmtRmfJi/g2A/TtI642DTNCzqRTIZYOk02lcJIJjETCcxkMnekUrkjk0HLZkloGnHDyB15t33c7cbw+8tmB9jnCkVRbtgh63K5eOCBB/D7/aRSKdnG/BZGCjqLgKjBMt+gStTduZFW57czPp+PrVu3lgy2Swk75USeYjHHLurY3TZzOXTKuXSKn7eUiJROpwkEAtTX1y/jFYW9e/eybt06IGf5j4DloomKwzStI2IYhHWdaU1jKptlKi/qTGUyTIrvs1mmslmmNY1pXSdsGIRNkzAwpSiEAwG2HDoEcMMWTdM0pa1TIllAmpubLWFlampqyQRTt9tNe3s7qqqSSqUYGhq6JQqer1mzZpYjsNJ8Um7jwF4kP5PJ0NbWxkc+8hEaGhqs586Qc+KMMiPWlDtGgRFgiJyQcx0YDATY+oEPsPGuu9B0vWTw7nK58Hg8RCKRJRPsFEWhvr4ej8djCXarPW1cIllNuFwuDh06hNfrJZFI8NJLL90WC35VVS13KczUdSw+xMZ9IBDI3QdKuuDjQLxI0InkN0yn02mmUyli+bVGNplEyx+ZdJp4JpO7v64TzbvgY+Ti+GRdHabfT7ZMxoD9PFVVfVPp1k1NTezatQtFUbh27ZqVViy5tVj+ZPbbENGpx/4hqoSo/WKaJtlsdtHbjt7qqKrKvffey/e//33reohr43Q6rUKUwuZeXCfHXh+huG5OqSJhonZOcUvzSo8tt3NbHNyn02nuvPNOAoHAkl9HO/X19bz3ve/l8uXLGIZBMi+8iJoNVieWfO0GXXRXURQyhmEV7hSdW0S7RdM00fNF2kReb0xRmAQce/fSsmEDqXR63v/X9oLUt4t1VyK5FXC5XFa7adH1au3atUsy19TU1BAIBIhGo0xPTxOPx99UYLlQKIrCli1brHQhMb6XCtjtc8N8a7S1trby8Y9/nKeeeoqLFy9aBTPj5Cz4UcCXPzzk2ps78gfkajGIujnVnZ2842d+hraOjllijn3zoKmpCb/fTyaTYXR0lNraWisWWUw8Hg8NDQ0MDg6STCYZGRmhs7Nz0V9XIpHcGrS1tbF9+3ZOnjxJd3c3V69e5c4771zu07opFEVh48aN1vfFDVqK5wjRnVek2sbIje9ucuO70zRxAKotrrU6HhoGKVXFq6pWt0Ml39lKrDMyuk5a10nl3TlRYNo00devz3XTKiHiFM8VXq/3Ta1FFEWx2piPj49z9OhR1q5du+zrGkkhUtBZYOyWtvnmK6qqaqWaSIdOjr1791JfX8/w8LB1m54PZu3Fi4sD7OLAGmYKINsPu5gzl6AjdpPt7f+EKl/Ofm937txoV5nFQFEUPvjBD/KDH/yAS5cuYSoKUTHBkBNorOKd+V2GLBS01RWCjoOZ1rpmvj25lq/inyRXuHO6qop3f+ITOJxOSKetRdNci0dx/cXfWiKRLBwNDQ1MTU0RiUSYmpqisbFxSYIyVVVpbW0lmUxa3bY2bty4rC68devW0dbWRnd3t3XbXIJOuQ0EgZgrTNMkEAjwwQ9+kK6uLl5++WUGBwdz84WiWIXpHcxub26aJoaqEmpo4J5772Xv/v24XK6SQo59vmltbbXmzGQyyejoKO3t7UtyLZubm0kkEkxNTTExMUFzc7OsByiRrBJEWn93dzdTU1McPnyYdevWreg25oqisHnzZlwuV0GDFjEGi41lMUcEAgHcbncu7icn2nvJiTlCsLfi5vxaQjdNNFUlYxh4FAWPoswIOvk25aZhoOWPtGGQyqdaTQMTDgc1O3eSyWTmVXaitrb2Tc+5wWCQu+++m6effprh4WFef/117rvvvlVtPrjVkILOIuByuQocHXMhRCDZqnmGjo4O7rnnHh5//HHrNuHSsQfXdkdHKVeOnVIdq4q7lszVFatY1ClXJFOkcgUCAXbu3Lm4F2uetLa28slPfpLf+73fI5FIoJETXwrEHGydWEwz11bXNHMTjWnOCDow4+bJ3z9Nzp0zraq0P/II+x94gGu9vQBWQbb5uNVUVbX+1hKJZOFwOBy0trYSj8fJZrOMjIywbt26JRGcq6uraWxsZGhoiGg0SjgcXtYWqPX19WzdupXu7u6CHVj7pkElRw7M7pYlEPOF0+lky5YtbNq0if7+fs6dO0dvby/T09M5gcYw0PLP43I6CYZCtLe3s2PHDjZv3ozX683tztoC9lIuUF3X2bFjBy0tLUQiERKJBOPj49TW1uL3+xf9WjqdTmprawmHw2SzWYaHh+ns7JTBvkSySqitrWX//v0899xzDA8Pc+bMGe6+++4VPQZs3bqVYDBYUMy/nOjvdrupq6tjeHgYk5wb0wMFMbPdBS/cORnTJJ0XdES3Q0v8MU0MIfwYBhly8XiMXNH8aGsrTZ2dJeeH4qYw2WyWNWvW3NQmyrZt2zh37hzXr1/n9ddfZ9u2bbKN+S2EFHQWAfGBsbd1rYQ9vUSmmeRwu928733v4+mnnyadTls7nyK4tQfbcwk6djGmlBDjdDoL3D72mkbFz1EsCtlbx9oDbVFwee/evQXdVJYTRVF49NFHOXXqFN/85jdzixfTZJoybXXJ7SJ7ybU0F211LUGHmba6WSCpKIQVBd9dd/Hh3/gNXG53QYHw+dTNEG41QIqbEskiEAwGqampYWJigqmpKaqrq5ekxpeiKDQ0NDAxMWGJScFgcEnSgkrhcDh429vextNPP10w1lQSdMrNNXZnTqnUXKfTyZo1a1i/fj26rhOPx4nFYqRSKQzDwOfzUVVVRTAYxOPxWI/PZDIlU3vt80w6nbbaybtcLtasWcOlS5dIp9MMDQ2xYcOGJVlU1dXVEYlEmJiYYHx8nFAotKzdHSUSydKhKAo7d+60OiGdOHGCzZs3F3SsXWm0t7ezZcsWjhw5Yt1md+gUzxGNjY3W/KaT2zAt64IXJQ0UhTTkNk2xuXlgRvwxTbKmaaXhRoFRVaXmvvvQFQU9L+qXqyEq1lCbN2++qevh9Xq5//77+ed//mcikQhHjhzhXe96l6x3eYsgBZ1FQNf1WYHeXIiAUtTSWcmq9kJx7733ctddd3H06NGC20WL1lJ1cyqlW5Wqn+NwOKw6R8LtM5/UreKAXQyiYtc0mUzicrl4+OGHlz3dyo7X6+U3f/M3GRsb4+mnny4QdTRmhBzRalHUeCiu8yCujOXOURSiioJr0yY++Qd/QGtHB3DjbjUxMYJsWy6RLAaqqtLe3k4kErHcFDU1NUsSlIk25sPDw8RiMcbGxmhra1v01y3HoUOHaG1tpa+vz7pNCCk3IugISon+xRsHqqoSCASoqqoq6QRNp9Nz1msTLtBMJkMqlWLPnj2WEycQCBAMBgmHw4TDYaLRaEG3lsVCLGimp6fRNI2xsTFCodAtNf9JJJLFw+fzcejQIR577DGmp6c5fvw4Dz300IodA/x+P4cOHeLVV18tiEfLCTput5vm5mb6+/uBXDw9zUy8bHfCCxd8Ou+A95gmbmZvmoqyBsIFHydXYD+9cSNr77yzouhv78jb2Ni4IHNtZ2cnW7Zs4Y033uDChQts377dargiWV6koLMI2Hcc5+u4seebzyc1ZTUQDAb56Ec/yuuvv17g0jFNk0wmU1bIsd+vnKBjL4YsLJM3UounlKBjT7dKJBLcf//9tLa2Lt0Fmyf19fV89rOfxel08uSTT5LNZjGASL4OTorcLoCfnKDjZaawm323wQCM/O5CTFGo3b2b//zZz7LNlmImJkFx3eZCpD5IJJLFw+v10tDQwPDwMIlEgomJCZqampbktVtbW4nFYkSjUcbGxqwuSctBW1sbb3vb2/jGN74BzMwd9k2D+dTNqTQ3iI2D4nlmrucqJerYNw6ECxTg/vvvt2IGUa8okUhY9YoCgcCSCHZVVVWW+ysSiTA5OVnQ7UsikdzebNiwgY0bN3Lp0iXeeOMNtm3bRkd+g2+loSgKDz30EF/5ylcIh8MVMwXEUV9fb6UUQy6enqS8Cz4FeEzT2jQt5YLX8/dPkutsNR0Msu0d70AzDIx8Hc9ym8tirti+fTs+n++mr4nT6eTee++lt7eXaDTK0aNHaW9vXzanrWQGKegsAqLAscjJn+9jYKbwriTHQw89xH333cePf/xjoLBAcbGoIygWdMpZ4EWgXVxcudQubKnnLE65EmJOMpnE4/HwyCOPWH/XW42mpib+6I/+iDvvvJOvfvWrTExMYJLLz82Qq9AvBB0PhYKOZR1VFDJA1udj7zvfyW/+zu+wZs2agtcRi4wbEWrEZ0bUlZJuNYlk4WlubiYSiRCPxxkeHiYUCi1JEUun00ldXR3xeJxMJsPY2Bjt7e3L8jl3Op188IMf5PHHH2d6etq6XWwaVBL4i6k034jA3+4CLU7rLfU8IoYo3jiwCzqldkiDwSANDQ0MDQ0Ri8WYnJyksbFxIS5ZRRRFoa2tjXg8TiqVYnh4mOrqahnsSySrBNHGvK+vj0QiweHDh3n/+9+/YseALVu2cM899/Dss88W3F5J9O/s7ETTNOLxOJATbibJCTnCaZNidowtHDpWvR1ygo54TBQIe71sfte7cFdXF7g5S20up1IpUqmU9TdZqDm2sbGRXbt2cfjwYa5du8b58+fZvXv3gjy35M0jbSCLgMvluuHCrmKwEx9ISY6qqir+43/8jyVrPAiV3G4rtCvSpW4r93v7zzd6iEEzkUiQSCRIpVK85z3vmSVu3GoEg0F+9Vd/lS9/+cs8/PDDBIPB3P+topBWFKYVhVFFYVhRGFQUBhSFfkWhT1G4rij0u91U7drFb/zxH/O5P/mTku/X5XJZYs583WrisyAKKUskkoXH7XbT2tqKoiikUimGhoaW7LXr6+sJBoOYpsno6CixWGzJXruYnTt38s53vnOWaCM2DexHqS6G5Y5Sc0sqlZo1b5SbV+yPLfU8yWQS0zR597vfPaujlKIoNDY24vF4ME2TsbExq1PLYuP1eq1CmclkksnJySV5XYlEcmvQ2trKrl27AOjp6aGnp2eZz+jN4/F4+PCHP4zX651zfhCHoiisW7euoINkllz61RgwXuKYyH8dsx2jwDAwBPQB/T4f7e98J7Xt7WXXM8VzTTKZZNeuXTQ3Ny/YNVEUhX379tHQ0ICu6xw7dmxZ53BJjlvTPrDCESrtjQo6ojiyrB1SyP79+/n3//7f84UvfIFsNlvg9LCLX8Xtxee7YzqfOgnlbPXFRSqTySR79uzhPe95z4pwljidTu655x52797N6dOnefLJJzl8+DB9fX2k02k0TSOZrzHkdDpxu900NDSwZ88e3vWud3HfffdVLHonxBnpVpNIbj2qq6upqqoiGo0yPT1NNBolGAwu+us6nU46OzuJx+Nomsbk5CSBQGBZUo1dLhe/9Eu/xMsvvzxL1NI0jXQ6XXCbPX20UqqV3QU6n3mm+PnFmGl/TruYFI/HOXToEFu3bi35vrxer1XPQbiwlmqTobm5menpaRKJBMPDwwSDwSXptiWRSJYfRVG4++67uXz5MpOTk7z00ku0t7cXCBwriQcffJCDBw/ywgsvFLjNxaZyKRen2+1mw4YNDA4OMjk5mZsfFIUYOXdOsQu+uCOWqLeTIVc3x9nYyO4HH6SxpYV0JlMyW6C4GLKo5fmud71rwedWexvzsbExjh8/zlvf+tYVse65XZGCziJgt1IXB4PlEOklsnX5bBwOBz//8z/PmTNneOaZZ2Yt9DVNmxVYFwfYxUWQi4PsUh2u5tstSwyeqVSKuro6fuEXfmHFTVwej4d77rmH/fv3E4lEGBgYoKenh8nJSWtSqK6uZu3ataxZs4ba2tp5WWjtDp35ipv2LldiYSSRSBYeh8NBW1sbPT09ZDIZBgcH2bx585IIKz6fj1AoxNTUFOPj4wQCgWWrt3LHHXfw8z//8/zpn/4pmXywLMhmszdU76a46P7NzjPl0npDoRAf+tCHKo6PDQ0NTE5OEovFrDpJS5VW19LSwrVr15a825ZEIll+gsEgBw4c4Omnn2ZkZIQzZ85w8ODBFTkGBAIBfvmXf5nXX3/dqo0jKDc/QE7U6ezspLq6msHBQVKpFJCroxMlV6tSdLayF0SGvJhjmig+H2vvuINtO3fi9fmseqKliu/b1yOilufDDz+8aDWMtm7dyoULF+jp6eHs2bPs2LFjSVJ7JaWRK6VFwN6G/EaCpxutu7OaCIVCfOpTn2JiYoITJ04AhR3EdF23WsAWCy4ul8tSr+faNZ1r57S4roEIspPJJH6/n1//9V9f0RXfVVWlpqaGmpoatm/fftPPJ2pGiOs/H+xuNZlyJZEsLqFQiFAoxPj4uOXUESkzi4m93komk7G6bS2HgKuqKh/96Ec5fvw4P/rRjwBmiTrFLs1KDlAxx4jOVvYOV8WCTjGlgvXiYsi6rvNv/s2/ob29veL7cjqdNDU1WQWSR0ZGWLNmzZIsqmpraxkbGyMSiRAOh0kkEituo0Mikbw5FEVh27ZtnD17lv7+fl577TW2bNlSsnzCSuDgwYO8//3v5+tf//qsTWX7ZqX9dtM0cblchEIhqqurCYfDjI+PE4/Hc+N6voNVSlGsmjkKufmoKhiks7OTDRs3EgwGURSFdF4QKjX3FGcLJBIJmpub+dmf/dlFK4jv9Xq59957GRgYIBqNcuTIER555BHZxnyZkILOIiCCNeCG3Dayw09l1qxZw+c//3n+03/6T1y6dAkoHDwNwyhZJKzYAi+6jZQSdSp1zrLb4It3TF0uF5/85CfZt2/fityBWCzEddV1fd5uNdHqXIqbEsnioygKLS0tRKNRy00RDAaXpIil3++nrq6O4eFhkskkkUhkScSkUlRXV/N7v/d7XLt2jStXrswqhi+C9nKuHDEv2LsolupwVezOuZGNA1Ef4ZFHHuFtb3vbvOaauro6JicnrcVEbW3tkrUxb2trs8Skvr4+Nm3aJB2XEskqQSz4f/CDHxAOh3n11Vf5qZ/6qRW54He5XPzqr/4qp06d4tSpUxXnB/HVfrhcLmpra2loaMiVMkgmSSaTZLNZa7PZ6/USDAatMVpsbtrrnxWXlih2cArRX1EUPvaxjy26gLZ27VruvPNOzpw5w6VLl9i+fTsbN25c1NeUlEbOrIuAXRwQO3tzBV5ut/v/b+9eY9s66z+Af8/xNbFzcew4du5p0qZdwwKl6dLQ/sulrJpKhxhMezOhbRKaVK5CQ0NMggkhBLwYk3hTCQnxBg1p4iYBQmNIILZ260bL1qZbkjaJFze+5OZ77Pj2fzE9B8exfZyLnTj9fqRoXW2fc+zG5/I9v+f3sEKnDIODg3jxxRfxve99D9evXwewMRFfW1vbEOikUinodDplqvJCpfDllsHn3jFNJBIwm8349re/jbNnzzLMyZP7eZQ7jCOTyfC7QFRF9fX1cDqdcLlciMVi8Pv9qtUfO0X0W4nH43C73aivr6/KsKBCBgYG8IMf/ADPPPMMfD7fhseTyeSGIb3imKDT6dbdPMi/cVCsEjRfoRN1UZ0Tj8cxMjKCJ554ouzATUxjHolE1vUrqsZFVUNDA6xWK3w+HyKRCILBYM3eoSeizTtw4AAGBgZw69Yt3Lp1C0NDQ3t+wpBi2tvb8dxzz+GrX/0q/H7/usfKDf21Wi20Wi2amprQ0tJS9BghJgUpZ7ZdcZzIbcz82GOPYWRkpOKfiSzLGB0dxezsLEKhEK5cuYLOzk4YDIaKr5vW4yxXFSBJknKyJPq7qBFfZvEaKkySJAwNDeEXv/gFPvWpTylTxOefGKdSKWUmkNXV1XV/Fv8v/i7/v/k/YvaqaDSKSCSi/ASDQbS0tOD555/Hgw8+WJN3HSotd9ryzb4mt/koEVVWS0sLjEYjstksFhcXlfH+lZY/25bX6921770kSThz5gyef/552Gy2gscWMbw3/9iR/1NoJsTcPxf6yV1m/nEnHA7j6NGjeOaZZzbduNpsNsNisUCSJCwtLWFpaWknP7aiJEmC0+mEwWBAJpOB1+vl+Q3RPUSr1WJsbAwmkwmJRAKvv/561Wbcq4QHHngA3/3ud9HY2Fjwxq+okCm0vy8242Gh/y/nWJF7jRKNRhGLxRAOh3Hq1Ck89thjVauGtNlsyugEt9uNW7duVWW9tB4rdCpAkiTli1RuhYEIdDbTPPZe1tPTg5///Oe4dOkSfvOb3yAUCik71/wySNEoTCTj+VU6uRU6+WXwhWYcERVAY2Nj+M53vsNmjyXkDj/c7Ixvm5nqnIi2R/RbcbvdWFtbg8fjQW9vb1X7rYTDYQSDQUSjUZjN5oqvtxBZlnH+/HnIsozvf//78Pl8G4ZDi+NAoWG9+ceZ3GbI5fZpE3dexYwliUQCo6OjeO6557bUOFoEK6FQCIlEAj6fDy0tLVU54RczI87PzyMWi8Hj8aCrq6vi6yWivaG1tRXDw8O4fPkyXC4XJicnMTQ0tNubtSWyLOORRx5BIBDACy+8oEzXnd9Tp9Aw3PxjRKkqTrXZdnN754jjRDwex+joKC5evIi6urqqfSaSJGF4eBjj4+Pw+/24evUq+vv7qzK0l/6HgU4FbKVCZ6t9d+5lVqsVzz77LB544AG8+OKLGB8fVzrO5598ix2faFBZrH9O/rCg3J2nmHWpra0NTz31FB599FE2eVQhLmaA/30X1C4Qxb+JKCMlouqw2WxYXV2F3+/HysoKWltbqxKsaDQaOJ1OrK6uKrNtDQwM7Mo05sCH+62HHnoIzc3N+OEPf6jcccwf3ivK4sVJtRjWm3/jQBxjit04EMvL78cj7mR/7nOfw9e//nVYLJYtvyej0agEK/F4HH6/H+3t7Vte3mY4HA4Eg0Flti2bzVbVCw4i2j2yLGNkZAQTExNYWlrClStX0Nvbu2uh/XZptVo8+eSTMBgM+NnPfoZgMLjhuiM/yBFDcovdWC4W6ADYcIO5UOifTCbxf//3f3j22We3dZzYKpPJhNHRUfz1r3/F0tIS/vOf/+DMmTO7dgy/FzHQqQDxRdxMDxDxJQbKr2SgD3esZ8+exfDwMH73u9/ht7/9LWZmZtZ97rkVO2LnKv6N1O6aih1nNpuF1WrF+fPn8eUvf5lVOWUSARpQflCp0+mUMcT8LhBVj0ajgcViwfLyMtLpdFWDlaamJjQ3N2NxcRGhUAihUAjNzc0VX28xsixjbGwMly5dwk9+8hO8+uqrSmP3QtU6otdNuSfrwPq+CLm9c8QJutVqxVe+8hV86Utf2pGeBHa7HYFAQOmT1NzcjPr6+m0vV41Go4HD4cD09LRS/dXX18djKNE9wmw248SJE3jllVewsLCAa9eu4fTp0zW7D9DpdHj88cfR3NyMH//4x7h79+6GUCd3lED+8aHcKs5chW4wr62tQZZlfOELX8DFixc3PRx3Jw0ODuLWrVu4ffs2bty4gaNHj8Jut+/a9txrGOhUiLh41Wg0yGQyqv1VREUCADaT2oLW1lY8/fTT+PznP4+//e1v+OMf/4j33ntPmco8V37ZopC/85QkCXq9Hj09PTh37hweeeQR9Pf3s1fOJojQbLPhpvi3YKBDVF2NjY2w2Wzw+XwIhUJYXl7e0jCfzcodFpRMJuH1emEymUqGSWoXA+VcLJR6jiRJ6OvrwwsvvIA//OEP+OUvf4mZmRnluFEo2EkmkxsqQQs13s+VP/2sRqPBpz/9aXzzm99Ef3//jgVqOp0OTqdTCVa8Xm/VgpXm5mY0NzdjZWUFgUAAwWBwVwM7Iqqu++67D+Pj4/jggw9w/fp1HDlyBK2trbu9WVum0Whw4cIF9Pb24kc/+hHeeust5dqv0Ay8Yt+u0+mg0WgKHiOK3VzOr84RoX9rayu+9rWv4cKFC1WZmbIUvV6PkydPwu12IxwO47XXXsPDDz/MmQ2rhJ9yhej1euXPm23wKO7U1WpyvVvEBcGTTz6JL37xi7hx4wb+/e9/480334TL5VL6BxT7XLPZLAwGA0wmExwOBz7+8Y/j9OnTGBkZURpk0ubkVzuV+xpxAVPLzfOIapEkSbDb7VhaWkIymcTCwgIsFktVgmyj0YiWlhZMT0/j5s2b8Hq9JacxF03xi8mtECxG9OwqJLd32smTJ3Hw4EG8+uqrePnll5Xmzfl3ZQVxrNlM7xydTofjx4/j4sWLOHz4MAwGAyKRSNHX5Sv1uAiU6uvrYTKZEAgEsLKyAqvVqgx/Ugu31IKlUtsojs/hcBjJZBIejwcNDQ28QUJ0jzAajTh9+jRefvllRCIRXLlyBefPn6/pfYAsyxgeHsalS5fw61//Gi+99NK6GRLzh2GJah0R4ORW6RQ7ThSaZVev1+OTn/wkvvGNb2BgYGDPXJ90dHTgvvvuw7Vr1zA1NQWXy8VpzKuEgU6FiJPIzczUI8sygsEgkskkTCaT8gUVJ2KlXles+iGbzSpDWIrJ7flTjNqJczknm9Uo2xfb0NjYiLGxMYyOjiKZTMLv98PlcsHlcmF+fh4rKyvKNK6NjY1oampCW1sbent70dXVhfb2dhiNRtXPntSJ3029Xl9WUCnLshL+MNknqj6j0ahMNx2NRrGwsACHw1G19a+urqKlpQXhcFgZElRovyGGQAHFb5yo7W82c8NFkiR89rOfxcjICK5evYpXXnkFk5OTShPMYssudXyUZRkWiwWjo6M4d+4cBgYGoNfrMTc3t2HdhcKj/MkAisk9/q6treH69etIJBK4c+cOBgYG1s20Weh95PZDK0bteJlKpRCJRDA9PY36+npYLJaq/l4R0e7q6urC4OAgbty4gYmJCXzkIx9BX1/fbm/WtlmtVnzrW9/CuXPn8Ktf/Qp///vfEQgE1j0nd3itqMYUQ6Zy+3jmhzm5rzEYDDh27BieeuopnD59GkajsarvU41Go8Ho6Chu376NUCiEy5cvo6OjY89t537Eq6UKyZ3lKplMqg6jSiQSWF1dRSAQwMTEBLRa7brpm4spdpJX6DmlqIUthcrE89dRahmyLBc96RXUTgZLnXCKdRQKnsRn09nZie7u7g0n2WKbxBCrZDJZdFpXUSpZilrZo1rAJt5LMfmN0jb7erXXisd34jkA1vXQSafTZQV7W5nunIh2jsPhQDgcXtdvpZInZdlsFvPz80p4JMsyjEYj9Hq9cmeyFLUJCMpZhloVodjn2e12XLhwAefOnYPb7cbNmzfxxhtvYHZ2VqkEzW0CL7ZLlmXo9XoYjUY4HA4cOXIEx44dw+DgIJqbm0vO7pfbC24rxHsXx+q2tja8//77mJ6eRlNT07pKqEqGY6urq7h58yaOHz+uDMflfp7o3qDRaDA2NqZUzYsL/txRDbVKo9FgaGgIP/3pT/HEE0/g97//Pf7xj39gfn6+YLV57gy6ucem/P2hVquF1WrF8ePH8eijj2JsbKwqvc+2qrm5GceOHcO//vUvuN1uvPvuuxgZGeF+vsIY6FSIwWAouzFyIpHAzMwMdDod7HY76uvrC1YmqIU2xZ6TWyVU7OQp9wRyqyeMO2krJ47l7iy2+/6227dhu6XzpU76BbUALrcJd7F1qFXHlDPcIR6PY2lpCW63e8PvtSg3zZfNZuH3+xGLxdDY2IhMJsNO+URVptfrYbfb4XK5EI/Hsbi4iI6OjoqclGWzWXg8Hni9XqXnXHd397rZOtTCls1UwxZ7fTnryDc0NISHHnoIqVQKwWAQLpcLfr8fXq8XkUgEoVBIGcprt9vR0tKC9vZ2tLW1wWAwbGiimU6nS74P0VyzGBGAlXoPyWQSFosFJpMJkUgEsizDarWum5ih1DaIYWjFiLvJhf5+YWEBhw8fxsDAwLpKZCK6N1itVtx///14/fXXMTc3h/HxcXzsYx/b7c3aMTqdDvfffz+OHj2Kp59+Gm+//TZee+01XL9+HT6fD8FgUJmcJZ/4O7PZDJvNhiNHjuDUqVMYGxtDZ2dnTfRYlSQJH/3oRzExMQGPx4O33noLhw8f5jTmFSZl98LV+z4UCARw+/ZtAEBfXx+sVmvB5yUSCUxPTyMSiQAAGhoa0N7eXvACVpyIlVLq5Ci3H0Cx54lmW6WWoXYntJxKHK1WW3QZYh2l3ovaSW+xE8rcdZRzN7YUta9OuRcYlf4KbvV97NRQBZ/Ph3feeQdGoxEnT55UrWBKJpO4evUqLBYLjh8/jiNHjtT0GGuiWpVOpzE1NYVwOAytVov+/v4dPykTlTkizNHpdOjt7a35hrn5FTXlVjRWSzabxdLSEmZmZgAATqdTCewqcUxaXl7G3bt3EY/HodFo0NHRgba2tj31mRBRdUQiEbz00kvw+/2wWq14/PHHa3Ya83JkMhnEYjF4PB7Mz8/jgw8+gN/vXzcsq66uDq2trejs7ERXVxecTieam5tVb9DuVe+//z7+9Kc/IZVK4cSJE/jMZz7Dm7MVxAqdChHVC6UCkvwwp7GxEQcOHNj1TuW7rZwgRO055QQ2aoFPqVLwcu/mqjX1VbsTqnY3Vq2vQTkBXDl3W0tVmYnPotg6Ghsb0djYCK1WC4PBUPAOg/i819bWMD09DY1Gg/b2djgcDoY5RLtEo9HA6XQiFospjWzNZvOOnZQVCnN6enpqPswByhsau5skSYLFYoHX60UsFsPi4iJsNhuMRuOOb/PKygrm5uawtram/E4xzCG6d5nNZpw8eRJ//vOfsby8jGvXruHUqVP79oJflmWYzWYcPHgQBw8eXBf059pPQ1D7+/vR19eHqakpjI+PY2hoCE6nc7c3a99ioFMhuXfnCu2gClXmMMz50F67k1nrdrtKaGVlBUajEbIs48CBAwXv8GezWcRiMbhcLgwMDAD4cCr6UjPcEFHliabxS0tLiEQiiMViO3IntViYkzvMiipLo9HA4XDA5XJhbW0Nd+/eRV9f345eVAUCAbhcLiSTyXVBPY/xRPe2Q4cOoaenB9PT07h27RoGBwfR1ta225tVFaVmA9wvdDodPvGJT2Bubg7RaBSXL1/Gww8/zOvcCtmfUegeICp0JEnaUN1QqDKnv7+fv+RUEblTIBb7yZ0ycad/gP81Oc5kMtBqtRt+xN3/eDwOrVYLu92O3t7efXu3hqhWiOmmDQYDMpmMcnG+HQxz9o6Wlhblcw8EAgiHwzu27EAggNnZWSXMcTqdDHOICMCH/b7GxsZgNBoRjUbxxhtvqPYcpdridDoxNDQEALhz547SioR2Hq+WKiT3YjZ3BxWPx3H79m1W5tA9I3f8b6ET+Wg0ipmZGYRCIWUK356eHk5ZTrRH1NfXw2azAYAyPGerRJjj8XiUgJdhzu4RM17pdDqk02l4PJ4duajKDXO0Wi26u7vhdDoZ5hCRorOzE4cPHwYATE5OKj29aH+QZRknTpyAxWJBMpnElStXEI/Hd3uz9iUGOhUiAp3cPirxeBxTU1OIRqMAPgxzWJlD+51Op1NO4vN79USjUczOziIajUKSJFitVvT29jLMIdpj7HY76urqkM1msbCwgEQisell5Ffm6PV69Pb2MszZZSaTCRaLBZIkIRKJbCuwAzaGOV1dXbDZbAxziGgdjUaD0dFRNDU1YW1tDZcvX+YF/z5jsVhw4sQJyLIMr9eLd955Z0/MprzfMNCpkNxmtclkUqnMWV1dhSRJDHPonpFbrZbb4Dk3zJFlGa2treju7maYQ7QH6XQ62O12yLKMeDyO+fn5TZ2U5YY56XQaBoMBfX19DHP2CKfTifr6emQyGfj9/i0PqwsGg+uGWXV1dRWd5ZOIqKWlBcPDw5AkCW63Gzdv3uQF/z4zNDSE9vZ2ZLNZvP322+tm96KdwSunCskPdCYnJxGPxyFJEpqamnDgwAFeuNI9QaPRbKhWyw9zbDYburq6OKMV0R5ms9kQCAQQCASwsrKC1tbWshok54c5RqMRPT09Oz4FOm2dwWCAxWJBLBZDPB6Hz+dDZ2fnppYRDAYxMzOjhDnd3d3KUD0iokIkScKxY8cwOTkJj8eDf/7zn9BqtdBoNAx29gFJkqDVauFwOPDuu+9iaWkJb775Jh588EH2ydxBTBQqRDRDTqVScLlcsNvtSpjT39/PC1e6Z4hAB/gw3IzFYgxziGqQLMtwOp2IRCJIpVLweDw4cOBAye9uocqcnp4eNDU1VXHLqRytra0IBoMIh8NYXFyExWKByWRSfV02m8XS0hLu3r27LsxhZQ4RlcNkMmF0dBR/+ctfMDExgWAwiLq6ut3eLNpB2WwWLpcLKysrOHv2LDKZDAOdHcRAp4JSqRQmJyfh8/lw9OhRdHR0oL6+Hj6fb93zci94i8nvPZJLlmXVoVtq6yhnG9SmE6/0dOPlLJtj9PceSZLWVavNzMwwzCGqUWazGRaLBYuLiwiFQlheXkZra2vB5xYKc3p7exnm7FE6nQ5dXV2YmJjA2toaPB4P+vv7Sx5Xs9ksFhcXMTc3h3Q6vS7M4fGYiMo1ODgIWZbx3//+F263e7c3hypgeHgYRqMRp0+f5iiVHcZPs4K0Wi3MZjNmZmYQiUSUE6R8aiWFaidF5QYdamGMWqCTO1tRIel0WjV4KrUOUZZXSm6D3UJKBVOSJCGbza7r41KIwWDY8jrE42La+kIymQw0Go3qv0cp5f57befxnQroJEmCwWDA4uIifD4f2traGOYQ1ShJktDe3o5QKIREIgGv1wuLxbJh353JZOD1ehnm1Jj6+nqYzWaEQiGEQiFEIhE0NDQUfK5okO12u5FKpZTZrDjMiog2S6vV4siRI2hvb1eG59P+I8tyWUO1aXMY6FSQw+FAIpFAQ0ODMuQqk8kUDXDE4+XIXUY5Y0w3+/xKKycoKLSdW33dTipnG8oJS9SUeh9q4RqAkqFRNpstGTqJdai9j2QyWXI79Xo9JEnCnTt3cPv2bTQ1NcHhcMBqtTLMIapRBoMBVqsVHo8H8Xgcfr8f7e3tyuOZTAYej0eZmpxhTu2QZRnt7e2IxWJIpVJwu904dOjQhn21qMy5e/fuujCHw6yIaDt4nCDaPCm7F67u97FkMol0Oq1Uh6TT6ZLPL6fKpdTjmUym5OPZbBbJZLLkc9LptLLNhV4vllGMWjMz8TmU+tXLZDKqn5VapY1aeCYa9ZZS6nHxWWz19eU+R/zulHp8q+tQK6XfKePj49Bqtejv74fT6UR3dzfDHKIalkwmMTU1hWg0Cr1ej0OHDqGurk4Jc1iZU7uy2Sy8Xi/m5uYgyzIOHTq0roG1CHPcbjeSySR0Oh16enrQ0tKyi1tNRER0b2KgQ7uinDBErVqpnDCl1DLUKqLK2ca1tbWSz1EL6LLZLBKJRNFliOFbxZYhwjG1kE8t/EqlUiUDuFJBoSRJ0Ov1JT+HSCSCdDqNQ4cOwWKxMMwh2geWl5cxPT2NTCYDm82G3t5epTInm82yAXINi8fjyuycJpMJBw8ehF6vBwClZ44Iczo7O4v2USIiIqLKYqBDtM9V4yuuFn6Jx9gEjWj/SKfTmJqaQjgchlarRVNTE1ZWVpBOp1FXV4e+vj6YTCY2x61RHo9HaU5qt9vR1dWFpaWldZU5XV1d7JlDRES0ixjoEBER0ZaEw2HcuXMHHo8Hs7OzcDgcaGpqQm9vb8km9rIsl6zUE5V/an2+thMWiT5j25m9sZwG9aWWkc1m92zglUql8N577yEej0Ov18Nut8Pr9bIyh4iIaA/h7XIiIiLaErPZDJPJhGg0ikQigYWFBdhsNiwuLqq+dqdmcCxGhCWlQhO1MEZteGg5szeqNahXm9FFbXZHWZZVqx/VwrFiszc2NDTA5/MhGo1idnYWTqeTYQ4REdEewkCHiIiItkSSJHR0dKCurg6Tk5NoaGhQeq0A5TeP32rPtHIb0xdbfm7z/UKBR27/sd1uLr8dauFYqe2cn5/HjRs3cO7cORiNRjidToY5REREewSHXBEREdG2pNNpJJPJDcFBOYGOmFmxGLVZD0WVS6n1qA3PEs3jiz1HPF5qHWqPqzWwlyQJWq22ZDVPqdkbxXaq9TPbbHgWj8fhdrtx5swZGI1GWCyWkq8nIiKi6mGgQ0RERKRiJ06X1Cpl1MKWTCZTchnlzKyo9nihYCqTyWyoviIiIqLdx0CHiIiIiIiIiKjGlO4GSEREREREREREew4DHSIiIiIiIiKiGsNAh4iIiIiIiIioxjDQISIiIiIiIiKqMQx0iIiIiIiIiIhqDAMdIiIiIiIiIqIaw0CHiIiIiIiIiKjGMNAhIiIiIiIiIqoxDHSIiIiIiIiIiGoMAx0iIiIiIiIiohrDQIeIiIiIiIiIqMYw0CEiIiIiIiIiqjEMdIiIiIiIiIiIagwDHSIiIiIiIiKiGsNAh4iIiIiIiIioxjDQISIiIiIiIiKqMQx0iIiIiIiIiIhqDAMdIiIiIiIiIqIaw0CHiIiIiIiIiKjGMNAhIiIiIiIiIqoxDHSIiIiIiIiIiGoMAx0iIiIiIiIiohrDQIeIiIiIiIiIqMYw0CEiIiIiIiIiqjEMdIiIiIiIiIiIagwDHSIiIiIiIiKiGsNAh4iIiIiIiIioxjDQISIiIiIiIiKqMQx0iIiIiIiIiIhqDAMdIiIiIiIiIqIa8/9mm2jfttOW/wAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_image_grid(\n", " {\n", " d: view(mol, direction=d, fixed_atom_size=False, padding=-1)\n", " for d in [\"tilt_x\", \"tilt_z\", \"along_z\"]\n", " },\n", " show_labels=False,\n", " rows=1,\n", ");" ] }, { "cell_type": "markdown", "id": "5043639e-c8cf-468b-a4f1-07bb2c1c20f0", "metadata": {}, "source": [ "We now proceed to configure the Quantum Espresso calculation. Initially, a `Settings` object is created to store all the necessary parameters. To ensure the reliability of our vibrational spectrum, we specify a `GeometryOptimization` task, guaranteeing that our structure resides at a local energy minimum, thus avoiding contributions from imaginary frequencies. Subsequently, we explicitly request the calculation of `NormalModes` (which yields the IR spectrum) and the `Raman` spectrum within the Properties section (`s.input.ams.Properties`).\n", "\n", "For the electronic structure calculation, we set an energy cutoff (`ecutwfc`) of `25 Ry`. While this value is intentionally low for illustrative purposes and to speed up the calculation, it is important to note that for production calculations, a cutoff of at least `40 Ry` is generally recommended. The optimal cutoff value, however, depends on the specific system being investigated. We specify a Monkhorst–Pack grid with dimensions of `4 × 4 × 3` for sampling the first Brillouin zone, aiming for relatively uniform sampling in the three directions.\n", "\n", "Additionally, we specify the pseudopotentials required for the calculation. Due to Quantum Espresso’s limitation in supporting only Norm-Conserving (NC) and LDA-type pseudopotentials for Raman spectra calculations, which are not included in our standard pseudopotential families, we directly specify the pseudopotential files: `QE/Be.pz-mt_fhi.UPF` for beryllium and `QE/O.pz-mt_fhi.UPF` for oxygen. The pseudopotential filenames indicate the use of the Perdew-Zunger LDA functional (`pz`), the Martins-Troullier generation method (`mt`), and their origin from the FHI-aims library (`fhi`).\n", "\n", "Finally, we instantiate an `AMSJob` object, combining the defined `Settings` and the `Molecule` object, and we assign the name `\"BeO\"` to the job. This job object encapsulates all the necessary information for the Quantum Espresso calculation and is ready to be executed." ] }, { "cell_type": "code", "execution_count": 4, "id": "a33d28b9-6829-4e76-bd79-04fc0ab6bc40", "metadata": {}, "outputs": [], "source": [ "from scm.plams import Settings, AMSJob\n", "\n", "s = Settings()\n", "\n", "s.input.ams.Task = \"GeometryOptimization\"\n", "\n", "s.input.ams.Properties.NormalModes = \"Yes\"\n", "s.input.ams.Properties.Raman = \"Yes\"\n", "\n", "s.input.QuantumEspresso.System.ecutwfc = 25.0\n", "s.input.QuantumEspresso.K_Points._h = \"automatic\"\n", "s.input.QuantumEspresso.K_Points._1 = \"4 4 3 0 0 0\"\n", "s.input.QuantumEspresso.Pseudopotentials.Files = [\n", " Settings(Label=\"Be\", Path=\"QE/Be.pz-mt_fhi.UPF\"),\n", " Settings(Label=\"O\", Path=\"QE/O.pz-mt_fhi.UPF\"),\n", "]\n", "\n", "job = AMSJob(settings=s, molecule=mol, name=\"BeO\")" ] }, { "cell_type": "markdown", "id": "38bc62f6-e18d-4f22-a9cc-842c6e6325f0", "metadata": {}, "source": [ "The `job.get_input()` function retrieves the content of the AMS input file generated during the previous setup. This enables users to inspect the input parameters before running the Quantum Espresso calculation. Reviewing the input file can be beneficial for advanced users who seek greater control over the calculation settings." ] }, { "cell_type": "code", "execution_count": 5, "id": "c88217c2-c2c7-472a-b7f0-320ba5de93c4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Properties\n", " NormalModes Yes\n", " Raman Yes\n", "End\n", "\n", "Task GeometryOptimization\n", "\n", "System\n", " Atoms\n", " Be 0.0000000000 1.5555413800 0.0007009000\n", " Be 1.3471383600 0.7777706900 2.1887103500\n", " O 0.0000000000 1.5555413800 1.6512462200\n", " O 1.3471383600 0.7777706900 3.8392556700\n", " End\n", " Lattice\n", " 2.6942767100 0.0000000000 0.0000000000\n", " -1.3471383600 2.3333120800 0.0000000000\n", " 0.0000000000 0.0000000000 4.3760188900\n", " End\n", "End\n", "\n", "Engine QuantumEspresso\n", " K_Points automatic\n", " 4 4 3 0 0 0\n", " End\n", " Pseudopotentials\n", " Files\n", " Label Be\n", " Path QE/Be.pz-mt_fhi.UPF\n", " End\n", " Files\n", " Label O\n", " Path QE/O.pz-mt_fhi.UPF\n", " End\n", " End\n", " System\n", " ecutwfc 25.0\n", " End\n", "EndEngine\n", "\n", "\n" ] } ], "source": [ "print(job.get_input())" ] }, { "cell_type": "markdown", "id": "ca7b5394-5421-46ee-a112-33d9d1ce7ab6", "metadata": {}, "source": [ "We call the `run()` method on the previously defined `AMSJob` object to start the Quantum Espresso calculation. This starts the execution of the job, and once it is completed, the results are stored in the results object. The output displays various stages of the job's progress, including `STARTED`, `RUNNING`, `FINISHED`, and finally, `SUCCESSFUL`, which indicates that the calculation has been completed without any errors." ] }, { "cell_type": "code", "execution_count": 6, "id": "7cf13d39-88ae-4e3d-a9a6-59aacb571d15", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10.04|12:27:34] JOB BeO STARTED\n", "[10.04|12:27:34] JOB BeO RUNNING\n", "[10.04|12:28:07] JOB BeO FINISHED\n", "[10.04|12:28:07] JOB BeO SUCCESSFUL\n" ] } ], "source": [ "results = job.run()" ] }, { "cell_type": "markdown", "id": "44df9c43-d7f6-4878-a549-e5f30745f78b", "metadata": {}, "source": [ "The results object offers specialized functions for extracting calculated vibrational frequencies and intensities for both IR and Raman spectra. In the code below, we use the methods `get_frequencies()`, `get_ir_intensities()`, and `get_raman_intensities()` to obtain these values. Afterward, the code prints the frequencies (in cm⁻¹), IR intensities (in km/mol), and Raman intensities (in A⁴/mol) in a nice table with three columns." ] }, { "cell_type": "code", "execution_count": 7, "id": "7fe34a86-4fc7-41ea-9579-52d4e608be5f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " freqs(cm-1) IR(km/mol) Raman(A^4/mol)\n", " 250.4 0.000 0.214\n", " 250.4 0.000 0.214\n", " 490.9 0.000 0.000\n", " 693.5 0.000 3.006\n", " 693.5 0.000 3.006\n", " 696.3 1043.861 13.505\n", " 741.3 990.978 4.991\n", " 741.3 990.978 4.991\n", " 830.8 0.000 0.000\n" ] } ], "source": [ "freqs = results.get_frequencies()\n", "ir_int = results.get_ir_intensities()\n", "raman_int = results.get_raman_intensities()\n", "\n", "print(f\"{'freqs(cm-1)':>15} {'IR(km/mol)':>15} {'Raman(A^4/mol)':>15}\")\n", "for freq, ir, raman in zip(freqs, ir_int, raman_int):\n", " print(f\"{freq:>15.1f} {ir:>15.3f} {raman:>15.3f}\")" ] }, { "cell_type": "markdown", "id": "ea6fa8db-8db4-423e-a4d3-c5d72d103f34", "metadata": {}, "source": [ "To visualize the IR spectrum, we use the `get_ir_spectrum` function, which applies broadening to the calculated absorptions in order to simulate the spectral lineshape observed in experiments. In this case, we use Lorentzian broadening with a width of 10 cm⁻¹ and focus on the spectral range from 200 to 1000 cm⁻¹. The `post_process=\"max_to_1\"` option normalizes the spectrum by setting the maximum intensity to 1.\n", "\n", "Then, we use matplotlib to visualize the results. We show the broadened IR spectrum as a curve (in blue) and small vertical bars (in red) to indicate the positions of the individual vibrational frequencies. Interestingly, the most prominent absorption in the spectrum results not from a single, high-intensity mode but rather from the overlap of two degenerate absorptions." ] }, { "cell_type": "code", "execution_count": 8, "id": "353095ec-5c94-4951-887f-a4939c44b4fe", "metadata": {}, "outputs": [ { "data": { "image/png": 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AAKB+CnXYJBOnAAAAkHAIqQAAAEg4hFQAAAAkHEIqAAAAEg4hFQAAAAmHkAoAAICEQ0gFAABAwiGkAgAAIOEQUgEAAJBwCKkAAABIOIRUAAAAJBxCKgAAABJOQofUgwcPavr06RoxYoSaN28ui8Wi+fPnh3x+QUGBJk2apKysLDVq1EhDhw7VunXr4tdgAAAAxERCh9Q9e/Zo1qxZ2rhxo37zm9+Eda7b7dZZZ52ll156Sdddd50eeugh5eXlaciQIfrhhx/i1GIAAADEgr2uG1CTnJwc5ebmqnXr1vrss880YMCAkM9dsmSJVq9erVdffVVjxoyRJF1wwQXq2rWrpk+frpdeeilezQYAAECUErqSmpKSotatW0d07pIlS9SqVSudd9553m1ZWVm64IIL9Oabb8rhcMSqmQAAyBhT100AjigJHVKjsX79evXr109Wq/9HHDhwoIqKirRp06Zqz83Ly9M333zj97V58+Z4NxkAUA8ZY3TdS+vU755l+urn/XXdHOCIccSG1NzcXOXk5ARs92zbtWtXtec+9dRT6tmzp9/X6NGj49VUAEA99v2vB/T2l7nKLyrTMyt/rOvmAEeMhB6TGo3i4mKlpKQEbE9NTfXur87kyZM1duxYv22bN28mqAIAAmzMLfR+X1BcWoctAY4sR2xITUtLCzrutKSkxLu/OtnZ2crOzo5b2wAAR47iUrf3+yTbEdtBCRx2R+z/mzwrA1Tl2damTZvD3SQAwBGouMzl/d7lZvIUECtHbEjt06eP1q1bJ7fb7bd97dq1Sk9PV9euXeuoZQCAI0mJT0h1M8MfiJkjIqTm5ubqu+++U1lZmXfbmDFj9Ouvv+q1117zbtuzZ49effVVnX322UHHqwIAEK7i0sqQWuYkpAKxkvBjUp944gkVFBR4Z+O/9dZb+vnnnyVJ119/vZo2bao77rhDCxYs0JYtW9SxY0dJ5SH1hBNO0OWXX65vv/1WLVu21FNPPSWXy6WZM2fW1ccBABxhfCuph0qdddgS4MiS8CH1r3/9q7Zt2+b9+bXXXvNWRy+77DI1bdo06Hk2m03/+c9/dNttt+nxxx9XcXGxBgwYoPnz56tbt26Hpe0AgCOf75hU3+8BRCfhQ+rWrVtrPWb+/PmaP39+wPZmzZrpueee03PPPRf7hgEAIP9gWuZy13AkgHAcEWNSAQCoK77d/YxJBWKHkAoAQBQcZZXVUyqpQOwQUgEAiEKZz9qopYRUIGYIqQAARKHMSSUViAdCKgAAUfANpmUuxqQCsUJIBQAgCr7d/S634dWoQIwQUgEAiIJvd79Elz8QK4RUAACi4HT7h1ImTwGxQUgFACAKVcehVq2sAogMIRUAgCiUBnT3MyYViAVCKgAAUaja3c+YVCA2CKkAAEShauWUMalAbBBSAQCIQtXKKZVUIDYIqQAARKFqKGWdVCA2CKkAAEShanc/IRWIDUIqAAARcgd5wxSz+4HYIKQCABChMnfg+FMqqUBsEFIBAIhQkIwasCQVgMgQUgEAiFCwQEolFYgNQioAABEKXkklpAKxQEgFACBCQSupTJwCYoKQCgBAhIJ17VNJBWKDkAoAQIRcJjCQMiYViA1CKgAAEXIG6dpndj8QG4RUAAAi5KaSCsQNIRUAgAgFG3/KmFQgNgipAABEyB0kkFJJBWKDkAoAQISopALxQ0gFACBCwaqmLhcTp4BYIKQCABAh1kkF4oeQCgBAhIIFUsakArFBSAUAIELBlqCikgrEBiEVAIAIBVvMn0oqEBuEVAAAIkQlFYgfQioAABEKPiaV2f1ALBBSAQCIULBASiUViA1CKgAAEQq2JKoryDhVAOEjpAIAECEqqUD8EFIBAIhQ0EoqIRWICUIqAAARclJJBeKGkAoAQISCLkEVrLwKIGyEVAAAIsRi/kD8EFIBAIiQbyBNtpf/SqW7H4gNQioAABFy+XT3p1SEVCqpQGwQUgEAiJBvIE2x2yQFn0wFIHyEVAAAIuQfUqmkArFESAUAIELBQipjUoHYIKQCABChYBOnqKQCsUFIBQAgQs5gldQgy1IBCB8hFQCACAWbOEUlFYgNQioAABEKvk4qs/uBWCCkAgAQIU9ItVoku83itw1AdAipAABEyLOYv91qld1aHlKZ3Q/EBiEVAIAIeSupVslmpZIKxBIhFQCACHkCaXkllXVSgVgipAIAECHfMameSqrTxcQpIBYIqQAARMhbSbVZK7v7DZVUIBYIqQAARMjpraRavCGVFaiA2CCkAgAQIVdFIrVbLbJZPLP7SalALBBSAQCIkGf4qc1qkc27TmodNgg4ghBSAQCIkKeSavOppLqopAIxQUgFACBCroo5UnarhXVSgRgjpAIAECFP1dRKSAVijpAKAECEKhfzt3hfi8oSVEBsEFIBAIiQy2cJKiuVVCCmCKkAAETI6V3M36eSSkgFYoKQCgBAhDyB1Ga1yFoxu99tJEOXPxC1hA6pDodDU6dOVZs2bZSWlqZBgwZp2bJlIZ27fPlyDR06VC1btlRmZqYGDhyoRYsWxbnFAICGxBtSLZWVVN/tACKX0CF1woQJevjhh3XppZfqsccek81m05lnnqlVq1bVeN6//vUvDRs2TKWlpZoxY4buu+8+paWlady4cXrkkUcOU+sBAEc6v0qqT0h1ElKBqNnrugHV+eSTT/TKK69o9uzZuvXWWyVJ48aNU8+ePTVlyhStXr262nOfeOIJ5eTk6P3331dKSook6eqrr1b37t01f/583XzzzYflMwAAjmy+IdW3kuqmux+IWsJWUpcsWSKbzaZJkyZ5t6WmpmrixIn6+OOPtWPHjmrPLSwsVLNmzbwBVZLsdrtatmyptLS0uLYbANBweJabsvmskypRSQViIWErqevXr1fXrl2VkZHht33gwIGSpA0bNqhdu3ZBzx0yZIgefPBBTZs2TePHj5fFYtFLL72kzz77TP/85z9rvXdeXp52797tt23z5s0RfhIAwJHKt5LqG1LdhFQgagkbUnNzc5WTkxOw3bNt165d1Z47bdo0bdmyRffdd5/uvfdeSVJ6erqWLl2qc845p9Z7P/XUU5o5c2aELQcANBROV+Vi/lRSgdhK2JBaXFzs113vkZqa6t1fnZSUFHXt2lVjxozReeedJ5fLpblz5+qyyy7TsmXLdMIJJ9R478mTJ2vs2LF+2zZv3qzRo0eH/0EAAEcsdzXd/VRSgeglbEhNS0uTw+EI2F5SUuLdX53rrrtOa9as0bp162S1lg+7veCCC9SjRw/deOONWrt2bY33zs7OVnZ2dhStBwA0BE7f7n4LlVQglhJ24lROTo5yc3MDtnu2tWnTJuh5paWlev7553XWWWd5A6okJSUlaeTIkfrss89UWloan0YDABoUtzekWv0qqayTCkQvYUNqnz59tGnTJhUWFvpt91RB+/TpE/S8vXv3yul0yuVyBewrKyuT2+0Oug8AgHB5K6kWEVKBGEvYkDpmzBjvWFIPh8OhefPmadCgQd6Z/du3b9d3333nPSY7O1uZmZl6/fXX/SqmBw8e1FtvvaXu3buzDBUAICZc1VVSWScViFrCjkkdNGiQxo4dqzvuuEN5eXnq0qWLFixYoK1bt+r555/3Hjdu3DitXLnS+55km82mW2+9VX/+8591wgknaNy4cXK5XHr++ef1888/68UXX6yrjwQAOMJUhlQqqUCsJWxIlaSFCxdq2rRpWrRokfLz89W7d2+9/fbbGjx4cI3n3XXXXerUqZMee+wxzZw5Uw6HQ71799aSJUt0/vnnH6bWAwCOdE6fSqqdkArEVEKH1NTUVM2ePVuzZ8+u9pgPPvgg6PZLLrlEl1xySZxaBgBA5RJUdqtFVgshFYilhB2TCgBAonO63JLKu/rtNkIqEEuEVAAAIuTJorYqlVTWSQWiR0gFACBCTrdPJdVnbW43s/uBqBFSAQCIUEVGLa+k+vxGdboIqUC0CKkAAETIW0m1UEkFYo2QCgBABIwxfmNSbb6VVMakAlEjpAIAEAHfGfx2q0U230oqIRWIGiEVAIAI+FZLrVaLbMzuB2KKkAoAQAR8x52WV1JZJxWIJUIqAAAR8K2W2gipQMwRUgEAiIC7ppDK7H4gaoRUAAAiUHMl1V0XTQKOKIRUAAAi4KoSUu1+IbUuWgQcWQipAABEoOoSVFYqqUBMEVIBAIiAb0i1WqikArFGSAUAIAJ+lVSbRVYLlVQglgipAABEwFljJZXZ/UC0CKkAAETAfzF/q9+YVN44BUSPkAoAQAScLt/Z/fKrpLpZJxWIGiEVAIAI+AZRm9Xqt04qlVQgeoRUAAAi4L+Yv/xCqpuQCkSNkAoAQAR8Z/DbrFbZLFRSgVgipAIAEAHftVA9i/l7ciqVVCB6hFQAACLg9KmketZI9VRTqaQC0SOkAgAQAd/1+u22ipBaMS7Vxex+IGqEVAAAIhC0kuoJqS5CKhAtQioAABHwX8yfSioQa4RUAAAi4L+Yf5WQyphUIGqEVAAAIuByB4ZUT0WViVNA9AipAABEwBWku98zNpUlqIDoEVIBAIiAbyXVSiUViDlCKgAAEfANqd5KqpVKKhArhFQAACLgWy31dPNTSQVih5AKAEAEfKulnsX8rSxBBcQMIRUAgAj4VkttVSqpLOYPRI+QCgBABIItQeXp9qeSCkSPkAoAQAT8J06V/zr1dPuzmD8QPUIqAAAR8F+CqvxPT7c/IRWIHiEVAIAI+C/mX/7rlNeiArFDSAUAIAJBK6mEVCBmCKkAAEQg2JhUQioQO4RUAAAi4L+Yf/mfNtZJBWKGkAoAQARcbrek8mBqqZgwZauoqPLGKSB6hFQAACLgKs+o3uqpJFWsQOX3NioAkSGkAgAQAW8l1eITUqmkAjFDSAUAIAKeSqrdt5Ja8VuVSioQPUIqAAAR8FRSrT4h1e6tpLrrpE3AkYSQCgBABDwz+P0rqeXfU0gFokdIBQAgAp61UK1BQiqVVCB6hFQAACLgdNVQSSWjAlEjpAIAEAFPd7//ElRUUoFYIaQCABABT3e/X0i1eV6LWidNAo4ohFQAACIQNKRaPCGVlApEyx7piUVFRVq2bJk++ugjffvtt9qzZ48sFotatmypY489Vr/97W91xhlnqFGjRrFsLwAACcEbUi2BY1JdTO8HohZ2JfWrr77ShAkT1Lp1a5177rl68skntXnzZlksFhljtGnTJj3xxBM699xz1bp1a02YMEFfffVVPNoOAECdCVpJJaQCMRNWJfXCCy/U0qVL1b9/f82YMUO/+93vdNxxx8lms/kd53K59O233+q9997TkiVL1LdvX40dO1Yvv/xyTBsPAEBdCRZSPTP9PZOqAEQurJBqtVr12WefqU+fPjUeZ7PZ1KtXL/Xq1Ut/+tOftGHDBj344IPRtBMAgITiDBJSrVRSgZgJK6RGWgnt06cPVVQAwBHFHeSNU3ZCKhAzzO4HACAClYv5V/4qtVoqX4tq6PIHokJIBQAgAjWNSfXdDyAycQup33//vaxWq+z2iFe5AgAgYXneKmW3BY5JLd9PSAWiEbcEmZ6ersGDB8vis34cAABHitoqqW66+4GoxC2ktmvXTh988EG8Lg8AQJ3yVErtQdZJ9d0PIDKMSQUAIAI1LeYvSW5CKhCVmFRSv/76a/3nP//R1q1bJUkdO3bUyJEj1atXr1hcHgCAhFNZSa2s91BJBWInqpDqcDh09dVXa9GiRTLGyFrxf1S326077rhDl156qZ577jklJyfHpLEAACQKKqlAfEXV3T916lQtXLhQ1157rTZu3KiSkhI5HA5t3LhR11xzjV588UVNmTIl4us7HA5NnTpVbdq0UVpamgYNGqRly5aFfP4//vEPnXjiiWrUqJEyMzN10kkn6f3334+4PQAAeHhn9/uGVAuVVCBWogqpL774ov7whz/oiSeeULdu3WS322Wz2dStWzc9+eSTuvTSS/Xiiy9GfP0JEybo4Ycf1qWXXqrHHntMNptNZ555platWlXruTNmzNDFF1+sdu3a6eGHH9a9996r3r17a+fOnRG3BwAAD5er5koq66QC0Ymqu7+srEwnnHBCtftPOukkvfXWWxFd+5NPPtErr7yi2bNn69Zbb5UkjRs3Tj179tSUKVO0evXqas9ds2aNZs2apb/97W+6+eabI7o/AAA18Y5JtRFSgXiIqpI6fPhwvfvuu9Xuf+eddzRs2LCIrr1kyRLZbDZNmjTJuy01NVUTJ07Uxx9/rB07dlR77qOPPqrWrVvrxhtvlDFGBw8ejKgNAABUp7YxqS7WSQWiElZI3bdvn9/XPffcoy1btui8887T//3f/2nbtm3atm2bli9frnPPPVfbtm3TPffcE1HD1q9fr65duyojI8Nv+8CBAyVJGzZsqPbc//u//9OAAQP0+OOPKysrS02aNFFOTo6eeOKJkO6dl5enb775xu9r8+bNEX0OAMCRqbbZ/VRSgeiE1d3fsmXLgDdIGWP01Vdf6c033wzYLkk9evSQ0+kMu2G5ubnKyckJ2O7ZtmvXrqDn5efna8+ePfroo4/0/vvva/r06Wrfvr3mzZun66+/XklJSbr66qtrvPdTTz2lmTNnht1mAEDDUdsbpwipQHTCCql33333YXvNaXFxsVJSUgK2p6amevcH4+na37t3r1555RVdeOGFkqQxY8aoV69euvfee2sNqZMnT9bYsWP9tm3evFmjR48O92MAAI5QwWb3Wy2EVCBWwgqpM2bMiFMzAqWlpcnhcARsLykp8e6v7jxJSkpK0pgxY7zbrVarLrzwQk2fPl3bt29X+/btq713dna2srOzo2k+AOAIF7SSaiOkArES8cSpoqIitWjRQrNnz45le7xycnKUm5sbsN2zrU2bNkHPa968uVJTU9WiRQvZbDa/fZ7gmZ+fH+PWAgAamsoxqcErqayTCkQn4pCanp4uu92uRo0axbI9Xn369NGmTZtUWFjot33t2rXe/cFYrVb16dNHu3fvVmlpqd8+zzjWrKys2DcYANBguN1Gnsn7Np+JU76TqNzM7geiEtUSVOeff76WLFninSQVS2PGjJHL5dLcuXO92xwOh+bNm6dBgwapXbt2kqTt27fru+++8zv3wgsvlMvl0oIFC7zbSkpKtHjxYh133HHVVmEBAAiFb5XUt4vfJ6PK6SKkAtGIajH/iy66SJMnT9bQoUN11VVXqWPHjkHHivbr1y/saw8aNEhjx47VHXfcoby8PHXp0kULFizQ1q1b9fzzz3uPGzdunFauXOkXlK+++mo999xz+uMf/6hNmzapffv2WrRokbZt2xbxywUAAPDwHW/qP7ufSioQK1GF1CFDhni///DDDwP2G2NksVjkcrkiuv7ChQs1bdo0LVq0SPn5+erdu7fefvttDR48uMbz0tLS9P7772vKlCl64YUXdOjQIfXp00f//ve/NXz48IjaAgCAh2dmv+Q/JtXmW0llTCoQlahC6rx582LVjqBSU1M1e/bsGidnffDBB0G3Z2dna/78+fFpGACgQauukuo7PtVNSAWiElVIHT9+fKzaAQBAveE3JtU3pDK7H4iZqCZOAQDQEPlXUnktKhAPUVVSpfJZ80uXLtW6deu0f/9+uX3G6UiSxWLxm+gEAEB9V20llZAKxExUIXXbtm0aOnSotm7dqszMTO3fv1/NmzdXQUGBXC6XWrZsqcaNG8eqrQAAJASXq7oxqT4hldn9QFSi6u6/7bbbtH//fq1Zs0abNm2SMUb/+Mc/dPDgQT344INKS0vTu+++G6u2AgCQEPxm99uqq6T69ywCCE9UIfX999/X5MmTNXDgQFkrxuQYY5SSkqLbbrtNp59+um666aZYtBMAgIRR/TqpviH1sDYJOOJEFVKLiorUsWNHSVJGRoYsFov279/v3X/iiSdq1apVUTUQAIBEU92YVCuVVCBmogqp7du3188//yxJstvtOuqoo7RmzRrv/m+//VapqanRtRAAgART3ex+KqlA7EQ1ceq0007Tm2++qenTp0uSJkyYoAceeED5+flyu91atGiRxo0bF5OGAgCQKKqtpFqopAKxElVIvf322/Xpp5/K4XAoJSVFd955p3bt2qUlS5bIZrPpkksu0cMPPxyrtgIAkBB8A2j1Y1KZ3Q9EI6qQ2r59e7Vv3977c2pqqp577jk999xzUTcMAIBE5XTVPiaVN04B0eGNUwAAhCmU2f1u1kkFohJWSH3ggQd08ODBsG9SWFioBx54IOzzAABIRH5jUqtZJ5VKKhCdsELqSy+9pHbt2mny5Mn64IMP5HK5qj22rKxMy5cv16RJk9S+fXu9/PLLUTcWAIBEUN3sft+Q6iakAlEJa0zql19+qZdeekl//etf9cwzzyglJUU9e/ZUp06d1KxZMxljlJ+fry1btujrr79WWVmZevXqpSeeeEKXXnppvD4DAACHVXWz+20WKqlArIQVUi0Wiy699FJdeumlWr9+vd544w19/PHHWrNmjfbu3StJatGihbp3766pU6fqnHPOUb9+/eLScAAA6kp1s/utVossFskYKqlAtCKe3d+3b1/17ds3lm0BACBiew86dN+/N6p/x+a6ZFD72k+IQnWVVKm8muo0hkoqECVm9wMAjghXLvxMr63fqTtf/0pFpc643qu62f2+P7NOKhAdQioA4IiwfnuB9/tfCx1xvZf/Oqn+v0o9lVUqqUB0CKkAgCPO7gPxDal+lVSbfyXVbiv/1ep08VpUIBqEVABAvWeqLJyfd6AkrveraUxqUkVoLaOSCkSFkAoAqPeKSv3X7S4sjveY1OCz+31/drkIqUA0CKkAgHqvoLjM7+d4T5yqqZLqGaNa5qa7H4hGVCF15MiReumll1RcXByr9gAAELZDDv9QetBRd7P7Pd39TiqpQFSiCqk//fSTLrvsMrVq1Urjx4/X8uXLA8YFAQAQb8VVuvurdv/Hmn8ltcrs/oqJUyxBBUQnqpD6/fffa+3atbr88sv13nvvafjw4Wrbtq1uu+02bdiwIUZNBACgZlVDadXKaqzVVEn1dP+XMbsfiErUY1IHDBigxx57TDt37tR//vMfnXbaaZozZ46OP/549ezZUw899JB+/vnnWLQVAICgSsoOcyXVVcOYVBvrpAKxELOJU1arVcOHD9eiRYu0fft2jRkzRt9++61uv/12dezYUWeccYb+/e9/x+p2AAB4Hf5KanmV1GKRrNVNnKKSCkQlprP7V61apWuuuUZdunTRq6++6q2k/u1vf9Pu3bs1atQo3X333bG8JQAAKq5SSa36c6x5qqRVq6i+2xiTCkTHHu0Fvv32W7344ot6+eWXtX37dmVnZ2v8+PH6wx/+oD59+niPu/HGGzVp0iQ9+eSTmjVrVrS3BQDAq7jKklMOZ3yrmJ4AWnU8quTT3c/sfiAqUYXUPn366KuvvlJKSorOOeccPfXUUxo+fLis1uAF2qFDh+q5556L5pYAAASoWjmNd0itrKQG/r5LsrFOKhALUYXUzMxMzZ07V2PHjlVGRkatx59zzjnasmVLNLcEACBASZl/IHTEubu/xkoq3f1ATEQVUhcuXKisrCylpaUF3V9cXKzdu3erffv2kqT09HR16NAhmlsCABCgtErltDTOk5acFVXSYGNSbd6JU4RUIBpRTZzq1KmTXn/99Wr3/+tf/1KnTp2iuQUAALWqGkodZXU3JrXyjVN09wPRiCqk1vZ2qbKysmrHpwIAECtVK6lxH5PqqmF2f8WYVNZJBaITdnd/YWGhCgoKvD/v3btX27dvDziuoKBAr7zyinJycqJqIAAAtQmopDoP05hUW/VjUp1MnAKiEnZIfeSRR7xLSFksFt1000266aabgh5rjNG9994bVQMBAKhNwJjUOpzd7w2pjEkFohJ2SB02bJgaN24sY4ymTJmiiy++WP369fM7xmKxqFGjRjr++OPVv3//mDUWAIBgqr7dyeF0yxgjiyWw0hkLNa+TysQpIBbCDqknnniiTjzxREnSoUOHdN5556lXr14xbxgAAKEKVjktdbmVYrfF5X41ze73TJxy0d0PRCWqJaimT58eq3YAABCxYCHV4YxfSK2pkmqjux+IibBC6qxZs2SxWHTXXXfJarWG9HpTi8WiadOmRdxAAABqE2xd1HiOS60ckxqsksobp4BYCCukzpgxQxaLRVOnTlVycrJmzJhR6zmEVABAvFVXSY0XT5XUWsMbp6ikAtEJK6S6q/yrsOrPAADUhWCV1Hi+GtUzUSupptn9bhPXyVvAkY6V9gEA9V51E6fixdPdn2Svfna/VDl2FUD4opo4FUxRUZFeeeUVORwOnXnmmerQoUOsbwEAgB9PZTPZbvUG1ni+GtXzytOg66T6LPDvdBvFae4WcMSLKqROnDhRa9eu1ddffy1JKi0t1QknnOD9uWnTpnr//ffVt2/f6FsKAEA1PMG0SYpde52lkuI7JtWzBmpSDW+ckng1KhCNqLr7V6xYofPOO8/780svvaSvv/5aixcv1tdff63WrVtr5syZUTcSAICaeENqamXtJZ6vRq1cJzXYmNTKbc44DjkAjnRRhdRffvlFHTt29P78xhtvqH///rr44ot13HHH6aqrrtLatWujbSMAADXyjD9t7BNS47oEVUUl1R6kkupbXeWtU0DkogqpjRo1UkFBgSTJ6XTqgw8+0PDhw737mzRpov3790fVQAAAauMJpI1TfCupcezur6ikJtmCjUn1qaSyCg4QsajGpPbr10/PPvushg4dqn/96186cOCAzj77bO/+H3/8Ua1atYq6kQAA1MRTSW2SmuTdFs/u/jJn9WNSfd9CxVqpQOSiCqn33Xefhg8frv79+8sYozFjxmjgwIHe/a+//rp++9vfRt1IAABq4jtxyiOus/s9Y1KDVFKTbEycAmIhqpDav39/fffdd1q9erUyMzN16qmnevcVFBRo8uTJftsAAIg1l9vIkwV9x6SWxXHSknd2f9A3TjFxCoiFqNdJzcrK0jnnnBOwPTMzUzfeeGO0lwcAoEa+E6QO15hU7zqpwcaksgQVEBMxWcz/wIED2rZtm/Lz82VM4P8hBw8eHIvbAAAQwDekNvIJqfF841SZu/rZ/X4TpxiTCkQsqpC6d+9eXXfddVq6dKlcrsAB6p53FgfbBwBALDh8fsf4VlLjuwRVxez+Wt44VcbsfiBiUYXUq666Sm+99ZZuuOEGnXLKKWrWrFms2gUAQEh81yJNTbLKZrXI5TZxG5PqOwY26DqpViqpQCxEFVLfe+893XzzzXrooYdi1R4AAMLiWzFNtluVbLOq2O2KWyXVN/wGWyfVbwkqKqlAxKJazD89Pd3vjVMAABxufiHVZlOy3RqwPZZ8J0MFWyfVbwkqKqlAxKIKqZdddplef/31WLUFAICw+VY2k+1Wb3UzXhOnfJeVsgcdk8obp4BYiKq7f8yYMVq5cqVGjBihSZMmqV27drLZbAHH9evXL5rbAABQLd+lppJsFqV4K6nxqWL6joENVkm188YpICaiCqknn3yy9/tly5YF7Gd2PwAg3gLGpNrjXEn1qY4GXSeVN04BMRFVSJ03b16s2gEAQER8u/tTKiZOSVKpMz4FEt/qqL2WN07F861XwJEuqpA6fvz4WLUDAICIlDr9Z9sn2S0B22N6vypjYKvyHQLgopIKRCyqiVO+cnNz9cUXX+jQoUOxuiQAALWqGho9ldSyOI0H9a+k1rIEFWNSgYhFHVLffPNNde/eXW3btlW/fv20du1aSdKePXvUt29fZv8DAOLKfwkqa9yXoPLtwg+6mL/POFXeOAVELqqQ+tZbb+m8885Ty5YtNX36dBlT+S/Gli1b6qijjtL8+fOjbSMAANUKqKTay1eZccRt4hSz+4HDIaqQOmvWLA0ePFirVq3SH//4x4D9J554otavXx/NLQAAqFFAJdUW3zGpta6T6vtaVMakAhGLKqR+/fXXuuCCC6rd36pVK+Xl5UV8fYfDoalTp6pNmzZKS0vToEGDgi51VZvf/e53slgsuu666yJuCwAgMVW3BFW8Ztb7jnUN1t3vtwQVs/uBiEX9WtSaJkr99NNPatGiRcTXnzBhgh5++GFdeumleuyxx2Sz2XTmmWdq1apVIV/jtdde08cffxxxGwAAia3qG6cql6CK/5jUJNZJBeImqpA6dOhQLViwQE6nM2DfL7/8omeffVbDhg2L6NqffPKJXnnlFT3wwAOaPXu2Jk2apPfff18dOnTQlClTQrpGSUmJ/vSnP2nq1KkRtQEAkPgO98Qp38X8g4ZU3+5+xqQCEYsqpN533336+eefNWDAAM2ZM0cWi0Xvvvuu/vznP6tXr14yxmj69OkRXXvJkiWy2WyaNGmSd1tqaqomTpyojz/+WDt27Kj1Gg899JDcbrduvfXWiNoAAEh8nolTFkv58k+e4BivN06V1bKYv81qkaVis5PZ/UDEolrMv1u3blq1apVuvPFGTZs2TcYYzZ49W5I0ZMgQPfnkk+rYsWNE116/fr26du2qjIwMv+0DBw6UJG3YsEHt2rWr9vzt27frL3/5i1544QWlpaWFde+8vDzt3r3bb9vmzZvDugYA4PDwVEyTbVZZLJbKMalxmzjlO7s/eK0nyWpVqcsdt7VagYYgqpAqST169NDy5cuVn5+vzZs3y+12q3PnzsrKyorqurm5ucrJyQnY7tm2a9euGs//05/+pL59++qiiy4K+95PPfWUZs6cGfZ5AIDDz1Mx9YRTz5/xW4Kq5nVSPdtLXUycAqIRVUidNWuWzjvvPPXs2VPNmjXTgAED/PZ/8803Wrp0qe6+++6wr11cXKyUlJSA7ampqd791VmxYoWWLl3qfbFAuCZPnqyxY8f6bdu8ebNGjx4d0fUAAPHjW0mVpBSfiVPGGFkswYNkpHyro0lBlqCSPBVWV9xWGAAagqhC6owZM9SlSxf17Nkz6P6vv/5aM2fOjCikpqWlyeFwBGwvKSnx7g/G6XTqhhtu0B/+8IeA0Byq7OxsZWdnR3QuAODw8obUigqq3xufXEbJ9tiGVGctb5zybUMp3f1AxKLu7q/Jvn37lJycHNG5OTk52rlzZ8D23NxcSVKbNm2Cnrdw4UJ9//33mjNnjrZu3eq378CBA9q6dauys7OVnp4eUbsAAImlrJrufs8+359jeT+p+pCaEucVBoCGIOyQ+r///U8ffPCB9+fXXnst6KSigoIC/eMf/1CvXr0ialifPn20YsUKFRYW+k2e8nTh9+nTJ+h527dvV1lZmX77298G7Fu4cKEWLlyo119/na57ADhCeMek2gJDaqnTrUaBI8eivF9ldTTFZgt6jOd1qXT3A5ELO6SuWLHCO6nIYrHotdde02uvvRb02OOOO05///vfI2rYmDFj9Ne//lVz5871LiHlcDg0b948DRo0yDuzf/v27SoqKlL37t0lSRdddFHQAHvuuefqzDPP1FVXXaVBgwZF1CYAQOLxVCuTgoXUOITEqm+4Cibea7UCDUHYIXXKlCm67rrrZIxRdna2nnnmGZ1//vl+x1gsFqWnp3snOUVi0KBBGjt2rO644w7l5eWpS5cuWrBggbZu3arnn3/ee9y4ceO0cuVKGVP+L9vu3bt7A2tVnTp1ooIKAEcYRw1jUuMREv3fOBW8uz/er2YFGoKwQ2paWpp30tKWLVuUlZUVt/GdCxcu1LRp07Ro0SLl5+erd+/eevvttzV48OC43A8AUP9UHZOacpgqqVaLZK9undQ4v1AAaAiimjjVoUOHWLUjqNTUVM2ePdv7goBgfMfH1sRTaQUAHFmqLkGVfJgqqdUt5O/bBgfd/UDEopryaIzRnDlzNHDgQLVs2VI2my3gy26P6wICAIAGrrrF/KX4hNSqwwuCobsfiF5UCXLKlCl6+OGH1adPH1122WVq1qxZrNoFAEBIypzlPWWe6qX/Oqnxq6Qmh1BJZeIUELmoQuqCBQt0/vnn65///Ges2gMAQFgOdyW16ssDgqGSCkQvqu7+4uJinXHGGbFqCwAAYatpCSpHHCupNY1JTaKSCkQtqpB6+umn69NPP41VWwAACFtAJTXOE6eq3i8Y1kkFohdVSH3qqae0Zs0a3X///dq7d2+s2gQAQMg8QTClmteixv5+5WNgQ6qkulhZBohUVCG1W7du+umnnzRt2jRlZ2erUaNGysjI8Ptq2rRprNoKAECAqmNEE6GSmuKtpLpifn+goYhq4tT5558viyX42zYAADgcSr1jRMt/H8V74lSZd13W6n//VU6copIKRCqqkDp//vwYNQMAgPC53EYut2cJKpukKq9FjUd3fwiVVE9g5o1TQOSi6u4HAKAu+Y45PVxLUIX2xqnywOwbogGEJ+xK6rp168K+Sb9+/cI+BwCA2vi+dtRTvUyxx7mS6qx9Mf8ke+VQgDKXWzarLebtAI50YYfU/v37hzwO1Rgji8Uil4uB4wCA2POtpHrCadJhmjiVVNMSVD5tcDjdSk0ipALhCjukzps3Lx7tAAAgbL4h1NPNb7NaZLNa5HKbuL5xKqWGSmpKnJfBAhqCsEPq+PHj49EOAADCFiykSuWVzGK3Ky4BMZw3TlVtI4DQMXEKAFBv+Y459Q2G8XzjU9V1WYOJ9wsFgIaAkAoAqLd8Q2iKvXLcpzekxqWSWvsbp+K9wgDQEBBSAQD1lqOG7v6q+2MltHVS/SdOAQgfIRUAUG85fF476jtZKV5vfDLG+CxBVfsbp8rbQEgFIkFIBQDUWzVNnCrfH9slEJ0+C/PXOCaViVNA1AipAIB6y+EMXCdVit/EqVJn8IlaVflXUnnjFBAJQioAoN4qrSaket4+FeuJU8FewxqMXyWVF9oAESGkAgDqLUcts/vLnLGtYjpCrKT6r5NKJRWIBCEVAFBvVTsmtSKwOmJcSXWUVV6vpled+i1BxcQpICKEVABAvVXt7H5bfMakVne/qpg4BUSPkAoAqLeqr6RWjEmN8ex+3+7+UCupLEEFRIaQCgCot/wW87cFVlJjPbO+pCzESipvnAKiRkgFANRbnu53m9Uiuy3+S1BVt+RVVUk+C/1TSQUiQ0gFANRbnhBaNTB6Q2qsJ075DB8Itbuf16ICkSGkAgDqLU8ArLpmaVK8Jk75zO5PSaqhkmplTCoQLUIqAKDeOtyV1BK/2f3VV1KtVkvlCwWopAIRIaQCAOothzek+gfGFJ9KqjGxmzzlV0mtYUyqFL9lsICGgpAKAKi3Smvp7pdiO8M/1CWofNvEmFQgMoRUAEC95ZnIVF13vxTbMaGhLuYvVYZY32WrAISOkAoAqLeqmzgVr9n1JWF096d5QiqVVCAihFQAQL3lqGbilO8YVUcM3zrluZa9yrqswaRQSQWiQkgFANRblWNS/ceHpvosD+Vb/YyWZ+JUbVVU3zYQUoHIEFIBAPVWdZVU30lNsQyJ3vvVMmlK8unuJ6QCESGkAgDqrdKK7veqY1LT4hRSPdcKrZJa3oZiQioQEUIqAKDeqnZMary6+yvuV9vyU5JvJZWJU0AkCKkAgHqrujdO+XX3x2HiVCiV1BTGpAJRIaQCAOqt6t44leo7uz8eY1LD6O4npAKRIaQCAOqt6t445Tu7P5ZjQitn99PdD8QbIRUAUG9V1/3uP7s/diHRE3h9x7xWxxOUmTgFRIaQCgCol5wut9ym/Ptk2+FZgspzrfTk2iupniEHLreJ6atZgYaCkAoAqJd8X3datbIZr8X8i0rLQ2paKLP7k+MTlIGGgpAKAKiXSn1CakAl1R6fgOjpuk9Lttd6rO+C/3T5A+EjpAIA6iX/Sqp/ZdNqtXgnU8VyCaricCqpSb4rDNDdD4SLkAoAqJdqqqRKUmpFSI1VQDTGeCuiIY1J9RtyQCUVCBchFQBQLzl8KqTBZtvHep3SUpdbroqZWmlhTJyS6O4HIkFIBQDUS47aKqkVITVWAbGktPJ+4U+corsfCBchFQBQL/mGz2CVzdQYv5a0tvtVd/+q5wIIDSEVAFAv+YbPYJXN1Bi/8amo1Fnj/apKidMKA0BDQUgFANRLnpn2kv/i/d5t9tiOSQ23kso6qUB0CKkAgHrJNzQGC6meyVQlzthUUn1DcSiV1Hi99QpoKAipAIB6yXdpqeBjUm0Vx8W+khrKElRpSUycAqJBSAUA1EvFIY9JjU1ILapleEHg/Zk4BUSDkAoAqJdK/Lr7A3+dpXln98emilkS7ux+Jk4BUSGkAgDqJb8xqfYaKqkxei2qbyU1lO5+v1ez0t0PhI2QCgColzwhNcVuldVqCdjvXcy/NEZjUsOcOCVVvpqVSioQPkIqAKBe8kycqm58qCcgOpxuGWOivt8hR+U6qY1S7CGdk55sDzgXQGgIqQCAeslT2ayuqpnis90Rg2WoDlYs5p9styopyGtYg2mcWhFSSwmpQLgIqQCAesnT3R9s0lT59thOXDpYUh40G4dYRZUqK64HHXT3A+EipAIA6qUSb0gNXkn1rbDGYgkoT5d9o5TQxqNKUuOKY+nuB8JHSAUA1Eue4FndclC+YfJQDCqZnmpo45SkkM9pxJhUIGKEVABAveStpAZZfkqqnLQkSUUxGBPqCZqNw6qklrfhQAkhFQgXIRUAUC951h49fJVUT3d/+GNSmTgFhI+QCgCol7zd/dWMSW0Up0pqRCGV7n4gbAkdUh0Oh6ZOnao2bdooLS1NgwYN0rJly2o977XXXtOFF16ozp07Kz09Xd26ddOf/vQnFRQUxL/RAIDDwrMEVUo1s/v9KqkxWNDfU0ltnBx6SG1SsQRVmcvIEaM3XwENRUKH1AkTJujhhx/WpZdeqscee0w2m01nnnmmVq1aVeN5kyZN0saNG3XZZZfp8ccf14gRI/TEE0/oxBNPVHFx8WFqPQAgnjxd6NUtCeU3JjUGlUzvmNTUMCqpybEdcgA0JKH/P+0w++STT/TKK69o9uzZuvXWWyVJ48aNU8+ePTVlyhStXr262nOXLFmiIUOG+G07/vjjNX78eC1evFhXXnllPJsOADgMiipCX3o1lU3f7v6DUYZUt9t4q7GRdPdL5eusNm+UHFU7gIYkYSupS5Yskc1m06RJk7zbUlNTNXHiRH388cfasWNHtedWDaiSdO6550qSNm7cGPO2AgAOr1KnW6Wu8olTjaqZOJXu091fFGV3v+/Ep0hm90vRB2WgoUnYSur69evVtWtXZWRk+G0fOHCgJGnDhg1q165dyNf75ZdfJEktW7as9di8vDzt3r3bb9vmzZtDvhcAIL6KfUJnejWVzSSbVcl2q0qd7qhn1/t21UdaSWWGPxCehA2pubm5ysnJCdju2bZr166wrvfggw/KZrNpzJgxtR771FNPaebMmWFdHwBw+IRa2WyUbFOp0+0dGhCpAyVl3u8zUsNYzJ9KKhCxhA2pxcXFSklJCdiemprq3R+ql156Sc8//7ymTJmiY445ptbjJ0+erLFjx/pt27x5s0aPHh3yPQEA8eO7pFR1Y1I9+/KLyqKuYu4v9gmpaaGH1CY+k6xYhgoIT8KG1LS0NDkcjoDtJSUl3v2h+PDDDzVx4kQNHz5c9913X0jnZGdnKzs7O/TGAgAOK//u9xoqqRX7oq2kFvpVUiPs7iekAmFJ2IlTOTk5ys3NDdju2damTZtar/HFF19o1KhR6tmzp5YsWSK7PWEzOQAgDIdCrKTG6o1PhcWV5zcNo5Lqu6Yqr0YFwpOwIbVPnz7atGmTCgsL/bavXbvWu78mP/74o0aMGKHs7Gz95z//UePGjePVVADAYeZXSa0ppFbsi3Y8aKTd/bF+NSvQkCRsSB0zZoxcLpfmzp3r3eZwODRv3jwNGjTIO7N/+/bt+u677/zO/eWXXzRs2DBZrVa9++67ysrKOqxtBwDEl9+Y1Bq6+z1jQg9GWcUsLI5s4pTdZlWKvfxXLbP7gfAkbP/3oEGDNHbsWN1xxx3Ky8tTly5dtGDBAm3dulXPP/+897hx48Zp5cqVMsZ4t40YMUI//fSTpkyZolWrVvm9oapVq1b63e9+d1g/CwAgtkKtpHpCqu+Y0kh4KqlpSTYl28Or7zRJtctxsJTufiBMCRtSJWnhwoWaNm2aFi1apPz8fPXu3Vtvv/22Bg8eXON5X3zxhSTpoYceCth36qmnElIBoJ4LtZLqqXr6jimNhCfkZqSF/2szIy1Jew6W+lVjAdQuoUNqamqqZs+erdmzZ1d7zAcffBCwzbeqCgA48vhWUtOTauruLw+pxWUulbncSrJFNsrNE3LDmTTlkVlxTkFxaUT3BhqqhB2TCgBAdTzjO1PsVtlrCJ6+lc9outs93f3hjEf1yExPliQVFFFJBcJBSAUA1DueN0A1qSU0+u4/EMW4VE9IjaqSSkgFwkJIBQDUO4UVVdHaxoj6LrwfzbjUfYfKu+qbNUoO+9ym6eUhdT9jUoGwEFIBAPWOZxLS4aikGmO0r6g8pLaIIKRmppWfc9DhVJnLHVEbgIaIkAoAqHc840tre0Wpb6U10mWoikpdKnWWh8tIKqmZ6ZVBmWoqEDpCKgCg3vFURWubyOS7vzDCiVOern5Jah5lSGVcKhA6QioAoN7xBM4mtVVSfUNqhFVMv5CaHklIrTxnP8tQASEjpAIA6h1vJbWW2fZNUu2yWsq/j7SK6RdSG0cyJpVKKhAJQioAoF4pdbpVUlY+RrRJSs2VVKvV4q1keiY/hSv6SmplSM0npAIhI6QCAOoV31n6tXX3S1KzipCYfyj6kBrRxKm0ynMKIgzKQENESAUA1Cu+b46qrbtfqpzstC/CkLr7oEOSlGy31rqaQDBNUu2yVAw5YHY/EDpCKgCgXvENqbWtkypJzSq66PMjrGLmFZZIkrKbpMjiSZthsFot3jdVRdoGoCEipAIA6hXfoOc73rM6lZXUyKqYeQfKK6nZTVIiOt+/DYRUIFSEVABAveIbUpuFEFI940gLikpljAn7fr96K6mpYZ/rkdW4PODurgi8AGpHSAUA1Cu+yzhlhjDb3jMj3+k2OuAIf0F/byU1I/JKalZFFTaPkAqEjJAKAKhXfLvMM0OYOOU7I39PmCGxpMzlHQMbTXe/pwpLJRUIHSEVAFCveJZxyki1y26r/ddYK58K6K+F4YVET1e/FGV3f0XALSp16WAE1VygISKkAgDqFc+C+KGuWdo6ozJc5h0oqeHIQDvzi73fH9UsLaxzfflWYammAqEhpAIA6hXPxKlQxqNKUqumlSH1l/3hhdSffUJq2yhCahYhFQgbIRUAUK94QmooM/ul8lenpiXZJEm/FIYbUoskSRaLlNM0ikqqz5CDcKu5QENFSAUA1Cv5FeudNguxkmqxWNS6opr6a5ghdUdFJbV1RqqS7ZH/yvQsQSVRSQVCRUgFANQbxhjtqXhNacvGoYVUqXLyVPjd/eWV1HbN0sM6r6pm6cmyW8vfVsUyVEBoCKkAgHqjsMQph9MtyX+cZ208k6fCnd3vGZMazXhUqfzVqC0rqqnhVnOBhoqQCgCoN3y7ysMJqZ7JU3kHSuR2h/bWqVKn2zuGNdqQKkltMsvbsKuguJYjAUiEVABAPeIbUsNZt9RTSS1zGe3zea1qTXYWFMvzFtW2UXb3+17Dd8UAANUjpAIA6o3dByOrpLbJrKyE7thXFNI5m/MOer/vnNUo5HtVx1ONzd1fIqfLHfX1gCMdIRUAUG/4dfc3Dj2kdm5ZGTJ/2n0opHM2/XrA+/0xrZqEfK/qeCqpLrdRbpgTuICGiJAKAKg3PGuMJtksapoW2jqpktS+RboqJtfrpz0Haz64giekts5IDete1WnXvLKaS5c/UDtCKgCg3vAsIZXdJFVWT+oMQYrd5q1kbtkTWiX1+1/KQ+oxrRqH2crgfMe1epa2AlA9QioAoN7ILSgPqUdlhj/b3jOuNJTufqfL7T2uWwy6+qXK2f0SlVQgFIRUAEC9sbNi+SbfwBeqThXjUrfsOVTrMlRb9hxSacXkpq4xCqkpdpv3pQKhTt4CGjJCKgCgXnC5jXfd0jYRVVLLu+0dTrc37FZn/fYC7/e92jYN+17V8QTlH3eHNi4WaMgIqQCAeiHvQIlcFRXQSELqsa0rK6Jf7dxf47Gfb8uXJDVOsceskipJXbLLg/LmvIMyJrSXCgANFSEVAFAv7PQZxxlJd3/Po5rKXjHZav32/BqP/bxif9/2mbKFMUGrNsdklwfeQ6UulqECakFIBQDUC76z8ts3D39x/dQkm7rnlIfEDTsKqj2uoKjUu5B/v/bNwr5PTY7Jrlwp4Ic8uvyBmhBSAQD1giek2qwWtW8e2WtK+7TLlFTe3V9WzVuf/vfDHu/3Azo2j+g+1eniE1I3E1KBGhFSAQD1giektmuWpmR7ZL+++rYrr4yWlLmrHZe67NtfJZWPRx3QKbaV1KwmKcpItUuSfvB5oxWAQIRUAEC94Fm3tFPL8Lv6PX7bpaX3+w++ywvYX+p064Pvy7ef2i1LKXZbxPcKxmKxqHvrDEnSN7sKY3pt4EhDSAUAJDy322jL3vKQ6llKKhKtm6aq51HlIfHdb34NmGH/wfd5OlDilCQNO65VxPepiWdJq+9+KZTD6YrLPYAjASEVAJDwdhYUq9RZPoY0mkqqJI3o0VqS9P2vBwK6/F9cu12S1CTFrjOOjU9I7V0RUstcRhtz6fIHqkNIBQAkvM0+i993jjKkjjm+nTyrSj374Rbv9nXb8/W/TbslSef2O0qNUuxR3ac6v2mb6f3+q58L4nIP4EhASAUAJLyvf66seB6bkxHVtVo3TdXve7eRJL31xS59tHmPDpSU6Y6lX0mSkm1WXXPq0VHdoyYdWqR7J09t2FHzSwWAhoyQCgBIeJ5u+XbN09SsUXLU17vld12VmlT+K/DyeZ/qtL+t1PcVs+0nDz06ojdahcpisahfh/JVA9Zu2Ru3+wD1HSEVAJDwvq4Iqb2OahqT63Vs2Uh/Oa+3rBap1OXW7gMOSdIZx2bruqFdYnKPmpx0dAtJ0s/5xdqxryju9wPqo/gMuAEAIEb2HHRoV8UrRHvGKKRK0ui+R6l101TN/2irCkvK9LvjWukPJ3SQ3Rb/+s1JR1cuhbX6xz26sHn7uN8TqG8IqQCAhPaFzytMY1VJ9Tihcwud0LlFTK8ZimNzMtQ0LUn7i8u0avNeXTiAkApURXc/ACChrf6xfNymzWpR3/axfQNUXbFZLTq54sUCH3yX511eC0AlQioAIKF5Qupv2jZV4zgtC1UXhvUoX4f1gMOp1T/uqePWAImHkAoASFh7Dzq0Mbf89aG+rzQ9Egztnq0kW/mCre98/UsdtwZIPIRUAEDC+t8Pu73fn1gHY0fjKSM1SacckyVJ+veXuSou5RWpgC9CKgAgYf3nq/IKY0aqXf07Nq/j1sTe2OPbSirv8n/nm9w6bg2QWI6cwT2IvSuvlDZvlrp0kZ57rq5bg4Zs/nxp61apY0dpwoS6bQsOm4MOp1ZWvKZ0WI/WSrbXUFfx/PfK7ZZOO+3w/12J8L+Xpx/bSs0bJWvfoVIt/HibRvc5ShaLJY4NjSH+f4k4o5KK6i1ZIq1cWf4nUJfmz5dmziz/Ew3Gf77K9c56P6tXTs0He/57tXp13fxdifC/l8l2qy4a0E6StH57gdb8tC8erYsP/n+JOCOkAgASjjFGiz7eJknKbpKik485siZN+bri5E5KqagSP/XB5jpuDZA4CKkAgISzbnuBvqp4FerFA9sr6TC8BaqutGycoosHli/m/+EPe/S/TbtrOQNoGI7c/9cDAOqtR5dvkiQl26y6ZNCR/zamyUOOVqNkmyRpxr++kcPJTH+AkAoASCgfbd6jD38oX9z+ooHt1CojtY5bFH/ZGam66YyukqSf9hzSo8t/qOMWAXWPkAoASBjFpS7d+fpXkqS0JJv+OLRLHbfo8Jnw2446NidDkvT0Bz96VzYAGipCKgAgYfzlvxu1bW+RJOm24d0aRBXVI8lm1ROX9FV6Rbf/dYvXed+2BTREhFQAQEL4x6fbtaBiRn/f9pkaf1LHum1QHTg6q7H+cn5vSeUL/I974ROCKhosQioAoM699cUu3fn615KkFo2S9feL+8pmrSeL2sfYqN+00V1nHitJ2n3AoQvmfKyPNu+p41YBhx8hFQBQZ4wxmvu/H3XjK+vlchul2K16+rLj1bZZel03rU5dNbizN6geKHHqsufX6uH3vleZy13HLQMOH0IqAKBO/LK/RJMWfa77//Od3KZ8otS8CQM0sFPzum5aQrhqcGc9emEfpditMkZ6/P3NGvnYh6yjigbDXtcNAAA0LIUlZVq4equeWfmTDjqckqS2zdL09KXHq1fbpnXcusQyuu9ROq5Nhq5/ab2+//WANucd1LgXPtGAjs109eCjdVr3bFkb6LAIHPkIqQCAw2JjbqFe/exnvfr5Dh0ocXq3/753ju4d3VOZ6cl12LrE1bVVE711/cma99EWPf5/P+hQqUufbs3Xp1s/U9tmaTqnTxuN7nOUjmnVpK6bCsQUIRUAEBelTrc27CjQiu/z9P7GPH3/6wG//cflZOj2kd01uGtWHbWw/ki2W3X1qUfr3H5H6YVVW7V4zTYdcDj1c36xnlzxo55c8aPaN0/X4K4tdXKXLB3foZmymqTUdbOBqCR0SHU4HLr77ru1aNEi5efnq3fv3rr33nv1u9/9rtZzd+7cqZtvvlnvvfee3G63hg4dqkceeUSdO3c+DC0HgIblkMOpLXsO6Ye8A/pix35t2FGgb3cVqjTIRJ8TO7fQxJM76fRjs2Wx0FUdjuwmqbp9ZHf9cejRem3dTr2xYafWby+QJG3fV6QX12zXi2u2S5LaNE1V77aZ6nlUhrpkN1bnrMbq0CJdKXZbHX4CIHQJHVInTJigJUuW6KabbtIxxxyj+fPn68wzz9SKFSt08sknV3vewYMHNXToUO3fv1933nmnkpKS9Mgjj+jUU0/Vhg0b1KJFi8P4KQCgfnO5jfYXl+nXwhL9sr9EvxSWKHd/iX7dX6KfC4r0Y94h/VJYUu35FovUu22mzuierdF9j1K75g175n4sNElN0viTOmr8SR21Zc8hLfv2F/1v0x59snWfSp3l/zDYtb9Eu/b/one++cV7ntUitWuerqMy09S6aapymqYqp2macpqmKrtJqjLTk9S8UbLSk238AwJ1LmFD6ieffKJXXnlFs2fP1q233ipJGjdunHr27KkpU6Zo9erV1Z771FNP6YcfftAnn3yiAQMGSJJGjhypnj176m9/+5vuv//+w/IZAKAuGGPkchuVutwqKXOrqNSpolJXxZdTxRXfF1f8XFTmUpHDpcKSMhUUlamguEz7i0pVUFz+c2FJmYwJ/f7JNqt6HJWh37TNVN/2mfptl5Zq2Ziu53jp1LKRJg0+WpMGH63iUpfWb8/XFz/v15c/F+iLHQXatb/yHxBuI23bW+R9q1d1ku1WNUtPUrP0ZDVvlKymaUlqlGJX4xS7GqXYlJ5sV+NWfdWop02Nm3VWox92Kz3ZrtQkq1LsNqXYrUrx/d5uJfQibAkbUpcsWSKbzaZJkyZ5t6WmpmrixIm68847tWPHDrVr167acwcMGOANqJLUvXt3nX766frnP/9JSEVMmCq/tYP9Eq+6KeCcgP1Vz6/9HoHtCu8a0bYx4ICKe7pN+bVMxTlGRjLlvySNTMW2imNM5TF+2z1/prWQadleJq2lzC+FMkZyV5wn+Z/r9rmnfK7ndge2xajyOr73lAm+3XN9l9t4/6z8XnIZI7e7yn7vNp/9Psd5vnf6XbPyPmUut0qdbpW63CpzuVXmMip1ln9fWrHPs73M6Zaj4rhwQmWkWjZOUZvMVHVq2UidWzbW0dnlf3bJbqxkOysc1oW0ZJtO6tJSJ3Vp6d1WWFKmn3Yf0o95B/XTnoPasueQdhWUV8XzDpTIHeTvSqnTrV8LHfq10FH9zTqdUf4lSc9/UmvbkivCqm9wTbJZZbNalGSzyO7zvc1qVZLVIrvNIrvVWvmnd1v58Z7vbRaLrFaLrBaLrBZ5v7dZLLJYVP69tXyfxef78uMtslp9vrdYZLNWHFexz/t9xfVlkSwqv7ZF5fsrv5fks8/q3WeRJ6f7/mwJuJb/+RaLxbs94BpB9gW2x/8Y+bXB871F6Um2hFspImFD6vr169W1a1dlZGT4bR84cKAkacOGDUFDqtvt1pdffqkrrrgiYN/AgQP13nvv6cCBA2rSpPpZkHl5edq9238dus2bN0fyMcL23Ic/ac7/fvL+HPiL5jAEI8+Gy+dKxshYLNKMd6u9QLDfhbEOY7U8hoDzQ7lG2GEMdec3V5R/SdKjH9ZtW45QFovUNC1JmWlJapqerMy0JGWm+//cKiNVrZumqFVGedcwQbR+yEhNUp92merTLjNgn9PlVt4Bh3L3F2vPwVIVFJVq36Gyij9LlV/xZ2GJU0UOpw46nDpU6pIrWLKtRamz/B9VB+Ss/WAcdmvvPF2tMlLruhl+Ejak5ubmKicnJ2C7Z9uuXbuCnrdv3z45HI5az+3WrVu1937qqac0c+bMSJodtaJSl3YfqOFfrodTSqPK70v4jwoQC1aLKqo45dUcTwWocptkt1pltUo2i0XJFdWmJJtVyXarkm1WJdksVX62KsluUbLNVvFn5TlpSValJ9uVlmxTerKt4k+70it+9nxf77tjx4yRNm+W3G7ptNOkjh3r5v5duhze+0bJbrOqTWaa2mSmhXyOMUYOp1sHF7yoQ9t36WCbtjo0/CwVlTpV6nTL4XSrpMwlR8X3DqdLjjKf751uOcrcKnG65HS5K3oMKnsOnBU9C06XW06XkdNdsc3zvatyf1nFnxFkZtQDCRtSi4uLlZISOIYpNTXVu7+68yRFdK7H5MmTNXbsWL9tmzdv1ujRo2ttd7R6HpWhiwe299tW9fdG1V8jwX6vWKocVfs1av7lFHh+zdcPfo+a7xlwiTDvGewThNvuwDbU3MbYPPuqP0f3v0VE9wzhf0///bX8byd5u9W8XUoV3WRVu7Uqu7x8uqSq2y5P111Fq6t0Z1mtld1Zftv92uF7neBdbNYautf8gqWn29Ba2S1YdZtvIPU8E8TBc8817PsfRhaLRalJNqVeOV4taz/8sDGmfJiRZ7iNu+Jnd8WQGs8+73EV243ne5/jPMNtvEN8jKkY6+07FKhy2FB1w5VqGjokv+MDz1fV7UGubWq5hgkYdlX5rCqfW/mfjVMSLxImXosqpKWlyeEIrCiWlJR491d3nqSIzvXIzs5WdnZ2WO2NldO6t9Jp3VvVyb0BAKivyseNlv9DEkeGhB1QlJOTo9zc3IDtnm1t2rQJel7z5s2VkpIS0bkAAABIDAkbUvv06aNNmzapsLDQb/vatWu9+4OxWq3q1auXPvvss4B9a9euVefOnWucNAUAAIC6l7AhdcyYMXK5XJo7d653m8Ph0Lx58zRo0CDvzP7t27fru+++Czj3008/9Quq33//vd5///2AsaYAAABIPAk7JnXQoEEaO3as7rjjDuXl5alLly5asGCBtm7dqueff9573Lhx47Ry5Uq/QcCTJ0/Ws88+q7POOku33nqrkpKS9PDDD6tVq1b605/+VBcfBwAAAGFI2JAqSQsXLtS0adO0aNEi5efnq3fv3nr77bc1ePDgGs9r0qSJPvjgA918882699575Xa7NWTIED3yyCPKyso6TK0HAABApCym6ormCOqbb75Rz5499fXXX6tHjx513RwAAIB6JdwslbBjUgEAANBwEVIBAACQcAipAAAASDiEVAAAACQcQioAAAASDiEVAAAACYeQCgAAgIRDSAUAAEDCIaQCAAAg4RBSAQAAkHAIqQAAAEg49rpuQH3hcDgkSZs3b67jlgAAANQ/ngzlyVS1IaSGaMeOHZKk0aNH121DAAAA6rEdO3aoX79+tR5nMcaYw9Ceeq+goEArV65Uu3btlJKSEtd7bd68WaNHj9Ybb7yhLl26xPVeiY5n4Y/n4Y/nUYln4Y/n4Y/n4Y/nUelwPguHw6EdO3bo1FNPVWZmZq3HU0kNUWZmps4555zDes8uXbqoR48eh/WeiYpn4Y/n4Y/nUYln4Y/n4Y/n4Y/nUelwPYtQKqgeTJwCAABAwiGkAgAAIOEQUgEAAJBwCKkJKCsrS9OnT1dWVlZdN6XO8Sz88Tz88Twq8Sz88Tz88Tz88TwqJfKzYHY/AAAAEg6VVAAAACQcQioAAAASDiEVAAAACYeQCgAAgIRDSAUAAEDCIaTG0aeffqrrrrtOPXr0UKNGjdS+fXtdcMEF2rRpU8CxGzdu1IgRI9S4cWM1b95cf/jDH7R79+6A49xutx566CF16tRJqamp6t27t15++eXD8XGi9s0332js2LHq3Lmz0tPT1bJlSw0ePFhvvfVWwLEN4XlUdd9998lisahnz54B+1avXq2TTz5Z6enpat26tW644QYdPHgw4DiHw6GpU6eqTZs2SktL06BBg7Rs2bLD0fyofPDBB7JYLEG/1qxZ43fskf4sfK1bt06jRo1S8+bNlZ6erp49e+rxxx/3O6YhPI8JEyZU+/fDYrFo586d3mMbwvP44YcfdNFFF6lt27ZKT09X9+7dNWvWLBUVFfkd1xCehSR9/vnnGjFihDIyMtSkSRMNGzZMGzZsCHrskfRMDh48qOnTp2vEiBFq3ry5LBaL5s+fH/TYePxODfWaUTGIm/PPP9+0bt3aXH/99ebZZ58199xzj2nVqpVp1KiR+eqrr7zH7dixw7Rs2dIcffTR5rHHHjP33XefadasmfnNb35jHA6H3zVvv/12I8lcddVVZu7cueass84ykszLL798uD9e2P7973+b4cOHmxkzZpi5c+eaRx991JxyyilGkpkzZ473uIbyPHzt2LHDpKenm0aNGpkePXr47Vu/fr1JTU01ffv2NU8//bS56667TEpKihkxYkTAdS666CJjt9vNrbfeaubMmWNOPPFEY7fbzYcffni4PkpEVqxYYSSZG264wSxatMjva/fu3d7jGsKz8Hj33XdNcnKyGTRokHn44YfN3LlzzdSpU81tt93mPaahPI/Vq1cH/L1YuHChSU9PN8cdd5z3uIbwPLZv324yMzNNhw4dzAMPPGDmzJljJkyYYCSZUaNGeY9rCM/CGGM+//xzk5qaao455hjz17/+1Tz00EOmY8eOJiMjw3z33Xd+xx5pz2TLli1Gkmnfvr0ZMmSIkWTmzZsXcFw8fqeGc81oEFLj6KOPPgr4H2vTpk0mJSXFXHrppd5t1157rUlLSzPbtm3zblu2bFlAePv5559NUlKS+eMf/+jd5na7zSmnnGLatm1rnE5nHD9NfDidTvOb3/zGdOvWzbutIT6PCy+80Jx22mnm1FNPDQipI0eONDk5OWb//v3ebc8++6yRZN59913vtrVr1xpJZvbs2d5txcXF5uijjzYnnnhi/D9EFDwh9dVXX63xuIbwLIwxZv/+/aZVq1bm3HPPNS6Xq9rjGsrzCObDDz80ksx9993n3dYQnsd9991nJJmvv/7ab/u4ceOMJLNv3z5jTMN4FsYYc+aZZ5pmzZqZPXv2eLft2rXLNG7c2Jx33nl+xx5pz6SkpMTk5uYaY4z59NNPqw2p8fidGuo1o0VIrQP9+vUz/fr18/6cnZ1txo4dG3Bc165dzemnn+79+cknnzSSzDfffON33EsvvWQkJdS/8MLx+9//3rRq1cr7c0N7HitXrjQ2m818+eWXASF1//79xm63+1XPjDHG4XCYxo0bm4kTJ3q33XbbbcZms/n9B9gYY+6//34jyWzfvj2+HyQKviG1sLDQlJWVBRzTUJ6FMcY8/fTTRpL59ttvjTHGHDx4MCCsNqTnEcy1115rLBaL2bJlizGm4TyPqVOnGkl+PQye7Var1Rw8eLDBPAtjjGnSpEnQ3xdnnXWWSU5ONgcOHDDGHPl/P2oKqfH4nRrqNaPFmNTDzBijX3/9VS1btpQk7dy5U3l5eerfv3/AsQMHDtT69eu9P69fv16NGjXSscceG3CcZ399cOjQIe3Zs0c//vijHnnkEf33v//V6aefLqnhPQ+Xy6Xrr79eV155pXr16hWw/6uvvpLT6Qx4HsnJyerTp0/A8+jatasyMjL8jvU8j+rGaCWSyy+/XBkZGUpNTdXQoUP12Wefefc1pGexfPlyZWRkaOfOnerWrZsaN26sjIwMXXvttSopKZHUsJ5HVWVlZfrnP/+pk046SR07dpTUcJ7HkCFDJEkTJ07Uhg0btGPHDv3jH//Q008/rRtuuEGNGjVqMM9CKh87mpaWFrA9PT1dpaWl+vrrryU1nL8fVcXjd2o414wWIfUwW7x4sXbu3KkLL7xQkpSbmytJysnJCTg2JydH+/btk8Ph8B7bqlUrWSyWgOMkadeuXfFsesz86U9/UlZWlrp06aJbb71V5557rp544glJDe95PPPMM9q2bZvuueeeoPtrex6+nzE3N7fa46TEfh7Jyck6//zz9dhjj+nNN9/Uvffeq6+++kqnnHKK9z94DeVZSOUTY5xOp8455xwNHz5cS5cu1RVXXKFnnnlGl19+uaSG9Tyqevfdd7V3715deuml3m0N5XmMGDFC99xzj5YtW6a+ffuqffv2uuiii3T99dfrkUcekdRwnoUkdevWTWvWrJHL5fJuKy0t1dq1ayXJO6muIT0TX/H4nRrONaNlj8lVEJLvvvtOf/zjH3XiiSdq/PjxkqTi4mJJUkpKSsDxqamp3mNSUlK8f9Z0XH1w0003acyYMdq1a5f++c9/yuVyqbS0VFLDeh579+7V3XffrWnTpikrKyvoMbU9D9/PWJ+fx0knnaSTTjrJ+/OoUaM0ZswY9e7dW3fccYfeeeedBvMspPJZu0VFRbrmmmu8s/nPO+88lZaWas6cOZo1a1aDeh5VvfTSS0pKStIFF1zg3daQnkfHjh01ePBgnX/++WrRooX+/e9/6/7771fr1q113XXXNahnMXnyZF177bWaOHGipkyZIrfbrXvvvdcbpDztb0jPxFc8fqeGc81oEVIPk19++UVnnXWWmjZtqiVLlshms0mSt5si2L86PN16nmPS0tJCOi7Rde/eXd27d5ckjRs3TsOGDdPZZ5+ttWvXNqjn8ec//1nNmzfX9ddfX+0xtT0P389Y359HVV26dNE555yj1157TS6Xq0E9C0/7Lr74Yr/tl1xyiebMmaOPP/5Y6enpkhrG8/B18OBBvfnmmxo+fLhatGjh3d5Q/n688sormjRpkjZt2qS2bdtKKv8HjNvt1tSpU3XxxRc3mGchSddcc4127Nih2bNna8GCBZKk/v37a8qUKbrvvvvUuHFjSQ3n70dV8fidGs41o0V3/2Gwf/9+jRw5UgUFBXrnnXfUpk0b7z5Pudzzrz5fubm5at68ufdfIzk5Ofrll19kjAk4TpLfdeuTMWPG6NNPP9WmTZsazPP44YcfNHfuXN1www3atWuXtm7dqq1bt6qkpERlZWXaunWr9u3bV+vzqPp3qbrjpMR+HtVp166dSktLdejQoQb1LDzta9Wqld/27OxsSVJ+fn6Deh6+3njjDRUVFfl19Uu1/7f0SHkeTz31lPr27esNqB6jRo1SUVGR1q9f32Cehcd9992nX3/9VR9++KG+/PJLffrpp3K73ZKkrl27Smo4fz+qisfv1HCuGS1CapyVlJTo7LPP1qZNm/T222/ruOOO89t/1FFHKSsry2+CiMcnn3yiPn36eH/u06ePioqKtHHjRr/jPGNvfI+tTzxdB/v3728wz2Pnzp1yu9264YYb1KlTJ+/X2rVrtWnTJnXq1EmzZs1Sz549ZbfbA55HaWmpNmzYEPA8Nm3apMLCQr9j68PzqM5PP/2k1NRUNW7cuEE9i+OPP16S/BaplyrHhGVlZTWo5+Fr8eLFaty4sUaNGuW3vaE8j19//dVv/KVHWVmZJMnpdDaYZ+GrWbNmOvnkk70TUJcvX662bdt6e+0a4jOR4pMxwrlm1GK2TgACOJ1OM2rUKGO3282///3vao+75pprTFpamt+yFsuXLzeSzNNPP+3dtmPHjmrXMDvqqKMSfl3QX3/9NWBbaWmp6devn0lLS/MuFdIQnsfu3bvN66+/HvDVo0cP0759e/P666+bL7/80hhjzIgRI0xOTo4pLCz0nv/cc88ZSea///2vd9uaNWsC1vYrKSkxXbp0MYMGDTp8Hy4CeXl5Ads2bNhgkpKS/BYobwjPwhhj1q1bZySZSy65xG/7xRdfbOx2u9m5c6cxpuE8D4+8vDxjt9vNH/7wh6D7G8Lz+P3vf2+Sk5PN999/77d99OjRxmq1Nti/G75eeeUVI8n89a9/9dt+JD+Tmpagisfv1FCvGS1CahzdeOONRpI5++yzA96WsmjRIu9x27dvNy1atDBHH320efzxx839999vmjVrZnr16mVKSkr8rnnbbbcZSWbSpEnm2Wef9b4NYvHixYf744Vt9OjR5rTTTjMzZszwvoGre/fuRpL529/+5j2uoTyPYIIt5v/555+blJQUv7ekpKammmHDhgWcP3bsWO9agHPmzDEnnXSSsdvtZuXKlYfrI0Rk6NCh5swzzzT33nuvmTt3rrnppptMenq6adq0qXetUGMaxrPwuOKKK4wkc8EFF5gnn3zSjB071kgyd9xxh/eYhvQ8jDHm73//u5Fk3nnnnaD7G8Lz8KyrnJ2dbWbNmmWefPJJM3LkSCPJXHnlld7jGsKzMKb8eZx++unmwQcfNM8995y58sorjc1mMyNGjAhYb/lIfCZ///vfzT333GOuvfZaI8mcd9555p577jH33HOPKSgoMMbE53dqONeMBiE1jk499VQjqdovX19//bUZNmyYSU9PN5mZmebSSy81v/zyS8A1XS6Xuf/++02HDh1McnKy6dGjh3nxxRcP10eKyssvv2zOOOMM06pVK2O3202zZs3MGWecYd58882AYxvC8wgmWEg1pvztOieddJJJTU01WVlZ5o9//KNfNcCjuLjY3HrrraZ169YmJSXFDBgwoNpf6InkscceMwMHDjTNmzc3drvd5OTkmMsuu8z88MMPAcce6c/Co7S01MyYMcN06NDBJCUlmS5duphHHnkk4LiG8jyMMeaEE04w2dnZNfaSNITnsXbtWjNy5EjTunVrk5SUZLp27Wruu+++gFDWEJ7F5s2bzbBhw0zLli1NSkqK6d69u3nggQeqfTXnkfZMOnToUG3G8Lzowpj4/E4N9ZrRsBhTZYQsAAAAUMeYOAUAAICEQ0gFAABAwiGkAgAAIOEQUgEAAJBwCKkAAABIOIRUAAAAJBxCKgAAABIOIRUAAAAJh5AKAACAhENIBQAAQMIhpAIAACDhEFIBAPXa008/rX79+ikpKUkzZsyo6+YAiBFCKgCgXsvJydGMGTN0/vnn13VTAMQQIRUAUKOHHnpI3bt3l9vtruumBDV69GiNGjVKmZmZQfc/88wzat++vRwOx+FtGICoEFIB1Bvz58+XxWIJ+nX77bfXdfOOSIWFhXrwwQc1depUWa3181fGhAkTVFpaqjlz5tR1UwCEwV7XDQCAcM2aNUudOnXy29azZ886as2R7YUXXpDT6dTFF19c102JWGpqqsaPH6+HH35Y119/vSwWS103CUAICKkA6p2RI0eqf//+IR9/6NAhNWrUKI4tOnLNmzdPo0aNUmpq6mG/98knn6yPPvoo6L677rpL9957b8jXuuCCC/TQQw9pxYoVOu2002LVRABxVD/7bgCgGjNmzJDFYtG3336rSy65RM2aNdPJJ5/s3b9z505dccUVatWqlVJSUtSjRw+98MILAddZtWqVBgwYoNTUVB199NGaM2eO99q+JkyYoI4dO1bbDl+h3Ntz3ubNmzVhwgRlZmaqadOmuvzyy1VUVBRwn507d2rixIlq06aNUlJS1KlTJ1177bUqLS3VihUrZLFY9Prrrwec99JLL8lisejjjz+u9llu2bJFX375pc4444yg+2u6t+9n2bRpky677DI1bdpUWVlZmjZtmowx2rFjh8455xxlZGSodevW+tvf/uZ3/VWrVskYE/QrnIAqSccff7yaN2+uN998M6zzANQdKqkA6p39+/drz549fttatmzp9/PYsWN1zDHH6P7775cxRpL066+/6oQTTpDFYtF1112nrKws/fe//9XEiRNVWFiom266SZL01VdfadiwYcrKytKMGTPkdDo1ffp0tWrVKuI2h3pvjwsuuECdOnXSAw88oHXr1um5555Tdna2HnzwQe8xu3bt0sCBA1VQUKBJkyape/fu2rlzp5YsWaKioiINGTJE7dq10+LFi3Xuuef6XX/x4sU6+uijdeKJJ1bb5tWrV0uS+vXrF7CvtnsnJyd7j73wwgt17LHH6i9/+Yv+/e9/695771Xz5s01Z84cnXbaaXrwwQe1ePFi3XrrrRowYIAGDx4c1rN1Op1yOp1yuVxyOp0qKSlRUlKSbDab33H9+vWrtjILIAEZAKgn5s2bZyQF/fKYPn26kWQuvvjigPMnTpxocnJyzJ49e/y2X3TRRaZp06amqKjIGGPM6NGjTWpqqtm2bZv3mG+//dbYbDZT9T+b48ePNx06dAi4l6cd4d7bc94VV1zhd9y5555rWrRo4bdt3Lhxxmq1mk8//TTg/m632xhjzB133GFSUlJMQUGBd19eXp6x2+1m+vTpAef5+vOf/2wkmQMHDgTsC+Xens8yadIk7z6n02natm1rLBaL+ctf/uLdnp+fb9LS0sz48eNrbFMwnvv4fs2bNy/guEmTJpm0tLSwrw+gbtDdD6DeefLJJ7Vs2TK/r6quueYav5+NMVq6dKnOPvtsGWO0Z88e79fw4cO1f/9+rVu3Ti6XS++++65Gjx6t9u3be88/9thjNXz48IjaG+q9a2r/Kaecor1796qwsFCS5Ha79cYbb+jss88OOj7XM9Rg3LhxcjgcWrJkiXffP/7xDzmdTl122WU1tnvv3r2y2+1q3Lix3/ZQ7+1x5ZVXer+32Wzq37+/jDGaOHGid3tmZqa6deumn376qcY2BTNjxoyA4QATJkwIOK5Zs2YqLi4OOmwCQOKhux9AvTNw4MBaJ05Vnf2/e/duFRQUaO7cuZo7d27Qc/Ly8rR7924VFxfrmGOOCdjfrVs3/ec//wm7vaHe25dvQJbKA5Yk5efnKyMjQ7t371ZhYWGtqxp0795dAwYM0OLFi72hcPHixTrhhBPUpUuXsD+L5/OEcm+Pqp+ladOmSk1NDRii0bRpU+3duzeiNoXCVAz7YHY/UD8QUgEckdLS0vx+9ixEf9lll2n8+PFBz+ndu3fYC9ZXF3hcLlfY9/ZVdTylhydohWPcuHG68cYb9fPPP8vhcGjNmjV64oknaj2vRYsWcjqdOnDggJo0aRL2fT2CfZZYfr5Q5efnKz09PeDvBoDEREgF0CBkZWWpSZMmcrlc1c5Wl8rDZVpamn744YeAfd9//33AtmbNmqmgoCBg+7Zt28K+dziysrKUkZGhr7/+utZjL7roIt1yyy16+eWXVVxcrKSkJF144YW1nte9e3dJ5bP8fUN0OPdOJFu2bNGxxx5b180AECLGpAJoEGw2m84//3wtXbo0aLjavXu397jhw4frjTfe0Pbt2737N27cqHfffTfgvKOPPlr79+/Xl19+6d2Wm5vrt+xTqPcOh9Vq1ejRo/XWW2/ps88+C9jvW5Fs2bKlRo4cqRdffFGLFy/WiBEjArrag/HM/K96/XDunUjWrVunk046qa6bASBEVFIBNBh/+ctftGLFCg0aNEhXXXWVjjvuOO3bt0/r1q3T8uXLtW/fPknSzJkz9c477+iUU07R5MmT5XQ69fe//109evTwC6NSeZVy6tSpOvfcc3XDDTeoqKhITz/9tLp27eo3GSrUe4fj/vvv13vvvadTTz1VkyZN0rHHHqvc3Fy9+uqrWrVqld+77MeNG6cxY8ZIku65556Qrt+5c2f17NlTy5cv1xVXXBHxvRPB559/rn379umcc86p66YACBEhFUCD0apVK33yySeaNWuWXnvtNT311FNq0aKFevTo4bf+aO/evfXuu+/qlltu0d133622bdtq5syZys3NDQipLVq00Ouvv65bbrlFU6ZM8a5t+sMPP/iF1FDvHY6jjjpKa9eu1bRp07R48WIVFhbqqKOO0siRI5Wenu537Nlnn61mzZrJ7XZr1KhRId/jiiuu0N13363i4mK/sZzh3DsRvPrqq2rfvj1vmwLqEYtJ1H4ZAEgwM2bM0MyZMxO2O7smTqdTbdq00dlnn63nn38+5PP279+vzp0766GHHvJbMqo+cTgc6tixo26//XbdeOONdd0cACFiTCoANABvvPGGdu/erXHjxoV1XtOmTTVlyhTNnj077JUPEsW8efOUlJQUsPYsgMRGJRUAQlQfK6lr167Vl19+qXvuuUctW7YMeGkAACQqKqkAcAR7+umnde211yo7O1sLFy6s6+YAQMiopAIAACDhUEkFAABAwiGkAgAAIOEQUgEAAJBwCKkAAABIOIRUAAAAJBxCKgAAABIOIRUAAAAJh5AKAACAhENIBQAAQMIhpAIAACDhEFIBAACQcAipAAAASDiEVAAAACSc/wefqGKlv6oJ4wAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ir_data = results.get_ir_spectrum(\n", " broadening_type=\"lorentzian\",\n", " broadening_width=10,\n", " min_x=200,\n", " max_x=1000,\n", " post_process=\"max_to_1\",\n", ")\n", "\n", "fig, ax = plt.subplots()\n", "ax.vlines(freqs, 0, len(freqs) * [ir_data[1].max() * 0.05], color=\"r\")\n", "ax.plot(*ir_data)\n", "ax.set_xlabel(\"Frequency (cm$^{-1}$)\")\n", "ax.set_ylabel(\"Intensity (arb.)\")\n", "ax.set_title(\"BeO IR Spectrum\")\n", "ax;" ] }, { "cell_type": "markdown", "id": "9d318c41-4f43-4078-ae2a-0eba4763c659", "metadata": {}, "source": [ "To visualize the Raman spectrum, we adopt a similar approach as with the IR spectrum, but using the `get_raman_spectrum` function. In contrast to the IR spectrum, the most prominent absorption in the Raman spectrum originates from a single, high-intensity mode rather than from the overlap of degenerate absorptions." ] }, { "cell_type": "code", "execution_count": 9, "id": "0dda8a36-0d97-408d-9301-0f7ba2467dd1", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "raman_data = results.get_raman_spectrum(\n", " broadening_type=\"lorentzian\",\n", " broadening_width=10,\n", " min_x=200,\n", " max_x=1000,\n", " post_process=\"max_to_1\",\n", ")\n", "\n", "fig, ax = plt.subplots()\n", "ax.vlines(freqs, 0, len(freqs) * [raman_data[1].max() * 0.05], color=\"r\")\n", "ax.plot(*raman_data)\n", "ax.set_xlabel(\"Frequency (cm$^{-1}$)\")\n", "ax.set_ylabel(\"Intensity (arb.)\")\n", "ax.set_title(\"BeO Raman Spectrum\")\n", "ax;" ] }, { "cell_type": "markdown", "id": "8ba03d0d-4f37-4cdc-bedf-ff2078af6084", "metadata": {}, "source": [ "The calculated IR and Raman spectra for beryllium oxide (BeO) can be compared to those available in [The Computational Raman Database](https://ramandb.oulu.fi/) (mpid: mp-2542). Our results are qualitatively consistent with those reported in this database. However, to reach more accurate results, it is essential to carefully optimize several parameters, such as using finer Brillouin zone sampling and a more appropriate energy cutoff." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 5 }