Commit 71a120bc by Maureen MUSCAT

Initial commit

parent 636d4f6c
......@@ -11,7 +11,8 @@
"- apply the filters on the dca matrix: create Pattern score matrix\n",
" - option: plot Pattern score matrix\n",
"\n",
"### prediction\n",
"\n",
"### Prediction\n",
"- use DCA score and Pattern score to determine the probability of contact for each (i,j)\n",
" - option: plot probability matrix\n",
" \n"
......@@ -34,7 +35,7 @@
},
{
"cell_type": "code",
"execution_count": 104,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
......@@ -45,7 +46,7 @@
" df=df[df[1]>len_d1]\n",
" matrix=np.array(df[2]).reshape(len_d1,len_d2)\n",
" if option ==True :\n",
" plt.imshow(m)\n",
" plt.imshow(matrix)\n",
" return matrix\n",
"\n",
"def compute_matrix_result_for_one_filter(matrice_dca,mat_f):\n",
......@@ -91,12 +92,12 @@
},
{
"cell_type": "code",
"execution_count": 105,
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -116,7 +117,7 @@
},
{
"cell_type": "code",
"execution_count": 109,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
......@@ -127,7 +128,7 @@
},
{
"cell_type": "code",
"execution_count": 110,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
......@@ -137,16 +138,16 @@
},
{
"cell_type": "code",
"execution_count": 111,
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f195f957a58>"
"<matplotlib.colorbar.Colorbar at 0x7f8f6a289898>"
]
},
"execution_count": 111,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
......@@ -164,7 +165,7 @@
}
],
"source": [
"correlation_matrix=np.array(df['best_corr {}'.format(v)]).reshape((shape))\n",
"correlation_matrix=np.array(df['best_corr {}'.format(v)]).reshape((dca_matrix.shape))\n",
"plt.imshow(correlation_matrix)\n",
"plt.title('Pattern score')\n",
"plt.colorbar()"
......@@ -179,7 +180,7 @@
},
{
"cell_type": "code",
"execution_count": 112,
"execution_count": 10,
"metadata": {
"scrolled": true
},
......@@ -187,14 +188,13 @@
"source": [
"# Load the classifiar and the min and max values for the pattern score \n",
"size_meff='big'\n",
"clf = pickle.load(open('{}-{}-linear-clf.sav'.format(v,size_meff) , \"rb\"),encoding='latin1') \n",
"\n",
"min_c,max_c=np.loadtxt('min_max_{}_{}'.format(v,size_meff))"
"clf = pickle.load(open('classifier/{}-{}-linear-clf.sav'.format(v,size_meff) , \"rb\"),encoding='latin1') \n",
"min_c,max_c=np.loadtxt('classifier/min_max_{}_{}'.format(v,size_meff))"
]
},
{
"cell_type": "code",
"execution_count": 113,
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
......@@ -204,7 +204,7 @@
},
{
"cell_type": "code",
"execution_count": 114,
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
......@@ -216,16 +216,16 @@
},
{
"cell_type": "code",
"execution_count": 115,
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f195f8f1e80>"
"<matplotlib.colorbar.Colorbar at 0x7f8f04a54630>"
]
},
"execution_count": 115,
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
......@@ -243,13 +243,57 @@
}
],
"source": [
"plt.imshow(probability.reshape(shape))\n",
"plt.imshow(probability.reshape(dca_matrix.shape))\n",
"plt.title('Probability of being a contact')\n",
"plt.colorbar()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Predicted contact map')"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"contact=probability>0.3\n",
"plt.imshow(contact.reshape(dca_matrix.shape))\n",
"plt.title('Predicted contact map')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"## to save results\n",
"df.to_csv('results.dat',index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
......
......@@ -35,7 +35,7 @@
},
{
"cell_type": "code",
"execution_count": 104,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
......@@ -46,7 +46,7 @@
" df=df[df[1]>len_d1]\n",
" matrix=np.array(df[2]).reshape(len_d1,len_d2)\n",
" if option ==True :\n",
" plt.imshow(m)\n",
" plt.imshow(matrix)\n",
" return matrix\n",
"\n",
"def compute_matrix_result_for_one_filter(matrice_dca,mat_f):\n",
......@@ -92,12 +92,12 @@
},
{
"cell_type": "code",
"execution_count": 105,
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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nT5uJv/Zk0IdjsbbKHmtL+VoYz8H5MDdJ5VAI7KpsKwAk581sbx4zN56641jOdUAOVeR+B2QODplrjoPvmi9QJ0E6Ho+RF24CAKoPmhAYyyFnV8J4FN8y12RntwUn+flR+VsWhhuQ+BV3XPqycfWZ865zz66/9oS5ejLnbD46+yQAOaDjbnO3fsD6qxh6y8Zb28VremDNzlq71P3ZKds6Xj5mz/3YmZr0z9qiAUcAKF0Na5nHKkliZ4kqSSmoG49TEDjAKQFWvhcTGNLrnQ3H8tesj2vH7B2SX5I1Ra+iZ5/9xzEoGhEREfEfE+ILPSIiImKHYFtZLt1cAqtHgqk28T0x0wrEPuBKMeSaUDW3NHFTUytmPq4fD6Z/4TpVQdqk3Fd1toDNUH7TTE3lI9cOWGo06yWPvkEyAfp3Mj+Vq8xVdVhagDnOyp1OLJrJ2Z01poEqPyYpDZsZGlpJidPiE6SCl79oPO/27nAOV83J3Gae+kbuN3Pa2+PheJrcVsyYyXCZPeEYMzsou0467aLal6pRdaUx65dWTyq9Y/PZmjJXUuO4pcirKybZtPVSumnd0qo17EobonPT611ps93fXzcXQopcD8qNzpOWfq9gJnzlZHDPlIjnniW9c1ezMWjuC2uWNesBcoMUpLIPyQhkl21Rrx4O89ElpcHMmrmtciR1kBhVuQpy540zzzxcNzVkY8xsq8K10AdPc8/8+d4uc72tHQ7XKF8hNUXK7Si/E56FAXcFrylZJ1yhil0PXBZxRRQMWQFx9By9A8TtkyIN/yFSlOxXjSJX0vpRkhd5w+bLSfWh5l5rV7LG6q3h78o3BwDmqDSmg6upMGcyBs0pcz+N/NBcz2ungtZ9fnGj2um9EHfoERERETsE8YUeERERsUOwrS6XRMujdH2woHPlYUvL5SLQSU7NvxVM2PSqRYXb+8w1UX7hGgCgdcgYAUPnKflDTKqh14wRwAqGbp2i/+PBpOJUci5btfxwMMlGTpPrgor3alJJatlM0upxa2ttauOQj732dv9zMk9MHlGs80lKqqEkCJVC4MK37VFitpCJr0UC2ITvUWV2TetmJUM1EwFLWukl7frsxkiQG0OV9Lx5ELD0SWN7jL8kxZTvUqh7/ajMwRKxBIhplCQXlRacTpKlzeUL05XxgfYDg0lEhXfELZUhmYJPn7TPrJh3R1gR5J7pDHNh8/D/LklUtIlJlF6jRC1RE2R2UP6KjYEmDA0UjiYFwtHXQtIKq5Kya2P94d10PKyP0iVi2QyUfgxrukdSDd0MsVTkvuxCYzmK1qS5asZ+ENxVzIDKXDHXX+1EaFf+oiWCNQ4yc0VcF+SiYzcg33f0pSCvUTts9xqQvhAXk6dCLK0xW7+5tzdKA3DpPJ+jQtnCpOG1k6TSdlpou7Bkc3jtFyz5avSd0Ja1B81l0xglV1PX3hEsAfF+EXfoERERETsE27pDd973da9be8KvqhaNBQZ1qBMNKpYsBXEbk7ZDKNNuWzmzvJthHmv9iJTQIo5oYdF+XdsHbGefuhV22wmyBgoUaMwthF/7+iyJJq0QX1usgdSqbRkzVFqPf32zagWcPN4/5tdJb3lISoPRTqA5RQFY4TI3x20nkaMCyq5DBW03SUvv5qzd1ZnwWXWbAaBw0/qwIvz7ZNP2AMUbtou6+rM2Hvv/NIxhu0y7oRUWkQrt5dJiORqXvNyXRbZq++36OSpenb8qBan3WeC4m7cdl+YmsBWj3wEAJ+ussdd2ednrFsROk/5149iU9Mt2aeUfXu5/ThwOAcoEpb239lFqfpLmSeYjc4vWOQUKNSDOliLnIDRJRqD//RJZNGs23pmlENTknS5bb2nV0KfnY/i7l+zCIiPgmLvOsuRkOTSlXB0HTWtPUjHwm6HfGhQGgNx1mw8V+ipcI8uEgpbJVZsPLQrPOQpcts0VZZ0RQaEwZ3NbPyESFZfNWrn1adspJ6etvyok16Y1y8QNDWizIN2ef09kC4GW6AOAdIX2092N658D9VvFPXfozrnfcc4tOufepGNjzrlvO+fekf+Pvtc1IiIiIiJ+9NiKy+V3ATz9rmNfAfCM9/4ogGfk3xERERERHyPuuaf33n/XOXfgXYc/D+Cz8vlrAL4D4Ne3csO+mqGYTnVKjeZACetTq/Z4apkCQMMbTU5W/GvPWKAldzWYWVqVHRhUFWTdcCfpvn7M+KjMTS3OBVdM6RXjjWoKPmCceS1VBwwGDFk9TwNqmQUzOQd45q0wVso5BoDhd0ghUUzwLuk2c2CsS3rl6npoTdq1WmUK7K6LK6xE8ghT5gJgV0z/mmSiT7xhfVw7LOn0ly3YnLxNOu+7gntkwBVFwV4n1+WU7sJV67em6wNA8YZowldZE97cCT0JiGmpOmAwGNsrCB+c0uKbszbfmR8aqT1ZC2uVtfQXfuFQ/3PphsgjkF46lyfM37Q+VCUFPtG0Me7myZ0luuKsGnrnJ8yVNHQprFnmPfey5Aak/qgbYCB4T/IEleOhvyx/UHvcFAxzi+FaCZLLSNbsvhyIV5fq2n6bu4lvnLFzHz00cB4w6C4rXpV7XaLSew9bW1ybSk8KT7s5Zu+F1hDlqtQ2rtn1YyMbjnHZuuI89ZFK5y0/JnPfIJdpd2Pw0lWorNwjFhR1cimWiuhmbb5TJDmg0hgfJDj6QYOiU957XenzAKbudqJz7svOuRedcy+229W7nRYRERER8SHxoVkuPqh73fWnxHv/Ve/9Ke/9qXR64646IiIiIuKjwQdluSw456a99zedc9MAFu/5DQDo+X66uJq9XMpqgAtMEX9otXSK8tdJrF/dIFwSjRkvnTFhaFCEvEtKgimK+Hd3B9PKERtkIPW4LqwIUuHjtPPEtRDZztyhCup7qQQWRcb7KnSJzaPdWsosT8UIuPL6/KeG5P72nWST0u25tJ6k/GeXjDWRrtj0V/aH8R4jaYP1Q+Y2Wp8O9y3dsPZz+UBOU24Kq2DlqP2As6HbZ3DcNPP09imLq4+9FlwDnIvQmCaGBjE40heCoehZ8ZJK/hXfDH+//ddMyXP4Arln5oM7r3GYeMA0Byu/+HD/c+la6GNq0VwTI+dtDKrTod+jL26UkgAGXQsJmbPCeeJoHzH3Y1dKqfXSNgela+bmUDXRdt7cBWNvmkuFnx8tu8YyBSDXnOYTsAuNXQON3WHscwskxUByF+xSVDbR8HnK43jyaP+zPiuZS/bK6D5AhWmWw7O89lNHrC95a1cub+uk+EaY23aJn0XrYlZcRazWuPKQudPyS2Ed1CnPhFlFLJMxfE7YOeSyvPVJm89dz0lewIjNF8s6KOuHn//MKrl9SZahMRrOHSL5hK3ig+7Qvwngi/L5iwD++ANeJyIiIiLiI8I99dCdc7+PEACdALAA4L8H8A0AfwBgH4DLAH7Je3/nbtdQlIdm/RNP/X0AQKcYfoVytLNL3TEfe3uX/dKpLnb5rN2ivs9+aXU3kSC+NpdvSkux4y7xaFm8i4MPSc30op0Vc0s1sNojDrenLEQtZ8e85xTxklnoS4WbuDQZ76A1oFedoWxEmq6kBGiSDfvVr+yz60+8vHG3rWJUANClQGE/c5E41jwuaQmaeqrXW5uwMSpSabzsguyMaLvQGwj2hnO7lHkIMog024+tkU7Zzq1NUT7CXLgXjyuPPQff+n0hnXb9HmeSarZi+D6N7aHiwD0BoDHJGv3h3OyCWZ0DfaD+ajk4DX4Cg7rhlaNh91e4Qbs0slBTy6L1TTttvhfvJJVswMJSbF1puzlgz7vuOw+Ga02+aHxtLgrO66S+O4yHBvQBIE3Bb21vikSytC4AYIWfC/MU2Ka54eLWauXzHHG77ETKaCYyhBaPX33A3jUsKsbrSAtVsxBYp0BrphqupcJzAAbeMf0MVxYCO2weBS6tp+U5uUzfd7/1lS3poW+F5fKFu/zpp+/13YiIiIiI7UNM/Y+IiIjYIdje1P9uDxlxf+SuCod0j7lOHFUwZ9Gi8pkQOPJ5M6fyV4lTK6YoV45vjJmbJCEuiR6VDuNAJweDfDK0R8uJAUCvSCI9hdDGpUesrZOvmtms12KtZE5d5nJ2bREK0vJUAJBcNDeJGwumYGHe2pe5bu3qTJSlfyRkRmYiB4OaQ+G3O0ml2nokwFQ+HczypacsOJit2PfVrG4XbQz7QmsA1g5QjoCwma5/wf4+9Q0qCXhO8gIotX/lGOlzu3DuxCs2FuwCyN2hoOaiSDWsk742uR5ak2GMszdIrI3GJSNuDndzyf5Ookw+a/3KrIk7gTjztUm718hKmIfOMKXwj9j3mXeck5T/LskYdMlNWLwm8gcccKfgffVYyLMovm1uSC4ZyCJWy6fCnKaJl81uEnVpcFnHdtmen5EL4b48rrUZ62NuiVxUEuxl1wOPdycTrqFBXwAoXKCSf5IDwG6W7CULMrf2WuC4PST8+mW7F/Py1T2q9RIAoMFl/NbCeGRJliJ1xqQc1j53zM6VEokdckkWr5j7JyWuWp+nwHN7o5Bej+Z76Aw9y/Tu03dIj95LW0XcoUdERETsEMQXekRERMQOwfa6XDq9vgqiVu9m0682bdFuVlBbf0DVFO1axRfm7B/d8IfOfuOFli6bG6QuPNoCVdlujdu9mqMp+p6YbC1ippDmtZr+aUp6bY5uVNFrTJhJWrhA2s8HzPxTlkl+hVw2xKhJrAWTbu1RYycMeSqBJTxt5hwzh5pL2038QPpD/PraUTNfO+PBTTI8Zy4hZg31xFQuLRNnv2DjpmY5AFT2ijZ0gRT9qsQUmg8utOpPWdq8J+tSXS0syXDnCXPNTT5HJvq+sDYy16iM35CtA2Ul1A7YMS1xBwB1kTfgWvQV4t+XzpM6n7B6XM9cexMvGfOj315y06zvI44zuYp0K5VetLlPke59n5dPrsHxV6yPXrdixJWu7rF75RfN9TDyZmhjc7f1khlOfZbUXSrWJ9rCprpt/HscKG56rvK4k9fNhcUl6jIiGbDykK3j0Vs2Bvosctp+aheteXZB3RGWCkljjL1KuSbi5siRomWyTuw1WRssQVH5rLlZlh61Mdr3Z2HuWLG1OU7Pnbi42L1UpzWnrs7sFXsX3PpPjD+frtNzK+OdXyLGzBYRd+gREREROwT35KF/lCgPz/rHf/K/BmBBDxYs4uBhk4oC18fDr+rQJQtC8G6+dCHsorhSTGrF+NzdMpdrDWCxKBbn0R0uC3n1SvxLLNYA7dqZG9vXnKZx5SoqNbIisktSUJrEhXLzdC0JrnWKG3dLgIlysYhQlTWcT1sfq3s3FsjmXbFmm2qWGgAk6V6ZivQ7Z3sADbQCg6JGeQlAstBYh4JgauXwuDCf2vWziW2M26M2B1xQeuitsPvkOe4x91t2j8yx5gzUfkUhFkKiwDLvCNf2hTZwBl91N1lnt8N1s/ObaxZ1KVi6dlAyc1+moCblSaweD1bC6CuWScoZxXouc6U5IMgFiFUPvXrI1h7PY2Y19DFB1h1ft7InjPeu71twsku64wwN0LM1wFZjejWseS5mXj1o1pciVaOMapqbzA3KwJbn1pFoWILqCfRJA03OT7E5SAoX/vajNi5Dl20MOYehLbUJ2GLqkJia5ofkbpM1QMQItWb5WGMXZcMTPT4rxdvZAvjen/76lnjocYceERERsUMQX+gREREROwTbzEP3lqYvZhCXWaoeIm7rbTNNSlTqTJGfp4DZoyHowsJSvU3MfS40XPkElZoi06p4JogG1Y6boBYjdy0EhlLL5tJZe8CCNuXzgRfNpl2F9NSZ815UmQFnQTg2vRaeDOb89PfNDTOQ+i/l4FKUyp5Ztc9cBmzouavh2IwFQtk85dJYdi0zi5NqIpP5nJmkwPIwFVOW9Gt2oTEHuzEuKdtT5q7IUgCoeUD1oK0t6YqtB3WzAIMuLLuW9aspsgntoo37ru+Ym0OD9BpcBYDsZfs7l0ob/2Eoe9grWb8L5JrI3Ahz35ohES6WZdhva2LsubDO2rttbbBme2E+jDfPS2Wfjdf4c8H94ahEXu2h6f5ndqd1S+F75eeu9I+1jtm5aQlKdim4zkH9yRdCMLY9aW6UJLlMWOiuPhPGhostZyjwu34k9Lf0tgV4S68iCi14AAAgAElEQVSZ9nnfVek2z6fwQ6TYKmuxPmtjVJu0+Srd1DbauPE6TY6EdTj5PStnye7X6j7qbzMsxtwCPYv0LNSnRQCQJA0qh61dymPvURCbSyEOFMIWqZBkaRMZg3sg7tAjIiIidgjiCz0iIiJih2BbXS5wFj1fejyYpaNnN9f85XJcaeHUsqIeR4vLolPtKDW6fthM6KYwN9yksQiYE5y7ZjICLTG9OcKdppJmqpPOaoulOWOTqPmZpmh8Y5y0tklBrSmV5pnHmyVSw8x3g6ma5Ps37HNnMoxhgsqBcXp2ktT7vOg0synOfdAyeOkVGyNmlqjZy8p0zDrKLBOPVjStmZWRX7d2r3wuVIaffIVSp0lpU7W+y5eIo33L5shTu3WeqjN27A61cfx0GBufoBR7Kl+4diycO3TezN/WrLnQVknSYKwWzk1WaT46G9kxzKJhTe3Cgh3XuU8y159K1/UVH1mjnLjZrWkp47ewubLkQHkzqQOgWv/AIIuleij0t/SqlVXMZMnlKO4PnRcASLbNjVG6aGOXFFkELp3HyIo8QpPcUlnyuKiLaYBlQwqiBVL1VB55u2h9nfiB6ayrYiszqArz9kzUd4Xr+iL1i6Q3EjO2TpRfv3bE1lbxur27lMveGt0ocQEA9ZngvmmTyye9bJ95vnqivpom1c+tIu7QIyIiInYI4gs9IiIiYodge10uPUvUGH0rmDAsOF+Y58ID1DRxc3AhB1axU2W36z9vtaonKQW48FKIqPucuRMGCgOQa0BVHvOXzYxkxooWyUiS4l6CTPDGVIhWJ9oWtS6fM1ZGm0p3qWmeWaTEB6qgrqXa8mSWI2lt6QmLpTNC3xnlcTGTTUvyNccpWYcE+ktX6xv+zklMGvFnZTpNmweA4hylhUspQS5CUjhnpvDI+dAuTa4BgBFK/igsbHSx9TIkmUCSA1pmLLNiczCcMVNWTfcypXdrSTUAyEpSDbOLWNlRGR6AsSmYe7C+18ZrdCm4mrj8YZsUFDlJqSfrb6Ds25qt/470MXuLCjpMU/KUMGLauyiBh9gghSukRirPDyfDabk8ABg+F+aOyyoytNgGu3zq45TuT0ldTVE5LVKyDzPZlJmSuU3FbMbNtaHzlb1u7opUzeYrdd0YSJ1pcVvRGHNhmn6hFJJUWHmI3HHPzAEAVj+9v3+sTGOozBZud5IKdzQm7b75hY2u4zY9y5owmKBkIS6rmL9h46EyG1os5/0g7tAjIiIidgi2l4fuPZzwV9vCc22M2a/n2AuWWuw6tvPQX/jWCAU1b1DQRXY7u140bniqYju29u7wq1zbZbsSTn0ef44K1srOx1MaNge59CewOWG/vqksp5qHE7jc3dJJ2xUMX6JdmPxaM581d4XKfGVDsKo9RhYCBU+Up50hcSPm77KmtYo55a/buTlOkReufpE4wb1xC1zlJMC69qDtlLlsFi5es88zwVLi3WX1hAXZCi8Gzen8sI1La8Z289ofLie2dsR2caMvmfCT6rezqFhvoACyWHfED04T31sFyLhkIQtXdfNUCFt2USqaBgBjC5yKHnZcvDbyl2yHz3PTmZD1TTvCHlkWKQn6s/DaQJkyWQe8629TUDVNc9sthGukVu2ZGFmhHeUFyVH4zAPYDHkRt7r1qPVr5s9JfIt46FUpQZdsEF/cbbRCOiSOx/OVvbYq1yTLZtnGu3nY1pFaNxzo5/HsFlWDnOogEBoP7hnoHwCsH7Y1Xx+zdo2I+Fb5nM1nhXTWNa8mS8F/trYHis5rv8gCWDtm99Wyg/xe2CruuUN3zu11zj3rnDvjnDvtnPs1OT7mnPu2c+4d+f/ova4VEREREfGjw1ZcLh0A/9B7fwLAUwB+1Tl3AsBXADzjvT8K4Bn5d0RERETEx4StFIm+CeCmfK44594CsAfA5wF8Vk77GoDvAPj197pWL51AQwNLWqIqYebaymPG1x6m9G7lM+eorBabyJqqXXnCgqIl4mB31Gwm069HPW9PmbmTEt3i1jAFF2+Tut7+YCrnb1J6OQUSy8Jn5qDr1J8budxnzA3SGQ/X4nJevaPGn1fXhJ+yY6wo2a92TtrtiZa1O1WntPSrGhgmDvcqKV02xTxkFwAFsxJyfOiMBaVYcc8ft8ASJCDGKeHZW6RTvT/MU22WFCDJukwvh7FjhciBclzE7c6sS14AHWNTNSWuhca0ufByN0lpU/qYecNKj7Uf2Gvff+2CXXc2BA39mgWA/aytubYEz1meoX7QDFcOgGoK+foBa1d63das6ru3p40T76nivAa0U1Xim7NCIWnJ94Qbze6jFLkRE4dmQ5vmN7oxGcV5e/4qD1i/0mvmChp/I1zjzkM2d1N/ZpID1//zsE52/wdzVWWWyWUoZdvWP2Gc+ewyrXkKOCekv2kiKNx53J6VpLxiUlR2cfwvzTVYPx7mLkmyEuksB9RBn8PYL5+06xdvUv6Hrj+WqyAV1hs/Hfoz821zK1eP2BiqUidgsgqdTVRi74X3FRR1zh0A8BiA5wBMycseAOYBTN3lO192zr3onHux3dpcVjQiIiIi4sNjyy9051wJwL8B8A+892v8Nx9E1Tf14Hvvv+q9P+W9P5XOFDc7JSIiIiLiI8CWWC7OuTTCy/z3vPd/JIcXnHPT3vubzrlpAIt3v4JeCPBi0SQlXb1+xEyzNJlG9b3GgFBGihZhAAZ5tu2pwJBgFgAL6KeU80tWe/E6uynI1L0a3CP1YxZNXz9M5rqaRnSt0hnr+vIng1merlIJLeL/Zq9YlFzT2dPk3tGiFQCw/LlQom34bTLx6b4tKX3XI2W8NEkipIn9oqa7I1dUb5KKiEyGNuQXzI3CjJq2uEcGSoNRanJyk4g8M0s4hV3dHGyqJ1gSYTLcixX7SlfsWr2ijWdKinukSXFy5Zj1a+xmGOP8BXN79YY3FvtonDxgf6eK8+nd5gbUyuzVT5jZXZ4zszp7NqTOtw8an5slDbhwhroRh75/ya5/yBQQeyOhD5lLtrbSBZvnvLBA6nusryxD4KkMXlrYRsy04GIaqorZJTdmJ7+RccOqpAniY3MOgLrsWNLDD9vzM/1dcqUq2jbPa6cC82TAPTVHc1em9Skqkj3iaxdv2prU8oM8n+1Zc+Uk1YVLuTDLx2zcpr9NrzSZu3bJ3CQs26Buttbk5oU/lBXEkgc9cqFlyD1Tk3dfdvlHUILOOecA/DaAt7z3/5z+9E0AX5TPXwTwx+/77hERERERHxnuWYLOOfcZAN8D8AbM5f+PEfzofwBgH4DLAH7Je39n04sIhgsz/qljXwr/kCvxTnxAK/yc8Vy7o6I1fMeCWY5+1ZsHwy4qTRrlWogYAJaePgwAGD23ebk7LlmmbchcmLdjI9bG5ZPhF56FlgZ2EFKursu7SCq1xlmdqnvM4B1XRrTjVw+QjvabZJlISbNe2sYtRdm0zVHauVwKu3wOVHKWrmZl8g5l+BLt9oU/fPthKsh7xvqVXjQrojkbLKYWZftyJl36hgRoKatv9aTthFONsCZzFEhNUOBr5UHb5RQlu5itBQ5o9+//pgXDunvN+urmpCjxqq2d1oTNQWOCxlDEmFJnLci3+DeP9z9Pvhh2n5xZzNzwgWxAuW+HdpdsKeqaaczY2lMtfr4HB9TTFZ57W9O6VjnTOrlqY6tEBc4b4GetcTCIqaVovSaXrC2VR2w8M6L7zc9E9UGb29yCjDMRFBxZ031Ljv7O861rAwCGXgwWUW/MxohLV7L2uSK/ZPfqCD+dg9idAgeZN3LHC6/a3Puy3at2LLwX1nfbfJevU/bz2yEYWn3AxoKfW7Z41PpPU3bzX3zvv9tSCbqtsFy+jwEHwwB++l7fj4iIiIjYHsTU/4iIiIgdgnu6XD5KDA3N+lOf/FUAxh2vHTMThHmfK4fNFJ15Jrhf6nspoEDp2ZpCm503M7FyzFLJNViafW2uf6x7ZI99n2QCNEBTeWiif4jL3akpzFXVc5fIPTQm3PJZcp1QcC97xbxS6w8FU7X0iulQ9yas3bW94Vpcfi1znYS+pHxZmoSOOsQNZ91uDcJVSM+5dGkjH7tDwlecmqya7QPpyBTk42BtXlwDWm4MAIqX7V4qEtWmeyUox0DdXlquEBiUMdgswMmCWwNcYAnoJTiARfdqzEiwt0ruDkqRTzRIME7S9Vk4jgO77bJw2lfsOylKW++MUBt17KiifeWgzV15LrgmOiXr99p++zz+hmrlUz7EbptbFury4r7gtcel7/quSspn4BKNxSth7rgk2wAp4IK5ATWg3Ry1MeJyktouDuAmSc9fJTPYTdncZc9SqmrXao0F11p9zO5VukHXEjJFu2iul9KcPSuJRuivBleBQc33kZesNJ26jVgWgmUA9L3B2uotkjdQDiDz6FMkv+Dq1u7WnjA3vE636nKJO/SIiIiIHYL4Qo+IiIjYIdhWtUXvLLKcWQ6pv/krxkhoTpvJuOslYqQsC5d4wVwb7ROWat4Vziwr0zFnPXcuMFb8LuOg9ijFN9Gwz2pJ5qgKPVc4Vz1z1kP3edIQl9JbRXJNrB+yfqXWzWxVvXKO0nO6b3YpmP4J4o5zebSUcK97WeIUk0lX20dqiVJCi5XlmEmgroFu3r7DP/dZ+f5mKeEA0NhlY98R9g1H7pu7zN2QuW2Mkv6tyCVS2xPOzV80F0Eva/3u7LI2ako2V1Bnrn1NyqslaL7ZNadcYHWXAIMlAQvP3ex/duIy4RT5yiGbz4R0gcvOrRJnva+9DiAt3G3tK38fMMZLL0kl8E5Tyb6r8iykrK21qc3T0nX98NrgvIDm7tAHzjsoXLd7aVnD/KK5olIrNof6fQBIyLovX7QxTpACYVWkLQpzlJuY2ijbwK69/GlzSfqSjVc3HzjhOdLob5eoxJuwfjpUzpL16TPiMVGmEzBY+q5HvH91reXouW+R21UZZeXXjbvOXKuGlJVLkLyDlqUDgMI79m5z4objd8xWEXfoERERETsE26uH3ukhdyv8steeOgIAyKzSjpHibakl+4X3xRBcqHzKduUcZEtVwhdZECtVIz3o3eGXnHdujrPeSN9aeeT8U9cmXrGKGnEQsEuWgXehranb1r78AmWC0m5AMym5sHOCKvd0yuF7rmJtzV40cZ+GaENzoIXB2Zda/YR14pmLXzsYdrLZO/b3Fu1mNIjFuzzmUBffsna1pAAyBzozyxYA0vFMVTYRNwKQlOxPDtwNFGMmy0K59O1xqmpD12X9dwUHwZQfz30ZCChOG8e6MZWV9lHR4pukfy2c8zSt3XyKM55tnnsilJW7Zd9fOml9KCzIfBF3nPtd+8RM+DsJcnHh8w4FAtProT/tGRLUoizi208Ey3X8B5Z7cfM/tazV3X8VAvG8TttjVPWJLJ7bT4Qd+Ohp61ftMN1Xgpqc3anrHACyN8LOnbOBu0esLSnKNWmXQ3uYb85VtnT9pyhrm61DtYbTxMnnued8gvR88Ci09pilmOGqSivh3OY+EgcjrXot4O0pa5Utvd6o7da1/gI/f1tF3KFHRERE7BDEF3pERETEDsG2ulx6mURfA7s4ty7HzFyqT5oZlp2nYM/BYMYUr7B+t5kz1SMhSJajoA0L8jR2BTcI84O5AHFqiYImYkqyO4Is3f511QQDBgMp68LfNWMNWD1EJcmWKKgiBZ3T63aMy92lhafaLVlbWU89K8JToCLX7HZKz1NKtfJ/qxSQzLJ2emgj95t1w1VfmwOSaXKj1I8Yb79fuJnkFypPmbtMU6qVBwwMBrT72wwyfzlIxkHiRE0DwyxSZX1ojYV+sVtKC4EDFOwiV9bqE2biJ+vkXrkc1t/aMXOjcLq8BhpVxAsYFLni9ZeUQCFz9YcvsrBUGAQO0LI7IS2uyvRVC6b5o6Zg7Wo8dqHv7MZsHDA3yMi5cHz9IeOej75tz5KuqcQ6adrTeHMxZA38cgnG3E0SntoXvpdi18Nly63wqsHPxZpJ5I1LQ2q+Qm7Z1oMSCQCgsSu4LBx5JFUuIxwXVxS5Vtb223xPvGg884a8gzhd3xNBIXMnPFesWd+kEorJugamrS1ctDuzyDkhUoi+vbkr9b0Qd+gREREROwTxhR4RERGxQ7CtLpdkvYPSm8G0dZpSTRzsofPGJWa9ZuWsdomjnaA05YHyZIL6uJmquTvBzmHXSKtMZcxGzXxU7md1j5mRhXkz45SlkqDvNKYo4i966azeN/om6X5TKrmyBhIVO7c7YtHulYfC2LCZNvqmmaeNJ4JeeuYORemJu9ojNkdyKYwha1P7DJUkE2ZIm7Xbr9q9lLnSGLN5KV+w+VI3C2CmcPWJfdYu5liLKVk9aO6bDKXQF18NvOPqSZNnYFO3cM3u1RZNd1Y15CryDUkL5/Tz3CKpOM4FZsf6TxzsHxt+3njPlSdmrF3iJixfMtcfqxL2cmGMWKaAFSOT18yt05sM5nqR5Bc2S9fv5G2d5a+Ym696WMz9hLlJ1vbb3A1fovsuh/XbGyLXyAJp5YtbqnCN1CCzG7nZnvIdBtLW6VlcORpYQeVXzd3GLsGMrC/mvOMOuS/3he/X9pILjdZ/4ZLx11WuoUs8c5UDACwXxZPoIp9beCfkObAbc4TYb5yOryyrArmHGnvNXZZYDs9CmlhsILeusvlYq7+Tc3QqsbzUPcPMri0i7tAjIiIidgjiCz0iIiJih2BbXS7wgOuI/dQVN0iS7CEyzZK3yf0iyT53HjRzZegKmX/CmqhPkVLaOTNlFRx5L102s705yYlDoV2lOfo+tUuZGavMdFjjau0bzbhEhdwRJTN7XS2Ywp5SjJnRMvx2MIG11BwAVH/ySP9z8YKYf8TgaFGJq5Uj9r2JF8P/WZUwf2Oj2iKzg7pjpBi5EM7NXqTEjj2WRMEJEx1NmqHECR57LSjAJbjYHdY+EMxuTlbihA12B2QuhFRrLs/G6fCpajieJBcYu5pu/tJRAMDMn1y1tpLiZYkU9TTZpTFlc1isUPESYbQwO4gTnrDHmEAdmefqjM13kZQCVfExT2PIaoh9dUhy0wxdpqQxch1Ujof+cGX5gaQvSpDp32rcWDCqcFin50RlKwAgaY8qRs+G9moyIACAJDuAkMQ0kFDI6fa5sE4KVzd3/7BbKn8pzE3tsK0NTsTquyyYMcMyHtPhWWHZCS6yUztC5epEebRLDKZUnSQ5DoS5ZQbVAKOlGPrFapDpH5zpf2781CfsupL815zYvJzdeyHu0CMiIiJ2CLaXh55NoXEoBHG0nNVAynWPg0JlvBtlKuycodTlblnEoJbsJ5HTidPCEXX0S185aLvP4XMWaKlPh+Prs3b/kbO246rPhL+XrpFmNqXre9kduqb9EjcP2M5sQPdbA5DEsWYdaE1R5xT+ApdSE0kDtmZ4t5UfsfsqRzp/nQpOU5BLxaDyr9yw71CJLUVnisTBSIe9TVrcuiPidH7WbG9NhXM7edsNZal2sFoJPkf8e9rFpW5ZHzQFntOsteA1YPz1ISrWrJrdADD5cjheOWnc8/LzVmZsnQK7pTdDADVNVhSnqDfG09J+Gwue7wTpfmdExK01bHPEu8PEeliza49a0DOzQrzl22GdJFc2lop797XyIjSXPm396h20YG9VyqfxjpOtp6RYIXlKkdfcDwAoXzcRtbo8N1zesPeAjaFKZ7DVmcoTD1zKLY69YpaJ26QAOWBcen5v8A5Zd9UcUB8QpJPAruN8CHpv8HX1fZW6SRroZLlrEXYOuLfpWvVdUoR9kfTaH7PyhX3xOwCu1ZH7431jK0Wic865551zrznnTjvn/qkcP+ice845d94596+dcxsLOUZEREREbBu24nJpAvic9/5RACcBPO2cewrAPwPwL7z3RwAsA/jSj66ZERERERH3wlaKRHsAatel5T8P4HMA/rYc/xqA/wHAb73XtRKdHjK3gymlqd6cPr78sJlxE9+/g3eDSzKxYl5lX7jWyBtmt9fJpaIuFy73lWpQGTLikfeywSRLUNY8q56pul3muplenA6/LqXrOJjWGqESWXR87YRIGlw185KVH3WMmA/OetB985HMyPYuM/e5dF6/tBeZwl3SCNdgkd9tgSDlJwNA9nwIPnYLnHpNLpUb5urJrEsfUsQN58rswmUfOW2uk9ak3UuDi6mqjXuaFBR7ZdJeF15xlzi9w69aMHXtkTAfPSoN1qV0fHVNlF8zpUHliANA6YzpW1dPhNT64hkrTVZ7wNLtS9fCeDMPnV0fnRHrowaRWXef3QXtqbCmym/buHIpNiekgtpxU4McyEdgN8SQpMCXqYQcQfM8mE+ubhgAqO8Jc6cl3YDBvAEOjhfmpL0UqOdd4+oj4brlOQr20vodORvWBD/fLBFROW5z0xgOV041rK/Db9OakhJwXKIut2CujfpsGI/cDWoLeXfSaxT4lXoB1RO77VqUe5FcCy7YO49ZMJlLao6eC6692oytgdwicd67FGAVnXXmzG8VW/qGcy7pnHsVwCKAbwO4AGDFe6+zeg3Anrt898vOuRedcy+2OrXNTomIiIiI+AiwpRe6977rvT8JYBbAkwAe2OoNvPdf9d6f8t6fyqQK9/5CRERERMQHwvtiuXjvV5xzzwL4FIAR51xKdumzAK6/97cHoUUAtJI6MKiaxm4M5RBrUYB3o6/wRkpq2RWqNC4pvEn+zgqZj3PGk+2NBvNy6TFK6+3YfVul8L3sNapYT+n2Km2gUW9gMFW9Q9xu5QW7V871j7VPPdj/rK6BNPGq0SEmgkgHOOL8KlsFAFKLZq73hsN9uUhB+g6bmqLGWCf2DqnFqQA/X59VD9lsbh0OZikzTxj5hXCPxrT9wCsjAQB6GSf9I/4/MYmUiQQASeFbr8+QW4vaMvxycI90h8ldQe4I5UNzLgCPUe2oMUcyy6LsSPIMrLa4fjAcL16xY6vHNrYVMI50l4teUH9TqjBIfanvMldOsinp+m+Zq2ggL4C+1xwNY5P5IUkPULELvWuNpBiSxHhRHnp6wdYTP5+pRWOJrT0aXFCli3asWyC+hEoalIhhVds49vxMrR+2Z5GlElLiKtX2AcZjB6zwRqJt60zdT4AVUuESeCxJkFont+x+KWVIbl8umNOV644/TwVoSBpAJUO4MM+1n7G/7/sGleyTdZJ6/5n/W2K5TDrnRuRzHsDPAngLwLMA/pac9kUAf/z+bx8RERER8VFhKzv0aQBfc84lEX4A/sB7/yfOuTMAvu6c+x8BvALgt+91IZ90Fuzw4Vd7fS8JCr3FHGkKXElmH3ORN+Om8i4OFPhq7Qm/hBrYA4DONJV8mrRfSuWEj75NmYXEyS1dkXvcpgzCIxY+cEkpIk0ZaZwlyTxz6M7lkaP9Q+n5Ffos1y9TdikFrpoTYeyKdE3Wlx/I1tMdOAVFefenAbsE8ao5g053PhwI9Qm7VnOvWSSrB0K/Jl6jFELK1mtLUDPH3Fva7aTFeqrN2u62cJ2ycVdtDHS3nSdxo16B+iCFghNXbSeb7xq3W7nEjVnSeV+zQGVugfjQyg8mMakeFWYeei3sznrDtiMce9mC++1x64+WUpv/rH1/+lkTtGrI+kzVbA44+NcekmfikPWFLQ8WdkpLCbbuo7bOOCDek7ErXLZdNWdwdyXLcf0hC8D2M1UBgDXyK6G9rG/Pz6WW7Esv2L1YNEyF1XinyWX2mkRgaBfDWa7HmvG2JpUAwOPCpfnUEkzfoRoAFOjkNZmqhHutHrH78zrRZ2Xuv7Ax2v1DKqotmb/8Xhu+SB4JXhtSgLs1uTEP5F7YCsvldQCPbXL8IoI/PSIiIiLixwAx9T8iIiJih2DbxbmUC94aCWZ5+bK5C9gcSVDZKdcQPedxDqKRiS5mHmtTZ1sUFFU3Q4dcCORGSczdtDaOBPOxuct4uJx635WU5dyymZkcgNLyZMV5aj9zgqlcV99tRDpFXJm9LiW08lRaL0VmYF+ci9wZKRo35j2rGTeQBt0hF5aM0YB7iCqsa7ms1UPmzhh/xcxmrqZeEhOe+fvZRUu9z90IrpgeB8vIhVbdL+4GMus5mJVes/FYPRbOHX3euOH1wzZ3qk9dfdz44qxnrun4eQoutvaTZEKGufrh3OWfMu30IeI990QjP7VgbrMuufPY3eZ6YbyHLlNJQjKx0+vBrVTZb2s+QWNUOCeB/LQ9wrVD5vZKr21c//Xd5gYZes3GqzMR1iwHH8vnOMVdrkP8+iQFLVm+Q11B+VdsPOtHzS2kAUwugZc/b6SEurh10os2rvnnjG/ROnm4/1nXEfPz+Vn04k5jV23xrAUt1X3oKUehMUHidRfMBZaUaw1dsnWeJGE2dd3t+5Y9Ex2SiGhKGUx24aUp6Jq6YO+g9tEZad/GOg/3QtyhR0REROwQxBd6RERExA7BtrpcfMKhWwi3zEpZrNXDZFISSWX0r4wdUJN02/Q6lXJrsTKcmE4pjsxz6nD4XoJYH+wG6e23dN6OmPYD6n5VimarYl7WzKkUKfmNSRX2Hmlqc7p8kiL+S48Ft01xwf6enyfXxaVgUrIZWTtAZrUwILgqeoLYNZ5K9q09KGpwpImdJg516lYwFTtT1G7i9adFmW5ojplGNgeNGdKHFyVAlmoYYCWJu6FB+to8t4rWsC3PoTPmxuiMb6wyXz9ibpIBVoO4CcrvmAnPmtpuNbjpfJnYC2dNG92lKY1/JPSxsMha3vYxuS6uwVEbi5UHjE2l+vYAkBSXYIK46YMMjHDhIpU/TBLjRU38JunfJ9rWr9XDNraa3zH0ipn13Qn7nroGhp63fjeOm/qkVrR3NIXMXEnWSK2wGPrb3G9ur9xrpvJYP7lvQ1s5Z0Pdp81ZO5YlZhWzWDrDwsyitbVO+vLZK2HsqnT97Jyd29gf3D6cS5C/aO8drl3QEhkMlqDojNiacSJ1oIwgAEitkKtUv0NuGB7P3iwxr+R557WxVXVMG/QAAA0SSURBVMQdekRERMQOwfYGRRMm8JNohd1Ids1+hfI3KXORfpV1x5eo2i8ec6i7wuH0xD9uD9MvtQQSu2ObixNxILA5KnxrikcUzxrvWAM8uat2jLnha5+cBTAoXsTBjTZVVRq6Er7HRa4d77AlSMzc8cJlKqgrWXWcicc74R5xs0tzYXd4tzHUXVKSrBH+e0+Ci9krm+9gWBwrdVX42MTRblOwKb0YdsXFs1TJhqyrpFQZak5QsGqPzV3+5cv9z7XPSqFsqhrFO1nl5VcP2Pd5bpJiPQ1ouz9o+t1s/ahFwjsvFhVLtML6U81vACjMU4UcChJrQK4+YY/g8AXKKJadHoue1Y7arrewLs8ErQ1Xp2dpiTIeJeuzPU3ZoRTUVIIBW5VZ0tVXnnh9wuYof8Esyd4IVbaSbFq2oDtHTHtdCQYJFu+iHf768WDdDJNF1iUxNs4JUe53nUXk6H2iVtvwObPOqieMJ65FojuUrd44aNYVC+klm+EZa47ZHOauGwmjn+FKZI01qmrWt7To+WTxroH5kKB684gF8reKuEOPiIiI2CGIL/SIiIiIHYJtdbkkml0ULoo5IcEc1if2JJLTI91vDUQOBCGYey1FVdm1kV0i3WMRgSq9YdxYDl5weTTlv66fMPO2x8JOYjKygFOVTCMVJeK04Q4VlmVXTUvKtmWpEHCHeOqqG55fML5rapF01o8HUzZDZjnrobP558Sk45TuAd6/mMiqEQ0MBmM1aNnZT8Wayfxlt9Hap/aHe5J5WTpPJrwErNnVxVIJyu8143awUPDtp61Q9tjrYT01SMph+QEbw/HXpbj1gpnHtf0WEGxIgLVAwljps8Z7dkWbu9aMuKXYPObUfHHPuKrNVzJHBa1JQ78rnPXyFSrszKUIJfimuugABrS6m1LgmF1g1QctsMZrXXXc4TcXlGsNSWHmZSqcvofkMATlOc4ZMTdfe4hE3GQc06T6kKTSdd3sJunsHFhubZT0qJOGOLtBdAzAFeYoIK7Bzh4VBS+eNv69ggucZ1ZpXKisYmY+dKhTtHHxNLcJKSmIpK2X4nXqt5BBWJKhU7D7NvbQcyu8eiZmbBVxhx4RERGxQxBf6BERERE7BNvLcnGu76rQVHHmZaZvcdTYmuYqwZxJkptDNc4BKy+WWKNUdVKA67t1yE3Dpe/QpjTmSeGGX+JSVmZGZcX04msVrlq7e+pGIHcCa153p61dyiRgpbUkVYZPSEkyVjhEhniuquBGGtDMekhWyJxfC26l9r4J+jupNApT5m4uMGXaLJ8w03D4HTPRuVxWV+QFUkSYYRkCNctXjphTZdd3qMyezOfA2iCzfegSMQXEnGZN7Owq8ZalfBmXBitfJn6wKPml522+O4eNg50iN4S6hdhdwetAGTXJZnLTv/NnzRdYO2Lj6XrU35qk61P5tJF3iIkjeuI+Z2PYGLX7FmmdZO+E/raI+cVuho6oFvIcZXk8xGWYWrJ1zq69zC1rlzJSeNw0BZ/B7jwez+xyGJfmbnOhcT2B+n7jlGvJvZQ1a6Bmgrr0WHs9WbfruvWw1tk1mWQ1VK49IGuSXYssR5HVZ2kTFVjA3Cf5SyY90BqhcnZzpN4qY9geMXbPVhF36BERERE7BPGFHhEREbFDsL2p/0nXF75XVgUXZFh+3NwBY39lTIOOlNZqjpuJk79OinkNYRdQsoKqCwKWFLPws7P9Y5rUAwDZzsYUW3Y3ZG6byacMDU1fB4DGbmJClEJ/hk+bC6F+wP5eIBXGrrJfyPxcPW6m7Nh3Qyo2F6qoH7XECI3iJ1ZtLFjBcMDNIZFzjrLzuUmRL+BCFSzgr6bk2OtUhoxcIsXzpjKnCTiuSVXkH6BCDHKtqW9ZSnhv3FxRyrThOW6PmvmZuUHFEcQ8rey3tTF0ycxmZQ2NvmHtZteHKhwyq4PVHF2DxqAU7sVsquYEKVqK+6Y5bmOYv2H+gJVTZmKXrob1malQoQiy1rUk3/gPjK3SJIaRls678gt2bOZ7VJyBSgk2JgJji8eTWV6lC6GNKw/Z2ht9nRLQhDlSPWb34iRATvzpP4M0xutHiRkij3uBGCBo2hivi9ro2CvG3ukXxQHQLhEbSVxrnETICW7sqlHwmk3KM87sImavtaZtPPRZYImK1jC5teT/tf2kmEnJbsq+qTy6ebIQJ0U2pj547eW4Q4+IiIjYIdh2ca62iCXllsKvlwr/AINa3Z0p2+X0NAWeNzO0S+pK4eXWCHHLqUyZWgOlmyTuxSI/I1ykVoS+KG2+Q0FN3YUxFzlzx3YY+evhvi3SaM6ubF4sWfn1zIsefc2CI5UnQmm7wjXSEr9sf9fAb+duwRPeeYgVkGxunoquBadVhCv8nXdecq27UGOZP695AZUHbEeXrm4cgwHLg1L7c8LL75C4WJLS6dvED9ZSatnV3oZjAJCStPIeBalZSiEtgbWBrQ0F8Vg4StGa5J0b8ecl0JmiYDTfd/g0zZ0IS2mqPDBoFeruL0nBfS4i3dgV5mbyVSrHR/M9oOku4PJtKSovqBbuyFmzJlyN+iCkgPwNW4cd1qcncSvd4XK5PQ10AkBjXPrFY0TSAQlZJr0sBfq5hB3t7LUEI+vm92iHreufayf0+eIA2kJQYL53do40/mk825NhDJhMMVDnQILEhas2RpVDtqaLN7S/9srlHAYmeeSvhPXJz9RWEXfoERERETsE8YUeERERsUPgvN+cN/kjuZlztwBUASzd69z7EBOI/bqfEPt1f+E/9n7t995P3uukbX2hA4Bz7kXv/altvek2IPbr/kLs1/2F2K+tIbpcIiIiInYI4gs9IiIiYofg43ihf/VjuOd2IPbr/kLs1/2F2K8tYNt96BERERERPxpEl0tERETEDkF8oUdERETsEGzrC90597Rz7pxz7rxz7ivbee+PEs65vc65Z51zZ5xzp51zvybHx5xz33bOvSP/H73XtX7c4JxLOudecc79ifz7oHPuOZmzf+2cy9zrGj+OcM6NOOf+0Dl31jn3lnPuUztkvv5bWYNvOud+3zmXux/nzDn3O865Refcm3Rs0/lxAf+79O9159zjH1/L3xt36df/LOvwdefc/+ecG6G//Yb065xz7j97v/fbthe6cy4J4F8C+DkAJwB8wTl3Yrvu/xGjA+Afeu9PAHgKwK9KX74C4Bnv/VEAz8i/7zf8GoC36N//DMC/8N4fAbAM4EsfS6s+PP43AN/y3j8A4FGEPt7X8+Wc2wPgvwFwynv/CQBJAL+M+3POfhfA0+86drf5+TkAR+W/LwP4rW1q4wfB72Jjv74N4BPe+0cAvA3gNwBA3iG/DOAh+c7/Ie/NLWM7d+hPAjjvvb/ovW8B+DqAz2/j/T8yeO9veu9fls8VhJfDHoT+fE1O+xqAv/nxtPCDwTk3C+DnAfwr+bcD8DkAfyin3Hd9AgDn3DCAvwbgtwHAe9/y3q/gPp8vQQpA3jmXAlAAcBP34Zx5778L4M67Dt9tfj4P4P/2AT8EMOKcm8aPITbrl/f+33vvVZnrhwBU1/vzAL7uvW967y8BOI/w3twytvOFvgfAVfr3NTl2X8M5dwDAYwCeAzDlvb8pf5oHsLn48Y8v/lcA/wiAyhGOA1ihxXe/ztlBALcA/F/iTvpXzrki7vP58t5fB/C/ALiC8CJfBfASdsacAXefn530LvkvAfyZfP7Q/YpB0Q8B51wJwL8B8A+892v8Nx/4oPcNJ9Q59zcALHrvX/q42/IjQArA4wB+y3v/GIKe0IB75X6bLwAQn/LnEX6wZgAUsdG83xG4H+fnXnDO/SaC+/b3PqprbucL/TqAvfTvWTl2X8I5l0Z4mf+e9/6P5PCCmn7y/8WPq30fAJ8G8IvOuTkEd9jnEPzOI2LOA/fvnF0DcM17/5z8+w8RXvD383wBwM8AuOS9v+W9bwP4I4R53AlzBtx9fu77d4lz7u8B+BsAfsVbMtCH7td2vtBfAHBUIvAZBOf/N7fx/h8ZxLf82wDe8t7/c/rTNwF8UT5/EcAfb3fbPii897/hvZ/13h9AmJu/8N7/CoBnAfwtOe2+6pPCez8P4Kpz7rgc+mkAZ3Afz5fgCoCnnHMFWZPar/t+zgR3m59vAvi7wnZ5CsAquWZ+7OGcexrBtfmL3nuuk/dNAL/snMs65w4iBH2ff18X995v238A/jpCVPcCgN/cznt/xP34DIL59zqAV+W/v47gc34GwDsA/hzA2Mfd1g/Yv88C+BP5fEgW1XkA/y+A7Mfdvg/Yp5MAXpQ5+waA0Z0wXwD+KYCzAN4E8P8glLe87+YMwO8jxAHaCBbVl+42Pwh1s/6lvEfeQGD5fOx9eB/9Oo/gK9d3x/9J5/+m9OscgJ97v/eLqf8REREROwQxKBoRERGxQxBf6BERERE7BPGFHhEREbFDEF/oERERETsE8YUeERERsUMQX+gREREROwTxhR4RERGxQ/D/A2eAWh1fxbBLAAAAAElFTkSuQmCC\n",
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UhzcKe0I3GAyGFoFt6AaDwdAisA3dYDAYWgS2oRsMBkOLwDZ0g8FgaBHYhm4wGAwtAtvQDQaDoUVwzQ3dOfdR59ykc+6QfNbjnHvCOXd87f/uVzuHwWAwGG49NvKE/icA3hH57IMAvuC93wPgC2t/GwwGg+E24pobuvf+KwBmIh+/C8Dja+3HAbz7Jo/LYDAYDNeJ18qhD3rvr8bZXgYwuN6Bzrn3O+eecc49Uyk3T11qMBgMhhvHDb8U9d57rFsoDvDef9h7f8B7fyCZuv5yaAaDwWDYGF7rhj7hnBsGgLX/18+UZTAYDIZNwWvd0D8D4L1r7fcC+PTNGY7BYDAYXis24rb4MQDfALDPOTfunHsfgN8C8Dbn3HEAb13722AwGAy3EdfMh+69f886X33fTR6LwWAwGG4AFilqMBgMLQLb0A0Gg6FFYBu6wWAwtAhsQzcYDIYWgW3oBoPB0CK4ppfLzYTzQLy4WgG81MtK6IlcMjhudh8rfg88xUraxTfsaLQXt4V9+p5barRP/CgjUgee5jGV/Fb+EYltreakYnmVX7Z9+WijPfED24I+PUdYvby4nQkna1lWQm97jhXKK9v6gv6xEquhuyqXIvf8+UY7XtoS9HGLrCTedaINzeBjrFaenl03iHddlPvDiN70WabymXgLq9EPTnHOXSdYsR1TrGwPALpSc2/f12h3f4MVzzOTYcX16Ud5nd5neL7iTspw6j7qyehnWPEdAMqjHFv11BnOZXtPo+2TXKdg/BGkzvH6hb28frRPNcPzxea5ToU9vY12Jk1p1FM8HgBm76V+9Xyb19T1SE0srjvOzCTHk5jl9dMp6nZhJLtun0pfrtHWuZW39gZ99LvYwop8nmq0VRapAvUcAKr97U3PtdKfbPo5ACwPc61L3dsb7bavnmy0Fx/d1Wi7atAdufOUW+ok4yB9J++hdHb97XD60WEeN8/5tB0T3djZFV6zVGafSwscm+f8F+8fDvqU27lWbeNlXC/sCd1gMBhaBLahGwwGQ4tgUykX74DamimWvkK6IlaoBMf1L9LcchWaN9lnTzfauWPN6QYA2P1xtms5TjF1TMzyVEjZ+DxNUVcUU6eN1xl8MkxZUxmg6ZS5KDSDnNu304xNTJA+AoDKCE00nyBNUriP1NDi1nCJBiZ4vrndNEOz03UexFNhpff6f7MHvh5mSy4JTdF9TEzhac557rt5zMBEWO+k0kvKoOvvSWH54f5GuziQDvr0ffUy+w9TTplT04326CUxw2WMAFBPct7pnWONdqGLa5OaIE2nsgSAzpPUAT135hKpjOkDHUGf/GXqar2T65Qdp7kfm6TM3FBIZfSKfpX2knJKXuE1UY9QaMLaqAwzjkpQkjnnx+VcCCmYtuPUz0lZz8EnLgR9Jt420mj3LbK/ylBlUY5QGe1CYU0+xkStXcepW3N7wvXoPsb9InWUNGZtjJRF+zfPNtq+jfJf7UQZlHcNNNpKe6qcAKCWogz7vsq9o97OOZeGqQPZsyEd5pYo6+JdlJlSS93fDGVb6+/kuftCemwjsCd0g8FgaBHYhm4wGAwtAtvQDQaDoUXgVutTbA462kf8Qw/8DACg1EcXp8xExEVplC6N8Yq4EH7jTKO98OYdUDjxjPLCK2Yvi1vUkHB850IusdzDa2ZPXuG5shznwr7OoE/+Is+9vIXnzk2Sf60n+Ju5PBRydEXht+NFznPoH8ir1fpCnrbSyesk58XlrEM+X1jfBW9DiOiEq/Hvwla+U8ifnGu06znKSd0mASBxTtzEujmfehtlHiuELlpLeyjr/Bly3ZNv5Oft5+mblr0YVsOqtnE8ySn2L42wfz0p7p3yTgcAahnyvokl4dOF10zNhXKeepDvVDrO8r1QapbHLezi+wQnrz0AIF6mnDu+RdfVK4+F7rIKPUf3C+SmV0Y5lpjcQ/VU+AyXPU93usW9fFeRP095Fgcjro4TdFW89CjXc/irPJfKov9gyC3HTlK/67vILQd650Id0vVY2kq96T7Idyq6tpnjE0H/eg/HgxPn2N7B91XlgdBdN1ajcIs91KfsBHUlcYVy0usDQOqpI422GyXXXxniWOKF0L+y2Me5JRepQ1/8x1971nt/ANeAPaEbDAZDi8A2dIPBYGgRbK7bYsyh2rZKO2Qv0myLui22n6IZEiuKSVIjr9L57GWsB6Up1G2x82t0a4q6LcaXxG1RTC03Q3Ox8/lwnOq22PncVPNzixmZPhvpL26L5Q72KeynK9cr3Ba/Rjpo8hG6vanbYrmLfW6K2+IQaZbkoqzHFVIu048wSk/HCADlPYx2TR4602jHxG1xaW8YZdd2iPJUt8Whf6DLms+K2+KwmNQI3RZTFznmcidl035U3OceCV0I1W2x3Cs0i1AzUbfFtkvUT6VwXJnX73mSrrf1iNtibIryVLfF7hf5uSuFJrpPChUhNFVmiuNfEXfGqNui9mk/xuts1G1RaZbpB7gGKovlbSGV0T7f3bTPRt0WVYa1UepQ5uXxRjvqtqjuz5UDexttdVtU3QBCt8Xup7nfBG6LQrOkJkPaL9bBuRXluNBtcTrok/HmtmgwGAwG2IZuMBgMLQPb0A0Gg6FFYBu6wWAwtAhsQzcYDIYWwW0LLCpIfuP8hTCoIyZv8kv9fFutgSDZyytBn6veMwCQnOF3Ggy0OCoJm94Svl3u+B2+kdbrXPwennfs00tBH81nnZQAAy85r6vtnGdiNhzz0l7xMPgigxCWH2XO8Nx4eM3gfJrEbFlkqMnBkmHO7Y2gOBR6JSxs59v/wX+kB8uJn6CXxq5PMLHT0V9gcAQADP4dgzKuvI6yrXbSM2fvRyPzlECp9AkGJtUG6fGiOfUXxkKvpZ6XxIuqyuvEr/A6xR305EhPht4f8/vpwVLq5JhnHqan0v7fD/u4M/QGcT305ChvZTs5zT5z94ZJzDKz1Pv0RQmG2kIvI83bD4TeYuqddepfcPy7P8Igm8V76RUCAPlzvE5snJ5Ffph532vZiEfYCmWgAVwagKWywAg9dgBgeQf1XvVbr6PXAICFvZIESwL3Ess8Tj3a4gthoJqTfc6dXSdJX0ck4V+Msl66i7qSE0+h+AQ9gyqRvPHjb+F9tONxBjP5HPX26E+HNRL2/im9hlS2FlhkMBgMdxiuuaE757Y65550zr3snHvJOfdza5/3OOeecM4dX/u/+1rnMhgMBsOtw0YCi6oAftF7f9A51w7gWefcEwB+EsAXvPe/5Zz7IIAPAvjla54tvmq+VrP8LSn1hkEEWQlKWOklZdD7VZpx5Ug5t8VRnqNLzLBqmuZy70s0w7J/Fw6rnhdzUQJBtn6ezv21fGh6JhYl+GSQlE01zzFrjmSfCcU98RBl0H6I84lVaDpXO0PZJK7QxK5KwMvcgzRj+79G07naF8kLvQHkDl0M/k5PSdCP5NjY9XGam9UumpGjnwrnmf/8i412z1Ni8osZ7BMhNbS0n/Ks5BmYpCW6ug7TPE1PR3J/iLkKicWZejODtlJLvH68FMrZy+n6n+F1Bj8pedrvCXOsJAe4hot30/xOz1Ifl3dynZaHwuepWI0yyHyLtECiY6zRrqXDcQa03wRpvx2/Sgpv6icfbrT7vhWWB3RSJq0iAWBX71Pglbl5vOSDufwQdXDoaR6nskA1LEGXf/oMr7mfQUqVPPUmGck5o/exBs4phVUe4ljyUvINAFZ28HkzW2E+9OIwaZbFbamgj+bWyU1SiZQKqbbxXNVcqMNjfyn57SUH+9Io+yvFArzyfr9eXPMJ3Xt/yXt/cK29COAwgBEA7wLw+NphjwN49w2NxGAwGAw3hOsK/XfOjQF4AMBTAAa991cfIy4DGFynz/sBvB8A0unOZocYDAaD4SZgwy9FnXNtAD4J4Oe994Gd4FddZZq6y3jvP+y9P+C9P5BK5psdYjAYDIabgA09oTvnkljdzP/Me/+ptY8nnHPD3vtLzrlhAJPrn2EV3rlGfvDOE+L6E0nOVRE+tv1s6NJ4FaXeiJvai3Sbq+XJUfU+GyaKuorCjtBaKLeR/+o6RJ4xLnz80lj4g5QvSjKms7xOfJDn9uKKtbw15LN3/wm57nqec9a86enFkJfLS/7kYi/n2XOI7l/1jmzTYzaK1Jnwd17XY+o+cnyDz5LP1+tkL0fcULuldqokk1L+tt4Vyja1xPcI+SOS+OwucvDqWpidCnVIoe8deg7xuWNpO9cjKqeVfsogN8n5p8/F1u0TX+BxXt41zI+JPo9zziN/G9FNlY24PS7s5Hq2nwllu7SN5+6URFPxXrrZqW5EoXJPjTMp28KDzN+t+fABwAu/PvJlviNS/VZZIBHqU1zWXWXY/iLXefF1oXul3sfFLZr0ihx4oiAJuIbDxGlas7faIbUPTvG8yYVIgrc016Ms9UY1h7m6K9dHwmtW+zi21ATl1F7j58vbQ1fJ9Az1OJq7fiPYiJeLA/ARAIe9978jX30GwHvX2u8F8OnrvrrBYDAYbho28oT+CICfAPCic+65tc9+FcBvAfgL59z7AJwF8CO3ZogGg8Fg2Ag2N1K0Y9QfOPABAGEe4no6pBX0O41+S1ykSVjrD/NnKxb2SSmwk5KjuM5zxc6G+dSd5C72y6SD6lsH5KDQfUsjFWviVqU5zLuPMJozczEsxVXtoik9cw/N1dwk559YDl2+1kNqvtz083Ln9VMuiZXwmslzjKrVaLjkRSl5tjs0kRUxMdk1Z7Tm4naRaRb6xV31RaGTxE1PTVKNJgVCN7HkDNdTo3jjV7geKzvDKL94ibqSOkMqYPyH6arY+2JYgm5hB2WtOcR9XvJaiw4WxkIdrosOJRcpkPmdPG/beJgPPftVuicWHrur0VaaJDNFmmZlaP0c20vDlG3nKXHJjeQJT81zDKor1Sz7qyz6ngspH6VsKhLhnVgW18B89JpSBrC3eZm2UreUHVwK5ZSalAjhUVIjKqdkJLpUx7k8QrktbuU8h77J8yYuhS6hi/eTttKydYrEhbD2ACoyH3HpfPLJX7VIUYPBYLiTYBu6wWAwtAg2tQQdvG+YOJosR01iIDSLC1to6qSzNKNX+kMqQU3U9BzbmrBHI97Kj7JkGgDUtAK8JElSs6vUFYqr4yTNrfgM28klMd+F0VrcH2ZHyE7QZO87SE/Q5W1S8m0p9N6o5sREVe8gMeWVGor23wiiXg3FPVoSj3LPCZ2Tlsr2OkYASE1IoimpeK5Jjl5R5T0lHkGiKyuD9LKpi+eCd2EYRBhtSx2KF2hWzz/IPvnxMHHa3B56f7QledyWJxkdW49E/vYdpFm9eD8TUlUkKlq9d4JyfgCg+dVEZqk+eqwgFBMW3343x3mW8ozNifeFeD2lZ0JaIVbmvZKZipx8DVHKRXWqKtHT+rnKIrq2Ognt4ySJWnIp1EG9j2sZ9nd1GZtcRqO1AaCyi/RW+gp1NYiC1XsIAOLNE9v1HKYM1ZtJKRYAiJd80+N075t/eCTo03aOaxi9DzcCe0I3GAyGFoFt6AaDwdAi2Hwvl4dWvVxcVfIT1yOBC2IFOflKK3f7+Pq/RUqTaJIh9Z55ZR85n4xHzaNXJCmSyvKu0pzyULogGkurSY7qcn29Zi3iAaRv4isdpDyUYlDUk9f/m62eA9Fz6NhiK6QMyj3NPQ+AMKhDgzL0vLVsaNbrcSonV1Y5yxhTEfNYvosVm1Nwcfm80h7J+V1sritKC2gOfgCIydiUylhXhyO56tWjqyZ0TrC20ftVdK2W4fk0B3xdAnt0jEDoKbSePJIRD6pKp+ZA19zo7BNbZ50AIC56ozqckM+rEX3Q71TvdN1V5q/YH+TeVX2EDDMayKP7kHrg6LlUh6P34HrrqVRhcF6Ee5fuL09+0bxcDAaD4Y6CbegGg8HQIrAN3WAwGFoEtqEbDAZDi8A2dIPBYGgR2IZuMBgMLYLNjRSt+4brYGGLurmFLmLqCuTFsyt/ktGUhbEw97BGj2mk5/IwXak6TjEaMOqCqG5aGvWoLl+VztBNTaO/qllxRSqK26NEe6WnwwQ9czsYDdl1jBFixX5GQ7YdCZP3FHYx2jR3ksmACjulZuIFJp1ypUg04gZQ6Q9zNGsNxO4XGCnpJZIue5rWJtitAAAXz0lEQVRjWd4rkY0A8sc4h2rPxoqcFCUitJoW1zqp85iek1z1I2HksK710jZGSqYWqGsz+3mN4c+Hyb20Jmglz7XtPkQdTE6FSaeqvZybJpxblLHlL3HMlbbQbTEzRb1LzlFXyj0cf6Ujkh//BMdTy/D6yUNnG+3Cw4yK1gRaQJhcqyz6rbq1tD9cT9XJie9lUrbBL0stW5FFfClMYqb3RELyiVcGGEWs+gQAMw9pzV25P8Ypp2o75Rx111XXy+S01OWVBHm1iNuiyqY4wGvGi+K6KhGpmtANAPLPszZv4eHRRltznpe7Qr3Va76am/V6sCd0g8FgaBHYhm4wGAwtgs2lXBypDk2GlBgPS3EtPETzJCYRpbE5UgnFnjDRVRDVtcLfqZ6DQllUJQe7lHxbHVvzHM0aFVbJhb9/Hcdo7i7sY25vpV90/LVI0qqOszRF48tiEmpUWS4d9KllYk2/q+b5+cx9lI1SBBuFj5QM65byflr+Kz0heeNlLPFIPnVXlgRMEj2nJb40shEA4kWOoSwMUEwYpOVhoYJkjABQbed4cpNa8oxr0CO56qNybjsp5xPdiM2RZqn1hLSfruHSVupDdlr0TqNj0xETf04ShElEaGJJoiFr4S1blfKAKkO/IlREXqMZw0jTIKI02Vy3ojn59bueo8Wmn6ssVB+BMDK862Xe09OvJ/0x+PUwOlXv46U9TLSlpeGSC1SO1JTUQQBQ6SW9WfMcZ6mHOqQ1DQAEj7sqt+AelKjPejQ6VdZQj1OaJncipJaUrkzNNo/+fjXYE7rBYDC0CGxDNxgMhhbB5lIuAvUe8bmQ/giqxmueKynllZ1a33sj+ra5GXzkLXhN3v5rwqH4As3yxHI4Tk20pGmZKzkOOiWOEGp2AeEb7akDNCPzUoIuNRPJC51qnle54zC9T2Ye6G56zEYRHWepv3luclcTUzxGeZZ6Qmopc55/1/LNEzi5SGKjsni5qGfN3D2UU/sZUhTBGBFJNCXlzFROyztJi6SuhHKq52iKKwVX2kuapeN4WFJQ17D7GHU4JnpS6ua8XuHdlRXZrFAHCyOcm3pYAKGua3788nexHJ3Oef6ekP5QT4pqL+ecUbogQg2pTpXFOyw3z3tFZZEohbLtOiJeWHKuztOVpp9HoVRVYlmSg+V4D9eyoadWelJyxU9RHtkS5RErR/aUEmmfwl562SwP8jrq2VPcHpYU9FmudefzLOMIuW/Ko+F65E6TIlU6baOwJ3SDwWBoEdiGbjAYDC0C29ANBoOhRbDJbouu4RIXF75P3dqAMIG+ujjFFzhcdcUCIm6Lyp/2kH9MLJETUy4XACriSlToF871FI+JFptwHRLtWiCXmZ7nmAt9PNdKf3jN7mfJ5Q3MkuOrtZF70yhBAKiKa5V+l5qkm1ZhgLLJR/pvBJmzoSuVT3M95l5HnjA7znmW+nidaH1OjQ7VCN3ECUbSLbx5R9AnK66G1XXkXJViFenLETe1Pq57ckmKZYgLYv4Iec3S1pDLzJyhK21sidfPXJI5D4Y87cA32KewQ9wWJZoxJnx8NJoxfplyL+5jfcq2U+u7ns7fxevEqpRH2/99rtH2eyjb9lNhdOvSmNROPc3vVLe0Bmf0u9R8pennKovxd5B/BoDcZcrTySuBYrdEXRZDvdXiFRrtm5qlbIOiFpFI8GA+hXA+jf6R2qdO9D4zSZ2uJ6lbFamRW24P94dsRQrAjPC+SU4XmrYBoDzI9bglNUWdcxnn3Lecc887515yzv3m2uc7nHNPOedOOOc+4ZxLXetcBoPBYLh12AjlUgLwFu/9fQDuB/AO59ybAPw2gN/13u8GMAvgfbdumAaDwWC4Fq5JufjVoqNXbbHk2j8P4C0Afmzt88cB/AaAP3jVk0lyrlKvRLhFEkil5mgSaS2+i29lFFXfCxGzSawlpUaSQr/M3UWXs/azIS2QWuAJcpJAKT5DU74+0hn0SVwmZZLK0qxUF7zecZqxx38p6obExEZpMSNzXz/Ba3SF0YipKzT3YgsSqdlOk3LLlxnlGJ8JTeyN4PAvDAV/7//1I/xDKBelszKnSV/4xPq1MmOdNCmru0grdDwXJsdavouyUVeuoNaluJe68UtB/1j7WKN94l9R7vv+kMeUtpFmyZwStzIAtS7SKbHT47z+fTvZ53TYZ/nugUY7e4F644oc5+kf4TPU2CdDk3ry7dsb7YG/Z3KtpQc0sVOo98tDPN/IZymD+uv28CBxx3PF8F5TuRd3MiFZ9jjd8SrDoTuezvviD4w02ls+d4HjElmoPgJhXVbV4fRlUo1uJZxnZYhj8PIYeuynuE4+LtG186EOVjvFRbTOeebOc3/Z9rkwER5EVtUB6q0mUUtM8/6qJ0I5XfzBrY328BMTaIZaf3vw95kfpH7v+ouV6OHXxIZeijrn4s655wBMAngCwEkAc977qzMeBzCyTt/3O+eecc49U6ksNzvEYDAYDDcBG9rQvfc17/39AEYBPAxg/0Yv4L3/sPf+gPf+QDK5sdSpBoPBYLh+OH+dkYTOuV8HsALglwEMee+rzrnvAvAb3vu3v1rfjvYR/9ADPwMASEi+Z58NvT/KEk0Xl0i2kiTiyV2MmCMyD/VgSczyuMW9pEwuPRq+0d7952I9yBvy4+8hrbD7E+E11VOnNCBeFUIZ6bnii83frgPAwl0SAXmCkXQ+Ho7TJ2lKaqSqtiGeAz7imbMR1CLroUjO0EQ+/07STCNP0qw+8Z7QjMxfkJzy8pu+sp3y2/+h0HrTeevbfk26paZ7VE7BmCdFnhnxMpFo0EB+CCM1lVo68VM00fd9KNSHpR00/7OXxSwX76pgXpFr6nXKnbxOcknWuR7er+rdpLpx9H1cg33/u3lyNQDIiHdQuZe0neZjr+VCf4d4IUyc1Qwqi/YjIeVSFvoiOc/rOLnXo3qrsqq1i9fTJdKedYk4j3rOOfE4QVHuwyz71NtCStSnJcJZPKqcJgwU75uoF9zsPsqz74Xm1OfRfxcmhbvrv1NXK93s/8V//LVnvfcHmp5EsBEvl37nXNdaOwvgbQAOA3gSwA+vHfZeAJ++1rkMBoPBcOuwET/0YQCPO+fiWP0B+Avv/Wedcy8D+Lhz7j8D+DaAj9zCcRoMBoPhGtiIl8sLAB5o8vkprPLp1wW3Zq1o4hmlXwAge148HsQkKgzQ+6KeWp9KSFyhKRxbpEmZmaZJm7sYSbQllmxFKJv2M5IIaDpiNkkggwY2ze+lSZmeIy2QzIbi1iRgiWUetzLM/tmL4TX1u9z55qXmZt9AKqTrpdDc3QiiuckhZn5JAikSmkNN6Y+I3ddxRjyNdnPd2o9QzlHKROfZ9jL1oToksjlFr4RaV5icS+mHwh56R2n+6ZpQHMmV0ESvpzWJmASyXKAZ7KohTaRrGNA8KZ6r2q2JzoLuAYWnJIdeP7YSeqks7CdV13aGupI/K9Sc6IYmLQNCuet3gSdKxAtNvytLnvHUFdJxKou514feH53HOE49V2kLaZr0RCjb2Dz/LgllUxujxwrWZ90CCim2QNlU+0SfZyLXnJHka0P0iJq9i336Pn+ax2yRsQBIrnCPiY/TawhSurHt5e0IUJeEddcfV2Sh/waDwdAqsA3dYDAYWgTX7eVyI2jvGPUHHv5ZAKFZnp4K8xloObeOl/kWOzZPU23hQOj23n6Mx62MihlVoBmZEC+TKGWjFcO1fJW+6V7aHpr1uYshVXQVyXMMvCjvYIDF8pbwjXbXi6QMlCLQ3CVaiR2I5M6Q77KXJJBFvEIKo2G+kY0gezlcj/k9PIfmtm5/gVTI8n4GAuWfPhP0L93DAIv0JclLMsX5z/6TPdoFncelGrysjeb8Xti5Pn2RmxA6S/MGaW5yyT8TzaviRT209GDXYY4/KtuAAlumbkw+Rqqw/5ukfHTNASB2kWb58sNjjXaQ46Qc8XJZUC8wHtf9t0cb7frOLY22egkBwPIWylbpucJW8YaJUhkyhNSi6KPmRhdZIJojRdZgWXLe5I/yvlneF+Z/Ub1Xmih9ljlj6lIvQT1pACA9IdSl5Dn3UjYvuh6K4iB1Lf/t8432yj3chzIvng/6oIdzK44wQFDzzyDCbi7tEtpphlTXl77wKzfHy8VgMBgM3xmwDd1gMBhaBLahGwwGQ4tgUzn0jrYR/8Z7fxoAUBwk31XoD7mr7qN0O1R3wB/6wycb7U9/4PuCPlWpCZpaaM6Znv0PPH7gz8J8y1rfsSq50WuSfzya5ztxVjjkB8gTx4JIMnFlmw37r4ibVlyOS4nbpbswFfQpCx+deomcXX2MPG2QnOwoEyZtFDVJmrV6cqkjKupy7h3kWcf+1/FGe/HNTGAFAJNv4HNDao7y7DlCjjD3pcNBn+IjzC6RuUg+dmWE10zPSsRffX09DuqVSi3bquQmTx0K+c/69sFGW3O4axRz9uDZoE/p9duaXl/XUznsaO1V5Ya/9PvMc/fOf/6T7NMdcuCTb+A5tv/ei432kf/CmqL7PsrrlyP9098g1145wPcYKo/S68J5pV88xz77mTgseYRJzFQWWrcUCBOs5Z4507RP+oVz2gW1HdRvTeyXO0vev9JP3jwRude0xkBiku9Baj28B6NugrFl6pe662q907TkzV/ZFfL+D/3XZxvtQ/+M7olaa7QwFib8Sy7ynlB30c8/9Z+MQzcYDIY7CbahGwwGQ4vA3BbXYG6LhLktmtuiuS2GMLdFg8FgMGwqbEM3GAyGFsFGsi3eNDgwn7O+UR5/W1hxffAZzZFM8+jwL9O82f3nId2hJasyl+V3SuiHs/+USZrGPhYpWVaSXMqX5M31Q2ONtua4BsJESTGpJO6Xpar3BE30PMJycpCczbEi56mLUk+Fv7l6vnqKSY9imqNaPD4SK6GHwUawMhyhliYqTY/zGfEckOu4VJg/O6kmptAsLs/rpOci+ciX2Celed+Fysh2SD7zakhFaN7wYh/XNn+SlEdmgsdoLnAgpK2SizTxlaqLyja22LxkWH6CxxVHaFJnLoSJ11Ru1QyvmVqkXa4UIgA8+cd/1Gg/9m/+baN96cfo5TL8RVI5yUgO9nSO89FkVIk+KXUY8VKpixdVfFmoLfEgUln4ZLjN1Dso60Bv5H6IyjbqtXIVM2+iR1a8wjVbHgrpk+HLnNvC/aRBVc49z17BekjNcQ56f6euiG5XwyRm9/85aa+DP/W6Rlvv9UpfSNt1fo2eU6V9W3C9sCd0g8FgaBHYhm4wGAwtgk2lXBTqVRALLZUgd3G9jeZZ/1PSJ5IXuibV5GNilmsu6tJemm319pBW0D4aLLE8RBH1Xgq9P9w6HkKuTd6wF3jeud0DwXF9s/KGXUuTyVjQG47Tyfminh1N8RqcmFJzIcUSl1zhSjks3E3KJzMtx/SG1FJcclnXh+gNoyW2lOIAXqU8nHhMxCRgKLEYlkWrtrF/4HEhcCWZpw8plyAHubSXtkuu+9lQB+vt4jWTpt7ESxI0JsFQ1QjNU9pNua30UtfTC5RNvBx6jNz7zR9vtPtFhvHiOgsfC5/hVO7q0aV684raA/qdzDP4XGThiqE+Tb6BNEP3MfE4STW//isg90r3515utGt3jzXanV+dCLosvpGBPZ0H5TuheXxkT0BVSuLJfuXaJRhJdbMnzPv+sW8zwG5/Reg1OW9hKKQnl7ZyDvmL1y71F4U9oRsMBkOLwDZ0g8FgaBHYhm4wGAwtgs3l0L1v8J4piRhMb+kPDnPj5LicuAIV3npPo91xOoygVG41PiPRiOIydd92Jg+a3BUmkGo7w/6L28ht56bEraoWulK5FXJchT2sJ6icqXK7fQcZzQoAhTFypm2HLjfatX5GmKUmwzqH1S09Tb9b2kfXz6RED0b7bwSV/pBL1CharYOqScj0OlobEgAy5zm36e+lm1l+QpJznZgJ+lQGKZtKG9dQE3KlLjCysTwSJjnSpGhTD5HbzE1r3UwenzsjOgMEjzoVeY+RnRT30ogrXWyJ71h8H8evCeY0IZiPRFCW2slVF/v53fDnGIFYGY3Urfw85509zftmcRvf16huZC+GY86ephtnvZvrpuvpM+E24YoU3Mo2RpRmz/FdhcqiuDO8v9suNdfPwl7OLXs2fO+h76sq7VKPeEyiYKUWsBsNr5mZErdiqQVc3c7jiv0hn52a5zxTFyWh1zDvQX2/EC+Ez8fxKxxPrYN7Sj1NfSp1hn36n6FO1/LheDYCe0I3GAyGFoFt6AaDwdAi2Nx86B2j/sBDH1i9sJjrLpLLWk1UjShVFyE1m6JQF0B1cYpGvIV95HxB/m9x64qFJnLgyqQ5t9WUVvlGRO0lCrQu19dram5zAEguSJSZREqqC58imnRqI0guhm5meg4dm7qOlntoBkf7+4TQYQX20fPWsqFZr8epnFxZ5SxjjLrWyXexIte9lhN3Qvlcc3RHvwtOW+X1lT4BQppHXR3X1eFIMihXY/+a0BzB2kbvV9G1WkaiPmWcdY3gLId6opHI68kjOR+6z1U6m1NwKo/YOusEAHHRG9XhhHxejeiDfqd6p+uuMn/F/iD3ruqjJseKRmXrPpRYFn5OzhW4X0fuwfXWsy7XD86LcO/S/eXJL/6qJecyGAyGOwm2oRsMBkOLYFMpF+fcFIBlANPXOrbF0Yc7WwZ3+vwBk4HN//rmv91733+tgzZ1QwcA59wzG+GCWhl3ugzu9PkDJgOb/62Zv1EuBoPB0CKwDd1gMBhaBLdjQ//wbbjm/2+402Vwp88fMBnY/G8BNp1DNxgMBsOtgVEuBoPB0CLY1A3dOfcO59xR59wJ59wHN/PatwPOua3OuSedcy87515yzv3c2uc9zrknnHPH1/7vvta5vpPhnIs7577tnPvs2t87nHNPrenBJ5xz15+F6DsIzrku59xfOeeOOOcOO+e+607SAefcL6zp/yHn3Mecc5lW1wHn3Eedc5POuUPyWdM1d6v4n2uyeME59+Brve6mbejOuTiADwH4fgB3A3iPc+7uzbr+bUIVwC967+8G8CYAH1ib8wcBfMF7vwfAF9b+bmX8HIDD8vdvA/hd7/1uALMA3ndbRrV5+D0Af+e93w/gPqzK4o7QAefcCIB/D+CA9/5eAHEAP4rW14E/AfCOyGfrrfn3A9iz9u/9AP7gtV50M5/QHwZwwnt/yntfBvBxAO/axOtvOrz3l7z3B9fai1i9kUewOu/H1w57HMC7b88Ibz2cc6MAfgDAH6397QC8BcBfrR3S6vPvBPA9AD4CAN77svd+DneQDmA1TXfWOZcAkANwCS2uA977rwCYiXy83pq/C8Cf+lV8E0CXc24YrwGbuaGPADgvf4+vfXZHwDk3BuABAE8BGPTeX1r76jKAwds0rM3A/wDwS2AapF4Ac977q1mJWl0PdgCYAvDHa7TTHznn8rhDdMB7fwHAfwNwDqsb+TyAZ3Fn6cBVrLfmN21vtJeimwDnXBuATwL4ee99UEnBr7oZtaSrkXPuBwFMeu+fvd1juY1IAHgQwB947x/AauqLgF5pcR3oxuoT6A4AWwDk8Uoq4o7DrVrzzdzQLwDYKn+Prn3W0nDOJbG6mf+Z9/5Tax9PXDWp1v6fvF3ju8V4BMAPOefOYJViewtW+eSuNfMbaH09GAcw7r1/au3vv8LqBn+n6MBbAZz23k957ysAPoVVvbiTdOAq1lvzm7Y3buaG/jSAPWtvt1NYfTHymU28/qZjjS/+CIDD3vvfka8+A+C9a+33Avj0Zo9tM+C9/xXv/aj3fgyr6/1F7/2PA3gSwA+vHday8wcA7/1lAOedc/vWPvo+AC/jDtEBrFItb3LO5dbuh6vzv2N0QLDemn8GwL9e83Z5E4B5oWauD977TfsH4J0AjgE4CeA/bua1b8c/AI9i1ax6AcBza//eiVUe+QsAjgP4PICe2z3WTZDFYwA+u9beCeBbAE4A+EsA6ds9vls89/sBPLOmB38NoPtO0gEAvwngCIBDAP4PgHSr6wCAj2H1nUEFq1ba+9Zbc6yWAPnQ2r74IlY9gl7TdS1S1GAwGFoE9lLUYDAYWgS2oRsMBkOLwDZ0g8FgaBHYhm4wGAwtAtvQDQaDoUVgG7rBYDC0CGxDNxgMhhaBbegGg8HQIvh/f01ctN4sOFgAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -117,7 +117,7 @@
},
{
"cell_type": "code",
"execution_count": 109,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
......@@ -128,7 +128,7 @@
},
{
"cell_type": "code",
"execution_count": 110,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
......@@ -138,16 +138,16 @@
},
{
"cell_type": "code",
"execution_count": 111,
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f195f957a58>"
"<matplotlib.colorbar.Colorbar at 0x7f8f6a289898>"
]
},
"execution_count": 111,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
......@@ -165,7 +165,7 @@
}
],
"source": [
"correlation_matrix=np.array(df['best_corr {}'.format(v)]).reshape((shape))\n",
"correlation_matrix=np.array(df['best_corr {}'.format(v)]).reshape((dca_matrix.shape))\n",
"plt.imshow(correlation_matrix)\n",
"plt.title('Pattern score')\n",
"plt.colorbar()"
......@@ -180,7 +180,7 @@
},
{
"cell_type": "code",
"execution_count": 118,
"execution_count": 10,
"metadata": {
"scrolled": true
},
......@@ -194,7 +194,7 @@
},
{
"cell_type": "code",
"execution_count": 119,
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
......@@ -204,7 +204,7 @@
},
{
"cell_type": "code",
"execution_count": 120,
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
......@@ -216,16 +216,16 @@
},
{
"cell_type": "code",
"execution_count": 121,
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f195f81afd0>"
"<matplotlib.colorbar.Colorbar at 0x7f8f04a54630>"
]
},
"execution_count": 121,
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
......@@ -243,14 +243,48 @@
}
],
"source": [
"plt.imshow(probability.reshape(shape))\n",
"plt.imshow(probability.reshape(dca_matrix.shape))\n",
"plt.title('Probability of being a contact')\n",
"plt.colorbar()"
]
},
{
"cell_type": "code",
"execution_count": 123,
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Predicted contact map')"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"contact=probability>0.3\n",
"plt.imshow(contact.reshape(dca_matrix.shape))\n",
"plt.title('Predicted contact map')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......
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